Connectivity and gene flow among Eastern Tiger Salamander

Connectivity and gene flow among
Eastern Tiger Salamander (Ambystoma
tigrinum) populations in highly modified
anthropogenic landscapes
Valorie R. Titus, Rayna C. Bell,
C. Guilherme Becker & Kelly
R. Zamudio
Conservation Genetics
ISSN 1566-0621
Volume 15
Number 6
Conserv Genet (2014) 15:1447-1462
DOI 10.1007/s10592-014-0629-5
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Conserv Genet (2014) 15:1447–1462
DOI 10.1007/s10592-014-0629-5
RESEARCH ARTICLE
Connectivity and gene flow among Eastern Tiger Salamander
(Ambystoma tigrinum) populations in highly modified
anthropogenic landscapes
Valorie R. Titus • Rayna C. Bell • C. Guilherme Becker
Kelly R. Zamudio
•
Received: 21 January 2014 / Accepted: 19 June 2014 / Published online: 8 July 2014
! Springer Science+Business Media Dordrecht 2014
Abstract Fragmented landscapes resulting from anthropogenic habitat modification can have significant impacts
on dispersal, gene flow, and persistence of wildlife populations. Therefore, quantifying population connectivity
across a mosaic of habitats in highly modified landscapes is
critical for the development of conservation management
plans for threatened populations. Endangered populations
of the eastern tiger salamander (Ambystoma tigrinum) in
New York and New Jersey are at the northern edge of the
species’ range and remaining populations persist in highly
developed landscapes in both states. We used landscape
genetic approaches to examine regional genetic population
structure and potential barriers to migration among
remaining populations. Despite the post-glacial demographic processes that have shaped genetic diversity in
tiger salamander populations at the northern extent of their
range, we found that populations in each state belong to
distinct genetic clusters, consistent with the large geographic distance that separates them. We detected overall
low genetic diversity and high relatedness within populations, likely due to recent range expansion, isolation, and
relatively small population sizes. Nonetheless, landscape
connectivity analyses reveal habitat corridors among
remaining breeding ponds. Furthermore, molecular
V. R. Titus (&)
Natural Resource Management, Green Mountain College, One
Brennan Circle, Poultney, VT 05764, USA
e-mail: valorie.titus@greenmtn.edu
R. C. Bell ! C. G. Becker ! K. R. Zamudio
Department of Ecology and Evolutionary Biology, Cornell
University, E145 Corson Hall, Ithaca, NY 14823, USA
estimates of population connectivity among ponds indicate
that gene flow still occurs at regional scales. Further
fragmentation of remaining habitat will potentially restrict
dispersal among breeding ponds, cause the erosion of
genetic diversity, and exacerbate already high levels of
inbreeding. We recommend the continued management and
maintenance of habitat corridors to ensure long-term viability of these endangered populations.
Keywords Ambystoma tigrinum ! Tiger salamander !
Landscape genetics ! Fragmentation
Introduction
Anthropogenic habitat fragmentation influences species
persistence and the adaptive potential of populations, and
also decreases the ability of organisms to disperse across
landscapes (Hunter 2002; Edenhamn et al. 2000; Spear
et al. 2005; Zamudio and Wieczorek 2007; Marsack and
Swanson 2009). This interruption to dispersal can then lead
to increased inbreeding (Frankham 2005) and smaller
effective population sizes (Frankham 1995; Wang et al.
2011), both of which ultimately result in reduced genetic
variation. Combined, these factors can contribute to genetic
erosion of populations and have negative consequences for
population persistence and resilience to environmental
change (Gibbs 1998; Hunter 2002; Andersen et al. 2004;
Frankham 2005; Spear et al. 2005; Rittenhouse and Semlitsch 2006). In landscapes that experience substantial
anthropogenic disturbance, quantifying current population
structure and connectivity is an essential first step in
understanding the likelihood of population persistence
(Edenhamn et al. 2000; Spear et al. 2005; Marsack and
Swanson 2009; Wang et al. 2009).
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Pond-breeding amphibian species are particularly vulnerable to habitat fragmentation due to reliance on specific
aquatic and terrestrial habitats (Rittenhouse and Semlitsch
2006; Cushman 2006; Greenwald et al. 2009a, b; Wang
et al. 2011). To complete their biphasic life cycle, most
amphibians require aquatic habitats that are non-randomly
distributed across the landscape and adjacent to upland
forested habitats (Becker et al. 2007). For instance, most
temperate frog species move to upland habitats or from
overwintering sites to summer refugia in the non-breeding
season (Lamoureux and Madison 1999; Lamoureux et al.
2002; Rittenhouse and Semlitsch 2007). Likewise, pondbreeding salamanders move between forested upland and
aquatic habitats; therefore, forested corridors between
wetlands are necessary to maintain population connectivity
(Greenwald et al. 2009a). Thus, landscape features, both
natural and anthropogenic, affect connectivity of amphibian populations and understanding the nature of these
barriers is important for their conservation (Spear et al.
2005; Zamudio and Wieczorek 2007; Greenwald et al.
2009a, b; Wang et al. 2011).
The eastern tiger salamander (Ambystoma tigrinum) is a
pond-breeding amphibian that occupies a highly developed
landscape in the Northeastern United States and may be
negatively affected by habitat fragmentation in this region.
Despite extensive landscape disturbance at the northeastern
extent of the species’ range, tiger salamanders persist in a
few remaining pine-barrens habitat patches in New York
and New Jersey. The historic range of the eastern tiger
salamander in New York included a few isolated populations west of the Hudson River and most of Long Island.
Presently, populations remain only on Long Island in
Suffolk County. In New Jersey, this species historically
occurred statewide but is now restricted to a few ponds in
the southern part of the state. Tiger salamanders are now
listed as endangered in both states and remaining populations are at risk of further declines due to continued urban
development.
Tiger salamander populations in the northeastern U.S.
are the result of post-pleistocene range expansion and
therefore show reduced genetic diversity compared to
historically stable populations further south (Church et al.
2003). Decreased genetic diversity and increased genetic
differentiation are a common and expected pattern in
range-edge populations, making it particularly challenging
to distinguish between the effects of historical demographic processes and current landscape disturbances on
the distribution of population genetic variation (Johansson
et al. 2006; Eckert et al. 2008; Dudaniec et al. 2012). We
were particularly interested in genetic diversity in this
region because these populations show the signature of
historical bottlenecks at range edge and yet persist in a
highly modified landscape with known population declines.
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Fig. 1 Land cover suitability for A. tigrinum in New York (a) and c
New Jersey (b). Landcover categories adapted from Compton et al.
(2007) and Greenwald et al. (2009b), see Table 4 Appendix. Surface
resistance depicting connectivity gradient across sampled ponds in
New York (c) and New Jersey (d)
We sampled a majority of the known remaining breeding
ponds in New York and New Jersey and genotyped individuals at microsatellite loci, to test the hypotheses that (1)
population genetic diversity is low in these range-edge
populations and (2) that highly modified and spatially
disjunct populations have suffered reduced connectivity in
this region. We interpreted these genetic results in light of a
landscape resistance model to identify potential corridors
for, and barriers to, migration in these historically recently
colonized, yet highly modified, environments (Lesica and
Allendorf 1995; Eckert et al. 2008).
Materials and methods
Population sampling
We collected tissue samples from adults, juveniles and egg
clutches at 17 breeding sites on Long Island, New York
(Ponds NY1–NY17) during the spring and summer months
of 2005–2008 and at nine sites in southern New Jersey
(Ponds NJ1–NJ9) in the spring of 2009 (Fig. 1). These sites
include many of the confirmed remaining breeding sites for
this species in these two states. We collected as many
tissue samples as possible from each locality (range 2–93,
mean 17). Due to the endangered status of this species, we
sampled non-destructively, by taking a single toe or a small
section of tail from adults or juveniles, or single eggs from
individual egg masses. Most of our ponds support natural
breeding aggregations, however, ponds NY1 and NY7 on
Long Island, potentially include animals that were relocated from surrounding wetlands in response to development or pond desiccation (Green, pers comm.).
Microsatellite data collection and analysis
We extracted genomic DNA from tissues using Chelex
extraction (Parra-Olea et al. 2007) and used the supernatant
as template for microsatellite amplification via polymerase
chain reaction (PCR). We genotyped samples at twelve
microsatellite loci (Table 1; Mech et al. 2003; Williams
and DeWoody 2004; Gopurenko et al. 2006; Parra-Olea
et al. 2007). Total PCR volume was 10 lL, including 1 lL
of DNA template (1–10 ng DNA), 0.2 lM of each primer,
0.05 U Taq polymerase, 19 PCR buffer with 1.5 mM
MgCl, and 0.1 mM dNTPs. Each forward primer was
labeled with a fluorescent dye (NED, PET, 6-FAM, or
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Table 1 Primer sequences and
amplification conditions for A.
tigrinum sampled in New York
and New Jersey
Conserv Genet (2014) 15:1447–1462
Primer
60.9
Forward/reverse primers
PCR annealing
temperature ("C)
Citation
TATTTATTGAACGTGAACCTACTGCTGAGAA
59.7
Gopurenko et al.
(2006)
55.3
Mech et al.
(2003)
59.7
Mech et al.
(2003)
59.7
Mech et al.
(2003)
59.7
Mech et al.
(2003)
53.3
Williams and DeWoody
(2004)
59.7
Parra-Olea et al.
(2007)
59.7
Parra-Olea et al.
(2007)
67.6
Parra-Olea et al.
(2007)
59.7
Parra-Olea et al.
(2007)
59.7
Parra-Olea et al.
(2007)
65.3
Parra-Olea et al. (2007)
AAAGTAACATTAGATTGGGGGAGGGATAGA
ATS4-11
GTGTTACCCACCTTTTTCTATCCC
GGCCGCACAACCTAAGTG
ATS4-20
TGTTTTGCCCTTATGTCG
GCCCAAATCCTAAAGAGTAAGT
ATS5-7
GGGCTTGAATCATGTAGTGG
GGGAAGACTAGATGGCAATAAC
ATS5-8
AGTCCCTCTCTATCTAATCTCG
ATTCTCCTGCCTGTATGTTT
Atex65
TTCTGAGCTGTCCATGTTCATATGC
CGCTAGGAAGTCACATTTACTTTGTC
Atig52.143
TCAGGCATCAGATTCGTTGTTA
TGTTTGTCGGATTTCGTTGTG
Atig52.115
AGCACAAGTTCTGAACCTTTCAC
CCGATCACTCGGGTTACTGT
At52.1
GACACCCACAATGCATTTCTACACC
GCTCTGGCCTTACCCTGCTATCC
At60.3
TTTGCCAATGTTTACCTGCCTGAAT
TGAGTCATGCCTTTCCTGGTGTAA
At52.6
TTACTCAATATCAGACTCCCCAAATGT
CCTATCCCTTCCCCAGCACTCC
At52.34
TGTACAGACAGGCAAGAGGTATTGACAGT
GTCTCCCACTTTAATTTCCCTCAGTTTTT
VIC). We used a 5 min initial denaturation at 94 "C; 35
cycles of 1 min denaturation at 94 "C, 1 min annealing at
the locus-specific temperature (Table 1), 1 min extension
at 72 "C; and a final extension of 72 "C for 5 min. Samples
were genotyped using an ABI Prism 3730 Genetic Analyzer (Applied Biosystems, Carlsbad, CA) and analyzed
using GENESCAN 500 LIZ size standard in GENEMAPPER version 3.5 software (Applied Biosystems).
To determine the presence of null alleles, which can bias
population genetic estimates, we used MICROCHECKER
version 2.2.3 (Van Oosterhout et al. 2004). We used the
program GENALEX version 6 (Peakall and Smouse 2006)
to calculate number of alleles, effective number of alleles,
and number of private alleles. We estimated observed (Ho)
and expected (He) heterozygosity to determine significant
deviations from Hardy–Weinberg equilibrium (HWE)
using ARLEQUIN version 3 (Schneider et al. 2000). At
three of our ponds, we sampled adults across multiple
years, which could potentially bias estimates of genetic
diversity (Savage et al. 2010), therefore at ponds L1, L3,
and L7 we also tested for deviations from HWE across
years before pooling samples in subsequent analyses. To
estimate the pairwise probability of linkage disequilibrium,
we used GENEPOP version 4.0.10 (Raymond and Rousset
1995) and implemented a Fisher’s exact test using 10,000
batches with 10,000 iterations per batch, following a 1,000
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step dememorization. We calculated pairwise relatedness
(r) using GENALEX version 6 (Queller and Goodnight
1989; Peakall and Smouse 2006) to estimate the degree of
inbreeding within populations and to assess for potential
bias in population genetic estimates due to our sampling of
mixed life stages (larvae, eggs, and adults). Due to the high
number of juveniles in our samples, we calculated effective
population size (Ne) for each breeding pond using COLONY version 2.0 (Jones and Wang 2009), which is based
on a sibship assignment model. In this method, the probabilities of all pairs of samples from a population being
full-sibs and half-sibs is calculated and used to determine
Ne. Both empirical data and simulation studies suggest that
the sibship assignment method is more accurate than other
common methods (e.g. heterozygote excess method, the
linkage disequilibrium method, and the temporal method;
Wang 2009).
Regional population structure and migration
We used Bayesian assignment in STRUCTURE version 2
(Pritchard et al. 2000) to identify genetic clusters among
breeding ponds in New York and New Jersey. To estimate
the number of genetic clusters (K) present across our
sampled breeding sites, we ran twenty independent runs of
4,000,000 iterations with a burn-in period of 1,000,000 for
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Conserv Genet (2014) 15:1447–1462
values of K ranging from 1 to 26. We implemented the
admixture model and assumed loci were independent
(Pritchard et al. 2000). We used STRUCTURE HARVESTER (Earl and vonHoldt 2011) to calculate DK (Evanno et al. 2005) and used CLUMPP version 1.1.2
(Jakobsson and Rosenberg 2007) and DISTRUCT version
1.0 (Rosenburg 2004) to average across runs and depict
inferred genetic clusters.
We used the genetic clusters identified in STRUCTURE
in an analysis of molecular variance in GENALEX version
6 (Peakall and Smouse 2006; AMOVA, Excoffier et al.
1992) to examine the contribution of three components of
variation: (1) among individuals within ponds, (2) among
ponds within genetic clusters, and (3) among genetic
clusters in different regions. We ran the AMOVA with all
sampled ponds and excluding ponds with substantial
admixture. To investigate the distribution of the genetic
variation as identified by the AMOVA, we calculated FST
for the entire population, and then subsequently for each
cluster using FSTAT 2.9.3 (Goudet 1995). We then calculated multi-locus estimates of FST (Weir and Cockerham
1984) and quantified population subdivision for all population pairs in NY and NJ (Goudet et al. 1996).
Landscape barriers to migration
To determine the effects of distance and landcover on
genetic connectivity across our sampled ponds in both New
York and New Jersey, we calculated two connectivity
indices: euclidean distance and surface resistance. We
define euclidean distance as the closest straight-line distance between each pair of populations. For our calculations of surface resistance, we characterized land cover
suitability according to known habitat requirements for
amphibians (Compton et al. 2007; Greenwald et al. 2009b;
Fig. 1a, b) based on a 30-m landcover raster (Fry et al.
2011). A 30 m landcover raster is suitable for this analysis
(Compton et al. 2007; Greenwald et al. 2009a, b) because
individual Ambystoma tigrinum routinely travel distances
over 30 m (Titus 2013 dissertation; Madison and Farrand
1998), as do other species of Ambystoma (e.g. Semlitsch
1998; Rothermel and Semlitsch 2002; McDonough and
Paton 2007; Zamudio and Wieczorek 2007), Habitats
where tiger salamanders are commonly found, including
deciduous forest and forested wetlands, were given a
resistance value of 1; habitats more likely to interrupt
salamander movement, such as cultivated land or mediumintensity development, were assigned higher resistance
values. Areas such as estuarine wetlands were assigned an
absolute resistance value because the chance of a salamander surviving in these habitat types is unlikely (See
Table 4 in Appendix). To confirm that land cover types in
both NY and NJ populations are randomly distributed
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spatially, we checked for spatial autocorrelation using
Global Morans’ Index.
We calculated surface resistance by employing our land
cover suitability classification as a conductance grid using
CIRCUITSCAPE v.3.3 (Accessed 23 March 2013) (McRae
and Shah 2009). We employed a cell connection scheme
connecting each node to four neighbors. Surface resistance
weights all possible paths between pairs of ponds and
produces a summary connectivity raster. We then used
single Mantel tests to correlate, in turn, euclidean distance
and surface resistance with pairwise multi-locus FST values
using PASSaGE v. 2.0 (Rosenberg and Anderson 2011). To
avoid the likely bias of multi-colinearity in partial mantel
tests (Guillot and Rousset 2013; Cushman et al. 2013) we
used independent single Mantel tests for the Euclidean and
surface resistance analyses.
Results
Microsatellite diversity and population relatedness
We genotyped 439 A. tigrinum individuals at 12 microsatellite loci (Table 1). We found no evidence of null
alleles and no linkage disequilibrium between loci in the
New York and New Jersey populations. Most loci and
ponds conformed to HWE (See Table 5 in Appendix) and
the few deviations were not consistent across ponds,
therefore we included all loci in subsequent analyses. We
found no significant interannual differences in HWE in
ponds L1 (Chi squared test = 20.067, p = 0.217), L3 (Chi
squared test = 15.567, p = 0.339), and L7 (Chi squared
test = 14.373, p = 0.571), therefore we pooled samples
across years at these sites in all subsequent analyses.
Breeding ponds in both New York and New Jersey
showed low allelic diversity and few private alleles
(Table 2). The microsatellite markers were not highly
polymorphic, ranging from fixed (1 allele, ATS5_8) to 13
alleles (ATS4_20) over all populations. The mean number
of alleles per locus within populations ranged from 1.1
(NY5) to 3.3 (NY1) among the New York ponds, and from
1.7 (NJ7) to 2.4 (NJ8) among New Jersey ponds (Table 2;
Fig. 2).
We estimated average pairwise relatedness for all individuals within each pond. Many sampled ponds showed
high degrees of relatedness (r) compared to the null distributions expected of panmictic populations (Fig. 3). Most
mean pond r estimates were lower than 0.25, the value
expected for half-sibs (Queller and Goodnight 1989).
Ponds NY2, NY5, and NY13 in New York (with average
relatedness of 0.55, 0.85, 0.77 respectively) and ponds NJ1,
NJ2, and NJ4 in New Jersey (with average relatedness of
0.45, 0.35, 0.37 respectively) showed the highest degree of
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Table 2 Summary statistics for
microsatellite loci sampled from
A. tigrinum breeding ponds in
New York (NY1–NY17) and
New Jersey (NJ1–NJ9)
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Pond
Na
Npa
N
Ne
Egg/larvae/adult
2005
NY1
3.3
0.25
93
25 (15–46)
NY2
1.9
0.00
10
9 (4–28)
NY3
2.7
0.08
42
NY4
2.2
0.25
8
NY5
1.1
0.08
12
NY6
1.9
0.00
5
NY7
3.0
0.00
NY8
2.4
0.08
NY9
2.5
NY10
1.2
NY11
NY12
20 (11–39)
0/9/9
2006
2007
2008
0/34/13
0/3/10
0/10/5
2009
0/10/0
0/20/0
–
0/9/4
0/2/7
1/6/1
5 (3–22)
0/12/0
–
0/0/2
0/3/0
56
21 (12–39)
0/35/9
0/1/3
15
35 (16–54)
0.00
21
18 (10–39)
0.00
2
3.0
1.6
0.17
0.00
31
2
NY13
1.3
0.00
5
Effective population size (Ne)
(±95 % confidence interval)
calculated using COLONY
version 2.0 (Jones and Wang
2009) from each pond with a
sample size N [ 10. The
number of samples collected for
each life stage (eggs, larvae, or
adults) is also noted. There were
no observable differences in
Hardy–Weinberg equilibrium at
ponds that were sampled in
multiple years
NY14
2.4
0.08
12
44 (17–71)
0/12/0
NY15
2.6
0.00
20
16 (8–35)
0/20/0
NY16
1.2
0.00
2
–
0/2/0
NY17
1.7
0.00
7
–
0/7/0
NJ1
2.1
0.08
19
19 (10–41)
NJ2
2.1
0.08
9
36 (14–58)
NJ3
1.8
0.25
4
–
4/0/0
NJ4
1.8
0.00
7
–
7/0/0
NJ5
1.9
0.08
7
–
7/0/0
NJ6
2.1
0.00
5
–
5/0/0
Na Average number of alleles,
Npa average number of private
alleles, N number of individuals
sampled
NJ7
1.7
0.00
5
–
5/0/0
NJ8
2.4
0.08
20
27 (15–57)
1/0/19
NJ9
1.9
0.00
20
27 (13–61)
20/0/0
relatedness. To determine if these high relatedness values
result from sampling non-adult life stages, we ran a
Spearman rank correlation and found no significant relationship between mean population relatedness and the
proportion of eggs and larvae sampled (Spearman
R = 0.316, p = 0.142). Therefore, the high estimates of
relatedness in some ponds accurately reflect smaller
breeding populations at those sites and not bias due to
sampling of particular life stages. We only estimated Ne for
populations where our sample size was N [ 10. Mean
estimates of Ne in both states were consistently low; New
York ranged from Ne = 5–44 with an average across ten
populations of Ne = 21 and New Jersey ranged from
Ne = 19–36 with an average across four populations of
Ne = 27 (Table 2).
Regional population structure and migration
Bayesian estimates of population structure revealed three
distinct genetic clusters: two clusters correspond to Long
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–
16 (9–33)
–
–
0/2/2
0/4/0
0/14/1
0/20/1
0/1/1
0/20/0
0/11/0
0/2/0
0/5/0
19/0/0
9/0/0
Island ponds and the third cluster corresponds to all ponds
in New Jersey (Fig. 4). Our DK values (Evanno et al. 2005)
indicated the best estimate for K was three genetic clusters
(DK = 427, for K = 3, while estimates of DK for all other
possible runs ranged from 0.09 to 272). Long Island ponds
NY 1-3 and NY 6-17 were assigned to a single genetic
cluster (mean 0.948; SD = 0.128), ponds NY4 and NY5
formed a second independent cluster (mean 0.854;
SD = 0.327), and all New Jersey ponds were assigned to a
third well-supported cluster (mean 0.935; SD = 0.147).
Mean membership coefficients for individuals in one of
these three clusters were very high, typically exceeding
90 % (overall mean 0.942; SD = 0.147).
The AMOVA (including all sampled ponds) indicated
that 60.2 % of genetic variation is attributable to differences within ponds, 3.1 % among ponds within clusters,
and 36.7 % of the variance was explained between the
three genetic clusters (Table 3). The AMOVA results are
consistent when pond NY5 (which exhibited substantial
admixture) is excluded from the analysis. Pairwise FST
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Fig. 2 Allelic richness and heterozygosity for A. tigrinum in 17 New
York (a) and 9 New Jersey (b) populations at 12 microsatellite loci.
Mean ± SD number of alleles (dark gray bars), mean ± SD number
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of effective alleles (light gray bars), mean ± SD number of private
alleles (white bars), and mean (±SD) heterozygosity (gray line) for
each population (across all loci)
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Fig. 3 Mean within-pond pairwise relatedness (r) estimates for A.
tigrinum. Expected relatedness intervals (black bars) assuming
panmixia among ponds were calculated based on the three regional
genetic clusters (NY1–NY17, NY4–NY5, NJ1–NJ9). Populations
with fewer than three samples were excluded from this analysis.
Populations where both adults and larvae or eggs were sampled are
represented by diamonds and populations where only larvae or eggs
were sampled are represented by circles
values ranged from 0 to 0.781 between NY ponds and
0–0.338 between NJ ponds (See Table 6 in Appendix). We
found high levels of differentiation among New York and
New Jersey ponds (average FST = 0.217), but slightly
lower pairwise divergence among ponds within each
cluster (average FST among NY ponds excluding ponds
NY4 and NY5 = 0.088, among NY Ponds NY4 and
NY5 = 0.157, among NJ ponds = 0.149).
Moran’s Index = 0.0058; Z-score = 0.1739; p = 0.862).
Reconstruction of surface resistance in both study regions
showed relatively high potential connectivity across ponds
in both New York (Fig. 1c) and New Jersey (Fig. 1d).
However, we found no relationship between connectivity
indices and FST values across populations in both New
York (euclidean distance: r = -0.044, p = 0.827; surface
resistance: r = -0.056, p = 0.786); and New Jersey
(euclidean distance: r = 0.120, p = 0.388; surface resistance: r = 0.266, p = 0.246). Due to the high FST values
between NY4 and NY5, and the remaining New York
ponds, we removed these ponds from this analysis to
determine if these values were influencing the outcome of
the Mantel test. Analyses excluding these ponds remain
consistent with previous results, indicating no relationship
between connectivity indices and FST in New York
(euclidean distance: r = 0.175, p = 0.352; surface resistance: r = 0.164, p = 0.396).
Landscape barriers to migration
Euclidean distances among sampling ponds in New York
averaged 12734 m, ranging between 59 and 49794 m. In
New Jersey, pairwise Euclidean distances averaged
10112 m, ranging between 49 and 22807 m (See Table 7
in Appendix). We confirmed the absence of spatial autocorrelation in land-cover across both landscapes (Global
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Fig. 4 Population structure of A. tigrinum collected from 26 ponds in
New York (NY1–NY17) and New Jersey (NJ1–NJ9) averaged across
20 replicates of K = 3. Breeding populations in New York belong to
two distinct clusters and New Jersey populations all belong to the
same genetic cluster
Table 3 Results of a hierarchical AMOVA to compare genetic variation within A. tigrinum breeding ponds, among ponds within the three
regional populations or clusters (New York, New York ponds NY4 and NY5, and New Jersey), and among the three regional populations
Source of variation
d.f.
Sum of squares
Fixation index
Percent variation
740
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p value
507.53
UST = 0.398
60.2
\0.001*
38.15
USC = 0.049
3.1
\0.001*
2
131.94
UCT = 0.367
36.7
\0.001*
729
487.45
UST = 0.404
59.60
\0.001*
22
2
33.45
125.35
USC = 0.043
UCT = 0.377
2.67
37.73
\0.001*
\0.001*
Including admixed populations
Within ponds
Among ponds within clusters
Among clusters
Excluding admixed populations
Within ponds
Among ponds within clusters
Among clusters
Population NY5 exhibited significant admixture, therefore we removed this population from the second analysis. An asterisk indicates significant
p values
Discussion
Genetic diversity and regional genetic structure
Our microsatellite analyses recovered very little genetic
diversity within the New York and New Jersey breeding
ponds. This finding is consistent with a previous phylogeographic study using mitochondrial markers (Church et al.
2003), which showed that northeastern tiger salamander
populations (including both New York and New Jersey)
descended from a few Pleistocene migrants from southern
regions (i.e. North Carolina). Therefore, low genetic
diversity in New York and New Jersey may be explained in
part by small founding population sizes as the species
extended its range northward (Garner et al. 2004; Hoban
et al. 2010). These historical biogeographical processes can
confound inferences of the genetic consequences of current/recent landscape changes (Zellmer and Knowles 2009;
Chiucci and Gibbs 2010) and thus must be considered in
evaluating the spatial distribution of genetic variation at
range-edges (Zellmer and Knowles 2009; Chiucci and
Gibbs 2010). Our analyses indicate that low genetic
diversity persists in these populations, even at more rapidly-evolving microsatellite markers, and this may have
consequences for long-term viability of these range-edge
populations. For instance, the genetic similarities among
breeding ponds may make the metapopulation more susceptible to declines or extinction in the face of widespread
disease or rapid environmental change (Amos and Balmford 2001; Frankham 2005; Spear et al. 2005). Furthermore, if these ponds, which were once interconnected as
the species expanded to its current northern limit (Church
et al. 2003), become isolated from one another, this may
increase the risk of inbreeding depression, particularly if
the site only supports a few breeding individuals (Frankham et al. 2002; Frankham 2005; Beebee 2010; Apodaca
et al. 2012). Our results show that tiger salamanders in
New York and New Jersey already exhibit high relatedness, therefore some degree of inbreeding may already be
occurring, despite evidence of pond-to-pond migration in
each region.
Estimated effective population sizes were relatively low,
with fewer than 44 breeding individuals at each pond.
Pond-breeding amphibians tend to exhibit effective population sizes of \100 (Beebee and Griffiths 2005) and our
population estimates are similar to population sizes reported for other salamander species (e.g. Funk et al. 1999;
Jehle and Arntzen 2002; Savage et al. 2010; Wang et al.
2011). In our study, low Ne could potentially be indicative
of small sample sizes at some sampled ponds, but even
ponds with large sample sizes exhibited low Ne (e.g. NY7;
N = 93, Ne = 25). Further investigation is needed to
123
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1456
determine if low values of Ne lead to decreased population
viability, reductions in population fitness, or a combination
of these factors (Lande 1998; Wang et al. 2011).
The northeastern range-edge populations of A. tigrinum
fall into three distinct genetic clusters, corresponding to
two groups of populations on Long Island and one in New
Jersey. Populations in New York and New Jersey are
highly differentiated from one another as evidenced by
high FST and AMOVA. These data are consistent with
other salamander studies (e.g. Routman 1993; Storfer 1999;
Tallmon et al. 2000; Curtis and Taylor 2003; Spear et al.
2005; Zamudio and Wieczorek 2007; Semlitsch 2008;
Greenwald et al. 2009b) where population connectivity is
high (low FST) at smaller geographic scales and population
divergence only occurs at larger scales due to landscape
barriers. Genetic structure among breeding ponds within
each region is minimal with the exception of two ponds in
New York that form a distinct genetic cluster. The two
differentiated ponds, NY4 and NY5, are centrally located
within 5–10 m of each other on Brookhaven National
Laboratory property, however, this area was drained and
excavated from 2005 to 2007 in a remediation effort, and it
is likely that part of this population was lost during this
process. Given the high rates of migration among ponds in
this region, and the presence of several breeding ponds
within 500 m of these two wetlands, if this genetic pattern
is a result of disturbance, then we predict that migrationdrift equilibrium will swamp this genetic signature in the
next few years as migrants from adjacent ponds colonize
these breeding sites.
Migration rates and landscape barriers
Our landscape analyses of connectivity among sites based
on habitat suitability show corridors that could potentially
enhance dispersal in each region (Fig. 1). Despite variance
in the quality of habitat surrounding ponds, we did not find
a correlation between landscape connectivity (measured as
surface resistance) and FST. Our low FST values for both
regions are consistent with recent genetic connectivity
between ponds, suggesting that even if average resistance
values between ponds were high at the time of the study,
there has still been recent gene flow between wetlands.
Anthropogenic habitat modification is known to cause
genetic isolation, while forested areas between breeding
ponds allow for high connectivity (Greenwald et al.
2009a). Radiotelemetry studies have also confirmed that
open fields may serve as barriers to some species of
Ambystoma (Gibbs 1998; Rittenhouse and Semlitsch 2006;
Titus 2013). Despite habitat changes in both regions, the
tiger salamander populations we sampled are still connected by relatively high quality habitat, and thus have
high connectivity (Fig. 1). Average FST within the main
123
Conserv Genet (2014) 15:1447–1462
New York population (with the exception of ponds NY4
and NY5) and within New Jersey is low, indicating that
gene flow between most of these breeding ponds is possible
in each state. Our results suggest that the observed low
heterozygosity and genetic divergence are a product of
small populations (possibly historically), not limited gene
flow. Nonetheless, further changes to these landscapes
could potentially create impassable barriers among breeding ponds, increase the potential for localized extinctions,
and reduce chances of recolonization. The long-term viability of the remaining populations will depend on maintaining this connectivity in an increasingly developed
region, and protecting migration corridors for population
management.
Conservation implications
Historical demographic expansion of tiger salamanders
into the Northeastern US has resulted in reduced genetic
variation overall in this region (Church et al. 2003), which
in turn likely influences observed landscape genetic patterns (Dudaniec et al. 2012). Our data suggest that this low
genetic diversity persists in range-edge populations, and
may act to exacerbate the consequences of habitat fragmentation (Lesica and Allendorf 1995; Dudaniec et al.
2012).
Our study highlights that individual tiger salamander
breeding ponds support very small numbers of breeding
adults, and are thus highly sensitive to perturbations that
limit migration from surrounding ponds, even those within
several hundred meters. For example, we found two
genetic clusters in New York, one of which is limited to
only two ponds; this drastic change in allele frequencies
over a small geographic scale and a short period of time
underscores the threats to amphibian populations dependent on wetlands and surrounding upland habitat (Beebee
2005; Spear et al. 2006; Beebee 2010). Minimizing disturbance to quality forested upland habitat surrounding
breeding ponds can reduce the chances of these changes in
allele frequencies by providing zones of protection for
localized breeding populations (Semlitsch 1998; Semlitsch
and Bodie 2003; Rittenhouse and Semlitsch 2006; Titus
2013).
Even with quality habitat surrounding a breeding pond,
corridors connecting these wetlands are critical to maintain
gene flow (Greenwald et al. 2009a, b; Wang et al. 2009;
Apodaca et al. 2012). Our landscape analyses indicate that
despite high habitat fragmentation, corridors still exist
among areas across the tiger salamander’s range in New
York and New Jersey. Although some regions along these
corridors experience minimal anthropogenic influences,
elevated mortality rates through marginal habitats could
reduce gene flow from one pond to another. For example,
Author's personal copy
Conserv Genet (2014) 15:1447–1462
subsidized predators (household pets and feral animals) in
developed areas, as well as open habitats, could also
increase mortality rates during migration and compromise
genetic connectivity among ponds (Loss et al. 2013; Loyd
et al. 2013). Therefore, maintaining and enhancing existing
forested corridors for continued migration is critical for
persistence of these range-edge populations.
Acknowledgments We thank Al Breisch and Dan Rosenblatt of the
New York State Department of Environmental Conservation, and
Dave Golden of the New Jersey Division of Fish and Wildlife, for
support with population sampling and permits. All sample were collected according to approved Brookhaven National Laboratory
Table 4 National Land Cover
Data (NLCD) categories and
corresponding resistance values
adapted from Compton et al.
(2007) and Greenwald et al.
(2009b)
A resistance value of 40 was
given to absolute, or
impenetrable, areas where A.
tigrinum cannot survive. These
values were derived based on
assigned resistance values
gathered from field researchers
with extensive experience with
Ambystoma species (Compton
et al. 2007; Greenwald et al.
2009b) and applied in previous
landcover models. We based our
categories on these values for
consistency with the existing
literature on Ambystomid
landscape resistance (Compton
et al. 2007; Greenwald et al.
2009b)
Class
1457
IACUC #347. This research was funded in part by State Wildlife
Grant T-2-2 from the U.S. Fish and Wildlife Service (USFWS) to the
New York State Department of Environmental Conservation (NYSDEC), an Upstate Herpetological Association Research Grant to VT,
and an NSF Population Evolutionary Processes award to KZ. RCB
was supported by a Cornell University Presidential Life Sciences
Fellowship and CGB was supported by a Fulbright/CAPES
Fellowship.
Appendix
See Tables 4, 5, 6, 7
Resistance
Deciduous Forest
1.0
Evergreen Forest
1.0
Mixed Forest
1.0
Palustrine Forested Wetland
1.0
Palustrine Emergent Wetland
3.0
Palustrine Shrub/Scrub Wetland
3.0
Scrub/Shrub
3.4
Development—open space
6.8
Low intensity developed
6.8
Grassland
9.2
Pasture/Hay
9.2
Cultivated Land
10.2
Bare Land
12.6
Development—medium intensity
12.6
High intensity developed
Palustrine Aquatic Bed
12.6
22.0
Water (open)
22.0
Estuarine
40
Unconsolidated shore
40
123
123
5
20
20
NJ7
NJ8
NJ9
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.00/0.53
.
.
.
4_11
.
.
.
.
.
.
.
.
.
.
.
0.06/0.06
.
.
.
.
.
.
.
.
.
.
0.00/0.67
.
.
.
5_7
.
0.55/0.51
0.42/0.40
0.80/0.53
0.20/0.56
0.57/0.53
0.43/0.36
0.50/0.43
0.22/0.31
0.58/0.49
0.75/0.54
.
0.15/0.15
0.50/0.50
0.20/0.47
0.67/0.53
0.60/0.60
0.60/0.71
0.75/0.61
0.13/0.13
0.58/0.43
0.20/0.19
0.00/0.36
0.33/0.33
0.33/0.30
.
0.25/0.54
.
.
.
.
0.00/0.67
.
.
.
0.00/0.11
0.33/0.32
.
0.33/0.60
0.17/0.41
0.17/0.53
0.06/0.16
.
.
.
.
0.40/0.51
0.67/0.51
0.04/0.04
.
.
.
.
.
.
.
0.00/0.67
.
.
0.01/0.11
52_1
.
0.74/0.64
0.05/0.19
.
.
0.58/0.57
0.08/0.58
0.41/0.53
0.00/0.67
0.71/0.75
0.50/0.50
0.25/0.25
0.15/0.15
.
0.50/0.50
0.35/0.51
0.53/0.50
0.55/0.52
0.50/0.43
0.13/0.33
0.57/0.74
.
0.77/0.70
0.00/0.11
.
0.25/0.67
0.10/0.28
0.07/0.07
0.75/0.54
0.54/0.54
0.25/0.54
.
.
0.14/0.58
.
0.22/0.48
0.59/0.80
0.02/0.02
0.54/0.57
0.75/0.68
0.64/0.56
Atex65
0.29/0.28
0.04/0.17
.
60_9
52_6
0.74/0.61
0.20/0.51
0.08/0.47
0.40/0.73
0.75/0.86
1.00/0.71
1.00/0.75
0.33/0.60
0.78/0.73
0.58/0.56
.
.
0.05/0.05
0.09/0.09
.
0.03/0.03
.
1.00/1.00
0.05/0.05
0.02/0.19
0.15/0.14
.
0.00/0.53
0.20/0.51
.
.
.
52_143
0.17/0.22
0.50/0.50
0.40/0.62
0.25/0.25
.
.
.
.
0.25/0.25
.
0.42/0.36
0.18/0.18
.
0.17/0.16
0.00/0.67
0.50/0.50
0.10/0.10
0.02/0.19
0.16/0.15
0.20/0.20
0.82/0.51
0.29/0.53
0.10/0.09
0.10/0.10
0.26/0.25
52_115
0.07/0.07
0.17/0.16
.
0.25/0.25
.
.
0.25/0.25
.
0.05/0.05
.
0.00/0.67
0.06/0.16
0.17/0.30
.
0.23/0.37
0.50/0.50
0.00/0.67
0.43/0.35
0.36/0.30
0.11/0.14
0.20/0.20
0.42/0.34
0.57/0.76
0.42/0.39
0.13/0.34
0.13/0.21
60_3
0.40/0.33
0.55/0.47
0.20/0.20
1.00/0.73
0.71/0.62
0.57/0.65
1.00/0.75
0.29/0.28
0.51/0.33
0.71/0.78
1.00/0.83
0.85/0.77
0.67/0.83
1.00/0.57
0.68/0.83
1.00/1.00
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
0.60/0.85
0.71/0.80
.
.
.
.
.
.
.
5_8
0.73/0.71
0.60/0.82
.
.
0.73/0.85
1.00/0.79
0.70/0.77
4_20
0.41/0.50
0.67/0.65
1.00/0.71
1.00/0.83
0.33/0.30
0.33/0.49
.
0.13/0.34
0.50/0.40
0.42/0.67
1.00/0.67
0.30/0.46
0.50/0.46
0.25/0.25
0.30/0.60
.
.
0.32/0.28
0.53/0.58
0.82/0.57
0.80/0.71
.
0.33/0.60
0.18/0.52
0.75/0.54
0.53/0.56
52_34
1458
The observed (Ho)/expected (He) heterozygosities were estimated from these individuals and heterozygosities in bold indicate populations that do not conform to Hardy–Weinberg expectations
for that particular locus. Monomorphic loci are indicated by a period
5
2
NY16
7
20
NY15
NJ6
12
NY14
NJ5
5
NY13
7
31
2
NY11
NY12
NJ4
2
NY10
4
21
NY9
NJ3
15
NY8
9
56
NY7
NJ2
5
NY6
7
12
NY5
19
8
NY4
NY17
42
NY3
NJ1
93
10
NY1
NY2
N
Pond
Table 5 Genetic variation at 12 microsatellite loci in populations of A. tigrinum collected from 2004 to 2008. N equals the number of individuals genotyped at each breeding site
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0.082
0.210
0.017
0.054
0.429
0.715
0.012
0.031
0.015
0.083
0.128
0.078
0.054
0.106
0.049
0.030
0.121
0.049
NY2
NY3
NY4
NY5
NY6
NY7
NY8
NY9
NY10
NY11
NY12
NY13
NY14
NY15
NY16
NY17
0
0.309
0.108
0.306
0.182
0.200
0.258
0.209
0.282
NJ1
NJ2
NJ3
NJ4
NJ5
NJ6
NJ7
NJ8
NJ9
0.351
0.240
0.359
0.308
0.347
0.161
0.403
0.079
0.437
0.349
0.445
0.069
0.133
0.041
0.090
0.047
0.338
0.144
0
NJ2
0.086
0.126
0.049
0.071
0.128
0.038
0.059
0.088
0.055
0.004
0.064
0.157
0.253
0.043
0
NY4
0.746
0.427
0
NY3
0.736
0.593
0.730
0.694
0.781
0.582
0.722
0.610
0.740
0.716
0.735
0.735
0
NY5
0.178
0.123
0.127
0.093
0.045
0.260
0
NJ3
0.064
0.174
0.008
0
0.148
0
0.061
0.094
0.059
0.086
0.020
0
NY6
0.034
0.116
0.031
0.021
0.083
0.069
0.082
0.234
0.073
0.116
0
NY7
0.261
0.226
0.208
0.059
0.145
0
NJ4
0.138
0.203
0.109
0.118
0.218
0.072
0.070
0.075
0.097
0
NY8
0.110
0.215
0.043
0.058
0.134
0.026
0.056
0.348
0
NY9
0.037
0.057
0.025
0
0
NJ5
0.300
0.222
0.246
0.225
0.487
0
0.238
0
NY10
0.062
0.142
0.048
0.066
0.135
0
0
0
0
0
0
NJ6
NY11
0
0.098
0
0
0.260
0
NY12
0
0
0.040
NJ7
0.036
0.262
0.039
0.059
0
NY13
0.056
0.013
0.016
0
NY14
0.031
0
NJ8
0.003
0.117
0
NY15
0
0
NY16
0
0
NJ9
NY17
Individuals were collected between 2004 and 2008 and genotyped at 12 microsatellite loci. in exact tests of differentiation, values in bold were statistically significant assuming significance
level of p \ 0.05 (adjusted p = 0.00368 for NY and p = 0.001389)
NJ1
Pond
0.093
0,172
0.048
0.082
0.153
0.178
0.097
0.125
0.069
0.074
0.712
0.279
0
0.072
0
NY1
NY2
NY1
Pond
Table 6 Average pairwise multi-locus FST between 17 New York and 9 New Jersey breeding ponds
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1459
123
123
46922
48915
4631
4613
4701
4209
1962
2232
1495
1221
1887
2013
1033
1148
0
NY3
22807
18401
18345
18357
4015
10658
3165
0
NJ2
46479
48423
4774
4743
5342
4827
2825
3192
2625
2365
994
994
111
0
NY4
46539
48492
4767
4736
5286
4762
2739
3111
2505
2241
1072
1068
0
NY5
19554
15279
15186
15199
2192
7558
0
NJ3
46822
48716
5607
5570
6326
5804
3819
4160
3498
3202
1489
0
NY6
45517
47449
4153
4121
5135
4599
2976
3462
3158
2977
0
NY7
12275
8066
7975
7982
7048
0
NJ4
47256
49323
4665
4672
4125
3714
1395
1368
358
0
NY8
47109
49188
4480
4481
3810
3408
1148
1042
0
NY9
19321
15092
15013
15018
0
NJ5
46321
48442
3682
3697
2776
2410
531
0
NY10
45990
48082
3344
3351
2781
2347
0
NY11
4393
232
49
0
NJ6
44221
46385
2039
2087
533
0
NY12
4380
0
192
NJ7
44110
46308
2328
2385
0
NY13
42645
44746
59
0
NY14
4414
0
NJ8
42647
44757
0
NY15
3185
0
NY16
0
0
NJ9
NY17
1460
Pairwise euclidean distances in New York averaged 12734 m, ranging between 59 and 49794 m. In New Jersey, pairwise Euclidean distances averaged 10112 m, ranging between 49 and
22807 m
17198
47604
NJ9
47826
NY17
49544
12787
12840
49794
NY16
5748
NJ7
NJ8
5728
NY15
5718
12801
5705
NY14
6004
5518
NJ6
5751
NY13
2969
5301
NY12
3279
NJ5
2990
NY11
3519
5258
3165
NY10
2660
2431
2247
NY9
2316
2095
NJ4
1894
NY8
NJ3
2043
NY7
1189
5578
1720
NY6
1068
0
1323
NY5
1113
NJ2
1412
NY4
0
1313
NJ1
552
1101
NY2
NY3
NJ1
0
NY1
NY2
Pond
NY1
Pond
Table 7 Pairwise euclidean distances (m) among sampling ponds in New York and New Jersey
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References
Amos W, Balmford A (2001) When does conservation genetics
matter? Heredity 87:257–265
Andersen LW, Fog K, Damgaard C (2004) Habitat fragmentation
causes bottlenecks and inbreeding in the European tree frog
(Hyla arborea). Proc R Soc Lond B 271:1293–1302
Apodaca JJ, Rissler LJ, Godwin JC (2012) Population genetic
structure and gene flow in a heavily disturbed habitat: implications for the management of the imperiled Red Hills salamander
(Phaeognathus hubrichti). Conserv Genet 13:913–923
Becker CG, Fonseca CR, Haddad CFB, Batista RF, Prado PI (2007)
Habitat-split and the global decline of amphibians. Science
318:1775–1777
Beebee TJC (2005) Conservation genetics of amphibians. Heredity
95:423–427
Beebee TJC (2010) Genetics in field ecology and conservation. In:
Dodd CK (ed) Amphibian conservation and ecology: a handbook
of techniques. Oxford University Press, New York, pp 407–427
Beebee TJC, Griffiths RA (2005) The amphibian decline crisis: a
watershed for conservation biology? Biol Conserv 125(271–285):
405
Chiucci JE, Gibbs HL (2010) Similarity of contemporary and
historical geneflow among highly fragmented populations of an
endangered rattlesnake. Mol Ecol 19:5435–5458
Church SA, Kraus JM, Mitchell JC, Church DR, Taylor DR (2003)
Evidence for multiple Pleistocene refugia in the postglacial
expansion of the eastern tiger salamander, Ambystoma tigrinum
tigrinum. Evolution 52:372–383
Compton BW, McGarigal K, Cushman SA, Gamble LR (2007) A
resistant-kernel model of connectivity for amphibians that breed
in vernal pools. Conserv Biol 21:788–799
Curtis JMR, Taylor EB (2003) The genetic structure of costal giant
salamanders (Dicamptodon tenebrosus) in a managed forest.
Biol Conserv 115:45–54
Cushman SA (2006) Effects of habitat loss and fragmentation on
amphibians: a review and prospectus. Biol Conerv 139:247–257
Cushman SA, Wasserman TN, Landguth EL, Shirk AJ (2013) Reevaluating causal modeling with mantel tests in landsacpe
genetics. Diversity 5:51–72
Dudaniec RY, Spear SF, Richardson JS, Storfer A (2012) Current and
historical drivers of landscape genetic structure differ in core and
peripheral salamander populations. PLoS One 7(5):e36769
Earl DA, vonHoldt BM (2011) STRUCTURE HARVESTER: a
website and program for 19 visualizing STRUCTURE output
and implementing the Evanno method. 4:359–361. Conserv
Genet Res 4:359–361
Eckert CG, Samis KE, Lougheed SC (2008) Genetic variation across
species’ geographical ranges: the central–marginal hypothesis
and beyond. Mol Ecol 17:1170–1188
Edenhamn E, Höggren M, Carlson A (2000) Genetic diversity and
fitness in peripheral and central populations of the European tree
frog Hyla arborea. Hereditas 133:115–122
Evanno G, Regnaut S, Goudet J (2005) Detecting the number of
clusters of individuals using the software structure: a simulation
study. Mol Ecol 14:2611–2620
Excoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular
variance inferred from metric distances among DNA haplotypes:
application to human mitochondrial DNA restriction data.
Genetics 131:479–491
Frankham R (1995) Effective population size ratios in wildlife: a
review. Genet Res 66:95–107
Frankham R (2005) Genetics and extinction. Biol Conserv
126:131–140
1461
Frankham R, Ballou JD, Briscoe DA (2002) Introduction to conservation genetics. Cambridge University Press, Cambridge
Fry J, Xian G, Jin S, Dewitz J, Homer C, Yang L, Barnes C, Herold
N, Wickham J (2011) Completion of the 2006 National Land
Cover Database for the Conterminous United States. PE&RS
77:858–864
Funk WC, Tallmon DA, Allendorf FW (1999) Small effective
population size in the long-toed salamander. Mol Ecol 8:
1633–1640
Garner TWJ, Pearman PB, Angelone S (2004) Genetic diversity
across a vertebrate species’ range: a test of the central-peripheral
hypothesis. Mol Ecol 13:1047–1053
Gibbs JP (1998) Distribution of woodland amphibians along a forest
fragmentation gradient. Landscape Ecol 13:263–268
Gopurenko D, Williams RN, McCormick CR, DeWoody JA (2006)
Insights into the mating habits of the tiger salamander (Ambystoma tigrinum tigrinum) as revealed by genetic parentage
analyses. Mol Ecol 15:1917–1928
Goudet J (1995) FSTAT (version 1.2): a computer program to
calculate F-statistics. J Hered 86:485–486
Goudet J, Raymond M, DeMeeus T, Rousset F (1996) Testing
differentiation in diploid 20 populations. Genetics 144:1933–
1940
Greenwald KR, Purrenhage JL, Savage WK (2009a) Landcover
predicts isolation in Ambystoma salamanders across region and
species. Biol Conserv 142:2493–2500
Greenwald KR, Gibbs HL, Waite TA (2009b) Efficacy of land-cover
models in predicting isolation of marbled salamander populations in a fragmented landscape. Conserv Biol 25:1232–1241
Guillot G, Rousset F (2013) Dismantling the Mantel tests. Methods
Ecol Evol 4:336–344
Hoban S, Borkowski DS, Brosi SL, McCleary TL, Thompson LM,
McLachlan JS, Pereira MA, Schlarbaum SE, Romero-Severson J
(2010) Range-wide distribution of genetic diversity in the North
American tree Juglans cinerea: a product of range shifts, not
ecological marginality or recent population decline. Mol Ecol
19:4876–4891
Hunter Guerry (2002) Amphibian distributions in a landscape of
forests and agriculture: an examination of landscape composition
and configuration. Conserv Biol 16:745–754
Jakobsson M, Rosenberg NA (2007) CLUMPP: a cluster matching
and permutation program for dealing with label switching and
multimodality in analysis of population structure. Bioinformatics
23:1801–1806
Jehle R, Arntzen JW (2002) Review: microsatellite markers in
amphibian conservation genetics. Herpetol J 12:9
Johansson M, Primmer CR, Merlia J (2006) History vs. current
demography: explaining the genetic population structure of the
common frog, Rana temporaria. Mol Ecol 15:975–983
Jones OR, Wang J (2009) COLONY: a program for parentage and
sibship inference from multilocus genotype data. Mol Ecol Res
10:551–555
Lamoureux VS, Madison DM (1999) Overwintering habitats of radioimplanted green frogs, Rana clamitans. J Herpetol 33:430–435
Lamoureux VS, Maerz JC, Madison DM (2002) Premigratory autumn
foraging forays in the green frog, Rana clamitans. J Herpetol
36:245–254
Lande R (1998) Anthropogenic, ecological and genetic factors in
extinction and conservation. Res Popul Ecol 40:259–269
Lesica P, Allendorf FW (1995) When are peripheral populations
valuable for conservation? Conserv Biol 9:753–760
Loss SR, Will T, Marra PP (2013) The impact of free-ranging
domestic cats on wildlife of the United States. Nat Commun
4:1396
123
Author's personal copy
1462
Loyd KAT, Hernandez SM, Carroll JP, Abernathy KJ, Marshall GJ
(2013) Quantifying free-roaming domestic cat predation using
animal-borne video cameras. Biol Conserv 160:183–189
Madison DM, Farrand L (1998) Habitat use during breeding and
emigration in radio-implanted tiger salamanders, Ambystoma
tigrinum. Copeia 1998:402-410
Marsack K, Swanson BJ (2009) A genetic analysis of the impact of
generation time and road- based habitat fragmentation on eastern
box turtles (Terrapene c. carolina). Copeia 4:647–652
McDonough C, Paton PWC (2007) Salamander dispersal across a
forested landscape fragmented by a golf course. J Wildl Manag
71:1163–1169
McRae BH, Shah VB (2009) Circuitscape user’s guide. The
University of California, Santa Barbara. http://www.circuits
cape.org
Mech SG, Storfer A, Ernst JA, Reudink MW, Maloney SC (2003)
Polymorphic microsatellite loci for tiger salamanders, Ambystoma tigrinum. Mol Ecol Notes 3:79–81
Parra-Olea G, Recuero E, Zamudio KR (2007) Polymorphic microsatellite markers for Mexican salamanders of the genus Ambystoma. Mol Ecol Notes 7:818–820
Peakall R, Smouse PE (2006) GENALEX 6: genetic analysis in
Excel. Population genetic software for teaching and research.
Mol Ecol Notes 6:288–295
Pritchard JK, Stephens M, Donnelly P (2000) Inference of population
structure using multilocus genotype data. Genetics 155:945–959
Queller DC, Goodnight KF (1989) Estimating relatedness using
genetic markers. Evolution 43:258–275
Raymond M, Rousset F (1995) GENEPOP (version 1.2): population
genetics software for exact tests and ecumenicism. J Hered
86:248–249
Rittenhouse TAG, Semlitsch RD (2006) Grasslands as movement
barriers for a forest-associated salamander: migration behavior
of adult and juvenile salamanders as a distinct habitat edge. Biol
Conserv 131:14–22
Rittenhouse TAG, Semlitsch RD (2007) Postbreeding habitat use of
wood frogs in a Missouri oak-hickory forest. J Herpetol
41:645–653
Rosenberg MS, Anderson CD (2011) Passage: pattern analysis,
spatial statistics, and geographic exegesis. V. 2. Methods Ecol
Evol 2:229–232
Rosenburg NA (2004) DISTRUCT: a program for the graphical
display of population structure. Mol Ecol Notes 4:137–138
Rothermel BB, Semlitsch RD (2002) An experimental investigation
of landscape resistance of forest versus old-field habitats to
emigrating juvenile amphibians. Con Biol 16(1324–502):1332
Routman E (1993) Population structure and genetic diversity of
metamorphic and paedomorphic populations of the tiger salamander, Ambystoma tigrinum. J Evol Biol 6:329–357
Savage WK, Fremier AK, Shaffer HB (2010) Landscape genetics of
alpine Sierra Nevada salamanders reveal extreme population
subdivision in space and time. Mol Ecol 19:3301–3314
Schneider S, Roessli D, Excoffier L (2000) ARLEQUIN: A software
program for population genetics data analysis (version 2.0).
123
Conserv Genet (2014) 15:1447–1462
Genetics and Biometry Lab, Department of Anthropology,
University of Geneva Switzerland
Semlitsch RD (1998) Biological delineation of terrestrial buffer zones
for pond-breeding salamanders. Conserv Biol 12:1113–1119
Semlitsch RD (2008) Differentiating migration and dispersal processes for pond-breeding amphibians. J Wildl Manag 72:
260–267
Semlitsch RD, Bodie JR (2003) Biological criteria for buffer zones
around wetlands and riparian habitats for amphibians and
reptiles. Conserv Biol 17:1219–1228
Spear SF, Peterson CR, Matocq MD, Storfer A (2005) Landscape
genetics of the blotched tiger salamander (Ambystoma tigrinum
melanostictum). Mol Ecol 14:2553–2564
Spear SF, Peterson CR, Matocq MD, Storfer A (2006) Molecular
evidence for historical and recent population size reductions of
tiger salamanders (Ambysotma tigrinum) in Yellowstone
National Park. Conserv Genet 7:605–611
Storfer A (1999) Gene flow and population subdivision in the
streamside salamander, Ambystoma barbouri. Copeia 1999:
174–181
Tallmon DA, Funk WC, Dunlap WW, Allendorf FW (2000) Genetic
differentiation among long- toed salamander (Ambystoma macrodactylum) populations. Copeia 2000:27–35
Titus VR, (2013) Movements, connectivity, and management:
conserving the New York State 23 endangered eastern tiger
salamander. Dissertation, Binghamton University
Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004)
MICRO-CHECKER: software for identifying and correcting
genotyping errors in microsatellite data. Mol Ecol Notes 4:
535–538
Wang IJ (2009) A new method for estimating effective population
sizes from a single sample of multilocus genotypes. Mol Ecol
18:2148–2164
Wang IJ, Savage WK, Shaffer HB (2009) Landscape genetics and
least-cost path analysis reveal unexpected dispersal routes in the
California tiger salamander (Ambystoma californiense). Mol
Ecol 18:1365–1374
Wang IJ, Johnson JR, Johnson BB, Shaffer HB (2011) Effective
population size is strongly correlated with breeding pond size in
the endangered California tiger salamander, Ambystoma californiense. Conserv Genet 12:911–920
Weir BS, Cockerham CC (1984) Estimating F-statistics for the
analysis of population structure. Evolution 38:1358–1370
Williams RN, DeWoody JA (2004) Fluorescent dUTP helps characterize 10 novel tetranucleotide microsatellites from an enriched
salamander (Ambystoma texanum) genomic library. Mol Ecol
Notes 4:17–19
Zamudio KR, Wieczorek AM (2007) Fine-scale spatial genetic
structure and dispersal among spotted salamander (Ambystoma
maculatum) breeding populations. Mol Ecol 16:257–274
Zellmer AJ, Knowles L (2009) Disentangling the effects of historic
vs. contemporary landscape structure on population genetic
divergence. Mol Ecol 18:3593–3602