fine-scale spatial genetic structure and dispersal among spotted

Molecular Ecology (2007)

16

, 257–274 doi: 10.1111/j.1365-294X.2006.03139.x

© 2006 The AuthorsJournal compilation © 2006 Blackwell Publishing Ltd

Blackwell Publishing Ltd

Fine-scale spatial genetic structure and dispersal among spotted salamander (

Ambystoma maculatum

) breeding populations

KELLY R. ZAMUDIO and ANIA M. WIECZOREK

*

Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY 14853-2701, USA

Abstract

We examined fine-scale genetic variation among breeding aggregations of the spottedsalamander (

Ambystoma maculatum

) to quantify dispersal, interpopulation connectivityand population genetic structure. Spotted salamanders rely on temporary ponds orwetlands for aggregate breeding. Adequate breeding sites are relatively isolated from oneanother and field studies suggest considerable adult site fidelity; therefore, we expected tofind population structure and differentiation at small spatial scales. We used microsatellitesto estimate population structure and dispersal among 29 breeding aggregations in TompkinsCounty, New York, USA, an area encompassing 1272 km

2

. Bayesian and frequency-basedanalyses revealed fine-scale genetic structure with two genetically defined demes: the Northdeme included seven breeding ponds, and the South deme included 13 ponds. Nine pondsshowed evidence of admixture between these two genetic pools. Bayesian assignment testsfor detection of interpopulation dispersal indicate that immigration among ponds is commonwithin demes, and that certain populations serve as sources of immigrants to neighbouringponds. Likewise, spatial genetic correlation analyses showed that populations ≤≤≤≤

4.8 kmdistant from each other show significant genetic correlation that is not evident at higherscales. Within-population levels of relatedness are consistently larger than expected if matingwere completely random across ponds, and in the case of a few ponds, within-populationprocesses such as inbreeding or reproductive skew contribute significantly to differentiationfrom neighbouring ponds. Our data underscore the importance of these within-populationprocesses as a source of genetic diversity across the landscape, despite considerablepopulation connectivity. Our data further suggest that spotted salamander breeding groupsbehave as metapopulations, with population clusters as functional units, but sufficientmigration among demes to allow for potential rescue and recolonization. Amphibianhabitats are becoming increasingly fragmented and a clear understanding of dispersal andpatterns of population connectivity for taxa with different ecologies and life histories iscrucial for their conservation.

Keywords

: connectivity, gene flow, metapopulation, microsatellites, migration, vernal ponds

Received 22 February 2006; revision received 28 July 2006; accepted 21 August 2006

Introduction

Amphibians often have patchy distributions due to habitatspecificity and strict ecophysiological requirements (Stebbins& Cohen 1995). This is particularly true for many pond-

breeding amphibians that link specific, and sometimesdistinct, environments for breeding, larval development andadult survival (Dunning 1992; Pope

et al

. 2000). Landscapescan differentially affect these species that link distinct habitatsto complete their life cycles because their populations maybe divided into demes at the scale of individual breedingaggregations, yet these may be interconnected and mayinteract as metapopulations through migration of adultsor juveniles during the nonbreeding season. Life historycharacteristics of individual species, specifically the relative

Correspondence: K. Zamudio, Fax: +1607 2558088, E-mail:[email protected]*Present address: Department of Tropical Plant and Soil Sciences,University of Hawaii at Manoa, Honolulu, HI 96822, USA

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K . R . Z A M U D I O and A . M . W I E C Z O R E K


importance of dispersal during certain life stages, will ultim-ately determine the pattern of population connectivity and thescale at which amphibians exhibit population structure.

Much of the focus in metapopulation studies has been onthe number and spatial arrangement of breeding habitatpatches and their ability to sustain viable populations undera range of dispersal scenarios (Gu

et al

. 2002; Baguette &Schtickzelle 2003; Ovaskainen & Hanski 2003). We knowconsiderably less about actual dispersal patterns andthe geographical scale at which gene flow is limited inamphibians. With increasing anthropogenic habitatmodification it is particularly important to understandthe genetic and demographic consequences of changesin landscape composition and configuration (Gibbs 1998;Guerry & Hunter 2002; Manel

et al

. 2003). Habitat fragmen-tation ranks among the major causes of species extinction(Wilcox & Murphy 1985; Andrèn 1994; Fahrig & Merriam1994), and one obvious consequence of fragmentation is thebreakdown of effective metapopulation dispersal processes(Gonzalez

et al

. 1998) which in turn decreases the probabilityof regional population persistence (Sjögren 1991). Likewise,changes in the supplementation and/or recolonizationpotential of isolated populations can influence local geneticdiversity and increase susceptibility to other threats such asdisease transmission, inbreeding and local extirpation, ordecrease the potential for adaptation to local environments(Pearman & Garner 2006; Spears

et al

. 2006).Recent studies of population structure in pond-breeding

amphibians have produced mixed results; some speciesexhibit genetic structure at very small spatial scales whileothers show panmixia among relatively distant breedingaggregations (e.g. Rowe

et al

. 2000; Tallmon

et al

. 2000;Newman & Squire 2001; Burns

et al

. 2004; Palo

et al

. 2004;Shaffer

et al

. 2004; Jehle

et al

. 2005). Although studies ofspatial structure and connectivity are becoming available formore species with differing vagilities, ecological tolerancesand population histories (Routman 1993; Scribner

et al

.1993; Hitchings & Beebee 1997; Squire & Newman 2002;Spear

et al

. 2005), we still lack basic information on the localdispersal capacity and scale of population differentiationfor most species (but see Lowe 2003; Jehle

et al

. 2005; Spear

et al

. 2005), limiting comparative analyses and general infer-ences of population structure and dynamics in amphibians.For the effective conservation of amphibian biodiversity,we ultimately need to understand the effects of landscapefeatures, such as fragmentation, permeability and con-figuration of habitat patches, on gene flow in taxa with con-trasting ecologies and life histories (Gibbs 1998; Guerry &Hunter 2002), thus permitting an estimate of the minimalrequirements for regional persistence of entire amphibiancommunities.

The spotted salamander (

Ambystoma maculatum

) isdistributed throughout eastern North America (Petranka1998) and is an ideal candidate for investigating landscape

genetics. Spotted salamanders, like many pond-breedingamphibians, depend on two types of habitat: fish-free vernalponds for breeding and moist upland forest for foragingand hibernation (Stebbins & Cohen 1995; Petranka 1998).Spotted salamanders are slow moving, have limited dispersalcapabilities (Madison 1997), do not move far from breedingsites during the year (Semlitsch 1998), and exhibit highbreeding site fidelity (Husting 1965; Whitford & Vinegar1966). Therefore, we expected to find populations or groupsof populations acting as independent breeding demes andreduced levels of interpopulation migration even at verylocalized scales (Marsh & Trenham 2001; Trenham 2001;Trenham

et al

. 2001; Funk

et al

. 2005; Jehle

et al

. 2005). Here,we assessed genetic variation among 29 breeding aggre-gations of

A. maculatum

in central New York State, USA. Ourobjectives were to determine whether gene flow due tomigration among breeding ponds maintains spatial connec-tivity among subpopulations in this regional assemblageand the geographical scale at which genetic differentiationis evident among these breeding sites. Combined, theseresults quantify potential genetic discontinuities at this finespatial scale, and further our understanding of the micro-evolutionary processes that generate genetic structure acrossnatural landscapes (Manel

et al

. 2003).

Materials and methods

Population sampling

Twenty-nine spotted salamander populations were sampledfrom vernal pools throughout Tompkins County, NewYork (Fig. 1) during the spring and early summer monthsof 1999 and 2000. We collected tissue samples from 15 to 30individuals from each breeding population. This species isan explosive breeder, and adults are only present at pondsduring a few nights each year (Tennessen & Zamudio2003; Savage & Zamudio 2005); larvae develop in pondsthroughout the late spring and early summer. We visited10 ponds during the two breeding seasons to sample adults,and completed sampling for the other ponds during thespring by collecting larvae from known breeding sites.Because cohorts can vary in genetic composition (Johnson& Black 1984; Sinsch 1992; Scribner

et al

. 1993), we avoidedsampling the same ponds at different times during the year;only three ponds included samples from both adults andlarvae (Appendix). Samples were frozen or preserved inabsolute ethanol for DNA isolation. Exact collection localitieswere determined using a global positioning system in thefield or large-scale maps of the area (Appendix; Fig. 1).

As a result of the glacial history of the Finger LakesRegion, Tompkins County can be divided into four regionsdue to two prominent landscape features (Mullins &Hinchey 1989; USGS 2000). Cayuga Lake is an elongatedglacial lake that bisects the county into eastern and western

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regions. In addition, a topographic divide, known as thePortage Escarpment, separates the flatter northern part ofthe county from the more topographically complex southernsegment. The topographic discontinuity between northernand southern halves of the county is reflected in verydifferent historical land use patterns (Tompkins CountyPlanning Office 2000). The northern region has been primarilyagricultural; the southern region has been harvested tovarious degrees but is currently characterized by a greateramount of forest cover (Smith

et al

. 1993). Amphibian breed-ing ponds are generally located within or in the vicinity ofdeciduous forest and previous studies have demonstrateda negative correlation between occupancy of breeding sitesand distance from forested uplands (Laan & Verboom1990; Pope

et al

. 2000). Therefore, in choosing localities wesampled widely across the physiographic and anthropogenicbarriers in Tompkins County.

DNA extraction and microsatellite amplification

Total genomic DNA was extracted by digestion in lysisbuffer with Proteinase K, followed by standard phenol–chloroform purification and ethanol precipitation (Sambrook& Russell 2001). DNA dilutions were used as templates foramplification of microsatellite loci via the polymerase chainreaction (PCR). Microsatellite loci for this species weredeveloped and characterized previously (Wieczorek

et al

.2002) and we employed 11 of those loci (Ama61, Ama5-1,Ama9-4, Ama11-2B, Ama4-10, AmaA, Ama3-3, Ama2C2,Ama12-7, Ama07, Ama34) in this study. The forward primerfor each locus was 5

′

-labelled with a fluorescent tag.Amplified products with different fluorescent labels ornonoverlapping size ranges were pooled and electrophoresedon a 5% polyacrylamide gel on an ABI 377 PRISM DNASequencer (PE Biosystems). Fragment sizes were determined

Fig. 1 Topographic map of Tompkins County, New York, with localities for 29 Ambystoma maculatum populations sampled for this study.Populations assigned (with membership coefficient ≥ 70%) to one of two genetic demes are represented by squares (North cluster) or circles(South cluster); admixed populations are represented by triangles.

260



and analysed with the TAMRA-500 size standard using

genescan

version 3.1 and

genotyper

version 2.1 (PEBiosystems).

Within-population patterns of genetic diversity

We calculated genetic diversity indices (number of alleles,effective number of alleles and number of private alleles)for each population using the program

genalex

version 6(Peakall & Smouse 2006). We used

arlequin

version 3(Schneider

et al

. 2000) to estimate observed (

H

O

) and expected(

H

E

) heterozygosities and to test genotypic frequenciesat each study site and locus for statistically significantdeviations from Hardy–Weinberg equilibrium (HWE).We used a Monte Carlo approximation of the Fisher’s exacttest (Guo & Thompson 1992) and a standard Bonferronicorrection for multiple comparisons (adjusted

P

value< 0.0001, for a table-wide significance of

α

= 0.05). TheMarkov chain algorithm was run for 100 000 steps following10 000 dememorization steps. We also estimated the pairwiseprobability of linkage disequilibrium using a Fisher’s exacttest implemented in

genepop

version 3.1d (Raymond &Rousset 1995) with 10 000 steps following a 1000 stepdememorization.

Three of our populations (populations BF, MB, RP)included collections of both adults and larvae; this couldpotentially bias the distribution of genotypes withinpopulations if we inadvertently sampled related individuals.Likewise, if reproductive skew and effective population sizesin this species vary among breeding groups (Tennessen &Zamudio 2003) it is possible that populations sampled onlyfor larvae could exhibit higher relatedness and bias esti-mates of interpopulation differentiation. To address this,we calculated pairwise relatedness between individualswithin populations using the relatedness estimator,

r

qg

, ofQueller & Goodnight (1989) implemented in

genalex

version 6 (Peakall & Smouse 2006). Significant differencesamong mean population relatedness were tested using apermutation test (Peakall & Smouse 2006); to limitcomputation time, we subsampled our data and randomlychose seven populations represented only by larvae, eightonly by adult samples, and the three populations withmixed collection (total 18 populations). We permutedgenotypes from all populations 999 times and derivedupper and lower 95% intervals for the expected range of

r

qg

,based on all populations. These intervals represent therange of

r

qg

that would be expected if reproduction wererandom across the sampled ponds. We also derived 95%CIs (determined by bootstrap resampling) for within-population estimates of mean relatedness. Population

r

qg

values that fall above the 95% expected values from per-mutations indicate that processes such as reproductiveskew, inbreeding, or drift are increasing relatedness, evenin the face of potential gene flow among ponds. If our

sampling strategy does in fact bias our analyses of localpopulation structure, we would expect higher

r

qg

valuesfor populations sampled as larvae and those with mixedlarval/adult samples.

Among-population patterns of genetic diversity

We performed a global test of overall population differ-entiation (not assuming HWE within populations) using

fstat

2.9.3 (Goudet 1995). This test permutes genotypesamong populations to create a null distribution forcomparison with observed levels of population differentiation(Goudet

et al

. 1996). We determined degree of populationsubdivision from multilocus estimates of

F

ST

(Weir &Cockerham 1984) for all population pairs (Goudet 1995).Pairwise significance tests for

F

ST

(Goudet

et al

. 1996) wereperformed by permutation and resampling of multilocusgenotypes among pairs of samples. Performing 8120 random-izations allowed for a table-wide significance at the 5%nominal level after standard Bonferroni corrections (adjusted

P

value = 0.0001). Pairwise population

F

ST

were used in twosubsequent analyses. We correlated

F

ST

with geographicaldistance to test for patterns of spatial subdivision andisolation by distance (IBD) using a Mantel test with 10 000randomizations (implemented in

fstat

2.9.3). Secondly, weused the population

F

ST

estimates in a Principal ComponentsAnalysis (PCA), to examine genetic clustering of populationsfrom throughout Tompkins County.

To test alternative hypotheses about barriers to geneflow and structure at this geographical scale, we estimated

F

-statistics for various hypothetical combinations of popu-lations, with confidence intervals inferred by bootstrappingover loci. We grouped populations according to four apriori hypotheses of potential population division: (i) all 29populations independently; (ii) isolation among populationsfrom streams in eight watersheds; (iii) isolation amongpopulations east and west of Cayuga Lake; and (iv) isola-tion between northern and southern populations (on eitherside of the Escarpment; Fig. 1). For each hypothetical group-ing we again used

fstat

to test for significance of overallpopulation differentiation (not assuming HWE within groupsor populations) and the significance of

F

IS

within each ofthe populations or pooled groups. Populations pooled ineach hypothetical scenario are listed in the Appendix.

Northern and southern segments of the study areahave had very different land use histories, prompting us toexamine in more detail differences in population con-nectivity and genetic diversity in populations of these twogeographical areas. For each geographical group of popu-lations we calculated average allelic richness,

H

O

and

F

ST

in

fstat

and used a permutation test to assess the significanceof differences among regions. The test permutes entirepopulations and allocates them to groups (keeping thenumber of populations in each group constant) to construct

G E N E T I C S T R U C T U R E O F S A L A M A N D E R B R E E D I N G A G G R E G A T I O N S

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a null distribution for the statistics of choice. We used atwo-tailed test to assess the significance of differencesbetween populations in both regions.

Bayesian estimates of population structure

We used Bayesian assignment techniques to test for popu-lation structure among breeding ponds in our sample andassessed the geographical scale of population differentiation,using the computer program

structure

version 2 (Pritchard

et al

. 2000). This method identifies clusters of geneticallysimilar individuals from multilocus genotypes withoutprior knowledge of their population affinities. The modelassumes

K

genetic clusters, each characterized by a set ofallele frequencies at each locus; the admixture model thenprobabilistically estimates the proportion of individualswith ancestry in each cluster. A series of pilot runs wereused to estimate Pr(X|

K

), where X represents the data,for

K

between 1 (the expected value if all populationsbelonged to the same breeding deme) and 29 (the maximumpossible number of populations). Using the options toignore population affiliation when clustering individuals,assuming independence among loci, and allowing admixture,we ran four independent runs of 1 000 000 iterations(following a burn-in period of 200 000) for each value of

K

(Pritchard

et al

. 2000). From these initial runs, we determinedthat the true value of

K

(with the highest posterior prob-ability) fell between 2 and 10. In this range LnProb(Data)increased rapidly and plateaued approximately between

K

= 4 or 5. We focused on this lower range of

K

for moredetailed analyses: we ran 10 replicates each with

K

rangingfrom 2 to 11, using the same parameters as the pilot study.In their instructions for use of

structure

, Pritchard & Wen(2003) warned about the computational difficulties ofestimating

K

and the difficulties of biological interpretationof estimates of numbers of populations derived from

structure

, especially in cases where LnProb values increasewith stepwise values of

K

. Incremental increases in the valueof LnProb(Data) can lead to overestimates of the number ofgenetic demes, therefore we calculated

∆

K

(Evanno

et al

.2005) by taking into account the shape of the log-likelihoodcurve with increasing

K

and variance among estimates inmultiple runs. Once the number of genetic clusters wasestablished, each individual was assigned to a cluster andwe estimated the overall membership of each sampledindividual in the clusters. Individual and population member-ship coefficients of ancestry in our inferred demes weregraphed in the program

distruct

version 1.0 (Rosenberg2004) to identify pure and admixed populations.

The population clusters identified in

structure

wereused in analysis of molecular variance (

amova

, Excoffier

et al

. 1992) to examine the distribution of genetic variationat three hierarchical levels: within populations, among popu-lations within genetic demes, and among genetic demes.

This test, implemented in

arlequin

version 3.0 (Schneider

et al

. 2000), partitions total genetic variance into covariancecomponents and calculates fixation indices (Wright 1965);the significance of fixation indices is determined by com-parison with a null distribution derived from permutinghaplotypes, individuals or populations at the appropriatehierarchical level (Excoffier

et al

. 1992). We performed two

amova

tests. First, we assigned each population to a clusterbased on their largest proportion of membership to a definedcluster; this test includes all populations regardless of theirlevel of admixture. In a second analysis, we eliminatedadmixed populations and examined hierarchical geneticdistribution using only relatively ‘pure’ populations, thosewhere the mean membership to one cluster was

≥

70%, andtherefore could be clearly assigned to one genetic deme.Although arbitrary, this assignment threshold for definingadmixed populations allows us to investigate their relativecontribution to the distribution of genetic variability ateach hierarchical level and also categorize populations forsubsequent analyses and graphical display.

Detecting recent migration and the geographical scale of genetic correlations

We used two different assignment tests to uncover potentialrecent immigrants among populations. We used

bayesass

version 1.3 (Wilson & Rannala 2003) and

geneclass

2 (Piry

et al

. 2004) to estimate recent migration rates and test forsignificant cases of assignment to populations other thanthe population of origin. Because larvae cannot migrate, weperformed these analysis using only individuals sampledas adults, resulting in analyses of 12 of our populations(population MB contained only five adults, and thereforewas also excluded due to insufficient sample size). Bothmethods use Bayesian techniques to calculate occurrenceor probability of individual assignment to source andnonsource populations. The partially Bayesian classificationmethod (Rannala & Mountain 1997) implemented in

geneclass

2 is the most accurate of the frequentist assign-ment methods (Cornuet

et al

. 1999), and we paired thiswith a Monte Carlo resampling method for computation ofassignment probability to each population (Paetkau

et al

.2004) using 10 000 simulated individuals. Misassign-ments with high probabilities (80–95%) were observationsunlikely to occur from a random combination of alleles,and thus were interpreted as migration events (or offspringof recent migrants). The accuracy of assignment tests hasbeen tested in simulations and studies where population oforigin is known (Berry

et al

. 2004); accuracy depends inpart on the stringency of the test used, therefore we usedboth 80% and 95% as assignment thresholds to explorethe effect on assignments with our data. We compared theresults of the assignment tests with estimates of migrationderived from the fully Bayesian method implemented in

262



bayesass

(Wilson & Rannala 2003). This method assumesthat first generation immigrants can be sampled anduses Markov chain Monte Carlo (MCMC) to estimatemean immigration rates among populations and theirconfidence intervals (CI).

bayesass

also estimates meanmigration rates (and CI) for data with insufficient informationfor estimating migration (Wilson & Rannala 2003), to serveas a control or comparison for values estimated fromempirical data. We performed 5 million iterations, excluded2 million of those as burn-in, and sampled the chain every2000 iterations; all other parameters were set at defaultvalues.

Finally, to specifically address the spatial scale of dispersaland deme size in this species, we performed a globalspatial autocorrelation analysis of 12 breeding populations(those composed of adult samples) in the program

genalex

version 6 (Peakall & Smouse 2006). This multivariate spatialautocorrelation analysis uses multilocus genetic data(Smouse & Peakall 1999) thus strengthening spatial signaland reducing the noise that results from individual single-locus analyses; these methods have recently been appliedto animal taxa with restricted dispersal and have contributedto our understanding of dispersal behaviour and dynamics(Peakall

et al. 2003; Double et al. 2005). Under restrictedgene flow, in the absence of selection, and if sampling hascaptured the geographical scale of positive genetic structure,populations should show significant positive spatial auto-correlation at short distances and these will decline to zerofollowed by higher order stochastic oscillations of positiveand negative values (Turner et al. 1982; Sokal & Wartenberg1983; Smouse & Peakall 1999). In cases of positive genetic

structure, the first x-intercept in the autocorrelogram (rplotted as a function of distance) provides an index of thespatial extent of nonrandom (positive) genetic structure(Peakall et al. 2003). We used individual pairwise geneticand geographical distance matrices derived from our datato calculate the autocorrelation coefficient r (Smouse &Peakall 1999) and statistical significance was tested by apermutation test that shuffles individual genotypes amonglocalities and computes the null distribution for r in casesof no genetic structure. We used 1000 permutations toestimate 95% confidence intervals for the populations inour adult dataset. As an alternative test, we also estimated,via bootstrapping, the 95% confidence interval for r, bydrawing with replacement from within relevant pairwisecomparisons within each distance class. Following Peakallet al. (2003), we reject the null hypothesis of no spatialautocorrelation only when r exceeds the 95% CI derivedfrom the among-population permutation test, and whenthe 95% CI about r (derived from bootstrapping) does notintercept the axis of r = 0.

Results

Within-population patterns of genetic diversity

Our microsatellite markers were highly polymorphic acrosspopulations sampled (Fig. 2). The number of alleles perlocus ranged from 13 (locus Ama 4-10) to 44 (locus Ama 3-3); within populations the mean number of alleles rangedfrom 4.9 to 9.2 (Fig. 2). A few populations showed slightlyhigher levels of diversity (GR, CEP, SP, RP, LO, JT) and

Fig. 2 Patterns of allelic richness and heterozygosity in 29 sampled populations of Ambystoma maculatum genotyped at 11 microsatellite loci.Bars represent mean ± SD number of alleles (black bars), mean ± SD number of effective alleles (grey bars), and mean ± SD number ofprivate alleles (white bars). Mean ± SD heterozygosities for each population (across all loci) are represented by the grey line. Populationsare arranged from north to south.

G E N E T I C S T R U C T U R E O F S A L A M A N D E R B R E E D I N G A G G R E G A T I O N S 263


most of these were populations in the more forestedsouthern region of the county. The effective number ofalleles (corrected for expected heterozygosity) allows formore meaningful comparisons of allelic diversity acrosspopulations with different allele distributions; the effectivenumber of alleles did not differ among our sampledpopulations, suggesting they may not vary significantly indemography, population size, or connectivity. Expectedheterozygosity ranged from 29% to 92%, with a mean of68.9% across all loci. Observed heterozygosity rangedfrom 7% to 100% with a mean of 53.4% over all sampledpopulations (Table S1, Supplementary material). On averageloci and populations conformed to HWE, with some locishowing deviations, but not at all populations (Table S1,Supplementary material). Two populations (BF and RT)showed deviation at 5 and 6 of the 12 loci, respectively.However, those loci did not show consistent deviationsacross all populations; therefore, we assume that processescausing this nonequilibrium were specific to those popula-tions, and continued to include those loci in subsequentanalyses. We detected no linkage disequilibrium betweenany of the 11 loci across all populations.

We estimated Queller & Goodnight’s (1989) index ofrelatedness among all sampled individuals in each popu-lation to determine whether our mixed sampling protocol(both adults and larvae) might bias estimates of populationdifferentiation. We found no pattern of increased within-population relatedness in populations with mixed, or onlylarval sampling (Fig. 3). Most populations showed signifi-cantly higher degrees of relatedness than expected fromthe null distribution created for all populations in the county.This pattern is expected if breeding aggregations are semi-isolated and migration is not sufficiently high to offset theincrease in relatedness that results from nonrandom mating.Although rqg estimates within most populations were

statistically higher than expected, values among populationsare similar and not exceedingly high (ranging from 0.02 to0.17; Fig. 3). Therefore, Ambystoma breeding aggregationsshow increased relatedness, likely due to nonrandommating among ponds and differential reproductive successwithin ponds (Tennessen & Zamudio 2003; Myers &Zamudio 2004). However, migration among ponds likelyreduces the chances of inbreeding in this system. The onlypopulation that deviated significantly from this pattern waspopulation MH; mean relatedness in the southernmostpopulation in our sample was 0.403, significantly higherthan other populations (Fig. 3). The sample for this popu-lation was composed of larvae, therefore we may havesampled sibs or half sibs if the breeding population wassmall or few individuals successfully reproduced there.

Among-population patterns of genetic diversity

Pairwise FST values ranged from 0 to 0.199 among all 29breeding aggregations (Table S2, Supplementary material),with an average FST = 0.073 (Table 1). These values representlow to moderate levels of population differentiation. Anoverall randomization test of population differentiationwas significant for each locus independently and for allloci combined (P < 0.001). Genetic distances (FST) amongpairs of populations were significantly correlated withgeographical distance between localities (Mantel test,P = 0.016). However, geographical distance explained lessthan 2% of the genetic variation (r2 = 0.014) in the countytherefore we did not detect a clear pattern of IBD at thisgeographical scale.

We used pairwise FST calculated in fstat (Table S2,Supplementary material) in a PCA to investigate the relativeposition of populations in multidimensional space. The firstthree principal components (PC) axes explain 28.27, 21.43

Fig. 3 Mean within-population pairwise relatedness, rqg, for a subset of populations included in this study. Gray bars are 95% upper andlower expected values for a null distribution generated from 999 permutations of data from all populations, and enclose the values expectedin breeding aggregations that are panmictic and show relatively even reproductive success. Populations from which we sampled onlyadults (black diamonds), only larvae (grey diamonds), or both adults/larvae (white circles) do not differ in mean relatedness, although mostdo differ from expectations under panmixia. The single outlier is population MH, with significantly higher rqg, likely due to inbreeding orvery small effective population size.

264 K . R . Z A M U D I O and A . M . W I E C Z O R E K


and 16.40% of the genetic variation among our popula-tions, respectively, for a total of 66.09%. Scattergrams of thethree axes show some correlation with geography (Fig. 4);the strongest signal is evidence of a southern group of

populations, concentrated in the highlands in the south-eastern section of the county. Most northern populationsare separate from these, primarily due to displacementalong PC Axis 1. Genetically intermediate populations are

Table 1 Population genetic structure inferred for various subdivisions of Ambystoma maculatum populations. Estimates of FST, FIS and FIT,confidence intervals (CI) and FST P values were calculated in fstat (Goudet 1995). We pooled populations according to membership in eightsampled watersheds, geographical position relative to Cayuga Lake (east/west), and geographical position relative to the PortageEscarpment (north/south). Significant overall FST are bold; numbers below FIS values indicate the number of populations (or groups ofpopulations) that showed significant FIS after Bonferroni correction, and the total number of population groups (significant/total). isNei’s corrected estimate of population differentiation adjusted for sample size

Grouping Statistic FST FIS FIT

29 populations Mean 0.073 (0.056–0.091) 0.215 (0.139–0.294) 0.272 (0.190–0.354) 0.077P value < 0.001 0/29

Watersheds Mean 0.037 (0.026–0.049) 0.248 (0.170–0.329) 0.276 (0.193–0.356) 0.046P value < 0.001 8/8

East/West Mean 0.016 (0.008–0.026) 0.265 (0.184–0.346) 0.277 (0.194–0.358) 0.016P value < 0.001 2/2

North/South Mean 0.013 (0.008–0.021) 0.266 (0.185–0.346) 0.275 (0.193–0.357) 0.0133P value < 0.001 2/2

′GST

′GST

Fig. 4 Scattergram of first three axes of aPCA of genetic variation (based on pairwisepopulation FST) in Ambystoma maculatumpopulations. Symbols represent the member-ship of populations in two genetic demesidentified by Bayesian clustering: the Southcluster (black circles) and the North cluster(white squares). Admixed populations (withless than 70% membership in either singleclade) are represented by grey triangles.Clustering of populations is concordantwith Bayesian assignment, but show acomplex geographical distribution (Fig. 1).



spread throughout the county, and not associated with acontact zone between north and south demes; these areintermediate in the PCA.

Comparison of F-statistics for populations pooled accord-ing to three alternative scenarios of population structureshowed that each pond contributes to genetic variation inthe landscape: with all 29 breeding aggregations consideredindependently, we calculated a significant FST and low FIS,suggesting that each pond represents a panmictic popula-tion. Pooling populations according to any of our scenarios(Table 1) resulted in lower FST, with less of the genetic varia-tion represented in each of the pooled subpopulations. Inaddition, the values of FIS became statistically significant in thepooled analyses, likely due to a Wahlund effect resultingfrom the combination of genetically distinct ponds (Table 1).

Our permutation tests for statistical comparisons of allelicrichness, observed heterozygosity (HO) and FST revealedno differences in genetic diversity or connectivity amongpopulations in the northern and southern regions of thestudy area. Allelic richness is almost identical betweenregions (north: 2.59, south: 2.53, P = 0.13). HO and FST areslightly higher among the more fragmented northernpopulations, but the difference was not statistically signif-icant (HO north: 0.556, south: 0.519, P = 0.07; FST north:0.077, south: 0.064, P = 0.44).

Bayesian estimates of population structure

The Bayesian estimates of population structure corroboratedour distance-based analyses. The model-based clusteringmethod implemented in structure suggested that themodel with K = 2 (where K is the number of geneticpopulation clusters) was substantially better than alternatemodels. The highest posterior probabilities for K variedamong multiple runs with the ‘best’ K ranging from 1 to 11,indicating that that posterior probability alone is not a good

metric for the choice of the number of breeding demes inthis case. Our values of LnProb(data) showed a pattern ofincremental increase with increasing K; the curve plateauedand continued to increase with larger values of K, leading topotential overestimates of the number of populations. Toaddress this problem, Evanno et al. (2005) proposed the use of∆K, which takes into account the shape of the log likelihoodcurve. For our data, ∆K = 2 was 125, the highest value, whereasestimates for all other possible runs were less than 30.

The genetic identity of individuals (the average per-individual proportion of ancestry) from each of the 29geographical populations into the two structure-definedclusters broadly corresponds to two geographical areas(Figs 1 and 5). Mean membership coefficients for all indi-viduals into one of these two demes were generally high,with a mean of 0.839 (SD = 0.130). The North cluster includesprimarily populations north of the escarpment, in theflatter northern region of the county; the South clusterpopulations are distributed throughout drainages in thetopographically complex southern region. Clusters includeunequal numbers of populations: seven ponds show highmembership coefficients in the North cluster and 13 in theSouth cluster (Fig. 1). Nine populations show evidence ofadmixture (Fig. 5); as in our PCA, admixed populations arespread throughout the county and do not fall along a clearcontact zone or limit to the distribution of demes.

Assuming that the genetic clusters inferred from theBayesian analyses represent panmictic demes, we estimatedFST and calculated an amova for populations in thesegenetic clusters. A hierarchical amova revealed that 92.5%of genetic variation resides within populations, 1.9% isdistributed among populations within clusters, and 5.6%of the variance can be explained by differentiation betweenthe two regional clusters (Table 2). Although the proportionof genetic variation accountable at higher levels is small(5.6%), all fixation indices are statistically significant. The

Fig. 5 Population structure inferred by Bayesian assignment of 592 individuals of Ambystoma maculatum. Spotted salamander populationsin Tompkins County can be assigned to two geographical genetic demes, each represented by a cluster of populations. The top figurerepresents individual membership coefficients in both genetic demes; the lower figure represents mean membership coefficient for eachsampled population. The north deme is well represented in most populations north of the Portage Escarpment, the south deme includesmost populations in the southern segment of the county. Nine populations in our sample showed evidence of mixed ancestry, with lessthan 70% mean membership coefficient to any deme (identified by triangles). Populations are ordered according to latitude (north to south).



results are similar if we exclude admixed populations(Table 2). Therefore, the genetic structure detected byclustering methods and population-based FST analyses ismirrored in the amova. Very little genetic variation can beattributed to differences among ponds within clusters, sug-gesting that they in fact represent clusters of populationsthat are genetically homogeneous. In addition, most of thegenetic variation among breeding aggregations at thisgeographical scale is found at the level of ponds.

Recent migration and the geographical scale of genetic correlations

Our Bayesian clustering analyses indicate that some popu-lations within demes have high membership coefficientsand therefore belong primarily to one genetic pool; however,many populations and individuals are admixed (Fig. 5), a

pattern that presumably results from dispersal among someof our sampled populations. These results are corroboratedby the Bayesian assignment analyses. Although only a smallproportion of the genotyped individuals could be assignedwith high certainty, both 80% and 95% assignment thresholdsin geneclass show significant assignments of individualsto ponds other than the pond of origin (Table 3). In mostof these cases of high gene flow, breeding ponds aregeographically close to other neighbouring populations andthus members of the same genetic deme. Populations BP,GS, NSG, SP and especially RW show highest assignmentrates from other populations and may be source populationsfor salamanders in neighbouring ponds. All of these pondsare located in the southeastern section of the county, theregion for which we have best sampling.

Results from bayesass analyses show similar results(Table 4). Only nine instances in the population immigration

Table 2 Results of hierarchical amova comparing genetic variation within breeding aggregations, among breeding populations within theNorth/South clusters of populations, and among clusters. Significance was tested against a null distribution of 10 000 random permu-tations. The test was performed twice, including and excluding admixed populations. Significant P values are indicated with an asterisk

Source of variation d.f. Sum of squares Fixation index Percent variation P value

Including admixed populationsWithin populations 1307 4088.61 φST = 0.075 92.51 < 0.001*Among populations within clusters 27 318.80 φSC = 0.057 5.58 < 0.001*Among clusters 1 49.18 φCT = 0.019 1.91 < 0.001*

Excluding admixed populationsWithin populations 884 2656.46 φST = 0.082 91.84 < 0.001*Among populations within clusters 18 174.88 φSC = 0.047 4.55 < 0.001*Among clusters 1 55.85 φCT = 0.036 3.61 < 0.001*

Table 3 Assignment tests for adult Ambystoma maculatum collected at 12 ponds in Tompkins County, New York. For each pond sampled(left column) we enumerate the number of samples assigned with 80% and 95% probability to either the pond of origin (along diagonal) orto other sampled ponds. Assignments and probabilities were calculated with geneclass2, using a partially Bayesian estimate (Rannala &Mountain 1997) and probability estimation by Monte Carlo resampling (Paetkau et al. 2004)

Samples assigned to:

AP BF BOG BP CNC CT GS HP NSG RP RW SP

80 95 80 95 80 95 80 95 80 95 80 95 80 95 80 95 80 95 80 95 80 95 80 95

Samples collected in: AP 3 0 1 0 2 0 10 1 0 0 2 0 5 0 0 0 6 2 0 0 19 11 7 3BF 0 0 1 0 0 0 0 0 1 0 0 0 1 1 0 0 2 1 0 0 7 3 1 0BOG 0 0 0 0 8 4 5 1 0 0 0 0 0 0 0 0 4 0 0 0 5 2 1 0BP 0 0 0 0 1 0 7 2 0 0 0 0 1 0 0 0 3 1 0 0 7 3 1 0CNC 2 1 4 0 0 0 5 2 2 1 1 0 4 3 1 1 5 1 0 0 11 7 3 2CT 0 0 1 0 0 0 5 2 0 0 4 1 4 0 0 0 2 1 0 0 10 2 2 1GS 1 1 3 1 0 0 7 5 1 0 3 0 4 2 1 1 5 2 1 1 13 6 3 1HP 2 1 2 1 2 0 9 3 1 0 0 0 2 1 3 2 2 2 1 0 13 7 8 4NSG 2 0 0 0 0 0 5 2 0 0 0 0 3 0 1 0 6 4 0 0 12 4 3 1RP 1 0 0 0 0 0 4 0 0 0 1 0 3 1 0 0 1 1 3 1 5 4 0 0RW 2 0 2 0 1 0 4 1 0 0 1 0 3 1 2 0 6 3 0 0 17 10 7 2SP 1 0 0 0 1 0 3 1 0 0 0 0 1 0 2 1 4 1 0 0 14 6 5 0

GE

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Table 4 Bayesian estimates of recent admixture among spotted salamander populations using the program bayesass version 1.3 (Wilson & Rannala 2003). Source populations are listedacross the top row; populations receiving immigrants are listed in the left-hand column. Each cell contains the mean migration rate and (confidence interval) for a pair of populations.Self-assignment rates are listed along the diagonal in italics. Most estimates of interpopulation migration are low, and do not differ significantly from values expected in cases of insufficientsignal in the data (noninformative CI: 1.17 × 1013, 0.110). Nine cases show detectable migration rates, with an estimated mean larger 0.110: these nine cases are immigrations from AP, HPand RW to other ponds, suggesting these three populations may serve as a source for more common exchange among ponds at this scale

AP BF BOG BP CNC CT GS HP NSG RP RW SP

AP 0.727 0.004 0.004 0.004 0.004 0.004 0.004 0.220 0.004 0.005 0.014 0.004(0.67–0.86) (0–0.03) (0–0.03) (0–0.027) (0–0.029) (0–0.029) (0–0.026) (0.08–0.31) (0–0.030) (0–0.033) (0–0.072) (0–0.026)

BF 0.014 0.684 0.005 0.005 0.005 0.005 0.004 0.026 0.005 0.013 0.226 0.004(0–0.076) (0.67–0.73) (0–0.031) (0–0.035) (0–0.035) (0–0.035) (0–0.033) (0–0.122) (0–0.035) (0–0.061) (0.12–0.31) (0–0.031)

BOG 0.277 0.003 0.678 0.003 0.004 0.004 0.004 0.007 0.004 0.005 0.006 0.004(0.22–0.32) (0–0.025) (0.67–0.71) (0–0.024) (0–0.026) (0–0.024) (0–0.024) (0–0.041) (0–0.025) (0–0.028) (0–0.038) (0–0.025)

BP 0.07 0.006 0.005 0.686 0.006 0.0057 0.006 0.178 0.006 0.008 0.015 0.006(0–0.252) (0–0.036) (0–0.036) (0.67–0.74) (0–0.042) (0–0.037) (0–0.036) (0.02–0.31) (0–0.043) (0–0.46) (0–0.079) (0–0.037)

CNC 0.010 0.004 0.004 0.004 0.681 0.004 0.004 0.004 0.027 0.005 0.250 0.004(0–0.038) (0–0.028) (0–0.026) (0–0.025) (0.67–0.71) (0–0.031) (0–0.028) (0–0.028) (0–0.094) (0–0.030) (0.17–0.31) (0–0.029)

CT 0.115 0.003 0.003 0.003 0.003 0.678 0.003 0.169 0.003 0.006 0.009 0.003(0–0.315) (0–0.020) (0–0.023) (0–0.022) (0–0.022) (0.67–0.71) (0–0.23) (0–0.320) (0–0.022) (0–0.036) (0–0.056) (0–0.022)

GS 0.031 0.004 0.004 0.004 0.005 0.005 0.678 0.080 0.004 0.105 0.075 0.004(0–0.144) (0–0.025) (0–0.023) (0–0.022) (0–0.027) (0–0.025) (0.67–0.71) (0–0.178) (0–0.025) (0.04–0.18) (0.02–0.15) (0–0.023)

HP 0.002 0.001 0.001 0.001 0.002 0.001 0.001 0.984 0.001 0.001 0.002 0.001(0–0.016) (0–0.014) (0–0.014) (0–0.015) (0–0.015) (0–0.014) (0–0.014) (0.95–0.99) (0–0.013) (0–0.014) (0–0.015) (0–0.014)

NSG 0.027 0.004 0.004 0.004 0.004 0.004 0.004 0.168 0.679 0.004 0.090 0.0040–0.103 0–0.026 0–0.025 0–0.024 0–0.028 0–0.024 0–0.030 (0.06–0.27) (0.67–0.71) 0–0.027 0.02–0.19 0–0.027

RP 0.002 0.002 0.001 0.002 0.002 0.002 0.002 0.002 0.002 0.980 0.002 0.002(0–0.016) (0–0.016) (0–0.017) (0–0.017) (0–0.019) (0–0.019) (0–0.019) (0–0.020) (0–0.018) (0.93–1.00) (0–0.018) (0–0.017)

RW 0.006 0.003 0.003 0.003 0.003 0.003 0.003 0.056 0.004 0.007 0.905 0.003(0–0.039) (0–0.025) (0–0.025) (0–0.023) 0–0.022 0–0.024 0–0.022 0–0.159 0–0.030 0–0.048 (0.78–1.00) 0–0.023

SP 0.006 0.004 0.004 0.004 0.004 0.004 0.008 0.251 0.004 0.004 0.028 0.6820–0.036 0–0.028 0–0.028 0–0.029 0–0.027 0–0.028 0–0.029 (0.17–0.31) 0–0.027 0–0.029 0–0.100 0.67–0.72



matrix show significant immigration rates. In these nine cases,the mean estimated number of immigrants falls outside ofthe confidence intervals expected in cases of insufficientsignal in the data. These incidences of migration includeemigrations from ponds AP, HP and RW. Primary receiv-ing populations are AP, BF, BOG, BP, CT, NSG and SP. Asin the geneclass analysis, most of the detectable dispersaloccurs between ponds in the southern deme, and primarilythose clustered in the southeastern region of the county,suggesting that the more intensive sampling in that regionrevealed the geographical scale at which dispersal unitesAmbystoma maculatum breeding aggregations.

Our fine-scale spatial autocorrelation analyses furtherresolve the scale of spatial connectivity among salamanderbreeding ponds. The autocorrelogram shows significantpositive genetic correlations among geographically closeponds (Fig. 6). The correlation coefficient clearly decreasesat increased distance classes, as would be expected in casesof restricted gene flow among subsets of geographicalsamples in the study. The x-intercept for r is 4.77 km, andthis corresponds to the distance among populations wheregenetic correlations are expected to cease. In other words,on average, distances below 4.8 km unite populations thatshare a higher proportion of genes, and populations moredistant than this threshold are genetically independent.

Discussion

Processes contributing to population structure

We found two statistically significant population clustersthat are generally geographically concordant. Our samplesspan 1272 km2 of heterogeneous landscape and each demeoccupies roughly half of the total collection area. Therefore,population differentiation is evident in this species even atthis localized scale. Levels of differentiation are not extreme

among populations or demes, especially as indexed bypairwise and overall population FST comparisons; nonethe-less, they are significantly different from zero, and amovareveals that more of the genetic variation can be explainedby differences among the north and south breeding demesthan by differences among ponds within each of thosepopulation groups.

It is likely that both historical and current micro-evolutionary processes have structured salamander breedingpopulations in Tompkins County. Despite species-specificcharacteristics that we expect to promote populationdifferentiation (Irwin 2002), such as site fidelity and lowvagility, it is in some ways surprising that populationstructure is as well defined as it is in this system. The entirestudy region only became inhabitable by spotted sala-manders after the last glacial maximum as the Laurentideice sheet receded from its maximum extent well south ofTompkins County (Ruddiman 1987; Mullins & Hinchey1989; Anderson et al. 1997). Rangewide studies of mito-chondrial variation in this species confirm that populationsthroughout the northeast are in fact the result of recentpopulation expansions, and that genetic diversity is lowamong these populations compared to older lineages(Phillips 1994; Zamudio & Savage 2003). Thus, the geneticstructure we detect today in Tompkins County is a resultof population dynamic processes that have occurred overmaximum 14 000 years (since the end of the Pleistocene;Mullins & Hinchey 1989). The marked topographicaldifferences between northern and southern sections of oursample area may explain some of the regional differenti-ation and the cohesiveness of populations in each cluster;however, we know of no obvious topographical or historicalbarriers to dispersal that would limit gene flow amongclusters of populations in north and south Tompkins County.Therefore, the genetic variation that has accumulated amongpopulations since their postglacial establishment in this

Fig. 6 Autocorrelogram of the spatial coefficient, r, as a function of distance. The null hypothesis of no spatial genetic structure is boundedby the 95% confidence intervals (dashed lines) derived from randomly permuting individual genotypes over geographical locations. Errorbars for mean r at each distance class were estimated with bootstrapping. Significant spatial genetic autocorrelation can be assumed whenmean r exceeds the 95% CI and the error bars for each distance class do not intercept the X-axis of r = 0. Not all distance classes arerepresented in comparisons among our breeding ponds, hence the x-axis is not to scale.



region likely represents a combination of natural limits todispersal and population-level processes imposed byspecies-specific behaviours.

Similar differentiation in allele frequencies over very smallgeographical scales has been observed in the Europeancommon frog, Rana temporaria (Palo et al. 2004), anotherwidespread pond-breeding amphibian with populationsthat resulted from postglacial dispersal. Our study of inter-population differentiation is similar to the study of thecommon frog in that the results emphasize a ‘paradox’increasingly noted in postglacial colonizers: we knowfrom their current presence in previously glaciated sites,that dispersal must have proceeded relatively rapidly fromsouthern populations that are in some cases quite distant.However, localized and fine-scale structure in postglacialpopulations suggests that dispersal must be limited amongthese newly established demes. The paradox lies in how anorganism vagile enough to recolonize vast expanses of post-glacial habitat could show limitations in interpopulationgene flow at small scales (Palo et al. 2004). Explanations forlocalized structure that do not rely exclusively on poordispersal capability include mechanisms such as (i) rapidlocal adaptation and exclusion of nonadapted migrants; (ii)small effective population sizes in northern populationsresulting from higher sex-ratio biases in breeding aggrega-tions; and (iii) reduction in continued migration rates frommore southern populations due to differential ‘penetrance’of new and occupied habitats (the ‘leading edge’ hypothesis,Hewitt 2000). Our data do not allow us to evaluate therelative roles of these factors in shaping genetic diversityin spotted salamanders. Nonetheless, it is clear thatcomparative studies of breeding dynamics and adaptationto environmental clines along latitudinal grades will un-doubtedly yield information about their role in populationdifferentiation in recently established populations.

Fine scale studies of wood frogs (Rana sylvatica) andColumbia spotted frogs (Rana luteiventris) (Newman &Squire 2001; Squire & Newman 2002; Funk et al. 2005),demonstrated lower estimates of migration and smallergenetic neighbourhood sizes in more topographicallycomplex regions, suggesting that landscape complexity andelevation may be important in structuring populations.Likewise, a recent study of gene flow in tiger salamanders(Ambystoma tigrinum) quantified the effect of variouslandscape variables on genetic differentiation; the resultsof that study showed a significant positive relationshipbetween differentiation (measured as FST), distance andelevation (Spear et al. 2005). Our results provide evidencethat these landscape features do not limit migration ordispersal of spotted salamanders to the same extent. Thetwo clusters of populations in our sample show similardegrees of cohesiveness relative to landscape features. Allnorthern populations form a genetic deme, despite thecentral barrier to dispersal imposed by Cayuga Lake, one

of the ‘Finger Lakes’ that lie along a north/south axis incentral Tompkins County (Fig. 1). The flat northern sectionof Tompkins County was historically woodland habitat(Smith et al. 1993) and presumably harboured a higherdensity of breeding sites that facilitated dispersal; manyof these are no longer evident due to large-scale habitatmodification of the region. Therefore, gene flow may havebeen more extensive and northern populations are membersof a genetically homogeneous assemblage occupyinglandscapes north of Cayuga Lake. Populations in the southare similarly united in a deme and measures of geneticdiversity and connectivity do not differ between the tworegions. Therefore, although topographical complexity in thesouthern region of the county may affect the distributionof wetlands and isolation among them, this effect is notsufficient to alter genetic diversity among populations ineach of these regions (Funk et al. 2005; Spear et al. 2005).

Land-use history can impact biodiversity at all levels,ranging from species diversity in communities to geneticdiversity within species (Gustafson et al. 2001; Vellend 2004).Agricultural land use can drastically reduce amphibianpopulations that rely on wetlands and upland forests eitherdue to direct reductions in population numbers, decreasedpopulation connectivity, or bottlenecks and founder eventsassociated with establishment of populations in abandonedagricultural lands (Marsh & Pearman 1997; Guerry &Hunter 2002; Vellend 2004). The amount of forested areawithin Tompkins County decreased dramatically from 1790(European settlement) to 1900; conversion to agriculturereduced forest cover from 100% to 19% during that century(Smith et al. 1993), with a primary impact in the northernparts of the county. The forests recovered to > 50% of thecounty area by 1980; therefore, approximately half ofthe forests in Tompkins County are secondary. Despitethese dramatic regional differences in land-use history,we found no evidence of decreased genetic diversity orconnectivity among populations in the north and southpopulation groups. The lack of genetic signal may result fromthe recency of the disturbance; spotted salamanders arelong-lived and show high site fidelity (Savage & Zamudio2005) therefore they may persist in isolated populations forsome time despite potentially negative changes in recruit-ment, population connectivity and gene flow. However,given the patchy distribution of suitable breeding sites in thefragmented northern section of the county and the fact thatmany salamanders avoid dispersing across highly modifiedlandscapes such as agricultural fields (Gibbs 1998;deMaynadier & Hunter 1999), it seems unlikely that thelevels of migration and gene flow we measured for thepopulations in the south can still occur among northernpopulations.

Our data offer more resolution on within- and among-population dynamics in the southern region of the county.In most of the southern ponds, more than 50% of the



genotyped individuals could be assigned to the south deme(Fig. 5). Three populations (RT, SM and MH) are clearexceptions and show evidence of substantial mixed geneticancestry in our Bayesian clustering analysis (Fig. 5). Admixedpopulations are expected in systems where geographicalstructure exists (i.e. gene flow is limited), but where thereare no absolute geographical barriers to dispersal; in thatcase we would expect admixed populations to be geo-graphically intermediate between the ranges occupied byeach deme. Alternatively, admixed populations may alsoappear if micro-evolutionary processes within populationsalter the frequency of certain genotypes and those changesare maintained, at least over short periods, due to limitedgene flow with neighbouring ponds. For example, if localselection varies over very short distances, favoured pheno-types are expected to change in frequency at a similargeographical scale. A second possibility is that mating isnot random or reproductive skew is very high withinpopulations, such that certain genotypes become commonover short time scales due only to the disproportionatecontribution of few individuals to larval cohorts. This couldexplain the genetic discontinuity between populationMH, our southernmost sample, and neighbouring ponds.Although this population is well within the range of thesouth deme, it has a very high membership coefficient in thenorth deme. This population also showed disproportion-ately high mean relatedness coefficients, suggesting thatour genotyped samples from this pond were close relativesand possibly offspring of adults with high north dememembership coefficients.

Combined, our population-level and clustering analysesindicate that salamander breeding populations are geneti-cally diverse, differentiated from each other, and maintaintheir own genetic identity. Populations do show similaritiesthat cluster them into larger genetic demes but these clustersof populations are not completely panmictic (Table 2),despite the presence of admixed individuals in mostpopulations (Fig. 5). Interestingly, these clusters seem tobe correlated with topography and complexity of relief,not with lakes or watersheds, which would seem likelybarriers to gene flow in salamanders.

Dispersal and the spatial scale of population structure

Our fine-scale sampling in the south deme allows us toindirectly estimate dispersal among salamander breedingdemes and evaluate the possibility that these regionalgenetic clusters behave as metapopulations. Our assignmenttests and estimates of immigration are generally concordant.Most breeding sites show some evidence of interponddispersal; however, certain ponds are clearly more likelyto be source populations for immigrants at this finescale. Indirect measures of dispersal are a useful tool;however, immigration rates estimated from genetic data

include assumptions about the number and directionof dispersing individuals, as well as a number of otherpopulation parameters that could alter the distribution ofalleles across neighbouring populations. The most obviousis the relative contribution of population-level processes(such as changes in absolute and effective population sizes,yearly stochasticity in larval recruitment, and even possiblelocal differences in mutation rates). Although most popu-lation genetic studies of natural systems do not address therelative contribution of these processes to fine scale geneticdifferentiation among populations, research progress inthis direction will help disentangle the relative contributionof these species-specific characteristics to overall patternsof population genetic structure (Storz et al. 2001a, b; Petitet al. 2001; Kraushaar et al. 2002; Rosenbaum et al. 2002).Due to the aggregate mating system in spotted salamandersextreme decreases in effective population sizes may occurdue to the high differential reproductive success (reproductiveskew) among individual (Tennessen & Zamudio 2003;Myers & Zamudio 2004). Likewise, larval mortality can bevery high in this species, reaching 100% in some cases, dueto environmental stochasticity and the importance ofpond hydroperiod for successful larval metamorphosis(Stenhouse 1987). We are coupling our regional populationgenetic study with a characterization of mating system andreproductive success in individual populations (Tennessen& Zamudio 2003; Myers & Zamudio 2004) to betterunderstand the interaction between dispersal and drift informing patterns of genetic diversity at this scale.

Combining the results of spatial genetic structure with ourindirect estimate of dispersal indicates that we captured inour sampling the geographical scale at which populationconnectivity contributes to genetic structure in this species.A significant pattern of IBD suggests that dispersal isrestricted among salamander breeding aggregations;however, the low correlation coefficient suggests thatprocesses other than distance may be altering the geneticcomposition of populations at various geographical scales.Spotted salamander populations in this landscape shownonrandom genetic structure at a scale of approximately4.8 km. Thus, our data suggest that population clusters, notindividual breeding populations, of salamanders behaveas metapopulations, rather than an assemblage of isolatedbreeding sites ( Jehle et al. 2005; Kinkead et al. 2006). Con-nectivity among breeding demes varies regionally, but notas a simple function of geographic distance. Demes showgeographical and genetic cohesiveness that is maintaineddespite evidence of interdeme migration. Future studies withsimilar sampling will allow us to evaluate the generality ofthese results in different landscapes and relative roles ofdispersal and within-populations processes in structuringamphibian populations. Future studies of habitat require-ments and the impacts of fragmentation on populationpersistence in this and other pond-breeding amphibians



should evaluate population dynamics at the appropriategeographical scale, one that includes interacting populationswithin genetic demes.

Acknowledgements

We thank M. F. Benard, E. H. Grant and W. K. Savage for assistancewith field collections, and C. Aquadro, M. Boggs, D. F. Boggs, J.Boggs, C. Gilbert, R. Harrison, E. Harrison, L. Rayor, R. Root, L.Stenzler, Cayuga Nature Center, Finger Lakes Land Trust andCornell Natural Lands for permission to sample ponds on theirproperty. L. Chan, H. Greene, J. Robertson, T. Beebee, and twoanonymous reviewers provided comments on the manuscript. W.K. Savage and M. Meixler helped with coordinates and drafting ofFig. 1. Molecular data were collected in the Evolutionary GeneticsCore Facility at Cornell University; we thank S. Bogdanowicz forassistance with laboratory procedures. This project was funded byResearch Grants (to K.Z.) from the National Science Foundation(DEB-9907798/DEB-0343526), the New York State BiodiversityResearch Institute and the President’s Council of Cornell Women.

Supplementary material

The supplementary material is available from http://www.blackwellpublishing.com/products/journals/suppmat/MEC/MEC3139/MEC3139sm.htm

Table S1 Genetic variation at 11 microsatellite loci in populations ofAmbystoma maculatum. For each breeding site, N equals the numberof individuals genotyped and from which the observed (HO) andexpected (HE) heterozygosities were estimated. Heterozygosities inbold indicate populations that do not conform to Hardy–Weinbergexpectations for that particular locus (Bonferroni corrected P-value <0.0011 for table-wide significance level of α = 0.05)

Table S2 Average pairwise FST between 29 Ambystoma maculatumbreeding aggregations genotyped at 11 microsatellite loci. Valuesin bold were statistically significant in exact tests of differentiation,assuming a table-wide significance level of P < 0.05 (adjustedP = 0.00012)

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Members of the Zamudio Laboratory study systematics, populationand conservation genetics, and the evolution of mating systemsin reptiles and amphibians. We are particularly interested inunderstanding the population biology of pond-breeding amphibiansand the processes contributing to differentiation within and amongpopulations in fragmented or patchy habitats. This work wascarried out while Ania Wieczorek was a postdoctoral associate atCornell University; currently her laboratory at the University ofHawaii studies population and conservation genetics.



Appendix

Coordinates of Ambystoma maculatum breeding aggregations sampled in Tompkins County, NY. Population acronyms are the same as thoseused in Fig. 1 and in the text; N is the sample size from each population (larvae/adults). Column ‘Group’ lists the membership of eachpopulation in pooled samples according to three hypothetical scenarios of population structure: East/West of Cayuga Lake, North/Southof the Portage Escarpment, and membership in one of eight drainages (d) sampled in Tompkins County.

Locality Acronym Latitude Longitude N Group

Aquadro Pond AP N 42 26 22.2 W 76 24 08.9 0/21 E/S/d5Brother Friel’s Pond BF N 42 25 55.9 W 76 19 30.9 18/17 E/S/d5Boggs’ Pond BOG N 42 31 41.33 W 76 49 31.75 0/25 W/N/d1Brown Road BR N 42 19 31.6 W 76 31 14.1 23/0 W/S/d4Bull Pasture BP N 42 27 29.3 W 76 27 33.0 0/16 E/N/d6Buttermilk Falls BUF N 42 24 28.6 W 76 30 43.1 26/0 E/S/d4Cady Lane CL N 42 30 37.1 W 76 18 50.8 15/0 E/N/d6CNC-Hammond Hill CNC N 42 26 01.6 W 76 18 21.2 0/21 E/S/d5Cornell Experimental Ponds CEP N 42 30 14.5 W 76 27 32.7 29/0 E/N/d6Connecticut Hill CT N 42 20 25.0 W 76 40 46.5 0/28 W/S/d3DEC Jeep Track JT N 42 19 02.0 W 76 38 43.3 25/0 W/S/d3Gilbert/Rayor Pond GR N 42 31 08.8 W 76 43 23.0 0/25 W/N/d2Grant/Brown Pond MB N 42 32 15.2 W 76 32 04.7 12/5 E/N/d7Green Pool GP N 42 32 39.5 W 76 40 46.8 24/0 W/N/d2Grove School GS N 42 22 01.0 W 76 22 26.4 0/25 E/S/d5Harrison Pond HP N 42 24 29.5 W 76 22 30.6 0/21 E/S/d5Little Orchard LO N 42 21 01.1 W 76 33 59.4 23/0 W/S/d4Michigan Hollow MH N 42 17 52.6 W 76 29 20.2 17/0 E/S/d4NYSEG NSG N 42 28 42.1 W 76 24 08.0 24/0 E/N/d6Old Man Pool OM N 42 36 13.9 W 76 25 10.7 20/0 E/N/d7Perry City Road PC N 42 29 31.0 W 76 37 31.6 17/0 W/N/d2Red Pine Trail RT N 42 24 15.8 W 76 34 53.8 24/0 W/S/d4Ringwood Pond RW N 42 27 03.2 W 76 21 52.0 0/26 E/S/d5Root Pond RP N 42 21 54.6 W 76 24 24.4 21/16 E/S/d5Shindagin Traps SH N 42 19 24.0 W 76 19 28.0 14/0 E/S/d5Steam Mill Road SM N 42 20 57.9 W 76 27 41.2 28/0 E/S/d4Stenzler Pond SP N 42 25 68.0 W 76 22 05.0 0/20 E/S/d5Yellow Barn YB N 42 27 02.4 W 76 19 51.4 23/0 E/S/d5Stauber Road STB N 42 37 21.9 W 76 18 13.4 21/0 E/N/d7

fine-scale spatial genetic structure and dispersal among spotted

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