ARTICLE

Conservation genetics of the endangered Spotted Turtle(Clemmys guttata) illustrate the risks of “bottleneck tests”Christina M. Davy and Robert W. Murphy

Abstract: Studies of population genetics in turtles have suggested that turtles do not experience genetic impacts of bottlenecksas strongly as expected. However, recent studies cast doubt on two commonly used tests implemented in the program BOTTLENECK,suggesting that these findings should be re-evaluated. The Spotted Turtle (Clemmys guttata (Schneider, 1792)) is endangered bothglobally and within Canada, but genetic data required to develop effective recovery strategies are unavailable. Here, we con-ducted the first study of population genetic structure in C. guttata. We then used multiple small populations of C. guttata asreplicates to test whether the commonly used program BOTTLENECK could detect the genetic signature of bottlenecks in ourstudy populations, which are all thought to have experienced significant declines in the past 2–3 generations (75 years). Turtles(n = 256) were genotyped at 11 microsatellite loci. A suite of Bayesian population genetics analyses and a principal coordinatesanalysis identified a minimum of 6 distinct genetic populations and a maximum of 10 differentiated subpopulations across thesampled Canadian range of C. guttata, which corresponded to demographically independent units. BOTTLENECK failed to detectpopulation declines. A literature review found that bottleneck tests in 17 of 18 previous genetic studies of tortoises andfreshwater turtles were based on suboptimal sampling, potentially confounding their results. High retention of genetic diversity(allelic richness and heterozygosity) in isolated populations of C. guttata and other turtle species is encouraging for speciesrecovery, but conclusions about the prevalence of genetic bottlenecks in such populations should be re-examined.

Key words: Clemmys guttata, landscape genetics, microsatellites, Ontario, STRUCTURE, TESS, assignment test, GENECLASS.

Résumé : Des études de la génétique des populations chez les tortues semblent indiquer que les impacts génétiques des goulotsd’étranglement sur ces dernières ne sont pas aussi forts que prévu. Des études récentes jettent toutefois un doute sur deux testsrépandus utilisés dans le programme BOTTLENECK, ce qui suggère que ces résultats devraient être réévalués. La tortue ponctuée(Clemmys guttata (Schneider, 1792)) est en voie de disparation a l’échelle tant planétaire que canadienne, mais les données génétiquesnécessaires a l’élaboration de stratégies de rétablissement efficaces ne sont pas disponibles. Nous avons réalisé la première étude dela structure de la génétique des populations chez C. guttata. Nous avons ensuite utilisé de multiples petites populations de C. guttatacomme réplicats pour vérifier si le programme d’usage courant BOTTLENECK peut déceler la signature génétique de goulotsd’étranglement dans les populations a l’étude, qui auraient toutes connu des diminutions significatives au cours des deux ou troisdernières générations (75 ans). Des tortues (n = 256) ont fait l’objet d’un génotypage sur 11 microsatellites. Un ensemble d’analysesbayésiennes de la génétique des populations et une analyse en coordonnées principales a permis de cerner au moins 6 populationsgénétiques distinctes et au plus 10 sous-populations différentiées dans l’aire de répartition canadienne de C. guttata échantillonnée, quicorrespondaient a des unités démographiquement indépendantes. BOTTLENECK n’a pas détecté les baisses de population. Une revuede la documentation a permis d’établir que les tests de la présence de goulots d’étranglement dans 17 études génétiques de tortuesterrestres et palustres sur 18 reposaient sur un échantillonnage sous-optimal, ce qui pourrait en avoir faussé les résultats. La forterétention de la diversité génétique (richesse allélique et hétérozygotie) dans des populations isolées de C. guttata et d’autres espèces detortues est encourageante en ce qui concerne le rétablissement de ces espèces, mais les conclusions concernant la prévalence degoulots d’étranglement génétiques dans ces populations devraient être réexaminées.

Mots-clés : Clemmys guttata, génétique du paysage, microsatellites, Ontario, STRUCTURE, TESS, test d’affectation, GENECLASS.

IntroductionDemographic and genetic factors both contribute to the persis-

tence or extinction of small populations (Gilpin and Soulé 1986;Lande 1988). Assessment of a population’s probability of extinc-tion should therefore include both demographic and genetic data.Although demographic data can be used to estimate populationviability (e.g., Enneson and Litzgus 2009; Shoemaker et al. 2013),the implications of genetic data may be less obvious, and the twofactors also interact. For example, a severe demographic bottle-neck may reduce a population to a nonviable size, and the genetic

diversity of the population may be simultaneously impacted by asudden loss of alleles. Genetic drift impacts the resulting smallpopulation more strongly than the original, large one resulting ina higher probability that rare alleles will be lost (Wright 1931), andsmall populations are more likely to experience significant in-breeding. This loss of diversity may ultimately reduce fitness andevolutionary potential (Wright 1922; Haldane 1926; Fisher 1949;Bouzat et al. 1998; Ewing et al. 2008).

The impact of fragmentation, bottlenecks, and genetic drift onsmall populations are documented in a range of wild populations(e.g., Frankham 1996; Bouzat et al. 1998; Ewing et al. 2008), but

Received 31 July 2013. Accepted 17 December 2013.

C.M. Davy* and R.W. Murphy. Department of Ecology and Evolutionary Biology, University of Toronto, 25 Willcocks Street, Toronto, ON M5S 3B2,Canada; Department of Natural History, Royal Ontario Museum, 100 Queen’s Park, Toronto, ON M5S 2C6, Canada.Corresponding author: Christina Davy (e-mail: purple_salamander@hotmail.com).*Present address: Trent University, DNA Building, 2140 East Bank Drive, Peterborough, ON K9J 7B8, Canada.

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Can. J. Zool. 92: 149–162 (2014) dx.doi.org/10.1139/cjz-2013-0188 Published at www.nrcresearchpress.com/cjz on xx xxx 2014.

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typically in species that meet the basic assumptions of classicalpopulation genetics models. Organisms with long life spans, over-lapping generations, and promiscuous mating systems violatesome of these assumptions (Frankham et al. 2002) and maytherefore experience the genetic effects of population fragmen-tation and decline differently. For example, most species of turtlehave delayed maturity, long generation times (often >25 years),long life spans, and polygamous or promiscuous mating systems(Congdon et al. 1993, 1994; Litzgus 2006; Davy et al. 2011; Liu et al.2013).

The expected genetic effects of recent, anthropogenic habitatand population fragmentation have not been detected in severalturtle species, despite empirical and anecdotal evidence of signif-icant population declines and fragmentation (Rubin et al. 2001;Kuo and Janzen 2004; Marsack and Swanson 2009; Pittman et al.2011). Isolated subpopulations remain genetically identical overmany years even when dispersal is severely restricted (Rubin et al.2001; Bennett et al. 2010), apparently owing to their long genera-tion times. Severe demographic bottlenecks are expected to leavea genetic signature (Wright 1931; Frankham 1996; Piry et al. 1999,Garza and Williamson 2001), but small populations of turtles thathave declined significantly in recent years often show no evidenceof genetic bottlenecks (Kuo and Janzen 2004; Mockford et al. 2005;Marsack and Swanson 2009; Spradling et al. 2010; Pittman et al.2011). One potential explanation is that the long life span of turtlesbuffers small populations against the genetic effects of habitatfragmentation and bottlenecks (Kuo and Janzen 2004; Marsackand Swanson 2009; Bennett et al. 2010) and the potential associ-ated loss of allelic diversity. However, while long life spans mayslow loss of genetic diversity, it is unlikely that they can trulyprevent it, and it is more likely that genetic structure in turtlesreflects historic rather than current processes.

The lack of evidence for genetic bottlenecks in turtles may alsobe related to difficulties in sampling rare or declining popula-tions. The commonly used program BOTTLENECK requires a min-imum of 10 loci and 30 individuals per tested population toachieve reasonable statistical power in tests of heterozygote ex-cess and in the qualitative mode-shift test (Piry et al. 1999). Suchsamples may be unattainable from tiny populations. Neverthe-less, these tests have been widely used, often with suboptimalsample sizes, and may have led to incorrect conclusions aboutgenetic impacts of population declines on threatened populationsof turtles and other long-lived species. Recent studies also suggestthat these tests may not accurately detect bottlenecks even whenthe sampling requirements are met (Cristescu et al. 2010; Peeryet al. 2012). Another commonly used test for bottlenecks is theM-ratio test of Garza and Williamson (2001), which tests for an-other expected signature of genetic bottlenecks, namely a declinein the number of alleles present in a population relative to therange in allele size. As with the mode shift and heterozygote excesstest, the M-ratio does not always correctly identify bottlenecks andall three tests are extremely sensitive to the assumed mutationmodel used in the analysis (Peery et al. 2012). Data from severelyreduced (bottlenecked) wild populations can serve to further ex-plore this problem.

The Spotted Turtle (Clemmys guttata (Schneider, 1792)) is globallyendangered due to habitat loss and illegal collection. This speciesoccurs in small, isolated populations across its range (COSEWIC2004; van Dijk 2011) precluding a genetic rescue effect (Tallmonet al. 2004). Low vagility may result in low levels of gene flowbetween isolated populations because this species has high sitefidelity and individuals rarely travel >2 km per year (Litzgus 1996;Seburn 2003; Rasmussen and Litzgus 2010). Individuals sampledin a small wetland in Indiana had reduced genetic diversity (mea-sured by DNA fingerprinting) compared with individuals in anearby large wetland, suggesting that declining populations mayhave reduced genetic diversity (Parker and Whiteman 1993). How-ever, there is no published information available about the ge-

netic population structure of C. guttata in either Canada or theUnited States, and C. guttata is currently managed as a single pop-ulation in Canada (COSEWIC 2004).

Historic data on population size are not available; therefore, theexact degree of decline of each occurrence is not known. However,an estimated >35% decline in the Canadian population over thepast 75 years (three generations) has reduced most occurrencesto <150 individuals (COSEWIC 2004). Thus, the remaining occur-rences provide a set of replicate, demographically independentpopulations likely to have undergone recent bottlenecks.

Here, we conduct the first analysis of genetic population structurein C. guttata and characterize the genetic structure of C. guttatapopulations in Canada. We also use data from these populationsto test whether traditional bottleneck tests (specifically, the twoheterozygote excess test and the qualitative mode-shift test imple-mented in the program BOTTLENECK) can detect bottlenecks inthreatened, recently reduced populations of long-lived organisms.Finally, we conduct a literature review to investigate the use andresults of the three “bottleneck” tests (qualitative mode-shift test,heterozygote excess test, and M-ratio test) in studies of other spe-cies of turtles, and we consider the implications of our results forrecovery of C. guttata and other endangered species.

Materials and methods

Sample collectionWe conducted mark–recapture surveys for C. guttata at 13 sites

across southern Ontario from April 2008 to October 2011. Oursamples represent most of the extant Canadian populations ofC. guttata, which are considered demographically independent (noexchange of individuals between occurrences; COSEWIC 2004;Fig. 1). Detailed location data were withheld to avoid increasedpressure on these populations. Turtles were captured by hand andmarked by shell notching (Cagle 1939). Blood (0.05–0.10 mL) wascollected from mature individuals by caudal venipuncture andstored on FTA cards (Whatman, Inc., Clifton, New Jersey, USA).Muscle was sampled from dead turtles encountered during sur-veys or dead on the road (DOR).

The number of C. guttata in Canada is currently estimated at2000 individuals (COSEWIC 2004), although this estimate is notrobust due to a lack of long-term data from most sites. We sam-pled 25–30 individuals/site where possible, or >10% of the esti-mated site population based on previous estimates of sitepopulation size (COSEWIC 2004; J.D. Litzgus, unpublished data).Where possible, we estimated adult population size (n) from ourmark–recapture data using the program MARK (White andBurnham 1999), with a modified closed-capture Schnabel model(data not shown). We supplemented our data through collabora-tion with researchers coordinating long-term mark–recaptureprojects at our sampling sites to maximize the number of captureevents at each site. We allowed the probability of detection (cap-ture) to vary among years to account for the varying survey effortand searcher efficiency.

Microsatellite genotypingDNA extraction followed Davy et al. (2012). Eleven microsatellite

loci originally developed for the Bog Turtle (Glyptemys muhlenbergii(Schoepff, 1801)) were amplified following the cycling parametersof King and Julian (2004), with locus-specific annealing tempera-tures shown in Table 1. Amplification followed the methods ofSchuelke (2000); using 4.0 �L of M13-labelled forward primer,0.66 �L each of pigtailed reverse primer (Eurofins MWG Operon)and a 6-carboxyfluorescein dye (6-FAM; Eurofins MWG Operon),and 1.0 �L of DNA eluate (6–9 ng). Fragment lengths were visual-ized using a 3730 DNA Analyzer (Applied Biosystems Inc., FosterCity, California, USA) with size standard GS(500) Liz (AppliedBiosystems). We scored genotypes with relative fluorescenceunit (RFU) peaks >200 using GENEMARKER (SoftGenetics, State

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College, Pennsylvania, USA), and repeated amplification forgenotypes with <200 RFU. Genotyping error was assessed by re-extracting DNA and re-genotyping approximately 3% (8/256) ofindividuals twice, using duplicate, independent blood samplesfrom the same individual where possible (Pompanon et al. 2005).Genotypes were checked for null alleles and long-allele drop-outusing MICRO-CHECKER version 2.2.3 (van Oosterhout et al. 2004).

Population genetics analysesWe used GENALEX version 6.0 (Peakall and Smouse 2006) to

quantify observed and expected heterozygosity (HO and HE, re-

spectively), probability of identity (PI), and PIsibs (which takes intoaccount the possibility that close relatives were sampled). Thesewere calculated for each locus, each site, and the entire data set.Linkage equilibrium and deviations from Hardy–Weinberg equi-librium (HWE) were tested in GENEPOP version 4.0.10 (Raymondand Rousset 1995; Rousset 2008) with sequential Bonferroni cor-rection of multiple pairwise comparisons (Rice 1989). Allelic andprivate allelic richness (Ar and Par, respectively) were adjustedfor unequal sample sizes by rarefaction in HP-RARE version 1.0(Kalinowski 2004, 2005).

Fig. 1. Approximate location of sampled sites of populations of Spotted Turtle (Clemmys guttata). LE, Lake Erie; LH, Lake Huron; BP, BrucePeninsula; GB, Georgian Bay; GH, Golden Horseshoe; HC, Hastings County; DOR, dead on road; EO, Eastern Ontario. Figure appears in colouron the Web.

Table 1. Summary statistics for 11 microsatellite loci originally developed for the Bog Turtle (Glyptemys muhlenbergii)(King and Julian 2004) and amplified in 256 Spotted Turtles (Clemmys guttata) from southern Ontario.

Locus Temp. (°C) n k Ne HO HE PI PIsibs

GmuA19 61.5 245 3 2.955 0.624 0.662 0.189 0.466GmuB08 58* 250 3 1.993 0.484 0.498 0.372 0.594GmuD16 58 250 11 5.710 0.768 0.825 0.052 0.35GmuD21 58* 242 9 4.225 0.769 0.763 0.08 0.388GmuD55 58* 245 10 4.498 0.702 0.778 0.073 0.379GmuD79 61.5 252 7 4.892 0.758 0.796 0.071 0.37GmuD87 61.5 238 18 13.309 0.891 0.925 0.01 0.29GmuD88 61.5 243 12 2.761 0.547 0.638 0.158 0.47GmuD107 54 240 15 8.641 0.858 0.884 0.024 0.313GmuD114 60 249 4 2.241 0.518 0.554 0.279 0.543GmuD121 56 254 10 3.936 0.657 0.746 0.1 0.402

Note: An asterisk indicates an initial touchdown of 1 °C/cycle from 10 °C above the annealing temperature, followed by a constantannealing temperature for the remaining cycles. Temp., annealing temperature (°C); n, number of individuals amplified at each locus;k, number of alleles; Ne, number of effective alleles; HO, observed heterozygosity; HE, expected heterozygosity; PI, probability ofidentity; PIsibs, probability of identity for siblings at a locus.

Davy and Murphy 151

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We calculated absolute differentiation among sites, Dest (Jost2008), using SMOGD version 1.2.5 (Crawford 2010). We also usedFSTAT (Goudet 1995) to quantify differentiation using FST withBonferroni correction for multiple pairwise comparisons, and as-sessed significance with 10 000 random permutations of the data.To avoid a lack of statistical power due to small sample sizes,sites <30 km apart (BP1 and BP2, GH1 and GH2, and GB1 and GB2)were combined for these calculations. Correlations between ge-netic distance (Dest) and Euclidean distance (Wright 1943) weretested using IBDWS version 3.23 (Jensen et al. 2005). Significancewas assessed with a Mantel test (Mantel 1967) with 30 000 random-izations. Geographic genetic structure was also visualized usingprincipal coordinates analysis (PCoA) of Dest in GENALEX.

We used three methods to determine the number of geneticallydistinct clusters represented by the data. Each method usedhas its own strengths, weaknesses, and assumptions (reviewed inFrançois and Durand 2010), and the combination of multipleanalyses allowed us to assess congruence among analyses anddetermine the most biologically likely solution. First, we usedSTRUCTURE version 2.3.4 (Pritchard et al. 2000) to test the relativeprobability of models ranging from 1 to 13 populations (K). EachMarkov chain Monte Carlo (MCMC) run involved 750 000 genera-tions with a burn-in of 75 000 generations, assuming correlatedallele frequencies (Falush et al. 2003) and historical admixturebetween populations (Pritchard et al. 2000). Eight runs were con-ducted at each value of K using the LOCPRIOR function to includesampling locations in the analysis. A further set of runs wereconducted without the LOCPRIOR function. Runs were compiledwith STRUCTURE HARVESTER version 0.6.92 (Earl and vonHoldt2012). We selected the most likely value of K by assessing theincrease in pr(X|K) (Pritchard et al. 2000), and using the ad hoc �Kmethod (Evanno et al. 2005). We used the Greedy and LargeKGreedyalgorithms in CLUMPP version 1.1.2 (Jakobsson and Rosenberg2007) to combine results from independent runs. Clusters identi-fied by STRUCTURE were visualized with DISTRUCT 1 version 1.1(Rosenberg 2004).

We also used TESS version 2.3.1 (Chen et al. 2007) and GENELANDversion 4.0.2 (Guillot et al. 2005) to analyze the data whileexplicitly considering spatial information. TESS assumed an ad-mixture model (Durand et al. 2009) and tested increasing values ofKmax (the maximum possible number of populations in the dataset) from 2 to 9, with 10 runs at each. Runs included 50 000 sweepswith a burn-in of 10 000 sweeps. Data were assessed to ensureconvergence. We considered the most likely K to occur at the point ofinflection of the decreasing deviance information criterion (DIC) val-ues and at which the number of distinct clusters stabilized (Chenet al. 2007). In GENELAND, 10 runs of 1 000 000 iterations each ex-plored a range of K from 1 to 15, assuming correlated allele fre-quencies among populations. A burn-in of 10% was applied afterprocessing. Finally, analysis of molecular variance (AMOVA) inARLEQUIN version 3.1 (Excoffier et al. 2005) quantified partition-ing of genetic variance within and among sampled sites and ge-netic clusters.

Finally, we conducted assignment tests in GENECLASS version2.0 (Piry et al. 2004) to determine the accuracy with which indi-viduals could be assigned to their site or genetic population oforigin based on the genotype data. Assignment tests used themethod of Rannala and Mountain (1997) with 100 000 simulatedindividuals. We first considered each sampled site as an indepen-dent population and then repeated the tests using the geneticpopulations identified by STRUCTURE and TESS. We also used theposterior probability of assignment of each individual to the clus-ters identified by STRUCTURE (i.e., the individual q matrix) todetermine whether individuals could be accurately assigned totheir site or populations of origin based on genetic data. Individ-uals were assigned to a cluster (site or population) when q > 0.5(Latch et al. 2006).

Analyses of genetic bottlenecksWe used BOTTLENECK to test for bottlenecks at sample sites in

the past 2Ne–4Ne generations (Piry et al. 1999). We tested for het-erozygote excess (Cornuet and Luikart 1996) using 1000 replicatesunder the two-phase model (TPM; Di Rienzo et al. 1994), withvariance of 12 among multiple steps. Parameters were set to 95%single-step mutations and 5% multiple-step mutations, and theWilcoxon test assessed statistical significance of heterozygote ex-cess as recommended for <20 loci (Piry et al. 1999). Qualitativemode shift tests (Luikart et al. 1998) were also performed. Samplessizes >29 were available from five sites, meeting the criteria rec-ommended for this program (Table 2), although the remainingsites were also tested. Finally, the heterozygote excess test andmode-shift test were also applied to the genetic clusters identifiedby STRUCTURE and TESS.

Summary of previous studies applying bottleneck tests todata from turtle populations

We used a Scholar’s Portal search (keywords: turtle, tortoise,BOTTLENECK, mode-shift test, heterozygote excess test, andM-ratio) to identify studies that had used these three tests to ana-lyze data from other turtle populations. We also ran a search forstudies with keywords turtle or tortoise that also cited Piry et al.(1999) or Garza and Williamson (2001). For each study, we notedthe number of loci used and the sample size available, as well asthe results of the three tests.

ResultsWe sampled 253 C. guttata from 13 sites (mean n = 19.5, SD = 10.6,

range = 4–35; Table 2) representing approximately 10% of the totalestimated Canadian population and most of the known Canadianrange. We also sampled three DOR individuals not found nearknown populations. Pairwise distances between sampled sitesranged from 3.2 to 670.0 km (mean = 277.2, SD = 147.5).

All duplicated genotypes were identical, indicating genotypingerror ≤1%. Number of alleles per locus and genetic diversity (HO,HE, Ar, and Par) of the 11 loci are shown in Tables 1–3. Sites LE1, GB,HC, and EO2 had private alleles, and several alleles were restrictedto only two or three sites. PI and PIsibs reached <0.001 with inclu-sion of four and eight loci, respectively.

MICRO-CHECKER identified homozygote excess across the en-tire data set but not in any individual sites, indicating effects ofpopulation genetic structure rather than null alleles. We found noevidence of long-allele drop-out. Exact tests detected no signifi-cant deviations from HWE in the overall data set or individualloci. We detected heterozygote excess at EO1 and EO2 (p < 0.05),but no significant heterozygote deficit at any site. We found noevidence of significant linkage disequilibrium between any locuspairs and all loci were included in further analyses.

STRUCTURE resolved an initial split between southeastern andsouthwestern Ontario, with a peak in �K at K = 2 (Figs. 2A–2E). TheDOR samples grouped not with nearby sites in this model but withsites in western Ontario. Smaller peaks at K = 5 and K = 8 indicatedfurther hierarchical population structure. At K = 5, the followingclusters were resolved: LH1 and LH2 (mean q = 0.80, SD = 13 0.14);BP1 and BP2 (mean q = 0.91, SD = 0.03); LE1, LE2, GH1, GH2, GB1,and GB2 (mean q = 0.77, SD = 0.10); HC (mean q = 0.97, SD = 0.01);and EO1 and EO2 (mean q = 0.86, SD = 0.18). These same fiveclusters were identified in the analyses without the LOCPRIORfunction (data not shown); the DOR samples from eastern Ontariowere again assigned more strongly to populations in southwest-ern Ontario than to nearby sites. At K = 8, sites EO1 and GB (GB1and GB2) became distinct, but no biologically relevant eighth clus-ter was apparent.

The DIC of the TESS analyses reached a point of inflection atfive clusters (mean DIC = 16 460.830, SD = 129.039; Fig. 2D).Although the DIC continued to decline past K = 5, no new clusterswere resolved at K = 6. The TESS and STRUCTURE clusters

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Table 2. Number of alleles (private alleles in parentheses), observed and expected heterozygosities (HO and HE, respectively), sample size (n), and estimated frequency of a null allele (for eachlocus across all populations) for 256 Spotted Turtles (Clemmys guttata) from southern Ontario organized by locus and populations (for definition of site acronyms see Fig. 1).

LH1 LH2 BP1 BP2 LE1 LE2 GH1 GH2 GB1 GB2 HC EO1 EO2* DOR1 DOR2

GmuB08 (estimated null allele frequency = 0.00)Number of alleles 2 2 2 2 2 3 2 2 2 3 2 2 2 — 1HO 0.414 0.433 0.500 0.556 0.533 0.455 0.480 0.333 0.333 0.647 0.438 0.571 0.471 — —HE 0.428 0.495 0.375 0.489 0.491 0.541 0.461 0.500 0.278 0.493 0.342 0.459 0.457 — —n 29 30 4 27 30 11 25 6 6 17 16 14 34 0 1

GmuD16 (estimated null allele frequency = 0.00)Number of alleles 7 8 5 6 8 8 8 4 4 9 6 5 5 (1) — 2HO 0.828 0.800 1.000 0.593 0.700 0.917 0.792 0.667 0.667 0.941 0.688 0.643 0.824 — —HE 0.815 0.782 0.688 0.763 0.812 0.813 0.821 0.653 0.625 0.794 0.693 0.617 0.734 — —n 29 30 4 27 30 12 24 6 6 17 16 14 34 0 1

GmuD55 (estimated null allele frequency = 0.134)Number of alleles 5 8 3 7 9 6 7 3 4 6 6 7 7 — 2HO 0.414 0.600 0.750 0.741 0.833 0.583 0.591* 0.400 0.750 0.882 0.688 0.929 0.853 — —HE 0.542 0.639 0.531 0.768 0.764 0.476 0.697 0.540 0.688 0.739 0.668 0.778 0.804 — —n 29 30 4 27 30 12 22 5 4 17 16 14 34 0 1

GmuD79 (estimated null allele frequency = 0.00)Number of alleles 5 6 4 7 6 5 7 4 3 5 4 4 5 — 1HO 0.759 0.800 0.750 0.815 0.871 0.727 0.750 0.429 0.667 0.765 0.750 0.929 0.647 — —HE 0.748 0.784 0.719 0.720 0.793 0.669 0.700 0.367 0.611 0.619 0.686 0.640 0.554 — —n 29 30 4 27 31 11 24 7 6 17 16 14 34 0 2

GmuD88 (estimated null allele frequency = 0.173)Number of alleles 6 6 3 5 7 (1) 4 7 2 4 (1) 3 3 4 6 (1) — 2HO 0.370* 0.517 0.500 0.577 0.667 0.545 0.720 0.000 0.500 0.400 0.188* 0.615 0.765 — —HE 0.517 0.650 0.531 0.649 0.655 0.442 0.694 0.278 0.514 0.464 0.365 0.559 0.707 — —n 27 29 4 26 30 11 25 6 6 15 16 13 34 0 1

GmuD107 (estimated null allele frequency = 0.00)Number of alleles 8 11 5 9 11 8 12 5 6 6 5 (1) 5 9 2 2HO 0.815* 0.833 1.000 0.913 0.867 0.917 0.818 0.571 1.000 0.882 0.800 0.786 0.935 — —HE 0.776 0.842 0.781 0.854 0.862 0.795 0.857 0.673 0.806 0.777 0.716 0.615 0.830 — —n 27 30 4 23 30 12 22 7 6 17 15 14 31 1 1

GmuD114 (estimated null allele frequency = 0.00)Number of alleles 3 4 3 3 3 3 2 2 2 3 2 2 4 — 1HO 0.379 0.533 0.500 0.444 0.500 0.750 0.478 0.333 0.500 0.529 0.375 0.643 0.706 — —HE 0.372 0.534 0.406 0.529 0.545 0.538 0.496 0.444 0.375 0.562 0.469 0.497 0.643 — —n 29 30 4 27 30 12 23 6 6 17 16 14 34 0 1

GmuD121 (estimated null allele frequency = 0.078)Number of alleles 7 6 2 4 6 7 7 3 5 5 3 3 5 2 (1) 2HO 0.724 0.933 0.250 0.519 0.613 0.545 0.615 0.667 0.833 0.588 0.750 0.643 0.588 — —HE 0.781 0.796 0.469 0.578 0.743 0.727 0.753 0.611 0.694 0.683 0.639 0.582 0.500 — —n 29 30 4 27 31 11 26 6 6 17 16 14 34 1 2

GmuD21 (estimated null allele frequency = 0.00)Number of alleles 5 7 1 6 7 5 6 4 4 5 (1) 5 5 6 1 2HO 0.852 0.852 0.000 0.538 0.800 0.900 0.769 0.714 0.833 0.600 0.688 0.917 0.912 — —HE 0.752 0.799 0.000 0.498 0.773 0.735 0.791 0.602 0.597 0.484 0.678 0.708 0.787 — —n 27 27 4 26 30 10 26 7 6 15 16 12 34 1 1

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corresponded, except that TESS grouped GB1 and GB2 with BP ratherthan with LE1, LE2, and GH. We therefore considered Georgian Bayas a separate, sixth population for all further analyses. GENELANDanalysis indicated K = 15 (log-likelihood = −3611.943), but five“ghost populations” were inferred and disregarded (Guillot et al.2005). The remaining 10 clusters corresponded to sampled sites,with pairs of sites separated by <30 km assigned to single clusters(BP1–BP2, GB1–GB2, GH1–GH2, and a cluster including HC and thethree nearby DOR samples). Thus, the three Bayesian analysesidentified five genetic populations with Georgian Bay implicatedas a sixth. Ten subpopulations corresponding to the GENELANDclusters nested within these populations. Placement of the DORsamples was again ambiguous, suggesting that they may belong tofurther, unsampled populations. The DOR samples were not in-cluded in further population-level analyses.

All pairwise FST values were significant except for LE2–GH. Pair-wise Dest (Table 4) was up to two times larger than FST, and rangedfrom 0.227 (LH1 vs. EO2) to near zero (0.014; LE2 vs. GH). Manteltests indicated significant isolation by distance (Z = 2687.4073,r = 0.3856, p = 0.018). Values of FIS were not significant after cor-rection for multiple comparisons.

AMOVA of the six inferred genetic populations showed thatvariation within populations accounted for 91.79% of the variationin the data (Table 5; AMOVA: �ST = 0.082, p < 0.0001). Significantvariation also occurred among the populations (�CT = 0.038,p < 0.0001) and among sites (�SC = 0.046, p < 0.0001). Each subpop-ulation occupied distinct coordinate space in PCoA, with the firstthree axes accounting for 73.4% of variation (Figs. 3A–3C).

Assignment tests in GENECLASS had a 66.3% success rate(167 individuals correctly assigned) when assigning individuals totheir sampling site (Table 6). When the genetic populations iden-tified by STRUCTURE and TESS were considered, assignment ac-curacy increased to 77% and 78.6%, respectively.

BOTTLENECK did not detect heterozygote excess in any sites orpopulations, regardless of sample size. Evidence of a mode shiftwas only detected at a site with inadequate sample size (n = 4;estimated census population size = 8). Our literature review iden-tified 18 studies of tortoises and freshwater turtles that used thetests available in BOTTLENECK or calculated the M-ratio to searchfor genetic signatures of recent bottlenecks (Table 7). Of these,14 studies used <10 loci and 16 studies (including ours) applied thetest to samples of <30 individuals. Some studies analyzing multi-ple populations included analyses of samples of both greater andless than 30 individuals. Only one study (Kuo and Janzen 2004)met both requirements for all tested samples.

DiscussionThe Canadian range of C. guttata includes at least 6 genetically

isolated populations, within which 10 subpopulations are nested.The significant genetic structure identified in our analyses cannotbe explained by either geographic proximity of sites or isolationby distance alone. Despite severe fragmentation and habitatloss, demographically and genetically independent populationsof C. guttata have retained heterozygosity comparable with that ofother studied turtle populations (Vargas-Ramirez et al. 2012).There is no evidence for significant inbreeding at this time, evenat sites where the census population size is below 50 individuals.However, low allelic richness at low-abundance sites suggests thatgenetic drift is occurring at non-negligible rates, leading to fur-ther differentiation among isolated populations. Despite docu-mented declines in the occurrence of C. guttata (COSEWIC 2004),two commonly used bottleneck tests failed to detect the geneticsignature of population declines, either because they were notpresent or because the test could not detect them. This corrobo-rates the findings of other, recent studies of these tests, reinforc-ing that the results of these tests should be interpreted cautiously.T

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Conservation genetics of C. guttataThe 10 sampled subpopulations of C. guttata meet the manage-

ment unit (MU) criteria of Moritz (1994) and Palsbøll et al. (2007):significantly divergent allele frequencies and no significant, cur-rent genetic exchange among populations. Effective recoveryplanning will need to consider differences in population size,habitat type, habitat quality, and specific threats to persistenceamong these units. The six genetic populations also meet two ofthe criteria for designatable units (DUs), the subspecific categori-zation recognized under Canadian law (Green 2005). However, thefinal criterion for DUs (variation in risk of extinction among po-tential units) is not met, as the risk of extirpation is high for allknown subpopulations of C. guttata (Enneson and Litzgus, 2009).Thus, categorization of Canadian populations of C. guttata asDUs may not be justifiable or necessary at this time, althoughthe population of C. guttata in Hastings County is particularlyvulnerable to stochastic events because it is so small (n < 50).The Hastings County population should be prioritized for protec-tion because it represents a distinct genetic unit within C. guttatathat may not be represented elsewhere. Further occurrences ofC. guttata would need to be genotyped to assign them to one of thegenetic units characterized here. There is no reason to think theycould be correctly assigned based solely on geographic location,and the ambiguous assignment of the DOR samples indicates thatfurther, unsampled genetic populations may exist.

Historic population sizes of C. guttata are not known (COSEWIC2004), so it is possible to argue that this species exists naturally intiny, isolated populations, and that the isolation and small size ofextant populations are not the result of large, recent declines.However, this hypothesis predicts that all sampled sites have beenisolated for many generations and should therefore be geneticallydifferentiated from neighbouring sites. Instead, our data showisolation of some neighboring sites (e.g., LH2 and BP), while othergroups of equally distant sites (e.g., LH1 and LH2; LE1, LE2, GH1,and GH2) still form single genetic clusters, suggesting that theywere only isolated from one another within the last few genera-

tions. This evidence suggests that these groups were once part oflarger, more continuous populations spread across the interven-ing landscape, and supports the hypothesis that the extant occur-rences of C. guttata represent a small remnant of the species’historic abundance in Canada.

The effects of genetic drift have likely been compounded overthe last century by severe anthropogenic habitat modification andcollection pressures leading to population declines. However, theunderlying genetic structure of C. guttata in Canada is probablynot anthropogenic in origin. Despite an overall pattern of isola-tion by distance, several neighbouring sites (LH2 and BP; LE1 andLH2) are significantly differentiated, providing evidence of histor-ical barriers between them. There is a substantial lag time fromthe isolation of two population fragments by a nonpermeablebarrier to the detectability of the barrier by Bayesian clusteringprograms such as STRUCTURE and TESS. Studies using simulateddata demonstrate that this lag time is ≥9 generations and maybe >100 generations; for GENELAND, a minimum of 3 generationsmay be required (Safner et al. 2011; Blair et al. 2012). This lag timeis probably longer in natural populations because most barriersare actually semipermeable to wildlife. Dating the exact origins ordrivers of differentiation is beyond the scope of this paper, but forC. guttata, nine generations translates to a minimum of 225 years.Thus, differentiation of the six genetic populations likely pre-dates contemporary anthropogenic landscape modifications inour study area. This hypothesis could be properly investigatedusing simulation studies and analyses of historic and current ratesof gene flow in this and other, co-occurring species.

Heterozygosity and allelic richness in populations of C. guttata arecomparable with those observed in sympatric, larger populations ofBlanding’s Turtles (Emydoidea blandingii (Holbrook, 1838)) and Snap-ping Turtles (Chelydra serpentina (L., 1758)) (Davy et al. 2013; Davy 2013).Allelic richness varies with census population size in C. guttata(Table 3), and the accelerated rate of genetic drift experienced bysmall populations will leave them vulnerable to further loss of allelesand resulting loss of adaptive potential. Nevertheless, we found no

Table 3. Genetic diversity (heterozygosity, allelic richness, and private allelic rich-ness) of sampled regions, genetic populations, and sites for 253 Spotted Turtles(Clemmys guttata) genotyped at 11 microsatellite loci (for definition of site acronymssee Fig. 1).

Region, genetic population,sampling site HO HE

Allelicrichness

Private allelicrichness

Southwestern Ontario 0.679 0.728 6.75 1.72Pop1 0.680 0.724 5.52 0.23

LH1 0.654 0.659 4.01 0.18LH2 0.718 0.71 4.44 0.25

Pop2 0.687 0.703 4.64 0.16BP1 0.614 0.509 3.18 0.19BP2 0.649 0.661 3.92 0.13

Pop3 0.644 0.662 6.88 0.57LE1 0.717 0.72 4.49 0.24LE2 0.691 0.663 4.37 0.35GH1 0.67 0.71 4.48 0.28GH2 0.51 0.546 3.35 0.1

Georgian Bay 0.708 0.651 5.31 0.35GB1 0.689 0.598 3.81 0.24GB2 0.713 0.647 3.98 0.35

Southeastern Ontario 0.718 0.707 6.11 1.08Pop4 0.625 0.595 3.34 0.18

HC 0.625 0.595 3.34 0.18Pop5 0.749 0.694 6.11 0.61

EO1 0.743 0.629 3.53 0.16EO2 0.742 0.673 4.1 0.35

Note: Allelic and private allelic richness are rarefied to account for variation in sample sizes(Kalinowski 2004). Pop1–Pop5 represent genetic clusters supported by both STRUCTURE andTESS analyses. Georgian Bay is considered independently. HO, observed heterozygosity averagedacross all loci; HE, expected heterozygosity averaged across all loci.

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Fig. 2. (A) Population structure inferred by STRUCTURE and TESS for increasing values of K. (B) STRUCTURE results: estimated naturallogarithm probability of the data (L(K)) at increasing values of K. (C) �K calculated from B. (D) TESS results: decreasing deviance informationcriterion (DIC) with increasing Kmax. (E) Geographic locations of the genetic populations identified by STRUCTURE and TESS at K = 5, markedwith an asterisk in A. The GB population is either genetically distinct from other sampled populations, or results from admixture betweenLE–GH and BP. Hypothesized dispersal routes for Spotted Turtle (Clemmys guttata) colonizing Canada after glacial retreat are indicated by blackarrows. For definitions of site acronyms see Fig. 1.

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evidence that loss of genetic diversity poses a direct, current threat tothe persistence of C. guttata in Canada. We did not detect significantinbreeding, and we are unaware of any evidence of inbreeding de-pression in these populations. Preservation of the remaining geneticdiversity in this species may improve their long-term probability ofpersistence, but recovery planning for C. guttata should focus ondemographic threats.

Genetic assignment tests for Canadian C. guttata show a poten-tial for repatriation of illegally collected individuals to theirgenetic populations of origin. Unfortunately, fine-scale discrimination(i.e., between subpopulations) is not accurate enough to justifyrepatriations (<70% accuracy for subpopulations). Profiling ofindividuals from additional sites may improve the accuracy ofthe test and allow assignment of individuals back to theirsource populations with greater confidence. Increased samplesizes and the incorporation of additional markers might alsoincrease the accuracy of identification. However, in some cases,we already sampled more than 80% of the census populationand increased sampling may not be feasible.

Biogeographic implicationsDirectly testing hypothesis of postglacial recolonization is outside

the scope of this study, but the high differentiation between C. gut-tata from southwestern and southeastern Ontario (Fig. 2A) may re-flect two distinct routes during colonization of the Great Lakesregion after the retreat of the Laurentide ice sheets, potentially fromtwo independent glacial refugia (Fig. 2E). A Pleistocene refugium hasbeen inferred for Wood Turtles (Glyptemys insculpta (LeConte, 1830)) inthe southern Appalachian Mountains (Amato et al. 2008), and mayalso have held C. guttata through the last ice age. If so, individualsfrom this refugium could have recolonized eastern Ontario from thesoutheast. A second potential refugium may have existed nearpresent-day southern Indiana, south of the last glacial maximum. Tothe southwest of our study area, a rich mid-Holocene fossil recordfrom Indiana, Ohio, and Michigan includes C. guttata and severalother species, and suggests rapid postglacial colonization of theGreat Lakes region from nearby refugia (Holman 1992). A similar

pattern of simultaneous postglacial colonization from the east andwest is also proposed for E. blandingii (Davy et al. 2013).

Genetic structure in C. guttata is reflected in other taxa across thislandscape. For example, barriers to gene flow among the Great Lakesalso occur for the walleye (Sander vitreus (Mitchill, 1818)) (Stepien et al.2009). Limited historic gene flow between central and southeasternOntario exists for smallmouth bass (Micropterus dolomieu Lacepède,1802) (Borden and Krebs 2009), S. vitreus (Stepien et al. 2009), channeldarters (Percina copelandi (Jordan, 1877)) (Kidd et al. 2011), and Chelydraserpentina (C.M. Davy, unpublished data). The presence of a geneti-cally distinct population of C. guttata (site HC) in the Moira Riverwatershed is also reflected in P. copelandi (Kidd et al. 2011), suggestinga unique colonization history for this area. Genetic differentiation ofpopulations on the Bruce Peninsula from nearby populations is alsodocumented in black bears (Ursus americanus Pallas, 1780) (Pelletieret al. 2011) and Massasauga Rattlesnakes (Sistrurus catenatus(Rafinesque, 1818)) (Gibbs et al. 1997). These broad-scale patternsapparently reflect historic processes and barriers to gene flowrather than influences of the current landscape.

Genetic structure of C. guttata differs unexpectedly from that ofthe Eastern Foxsnake (Pantherophis gloydi (Conant, 1940)), a co-occurring marshland–prairie specialist that shows significant ge-netic structure along the north shore of Lake Erie (Row et al. 2010).Pantherophis gloydi shares the marshland habitat preferences ofC. guttata but can also exploit a variety of other open habitats. Rowet al. (2010) suggested that differentiation among populations ofP. gloydi results from reduced dispersal due to habitat conversion,but the distribution of C. guttata in the same area has been morestrongly reduced without causing similar genetic differentiation.The difference in population structure between these species likelyreflects their generation times: approximately 5 years for P. gloydiversus >25 years for C. guttata (COSEWIC 2004, 2008). This differ-ence could allow populations of P. gloydi to express genetic effectsof recent population fragmentation faster than the longer livedC. guttata.

Table 4. Pairwise values FST (below the diagonal) and Dest (above the diagonal; Jost 2008) for 13 putative subpopula-tions of 253 Spotted Turtles (Clemmys guttata) sampled across southern Ontario (for definition of site acronyms seeFig. 1).

LH1 LH2 BP LE1 LE2 GB GH HC EO1 EO2

LH1 0.039 0.16 0.104 0.1 0.095 0.072 0.161 0.171 0.229LH2 0.031 0.137 0.067 0.04 0.068 0.065 0.111 0.142 0.164BP 0.091 0.069 0.102 0.052 0.104 0.088 0.15 0.084 0.161LE1 0.061 0.041 0.058 0.045 0.062 0.021 0.118 0.133 0.142LE2 0.067 0.042 0.047 0.032 0.077 0.013 0.144 0.094 0.172GB 0.071 0.049 0.065 0.044 0.052 0.06 0.136 0.12 0.098GH 0.051 0.045 0.057 0.022 0.009 0.043 0.123 0.089 0.116HC 0.134 0.095 0.11 0.097 0.124 0.12 0.104 0.157 0.226EO1 0.112 0.078 0.082 0.067 0.079 0.082 0.055 0.138 0.102EO2 0.107 0.078 0.096 0.078 0.082 0.078 0.073 0.135 0.089

Note: Sites in GH, GB, and BP were analyzed together. The FST value in italic type is not significant (p > 0.05) after correction formultiple comparisons.

Table 5. Hierarchical analysis of molecular variance (AMOVA) for putative populations ofSpotted Turtle (Clemmys guttata) in Canada conducted in ARLEQUIN.

Source of variationSum ofsquares

Variancecomponents (�2)

Percentvariation

Among populations 102.237 0.158 3.829Among subpopulations within populations 59.966 0.180 4.384Within subpopulations 1637.154 3.778 91.787

Total 1799.357 4.116

Note: Each source of variation was significant (p < 0.0001). Tested populations were those identified byboth STRUCTURE and TESS analyses with Georgian Bay (GB) treated as a separate, sixth population. Sub-populations refer to sampling sites except GB, Golden Horseshoe (GH), and Bruce Peninsula (BP), which aretreated as single subpopulations.

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Conservation genetics of long-lived, iteroparous organismsHigh retention of heterozygosity also occurs in other frag-

mented turtle populations (reviewed by Vargas-Ramirez et al.2012; however, empirical estimates of population size for most

species reviewed are unavailable). High diversity in threatenedpopulations can give managers time to stabilize populations be-fore loss of diversity affects population viability (Kuo and Janzen2004; Marsack and Swanson 2009). Furthermore, heterozygosity is

Fig. 3. Principal coordinates analysis plot based on Dest (Table 4), showing structure among populations of Spotted Turtle (Clemmys guttata)(A, B) but less resolution of structure based on genetic distance among individuals (C). For definitions of site acronyms see Fig. 1. Figureappears in colour on the Web.

Table 6. Assignment of individual Spotted Turtle (Clemmys guttata) by GENECLASS analysis based on sampling sites;66.3% of individuals were assigned correctly (for definition of site acronyms see Fig. 1).

Assigned to

Sample source LH1 LH2 BP1 BP2 LE1 LE2 GH1 GH2 GB1 GB2 HC EO1 EO2

LH1 20 6 1 2LH2 1 26 2BP1 2 2BP2 1 18 8LE1 2 26 2LE2 1 2 3 6GH1 1 3 1 5 1 15GH2 1 4 2GB1 2 1 1 2GB2 3 3 2 9HC 4 1 11EO1 1 1 1 9 2EO2 1 1 2 30

Note: Shaded areas indicate clustering of sites in genetic populations supported by both STRUCTURE and TESS.

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Table 7. Summary of bottleneck tests in published studies of population genetics of tortoises and freshwater turtles.

Number of

Species LociIndividuals(mean)

Mode shiftdetected?

Significant heterozygosityexcess detected?

Significantly decreasedM-ratio detected? Reference

Geometric Tortoise, Psammobates geometricus(L., 1758)

8 25–28 (26.3) No No 3 of 3 tests significant Cunningham et al.2002

Morafka’s Desert Tortoise, Gopherus morafkai Murphy,Berry, Edwards, Leviton, Lathrop and Riedle, 2011

7 9–38 (18.8) — No No Edwards et al. 2004

Ornate Box Turtle, Terrapene ornata(Agassiz, 1857)

11 73–74 (73.5) No No No Kuo and Janzen 2004

Blanding’s Turtle, Emydoidea blandingii 5 27–43 (36.7) No No — Mockford et al. 2005Gopher Tortoise, Gopherus polyphemus

(Daudin, 1802)9 11–26 (19.1) — 5 of 14 tests significant — Schwartz and Karl

2005Diamondback Terrapin, Malaclemys terrapin

(Schoepff, 1793)6 12–56 (24.8) — No No Hauswaldt and Glenn

2005South American River Turtle, Podocnemis expansa

(Schweigger, 1812)9 16–37 (26.6) — 5 of 11 tests significant 11 of 11 tests significant Pearse et al. 2006

Desert Tortoise, Gopherus agassizii(Cooper, 1863)

11 18–83 (41.9) No 2 of 15 tests significant No Murphy et al. 2007

Yellow-spotted River Turtle, Podocnemis unifilis(Troschel, 1848)

5 14–55 (28.4) No No Yes, 10 of 11 significant Escalona et al. 2009

Eastern Box Turtle, Terrapene carolina carolina(L., 1758)

8 40–70 (54.3) No 2 of 3 tests significant No Marsack and Swanson2009

Alligator Snapping Turtle, Macrochelys temmincki(Troost in Harlan, 1835)

7 ≥10 per testedpopulation

Yes, 4 of 12 testssignificant

No Yes, 10 of 12 significant Echelle et al. 2010

Wood Turtle, Glyptemys insculpta 9 51 and 80 No No No Spradling et al. 2010Bog Turtle, Glyptemys muhlenbergii 18 8.9–34.7 (15.5)* — 2 of 6 tests significant — Pittman et al. 2011Gopherus polyphemus 9 11–40 (18.8) — No — Richter et al. 2011European Pond Turtle, Emys orbicularis

(L., 1758)7 23–36 (29.7) No 3 of 9 tests significant 5 of 9 tests significant Velo-Antón et al. 2011

Magdalena River Turtle, Podocnemis lewyana(Duméril, 1852)

10 4–49 (21) 2 of 3 tests significant† 7 of 7 tests significant 7 of 7 tests significant Vargas-Ramirez et al.2012

Marginated Tortoise, Testudo marginata(Schoepff, 1792)

11 18 No Significant under IAM but notunder TPM (Piry et al. 1999)

Yes Perez et al. 2011

Argentine Tortoise, Chelonoidis chilensis(Gray, 1870)

10 21 — No — Fritz et al. 2012

Spotted Turtle, Clemmys guttata 11 4–35 In a single tested site No — This study

Note: “Number of loci” is the number of loci used in bottleneck tests (in some cases this was lower than the total number amplified). “Number of individuals” is the minimum–maximum (mean in parentheses)number of individuals genotyped per tested population. When only populations above a certain size limit were used, only these populations were included in the summary. Values of loci and individuals in boldfacetype indicate that the minimum sampling recommendations for tests in BOTTLENECK were met. IAM, infinite allele model; TPM, two-phase model.

*Pittman et al. (2011) present mean sample size per locus.†Tests of seven sites were not significant; when sites were grouped into three populations, two of these demonstrated a significant mode shift.

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often an important predictor of individual fitness (Frankham et al.2002). Direct comparison of heterozygosity among species may becomplicated by interspecific differences in behaviour. For exam-ple, inbreeding avoidance can lead to excess heterozygosity, asdemonstrated in other reptiles (Stow and Sunnucks 2004). A com-parative approach among co-occurring species could be used totest the role of behaviour in determining “baseline” levels ofheterozygosity among species. However, heterozygosity may notbe an appropriate measure of genetic “health” in long-lived, iter-oparous organisms that retain high heterozygosity despite declines,because it may not accurately reflect the absolute reduction of ge-netic diversity in small populations. Allelic richness may provide amore useful measure for comparing absolute genetic diversityamong small and declining populations of long-lived organisms(Petit et al. 1995).

Bottleneck tests and long-lived organismsOur evaluation of bottleneck tests raises two concerns: (1) the

repeated use of these tests in the literature with sample sizes ornumber of loci below the recommended lower limit (Table 7) and(2) the ability of the tests to detect known declines even whenadequate sampling is possible (Cristescu et al. 2010; Peery et al.2012; this study). We suggest that the genetic impacts of bottle-necks in declining populations of long-lived organisms such asturtles require further consideration under a framework thatdoes not rely on BOTTLENECK or the M-ratio (e.g., see Kuo andJanzen 2003). Genetic consequences of bottlenecks can eventuallycomplicate species recovery (Ewing et al. 2008; Swinnerton et al.2004) even if the genetic signature of the bottleneck is not yetdetectable. In the case of severely bottlenecked populations ofturtles, tests may fail to detect bottlenecks because insufficienttime has passed since the bottleneck event (Mockford et al. 2005)or because long, overlapping generations may prevent a detect-able genetic signal from arising for many years (Marsack andSwanson 2009; Bennett et al. 2010). Tests may also fail because oflimitations inherent in the method (Peery et al. 2012). In all threecases, bottlenecked populations are still vulnerable and incorrectconclusions about the genetic health of threatened populationscan slow recovery efforts. We recommend caution when consid-ering previous conclusions that populations of long-lived organ-isms such as turtles are not genetically impacted by bottlenecks(e.g., Kuo and Janzen, 2004; Mockford et al. 2005; Marsack andSwanson 2009; Spradling et al. 2010; Pittman et al. 2011). Theseconclusions should be re-evaluated as more appropriate tests be-come available.

AcknowledgementsThis research was generously supported by the Government of

Ontario (Species at Risk Stewardship Fund grant to C.M.D. andR.W.M.), Wildlife Preservation Canada (Canada Collection grant toC.M.D.), and the Natural Sciences and Engineering Research Coun-cil of Canada (NSERC Discovery Grant to R.W.M.; Canada GraduateScholarship to C.M.D.). Thanks go to S.M. Coombes and manyvolunteers for assistance with fieldwork. Site access and furthersamples were provided by C. Brdar, M. Cairns, J. Cebek,S.D. Gillingwater, J.D. Litzgus, M.L. Rasmussen, D.C. Seburn,K.T. Yagi, A.R. Yagi, the Ausable Bayfield Conservation Authority,the Nature Conservancy of Canada, Ontario Parks, Ontario Na-ture, Ontario Hydro, and Parks Canada. Genotyping costs wereoffset by the support of the Schad Foundation. Research followedanimal use protocols ROM2008-11, ROM2009-02, ROM2009-21, andROM2010-14 from the Animal Care Committee of the Royal On-tario Museum; permits 1045769, 1049600, 1062210, 1067079, SR-B-001-10, and AY-B-013-11 from the Government of Ontario; andauthorizations from Ontario Parks and Parks Canada. J.D. Litzgus,S.C. Lougheed, D.A. McLennan, D.C. Seburn, C.C. Wilson, and twoanonymous reviewers provided valuable comments on earlier ver-sions of the manuscript.

ReferencesAmato, M.L., Brooks, R.J., and Fu, J. 2008. A phylogeographic analysis of popula-

tions of the wood turtle (Glyptemys insculpta) throughout its range. Mol. Ecol.17: 570–581. doi:10.1111/j.1365-294X.2007.03580.x. PMID:17971089.

Bennett, A.M., Keevil, M., and Litzgus, J.D. 2010. Spatial ecology and populationgenetics of northern map turtles (Graptemys geographica) in fragmented andcontinuous habitats in Canada. Chelonian Conserv. Biol. 9: 185–195. doi:10.2744/CCB-0824.1.

Blair, C., Weigel, D.E., Balazik, M., Keeley, A.T.H., Walker, F.M., Landguth, E.,Cushman, S., Murphy, M., Waits, L., and Balkenhol, N. 2012. A simulation-based evaluation of methods for inferring linear barriers to gene flow.Mol. Ecol. Resour. 12: 822–833. doi:10.1111/j.1755-0998.2012.03151.x. PMID:22551194.

Borden, W.C., and Krebs, R.A. 2009. Phylogeography and postglacial dispersal ofsmallmouth bass (Micropterus dolomieu) into the Great Lakes. Can. J. Fish.Aquat. Sci. 66(12): 2142–2156. doi:10.1139/f09-155.

Bouzat, J.L., Cheng, H.H., Lewin, H.A., Westemeier, R.L., Brawn, J.D., andPaige, K.N. 1998. Genetic evaluation of a demographic bottleneck in theGreater Prairie Chicken. Conserv. Biol. 12: 836–843. doi:10.1111/j.1523-1739.1998.97164.x.

Cagle, F. 1939. A system of marking turtles for future identification. Copeia,1939: 170–173. doi:10.2307/1436818.

Chen, C., Durand, E., Forbes, F., and François, O. 2007. Bayesian clustering algo-rithms ascertaining spatial population structure: a new computer programand a comparison study. Mol. Ecol. Notes, 7: 747–756. doi:10.1111/j.1471-8286.2007.01769.x.

Congdon, J.D., Dunham, A.E., and van Loben Sels, R.C. 1993. Delayed sexualmaturity and demographics of Blanding’s turtles (Emydoidea blandingii): im-plications for conservation and management of long-lived organisms.Conserv. Biol. 7: 826–833. doi:10.1046/j.1523-1739.1993.740826.x.

Congdon, J.D., Dunham, A.E., and van Loben Sels, R.C. 1994. Demographics ofcommon snapping turtles (Chelydra serpentina): implications for conservationand management of long-lived organisms. Am. Zool. 34: 397–408. doi:10.1093/icb/34.3.397.

Cornuet, J.M., and Luikart, G. 1996. Description and power analysis of two testsfor detecting recent population bottlenecks from allele frequency data.Genetics, 144: 2001–2014. PMID:8978083.

COSEWIC. 2004. COSEWIC assessment and update status report on the SpottedTurtle Clemmys guttata in Canada. Committee on the Status of EndangeredWildlife in Canada, Ottawa, Ont. Available from http://www.sararegistry.gc.ca/default_e.cfm [accessed 8 September 2012].

COSEWIC. 2008. COSEWIC assessment and update status report on the EasternFoxsnake Elaphe gloydi, Carolinian population and Great Lakes/St. Lawrencepopulation, in Canada. Committee on the Status of Endangered Wildlife inCanada, Ottawa, Ont. Available from http://www.sararegistry.gc.ca/default_e.cfm [accessed 8 September 2012].

Crawford, N.G. 2010. SMOGD: software for the measurement of genetic diversity.Mol. Ecol. Resour. 10: 556–557. doi:10.1111/j.1755-0998.2009.02801.x. PMID:21565057.

Cristescu, R., Sherwin, W.B., Handasyde, K., Cahill, V., and Cooper, D.W. 2010.Detecting bottlenecks using BOTTLENECK 1.2.02 in wild populations: theimportance of the microsatellite structure. Conserv. Genet. 11: 1043–1049.doi:10.1007/s10592-009-9949-2.

Cunningham, J., Baard, E.H.W., Harley, E.H., and O’Ryan, C. 2002. Investigationof genetic diversity in fragmented geometric tortoise (Psammobates geometricus)populations. Conserv. Genet. 3: 215–223.

Davy, C.M. 2013. Conservation genetics of freshwater turtles. Ph.D. thesis, De-partment of Ecology and Evolutionary Biology, University of Toronto, To-ronto, Ont.

Davy, C.M., Edwards, T., Lathrop, A., Bratton, M., Hagan, M., Henen, B., Nagy, K.,Stone, J., Hillard, L.S., and Murphy, R.W. 2011. Polyandry and multiple pater-nities in the threatened Agassiz’s desert tortoise, Gopherus agassizii: conserva-tion implications. Conserv. Genet. 12: 1313–1322.

Davy, C.M., Leifso, A.E., Conflitti, I.M., and Murphy, R.W. 2012. Characterizationof 10 novel microsatellite loci and cross-amplification of two loci in thesnapping turtle (Chelydra serpentina). Conserv. Genet. Resour. 4: 695–698. doi:10.1007/s12686-012-9624-7.

Davy, C.M., Bernardo, P.H., and Murphy, R.W. 2013. A Bayesian approach toconservation genetics of Blanding’s turtle (Emys blandingii) in Ontario, Can-ada. Conserv. Genet. doi:10.1007/s10592-013-0540-5.

Di Rienzo, A., Peterson, A.C., Garza, J.C., Valdes, A.M., Slatkin, M., andFreimer, N.B. 1994. Mutational processes of simple-sequence repeat loci inhuman populations. Proc. Natl. Acad. Sci. U.S.A. 91: 3166–3170. doi:10.1073/pnas.91.8.3166. PMID:8159720.

Durand, E., Jay, F., Gaggiotti, O.E., and François, O. 2009. Spatial inference ofadmixture proportions and secondary contact zones. Mol. Biol. Evol. 26:1963–1973. doi:10.1093/molbev/msp106. PMID:19461114.

Earl, D.A., and vonHoldt, B.M. 2012. STRUCTURE HARVESTER: a website andprogram for visualizing STRUCTURE output and implementing the Evannomethod. Conserv. Genet. Resour. 4: 359–361. doi:10.1007/s12686-011-9548-7.

Echelle, A.A., Hackler, J.C., Lack, J.B., Ballard, S.R., Roman, J., Fox, S.F.,Leslie, D.M., and Van Den Bussche, R.A. 2010. Conservation genetics of thealligator snapping turtle: cytonuclear evidence of range-wide bottleneck

160 Can. J. Zool. Vol. 92, 2014

Published by NRC Research Press

Can

. J. Z

ool.

Dow

nloa

ded

from

ww

w.n

rcre

sear

chpr

ess.

com

by

Tre

nt U

nive

rsity

on

01/3

1/14

For

pers

onal

use

onl

y.

effects and unusually pronounced geographic structure. Conserv. Genet. 11:1375–1387. doi:10.1007/s10592-009-9966-1.

Edwards, T., Schwalbe, C.R., Swann, D.E., and Goldberg, C.S. 2004. Implicationsof anthropogenic landscape change on inter-population movements of thedesert tortoise (Gopherus agassizii). Conserv. Genet. 5: 485–499. doi:10.1023/B:COGE.0000041031.58192.7c.

Enneson, J.J., and Litzgus, J.D. 2009. Stochastic and spatially explicit populationviability analyses for an endangered freshwater turtle, Clemmys guttata. Can.J. Zool. 87: 1241–1254. doi:10.1139/Z09-112.

Escalona, T., Engstrom, T.N., Hernandez, O.E., Bock, B.C., Vogt, R.C., andValenzuela, N. 2009. Population genetics of the endangered South Americanfreshwater turtle, Podocnemis unifilis, inferred from microsatellite DNA data.Conserv. Genet. 10: 1683–1696. doi:10.1007/s10592-008-9746-3.

Evanno, G., Regnaut, S., and Goudet, J. 2005. Detecting the number of clusters ofindividuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14:2611–2620. doi:10.1111/j.1365-294X.2005.02553.x. PMID:15969739.

Ewing, S.R., Nager, R.G., Nicoll, M.A.C., Aumjaud, A., Jones, C.G., and Keller, L.F.2008. Inbreeding and loss of genetic variation in a reintroduced populationof Mauritius Kestrel. Conserv. Biol. 22: 395–404. doi:10.1111/j.1523-1739.2008.00884.x. PMID:18294297.

Excoffier, L., Laval, G., and Schneider, S. 2005. ARLEQUIN ver. 3.0: an integratedsoftware package for population genetics data analysis. Evol. Bioinform.Online, 1: 47–50. PMID:19325852.

Falush, D., Stephens, M., and Pritchard, J.K. 2003. Inference of population struc-ture using multilocus genotype data: linked loci and correlated allele fre-quencies. Genetics, 164: 1567–1587. PMID:12930761.

Fisher, R.A. 1949. The theory of inbreeding. Oliver and Boyd, Edinburgh, Scot-land.

François, O., and Durand, E. 2010. Spatially explicit Bayesian clustering modelsin population genetics. Mol. Ecol. Resour. 10: 773–784. doi:10.1111/j.1755-0998.2010.02868.x. PMID:21565089.

Frankham, R. 1996. Relationship of genetic variation to population size in wild-life. Conserv. Biol. 10: 1500–1508. doi:10.1046/j.1523-1739.1996.10061500.x.

Frankham, R., Ballou, J.D., and Briscoe, D.A. 2002. Introduction to conservationgenetics. Cambridge University Press, Cambridge, UK.

Fritz, U., Alcalde, L., Vargas-Ramírez, M., Goode, E.V., Fabius-Turoblin, D.U., andPraschag, P. 2012. Northern genetic richness and southern purity, but justone species in the Chelonoidis chilensis complex. Zool. Scr. 41: 220–232. doi:10.1111/j.1463-6409.2012.00533.x.

Garza, J.C., and Williamson, E.G. 2001. Detection of reduction in population sizeusing data from microsatellite loci. Mol. Ecol. 10: 305–318. doi:10.1046/j.1365-294x.2001.01190.x. PMID:11298947.

Gibbs, H.L, Prior, K.A., Weatherhead, P.J., and Johnson, G. 1997. Genetic struc-ture of populations of the threatened eastern massasauga rattlesnake,Sistrurus c. catenatus: evidence from microsatellite DNA markers. Mol. Ecol. 6:1123–1132. doi:10.1046/j.1365-294X.1997.00284.x. PMID:9421918.

Gilpin, M.E., and Soulé, M.E. 1986. Minimum viable populations: the processes ofspecies extinctions. In Conservation biology: the science of scarcity and di-versity. Edited by M. Soulé. Sinauer Associates, Inc., Sunderland, Mass.pp. 13–34.

Goudet, J. 1995. FSTAT (version 1.2): a computer program to calculate F-statistics.J. Hered. 86: 485–486.

Green, D.M. 2005. Designatable units for status assessment of endangered spe-cies. Conserv. Biol. 19: 1813–1820. doi:10.1111/j.1523-1739.2005.00284.x.

Guillot, G., Mortier, F., and Estoup, A. 2005. GENELAND: a program for landscapegenetics. Mol. Ecol. Notes, 5: 712–715.

Haldane, J.B.S. 1926. A mathematical theory of natural and artificial selection.Math Proc. Cambridge Philos. Soc. 23: 363–372. doi:10.1017/S0305004100015176.

Hauswaldt, J.S., and Glenn, T.C. 2005. Population genetics of the diamondbackterrapin (Malaclemys terrapin). Mol. Ecol. 14: 723–732. doi:10.1111/j.1365-294X.2005.02451.x. PMID:15723664.

Holman, J.A. 1992. Late Quaternary herpetofauna of the central Great Lakesregion, U.S.A.: zoogeographical and paleoecological implications. Quat. Sci.Rev. 11: 345–351. doi:10.1016/0277-3791(92)90005-S.

Jakobsson, M., and Rosenberg, N.A. 2007. CLUMPP: a cluster matching and per-mutation program for dealing with label switching and multimodality inanalysis of population structure. Bioinformatics, 23: 1801–1806. doi:10.1093/bioinformatics/btm233. PMID:17485429.

Jensen, J.L., Bohonak, A.J., and Kelley, S.T. 2005. Isolation by distance, web ser-vice. BMC Genet. 6: 13. v.3.23 http://ibdws.sdsu.edu/. PMID:15760479.

Jost, L. 2008. GST and its relatives do not measure differentiation. Mol. Ecol. 17:4015–4026. PMID:19238703.

Kalinowski, S.T. 2004. Counting alleles with rarefaction: private alleles andhierarchical sampling designs. Conserv. Genet. 5: 539–543. doi:10.1023/B:COGE.0000041021.91777.1a.

Kalinowski, S.T. 2005. HP-Rare: a computer program for performing rarefactionon measures of allelic diversity. Mol. Ecol. Notes, 5: 187–189.

Kidd, A., Reid, S., and Wilson, C. 2011. Local and regional population geneticstructure of the threatened channel darter in Ontario and Quebec. Posterpresentation at American Fisheries Society 41st Annual Meeting, Seattle,Washington, 4–8 Sept. 2011. Available from https://afs.confex.com/afs/2011/webprogram/Paper6540.html [accessed 18 January 2014].

King, T.L., and Julian, S.E. 2004. Conservation of microsatellite DNA flanking

sequences across 13 emydid genera assayed with novel bog turtle (Glyptemysmuhlenbergii) loci. Conserv. Genet. 5: 719–725. doi:10.1007/s10592-004-1854-0.

Kuo, C.H., and Janzen, F.J. 2003. BOTTLESIM: a bottleneck simulation programfor long-lived species with overlapping generations. Mol. Ecol. Notes, 3: 669–673. doi:10.1046/j.1471-8286.2003.00532.x.

Kuo, C.H., and Janzen, F.J. 2004. Genetic effects of a persistent bottleneck on anatural population of ornate box turtles (Terrapene ornata). Conserv. Genet. 5:425–437. doi:10.1023/B:COGE.0000041020.54140.45.

Lande, R. 1988. Genetics and demography in biological conservation. Science,241: 1455–1460. doi:10.1126/science.3420403.

Latch, E.K., Dharmarajan, G., Glaubitz, J.C., and Rhodes, O.E. 2006. Relativeperformance of Bayesian clustering software for inferring population sub-structure and individual assignment at low levels of population differentia-tion. Conserv. Genet. 7: 295–302. doi:10.1007/s10592-005-9098-1.

Litzgus, J.D. 1996. Life history and demography of a northern population ofspotted turtles, Clemmys guttata. M.Sc. thesis, University of Guelph, Guelph,Ont.

Litzgus, J.D. 2006. Sex differences in longevity in the Spotted Turtle (Clemmysguttata). Copeia, 2006: 281–288. doi:10.1643/0045-8511(2006)6[281:SDILIT]2.0.CO;2.

Liu, Y., Davy, C.M., Shi, H.T., and Murphy, R.W. 2013. Sex in the half-shell: areview of the functions and evolution of courtship behavior in freshwaterturtles. Chelonian Conserv. Biol. 12: 84–100. doi:10.2744/CCB-1037.1.

Luikart, G., Allendorf, F.W., Cornuet, J.M., and Sherwin, W.B. 1998. Distortion ofallele frequency distributions provides a test for recent population bottle-necks. J. Hered. 89: 238–247. doi:10.1093/jhered/89.3.238. PMID:9656466.

Mantel, N. 1967. The detection of disease clustering and a generalized regressionapproach. Cancer Res. 27: 209–220. PMID:6018555.

Marsack, K., and Swanson, B.J. 2009. A genetic analysis of the impact of genera-tion time and road-based habitat fragmentation on Eastern Box Turtles(Terrapene c. carolina). Copeia, 2009: 647–652. doi:10.1643/CE-08-233.

Mockford, S.W., McEachern, L., Herman, T.B., Snyder, M., and Wright, J.M. 2005.Population genetic structure of a disjunct population of Blanding’s turtle(Emydoidea blandingii) in Nova Scotia, Canada. Biol. Conserv. 123: 373–380.doi:10.1016/j.biocon.2004.11.021.

Moritz, C. 1994. Defining ‘evolutionarily significant units’ for conservation.Trends Ecol. Evol. 9: 373–375. doi:10.1016/0169-5347(94)90057-4. PMID:21236896.

Murphy, R.W., Berry, K.H., Edwards, T., and McLuckie, A.M. 2007. A geneticassessment of the recovery units for the Mojave population of the deserttortoise, Gopherus agassizii. Chelonian Conserv. Biol. 6: 229–251. doi:10.2744/1071-8443(2007)6[229:AGAOTR]2.0.CO;2.

Palsbøll, P.J., Bérubé, M., and Allendorf, F.W. 2007. Identification of manage-ment units using population genetic data. Trends Ecol. Evol. 22: 11–16. doi:10.1016/j.tree.2006.09.003. PMID:16982114.

Parker, P.G., and Whiteman, H.H. 1993. Genetic diversity in fragmented popu-lations of Clemmys guttata and Chrysemys picta marginata as shown by DNAfingerprinting. Copeia, 1993: 841–846. doi:10.2307/1447248.

Peakall, R., and Smouse, P.E. 2006. GENALEX 6: genetic analysis in Excel. Popu-lation genetic software for teaching and research. Mol. Ecol. Notes, 6: 288–295. doi:10.1111/j.1471-8286.2005.01155.x.

Pearse, D.E., Arndt, A.D., Valenzuela, N., Miller, B.A., Cantarelli, V., andSites, J.W., Jr. 2006. Estimating population structure under nonequilibriumconditions in a conservation context: continent-wide population genetics ofthe giant Amazon river turtle, Podocnemis expansa (Chelonia; Podocnemidi-dae). Mol. Ecol. 15: 985–1006. doi:10.1111/j.1365-294X.2006.02869.x. PMID:16599962.

Peery, M.Z., Kirby, R., Reid, B.N., Stoelting, R., Doucet-Bëer, E., Robinson, S.,Vásquez-Carrillo, C., Pauli, J.N., and Palsbøll, P.J. 2012. Reliability of geneticbottleneck tests for detecting recent population declines. Mol. Ecol. 21: 3403–3418. doi:10.1111/j.1365-294X.2012.05635.x. PMID:22646281.

Pelletier, A., Obbard, M.E., White, B.N., Doyle, C., and Kyle, C.J. 2011. Small-scalegenetic structure of American black bears illustrates potential postglacialrecolonization routes. J. Mammal. 92: 629–644. doi:10.1644/10-MAMM-A-212.1.

Perez, M., Leblois, R., Livoreil, B., Bour, R., Lambourdiere, J., Samadi, S., andBoisselier, M. 2011. Effects of landscape features and demographic history onthe genetic structure of Testudo marginata populations in the southernPeloponnese and Sardinia. Biol. J. Linn. Soc. 105: 591–606. doi:10.1111/j.1095-8312.2011.01805.x.

Petit, R.J., El Mousadi, A., and Pons, O. 1995. Identifying populations for conser-vation on the basis of genetic markers. Conserv. Biol. 12: 844–855. doi:10.1111/j.1523-1739.1998.96489.x.

Piry, S., Luikart, G., and Cornuet, J.M. 1999. BOTTLENECK: a program for detect-ing recent effective population size reductions from allele frequency data.J. Hered. 90: 502–503.

Piry, S., Alapetite, A., Cornuet, J.M., Paetkau, D., Baudouin, L., and Estoup, A.2004. GENECLASS2: a software for genetic assignment and first-generationmigrant detection. J. Hered. 95: 536–539. doi:10.1093/jhered/esh074. PMID:15475402.

Pittman, S.E., King, T., Faurby, S., and Dorcas, M.E. 2011. Genetic and demo-graphic status of an isolated bog turtle (Glyptemys muhlenbergii) population:implications for the conservation of small populations of long-lived animals.Conserv. Genet. 12: 1589–1601.

Davy and Murphy 161

Published by NRC Research Press

Can

. J. Z

ool.

Dow

nloa

ded

from

ww

w.n

rcre

sear

chpr

ess.

com

by

Tre

nt U

nive

rsity

on

01/3

1/14

For

pers

onal

use

onl

y.

Pompanon, F., Bonin, A., Bellemain, E., and Taberlet, P. 2005. Genotyping errors:causes, consequences and solutions. Nat. Rev. Genet. 6: 847–846. doi:10.1038/nrg1707. PMID:16304600.

Pritchard, J.K., Stephens, M., and Donnelly, P.J. 2000. Inference of populationstructure using multilocus genotype data. Genetics, 155: 945–959. PMID:10835412.

Rannala, B., and Mountain, J.L. 1997. Detecting immigration by using multilocusgenotypes. Proc. Natl. Acad. Sci. U.S.A. 94: 9197–9221. doi:10.1073/pnas.94.17.9197. PMID:9256459.

Rasmussen, M.L., and Litzgus, J.D. 2010. Habitat selection and movement pat-terns of Spotted Turtles (Clemmys guttata): effects of spatial and temporalscales of analyses. Copeia, 2010: 86–96. doi:10.1643/CE-09-141.

Raymond, M., and Rousset, F. 1995. GENEPOP (version 1.2): population geneticssoftware for exact tests and ecumenicism. J. Hered. 86: 248–249.

Rice, W.R. 1989. Analyzing tables of statistical tests. Evolution, 43: 223–225.doi:10.2307/2409177.

Richter, S.C., Jackson, J.A., Hinderliter, M., Epperson, D., Theodorakis, C.W., andAdams, S.M. 2011. Conservation genetics of the largest cluster of federallythreatened Gopher Tortoise (Gopherus polyphemus) colonies with implica-tions for species management. Herpetologica, 67: 406–419. doi:10.1655/HERPETOLOGICA-D-10-00044.1.

Rosenberg, N.A. 2004. DISTRUCT: a program for the graphical display of popu-lation structure. Mol. Ecol. Notes, 4: 137–138. doi:10.1046/j.1471-8286.2003.00566.x.

Rousset, F. 2008. GENEPOP’007: a complete reimplementation of the GENEPOPsoftware for Windows and Linux. Mol. Ecol. Resour. 8: 103–106. PMID:21585727.

Row, J.R., Blouin-Demers, G., and Lougheed, S.C. 2010. Habitat distribution in-fluences dispersal and fine-scale genetic population structure of eastern fox-snakes (Mintonius gloydi) across a fragmented landscape. Mol. Ecol. 19:5157–5171. doi:10.1111/j.1365-294X.2010.04872.x. PMID:20977510.

Rubin, C.S., Warner, R.E., Bouzat, J.L., and Paige, K.N. 2001. Population geneticstructure of Blanding’s turtles (Emydoidea blandingii) in an urban landscape.Biol. Conserv. 99: 323–330. doi:10.1016/S0006-3207(00)00198-1.

Safner, T., Miller, M.P., McRae, B.H., Fortin, M.J., and Manel, S. 2011. Comparisonof Bayesian clustering and edge detection methods for inferring boundariesin landscape genetics. Int. J. Mol. Sci. 12: 865–889. doi:10.3390/ijms12020865.PMID:21541031.

Schuelke, M. 2000. An economic method for the fluorescent labeling of PCRfragments. Nat. Biotechnol. 18: 233–234. doi:10.1038/72708. PMID:10657137.

Schwartz, T.S., and Karl, S.A. 2005. Population and conservation genetics of thegopher tortoise (Gopherus polyphemus). Conserv. Genet. 6: 917–928. doi:10.1007/s10592-005-9078-5.

Seburn, D.C. 2003. Population structure, growth, and age estimation of SpottedTurtles, Clemmys guttata, near their northern limit: an 18-year follow-up. Can.Field-Nat. 117: 436–439.

Shoemaker, K.T., Breisch, A.R., Jaycox, J.W., and Gibbs, J.P. 2013. Reexaminingthe minimum viable population concept for long-lived species. Conserv. Biol.27: 542–551. doi:10.1111/cobi.12028. PMID:23458501.

Spradling, T.A., Tamplin, J.W., Dow, S.S., and Meyer, K.J. 2010. Conservationgenetics of a peripherally isolated population of the wood turtle (Glyptemysinsculpta) in Iowa. Conserv. Genet. 5: 1667–1677. doi:10.1007/s10592-010-0059-y.

Stepien, C.A., Murphy, D.J., Lohner, R.N., Sepulveda-Villet, O.J., andHaponski, A.E. 2009. Signatures of vicariance, postglacial dispersal andspawning philopatry: population genetics of the walleye Sander vitreus. Mol.Ecol. 18: 3411–3428. doi:10.1111/j.1365-294X.2009.04291.x. PMID:19659479.

Stow, A.J., and Sunnucks, P. 2004. Inbreeding avoidance in Cunningham’sskinks (Egernia cunninghami) in natural and fragmented habitat. Mol Ecol 13:443–447. doi:10.1046/j.1365-294X.2003.02060.x.

Swinnerton, K., Groombridge, J.J., Jones, C.G., Burn, R.W., and Mungroo, Y. 2004.Inbreeding depression and founder diversity among captive and free-livingpopulations of the endangered pink pigeon Columba mayeri. Anim. Conserv.7: 353–364. doi:10.1017/S1367943004001556.

Tallmon, D.A., Luikart, G., and Waples, R.S. 2004. The alluring simplicity andcomplex reality of genetic rescue. Trends Ecol. Evol. 19: 489–496. doi:10.1016/j.tree.2004.07.003. PMID:16701312.

van Dijk, P.P. 2011. Clemmys guttata. IUCN red list of threatened species. Version2012.1. Available from http://www.iucnredlist.org [accessed 21 August 2012].

van Oosterhout, C., Hutchinson, W.F., Wills, D.P.M., and Shipley, P. 2004.MICRO-CHECKER: software for identifying and correcting genotyping errorsin microsatellite data. Mol. Ecol. Notes, 4: 535–538. doi:10.1111/j.1471-8286.2004.00684.x.

Vargas-Ramirez, M., Stuckas, H., Castano-Mora, O.V., and Fritz., U. 2012. Ex-tremely low genetic diversity and weak population differentiation in theendangered Colombian river turtle Podocnemis lewyana (Testudines: Podocne-mididae). Conserv. Genet. 13: 65–77. doi:10.1007/s10592-011-0263-4.

Velo-Antón, G., Becker, C.G., and Cordero-Rivera, A. 2011. Turtle carapace anom-alies: the roles of genetic diversity and environment. PLoS ONE, 6: e18714.doi:10.1371/journal.pone.0018714. PMID:21533278.

White, G.C., and Burnham, K.P. 1999. Program MARK: survival estimation frompopulations of marked animals. Bird Study, 46(Suppl. 1): 120–138. doi:10.1080/00063659909477239.

Wright, S. 1922. Coefficients of inbreeding and relationship. Am. Nat. 56: 330–338. doi:10.1086/279872.

Wright, S. 1931. Evolution in Mendelian populations. Genetics, 16: 97–159. PMID:17246615.

Wright, S. 1943. Isolation by distance. Genetics, 28: 114–138. PMID:17247074.

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