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
ii
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.
iii
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.
v
TABLE OF CONTENTS
ABSTRACT ………………………………………………………………
ii
ACKNOWLEDGEMENTS ………………………………………………
v
LIST OF TABLES
viii
………………………………………………………
LIST OF FIGURES ………………………………………………………
ix
Chapter One:
Study Area
………………………………………
1
Chapter Two:
Eastern Coyote (Canis latrans) Survivorship
and Mortality Causes in a Suburban Landscape …..
6
………………………………………………………
8
METHODS
Trapping and Radio Telemetry
Survival and Mortality
………………………
8
………………………………
9
Mortality Correlates ………………………………………
Road crossing rates ………………………………
Roads in home ranges ………………………………
Home range habitat composition
………………
RESULTS
………………………………………………………
Trapping and Radio Telemetry
11
11
12
12
13
………………………
13
………………………………
14
Mortality Correlates ………………………………………
Road crossing rates ………………………………
Roads in home ranges ………………………………
Home range habitat composition
………………
26
26
27
27
Survival and Mortality
DISCUSSION
………………………………………………
27
Management Implications and Conclusions ………………
31
Chapter Three:
METHODS
Eastern Coyote (Canis latrans) Home Range and
Habitat Selection in a Suburban Landscape ………
33
………………………………………………………
36
vi
Trapping and Animal Handling
………………………
36
………………………………………
37
………………………………
39
Habitat Use and Selection
………………………………
Johnson’s second-order selection
………………
Johnson’s third-order selection
………………
40
41
42
Radio Telemetry
Home Range Analysis
RESULTS
………………………………………………………
Trapping and Animal Handling
………………………
42
………………………………………
43
………………………………
44
Habitat Use and Selection
………………………………
Johnson’s second-order selection
………………
Johnson’s third-order selection
………………
45
45
49
Radio Telemetry
Home Range Analysis
DISCUSSION
………………………………………………
Radio Telemetry and Home Range Size
Habitat Selection
52
………………
52
………………………………………
53
Management Implications
………………………………
55
Ecological Implications
………………………………
56
………………………………………………………
57
………………………………………………………………
66
BIBLIOGRAPHY
APPENDIX
42
vii
LIST OF TABLES
CHAPTER ONE
Table I.
Categorical land use classifications of 9 identified land-uses
within the suburban Albany Pine Bush study area ………
5
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 ….
18
26
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 ………
viii
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 ………………………
3
4
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
………
14
15
16
17
19
20
21
22
23
24
25
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 ……
ix
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 =
1
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.
2
Figure 1. Map of the Albany Pine Bush Preserve boundaries and effective study area
positioned between urban and rural lands.
3
Figure 2. Map of the effective Albany Pine Bush study area showing the distribution of
available habitat.
4
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
6
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).
7
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,
8
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
9
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
10
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 <40 mph speed limits; 4) TOTAL = all roads within the study area. Interstate roads
typically have high traffic volume and velocity and may pose a barrier to coyote
movement. FSC roads typically have surges of traffic volume and moderate traffic
velocities, thus posing a risk to coyotes attempting to cross. All local roads (LOC) have
infrequent traffic and low speeds, thus present the least threat to a crossing coyote. The
11
TOTAL group was used to determine if an overall risk effect existed for coyotes crossing
all roads within the study area.
Road crossing rates were determined by the number of crosses/hr of radio
tracking. I compared road-crossing rates between 3 coyote groupings: Vehicle Killed,
Shot, and Other (alive or killed from other mortality sources). I tested between coyote
groups using One-Way ANOVA and Tukey’s HSD post-hoc test with alpha = 0.05 for
each road category. Under the hypothesis that Vehicle Killed coyotes crossed roads
greater than all other coyotes, I used 1-tailed t-tests to test between crossing rates of
Vehicle Killed coyotes against the combined rates of Shot and All Other coyotes. Tests
were considered significant at P ≤ 0.05.
Roads in home ranges: To determine if road density within home ranges impacted
coyote survival, I calculated the total length of roads within each coyote’s home range
(95% FK; Chapter 3). All roads were grouped as mentioned above. I tested between 3
coyote groupings: Vehicle Killed, Shot, and Other using One-Way ANOVA and Tukey’s
HSD post-hoc test (alpha = 0.05) for each road category.
Home range habitat composition: Natural, open, agriculture, commercial, and
residential habitats were identified within the study site (Chapter 3). I made comparisons
of area measures for these 5 available habitats within each 95% FK home range (Chapter
3). For each habitat type I measured total area, mean patch size, and maximum patch size
and tested these measures between 3 coyote groups (Vehicle Killed, Shot, and Other)
using One-Way ANOVA and Tukey’s HSD post-hoc test with alpha = 0.05.
12
RESULTS
Trapping and Radio Telemetry
I captured and radio tracked 21 coyotes for a total of 1781 locations over three
years. Fifty percent of 12 captured females showed signs of lactation. Six of 7 adults
had enlarged dark teats indicating they had reproduced and given birth. Three pups, 2
yearlings, and 1 adult female did not lactate during the study; indicated by small (1–2 mm
in length) white to pink nipples. Individual coyotes were radio collared and monitored
for 4–784 days (median = 105, mean = 175 ± 182 [SD]). One female (c06) was tracked
26 months and a second female (c24) was tracked for 14 months. The remaining animals
were tracked <1 year (Figure 3). One yearling male (c03) coyote’s signal disappeared
shortly after the transmitter began functioning improperly and was never relocated or
trapped again. A female pup (c20) disappeared immediately after being trapped and was
never relocated during the study. A third coyote (male pup, c37) dispersed from the
study site and was recovered after being shot ca. 285 km north of his natal home range.
These 3 animals were censored from the dataset on the date of the last confirmed
radiolocation within the study area. Three other coyotes remained alive at the end of the
study and were censored accordingly.
13
20
*
R
Female
Male
D
S
INDIVIDUAL COYOTES
*
15
*
R
R
S
M
10
R
S
S
R
S
5
R
P
?
R
S
0
1
9
17
25
33
41
49
57
65
73
81
89
97 105 113 121 129 137 145 153
3 YEARS (WEEKS)
Figure 3. Radio-tracking periods, beginning at the time of capture, for individual coyotes
inhabiting the Albany Pine Bush study area, 1 April 2001–31 March 2004 (Symbols
represent fate of individual coyotes: “S” = shot, “R” = road-killed, “?” =
unknown/missing, “P” = poisoned, “M” = mange, “D” = Dispersed, and “*” = alive at
end of study).
Survival and Mortality
Annual survival rates and functions (Figure 4, 5, 6) were similar for all 3 years
(Table II) and were pooled giving a final survival rate of 0.23 ± 0.14 for dependent
individuals (Figure 7) and 0.20 ± 0.14 for independent individuals (Figure 8). I used the
independent dataset for the remainder of the survival and mortality estimates. Survival
rates and functions between sexes (Figure 9), age classes (Figure 10) capture method
(Figure 11) did not differ. Coyotes with home ranges inside and outside of the preserve
boundaries (Figure 12) had similar survival rates and functions. Seasonal survival rates
were similar for Spring = 0.68 ± 0.28, Summer = 0.78 ± 0.23, Fall = 0.58 ± 0.25, and
Winter = 0.66 ± 0.31 (Figure 13).
14
1.00
0.90
0.80
SURVIVLA 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 4. Year 1: annual survival function (±95% CI) for radio-tracked coyotes (n = 7)
inhabiting the Albany Pine Bush study area, 1 April 2001–31 March 2002.
15
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 5. Year 2: annual survival function (±95% CI) for radio-tracked coyotes (n = 8)
inhabiting the Albany Pine Bush study area, 1 April 2002–31 March 2003.
16
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 6. Year 3: annual survival function (±95% CI) for radio-tracked coyotes (n = 10)
inhabiting the Albany Pine Bush study area, 1 April 2003–31 March 2004.
17
Table II. Results of paired comparisons between years for annual survival rates (Z-test)
and functions (X2) from the suburban Albany Pine Bush study area, 7 April 2001–31
March 2004.
Test result
between rates
Paired comparisons
Test result
between survival
functions
Year
Rate
Year
Rate
Zstatistic
P
value
X2
P value
1
2
3
0.15 (0.19)
0.15 (0.19)
0.06 (0.07)
2
3
3
0.06 (0.19)
0.29 (0.28)
0.29 (0.28)
0.87
0.86
1.61
0.384
0.390
0.107
0.183
0.650
1.16
>0.05
>0.05
>0.05
18
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
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.
19
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.
20
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.
21
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.
22
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.
23
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
9
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.
24
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
13
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
25
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
26
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
27
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 <1.0 (approximately 0.66–0.85, depending on mean litter size)
indicating a declining population. If this declining rate were constant over multiple
years, as it appeared to be over the three years of this study, the local population would
require immigration from surrounding source-populations to maintain a presence.
Together, the two major mortality sources, vehicles (n = 7) and shooting (n = 6),
accounted for 86.7% of all coyote mortalities in this study. While each has been reported
as a major mortality factor elsewhere, no study has found them to sum up as severely
(Chamberlain 2001, Gese et al. 1989, Grinder and Krausman 2001a, Kamler and Gipson
2004, Riley et al. 2003, Roy and Dorrance 1985). When examined individually, these
factors were considerably harsh in the APB. Using only coyote mortalities from vehicles
and censoring all other coyote mortalities at the time of departure, the survival rate would
be approximately 0.47, still lower than many reported survival rates.
I found that coyotes that were eventually killed by vehicles crossed roads more
frequently than all other coyotes. This was not a result of greater road densities in their
home ranges, suggesting that this difference is caused more by behavioral traits of
individual coyotes than the specific landscape inhabited. In fact, shot coyotes had greater
28
lengths of FSC roads in their home ranges than vehicle killed, and other coyotes.
Similarly in southern California, Riley et al. (2003: 574) found no relationship between
coyote exposure to varying levels of urbanization and vehicle deaths of coyotes and
suggested “Animals most exposed to urban areas may also gain familiarity with roads
and develop the ability to safely navigate them.”
Coyotes killed by shooting had larger maximum and mean open habitat patch size
within their home range than all other coyotes that were not shot. Though there was not a
direct relationship to coyotes being shot in these patches, these measures may reveal
differences in habitat use that increase visual exposure of coyotes to people with firearms.
I failed to find a difference in residential habitat use between shot and all other coyotes.
There was no relationship to support that coyotes would more likely be shot in home
ranges encompassing less residential areas, as people might be more likely to shoot at a
coyote in areas with lower human density.
Study areas in southern California and APB, New York were spatially similar in
fragmentation of undeveloped habitat within human altered landscapes, although
differences in vegetative cover are great. Riley et al. (2003) found that southern
Californian coyote home ranges (n = 40) encompassed larger portions of natural areas
(73.0%) than developed habitat (17.6%) and altered open habitats (9.1%). Though
coyotes may have been associated with developed lands, they primarily inhabited natural,
undeveloped habitats (Riley et al. 2003). In APB, coyote home ranges were similar in
that the largest proportion was natural habitat (63.2%) and lesser proportions consisted of
developed habitat (18.0%, residential and commercial combined) and altered open
habitats (18.8%; Chapter 3). Despite the similarity in home range compositions, survival
29
rates were drastically higher in southern California. Comparing mortality causes, it
appears that these Californian coyotes were better at crossing roads without being killed,
and were exposed to less hunting pressure. Related to this, coyotes have been so
successful at habituating to urbanized lands in California that problematic coyotes are
frequently seen in residential areas, and occasionally prey on pets and attack people
(Timm et al. 2004).
Low APB coyote survivorship may be due to the recent arrival of coyotes to the
area. Riley et al. (2003) suggested that urban-exposed coyotes require a period of
familiarization to the challenges of living in an urbanized landscape before successfully
expanding into these areas. Locally, species inventories conducted in 1976 and 1989 did
not list coyotes as present in the Albany Pine Bush Preserve (Schneider et al. 1991).
Coyotes may not have been detected, perhaps due to low density, or have since
immigrated into the Preserve. High mortality from roads and hunting pressure may act to
slow this familiarization process by killing individuals before they learn how to survive
in suburbia, and regularly creates vacancies to be filled by naïve individuals from the
surrounding rural areas. Thus their recent arrival and the exceptionally harsh
environment may explain why APB coyotes do not posses the behavioral traits needed to
inhabit urbanized areas that are demonstrated in many western populations.
Eastern coyotes may also be limited in their ability to adapt to urban
environments by their genetics. Thurber and Peterson (1991) argued that large
northeastern coyotes, averaging approximately 5.5 kg larger than southwestern coyotes,
result from a phenotypic selection in response to greater prey abundance and diversity,
and larger prey size in northeastern ecosystems. Non-exclusively, larger eastern coyotes
30
may be due, in part, to introgression of wolf genes from hybridizations that occurred
during the expansion period (see Gommper 2002a). A recent unpublished report found
that 22% (n = 100) of coyotes sampled from Maine had >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 <1.0, such that
the area must require immigration to maintain the coyote population. Thus, suburban
Albany functions as an ecological sink and an ecological trap for coyotes. The
31
surrounding natural and rural areas appear to be a sufficient source population, as coyotes
were present in the area throughout the 3-year study.
While these data indicate a hazardous environment for eastern coyotes, local
human-residents may take solace. Urban coyote populations with high survival rates are
more likely to produce individual coyotes that become a nuisance or even dangerous to
people. This apparently is the result of habituated animals, occasionally fed by a few
people, which regularly use human-dominated landscapes and come in close contact with
people (Riley et al. 2003, Way et al. 2004, in press). One solution, used by wildlife
managers, to curb coyote conflict is to reduce their habituation by lethal removal utilizing
nuisance wildlife-trappers, extending trapping and hunting seasons, and surgical
sterilization (Bromley and Gese 2001, Sacks et al. 1999, Wagner and Conover 1999).
The Albany area apparently has sufficient coyote hunters (illegal and legal) to
eliminate bold coyotes and prevent nuisance issues. This culling, in addition to urbanrelated mortality sources, reduced annual survival below the limits of a self-sustaining
coyote population, requiring immigration to maintain a local population. This high level
of mortality may limit coyotes from gaining familiarity in suburban areas, such as roadcrossing skills, and using human-dominated landscapes. This, in turn, maintains coyote
avoidance of people while keeping coyote ecology focused to natural lands and natural
foods.
32
Eastern Coyote (Canis latrans) Home Range and Habitat Selection
in a Suburban Landscape
Chapter Three
Anthropogenic changes to the landscape of northeastern USA have been intensive
and wide ranging. Throughout the 1800’s, people hunted and trapped wolves (Canis
lupus) and mountain lions (Felis concolor) to eliminate these populations. Concurrently,
the region’s forests were intensively logged and converted into agricultural production
(Foster et al. 2004, Rhodes 1991). At the turn of the 20th century, nearly all of New York
State had been denuded of historical forests (Foster et al. 2004) as well larger native
predators (Nowak 2002). Soon after, much of the land was quickly abandoned for more
arable lands of the Midwest and natural succession reestablished forested lands
throughout much of northeastern USA (Foster et al. 2004, Klyza 2001, McKibben 2000).
However, trophic roles remain unbalanced, as wolves and mountain lions have not
reestablished populations.
Wolves in particular are sensitive to anthropogenic change over large scales as
road densities, moderately fragmented natural lands, and reduced prey densities prevent
their natural return to an area (Mladenoff et al. 1995). Their absence can affect entire
ecosystems as herbivore populations may increase in density, or alter their behavior, and
negatively impact the floral composition of a landscape through over browsing (Ripple
and Beschta 2003). In the northeast, however, in the absence of two larger native
predators, coyotes have exploited the vacant top-predator niche.
Historical evidence indicates that coyotes were once limited eastwardly in
geographic range to the central plains of North America (Gommper 2002b, Nowak 2002).
33
Following the anthropogenic changes described above, coyotes expanded their range.
Coyotes reached the northeast in the early 1900’s indicated by rare observations in
southeastern Canada and later New York (Severinghaus 1974a). Initial reports of coyotes
occurred in northern New York near the St. Lawrence River valley in the 1940’s (Fener
2001, Severinghaus 1974a). Coyote populations increased rapidly, and by the early
1970’s had spread across all of New York State with the exception of Long Island and
Manhattan. Their movement into urban areas apparently lagged by a few years, for
example, coyotes were not included in species inventories conducted in 1976 and 1989 in
the Albany Pine Bush Preserve, (Schneider et al. 1991), the site of this study. The
northeastern coyote population now inhabits a diverse range of habitats from remote
wilderness (Brundige 1993), rural (Person and Hirth 1991) to urbanized landscapes (Way
et al. 2001). Though the scale of anthropogenic change in the northeast appears to limit
wolves from re-establishing populations, there is little information if eastern coyote
populations are spatially limited in humanized landscapes.
Many studies have been conducted on coyotes in western North America within
natural and rural habitats (Andelt 1985, Atkinson and Shackleton 1991, Gese 2001, Roy
and Dorrance 1985, Thurber and Peterson 1991), and urbanized areas (Grinder and
Krausman 2001b, Quinn 1997, Riley et al. 2003). Grinder and Krausman (2001b) found
that coyotes inhabiting Tucson, Arizona incorporated larger proportions of residential and
other developed areas than natural areas. Coyote use of residential areas can lead to
negative interactions with humans. Timm et al. (2004) reported an increasing trend from
1978 to 2003 in the frequency of coyotes negatively interacting with and attacking people
and pets in California for a total of 89 incidents. During this time, urban coyotes became
34
increasingly habituated to people, leading to the eventual hazards and nuisance issues in
the west (Timm et al. 2004).
Coyotes in the northeast exhibit larger body sizes and weights than coyotes
inhabiting western states (see Gommper 2002a, Thurber and Peterson 1991). This may
be an adaptation to diverse foods items and preying on white-tailed deer (Odocoileus
virginianus) in northeastern ecosystems (Brundige 1993, Long et al. 1998, Thurber and
Peterson 1991) and may be related to hybridization with wolves during colonization
(Wilson et al. 2004). How this change in ecology, and possibly genetics, affects their
space-use or ability to adapt to humanized landscapes remains poorly understood as few
studies have examined coyote ecology in the northeast (Gommper 2002a).
Although coyotes now inhabit most northeastern suburban landscapes, little is
known about their ecology or space use within these environments. One study in a
developed area of Cape Cod, Massachusetts found coyotes to frequently use human
modified lands at night while preferring natural lands for daytime resting sites and
denning (Way et al. 2001, 2004). This suggests that eastern coyotes have begun to
habituate to people and may eventually lead to coyotes attacking pets and people as in
California (Timm et al. 2004). The potential of coyotes to negatively impact people in
other eastern suburban environments has not been examined.
I studied eastern coyotes in a suburban landscape of Albany County, New York
using radio telemetry to address the status of coyotes as a new predator in suburban
landscapes, and the potential to conflict with humans in developed areas of northeastern
USA. The objective of this study was to obtain estimates of annual and seasonal home
35
range size and to quantify annual habitat use and selection within this fragmented
suburban landscape.
METHODS
Trapping and Animal Handling
I trapped coyotes using modified padded foothold traps (No. 3 Victor Softcatch
Coilspring) and non-locking neck snares. Modifications to foothold traps included, 2
additional swivel points (total = 3), double stake anchor plates, and a shock spring
attached along the anchor chain. Neck snares were made using hardware available from
commercial trapping companies. I fastened preprinted metal identification tags to the
anchor chains and snare anchors in accordance with New York State trapping regulations.
Prior to deploying traps, all traps were boiled once in clean water, and once in a
commercially available trap die mixed with water. All foothold traps were waxed to
reduce oxidation, lubricate traps, and to reduce human scent deposited while handling
traps.
I trapped in areas exhibiting coyote sign, and avoided trapping in locations with
high human and pet activities. In accordance with Albany Pine Bush Commission
regulations, traps set within the Preserve were positioned greater than 15 m (50 feet) from
all official trails to minimize negative effects on people and pets. All traps were checked
once per day beginning at 0700 hr, and occasionally prior to dusk when trapping near
moderate human and pet activity areas. Upon detecting a captured coyote, I physically
restrained the animal using a snare pole and Y-stick (a sapling tree cut at 1.5 m long with
a natural branching at one end. Each branch of the “Y” was cut at 8 cm long.). During
the first half of the project, I chemically immobilized trapped coyotes with Telazol® (5.0
36
mg/kg body mass) and monitored vital signs (heart and respiration rate, body
temperature, and capillary refill) at the time of onset and each 5 minute interval after until
initial awakening. When additional field assistants were available, I typically physically
immobilized coyotes with muzzles (ace bandages) and leg hobbles (rope) for the duration
of the handling process. Once safely immobilized, coyotes were immediately fitted with
a radio collar equipped with 3 signal pulse rates: inactivity, activity, and mortality (mort.
>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 (<1 yr),
yearlings (≥1 yr), or adults (≥2 yrs) by tooth wear (Gier 1968). All coyotes were
assigned identification numbers for record keeping. The first 7 coyotes trapped were not
ear tagged while all coyotes trapped after June 2002 received plastic sheep-sized
livestock eat tags. Lastly, I examined all animals for general health, external parasite
loads, wounds and scars, and other uniquely identifying traits. Chemically immobilized
coyotes were held in large cage traps until fully recovered from the drug effects. All
animals were released promptly at the trap site and relocated the following day to detect
possible trap mortalities. This study was conducted in accordance with the approved
SUNY Albany IUCAC protocol #03-05.
Radio Telemetry
I radio-tracked coyotes from 7 April 2001–18 December 2003. Coyotes were
remotely located by triangulation using a vehicle mounted 4-element Yagi antenna
system. I triangulated from a set of telemetry stations, with previously GPS measured
coordinates, along roads and access ways as near as possible to the coyote to improve
triangulation error. Coyotes were typically located once per diurnal period five days per
37
week after morning trap checks and before dusk. Because coyotes were mostly active at
night, I collected 4–5 successive locations at ≥1 hr intervals for groups of coyotes either
north or south of I-90. Bearings for each location were collected within a maximum of
20 minutes, though typically ≤12 minutes. I considered successive nocturnal locations as
biologically independent as the sampling interval provided sufficient time for coyotes to
move to any section of their home ranges (Lair 1987; McNay et al. 1994). Coyote
movements between successive locations collected at 60–120 minute intervals ranged
from 0–4612 m for females, and 0–4156 m for males. Collecting successive locations
systematically during activity periods has been shown to increase the biological relevance
of home range estimates (De Solla et al. 1999).
I calculated precision of bearings to be ±3.5° based on 25 bearings to each of 5
test collars. Linear distance from estimated locations to the known transmitter locations
averaged 102.7 m ± 49.0 SD. I used Locate II (Nams 1990) to analyze all triangulations
using ≥2 bearings intersecting from 45–135 degrees. Mean error polygon size was 3.86
ha (0–100 ha). Locations with error polygons ≤100 ha were used for analysis to prevent
under representation of coyote habitat use of interior natural patches.
I defined the study year as beginning on 1 April and ending on 31 March of the
following calendar year. Each year was divided 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 beginning of the year and Spring season was synchronized to
the apparent local variation in time of parturition.
38
Home Range Analysis
Coyote locations were imported and analyzed in ArcView GIS (ESRI) to generate
home ranges using the Home Range Extension (HRE; Blue Sky Telemetry
http://www.blueskytelemetry.com/hre.asp). I selected the fixed kernel (FK) home range
(Worton 1989) based on its non parametric technique of modeling an animal’s home
range utilization distribution, freedom from the assumption of bivariate normal
distributions of locations, and the ability to accurately reproduce known hypothetical
home range utilization distributions (Seamen and Powell 1996, Worton 1989). Kernel
methods are also capable of identifying multiple activity centers (Worton 1989), which is
a desirable quality in a suburban landscape where all land classes may not be used
evenly. FK home ranges were generated using a 70 × 70 grid and the least-squares crossvalidation (lscv) bandwidth (Worton 1995, Seaman and Powell 1996, Seaman et al.
1999). I selected the minimum convex polygon (MCP) method (Mohr 1947) as a
secondary home range estimate for general comparisons to other studies due to its
continued use in the literature.
I used 100% of each animal’s locations for home range analysis with the
exception of one adult female (c02). The female was located 2.37 km from her home
range of 7 months; therefore I excluded those 4 locations from analyses. I calculated
95% and 50% contours for MCP and FK annual home ranges. I constructed area
observation curves (Odum and Kuenzler 1955) for the FK method, using the respective
annual home range bandwidth, and MCP method to determine if home range size
approached an asymptote with each addition of 10 locations. Coyotes with area
observation curves approaching an asymptote, or were radio tracked >3 seasons were
39
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.
40
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
41
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
42
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
<12 Months
3
3
6
Age class
>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
43
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
--
44
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).
45
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.
46
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.
47
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.
48
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 <11%. Coyotes used agriculture (t15 =
0.515, P = 0.614) and open (t15 = 1.516 P = 0.150) habitats similar to random use while
commercial (t15 = 4.727, P < 0.001) and residential (t15 = 3.957, P = 0.001) habitats were
used less than expected. Ranking habitats by the mean difference between coyote
locations and expected random use of habitats indicates coyote’s preferred natural habitat
within home ranges (Figure 18).
90.0
80.0
Coyote Use of Home Range
Study Area Availability
70.0
Percent
60.0
50.0
40.0
30.0
20.0
10.0
0.0
Natural
Open
Agriculture
Commercial
Residential
Figure 17. Third-order coyote habitat use (% locations ± se) vs. home range availability
(n = 17) of five identified habitats within the Albany Pine Bush study area, 7 April 2001
– 18 December 2003.
49
15.0
Difference
10.0
5.0
0.0
-5.0
-10.0
Natural
Open
Agriculture
Commercial
Residential
Figure 18. Third-order mean difference (percent ± se) between coyote use (% locations
± se ) and available habitats within home ranges (n = 17) in the Albany pine bush study
area, 7 April 2001–18 December 2003.
At this scale, compositional analysis confirmed that habitat use within home
ranges was not random (Wilk’s lambda = 0.574, F3,16 = 6.715, P < 0.001) (Figure 19).
Natural habitat was used in the greatest proportion within home ranges and selected
significantly more than all other available habitats (Table VIII).
50
4
3
Coyote Use of Home Range
Home Range Availability
Log-ratios
2
1
0
-1
-2
-3
Natural
Open
Agriculture
Commercial
Figure 19. Third-order log-ratios of compositional habitat analysis for coyote habitat-use
within home ranges (n = 17) vs. home range availability referenced to residential habitat,
7 April 2001–18 December 2003.
Table VIII. Third-order matrix ranking for compositional analysis: simplified matrix
ranking of coyote habitat selection within home ranges 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
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.
51
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,
52
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.
53
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
54
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.
55
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.
56
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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.
67
Coyote c02: Adult female with 116 locations collected during year 1.
68
Coyote c03: Juvenile male with 155 locations collected during year 1.
69
Coyote c06: Adult female with 58 locations collected during year 1.
70
Coyote c06: Adult female with 93 locations collected during year 2.
71
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.
75
Coyote c27: Adult male with 101 locations collected during year 3.
76
Coyote c29: Yearling male with 102 locations collected during May–September of
year 3.
77
Coyote c29: Yearling male with 102 locations collected during September–December of
year 3.
78
Coyote c34: Adult female with 134 locations collected during year 3.
79
Coyote c36: Adult female with 50 locations collected during year 3.
80
Coyote c37: Pup male with 89 locations collected during year 3.
81
Coyote c38: Pup male with 88 locations collected during year 3.
82
Coyote c39: Pup female with 66 locations collected during year 3.
83