bogan_thesis_2004

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coyotes survive in suburban areas in this region and little is known of their ecological roles or ... with many hours of radio tracking and trapping (finally trapping a coyote before leaving ...... with a clipped antenna in an attempt to hide the animal.

Eastern Coyote Home Range, Habitat Selection and Survival in the Albany Pine Bush Landscape

Abstract of a thesis presented to the Faculty of the University at Albany, State University of New York in partial fulfillment of the requirements for the degree of

Master of Science College of Arts and Sciences Department of Biological Science

Daniel A. Bogan 2004

ABSTRACT In the northeast USA, top mammalian predators were extirpated through persecution and habitat loss. The coyote (Canis latrans) expanded into the northeast taking advantage of this vacant predator niche. Since 1970, coyotes have been widespread across all of mainland New York, yet no study has examined how well coyotes survive in suburban areas in this region and little is known of their ecological roles or potential to conflict with people. This information is important because in western states coyotes have high survival rates, a high degree of urban association and cause conflict with people. I studied survivorship and correlates of cause-specific mortality of coyotes using radio telemetry. The annual survival rate was 0.20 ± 0.14. There were no differences in survival rates between sexes, age classes, home range location, or capture methods. Collisions with vehicles (n = 7) and shooting (n = 6) accounted for the 2 major mortality factors. Coyotes that were killed by vehicles crossed roads more often than all other coyotes, though they did not have more roads within their home ranges. Coyotes that were shot had a larger mean and maximum open habitat patch size within their home ranges. High exploitation of the local coyote population may cause coyotes to avoid human-developed lands thus reducing the potential for negative interactions with people. I concurrently studied home range and habitat selection of coyotes in the suburban Albany Pine Bush landscape. Fixed kernel and minimum convex polygon (95%) home ranges (n = 17) averaged 6.81 km2 and 5.75 km2, respectively. Habitat analysis revealed that coyotes selected for natural habitat and avoided residential and commercial lands when locating a home range area and moving within the home range. Compositional

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analysis additionally ranked natural habitat as the most selected habitat at 2 spatial scales of selection (62.3% and 74.5%). Coyotes lived in small home ranges and primarily used the remaining natural lands in the suburban landscape. These results indicate that local coyotes maintain a natural ecological role and under existing conditions do not currently pose a threat to people and pets living adjacent to natural lands.

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Eastern Coyote Home Range, Habitat Selection and Survival in the Albany Pine Bush Landscape

A thesis presented to the Faculty of the University at Albany, State University of New York in partial fulfillment of the requirements for the degree of

Master of Science College of Arts and Sciences Department of Biological Sciences

Daniel A. Bogan 2004

ACKNOWLEDGEMENTS I am grateful to my thesis advisor, Dr. Roland Kays, for providing guidance and unyielding energy and inquisitiveness for this research project. Thank you to Dr. George Robinson for providing academic and scientific counsel throughout my pursuit of this degree. Thank you to Dr. Stanley Gehrt for serving as a valued committee member. I would like to thank the technicians, and volunteers that assisted this study. It has been a pleasure working with all of you. Jared Wagner worked many “intrinsically” rewarding hours trapping foxes (?) and radio tracking coyotes. Laura Robinson assisted with many hours of radio tracking and trapping (finally trapping a coyote before leaving for U. of Maine). Bill Lang brought skill in trapping, and performed radio telemetry like clockwork. Thank you to Joe Bopp, Adam Fox, Sam Franklin, and Jessica Walsh. The Biodiversity Research Institute of the State of New York provided major grant funding, and the New York State Museum provided equipment and funding necessary to conduct this research. A great thanks is owed to the staff of both organizations. Thank you to the staff of the Albany Pine Bush Preserve Commission for permitting our research activities and providing valuable assistance. Thank you to Karl Parker and Alan Hicks of the New York State Department of Environmental Conservation. Thank you to the Town of Guilderland Parks and Recreation Department, and private landowner, Victoria Wells, for allowing trapping on their lands. Thank you to the graduate students in the often-overlapping programs in Ecology, Evolution and Behavior, and Biodiversity Conservation and Policy, Department of Biological Sciences. Last, I would like to thank my friend and fellow graduate student, Amielle DeWan, for countless discussions of our research projects.

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TABLE OF CONTENTS ABSTRACT ………………………………………………………………

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ACKNOWLEDGEMENTS ………………………………………………

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LIST OF TABLES

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………………………………………………………

LIST OF FIGURES ………………………………………………………

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Chapter One:

Study Area

………………………………………

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Chapter Two:

Eastern Coyote (Canis latrans) Survivorship and Mortality Causes in a Suburban Landscape …..

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………………………………………………………

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METHODS

Trapping and Radio Telemetry Survival and Mortality

………………………

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………………………………

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Mortality Correlates ……………………………………… Road crossing rates ……………………………… Roads in home ranges ……………………………… Home range habitat composition ……………… RESULTS

………………………………………………………

Trapping and Radio Telemetry

11 11 12 12 13

………………………

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………………………………

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Mortality Correlates ……………………………………… Road crossing rates ……………………………… Roads in home ranges ……………………………… Home range habitat composition ………………

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Survival and Mortality

DISCUSSION

………………………………………………

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Management Implications and Conclusions ………………

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Chapter Three:

METHODS

Eastern Coyote (Canis latrans) Home Range and Habitat Selection in a Suburban Landscape ………

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………………………………………………………

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Trapping and Animal Handling

………………………

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………………………………………

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………………………………

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Habitat Use and Selection ……………………………… Johnson’s second-order selection ……………… Johnson’s third-order selection ………………

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Radio Telemetry Home Range Analysis

RESULTS

………………………………………………………

Trapping and Animal Handling

………………………

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………………………………………

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………………………………

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Habitat Use and Selection ……………………………… Johnson’s second-order selection ……………… Johnson’s third-order selection ………………

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Radio Telemetry Home Range Analysis

DISCUSSION

………………………………………………

Radio Telemetry and Home Range Size Habitat Selection

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………………

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Management Implications

………………………………

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Ecological Implications

………………………………

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BIBLIOGRAPHY APPENDIX

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LIST OF TABLES CHAPTER ONE Table I.

Categorical land use classifications of 9 identified land-uses within the suburban Albany Pine Bush study area ………

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CHAPTER TWO Table II.

Table III.

Paired comparisons between years for coyote survival rates and functions from the suburban Albany Pine Bush study area ……………………………………………… Comparisons of mean road-crossing rates, by road class, for coyotes killed by vehicles verses all other coyotes ….

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CHAPTER THREE Table IV. Table V. Table VI. Table VII. Table VIII.

Number of female and male coyotes trapped by age class… Percent of radio-telemetry activity readings by diurnal period Coyote home range sizes ……………………………… Second-order compositional habitat ranking matrix ……… Third-order compositional habitat ranking matrix ………

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43 43 44 48 51

LIST OF FIGURES CHAPTER ONE Figure 1. Figure 2.

Map of the Albany Pine Bush Preserve boundaries and effective study area positioned between urban and rural lands ……… Map of the effective Albany Pine Bush study area showing the distribution of available habitat ………………………

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CHAPTER TWO Figure 3. Figure 4. Figure 5. Figure 6. Figure 7. Figure 8. Figure 9. Figure 10. Figure 11. Figure 12. Figure 13.

Radio-tracking periods and fates for individual coyotes inhabiting the study area ……………………………… Year 1: annual survival for coyotes ……………………… Year 2: annual survival for coyotes ……………………… Year 3: annual survival for coyotes ……………………… Year-specific annual survival function for coyotes (dependent individuals) ……………………………… Year-specific annual survival function for coyotes (independent individuals) ……………………………… Annual survival functions for female and male coyotes … Annual survival functions for coyotes by age class ……… Annual survival functions for coyotes by capture method… Annual survival functions for coyotes living inside or outside the Albany Pine Bush Preserve ……………………… Annual survival functions of coyotes by season ………

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CHAPTER THREE Figure 14. Figure 15. Figure 16. Figure 17. Figure 18. Figure 19.

Second-order coyote habitat use vs. availability ……… Second-order difference between coyote habitat use and availability ……………………………… Second-order compositional habitat analysis log-ratios…… Third-order coyote habitat use vs. availability ……… Third-order difference between coyote habitat use and availability ……………………………… Third-order compositional habitat analysis log-ratios ……

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46 47 48 49 50 51

Study Area Chapter One The 114.75 km2 Albany Pine Bush study area (APB) is located between the cities of Albany and Schenectady, New York, USA (42° 30´ N, 73° 52´). This suburban area is positioned within the interface between urban and rural lands (Figure 1). The initial trapping effort focused on the Albany Pine Bush Preserve (11.9 km2; N. Gifford, Albany Pine Bush Commission, personal communication), although, outside of the Preserve, substantial natural lands exist within the study area (Figure 1). The natural components of the Preserve are a complex assemblage of remnant patches of inland pitch pine (Pinus rigida) and scrub-oak (Quercus ilicifolia and Q. prinoides) barrens, pitch pine-oak (Q. spp.) forest, successional northern and southern hardwoods, and other natural cover types (see Barnes 2003, Schneider et al. 1991). These natural areas are fragmented by adjacent publicly and privately owned human land-uses and multi-class roads (local = 334.7 km, county = 15.4 km, state = 43.6 km, federal = 17.4 km, and multi-lane interstates and interstate exchanges = 65.7 km). Total road density is 4.15 km/km2 for the entire study area. I used a GIS database (Arc GIS, ESRI) to identify the effective study area, as defined by coyote movements, by plotting all radio telemetry locations and creating a buffer around all locations at a 1-km radius (Figure 2). I selected the 1-km radius arbitrarily to identify areas that could have been used by coyotes. One coyote was opportunistically captured and radio tracked 4.5–6.4 km away from the main group of coyotes. Using the 1-km radius resulted in a disjunctive study area composed of the main area of focus = 103.83 km2 and a satellite area = 10.92 km2. The entire study area =

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114.75 km2 encompassing the Albany Pine Bush Preserve and the surrounding suburban landscape. I used high-resolution aerial photographs (pixel = 0.3 m) as the base layer to digitize the study area at a resolution of 0.01 ha and assigned each polygon into one of 9 land-use classifications (photos taken in 2001, released in 2003; NYS GIS Clearinghouse; www.nysgis.state.ny.us). Categorical land classifications were determined by type of human land-use, and structural characteristics (Table I). I gained permission to access all lands before commencing research activities.

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Figure 1. Map of the Albany Pine Bush Preserve boundaries and effective study area positioned between urban and rural lands.

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Figure 2. Map of the effective Albany Pine Bush study area showing the distribution of available habitat.

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Table I. Categorical land use classifications and descriptions for 9 identified land-uses within the suburban Albany Pine Bush landscape (114.75 km2). Class Natural

Description All natural areas including dense brush/sapling trees to closed canopy forest, and pine barrens.

Open

Municipal parks, golf courses, athletic fields and capped landfills.

8.8

Agriculture

Crop, pasture, and hay lands.

7.0

Residential

Maintained areas surrounding and including one or more houses and apartment buildings, typically providing limited green spaces (lawns).

24.3

Commercial

Business areas typically with paved parking lots and limited green spaces such as malls, shopping centers, restaurants, and pubic and private office buildings.

9.4

Industrial

Business areas with operating heavy equipment; strip mines.

2.3

Railway

Operational, and abandoned train corridors.

0.5

Roads

Interstate, Federal, State, County, and local road class.

2.5

Water

Reservoirs, ponds, rivers streams, and permanently flooded wetlands.

1.9

5

% AREA 43.3

Eastern Coyote (Canis latrans) Survivorship and Mortality Causes in a Suburban Landscape Chapter Two In northeastern USA, wolves (Canis lupus) and mountain lions (Felis concolor) were extirpated after extensive persecution and habitat perturbations (Foster et al. 2004, Gommper 2002a, Nowak 2002, Rhodes 1991). The coyote (Canis latrans) has since expanded their range as the de facto top predator and is common within this region. This expansion reached the northeast by the early 1900’s (Severinghaus 1974a, b). By the 1940’s, coyotes were observed within northern New York State, and by the 1970’s they spread south and west across the entire state with the exception of Manhattan and Long Island (Fener 2001). Though coyote populations inhabit urbanized landscapes in many parts of the USA (Grinder and Krausman 2001a, Quinn 1997, Riley et al. 2003, Timm et al. 2004, Way et al. 2001) there is no information on the degree to which northeastern coyote populations are limited by humanized landscapes. Human-related mortalities from poisons, vehicles, shooting (illegal and sport hunting), trapping, and control efforts have been identified in many studies as major mortality sources for coyote populations across North America (Andelt 1985, Chamberlain 2001, Grinder and Krausman 2001a, Knowlton et al. 1999, Riley et al. 2003, Roy and Dorrance 1985). Coyotes also die from predation (Riley et al. 2003) and intraspecific aggression, though these are typically less important (Brundige 1993, Patterson and Messier 2001, Okoniewsky 1982). An intuitive relationship exists between cause-specific mortalities and the level of human land-use within the landscape. Collisions with vehicles more often kill coyotes in urban landscapes (Grinder and

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Krausman 2001a, Kamler and Gipson 2004, Riley et al. 2003), whereas coyotes in rural areas more often die from hunting (Chamberlain 2001, Gese et al. 1989, Roy and Dorrance 1985). However no study has examined coyote mortality causes and survivorship in northeastern USA where much of their biology is different from western coyotes. Northeastern coyotes average larger in body mass than western coyotes (Thurber and Peterson 1991). Coyote-wolf hybridization in northeastern ecosystems has been suggested as a possible explanation (see Gommper 2002a). Recent genetic research confirms that many coyotes from New England and northern New York have hybridized with wolves (Wilson et al. 2004 [unpublished report]). Compared with most western populations, eastern coyote have been documented to prey on white-tailed deer (Odocoileus virginianus; Brundige 1993, Long et al. 1998) and contain large proportions of deer in their diets (Bogan and Kays, unpublished data, Parker 1986, Samson and Crete 1997). These findings suggest that eastern coyotes may exhibit a different ecology than western coyotes, though so few studies have examined coyote ecology in the northeast (Gommper 2002a) that this conclusion is preliminary. Some have speculated that altered northeastern coyote ecology is rooted in a shift in behavior towards that of the extirpated large predators (Thurber and Peterson 1991, Wilson et al. 2004). Urban landscapes support wildlife (prey) populations and offer anthropogenic foods, which attract coyotes into the area from surrounding natural lands (Fedrianni et al. 2001, Timm et al. 2004). After colonizing an urbanized area, coyotes increasingly traverse roads and residential areas, and may enter into conflict with people by threatening and attacking pets and small children (Riley el al. 2003, Timm et al. 2004).

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The lack of human persecution (Kitchen et al. 2000) in urban areas may allow coyotes to habituate to people, resulting in this type of conflict. Though coyotes have never been studied in an urban environment with ongoing hunting and trapping. In contrast, wolves are less adept at living near people and do not inhabit urbanized landscapes (Mladenoff et al. 1995, Treves et al. 2004). If eastern coyotes are hybrids with a substantial proportion of wolf genes, as suggested by White et al. (1994 [unpublished report]), they may be less adapted to inhabit urban landscapes than purebred western coyotes. To understand how these new top predators adapt their ecology and behavior to human-dominated landscapes, I studied eastern coyotes in a suburban landscape of New York using radio telemetry. The objective of this study was to identify cause-specific mortalities and estimate annual and seasonal survival rates. Additionally, I monitored movement patterns and habitat use to identify relationships between cause-specific mortality factors and coyote space use. METHODS Trapping and Radio Telemetry Coyotes were captured with modified #3 padded footholds (Victor Soft Catch) and neck snares (Chapter 3) in accordance with SUNY Albany IACUC protocol #03-05. At the time of trapping and mortality recoveries, I inspected all females for physical indications of reproduction indicated by teat condition. I fitted coyotes with radio transmitter collars equipped with 8 hr mortality sensors (ATS, Isanti, MI). Telemetry error estimates were established (Chapter 3). Coyote mortality events were monitored from 7 April 2001 to 31 March 2004. Between 7 April 2001 and 18 December 2003,

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coyotes were located once per diurnal period five days per week, commencing after trap checks and before dusk. Groups of coyotes were radio tracked during nocturnal activity periods one night per week collecting 5 successive locations no less than 1 hr. apart. Nocturnal tracking sessions began at or shortly following sunset. Between 19 December 2003 and 31 March 2004, coyotes were located ≥2 times per week to monitor survivorship. While collecting bearings, I monitored radio signals to detect mortality events. If a mortality signal was detected I visually confirmed the animal’s status. For all mortalities, I recorded the location (UTM coordinates), site characteristics, collar condition, and general animal condition at the recovery site. Necropsies were performed by the New York State Dept. of Environmental Conservation Pathology Laboratory to assess the exact cause of death. The study period was partitioned into 3 years. Each year began on 1 April starting in 2001 and ended on 31 March, terminating in 2004. Each year was further split into 4 seasons: Spring (1 April – 30 June), Summer (1 July – 30 September), Fall (1 October – 31 December), and Winter (1 January – 31 March). The study year and Spring season began at the approximate time of parturition. Later seasons occurred approximately with local climate changes. Survival and Mortality I calculated survival functions based on weekly survival rates using the KaplanMeier method (Kaplan and Meier 1958) modified by Pollack et al. (1989). This method allows for staggered entry of sampled animals, which is advantageous when animals are added to the study at varying times. Animals added later in the study are assumed to have the same survival function as previously added animals and requires an adequate

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sample size of 40–50 individuals (Pollock et al. 1989). This method also assumes animals are a random sample, with independent fates. It is generally assumed that capturing and equipping a radio collar has negligible affects on the behavior and survival of an animal. Individuals were censored from the survival analysis on the date of the last confirmed location in the study area in the event that they dispersed from the study site, or were no longer located by radio telemetry (i.e. missing signal: undocumented dispersal, radio failure, or destroyed transmitters where the animal was not recovered). I estimated annual survival functions and rates for each study year and constructed 95% confidence intervals for each function (Pollock et al. 1989). All survival rates are reported with 95% confidence intervals when available. I compared annual survival functions using log-rank tests, and annual survival rates using Z-tests (Pollock et al. 1989). No significant difference was detected between functions and rates between years, therefore I pooled 3 years of data using statistically dependent individuals and again for independent individuals to improve annual survival estimates. Dependent individuals included coyotes monitored during multiple years and registered as >1 individual when calculating pooled survival. Whereas survival calculations for independent individuals used one year with the longest dataset for each coyote to avoid repeated entry into the survival calculation. Using the statistically independent pooled data, I tested differences in annual pooled-survival functions and rates between sexes, and age classes. As a precautionary action, I tested annual survival rates and functions between capture methods to identify possible trapping method affects on survival. I further tested survival between coyotes with home ranges positioned inside and outside of Albany Pine Bush Preserve boundaries

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to investigate if the Preserve serves as a refuge for coyotes. I then calculated and compared seasonal survival functions and rates. I conducted all tests using alpha = 0.05. Due to small sample size of individual coyotes for survival estimates, I additionally report the length of time (median, mean, Std. Dev. and range in days) that animals were radio-monitored. Using the independent pooled data, I calculated marginal annual survival-functions for each mortality source using all other coyotes as censored from this analysis at the time of departure from the study or at the end of the study (Pollock et al. 1989). Mortality Correlates I investigated coyote habitat use and movement as potential variables correlated with specific mortality sources. The movement study ended on 18 December 2003 therefore I used the known fates of coyotes at the end of the movement study for the remaining space use and movement analyses. Road crossing Rates: I determined the number of road crossings and the total length of time for all nocturnal tracking sessions (time from first location to the time of the last location for each animal). I categorized roads into 4 classes: 1) INT = interstate and interstate ramps with 55–65 mph speed limits; 2) FSC = federal, state, and county having 40–55 mph speed limits; 3) LOC = all neighborhood, village, town, city roads with 0.05 >0.05

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1.00 0.90 0.80

SURVIVAL RATE

0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 1

3

5

7

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11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51

WEEK

Figure 7. Year-specific annual survival function (±95% CI) for dependent individuals (n = 25; individual coyotes tracked >1 year registered as >1 animal) pooled between years for coyotes (N = 20) inhabiting the Albany Pine Bush study area, 1 April 2001–31 March 2004.

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1.00 0.90 0.80

SURVIVAL RATE

0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 1

3

5

7

9

11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51

WEEKS

Figure 8. Year-specific annual survival function (±95% CI) for independent individuals (n = 20; individual coyotes tracked >1 year registered as only 1 individual) pooled between years for coyotes (N = 20) inhabiting the Albany Pine Bush study area, 1 April 2001–31 March 2004.

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1.00 0.90 0.80

Male Female

SURVIVAL RATE

0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 1

3

5

7

9

11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51

WEEKS

Figure 9. Year-specific annual survival functions for female (n = 11) and male (n = 9) coyotes inhabiting the Albany Pine Bush study area, 1 April 2001–31 March 2004.

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1.00 0.90

Pup Juvenile Adult

0.80

SURVIVAL RATE

0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 1

3

5

7

9

11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51

WEEKS

Figure 10. Year-specific annual survival functions for pup (n = 5), juvenile (n = 4), and adult (n = 11) coyotes inhabiting the Albany Pine Bush study area, 1 April 2001–31 March 2004.

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1.00 0.90 0.80

Snare Foot hold

SURVIVAL RATE

0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 1

3

5

7

9

11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51

WEEKS

Figure 11. Year-specific annual survival functions for coyotes captured by snare (n = 13) and foot hold (n = 6) inhabiting the Albany Pine Bush study area, 1 April 2001–31 March 2004.

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1.00 0.90 0.80

Preserve Not preserve

SURVIVAL RATE

0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 1

3

5

7

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11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51

WEEKS

Figure 12. Year-specific annual survival functions for coyotes from Albany Pine Bush study area with home ranges boundaries inside (n = 10) or outside (n = 10) of the Albany Pine Bush Preserve, 1 April 2001–31 March 2004.

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1.00 0.90 0.80

SURVIVAL RATE

0.70 0.60 0.50 0.40 0.30 0.20 0.10

Spring

Summer

Fall

Winter

0.00 1

2

3

4

5

6

7

8

9

10

11

12

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WEEKS

Figure 13. Year-specific annual survival functions of coyotes inhabiting the Albany Pine Bush study area for spring (n = 4), summer (n = 7), fall (n = 9), and winter (n = 5), 1 April 2001–31 March 2004.

I confirmed 15 mortalities of radio-collared coyotes during the study. Seven coyotes were hit by vehicles and found dead along roadsides. Six coyotes were shot and killed. One coyote died from toxins (anti-coagulant rodenticide) and one coyote died from complications due to extensive mange (Sarcoptes scabieilatin). Only one shotcoyote was killed during regulated hunting seasons (by a bow hunter). Necropsy results indicated that some shot animals may not have died instantly and could have traveled long distances before succumbing to blood loss from injuries. Thus it was difficult to determine the exact location where most individuals were shot. One adult female coyote that was shot was recovered along a roadside 4-km from the nearest edge of her 95% FK home range with the transmitter antenna clipped. The bullet passed through the heart and

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lungs and created a large exit wound, suggesting that she could not have traveled 4-km after being shot. I suspect that she was shot dead and then transported to the roadside with a clipped antenna in an attempt to hide the animal. Marginal annual survival rates per cause-specific mortality were approximately 0.47 ± 0.28 for Vehicle Killed, 0.50 ± 0.28 for Shot, 0.92 ± 0.20 for Mange, and 0.91 ± 0.22 for Toxins. Survival rates from both shooting and vehicles were greater than toxins and mange (X2 ≥ 3.571, P ≤ 0.05). Mortality Correlates Road crossing rates: Mean road crossing rates (crosses/hr) between coyote groups were similar (Vehicle Killed = 1.001, Shot = 0.312, Other = 0.158; F2,14 ≤ 3.661, P ≥ 0.053), however pair wise comparisons showed that Vehicle Killed coyotes crossed the TOTAL roads more than Other coyotes (Tukey’s, P = 0.047), but not more than Shot coyotes (Tukey’s, P = 0.155). Mean road crossing rates for Vehicle Killed coyotes were greater than the combined Shot and Other coyotes for INT, FSC, and TOTAL road classes (Table III). Table III. Mean Road Crossing Rates (crosses/hour) of coyotes hit killed by vehicles and combined Shot and Other coyotes in the Albany pine bush study area (7 April 2001– 18 December 2003). Road classes are: INT = interstate highways and ramps; FSC = federal, state, and county routes; LOCAL = neighborhood roads, etc; TOTAL = all roads combined.

Vehicle killed Shot, Other t15 P value

n 4 13

INT 0.434 0.005 2.800 0.007

FSC 0.410 0.057 2.134 0.025

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Road class LOCAL 0.157 0.151 0.070 0.151

TOTAL 1.001 0.213 2.652 0.009

Roads in home ranges: Coyote groups had different lengths of FSC roads within home ranges (F2,14 = 5.353, P = 0.019). Shot coyotes (mean = 3.73 km) had more length of roads within their home ranges than did Vehicle Killed (mean = 0.47 km; Tukey’s, P = 0.043) and Other coyotes (mean = 1.33 km; Tukey’s, P = 0.027). All additional road classes were similar in length between Vehicle killed, Shot and Other coyote-group home ranges (F2,14 ≤ 0.289, P ≥ 0.754) Home range habitat composition: Total area of all habitats were used similarly between coyote-group home ranges (F2,14 ≤ 4.031, P ≥ 0.068). Mean patch size of open habitat within coyote-group home ranges were different (F2,14 = 4.027, P = 0.042) as Shot coyotes (mean = 3.57 ha) had greater mean open patches within home ranges than Vehicle Killed coyotes (mean = 0.99 ha; Tukey’s, P = 0.049). Mean patch size for all other habitats within home ranges were similar between coyote groups (F2,14 ≤ 2.772, P ≥ 0.102). Maximum patch size for open habitat with home ranges were different between coyote groups (F2,14 ≤ 8.812, P ≥ 0.003) as Shot coyotes (mean = 33.34 ha) had larger maximum patches within home ranges than Vehicle Killed (mean = 7.41 ha; Tukey’s, P = 0.15) and Other coyotes (mean = 8.66; Tukey’s, P = 0.003). All other habitats had similar maximum patch sizes between coyote-group home ranges (F2,14 ≤ 8.812, P ≥ 0.003). DISCUSSION The pooled annual survival rate (0.20 ± 0.14) was lower than any other reported coyote population, including urban (0.72–0.74; Grinder and Krausman 2001a, Riley et al 2003) and rural environments (0.58–1.00; Chamberlain 2001, Roy and Dorrance 1985). Albany coyotes may have exhibited significantly lower survival rates than coyotes

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inhabiting rural lands of central Alberta, Canada (0.38; Roy and Dorrance 1985) however no error estimate was reported to make a valid comparison. Coyote population dynamics are highly plastic, allowing for a variety of survival strategies such as increased litter size and decreased age of first breeding, which permit coyotes to thrive despite harsh population limitations. Based on Sterling et al.’s (1983) model-estimates for coyote finite rates of increase (λ; calculated from proportions of breeding females, survival estimates, and mean litter sizes established from published studies), APB λ is 5% wolf ancestry and were similar in genetic structure to coyotes sampled in northern New York (Wilson et al. 2004). However, the weight-wolf relationship may not be simple, as Wilson et al. (2004) noted that their smallest tested coyote (Female, 12.3 kg) had the highest degree of eastern Canadian wolf (Canis lycaon) ancestry (89%). Although the genetic structure of APB coyotes is not known, it is possible that their phenotype, influenced by wolf genes, be less adapted to an urban environment than purebred western coyotes. Wolves do not persist well in human dominated areas, as they are sensitive to road densities, modified habitat, and persecution from people (Carroll et al. 2003, Mladenoff and Sickley 1998). Genotyping APB coyotes could test this hypothesis. Management Implications and Conclusions: Within the suburban APB landscape, resident coyotes maintained smaller than typical home ranges, composed primarily of natural habitats. Due to habitat fragmentation, individual natural patches were too small to support an entire coyote home range. Thus, local coyotes exposed themselves to mortality risks from being forced to cross roads within their home range, and increased exposure to people in agricultural, open, residential, and commercial lands. This exposure resulted in an annual survival rate of 0.20 primarily due to the combination of shooting and vehicle collisions. Population models suggest that this population’s finite rate of increase is 8 hrs of inactivity; Advanced Telemetry Systems, Isanti MI). I weighed and measured the animal, assessed sex and reproductive condition, and aged animals as pups (3 seasons were

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used for annual home range analysis. Seasonal FK home ranges, using the annual bandwidth, and MCP home ranges were calculated for individuals having ≥30 locations per season (Seaman et al. 1999) to investigate if home ranges varied in size between seasons. Differences in 95% and 50% annual home range sizes were tested between sexes, and age classes using t-tests. Pups and yearlings were combined due to low sample sizes. I used One-Way ANOVA to test for between-season differences in 95% and 50% home range size with alpha level = 0.05. For the remaining sections of this chapter, all reported error measures are standard errors. Habitat Use and Selection Rosenzweig and Abramsky (1986) characterized the process that results in an animal’s demonstrated use of resources. Initially, an animal perceives a “resourcespecific differential fitness” among multiple available resources. This leads to the development of preference, giving way to resource selection and is ultimately displayed as resource use. This process may occur at multiple scales and researchers must clearly state the scale of selection being studied (Levin 1992). Johnson (1980) suggested a standard methodology to identify the scale being studied using 4 orders of selection. The first-order selection determines the cumulative selection of geographic range by individuals of a species. Second-order selection influences an individual’s selection of home range within a region. Third-order selection determines the amount of use of each resource within the home range. Fourth-order selection applies to the feeding strategy at a particular site. I examined second and third order selection for 95% annual FK home ranges.

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Johnson’s second-order selection: I determined the total percent of available habitats within the study area and within each annual 95% FK home range. Roads, railways, and water were excluded from use and selection analyses as these land classes are not reasonable habitat types or have dimensions smaller than our radio tracking error. Commercial and industrial areas were combined for analysis due to the limited availability of industrial lands within the study area (Table I). The remaining five land classes were identified within the study area as available habitat classes. Random use of the study area by coyotes would produce similar proportions to the available habitat amounts. I tested for overall coyote habitat use departing from expected random use of the study area using Two-Way ANOVA. I then tested each habitat class individually to identify which habitats were used dissimilarly to random expectation with t-tests. I ranked use of land classes by the difference between mean coyote home range use and study area availability. Habitat use was further investigated using compositional analysis to reveal selection relative to all available habitats (Aebischer et al. 1993). Compositional analysis is similar to the principles of MANOVA. This method uses the animal as the experimental unit, categorical data (i.e., habitat), and assumes multivariate normality. Based on the unit-sum constraint, that all proportions of available habitats sum to one composition, this method accounts for the use of one habitat influencing use of another by comparing log-ratios of habitats and tests this against random use. The result leads to a ranking matrix that indicates a relative selection order of habitat types. Following Aibescher et al. (1993), I calculated the differences of log-ratios between habitats using residential as the reference habitat. I tested for overall selection

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using Wilk’s lambda statistic (MANOVA) against the hypothesis (Ho: d = 0). If the null hypothesis was rejected, further tests of each habitat class were tested with t-tests (alpha = 0.05) to reveal specific habitat use deviating from random use. I then constructed simplified ranking matrices to examine relative selection order (Aibescher et al. 1993). Johnson’s third-order selection: I classified each coyote location by the habitat type present at each position and calculated the percent of locations within each land class. I used Two-Way ANOVA to test for departure from random use between the proportions of locations in each habitat compared to expected use of habitat proportions in each home range. I tested each land class individually to identify where coyote use departed from random use of the home range by paired t-tests. I then ranked use of land classes by the mean difference between coyote use and random use within home range. Using compositional analysis, I investigated proportional use by telemetry locations relative to use of other habitats within home range compared to random use of the home range (Aibescher et al. 1993). I tested for overall selection using Wilk’s lambda statistic (MANOVA) against the hypothesis (Ho: d = 0). If the null hypothesis was rejected, further tests of individual habitat classes were tested by t-tests alpha level = 0.05 to reveal specific habitat use departing from random use. I then constructed simplified ranking matrices to examine relative selection order. RESULTS Trapping and Animal Handling I captured a total of 21 coyotes within the study area (Table IV). Snares captured 14 animals (8F, 6M), while foothold traps captured 6 (3 F, 3 M). One additional female coyote was pulled from a water retention pond with steep ice-covered banks on the

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property of Albany International Airport. She was held in captivity for 4 days by the NYS DEC and exhibited good health; therefore she was radio collared and released near the recovery site. She was later radio tracked for 12 months near the airport. I included her data for analysis despite capture method, and her failure to return to the airport. Seven adult females and 3 adult males averaged 13.44 (10.15 – 16.0) kg and 17.35 (15.5 – 18.75) kg respectively.

Table IV. Number of female and male coyotes trapped, by age class, in the suburban Albany Pine Bush study area, 7 April 2001–30 November 2003.

Sex Female Male Total

1 Year 2 3 5

>2 Years 7 3 10

Total 12 9 21

Radio Telemetry I radio tracked 21 coyotes for a total of 1781 locations (Table V), collecting >30 locations for 14 animals (8 F, 6 M). Only one female (c06) was tracked 26 months and a second female (c24) was tracked for 14 months. The remaining animals were tracked ≤1 year. Table V. Percentages of active and inactive radio-telemetry locations by diurnal period for radio-tracked coyotes in the Albany Pine Bush study area, 7 April 2001–18 December 2003.

Diurnal period Day Night Total

Activity Reading Active % Inactive % 12 40 31 17 43 57

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Total % 52 48 100

n 920 861 1781

Home Range Analysis I generated 17 annual home ranges for 14 resident coyotes using FK and MCP (Table VI). One male coyote (c29) abandoned a well-defined home range (n = 102 locations collected during 5 months) and joined a neighboring female two weeks after the death of her mate. I collected 65 locations for his second home range over 4 months, which approximated the female’s home range size. Both home ranges reached an asymptote at 30 locations for the first, and 50 locations for the second measurement. I used the male’s 2 distinct home ranges within one year for annual home range analysis. I generated 3 annual home ranges for the female coyote that was tracked for 26 months and used each for analyses. Winter seasonal home ranges were excluded from comparisons as only one animal had >30 locations during this period. Table VI. Coyote home range sizes from the Albany Pine Bush study area, April 7, 2001–December 18, 2003.

N

Fixed Kernel 95% 50% Mean SE Mean SE

Annual Total Female Male

17 10 7

6.81 5.83 8.22

1.07 1.30 1.80

1.39 1.14 1.76

0.22 0.26 0.36

5.74 5.17 6.58

0.89 1.09 1.53

1.12 0.98 1.32

0.21 0.28 0.34

Age Class Pup Yearling Adult

3 5 9

2.49 9.57 6.72

0.72 1.97 1.39

0.57 2.14 1.26

0.20 0.37 0.26

1.85 6.45 6.66

0.24 1.32 1.31

0.36 1.58 1.12

0.11 0.37 0.31

Seasonal Spring Summer Fall Winter

6 8 9 1

2.24 4.36 5.18 0.43

0.78 1.23 1.53 --

0.43 0.89 1.06 0.10

0.13 0.03 0.35 --

2.94 4.97 4.11 0.67

0.79 1.33 1.09 --

0.20 0.99 0.88 0.13

0.06 0.34 0.24 --

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Minimum Convex Polygon 95% 50% Mean SE Mean SE

An adult female (c24) maintained the smallest annual 95% FK home range of 0.84 km2 while an adult male (c29) had the largest annual 95% FK home range (14.97 km2). There were no differences between the size of female and male annual 95% home ranges (FK: t15 = 1.104, P = 0.287; MCP: t15 = 0.772, P = 0.452). Female and male annual core home ranges were similar in size for 50% FK (t15 = 1.448, P = 0.168) and 50% MCP (t15 = 0.776, P = 0.450). Home range size for pups and yearlings was similar to adults (95% FK: t15 = 0.471, P = 0.664; 95% MCP: t15 = 1.342, P = 0.200; 50% FK: t15 = 0.210, P = 0.836; 50% MCP: 50% t15 = 1.073, P = 0.300). Spring, Summer, and Fall seasonal home ranges were al similar in size (95% FK: F2,20 = 1.169, P = 0.331; 95% MCP: F2,20 = 0.692, P = 0.512; 50% FK: F2,20 = 0.985, P = 0.391; 50% MCP: F2,20 = 2.282, P = 0.128). Habitat Use and Selection Johnson’s second-order selection: Natural habitat was the largest portion (63.2%) of coyote home ranges followed by similar proportions of agricultural, open, commercial, and residential lands (Figure 14). Coyote home range selection was not random within the study area (F9 = 234.9, P < 0.001) primarily because of the selection for natural habitat (t15 = 7.456, P < 0.001) and avoidance of both commercial habitat (t15 = 4.293, P = 0.001) and residential habitat (t15 = 8.838, P < 0.001). Agricultural (t15 = 1.678, P = 0.114) and open (t15 = 1.060, P = 0.306) habitats were used similar to random use. Ranking habitat classes by the mean difference between home range use and random use of the study area indicates coyotes’ preferred natural habitat within the study area (Figure 15).

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70.0

60.0

Coyote Use Study Area Availability

Percent

50.0

40.0

30.0

20.0

10.0

0.0 Natural

Open

Agriculture

Commercial

Residential

Figure 14. Second-order coyote home-range (n = 17) habitat use vs. study area availability (percent ± se) of five identified habitats within the Albany Pine Bush study area, 7 April 2001 – 18 December 2003.

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25.0 20.0 15.0

Percent

10.0 5.0 0.0 -5.0 -10.0 -15.0 -20.0

Natural

Open

Agriculture

Commercial

Residential

Figure 15. Second-order mean difference (percent ± se) between coyote home range (n = 17) use and available habitats within the Albany pine bush study area, 7 April 2001–18 December 2003.

Compositional analysis also revealed that home range selection was not random within the study area (Wilk’s lambda = 0.758, F3,16 = 6.793, P < 0.001; Figure 16). At this selection scale, the ranking matrix indicated that coyotes primarily selected for natural habitat followed by agricultural, open, commercial, and least for residential habitat (Table VII). The ranking matrix indicates that natural habitat was used in the greatest proportion in the study area and selected greater than open, commercial and residential habitats. Agricultural habitat was used less but selected statistically equal to natural habitat in coyote home ranges.

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3

2 Coyote Use Study Area

Log-ratios

1

0

-1

-2

-3

-4 Natural

Open

Agriculture

Commercial

Figure 16. Second-order log-ratios of compositional habitat analysis for coyote home range (n = 17) habitat use vs. study area availability referenced to residential habitat, 7 April 2001–18 December 2003.

Table VII. Second-order matrix ranking for compositional analysis: simplified matrix ranking of coyote home range selection within the study area of 5 habitat classes examined by compositional analysis within the Albany pine bush study area, 7 April 2001–18 December 2003. Triple signs represent significant differences (t-test, P = 0.05); single signs represent non-significant results (t-test, P > 0.05). Single signs indicate greater/lesser proportional use, though not significant, relative to the comparative (columns) habitats. Comparison land use Land use Ag Com For Opn Res Rank Ag 3 + – + +++ Com 1 – ––– ––– + For1 4 + +++ +++ +++ Opn 2 – +++ ––– +++ Res 0 +++ – ––– ––– * For = Forest, Opn = Open, Com = Commercial, Res = Residential, Ag = Agriculture 1 Example: Forest was used more, yet not significantly more, than Agriculture.

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Johnson’s third-order selection: The locations of individual coyotes were not distributed randomly in the habitats available within their respective home ranges (F9 = 184.6, P < 0.001; Figure 17). Coyotes used natural habitat greater than expected (t15 = 6.284, P < 0.001) as an average of 74.5% of a coyote’s locations were within natural areas. The remaining habitats were each used 0.05). Single signs indicate greater/lesser proportional use, though not significant, relative to the comparative (columns) habitats. Comparison land use Land use Ag Com For Opn Res Rank Ag 2 + ––– – + Com 1 – ––– ––– + 1 For 4 +++ +++ +++ +++ Opn 3 + +++ ––– +++ Res 0 – – ––– ––– * For = Forest, Opn = Open, Com = Commercial, Res = Residential, Ag = Agriculture 1 Example: Forest was selected more than all other habitat types within home ranges.

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DISCUSSION Radio Telemetry and Home Range Size Of the 21 trapped and radio-collared coyotes, I identified 14 as residents based on their use of small home ranges, and asymptotic area-observation curves. The 7 remaining individuals died or disappeared before collecting sufficient data for spatial analysis. Six of these 7 remaining coyotes died from human related mortalities and I lost radio-contact (i.e. long distance dispersal or radio failure) with one coyote before collecting sufficient spatial data (Chapter 2). I found no difference in home range size for comparisons between sexes, ages or seasons for both home range methods. Stability between seasonal home range sizes may partially be explained by territorial-behavior of preferred habitats. Given constant prey densities, resident coyotes maintain and defend similar pack territories over long periods of time (Gese 2001, Kitchen et al. 2000). Eastern coyotes typically have larger home ranges than found in this study. In the wilderness areas of the Adirondack Park, NY, coyotes inhabited larger MCP annual home ranges that averaged 139.8 km2 for females and 86.0 km2 for males (Brundige 1993). In the rural landscape west of Lake Champlain coyotes occupied non-denning seasonal MCP home ranges averaging (mean ± se) 14.3 ± 0.3 km2 for females and 18.9 ± 2.3 km2 for males (Kendrot 1998). Kendrot (1998) also found smaller denning home ranges for females (mean = 6.8 ± 5.2 km2) and males (mean = 7.2 ± 1.9 km2) that were larger than equivocal seasonal (spring) home ranges (mean = 2.94 ± 0.79 km2) documented in this study. In rural Vermont, along the east side of Lake Champlain coyotes also exhibited larger 95% MCP home ranges (mean = 16.4 ± 2.3 km2; Person 1988) than in the APB. Coyotes also used larger home ranges in an urbanized landscape of Cape Cod,

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Massachusetts in which males (39.1 ± 0.3 km2) used larger home ranges than females (23.6 ± 6.7 km2; Way et al. in press). Many studies in western North America report larger average home range areas in natural and rural areas for resident (range 10.8–15.1 km2) and transient coyotes (106.5– 204.0 km2) than found in this study (Atkinson and Shackleton 1991, Gese et al 1988, Roy and Dorrance 1985). In urban Tucson, AZ, 95% MCP home ranges averaged 12.6 ± 3.5 km2 (range 1.7–59.7 km2) for resident coyotes, while transients had larger home ranges from 58.3–180.2 km2 (Grinder and Krausman 2001b). Only one study by Riley et al. (2003) documented smaller average home ranges than in this study, in which urban coyotes in Southern California used home ranges averaging 2.84 ±0.66 km2 for females and 6.17 ± 1.59 km2 males. These two western studies of urban coyotes contrast each other in home range size, but more importantly, in coyote habitat use. Habitat Selection APB coyotes showed preference for natural habitat and avoidance of residential and commercial habitat at both selection-scales examined, and with both analytical techniques. Coyotes also selected agricultural habitat at the larger second-order, though the importance of agricultural land is diminished by the low proportional-availability within the study area. Coyotes exhibited stronger selection at the finer, third-order, “within home range” scale than the larger, second-order, “within study area” scale. Thus, it is probable that selection at the second-order may be more a byproduct of coyote selection at the third-order scale; home ranges are generated, at distance, around sets of individual locations and may be less sensitive to preferred habitats in general.

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The habitat-selection results of this study are very similar to those of Riley et al. (2003) in which coyote home ranges in southern California contained a larger proportion of natural areas (73%) than developed habitat (17.6%) and altered open habitats (9.1%). Though differences in vegetation types are great between Albany County, New York and southern California, these study areas were spatially similar in interspersion of natural, undeveloped habitats within human-altered habitats. In both sites, coyotes primarily inhabited natural habitats associated with developed lands. Coyotes in Cape Cod primarily used natural habitats for den sites and diurnal resting sites (26%), while using residential (26%) and altered (48%) habitats during activity periods (Way et al. 2004). Similarly, Quinn (1997) found coyotes in urban King County, Washington used large amounts of urban lands, although they preferred natural forest where available. Mixed vegetation areas, consisting of ≥50% non-native plant cover and ≤70% buildings and impervious surfaces, was the dominant habitat type in the study area (40%), as well as coyote home ranges (35%). Though coyotes adapt to using urban lands, they remain reliant on natural habitats. Riley et al. (2003) found that coyote home-range size increased with increasing use of non-natural habitats. In Tucson, AZ, coyotes had large home ranges and used more developed lands than in southern California and Albany County, NY (Grinder and Krausman 2001b, Riley et al. 2003). Grinder and Krausman (2001b) identified natural, park and residential as the primary habitats within their study area, while natural habitat included low density housing areas, state and federal parks, and croplands with privately owned natural open space. By definition, this habitat class is not directly comparable to other studies that mapped natural areas at higher resolution. The actual percent of truly

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natural habitat is not known within the study area and the actual use of human-modified lands may be underrepresented in Tucson, AZ. More importantly, coyotes in Tucson, AZ primarily used non-natural habitats in large home ranges, and accordingly, support the findings of Riley et al. (2003). Management Implications Small home range size with a low degree of urban association in the APB coyote population has important management implications. Coyotes are increasingly causing nuisance issues in southern California (e.g. killing pets, and attacking people, Timm et al. 2004) despite the limited use of developed lands found by Riley et al. (2003). Nuisance issues may result from specific problematic animals that habituate to people and are not representative of the local population as a whole (Riley et al. 2003, Timm et al. 2004). Timm et al. (2004) describe the trend in which coyotes become increasingly habituated to people in southern California, which gives rise to problematic coyotes. The initial step involves coyotes incorporating residential areas into their home ranges during nocturnal movements and foraging activities followed by an escalation in use during daylight periods. My spatial data show that coyotes in Albany County are tolerant of living within urbanized landscapes, but primarily remain in natural areas. This suggests that their ecological interactions remain focused on natural prey and food items, additionally confirmed by fecal-based dietary studies (Bogan and Kays, unpublished data). They have not began to incorporate significant amounts of developed lands into home ranges for movement and foraging excursions, and thus do not pose an obvious risk to human interests.

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Increased urban association increases home range size (Riley et al. 2003) and elevates a coyote’s potential to become a nuisance (Timm et al. 2004) resulting also in greater conspicuous coyote movements across large urbanized areas. As such, this may prove beneficial in identifying problematic coyotes for removal and allow wildlife managers to follow individuals returning to natural areas where removal techniques can be practiced. Ecological Implications While habitat fragmentation and isolation caused by human development has been shown to exclude large carnivores (Crooks 2002), coyotes maintained a population throughout the study that preferred natural habitat to human-developed lands. Loss of large predators has been implicated with unnatural increases in mesopredator and domestic cat populations, which in turn may cause a decline in native small mammal and bird populations (Crooks and Soule 1999). DeWan (2002) failed to find a difference in small mammal abundance between large and small natural patches in the Albany Pine Bush Preserve, suggesting that there was not a differential predator-effect between sites. As coyotes primarily used natural habitat, potential ecological effects of mesopredators may impact small mammal and bird populations greater in the surrounding humandeveloped lands than in natural habitats. Radio-tracking data from indoor/outdoor domestic cats directly bordering the Albany Pine Bush Preserve seldom ventured far from residential areas, remaining mostly in their own yard, or along the edge of natural habitat fragments (Kays and DeWan 2004). These data suggest, despite their low survival, there are sufficient coyotes in the area to prevent mesopredator release, thus contributing to an ecological balance in this suburban natural area.

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Appendix A. Annual Home Ranges The following are fixed kernel (FK) and minimum convex polygon (MCP) annual home range estimates (n = 17) for 14 coyotes from the Albany Pine Bush study, 1 April 2001– 18 December 2003. Radio telemetry and home range estimation methods are described in Chapter 2. Figures are labeled by coyote identification number and listed in chronological order in which individuals were added to the study.

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Coyote c01: Adult female with 58 locations collected during year 1.

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Coyote c02: Adult female with 116 locations collected during year 1.

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Coyote c03: Juvenile male with 155 locations collected during year 1.

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Coyote c06: Adult female with 58 locations collected during year 1.

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Coyote c06: Adult female with 93 locations collected during year 2.

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Coyote c06: Adult female with 185 locations collected during year 3.

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Coyote c14: Pup female with 32 locations collected during year 1.

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Coyote c22: Adult male with 92 locations collected during year 2.

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Coyote c24: Adult female with 195 locations collected during year 3.

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Coyote c27: Adult male with 101 locations collected during year 3.

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Coyote c29: Yearling male with 102 locations collected during May–September of year 3.

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Coyote c29: Yearling male with 102 locations collected during September–December of year 3.

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Coyote c34: Adult female with 134 locations collected during year 3.

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Coyote c36: Adult female with 50 locations collected during year 3.

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Coyote c37: Pup male with 89 locations collected during year 3.

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Coyote c38: Pup male with 88 locations collected during year 3.

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Coyote c39: Pup female with 66 locations collected during year 3.

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