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Quantifying the effect of hiking disturbance on American pika (Ochotona princeps) foraging behaviour Stafl, Natalie L 2013

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Quantifying the effect of hiking disturbance on American pika (Ochotona princeps) foraging behaviour by Natalie L. Stafl  BSc., University of British Columbia, 2009  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in The Faculty of Graduate Studies (Zoology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) May 2013 c Natalie L. Stafl 2013  Abstract An animal’s need to balance energy intake with predator avoidance results in trade-offs predicted by optimum foraging theory. These trade-offs may include reducing foraging activity if 1) perception of predation risk is high or 2) abiotic conditions are suboptimal for foraging. Human disturbances such as hikers can influence an animal’s perception of predation risk, yet little is known about how hiking affects the foraging behaviour of species in the alpine zone. American pikas (Ochotona princeps) are small, foodhoarding mammals whose foraging ability is restricted by heat. If pikas’ ability to forage is further decreased by hiking disturbance it could negatively impact their survival due to reduced food storage. To quantify the effect of hikers on foraging time, I evaluated multiple hypotheses of risk avoidance by simulating disturbance events for 48 pikas in Glacier National Park, BC. I assessed pikas’ response to hikers using four indicators of risk behaviour: alert distance (DA ), flight initiation distance (DF ), exit delay (TE ) and delay in return to forage (TR ). To test if temperature or distance to trail was more influential to pikas’ foraging activity, I conducted behavioural observations on 17 pikas. All simulated disturbance events elicited anti-predator response behaviours in foraging pikas, reducing time available to forage and increasing time spent alert and vigilant. Distance to trail was an important predictor ii  of TE and TR . Pikas near trails (<50 meters) exhibited increased tolerance to human disturbance losing an average of 4.1 (SE=0.6, n=19) minutes of foraging time per disturbance event compared to pikas with territories >100 m away from trails, which lost an average of 13.2 (SE=1.7, n=16) minutes of foraging time per disturbance event. Temperature, not distance to trail, was the strongest predictor of pikas’ foraging activity over a 4-6 hour period. This suggests that human disturbance may be partially mitigated by pikas’ behavioural adaptation at less frequented sites. Monitoring pika populations near and away from trails would be well-advised given projected trends in warming climate and potential increases in hiking traffic.  iii  Preface This work was funded by a fellowship and research grant from NSERC (National Science and Engineering Research Council), a fellowship from the UBC Department of Zoology and a research grant from the Alpine Club of Canada Environment Fund. Approval for this research to be carried out in National Parks was given by the Parks Canada Agency: permit # MRGNP2011-9255. The UBC Animal Care Committee approved this research (Certificate #: 4617-11) and no animals were harmed during the course of this study.  iv  Table of Contents Abstract  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ii  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  iv  Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . .  v  Preface  List of Tables  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii  List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . .  ix  1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  1  1.1  Study system . . . . . . . . . . . . . . . . . . . . . . . . . . .  2  1.2  Field sites  4  1.3  Structure of this thesis  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  6  2 Hikers as agents of disturbance in a warming climate . . .  7  2.1  Introduction  . . . . . . . . . . . . . . . . . . . . . . . . . . .  7  2.2  Methods  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  10  2.2.1  Study system  . . . . . . . . . . . . . . . . . . . . . .  10  2.2.2  Field sites  . . . . . . . . . . . . . . . . . . . . . . . .  11 v  2.2.3  Data collection . . . . . . . . . . . . . . . . . . . . . .  2.2.4  Statistical analysis  11  . . . . . . . . . . . . . . . . . . .  14  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  16  2.3  Results  2.4  Discussion  . . . . . . . . . . . . . . . . . . . . . . . . . . . .  28  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  38  3.1  Implications for management . . . . . . . . . . . . . . . . . .  39  3.2  Future directions . . . . . . . . . . . . . . . . . . . . . . . . .  40  Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  42  3 Conclusion  vi  List of Tables 2.1  Predicted associations of covariates and pikas’ anti-predator responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  2.2  10  Summary of response and predictor variables for trail disturbance trials . . . . . . . . . . . . . . . . . . . . . . . . . . . .  18  2.3  Hiking traffic at each field site . . . . . . . . . . . . . . . . . .  19  2.4  Predictors of alert distance . . . . . . . . . . . . . . . . . . .  23  2.5  Predictors of flight initiation distance . . . . . . . . . . . . . .  24  2.6  Predictors of exit delay . . . . . . . . . . . . . . . . . . . . . .  25  2.7  Predictors of return to forage . . . . . . . . . . . . . . . . . .  26  2.8  Predictors of the ratio between flight initiation distance and  2.9  alert distance . . . . . . . . . . . . . . . . . . . . . . . . . . .  27  Predictors of pikas’ foraging activity . . . . . . . . . . . . . .  28  vii  List of Figures 1.1  Map of field site locations . . . . . . . . . . . . . . . . . . . .  2.1  Diagram of a pika’s behavioural response to human disturbance. 12  2.2  Pikas’ anti-predator responses to supported predictor variables 20  2.3  The ratio of flight initiation distance to alert distance as it scales with distance to trail . . . . . . . . . . . . . . . . . . .  2.4  5  21  The relationship between pikas’ foraging activity and average daily temperature . . . . . . . . . . . . . . . . . . . . . . . . .  29  viii  Acknowledgements I would like to acknowledge and thank my supervisor, Mary O’Connor for her support despite working in a somewhat saltier system. Her insights, perspectives and feedback at all stages of the project helped to clarify my work and structure the questions I set out to answer. I am grateful to my committee members: Peter Arcese and Chris Harley, who provided constructive comments during the review process and were generous with their guidance during committee meetings. I am lucky to have lab mates, Ross Whippo, Joey Bernhardt and Matt Siegle, who inspire me with their creativity when approaching new problems. I am appreciative of the agencies that have supported this research project including: The National Science and Engineering Research Council (NSERC), the Alpine Club of Canada and Mount Revelstoke and Glacier National Parks. I would like to thank to Gregg Walker and Alice Weber of Parks Canada for helping to securing equipment, gear and permits to conduct research within the Parks and for connecting me with local high school students who contributed to the project rain or shine through the Glacier Adventure Stewardship Program. Finally, this work would not have been possible without my excellent, ix  hard-working and fun field assistants and volunteers: Madeleine MartinPreney, Katherine Loewen, Claire Edwards, Julia Amerongen-Maddison and Breanne Johnson.  x  Chapter 1  Introduction Foraging ecology is the study of how animals acquire energy in their natural environments (Stephens et al., 2007). Much thought has been given to the decision making process in which animals choose when, how and what to forage. This framework for assessing risk and rewards is known as optimal foraging theory (OFT) (MacArthur & Pianka, 1966). One classic prediction of OFT is that animals will forage less when the risk of mortality due to predation is high (Lima & Dill, 1990). Similarly, animals in a resource rich environment have more flexibility in their decision making and can be more selective about when and how they choose to forage (Beale & Monaghan, 2004). Animals optimize their access to resources based on a number of factors that balance resource availability with the need to avoid predation, stress and unnecessary energetic expenditure (Lima & Dill, 1990). A fundamental trade-off exists between the need to avoid predation and the need to maximize energy intake (Lima & Dill, 1990). These behavioural decisions have consequences for fitness and survival and ultimately influence the persistence of individuals and populations. When humans are present and interacting in an animal’s natural envi-  1  ronment in some cases they can be perceived as equivalent to predators in the animal’s assessment of risk (Frid & Dill, 2002). The risk-disturbance hypothesis suggests that upon encountering human disturbance stimuli, animals use the same economic principals used by prey when encountering predators (Frid & Dill, 2002). The risk-disturbance hypothesis can be used as a framework for understanding how animals will respond to disturbances based on risks and rewards. If animals perceive human disturbance as equivalent to predation risk, activities such as foraging might be compromised, affecting the energy budget of the individual (Frid & Dill, 2002). This could ultimately affect populations or lead to occupation of alternate territory if it were available (Gill et al., 2001).  1.1  Study system  My research focuses on American pikas (Ochotona princeps); small lagomorphs that live in alpine and subalpine environments. Pikas are ideal model organisms for studying foraging behaviour as their foraging activity is easily quantifiable and their accumulated food stores are directly linked to fitness (Dearing, 1997; Morrison, 2007). Foraging preference, sensitivity to predation and landscape level population change have been documented in this species but little is known about how pikas respond to hiking traffic (Holmes, 1991; Hudson et al., 2008; Rodhouse et al., 2010). Pikas can be found in mountainous areas from the Great Basin in the United States to Northern British Columbia, Canada. Their habitat mainly consists of rocky boulder fields (talus slopes) in the alpine and subalpine  2  zones. Boulder fields provide protection from predators and serve as a refuge from warmer daytime temperatures (Holmes, 1991; MacArthur & Wang, 1973). Foraging success and food hoarding is essential for pikas’ survival (Morrison, 2007). Pikas do not hibernate and instead stockpile food throughout the summer for overwinter subsistence (Dearing, 1997). Depending on the latitude and elevation, often the snow free period lasts little more than a few months from mid July into early October. This short, strongly seasonal environment makes foraging and stockpiling food critical for pikas (Morrison, 2007). Pikas stockpile food in one or several haypiles (Dearing, 1997). Individuals defend haypiles and rarely forage greater than 10 meters from the talus (Barash, 1973; McIntire & Hik, 2005; Morrison et al., 2004). American pikas’ perception of predation risk strongly structures their foraging decisions (Holmes, 1991; Morrison et al., 2004). In more exposed meadows, further from the talus, pikas decreased the amount of food removed from experimental cafeteria trials (Morrison et al., 2004). Similarly, when cover was artificially extended, pikas ventured further out into the meadow complex (Holmes, 1991). It is unknown how pikas perceive and evaluate risk associated with human traffic. Recreational trail networks provide access to the alpine and can intersect American pika habitat. Hikers have the potential to disturb pikas during a sensitive, limited foraging period. If pikas equate human disturbance with predation risk, hiking traffic may have energetic and fitness consequences for pika populations. In addition to biotic disturbances, physical stress, such as temperature, 3  can restrict pikas’ foraging activity. Pikas are extremely sensitive to heat and are unable to thermoregulate when exposed to temperatures higher than 25◦ C (MacArthur & Wang, 1973; Smith, 1974). Climate change is projected to cause preferential warming in arctic and alpine environments in the coming decades (ACIA, 2005). This projected warming could have large repercussions for pika habitat suitability. Local extirpations of pika populations at some lower elevation sites in the Great Basin have been attributed to warming climate although some populations do persist in areas outside of the projected bioclimatic envelope (Beever et al., 2003; Simpson, 2009).  1.2  Field sites  All field work for this project was carried out in the Columbia Mountains of British Columbia, Canada during the summer of 2012. I had two sites in Glacier National Park and one site in Mount Revelstoke National Park (Figure 1.1). At all sites I surveyed there were pikas both near and away from trails experiencing different exposure levels to hiking traffic within their territories. All study areas were located between 1755 and 2157 meters of elevation. Avalanche Crest trail (51◦ 16’16” N; 117◦ 28’45” W) and Abbott Ridge trail (51◦ 15’00” N; 117◦ 28’45” W) were located in Glacier National Park. Eva Lake trail (51◦ 03’52” N; 118◦ 06’42” W) was located in Mount Revelstoke National Park.  4  Figure 1.1: Map of field site locations in the Columbia Mountains of British Columbia, Canada (Parks Canada Agency, 2013). Red crosses represent field sites.  5  1.3  Structure of this thesis  The goal of this thesis is to quantify the effect of human recreation and temperature on the foraging behaviour of American pika. This thesis is organized into three chapters: the introduction, given here, one data chapter and a concluding chapter. In Chapter 2, I report the results of simulated hiker disturbance events and behavioural observations of pikas’ activity to test: 1) if pikas assess human disturbance as they would predation risk and 2) if temperature or proximity to trail best predict pikas’ foraging activity. I use the risk-disturbance hypothesis proposed by Frid and Dill (2002) to structure my assessment of pikas’ behavioural response to disturbance and put it in context of overall foraging activity. Chapter 3 concludes this thesis and explores potential directions for future research and the implications my findings have for managing American pikas.  6  Chapter 2  Hikers as agents of disturbance in a warming climate 2.1  Introduction  Recreation networks often include alpine areas and intersect pika habitat, however, the effect of hiking traffic on the behaviour of American pika (Ochotona princeps) is unknown. Animals assess risk caused by human disturbance in the same way they assess risk associated with predator encounters (Frid & Dill, 2002). If pikas perceive humans as predators, optimal foraging theory suggests that pikas would change their foraging behaviour to increase vigilance and other anti-predator behaviours at a cost of stockpiling food (Lima & Dill, 1990). Lower stockpiles for the winter could imply reduced survival as pikas depend on accumulated food stores to overwinter (Morrison, 2007). In addition to biotic disturbances, the abiotic environment can also constrain pikas’ foraging time. Pikas are extremely sensitive to heat and are  7  unable to thermoregulate when exposed to temperatures higher than 25◦ C (MacArthur & Wang, 1973; Smith, 1974). Warm temperatures can restrict pikas to the talus and limit pikas’ ability to forage (Smith, 1974). Given this context, human disturbance and pikas’ perception of risk associated with hiking groups could further limit the time available to collect food. This could have implications for food hoarding and overwinter survival. In recent years pikas have received considerable attention as there have been documented extirpations in some low elevation populations (Beever et al., 2003). Global climate change has been implicated as a driver in these shifts although human disturbance could not be ruled out (Beever et al., 2003, 2010). The relative importance of thermal limitations and hiker disturbance in relation to pikas’ foraging activity is not well understood. I tested the hypothesis that pikas respond to human disturbance with the same behaviours that they exhibit in response to predator encounters and that these responses cost foraging time. Predation risk theory suggests that anti-predator behaviour can be influenced by three general categories of behavioural predictors: characteristics of the predator, characteristics of the prey and environmental factors such as temperature (Frid & Dill, 2002; Lima & Dill, 1990). I quantified pikas’ responses to each type of behavioural predictor to determine if hikers elicit anticipated anti-predator behaviours. Covariates and predicted associations with pikas’ anti-predator responses are listed in Table 2.1 and further explained in the methods section. Prey that actively engage in vigilance or other predator monitoring behaviours do so at the cost of other activities such as foraging (Cooper, 2005). To document this potential cost I assessed two measures of pikas’ moni8  toring behaviour: alert distance (DA ) and flight initiation distance (DF ). Flight initiation distance is a measure of the degree to which animals tolerate risk (Ydenberg & Dill, 1986). It describes the closest distance that an approacher can get before an animal begins to flee and has been used extensively as a proxy for how much risk an animal is prone to accept (Cooper & Frederick, 2007). I also assessed the ratio of DF /DA as they could be correlated responses. Animals that repeatedly receive non-threatening disturbance stimuli should postpone flight initiation in favour of monitoring to avoid the opportunity costs and energetic consequences of fleeing (Sirot, 2010). The ratio of DF /DA deals with that correlation. Time lost foraging due to disturbance is an opportunity cost that directly relates to fitness of an animal. To capture this cost, I assessed exit delay (TE ) and the delay in return to forage (TR ) after the initial disturbance (Figure 2.1). I predicted that pikas’ foraging activity would be negatively affected by human disturbance due to their assessment of predation risk and the associated opportunity cost of time lost foraging (Frid & Dill, 2002). I also predicted that pikas near trails would have a higher tolerance to human disturbance and adapt their behaviour to minimize this opportunity cost as repeated exposure to non-lethal stimulus can lead to habituation (Taylor & Knight, 2003). I expected high temperatures would increase pikas’ anti-predator response to disturbance stimuli as multiple stressors, such as suboptimal abiotic conditions and hiking disturbance could act together to dissuade pikas from foraging (Smith, 1974). To put these behavioural responses in context of overall foraging activity, I tested the relative importance of temperature and distance to trail on pikas’ foraging activity. 9  Table 2.1: Predicted associations of covariates and anti-predator responses. Covariates are broken down into three categories suggested by predation risk theory. Response variables include alert distance (DA ), flight initiation distance (DF ), exit delay (TE ), return to forage (TR ) and the ratio of flight initiation distance to alert distance (DF /DA ). Category Characteristics of the predator Characteristics of the prey Environmental factors  2.2 2.2.1  Covariate  Response  directness of approach start distance distance to trail distance to refuge temperature  DA + + + + +  DF + + + + +  TE + n/a + + +  TR + n/a + + +  DF /DA + n/a + + +  Methods Study system  American pikas (Ochotona princeps) are small lagomorphs that live in the alpine. They can be found in mountainous areas from the Great Basin in the United States to Northern British Columbia, Canada. Their habitat mainly consists of rocky boulder fields (talus slopes) in subalpine and alpine zones although there are populations that persist at lower elevations (Simpson, 2009). Boulder fields provide protection from predators and serve as a refuge from warmer daytime temperatures (Holmes, 1991; Smith, 1974). Pikas are territorial, central-place foragers that stockpile food in one or several haypiles (Dearing, 1997). Individuals defend haypiles and rarely forage greater than 10 meters from the talus (Barash, 1973; McIntire & Hik, 2005; Morrison et al., 2004). Pikas do not hibernate and instead stockpile food for overwinter subsistence (Dearing, 1997). Depending on the latitude and elevation of the habitat, the snow free period lasts little more than a few months from mid-July into early October. This short, strongly seasonal 10  environment makes foraging and stockpiling foods all the more important.  2.2.2  Field sites  Disturbance trials were conducted at three sites in Mount Revelstoke and Glacier National Parks in the Columbia Mountains of British Columbia, Canada. The sites in Glacier National Park were located near Avalanche Crest trail (51◦ 16’16” N ; 117◦ 28’45” W) and Abbott Ridge trail (51◦ 15’00” N; 117◦ 28’45” W). The site in Mount Revelstoke National Park was located near the Eva Lake trail (51◦ 03’52” N; 118◦ 06’42” W). All foraging activity observations were carried out at the Abbott Ridge field site.  2.2.3  Data collection  To test my predictions, I simulated hiker disturbance under various conditions and quantified four anti-predator behavioural responses for each event (Figure 2.1). I set up infrared trail counters 1.3 meters off the ground next to trails leading to each of my field sites to record the number of hiking groups that pikas near trails encountered over the observation period. I defined a hiking group as any number of people passing by a trail counter within two minutes of each other. I compared data from the trail counters with notes from physical observations to ensure accuracy. I conducted disturbance trials from September 18th to September 28th 2012 to minimize the effects of seasonality. I initially surveyed patches of talus for pika occupation near and away from trails at each site. Following the survey, pikas in each group were randomly selected for disturbance trials. Once a target pika was identified, two observers positioned themselves 11  Pika actively foraging  Seconds  Disturbance event  Meters Alert distance (DA )  Pika becomes alert Exit delay (TE )  Pika takes flight  Flight initiation distance (DF )  Pika takes cover Return to forage (TR )  Pika emerges from cover Pika resumes foraging  Figure 2.1: Diagram of a pika’s behavioural response to human disturbance. Four anti-predator responses: alert distance (DA ), flight initiation distance (DF ), exit delay (TE ) and return to forage (TR ) were measured and recorded for each pika. The ratio between DF and DA was assessed to get a measure of pikas’ wariness.  12  Ratio (DF / DA)  within sight of the pika territory and monitored the individual until it began foraging in a meadow complex. Individual pikas were never sampled more than once. When the target pika was observed foraging for more than a minute, one observer continuously approached at a constant rate of approximately 1 m/s. They approached the pika either tangentially (at a 90 degree angle within two metres of the pika’s original location, as if passing by on a trail) or directly. Markers were dropped without interrupting the approach 1) at the observer’s starting location, 2) when the pika first became alert to the approaching observer (DA ), 3) when the pika first began its flight response (DF ) and 4) at the original position of the pika in the meadow when the trial commenced (distance to refuge). The second observer remained at the starting location and began timing, with a stopwatch, as soon as the pika became alert. After the pika took flight and entered the talus both observers watched for and signalled when the pika first emerged from cover. The delay in the time it took the pika to emerge from the talus was then recorded (TE ). The observers continued to monitor the pika until the individual resumed foraging in the meadow complex (TR ). Distance to nearest trail was measured using GPS units to the nearest meter from each pika’s main haypile. All other measurements were made with a 60 meter measuring tape to the nearest tenth of a meter. Of the 48 pikas tested, three did not return to forage due to interrupted trials when a hiker not associated with the project walked by. When this occurred the trial was stopped and all other responses were measured and recorded. Temperature was recorded pre-trial as ambient temperature in the shade 0.5 meters above ground. 13  Foraging activity To examine the effect of temperature and distance to trail on pikas’ foraging activity I used data from 17 pikas observed for 4-6 hours each. Two field biologists collectively observed individual pikas over the period of August 21st to September 20th, 2012 for a total of 98 hours of observation time. All observations were conducted at the Abbott Ridge field site between 2008 to 2196 meters of elevation. Once an active pika was selected for observation, the observer positioned themselves in the talus >50 m away from the pika territory and waited a minimum of ten minutes or until the pika no longer visibly responded to the observer’s presence; whichever took longer. For each minute during the observation period the pika’s activity was recorded as foraging (eating or haying), vigilance (active monitoring), underground, territorial (marking territory or chasing individuals), travelling or other. The ’other’ category ranged from 0% to 1.1% of the total observation time and mainly consisted of pikas grooming. To ensure consistency between observers, each observer independently recorded the activity of the same pika for 2 hours. Responses did not vary by more than 2% between observers. Temperature (◦ C) was recorded in the shade 0.5 meters off the ground and averaged over the observation period.  2.2.4  Statistical analysis  I evaluated support for my predictions using mixed linear models and the information theoretic approach. I used linear mixed effects models (package nlme) to determine which covariates exerted the most influence on pikas’  14  anti-predator responses (Pinheiro et al., 2012). I measured four responses: DA , DF , TE and TR as well as the ratio of DF to DA in order to capture measures of wariness and the time lost foraging due to disturbance for each pika. I modelled each response separately and included five covariates corresponding to my predictions: direction of approach (direct or tangential), distance to trail (m), distance to refuge (m), start distance (m) and temperature (◦ C). All responses were log transformed to meet assumptions of normality. I included distance to trail rather than hiking traffic in the models as the terms were collinear. Distance to trail more accurately represented the experience of individual pikas that were mid-distance and far from trails where quantifying human traffic would have been impractical using trail counters. When the terms were substituted they exhibited the same patterns. Unequal variance in start distance and field site were accounted for with the varIdent function in the nlme package in R (Pinheiro et al., 2012). There were no significant differences in pikas’ anti-predator responses between field sites. I included field site as a random effect because I had multiple measurements at each location. For each response, I first included all predictors and then analysed all subsets using the R package MuMIn (Barton, 2012). Interaction terms were left out due to small sample size and lack of a priori evidence that suggested their inclusion. I tested for collinearity between five predictor variables by comparing the variance inflation factor (VIF) values for each covariate using the CAR package in R (Fox et al., 2010). Generally VIF values greater than 5 are considered to be evidence of collinearity (Fox & Monette, 1992). All the 15  VIF values associated with my five chosen predictors were less than 2. To compute the relative effect of distance to trail and temperature on overall foraging behaviour I built linear models in R. I included distance to trail as a proxy for human disturbance events and average ambient temperature in the shade 0.5 meters of the ground as predictors of overall foraging activity. Observations were conducted between 8 am to 6 pm. There was no evidence of auto-correlation between time blocks nor a significant difference in foraging activity between time periods so I did not include time as a predictor in the model. I used the information theoretic approach to compare models and assess their weight. I started out with a full mixed effects model that included all five predictor variables but no interactions. Second order Akaike Information Criterion (AICc) were computed for all subsequent parsimonious models and ranked by lowest AICc with the R package MuMin (Barton, 2012; Burnham & Anderson, 2002). Of all possible subsets, I selected models that fell within 4 delta AICc (”strong support”) of the top model (Burnham & Anderson, 2002). With the established subset of models, I used model averaging techniques to estimate beta parameters and confidence intervals for all terms included. All analyses were completed in R version 2.15.2 (R Core Development Team, 2012).  2.3  Results  Disturbance trials were conducted on pikas with haypiles ranging from 0 to 490 meters away from trails (Table 2.2). Eva lake had the most hiking  16  traffic per day (mean 21, SE=2) followed by Abbott Ridge (mean 8, SE=1) and Avalanche Crest (mean 20, SE=1) (Table 2.3). On average, pikas took flight 21.2 meters (SE=2.0) from an approacher and did not return to forage for 8.6 minutes (SE=1.0) after experiencing a disturbance (Table 2.2). The models for DA and DF both showed strong support for the positive effect of start distance on pikas’ wariness (Tables 2.4, 2.5). The models for DA and TR showed strong support for inflated responses to direct approaches (Tables 2.4, 2.7). Models of TE , TR and the ratio of DF /DA all showed support for decreasing responses with individuals’ proximity to trail (Tables 2.6, 2.7, 2.8). None of the models showed support for the effect of distance to refuge or temperature on pikas’ anti-predator responses. Pikas near trails (<50 meters) had an average TR of 4.1 minutes (SE=0.6, n=19) of foraging time lost per hiking disturbance while pikas >100 m away from trails experienced an average of 13.2 (SE=1.7, n= 16) minutes of foraging time lost per disturbance.  17  Table 2.2: Summary of response and predictor variables for trail disturbance trials. Mean alert distance (DA ), flight initiation distance (DF ), exit delay (TE ), return to forage (TR ), start distance, distance to refuge, distance to trail and temperature are reported with their standard deviation, range and associated sample sizes. Type of approach  DA (m)  Direct SD Range Tangential SD Range All SD Range  Response 30.7 12.8 14.7-54.3 30.4 15.1 11.9-81.4 30.5 14.1 11.9-81.4  DF (m) Variables 22.9 12.2 4.7- 52.2 20.1 15.3 5.5- 81.4 21.2 14.1 4.7-81.4  TE (sec) 173 190 13-738 90 92 12-461 123 143 12- 738  TR (sec)  Start distance (m)  764 486 53-1725 354 219 64-977 516 399 53-1725  Predictor Variables 34.1 1.1 13 0.6 18.0-60.2 0.3-2.7 39.2 1.3 18.3 0.9 15.9-91.4 0.2-3.7 37.2 1.2 16.4 0.8 15.9-91.4 0.2-3.7  Distance to refuge (m)  Distance to trail (m)  Temperature (◦ C)  Sample size  154 137 10-453 86 105 0-490 113 122 0-490  12.1 3.6 5.3-19.3 13.5 3.1 4.6-19.3 12.9 3.3 4.6-19.3  29 (TR =26)  19 (TR =17)  48 (TR =45)  18  Table 2.3: Number of hiking groups per day visiting each field site. Hiking traffic was recorded from August 1st 2012 to September 30th 2012. Group was defined as any individual hiking within two minutes of each other. Field site  Maximum  Minimum  Mean  SD  Avalanche Crest Abbott ridge Eva lake  20 21 48  0 0 2  7 8 21  5 5 13  Total  48  0  12  11  Alert distance ranged from 11.9 to 81.4 meters (Table 2.2). All six models in the subset included predictors for type of approach, start distance and a non-zero intercept (Table 2.4). There was strong support for a negative effect of tangential approach and a positive effect of start distance on DA as the 95% confidence intervals for neither of the predictors overlapped zero (Figure 2.2(a), Table 2.4). Flight initiation distance ranged from 4.7 to 81.4 meters (Table 2.5). Start distance and a non-zero intercept were included in all eleven of the candidate models and had a positive influence on DF (Figure 2.2(b), Table 2.5). Direction of approach appeared in six of the eleven candidate models and there was a trend for pikas tangentially approached to have smaller flight initiation distances, however it and all other predictors in the model had 95% confidence intervals that overlapped zero and were not strongly supported. Exit delay ranged from 12 to 738 seconds (Table 2.6). The top model included an intercept term and a predictor for distance to trail which appeared in eight of the eleven top models (Table 2.6). There was strong support for the inclusion of a non-zero intercept and a positive effect of distance to trail 19  5  5  3 1  2  Log DF(m)  3 2  Approach  1  Log DA(m)  4  (b)  4  (a)  0  0  Direct Tangential  20  40  60  80  20  60  80  (d)  4 2  4  6  Log TR(sec)  (c)  6  8  Initial start distance (m)  2  Log TE(sec)  8  Initial start distance (m)  40  Approach  0  0  Direct Tangential  0  100  300  Distance to trail (m)  500  0  100  300  500  Distance to trail (m)  Figure 2.2: Alert distance (a), flight initiation distance (b), exit delay (c) and return to forage (d) in response to predictors with strong support from the candidate set of models. The slopes and intercepts of the lines drawn are model-averaged estimates from the subset of models within four ∆ AICc of the top model; n=48 pikas for (a)(b)(c) and n=45 for (d). All responses were log transformed to meet the assumptions of the models.  20  1.0 0.8 0.6 0.4 0.2  Ratio of DF DA  0  100  200  300  400  500  Distance to trail (m) Figure 2.3: The ratio of flight initiation distance to alert distance (DF /DA ) as it scales with distance to trail. Intercept and slope values are from model averaged estimates of all models within 4 ∆ AICc of the top model; n=48 pikas. on exit delay (Figure 2.2(c)). All other predictors were not supported by the data. Return to forage ranged from 53 to 1725 seconds (Table 2.7). There was strong support for a non-zero intercept and a positive relationship of TR with increasing distance to trail. Distance to trail was included in all models in the subset (Table 2.7). Tangential approach was negatively associated with TR and was included as a predictor term in seven of the eight candidate models (Table 2.7, Figure 2.2(d)). Distance to refuge, initial start distance and temperature were less supported by the model and did not have a strong effect on TR .  21  There were twelve models included in the subset for the ratio of DF / DA (Table 2.8). The only predictors that were supported by the models were a non-zero intercept and distance to trail. Distance to trail appeared in eight of the twelve models I evaluated and showed a positive relationship with DF / DA (Figure 2.3). Higher ratios indicate a lower tolerance to disturbance.  22  Table 2.4: Predictors of six mixed effects models with ∆ AICc of <4 for alert distance (DA ) in log meters of 48 pikas based on distance to trail (m), type of approach (direct or tangential), start distance (m), temperature (◦ C) and distance to refuge (m). Variables included in models are indicated with a plus sign (+). Number of parameters used in each model, AICc, ∆ AICc (AICc of model - AICc of best model) and normalized Akaike weights are shown below each model. Model-averaged estimates of parameters (Beta), unconditional standard errors (SE) and 95% confidence intervals (CI) shown for each predictor. Bold font indicates that the 95% CI of a predictor did not include 0. Predictors Intercept Tangential approach Start distance (m) Temperature (/circ C) Distance to refuge (m) Distance to trail (m) Number of parameters AICc Delta AICc Normalized AICc weights  Model rank 1 + + +  2 + + + +  3 + + + +  3 -14.200 0.000 0.307  4 -14.100 0.100 0.303  4 -12.400 1.800 0.127  4 + + + + + 5 -12.200 2.000 0.112  5 + + +  6 + + + +  +  +  4 -11.500 2.700 0.082  5 -11.200 3.000 0.069  Beta  SE  95% CI  2.421 -0.176 0.025 0.013 -0.033 0.000  0.135 0.055 0.002 0.008 0.032 0.000  Lower CI 2.151 -0.287 0.022 -0.002 -0.098 -0.001  Upper CI 2.690 -0.064 0.029 0.028 0.032 0.000  23  Table 2.5: Predictors of eleven mixed effects models with ∆ AICc of <4 for flight initiation distance (DF ) in log meters of 48 pikas based on distance to trail (m), type of approach (direct or tangential), start distance (m), temperature (◦ C) and distance to refuge (m). Variables included in models are indicated with a plus sign (+). Number of parameters used in each model, AICc, ∆ AICc (AICc of model - AICc of best model) and normalized Akaike weights are shown below each model. Model-averaged estimates of parameters (Beta), unconditional standard errors (SE) and 95% confidence intervals (CI) shown for each predictor. Bold font indicates that the 95% CI of a predictor did not include 0. Predictors Intercept Start distance (m) Tangential approach Distance to trail (m) Temperature (◦ C)) Distance to refuge (m) Number of parameters AICc Delta AICc Normalized AICc weights  Model rank 1 + + +  2 + + + +  3 + +  4 + + +  +  5 + +  +  + +  4 75.500 1.900 0.093  4 76.200 2.600 0.065  6 + +  Beta 7 + + +  8 + + + + +  9 + +  4 74.300 0.700 0.162  3 74.400 0.800 0.155  2 76.300 2.700 0.062  4 76.400 2.800 0.057  5 76.700 3.100 0.051  3 77.000 3.400 0.042  +  11 + + + +  +  +  4 77.200 3.600 0.039  5 77.300 3.700 0.037  +  + 3 73.600 0.000 0.237  10 + +  2.125 0.023 -0.260 0.001 -0.016 -0.020  SE  95% CI  0.221 0.004 0.132 0.001 0.016 0.074  Lower CI 1.683 0.015 -0.526 0.000 -0.048 -0.170  Upper CI 2.567 0.031 0.007 0.002 0.016 0.131  24  Table 2.6: Predictors of eleven mixed effects models with ∆ AICc of <4 for exit delay (TE ) in log seconds of 48 pikas based on distance to trail (m), type of approach (direct or tangential), start distance (m), temperature (◦ C) and distance to refuge (m). Variables included in models are indicated with a plus sign (+). Number of parameters used in each model, AICc, ∆ AICc (AICc of model - AICc of best model) and normalized Akaike weights are shown below each model. Model-averaged estimates of parameters (Beta), unconditional standard errors (SE) and 95% confidence intervals (CI) shown for each predictor. Bold font indicates that the 95% CI of a predictor did not include 0. Predictors Intercept Distance to trail (m) Temperature (◦ C) Distance to refuge (m) Tangential approach Start distance (m) Number of parameters AICc Delta AICc Normalized AICc weights  Model rank 1 + +  2 + + +  3 + +  4 +  5 +  6 + +  Beta 7 + +  8 +  + + +  9 + + + +  +  3 140.800 1.200 0.147  3 141.800 2.200 0.092  1 141.900 2.300 0.088  2 142.100 2.500 0.080  3 142.200 2.600 0.076  3 142.200 2.600 0.073  11 + + + +  + 2 139.600 0.000 0.277  10 + + +  + 2 143.000 3.400 0.049  4 143.400 3.800 0.040  4 143.500 3.900 0.040  4 143.500 3.900 0.039  4.314 0.002 -0.050 -0.229 0.071 -0.003  SE  95% CI  0.499 0.001 0.038 0.290 0.157 0.008  Lower CI 3.320 0.000 -0.126 -0.813 -0.245 -0.019  Upper CI 5.308 0.005 0.027 0.354 0.387 0.013  25  Table 2.7: Predictors of eight mixed effects models with ∆ AICc of <4 for return to forage (TR ) in log seconds of 45 pikas based on distance to trail (m), type of approach (direct or tangential), start distance (m), temperature (◦ C) and distance to refuge (m). Variables included in models are indicated with a plus sign (+). Number of parameters used in each model, AICc, ∆ AICc (AICc of model - AICc of best model) and normalized Akaike weights are shown below each model. Model-averaged estimates of parameters (Beta), unconditional standard errors (SE) and 95% confidence intervals (CI) shown for each predictor. Bold font indicates that the 95% CI of a predictor did not include 0. Predictors Intercept Distance to trail (m) Tangential approach Start distance (m) Temperature (◦ C) Distance to refuge (m) Number of parameters AICc Delta AICc Normalized AICc weights  Model rank 1 + + +  2 + + +  3 + + + +  + 4 107.800 0.000 0.252  3 108.100 0.300 0.225  4 108.200 0.400 0.215  4 + + + + + 5 109.900 2.100 0.089  5 + + +  6 + + +  7 + + + +  + +  +  +  5 110.400 2.600 0.069  4 110.500 2.700 0.067  5 111.200 3.400 0.048  8 + +  + 3 111.700 3.900 0.035  Beta  SE  95% CI  6.081 0.003 -0.604 -0.059 0.009 0.068  0.554 0.210 0.001 0.036 0.006 0.106  Lower CI 4.979 -1.028 0.001 -0.131 -0.003 -0.147  Upper CI 7.183 -0.179 0.004 0.013 0.021 0.283  26  Table 2.8: Predictors of twelve mixed effects models with ∆ AICc of <4 for the ratio between flight initiation distance and alert distance (DF /DA ) of 48 pikas based on distance to trail (m), type of approach (direct or tangential), start distance (m), temperature (◦ C) and distance to refuge (m). Variables included in models are indicated with a plus sign (+). Number of parameters used in each model, AICc, ∆ AICc (AICc of model - AICc of best model) and normalized Akaike weights are shown below each model. Model-averaged estimates of parameters (Beta), unconditional standard errors (SE) and 95% confidence intervals (CI) shown for each predictor. Bold font indicates that the 95% CI of a predictor did not overlap 0. Predictors Intercept Distance to trail (m) Temperature (◦ C) Tangential approach Distance to refuge (m) Start distance (m) Number of parameters AICc Delta AICc Normalized AICc weights  Model rank 1 + +  2 + + +  3 + +  4 + +  5 + +  6 +  Beta 7 +  8 +  + +  +  9 + + + +  10 + + +  + 3 6.500 1.000 0.144  3 7.300 1.800 0.096  3 7.600 2.100 0.081  12 + + +  + +  2 5.500 0.000 0.239  11 + + +  3 7.800 2.300 0.075  + 2 8.000 2.500 0.069  1 8.100 2.600 0.063  2 8.300 2.800 0.057  4 8.700 3.200 0.047  4 8.800 3.300 0.046  4 8.900 3.400 0.044  3 9.100 3.600 0.040  0.733 0.001 -0.013 -0.076 -0.030 -0.001  SE  95% CI  0.135 0.000 0.009 0.072 0.041 0.002  Lower CI 0.464 0.000 -0.032 -0.221 -0.112 -0.006  Upper CI 1.002 0.001 0.006 0.069 0.053 0.003  27  Foraging activity Temperature was strongly supported as a predictor of pikas’ foraging activity. Warmer temperatures reduced the proportion of time pikas spent foraging (Table 2.9). Models that included distance to trail received very little support and the term was not included in the top model. Table 2.9: Predictors of pikas’ foraging activity (n=17). Average temperature was recorded in the shade 0.5 m off the ground in ◦ C. Model estimates of parameters (Beta), unconditional standard errors (SE) and 95% confidence intervals (CI) shown for each predictor. The number of parameters (K) is given. Bold font indicates that the 95% CI of a predictor did not include 0. Predictors Beta SE 95% CI K AICc Intercept Temperature  2.4  0.684 -0.032  0.086 0.006  Lower CI 0.501 -0.046  Upper CI 0.867 -0.018  2  -19.6  Discussion  Pikas displayed wariness and lost foraging time with each simulated hiking disturbance. Despite this effect, there was strong support for a trend of decreasing opportunity cost and monitoring with decreasing distance to trial (Tables 2.6, 2.7, 2.8). This suggests that while human disturbance resulted in a loss of foraging time, pikas were able to adapt their behaviour to minimize this disruption. This conclusion is supported by my behavioural observations which indicated that temperature, not isolation from hiking trails, was the best predictor of pikas’ foraging activity (Table 2.9).  28  0.5 0.4 0.3 0.2 0.1 0.0  Proportion of time spent foraging  8  10  12  14  16  18  20  Temperature (°C) Figure 2.4: The relationship between the proportion of time pikas spent foraging to average temperature in the shade 0.5 metres off the ground (◦ C) over a 4-6 hour period. Dotted lines represent 95% confidence intervals, n=17 pikas.  29  Did characteristics of the predator influence pikas’ responses? Characteristics of the predator: directness of approach and start distance, were strong predictors of at least two anti-predator responses in pikas. In a meta-analysis conducted by Stankowich & Blumstein (2005), directness of approach was found to be an important predictor of anti-predator responses across a wide spectrum of taxa. Among lizards and various species of birds Cooper (2005) and Blumstein et al. (2003) found that predator start distance influenced measures of wariness. The directness with which a predator approaches can be interpreted by the prey as the intent of the predator (Kramer & Bonenfant, 1997). Direct approaches were associated with higher risk in both marmots and black iguanas (Burger & Gochfeld, 1990; Kramer & Bonenfant, 1997). Directness of approach affected pikas’ alert distance and return to forage (Table 2.4, 2.7). In both cases tangentially approached pikas showed decreased responses suggesting that they were perceived as less risky than direct approaches. A positive effect of start distance on anti-predator responses can be attributed either to the effort an animal puts into vigilance behaviour or an artefact of poor methodology (Blumstein et al., 2003; Cooper, 2008). In a number of bird species the association of DF to start distance has been explained as a function of their wariness behaviour (Blumstein et al., 2003). This is consistent with risk avoidance strategies as animals in higher risk circumstances are likely to invest more in wariness activities and show inflated responses to increasing start distances (Cooper, 2005). Start distance played an important role in determining pikas’ wariness responses (DF and  30  DA ). A similar trend of the influence of start distance on DF and DA has been observed in grey squirrels and striped plateau lizards (Cooper, 2005; Engelhardt & Weladji, 2011). To test whether the effect of start distance in my study could be attributed to pikas’ perception of risk or an artefact of experimental methodology I examined the ratio of flight initiation distance to alert distance to see if pikas further from trails would be more wary than pikas nearer to trails. This prediction was supported in my subset of candidate models. Pikas further from trails had a lower tolerance for human disturbance. After they became alert, pikas closer to trails spent more time being vigilant before they took flight than pikas further from trails (Figure 2.3). The magnitude of pikas’ responses to human disturbance could be biologically significant. The longest time it took for a pika I observed to resume foraging after a human disturbance was 28.3 minutes and the mean TR was 8.6 (SE= 1.0) minutes. During the course of my trials and observations, I noticed three instances of pikas encountering short-tailed weasels (Mustela erminea) and recorded the time it took for each pika to resume foraging after the encounter. The longest time it took for a pika to resume foraging after encountering a weasel was 28.9 minutes (mean 23.0, SE=3.0, n=3). Although these predator events were infrequent, the magnitude of pikas’ responses to their natural, most effective predator was larger than their responses to more frequently encountered hikers. This suggests that pikas are able to differentiate between characteristics associated with hikers and their natural predators. Characteristics of the hiker, such as start distance and directness of 31  approach influenced pikas’ anti-predator response behaviours. Although not manipulated in these trials, predator group size and speed of approach, both of which vary among hiking groups in the alpine, have been recognized as characteristics that could affect animal flight response (Cooper & Frederick, 2007; Stankowich & Blumstein, 2005).  Did characteristics of the individual affect pikas’ responses? Experience of the individual affected the magnitude of pikas’ responses to disturbance stimuli. Distance to trail, a proxy for individual experience, was strongly supported as a predictor of the opportunity costs of lost foraging time (TE and TR ) as well as a measure of wariness (DF /DA ). Decreased responses in pikas nearer to trails could represent an increased, possibly learned, tolerance to human disturbance. Pikas nearer trails may have become accustomed to repeated non-threatening hiking disturbances which could explain their higher tolerance for human approach (Sirot, 2010). Numerous studies with marmots, birds, ungulates and other taxa have documented deflated responses to human disturbance in animals experiencing repeated non-lethal disturbance stimuli (Burger & Gochfeld, 1990; Griffin et al., 2007; Sirot, 2010; Stankowich, 2008). Pikas further from trails did not have the experience to separate human approach from predator stimulus so they tended to react more severely. True habituation cannot be established without monitoring individuals over the course of their exposure to human disturbance over time, however, evaluating anti-predator behaviours in animals exposed to varying levels of disturbance can increase our understanding of their tolerance to disturbance events (Bejder et al., 2009; Sirot, 2010; 32  Stankowich & Blumstein, 2005). Distance to refuge was not supported as a strong predictor in any of the responses I recorded. This is contrary to my prediction and numerous studies of other taxa and pikas which suggest that foraging further afield leads to a higher perception of risk (Holmes, 1991; Stankowich & Blumstein, 2005). The relatively low range of distance to refuge (0.2-3.7 m) captured in my study may have contributed to a lack of response. Holmes (1991) found that American pikas would limit their exposure to predation risk by remaining close to cover. He found that female pikas in their second week above ground foraged an average of 4.1 (SE=1.3) meters from the talus; my study did not capture the full upper end of this range (Holmes, 1991). Experience of the individual affected the magnitude of pikas’ anti-predator responses, however distance to refuge did not. Variation among individual pikas may have influenced the behavioural responses of some of the pikas. Age, sex, reproductive status and condition of the animal can all affect the behavioural response to human disturbance (Griffin et al., 2007; Lima & Dill, 1990). All animals in my study were adults, ruling out age effects but I was unable to determine sex and reproductive status without more intrusive capturing and handling methods which may have affected the individual response.  Did temperature affect pikas’ anti-predator behaviours? Contrary to my prediction, there was not strong support for the influence of temperature on pikas’ anti-predator responses to human disturbance. Most disturbance trials were conducted at cooler temperatures ranging from 4.6◦ C 33  to 19.3◦ C (mean= 12.9, SE=0.5) so it could be that the temperature variation in my study did not capture many warmer, suboptimal conditions for foraging. When conducting a meta-analysis on flight initiation distance, Stankowich & Blumstein (2005) found no consistent response of DF to temperature across taxa. However, when I considered overall pika activity, temperature was the strongest predictor for foraging activity suggesting it exerts an effect on a different behavioural scale.  Predictors of pikas’ foraging activity At my field site, temperature was the most important and only significant predictor of pikas’ foraging activity over a one month period (Table 8). For every degree increase in average daily temperature pikas decreased foraging by 3% over a 4-6 hour period (Figure 2.4). This is consistent with behavioural observations conducted by Smith (1974) who noted that temperature appeared to be restricting pikas’ foraging activity. If climate follows projected warming trends, my data suggests that pikas may have less time available to forage which could impact their ability to store food (ACIA, 2005). Warmer temperatures could also have metabolic consequences that may increase resource requirements and affect herbivore-plant interactions (O’Connor, 2009). While human disturbance did affect pikas’ anti-predator behaviours and reduce foraging time, distance to trail and therefore general proximity to hiking traffic did not predict pikas’ foraging activity observed over a 4-6 hour period at the Abbott ridge field site. Pikas near trails exhibited an increased tolerance to human approach (Table 2.6, 2.7, 2.8). This behavioural 34  adaptation could have minimized the influence of human disturbance and help explain why, on a larger time scale, distance to trail was not supported as a predictor of pikas’ foraging activity. Over the observation period, pikas near trails at Abbott ridge encountered an average of 8 (SE=1) hiking groups per day. Given that pikas near trails lost an average of 4.1 (SE=0.6) minutes of foraging time per hiking disturbance this would result in 33 minutes of total foraging time lost. Over this same period, there was an average of 13.0 (SE= 0.0) hours of daylight (Environment Canada, 2013). Disregarding other factors, human traffic under this scenario would represent a 4% decrease in overall foraging time available to pikas. Compared to the strong negative association of pikas’ foraging activity with temperature, it is unsurprising that distance to trail was not supported in the foraging activity model. All foraging activity observations were conducted at the Abbott ridge site with relatively low amounts of hiking traffic. It could be that at a field site with more hiking traffic distance to trail would have a larger effect on pikas’ foraging activity. The maximum number of hiking groups observed in one day at any of the field sites was 48, which could represent a loss of 3.3 hours or 25% of total daylight hours available for foraging. Combined with warmer temperatures and the foraging constraints they impose, these upper limits of hiking traffic could meaningfully restrict pika foraging.  Implications for conservation Although pikas assessed hikers as equivalent to predation risk in the disturbance trials and there was an opportunity cost to foraging time associ35  ated with human disturbance, individuals with prior experience to hikers were able to behaviourally mitigate this impact by adjusting their response. When examining pikas’ activity budgets, temperature, not distance to trail, was the best predictor of pikas’ foraging activity. Understanding how disturbances are mitigated on different time scales is important in assessing human impact. If activity budgets had been left unexamined, we could have reached spurious conclusions about the relative importance of different factors when it comes to managing a sensitive species. My results suggest that warming climate may lead to less time available for pika foraging and food storage, confirming the observations of Smith (1974). This has major implications for pikas’ fitness and persistence in certain environments. Extirpations attributed to warming have been documented in some low elevation pika populations in the US (Beever et al., 2003). Despite this trend, further research on pikas’ behavioural adaptation needs to be done as there are more populations persisting at lower elevations than predicted by the bioclimatic envelop for the species (Rodhouse et al., 2010; Simpson, 2009). This could be attributed to behavioural plasticity in the timing of forage; when documenting pikas’ foraging patterns Smith (1974) suggested that pikas could potentially adapt their behaviour and even forage partially at night. The interaction between human recreation in the alpine and pikas’ foraging activity needs to be examined further given the context of a warming climate. When foraging time is limited due to abiotic constraints such as temperature, human disturbance could further hinder the ability of pikas to accumulate adequate food stores. Pikas near trails had a higher tolerance to 36  hiking disturbance, however they did not fully eliminate their anti-predator responses. This suggests that there is a limit to the extent to which animals can habituate to human disturbance stimuli as proposed by Frid & Dill (2002). Understanding more about pikas’ sensitivity to human disturbance and the extent to which hiking traffic impacts their foraging activity will be important for park managers as pikas may experience less time available for foraging in a warmer climate.  37  Chapter 3  Conclusion Understanding how animals are affected by abiotic conditions and biotic interactions is a long standing goal of ecology. Animals must make decisions whether or not to forage by balancing predation risk and heat stress and when novel disturbance agents such as hikers are introduced, foraging decisions become more complex (Frid & Dill, 2002; Lima & Dill, 1990). I explored one species, American pika (Ochotona princeps) in a subalpine community to understand how hiking traffic affected its foraging behaviour. American pikas in Glacier National Park encountered hikers on a daily basis which affected pikas’ perception of risk and elicited pikas’ anti-predator behaviours that took away time from foraging (Chapter 2). Despite this adverse effect there was evidence that pikas nearer to trails changed their behaviour to mitigate this impact (Chapter 2). Temperature was a strong predictor of pikas’ foraging activity even though it was not a strong predictor of pikas’ anti-predator behaviour in response to human disturbance (Chapter 2).  38  3.1  Implications for management  The impact that human recreation has on our environment is complex, especially when it comes to wildlife responses and behaviours. Many trail impact studies focus on metrics such as flight initiation distance (DF ) but they may not tell the complete story. For each species, determining the link between human disturbance and survival is necessary for choosing a biologically relevant metric of impact. In a food hoarding species, quantifying the opportunity cost of time lost foraging associated with human disturbance can link human recreation directly to stockpiling success and survival. For every encounter with hikers, American pikas lost 53 seconds to 28.8 minutes of foraging time (mean=8.6 minutes, SE=1.0) (Chapter 2). Pikas near trails that were repeatedly exposed to non-threatening human disturbance stimuli mitigated this impact by delaying their flight response in favour of monitoring and minimizing the opportunity cost of time lost foraging (Chapter 2). However, pikas did not completely eliminate anti-predator behaviours in response to human disturbance suggesting that hiking traffic could have a biologically significant impact in areas with more frequent human recreation. Wildlife managers should broaden the scope of their focus to include not only metrics of human disturbance but other factors in the environment that could potentially influence overall animal activity. In the case of pikas, temperature was the main predictor of overall activity however it did not play a role in individual response to disturbance stimuli (Chapter 2). If pikas’ activity budgets had been left unexamined, it would have been possible to  39  come to spurious conclusions about the relative importance of different factors when it comes to managing pika populations and disregard the role of temperature on pikas’ foraging activity. Climate projections suggest that in the coming decades hotter days will become more frequent in the alpine (ACIA, 2005). Pikas’ foraging activity was strongly linked to temperature; pikas spent less time foraging on warmer days (Chapter 2). This suggests that warming climate will restrict pikas’ time available to forage. When pikas’ foraging activity is constrained by the abiotic environment, the additional loss of foraging time due to hiking disturbance could affect pikas’ ability to store food and survive the winter. In order to detect and document this change over time, monitoring pika populations for changes in abundance or distribution near and away from trails would be prudent.  3.2  Future directions  Understanding the impact of human recreation on the environment is a challenging pursuit in conservation biology. In future studies, it would be interesting to examine whether pikas are preferentially utilizing habitats away from trails and explore the upper limits of how much human traffic pikas can withstand before the disturbance begins to affect their overall foraging activity and lead to less stockpiling of food. Comparing pikas’ demographic information in near trail and back-country populations to see if human disturbance is influencing survival and reproduction would help illuminate the population level consequences of human disturbance. Further  40  work on pikas’ behavioural plasticity would help us understand to what extent pikas can adapt to warming temperatures by foraging earlier in the day and later in the evenings and whether or not this would influence their susceptibility to predation. Increasing our understanding of how metabolic requirements of herbivores in the alpine could change with warming climate could help us predict future herbivore-plant interactions. This information could help us project the future distribution and persistence of alpine communities and conceptualize some impacts of climate change.  41  Bibliography ACIA (2005). Arctic Climate Impact Assessment. Cambridge University Press. Barash, D. P. (1973). 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