landscape ecology of eastern coyotes based ...people.cst.cmich.edu/gehri1tm/bio 691 - landscape...

Ecological Applications, 18(4), 2008, pp. 1014–1027� 2008 by the Ecological Society of America

LANDSCAPE ECOLOGY OF EASTERN COYOTES BASED ON LARGE-SCALEESTIMATES OF ABUNDANCE

ROLAND W. KAYS,1,4 MATTHEW E. GOMPPER,2 AND JUSTINA C. RAY3

1New York State Museum, CEC 3140, Albany, New York 12230 USA2Department of Fisheries and Wildlife Sciences, University of Missouri, Columbia, Missouri 65211-7240 USA

3Wildlife Conservation Society Canada, 720 Spadina Avenue #600, Toronto, Ontario M5S2T9 Canada

Abstract. Since their range expansion into eastern North America in the mid-1900s,coyotes (Canis latrans) have become the region’s top predator. Although widespread acrossthe region, coyote adaptation to eastern forests and use of the broader landscape are not wellunderstood. We studied the distribution and abundance of coyotes by collecting coyote fecesfrom 54 sites across a diversity of landscapes in and around the Adirondacks of northern NewYork. We then genotyped feces with microsatellites and found a close correlation between thenumber of detected individuals and the total number of scats at a site. We created habitatmodels predicting coyote abundance using multi-scale vegetation and landscape data andranked them with an information-theoretic model selection approach. These models allow usto reject the hypothesis that eastern forests are unsuitable habitat for coyotes as theirabundance was positively correlated with forest cover and negatively correlated with measuresof rural non-forest landscapes. However, measures of vegetation structure turned out to bebetter predictors of coyote abundance than generalized ‘‘forest vs. open’’ classification. Thebest supported models included those measures indicative of disturbed forest, especially moreopen canopies found in logged forests, and included natural edge habitats along water courses.These forest types are more productive than mature forests and presumably host more prey forcoyotes. A second model with only variables that could be mapped across the regionhighlighted the lower density of coyotes in areas with high human settlement, as well aspositive relationships with variables such as snowfall and lakes that may relate to increasednumbers and vulnerability of deer. The resulting map predicts coyote density to be highestalong the southwestern edge of the Adirondack State Park, including Tug Hill, and lowest inthe mature forests and more rural areas of the central and eastern Adirondacks. Together,these results support the need for a nuanced view of how eastern coyotes use forested habitats.

Key words: abundance; Adirondack State Park, New York; Canis latrans; eastern coyote; fecal DNA;landscape ecology; noninvasive survey.

INTRODUCTION

Ecologists increasingly recognize the importance of

studying animal abundance over broad spatial scales to

provide a better understanding of relationships between

animals, natural habitats, and human disturbance

(Baum et al. 2003, Karanth et al. 2004). Conducting

these landscape-scale studies is problematic, however,

for animals that are difficult to census. The abundances

of mammalian carnivores, for example, are rarely

estimated because of their low densities, use of large

areas, and shy nature. As a result, landscape models for

these species generally derive from small scale studies of

a few individuals, or broader scale categorical (presence–

absence) rather than continuous (population size) data

(Fuller et al. 2001, Zielinski et al. 2005). These small-

scale or coarse data may fail to capture many of the

nuances of habitat use or be unrepresentative of larger

areas. Here we show how noninvasive survey techniques

may be used to assay the abundance of coyotes (Canis

latrans) across a large geographic landscape, and how

these data can be used to compare diverse predictive

models of their landscape use.

The assemblage of mammalian predators in north-

eastern North America has undergone extensive reor-

ganization over the past 150 years (Ray 2000).

Following the extirpation of native large carnivores,

coyotes expanded their range into the Northeast, such

that over the past half-century the species has become

widespread across the region (Parker 1995, Gompper

2002). The ecological consequences of this range

expansion remain unclear, and this lack of clarity is

amplified by uncertainty regarding the fundamental

landscape ecology of coyotes. Given their origin in open

landscapes of central and western North America the

relative suitability of eastern forest habitat for coyotes

has been repeatedly questioned (Crete et al. 2001, Richer

et al. 2002). Although coyotes have been recorded in

virtually all habitat types in New England they have

been predominantly associated with more open, human-

Manuscript received 23 February 2007; revised 5 November2007; accepted 19 November 2007. Corresponding Editor: M.Friedl.

4 E-mail: [email protected]

1014

modified habitats (Oehler and Litvaitis 1996, Way et al.

2004). The hypothesis that eastern forests are poor

habitat for coyotes is supported by results from eastern

Quebec where, compared with coyotes in forested areas,

individuals living in rural areas are more active at dens

(Tremblay et al. 1998), have smaller home ranges (Crete

et al. 2001) and higher densities (Richer et al. 2002),

presumably because of differences in available food. On

the other hand, Vermont and New York animals

preferred forests over rural areas (Person and Hirth

1991, Kendrot 1998) and coyotes in New Brunswick

forests showed no signs of poor nutrition (a sign of

suboptimal conditions [Dumond and Villard 2000]).

Furthermore, forest coyotes have marginally higher

annual survival than rural animals and demonstrate no

apparent selectivity for open habitats within their home

range (Crete et al. 2001). Overall, it is unclear from

previous research whether forest in northeastern North

America should generally be considered quality habitat

for coyotes.

The extent to which coyotes use the forests that

dominate eastern North America is important when

gauging their ecological role in the region and directly

reflects on broader conservation issues and evolutionary

questions. Some have suggested that Eastern coyotes

have filled a wolf (C. lupus) niche in the region because

of their regular predation on ungulates (Mathews and

Porter 1992, Ballard et al. 1999). However, if forests

have low carrying capacity for coyotes, as suggested by

Crete et al. (2001), then their ecological impact there

may be reduced, with the wolf niche remaining open in

the heavily forested regions typical of much of

northeastern North America. Thus, from a conservation

perspective, the potential for eastern coyotes to fill the

top-predator niche has a direct bearing on the proposed

reintroduction of wolves to the region (Mladenoff and

Sickley 1998, Paquet et al. 1999). Additionally, if eastern

coyotes have adapted to forested habitats, such a niche

shift from their origin as open-country animals raises

questions about the evolutionary mechanisms underly-

ing this adaptation, as has previously been discussed in

regards to apparent increases in body size in parts of

eastern North America (Thurber and Peterson 1991,

Lariviere and Crete 1993, Peterson and Thurber 1993,

Gompper 2002, Kyle et al. 2006).

To more fully understand the landscape ecology of

coyotes in forested regions of northeastern North

America, we report here on our survey of coyote

abundance across rural and forested landscapes in

northern New York. Our use of a noninvasive censusing

approach enabled us to estimate the variability in

abundance over large areas and thereby build land-

scape-level models of habitat use. These abundance-

based models allow us to evaluate the general suitability

of rural vs. forested habitats within the study area, as

well as the more specific features that explain patterns of

coyote abundance in a region. In particular, if eastern

forests represent unsuitable habitat we predict that

coyote abundance will be correlated negatively with

forest cover across the landscape and positively withanthropogenic features that describe the open rural

landscapes of this region.

METHODS

Field surveys

Fieldwork took place from 1998 to 2000 across 54sites in and around Adirondack State Park (ADK,

;25 000 km2) in northern New York, USA. ADK is thelargest park in the contiguous United States and

contains a broad range of habitat types, managementunits, human population densities, and resource extrac-

tion intensities (Jenkins and Keal 2004). Field sites werespread throughout the park and surrounding regions in

a variety of forested and open-canopy landscapes. Thesesites varied in their extent of anthropogenic modifica-

tion, from those characterized by relatively low levels offragmentation and human use to those situated insuburbanized landscapes or subject to intensive resource

extraction (forestry) and agriculture. We selected 54 sitesin three general landscape types broadly representative

of northern New York: interior forest landscapescharacteristic of much of the mid-elevation wildland

habitats of central ADK (n¼31 sites), logged landscapeswhere active forest extraction activities were taking place

(n¼ 12 sites), and suburban/agricultural landscapes (n¼11 sites) on the outskirts of the study area where the

landscape has undergone the most extensive changes inthe region due to a long history of human use.

At each site a 5-km transect was marked along hikingtrails or unpaved roads. We only used transects that had

forest cover immediately around them, although somesites were more broadly surrounded by rural or

suburban landscapes. We surveyed coyote presencealong each transect using two noninvasive techniques:

camera traps and scat surveys, with the later supple-mented by analyses of fecal-DNA. Data from camera

traps was found to be unreliable for identifying thepresence of coyotes (Gompper et al. 2006) and thus alldata presented in this study are derived from scat

surveys. Each 5-km transect was cleared of scats and wasthen walked once monthly for three consecutive summer

months. All non-bear (Ursus americanus) scats werecollected in paper bags and stored immediately at

�208C.

Genetic analyses

Species identification analyses were conducted on all

scats collected from a subset of sites (n ¼ 31 sites) thathad five or more suspected coyote scats. DNA was

extracted with Qiagen QIAmp DNA Stool Mini kit(Qiagen, Valencia, California, USA). To calibrate

subsequent PCR, we quantified the amount of DNA ina subset of 23 fecal samples using a Nanodropspectrophotometer (NanoDrop Technologies, Willming-

ton, Delaware, USA). To confirm species of origin, weused the MDO mitochondrial DNA PCR primers to

June 2008 1015COYOTE FOREST USE

amplify regions of genomic DNA derived from coyote

(Gompper et al. 2006; J. E. Maldonado, unpublished

data). We sequenced non-coyote DNA samples on an

ABI Prism 3700 DNA Analyzer (Applied Biosystems,

Foster City, California, USA) using the same mitochon-

dria-specific primer pair for the sequencing reactions.

Species identifications were made through alignment of

generated sequences with either a candidate species’

sequence found in Genebank, or with sequence derived

from the New York State Museum tissue collection. In

total, we tested 472 fecal samples and obtained sufficient

genetic material to confirm species of origin for 377

(79.9%), of which 335 (88.9%) were coyote.

DNA from coyote fecal samples was subsequently

genotyped using three microsatellite primer pairs

(FH2001, FH2062, and FH2140 [Mellersh et al. 1997,

Kohn et al. 1999]) to PCR-amplify canid-specific

tetranucleotide repeat microsatellite markers. We select-

ed these three loci because they had a high polymor-

phism information content, providing a low probability

of random match between multilocus genotypes within

populations (Kohn et al. 1999). Although three loci are

fewer than recommended for typical density estimates

(Waits et al. 2001), these loci provide sufficient precision

for the objectives of our study since we are not

attempting to compare the source individual for a fecal

sample across sites, but rather require only to derive an

accurate index of population abundance from the

relatively small number of fecal samples collected from

each site. After preliminary trials, we slightly modified

the 2140 primer to address artifactual ‘‘þA’’ addition to

PCR products by using the following primer sequences:

forward, 5-GTATGATGAGGGGAAGCCA-3; reverse,

5-GTTTCTTTGACCCTCTGGCATCTAGGA-3. The

forward primer of each pair was labeled with a

fluorescent dye as follows: 2001 (6-FAM), 2062

(HEX), and 2140 (NED). The final concentrations of

PCR components in 25-lL reactions were: 1X buffer

(Qiagen); 0.2 mmol each dNTP; 1.5 mmol MgCl2; 0.5

lmol each primer; 0.02 U/lL Taq polymerase (Qiagen);

‘‘TaqStart’’ antibody (Invitrogen, Carlsbad, California,

USA), 14:1 (antibody : Taq) molar ratio, for ‘‘hot start’’

PCR initiation; and 2.0 lL fecal DNA extraction (as

prepared from Qiagen Stool kit above). We ran negative

controls (lacking template DNA) with each set of

reactions. The PCR thermalcycling program used was

an initial cycle of 958C for 3 minutes; followed by 40

cycles of 948C for 30 s, 588C for 30 s, 728C for 45 s; and a

final cycle of 728C for 2 minutes. For 2062 primers, a

608C annealing temperature was used. For PCR

amplifications carried out using MDO primers, 0.4 lmol

of each primer was used (final concentration), and the

thermalcycling program was an initial cycle of 958C, for

3 minutes; 35 cycles of 948C for 30 s, 538C for 30 s, 728C

for 60 s; and a final cycle of 728C for 5 minutes. The

allele sizes of amplified genetic markers were determined

using an ABI 3100 Genetic Analyzer and Genotyper

v3.7 software (Applied Biosystems, Foster City, Cal-

ifornia, USA) at the Wadsworth Center, New York

State Department of Health, Albany, New York, USA.

Tissue samples from 39 adult coyotes obtained from

trappers in Otsego County, New York (;30 km south of

ADK) were used to assess the appropriateness of the loci

for the noninvasive analyses. We assumed these

individuals to be unrelated and used the program

GIMLET 1.3.3 (Valiere 2002) to calculate the probabil-

ity of identity (PID) (Paetkau and Strobeck 1994, Waits

et al. 2001) for unrelated (PIDun corrected for samples

size) and sibling (PIDs) individuals, thereby giving lower

and upper bounds for PID. Programs MICRO-CHECK-

ER 2.2.3 (Van Oosterhout et al. 2004) and GENEPOP

3.4 (Raymond and Rousset 1995) were used to quantify

heterozygosity and examine for possible Hardy-Wein-

berg and linkage disequilibrium.

Each coyote fecal sample was genotyped using

replicated amplifications that required each allele to be

seen at least twice prior to identifying a consensus

genotype for the sample. Likewise, homozygote geno-

types were amplified twice. If genotypes were not

confirmed by seeing each allele twice after two ampli-

fications the sample was removed from our analysis.

This approach is similar to the filtering method used by

Adams and Waits (2007), who found a two-amplifica-

tion approach for homozygotes to ultimately produce

results comparable to more conservative repeated

amplification approaches, while significantly reducing

the effort and cost. This approach is particularly

appropriate for our study because at each site we

compare only a relatively small number of scat samples.

Furthermore, the use of tetranucleotide repeat micro-

satellites facilitated the identification of false alleles and

thus the grouping of identical and near-identical

genotypes. We calculated two measures of the mean

per-replicate probability of allelic dropout using pro-

gram GIMLET 1.3.3 (Valiere 2002). Following Prugh et

al. (2005),

PDj� ¼Djk

Ahetj

or PDj ¼Djk

Aj

where Djk is the number of amplifications with a missing

allele at locus j in replicate k, Ahetj is the number of

genotypes heterozygous at locus j, and Aj represents the

total number of consensus genotypes (including homo-

zygotes). False allele probabilities were calculated as

PFj ¼Fjk

Aj

where Fjk is the number of amplifications resulting in a

false allele at locus j in replicate k. Under a rule that

requires seeing each allele twice to confirm a genotype,

ðPDjÞ2 and ðPFjÞ2 represent the likelihood of a dropout

or false allele for each loci when replicated, and the sum

of these measures, Perr, represents the total probability

of an erroneous genotype at locus j. Perr, can be summed

across loci to calculate the total probability of an

ROLAND W. KAYS ET AL.1016 Ecological ApplicationsVol. 18, No. 4

erroneous multilocus consensus genotype for any sample

(Prugh et al. 2005).

Only samples for which all loci were resolved with all

alleles seen at least twice were included in calculations of

population size. Population size per site was calculated

as the number of unique genotypes per transect and then

regressed as a dependent variable against the number of

fecal samples collected and fully genotyped per site. The

ensuing regression equation was used to extrapolate the

number of coyote individuals per site based on the

number of putative coyote scats (Gompper et al. 2006)

collected per site.

Habitat modeling

We used an information-theoretic approach for

habitat model development and selection (Burnham

and Anderson 2002), using the number of coyote

individuals as the response variable and employing

multiple linear regression techniques. We formed five a

priori hypotheses to explain how local and landscape

habitat attributes would influence coyote abundance

based on relationships reported in the literature and our

experience in the study area (Table 1). To frame these

hypotheses we selected variables reflecting forest type,

open areas, edge habitats, and anthropogenic distur-

bance, since these have been shown to be important for

within-home-range habitat selection of eastern coyotes

(Caturano 1983, Major and Sherburne 1987, Litvaitis

and Harrison 1989, Person and Hirth 1991, Brundige

1993, Oehler and Litvaitis 1996, Kendrot 1998, Trem-

blay et al. 1998, Crete et al. 2001, Richer et al. 2002,

Way et al. 2004). Included among these were direct

metrics of vegetation structure measured at each site,

reflecting the openness and disturbance of a forest more

accurately than regional GIS layers. GIS layers were

downloaded from the New York State GIS clearing-

TABLE 1. Variables used for landscape models arranged under headings that represent five primary hypotheses that might explainhow local and landscape habitat attributes influence coyote abundance.

Variable type and name Variable description Scale Source and/or method

Local vegetation structure

HEIGHT mean height of trees � 10 cm dbh local field measurementVOLCWD mean volume of coarse woody debris local field measurementCANOPEN mean canopy openness local field measurementBASNAG mean basal area of snags local field measurement

Land cover: natural edges

dtWATER distance to nearest lake, river, orstream

local GIS (NYS 1:24 km Hydrography NetworkCoverages)

SHORE shoreline density for lakes, rivers, andstreams

landscape GIS (NYS 1:24 km Hydrography NetworkCoverages)

WETLAND proportion of area in wetland landscape GIS (NY-GAP Land Cover Map 8.6)NATFRAG average size of natural fragment as

delimited by roads, agriculture, orhuman developments

landscape GIS (NY-GAP Land Cover Map 8.6)

Land cover: forest type

FORCOV proportion of area in forest cover landscape GIS (NY-GAP Land Cover Map 8.6)CON proportion of area in conifer forest landscape GIS (NY-GAP Land Cover Map 8.6)DEC proportion of area in deciduous

forestlandscape GIS (NY-GAP Land Cover Map 8.6)

MIX proportion of area in mixed forest landscape GIS (NY-GAP Land Cover Map 8.6)

Anthropogenic

dtLOGRD distance to nearest logging road local GIS (digitized from USGS 100-km maps ascompiled from USGS 1:24 kmtopographic maps dated 1945–1980)

dtPAVED distance to nearest paved road local GIS (NYS ALIS roads layer)dtHOUSE distance to nearest house local GIS (NYS Office for Real Property Services

from the year 2000)HOUSE house density landscape GIS (NYS Office for Real Property Services

from the year 2000)PAVED density of paved county roads landscape GIS (NYS ALIS roads layer)LOGRD logging road density landscape GIS (digitized from USGS 100-km maps as

compiled from USGS 1:24 kmtopographic maps dated 1945–1980)

Physical

TRI terrain ruggedness index landscape calculated from digital elevation model(Riley et al. 1999)

SNOW snowfall average, winters 2002–2003and 2003–2004

landscape GIS (National Operational HydrologicRemote Sensing Center 2004)

Notes: Local-scale parameters represent averaged measures from nine stations distributed along each transect. Landscape-scaleparameters derive from GIS assessments of the landscape around trails using four different buffer scales: 0.5, 1, 5, and 10 km.


house except for the GAP land use data which came

from the New York State Gap Analysis Program

(Cornell University, Ithaca, New York) and the snowfall

data, which was an average of actual snowfall from

winters 2002–2003 and 2003–2004 (National Operation-

al Hydrologic Remote Sensing Center 2004). No

remotely sensed snow data were available from the

exact years of our field surveys, so we consider these

averaged data an index of typical snowfall patterns for

the region. We avoided false impressions of local

accuracy by only using the snow variable after averaging

over larger scales (5-km and 10-km buffers). We

calculated the terrain ruggedness index (TRI) from a

digital elevation model following Riley et al. (1999). We

included a hypothesis that topography and snow cover

would impact summer coyote density since such factors

were highly variable in the study area and have been

shown to influence carnivores in general (Carroll 2007)

and were hypothesized to have slowed coyote coloniza-

tion of the area (Fener et al. 2005).

We had no direct measures of prey abundance across

the study area. Coyote diet across northeastern North

America consists of a combination of white-tailed deer

(Odocoileus virginianus) and smaller prey (chiefly lepor-

ids), as well as seasonal fruit (Hamilton 1974, Brundige

1993, Patterson et al. 1998, Gompper 2002; R. Kays, J.

Ray, and M. Gompper, unpublished data). Measures of

small prey abundance across the study area do not exist,

and indirect measures of deer abundance through

hunting returns (Nesslage and Porter 2001) were too

coarse and geographically limited for the purposes of

our analyses. We did not use measures of greenness,

derived from MODIS satellite imagery, as a surrogate

for prey abundance as others have (e.g., Mace et al.

1999, Carroll 2007), because of its high correlation with

deciduous cover in this landscape, but used instead

measures of vegetation structure that have been shown

to be related to prey abundance.

We initially considered 32 landscape variables, but

discarded many either because they were highly corre-

lated with others (Pearson’s correlation . 0.70), or

because inherent heteroscedacity. We transformed (log,

natural log, or square root) a subset of the final variable

set to stabilize and normalize their variances. In all, we

classified the 21 habitat variables a priori to test the five

classes of hypotheses (Table 1). Nine of these were local-

scale variables representing the averages of measures

taken at each of nine stations along each transect. The

remainder were landscape-scale variables measured

using ARCGIS in buffers surrounding each trail at four

scales (500 m, 1 km, 5 km, and 10 km), except for

snowfall, which was only averaged over 5-km and 10-km

scales. We selected the larger scales as an approximate

upper extreme for the radius of a coyote home range

(Brundige 1993) and the two smaller scales to capture

more fine-grained habitat selection. Models with inter-

action terms were not included to restrict the number of

candidate models and because we knew of no strong

biological explanations for putative interactions.

We used a multi-scale, three-stage modeling process to

test hypotheses regarding coyote abundance and to

select top models. First, we grouped variable sets into a

priori models representing the five hypotheses listed in

Table 1, as well as five additional combinations of

hypotheses drawn from the literature: all land cover, all

land cover þ vegetation structure, all land cover þphysical, vegetation structure þ anthropogenic, and

anthropogenic þ physical. These models were then

tested with data collected at the four scales, except for

the local vegetation data, which could not be extrapo-

lated past the local scale, giving a total of 37 total

variable sets. For each set, we used a combined process

of stepwise selection (P to enter at 0.05 and leave set at

0.10) and Akaike’s Information Criterion (AIC; Burn-

ham and Anderson 2002) to derive one model for each

variable set. For the latter, we ranked all possible models

of up to three terms based on AIC adjusted for small

sample size (AICc). We then used the AICc differences

between the best model and the other candidate models

(Di ¼ AICci� minimum AICc) to determine the relative

ranking of each model. In all cases, the result generated

from the stepwise analysis selected a model for which Di

� 2 (Burnham and Anderson 2002) we selected the mostparsimonious model (fewest parameters) that included

variables from the given hypothesis. At the end of stage

one we had models describing the explanatory power of

37 different hypothesis-scale combinations to predict

coyote abundance.

In the second stage, we ranked the candidate models

against one another based on AICc and Di as previously

described. We also calculated AICc weights, to assess the

strength of evidence that any particular model was the

best model in our set. The ranking of models in stage

two allowed for the direct comparison between hypoth-

eses and between scales.

In the third stage, we produced one best model to

describe coyote abundance in the region. To do so, we

investigated whether any of the top models that emerged

from stage 2 could be improved by forcing additional

variables from the other top models (�2 AIC), which

often represented variables from different hypotheses or

different scales (following Holloway andMalcolm 2006).

We then rescaled AIC values relative to the best model

(i.e., the model with the minimumAIC), and recalculated

AIC weights to settle on a final model. We examined the

relative importance of each variable by summing the

AICc weights across the top models emerging from stage

2 for any model including the variable in question

(Burnham and Anderson 2002). To map our model

results, we repeated the three-stage modeling process

using only variables for which we had region-wide GIS

coverage (i.e., excluding vegetation structure measures).

All analyses were performed using SAS (version 8.2; SAS

Institute, Cary, North Carolina, USA).


RESULTS

Coyote abundance estimates

Thirty-six Otsego County coyotes obtained from

trappers were genotyped at all three loci and three

individuals were genotyped at two loci. The number of

alleles per locus varied from four to nine, and mean

allele frequency varied from 0.11 to 0.25 (Tables 2 and

3). Average observed and expected heterozygosity was

0.65 and 0.74, respectively. Departures from Hardy-

Weinberg equilibrium were not significant (P . 0.05) for

each locus, and no cases of linkage disequilibrium were

detected (P . 0.05). The PIDun using all loci was 2.92 3

10�7 and the PIDs was 3.96 3 10�3.

We collected a total of 645 fecal samples from the 54

study sites. Four of the sites had no putative coyote

feces. We attempted to extract DNA from 472 of those

samples from 31 sites with more than five suspected

coyote’s feces. A subsample of extracts from 23 scat

samples had an average of 1.58 ng/uL of DNA. Of the

472 samples extracted we were able to genetically

identify the species of origin for 377 samples, with two

additional samples giving a genotype of unknown

species origin, and 93 failing to amplify. This resulted

in an 80.3% amplification rate for the MDO species ID

primer. Most (88.9%) of the identified feces were from

coyotes (Fig. 1). We attempted to genotype all 335

coyote feces for the microsatellite loci, reamplifying

samples until each allele was seen twice or the sample

failed twice. Marker 2001 amplified at a rate of 78.6% (n

¼2010 attempted amplifications), marker 2062 amplified

at a rate of 82.1% (n¼ 2016), and marker 2140 amplified

at a rate of 82.6% (n ¼ 2346). Average per-locus drop-

out rate was 0.129 6 0.014 (mean 6 SE), and the

average per-locus, per-replicate false allele rate was

0.0029 6 0.0011 (n ¼ 2382). Probability of obtaining a

false three-locus genotype following replication was

3.53% (Table 4), implying approximately nine incorrect-

ly genotyped samples from our pool of 259 samples.

Using an approach that requires seeing every allele

twice, we obtained 122 samples with usable genotypes

for all three microsatellites, 47 for two markers, 53 for

one marker, and 76 with no markers amplifying. In

addition, 37 samples amplified for a locus only once, and

were therefore not used because we were unable to verify

the genotype. Across the 25 sites with successfully

genotyped feces, mean number of coyote fecal samples

examined per site was 4.8 (range: 1–29) and mean

number of identified genotypes was 3.12 (range: 1–13).

The number of coyote genotypes was closely correlated

with the number of fecal samples (Fig. 2), indicating that

fecal counts can be used to census relative coyote

densities across sites. The relationship was best fit by the

power function y ¼ 1.0914x0.727 (r2 ¼ 0.93, P , 0.001).

Applying this formula to identify the number of coyotes

across all 54 sites revealed an average of 4.0 coyotes per

transect (64.0 [SD]; range 0–21).

Landscape models

The first stage of habitat model selection produced

candidate models for each scale resulting in 24 models

that described the explanatory power of 37 different

hypothesis-scale combinations predicting coyote abun-

dance (Table 5). Some model selection procedures

produced identical models for different sets (notably

WETLAND500 was repeated twice, SHORE10km three

times, VOLCWD þ CANOPEN þ BASNAG emerged

three times at different scales, as did CANOPEN þdtPAVED). In the case of land cover-forest hypotheses

at all scales, no significant models were produced.

Following stage 2 of the model selection process, four

models received substantial support (emerging from the

top nine hypotheses), with AIC scores within two units

of one another, all of which contained vegetation

TABLE 2. Variability of three microsatellites used to genotype Otsego County coyotes (n ¼ 39coyotes) obtained from trappers.

Locus No. alleles Allele frequency�

Heterozygosity

PIDun PIDsObserved Expected

FH2001 5 0.20 6 0.15 0.69 0.71 6.558 3 10�3 1.580 3 10�1

FH2062 4 0.25 6 0.11 0.64 0.71 7.890 3 10�4 6.744 3 10�2

FH2140 9 0.11 6 0.11 0.62 0.79 5.641 3 10�2 3.714 3 10�1

Note: Values represent allelic diversity and frequency, observed and expected heterozygosity,and the probability of identity of unrelated and related individuals.

� Mean 6 SD.

TABLE 3. Allele frequencies for three microsatellites amplified in Otsego County coyote tissuesobtained from trappers (n ¼ 39 coyotes).

Locus

Allelles

a b c d e f g h i

FH2001 0.038 0.077 0.218 0.397 0.269FH2062 0.141 0.385 0.295 0.179FH2140 0.250 0.069 0.083 0.069 0.014 0.125 0.333 0.042 0.014


structure variables (vegetation structure, vegetation

structure þ land cover at all scales, and vegetation

structure þ anthropogenic at all scales). Three such

models demonstrated the highest AICc weights. Models

without vegetation structure variables had DAIC values

. 7 and AIC weights , 0.01. Models using the same

variables at different scales produced similar top models.

The exceptions were the inclusion of FORCOV at the 1-

km scale within the top model, whereas this variable was

unimportant at any other scale. STRSHORE replaced

WETLAND as an important variable at the two largest

scales.

In the final stage of the modeling process we produced

34 additional models by forcing additional variables into

the four top models emerging from stage 2. The top

three (Table 6) were within two AIC points of each

other, and therefore are viewed as roughly equal, while

all lesser models had Di . 2.6. We selected model 2 as

the optimal model because it is more parsimonious than

model 1 and was ranked only marginally lower. This

optimal model was:

Coyote abundance ¼ �12:16147þ 4:60206 3 VOLCWD

þ 6:73530 3 CANOPEN

� 16:84135 3 BASNAG

þ 15:20426 3 WETLAND1k

þ 14:88565 3 FORCOV1k:

This equation indicates coyote abundance is positively

associated with canopy openness and volume of coarse

woody debris at the stand level, and positively associ-

ated with amount of forest cover and wetland habitat

within a 1-km buffer of a trail, but negatively associated

with basal area of snags at the stand level. In alternative

models (Table 6), dtPAVED or SHORE10K were also

positively associated with coyote abundance. To rank

the relative importance of all variables across these 34

stage-3 models we summed the AIC weights for models

that included the variable in question, obtaining the

following: CANOPEN ¼ 1.0, WETLAND1K ¼ 0.699,

FORCOV1K¼ 0.674, SHORE10K ¼ 0.574, VOLCWD

¼ 0.559, BASNAG ¼ 0.559, dtPAVED ¼ 0.454.

When vegetation structure variables were eliminated

from the analyses to produce models that could be

mapped across the study area, the top nine models from

stage two were no longer considered, and variables such

as TRI, SNOW, and HOUSE, emerged as the most

important (model 10 and below; Table 5), with

dtPAVED remaining the only variable in common. In

the final stage of constructing models that could be

mapped we built 22 models, of which the top four are

presented in Table 6. All lesser models had Di . 2.4. The

model with the highest AIC score and weight emerging

from this process was:

Coyote abundance ¼ �9:72937þ 4:62711 3 dtPAVED

� 0:16438 3 TRI500

þ 0:01240 3 SNOW10k

� 3:76598 3 HOUSE5k

� 5:70695 3 dtHOUSE:

This equation shows that in the absence of vegetation

structure data, coyote abundance is positively associated

with distance to roads and negatively associated with

FIG. 1. Source species and percentage of 377 genotypedfecal samples collected from 54 independent transects in andaround the Adirondacks (New York, USA) during the summersof 2000–2002.

TABLE 4. Genotyping error rates for fecal samples.

Locus PDj�� n� PDj� n� PFj� n� ðPDjÞ2 ðPFjÞ2 Perr

2001 0.203 6 0.035 102 0.101 6 0.019 206 0.001 6 0.001 834 0.0102 1 3 10�6 0.010202140 0.143 6 0.022 132 0.092 6 0.015 205 0.003 6 0.002 960 0.0085 9 3 10�6 0.008472062 0.201 6 0.030 137 0.129 6 0.020 213 0.003 6 0.002 884 0.0166 9 3 10�6 0.01665

Total 0.0353 1.9 3 10�5 0.03533

Notes: PDj� is the per-sample dropout rate including heterozygotes only. PDj is the per-sample dropout rate including all data.PFj is the false allele rate for all samples and all replicates. ðPDjÞ2 and ðPFjÞ2 represent the probability of a dropout or falseallele, respectively, in the multilocus genotype under a see-each-allele-twice rule. Perr is the total probability of obtaining anerroneous genotype.

� Values shown are means 6 SE.� Number of fecal samples.


topographic relief at small scales, housing density at

larger scales, and distance to house, with a slightly

positive relationship to snow depth at large scales. The

relative importance of these variables, as represented by

summed AIC weights, were: dtPAVED ¼ 1.0, TRI ¼0.960, SNOW ¼ 0.734, HOUSE ¼ 0.703, dtHOUSE ¼0.683. Scale had no affect on these relative relationships.

Using this top model to map predicted coyote abun-

dance across northern New York shows a patchwork of

high and low densities, with lowest abundance toward

the center of ADK and higher abundance along its

periphery, especially in the southwest portion of the

park and in the Tug Hill area (Fig. 3).

DISCUSSION

Broadscale abundance surveys

We found coyotes to be virtually ubiquitous in the

rural and forested landscapes of northern New York,

but quite variable in their local abundance. This

variation would have been lost had we documented

only the presence or putative absence of coyotes since

only four of our 54 transects did not record coyote

FIG. 2. Regression plot of the number of coyote fecalsamples collected and genotyped at a site vs. the number ofcoyote individuals identified through genetic fingerprinting ofthe same samples. The relationship was best fit by the powerfunction y ¼ 1.0914x0.727 (r2 ¼ 0.93, P , 0.001).

TABLE 5. Best model results predicting coyote abundance from stages 1 and 2 of overall model selection criteria.

Modelno. Model type Scale K

AdjustedR2 AICc DAICc AICwt Variables in model

1 Veg struc þ Land cover-All local þ 1 km 4 0.352 164.813 0.000 0.156 CANOPEN, FORCOV1K,WETLAND1K

2 Veg struc local 4 0.351 164.892 0.079 0.150 VOLCWD, CANOPEN, BASNAG3–6 Veg struc þ Anthro local þ

all scales4 0.351 164.892 0.079 0.150 CANOPEN, dtPAVED

7 Veg struc þ Land cover-All 10 km 3 0.313 165.617 0.804 0.105 CANOPEN, SHORE10K8 Veg struc þ Land cover-All 5 km 3 0.291 167.341 2.528 0.044 CANOPEN, SHORE5k9 Veg struc þ Land cover-All 500 m 4 0.305 168.580 3.767 0.024 CANOPEN, FORCOV500,

WETLAND50010 Anthro þ Physical local þ 10 km 4 0.262 171.854 7.041 ,0.01 dtPAVED, SNOW10k, TRI10k11 Anthro þ Physical local þ 500 m 3 0.219 172.575 7.762 ,0.01 dtPAVED, TRI50012 Anthro local þ 5 km 4 0.237 173.685 8.872 ,0.01 dtPAVED, dtHOUSE, HOUSE5k13 Anthro þ Physical local þ 5 km 4 0.237 173.685 8.872 ,0.01 dtPAVED, dtHOUSE, HOUSE5k14 Anthro þ Physical local þ 1 km 3 0.200 173.870 9.057 ,0.01 dtPAVED, TRI1k15 Abiotic þ Landcover-All 1 km 4 0.226 174.439 9.626 ,0.01 WETLAND1K, FORCOV1K,

TRI1k16 Anthro local þ 10 km 4 0.224 174.581 9.768 ,0.01 dtPAVED, dtHOUSE, HOUSE10k17 Land cover-All 10 km 2 0.149 174.976 10.163 ,0.01 SHORE10K18 Land cover-Natural edge 10 km 2 0.149 174.976 10.163 ,0.01 SHORE10K19 Land cover-All þ Abiotic 10 km 2 0.149 174.976 10.163 ,0.01 SHORE10K20 Land cover-All 1 km 3 0.179 175.300 10.487 ,0.01 WETLAND1K, FORCOV1K21 Anthro local þ 500 m 3 0.172 175.722 10.909 ,0.01 dtPAVED, dtHOUSE22 Anthro local þ 1 km 3 0.172 175.722 10.909 ,0.01 dtPAVED, dtHOUSE23 Land cover-Natural edge 1 km 2 0.133 176.000 11.187 ,0.01 WETLAND1K24 Land cover-All 5 km 4 0.198 176.374 11.561 ,0.01 SHORE5k, WETLAND5k, DEC5K25 Land cover-All þ Abiotic 5 km 4 0.198 176.374 11.561 ,0.01 SHORE5k, WETLAND5k, DEC5K26 Physical 10 km 3 0.151 177.065 12.252 ,0.01 SNOW10k, TRI10k27 Land cover-Natural edge 5 km 3 0.145 177.450 12.637 ,0.01 SHORE5k, WETLAND5k28 Land cover-All 500 m 2 0.103 177.839 13.026 ,0.01 WETLAND50029 Land cover-Natural edge 500 m 2 0.103 177.839 13.026 ,0.01 WETLAND50030 Land cover-All þ Abiotic 500 m 3 0.138 177.899 13.086 ,0.01 WETLAND500, TRI50031 Physical 500 m 2 0.095 178.266 13.453 ,0.01 TRI50032 Physical 1 km 2 0.074 179.506 14.693 ,0.01 TRI1k33 Physical 5 km 2 0.051 180.867 16.054 ,0.01 TRI5k

34–37 Land cover-Forest all scales (no significant model)

Notes: All models are significant at P , 0.01 except models 34–37, which were not significant. K indicates the number ofvariables used in the model (including the intercept), AICc values are Akaike’s Information Criterion adjusted for small sample size,DAICc is the difference in AICc score from the top model, AICwt is the Aikaike weight. Variables are described in Table 1.


presence. The transects that detected the most animals

probably reflect the frequent use of an area by two to

three packs, perhaps using areas nearby as travel

corridors or rendezvous sites, while the lower abundance

sites reflect rarer visits by coyotes, probably from one

broad-ranging pack. Understanding the patterns and

potential causes of this variation in coyote abundance

over the broader landscape is important for document-

ing the extent to which the species has become

established as the region’s top predator.

Large carnivores are generally wary, wide-ranging,

and relatively rare, which makes these species difficult to

detect, let alone quantify their abundance across large

landscapes. On the other hand, adaptable generalists like

coyotes can be present in almost all parts of their range,

such that measures of abundance (rather than presence/

absence) are needed to understand their habitat prefer-ences. Fortunately, coyotes indirectly reveal their

presence by leaving feces along trails, and the results

of this study confirm that the abundance of feces is

directly related to the number of individual coyotes

inhabiting the site. This simple and direct index of

coyote abundance allows us to identify the factors that

affect coyote abundance, and predicatively map these

relationships across the entire region, something rarely

done for top predators (Karanth et al. 2004). Although

several noninvasive surveying techniques have a similar

potential to overcome the limitations of presence/

absence data by allowing estimates of absolute or

relative abundance across broad landscapes withoutthe necessity of capturing the animal (Karanth and

Nichols 1998, Gompper et al. 2006, Herzog et al. 2007)

these techniques remain poorly developed for censusing

population size of species that do not have markings

that are individually distinct, or that exist over very large

landscapes, such as coyotes.

Coyotes in forested ecosystems

Coyotes entered eastern North America from the

Midwest, where they evolved in the open habitats of the

Great Plains (Parker 1995, Gompper 2002). Studies of

individual populations have provided conflicting in-

sights on the extent to which coyotes have adapted to

the forested systems that dominates the Northeast

(Person and Hirth 1991, Kendrot 1998, Crete et al.

2001, Richer et al. 2002). We found coyote abundance to

be positively related to the amount of forest cover within

the surrounding 1 km and negatively related to levels of

rural human settlement, thereby rejecting the hypothesis

that eastern forests are unsuitable habitat for eastern

coyotes.

More importantly, our abundance-based models

reveal that specific characteristics of the forest explain

more variation than coarse-scale habitat delineations

(open vs. forested). Our data always favored hypotheses

that included measures of vegetation structure measured

at the forest stand level. Hence, landscapes with the

highest abundance of coyotes were forests characterized

by open canopy and less woody structure (e.g., more

open canopy, less coarse woody debris) with abundantnatural edges (e.g., wetlands and shoreline). Thus at a

broad spatial scale, forested habitat was correlated with

higher densities of coyotes, but on a finer spatial scale,

coyotes were more abundant where the degree of forest

canopy openness increased. The natural edges and

disturbed forests favored by coyotes, with more open

canopies and less downed woody debris, may serve as

refuges from human hunters compared to areas with

more open landscapes. Such hunter-driven habitat

preference has been observed in ungulates (Kilgo et al.

1998, Millspaugh et al. 2000), and coyotes in northern

New York appear to face high hunting pressure(Kendrot 1998). Even more importantly, however,

natural edges and disturbed forest are likely to provide

increased prey availability relative to mature forests.

Habitat selection by coyotes is typically influenced byprey availability (Ozoga and Harger 1966, Hilton 1978,

Litvaitis and Shaw 1980, Andelt and Andelt 1981).

Although we were not able to directly measure this at

the scale of our predator surveys, the patterns of coyote

abundance we observed mirror likely patterns in the

distribution of preferred prey. The diet of coyotes in

northern New York (Hamilton 1974, Chambers 1987;

R. Kays, J. Ray, and M. Gompper, unpublished data) is

dominated by snowshoe hare and white-tailed deer, with

fruit (e.g., raspberry [Rubus spp.] and blueberry [Vacci-

nium spp.]), playing an important role in the summer.

These food items are more abundant in younger, more

TABLE 6. Best model results predicting coyote abundance from stage 3 of overall model selection criteria.

Model no. Variables in model AICc DAICc AICwt

All variables

1 VOLCWD, CANOPEN, BASNAG, WETLAND1K, FORCOV1K, SHORE10K 161.692 0 0.1482 VOLCWD, CANOPEN, BASNAG, WETLAND1K, FORCOV1K 162.303 0.610 0.1093 VOLCWD, CANOPEN, BASNAG, WETLAND1K, FORCOV1K, dtPAVED 163.613 1.920 0.057

Without vegetation structure variables

1 dtPAVED, HOUSE5K, dtHOUSE, TRI500, SNOW10K 168.995 0 0.2152 dtPAVED, HOUSE5K, dtHOUSE, TRI10K, SNOW10K 170.157 1.162 0.1203 dtPAVED, HOUSE5K, dtHOUSE, TRI1K, SNOW10K 170.822 1.827 0.08634 dtPAVED, HOUSE5K, dtHOUSE, TRI500 171.044 2.049 0.0772

Notes:AICc values are Akaike’s Information Criterion adjusted for small sample size, DAICc is the difference in AICc score fromthe top model, and AICwt is the Aikaike weight. Variables are described in Tables 1 and 5.


open forest, as well as in more disturbed habitats in the

area (Oosting 1956, Severinghaus and Brown 1956),

with lower densities of such prey in large tracts of

closed-canopy forests (Bittner and Rongstad 1982,

Litvaitis et al. 1985). Deer may also be more vulnerable

near frozen lakes and forest edges in winter (Patterson

and Messier 2003), and if coyote hunting strategies for

killing adult deer follow those of wolves and elk (Cervus

elaphus), hunting in forested areas may be more

successful than in open habitats (Creel et al. 2006).

Our finding that forest type is more important in

predicting coyote abundance than more generalized

habitat categories (i.e., open vs. forest) may help explain

contradictory results of past research. Several studies

suggesting that forested habitats are suboptimal for

coyotes were carried out in boreal forest systems

(Samson and Crete 1997, Tremblay et al. 1998, Crete

et al. 2001). It may be that coyote use of these forested

systems differs significantly from their use of the

younger or more deciduous forests of northern New

York (Richer et al. 2002). Indeed, two local studies in

landscapes directly comparable to our study area found

coyotes preferred forests (Person and Hirth 1991,

Kendrot 1998). These differing results indicate the

importance of including detailed descriptions of forest

type when analyzing the habitat use of wide-ranging

carnivores such as coyotes.

Coyotes and human activities

The specific forest characteristics we found most

important in predicting coyote abundance were mea-

sures indicative of forest disturbance, especially the open

canopies and lower levels of coarse woody debris that

result from selective logging. The eastward expansion of

coyotes coincided with the forest conversion due to

logging and agriculture (Parker 1995, Fener et al. 2005).

Yet aside from Major and Sherburne’s (1987) report of

summer use of clearcuts, there has been little study of

coyote habitat use as a function of timber harvest.

Nearly all current eastern forests have undergone

periods of deforestation and reforestation associated

with logging and land conversion (Foster 1992, Foster et

al. 1998), and logging continues to be the primary

disturbance factor in some forests throughout Northeast

FIG. 3. Predicted coyote abundance across northern New York showing the highest densities (red) in areas with lower roaddensities lacking steep terrain, especially where snow depth can be greater. The 54 study sites where coyote abundance wasmeasured through fecal surveys are indicated in black; lakes and rivers are blue.


North America (Jenkins and Keal 2004). Our results

show that coyotes are most abundant in these disturbed

forests, and suggest that this anthropogenic activity may

in turn have strong indirect effects on local predator

assemblages by influencing the dynamics of intraguild

competition (Crooks and Soule 1999).

Our hypothesis that aspects of human settlement

affect coyote distribution was supported, particularly

when local-scale variables related to vegetation structure

were excluded from the models. Coyote densities were

highest far from roads in areas with low house density,

but within these areas coyotes were actually more

common closer to houses. Among these models, distance

to roads was by far the most important parameter

(highest AIC weight). These nuanced findings probably

reflect the higher risk of mortality leading to lower

densities for coyotes in highly settled areas at larger

scales (Major 1983, Kendrot 1998, Bogan 2004), offset

at local scales by the abundant prey in disturbed habitats

near human residences. When vegetation structure

variables were included in the analyses, only one

anthropogenic variable (dtROADS) received enough

support to be included in our final stage of model

building, and had the lowest summed AIC weights in

this final stage.

There have been substantial changes to land cover and

land use patterns in northeastern North America since

European settlement. Over the past century, logging and

agriculture have declined in the region while rates of

wildland development and tourism have increased

(Foster et al. 1998, Jenkins and Keal 2004, Glennon

and Krester 2005). The combined result of these land use

changes in northern New York are extensive tracts of

maturing forests, especially on public lands. Our study

suggests that initial stages of reforestation are likely to

be associated with increased coyote populations, but as

forests mature and support less potential prey, coyote

numbers in these areas should decline. Private lands

where more active logging is taking place, on the other

hand, are likely to produce more coyote food, and

therefore support more coyotes.

One benefit of using large-scale landscape variables to

model coyote abundance is that the results can be

projected over large regions (Fig. 3). The results

graphically illustrate the relationships described by our

best model that could be mapped, namely higher

summer coyote densities away from settled regions,

especially where local terrain is moderate and snowfall is

high. This model fits with established coyote–human

interactions in rural areas and also suggests that, since

coyotes are not known to be seasonally migratory, deep

snow might indirectly constitute favorable summer

habitat for coyotes by making deer more vulnerable,

as has been observed in studies of coyote–deer

interactions (Patterson et al. 1998, Patterson and

Messier 2000, 2003). An important caveat, however, is

that our mapping was carried out without the fine-scale

vegetation structure variables that were most important

for predicting coyote abundance. Thus, additional

variation should be expected within the context of the

mapped predictions with higher coyote abundance in

more disturbed forests.

Top-down effects of large canids

With the extinction of the wolf in the region in the late

1800s (Coleman 2004), coyotes are now the top predator

in the region, adding two interesting perspectives to our

results. First, if the ecological effects of top predators

vary positively with their abundance, coyote-driven top-

down direct and indirect effects should be stronger

where their numbers are greater (Ripple and Beschta

2004, Ray et al. 2005). Second, the extent to which

eastern coyotes have filled the wolf niche has implica-

tions for proposed reintroductions of wolves to the

region.

Predators have the potential to drive community and

ecosystem processes (Ray et al. 2005), as has been

demonstrated for coyotes in southern California

(Crooks and Soule 1999). The mechanisms by which

these effects occur include direct and fear-mediated

changes to prey numbers and behavior as well as indirect

effects derived from the altered impact of prey on the

landscape (Post et al. 1999, Ripple and Beschta 2004,

Croll et al. 2005, Ray et al. 2005, Creel et al. 2006).

However the circumstances that determine if and when

(at what abundance) predators become drivers of

ecosystem processes are still poorly understood. If

greater coyote abundance equates to greater strengths

of trophic interactions in the Northeast we might expect

that the top-down effects of this recent arrival would be

most strongly observed in younger and more disturbed

forests with greater proportions of edge habitat, where

our models suggest higher numbers of coyotes. The

existence of these habitats is strongly influenced by the

extent of extractive logging in the Northeast, leading to

the testable prediction that coyote-driven top-down

effects are ephemeral; as habitats shift from open areas

to younger, more productive forest, the effects of

coyotes on the local animal and plant community

should increase. However as these younger forests

mature and become more closed-canopy and less

productive for coyote prey species, the top-down

influence of coyotes should also decrease.

The potential to reintroduce wolves into northeastern

forests is periodically raised (Paquet et al. 1999, Sharpe

et al. 2001, U.S. Fish and Wildlife Service 2003). The

ecological similarity between eastern coyotes and wolves

is a concern for any such plan, with some questioning

the extent to which eastern coyotes have already filled

the wolf niche (Mathews and Porter 1992, Ballard et al.

1999, Sage 2001). Although eastern coyotes eat more

ungulate prey than western coyotes, their consumption

of large prey is still much less than that of wolves, with

coyotes eating more fruit and small and medium-sized

prey instead, and never taking large prey such as moose

(Alces alces) (Potvin et al. 1988, Gompper 2002, Chavez


and Gese 2005). Our data show that eastern coyotes can

flourish in rural, human-dominated landscapes and also

take advantage of disturbed forests, presumably because

of the diverse food there; these relationships drive the

geographic patterns shown in our predictive map (Fig.

3). These patterns are clearly different than those

predicted for wolves, which are more sensitive to road

density and track ungulate populations, but are not

sensitive to densities of smaller prey or fruit (Harrison

and Chapin 1998, Mladenoff and Sickley 1998). Within

the Adirondack Park, wolves would presumably do

better than coyotes in the mature wilderness forest,

spatially tracking deer, beaver (Castor canadensis), and

the growing moose populations there. These predicted

differences in spatial ecology underscore the idea that

eastern coyotes have not filled the wolf niche in the

region, similar to the conclusion of Crete et al. (2001)

based on a comparison of the foraging ecology and

energetic physiology of the two species.

CONCLUSIONS

We have shown how noninvasive survey techniques

can be used over a large landscape to collect the

necessary data for modeling the habitat ecology of a

wide-ranging and difficult-to-observe predator species

based on population abundance. This approach reveals

not only that coyote abundance is greater in forested

landscapes, but also provides the means of testing

alternative models for how variance in these habitats

affects abundance, ultimately allowing a more nuanced

view of how coyotes use forested systems. Such an

approach provides a more detailed and biologically

relevant view of the spatial patterns of top predator

populations, and represents a necessary first step for

addressing several important fundamental and applied

questions such as how the arrival of a novel top predator

influences community and ecosystem structure and

function, and how the species is likely to respond to

future changes in eastern forests.

ACKNOWLEDGMENTS

Funding for this work came from the Wildlife ConservationSociety, the Geraldine R. Dodge Foundation, the NationalGeographic Society, Columbia University, the New York StateMuseum, the New York Biodiversity Research Institute, theNew York Department of Environmental Conservation, andthe National Science Foundation (DEB 0347609). The AlbanyPine Bush Preserve Commission, Adirondack Park Agency,New York State Department of Environmental Conservation,and Saratoga National Historical Park provided permitting andlogistic support. Field and laboratory work benefited from theassistance of J. Bopp, H. Bull, J. Cryan, A. DeWan, S.Franklin, R. Gill, C. Herzog, E. Hellwig, J. Isabelle, R. Keefe,N. Korobov, S. LaPoint, J. Richardson, L. Robinson, D.Ruggeri, and G. Woolmer. J. Malcolm and G. Holloway gavevaluable advice with habitat modeling. Numerous privatelandowners generously gave permission to conduct surveys,including the Adirondack League Club, International PaperCompany, The Nature Conservancy, Brandreth Park, RossPark, Domtar Industries, Gulf View Hunting Club, MinerAgricultural Institute, and the Dragoon and DeCosse families.

Thanks to two anonymous reviewers for their helpful commentsthat improved the manuscript.

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