How a Central-Place Forager (Callospermophilus Lateralis) Modifies Its Movement Behavior to Navigate a Risky Landscape and Maximize Fitness

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University of Nevada, Reno

How a Central-Place Forager (Callospermophilus lateralis) Modifies its Movement Behavior to Navigate a Risky Landscape and Maximize Fitness

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Biology

Kira Leigh Hefty

Dr. Kelley M. Stewart & Dr. Stephen B. Vander Wall/Thesis Advisors

August 2016

THE GRADUATE SCHOOL

We recommend that the thesis prepared under our supervision by

KIRA LEIGH HEFTY

Entitled

How a Central-Place Forager (Callospermophilus lateralis) Modifies its Movement Behavior to Navigate a Risky Landscape and Maximize Fitness

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

MASTER OF SCIENCE

Stephen B. Vander Wall, Ph. D. & Kelley M. Stewart, Ph. D., Co-Advisors

William S. Longland, Ph. D., Committee Member

Marjorie D. Matocq, Ph. D., Graduate School Representative

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

August, 2016 i

Thesis Abstract

All animals need to optimize their foraging efforts to survive. Maximizing fitness, however, is more complicated than simply maximizing the net energy gained from foraging. While individuals move throughout their home ranges, they must also adopt strategies to reduce competition and predation. These conflicting behaviors can affect differential selection of food patches and how much time individuals may spend foraging in safe versus risky patch types.

Golden-mantled ground squirrels (Callospermophilus lateralis) are granivorous rodents that are widespread throughout mountainous regions of western North America. C. lateralis primarily depends on the ephemeral availability of seeds to survive the summer and winter torpor. Unlike their scatter-hoarding and pilfering competitors, C. lateralis larder-hoards seeds year-round in a central burrow. C. lateralis cannot always be at the burrow to defend the larder from pilferers, however, because they must leave to participate in other activities such as foraging.

Additionally, when away from the burrow, individuals incur the cost of vigilance to avoid predation. Therefore, individuals must balance conflicting behaviors of larder defense, foraging activity, and antipredator vigilance to maximize fitness. I used resource selection functions and time local convex hull metrics to analyze the spatial and spatiotemporal characteristics of movement behavior for 10 C. lateralis individuals in 2014. I used 5 gram GPS loggers which recorded locations on a narrow time interval. Results suggested that seed availability, distribution of available seeds, and current energetic requirements were large factors in predicting where and how individuals traveled throughout their home ranges. Following winter torpor, energetic requirements were highest. The first seeds available existed in distinct clumps, not necessarily close to the burrow. Individuals traveled farther to access those profitable

ii patches and selected matrices that were associated with high predation risk, but low energetic cost of travel. Individuals also spent more time in those high quality patches and less time vigilant at the burrow. When no seeds were available, individuals avoided risky matrices and stayed close to the burrow. Right before winter torpor, seeds were more randomly dispersed and individual movement patterns suggested that individuals gathered seeds closer to the burrow. Individuals may be more likely to stay closer to the burrow at this time of year to defend the larder from pilferers. Spatial analyses provided key insights as to which patches may be important to individuals seasonally, while spatiotemporal analyses indicated how much time and how frequently individuals visited these patches. Both analyses were important for understanding how individuals may be using behavioral trade-offs to maximize fitness in a dynamic and stressful environment. These same questions and analyses can be applied to many animals in many different ecosystems, and could be particularly important when making management decisions regarding species of conservation concern.

iii

Acknowledgments

I would first like to thank my co-advisors, Dr. Stephen Vander Wall and Dr. Kelley Stewart. Dr.

Vander Wall provided expertise on the foraging behavior of my study subject and associated competitors, as well as helped me organize my original proposal ideas. Dr. Stewart greatly helped me refocus and refine my project with analysis suggestions. Beyond providing her research expertise, Dr. Stewart also provided opportunities for me to network with other professionals to help me succeed beyond my brief time at the University of Nevada, Reno.

I would also like to thank my committee members, Dr. Marjorie Matocq, and Dr. William

Longland. Their support, expertise, and encouragement were also invaluable as I was organizing and editing my proposal and thesis. There were also several faculty members who provided support in various aspects of my research. In particular, GIS gurus Dr. Jeremy Smith and Tom

Dilts, MS, both provided great technical support and suggestions as I began analyzing my data.

I would also like to thank the Department of Biology at the University of Nevada, Reno, for providing me with funding from the Whittell Forest Graduate Fellowship fund. Using GPS technology is expensive, but very worthwhile. I would not have been able to complete my research without this support.

I want to thank the members of my two labs for providing field support, academic support, and friendship throughout the course of my project. In my first field season, both Dr.

Jacob Dittel and Meredith Reia helped me wrangle and restrain squirrels in the field, sometimes coming away with bloody fingers for their efforts. All members of my lab were also great friends who supported me and provided encouragement.

Friends outside my lab group and family also provided incredible support, particularly when times were frustrating or difficult. My parents, Bryan and Linda Hefty, always believed in me, and even provided financial support when I received no equipment funding my second year.

Everyone believed I could succeed and were always willing to support me in any way they could.

I am incredibly grateful for my family and everyone I met during my time at UNR. I am also grateful for two of the best field and hiking companions anyone could ask for—my two dogs

James Bond and Rooney. While they didn’t understand any of what I was trying to accomplish here, they kept my spirits up when it would take me weeks to recapture a squirrel or when I had to dig up a burrow to retrieve a GPS logger.

Finally, I would especially like to thank Jeffrey Gicklhorn, who has been a great partner to me during my last and most difficult year during my degree. I am incredibly grateful for both his professional insight and emotional support. His corny puns and optimistic nature always made me smile, even after a frustrating day in the office. I am proud of him and what he has been able to accomplish so far in his degree and am so excited for us to be able to grow together beyond our time in Reno.

TABLE OF CONTENTS

THESIS ABSTRACT ...... vi

ACKNOWLEDGMENTS ...... iii

THESIS INTRODUCTION ...... 1

Literature ...... 7

CHAPTER 1: Forage or perish: How a small granivorous rodent balances conflicting behavioral demands to survive in a stressful environment ...... 11

ABSTRACT ...... 11

INTRODUCTION ...... 12

METHODS ...... 16

RESULTS...... 21

DISCUSSION ...... 23

LITERATURE ...... 30

TABLES & FIGURES ...... 40

CHAPTER 2: Spatiotemporal analysis of movement behavior of a central-place forager in an environment of conflicting demands ...... 49

ABSTRACT ...... 49

INTRODUCTION ...... 50

METHODS ...... 55

RESULTS...... 59

DISCUSSION ...... 60

LITERATURE ...... 64

FIGURES ...... 69

THESIS SUMMARY ...... 73

Literature ...... 78

Thesis Introduction

All animals need to eat to maximize fitness. Healthy individuals are more likely to have successful reproduction, increased resilience in stressful and unpredictable environments, and increased chance of survival during extended periods of food scarcity (Millar & Hickling 1990).

For example, female Eurasian red squirrels in food rich environments have larger litter sizes and live longer than females in poorer quality habitat (Wauters & Dhondt 1995). Optimal foraging exists when individuals increase their net energetic gain of foraging by minimizing energetic costs associated with travel and handling time (MacArthur & Pianka 1966). According to the marginal value theorem, the longer an individual remains in a foraging patch, the more depleted it becomes (Charnov 1976). Therefore, optimal foraging lies within the balance between transit time and time spent in a depleting patch. This model has been used in several studies to determine how individuals are making foraging decisions in complex landscapes, however, the assumptions of this model and its applicability to these complex decisions has been criticized in recent years (Nonacs 2001; Price & Correll 2001; Verdolin 2006). Individuals don’t always make perfect decisions while foraging, and the decisions they do make can be influenced by several competing demands (Brown 1999; Morris 2009).

In addition to incurring the energetic cost of transit time between food patches, individuals must also balance time allocated to other behaviors such competition for resources, predator avoidance, food-guarding, exposure to abiotic elements, and mate search. These competing behaviors could affect how much time individuals spend in food patches and which patches they select (Brown 1999; Newman 1991). Specifically, when energetic requirements are high, some species have been shown to take greater risks in their search for food, which may

involve spending more time in a food patch or traveling farther to access a food patch.

Specifically, hungry individuals often have reduced antipredator vigilance behavior, which has been argued to increase mortality rates in comparison to satiated individuals (Bachman 1993;

Sih 1980). Other species consistently try to modify their behavior while foraging to offset risks associated with traveling through a stressful environment. For example, yellow-bellied marmots are able to increase the amount of time they spend foraging in riskier patches by participating in antipredator vigilance activity (Armitage & Salsbury 2016).

Models based on the marginal value theorem, such as giving up densities (GUDs), are limited by the assumptions and restrictions of the theorem (Nonacs 2001; Price & Correll 2001;

Verdolin 2006). Giving-up-densities (GUDs) have been extensively used to measure how the threat of predation affects how quickly an individual will give up a foraging effort (Brown 1988).

What these models often do not consider is the myriad of other behaviors and risks individuals also undertake when they leave a point of refuge in search of food. Results from GUDs are restricted to very specific sets of environmental conditions, either simulated in a lab environment or at the time in which a field experiment took place. Additionally, there can be great variation in the risk an individual is willing to take dependent on age, experience, and energetic requirements. For example, juvenile thirteen-lined ground squirrels, which experience higher mortality rates than adults, devoted more time to foraging and less time to vigilance when nutritional needs were not met (Arenz & Leger 2000). While the simplicity of the application of GUDs makes it great to measure dichotomous trade-offs in behavior, this simplicity in design also makes it difficult to generalize results to all individuals within a study population and across species of interacting communities (Price & Correll 2001).

Resource selection functions and spatiotemporal analyses of resource use allows researchers to address similar questions, but without the constraints of the assumptions made by the marginal value theorem underlying many other optimal foraging theory models. Rather, interpretations are made from directly observing the activity patterns of individuals as they naturally move throughout their home ranges and therefore encounter dynamic levels of risk

(Boyce et al. 2002). Spatial analysis of resource selection or avoidance has been used to identify which patches are important to individuals of a population and how the importance of those resources may change seasonally (Chetkiewicz & Boyce 2009; Gustine & Parker 2008). Analyzing temporal partitioning of patch use further describes how animals are using resources, which also aids in addressing questions of how individuals balance conflicting behaviors to maximize foraging efforts (Lyons et al. 2012). Incorporating the serial correlation of GPS data allows researchers to measure rates of revisitation and visit duration and correlate those metrics to changes in habitat type, predation risk, and seasonal fluxes in food availability. For example, time local convex hull metrics were used to address which areas grey seals (Halichoerus grypus) sampled most intensively and how those areas fluctuated as a possible correlate of the seasonal availability of prey (Baker et al. 2015).

One particular group of species that dynamically adopts behavioral trade-offs in order to optimize foraging efforts is the granivore guild. By definition, members of a guild include groups of species that use the same resources in a similar manner (Root 1967; Simberloff and Dayan

1991). Species in the granivore guild primarily depend on seeds to survive throughout the summer and across time periods when food is scarce. In the arid eastern Sierra Nevada

Mountains, level of competition between these species is high when seeds are available, causing species to differentially select foraging patches and use different foraging strategies. Small

rodents in this guild use either a pilferage tolerance or a pilferage avoidance technique for harvesting and caching food (Vander Wall & Jenkins 2003). In the pilferage tolerance strategy, species such as yellow-pine chipmunks (Tamias amoenus) scatter-hoard seeds to avoid mass loss of food stores and use their acute olfactory sense to pilfer from caches made by conspecifics and other species (Downs and Vander Wall 2009). These caches are subject to pilfering and are often recached throughout the foraging season (Vander Wall 1994). In one study, chipmunks pilfered from 95% of primary caches containing Sierra bush chinquapin

(Castanopsis sempervirens) seeds and recached 33% of the seeds into secondary caches (Roth and Vander Wall 2005). In contrast, other rodents larder-hoard food, space caches further apart, or defend caches (Huang et al. 2011). This is the pilferage avoidance strategy. Species using this strategy incur the added cost of needing to allocate time to defending food stores. Golden- mantled ground squirrels (Callospermophilus lateralis) are one of the species that uses this strategy to reduce competition for food when seeds are available. Unlike their competitors, they are not effective pilferers (Vander Wall et al. 2009). They are also an asocial species, so their larder remains unprotected when the individual must leave to participate in other activities, including foraging (Ferron 1985). Their inability to reciprocate pilfering behavior puts C. lateralis at a competitive disadvantage. This strategy could be potentially problematic for C. lateralis, particularly when food sources are limited and unpredictable.

In the arid eastern slope of the Sierra Nevada Mountains, understory habitat for the granivore guild is composed primarily of four seed-bearing shrub species: greenleaf manzanita

(Arctostaphylos patula), Sierra bush chinquapin (Castanopsis sempervirens), tobacco bush

(Ceanothus velutinus), and antelope bitterbrush (Purshia tridentata) (Vander Wall 1998).

Granivores may additionally harvest seeds from overstory pine tree species, predominantly

Jeffrey pine (Pinus jeffreyi), in the fall. Seed crops from both understory and overstory plants are ephemeral and unpredictable (Vander Wall 1994; Vander Wall 2002). Between seasons of seed availability, food is scarce, particularly for C. lateralis inhabiting open canopy forest habitat with little to no understory forb or grass cover (Bihr & Smith 1998). Therefore, when these profitable seeds are available, the level of competition is heightened. This is when differential foraging and caching strategies become important to reduce competition (Chesson 2000; Kotler et al. 2002;

Schoener 1974).

C. lateralis must simultaneously balance the need to leave the burrow to forage, avoid predators, and defend the larder from pilferers. In an environment with an ephemeral availability of food, this is an ideal species to study when trying to determine how individuals modify their movement patterns to balance dynamic and conflicting behaviors. To investigate this idea, I hypothesized that spatial and spatiotemporal partitioning of patch use would change within and across seasons of differential food availability. I used data collected from GPS loggers on 10 different squirrels in 2014 to measure changes in distance traveled from the burrow, proximity to locations of refuge from predators, and vegetative canopy type selection. I used resource selection functions to assess the changes in spatial selection or avoidance of these parameters between seasons of seed availability and when seeds were not available. To measure spatiotemporal resource partitioning, I used time local convex hull metrics to assess changes in rate of revisitation, time spent in patches, and variable distances traveled from the burrow and refuge locations. In seasons of seed availability and when individuals are in an energy deficit, I predicted that movement patterns should indicate increased prioritization of foraging efficiency and larder defense. In times when food is scarce and individuals are no

longer in an energy deficit, I predicted that individuals should prioritize behaviors associated with energetic conservation and predator avoidance.

Literature Cited Arenz, CL, & Leger, DW. 2000. Antipredator vigilance of juvenile and adult thirteen-lined ground

squirrels and the role of nutritional need. Animal Behaviour 59: 535-541.

Armitage, KB, & Salsbury, CM. 2016. Pattern and variation of the time budget of yellow-bellied

marmots. Ethology, Ecology, and Evolution 28: 329-365.

Bachman, GC. 1993. The effect of body condition on the trade-off between vigilance and

foraging in Belding’s ground squirrels. Animal Behavior 46: 233-244.

Baker, LL, Mills Flemming, JE, Jonsen, ID, Lidgard, DC, Iverson, SJ, & Bowen, WD. 2015. A novel

approach to quantifying the spatiotemporal behavior of instrumented grey seals used to

sample the environment. Movement Ecology 3: doi:10.1186/s40462-015-0047-4.

Bihr, KJ, & Smith, RJ. 1998. Location, structure, and contents of burrows of Spermophilus

lateralis and Tamias minimus, two ground-dwelling Sciurids. Southwestern Naturalist

43: 352-362.

Boyce, MS, Vernier, PR, Nielsen, SE, & Schmiegelow, FKA. 2002. Evaluating resource selection

functions. Ecological Modelling 157: 281-300.

Brown, JS. 1988. Patch use as an indicator of habitat preference, predation risk and competition.

Behavioral Ecology and Sociobiology 22: 37-47.

Charnov, EL. 1976. Optimal foraging, the marginal value theorem. Theoretical Population

Biology 9: 129-136.

Chesson, P. 2000. Mechanisms of maintenance of species diversity. Annual Review of Ecology

and Systematics 31: 343-366.

Chetkiewicz, CLB, & Boyce, MS. 2009. Use of resource selection functions to identify

conservation corridors. Journal of Applied Ecology 46: 1036-1047.

Ferron, J. 1985. Social behavior of the golden-mantled ground squirrel (Spermophilus lateralis).

Canadian Journal of Zoology 63: 2529-2533.

Gustine, DD, & Parker, KL. 2008. Variation in the seasonal selection of resources by woodland

caribou in northern British Columbia. Canadian Journal of Zoology 86: 812-825.

Huang, Z, Wang, Y, Zhang, H, Wu, F, & Zhang, Z. 2011. Behavioural responses of sympatric

rodents to complete pilferage. Animal Behaviour 81: 831-836.

Kotler, BP, Brown, JS, Dall, SRX, Gresser, S, Ganey, D, & Bouskila, A. 2002. Foraging games

between gerbils and their predators: temporal dynamics of resource depletion and

apprehension in gerbils. Evolutionary Ecology Research 4: 495-518.

Lyons, AJ, Turner, WC, & Getz, WM. 2012. Home range plus: a space-time characterization of

movement over real landscapes. Movement Ecology 1: 2-10.1186/2051-3933-1-2.

Millar, JS, & Hickling, GJ. 1990. Fasting endurance and the evolution of mammalian body size.

Functional Ecology 4: 5-12.

MacArthur, RH, & Pianka, ER. 1966. On optimal use of a patchy environment. The American

Naturalist 100: 603-609.

Morris, DW. 2009. Apparent predation risk: tests of habitat selection theory reveal unexpected

effects of competition. Evolutionary Ecology Research 11: 209-225.

Newman, JA. 1991. Patch use under predation hazard-foraging behavior in a simple stochastic

environment. Oikos 61: 29-44.

Nonacs, P. 2001. State dependent behavior and the marginal value theorem. Behavioral Ecology

12: 71-83.

Price, MN, & Correll, RA. 2001. Depletion of seed patches by Merriam’s kangaroo rats: are GUD

assumptions met? Ecology Letters 4: 334-343.

Pyke, GH, Pulliam, HR, & Charnov, EL. 1977. Optimal foraging: a selective review of theory and

tests. The Quarterly Review of Biology 52:137-154.

Roth, J.K., and Vander Wall, S.B. 2005. Primary and secondary seed dispersal of bush chinquapin

(Fagaceae) by scatterhoarding rodents. Ecology 86: 2428-2439.

Root RB. 1967. The niche exploitation pattern of the blue-gray gnatcatcher. Ecological

Monographs 37: 317-350.

Schoener, TW. 1974. Resource partitioning in ecological communities. Science 185: 27-39.

Simberloff, D., and Dayan, T. 1991. The guild concept and the structure of ecological

communities. Annual Review of Ecology and Systematics 22: 115-143.

Sih, A. 1980. Optimal behavior: can foragers balance two conflicting demands? Science 210:

1041-1043.

Vander Wall, S.B. 1994. Seed fate pathways of antelope bitterbrush: dispersal by seed-caching

yellow pine chipmunks. Ecology 75: 1911-1926.

Vander Wall, SB. 1998. Foraging success of granivorous rodents: effects of variation in seed and

soil water on olfaction. Ecology 79: 233-241.

Vander Wall, SB. 2002. Masting in animal-dispersed pines facilitates seed dispersal. Ecology 83:

3508-3516.

Vander Wall, SB, Enders, MS, & Waitman, BA. 2009. Asymmetrical cache pilfering between

yellow pine chipmunks and golden-mantled ground squirrels. Animal Behaviour 78: 555-

561.

Vander Wall, SB, & Jenkins, SH. 2003. Reciprocal pilferage and the evolution of food-hoarding

behavior. Behavioral Ecology 15: 656-667.

Verdolin, JL. 2006. Meta-analysis of foraging and predation risk trade-offs in terrestrial systems.

Behavioral Ecology and Sociobiology 60: 457-464.

Wauters, LA, & Dhondt, AA. 1995. Lifetime reproductive success and its correlates in female

Eurasian red squirrels. Oikos 72: 402-410.

Chapter 1 Forage or perish: how a small granivorous rodent balances conflicting behavioral demands to survive in a stressful environment

ABSTRACT

Members of the granivore guild in the eastern Sierra Nevada use behavioral trade-offs to optimize foraging efforts and survive in a landscape of unpredictable and limited food availability. Golden-mantled ground squirrels (Callospermophilus lateralis) are larder-hoarding rodents that must meet multiple demands, such as the need to forage, defend food stores from pilferers, and avoid predators. How individuals balance these conflicting demands can be measured by differential selection of habitat patches. We hypothesized that distance traveled from the home burrow and refuge locations as well as selection or avoidance of vegetative cover type would change seasonally dependent on these dynamic demands. In 2014, we used data collected from GPS loggers to model selection or avoidance of those parameters in two seasons of seed availability and one season in which no seeds were available. Individuals selected intermediate distances to their burrows during seasons of seed availability, indicating trade-offs between foraging away from the burrow and minimizing risks of predation and loss of

food stores to pilferers. Distance to refuge locations also indicated individuals may use refuge locations to reduce predation risk while foraging away from the burrow. Selection for vegetative canopy cover predominantly reflected seed availability by season, however, when energetic demands were high, individuals selected riskier open canopy patch types, suggesting a trade-off of foraging efficiency over predator avoidance. These results indicate that individuals differentially selected habitat patches to balance seasonally dynamic conflicting demands. In the future, it will be important to measure resource selection across multiple time-steps to gain a more comprehensive view of dynamic behavioral decisions and habitat requirements of this and other species.

INTRODUCTION

Golden-mantled ground squirrels (Callospermophilus lateralis) are a widespread small mammal species existing in the mountainous regions of western North America. They are members of the granivore guild, which includes species that compete for the same seed sources each year

(Simberloff & Dayan 1991). In the arid eastern Sierra Nevada, competition among granivores is high when seeds are available (Vander Wall 1994). In this environment, seed availability is ephemeral and most seed species are not available at overlapping times during the year. These characteristics have caused species to evolve differential foraging and hoarding strategies

(Vander Wall 2000; Vander Wall et al. 2009). C. lateralis use a larder-hoarding strategy, in which they place all of their seeds in a burrow and defend that burrow from competitors such as yellow-pine chipmunks (Tamias amoenus). T. amoenus scatter-hoards seeds to avoid mass loss of food stores and also use their acute olfactory sense to pilfer from caches made by

conspecifics and species (Downs and Vander Wall 2009). C. lateralis cannot reciprocate this pilfering behavior, which makes larder-defense their primary strategy to collect and conserve food (Vander Wall et al. 2009). Because C. lateralis are solitary, individuals must often trade-off larder defense to participate in other activities such as foraging when these ephemeral seeds are available (Ferron 1985). Additionally, C. lateralis also incurs the risk of predation while out foraging. Therefore, individuals must also at times forego foraging for antipredator vigilance or vice versa. Like many other prey species in stressful environments, C. lateralis must balance the time and effort allocated to all of these competing demands to maximize fitness (Brown 1999).

Using C. lateralis as a study subject provides a unique opportunity to analyze these trade-offs in an environment characterized by a highly ephemeral and profitable food source shared by many species. The site selected for this study had an understory consisting almost solely of scattered patches of antelope bitterbrush (Purshia tridentata) and an overstory that was dominated by mature Jeffrey pine (Pinus jeffreyi) trees. Those two species of plants were the only seed-producing plants in the site that supported populations of C. lateralis and granivorous competitors. Bitterbrush seeds, when available in July, are restricted to stands of shrubs existing primarily under open (e.g., no overstory cover) canopy (Dyer et al. 7/8/2014).

Jeffrey pine seeds are scattered by the wind in early September and, although some trees may produce more seeds than others, are scattered fairly randomly across the site, meaning individuals do not have to select particular patches to access these seeds. Depending on the quantity of seeds produced in a given year, individuals may expand or contract their search area, or change their caching strategies to harvest and store as many of these profitable seeds as possible (Castleberry et al. 2001; Vander Wall 2002). These temporally dynamic behavioral

strategies are still poorly understood, and often neglected in the analysis of resource use and movement behavior (Lima & Zollner 1996; Mooij & DeAngelis 2003).

Optimal foraging theory suggests that individual behaviors should minimize the energetic cost of foraging while maximizing energy gained from food gathered (MacArthur &

Pianka 1966; Pyke et al. 1977). Cost incurred by the animal while traveling through its home range is not purely energetic, however, and changes dependent on species, habitat composition, and year. As with C. lateralis, additional costs associated with dynamically changing demands include predator pressure, exposure to abiotic elements in unsuitable matrices, and competitive interactions (Bonenfant & Kramer 1996, Fuller & Harrison 2010, Harvey & Fortin 2013). The matrix through which C. lateralis moves and the resources it selects should then represent the lowest cumulative cost to each individual. Additionally, which resources individuals select and which behavioral trade-offs become important could also change seasonally dependent on current energetic requirements and distribution of available food. For example, how sensitive individuals are to risk of predation may change dependent on their energetic demands (Brown

1999, Dill and Fraser 1984). Juvenile thirteen-lined ground squirrels devoted more time to foraging and less time to vigilance in risky patches when nutritional needs were not met (Arenz

& Leger 2000). Antipredator vigilance behaviors for brush-tailed rock-wallabies (Petrogale penicillata) decreased on cold nights, purportedly due to higher energy requirements (Carter &

Goldizen 2003). Energetic demands for C. lateralis vary dependent on length of time between seasons of seed availability. In particular, energetic demands are highest when the larder has been emptied prior to the availability of bitterbrush in July. In order to minimize predation risk when energetic demands are high, C. lateralis may travel between patches at high speeds and increase their harvest rates (Smith 1995). Exploiting these risky patches could also reduce

competition for C. lateralis while harvesting seeds. In general, other species which are larger than their competitors have been shown to take advantage of reduced competition in riskier patch types because of their increased ability to flee from predators (Cohen et al. 1993, Kotler et al. 1994, Smith 1995).

When energetic demands are lower, anti-predator behavior may become more important, causing individuals to forego foraging activity when risk of predation is high. Many studies have provided evidence that granivorous rodents adjust their foraging behavior when they detect a predator (Brinkerhoff 2005; Kotler 1992; Carthey et al. 2015). Another burrowing rodent, the woodchuck (Marmota monax), gave up foraging more readily upon perceiving a predator threat when further from the cover of their burrow (Bonenfant & Kramer 1995).

Additionally, fox squirrels (Sciurus niger) were more likely to abandon a seed patch in response to predator threat when further from refuge (trees) than when they were closer to refuge

(Thorson et al. 1998).

Additionally when energetic demand is lower, individuals may want to prioritize defending the larder. In high mast years for Jeffrey pine trees, rodents remove seeds more rapidly and re-cache them less frequently (Vander Wall 2002). Additionally during those high mast years, pine seeds predominate T. amoenus winter larders (Kuhn and Vander Wall 2009), indicating there may be more pilfering pressure on C. lateralis larders during this time. Perceived pilferage risk has even been shown to influence a shift in behavior of rodents from scatter- hoarding to larder-hoarding, indicating that individuals may benefit from defending a single seed cache (Dally 2006; Huang et al. 2011).

In an environment of ephemeral food availability, are C. lateralis using dynamic behavioral trade-offs to balance conflicting demands of predator avoidance, larder-defense, and foraging? This study addresses this question by using resource selection functions to identify differential selection of resources between seasons of seed availability and seasons when seeds are not available. I formulated three predictions: 1) In all seasons, C. lateralis should select foraging sites closer to the burrow to protect the larder from pilferers and to reduce the risk of predation, 2) During seasons of seed availability, C. lateralis should select patches in which probable encounter rate with food is highest, and 3) When individuals are away from the burrow, they should remain within close proximity to points of refuge such as boulders and stumps to escape predators.

METHODS

Study Site

This study was conducted within a 1.3 km2 area in the Whittell Forest and Wildlife Area in Little

Valley, Washoe County, about 30 km south of Reno, Nevada, USA (39°15’0”N, 119°52’35”W).

This study site is owned by the University of Nevada, Reno, and comprises 1,073 hectares with elevation about 1975 m. Dominant woody vegetation includes Jeffrey pine (Pinus jeffreyi), lodgepole pine (Pinus contorta), antelope bitterbrush (Purshia tridentata), greenleaf manzanita

(Arctostaphylos patula), tobacco bush (Ceanothus velutinus), and Sierra bush chinquapin

(Castanopsis sempervirens). The portion of Little Valley selected for this study had a dominant open tree canopy of Jeffrey pine and a patchy understory of antelope bitterbrush.

Animal Capture and Handling

During July-October, 2014, 10 adult C. lateralis (six males and four females) were selected for study. Most individuals were used more than once, dependent on my ability to recapture individuals. GiPSy 5 global positioning system (GPS) loggers were supplied by

TechnoSmart Europe s.r.l.,and very high frequency (VHF) transmitters were supplied by

Advanced Telemetry Systems Inc. were epoxied to the loggers to track individuals and retrieve them. Squirrels were captured in Tomahawk traps and handled in accordance with a protocol approved by the Institutional Animal Care and Use Committee (IACUC, #A07/08-30) that was in keeping with guidelines established by the American Society of Mammalogists for use of wild mammals in research (Sikes et al. 2016).

Data were collected in three different seasons. Those seasons were delineated by timing and type of seeds that were available: 1) bitterbrush seed season (bitterbrush seeds available

July 21-31, 2014), 2) interval season (no seeds available, August 1- September 11, 2014), and 3) pine seed season (Jeffrey pine seeds available, September 12-19, 2014). GPS loggers were programmed to collect 5 locations simultaneously every 5 minutes. Data were further processed at the end of the season by averaging the location of the 5 simultaneous fixes to create one more accurate point. On this schedule, the battery lasted for 4 days. After that time, loggers were retrieved from the squirrels and data were downloaded.

Habitat Modeling

All GPS locations collected from all individuals were used to create 95% fixed kernel density home range polygons using the Geospatial Modelling Environment (Beyer 2012). Home

range estimates were smoothed using the least squares cross-validation (LSCV), which is associated with the least biased estimates of home range size, particularly with smaller sample sizes (Seaman 1999). All home range polygons were imported into ArcMap 10.3 and inspected for accuracy and to ensure outlying points did not cause unrealistic elongation of the estimated home range.

A vegetation raster with 1-m resolution was created in ArcMap 10.3 that described three dominant canopy cover types: open, pine, and antelope bitterbrush (Figure 1). Open canopy was categorized by no overstory and no understory vegetative cover. Pine canopy was categorized by no bitterbrush understory and pine as an overstory cover. Bitterbrush canopy was categorized by a dominant understory cover of bitterbrush, regardless of whether there was an overstory cover of pine or not. Open and pine canopy cover were defined via digitization while bitterbrush cover, burrows, and refuge locations were mapped at the study site using a

Trimble GeoExplorer 7x. 1 meter resolution images from the National Agriculture Imagery

Program (NAIP) were used to digitize areas of pine canopy cover. In the field, bitterbrush shrubs were mapped as a continuous stand if <1 meter apart. Refuge locations consisted of stumps, boulders, and fallen logs under which rodent tunnels were found (Figure 2). These locations were mapped as either points (<1 meter in diameter) or polygons (> 1 meter in diameter). If refuge locations such as boulders were <1 meter apart, they were mapped as a continuous polygon. Distance to the nearest refuge area was calculated using the Near tool in ArcMap.

Separate rasters were created for canopy cover and distance to the home burrow. Distance from the home burrow was represented using a Euclidean distance raster with each burrow as the point of origination (Figure 3).

Resource selection functions (RSFs) were used to determine the probability of selection or avoidance of a particular resource (Manly et al. 2002). These functions can be used to model resource use within and between seasons. RSFs, though relatively simplistic in nature, are often used to inform movement connectivity analyses and are flexible in their ability to analyze changes in habitat selection over time. Because RSFs are usually informed by GPS or telemetry data, the model output provides useful information needed to make inferences about behavioral decisions made by a target species. In a way, RSFs may provide a baseline from which other applied and experimental models can draw to make conservation decisions (Chetkiewicz et al. 2006; Hebblewhite & Merrill 2008; Johnson et al. 2004). Though, RSFs have most commonly been used in studies of large mammals, they can be very helpful in determining resource use by small mammals that experience unique habitat constraints (Hough & Dieter

2009; Barker & Derocher 2010; Perkins & Conner 2004; Doumas & Koprowski 2013). To create the resource selection function, categories of used and available points were designated (McKee et al. 2015, Long et al. 2014). Points logged by squirrels represented habitat used by each squirrel (used points) while habitat available to that squirrel was represented by creating random points to represent availability. Random points were generated within each home range for each squirrel generally at a 3:1 ratio of randomly generated points and used points. Random points were generated so that they proportionally represented the vegetative cover types within the home range. When used points were few (<60), to achieve these proportional random points, the ratio had to be increased to 5:1. Raster values for distance to burrow and refuge were extracted for both used and random points.

Resource selection was evaluated for all individuals at the scale of the home range for each individual (third order scale Manly et al. 2002). Similarly, to represent variation in selection

of resources among seasons, three different mixed-effect logistic regression models, one per season, were designed, all including the same fixed effects of canopy cover and distance to burrow. All statistics were calculated in R version 3.1.2 using the lme4 package (R Development

Core Team 2011, Gillies et al. 2006, Bolker et al. 2009). Individual squirrels were modeled as a random effect to account for variation in number of locations collected among individuals (Long et al. 2014; McKee et al. 2015). Non-linearity was inspected for the continuous variable distance to burrow graphically using frequency histograms. When non-linearity was suspected, I included the continuous variable as both a linear and non-linear (quadratic) term (Hosmer & Lemeshow

2000). I then selected the best fit model using Akaike Information criterion adjusted for small sample sizes (AICc, Burnham and Anderson, 1998). Competitive models within each season were averaged, and averaged estimates were used to make inferences (Burnham & Anderson 1998).

Additionally, continuous variables were standardized to allow for direct comparison among parameter estimates (Neter et al. 1996; Stewart et al. 2015). For the discrete variable of canopy type, I chose pine canopy as the intercept because it comprised the majority of the study area. Additionally, most individuals make their burrows under open or no cover and would not use pine canopy as shelter while trying to avoid a predator (Shick et al. 2006). Positive values indicated avoidance for or distances further from a covariate while negative values indicated positive selection for or distances nearer to a covariate (Stewart et al. 2015). Because I was not using information from these models to predict habitat use, I did not run a k-fold cross- validation, which is often used to evaluate the predictive power of the model (Boyce et al.

2002).

RESULTS

Summary Statistics

I used the non-parametric Kruskal-Wallis test to analyze differences among distance to burrow and distance to refuge between seasons. A post-hoc Dunn’s test was used to test pairwise comparisons between seasons. All seasons were significantly different from each other for both distance to burrow (Kruskal-Wallis chi-squared = 180.36, df = 2, p-value<0.001) and distance to refuge (Kruskal-Wallis chi-squared = 242.06, df = 2, p-value< 0.001). In general, individuals traveled farthest from the burrow during the bitterbrush seed season (66±52 m), and remained relatively close to the burrow during the interval season (48±26 m) and Jeffrey pine seed season

(46±57 m). Individuals were active closer to refuge locations on average during the bitterbrush seed season (9±9 m) and Jeffrey pine seed season (7±16m) and traveled further from refuge locations during the interval season (12±13 m).

Resource Selection

A full model that included season as a fixed variable indicated that there were significant differences among seasons. Therefore, separate models were run for each season.

While all seasons included multiple models with Δ AICc scores<2, some of these top models included uninformative parameters (Arnold 2010; Burnham and Anderson 2002). During both the bitterbrush seed and interval seasons, there were three models with Δ AICc scores<2, but the additional parameters in the 2nd and 3rd best models were not significant in the model overall and did not improve model fit (Arnold 2010). Therefore, models were not averaged during these two seasons. In some models, distance to the burrow was best modeled as

quadratic. What this means is that individuals selected distances from the burrow that were intermediate—not directly adjacent to the burrow nor at great distances from the burrow. The top model for the bitterbrush seed season included significant (p<0.0001) selection for quadratic distance to burrow, linear distance to refuge locations (β=-0.7318 SE=0.0558), bitterbrush canopy cover (β=0.5929 SE=0.1510), and open canopy cover (β=0.7487 SE=0.0537)

(Table 1). During the season in which no seeds were available, the top model included significant selection for linear distance to burrow (β=-0.8450 SE=0.0298), quadratic distance to refuge, and avoidance of both bitterbrush canopy cover (β=-0.2373 SE=0.0647), and open canopy cover (β=-

0.1494 SE=0.0439) (Table 2). All parameters were significant (p<0.05). The pine seed season had four competitive models which were averaged (Table 3). All competitive models contained quadratic forms of standard distance. The second model contained linear distance to refuge while the third model contained quadratic distance to refuge. The fourth model was the only model to include canopy cover. When averaged, there was significant selection for quadratic distance to burrow, and linear distance to refuge (β =-0.3660 SE=0.1646).

While distance to burrow was best modeled as a quadratic effect for both foraging seasons, the quadratic form of distance to burrow did not significantly improve the fit of the model for the season in which seeds were not available. This result indicates that individuals were active at more intermediate distances from the burrow during seasons of seed availability rather than during the interval season in which individuals selected distances that were closer to the burrow. During all seasons, individuals selected distances closer to the burrow than available. However, this pattern was more pronounced during seasons of seed availability compared with the season of no seed availability. This result was evidenced the most during the pine seed season, the last food available before winter torpor (Figure 4). Selection for nearer

distances to refuge was present in all seasons, however, intermediate distances to the burrow were selected in both bitterbrush seed and pine seed seasons. Selection for closer distances to refuge was stronger during bitterbrush seed season compared to pine seed season (Figure 5).

During bitterbrush seed season, individuals significantly selected both open and bitterbrush canopy cover, though they selected open canopy cover slightly more than bitterbrush (Figure 6). During the season of no seed availability, individuals significantly avoided both open and bitterbrush canopy cover. During pine seed season, individuals showed no significant selection for or avoidance of either open or bitterbrush canopy cover relative to pine canopy cover.

DISCUSSION

The ability to balance conflicting behaviors is key to surviving in highly competitive and risky landscapes (Lima 1985; Pyke et al. 1977; Rothley et al. 1997). For C. lateralis, this balance means optimizing foraging efforts to meet energetic requirements while also minimizing the risk of predation and loss of food stores to pilferers. Individuals modified travel distances from the burrow and points of refuge, as well as differentially selected vegetative canopy cover by season in order to balance these conflicting demands. Contrary to our first prediction, during seasons of seed availability individuals strongly selected intermediate distances to the burrow, in contrast to selecting distances linearly closer to the burrow during the season of no seed availability. As predicted, selection for proximity to refuge occurred during seasons of seed availability when individuals foraged away from the burrow. Finally, selection for vegetative canopy cover reflected seed availability by season, also as predicted.

Risk of starvation and predation are two of the greatest threats to the survival of C. lateralis as well as other small rodents (Lima and Dill 1990). Nevertheless, how species choose to balance decisions regarding these pressures is largely dependent on the dynamic characteristics of seed density and energy requirements of the individual (Hilton et al. 1999; Sansom et al.

2009; Somers et al. 2012, Taraborelli et al. 2003; Walther & Gosler 2001). For central-place foraging species, the burrow acts as a protective shelter from predators (Andino et al. 2016;

Crowell et al. 2016; Hayes et al. 2007; Hendrie et al. 1998; Lagos et al. 2009). For C. lateralis, proximity to the burrow is also important to protect the larder from pilferers. While selection for intermediate distances from the burrow was evidenced for both seasons of seed availability, there was variation in the proximity of those intermediate distances traveled from the burrow

(see Figure 4). Individuals traveled furthest from the burrow on average during bitterbrush seed season compared with pine season. Bitterbrush is the first seed to become available in the summer and individuals are obligated to travel from the burrow to bitterbrush patches in which seeds are available. When and how individuals choose to maintain foraging activity in risky environments (i.e. at great distances from burrow) is dependent on seed availability and energy requirements of the individual (Kotler et al. 2004; Lima 1998, Suselbeek 2014). Some species choose to forego a predator avoidance strategy when energy requirements are high. For example, common redshanks (Tringa totanus) have been shown to prioritize foraging over predator avoidance when low temperatures caused increased metabolic costs in individuals

(Hilton et al. 1999). Also, tadpoles deprived of resources were more likely to maintain high levels of activity in predator-saturated environments (Anholt & Werner 1995).

In contrast to the longer distances traveled on average in bitterbrush season, individuals remained closest to the burrow on average during pine seed season (see Figure 4). During years

with high pine mast, pines seeds predominate other seed species in T. amoenus winter larders in Little Valley (Kuhn and Vander Wall 2009), indicating these seeds are highly valuable for winter survival. Unlike bitterbrush seeds, pine seeds are dispersed randomly by wind events, so individuals are not obligated to travel long distances if they encounter seeds closer to the burrow. Pilfering pressure may also be higher on individuals at this time during the year, as the larder becomes larger and more valuable. In a study testing the pilfering rates of red squirrels

(Tamiasciurus hudsonicus), squirrels with larger middens were pilfered from at a higher rate than squirrels with smaller middens (Gerhardt 2005). Before pine seed availability, Bihr and

Smith (1998) excavated C. lateralis burrows in July and August and reported that burrows did not contain large amounts of seeds. This result may indicate that individuals return to their burrows for shelter during bitterbrush seed season rather than to defend the larder, then switch to a larder-defense strategy during pine seed season when the larder is more valuable.

Although C. lateralis selected distances closest to the burrow within the pine seed season, they occasionally made longer trips from the burrow than in any other season (see

Figure 4). Pine seeds are incredibly important to C. lateralis at the onset of winter because pine seeds are high in crude protein and fat, more so than any other seed available to them throughout the year (Vander Wall 1994). Therefore, individuals may be willing to make a few long trips in order to harvest more seeds when seeds nearer to the burrow become depleted.

During seasons of seed availability, distance from burrow was best modeled as a quadratic relationship, indicating that individuals foraged primarily at intermediate distances from the burrow. Small mammal species must adopt resource use strategies that balance foraging and predator avoidance (Bonenfant & Kramer 1995; Thorson et al. 1998). This balance may best be achieved for C. lateralis by concentrating activity within an intermediate distance

from the burrow, where they may still forage, but still be able to return to the burrow frequently to return seeds to the larder as well as defend the larder from pilferers and avoid predation. This effect was not maintained when seeds were not available. Following bitterbrush season, individuals may simply be less inclined to travel frequently from the burrow when there are few seeds to collect. A forager’s harvest rate should decrease as resources in a patch become depleted (Brown 1999). A study of C. lateralis in Chelan County, Washington, indicated that C. lateralis reached peak body mass in August, well before winter torpor (Boswell et al.

1994). Field observation of C. lateralis in Little Valley also indicated a similar pattern, possibly signifying that individuals have gained enough mass to survive the summer and only need to gather pine seeds to supplement themselves during the winter.

If individuals are obligated to travel farther from the burrow to gather seeds, to reduce the risk of predation, proximity to refuge locations may be important. Although there are certainly more points of refuge than were detected in the study, overall, close proximity to locations of refuge may allow individuals to maximize their time foraging by reducing their risk of predation. As expected, when seeds were available, individuals selected distances closer to refuge locations (see Figure 2). Individuals made longer trips from the burrow during these seasons. Many other central-place foraging species have been shown to forage near to areas of cover when away from the nest or burrow (Bonenfant & Kramer 1995; Thorson et al. 1998). For example, small rodents in coastal California were less likely to decrease foraging activity when perceiving a predation risk when nearer to patches of protective cover (Johnson & De León

2015). These studies, as well as similar studies, used giving up densities (GUDs) to measure behavioral trade-offs, a technique rooted in optimal foraging theory (Brown 1988). Fox squirrels

(Sciurus niger) and grey squirrels (Sciurus carolinensis) fed nearer to trees in savannas and open

woodlands, which allowed them to reduce their GUDs when they perceived a predator threat

(Brown et al. 1992, Bowers et al. 1993, Brown and Morgan 1995). While GUDs have been extensively used to describe trade-offs associated with predator avoidance and foraging activity, because they are based on the optimal foraging theory, they are not robust to effects that other behavioral demands may have on the decisions made by individuals (Price & Correll 2001). GUDs infer decisions individuals make by using indirect indices of activity, whereas resource selection functions use direct measures of activity to make these inferences.

Although it may seem surprising that individuals select open canopy cover during the bitterbrush seed season, there are several reasons why individuals may want to use open canopy cover types while foraging. In particular, antelope bitterbrush grows most commonly in shrub stands with little to no overstory cover (Dyer et al. 7/8/2014). Additionally, traveling through open canopy is also more cost effective and may allow them to return to bitterbrush patches and harvest seeds more quickly (Kenagy & Hoyt 1989). Larger-bodied members of the granivore guild have even been shown to exploit riskier open areas to avoid competitive exclusion, and are able to do so because of their increased locomotion ability (Blumstein et al.

2004; Smith 1995; Vasquez 2002). Unlike their smaller-bodied competitors, larger desert rodents have been shown to select open canopy foraging patches of higher predation risk if the quality of seed is high (Kotler 1984). This differential selection of foraging patches also promotes coexistence between species within the granivore guild (Schoener 1974).

In contrast to the bitterbrush seed season, both open and bitterbrush canopy were significantly avoided during the interval season (see Figure 6). When seeds are not available, individuals should conserve energy, protect their larder, and avoid predation. During pine seed season, bitterbrush and open canopy was neither significantly selected nor avoided in

proportion to their availability. Pine seeds are scattered at random by the wind, so seed encounter rate could be just as likely near to the burrow as well as further from the burrow until availability of seeds near the burrow are depleted.

The conclusions made in this study are limited by small sample sizes. While there are great benefits to using GPS technology, it is many times more expensive than traditional VHF telemetry. As GPS equipment becomes smaller in size, more affordable, and thus more accessible for small mammal studies, conducting such analyses should be more feasible. One disadvantage of these units currently is that they only log data, requiring retrieval of the unit, and battery life can be relatively short depending on the number and frequency of fixes collected. Nevertheless, using GPS technology allows animals the ability to behave normally without the interruption of researchers actively tracking them. They also save time in the field, which can ultimately save money in travel expenditures. The most time-consuming aspect of using these units was trying to recapture the individuals wearing loggers. Moreover, burrowing animals present unique challenges to both GPS and telemetry equipment. Antennas can snap off in tunnels and animals can lose the logger altogether squeezing through narrow places. Loggers were shed inside burrow tunnels on a few occasions and were retrieved if they still emitted a

VHF signal. If the telemetry antenna was snapped off, the logger could not be tracked and retrieved. Two study animals disappeared from the site, mostly likely due to predation events, though one logger from one individual was found on top of a boulder inside the study site.

Overall, from field observation, loggers did not appear to inhibit movement or behavior of individuals in the field. Normal foraging activity, grooming, and pursuit of competitors was still observed. In the future, it would be advantageous to have lower profile units that have longer battery life, accurate internal antennas, and have the ability to emit a VHF signal.

Implications

C. lateralis adopt behavioral strategies aligning with their dynamic nutritional demands throughout the year. Members of the granivore guild in the eastern Sierra Nevada live in an environment of unique challenges: an unpredictable food source that is available for short periods of time each year. The availability of these seed sources are dependent on a healthy open forest canopy, a forest structure which is under threat due to current fire suppression.

Species with small populations, small home ranges, and limited dispersal capability may fall victim to diminishing and degrading habitat (Caughley 1994; Penaranda & Simonetti 2015,

Sherman & Runge 2002). Additionally, research has shown that, in comparison to regular forest thinning and prescribed burning, enclosed forest types lead to more destructive fires which have far more negative impacts on small mammal species (Kalies et al. 2010). Nevertheless, because of their ability to disperse seeds, granivorous rodents have been shown to play a key role in restoring biotic communities following fire (Briggs et al. 2009; Longland & Clements 1995; Steele et al. 2014; Wolff 2007). The decline of these species could impact the ability of the vegetative community to naturally restore itself post-fire. Because of its widespread distribution and ease of sampling, C. lateralis could be used as an umbrella species, to the effect that regular monitoring and conservation of their populations would also benefit the long-term survival of other members of this important guild. Specifically, fluctuations in C. lateralis populations likely are driven by decreased habitat suitability, specifically the loss of open tree canopy (Shick et al.

2006; McKeever 1964; Bartels & Thompson 1993; Hayward & Hayward 1995). Conducting regular studies such as this are essential for monitoring these important species and also ensuring the health of the habitat they occupy.

LITERATURE

Abramsky, Z, Strauss, E, Subach, A, Riechman, A, & Kotler, BP. 1996. The effect of barn owls

(Tyto alba) on the activity and microhabitat selection of Gerbillus allenbyi and G.

pyramidum. Oecologia 105: 313-319.

Anholt, BR, & Werner, EE. 1995. Interaction between food availability and predation mortality

mediated by adaptive behavior. Ecology 76: 2230-2234.

Arenz, CL, & Leger, DW. 2000. Antipredator vigilance of juvenile and adult thirteen-lined ground

squirrels and the role of nutritional need. Animal Behaviour 59: 535-541.

Arnold, TW. 2010. Uninformative parameters and model selection using Akaike’s Information

Criterion. Journal of Wildlife Management 74: 1175-1178.

Barker, OE, & Derocher, AE. 2010. Habitat selection by arctic ground squirrels (Spermophilus

parryii). American Society of Mammalogists 91: 1251-1260.

Bartels, MA, & Thompson, DP. 1993. Spermophilus lateralis. Mammalian Species 440:1-8.

Bedoya-Perez, MA, Carthey, AJR, Mella, VSA, McArthur, C, & Banks, PB. 2013. A practical guide

to avoid giving up on giving up densities. Behavioral Ecology and Sociobiology 87: 1541-

1553.

Béllisle, M. 2005. Measuring landscape connectivity: the challenge of behavioral landscape

ecology. Ecology (Durham) 86: 1988-1995.

Beyer, HL. 2012. The Geospatial Modelling Environment (150pp)

Bihr, K. J., and Smith, R.J. 1998. Location, structure, and contents of burrows of Spermophilus

lateralis and Tamias minimus, two ground-dwelling Sciurids. The Southwestern

Naturalist 43: 352-362.

Blake, BH. 1972. The annual cycle of fat storage in two populations of golden-mantled ground

squirrels. Journal of Mammalogy 53: 157-167.

Bolker, BM, Brooks, ME, Clark, CJ, Geange, SW, Poulsen, JR, Henry, HH, & White, JS. 2009.

Generalized linear mixed models: a practical guide for ecology and evolution. Trends in

Ecology and Evolution 24: 127-135.

Boswell, T, Woods, SC, & Kenagy, GJ. 1994. Seasonal changes in body mass, insulin, and

glucocorticoids of free-living golden-mantled ground squirrels. General and Comparative

Endocrinology 96: 339-346.

Bowers, MA, Jefferson, JL, & Kuebler, MG. 1993. Variation in giving-up densities of foraging

chipmunks (Tamias striatus) and squirrels (Sciurus carolinensis). Oikos 66: 229-236.

Boyce, MS, Vernier, PR, Nielson, SE, & Schmiegelow, FKA. 2002. Evaluating resource selection

functions. Ecological Modelling 157: 281-300.

Briggs, JS, Vander Wall, SB, & Jenkins, SH. 2009. Forest rodents provide directed dispersal of

Jeffrey pine seeds. Ecology 90: 675-687.

Brinkerhoff, RJ. 2005. Corridors and olfactory predator cues affect small mammal behavior.

Journal of Mammalogy 86: 662-669.

Brown, JS. 1988. Patch use as an indicator of habitat preference, predation risk and competition.

Behavioral Ecology and Sociobiology 22: 37-47.

Brown, JS. 1999. Vigilance, patch use and habitat selection: Foraging under predation risk.

Evolutionary Ecology Research 1: 49-71.

Brown, JS, Morgan, RA, & Dow, BD. 1992. Patch use under predation risk. II: a test with fox

squirrels, Sciurus niger. Annales Zoologici Fennici 29: 311-318.

Brown, JS, & Morgan, RA. 1995. Effects of foraging behavior and spatial scale on diet selectivity-

a test with fox squirrels. Oikos 74: 122-136.

Burnham, K. P. and Anderson, D. R. 2002. Model Selection and Multimodel Inference: a Practical

Information-theoretic Approach, second ed. – Springer.

Carter, K, and Goldizen, AW. 2003. Habitat choice and vigilance behaviour of brush-tailed rock-

wallabies (Petrogale penicillata) within their nocturnal foraging ranges. Wildlife

Research 30: 355-364.

Carthey, AJ, & Banks, PB. 2015. Foraging in groups affects giving-up densities: solo foragers quit

sooner. Oecologia 178: 707-713.

Castleberry, SB, Ford, MW, Wood, PB, Castleberry, NL, & Mengak, MT. 2001. Movements of

Allegheny woodrats in relation to timber harvesting. The Journal of Wildlife

Management 65: 148-156.

Caughley, G. 1994. Directions in conservation biology. Journal of Animal Ecology 63: 215-244.

Chetkiewicz, CLB, St. Clair, CC, & Boyce, MS. 2006. Corridors for conservation: integrating

pattern and process. Annual Review of Ecology Evolution and Systematics 37: 317-342.

Cohen, JE, Pimm, SL, Yodzis, P, & Saldaña, J. 1993. Body sizes of animal predators and prey in

food webs. Journal of Animal Ecology 62: 67-78.

Converse, SJ, Block, WM, & White, GC. 2006. Small mammal population and habitat responses

to forest thinning and prescribed fire. Forest Ecology and Management 228: 263-273.

Crowell, MM, et al. 2016. Selection of food patches by sympatric herbivores in response to

concealment and distance from a refuge. Ecology and Evolution 6: 2865-2876.

Dally, JM, Clayton, NS, & Emery, NJ. 2006. The behaviour and evolution of cache protection and

pilferage. Animal Behaviour 72: 13-23.

Dill, LM, and Fraser, AHG. 1984. Risk of predation and the feeding behavior of juvenile coho

salmon (Oncorhynchus kisutch). Behavioral Ecology and Sociobiology 16: 65-71.

Doumas, SL, & Koprowski, JL. 2013. Effect of heterogeneity in burn severity on Mexican fox

squirrels following the return of fire. International Journal of Wildland Fire 22: 405-413.

Downs, C.J., and Vander Wall, S.B. 2009. High relative humidity increases pilfering success of

yellow pine chipmunks. American Society of Mammalogists 90: 796-802.

Dyer, D, O’Beck, R, & Noller, GL. 07/08/2014. USDA & NRCS Plant Guide: Antelope Bitterbrush

(Purshia tridentata (Pursh) DC. Retrieved from

https://plants.usda.gov/plantguide/pdf/pg_putr2.pdf

Ferron, J. 1985. Social behavior of the golden-mantled ground squirrel (Spermophilus lateralis).

Canadian Journal of Zoology 63: 2529-2533.

Gerhardt, F. 2005. Food pilfering in larder-hoarding red squirrels (Tamiasciurus hudsonicus).

Journal of Mammalogy 86: 108-114.

Gillies, CS, Hebblewhite, M, Nielsen, SE, Krawchuk, MA, Aldridge, CL, Frair, JL, Saher, DJ, Stevens,

CE, & Jerde, CL. 2006. Application of random effects to the study of resource selection

by animals. Journal of Animal Ecology 75: 887-898.

Hayward, GD, & Hayward, PH. 1995. Relative abundance and habitat associations of small

mammals in Chamberlain Basin, Central Idaho. Northwest Science 69: 114-125.

Hebblewhite, M, & Merrill, E. 2008. Modelling wildlife-human relationships for social species

with mixed-effects resource selection models. Journal of Applied Ecology 45: 834-844.

Hernandez-Pliego, J, Rodriguez, C, & Bustamante, J. 2014. Gone with the wind: seasonal trends

in foraging movement directions for a central-place forager. Current Zoology 60: 604-

615.

Hilton, GM, Ruxton, GD, & Cresswell, W. 1999. Choice of foraging area with respect to predation

risk in redshanks: the effects of weather and predator activity. Oikos 87: 295-302.

Hough, MJ, & Dieter, CD. 2009. Resource selection habitat model for northern flying squirrels in

the Black Hills, South Dakota. American Midland Naturalist 162: 356-372.

Hosmer, DW, & Lemeshow, S. 2000. Applied Logistic Regression. John Wiley and Sons. New

York. USA

Jesmer, BR, Van Vuren, DH, Wilson, JA, Kelt, DA, & Johnson, ML. 2011. Spatial organization in

female golden-mantled ground squirrels. The American Midland Naturalist 65: 162-168.

Johnson, CJ, Seip, DR, & Boyce, MS. 2004. A quantitative approach to conservation planning:

using resource selection functions to map the distribution of mountain caribou at

multiple spatial scales. Journal of Applied Ecology 41: 238-251.

Johnson, MD, & De León, YL. 2015. Effect of an invasive plant and moonlight on rodent foraging

behavior in a coastal dune ecosystem. PLoS One 10: e0117903.

Kalies, EL, Chambers, CL, & Covington, WW. 2010. Wildlife responses to thinning and burning

treatments in southwestern conifer forests: a meta-analysis. Forest Ecology and

Management 259: 333-342.

Kenagy, G.J., and Hoyt, D.F. 1989. Speed and time-energy budget for locomotion in golden-

mantled ground squirrels. Ecology 70: 1834-1839.

Kenagy, GJ, Stevenson, RD, & Masman, D. 1989. Energy requirements for lactation and postnatal

growth in captive golden-mantled ground squirrels. Physiological Zoology 62: 470-487.

Knowlton, JL, & Graham, CH. 2010. Using behavioral landscape ecology to predict species’

responses to land-use and climate change. Biological Conservation 143: 1342-1354.

Kotler, BP. 1984. Risk of predation and the structure of desert rodent communities. Ecology 65:

689-701.

Kotler, BP. 1992. Behavioral resource depression and decaying perceived risk of predation in

two species of coexisting gerbils. Behavioral Ecology and Sociobiology 30: 239–244.

Kotler, BP, Brown, JS, & Mitchell, W. 1994. The role of predation in shaping the behavior,

morphology, and community organization of desert rodents. Australian Journal of

Zoology 42: 449-466.

Kotler, BP, Brown, JS, & Bouskila, A. 2004. Apprehension and time allocation in gerbils: the

effects of predatory risk and energetic state. Ecology 85: 17-922.

Kuhn, K.M., and Vander Wall, S.B. 2008. Linking summer foraging to winter survival in yellow

pine chipmunks (Tamias amoenus). Oecologia 157: 349-360.

Lagos, PA, Meier, A, Ortiz, L, Castro, RA, Bozinovic, F, & Ebensperger, LA. 2009. Flight initiation

distance is differentially sensitive to the costs of staying and leaving food patches in a

small-mammal prey. Canadian Journal of Zoology 87: 1016-1023.

Lima, SL. 1985. Maximizing feeding efficiency and minimizing time exposed to predators: a

trade-off in the black-capped chickadee. Oecologia 66: 60-67.

Lima, SL, & Zollner, PA. 1996. Towards a behavioral ecology of ecological landscapes. Trends in

Ecology and Evolution 11: 131-136.

Lima, SL. 1998. Initiation and termination of daily feeding in dark-eyed juncos: influences of

predation risk and energy reserves. Oikos 53: 3-11.

Longland, WS, & Clements, C. 1995. Use of fluorescent pigments in studies of seed caching by

rodents. Journal of Mammalogy 76: 1260-1266.

MacArthur, RH, & Pianka, ER. 1966. On optimal use of a patchy environment. The American

Naturalist 100: 603-609.

Mandelik, Y, Jones, M, & Dayan, T. 2003. Structurally complex habitat and sensory adaptations

mediate the behavioural responses of a desert rodent to an indirect cue for increased

predation risk. Evolutionary Ecology Research 5: 501-515.

Manly, BFJ, McDonald, LL, Thomas, DL, McDonald, TL, & Erikson, W. 2002. Resource Selection by

Animals: Statistical Design and Analysis for Field Studies, 2nd Edition, Kluwer Press, New

York.

McKeever, S. 1964. The biology of golden-mantled ground squirrel, Citellus lateralis. Ecological

Monographs 34: 383-401.

Mooij, WM, & DeAngelis, DL. 2003. Uncertainty in spatially explicit animal dispersal models.

Ecological Applications 13: 794-805.

Neter, JM, et al. 1996. Applied Linear Statistical Methods, 4th Edition. McGraw-Hill.

Penaranda, DA, & Simonetti, JA. 2015. Predicting and setting conservation priorities for Bolivian

mammals based on biological correlates of the risk of decline. Conservation Biology 29:

834-843.

Perkins, MW, & Conner, LM. 2004. Habitat use of fox squirrels in southwestern Georgia. Journal

of Wildlife Management 68: 509-513.

Pyke, GH, Pulliam, HR, & Charnov, EL. 1977. Optimal foraging: a selective review of theory and

tests. The Quarterly Review of Biology 52: 137-154.

R Development Core Team. 2011. R Development Core Team, R: a Language and Environment

for Statistical Computing.-R Foundation for Statistical Computing.

Rothley, KD, Schmitz, OJ, & Cohon, JL. 1997. Foraging to balance conflicting demands: novel

insights from grasshoppers under predation risk. Behavioral Ecology 8: 551-559.

Sansom, A, Lind, J, & Cresswell, W. 2009. Individual behavior and survival: the roles of predator

avoidance, foraging success, and vigilance. Behavioral Ecology 20: 1168-1174.

Sawyer, SC. 2011. Placing linkages among fragmented habitats: do least-cost models reflect how

animals use landscapes? Least-cost modelling for habitat linkage design. The Journal of

Applied Ecology 48: 668-678.

Schoener, TW. 1974. Resource partitioning in ecological communities. Science 185: 27-39.

Sih, A. 1980. Optimal behavior: can foragers balance two conflicting demands? Science 210:

1041-1043.

Seaman, DE. 1999. Effects of sample size on kernel home range estimates. The Journal of

Wildlife Management 63: 739-747.

Sherman, PW, & Runge, MC. 2002. Demography of a population collapse: the northern Idaho

ground squirrel (Spermophilus brunneus brunneus). Ecology 83: 2816-2831.

Shick, KR, Pearson, DE, & Ruggiero, LF. 2006. Forest habitat associations of the golden-mantled

ground squirrel: implications for fuels management. Northwest Science 80: 133-139.

Sikes, RS, Gannon, WL, et al. 2016. 2016 Guidelines of the American Society of Mammalogists

for the use of wild mammals in research and education. Journal of Mammalogy 97:663–

688.

Simberloff, D, & Dayan, T. 1991. The guild concept and the structure of ecological communities.

Annual Review of Ecology and Systematics 22: 115-143.

Smith, RJ. 1995. Harvest rates and escape speeds in 2 coexisting species of montane ground-

squirrels. Journal of Mammalogy 76:189-195.

Somers, N, Milotic, T, & Hoffman, M. 2012. The impact of sward height, forage quality and

competitive conditions on foraging behaviour of free-ranging rabbits (Oryctolagus

cuniculus L.). Belgian Journal of Zoology 142: 74-85.

Steele, MA, Contreras, TA, Hadj-Chikh, LZ, Agosta, SJ, Smallwood, PD, & Tomlinson, CN. 2014. Do

scatter hoarders trade off increased predation risks for lower rates of cache pilferage?

Behavioral Ecology 25: 206-215.

Stevenson-Holt, CD, Watts, K, Bellamy, CC, Nevin, OT, & Ramsey, AD. 2014. Defining landscape

resistance values in least-cost connectivity models for the invasive grey squirrel: a

comparison of approaches using expert-opinion and habitat suitability modelling. PLOS

One 9: e112119.

Stewart, KM, Walsh, DR, Kie, JG, Dick, BL, & Bowyer, RT. 2015. Sexual segregation in North

American elk: the role of density dependence. Ecology and Evolution 5: 709-721.

Taraborelli, PA, Dacar, M, & Giannoni, SM. 2003. Effect of plant cover on seed removal by

rodents in the Monte Desert (Mendoza, Argentina). Austral Ecology 28: 651-657.

Thorson, JM, Morgan, RA, Brown, JS, & Norman, JE. 1997. Direct and indirect cues of predatory

risk and patch use by fox squirrels and thirteen-lined ground squirrels. Behavioral

Ecology 9: 151-157.

Vander Wall, S.B. 1994. Seed fate pathways of antelope bitterbrush: dispersal by seed-caching

yellow pine chipmunks. Ecology 75: 1911-1926.

Vander Wall, S.B. 2000. The influence of environmental conditions on cache recovery and cache

pilferage by yellow pine chipmunks (Tamias amoenus) and deer mice (Peromyscus

maniculatus). Behavioral Ecology 11: 544-549.

Vander Wall, S.B. 2002. Masting in animal-dispersed pines facilitates seed dispersal. Ecology 83:

3508-3516.

Vander Wall, SB, Hager, ECH, and Kuhn, KM. 2005. Pilfering of stored seeds and the relative

costs of scatter-hoarding versus larder-hoarding in yellow pine chipmunks. Western

North American Naturalist 65: 248-257.

Vander Wall, SB, Enders, MS, & Waitman, BA. 2009. Asymmetrical cache pilfering between

yellow pine chipmunks and golden-mantled ground squirrels. Animal Behaviour 78: 555-

561.

Verdolin, JL. 2006. Meta-analysis of foraging and predation risk trade-offs in terrestrial systems.

Behavioral Ecology and Sociobiology 60: 457-464.

Walther, BA, & Gosler, AG. 2001. The effects of food availability and distance to protective cover

on the winter foraging behaviour of tits (Aves: Parus). Oecologia 129: 312-320.

Wolff, JO. 2007. Social biology of rodents. Integrative Zoology 2: 193-204.

Table 1: Top model (lowest Δ AIC) for our resource selection function during bitterbrush seed season. Six individuals were studied July 15-31, 2014, in the Whittell Forest and Wildlife Area, NV, USA. Category Parameter β SE Confidence Interval Significance Level (0.05) Lower Upper

Intercept -- -1.9819 1.6683 -5.2519 1.2880 -- Canopy Cover Bitterbrush 0.5929 0.1510 0.2970 0.8889 *** Open 0.7487 0.0537 0.6434 0.8540 *** Continuous Distance Linear to Burrow -1.5257 0.0891 -1.7003 -1.3510 *** Quadratic to Burrow 0.7973 0.1016 0.5981 0.9965 *** Linear to Refuge -0.7318 -0.7318 -0.8411 -0.6224 ***

Table 2: Top model (lowest Δ AIC) for our resource selection function during the interval season in which seeds were not available. Seven individuals were studied August 1-September 6, 2014, in the Whittell Forest and Wildlife Area, NV, USA.

Category Parameter β SE Confidence Interval Significance Level (0.05) Lower Upper

Intercept -- -2.0258 0.1895 -2.3972 -1.6544 *** Canopy Cover Bitterbrush -0.2373 0.0647 -0.3641 -0.1105 *** Open -0.1494 0.0439 -0.2353 -0.0635 *** Continuous Distance Linear to Burrow -0.8450 0.0298 -0.9033 -0.7866 *** Quadratic to Refuge -0.1081 0.0277 -0.1624 -0.0538 ***

Table 3: Top model (lowest Δ AIC) for our resource selection function during the pine seed season in which seeds were not available. Seven individuals were studied September 7-September 15, 2014, in the Whittell Forest and Wildlife Area, NV, USA.

Category Parameter β SE Confidence Interval Significance Level (0.05)

Lower Upper Intercept -- -2.0258 0.1895 -2.3972 -1.6544 *** Continuous Distance Linear to Burrow -4.2398 0.1883 -4.6089 -3.8707 *** Quadratic to Burrow 3.2063 0.1696 2.8739 3.5387 *** Linear to Refuge -0.3472 0.1638 -0.6683 -0.0261 *

0 0.075 0.15 km

Figure 1: Vegetative canopy cover layer used to designate selection or avoidance of habitat patch types. Open and Jeffrey pine canopy cover types were digitized from 1.0 m resolution aerial images from the National Agriculture Imagery Program (NAIP) and bitterbrush data were collected in the field using a Trimble Geo 7x. Study site was located within the Whittell Forest and Wildlife Area, NV.

Refuge

0 0.075 0.15 km

Figure 2: Refuge locations mapped in the field with a Trimble Geo 7x. Refuge locations consisted of stumps, boulders, fallen trees, and brush piles. Study site was located within the Whittell Forest and Wildlife Area, NV.

Euclidean Distance (m) Burrow Incident Points

High : 395

Low : 0

Figure 3: Euclidean distance rasters used to calculate distance (m) traveled from the burrow for each point. Rasters were created using the Euclidean distance tool in ArcMap 10.3. A) was taken during bitterbrush seed season, B) was taken during the interval season, and C) was taken during pine seed season.

Available A Used

Point Density Point

Distance to Burrow (m)

Figure 4: Point density plots of distance to burrow (m) from all three seasons: A) Bitterbrush, B) Interval, and C) Pine. The dark gray indicates used points (gained from GPS loggers) and the light gray indicates randomly generated available points.

A B Available

Used

Point Density Point

Distance to Refuge (m)

Figure 5: Density plots of distance to refuge (m) from all three seasons: A) Bitterbrush and B) Pine. The dark gray indicates used points (gained from GPS loggers) and the light gray indicates randomly generated available points.

Figure 6: Model generated beta estimates for categorical vegetative canopy cover for two seasons: A) Bitterbrush and B) Interval. Positive values indicate selection, negative values indicate avoidance. Jeffrey pine canopy cover was used as a reference for both seasons.

Chapter 2

Spatiotemporal analysis of movement behavior of a central-place forager in an environment of conflicting demands

ABSTRACT

Time allocated to competing behaviors in central-place foraging species plays a large role in determining long-term survival. Individuals must maximize their energy intake each day while also devoting time to antipredator behavior as well as travel between patches and to-and-from their burrows. Golden-mantled ground squirrels (Callospermophilus lateralis) are larder- hoarding rodents that rely on an ephemeral availability of food each year, which they compete over with other granivores. To avoid competition, this species must exploit riskier patch types in order to access food. We used time local convex hull analyses to determine how this species balances time allocated to conflicting behaviors to maximize fitness early in the year.

Specifically, we predicted individuals would maximize duration of time spent at distances farther from the burrow, as a function of intermittent antipredator vigilance activity and seed patch exploitation. We also predicted individuals would have high rates of revisitation, but low visit duration in risky canopy cover types that serve as corridors connecting high value seed patches.

Our results supported our predictions and suggest that inclusion of temporal data in spatial

analyses is important to fully understand the dynamic behavioral trade-offs incurred by individuals as they travel throughout their home ranges.

INTRODUCTION

All species must optimize their foraging efforts by balancing the energy gained while foraging with the energetic cost of foraging and predation risk (Brown 1988). Species that forage on the same food sources each year have evolved several strategies to mediate these costs while also avoiding competitive exclusion. These strategies include temporal or spatial differentiation in resource partitioning (Chesson 2000; Kotler et al. 2002; Schoener 1974). For example, in deserts, nocturnal granivorous rodents avoid interference competition with their diurnal competitors and also reduce predation risk by modifying their foraging activity according to different moon phases (Kronfeld-Schor & Dayan 2003; Upham & Hafner 2013). Other species mediate interspecific competition by exploiting different parts of a foraging patch to reduce competition, sometimes resulting in species exploiting riskier patch types (MacArthur & Levins 1964;

Wondolleck 1978). Morphological, physiological, and behavioral adaptations caused by competition allows species to partition dietary niches in this manner (Kotler 1984; Smith 1991;

Thompson 1982). Some of these adaptations include larger body size, increased locomotion, and increased bouts of vigilance during foraging. Yellow-bellied marmots (Marmota flaviventris), a large-bodied rodent capable of exploiting riskier patches, spend 63% of their time vigilant to reduce predation risk while foraging (Armitage & Salsbury 2016). Other species, such as degus

(Octodon degas), can increase their travel speed in risky patches (Vasquez et al. 2002). Larger

species in heteromyid communities additionally increase their harvest rates in risky patches

(Price & Heinz 1984). Gerbils exploiting riskier patches in the Negev desert are larger than their competitors and concurrently have increased locomotion and harvests rates (Ovadia et al.

2001).

For species exploiting these risky patches, balancing time allocation to foraging and antipredator vigilance become two very important factors in deciding when to continue and when to quit foraging activity (Newman 1991). Particularly for these species, continuing to forage in risky patches should correlate to higher feeding rates (Pulliam et al. 1982, Dehn 1990,

McNamara and Houston 1992). As soon as food sources become depleted, the costs associated with foraging in risky patches outweigh the benefits and individuals should cease foraging activity (Brown 1988). However, abandoning a foraging patch too soon incurs the cost of lost opportunity to individuals. Increasing time spent vigilant while foraging to avoid predation then becomes particularly important in minimizing cost associated with lost opportunities so that individuals can maximize time allocation in foraging patches and meet their energetic requirements (Brown 1999). Species that exploit risky patches that do not increase vigilance activity face increased mortality risk. Juvenile and hungry individuals often have reduced antipredator vigilance behavior, which has been argued to increase mortality rates in comparison to satiated adults (Bachman 1993; Sih 1980). Juvenile thirteen-lined ground squirrels, which experience higher mortality rates than adults, also devoted more time to foraging and less time to vigilance when nutritional needs were not met (Arenz & Leger 2000).

Balancing time spent in antipredator vigilance, time spent foraging, and time spent traveling between patches becomes critically important to these granivorous rodents. Optimal foraging theory is established on the basis of the equilibrium of time devoted to searching for

and harvesting food (MacArthur & Pianka 1966; Pyke et al. 1977). If too much time is allocated to antipredator vigilance, individuals may not be able to gather enough food to meet their energetic needs. If too much time is spent foraging in absence of vigilance or traveling through risky matrices, individuals increase their risk of predation. Individuals must optimize their foraging efforts when they are able to modify their timing of movement behavior in a way that increases efficiency of foraging while minimizing the associated risks (Brown 1999). While this balance of behaviors has commonly been argued, there are few methods that accurately measure how individuals allocate time spent in each behavior. Giving-up densities (GUDs) are one method that have been extensively used to measure trade-offs between behaviors such as foraging effort and predator avoidance by measuring seed density left in patches or experimental seed trays placed at various distances within and between previously selected areas of interest (Brown 1988). However, GUDs do not measure the amount of time individuals spend at the seed tray or how frequently individuals may revisit that seed tray to reduce their risk of predation. GUDs are also founded on the marginal value theorem, which was designed to measure how predators optimize their foraging efforts by how much time they spend in depleting patches (Charnov 1976), which was then modified by Brown (1988) to incorporate predation risk, searching and processing costs, thermoregulatory costs, and missed opportunity costs. Nevertheless, time is often not explicitly measured in studies using GUDs, particularly time spent in antipredator behaviors, such as vigilance, that may allow animals to spend more time in a foraging patch than otherwise predicted by the theoretical model (Nonacs 2001; Price

& Correll 2001; Verdolin 2006). In contrast, activity budgets collected via direct observation strictly describe time allocation of specific behaviors of interest. These studies are limited,

however, by the observer’s ability to perceive all activity, something that is particularly difficult to do in secretive species or visually obscured environments (Christiansen et al. 2013).

The use of GPS technology has allowed researchers to analyze movement behavior beyond the constraints of direct observation and biases of theoretical models. Nevertheless, though time is an inherent aspect of GPS data, it is often disregarded in final analyses.

Previously, use of regularly collected GPS data were critiqued for their serial autocorrelation, however, in recent years this autocorrelation has been recognized as an important factor in detecting patterns in movement behavior (Benhamou 2011; Boyce et al. 2010). Methods developed in response to the increased demand to incorporate serial correlation, specifically in home range analyses, include Brownian bridge movement models, movement based kernel density, and time geography density estimation. Another emerging method is the time local convex hull method (Lyons et al. 2013). T-LoCoH is a non-parametric method for aggregating hulls created around each point in a dataset. Rather than classifying resource strictly on static point density, T-LoCoH uses the time stamps from each point collected to supplement the partitioning of patch use by time. This method allows the researcher to infer the amount of time spent in a patch and how many times an individual revisits a patch. This spatiotemporal data can be incredibly informative when determining how individuals allocate time to foraging to maximize fitness while concurrently reducing the risk of predation and avoiding competitive exclusion.

Golden-mantled ground squirrels (Callospermophilus lateralis) were used as a study subject for this analysis. They are members of the granivore guild in the eastern Sierra Nevada.

Members of this guild compete for the same limited source of seeds each year and are subject to predation risk and starvation (Simberloff & Dayan 1991). These species use dynamic

behavioral trade-offs to minimize these costs and increase their fitness. C. lateralis are central- place foragers who store seeds in one burrow throughout the year, in contrast to their dominant competitors—predominantly yellow-pine chipmunks (Tamias amoenus)—who scatter-hoard seeds prior to bringing them into a larder preceding winter torpor (Bartels &

Thompson 1993; Vander Wall 1994). C. lateralis are also larger than most of their competitors and are capable of faster travel speeds between patches and faster harvesting rates (Smith

1995). These adaptations could indicate antipredator behavior as well as a larder-defense strategy, because their larder is prone to pilfering attempts from competitors with superior olfactory senses (Downs and Vander Wall 2009; Vander Wall et al. 2009). These attributes have evolved in response to high levels of competition and are commonly attributed to species that are capable of foraging in riskier patch types (Kotler 1984; Smith 1991; Thompson 1982).

Therefore, C. lateralis must balance time spent in foraging, predator avoidance, and larder defense in order to maximize fitness.

This study was conducted during the first available seed harvest of a foraging year. Prior to this season, individuals survived predominantly on seeds left in the burrow from the previous year and scant availability of forbs within the study site. Individuals were likely in an energy deficit at the beginning of this study, suggesting they may trade-off predator avoidance for foraging. However, antipredator vigilance behavior could still play a large role in influencing movement behavior between and within foraging patches. To address how individuals balance time allocated to these conflicting strategies, we formulated three predictions: 1) Because the larder is likely empty and individuals are in an energy deficit, individuals will seek to maximize their foraging efforts by spending longer periods in foraging patches, 2) Risky matrices that individuals must repeatedly travel through in order to access seed patches will be characterized

by low visit duration and high rates of revisitation, and 3) Duration of time spent in areas further from the burrow and locations of refuge will increase because of the clumped distribution of seed patches at greater distances from the burrow and the need to maintain antipredator vigilance when escape is difficult.

METHODS

Study Site

This study was conducted within a 1.3 km² area in the Whittell Forest and Wildlife Area in Little

Valley, Washoe County, about 30 km south of Reno, Nevada, USA (39°15’0”N, 119°52’35”W).

(Arctostaphylos patula), tobacco bush (Ceanothus velutinus), and Sierra bush chinquapin

(Castanopsis sempervirens). The portion of Little Valley selected for this study had a dominant open tree canopy of Jeffrey pine and a patchy understory of antelope bitterbrush. During the time in which this study was conducted, antelope bitterbrush was the only shrub producing seeds.

Animal Handling and GPS Data

During July 15-31, 2014, six adult C. lateralis were captured in Tomahawk traps and handled in accordance with a protocol approved by the Institutional Animal Care and Use Committee

(IACUC, #A07/08-30) that was in keeping with guidelines established by the American Society of

Mammalogists for use of wild mammals in research (Sikes et al. 2016). GiPSy 5 GPS loggers supplied by TechnoSmart Europe s.r.l. and very high frequency (VHF) transmitters supplied by

Advanced Telemetry Systems Inc. were used to record movement behavior of individuals and retrieve them. GPS loggers were attached to squirrels using zip-ties that were threaded through soft cords of fabric, which were then secured across the neck and chest of individuals. GPS loggers were programmed to collect one fix every ten seconds only during periods of activity when the squirrel was aboveground. On this schedule, batteries in the loggers lasted 2-3 days.

Distance and Vegetative Canopy Cover Type Parameter Designation

A vegetation raster with 1-meter resolution was created in ArcMap 10.3 that categorized three dominant canopy cover types: open, pine, and antelope bitterbrush. Open canopy was categorized by no overstory and no understory cover. Pine canopy was categorized by a dominant overstory of Jeffrey pine and no understory cover. Bitterbrush canopy was categorized by a dominant bitterbrush understory regardless of whether or not pine was present as overstory or not. Open and pine canopy cover were defined via digitization while bitterbrush cover, burrows, and refuge locations were mapped at the study site using a Trimble

GeoExplorer 7x. One meter resolution images from the National Agriculture Imagery Program

(NAIP) were used to digitize areas of pine canopy cover. In the field, bitterbrush shrubs <1 meter apart were mapped as a continuous stand. Refuge locations consisted of stumps, boulders, and fallen logs under which rodent tunnels were found. These locations were mapped as either points (<1 meter in diameter) or polygons (> 1 meter in diameter). If refuge locations such as boulders were <1 meter apart, they were mapped as a continuous polygon. Distance to the

nearest refuge area was calculated using the Near tool in ArcMap for each point from each individual. Separate rasters were created for canopy cover and distance to the home burrow.

Distance from the home burrow was represented using a Euclidean distance raster with each burrow as the point of origination. Values from these rasters were extracted to each point for each individual.

Time Local Convex Hull and Statistical Analysis

Within the T-LoCoH format, I designed methods to measure revisitation rates and visit duration using GPS data. T-LoCoH is not a method that is entirely preconfigured, meaning much of the analysis requires appropriate knowledge of the study subject and how it uses its habitat in order to design code and obtain meaningful results. All code was run in program R version 3.1.2

(R Development Core Team 2011) using the T-LoCoH package (Lyons & Getz R Development

Core Team 2014). T-LoCoH operates similarly to the previously developed local convex hull method, in that it designs hulls around clusters of points defined by the analyst using either the k (nearest neighbor), r (fixed radius), or a (adaptive radius) method. Distinctively, T-LoCoH uses time to also define how nearest neighbors are selected and how hulls are sorted. This method is accomplished by using time-scaled distance, which incorporates the use of a spatiotemporal scaling parameter (for additional information, see Lyons et al. (2013)).

To begin the analysis, the analyst selects a scaling parameter s, which determines to what degree temporal influence is used to define nearest neighbors that are used to create local hulls. Values of s increasing from zero indicate an increase in the amount of temporal influence.

It is generally suggested to select a value of s so that 40-80% of hulls created are time-selected

(Lyons & Getz 2014). In my analysis, I selected values of s closest to 60%. Additionally, nearest

neighbor selection is determined using either the k, r, or a method, as mentioned above. I used the a, or adaptive, method to construct hulls. In this method, the cumulative distance between a parent point and its neighbors is defined and used to construct hulls. This method is recommended because it minimizes spurious massive hulls or incredibly small hulls that are a result of defining a set number of neighbors to be included in the creation of a hull, as occurs in the commonly used k-method. Values of a are determined via visual analysis of hulls generated and using knowledge of the study subject. I selected 190 as my value for a for all squirrels, which was largely dependent on the size of the area used by each individual, the dispersion of points within that area, and if I saw that hulls avoided encompassing spurious data points while also covering holes within the core areas of activity. Lastly, an inter-visit gap (IVG) value was assigned to define which points were considered revisits. I chose an IVG of 5 minutes based on field observation of individuals foraging in the field. Therefore, separate visits were identified when an individual left a hull and then returned after a period of at least 5 minutes had passed.

Rates of revisitation (number of separate visits to a hull: nsv) as well as visit duration

(mean number of locations per visit to a hull: mnlv) were then assigned to each point within each hull. These values were used to determine if distance parameters to the burrow and refuge locations were correlated with high or low occurrences of revisitation using Spearman’s correlation coefficient. To determine if there was difference in revisitation and visit duration among vegetative canopy cover types, a random effects ANOVA was used, with individual squirrel as the random effect. Revisitation to and duration of time spent in bitterbrush patches was of particular interest because this was the only seed available at the time of this study.

RESULTS

Distance Parameters

Revisitation rates were identified as the number of separate visits to a hull following a inter-visit gap of 60 seconds (nsv). Visit duration was identified as the mean number of locations per visit to a hull within and inter-visit gap of 60 seconds (mnlv). Correlation coefficients were fairly small overall, but displayed distinctly different patterns between duration and revisitation rates— where revisitation was negatively correlated with both distance parameters and visit duration was positively correlated with distance to burrow, though not strongly correlated with distance to refuge (Figure 1). There was a significant negative correlation between visit duration (mnlv) and revisitation rate (nsv) across all patches (Spearman’s rho=-0.29, p<0.001). As individuals traveled farther from the burrow, visit duration increased (Spearman’s rho=0.17, p<0.001), while revisitation rate decreased (Spearman’s rho=-0.39, p<0.001). Revisitation rate was also negatively correlated with distance to refuge (Spearman’s rho=-0.23, p<0.001). There was no significant correlation between visit duration and distance from refuge (Spearman’s rho=0.13, p>0.05). Small correlation factors were likely due to the non-linear nature of the distance to burrow variable, given individuals concentrated efforts in two dominant locations: right near the burrow, and at about ~150 from the burrow, creating a bimodal distribution (Figure 2).

Vegetative Canopy Cover Type

Results from the random effects ANOVA indicated that there were significant differences in revisitation rate (number of separate visits to hull: nsv) between bitterbrush, open, and pine canopy types (F: 23.36, df: 2, p<0.001). Bitterbrush had the lowest overall revisitation rate

(4.6±3.0 nsv) in comparison to open (mean: 6.4±3.5 nsv) and pine (mean: 6.4±3.8 nsv) vegetative canopy cover types, open and pine were not significantly different from each other

(Figure 3). Statistical results also indicated significant differences among all vegetative canopy cover types for visit duration (mean number of locations per visit to a hull: mnlv) (F: 5.56, df: 2, p=0.004). Bitterbrush had the highest visit duration (mean: 9.5±5.2 mnlv) in comparison to open

(mean: 8.1±5.4 mnlv) and pine (mean: 7.6±5.5 mnlv) vegetative canopy types (Figure 4).

DISCUSSION

How animals maximize their fitness is largely dependent on how they allocate their time to conflicting behaviors of predator avoidance and foraging effort. For species that depend on the same resources, competition causes some guild members to exploit riskier patch types to avoid competitive exclusion (Kotler 1984; Smith 1991; Thompson 1982). C. lateralis is a species that faces high levels of competition with other members of the granivore guild (Vander Wall et al.

2009) which it must balance with the need to avoid predation and fulfill its nutritional requirements. After further analysis, C. lateralis may use a time management strategy that allows it to balance these conflicting behaviors. Overall, individuals increased time devoted to patches further from the burrow, indicating they may be maximizing their foraging efforts rather than standing vigilant at the burrow to ward off pilferers. Concurrently, individuals spent more time in safer patch types such as bitterbrush canopy cover in comparison to riskier patch types such pine and open canopy cover, which had higher rates of revisitation, but lower visit duration.

During the time at which this study took place, antelope bitterbrush was the primary source of food, which may aid in explaining why individuals were willing to spend more time further from the burrow, and specifically in bitterbrush patches. Other species have been shown to maximize foraging efforts over other activities, even in risky patches, when their energetic demands are high (Brown 1989; Kotler et al. 1991). For example, hungry coho salmon

(Oncorhynchus kisutch) made longer trips into riskier areas, facing higher mortality risks (Dill &

Fraser 1984). This trade-off may happen seasonally, or could occur over shorter time-scales.

Antipredator vigilance levels for brush-tailed rock-wallabies (Petrogale penicillata) decreased on cold nights, purportedly due to higher energy requirements (Carter & Goldizen 2003). While spending more time at distances farther from the burrow may inhibit the ability of C. lateralis to ward off pilferers, a larder-defense strategy may not be necessary at this time of year, because the larder has presumably been emptied during winter torpor. Bihr and Smith (1998) excavated

C. lateralis burrows in July and August and reported that burrows contained few seeds.

Additionally, it would not be energetically efficient to frequently revisit profitable patches at the periphery of the home range, rather, individuals should stay in distant patches long enough to deplete them before moving on. Later in the season, when their larders are presumably fuller, individuals may shift their behavior to a larder-defense strategy, spending more time at the burrow.

While individuals may maximize their foraging efforts by spending more time away from the burrow, they may also be concurrently increasing their risk of predation. To minimize this risk, individuals may choose to increase their time in antipredator vigilance behavior to maintain foraging activity (Brown 1999). Guinea pigs spent longer bouts foraging when further from cover and had higher rates of vigilance to compensate for the increased risk of predation in these

patches (Cassini 1991). Therefore, the increase in visit duration observed with increased distance from burrow could also potentially be attributed to individuals taking intermittent pauses in foraging activity to scan for predators.

Traveling between patches in risky matrices also presents a dilemma to foraging species.

To minimize this cost, individuals have been shown to alter their travel speeds, interrupt travel with episodes of vigilance, or use indirect routes to remain close to protective cover (Brown

1999, Kotler 1984, Thompson 1982). C. lateralis revisited riskier open and pine canopy cover more frequently than bitterbrush canopy cover types, but spent more time under bitterbrush canopy than either pine or open cover types. This behavior may indicate that individuals maximize their travel speed between patches. Yellow-bellied marmots (Marmota flaviventris) run fastest across low grasses and bare ground while traveling between foraging patches

(Blumstein 2004). Degus (Octodon degas) traveled 1.82 times faster in open habitat as compared to shrub habitat (Vasquez 2002). Kenagy & Hoyt (1989) found that the closely related

Cascade golden-mantled ground squirrel (Callospermophilus saturatus) travel greater distances during the day at their maximum aerobic speeds while moving around their home ranges. This travel speed was made possible by the frequent use of open habitat and was actually found to be more energy efficient than traveling the same distances at a walking speed. This suggests that C. lateralis may similarly be using these areas as energetically cost-effective corridors in order to access foraging patches. Overall, minimizing time spent in less desirable matrices may allow individuals to reduce predation risk, maximize their harvest rates, and decrease energetic costs associated with travel.

In conclusion, the incorporation of temporal data into movement behavior analyses aids in describing how individuals might use behavioral trade-offs to maximize their fitness.

Pinpointing areas of high visit duration may help identify key habitat requirements for species seasonally and throughout a year. While spatial data are still commonly used to describe habitat utilization, it is difficult to ascertain behavioral tendencies from mere clusters of static data.

Inclusion of temporal data allows researchers to expand their existing knowledge of spatial patch use in order to make more informed inferences about core habitat requirements and potential risks to species survival.

LITERATURE

Arenz, CL, & Leger, DW. 2000. Antipredator vigilance of juvenile and adult thirteen-lined ground

squirrels and the role of nutritional need. Animal Behaviour 59: 535-541.

Armitage, KB, & Salsbury, CM. 2016. Pattern and variation of the time budget of yellow-bellied

marmots. Ethology, Ecology, and Evolution 28: 329-365.

Bachman, GC. 1993. The effect of body condition on the trade-off between vigilance and

foraging in Belding’s ground squirrels. Animal Behavior 46: 233-244.

Bartels, MA, & Thompson, DP. 1993. Spermophilus lateralis. Mammalian Species 440: 1-8.

Benhamou, S. 2011. Dynamic approach to space and habitat use based on biased random

bridges. PloS One 6: e14592.

Bihr, K.J, & Smith, RJ. 1998. Location, structure, and contents of burrows of Spermophilus

lateralis and Tamias minimus, two ground-dwelling Sciurids. The Southwestern

Naturalist 43: 352-362.

Blumstein, DT, Runyan, A, Seymour, M, Nicodemus, A, Ozgul, A, Ransler, F, Im, S, Stark, T,

Zugmeyer, C, & Daniel, JC. 2004. Locomotor ability and wariness in yellow-bellied

marmots. Ethology 110: 615-634.

Brown, JS. 1988. Patch use as an indicator of habitat preference, predation risk, and

competition. Behavioral Ecology and Sociobiology 22: 37-47.

Brown, JH, & Zeng, Z. 1989. Comparative population ecology of eleven species of rodents in the

Chihuahua Desert. Ecology 70: 1507-1525.

Brown, JS. 1999. Vigilance, patch use and habitat selection: Foraging under predation risk.

Evolutionary Ecology Research 1: 49-71.

Carter, K, & Goldizen, AW. 2003. Habitat choice and vigilance behaviour of brush-tailed rock-

wallabies (Petrogale penicillata) within their nocturnal foraging ranges. Wildlife

Research 30: 355-364.

Cassini, MH. 1991. Foraging under predation risk in the wild guinea-pig (Cavia aperea). Oikos 62:

20-24.

Charnov, EL. 1976. Optimal foraging, the marginal value theorem. Theoretical Population

Biology 9: 129-136.

Chesson, P. 2000. Mechanisms of maintenance of species diversity. Annual Review of Ecology

and Systematics 31: 343-366.

Christiansen, F, Rasmussen, MH, & Lusseau, D. 2013. Inferring activity budgets in wild animals to

estimate the consequences of disturbances. Behavioral Ecology 24: 1415-1425.

Dehn, MM. 1990. Vigilance for predators-detection and dilution effects. Behavioral Ecology and

Sociobiology 26: 337-342.

Dill, LM, and Fraser, AHG. 1984. Risk of predation and the feeding behavior of juvenile coho

salmon (Oncorhynchus kisutch). Behavioral Ecology and Sociobiology 16: 65-71.

Downs, C.J., and Vander Wall, S.B. 2009. High relative humidity increases pilfering success of

yellow pine chipmunks. American Society of Mammalogists 90: 796-802.

Kenagy, G.J., and Hoyt, D.F. 1989. Speed and time-energy budget for locomotion in golden-

mantled ground squirrels. Ecology 70: 1834-1839.

Kotler, BP. 1984. Risk of predation and the structure of desert rodent communities. Ecology 65:

689-701.

Kotler, BP, Brown, JS, & Hasson, O. 1991. Owl predation on gerbils: the role of body size,

illumination, and habitat structure on rates of predation. Ecology 72: 2249- 2260.

Kotler, BP, Brown, JS, Dall, SRX, Gresser, S, Ganey, D, & Bouskila, A. 2002. Foraging games

between gerbils and their predators: temporal dynamics of resource depletion and

apprehension in gerbils. Evolutionary Ecology Research 4: 495-518.

Kronfeld-Schor, N, & Dayan, T. 2003. Partitioning of time as an ecological resource. Annual

Review of Ecology, Evolution, and Systematics 34: 153-181.

Lyons, AJ, Turner, WC, & Getz, WM. 2012. Home range plus: a space-time characterization of

movement over real landscapes. Movement Ecology 1: 2-10.1186/2051-3933-1-2.

Lyons A, Getz W. R Development Core Team: T-LoCoH: Time Local Convex Hull Homerange and

Time Use Analysis. 2014. R package version 1.16.

MacArthur, RH, & Levins, R. 1964. Competition, habitat selection, and character displacement in

patchy environment. Proceedings of the National Academy of Sciences of the United

States of America 51: 1207-1210.

MacArthur, RH, & Pianka, ER. 1966. On optimal use of a patchy environment. The American

Naturalist 100: 603-609.

McNamara, JM, & Houston, AI. 1992. Evolutionarily stable levels of vigilance as a function of

group-size. Animal Behaviour 43: 641-658.

Newman, JA. 1991. Patch use under predation hazard-foraging behavior in a simple stochastic

environment. Oikos 61: 29-44.

Nonacs, P. 2001. State dependent behavior and the marginal value theorem. Behavioral Ecology

12: 71-83.

Ovadia, O, Ziv, Y, Abramsky, Z, Pinshow, B, & Kotler, BP. 2001. Harvest rates and foraging

strategies in Negev Desert gerbils. Behavioral Ecology 12: 219-226.

Price, MN, & Correll, RA. 2001. Depletion of seed patches by Merriam’s kangaroo rats: are GUD

assumptions met? Ecology Letters 4: 334-343.

Price, MV, & Heinz, KM. 1984. Effects of body size, seed density, and soil characteristics on rates

of seed harvest by heteromyid rodents. Oecologia 61: 420-425.

Pulliam, HR, Pyke, GH, & Caraco, T. 1982. The scanning behavior of juncos-a game-theoretical

approach. Journal of Theoretical Biology 95: 89-103.

Pyke, GH, Pulliam, HR, & Charnov, EL. 1977. Optimal foraging: a selective review of theory and

tests. The Quarterly Review of Biology 52:137-154.

R Development Core Team. 2011. R Development Core Team, R: a Language and Environment

for Statistical Computing.-R Foundation for Statistical Computing.

Schoener, TW. 1974. Resource partitioning in ecological communities. Science 185: 27-39.

Sih, A. 1980. Optimal behavior: can foragers balance two conflicting demands? Science 210:

1041-1043.

Sikes, RS, Gannon, WL, et al. 2016. 2016 Guidelines of the American Society of Mammalogists

for the use of wild mammals in research and education. Journal of Mammalogy 97: 663–

688.

Simberloff, D, & Dayan, T. 1991. The guild concept and the structure of ecological communities.

Annual Review of Ecology and Systematics 22: 115-143.

Smith, RJ. 1995. Harvest rates and escape speeds in 2 coexisting species of montane ground-

squirrels. Journal of Mammalogy 76: 189-195.

Thompson, SD. 1982. Microhabitat utilization and foraging behavior of bipedal and quadrupedal

heteromyid rodents. Ecology 63: 1303-1312.

Upham, NS, & Hafner, JC. 2013. Do nocturnal rodents in the Great Basin Desert avoid

moonlight? Journal of Mammalogy 94: 59-72.

Vander Wall, S.B. 1991. Mechanisms of cache recovery by yellow pine chipmunk. Animal

Behaviour 41: 851-863.

Vander Wall, S.B. 1994. Seed fate pathways of antelope bitterbrush: dispersal by seed-caching

yellow pine chipmunks. Ecology 75: 1911-1926.

Vander Wall, SB, Enders, MS, Waitman, BA. 2009. Asymmetrical cache pilfering between yellow

pine chipmunks and golden-mantled ground squirrels. Animal Behaviour 78: 555-561.

Vasquez, RA, Ebensperger, LA, & Bozinovic, F. 2002. The influence of habitat on travel speed,

intermittent locomotion, and vigilance in a diurnal rodent. Behavioral Ecology 13: 182-

187.

Verdolin, JL. 2006. Meta-analysis of foraging and predation risk trade-offs in terrestrial systems.

Behavioral Ecology and Sociobiology 60: 457-464.

Wondolleck, JT. 1978. Forage-area separation and overlap in heteromyid rodents. Journal of

Mammalogy 59: 510-518.

Figure 1: Spearman correlation matrix of relationships between revisitation rate (number of separate visits to a hull after an inter-visit gap of 60 seconds: nsv), visit duration (mean number of locations per visit within an inter-visit gap of 60 seconds: mnlv), distance to burrow (m), and distance to refuge locations (m) for Callospermophilus lateralis in the Whittell Forest and Wildlife Area July 15-31, 2014.

Revisitation Rate Revisitation (nsv)

(nsv)

Visit Duration Visit

Distance to Burrow Figure 2: Revisitation rate(m) (number of separate visits: nsv) and visit duration (mean number of locations per visit: mnlv) correlated to distance traveled from burrow (m). Linear correlation is represented by the dotted line while the best fit line is in blue. Linear correlation was not appropriate as distance to burrow had a bimodal distribution.

) RevisitationRate (nsv

Figure 3: Relationship between revisitation rate (number of separate visits to a hull after an inter-visit gap of 60 seconds: nsv) and vegetative canopy cover type for Callospermophilus lateralis in the Whittell Forest and Wildlife Area July 15-31, 2014.

) Visit Duration Visit (mnlv

Figure 4: Relationship between visit duration (mean number of locations per visit within an inter-visit gap of 60 seconds: mnlv) and vegetative canopy cover type for Callospermophilus lateralis in the Whittell Forest and Wildlife Area July 15-31, 2014.

Thesis Summary

All animals need to optimize their foraging efforts to survive in stressful environments (Brown

1988). Researchers have tried to model how animals optimize their efforts by using methods based on the marginal value theorem, indicating animals should reduce the cost of traveling to maximize the net energy gained from staying in a food patch (Charnov 1976). While this model may depict simple choices made while foraging, there are many other decisions animals have to make while traveling throughout their home ranges. Individuals also have to balance costs associated with competition in harvesting and storing food, antipredator behavior, exposure to abiotic elements, and mate search. Because of these factors, the marginal value theorem often inaccurately predicts how animals behave within their home ranges (Nonacs 2001; Price &

Correll 2001; Verdolin 2006).

GPS data have allowed researchers to incorporate more of these behavioral decisions and trade-offs associated with these decisions into their models (Gurarie et al. 2009; de Weerd, et al. 2015). By using analyses such as resource selection functions and time local convex hull metrics, researchers are not constrained by the assumptions of the marginal value theorem.

Resource selection functions allow researchers to objectively identify areas of importance to individuals of a population and how patch selection or avoidance may change throughout the year. Indicating which patches are important at which times of year, managers can also better design conservation and management plans regarding a species (Boyce et al. 2002; Chetkiewicz

& Boyce 2009). More recently, researchers are now able to assess just how individuals are using these patch types by incorporating the time stamps taken with each GPS point. Time local convex hull metrics use this serial correlation to calculate rates of revisitation and visit duration to patch types (Lyons et al. 2012). Therefore, while there may be a high density of points within

a certain patch type, the duration of time animals actually spend there may be less than in other patch types categorized by lower point density. High duration of time in or rate of revisitation to patches could indicate presence of an important food source or protective cover. For example,

Lyons et al. (2012) found that areas of high revisitation were correlated to water resources.

Other areas of low visit duration but perhaps high revisitation could indicate high risk matrices over which individuals must cross to reach profitable patches. In patchy habitats associated with an ephemeral availability of food and when individuals are aware of the location of high quality patches, this type of higher risk movement is possible (Fahrig 2007). To reduce risk of predation, animals may modify their travel speed or antipredator vigilance behavior. Some species, such as degus (Octodon degas), can increase their travel speed in risky patches (Vasquez et al. 2002).

Additionally, species that experience high levels of competition when profitable food sources are available, may differentially select patch types, causing some species to forage in areas that may incur greater predation risk (MacArthur & Levins 1964; Wondolleck 1978).

Members of the granivore guild in the arid eastern Sierra Nevada experience increased levels of competition when ephemeral foods are available. To reduce competition, species have evolved different foraging and harvesting techniques (Vander Wall & Jenkins 2003). Golden- mantled ground squirrels (Callospermophilus lateralis) larder-hoard seeds year round and forage in a habitat characterized by open tree canopy. This species presented a unique opportunity to study the dynamic behavioral trade-offs made by individuals. They compete directly with other granivores, particularly yellow-pine chipmunks (Tamias amoenus), which use their olfactory senses to retrieve caches of seeds they have made and also pilfer from caches made by others

(Downs and Vander Wall 2009). Field and lab experiments have also indicated that T. amoenus is capable of pilfering from C. lateralis (Vander Wall et al. 2009). C. lateralis cannot reciprocate this

pilfering activity, and therefore must be at the burrow to defend its larder of food. Seed availability is ephemeral and within this arid open tree canopy habitat type, food choice is limited, particularly when seeds are not available. In addition to defending the larder, C. lateralis must also forage and avoid predators. Optimizing trade-offs between these conflicting behaviors across seasons of variable food availability is key to the long-term survival of this species.

I used 5 gram GPS loggers to analyze foraging behavior of ten C. lateralis individuals in

2014. I collected data in two seasons of seed availability and one season in which seeds were not available. I used resource selection functions and time local convex hull metrics to analyze how selection or avoidance of patches changes throughout a year and how rates of revisitation and visit duration were associated with these same patch types within one season of seed availability. For all analyses, I used the parameters of distance traveled from the burrow, distance to nearest refuge location, and habitat type. Results from resource selection functions indicate that seed availability largely influences the movement patterns of individuals. When seeds were available, intermediate distances from the burrow (distances neither directly approximate to nor at very great distances from the burrow) were significantly selected as well as distances within close proximity to refuge locations. Selection or avoidance of habitat type differed between each of the three seasons and was greatly influenced by which seed type was available. My time local convex hull results for the season in which only antelope bitterbrush

(Purshia tridentata) was available indicated that individuals spend more time in bitterbrush patches than patches characterized by either open or only tree-covered canopy. The latter two habitat types were characterized by low visit duration and high revisitation, indicating individuals need to traverse these areas frequently to access the high quality bitterbrush patches. As distance from the burrow increased, so did duration of time, indicating individuals

may be attempting to conserve energy by infrequently revisiting patches on the outer-edges of their home range. C. lateralis selected and had higher rates of revisitation to open patches during the season in which bitterbrush seeds were available. Individuals may also prioritize foraging over antipredator vigilance during this season, as they are likely in an energy deficit.

Other species have been shown to reduce antipredator behavior in search of food when their current energy requirements are high (Bachman 1993; Dill & Fraser 1984; Sih 1980).

Additionally, C. lateralis are larger than their competitors, T. amoenus, indicating they may be able exploit these riskier patch types where risk of predation is higher (Kotler 1984; Smith 1995;

Thompson 1982). This strategy changed later in the year as the availability and the distribution of available seeds changed. Individuals stayed closer to the burrow and avoided open and bitterbrush habitat types when no seeds were available. When Jeffrey pine seeds were available, individuals neither selected nor avoided bitterbrush or open canopies. This was most likely due to the more randomly dispersed nature of pine seeds. Also during this season, defending the larder becomes more important, so that individuals can conserve enough seeds to survive winter torpor and reproduce successfully in the spring. Overall, individuals selected distances closer to the burrow than during bitterbrush season, indicating they are maximizing their foraging efforts closest to the burrow. Earlier in the year, when bitterbrush seeds are available, it is more likely that individuals are consuming these seeds to meet their current energetic requirements, meaning larder defense is not a priority as it is when Jeffrey pine seeds become available. Bihr and Smith (1998) excavated C. lateralis burrows in July and August and reported that burrows contained few seeds. Excavations of T. amoenus winter larders revealed that Jeffrey pine seeds accounted for the majority of caloric content (Kuhn & Vander Wall 2009), indicating these seeds may be particularly important to surviving winter torpor.

Overall, how individuals used behavioral trade-offs to maximize their fitness changed dependent on energetic requirements, level of perceived competition, and seed availability.

These results indicate that there are many factors that influence foraging behavior, supporting one of the central critiques of the marginal value theorem (Price & Correll 2001). These behaviors should be incorporated into movement models to more accurately describe habitat requirements on a long-term timescale. Though this study was completed using a fairly widespread species, these same questions and analyses could be applied to other species of conservation concern. Understanding all the factors that impact how these species select or avoid habitat patches is important for guiding management decisions. While attempting to incorporate dynamic behavioral decisions can be complicated, complete lack of any behavioral data has led to mismanagement and misplacement of key corridors for many species (Knowlton

& Graham 2010). Resource selection functions and time local convex hull analysis provide a means to provide these data free of the constraints of classic modeling techniques and better interpret how individuals alter their movement patterns as they make dynamic trade-offs throughout the year.

Literature Cited

Bachman, GC. 1993. The effect of body condition on the trade-off between vigilance and

foraging in Belding’s ground squirrels. Animal Behavior 46: 233-244.

Boyce, MS, Vernier, PR, Nielson, SE, & Schmiegelow, FKA. 2002. Evaluating resource selection

functions. Ecological Modelling 157: 281-300.

Brown, JS. 1988. Patch use as an indicator of habitat preference, predation risk and competition.

Behavioral Ecology and Sociobiology 22: 37-47.

Charnov, EL. 1976. Optimal foraging, the marginal value theorem. Theoretical Population

Biology 9: 129-136.

Chetkiewicz, CLB, & Boyce, MS. 2009. Use of resource selection functions to identify

conservation corridors. Journal of Applied Ecology 46: 1036-1047. de Weerd, N, van Langevelde, F, van Oeveren, H, Nolet, BA, Kölzsch, A, Prins, HHT, & de Boer,

WF. 2015. Deriving animal behaviour from high-frequency GPS: tracking cows in open

and forested habitat. PLOS One 10: e0129030.

Dill, LM, and Fraser, AHG. 1984. Risk of predation and the feeding behavior of juvenile coho

salmon (Oncorhynchus kisutch). Behavioral Ecology and Sociobiology 16: 65-71.

Downs, C.J., and Vander Wall, S.B. 2009. High relative humidity increases pilfering success of

yellow pine chipmunks. American Society of Mammalogists 90: 796-802.

Fahrig, L. 2007. Non-optimal animal movement in human-altered landscapes. Functional

Ecology 21: 1003-1015.

Gurarie, E, Andrews, RD, & Laidre, KL. 2009. A novel method for identifying behavioural changes

in animal movement data. Ecology Letters 12: 395-408.

Kuhn, KM, & Vander Wall, SB. 2009. Formation and contents of yellow-pine chipmunk (Tamias

amoenus) winter larders. Western North American Naturalist 69: 309-318.

Knowlton, JL, & Graham, CH. 2010. Using behavioral landscape ecology to predict species’

responses to land-use and climate change. Biological Conservation 143: 1342-1354.

Kotler, BP. 1984. Risk of predation and the structure of desert rodent communities. Ecology 65:

689-701.

Lyons, AJ, Turner, WC, & Getz, WM. 2012. Home range plus: a space-time characterization of

movement over real landscapes. Movement Ecology 1: 2-10.1186/2051-3933-1-2.

MacArthur, RH, & Levins, R. 1964. Competition, habitat selection, and character displacement in

patchy environment. Proceedings of the National Academy of Sciences of the United

States of America 51: 1207-1210.

Nonacs, P. 2001. State dependent behavior and the marginal value theorem. Behavioral Ecology

12: 71-83.

Ovadia, O, Ziv, Y, Abramsky, Z, Pinshow, B, & Kotler, BP. 2001. Harvest rates and foraging

strategies in Negev Desert gerbils. Behavioral Ecology 12: 219-226.

Price, MN, & Correll, RA. 2001. Depletion of seed patches by Merriam’s kangaroo rats: are GUD

assumptions met? Ecology Letters 4: 334-343.

Sih, A. 1980. Optimal behavior: can foragers balance two conflicting demands? Science 210:

1041-1043.

Smith, RJ. 1995. Harvest rates and escape speeds in 2 coexisting species of montane ground-

squirrels. Journal of Mammalogy 76: 189-195.

Thompson, SD. 1982. Microhabitat utilization and foraging behavior of bipedal and quadrupedal

heteromyid rodents. Ecology 63: 1303-1312.

Vander Wall, SB, Enders, MS, & Waitman, BA. 2009. Asymmetrical cache pilfering between

yellow pine chipmunks and golden-mantled ground squirrels. Animal Behaviour 78: 555-

561.

Vander Wall, SB, & Jenkins, SH. 2003. Reciprocal pilferage and the evolution of food-hoarding

behavior. Behavioral Ecology 15: 656-667.

Vasquez, RA, Ebensperger, LA, & Bozinovic, F. 2002. The influence of habitat on travel speed,

intermittent locomotion, and vigilance in a diurnal rodent. Behavioral Ecology 13: 182-

187.

Verdolin, JL. 2006. Meta-analysis of foraging and predation risk trade-offs in terrestrial systems.

Behavioral Ecology and Sociobiology 60: 457-464.

Wondolleck, JT. 1978. Forage-area separation and overlap in heteromyid rodents. Journal of

Mammalogy 59: 510-518.