Mesocarnivore responses to changes in habitat and resource availability in California

by

Allison Lynn Bidlack

B.S. (University of Michigan) 1992 M.S. (University of Alaska Fairbanks) 2000

A dissertation submitted in partial satisfaction of the

requirements for the degree of

Doctor of Philosophy

in

Environmental Science, Policy and Management

in the

Graduate Division

of the

University of California, Berkeley

Committee in charge:

Professor Wayne M. Getz, Co-Chair Professor Adina M. Merenlender, Co-Chair Professor Wayne P. Sousa

Fall 2007

Mesocarnivore responses to changes in habitat and resource availability in California

Copyright 2007

by

Allison Lynn Bidlack

ABSTRACT

Mesocarnivore responses to changes in habitat and resource availability in California

by

Allison Lynn Bidlack

Doctor of Philosophy in Environmental Science, Policy and Management

University of California, Berkeley

Professor Wayne M. Getz, Co-Chair Professor Adina M. Merenlender, Co-Chair

Effective conservation of mammalian carnivores requires knowledge of their ecologies in both natural and altered habitats, especially in regions experiencing substantial human population growth. The research presented in this dissertation addresses the relationship between carnivores and habitat change, and investigates the magnitude of temporal and spatial population fluctuations and the processes behind them in both natural and urbanizing systems. To examine these fluctuations in carnivore populations, and the contribution of long-term studies to our understanding of these changes over time, I compared San Joaquin kit fox population numbers with rainfall and prey data collected over 35 years on the Carrizo Plain, California. Seasonal rainfall levels were a poor predictor of interannual kit fox numbers, although I did find a correlation between fox numbers and jackrabbit numbers over time. A spatial congruence between giant kangaroo rat distribution and kit fox distribution was also evident, and both species appeared to be expanding their ranges northward within the portion of the park that had previously been cultivated. Urbanization can also lead to distributional changes and increases in invasive species, threatening native biodiversity.

Given this risk, I focused on the relationship between invasive red fox and habitat correlates in the San Francisco Bay area. I found that red foxes were widespread but not locally abundant, and were strongly correlated with urban development and high road densities. No correlation between red fox distribution and other native canids was apparent. Other common mesocarnivores are also responding to urbanization in the San

Francisco Bay Area. Coyotes, bobcats and gray foxes were detected at different densities along an urban-to-rural gradient. Both total carnivore abundance and number of species detected were highly negatively correlated with levels of urban development and road density, and coyotes and bobcats tended to avoid urban areas. Coyotes were detected more often in open habitats such as grasslands, while gray foxes strongly preferred hardwood forest. The extent of habitat at different landscape scales was also important, with coyotes reacting at the largest scale, and gray foxes at the smallest.

TABLE OF CONTENTS

List of figures…………………………………………………………………… ii

List of tables……………………………………………………………….…… iv

Acknowledgments……………………………………………………………… v

CHAPTER 1 Introduction……………………………...……...... ………. 1

CHAPTER 2 San Joaquin kit fox (Vulpes macrotis mutica) population dynamics on the Carrizo Plain, California, 1970-2005….. 18

CHAPTER 3 Distribution and habitat use of red fox (Vulpes vulpes) in the San Francisco Bay Area ………..…………………………. 60

CHAPTER 4 Effects of urbanization on mesocarnivores in the San Francisco Bay Area ……………….……………...... 100

CHAPTER 5 Conclusions and directions for future research….....…..... 143

APPENDIX A Distribution of non-native red foxes in East Bay oak woodlands ……………………………………...... 153

APPENDIX B Dog training and detection tests ………………….………. 169

APPENDIX C Characterization of a western North American carnivore community using PCR-RFLP of cytochrome b obtained from fecal samples ………………………………………… 180

i

LIST OF FIGURES

Chapter 2 Figure 1 Map of the Carrizo Plain National Monument and survey transects………………………………………………………… 47 Figure 2 Effective precipitation (November-April) at New Cuyama Fire Station from 1973-2005…..……………………………………. 48 Figure 3 Kit fox, rodent, and jackrabbit counts from the Elkhorn Plain surveys 1970-2005………………………………………………………. 49 Figure 4 Correlation of fox counts, growth rates, and survival against lagged effective rainfall………………………………………………… 50 Figure 5 Elkhorn kit fox sightings 1970-2005 (a); Soda Lake kit fox sightings 1989-2005 (b)…………………………………………………... 51 Figure 6 Elkhorn kit fox sightings (a); Soda Lake kit fox sightings (b); separated into Fall/Winter and Spring/Summer………………... 52 Figure 7 Results of logistic regression of kit fox sightings along Elkhorn and Soda Lake transects from 1989-2005………………………….. 53 Figure 8 Kit fox detections along survey routes in 2001 and 2006, superimposed on giant kangaroo rat distribution……………………………… 54

Chapter 3 Figure 1 Map of San Francisco Bay study area………………………… 87 Figure 2 Red fox survey/sightings locations……………………………. 88 Figure 3 Map of red fox detections during scat surveys by Reed and Bidlack………………………………………………………… 89 Figure 4 Map of all red fox detections………………………………….. 90

Chapter 4 Figure 1 Map of carnivore survey sites in six counties within the San Francisco Bay Area………………………………………………………. 124

Appendix A Figure 1 Location map of survey transects in the East Bay……………. 168

ii

Appendix B Figure 1 Percentage of scats placed at different widths from the center line found by detection dog on road and pasture transects………………… 178 Figure 2 Scat detection success regressed against temperature………….. 179

Appendix C Figure 1 Schematic of cutting patterns created by restriction enzymes HpaII, DdeI and HpyCh4V………………………………………………….. 189 Figure 2 Agarose gel restriction enzyme banding patterns for seven species…………………………………………………………. 190

iii

LIST OF TABLES

Chapter 2 Table 1 Missing survey dates for Elkhorn and Soda Lake routes……….. 55 Table 2 Kit fox and jackrabbit population variables regressed against predictors………………………………………………………… 56 Table 3 P-values of t-tests of mean differences between above- and below- average rainfall years……………………………………………. 57 Table 4 Results of X2 contingency analyses of seasonal differences in sightings along the Soda Lake and Elkhorn survey routes………………… 58

Chapter 3 Table 1 Results of scat survey transects…………………………………. 91 Table 2 List of predictor variables used in logistic regressions………….. 96 Table 3 Summary of transects and scat detections by county…………… 97 Table 4 Results of univariate logistic regression analysis……………….. 98 Table 5 The best multiple logistic regression model for red fox presence. 99

Chapter 4 Table 1 Results of scat survey transects…………………………………. 125 Table 2 List of predictor variables used in logistic and linear regressions 129 Table 3 Results of univariate logistic regressions for coyotes (a); gray foxes (b); bobcats (c)………………………………………………………. 130 Table 4 Results of multiple logistic regressions for coyote (a); gray foxes (b); bobcats (c)………………………………………………………. 133 Table 5 Results of univariate linear regressions for coyote (a); gray foxes (b); bobcats (c)………………………………………………………. 136 Table 6 Results of multiple linear regressions for coyotes and bobcats..... 139 Table 7 Results of univariate linear regressions for scats (a); species (b).. 140 Table 8 Results of multiple linear regressions for all species combined…. 142

iv

ACKNOWLEDGMENTS

As anyone who has completed a thesis or dissertation knows, it is only through the support of faculty advisors, colleagues, classmates, friends, family members and other important folks that one gets through the process. I would first and foremost like to thank my committee members, Wayne Getz, Adina Merenlender and Wayne Sousa, for all of their support, advice and encouragement. I benefited greatly from their experience and clear thinking.

I would also like to thank all the land management agencies and land trusts that gave me access to their properties for this research: California Parks and Recreation;

East Bay Regional Park District; Marin County Open Space District; Muir Heritage

Land Trust; Napa County Land Trust, including the Wantrup Wildlife Sanctuary;

Pepperwood Reserve (California Academy of Sciences); Sonoma County Agricultural

Preservation and Open Space District, particularly the Audubon California Mayacamas

Mountains Sanctuary; Sonoma County Regional Parks; and Sonoma Land Trust.

Thanks especially to all the private landowners who graciously allowed me on their property, and more often than not, gave me a tour and some lemonade, and treated me to good conversation.

Many people generously helped me in a variety of ways, and I am grateful to all of them. Colin Brooks and Shane Feirer were invaluable in teaching me about GIS, and helping me in my data analysis. I literally could not have done this research without them. Per Palsbøll and Martine Berube kindly let me use their lab for my DNA extractions and genotyping, and were always available to help when I ran into

v problems. I am also deeply indebted to Bob Stafford of the California Department of

Fish and Game for his willingness to share data and his help in making sure I had the information and access I needed for the kit fox project. His infectious enthusiasm and broad knowledge about all things that bloom, fly, hop and slither on the Carrizo Plain helped instill in me a great love for this very special place.

Aimee Hurt of Working Dogs for Conservation taught me how to find and train a scat detection dog. Her patience and good humor, combined with her ability to communicate with both humans and dogs, made everything seem easy. The Hopland

Research Center staff were extremely accommodating about housing and caring for my detection dog for the two years he lived there. My dog, Seth, was a bundle of energy and mischief, and he deserves credit for his hard work and endless eagerness to find scat. May your life be filled with treats and bunny chases, Seth.

Joe DiDonato of the East Bay Regional Park District was very helpful sharing information with me and in giving me complete access to EBRPD lands. The Lindsay

Wildlife Museum, the California Living Museum, Coyote Point Museum, and the

Folsom Zoo all collected and contributed scat samples from their captive carnivores for the scat detection dog training; I appreciate the dirty work this entailed. Lastly, this research was partially supported by funding from the American Society of

Mammalogists. Thanks for all the help!

I owe many thanks to Sarah Reed, who was my partner in crime through much of my graduate program. We had many good laughs over the years “exercising our nerds” in and out of class. She helped in all aspects of my research, from dog training

vi to labwork to data analysis, and was a constant source of empathy and encouragement.

I hope we have many more years of friendship to come.

My friends in the Getz and Merenlender labs provided constructive criticism, fresh ideas, and perspective when I needed it. Thanks especially to Holly Ganz, Emily

Heaton, Karen Levy, Jamie Lloyd-Smith, Leo Polansky, Jessica Redfern, Adena

Rissman, Sadie Ryan, Wendy Turner, and George Wittemyer. I owe these people, as well as the many other friends I made at Berkeley, a debt of gratitude for all the emotional support they provided throughout this process.

My family was always a source of strength, and they never doubted my ability to get through. Thanks to my mother, who remembered all too well her graduate years, and who always knew the right thing to say to keep me going. Last, but certainly not least, I thank my husband, Rich, for his patience, advice, good cooking, sense of humor, tough love, and for his unwavering faith in me. We did it!

vii

CHAPTER 1

INTRODUCTION

The conservation and management of mammalian carnivores is a complex challenge, especially in regions of the world that are experiencing intense human population growth. Predator populations throughout the world have been impacted by over-hunting, disease, urbanization, invasive species, and fragmentation and loss of habitat (Gittleman et al., 2001). Carnivores may be particularly vulnerable to extinction because of their relatively large home range sizes, low densities, prey requirements, and potential for direct conflicts with humans and their domestic animals. Effective conservation of these animals requires knowledge of their ecologies in both natural and altered habitats, and requires that researchers investigate patterns and processes at large temporal and spatial scales.

Carnivore populations fluctuate in abundance and spatial distribution over time due to changes in resource availability, disease, competition, and predation (Turchin,

2003). These drivers can be the result of naturally occurring phenomena, like changes in weather patterns, but can also arise from human actions, such as habitat conversion and the introduction of exotic species. Many factors can act on a population at one time, producing complex population dynamics and distribution patterns. Additionally, drivers are frequently interrelated; for example, habitat fragmentation is linked with the decline of carnivore populations, which creates barriers to dispersal, destroys habitat for native species and provides resources for competing exotics (Smallwood, 1994;

Sunquist & Sunquist, 2001).

1 In my research, I investigate the impacts of resource changes on mesocarnivore population densities and distributions in California. These resource fluctuations include changes in prey availability, and changes in land use due to urbanization. California is one of the world’s biodiversity hotspots; however, less than 25% of the original vegetation remains (Myers et al., 2000), and the state’s human population is growing by

1.6 % per year (LAO, 2000). As such, the issues surrounding predator conservation in

California are illustrative of the problems facing carnivores throughout the world.

Responses to prey availability

Temporal responses to prey availability are well documented for many carnivores; for example, wolf numbers fluctuate with moose density on Isle Royale

(Vucetich & Peterson, 2004), lynx and snowshoe hares cycle together (Elton &

Nicholson, 1942), and other taxa such as mustelids, hyenas, and fox populations rise and fall with their prey (Korpimaki et al., 1991; Jedrzejewski et al., 1995; Kaikusalo &

Angerbjörn, 1995; Honer et al., 2005; Gilg et al., 2006). Density differences in areas with differing prey abundance are also common (e.g., Fuller, 1989; Karanth et al., 2004;

Marker & Dickman, 2005). Prey abundance is often driven by changes in resource availability, which can be caused by environmental factors such as variability in weather patterns, or anthropogenic factors such as overhunting or habitat restoration.

Often, only long-term datasets will elucidate the temporal and spatial relationships between predator and prey. The strength of relationships between seasonally fluctuating resources and population sizes of consumers obtained from short- term studies may be misleading in systems influenced by long-term cycles or global

2 climate change (Brown & Heske, 1990; Ernest et al., 2000; Brown & Ernest, 2002).

For example, long-term studies of rodent dynamics in response to plant productivity and rainfall in the southwestern United States indicate that populations respond differentially to resource availability and to precipitation, creating complex relationships among variables over time (Brown & Heske, 1990; Ernest et al., 2000).

Spatial changes in predator and prey distributions can also be extremely difficult to detect over short periods of time. Long-term studies improve our understanding of the relationship between carnivore density and prey abundance, and lead to better predictions of predator population dynamics and habitat requirements (Fuller & Sievert,

2001).

Responses to Urbanization

Carnivore population numbers and distributions also vary with habitat disturbance and loss. Predators alter their movements in response to habitat fragmentation in order to avoid humans and to locate shelter and prey (Sunquist &

Sunquist, 2001). They may avoid open or deforested areas (Hargis et al., 1999; Hilty &

Merenlender, 2004), establish home ranges away from development (Riley et al., 2003) and primarily utilize natural or forested corridors for dispersal (Beier, 1995). Despite these behavioral adaptations, predator populations tend to decline with habitat loss, and species with large home range sizes are disproportionately impacted due to edge-related mortality (e.g., hunting, roadkill; Woodroffe & Ginsberg, 1998).

Habitat loss and fragmentation driven by urban development is one of the primary challenges to preserving carnivore populations in the United States today

3 (Hansen et al. 2005; McKinney 2002). Residential development covers as much as five times the land area it did in 1950 (Brown et al. 2005), and the wildland-urban interface comprises almost 10% of the land surface of the lower 48 states (Radeloff et al. 2005).

Urbanization impacts both individual carnivore species and the predator community as a whole. Some generalist species, such as raccoons and striped skunks, are able to adapt to urban environments, and their populations may increase across rural-to-urban gradients. Urban-associated species often include exotic invasives that are highly tolerant of human activities (Odell & Knight, 2001; Maestas et al., 2003). Other taxa, such as pumas and grizzly bears, will not occur in urban areas, due to specialized prey requirements, home range sizes, or behavioral restrictions.

These species-level impacts translate to changes in carnivore communities over rural-to-urban gradients, often with native species diversity decreasing and non-native species diversity increasing (Crooks, 2002; Prange & Gehrt, 2004; Hansen et al., 2005).

Interspecific relationships can be altered in urban areas, with some prey species being heavily impacted by non-native carnivores such as red foxes and feral cats (Crooks &

Soulé, 1999). This is especially problematic when large carnivores are eliminated from a system, releasing smaller meso-predators from competition (“meso-predator release”;

Crooks & Soulé, 1999). Invasive species can also compete for food and shelter with native fauna at the urban-wildland interface (Smallwood, 1994). Importantly, carnivore communities may continue to change decades after development or fragmentation first occurs due to system inertia (Hansen et al., 2005). An understanding of the response of carnivores to urbanization requires both fine- and large-scale ecological studies, as well

4 as long-term research to monitor changes over time. This research is vital to the maintenance of native carnivore diversity in much of North America.

Organization of the Dissertation

The research presented in this dissertation addresses the relationship between carnivores and habitat change, and investigates the magnitude of temporal and spatial fluctuations and the drivers behind them in both natural and urbanizing systems. The goals of my dissertation are to: 1) investigate temporal and spatial drivers of carnivore population fluctuations in a minimally-altered system; 2) examine the distribution and habitat correlates of an invasive carnivore in an urban system; and 3) explore the impacts of urbanization on a suite of native carnivores. Because carnivores can be difficult to study due to their elusive nature and low population sizes, I utilized a combination of field, laboratory and computational methods to complete this research.

Spotlighting and scat surveys were used to detect predators, and DNA genotyping was used to positively identify scats to species. All data were entered into a geographic information system (GIS), and parametric statistics were used to investigate population fluctuations over time and to correlate habitat variables obtained from the GIS with carnivore detections.

In the first study (Chapter 2) I examine a 35-year kit fox spatial survey dataset from the Carrizo Plain National Monument to investigate the magnitude and drivers of kit fox population fluctuations over time and space. The San Joaquin kit fox (Vulpes macrotis mutica) is a federally endangered species endemic to the grassland and desert scrub environment of south-central California. Much of the original habitat of the kit

5 fox has been lost to agricultural land conversion and urban development (Williams et al., 1998), and habitat destruction and fragmentation are continuing. There are no reliable estimates of the current population size of this species, although ballpark figures generally fall under 10,000 animals (Williams et al., 1998), and it is known that local populations exhibit large density fluctuations over time (Cypher et al., 2000).

The Carrizo Plain is a desert ecosystem, and home to one of the last remaining

San Joaquin kit fox populations. Productivity in such arid systems is ultimately driven by rainfall (Rosenzweig, 1968); however, resource variability and patterns of productivity may not directly translate up trophic levels (Ernest et al., 2000; Brown &

Ernest, 2002). Mammalian predator populations, like the kit fox, may not fluctuate in synchrony with rainfall or with their primary prey in these systems. Several short-term demographic studies have been conducted on San Joaquin kit foxes, and the relationships between numbers of foxes (and fox population growth rates) and prey densities and rainfall is unclear (Ralls & Eberhardt, 1997; Cypher et al., 2000; White et al., 2000). However, though there may be little temporal synchrony between consumer populations and resources, the spatial patterning of animals still may be linked with that of their primary food source. Long-term datasets, like the one used in this chapter, can contribute substantially to our understanding of population fluctuations. I compare the

35-year survey record of kit fox detections with those of prey populations and rainfall and investigate seasonal and inter-annual changes in kit fox distribution. The spatial component of these data also provides a unique opportunity to explore relationships between predator and prey and shared distributional changes over time.

6 In the second study (Chapter 3) I focus on the distribution and habitat use of non-native red foxes in the San Francisco Bay area. Non-native red foxes (Vulpes vulpes) were introduced into lowland California in the late nineteenth and early twentieth centuries for fur farming and hunting, and since the mid-seventies, they have invaded the region surrounding the San Francisco Bay (Lewis et al., 1993). Red foxes are often associated with heterogeneous or fragmented landscapes (Catling & Burt,

1995; Larivière & Pasitschniak-Arts, 1996; Oehler & Litvaitis, 1996; Kurki et al.,

1998), and are increasingly found in urban and suburban areas in North America,

Europe, and Australia, where they can attain very high densities (e.g., MacDonald &

Newdick, 1982; Adkins & Stott, 1998, and references therein). Because invasive species are often associated with disturbed ecosystems, they may be able to establish in natural habitat fragments more easily if there is a complex interface between developed or cultivated areas and wildlands (Smallwood, 1994; Theobald et al., 1997; Odell &

Knight, 2001; McKinney, 2002; Maestas et al., 2003; Hansen et al., 2005).

The San Francisco Bay Area is characterized by a diverse mix of human- dominated landscapes and wildlands, and most of the projected growth in the next 25 years is expected to occur on the urban fringe (Association of Bay Area Governments,

2007). If red fox dispersal and successful establishment is predicated on landscape conversion to housing and agriculture (Harris & Rayner, 1986; Adkins & Stott, 1998), these development pressures may lead to the continued red fox invasion of northern coastal California. Red foxes have been implicated in the decline in California of several federally threatened and endangered bird species, and they may have deleterious effects on native mammals as well (U.S. Fish and Wildlife Service, 1990; U.S. Fish and

7 Wildlife Service and U.S. Navy, 1990; Ralls & White, 1995; Clark, 2001). The relationship between red foxes and other wild canids is complex: coyotes (Canis latrans) are known to compete directly and indirectly with red foxes (Voigt & Earle,

1983; Sargeant et al., 1987; Harrison et al., 1989; Sargeant & Allen, 1989; Lavin et al.,

2003), and red foxes may displace gray foxes (Urocyon cinereoargenteus) in some areas (J. DiDonato, pers comm.). I use scat surveys along with other red fox detection datasets to examine red fox presence along a gradient of habitats from urban-isolated sites to wildlands in a variety of land cover types. I investigate the relationship between patterns and types of land development and red fox invasion in the Bay Area, the effect of red fox presence on native gray foxes, and the impact of coyotes on red fox presence in these areas.

In the final study (Chapter 4) I examine the distribution and habitat use of three sympatric carnivores in the San Francisco Bay metropolitan area, the fifth largest urban area in the United States. Coyotes, bobcats (Lynx rufus) and gray foxes are common mesocarnivores in northern coastal California wildlands, and generally occupy different habitat niches (Bekoff, 1977; Fritzell & Haroldson, 1982; Larivière & Walton, 1997).

However, predators will shift their distributions and habitat use in response to disturbance and fragmentation in altered systems, such as urban and agricultural areas.

Some small generalist carnivores, such as striped skunks (Mephitis mephitis) and raccoons (Procyon lotor), are well-known and quite visible urban adapters (Prange &

Gehrt, 2004), while larger carnivores, like coyotes and mountain lions (Puma concolor), tend to avoid urban areas (Beier, 1995; Crooks, 2002; Gosselink et al., 2003; Riley et al., 2003; Atwood et al., 2004). I survey 70 sites along an urban-to-rural gradient to

8 assess presence and relative abundance of each species and compare these data with environmental variables such as cover type, road density, and habitat connectivity at multiple scales. Examining a suite of carnivores along an urban-to-rural gradient may shed light on differences in scale and habitat use among the species, and help in understanding what is necessary to maintain a complete carnivore guild in an urbanizing system.

9 Literature Cited

Adkins, C.A.; Stott, P. 1998. Home ranges, movements and habitat associations of red foxes Vulpes vulpes in suburban Toronto, Ontario, Canada. Journal of Zoology London 244: 335-346.

Association of Bay Area Governments. 2007. www.abag.ca.gov/index.html

Atwood, T.C.; Weeks, H.P.; Gehring, T.M. 2004. Spatial ecology of coyotes along a suburban-to-rural gradient. Journal of Wildlife Management 68: 1000-1009.

Beier, P. 1995. Dispersal of juvenile cougars in fragmented habitat. Journal of Wildlife Management 59: 228-237.

Bekoff, M. 1977. Canis latrans. Mammalian Species 79: 1-9.

Brown, J.H.; Ernest, S.K.M. 2002. Rain and rodents: complex dynamics of desert consumers. Bioscience 52: 979-987.

Brown, D.G.; Johnson, K.M.; Loveland, T.R.; Theobald, D.M. 2005. Rural land-use trends in the coterminous United States, 1950-2000. Ecological Applications 15: 1851-1863.

Crooks, K. 2002. Relative sensitivities of mammalian carnivores to habitat fragmentation. Conservation Biology 16: 488-502.

Cypher, B.L.; Warrick, G.D.; Otten, M.R.M.; O’Farrell, T.P.; Berry, W.H.; Harris, C.E.; Kato, T.T.; McCue, P.M.; Scrivner, J.H.; Zoellick, B.W. 2000. Population dynamics of San Joaquin kit foxes at the Naval Petroleum Reserves in California. Wildlife Monographs 145: 1-43.

10 Catling, P.C.; Burt, R.J. 1995. Why are red foxes absent from some eucalypt forests in eastern New South Wales? Wildlife Research 22: 535-546.

Clark, H.O. 2001. Endangered San Joaquin kit fox and non-native red fox: interspecific competitive interactions. Fresno, CA: California State University. MS. thesis.

Crooks, K.R.. 2002. Relative sensitivities of mammalian carnivores to habitat fragmentation. Conservation Biology 16: 488-502.

Crooks, K.R; Soulé, M.E. 1999. Mesopredator release and avifaunal extinctions in a fragmented system. Nature 400: 563-566.

Elton, C.; Nicholson, M. 1942. The ten-year cycle in numbers of the lynx in Canada. Journal of Animal Ecology 11: 215-244.

Ernest, S.K.M.; Brown, J.H.; Parmenter, R.R. 2000. Rodents, plants, and precipitation: spatial and temporal dynamics of consumers and resources. Oikos 88: 470-482.

Fritzell, E.K.; Haroldson, K.J. 1982. Urocyon cinereoargenteus. Mammalian Species 189: 1-8.

Fuller, T.K.; Sievert, P.R. 2001. Carnivore demography and the consequences of changes in prey availability. Pages 163-178 in Carnivore Conservation, Gittleman, J.L.; Funk, S.M.; MacDonald, D.W.; Wayne, R.K. (Eds.) Cambridge: Cambridge University Press.

Fuller, T.K. 1989. Population dynamics of wolves in north-central Minnesota. Wildlife Monographs 110: 1-39.

11 Gilg, O.; Sittler, B.; Sabard, B.; Hurstel, A.; Sane, R.; Delattre, P.; Hanski, L. 2006. Functional and numerical responses of four lemming predators in high arctic Greenland. Oikos 113: 193-216.

Gittleman, J.L.; Funk, S.M.; MacDonald, D.W.; Wayne, R.K., eds. 2001. Carnivore Conservation. Cambridge: Cambridge University Press.

Gosselink, T.E.; Van Deelen, T.R.; Warner, R.E.; Joselyn, M.G. 2003. Temporal habitat partitioning and spatial use of coyotes and red foxes in east-central Illinois. Journal of Wildlife Management 67: 90-103.

Hargis, C.D.; Bissonette, J.A.; Turner, D.L. 1999. The influence of forest fragmentation and landscape pattern on American martens. Journal of Applied Ecology 36: 157-172.

Harris, S.; Rayner, J.M.V. 1986. Urban fox (Vulpes vulpes) population estimates and habitat requirements in several British cities. The Journal of Animal Ecology 55: 575-591.

Harrison, D.J.; Bissonette, J.A.; Sherburne, J.A. 1989. Spatial relationships between coyotes and red foxes in eastern Maine. Journal of Wildlife Management 53: 181-185.

Hilty, J.A.; Merenlender, A.M. 2004. Use of riparian corridors and vineyards by mammalian predators in Northern California. Conservation Biology 18: 126- 135.

Honer, O.P.; Wachter, B.; East, M.L.; Runyaro, V.A.; Hofer, H. 2005. The effect of prey abundance and foraging tactics on the population dynamics of a social, territorial carnivore, the spotted hyena. Oikos 108: 544-554.

12 Jedrzejewski, W.; Jedrzejewski, B.; Szymura, L. 1995. Weasel population response, home range, and predation on rodents in a deciduous forest in Poland. Ecology 76: 179-195.

Kaikusalo, A.; Angerbjörn, A. 1995. The arctic fox population in Finnish Lapland during 30 years, 1964-1993. Annales Zooloogici Fennici 32: 69-77.

Karanth, K.U.; Nichols, J.D.; Kumar, N.S. ; Link, W.A.; Hines, J.E. 2004. Tigers and their prey: predicting carnivore densities from prey abundance. Proceedings of the National Academy of Science 101: 4854-4858.

Korpimaki, E.; Norrdahl, K. ; Rinta-Jaskari, T. 1991. Response of stoats and least weasels to fluctuating food abundances: is the low phase of the vole cycle due to mustelid predation? Oecologia 88: 552-561.

Kurki, E.G.; Nikula, A.; Helle, P.; Linden, H. 1998. Abundances of red fox and pine marten in relation to the composition of boreal forest landscapes. Journal of Animal Ecology 67: 874-886.

Lariviere, S.; Pasitschniak-Arts, M. 1996. Vulpes vulpes. Mammalian Species 1-11.

Lavin, S.R.; Van Deelen, T.R.; Brown, P.W.; Warner, R.E.; Ambrose, S.H. 2003. Prey use by red foxes (Vulpes vulpes) in urban and rural areas of Illinois. Canadian Journal of Zoology 81: 1070-1082.

Legislative Analyst’s Office. 2000. http://www.lao.ca.gov/laoapp/main.aspx

Lewis, J.C.; Sallee, K.L.; Golightly Jr., R.T. 1993. Introduced red fox in California. Non-Game Bird and Mammal Section Report 93-10. Sacramento: California Department of Fish and Game.

MacDonald, D.W.; Newdick, M.T. 1982. The distribution and ecology of foxes, Vulpes vulpes (L.) in urban areas. Pages 123-135 in Urban Ecology: proceedings of the

13 Second European Ecological Symposium, Berlin, September 8-12, 1980. Bornkamm, R.; Lee, J.A.; Seaward, M.R.D. (Eds). Oxford: Blackwell.

Maestas, J.D.; Knight, R.L.; Gilgert, W.C. 2003. Biodiversity across a rural land-use gradient. Conservation Biology 17: 1425-1434.

Marker, L.L.; Dickman, A.J. 2005. Factors affecting leopard (Panthera pardus) spatial ecology, with particular reference to Namibian farmlands. South African Journal of Wildlife Research 35: 105-115.

McKinney, M.L. 2002. Urbanization, biodiversity, and conservation. BioScience 52: 883-890.

Myers, N.; Mittermeier, R.A.; Mittermeier, C.G.; da Fonseca, G.A.B.; Kent, J. 2000. Biodiversity hotspots for conservation priorities. Nature 403: 853-858.

Odell, E.A.; Knight, R.L. 2001. Songbird and medium-sized mammal communities associated with exurban development in Pitkin County, Colorado. Conservation Biology 15: 1143-1150.

Oehler, J.D.; Litvaitis, J.A. 1996. The role of spatial scale in understanding responses of medium-sized carnivores to forest fragmentation. Canadian Journal of Zoology 74: 2070-2079.

Prange, S.; Gehrt, S.D. 2004. Changes in mesopredator-community structure in response to urbanization. Canadian Journal of Zoology 82: 1804-1817.

Radeloff, V.C.; Hammer, R.B.; Stewart, S.I.; Fried, J.S.; Holcomb, S.S.; McKeefry, J.F. 2005. The wildland-urban interface in the United States. Ecological Applications 15: 799-805.

Ralls, K.; Eberhardt, L.L. 1997. Assessment of abundance of San Joaquin kit foxes by spotlight surveys. Journal of Mammalogy 78: 65-73.

14 Ralls, K.; White, P.J. 1995. Predation on San Joaquin kit foxes by larger canids. Journal of Mammalogy 76: 723-729.

Randa, L.A.; Yunger, J.A. 2006. Carnivore occurrence along an urban-rural gradient: a landscape-level analysis. Journal of Mammalogy 87: 1154-1164.

Riley, S.P.D.; Sauvajot, R.M.; Fuller, T.K.; York, E.C.; Kamradt, D.A.; Bromley, C.; Wayne, R.K. 2003. Effects of urbanization and habitat fragmentation on bobcats and coyotes in southern California. Conservation Biology 17: 566-576.

Rosenzweig, M.L. 1968. Net primary productivity of terrestrial communities: prediction from climatological data. American Naturalist 102: 67-74.

Sargeant, A.B.; Allen, S.H.; Hastings, J.O. 1987. Spatial relations between sympatric coyotes and red foxes in North Dakota. Journal of Wildlife Management 51: 285-293.

Sargeant, A.B.; Allen, S.H. 1989. Observed interactions between coyotes and red foxes. Journal of Mammalogy 70: 631-633.

Smallwood, K.S. 1994. Site invasibility by exotic birds and mammals. Biological Conservation 69: 251-259.

Sunquist, M.E.; Sunquist, F. 2001. Changing landscapes: consequences for carnivores. Pages 399-418 in Carnivore Conservation, Gittleman, J.L.; Funk, S.M.; MacDonald, D.W.; Wayne, R.K. (Eds). Cambridge: Cambridge University Press.

Theobald, D.M.; Miller, J.R.; Hobbs, N.T. 1997. Estimating the cumulative effects of development on wildlife habitat. Landscape and Urban Planning 39: 25-36.

15 Turchin, P. 2003. Complex Population Dynamics: a theoretical/empirical synthesis. Monographs in Population Biology. Princeton: Princeton University Press.

U.S. Fish and Wildlife Service; U.S. Navy. 1990. Endangered species management and protection plan, Naval Weapons Station - Seal Beach and Seal Beach National Wildlife Refuge. Final Environmental Impact Statement. Portland, OR.

U.S. Fish and Wildlife Service. 1990. Predator management plan and environmental assessment, San Francisco Bay National Wildlife Refuge. Draft report. Newark, CA.

Voigt, D.R.; Earle, B.D. 1983. Avoidance of coyotes by red fox families. Journal of Wildlife Management 47: 852-857.

Vucetich, J.A.; Peterson, R.O. 2004. Long-term population and predation dynamics of wolves on Isle Royale. Pages 281-292 in Biology and Conservation of Wild Canids, Macdonald, D.; Sillero-Zubiri, C. (Eds). Oxford University Press.

White, P.J.; Ralls, K. 1993. Reproduction and spacing patterns of kit foxes relative to changing prey availability. Journal of Wildlife Management 57: 861-867.

White, P.J.; Berry, W.H.; Eliason, J.J.; Hanson, M.T. 2000. Catastrophic decrease in an isolated population of kit foxes. The Southwestern Naturalist 45: 204-211.

Williams, D.F.; Cypher, E.A.; Kelly, P.A.; Miller, K.J.; Norvell, N.; Phillips, S.E.; Johnson, C.D.; Colliver, G.W. 1998. Recovery plan for upland species of the San Joaquin Valley, California. Portland: US Fish and Wildlife Service.

Woodroffe, R.; Ginsberg, J.R. 1998. Edge effects and the extinction of populations inside protected areas. Science 280: 2126-2128.

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17 CHAPTER 2

SAN JOAQUIN KIT FOX (VULPES MACROTIS MUTICA) POPULATION DYNAMICS ON THE CARRIZO PLAIN, CALIFORNIA, 1970-2005

Introduction

Natural populations tend to fluctuate in abundance and spatial distribution over time due to changes in resource availability, disease, competition, predation, and stochastic variations in reproduction (Turchin, 2003). Mammalian carnivores respond to temporal changes in prey abundance, and are also influenced by geographic variation in availability of food resources; indeed, these may be the major factors influencing carnivore population viability (Fuller & Sievert, 2001). Temporal responses to prey availability are well documented for many carnivores (e.g., Elton & Nicholson, 1942;

Korpimaki et al., 1991; Jedrzejewski et al., 1995; Kaikusalo & Angerbjörn, 1995;

Honer et al., 2005; Gilg et al., 2006), as are density differences in areas with differing prey abundance (e.g., Fuller, 1989; Karanth et al., 2004; Marker & Dickman, 2005).

However, a more specific understanding is needed of how temporal changes in prey distribution (rather than prey abundance) affect the population density and demography of particular carnivores, especially in the context of global land use and climate change.

Drivers of predator population density and distribution vary over time, and complex interactions among them can confound our conclusions based on data from short-term studies. Long-term studies, on the other hand, can contribute substantially to our understanding of population fluctuations and often lead to different insights than those gained by shorter-term studies of individual survival and reproduction (Grant &

18 Grant, 1996; Ernest et al., 2000; Brown & Ernest, 2002). In particular, the strength of relationships between seasonally fluctuating resources and population sizes of consumers obtained from short-term studies may be misleading in systems influenced by long-term cycles or global climate change. Furthermore, although there may be little temporal synchrony between consumer populations and resources, the spatial patterning of animals still may be linked with that of their primary food source, and these changes are often only perceived over long periods of time.

Long-term ecological datasets are relatively rare, particularly in regards to endangered species populations. Fortunately, we have 35 years of spatial survey data for the endangered San Joaquin kit fox (Vulpes macrotis mutica), a federally listed species endemic to the grassland and desert scrub environment of south-central

California. Productivity in such arid systems is ultimately driven by rainfall

(Rosenzweig, 1968); however, resource variability and patterns of productivity may not directly translate up trophic levels (Ernest et al., 2000; Brown & Ernest, 2002). For example, long-term studies of rodent dynamics in response to plant productivity and rainfall in the southwestern United States indicate that populations respond differentially to resource availability and to precipitation, creating complex relationships among variables (Brown & Heske, 1990; Ernest et al., 2000). Plant growth generally responds to rainfall immediately, while consumer populations may take up to one year to show evidence of increase, and may depend on the precipitation totals over multiple rainy seasons (Brown & Heske, 1990; Ernest et al., 2000). Further up the trophic scale, mammalian predator populations may not fluctuate in synchrony

19 (concurrent or delayed) with rainfall or vegetation biomass in these systems, although they may vary with prey populations.

The San Joaquin kit fox is native to the grass- and shrublands of the San Joaquin

Valley and surrounding foothills in California. This small fox primarily feeds on nocturnal rodents (especially kangaroo rats, Dipodomys spp.), but will also eat leporids, diurnal ground squirrels, birds, insects, and reptiles (Cypher et al., 2000). Kit foxes may live approximately six to seven years, with males and females living in pairs during the breeding and pupping season (McGrew, 1979). Breeding occurs December-

February, with pups born in February-April (McGrew, 1979; Cypher et al., 2000).

Juveniles disperse in September-October, and generally do not breed their first year

(Mcgrew, 1979; Cypher et al., 2000). V. m. mutica is morphologically and genetically distinct from other kit and swift foxes in southern California and elsewhere in North

America (Waithman & Roest, 1977; Mercure et al., 1993). Much of the original habitat of the kit fox has been lost to agricultural land conversion and urban development

(Williams et al., 1998), and habitat destruction and fragmentation are continuing. In fact, recent research indicates that kit fox populations are somewhat isolated (Schwartz et al., 2005). There are no reliable estimates of the current population size of this species, although ballpark figures generally fall under 10,000 animals (Williams et al.,

1998), and it is known that local populations exhibit large density fluctuations over time

(Cypher et al., 2000). The US Fish and Wildlife Service has designated three “core” populations as necessary to the continued survival of the species (Williams et al., 1998): those in the Carrizo Plain National Monument, western Kern county, and the Ciervo-

Panoche Natural Area.

20 Several demographic studies have been conducted on San Joaquin kit foxes, as well as on kit foxes in other parts of the American Southwest. These studies have led to contradictory conclusions regarding the drivers of population fluctuations, such as temporal changes in prey abundance, which are important to many mammalian carnivore populations (Fuller & Sievert, 2001). Numbers of foxes, as well as fox population growth rates, were found to be positively correlated with prey densities on the Naval Petroleum Reserves (NPRC) in western Kern County (Cypher et al., 2000), but there seemed to be no association among fox counts, leporids, and rodents on the

Carrizo Plain and at Camp Roberts (Ralls & Eberhardt, 1997; White et al., 2000).

However, there is good evidence across multiple studies that kit foxes have lower reproductive success during periods of low prey availability (Egoscue, 1975; White &

Ralls, 1993; White et al., 1996; Spiegel & Tom, 1996; Cypher et al., 2000).

Interestingly, there appears to be some support for a correlation between rainfall and kit fox numbers in two studies (Ralls & Eberhardt, 1997; Cypher et al., 2000), which is explained by the authors as a result of vegetative responses to increased rainfall, leading to larger primary consumer populations on which kit foxes feed. These results and assumptions are somewhat surprising given the two trophic levels separating kit foxes and rainfall, and are at odds with research concerning population responses of rodents to rainfall in an arid environment (Ernest et al., 2000).

In this paper we examine a 35-year kit fox survey dataset from the Carrizo Plain to investigate the magnitude and drivers of population fluctuations over time and space.

Will a long-term dataset provide more insight into these drivers than shorter-term studies? We compare the records of kit fox detections with those of prey populations

21 and rainfall, and sub-sample the data at various time scales to examine the impact of using short-term data as opposed to long-term data on our conclusions. We investigate habitat use and seasonal and inter-annual changes in kit fox distribution, and compare the spatial distribution of kit foxes with that of kangaroo rats using a geographic information system (GIS). The spatial component of these data provides us with a unique opportunity to explore relationships between predator and prey and shared distributional changes over time. We compare the insights gained from these data with those gained from other studies and discuss how this may inform management decisions.

Methods

Study Area

The Carrizo Plain National Monument (CPNM) in southern California extends

60 kilometers northwest-southeast and encompasses over 1,000 km2, including the southern portion of the Carrizo Plain itself, as well as portions of the Temblor and

Caliente mountain ranges (Figure 1). Elevation in the monument ranges from 600 meters above sea level on the valley floor to over 1500 meters in the Caliente Range. It is characterized by hot, dry summers and cool, wet winters. Precipitation averages 250 mm per year, with most rain falling between November and April (Western Regional

Climate Center [WRCC], 2007). Temperatures range from 16 C to 30 C in summer and from 7 C to 16 C in winter.

The valley vegetation is predominantly non-native annual grasslands mixed with desert saltbush scrub communities, with juniper-oak woodlands in the mountains

22 surrounding the plain. The plain itself is dominated by a 1200 ha flat alkali pan, which is seasonally filled with water. Dryland farming for wheat and barley occurred until

1989 across 11,000 ha, and cattle and sheep grazing still occurs on a limited spatial and temporal basis.

The Carrizo Plain supports a variety of wildlife, including kangaroo rats, jackrabbits, elk, pronghorn antelope, coyotes, rattlesnakes, horned lizards, and numerous birds of prey. Several threatened and endangered animal species exist here, including the San Joaquin kit fox, the giant kangaroo rat (Dipodomys ingens), San

Joaquin antelope squirrel (Ammospermophilus nelsoni), the blunt-nosed leopard lizard

(Gambelia silus), and the longhorn fairy shrimp (Branchineta longiantenna). The

Carrizo encompasses the largest remaining habitat patch for these species and also provides a haven for several federally listed plant species including the California jewelflower (Caulanthus californicus), Hoover´s wooly-star (Eriastrum hooveri) and

San Joaquin wooly-threads (Lembertia congdonii).

Survey Methods

Quarterly night-time spotlighting surveys by the California Department of Fish and Game are conducted along two routes in the CPNM. The Elkhorn route is approximately 61 km long and follows roads along the Elkhorn Plain on the eastern side of the CPNM, and the Soda Lake route runs along Soda Lake Road in the Carrizo Plain for 72 km (Figure 1). The Elkhorn route has been surveyed since 1970, while the Soda

Lake route was started in 1989. Spring counts are generally performed in March, summer counts in June, fall counts in September and winter counts in December. A

23 spotlighting team consists of at least two people driving the spotlighting route, each sweeping the area with million-candlepower hand-held spotlights (however, the power of the spotlights has increased over the years, beginning with 100,000-candlepower lights). When carnivore eyeshine is detected, a note is made of the mileage, number of animals and species. Counts of leporids and small rodents are also opportunistically noted along the transects (without mileage data), but rodents are not identified to species.

Population Dynamic Analyses

For the population dynamic analysis we focused only on the counts of kit foxes, nocturnal rodents (the majority of which are kangaroo rats) and jackrabbits along the

Elkhorn route from 1970 until 2005. The time series has several gaps over this 35 year history, due to a variety of causes such as impassable roads and inclement weather

(Table 1). Most data gaps are single quarterly counts, although there are two full years missing in 1994 and 1995. These years were left out of the analysis; however, the single gaps were filled using the corresponding seasonal count means across all other years for each species. This allowed us to calculate yearly averages for each population, using values from summer, fall and winter of one calendar year and spring of the following year (e.g., yearly average count for kit foxes in 1974 incorporates counts from summer, fall and winter of 1974 and spring of 1975). Rainfall data from

New Cuyama (south of CPNM) from 1974 through 2006 was obtained from the WRCC

(2007). Although this record is collected by a weather station on the other side of the

Caliente Range from the Carrizo Plain, it is highly correlated with the much shorter

24 weather record from the CPNM (1992-2006; r = 0.97). Because most rain falls in a six- month period every year, we calculated the “effective” rainfall each year from

November to April (e.g., effective rainfall for 1974 includes November and December of 1974 and January through April of 1975; Figure 2).

All statistical analyses were performed in JMP 6.0 (SAS Institute Inc., Cary

NC). The population time series were first analyzed for consistency within and between years. We examined intra- and inter-annual variability in counts; high intra-annual compared with inter-annual variability would suggest noise due to observation error, rather than true process variation. Autocorrelation between seasons and years was investigated, and we also looked for evidence of long-term trends in abundance and multiyear cycles using partial autocorrelation periodograms and spectral analysis.

Relationships between kit fox numbers (and population growth rates) and prey species and rainfall, as well as between prey numbers (and growth rates) and rainfall, were investigated by cross-correlating the various time series separated by time lags ranging from zero to two years. Effective rainfall over two years was also summed for correlations. We classified precipitation as above or below the long-range average for t-tests of differences in fox and prey numbers between good and bad rain years (also with one- and two-year lags). Fox numbers were included in tests as 1) average annual counts, 2) summer counts only (to look at pup numbers), 3) population growth rate

(ln(Nt/Nt-1)) from one year to the next using average annual counts, 4) winter counts only (to look at adult numbers), 5) population growth rate from one winter to the next, and 6) the ratio of winter to summer counts, which includes pup mortality and dispersal.

25 For these analyses, we assumed there was relatively little movement of foxes into or out of the Carrizo Plain.

All of the above analyses were conducted using the full 35 year dataset. To investigate how our results from the cross-correlations would be impacted if we had fewer years of data we subsampled the data into several overlapping multi-year blocks in a “moving window” type of sampling. We had nine 5-year blocks, five 10-year blocks, three 15-year blocks, two 20-year blocks, and one 27-year block. Within each of these blocks, each of the six fox demographic variables were regressed against effective rainfall from the previous year. Data blocks were overlapping and thus non- independent; however, no comparative statistical tests were run among blocks.

Spatial Distributional Analysis

All GIS analyses were performed using ArcGIS 9.2 (ESRI, Redlands CA). Both the Elkhorn and Soda Lake datasets were used in our investigation of kit fox distribution along the transect lines. First, habitat use/availability analyses were conducted by comparing the numbers of fox detection locations within each habitat type along the transects against the amount of that habitat available. We used the California

Department of Forestry’s Multi-Source Land Cover Data layer (FRAP, 2003) and limited our analysis to the years 1997-2006 because this layer represents only a snapshot in time of the vegetative cover on the Carrizo. Habitat types were classified as herbaceous (grasslands), desert, or shrub and a X2 analysis was used to compare habitat use versus availability. Next, seasonal distributional changes in kit fox counts were investigated using a Pearson Χ2 two-way contingency test, and nominal logistic

26 regression was used to examine changes over years in fox distribution along the transects. For both tests, kit fox locations were binned into four equal segments along each of the transects, thus creating a categorical (rather than continuous) response variable.

In 2001 and 2006 we conducted aerial surveys of giant kangaroo rat precincts in

CPNM. Precincts are highly visible areas surrounding kangaroo rat burrows that have been grazed nearly bare by the rodents. GPS locations of the boundaries of precinct aggregations were mapped in a GIS and the points were used to delineate an approximate range for giant kangaroo rats in the CPNM. Locations of kit fox sightings for that year were then mapped onto these polygons to investigate if there was any spatial correlation between kangaroo rat presence and fox detection during these years.

Results

Population dynamics

Average yearly kit fox counts on the Elkhorn Plain fluctuated greatly over the

35 year time period, from a low of 6.5 to a high of 39.5 (Figure 3). Autocorrelation between seasonal counts was significant (r = 0.23, p = 0.01) and autocorrelation between years was high (r = 0.6, p = 0.0003). Intra-annual variation was lower than inter-annual variation in counts (s.d. for all years combined = 8.86, median s.d. per year

= 6.83). Average yearly jackrabbit counts varied from 11 to 84 and rodents varied from

8.5 to 323. Jackrabbit counts were autocorrelated from one season to the next (r = 0.25, p = 0.005), and from year to the next (r = 0.41, p = 0.021). Intra-annual variation was also lower than inter-annual variation in jackrabbit counts (s.d. for all years combined =

27 19.52, median s.d. per year = 18.29). However, there was great intra-annual variability in the rodent counts (s.d. for all years combined = 84.41, median s.d. per year = 93.67), and autocorrelation between kangaroo rat seasonal counts was non-significant (r = 0.05, p = 0.59). These results suggest that spotlighting may not be the best method for detecting changes in rodent abundance. Given the poor quality of the rodent data, we did not use them for any further analyses. No long-term trends (increasing or decreasing) were evident for any of the species, nor was there evidence for inter-annual cycles.

For foxes, the average annual finite growth rate was not significantly different from one, although the annual rates of growth varied from year to year (1.09 ± 0.44 s.d.). Summer counts of foxes tended to be higher than the counts from other seasons (p

< 0.01, Tukey HSD test), reflecting the emergence of pups from maternity dens in the spring. The growth rate from one winter count to the next was 1.19 ± 0.74, while the change from summer to winter counts was 0.61 ± 0.29. This difference was statistically significant (p = 0.0002) and indicates that the winter counts truly reflect counts of adults, which have a higher survival rate than juveniles.

Fox counts were positively correlated with counts of jackrabbits in the same year (r = 0.44, p = 0.011), and with jackrabbits in the previous year (r = 0.38, p = 0.035;

Table 2). Annual growth rate of foxes was correlated with numbers of jackrabbits in the same year (r = 0.52, p = 0.0025; Table 2), although kit fox growth rate based on winter numbers alone was not correlated with jackrabbits. Summer counts of foxes were positively correlated with counts of jackrabbits in the same year (r = 0.47, p = 0.006), and with the previous year’s jackrabbit numbers (r = 0.43, p = 0.012), while the ratio of

28 winter to summer fox counts was negatively correlated with the previous year’s jackrabbit counts (r = -0.43, p = 0.012; Table 2).

Neither annual average jackrabbit counts nor growth rates were correlated with rainfall from any previous years. There was also no correlation between annual fox counts or winter counts and effective rainfall, but kit fox annual growth rate was negatively correlated with the current year’s precipitation (r = -0.43, p = 0.024; Table

2). Summer fox counts were higher following years of below-average rainfall (p =

0.043; Table 3). There was a positive correlation between the winter to summer ratio of counts and the previous year’s rainfall (r = 0.44, p = 0.014) and between this ratio and rainfall during the previous two years (r = 0.42, p = 0.024; Table 2). The winter to summer fox ratio was higher following years of above-average rainfall (p = 0.003;

Table 3).

Correlative analysis using subsamples of the full dataset led to widely varying results depending on the years included (Figure 4). Shorter datasets tended to result in both positive and negative correlations between the same variables depending on the years included, and the more years included in the dataset, the lower the variance in r- values.

Spatial analyses

Kit fox sightings were not distributed uniformly along the two routes. Sightings along the Soda Lake route over all years were clustered in a unimodal distribution, with the peak around 34 km (Figure 5a). Detections along the Elkhorn route had a bimodal distribution, with peaks near 28 km and 45 km (Figure 5b). Analysis of habitat

29 use/availability revealed no significant patterns of land cover preference along the routes (e.g., grassland vs. shrub), or detection bias between habitat types. We did not include ruggedness of the terrain as an explanatory variable for the very reason that ruggedness may co-vary with both use and detectability. Contingency tests of the kit fox sightings along the Elkhorn route indicated that the spatial distribution of fall and winter sightings were more similar to each other than to summer or spring. In contrast, contingency tests of the detection distributions along the Soda Lake route revealed no significant seasonal differences (Table 4; Figure 6).

Logistic regression of detection locations across years revealed significant changes in fox distributions on both routes (Elkhorn: p = 0.018; Soda Lake: p < 0.0001;

Figure 7). A comparison of sightings from 1989 through 2005 on both routes revealed much greater changes on the Soda Lake route than the Elkhorn route. Sightings along the Elkhorn route stayed relatively constant over this time period, as illustrated by nearly horizontal logistic regression lines (Figure 7). Sightings along the Soda Lake route, however, changed considerably over the same time span. In 1989, nearly 1/3 of the kit fox sightings were located in the southern quarter of the route, and by 2005 there were virtually no sightings in the southern quarter. In contrast, in 1989 approximately

1/4 of the sightings were located along the northern half of the route, but by 2005 nearly

2/3 of the sightings were located along this portion of the route (Figure 7).

An examination of the kangaroo rat precinct maps overlaid with the kit fox detections from 2001 and 2006 showed a strong correlation between kangaroo rat presence and fox detection along the Soda Lake route (Figure 8). Giant kangaroo rat precinct area expanded 83% between the two surveys, and the distribution of kit fox

30 sightings between the two surveys corresponded spatially with the spread of kangaroo rats. In both years, there were very few kit fox sightings outside of the surveyed kangaroo rat distribution (Figure 8).

Discussion

Long-term population dynamic datasets are rare, and particularly so for endangered species. In many cases, regulations require wildlife or land managers to periodically survey populations of conservation concern, but researchers often do not have knowledge of or access to these records, and therefore cannot utilize the data that have been collected. Frequently, survey data is not gathered in a rigorous or systematic way over time, and there may be temporal gaps or oversights in data collection, all of which can make analysis and interpretation challenging. However, a great amount of money and effort is expended on population monitoring in many areas, and attempts should be made to use the data available. Long term data, even with its potential problems, can inform researchers and managers about drivers of population declines and increases, changes in prey abundance and other resources, and natural fluctuations in species distributions and habitat use.

The kit fox survey data we used was collected over 35 years by employees of the California Department of Fish and Game. Spotlighting was used as it is the most efficient method for rapid assessment of population trends over large regions and over long periods of time. Over the entire the time period, the surveys were supervised by two people in succession, lending a remarkable consistency to the data collection, and both employees are adept at identifying kit foxes as well as other species that inhabit the

31 CPNM. Nevertheless, there are a number of limitations to the data, such as seasonal gaps in collection due to inclement weather or the misplacement of datasheets, irregularities in the start-point of the Elkhorn survey route, and some imprecision in the marking of kit fox detection localities. Even accounting for these problems, however, we believe the kit fox and jackrabbit data are of generally good quality. Intra-annual variation was lower than inter-annual variability, and there was a high level of autocorrelation between counts between seasons, suggesting that quarterly counts are a reasonable method of assessing changes in relative abundance of foxes and jackrabbits over time.

The kit fox and jackrabbit data appear to be more robust than the rodent data.

There was a great deal of noise within the rodent survey data, with intra-annual variability rivaling inter-annual fluctuations. This was likely a result of both observer error (rodents are much harder to observe and count during spotlighting than larger animals) and natural population density fluctuations. These survey data indicate that spotlighting may not be a good method of estimating relative abundance of rodents over time. This is unfortunate because nocturnal rodents are an important part of the diet of kit foxes (Egoscue, 1962; Laughrin, 1970; White et al., 1996; Spiegel et al., 1996;

Cypher et al., 2000). Without accurate counts, it is impossible to investigate relationships between rodent and fox population dynamics. Likewise, this dataset does not include information about vegetation changes or predator numbers (spotlighting also tends not be effective in counting coyotes since they avoid vehicle headlights). Thus, we are missing information on trophic levels both below and above kit foxes.

32 Population dynamics

The kit fox population on the Carrizo Plain appears to be persistent, although it exhibits large variations in size over time. These fluctuations are similar in magnitude to those found at Elk Hills over a 15 year period (Cypher et al., 2000). We found that summer counts of foxes tended to be higher than other seasons, which likely reflects the emergence of pups between the spring and summer surveys. Additionally, the annual growth rate when calculated using winter counts (before pup emergence) was similar to the annual growth rate when calculated using annual averages. Therefore, change from winter to winter probably represents adult survival plus the addition of new yearlings.

In contrast, the rate of change from summer to winter counts was much higher, even considering the shorter time frame, most likely reflecting the lower survival rates of juveniles, which has been well-documented (Disney & Spiegel, 1992; Ralls & White,

1995; Cypher & Spencer, 1998; Cypher et al., 2000). It is possible, however, that this rate also reflects the dispersal of young foxes out of the study area (Egoscue, 1962;

Koopman et al., 1998). Throughout this analysis we have assumed that the Carrizo population is closed because suitable habitat for kit foxes is fragmented outside of the

Monument, and kit fox detections sharply decline at the edges of the park. Accurate estimates of parameters such as population growth rate and juvenile survival depend on this assumption; however, there may be a small number of foxes moving into and out of the area (Schwartz et al., 2005), which could affect our rate estimates. Overall, these data do suggest that spotlighting surveys for kit fox in large populations like the one in

CPNM can result in accurate relative abundance estimates and can give insight into population dynamics and life history parameters.

33 Kit fox numbers were correlated with those of black-tailed jackrabbits. Foxes may be impacted both directly and indirectly by the density of jackrabbits on the

CPNM. Foxes are known to prey on jackrabbits and in fact, they may provide a substantial portion of their diet (Cypher and Spencer, 1998). Multiple studies have also shown that adult foxes may produce more young in years of higher prey availability

(Egoscue, 1975; White & Ralls, 1993; White et al., 1996; Spiegel & Tom, 1996; Cypher et al., 2000). Therefore, fox numbers, growth rates and reproductive success might be expected to increase with increasing jackrabbits. Indeed, we found that fox numbers tracked those of jackrabbits on the CPNM, and a previous study at the NPRC also showed that kit fox numbers were correlated with those of leporids (Cypher and

Spencer, 1998). Likewise, our study revealed that the abundance of pups (as estimated by summer counts) is correlated with higher numbers of jackrabbits in previous years.

Importantly however, coyotes also prey on jackrabbits (Cypher et al., 1994; Bartel &

Knowlton, 2005) and are the predominate predator of kit foxes (Berry et al., 1987;

Standley et al., 1992; Ralls & White, 1995; Cypher & Spencer, 1998). Large numbers of jackrabbits may then result in higher population levels of coyotes, thus lowering kit fox numbers. Our results indicate that the ratio of winter to summer fox counts may be lower following years of high jackrabbit abundance. Juvenile kit foxes tend to have higher rates of mortality than adults (Disney & Spiegel, 1992; Standley et al., 1992;

Cypher & Spencer, 1998; Cypher et al., 2000) and it is possible that coyotes have a large impact on juvenile survival on the CPNM (Ralls & White, 1995). Unfortunately, we have no reliable information on coyote abundances over time on the Carrizo.

34 We found very little correlation between rainfall and kit fox numbers, contrary to previous studies (Ralls & Eberhardt, 1997; Cypher et al., 2000). Most of the significant correlations involved estimated pup abundance and survival. There appear to be more pups born following high rainfall years, perhaps suggesting that high rainfall results in increased ecosystem productivity, leading to higher reproductive success for females. Multiple studies have shown that female kit foxes will produce more young in years of high prey availability (Egoscue, 1975; White & Ralls, 1993; White et al., 1996;

Spiegel & Tom, 1996; Cypher et al., 2000). The data also indicated a relationship between the winter to summer fox ratio and higher rainfall in previous years, suggesting that pup survival may be increased during periods of high productivity. It is important to note, however, that these are only conjectures because we have no data on total prey numbers or vegetative responses to rainfall in this system. Furthermore, as Brown and

Ernest (2002) discuss, rodent responses to rainfall in arid systems are “complex and non-linear,” and our inability to uncover statistically significant relationships between precipitation and consumer populations may be due to the high temporal and spatial variability in rainfall patterns, and differing impacts of extreme precipitation events on consumer populations. Significant correlations between secondary consumers and precipitation patterns should therefore be even more unlikely to occur.

We had hoped that this dataset would provide new insight into correlations among kit foxes, prey, and rainfall because of the length of time data were collected.

However, this was not the case. In fact, the more years we included in any analyses, the weaker any relationships became, and perceived correlations among variables depended highly on how many and which years were included in the analysis. These results were

35 similar to those of Ernest et al. (2000) and Brown and Ernest (2002), and suggest that correlations fortuitously occurring over the short term may not hold up over the long term due to the variability in natural systems. Further, the inclusion of more years may avoid the problem of outlier years that sometimes drive strong but misleading correlations.

Spatial analyses

Kit fox sightings were not distributed uniformly along the two transect routes.

In both cases, they were clustered towards the middle of the routes, and farther from the edges of the CPNM. We found no vegetation differences that would explain this clustering. It is possible that we are seeing an edge effect in the habitat use and distribution of kit foxes in this area. In California Valley, to the north and northwest of the transects, habitat suitable for kit foxes is scarce. Desert scrub has been replaced by dryland farms, livestock pastures, and housing subdivisions. To the south there is a major east-west two lane highway (Route 166), the Cuyama River valley (which has seen a dramatic conversion in recent years from grazed pastures to row crops and orchards), and a large gravel quarry. In addition, the presence of domestic dogs may impact kit fox populations on the edges of the Monument. Dogs are known to kill foxes

(Disney & Spiegel, 1992; Ralls & White, 1995) and may discourage them to utilize areas that otherwise would be suitable.

We found seasonal differences in habitat use along the Elkhorn transect, but not the Soda Lake transect (Table 4; Figure 6). On the Elkhorn route, the distribution of kit fox sightings tended to be more diffuse during the fall and winter surveys, possibly

36 reflecting the dispersal of juvenile foxes away from their natal dens. However, we do not have enough detailed information to determine if habitat use during the spring and summer surveys is more restricted because of certain factors related to denning sites

(e.g., land cover, soils, or topography).

Inter-annual changes in kit fox distributions were apparent on both transects, but were especially evident on the Soda Lake route (Figure 7). When the surveys were first started on this route in 1989, there were many fox sightings towards the southern end of the transect, and less than 25% of the sightings were in the northern half of the route.

By 2005, detections of kit foxes had dramatically altered, with virtually no animals seen in the southern portion, and over 70% sighted in the northern half of the route. During the same time period, detections along the Elkhorn route also changed significantly, but to a much lesser degree than on the Soda Lake route (p = 0.018 compared with p <

0.0001). Dryland farming was discontinued on the Carrizo Plain in 1989, but until that date had been the dominant land use of much of the northwestern part of the valley floor and foothills near Soda Lake. Disking the soil every year precluded the use of this habitat by giant kangaroo rats, and therefore these rodents were rare or absent in this area of the Plain. Since 1989, kangaroo rats have been steadily spreading north and are now a common site around Soda Lake and to the north and west. Concomitant with this spread, kit foxes appear to have moved north as well. In contrast, dryland farming was not practiced extensively in the eastern portion of the Plain or along the Elkhorn route, and both kangaroo rats and kit foxes have been found along this transect since the surveys began in 1970.

37 Kit foxes prey extensively on kangaroo rats throughout their range, and Grinnell

(1937) noted the strong geographic association of kit foxes with these rodents. It is unclear whether kit foxes have a preference for kangaroo rats when other prey are available (Cypher et al., 2000), or to what extent kit foxes are reliant on kangaroo rats as a source of food (Speigel et al., 1996; Koopman, 1995). However, in areas where kit foxes fed primarily on diurnal rodents such as ground squirrels, populations of this predator no longer exist (Orloff et al., 1986; Logan et al., 1992; however, see Moonjian,

2007). The parallel spread of foxes and kangaroo rats along the Soda Lake route suggests a strong spatial association between these two taxa, and is supported by the information available concerning kangaroo rat precinct expansion between 2001 and

2006. Although the mapped kangaroo rat distributions are only rough approximations of the actual range, they provide some indication of the spread of kangaroo rats within this time period (Figure 8). Kit fox detection localities closely paralleled the range of giant kangaroo rat distribution during both years, and the northerly spread of foxes along Soda Lake is apparent. Unfortunately, the abundance of kit foxes increased between the two years, somewhat confounding the spatial pattern. More precise mapping of kangaroo rat distribution is needed, as are future aerial surveys to monitor the ongoing changes in precinct areas. The strong spatial association between these two species contrasts markedly with the findings of Warrick & Cypher (1998), who found no evidence for a positive spatial relationship between kit foxes and lagomorphs.

38 Conclusions

This study stresses the importance of long-term data collection, particularly in regards to the ecology and management of endangered species. Although these surveys were originally intended as a coarse-grain monitoring tool, they have proved to be important in investigating drivers of kit fox population density and habitat use. It is essential that these surveys continue, and that more in-depth data is collected.

Currently, fox locations are marked using a range finder and GPS, a marked improvement over the mileage method used for most of the collection period. This will eventually allow researchers to estimate population size using a detection probability function. Additionally, information on vegetation change and prey numbers is critical to understanding this system. Permanent vegetation plots which can be surveyed several times per year should be established throughout the CPNM. Rodent population density and distribution should also be monitored using track plates or trapping grids in established plots. Lastly, population numbers of predators of kit foxes, especially coyotes, should be monitored using track plates, hair snares, or camera traps.

The long-term spatial data from the CPNM provides us with a unique glimpse into the dynamics of a persistent population of San Joaquin kit fox in one of its remaining core habitats. An examination of this long-term dataset revealed that kit fox population densities are quite variable over time, although we were not able to consistently correlate these fluctuations with any abiotic or biotic factors. A better understanding of the drivers of these population fluctuations is vital to managing this vulnerable species, especially in areas where they are currently rare. Furthermore, because kit fox numbers tend to vary over time, populations may be vulnerable to

39 extinction at low densities. Therefore, managers may require flexibility in restricting resource access, such as off-road vehicle use or spotlight hunting, during periods of low population density. Our study also revealed large changes in kit fox distribution on

CPNM over time, which was likely linked to changes in land use and kangaroo rat distribution. Again, this may be important to long-term land use planning in regards to kit fox management and conservation; lands currently devoid of kit fox may provide necessary future habitat, particularly in the event of global climate change. At present, the CPNM represents one of the last large intact habitat patches for kit fox where natural processes can take place, and continuing study of its foxes can only benefit this endangered species.

40 Literature Cited

Bartel RA & FF Knowlton. 2005. Functional feeding responses of coyotes, Canis latrans, to fluctuating prey abundance in the Curlew Valley, Utah, 1977-1993. Canadian Journal of Zoology 83:569-578.

Berry WH, Scrivner JH, O’Farrell TP, Harris CE, Kato TT & PM McCue. 1987. Sources and rates of mortality of the San Joaquin kit fox, Naval Petroleum Reserve #1, Kern County, California, 1980-1986. US Department of Energy Topical Report No. EGG 10282-2154.

Brown JH & SKM Ernest. 2002. Rain and rodents: complex dynamics of desert consumers. Bioscience 52:979-987.

Brown JH & EJ Heske. 1990. Temporal changes in a Chihuahuan Desert rodent community. Oikos 59:290-302.

California Department of Forestry. 2003. Fire Resource and Assessment Program. http://frap.cdf.ca.gov/data/frapgisdata/select.asp

Cypher BL, Warrick GD, Otten MRM, O’Farrell TP, Berry WH, Harris CE, Kato TT, McCue PM, Scrivner JH & BW Zoellick. 2000. Population dynamics of San Joaquin kit foxes at the Naval Petroleum Reserves in California. Wildlife Monographs 145:1-43.

Cypher BL & KA Spencer. 1998. Competitive interactions between coyotes and San Joaquin kit foxes. Journal of Mammalogy 79:204-214.

Disney M & LK Spiegel. 1992. Sources and rates of San Joaquin kit fox mortality in western Kern County, California. Transactions of the Western Section of the Wildlife Society 28:73-82.

41

Egoscue HJ. 1962. Ecology and life history of the kit fox in Tooele County, Utah. Ecology 43:481-497.

Egoscue HJ. 1975. Population dynamics of the kit fox in western Utah. Bulletin of the Southern California Academy of Sciences 74:122-127.

Elton C & M Nicholson. 1942. The ten-year cycle in numbers of the lynx in Canada. Journal of Animal Ecology 11:215-244.

Ernest SKM, Brown JH, RR Parmenter. 2000. Rodents, plants, and precipitation: spatial and temporal dynamics of consumers and resources. Oikos 88: 470-482.

Fuller TK. 1989. Population dynamics of wolves in north-central Minnesota. Wildlife Monographs 110:1-39.

Fuller TK & PR Sievert. 2001. Carnivore demography and the consequences of changes in prey availability. Pages 163-178 in Carnivore Conservation, Gittleman JL, Funk SM, MacDonald DW & RK Wayne, (Eds). Cambridge: Cambridge University Press.

Gilg O, Sittler B, Sabard B, Hurstel A, Sane R, Delattre P & L Hanski. 2006. Functional and numerical responses of four lemming predators in high arctic Greenland. Oikos 113:193-216.

Grant PR, R Grant. 1996. Finch communities in a climatically fluctuating environment. Pages 343-384 in Long-term Studies of Vertebrate Communities, ML Cody & JA Smallwood, (Eds). San Diego: Academic Press.

42 Grinnell J, Dixon JS & JM Linsdale. 1937. Fur-bearing Mammals of California. Berkeley: University of California Press.

Honer OP, Wachter B, East ML, Runyaro VA & H Hofer. 2005. The effect of prey abundance and foraging tactics on the population dynamics of a social, territorial carnivore, the spotted hyena. Oikos 108:544-554.

Jedrzejewski W, Jedrzejewski B & L Szymura. 1995. Weasel population response, home range, and predation on rodents in a deciduous forest in Poland. Ecology 76:179-195.

Kaikusalo A & A Angerbjörn. 1995. The arctic fox population in Finnish Lapland during 30 years, 1964-1993. Annales Zooloogici Fennici 32:69-77.

Karanth KU, Nichols JD, Kumar NS, LinkWA & JE Hines. 2004. Tigers and their prey: predicting carnivore densities from prey abundance. Proceedings of the National Academy of Science 101:4854-4858.

Koopman ME. 1995. Food habits, space use, and movements of the San Joaquin kit fox on the Elk Hills Naval Petroleum Reserves in California. MS Thesis, University of California, Berkeley.

Koopman ME, Scrivner JH & TT Kato. 1998. Patterns of den use by San Joaquin kit foxes. Journal of Wildlife Management 62:373-379.

Korpimaki E, Norrdahl K & T Rinta-Jaskari. 1991. Response of stoats and least weasels to fluctuating food abundances: is the low phase of the vole cycle due to mustelid predation? Oecologia 88:552-561.

43 Laughrin L. 1970. San Joaquin kit fox: its distribution and abundance. California Department of Fish and Game Wildlife Management Branch Administrative Report No. 70-2.

Logan CG, Berry WH, Standley WG & TT Kato. 1992. Prey abundance and food habits of San Joaquin kit fox (Vulpes velox macrotis) at Camp Roberts Army National Guard training site. US Department of Energy Topical Report No. EGG 10617- 2158.

Marker LL & AJ Dickman. 2005. Factors affecting leopard (Panthera pardus) spatial ecology, with particular reference to Namibian farmlands. South African Journal of Wildlife Research 35:105-115.

McGrew JC. 1979. Vulpes macrotis. Mammalian Species 123:106.

Mercure A, Ralls K, KP Koepfli & RK Wayne. 1993. Genetic subdivision among small canids: mitochondrial DNA differentiation of swift, kit and arctic foxes. Evolution 47:1313-1328.

Moonjian JM. 2007. A current distribution and a dietary analysis of San Joaquin kit fox in San Luis Obispo County. MS Thesis, California Polytechnic State University, San Luis Obispo.

Orloff, SG, Hall F & L Spiegel. 1986. Distribution and habitat requirements of the San Joaquin kit fox in the Northern extreme of their range. Transactions of the Western Section of the Wildlife Society 22:60-70.

Ralls K & LL Eberhardt. 1997. Assessment of abundance of San Joaquin kit foxes by spotlight surveys. Journal of Mammalogy 78:65-73.

44 Ralls K & PJ White. 1995. Predation on San Joaquin kit foxes by larger canids. Journal of Mammalogy 76:723-729.

Rosenzweig ML. 1968. Net primary productivity of terrestrial communities: prediction from climatological data. American Naturalist 102:67-74.

Schwartz MK, Ralls K, Williams DF, Cypher BL, Pilgrim KL & RC Fleischer. 2005. Gene flow among San Joaquin kit fox populations in a severely changed ecosystem. Conservation Genetics 6:25-37.

Speigel LK, Cypher BL & TC Dao. 1996. Diet of San Joaquin kit fox at three sites in western Kern County, California. Pages39-52 in Studies of the San Joaquin Kit Fox in Undeveloped and Oil-developed Areas, LK Speigel, (Ed). Sacramento: California Energy Commission.

Speigel LK & J Tom. 1996. Reproduction of San Joaquin kit fox in undeveloped and oil-developed habitats of Kern County, California. Pages 53-69 in Studies of the San Joaquin Kit Fox in Undeveloped and Oil-developed Areas, LK Speigel, (Ed). Sacramento: California Energy Commission.

Standley WG, Berry WH, O’Farrell TP & TT Kato. 1992. Mortality of San Joaquin kit fox (Vulpes velox macrotis) at Camp Roberts Army National Guard Training Site, California. US Department of Energy Topical Report No. EGG 10627- 2157.

Turchin P. 2003. Complex Population Dynamics: a theoretical/empirical synthesis. Monographs in Population Biology. Princeton: Princeton University Press.

Waithman J & A Roest. 1977. A taxonomic study of the kit fox, Vulpes macrotis. Journal of Mammalogy 58:157-164.

45

Warrick GD & BL Cypher. 1998. Factors affecting the spatial distribution on San Joaquin kit foxes. Journal of Wildlife Management 62:707-717.

Western Regional Climate Center. 2007. http://www.wrcc.dri.edu

White PJ & K Ralls. 1993. Reproduction and spacing patterns of kit foxes relative to changing prey availability. Journal of Wildlife Management 57:861-867.

White PJ, Vanderbilt White CA & K Ralls. 1996. Functional and numerical responses of kit foxes to a short-term decline in mammalian prey. Journal of Mammalogy 77:370-376.

White PJ, Berry WH, Eliason JJ & MT Hanson. 2000. Catastrophic decrease in an isolated population of kit foxes. The Southwestern Naturalist 45:204-211.

Williams DF, Cypher EA, Kelly PA, Miller KJ, Norvell N, Phillips SE, Johnson CD & GW Colliver. 1998. Recovery plan for upland species of the San Joaquin Valley, California. Portland: US Fish and Wildlife Service.

46

Figure 1. Map of the Carrizo Plain National Monument. CPNM boundary in solid. Elkhorn Plain and Soda Lake survey routes are

47 shown with dashed lines.

Figure 2. Effective precipitation (November-April) at New Cuyama Fire Station from 1973-2005, where dotted line is mean.

48

Figure 3. Kit fox, rodent, and jackrabbit counts from the Elkhorn Plain surveys 1970- 2005, with standard deviation bars around the yearly means.

49 Annual Fox Counts Annual Fox Growth Rate

1 1 0.5 0.5

0 0 r-value -0.5 r-value -0.5 -1 -1 0 5 10 15 20 25 30 0 5 10 15 20 25 30 Years in dataset Years in dataset

Summer Fox Counts Pup Survival

1 1 0.5 0.5

0 0 r-value -0.5 r-value -0.5 -1 -1 0 5 10 15 20 25 30 0 5 10 15 20 25 30 Years in Dataset Years in dataset

Winter Fox Counts Annual Growth Rate (winter counts)

1.5 1 1 0.5 0.5 0

r-value r-value 0 -0.5 -1 -0.5 0 5 10 15 20 25 30 0 5 10 15 20 25 30 Years in dataset Years in dataset

Figure 4. Correlation of fox counts, growth rates, and survival against lagged (1 year) effective rainfall using various subsets of data.

50

(a)

(b)

Figure 5. a) Elkhorn kit fox sightings 1970-2005; b) Soda Lake kit fox sightings 1989- 2005.

51 (a)

(b)

Figure 6. a) Elkhorn kit fox sightings; b) Soda Lake kit fox sightings. Sightings separated into Fall/Winter and Spring/Summer.

52

the Elkhornroutestayed nearlyc along theSodaLakeroute shiftedaway theso the routes.(a)SodaLakeroute, sightings fallingineachofthefourbins,with thelowestbin being thenorthern endof into oneoffour“bins”.Thelinesrepr Lake transectsfrom 1989-2005.Routeswere Figure 7 Cumulative proportion of sightings Cumulative proportion of sightings occurring within survey quarters occurring within survey quarters . Resultsoflogisticregression 0.00 0.25 0.50 0.75 1.00

01/01/1989 Soda LakeSurvey Date 4

Elkhorn Survey Date 01/01/1991 4 01/01/1993 3

01/01/1995 3

01/01/1997 2 p <0.0001;(b)Elkhornroute, onstant overthesame time period. 2 01/01/1999 esent changesincumulative proportionsof 01/01/2001 1 1 kit foxsightingsalongElkhornandSoda 01/01/2003 uthern end(Bin4),while sightingsalong quartered andsightings 01/01/2005

p =0.018.Sightings werecategorized 53

Figure 8a. Kit fox detections along survey routes in 2001, superimposed on giant kangaroo rat distribution. Hatched area is giant kangaroo rat range in 2001, dark gray is giant kangaroo rat range in 2006.

54

Figure 8b. Kit fox detections along survey routes in 2006, superimposed on giant kangaroo rat distribution. Hatched area is giant kangaroo rat range in 2001, dark gray is giant kangaroo rat range in 2006.

55 Table 1. Missing survey dates for Elkhorn and Soda Lake routes. SP = spring; SU = summer; FA = fall; WI = winter.

Elkhorn Soda Lake Year 1970 SP,WI 1971 SP 1978 SP 1979 SU 1980 FA,WI 1989 FA 1994 SP,SU,FA,WI SP,SU,FA,WI 1995 SP,SU,FA,WI SP,SU,FA,WI 1997 WI WI 1999 SU 2003 FA

56 Table 2. Kit fox and jackrabbit population variables regressed against predictors. Numbers are r-values for each time lag.

Sum, 2 year Jackrabbit annual Effective average Effective precipitation Precip. Time Lag t t-1 t-2 t t-1 t-2 t, t-1 t-1,t-2

Dependent Variable Kit Fox Annual Average 0.44* 0.38* 0.29 -0.17 -0.09 0.22 -0.20 0.08 Kit Fox Summer Counts 0.47** 0.43* 0.29 -0.17 -0.35 0.13 -0.36 -0.15 Kit Fox Growth Rate 0.52** -0.11 -0.07 -0.43* -0.09 0.35 -0.36 0.18 Kit Fox Winter Counts 0.25 0.23 0.26 -0.10 0.09 0.13 -0.01 0.14 Kit Fox Growth Rate 0.34 -0.09 0.01 -0.24 0.06 0.13 -0.14 0.11 (winter) Kit Fox Pup Survival -0.31 -0.43* 0.27 0.16 0.44* -0.08 0.42* 0.25 Jackrabbit Annual - -0.18 -0.24 0.26 -0.29 0.02 Average Jackrabbit Growth Rate - -0.23 -0.10 0.42* -0.23 0.19 *p < 0.05, **p < 0.01

57 Table 3. P-values of t-tests of mean differences between above- and below-average rainfall years, with two time lags. P-values ≤ 0.05 in bold.

Effective precipitation Time Lag t t-1 t-2

Kit Fox Annual Average 0.3 0.92 0.34 Kit Fox Summer Counts 0.18 0.04 0.64 Kit Fox Growth Rate 0.07 0.97 0.39 Kit Fox Winter Counts 0.83 0.54 0.37 Kit Fox Growth Rate (winter) 0.69 0.79 0.46 Kit Fox Pup Survival 0.05 0.003 0.52 Jackrabbit Annual Average 0.16 0.2 0.17 Jackrabbit Growth Rate 0.39 0.84 0.05

58 Table 4. Results of X2 contingency analyses of seasonal differences in sightings along the Soda Lake and Elkhorn survey routes. Elkhorn results above diagonal; Soda Lake below diagonal. Significant p-values in bold.

Summer Fall Winter Spring Summer 0.0016 0.0023 0.1446 Fall 0.9906 0.5136 0.0009 Winter 0.7520 0.7112 0.0177 Spring 0.3094 0.2791 0.2506

59 CHAPTER 3

DISTRIBUTION AND HABITAT USE OF RED FOX (VULPES VULPES) IN THE SAN FRANCISCO BAY AREA

Introduction

Invasive species are often associated with disturbed ecosystems, and may be able to establish in natural habitat fragments more easily if there is a complex interface between developed or cultivated areas and wildlands. Urban, suburban and exurban development lead to reduced native biodiversity and increases in invasion by exotic species (Odell & Knight 2001; Maestas et al. 2003; Smallwood 1994; Hansen et al.

2005; McKinney 2002). The wildland-urban interface comprises almost 10% of the land surface of the coterminous United States (Radeloff et al. 2005), and exurban development is the fastest growing form of land use (Brown et al. 2005), posing a huge challenge to preserving biodiversity and reducing the impacts of non-natives in adjacent wildlands. The link between suburban residential development and invasive spread may be especially significant in areas with high biodiversity that are undergoing rapid anthropogenic change, as is the case in northern California (Abbitt et al. 2000).

Since the mid-seventies, non-native red foxes (Vulpes vulpes) have invaded the region surrounding the San Francisco Bay in California (J. DiDonato, pers comm.;

Lewis et al. 1993). The San Francisco Bay Area (hereafter, the Bay Area) is characterized by a diverse mix of high- and low-density housing, vineyards, hardwood forest, wetlands and grasslands, resulting in a complex interface between human-

60 dominated landscapes and wildlands. Of particular importance are the region’s oak woodlands, diverse habitats which are home to ten species of oaks and provide resources to over 330 species of vertebrates. Most of the undeveloped oak woodlands in the Bay Area are under private ownership and are subject to intense vineyard and residential development pressure. Hence, they are under significant threat of invasion by many exotic species, including red foxes. If red fox dispersal and successful establishment is predicated on landscape conversion to housing and agriculture (Harris

& Rayner 1986; Adkins & Stott 1998), these development pressures may lead to the continued red fox invasion of northern coastal California.

Red foxes are the most widespread non-domesticated carnivore in the world, and have been enormously successful invading many habitats (Larivière & Pasitschniak-

Arts 1996). They occur on six continents and have continued to increase their range over the last century, both naturally and due to introduction by humans (Aubry, 1983;

Lewis et al. 1993; Macpherson 1964; Marsh 1938). Red foxes are often associated with heterogeneous or fragmented landscapes (Catling & Burt 1995; Kurki et al. 1998;

Larivière & Pasitschniak-Arts 1996; Oehler & Litvaitis 1996), and are increasingly found in urban and suburban areas in North America, Europe, and Australia, where they can attain very high densities (e.g., MacDonald & Newdick 1982; Adkins & Stott 1998, and references therein). Red foxes often negatively impact native fauna in areas where they have been introduced; for example, in Australia their arrival is linked to the decline and local extirpation of many endemic prey species (Burbidge & Manly 2002; Burbidge

& McKenzie 1989; Dickman 1996; Kinnear et al. 1988; Kinnear et al. 1998).

61 Red foxes are present through much of North America, where predation and competition by coyotes (Canis latrans) may be important in shaping red fox distributions and habitat use. Coyotes are known to kill red foxes (Sargeant & Allen

1989), and red foxes will avoid areas with high densities of coyotes (Voigt & Earle

1983; Sargeant et al. 1987; Harrison et al. 1989). Coyotes may also competitively displace red foxes through scramble competition for food resources (Lavin et al. 2003).

However, coyotes tend to avoid urban or developed areas (Gosselink et al. 2003; Odell

& Knight 2001; Randa & Yunger 2006), and these regions might provide a refuge from coyote predation for red foxes. Indeed, red foxes are present in and around several urban centers, including Oakland, Toronto, Chicago, Los Angeles, and Madison

(Adkins & Stott 1998; Randa & Yunger 2006; Lewis et al. 1993; Ables 1969). Urban and suburban development may create refuges for red foxes that allow them to reach high densities, and facilitate their invasion into natural areas bordering the wildland- urban interface.

Non-native red foxes were introduced into lowland California in the late nineteenth and early twentieth centuries for fur farming and hunting. Foxes were imported and released in Orange County in southern California from 1905 to 1919

(Sleeper 1987). Commercial fur farming became very popular in the mid twentieth century, with approximately 125 fox farms throughout California by the early 1940s

(Vail 1942), and many foxes undoubtedly escaped or were released from these farms

(Lewis et al. 1993). Gray (1975) documented red foxes occupying 16 counties in northern California, with most sightings occurring in the Sacramento Valley. A localized population of these animals in the Sacramento Valley was first described by

62 Grinnell et al. (1937), who believed them to be distinct from the native Sierra Nevada red fox (V. v. necator); however, recent genetic work has revealed that these foxes are indeed native, and are distinct from the foxes found in the Bay Area (Sacks et al. 2007).

Gray (1975) also described a population from Los Angeles County, and stated that there were unconfirmed sightings in other southern California localities. Most recently,

Lewis et al. (1993) documented the presence of red foxes in 36 counties throughout the state; the North Coast, the Modoc Plateau, and the Mojave Desert were the only regions where they were not found.

Red foxes have been implicated in the decline in California of several federally threatened and endangered bird species, including the light-footed clapper rail (Rallus longirostris levipes), the California clapper rail (R. longirostris obsoletus), the

California least tern (Sterna antillarum browni), and the western snowy plover

(Charadrius alexandrinus nivosus; U.S. Fish and Wildlife Service 1990; U.S. Fish and

Wildlife Service and U.S. Navy 1990). While depredations of bird populations have been well-documented, less is known about red fox impacts to mammalian prey species and native carnivores. Anecdotal evidence suggests that red foxes have replaced gray foxes (Urocyon cinereoargenteus) in some areas (J. Patton and R. Barrett, pers comm), and that when red fox numbers are reduced, gray fox populations rebound (J. DiDonato, pers comm). Interestingly, gray foxes do not have the same impact on ground-nesting birds as red foxes. Red foxes have also killed several federally endangered San Joaquin kit foxes (Vulpes macrotis mutica; Clark 2001; Ralls & White 1995), and conservationists are concerned over possible introgression between non-native red foxes and the Sierra Nevada red fox (Perrine 2005).

63 My goals in this study were to 1) assess the general distribution and habitat use of red foxes throughout the Bay Area, particularly in wildlands; 2) examine how red fox presence correlates with residential and agricultural development; 3) investigate the relationship between red and gray foxes; and 4) evaluate how red fox distribution may be affected by the presence of coyotes. I examined a gradient of habitats from urban- isolated sites to wildlands in a variety of land cover types, and at a latitudinal gradient from south to north (from areas with red fox to those purportedly without). I used a mixture of red fox, gray fox and coyote presence data (scat and sightings) collected by me and other researchers to assess red fox distribution and habitat use in the region.

Using these data, I investigated the relationship between patterns and types of land development and red fox invasion in the Bay Area, the effect of red fox presence on native gray foxes, and the impact of coyotes on red fox presence in these areas.

Methods

Study Area

The San Francisco Bay metropolitan area is the fifth largest urban area in the

United States, with over 7 million people. The area’s population is expected to grow by

23% in the next 25 years, and much of this growth is expected to occur on the urban fringe within commuting distance of the major cities (Association of Bay Area

Governments 2007). This region of northern coastal California is characterized by a

Mediterranean climate of warm, dry summers and cool, wet winters. The dominant habitat types surrounding the metropolitan Bay Area are salt marsh wetlands, oak woodlands intermixed with annual grasslands, and at higher elevations, chaparral and

64 chemise scrub communities. Human altered landscapes include a mix of high- and low- density housing, agriculture (including row crops, orchards, and vineyards), and grazed rangelands. I focused on four North Bay counties, Marin, Sonoma, Napa, and

Mendocino, and on three East Bay counties, Alameda, Contra Costa, and San Joaquin

(Fig. 1). Red foxes have been sighted throughout the East Bay, but are thought to occur only in the very southern end of the North Bay (Lewis et al. 1993). Other mesocarnivores such as coyotes, bobcats (Lynx rufus) and gray foxes are present throughout the study area.

Datasets

Four sets of red fox data were used in this analysis (Fig. 2). One was collected by Lewis et al. (1993), and represents the locations of red fox sightings in the study area from 1970 through the early nineties. These data are based on a survey of wildlife professionals throughout the state and include 59 red fox sighting localities in six counties (there were no sightings in Mendocino County). The second dataset was collected by Clark et al. (2003) and represents seven locations of red fox roadkill, sightings, and molecularly identified scats collected during surveys for foxes (primarily kit foxes) in the East Bay. The third and fourth datasets were collected by me and a colleague, Sarah Reed, and represent presence or absence of positively identified red fox scats along transects at survey sites throughout the Bay Area. Scat collection has been shown to be a good method for estimating presence and relative abundance of canids (Harrison et al. 2002; Harrison et al. 2004). Site selection and survey methods for these two datasets follow.

65 Site Selection

I chose sites along an urban-to-rural gradient using a geographic information system (GIS) and road maps of the Bay Area. I looked for sites completely surrounded by high density residential or industrial development, and for sites that I designated the urban “fringe”: large areas of open space directly bordering high density urban areas. I chose these sites regardless of land cover, but in all cases the parcels were open to the public. I also chose oak woodland-dominated rural properties under a variety of public and private ownerships (see Appendix A for a full description of selection methods;

Bidlack et al. 2007a). Ultimately, 70 sites were selected to survey, and some properties were sufficiently large, with easy road or trail access, to allow for two survey transects to be conducted at a minimum separation distance of 2 km (to insure sampling independence). Consequently, a total of 82 transects were eventually surveyed between

June and September 2005 and 2006 (Table 1; Fig. 2). Transect start points and directions were chosen randomly within each site using trail or road maps. Transects were up to 2.5 km in length, although length varied because of the small size and limited trails in many of the sites. Each site was surveyed once.

Sarah Reed surveyed 47 sites in Marin, Sonoma, Mendocino and Napa counties for her study of carnivore relative abundance in relation to recreational use and habitat connectivity (Reed 2007). She chose sites with a variety of land cover types, in differing proximities to residential and agricultural development, and with varying degrees of public access (Table 3; Fig. 2). She walked multiple short transects per site along hiking trails, fire roads and game trails, but visited each site only once. I considered all of her transects within one site as one transect in my analysis.

66 Survey Methods and Data Collection

Surveys for red fox were performed using both visual surveys for carnivore scat, and a detection dog trained to locate the scats of red foxes, coyotes, gray foxes, bobcats, and pumas. Scat sniffing dogs have proven to be both remarkably efficient and precise in their searching. In prior research, detection dogs have correctly identified

100% of target carnivore scats in a matrix of sympatric species (Smith et al. 2001;

Smith et al. 2003) and have detected the presence of target species regardless of species density or vegetation type (Smith et al. 2005). I trained a candidate detection dog at the

Hopland Research and Extension Center over a period of about eight weeks in 2004.

The training consisted of a series of progressively more difficult tasks designed to teach the dog to search for and to locate target scats in a natural environment, using positive reinforcement (see Appendix B for training methods).

A pilot survey indicated that in this region, most canid scats are deposited along hiking and game trails and dirt roads (Bidlack unpublished data). Therefore, all field surveys were conducted along fire roads and hiking and game trails. In a controlled test at the end of the training, my dog detected 100 percent of scats placed within 10 m of the centerline of a transect along an unpaved road, and 98 percent of scats within 20 m

(see Appendix 2). Because my dog worked most efficiently during cool weather, the dog-assisted surveys were conducted in the early morning, although visual surveys by me and Sarah were conducted during both mornings and afternoons.

Transect and scat location data were collected using a Trimble GeoExplorer III

GPS unit (Trimble Navigation Ltd., Sunnyvale CA). Transect location and approximate length were recorded using the GPS to collect point location information every five

67 seconds. When a scat was found, its location was also recorded using the GPS.

Collected scats were individually bagged with a desiccant pack (DesiPak; Texas

Technologies, Inc., Cedar Park TX), given an identification number and subsequently stored at -80 C.

Molecular methods

Scats were identified to species using molecular genetic methods. DNA was extracted from each scat with a QIAamp DNA stool kit (Qiagen Inc., Valencia CA), using a small sub-sample (~500 g) that was removed from the outer surface or end of each feces. Polymerase chain reaction (PCR) was then used to amplify a 196 base pair segment of the mitochondrial cytochrome b gene. PCR was performed using 2 μl 1:50 extraction dilution in a 20 μl total reaction volume containing 1× reaction buffer

(Qiagen Inc.), 1.5 mM MgCl2, 200 µM each dNTP, 0.5 µM each primer, and 1 U Taq polymerase (Qiagen Inc.). Thermalcycling was initiated at 94˚C for 2 min, followed by

40 cycles of 94˚C for 1 min, 54˚C for 1 min and 72˚C for 2 min. All PCR reactions included at least one negative control to monitor for contamination. Following DNA amplification, I first digested the PCR products with the restriction enzymes HpaII and

DdeI to separate puma, bobcat, raccoon and skunk, and then digested the samples identified as canid with HpyCh4V to differentiate among red fox, gray fox and coyote.

The 10 µl reaction volume contained 8 µl PCR product, 1 µl digest buffer, 0.5 µl H2O, and 2.5 U each restriction enzyme. Products were digested for 4 h, following the manufacturer’s instructions (New England Biolabs Inc., Ipswich MA). I electrophoresed the products for 45 minutes on a 2% agarose gel and visualized the

68 predicted cutting patterns using ethidium bromide and UV light. Complete laboratory protocols are reported in Appendix C (Bidlack et al., 2007b).

Data analysis

All GIS analyses were performed using ArcGIS 9.2 (ESRI, Redlands CA) and all statistical analyses were performed in JMP 6.0 and SAS 9.1 (SAS Institute Inc., Cary

NC). Red fox sighting locations recorded in the UTM (Universal Transverse Mercator) coordinate system in Lewis et al. (1993) were imported into an ArcMap project and re- projected in the Teale-Albers NAD27 projection. Howard Clark provided me with the latitude and longitude (in decimal degrees) of each of the red fox sightings from his study (Clark et al. 2003), and these points were also imported into the ArcMap project.

Collected GPS transect and scat locations were differentially corrected and processed with GPS Pathfinder Office 3.0 (Trimble Navigation Ltd., Sunnyvale CA). I exported point and transect files into ArcMap and used the Smooth Line tool in ArcToolbox to minimize GPS errors along the transect lines and to obtain more accurate estimates of transect lengths.

First, I used the scat data to investigate the relationship between red and gray foxes. A t-test was run to examine differences in gray fox relative abundance at sites with and without red fox.

All four datasets were included in a multiple logistic regression analysis of red fox distribution and habitat relationships. The Lewis et al. (1993) and Clark et al.

(2003) data represents only presence, while the scat data represents both presence and absence (or more correctly, non-detection). Although the data represent different types

69 of detections, collected in different ways, utilizing red fox presence as the binomial response variable in a logistic regression allowed me to combine these data in a meaningful way. Logistic regression is one member of the family of generalized linear modeling techniques, and is distinguished by the fact that the response variable is discrete, taking on two or more possible values. Like linear regression, the goal of logistic regression model building is to find the best-fitting and most parsimonious model to describe the relationship between a response variable (in this case, red fox presence/absence) and a suite of independent variables (in this case, habitat characteristics).

First, in ArcMap I created three concentric and nested buffers around each survey transect line at distances of 100m, 500m and 1000m. For the Lewis et al. (1993) and Clark et al. (2003) data points, I created three concentric buffers around each point with areas equaling the mean area covered by the corresponding transect buffers

(hereafter, I will also refer to these buffers as 100m, 500m and 1000m for ease of communication). I then extracted ten different habitat variables at each point or transect site (Table 2). I calculated mean elevation within the 100m buffer using a 30 meter digital elevation model (DEM). Land cover characteristics of each of the three site buffers were calculated using the CalVeg polygon layer (USDA Forest Service 2006). I calculated the relative proportions of the buffer areas covered by hardwood forest, grasslands, agriculture and urban development. I used TIGER data (US Census Bureau) to calculate road density around the sites by calculating the total length of roads in each buffer divided by the buffer area. Because buffer road density did not always provide an accurate description of development surrounding sites, particularly for sites located

70 in wildlands contiguous with high density urban areas, I also calculated a distance-to- urban-road-density metric, as follows. A state road density coverage was compared with the urban polygons in the CalVeg layer to determine the road density of these areas, and a road density of 7.5 km/km2 was classified as urban. This raster was then used to create a Euclidean distance coverage and the distance from sites to the nearest urban area was calculated. Similarly, distance to nearest water sources was calculated for each site using the rasterized National Hydrography Dataset (USGS) layer, which contains lakes and ponds, rivers, reservoirs, wetlands, and coastlines. I also computed the diversity of land cover within each buffer around each site using the Shannon diversity index

H = -Σ pi*lnpi ,

where pi is the proportion of total area in the ith cover type. Lastly, I calculated the relative abundance of coyotes and gray foxes (number of scats per km) at each transect

(no information on coyote or gray fox abundance was available for the Lewis et al.

(1993) and Clark et al. (2003) sites).

For the logistic regression, all data points from the Lewis et al. (1993) and Clark et al. (2003) studies were coded as “red fox present,” while all transects from the scat surveys were coded as either “red fox present” or “red fox absent.” To build an appropriate logistic model, I followed the procedure outlined in Hosmer & Lemeshow

(2000). I first ran exploratory univariate logistic regressions with each independent

71 variable. Variables with p-values less than 0.25 were kept for further investigation. For significant variables differentiated only by the buffer width (i.e., road density within

100m, 500m, and 1000m), the variable with the highest G statistic, or greatest difference in log-likelihood from a model containing only a constant, was retained. To test for variable independence, I examined a correlation matrix of the remaining variables, and checked that the tolerance (1 – r2 of each variable regressed against all the rest) for each variable was less than 0.1, indicating non-collinearity (Quinn &

Keough 2002). I investigated the distributions of each independent variable for each response category to determine the appropriate transformation of that variable (Kay &

Little 1987; Robertson et al. 1994). Once transformations were determined, I created a full model with all variable transformations and made note of estimated variable coefficients and p-values, as well as overall model fit. I followed a process of variable and model selection based on an examination of p-values and 95% confidence intervals of estimated coefficients (i.e., whether the CI includes zero), the impact on these values of entering or removing variables, and the AICc value of each model (Burnham &

Anderson 2002). Lastly, I examined the goodness of fit of the final models using the receiving operating characteristic (ROC).

Results

Scat surveys

Sarah Reed and I collected a total of 1900 scats from 130 transects, with a mean of 1866 meters per transect. There was a mean of 14.5 (± 11.4 sd) scats found per transect, with a range of zero to 54. Using molecular methods we identified 1631

72 (85.8%) scats to species: we identified 893 (54.7%) coyote scats, 258 gray fox scats

(15.8%), and 32 (3.6%) red fox scats. The remainder of the identified scats were deposited by bobcats, striped skunks, pumas and domestic cats. Coyotes were found in all surveyed counties on 103 (79.2%) transects, and gray foxes in all counties on 48

(36.9%) transects, while red foxes were found only in Marin, Sonoma, Alameda and

Contra Costa counties on 16 (12.3%) transects (San Joaquin County was not included in the scat surveys; Tables 1 & 3; Fig. 3).

Red Fox Interspecific and Habitat Interactions

Gray fox relative abundance was not related to red fox presence (p = 0.85), nor was red fox detection correlated with coyote or gray fox abundance (Table 4). A total of 190 sites from the four datasets were included in the logistic regression of habitat variables. The univariate analysis indicated that distance to water and amount of agricultural land cover were not correlated with presence or absence of red foxes (Table

4). Variable diagnostics suggested that road density and urban land cover were most important at the 100m buffer level, while hardwood forest, grassland vegetative cover, and habitat diversity were most important at the 1000m buffer level. Habitat diversity was highly correlated with hardwood forest and grassland cover (r > 0.75) because of the equation used in calculating the Shannon index. Models including the diversity index instead of hardwood or grassland cover performed less well (data not shown); therefore, the Shannon index variable was removed from the remaining models. Model building resulted in one best model (Table 5), which included road density and proportion of urban development within the 100m buffer, and amount of hardwood

73 forest and grassland within 1000m of the transect. The ROC value of 0.875 indicates that this model has high explanatory power.

At a relatively small scale, red foxes are strongly and positively associated with the presence of roads, and are therefore more likely to occur in areas with roads than in equivalent areas without roads. The univariate analyses predict that red foxes will occur with > 50% frequency when road density within a 100m buffer is higher than 5.9 km/km2. Likewise, red foxes are strongly and positively associated with the amount of urban development within 100m of the transect; in fact, red foxes will occur more than

50% of the time if urban development covers more than 23% of the area immediately surrounding a transect. At a relatively large scale, red foxes are strongly and positively associated with grasslands, and are more likely to be detected when grasslands cover more than 54% of the 1000m buffer surrounding a transect. They are also more likely to be found in areas without hardwood forest cover.

Discussion

The San Francisco Bay region, with its mix of urban, suburban and agricultural development within a naturally heterogeneous landscape of oak woodlands, grasslands and wetlands, seems to provide a highly suitable habitat for red foxes. Most previous research suggests that these adaptable animals are closely associated with disturbed or heterogeneous habitats. In rural Australia red foxes only occur in forested patches close to areas that have been cultivated, heavily grazed, burned, logged, or converted to residential development (Catling & Burt 1995), and in rural Europe red foxes are most abundant in landscapes including 20 to 30 percent agricultural land (Kurki et al. 1998).

74 Urban areas may also provide reliable sources of food, water and cover for foxes. For instance, in Orange County in southern California, local residents provided red foxes with supplemental food at every site studied (Lewis et al. 1993). Within many urbanized areas, red foxes tend to preferentially utilize low density residential neighborhoods and open spaces, and they may reach very high densities in these habitats (Marks & Bloomfield 2006; Harris & Rayner 1986; Adkins & Stott 1998;

Lewis et al. 1993).

Scat surveys indicate that while red foxes are widespread in the Bay Area

(occurring in six of seven counties surveyed), they are generally rare. In fact, of 1631 scats identified to species, only 32 were confirmed from red fox. This was surprising, given the apparent suitability of habitat, and the fact that red foxes have been present in the Bay Area for nearly forty years. I had personally observed foxes in two parks in

Alameda and Contra Costa counties and had been told of other populations inhabiting suburban golf courses and neighborhoods. The data do suggest that these foxes may be locally common in some areas, and that their distribution among and within counties is uneven. For example, a quarter of all red fox scats were found along one transect in

Alameda County, and another four were found along one transect in Sonoma County.

Most fox scats were found in Alameda County, followed by Marin, Sonoma and Contra

Costa counties (Table 3). This uneven distribution may reflect the habitat preferences of red foxes, in regards to vegetative land cover and urbanization. Logistic regression using all four datasets revealed that foxes tend to only occur in regions containing more grassland than hardwood forest and in sites very near or surrounded by high density

75 urban development and roads. In this area, red foxes rely heavily on the cover and resources provided by the built environment.

I found no relationship between red fox presence and gray fox or coyote relative abundance, and it appears that neither species of fox is excluding the other from any areas. Gray foxes usually occupy forested habitats (Fritzell & Haroldson 1982), and in this system gray foxes are strongly correlated with hardwood forest cover (see Chapter

4). Although both foxes co-occurred at six sites, it appears that they generally occupy different habitats. As in the Bay Area, Gosselink et al. (2003) found that red foxes in

Illinois preferentially used open areas (such as roadsides and crop fields) and avoided woodlands and other high-cover habitats. They attribute some of this habitat selection as a response to the presence of coyotes in more rural and high-cover habitats. Coyotes are known to kill red foxes (Sargeant & Allen 1989), and red foxes will avoid areas with high densities of coyotes (Voigt & Earle 1983; Sargeant et al. 1987; Harrison et al.

1989). Habitat partitioning between the two species is well-established (Major &

Sherburne 1987; Theberge & Wedeles 1989), and coyotes may competitively displace red foxes by indirect competition for food resources (Lavin et al. 2003). I thought that the high numbers of coyotes in the Bay Area would exclude red foxes from some more rural areas. However, coyote scats were found on 79% of all transects, and both coyotes and foxes were found on 12 of the 16 transects where fox scats were detected. I found no correlation between coyote presence or relative abundance and red fox presence. Nevertheless, coyotes tend to avoid urban or developed areas (Gosselink et al. 2003; Randa & Yunger 2006; also see Chapter 4), and these regions might indeed

76 provide a refuge for red foxes. More research into the interactions between these two species is needed in this region to rigorously test this hypothesis.

The predictive power of the red fox logistic model would be improved if I had finer-scale habitat data. For instance, information on parcel size and number of housing units per parcel would provide better resolution than road density or the CalVeg land use layer; unfortunately, these data are not available for all the counties I studied. More detailed information regarding water sources, vegetation structure and species composition would also help in modeling red fox habitat preferences. Lastly, data on prey densities and other food resources would likely help in explaining fox abundance at different sites.

Whether due to their dependence on urban resources or avoidance of coyotes, red foxes apparently have not been particularly successful in invading natural areas far from urban development. This is encouraging information concerning the potential impact of these non-native carnivores on the biodiversity of northern California wildlands. And while it may be problematic to extrapolate these results to other areas of the state, perhaps we have less to fear from future red fox impacts to native and endangered California species than history would indicate. Importantly, however, though they may not be able to persist in wildlands far from developed areas, red foxes may survive in such habitats that are close to human development. They may also be able to diffuse through natural areas, using anthropogenic structures such as roads and buildings as cover and food sources. This pattern has been observed in other human- associated species (for example, domestic cats, cowbirds; Hejl et al. 2002; Maestas et al. 2003; Morrison & Caldwell 2002; Odell & Knight 2001). Urban, suburban and

77 exurban development may ultimately lead to reduced native biodiversity and increases in invasion by exotic species if certain development thresholds are passed (Maestas et al. 2003; Smallwood 1994; Hansen et al. 2005).

It also appears that red foxes may still be expanding their distribution in the state. They were first documented in Contra Costa, Napa and Sonoma counties in the mid 1970s, and spread to Alameda and western Marin in the early 1980s (Lewis et al.

1993). When compared with the distribution of red fox sightings from Lewis et al.

(1993), there has been an apparent northward range expansion by red foxes into eastern

Marin and central Sonoma counties since the 1990s (Fig. 4). This may be related to the rapid urbanization of these counties in recent decades, especially along the Interstate

101 corridor. The spatial pattern of fox detections in these northern counties is similar to that seen along the Interstate 580 corridor in Alameda and San Joaquin counties as reported in Lewis et al. (1993; Fig. 4). Disturbingly, continuing residential development from the Bay Area into the Sacramento Valley may lead to interactions and genetic introgression between non-native red fox and the native subspecies there. If red fox occupancy patterns reflect a reliance on human development, then conserving large patches of undeveloped land in the Bay Area may help buffer these habitats against invasion by red fox.

In conclusion, these data suggest that in the Bay Area red foxes have successfully established populations only in areas near human development. It is unclear why red foxes are so rare in this region, given their generally adaptable nature.

Further research is needed to better quantify the habitat correlates of red fox as well as to tease apart the relationships between foxes and coyotes. In this way we may gain

78 insight into the drivers of the red fox invasion, and the possible impact of future development on the spread of this exotic species.

79 Literature Cited

Ables, E.D. 1969. Home-range studies of red foxes (Vulpes vulpes). Journal of Mammalogy 50: 108-120.

Abbitt, R.F.; Scott, J.M.; Wilcove, D.S. 2000. The geography of vulnerability: incorporating species geography and human development patterns into conservation planning. Biological Conservation 96:169-175.

Adkins, C.A.; Stott, P. 1998. Home ranges, movements and habitat associations of red foxes Vulpes vulpes in suburban Toronto, Ontario, Canada. Journal of Zoology London 244: 335-346.

Association of Bay Area Governments. 2007. www.abag.ca.gov/index.html

Aubry, K.B. 1983. The Cascade red fox: distribution, morphology, zoogeography and ecology. Seattle, Washington: University of Washington. PhD. dissertation.

Bidlack, A.L.; Merenlender, A.; Getz, W.M. 2007a. Distribution of non-native red foxes in East Bay oak woodlands. In Sixth Symposium on Oak Woodlands: Today's Challenges, Tomorrow's Opportunities, October 9-12, 2006, Rohnert Park, California. USDA Forest Service General Technical Report.

Bidlack, A.L.; Reed, S.E.; Palsbøll, P.J.; Getz, W.M. 2007b. Characterization of a western North American carnivore community using PCR-RFLP of cytochrome b obtained from fecal samples. Conservation Genetics 8: 1511-1513.

Brown, D.G.; Johnson, K.M.; Loveland, T.R.; Theobald, D.M. 2005. Rural land-use trends in the coterminous United States, 1950-2000. Ecological Applications 15: 1851-1863.

80 Burbidge, A.A.; McKenzie, N.L. 1989. Patterns in the modern decline of Western Australia's vertebrate fauna: causes and conservation implications. Biological Conservation 50: 143-198.

Burbidge, A.A.; Manly, B.F. 2002. Mammal extinctions on Australian islands: causes and conservation implications. Journal of Biogeography 29: 465-473.

Burnham, K.P.; Anderson, D.R. 2002. Model Selection and Multimodel Inference: a practical information-theoretic approach. New York: Springer.

Catling, P.C.; Burt, R.J. 1995. Why are red foxes absent from some eucalypt forests in eastern New South Wales? Wildlife Research 22: 535-546.

Clark, H.O. 2001. Endangered San Joaquin kit fox and non-native red fox: interspecific competitive interactions. Fresno, CA: California State University. MS. thesis.

Clark, H.O.; Smith, D.A.; Cypher, B.L.; Kelly, P.A. 2003. Detection dog surveys for San Joaquin kit foxes in the northern range. Fresno, California: Endangered Species Recovery Program, 37 p. Contract number 4600013447.

Dickman, C.R. 1996. Impact of exotic generalist predators on the native fauna of Australia. Wildlife Biology 2: 185-195.

Fritzell, E.K.; Haroldson, K.J. 1982. Urocyon cinereoargenteus. Mammalian Species 189: 1-8.

Gosselink, T.E.; Van Deelen, T.R.; Warner, R.E.; Joselyn, M.G. 2003. Temporal habitat partitioning and spatial use of coyotes and red foxes in east-central Illinois. Journal of Wildlife Management 67: 90-103.

Gray, R.L. 1975. Sacramento Valley red fox survey. Non-game wildlife investigation progress report. Sacramento: California Department of Fish and Game.

81 Grinnell, J.; Dixon, J.S.; Linsdale, J.M. 1937. Furbearing mammals of California. Berkeley: University of California Press.

Hansen, A.J.; Knight, R.L.; Marzluff, J.M.; Powell, S.; Brown, K.; Gude, P.H.; Jones, K. 2005. Effects of exurban development on biodiversity: patterns, mechanisms, and research needs. Ecological Applications 15: 1893-1905.

Harris, S.; Rayner, J.M.V. 1986. Urban fox (Vulpes vulpes) population estimates and habitat requirements in several British cities. The Journal of Animal Ecology 55: 575-591.

Harrison, D.J.; Bissonette, J.A.; Sherburne, J.A. 1989. Spatial relationships between coyotes and red foxes in eastern Maine. Journal of Wildlife Management 53: 181-185.

Harrison, R.L.; Barr D.J.; Dragoo, J.W. 2002. A comparison of population survey techniques for swift foxes (Vulpes velox) in New Mexico. American Midland Naturalist 148:320-337.

Harrison, R.L.; Clarke, P-G.S.; Clarke, C.M. 2004. Indexing swift fox populations in New Mexico using scats. American Midland Naturalist 151: 42-49.

Hejl, S.J.; Mack, D.E.; Young, J.S.; Bednarz, J.C.; Hutto, R.L. 2002. Birds and changing landscape patterns in conifer forests of the north-central Rocky Mountains. Studies in Avian Biology 25:113-129.

Hosmer, D.W.; Lemeshow, S. 2000. Applied Logistic Regression. New York: John Wiley & Sons.

Kay, R.; Little, S. 1987. Transformations of the explanatory variables in the logistic regression model for binary data. Biometrika 74: 495-501.

82 Kinnear, J.E.; Onus, M.L.; Bromilow, R.N. 1988. Fox control and rock wallaby population dynamics. Australian Wildlife Research 15: 435-477.

Kinnear, J.E.; Onus, M.L.; Sumner, N.R. 1998. Fox control and rock wallaby dynamics - II. An update. Wildlife Research 25: 81-88.

Kurki, E.G.; Nikula, A.; Helle, P.; Linden, H. 1998. Abundances of red fox and pine marten in relation to the composition of boreal forest landscapes. Journal of Animal Ecology 67: 874-886.

Lariviere, S.; Pasitschniak-Arts, M. 1996. Vulpes vulpes. Mammalian Species 1-11.

Lavin, S.R.; Van Deelen, T.R.; Brown, P.W.; Warner, R.E.; Ambrose, S.H. 2003. Prey use by red foxes (Vulpes vulpes) in urban and rural areas of Illinois. Canadian Journal of Zoology 81: 1070-1082.

Lewis, J.C.; Sallee, K.L.; Golightly Jr., R.T. 1993. Introduced red fox in California. Non-Game Bird and Mammal Section Report 93-10. Sacramento: California Department of Fish and Game.

MacDonald, D.W.; Newdick, M.T. 1982. The distribution and ecology of foxes, Vulpes vulpes (L.) in urban areas. Pages 123-135 in Urban Ecology: proceedings of the Second European Ecological Symposium, Berlin, September 8-12, 1980. Bornkamm, R.; Lee, J.A.; Seaward, M.R.D. (Eds). Oxford: Blackwell.

MacPherson, A.H. 1964. A northward range extension of the red fox in the eastern Canadian arctic. Journal of Mammalogy 45: 138-140.

Maestas, J.D.; Knight, R.L.; Gilgert, W.C. 2003. Biodiversity across a rural land-use gradient. Conservation Biology 17: 1425-1434.

Major, J.T.; Sherburne, J.A. 1987. Interspecific relationships of coyotes, bobcats, and red foxes in western Maine. Journal of Wildlife Management 51: 606-616.

83 Marsh, D.B. 1938. The influx of the red fox and its colour phases into the barren lands. Canadian Field-Naturalist 52: 60-61.

McKinney, M.L. 2002. Urbanization, biodiversity, and conservation. BioScience 52: 883-890.

Morrison, M.L.; Caldwell, H.D. 2002. Geographic variation in cowbird distribution, abundance and parasitism. Studies in Avian Biology 25: 65-72.

Marks, C.A.; Bloomfield, T.E. 2006. Home-range size and selection of natal den and diurnal shelter sites by urban red foxes (Vulpes vulpes) in Melbourne. Wildlife Research 33: 339-347.

Odell, E.A.; Knight, R.L. 2001. Songbird and medium-sized mammal communities associated with exurban development in Pitkin County, Colorado. Conservation Biology 15: 1143-1150.

Oehler, J.D.; Litvaitis, J.A. 1996. The role of spatial scale in understanding responses of medium-sized carnivores to forest fragmentation. Canadian Journal of Zoology 74: 2070-2079.

Perrine, J.D. 2005. Ecology of red fox (Vulpes vulpes) in the Lassen Peak region of California, U.S.A. Berkeley, CA: University of California. PhD. dissertation.

Quinn, G.P.; Keough, M.J. 2002. Experimental Design and Data Analysis for Biologists. Cambridge: Cambridge University Press.

Radeloff, V.C.; Hammer, R.B.; Stewart, S.I.; Fried, J.S.; Holcomb, S.S.; McKeefry, J.F. 2005. The wildland-urban interface in the United States. Ecological Applications 15: 799-805.

Ralls, K.; White, P.J. 1995. Predation on San Joaquin kit foxes by larger canids. Journal of Mammalogy 76: 723-729.

84 Randa, L.A.; Yunger, J.A. 2006. Carnivore occurrence along an urban-rural gradient: a landscape-level analysis. Journal of Mammalogy 87: 1154-1164.

Reed, S.E. 2007. Natural and human disturbance factors influencing carnivore distributions and implications for conservation. Berkeley, CA: University of California. PhD. Dissertation.

Robertson, C.; Boyle, P.; Hsieh, C-C.; MacFarlane, G.J.; Maisonneuve, P. 1994. Some statistical considerations in the analysis of case-control studies when the exposure variables are continuous measurements. Epidemiology 5: 164-170.

Sacks, B.; Aubry, K.; Perrine, J.; Pollinger, J.; Statham, M.; Wayne, B.; Wisely, S. 2007. Is the Sacramento Valley red fox an overlooked native population? Presentation at the Western Section of the Wildlife Society Annual Conference.

Sargeant, A.B.; Allen, S.H.; Hastings, J.O. 1987. Spatial relations between sympatric coyotes and red foxes in North Dakota. Journal of Wildlife Management 51: 285-293.

Sargeant, A.B.; Allen, S.H. 1989. Observed interactions between coyotes and red foxes. Journal of Mammalogy 70: 631-633.

Sleeper, J. 1987. Bears to briquets: a history of Irvine Park 1897-1997. Trabuco Canyon, CA: California Classics.

Smallwood, K.S. 1994. Site invasibility by exotic birds and mammals. Biological Conservation 69: 251-259.

Smith, D.A.; Ralls, K.; Davenport, B.; Adams, B.; Maldonado, J.E. 2001. Canine assistants for conservationists. Science 291: 435.

Smith, D.A.; Ralls, K.; Hurt, A.; Adams, B.; Parker, M.; Davenport, B.; Smith, M.C.; Maldonado, J.E. 2003. Detection and accuracy rates of dogs trained to find

85 scats of San Joaquin kit foxes (Vulpes macrotis mutica). Animal Conservation 6: 339-346.

Smith, D.A.; Ralls, K.; Cypher, B.L.; Maldonado, J.E. 2005. Assessment of scat- detection dog surveys to determine kit fox distribution. Wildlife Society Bulletin 33: 897-904.

Theberge, J.B.; Wedeles, C.H.R. 1989. Prey selection and habitat partitioning in sympatric coyote and red fox populations, southwest Yukon. Canadian Journal of Zoology 67: 1285-1290.

USDA Forest Service. 2006. CALVEG/EVEG. http://www.fs.fed.us/r5/rsl/clearinghouse

U.S. Fish and Wildlife Service; U.S. Navy. 1990. Endangered species management and protection plan, Naval Weapons Station - Seal Beach and Seal Beach National Wildlife Refuge. Final Environmental Impact Statement. Portland, OR.

U.S. Fish and Wildlife Service. 1990. Predator management plan and environmental assessment, San Francisco Bay National Wildlife Refuge. Draft report. Newark, CA.

Vail, E.L. 1942. Fox ranching in southern California. California Fish and Game 28: 87-88.

Voigt, D.R.; Earle, B.D. 1983. Avoidance of coyotes by red fox families. Journal of Wildlife Management 47: 852-857.

86

Figure 1. Map of San Francisco Bay study area. Gray areas represent urban development. 87

Figure 2. Red fox survey/sightings locations. Dotted pentagons are scat survey transects by Bidlack; squares are transect sites by Reed; circles are red fox sightings reported by Lewis et al. (1993); triangles are red fox sightings, scats and roadkills reported by Clark et al. (2003).

88

Figure 3. Map of red fox detections during scat surveys by Reed and Bidlack.

89

Figure 4. Map of all red fox detections. Stars represent locations of red fox scats collected by Reed and Bidlack; circles are red fox sightings1970-1992 reported in Lewis et al. (1993). There has been an apparent northward range expansion into eastern Marin and southern Sonoma counties since the 1990s.

90 Table 1. Results of scat survey transects, with length, number of scats found, number of scats identified using molecular methods, and number of scats identified from coyotes, gray foxes and bobcats.

# # Gray # Red Date Length Scats # Scats # Scats # Coyote Fox Fox Transect Collector County (Month/Day/Year) (m) Found ID'd ID'd/km Scats/km Scats/km Scats ACAL ALB Contra Costa 7/25/2006 1175 2 2 1.70 0.85 0 0 ALBA ALB Alameda 7/5/2006 755 1 1 1.32 1.32 0 0 ALMA ALB Sonoma 8/3/2006 1440 6 5 3.47 2.08 0 1 ALST SER Napa 9/26/2005 2279 8 8 3.51 3.51 0 0 ANNA SER Sonoma 8/16/2005 4495 8 7 1.56 1.11 0 1 BAUN SER Marin 7/11/2005 1952 15 11 5.64 4.61 0 0 BDM1 ALB Contra Costa 7/25/2005 1168 28 28 23.97 23.97 0 0 BDM2 ALB Contra Costa 8/25/2005 1875 35 32 17.07 16.00 0 0 BERA SER Sonoma 8/24/2005 2805 8 7 2.50 2.14 0 0 BOTH ALB Marin 7/27/2006 1467 2 1 0.68 0 0 0 BOUV SER Sonoma 9/2/2005 1903 40 31 16.29 11.56 1.05 0 BOYD ALB Marin 7/28/2006 1348 4 4 2.97 0.74 0.74 0 BRIO1 ALB Contra Costa 7/5/2005 2027 17 17 8.39 5.92 2.47 0 BRIO2 ALB Contra Costa 7/6/2005 2034 6 6 2.95 2.95 0.0 0 CAAL ALB Marin 7/27/2006 1437 9 2 1.39 0 1.39 0 CARN ALB Alameda 8/29/2005 1841 11 10 5.43 1.63 0 0 CAWH SER Marin 6/27/2005 3747 2 1 0.27 0 0 0 CHAB ALB Alameda 7/19/2006 853 17 13 15.24 4.69 10.55 0 CHCA ALB Marin 7/28/2006 992 6 6 6.05 6.05 0 0 CLAR ALB Alameda 7/20/2006 1383 17 17 12.29 0 11.57 0 CLAY ALB Contra Costa 7/17/2005 1864 18 18 9.66 8.58 0 0 COMA ALB Marin 7/26/2006 591 1 0 0 0 0 0 COOK SER Sonoma 8/4/2005 1857 15 13 7.00 5.92 0 0 COOL ALB Mendocino 10/4/2005 1826 16 14 7.67 7.67 0 0

91 COYO ALB Alameda 7/6/2006 1314 6 6 4.57 0.76 3.04 0 Table 1 (cont.)

# # Gray # Red Date Length Scats # Scats # Scats # Coyote Fox Fox Transect Collector County (Month/Day/Year) (m) Found ID'd ID'd/km Scats/km Scats/km Scats CRAN SER Sonoma 8/4/2005 3456 13 13 3.76 0.87 0 0 CRCR ALB Sonoma 8/4/2006 1135 1 1 0.88 0 0 0 CULL1 ALB Alameda 7/11/2006 722 8 7 9.70 4.16 0 0 CULL2 ALB Alameda 7/11/2006 432 1 1 2.31 0 2.31 0 CUMM ALB Sonoma 10/5/2005 1252 54 48 38.34 23.96 13.58 0 DEER ALB Marin 8/1/2006 1186 18 18 15.18 13.49 0 0 DONE ALB Alameda 7/6/2006 542 6 6 11.07 1.85 9.23 0 FAOS SER Sonoma 7/25/2005 2889 17 17 5.89 4.15 0.69 0 FERN ALB Contra Costa 7/20/2006 432 1 0 0 0 0 0 FOOT ALB Sonoma 8/2/2006 980 5 4 4.08 0 4.08 0 FOOT SER Sonoma 9/9/2005 3820 6 5 1.31 0.52 0.52 0 FPAN SER Sonoma 6/30/2005 1633 42 30 18.37 8.57 0 0 GAR1 ALB Alameda 7/27/2005 1937 30 30 15.49 14.97 0 1 GAR2 ALB Alameda 7/28/2005 1867 21 19 10.18 4.82 4.82 0 GEAR ALB Sonoma 9/20/2005 1637 24 17 10.38 6.72 0 0 GERA SER Sonoma 8/16/2005 1698 16 14 8.24 1.77 0.59 4 GGNR ALB Marin 7/27/2006 999 5 5 5.01 0 0 0 GIAC ALB Marin 7/31/2006 1145 13 12 10.48 1.75 6.11 0 GLOA SER Sonoma 7/19/2005 1682 42 36 21.40 11.29 2.38 0 GRAD ALB Marin 9/9/2005 1774 26 23 12.97 2.82 2.25 0 GRRA SER Marin 8/25/2005 1461 42 35 23.95 6.84 0 1 HAYW ALB Alameda 7/4/2006 2087 16 15 7.19 0.96 0.48 8 HEPU ALB Sonoma 8/4/2006 1433 5 5 3.49 0 0 0 HILL ALB Contra Costa 7/11/2006 790 12 12 15.19 0 13.92 0 HOOD SER Sonoma 7/29/2005 3015 20 16 5.31 0.66 0 0 HREC ALB Mendocino 9/14/2005 1921 14 9 4.69 3.12 0 0 92 HREC SER Mendocino 9/30/2005 1842 22 21 11.40 8.14 0.54 0 Table 1. (cont.)

# # Gray # Red Date Length Scats # Scats # Scats # Coyote Fox Fox Transect Collector County (Month/Day/Year) (m) Found ID'd ID'd/km Scats/km Scats/km Scats HUCK ALB Contra Costa 7/21/2006 986 5 5 5.07 0 3.04 1 INTR ALB Marin 9/6/2005 1815 13 12 6.61 3.31 0 0 INVA SER Marin 8/25/2005 3637 2 2 0.55 0.55 0 0 JALO SER Sonoma 7/25/2005 4221 13 12 2.84 1.90 0 0 JARA SER Sonoma 8/24/2005 2130 22 18 8.45 5.63 0 0 KECO SER Sonoma 8/16/2005 791 4 4 5.06 1.26 3.79 0 KING ALB Alameda 7/19/2006 418 4 2 4.78 0 0 2 LAFA ALB Contra Costa 7/24/2006 1445 4 2 1.38 0.69 0 0 LARA SER Sonoma 7/26/2005 1877 31 30 15.98 10.12 2.66 0 LEON ALB Alameda 7/18/2006 1651 7 2 1.21 0 1.21 0 LIME ALB Contra Costa 7/25/2006 1474 6 6 4.07 4.07 0 0 LOAL ALB Marin 8/30/2005 1971 13 13 6.60 0.51 4.57 0 LOAL SER Marin 8/2/2005 3646 9 6 1.65 0.82 0 0 LOPA SER Marin 6/7/2005 2738 9 12 2.56 2.19 0.37 0 LORA SER Marin 8/2/2005 1588 15 7 7.56 6.30 0 0 LOVE ALB Marin 7/31/2006 1116 10 7 6.27 3.58 0 0 LUVA SER Marin 7/8/2005 2394 18 15 6.27 2.09 0 3 MATT SER Sonoma 7/21/2005 1711 21 16 9.35 4.09 1.17 0 MCRA SER Sonoma 7/28/2005 2012 35 27 13.42 5.97 0 0 MCRD ALB Sonoma 9/27/2005 1669 17 15 8.99 4.79 3.00 0 MCRM ALB Sonoma 10/8/2005 1836 25 25 13.62 12.53 0.54 0 MGRA SER Marin 8/8/2005 1945 43 41 21.08 8.74 0 1 MILL ALB Contra Costa 7/3/2006 1930 11 9 4.66 0.52 2.59 0 MODI ALB Sonoma 9/12/2005 1894 25 24 12.67 5.81 1.06 0 MOON ALB Sonoma 9/10/2005 1783 32 31 17.39 17.39 0 0 MOOR SER Sonoma 9/12/2005 2292 20 16 6.98 6.54 0 0 MORA ALB Contra Costa 7/21/2006 852 9 5 5.87 0 4.69 1 93 MOTE ALB Contra Costa 7/7/2005 1786 15 15 8.40 4.48 3.36 0 Table 1. (cont.)

# # Gray # Red Date Length Scats # Scats # Scats # Coyote Fox Fox Transect Collector County (Month/Day/Year) (m) Found ID'd ID'd/km Scats/km Scats/km Scats MTBU ALB Marin 8/1/2006 1419 2 1 0.70 0 0 0 MTBU SER Marin 7/5/2005 4224 20 18 4.26 1.89 0 0 MTDI ALB Contra Costa 7/7/2005 1642 18 18 10.96 10.96 0 0 NUBA SER Sonoma 7/7/2005 1678 18 14 8.34 6.55 0 0 OHLO ALB Alameda 8/23/2005 1180 48 42 35.59 0.85 34.75 0 OYST ALB Alameda 7/6/2006 1300 14 13 10.00 0.77 0 0 PEPP SER Sonoma 7/13/2005 2036 26 22 10.80 3.93 0.98 0 PEPP1 ALB Sonoma 9/11/2005 1620 21 20 12.35 4.32 3.70 0 PEPP2 ALB Sonoma 9/13/2005 1798 26 22 12.24 1.67 0.56 0 PLEA1 ALB Alameda 7/13/2005 1905 4 4 2.10 2.10 0 0 PLEA2 ALB Alameda 7/29/2005 2221 28 24 10.81 6.75 1.80 0 PTIS ALB Contra Costa 7/5/2006 934 4 1 1.07 0 0 0 PTPI ALB Contra Costa 7/3/2006 2475 1 1 0.40 0 0 1 RARA ALB Sonoma 8/3/2006 1639 5 4 2.44 0 0 0 RARA SER Sonoma 8/15/2005 3391 4 4 1.18 0.29 0 0 RING1 ALB Marin 7/26/2006 1395 1 1 0.72 0 0 0 RING2 ALB Marin 7/26/2006 995 2 2 2.01 0 0 0 ROVA ALB Contra Costa 7/12/2005 2035 14 14 6.88 6.88 0 0 RUCR SER Marin 7/11/2005 3831 15 11 2.87 1.57 0 3 RUSH ALB Marin 8/1/2006 1763 9 8 4.54 4.54 0 0 SANP ALB Marin 7/28/2006 1243 1 1 0.80 0 0 0 SHEP ALB Alameda 7/19/2006 902 0 0 0 0 0 0 SHIL ALB Sonoma 8/2/2006 1493 5 5 3.35 3.35 0 0 SHIL SER Sonoma 7/12/2005 3453 22 21 4.63 2.03 0 0 SKRA ALB Contra Costa 8/22/2005 2093 31 31 14.81 4.78 9.08 0 SKWP SER Napa 8/30/2005 3811 12 16 3.15 2.89 0 0 SLHO ALB Marin 7/28/2006 1236 2 2 1.62 0.81 0 0 94 SOVA SER Sonoma 7/19/2005 3764 18 12 3.72 3.45 0.27 0 Table 1. (cont.)

# # Gray # Red Date Length Scats # Scats # Scats # Coyote Fox Fox Transect Collector County (Month/Day/Year) (m) Found ID'd ID'd/km Scats/km Scats/km Scats SPLA ALB Sonoma 8/3/2006 1624 7 7 4.31 3.08 0.62 0 SPLK SER Sonoma 7/7/2005 3660 0 14 0 0 0 0 STBY SER Napa 9/8/2005 1705 24 0 12.31 9.38 0 0 STHI ALB Marin 7/26/2006 1258 1 1 0.79 0 0 0 STWA SER Marin 7/18/2005 2574 3 1 0.39 0.39 0 0 SUGA ALB Contra Costa 7/25/2006 935 8 8 8.56 8.56 0 0 SUNO1 ALB Alameda 7/14/2005 2035 13 10 4.91 2.46 0 0 SUNO2 ALB Alameda 8/19/2005 1841 19 16 8.69 5.43 0 0 TELI ALB Marin 7/31/2006 1316 5 4 3.04 0.76 0 0 TELI SER Marin 8/29/2005 3515 15 13 3.70 2.56 0 0 TEME ALB Alameda 7/11/2006 726 2 0 0 0 0 0 TODD SER Sonoma 8/15/2005 2384 15 12 5.03 2.94 0.42 0 TURA ALB Napa 10/9/2005 1808 10 9 4.98 0.55 0 0 TURA SER Napa 8/30/2005 2321 20 18 7.75 6.46 0 0 VARG ALB Alameda 8/21/2005 1783 25 20 11.22 2.24 5.05 2 VEHI SER Marin 7/18/2005 1682 12 11 6.54 2.38 0 1 WANT ALB Napa 10/3/2005 1783 16 9 5.05 2.80 0 0 WAWP SER Napa 9/1/2005 1892 33 24 12.69 11.10 0 0 WEHI SER Napa 9/8/2005 3792 11 30 2.90 0.26 2.64 0 WEST SER Sonoma 9/9/2005 1254 32 11 23.92 14.35 0.80 0 WHRO SER Sonoma 7/26/2005 2182 17 16 7.33 5.04 1.37 0 WILD ALB Contra Costa 7/20/2006 1198 8 8 6.68 0.83 2.50 1 95

Table 2. List of predictor variables used in logistic regressions.

Predictor Variable Description COY Relative abundance of coyotes (scats/km) GRAY Relative abundance of gray foxes (scats/km) ELEV Average elevation within 100m buffer DH2O Distance to nearest water source (m) DURB Distance to nearest urban area (m) RDS100, RDS500, RDS1000 Road density (km2/km) within 100m, 500m, 100m buffers AGR100, AGR500, AGR1000 Proportion of 100m, 500m, 1000m buffers in agriculture HWD100, HWD500, HWD1000 Proportion of 100m, 500m, 1000m buffers in hardwood forest HEB100, HEB500, HEB1000 Proportion of 100m, 500m, 1000m buffers in grassland URB100, URB500, URB1000 Proportion of 100m, 500m, 1000m buffers in urban SHAN100, SHAN500, SHAN1000 Diversity index within 100m, 500m, 1000m buffers

96

Table 3. Summary of transects and scat detections by county.

County # of transects # of red fox scats Alameda 22 13 Contra Costa 21 4 Marin 34 9 Mendocino 3 0 Napa 8 0 Sonoma 42 6

97

Table 4. Results of univariate logistic regression analysis. Coeff is the estimated variable coefficient; G is the G statistic, the difference in log-likelihood from a model containing only a constant.

Variable Coeff. St Err G p COY 0.067 0.074 0.982 0.322 GRAY 0.045 0.098 0.273 0.601 ELEV 0.004 0.001 10.435 0.001 DURB 0.0001 0.0004 6.168 0.013 DH2O -0.0002 0.001 0.177 0.674 RDS100 -0.156 0.041 23.788 <0.0001 RDS500 -0.140 0.042 16.718 <0.0001 RDS1000 -0.115 0.038 11.255 0.001 AGR100 -1.362 1.642 0.753 0.394 HWD100 3.915 0.669 46.819 <0.0001 HEB100 -1.355 0.467 8.728 0.003 URB100 -3.414 0.876 21.942 <0.0001 AGR500 1.633 1.699 1.066 0.302 HWD500 4.989 0.800 52.818 <0.0001 HEB500 -2.848 0.561 29.689 <0.0001 URB500 -1.118 0.632 3.177 0.075 AGR1000 1.788 1.489 1.691 0.194 HWD1000 5.532 0.866 55.723 <0.0001 HEB1000 -3.683 0.630 42.988 <0.0001 URB1000 -0.471 0.602 0.609 0.435 SHAN100 1.847 0.515 14.235 0.0002 SHAN500 3.064 0.571 37.589 <0.0001 SHAN1000 3.251 0.551 48.818 <0.0001

98 Table 5. The best multiple logistic regression model for red fox presence. Trans represents the variable scale or transformation; Coeff is the estimated variable coefficient; G is the G statistic, the difference in log-likelihood from a model containing only a constant; Lower and Upper 95% are the confidence intervals for the coefficient estimates.

Lower Upper Variable Trans Coeff. St Err G p 95% 95% Intercept 2.067 0.548 14.254 <0.001 1.012 3.180 RDS100 x -0.193 0.064 8.967 0.003 -0.327 -0.073 URB100 x -2.478 1.271 3.801 0.051 -5.143 -0.132 HWD1000L 0/1 -1.208 0.300 16.227 <0.001 -1.841 -0.648 HEB1000 x -3.686 0.894 17.001 <0.0001 -5.528 -1.999 -lnL = 75.819 AICc = 161.966 ROC = 0.875

99 CHAPTER 4

EFFECTS OF URBANIZATION ON MESOCARNIVORES IN THE SAN FRANCISCO BAY AREA

Introduction

Habitat loss and fragmentation driven by urban development is one of the primary challenges to preserving biodiversity in the United States today (Hansen et al.

2005; McKinney 2002). Urban and exurban development covers as much as five times the land area it did in 1950 (Brown et al. 2005), and the wildland-urban interface comprises almost 10% of the land surface of the lower 48 states (Radeloff et al. 2005).

Urbanization destroys and fragments existing natural habitats, eradicates species, and facilitates the invasion of non-natives into urban-adjacent wildlands (McKinney 2002).

In fact, Czech and colleagues (2000) estimate that more than 50% of the federally listed threatened and endangered species in the United States are in jeopardy due to urbanization. Although many taxa are impacted by urbanization, mammalian carnivores may be particularly vulnerable because of their relatively large home range sizes, low densities, prey requirements, and potential for direct conflicts with humans and their domestic animals.

Carnivores vary in their responses to urbanization due to their range of body sizes, social structure, and habitat use. Some small generalist carnivores, such as striped skunks (Mephitis mephitis) and raccoons (Procyon lotor), are well-known and quite visible urban adapters (Prange & Gehrt 2004), and gray foxes (Urocyon cinereoargenteus) have been detected in habitat fragments surrounded by urban

100 development (Crooks 2002). Larger carnivores, like coyotes (Canis latrans) and mountain lions (Puma concolor), tend to avoid urban areas, although they may utilize wildland remnants within the urban matrix and disperse through residential areas (Beier

1995; Crooks 2002; Gosselink et al. 2003; Riley et al. 2003; Atwood et al. 2004).

Unlike studies of individual species, examining a suite of carnivores along an urban-to- rural gradient may shed light on differences in scale and habitat use among the species, and help in understanding what is necessary to maintain a complete carnivore guild in an urbanizing system.

Coyotes, bobcats and gray foxes are common mesocarnivores in North America, with differing resource needs, home range sizes, and levels of sociality. My goals in this study were to examine the distribution and habitat use of these three sympatric carnivores in a large metropolitan region, and to assess their responses to urbanization.

I surveyed dozens of sites along an urban-to-rural gradient to assess presence and relative abundance of each species and compared these data with environmental variables such as cover type, road density, and habitat connectivity at multiple scales.

More knowledge about the habitat needs of these elusive animals in an urbanizing landscape may help in ensuring that future development incorporates measures to foster and maintain native biodiversity.

Methods

Study Area

The San Francisco Bay metropolitan area (hereafter, the Bay Area) is the fifth largest urban area in the United States, with over 7 million people. The area’s

101 population is expected to grow by 23% in the next 25 years, and much of this growth is expected to occur on the urban fringe within commuting distance of the major cities

(Association of Bay Area Governments 2007). Its location in one of the world’s biodiversity hotspots (Myers et al. 2000; Conservation International 2007) makes it an ideal site to study the conservation impacts of urbanization. This region of northern coastal California is characterized by a Mediterranean climate of warm, dry summers and cool, wet winters. The dominant habitat types surrounding the San Francisco Bay are salt marsh wetlands, oak woodlands intermixed with annual grasslands, and at higher elevations, chaparral and chemise scrub communities. Human altered landscapes include a mix of high- and low-density housing, agriculture (including row crops, orchards, and vineyards), and grazed rangelands. I focused on four North Bay counties,

Marin, Sonoma, Napa, and Mendocino, and on two East Bay counties, Alameda and

Contra Costa (Fig. 1). Coyotes, bobcats and gray foxes are present throughout the study area.

Site Selection

I chose sites along an urban-to-rural gradient using a geographic information system (GIS) and road maps of the Bay Area. I looked for sites completely surrounded by high density residential or industrial development, and for sites that I designated the urban “fringe”: large areas of open space directly bordering high density urban areas. I chose these sites regardless of land cover, but in all cases the parcels were open to the public. I also chose oak woodland-dominated rural properties under a variety of public and private ownerships (see Appendix A for a full description of selection methods;

102 Bidlack et al. 2007a). Ultimately, 70 sites were selected to survey, and some properties were sufficiently large, with easy road or trail access, to allow for two survey transects to be conducted at a minimum separation distance of 2 km (to insure sampling independence). Consequently, a total of 82 transects were eventually surveyed between

June and September 2005 and June and September 2006 (Table 1; Fig. 1). Transect start points and directions were chosen randomly within each site using trail or road maps.

Transects were up to 2.5 km in length, although length varied because of the small size and limited trails in many of the sites. Each site was surveyed once.

Survey Methods and Data Collection

Surveys for mesocarnivores were performed using both visual and detection dog-assisted surveys for carnivore scat. Scat collection has been shown to be a good method for estimating presence and relative abundance of carnivores (Harrison et al.

2002; Harrison et al. 2004), and dogs have been used in field studies to detect presence and distribution of several species, including bears, kit foxes, bobcats and fishers (Smith et al., 2003; Wasser et al., 2004; Long et al. 2007). I trained a candidate detection dog to locate the scats of wild felids and canids at the Hopland Research and Extension

Center over a period of about eight weeks in 2004. The training consisted of a series of progressively more difficult tasks designed to teach the dog to search for and to locate target scats in a natural environment, using positive reinforcement (see Appendix B for training methods). In a controlled test at the end of the training, my dog detected 100% of scats placed within 10 m of the centerline of a transect along an unpaved road, and

98% of scats within 20 m (see Appendix B). A pilot survey indicated that in this

103 region, most carnivore scats are deposited along dirt roads and game and hiking trails

(Bidlack unpublished data). Therefore, all visual and dog-assisted field surveys were conducted along fire roads and hiking and game trails. Because my dog worked most efficiently during cool weather, the dog-assisted surveys were conducted in the early morning, although visual surveys were conducted during both mornings and afternoons.

Transect and scat location data were collected using a Trimble GeoExplorer III

GPS unit (Trimble Navigation Ltd., Sunnyvale CA). Transect location and approximate length were recorded using the GPS to collect point location information every five seconds. When a scat was found, its location was also recorded using the GPS.

Collected scats were individually bagged with a desiccant pack (DesiPak; Texas

Technologies, Inc., Cedar Park TX), given an identification number and subsequently stored at -80 C.

Molecular methods

Scats were identified to species using molecular genetic methods. DNA was extracted from each scat with a QIAamp DNA stool kit (Qiagen Inc., Valencia CA), using a small sub-sample (~500 g) that was removed from the outer surface or end of each feces. Polymerase chain reaction (PCR) was then used to amplify a 196 base pair segment of the mitochondrial cytochrome b gene. PCR was performed using 2 μl 1:50 extraction dilution in a 20 μl total reaction volume containing 1× reaction buffer

(Qiagen Inc.), 1.5 mM MgCl2, 200 µM each dNTP, 0.5 µM each primer, and 1 U Taq polymerase (Qiagen Inc.). Thermalcycling was initiated at 94˚C for 2 min, followed by

40 cycles of 94˚C for 1 min, 54˚C for 1 min and 72˚C for 2 min. All PCR reactions

104 included at least one negative control to monitor for contamination. Following DNA amplification, I first digested the PCR products with the restriction enzymes HpaII and

DdeI to separate puma, bobcat, raccoon and skunk, and then digested the samples identified as canid with HpyCh4V to differentiate among red fox, gray fox and coyote.

The 10 µl reaction volume contained 8 µl PCR product, 1 µl digest buffer, 0.5 µl H2O, and 2.5 U each restriction enzyme. Products were digested for 4 h, following the manufacturer’s instructions (New England Biolabs Inc., Ipswich MA). I electrophoresed the products for 45 minutes on a 2% agarose gel and visualized the predicted cutting patterns using ethidium bromide and UV light. Complete laboratory protocols are reported in Appendix C (Bidlack et al., 2007b).

Data analysis

Collected GPS transect and scat locations were differentially corrected and processed with GPS Pathfinder Office 3.0 (Trimble Navigation Ltd., Sunnyvale CA). I exported corrected point and transect files into ArcMap (ESRI, Redlands CA) using the

Teale-Albers NAD27 projection. I then used the Smooth Line tool in ArcToolbox to minimize GPS errors along the transect lines and to obtain more accurate estimates of transect lengths. All GIS analyses were performed using ArcGIS 9.2 (ESRI) and all statistical analyses were performed in JMP 6.0 (SAS Institute Inc., Cary NC).

I first used logistic regression to investigate how species presence relates to certain habitat characteristics like vegetative cover and urban development. Logistic regression is one member of the family of generalized linear modeling techniques, and is distinguished by the fact that the response variable is discrete, taking on two or more

105 possible values. Like linear regression, the goal of logistic regression model building is to find the best-fitting and most parsimonious model to describe the relationship between a response variable (in this case, carnivore scat detection/non-detection) and a suite of independent variables (in this case, habitat characteristics). Second, for areas where carnivores were detected, I used linear regression to examine the environmental correlates of relative abundance of each species, the relative abundance of all species combined, and the number of species detected. Relative abundance was measured using scats per kilometer as a proxy.

Habitat variables (Table 2) were assembled in ArcMap. First, I created three concentric and nested buffers around each survey transect line at distances of 100 m,

500 m and 1000 m. The relative proportions of the buffer areas covered by hardwood forest, grasslands, and urban development were calculated using the CalVeg polygon layer (USDA Forest Service 2006). I used TIGER data (US Census Bureau) to calculate road density around the transects by calculating the total length of roads in each buffer divided by the buffer area. Because buffer road density did not always provide an accurate description of development surrounding transects, particularly for sites located in wildlands contiguous with high density urban areas, I also calculated a distance-to-urban-road-density metric, as follows. A state road density coverage was compared with the urban polygons in the CalVeg layer to determine the road density of these areas, and a road density of 7.5 km/km2 was classified as urban. This raster was then used to create a Euclidean distance coverage and the distance from transects to the nearest urban area was calculated. Lastly, the total area of the site was included as a habitat variable.

106 I also calculated two area-informed connectivity metrics for each site to measure the effect of habitat connectivity on species presence and relative density within the parcel. The first was a measure of total habitat area within a 2500 m buffer area around each site, divided by the buffer area. This metric has performed well in tests of habitat connectivity (Bender et al. 2003). Second, I calculated the median patch size within the

2500 m buffer around each site. This metric was developed by Reed (2007) and was highly correlated with carnivore abundance in the same northern California system. In both cases, I used the CalVeg layer to define habitat patches, including hardwood forest, grasslands, shrublands, mixed conifer/hardwood forest, and conifer forest as habitat.

Urban areas, agriculture, and water were classified as non-habitat. Habitat polygons were included regardless of size, since all three species were detected in sites as small as 17 hectares (with 15.5 ha being the smallest site surveyed), and because I was interested in patches that could be used for cover by a dispersing animal.

For the logistic regression, all sites and transects where scats were found for a particular species were coded as “present” for that species, while all other sites and transects were coded as “absent.” I first ran exploratory univariate logistic regressions for each species with each variable that was differentiated only by the buffer width (i.e., road density within 100 m, 500 m, and 1000 m). The variable with the highest G statistic, or greatest difference in log-likelihood from a model containing only a constant, was retained. To test for variable independence, I examined a correlation matrix of the remaining variables, and checked that the tolerance (1 – r2 of each variable regressed against all the rest) for each variable was greater than 0.1. This indicates that collinearity with other variables is sufficiently weak to permit the variable in question to

107 be used in a multiple regression (Quinn & Keough 2002). I also investigated the distributions of each independent variable for each response category to determine the appropriate transformation of that variable (Kay & Little 1987; Robertson et al. 1994).

Once transformations were determined, I created a full model with all non-collinear variables transformed, noting estimates of the variable coefficients and p-values, and overall model fit using the G statistic (Hosmer & Lemeshow 2000). I examined all subsets of the full model and followed a process of model selection based on an inspection of p-values and 95% confidence intervals of estimated coefficients (i.e., whether the CI includes zero), the impact on these values of entering or removing variables, the AICc value of each model (Burnham & Anderson 2002), and the value of the receiver operating characteristic (ROC). The ROC is a measure of the model’s ability to discriminate between two outcomes; values close to 0.5 indicate no discrimination, while values between 0.8 and 0.9 indicate excellent discrimination

(Hosmer & Lemeshow 2000).

Linear regressions were performed in a similar manner to the logistic regressions. The response variables of coyote, gray fox and bobcat relative abundance were log transformed to fulfill the assumptions of normality. Carnivore relative abundance (the sum of all species’ scat/km) was also log transformed, although number of species detected was not. Simple univariate regressions for each response variable against each predictor variable were run, distributions of residuals from these regressions were tested for normality, and predictor variables were transformed as necessary. For variables only differentiated by the buffer area, I chose the one with the

2 highest r adj value for subsequent multiple regressions. As described above, to test for

108 variable independence, I examined a correlation matrix of the remaining variables, and checked the tolerance for each variable, keeping only those with values less than 0.1

(Quinn & Keough 2002). Finally, all model subsets using the remaining variables were examined, and the best model (or models) based on AICc values was retained.

Results

Scat Surveys

I collected a total of 1008 scats from 82 transects in 70 sites throughout the Bay

Area. Using molecular methods I identified 893 scats (88.6 %) to species. I found 467 coyote (52.3 %), 217 gray fox (24.3%) and 181 bobcat (20.3 %) scats. The remainder of the identified scats (4.1 %) were deposited by red foxes, striped skunks, and pumas.

Coyotes were detected at 50 sites, gray foxes at 30 sites and bobcats at 48 sites (Table

1).

Logistic Regressions

Distance from urban areas and road density were the primary predictors of coyote presence (Table 3a). Coyote presence was positively associated with increasing distance from urban areas, and negatively associated with increasing road density. The logistic curves indicate that there is a greater than 50% probability of coyote detection along a transect only when the transect is at least 174 meters from urban areas, and when the road density surrounding the survey line is less than 6.4 km/km2 (Table 3a).

Coyotes were also unlikely to be detected if there was greater than 50% urban area within a 1000m buffer around a transect, and site area was important as well: the larger

109 the parcel, the more likely coyotes were detected (Table 3a). Competing models suggested that distance to urban areas is the most important predictor variable for coyote presence, followed by road density within the 1000m buffer (Table 4a). Both site area and median habitat patch size appeared in several models, although the confidence intervals for these variable coefficients straddle zero (Table 4a). All of the best logistic models resulted in ROC values greater than 0.8, indicating high explanatory power (Table 4a).

Gray fox presence was positively correlated with the amount of hardwood forest within 100m of the transect, and was negatively associated with amount of grassland

(Table 3b). In fact, gray foxes were only likely to be detected on a transect if there was greater than 61% hardwood cover immediately surrounding that survey line (Table 3b).

Model building resulted in seven best models, all of which included either hardwood forest or grassland area (Table 4b). Amount of urban area and median patch size also appeared in several models, although the confidence intervals for these variable coefficients straddle zero (Table 4b). ROC values for all models were greater than 0.6, indicating high model discrimination.

Bobcat presence was weakly predicted by all variables in the univariate regressions (Table 3c); however, multiple logistic regression indicated that distance to urban areas was a reliable positive indicator of bobcat detection (Table 4c).

Surprisingly, site area was consistently negatively correlated with bobcat presence, although this may be a result of the interaction between site area and distance to urban areas (Table 4c). ROC values were greater than 0.7 for all models.

110 Linear Regressions

The relative abundance of coyotes was negatively correlated with urban development and road density, and positively correlated with all other habitat variables

(Table 5a). Multiple regression resulted in two competing models, both of which included the amount of grassland within 1000m of the transect, and the amount of habitat within the site buffer as important predictor variables for coyote abundance

(Table 6). The second model also included site area as a predictor, and coyote abundance was positively correlated with all of these variables. Both models explained

2 nearly 30% of the variance in the data (r adj > 0.25; Table 6).

Gray fox relative abundance was not correlated with any habitat variables; in

2 fact all but one of the r adj values from the univariate regressions were negative (Table

5b). Therefore, I did not perform any multiple regressions on the gray fox data.

Like coyotes, the relative abundance of bobcats in this system was negatively correlated with road density and urban areas (Table 5c). Multiple regression resulted in two competing models, both of which included road density within 500m of the transect and amount of grassland within 100 m of the transect as important negative predictor variables for bobcat abundance (Table 6). The second model also included amount of hardwood forest within 100 m of the transect as a predictor variable. Both models explained nearly 30% of the variation in the bobcat abundance data (Table 6).

Total carnivore abundance was negatively correlated with urban development and road density (Table 7a). Model building resulted in four competing models, all of which included median habitat patch size as a negative predictor variable (Table 8).

Site area and distance from urban areas were both included in several models and were

111 positive predictors of total carnivore abundance (Table 8). The number of species detected at each transect was also strongly correlated with urban development and road density (Table 7b), and road density was an important negative predictor variable in all but one of the five competing multiple regression models (Table 8). Distance from urban areas, median habitat patch size, and amount of hardwood forest within 500 m of the survey transect were included in several models (Table 8).

Discussion

My scat surveys showed that coyotes, gray foxes and bobcats are widely distributed throughout the Bay Area in a variety of habitats. However, the three species appear to respond independently and differently to urbanization. Coyotes had the strongest negative relationship with urban development. They were detected more often in suburban or rural areas, with road densities less than 6.4 km/km2 (Table 3a), and site area was an important positive predictor for coyote presence; the larger the site, the more likely coyotes would be detected. Coyotes were also strongly correlated with the amount of natural habitat surrounding a site (Table 6), suggesting that coyotes require large areas of natural habitat. Similarly, bobcats were more likely to occur further from urban areas (Table 4c), and their abundance was negatively impacted by road density

(Table 6). Gray foxes in contrast to coyotes and bobcats, did not seem particularly impacted by urbanization. Gray fox presence was most highly associated with amount of hardwood forest cover, but was only weakly correlated with urban development

(Table 4b).

112 Other studies have found similar responses by these three species to urbanization. Gosselink and colleagues (2003) found no coyotes in urban areas along an urban-to-rural gradient in Illinois, and Riley and colleagues (2003) found that coyote home ranges in a fragmented urban landscape in southern California consisted predominantly of natural habitat. The probability of coyote occurrence in urban habitat fragments tends to decline with patch size and isolation, and coyote detection generally increases with distance from development and with lowered densities of housing (Odell

& Knight 2001; Crooks 2002; Randa & Yunger 2006). Previous research also indicates that bobcats avoid urban areas (Crooks 2002), and that bobcat home ranges tend to be composed predominantly of natural areas within a matrix of urban and suburban development (Riley et al. 2003). In contrast, Crooks (2002) detected gray foxes in urban habitat fragments as small as 2 ha, and found that their occurrence and relative abundance tend to increase with decreasing fragment size and increasing isolation.

Coyotes, bobcats and gray foxes vary in body size and average home range size.

Although all three species are habitat generalists, the relatively large body size (13.5 kg; range 7 – 20 kg; Nowak 1999), home range size (4.96 km2; southern California; Riley et al. 2003) and prey requirements of coyotes may prevent them from inhabiting high density urban areas (Crooks 2002). Bobcats are somewhat smaller, averaging 9.7 kg

(range 4.1 – 15.3 kg; Nowak 1999), and having home ranges varying from a mean of

1.72 km2 for females to a mean of 3.03 km2 for males (southern California; Riley et al.

2003). Despite their smaller size, the exclusively carnivorous diet of bobcats may prevent them from utilizing built-up areas, unlike another similar-sized generalist carnivore, the raccoon. Gray foxes are the smallest of the three species I studied, with

113 body size averaging 4.4 kg (range 1.8 – 7 kg; Nowak, 1999), and home ranges averaging 0.69 km2 (northern California; Riley 1999). They do not require much space for persistence, and like coyotes, also have an omnivorous diet. Thus, gray foxes may be able to take advantage of small urban habitat remnants and the many food sources available in an urban area, like pet food and garbage.

Total carnivore abundance and number of species detected were negatively correlated with urban development and road density (Tables 7 & 8). Specifically, abundance was positively correlated with distance from urban areas and total site area, while number of species detected was negatively correlated with road density.

However, these relationships may have been driven by the preponderance of coyote and bobcat scats collected, and the strong correlation between the abundance of these species and urbanization. I may have found a different pattern if I had used methods that were better for detecting urban-tolerant species such as raccoons, striped skunks, and feral cats (Crooks 2002). I also found a consistent negative correlation between carnivore abundance (and number of species) and median habitat patch size, which is inconsistent with previous work (Reed 2007).

The presence and relative abundance of the three carnivores studied varied with vegetative land cover in addition to urban metrics. Coyote abundance was positively correlated with both hardwood forest and grassland, and the amount of grassland surrounding the survey transect was one of the most important predictor variables in the analysis of relative abundance (Tables 5 & 6). Bobcats and gray foxes were positively correlated with hardwood forest cover, and negatively correlated with grassland (Tables

4b, 5c & 6). These results are generally consistent with the known habitat preferences

114 of coyote and gray foxes, and bobcats inhabit a variety of habitats throughout North

America (Bekoff 1977; Fritzell & Haroldson 1982; Larivière & Walton 1997). In accordance with the differences in size and home range among the species, the scale at which habitat variables were important varied. In particular, coyote presence was highly correlated with variables within the largest transect buffer (1000 m), whereas gray foxes responded to habitat variables within 100 m of the survey transect. Bobcats seemed to respond at multiple scales, perhaps reflecting their intermediate body size and home ranges.

The logistic models seemed to describe the data better than the linear models.

There may be two reasons for this. One, the logistic models used more of the data than the linear models, because the linear models did not include transects or sites where the species were not detected. Two, although scat collection is a good proxy for relative abundance, there may be more noise in the data because it is impossible to tell from the methods I used how many individuals are depositing those scats. Canids in particular tend to deposit scats in latrine sites, and there may be many individual scats from the same animal at a single site. The explanatory power of both types of models in regards to habitat variables would be improved if I had finer-scale data at both the transect and site level. For instance, information on parcel size and number of housing units per parcel would provide better resolution than road density or the CalVeg land use layer; unfortunately, these data are not available for all the counties I studied. More detailed information regarding vegetation structure and species composition would also help in modeling species’ habitat preferences. Lastly, given that I am studying predators, data on prey densities would likely help in explaining carnivore abundance at different sites.

115 Although I detected all three carnivores across the urban-to-rural gradient, and in several very small sites (<20 ha), presence does not necessarily mean that the site supports resident animals. It is possible that my sampling captured dispersing animals, rather than residents. Juveniles, particularly males, of all three species are known to disperse widely, and it is likely that some collected scats were those of itinerant animals. Repeated surveys of each transect would lead to more accurate estimates of occupancy (MacKenzie et al. 2006).

Northern coastal California currently supports a relatively intact carnivore community; however, current growth rates and concurrent habitat loss, fragmentation, and spread of non-native species may lead to a loss of some native species. Larger carnivores such as coyotes, black bears, and pumas are already rare or absent from urban areas. Preliminary data from another study in California oak woodlands suggests that there are far fewer native carnivores in natural areas that have heavy recreational usage compared with non-public-access areas (Reed 2007), and in a survey of carnivores utilizing riparian corridors in a vineyard-dominated landscape in Sonoma

County, only six native species were detected, out of a possible 11 (Hilty &

Merenlender 2004). Carnivore community structure changes across rural-to-urban gradients (Maestas et al. 2003; Prange & Gehrt 2004). Non-native carnivores like red foxes and feral cats are closely associated with urban areas (see Chapter 3; Crooks

2002), but are capable of spreading into adjacent wildlands (Odell & Knight 2001; Hilty

& Merenlender 2004). Feral cats in particular may be able to out-compete bobcats for prey by their explosive growth rate (Reed, unpublished data), and can act as vectors for

116 disease (Riley et al. 2004). Gray foxes also can contract disease from interactions with domestic dogs in wildlands bordering urban development (Riley et al. 2004).

It is possible that competitive interactions among native species may also be affected by habitat fragmentation due to urbanization. It is unclear whether urban- adapters succeed because other, less adaptable species disappear from urban areas, or if urban-adapters actually out-compete these other species. Previous studies of coyotes, bobcats and gray foxes in wildlands (Major & Sherburne 1987; Neale & Sacks 2001;

Chamberlain & Leopold 2005; Reed 2007) suggest little competition among the three species (but see Fedriani et al. 2000). This lack of obvious competition for resources may be due to the differences in body size or diet, or to differences in habitat use; however, these relationships may shift in fragmented habitats. I did not investigate correlations among native species presence or relative abundance; future work will include an examination of interspecific interactions, specifically focusing on the impacts of habitat change on carnivore community dynamics.

Although it does appear that some species, like gray foxes, are currently persisting in small fragments of habitat within the Bay Area urban matrix, we may see continuing decreases in native species abundance and richness over time (Hansen et al.

2005). This trend is driven by increases in non-native species abundance, continuing replacement of native vegetation by exotics, and increasing recreational and development pressure on remaining natural areas and open spaces. Ensuring the future viability of carnivore populations in this region will require growth planning that considers habitat characteristics at the appropriate landscape scale important to different species. It will also necessitate restoration efforts in areas impacted by non-native

117 plants and animals. Studies like this one help in determining the resource needs of carnivores of varying body size, home range and dietary requirements.

118 Literature Cited

Association of Bay Area Governments. 2007. www.abag.ca.gov/index.html

Atwood, T.C.; Weeks, H.P.; Gehring, T.M. 2004. Spatial ecology of coyotes along a suburban-to-rural gradient. Journal of Wildlife Management 68: 1000-1009.

Beier, P. 1995. Dispersal of juvenile cougars in fragmented habitat. Journal of Wildlife Management 59: 228-237.

Bekoff, M. 1977. Canis latrans. Mammalian Species 79: 1-9.

Bender, D.J.; Tischendorf, L.; Fahrig, L. 2003. Using patch isolation metrics to predict animal movement in binary landscapes. Landscape Ecology 18: 17-39.

Bidlack, A.L.; Merenlender, A.; Getz, W.M. 2007a. Distribution of non-native red foxes in East Bay oak woodlands. In Sixth Symposium on Oak Woodlands: Today's Challenges, Tomorrow's Opportunities, October 9-12, 2006, Rohnert Park, California. USDA Forest Service General Technical Report.

Bidlack, A.L.; Reed, S.E.; Palsbøll, P.J.; Getz, W.M. 2007b. Characterization of a western North American carnivore community using PCR-RFLP of cytochrome b obtained from fecal samples. Conservation Genetics 8: 1511-1513.

Brown, D.G.; Johnson, K.M.; Loveland, T.R.; Theobald, D.M. 2005. Rural land-use trends in the coterminous United States, 1950-2000. Ecological Applications 15: 1851-1863.

Burnham, K.P.; Anderson, D.R. 2002. Model Selection and Multimodel Inference: a practical information-theoretic approach. New York: Springer.

119 Chamberlain, M.J.; Leopold, B.D. 2005. Overlap in space use among bobcats (Lynx rufus), coyotes (Canis latrans) and gray foxes (Urocyon cinereoargenteus). American Midland Naturalist 153: 171-179.

Conservation International 2007. www.biodiversityhotspots.org

Crooks, K. 2002. Relative sensitivities of mammalian carnivores to habitat fragmentation. Conservation Biology 16: 488-502.

Czech B.; Krausman P.R.; Devers P.K. 2000. Economic associations among causes of species endangerment in the United States. BioScience 50: 593–601.

Fedriani, J.M.; Fuller, T.K.; Sauvajot, R.M.; York, E.C. 2000. Competition and intraguild predation among three sympatric carnivores. Oecologia 125: 258-270.

Fritzell, E.K.; Haroldson, K.J. 1982. Urocyon cinereoargenteus. Mammalian Species 189: 1-8.

Gosselink, T.E.; Van Deelen, T.R.; Warner, R.E.; Joselyn, M.G. 2003. Temporal habitat partitioning and spatial use of coyotes and red foxes in east-central Illinois. Journal of Wildlife Management 67: 90-103.

Hansen, A.J.; Knight, R.L.; Marzluff, J.M.; Powell, S.; Brown, K.; Gude, P.H.; Jones, K. 2005. Effects of exurban development on biodiversity: patterns, mechanisms, and research needs. Ecological Applications 15: 1893-1905.

Harrison, R.L.; Barr D.J.; Dragoo, J.W. 2002. A comparison of population survey techniques for swift foxes (Vulpes velox) in New Mexico. American Midland Naturalist 148:320-337.

Harrison, R.L.; Clarke, P-G.S.; Clarke, C.M. 2004. Indexing swift fox populations in New Mexico using scats. American Midland Naturalist 151: 42-49.

120

Hilty, J.A.; Merenlender, A.M. 2004. Use of riparian corridors and vineyards by mammalian predators in Northern California. Conservation Biology 18: 126- 135.

Hosmer, D.W.; Lemeshow, S. 2000. Applied Logistic Regression. New York: John Wiley & Sons.

Kay, R.; Little, S. 1987. Transformations of the explanatory variables in the logistic regression model for binary data. Biometrika 74: 495-501.

Larivière, S.; Walton, L.R. 1997. Lynx rufus. Mammalian Species 563: 1-8.

Long, R.A.; Donovan, T.M.; Mackay, P.; Zielinski ,W.J.; Buzas, J.S.. 2007. Effectiveness of scat detection dogs for detecting forest carnivores. Journal of Wildlife Management 71: 2007-2017.

MacKenzie, D.I.; Nichols, J.D.; Royle, J.A.; Pollock, K.H.; Bailey, L.L.; Hines, J.E. 2006. Occupancy Estimation and Modeling: inferring patterns and dynamics of species occurrence. London: Elsevier.

Major, J.T.; Sherburne, J.A. 1987. Interspecific relationships of coyotes, bobcats, and red foxes in western Maine. Journal of Wildlife Management 51: 606-616.

McKinney, M.L. 2002. Urbanization, biodiversity, and conservation. BioScience 52: 883-890.

Myers, N.; Mittermeier, R.A.; Mittermeier, C.G.; da Fonseca, G.A.B.; Kent, J. 2000. Biodiversity hotspots for conservation priorities. Nature 403: 853-858.

Neale, J.C.C.; Sacks, B.N. 2001. Food habits and space use of gray foxes in relation to sympatric coyotes and bobcats. Canadian Journal of Zoology 79: 1794-1800.

121 Nowak, R.M. 1999. Walker’s Mammals of the World. Baltimore: Johns Hopkins University Press.

Odell, E.A.; Knight, R.L. 2001. Songbird and medium-sized mammal communities associated with exurban development in Pitkin County, Colorado. Conservation Biology 15: 1143-1150.

Prange, S.; Gehrt, S.D. 2004. Changes in mesopredator-community structure in response to urbanization. Canadian Journal of Zoology 82: 1804-1817.

Quinn, G.P.; Keough, M.J. 2002. Experimental Design and Data Analysis for Biologists. Cambridge: Cambridge University Press.

Radeloff, V.C.; Hammer, R.B.; Stewart, S.I.; Fried, J.S.; Holcomb, S.S.; McKeefry, J.F. 2005. The wildland-urban interface in the United States. Ecological Applications 15: 799-805.

Randa, L.A.; Yunger, J.A. 2006. Carnivore occurrence along an urban-rural gradient: a landscape-level analysis. Journal of Mammalogy 87: 1154-1164.

Reed, S.E. 2007. Natural and human disturbance factors influencing carnivore distributions and implications for conservation. Berkeley, CA: University of California. PhD. Dissertation.

Riley, S.P.D. 1999. Spatial organization, food habits and disease ecology of bobcats (Lynx rufus) and gray foxes (Urocyon cinereoargenteus) in national park areas in urban and rural Marin County, California. Davis, CA: University of California. PhD Dissertation.

Riley, S.P.D.; Foley, J.; Chomel, B. 2004. Exposure to feline and canine pathogens in bobcats and gray foxes in urban and rural zones of a national park in California. Journal of Wildlife Diseases 40: 11-22.

122 Riley, S.P.D.; Sauvajot, R.M.; Fuller, T.K.; York, E.C.; Kamradt, D.A.; Bromley, C.; Wayne, R.K. 2003. Effects of urbanization and habitat fragmentation on bobcats and coyotes in southern California. Conservation Biology 17: 566-576.

Robertson, C.; Boyle, P.; Hsieh, C-C.; MacFarlane, G.J.; Maisonneuve, P. 1994. Some statistical considerations in the analysis of case-control studies when the exposure variables are continuous measurements. Epidemiology 5: 164-170.

Smith, D.A.; Ralls, K.; Hurt, A.; Adams, B.; Parker, M.; Davenport, B.; Smith, M.C.; Maldonado, J.E. 2003. Detection and accuracy rates of dogs trained to find scats of San Joaquin kit foxes (Vulpes macrotis mutica). Animal Conservation 6: 339-346.

USDA Forest Service. 2006. CALVEG/EVEG. http://www.fs.fed.us/r5/rsl/clearinghouse

Wasser, S.K.; Davenport, B.; Ramage E.R.; Hunt, K.E.; Parker, M.; Clarke, C.; Stenhouse, G. 2004. Scat detection dogs in wildlife research and management: application to grizzly and black bears in the Yellowhead Ecosystem, Alberta, Canada. Canadian Journal of Zoology 82: 475-492.

123

Figure 1. Map of carnivore survey sites in six counties within the San Francisco Bay Area. County codes: ME, Mendocino; SO, Sonoma; MA, Marin; NA, Napa; CC, Contra Costa; AL, Alameda. 124

Table 1. Results of scat survey transects, with length, number of scats found, number of scats identified using molecular methods, number of scats identified from coyotes, gray foxes and bobcats, and total number of species detected.

# # Gray Date Length Scats # Scats # Scats # Coyote Fox # Bobcat # Transect County (Month/Day/Year) (m) Found ID'd ID'd/km Scats/km Scats/km Scats/km Species* ACAL Contra Costa 7/25/2006 1175 2 2 1.70 0.85 0 0.85 2 ALBA Alameda 7/5/2006 755 1 1 1.32 1.32 0 0 1 ALMA Sonoma 8/3/2006 1440 6 5 3.47 2.08 0 0.69 3 BDM1 Contra Costa 7/25/2005 1168 28 28 23.97 23.97 0 0 1 BDM2 Contra Costa 8/25/2005 1875 35 32 17.07 16.00 0 1.07 2 BOTH Marin 7/27/2006 1467 2 1 0.68 0 0 0.68 1 BOYD Marin 7/28/2006 1348 4 4 2.97 0.74 0.74 1.48 3 BRIO1 Contra Costa 7/5/2005 2027 17 17 8.39 5.92 2.47 0 2 BRIO2 Contra Costa 7/6/2005 2034 6 6 2.95 2.95 0.00 0 1 CAAL Marin 7/27/2006 1437 9 2 1.39 0 1.39 0 1 CARN Alameda 8/29/2005 1841 11 10 5.43 1.63 0 3.80 2 CHAB Alameda 7/19/2006 853 17 13 15.24 4.69 10.55 0 2 CHCA Marin 7/28/2006 992 6 6 6.05 6.05 0.00 0 1 CLAR Alameda 7/20/2006 1383 17 17 12.29 0 11.57 0 2 CLAY Contra Costa 7/17/2005 1864 18 18 9.66 8.58 0 1.07 2 COMA Marin 7/26/2006 591 1 0 0 0 0 0 0 COOL Mendocino 10/4/2005 1826 16 14 7.67 7.67 0 0 1 COYO Alameda 7/6/2006 1314 6 6 4.57 0.76 3.04 0.76 3 CRCR Sonoma 8/4/2006 1135 1 1 0.88 0 0 0.88 1 CULL1 Alameda 7/11/2006 722 8 7 9.70 4.16 0 5.54 2 CULL2 Alameda 7/11/2006 432 1 1 2.31 0 2.31 0 1 CUMM Sonoma 10/5/2005 1252 54 48 38.34 23.96 13.58 0.80 3

125 DEER Marin 8/1/2006 1186 18 18 15.18 13.49 0 1.69 2 DONE Alameda 7/6/2006 542 6 6 11.07 1.85 9.23 0 2 Table 1 (cont.)

# # Gray Date Length Scats # Scats # Scats # Coyote Fox # Bobcat # Transect County (Month/Day/Year) (m) Found ID'd ID'd/km Scats/km Scats/km Scats/km Species* FERN Contra Costa 7/20/2006 432 1 0 0 0 0 0 0 FOOT Sonoma 8/2/2006 980 5 4 4.08 0 4.08 0 1 GAR1 Alameda 7/27/2005 1937 30 30 15.49 14.97 0 0 2 GAR2 Alameda 7/28/2005 1867 21 19 10.18 4.82 4.82 0.54 3 GEAR Sonoma 9/20/2005 1637 24 17 10.38 6.72 0 3.67 2 GGNR Marin 7/27/2006 999 5 5 5.01 0 0 5.01 1 GIAC Marin 7/31/2006 1145 13 12 10.48 1.75 6.11 2.62 3 GRAD Marin 9/9/2005 1774 26 23 12.97 2.82 2.25 7.33 4 HAYW Alameda 7/4/2006 2087 16 15 7.19 0.96 0.48 0.48 5 HEPU Sonoma 8/4/2006 1433 5 5 3.49 0 0 3.49 1 HILL Contra Costa 7/11/2006 790 12 12 15.19 0 13.92 1.27 2 HREC Mendocino 9/14/2005 1921 14 9 4.69 3.12 0 1.56 2 HUCK Contra Costa 7/21/2006 986 5 5 5.07 0 3.04 1.01 3 INTR Marin 9/6/2005 1815 13 12 6.61 3.31 0 3.31 2 KING Alameda 7/19/2006 418 4 2 4.78 0 0 0 1 LAFA Contra Costa 7/24/2006 1445 4 2 1.38 0.69 0 0.69 2 LEON Alameda 7/18/2006 1651 7 2 1.21 0 1.21 0 1 LIME Contra Costa 7/25/2006 1474 6 6 4.07 4.07 0 0 1 LOAL Marin 8/30/2005 1971 13 13 6.60 0.51 4.57 1.52 3 LOVE Marin 7/31/2006 1116 10 7 6.27 3.58 0 2.69 2 MCRD Sonoma 9/27/2005 1669 17 15 8.99 4.79 3.00 1.20 3 MCRM Sonoma 10/8/2005 1836 25 25 13.62 12.53 0.54 0.54 3 MILL Contra Costa 7/3/2006 1930 11 9 4.66 0.52 2.59 1.55 3 MODI Sonoma 9/12/2005 1894 25 24 12.67 5.81 1.06 5.81 3 MOON Sonoma 9/10/2005 1783 32 31 17.39 17.39 0 0 1

126 MORA Contra Costa 7/21/2006 852 9 5 5.87 0 4.69 0 2 MOTE Contra Costa 7/7/2005 1786 15 15 8.40 4.48 3.36 0.56 3 Table 1 (cont.)

# # Gray Date Length Scats # Scats # Scats # Coyote Fox # Bobcat # Transect County (Month/Day/Year) (m) Found ID'd ID'd/km Scats/km Scats/km Scats/km Species* MTBU Marin 8/1/2006 1419 2 1 0.70 0 0 0.70 1 MTDI Contra Costa 7/7/2005 1642 18 18 10.96 10.96 0 0 1 OHLO Alameda 8/23/2005 1180 48 42 35.59 0.85 34.75 0 2 OYST Alameda 7/6/2006 1300 14 13 10.00 0.77 0 9.23 2 PEPP1 Sonoma 9/11/2005 1620 21 20 12.35 4.32 3.70 4.32 3 PEPP2 Sonoma 9/13/2005 1798 26 22 12.24 1.67 0.56 10.01 3 PLEA1 Alameda 7/13/2005 1905 4 4 2.10 2.10 0 0 1 PLEA2 Alameda 7/29/2005 2221 28 24 10.81 6.75 1.80 1.80 3 PTIS Contra Costa 7/5/2006 934 4 1 1.07 0 0 1.07 1 PTPI Contra Costa 7/3/2006 2475 1 1 0.40 0 0 0 1 RARA Sonoma 8/3/2006 1639 5 4 2.44 0 0 2.44 1 RING1 Marin 7/26/2006 1395 1 1 0.72 0 0 0.72 1 RING2 Marin 7/26/2006 995 2 2 2.01 0 0 2.01 1 ROVA Contra Costa 7/12/2005 2035 14 14 6.88 6.88 0 0 1 RUSH Marin 8/1/2006 1763 9 8 4.54 4.54 0 0 1 SANP Marin 7/28/2006 1243 1 1 0.80 0 0 0.80 1 SHEP Alameda 7/19/2006 902 0 0 0 0 0 0 0 SHIL Sonoma 8/2/2006 1493 5 5 3.35 3.35 0 0 1 SKRA Contra Costa 8/22/2005 2093 31 31 14.81 4.78 9.08 0.96 3 SLHO Marin 7/28/2006 1236 2 2 1.62 0.81 0 0.81 2 SPLA Sonoma 8/3/2006 1624 7 7 4.31 3.08 0.62 0.62 3 STHI Marin 7/26/2006 1258 1 1 0.79 0 0 0.79 1 SUGA Contra Costa 7/25/2006 935 8 8 8.56 8.56 0 0 1 SUNO1 Alameda 7/14/2005 2035 13 10 4.91 2.46 0 2.46 2 SUNO2 Alameda 8/19/2005 1841 19 16 8.69 5.43 0 3.26 2

127 TELI Marin 7/31/2006 1316 5 4 3.04 0.76 0 2.28 2 TEME Alameda 7/11/2006 726 2 0 0 0 0 0 0 Table 1 (cont.)

# # Gray Date Length Scats # Scats # Scats # Coyote Fox # Bobcat # Transect County (Month/Day/Year) (m) Found ID'd ID'd/km Scats/km Scats/km Scats/km Species* TURA Napa 10/9/2005 1808 10 9 4.98 0.55 0 2.77 3 VARG Alameda 8/21/2005 1783 25 20 11.22 2.24 5.05 2.80 4 WANT Napa 10/3/2005 1783 16 9 5.05 2.80 0 1.68 2 WILD Contra Costa 7/20/2006 1198 8 8 6.68 0.83 2.50 2.50 4 * Number of species includes skunks, pumas, and red fox

128

Table 2. List of predictor variables used in logistic and linear regressions.

Predictor Variable Description 2 AREA Area of site (m )

DURB Distance from transect to nearest urban area (m) 2 RDS100, RDS500, RDS1000 Road density (km/km ) within 100m, 500m, 100m transect buffers Proportion of 100m, 500m, 1000m transect buffers in hardwood HWD100, HWD500, HWD1000 forest

HEB100, HEB500, HEB1000 Proportion of 100m, 500m, 1000m transect buffers in grassland

URB100, URB500, URB1000 Proportion of 100m, 500m, 1000m transect buffers in urban

PBUFF Proportion of 2500m site buffer in habitat 2 PMED Median patch size within 2500m site buffer (m )

129

Table 3a. Results of univariate logistic regressions for coyotes. Coeff is the estimated variable coefficient; G is the G statistic, the difference in log-likelihood from a model containing only a constant; 50% prob. represents the variable level at which the species is detected or not with a 50% probability; 95% C.I. is the 95% confidence interval for this measure.

Variable Coeff. St Err G 50% prob. 95% C.I. AREA <0.001 <0.001 8.539 0 - DURB 0.001 <0.001 18.612 174m 0 – 703m RDS100 -0.177 0.095 3.593 6.6 km/km2 - RDS500 -0.315 0.102 12.229 5.8 km/km2 3.9 - 11.2 km/km2 RDS1000 -0.360 0.099 17.733 6.4 km/km2 4.7 - 9.8 km/km2 HWD100 0.631 0.851 0.558 0 - HWD500 2.060 1.092 3.784 0 - HWD1000 3.143 1.279 6.804 6% 0 - 23.6% HEB100 1.257 0.918 1.952 0 - HEB500 1.864 1.192 2.653 0 - HEB1000 2.197 1.469 2.493 0 - URB100 -4.162 1.903 5.697 27.9% 14 – 100% URB500 -3.666 1.292 9.568 41.9% 26 – 98.7% URB1000 -3.320 1.097 10.294 51.3% 34 – 100% PMED <0.001 <0.001 1.642 66.9 ha - PBUFF 3.655 1.865 4.066 16.7% -

130 Table 3b. Results of significant univariate logistic regressions for gray foxes. Coeff is the estimated variable coefficient; G is the G statistic, the difference in log-likelihood from a model containing only a constant; 50% prob. represents the variable level at which the species is detected or not with a 50% probability; 95% C.I. is the 95% confidence interval for this measure.

Variable Coeff. St Err G 50% prob. 95% C.I. AREA <0.001 <0.001 0.201 0 - DURB <0.001 <0.001 1.015 7618m - RDS100 0.161 0.094 3.068 4.6 km/km2 - RDS500 -0.058 0.084 0.489 0 - RDS1000 -0.064 0.077 0.701 0 - HWD100 2.518 0.886 9.068 61.7% 40.7 – 100% HWD500 2.648 1.063 6.808 54.5% 34.9 – 100% HWD1000 2.133 1.121 3.81 55.8% - HEB100 -2.243 0.924 6.523 21.3% 0 -44.5% HEB500 -2.407 1.154 4.841 16.1% 0 -37.6% HEB1000 -2.494 1.389 3.624 12.2% - URB100 -2.906 2.109 2.347 0 - URB500 -1.308 1.187 1.302 0 - URB1000 -1.086 1.012 1.199 0 - PMED <0.001 <0.001 4.374 10.7 ha - PBUFF 1.399 1.723 0.672 70.9% -

131 Table 3c. Results of significant univariate logistic regressions for bobcats. Coeff is the estimated variable coefficient; G is the G statistic, the difference in log-likelihood from a model containing only a constant; 50% prob. represents the variable level at which the species is detected or not with a 50% probability; 95% C.I. is the 95% confidence interval for this measure.

Variable Coeff. St Err G 50% prob. 95% C.I. AREA <0.001 <0.001 2.308 2838ha - DURB <0.001 <0.001 2.668 0 - RDS100 -0.053 0.092 0.323 14.7 km/km2 - RDS500 -0.097 0.081 1.149 10.02 km/km2 - RDS1000 -0.08 0.075 1.123 12.3 km/km2 - HWD100 -0.024 0.818 0.001 0 - HWD500 0.246 1.003 0.06 0 - HWD1000 -0.353 1.103 0.102 0 - HEB100 0.464 0.865 0.289 0 - HEB500 0.772 1.086 0.516 0 - HEB1000 1.609 1.364 1.489 0 - URB100 -1.154 1.628 0.497 66.5% - URB500 -1.125 1.103 1.037 78.5% - URB1000 -0.917 0.981 0.87 98.3% - PMED <0.001 <0.001 0.266 0 - PBUFF 2.49 1.775 2.019 12.1% -

132

Table 4a. Results of multiple logistic regressions for coyote. ROC is the Receiver Operating Characteristic; Trans represents the variable scale or transformation; Coeff is the estimated variable coefficient; Lower and Upper 95% are the confidence intervals for the coefficient estimates.

Lower Upper Model -lnL AICc ΔAICc ROC Variable Trans. Coeff. St. Err. 95% 95% 1 31.215 71.036 0 0.841 DURB x1/3 0.169 0.073 0.035 0.326 AREA ln(x) 0.431 0.261 -0.059 0.979 PMED x1/3 -0.096 0.064 -0.239 0.015 Intercept -4.096 3.998 -12.315 3.619 2 32.605 71.578 0.542 0.824 AREA ln(x) 0.449 0.258 -0.033 0.996 DURB x1/3 0.174 0.073 0.042 0.33 Intercept -7.046 3.533 -14.576 -0.479 3 32.688 71.734 0.698 0.827 DURB x1/3 0.231 0.065 0.117 0.374 PMED x1/3 -0.102 0.064 -0.243 0.009 Intercept 1.857 1.795 -1.459 5.659 4 34.268 72.716 1.68 0.803 DURB x1/3 0.232 0.066 0.117 0.377 Intercept -0.999 0.528 -2.119 -0.015 5 33.276 72.918 1.88 0.821 RDS1000 x1/3 -1.96 0.896 -3.879 -0.313 AREA ln(x) 0.412 0.275 -0.112 0.986 Intercept -2.223 4.835 -11.876 7.408

133

Table 4b. Results of multiple logistic regressions for gray fox. ROC is the Receiver Operating Characteristic; Trans represents the variable scale or transformation; Coeff is the estimated variable coefficient; Lower and Upper 95% are the confidence intervals for the coefficient estimates.

Lower Upper Model -lnL AICc ΔAICc ROC Variable Trans. Coeff. St. Err. 95% 95% 1 42.25 90.858 0 0.701 HEB100 arcsine√x -1.754 0.752 -3.331 -0.351 PMED x1/3 -0.083 0.045 -0.181 -0.001 Intercept 2.963 1.334 0.538 5.859 2 42.557 91.472 0.614 0.715 HWD100 arcsine√x 1.565 0.705 0.248 3.044 PMED x1/3 -0.066 0.044 -0.161 0.015 Intercept 0.252 1.385 -2.407 3.127 3 41.568 91.742 0.884 0.726 HEB100 arcsine√x -1.915 0.775 -3.546 -0.472 URB100 arcsine√x -1.295 1.144 -3.721 0.846 PMED x1/3 -0.069 0.046 -0.169 0.015 Intercept 2.294 1.325 0.511 5.805 4 43.811 91.798 0.94 0.702 HWD100 arcsine√x 1.785 0.693 0.497 3.246 Intercept -1.685 0.568 -2.899 -0.645 5 42.845 92.048 1.19 0.699 HEB100 arcsine√x -2.016 0.761 -3.168 -0.601 URB100 arcsine√x -1.749 1.116 -4.129 0.317 Intercept 1.197 0.611 0.043 2.466 6 44.205 92.586 1.728 0.673 HEB100 arcsine√x -1.812 0.735 -3.351 -0.437 Intercept 0.767 0.535 -0.255 1.867 7 42.031 92.668 1.81 0.717 HWD100 arcsine√x 0.697 1.067 -1.335 2.938 HEB100 arcsine√x -1.186 1.148 -3.466 1.118 PMED x1/3 -0.075 0.046 -0.175 0.009

134 Intercept 1.878 2.108 -2.234 6.165 Table 4c. Results of multiple logistic regressions for bobcat. ROC is the Receiver Operating Characteristic; Trans represents the variable scale or transformation; Coeff is the estimated variable coefficient; Lower and Upper 95% are the confidence intervals for the coefficient estimates.

Lower Upper Model -lnL AICc ΔAICc ROC Variable Trans. Coeff. St. Err. 95% 95% 1 39.749 88.104 0 0.73 DURB x1/3 0.182 0.063 0.066 0.316 AREAn( lx) -0.602 0.249 -1.131 -0.141 DURBxAREA -0.054 0.031 -0.117 0.005 Intercept 8.371 3.449 2.017 15.713 2 41.362 89.082 0.978 0.718 DURB x1/3 0.149 0.058 0.042 0.273 AREA ln(x) -0.474 0.222 -0.934 -0.055 Intercept 6.393 3.018 0.686 12.644 3 40.283 89.172 1.068 0.729 DURB x1/3 0.136 0.059 0.027 0.259 AREA ln(x) -0.613 0.259 -1.17 -0.138 HEB1000 arcsine√x 2.154 1.535 -0.682 5.381 Intercept 7.392 3.297 1.275 14.38 135 Table 5a. Results of coyote univariate linear regressions.

sign of 2 Variable Trans. r adj p slope AREA ln(x) 0.113 0.011 + DURB x1/3 0.046 0.076 + RDS100 -0.012 0.387 - RDS500 0.079 0.028 - RDS1000 0.118 0.009 - HWD100 -0.014 0.573 - HWD500 -0.021 0.876 + HWD1000 -0.008 0.440 + HEB100 0.042 0.085 + HEB500 0.153 0.003 + HEB1000 0.196 0.001 + URB100 0.089 0.021 - URB500 0.129 0.007 - URB1000 0.134 0.006 - PMED -0.016 0.624 + PBUFF 0.082 0.026 +

136 Table 5b. Results of gray fox univariate linear regressions.

sign of 2 Variable Trans. r adj p slope AREA -0.01 0.405 + DURB -0.036 0.806 - RDS100 -0.037 0.845 + RDS500 -0.038 0.897 - RDS1000 -0.038 0.930 + HWD100 0.018 0.235 + HWD500 -0.019 0.485 + HWD1000 -0.008 0.385 + HEB100 -0.009 0.399 - HEB500 -0.027 0.601 - HEB1000 -0.037 0.844 - URB100 -0.027 0.596 - URB500 -0.035 0.777 + URB1000 -0.037 0.900 - PMED -0.012 0.417 - PBUFF -0.038 0.957 +

137 Table 5c. Results of bobcat univariate linear regressions.

sign of 2 Variable Trans. r adj p slope AREA ln(x) 0.028 0.133 + DURB x1/3 0.041 0.092 + RDS100 -0.022 0.883 - RDS500 x1/3 0.122 0.009 - RDS1000 x1/3 0.072 0.038 - HWD100 0.102 0.016 + HWD500 0.112 0.012 + HWD1000 0.099 0.018 + HEB100 0.115 0.011 - HEB500 0.036 0.106 - HEB1000 -0.008 0.434 - URB100 -0.017 0.637 - URB500 0.002 0.305 - URB1000 <0.001 0.320 - PMED -0.016 0.592 - PBUFF 0.019 0.177 +

138 Table 6. Results of multiple linear regressions for coyotes and bobcats.

2 COYOTE Model -lnL AICc ΔAICc r adj Best ln(COY) = -0.69 + 2.64*HEB1000 + 2.12*PBUFF 44.022 94.577 0 0.277 Model

Competing ln(COY) = -2.17 + 2.31*HEB1000 + 1.89*PBUFF 43.316 95.541 0.964 0.282 Model + 0.11*ln(AREA)

BOBCAT Best ln(BOB) = 1.86 - 0.66* (RDS500)1/3 - 30.189 66.936 0 0.299 Model 1.28*HEB100

Competing ln(BOB) = 2.19 - 0.74* (RDS500)1/3 - 29.985 68.922 1.986 0.289 Model 1.52*HEB100 - 0.37*HWD500

139 Table 7a. Results of univariate linear regressions for scats.

sign of 2 Variable Trans. r adj p slope AREA ln(x) 0.117 0.003 + DURB x1/3 0.138 0.001 + RDS100 -0.012 0.673 - RDS500 0.083 0.01 - RDS1000 0.14 0.001 - HWD100 0.026 0.101 + HWD500 0.055 0.031 + HWD1000 0.075 0.013 + HEB100 -0.01 0.510 - HEB500 -0.015 0.927 + HEB1000 -0.009 0.521 + URB100 arcsine√x 0.034 0.066 - URB500 arcsine√x 0.089 0.008 - URB1000 0.085 0.009 - PMED x1/3 0.086 0.009 - PBUFF 0.072 0.015 +

140 Table 7b. Results of univariate linear regressions for species.

sign of 2 Variable Trans. r adj p slope AREA ln(x) 0.029 0.080 + DURB x1/3 0.099 0.004 + RDS100 -0.01 0.575 - RDS500 x1/3 0.105 0.003 - RDS1000 0.125 0.002 - HWD100 0.022 0.115 + HWD500 0.05 0.034 + HWD1000 0.031 0.077 + HEB100 -0.013 0.723 - HEB500 -0.015 0.990 + HEB1000 -0.012 0.675 + URB100 arcsine√x 0.033 0.069 - URB500 arcsine√x 0.057 0.025 - URB1000 0.069 0.015 - PMED x1/3 0.05 0.033 - PBUFF 0.018 0.136 +

141 Table 8. Results of multiple linear regressions for all species combined.

TOTAL SPECIES 2 ABUNDANCE Model -lnL AICc ΔAICc r adj Best Model ln(SCAT) = 0.29 + 0.04* (DURB)1/3 - 59.927 128.489 0 0.243 0.05* (PMED)1/3 + 0.15*ln(AREA)

Competing Model ln(SCAT) = 2.23 + 0.06* (DURB) 1/3 - 61.494 129.363 0.874 0.219 1 0.04* (PMED) 1/3

Competing Model ln(SCAT) = -0.67 - 0.05* (PMED) 1/3 + 61.717 129.809 1.32 0.215 2 0.24*ln(AREA)

Competing Model ln(SCAT) = 0.48 - 0.04* (PMED) 1/3 + 60.911 130.457 1.968 0.221 3 0.17*ln(AREA) - 0.06*RDS1000

# SPECIES Best Model SPEC = 3.13 - 0.03* (PMED) 1/3 - 70.587 147.532 0 0.149 0.1*RDS1000

Competing Model SPEC = 2.45 - 0.04* (PMED) 1/3 + 70.774 147.906 0.374 0.145 1 0.05* (DURB) 1/3

Competing Model SPEC = 2.35 - 0.16*RDS1000 72.118 148.413 0.881 0.125 2

Competing Model SPEC = 2.79 - 0.06*RDS1000 - 0.03* 70.098 148.802 1.27 0.149 3 (PMED) 1/3 + 0.03* (DURB) 1/3

Competing Model SPEC = 2.86 - 0.09*RDS1000 - 0.03* 70.219 149.044 1.512 0.146 4 (PMED) 1/3 + 0.41*HWD500

Competing Model SPEC = 2.09 - 0.1*RDS1000 + 71.464 149.286 1.754 0.128 5 0.55*HWD500

142

CHAPTER 5

CONCLUSIONS AND DIRECTIONS FOR FUTURE RESEARCH

The research presented in this dissertation addresses the relationship between carnivores and habitat change, with investigations into the magnitude and drivers of temporal and spatial fluctuations, and the patterns of invasion and habitat use by carnivores in urbanizing systems. The knowledge gained from these studies is valuable in the context of managing the particular species and systems examined. It also gives us some insight into carnivore ecology and study design at a more general level.

In the first study I examined population fluctuations of San Joaquin kit fox on the Carrizo Plain, using a 35-year survey dataset. This small fox is federally endangered, and the Carrizo population represents one of the last remnants of this species in their native desert environment (Williams et al., 1998). I compared kit fox population numbers with rainfall and prey data to investigate the drivers of the changes in fox numbers over time. This long-term dataset revealed no correlation between rainfall and kit fox numbers, although I did find a correlation between fox numbers and jackrabbit numbers over time. Although this system is ultimately driven by rainfall

(Rosenzweig, 1968), precipitation patterns may not translate directly up trophic levels

(Ernest et al., 2000; Brown & Ernest, 2002), and rainfall amounts are a poor predictor of interannual kit fox numbers. Spatially, kit foxes were not evenly distributed along the survey routes, and appeared to be less common near the edges of the Monument,

143 indicating subtle habitat preferences and/or edge effects due to development pressures outside the park. There were also small seasonal differences in kit fox distribution, with foxes less clustered during fall and winter counts, perhaps due to dispersal of juveniles.

Lastly, there was spatial congruence between giant kangaroo rat distribution and kit fox distribution along both routes, and both species appeared to be expanding their ranges northward within the portion of the park that had previously been cultivated.

This study highlights the fact that predator populations may vary tremendously over time, and that long-term studies are key to uncovering these distributional patterns and the processes driving them. Understanding natural variability is important for managers faced with making decisions based on population numbers and distribution.

Predators may utilize different portions of their habitat seasonally, as well as over a period of years, affecting management practices such as grazing rotations, burning, and hunting. Decisions concerning habitat preservation or land purchasing will also be affected by fluctuations in predator numbers and distributions; for example, land may not be considered valuable if the species of concern happens to be absent because of temporary low population numbers. Finally, if the relationship between predator and prey is not fully understood, then important prey populations may not be properly included in carnivore management plans.

In the second study I focused on the relationship between invasive red fox and urbanization in the San Francisco Bay area. Red foxes were introduced to lowland

California in the last century for fur farming and hunting, and have had devastating impacts on several species of ground-nesting birds (U.S. Fish and Wildlife Service,

1990; U.S. Fish and Wildlife Service and U.S. Navy, 1990; Lewis et al., 1993). They

144 have continued to expand their range and may negatively impact other taxa (Ralls &

White, 1995; Clark, 2001). Contrary to my assumption going in, I found that red foxes were surprisingly rare in the region, although they were fairly widespread. They appeared to be somewhat localized, and were strongly correlated with urban development and high road densities. They were also associated with open habitats as opposed to hardwood forest. Finally, there was no correlation between red foxes and native gray foxes, or between red foxes and coyotes.

These data suggest that in the Bay Area red foxes have successfully established populations only in areas near human development. Many invasive species utilize human-altered habitats as resource and refuge, and can negatively impact native species living in urban areas and in wildlands surrounding development. Knowledge of development thresholds or configurations preferential to invasive species may help in reducing the populations of these species in urbanizing areas, and hence, their impacts to native biodiversity.

In the final study I investigated the responses of three common mesocarnivores to urbanization. Northern California currently has a relatively complete carnivore community; however, this part of the state contains the fifth largest metropolitan area in the United States, and is projected to grow by nearly 25% in the next 25 years

(Association of Bay Area Governments, 2007). If we want to maintain carnivores in this system, an understanding of their responses to urbanization is vital. I examined the habitat use of coyotes, bobcats and gray foxes along an urban-to-rural gradient in a variety of habitat types. All three carnivores were found throughout the study region, although coyotes and bobcats tend to avoid urban areas, while gray foxes occur across

145 the gradient. Coyotes were detected more often in open habitats such as grasslands, while gray foxes strongly preferred hardwood forest. Scale seemed to be important as well; each species responded differently to habitat variables at different scales, with coyotes reacting at the largest scale, and gray foxes at the smallest. This is most likely a result of body and home range sizes (Crooks, 2002). Both total carnivore abundance and number of species detected were highly negatively correlated with levels of urban development and road density.

Because carnivores respond differentially to urbanization, it is important to study many species to fully understand the impacts of human development on this group of animals. How animals respond to development may relate to their body or home range size, their prey requirements, their social structure, their interactions with domestic animals, or their perception by humans. Carnivores may respond to habitat alteration at scales appropriate to these characteristics, making an urban matrix habitable by one, useless to another. Ensuring the future viability of carnivore populations in urban areas requires growth planning that considers habitat characteristics at the appropriate landscape scale important to different species.

Future Research

Effective conservation of carnivores requires specific knowledge of their ecologies in both natural and altered habitats, and requires that researchers investigate fundamental patterns and processes at large temporal and spatial scales. Specifically, because most studies are of short duration (3-5 years), we know too little about the magnitude and long-term drivers of most predator population fluctuations in natural

146 systems (with a few exceptions; e.g., lynx-snowshoe hare cycles). How much do predator densities vary over time? Are population size changes driven by bottom-up forces? How tightly linked are predators with their primary prey? How strongly and in what ways are predators affected by climate cycles? Short-term studies are inappropriate for answering these questions and may result in misleading or incorrect conclusions.

Kit foxes on the Carrizo Plain are an ideal subject for long-term study. They inhabit a fairly simple system that is driven by strong climatic forces, may have a nearly closed population (Schwartz et al., 2005), and exhibit large population fluctuations. At present, the CPNM represents one of the last large intact habitat patches for kit fox where natural processes can take place, and continuing study of its foxes is necessary for the successful management of this endangered species. It is essential that the spotlighting surveys continue, and that more in-depth data is collected. Currently, fox locations are marked using a range finder and GPS, a marked improvement over the mileage method used for most of the collection period. This will eventually allow researchers to estimate population size using a detection probability function.

Information on vegetation change and prey numbers is also critical to understanding this system. Permanent vegetation plots which can be surveyed several times per year should be established throughout the CPNM. Rodent population density and distribution should also be monitored using track plates or trapping grids in established plots. Further research is needed on the importance of kangaroo rats in the kit fox diet, which could be accomplished by analyzing scat samples collected over multiple seasons and years. Additionally, population numbers of predators of kit foxes, especially

147 coyotes, should be monitored using track plates, hair snares, or camera traps

(spotlighting also tends not be effective in counting coyotes since they avoid vehicle headlights). These data will shed light on whether kit fox population parameters are tied to rainfall patterns, or the resulting vegetation or consumer dynamics; and in what ways kit foxes are impacted by coyotes in good and bad prey years.

We also know too little about the ecology of carnivores in altered or urbanizing systems. Invasive species such as red foxes and feral cats may benefit from such habitat change, while other native species may decline with increasing development pressure.

How are carnivore responses to urbanization modified by the natural habitat matrix?

How does habitat fragmentation affect community dynamics? Does the carnivore community itself affect individual species’ responses to development? Are there certain development patterns that facilitate carnivore persistence, or discourage invasion by exotic species? As in natural systems, understanding the responses of carnivores to urbanization requires studies of both pattern and process over large temporal and spatial scales.

My second study examined the pattern of non-native red fox distribution and habitat use in the Bay Area, and further research is needed to better quantify the habitat correlates of red fox. First, additional surveys for red fox are needed, including more sites in a greater variety of habitat and land cover types. Finer-scale land cover data is also necessary. For instance, information on parcel size and number of housing units per parcel would provide better resolution than road density or the CalVeg land use layer. More detailed information regarding water sources, vegetation structure and species composition would also help in modeling red fox habitat preferences; such data

148 would require thorough on-the-ground vegetation surveys. Data on prey densities and other food resources would likely help in explaining fox abundance at different sites, and could be accomplished using track plates or live-trapping rodents, performing bird surveys, and interviewing local residents about their inadvertent or deliberate feeding of wildlife. In addition, we need to tease apart the relationship between red foxes and coyotes in this system, which may require creative ways of investigating dynamical species processes. Lastly, the invasion of the Bay Area by red foxes is occurring over a timescale of decades; therefore, multiple studies over time, or a continuing study is essential for understanding this long-term process. Molecular population genetics could be used to investigate the origins and rate of spread of the region’s red foxes.

Like invasive carnivores, native predators respond in different ways to urbanization, and additional research would help in understanding these responses. I included three common species in my third study; however, it would be useful to examine the habitat use and distribution of rarer species, such as badgers, pumas, and weasels. I also concentrated on species that are easily studied using scat as a proxy for abundance. Species such as raccoons and skunks are urban adapters, and yet do not deposit many scats along trails, and therefore, it would be necessary to utilize other methods of carnivore detection such as track plates or cameras to detect these taxa. The inclusion of rare species and urban-adapters, and the inclusion of species across the body size continuum, may shed light on why urbanization impacts some species and not others. As with the red fox study, finer-scale land cover and food resource data would improve models of wildlife-habitat relationships, and long-term studies would confirm

149 whether carnivore community composition is stable in natural fragments within the urban matrix.

Importantly, repeated transect surveys would lead to more accurate estimates of occupancy for all species (MacKenzie et al., 2006), and many additional transects are needed, including more sites in a greater variety of habitat and land cover types. Future work should also include an examination of interspecific interactions, specifically focusing on the impacts of habitat change on carnivore community dynamics. This could involve either detailed statistical analyses of carnivore associations over a broad area, or a carefully planned observational study in a series of temporally or spatially paired sites.

150 Literature Cited

Association of Bay Area Governments. 2007. www.abag.ca.gov/index.html

Brown, J.H.; Ernest, S.K.M. 2002. Rain and rodents: complex dynamics of desert consumers. Bioscience 52: 979-987.

Clark, H.O. 2001. Endangered San Joaquin kit fox and non-native red fox: interspecific competitive interactions. Fresno, CA: California State University. MS. thesis.

Crooks, K. 2002. Relative sensitivities of mammalian carnivores to habitat fragmentation. Conservation Biology 16: 488-502.

Ernest, S.K.M.; Brown, J.H.; Parmenter, R.R. 2000. Rodents, plants, and precipitation: spatial and temporal dynamics of consumers and resources. Oikos 88: 470-482.

Lewis, J.C.; Sallee, K.L.; Golightly Jr., R.T. 1993. Introduced red fox in California. Non-Game Bird and Mammal Section Report 93-10. Sacramento: California Department of Fish and Game.

MacKenzie, D.I.; Nichols, J.D.; Royle, J.A.; Pollock, K.H.; Bailey, L.L.; Hines, J.E. 2006. Occupancy Estimation and Modeling: inferring patterns and dynamics of species occurrence. London: Elsevier.

Ralls, K.; White, P.J. 1995. Predation on San Joaquin kit foxes by larger canids. Journal of Mammalogy 76: 723-729.

Rosenzweig, M.L. 1968. Net primary productivity of terrestrial communities: prediction from climatological data. American Naturalist 102: 67-74.

151 Schwartz, M.K.; Ralls, K.; Williams, D.F.; Cypher, B.L.; Pilgrim, K.L.; Fleischer, R.C. 2005. Gene flow among San Joaquin kit fox populations in a severely changed ecosystem. Conservation Genetics 6: 25-37.

U.S. Fish and Wildlife Service; U.S. Navy. 1990. Endangered species management and protection plan, Naval Weapons Station - Seal Beach and Seal Beach National Wildlife Refuge. Final Environmental Impact Statement. Portland, OR.

U.S. Fish and Wildlife Service. 1990. Predator management plan and environmental assessment, San Francisco Bay National Wildlife Refuge. Draft report. Newark, CA.

Williams, D.F.; Cypher, E.A.; Kelly, P.A.; Miller, K.J.; Norvell, N.; Phillips, S.E.; Johnson, C.D.; Colliver, G.W. 1998. Recovery plan for upland species of the San Joaquin Valley, California. Portland: US Fish and Wildlife Service.

152 APPENDIX A

DISTRIBUTION OF NON-NATIVE RED FOXES IN EAST BAY OAK WOODLANDS

In press:

Bidlack, A.L.; Merenlender, A.; Getz, W.M. 2008. Distribution of non-native red foxes in East Bay oak woodlands. In Sixth Symposium on Oak Woodlands: Today’s Challenges, Tomorrow’s Opportunities, October 9-12, 2006. USDA Forest Service Technical Report.

Abstract

European red foxes (Vulpes vulpes) were introduced into lowland California in the

1880s for fur farming and hunting. The introduced foxes quickly spread throughout much of the state and have been implicated in the decline of several federally threatened and endangered ground-nesting bird species. Red foxes have been present in the East

Bay for 25 to 30 years and they are regularly sighted in coastal wetlands and in the

Oakland and Berkeley hills. We were interested in documenting the extent of the invasion away from human-dominated habitats into oak woodlands in the East Bay, as foxes may negatively impact both prey populations and native carnivores such as gray foxes. We surveyed fire roads and hiking trails in core oak woodland sites in Contra

Costa and Alameda counties for carnivore scat. All scat samples were collected, and their locations entered into a GIS. To positively distinguish the scat to species, DNA was extracted, amplified, and identified using PCR and RFLP. Four carnivore species were identified, including coyote, gray fox, red fox, and bobcat. Red foxes were only

153 detected in woodlands that were adjacent to urban or suburban development, and were not detected in more rural sites. They may be dependent on human-dominated systems for resources and cover, and the high numbers of coyotes present in the East Bay may be excluding them from some areas. This research is providing managers essential information about red fox distribution, habitat requirements, and interactions with other carnivores, which can be used to better monitor and eventually control red fox invasions and subsequent impacts to native species.

154 Introduction

Red foxes (Vulpes vulpes) are the most widespread non-domesticated carnivore in the world, and have been enormously successful invading many habitats (Larivière and Pasitschniak-Arts 1996). They occur on six continents and have continued to increase their range over the last century (Lewis and others 1993; Macpherson 1964;

Marsh 1938). Red foxes have also had tremendous impacts on native fauna in areas where they have been introduced; in Australia their arrival is linked to the decline and local extirpation of many native prey species (Burbidge and Manly 2002; Burbidge and

McKenzie 1989; Dickman 1996; Kinnear and others 1988; Kinnear and others 1998).

Nonnative red foxes were introduced into lowland California in the late nineteenth century for fur farming and hunting. A localized population of these animals in the Sacramento Valley was first described by Grinnell and others (1937), who believed them to be distinct from the native Sierra Nevada red fox (V. v. necator).

Residents from five counties described hunting and trapping these valley foxes beginning in about 1896 (Grinnell and others 1937). Foxes were also imported and released in Orange County for hunting from 1905 to 1919 (Sleeper 1987). Commercial fur farming became very popular in the mid twentieth century, with approximately 125 fox farms throughout California by the early 1940s (Vail 1942), and many foxes undoubtedly escaped or were released from these farms (Lewis and others 1993). Gray

(1975) documented red foxes occupying 16 counties in northern California, with most sightings occurring in the Sacramento Valley. Gray (1975) also described a population from Los Angeles County, and stated that there were unconfirmed sightings in other southern California localities. Most recently, Lewis and others (1993) documented the

155 presence of red foxes in 36 counties throughout the state, excluding the North Coast, the

Modoc Plateau, and the Mojave Desert.

Red foxes have been implicated in the decline in California of several federally threatened and endangered bird species, including the light-footed clapper rail (Rallus longirostris levipes), the California clapper rail (R. longirostris obsoletus), the

California least tern (Sterna antillarum browni), and the western snowy plover

(Charadrius alexandrinus nivosus; U.S. Fish and Wildlife Service 1990; U.S. Fish and

Wildlife Service and U.S. Navy 1990). While depredations of bird populations have been well-documented, less is known about red fox impacts to mammalian prey species and native carnivores. Anecdotal evidence suggests that red foxes have replaced gray foxes (Urocyon cinereoargenteus) in some areas (J. Patton and R. Barrett, pers comm), and that when red fox numbers are reduced, gray fox populations rebound (J. DiDonato, pers comm). Red foxes have also killed several federally endangered San Joaquin kit foxes (Vulpes macrotis mutica; Clark 2001; Ralls and White 1995), and there is concern over possible introgression between nonnative red foxes and the Sierra Nevada red fox

(Perrine 2005).

Red foxes are often associated with human-dominated landscapes and may sustain very high densities in these heterogeneous habitats (Catling and Burt 1995;

Kurki and others 1998; Larivière and Pasitschniak-Arts 1996; Oehler and Litvaitis

1996). In urban Orange County, red foxes utilize open spaces such as golf courses, airports and cemeteries (Lewis and others 1993). In North America, predation and competition by coyotes (Canis latrans) may also be important in shaping red fox distributions (Harrison and others 1989; Sargeant and others 1987; Sargeant and Allen

156 1989; Voigt and Earle 1983). Coyotes tend to avoid urban or developed areas

(Gosselink and others 2003; Odell and Knight 2001), and these regions might provide a refuge from coyote predation for red foxes.

Since the mid-seventies, nonnative red foxes have been present and locally increasing in number around the San Francisco Bay (J. DiDonato, pers comm.; Lewis and others 1993). Counties surrounding the Bay Area support a diverse mix of high- and low-density housing, vineyards, oak woodlands, and grasslands, resulting in a complex interface between human-dominated landscapes and wildlands. However, most of the undeveloped land is under private ownership and is subject to intense vineyard and residential development pressure. If red fox dispersal and successful establishment is predicated on landscape conversion, these development pressures may lead to the continued red fox invasion of northern coastal California oak woodlands. Our objective was to evaluate the abundance of red foxes in oak woodlands in Contra Costa and

Alameda counties, in order to assess the extent of their spread from human-dominated habitats into wildlands in the East Bay.

Methods

We used a geographic information system (GIS) to select core oak woodland sites in Contra Costa and Alameda counties. Using the State of California Fire and

Resource Assessment Program vegetation classification layer, along with the Bay Area

Open Space Council open space layer, we examined the land cover within a 5 km2 circle around the centroid of each open space polygon. We defined core woodland areas as those circles that contained 25 percent to 75 percent combined hardwood, conifer and

157 shrub cover, of which at least 50 percent was hardwood. These constrictions ensured that grasslands and closed-canopy forests were not included in our site selection and that any cover was predominantly hardwood rather than shrub or conifer. We also eliminated properties with greater than 5 percent water, wetland, urban, or agriculture area. This provided us with 15 oak woodland dominated properties, under a variety of ownerships, for which we eventually obtained permission to survey 12. Six of these properties were sufficiently large that two survey transects were conducted, making a total of 18 transects (fig. 1).

Surveys for red fox were performed using a detection dog trained to locate the scats of all canids and felids, excluding domestic dog. Scat sniffing dogs have proven to be both remarkably efficient and precise in their searching. In prior research, detection dogs have correctly identified 100 percent of target carnivore scats in a matrix of sympatric species (Smith and others 2001, 2003) and have detected the presence of target species regardless of species density or vegetation type (Smith and others 2005).

In a controlled test, our dog detected 100 percent of scats placed within 10 m of the centerline of a transect along an unpaved road, and 98 percent of scats within 20 m

(Reed and Bidlack 2006). A pilot survey indicated that in these oak woodland systems, most canid scats are deposited along trails, with surveying and collection off-trail extremely difficult and inefficient (Bidlack Unpublished Data). Therefore, surveys were conducted along fire roads, and hiking and game trails. Transect start points and direction were chosen randomly within the site, which means that the land cover around the transect may differ slightly from that around the centroid of the site. Each transect was two km long, and all transects were separated by at least 2.5 km to increase

158 independence of sampling (red fox home ranges vary widely, but average five square km [Larivière and Pasitschniak-Arts 1996]). Due to time and financial constraints, each transect was surveyed only once.

Collected scats were individually bagged and given an identification number and a GPS location and subsequently stored at -80 C. Scats were later identified to species using molecular genetic methods. DNA was extracted from each scat using a small sub-sample (~500 g) that was removed from the outer surface or end of each feces and used in a QIAamp DNA stool kit (Qiagen). Polymerase chain reaction (PCR) was then used to amplify a 196 base pair segment of the mitochondrial cytochrome b gene.

Restriction fragment length polymorphism (RFLP) analysis using three restriction enzymes (HpaII, DdeI and HpyCh4V) to cut this fragment was used to separate coyote, gray fox, red fox and bobcat scats (for protocols see Bidlack and others 2006).

Results and Discussion

Eighteen two-km transects were surveyed during the summer of 2005. We collected a total of 383 scats, with a mean of 21.3 scats found per transect, and with a range of five to 48. There was a mean of 12.48 (± 8.83 s.d.) scats located per km. Using molecular methods we identified 353 scats (92 percent) to species. Coyote scat was the most prevalent at 222 (62 percent), followed by gray fox at 93 (26 percent), bobcat at 35

(10 percent), and red fox at three (1 percent). Coyotes were detected at all sites, while bobcats and gray fox were detected at 11 and seven sites, respectively. Red fox scats were found at only two sites, Vargas Plateau and Garin/Dry Creek, both in Alameda

County (fig. 1).

159 Our study indicates that red foxes have not been particularly successful in invading core oak woodlands in the East Bay. This may be the result of several factors, one of which is the amount and pattern of development. Most previous research in other regions suggests that red foxes are closely associated with disturbed or heterogeneous habitats. In Australia red foxes only occur in forested patches close to areas that have been cultivated, heavily grazed, burned, logged, or converted to residential development

(Catling and Burt 1995), and in Europe red foxes are most abundant in landscapes including 20 to 30 percent agricultural land (Kurki and others 1998). Urban areas may provide reliable sources of food, water and cover for foxes; in fact, local residents provided red foxes with supplemental food at every site studied in Orange County

(Lewis and others 1993). The two sites where red fox scats were found in our study were both near developed areas. Vargas Plateau is a working cattle ranch, and

Garin/Dry Creek Park directly abuts a dense residential neighborhood, unlike all the other sites (some of which are adjacent to low density suburban developments). Another recent survey for foxes conducted in grasslands (<25 percent canopy cover) in eastern

Alameda and Contra Costa counties indicates that red foxes may persist only near permanent water sources such as reservoirs and canals (Clark and others 2003; Smith and others 2006). This study surveyed 17 sites in these two counties and detected red foxes at only three sites, again confirming that this species has on the whole not been successful in invading wildlands in northern California (Clark and others 2003; Smith and others 2006). Red foxes may not be able to persist in wildlands far from developed areas, though they may survive in the same types of habitats close to human development. This pattern has been observed in other human-associated species (for

160 example, domestic cats, cowbirds; Hejl and others 2002; Maestas and others 2003;

Morrison and Caldwell 2002; Odell and Knight 2001). If red fox occupancy patterns reflect a reliance on human development, then conserving large patches of undeveloped oak woodland habitat in the East Bay may help buffer these habitats against invasion by red fox.

The high numbers of coyotes in the East Bay may also be excluding red foxes from some areas, or keeping their abundance low. Coyotes are known to kill red foxes

(Sargeant and Allen 1989), and red foxes will avoid areas with high densities of coyotes

(Harrison and others 1989; Sargeant and others 1987; Voigt and Earle 1983). We found coyote scats on all of our transects, indicating that they are ubiquitous in oak woodlands in the Bay Area, and there was no relationship between coyote abundance and distance of the transect from developed areas. However, we found coyote scats on the same transects with red fox scats. Currently, the relationship between these two species is still unclear, and it is impossible to differentiate between coyote presence and lack of development as impediments to red fox spread into core woodlands. Similarly, we remain uncertain concerning the interaction between gray and red foxes in these sites, in view of the fact that we found gray fox scats at both of the sites that also supported red fox.

Recently, presence/absence surveys have come under criticism for a general lack of detection rate estimates, leading to biased approximations of habitat occupancy

(MacKenzie and others 2006, and papers cited therein). However, surveys performed by scat detection dogs are likely to detect more species and more samples per species, especially for rare taxa, than other non-invasive survey methods such as searching by

161 humans, track plates, remote cameras, and hair snares (Harrison and others 2002; Smith and others 2001). We made the assumption that all species were defecating on roads and trails, and that there would be no detection bias by the dog among the target species. Our findings concerning red fox occurrence are corroborated by the results of

Smith and others (2006), who surveyed some of the same properties (Black Diamond

Mines, Round Valley, Carnegie State Recreation Area) using a scat-detection dog, with negative results. Nevertheless, our surveys may be an underestimate of species presence, given that transects were only surveyed once.

Our preliminary data suggest that in the East Bay red foxes have successfully established populations only in core oak woodlands adjacent to human development.

Further research is needed to better quantify the habitat correlates of red fox as well as to tease apart the relationships among the three canid species in the area. In this way we may gain insight into the drivers of the red fox invasion, and the possible impact of future development on the spread of this exotic species.

Acknowledgments

We thank the East Bay Regional Park District, the Muir Heritage Land Trust, and California State Parks for permission to survey their properties. We are also grateful to Dr. Per Palsbøll for generously allowing us to use his genetics laboratory. This work was supported by an American Society of Mammalogists Grant-in-aid of Research to

ALB.

162 Literature Cited

Bidlack, A.L.; Reed, S.E.; Palsbøll, P.J.; Getz, W.M. 2007. Characterization of a western North American carnivore community using PCR-RFLP of cytochrome b obtained from fecal samples. Conservation Genetics 8: 1511-1513.

Burbidge, A.A.; McKenzie, N.L. 1989. Patterns in the modern decline of Western Australia's vertebrate fauna: causes and conservation implications. Biological Conservation 50: 143-198.

Burbidge, A.A.; Manly, B.F. 2002. Mammal extinctions on Australian islands: causes and conservation implications. Journal of Biogeography 29: 465-473.

Catling, P.C.; Burt, R.J. 1995. Why are red foxes absent from some eucalypt forests in eastern New South Wales? Wildlife Research 22: 535-546.

Clark, H.O. 2001. Endangered San Joaquin kit fox and non-native red fox: interspecific competitive interactions. Fresno, CA: California State University; 84 p. M.S. thesis.

Clark, H.O.; Smith, D.A.; Cypher, B.L.; Kelly, P.A. 2003. Detection dog surveys for San Joaquin kit foxes in the northern range. Fresno, California: Endangered Species Recovery Program. 37 p. Contract number 4600013447.

Dickman, C.R. 1996. Impact of exotic generalist predators on the native fauna of Australia. Wildlife Biology 2: 185-195.

Gosselink, T.E.; Van Deelen, T.R.; Warner, R.E.; Joselyn, M.G. 2003. Temporal habitat partitioning and spatial use of coyotes and red foxes in east-central Illinois. Journal of Wildlife Management 67: 90-103.

Gray, R.L. 1975. Sacramento Valley red fox survey. Non-game wildlife investigation progress report. Sacramento: California Department of Fish and Game. 6 p.

163 Grinnell, J.; Dixon, J.S.; Linsdale, J.M. 1937. Furbearing mammals of California. Berkeley: University of California Press; 777 p.

Harrison, D.J.; Bissonette, J.A.; Sherburne, J.A. 1989. Spatial relationships between coyotes and red foxes in eastern Maine. Journal of Wildlife Management 53: 181-185.

Harrison, R.L.; Barr D.J.; Dragoo, J.W. 2002. A comparison of population survey techniques for swift foxes (Vulpes velox) in New Mexico. American Midland Naturalist 148:320-337.

Hejl SJ, Mack DE, Young JS, Bednarz JC & RL Hutto. 2002. Birds and changing landscape patterns in conifer forests of the north-central Rocky Mountains. Studies in Avian Biology 25:113-129.

Kinnear, J.E.; Onus, M.L.; Bromilow, R.N. 1988. Fox control and rock wallaby population dynamics. Australian Wildlife Research 15: 435-477.

Kinnear, J.E.; Onus, M.L.; Sumner, N.R. 1998. Fox control and rock wallaby dynamics - II. An update. Wildlife Research 25: 81-88.

Kurki, E.G.; Nikula, A.; Helle, P.; Linden, H. 1998. Abundances of red fox and pine marten in relation to the composition of boreal forest landscapes. Journal of Animal Ecology 67: 874-886.

Lariviere, S.; Pasitschniak-Arts, M. 1996. Vulpes vulpes. Mammalian Species 1-11.

Lewis, J.C.; Sallee, K.L.; Golightly Jr., R.T. 1993. Introduced red fox in California. Non-Game Bird and Mammal Section Report 93-10. Sacramento: California Department of Fish and Game. 70 p.

164 MacKenzie, D.I.; Nichols, J.D.; Royle, J.A.; Pollock, K.H.; Bailey, L.L; Hines, J.E. 2006. Occupancy Estimation and Modeling: inferring patterns and dynamics of species occurrence. Oxford: Elsevier, Inc.; 324 p.

MacPherson, A.H. 1964. A northward range extension of the red fox in the eastern Canadian arctic. Journal of Mammalogy 45: 138-140.

Maestas, J.D.; Knight, R.L.; Gilgert, W.C. 2003. Biodiversity across a rural land-use gradient. Conservation Biology 17: 1425-1434.

Marsh, D.B. 1938. The influx of the red fox and its colour phases into the barren lands. Canadian Field-Naturalist 52: 60-61.

Morrison ML & HD Caldwell. 2002. Geographic variation in cowbird distribution, abundance and parasitism. Studies in Avian Biology 25:65-72.

Odell, E.A.; Knight, R.L. 2001. Songbird and medium-sized mammal communities associated with exurban development in Pitkin County, Colorado. Conservation Biology 15: 1143-1150.

Oehler, J.D.; Litvaitis, J.A. 1996. The role of spatial scale in understanding responses of medium-sized carnivores to forest fragmentation. Canadian Journal of Zoology 74: 2070-2079.

Perrine, J.D. 2005. Ecology of red fox (Vulpes vulpes) in the Lassen Peak region of California, U.S.A. Berkeley, CA: University of California. Ph.D. dissertation.

Ralls, K.; White, P.J. 1995. Predation on San Joaquin kit foxes by larger canids. Journal of Mammalogy 76: 723-729.

Reed, S.E.; Bidlack, A.L. 2006. Optimizing field surveys using wildlife detection dogs. Unpublished draft.

165 Sargeant, A.B.; Allen, S.H.; Hastings, J.O. 1987. Spatial relations between sympatric coyotes and red foxes in North Dakota. Journal of Wildlife Management 51: 285-293.

Sargeant, A.B.; Allen, S.H. 1989. Observed interactions between coyotes and red foxes. Journal of Mammalogy 70: 631-633.

Sleeper, J. 1987. Bears to briquets: a history of Irvine Park 1897-1997. Trabuco Canyon, CA: California Classics; 32 p.

Smith, D.A.; Ralls, K.; Davenport, B.; Adams, B.; Maldonado, J.E. 2001. Canine assistants for conservationists. Science 291: 435.

Smith, D.A.; Ralls, K.; Hurt, A.; Adams, B.; Parker, M.; Davenport, B.; Smith, M.C.; Maldonado, J.E. 2003. Detection and accuracy rates of dogs trained to find scats of San Joaquin kit foxes (Vulpes macrotis mutica). Animal Conservation 6: 339- 346.

Smith, D.A.; Ralls, K.; Cypher, B.L.; Maldonado, J.E. 2005. Assessment of scat- detection dog surveys to determine kit fox distribution. Wildlife Society Bulletin 33: 897-904.

Smith D.A.; Ralls, K.; Cypher, B.L.; Clark, H.O.; Kelly, P.A.; Williams, D.F.; Maldonado, J.E. 2006. Relative abundance of endangered San Joaquin kit foxes (Vulpes macrotis mutica) based on scat-detection dog surveys. Southwestern Naturalist 51: 210-219.

U.S. Fish and Wildlife Service; U.S. Navy. 1990. Endangered species management and protection plan, Naval Weapons Station - Seal Beach and Seal Beach National Wildlife Refuge. Final Environmental Impact Statement. Portland, OR. 591 p.

166 U.S. Fish and Wildlife Service. 1990. Predator management plan and environmental assessment, San Francisco Bay National Wildlife Refuge. Draft report. Newark, CA. 26 p.

Vail, E.L. 1942. Fox ranching in southern California. California Fish and Game 28: 87- 88.

Voigt, D.R.; Earle, B.D. 1983. Avoidance of coyotes by red fox families. Journal of Wildlife Management 47: 852-857.

167

Figure 1. Location map of survey transects in the East Bay. BDM1 and BDM2, Black Diamond Mines Regional Park; BR1 and BR2, Briones Regional Park; CAR, Carnegie State Vehicular Recreation Area; CLAY, Clayton Land Bank; GDC1 and GDC2, Garin/Dry Creek Regional Park; MT1 and MT2, Morgan Territory Regional Park; OHL, Ohlone Wilderness; PLR1 and PLR2, Pleasanton Ridge Regional Park; RV, Round Valley Regional Park; SR, Sky Ranch; SUN1 and SUN2, Sunol Regional Wilderness; and VAR, Vargas Land Bank. Sunburst symbol represents sites where red foxes were found. Dark gray color represents urban areas, light gray is oak woodlands.

168 APPENDIX B

DOG TRAINING AND DETECTION TESTS

Introduction

Scat detection dogs are quickly becoming a common and extremely useful tool in wildlife studies. Dogs have been used in field studies to detect presence and distribution of several species, including bears, kit foxes, bobcats and fishers (Wasser et al. 2004; Smith et al. 2003, Long et al. 2007). They can improve the efficiency of human observer off-road surveys by 900% (Meadows 2000) and can correctly identify target scats in a matrix of sympatric species (Smith et al. 2001; Smith et al. 2003) regardless of species density or vegetation type (Smith et al. 2005). Detection dogs are thus greatly improving our ability to study wild animal populations in a non-invasive way, and are particularly useful for studies of rare or widely dispersed species. Because of these advantages, I chose to train and use a scat detection dog in my dissertation research on mesocarnivore distribution and habitat use in northern California. In order to evaluate the abilities of my detection dog, I conducted a series of experimental transects and pilot tests to investigate the dog’s detection width, detection differences in field and road survey contexts, and the effects of fatigue, temperature, wind speed and humidity on his detection and movement rate.

169 Methods

Dog Training

The dog I trained was a two year old male pit bull terrier mix named Seth, which

I obtained from the East Bay SPCA in Dublin, California after he was run through a series of attention and drive tests. I trained the dog at the Hopland Research and

Extension Center (HREC) in Hopland California, following the methods of Aimee Hurt of Working Dogs for Conservation. Training scats were obtained from local wildlife sanctuaries and zoos, and an effort was made to collect scats from multiple individuals per target species to teach the dog to generalize to species rather than to focus on scat from one individual. Additionally, I tried to only obtain scats from animals that had been fed a wild or semi-wild diet, rather than processed feed. The training proceeded in several steps over a period of approximately eight weeks in early 2004. First, the dog was introduced to a series of boxes, each with a hole in the top. One of the boxes contained a known target scat. Seth was led to each box in turn, and rewarded with a few moments of ball-play immediately upon stopping at the scat box. He quickly learned to search each box to find the scat in order to receive his reward. The dog was taught to signal a detection by sitting or lying down next to the box containing the scat until I approached. Next, the scat was placed in different boxes, and the box configuration was varied to ensure that Seth was actually searching, and not remembering the previous location of the scat. Non-target scats (like domestic dog) were also included to teach the dog to ignore them. Once Seth understood this “game,” the boxes were removed and a scat was placed randomly in the yard, thus removing any

170 visual search cues. These searches were first conducted in very controlled circumstances (the fenced yard) until Seth could locate scats anywhere in the yard.

Next, several scats were placed along a dirt road and the dog was trained to search along this transect. These searches became progressively more difficult, with more scats being placed at farther and farther intervals along the search transect. In order to reduce my scent on the trail, I would either place the scats along the road from a car, or have another researcher place the scats along the road up to three hours before beginning the search. Having another person place the scats also eliminated my ability to subconsciously direct the dog to the scat area. Lastly, scats were placed off-road in widening widths, as well as randomly in open fields away from any roads. Dozens of repetitions at each of these steps were conducted over a period of weeks until Seth was capable of locating scats of wild animals along transects in the field.

Detection width tests

HREC served as the site for the detection width tests. Two straight 200 meter transects were marked out using a GPS unit and flagging tape. One transect was laid along a section of dirt road, while the second transect was laid out in a fenced ungrazed pasture approximately 350 meters from the first transect. Both transects were located in mixed oak woodland with an herbaceous understory of annual grasses and forbs.

Three scats from the same individual (a gray fox from the Lindsay Wildlife

Museum, Walnut Creek, California) were placed at random intervals along each transect. A random number generator in Microsoft Excel was used to choose distances along the transect in ten meter intervals; right or left side; and a distance from the

171 centerline of the transect in five meter intervals. Scats were placed along the transects anywhere from 30 minutes to several hours before the dog was allowed to search. Scats that were not located by the dog on an out-and-back search were left along the transects for detection in future runs. In order to minimize the possibility of the dog following my scent trail as I placed the scat, rather than air-scenting for the scat, I walked a circuitous and excessively long route. This ensured that my scent was all over the area, and not localized in the specific areas of the scat.

Each “run” consisted of scat placement and relocation by the dog. One to two runs were performed per day, over a non-continuous period of 37 days during the winter of 2005, for a total of fifteen runs on each transect. Runs were not performed in the rain. The date and time of each placement and relocation was noted, along with the temperature, wind speed and relative humidity (rounded to the nearest hour and obtained from a meteorological station on HREC).

Field surveys

Surveys for canid scat were conducted in five East Bay Regional Parks during

August and September 2004. A total of 13 surveys were conducted along dirt trails and roads open to the public. Because of high ambient temperatures during these summer months, surveys were started in the early morning. All scats that the dog detected were collected and given an identification number. Transect and scat location data were collected using a Trimble GeoExplorer III GPS unit (Trimble Navigation Ltd.,

Sunnyvale CA). Transect location and approximate length was recorded using the GPS to collect point location information every five seconds. When a scat was found, its

172 location was also recorded using the GPS. Local weather data (temperature, relative humidity and wind speed) for the survey time period were collected from the California

Irrigation Management Information System website (CIMIS 2005).

GPS locations were differentially corrected and processed in GPS Pathfinder

Office 3.0 (Trimble Navigation Ltd., Sunnyvale CA). Distance along the survey route of located scats was calculated, along with detection width, in ArcGIS 9.2 (ESRI,

Redlands CA). Correlations between search success and environmental factors were performed in JMP 6.0 (SAS Institute, Cary NC).

Results

Detection tests

The dog located a total of 79 of 90 scats placed (88%) and detection was higher on the road transect (91%) versus the pasture transect (84%). The per-search average detection was 48% and 51% on the road and pasture transects, respectively; however, the variance in per-search detection was higher in the pasture transects (0.016 road;

0.084 pasture). All scats placed at zero, five and ten meters were located on both the road and pasture transects. Seth exhibited a slight reduction in detection at 15 and 20 meters and a severe reduction at 25 meters (< 30% success; Figure 1).

Air temperatures during transect searches ranged from 5.5° C to 21.8° C. When the ratio of found scats to present scats was regressed against temperature, there was a significant negative relationship in the pasture transect (p = 0.004, r = - 0.702; Figure

2a), but not the road transect (p = 0.235, r = 0.326; Figure 2b). Relative humidity and

173 temperature were highly negatively correlated (p < 0.0001, r = - 0.93), and thus humidity had a significant positive relationship with detection success in the pasture transect (p = 0.003, r = 0.716), but not the road transect. Wind speed ranged from 1 m/s to 3.4 m/s and was not a significant factor in search success for either transect.

Detection width was also not affected by temperature, humidity or wind speed.

Field surveys

192 scats were collected during the field surveys. The survey transects ranged from 1.7 km to 4.1 km, with an average of 3 km, and traversed habitats including annual grasslands, chaparral, and oak woodlands. The mean rate of movement along each transect was 1.7 km/hr, and the mean rate of scat detection was 7.4 scats per hour or 4.7 scats per kilometer. The rate at which Seth moved between scat detections did not vary with increasing temperature, or with increasing length of transect, but did decrease with increasing numbers of scats found along transect length (p = 0.004, r = - 0.215).

Mean width of detection was 4.36 meters, with a maximum detection distance from the centerline of 28 meters. There was no difference between detection widths on dirt trails versus dirt roads, and temperature, relative humidity and wind speed did not affect detection width.

Discussion

Seth had a high overall rate of detection during the placed-scat tests, indicating he was proficient at locating target scats in a complex natural environment. Although he exhibited a relatively low detection rate per test, this resulted from increasing

174 numbers of scats placed far from the transect that were never found. These detection tests also revealed that Seth had a discrete detection threshold of approximately 20 meters from the transect centerline. This threshold is important for designing comprehensive species surveys, or for modeling species density based on detection probability functions.

Lower humidity may directly result in fewer scent molecules in the air, and as expected, high temperatures and low relative humidity negatively affected Seth’s search success. However, this impact was not consistent between the pasture and road detection tests. Perhaps the road acted as a “guide” for his searching, or the pasture transect was more complex, leading to differences in detection. While temperature had some effect on detection success, it did not seem to affect Seth’s detection width.

Width may therefore be more a function of the dog’s movements than ambient environmental conditions.

I expected the dog to exhibit differing searching patterns on the road versus the pasture detection tests. Indeed, at the beginning of the trials, he tended to search in a straight-line pattern along the road, while searching in wider “loops” on the pasture transect. These qualitative differences disappeared by the end of the trial period, suggesting that he learned that scats were placed equally far from the transect in both the pasture and road conditions. Consequently, I was surprised by the narrowness of the detection corridor along the field survey transects, especially in light of the wide detection area shown in the placed-scat transects. This could be a result of the trail acting as a linear visual guide for the dog, and to the fact that a trail is often the path of least resistance through heavy brush or other vegetation. It could also be caused by the

175 high degree of reward available along the trails in the field surveys; there was not much incentive for Seth to search widely when the trails were replete with scats.

Interestingly, environmental conditions did not affect Seth’s search rate or search width in the field surveys; however, the more scats that he detected, the slower was his rate of movement between detections. This was likely due to the fact that Seth would get fatigued by the ball play that was his reward for scat detection.

Seth was capable of, and proficient at searching for, detecting, and signaling on scats of the appropriate species (for molecular identification methods, see Appendix 3).

He often signaled on scats that were hidden by dirt or leaf litter, and that were off-trail, increasing my detection and collection ability alone. However, the use of a detection dog for a mesocarnivore study in this system may not be ideal. First, many land owners and public management agencies are unwilling to allow dogs off-leash on their properties, for livestock and wildlife protection reasons. This severely limits the use of this method in many areas. Second, mesocarnivores such as coyotes and bobcats are quite common in this system, reducing the usefulness of a detection dog in assessing the relative abundance of these species. Search dogs may be more valuable when used in surveys of rare or wide-ranging species (e.g., Wasser et al 2004; Smith et al. 2003). For this reason, a dog may indeed be useful in surveys for species such as red fox that are invading the Bay Area.

176 Literature Cited

California Irrigation Management Information System. 2005. http://wwwcimis.water.ca.gov/cimis/data.jsp

Long RA, Donovan TM, Mackay P, Zielinski WJ, & JS Buzas. 2007. Effectiveness of scat detection dogs for detecting forest carnivores. Journal of Wildlife Management 71: 2007-2017.

Meadows R. 2002. Scat sniffing. Zoogoer 31:5.

Smith DA, Ralls K, Davenport B, Adams B, & JE Maldonado. 2001. Canine assistants for conservationists. Science 291: 435.

Smith DA, Ralls K, Hurt A, Adams B, Parker M, Davenport B, Smith MC, & JE Maldonado. 2003. Detection and accuracy rates of dogs trained to find scats of San Joaquin kit foxes (Vulpes macrotis mutica). Animal Conservation 6: 339- 346.

Smith DA, Ralls K, Cypher BL, & JE Maldonado. 2005. Assessment of scat-detection dog surveys to determine kit fox distribution. Wildlife Society Bulletin 33: 897- 904.

Wasser SK, Davenport B, Ramage ER, Hunt KE, Parker M, Clarke C & G Stenhouse. 2004. Scat detection dogs in wildlife research and management: application to grizzly and black bears in the Yellowhead Ecosystem, Alberta, Canada. Canadian Journal of Zoology 82: 475-492.

177

Figure 1. Percentage of scats placed at different widths from the center line found by detection dog on road and pasture transects.

178

a)

b)

Figure 2. Scat detection success regressed against temperature. a) percent of scats found on road transect; b) percent of scats found on pasture transect.

179 APPENDIX C

CHARACTERIZATION OF A WESTERN NORTH AMERICAN CARNIVORE COMMUNITY USING PCR-RLFP OF CYTOCHROME B OBTAINED FROM FECAL SAMPLES

Published in:

Bidlack, A.L.; Reed, S.E.; Palsbøll, P.J.; Getz, W.M. 2007. Characterization of a western North American carnivore community using PCR-RFLP of cytochrome b obtained from fecal samples. Conservation Genetics 8: 1511-1513.

Reprinted with kind permission from Springer Science and Business Media

Key words: rapid assessment, non-invasive sampling, species identification, fecal DNA, carnivores

Abstract

We developed a simple and reliable method to identify carnivore scats to species using PCR and RFLP of a portion of the mtDNA cytochrome b gene, which works for seven of the most common carnivores in western North America. We identified a short

(196 bp) polymorphic region of cytochrome b which would be easily amplifiable even from degraded DNA, developed a primer set, and isolated a set of three restriction enzymes (HpaII, DdeI, HpyCH4V) that would identify the seven target species. In order to test whether this protocol would effectively identify scats we had obtained in the field we collected 243 carnivore scats from 12 sites in the San Francisco Bay area.

85% (206) of our samples successfully amplified and were subsequently identified to

180 species using our RFLP protocol. We selected 108 of these samples to sequence; our species identifications based on sequencing were identical to those obtained using our

PCR-RFLP method. Our PCR-RFLP method is a simple and efficient means to identify carnivore scats to species, eliminating the need for sequencing, which is costly and requires more laboratory equipment. It allows for rapid and noninvasive assessment of multiple carnivore taxa and is particularly useful for surveying populations across many sites.

181 Non-invasive methods of surveying wildlife populations, such as the molecular analysis of scat or hair samples, are useful in determining species distributions, and investigating phylogeographic patterns and population structure (DeYoung and

Honeycutt 2005; Waits and Paetkau 2005). These techniques can be especially time and cost-effective when surveying populations over large spatial scales. Several methods have been developed for identifying hair and scats of felids, canids, and mustelids to species, using various markers (Paxinos et al. 1997; Mills et al. 2000;

Riddle et al. 2003); however, we needed a more consistent and efficient method for rapid assessment of the carnivore community as whole. We developed a simple and reliable method to identify carnivore scats to species using PCR and RFLP of a portion of the mtDNA cytochrome b gene, which successfully differentiates seven of the most common and widespread carnivores in western North America. This protocol serves as a basic framework for community or guild-level analysis which can be appropriately modified depending on the full suite of species present in a study area.

Surveying mitochondrial DNA sequences of coyote (Canis latrans), red fox

(Vulpes vulpes), gray fox (Urocyon cinereoargenteus), bobcat (Lynx rufus), puma (Puma concolor) raccoon (Procyon lotor), and striped skunk (Mephitis mephitis) deposited in

GenBank (Accession Nos. AF028040, AY928669, AF028156, AY499332, AF499775,

X94930, X94927), we identified a short (196 bp) polymorphic region of cytochrome b which would distinguish these seven species. We developed a primer (HCarn200; 5’-

ATTCAGCCRTARTTAACGTC–3’) to use in conjunction with CanidL1 (Paxinos et al.

1997) to amplify this fragment. Our primers were not species-specific, for the very reason that we wanted to amplify DNA from a suite of species from four taxonomic

182 families. We imported the GenBank sequences into NebCutter (v. 2.0, New England

Biolabs) and isolated a set of three restriction enzymes (HpaII, DdeI, HpyCH4V) that would identify the seven target species (Figure 1). A double digest of HpaII and DdeI cuts all species except for the canids, and provides unique restriction patterns for puma, bobcat, raccoon, and skunk. A second digest with HpyCH4V then distinguishes red fox from gray fox. None of the enzymes cut coyote; therefore, if no cuts were observed for a sample, then we assigned that sample as coyote.

To test the performance of this protocol, we obtained frozen tissue samples from individuals of each species from the Museum of Vertebrate Zoology at the University of

California, Berkeley (catalog numbers: C. latrans, 152177, 208488; L. rufus, 188623,

196060; U. cinereoargenteus, 154021; V. vulpes, 208489, REJ 1529; P. concolor,

155331, 193138; M. mephitis, 198469, 208492; P. lotor, 191164, 191174). DNA was extracted from each sample using the DNeasy Tissue kit (Qiagen). PCR was performed using 2 μl 1:50 extraction dilution in a 20 μl total reaction volume containing 1× reaction buffer (Qiagen), 1.5 mM MgCl2, 200 µM each dNTP, 0.5 µM each primer, and

1 U Taq polymerase (Qiagen). Thermalcycling was initiated at 94˚C for 2 min, followed by 40 cycles of 94˚C for 1 min, 54˚C for 1 min and 72˚C for 2 min. All PCR reactions included at least one negative control to monitor for contamination.

Following DNA amplification, we first digested PCR products with HpaII and DdeI to identify puma, bobcat, raccoon and skunk, and then digested the samples identified as canid with HpyCh4V. The 10 µl reaction volume contained 8 µl PCR product, 1 µl digest buffer, 0.5 µl H2O, and 2.5 U each restriction enzyme. Products were digested for 4 h, following the manufacturer’s instructions (New England Biolabs). We

183 electrophoresed the products for 45 minutes on a 2% agarose gel and visualized the predicted cutting patterns using ethidium bromide and UV light (Figure 2).

In order to test whether this protocol would effectively identify scats obtained in the field we collected 243 carnivore scats along trails and fire roads from 12 sites in the

San Francisco Bay area. Scat samples were placed in paper or plastic bags with a clay desiccant pack (Texas Technologies) and placed in a –80˚C freezer for storage. A small amount (~500mg) from the outer surface or end of each scat was removed up to six months later for DNA extraction. QIAamp DNA Stool kits (Qiagen) were used for extracting DNA and extractions were performed in a quasi-clean laboratory, with precautions taken to prevent contamination. Extracted DNA was diluted to 1:50 before

PCR. The PCR protocols were the same as above and were performed in a PCR product-free area after UV-radiation of materials. 85% (206) of our samples successfully amplified and were subsequently identified to species using our RFLP protocol as above. We identified 86 coyote, 69 bobcat, 43 gray fox, three red fox, four puma, and one striped skunk scats.

We selected 108 of these samples to sequence, using our primer set, to confirm the accuracy of our species identifications. We used an ABI 3730 automated sequencer using the BigDye Terminator (v 3.1) Cycle Sequencing Kit (Applied Biosystems).

Sequences were edited and aligned by eye in BioEdit (v. 7.0.3; Hall 1999; GenBank

Accession Nos.: EF 373765 – EF 373872). Our species identifications based on sequencing were identical to those obtained using our PCR-RFLP method.

During our initial trials, we found that the primer combination of CanidL1 and

HCarn200 amplified at a much higher rate than the commonly used mixture of CanidL1

184 and H15149 (Kocher et al. 1989; Paxinos et al. 1997; Riddle et al. 2003). The latter combination resulted in less than 35% amplification success, presumably because the longer fragment (412 bp) is harder to amplify from scat samples that are often degraded.

Interestingly, there was no indication of contamination by non-target prey DNA, despite large amounts of deer and lagomorph hair in many of the scat samples, and the fact that our primers would probably amplify DNA from these species, based on inspection of sequences on GenBank. Our results suggest that it may be unnecessary to design species-specific mtDNA primers for carnivores in order to avoid amplifying prey species, and support the assertion by Foran et al. (1997) that ingested animal matter may be adequately degraded by passing through the digestive tract, eliminating cross- contamination (but see Murphy et al. 2003).

Prior knowledge of the potential presence of carnivores in addition to those we have included (e.g., gray wolf, black bear, grizzly bear, lynx, ringtail, coati, spotted skunk, hog-nosed skunk, kit fox, swift fox, badger, wolverine, marten, fisher, long- tailed weasel and mink) will be important and may require the inclusion of additional restriction enzymes, particularly if there are closely related taxa present. For example, feral domestic cats and free-roaming domestic dogs could be present in our study sites.

The presence of domestic cats will require the use of another enzyme (BslI) to differentiate these scats from those of bobcats. Domestic dog and raccoon samples are indistinguishable using our method, and therefore a second double digest using DdeI and HpyCH4V is necessary to separate them. Similarly, none of the restriction enzymes we used cut DNA from coyote, and therefore, there might be an opportunity for misidentification (false positives for coyote) if scat from other species lacking

185 restriction sites were unintentionally included in the analysis. No reasonable number of restriction enzymes will serve to differentiate all North American carnivores. We chose to limit our analysis to the most widespread and common species, but the method we describe is flexible and can easily be modified to incorporate other locally-present taxa on a case-by-case basis.

Our PCR-RFLP method is a simple and efficient means to identify carnivore scats to species, eliminating the need for sequencing, which is costly and requires more laboratory equipment. It allows for rapid and noninvasive assessment of multiple carnivore taxa and is particularly useful for surveying populations across many sites.

We are currently using this protocol for assessing carnivore community responses to recreation and development pressures in natural areas in and around the San Francisco

Bay area.

Acknowledgments

We thank C. Conroy (Museum of Vertebrate Zoology) for providing tissue samples and M. Rew for sequencing assistance. Thanks to A. Merenlender, S. Mills and an anonymous reviewer for helpful comments on the manuscript. This study was supported by an American Society of Mammalogists Grant-in-Aid of Research (ALB) and a NSF Graduate Research Fellowship, Budweiser Conservation Scholarship, Phi

Beta Kappa Doctoral Fellowship and a Berkeley Sigma Xi Grant-in-Aid of Research

(SER).

186 References

DeYoung RW, Honeycutt RL (2005) The molecular toolbox: genetic techniques in wildlife ecology and management. J. Wildlife Manag., 69, 1362-1384.

Foran DR, Crooks KR, Minta SC (1997) Species identification from scat: an unambiguous genetic method. Wildlife Soc. Bull., 35, 835-839.

Hall TA (1999) BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucl. Acids. Symp. Ser., 41, 95-98.

Kocher TD, Thomas WK, Meyer A, et al. (1989) Dynamics of mitochondrial DNA evolution in animals: amplification and sequencing with conserved primers. Proc. Nat. Acad. Sci., USA 86, 6196-6200.

Mills LS, Pilgrim KL, Schwartz MK, McKelvey KS (2000) Identifying lynx and other North American felids based on MtDNA analysis. Conserv. Genet., 1, 285-288.

Murphy MA, Waits LP, Kendall KC (2003) The influence of diet on faecal DNA amplification and sex identification in brown bears (Ursus arctos). Mol. Ecol., 12, 2261-2265.

Paxinos E, McIntosh C, Ralls K, Fleischer R (1997) A noninvasive method for distinguishing among canid species: amplification and enzyme restriction of DNA from dung. Mol. Ecol., 6, 483-486.

Riddle AE, Pilgrim KL, Mills LS, McKelvey KS, Ruggiero LF (2003) Identification of mustelids using mitochondrial DNA and non-invasive sampling. Conserv. Genet., 4, 241-243.

187 Waits LP, Paetkau D (2005) Noninvasive genetic sampling tools for wildlife biologists: a review of applications and recommendations for accurate data collection. J. Wildlife Manag., 69, 1419-1433.

188

Figure 1. Schematic of cutting patterns created by restriction enzymes HpaII, DdeI and HpyCh4V. The left panel represents a double digest with HpaII and DdeI to separate out the canids and differentiate the other four species. On the right is the restriction pattern for the canids after a second digest using HpyCh4V.

189

Figure 2. Agarose gel restriction enzyme banding patterns for seven species. a) HpaII/DdeI double digest of all species; b) HpyCH4V digest of canids.

190