Arts and Sciences, Irving K. Barber School of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Newbury, Roberta Kay
Doctor of Philosophy - PhD
University of British Columbia
The bobcat (Lynx rufus) is a native North American felid that is an economically valuable furbearer. This mesocarnivore is an important species in ecosystem structuring, exerting top-down control on rodent populations. Bobcats in northern latitudes face seasonal challenges such as deep snows, cold, and food scarcity. I used laboratory, modeling, and field methods to investigate bobcat ecology in a northern peripheral population where I evaluated, (1) winter diet, (2) modeled energetics and determined overwinter prey requirements, (3) determined home range size and habitat selection, and (4) determined seasonal movement distances and shape of movements in relation to habitat. Bobcats consumed 5 major prey groups: deer (Odocoileus spp.), snowshoe hares (Lepus americanus), red squirrels (Tamiasciurus hudsonicus), Cricetidae (rodents), and Tetraoninae (grouse). Squirrels accounted for 54% of biomass consumed, followed by Cricetidae (24.5%), hares (12.2%), deer (8.5%), and grouse (<1%). Bobcats in northwest Montana appeared to be dependent on squirrels and other rodents in the winter. I developed a strict, but realistic, winter energetics model for bobcats from field data on average movements, body mass, and observed diet of bobcats in northwest Montana. Bobcat daily energy expenditures were estimated at ~2.35×basal metabolic rate. This model predicted that over winter, a 10.5 kg bobcat would need ~5 kg of deer, 15 snowshoe hares, 338 red squirrels, 19 woodrats, and 547 small rodents. Male bobcat annual home ranges were 90.0 ± 12.0 km² and females were 42.2 km². Seasonal home ranges, within sex, did not differ significantly in size or relative habitat composition. Home range composition did differ from availability across the study site, with open habitats being chosen less and lodgepole pine (Pinus contorta) habitats been chosen more. Locations within the home range were found in most habitats according to availability. Male bobcats moved greater daily distances across seasons than did the female bobcat. Movement distances were significantly less in winter for both. Fractal dimension of movement pathways show male bobcats moved in a more linear fashion, while the female exhibited more convoluted movements across all seasons. Males and the female differed in habitats selected along movement paths, but varied little across seasons.
BEHAVIORAL ECOLOGY OF THE BOBCAT IN A REGION WITH DEEP WINTER SNOWS by ROBERTA KAY NEWBURY B.S., University of Montana, 2001 M.S., Eastern Illinois University, 2005 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) June 2013 © Roberta Kay Newbury, 2013 ii ABSTRACT The bobcat (Lynx rufus) is a native North American felid that is an economically valuable furbearer. This mesocarnivore is an important species in ecosystem structuring, exerting top-down control on rodent populations. Bobcats in northern latitudes face seasonal challenges such as deep snows, cold, and food scarcity. I used laboratory, modeling, and field methods to investigate bobcat ecology in a northern peripheral population where I evaluated, (1) winter diet, (2) modeled energetics and determined overwinter prey requirements, (3) determined home range size and habitat selection, and (4) determined seasonal movement distances and shape of movements in relation to habitat. Bobcats consumed 5 major prey groups: deer (Odocoileus spp.), snowshoe hares (Lepus americanus), red squirrels (Tamiasciurus hudsonicus), Cricetidae (rodents), and Tetraoninae (grouse). Squirrels accounted for 54% of biomass consumed, followed by Cricetidae (24.5%), hares (12.2%), deer (8.5%), and grouse (<1%). Bobcats in northwest Montana appeared to be dependent on squirrels and other rodents in the winter. I developed a strict, but realistic, winter energetics model for bobcats from field data on average movements, body mass, and observed diet of bobcats in northwest Montana. Bobcat daily energy expenditures were estimated at ~2.35×basal metabolic rate. This model predicted that over winter, a 10.5 kg bobcat would need ~5 kg of deer, 15 snowshoe hares, 338 red squirrels, 19 woodrats, and 547 small rodents. Male bobcat annual home ranges were 90.0 ± 12.0 km 2 and females were 42.2 km 2 . Seasonal home ranges, within sex, did not differ significantly in size or relative habitat composition. Home range composition did differ from availability across the study site, with open habitats being chosen less and lodgepole pine (Pinus contorta) habitats been chosen more. Locations within the home range were found in most habitats according to availability. Male bobcats moved greater daily distances across seasons than did the female bobcat. Movement distances were significantly less in winter for both. Fractal dimension of movement pathways show male bobcats moved in a more linear fashion, while the female exhibited more convoluted movements across all seasons. Males and the female differed in habitats selected along movement paths, but varied little across seasons. iii PREFACE The methods used for animal capture and handling in this study adhere to a strict Animal Care and Use protocol following guidelines set forth by the American Society of Mammalogists’ Animal Care and Use Committee (Sikes et al. 2011). Permits and approval for this research were obtained from Montana State Fish, Wildlife, and Parks (#’s 2009-59, 2010-002, 2011-003) and the University of British Columbia’s Animal Care Committee (A07-0676-R001). Permission to conduct research on National Forest System lands was covered under CCS Agreement 07-CS-11011000-009 with the USDA Forest Service for snowshoe hare (Lepus americanus), Canada lynx (Lynx canadensis), and bobcat (Lynx rufus) research on the Tally Lake Ranger District of the Flathead National Forest and Libby and Fortine Ranger Districts of the Kootenai National Forest, Montana. iv TABLE OF CONTENTS ABSTRACT…..........................................................................................................................ii PREFACE….............................................................................................................................iii TABLE OF CONTENTS…......................................................................................................iv LIST OF TABLES………………………………………………………………………...…vii LIST OF FIGURES…………………………………………………………………………..ix ACKNOWLEDGMENTS...….……………………………………………………………….x DEDICATION……………………………………………………………………………….xii CHAPTER 1: Bobcat ecology at the northern periphery of their geographic distribution…...1 1.1. Introduction……....................................................................................................1 CHAPTER 2: Winter diet of bobcats…………………………………………………………6 2.1. Literature review and objectives………………………………………………....6 2.2. Study Area………………………………………………………………………10 2.3. Methods 2.3.1. Sample collection……………………………………………………..11 2.3.2. Sample analysis……………….………………………………………12 2.3.3. Prey species identification……………………………………………13 2.3.4. Trap bait and trapper surveys.………………………………………...13 2.3.5. Statistical analyses…………………………………………………….14 2.3.6. Comparison of bobcat winter diets.……………………………...…...15 2.3.7. Biomass calculations.…………………………………………………15 2.3.8. Dietary niche breadth and overlap with Canada lynx.………………..15 2.4. Results…………………………………………………………………………..16 2.5. Discussion………………………………………………………………………17 2.6. Implications of bobcat winter diet.……………………………………………..21 CHAPTER 3: An energetics model for bobcats in a deep snow environment......................32 3.1. Literature review and objectives………………………………………………..32 3.2. Study Area………………………………………………………………………36 3.3. Methods 3.3.1. Bobcat capture and handling…………………………………………37 3.3.2. Bobcat movement and activities………………………….…………..38 3.3.3. Winter energetics model for bobcats……………….…………………39 3.3.4. Model application……………………………………………………..42 3.3.5. Winter prey requirements….………………………………………….42 v 3.4. Results 3.4.1. Bobcat capture data..…………………………………………….……43 3.4.2. Baseline winter model…………….……………..………....…………43 3.4.3. Energy balance of radiocollared bobcats………...…………………....43 3.4.4. Overwinter prey requirements for bobcats….………………...………45 3.5. Discussion 3.5.1. Baseline winter model overview…………………………...…………46 3.5.2. Bobcat ability to meet DEE..................................................................46 3.5.3. Implications for bobcats in extreme environments.…………………..49 3.6. Implications for bobcat winter energetics..……………………………………..53 CHAPTER 4: Resource selection and home range characteristics………………………….63 4.1. Literature review and objectives………………………………………………..63 4.2. Study Area………………………………………………………………………66 4.3. Methods 4.3.1. Bobcat capture and handling………………………………….………67 4.3.2. GIS basemap development…………………………….….…………..68 4.3.3. Seasonal resource selection and home ranges...………………………68 4.3.4. Latitudinal comparisons………………………………………………69 4.3.5. Statistical analyses…………………………………………….………69 4.4. Results 4.4.1. Bobcat capture data…...…………………………………..………….70 4.4.2. Seasonal home ranges………………………………….…………..…71 4.4.3. Comparison of male and female home range composition….………..71 4.4.4. Hierarchical habitat selection……………………….……………...…72 4.4.5. Latitudinal comparisons…………………………………….……...…73 4.5. Discussion………………………………………………………………………74 4.5.1. Home ranges...…………………………………………………….…..74 4.5.2. Habitat selection………………………………………………………77 4.5.3. Sample size considerations………………………………….………...78 4.6. Implications of bobcat seasonal home ranges and habitat selection…..………..80 CHAPTER 5: Seasonal movement patterns of bobcats……………………………………..97 5.1. Literature review and objectives………………………………………………..97 5.2. Study area…………...…………………………………………………………100 5.3. Methods 5.3.1. Capture and handling…...……………………………………………101 5.3.2. Movements and movement pathways……….………………………102 5.3.3. Statistical analyses…………………………………………….……..103 5.4 Results 5.4.1. Daily movements………………………………………………….…104 5.4.2. Weekly movements………………………………………………….105 5.4.3. Habitat type chosen along movement paths….……………………...106 5.5. Discussion……………………………………………………………………..107 5.5.1. Movement distances and rates………………….……………………108 5.5.2. Movement tortuosity………………………………………...………110 vi 5.6. Implications of bobcat seasonal movement patterns ....…………………….…111 CHAPTER 6: Conclusions...................................................................................................130 6.1. Overview………………………………………………………………………130 6.2. Bobcat energy balance in winter………………………………………………131 6.3. A generalist in a specialist’s world…………………………………………….135 6.4. Implications for the future……………………………………………………..138 REFERENCES CITED…………………………………………………………...………..140 APPENDICES……………………………………………………………………..……….163 APPENDIX A: Bobcat genetics in the Salish Mountains of Montana...…………..163 APPENDIX B: Bobcat and Canada lynx tracking and trapping effort…………….166 APPENDIX C: GIS source information...................................................................168 APPENDIX D: Seasonal elevation of bobcat locations…………………………....170 APPENDIX E: Differences in home range estimators……………………………..180 vii LIST OF TABLES Table 2.1. Prey species available on TLRD………………………………………………...22 Table 2.2. Total detections of prey species in diet samples…………………………………23 Table 2.3. Selected winter diet studies for northern latitude bobcats……………………….24 Table 2.4. Conversion for % biomass of prey in diet……………………………………….26 Table 2.5. Selected winter diet studies for Canada lynx……………………………………27 Table 2.6. Bobcat and lynx dietary overlap…………………………………………………28 Table 3.1. Activity scenarios and bobcat DEE……………………………………………...54 Table 3.2. Mass and daily movements of individual bobcats on TLRD……………………55 Table 3.3. Energy assimilation for bobcats by prey type…………………………………...56 Table 3.4. Winter energetics and predicted DEE……………………………………………57 Table 3.5a. Bobcat winter prey requirements for different timeframes…………………….58 Table 3.5b. Bobcat winter prey requirements for comparison with Powers et al. (1989)…..60 Table 4.1. Habitat composition of study areas in northwest Montana……………………...81 Table 4.2. Bobcat home range sizes………………………………………………………...82 Table 4.3. Effect of sex and season on home range composition…………………………...83 Table 4.4. Hierarchical habitat selection within bobcat home ranges………………………85 Table 4.5. Comparisons of habitat types for 2 nd and 3 rd order habitat selection……………87 Table 4.6. Home range studies used in latitudinal meta-analysis…………………………...89 Table 4.7. Latitudinal comparisons for male and female bobcats…………………………..90 Table 5.1. Effect of sex and season on daily movements………………………………….113 Table 5.2. Average daily movements by season…………………………………………...114 Table 5.3. Comparison of daily movements by season……………………………………115 Table 5.4. Effect of sex and season on shape and distance of weekly movement paths…..116 Table 5.5. Comparisons of seasonal effect on weekly movement distance………………..117 Table 5.6. Average Fractal dimension (shape) and distance of movement paths………….118 Table 5.7. Effect of sex and season on habitat type selected along movement paths…...…119 Table 5.8. Average proportion of habitat type used along movement paths by sex……….121 Table 5.9. Comparison of seasonal habitat use along movement paths……………...……123 viii Table B.1. Bobcat and lynx survey and trapping effort…………………………………..167 Table C.1. GIS source information.....................................................................................168 Table D.1. Habitat type of individual locations by season……………………………….174 Table D.2. Elevation of individual bobcat locations by season…………………………..176 ix LIST OF FIGURES Figure 2.1. Bobcat and Canada lynx track photo comparison……………………………...29 Figure 2.2. Prey consumed by TLRD and northern latitude bobcats.....................................30 Figure 2.3. Standardized niche breadth of bobcat and lynx populations…………………...31 Figure 3.1. Energy expenditure by activity type……………………………………………61 Figure 3.2. Key to movement and behavioral strategies……………………………………62 Figure 4.1. Study area map…………………………………………………………………91 Figure 4.2. Locations of snowshoe hare mark-recapture grids on TLRD………..….……...92 Figure 4.3. Proportion of bobcat GPS locations by habitat type…………………….……...93 Figure 4.4. Bobcat seasonal home ranges in areas of snow persisting to May 15….………94 Figure 4.5. Snowmobiling to locate collared bobcats on May 5, 2010 at 1500m……….….95 Figure 4.6. Annual and seasonal home ranges for M1...........................................................96 Figure 5.1. Daily movement distance for male and female bobcats by season…………...124 Figure 5.2. Daily movement rate for male and female bobcats by season………………..125 Figure 5.3. Shape of weekly movements paths for male and female bobcats by season….126 Figure 5.4. Comparison of male and female Fractal D for weekly movements..…………127 Figure 5.5. Weekly movement distance for male and female bobcats by season…………128 Figure 5.6. Fractal dimension of movement paths for individual bobcats...........................129 Figure D.1. Average elevation of male and female bobcats for seasonal locations.............177 Figure D.2. Average seasonal elevation of individual bobcats………..…………………..178 x ACKNOWLEDGMENTS I am in debt to a great many people who believed in me and in this research. Without their assistance and support, I would never have succeeded in this endeavor. If I miss anyone in the following paragraphs, I apologize! I extend my sincere appreciation and thanks to Tim Thier and Jim Williams at Montana Fish, Wildlife, and Parks. In particular, Tim went above and beyond the call of duty. He was instrumental in bobcat carcass collection, and loaned me many major and minor pieces of equipment over the years. Without his assistance, I would never have accomplished the fieldwork required to present this thesis. Additionally, Kent Laudon (MT FWP) searched for missing bobcats on several occasions while conducting wolf telemetry flights. I owe him thanks also. Amy Jacobs, with the USDA Forest Service Tally Lake Ranger District, is owed profuse thanks for assisting with maps, lynx sighting data, and procuring access to conduct this research on Forest Service lands. Brian Manning of Montana Department of Natural Resources and Conservation deserves a special acknowledgment for allowing us to use the Stillwater State Forest Unit Office near Olney. Brian allowed us to park a field trailer there, and gave us access to kitchen and bathroom facilities. Thank you, Brian, for all you have done over the years. I am indebted to the wonderful people at Alpine Animal Hospital in Whitefish: Dr. Hugh Rogers, whom I can only thank far too late as he has gone on to a better place; Dr. Al Barton for support and veterinary advice throughout the project; and the wonderful Julie Ann King for having the patience to teach me blood draw techniques. Next, I thank Neil Anderson for access to MT FWP Wildlife Laboratory facilities in Bozeman, Montana, where carcasses were stored and necropsied. I am indebted to Dave Dyer, University of Montana Zoological Museum curator in Missoula, Montana, for access to Museum facilities, collections, equipment, and for expert assistance with specimen identification. I also extend my thanks to the University of Montana Historical Archaeology Lab for equipment loaned during diet analyses. Additionally, I need to thank those who were gracious enough to open their homes to me while I was working on campus, in transit to and from campus, or while working in the lab: Joyce and Dennis Boon, Julie and Stuart Cunningham, Jeanne Franz, Emily Herdman, Natalie Melaschenko, F. Ron Newbury, Corinne Spencer, Karen Hodges, and Katy and Rory Williams. Donna Badewitz and Trevor Fero were most gracious to volunteer time in the lab. Mike Kroeger, Kristi Drake, Brandon Nickerson, Kristen Kirkby, and Jessica Schulz also provided field assistance during the early stages of field research. I extend my appreciation to all the anonymous trappers who donated bobcat carcasses to my study and who took the time to respond to my trap bait survey. During this research, I was financially supported by a grant in aid of research from xi Sigma Xi and fellowships and scholarships from UBC Okanagan. A research grant from the Natural Sciences and Engineering Research Council of Canada to Karen Hodges provided research funding. My advisor, Karen Hodges, has been patient, wise, and supportive throughout. She was brave enough to allow me to be—perhaps overly—ambitious with respect to research goals. She was gracious enough to house me for a semester, and I have greatly appreciated all she has done for me over the years. My committee members, L. Scott Mills, Robert Lalonde, Mike Russello, and Rebecca Tyson, have been instrumental in improving each chapter, as they are a fine group of researchers coming from a wide scientific background. My field technicians were the most amazing people to work with, and I am so happy to have them in my life. I was incredibly lucky to have worked with Imogene Davis, Tyler Parks, Jaclyn Comeau, Ethan Schniedermeyer, Jodi Berg, and Mark Cancellare. Not only do these folks have top-notch skills, they were incredibly dedicated to my research. Through their skill, perseverance, and general all around toughness, this project succeeded. Not only did they each work harder than could be imagined, they did so with unfailing humor. This group was the best to spend many, many long hours with, be it snowmobiling, building traps, unsticking stuck snowmobiles, starting cold snowmobiles, crossing raging rivers, waiting for a bobcat to recover from anesthesia, climbing cliffs, or building a snowman. We worked hard, but we had fun along the way! Their friendships mean the world to me, and I have tried to write my gratitude into the following pages. My greatest debt is owed to my family. My mom and dad, Pamela and Allen Whitaker, obviously, deserve a great deal of credit. For one, they allowed me to run wild as a kid in the very forest where these bobcats live. To Anna Riley for everything that she is and has done for us, Cynthia Riley Augé for listening to me always, and F. Ron Newbury for his encouragement in all things. Jim Riley deserves special thanks for helping me weld trap drop gates; they were one of the few things that did not break on this project! Most importantly, my immeasurable love and appreciation goes to Peter, Isabeau, and Alora, who were there for the entire journey. Isabeau and Alora, I am so proud of you, and I am honored to be your mother. You have encouraged me throughout, when it is my job to encourage you. Peter has unfailingly supported me. Being a graduate student is hard, being the spouse of one is even more so. He helped in all aspects of fieldwork, but was also my staunchest supporter. He built traps, saw our house, driveway, and garage overrun with field equipment, allowed field techs to sleep on our couch, drove people to and from the airport, picked up road killed deer, stayed out long hours with me searching for radiocollars, handling animals, moving traps, and spent countless more hours listening to me talk about bobcats. I love you. My last acknowledgement is for the bobcats: the epitome of wild. They are elusive, unbridled, tough, breathtakingly beautiful, and completely amazing. My admiration and respect for these animals, particularly the infamous M1, knows no bounds. xii DEDICATION In memory of Donald A. Riley whose love flows through my family. (July 2, 1932—March 20, 2011) 1 CHAPTER 1 BOBCAT ECOLOGY AT THE NORTHERN PERIPHERY OF THEIR GEOGRAPHIC DISTRIBUTION 1.1. INTRODUCTION Bobcats (Lynx rufus) are common North American felids that make use of a wide variety of habitat types and prey species. This native felid plays an important ecological role in the ecosystems it inhabits. As a common mesocarnivore (a carnivore <15 kg on average), the bobcat may drive community structure (Roemer et al. 2009) through top-down control of rodent populations, and may function in a role similar to larger, apex predators. The scenario where the bobcat functions as a top ecosystem predator in North America is becoming more common as human actions (e.g. direct persecution, habitat loss, habitat conversion) have caused substantial declines in populations of large carnivores (Laliberte and Ripple 2004). As a result, many mesopredators have become default apex predators (Crooks and Soulè 1999). In fact, in some regions, bobcats are the apex predator, where they exert a stabilizing effect on the ecosystem by suppressing mesopredators. For example, bobcats prey heavily on rodents, such as cotton rats (Sigmodon hipsidus) that may be detrimental to local gamebird populations (Godbois et al. 2003). In addition to playing an important, and often poorly understood, role in ecosystem functioning, the bobcat is culturally (Hansen 2007) and economically important. The bobcat is listed in Appendix II of CITES, with this designation indicating that although bobcats are not Threatened with extinction, hunting—primarily through trapping for fur—must be closely monitored. The bobcat is the most heavily traded felid species on the international market (United Nations Environment Programme-World Conservation Monitoring Centre 2009), with ~50,000 pelts exported from the U.S. every year. Bobcat pelts are sought from bobcats that inhabit the mountainous western U.S., as high elevations and cold winters make the bobcat’s fur longer, softer, and more luxurious. A bobcat pelt of the highest quality can fetch over $500 (Montana Trappers Association 2013). Bobcat fur is used in trim, coats, and jackets, with a calf-length coat demanding a price well over $10,000 USD. 2 Bobcats are widely distributed throughout the United States, but are less common in the southern portions of the Canadian provinces and the northern and central parts of Mexico (Anderson 1987). Deep snows and extreme cold winter conditions may limit the northern distribution of bobcats above 50° N latitude. North of the 50 th parallel, Canada lynx (Lynx canadensis) replace bobcats as the dominant Lynx spp. on the landscape. Lynx are deep snow specialists, with large snowshoe-like feet that allow lynx to float on deep powdery snow. Lynx (8-11 kg) are similar in size to bobcats (6-16 kg), but lynx specialize on snowshoe hares (Lepus americanus). However, bobcats at the northern extent of their distribution overlap with lynx at the southern extent of their geographic distributions; these two species may overlap extensively in habitat and food requirements where they are sympatric (Buskirk et al. 2000b). Bobcats have small feet that are not well adapted to walking on snow, leaving them at a relative disadvantage in environments with winter snows where a bobcat sinks more than 20 cm. Additionally, bobcats begin to thermoregulate slightly below 0°C (-2.2°C, Mautz and Pekins 1989). Coupled with high foot loadings and a high lower critical body temperature (TLC), bobcats expend large amounts of energy in geographical locations with cold, snowy winters. However, snow conditions in southern lynx habitats (i.e. northern bobcat habitats) may be subject to more freezing and thawing than in northern lynx habitats (Buskirk et al. 2000a), with such differences largely dependent on elevation, aspect, and local weather conditions (Ruediger et al. 2000). Increased crusting and compaction of snow at the southern reaches of lynx range may reduce the competitive advantage that lynx have over other species such as bobcats in soft snow, due to the lynx’s long legs and low foot loadings (Buskirk et al. 2000b). Past research indicates that bobcats are limited by deep snow conditions (Litvaitis et al. 1986b). For instance, bobcats have been shown to reduce movements in winter (Koehler and Hornocker 1989, Apps 1996) and contract winter home range size as compared to other seasons (Koehler and Hornocker 1989). Additionally, bobcats may use different habitats in winter that provide snow-free access to prey (Bailey 1974, Koehler and Hornocker 1989), but have increased vulnerability to harvest mortality (Petraborg and Gunvalson1962, Bailey 1974, Knick 1990). Though bobcats may not be physiologically or morphologically well adapted to deep snow environments, behavioral plasticity may allow bobcats to use behavioral 3 strategies in such environments to offset innate limitations that place them at a disadvantage in northern latitudes. Indeed, bobcats are flexible in many aspects of their general ecology, including home range size, home range overlap with conspecifics, dietary preferences, habitat selection, and responses to anthropogenic landscape modification. Often generalist species that are widely distributed and use a wide range of habitats may be able to withstand extremes in environmental conditions (Coltrane and Barboza 2010), partly through local population adaptation (Reding 2011). Bobcats appear to be expanding their distribution north in parts of British Columbia and Alberta, Canada, in response to the increasing fragmentation of northern boreal forests as the landscape is converted for logging, farming, and human settlement (Hall 1981, Boyle 1987), though little information is available on current distribution and population status above 50°N (Hatler et al. 2008). Harvest, museum, and sighting records for bobcats above 51°N in British Columbia are uncommon (Hatler et al. 2008). Coupled with human alteration of a landscape that creates habitat conditions that favor a generalist species, climate change impacts could potentially favor bobcats in northern latitudes, if the frequency of freezing and thawing cycles in areas of deep, powdery snow are increased. Freezing and thawing leads to crusting and compaction of snow, effectively eliminating high energy expenditures associated with winter movements for bobcats and removing the advantage the deep snow specialist lynx has over the generalist bobcat. Bobcats in northern latitudes face many challenges such as deep snows, cold temperatures, and possible food scarcity; thus, survival strategies for bobcats depend largely on the behavioral and physiological plasticity demonstrated by individual animals. Determining dietary niche in atypical environments and during times of limited resources can inform our understanding of the behavioral plasticity, prey switching ability, and potential for competitive interaction among sympatric congeners. Winter diet and dietary niche breadth of bobcats is unknown in Montana in a deep snow environment, where the Threatened Canada lynx is both sympatric and better adapted ecologically and morphologically to winter conditions experienced in this area. Critical information that shapes our understanding of the basic behavioral ecology of a predator are diet and energetic requirements, home range size, movement patterns, and habitat. Movement patterns and space use by predators depend not only on the sex of the individual, but also on other factors such as the perceptual range (fine- 4 or coarse-grained) of the species, behavioral plasticity of individuals, response to habitat complexity and landscape barriers, and time of year. Hence, investigation of movement distances and movement pathways can inform our understanding of foraging strategies and behavorial adaptations to meeting energetic requirements. Winter in northern latitudes can be extreme and presents specific challenges to bobcat ability to meet energetic requirements during cold winters characterized by deep, persistent snows. Such extremes in seasonal conditions strongly impact bobcat population density in northern latitudes. Bobcat populations inhabiting an area of deep winter snows in northwest Montana offer an effective opportunity to determine how bobcat behavioral flexibility compensates for limiting morphological and physiological factors; this approach could be applied to other generalist species as well to evaluate how peripheral populations cope with strenuous conditions. The population of bobcats in the Salish Range of Montana are likely to be, in part, one of the main source populations supplying individuals expanding bobcat range northward. Understanding of the behavioral strategies of this well established population near the periphery of bobcat northern distribution is an excellent starting point for understanding the breadth of behavioral ability that peripheral populations must possess in order to succeed in atypical environments. I conducted a 4-year field study from 2008-2011 on the Tally Lake Ranger District of the Flathead National Forest in northwestern Montana to investigate behavioral ecology of bobcats in an area of deep winter snows. Specifically, bobcats were fitted with GPS/VHF radiocollars that obtained high accuracy location data every 3 hours to investigate bobcat movements, habitat selection, and home range requirements on a seasonal basis. There are two overriding questions that directed the course of this research. (1) Are bobcats limited by deep snow environments? (2) What behavioral strategies do bobcats in northern latitudes use that allow them to be successful in environments that they are not well suited for morphologically? In this thesis, I offer four data chapters that collectively address bobcat ecology in northwestern Montana. Chapter 2 examines two hypotheses: (1) Winter diet of bobcats in northwest Montana does not differ from other bobcat populations in northern latitudes. (2) Winter diet of bobcats in northwest Montana does not overlap significantly with Canada lynx. In this chapter, I test the winter diet composition of a bobcat population in northwestern Montana to 5 determine if bobcats in this region showed facultative specialization on snowshoe hares, the primary prey of the closely related lynx. Winter diet of northwest Montana bobcats is also compared to the winter diet of 13 other bobcat populations in northern latitudes to test whether northern bobcats have similar or different diets. Chapter 3 examines (1) whether bobcats in northwest Montana are able to meet daily energy expenditures on the diets they obtain, and (2) what are the behavioral strategies bobcats engage in to offset caloric expenditures from thermoregulatory requirements and the high energetic cost of moving in deep snow conditions? To investigate if individual bobcats were able to maintain energy balance, I develop an energetics model from field data on average movements, body mass, and observed diet of bobcats in northwest Montana to determine overwinter prey requirements. Additionally, I determine distances and activity levels that would enable bobcats to stay in energy balance. Chapter 4 examines three hypotheses: (1) bobcat home range size depends on season, (2) bobcat habitat use depends on season, and (3) home range size depends on latitude. Basic ecological knowledge of the bobcat’s habitat use and home range distribution are lacking for bobcats in northwest Montana where bobcats inhabit mountainous habitats receiving >300 cm of snow annually. I use GPS radiocollar data from bobcats in a deep snow environment in northwest Montana to evaluate home range size and habitat selection seasonally. Additionally I conduct a meta-analysis of home range size across latitudinal gradients to determine variation in home range size across bobcat distribution. Chapter 5 also tests three hypotheses: (1) bobcat movement patterns (shape and distance) depend on sex, (2) bobcat movement patterns depend on season, and (3) bobcat movement patterns depend on habitat type. To investigate movements of bobcats in northwest Montana, I evaluate daily bobcat movements, shape of weekly movements using fractal dimension, and habitats selected along movement pathways with respect to both season and sex of individual animals. Last, Chapter 6 synthesizes my results to present a comprehensive picture of the behavioral ecology of bobcats in an area experiencing long, cold winters characterized by deep, persistent snow conditions. I present implications for the future of the bobcat in northern latitudes and use of this bobcat population as a model for the general ecology and behavioral plasticity of bobcats as they expand their geographic distribution north of 50°N. 6 CHAPTER 2 WINTER DIET OF BOBCATS 2.1. LITERATURE REVIEW AND OBJECTIVES Knowledge of the dietary needs of a species is important in informing the ecological niche of the animal, as resource partitioning often limits niche overlap in sympatric species that occupy a common trophic level or role in the ecological community (de Azevedo 2008). Dietary niche is strongly related to habitat requirements, home range size, and intra- and interspecific interactions, including habitat preferences and competition within and between species (Schoener 1986, Calder 1996, de Azevedo 2008). Thus, dietary partitioning should reduce competitive interactions between species (Schoener 1986); indeed, how resources are partitioned may determine the diversity of coexisting species (Pianka 1974). A key aspect of understanding dietary niche is determining if the animal functions as a dietary generalist or specialist. The ecological role of a species is of utmost conservation importance, as many specialist species are declining and face increased extinction risk, relative to congener generalists (Devictor et al. 2008, Clavel et al. 2011). Typically, generalist predators exert a stabilizing effect on community dynamics, whereas specialist predators exert destabilizing effects, often producing cyclic population dynamics typified by peaks and crashes in both predator and prey numbers (May 1977, Roth et al. 2007). Here, a generalist species is defined as a species with a broad ecological (dietary) niche, in that the animal exploits a wide variety of resources and habitats, a so-called “jack-of-all-trades” (MacArthur 1972, Richards et al. 2006, Devictor et al. 2008). A specialist consumes a narrow range of prey items, with one particular prey item being overwhelmingly predominant in the diet, such that the predator in question is often considered an obligate predator on that prey type. Such specialization is often used to predict the adaptive responses of the animal in a patchy, dynamic landscape (Levins 1968, Devictor et al. 2010). Specialization is a versatile and ephemeral concept in ecology, given the multi- dimensional and multi-scale nature of niches, and it is recognized that individuals, and by extension populations, may cover the full spectrum between generalization and specialization 7 (Devictor at al. 2010). For example, when resources are abundant in a landscape, an animal may become highly specialized on one type of food or habitat (Schoener 1974a). Many studies have found that individualized specialization in diet is widespread among generalist predators, with populations being composed of individual specialists (Bolnick et al. 2003, Poore and Hill 2006, Martins et al. 2008, Woo et al. 2008). Woo et al. (2008) found that specialization in a generalist predator might last from days to years, and that this adaptive, or facultative, specialization resulted in higher fitness when resources were homogenous or predictable, both numerically and spatially. Individuals that retained a generalist diet had higher reproductive fitness when the environment was more heterogeneous and resource location and abundance less predictable. Thus, niche breadth is primarily dependent upon prey abundance and landscape heterogeneity and productivity (Svanbäck and Persson 2004, Panzacchi et al. 2008, Tinker et al. 2008). Individual specialization or ‘facultative specialization’ could occur in a generalist predator, in the following situations: (1) one type of prey is locally abundant, (2) resources become limited, e.g. seasonal variation in prey availability, (3) the landscape becomes increasingly homogeneous, thereby providing less diversity of prey types, or (4) one prey type is energetically more beneficial, when considering encounter rates, capture rates, and calories obtained (Brown et al. 2004, Malo et al. 2004, Roth et al. 2007). In essence, a generalist becomes a facultative specialist when it preys upon the most common prey type in the landscape. Generalist species often have high behavioral plasticity (Tuomainen and Candolin 2011), and part of this plasticity is manifested in the ability to switch prey types when a certain food type becomes locally more numerous. Though a generalist may engage in facultative specialization, the plastic behavior of a generalist predator still allows it to use other prey species. Conversely, resource partitioning models predict that when food becomes limited, breadth of both food and habitat niches should increase (Schoener 1971, Krebs et al. 1977, Neale and Sacks 2001). Thus, generalists may have higher fitness than specialists in certain environmental settings, even when fitness costs associated with the use of many habitat and prey types are high (van Tienderen 1991). Other factors thought to contribute to generalist versus specialist tendencies include the behavioral plasticity of the individual in response to human-induced landscape change (Tuomainen and Candolin 2011), local adaptation of 8 populations (Ravigne et al. 2009), and the functional morphology of the animal in question (i.e. is it morphologically adapted to certain environments or prey to an extent that allow it to specialize?) (Ferry-Graham et al. 2002). Species that display elevated behavioral plasticity are often generalists, while those that display high morphological adaptations are typically specialists. Local adaptation of a population is generally one of the first steps towards specialization. In this chapter, I address the dietary overlap of the sympatric generalist bobcat (Lynx rufus) and specialist Canada lynx (Lynx canadensis) in North America, particularly where the geographic ranges of the two species meet along the US/Canada border. Specifically, in the mountainous portions of Montana, bobcats potentially overlap in habitat and food requirements with the federally Threatened Canada lynx (USFWS 2000, CITES 2004), but morphological differences between species often drive specialization (Schoener 1974b, Ferry-Graham et al. 2002). Snowshoe hare (Lepus americanus) constitute the majority of the lynx’s diet throughout their geographic range (Mowat et al. 2000), and lynx have morphological adaptations including large feet that reduce foot-loading and sinking depth in snow, in conjunction with long hind legs that allow for leaps and bounds through deep snow—facilitating pursuit and capture of hares in winter. However, recent research has shown that lynx diet may vary with latitudinal gradient, as lynx located in southern environs often make higher use of alternative prey, such as red squirrels (Tamiasciurus hudsonicus) (Roth et al. 2007, but see Squires and Ruggiero 2007). Bobcats have small feet that are not well adapted to walking on soft snow, ostensibly giving lynx a competitive advantage in environments with deep winter snow (Buskirk et al. 2000b). Despite these morphological differences, in some areas in New England and adjoining Canadian provinces, bobcats consume >50% hares in winter (Litvaitis et al. 1986a, Litvaitis and Harrison 1989, Matlack and Evans 1992, Pollack 1951). Bobcats may out-compete lynx unless deep snows provide lynx with a foraging advantage (Parker and Smith 1983), as bobcats are more plastic in dietary and habitat preference. The potential for bobcats to out-compete lynx is notable, as snow conditions in southern lynx habitats may be subject to more freezing and thawing than in northern lynx habitats (Buskirk et al. 2000a). Snow conditions are largely dependent on elevation, aspect, and local weather conditions (Ruediger et al. 2000). Increased crusting and compaction of 9 snow at the southern reaches of lynx range may reduce the competitive hunting advantage that lynx have over species such as bobcats and coyotes (Canis latrans) in soft snow (Buskirk et al. 2000b). Northwestern Montana typically has high average annual snowpack, with snow often persisting in the mountains through May and June, which should favor lynx over bobcats. The bobcat is typically a generalist predator throughout its geographic range; however, regional differences in bobcat diet are common (McCord and Cordoza 1982, Fuller et al. 1985, Litvaitis et al. 1986b). A firm grasp of winter diet of bobcats living in extreme winter conditions may be particularly important to understanding behavioral plasticity in this species, as snow depth influences bobcat movements (McCord 1974) and habitat use (Bailey 1974). Bobcats in northwest Montana may show dissimilarity in winter diet compared with bobcats in other northern latitudes due to differences in winter conditions (i.e. bobcats in Midwestern northern latitudes are likely to experience fewer days with snow on the ground). For example, deep snows may impact the ability of bobcats to capture certain types of prey, such as snowshoe hares, making northwest Montana bobcats more dependent on other prey types in winter. Additionally, winter conditions influence the number of prey species and the density and distribution of prey (Harestad and Bunnell 1979). Areas that experience harsh winter conditions are likely to have fewer prey species and fewer prey individuals available. In such areas, the prey ‘menu’ may in fact be limited for the bobcat, initiating potential facultative specialization on the most plentiful and catchable food source. The subspecies of bobcat (Lynx rufus pallescens) in northwest Montana is one of the largest subspecies of bobcat across their geographic range, being distributed mainly along the Rocky Mountains of Colorado, Wyoming, Montana, Idaho, Alberta, and British Columbia (Larivière and Walton 1997). It is plausible that individuals of this subspecies may in fact show high phenotypic and behavioral plasticity, allowing them to cope with long, harsh winters that they regularly experience is this geographic region. Furthermore, northwest Montana is an area of historic and modern lynx occurrence. In particular, a well-studied lynx population is located 200 km southeast from TLRD, and is the closest lynx population available for dietary comparison with bobcats in northwest Montana; these congeners are most likely to interact with one another on the landscape. Furthermore, the Salish Range is part of lynx critical habitat in Montana (USFWS 2009). My main objectives with this 10 chapter are to: (1) determine bobcat winter diet in northwest Montana to examine the degree of specialization by prey type, primarily snowshoe hares, (2) compare this diet to other bobcats in northern latitudes, and (3) compare bobcat diets to lynx populations to assess potential dietary overlap. 2.2. STUDY AREA The Salish Mountains, centered at 48° 12' N, 114° 48' W, are located in northwest Montana and are bounded by the Whitefish and Mission Ranges to the east, and the Cabinet and Purcell Ranges to the west. The Salish Range starts near Eureka, extending south to Plains and St. Ignatius. The Salish Range encompasses 10,684 km 2 with >30 peaks over 1828 m, 10 of which are located in my study area. Additionally, there are >30 peaks between 1524-1828 m, many of which are on my study site. My specific study site is the Tally Lake Ranger District (TLRD) of the Flathead National Forest, Montana, USA (48°30´0˝N, 114°45´0˝W), located in the center of the Salish Range. Elevations range from 945 m to 2008 m. Temperatures range from -42 to 38° C and mean annual precipitation is 58 cm at 975 m in Olney, Montana, on the northeast edge of the TLRD (National Oceanic and Atmospheric Administration 2013). Average annual snowfall averages 5 m at mid-elevations (1491 m) to over 8 m above 2000 m (data courtesy of the nearby Whitefish Mountain Resort 2013). Forested areas of TLRD are dominated by moist, coniferous forests composed of western larch (Larix occidentalis), lodgepole pine (Pinus contorta), Douglas fir (Pseudotsuga menziesii), subalpine fir (Abies lasiocarpa), and Engelmann spruce (Picea engelmannii). Lower elevations are primarily composed of older, multi-layered forests of western larch, Douglas fir, and lodgepole pine. Lodgepole pine forests form 30% of the landscape and an additional 30% is formed by Douglas fir/larch associations. Subalpine fir forests constitute 20% of the area (Flathead National Forest 2006). The remaining area is composed of Ponderosa pine (Pinus ponderosa), western red cedar (Thuja plicata)/western hemlock (Tsuga heterophylla), grand fir (Abies grandis), and whitebark pine (Pinus albicaulis)/subalpine larch (Larix lyallii) communities. In general, mature forests occur along riparian strips in upland areas or in small patches throughout the district. Fire, insects, and disease are the predominant natural disturbances. The primary human disturbance is 11 timber harvest in the forested uplands (Flathead National Forest 2006). On TLRD during winter, snowshoe hares, red squirrels, grouse (Falcipennis canadensis and Bonasa umbellus), bushy-tailed woodrats (Neotoma cinerea), a variety of small mammals (mice and vole sub-families Neotominae and Arvicolinae respectively), and carrion are possible food sources. Deer (Odocoileus virginianus and O. hemionus) are uncommon on the higher elevations of the study area in winter. 2.3 METHODS 2.3.1. SAMPLE COLLECTION Bobcat scats were collected throughout TLRD during winter (Dec.-Feb. 2009-2011) when encountered along snowmobile tracks or while backtracking a bobcat. Appearance of the scat and the presence of bobcat tracks were used to confirm the scat was from a bobcat. Scats were also collected from live-trapped bobcats (see Chapter 3). Scats collected from traps were assumed to be from the cat's meal prior to ingesting trap bait. Any fur from trap bait that was frozen or stuck to the outside of scats was removed. I was confident in bobcat track identification on the study area; track identification can be challenging and I took great care in species ID from tracks (Figure 2.1). Additionally, over the course of 4 winter field seasons, I put in >8500 km of snowtrack survey effort via snowmobiles, and 3 winter field seasons resulting in 2058 live trap nights. During 4 years of winter field effort, no lynx were captured and no lynx tracks were detected in the snow. Bobcat sign was found at all elevations and habitats throughout TLRD. Furthermore, all bobcat DNA (referenced in Chapter 4 bobcat handling) collected in this study was tested to confirm species ID and investigate potential hybridization between bobcats and lynx (Appendix A). These samples included 10 individuals captured and handled, and 47 bobcat carcasses collected from fur trappers (detailed below). Samples were tested using the approach described by Schwartz et al. (2004), and compared to a large reference library of known bobcat and lynx individuals. The genetic analyses showed that all mtDNA came from bobcat origin, and that all samples tested as pure bobcat (Newbury, Schwartz, and Hodges, unpublished data). Field effort and genetic results strongly support that lynx are not sympatric with bobcats in this area of the Salish Range. A more detailed description of methods can be found in Appendix A. 12 Bobcat carcasses from TLRD and the broader Salish Mountain range immediately surrounding TLRD were collected from local fur trappers during the bobcat trapping season. The trapping season runs from Dec. 1-Feb. 15; however, all carcasses were collected in December 2009 and 2010, as the bobcat quota is inevitably filled by the end of December. In Region 1 of northwest Montana, which encompasses TLRD and the Salish Range, the bobcat quota is 250 animals; I collected 30 carcasses in 2009 (12% of 251 animals taken) and 17 carcasses in 2010 (6% of 278 animals taken). Trappers are not required to turn in carcasses, so all carcasses were donated voluntarily. Necropsies conducted during July 2010 took place at the Montana Fish, Wildlife, and Parks Wildlife Laboratory in Bozeman, MT; necropsies conducted during April 2011 took place in the Philip L. Wright Zoological Museum preparatory lab at the University of Montana in Missoula. Stomachs were opened and all contents removed. Colon contents were collected from the section of large intestine within 6 inches of the rectum, such that colon samples were basically the same as scat samples. Samples were stored at -23°C until 24-48 hours prior to analysis, when they were allowed to thaw at room temperature. 2.3.2. SAMPLE ANALYSIS All scat and colon samples were oven-dried for ~12 hours, or until sample weight remained constant. Sample contents were analyzed following Reynolds and Aebischer (1991). Dry weight of each sample was recorded, then samples were broken down in water and rinsed through a 0.5 mm sieve to separate microscopic from macroscopic fragments. A sieve of 0.5 mm mesh captured even the smallest rodent (Peromyscus, Microtus, and Myodes spp.) bones and teeth (personal observation). For the purposes of this study, the microscopic portion of samples was not examined. The microscopic portion of fecal samples is often examined when the diet of a species includes invertebrates; since bobcats are not known to eat insects or worms on any regular basis, I discarded the microscopic portion of samples. Each sample was sorted into general categories such as fur, bone, feather, and incidental ingestion (e.g. pine needles) and allowed to air dry overnight to aid in identification of prey species. Following thawing, stomach samples were immediately rinsed (following Litvaitis et al. 1984) through a 0.5 mm sieve and sorted to broad categories such as fur, bone, feather, and incidental ingestion, and identified as described below. 13 2.3.3. PREY SPECIES IDENTIFICATION Prey items were identified to species when possible. Diagnostic hair, teeth, and bones were used to identify prey species, with the aid of a dissecting microscope as needed. Bones and fur present in samples were compared to specimens in the Philip L. Wright Zoological Museum at the University of Montana in Missoula for species confirmation. When identification was not possible through macroscopic techniques, as when no diagnostic teeth or bones were present, hairs were identified by using a compound microscope, reference hairs, and a key to mammalian guardhairs (Moore et al. 1974). This approach typically was necessary for mice and voles. All other species could be identified macroscopically. Small rodents such as mice and vole species could not be differentiated due to severe degradation of hair and bone in samples (personal observation; Dave Dyer, museum curator, personal communication); thus these species were grouped by sub-family or family. Dry weight for each prey species in scat and colon samples was taken. Final classifications were: Cervidae (Odocoileus spp.), Leporidae (Lepus americanus), Sciuridae (Tamiascurius hudsonicus), Tetraoninae (grouse sub-family), and Cricetidae (Table 2.1). Cricetidae included unknown Cricetidae (typically these samples were degraded), Neotominae (deer mice and woodrats), and Arvicolinae (voles). 2.3.4. TRAP BAIT AND TRAPPER SURVEYS I accounted for trap bait in samples in two ways. Since scats were collected in areas where I was using deer as my primary bait for trapping animals, I excluded deer found in scats collected from live-trapped animals and from scats found along tracks and roads in the general trapping area. Second, to account for trap bait in stomach and colon samples from bobcat carcasses, I sent out surveys to trappers who had turned in bobcat carcasses. Trapper response was ~50%. I excluded bait from samples for which I had a trapper’s response. For samples from bobcats turned in by trappers that did not respond, I excluded items ingested that were highly suspected to be trap bait, e.g. domestic chicken. However, I did not categorically exclude items such as deer meat from samples where the trapper did not respond because for some samples for which a trapper did respond, deer fur/meat was contained in the sample, but the trapper had not used that as bait. I acknowledge that exclusion of trap bait may complicate 14 dietary comparison with other bobcat populations in northern latitudes as I could not determine if trap bait was or was not included in those dietary studies. I chose to exclude bait, as my goal with these analyses was to evaluate diet of prey that bobcats obtained without human interference. I believe my approach is valid, as I did not systematically eliminate certain prey types, such as deer, from the analysis. If an effect is present, the proportion of trap bait included in my analyses as a result of non-response from trappers would inflate proportions of deer and snowshoe hare in bobcat winter diet in Montana, as deer and hare are the most common type of ‘wild’ bait used by trappers, following items such as domestic chicken. Though deer and hare are low proportions of bobcat winter diet in my study, this possible bias should be kept in mind with respect to results presented. 2.3.5. STATISTICAL ANALYSES Once prey species were identified, the proportion of each sample composed of that species was visually estimated following Reynolds and Aebischer (1991). 83% of samples were composed of one prey species. The occurrence of a prey species in each sample was counted as one occurrence; each prey type was either present or absent in a sample. I did not attempt to quantify the number of individuals in each sample, given the degraded quality of bone and fur. Items that were incidentally ingested (coarse woody debris, bobcat fur presumably from grooming, rocks, vegetative matter, etc.) and ingested trap bait were excluded from analysis. Analyses are based on samples as displayed in Table 2.2. I calculated Absolute Frequency of Occurrence (AFO) of each prey species found (number of occurrences of a given prey type/total number of samples), which does not artificially inflate sample size (Wright 2010), and Relative Frequency of Occurrence (RFO) (number of occurrences of a given prey type/total number of prey species occurrences). RFO accounts for more than one prey type being found in each sample (Ackerman et al. 1984). Statistics were computed for all bobcats combined, and the total sample size is the number of scats, colon, and stomach samples combined (see Table 2.2). 15 2.3.6. COMPARISON OF BOBCAT WINTER DIETS Using a G-test for independence, I compared my AFO results to the average proportions of each prey category reported in 12 winter bobcat diet studies in northern latitudes (as summarized in Table 2.3). A P-value <0.05 was accepted as significant for this test. In studies from which prey frequency data could be extracted, I grouped diet into 6 categories: cervids, leporids, tree squirrels, other rodents, birds, and other. I excluded other from analyses, as my study did not find any of the prey species grouped in other, e.g. beaver (Castor canadensis) and mesopredators. 2.3.7. BIOMASS CALCULATIONS I calculated % biomass for each of the prey species in bobcat diets in northwest Montana, using Baker et al.’s (1993) regression equation which relates dry weight of each prey type to biomass of prey type. First, average weight of prey species was determined from the literature (Table 2.1). Following Baker et al.’s (1993) regression equation y = 16.63 + 4.09x, where x is the average weight of each prey type, and y is the conversion factor, I calculated conversion factors for each prey type (Table 2.4), except deer. Intake and digestibility of prey items does not increase linearly with increasing prey size, so I used the conversion factor for deer empirically derived by Baker et al. (1993) from the above regression equation. Total dry weight for each prey species found in bobcat scat and colon samples was summed and an average dry weight for an individual occurrence was determined (Table 2.4). For example, when deer was detected in a sample, the average weight was 5.5 g. Dry weights per prey type in stomach samples were not determined because stomach samples were not dried prior to analysis. To incorporate stomach samples into biomass, I used the average dry weight from scat and colon samples; thus, each stomach sample that contained deer was assigned a value of 5.5 g of deer. Total dry weight per prey type was summed, multiplied by its conversion factor, and then divided by total weight summed across all prey types to determine percent biomass (Baker et al. 1993). 2.3.8. DIETARY NICHE BREADTH AND OVERLAP WITH CANADA LYNX Winter niche breadth for Montana bobcats and other bobcats in northern latitudes (Table 2.3) was calculated (using AFO) in Levin’s (1968) measure of niche breadth (B = (1/ 16 ∑ p2j), where B=Levin’s measure of niche breadth; pj = fraction of items in the diet that are of food category j). I converted to a standardized dietary breadth on a scale of 0 to 1 following Hurlbert’s (1978) measure, as follows: BA = (B − 1)/(n − 1), where BA= standardized niche breadth; B = Levin’s measure of niche breadth; and n = number of possible resources (Krebs 1998). I used my general categories of prey types as the number of ‘resources’; therefore resources = 5. Winter niche breadth was calculated for Canada lynx populations in the western United States and Canada (Table 2.5). Niche overlap for lynx populations, northwest Montana bobcats, and other bobcats was calculated in EcoSim 7.72 (Gotelli and Entsminger 2011) using Pianka’s (1974) index where α = ∑ piqi/ (∑ pi 2 ∑ qi 2 ) 1/2 ; pi is the proportion of prey type i in the diet of the first species, and qi is the proportion of the same prey type in the diet of the second species. The index ranges from 0-1, from no overlap to complete overlap. I ran 1000 simulations to determine if the probability of observed overlaps was greater or less than expected by chance. 2.4. RESULTS Hares constituted 17.9% (absolute frequency of occurrence) of bobcat winter diet in the Salish, while squirrels and rodents composed 48.7% and 34.6% of samples (Table 2.2). Red squirrels accounted for 54% of biomass consumed by bobcats in the Salish Range, followed by other rodents (24.5%), snowshoe hare (12.2%), deer (8.5%), and grouse (<1%) (Table 2.4). Biomass results largely agreed with AFO rankings of prey types; however, in AFO rankings, a larger portion of grouse was consumed than deer. Biomass calculations show deer constituted more dietary biomass than grouse because of the large differences in body mass of these prey types. Squirrels were detected in 38 samples, other rodents in 27 samples, hares in 14 samples, grouse in 10 samples, and deer in 5 samples (Table 2.2). Based on the average proportion for each prey type found in studies listed in Table 2.3, the expected fraction of prey types in the winter diet of bobcats in northern latitudes was: deer (29.1%), Leporidae (39.4%), tree squirrels (7.9%), other rodents (27.2%), birds (9.8%). Totals do not add to 100% as most studies report AFO, which does not sum to 1, as the number of samples is the denominator (as opposed to number of detections as in RFO), and more than one prey type can be detected in each sample. Winter diets of bobcats in northwest Montana were 17 significantly different from the average winter diet of bobcats in other northern latitudes (G = 45.05, df = 4, p < 0.0001). Montana bobcats consumed far more squirrels than did bobcats elsewhere, but fewer lagomorphs (Figure 2.2). Winter dietary niche breadth for Montana bobcats was 2.44 and standardized niche breadth was 0.36; other northern bobcats had a niche breadth of 3.03, with a standardized niche breadth of 0.51. Dietary overlap between Montana bobcats and bobcats in other northern latitudes was 0.64 (variance=0.01, p=0.81), within EcoSim 7.72 (Gotelli and Entsminger 2011) using Pianka’s (1974) index for niche overlap. Bobcats in Montana had a narrower dietary niche breadth than bobcats in other northern latitudes, but the difference was not significant. Lynx populations had a niche breadth ranging from 1.44-2.60, with Montana lynx having the lowest breadth and Yukon lynx having the highest. Standardized niche breadth for lynx populations ranged from 0.11-0.40 (Figure 2.3). Dietary overlap between lynx populations ranged from 0.89 (Montana and Yukon populations) to 0.99 (British Columbia and Yukon) (variance=0.01, p<0.0001), indicating that lynx populations across their western range share the same diet (Table 2.6). Dietary overlap for Montana bobcats and lynx in Montana was 0.40 (variance=0.05, p=0.43); lynx in the Yukon=0.75 (variance=0.04, p=0.23); in Washington=0.51 (variance=0.05, p=0.42); and in British Columbia=0.74 (variance=0.04, p=0.23). Dietary overlap for bobcats from other northern latitudes and lynx in Montana was 0.71 (variance=0.04, p=0.18); lynx in the Yukon=0.72 (variance=0.02, p=0.28); in Washington=0.75 (variance=0.03, p=0.12); and in British Columbia=0.68 (variance=0.03, p=0.40). Bobcats in Montana showed more dietary overlap with lynx populations in British Columbia and the Yukon, than lynx populations in Montana and Washington. 2.5. DISCUSSION Bobcats in northwest Montana did not facultatively specialize on snowshoe hares. If anything, northwest Montana bobcats could be considered facultative specialists on red squirrels, as 49% of prey individuals and 54% of prey biomass consisted of red squirrels. The winter diet of bobcats in northwest Montana differed from winter diets of bobcats in other northern latitudes. Specifically, Montana bobcats ate significantly more squirrels and 18 fewer snowshoe hares than did bobcats from other northern forests. Indeed, red squirrels composed over half of the biomass consumed by bobcats in this area, with other rodents comprising nearly a quarter of the biomass; these two groups of rodents total nearly 80% of the biomass consumed by bobcats in northwest Montana. Such dependence on rodents reflects bobcat diets across their range (McCord and Cardoza 1982, Anderson 1987, Rolley 1987, Larivière and Walton 1997, Tewes et al. 2002), but is atypical compared to bobcats in other northern forests. Bobcats in northwest Montana did not show significant statistical overlap in diet with lynx populations in western North America; however, there is the potential for biologically significant overlap in diets, as the top prey items for lynx and bobcats always consisted of red squirrels, snowshoe hares, and other rodents. There is evidence for dietary partitioning between bobcats and lynx in Montana, as the frequency of occurrence of these top prey items in bobcat and lynx diets appeared to be the reciprocal of each other. For example, lynx consumed large proportions of snowshoe hare and small proportions of red squirrels, whereas bobcats consumed smaller proportions of hares and larger proportions of red squirrels. Rodents (squirrels and other rodents combined) comprise, on average, ~39% of bobcat diet in 12 studies in northern latitudes. Koehler and Hornocker (1989) studied winter diet of bobcats in Idaho in an area ~640 km south of TLRD that is very similar in vegetation, topography, and climate to what bobcats in the Salish Mountains of Montana experience. They found that rodents comprised ~90% of bobcat diet, while Toweill and Anthony (1988) reported that rodents constituted 43% of bobcat winter diet in Oregon, and Knick et al (1984) reported that rodents were 59% and 43% of the diet of western and eastern Washington bobcats in winter. Winter diet of bobcats in Montana is most similar to that of bobcats in Idaho, likely due to similarities in regional topography, vegetation, climate, and prey types. The large majority of prey eaten by bobcats in winter weighed <1 kg, and the ~1.5 kg snowshoe hare was not a dominant prey species. This study reports one of the lower percentages of lagomorphs, 17.9%, in bobcats’ winter diet. The lowest proportions of Leporids reported was 1.5% occurrence in samples by Koehler and Hornocker (1989) in Idaho, and 15% by McClean et al. (2005) in Pennsylvania. Other studies report from 20% in eastern Washington (Knick et al. 1984) to 71% in Nova Scotia (Matlack and Evans 1992), with an average of ~39% Leporids consumed in northern latitudes. Leporids consumed were 19 primarily snowshoe hare, though eastern cottontails (Sylvilagus floridanus) composed part of the bobcat diet in some eastern studies, particularly McClean et al. (2005) and Litvaitis et al. (1984). Snowshoe hares occur throughout the Salish Range; hare densities from 2001-2009 across 13 long term mark recapture sites located on the northern half of TLRD were 0.77 ± 0.07 hares/ha (Range: 0-3.17) (Hodges and Mills, unpublished data). Five of these long term sites have average hare densities >0.80 hares/ha on an annual basis, indicating locally abundant hare densities. Thus, hares are an available winter food source for bobcats in this region. It is possible that bobcats in this study did not consume a large proportion of Leporids across the Salish as winter conditions at that time (depth and lack of hard crust) may not have been conducive to capturing snowshoe hares. For example, coyotes captured proportionally fewer hares in winter than during non-snow seasons, obstensibly due to snow conditions and sinking depth in snow (O’Donoghue et al. 1998). Other prey types such as squirrels may have been more numerous or energetically beneficial to bobcats with respect to ease of capture, numbers encountered, and calories obtained versus energy expended. Niche breadth calculations for winter bobcat diet in Montana show a standardized niche breadth of 0.36, whereas bobcats in other northern latitudes show a broader niche breadth of 0.51, based on average proportion of prey types across studies. Indeed, Montana bobcats did not show significant dietary overlap with other bobcats in northern latitudes, pointing towards potential facultative specialization on rodents in Montana. Calculations of standardized niche breadth, however, are similar to values for other cat species considered to be generalist predators, including puma (Puma concolor) in Brazil (0.47; de Azevedo 2008) and Peru (0.49; Emmons 1987); jaguar (Panthera onca) in Brazil (0.41; de Azevedo 2008); and pampas cats (Leopardus pajeros, 0.44) and Geoffroy’s cats (L. geoffroyi, 0.52; Berg 2007). Other studies of niche breadth have used the non-standardized niche breadth measure; for example, winter diet of bobcats in California had B=8.97 (Neale and Sacks 2001), much higher than the value for bobcats in Montana B=2.44. Other northern latitudes had B=3.03, thus indicating more prey species were available, and used, in California in winter, and that more northern bobcats have a more specialized winter diet. Dietary overlap (measured as % frequency of occurrence) was not statistically significant between bobcats and lynx in northwest Montana, or between other lynx 20 populations in the west and Montana bobcats. Given the dietary overlaps observed and the biological importance of alternative prey species to lynx, it is possible that bobcats could displace lynx through direct food competition for alternate prey. In the Yukon, Washington, BC, Alberta, and Montana, lynx consumed 50-82% snowshoe hare and 13-35% red squirrel (O’Donoghue et al. 1998, Apps 2000, Aubry et al. 2000, Squires and Ruggiero 2007). Bobcats consume a large proportion of red squirrels, which could negatively affect lynx in the southern periphery of their range if dietary overlap is biologically significant to lynx and bobcats in this region. Although bobcats in northwest Montana did not consume large proportions of hares in winter, they could impact lynx through use of hares in non-snow seasons and alternate prey items such as red squirrels year round. Note that only one lynx dietary study (O’Donoghue et al. 1998) spans an entire snowshoe hare population cycle, and thus averages prey consumption by lynx over the course of the cycle (see Table 2.5 for years of diet analysis). Apps (2000) reports prey consumption for lynx consistent with snowshoe hare lows, and the years of his study (1996-1998) correspond with a time of known hare population lows. The lynx diet studies in Montana and Washington took place without knowledge of relative hare numbers. Hare cycles in southern lynx range still remain a highly debated topic; however, prey consumption by lynx in Montana and Washington seemed to indicate relatively high hare abundance. O’Donoghue et al. (1998) showed that lynx increasingly depredated red squirrels as snowshoe hare numbers declined, though AFO of hares and red squirrels consumed during this study as compared to northwest Montana bobcats was averaged across an entire hare cycle. Indeed, Roth et al. (2007) have demonstrated a latitudinal gradient in lynx diet, with individuals located at the southern periphery of lynx geographic range showing a more diverse diet than those cats found in northern boreal forests. This pattern suggests that lynx may use alternate prey more frequently in their southern range, as hare numbers in these areas are more reflective of hare densities at cyclic hare lows in northern boreal forests; during hare cyclic lows, lynx can and will switch to alternate prey. Lynx and bobcat range overlap near the US/Canada border. Bobcats at this latitude, including those in this study, show a dietary niche breadth more similar to lynx than to other bobcats located farther south. Bobcats thus echo the lynx gradient in dietary flexibility, with northern bobcats having narrower dietary habits than their conspecifics to the south. This 21 narrowing of diet breadth may be due to more limited resources and lowered habitat productivity as compared to southern bobcat range. However, this pattern presents a unique challenge for lynx conservation and recovery in the contiguous United States, as bobcats and lynx may be in direct competition for both primary and alternate prey in areas where their ranges overlap. 2.6. IMPLICATIONS OF BOBCAT WINTER DIET Bobcats in northwest Montana appear to be highly adaptable, generalist predators, even in an environment characterized by long winters with deep, persistent snows (see Chapter 4). Rodents constituted nearly 80% biomass of bobcat winter diet, while snowshoe hares were ~12% of the dietary biomass, which appears to be the opposite of the regional lynx diet. The top diet items for lynx and bobcats in the west are nearly always the same 3 items: snowshoe hares, red squirrels, and other rodents. There is the potential for significant dietary overlap of alternate prey items, primarily red squirrels, which are important to lynx during periods of hare lows. However, bobcats do not act as facultative specialists on snowshoe hares during the winter in northwest Montana, and do not appear to be displacing Montana lynx populations from the area through direct competition for food in winter. Other factors such as habitat fragmentation, other natural and human disturbance, and local site conditions are likely responsible for the distribution of these congener felid species in western Montana. 22 Table 2.1. List of prey species potentially present in the Salish Range and Tally Lake Ranger District in winter. Distributions within Montana based upon Foresman (2001). Types of Prey Common name Average body mass (kg) (for each group combined) Cervidae Odocoileus hemionus Mule deer 60.0 a Odocoileus virginianus Whitetail deer Leporidae Lepus americanus Snowshoe hare 1.4 Sciuridae Tamiasciurus hudsonicus American red squirrel 0.195 Tetraoninae Bonasa umbellus Ruffed grouse 0.539 Dendragapus obscurus Blue grouse (uncommon) (Not used) Falcipennis canadensis Spruce grouse Cricetidae 0.038 b Arvicolinae* Microtus longicaudus Long-tailed vole Microtus montanus Montane vole Microtus pennsylvanicus Meadow vole Microtus richardsoni Water vole Myodes gapperi Southern Red-backed vole Ondatra zibethicus Muskrat 1.136 Phenacomys intermedius Heather vole Synaptomys borealis Northern bog lemming Neotominae† Neotoma cinerea Bushy-tailed woodrat 0.336 Onchomys leucogaster Northern Grasshopper mouse Peromyscus leucopus White-footed mouse Peromyscus maniculatus Deer mouse Reithrodontomys megalotis Western harvest mouse a Median mass for deer to account for differences in mass between sex and age classes. I chose median, as average was much higher, due to larger individuals, such as mature males pulling mass towards the high end. Additionally, it is more likely that a bobcat would target smaller deer to predate. b Average weight used for all Cricetidae, excluding muskrats and woodrats, which are an order of magnitude larger. *Myodes gapperi and Microtus spp. are most common on the study site. Ondatra zibethicus are also common in the area, but were easy to identify in remains compared to the smaller Arvicolids. † Neotoma cinerea and Peromyscus maniculatus are most common on TLRD and were easy to distinguish from one another in remains. 23 Table 2.2. Total detections of prey species displayed (%) as absolute frequency of occurrence (AFO) and relative frequency of occurrence (RFO) in winter diet of Montana bobcats. Scats were collected between December 2009-April 2010, and December 2010- March 2011. Bobcat carcasses were collected in Dec. 2009 and 2010 (M=28,F=19). After exclusion of trap bait and incidentally ingested items, samples totaled 78 (scat=16, colon=37, stomachs=25). Prey Total detections .Overall AFO Overall RFO (prey detections/total samples) (prey detections/ total detections) N=78 N=94 Odocoileus spp. 5 6.4 5.3 L. americanus 14 17.9 14.9 T. hudsonicus 38 48.7 40.4 Tetraoninae (grouse) 10 12.8 10.6 Arvicolinae a 10 12.8 10.6 Neotominae b 10 12.8 10.6 Unknown Cricetidae c 7 9.0 7.4 Total Cricetidae 27 34.6 28.7 a One Arvicoline sample was identifiable as Ondatra zibethicus in a female bobcat stomach. All others were voles. b 8 of 10 Neotominae samples were Neotoma cinerea. Other Neotominae samples were Peromyscus maniculatus c Unknown Cricetidae samples were those that had no diagnostic bones/teeth allowing classification into sub- family; however, fur indicated a Cricetid. 24 Table 2.3. Selected winter bobcat diet studies in the northern US and southern Canada (1939-2005) using Absolute Frequency of Occurrence (AFO). Observed winter diet of bobcats in northwest Montana was compared to the average proportion (%) of prey types consumed in these 12 studies. Location Sample N Cervidae a Lagomorpha b Sciuridae c Rodentia d Aves e Other f Reference Vermont Stomach 140 32.0 31.0 13.0 45.0 16.0 28.0 Hamilton &Hunter 1939 W. Washington Stomach 324 7.0 26.0 11.0 48.0 7.0 9.0 E. Washington Stomach 123 11.0 20.0 17.0 26.0 15.0 5.0 Knick, Sweeney, Alldredge, & Brittell 1984 Idaho Scat 135 43.8 a 1.5 2.2 88.1 3.7 0.0 Koehler & Hornocker 1989 New Hampshire Intestines 388 22.4 48.9 18.8 11.9 0.0 0.0 Litvaitis, Stevens, & Mautz 1984 Maine Intestines 230 12.4 50.6 5.0 12.4 12.9 8.8 Litvaitis, Clark, & Hunt 1986a Maine Scat 346 29.4 a 64.7 0.0 14.7 5.9 2.9 Litvaitis & Harrison 1989 Nova Scotia Stomach 666 17.1 71.0 4.8 16.4 6.6 3.7 Matlack & Evans 1992 Pennsylvania Stomach 85 42.0 15.0 3.0 21.0 39.0 6.0 McClean, McKay, & Lovallo 2005 New England Stomach, colon 208 32.2 60.1 11.5 12.1 5.3 18.8 Scat 250 28.0 52.0 11.2 16.0 3.6 10.4 Pollack 1951 Minnesota Stomach 50 35.0 44.1 0.9 4.3 1.6 15.4 Rollings 1945 Oregon Scat 499 35.0 a 38.0 11.0 32.0 7.0 4.0 Toweill & Anthony 1988 Maine Stomach, colon 88 40.9 21.6 14.0 20.0 12.0 21.0 Westfall 1956 25 Table 2.3 continued. Selected winter bobcat diet studies in the northern US and southern Canada (1939-2005) using Absolute Frequency of Occurrence (AFO). Observed winter diet of bobcats in northwest Montana was compared to the average proportion (%) of prey types consumed in these 12 studies. Location Sample N Cervidae a Lagomorpha b Sciuridae c Rodentia d Aves e Other f Reference Average Proportion Total N=3532 29.1 39.4 7.9 27.2 9.9 9.8 a Cervidae was typically Odocoileus spp. Exceptions are as follows: Toweill & Anthony 1988 ungulate estimate includes 4% elk (Cervus elaphus). Litvaitis & Harrison 1989 includes 5.9% moose (Alces alces). Koehler and Hornocker 1989 ungulate estimate includes 15.6% bighorn sheep (Ovis canadensis) and 1.5% unknown ungulate. b Lagomorpha was represented by Lepus americanus and Sylvilagus spp. c Sciuridae was represented by tree squirrels: Eastern grey squirrel (Sciurus carolinensis), American red squirrel, and Northern flying squirrel (Glaucomys sabrinus). Some studies reported ground squirrels in bobcat winter diet. As ground squirrels were not detected and are not available to Montana bobcats in the winter (ground squirrels hibernate), in order to keep categories consistent, I placed ground squirrels reported in other studies with other rodents in Rodentia. d Rodentia included rodents <2 kg in body mass. Neotominae, Arvicolinae, ground squirrels, and mountain beaver (Aplodontia rufa) were included. e Aves includes all birds reported. f Other includes large rodents >2 kg, i.e. beaver (Castor canadensis), woodchuck (Marmota monax), and marmots (Marmota spp.). Meso-mammals consumed were raccoons (Procyon lotor), porcupine (Erethizon dorsatum), skunks (Spilogale and Mephitis spp.), and opossum (Didelphis virginiana). Carnivores reported consumed were lynx (Lynx canadensis), bobcat (Lynx rufus), mink (Neovison vison), fox (Vulpes vulpes), domestic cat (Felis catus), and otter (Lontra canadensis). Also consumed were fish, vegetation, and berries. 26 Table 2.4. Values used to calculate % biomass of prey in bobcat winter diets in Montana. Conversion factors to biomass from dry weight are calculated from the regression equation y = 16.63 + 4.09x presented by Baker et al. (1993). Absolute frequency of occurrence (AFO) of prey types is presented again for ease of comparison to % biomass calculations. Species Conversion factor Avg. g AFO % biomass per sample consumed Deer 27.0* 5.5 6.4 8.5 Hare 22.4 3.4 17.9 12.2 Squirrel 17.4 7.3 48.7 54.0 Cricetidae† 34.6 24.5 Woodrat 18.0 3.1 Muskrat 21.3 10.4†† Arvicolinae 16.8 10.4 Neotominae 16.8 1.8 a Unk. Cricetid 16.8 1.8 Grouse 18.8 0.4 12.8 <1.0 * I used the conversion factor empirically derived by Baker et al. (1993) rather than calculating my own from average deer weight. Digestibility and intake do not increase linearly with increasing prey size, and the conversion factor presented by Baker et al. (1993) was more reliable than using a regression equation. †Within Cricetidae, I separated out woodrats and muskrats due to their larger size. Biomass was figured by summing the totals calculated across sub-categories. ††The one muskrat detected was in a stomach sample, and therefore had no dry weight associated with it. I used average weight within the Arvicolids to calculate muskrat biomass. a There were no Neotomid samples within scats and colons from which to calculate average dry weight per sample. I applied the calculated average dry weight of Unknown Cricetids to the Neotomid group. 27 Table 2.5. Selected winter Canada lynx diet studies in the northwest US and Canada for which dietary data could be extracted. Lynx populations were chosen based on proximity to bobcats in the Salish Mountains. Observed winter diet of lynx is reported in % frequency of occurrence. Distance from Tally Lake Ranger District, Flathead National Forest, Montana is shown in parentheses. Location/ Years Sample N Deer Snowshoe hare Tree squirrels Rodents Aves Distance Reference Yukon (2960 km) 1986-1995 Kills 502 0.0 50.2 34.7 11.0 0.0 O’Donoghue et al. 1998 British Columbia (400 km) 1996-1998 Kills 137 0.0 52.0 35.0 a 3.0 0.7 Apps. 2000 Washington (400 km) 1985-1987 Scats 29 7.0 79.0 24.0 3.0 0.0 Aubry et al. 2000 Montana* 1998-2002 Kills 84 2.4 82.1 13.1 a 0.0 2.4 (200 km) Squires & Ruggerio. 2007 *Excluded two kills that were least weasel (Mustela nivalis). a Includes flying squirrels (Glaucomys sabrinus). 28 Table 2.6. Pairwise comparison of northwest Montana bobcat and other northern latitude bobcats (N. Bobcat) and lynx diets for various lynx populations using EcoSim 7.72 (Gotelli and Ensminger 2011) to calculate niche using Pianka’s (1974) index. Lynx dietary overlap is high (88-99%) between locations. Montana bobcats show the greatest dietary similarity with Yukon and BC lynx; however, Montana bobcat and other northern bobcat diet overlap with each lynx population is not significant. * denotes significant dietary overlap between lynx population. MT Bobcat N. Bobcat Yukon Lynx WA Lynx BC Lynx MT Lynx MT Bobcat X 0.64 0.75 0.51 0.74 0.40 N. Bobcat X 0.72 0.75 0.68 0.71 Yukon Lynx X 0.94* 0.99* 0.89* WA Lynx X 0.95* 0.99* BC Lynx X 0.91* 29 Figure 2.1. Left photo shows a bobcat (Lynx rufus) track. Chapstick for reference is 6.7 cm long. These tracks lead to a bobcat captured in a box trap on TLRD. On the right is a Canada lynx (Lynx canadensis) track in the Yaak River drainage of the Kootenai National Forest, ~80 km west of TLRD. Lens cap in this picture is 5.9 cm in diameter. Note that although snow conditions are different the bobcat toe and heel pads are well defined, and the heel pad is large compared to the size of the track. On the other hand, the lynx track has a very small heel pad relative to track size. 30 Figure 2.2. Prey consumed in winter by bobcats in northwest Montana (this study) versus other studies (taken from Table 2.3). Tree squirrel category is red squirrel for Montana bobcats, but in other nothern latitudes includes eastern grey squirrel and northern flying squirrel. Error bars are the exact 95% binomial confidence intervals. 31 Figure 2.3. Standardized niche breadth for northwest Montana bobcats, bobcats in other northern latitudes (N. Bobcats), and lynx populations in the western US and Canada. The Montana bobcats occupy a similar dietary niche with Yukon and British Columbia lynx populations, but overlaps with Montana and Washington lynx populations are much lower. The Montana and Washington lynx display a more specialized diet than other lynx populations. 32 CHAPTER 3 AN ENERGETICS MODEL FOR BOBCATS IN A DEEP SNOW ENVIRONMENT 3.1. LITERATURE REVIEW AND OBJECTIVES Metabolism is a uniquely biological process that obeys physical and chemical principles, which govern the transformation of energy and materials to useable forms following a complex network of biochemical reactions (Brown et al. 2004). The relationship between basal metabolic rate (BMR) and body size has long been studied (Kleiber 1932), and the impacts of phylogeny (Muñoz-Garcia and Williams 2005), diet (McNab 1980, 1986), and body shape (Powell et al. 1985) have been accounted for within taxonomic groups . Basal metabolic rate and daily energy expenditure (DEE) are closely linked, with DEE accounting for activities such as foraging and metabolic regulation when an animal is outside of its thermoneutral zone (Ricklefs et al.1996), as in extreme temperatures. On average, DEE is estimated to be 2-3 × BMR (Brown et al. 2004, Nagy 2005, Savage et al. 2004). Carnivorous mammals that primarily eat vertebrates have high basal metabolic rates (Iverson 1972, McNab 1980, McNab 1986) compared to the traditional BMR and body mass relationship (Kleiber 1932, 1961). These species tend to have large home ranges in relation to body mass (Harestad and Bunnell 1979, Gittleman and Harvey 1982), reflecting higher movements. This relationship between body mass and home range size has implications for home range size of individuals in different geographic contexts. For example, individuals of a species that are located in northern latitudes are often larger than conspecifics farther south, a phenomenon known as Bergmann’s Rule (Mayr 1963, see Meiri 2011). In addition to latitudinal variation in body size, mountainous locations also tend to produce larger individuals (Foster 1964, Grant 1965), both in predator and prey species. Larger body size in colder climates is advantageous as it decreases an animal’s surface-to-volume ratio, by which the animal loses less body heat to its surroundings, an important adaptation in winter environments. Even though thermal conductance is reduced as body size increases, energetic requirements are elevated, which has strong effects for strict carnivores (felids in particular), due to the energetic costs of finding, attacking, and subduing 33 prey. Larger body size makes the habitat a predator occupies less productive and patchier than it would be for smaller, non-carnivorous animals (Harestad and Bunnell 1979). For larger predators, the landscape will inherently contain fewer suitable prey, thereby requiring the animal to range over a larger area in order to satisfy energetic requirements. Hence, body size of predators is regulated by the frequency distribution of prey size and the presence of competitors consuming the same prey, as well as the latitudinal variation in the body size of prey and competitors (McNab 1971). The comparatively high BMR seen in vertebrate-eating carnivorous mammals thus derives from the combined influences of body mass, food habits, behavior, and climate. Phylogeny also has a moderate influence on BMR, as food habits and phylogenetic relationships are often highly correlated (McNab 1986). Within Carnivora, BMR is significantly correlated with Family (e.g. Felidae, Canidae) only when food habits are not included in the analysis. However, as food habits are correlated with Family Carnivora, there is no simple cause and effect relationship for BMR and the other variables (McNab 1992, 2000). Consistent with the preceding, McNab (2000, 2002) found that felids, which are hyper-carnivorous, have higher than expected BMRs based on body mass. McNab (2000) found that felids had 28% greater than expected BMR based on the all-mammal curve. Concurrently, the average body temperatures of felid species are also quite high (range: 37.0 to 38.4°C), which is expected based on high metabolic rate (McNab 2000). More specifically, mean basal rate for bobcats (Lynx rufus) was 180% the expected all-mammal curve. Using the standard equation for BMR, a bobcat weighing 10 kg would require a minimum 394 kcal/day (BMR = 70 × W 0.75 = 70 × 10 0.75 = 394), whereas using the all-felid estimate, a bobcat of the same size would require 504 kcal/day (1.28 × 394 = 504). The felid estimate is largely in agreement with the fasting winter metabolic rate estimated for bobcats of 85 kcal/kg W 0.75 (or 478 kcal/day), a value 21% greater than the all-mammal curve (Gustafson 1984, Mautz and Pekins 1989). The McNab (2000) estimate gives a 5% greater energy requirement (504 kcal/478 kcal) than Mautz and Pekins’ (1989) resting winter metabolic rate for bobcats. Both of these estimates may still be low, as bobcats displayed basal rates that were highest for all felid species examined, though high basal rate in this instance may have been due to anxiety in the wild caught individuals examined in both 34 studies. Understanding the basic energetic requirements of a wildlife species allows us to determine if an animal is well suited to a given landscape (Laundré 2005). Winter in northern latitudes presents many challenges for wildlife, including snow, cold temperatures, and often, food scarcity (Hodges et al. 2006). Animals can deal with harsh conditions in two main ways, through behavioral and physiological adaptations. Physiological adaptations include hibernation (slowing of metabolism), increase in body fat, and increases in fur length and density (increasing insulative properties of seasonal coat). Morphological and physiological plasticity may represent an adaptive response to a variable environment (Kingsolver and Huey 1998). Behavioral responses to the challenges imposed by winter conditions include seasonal migrations, reducing overall daily movements, changing hunting techniques from chase to ambush to reduce energy expenditures, eating the most plentiful prey type, eating the most energetically beneficial prey type, moving during daylight hours to take advantage of warmer temperatures, ceasing movements during wind or extreme cold, and selecting microhabitats that provide temperature refugia. Individual behavioral responses could be coupled with other adaptations to seasonal changes in northern environments near the edge of a species’ geographic distribution. An environment far different from that typically experienced by individuals at the core of a species’ distribution could directly drive changes in individual behavior, morphology, and physiology. Individuals demonstrating such a plastic response to this highly variable local environment will have higher fitness in the new setting than others that do not show a similar response (Price et al. 2003). Plastic responses may be coupled with genetic changes, leading to locally adapted populations, and given enough time, subspecies designation, or eventual speciation. Bobcats do not migrate or hibernate. They likely employ a strategy that couples behavioral modifications such as becoming more active during daylight hours, with a physiological strategy that incorporates increased mass (i.e. fat storage) by late fall, and increased insulation. Insulation can be increased by growing denser, longer fur to minimize mass-specific metabolism, a strategy that is more effective for large mammals (Steudel et al. 1994), as the length of fur an animal is capable of growing depends on the size of the animal. Bobcats appear to follow Bergmann’s Rule of increased body size in northern latitudes 35 (McNab 1971) and display seasonal increases in mass prior to winter, thereby allowing bobcats in northern climates to maintain larger fat stores than bobcats in southern latitudes. These relatively larger fat stores could tide northern bobcats through severe cold spells and periods of food shortage (Millar and Hickling 1990). In northwest Montana, bobcats use an environment that is characterized by extreme winter temperatures and deep, persistent snows. Many aspects of bobcat ecology have been studied, but generally in geographic areas that are ‘normal’ for bobcat distribution. Studies that involve bobcats in areas with severe winter conditions have concluded that bobcats are limited by snow and extreme cold temperatures (Petraborg and Gunvalson 1962, Bailey 1974, McCord 1974, Hamilton 1982, McCord and Cordoza 1982, Major 1983, Litvaitis et al. 1986a, 1986b, Koehler and Hornocker 1989, Matlack and Evans 1992). Some aspects of bobcat energetics have been investigated, including impacts of seasonal temperatures on metabolic rate (Mautz and Pekins 1989), the influence of season on movements (Koehler and Hornocker 1989), and the assimilation of prey types into metabolizable energy (Powers et al. 1989); however, none have specifically linked a model of energy expenditures and prey requirements explicitly with observed diet. Moreover, none have examined daily energy expenditure (DEE) and known winter diet items using the McNab (2000) all-felid basal metabolic rate. Coupling a mathematical model of DEE based on varied parameters such as movement distances, time spent hunting, ambient temperature, and body mass with known diet composition of bobcats in a particular geographic area can allow determination of energy balance in the atypical (for bobcats) winter environment of northwest Montana. My goals with this chapter are to develop a strict, but realistic energetics model based on average movement distances, body mass, and observed diet of bobcats in northwest Montana to investigate if individual bobcats are able to maintain energy balance in the winter in mountainous northwest Montana. My main objectives with this chapter are to (1) determine if bobcats in northwest Montana are able to meet daily energy requirements on the observed diet, and (2) determine the conditions (movement distances and time spent active) under which bobcats would be unable to stay in energy balance on the observed winter diet. 36 3.2. STUDY AREA The Salish Mountains, centered at 48° 12' N, 114° 48' W, are located in northwest Montana and are bounded by the Whitefish and Mission Ranges to the east, and the Cabinet and Purcell Ranges to the west. The Salish Range starts near Eureka, extending south to Plains and St. Ignatius. The Salish Range encompasses 10,684 km 2 with >30 peaks over 1828 m, 10 of which are located in my study area. Additionally, there are >30 peaks between 1524-1828 m, many of which are on my study site. My specific study site is the Tally Lake Ranger District (TLRD) of the Flathead National Forest, Montana, USA (48°30´0˝N, 114°45´0˝W), located in the center of the Salish Range. Elevations range from 945 m to 2008 m. Temperatures range from -42 to 38° C and mean annual precipitation is 58 cm at 975 m in Olney, Montana, on the northeast edge of the TLRD (National Oceanic and Atmospheric Administration 2013). Average annual snowfall averages 5 m at mid-elevations (1491 m) to over 8 m above 2000 m (data courtesy of the nearby Whitefish Mountain Resort 2013). Forested areas of TLRD are dominated by moist, coniferous forests composed of western larch (Larix occidentalis), lodgepole pine (Pinus contorta), Douglas fir (Pseudotsuga menziesii), subalpine fir (Abies lasiocarpa), and Engelmann spruce (Picea engelmannii). Lower elevations are primarily composed of older, multi-layered forests of western larch, Douglas fir, and lodgepole pine. Lodgepole pine forests form 30% of the landscape and an additional 30% is formed by Douglas fir/larch associations. Subalpine fir forests constitute 20% of the area. The remaining area is composed of Ponderosa pine (Pinus ponderosa), western red cedar (Thuja plicata)/western hemlock (Tsuga heterophylla), grand fir (Abies grandis), and whitebark pine (Pinus albicaulis)/subalpine larch (Larix lyallii) communities. In general, mature forests occur along riparian strips in upland areas or in small patches throughout the district. Fire, insects, and disease are the predominant natural disturbances. The primary human disturbance is timber harvest in the forested uplands (Flathead National Forest 2006). On TLRD during winter, snowshoe hares (Lepus americanus), red squirrels (Tamiasciurus hudsonicus), grouse (Falcipennis canadensis and Bonasa umbellus), bushy- tailed woodrats (Neotoma cinerea), a variety of small mammals (mice and vole sub-families Neotominae and Arvicolinae respectively), and carrion are possible food sources for bobcats. 37 Deer (Odocoileus virginianus and O. hemionus) are uncommon on the higher elevations of the study area in winter and so are not expected to comprise a significant source of food. 3.3. METHODS 3.3.1. BOBCAT CAPTURE AND HANDLING Following Maletzke (2004), I waited a minimum of 12 hours from the last snowfall to allow animals time to move through the study site. I conducted track surveys via snowmobile to detect bobcat presence. I then situated traps throughout the main study area based on presence of recent bobcat sign. Bobcats were trapped using commercially available box traps or box traps constructed after Kolbe et al. (2003), but modified to capture bobcats and to operate effectively given frequent freeze and thaw cycles. Each trap was baited with roadkilled deer and commercially available cat lures. Captured animals were immobilized with Telazol® at 5 mg/kg estimated body mass administered intramuscularly with an extendable pole-syringe. Telazol® does not require the administration of a protagonist drug, and both immobilization (<5 minutes on average) and complete recovery (2-3 hours from injection) of bobcats was quick. Immobilized bobcats were sexed, weighed, eartagged (National Band and Tag Co., 1005-4 Monel, sequential numbering), a DNA sample obtained (blood and hair), and subjectively aged (kitten: <1 year, juvenile: 1-2 years, adults: >2 years) based on skeletal measurements and tooth wear. Vital rates of immobilized animals (core body temperature and respiration) were closely monitored to ensure the well-being of the individual. Individual bobcats weighing >4 kg (such that the collar was <5% of the bobcat’s body weight) were fitted with a Lotek GPS_3300SL (Lotek Eng. Inc., Newmarket, ON) or Sirtrack Model TGC181 GPS/VHF satellite telemetry collar (Sirtrack®, Hawkes Bay, New Zealand) with mortality sensor. These collars took one location every 3 h. All animals were released at the point of capture. These methods adhered to a strict protocol for trapping and handling following guidelines set forth by the American Society of Mammalogists’ Animal Care and Use Committee (Sikes et al. 2011), and permits obtained from Montana State Fish, Wildlife, and Parks (#’s 2009-59, 2010-002, 2011-003) and the University of British Columbia’s Animal Care Committee (A07-0676-R001). 38 3.3.2. BOBCAT MOVEMENTS AND ACTIVITIES Actual weights of radiocollared bobcats, average daily distances moved, and estimated time spent active were entered in the model to determine average DEE of bobcats on the study site. Weights were obtained from anesthetized bobcats at the time of capture. Average daily distances moved were determined in Microsoft Excel (2010) by computing distance between consecutive UTM coordinates. Distances between consecutive movements were summed and divided by the number of days in the winter season, or the number of days that the bobcat was radiocollared in that season, to obtain average daily movement distances. For purposes of this study winter consisted of the 90 days from 1 December to 28 February. All bobcats spent <90 radiocollared days in winter, as they were collared either later in the season or the radiocollar failed before the end of winter. To define if a bobcat remained in overwinter energy balance, average distance moved, time spent active, hunting, and eating were estimated from field data or from the literature. Movement distances in the model were varied from 0-19 km, based upon low and high 24-h movement distances observed for collared bobcats and a median movement distance for winter of 9 km (see Chapter 4). These distances are comparable to winter distances reported for various Lynx spp. in winter: Iberian lynx (Lynx pardinus), 11.1 km (Aldama et al. 1991); Canada lynx (Lynx canadensis), 7 km (Moen et al. 2008), 8.8 km (Parker et al. 1983), 23.3 km (Apps 2000), and bobcat, 11.7 km (Erickson 1955), 11.2 km (Rollings 1945), 8 km (Bailey 1974), and 8.7 km (Sullivan 1995). Time spent active, hunting, and eating were based on felid activity patterns reported in the literature. Average time spent active was difficult to estimate from GPS collar data due to the programmed duty cycle of one location every 3 h. Time spent active was varied from 0 to 20 h, though an average of 16 h active was used based upon Aldama et al.’s (1991) observation of Iberian lynx activity levels. Time spent hunting was varied from 0 to 2 h, which may be an extreme high end estimate. An average time spent hunting of 2 minutes (0.033 h) was based upon the convention of 30 seconds per chase that a wild Iberian lynx gives to a hare, again following Aldama et al. (1991). Considering average hunting success to be 25%, 4 hunting attempts are required before prey is captured such that 30 seconds × 4 attempts = 2 minutes. Hunting success of 25% is well within the range of hunting success of 9-36% and 14-55% given for Canada lynx (Brand et al. 1976), Parker et al. 1983); there is no reason to suspect that 39 bobcats differ from closely related lynx species in this respect. Time spent eating prey was varied from 0 to 4.5 h (to account for large prey items such as deer), with an average time spent consuming prey of 1 h based upon estimates of time spent eating by captive Iberian lynx (Aldama et al. 1991). 3.3.3. WINTER ENERGETICS MODEL FOR BOBCATS For this model, I used the same framework as Aldama et al.’s (1991) Iberian lynx energetics model, which was based on Powell’s (1979) fisher (Martes pennanti) energetics model. Thus, total energetic cost (CT) for non-reproductive animals (Aldama et al. 1991) is defined as: CT = Cr + Cl + Ch + Ce + Cth (Eqn. 1) The cost of resting (Cr), locomotion (Cl), hunting (Ch), eating (Ce), and thermoregulation (Cth), are defined by the following equations: (1) Cost of resting. [Cr = 85 × W 0.75 kcal/day where W is mass in kg (Mautz and Pekins 1989).] Cr is equivalent to basal metabolic rate (BMR) for an individual, and is typically expressed as 70 × W 0.75 I have opted to use the costs of thermoregulation as resting BMR, as average winter temperatures on my study site require bobcats to thermoregulate while at rest throughout the winter; therefore Cr is replaced by Cth. Other activities such as moving (Cl), hunting (Ch), and eating were assumed to generate enough body heat in the animal that additional thermoregulatory costs were not incurred when engaged in those activities. a. Winter was defined here as December-February. Though the period of snow cover often lasts longer in northwest Montana, these months are the coldest. Average daily temperatures during these months are typically below the lower critical temperature for bobcats as determined by Mautz and Pekins (1989), where winter BMR was estimated to be 85 kcal/kg W 0.75 , based on a Tc = lower critical temperature of -2.2ºC. Mean winter temperature was -6ºC in 40 their study. b. I assumed that bobcats thermoregulate throughout the winter, such that Cr is replaced by Cth.. Other activities such as locomotion, hunting, and eating are assumed to generate heat, thus replacing thermoregulatory costs. Average daily temperatures in winter for TLRD at low elevation (932 m) were -4.3ºC and average minimum daily temperatures were -8.4ºC. Most of the bobcats in this study lived at 1300-1700 m, and likely experienced far colder winter conditions. Average wind speeds were 6.8 kph; therefore, average daily temperature with windchill was -7ºC, and the average minimum temperature with windchill was -12ºC. Although windchill is calibrated to humans, it is simply used here to illustrate that temperatures experienced were colder than ambient temperatures as wind speed is expected to affect thermal conductance, and thermoregulatory costs in bobcats. Minimum temperatures for Winter 2011 were -24.4ºC (December), -23.3ºC (January), -25ºC (February) at 932 m. Including maximum wind speeds of 51.5, 64, and 56 kph for each month respectively, windchill temperatures would have dipped to -40ºC, -41.7ºC, and -42.8ºC (NOAA 2013). Given these temperatures and wind speeds, it is a reasonable assumption that bobcats experienced 24-h thermoregulatory costs. (2) Cost of locomotion. [Cl = 5.8 × W 0.75 × t2 + 2.6 × W 0.6 × d], where t2 is time spent in motion (walking, foraging, territorial marking), and d is daily distance traveled in km (Taylor et al. 1970). I acknowledge that the equation for the cost of locomotion is imperfect, as the formula assumes constant effort and speed; this equation for locomotion is standard in the energetics literature (see Powell 1979, Aldama et al. 1991, Laundré 2005). Undoubtedly, if a bobcat were to move at a faster rate, it would expend greater energy than moving the same distance but at a slower speed. Here I assume that such costs between rate and time spent moving are negligible. 41 (3) Cost of hunting. [Ch = 78.3 × W 0.84 × t3], where t3 is the time (hr) spent chasing, attacking, and subduing prey (Calder 1984). (4) Cost of eating. [Ce = (5.8 × W 0.75 + 3.12 × W 0.6 ) × t4)], where t4 is time spent eating (Ackerman et al. 1986). Cth replaces Cr and is only used once in the model. Thus, Eqn. 1 becomes: CT = Cth + Cl + Ch + Ce (Eqn. 2) Hence, I defined daily energy expenditure as: CT = (85 × W 0.75 kcal/day) + (5.8 × W 0.75 × t2 + 2.6 × W 0.6 × d) (Eqn. 3) + (78.3 × W 0.84 × t3) + ((5.8 × W 0.75 + 3.12 × W 0.6 ) × t4) In order to incorporate McNab’s (2000) all-felid BMR curve, which indicates 5% greater BMR for bobcats than Mautz and Pekins (1989) calculated for winter BMR, I modified the equation further in the following way: CT = (1.05 × 85 × W 0.75 kcal/day) + (5.8 × W 0.75 × t2 + 2.6 × W 0.6 × d) (Eqn. 4) + (78.3 × W 0.84 × t3) + ((5.8 × W 0.75 + 3.12 × W 0.6 ) × t4) This model would also be applicable to females during gestation, as this period has been shown to increase caloric requirements in pumas (Puma concolor) by only 38.5 kcal per day (Laundré 2005). The majority of kittens in northwest Montana are born during May and June (Brainerd 1985), which means that most female bobcats are not gestating until mid-March. Lactation and the period where kittens are dependent on the mother increases energetic costs markedly (Laundré 2005). 42 3.3.4. MODEL APPLICATION Sensitivity for the energetic expenditures model was calculated using Microsoft Excel (2010). Parameters were bobcat mass (W), distance moved (d), time spent moving (t2), time spent hunting (t3), and time spent eating (t4). Parameters were varied using a range of realistic values for 11 different scenarios as displayed in Table 3.1 to estimate energetic requirements for certain activities that a bobcat would experience in the wild. Basic model performance was evaluated against a range of realistic parameter combinations to determine which combinations of activity allowed bobcat energy balance. These general results were compared to field data. Hence, field data for individual bobcats were compared to model estimates of DEE for a bobcat of the same mass modeled with the median estimates of d = 9 km, time active = 16 h, time hunting = 0.033 h, time hunting = 1 h, to determine overwinter energy balance (Tables 3.2-3.4). Hereafter, this baseline model will be referred to as the Baseline Winter Model (BWM). Substituting median estimates for time spent engaged in various activities and daily distance moved, the equation for the BWM becomes: CT = (89.25W 0.75 ) + (92.8W 0.75 + 23.4W 0.6 ) + (2.58W 0.84 ) + (5.8W 0.75 + 3.12W 0.6 ) (Eqn. 5) Simplifying the BWM gives: CT = (26.52W 0.6 ) + (187.85W 0.75 ) + (2.58W 0.84 ) (Eqn. 6) 3.3.5. WINTER PREY REQUIREMENTS I based daily and overwinter prey requirements on the observed diet of bobcats (% biomass) in northwest Montana: Sciuridae (54%), Cricetidae (24.5%), Leporidae (12.2%), Cervidae (8.5%), and Tetraoninae (<1%) (See Chapter 2). I based calculations of energy obtained from each prey type, with the exception of Tetraoninae, on estimates of gross energy intake by bobcats as calculated by Powers et al. (1989). Tetraoninae were excluded as they comprised <1% of bobcat diet. DEEs of bobcats were then divided by metabolizable kcal obtained from each prey type to determine numbers of prey individuals required for a bobcat to remain in energy balance (Table 3.3) and which combinations of prey types could be used to meet DEE. 43 3.4. RESULTS 3.4.1. BOBCAT CAPTURE DATA Eight bobcats (6M, 2F) were radiocollared. Of these 8 individuals, collars from 4 males and 1 female were recovered, with a total of 7,664 GPS locations used for energetics modeling, home range and habitat selection (Chapter 4), and seasonal movements (Chapter 5). 3.4.2. BASELINE WINTER MODEL The winter energetics model calculated DEE of ~2.35×BMR for bobcats in northwest Montana (Table 3.5). Bobcats could meet DEE through any combination of activity and movement parameters, but energy balance was based on median parameters of 9 km moved daily, 16 h active, 1 h eating, and 2 minutes spent hunting in every 24 h period (see Eqn. 6). The energetics model was most sensitive to variations in time hunting, as the cost of attacking and subduing prey is high (Figure 3.1). For example, if a bobcat spent 1 h moving, or 1 h eating, it only expended 44.5 and 46.6 kcal respectively, but if it spent 1 h hunting (attacking and subduing prey in a 24 h period), then it expended 564.4 kcal. In short, five minutes spent hunting expends approximately the same number of kcal as 1 h spent active or 1 h spent eating. Thus, small increases in time spent hunting disproportionately increases kcal expended in that activity as compared to other activities. In terms of food requirements, 1-h of any activity other than hunting would require just over 1 small rodent to satisfy caloric expenditures, whereas the same time spent hunting would require over 14 rodents, over 3 squirrels, or nearly 2 woodrats. These are considerable additional calories required by increased hunting time, and thus likely a strong need to minimize time spent hunting. This model suggests that short hunting time and/or high hunting success as compared to hunting attempts is mandatory for bobcats to remain in overwinter energy balance. 3.4.3. ENERGY BALANCE OF RADIOCOLLARED BOBCATS I found that bobcats on TLRD stayed in energy balance throughout the winter, by reducing daily km moved (Table 3.2), relative to movements in other seasons (Chapter 5, Table 5.2). Bobcats that exceeded 9 km median daily movement distance were interpreted as exceeding energy balance, if, as assumed, activity levels scaled to movement distances (i.e. 44 greater movement distance translates to greater time spent active). Bobcats live-trapped routinely had greater mass early in the winter than when they were recaptured towards the end of the winter, likely reflecting use of fat stores. As winter deepened and bobcats were harder pressed to meet day-to-day energetic requirements, these animals, though leaner, still appeared healthy. No starvation mortality of study animals occurred; of three observed mortalities, two were human caused (trapping) and one was due to predation. Overwinter mass loss agrees with the Baseline Winter Model results that animals that exceeded average activity parameters exceeded energy balance. Overwinter mass loss was observed; however, bobcats did appear able to regain mass, signaling that while they were out of energy balance on some occasions and lost weight, individuals were able to regain mass by obtaining prey greater than required to simply maintain weight. For example, M1’s mass upon initial capture (December 12, 2009) was 14 kg. Upon subsequent recaptures (January 22 to draw blood) he weighed 12.5 kg and 13 kg on February 2, 2010 when his radiocollar was changed. For example, M1 exceeded the average movement distance of 9 km for 5 of 87 days (5.7%) he was collared in a single winter season, including one 24-h period in which he moved 16.3 km (Table 3.2). Using the BWM for M1, with mass of 14 kg at initial capture on December 12, 2009, this bobcat’s DEE would be 1512 kcal (Eqn. 6). When considering the maximum daily movement of 16.3 km for M1 (Table 3.2), this bobcat would expend 1605 kcal as predicted by the BWM, if all other variables except d (distance moved) are held constant. The average temperature during this 24-h period was -14.4°C, with sustained winds of 24 kph, and new snowfall of 21.3 cm at 904 m. M1 remained at an average elevation of 1453 m throughout the winter (Chapter 4). This large movement would leave M1 with a caloric deficit of 93 kcal; in order to offset this caloric deficit, M1 could make the following physiological or behavioral choices to offset the kcal over-expenditure. 1) he could use body fat reserves to offset the loss, or 2) he could spend 2 hours less being active during the 24-h period, or 3) he could change energy intake by consuming more kcal (e.g. he could consume just over 2 more rodents or 1 more squirrel in that time period). Behavioral plasticity could allow M1 to remain in energy balance as modifications in activity levels and foraging choices would allow him to meet increased energetic requirements resulting from large movements. Such foraging decisions (what to eat and how to hunt, e.g. chase vs. ambush) and activity choices (fewer hours spent 45 active or moving faster through the landscape) are displayed in Figure 3.2. 3.4.4. OVERWINTER PREY REQUIREMENTS FOR BOBCATS Based on prey intake proportional to observed bobcat diet (Chapter 2), the daily diet of a 10.5 kg bobcat in northwest Montana consists of 3.8 squirrels, 0.2 woodrats, 5.9 rodents, 0.2 snowshoe hares, and ~50 g of deer per day (Table 3.5a). Overwinter energy expenditure (Dec-Feb) for this bobcat would total 110,070 kcal. Based on the observed diet of bobcats in this area, a 10.5 kg bobcat would need to consume 4.5 kg of deer, 15 snowshoe hares, 328 squirrels, 19 woodrats, and 530 rodents to remain in energy balance (Table 3.5a). To be comparable to overwinter prey requirements estimated by Powers et al. (1989), a 15 kg bobcat during a 120-day winter period (Dec-March), at rest would minimally require 81,600 kcal (resting BMR of 680 kcal per day) (Table 3.5b). A direct comparison of prey requirements is difficult as hares and squirrels in their study had higher average mass, while rodent species had lower average mass than those in my study. Additionally, Powers et al. (1989) estimated less use of deer carcasses by bobcats than I did. Using average prey masses for my study, a bobcat would consume 39.0 kg of deer (65% of a 60 kg deer), 87 snowshoe hares, 2,033 rodents, or 450 squirrels to fulfill BMR energy requirements. However, such overwinter prey estimates are inadequate to sustain a bobcat engaged in normal activities. Under my overwinter energetics model for bobcats, a 15 kg bobcat would need 191,040 kcal given median movement and activity patterns for a120-d winter period; this overwinter energy expenditure is 2.34 × BMR, which is in line with daily energy expenditures of 2-3 × fasting metabolic rate for free-ranging large mammals (Moen 1973, Robbins 1983). An overwintering bobcat of this mass would therefore need to consume 91.3 kg of deer (1.5 individual deer weighing 60 kg), 203 snowshoe hares, 612 woodrats, 1,053 red squirrels, or 4,912 rodents. Based on the observed diet for bobcats, overwinter prey requirements would more realistically be composed of 7.8 kg of deer, 25 snowshoe hares, 569 squirrels, 919 rodents, and 32 woodrats (Table 3.5). 46 3.5. DISCUSSION 3.5.1. BASELINE WINTER MODEL OVERVIEW The Baseline Winter Model is very strict. Movement parameters estimated for modeling purposes reflected a high—but realistic—estimate of daily average movement distances of bobcats throughout the winter. Collared bobcats moved less than the median distance the BWM used to determine energy balance, and it is plausible that bobcats were able to obtain sufficient prey to satisfy prey requirements for such energy expenditures, given the observed winter diet of bobcats in this area. Model estimates of movement distances reflected generous 24-h movements for bobcats; male bobcats moved on average < 5 km per day in this study, while the female bobcat moved just over 2 km per day during winter (Chapter 4). Bailey (1974) reports that the majority of male bobcats moved < 4.8 km in winter, and females typically moved <3.2 km; however, longer daily distances of up to 8 km (Bailey 1974), 11.7 km (Erickson 1955), and 11.2 km (Rollings 1945) have been reported. One male bobcat moved up to 16.3 km in 24-h in this study; median movements of 9 km used in the energetics model are at the higher end of realistic. With reduced winter movement distances, bobcats will further reduce DEE, and prey requirements will decrease accordingly. Furthermore, this model assumed that bobcats have higher than average BMR compared to the majority of mammals based on McNab’s (2000) estimates of BMR for felids, even compared to the high winter resting BMR found by Mautz and Pekins (1989) of 85 kcal/kg W 0.75 . The model constructed required that bobcats spend 5% more energy on thermoregulatory, movement, and hunting activities than previous energetics models. 3.5.2. BOBCAT ABILITY TO MEET DEE I constructed a strict winter energetics model that evaluates the bobcat’s ability to meet DEE in the harshest conditions. This model thus includes several assumptions that could be relaxed. First, the model is sensitive to minor changes in time spent hunting, as compared to time engaged in other activities (Figure 3.1). This parameter, cost of hunting, Ch = 78.3×W 0.84 ×t3, uses time (hr) spent attacking and subduing prey (Calder 1984), to determine kcal expended in the act of predation. This parameter by definition must include the cost of chasing, capturing, and killing prey species. Considering that nearly 80% of 47 bobcat diet in this study involves prey <1 kg in mass, it is unlikely that each act of predation involves a large energy expenditure on the part of a hunting bobcat. For example, a bobcat killing a Cricetid rodent will pounce and bite quickly, with most of these prey items being consumed in one or two bites (Hamilton and Hunter 1939). It may be more realistic to eliminate this model term for bobcats pursuing prey that are < 1 kg. Such energy expenditure, as calculated by the Calder (1984) portion of the energetics equation, is likely when a bobcat engages in a long chase or targets a larger prey animal, such as a snowshoe hare or a deer. Bobcats are capable of killing deer, and even have an advantage in deep snow. However, a bobcat risks serious injury or death in attacking and killing a deer. If an injury impacts the bobcat’s ability to hunt, the injury could lead to eventual starvation if the bobcat was unable to capture prey. The energetic cost of killing a deer is likely even higher than estimated by Calder (1984); however, the caloric return is so high, energetic benefits likely far outweigh costs. The time it takes for a bobcat to kill a deer ranges from minutes (Young 1928, Newsom 1930) to hours (Rollings 1945). Time spent killing a deer could place energetic expenditures from as few as 5 kcal (2 minutes used in this model) to 2822 kcal for 5 hours spent killing a deer. Even such a large energetic expenditure is offset by consuming just 1.35 kg of deer, which a bobcat can easily consume in one meal. Deer obtained as carrion obviously carry no hunting cost, and 1 kg of deer provides a bobcat with 2091 kcal. Bobcats can reduce energetic demands by ambush hunting, rather than actively chasing prey, a technique they likely employ for the 1400 g snowshoe hare. Canada lynx switch hunting strategies when prey density changes; lynx use an ambush strategy increasingly during cyclic hare lows, a strategy that conserves critical energy reserves (O’Donoghue et al. 1998). A switch in hunting strategy makes intuitive behavioral sense as the cost of hunting is high (Figure 3.1). Additionally, bobcats are not likely to pursue hares far through deep snow, where the bobcat is at an extreme disadvantage in the chase, should the hare escape the initial attack. Indeed, there is evidence that bobcats quickly abandon the chase should the initial jump after the hare prove unsuccessful (Marston 1942). It is interesting to examine when a bobcat should abandon pursuit of a hare in deep, soft snow. A 1400 g snowshoe hare will provide a bobcat with 941 kcal. Lynx pursued hares farther during cyclic hare lows in the Yukon, for an approximate average distance of 15 m in 48 a successful kill, and ~24 m in an unsuccessful hunting attempt (O’Donoghue et al. 1998). If a bobcat will chase hares for the same distance, to a maximum of 24 m, based on an average chase time of 30 seconds (Aldama et al. 1991), a bobcat would expend a minimum of 4.5 kcal; however, this estimate does not account for the effort required by a bobcat leaping and sinking in deep snow. The cost of chasing a hare in soft snow will be higher for a bobcat than for a lynx, as bobcats have higher foot loadings than lynx (Buskirk et al. 2000b). Although 4.5 kcal is not a large caloric expenditure, and a bobcat could theoretically chase the hare longer, it seems more plausible that the chase is abandoned because the individual bobcat assesses that it is not likely to capture the prey. In the case of deep soft snow, it is likely that the energetic expenditure is greater than the 4.5 kcal estimated above. However, bobcats may engage in longer chases of hares should snow conditions be conducive to a bobcat successfully chasing and catching a hare, as in the frequent time periods in northwest Montana of hard, crusty snow. In fact, Canada lynx distribution and hunting success may be directly correlated to snow conditions (Stenseth et al. 2004); lynx hunting success increases with deep, soft snows as compared to similar sized competitors such as bobcats and coyotes (Canis latrans). It is logical to think that the opposite would be true for bobcats; thus, bobcats at the northern extent of their geographic distribution and that are sympatric with lynx populations benefit from snow crusting and compaction cycles with regard to hunting success. A second assumption of the model that could be relaxed is the assumption of constant over-winter thermoregulation. Bobcats are likely to make behavioral decisions that affect their thermoregulatory costs on days with more extreme conditions. Behavioral changes include reducing movements during extreme cold and snow periods, seeking shelter from winds, and choosing resting sites that are protected from the elements. Additionally, a lower critical temperature (TLC) of -2.2º C (Mautz and Pekins 1989) for bobcats seems unduly high, particularly for bobcats that inhabit extreme winter environments. Mautz and Pekins (1989) state that in their study bobcats were held in cages that exposed the cats to the elements far more than would be experienced in the wild, which may have resulted in an abnormally high lower critical temperature. Bobcats in northwest Montana may be acclimated to colder average temperatures. Indeed, Canada lynx are thermoneutral to <-10°C (Casey et al. 1979). I used mean monthly 49 temperatures in this model, which were below the TLC of -2.2°C for bobcats, and hence required thermoregulation through the winter. However, taken on a daily basis, of 90 winter days, 46 days had mean temperatures that were above the TLC of -2.2°C; 27 days had reported minimum temperatures that were still above the TLC of -2.2°C. If bobcats in northwest Montana have a TLC similar to Canada lynx of -10°C, then of 90 winter days, 79 days had mean temperatures that were above a TLC of -10°C, while 61 days had reported minimum temperatures that were higher than -10°C. These temperatures come from a mild winter (2009-2010) in northwest Montana, and reflect temperature data available for valley floor (914 m) elevations. However, radiocollared bobcats were located at higher elevations and likely experienced temperatures far lower than reported for valley elevations during this winter, and will likely experience far colder temperatures during harsher winters. The Baseline Winter Model uses a relatively high TLC to incorporate thermoregulatory costs, and the results of this model show that bobcats are able to remain in energy balance overwinter even when required to thermoregulate 24-h per day. Thus, bobcats should be able to withstand extreme winter temperatures for long periods, given the ability to find sufficient prey. If one were to use the standard metabolic rate for a bobcat in its thermoneutral zone of 79 kcal/kg W 0.75 as reported by Mautz and Pekins (1989), rather than the winter BMR of 85 kcal/kg W 0.75 (as used in this model to assume thermoregulation), a 10.5 kg bobcat would require 35 kcal/day fewer, for temperatures above -10°C. This small daily kcal savings would result in an overwinter energetics savings of 2765 kcal (79 days above -10°C × 35 kcal/day). This overwinter energetics savings is equivalent to 3 fewer snowshoe hares, 15 fewer squirrels, 69 fewer rodents, or 9 fewer woodrats that a bobcat needs to capture in a winter to stay in energy balance. By adjusting the TLC lower, a bobcat could, in theory, reduce daily prey requirements, such that there is less time required in prey acquisition. 3.5.3. IMPLICATIONS FOR BOBCATS IN EXTREME ENVIRONMENTS There are several important implications of the dietary-based energetics model for bobcats in extreme environments. As bobcats may have high thermoregulatory costs in winter environments (McNab 2000, Mautz and Pekins 1989) such as those experienced by bobcats in northwest Montana, it is important to evaluate how bobcats are able to succeed in 50 a seemingly atypical setting. Bobcats in northern latitudes have large home ranges, as reflected in this study (Chapter 4) and others (Bailey 1974; Knick 1990; Koehler and Hornocker 1989, Marston 1942). A number of factors like larger body size, high energetic requirements, and habitat productivity can explain large home range size (Bailey 1974, Harestad and Bunnell 1979, Gittleman and Harvey 1982, Lindstedt et al. 1986). Bobcats in northwest Montana have all three factors—large body size, high energetic needs, and lower prey availability relative to season and latitude—which have strong implications for bobcat population density, landscape carrying capacity relative to bobcats, bobcat predation rates, and levels of intra- and interspecific competition (Brown et al. 2004). Based on caloric needs and observed diet, most bobcats on TLRD require about 10 prey items per day, if prey taken are proportional to observed diet and consist solely of squirrels and rodents. If a bobcat consumes ~10 prey items per day, on average a bobcat makes one kill of prey < 1 kg every 2 h 24 min. A high kill rate for small prey is possible for a bobcat; previous studies examining the stomach contents of bobcats have noted multiple prey items in a stomach (e.g. Hamilton and Hunter 1939, Litvaitis et al. 1984, McClean et al. 2005). In my study (Chapter 2), 5 of 25 (20%) stomachs had ≥2 prey types. In an examination of 140 bobcat stomachs collected during fall and winter in Vermont, Hamilton and Hunter (1939) report one stomach containing a gray squirrel (Sciurus carolinensis), a flying squirrel (Glaucomys sabrinus), and a deer mouse (Peromyscus maniculatus); one stomach with red squirrel, mink (Neovison vison), muskrat (Ondatra zibethicus), and fish; one stomach with a chipmunk (Tamias striatus), red squirrel, a deer mouse, three field mice (subfamily Arvicolinae), and a small bird; several stomachs containing 2-3 mice each; and one stomach containing 5 voles. A domestic cat’s stomach empties in <12-h (Julie Ann King, veterinary technician, personal communication). There is no reason to suspect a large difference in digestion rates between domestic cats and bobcats. Given the regular occurrence of multiple food items in bobcat stomachs, bobcats appear capable of meeting daily energy requirements through capture of multiple prey animals within short periods of time. Bobcat kill rates are thus likely high for prey <1 kg. Kill rates would be lower for larger prey (woodrat, hare, and deer). For example, a 10.5 kg bobcat requires 1223 kcal/day under this strict winter thermoregulation model. This kcal requirement could be satisfied by taking 1.3 snowshoe 51 hares/day, a number well in line with that consumed by coyotes (0.3-2.3 hares/day) and lynx (0.3-1.2 hares/day) in the Yukon (O’Donoghue et al. 1998). On a hare-only diet of 1.3 hares/day, a 10.5 kg bobcat would need to kill once every 18 h 30 min. Based on the results from the BWM, I offer the following speculations on bobcat behavior, impact of local site conditions, and implications for bobcat populations and general ecology in deep snow environments. Bobcats in this area of Montana may not be as limited by extreme temperatures and deep snow conditions as has been previously thought. Additionally, this population of bobcats may be maintained by high prey densities, microhabitat refugia, and winter conditions of cyclic snow crusting and compaction that allow them to thrive in this environment. Varying snow conditions throughout the winter may in fact mean that winter activity is less energetically expensive for bobcats in northwest Montana than modeled because hard, crusty snow allows for easier foraging and movement than that expected in deep, soft snow. Snow conditions that are favorable to bobcat foraging success, coupled with habitats that allow for escape from extreme weather conditions would enable bobcats to expend less energy on thermoregulation. The BWM deals specifically with average metabolic stresses and dietary requirements of bobcats to remain in daily and overwinter energy balance. However, bobcats likely face day-to-day variability in individual ability to meet average metabolic needs. This daily variation is likely affected by prey availability, the bobcat’s ability to successfully capture sufficient prey, and abiotic conditions such as extreme cold snaps and snowstorms. Essentially, how long can a bobcat experience a "run of bad luck", relative to the distribution of poor hunting days or extra cold days where metabolic needs are great? Certainly, there is a critical point where winter conditions coupled with the individual’s hunting success or lack thereof, reach a “point of no return”, wherein the bobcat loses physical condition and starvation ensues. A complete examination of this question is beyond the scope of this chapter, but I recognize that chance (with respect to hunting success) and extreme weather events will affect bobcats in deep snow environments. There is evidence in my study that bobcats employ behavioral strategies that function as a bet-hedging approach to conserving energy in winter. It is plausible that bobcats minimize daily distance traveled in winter (Chapter 5) as a variance reduction strategy, meaning that movement distance is a variable that bobcats can control. In December 2009, 52 TLRD experienced an extreme cold spell where daily high temperatures did not exceed -20 ºC for 10 consecutive days; there was little to no activity of wildlife species in this area based on tracks detected (personal observation). When the cold spell broke, and overnight temperatures exceeded -20 ºC, bobcats resumed activity on the study site. I am not implying that bobcats did not hunt or move during this period, but that movements were so greatly reduced that we did not locate any tracks indicating bobcats were active in the area. Thus, it is possible that calories conserved by reducing movements on a daily basis can be “saved” for use during extreme weather conditions, increasing the chances that a bobcat can outlast the “run of bad luck”. Finally, energetics modeling can be used to inform our understanding of competition between species (Brown et al. 2004), particularly sympatric congeners. Bobcats overlap in habitat and diet with the federally Threatened Canada lynx, particularly in northwestern Montana (Smith 1984, Brainerd 1985, Ruediger et al. 2000); however, bobcats have small feet that are not well adapted to walking on snow, ostensibly giving lynx a competitive advantage in environments with deep winter snow (Buskirk et al. 2000b). However, snow conditions in southern lynx habitats may be subject to more freezing and thawing than in northern lynx habitats (Buskirk et al. 2000a), with such differences largely dependent on elevation, aspect, and local weather conditions (Ruediger et al. 2000). Increased crusting and compaction of snow at the southern reaches of lynx range may reduce the competitive advantage that lynx have over other species such as bobcats and coyotes in soft snow, due to the lynx’s long legs and low foot loadings (Buskirk et al. 2000b). Given my field observations pertaining to TLRD regarding bobcat presence and lynx absence, land management practices, road densities, and differences in local site conditions all play a critical part in the complexities of lynx and bobcat distribution in this area (Chapter 4 and 5). Climate change will have important impacts for interspecific competition between lynx and bobcats, as areas with deep, soft winter snow are likely to experience increased crusting and compaction cycles. Northwest Montana provides the perfect laboratory to study the interaction of these congeners to determine behavioral and phenotypic plasticity. Such a research focus would allow prediction of areas where lynx are likely to persist and bobcats likely to expand. 53 I suggest further studies that specifically investigate winter snow conditions with emphasis on determining number of days with hard, compacted snow versus powdery snow in conjunction with estimates of primary and alternate prey densities. Given that bobcats are able to withstand and thrive in harsh winter conditions in close proximity to known lynx populations in Montana such changes in local site conditions may increase the chance that bobcats could displace lynx from potential habitat. 3.6. IMPLICATIONS FOR BOBCAT WINTER ENERGETICS Bobcats in northwest Montana remained in overwinter energy balance based on an energetics model incorporating all-felid basal metabolic rate and estimated activity parameters. Calculation of overwinter prey requirements were based on the observed bobcat diet described in Chapter 2. These prey requirements indicate that bobcats on average make a kill of prey <1 kg every ~2.5 h. On average, bobcat DEE was 2.35×BMR, well within normal range of 2-3×BMR reported in the literature. Bobcats reduce daily movement distances (Chapter 5) and may seek microhabitats to further decrease daily energetic expenditures. Bobcat energy balance in a harsh winter setting has important implications for potential competition with the federally Threatened Canada lynx, as snow crusting and compaction cycles may subsidize bobcat success in this atypical environment in northwest Montana. Climate change may favor bobcats by increasing the number of winter days where the low foot loadings of the lynx do not provide an advantage. The number of days where snow is hard and crusty could increase, which may lessen the competitive advantage that large snowshoe-like feet give lynx in deep, soft snows, increasing the chance that bobcats could displace lynx from additional locations in Montana. 54 Table 3.1. Overview of the activity scenarios used to calculate bobcat winter DEE; mass was held constant for each scenario. The Baseline Winter Model (Eqn. 6) is shown first, followed by each scenario. I modeled masses of 4.5-15 kg in 1.5 kg intervals, for a total of 8 different bobcat masses. DEE was calculated for that mass dependent on each scenario. Scenario 1 represents basal metabolic rate for a bobcat, while Scenario 11 represents an extreme in activity level. Each subsequent line shows increases in activity parameters, reflecting realistic estimates of activity for bobcats to give a range of DEE from no activity to intense activity levels. Scenario Distance moved (km) Time spent active (h) Time spent hunting (h) Time spent eating (h) (Number) (Cl) (Cl) (Ch) (Ce) BWM 9 16 0.033 1 1 0 0 0 0 2 1 1 0.008 0.1 3 3 2 0.017 0.5 4 5 4 0.033 1 5 7 8 0.050 1.5 6 9 10 0.083 2 7 11 12 0.167 2.5 8 13 14 0.500 3 9 15 16 1 3.5 10 17 18 1.5 4 11 19 20 2 4.5 55 Table 3.2. Mass and daily winter movement distances of radiocollared bobcats on TLRD. DEE was calculated directly from the winter energetics model Eqn. 4, using low, average, and high movements distances. All other activity levels were held constant as shown for BWM in Table 3.1. Male bobcats are indicated by “M”, while female bobcats are shown with “F”. For male bobcats with unknown daily movement distances, average daily movement distance of collared males (x̄ = 3.11 km) was used to calculate DEE. For female bobcats with unknown daily movement distances, average movement of F2 (x̄ = 2.21 km) was used to calculate DEE. UNK denotes unknown information. Bobcat ID Mass Average daily km ± SE Average DEE Range of daily movement DEE range (kg) (kcal) (km) (kcal) M1 14.0 4.42 ± 0.322 1454 ± 4.5 0.057—16.30 1399—1605 M2 9.5 2.15 ± 0.277 1067 ± 3 0.133—6.08 1047—1107 M3 14.5 2.75 ± 1.43 1471 ± 18.5 0.781—5.55 1446—1508 F2 11.0 2.21 ± 0.532 1191 ± 5.5 0.203—4.94 1169—1221 M4* 11.0 UNK 1201 M5* 9.0 UNK 1034 M6* 12.0 UNK 1282 M7* 11.0 UNK 1201 F1* 7.75 UNK 917 F3* 9.0 UNK 1026 *Average DEE was estimated using average male or female movement distance in the winter energetics model (Eqn. 4), with time spent active, hunting, and eating held constant as in BWM. 56 Table 3.3. Energy obtained by bobcats from prey types available in northwest Montana. Prey are presented by % biomass in diet (Chapter 2), digestible energy obtained from each prey type, metabolizable energy gained from each prey type, and kcal obtained from that prey type. The number of individual prey animals required to satisfy the daily energy requirements of a 10.5 kg bobcat engaged in average activity patterns (BWM scenario Table 3.1) during winter on the observed % biomass of that prey type in the northwest Montana bobcat winter diet. % biomass consumed Species Avg. Dry matter Digestible Metabolizable kcal obtained Number c % Rank weight (kg) content a energy a energy % a needed/day % (kcal/g) 10.5 kg bobcat Deer 8.5 4 60.000 39.0 6.25 85.8 2091.4 b (~50 g) e Snowshoe hare 12.2 3 1.400 29.7 3.61 62.7 941.2 0.2 Squirrel 54.0 1 0.195 25.9 4.80 74.9 181.6 3.8 Cricetidae 19.3 2 0.038 33.3 4.43 71.6 40.1 6.1 -Woodrat d 5.2 0.336 25.9 4.80 74.9 312.9 0.2 Grouse <1.0 5 0.539 --- --- ---- ---- ---- a From Powers et al. (1989). b Assumes that a bobcat consumes 1 kg of deer during a feeding. c Required to satisfy DEE of 1223 kcal/day for a 10.5 kg bobcat as calculated from winter energetics model assuming average movement distances 9 km, 16 hours active, 0.033 hours hunting, 1 hour eating. d Woodrats (Neotoma cinerea) composed 5.2% of total biomass consumed by bobcats in winter. For comparison to other studies in Chapter 1, woodrats were grouped within the broader Cricetid family. However, I have chosen to break woodrats out separately here, as they are a larger prey item available to bobcats in northwest Montana, being 2x as heavy as squirrels, and providing roughly 1.7x as many calories as a red squirrel. As dry matter content, digestible energy, and metabolizable energy have not been computed for bobcats consuming woodrats (in Powers et al. 1989), I assumed that these parameters would be comparable to a red squirrel. e Deer consumed is presented in amount needed to satisfy caloric requirements from observed diet. Deer is 8.5% biomass of observed diet, which equals 104 kcal of 12623 kcal needed daily by a 10.5 kg bobcat. This is equivalent to 50 g of deer per day. All other numbers are presented in whole animal counts. 57 Table 3.4. Winter energetics model showing predicted DEE per bobcat mass. Shown are kcal expenditures for bobcats at rest with thermoregulatory costs (Scenario 1 Table 3.1). Next presented are the range of caloric expenditures for Scenarios 1-11 in Table 3.1. The BWM scenario using Eqn. 6 is shown, as this is assumed to be the caloric expenditure below which a bobcat can remain in energy balance on a daily basis in winter. Lastly, DEE for energy balance (BWM) is shown as a function of Scenario 1, where a bobcat is at rest. Resting energetic costs are a larger portion of DEE for smaller bobcats than for larger bobcats. This model assumes that bobcats can obtain prey on a daily basis as shown in Table 3.3. Meeting DEE is possible through variation of activity and movement parameters and any combination of prey that satisfies daily kcal requirements. Mass (kg) Resting bobcat (kcal) kcal range Baseline Winter Model DEE as a function of BMR (Scenario 1) (Scenario 1-11) (Eqn. 6 ) (BWM/Scenario 1) 4.5 276 276-1425 655 2.37× 6.0 342 342-1778 809 2.37× 7.5 404 404-2112 954 2.36× 9.0 464 464-2431 1092 2.35× 10.5 521 521-2738 1223 2.35× 12.0 575 575-3036 1350 2.35× 13.5 629 629-3326 1472 2.34× 15.0 680 680-3608 1592 2.34× 58 Table 3.5a. Bobcat prey requirements for different time periods based upon the observed diet (Chapter 2) of bobcats in northwest Montana with respect to BWM (Eqn. 6). All prey types, with the exception of deer, are presented in whole animal counts. Deer are presented in g or kg, depending on the timeframe of dietary requirements. Time period Bobcat mass DEE a Prey type b 1 day 7 days 30 days 90 days 120 days (kg) (kcal) 4.5 655 Deer 30.0 g 190.0 g 798.6 g 2.4 kg 3.2 kg Snowshoe hare 0.1 0.6 2.5 7.6 10.2 Red squirrel 2.0 13.6 58.4 175.3 233.7 Cricetidae 3.2 22.1 94.5 283.4 377.9 Woodrat 0.1 0.8 3.3 9.8 13.1 6 809 Deer 32.9 g 230.0 g 99.0 g 3.0 kg 3.9 kg Snowshoe hare 0.1 0.7 3.2 9.4 12.6 Red squirrel 2.4 16.8 72.2 216.5 288.7 Cricetidae 3.9 27.2 116.7 350.1 466.8 Woodrat 0.1 0.9 4.0 12.1 16.1 7.5 954 Deer 38.3 g 270.0 g 1.2 kg 3.5 kg 4.7 kg Snowshoe hare 0.1 0.9 3.7 11.1 14.8 Red squirrel 2.8 19.9 85.1 255.3 340.5 Cricetidae 4.6 32.1 137.6 412.8 550.4 Woodrat 0.2 1.1 4.8 14.3 19.0 9 1092 Deer 44.4 g 310.0 g 1.3 kg 4.0 kg 5.3 kg Snowshoe hare 0.1 1.0 4.3 12.7 17.0 Red squirrel 3.2 22.7 97.4 292.3 389.7 Cricetidae 5.3 36.8 157.5 472.5 630.1 Woodrat 0.2 1.3 5.4 16.3 21.8 10.5 1223 Deer 50.0 g 350.0 g 1.5 kg 4.5 kg 6.0 kg Snowshoe hare 0.2 1.1 4.8 14.3 19.0 Red squirrel 3.8 26.3 109.1 327.3 436.4 Cricetidae 5.9 41.2 176.4 529.2 705.6 Woodrat 0.2 1.4 6.1 18.3 24.4 59 Table 3.5a continued. Bobcat prey requirements for different time periods based upon the observed diet (Chapter 2) of bobcats in northwest Montana with respect to BWM (Eqn. 6). All prey types, with the exception of deer, are presented in whole animal counts. Deer are presented in g or kg, depending on the timeframe of dietary requirements. Time period Bobcat mass DEE a Prey type b 1 day 7 days 30 days 90 days 120 days (kg) (kcal) 12 1350 Deer 54.9 g 380.0 g 1.7 kg 4.9 kg 6.6 kg Snowshoe hare 0.2 1.2 5.3 15.7 21.0 Red squirrel 4.0 28.1 120.4 361.3 481.8 Cricetidae 6.5 45.4 194.7 584.2 778.9 Woodrat 0.2 1.6 6.7 20.2 26.9 13.5 1472 Deer 59.8 g 420.0 g 1.8 kg 5.4 kg 7.2 kg Snowshoe hare 0.2 1.3 5.7 17.2 22.9 Red squirrel 4.4 30.6 131.3 394.0 525.3 Cricetidae 7.1 49.5 212.3 637.0 849.3 Woodrat 0.2 1.7 7.3 22.0 29.4 15 1592 Deer 64.7 g 450.0 g 1.9 kg 5.8 kg 7.8 kg Snowshoe hare 0.2 1.4 6.2 18.6 24.8 Red squirrel 4.7 33.1 142.0 426.1c 568.1 Cricetidae 7.7 53.63 229.6 688.9 918.6 Woodrat 0.3 1.9 7.9 23.8 31.8 a DEE based on daily movements of 9 km, 16 hours active, 0.033 hours spent hunting per day, and 1 hour spent eating per day (Eqn. 6) for bobcats of different mass. . b All prey types are presented in animal counts, with the exception of deer, which is presented in grams or kilograms. 60 Table 3.5b. Single prey requirements for 10.5 and 15.0 kg bobcats with respect to BWM. Presented are the whole numbers per day of a single prey type a bobcat would require for a 24-h period and for a 120 day winter period, for comparison with Powers et al. (1989). For example, a 10.5 kg bobcat would need to consume ~7 red squirrels per day to satisfy DEE of 1223 kcal as calculated by Eqn. 6. Again, deer is presented in g and kg. Time period Bobcat mass Prey type 1 day 120 days (kg) 10.5 DEE = 1223 kcal 146,760 kcal Deer 584.8 g 54.3 kg (0.91 deer) Snowshoe hare 1.3 203.0 Red squirrel 6.7 808.2 Cricetidae 30.5 3656.2 Woodrat 3.9 469.1 15.0 DEE = 1592 kcal 191,040 kcal Deer 760.0 g 91.3 kg (1.5 deer) Snowshoe hare 1.7 203.0 Red squirrel 8.8 1,052.1 Cricetidae 39.7 4,759.3 Woodrat 5.1 610.6 61 Figure 3.1. Energy expenditure for increasing time spent hunting, active, or engaged in eating for bobcats. Small changes in time spent hunting increase energetic expenditures far more rapidly than do large changes in time spent eating or active by bobcats. Increases in time spent hunting within the winter energetics model have a greater impact on bobcat daily energy expenditures; the model is most sensitive to changes in this parameter. Time spent hunting does assume that the bobcat chases prey in order to subdue it, rather than engaging in ambush hunting tactics. 62 BOBCAT MOVEMENT AND FORAGING PARAMETERS Is the bobcat engaged in activities that are less than or equal to all median parameters for the winter energetics model? If so, then the bobcat is in energy balance, If not, then…. Is the bobcat moving more than the median distance of 9 km per day? If yes, then the bobcat can reduce movement distance or increase prey intake If no, then… Is the bobcat hunting (attacking, subduing, and killing) prey more than 2 minutes (0.033 h) per day? If yes, then does the prey obtained offset the high energetic expenditure of spending more time engaged in hunting this prey? If yes, then energy balance should be obtained from high calories obtained from this prey (such as deer). If no, then… Can the bobcat reduce hunting time or pursue another type of prey? If yes, then bobcat should pursue this option. If no, then the bobcat must reduce movement distances substantially or… Is the bobcat active more than 16 h per day? If yes, then the bobcat can reduce time spent active, reduce movement distance, or reduce time spent hunting to offset energetic expenditures. If no, then the bobcat is either in energy balance or needs to reduce other parameters to maintain energy balance. Is the bobcat eating more than 1 h per day? If yes, then does energy from prey offset the cost of eating and maintain energy balance, or offset demands of other movement parameters? If yes, then bobcat does not need to reduce time spent eating. If no, then bobcat needs to reduce time spent eating or find larger prey. If no, then bobcat is either in energy balance or needs to reduce other movement and foraging parameters. IF BOBCAT FAILS TO ADJUST BEHAVIOR THAT CAUSES AN ENERGY DEFICIT, THEN THE BOBCAT WILL SUFFER OVERWINTER MASS LOSS, OR EVEN EVENTUAL STARVATION. Figure 3.2. Key depicting simplified movement and behavioral strategies a bobcat could employ to stay in energy balance over winter. 63 CHAPTER 4 RESOURCE SELECTION AND HOME RANGE CHARACTERISTICS 4.1. LITERATURE REVIEW AND OBJECTIVES The two most crucial components in predator ecology for interpreting an individual’s location in space and time are home range and habitat use (Kernohan et al. 2001). The spatial arrangement of resources across the landscape is the predominant factor that affects where individuals occur (Azevedo and Murray 2007). Resources are critical to individual survival and can encompass mates, denning sites, prey base, and refugia from competitors and predators (Donovan et al. 2011). Wide-ranging predators are often of conservation and management concern and a basic understanding of their habitat requirements and space use is critical for effective management (Hansen 2007, Donovan et al. 2011). Bobcat (Lynx rufus) home range size is influenced by a wide variety of factors including sex (Larivière and Walton 1997, Cochrane et al. 2006, Hansen 2007), geographic region (Anderson 1987, Ferguson et al. 2009), season (Koehler and Hornocker 1989, Cochrane et al. 2006), habitat quality (Harestad and Bunnell 1979, Rucker et al. 1989, Conner et al. 2001), and prey availability and abundance (Knick 1990, Anderson and Lovallo 2003). Carnivorous mammals that primarily eat vertebrates have high basal metabolic rates (Iverson 1972, McNab 1980, McNab 1986), and tend to have large home ranges in relation to body mass (Harestad and Bunnell 1979, Gittleman and Harvey 1982). Larger individuals tend to have larger home ranges than smaller individuals, and in most mammalian species, males tend to have larger home ranges than females. Carnivores also have high latitudinal variation in home range size, with the bobcat (Lynx rufus) among the most variable (Elizalde- Arellano 2012). For example, bobcat home ranges have been reported to be 64.6 and 199.1 km 2 for female and male bobcats in southeast British Columbia (Apps 1996) at 49°30’ to 50°30’ N latitude, and 14.8 and 43.2 km 2 for females and males, respectively, in Oklahoma (Rolley 1985), at 34°30’ N, a distance of ~3100 km. Thus, home range sizes for females are 4.4× larger, and males are 4.6× larger in British Columbia than in Oklahoma. Generalities that 64 apply to bobcats are that males typically have larger home ranges than do females (but see Elizalde-Arellano 2012), and adult animals typically have larger home ranges than do juveniles. Bobcats that live in northern latitudes tend to have larger home ranges than do those in southern locations. Bobcat home ranges during winter months tend to contract (Fuller et al. 1985, Koehler and Hornocker 1989, but see Fendley and Buie 1986), with summer home ranges 4× larger than winter ranges. In winter, snow depth may affect habitat use (Bailey 1974, Hamilton 1982) as deep snow restricts bobcat movement (McCord 1974, Litvaitis et al. 1986b, Koehler and Hornocker 1989). In particular, Koehler and Hornocker (1989) found that bobcats shifted habitat use to areas that were lower in elevation, were on south— southwest aspects that were relatively snow-free in winter, where less energy was required for movement and thermoregulation. Aside from reducing energetic costs, this shift in habitat use focused on habitat types (dry or xeric sites) where prey was available and accessible, but supplied terrain that offered escape from predators (Koehler and Hornocker 1989). Thus, bobcats in areas of deep, persistent snows are more likely to demonstrate seasonal changes in home range size and differential use of habitats. Furthermore, bobcat social organization influences home range size (Bailey 1974, Nielsen and Woolf 2001) and can result in differences in space use and resource selection among individuals and populations. Typical bobcat population organization involves greater male-female overlap, and less female-female or male-male overlap. However, females generally maintain a home range exclusive of other females (Bailey 1974, Buie et al. 1979, McCord and Cardoza 1982, Anderson 1987). Males overlap several female home ranges and there is often partial overlap of male-male home ranges (Litvaitis et al. 1987, Koehler and Hornocker 1989, Nielsen and Woolf 2001). However, there are many exceptions to these generalizations, as some studies have reported high female-female overlap (Zezulak and Schwab 1979, Chamberlain and Leopold 2001, Diefenbach et al. 2006) and males excluding all other males from territories (Zezulak and Schwab 1979). For example, Apps (1996) reported nearly 100% overlap of same sex bobcats during winter, although these individuals were temporally separated. Such patterns of space use and home range overlap are strongly influenced by genetic relatedness of individuals, with more closely related individuals more likely to share overlapping territories 65 and core areas, particularly in the case of resident female bobcats (Janečka et al. 2006, Kapfer 2012). Bobcat population density and spatial organization play a key role in bobcat reproductive success, which is associated with habitat selection and use, prey availability, and maintenance of exclusive core home range areas (Benson et al. 2006, Diefenbach et al. 2006, Janečka et al. 2006). Bobcat populations tend to have a 1:1 sex ratio (Larivière and Walton 1997). However, female bobcat home range size is inversely related to habitat productivity (Ferguson et al. 2009), as females receive no assistance from males in raising young and consequently are limited by prey abundance and distribution within the landscape (Ferguson et al. 2009). Male home range size relates to multiple factors including access to multiple females (Anderson 1988, Sandell 1989), distribution of other males in the area (Lovallo and Anderson 1996, Benson et al. 2004), and prey resources (Litvaitis et al. 1987), which explain the larger home ranges of males as compared to females. Bobcat home ranges in northwest Montana may be situated to avoid deep snows and to maximize ability to move in winter; therefore, bobcats may chose home ranges in areas that have high road densities, if bobcats use roads as travel corridors. If this scenario does facilitate bobcat movement, bobcat home ranges should be situated in areas of high road density as compared to the broader study area, and should show differential habitat selection across seasons. Furthermore, an investigation of bobcat home range size based on latitude can further inform our understanding of the broad behavioral plasticity that this species exhibits. Investigation of these important behavioral adaptations should be examined in northwest Montana, to determine if certain habitat types and anthropogenic features facilitate bobcat populations in an area with severe, and often prolonged, winters. My main objectives with this chapter are to test three main hypotheses: (1) bobcat home range size depends on season, (2) bobcat habitat use depends on season, and (3) bobcat annual home range size depends on latitude. I predict that during winter in northwest Montana, home range size will contract as bobcat movements are limited by snow and suitable winter habitat. Furthermore, I predict that bobcats will use certain habitat types preferentially during winter in northwest Montana as these habitats provide refugia from cold temperatures and deep snow, and provide a better prey base than other habitats (e.g. habitat use will shift preferentially to 66 spruce-fir (Picea engelmannii/Abies lasiocarpa) habitat types). Lastly, I predict that bobcats in this study will have home ranges most similar to bobcats in northern latitudes and will be larger than bobcat home ranges in southern locations. 4.2. STUDY AREA Bobcats were radiocollared in a study site nested within a larger region; the study site and larger area are of similar habitat composition. Region 1 (R1) is a state wildlife management district under the jurisdiction of Montana Fish, Wildlife, and Parks in northwest Montana (Figure 4.1), covering 34,540.5 km 2 . However, modeling for bobcat resource selection was done on a smaller area within R1, which is more representative of the area where GPS data for bobcats was obtained. This area will be referred to as the Northwest area (NW); the NW is 8989.5 km 2 , which represents ~26% of R1. The NW area has a road density of 1.36 km -1 . The NW is bounded by British Columbia, Canada on the north, Idaho on the west, Hwy 2 on the south, and Hwy 93 on the east. The specific study area of Tally Lake Ranger District (TLRD) bounded by the NW area totals 1136.9 km 2 , and represents ~13% of the NW, and just over 3% of R1. TLRD has a road density of 1.22 km -1 . The areas were classified into 13 habitat types using MSDI Landcover 2010 (Table 4.1) Bobcat GPS locations were collected on the Tally Lake Ranger District (TLRD) of the Flathead National Forest and Rexford and Libby Ranger Districts of the Kootenai National Forest, Montana, USA (48°30´0˝N, 114°45´0˝W). The majority of fieldwork took place on TLRD, and I will collectively refer to the broader area bobcats used as TLRD for sake of brevity. Elevations throughout the TLRD range from 945 m to 2008 m. Average annual temperatures range from -42 to 38° C and mean annual rainfall is 58 cm at 975 m in Olney, Montana, on the northeast edge of the TLRD. Winter temperatures range from -42 to 7° C, and annual snowfall typically exceeds 300 cm at mid-elevations (>1300 m) and can exceed 700 cm at elevations >2000 m (National Oceanic and Atmospheric Administration 2013). Forested areas of TLRD are dominated by moist, coniferous forests composed of western larch (Larix occidentalis), lodgepole pine (Pinus contorta), Douglas fir (Pseudotsuga menziesii), subalpine fir (Abies lasiocarpa), and Engelmann spruce (Picea engelmannii). Lower elevations are primarily composed of older, multi-layered forests of western larch, 67 Douglas fir, and lodgepole pine. Ponderosa pine (Pinus ponderosa), western red cedar (Thuja plicata)/western hemlock (Tsuga heterophylla), grand fir (Abies grandis), and whitebark pine (Pinus albicaulis)/subalpine larch (Larix lyallii) communities are also found on TLRD. However, these forest associations compose small proportions of the district. In general, old growth forest occurs along riparian strips in upland areas or in small patches throughout the district. Fire, insects, and disease are the predominant natural disturbances in this area; however, increasing human use and development are also dominant forces in shaping this landscape. The forested uplands in this ranger district are highly productive, and have been heavily harvested (Flathead National Forest 2006). 4.3. METHODS 4.3.1. BOBCAT CAPTURE AND HANDLING Field work took place between January 2008 and April 2011; for a full break down of field and trapping effort please see Appendix B. Trap sites were situated on TLRD based on presence of recent bobcat sign and distributed across the north end of the main study area to overlap with long-term snowshoe hare mark-recapture sites (Figure 4.2, Hodges and Mills, ongoing research). I trapped bobcats using commercially available box traps and homemade box traps modified from Kolbe et al. (2003) and Washington Department of Game and Fish employees for Canada lynx (Lynx canadensis). Each trap set was baited with roadkilled deer, scented with commercially available cat lures, and visually lured with grouse feathers or blank CDs that were hung from fishing line on a swivel. Captured animals were immobilized with Telazol® at 5 mg/kg estimated body mass administered intramuscularly with an extendable pole-syringe. Telazol® does not require the administration of a protagonist drug, and both immobilization (<5 minutes on average) and complete recovery (2-3 hours from injection) were quick. Anesthetized individuals were sexed, weighed, eartagged (National Band and Tag Co., 1005-4 Monel, sequential numbering), a DNA sample obtained (blood and hair), and aged (kitten: <1 year, juvenile: 1- 2 years, adults: >2 years) based on skeletal measurements and tooth wear. Vital rates (temperature, respiration, and pulse) of immobilized animals were closely monitored to ensure the well-being of the individual. Individual bobcats weighing >4kg were fitted with a Lotek GPS_3300SL (Lotek Eng. 68 Inc., Newmarket, ON) or Sirtrack Model TGC181 GPS/VHF satellite telemetry collar, with locations taken every 3 h. All animals were released at the point of capture. These methods adhered to a strict protocol for trapping and handling following guidelines set forth by the American Society of Mammalogists’ Animal Care and Use Committee (Sikes et al. 2011), Montana State Fish, Wildlife, and Parks permits (2009-059, 2010-002, 2011-003), and the University of British Columbia’s Animal Care Committee (A07-0676-R001). 4.3.2. GIS BASEMAP DEVELOPMENT A basemap of TLRD, NW area, and R1 of Montana was constructed in ArcMap 10.0 and 10.1 using publicly available GIS layers through the Montana Geographic Information Clearinghouse and the USDA Forest Service Northern Region Geospatial Library. All pertinent layers were clipped to R1, NW area, and TLRD as needed. Relevant layers included MSDI Land Cover 2010, roads (major highways, secondary roads, and forest roads), digital elevation model (DEM) for Region 1 with 30-m resolution, snow persistence through May 15 annually, Canada lynx habitat for Forest Service lands, Canada lynx linkage zones, land ownership, fire history, and watershed information (Appendix C). Land cover types were consolidated to 13 biologically relevant categories for bobcats in northwest Montana, and percent of each habitat type in TLRD, NW, and R1 calculated. 4.3.3. SEASONAL RESOURCE SELECTION AND HOME RANGES GPS radiocollar locations from individual bobcats were imported to ArcMap 10.x, where elevation (based on DEM, see Appendix D) and habitat type (based on consolidated land cover layer) were determined for each location using the XTools Pro Identity function. I calculated proportion of total locations in each habitat type per season for each bobcat in Excel (2010). Seasons were defined as winter (December 1-February 28), spring (March 1- May 31), summer (June 1-August 31), and fall (September 1-November 30). These time frames were chosen as the months defined as winter reflect the coldest temperatures and greatest snowfall, spring reflected breeding and birth of kittens (note that snow persisted in most areas well into April, and in some areas through mid-May), summer represented the snow free and kitten rearing season, and fall represented cooling temperatures and initial snowfall. 69 Location files for individual bobcats were imported into Biotas™ 2.0a (Ecological Software Solutions, LLC 2012) for construction of fixed kernel home range estimates. Aside from delimiting home range size and boundaries, kernel home range estimators provide a probability density function (Silverman 1986), and reflects where the individual is most likely to occur within the boundaries of its home range (Kernohan et al. 2001, Marzluff et al. 2004). I constructed annual 95% and 50% (core) fixed kernel home ranges for each individual. Seasonal 95% fixed kernel home ranges were built for each individual with ≥ 30 locations per season. Kernels were constructed using a window width specified as ‘Specific Functions (ad hoc)’ and options for optimal edge length and automatic range estimation. All home range shapefiles were imported into ArcGIS 10.x and area of each habitat type, road density, and snow persistence was extracted for individual and seasonal home ranges. The proportion of each habitat type in each home range was calculated in Excel (2010). 4.3.4. LATITUDINAL COMPARISONS I conducted a literature review to extract information on male and female bobcat home range size by 5° latitudinal bands (Table 4.6). Latitudinal bands began at 25°N and went to 50°N, for 5 intervals. Five studies were selected for each band, with every attempt to select evenly from across the North American continent, west to east. For each band, I calculated an average home range size for that latitudinal band, using the number of individual males and females in each study to weight home range estimates. The type of home range estimator used depends on the type of data and the objectives of the study. Study methods varied widely in home range estimator used, and I calculated averages of averages; however, I believe that this approach can be informative to evaluate general patterns of home range size depending on latitude (Appendix E). 4.3.5. STATISTICAL ANALYSES Elevation and proportional habitat use of individual locations were calculated in Excel (2010). Seasonal home range composition was also calculated in Excel (2010) for each individual with a suitable number (≥30) of seasonal locations. Differences in seasonal home range size were tested using ANOVA (PopTools extension in Excel 2010) for all bobcats pooled. Next, seasonal home range composition was compared between male and 70 female bobcats to test for sex differences in habitat selection across seasons using MANOVA in SPSS 21.0. I also used MANOVA to test for hierarchical habitat selection where (1) home range composition was compared to study site composition (2 nd order habitat selection) and (2) where the proportion of an individual’s seasonal locations in each habitat type were compared to proportion of habitat type available to that individual in seasonal home ranges (3 rd order habitat selection). An α ≤ 0.05 was accepted as a statistically significant result for all analyses. 4.4. RESULTS 4.4.1. BOBCAT COLLAR DATA Eight bobcats (6M, 2F) were radiocollared. One female bobcat was killed by coyotes a few days after collaring; no collar data was collected for this individual. I did not recover collars on two males; hence, collar data is based on 4M, 1F. The female bobcat had sufficient data for home range construction in all seasons, with >30 locations per season (Appendix D, Table D.2). One male bobcat only had locations for spring before his radiocollar failed. Two male bobcats had sufficient data for all seasons, and one male bobcat had sufficient data for all seasons except winter. The female (F2) was collared from 19-Feb-2010 to 7-Dec-2010. M1 was collared from 12-Dec-2009 to 16-Dec-2010; M2 was collared from 21-January-2010 to 21-December- 2010; M3 was collared from 10-March-2010 to 21-January-2011; M4 was collared from 14- March-2010 to 22-December-2010 (though his collar failed in mid-summer). All of these collars failed before recovery. Lastly, M5 was collared 26-March-2010 and M6 was collared 6-April-2010. Neither collar was recovered. See Table D.2 for a complete breakdown of points per season for individual bobcats. A total of 7,664 GPS locations were collected from radiocollared bobcats, with the highest proportion of locations occurring in lodgepole (19.0%), wet spruce-fir (15.0%), ponderosa (13.8%), and wet site mixed coniferous stands (12.7%) (Table 4.1 and Figure 4.3). Of 13 habitat types available across the study area, the 4 habitats above constituted 60.5% of all bobcat locations (Figure 4.3). 71 4.4.2. SEASONAL HOME RANGES Seasonal home ranges did not change size significantly for northwest Montana bobcats (F3,12 = 0.10, p = 0.957); thus bobcats did not reduce winter home range size as compared to home range size in other seasons. For all bobcats combined, winter home ranges (N = 3) were 65.3 ± 37.5 km 2 , spring home ranges (N = 5) were 74.2 ± 16.7 km 2 , summer home range (N = 4) were 81.3 ± 13.9 km 2 , and fall home ranges (N = 4) were 71.9 ± 10.9 km 2 . The 95% fixed kernel annual home range for the female bobcat was 42.2 km 2 . The core (50%) annual home range for the female bobcat was 9.8 km 2 (Table 4.2) Her seasonal home range size for 95% fixed kernel estimates were 20.3 km 2 winter, 47.2 km 2 spring, 48.4 km 2 summer, and 47.7 km 2 fall. The female’s winter home range was constructed from 48 locations. These locations were collected in a 9-day period; although the number of locations is acceptable for home range construction (White and Garrott 1990), I believe that the timeframe was not long enough to allow her to use her entire home range, as winter movements of bobcats are greatly reduced from other seasons (Chapter 5). The average 95% fixed kernel annual home range for male bobcats was 90.0 ± 12.0 km 2 . The core (50%) annual home range for male bobcats was 21.9 ± 5.8 km 2 (Table 4.2). Winter 95% fixed kernel home ranges could be constructed for only two male bobcats; these home ranges were 35.9 km 2 and 140.0 km 2 , with an average of 87.9 ± 51.9 km 2 . The smaller home range belonged to a juvenile male, who was ~8 months old at the time of collaring. The relatively small size of the juvenile’s home range during winter is very similar to that of the adult collared female in this study, and may indicate that the male kitten was not yet independent of his mother, an unknown, uncollared individual, and was likely captured within her home range. Male spring 95% home ranges (N=4) were 80.9 ± 19.7 km 2 , summer 95% home ranges (N=3) were 92.3 ± 11.9 km 2 , and fall 95% home ranges (N=3) were 80.0 ± 10.4 km 2 . 4.4.3 COMPARISON OF MALE AND FEMALE HOME RANGE COMPOSITION Male bobcats did not differ significantly from the female bobcat in composition of home ranges (F8,1 = 122.57, p = 0.070). Seasonal differences in home range composition 72 were not significant (F24,9 = 0.65, p = 0.810), and the interaction of sex*season was not significant (F24,9 = 0.72, p = 0.756). However, univariate tests did show that male home ranges contained greater proportions of dry site Engelmann spruce-subalpine fir stands, grassland habitats, Ponderosa stands, shrub habitats, and wet site Engelmann spruce- subalpine fir habitats, while the female’s home range contained greater proportions of lodgepole stands, wet site mixed conifer species stands, and riparian habitats than did the males’ home ranges (Table 4.3). 4.4.4. HIERARCHICAL HABITAT SELECTION Road density did not differ among bobcat home ranges, TLRD, and NW area (F2,21 = 0.586, p = 0.565); therefore, bobcats were not selecting home ranges that had a higher or lower density of roads than was available across the general study area and the broader landscape. The female’s seasonal 95% home ranges had a road density of 1.41 km-1 winter, 1.28 km -1 spring, 1.87 km -1 summer, and 1.09 km -1 fall. Male seasonal 95% home ranges had a road density of 0.90 ± 0.44 km -1 winter, 1.63 ± 0.20 km -1 spring, 1.47 ± 0.40 km -1 summer, and 1.62 ± 0.19 km -1 fall. The female bobcat’s winter home range was composed of <1% area where winter snows persisted through May 15, while her spring home range was composed of 1.4%, summer 0.8%, and fall 1.4% of areas that had snow persisting through May 15 annually; however, this bobcat lived at lower elevation than all but one radiocollared male for which data is available. An adult female was captured at higher elevation (~1500 m) in January 2010; however, she was killed by coyotes before sufficient data could be collected to build a winter home range for her. Another adult female was captured at higher elevations (~1450 m) in January 2011 when radiocollars were being recovered. Amongst the males that lived at higher elevations (M1, M2, M3), winter home ranges were composed of 15.9 ± 7.8% areas where snows persisted to May 15 annually, 24.6 ± 0.9% spring, 30.5 ± 5.2% summer, and 28.5 ± 6.1% areas with snows persisting through May 15 (Figure 4.4 and 4.5). Areas of snow persisting through May 15 total 102.0 km 2 , which is only 9.0% of the total area in TLRD. If bobcats were avoiding areas of deep, persistent winter snows, bobcats would not be found in higher areas of the study site, particularly in 73 winter. Bobcats did not demonstrate biologically significant differences in elevation of locations by season (Appendix D). Habitat selection (Johnson 1980) differed significantly for both 2 nd order (home range to study area) and for 3 rd order habitat selection (GPS locations to home range) (F26,26 = 10.267, p < 0.001), but did not differ across seasons (F39,42 = 0.490, p = 0.987) or for the interaction of 2 nd and 3 rd order selection*season (F78, 102 = 0.586, p = 0.993). Specifically hierarchical habitat selection differed in burned habitats, lodgepole stands, dry site mixed species coniferous stands, wetland areas, and other habitat types (Table 4.4). Additionally, harvested areas showed marginal significant differences, as did use of shrub habitats (Table 4.4). Specifically, bobcat home ranges had significantly lower proportions of burned areas, harvested areas, dry site mixed coniferous species stands, and other habitat types than were available across TLRD. Home ranges were composed of lower proportions of wetland habitats than were available to bobcats on TLRD. Additionally, home ranges were composed of significantly greater proportions of lodgepole pine habitats than were available on TLRD. Home ranges were composed of greater proportions of Ponderosa pine habitats and shrub habitats than were available on TLRD. All other habitats composed home ranges as expected compared to availability across the study area (Table 4.5). Individual locations within home ranges showed that lodegpole pine stands were used significantly less than was available within home ranges, but to a greater extent than was widely available across TLRD. Wetland habitats were used significantly less than were available in home ranges. Additionally, shrub habitats were used somewhat less than was available within home ranges. Burned areas were used somewhat less than available within home ranges. All other habitats within home ranges were used according to availability (Table 4.5). 4.4.5. LATITUDINAL COMPARISONS Male and female bobcats in the most northern latitudes (45-50°N) had the largest home ranges (Table 4.7). Home ranges for both sexes were consistently smaller in each interval as latitude decreased, until 25-30°N was reached. Bobcats at 25-30°N had larger home ranges than did individuals living at 30-35°N, possibly indicating that bobcats at the 74 southern periphery of their geographic distribution require larger home ranges than do bobcats in ‘core’ latitudes, if habitat productivity is lower in these more arid environments. Additionally, female bobcats at 25-30°N also had larger home ranges than did females at 35- 40°N. Male bobcats at 45-50°N had home ranges 174% larger than males at 40-45°N, while females 45-50°N had home ranges 159% larger than females at 40-45°N. Males always had larger average home ranges than did females in the same latitudinal band. Within the most northern band, male home ranges were 277% larger than females’ ranges. 4.5. DISCUSSION Bobcats on TLRD do not appear to avoid deep snow or be limited in home range or habitat use during winter; instead, they maintained their home range and habitat use throughout the year. First, bobcats on my study area did not change home range size on a seasonal basis. Winter home ranges were similar in size to other seasonal home ranges, and of three bobcats for which winter data is available, one of the male bobcats maintained a larger range in winter than he did in any other season. Second, bobcats in this study did not shift habitat use during winter months to use relatively snow-free habitats, as none were available; all habitats were used in a similar manner throughout the year by resident bobcats. Last, bobcat home range size is strongly tied to latitude, with both male and female bobcats having much larger home ranges at the northern extent of their geographic distribution than in more southerly latitudes. Thus, if my results are typical of individual bobcats in northwest Montana, bobcats in this region likely approach winter with different behavioral strategies for coping with harsh conditions than has been demonstrated by bobcats in other regions, which shifted habitat use in winter to avoid deep snow areas. 4.5.1. HOME RANGES Bobcats in northern latitudes near the edge of their geographic distribution have far larger home ranges than do bobcats in the core area of their distribution. The difference is drastic; male bobcats at 45-50°N have home ranges that are over 1000% (10×) larger than males at 30-35°N. Females differ between these latitudes by 565%. Bobcats towards the southern periphery of their distribution show an increase in home range size as compared to 75 bobcats in core areas, possibly due to decreased habitat productivity at the southern periphery as compared to the core distribution. Bobcats at the northern extent of the species distribution have home ranges that are 574% and 271% larger compared to individuals at the southern periphery (25-30°N) of their distribution, for males and females respectively. Large home ranges at the northern periphery of bobcat distribution are likely a consequence of the extreme winter conditions and lower habitat productivity that these individuals encounter. Female bobcat home range size is inversely related to environmental productivity, while male home range size scaled isometrically to female home range size (Ferguson et al. 2009). Females require a greater area in northern latitudes in order to satisfy their caloric needs due to lower habitat productivity. Males maintain large home ranges for the same reasons, but attempt to overlap as many females as possible; however, home range size of males is constrained by the area a male can successfully defend from other males. Home ranges for bobcats in my study are among the largest reported. They are most similar to estimates for other northern populations, e.g. bobcats in western and central Montana (Brainerd 1985, Knowles 1985), southeastern British Columbia (Apps 1996), and Maine (Litvaitis 1986b,1987). In addition to occurring at similar latitudes, these studies, with the exception of Knowles (1985) occurred in mountainous terrain, characterized by long winters with deep snow. Indeed, Brainerd’s (1985) study site is ~200 km south, while App’s (1996) study site is ~200 km north of TLRD. Bobcats in mountainous areas may occupy larger home ranges in response to low prey densities (Litvaitis et al. 1986b, Riley et al. 2003). Indeed snowshoe hare (Lepus americanus) densities < 0.5 hares/ha partially explained large home range sizes for Maine bobcats (Litvaitis et al. 1986b). Hare densities from 2001-2009 across 13 mark-recapture sites on TLRD were 0.77 ± 0.07 hares/ha (Range: 0-3.17) (Hodges and Mills, unpublished data); however, hare densities may not have a large influence on bobcat home range sizes on TLRD, as hares were only 12.2% biomass of bobcat winter diet in the Salish Mountains of Montana (Chapter 2). Other studies have reported that bobcats used lower elevations and greatly reduced home range size in winter (¼ the size of summer ranges), due to restricted movements and access to areas during winter (Koehler and Hornocker 1989). Bobcats in my study did not constrict home range size in the winter, nor did they shift seasonal home range locations 76 spatially or use different habitats seasonally to compensate for winter conditions (Figure 4.5). Litvaitis et al. (1986b) suggest that large home ranges in Maine bobcats are partial compensation for restricted access to mountainous portions of individual home ranges in deep winter snows; however, bobcats on TLRD used all areas and habitats within their home ranges over the course of the winter. Though bobcats did use slightly lower elevations in winter (Appendix D), the change in elevations used between winter and summer was only 8 m, which does not make a large difference in areas receiving over 300 cm of snow every winter. Even though daily movement distances were reduced in winter (Chapter 5), bobcats continued to use their entire home range in deep winter snows. Deep snow environments are costly from an energetic perspective for bobcats, yet radiocollared bobcats on TLRD continue to use their entire home range over the course of the winter without reducing home range size. Bobcats have greater energetic costs moving through deep soft snow than do lynx, due to the high foot loading of a bobcat relative to a lynx (Buskirk et al. 2000a). However, TLRD and the Salish Mountains are characterized by dramatic fluctuations in freezing and thawing over the course of the winter, which leads to marked changes in snow conditions, sometimes on a daily basis. There is noticeable crusting and compaction of snow layers, such that snow becomes hard enough for humans to walk on without the use of snowshoes. Such snow conditions likely enable bobcats to make regular use of their home ranges as costs of winter movement are reduced due to hard, crusted snow. I suggest that reduced movements are a behavioral strategy to reduce energetic costs throughout winter, both due to thermoregulatory requirements (Chapter 3) and reduced prey availability. If bobcats reduce movements during deep soft snow periods, and take advantage of snow crusting and compaction events, travel costs would be greatly reduced for these individuals. Furthermore, there is strong evidence that coyotes (Canis latrans) preferentially travel on areas of harder snow than are found at random within their home ranges (Kolbe et al.2007). It is possible that bobcats engage in a similar movement strategy, including taking advantage of snowmobile trails (personal observation); this use of more compacted snow is an avenue for future investigation regarding bobcat behavioral adaptation to deep snow conditions. 77 4.5.2. HABITAT SELECTION Bobcats on TLRD exhibited 2 nd order habitat selection across the study site, but habitat selection did not vary seasonally. Bobcat home ranges were composed of significantly greater proportions of lodgepole pine habitats than were available on TLRD, and there was weak selection towards favoring Ponderosa pine and shrub habitats within home ranges. These habitat types likely reflected prey density and accessibility for bobcats. Conversely, bobcats chose home range with less dry site mixed coniferous species stands and other habitats (mostly anthropogenic structures and gravel pits) than were available across TLRD. Additionally, bobcats chose home ranges that avoided open habitats with little cover, such as burned and harvested areas. Avoidance of open areas is interesting because winter timber harvest was ongoing in the northern section of TLRD during the course of this study, and during Summer 2007, 30,000 acres of TLRD burned in a lightning caused wildfire. This burn area was open and exposed in winter and was salvage logged during 2008-2010. During winter surveys through this area, I found only the tracks of coyotes and wolves (Canis lupus). Bobcats rarely used these open areas, although part of this avoidance may have been in response to human disturbance in the area, i.e. active logging operations. Avoidance of open areas and dry site coniferous stands may reflect behavioral choices aimed at reducing travel and thermoregulatory costs in winter (McNab 2000) Additionally, avoidance of these areas could reflect lower preferred prey availabilities (Harestad and Bunnell 1979, Litvaitis et al. 1986b) and behavioral strategies that reduce vulnerability to potential predators (Wilson 2010) in open areas lacking cover. Bobcats often escape threats by climbing trees, and burned and harvested areas have few climbable trees, while dry site coniferous stands typically have little horizontal cover in the form of understory shrub species, which serve dual functions of concealment from predators and prey (Maletzke et al. 2008, Squires et al. 2008, Vashon et al. 2008). Bobcats in my study did not exhibit strong 3 rd order habitat selection, which compares actual locations to proportion of habitat type within the individual’s home range. Lodgepole pine habitats were used somewhat less than were available to bobcats within their home range, but actual bobcat locations were located in lodgepole habitats in accordance with their availability across the study site. Wetland habitats were generally avoided within the home 78 range. All other habitat types were used as available within a bobcat’s home range. Other studies have shown that bobcats exhibit strong seasonal changes in habitat selection (Fuller et al. 1985, Litvaitis et al. 1986b, Koehler and Hornocker 1989), as do Canada lynx (Squires et al. 2010, Vashon et al. 2008). Specifically, in these other studies, bobcats switched habitat use to areas that were snow free and offered more numerous or vulnerable prey. However, I did not detect significant differences in habitat composition of home ranges between seasons, though I have a small sample size that impacted my ability to detect differences in seasonal habitat use. Other than an avoidance of more open habitats, and a preference for lodgepole pine stands, bobcats on TLRD situated and used home ranges proportional to availability throughout the year. Bobcats likely view the landscape on a coarse-grained scale (Chapter 5) due to large perceptual range and high mobility, and therefore 2 nd order habitat selection was made at this larger scale. Bobcats exhibited weaker 3 rd order habitat selection; it is entirely possible that they select habitats on a fine scale with respect to biotic and abiotic variables at scales that were not measured in this study. On TLRD, bobcats did not have snow-free habitats available during winter, and showed no avoidance of habitat type due to snow. Bobcat success in this specific area likely depends on behavioral decisions at fine-scales (e.g. choosing areas of compacted snow for travel) and the capability of this generalist predator to fulfill its energetic needs on prey <1 kg (Chapter 2), while covering a large home range in difficult winter conditions. 4.5.3. SAMPLE SIZE CONSIDERATIONS As is common in other studies of Lynx spp., I captured more males than females (Table 4.6 for other bobcat studies). Data presented in my study are based on 4 males and 1 female bobcat. Fourteen of 25 studies (56%) in Table 4.6 present data from 5 or fewer males, while 2 of the studies (8%) present data from 1 male. Sixteen of 25 (64%) studies present data from 5 or fewer females, with 4 of the studies (16%) presenting data from 1 female. Although low sample size is common in studies of highly vagile carnivores such as the bobcat, the data I present and the reasoned conclusions I draw from analyses of bobcat radiocollar data should be considered in light of number of individuals monitored, particularly with respect to conclusions drawn from the female bobcat. 79 Sample size and high individual variation for bobcats in my study does lead to low statistical power to detect differences in home range size and overall habitat selection. There is the chance that the bobcats captured in this study display different preferences in habitats used than the broader bobcat population in this area; however, individual bobcats demonstrated behaviors similar to each other with respect to habitats used and movement patterns (Chapter 5). Individual bobcats did vary in habitat selection from one another, and individual bobcats did display variation in habitat selection and home range size across seasons. Only a larger sample size would allow me to evaluate the range of variation typical of bobcats in this region. However, there is no reason to suspect that these individuals, both males and female, are not representative of other bobcats in the region. Specifically, with results based on one female bobcat, home range size reported for females could be biased high if this specific female had an unusually large home range. Conversely, this female could have had a very small home range compared to other females. In either scenario, this female had a much smaller home range than did male bobcats in my study. I have no independent reason to suspect that this female was abnormal in her home range and habitat selection behavior. This female’s home range is very similar to home ranges reported for female bobcats in studies that were ~200 km north and south (Apps 1996, Brainerd 1985 respectively) of my study area. This female was an adult, resident animal, and is likely to be representative of reproductively active, dominant female bobcats in northwest Montana. The same caveats apply to the male bobcats in this study. Two male bobcats were dominant, adult males (M1 and M3) and had larger home ranges than the younger males (M2, a juvenile and M4, young adult) (Table 4.2). I did not recover radio collars from M5, a young adult male, and M6, a dominant, adult male. Sex, age, and dominance status do affect home range size of animals, and these differences should be kept in mind, as all males were pooled across age classes. Average home range size (for all males with recovered collars) were comparable to home ranges reported for other northern latitude male bobcats. The males in my study are likely to be representative of other males in this population given the reasons explained for the female bobcat. 80 4.6. IMPLICATIONS OF BOBCAT SEASONAL HOME RANGES AND HABITAT SELECTION Bobcats on TLRD did not shift seasonal home range locations to lower elevations nor did home range size contract in winter. Annual home ranges on TLRD were among the largest reported for bobcats in North America, but were comparable to other bobcat populations living in northern latitudes with significant winter snows. Bobcats in northern latitudes have home ranges that are 500-1000% larger than bobcat home ranges in more southern locations. Winter home ranges were as large as, or larger than, other seasonal home ranges on TLRD, and if my results are typical of the broader population of northwest Montana, it is reasonable to assume that other bobcats in the region do not contract home range size in winter. On TLRD, bobcats moved shorter daily distances in winter (Chapter 5), but still maintained large home ranges, likely due to low prey densities, prey accessibility, and high energetic costs of moving in deep snow; therefore, bobcats traversed their entire home range fewer times in winter than in summer. Reduced daily movements may be a behavioral modification employed by bobcats to reduce overwinter energetic expenditures in deep snows and cold temperatures. Bobcats in my study did show coarse-grain habitat selection at the level of home ranges, with general avoidance of open habitats, and strong selection for lodgepole pine stands, but typically used habitats as they were available across the landscape and within home ranges. Thus it is likely that bobcats in northwest Montana do not select different habitats seasonally in order to make use of snow-free areas, as there are often no snow-free habitat available to these individuals. 81 Table 4.1. Habitat composition of Region 1, Northwest Modeling area (NW), and Tally Lake Ranger District (TLRD) in northwest Montana, as compared to the percent of bobcat GPS locations (N = 7,664) in each habitat type. R1 covers 34,000 km 2 , NW covers nearly 9,000 km 2 , and TLRD covers just over 1100 km 2 . Each particular area differs in compositional ranking of habitat types, though the differences between areas (with TLRD nested in NW, which is nested in R1), are not significant (Gadj = 25.80, df = 24, P = 0.36). Note that R1 has much higher proportions of other habitats, which include towns, agricultural areas, and other human developments such as mining areas and gravel pits. Percent land area in habitat type Percent bobcat R1 NW TLRD GPS locations MAJOR HABITAT TYPE Mixed dry 20.8 24.0 25.4 12.1 Mixed wet 15.8 17.6 17.7 12.7 Harvest 4.9 9.6 14.1 10.6 Lodgepole 3.8 6.1 11.3 19.0 Wet Spruce-fir 11.6 13.7 10.9 15.0 Dry Spruce-fir 13.6 7.4 4.9 9.3 Ponderosa 1.8 5.0 4.8 13.8 Other a 12.0 4.1 3.3 1.2 Grassland 10.2 5.9 2.4 2.4 Burn 1.6 2.6 2.2 0.0 Riparian 1.6 1.0 1.3 2.3 Wetland 1.2 1.0 1.0 0.5 Shrub 1.2 1.2 0.4 1.1 a Other habitat classification consists of agricultural areas, alpine habitats, aspen, mixed aspen-conifer stands, Douglas fir, Great Plains Saline wetland habitat types, areas developed by people (towns), mining areas, non- native vegetation types, exposed rock, sage steppe habitats, subalpine habitats, and open water. 82 Table 4.2. Annual and seasonal home range size for bobcats on TLRD. All home ranges are shown in km 2 for fixed kernel estimates of home range size. Seasonal home range estimates are for 95% fixed kernel estimates for individuals with ≥30 locations in that season. Radiocollars for M5 and M6 were not recovered. Age is A = adult, J = juvenile. UNK denotes information that is unknown. Bobcat Total N Age Annual 95 Annual 50 Winter Spring Summer Fall ID locations F2 1,599 A 42.2 9.8 20.3 (1.9) 47.2 (10.6) 48.4 (6.6) 47.7 (14.6) M1 2,123 A 123.6 27.0 139.8 (20.9) 101.6 (17.1) 101.2 (28.1) 100.7 (23.2) M2 1,946 J 72.2 11.5 35.9(2.6) 29.4 (4.0) 107.1 (17.6) 71.8 (7.6) M3 1,501 A 91.1 35.9 N/A 120.0 (20.6) 68.7 (39.8) 67.7 (13.3) M4 495 A 72.6 13.0 N/A 72.6 (13.2) N/A N/A M5 X A UNK UNK UNK UNK UNK UNK M6 X A UNK UNK UNK UNK UNK UNK 83 Table 4.3. MANOVA and univariate ANOVAs for the effects of sex and season on home range composition of male and female bobcats in northwest Montana. The effect of sex and season were marginally significant with respect to bobcats throughout the year; male and female bobcats displayed differences in habitat selection on a seasonal basis. Degrees of freedom (df) for sex are df = 1, 8, for season df = 3, 8. Bold text refers to significant results at p < 0.05. Source d.f. F P (a) Multivariate analysis Sex 8,1 122.57 0.070 Season 24,9 0.65 0.810 Sex*Season 24,9 0.72 0.756 Source SS F P (b) Univariate analyses Burn Sex 0.05 0.96 0.357 Season 0.05 0.32 0.813 Error 0.38 Dry Spruce fir Sex 83.35 7.92 0.023 Season 0.62 0.02 0.996 Error 84.19 Grassland Sex 10.47 6.23 0.037 Season 0.23 0.05 0.986 Error 13.44 Harvest Sex 2.85 0.96 0.355 Season 1.14 0.13 0.940 Error 23.67 Lodgepole Sex 98.67 6.68 0.032 Season 48.31 1.09 0.407 Error 118.11 Mixed dry Sex 100.10 3.26 0.108 Season 43.01 0.47 0.713 Error 245.36 Mixed wet Sex 736.30 39.14 <0.001 Season 31.54 0.56 0.657 Error 150.48 Ponderosa Sex 570.47 46.32 <0.001 Season 15.52 0.42 0.744 Error 98.54 Riparian Sex 123.79 801.83 <0.001 Season 0.75 1.63 0.259 Error 1.24 Shrub Sex 13.99 11.37 0.010 Season 0.22 0.06 0.980 Error 9.85 Wetland Sex 0.02 0.19 0.676 Season 0.14 0.37 0.779 Error 1.00 84 Table 4.3 continued. MANOVA and univariate ANOVAs for the effects of sex and season on home range composition of male and female bobcats in northwest Montana. The effect of sex and season were marginally significant with respect to bobcats throughout the year; male and female bobcats displayed differences in habitat selection on a seasonal basis. Degrees of freedom (df) for sex are df = 1, 8, for season df = 3, 8. Bold text refers to significant results at p < 0.05. Source SS F P (b) Univariate analyses Wet spruce fir Sex 264.85 17.60 0.003 Season 40.85 0.91 0.480 Error 120.40 Other Sex 0.01 0.00 0.949 Season 0.16 0.05 0.984 Error 8.88 85 Table 4.4. MANOVA and univariate ANOVAs for the effect of 2 nd and 3 rd order habitat selection for bobcat home ranges in northwest Montana. Season did not influence home range composition for bobcats, but bobcats selected habitats differently from what was available within home ranges (3 rd order) and from what was available across the study site (2 nd order). For example, burned areas were used differently than availability. Degrees of freedom are 2,24 for each habitat type in univariate analyses. Bold text refers to significant results at p < 0.05. Source d.f. F P (a) Multivariate analysis 2 nd and 3 rd order 26,26 10.27 <0.001 Season 39, 42 0.49 0.987 2 nd and 3 rd order*Season 78,102 0.59 0.993 Dependent variable Source SS F P Burn Contrast 16.04 390.17 <0.001 Error 0.49 Dry Spruce fir Contrast 53.28 0.86 0.436 Error 744.34 Grassland Contrast 0.91 0.12 0.899 Error 101.64 Harvest Contrast 47.52 2.81 0.080 Error 202.65 Lodgepole Contrast 464.76 5.97 0.008 Error 934.59 Mixed dry Contrast 556.99 9.33 0.001 Error 717.26 Mixed wet Contrast 102.90 0.42 0.660 Error 2922.11 Ponderosa Contrast 233.22 1.61 0.220 Error 1736.46 Riparian Contrast 29.72 0.59 0.563 Error 605.25 Shrub Contrast 14.04 2.54 0.100 Error 66.31 Wetland Contrast 1.42 7.36 0.003 Error 2.32 Wet spruce fir Contrast 56.81 0.52 0.600 Error 1305.62 86 Table 4.4 continued. MANOVA and univariate ANOVAs for the effect of 2 nd and 3 rd order habitat selection for bobcat home ranges in northwest Montana. Season did not influence home range composition for bobcats, but bobcats selected habitats differently from what was available within home ranges (3 rd order) and from what was available across the study site (2 nd order). For example, burned areas were used differently than availability. Degrees of freedom are 2,24 for each habitat type in univariate analyses. Bold text refers to significant results at p < 0.05. Other Contrast 23.43 4.89 0.017 Error 57.55 87 Table 4.5 Pairwise comparisons of proportion of habitat type bobcat locations (LOC) were found in, compared to composition of home ranges (HR) and across the study site (TLRD). Habitats that differ significantly in 2 nd order (HR to TLRD) selection are marked with *, while habitats that differ significantly in 3 rd order (LOC to HR) are marked with †. Mean difference (+) or (-) indicates if the composition of interest (LOC, HR, TLRD), is less than or more than for the area being compared. For example, within shrub habitats, HR area shrub habitat proportion is greater compared to proportion of actual bobcat locations (LOC) in shrub areas. This difference is marginally significant, with p = 0.066. The interpretation is that individual bobcat locations occurred less than expected in shrub habitats as compared to what was available to bobcats within their home ranges, indicating avoidance of shrub areas. Bold text refers to significant results at p < 0.05. Habitat Area Compared to Mean Difference SE 95% Confidence Interval P Burn* HR LOC 0.08 0.05 -0.03 – 0.19 0.127 TLRD -2.08 0.08 -2.25 – -1.92 <0.001 LOC HR -0.08 0.05 -0.19 – 0.03 0.127 TLRD -2.17 0.08 -2.33 – -2.00 <0.001 Dry Spruce fir HR LOC -1.24 2.00 -5.37 – 2.89 0.541 TLRD 2.80 3.12 -3.65 – 9.24 0.380 LOC HR 1.24 2.00 -2.89 – 5.37 0.541 TLRD 4.04 3.12 -2.41 – 10.48 0.208 Grassland HR LOC -0.22 0.74 -1.75 – 1.30 0.766 TLRD -0.50 1.15 -2.88 – 1.89 0.672 LOC HR 0.22 0.74 -1.30 – 1.75 0.766 TLRD -0.27 1.15 -2.65 – 2.11 0.816 Harvest* HR LOC 0.34 1.04 -1.81 – 2.50 0.747 TLRD -3.46 1.63 -6.82 – -0.09 0.044 LOC HR -0.34 1.04 -2.50 – 1.81 0.747 TLRD -3.80 1.63 -7.16 – -0.43 0.029 Lodgepole*† HR LOC 4.82 2.24 0.19 – 9.45 0.042 TLRD 11.38 3.50 4.15 – 18.60 0.003 LOC HR -4.82 2.24 -9.45 – -0.19 0.042 TLRD 6.56 3.50 -0.67 – 13.78 0.073 Mixed dry* HR LOC -1.63 1.97 -5.69 – 2.42 0.414 TLRD -13.12 3.07 -19.45 – -6.79 <0.001 LOC HR 1.63 1.97 -2.42 – 5.69 0.414 TLRD -11.49 3.07 -17.82 – -5.16 0.001 88 Table 4.5 continued. Pairwise comparisons of proportion of habitat type bobcat locations (LOC) were found in, compared to composition of home ranges (HR) and across the study site (TLRD). Habitats that differ significantly in 2 nd order (HR to TLRD) selection are marked with *, while habitats that differ significantly in 3 rd order (LOC to HR) are marked with †. Mean difference (+) or (-) indicates if the composition of interest (LOC, HR, TLRD), is less than or more than for the area being compared. For example, within shrub habitats, HR area shrub habitat proportion is greater compared to proportion of actual bobcat locations (LOC) in shrub areas. This difference is marginally significant, with p = 0.066. The interpretation is that individual bobcat locations occurred less than expected in shrub habitats as compared to what was available to bobcats within their home ranges, indicating avoidance of shrub areas. Bold text refers to significant results at p < 0.05. Habitat Area Compared to Mean Difference SE 95% Confidence Interval P Mixed wet HR LOC -1.35 3.97 -9.54 – 6.83 0.736 TLRD -5.68 6.18 -18.45 – 7.09 0.368 LOC HR 1.35 3.97 -6.83 – 9.54 0.736 TLRD -4.33 6.19 -17.10 – 8.45 0.491 Ponderosa HR LOC -1.66 3.06 -7.97 – 4.65 0.591 TLRD 6.90 4.77 -2.95 – 16.75 0.161 LOC HR 1.66 3.06 -4.65 – 7.97 0.591 TLRD 8.56 4.77 -1.28 – 18.41 0.085 Riparian HR LOC -1.52 1.81 -5.24 – 2.21 0.410 TLRD 1.08 2.82 -4.73 – 6.89 0.705 LOC HR 1.52 1.81 -2.21 – 5.24 0.410 TLRD 2.59 2.82 -3.22 – 8.41 0.366 Shrub HR LOC 1.15 0.60 -0.08 – 2.38 0.066 TLRD 1.61 0.93 -0.31 – 3.54 0.097 LOC HR -1.15 0.60 -2.38 – 0.08 0.066 TLRD 0.46 0.93 -1.46 – 2.39 0.626 Wetland† HR LOC 0.27 0.11 0.04 – 0.51 0.022 TLRD -0.35 0.18 -0.71 – 0.01 0.056 LOC HR -0.27 0.11 -0.51 – -0.04 0.022 TLRD -0.62 0.18 -0.98 – -0.26 0.002 Wet Spruce fir HR LOC 1.35 2.65 -4.12 – 6.82 0.615 TLRD 4.15 4.14 -4.39 – 12.68 0.326 LOC HR -1.35 2.65 -6.82 – 4.12 0.615 TLRD 2.79 4.14 -5.74 – 11.33 0.506 Other* HR LOC -0.38 0.56 -1.53 – 0.77 0.505 TLRD -2.70 0.87 -4.49 – -0.91 0.005 LOC HR 0.38 0.56 -0.77 – 1.53 0.505 TLRD -2.32 0.87 -4.12 – -0.53 0.013 89 Table 4.6. Location and details of home range estimates for bobcats in different latitudinal band. IP refers to irregular polygon, MCP to minimum convex polygon (and variant names of such), MMAM is modified minimum area method, OCP is outer convex polygon, and UD is utilization distribution, which also refers to kernel estimates. Where % MCP is not indicated in the study, I simply refer to MCP as the general method. The majority of these studies were compiled in a review by Ferguson et al. 2009, and I would like to acknowledge their work in much of the compilation of information. I could not determine number of male and female bobcats in Brainerd’s (1985) study, so 9 were assigned male and 8 were assigned female for weighting in home range size. Home range size (km 2 ) Number of individuals Latitude Area Male Female Male Female Method Reference 45-50°N SE British Columbia 138.5 55.7 7 5 100MCP Apps 1996 Western Montana 79.0 58.6 (17 total) 100MCP Brainerd 1985 Northwest Montana 90.0 42.2 4 1 95Fixed kernel Newbury 2013 North-central Montana 83.3 17.8 1 1 MMAM Knowles 1985 Northern Wisconsin 65.9 32.2 6 5 95MCP Lovallo and Anderson 1996 East central Maine 167.9 27.5 4 1 95MCP Major 1983 40-45°N Northern California 73.8 42.7 3 4 IP Zezulak 1980 East central Idaho 25.0 20.9 5 11 95MCP Knick 1990 Central South Dakota 30.0 26.7 2 6 OCP Fredrickson and Mack 1995 Northern New York. 143.9 32.5 2 1 95MCP Fox 1990 Northwest Vermont 70.9 22.9 10 4 Fixed kernel UD Donovan et al. 2011 35-40°N West central California 10.5 3.7 7 11 95MCP Riley 2006 South eastern Colorado 75.4 29.3 11 5 95MCP Anderson 1987 North central Kansas 20.0 7.5 1 3 95MCP Kamler and Gipson 2000 Southern Illinois 35.7 11.9 22 30 95MCP Nielsen and Woolf 2001 Western Kentucky 9.0 7.7 2 3 95MCP Painter 1991 30-35°N Southern California 3.0 1.7 13 11 95MCP Riley et al. 2003 South central Arizona 9.1 4.8 2 3 MMAM Lawhead 1984 South central 20.2 12.3 15 27 95MCP Conner et al. 2001 Mississippi South central Georgia 8.2 5.2 7 22 95MCP Cochrane et al. 2006 Eastern coastal 8.1 5.7 2 6 95MCP Griffin 2001 South Carolina 25-30°N Chihuahua Mexico 24.7 27.1 4 4 MCP Elizalde-Arrellano et al. 2012 Gulf Coast Texas 9.9 7.1 8 4 95MCP Mock 2004 Gulf Coast Texas 3.5 1.2 3 1 95MCP Bradley and Fagre 1988 Central Florida 29.0 16.7 3 5 95MCP Wassmer et al. 1988 South central Florida 37.6 14.0 4 4 95MCP Maher 1996 90 Table 4.7. Latitudinal comparison using information in Table 4.6 to calculate an average home range size for each latitudinal gradient, weighted by number of individuals in each study. All home ranges are reported in km 2 . Additionally comparisons are given for male:female home range size in each gradient. Additionally, same sex comparisons are made between latitudinal intervals, with male and female home ranges in northern latitudes being consistently larger than more southern latitudes, with the exception of the 25-30°N band. Male % female % lower latitude (M) (F) Latitude Male N Female N In same interval (same sex comparison) 45-50 102.9 27 49.8 20 220 161 189 40-45 63.8 22 26.3 26 242 159 232 35-40 40.1 43 11.3 52 354 361 152 30-35 11.1 39 7.4 69 149 57 48 25-30 19.4 22 15.4 18 126 --- --- 91 Figure 4.1. Region 1 in northwest Montana is represented by the gray shaded area on the map. TLRD is the smallest bordered area within R1, while the broader area modeled for bobcat resource selection is the bordered area (NW) encapsulating TLRD. This bounded area is bordered on the south by Hwy 2 and on the east by Hwy 93 where they intersect in Kalispell, MT. The northern boundary is with British Columbia, Canada, and western boundary is Idaho. 92 Figure 4.2. Locations of 13 snowshoe hare mark-recapture grids on TLRD in northwest Montana (Hodges and Mills, unpublished data). Annual 95% fixed kernel home ranges for radiocollared bobcats are overlaid on the map to show location of bobcats in relation to snowshoe hare density estimates. 93 Figure 4.3. Proportion of bobcat GPS locations (N=7,664) by habitat type compared to habitat proportions in Tally Lake Ranger District (TLRD), Flathead National Forest, Montana. 94 Figure 4.4. Winter and spring home ranges for three male bobcats (M1, M2, and M3) that had established home ranges in the higher elevations of TLRD. Home ranges are shown overlaid on areas where snow persisted annually through May 15. Bobcats did not shift home ranges or habitat use in order to avoid deep, persistent snows. The pink star marks the location for Figure 4.5. 95 Figure 4.5. 5 May 2010 snowmobiling to locate radiocollared bobcats on TLRD, elevation ~1530 m. Both M1 and M2 were located with strong signals on this day. Photo by R. Newbury. 96 Figure 4.6. Annual and seasonal home ranges for M1. 97 CHAPTER 5 SEASONAL MOVEMENT PATTERNS OF BOBCATS 5.1. LITERATURE REVIEW AND OBJECTIVES Understanding how and why animals move through landscapes in the way that they do is key to effective conservation and management of wildlife populations (Jacoby et al. 2012). Temporal patterns of landscape and home range navigation are a result of successful behavioral decisions in response to biological, physical, and environmental stimuli (Patterson et al. 2008, Jacoby et al. 2012). The study of animal movements has provided important insights into foraging strategies (Humphries et al. 2010) and how animals use landmarks within their home range to navigate familiar routes (Biro et al. 2007). Indeed, fitness is increased for individuals that are familiar with a given area, as opposed to those that are in unfamiliar territory (Spencer 2012). Learning and memory in animals is of great importance in animal movements and concurrent space use of home ranges (Van Moorter et al. 2009, Boyer and Walsh 2010). Home range formation—fidelity to a defined area in the landscape—is an emergent property of movement decisions made by an animal that benefits from spatial information (Spencer 2012); basically an animal learns from and remembers movement paths and associated behavorial decisions. Spatial learning strengthens the animal’s knowledge of the area it inhabits, thereby increasing fitness as compared to transient individuals. Cognitive maps of locations within an animal’s territory have been widely demonstrated in mammalian species (Gallistel 1990). Individuals have expectations of what resources may be found within certain places (O’Keefe and Nadel 1978, Spencer 2012) within their territory, and develop spatial and temporal patterns of movement within their home ranges. Movement patterns of individual animals likely depend on naturally occurring landscape barriers, the animal’s acquired knowledge of its environment (i.e. spatial memory), and anthropogenic landscape changes (Senft et al. 1987, Wiens and Milne 1989, With 1994, Turchin 1998, Pace 2001, Atkinson et al. 2002, Crooks 2002). Individual movement patterns are influenced by innate physiological factors, such as hunger, sex-specific reproductive 98 strategies, and the sensory capabilities of the organism in question (Bell 1991, Zollner and Lima 1997). Sensory capabilities are often much greater in large bodied species compared to small bodied species (Boyer and Walsh 2010). For example, a bobcat (Lynx rufus) likely has far greater sensory capabilities than a deer mouse (Peromyscus maniculatus), indicating greater spatial memory in the bobcat, greater capacity of movement, and the ability to view the landscape on a coarse-grained scale. A coarse-grained scale can be defined as the perceptual ability to develop a cognitive map of landscape components on a broad spatial and temporal scale, such as river drainages or mountain ranges or across years (Wolf et al. 2009, Gautestad et al. 2013). Not only does the animal perceives its surroundings on a large spatial and temporal scale, but it also has the ability to move regularly through this large area, as opposed to just perceiving and moving about in the immediate stand of trees the animal is located in at that moment. Therefore, evaluating how a predator navigates its home range with respect to sex of the animal and the time of year can yield important information about the behavioral ecology of a species. Knowledge of animal movement patterns can elucidate the abiotic and biotic factors an animal may be reacting to, such as slope of the mountainside, location of conspecifics, location of prey, or habitat type the animal encounters. For example, prior research on Canada lynx (Lynx canadensis) that investigated Fractal D of fine-scale movement behaviors through snowtracking of collared individuals indicates that lynx exhibit strong coarse-grained resource selection (Fuller and Harrison 2010), selection of habitats on a broader, landscape scale. Indeed, the perceptual range of an animal defines the spatial extent of the overall landscape that the animal can detect and that is accessible by movement (Olden et al. 2004), which in large part is dependent on the spatial memory of the animal in question (Atkinson et al. 2002). Additionally, male and female lynx were found to respond differently to landscape complexity within their home ranges with female lynx exhibiting more convoluted movements as they used a smaller home range area with greater intensity than did males (Fuller and Harrison 2010); thus female lynx were more sensitive to fine-scale habitat variation across space. Quantifying animal movement patterns is useful as path shape may reflect habitat quality (Crist et al. 1992, Stapp and Van Horne 1997, Etzenhouser et al. 1998, Gillis and Nams 1998) and energy expenditure (Wiens et al. 1995). For example, when some species 99 are in high quality habitat, they maximize foraging efficiency by decreasing their speed of travel and increasing the tortuosity of their paths (Crist et al. 1992, Stapp and Van Horne 1997, Etzenhouser et al. 1998, Gillis and Nams 1998). For example, Canada lynx vary the tortuosity of their movements depending on habitat type (DeCesare et al. 2005), and prey density in relation to habitat factors (Fuller and Harrison 2010). Additionally, movement patterns are influenced by the degree of habitat heterogeneity and habitat composition in the landscape (With, 1994, Bascompte and Vilà 1997, Edwards et al. 2001), as animals navigate a heterogeneous landscape that is composed of areas that are more or less suitable to their needs (Barraquand and Benhamou 2008). Movement patterns of animals can also be studied from a fractal perspective, with a fractal dimension of D=1.00 indicating a straight-line movement, whereas D=2.00 is the fractal dimension predicted by a random walk (Sugihara and May 1990). Straight line movements may indicate that an area is simply being traveled through and the animal is not foraging there, whereas a more convoluted path suggests that an animal is spending increased time in that habitat patch. The degree of tortuosity of a movement path could be a good measure of the relative importance of certain habitats in terms of hunting opportunities, safety from predation, location of mates, or other ecologically meaningful factors (Nams and Bourgeois 2004). Furthermore, the use of fractal dimension has become a common approach to the study of animal movement patterns (Nams 1996, 2006, Nams and Bourgeois 2004). These estimators are particularly useful since the hierarchical nature of habitats and the patchy distribution of habitats and resources cause the shape and configuration of movement paths to differ through space and time (Nams 2004). Such an approach to quantifying bobcat movements is useful because habitats used by bobcats in northwest Montana likely include those that are used primarily for traveling between foraging sites and others that are used primarily for hunting due to the natural and anthropogenic-caused patchiness of the landscape. We might expect to see bobcats foraging in more open lodgepole pine (Pinus contorta) stand in summer, but less during the winter. Summer movements through this habitat could result in high foraging success, but winter may bring foraging trade-offs in the same habitat, as deep snows and greater exposure to severe winter conditions would increase the energetic expenditure required to forage in this habitat. Thus, bobcats may shift the 100 shape of movements and habitats selected seasonally, but bobcats may also reduce movement distance and rate in order to conserve energy in winter (Chapter 2). Bobcats are likely to be similar to Canada lynx in terms of coarse-grained habitat selection, large perceptual range of the landscape, and great capacity for spatial memory given the size of bobcat home ranges (Chapter 4). Therefore, coupling an evaluation of movement distances, movement rates, and shape of coarse-grained (large scale) movements through fractal analysis with habitats selected along pathways, the sex of the animal, and season may provide powerful insights into behavioral decisions made by bobcats in northwest Montana. For example, if bobcats are limited by deep snow during the winter, we may expect reduced movements with higher tortuosity, primarily found in preferred habitats that provide sufficient cover and hunting opportunities. My main objective with this chapter is to test three hypotheses: (1) bobcat movement patterns (distance and shape) depend on the sex of the individual, (2) bobcat movement patterns depend on season, and (3) bobcat movement patterns depend on habitat type. I predict that male bobcats will move greater daily distances than females in all seasons, but that both males and females will show reduced movements in winter as compared to other seasons. Furthermore, I predict that male bobcats will have straighter weekly movements than will female bobcats, but that movements will be more convoluted for both male and female bobcats in winter than other seasons. If bobcats move less in winter, they are likely to make greater use of a smaller area, thus leading to greater tortuosity of movements. Next, I predict that bobcats select preferred habitats such as lodgepole pine (Chapter 4) along paths, while avoiding open habitats, but that selection of these habitats does not vary across seasons along movement paths. Lastly, I predict that habitat type can be used to predict the shape of movements, with preferred habitats leading to more tortuous paths, and avoided habitats producing straighter paths. 5.2. STUDY AREA Bobcat GPS locations were collected on the Tally Lake Ranger District (TLRD) of the Flathead National Forest and Rexford and Libby Ranger Districts of the Kootenai National Forest in the Salish Mountains of northwest Montana, USA (48°30´0˝N, 114°45´0˝W). The majority of fieldwork took place on TLRD, and I will collectively refer to 101 the broader area (Kootenai National Forest Ranger Districts included) bobcats used as TLRD for sake of brevity. Elevations throughout the TLRD range from 945 m to 2008 m. Winter (Dec.-Feb.) temperatures range from -42 to 38° C and mean annual rainfall is 58 cm at 975 m in Olney, Montana, on the northeast edge of the TLRD. Winter temperatures range from -42 to 7° C, and annual snowfall typically exceeds 300 cm at mid-elevations (>1300 m) and can exceed 700 cm at elevations >2000 m (National Oceanic and Atmospheric Administration 2013). Forested areas of TLRD are dominated by moist, coniferous forests composed of western larch (Larix occidentalis), lodgepole pine, Douglas fir (Pseudotsuga menziesii), subalpine fir (Abies lasiocarpa), and Engelmann spruce (Picea engelmannii). Lower elevations are primarily composed of older, multi-layered forests of western larch, Douglas fir, and lodgepole pine. Ponderosa pine (Pinus ponderosa), western red cedar (Thuja plicata)/western hemlock (Tsuga heterophylla), grand fir (Abies grandis), and whitebark pine (Pinus albicaulis)/subalpine larch (Larix lyallii) communities are also found on TLRD. However, these forest associations are rare. In general, old growth forest occurs along riparian strips in upland areas or in small patches throughout the district. Fire, insects, and disease are the predominant natural disturbance factors in this area; however, increasing human use and development are also dominant forces in shaping this landscape. The forested uplands in this ranger district are highly productive, and are frequently harvested (Flathead National Forest 2006) 5.3. METHODS 5.3.1. CAPTURE AND HANDLING Trap sites were situated on TLRD based on presence of recent bobcat sign and distributed across the north end of the main study area, with trapping locations targeted at capturing bobcats that used areas with long-term snowshoe hare mark-recapture sites (Hodges and Mills, unpublished data). I trapped bobcats using commercially available box traps or box traps modified from Kolbe et al. (2003) and Washington Department of Game and Fish employees for Canada lynx. Each trap set was baited with roadkilled deer, scented with commercially available cat lures, and visually lured with grouse feathers or blank cds that were hung from fishing line on a swivel. 102 Captured animals were immobilized with Telazol® at 5 mg/kg estimated body mass administered intramuscularly with an extendable pole-syringe. Telazol® does not require the administration of a protagonist drug, and both immobilization (<5 minutes on average) and complete recovery (2-3 hours from injection) were quick. Anesthetized individuals were sexed, weighed, eartagged (National Band and Tag Co., 1005-4 Monel, sequential numbering), a DNA sample obtained (blood and hair), and aged (kitten: <1 year, juvenile: 1- 2 years, adults: >2 years) based on skeletal measurements and tooth wear. Vital rates (temperature, respiration, and pulse) of immobilized animals were closely monitored to ensure the well-being of the individual. Individual bobcats weighing >4kg were fitted with a Lotek GPS_3300SL (Lotek Eng. Inc., Newmarket, ON) or Sirtrack Model TGC181 GPS/VHF satellite telemetry collar. The GPS duty cycle on collars was set to record a location every 3 h if a satellite fix could be obtained. Data was stored on board the collar, necessitating collar recovery in order for data to be downloaded. See Chapter 4 for a complete description of number of bobcats collared, length of monitoring, and number of GPS points collected. All animals were released at the point of capture. These methods adhered to a strict protocol for trapping and handling following guidelines set forth by the American Society of Mammalogists’ Animal Care and Use Committee (Sikes et al. 2011), Montana State Fish, Wildlife, and Parks permits (2009- 059, 2010-002, 2011-003), and the University of British Columbia’s Animal Care Committee (A07-0676-R001). 5.3.2. MOVEMENTS AND MOVEMENT PATHWAYS Average daily distances moved were determined in Microsoft Excel (2010) by computing distance between consecutive UTM coordinates. Distances between consecutive movements were summed and divided by the number of days in the winter season, or the number of days that the bobcat was radiocollared in that season, to obtain average daily movement distances. Daily movement distances were divided by 24 h to obtain an average hourly movement rate. Movement data are available for only one female bobcat, and here she is used as representative of the female bobcat population. Weekly movement pathways were constructed in ArcGIS 10 and 10.1 by converting all locations taken at 3 h intervals that occurred in a weekly period for an individual bobcat to 103 polylines, and calculating the length of the feature. The habitat type that each point fell within was determined with XTools Pro 9.1, Identity function. Average weekly movement distances and rates were determined for each season for bobcats. The Fractal D of each pathway was calculated in program Fractal (Nams 2006) using the Fractal Mean estimator and accepting program defaults (window range = 0.25; range of spatial scales = auto). The Fractal D was chosen as an appropriate method for analysis of bobcat movements as the approach used by Nams (2004) solves the problem of non-independence of the log of path length measurement that is encountered in traditional fractal methods proposed by Mandelbrot (1967) and Milne (1991). The Fractal D estimator solves this problem by estimating fractal dimension at different spatial scales, giving a measure of variance, and combining data from many separate path segments gathered at a variety of spatial scales (Nams 2006). Fractal estimators are particularly useful in evaluating the shape of bobcat movements because the hierarchical nature of habitats and the patchy distribution of habitats and resources in the study area. This landscape complexity can cause the shape and configuration of movement paths to differ through space and time (Nams 2004). Habitat types were consolidated from 26 original habitat types identified in MSDI Landcover 2010 raster files (Appendix D). Habitat classifications that were rare on TLRD were collapsed to single types. For example, ‘Other’ includes agricultural areas, alpine habitats, aspen (Populus tremuloides), mixed aspen-conifer stands, Douglas fir, Great Plains Saline wetland habitat types, areas developed by people (towns), mining areas, non-native vegetation types, exposed rock, sage steppe habitats, subalpine habitats, and open water. Even with this consolidation, Other constitutes only 3.3% of TLRD. Additionally, original cover types designated intensity of harvested areas into 3 categories; these were collapsed to a single Harvest cover type. Proportion of bobcat locations in each of 13 habitat types was determined from total number of locations in the movement path. 5.3.3. STATISTICAL ANALYSES Daily movement distances and rates were analyzed in SPSS 21.0 with a general linear model using a multivariate analysis of variance (MANOVA). Specifically, I analyzed if movement distance and rate of movement were dependent on sex and season, with daily temperature (mean, minimum, and maximum temperature), sustained windspeed, and 104 snowfall as model covariates. An α ≤ 0.05 was accepted as a statistically significant result. Fractal D of weekly movement pathways and weekly movement distances were analyzed in SPSS 21.0 first using univariate ANOVA to determine if Fractal D and weekly movement distances were significantly different between sexes and seasons to evaluate if all data for movement pathways could be pooled. As data for male and female bobcats could not be pooled, I analyzed weekly movements using the same approach as for daily movements, with a general linear model using MANOVA. I analyzed if Fractal D (shape of movement) and distance of weekly movements were dependent on sex and season. Again, α ≤ 0.05 was accepted as a statistically significant result. Lastly, I analyzed if shape of movements (Fractal D) could be predicted by sex of the animal, season, and the habitat type a bobcat moved through using linear regression with all habitat types, sex, and season entered in the model as predictor variables. Reported statistics are x̄ ± SE unless otherwise stated. 5.4. RESULTS 5.4.1. DAILY MOVEMENTS Male bobcats moved significantly greater daily distances at a higher rate of speed than the female bobcat (F1, 1214 = 29.01, p < 0.001). Additionally, bobcats significantly reduced daily movement distances and rate of movement dependent on season (F3,1214 = 5.65, p < 0.001); however, the interaction of sex and season was not a significant factor (F3,1214 = 0.81, p = 0.490) (Table 5.1). Specifically, both male and female bobcats showed reduced movement distances and rate of movement in winter, and greater movement in summer. (Table 5.2, Figure 5.1, 5.2). The covariates (mean temperature, minimum temperature, maximum temperature, sustained windspeed, and snowfall) included in the model were not significant, and therefore were dropped from further analyses. In pairwise comparisons of seasonal movement distances and rates, bobcat moved significantly less during winter than during all other seasons; additionally, bobcats reduced movement in fall as compared to summer (Table 5.3, Figure 5.1, 5.2). Spring and fall movement distances and rates are nearly significantly different from one another (Mean difference = 553.2 ± 315.0, p = 0.079); bobcats move greater distances in spring at a higher rate of speed than they do in fall. This could be a biologically significant behavioral pattern 105 in bobcats, as spring (March-May) would see snows melting, and many prey species giving birth or becoming active again in warmer temperatures. Reduction in fall movements could indicate a behavioral switch to reduced movements on the part of bobcats to conserve energy as temperatures fall and snow begins to accumulate. 5.4.2. WEEKLY MOVEMENTS Fractal D of for all pathways was significantly different between male bobcats and the female bobcat (F1, 176 = 18.02, p < 0.001); the female bobcat moved with higher tortuosity than male bobcats. The female had a D = 1.18 ± 0.018 (range: 1.06-1.67), while male bobcats had D = 1.12 ± 0.005 (range: 1.04-1.44). The female bobcat showed much greater variation in the shape of her movements than did male bobcats; thus, male and female pathways could not be pooled. Weekly movement distances were also significantly different between male bobcats and the female bobcat (F1, 176 = 7.99, p = 0.005); male bobcats moved greater distances on a weekly basis than did the female, a result supported by greater daily movement distances in male bobcats. The female bobcat moved 31.9 ± 1.1 km per week (range: 17.0-47.8), while male bobcats moved 41.6 ± 1.7 km per week (range: 5.1-91.5). The interaction of Sex*Season was not a significant factor in the MANOVA (F6, 340 = 0.84, p = 0.537) (Table 5.4), as male and female bobcats reacted similarly across seasons. Fractal D and distance of weekly movement paths were not significantly different by sex (F2, 169 = 2.18, p = 0.116), but were significantly different for season (F6, 340 = 2.62, p = 0.017) in multivariate tests. Univariate tests within the MANOVA show that the effect of sex on Fractal D was not significant (F1,170 = 1.56, p =0.213), and that season did not affect shape of movements (F3, 170 = 1.65, p = 0.18). However, movement distance was significantly different between sexes (F1, 170 = 3.86, p = 0.051), but season had a weaker effect on movement distance (F1, 170 = 2.29, p = 0.08). Specifically, male bobcats displayed little variation in the shape of movements over the course of the year, while the female bobcat showed a weak trend towards increasingly convoluted movements in spring and summer, with straighter movement (less tortuous) movements in fall and winter, though pathway data from the female bobcat is limited to one path for winter (Table 5.6, Figure 5.3, 5.4). Also, male and female bobcats showed a pattern 106 of reduced movement distances in winter and fall as compared to spring and summer. Fractal D by season was not significantly different, but bobcat weekly movement distances were significantly less during winter than during all other seasons (Table 5.5, Figure 5.5). Sample size considerations also apply for movement data (see Chapter 4). The data are forone female bobcat and 4 male bobcats. Individual bobcats did differ from each other with respect to seasonal movement paths, particularly M2, a juvenile male bobcat (Figure 5.6). This young male displayed movements more similar to the female bobcat across seasons; it is possible that during winter (he was collared in January 2010) and spring he was still partially dependent on his mother. His movement patterns were more convoluted than those of adult males. Additionally, M2’s home range (Chapter 4) was smaller than those of adult males in winter and spring, and wase more similar to the female’s home range size in that season. 5.4.3. HABITAT TYPE CHOSEN ALONG MOVEMENT PATHS The type of habitat chosen differed significantly between movement paths for pooled male bobcats and movement paths of a female bobcat (F12, 159 = 29.50, p < 0.001), and habitat chosen along pathways also differed depending on season (F36, 483 = 1.91, p = 0.001), (Table 5.7). Furthermore, the interaction of Sex*Season was significant in determining habitat chosen (F36, 483 = 1.62, p = 0.014). Specifically, male bobcats used greater proportions of dry spruce fir, grassland, Ponderosa, and wet spruce fir habitats than the female bobcat, while the female used mixed dry and wet coniferous stands and riparian habitats to a greater extent than males for movement paths (Table 5.8). Habitat selection of movement paths also varied significantly by season in the proportion of mixed dry conifer stands chosen (F3, 170 = 3.91, p = 0.010) and riparian habitats used (F3, 170 = 14.34, p < 0.001) (Table 5.7). Bobcats selected mixed dry stands significantly more in winter than all other seasons (Table 5.9) for weekly movements. Riparian habitats were also selected far more in winter than all other seasons, and more in fall and summer than in spring. Fractal D could be predicted by sex, season, and habitat type (F14, 163 = 2.10, p = 0.014,); however, this model explained little of the variation (R 2 = 0.15) seen in the shape of weekly movement pathways. Sex was the only significant predictor (t = 2.633, p = 0.01), 107 while season was not a significant predictor in the model (t = -1.391, p = 0.17). All habitat variables were non-significant in predicting the shape of bobcat movements (range: p=0.29- 0.99). Thus, the best predictor of the shape of movement pathways in bobcats is the sex of the animal. 5.5. DISCUSSION Movement patterns of bobcats on TLRD were dependent upon sex and season, while habitat type had little effect on how bobcats navigated the landscape. Specifically, male bobcats moved farther in a more linear fashion than a female bobcat, but all bobcats responded to time of year in a similar way, with movements being reduced in winter. Male bobcats moved significantly farther on a daily and weekly basis than did the female bobcat in all seasons; however, all bobcats significantly reduced movements in winter. All animals showed a pattern of low winter movement, increased spring movements, movement distances maximal in summer, and decreasing fall movements as winter approached. Fall movements were less than spring movement, an interesting pattern as spring (March-May) often had snow on the ground, while fall (September-November) was less likely to have the same amount of snow. However, spring movements can be associated with mate searching behavior, as bobcats breed in March. Male bobcats moved along much straighter paths throughout the year than did the female bobcat, but differences between the sexes were not significant on a seasonal basis. However, males did show a pattern of more convoluted movement paths in winter compared to other seasons, while the female bobcat displayed the most tortuous paths during summer, when she was rearing kittens (Figure 5.4). More convoluted movements for the female bobcat makes intuitive sense as she: (1) had a much smaller home range that she would use more intensively, (2) would need to return often to her den to care for young kittens, (3) would be constrained in movement decisions when accompanied by young kittens as she would need to move more slowly to enable kittens to keep up, and (4) would need to select areas where kittens were well concealed and escape cover was close. Male and female bobcats did select different habitats along movement paths on a seasonal basis. Male bobcats on TLRD appeared to use drier habit and more open habitats more often than did the female bobcat, but seasonal differences in habitats selected along 108 movement paths were driven in large part by the female bobcat. She changed her use of riparian habitats markedly across seasons, using riparian areas the most in winter. However, all bobcats selected mixed dry site coniferous stands much more in winter than they did during other seasons. These habitat were selected more in winter than other seasons, possibly reflecting greater importance of these habitat types as winter foraging habitats than they provided in other seasons. It is interesting that habitats selected along movement paths were different by sex and season, as seasonal home range habitat selection and location of individual GPS locations did not show this pattern; indeed, 3 rd order habitat selection (Johnson 1980) indicated that bobcats used habitats within home ranges based on expectation (Chapter 4). Use of habitats based upon availability within home ranges could indicate that while bobcats are choosing and using home ranges on a coarse-grained temporal scale, that bobcats are selective of habitat type within their home range during a relatively short time span. For example, individuals may selectively use riparian areas (or other habitat type) if that area yields a rich food source that the bobcat discovers opportunistically, such as a deer carcass. Habitat selected along movement paths was a poor predictor of the shape of movements (Fractal D), while the sex of the animal was a strong predictor of path tortuosity. This model to predict the shape of bobcat movement paths explained little variation seen in path tortuosity though other variables not measured likely influenced how a bobcat moved through the landscape. Additionally, the scale of movement may have been too large and the frequency of bobcat relocations too infrequent to capture what may inherently be fine-scale movement decisions. Movement decisions likely depended on factors such as within-patch metrics like canopy closure, horizontal (shrub) cover, microsite prey densities, and bobcat sex and age. I suggest further study with collars programmed to collect relocations on a finer temporal scale, a larger female sample size, and evaluation of microsite variables. 5.5.1. MOVEMENT DISTANCES AND RATES Daily and weekly movement distances of bobcats in my study varied significantly between male and female bobcats and across seasons. Winter movement distances were greatly reduced compared to all other seasons; in fact, daily winter movements were almost half summer movements. Male bobcats moved on average < 5 km per day in this study in 109 winter, while the female bobcat moved just over 2 km per day during winter. Bailey (1974) reports that the majority of male bobcats moved < 4.8 km in winter, and females typically moved <3.2 km; however, longer winter daily distances of up to 8 km (Bailey 1974), 11.7 km (Erickson 1955), and 11.2 km (Rollings 1945) have been reported. My results of decreased winter movements are consistent with the findings of other winter bobcat studies, where snow was thought to restrict bobcat movements (Bailey 1974, McCord 1974, Fuller et al. 1985, Litvaitis et al. 1986b). One male bobcat moved up to 16.3 km in 24-h during winter in this study; the minimum winter daily movement distance for this bobcat, M1, was 5.0 km, including 16 days where his movements exceeded 7 km. Five of those days include movements exceeding 9 km. M1’s winter locations were at an average elevation of 1453 m (Appendix D), hence this male was making large movements in deep snow conditions. These daily movement distances are comparable to maximum winter distances reported for various Lynx spp. in winter: Iberian lynx (Lynx pardinus), 11.1 km (Aldama et al. 1991); Canada lynx, 7 km (Moen et al. 2008), 8.8 km (Parker et al. 1983), 23.3 km (Apps 2000), and bobcat, 11.7 km (Erickson 1955), 11.2 km (Rollings 1945), 8 km (Bailey 1974), and 8.7 km (Sullivan 1995). On average Lynx spp. tend to reduce winter movements, but they are still capable of large movements even in deep snow. As bobcats do not have the low foot loadings of lynx, they sink farther in deep snow; such long winter movements are more energetically costly for bobcats (Chapter 3). Male bobcats in my study moved faster than the female bobcat, and seasonal rate of movements were much lower in winter for both sexes than in other seasons. Movement rates reported in my study are similar to those reported in other bobcat studies. Male bobcats consistently move greater distances at higher rates of speed than females, with males moving 6.2 ± 1.5 km (258 m/h) and females moving 3.8 ± 1.4 km (158 m/h) (Larivière and Walton 1997). Bobcats moved 4.9 ± 0.7 km per day, at a rate of 0.3 ± 0.4 km/h in the Chihuahuan Desert, Mexico (Elizalde-Arellano et al. 2012). Reports of daily movement distances of bobcats range from 1-3 km/day (42 m/h to 125 m/h) in Oregon (Witmer and DeCalesta 1986) and 4.9 km/d (204 m/h) in north-central Montana (Knowles 1985). Bailey (1974) reported average net distance between daily radio locations of female and male bobcats of 1.2 and 1.8 km respective, which gives movement rates of 50 m/h and 75 m/h. Kapfer (2012) reported 110 movement rates of 108 m/h and 93 m/h for two adult female bobcats in east-central Minnesota. Even though movement distances and rates were reduced in winter, and were largely comparable to other bobcat studies, I found no significant difference in home range size across seasons for radiocollared bobcats (Chapter 4). In fact, winter home ranges of male bobcats were only slightly smaller than summer home ranges, yet larger than spring and fall home ranges. This result implies that bobcats in northwest Montana still used the same area, but simply took longer to travel throughout their home range in winter, if the results I present are representative of other bobcats in the broader population. Additionally, there are no snow-free habitats available to bobcats on TLRD during winter. Snowfall on TLRD and the Salish Mountains typically exceeds 300 cm at mid- elevations (>1300 m) and >700 cm above 2000 m (National Oceanic and Atmospheric Administration 2013). Bobcats in northwest Montana were found at 1456 ± 4.9 m (range 1001-1794 m) in winter, while they were found at 1468 ± 3.5 m (range 987-1995 m) during the summer (Appendix D). As mean elevation difference is only 12 m between seasons, it is not likely to make a large difference in snow depth in winter. Therefore, I propose that bobcats reduce movements in winter as a behavioral adaptation to reduce energetic costs of moving in deep snow (Chapter 2), but that large home ranges (Chapter 4) are required due to prey availability in areas that experience severe winter conditions. Bobcats do not appear negatively impacted by deep snow environments with respect to population success and distribution in northwest Montana, though results and conclusions drawn should be regarded cautiously due to low sample size in my study (Chapter 4). 5.5.2. MOVEMENT TORTUOSITY Male bobcats on TLRD showed little variation in shape of movements across seasons. The female bobcat exhibited movements with greater tortuosity than did male bobcats, which implies more intense use of her much smaller home range (Chapter 4). The female’s movements were more convoluted in spring and summer, when she was rearing kittens. Higher fractal dimension of movements is associated with the presence of young accompanying a female (Saeki et al. 2007, Webb et al. 2009, Fuller and Harrison 2010). Comparatively more linear movements in males and non-reproductive females were 111 associated with greater capacity for movement, mate searching and territorial patrolling behaviors (Bascompte and Vila 1997, Saeki et al. 2007, Webb et al. 2009, Fuller and Harrison 2010). Larger, highly mobile animals with greater perceptual scale exhibit lower path tortuosity than smaller species (With 1994), which is reflected in average Fractal D of bobcat movement paths <1.20 across seasons. I likely measured bobcat movements at a grain corresponding to bobcat responses to habitat patches within their home range, thus reflected in higher fractal dimension of movement paths for bobcats, than reported for lynx by Fuller and Harrison (2010). Higher fractal dimension at broader scales implies higher turning angles to remain within a predefined space, i.e. the home range. Bobcats in my study covered large home ranges (Chapter 4) comparable to Canada lynx (Vashon et al. 2008), which exhibit strong landscape-scale (coarse-grained) habitat selection (Hoving et al. 2004). Fuller and Harrison (2010) detected a difference in how lynx (both male and female) responded to landscape scale. Movement paths of lynx were straighter at fine spatial scales than at broader scales; movement paths became increasingly complex as Canada lynx moved from within-patch to between patch behaviors with respect to habitat heterogeneity, thus it is likely that I evaluated bobcat movements at the scale where bobcats were responding to a complex, patchy habitat configuration. 5.6. IMPLICATIONS OF BOBCAT SEASONAL MOVEMENT PATTERNS Bobcat movement patterns (distance and shape) depend on both the sex of the animal and the time of year, but movement patterns were not affected by habitat type for individuals on TLRD. Daily movements differed significantly between male and female bobcats, with males moving greater distances than females in all seasons. Daily movements were significantly reduced in winter, a probable behavioral adaptation to conserve energy in the deep snow and cold. Additionally, evaluation of coarse-grained movement pathways showed significant differences in the shape of bobcat movements between the sexes; the female bobcat’s movements exhibited greater tortuosity across seasons than did those of male bobcats. Male bobcats displayed little variation in shape of movements across seasons. The shape of bobcat movements is likely related to greater home range size, mate searching, and territorial patrolling in males, while the female exhibited more convoluted movements as she 112 used her much smaller home range more intensively, and was constrained in her movement ability while raising three kittens. Habitats selected along movement paths differed between sexes, but habitats selected along movement paths varied little across seasons. Riparian areas and dry site mixed conifer stands were used more in winter, possible because of increased hunting opportunity. Bobcats on TLRD demonstrated movement patterns similar to reported movement distances and rate of movement from other bobcat studies in different regions. However, bobcats on TLRD also appeared to exhibit movement behaviors that were comparable to the closely related Canada lynx during both non-snow and snow seasons. Thus, if bobcats in my research were typical of the broader bobcat population, bobcats in northwest Montana may demonstrate a mixture of behaviors characteristic of both bobcats and lynx that allow them to be successful in deep winter snows of this region. 113 Table 5.1. MANOVA and univariate ANOVAs for the effects of sex and season on the 24-h movement distances and rate of movement of bobcats in northwest Montana. The effect of sex and season were significant with respect to effects on movement distance and movement rate of bobcats; however, the interaction of sex and season did not have a significant impact on either movement distance or rate of movement of bobcats throughout the year. Degrees of freedom for sex = 1,1214, while season = 3, 1214. Bold text denotes significance at p<0.05. Source d.f. F P (a) Multivariate analysis Sex 1, 1214 29.01 <0.001 Season 3, 1214 5.65 0.001 Sex*Season 3, 1214 0.81 0.49 Source SS F P (b) Univariate analyses Movement distance Sex 324,754,010.5 29.01 <0.001 Season 189,740,281.6 5.65 <0.001 Error 13,589,471,198.3 Movement rate Sex 563,809.0 29.01 <0.001 Season 329,410.2 5.65 0.001 Error 23,592,831.9 114 Table 5.2. Average daily movement distances and rate of movement by season for bobcats in northwest Montana. Sex Number of days Season Movement distance Movement rate ± SE (km) ± SE (m/h) Male 134 Winter 4.1 ± 0.4 172.6 ± 15.9 319 Spring 6.6 ± 0.2 275.1 ± 8.3 276 Summer 7.1 ± 0.3 296.7 ± 10.6 238 Fall 5.9 ± 0.2 244.3 ± 9.2 Female 10 Winter 2.7 ± 1.1 110.4 ± 44.9 91 Spring 4.8 ± 0.4 198.9 ± 14.9 90 Summer 4.8 ± 0.4 198.0 ± 16.1 69 Falll 4.4 ± 0.4 183.6 ± 16.9 115 Table 5.3. Pairwise comparisons of seasonal 24-h movement distances and rates of movement for bobcats in northwest Montana. Bold text denotes significance at p<0.05. Dependent Variable Season Season Mean Difference SE P 95% Confidence Interval Movement distance Winter Spring Summer Fall -2292.8 -2541.8 -1739.6 618.9 693.4 627.8 <0.001 <0.001 0.006 -3507.0 – -1078.6 -3902.2 – -1181.5 -2971.3 – -508.0 Spring Winter Summer Fall 2292.8 -249.0 553.2 618.9 353.0 315.0 <0.001 0.481 0.079 1078.6 – 3507.0 -941.7 – 443.6 -64.9 – 1171.2 Summer Winter Spring Fall 2541.8 249.0 802.2 693.4 353.0 350.4 <0.001 0.481 0.022 1181.5 – 3902.2 -443.6 – 941.7 114.8 – 1489.6 Fall Winter Spring Summer 1739.6 -553.2 -802.2 627.8 315.0 350.4 0.006 0.079 0.022 508.0 – 2971.3 -1171.2 – 64.9 -1489.6 – -114.8 Movement rate Winter Spring Summer Fall -95.5 -105.9 -72.5 25.8 28.9 26.2 <0.001 <0.001 0.006 -146.1 – -44.9 -162.6 – -49.2 -123.8 – -21.2 Spring Winter Summer Fall 95.5 -10.4 23.0 25.8 14.7 13.1 <0.001 0.481 0.079 44.9 – 146.1 -39.2 – 18.5 -2.7 – 48.8 Summer Winter Spring Fall 105.9 10.4 33.4 28.9 14.7 14.6 <0.001 0.481 0.022 49.2 – 162.6 -18.5 – 39.2 4.8 – 62.1 Fall Winter Spring Summer 72.5 -23.0 -33.4 26.2 13.1 14.6 0.006 0.079 0.022 21.2 – 123.8 -48.8 – 2.7 -62.1 – -4.8 116 Table 5.4. MANOVA and univariate ANOVAs for effects of sex and season on the weekly movement pathways (measured with Fractal D) and weekly movement distance of bobcats in northwest Montana. Season had the only significant impact on the shape of weekly bobcat movements in multivariate tests; however univariate tests show that movement distance is affected by both sex of bobcat and season. Degrees of freedom for sex = 1,170, while season = 3, 170. Bold text denotes significance at p<0.05. Source d.f. F P (a) Multivariate analysis Sex 2, 169 2.18 0.116 Season 2, 169 2.62 0.017 Sex*Season 6, 340 0.84 0.537 Source SS F P (b) Univariate analyses Fractal D Sex 0.01 1.56 0.213 Season 0.03 2.29 0.180 Error 0.86 Movement distance Sex 1,150,679,431.5 3.86 0.051 Season 2,049,589,719.1 2.29 0.080 Error 50,672,879,915.2 117 Table 5.5. Post hoc Bonferroni corrected comparisons of seasonal effects on average weekly movement distances of bobcats in northwest Montana. Bold text denotes significance at p<0.05. Season Season Mean Difference SE P 95% Confidence Interval Winter Spring Summer Fall -16860.0 4312.9 0.001 -28373.2 – -5346.8 -22013.3 4391.0 <0.001 -33735.0 – -10291.5 -12335.8 4491.4 0.040 -24325.6 – -346.0 Spring Winter Summer Fall 16860.0 4312.9 0.001 5346.8 – 28373.2 -5153.3 3284.0 0.711 -13919.7 – 3613.2 4524.2 3417.0 1.000 -4597.5 – 13645.9 Summer Winter Spring Fall 22013.3 4391.0 <0.001 10291.5 – 33735.0 5153.3 3284.0 0.711 -3613.2 – 13919.7 9677.4 3515.1 0.039 293.9 – 19061.0 Fall Winter Spring Summer 12335.8 4491.4 0.040 346.0 – 24325.6 -4524.2 3417.0 1.000 -13645.9 – 4597.5 -9677.4 3515.1 0.039 -19061.0 – -293.9 118 Table 5.6. Average Fractal D and distance of weekly movement paths for bobcats in northwest Montana. Paths were constructed by determining distances between consecutive GPS locations that occurred within a weekly period. For example, there are 22 weekly paths for male bobcats in winter, and males moved ~24.6 km during that weekly period. Sex N Paths Season Fractal D ± SE Weekly movement (N) distance ± SE (km) Male (3) 22 Winter 1.14 ± 0.02 24.6 ± 3.8 (4) 47 Spring 1.12 ± 0.01 44.0 ± 2.6 (3) 39 Summer 1.12 ± 0.01 50.5 ± 2.8 (3) 35 Fall 1.11 ± 0.01 38.9 ± 2.9 Female (1) 1 Winter 1.06 22.1 13 Spring 1.17 ± 0.02 31.9 ± 4.8 13 Summer 1.20 ± 0.02 34.5 ± 4.8 10 Falll 1.16 ± 0.02 29.6 ± 5.5 119 Table 5.7. MANOVA and univariate ANOVAs for effects of sex and season on habitat type selected by bobcats along weekly movement paths. Degrees of freedom for sex = 1,170, and season = 3, 170 in univariate analyses. Bold text denotes significance at p<0.05. Source d.f. F P (a) Multivariate analysis Sex 12, 159 29.50 <0.001 Season 36, 483 1.91 0.001 Sex*Season 36, 483 1.62 0.014 Source SS F P (b) Univariate analyses Burn Sex 0.00 0.06 0.807 Season 0.00 0.18 0.910 Error 0.00 Dry Spruce fir Sex 0.07 12.22 0.001 Season 0.02 0.93 0.426 Error 0.93 Grassland Sex 0.01 4.05 0.046 Season 0.00 0.27 0.840 Error 0.22 Harvest Sex 0.02 2.65 0.105 Season 0.03 1.58 0.196 Error 1.02 Lodgepole Sex 0.03 2.13 0.147 Season 0.07 1.68 0.173 Error 2.36 Mixed dry Sex 0.04 5.46 0.021 Season 0.08 3.91 0.010 Error 1.21 Mixed wet Sex 0.58 69.31 <0.001 Season 0.00 0.11 0.954 Error 1.43 Ponderosa Sex 0.33 43.72 <0.001 Season 0.01 0.56 0.644 Error 1.26 Riparian Sex 0.15 195.13 <0.001 Season 0.03 14.34 <0.001 Error 0.13 Shrub Sex 0.00 3.17 0.077 Season 0.00 0.51 0.678 Error 0.07 Wetland Sex 0.00 0.29 0.590 Season 0.00 0.70 0.556 Error 0.02 120 Table 5.7 continued. MANOVA and univariate ANOVAs for effects of sex and season on habitat type selected by bobcats along weekly movement paths. Degrees of freedom for sex = 1,170, while season = 3, 170 in univariate analyses. Bold text denotes significance at p<0.05. Source SS F P (b) Univariate analyses Wet spruce fir Sex 0.18 19.77 <0.001 Season 0.02 0.67 0.570 Error 1.51 Other Sex 0.00 1.81 0.180 Season 0.00 1.12 0.342 Error 0.11 121 Table 5.8. Average proportion of habitat type used along weekly movement paths for male bobcats and the female bobcat. Locations per path averaged 42 ± 1 (SE) locations for male bobcats, and 42 ± 2 locations for the female bobcat. Male bobcats used greater proportions of dry spruce fir, grassland, Ponderosa, and wet spruce fir habitats than the female bobcat, while the female used mixed dry and wet coniferous stands and riparian habitats to a significantly greater extent than males for movement paths. Bold text denotes significance at p<0.05. Habitat type Sex Average proportion of locations along weekly movement paths SE P 95% Confidence Interval Burn F 0.00 0.00 0.807 -0.00 – 0.00 M 0.00 0.00 0.00 – 0.00 Dry Spruce-fir F 0.03 0.02 0.001 -0.01 – 0.07 M 0.11 0.01 0.10 – 0.12 Grassland F 0.01 0.01 0.046 -0.01 – 0.03 M 0.03 0.00 0.02 – 0.03 Harvest F 0.08 0.02 0.105 0.03 – 0.12 M 0.11 0.01 0.10 – 0.13 Lodgepole F 0.23 0.03 0.147 0.16 – 0.29 M 0.18 0.01 0.16 – 0.20 Mixed dry conifer F 0.18 0.02 0.009 0.13 – 0.23 M 0.12 0.01 0.11 – 0.14 Mixed wet conifer F 0.30 0.03 <0.001 0.25 – 0.35 M 0.08 0.01 0.06 – 0.09 Ponderosa F 0.01 0.02 <0.001 -0.04 – 0.06 M 0.18 0.01 0.16 – 0.19 Riparian F 0.12 0.01 <0.001 0.11 – 0.14 M 0.01 0.00 0.00 – 0.01 Shrub F 0.00 0.01 0.077 -0.01 – 0.01 M 0.01 0.00 0.01 – 0.01 122 Table 5.8 continued. Average proportion of habitat type used along weekly movement paths for male bobcats and the female bobcat. Locations per path averaged 42 ± 1 (SE) locations for male bobcats, and 42 ± 2 locations for the female bobcat. Male bobcats used greater proportions of dry spruce fir, grassland, Ponderosa, and wet spruce fir habitats than the female bobcat, while the female used mixed dry and wet coniferous stands and riparian habitats to a significantly greater extent than males for movement paths. Bold text denotes significance at p<0.05. Habitat type Sex Average proportion of locations along weekly movement paths SE P 95% Confidence Interval Wetland F 0.00 0.00 0.590 -0.00 – 0.01 M 0.00 0.00 0.0 0.01 Wet Spruce-fir F 0.05 0.03 <0.001 0.00 – 0.01 M 0.17 0.01 -0.01 – 0.10 Other F 0.00 0.01 0.180 0.15 – 0.19 M 0.01 0.00 -0.01 – 0.02 123 Table 5.9. Pairwise comparisons of seasonal habitat use along movement paths for bobcats in northwest Montana. Bold text denotes significance at p<0.05. Dependent Variable Season Season Mean Difference SE P 95% Confidence Interval Mixed dry stands Fall Spring -0.03 0.02 0.139 -0.07 – 0.01 Summer -0.03 0.02 0.158 -0.07 -- 0.01 Winter -0.15 0.05 0.001 -0.24 – -0.06 Spring Fall 0.03 0.02 0.139 -0.01 – 0.07 Summer 0.00 0.02 0.952 -0.04 – 0.04 Winter -0.12 0.05 0.007 -0.21 – -0.03 Summer Fall 0.03 0.02 0.158 -0.01 – 0.07 Spring -0.00 0.02 0.952 -0.04 – 0.04 Winter -0.12 0.05 0.007 -0.21 – -0.04 Winter Fall 0.15 0.05 0.001 0.06 – 0.24 Spring 0.12 0.05 0.007 0.03 – 0.21 Summer 0.12 0.05 0.007 0.04 – 0.21 Riparian Fall Spring 0.03 0.01 <0.001 0.01 – 0.04 Summer 0.01 0.01 0.186 -0.00 – 0.02 Winter -0.06 0.02 <0.001 -0.09 – -0.03 Spring Fall -0.03 0.01 <0.001 -0.04 – -0.01 Summer -0.02 0.01 0.007 -0.03 – -0.01 Winter -0.09 0.02 <0.001 -0.12 – -0.06 Summer Fall -0.01 0.01 0.186 -0.02 – 0.00 Spring 0.02 0.01 0.007 0.01 – 0.03 Winter -0.07 0.02 <0.001 -0.10 – -0.04 Winter Fall 0.06 0.02 <0.001 0.03 – 0.09 Spring 0.09 0.02 <0.001 0.06 – 0.12 Summer 0.07 0.02 <0.001 0.04 – 0.10 124 Figure 5.1. Daily (24-h) movement distance for bobcats across seasons in northwest Montana. Movement data is available for one female and 3 male bobcats, with the exception of spring, which had 4 male bobcats. Error bars represent 1SE. 125 Figure 5.2. Daily (24-h) rate of movement for bobcats across seasons in northwest Montana. Movement data is available for one female and 3 male bobcats, with the exception of spring, which had 4 male bobcats. Error bars represent 1SE. 126 Figure 5.3. Fractal dimension of weekly movement paths for bobcats across seasons in northwest Montana. Movement data is available for one female and 3 male bobcats, with the exception of spring, which had 4 male bobcats. Female paths are N = 1 in winter, 13 in spring, 13 in summer, and 10 in winter. Male paths are 21 in winter, 46 in spring, 39 in summer, and 35 in fall. Error bars represent 1SE. 127 Figure 5.4. Comparison of a male and female weekly paths during the week of August 4-10, 2010. F2’s movements are represented in the left panel, where she covered an area of ~8 km 2 . Her Fractal D = 1.20, with 46 locations during this week. She covered 35.9 km, with an average distance of 780 m between 3 h relocations. M1’s movements are on the right panel, where he covered an area ~64 km 2 . His Fractal D = 1.07, with 44 locations, He traveled 62.4 km, with an average distance of 1419 m between relocations. 128 Figure 5.5. Weekly movement distance for bobcats across seasons in northwest Montana. Movement data is available for one female and 3 male bobcats, with the exception of spring, which had 4 male bobcats. Error bars represent 1SE. 129 Figure 5.6. Fractal dimension of weekly movement paths for individual bobcats across all seasons. Data are for one female bobcat (F2) and 4 male bobcats. F2 had 1 winter path, 13 spring, 13 summer, and 10 fall paths. M1 had 13 paths for each season. M2 had 8 winter and 13 paths each for remaining seasons. M3 had 1 winter path, 13 spring and summer paths each, and 9 fall paths. M4 had 8 spring paths. Error bars represent 1SE. 130 CHAPTER 6 CONCLUSIONS 6.1. OVERVIEW In this thesis, I have examined the idea that bobcats (Lynx rufus) in northwest Montana exhibit behavioral and phenotypic plasticity that allow them to overwinter in a deep snow environment. The bobcat can serve as an effective model species for understanding the behavioral flexibility and potential range expansion of other generalist species, and may elucidate how similar mesocarnivores will respond to anthropogenic impacts on ecosystems. Potentially, behavioral adaptations could allow bobcats to expand their habitat niche at the northern extent of their geographic distribution if individuals are able to employ strategies that allow them to use environments that they are not well suited for physiologically and morphologically. Behavioral flexibility with respect to home range and habitat use, movement capabilities, and generalized prey use and prey switching abilities may increase direct competition between bobcats and Canada lynx (Lynx canadensis) for food and space where these species are currently sympatric, and as bobcats expand their distribution north. My results suggest that harsh winter environments do not necessarily limit bobcat distribution and home range dynamics. Deep winter snow does affect some aspects of bobcat ecology, but bobcats appear to employ a mix of generalist and specialist behaviors unique to this population of bobcats in northwest Montana that allow them to overwinter successfully in a mountainous setting that receives >300 cm of snow. For example, bobcats in my research did not reduce winter home range size, nor did they switch habitat use in the winter. The lack of habitat use shifts and contraction of winter home ranges is in contrast to past research, which indicates that bobcats are limited by deep snow (Bailey 1974, Koehler and Hornocker 1989, Litvaitis et al. 1986b, Apps 1996, Kapfer 2012). Deep winter snows reduced home range size (Fuller et al. 1985, Koehler and Hornocker 1989), caused bobcats to seek snow free habitats (Koehler and Hornocker 1989), and may have limited northern geographic distribution (Kapfer 2012). 131 6.2. BOBCAT ENERGY BALANCE IN WINTER Bobcats, like all organisms, face the challenge of meeting daily energy requirements—caloric needs—to sustain them. The individual is out of energy balance when daily energy expenditure exceeds daily energy intake; the bobcat will burn stored fat reserves. If DEE exceeds daily energy intake frequently enough, fat stores will deplete, and the ultimate result for the animal is starvation. Winter is a time of particular challenge for a predator in a deep snow area, as movement in cold temperatures and soft snows greatly increases energetic expenditures, while prey species and prey numbers are reduced, as many prey species hibernate and are largely unavailable as a source of energy. Bobcats face this seasonal challenge in northwest Montana where the balance of energy expenditure versus energy intake is tipped more strongly towards energy expenditure. Past research has indicated the bobcats begin thermoregulating at -2.2°C (Mautz and Pekins 1989). Coupled with high foot loadings that cause bobcats to sink in soft snows, winter conditions can greatly increase bobcat caloric requirements during the winter when prey is scarce and it is most difficult to satisfy energy demands in extreme environments. Knowledge of the behavioral strategies bobcats employ to overwinter successfully in such areas will better inform our understanding of the behavioral plasticity that allows bobcats to expand their distribution and make use of conditions thought to be limiting to the species. One of my main goals in this thesis was to investigate if bobcats depended on snowshoe hares (Lepus americanus) as their main prey during winter in northwest Montana. The winter diet of various bobcat populations in northern latitudes is composed of up to 71% (x̄ = 39.4%) lagomorphs (snowshoe hare and Sylvilagus spp.) (Chapter 2). Winter diet in percent biomass for bobcats in northwest Montana was composed primarily of 54% red squirrel (Tamiascuirus hudsonicus), while snowshoe hare was only 12.2%. Bobcats in northwest Montana did not depend on snowshoe hares in winter during the time of this study. Bobcats mostly ate prey <1.0 kg, with this prey category being 78.5% of winter biomass consumed. Indeed, bobcats could be considered a facultative squirrel specialist during winter in northwest Montana. Animals can be considered facultative specialists if they have a lower realized dietary niche during at least one spatial or temporal scale, but have a broad enough fundamental dietary niche that allows them to expand their diet during times of less difficult environmental conditions (Shipley et al. 2009). Use of such small prey 132 throughout the winter would necessitate a kill rate by bobcats of one prey item every 2.5 hours, which is plausible given the hunting behavior of bobcats and other felids. Winter dietary requirements have strong implications for bobcat energetics and ability to meet daily energy needs. Snowshoe hare are common throughout TLRD and the Salish Range, but deep winter snows may reduce the value of this prey species to bobcats on a seasonal basis. It is possible that hares consumed in winter were taken opportunistically through ambush hunting techniques where little energy is expended in capturing hares. However, bobcats may not chase hares farther than one or two bounds, as a hare is likely to escape a bobcat in soft, powdery snow. A bobcat would sink deep into the snow in such conditions, expend large amounts of energy, and be unlikely to succeed in such a chase past the initial attack. Low consumption of snowshoe hares by bobcats in northwest Montana during winter may reflect behavioral choices on the part of bobcats that allow individual animals to conserve critical energy in winter (Chapter 3). In short, snowshoe hares may be difficult for bobcats to catch in deep snow, and other prey, such as red squirrels, offer a greater energy return to a bobcat. Squirrels often wander to the forest floor to visit food caches and movement in deep snow is difficult for squirrels, leaving them more vulnerable to bobcat predation. Additionally, bobcats are excellent climbers, and I observed numerous sets of bobcat tracks chasing squirrel tracks up a tree, with the claw marks of the bobcat evident on the tree. Bobcats made other behavioral adaptations that led to overwinter success in the harsh winter environment of northwest Montana. Bobcats significantly reduced daily movement distances in winter as compared to other seasons. Winter movements were 56 and 58% of summer movements for females and males, respectively. Other studies have attributed reduced winter movement to limitation in deep snow (Koehler and Hornocker 1989, Litvaitis et al. 1986b); however, I would argue that this is not a limitation in areas of deep snow, but rather a behavioral strategy that reduces daily energetic costs associated with movements. Reducing daily distance moved allows bobcats to reduce energy expenditures and ‘save’ those calories for potentially lean hunting times, severe cold snaps, or winter storms that deliver copious snow. Movements in deep snow conditions are energetically costly for bobcats, and reduced 133 winter movements are likely a long-term behavioral strategy that conserves small amounts of energy on a daily basis. Energy conservation on a daily basis would allow bobcats to meet overwinter energy requirements, thus increasing the chances of surviving the winter. In essence, reduction of daily movements may be part of a bet-hedging strategy, wherein bobcats reduce energetic expenditures on a daily basis to save energy reserves for periods of higher energetic demand, where hunting success may be low or a particularly long cold spell or severe snowfall occurs. Furthermore, bobcats employ other behavioral strategies to conserve energy in winter. I observed individual bobcats (collared and uncollared) moving during the daytime. For example, several bobcats were seen sitting in sunny spots on snowmobile trails and roads. Additionally, telemetry often indicated that collared bobcats were active during daytime hours, and bobcat tracks crossed trails after we had been through an area earlier in the day. Movement during daylight hours and sunning would reduce thermoregulatory costs. Bobcats couple behavioral adaptations to winter with a variety of physiological adaptations. Mass gain prior to the onset of winter is a common adaptation in birds and mammals that increases the chances of winter survival (Ydenberg et al. 2010, Mustonen et al. 2012). Bobcats gain mass prior to the onset of winter. Although I did not explicitly measure body fat, bobcats necropsied from December fur trapping had copious fat around internal organs; some individuals had fat deposits that were several inches thick. Additionally, some radiocollared individuals exhibited mass loss and subsequent gain over the course of winter, indicating that fat reserves were used and restored throughout the winter. Such fat deposits are a critical component of body condition in predators because it is a direct source of energy between meals (Caughley and Sinclair 1994, Robitaille and Cobb 2003, Robitaille et al. 2012). Indeed, individuals with larger fat reserves have better fasting endurance (Schulte-Hostedde et al. 2005), which gives these individuals a selective advantage when energy demands are high and prey consumption does not meet short term energy needs (Buskirk and Harlow 1989). Prior research indicates that areas with high elevation, low temperatures, low local prey abundance, and deep snows that restrict travel may lead to starvation in bobcats due to lack of hunting success and subsequent use of fat reserves (Petraborg and Gunvalson 1962, Matlack 1984, Matlack and Evans 1992). However, I observed no overwinter starvation by bobcats during the course of my study. Bobcats used 134 average winter elevations of 1456 m, which was only 12 m lower than summer elevations, snows exceeded >300 cm at 1300 m over the winter, and temperatures of -45°C were observed, and yet bobcats displayed patterns of mass loss and subsequent regain over the course of winter. Bobcats are thought to have a very high lower critical temperature of -2.2°C (Mautz and Pekins 1989). At ambient temperatures lower than this TLC, bobcats have increased energetic costs due to thermoregulation. Yet, it is possible that bobcats that are routinely exposed to harsh winter conditions have a lower TLC, thereby reducing winter thermoregulatory costs. Animals that use a wide range of habitats (i.e. generalists) may be able to withstand extremes in environmental conditions and prey availability (Coltrane and Barboza 2010). Western bobcats, including Lynx rufus pallescens, have a distinct evolutionary history, and may be well adapted to the particular areas and habitats they live in (Reding 2011), meaning that local population adaptation to severe winter conditions is a possibility in this region. Bobcats in northwest Montana did not reduce winter home range size or selectively use different habitats in winter. Latitudinal variation in bobcat home range size indicates that bobcats near the northern periphery of their geographic distribution require large home ranges to satisfy caloric requirements, particularly in winter. Other studies have shown that bobcats shift habitat use in winter to take advantage of snow free areas that reduce energetic costs of movement and provide easier access to prey (Koehler and Hornocker 1989). However, there are no snow free areas available to bobcats in my study. My results suggest that bobcats in northwest Montana balance the high energetic costs of moving through deep snows by moving less in winter than in other seasons. Large home ranges may compensate for increased time spent in a particular location, which would invariably lead to depleted prey numbers on a fine scale. Large home ranges were still used over the course of the winter, but each area was visited less frequently than during snow-free months, when movement distances and rate were increased. It is plausible that when navigating their home ranges, bobcats chose areas where snow was more compact in order to reduce movement costs, and bobcats took advantage of snow crusting and compaction events that would greatly reduce the costs of moving through soft snow. Bobcats were habitat generalists and did not shift habitats in winter, suggesting that 135 home ranges were situated to provide optimal foraging, escape, and thermal cover throughout the year. Bobcats did not alter how they moved through the landscape on a seasonal basis. One might expect that if bobcats were restricted in movement or to certain habitats in winter, that movements would become more convoluted to account for the increased need to turn in order to remain in a smaller space. How bobcats moved through their landscape varied little over the seasons, indicating that snow conditions were not restricting individuals to certain parts of their home range. In fact, bobcats moved throughout their home range continually over the course of the year, regardless of season. My research suggests that bobcats are not limited in distribution or movements by areas that have deep winter snows per se, but that bobcats can successfully use habitats that experience harsh winters if a number of factors are present. These factors are: (1) a relative abundance of prey <1 kg (squirrels and other rodents) that are available to bobcats during the winter, and (2) areas that experience cyclic freezing and thawing that leads to hard, crusted snow which reduces cost of winter movements. Locally adapted bobcat populations in northwest Montana may employ behavioral strategies that coupled with physiological adaptations, increase their chances of enduring long, persistent winters. This population of bobcat in northwest Montana are likely to be a source population for bobcats in British Columbia and potentially Alberta. Individuals bobcats in this study demonstrate behavorial and phenotypic plasticity that can account for the current range expansion of bobcats (Sunquist and Sunquist 2002), particularly north of 50° latitude in British Columbia and Alberta (Alexander and Gailus 2005, Lobo and Millar 2010). Thus this population of bobcats is a suitable model for evaluating areas in British Columbia and Alberta where bobcats may be able to colonize and establish local populations. 6.3. A GENERALIST IN A SPECIALIST’S WORLD Bobcats are a widespread North American felid, with a range extending north into the southern Canadian provinces and south into central Mexico (Anderson 1987). Bobcats are both habitat and dietary generalists, being highly flexible in habitat requirements and prey requirements across their large range. Bobcat populations appear secure throughout North America, with evidence of a large-scale population increase for data collected between 1996- 2008 (Roberts and Crimmins 2010). There have been indications that bobcats may be 136 expanding their distribution north, particularly in British Columbia, Canada, due to habitat fragmentation and forest clearing (Hall 1981, Boyle 1987, Nowell and Jackson 1996, Sunquist and Sunquist 2002), though recent reports from 6 of 7 Canadian provinces only indicate stable bobcat populations (Roberts and Crimmins 2010). Forest fragmentation may favor the generalist bobcat, which is capable of using many different types of habitats. Furthermore, bobcats can make use of small habitat patches, and have the behavioral flexibility to adapt to a high degree of edge habitat. Such habitat fragmentation and forest clearing may favor bobcats if these activities increase prey populations preferred by bobcats. However, little is known about current bobcat range expansion (Hatler et al. 2008), though there is evidence of bobcats occurring above 50°N (Lobo and Millar 2010). Canada lynx, the closest relative of bobcats, are a specialist predator, and are highly dependent on snowshoe hares as their primary prey, particularly during high phases in the snowshoe hare population cycle. Hares comprise 75-100% of lynx diets (Ruggiero et al. 2000), though lynx may engage in more prey switching in patchy southern habitats (Quinn and Parker 1987, Lewis and Wenger 1998), with red squirrels being a main alternate prey species. Lynx were listed as federally Threatened in the lower 48 states in (USFWS 2000) under the Endangered Species Act. The initial listing of lynx was in response to declining lynx numbers, a probable result of multiple human activities including anthropogenic habitat fragmentation (Ruggiero et al. 2000). Considering the dietary specialization of lynx and the protected status of the species, one of the main goals of my research was to determine if winter dietary competition between lynx and bobcats was occurring in northwest Montana. Bobcats in northwest Montana demonstrate a more specialized winter diet with narrower niche breadth than do other bobcat populations in northern latitudes. Montana bobcat diet is most similar to the dietary breadth of Canada lynx populations in the Rocky Mountains, particularly lynx populations in British Columbia. In this region, diets of Lynx spp. are composed of three main prey categories. Order of importance of these categories for bobcats in northwest Montana are squirrels, other rodents, and hares, while for lynx populations within 500 km of my study area, snowshoe hares, red squirrels, and other rodents are dietary preferences. The relative frequency of these prey items varies widely between lynx and bobcats, but as they depend largely on the same main prey types, the chances of competition are increased. 137 In addition to potential dietary competition in the Intermountain West, bobcats and lynx overlap greatly in habitat and elevational use. In the western United States, 83% of lynx occurrences were associated with Rocky Mountain Coniferous Forest, with 77% of occurrences within the 1500-2000 m elevation zone (McKelvey et al. 2000). Lynx were primarily associated with lodgepole pine (Pinus contorta), subalpine fir (Abies lasiocarpa), and Engelmann spruce (Picea engelmannii) habitat types (Aubry et al. 2000). In extreme northwestern Montana, Western Red Cedar (Thuja plicata)/Western hemlock (Tsuga heterophylla) habitat types are also often used (Ruediger et al. 2000). These habitat types and suitable snowshoe hare densities (Hodges and Mills, in prep) are available to lynx on TLRD. Additionally, snow conditions are generally more conducive to lynx with their large snowshoe-like feet than to bobcats; however, TLRD is heavily fragmented. Nearly 15% of the land area in TLRD is currently classified as areas of timber harvest. TLRD is nested within Region 1, the larger wildlife management area under the jurisdiction of Montana Fish, Wildlife, and Parks. Region 1 has just 5% of its total area in timber harvest (see Table 4.1). Habitats types occurring in TLRD that are preferred by lynx are often small and patchy. This area is highly disturbed, and although prey and habitats are available to lynx in the area, the landscape composition is more conducive to the generalist bobcat that is highly adaptable to fragmented landscapes. Coupled with high habitat fragmentation, snow conditions on TLRD may not confer an advantage to lynx over bobcats. Snow conditions in southern lynx habitats may be subject to more freezing and thawing than in northern lynx habitats (Buskirk et al. 2000a), with such differences largely dependent on elevation, aspect, and local weather conditions (Ruediger et al. 2000). Increased crusting and compaction of snow at the southern reaches of lynx range may reduce the competitive advantage that lynx have over other species such as bobcats and coyotes (Canis latrans) in soft snow (Buskirk et al. 2000b). The Salish Range where my study was conducted is prone to freezing and thawing events, which produce snow conditions that allow bobcats to successfully overwinter. High habitat fragmentation, increasing human disturbance, and the lack of consistently deep, soft winter snows, may allow bobcats to competitively exclude lynx in these settings. 138 6.4. IMPLICATIONS FOR THE FUTURE My research answered important questions about bobcat behavioral ecology in northwest Montana. My findings demonstrate the behavioral flexibility of this species in atypical settings. Bobcats are not limited by snow per se, as they still use large winter home ranges, do not shift winter habitats to avoid snow, and navigate the landscape in a similar fashion across seasons. Their ability to facultatively specialize on certain prey in winter and ability to meet energy requirements even when reducing winter movements exhibits that highly plastic behavior increases the success of bobcats in winter environments of northwest Montana. There have been recent proposals by the U.S. to remove the bobcat from CITES Appendix II; these proposals did not receive support (CITES 2010). Protection of the bobcat is vitally important for this species as the last population estimate for bobcats was conducted over 25 years ago. Current harvest management is based solely on annual fur returns, and not on actual population estimation methods, and there is evidence of regional population declines (Litvaitis et al. 2006). In addition to population numbers, we do not know relative population health; for example, rodenticide poisoning of bobcats through bioaccumulatory effects (e.g. when they consume poisoned rodents) has been linked with mange-associated mortality in California (Riley et al. 2007). These unintended and unforeseen human impacts on bobcat populations need to be closely monitored as bobcats may be vulnerable to the effects of increasing habitat fragmentation and urbanization (Riley et al. 2003). Furthermore, bobcats face continued trapping pressure, and may be particularly vulnerable to trapping in northern latitudes with harsh winters (Petraborg and Gunvalson 1962, Koehler and Hornocker 1989). It is important to build on our knowledge of bobcat ecology to further our understanding of how human impacts and climate change will affect bobcats, lynx, and the interaction between these closely related species. In light of aspects of bobcat behavior that I have demonstrated, there is explicit need for fine scale evaluation of bobcat movements in winter to determine if crusting and compaction of snow plays a key role in bobcat ability to successfully overwinter in northern latitudes. Such investigation of snow conditions used by bobcats should be coupled with evaluation of snow conditions where Canada lynx are known to occur within the region, to determine if there are real differences in site conditions that 139 favor lynx over bobcats. A model of regional snow conditions coupled with climate change models would enable researchers to predict areas that bobcats would be most likely to colonize, and determine which lynx populations are most likely to be impacted in the long term. Now is the time to be proactive with respect to bobcat conservation and management. 140 REFERENCES CITED Ackerman, B.B, Lindzey F.G., and T.P. Hemker. 1984. Cougar food habits in southern Utah. Journal of Wildlife Management 48:147-155. Ackerman, B.B., F.G. Lindzey, and T.P. Hemker. 1986. Predictive energetics model for cougars. Pages 333-352 in S.D. Miller and D.D. Everett, eds. Cats of the world. National Wildlife Federation, Washington, D.C. Aldama, J.J., J.F. Beltran, and M. Delibes. 1991. Energy expenditure and prey requirements of free-ranging Iberian lynx in southwestern Spain. Journal of Wildlife Management 55:635-641. Alexander, S., and J. Gailus. 2005. A GIS-based approach to restoring connectivity across Banff’s Trans-Canada Highway (Technical Report #4). Yukon to Yellowstone Conservation Initiative, Canmore, Alberta. 45 pp. Anderson, E.M. 1987. A critical review and annotated bibliography of literature on the bobcat. Colorado Division of Wildlife Special Report 62. Denver, Colorado. Anderson, E. M. 1987. Bobcat predation on a red-tailed hawk. Southwestern Naturalist 32:149–150. Anderson, E.M. 1988. Effects of male removal on spatial distribution of bobcats. Journal of Mammalogy 69:637-641. Anderson, E.M., and M.J. Lovallo. 2003. Bobcat and lynx. Pp 758-786 in G.A. Feldhamer, B.C. Thompson, and J.A. Chapman (Eds.). Wild Mammals of North America. Johns Hopkins University Press, Baltimore, Maryland. Apps, C.D. 1996. Bobcat (Lynx rufus) habitat selection and suitability assessment in south east British Columbia. M.S. Thesis, University of Calgary, Calgary, Alberta, Canada. 145 pp. Apps, C.D. 2000. Space-use, diet, demographics, and topographic associations of lynx in the southern Canadian Rockies. Pp 351-371, Chapter 12 in L.F. Ruggiero, K.B. Aubry, S.W. Buskirk, G.M. Koehler,C.J. Krebs, K.S. McKelvey, and J.R. Squires (Eds.). Ecology and conservation of lynx in the United States. University of Colorado Press, Denver, CO. 141 Atkinson, R.P.D., C.J. Rhodes, D.W. Macdonald, and R.M. Anderson. 2002. Scale-free dynamics in the movement patterns of jackals. Oikos 98:134-140. Aubry, K.B., G.M. Koehler, and J.R. Squires. 2000. Ecology of Canada lynx in southern boreal forests. Chapter 13 in L.F. Ruggiero, K.B. Aubry, S.W. Buskirk, G.M. Koehler,C.J. Krebs, K.S. McKelvey, and J.R. Squires (Eds.). Ecology and conservation of lynx in the United States. University of Colorado Press, Denver, CO. Azevedo, F.C.C., and D.L. Murray. 2007. Evaluation of potential factors predisposing live- stock to predation by jaguars. Journal of Wildlife Management 71:2379-2386. Bailey, T.N. 1974. Social organization in a bobcat population. Journal of Wildlife Management 38:435-446. Baker, L.A., R.J. Warren, and W.E. James. 1993. Bobcat prey digestibility and representation in scats. Proceedings of the Annual Conference of the Southeast Association of Fish and Wildlife Agencies 47:71-79. Barraquand, F., and S. Benhamou. 2008. Animal movements in heterogeneous landscapes: identifying profitable places and homogeneous movement bouts. Ecology 89:3336– 3348. Bascompte, J., and C. Vila. 1997. Fractals and search paths in mammals. Landscape Ecology 12:213-221. Bekoff, M., and L.D. Mech. 1984. Computer simulation: Simulation analyses of space use: Home range estimates, variability, and sample size. Behavior Research Methods, Instruments, and Computers 16: 32–37. Bell, W.J. 1991. Searching behavior: the behavioral ecology of finding resources. Chapman and Hall, New York, New York, USA. 372 pp. Benson, J.F., M.J. Chamberlain, and B.D. Leopold. 2004. Land tenure and occupation of vacant home ranges by bobcats (Lynx rufus). Journal of Mammalogy 85:983-988. Benson, J.F., M.J. Chamberlain, and B.D. Leopold. 2006. Regulation of space use in a solitary felid: population density or prey availability. Animal Behavior 71:685-693. Berg, J.E. 2007. The carnivore assemblage of La Payunia Reserve, Patagonia, Argentina: dietary niche, prey availability, and selection. M.S. thesis. University of Montana, Missoula, MT. 50 pp. 142 Biro, D., R. Freeman, J. Meade, S. Roberts, and T. Guilford. 2007. Pigeons combine compass and landmark guidance in familiar route navigation. Proceedings of the National Academy of Sciences 104:7417-7476. Bolnick, D.I., R. Svanbäck, J.A. Fordyce, L.H. Yang, J.M. Davis, C.D. Hulsey, and M.L. Forrister. 2003. The ecology of individuals: incidence and implications of individual specialization. The American Naturalist 161:1–28. Boulanger, J. G., and G.C. White. 1990. A comparison of home-range estimators using Monte Carlo simulation. Journal of Wildlife Management 54:310–315. Boyer, D., and P.D. Walsh. 2010. Modelling the mobility of living organisms in heterogeneous landscapes: does memory improve foraging success? Philosophical Transactions of the Royal Society, A. Mathematical, Physical & Engineering Sciences 368:5645-5659. Boyle, K.A. 1987. Habitat suitability index models: bobcat. Biol. Rep. 82 (10.147). Washington, D.C. Department of the Interior, Fish and Wildlife Service. 16 pp. Bradley, L.C., and D.B. Fagre. 1988. Coyote and bobcat responses to integrated ranch management practices in south Texas. Journal of Range Management 41:322-327. Brainerd, S. M. 1985. Reproductive ecology of bobcats and lynx in western Montana. M.S. Thesis, University of Montana, Missoula, MT. 85 pp. Brand, C.J., L.B. Keith, and C.A. Fischer. 1976. Lynx responses to changing snowshoe hare densities in central Alberta. Journal of Wildlife Management 40:416-428. Brown, J.H., J.F. Gillooly, A.P. Allen, V.M. Savage, and G.B. West. 2004. Toward a metabolic theory of ecology. Ecology 85:1771-1789. Buie, D.E., T.T. Fendley, and H. McNab. 1979. Fall and winter home ranges of adult bobcats on the Savannah River Plant, South Carolina. Pp. 42–46, In L.G. Blum and P.C. Escherich (Eds.). Proceedings of the Bobcat Research Conference. National Wildlife Federation Scientific and Technical Series 6. National Wildlife Federation, Washington, DC. Buskirk, S.W., and H.J. Harlow. 1989. Body-fat dynamics of the American marten (Martes americana) in winter. Journal of Mammalogy 70:191-193. Buskirk, S.W., L.F. Ruggiero, K.B. Aubry, D.E. Pearson, J.R. Squires, and K.S. McKelvey. 2000a. Comparative ecology of lynx in North America. Chapter 14 in L.F. Ruggiero, 143 K.B. Aubry, S.W. Buskirk, G.M. Koehler, C.J. Krebs, K.S. McKelvey, and J.R. Squires, (Eds.). Ecology and conservation of lynx in the United States. University of Colorado Press, Denver, CO. Buskirk, S.W., L.F. Ruggiero, and C.J. Krebs. 2000b. Habitat fragmentation and interspecific competition: implication for lynx conservation. Chapter 4 in L.F. Ruggiero, K.B. Aubry, S.W. Buskirk, G.M. Koehler, C.J. Krebs, K.S. McKelvey, and J.R. Squires (Eds.). Ecology and conservation of lynx in the United States. University of Colorado Press, Denver, CO. Calder, W.A., III. 1984. Size, function and life history. Harvard University Press, Cambridge, Massachusetts, USA. Calder, W.A. 1996. Size, function, and life history. Dover Publications, Mineola, NY. 448 pp. Carmichael L.E., W. Clark, and C. Strobeck. 2000. Development and characterization of microsatellite loci from lynx (Lynx canadensis) and their use in other fields. Molecular Ecology 9:2197-2198. Casey, T.M., P.C. Withers, and K.K. Casey. 1979. Metabolic and respiratory responses of arctic mammals to ambient temperature during the summer. Comparative Biochemis try and Physiology 64:331–341. Caughley, G., and A.R.E. Sinclair. 1994. Wildlife ecology and management. Blackwell Science, Cambridge,Massachusetts, USA, 334 pp. Chamberlain, M.J. and B.D. Leopold. 2001. Spatio-temporal relationships among adult bobcats in central Mississipppi. Pp. 45-50 in A.C. Woolf, C.K. Nielsen, and R.D. Bluett (Eds.). Proceedings of a symposium on current bobcat research and implications for management. The Wildlife Society 2000 Conference, Nashville, Tennessee. Clavel, J., Jullliard, R., and V. Devictor. 2011. Worldwide decline of specialist species: toward a global functional homogenization? Frontiers in Ecology and Environment 9:222–228. Cochrane, J.E., J.D. Kirby, I.G. Jones, L.M. Conner, and R.J. Warren. 2006. Spatial organization of adult bobcats in a longleaf pine-wiregrass ecosystem in southwestern Georgia. Southeastern Naturalist 5:711-724. 144 Coltrane, J.A., and P.S. Barboza. 2010. Winter as a nutritional bottleneck for North American porcupines (Erithizon dorsatum). Journal of Comparative Physiology B 180:905-918. Conner, L.M., M.J. Chamberlain, and B.D. Leopold. 2001. Bobcat home-range size relative to habitat quality. Proceedings of the Annual Conference of the Southeastern Association of Fish and Wildlife Agencies 55:418-426. Convention on International Trade in Endangered Species of Wild Fauna and Flora. 2004. Amendments to Appendices I and II of CITES: Deletion of bobcat (Lynx rufus) from Appendix II. Thirteenth Meeting of the Conference of the Parties. Bangkok, Thailand, October 2–14, 2004. Convention on International Trade in Endangered Species of Wild Fauna and Flora. 2010. Consideration of proposals for amendment of Appendices I and II: Removal of Lynx rufus (bobcat) from Appendix II. Fifteenth Meeting of the Conference of the Parties. Doha, Qatar, 13-25 March 2010. Crist, T.O., D.S. Guertin, J.A. Wines, and B.T. Milne. 1992. Animal movement in heterogeneous landscapes: an experiment with Eleodes beetles in shortgrass prairie. Functional Ecology 6:536-544. Crooks, K.R. 2002. Relative sensitivity of mammalian carnivores to habitat fragmentation. Conservation Biology 16:488-502. Crooks K.R., and M.E. Soulé. 1999. Mesopredator release and avifaunal extinctions in a fragmented system. Nature 400:563–566. de Azevedo, F.C.C. 2008. Food habits and livestock depredation of sympatric jaguars and pumas in the Iguaçu National Park Area, South Brazil. Biotropica 40:494-500. DeCesare, N.J., J.R. Squires, and J.A. Kolbe. 2005. Effect of forest canopy on GPS-based movement data. Wildlife Society Bulletin 33:935-941. Devictor, V., R. Julliard, J. Clavel, F. Jiguet, A. Lee, D. Couvet. 2008. Functional biotic homogenization of bird communities in disturbed landscapes. Global Ecology and Biogeography 17:252–261. Devictor, V., J. Clavel, R. Julliard, S. Lavergne, D. Mouillot, W. Thuiller, P. Venail, S. Villeger, and N. Mouquet. 2010. Defining and measuring ecological specialization. Journal of Applied Ecology 47:15-25. 145 Diefenbach, D.R., L.A. Hansen, R.J. Warren, and M.J. Conroy. 2006. Spatial organization of bobcats reintroduced to an island. Journal of Mammalogy 87:394-401. Donovan, T.M., M. Freeman, H. Abouelezz, K. Royar, A. Howard, and R. Mickey. 2011. Quantifying home range habitat requirements for bobcats (Lynx rufus) in Vermont, USA. Biological Conservation 114:2799-2809. Edwards, M.A., Forbes, G.J., and J. Bowman. 2001. Fractal dimension of ermine, Mustela erminea (Carnivora: Mustelidae), movement patterns as an indicator of resource use. Mammalia 65:220-225. Elizalde-Arellano, C., J.C. López-Vidal, L. Hernández, J.W. Laundré, F.A. Cervantes, and M. Alonso-Spilsbury. 2012. Home range size and activity patterns of bobcats (Lynx rufus) in the southern part of their range in the Chihuahuan Desert, Mexico. American Midland Naturalist 168:247-264. Emmons, L.H. 1987. Comparative feeding ecology of felids in a Neotropical rainforest. Behavioral Ecology and Sociobiology 20: 271–283. Erickson, A.W. 1955. An ecological study of the bobcat in Michigan. M.S. Thesis. Michigan State University. 133 pp. Etzenhouser, M.J., M.K. Owens, D.E. Spalinger, and S.B. Murden. 1998. Foraging behavior of browsing ruminants in a heterogeneous landscape. Landscape Ecology 13:55-64. Fendley, T.T., and D.E. Buie. 1986. Seasonal home range and movement patterns of the bobcat on the Savannah River Plant. Pp 237-259 in S.D. Miller and D.D. Everett (Eds.). Cats of the World: Biology, Conservation, and Management. National Wildlife Federation, Washington, D.C. Ferguson, A.W., N.A. Currit, and F.W. Weckerly. 2009. Isometric scaling in home-range size of male and female bobcats (Lynx rufus). Canadian Journal of Zoology 87:1052- 1060. Ferry-Graham, L.A., D.I. Bolnick, and P.C. Wainwright. 2002. Using functional morphology to examine the ecology and evolution of specialization. Integrative and Comparative Biology 42:265–277. Flathead National Forest. 2006. Proposed Land Management Plan, Appendix 58, Flathead Forest Plan Amendment 21, Appendix IV, Subbasins and Geographic Unit Descriptions. 146 Foresman, K.R. 2001. The wild mammals of Montana. Special Publication No. 12. American Society of Mammalogists. 278 pp. Foster, J.B. 1964. Evolution of mammals on islands. Nature 202:234-235. Fox, L.B. 1990. Ecology and population biology of the bobcat, Felis rufus in New York. Ph.D. Dissertation, State University of New York, Syracuse, New York. Fredrickson, L., and J.L. Mack. 1995. Mortality, home ranges, movements, and habitat preferences of South Dakota bobcats, 1990-94. Rep. No. 7544, South Dakota Department of Game, Fish, and Parks, Pierre, South Dakota. Fuller, A.K., and D.J. Harrison. 2010. Movement paths reveal scale-dependent habitat decisions by Canada lynx. . Journal of Mammalogy 91:1269-1279. Fuller, T.K., W.E. Berg, and D.W. Kuehn. 1985. Bobcat home range size and daytime cover- type use in northcentral Minnesota. Journal of Mammalogy 66:568-571. Gallistel, C. R. 1990. The organization of learning. MIT Press, Cambridge, Massachusetts. Gautestad, A.O., L.E. Loe, and A. Mysterud. 2013. Inferring spatial memory and spatiotemporal scaling from GPS data: comparing red deer Cervus elaphus movements with simulation models. Journal of Animal Ecology. Early online view: doi: 10.1111/1365-2656.12027. Gillis, E.A., and V.O. Nams. 1998. How red-backed voles find habitat patches. Canadian Journal of Zoology 76:791-794. Gittleman, J.L., and P.H. Harvey. 1982. Carnivore home range size, metabolic needs and ecology. Behavioral Ecology and Sociobiology 10:57-63. Godbois, I.A., L.M. Conner, and R.J. Warren. 2003. Bobcat diet on an area managed for northern bobwhite. Proceedings of the Southeastern Association of Fish and Wildlife Agencies 57:222-227. Gotelli, N.J. and G.L. Entsminger. 2011. EcoSim: Null models software for ecology. Version 7.72. Acquired Intelligence Inc. & Kesey-Bear. Jericho, Vermont. http://garyentsminger.com/ecosim.htm. Grant, P.R. 1965. The adaptive significance of some size trends in island birds. Evolution 19:355-367. Griffin, J.C. 2001. Bobcat ecology on developed and less-developed portions of Kiawah Island, South Carolina. M.S. thesis, The University of Georgia, Athens. 147 Gustafson, K.A. 1984. The winter metabolism and bioenergetics of the bobcat in New York. M.S. Thesis, University of New York, Syracuse. 112pp. Hall, R.E. 1981. The mammals of North America. 2nd ed. Volume 2, New York: John Wiley and Sons. 1271 pp. Hamilton, D.A. 1982. Ecology of the bobcat in Missouri. M.S. thesis. University of Missouri—Columbia. 132 pp. Hamilton, Jr., W.J., and R.P. Hunter. 1939. Fall and winter food habits of Vermont bobcats. Journal of Wildlife Management 3:99-103. Hansen. K. 2007. Bobcat, master of survival. Oxford University Press, New York, USA. 212 pp. Harestad, A.S., and F.L. Bunnell. 1979. Home range and body weight—a reevaluation. Ecology 60:389-402. Hatler et al. 2008. Bobcat distribution in British Columbia. In Klinkenberg, Brian. (Editor) 2012. E-Fauna BC: Electronic Atlas of the Fauna of British Columbia [efauna.bc.ca]. Lab for Advanced Spatial Analysis, Department of Geography, University of British Columbia, Vancouver. [Accessed: 4/23/2013 12:26:47 PM] Hodges, K.E., R. Boonstra, and C.J. Krebs. 2006. Overwinter mass loss of snowshoe hares in the Yukon: starvation, stress, adaptation or artifact? Journal of Animal Ecology 75:1-13. Homyack, J.A., J.H. Vashon, C.Libby, E.L. Lindquist, S. Loch, D.F. McAlpine, K.L. Pilgrim, and M.K. Schwartz. 2008. Canada lynx-bobcat (Lynx canadensis × L. rufus) hybrids at the southern periphery of lynx range in Maine, Minnesota and New Brunswick. The American Midland Naturalist 159:504-508. Hoving, C.L., D.J. Harrison, W.B. Krohn, W.J. Jakubas, and M.A. McCullough. 2004. Canada lynx Lynx canadensis habitat and forest succession in northern Maine, USA. Wildlife Biology 10:285-294. Humphries, N. E., et al. 2010. Environmental context explains Levy and Brownian movement patterns of marine predators. Nature 465:1066-1069. Hurlbert, S.H. 1978. The measurement of niche overlap and some relatives. Ecology 59:67- 77. Iverson, J.A. 1972. Basal energy metabolism of mustelids. Journal of Comparative 148 Physiology 81:341-344. Jacoby, D.M.P., E.J. Brooks, D.P. Croft, D.W. Sims. 2012. Developing a deeper understanding of animal movements and spatial dynamics through novel application of network analyses. Methods in Ecology and Evolution 3:574-583. Janečka, J.E., T.L. Blankenship, D.H. Hirth, M.E. Tewes, C.W. Kilpatrick, and L.I. Grass man Jr. 2006. Kinship and social structure of bobcats (Lynx rufus) inferred from microsatellite and radio-telemetry data. Journal of Zoology 269:494-501. Johnson, D.H. 1980. The comparison of usage and availability measurements for evaluating resource preference. Ecology 61:65-71. Kamler, J.F., and P.S. Gipson. 2000. Home range, habitat selection, and survival of bobcats, Lynx rufus, in a prairie ecosystem in Kansas. Canadian Field Naturalist 114:388-394. Kapfer, P.M. 2012. Bobcat (Lynx rufus) spatial ecology and harvest in Minnesota. Ph.D. Dissertation, University of Minnesota. 107 pp. Kernohan, B.J., Gitzen, R.A., and J.J. Millspaugh, 2001. Analysis of animal space use and movements. In: Millspaugh, J.J., J.J. Marzluff. (Eds.), Radio Tracking and Animal Populations. Academic Press, San Diego. Pp. 474. Kingsolver, J.G., and R.B. Huey. 1998. Evolutionary analyses of morphological and physiological plasticity in thermally variable environments. American Zoologist 38:545-560. Kinley, T.A. 1992. Ecology and management of bobcats (Lynx rufus) in the East Kootenay district of British Columbia. M.S. Thesis, University of Calgary, Calgary, Alberta, Canada. Kleiber, M. 1932. The theories of the relation between body size and metabolism. Hilgardia 6:322-353. Kleiber, M. 1961. The fire of life. An introduction to animal energetics. John Wiley and Sons, New York, USA. Knick, S.T. 1990. Ecology of bobcats relative to exploitation and a prey decline in southeastern Idaho. Wildlife Monographs 108:3-42. Knick, S. T., S. J. Sweeney, J. R. Alldredge, and J. D. Brittell. 1984. Autumn and winter food habits of bobcats in Washington State. Great Basin Naturalist 44:70–74. 149 Knowles, P.R. 1985. Home range size and habitat selection of bobcats, Lynx rufus, in north- central Montana. Canadian Field Naturalist 99:6-12. Koehler, G.M., and M.G. Hornocker. 1989. Influences of season on bobcats in Idaho. Journal of Wildlife Management 53:197-202. Kolbe, J.A., J.R. Squires, and T.W. Parker. 2003. An effective box trap for capturing lynx. Wildlife Society Bulletin 31:980-985. Kolbe, J.A., J.R. Squires, D.H. Pletscher, and L.F. Ruggiero. 2007. The effect of snowmobile trails on coyote movements within lynx home ranges. Journal of Wildlife Management 71:1409-1418. Krebs, J.R., J.T. Erichsen, M.I. Webber, and E.L. Charnov. 1977. Optimal prey selection in the great tit (Parus major). Animal Behaviour 25:30-38. Krebs, C.J. 1998. Ecological methodology. Benjamin Cummings. California. 624 pp. Laliberte, A.S., and W.J. Ripple. 2004. Range contractions of North American carnivores and ungulates. BioScience 54:123–138. Larivière, S., and L. R. Walton. 1997. Lynx rufus. Mammalian Species 563:1–8. Laundré, J.W. 2005. Puma energetics: a recalculation. Journal of Wildlife Management 69:723-732. Lawhead, D.N. 1984. Bobcat Lynx rufus home range, density, and habitat preference in south-central Arizona. Southwestern Naturalist 29:105-113. Levins, R. 1968. Evolution in changing environments: some theoretical explorations. Princeton University Press, Princeton, New Jersey. 120 pp. Lewis, L., and C.R. Wenger. 1998. Idaho’s Canada lynx: pieces of the puzzle. Idaho Bureau of Land Management Technical Bulletin No. 98-11. Linstedt, S.L., B.J. Miller, and S.W. Buskirk. 1986. Home range, time, and body size in mammals. Ecology 67:413-418. Litvaitis, J. A., C. L. Stevens, and W. W. Mautz. 1984. Age, sex, and weight of bobcats in relation to winter diet. Journal of Wildlife Management 48:632–635. Litvaitis, J. A., A. G. Clark, and J. H. Hunt. 1986a. Prey selection and fat deposits of bob cats (Felis rufus) during autumn and winter in Maine. Journal of Mammalogy 67:389–392. 150 Litvaitis, J. A., J.A. Sherburne, and J.A. Bissonette. 1986b. Bobcat habitat use and home range size in relation to prey density. Journal of Wildlife Management 50:110-117. Litvaitis. J.A., J.T. Major, and J.A. Sherburne. 1987. Influence of season and human- induced mortality on spatial organization of bobcats (Felis rufus) in Maine. Journal of Mammalogy 68:100-106. Litvaitis, J.A., and D. J. Harrison. 1989. Bobcat-coyote niche relationships during a period of coyote population increase. Canadian Journal of Zoology 67:1180-1188. Litvaitis, J.A., J.P. Tash, and C.L. Stevens. 2006. The rise and fall of bobcat populations in New Hampshire: relevance of historical harvests to understanding current patterns of abundance and distribution. Biological Conservation 128:517-528. Lobo, N., and J.S. Millar. 2010. Photographic evidence of bobcats, Lynx rufus, in the Kananaskis Valley in southwestern Alberta. The Canadian Field Naturalist 124:260- 262. Lovallo, M.J., and E.M. Anderson. 1996. Bobcat (Lynx rufus) home range size and habitat use in northwest Wisconsin. American Midland Naturalist 135:241-252. MacArthur, R.H. 1972. Geographical Ecology. Harper & Row, New York, NY. 288 pp. Maher, D.S. 1996. Comparative ecology of the bobcat, black bear, and Florida panther in south Florida. Ph.D. dissertation, University of Florida, Gainesville. Major, J.T. 1983. Ecology and interspecific relationships of coyotes, bobcats, and red foxes in western Maine. Ph.D. Dissertation, University of Maine, Orono. 64 pp. Maletzke, B. 2004. Winter habitat selection of lynx (Lynx canadensis) in northern Washington. M.S. Thesis. Washington State University, Pullman, WA. 48 pp. Maletzke, B. T., G. M. Koehler, R. B. Wielgus, K. B. Aubry, and M. A. Evans. 2008. Habitat conditions associated with lynx hunting behavior during winter in northern Washington. Journal of Wildlife Management 72:1473–1478. Malo, A.F., J. Lozano, D.L. Huertas, and E. Virgós. 2004. A change of diet from rodents to rabbits (Oryctolagus cuniculus). Is the wildcat (Felis silvestris) a specialist predator? Journal of Zoology, 263:401-407. Marston, M.A. 1942. Winter relations of bobcats to white-tailed deer in Maine. Journal of Wildlife Management 6:329-337. 151 Martins, E.G., M.S. Araujo,V. Bonato, and S.F.D. Reis. 2008. Sex and season affect individual-level diet variation in the Neotropical marsupial Gracilinanus microtarsus (Didelphidae). Biotropica 40:132–135. Marzluff, J.M., J.J. Millspaugh, and M.S. Handock. 2004. Relating resources to a probabilistic measure of space use: forest fragments and Stellar’s jays. Ecology 85:1411–1427. Matlack, C.R., and A.J. Evans. 1992. Diet and condition of bobcats (Lynx rufus) in Nova Scotia during autumn and winter. Canadian Journal of Zoology 70:1114-1119. Mautz, W.W., and P.J. Pekins. 1989. Metabolic rate of bobcats as influenced by seasonal temperatures. Journal of Wildlife Management 53:202-205. May, R.M. 1977. Predators that switch. Nature 269:103-104. Mayr, E. 1963. Animal species and evolution. Belknap Press, Cambridge, Massachusetts, USA. McCord, C.M. 1974. Selection of winter habitat by bobcats (Lynx rufus) on the Quabbin Reservation, Massachusetts. Journal of Mammalogy 55:428-437. McCord, C. M., and J. E. Cardoza. 1982. Bobcat and lynx: Felis rufus and F. lynx. Pages 728–768 in J. A. Chapman, and G. A. Feldhamer (Eds.). Wild Mammals of North America: biology, management, and economics. Johns Hopkins University Press, Baltimore, Maryland. McKelvey, K.S., K.B. Aubry, and Y.K. Ortega. 2000. History and distribution of lynx in the contiguous United States. Chapter 8 in L.F. Ruggiero, K.B. Aubry, S.W. Buskirk, G.M. Koehler, C.J. Krebs, K.S. McKelvey, and J.R. Squires, eds. Ecology and conservation of lynx in the United States. University of Colorado Press, Denver, CO. McLean, M.L., T.S. McCay, and M.J. Lovallo. 2005. Influence of age, sex, and time of year on diet of the bobcat (Lynx rufus) in Pennsylvania. American Midland Naturalist153:450- 453. McNab, B.K. 1971. On the ecological significance of Bergmann’s Rule. Ecology 52:845- 854. McNab, B.K. 1980. On estimating thermal conductance in endotherms. Physiological Ecology 53:145-156. McNab, B.K. 1986. The influence of food habits on the energetics of eutherian mammals. 152 Ecological Monographs 56:1-19. McNab, B.K. 1992. The comparative energetics of rigid endothermy: the Arvicolidae. Journal of Zoology, London 227:585-606. McNab, B.K. 2000. The standard energetics of mammalian carnivores: Felidae and Hyaenidae. Canadian Journal of Zoology 78:2227-2239. McNab, B.K. 2002. The physiological ecology of vertebrates: a view from energetics. Comstock/Cornell University Press, Ithaca, New York, USA. Meiri, S. 2011. Bergmann’s Rule—what’s in a name? Global Ecology and Biogeography 20:203-207. Millar, J.S., and G.J. Hickling. 1990. Fasting endurance and the evolution of mammalian body size. Functional Ecology 4:5-12. Mills L.S., J.J. Citta, K.P. Lair, M.K. Schwartz, and D.A. Tallmon. 2000. Estimating animal abundance using noninvasive DNA sampling: Promise and pitfalls. Ecological Applications 10:283-294. Mills L.Ss, K. Pilgrim, M.K. Schwartz, K. McKelvey. 2001. Identifying lynx and other North American felids based on mtDNA analysis. Conservation Genetics 1:285-289. Mock, J.M. 2004. Spatial patterns and habitat use of an unharvested bobcat population in Aransas National Wildlife Refuge. M.S. thesis, Texas A&M University—Kingsville, Kingsville. Moen, A.N. 1973. Wildlife ecology, an analytical approach. W.H. Freeman and Co., San Francisco, California, USA. Moen R., C.L. Burdett, and G.J. Niemi. 2008. Movement and habitat use of Canada lynx during denning in Minnesota. Journal of Wildlife Management 72:1507-1513. Montana Trapper’s Association. 2013. MTA home page. <www.montanatrappers.org> Accessed 23 Jun 2013. Moore, T.D., L.E. Spence, and C.E. Dugnolle. 1974. Identification of the dorsal guard hairs of some mammals of Wyoming. Wyoming Game Fish Dept. Bull. No. 14. 177 pp. Mowat, G., K.G. Poole, and M. O’Donoghue. 2000. Ecology of lynx in northern Canada and Alaska. Chapter 9 in L.F. Ruggiero, K.B. Aubry, S.W. Buskirk, G.M. Koehler, C.J. Krebs, K.S. McKelvey, and J.R. Squires (Eds.). Ecology and conservation of lynx in the United States. University of Colorado Press, Denver, CO. 153 Muñoz-Garcia, A., and J.B. Williams. 2005. Basal metabolic rate in carnivores is associated with diet after controlling for Phylogeny. Physiological and Biochemical Zoology 78:1039-1056. Mustonen, A-M., T. Lempiäinen, M. Aspelund, P. Hellstedt, K. Ikonen, J. Itämies, V. Vähä, J. Erkinaro, J. Asikainen, M. Kunnasranta, P. Niemelä, J. Aho, and P. Nieminen. 2012. Application of change-point analysis to determine winter sleep patterns of the raccoon dog (Nyctereutes procyonoides) from body temperature recordings and a multi- faceted dietary and behavioral study of wintering. BMC Ecology 12:27 available online at http://www.biomedcentral.com/1472-6785/12/27. Nagy, K.A. 2005. Field metabolic rate and body size. Journal of Experimental Biology 208:1621-1625. Nams, V.O. 1996. The VFractal: a new estimator for fractal dimension of animal movement paths. Landscape Ecology 11:289-297. Nams, V.O. 2004. Using animal movement paths to measure response to spatial scale. Oecologia 143:179-188. Nams, V.O. 2006. Improving accuracy and precision in estimating fractal dimension of animal movement paths. Acta Biotheoretica 54:1-11. Nams, V.O. and M. Bourgeois. 2004. Using fractal analysis to measure habitat use at different spatial scales: an example with marten. Canadian Journal of Zoology 82:1738-1747. National Oceanic and Atmospheric Administration. 2013. Daily and monthly temperatures, precipitation, snowfall, and wind speeds. Neale, J.C.C., and B.N Sacks. 2001. Resource utilization and interspecific relations of sympatric bobcats and coyotes. Oikos 94:236-249. Newsom, W.M. 1930. The common bobcat a deer killer. American Game19:42-50. Nielsen, C.K., and A. Woolf. 2001. Survival of unexploited bobcats in southern Illinois. Journal of Wildlife Management 66:833-838. Nowell, K. and P. Jackson.1996. Wild Cats.Status Survey and Conservation Action Plan. IUCN/SSC Cat Specialist Group, Gland, Switzerland and Cambridge, UK. 154 O’Donoghue, M., S. Boutin, C.J. Krebs, G. Zuleta, D.L. Murray, and E.J. Hofer. 1998. Functional responses of coyotes and lynx to the snowshoe hare cycle. Ecology 79:1193-1208. O’Keefe, J., and L. Nadel. 1978. The hippocampus as a cognitive map. Oxford University Press, Oxford, United Kingdom. Olden, J.D., R.L. Schooley, J.B. Monroe, and N.L. Poff. 2004. Context-dependent perceptual ranges and their relevance to animal movements in landscapes. Journal of Animal Ecology 73:1190-1194. Pace, R.M., III. 2001. Estimating and visualizing movement paths from radio-tracking data. Pages 189-206 in J.J. Millspaugh and J.M Marzluff (Eds.). Radio tracking and animal populations. Academic Press, San Diego, California, USA. Painter, D.J. 1991. Home range characteristics of bobcats in the Land between the Lakes, Kentucky. M.S. thesis, Eastern Kentucky University, Richmond. Panzacchi, M., J.D.C. Linnell, J. Odden, M. Odden, and R. Andersen. 2008. When a generalist becomes a specialist: patterns of red fox predation on roe deer fawns under contrasting conditions. Canadian Journal of Zoology 86:116-126. Parker, G.R., J.W. Maxwell, L.D. Morton, and G.E.J. Smith. 1983. The ecology of the lynx (Lynx canadensis) on Cape Breton Island. Canadian Journal of Zoology 61:770-786. Parker, G. R., and G. E. J. Smith. 1983. Sex- and age-specific reproductive and physical parameters of the bobcat (Lynx rufus) on Cape Breton Island, Nova Scotia. Canadian Journal of Zoology 61:1771–1782. Patterson, T.A., L. Thomas, C. Wilcox, O. Ovaskainen, and J. Matthiopoulos, J. 2008. State-space models of individual animal movement. Trends in Ecology & Evolution 23:87-94. Petraborg, W.H., and V.E. Gunvalson. 1962. Observations on bobcat mortality and bobcat predation on deer. Journal of Mammalogy 43:430-431. Pianka, E.R. 1974. Niche overlap and diffuse competition. Proceedings of the National Academy of Sciences 71:2141-2145. Pollack, E.M. 1951. Food habits of bobcats in the New England states. Journal of Wildlife Management 15:209-213. 155 Poole, K.G. 1994. Characteristics of an unharvested lynx population during a snowshoe hare decline. Journal of Wildlife Management 58:608-618. Poore, A., and N. Hill. 2006. Sources of variation in herbivore preference: among- individual and past diet effects on amphipod host choice. Marine Biology 149:1403- 1410. Powell, R.A. 1979. Ecological energetics and foraging strategies of the fisher (Martes pennanti). Journal of Animal Ecology 48:195-212. Powell, R.A. 2000. Animal home ranges and territories and home range estimators. Pp 65- 110, Chapter 3 in L. Boitani and T.K. Fuller (Eds.). Research Techniques in Animal Ecology. Columbia University Press, New York. Powell, R.A., T.W. Clark, L. Richardson, and S.C. Forrest. 1985. Black-footed ferret Mustela nigripes energy expenditure and prey requirements. Biological Conservation 34:1-15. Powell, R. A. 1987. Black bear home range overlap in North Carolina and the concept of home range applied to black bears. International Conference on Bear Research and Management 7:235–242. Powers, J.G., W.M. Pekins, and P.J. Mautz. 1989. Nutrient and energy assimilation of prey by bobcats. Journal of Wildlife Management 53:1004-1008. Price, T.D., A. Qvarnström, and D.E. Irwin. 2003. The role of phenotypic plasticity in driving genetic evolution. Proceedings Biological Sciences 270:1433-1440. Quinn, N.W.S., and G. Parker. 1987. Lynx. Pages 682-695 in Wild furbearer management and conservation in North America. M. Novak, J.A. Baker, M.E. Obbard, and B. Malloch, eds. Ministry of Natural Resources, Ontario, Canada. Ravingné, V., U. Dieckmann, and I. Olivieri. 2009. Live where you thrive: joint evolution of habitat choice and local adaptation facilitates specialization and promotes diversity. The American Naturalist 174:e-article. Reding, D.M. 2011. Patterns and processes of spatial genetic structure in a mobile and continuously distributed species, the bobcat (Lynx rufus). PhD dissertation, Iowa State University, Des Moines, Iowa. 195 pp. 156 Reynolds, J.C, and N.J. Aebischer. 1991. Comparison and quantification of carnivore diet by faecal analysis: a critique, with recommendations, based on a study of the Fox Vulpes vulpes. Mammal Review 21:97-122. Richards, C.L, O.Bossdorf, N.Z. Muth, J. Gurevitch, and M. Pigliucci. 2006. Jack of all trades, master of some? On the role of phenotypic plasticity in plant invasions. Ecology Letters 9:981-993. Ricklefs, R.E., M. Konarzewski, and S. Daan. 1996. The relationship between basal metabolic rate and daily energy expenditure in birds and mammals. The American Naturalist 147:1047-1071. Riley, S.P.D. 2006. Spatial ecology of bobcats and gray foxes in urban and rural zones of a national park. Journal of Wildlife Management 70:1425-1435. Riley, S.P.D., R.M. Sauvajot, T.K. Fuller, E.C. York, D.A. Kamradt, C. Bromley, and R.K. Wayne. 2003. Effects of urbanization and habitat fragmentation on bobcats and coyotes in southern California. Conservation Biology 17:566-576. Riley, S.P.D., C. Bromley, R.H. Poppenga, F.A. Uzal, L. Whited, and R.M. Sauvajot. 2007. Anticoagulant exposure and notoedric mange in bobcats and mountain lions in urban southern California. Journal of Wildlife Management 71:1874-1884. Robbins, C.T. 1983. Wildlife feeding and nutrition. Academic Press, New York, New York, USA. Roberts, N.M., and S.M. Crimmins. 2010. Bobcat population status and management in North America: evidence of large-scale population increase. Journal of Fish and Wildlife Management 1:169-174. Robitaille, J-F., and E.W. Cobb. 2003. Indices to estimate fat deposits in American marten Martes americana. Wildlife Biology 9: 113-121. Robitaille, J-F., L. Villano, T.S. Jung, H.P. Slama, and M.P. Oakley. 2012. Fat dynamics and development of body condition indices for harvested populations of wolverine Gulo gulo. Wildlife Biology 18:35-45. Roemer, G.W., M.E. Gompper, and B. Van Valkenburgh. 2009. The ecological role of the mammalian mesocarnivore. Bioscience 59:165-173. Rolley, R.E. 1985. Dynamics of a harvested bobcat population in Oklahoma. Journal of Wildlife Management 49:283-292. 157 Rolley, R. E. 1987. Bobcat. Pages 671–681 in M. Novak, J. A. Baker, M. E. Obbard, and B. Malloch, eds. Wild furbearer management and conservation in North America. Ministry of Natural Resources, Ontario, Canada. Rollings, C.T. 1945. Habits, foods and parasites of the bobcat in Minnesota. Journal of Wildlife Management 9:131-145. Roth, J.D., J.D. Marshall, D.L. Murray, D.M. Nickerson, and T.D. Steury. 2007. Geographical gradients in diet affect population dynamics of Canada lynx. Ecology 88:2736-2743. Rucker, R.A., M.L. Kennedy, G.A. Heidt, and M.J. Harvey. 1989. Population density, movements, and habitat use of bobcats in Arkansas. Southwestern Naturalist 34:101- 108. Ruediger, B., J. Claar, S. Gniadek, B. Holt, L. Lewis, S. Mighton, B. Naney, G. Patton, T. Rinaldi, J. Trick, A. Vandehey, F. Wahl, N. Warren, D. Wenger, and A. Williamson. 2000. Canada lynx conservation assessment and strategy. USDA Forest Service, USDI Fish and Wildlife Service, USDI Bureau of Land Management, and USDI National Park Service. Missoula, MT. 132 pp. Ruggiero, L.F., K.B. Aubry, S.W. Buskirk, G.M. Koehler, C.J. Krebs, K.S. McKelvey, and J.R. Squires, eds. 2000. Ecology and conservation of lynx in the United States. University of Colorado Press, Denver, CO. 480 pp. Saeki, M., P.J. Johnson, and D.W. Macdonald. 2007. Movements and habitat selection of raccoon dogs (Nyctereutes procyonoides) in a mosaic landscape. Journal of Mammalogy 88:1098-1111. Sandell, M. 1989. The mating tactics and spacing patterns of solitary carnivores. Pp. 164- 182. J.L. Gittleman (Ed.) in Carnivore behavior, ecology, and evolution. Cornell University Press, Ithaca, New York. Savage, V. M., J. F. Gillooly, W. H. Woodruff, G. B. West, A. P. Allen, B. J. Enquist, and J. H. Brown. 2004. The predominance of quarter-power scaling in biology. Functional Ecology 18:257-282. Schoener, T.W. 1971. Theory on feeding strategies. Annual Review of Ecology and Systematics 2:369-404. 158 Schoener, T.W. 1974a. The compression hypothesis and temporal resource partitioning. Proceedings of the National Academy of Sciences 71:4169-4172. Schoener, T.W. 1974b. Resource partitioning in ecological communities. Science 185:27- 39. Schoener,T.W. 1986. Resource partitioning. In: Kikkawa, J. and Anderson, D. J. (Eds.), Community ecology: pattern and process. Blackwell, pp. 91-126. Schulte-Hostedde, A.I., B. Zinner, J.S. Millar, and G.J. Hickling, 2005. Restitution of mass/size residuals: validating body condition indices. Ecology 86:155-163. Schwartz, M.K., K.L. Pilgrim, K.S. McKelvey, E.L. Lindquist, J. J. Claar, S. Loch, and L.F. Ruggerio. 2004. Hybridization between Canada lynx and bobcats: genetic results and management implications. Conservation Genetics 5:349–355. Senft, R.L., Coughenour, M.B., Bailey, D.W., Rittehhouse, L.R., Sala, O.E., and D.M. Swift. 1987. Large herbivore foraging and ecological hierarchies. BioScience 37:789-799. Shipley, L.A., J.S. Forbey, and B.D. Moore. 2009. Revisiting the dietary niche: when is a mammalian herbivore a specialist? Integrative and Comparative Biology 49:274-290. Sikes, R.S., W.L. Gannon, and the Animal Care and Use Committee of the American Society of Mammalogists. 2011. Guidelines of the American Society of Mammalogists for the use of wild animals in research. Journal of Mammalogy 92:235-253. Silverman, B.W. 1986. Density estimation for statistics and data analysis. Chapman and Hall, London, UK. Smith, D. S. 1984. Habitat use, home range, and movements of bobcats in western Montana. M.S. Thesis, Univ. of Montana, Missoula, MT. 58 pp. Spencer, W.D. 2012. Home ranges and the value of spatial information. Journal of Mammalogy 93:929-947. Squires, J.R., and L.F. Ruggiero. 2007. Winter prey selection of Canada lynx in northwestern Montana. Journal of Wildlife Management 71:310-315. Squires, J.R., N.J. DeCesare, J.A. Kolbe, and L.F. Ruggiero. 2008. Hierarchical den selection of Canada lynx in western Montana. Journal of Wildlife Manage 72:1497- 1506. 159 Squires, J.R., N.J. Decesare, J.A. Kolbe, and L.F. Ruggiero. 2010. Seasonal resource selection of Canada lynx in managed forests of the Northern Rocky Mountains. The Journal of Wildlife Management 74:1648–1660 Stapp, P., and B. Van Horne. 1997. Response of deer mice (Peromyscus maniculatus) to shrubs in shortgrass prairie: linking small-scale movements and spatial distribution of individuals. Functional Ecology 11:644-651. Steudel, K., W.P. Porter, and D. Sher. 1994. The biophysics of Bergmann’s rule: a comparison of the effects of pelage and body size variation on metabolic rate. Canadian Journal of Zoology 72:70-77. Stenseth, N.C., A. Shabbar, K. Chan, S. Boutin, E.K. Rueness, D. Ehrich, J.W. Hurrell, O.C. Lingjaerde, and K.S. Jakobsen. 2004. Snow conditions may create an invisible barrier for lynx. PNAS 101:10632-10634. Sugihara, G. and R.M. May. 1990. Applications of fractals in ecology. Trends in Ecology and Evolution 5:79-86. Sullivan, K.J. 1995. Diel activity and movement patterns of adult bobcats in central Mississippi. M.S. thesis. Mississippi State University. 120 pp. Sunquist, M. and F. Sunquist.2002. Wild Cats of the World.University of Chicago Press. Svanbäck, R. and L. Persson. 2004. Individual diet specialization, niche width and population dynamics: implications for trophic polymorphisms. Journal of Animal Ecology 73:973–982. Taylor, C.R., K. Schmidt-Neilsen, and J.L. Raab. 1970. Scaling of energetic cost of running to body size in mammals. American Journal of Physiology 219:1004-1007. Tewes, M.E., J.M. Mock, and J.H. Young. 2002. Bobcat predation on quail, birds, and mesomammals. Pages 65–70 in S. J. DeMaso, W. P. Kuvlesky, Jr., F. Hernańdez, and M. E. Berger (Eds.). Quail V: Proceedings of the Fifth National Quail Symposium. Texas Parks and Wildlife Department, Austin, TX. Tinker, M.T., G. Bentall, and J.A. Estes. 2008. Food limitation leads to behavioral diversification and dietary specialization in sea otters. Proceedings of the National Academy of Sciences, USA105:560–565. Toweill, D.E., and R.G. Anthony. 1988. Annual diet of bobcats in Oregon’s Cascade Range. Northwest Science 62:99-103. 160 Tuomainen, U., and U. Candolin. 2011. Behavioural responses to human-induced environmental change. Biological Reviews 86:640-657. Turchin, P. 1998. Quantitative analysis of movement: measuring and modeling population redistribution in animals and plants. Sinauer Associates, Sunderland, Massachusetts, USA. United Nations Environment Programme-World Conservation Monitoring Centre. 2009. UNEP-WCMC home page. <http://www.unep-wcmc.org/>. Accessed 23 Jun 2013. United States Fish and Wildlfie Service. 2009. Endangered and Threatened Wildlife and Plants; Revised Designation of Critical Habitat for the Contiguous United States Distinct Population Segment of the Canada Lynx; Final Rule. Department of the Interior, Fish and Wildlife Service, Federal Register 50 CFR Part 17. 88 pp. United States Fish and Wildlife Service. 2000. Endangered and Threatened Wildlife and Plants; Determination of Threatened Status for the Contiguous U.S. Distinct Population Segment of the Canada Lynx and Related Rule; Final Rule. Department of the Interior, Fish and Wildlife Service, Federal Register 50 CFR Part 17. 36 pp. Van Moorter, B., D. Visscher, S. Benhamou, L.B., Orger, M.S. Boyce, and J.M. Gaillard. 2009. Memory keeps you at home: a mechanistic model for home range emergence. Oikos 118:641-652. van Tienderen, P.H. 1991. Evolution of generalists and specialist in spatially heterogeneous environments. Evolution 45:1317-1331. Vashon, J.H., A.L. Meehan, W.J. Jakubas, J.F. Organ, A.D. Vashon, C.R. McLaughlin, G.J. Matula, Jr., and S.M. Crowley. 2008. Spatial ecology of a Canada lynx population in northern Maine. Journal of Wildlife Management 72:1479-1487. Ward, R.M.P., and C.J. Krebs. 1985. Behavioural responses of lynx to declining snowshoe hare abundance. Canadian Journal of Zoology 63:2817-2824. Wassmer, D.A., D.D. Guenther, and J.N. Layne. 1988. Ecology of the bobcat in south- central Florida. Bulletin of the Florida State Museum of Biological Sciences 33:159- 228. Webb, S.L., S.K. Riffell, K.L. Gee, and S. Demaris. 2009. Using fractal analyses to characterize movement paths of white-tailed deer and response to spatial scale. Journal of Mammalogy 90:1210-1217. 161 Westfall, C. Z. 1956. Foods eaten by bobcats in Maine. Journal of Wildlife Management 20:199–200. White, G. C., and R.A. Garrott. 1990. Analysis of wildlife radio-tracking data. New York: Academic Press. Whitefish Mountain Resort. 2013. Snowfall statistics. Whitefish, Montana. Wiens, J.A. and B.T. Milne. 1989. Scaling of “landscapes” in landscape ecology, or, landscape ecology from a beetle’s perspective. Landscape Ecology 3:87-96. Wiens, J.A., T.O. Crist, K.A. With, and B.T. Milne. 1995. Fractal patterns of insect movement in microlandscape mosaics. Ecology 76:663-666. Wilson, R.R. 2010. Identifying and understanding the spatial distribution of bobcat and coyote behavior. Ph.D. dissertation Utah State University. 133 pp. With, K.A. 1994. Using fractal analysis to assess how species perceive landscape structure. Landscape Ecology 9:25-36. Witmer, G.W. and D.S. DeCalesta. 1986. Resource use by unexploited sympatric bobcats and coyotes in Oregon. Canadian Journal of Zoology 64:2333-2338. Wolf, M., J.L. Frair, E. Merrill, and P. Turchin. 2009. The attraction of the known: the importance of spatial familiarity in habitat selection in wapiti Cervus elaphus. Ecography 32: 401–410. Woo, K.J., K.H. Elliott, M. Davidson, A.J. Gaston, and G.K. Davoren. 2008. Individual specialization in diet by a generalist marine predator reflects specialization in foraging behavior. Journal of Animal Ecology 77:1082–1091. Wright, B.E. 2010. Use of chi-square tests to analyze scat-derived diet composition data. Marine Mammal Science 26:395-401. Ydenberg, R.C., D. Dekker, G. Kaiser, P.C.F. Shepherd, L.E. Ogden, K. Rickards, And D.B. Lank. 2010. Winter body mass and over-ocean flocking as components of danger management by Pacific dunlins. BMC Ecology 10 available online http://www.biomedcentral.com/1472-6785/10/1. Young, S.P. 1928. Bobcat kills deer. Journal of Mammalogy 9:64-65. Zezulak, D.S. 1980. Northeastern California bobcat study. Final report, Project E-W-2, Study IV, California Department of Fish and Game, Sacramento, California. Zezulak, D.S., and R.G. Schwab. 1979. A comparison of density, home range and habitat 162 utilization of bobcat populations at Lava Beds and Joshua Tree National Monuments, California. Proceedings of the Bobcat Research Conference, National Wildlife Federation Scientific and Technical Series 6:74-79. Zollner, P.A., and S.L. Lima. 1997. Landscape-level perceptual abilities in white-footed mice: perceptual range and the detection of forested habitat. Oikos 80:51-60. 163 APPENDICES APPENDIX A BOBCAT GENETICS IN THE SALISH MOUNTAINS OF NORTHWEST MONTANA Bobcats (Lynx rufus) and Canada lynx (Lynx canadensis) hybridization has been documented in the wild (Schwartz et al. 2004, Homyack et al. 2008), and there is evidence that hybrids are fertile (Homyack et al. 2008). Seven confirmed hybrids have been detected in two states and one province: Maine (N=2), Minnesota (N=3), and New Brunswick (N=2). Of these 7 individuals, 2 were male and 5 were female. All animals were identified as having lynx mtDNA, i.e. the mother of the individual was a lynx. It is unknown if hybridization is a common or uncommon occurrence between lynx and bobcats where these species overlap in distribution, and determining the frequency of hybridization is important as hybridization could limit the distribution and recovery of lynx in the contiguous United States, where lynx are protected as Threatened under the Endangered Species Act (Schwartz et al. 2004). Bobcats and lynx are sympatric in northwest Montana, and the potential for hybrid events is present. Furthermore, the area where this research was conducted (Tally Lake Ranger District) has a history of lynx sightings. Thirty years of lynx sighting data for TLRD were obtained from the Flathead National Forest, and includes sightings of a female lynx with kittens in 1999. Several sightings occurred between 2007-2011 when I conducted fieldwork; however, none of these contemporary sightings occurred during winter, so species ID could not be verified using tracks and no photographic evidence was obtained by the individual(s) reporting the sighting. One putative lynx sighting during May 2010 took place on a day where a radiocollared bobcat was within 50 m of the reported sighting on that date and time. The area with the reported female lynx with kittens and 5 additional lynx sightings in the past 14 years is centered in the area where 4 radiocollared male bobcats, 1 radiocollared female bobcat, 1 eartagged male bobcat, and 1 eartagged female bobcat were located. All bobcat samples (referenced in Chapter 4 bobcat handling) collected in this study 164 were tested to confirm species ID and investigate potential hybridization between bobcats and lynx. These samples included 10 individuals captured and handled, and 47 bobcat carcasses collected from fur trappers throughout the Salish Mountains for diet analysis. Samples were sent to the Wildlife Genetics Laboratory at the USDA Forest Service Rocky Mountain Research Station in Missoula, Montana for investigation of bobcat/lynx hybridization. Samples were tested using the approach described by Schwartz et al. (2004), and compared to a large reference library of known bobcat and lynx individuals. This approach was initially developed as part of the National Lynx survey to non-invasively determine Canada lynx distribution across the United States, and is specifically designed to distinguish amongst the four felid species in North America (lynx, bobcat, cougar [Puma concolor], domestic cat [Felis catus]) using mtDNA (Mills et al. 2000). The Mills et al. (2000) protocol in blind tests, which Schwartz et al. (2004) follows, correctly identified felids to species 100% of the time. Specifically, microsatellite markers Lc106 and Lc110 (Carmichael et al. 2001), were amplified as these alleles are non-overlapping in bobcats and lynx (Schwartz et al. 2004). Lc110 is fixed at 80 base pairs (bp) in bobcats, and has frequencies between 91 and 103 in lynx, while Lc106 has alleles between 98 and 110 bp in lynx and 88 to 90 bp in bobcats (Carmichael et al. 2001). Geographical consistency of allele frequencies for these microsatellites was evaluated on 108 known pure lynx samples and 79 known pure bobcat samples (Schwartz et al. 2004). Tests using 16sRNA to investigate mtDNA for possible hybridization with lynx and direction of hybridization (Mills et al. 2001) and the analysis at microsatellites (Schwartz et al. 2004) revealed that all mtDNA came from bobcat origin, and that all samples tested as pure bobcat (Newbury, Schwartz, and Hodges, unpublished data). Field effort (Appendix B) and genetic results strongly suggest that lynx are not sympatric with bobcats in this area of the Salish Range, and the majority of putative lynx sightings in the Salish Mountains, particularly on the Tally Lake Ranger District, are false positives, i.e. these animals are bobcats that were misidentified as lynx. Genetic testing for hybridization in this study is the first large scale effort to determine levels of hybridization between bobcats and lynx where their ranges overlap. Hybridization may be an uncommon phenomenon between lynx and bobcats; however, if 165 lynx are absent or transient in the Salish Mountains of northwest Montana, it would be advantageous to test for hybridization in areas where lynx and bobcats are known to co-occur, to determine if increased levels of hybridization are found in these areas. Areas where lynx and bobcats are known to occur within the same drainages are the Glacier View Ranger District of the Flathead National Forest to the east of the Salish, and the Purcell Mountains (Yaak River drainage) in the Kootenai National Forest to the west of the Salish. 166 APPENDIX B BOBCAT AND CANADA LYNX TRACKING AND TRAPPING EFFORT Field work took place from January 2008 until April 2011. During this time field effort was focused on 4 winter field seasons totaling 16 months, 2 summer field seasons totaling 6 months, and 3 months spent in the lab. Additionally, once animals were collared during Winter 2009-2010, monitoring of animals took place year round. Winter 2008 was a survey effort, as the project at that time was focused on Canada lynx (Lynx canadensis). This winter I experienced numerous logistical problems and the appropriate animal handling permits had not come through. Winter 2009 saw the ability to trap and handle Canada lynx, but not bobcats. I captured 7 bobcats (Lynx rufus), which were released without handling. Trapping efforts during 2009-2011 were focused on the northwest region of TLRD, to specifically capture bobcats that were located in the same area as 13 snowshoe hare (Lepus americanus) mark-recapture sites (Hodges and Mills, unpublished data). Winter 2009-2010 resulted in the capture and collaring of 8 bobcats (6M, 2F); however, one female was killed by coyotes, and I was unable to trap another bobcat before the end of field season and beginning of bear emergence in order to place that collar out in the field. Winter 2010-2011 resulted in the failure of radiocollars to release properly, necessitating retrapping animals to recover data. Two bobcats (1M, 1F) were harvested by local furtrappers, with collars returned to me. I recaptured 3 male bobcats to recover the radiocollars. Two collared males remain at large. Additionally, I captured an unmarked male and an unmarked female bobcat during this season. Over the course of 4 winter field seasons, I put in >8500 km of snowtrack survey effort via snowmobiles, and 3 winter field seasons resulted in 2058 live trap nights. During 4 years of winter field effort, no lynx were captured and no lynx tracks were detected in the snow (Table B.1). Surveys were conducted throughout all areas of TLRD, with effort made to survey the entire study site at least once per week. Bobcat sign was found at all elevations and habitats throughout TLRD. 167 Table B.1. Survey and trapping effort on Tally Lake Ranger District by year showing number of bobcat and lynx captures. Field season Month/ Survey Trap nights Lynx Bobcat* Year distance (km) captures captures 1 a 01-04/2008 2800 0 0 0 2 b 01-04/2009 2720 450 0 7 3 c 12/2009-04/2010 2610 968 0 8 4 c† 12/2010-04/2011 2100 640 0 5 *Bobcat captures are reported as individual animals captured and does not include recaptures of individuals. a This research was initiated as a Canada lynx study, and though federal permits were in place to trap and collar lynx, state permits had not been obtained yet, so no trapping effort took place. b In Field Season 2, trapping permits for lynx were in place, but no lynx tracks were found, not lynx captured. However, 7 individual bobcats were captured, but state permits were not in place to handle bobcats. These individuals were released without handling. c Trapping and handling permits were in place for both bobcat and lynx during Seasons 3 and 4. Only bobcat tracks were encountered across the study area and only bobcats captured. †Trapping during Season 4 was focused on retrieving radiocollars off known individuals, as the release mechanism on all collars had failed. 168 APPENDIX C Table C.1. GIS source information for basemap layers used to overlay bobcat (Lynx rufus) home ranges and quantify resource selection in a patchy landscape. Citations are provided for layers that had specific sources, while a link to the GIS files and metadata are provided for all GIS coverages used. GIS layer Source and Citation Website for access Montana Land Cover Framework 2010 Montana Geographic Information Clearinghouse http://nris.mt.gov/gis/default.asp http://nris.mt.gov/nsdi/nris/mdb/MSDI_Land_Cover_2010.zip  Montana Natural Heritage Program (MTNHP). 2010. Montana Land Cover/Land Use Theme. Based on classifications originally developed by the University of Idaho, Sanborn and the MTNHP for the Pacific Northwest ReGAP project. Helena, Montana. Roads Flathead and Kootenai National Forest Geospatial Information Tally Lake Ranger District Flathead National Forest GIS Specialist http://www.fs.usda.gov/main/flathead/landmanagement/gis Montana Roads from TIGER/Line files, 2000 http://gisportal.msl.mt.gov/geoportal/catalog/main/home.page  U.S. Census Bureau, Geography Division. 2001. Montana Roads from TIGER/Line Files, 2000. Helena, Montana, Montana State Library. DEM 30m USDA Forest Service Northern Region Geospatial Library http://www.fs.usda.gov/main/r1/landmanagement/gis https://fs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb5381449.zip Watershed Flathead and Kootenai National Forest Geospatial Information Tally Lake Ranger District Flathead National Forest GIS Specialist http://www.fs.usda.gov/main/flathead/landmanagement/gis Snow persistence USDA Forest Service Northern Region Geospatial Library http://www.fs.usda.gov/main/r1/landmanagement/gis  Copeland, J. 2009. North America Persistent Spring Snow Cover 2000_2006. RMRS Forest Sciences Lab, Missoula MT. https://fs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb5299440.zip Canada lynx (Lynx canadensis) habitat for Forest Service lands USDA Forest Service Northern Region Geospatial Library http://www.fs.usda.gov/main/r1/landmanagement/gis https://fs.usda.gov/Internet/FSE_DOCUMENTS/fsp5_030436.zip 169 Table C.1 continued. GIS source information for basemap layers used to overlay bobcat home ranges and quantify resource selection in a patchy landscape. Citations are provided for layers that had specific sources, while a link to the GIS files and metadata are provided for all GIS coverages used. GIS layer Source and Citation Website for access Canada lynx linkage zones USDA Forest Service Northern Region Geospatial Library http://www.fs.usda.gov/main/r1/landmanagement/gis https://fs.usda.gov/Internet/FSE_DOCUMENTS/fsp5_030435.zip Land ownership Flathead and Kootenai National Forest Geospatial Information Tally Lake Ranger District Flathead National Forest GIS Specialist http://www.fs.usda.gov/main/flathead/landmanagement/gis Fire history Flathead and Kootenai National Forest Geospatial Information Tally Lake Ranger District Flathead National Forest GIS Specialist 170 APPENDIX D SEASONAL ELEVATION AND HABITAT TYPE OF INDIVIDUAL BOBCAT GPS LOCATIONS Bobcats (Lynx rufus) often use lower elevations in winters, to take advantage of decreased snow depth, or access snow free habitats (Koehler and Hornocker 1989). Additionally, bobcats are thought to be restricted from using higher elevations in winter due to increased snow depth with increased elevation (Litvaitis et al. 1986b, Apps 1996). Thus seasonal differences in elevation of bobcat locations could indicate limitation by deep snow habitats, or behavioral strategies to mitigate high winter energy expenditures. A total of 7,664 GPS locations were collected from 5 radiocollared bobcats (4M, 1F) between 12 December 2009 and 9 December 2010. Location accuracy was high; error was 4.00 ± 0.07 m, based on a random subsample of 1,855 locations. Given such high location accuracy, I was confident in habitat classification as assigned in GIS. I acknowledge that female bobcats may differ from male bobcats in habitat selection; however, as only one female was collared, her data is pooled with the males to provide a broader interpretation of which habitats bobcats select on a seasonal basis. All bobcats showed wide variation in habitat selection across individuals. Winter locations (N = 719) were at 1456 ± 6.1 m in elevation (range 1001-1794 m). Winter locations were predominantly in mixed species dry coniferous (20.7%), Ponderosa pine (Pinus ponderosa) (19.6%), Lodgepole pine (Pinus contorta) (17.2%), and wet site Engelmann Spruce (Picea engelmannii)-Subalpine fir (Abies lasiocarpa) (9.7%) stands. Remaining winter locations were divided amongst several habitat types, such as dry site Engelmann Spruce-Subalpine fir, mixed wet (mesic) coniferous stands, and harvested areas. Spring locations (N = 2,757) were at 1429 ± 3.1 m (range 1015-1892 m), and were predominantly found in Lodgepole (18.3%), mixed wet coniferous (14.6%), wet site Engelmann spruce-subalpine fir (13.8%), and mixed species dry site coniferous stands (12.9%). Summer locations (N = 2,181) were at 1468 ± 3.5 m (range 987-1995 m), and were located in Lodgepole (18.9%), wet site Engelmann spruce-subalpine fir (15.9%), Ponderosa 171 (15.3%), and mixed species dry site coniferous stands (10.5%). Lastly, fall locations (N = 2,007) occurred at 1491 ± 3.6m (range 1032-1898 m), and were located in Lodgepole (20.6%), wet site Engelmann spruce-subalpine fir (17.5%), harvested (13.8%), and Ponderosa (12.8%) stands. Table D.1 provides a full seasonal breakdown of GPS collar habitat locations. Seasonal locations were found at significantly different elevations (F3,7660 = 59.76, p < 0.001), when all individuals were pooled. Pairwise comparisons of seasons show that elevation of winter and summer locations were not significantly different (Mean difference = -12.10, SE = 6.99, p = 0.08). However, winter locations were at significantly higher elevation than spring locations (Mean difference = 26.68, SE = 6.81, p < 0.001) and were at significantly lower elevation than fall locations (M = -35.52, SE = 7.07, p < 0.001). Spring locations were at significantly lower elevation than both summer (M = -38.78, SE = 4.66, p < 0.001) and fall (M = -62.20, SE = 4.77, p < 0.001) locations. Summer locations were significantly lower in elevation than fall locations (M = -23.42, SE = 5.03, p < 0.001) (Figure D.1). However, elevational data should be interpreted with caution as two collared bobcats (1M, 1F) were collared at low elevations (valley floor of 900-1200 m) as compared to the other bobcats (1400-1600 m). These low elevation individuals are strongly represented in spring locations, but of these two animals, only the female bobcat is represented in winter by 48 locations, skewing winter elevations to animals that were collared at mid- to high- study site elevations. It is more informative to look at within individual seasonal variation in elevation. All individuals with locations in four seasons showed a trend of lower winter and spring elevations as compared to locations at higher elevations in summer and fall (Figure D.2), except one male, with higher winter locations than spring locations (Table D.2). However, two individuals (1M, 1F) have winter season locations weakly represented; M3 has only 18 winter locations, and F2 has only 48 winter locations. M4 is represented only for the spring season, as his collar malfunctioned and failed after 2 months, and thus he is not included in Figure D.2. In general, the GPS collar locations demonstrate that bobcats are capable of using all elevations throughout the year on the study site; those individuals that had established home ranges at higher elevation were able to successfully overwinter in deep snow habitats. 172 SEASONAL CHANGES IN ELEVATION The average annual elevation of all bobcat locations on the study site was 1461 ± 2.1 m (N = 7,664). Bobcats used lower elevations during winter and spring (snow on ground) than during summer and fall (typically snow free). Average elevation of winter locations (N = 719) across all bobcats was 1456 ± 4.9 m (range 1001-1794 m), while summer locations (N = 2,181) were located at 1468 ± 3.5 m (range 987-1995 m). The difference in elevation between the main snow vs. snow free season is only 12 m; thus, differences between summer and winter elevations used may not have biological significance. A difference of 12 m lower in elevation is not likely to affect snow conditions or snow depth much as this area receives > 3 m of snow over the course of the winter at mid-elevations. Overall, bobcats do not demonstrate a large shift in elevations used throughout the year. Spring locations were at the lowest elevation of all seasons, with locations being at 1429 ± 3.1 m (range 1015-1892 m). Rather than avoidance of deep snow, it is likely that bobcats were taking advantage of lower sites because prey species were more easily found in these areas as spring progressed and snow melted. It is plausible that the birth pulse of preferred prey such as red squirrels (Tamiasciurus hudsonicus) (Chapter 1), likely drew bobcats to these locations, as vulnerable juveniles emerged. Koehler and Hornocker (1989) reported a significant difference in winter and summer elevation of bobcat locations (1,365.5 vs 1,852.6 m) across their Idaho study site. This winter elevation is ~90 m lower than bobcats in my study selected; however, the actual elevation that Idaho bobcats and northwest Montana bobcats were located at in winter is quite similar (1366 m in Idaho vs. 1456 m in northwest Montana). Summer elevations in Koehler and Hornocker (1989) are much higher than elevations that were available to bobcats on TLRD; the Idaho study site had elevations up to 3,048 m, over 1000 m greater than on my study site. Winters were severe in the Idaho study, and bobcats used snow-free aspects at lower elevations, where energetic costs were reduced, with respect to travel and thermoregulation (Koehler and Hornocker 1989). Several other studies have concluded that snow restricts bobcat movements in winter (Bailey 1974, McCord 1974, Fuller et al. 1985, Litvaitis et al. 1986b), and may in fact be one of the limiting factor in bobcat distribution (Litvaitis et al. 2006, Kapfer 2012). Apps (1996) and Kinley (1992) showed that bobcats in the East Kootenay district of southeastern British Columbia, which constitutes the northern 173 border of northwest Montana were restricted to locations below 1200 m. Bobcats in northwest Montana did show greatly reduced movements in winter (Chapter 5), with locations on average being located at lower elevations than during the snow-free seasons; however, individuals in my study did not have the option of seeking snow free habitats or aspects in winter. 174 Table D.1. A full seasonal breakdown of GPS collar habitat locations by season. Habitat types were ranked in descending order from the habitat used most during that season. All bobcat locations (male and female) were pooled for each season, and raw numbers per habitat type tabulated to determine % of locations found in each habitat type per season. If a habitat type is not listed for a particular season, this indicates that % of locations in that habitat type for that season was 0%. Season Habitat type Number of locations Percent of total Winter Mixed dry 149 20.7 (N = 719) Ponderosa 141 19.6 Lodgepole 124 17.3 Wet Spruce-fir 70 9.7 Dry Spruce-fir 69 9.6 Harvest 68 9.5 Mixed wet 62 8.6 Grassland 15 2.1 Riparian 15 2.1 Shrub 6 0.8 Spring Lodgepole 504 18.3 (N = 2,757) Mixed wet 402 14.6 Wet Spruce-fir 380 13.8 Mixed dry 356 12.9 Ponderosa 330 12.0 Dry Spruce-fir 292 10.6 Harvest 251 9.1 Grassland 87 3.2 Other 54 2.0 Riparian 50 1.8 Shrub 32 1.2 Wetland 18 0.7 Burn 1 0.0 Summer Lodgepole 412 18.9 (N = 2,181) Wet Spruce-fir 346 15.9 Ponderosa 333 15.3 Mixed_wet 279 12.8 Mixed_dry 229 10.5 Harvest 215 9.9 Dry Spruce-fir 212 9.7 Riparian 57 2.6 Grassland 42 1.9 Other 29 1.3 Wetland 14 0.6 Shrub 13 0.6 175 Table D.1 continued. A full seasonal breakdown of GPS collar habitat locations by season. Habitat types were ranked in descending order from the habitat used most during that season. All bobcat locations (male and female) were pooled for each season, and raw numbers per habitat type tabulated to determine % of locations found in each habitat type per season. If a habitat type is not listed for a particular season, this indicates that % of locations in that habitat type for that season was 0%. For example, no locations were found in burned areas in winter. Season Habitat type Number of locations Percent of total Fall Lodgepole 413 20.6 (N = 2,007) Wet Spruce-fir 351 17.5 Harvest 277 13.8 Ponderosa 257 12.8 Mixed_wet 233 11.6 Mixed_dry 193 9.6 Dry Spruce-fir 143 7.1 Riparian 56 2.8 Grassland 38 1.9 Shrub 30 1.5 Other 9 0.5 Wetland 7 0.4 176 Table D.2. A complete breakdown of number of locations per season, mean ± 1SE elevation per season, and range of location elevations for individual radiocollared bobcats on TLRD. Bobcat ID Season Number of locations Elevation (m) ± SE Range F1 Winter 48 1242.4 ± 17.3 1113-1584 Spring 559 1321.5 ± 6.6 1032-1698 Summer 530 1294.5 ± 6.1 1045-1732 Fall 462 1330.8 ± 6.9 1032-1689 M1 Winter 454 1452.8 ± 5.7 1001-1794 Spring 572 1499.5 ± 4.0 1273-1803 Summer 526 1513.9 ± 4.8 1286-1817 Fall 571 1514.8 ± 4.4 1001-1800 M2 Winter 199 1529.7 ± 5.6 1369-1780 Spring 585 1496.0 ± 3.2 1413-1892 Summer 558 1514.7 ± 5.2 987-1995 Fall 604 1539.1 ± 4.4 1315-1837 M3 Winter 18 1274.1 ± 23.2 1227-1552 Spring 546 1518.7 ± 6.7 1215-1881 Summer 567 1540.3 ± 6.9 1272-1881 Fall 370 1576.0 ± 8.6 1273-1898 M4 Spring 495 1289.9 ± 8.1 1015-1776 177 Figure D.1. Average elevation (m) of seasonal locations for male and female bobcats pooled. Spring locations were found at the lowest elevations, but two bobcats (1M,1F) that were radiocollared at lower elevation are strongly represented in spring season (March-May). Winter locations (December-February) are found at lower elevations than summer (June- August) and fall locations. Bobcats were located, on average, at the highest elevations in fall (September-November). 178 Figure D.2. Average seasonal elevation of locations per individual bobcat. Only one bobcat was located at higher elevation in the winter than during the spring (M2), but typically bobcats were located at lowest elevations in winter, with and consistently used higher elevations in spring, summer, and fall as snow free habitats were accessed. However, when looking at individual bobcats, selection of location by elevation varied markedly between individuals. 179 Figure D.2. Average seasonal elevation of locations per individual bobcat. F2 M1 M2 M3 180 APPENDIX E DIFFERENCES IN HOME RANGE ESTIMATORS Home range estimators have different underlying statistical assumptions (Boulanger and White 1990). Parametric estimators include utilization distributions and kernel density estimators, while nonparametric estimators include minimum convex polygons (MCP), outer convex polygons, minimum modified area method, and harmonic mean home range methods. Home range estimators are sensitive to the type of data used and the number of locations used in home range construction. The majority of studies in Table 4.6 use nonparametric methods, particularly the MCP method. MCP home ranges provide only rudimentary information regarding an animal’s space use, and are highly sensitive to extreme data points (Powell 2000), leading to overly large home range estimates that can include large, unused areas. Furthermore, MCPs only approach asymptotic values of home range area and outline with large sample sizes (≥100 locations) (Bekoff and Mech 1984, Powell 1987, White and Garrott 1990), so home ranges constructed with fewer points may underestimate home range size. MCPs also assume that all areas of the home range are used with equal intensity (Powell 2000). Thus, MCP home range estimates in Table 4.6 may be biased large if they include large areas of unused space, but may be small if fewer than 100 locations were used in home range construction. Kernel density estimators weight home range size based on intensity of space use (Powell 2000); hence, an area in a home range with many locations will be given greater weight as the animal spends more time in that area. Utilization distributions estimate the smallest area that an animal spends a fixed percent of its time within (Powell 2000). Use of a 95% fixed kernel estimate excludes 5% of locations, and will exclude areas an animal may never visit again or occasional extraterritorial forays. Kernel density estimates are likely to reflect actual space use more accurately than MCP estimates, but may result in smaller home range estimates than MCP methods. Kernel density home range estimates are a method to assess intensity of space use. Therefore, I used fixed kernel density estimators as these were better suited than MCP home 181 range estimates to deal with the large number of bobcat locations in my study. Kernel density home ranges gave a much more accurate description of space use on my study area, and captured the importance of certain habitat types to bobcats in this region.
Thesis/Dissertation
eng
Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Behavioral ecology of the bobcat in a region with deep winter snows.
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