University of Nevada, Reno Effects of Fire on Desert Tortoise (Gopherus Agassizii) Thermal Ecology a Dissertation Submitted in P

University of Nevada, Reno

Effects of fire on desert tortoise (Gopherus agassizii) thermal ecology

A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Ecology, Evolution, and Conservation Biology

Sarah J. Snyder

Dr. C. Richard Tracy/Dissertation Advisor

May 2014

THE GRADUATE SCHOOL

We recommend that the dissertation prepared under our supervision by

SARAH J. SNYDER

Entitled

Effects of fire on desert tortoise (Gopherus agassizii) thermal ecology

be accepted in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

C. Richard Tracy, Ph.D., Advisor

Kenneth Nussear, Ph.D., Committee Member

Peter Weisberg, Ph.D., Committee Member

Lynn Zimmerman, Ph.D., Committee Member

Lesley DeFalco, Ph.D., Graduate School Representative

David W. Zeh, Ph. D., Dean, Graduate School

May, 2014

ABSTRACT

Among the many threats facing the desert tortoise (Gopherus agassizii) is the destruction and alteration of habitat. In recent years, wildfires have burned extensive portions of tortoise habitat in the Mojave Desert, leaving burned landscapes that are virtually devoid of living vegetation. Here, we investigated the effects of fire on the thermal ecology of the desert tortoise by quantifying the thermal quality of above- and below-ground habitat, determining which shrub species are most thermally valuable for tortoises including which shrub species are used by tortoises most frequently, and comparing the body temperature of tortoises in burned and unburned habitat. To address these questions we placed operative temperature models in microhabitats that received filtered radiation to test the validity of assuming that the interaction between radiation and radiation-absorbing properties of the model can result in a single, mean radiant absorptance regardless of whether the incident solar radiation is direct unfiltered or filtered by plant canopies, using the desert tortoise as a case study. We found that operative temperatures were nearly identical within microhabitats no matter the absorptance used in the model, which supports the use of a single mean absorptance in modeling operative temperature for animals in a variety of habitats. Using validated models, we calculated indices of thermal habitat quality, and also the hours tortoises could be active within their preferred body temperature range each day across the tortoise activity season. The thermal quality index was similar between burned and unburned habitat, but unburned habitat was more thermally heterogeneous, and it provided slightly longer activity times for tortoises within their preferred body temperature range as long as they could access all thermal microhabitats in their home range, and they were not ii constrained by habitat configuration or microhabitat abundance. However, the differences found were small and likely not biologically significant. Living Yucca species provided the best thermal microhabitats for tortoises during the summer and were used most frequently by tortoises in burned and unburned habitat. We found that burrows in burned and unburned habitats were of similar thermal quality regardless of whether shrubs were present near the burrow mouth. Finally, body temperatures of tortoises using burned and unburned habitat were similar, but tortoises in burned habitat had slightly higher minimum body temperatures. The small magnitude differences detected in minimum body temperature would likely not affect physiological performance. Taken together, these results suggest that burned habitat may be thermally suitable for desert tortoises but direct mortality from fire, and change in nutritional resources, should also be considered when evaluating the effects of fire, and the suitability of burned habitat for tortoise population persistence.

iii

ACKNOWLEDGMENTS

This project was truly a collaborative effort made possible by a partnership between the University of Nevada, Reno (UNR) and the United States Geological Survey

(USGS). In addition to funding, USGS provided me with countless helpers and hours of manpower, prudent advice, and inspiration during all stages of this project. I would especially like to thank Ken Nussear, Todd Esque, Lesley DeFalco, Kristina Drake, Rich

Inman, Katherine Nolte, and Andrew Modlin for their guidance and patience throughout my tenure at UNR. I would like to thank UNR, USGS, and Coyote Springs Investment,

LLC for generous funding that supported this research.

This project required the help of many people, from painting 300 metal bowls, to tracking tortoises in the hot desert from sundown to sunup, and I have many people to thank for their time and assistance. Many Student Conservation Association (SCA) interns and USGS employees provided hours of work in the field including C. Aiello, P.

Babbin, S. Bajwa, T. Barwise, P. F. Berntson, F. Chen, J. Curran, Cosman, K. Drake, E.

Driver, E. Frankel, J. Gleave, G. Howerton, R. Inman, B. Jacobs, E. J. Jay, Jeffreys, A.

Jones, M. Kang, A. Klapperich, R. Kunicki, D. Martin, A. Modlin, S. Ng, J. Niedbala, K.

Nolte, K. Oddenino, C. Phillips, B. Reyonlds, J. Steiner, A. Terry, and V. Vanzerr. I’d also like to thank Rich Inman for his help using the spectroradiometer, Martyn Drabik-

Hamshare for his help in the field collecting irradiance data, and Fran Sandmeier, Dick

Tracy, and Chava Weitzman for collecting iButtons from the field. I’d like to acknowledge all of the help I had from friends who attended my infamous “bowl sanding iv parties” and especially Jenny Todd for her help constructing operative temperature models.

I sought much-needed statistical advice from many individuals including Ken

Nussear, Rich Inman, Kevin Burls, and Matt Forister. My wonderful committee members, Dick Tracy, Ken Nussear, Lesley DeFalco, Lynn Zimmerman, and Peter

Weisberg have given me valuable feedback in the form of reading earlier drafts of manuscripts to giving advice on my research design. Their guidance has helped me to become a competent ecologist and conservation biologist, and for that I am very grateful.

The professors I worked for as a teaching assistant, including Elena Pravasudova,

Charlotte Borgeson, Tom Kidd, Pamela Sandstrom, Dick Tracy, and Patricia Berninsone have been exceptionally understanding as I have tried to juggle dissertation work with my teaching responsibilities. These individuals and my teaching experiences have inspired me to pursue a career in undergraduate education. Administrative staff, faculty, and students in the EECB program and Biology Department at UNR have been influential in my success as a graduate student by filling many capacities over the past seven years.

I would like to thank the Tracy lab members, new and old, for their camaraderie, humor, and continual support. I believe Tracy lab members share a unique and unrivaled bond that is everlasting. Thanks to Bridgette Hagerty, Fran Sandmeier, Chris Geinger,

Rich Inman, Tia Pilikian, Mike Pesa, Amy Barber, Stephanie Wakeling, Nichole

Maloney, Lee Lemenager, Chava Wietzman, Jenny Todd, Michelle Gordon, John Gray, and honorary member Molly Bechtel. Additionally, I don’t think I would have survived my early years as a graduate student without the sage advice from Cynthia Downs on how to navigate paperwork, and advice from Bridgette Hagerty, Fran Sandmeier, and v

Chris Geinger on the inner workings of the Tracy lab. My dwindling cohort of graduate students and new students that have entered the program since I started have been my

Nevada family and, while they are too numerous to list in full, I can’t begin to thank them enough. I would like to especially thank Kevin Badik and Kevin Burls who were my first wonderful roommates in Reno and have remained close buddies ever since. Over the past several years Cynthia Scholl, Chava Weitzman, and Kevin Burls have seriously kept me going and introduced new joy into my life and for that I will be eternally grateful. My best friends from undergrad, Alli Clearwater and Shane Eaton, have always provided me with necessary distraction and adventure when work became overwhelming, which allowed me to persevere.

Of special note I would first like to thank my major professor, Dick Tracy, who has challenged, inspired, and guided me as a graduate student. Dick has been my greatest advocate and has pushed me to become a critical thinker and passionate scientist and teacher. No student could ask for a more supportive, generous, and caring mentor. No thanks would be complete without acknowledging my truly wonderful family. My parents and grandparents have been my greatest supporters and have believed in me when

I had doubts. Thanks to my parents for introducing me to the beauty of the natural world as a child, to my father for his unconditional understanding, and to my mother who first planted the seed that I ought to pursue a career in wildlife biology. Finally, I would like to thank the majestic tortoise, which ambles across the dry, desolate scrub as a symbol of the rugged Nevada desert, that I called home.

TABLE OF CONTENTS

ABSTRACT ……………………...... i

ACKNOWLEDGEMENTS …………………………...…………………………...…….iii

LIST OF TABLES …………………...…………………………………………………..ix Chapter 1…………………………………………...…………………………………ix Chapter 2 ……………………………………………..………………………………ix Chapter 3 …………………………………………...…………………………………x

LIST OF FIGURES ……………………………………………………………..……….xi Chapter 1……………………………………………………………………...………xi Chapter 2 ………………………………………………………………..……………xi Chapter 3 ……………………………………………………………………………xiii

INTRODUCTION …………………………………………………………….………….1 Significance and applications for management ……………………………...……….5 Approach ……………………………………………………………………….……..6

CHAPTER 1. MODELING OPERATIVE TEMPERATURE: EFFECTS IMPOSED BY HABITATS THAT FILTER INCIDENT RADIATION ………………………...... …..13 ABSTRACT …………………………………………………………………………13 INTRODUCTION………………………………………………...…………………14 METHODS ……………………………………………………………...…………..17 Spectral measurements …………………………………………………………..17 Light quality ……………………………………………………………...……...18 Animal absorptance ……………………………………………………………..19 Desert tortoise case study ……………………………………………………….20 RESULTS ……………………………………………………………...…………....21 Light quality ……………………………………………………………...……...21 Animal absorptance ……………………………………………………………..22 Desert tortoise case study ………………………………………………………..22 DISCUSSION...………………………………………………………...…………....23 Light quality ……………………………………………………………...……...23 Animal absorptance ……………………………………………………………..24 Desert tortoise case study ………………………………………………………..25 Conclusion ………………………………………………………………………26 ACKNOWLEDGEMENTS ……………………………………………………….…….26 LITERATURE CITED ………………………………………………………………….27 TABLES …………………………………………………………………………...……32 vii

FIGURE LEGENDS …………………………………………………………………….35 FIGURES ………………………………………………………………………………..36

CHAPTER 2. THERMAL QUALITY OF BURNED AND UNBURNED HABITAT FOR THE DESERT TORTOISE (GOPHERUS AGASSIZII)……………..……...... …..39 ABSTRACT …………………………………………………………………………39 INTRODUCTION………………………………………………...…………………40 METHODS ……………………………………………………………...…………..46 Study site ………………………………….……………………………………..46 Vegetation surveys ………………………………………………………….…...47 Operative temperature models …………………………………………………..48 Comparison of burned and unburned thermal habitat quality ……….………….50 Thermal characteristics of microhabitats ……………………….……………….53 RESULTS ……………………………………………………………...…………....54 Vegetation surveys ………………………………………………………….…...54 Comparison of burned and unburned thermal habitat quality ……….………….55 Thermal characteristics of microhabitats ……………………….……………….56 DISCUSSION...………………………………………………………...…………....58 Vegetation surveys ………………………………………………………….…...58 Comparison of burned and unburned thermal habitat quality ……….………….59 Thermal characteristics of microhabitats ……………………….……………….63 Conservation implications ………………………………………………………65 ACKNOWLEDGEMENTS ……………………………………………………….…….68 LITERATURE CITED ………………………………………………………………….69 TABLES …………………………………………………………………………...……77 FIGURE LEGENDS …………………………………………………………………….87 FIGURES ………………………………………………………………………………..90

CHAPTER 3. THERMOREGULATION BY TORTOISES IN BURNED AND UNBURNED HABITAT ……………..………………………………………...... …..112 ABSTRACT ……………………………………………..…………………………112 INTRODUCTION………………………………………………...…………..……113 METHODS ……………………………………………………………...…………116 Study site ………………………………….………………………………...….116 Vegetation and weather data collection ………………………………..….…...116 Tortoise capture, transmitter attachment, and data logger attachment ..…….....117 Tortoise tracking and data collection ……….………………………………….118 Burrow temperature ……………………….…………………………………...119 Data analyses …………………………………………………………………..120 RESULTS ……………………………………………………………...…...……...123 Burrow temperature …………………………………………………………....123 Vegetation use ……….…………………………………………………..…….124 Body temperature ……………………….………………………………….….125 DISCUSSION...………………………………………………...…………….…....127 viii

Burrow temperature …………………………………………………………....127 Vegetation use ……….…………………………………………………..……..129 Conclusion …………………………………………………………….……….134 ACKNOWLEDGEMENTS ……………………………………………………………136 LITERATURE CITED ………………………………………………………………...137 TABLES …………………………………………………..……………………...……144 FIGURE LEGENDS …………………………………………………………………...153 FIGURES ……………………………………………………………………………....155

CONCLUSION ………………………………………………………………………...164

LIST OF TABLES

Chapter 1

Table 1 Characteristics of plant species sampled as categorized into microhabitat types ……………………………………………………………………………………..32

Table 2 Integrated mean absorptance for desert reptiles in unfiltered skylight compared to canopy-filtered microhabitats ……………………………………………..33

Table 3 Minimum, maximum, and mean operative temperatures for a tortoise calculated by integrating unfiltered skylight, tropical S. podophyllum, and desert yucca Y. brevifolia spectra with tortoise carapace (animal) and paint absorptivity curves ……….34

Chapter 2

Table 1 Mean percent cover (± one standard deviation) by taxa in burned and unburned habitat during 2006 and 2011 ………………………………………………...77

Table 2 Comparison of living taxa and open microhabitat sites during spring as number of hours a tortoise could be active daily within Tp-range ………………………...78

Table 3 Comparison of living taxa and open microhabitat sites during summer as number of hours a tortoise could be active daily within Tp-range ………………………...79

Table 4 Comparison of living taxa and open microhabitat sites during fall as number of hours a tortoise could be active daily within Tp-range ………………………...80

Table 5 Comparison of dead taxa and open microhabitat sites during spring as number of hours a tortoise could be active daily within Tp-range ………………………...81

Table 6 Comparison of dead taxa and open microhabitat sites during fall as number of hours a tortoise could be active daily within Tp-range ………………………...82

Table 7 Comparison of dead taxa and open microhabitat sites during summer as number of hours a tortoise could be active daily within Tp-range ………………………...83

Table 8 Comparison of living and dead shrubs within taxa during spring as number of hours a tortoise could be active daily within Tp-range …………………………………84

Table 9 Comparison of living and dead shrubs within taxa during fall as number of hours a tortoise could be active daily within Tp-range …………………………………….85

Table 10 Comparison of living and dead shrubs within taxa during summer as number of hours a tortoise could be active daily within Tp-range ………………………...86

Chapter 3

Table 1 Number of tortoises in the study (n) per year categorized by their habitat usage (the remaining percent of locations were in unburned habitat) …………………144

Table 2 Mean percent availability and tortoise use (± one standard error) by taxa in burned and unburned habitat …………………………………………………………...145

Table 3 Ranking matrix for tortoises in burned habitat comparing microhabitat use and availability …………………………………………………………………………146

Table 4 Ranking matrix for tortoises in unburned habitat comparing microhabitat use and availability …………………………………………………………………….147

Table 5 Model comparison ranked by ΔAIC describing minimum tortoise Tb …148

Table 6 Model comparison ranked by ΔAIC describing maximum tortoise Tb ...149

Table 7 Model comparison ranked by ΔAIC describing mean tortoise Tb ……...150

Table 8 Model comparison ranked by ΔAIC describing tortoise Tb range ……..151

Table 9 Model comparison ranked by ΔAIC describing the proportion of recorded body temperatures that fell within Tp-range (25°C - 35°C) ……………………………...152

LIST OF FIGURES

Chapter 1

Figure 1 Mean percent transmittance across the spectrum by microhabitat type....36

Figure 2 Nonmetric multi-dimensional scaling biplot illustrating how light energy is partitioned among bands under different microhabitats ……………………………...37

Figure 3 Mean absorptances for a tortoise (open circles) and a tortoise Te model (shaded circles) calculated by integrating absorptivity curves of a tortoise carapace and paint with microhabitat spectra ……………………………………...... …………..…38

Chapter 2

Figure 1 Relationship between Te obtained using physical models and calculated using a mathematical model for open microhabitats on 10% of randomly selected sampling days. Solid line is y=x and broken line is the best-fit regression …………...... 90

Figure 2 Daily thermal habitat quality index calculated for each plot across the sampling period …………………………….……………………………………………91

Figure 3 Hours available to tortoises within Tp-range per day in each plot across the sampling period …………………………………………………………………………92

Figure 4 Mean perennial coverage in burned and unburned plots one to six years post-fire in relationship to mean annual precipitation from October of the previous year through March of the current year ………………………………...…………………….93

Figure 5 Mean perennial coverage of living and dead taxa in burned and unburned plots one to six years post-fire ……………………………………...…………………...94

Figure 6 Mean coverage in burned plots of taxa that comprise at least 2% of area during any year ………………………………………………………………………….95

Figure 7 Mean coverage in unburned plots of taxa that comprise at least 2% of area during any year ………………………………………………………………………….96

Figure 8 Thermal habitat quality index plotted as three day averages and using mean cover data from 2006 through 2011 …………………………………………….97 xii

Figure 9 Mean daily thermal habitat quality index yearly from 2006 to 2011 for burned (black circles) and unburned habitat (gray circles) ……………………..……….98

Figure 10 Mean difference in thermal habitat quality index between burned and unburned habitat across the season ……………………………………………………...99

Figure 11 Hours available to tortoises within Tp-range as three day averages and using mean cover data from 2006 through 2011 ……………………………………………..100

Figure 12 Mean hours available to tortoises within Tp-range per day yearly from 2006 to 2011 for burned (black circles) and unburned habitat (gray circles) ………....……..101

Figure 13 Mean difference in hours available to tortoises within Tp-range per day between burned and unburned habitat across the season ………………………..……..102

Figure 14 Mean hours available to tortoises within Tp-range per day in each microhabitat …………………………………………………………………………..103

Figure 15 Mean monthly hours available to tortoises within Tp-range per day in open burned (black circles) and open unburned (gray circles) microhabitats ……………….104

Figure 16 Mean monthly hours available to tortoises within Tp-range per day under dead (black triangles) and living (gray triangles) Ambrosia dumosa (AMDU) compared to open burned (black circles) and open unburned (gray circles) microhabitats ....……105

Figure 17 Mean monthly hours available to tortoises within Tp-range per day under dead (black triangles) and living (gray triangles) Larrea tridentata (LATR) compared to open burned (black circles) and open unburned (gray circles) microhabitats ...... …….106

Figure 18 Mean monthly hours available to tortoises within Tp-range per day under dead (black triangles) and living (gray triangles) Psorothamnus fremontii (PSFR) compared to open burned (black circles) and open unburned (gray circles) microhabitats ………………………………………………………………………………..…………107

Figure 19 Mean monthly hours available to tortoises within Tp-range per day under dead (black triangles) and living (gray triangles) Yucca brevifolia (YUBR) compared to open burned (black circles) and open unburned (gray circles) microhabitats ...... 108

Figure 20 Mean monthly hours available to tortoises within Tp-range per day under dead (black triangles) and living (gray triangles) Yucca schidigera (YUSC) compared to open burned (black circles) and open unburned (gray circles) microhabitats ……...... 109

Figure 21 Mean monthly hours available to tortoises within Tp-range per day under living (gray triangles with broken lines) Ephedra nevadensis (EPNE), living (gray xiii

triangles with solid lines) Lycium sp. (LYSP), living (gray squares with broken lines) Krameria sp. (KRSP), and living (gray squares with solid lines) Sphaeralcea ambigua (SPAM), compared to open burned (black circles) and open unburned (gray circles) microhabitats ………………….………………………………………………………..110

Figure 22 Mean monthly range of hours available to tortoises within Tp-range per day in burned (black polygon) and unburned (gray polygon) habitat based on microhabitats that comprise at least 2% of area in each habitat during any one year ………….....…..111

Chapter 3

Figure 1 Top- tortoise with GPS logger and VHF radio transmitter, Bottom- attachment site for iButton temperature logger ….....………………………………….155

Figure 2 Tortoise location recorded by GPS loggers (yellow) and with VHF radio tracking (blue) in 2009 and 2010. Burned habitat is in red. A 200 m buffer around the fire perimeter is in light green ……………………………………………………………...156

Figure 3 Minimum daily Tb of tortoises in burned (black) and unburned (gray) habitat plotted as averages per habitat type per day …………………………………...157

Figure 4 Maximum daily Tb of tortoises in burned (black) and unburned (gray) habitat plotted as averages per habitat type per day …………………………....……...158

Figure 5 Mean daily Tb of tortoises in burned (black) and unburned (gray) habitat plotted as averages per habitat type per day …………………………………………...159

Figure 6 Daily Tb range of tortoises in burned (black) and unburned (gray) habitat plotted as averages per habitat type per day …………………………………………...160

Figure 7 Percent of daily Tb that fell within tortoise Tp-range (25°C - 35°C) for tortoises in burned (black) and unburned (gray) habitat plotted as averages per habitat type per day ...…………………………………………………………………………..161

Figure 8 Daily measures of mean (dashed line), minimum (dotted line), and maximum (solid line) temperature within tortoise burrows plotted as averages per habitat type per month. Burrows in burned habitat are black and burrows in unburned habitat are gray ………...…………………………………………………………………………..162

Figure 9 Daily percent of time Tburrow range overlapped tortoise Tp-range (25°C - 35°C) plotted as averages per habitat type per month. Burrows in burned habitat are black and burrows in unburned habitat are gray ………...... ………………………………….163

INTRODUCTION 1

The Mojave population of the desert tortoise (Gopherus agassizii) was listed as 2 threatened in 1990 under the Endangered Species Act of 1973 after the observation that 3 desert tortoise populations were declining in many areas and that regulations were 4 insufficient in protecting tortoises and their habitat (Fish and Wildlife Service 1994). The 5 cumulative impacts of many direct and indirect human activities greatly contributed to 6 population declines. Of particular concern was the widespread destruction, degradation, 7 and fragmentation of habitat resulting from the introduction of invasive plant species, 8 human land use, and urbanization (Fish and Wildlife Service 1994). These alterations to 9 the landscape reduced the suitability of existing habitat areas and prevented movement of 10 tortoises among populations making them more vulnerable to extinction. Listing the 11

Mojave desert tortoise prevented “taking” tortoises and facilitated, designation of critical 12 habitat within each of six distinct recovery units (Desert Tortoise Recovery Plan 1994). 13

In 2005, the Southern Nevada fire complex burned over 750,000 acres, including 14 approximately 400,000 acres of desert tortoise habitat and 65,183 acres of designated 15 critical habitat (U. S. Fish and Wildlife Service 2006). This area represented 132% of the 16 total area burned during the previous 25-year period (Brooks and Matchett 2006). Among 17 the many factors supporting an increase in widespread fires was the invasion of alien 18 grasses. Annual grasses provide standing dead material that ignites quickly, and fuels 19 fires that spreads rapidly due to the grasses’ large surface-to-volume ratio and low 20 moisture content. Annual grass populations also recover more readily after fires than do 21 native plant species, setting into motion a grass/fire cycle (D’Antonio and Vitousek 22

1992). Fire return intervals may also decrease with alien grass dominance such that areas 23

2 may reburn three times during a period of 60 years (Brooks and Matchett 2006). The 24

2005 fire season was preceded by three years of abnormally high rainfall, which may 25 have stimulated growth of non-native annual grasses, creating large amounts of fine fuel 26

(Brooks and Matchett 2006). These patterns suggest a continued threat of fires to desert 27 tortoise habitat in the Mojave, where fires have historically been infrequent and small in 28 scale (Brooks 1999). 29

The observed increase in fire intensity and severity may have several negative 30 consequences for the desert tortoise. Fires can cause direct tortoise mortality (Esque et al. 31

2003, Lovich et al. 2011), which may have population-level impacts for this long-lived 32 species with low recruitment. Indirectly, fires may impact tortoises by fragmenting 33 habitat and by changing the plant communities that make up both the food and shelter 34 components of tortoise habitat (Fish and Wildlife Service 1994) for decades or longer 35

(Abella 2009). Burned habitat may be of lower nutritional value and thermal quality than 36 unburned habitat (Esque et al. 2003, Lovich et al. 2011, Morafka and Berry 2002), which 37 could impact tortoise body condition or reproductive output (Lovich et al. 2011), thus 38 ultimately reducing fitness. The impact of fire on tortoises is complex and multifaceted, 39 encompassing both direct and indirect effects, and may vary among sites. Here, we 40 narrowed our scope to address the impact of fire on the thermal ecology of desert 41 tortoises at a site, which had burned during the Southern Nevada fire complex in 2005. 42

We mechanistically modeled the thermal environment in burned and unburned habitat, 43 observed tortoise behavior, and recorded tortoise body temperature in both burned and 44 unburned habitat. 45

In the Mojave Desert, climate is extremely variable and surface temperatures can 46 become lethally high (Zimmerman et al. 1994), so for tortoises, thermoregulation is 47 crucial for maintaining body temperature (Tb) within optimal or tolerable physiological 48 limits. Body temperature is important because it influences various physiological 49 processes such as metabolic rate (Bennett and Dawson 1976), digestion (Stevenson et al. 50

1985, Zimmerman and Tracy 1989), and locomotion (Hertz et al. 1983, Stevenson et al. 51

1985, Waldschmidt and Tracy 1983), which tend to correlate with fitness (Christian and 52

Tracy 1981). In ectotherms, Tb is a function of the interaction between an animal, and the 53 microhabitat the animal selects (Huey 1991). Therefore, Tb is determined, in part, by the 54 microhabitats available to an animal in its habitat, the properties of the microhabitats, and 55 how the animal behaviorally exploits its habitat (Huey 1991). Thermal habitat quality can 56 be described in terms of how well the habitat provides physiologically suitable conditions 57 for species (Huey 1991), and this value can be mathematically quantified using operative 58 temperature models to compare quality between habitats (Tracy and Christian 1986). 59

Tortoises are able to exploit their thermal habitats using a suite of unique 60 adaptations. Their carapace can serve as a heat sink to buffer against temperature 61 extremes (McGinnis and Voigt 1971), they can adopt seasonal activity patterns to avoid 62 above-ground activity when conditions are unfavorable and resources are scarce 63

(Woodbury and Hardy 1948), and tortoises are adept at digging burrows that can be used 64 in the winter as hibernacula, and in the summer when operative surface temperatures 65 surpass approximately 45°C (Zimmerman et al. 1994). When active above ground 66 tortoises rely on patchily distributed perennial shrubs as thermal refugia. Perennial shrubs 67 provide important thermal heterogeneity on the landscape (Attum et al. 2013, Hillard 68

1996, Lagarde et al. 2012). Using shrubs as shade resources, can allow tortoises to be 69 active above ground without returning to underground burrows when the unshaded 70 environment does not permit tortoises to maintain body temperature within tolerable 71 physiological limits (Nussear 2004, Zimmerman et al. 1994). 72

In the Mojave, fires of high-intensity generally burn perennial shrubs above, and 73 sometimes below, ground (Esque et al. 2003). Fires can leave a burned landscape 74 denuded of living vegetation, and dominated by charred dead limbs and annual plants 75

(personal observation). Mojave Desert perennials have low rates of re-sprouting (Abella 76

2009), and specific recruitment requirements (Reynolds et al. 2012), which can inhibit 77 vegetation recovery after fire. It can take decades for vegetative cover to return to pre-fire 78 characteristics, and burned species composition may never converge with unburned 79 composition, creating a landscape dominated by post-fire colonizers (Abella 2009). If the 80 thermal properties of microhabitats available to tortoises in burned and unburned habitat 81 differ, fire could indirectly impact tortoises in burned areas indefinitely by restricting the 82 ability of tortoises to thermoregulate. Additionally, differences in plant cover may change 83 the thermal environment available to tortoises below ground in burrows, where tortoises 84 spend the majority of their life (Nagy and Medica 1986). Tortoises construct burrows 85 more frequently under shrubs than in the open (Berry and Turner 1986, Burge 1978, 86

Wilson et al. 1999), and with openings that generally face in an eastern or northern 87 direction (Bulova 1994, Burge 1978, Hazard and Morafka 2004, but see Berry and Turner 88

1986). Shrub cover may affect the thermal environment of burrows by blocking radiation 89 from entering the burrow mouth, depending on the direction the burrow faces, or by 90 influencing convection into the burrow. If changes in the thermal environment above 91

5 and/or below ground impact tortoise thermoregulation, it would manifest as differences in 92 body temperatures between tortoises in burned and unburned habitat. Since body 93 temperature influences physiological performance, it is possible that thermal differences 94 between burned and unburned habitat could have long lasting fitness consequences for 95 this threatened species. 96

SIGNFICANCE AND APPLICATIONS FOR MANAGEMENT 98

While previous studies have investigated some direct (Esque et al. 2003, Lovich 99 et al. 2011) and indirect (Lovich et al. 2011) impacts of fire on desert tortoises, and many 100 investigators have speculated that fire alters the thermal environment for tortoises (Esque 101 et al. 2003, Lovich et al. 2011, Morafka and Berry 2002), to our knowledge this is the 102 first study to evaluate the effects of fire mechanistically in relation to the thermal ecology 103 of tortoises. After fires, managers are faced with complicated decisions including whether 104 or not burned habitat is suitable for desert tortoises and thus should be protected, the 105 temporal effects of landscape changes on habitat quality, whether burned habitat should 106 be restored, and if so, the actions needed to restore/revegetate habitat to suit the 107 requirements of the tortoise. Because tortoise populations have exhibited declines, the 108 decisions made regarding habitat management have important impacts on the future of 109 this species. The findings of our study should help guide management decisions by 110 quantifying, in burned and unburned habitat, (1) above-ground thermal quality across 111 time, (2) which shrub species provide the best thermal resources to tortoises and which 112 are used to a greater degree, (3) below-ground thermal quality in burrows , and (4) body 113 temperatures of tortoises. It is important to consider context when applying the findings 114

6 of this study because landscapes and fire histories differ across the distribution of this 115 species. Additionally, this study addresses only the thermal effects of fire on the desert 116 tortoise and other considerations, including the effects of fire on direct mortality and 117 forage availability, as well as indirect effects of shifting animal associations should be 118 taken into account when deciding how best to manage burned habitat for this species. 119

Our study also contributes to the biophysical ecology and thermal biology 120 literature by providing justification for using a method that has been widely accepted, but 121 previously untested in operative temperature modeling. We challenged the common 122 practice of building operative-temperature models assuming that the interaction between 123 radiation and radiation-absorbing properties of the model can result in a single, mean 124 absorptance regardless of whether the incident light is direct unfiltered solar radiation or 125 solar radiation filtered by plant canopies using the desert tortoise as a case study. These 126 findings may be applied to many systems and used to validate other biophysical studies. 127

128

APPROACH 129

This dissertation was part of a large collaborative effort with the United States 130

Geological Survey (USGS) to assess the impact of fire on desert tortoises. Our 131 contribution to the project has been in investigating the thermal properties of burned 132 habitat, how tortoises use burned habitat, and how burned habitat can affect tortoise body 133 temperature (covered in chapters two and three). To quantify the thermal quality of 134 burned and unburned habitat, I recorded operative temperature using models that were 135 placed in microhabitats receiving filtered solar radiation. In chapter one, I test a method 136

7 used in operative temperature modeling to defend the results presented in the following 137 chapters. In three chapters I address the following objectives: 138

Chapter 1. Modeling operative temperature: Effects imposed by habitats that filter 139

incident radiation 140

• Determine how light filtered through plant canopies differs in quality among 141

microhabitat types. 142

• Determine if differences in light quality among microhabitat types changes 143

integrated mean absorptance for a variety of desert reptiles. 144

• Use the desert tortoise (Gopherus agassizii) as a case study, I: 145

• Investigate whether mean absorptance diverges between real animals and 146

operative temperature models (painted to match the mean absorptance of 147

a tortoise carapace based on clear day unfiltered solar integration) in 148

microhabitats with filtered light. 149

• Assess the magnitude of error associated with modeling operative 150

temperature in microhabitats with filtered light using standard methods 151

for calculating absorptance. 152

153

Chapter 2. Thermal quality of burned and unburned habitat for the desert tortoise 154

(Gopherus agassizii) 155

• Determine whether thermal quality (as calculated by index of thermal quality) 156

differs between burned and unburned habitat for the desert tortoise over the 157

season in which tortoises are active. 158

• Determine whether the quantity of time during which tortoises can be active 159

within their preferred body temperature range (if all microhabitats are 160

accessible) differs between burned and unburned habitat. 161

• Determine whether the thermal quality index, or available activity hours, change 162

between one and six years post-fire as the perennial vegetation cover undergoes 163

secondary succession. 164

• Compare the thermal value of microhabitats for tortoises. 165

• Suggest conservation implications of these findings, and provide 166

recommendations as to how burned habitat can be best managed/restored to 167

meet the thermal needs of tortoises. 168

169

Chapter 3. Thermoregulation by tortoises in burned and unburned habitat 170

• Assess the thermal characteristics of burrows in burned and unburned habitat 171

with and without vegetative cover. 172

• Evaluate vegetative cover used by tortoises in relation to cover availability. 173

• Compare body temperature among tortoises using burned and unburned habitat, 174

to evaluate the suitability of burned landscapes as tortoise habitat. 175

176

177

178

179

180

181

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Bennett, A. F. and W. R. Dawson. 1976. Metabolism. In C. Gans and W. R. Dawson 188

(Eds.), Biology of the Reptilia, Vol. 5. Academic Press, New York, pp. 127-223. 189

Berry, K. H. and F. B. Turner. 1986. Spring activities and habits of juvenile desert 190

tortoises, Gopherus agassizii, in California. Copeia 1986:1010- 1012. 191

Brooks, M. L. 1999. Alien annual grasses and fire in the Mojave Desert. Madrono 46: 13- 192

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D'Antonio, C. M. and P. M. Vitousek. 1992. Biological invasions by exotic grasses, the 204

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vertebrates. Southwestern Naturalist 48: 103-111. 209

Fish and Wildlife Service. 1994. Desert tortoise (Mojave population) Recovery Plan. U.S. 210

Fish and Wildlife Service, Portland, Oregon. 73 pages plus appendices. 211

Hazard, L. C. and D. J. Morafka. 2004. Characteristics of burrows used by neonate desert 212

tortoises during hibernation. Journal of Herpetology 38: 443-447. 213

Hertz, P. E. R. B. Huey, and E. Nevo. 1983. Homage to Santa Anita: Thermal sensitivity 214

of sprint speed in agamid lizards. Evolution 37:1075-1084. 215

Hillard, S. 1996. The importance of the thermal environment to juvenile desert tortoises. 216

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137: S91–115. 219

Lagarde, F., T. Louzizi, T. Slimani, H. El Mouden, K. Ben Kaddour, S. Moulherat, and X. 220

Bonnet. 2012. Bushes protect tortoises from lethal overheating in arid areas of 221

Morocco. 2012. Environmental Conservation 39: 172-182. 222

Lovich, J. E., J. R. Ennen, S. V. Madrak, C. L. Loughram, K. P. Meyer, T. R. Arundel, 223

and C. D. Bjurlin. 2011. Long-term post-fire effects on spatial ecology and 224

reproductive output of female Agassiz’s desert tortoises (Gopherus agassizii) at a 225

wind energy facility near Palm Springs, California, USA. 2011. Fire Ecology 7: 226

75-87. 227

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Gopherus agassizii. Comparative Biochemical Physiology 40A: 119-126. 229

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an exaptive opportunist? Implications for tortoise conservation. Chelonian 231

Conservation and Biology 4: 263-287. 232

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southern Nevada. Herpetologica 42: 73-92. 234

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tortoise Gopherus agassizii. University of Nevada, Reno. PhD in Ecology, 236

Evolution, and Conservation Biology, 210 pp. 237

Reynolds, M. B., L. A. Defalco, and T. C. Esque. 2012. Short seed longevity, variable 238

germination conditions, and infrequent establishment events provide a narrow 239

window for Yucca brevifolia (Agavaceae) recruitment. 2012. American Journal of 240

Botany 99; 1647-1654. 241

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locomotion, 243 tongue flicking, digestion and oxygen consumption in the wandering garter snake. 244

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thermal niche axes. Ecology 67: 609-615. 247

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County, Utah. Service File No. 1-5-05-F 526. April 12, 2006. Prepared by the 251

Southern Nevada Field Ofﬁce, Las Vegas, Nevada. 68 pp. 252

Wilson, D. S, C. R. Tracy, K. A. Nagy, and D. J. Morafka. 1999. Physical and 253

microhabitat characteristics of burrows used by juvenile desert tortoises 254

(Gopherus agassizii). Chelonian Conservation and Biology 3: 448-453. 255

256 Waldschmidt, S. and C. R. Tracy. 1983. Interactions between a lizard and its thermal 257

environment: implications for sprint performance and space utilization in the 258

lizard Uta stansburiana. Ecology 64: 476-484. 259

Woodbury, A. M. and R. Hardy. 1948. Studies of the desert tortoise, Gopherus agassizii. 260

Ecological Monographs 18: 146-200. 261

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Salice. 1994. Thermal ecology of desert tortoises in the eastern Mojave Desert: 263

seasonal patterns of operative and body temperatures, and microhabitat 264

utilization. Herpetological Monographs 8: 45-59. 265

Zimmerman, L. C. and C. R. Tracy. 1989. Interactions between the Environment and 266

Ectothermy and Herbivory in Reptiles. Physiological Zoology 62: 374-409. 267

268

269

CHAPTER 1. MODELING OPERATIVE TEMPERATURE: EFFECTS 270

IMPOSED BY HABITATS THAT FILTER INCIDENT RADIATION 271

272

ABSTRACT 273

We challenged the common practice of using a single mean absorptance based on 274 unfiltered skylight spectra to model operative temperature for animals in filtered light 275 habitats by examining the effects of plant canopies on light transmittance. To assess 276 differences in light filtration over a range of microhabitats, spectra were recorded under 277 canopies of desert plants, tropical plants, and under unfiltered skylight. Spectra were then 278 integrated with absorptivity curves of desert reptiles to determine if differences in light 279 quality among microhabitat types changed integrated mean absorptance. Finally, we used 280 the desert tortoise (Gopherus agassizii) as a case study to investigate the effects of 281 filtered microhabitats on paint choice for physical operative temperature models and 282 determined the magnitude of error that could result from discrepancies between paint and 283 animal absorptance. We found that light energy was partitioned similarly among 284 microhabitats with like canopy types and that most variation was explained by 285 differences in transmittance between the visible and near infrared wavelengths. Mean 286 absorptance for reptiles was similar among microhabitats with the greatest differences 287 observed between animals in unfiltered skylight and under tropical canopies. In most 288 microhabitats paint and animal absorptances differed, but operative temperatures were 289 nearly identical within microhabitats no matter the absorptance used in the model. The 290 findings of this study support the use of a single mean absorptance in modeling operative 291 temperature for animals in a variety of habitats. 292

1. INTRODUCTION 293

The thermal environment for living organisms can be defined by a suite of 294 heat-transfer processes (e.g., convection, radiation, conduction, evaporation) that 295 ultimately determine the body temperature of ectothermic animals (Gates 1980). 296

Operative temperature (Te) is defined as the temperature attained by an inanimate object, 297 of zero heat capacity, with the same size, shape, and properties important to radiative heat 298 exchange as the animal for which the measurement is made (Bakken and Gates 1975). In 299 other words, Te integrates the mechanisms of energy transfer to produce a single index 300 that represents the steady-state body temperature of a specific animal experiencing a 301 given set of environmental conditions (Bakken et al. 1985, Tracy 1982) and can be 302 calculated either mathematically, or using physical models that replicate the properties of 303 animals (Bakken, 1992, Tracy 1982). There is a vast literature on how to calculate or 304 measure operative temperatures (e.g. Bakken and Gates 1975, Bakken et al. 1985, 305

Dzialowski 2005, O’Connor et al. 2000, Tracy et al. 2007), and a similarly rich literature 306 demonstrating the use of operative temperature as a valuable metric in ecological 307 applications (e.g. Bakken 1992, Christian and Tracy 1985, Grant and Dunham 1988, 308

Hertz 1992a, Hertz 1992b). 309

During daylight hours, solar radiation is the greatest contributor to the overall 310 energy balance of ectotherms. Solar radiation includes ultraviolet, visible, and short-wave 311 infrared radiation and can be incident on an animal in the form of either direct radiation 312 or scattered skylight (Gates 1980). The amount of energy gained through solar radiation 313 is not only contingent upon the intensity of radiation, but also on the area and absorptivity 314

(the fraction of incident radiation absorbed) of the surface to incident radiation (Gates 315

1980, Tracy 1982). The absorptivity of an animal varies across the solar spectrum (Norris 316

1967, Porter 1967), and the color of the animal is often not a predictor of its absorptive 317 properties in non-visible regions (Nussear et al. 2000). 318

Mean absorptance is calculated by integrating the absorptivity of an animal at 319 each wavelength multiplied by the wavelength’s intensity across the spectrum (generally 320 from 300 nm to 2500 nm), and dividing it by the total solar energy (Gates 1980). Thus, 321 mean absorptance depends on both the absorptivity of the animal and the spectrum of 322 incident light with which absorptivity is integrated (Nussear et al. 2000). Historically, this 323 spectrum has been assumed to be that of a clear day with unfiltered solar radiation (Gates 324

1980). The mean absorptance based upon a single integration has been used in 325 mathematical calculations and is mimicked in physical models by using paint that 326 matches the mean absorptance of an animal (Bakken et al. 1985, Bakken 1992). 327

However, paints composed of carbon pigment have a flat reflective signature across the 328 solar spectrum whereas melanin is more reflective in the near infrared region (Harlow et 329 al. 2010). This leads to a mismatch in the shape of the absorptivity curve between paint 330 and integument, even though there is a similar integrated mean absorptance, which could 331 introduce error into operative temperature modeling. 332

Historical mean absorptance calculations may accurately represent radiant energy 333 gain for animals subject to clear day unfiltered radiation, but do not necessarily predict 334 absorbed radiation by animals in filtered light microhabitats where light is scattered and 335 reflected (O’Connor and Spotila 1992). Even so, operative temperatures are often 336 calculated using the same mean absorptance values for individuals in microhabitats 337 receiving unfiltered solar radiation as those in other microhabitats receiving radiation 338

16 filtered through clouds, shrub canopies, forest canopies, and other forms of shade (e.g. 339

Bauwens et al. 1996, Belliure et al. 1996, Christian and Bedford 1995, Diaz and Diaz 340

2004, Grant and Dunham 1988, Harlow et al. 2010, Shoemaker and Gibbs 2010). Light 341 transmitted through vegetation is likely to differ in quality (i.e. the distribution of energy 342 across the spectrum), not just quantity, from unfiltered solar radiation, as many studies 343 have found that proportion of understory light in different wavebands (e.g. red:far-red 344 ratios) varies with canopy species (Federer and Tanner 1966, Muraoka et al. 2001, Sattin 345 et al. 1994), canopy cover (Capers and Chazdon 2004, Grant 1997, Lee 1987, Pecot et al. 346

2005, Rossi et al. 2001), canopy architecture (Skalova et al. 1999), and leaf water content 347

(Serrano and Penuelas 2005). These relationships also depend on sky condition (cloudy 348 or sunny) (Capers and Chazdon 2004, Pecot et al. 2005). 349

Dzialowski (2005) reviewed the use and accuracy of operative temperature 350 models and stressed, among other factors, the importance of choosing an appropriate 351 paint for physical models that matches the absorptivity of the study animal. However, to 352 our knowledge, the biophysical ecology literature has yet to validate the use of a single 353 mean animal absorptance for deriving operative temperatures for animals in all light 354 environments. Here, we address this issue by investigating the effects of light filtered by 355 plant canopies on absorptance calculations for desert reptiles. The objectives of this paper 356 are to: 357

358

1. Determine how light filtered through plant canopies differs in quality among 359

microhabitat types. 360

361

2. Determine if differences in light quality among microhabitat types changes 362

integrated mean absorptance for a variety of desert reptiles. 363

364

3. Use the desert tortoise (Gopherus agassizii) as a case study to: 365

i. Investigate whether mean absorptance diverges between real animals and 366

operative temperature models (painted to match the mean absorptance of a 367

tortoise carapace based on clear day unfiltered solar integration) in 368

microhabitats with filtered light. 369

ii. Reveal the magnitude of error associated with modeling Te in microhabitats 370

with filtered light using standard methods for calculating absorptance. 371

372

2. MATERIALS AND METHODS 373

2.1 Spectral measurements 374

Measurements of solar spectra were taken in the Mojave Desert in southern 375

Nevada on April 17 and 19, 2012 under clear sky conditions within 1.5 hours pre- and 376 post- solar noon (11:10 to 14:10) using an ASD FieldSpec 3 portable spectroradiometer 377 with cosine-corrected irradiance receptor. The spectroradiometer recorded irradiance in 378

W/(m2 • nm) from 350 nm to 2500 nm at a resolution of 1 nm. To examine differences in 379 light filtration over a range of microhabitat types, we recorded spectra under canopies of 380 native desert plant species, non-native tropical plant species, and under unfiltered 381 skylight (Table 1). The native species chosen exhibited a range in canopy architecture 382 representative of Mojave Desert vegetation and were grouped into three microhabitat 383 types based on conformation: shrub canopy, sub-shrub canopy, and yucca canopy. 384

Tropical species had larger leaves and more leaf overlap than any of the desert 385 species sampled and were grouped as a single microhabitat type. We chose these species 386 to represent contrasting closed canopy habitats. We measured spectra under naturally 387 occurring native and potted non-native species in the same field location. To eliminate 388 any bias due to soil reflectance, soil in the pots of the tropical species was covered with 389 the same substrate found under the native species. Each spectrum was recorded by 390 placing the cosine receptor horizontally level to the ground using a tripod. To determine 391 the quality of filtered light under plant canopies, we recorded five shaded spectra in 392 different spots under each plant. A paired spectrum was also recorded next to each plant 393 in unfiltered skylight to compare the quality of light in canopy-filtered microhabitats to 394 that of unfiltered skylight. Recording an unfiltered skylight spectrum after each set of 395 readings below a plant canopy accounted for changes in radiation during the sampling 396 period due to sun position or the presence of thin clouds. 397

398

2.2 Light quality 399

Irradiance was measured between 350 nm and 1800 nm. Data between 1350 nm 400 and 1400 nm and above 1800 nm were omitted from analyses due to instrument noise. 401

For each replicate plant, only the shaded spectrum with the lowest total transmitted 402 energy was selected for analysis to maximize potential effects of canopy light filtration. 403

We calculated total radiant energy for each spectrum by summing energy at all 404 wavelengths. Percent transmittance was calculated for each plant canopy by dividing the 405 total radiant energy transmitted through the canopy by the total radiant energy in the 406 paired unfiltered skylight spectrum (Table 1). Mean percent transmittance at each 407

19 wavelength was also calculated for each microhabitat type (Figure 1). Radiant energy 408 was then divided into four bands: ultraviolet (UV) 350 nm to 400 nm, visible (VIS) 400 409 nm to 700 nm, near infrared (NIR) 700 nm to 1400 nm, or short-wave infrared (SWIR) 410

1400 nm to 1800 nm (Metzger 2012). The proportion of total radiant energy contained in 411 each band was calculated for each spectrum. We used nonmetric multidimensional 412 scaling (NMDS), which is robust to non-normal data, to visualize how light energy was 413 partitioned among bands in different microhabitats using raw proportions. All indices 414 were equally good at separating microhabitats using rank correlation so NMDS was 415 calculated using Bray-Curtis dissimilarity. We conducted a permutational multivariate 416 analysis of variance (PERMANOVA) to test if grouping species into microhabitat types 417 explained differences in how canopy-filtered light energy was partitioned. NMDS and 418

PERMANOVA analyses were conducted using the Vegan package ver 2.0-9 in program 419

R (Oksanen et al. 2012). 420

421

2.3 Animal absorptance 422

Spectral absorptivity data for desert reptiles were obtained from published figures 423

(Norris 1967, Porter 1967) using the program GraphClick (Arizona Software, ver 3.0.) 424 and from the primary author of Nussear et al. (2000). A mean absorptance value for each 425 reptile was calculated for each spectrum following Gates (1980) by integrating the 426 published absorptivity curves with the measured irradiance curves for all microhabitats. 427

We used linear models with Dunnett’s post hoc comparisons (Package multcomp ver 1.2- 428

21 in R 3.0.2) for each reptile species to determine whether absorptance values for 429

20 animals in canopy-filtered microhabitats differed significantly from absorptance values 430 for animals in unfiltered skylight. 431

432

2.4 Desert tortoise case study 433

Spectral absorptivity curves of several paint samples were measured from 350 nm 434 to 1800 nm using a spectrophotometer with a reflecting sphere attachment (Model 5420, 435

Beckman Inc., Fullerton, CA). Paint absorptivity curves were integrated with a clear day 436 unfiltered solar radiation spectrum to obtain a mean absorptance for each sample. The 437 process was also repeated using the average absorptivity curve of a desert tortoise 438 carapace (Nussear et al. 2000). For analysis, we chose the paint that most closely 439 matched the mean absorptance of the tortoise carapace to represent the absorptivity of a 440

Te model (paint 83.2%, tortoise 82.2%). The absorptivity curves of the paint and tortoise 441 carapace were integrated with each microhabitat spectrum yielding a mean absorptance 442 value for a painted Te model and for a tortoise in each microhabitat. Linear mixed effect 443 models (Package nlme ver 2.1-111 in R 3.0.2) were conducted pairwise, for each 444 microhabitat, to determine whether the absorptance of the Te model and the tortoise were 445 statistically different. We included plant replicate as a random effect to account for the 446 repeated use of spectra in calculating Te model and tortoise absorptances. 447

Te calculations were made using a mathematical model parameterized with 448 conditions that would maximize differences in absorptance (e.g., intense radiation in the 449 open, minimal convection, large tortoise body size). Weather data were obtained from a 450

HOBO weather station (located in desert tortoise habitat where irradiance was measured) 451 equipped with a solarimeter and thermistors located 100 cm above ground surface and 10 452

cm below ground surface. We calculated hourly Te during daylight hours for a 38 cm 453 desert tortoise in southern Nevada using solar radiation, air temperature, and ground 454 temperature measured on the summer solstice (June 21, 2010). Wind speed was 455 artificially adjusted to 1 m/s for all hours to reduce energy exchange via convection. We 456 modeled Te for three microhabitats: unfiltered skylight (control), tropical Syngonium 457 podophyllum (tortoise absorptance lowest and paint absorptance highest), and desert 458

Yucca brevifolia (tortoise absorptance highest and paint absorptance lowest). For each 459 microhabitat, Te was modeled using absorptance values that were calculated by 460 integrating the absorptivity curves of the paint and tortoise carapace with filtered and 461 unfiltered spectra. In filtered microhabitats, all parameters were kept constant, but solar 462 radiation was reduced according to percent transmittance for that species (Table 1). We 463 compared daily minimum, maximum, and mean Te within each microhabitat using linear 464 models with Tukey’s post hoc comparisons (Package multcomp ver 1.2-21 in R 3.0.2). 465

466

3. RESULTS 467

3.1 Light quality 468

Mean percent of transmitted light varied both among microhabitat types and 469 across the spectrum with clear breaks between the VIS and NIR bands and the NIR and 470

SWIR bands (Figure 1). Stress was minimal (S=0.01) in a two dimensional NMDS biplot 471 indicating a good representation the data (Figure 2). The way in which light energy was 472 partitioned into bands varied significantly among microhabitat types (PERMANOVA, 473

P<0.01). Most of the variation occurred across the VIS and NIR gradient associated with 474 axis NMDS1 while little variation occurred across the UV and SWIR gradient associated 475

22 with axis NMDS2 (Figure 2). Light filtered through tropical canopies had a higher 476 proportion of energy in the NIR and SWIR bands and a lower proportion of energy in the 477

VIS and UV bands, while light filtered through desert yucca canopies had a lower 478 proportion of energy in the NIR and SWIR bands and a higher proportion of energy in the 479

VIS and UV bands. Light filtered through desert shrub and sub-shrub canopies was 480 partitioned intermediately between the tropical and desert yucca canopies (Figure 2). 481

482

3.2 Animal absorptance 483

Mean absorptance for reptiles in unfiltered skylight ranged from 52.4% in 484

Crotalus cerastes to 96.7% in Sceloporus occidentalis (Table 2). Absorptance values, 485 calculated using canopy-filtered spectra, differed from absorptance values calculated 486 using unfiltered skylight spectra by up to 8.3%. The direction and magnitude of 487 absorptance differences depended on both the species of reptile and the species of plant. 488

Calculated absorptance for all of the reptiles was similar to unfiltered skylight under the 489 canopy of only one desert shrub (Krameria spp.) (P>0.05, Table 2). The greatest 490 differences in absorptance were observed between unfiltered skylight and tropical 491 canopies. Calculated absorptance differed between all four tropical species and unfiltered 492 skylight for all reptiles (P<0.05, Table 2). However, even these differences were 493 relatively minor (0.6% to 8.3%). 494

495

3.3 Desert tortoise case study 496

Mean absorptance of the desert tortoise and Te model differed significantly in all 497 filtered microhabitats (P<0.05) except under the desert yucca Y. schidigera (F1,5 = 1.91, 498

P=0.23, Figure 3). The absorptance of the Te model was higher than the absorptance of 499 the tortoise carapace for all microhabitats except under the desert yucca Y. brevifolia. The 500 greatest differences in absorptance were found under tropical plant canopies with a 501 maximum difference of 8.5% under S. podophyllum. 502

Calculated daily measures of Te for the desert tortoise had no variance so even 503 minor differences were statistically significant (Table 3). Minimum, maximum, and mean 504

Te were all significantly different between paint and tortoise carapace in the unfiltered 505 skylight microhabitat (P<0.05). All measure of Te were identical under Y. brevifolia 506 while maximum and mean Te were statistically different under S. podophyllum. Te 507 differences within microhabitats were all 0.2°C or less (Table 3). 508

509

4. DISCUSSION 510

4.1 Light quality 511

The distribution of transmitted light energy across the four light band ranges we 512 considered was similar among species of like canopy type (tropical and desert sub- 513 categories). Under tropical plant canopies, transmitted light quality was indicative of 514 direct filtration through leaves. Less light was transmitted in the UV and VIS bands, 515 while proportionately more light was transmitted in the NIR and SWIR bands. This 516 pattern is consistent with the rapid change in leaf reflectance between red and near 517 infrared regions (Grant 1997). Leaf pigments absorb much of the photosynthetically 518 active radiation, thus intercepting visible light, while leaves reflect and/or transmit large 519 portions of near infrared radiation to prevent overheating (Gates 1980). 520

Desert shrubs tended to have smaller leaves and more non-green branches than 521 tropical species, which produced canopies that were more open and intercepted less light. 522

Light transmitted through desert shrub canopies was more similar to unfiltered skylight, 523 which could be attributed to the larger amount of unscattered direct and diffuse radiation 524 penetrating the canopy through gaps instead of being filtered through plant materials 525

(Endler 1993). Also, non-green plant parts absorb less light in the visible region and more 526 light in the near infrared region than green plant parts (Serrano et al. 2000). The light 527 quality under desert shrubs was consistent with expectations for transmittance through 528 canopies with higher ratios of branches to leaves. The architecture and leaf size of desert 529 sub-shrubs was intermediate between the desert and tropical canopies as the quality of 530 transmitted light. 531

Desert yuccas had dense canopies that restricted virtually all light transmittance, 532 as is the case with other desert succulents (Gates 1980). Thus, the light detected from the 533 ground below desert yuccas was primarily composed of scattered light reflected from 534 surrounding surfaces, resulting in low and even energy transmittance across the spectrum. 535

The similarities in light filtration observed among like species are probably due to a 536 combination of shared characteristics including (1) how leaves absorb, reflect, and 537 transmit radiation for physiological purposes, (2) the ratio of green to non-green plant 538 parts in the canopy, (3) the light scattering properties of canopy structure and 539 architecture, and (4) the degree of canopy closure. 540

541

4.2 Animal absorptance 542

In general, mean absorptance calculations for a single animal were similar among 543 all microhabitat types. Calculations for animals differed the most between tropical 544 canopies and unfiltered skylight. Gates (1980) calculated a maximum difference in 545 absorptance of 1.9% for reptiles in light of differing quality (low vs. high sun), which is 546 less than the maximum difference found here. While significant differences in animal 547 absorptance existed between unfiltered skylight and many of the light microhabitats, only 548 under dense tropical foliage did calculated absorptances differ from unfiltered skylight by 549 more than 5%. Observed differences could be magnified in situations where within- 550 canopy light scattering changes absorptance spectra more drastically, such as in forested 551 habitats where canopies are denser and more complexly layered. 552

553

4.3 Desert tortoise case study 554

Differences in absorptance between a desert tortoise carapace and painted Te 555 models were greatest under tropical canopies and least under desert yucca canopies. 556

Differences were primarily due to the variability in calculated tortoise carapace 557 absorptance among canopy-filtered microhabitats. This variability did not exist in 558 calculated paint absorptance, as a result of the flatter paint absorptivity curve, and led to 559 the observed discrepancies between tortoise and Te model absorptance. Operative 560 temperatures were nearly identical when calculated using the tortoise carapace and paint 561 absorptance, even when parameters were set to maximize differences in Te. This result 562 was especially evident in canopy-filtered microhabitats where there was less radiant 563 energy contributing to Te. Even if differences in absorptance are great, in microhabitats 564 where little light is transmitted other components of the energy budget become more 565

26 influential in determining the operative temperature than absorbed radiation. Differences 566 in Te were smaller than those previously reported for models of dissimilar absorptance in 567 unfiltered skylight, however, the absorptance values compared in this study were also 568 more similar within microhabitats than values compared elsewhere (Bakken and Gates 569

1975, Shine and Kearney 2001). 570

571

4.4 Conclusion 572

Although researchers frequently use operative temperatures to investigate 573 biophysical questions concerning animals in habitat that filter incident light, this is the 574 first study to assess the influence of canopy light filtration on the calculated absorptance 575 of animals or Te models. We found differences in the quality of light filtered through 576 plant canopies, but these differences had small effects on animal absorptance and 577 virtually no effect on calculated operative temperatures. Thus, our study supports the 578 common practice of integrating animal absorptivity curves with unfiltered clear day solar 579 radiation to ascertain a mean animal absorptance, even when operative temperature is 580 modeled for animals in filtered light environments. 581

582

ACKNOWLEDGMENTS 583

We would like to thank Martyn Drabik-Hamshare for his help in the field collecting 584 irradiance data and Richard D. Inman for guidance in operating the spectroradiometer. 585

We thank Lesley A. DeFalco and Peter J. Weisberg for helpful discussion and comments 586 on an earlier draft of the manuscript. This project was supported by Coyote Springs 587

Investment, LLC. 588

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686

TABLES 687 688 689 Table 1. Characteristics of plant species sampled as categorized into microhabitat types. 690 Means are ± one standard deviation. 691 692 693 694 695 696 697 698

100 699 8.7 ± 5.8 9.9 ± 1.4 9.8 ± 2.8 6.3 ± 1.5 4.6 ± 1.5 9.1 ± 2.5 11.5 ± 4.2 13.9 ± 8.5 23.8 ± 6.3 18.4 ± 3.0 10.0 ± 3.1 10.1 ± 2.2

26.1 ± 10.5 700 Meanpercent 701 transmittance(%) 702 703 53.3 ± 7.7 31.3 ± 1.5 25.0 ± 6.9 46.0 ± 6.9 29.0 ± 7.0 36.7 ± 4.7 105 ± 14.4 76.3 ± 19.1 49.0 ± 20.7 57.2 ± 14.8 134.3 ± 28.7 323.2 ± 68.5 157.7 ± 37.1 Mean height (cm) Meanheight 39.7 ± 4.1 54.2 ± 7.1 38.3 ± 6.3 56.2 ± 4.9 79.2 ± 27.5 64.7 ± 13.2 93.9 ± 16.6 115.0 ± 28.0 109.2 ± 19.2 181.5 ± 44.0 181.9 ± 54.1 201.3 ± 57.4 146.2 ± 42.4 Meandiameter (cm) 6 6 6 6 6 6 6 6 6 3 6 6 6 75 Samplesize Microhabitat type Microhabitat Desertshrub canopy Desertshrub canopy Desertshrub canopy Desertshrub canopy Desertshrub canopy Desertshrub canopy Desertsub-shrub canopy Desertyucca canopy Desertyucca canopy canopy Tropical canopy Tropical canopy Tropical canopy Tropical Unfilteredskylight Species Ambrosiadumosa Ephedranevadensis Krameriaspp. Larreatridentata spp. Lycium Psorothamnusfremontii Sphaeralceaambigua brevifolia Yucca schidigera Yucca Caladiumbicolor Ficuslyrata Perillahybrida Syngoniumpodophyllum

Table 2. Integrated mean absorptance for desert reptiles in unfiltered skylight compared 704 to canopy-filtered microhabitats (abbreviations given across top). For each reptile, 705 significant differences between canopy-filtered microhabitat absorptances and the 706 reference unfiltered skylight absoprtance (first column) are denoted with an 707 asterisk (P<0.05). Positive values indicate that the filtered microhabitat 708 absorptance is higher than the unfiltered skylight absorptance. Absorptance 709 differences greater than 5% are shaded. Plant species abbreviations are: Ambrosia 710 dumosa (AMDU); Ephedra nevadensis (EPNE); Krameria spp. (KRSP); Larrea 711 tridentata (LATR); Lycium spp. (LYSP); Psorothamnus fremontii (PSFR); 712 Sphaeralcea ambigua (SPAM); Yucca brevifolia (YUBR); Yucca schidigera 713 (YUSC), Caladium bicolor (CABI), Ficus lyrata (FILY), Perilla hybrid (PEHY), 714 and Syngonium podophyllum (SYPO). 715 716 717 718 - 6.9* - 3.4* - 6.6* - 1.1* - 2.0* - 0.8* - 8.3* - 2.8* - 5.8* - SYPO + 2.0* + 4.1* + 6.6* + 3.0* + - 2.6* - 719 720 - 5.8* - 2.7* - 5.3* - 2.2* - 1.0* - 1.4* - 0.6* - 6.8* - 2.3* - 4.9* - PEHY + 1.8* + 3.7* + 5.8* + 2.7* + 721 FILY - 6.2* - 2.8* - 5.8* - 2.4* - 1.2* - 1.6* - 0.7* - 7.2* - 2.5* - 5.3* -

+ 1.8* + 3.9* + 6.2* + 2.7* + 722 723 CABI - 6.0* - 2.8* - 5.7* - 2.4* - 1.0* - 1.6* - 0.8* - 6.9* - 2.4* - 4.9* - + 1.4* + 3.4* + 5.2* + 2.4* + 724 725 + 0.7 + 1.0 + 0.5 + 0.7 + 1.5 + 0.7 + 0.2 + 0.5 + 1.3 + 0.5 + 0.5 + YUSC + 0.8* + 0.7* + 0.7* + 0.0 0.0 - 0.5 - YUBR + 0.6* + 2.8* + 1.3* + 3.1* + 1.5* + 0.5* + 1.3* + 1.2* + 3.4* + 1.3* + 1.9* + - 0.3 - - 3.7* - 1.6* - 3.3* - 1.4* - 0.7* - 0.9* - 4.3* - 1.5* - 3.3* - SPAM + 1.3* + 2.5* + 4.0* + 1.7* + 0.0 +1.1 - 0.1 - 0.2 - + 0.2 + 0.4 + 0.1 + 0.4 + 0.4 + 0.5 + 0.2 + 0.1 + PSFR + 0.6* + 0.8* + - 1.2 - 0.7 - 1.0 - 0.5 - 0.1 - 0.4 - 0.1 - 1.5 - 0.5 - 1.1 - + 0.4 + 1.1 + LYSP + 0.7* + 0.6* + - 0.5 - 0.1 - LATR +1.5* - 2.5* - 1.0* - 2.1* - 0.9* - 0.5* - 2.9* - 1.1* - 2.5* - + 1.2* + 2.1* + 3.4* + 0.0 0.0 - 0.5 - 0.2 - 0.4 - 0.2 - 0.1 - 0.6 - 0.2 - 0.6 - + 0.3 + 0.6 + 0.9 + 0.4 + KRSP - 0.6 - 0.2 - - 2.5* - 1.2* - 2.2* - 1.0* - 0.4* - 3.0* - 1.1* - 2.4* - EPNE + 0.9* + 1.8* + 2.8* + 1.3* + Difference in absorptance between canopy-filtered microhabitats listed by species and unfiltered skylight (%) skylight speciesunfiltered by and microhabitatslisted Differenceabsorptancecanopy-filtered between in - 0.6 - 0.2 - - 2.3* - 1.1* - 2.1* - 0.9* - 0.4* - 2.8* - 1.0* - 2.2* - + 0.8* + 1.5* + 2.5* + 1.1* + AMDU 80.8 66.0 80.6 65.4 65.9 67.3 82.6 96.7 68.2 83.2 88.8 52.4 80.2 82.2 Integrated Nussear2000al., et c absorptance(%) Unfilteredskylight a b b a b b a c a b a b Porter, 1967; Porter, b b

a Desertspeciesreptile Norris,1967; Aspidoscelistigris Callisaurusdraconoides Crotaphytuscollaris Dipsosaurusdorsalis Holbrookiamaculata Sauromalusvarius Sceloporusmagister Sceloporusoccidentalis Umanotata Umascoparia Utastansburiana Crotaluscerastes Salvadorahexalepis Gopherusagassizii a Animal type Animal

Table 3. Minimum, maximum, and mean operative temperatures for a tortoise calculated 726 by integrating unfiltered skylight, tropical S. podophyllum, and desert yucca Y. brevifolia 727 spectra with tortoise carapace (animal) and paint absorptivity curves. Significant 728 differences within microhabitat are denoted with an asterisk and similar values are 729 grouped by like superscript. Values are ± one standard deviation. 730 731 732 733 Mean Absorptance Integrating Light Minimum Maximum Mean Microhabitat absorptance curve spectrum quantity temp (°C) temp (°C) temp (°C) (%) 13.3 ± 0.00*a 49.3 ± 0.00*a 37.4 ± 0.00*a Unfiltered skylight Animal Unfiltered skylight 82.2 ± 0.06 Full Paint Unfiltered skylight 83.2 ± 0.03 Full 13.4 ± 0.00*b 49.5 ± 0.00*b 37.6 ± 0.00*b Animal S. podophyllum 76.4 ± 0.72 Reduced 12.2 ± 0.00 29.3 ± 0.00*a 24.7 ± 0.00*a S. podophyllum Animal Unfiltered skylight 82.2 ± 0.06 Reduced 12.2 ± 0.00 29.4 ± 0.00*b 24.7 ± 0.00*a Paint S. podophyllum 85.0 ± 0.20 Reduced 12.2 ± 0.00 29.5 ± 0.00*c 24.5 ± 0.00*b Animal Y. brevifolia 84.0 ± 1.17 Reduced 12.1 ± 0.00 28.8 ± 0.00 24.3 ± 0.00 Y. brevifolia Animal Unfiltered skylight 82.2 ± 0.06 Reduced 12.1 ± 0.00 28.8 ± 0.00 24.3 ± 0.00 Paint Y. brevifolia 82.7 ± 0.35 Reduced 12.1 ± 0.00 28.8 ± 0.00 24.3 ± 0.00 734 735

FIGURE LEGENDS 736 737 Figure 1. Mean percent transmittance across the spectrum by microhabitat type (desert 738 shrub canopy solid black, desert sub-shrub canopy dashed black, desert yucca 739 canopy solid gray, and tropical canopy dotted gray). Light band ranges are shown 740 at the bottom (ultraviolet “UV”, visible “VIS”, near infrared “NIR”, and 741 shortwave infrared “SWIR”). 742 743 Figure 2. Nonmetric multi-dimensional scaling biplot illustrating how light energy is 744 partitioned among bands under different microhabitats. Filled circles are 745 unfiltered skylight, open squares are desert shrub species, filled squares are desert 746 sub-shrub species, filled triangles are desert yucca species, and open circles are 747 tropical species. Lines with arrows represent fitted vectors. 748 749 Figure 3. Mean absorptances for a tortoise (open circles) and a tortoise Te model (shaded 750 circles) calculated by integrating absorptivity curves of a tortoise carapace and 751 paint with microhabitat spectra. Significant difference in absorptance between 752 tortoise and Te model within a microhabitat type is indicated with an asterisk 753 (P<0.05). Error bars represent one standard deviation. 754 755

FIGURES 756 757 758 759 Figure 1. 760 761 762 763 764 765 766 30 Desert shrub canopy Desert sub-shrub canopy 25 Desert yucca canopy Tropical canopy

15 % Transmittance %Transmittance 10

UV VIS NIR SWIR 0 350 550 750 950 1150 1350 1550 1750 Wavelength (nm) 767 768 769 770

Figure 2. 771 772 773 774 775 776 777

Unfiltered skylight Desert shrub SWIR Desert sub-shrub Desert yucca 0.4 Tropical 0.2

VIS

0.0 NIR NMDS2 -0.2

-0.4 UV

-0.6 Stress=0.01

-0.4 -0.2 0.0 0.2 0.4 0.6 0.8

NMDS1 778 779 780 781 782 783

Figure 3. 784 785 786 787 788 789 790 86.00 *" *

* 84.00 *

82.00

80.00 % Absorptance %

78.00 Te Model Absorptance

Tortoise Carapace Absorptance

76.00 0" 1" 2" 3" 4" 5" 6" Unfiltered skylight Desert shrub Desert sub-shrub Desert yucca Tropical 791 792 793 794 795

CHAPTER 2. THERMAL QUALITY OF BURNED AND UNBURNED HABITAT 796

FOR THE DESERT TORTOISE (GOPHERUS AGASSIZII) 797

798

ABSTRACT 799

Recently, wildfires have burned extensive portions of the Mojave Desert, raising 800 concern about the effects of fire on desert tortoises (Gopherus agassizii) and their habitat. 801

The aim of this study was to quantify the thermal quality of burned and unburned habitat 802 to assess the mechanistic impact of fire on desert tortoise thermal biology. Perennial 803 cover was quantified for six years in burned and unburned tortoise habitat in southern 804

Nevada following a fire in 2005, and operative temperature models were deployed under 805 common shrub and open microhabitats during the season in which tortoises were active. 806

Data were used to calculate daily indices of thermal habitat quality, and the available 807 hours during which tortoises could achieve their preferred body temperature in burned 808 and unburned habitat. The indices were used to discern differences in thermal quality 809 among microhabitats. The thermal quality index was similar between burned and 810 unburned habitat, but unburned habitat was more thermally heterogeneous, and it 811 provided slightly longer activity times for tortoises within their preferred body 812 temperature range as long as they could access all thermal microhabitats in their home 813 range, and they were not constrained by habitat configuration or microhabitat abundance. 814

Several living and dead shrub species provided microhabitats of adequate thermal quality 815 to tortoises, including the post-disturbance colonizing species Sphaeralcea ambigua, 816 which provided relatively high coverage in burned habitat shortly after the fire. Our study 817 suggests that burned habitat with similar fire history and colonization patterns as our 818

40 study site should be regarded as valuable to desert tortoises and it should be protected as 819 suitable habitat that can be thermally exploited by tortoises with little additional 820 restoration effort. 821

822

1. INTRODUCTION 823

The Mojave population of the desert tortoise (Gopherus agassizii) was listed as 824 threatened in 1990 under the Endangered Species Act of 1973 after the observation that 825 desert tortoise populations were declining in many areas. Habitat loss, degradation, and 826 fragmentation were among the many concerns addressed in the Desert Tortoise Recovery 827

Plan (Fish and Wildlife Service 1994), and the listing as threatened led to the designation 828 of critical habitat for the species. In recent years, wildfires in the Mojave Desert have 829 become of increasing concern, as more desert tortoise habitat is being burned annually in 830 a system with historically infrequent and small-scale fires (Brooks 1999). In 2005, the 831

Southern Nevada fire complex burned over 750,000 acres, including approximately 832

400,000 acres of desert tortoise habitat and 65,183 acres of designated critical habitat (U. 833

S. Fish and Wildlife Service 2006). This area represented 132% of the total area burned 834 during the previous 25-year period (Brooks and Matchett 2006). The increase in fire 835 frequency, and size, can be at least partially attributed to the invasion of alien grasses, 836 which ignite quickly, encourage fire spread, and recover more readily after fire than does 837 native vegetation, setting into motion a grass/fire cycle (D’Antonio and Vitousek 1992). 838

Threats posed by fire include direct tortoise mortality, habitat fragmentation, and 839 alterations to the plant communities that make up both food and shelter for desert 840 tortoises (Fish and Wildlife Service 1994). Therefore, the observed increase in fire 841

41 intensity and severity may have several negative consequences for this species. Acute 842 mortality of adult tortoises has been documented (Esque et al. 2003, Lovich et al. 2011), 843 and could have population-level impacts for this long-lived species with low recruitment. 844

Additionally, burned habitat may be of lower nutritional and/or thermal quality than 845 unburned habitat (Esque et al. 2003, Lovich et al. 2011, Morafka and Berry 2002). 846

Tortoises with home ranges in burned habitat may be indirectly affected by fire if 847 reproductive output or body condition decreases as the result of habitat quality reduction 848

(Lovich et al. 2011). The aim of this study was to quantify the thermal quality of burned 849 and unburned habitat with respect to the desert tortoise, and to assess the mechanistic 850 impact of fire on desert tortoise thermal biology. 851

Thermal habitat quality can be described in terms of how well the habitat provides 852 physiologically suitable conditions for species (Huey 1991). Ectotherms interact with 853 their environment through behavioral, physiological, and morphological changes (Tracy 854

1982). The heat exchanged between an ectotherm and its environment determines the 855 ectotherm’s body temperature. In reptiles, heat is primarily exchanged through 856 convection, conduction, and the absorption or dissipation of radiant energy (Tracy 1982). 857

Many physiological processes, such as locomotor ability (e.g. Waldschmidt and Tracy 858

1983), digestive rate and efficiency, and metabolism, are temperature-dependent and can 859 influence an ectotherm’s ability to avoid predators, capture prey, and achieve energy 860 balance necessary for growth and reproduction (Huey 1991). Therefore, the body 861 temperatures achievable to an organism in its habitat can directly affect the fitness of that 862 organism (Christian and Tracy 1981, Jayne and Bennett 1990). To assess the thermal 863 quality of a habitat quantitatively one must integrate (a) the relationship between body 864

42 temperature and physiological performance for the study species, and (b) the distribution 865 of temperatures available to that species in the habitat. 866

The relationship between body temperature and a physiological performance can 867 be described using a physiological performance curve, which describes the relationship 868 between body temperature and performance, the body temperature (Tb) at which 869 performance is optimal (To), the breadth over which a performance can be performed at a 870 certain level (B), and critical thermal maximum (CTMax) and minimum (CTMin) beyond 871 which performance is unattainable (Anguilletta et al., 2002; Huey and Stevenson, 1979). 872

Within a species, performance curves may differ depending on the particular 873 physiological process (e.g. Stevenson 1985). Therefore, it is best to assess thermal habitat 874 quality in terms of the physiological process that is most likely to affect fitness for the 875 species of study. Unfortunately, relationships between individual physiological processes 876 and fitness are often unknown and difficult to ascertain. In these instances, preferred 877 body temperature (Tp) may be an appropriate substitute for To (Huey and Stevenson 878

1979), as a measure that reflects trade-offs in optimizing multiple physiological 879 processes, eg. digestive efficiency vs. passage rate (Dorcas et al. 1997). Evolutionary 880 theory predicts that the temperature an animal prefers should match the temperature that 881 confers the greatest fitness advantage such that Tp and To are likely coadapted, although 882 evidence is mixed (Angilletta et al. 2002), and Tp may vary with respect to ecological 883 context (Huey 1991). 884

The distribution of temperatures available to an organism in a habitat can be 885 determined by creating a thermal map of operative temperatures (Te) (Bakken 1992, 886

Bakken and Gates 1975). Te represents the steady-state body temperature of an organism 887

experiencing a given set of environmental conditions, and Te can be calculated 888 mathematically or estimated using physical models that replicate certain properties of an 889 organism (Bakken, 1992, Bakken et al. 1985, Tracy 1982). The use of physical models 890 permits Te to be recorded at fine spatial and temporal scales across a landscape with 891 limited instrumentation. Habitats can be characterized either by distributing models 892 randomly across a landscape (Diaz 1997), by capturing the most extreme microhabitats 893 available (Christian and Weaver 1996), or by stratifying sampling across various 894 microhabitat types (Grant and Dunham 1988, Waldschmidt and Tracy 1983) to capture 895 the range of Te available to an animal at any given time. Thermal maps are useful in 896 elucidating changes that occur in the thermal environment daily, seasonally, and annually 897

(Diaz and Cabezas-Diaz 2004). 898

Tracy and Christian (1986) used thermal mapping and physiological performance, 899 in the form of sprint rate, to develop a spatio-temporal index of home range quality. This 900 index is expressed as the product of the time (in hours) in which and area (in square 901 meters) over which an animal could achieve a range of optimal body temperatures 902

(weighted by performance) integrated across a day. The habitat quality index can be used 903 to determine the time and space proportions of a home range that are most thermally 904 suitable and to differentiate thermal home range quality among individuals or between 905 seasons (Tracy and Christian 1986, Waldschmidt and Tracy 1983). 906

Limitations of this index have been outlined elsewhere (see Bakken, 1992, Huey 907

1991). Perhaps, most problematic is that, by definition, the index must increase as a 908 function of home-range size, even if individuals with differing home range sizes have 909 home ranges of the same thermal quality (Huey 1991). However, this index can be altered 910

44 in such a way as to make it unitless (and instantaneous, if desired) (reported as a personal 911 communication with Grant 1988 in Huey 1991). In this modification, both space and time 912 are calculated as proportions yielding an index that represents the fractions of space and 913 fraction of time available for an animal to achieve particular body temperatures. A 914 proportional index is also useful because it is not limited to assessing the habitat quality 915 of animal home ranges, but it can be used to compare the thermal quality of habitats in 916 different portions of a species’ geographic range or among sites that have been exposed 917 to different environmental impacts or perturbations. 918

Here, we used the proportional modification of the home range quality index, as 919 well as an estimation of potential activity hours available, to compare the thermal quality 920 of burned and unburned habitat for the desert tortoise in the Mojave Desert. Thermal 921 resources above ground are limited for tortoises in the Mojave Desert, so modifications to 922 their thermal environment could change tortoise activity patterns or thermoregulation 923 precision. The desert tortoise possesses a unique suite of adaptations allowing it to 924 survive in a thermally restrictive environment. For example, tortoises use of their 925 carapace as a heat sink to buffer against temperature extremes (McGinnis and Voigt 926

1971), adopt seasonal activity patterns, thereby avoiding above-ground activity when 927 conditions are unfavorable and resources are scarce (Woodbury and Hardy 1948), and are 928 adept at digging burrows that they use in the winter as hibernacula, and in the summer 929 when operative surface temperatures surpass approximately 45°C (Zimmerman et al. 930

1994). Nevertheless, during the majority of the activity season when tortoises acquire 931 energy by foraging and participate in reproductive activities, surface temperatures can 932 reach lethally high limits for tortoises during midday (Zimmerman et al. 1994). Tortoises 933

45 are able to withstand periods of intense heat by seeking temporary refuge in burrows 934 below ground, and by using woody shrubs as shade shelters to protect them from intense 935 solar radiation (Bulova 2002, Nussear 2004, Zimmerman et al. 1994). 936

Perennial shrubs provide microhabitats where tortoises can escape thermal 937 extremes while expending less energy than would be necessary to dig a new burrow or to 938 return to a pre-existing burrow. Seeking thermal refuge under shrubs also allows tortoises 939 to be active above-ground and thus have access to food resources and social interaction 940

(Nagy and Medica 1986, Woodbury and Hardy 1948). Larger shrubs afford tortoises a 941 more stable thermal environment, which tortoises may exploit when temperatures in other 942 microhabitats exceed lethal limits, while temperatures beneath small shrubs fluctuate 943 more widely and offer thermal heterogeneity (Attum et al. 2013, Lagarde et al. 2012). 944

Both the hottest and coolest microhabitats available to tortoises above-ground are often 945 under the canopy of shrubs (Hillard 1996), thus shrubs are important resources for 946 thermoregulation. 947

In the Mojave, fires of high intensity burn perennial shrubs above, and sometimes 948 below ground (Esque et al. 2003), leaving a burned landscape denuded of living 949 vegetation and dominated by charred dead limbs and annuals (personal observation). 950

Mojave Desert perennials have low rates of resprouting (Abella 2009), and specific 951 recruitment requirements (Reyonolds et al. 2012) that inhibit plant recovery after fire. It 952 can take decades for vegetative cover to return to pre-fire levels and burned species 953 composition may never converge with unburned composition, creating a landscape 954 dominated by post-fire colonizers (Abella 2009). 955

The purpose of this study was to assess the impact of a burned landscape on the 956 thermoregulatory opportunities available for desert tortoises. Here, we address the 957 following questions: 958

1) Does the thermal quality differ between burned and unburned habitat 959

for the desert tortoise over the season in which tortoises are active? 960

2) Does the quantity of time during which tortoises can be active within 961

their preferred body temperature range (if all microhabitats are 962

accessible) differ between burned and unburned habitat? 963

3) Does the thermal quality index, or available activity hours, change 964

between one and six years post-fire as the perennial vegetative cover 965

undergoes secondary succession? 966

4) Which microhabitats are most thermally valuable for tortoises? 967

5) What are the conservation implications of these findings to the desert 968

tortoise, and how can burned habitat best be managed or restored to 969

meet the thermal needs of this species? 970

2. METHODS 971

2.1 Study Site 972

The study was conducted in Coyote Springs Valley in Southern Nevada, along 973

Interstate 93 approximately 20 miles northeast of Las Vegas. This site is within the 974

Mormon Mesa Desert Wildlife Area (Fish and Wildlife Service 1994). A portion of this 975 study site was burned as part of the Southern Nevada Fire complex in 2005 in three 976 separate fire events (Dry Middle, Dry Rock, and Garnet). In total, these fires burned over 977

5,700 acres. The site is located on a gently sloping bajada at an elevation of 800 to 900 m 978

47 and is characterized as Mojave Desert scrub habitat dominated by Larrea tridentata, 979

Ambrosia dumosa, and Yucca brevifolia. 980

981

2.2 Vegetation Surveys 982

Following the 2005 fire, eighteen 400 m x 400 m monitoring plots were 983 established, with nine plots located in burned habitat and nine plots located in adjacent 984 unburned habitat (see Scoles-Sciulla et al. 2011 for more specific details). Three 985 permanent 100-m line transects were established 10 m from the center of each monitoring 986 plot, and they were oriented at 60°, 80°, and 300° from north. Transect lines were used to 987 estimate perennial shrub cover by canopy intercept. Each perennial plant was identified 988 to species, or genus for taxa not easily identified to species (hereafter “taxa”), and 989 categorized as living or dead (hereafter “life class”). Most dead vegetation in the burned 990 habitat had succumbed to fire. Cover data from transects within each plot were pooled to 991 estimate cover composition. Percent cover was determined for each shrub taxa/life class 992 combination (hereafter “microhabitat”) in each burned and unburned plot. Only 993 combinations that comprised at least 1% total area of a landscape (burned or unburned) 994 were used in habitat quality and microhabitat analyses, which included nine living shrub 995 taxa, and five dead shrub taxa for a total of fourteen microhabitats. The proportion of the 996 site that was open area (with no canopy cover), total living cover, total dead cover, and 997 total combined cover were also calculated for each plot, and rounded to the nearest 998 percent. Perennial cover was estimated annually from 2006 to 2011. 999

Linear mixed effect models (Package nlme ver 2.1-111 in R 3.0.2) with Tukey’s 1000 post hoc comparisons (Package multcomp ver 1.2-21 in R 3.0.2) were used to explore 1001

48 differences in cover between burned and unburned habitat among years (Pinheiro and 1002

Bates 2000). We ran separate models for burned and unburned habitat, each of which 1003 included life class, year, and a life class by year interaction as fixed effects, plot as a 1004 random effect to account for repeated measures over years, and cover (arc square root 1005 transformed proportion of total area) as a response variable. A third model, which 1006 included habitat type, year, and a habitat type by year interaction as fixed effects, plot as 1007 a random effect, and combined cover as a response variable was used to differentiate 1008 changes in total cover among habitat types across time. 1009

1010

2.3 Operative temperature models 1011

We constructed physical Te models from metal mixing bowls, similar in size and 1012 shape to an adult desert tortoise (27 cm diameter), and painted to match the average 1013 integrated spectral absorptance of a tortoise carapace (84%) (Nussear et al. 2000). Models 1014 were enclosed on the bottom with a plastic dinner plate, and elevated 3.5 cm above 1015 ground with wooden “legs”. Placement of the temperature sensor in the model was 1016 determined by comparing temperatures obtained from thermocouples distributed 1017 throughout the inside of the model to operative temperatures calculated using a 1018 mathematical biophysical model parameterized with data from a nearby HOBO weather 1019 station, since temperature gradients may exist inside physical models (O’Connor et al. 1020

2000). Nine thermocouples were attached with epoxy to sites across the inner surface of 1021 the bowl, one thermocouple was suspended in the middle of the airspace within the 1022 model, and one thermocouple was attached to the plate on the bottom interior of the 1023 model. Temperatures obtained from the thermocouple attached to the plate most closely 1024

49 matched calculated operative temperature so this site was chosen for sensor placement. 1025

Each Te model was outfitted with an iButton temperature logger (Thermochron 1026

DS1921G, range -40°C to 85°C, accuracy ± 1°C, resolution 0.5°C) attached to the inside 1027 surface of the plate with the side closest to the thermistor oriented towards in the inside of 1028 the model. We configured and downloaded iButtons using Thermodata software (ver 1029

3.0). 1030

Te models were used to create a thermal map of burned and unburned habitat for 1031 the desert tortoise. Five shrub individuals were chosen at random from each of the 1032 fourteen microhabitats whose proportion of cover accounted for 1% or more of burned or 1033 unburned habitat. Greatest canopy width, width perpendicular to greatest canopy width, 1034 and height of main branch were measured for each individual. Shrubs covered the range 1035 of sizes used by tortoises as cover sites (unpublished data). We placed four Te models 1036 under each shrub, as close to the central stem as possible, at each of four cardinal 1037 directions (N,E,S,W) to capture the range of thermal environment afforded by the shrub 1038 at any one time. Ten models were also placed in the open, outside of shrub canopies, five 1039 each in burned and unburned habitat. A total of 290 tortoise models were distributed 1040 across the study site. We programmed iButtons to record a temperature every 30 minutes 1041 and downloaded them periodically. Models were deployed from April to October of 2011 1042 to create a thermal map of burned and unburned habitat and to compare the thermal 1043 characteristics of microhabitats over the season of tortoise activity. 1044

Te models were validated by comparing the average Tes of models in the open to 1045

Tes calculated from a biophysical model. Model calculations were made using data on air 1046 temperature, ground temperature, solar radiation, and wind speed obtained from nearby 1047

weather stations. Te was calculated hourly for 10% of sampling days selected randomly 1048

2 (18 days). Physical model Te and calculated Te (Figure 1) were highly correlated (r =0.97, 1049 y=0.94x + 2.63) and similar to model accuracy reported in other studies (Diaz, 1997; 1050

Lagarde et al., 2012). At lower temperatures, physical model temperatures are slightly 1051 higher than calculated temperatures. Over the range of temperatures applicable to this 1052 study (20°C to 45°C) the best-fit regression line never deviates from y=x by more than 1053

1.5°C (Figure 1). 1054

1055

2.4 Comparison of burned and unburned thermal habitat quality 1056

Thermal habitat quality index 1057

The home range thermal quality index derived by Tracy and Christian (1986) was 1058 transformed into a proportional index and further altered to address the questions in our 1059 study as summarized in the following equation: 1060

!"#$ ! !" � = �(�, �, �) �� !!!"#$ !!! !!! where I is the daily thermal habitat quality index expressed as a proportion between 0 and 1061

1, t is the time from dawn to dusk over which the index is integrated (representing times 1062 when tortoises are most active and differences between burned and unburned habitat is 1063 most likely), a is the proportion of each microhabitat available to the tortoise at any one 1064 time t (which is either 0 or 1 under any individual shrub), m is the microhabitat type (14 1065 shrub types plus open burned and open unburned microhabitats), and A is the proportion 1066 of each microhabitat that comprises a plot, as calculated from vegetation cover surveys. 1067

In this equation, a is a binary value determined by whether the temperature of a 1068 microhabitat falls within or outside an estimated preferred body temperature range (Tp- 1069 range) for the desert tortoise. In a temperature gradient experiment, Naegle (1976) found 1070 preferred body temperature to be 29.2°C for the desert tortoise and temperatures between 1071

24.6°C and 34.5°C accounted for 64.4% of all body temperatures recorded. In the field, 1072

Zimmerman et al. (1994), found that tortoises on the surface exhibited body temperatures 1073 that ranged from 25.8°C to 34.6°C with a median body temperature of 30.2°C, McGinnis 1074 and Voigt (1971) found that tortoise body temperature ranged from 28.8°C to 38.0°C 1075 with a mean of 32.3°C during a time of year when tortoises were more active, and 1076

Brattstrom (1965) found mean body temperature of active tortoises to be 30.6°C. 1077

Therefore, we chose 25°C to 35°C to represent the range of preferred body temperatures 1078 for the tortoise where activity above-ground is most likely. Instead of weighting a by a 1079 measure of physiological performance, a=1 when any Te under an individual shrub is 1080 between 25°C and 35°C (habitat is available for activity within Tp-range) and a=0 when all 1081

Tes under an individual shrub are below 25°C or above 35°C (habitat is not available for 1082 activity within Tp-range). At every time t, a was calculated for each shrub individual by 1083 determining the minimum and maximum Te available under that shrub (Te-range). If there 1084 was any overlap between Te-range and Tp-range, then a was set to 1 for that shrub individual. 1085

For each microhabitat type, a could range between 0 and 1 depending on the number of 1086 individual shrubs where Te-range and Tp-range overlapped. For instance, if 2 out of 5 shrubs 1087 were available within Tp-range, then a was set to 0.4 for that microhabitat type. 1088

I was calculated daily from April through October for each plot in burned and 1089 unburned habitat annually using Te data from 2011, and using data on vegetation cover 1090

from 2006 (1 year subsequent to fire) through 2011 (6 years since fire). Keeping Te 1091 constant and changing cover according to year allowed differences in the thermal 1092 environment, due to changes in perennial vegetation post-fire, to be discerned, without 1093 confounding effects with changes in yearly weather over the course of the study. 1094

Available activity hours 1095

We also calculated the number of hours a tortoise could be active daily in burned 1096 and unburned habitat between 25°C and 35°C, if the tortoise was resourceful and could 1097 access any microhabitat on the landscape that provided temperatures within Tp-range 1098 regardless of its abundance. This measure is not weighted by cover proportions, and thus, 1099 is only a measure of temporal suitability. It is calculated as follows in much the same 1100 manner as I: 1101

!"#$ !.! � = �� !!!"#$ !!!

1102 where H is the number of hours available to a tortoise within Tp-range each day, t is the 1103 time from dawn to dusk over which the index is integrated, and i is a binary expression of 1104 the daily thermal quality index at any one half hour timestep. If the daily thermal quality 1105 index is 0 at time t, i is 0, and if the daily thermal quality index is > 0 at time t, i is 0.5. H 1106 was calculated daily from April through October for each plot in burned and unburned 1107 habitat annually using Te data from 2011, and from data on vegetative cover from 2006 1108 through 2011. 1109

Statistical Analyses 1110

Linear mixed effect models (Package nlme ver 2.1-111 in R 3.0.2) were used to 1111 explore differences in thermal quality and available activity hours between burned and 1112 unburned habitat among years. Separate models were run for spring (April and May), 1113 summer (June, July, and August), and fall (September and October) (Figures 2 and 3). 1114

Spring and fall may be thermally limiting to tortoises because available temperatures fall 1115 below Tp-range while summer is thermally limiting to tortoises because available 1116 temperatures fall above Tp-range. Models included habitat type (burned or unburned), year, 1117 and a habitat type by year interaction as fixed effects, plot as a random effect to account 1118 for repeated measures over time, and either index (as a proportion between 0 and 1 arcsin 1119 square root transformed) or hours available as a response variable. Days with 1120 precipitation were very uncommon at the study site, and thus, were excluded from the 1121 analyses due to aberrant values. 1122

1123

2.5 Thermal characteristics of microhabitats 1124

Number of hours available in each of 14 shrub microhabitats and two open 1125 microhabitats were calculated for each day from April to October, using Te model data 1126 from 2011 to categorize individual microhabitats as falling within or outside of Tp-range. 1127

Linear mixed effect models with Tukey’s post hoc comparisons (Package multcomp ver 1128

1.2-21) were used to evaluate the thermal quality among microhabitat types during 1129 spring, summer, and fall. Comparisons were made among living shrub taxa (and open 1130 microhabitats) and among dead shrub taxa (and open microhabitats) in separate models 1131 that included microhabitat as a fixed effect, individual microhabitat as a random effect to 1132 account for repeated measures over time, and number of hours available within Tp-range as 1133

54 the response variable. To determine whether life class altered the thermal characteristics 1134 of shrubs, taxa with living and dead representatives were compared in individual models 1135 with life class as a fixed effect, individual shrub as a random effect, and number of hours 1136 available within Tp-range as the response variable. 1137

1138

3. RESULTS 1139

3.1 Vegetation Surveys 1140

Dead Larrea tridentata was the dominant cover taxon by area in burned habitat, 1141 with Sphaeralcea ambigua increasing in abundance by the end of the study while living 1142 and dead Ambrosia dumosa and living L. tridentata dominated unburned habitat (Table 1143

1). Taxa composition and percent cover varied greatly among plots of the same habitat 1144 type within year. Mean combined total cover in burned plots ranged from 9% to 12.7% of 1145 area (2008 and 2006, respectively) over the course of the study while mean cover in 1146 unburned plots was much higher, ranging from 23.9 to 30.6% of area (2008 and 2006, 1147 respectively) (Figure 4). The combined total cover was higher in unburned habitat (F1,16 = 1148

149.92, P < 0.01) and differed by year (F5,85 = 7.06, P < 0.01). Changes in total cover 1149 were correlated with annual precipitation (Figure 4) with cover being higher in 2006 than 1150 in all other years except 2011 and also higher in 2011 than in 2008 (P < 0.01). There was 1151 no interaction between habitat type and year so the interaction term was removed from 1152 the model to calculate F-statistics for main effects. 1153

In burned habitat, there was significantly more dead cover (11.1 to 6.6% more 1154 area annually) than living cover (F1,88 = 49.14, P < 0.01) and cover differed among years 1155

(F5,88 = 3.41, P < 0.01). In unburned habitat, there was significantly more living cover 1156

(25.2 to 19.7% more area annually) than dead cover (F1,88 = 258.85, P < 0.01).and cover 1157 was the same across years (F5,88 = 1.63, P = 0.16; Figure 5). An interaction between life 1158 class and year was observed for both habitat types. In burned plots, dead cover generally 1159 decreased while living cover generally increased with time since the fire in 2005 (F5,88 = 1160

4.70, P < 0.01). This is congruent with patterns observed for the most common taxa in 1161 burned plots, where dead L. tridentata cover decreased and living S. ambigua cover 1162 increased over time (Figure 6). In unburned plots, dead cover fluctuated annually while 1163 living cover decreased from 2006 to 2009 and increased from 2009 to 2011, with a net 1164 decrease in cover over time (F5,88 = 3.44, P < 0.01) which was less closely tied to patterns 1165 observed in dominant cover taxa (Figure 7). 1166

1167

3.2 Comparison of burned and unburned thermal habitat quality 1168

The interaction between habitat type and year was not significant in any of the 1169 models so the interaction term was removed from all models to calculate F-statistics for 1170 main effects. The calculated thermal habitat quality index was higher in unburned habitat 1171 in the spring (F1,16 = 7.49, P = 0.01; Figure 8) but did not differ between burned and 1172 unburned habitats in summer (F1,16 = 2.67, P = 0.12) or fall (F1,16 = 0.88, P = 0.36) or 1173 among years (spring F5,5377 = 0.49, P = 0.78; summer F5,8941= 0.17, P = 0.97; fall F5,4621 = 1174

0.10, P = 0.99; Figure 9). While index values were variable from day to day, indices 1175 varied little among plots within habitat type for a given day (Figures 8). Mean monthly 1176 index values averaged across years were highest in October (burned = 0.561, unburned = 1177

0.566) and lowest in August (burned = 0.204, unburned = 0.213) and indices were most 1178 different in April (unburned index 0.023 higher than burned index). Differences in daily 1179

56 indices between burned and unburned habitat were more variable in spring and fall and 1180 more stable during summer (Figure 10). On more days mean habitat quality index was 1181 higher in unburned habitat than in burned habitat (unburned = 111 days, burned = 65 1182 days, χ 2 = 12.02, P < 0.01). 1183

Number of activity hours available was higher in unburned habitat than in burned 1184 habitat during all seasons (spring F1,16 = 29.69, P < 0.01; summer F1,16 = 8.43, P = 0.01; 1185 fall F1,16 = 13.58, P < 0.01; Figure 11). Number of activity hours differed among years in 1186 summer (F5,8941 = 3.54, P < 0.01) but not in spring (F5,5377 = 0.49, P = 0.78) or fall 1187

(F5,4621= 1.27, P = 0.27). Difference in years was driven by a greater number of hours in 1188

2006 than in all other years (P < 0.05) except 2011 (P = 0.10; Figure 12). Mean available 1189 hours across years were highest in May (burned = 11.7, unburned = 12.0) and lowest in 1190

August (burned = 5.5, unburned = 6.3). The greatest monthly difference averaged across 1191 years occurred in July, during which time there was an average of 1.0 more hours 1192 available in unburned habitat than in burned habitat. Differences in daily hours available 1193 between burned and unburned habitat were highest in summer (Figure 13). On more days 1194 mean activity hours available was higher in unburned habitat than in burned habitat 1195

(unburned = 157 days, burned = 7 days, χ2 = 12.02, P < 0.01). 1196

1197

3.3 Thermal characteristics of microhabitats 1198

Among living taxa and open microhabitats, mean hours available within Tp-range 1199 differed in spring (F10,43 = 14.52, P < 0.01), summer (F10,44 = 14.52, P < 0.01), and fall 1200

(F10,43 = 23.85, P < 0.01). In the spring, the open microhabitats provided significantly 1201 fewer hours than all shrub microhabitats (between 1.33 and 3.06 fewer hours, P < 0.01; 1202

Table 2) while all shrub microhabitats provided similar thermal environments for 1203 tortoises except for Krameria sp. which provided significantly fewer hours of activity 1204 than Lycium sp. (difference= 1.45 hours, P < 0.01). In the summer, the open 1205 microhabitats provided significantly fewer hours than all shrub microhabitats (P < 0.01), 1206 except for open burned microhabitats that did not differ from Krameria sp. (P = 0.07; 1207

Table 3). Among shrub microhabitats in summer there was considerable variability. The 1208 thermal environments under Yucca species, Ephedra nevadensis and Psorothamnus 1209 fremontii were statistically similar and provided the most hours for tortoise activity (7.20 1210 to 6.72 hours) while Krameria sp. provided the fewest hours (4.80 hours). Fall patterns 1211 were similar to spring patterns. The open microhabitats provided significantly fewer 1212 hours than all shrub microhabitats (between 1.49 and 2.77 fewer hours, P < 0.01; Table 1213

4). Some differences in thermal quality were observed between the shrubs providing the 1214 most and fewest hours of activity for tortoises. Y. schidigera was the highest quality 1215 microhabitat (8.05 hours) and S. ambigua was the poorest quality microhabitat (7.06 1216 hours) but the difference in hours provided by these microhabitats was only 0.99 hours. 1217

Among dead taxa and open microhabitats, mean hours available within Tp-range 1218 differed in spring (F6,28 = 19.21, P < 0.01), summer (F6,28 = 32.62, P < 0.01), and fall 1219

(F6,425= 15.67, P < 0.01). In spring and fall, all shrub microhabitats were of similar 1220 thermal quality and provided more hours than open microhabitats (P < 0.01; Tables 5 and 1221

6). In summer, Y. brevifolia and Y. schidigera provided the same number of hours (6.21 1222 to 6.22; Table 7). A. dumosa, L. tridentata, and P. fremontii were statistically similar and 1223 provided fewer hours than the Yucca species. 1224

Dead and living shrubs of the same taxa provided similar thermal environments in 1225 the spring (Table 8) and fall (Table 9), except that living P. fremontii provided 1226 significantly more hours than dead P. fremontii in the fall (difference= 1.01 hours, P < 1227

0.01). In summer living A. dumosa, L. tridentata, and P. fremontii provided more hours 1228 for tortoises activity than did their dead counterparts (0.54 to 1.61 more hours) but living 1229 and dead Yucca species were of similar thermal quality (Table 10). When averaged 1230 across all seasons, within a species unburned shrubs tended to provide more hours than 1231 burned shrubs (Figure 15). For all microhabitats, hours available varied by month with 1232 highest values generally in May and lowest values in August, but patterns were variable 1233

(Figures 15 to 21). While hours available in open microhabitats were similar, open 1234 burned habitat provided slightly more activity time for tortoises than open unburned 1235 habitat (Figure 15). The most common microhabitats in unburned plots provided a wider 1236 range of mean hours available per month than did the most common microhabitats in 1237 burned plots (Figure 22). 1238

1239

4. DISCUSSION 1240

4.1 Vegetation Surveys 1241

Total perennial cover was positively correlated with annual precipitation recorded 1242 in rain gauges in the study plots the previous fall and winter. Quantity of precipitation 1243 during this time interval predicts perennial growth the following spring in the Mojave 1244

Desert (Beatley 1974) as does timing of precipitation events (Scoles-Sciulla et al. 2011). 1245

Total cover was greater in 2006 than in all other years during this study, which could be 1246 attributed to unusually high precipitation during 2004 and 2005, which stimulated growth 1247

59 of herbaceous tissues in perennial plants, as well as annual plants, fueling the 2005 1248

Southern Nevada fire complex (Brooks and Matchett 2006). A decrease in live 1249 vegetation cover in unburned plots, subsequent to 2006, may reflect the shedding of plant 1250 tissues when precipitation was not high enough to maintain growth. In burned plots, a 1251 decrease in dead cover could be due to deterioration of burned tissues following the fire. 1252

The primary colonizer of burned habitat was S. ambigua, which has been 1253 demonstrated to be more abundant after fire (Abella 2009) and other disturbances 1254

(Carpenter et al. 1986). An increase in S. ambigua was associated with a decrease in dead 1255

L. tridentata, the dominant living taxa in unburned habitat. Perennial cover and species 1256 composition in burned plots did not approach pre-burn conditions after six years, which is 1257 typical of arid systems with long-lived species adapted to infrequent and small scale 1258 disturbance (Abella 2009). 1259

1260

4.2 Comparison of the thermal environment in burned and unburned habitat 1261

We found that the thermal habitat quality index did not differ in response to 1262 yearly changes in annual perennial cover and was similar between burned and unburned 1263 habitat in summer and fall. Although total cover was significantly higher in unburned 1264 plots than in burned plots, and it changed over time, the open microhabitat dominated 1265 both burned and unburned areas (> 70% area). Thus, it was likely a driving force 1266 influencing the thermal habitat quality index. Open habitat was of slightly higher quality 1267 in burned habitat while all other microhabitats were of higher quality than the open 1268 microhabitats. It appears that a trade-off may exist between the proportion of open 1269 habitat, and the quality of open habitat that results in similar thermal quality indices 1270

60 between habitat types. We speculate that the difference in thermal quality in open habitats 1271 is a reflection of differing soil albedo or wind penetration within the matrix of vegetation 1272 in burned and unburned habitats. The thermal habitat quality index was slightly higher in 1273 unburned habitat than in burned habitat in the spring. In the spring, the thermal quality 1274 index is primarily constrained by operative temperatures below the preferred range. 1275

Higher proportion of perennial shrub cover and higher species diversity in unburned 1276 habitat may provide more warmer microhabitat sites for tortoises during times of the day 1277 when open microhabitats are too cool. There were more days during which the thermal 1278 habitat quality index was higher in unburned habitat than in burned habitat, but the mean 1279 difference was very small (0.032), and probably not biologically meaningful to a tortoise. 1280

We did find a difference in the number of hours available to tortoises within Tp- 1281 range between habitat types for all seasons. The greatest differences in activity hours 1282 between burned and unburned habitat were observed in summer, when activity is 1283 constrained by hot temperatures during much of the day. When averaged across the 1284 activity season, and across years, there were 0.6 more hours per day available to tortoises 1285 in unburned habitat than in burned habitat and the vast majority of days permitted 1286 tortoises to be active longer at preferred temperatures in unburned habitat, due to more 1287 heterogeneity in unburned plots. Available hours were based solely on microhabitat 1288 composition within plots, and calculations assumed that a tortoise could access any 1289 microhabitat at any time. Because species composition among plots varied, activity hours 1290 also varied widely depending on which combinations of plant taxa were present in a plot. 1291

During the summer when activity hours in burned and unburned plots were most 1292 different, the thermal environment also differed most among microhabitats and between 1293

61 burned and unburned shrubs of the same species. Any differences in cover composition 1294 among plots or habitat types would be reflected most during the summer season. More 1295 activity hours were available in 2006 than in all other years, which could be attributed to 1296 a change in plant composition between 2006 and 2007. It is possible this change in taxa is 1297 an artifact of sampling method, where shedding of herbaceous tissues due to a reduction 1298 in precipitation, caused shrubs to be smaller such that they no longer intersected with 1299 permanent line transects. If this were the case, shrubs that still existed in plots could have 1300 been unaccounted for with vegetation surveys, which would explain the correlation 1301 between activity hours and total cover. 1302

The thermal habitat quality index, and number of potential activity hours, in both 1303 habitats were highest in the spring and fall, corresponding to times when tortoises are 1304 most active, and lowest in summer when tortoise activity is restricted by lethally-high 1305 mid-day temperatures (Zimmerman et al. 1994). Our data are similar to operative 1306 temperature data published by Zimmerman et al. (1994), however, according to our index 1307 and activity hours, using 2011 temperature data, the thermal environment was most 1308 restrictive in August while their models indicated greatest activity restriction in July. 1309

Interestingly, potential advantage in activity hours conferred to tortoises using unburned 1310 habitat was greatest in July, the second most thermally restrictive month, during which 1311 time tortoises could be active, on average, 8.5 hours, if they used unburned habitat, 1312 compared to 7.5 hours if they used burned habitat. It is important to note that the 1313 calculation of the available activity hours does not take into account the rarity and 1314 distribution of microhabitats on the landscape and assumes that a tortoise is able to travel 1315 among each microhabitat as it becomes thermally available in a plot. If tortoises are able 1316

62 to satisfy these assumptions, then tortoises in unburned habitat could be active above 1317 ground longer, especially during thermally restrictive seasons. Because the thermal 1318 quality index is similar between burned and unburned habitats during the activity season 1319 for desert tortoises, but hours of activity differ, it is likely that thermal refugia that extend 1320 activity hours in unburned habitat are relatively rare on the landscape. 1321

It is unknown whether an increase in activity time on such a scale would be of any 1322 consequence to desert tortoises. Tortoises spend a large portion of their lives in burrows 1323

(Bulova 1994, Nagy and Medica 1986, Woodbury and Hardy 1948) and above-ground 1324 activity is difficult to predict, even when environmental variables that may influence 1325 activity are accounted for (Nussear and Tracy, 2007). When tortoises are most active they 1326 may only spend three hours out of their burrows and only emerge every four days (Nagy 1327 and Medica 1986). Our data show that even during the most inhospitable times of the 1328 activity season, tortoises would have the opportunity to be active on most days for at least 1329 three hours as long as they had access to some type of shrub cover in burned or unburned 1330 habitat. During the spring and fall tortoises could exclusively use open microhabitat for 1331 much longer periods of activity. Esque (1994) observed feeding bouts lasting over five 1332 hours, but mean length of a feeding bout was never greater than 75 minutes while Nagy 1333 and Medica (1986) observed feeding bouts lasting only 0.57 hours in duration. Therefore, 1334 it is unlikely that fewer hours available in burned habitat would be a limiting factor in 1335 determining foraging time and energy acquisition. Additionally, to take advantage of 1336 more activity hours in unburned habitat, tortoises would have to expend energy to locate 1337 potentially rare microhabitats that offer suitable temperatures and not necessarily optimal 1338 foraging opportunities (Tracy et al. 2006). Remaining below-ground in burrows is often 1339

63 less energetically expensive, and limits evaporative water loss in desert tortoises by 1340 reducing metabolic rates (Naegle 1976). 1341

1342

4.3 Thermal characteristics of microhabitats 1343

Shrub microhabitats provided tortoises with significantly more potential activity 1344 time within Tp-range than open habitat during all seasons, and on average, extended activity 1345 time by between one and three hours. In spring and fall, most shrub species provided 1346 similar thermal environments, regardless of whether they were living or dead. However, 1347 during summer the thermal quality of shrub microhabitats varied among living and dead 1348 shrubs of different taxa and between living and dead shrubs of the same taxa. In the 1349 spring, Lycium sp. provided the most activity hours and but the advantage conferred by 1350 this species deteriorated in summer and fall. Likewise, S. ambigua provided a mediocre 1351 thermal environment in the spring and summer, and provided the fewest activity hours of 1352 any shrub microhabitat in the fall. These changes in thermal quality are likely due to 1353 changes in foliage cover throughout the year. Foliage in Lycium sp. is reduced after the 1354 spring and S. ambigua loses virtually all of its leaves by the fall. 1355

Among living taxa, Yucca species, E. nevadensis, and P. fremontii, provided the 1356 best microhabitats for tortoises, especially in the summer,. Shrub microhabitats that 1357 transmit less light through their canopies tended to be of higher thermal quality (Chapter 1358

1). Previous studies have found that tortoises used E. nevadensis and Y. schidigera as 1359 shade resources disproportionately more frequency than their presence on the landscape, 1360 and they avoided using the shade resources of some common plant taxa, such as A. 1361 dumosa (Nussear 2004), that we found to be of lower thermal quality. L. tridentata is the 1362

64 most common taxon at the Coyote Springs site and highly associated with desert tortoise 1363 habitat (Fish and Wildlife Service 1994), but that shrub species was found to be a 1364 relatively poor thermal refuge for tortoises when compared to the microhabitats afforded 1365 by other shrub species. Among dead taxa, Yucca species were the most valuable thermal 1366 resources for tortoises and were of similar thermal quality to their living counterparts. 1367

While shrub microhabitats were scarcer (and often dead) in burned habitat, 1368 several that were present provided shelter of only slightly lower thermal quality than 1369 shrubs found in unburned habitat. However, when we considered only the most common 1370 taxa, we found that shrub microhabitats in unburned habitat were more thermally 1371 heterogenous, and could be exploited by tortoises to achieve Tp-range more efficiently, and 1372 for longer periods of time, than was possible by shrub microhabitats in burned habitat. Of 1373 particular note, was the aggressive colonization of S. ambigua as a thermal resource in 1374 burned habitat. Typically, this species is a perennial herb reaching only 50 to 100 cm in 1375 height (Jepson Flora Project 2013), and it is a preferred food plant of the desert tortoise 1376

(Esque 1994). However, in burned habitat at the Coyote Springs site, this species reached 1377 heights of 120 cm, and grew to over 200 cm in diameter and was frequently used as a 1378 cover site by tortoises (personal observation). We found that S. ambigua was of similar 1379 thermal quality to mediocre thermal microhabitats found in unburned areas during the 1380 thermally limiting summer season, and could serve as an interim resource during 1381 vegetative recovery in as few as two years after fire. In four years, S. ambigua covered 1382 the same percentage of area in burned habitat as the top five thermal microhabitats 1383 combined in unburned habitat. 1384

1385

4.4 Conservation implications 1386

Reptile responses to fire vary, with evidence suggesting that the abundance of 1387 some species does not change or increases following fire (Nicholson et al. 2006) while 1388 the abundance of other species decreases (Sanz-Augilar et al. 2011), even among 1389 sympatric taxa (Webb and Shine 2008). Still other species may be associated with 1390 particular successional stages, and fluctuate in response to vegetation changes (Smith et 1391 al. 2012). For tortoises, effects of fire on survival and persistence are equally mixed and 1392 vary by species. Testudo hermanni hermanni appears to suffer high rates of direct 1393 mortality with fire (Hailey 2000), and has decreased survival probability in burned 1394 habitats (Couterier et al. 2011). Indeed, this species has a high probability of movement 1395 from burned to unburned habitat immediately following fire (Couterier et al. 2011). 1396

Testudo gracea exhibits direct and delayed reductions in survival following fire, which 1397 likely leads to population crashes with short fire-return intervals (Sanz-Aguillar et al. 1398

2010). Conversely, Gopherus polyphemus is more abundant in frequently burned 1399 landscapes, preferring the open habitat structure created by fires (Ashton et al. 2008, 1400

McCoy et al. 2013). Direct mortality has been documented in G. agassizii, potentially 1401 reducing populations by as much as 11% (Esque et al. 2003). However, Lovich (2011) 1402 found that reproductive output was similar between G. agassizii using burned and 1403 unburned habitat. 1404

Impacts of fire on reptiles are typically assessed using direct measurements of 1405 mortality and survivorship (Esque et al. 2003, Hailey, 2000), or through correlational 1406 studies that examine animal presence, or abundance, with fire driven habitat 1407 modifications, including changes in vegetation composition, canopy structure, substrate, 1408

66 and shelter abundance (Lindenmayer et al. 2008, Santos and Poquet 2010). Here, we 1409 assessed the impact of fire on desert tortoises by examining the mechanistic relationship 1410 between habitat composition and thermal quality. We found that the thermal quality 1411 index, which takes into account the proportion of habitat that would allow tortoises to 1412 reach body temperature within a preferred range, was similar between burned and 1413 unburned habitat, but that unburned habitat was more thermally heterogeneous and 1414 provided an average of 0.6 more hours daily that tortoises could be active at body 1415 temperatures within Tp-range, if they could access all microhabitats available in their home 1416 range, and they were not constrained by habitat configuration or microhabitat abundance. 1417

A difference of this small magnitude is likely imperceptible to a tortoise, and it would not 1418 be physiologically limiting because tortoises are often inactive in burrows even when 1419 conditions would permit optimal activity (Nagy and Medica, 1986, Nussear and Tracy 1420

2007). This assumes that tortoises could seek refuge in underground burrows of similar 1421 thermal quality in both habitats. Additionally, we discovered that the aggressive 1422 colonization of S. ambigua, which grew to provide surprisingly high vegetative cover, 1423 provided a remarkably good replacement of shade resources in burned habitats, and that 1424 several other species provided microhabitats that were of fairly high thermal quality in 1425 burned habitat shortly after the fire. 1426

Our study suggests that burned habitat should be protected as suitable thermal 1427 habitat for the desert tortoise, and burned areas can be thermally exploited by tortoises 1428 with little additional restoration effort. If burned areas are seeded with perennial species, 1429 good long-term choices for thermal cover are Y. brevifolia, Y schidigera, E. nevadensis, 1430 and P. fremontii, although all shrub species, even dead individuals, provide some amount 1431

67 of thermal protection for tortoises and most shrub microhabitats provide similar thermal 1432 protection during the spring and fall. Shrub microhabitats permit tortoises to achieve 1433 body temperatures within Tp-range for longer periods of time per day, thereby optimizing 1434 physiological performance with potential consequences to fitness. Our findings support 1435 those of Lovich et al. (2011), who found that desert tortoises remained on burned habitat 1436 fifteen years after fire with no effect on reproductive output or body condition. They also 1437 observed tortoises in burned habitat shifted cover use as perennial composition changed 1438 with time. 1439

While burned habitat does not appear to be thermally limiting to tortoises, fire still 1440 may still pose a considerable threat to this species by directly impacting population size 1441 and growth (Esque et al. 2003). Additionally, annual plants and grasses that serve as food 1442 resources for tortoises often change in abundance and species composition following fire 1443

(Scoles-Sciulla,et al. 2011). Annual plants may attain greater biomass in burned habitat 1444

(Scoles-Sciulla,et al. 2011) but the community of annuals may be different from that in 1445 unburned habitat. Some species of non-native annual may establish more quickly 1446 following fire and outcompete native species by having seed banks that escape lethal 1447 temperatures during fires (Esque, 2004). Non-native grasses can capitalize on increased 1448 soil nitrogen following fire during wet years and reach high levels of production (Esque 1449

2004). Shifts from diets dominated by forbs to diets dominated by grass could result in 1450 deficiencies of some nutrients for tortoises (Hazard et al. 2010). However, the effects of 1451 non-native and native diets on tortoise fitness have not been well studied. 1452

It is necessary to exercise caution in generalizing these results to tortoise 1453 populations in other parts of the species range. This study only investigated the thermal 1454

68 quality of burned and unburned habitat for one tortoise population as a result of a single 1455 fire season. Habitats with dissimilar perennial composition and coverage and fires of 1456 different frequency and severity might produce different results. Elzer et al. (2013) found 1457 differences in the amount of time snakes could be active within Tp-range based on fire 1458 frequency and canopy openness in one region, but they did not find a relationship 1459 between fire and thermal quality in another nearby region. Although we predict that 1460 operative temperature data from other years would reveal similar patterns, all thermal 1461 quality measures were extrapolated from the 2011 season. This study was also limited to 1462 inferring habitat quality based solely on the thermal environment above-ground, and did 1463 not address aspects of the thermal environment below-ground, the influence of perennial 1464 cover changes on predation, or the abundance of other important resources, such as cover 1465 and composition of annual plants in burned and unburned habitat. Further investigation is 1466 needed to understand comprehensively the impact of fire on desert tortoise habitat. 1467

1468

ACKNOWLEDGMENTS 1469

We would like to thank the many SCA interns and USGS employees that helped collect 1470 massive amounts of field data. We thank Dick Tracy, Ken Nussear, Lesley DeFalco, 1471

Lynn Zimmerman and Peter Weisberg for helpful discussion and comments on an earlier 1472 draft of the manuscript. This project was supported by Coyote Springs Investment, LLC. 1473

1474

1475

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1643

TABLES 1644 1645 1646 1647 Table 1. Mean percent cover (± one standard deviation) by taxa in burned and unburned 1648 habitat during 2006 and 2011. Taxa abbreviations are: Ambrosia dumosa 1649 (AMDU); Ephedra nevadensis (EPNE); Krameria sp. (KRSP); Larrea tridentata 1650 (LATR); Lycium sp. (LYSP); Psorothamnus fremontii (PSFR); Sphaeralcea 1651 ambigua (SPAM); Yucca brevifolia (YUBR); Yucca schidigera (YUSC). 1652 1653 1654 1655

Burned-2006 Unburned- 2006 Burned- 2011 Unburned- 2011 AMDU- Dead 0.57 ± 0.56 4.99 ± 2.16 0.06 ± 0.11 2.23 ± 1.62 AMDU- Living 0.41 ± 0.42 6.72 ± 3.91 0.53 ± 0.60 7.12 ± 3.53 EPNE- Living 0.11 ± 0.18 1.12 ± 0.76 0.08 ± 0.11 1.48 ± 1.13 KRSP-Living 0.04 ± 0.07 1.80 ± 1.36 0.00 ± 0.00 0.90 ± 0.79 LATR- Dead 9.53 ± 3.27 0.29 ± 0.27 6.81 ± 3.12 1.68 ± 0.79 LATR- Living 0.70 ± 0.74 12.91 ± 5.39 0.22 ± 0.33 10.18 ± 4.82 LYSP- Living 0.01 ± 0.03 1.13 ± 0.99 0.06 ± 0.09 0.98 ± 0.68 PSFR- Dead 0.59 ± 0.83 0.04 ± 0.09 0.23 ± 0.39 0.07 ± 0.13 PSFR- Living 0.24 ± 0.49 0.89 ± 1.03 0.26 ± 0.46 0.63 ± 0.86 SPAM- Living 0.03 ± 0.07 0.00 ± 0.00 1.71 ± 1.55 0.00 ± 0.00 YUBR- Dead 0.04 ± 0.10 0.02 ± 0.07 0.03 ± 0.05 0.00 ± 0.00 YUBR- Living 0.06 ± 0.17 0.11 ± 0.21 0.00 ± 0.00 0.17 ± 0.43 YUSC- Dead 0.44 ± 0.61 0.11 ± 0.12 0.20 ± 0.15 0.30 ± 0.51 YUSC-Living 0.13 ± 0.26 0.62 ± 0.72 0.07 ± 0.11 0.47 ± 0.49 OPEN- Burned 87.09 ± 3.28 ----- 89.76 ± 3.73 ----- OPEN- Unburned ----- 69.26 ± 4.23 ----- 73.74 ± 5.34 1656

1657

Table 2. Comparison of living taxa and open microhabitat sites during spring. Mean 1658 number of hours a tortoise could be active daily within Tp-range is listed across the 1659 top under each microhabitat type. Values in matrix are the mean of the row 1660 subtracted from the mean of the column so that positive values indicate an 1661 advantage in mean number of hours available in the microhabitat in the column 1662 as compared to that of the row and vice versa for negative values. Significant 1663 differences (P<0.05) are denoted with an asterisk. Microhabitat abbreviations 1664 are: Ambrosia dumosa (AMDU); Ephedra nevadensis (EPNE); Krameria sp. 1665 (KRSP); Larrea tridentata (LATR); Lycium sp. (LYSP); Psorothamnus fremontii 1666 (PSFR); Sphaeralcea ambigua (SPAM); Yucca brevifolia (YUBR); Yucca 1667 schidigera (YUSC). 1668 1669 1670 1671 1672 AMDU EPNE KRSP LATR LYSP PSFR SPAM YUBR YUSC OPEN OPEN 1673 Burned Unburned Average hours 9.03 8.99 7.72 8.30 9.17 8.5 8.51 8.04 8.68 6.39 6.11 1674 AMDU 0 -0.04 -1.31 -0.73 0.14 -0.53 -0.52 -0.99 -0.35 -2.64* -2.92* EPNE 0.04 0 -1.27 -0.69 0.18 -0.49 -0.48 -0.95 -0.31 -2.60* -2.88* 1675 KRSP 1.31 1.27 0 0.58 1.45* 0.78 0.79 0.32 0.96 -1.33* -1.61* LATR 0.73 0.69 -0.58 0 0.87 0.20 0.21 -0.26 0.38 -1.91* -2.19* 1676 LYSP -0.14 -0.18 -1.45* -0.87 0 -0.67 -0.66 -1.13 -0.49 -2.78* -3.06* PSFR 0.53 0.49 -0.78 -0.20 0.67 0 0.01 -0.46 0.18 -2.11* -2.39* 1677 SPAM 0.52 0.48 -0.79 -0.21 0.66 -0.01 0 -0.47 0.17 -2.12* -2.40* 1678 YUBR 0.99 0.95 -0.32 0.26 1.13 0.46 0.47 0 0.64 -1.65* -1.93* YUSC 0.35 0.31 -0.96 -0.38 0.49 -0.18 -0.17 -0.64 0 -2.29* -2.57* 1679 OPEN-Burned 2.64* 2.60* 1.33* 1.91* 2.78* 2.11* 2.12* 1.65* 2.29* 0 -0.28 OPEN-Unburned 2.92* 2.88* 1.61* 2.19* 3.06* 2.39* 2.40* 1.93* 2.57* 0.28 0 1680

1681

1682

1683

1684

1685

1686

1687

Table 3. Comparison of living taxa and open microhabitat sites during summer. Mean 1688 number of hours a tortoise could be active daily within Tp-range is listed across the 1689 top under each microhabitat type. Values in matrix are the mean of the row 1690 subtracted from the mean of the column so that positive values indicate an 1691 advantage in mean number of hours available in the microhabitat in the column 1692 as compared to that of the row and vice versa for negative values. Significant 1693 differences (P<0.05) are denoted with an asterisk. Microhabitat abbreviations 1694 are: Ambrosia dumosa (AMDU); Ephedra nevadensis (EPNE); Krameria sp. 1695 (KRSP); Larrea tridentata (LATR); Lycium sp. (LYSP); Psorothamnus fremontii 1696 (PSFR); Sphaeralcea ambigua (SPAM); Yucca brevifolia (YUBR); Yucca 1697 schidigera (YUSC). 1698 1699

1700

AMDU EPNE KRSP LATR LYSP PSFR SPAM YUBR YUSC OPEN OPEN Burned Unburned Average hours 5.68 6.86 4.80 5.19 5.56 6.72 5.81 7.2 6.87 3.87 3.44 AMDU 0 1.18* -0.88 -0.49 -0.12 1.04* 0.13 1.52* 1.19* -1.81* -2.24* EPNE -1.18 0 -2.06* -1.67* -1.30* -0.14 -1.05* 0.34 0.01 -2.99* -3.42* KRSP 0.88 2.06* 0 0.39 0.76 1.92 1.01* 2.40* 2.07* -0.93 -1.36* LATR 0.49 1.67* -0.39 0 0.37 1.53* 0.62 2.01* 1.68* -1.32* -1.75* LYSP 0.12 1.30* -0.76 -0.37 0 1.16* 0.25 1.64* 1.31* -1.69* -2.12* PSFR -1.04* 0.14 -1.92 -1.53* -1.16* 0 -0.91 0.48 0.15 -2.85* -3.28* SPAM -0.13 1.05* -1.01* -0.62 -0.25 0.91 0 1.39* 1.06* -1.94* -2.37* YUBR -1.52* -0.34 -2.40* -2.01* -1.64* -0.48 -1.39* 0 -0.33 -3.33* -3.76* YUSC -1.19* -0.01 -2.07* -1.68* -1.31* -0.15 -1.06* 0.33 0 -3.00* -3.43* OPEN-Burned 1.81* 2.99* 0.93 1.32* 1.69* 2.85* 1.94* 3.33* 3.00* 0 -0.43 OPEN-Unburned 2.24* 3.42* 1.36* 1.75* 2.12* 3.28* 2.37* 3.76* 3.43* 0.43 0 1701

1702

1703

1704

1705

1706

1707

Table 4. Comparison of living taxa and open microhabitat sites during fall. Mean number 1708 of hours a tortoise could be active daily from within Tp-range is listed across the 1709 top under each microhabitat type. Values in matrix are the mean of the row 1710 subtracted from the mean of the column so that positive values indicate an 1711 advantage in mean number of hours available in the microhabitat in the column 1712 as compared to that of the row and vice versa for negative values. Significant 1713 differences (P<0.05) are denoted with an asterisk. Microhabitat abbreviations 1714 are: Ambrosia dumosa (AMDU); Ephedra nevadensis (EPNE); Krameria sp. 1715 (KRSP); Larrea tridentata (LATR); Lycium sp. (LYSP); Psorothamnus fremontii 1716 (PSFR); Sphaeralcea ambigua (SPAM); Yucca brevifolia (YUBR); Yucca 1717 schidigera (YUSC). 1718 1719

1720

AMDU EPNE KRSP LATR LYSP PSFR SPAM YUBR YUSC OPEN OPEN Burned Unburned Average hours 7.71 8.19 7.12 7.34 7.70 8.00 7.06 7.89 8.05 5.57 5.28 AMDU 0 0.48 -0.59 -0.37 -0.01 0.29 -0.65 0.18 0.34 -2.14* -2.43* EPNE -0.48 0 -1.07* -0.85 -0.49 -0.19 -1.13* -0.30 -0.14 -2.62* -2.91* KRSP 0.59 1.07* 0 0.22 0.58 0.88* -0.06 0.77 0.93* -1.55* -1.84* LATR 0.37 0.85 -0.22 0 0.36 0.66 -0.28 0.55 0.71 -1.77* -2.06* LYSP 0.01 0.49 -0.58 -0.36 0 0.30 -0.64 0.19 0.35 -2.13* -2.42* PSFR -0.29 0.19 -0.88* -0.66 -0.30 0 -0.94* -0.11 0.05 -2.43* -2.72* SPAM 0.65 1.13* 0.06 0.28 0.64 0.94* 0 0.83 0.99* -1.49* -1.78* YUBR -0.18 0.30 -0.77 -0.55 -0.19 0.11 -0.83 0 0.16 -2.32* -2.61* YUSC -0.34 0.14 -0.93* -0.71 -0.35 -0.05 -0.99* -0.16 0 -2.48* -2.77* OPEN-Burned 2.14* 2.62* 1.55* 1.77* 2.13* 2.43* 1.49* 2.32* 2.48* 0 -0.29 OPEN-Unburned 2.43* 2.91* 1.84* 2.06* 2.42* 2.72* 1.78* 2.61* 2.77* 0.29 0

1721

1722

1723

1724

1725

1726

1727

Table 5. Comparison of dead taxa and open microhabitat sites during spring. Mean 1728 number of hours a tortoise could be active daily within Tp-range is listed across the 1729 top under each microhabitat type. Values in matrix are the mean of the row 1730 subtracted from the mean of the column so that positive values indicate an 1731 advantage in mean number of hours available in the microhabitat in the column 1732 as compared to that of the row and vice versa for negative values. Significant 1733 differences (P<0.05) are denoted with an asterisk. Microhabitat abbreviations 1734 are: Ambrosia dumosa (AMDU); Ephedra nevadensis (EPNE); Krameria sp. 1735 (KRSP); Larrea tridentata (LATR); Lycium sp. (LYSP); Psorothamnus fremontii 1736 (PSFR); Sphaeralcea ambigua (SPAM); Yucca brevifolia (YUBR); Yucca 1737 schidigera (YUSC). 1738 1739

1740

1741

AMDU LATR PSFR YUBR YUSC OPEN OPEN Burned Unburned Average hours 7.83 7.83 8.21 8.19 8.35 6.39 6.11 AMDU 0 0 0.38 0.36 0.52 -1.44* -1.72* LATR 0 0 0.38 0.36 0.52 -1.44* -1.72* PSFR -0.38 -0.38 0 -0.02 0.14 -1.82* -2.10* YUBR -0.36 -0.36 0.02 0 0.16 -1.80* -2.08* YUSC -0.52 -0.52 -0.14 -0.16 0 -1.96* -2.24* OPEN-Burned 1.44* 1.44* 1.82* 1.80* 1.96* 0 -0.28 OPEN-Unburned 1.72* 1.72* 2.10* 2.08* 2.24* 0.28 0

Table 6. Comparison of dead taxa and open microhabitat sites during fall. Mean number 1742 of hours a tortoise could be active daily within Tp-range is listed across the top 1743 under each microhabitat type. Values in matrix are the mean of the row 1744 subtracted from the mean of the column so that positive values indicate an 1745 advantage in mean number of hours available in the microhabitat in the column 1746 as compared to that of the row and vice versa for negative values. Significant 1747 differences (P<0.05) are denoted with an asterisk. Microhabitat abbreviations 1748 are: Ambrosia dumosa (AMDU); Ephedra nevadensis (EPNE); Krameria sp. 1749 (KRSP); Larrea tridentata (LATR); Lycium sp. (LYSP); Psorothamnus fremontii 1750 (PSFR); Sphaeralcea ambigua (SPAM); Yucca brevifolia (YUBR); Yucca 1751 schidigera (YUSC). 1752 1753

1754

AMDU LATR PSFR YUBR YUSC OPEN OPEN Burned Unburned Average hours 7.12 6.82 6.99 7.03 7.35 5.57 5.28 AMDU 0 -0.30 -0.13 -0.09 0.23 -1.55* -1.84* LATR 0.30 0 0.17 0.21 0.53 -1.25* -1.54* PSFR 0.13 -0.17 0 0.04 0.36 -1.42* -1.71* YUBR 0.09 -0.21 -0.04 0 0.32 -1.46* -1.75* YUSC -0.23 -0.53 -0.36 -0.32 0 -1.78* -2.07* OPEN-Burned 1.55* 1.25* 1.42* 1.46* 1.78* 0 -0.29 OPEN-Unburned 1.84* 1.54* 1.71* 1.75* 2.07* 0.29 0

Table 7. Comparison of dead taxa and open microhabitat sites during summer. Mean 1755 number of hours a tortoise could be active daily within Tp-range is listed across the 1756 top under each microhabitat type. Values in matrix are the mean of the row 1757 subtracted from the mean of the column so that positive values indicate an 1758 advantage in mean number of hours available in the microhabitat in the column 1759 as compared to that of the row and vice versa for negative values. Significant 1760 differences (P<0.05) are denoted with an asterisk. Microhabitat abbreviations 1761 are: Ambrosia dumosa (AMDU); Ephedra nevadensis (EPNE); Krameria sp. 1762 (KRSP); Larrea tridentata (LATR); Lycium sp. (LYSP); Psorothamnus fremontii 1763 (PSFR); Sphaeralcea ambigua (SPAM); Yucca brevifolia (YUBR); Yucca 1764 schidigera (YUSC). 1765 1766 1767

1768

AMDU LATR PSFR YUBR YUSC OPEN OPEN 1769 Burned Unburned Average hours 4.60 4.65 5.11 6.21 6.22 3.87 3.45 AMDU 0 0.05 0.51 1.61* 1.62* -0.73 -1.15* LATR -0.05 0 0.46 1.56* 1.57* -0.78* -1.20* PSFR -0.51 -0.46 0 1.10* 1.11* -1.24* -1.66* YUBR -1.61* -1.56* -1.10* 0 0.01 -2.34* -2.76* YUSC -1.62* -1.57* -1.11* -0.01 0 -2.35* -2.77* OPEN-Burned 0.73 0.78* 1.24* 2.34* 2.35* 0 -0.42 OPEN-Unburned 1.15* 1.20* 1.66* 2.76* 2.77* 0.42 0

Table 8. Comparison of living and dead shrubs within taxa during spring with mean 1770 number of hours a tortoise could be active daily within Tp-range listed for each 1771 microhabitat. Difference is mean hours available under dead shrubs subtracted 1772 from mean hours available under living shrubs. Significant differences (P<0.05) 1773 are denoted with an asterisk. Microhabitat abbreviations are: Ambrosia dumosa 1774 (AMDU); Larrea tridentata (LATR); Psorothamnus fremontii (PSFR); Yucca 1775 brevifolia (YUBR); Yucca schidigera (YUSC). 1776 1777

1778

1779 Living Dead Difference AMDU 9.03 7.83 1.20 LATR 8.30 7.83 0.47 PSFR 8.50 8.21 0.29 YUBR 8.04 8.19 -0.15 YUSC 8.68 8.35 0.33

Table 9. Comparison of living and dead shrubs within taxa during fall with mean number 1780 of hours a tortoise could be active daily within Tp-range listed for each 1781 microhabitat. Difference is mean hours available under dead shrubs subtracted 1782 from mean hours available under living shrubs. Significant differences (P<0.05) 1783 are denoted with an asterisk. Microhabitat abbreviations are: Ambrosia dumosa 1784 (AMDU); Larrea tridentata (LATR); Psorothamnus fremontii (PSFR); Yucca 1785 brevifolia (YUBR); Yucca schidigera (YUSC). 1786 1787

1788

1789 Living Dead Difference AMDU 7.71 7.12 0.59 LATR 7.34 6.82 0.52 PSFR 8.00 6.99 1.01* YUBR 7.89 7.03 0.86 YUSC 8.05 7.35 0.70*

Table 10. Comparison of living and dead shrubs within taxa during summer with mean 1790 number of hours a tortoise could be active daily within Tp-range listed for each 1791 microhabitat. Difference is mean hours available under dead shrubs subtracted 1792 from mean hours available under living shrubs. Significant differences (P<0.05) 1793 are denoted with an asterisk. Microhabitat abbreviations are: Ambrosia dumosa 1794 (AMDU); Larrea tridentata (LATR); Psorothamnus fremontii (PSFR); Yucca 1795 brevifolia (YUBR); Yucca schidigera (YUSC). 1796 1797

1798

1799 Living Dead Difference AMDU 5.68 4.60 1.08* 1800 LATR 5.19 4.65 0.54* PSFR 6.72 5.11 1.61* YUBR 7.20 6.21 0.99 YUSC 6.87 6.22 0.65

FIGURE LEGENDS 1801 1802 Figure 1. Relationship between Te obtained using physical models and calculated using a 1803 mathematical model for open microhabitats on 10% of randomly selected 1804 sampling days. Solid line is y=x and broken line is the best-fit regression. 1805 1806 Figure 2. Daily thermal habitat quality index calculated for each plot across the sampling 1807 period. Small filled black circles are burned plots and large open gray circles 1808 are unburned plots. 1809 1810 Figure 3. Hours available to tortoises within Tp-range per day in each plot across the 1811 sampling period. Small filled black circles are burned plots and large open gray 1812 circles are unburned plots. 1813 1814 Figure 4. Mean perennial coverage in burned and unburned plots one to six years post- 1815 fire in relationship to mean annual precipitation from October of the previous 1816 year through March of the current year. Cover in burned plots is indicated by a 1817 solid black line, cover in unburned plots is indicated by a solid gray line, and 1818 precipitation is indicated by a dotted black line and open circles. Error bars are 1819 standard errors. 1820 1821 Figure 5. Mean perennial coverage of living and dead taxa in burned and unburned plots 1822 one to six years post-fire. Burned living cover is indicated by a solid black line, 1823 burned dead cover is indicated by a broken black line, unburned living cover is 1824 indicated by a solid gray line, and unburned dead cover is indicated by a 1825 broken gray line. Error bars are standard errors. 1826 1827 Figure 6. Mean coverage in burned plots of taxa that comprise at least 2% of area during 1828 any year. Dead Larrea tridentata (LATR) is shown with black squares and 1829 living Sphaeralcea ambigua (SPAM) is shown with gray stars. Error bars are 1830 standard errors. 1831 1832 Figure 7. Mean coverage in unburned plots of taxa that comprise at least 2% of area 1833 during any year. Dead Ambrosia dumosa (AMDU) is shown with black 1834 diamonds, living Ambrosia dumosa (AMDU) is shown with gray diamonds, 1835 and living Larrea tridentata (LATR) is shown with gray squares. Error bars are 1836 standard errors. 1837 1838 Figure 8. Thermal habitat quality index plotted as three day averages and using mean 1839 cover data from 2006 through 2011. Solid black line is mean burned habitat 1840 index and broken gray line is mean unburned habitat index. Error bars are 1841 standard errors. 1842 1843

Figure 9. Mean daily thermal habitat quality index yearly from 2006 to 2011 for burned 1844 (black circles) and unburned habitat (gray circles). Error bars are standard 1845 errors. 1846 1847

Figure 10. Mean difference in thermal habitat quality index between burned and 1848 unburned habitat across the season. Difference is calculated by subtracting the 1849 mean burned index from the mean unburned index so that positive values 1850 indicate an advantage conferred by unburned habitat and negative values 1851 indicate an advantage conferred by burned habitat. 1852 1853 Figure 11. Hours available to tortoises within Tp-range as three day averages and using 1854 mean cover data from 2006 through 2011. Solid black line is mean burned 1855 habitat index and broken gray line is mean unburned habitat index. Error bars 1856 are standard errors. 1857 1858 Figure 12. Mean hours available to tortoises within Tp-range per day yearly from 2006 to 1859 2011 for burned (black circles) and unburned habitat (gray circles). Error bars 1860 are standard errors. 1861 1862

Figure 13. Mean difference in hours available to tortoises within Tp-range per day between 1863 burned and unburned habitat across the season. Difference is calculated by 1864 subtracting the mean number of burned hours from the mean number of 1865 unburned hours so that positive values indicate an advantage conferred by 1866 unburned habitat and negative values indicate an advantage conferred by 1867 burned habitat. 1868 1869

Figure 14. Mean hours available to tortoises within Tp-range per day in each microhabitat. 1870 Dead shrubs are shown with black circles, living shrubs are shown with gray 1871 circles, open microhabitat in the burned area is shown with a black triangle, 1872 and open microhabitat in the unburned area is shown with a gray triangle. 1873 Microhabitat abbreviations are: Ambrosia dumosa (AMDU); Ephedra 1874 nevadensis (EPNE); Krameria sp. (KRSP); Larrea tridentata (LATR); 1875 Lycium sp. (LYSP); Psorothamnus fremontii (PSFR); Sphaeralcea ambigua 1876 (SPAM); Yucca brevifolia (YUBR); Yucca schidigera (YUSC). Error bars are 1877 standard errors. 1878 1879 Figure 15. Mean monthly hours available to tortoises within Tp-range per day in open 1880 burned (black circles) and open unburned (gray circles) microhabitats. Error 1881 bars are standard errors. 1882 1883 Figure 16. Mean monthly hours available to tortoises within Tp-range per day under dead 1884 (black triangles) and living (gray triangles) Ambrosia dumosa (AMDU) 1885

compared to open burned (black circles) and open unburned (gray circles) 1886 microhabitats. Error bars are standard errors. 1887 1888 Figure 17. Mean monthly hours available to tortoises within Tp-range per day under dead 1889 (black triangles) and living (gray triangles) Larrea tridentata (LATR) 1890 compared to open burned (black circles) and open unburned (gray circles) 1891 microhabitats. Error bars are standard errors. 1892 1893 Figure 18. Mean monthly hours available to tortoises within Tp-range per day under dead 1894 (black triangles) and living (gray triangles) Psorothamnus fremontii (PSFR) 1895 compared to open burned (black circles) and open unburned (gray circles) 1896 microhabitats. Error bars are standard errors. 1897 1898 Figure 19. Mean monthly hours available to tortoises within Tp-range per day under dead 1899 (black triangles) and living (gray triangles) Yucca brevifolia (YUBR) 1900 compared to open burned (black circles) and open unburned (gray circles) 1901 microhabitats. Error bars are standard errors. 1902 1903 Figure 20. Mean monthly hours available to tortoises within Tp-range per day under dead 1904 (black triangles) and living (gray triangles) Yucca schidigera (YUSC) 1905 compared to open burned (black circles) and open unburned (gray circles) 1906 microhabitats. Error bars are standard errors. 1907 1908 Figure 21. Mean monthly hours available to tortoises within Tp-range per day under living 1909 (gray triangles with broken lines) Ephedra nevadensis (EPNE), living (gray 1910 triangles with solid lines) Lycium sp. (LYSP), living (gray squares with 1911 broken lines) Krameria sp. (KRSP), and living (gray squares with solid lines) 1912 Sphaeralcea ambigua (SPAM), compared to open burned (black circles) and 1913 open unburned (gray circles) microhabitats. Error bars are standard errors. 1914 1915 Figure 22. Mean monthly range of hours available to tortoises within Tp-range per day in 1916 burned (black polygon) and unburned (gray polygon) habitat based on 1917 microhabitats that comprise at least 2% of area in each habitat during any one 1918 year. 1919 1920 1921

1922

FIGURES 1923 1924 1925 1926 Figure1. 1927 1928

1929

30 Best fit regression: y = 0.94x + 2.63 R² = 0.97 Model Te (°C) Te Model

10 y = x

0 0 10 20 30 40 50 60 Calculated Te (°C)

Figure 2. 1930

1931

1932

1.0 Spring Summer Fall 0.9

0.8

0.7

0.6

0.5 Burned 0.4 Unburned

0.3 Thermal Habitat Quality Index Index ThermalHabitatQuality 0.2

0.1

0.0 90 140 190 240 290 Julian Date

Figure 3. 1933

1934

1935

1936

16 Spring Summer Fall

12 p-range T 10

8 Burned Unburned 6

Hours Available within within Available Hours 4

0 90 140 190 240 290 Julian Date

Figure 4. 1937 1938

1939

0.35 600

0.30 500

0.25 400

0.20 300 Burned 0.15 Unburned Precipitation 200 (mm) Precipitation Proportion of Total Area Total of Proportion 0.10

100 0.05

0.00 0 2005 2006 2007 2008 2009 2010 2011 2012 Year

Figure 5 1940 1941

1942

0.30

0.25

0.20

Burned-Living 0.15 Burned-Dead Unburned-Living

0.10 Unburned-Dead Proportion of Total Area Total of Proportion

0.05

0.00 2005 2006 2007 2008 2009 2010 2011 2012 Year

Figure 6. 1943 1944

1945

0.30

0.25

0.20

0.15 LATR-Dead SPAM-Living

0.10 Proportion of Total Area Total of Proportion

0.05

0.00 2006 2007 2008 2009 2010 2011 Year

Figure 7. 1946 1947

1948

0.30

0.25

0.20

0.15 AMDU-Dead AMDU-Living LATR-Living 0.10 Proportion of Total Area Total of Proportion

0.05

0.00 2006 2007 2008 2009 2010 2011 Year

Figure 8. 1949 1950 1951

1.0 Burned 0.9 Unburned

0.8

0.7

0.6

0.5

0.4

0.3 Thermal Habitat Quality Index ThermalHabitatQuality 0.2

0.1

0.0 90 140 190 240 290 Julian Date

Figure 9. 1952 1953

1954 0.330

0.325

0.320 Burned Unburned

Thermal Habitat Quality Index Index ThermalHabitatQuality 0.315

0.310 2005 2006 2007 2008 2009 2010 2011 2012 Year

Figure 10. 1955 1956

1957

0.25 Spring Summer Fall 0.20

0.15

0.10

0.05 UnburnedAdvantage

0.00 80 130 180 230 280 -0.05

-0.10

-0.15 Difference in Thermal Habitat Quality Index Index DifferenceThermalHabitatQuality in

-0.20 BurnedAdvantage

-0.25 Julian Date

100

Figure 11. 1958 1959 1960

15 Burned Unburned 13

p-range 11 T

7 Hours Available within within Available Hours

3 90 140 190 240 290 Julian Date

101

Figure 12. 1961 1962

1963

1964

10.0 1965

9.8 1966 9.6

p-range 9.4

9.2

Burned 9.0 Unburned

8.8 Hours Available within T within Available Hours 8.6

8.4

8.2 2005 2006 2007 2008 2009 2010 2011 2012 Year

102

Figure 13. 1967

1968

1969

1970

3 Spring Summer Fall

2 p-range

1 UnburnedAdvantage

0 80 130 180 230 280

-1

Difference in Hours Available within T within Available DifferenceHours in -2 BurnedAdvantage

-3 Julian Date

103

Figure 14. 1971 1972

1973

1974 9.0 1975

8.0

p-range 7.0

6.0

5.0 Hours Available within T within Available Hours

4.0

3.0 AMDU EPNE KRSP LATR LYSP PSFR SPAM YUBR YUSC OPEN

104

Figure 15. 1976 1977 1978

p-range 8

Hours Available within T within Available Hours OPEN-Burned 2 OPEN-Unburned

0 3 4 5 6 7 8 9 10 11 Month

105

Figure 16. 1979 1980 1981

p-range 8

Hours Available within T within Available Hours OPEN-Burned OPEN-Unburned 2 AMDU-Dead AMDU-Living 0 3 4 5 6 7 8 9 10 11 Month

106

Figure 17. 1982 1983 1984

p-range 8

Hours Available within T within Available Hours OPEN-Burned OPEN-Unburned 2 LATR-Dead LATR-Living 0 3 4 5 6 7 8 9 10 11 Month

107

Figure 18. 1985 1986 1987

p-range 8

Hours Available within T within Available Hours OPEN-Burned OPEN-Unburned 2 PSFR-Dead PSFR-Living 0 3 4 5 6 7 8 9 10 11 Month

108

Figure 19. 1988 1989

p-range 8

Hours Available within T within Available Hours OPEN-Burned OPEN-Unburned 2 YUBR-Dead YUBR-Living 0 3 4 5 6 7 8 9 10 11 Month

109

Figure 20. 1990 1991 1992

p-range 8

Hours Available within T within Available Hours OPEN-Burned OPEN-Unburned 2 YUSC-Dead YUSC-Living 0 3 4 5 6 7 8 9 10 11 Month

110

Figure 21. 1993 1994 1995

p-range 8

OPEN-Burned 4 OPEN-Unburned

Hours Available within T within Available Hours EPNE-Living LYSP-Living 2 KRSP-Living SPAM-Living 0 3 4 5 6 7 8 9 10 11 Month

111

Figure 22. 1996 1997 1998 1999 2000 2001 2002 2003 2004 12 Burned 2005 2006 Unburned 2007 10 2008 2009

-range 2010 p 8 T 2011 2012 6 2013 2014 2015 4 2016 2017 Hours Available within within Available Hours 2018 2 2019 2020 2021 0 3 4 5 6 7 8 9 10 11 2022 Month 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040

112

CHAPTER 3. THERMOREGULATION BY TORTOISES IN BURNED AND 2041

UNBURNED HABITAT 2042

2043

ABSTRACT 2044

Wildfires in the Mojave Desert are consequential to desert tortoises (Gopherus 2045 agassizii), not simply because they are a source of direct mortality, but also because they 2046 alter the habitat including microhabitats that tortoises use in behavioral thermoregulation. 2047

Here we assessed the thermal characteristics of burrows in burned and unburned habitat 2048 with and without vegetative cover, evaluated vegetative cover use by tortoises in 2049 comparison to cover availability, and compared body temperatures among tortoises using 2050 burned and unburned habitat, to evaluate the suitability of burned landscapes as tortoise 2051 habitat. We analyzed temperatures inside burrows in burned and unburned habitat, with 2052 and without vegetative cover, by inserting temperature-sensing data loggers into burrows, 2053

30 cm from the burrow mouth and at the terminus of the burrow. Tortoises in burned and 2054 unburned habitat were outfitted with GPS loggers, radio transmitters, and temperature- 2055 sensing data loggers to record body temperatures. Tortoises were tracked during 2009 2056 and 2010 to record vegetation cover use, and to determine the ways in which body 2057 temperature varied with habitat use. Perennial plant cover and composition were 2058 estimated from plots in burned and unburned habitat. The thermal characteristics of 2059 burrows did not differ between burned and unburned habitat and temperature did not vary 2060 with vegetative cover. Tortoises in unburned habitat selected vegetative cover according 2061 to thermal quality while tortoises in burned habitat selected living shrubs over dead 2062 shrubs even when dead shrubs provided microhabitats of higher thermal quality. The only 2063

113 body temperature metric that varied between habitat types was minimum body 2064 temperature, which was slightly lower in burned habitat. Our study suggests that burned 2065 habitat is of comparable thermal quality to unburned habitat for tortoises, and that burned 2066 habitat does not impose constraints on tortoise thermoregulation. 2067

2068

1. INTRODUCTION 2069

Larger and more frequent wildfires have burned vast areas of habitat used by the 2070 threatened Mojave desert tortoise (Gopherus agassizii). In 2005 alone, approximately 2071

400,000 acres of desert tortoise habitat and 65,183 acres of designated critical habitat 2072 were burned as part of the Southern Nevada fire complex (U. S. Fish and Wildlife 2073

Service 2006), due in part to the invasion of flammable and resilient alien grasses into 2074 desert ecosystems (D’Antonio and Vitousek 1992). High intensity fires kill perennial 2075 shrubs above and below ground (Esque et al. 2003), leaving primarily dead individuals 2076

(Scoles-Sciulla et al. 2011) and changing the perennial cover composition on the 2077 landscape for decades or longer (Abella 2009). 2078

Perennial shrubs provide important thermal heterogeneity on the landscape 2079

(Attum et al. 2013, Hillard 1996, Lagarde et al. 2012). Exploiting shrubs as thermal 2080 resources, allows tortoises to be active above ground without returning to underground 2081 burrows when the open environment does not permit tortoises to maintain body 2082 temperature within physiological limits (Nussear 2004, Zimmerman et al. 1994). Despite 2083 changes in perennial shrub composition and proportion of perennial cover after wildfires, 2084 for the desert tortoise, overall above ground thermal quality is similar between burned 2085 and unburned habitat, with unburned habitat providing a slight advantage to tortoises in 2086

114 the number of hours tortoises can be active above ground within their preferred 2087 temperature range (Chapter 2). However, it is also possible that fires could impact the 2088 thermal environment of burrows below ground. 2089

Above ground activity is essential for foraging and dispersal (Nagy and Medica 2090

1986, Woodbury and Hardy 1948), but tortoises often spend the majority of each day 2091 underground (Nagy and Medica 1986), even when above ground temperatures are 2092 suitable for activity (Nussear and Tracy 2007). The environment within burrows may be 2093 more conducive to slowing evaporative water loss (Bulova 2002), reducing energy 2094 expenditure (Nagy and Medica 1986), and reducing predation (Esque et al. 2010). 2095

Additionally, temperatures within burrows are usually more stable than that in the surface 2096 environment (Pike and Mitchell 2013, Zimmerman et al. 1994) so tortoises in burrows 2097 need not shuttle between microhabitats to thermoregulate. Tortoises construct burrows 2098 more frequently under shrubs than in the open (Berry and Turner 1986, Burge 1978, 2099

Wilson et al. 1999) and with openings that face in an eastern or northern direction 2100

(Bulova, 1994, Burge 1978, Hazard and Morafka 2004, but see Berry and Turner 1986). 2101

Shrub cover, which is different in burned and unburned habitat, may affect the thermal 2102 environment of burrows by blocking radiation from entering the burrow mouth, 2103 depending on the direction the burrow faces, or by influencing convection into the 2104 burrow. Additionally, any differences in soil texture or moisture between burned and 2105 unburned habitat could change the thermal environment below ground (Campbell 1977, 2106

Rose 1966). 2107

For desert-dwelling ectotherms living in extremely variable climates, 2108 thermoregulation is crucial for maintaining body temperature (Tb) within physiological 2109

115 limits. Body temperature is important because it influences such physiological processes 2110 as metabolic rate (Bennett and Dawson 1976), digestion (Stevenson et al. 1985, 2111

Zimmerman and Tracy 1989), and locomotion (Hertz et al. 1983, Stevenson et al. 1985, 2112

Waldschmidt and Tracy 1983), which may affect fitness (Christian and Tracy 1981). In 2113 ectotherms, Tb is a function of the interaction between an animal and the microhabitat the 2114 animal selects (Huey 1991). Therefore, Tb results, in part, upon the microhabitats 2115 available to an animal in its habitat, the properties of the microhabitats, and how the 2116 animal behaviorally exploits its habitat (Huey 1991). 2117

Because the composition of microhabitats in burned and unburned habitats differ, 2118 and the thermal properties of burrows may differ, it is conceivable that tortoises using 2119 burned and unburned habitat could interact with their habitat in such a way as to change 2120 their Tb, which could have significant physiological consequences. Differences in Tb 2121 between tortoises using burned and unburned habitat could arise due to differences in the 2122 thermal environments of burrows, the amount of time tortoises are active above, or how 2123 tortoises are perceiving and using the microhabitats available to them in each habitat. 2124

Here we address these hypotheses by (1) assessing the thermal characteristics of burrows 2125 in burned and unburned habitat with and without vegetative cover, (2) evaluating 2126 vegetative cover use by tortoises in comparison to cover availability, and (3) comparing 2127

Tb. among tortoises using burned and unburned habitat, to evaluate the suitability of 2128 burned landscapes as tortoise habitat. 2129

2130

2. METHODS 2131

2.1 Study Site 2132

116

Tortoises were studied at Coyote Springs Valley, within the Mormon Mesa Desert 2133

Wildlife Area (Fish and Wildlife Service 1994), along Interstate Highway 93 2134 approximately 20 miles northeast of Las Vegas, Nevada. Tortoises were clustered into a 2135 northern and southern group spanning approximately 56 square kilometers of burned and 2136 unburned habitat. At the site, over 23 square kilometers burned in 2005 in three separate 2137 fire events (Dry Middle, Dry Rock, and Garnet). The perimeter of the fire was mapped by 2138 the BLM and further refined by USGS using satellite imagery and global position system 2139

(GPS) technology. The site is located on a bajada at an elevation of 800 to 900 m and is 2140 characterized as Mojave Desert scrub habitat dominated by Larrea tridentata, Ambrosia 2141 dumosa, and Yucca brevifolia. Tortoises at this site belong to the Northern Mojave 2142 population cluster (Hagerty and Tracy 2010). 2143

2144

2.2 Vegetation and weather data collection 2145

Perennial vegetation at the study site was characterized in both 2009 and 2010 2146 using three permanent line transects established in nine 400 m x 400 m plots in unburned 2147 habitat and nine 400 m x 400 plots in burned habitat (see Scoles-Sciulla et al. 2011 and 2148

Chapter 2 for full description). Percent perennial cover by species/genus (living or dead) 2149 was estimated for each plot and averaged across years. We used data on perennial cover 2150 to estimate the relative availability of vegetated microhabitats to tortoises. 2151

Weather data were recorded every 30 minutes throughout the study using a 2152

HOBO weather station (Onset Computer Corporation) located at the site that was 2153 equipped with a solarimeter, wind speed sensor, and thermistors located 100 cm above 2154 ground surface and 10 cm below ground surface. 2155

117

2156

2.3 Tortoise capture, transmitter attachment, and data logger attachment 2157

Sixteen tortoises were initially captured during the fall of 2006 by walking 2158 transects through burned and unburned areas. Ten additional tortoises were captured in 2159

August of 2007 with a total of 11 tortoises captured in burned habitat and 15 tortoises 2160 captured in unburned habitat. In subsequent years, additional tortoises were captured 2161 opportunistically during tracking of tortoises already in the study, and this allowed us to 2162 maintain a sample size of at least 20 tortoises at all times by replacing individuals which 2163 died or went missing. Data for this study were collected April to November during 2009 2164 and 2010. 2165

Upon initial capture, tortoises were sexed, uniquely numbered with a vinyl tag 2166 attached to a vertebral scute with epoxy, and weighed to the nearest 5 g using a portable 2167 scale. Maximum carapace length, midline plastron length, maximum height, and carapace 2168 width between the fifth and sixth marginal scute were recorded for each tortoise. Only 2169 tortoises with a carapace length that were at least 180 mm were used in the study. 2170

Tortoise measurements and mass were recorded yearly subsequent to capture. 2171

We attached a VHF radio transmitter (Holohil Systems Ltd.) to the first costal 2172 scute of each tortoise using putty epoxy as described by Boarman et al. (1998). 2173

Transmitters were replaced periodically due to expected battery failure. An iButton 2174 temperature logger (Thermochron DS1921G, range -40°C to 85°C, accuracy ± 1°C, 2175 resolution 0.5°C) was also adhered to the shell of each tortoise at the junction of the tail 2176 and underside of the caudal scute using indoor/outdoor-mounting tape. It has been 2177 demonstrated that this location provides the best external approximation of cloacal 2178

118 temperature, and allows the temperature sensor to remain attached to an animal for an 2179 extended period of time (Nussear et al. 2002). We programmed temperature loggers to 2180 record a temperature every 15 minutes using Thermodata software (ver 3.0) and 2181 downloaded and replaced loggers every three weeks as memory capacity was met. 2182

Additionally, a sample of tortoises was outfitted with GPS data loggers (Advanced 2183

Telemetry Systems) as part of a prototyping study. GPS loggers were attached to the 2184 second costal scute using putty epoxy and were programmed to record a location 7 to 9 2185 times per day. The total mass of instrumentation ranged from 110 g to 120 g and never 2186 exceeded 10% of an animal’s total body mass (Figure 1). 2187

2188

2.4 Tortoise tracking and data collection 2189

VHF receivers were used to physically locate tortoises throughout their period of 2190 activity. In 2009, tortoises were intensively tracked throughout day and night for five 2191 days every third week from May to August. In 2010, tortoises were tracked during 2192 daylight hours, when tortoise activity was most probable, for five days over three 2193 consecutive weeks from April to September. During periods of intensive tracking 2194 individual tortoises were relocated between two and five times per day. Tortoises were 2195 also located periodically throughout the activity season independent of intensive tracking. 2196

When a tortoise was encountered during tracking, we recorded the GPS location 2197 where the tortoise was found, the habitat type (burned or unburned), and microhabitat the 2198 tortoise was using. Microhabitat was classified into four categories (burrow, open, pallet, 2199 and vegetation). When we located a tortoise under shrub vegetation, the species of the 2200 shrub was recorded, as well as the height of the main branch, the width of the shrub in 2201

119 two perpendicular dimensions, the distance between the center of the shrub and the 2202 tortoise, the azimuth of the shrub to the tortoise, and whether the shrub was living or 2203 dead. 2204

2205

2.5 Burrow temperature 2206

Burrows that were previously occupied by tortoises were selected for thermal 2207 analyses. Burrows were categorized by the absence or presence of vegetation (living or 2208 dead) cover originating within 1 m of the burrow mouth, and the habitat type in which 2209 they occurred. Thirty burrows that were constructed in loose soil were chosen to 2210 represent the four vegetation-by-habitat type combinations that were most common 2211

(burned habitat with no vegetation, burned habitat with dead vegetation, unburned habitat 2212 with living vegetation, or unburned habitat with no vegetation) as evenly as possible. We 2213 recorded the burrow direction (categorized as N, E, S, or W), length, overburden depth 2214

(distance between the base of the mouth and top of the soil), and average inclination (for 2215 method see Burge 1978). An approximation of maximum burrow depth was calculated by 2216 adding the overburden depth to the product of burrow length multiplied by the sin of the 2217 inclination angle (in radians). To capture the range of temperatures available to tortoises 2218 in burrows, we constructed thin wooden sticks the length of each burrow. We attached 2219 two iButton temperature loggers to each stick to record burrow temperature 30 cm from 2220 the mouth and at the burrow terminus. Sticks were staked in place and inserted into 2221 burrows so that iButtons recorded the air temperature at the ground level of the burrow 2222

(Tburrow). Sticks were unobtrusive and tortoises continued to use burrows with 2223

120 instrumentation. Temperatures were recorded every hour from August to October of 2224

2009, and from April to August of 2010. 2225

2226

2.5 Data analyses 2227

Burrow temperature 2228

Burrow temperatures from 2009 to 2010 were pooled to examine the thermal 2229 environment within burrows during the tortoise activity season. For each burrow we used 2230 temperatures from two loggers to calculate daily minimum, maximum, and mean Tburrow. 2231

We also calculated the number of times Tburrow range overlapped preferred body 2232 temperature for the tortoise Tp-range (25°C - 35°C) as a proportion of total temperatures 2233 logged per day to serve as a proxy for thermal quality. Measuring operative temperatures 2234 in burrows would have required blocking burrow entrances to tortoises with a physical 2235 model. We are aware that significant energy exchange via convection or conduction 2236 could have affected the accuracy of the thermal quality estimate. Because the thermal 2237 environment in burrows is stable from day-to-day, we averaged daily Tburrow by week to 2238 reduce our large sample size. We conducted separate linear mixed-effect models 2239

(Package nlme ver 2.1-111 in R 3.0.2) using minimum, maximum, mean, and proportion 2240 of daily Tburrow within Tp-range (arcsin square root transformed) as response variables to 2241 determine what burrow characteristics best predicted temperature. Each model included 2242 week of year squared as a covariate to account for seasonal variation in temperature, and 2243 habitat type (burned or unburned), vegetation type (living, dead, or absent), burrow 2244 direction, burrow length, and burrow depth as factors. In each model we included burrow 2245

121 number as a random effect to account for repeated measures of the same burrow over 2246 time. 2247

2248

Vegetation use 2249

We used compositional analysis (Aebischer et al. 1993) on pooled data from 2009 2250 and 2010 to examine vegetated microhabitat selection by tortoises in both habitat types 2251 by comparing plant taxa use with plant taxa availability on a per tortoise basis. Radio- 2252 tracked tortoises encountered in vegetated microhabitats were categorized as occurring in 2253 either burned or unburned habitat. We divided the number of times each tortoise was 2254 found under each shrub taxa type by the total number of encounters recorded for the 2255 tortoise in vegetated microhabitats to determine percent usage. Observations that 2256 occurred during dawn, dusk, and night were removed from the analyses, as were records 2257 where shrub taxon was ambiguous. Tortoises with fewer than five encounters in 2258 vegetated microhabitats per habitat type were removed from the analyses. When multiple 2259 encounters were recorded for a tortoise on the same day under the same shrub individual 2260

(determined by GPS location and shrub dimensions), any duplicate records were omitted. 2261

Taxa that comprised less than 5% of all encounters in a habitat type were combined into a 2262 category called “Other”. Microhabitat availability was determined for burned and 2263 unburned habitat by averaging perennial plant cover data across plots. For each taxon, 2264 percent total cover was divided by percent of area vegetated to yield the proportion of 2265 vegetated area occupied. 2266

The adehabitatHS package in R (ver 0.3.8 in R 3.0.2) was used to test whether 2267 taxa were non-randomly selected by tortoises (using a Wilks lambda) and to rank relative 2268

122 taxa use (by testing pairwise log-ratio differences). Significance was tested at the 0.05 2269 alpha levels with 999 randomized iterations. We substituted 0.01 for zero values in all 2270 matrices (Aebischer et al. 1993). 2271

2272

Body temperature 2273

Daily Tb was characterized by calculating minimum, maximum, and mean Tb for 2274 each tortoise from May to October in 2009 and from April to October in 2010, using only 2275 body temperature logged between dawn and dusk. We also calculated Tb range to 2276 estimate daily Tb variation. Because tortoises active above ground have more variable 2277 body temperatures than tortoises exclusively using burrows (Zimmerman 1994), 2278 temperature range, in combination with maximum and minimum temperature, was also 2279 used to elucidate potential activity differences among tortoises. To capture relative 2280

“accuracy” of thermoregulation (Hertz et al. 1993), given that both burned and unburned 2281 habitats provided similar operative temperatures to tortoises (Chapter 2), we calculated 2282 the percentage of recorded body temperatures that fell within the preferred range of body 2283 temperature for desert tortoises (Tp-range= 25°C - 35°C, see Chapter 2) per tortoise per 2284 day. We classified tortoises as occupying either burned habitat or unburned habitat each 2285 day using a combination of encounters of tortoises by radio-tracking, and GPS-logger 2286 records, in conjunction with ArcGIS (ver 10.0). We created a 200 m buffer area on either 2287 side of the burned perimeter and excluded any tortoise locations that fell within the buffer 2288 to eliminate days when tortoises had a higher probability of moving between habitat 2289 types (Figure 2). Mean daily movement of desert tortoises (Duda et al.1999, Franks et al. 2290

2011), and movement between shelter sites is generally less than 200 m (Bulova 1994). 2291

123

We also eliminated tortoises with fewer than 10 days of recorded body temperature 2292 outside of the perimeter buffer. 2293

To determine whether daily metrics of Tb were different between the tortoises 2294 using burned and unburned habitat, we conducted separate linear mixed effect models 2295

(Package nlme ver 2.1-111 in R 3.0.2) using minimum, maximum, mean, range, and 2296 proportion of daily Tb within Tp (arcsin square root transformed) as response variables. 2297

Each model included maximum daily ground temperature (Tg) as a covariate. Year, 2298 tortoise sex, and tortoise mass were included as factors, and individual tortoise was 2299 treated as a random factor to account for repeated measurements over time. We used a 2300 model selection approach (Package MuMin ver 1.9.5) to determine the set of factors in 2301 our mixed model that best explained measures of tortoise Tb. 2302

2303

3. RESULTS 2304

3.1 Burrow temperature 2305

Temperatures were recorded in a total of 30 burrows, with 18 burrows in burned 2306 habitat (dead vegetation n= 8, vegetation absent n= 10) and 12 burrows in unburned 2307 habitat (living vegetation n=8, vegetation absent n= 4). Burrow lengths ranged from 30 2308 cm to 130 cm (mean burned= 79.9 cm, mean unburned= 99.5 cm) and depths ranged 2309 from 39 cm to 76 cm (mean burned= 50.2 cm, mean unburned= 54.4 cm). The majority 2310 of burrows sampled faced easterly with no burrows facing westerly (east n= 15, north n= 2311

10, south n= 5). Week of year squared was a significant factor (P < 0.05) in all models. 2312

Daily minimum, maximum, and mean Tburrow did not differ by habitat type, vegetation 2313 type, burrow direction, burrow length, or burrow depth, nor did the proportion of day 2314

124

burrows were within Tp-range. Temperatures in burrows were lowest in April (mean Ta= 2315

16.9°C) and highest in summer, with peak temperatures in July (mean Ta= 35.5°C) 2316

(Figure 8). Burrows were of relatively good thermal quality June through September and 2317 provided the best thermal quality in June (mean available time within preferred Tp-range = 2318

52.4%) and the poorest thermal quality in April (mean available time within preferred Tp- 2319 range = 0.8%) (Figure 9). 2320

2321

3.2 Vegetation use 2322

Tortoises clearly used both habitat types, but the percentage of tortoise locations 2323 within burned habitat varied by individual tortoise (Table 1). Of the 2193 tortoise 2324 encounters in burned habitat from 2009 to 2010, tortoises were found in vegetated 2325 microhabitats for 18.6% of the observations. Tortoises were encountered 2557 times in 2326 unburned habitat, with 21.3% of observations in vegetated microhabitats. 2327

In burned habitat, a total of 192 unique encounters (n= 13 tortoises) with tortoises 2328 in eight plant taxa types were included in analyses and in unburned habitat, a total of 334 2329 unique encounters (n= 18 tortoises) in six taxa types were included (Table 2). In burned 2330 habitat, vegetated microhabitat selection was non-random (λ= 0.12, P= 0.02) and taxa 2331 were ranked in order from most to least used as: Yucca brevifolia (living) > Larrea 2332 tridentata (living) > Ambrosia dumosa (living) > Sphaeralcea ambigua (living) > Yucca 2333 schidigera (dead) > Y. brevifolia (dead) > Other > L. tridentata (dead). Y. brevifolia 2334

(living) use was significantly greater than use of all other microhabitats (Table 3), 2335 accounting for 8.84% of encounters while being barely detected in burned plots (Table 2336

2). The most abundant taxon, L. tridentata (dead), comprised 53.38% of perennial cover 2337

125 but only accounted for 14.87% of encounters while the second most abundant taxa, S. 2338 ambigua (living), was used by tortoises similarly to its availability (29.64% use vs. 2339

26.51% available). 2340

In unburned habitat, tortoises also selected taxa non-randomly (λ= 0.18, P< 0.01) 2341 and taxa were ranked in order from most to least used as: Y. brevifolia (living) > Y. 2342 schidigera (living) > Other > Lycium sp. (living) > A. dumosa (living) > L. tridentata 2343

(living). Microhabitat use was clearly divided between two groups, with no detectable 2344 difference between the two top-ranking taxa and no difference among the four bottom- 2345 ranking taxa, but a significant difference between groups (Table 4). Y. brevifolia (living) 2346 accounted for 11.90% of use but only 0.39% of habitat available and Y. schidigera 2347

(living) accounted for 19.02% of use but only 1.60% of habitat available (Table 2). 2348

“Other” was the most used microhabitat category (24.95%) but was also the second most 2349 abundant in unburned plots (30.18%), following L. tridentata (living) (36.35%). 2350

2351

3.3 Body temperature 2352

In 2009, we recorded a total of 236 body temperature days (burned= 111, 2353 unburned= 125) on 13 tortoises (M= 8, F=5), and in 2010, we recorded a total of 954 2354 body temperature days (burned= 273, unburned= 681) on 15 tortoises (M= 10, F=5). Our 2355 sample size was reduced due to loss of iButtons and exclusion of data from tortoises 2356 occupying edge habitat. Because our 2010 sample included over twice as many unburned 2357 days, we randomly selected a subset of unburned days for analyses so that there were 273 2358 days recorded in each habitat type. Tortoise mass ranged from 2285 g to 5035 g (mean 2359

M= 4151 g, mean F= 2762 g). 2360

126

Maximum Tg was a significant variable in all models (P < 0.01), with a relative 2361 importance of 1 and sex was not significant in any model (P > 0.38). Most Tb metrics had 2362 several models within 2 ΔAIC units of the top model. Minimum Tb was lowest in spring 2363 and highest in summer (Figure 3). Minimum Tb was significantly higher in unburned 2364 habitat (β = 1.42 ± 0.86, F1,750 = 10.37, P < 0.01) with maximum daily difference of 2365

3.0°C and marginally higher in 2009 (β = -0.72 ± 0.70, F1,750 = 4.13, P= 0.04). We found 2366 a small positive correlation between mass and minimum Tb (β < 0.01, F1,17 = 8.47, P < 2367

0.01). Habitat type, mass, and maximum Tg were factors in all models within 2 ΔAIC 2368 units of the top minimum Tb model (Table 5). Maximum Tb remained fairly stable across 2369 the season (Figures 4) and was generally bounded within 30°C and 40°C. The only 2370 significant predictor of maximum Tb was maximum Tg with the top model including mass 2371 and maximum Tg (Table 6). Mean Tb generally tracked maximum Tg, and was lower in 2372 the spring (Figure 5), likely driven by lower minimum temperatures during this season. 2373

Mean Tb was similar between burned and unburned habitats, but it was higher in 2010 (β 2374

= 0.80 ± 0.21, F1,750 = 12.66, P < 0.01). The top model for mean Tb included year and 2375 maximum Tg, as did all models with 2 ΔAIC units (Table 7). 2376

Tb range was greater in spring and fall (Figure 6), with a remarkably high average 2377 range in April of 19.5°C. On several occasions, daily ranges between 20°C and 30°C 2378 were observed. Range was significantly higher for tortoises using burned habitat than for 2379 tortoises using unburned habitat (β = -1.33 ± 0.02, F1,750 = 6.1, P < 0.01) and we found a 2380 small negative correlation between mass and Tb range (β > -0.01, F1,17 = 8.46, P < 0.01). 2381

The top model for Tb range included habitat type, mass, year, and maximum Tg, although 2382

127 it was separated by a second model, without year, by only 0.6 ΔAIC units (Table 8). 2383

Proportion of daily Tb within Tp-range was highly variable day-to-day and across seasons, 2384 with most variability occurring in spring and fall (Figure 7). Tortoises could only stay 2385 within Tp-range for an average of 51.7% of the time in May, but in July and August tortoise 2386

Tb was within Tp-range during 90% of the day. The top model that best described 2387 proportion of daily Tb within Tp-range included only maximum Tg, which was also the only 2388 significant factor in the mixed model (Table 9). 2389

2390

4. Discussion 2391

4.1 Burrow temperature 2392

Daily minimum, maximum, mean and thermal quality were similar between 2393 burrows in burned and unburned habitat. This finding is in contrast to another study that 2394 found higher temperatures in burrows used by the toad Bufo boreas in severely burned 2395 habitat than in unburned habitat even after three years of vegetation regrowth (Hosack et 2396 al. 2009). However, the temperature of toad burrows were recorded using operative 2397 temperature models at the burrow mouth and, in difference, we recorded temperatures 2398 fully within burrows, which may explain why we did not observe correlations between 2399 any of the factors we measured and burrow temperature. Our objective was to capture the 2400 range of temperatures available to tortoises within a burrow, because animals often move 2401 within burrows to thermoregulate (Whitaker and Shine 2002), which obscured 2402 differences in the thermal environment of the burrow mouth. 2403

Desert tortoises (Berry and Turner 1986, Burge 1978, Wilson et al. 1999), as well 2404 as some mammals (Gea-Izquierdo 2005, Pallomares 2003, Hayes et al. 2006) 2405

128 predominantly construct burrows under shrub cover. It has been speculated that this 2406 placement choice is driven by the influences of shrubs on the thermal environment of 2407 burrows (Burge 1978). However, we found no differences in temperature among burrows 2408 with living, dead, or no shrub cover. Therefore, differences in the availability of shrubs in 2409 burned and unburned habitat, as sites for burrow construction, seem unlikely to influence 2410 the thermal environment available to tortoises below ground for thermoregulation. Roots 2411 of shrubs perhaps are used to increase the structural integrity of soil burrows. Root 2412 masses under burrows could aid in burrow stability, and might allow burrows to persist 2413 longer before collapse (Pallomares 2003). Over time, the roots of burned shrubs will 2414 decompose, and provide less soil structure, perhaps leading to burrows constructed in 2415 burned habitat being less structurally sound. This would require tortoises to dig new 2416 burrows more frequently. Alternatively, burrows located under shrubs may afford 2417 tortoises greater protection from predators (Hayes et al. 2006) or provide greater burrow 2418 humidity (Bulova 2002). If tortoises in burned habitat have fewer shrubs under which to 2419 construct burrows, they could be more susceptible to predation or lose more water via 2420 evaporation. 2421

We also did not find an effect of direction of the burrow opening on burrow 2422 temperature, another factor which has been speculated to influence the thermal 2423 environment in tortoise burrows (Berry and Turner 1986, Burge 1977, Hazard and 2424

Morafka 2004, Wilson et al. 1999), however, we were unable to locate any burrows 2425 meeting our criteria that faced westerly. Additionally, our calculations were made using 2426 temperatures completely within the burrow that were not influenced by direct solar 2427 radiation. The thermal environment at the mouth of the burrow, which is exposed to solar 2428

129 radiation, is more likely to vary with direction. Burrow depth and length were not 2429 correlated with any of the temperature measurements that we calculated. Minimum and 2430 maximum daily temperatures were most likely recorded at the position 30 cm from the 2431 mouth in all burrows regardless of burrow length or depth, because temperature is more 2432 variable closer to the surface (Pike and Mitchell 2013). We might expect deeper or longer 2433 burrows to have lower mean temperatures, however, beyond 60 cm burrow temperature 2434 varies little (Pike and Mitchell 2013), so small differences in deep burrow temperatures 2435 were likely undetectable in our analysis. 2436

Burrows provided the best thermal environments for tortoises between June and 2437

September, when Tburrow fell within tortoise Tp-range for 40%-60% of the day. This period 2438 also corresponds with the time when above ground operative temperatures exceed lethal 2439 limits for tortoises (Zimmerman et al. 1994) and the thermal quality of above ground 2440 habitat is poorest (Chapter 2), and when tortoises retreat to burrows during mid day 2441

(Zimmerman et al. 1994). Even during the hottest months, mean maximum Tburrow did not 2442 exceed the critical thermal maximum for a tortoise (Brattstrom 1965, Naegle 1976), 2443 solidifying the importance of burrows as thermal refuges for tortoises during peak 2444 temperatures. 2445

2446

4.2 Vegetation use 2447

Tortoises in both burned and unburned habitat chose vegetated shelter sites non- 2448 randomly with regard to proportion of cover on the landscape. In unburned habitat, 2449 tortoises appear to have chosen shrub taxa according to their thermal value. Tortoises 2450 used Y. brevifolia (living) and Y. schidigera (living) at equally high frequencies and 2451

130

Lycium sp. (living), A. dumosa (living), and L. tridentata (living) at similarly low 2452 frequencies. Y. brevifolia and Y. schidigera provide the most hours to tortoises within Tp- 2453 range, while A. dumosa and L. tridentata provide the fewest hours to tortoises within Tp- 2454 range among taxa included in the analysis (Chapter 2). Interestingly, the use of E. 2455 nevadensis and P. fremontii accounted for less than 5% of encounters, despite their 2456 relatively high thermal quality, and their abundance. In another study, E. nevadensis was 2457 used by tortoises disproportionately more frequency than its presence on the landscape 2458

(Nussear 2004). 2459

Moorish tortoises (Testudo graeca soussensis) (Lagarde et al. 2012) and Egyptian 2460 tortoises (Testudo kleinmanni) (Attum et al. 2013) living in scrub habitats, preferentially 2461 select larger or taller shrubs over smaller shrubs of the same species as cover sites. These 2462 larger shrubs are thermally advantageous, because they provide tortoises with more stable 2463 temperatures and lower temperatures during the hot summer (Attum et al. 2013, Lagarde 2464 et al. 2012). This is congruent with interspecific shrub species selection by desert 2465 tortoises in our study. The largest and tallest species sampled in our study (Y. brevifolia 2466 and Y. schidigera) also provided the best thermal cover sites and were used at a higher 2467 frequency by desert tortoises. The two other species of higher thermal quality (E. 2468 nevadensis and P. fremontii) that were used less frequently by tortoises were also smaller 2469 shrub species, perhaps indicating that tortoises are choosing shrubs based on size rather 2470 than species or thermal quality per se. 2471

In burned habitat, tortoises chose living shrubs over dead shrubs, despite the 2472 predominance of dead shrubs in burned habitat, and the relatively high thermal quality of 2473 some dead shrubs. Tortoises preferred living Y. brevifolia to all other shrub taxa. While 2474

131 dead Y. brevifolia is only slightly poorer in thermal quality than living Y. brevifolia, and it 2475 is more common in burned habitat (Chapter 2), it was used relatively less. The thermal 2476 quality of dead Y. brevifolia is also superior to living L. tridentata - the second most used 2477 shrub in burned habitat - by providing over two more hours of activity within tortoise Tp- 2478 range on average (Chapter 2). 2479

Dead shrubs may be viewed as novel thermal resources by some tortoises, 2480 because dead shrubs lack certain cues associated with cover site selection (Hilden 1965). 2481

The cues that tortoises use to select cover sites may be dependent on tortoises’ 2482 experiences with unburned habitat early in life (Wiens 1970). Rittenhouse et al. (2008), 2483 found that translocated box turtles used predisposed cues in selecting habitat at 2484 translocated sites. Desert tortoises translocated into habitats with atypical vegetation 2485 assemblages move considerable distances to reach areas with typical Mojave vegetation 2486

(Nussear 2012), perhaps in search of familiar cover sites. However, Asbury and Adolph 2487

(2007) found that western fence lizards from different populations were behaviorally 2488 plastic in selecting microhabitats. In a common environment, lizards choose 2489 microhabitats based upon thermal quality, regardless of the microhabitats they selected in 2490 their native range. Also, in our study, S. ambigua was a novel shade shrub in burned 2491 habitat, but it was used in proportion to its abundance by tortoises. Similarly, Lovich et 2492 al. (2011) observed that desert tortoises in burned habitat switched from using L. 2493 tridentata to using Encelia farinosa as cover sites, after the previously abundant and 2494 familiar L. tridentata suffered high rates of mortality during a fire and was replaced by E. 2495 farinosa. Therefore, the reasons behind the shrub use patterns observed in burned habitat 2496 remain unclear. 2497

132

Choosing shrubs of high thermal quality, like Y. brevifolia, could be energetically 2498 beneficial because tortoises can remain stationary for longer periods of time within Tp- 2499 range, without expending energy to move to another microhabitat. However, choosing 2500 shrubs of higher thermal quality could be energetically disadvantageous if they are rare 2501 and require tortoises to move longer distances. Tortoises in burned habitat could take 2502 advantage of the high thermal quality of living Y. brevifolia without incurring energetic 2503 costs, by centering areas of activity within their home ranges around patches of 2504 vegetation that escaped fire, however our study did not specifically address this 2505 hypothesis. 2506

2507

4.3 Body temperature 2508

All metrics of tortoise body temperature were correlated with maximum Tg and 2509 did not differ between sexes. The proportion of daily Tb within Tp-range was similar 2510 between tortoises in burned and unburned habitat, indicating that tortoises were able to 2511 use burned and unburned habitats effectively to keep their body temperatures within 2512 preferred ranges for similar amounts of time. We found that proportion of daily Tb within 2513

Tp-range was highly variable from day-to-day in spring and fall and more stable in the 2514 summer, which was similar to the pattern observed for thermal quality of habitat above 2515 ground (Chapter 2). However, proportion of daily Tb within Tp-range was inversely related 2516 to above ground thermal habitat quality across the season, demonstrating that tortoises 2517 are better able to attain preferred body temperatures, within narrower ranges, during the 2518 summer by using burrows, when below ground habitat quality is best. Narrower Tb ranges 2519 during summer are consistent with other G. agassizii studies, as well as Tb patterns in G. 2520

133 polyphemus (Pike and Mitchell 2013), Chelonoidis carbonaria (Noss et al. 2013), 2521

Homopus signatus (Loehr 2012), Stignochelys pardalis (McMaster and Downs 2013), T. 2522 kleinmanni (Attum et al. 2013), among others. 2523

We found small yearly differences in minimum and mean Tb between years, with 2524 a higher minimum Tb in 2009, and a higher mean Tb in 2010. These minor differences 2525 could be attributed to the larger sampling period in 2010, or weather variation among 2526 years. We also found a small but positive correlation between body mass and minimum 2527

Tb and a small but negative correlation between body mass and maximum Tb. Tortoises 2528 with greater mass have more thermal inertia, so it is expected that larger tortoises should 2529 exhibit a smaller range in Tb (O’Connor 1999). 2530

Tortoises in burned habitat had significantly lower minimum Tb and significantly 2531 larger range in Tb. Tortoise Tb is generally lowest in early morning and also lower for 2532 tortoises using epigeal sleeping sites (Zimmerman et al. 2004), therefore, differences in 2533 minimum Tb could be attributed to differences in sleeping site selection. It is possible that 2534 temperatures of burrows in burned and unburned areas, while similar during the day, are 2535 different at night, forcing tortoises above ground to avoid the lagging wave of heat 2536 penetrating into soil, or tortoises in burned habitat could be selecting sleeping sites 2537 differently from tortoises in unburned habitats for other reasons. The observed difference 2538 in Tb range can be attributed to lower minimum Tb for tortoises in burned habitat. If 2539 tortoises in burned habitat also exhibited higher maximum Tb we might assume that 2540 tortoises were active above ground more often, because Tb of tortoises above ground are 2541 more variable than those of tortoises in burrows (Zimmerman et al. 1994), however, we 2542 did not observe this trend. 2543

134

The difference in monthly minimum Tb was, at most, 3.0°C and the greatest 2544 difference in Tb range was 2.8°C. Tortoise Tb range fluctuated greatly, with daily ranges 2545 sometimes between 20°C and 30°C. It is unlikely that differences in range and minimum 2546

Tb, at the magnitude observed, are physiologically relevant to tortoises, which are not 2547 precise thermoregulators and, instead, generally avoid body temperatures above and 2548 below their critical temperatures while allowing body temperatures to fluctuate daily and 2549 seasonally within the constraints of available operative temperatures (Attum et al. 2013, 2550

Loehr 2012, McMaster and Downs 2013, Noss et al. 2013, Pike and Mitchell 2013, 2551

Zimmerman 1994). Additionally, tortoises in burned habitat were able to keep Tb within 2552

Tp-range for the same proportion of the time as tortoises in unburned habitat, despite 2553 starting with the day with lower minimum Tb. 2554

2555

4.4 Conclusion 2556

Overall quality of habitat available to tortoises both above ground, and in 2557 burrows, is similar between burned and unburned habitat. Tortoises choose microhabitat 2558 sites differently depending on habitat type. Tortoises in unburned habitat appear to 2559 choose shrub cover speices according to thermal quality, while tortoises in burned habitat 2560 prefer living shrubs to dead shrubs, regardless of thermal quality. Living Y. brevifolia 2561 appears to be an important thermal resource for tortoises in both burned and unburned 2562 habitat. Y. brevifolia provides the best thermal cover above ground and is preferred by 2563 tortoises compared to other shrub taxa. S. ambigua is another important thermal resource 2564 for tortoises in burned habitat. Tortoises select S. ambigua in proportion to its presence 2565 on the landscape, but this colonizer species grows to large size and it attains high 2566

135 coverage within burned habitats after fire, and thus, this species provides thermal quality 2567 comparable to other living shrub taxa. 2568

In general, tortoise body temperatures were similar among tortoises using burned 2569 and unburned habitat. However, differences could occur at smaller temporal scales, 2570 which were not detectable in this study. Lower minimum Tb observed in tortoises using 2571 burned habitat could indicate differences in sleeping site selection between tortoises in 2572 burned and unburned habitat. Further study is necessary to determine whether thermal 2573 differences in burrow or above ground habitat direct changes in tortoise behavior at night. 2574

Our study suggests that burned habitat is of comparable thermal quality to 2575 unburned habitat for tortoises, and that burned habitat does not impose constraints on 2576 tortoise thermoregulation at this site. Our findings are corroborated by Lovich et al. 2577

(2011) who found that desert tortoises continued to use burned habitat after fire, often 2578 exploiting colonizing shrub species for cover, even when unburned habitat was accessible 2579 via short-distance movement from tortoise activity areas. Likewise reproductive output 2580 and body condition remained stable for tortoises using burned habitat years after fire, 2581 suggesting that fire may not severely impact post-fire fitness, although direct mortality 2582 from fire could be detrimental to tortoise populations (Lovich et al. 2011). Also, the 2583

Mediterranean tortoises, T. graeca, does not migrate from burned habitat to easily 2584 accessible unburned habitat and maintains similar home range size whether in burned or 2585 unburned areas (Sanz-Aguillar 2011), suggesting that these tortoises are also able to meet 2586 their thermal needs by exclusively exploiting burned habitat. 2587

While our findings indicate that burned habitat is thermally suitable for desert 2588 tortoises, every fire site should be evaluated for restoration based upon perennial cover, 2589

136 as it is possible that the thermal quality of habitat in other areas could be more 2590 significantly impacted by wildfire. Sites without Y. brevifolia or where S. ambigua does 2591 not rapidly colonize after fire could be more thermally limiting for tortoises. Shrub 2592 recolonization patterns, and presence of dead shrubs will also likely change over longer 2593 periods of time and could be affected by fire intervals and re-burning. However, it is also 2594 possible that if thermal quality of burned habitat is reduced, tortoises can compensate for 2595 reduced thermal quality above ground by increasing the use of burrows, resulting in 2596 minimal physiological effects. When assessing the overall quality of burned habitat for 2597 tortoises it is necessary to consider the quality of thermal resources, food resources, and 2598 how these components of habitat interact to affect net energy gain and ultimately, fitness. 2599

Our study only addresses one resource important for tortoise population persistence. 2600

Further investigation is necessary to predict the long-term impacts of fire on this 2601 threatened desert reptile. 2602

2603

ACKNOWLEDGMENTS 2604

We would like to thank the many SCA interns and USGS employees that helped collect 2605 massive amounts of field data. We thank Dick Tracy, Ken Nussear, Lesley DeFalco, 2606

Lynn Zimmerman and Peter Weisberg for helpful discussion and comments on an earlier 2607 draft of the manuscript. This project was supported by Coyote Springs Investment, LLC. 2608

2609

2610

2611

2612

137

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144

TABLES

Table 1. Number of tortoises in the study (n) per year categorized by their habitat usage (the remaining percent of locations were in unburned habitat).

2009 2010

% Locations Burned n % Locations Burned n 0 to 20 9 0 to 20 13

21 to 40 4 21 to 40 5 41 to 60 1 41 to 60 3

61 to 80 0 61 to 80 1 81 to 100 11 81 to 100 6

Total 25 Total 28

145

Table 2. Mean percent availability and tortoise use (± one standard error) by taxa in burned and unburned habitat. Taxa that were not used by tortoises at a frequency of 5% were combined into the category labeled “Other”. Taxa abbreviations are: Ambrosia dumosa (AMDU); Larrea tridentata (LATR); Sphaeralcea ambigua (SPAM); Yucca brevifolia (YUBR); Yucca schidigera (YUSC).

Burned Habitat Unburned Habitat Species Availability (%) Use (%) Availability (%) Use (%) AMDU- Living 3.68 ± 1.08 10.61 ± 3.38 27.82 ± 3.02 18.43 ± 3.28 LATR- Dead 53.38 ± 6.53 14.87 ± 4.18 ------LATR- Living 0.78 ± 0.55 15.18 ± 6.68 36.35 ± 4.28 19.59 ± 3.28 LYSP- Living ------3.67 ± 0.90 6.27 ± 2.02 SPAM- Living 26.51 ± 6.52 29.64 ± 24.07 ------

YUBR- Dead 1.77 ± 0.45 4.55 ± 1.85 ------YUBR- Living 0.01 ± 0.00 8.84 ± 3.62 0.39 ± 0.26 11.90 ± 3.12 YUSC- Dead 1.89 ± 0.78 6.71 ± 2.93 ------YUSC- Living ------1.60 ± 0.56 19.02 ± 4.41 Other 11.97 ± 3.45 9.29 ± 2.66 30.18 ± 3.51 24.95 ± 4.42

146

Table 3. Ranking matrix for tortoises in burned habitat comparing microhabitat use and availability. A positive sign (+) indicates that the taxon in the row is used more than the taxa in the column and vice versa for a negative sign (−). Significant differences in use are denoted with three signs (P< 0.05). Taxa that were not used by tortoises at a frequency of 5% were combined into the category labeled “Other”. Taxa abbreviations are: Ambrosia dumosa (AMDU); Larrea tridentata (LATR); Sphaeralcea ambigua (SPAM); Yucca brevifolia (YUBR); Yucca schidigera (YUSC).

AMDU LATR LATR SPAM YUBR YUBR YUSC Other Living Dead Living Living Dead Living Dead AMDU- Living 0 +++ − + + −−− + + LATR- Dead − 0 −−− −−− − −−− −−− − LATR- Living + +++ 0 + + −−− + +++ SPAM- Living − +++ − 0 + −−− + + YUBR- Dead − + − − 0 −−− − + YUBR- Living +++ +++ +++ +++ +++ 0 +++ +++ YUSC- Dead − +++ − − + −−− 0 + Other − + −−− − − −−− − 0

147

Table 4. Ranking matrix for tortoises in unburned habitat comparing microhabitat use and availability. A positive sign (+) indicates that the taxon in the row is used more than the taxa in the column and vice versa for a negative sign (−). Significant differences in use are denoted with three signs (P< 0.05). Taxa that were not used by tortoises at a frequency of 5% were combined into the category labeled “Other”. Taxa abbreviations are: Ambrosia dumosa (AMDU); Larrea tridentata (LATR); Lycium sp. (LYSP); Yucca brevifolia (YUBR); Yucca schidigera (YUSC).

AMDU LATR LYSP YUBR YUSC Other Living Living Living Living Living AMDU- Living 0 + − −−− −−− − LATR- Living − 0 − −−− −−− − LYSP- Living + + 0 −−− −−− − YUBR- Living +++ +++ +++ 0 + +++ YUSC- Living +++ +++ +++ − 0 +++

Other + + + −−− −−− 0

148

Table 5. Model comparison ranked by ΔAIC describing minimum tortoise Tb. Variables include “Habitat Type” (burned or unburned); “Mass”; “Sex”; “Year” (2009 or 2010); and “TgMax” (maximum ground temperature). All models contain a random effect of individual tortoise.

Variables AIC ΔAIC Weight Habitat Type + Mass + Year + TgMax 4269.27 0.00 0.43

Habitat Type + Mass + Sex + Year + TgMax 4270.47 1.20 0.24

Habitat Type + Mass + TgMax 4271.11 1.84 0.17 Habitat Type + Mass + Sex + TgMax 4272.61 3.34 0.08

Habitat Type + Sex + Year + TgMax 4275.91 6.64 0.02 Mass + Year + TgMax 4276.29 7.01 0.01

Habitat Type + Sex + TgMax 4276.38 7.11 0.01 Mass + TgMax 4277.23 7.95 0.01

Habitat Type + Year + TgMax 4277.45 8.18 0.01 Habitat Type + TgMax 4277.52 8.25 0.01

Mass + Sex + Year + TgMax 4277.92 8.65 0.01

….. ….. ….. ….. Null 4753.76 484.49 0.00

149

Table 6. Model comparison ranked by ΔAIC describing maximum tortoise Tb. Variables include “Habitat Type” (burned or unburned); “Mass”; “Sex”; “Year” (2009 or 2010); and “TgMax” (maximum ground temperature). All models contain a random effect of individual tortoise.

Variables AIC ΔAIC Weight Mass + TgMax 3605.53 0.00 0.21

Sex + TgMax 3606.27 0.74 0.15 Mass + Sex + TgMax 3607.28 1.76 0.09

Habitat Type + Mass + TgMax 3607.44 1.91 0.08 Mass + Year + TgMax 3607.50 1.97 0.08

Habitat Type + Sex + TgMax 3608.00 2.47 0.06

Sex + Year + TgMax 3608.10 2.57 0.06 TgMax 3608.12 2.59 0.06

Habitat Type + Mass + Sex + TgMax 3609.18 3.65 0.03 Mass + Sex + Year + TgMax 3609.24 3.71 0.03

Habitat Type + Mass + Year + TgMax 3609.40 3.87 0.03 Habitat Type + TgMax 3609.47 3.94 0.03

Year + TgMax 3609.75 4.22 0.03

Habitat Type + Sex + Year + TgMax 3609.80 4.27 0.03 Habitat Type + Year + TgMax 3611.04 5.52 0.01

Habitat Type + Mass + Sex + Year + TgMax 3611.12 5.59 0.01 ….. ….. …… …..

Habitat Type + Mass + Sex 3636.98 31.46 0.00

150

Table 7. Model comparison ranked by ΔAIC describing mean tortoise Tb. Variables include “Habitat Type” (burned or unburned); “Mass”; “Sex”; “Year” (2009 or 2010); and “TgMax” (maximum ground temperature). All models contain a random effect of individual tortoise.

Variables AIC ΔAIC Weight

Year + TgMax 3034.91 0.00 0.25

Habitat Type + Year + TgMax 3035.19 0.28 0.22 Habitat Type + Mass + Year + TgMax 3036.38 1.47 0.12

Mass + Year + TgMax 3036.62 1.71 0.11 Habitat Type + Sex + Year + TgMax 3036.64 1.74 0.11

Sex + Year + TgMax 3036.65 1.75 0.11 Habitat Type + Mass + Sex + Year + TgMax 3038.37 3.47 0.04

Mass + Sex + Year + TgMax 3038.60 3.69 0.04 ….. ….. ….. …..

Null 3698.80 663.89 0.00

151

Table 8. Model comparison ranked by ΔAIC describing tortoise Tb range. Variables include “Habitat Type” (burned or unburned); “Mass”; “Sex”; “Year” (2009 or 2010); and “TgMax” (maximum ground temperature). All models contain a random effect of individual tortoise.

Variables AIC ΔAIC Weight

Habitat Type + Mass + Year + TgMax 4637.90 0.00 0.29 Habitat Type + Mass + TgMax 4637.97 0.06 0.28

Habitat Type + Mass + Sex + Year + TgMax 4639.69 1.78 0.12 Habitat Type + Mass + Sex + TgMax 4639.87 1.96 0.11

Mass + TgMax 4640.91 3.01 0.06

Mass + Year + TgMax 4641.43 3.53 0.05 Mass + Sex + TgMax 4642.89 4.99 0.02

Mass + Sex + Year + TgMax 4643.37 5.46 0.02 Habitat Type + Sex + TgMax 4643.99 6.09 0.01

Sex + TgMax 4644.60 6.69 0.01 Habitat Type + Sex + Year + TgMax 4645.06 7.16 0.01

Sex + Year + TgMax 4645.82 7.91 0.01 ….. ….. ….. …..

Habitat Type 4903.24 265.33 0.00

152

Table 9. Model comparison ranked by ΔAIC describing the proportion of recorded body temperatures that fell within Tp-range (25°C - 35°C). Variables include “Habitat Type” (burned or unburned); “Mass”; “Sex”; “Year” (2009 or 2010); and “TgMax” (maximum ground temperature). All models contain a random effect of individual tortoise.

Variables AIC ΔAIC Weight TgMax -76.73 0.00 0.19

Mass + TgMax -75.97 0.75 0.13 Year + TgMax -75.40 1.33 0.10

Habitat Type + TgMax -75.09 1.64 0.08 Habitat Type + Mass + TgMax -74.96 1.77 0.08

Mass + TgMax -74.95 1.78 0.08

Sex + Mass + TgMax -74.42 2.30 0.06 Sex + Year + TgMax -74.31 2.41 0.06

Habitat Type + Year + TgMax -73.72 3.00 0.04 Mass + Year + TgMax -73.53 3.19 0.04

Habitat Type + Mass + Sex + TgMax -73.50 3.22 0.04 Habitat Type + Sex + TgMax -73.45 3.27 0.04

Habitat Type + Mass + Year + TgMax -73.19 3.54 0.03 Mass + Sex + Year + TgMax -72.70 4.02 0.02

Habitat Type + Sex + Year + TgMax -71.97 4.76 0.02

Habitat Type + Mass + Sex + Year + TgMax -71.68 5.05 0.01 ….. ….. ….. …..

Habitat Type 308.34 385.06 0.00

153

FIGURE LEGENDS

Figure 1. Top- tortoise with GPS logger and VHF radio transmitter, Bottom- attachment site for iButton temperature logger.

Figure 2. Tortoise location recorded by GPS loggers (yellow) and with VHF radio tracking (blue) in 2009 and 2010. Burned habitat is in red. A 200 m buffer around the fire perimeter is in light green.

Figure 3. Minimum daily Tb of tortoises in burned (black) and unburned (gray) habitat plotted as averages per habitat type per day. 2009 data are plotted as triangles and 2010 data are plotted as circles. Maximum Tg in 2009 is shown with a dotted line and maximum Tg in 2010 is shown with a solid line.

Figure 4. Maximum daily Tb of tortoises in burned (black) and unburned (gray) habitat plotted as averages per habitat type per day. 2009 data are plotted as triangles and 2010 data are plotted as circles. Maximum Tg in 2009 is shown with a dotted line and maximum Tg in 2010 is shown with a solid line.

Figure 5. Mean daily Tb of tortoises in burned (black) and unburned (gray) habitat plotted as averages per habitat type per day. 2009 data are plotted as triangles and 2010 data are plotted as circles. Maximum Tg in 2009 is shown with a dotted line and maximum Tg in 2010 is shown with a solid line.

Figure 6. Daily Tb range of tortoises in burned (black) and unburned (gray) habitat plotted as averages per habitat type per day. 2009 data are plotted as triangles and 2010 data are plotted as circles. Maximum Tg in 2009 is shown with a dotted line and maximum Tg in 2010 is shown with a solid line.

Figure 7. Percent of daily Tb that fell within tortoise Tp-range (25°C - 35°C) for tortoises in burned (black) and unburned (gray) habitat plotted as averages per habitat type per day. 2009 data are plotted as triangles and 2010 data are plotted as circles. Maximum Tg in 2009 is shown with a dotted line and maximum Tg in 2010 is shown with a solid line.

Figure 8. Daily measures of mean (dashed line), minimum (dotted line), and maximum (solid line) temperature within tortoise burrows plotted as averages per habitat type per month. Burrows in burned habitat are black and burrows in unburned habitat are gray. Error bars are standard errors.

154

Figure 9. Daily percent of time Tburrow range overlapped tortoise Tp-range (25°C - 35°C) plotted as averages per habitat type per month. Burrows in burned habitat are black and burrows in unburned habitat are gray. Error bars are standard errors.

155

FIGURES

Figure 1.

156

Figure 2.

(! (! (! (!!(!(! (! (!(! (!(! (! (! ((! GPS Transmitter Locations (!(!(! (! (! (!(! (! (! Radio Transmitter Locations

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(! 0 0.5 1 2 Kilometers

157

Figure 3.

35 120 2009 Burned 30 110 2010 Burned 2009 Unburned 100 2010 Unburned 25 90 2009 Maximum Tg 2010 Maximum Tg (°C)

b 20 80 T

70 15

Minimum Minimum 60 (°C)

g 10 50 T

40 5 Maximum 30

0 20 50 100 150 200 250 300 Julian Date

158

Figure 4.

45 120 2009 Burned 40 110 2010 Burned 2009 Unburned 35 100 2010 Unburned

30 90 2009 Maximum Tg 2010 Maximum Tg (°C)

b 80

T 25

70 20 60 Maximum (°C)

15 g 50 T 10 40 Maximum 5 30

0 20 50 100 150 200 250 300 Julian Date

159

Figure 5.

40 120 2009 Burned 35 110 2010 Burned 2009 Unburned 100 30 2010 Unburned

90 2009 Maximum Tg 25 2010 Maximum Tg 80 (°C) b T 20 70 Mean 15 60 (°C)

g 50 T 10 40

5 Maximum 30

0 20 50 100 150 200 250 300 Julian Date

160

Figure 6.

40 120 2009 Burned 30 110 2010 Burned 2009 Unburned 20 100 2010 Unburned 2009 Maximum Tg 90 2010 Maximum Tg 10 80 (°C)

b T 0 70 50 100 150 200 250 300 Range -10 60 (°C)

50 g -20 T 40 -30 30 Maximum

-40 20 Julian Date

161

Figure 7.

100 120 2009 Burned 90 110 2010 Burned 80 2009 Unburned 100 2010 Unburned 70 90 2009 Maximum Tg 2010 Maximum Tg 25 to 3525 °C to

60 b 80 T

50 70

40 60 (°C)

g 30 50 T Percentageday of 20 40 Maximum 10 30

0 20 50 100 150 200 250 300 Julian Date

162

Figure 8.

(°C) Burned- Min Temp 25 Burned- Mean Temp burrow

T Burned- Max Temp 20 Unburned- Min Temp Mean 15 Unburned- Mean Temp Unburned- Max Temp 10

0 3 4 5 6 7 8 9 10 11 Month

163

Figure 9.

100

p-range 80 T

60 within tortoise within 50 Burned burrow

T 40 Unburned

20 Percentday of 10

0 3 4 5 6 7 8 9 10 11 Month

164

CONCLUSION

We successfully utilized operative temperature models to evaluate the thermal quality of microhabitats for tortoises in burned and unburned habitats, by first validating the use of a single mean absorptance in filtered light conditions. Contrary to our predictions, we found that small differences existed between the thermal environment in burned and unburned habitat but that these differences were relatively minor and likely not detectable by tortoises. We found that living and dead Yucca species provided the best thermal microhabitats for tortoises, especially during the summer, but that tortoises preferentially chose living Yucca shrubs over dead individuals as cover sites. Sphaeraclea ambigua also colonized burned habitat soon after fire and provided an abundant and alternative shade resource that was used by tortoises in proportion to its abundance.

Differences in temperature were not detected between burrows in burned and unburned habitat and, in general, tortoises in burned and unburned habitat exhibited similar body temperatures, indicating that tortoise thermoregulation was not inhibited by structural changes in habitat composition following fire. Taken together, our results suggest that the tortoises at our study site were not adversely affected by thermal changes resulting from fire. However, the impacts of fire on the thermal environment could vary at different sites across the range of the tortoise. Also, fire is responsible for direct tortoise mortality and could impact the availability of other resources, such as forage quantity, composition, and nutritional value. Further studies are needed to address the multifaceted impacts of wildfire on this species, as fires continue to threaten tortoise habitat in the Mojave Desert.