Assessment of Population Structure of Coyotes in
East-Central Alabama using Microsatellite DNA
Dalinda L. Damm, James B. Armstrong, Wendy M. Arjo, and Antoinette J. Piaggio
Southeastern Naturalist, Volume 14, Issue 1 (2015): 106–122
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2015 SOUTHEASTERN NATURALIST 14(1):106–122
Assessment of Population Structure of Coyotes in
East-Central Alabama using Microsatellite DNA
Dalinda L. Damm1,*, James B. Armstrong2, Wendy M. Arjo3, and
Antoinette J. Piaggio4
Abstract - Canis latrans (Coyotes) are a management concern in the southeastern US
because of their potential impacts on agriculture, other wildlife species, and human health
and safety. This region is part of a recent range expansion by Coyotes, and information
about their population structure in the southeastern US is lacking. In this study, we used
microsatellite DNA to assess genetic diversity and population structure among Coyotes in
east-central Alabama. We detected high genetic diversity (HE = 0.78) and no population
structure across the total sampling area. Additionally, we investigated population structure
between urban and rural groups. We detected low but significant population structure between
these groups, which may be biologically meaningful. We discuss the implications
of this result in the context of potential management strategies. Overall, our study sought
to provide information about the molecular ecology of Coyotes within a region of recent
range expansion.
Introduction
Historically, Canis latrans Say (Coyote) was native to the Central Plains region
of the US, including Texas, Oklahoma, Kansas, and Nebraska (Nowak 1978,
Parker 1995, Young and Jackson 1951). Within the last 200 years, Coyotes have
expanded their range first into the western US, followed by an eastward expansion
predominantly occurring over the last century (Brady and Campbell 1983, French
and Dusi 1979, Gipson et al. 1974, Hill et al. 1987, Parker 1995, Wooding and
Hardinsky 1990). The Coyote’s successful range expansion has likely been facilitated
by its behavioral plasticity and capacity for high reproduction (Bekoff 1978).
Colonization of the southeastern US by Coyotes began in the early 1960s, with the
range-expansion front crossing over southern portions of the Mississippi River,
and moving east. This trajectory of Coyote population expansion throughout the
Southeast occurred mainly within in the last 30 years (Parker 1995). Coyotes are a
management concern within this region for many reasons. However, there is a lack
of information regarding the population structure of Coyotes in the southeastern US
(Mastro et al. 2012) that could limit the development of effective strategic management
plans for the species.
Management concerns regarding Coyotes in Alabama are representative of
problems observed across the southeastern US. Issues requiring management
1Auburn University, School of Forestry and Wildlife Sciences, 602 Duncan Drive, Auburn,
AL 36849. 2Auburn University, School of Forestry and Wildlife Sciences, 602 Duncan
Drive, Auburn, AL 36849. 3AGEISS, Inc., 1401 Marvin Road NE, Suite 307, #422, Lacey,
WA 98516. 4USDA, APHIS, WS, National Wildlife Research Center, 4101 LaPorte Avenue,
Fort Collins, CO 80521. *Corresponding author - Dalinda_Damm@yahoo.com.
Manuscript Editor: Michael Conner
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of the species within this region include depredation on livestock, damage to
crops, perceptions of competition between Coyotes and hunters for game resources
including potential effects of Coyotes on Odocoileus virginianus Zimmermann
(White-tailed Deer) fawn recruitment, and threats to human and aircraft safety at
airports (Houben 2004; Howze et al. 2009; Jones 1987; Kilgo et al. 2012, 2014;
USDA 2002). Damage to livestock and crops by Coyotes has been documented
in the state (Armstrong and Walters 1995, Connolly 1992, Dunn and Smith 2011,
Philipp and Armstrong 1995, USDA 2014), and Armstrong and Smith (2014)
reported that the number of damage complaints from Coyote activities has risen
sharply with the increase in Coyote numbers in Alabama. Recent studies have
suggested that Coyote depredation has contributed to reduced White-tailed Deer
fawn recruitment in portions of Alabama (Jackson and Ditchkoff 2013, VanGilder
et al. 2009). Coyote impacts are not limited to rural Alabama; Coyotes have also
become a problem in urban areas. Complaints from the public concerning Coyotes
have shifted in recent years from primarily reports of agricultural damage in rural
areas, to urban-specific issues, such as attacks on pets and other negative human
interactions (Armstrong 2011). In December 2013, the city of Auburn, AL, entered
into an agreement with USDA-Wildlife Services (USDA-WS) to reduce a Coyote
population around a city park, which included trapping efforts, habitat reduction,
and public education (USDA 2013). In addition, USDA-WS has carried out
management efforts to reduce the potential human safety threat posed by Coyotes
at several Alabama airports (W. Gaston, USDA/APHIS/WS, Auburn, AL, pers.
comm.). Overall, Coyotes constitute a significant wildlife management and damage
issue within the southeastern US, including Alabama.
Information about the ecology of Coyotes is essential to gain a better understanding
of the species and to develop effective management strategies. Traditionally,
management units, commonly defined as demographically autonomous groups,
have been based on elements like administrative or geographic barriers, habitat
characteristics, and geographic distribution of a species (DeYoung and Honeycutt
2005, Lackey 1998, PalsbØll et al. 2006, Wallace et al. 2010). The identification of
management units is a crucial component of conservation and management plans,
because they define a discrete section for focusing monitoring and management
actions (Moritz 1994, PalsbØll et al. 2006, Schwartz et al. 2007). More recently,
management units have been identified as groups that exhibit significant genetic
differentiation (Moritz 1994, PalsbØll et al. 2006). However, meaningful management
units can be difficult to define for Coyotes because of their high capacity
for dispersal, migratory tendencies, and contiguous distribution across their range
(DeYoung and Honeycutt 2005, Diniz-Filho and Telles 2002). Further, Coyotes are
characterized as habitat and foraging generalists (Bekoff 1978), which allows them
to thrive in diverse environments, and may also limit characterization of practical
management units. Nonetheless, Coyote populations can be influenced by many
factors including fragmentation of the landscape and habitat, inter-specific competition,
and natural geographic and man-made barriers, including increased levels of
urbanization (Arjo and Pletscher 1999; Atwood et al. 2004; Berger and Gese 2007;
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Gehrt et al. 2009; Randa and Yunger 2006; Rashleigh et al. 2008; Riley et al. 2006;
Sacks et al. 2004, 2005). Modern genetic methods to assess population structure
can be a useful tool for wildlife biologists studying a highly adaptable species, such
as the Coyote, and can be used to provide information to assist in defining management
units (DeYoung and Honeycutt 2005, Honeycutt 2000).
Several studies using DNA to investigate population structure of Coyotes have
identified significant population differentiation. Williams et al. (2003) detected low
levels of genetic structure between Coyotes grouped by age after a transition from
selective to non-selective removal-based management practices in northern California.
Other studies identified Coyote population structure related to the presence
of a major freeway or based on microhabitat breaks and other habitat-specific delineations
in California (Riley et al. 2006; Sacks et al. 2004, 2005, 2008). Monzόn
(2014) detected population structure within eastern Coyotes at what was considered
a “contact zone” between 2 fronts of colonization. Coyotes sampled in New York
showed significant population structure, which could be due to deer densities and
human land-use (Monzόn 2014). Another study detected genetic differentiation at
a broader geographic scale between eastern and western Coyote populations (Way
et al. 2010). Rashleigh and others (2008) conducted a study around the Cleveland,
OH, area where they detected population differentiation among groups separated
by the downtown area and 2 major interstates. To date, no study employing genetic
data to examine Coyote genetic population structure has been completed in the
southeastern US.
This study addressed the evident need for information regarding the molecular
ecology of Coyotes within the southeastern US. Our goal was to use nuclear DNA
(i.e., microsatellites) to assess genetic diversity and population structure among
Coyotes in east-central Alabama. We hypothesized that we would detect high levels
of genetic diversity and low levels of population structure among these Coyotes due
to the biological profile of the species (i.e., high mobility and reproductivity, and
continuous dispersal). In addition to our main objective, we also investigated population
structure between urban and rural groups. Other studies (Atwood et al. 2004;
Gehrt et al. 2009, 2011; Randa and Yunger 2006; Riley et al. 2003) have found
population differentiation between urban Coyotes and surrounding populations.
Therefore, we chose to examine if Coyotes captured within, and in close proximity
to, the city of Auburn, AL, experienced reduced gene flow with Coyotes from surrounding
rural areas. Overall, our study sought to provide basic information about
the molecular ecology of Coyotes within a region of recent population expansion
for this species, as well as to provide information that could inform development
of management strategies.
Field-site Description
Our study area encompassed a 100-km radius around the Auburn/Opelika Metroplex
Statistical Area (MSA) in east-central Alabama. We collected samples from
Chambers, Coosa, Lee, Macon, Montgomery, Russell, and Tallapoosa counties. With
a population of 130,516 people in 2008 (first year of our study), the Auburn/Opelika
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MSA was considered the fastest-growing metropolitan area in Alabama since 1990
(US Census Bureau 2001). The landscape directly adjacent to metropolitan sections
is a mixture of agricultural, forested, ranching, and farming lands.
Methods
Sample collection and DNA extraction
With assistance from USDA-Wildlife Services (Auburn, AL) and a host of local
volunteers, we opportunistically collected samples (n = 74) from live-captured, targeted/
hunter-harvested, and vehicle-killed animals from April 2008 to May 2009.
We obtained tissue samples from live-captured individuals during a telemetry study
concurrently conducted at Auburn University (Jantz 2011). We sampled tissue from
the ear of each live Coyote using a commercial-grade ear-notcher. We collected
ample skin or muscle tissue as available from deceased Coyotes. We stored tissue
samples in an EDTA/DMSO buffer solution saturated with NaCl for preservation
(Seutin et al. 1991). We extracted DNA from each sample using a DNeasy® Tissue
Kit (QIAGEN Inc., Valencia, CA) following the manufacturer’s protocol. All collection
protocols were approved by Auburn University Institutional Animal Care
and Use Committee (Protocol# 2007-1244).
Laboratory protocol
We amplified 10 microsatellite markers (FH2001, FH2096, FH2137, CXX140,
FH2054, FH2010, FH2159, CX2235, FH2100, FH2062; Breen et al. 2001; Francisco
et al. 1996; Ostrander et al. 1993, 1995) using 3 multiplexed polymerase
chain reactions (PCRs; Table 1). We ran each reaction with optimized amounts of
PCR water, GeneAmp 10X PCR Buffer II (Applied Biosystems, Inc., Foster City,
CA), 25 mM MgCl2 (Panel A: 1.0 μl, Panel B: 0.8 μl, Panel C: 0.7 μl; Applied Biosystems,
Inc.), 1.0 μl dNTP (Promega, Madison, WI; 10 mM), primers (Table 1;
1 μM), 0.1 μl Amplitaq Gold (Applied Biosystems, Inc.; 5 U/μL), and 0.4 μl BSA
(Promega; 10 mg/ml). The multiplexed PCR-amplification process included an
initial denaturation cycle of 10 minutes at 95 °C followed by 52 cycles of 94 °C for
30 seconds, panel-specific annealing temperatures for 30 seconds (Panel A = 51 °C,
Panel B = 50 °C, Panel C = 59 °C), and extension at 72 °C for 45 seconds. A final
extension was accomplished in one 7-minute cycle at 72 °C.
We sent amplification products to the Wildlife Genetics Lab at the USDA-WS
National Wildlife Research Center in Fort Collins, CO, for visualization on an ABI
3130 Genetic Analyzer (Applied Biosystems, Inc.). We binned the visualized data
using GeneMapper Software v4.0 (Applied Biosystems, Inc.) and exported it using
GMConvert (Faircloth 2006). We employed CONVERT v1.31 (Glaubitz 2004)
to transform the raw data files into the proper input files for various downstream
statistical analysis software.
Population assignment
We categorized individuals into 3 groups (i.e., urban, rural, and buffer/interface)
to investigate the existence of population structure among Coyotes sampled in this
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study. We created a point shapefile within ArcGIS (Esri) from coordinates taken
at the location where we obtained each Coyote sample. We made the assumption
that the location where an individual was sampled corresponded to the habitat/
landscape type with which they were most likely to be associated the majority of
the time. We assigned each point to a category of either urban or rural based on
Alabama Gap Analysis Project (AL-GAP) landcover data (Kleiner et al. 2007)
and TIGER/Line census-block data from the 2000 US Census Bureau (US Census
Bureau 2002). The US Census Bureau classifies any census-block group having a
population density of at least 1000 people per square mile with surrounding blocks
having at least 500 people per square mile as urban. We defined sites outside of
those constraints as rural. We performed zonal statistics using the Spatial Analyst
Tools in ArcGIS (Esri) across the total study area to determine majority landcover
type per census block, based on AL-GAP landcover data (Kleiner et al. 2007). We
selected landcover types of low-, medium-, and high-intensity development and
open developed areas (i.e., impervious surfaces, golf courses) and reclassified
them as urban. We then performed a spatial query to select attributes from both
the census and landcover data layers. We combined all polygons that had been
classified as urban based on both census and landcover type into a single urban
polygon. We then applied a 4.22-km buffer to the urban polygon, the approximate
diameter of a rural Coyote home range calculated for the study area (Jantz 2011).
We excluded any Coyote falling within this “buffer/interface” area from the urban/
rural comparison analysis in an attempt to eliminate individuals that could not be
assigned definitively to either the urban or rural group as defined within this study.
We deemed any Coyote sampled at a point that was within the urban polygon to be
an urban Coyote. Lastly, we classified all individuals not categorized as urban and
not collected within the buffered interface area as rural (Fig. 1). Final sample sizes
for each population were: urban (n = 8), buffer/interface (n =16) and rural (n = 50).
Genetic statistical analyses
We completed statistical analyses using the following datasets: (1) total dataset
(n = 74; total number of individuals), (2) total urban dataset (n = 8; all assigned
urban individuals), (3) total rural dataset (n = 50; all assigned rural individuals),
and (4) iterative rural datasets of randomly subsampled individuals (n = 8; from the
total rural dataset).
We used the program MICRO-CHECKER 2.2.3 (Oosterhout et al. 2004) to test
for evidence of genotyping errors, such as null alleles and scoring errors. We utilized
the program FSTAT 2.9.3 (Goudet 2001) to examine the microsatellite loci
for linkage disequilibrium. We tested for violations of Hardy-Weinberg equilibrium
and assessed genetic diversity and allelic richness in ARLEQUIN 3.1 (Excoffier et
al. 2005). We performed sequential Bonferroni tests for Hardy-Weinberg equilibrium
estimates across loci to correct for biased significance of data within tables
(Rice 1989). We performed all of these tests using the total dataset.
We used BAPS 5.2 (Corander et al. 2008), a Bayesian clustering program, to
test for genetic differentiation using the total dataset without a priori populationSoutheastern
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membership information using the spatial clustering of individuals algorithm.
BAPS works by assigning individuals into population clusters (K) based on genetic
structure detected from allelic frequency data and spatial proximity. These tests
detect population differentiation among all individuals; we performed 10 iterations
for each of K = 1–10. We also used BAPS, incorporating both genotypic and GPSpoint
data, to assess spatial clustering of groups over 10 iterations of both K = 1
and K = 2 to determine if differentiation between the a priori selected total urban
and total rural datasets could be detected.
We employed ARLEQUIN 3.1 (Excoffier et al. 2005) to calculate genetic diversity
measures and pairwise FST (Weir and Cockerham 1984) for the total urban and
total rural datasets. However, the unequal sample sizes produced by categorizing
individuals as either rural (n = 50) or urban (n = 8) in ArcGIS (Esri) were a concern
because pairwise FST does not perform well with unequal sample sizes (Cockerham
1973). Thus, to calculate pairwise FST (Weir and Cockerham 1984) using equalized
sample sizes, we constructed the iterative rural datasets by randomly sampling,
with replacement, 8 individuals from the total rural dataset 100 times. Then, we calculated
pairwise FST between each iterative rural dataset (n = 8) and the total urban
dataset (n = 8) using ARLEQUIN 3.1 (Excoffier et al. 2005). We also subsampled
Figure 1. Sampling classifications within the Alabama study site: urban = grey, buffer/interface
= simple hatch, and rural = white. The black dots represent the locations of sampled
Coyotes.
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10 of the 100 randomly selected iterative rural datasets to examine possible sampling
bias in measures of heterozygosity.
We used ARLEQUIN 3.1 (Excoffier et al. 2005) to calculate expected (HE) and
observed (HO) heterozygosity measures for the 10 randomly selected iterative rural
datasets to compare to the total urban dataset. We then plotted heterozygosity
measures graphically to assess whether the heterozygosity estimates from the total
rural dataset were within the same distribution as 10 of the iterative rural datasets.
If the estimates fell within the same distribution, we concluded that no sampling
bias existed in heterozygosity measures, and thus comparisions of the total urban
and total rural datasets was not biased by unequal sample sizes.
Results
We successfully genotyped 74 individuals sampled within the study area. Of
the 74 total Coyotes sampled, 30 (40.5%) were either captured or targeted, and 44
(59.5%) were vehicle-killed. We recognize the potential for bias if a large portion
of the Coyotes assigned to the urban and rural groups were sampled during dispersal
season when Coyotes were more transient and moved through the landscape.
However, only 38% (n = 28) of the Coyotes within the total dataset were sampled
during a common dispersal time period (i.e., 1 September–31 December) (Holzman
et al. 1992). We categorized 17 Coyotes sampled during a dispersal time period as
rural and 4 as urban. The remaining 7 Coyotes collected during this time period fell
within the buffer/interface area, and were excluded from the urban/rural comparison.
Of the 21 Coyotes that we sampled during dispersal season and categorized as
either rural or urban, 12 were vehicle-killed and 9 were captured/targeted.
One microsatellite marker, CX2235, showed evidence of null alleles at P ≤ 0.05,
which was a concern because null alleles can be evidence of reduced primer annealing,
competition among target alleles of various lengths during amplification, or
poor template quality (Dakin and Avise 2004, Wattier et al. 1998). However, we retained
CX2235 in the study because the occurrence of null alleles at this locus was
less than 0.20, which is considered uncommon or rare (Dakin and Avise 2004). All 10 loci
were used in the analyses, with no loci having more than 5% missing data. We did
not detect linkage disequilibrium across loci over all samples. All loci were polymorphic
with 1 locus, CXX140, violating Hardy-Weinberg equilibrium (Table 1).
This locus had a significantly lower HO (0.82) than HE (0.83) after sequential
Bonferroni corrections (P = 0.001), which suggested significant homozygosity.
This result might indicate the presence of allelic dropout, null alleles, linkage of
alleles, or inbreeding. However, we detected neither null alleles nor allelic dropout.
Also, the tests for linkage disequilibrium were not significant, suggesting the
markers evolved independently within our sample. Lastly, if the violation was a
consequence of inbreeding, we would have expected to observe such a phenomenon
at many or all loci and not just at a single locus (Selkoe and Toonen 2006).
It is important to note the difference between the observed and expected values of
heterozygosity is quite low (0.01). Three of the 9 total alleles detected at CXX140
represent 68.92% of the total allelic frequency for the locus, thus demonstrating a
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deficiency of heterozygotes in the population at this locus. We retained the locus
in the study because it was variable and did not seem to suffer from linkage disequilibrium
or null alleles. Expected heterozygosity among all samples was 0.78,
and allelic richness ranged from 5 to 22 alleles per locus (Table 1), indicating that
genetic diversity was high.
Without an a priori population designation, BAPS 5.2 (Corander et al. 2008)
detected a single genetic cluster among all individuals (K = 1), which suggested
no differentiation among individuals within 100 km of the Auburn/Opelika MSA.
We further assessed genetic differentiation using datasets (i.e., total urban, total
rural, and iterative rural) comprised of rural and urban populations assigned a
priori. The clustering program BAPS 5.2 (Corander et al. 2008) detected no genetic
differentiation between the total urban and total rural datasets. An estimate
of pairwise FST between the total urban and total rural datasets showed significant
genetic differentiation (FST = 0.03; P = 0.01). Additionally, estimates of pairwise
FST between the total urban and iterative rural datasets indicated genetic differentiation
between the groups. Sixty-five percent of the 100 iterative pairwise FST
comparisons were significant at P ≤ 0.05 (Table 2). All significant FST estimates
were low (0.01–0.06; P < 0.05).
Heterozygosity measures for the total rural dataset fell within the distribution of
the values generated from the 10 randomly selected iterative rural datasets; thus,
we concluded that there was no sampling bias in heterozygosity measures. Thus, we
report the gene diversity estimates for both the total rural (0.78) and the total urban
(0.71) datasets (Fig. 2).
Discussion
The main goal of this study was to evaluate population structure and genetic
diversity of Coyotes sampled within a portion of east-central Alabama. Our effort
Table 1. Per-locus information: microsatellite panel/primers information and genetic diversity indices
(allelic richness, heterozygosity) for total sample of Coyotes (n = 74). AR = allelic richness; HE =
expected heterozygosity; HO = observed heterozygosity.
Primer/locus details
Approximate Genetic diversity indices
Multiplex Locus Color Type allele-size range AR HE HO
A FH2001 Fam Tetra 122–158 10 0.76 0.70
A FH2096 Hex Tetra 89–109 5 0.60 0.57
A FH2137 Ned Tetra 158–194 14 0.89 0.88
A CXX140 Hex Di 130–154 9 0.83 0.82
B FH2054 Ned Tetra 135–175 9 0.76 0.85
B FH2010 Hex Tetra 221–237 5 0.74 0.66
B FH2159 Fam Tetra 155–206 22 0.94 0.91
C CX2235 Fam Tetra 136–176 8 0.81 0.72
C FH2100 Hex Tetra 142–176 5 0.72 0.72
C FH2062 Ned Tetra 129–145 6 0.76 0.73
Mean 9.3 0.78 0.76
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Iteration FST P-value
1* 0.03 0.01
2* 0.03 0.01
3* 0.06 0.00
4 0.02 0.09
5 0.01 0.17
6* 0.04 0.01
7* 0.03 0.03
8* 0.04 0.01
9* 0.05 0.01
10* 0.03 0.02
11* 0.03 0.02
12* 0.02 0.04
13 0.02 0.08
14* 0.03 0.02
15* 0.05 0.01
16 0.02 0.06
17* 0.03 0.03
18 0.03 0.07
19* 0.06 less than 0.001
20 0.02 0.10
21 0.02 0.09
22* 0.04 less than 0.001
23* 0.04 0.01
24* 0.03 0.03
25 0.02 0.10
26* 0.04 0.02
27* 0.05 0.01
28* 0.04 0.03
29 0.02 0.06
30 0.02 0.07
31* 0.03 0.04
32* 0.03 0.04
33 0.02 0.09
34 0.02 0.06
35 0.02 0.07
36 0.02 0.08
37* 0.03 0.02
38 0.02 0.06
39* 0.04 0.01
40* 0.05 less than 0.001
41 0.03 0.06
42* 0.04 0.01
43* 0.05 0.00
44* 0.03 0.04
45* 0.03 0.02
46* 0.03 0.01
47 0.02 0.07
48 0.02 0.07
49* 0.04 0.01
50 0.02 0.08
Table 2: Pairwise FST values with P-values calculated using a subsample (n = 8) of the rural Coyote
population against the total urban population (n = 8) near Auburn, AL. * denotes P ≤ 0.05.
Iteration FST P-value
51 0.02 0.06
52* 0.03 0.02
53* 0.04 0.01
54* 0.03 0.02
55* 0.04 0.01
56* 0.04 less than 0.001
57* 0.05 less than 0.001
58 0.02 0.06
59* 0.03 0.04
60 0.01 0.14
61 0.02 0.07
62* 0.02 0.04
63* 0.04 less than 0.001
64* 0.03 0.02
65 0.01 0.18
66* 0.04 0.01
67* 0.03 0.03
68* 0.04 0.02
69* 0.05 less than 0.001
70* 0.04 0.01
71* 0.04 0.02
72* 0.04 0.01
73* 0.03 0.03
74* 0.03 0.03
75* 0.03 0.03
76 0.02 0.08
77 0.02 0.11
78* 0.05 less than 0.001
79* 0.05 0.01
80* 0.03 0.04
81* 0.03 0.02
82* 0.04 0.01
83* 0.03 0.03
84 0.03 0.06
85* 0.03 0.02
86* 0.03 0.02
87 0.02 0.08
88 0.02 0.07
89 0.02 0.08
90* 0.03 0.04
91 0.01 0.09
92* 0.03 0.04
93 0.02 0.05
94 0.01 0.13
95* 0.03 0.04
96* 0.03 0.05
97* 0.05 0.01
98* 0.03 0.04
99 0.02 0.08
100 0.02 0.06
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provided baseline genetic information about Coyotes within an area of recent range
expansion for this species (Parker 1995). Coyotes across the study area (i.e., total
dataset) showed high levels of genetic diversity and no significant population
structure. This finding is consistent with other studies completed across the US
and Canada using autosomal microsatellite DNA, which have shown that Coyotes
maintain a high level of genetic diversity and gene flow across their range (Riley et
al. 2006; Roy et al. 1994; Sacks et al. 2004, 2008).
We observed weak genetic differentiation (Balloux and Lugon-Moulin 2002,
Wright 1978) between the urban and rural groups. Low, but significant, FST estimates
resulted from pairwise comparisons of the total urban and total rural
datasets, as well as among a majority of the total urban and iterative rural dataset
comparisons. However, we urge caution in considering this evidence of genetic
differentiation as being biologically meaningful because the observed FST values
were very low. These results conflict with the BAPS results and the biology of Coyotes,
which together suggest a high level of genetic diversity resulting in little or no
population structure. Comparisons between the total rural and total urban datasets
were extremely skewed with 50 rural individuals compared to 8 urban individuals
in the dataset. Further, 35% of the pairwise FST estimates comparing groups within
the total urban and iterative rural datasets were not significant. We acknowledge that
the exclusion of the 16 buffer/interface individuals from the urban/rural comparison
analysis might have removed some allelic diversity that could have demonstrated
a connection between the rural and urban groups tested. This potential elimination
of information may have strengthened the results suggesting significant population
structure. However, removing the Coyotes that fell within the buffer/interface area
was necessary to account for the individuals that could not clearly be assigned to
Figure 2. Scatter plot showing results of iterative testing of subsamples (grey squares) from
the rural Coyote population compared to estimates for the total rural dataset (n = 50; black
triangle) and total urban dataset (open diamond).
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either the urban or rural group. It is also possible that our reduction in sample size
through subsampling the rural group masked shared allelic diversity between the total
urban and iterative rural groups and thus led to low but significant FST estimates.
It is also true that the high rates of mutation that occur in microsatellites can lead
to an underestimation of FST estimates (Balloux and Lugon-Moulin 2002, Hedrick
1999). In a study of Coyote populations in southern California, population differentiation
was detected between populations on either side of a major freeway. The
FST estimates (0.03–0.04) that characterized this differentiation were significant and
low (Riley et al. 2006), similar to those observed in this study. Further, it has been
demonstrated that in some systems, such as fish populations, low but significant FST
reflects biologically meaningful population structure (Knutsen et al. 2011). Therefore,
consideration of the relevance of potential population differentiation between
urban Auburn, AL, Coyotes and those from surrounding rural areas is warranted.
We acknowledge the possibility for bias within the urban/rural comparison analysis
introduced by including potentially dispersing Coyotes in the urban and rural
populations tested. It could be suggested that vehicle-killed Coyotes might include a
large proportion of dispersing animals because they are less familiar with their surroundings
during dispersal activities (Bekoff and Gese 2003). We did not take into
account whether an individual was sampled as a vehicle-killed or captured/targeted
individual when delineating urban and rural Coyotes. However, there was not an
overwhelming majority of vehicle-killed Coyotes included within this study, nor
was there a majority of the Coyotes sampled during a common dispersal time period.
Of the 8 Coyotes making up the total urban dataset investigated within this
study, 6 individuals were sampled in close proximity to the Auburn University
Regional Airport (formerly known as the Auburn-Opelika Robert G. Pitts Airport),
suggesting that Coyotes may be concentrated within the area. Coyotes have
been identified as the most common carnivore species threat to aircraft, and in
most cases, the mammalian species second only to deer (predominately Whitetailed
Deer) (Cleary and Dolbeer 2005, Cleary et al. 2006, Dolbeer and Wright
2009, Dolbeer et al. 2000). Coyotes specifically are drawn to airports because
the facilities have ample water sources and large, open grassland areas that are
advantageous for hunting prey species (Cleary and Dolbeer 2005, Dolbeer and
Wright, 2009, Dolbeer et al. 1993). It may also be that these Coyotes were isolated
in the area due to anthropogenic barriers such as highways, and the increased
commercial development within 2 miles of the airport that has drastically altered
the landscape over the last several years. The 2 major highways that intersect in
the Auburn/Opelika MSA, Interstate 85 and Alabama State Highway 280, converge
approximately 2.5 km from the airport. Alabama State Highway 280 runs
to the east and north of the Auburn University Regional Airport, while Interstate
85 more closely borders the airport to the south. Riley et al. (2006) found that
freeways in California served as barriers to gene flow between Coyote populations.
We speculate that reproductive opportunities between this subset of urban
Coyotes and rural individuals could be limited, thus leading to reduced gene flow
and increased levels of genetic differentiation. If this were true, it is possible that
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2015 Vol. 14, No. 1
a group of Coyotes, like those observed near the Auburn University Regional Airport,
could be considered a viable management unit.
Coyotes exhibit high capacity for dispersal and continuous distribution across
their range, are transient in nature, and are habitat generalists (Bekoff and Gese
2003), thus we expected to observe high levels of population diversity and low
population structure. Indeed, when examining population diversity across the
entire study area we detected high levels of genetic diversity and no population
structure. These findings suggest that Coyotes within east-central Alabama move
large distances and interact in a way that does not promote population subdivision.
We conclude that defining distinct groups, or management units, for Coyotes based
solely on genetic data may not be viable at the broader spatial scale used within this
study (i.e., 100-km radius of the Auburn/Opelika MSA). Therefore, management
strategies should be applied uniformly to all Coyotes being managed within the area
investigated in this study. When examining genetic differentiation at a finer scale
(i.e., within the Auburn/Opelika MSA) while incorporating landscape and human
population data, we may have detected weak population structure between urban
and rural Coyotes. To better understand population structure of Coyotes in urban areas
of Alabama and across the southeastern US, further research efforts are needed.
In fact, the results of this study may differ from what might be observed within
other metropolitan areas of Alabama. A larger sample of urban Coyotes within the
Auburn/Opelika MSA, as well as a comparison between other small urban areas
and larger metropolitan areas within Alabama, and throughout the southeastern US,
would likely provide better information to wildlife biologists for use in generating
effective management strategies for the species. This study provides baseline
information regarding population genetics of Coyotes in east-central Alabama and
the utility of using genetic techniques to assist in the delineation of management
units, a practice that is becoming more important as we observe an increase in
human-Coyote conflicts and thus the need to manage Coyote populations across the
southeastern US.
Acknowledgments
We thank The Center for Forest Sustainability and Auburn University for funding this
study. We thank the USDA-National Wildlife Research Center’s Genetics Lab and the Kenny
Brock Lab at the Auburn University School of Veterinary Medicine for support with data
collection and analyses. Also, thanks to all of the individuals and agencies that collected
carcasses and tissue samples for this project, including Alabama USDA-Wildlife Services.
Thank you to Dr. Todd Steury for helping tremendously with statistical analyses, Amy
Silvano for assistance with ArcGIS, and Karen Tenaglia-Hoksbergen, Dr. Scott Santos, and
Philip Damm for frequent help and guidance. Lastly, we appreciate the comments provided
by reviewers of this manuscript during consideration of publication.
Literature Cited
Arjo, W.M., and D.H. Pletscher. 1999. Behavioral responses of Coyotes to wolf recolonization
in northwestern Montana. Canadian Journal of Zoology 77:1919–1927.
Southeastern Naturalist
D.L. Damm, J.B. Armstrong, W.M. Arjo, and A.J. Piaggio
2015 Vol. 14, No. 1
118
Armstrong, J.B. 2011. Changes in wildlife-damage management in Alabama: 1990–2011.
Proceedings of Vertebrate Pest Conference 25:315–316.
Armstrong, J.B, and M.D. Smith. 2014. Coyote control in Alabama. Publication of Alabama
Cooperative Extension System (Alabama A&M University and Auburn University)
(ACES). ANR-0587. Available online at http://www.aces.edu/pubs/docs/A/ANR-0587/
ANR-0587.pdf. Accessed 8 October 2014.
Armstrong, J.B., and N.K. Walters. 1995. Using a toll-free telephone hotline to assess
Coyote depredation in Alabama. Proceedings of Annual Conference of the Southeastern
Association of Fish and Wildlife Agencies 49:537–544.
Atwood, T.C., H.P. Weeks, and T.M. Gehring. 2004. Spatial ecology of Coyotes along a
suburban-to-rural gradient. Journal of Wildlife Management 68:1000–1009.
Balloux, F., and N. Lugon-Moulin. 2002. The estimation of population differentiation with
microsatellite markers. Molecular Ecology 11:155–165.
Bekoff, M. (Ed.). 1978. Coyotes: Biology, Behavior, and Management. The Blackburn
Press, Caldwell, NJ. 384 pp.
Bekoff, M., and E.M Gese. 2003. Coyote. Pp. 467–481, In G. Feldhamer, B.C. Thompson,
and J.A. Chapman (Eds.). Wild Mammals of North America. John Hopkins Press, Baltimore,
MD. 1232 pp.
Berger, K.M., and E.M. Gese. 2007. Does interference competition with wolves limit the
distribution and abundance of Coyotes? Journal of Animal Ecology 76:1075–1085.
Brady, J.R., and W.H. Campbell. 1983. Distribution of Coyotes in Florida. Florida Field
Naturalist 11:40–41.
Breen, M., S. Jouquand, C. Renier, C.S. Mellersh, C. Hitte, N.G. Holmes, A. Chéron, N.
Suter, F. Vignaux, A.E. Bristow, C. Priat, E. McCann, C. André, S. Boundy, P. Gitsham,
R. Thomas, W.L. Bridge, H.F. Spriggs, E.J. Ryder, A. Curson, J. Sampson, E.A. Ostrander,
M.M. Binns, and F. Galibert. 2001. Chromosome-specific single-locus FISH
probes allow anchorage of an 1800-marker integrated radiation-hybrid/linkage map of
the Domestic Dog genome to all chromosomes. Genome Research 11:1784–1795.
Cleary, E.C., and R.A. Dolbeer. 2005. Wildlife hazard management at airports: A manual
for airport personnel. USDA National Wildlife Research Center, Fort Collins, CO.
348 pp.
Cleary, E.C., R.A. Dolbeer, and S.E. Wright. 2006. Wildlife strikes to civil aircraft in the
United States 1990–2005. Other bird strike and aviation materials. Serial Report Number
12. Washington, DC. 64 pp.
Cockerham, C.C. 1973. Analyses of gene frequencies. Genetics 74:679–700.
Connolly, G. 1992. Coyote damage to livestock and other resources. Pp. 161–169, In A.H.
Boer. (Ed.). Ecology and Management of the Eastern Coyote. University of New Brunswick,
Fredericton, NB, Canada. 194 pp.
Corander, J., J. Sirén, and E. Arjas. 2008. Bayesian spatial modeling of genetic population
structure. Computational Statistics 23:111–129.
Dakin, E.E., and J.C. Avise. 2004. Microsatellite null alleles in parentage analysis. Heredity
93:504–509.
DeYoung, R.W., and R.L. Honeycutt. 2005. The molecular toolbox: Genetic techniques
in wildlife ecology and management. Journal of Wildlife Management 69:1362–1384.
Diniz-Filho, J.A.F., and M.P.D.C. Telles. 2002. Spatial autocorrelation analysis and the
identification of operational units for conservation in continuous populations. Conservation
Biology 16:924–935.
Dolbeer, R.A., and S.E. Wright. 2009. Safety-management systems: How useful will the
FAA National Wildlife Strike Database be? Human–Wildlife Conflicts 3:167–178.
Southeastern Naturalist
119
D.L. Damm, J.B. Armstrong, W.M. Arjo, and A.J. Piaggio
2015 Vol. 14, No. 1
Dolbeer, R.A., J.L. Belant, and J.L. Sillings. 1993. Shooting gulls reduces strikes with
aircraft at John F. Kennedy International Airport. Wildlife Society Bulletin 21:442–450.
Dolbeer R.A., S.E. Wright, and E.C. Cleary. 2000. Ranking the hazard level of wildlife species
to aviation. Wildlife Society Bulletin 28:372–378.
Dunn A.C., and M.D. Smith. 2011. The Coyote: Facts and myths about living with this wild
canid. Publication of Alabama Cooperative Extension System, Alabama A&M University
and Auburn University (ACES). ANR-1413. Available online at http://www.aces.
edu/pubs/docs/A/ANR-1413/ANR-1413.pdf. Accessed 8 October 2014.
Excoffier, L., G. Laval, and S. Schneider. 2005. Arlequin version 3.1: An integrated software
package for population-genetics data analysis. Evolutionary Bioinformatics Online
1:47–50.
Faircloth, B.C. 2006. GMCONVERT: File conversion for GENEMAPPER output files.
Molecular Ecology Notes 6:968–970.
Francisco, L.V., A.A. Langston, C.S. Mellersh, C.L. Neal, and E.A. Ostrander. 1996. A class
of highly polymorphic tetranucleotide repeats for canine genetic mapping. Mammalian
Genome 7:359–362.
French, T.W., and J.L. Dusi. 1979. Status of the Coyote and Red Wolf in Alabama. Journal
of the Alabama Academy of Sciences 54:222.
Gehrt, S.D., C. Anchor, and L.A. White. 2009. Home range and landscape use of Coyotes in a
metropolitan landscape: Conflict or coexistence? Journal of Mammalogy 90:1045–1057.
Gehrt, S.D., J.L. Brown, and C. Anchor. 2011. Is the urban Coyote a misanthropic synanthrope?
The case from Chicago. Cities and the Environment (CATE) 4:3.
Gipson, P.S., J.A. Sealander, and J.E. Dunn. 1974. The taxonomic status of wild Canis in
Arkansas. Systematic Zoology 23:1–11.
Glaubitz, J.C. 2004. Convert: A user-friendly program to reformat diploid genotypic data
for commonly used population genetic software packages. Molecular Ecology Notes
4:309–310.
Goudet, J. 2001. FSTAT ver. 2.9.3, A program to estimate and test gene diversities and fixation
indices. University of Lausanne, Switzerland. Available online at http://www.unil.
ch/izea/softwares.fstat.html. Accessed 25 March 2009.
Hedrick, P.W. 1999. Perspective: Highly variable loci and their interpretation in evolution
and conservation. Evolution 53:313–318.
Hill, E.P., P.W. Sumner, and J.B. Wooding. 1987. Human influences on range expansion of
Coyotes in the southeast. Wildlife Society Bulletin 15:521–524.
Holzman, S., M.J. Conroy, and J. Pickering. 1992. Home range, movements, and habitat
use of Coyotes in south-central Georgia. Journal of Wildlife Management 56:139–146.
Honeycutt, R.L. 2000. Genetic applications for large mammals. Pp. 233–252, In S. Demarais
and P.R. Krausman, (Eds.). Ecology and Management of Large Mammals in North
America. Prentice Hall, Upper Saddle River, NJ. 778 pp.
Houben, J.M. 2004. Status and management of Coyote depredations in the eastern United
States. Sheep and Goat Research Journal 19:16–22.
Howze, M.B., L.M. Conner, R.J. Warren, and K.V. Miller. 2009. Predator removal and
White-tailed Deer recruitment in southwestern Georgia. Proceedings of Annual Conference
of the Southeastern Association of Fish and Wildlife Agencies 63:17–20.
Jackson, A.M., and S.S. Ditchkoff. 2013. Survival estimates of White-tailed Deer fawns at
Fort Rucker, Alabama. The American Midland Naturalist 170:184–190.
Jantz, H.E. 2011. Home range, activity patterns, and habitat selection of the Coyote (Canis
latrans) along an urban–rural gradient. M.Sc. Thesis. Auburn University, Auburn, AL.
107 pp.
Southeastern Naturalist
D.L. Damm, J.B. Armstrong, W.M. Arjo, and A.J. Piaggio
2015 Vol. 14, No. 1
120
Jones, E.J. 1987. Coyote damage in the southeastern United States. Proceedings of the
Eastern Wildlife Damage Control Conference 3:320.
Kilgo, J.C., H.S. Ray, M. Vukovich, M.J. Goode, and C. Ruth. 2012. Predation by Coyotes
on White-tailed Deer neonates in South Carolina. The Journal of Wildlife Management
76:1420–1430.
Kilgo, J.C., M. Vukovich, H.S. Ray, C.E. Shaw, and C. Ruth. 2014. Coyote removal, understory
cover, and survival of White-tailed Deer neonates. The Journal of Wildlife
Management 78:1261–1271.
Kleiner, K.J., M.D. Mackenzie, A.L. Silvano, J.A. Grand, J.B. Grand, J. Hogland, E.R. Irwin,
M.S. Mitchell, B.D. Taylor, T. Earnhardt, E. Kramer, J. Lee, A.J. McKerrow, M.J.
Rubino, K. Samples, A. Terando, and S.G. Williams. 2007. GAP land-cover map of ecological
systems for the state of Alabama (provisional). Alabama Gap Analysis Project.
Available online at www.auburn.edu\gap. Accessed 7 July 2009.
Knutsen, H., E.M. Olsen, P.E. Jorde, S.H. Espeland, C. Andre, and N.C. Stenseth. 2011.
Are low but statistically significant levels of genetic differentiation in marine fishes
“biologically meaningful”? A case study of coastal Atlantic Cod. Molecular Ecology
20:768–783.
Lackey, R.T. 1998. Seven pillars of ecosystem management. Landscape and Urban Planning
40:21–30.
Mastro, L.L, E.M. Gese, J.K. Young, and J.A. Shivik. 2012. Coyote (Canis latrans), 100+
years in the East: A literature review. Addendum to the Proceedings of the 14th Wildlife
Damage Management Conference 14:129–131.
Monzόn, J. 2014. First regional evaluation of nuclear genetic diversity and population
structure in northeastern Coyotes (Canis latrans). F1000Research 3:66.
Moritz, C. 1994. Defining “evolutionary significant” units for conservation. TRENDS in
Evolution and Ecology 9:373–375.
Nowak, R.M. 1978. Evolution and taxonomy of Coyotes and related Canis. Pp. 3–16, In
M. Bekoff, (Ed.). Coyotes: Biology, Behavior, and Management. The Blackburn Press,
Caldwell, NJ. 384 pp.
Oosterhout, C.V., W.F. Hutchinson, D.P.M. Wills, and P. Shipley. 2004. MICRO-CHECKER:
Software for identifying and correcting genotyping errors in microsatellite data.
Molecular Ecology Notes 4:535–538.
Ostrander, E.A., G.F. Sprague, and J. Rine. 1993. Identification and characterization of
dinucleotide repeat (CA)n markers for genetic mapping in Dog. Genomics 16:207–213.
Ostrander, E.A., F.A. Mapa, M. Yee, and J. Rine. 1995. One hundred and one new simple
sequence repeat-based markers for the canine genome. Mammalian Genome 6:192–195.
Palsbøll, P.J., M. Bérubé, and F.W. Allendorf. 2006. Identification of management units using
population genetic data. Trends in Ecology and Evolution 22:11–16.
Parker, G. 1995. Eastern Coyote: The Story of Its Success. Nimbus Publishing Limited,
Halifax, NS, Canada. 264 pp.
Phillip, M.C., and J.B. Armstrong. 1995. Perceptions and knowledge of Alabama fruit and
vegetable producers towards Coyotes. Eastern Wildlife Damage Control Conference
6:175–181.
Randa, L.A., and J.A. Yunger. 2006. Carnivore occurrence along an urban–rural gradient:
A landscape-level analysis. Journal of Mammalogy 87:1154–1164.
Rashleigh, R.M., R.A. Krebs, and H. van Keulen. 2008. Population structure of Coyote
(Canis latrans) in the urban landscape of the Cleveland, Ohio area. The Ohio Journal
of Science 108:54–59.
Rice, W.R. 1989. Analyzing tables of statistical tests. Evolution 43:223–225.
Southeastern Naturalist
121
D.L. Damm, J.B. Armstrong, W.M. Arjo, and A.J. Piaggio
2015 Vol. 14, No. 1
Riley, S.P.D., R.M. Sauvajot, T.K. Fuller, E. C. York, D. A. Kamradt, C. Bromley, and R.K.
Wayne. 2003. Effects of urbanization and habitat fragmentation on Bobcats and Coyotes
in southern California. Conservation Biology 17:566–576.
Riley, S.P.D., J.P. Pollinger, R.M. Sauvajot, E.C. York, C. Bromley, T.K. Fuller, and R.K.
Wayne. 2006. A southern California freeway is a physical and social barrier to gene flow
in carnivores. Molecular Ecology 15:1733–1741.
Roy, M.S., E. Geffen, D. Smith, E.A. Ostrander, and R.K. Wayne. 1994. Patterns of differentiation
and hybridization in North American wolflike canids, revealed by analysis of
microsatellite loci. Molecular Biology and Ecology 11:553–570.
Sacks, B.N., S.K. Brown, and H.B. Ernest. 2004. Population structure of California Coyotes
corresponds to habitat-specific breaks and illuminates species history. Molecular Ecology
13:1265–1275.
Sacks, B.N., B.R. Mitchell, C.L. Williams, and H.B. Ernest. 2005. Coyote movements
and social structure along a cryptic population genetic subdivision. Molecular Ecology
14:1241–1249.
Sacks, B.N., D.L. Bannasch, B.B. Chomel, and H.B. Ernest. 2008. Coyotes demonstrate
how habitat specialization by individuals of a generalist species can diversify populations
in a heterogeneous ecoregion. Molecular Biology and Ecology 25:1384–1394.
Schwartz, M.K., G. Luikart, and R.S. Waples. 2007. Genetic monitoring as a promising tool
for conservation and management. Trends in Ecology and Evolution 22:25–33.
Selkoe, K.A., and R.J. Toonen. 2006. Microsatellites for ecologists: A practical guide to
using and evaluating microsatellite markers. Ecology Letters 9:615–629.
Seutin, G., B.N. White, and P.T. Boag. 1991. Preservation of avian blood and tissue samples
for DNA analyses. Canadian Journal of Zoology 69:82–90.
US Census Bureau. 2001. Census 2000 redistricting data 2001: Summary file and 1990 census.
Available online at http://www.census.gov/population/www/cen2000/briefs/phc-t3/
tables/tab05.pdf. Accessed 13 April 2010.
US Census Bureau, Geography Division. 2002. Census 2000 urban and rural classification.
Available online at http://www.census.gov/geo/www/tiger. Accessed 15 July 2009.
US Department of Agriculture (USDA). 2002. Environmental assessment: Reduction of
Coyote damage to livestock and other resources in Louisiana. Available online at: http://
www.aphis.usda.gov/regulations/pdfs/nepa/LAcoyote.pdf. Accessed 8 October 2014.
USDA. 2013. Cooperative services agreement with the City of Auburn, AL. Available from
the City of Auburn.
USDA. 2014. Environmental assessment: Mammal-damage management in Alabama.
Available online at: http://www.aphis.usda.gov/wildlife_damage/downloads/nepa/
AL_Mammal_EA_FINAL.pdf. Accessed 8 October 2014.
VanGilder, C.L., G.R. Woods, and K.V. Miller. 2009. Effects of an intensive predator removal
on White-tailed Deer recruitment in northeastern Alabama. Proceedings of Annual
Conference of the Southeastern Association of Fish and Wildlife Agencies 63:11–16.
Wallace B.P., A.D. DiMatteo, B.J. Hurley, E.M. Finkbeiner, A.B. Bolten, M.Y. Chaloupka,
B.J. Hutchinson, F.A. Abreu-Grobois, D. Amorocho, K.A. Bjorndal, J. Bourjea, B.W.
Bowen, R. Briseño Dueñas, P. Casale, B.C. Choudhury, A. Costa, P.H. Dutton, A. Fallabrino,
A. Girard, M. Girondot, M.H. Godfrey, M. Hamann, M. López-Mendilaharsu,
M.A. Marcovaldi, J.A. Mortimer, J.A. Musick, R. Nel, N.J. Pilcher, J.A. Seminoff, S.
Troëng, B. Witherington, and R.B. Mast. 2010. Regional management units for marine
turtles: A novel framework for prioritizing conservation and research across multiple
scales. PLoS ONE 5:e15465.
Southeastern Naturalist
D.L. Damm, J.B. Armstrong, W.M. Arjo, and A.J. Piaggio
2015 Vol. 14, No. 1
122
Wattier, R., C.R. Engel, P. Saumitou-Laprade, and M. Valero. 1998. Short-allele dominance
as a source of heterozygote deficiency at microsatellite loci: Experimental evidence at
the dinucleotide locus Gv1CT in Gracilaria gracilis (Rhodophyta). Molecular Ecology
7:1569–1573.
Way, J.G., L. Rutledge, T. Wheeldon, and B.N. White. 2010. Genetic characterization of
eastern “Coyotes” in eastern Massachusetts. Northeastern Naturalist 17:189–204.
Weir, B.S., and C.C. Cockerham. 1984. Estimating F-statistics for the analysis of population
structure. Evolution 38:1358–1370.
Williams, C.L., K. Blejwas, J.J. Johnston, and M.M. Jaeger. 2003. Temporal genetic variation
in a Coyote (Canis latrans) population experiencing high turnover. Journal of Mammalogy
84:177–184.
Wooding, J.B., and T.S. Hardinsky. 1990. Coyote distribution in Florida. Florida Field
Naturalist 18:12–13.
Wright, S. 1978. Evolution and the Genetics of Populations. Volume 4. Variability Within
and Among Natural Populations. University of Chicago Press, Chicago, IL. 590 pp.
Young, S.P., and H.H.T. Jackson. 1951. The Clever Coyote. Lincoln and London, University
of Nebraska Press, Lincoln, NE. 411 pp.