From refugia to rookeries: Phylogeography of Atlantic green turtles

Journal of Experimental Marine Biology and Ecology 461 (2014) 306–316

Contents lists available at ScienceDirect

Journal of Experimental Marine Biology and Ecology journal homepage: www.elsevier.com/locate/jembe

From refugia to rookeries: Phylogeography of Atlantic green turtles Eugenia Naro-Maciel a,⁎, Brendan N. Reid b, S. Elizabeth Alter c, George Amato d, Karen A. Bjorndal e, Alan B. Bolten e, Meredith Martin d, Campbell J. Nairn f, Brian Shamblin f, Oscar Pineda-Catalan d a

Biology Department, College of Staten Island, City University of New York, 2800 Victory Boulevard, Staten Island, NY 10314, USA Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI 53704, USA Biology Department, York College, City University of New York, Jamaica, NY 11451, USA d Sackler Institute for Comparative Genomics, American Museum of Natural History, New York, NY 10024, USA e Department of Biology and Archie Carr Center for Sea Turtle Research, University of Florida, Gainesville, FL 32611, USA f Daniel B. Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30605, USA b c

a r t i c l e

i n f o

Article history: Received 16 June 2014 Received in revised form 28 August 2014 Accepted 30 August 2014 Available online xxxx Keywords: Bayesian skyline plots Chelonia mydas Marine glacial refugia Microsatellite mtDNA Population structure

a b s t r a c t Investigating species' distribution and abundance over time is central to evolutionary biology, and provides important context for conservation and management. With respect to population genetic structure in green sea turtles (Chelonia mydas), certain processes such as female philopatry to natal rookeries are well understood, while others, such as male philopatry and historical changes in distribution and abundance, remain relatively understudied. Further, although inferences from mitochondrial DNA and nuclear microsatellites have both been critical in identifying management units, comparisons of these units based on both markers are still rare. Here we analyzed novel data from fifteen microsatellite markers gathered at six green turtle rookeries in the western Atlantic as well as previously published mitochondrial sequences from 13 regional rookeries. We detected low, but significant, population structure at microsatellite loci, which coincides with previous delineations of local and regional management units as well as reports of male philopatry. However, we also detected a discord between nuclear and mitochondrial data, in which two tropical rookeries (Aves Island, Venezuela; and Matapica, Surinam) clustered with the Caribbean and Mediterranean based on microsatellite data, but displayed a mitochondrial lineage characteristic of the southern Atlantic and Africa. To investigate the possible causes of this discord, we used both classical and Bayesian methods to estimate historical migration rates and the timing and magnitude of changes in population size. We detected a strong barrier to dispersal between the northern and southern Atlantic, as well as an expansion in the southern mitochondrial lineage during the Wisconsin glacial period and a later expansion in the northern lineage following the Last Glacial Maximum. We propose that the Aves and Surinam rookeries were colonized by females from a southern glacial refugium, after which they experienced male-biased gene flow from the Caribbean. This study highlights the utility of incorporating data from multiple types of molecular markers in accurately identifying conservation units and in elucidating the complex historical and contemporary processes underlying population genetic structure in marine species. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Historical distribution and abundance data provide fundamental context for evolutionary, ecological, and behavioral marine research, as well as for conservation of threatened and endangered species. While this context is still lacking for many taxa, recent research has identified a number of historical patterns that are consistent across a broad array of marine organisms. During glacial and interglacial cycles, climate-related changes impacted several thermophilic marine species that retreated from the ice into more hospitable glacial refugia during colder periods (McMillen-Jackson and Bert, 2004; Naro-Maciel et al., 2011; Tolley et al., 2005). In warmer times, when the ice melted and ⁎ Corresponding author. Tel.: +1 1 718 982 3871. E-mail address: [email protected] (E. Naro-Maciel).

http://dx.doi.org/10.1016/j.jembe.2014.08.020 0022-0981/© 2014 Elsevier B.V. All rights reserved.

sea levels were substantially higher (Lambeck et al., 2002), spatial and demographic expansions occurred from these areas. Marine population structure is further often strongly impacted by ocean currents, but other factors, such as complex migratory behaviors, may play significant yet poorly understood roles (Bowen and Karl, 2007; Cowen et al., 2007; Naro-Maciel et al., 2014). Genetic analysis is a powerful tool for elucidating population history and structure with respect to geography, with conservation implications that include designating management units and priorities, and responding to climate change. This approach can be particularly informative when considering highly migratory or cryptic endangered marine organisms such as green sea turtles (Bowen and Karl, 2007). Green turtles (Chelonia mydas) are key elements of diverse tropical and temperate ecosystems (Bjorndal and Jackson, 2003). Like all marine chelonians, they hatch from eggs on nesting beaches and then enter the

E. Naro-Maciel et al. / Journal of Experimental Marine Biology and Ecology 461 (2014) 306–316

ocean. This oceanic phase is known as the “lost years” because turtle location is largely unknown (Carr, 1967), although a testable hypothesis for green turtles has recently been proposed (Putman and Naro-Maciel, 2013). Juveniles leave the open ocean for coastal feeding grounds at ~ 3–5 years of age (Reich et al., 2007) and generally forage in mixed aggregations drawn from various rookeries (Bowen and Karl, 2007). Adults undergo breeding migrations between often distant feeding and nesting habitats. Mating occurs offshore of the rookery and/or during overlapping reproductive migrations (FitzSimmons et al., 1997a, 1997b). Many females return about every 2–4 years to nest in the area of their birth, a behavior known as natal homing (Carr, 1967). Due to their cryptic marine habitat, less is known about philopatry and reproductive behavior in males, although they are thought to breed more frequently than females and are reproductively active for about a month (Limpus, 1993). Throughout their lives green turtles face diverse threats including harvest, fishery impacts, habitat loss, pollution, disease, and climate

307

change (Wallace et al., 2011) and are classified as globally endangered (IUCN, 2014). Vast reductions from hunting over the past millennium are known from the Caribbean and other areas, emphasizing the importance of protecting diverse rookeries (Bjorndal and Jackson, 2003; IUCN, 2014; McClenachan et al., 2006; Seminoff, 2004). Even so, in recent decades Atlantic rookeries including Ascension, Tortuguero, and Trindade (Fig. 1) have increased (Almeida et al., 2011; Broderick et al., 2006; Chaloupka et al., 2008). Currently these rookeries, along with many others that are genetically distinct, are considered independent management units (MUs). In response to the wide-ranging threats and migrations of marine turtles, regional management units (RMUs) were also developed to protect potentially independently evolving rookery clusters from shared threats, although only limited nuclear data were available at the time (Wallace et al., 2010). Genetic research has contributed greatly to our understanding of sea turtle conservation and ecology over the years (Bowen and Karl, 2007). In particular, data from genetic markers with different modes

Fig. 1. Distribution of Atlantic control region haplotype lineages among rookeries. The spatial distribution of the lineages among rookeries reveals a distinct north–south gradient with mixing of lineages in central rookeries. The northern lineage is shown in black and the southern lineage is shown in white. The lineage of each haplotype is given in Supplementary Table 1. The “*” symbol indicates rookeries in the northern microsatellite cluster. Rookeries with fewer than 10 samples or 20 females nesting annually were not included in the analyses. Rookeries assessed (with selected decimal coordinates) were: Florida, USA (FL; 27.48, −80.30) and Quintana Roo, Mexico (MX; 20.52, −86.93) (Encalada et al., 1996); Aves Island, Venezuela (AV; 15.67, −63.62) and Matapica, Surinam (SU; 5.95, −54.92) (Bolker et al., 2007; Encalada et al., 1996); Northern Cyprus and Turkey (MED; 34.75, 33.33 and 36.82, 35.75; Bagda et al., 2012; Encalada et al., 1996; Kaska, 2000); Cuba (CB; 21.64, −82.24; Ruiz−Urquiola et al., 2010); Tortuguero, Costa Rica (CR; 10.58, −83.52;Bjorndal et al., 2005; Encalada et al., 1996); Ascension Island, UK (AI; −7.95, −14.37); Poilao, Guinea Bissau (GB; 10.87, −15.70; Encalada et al., 1996; Formia et al., 2006); Bioko Island, Equatorial Guinea (BK; 3.25, 8.53); Sao Tome (ST; 1.00, 7.00; Formia et al., 2006); Trindade Island, Brazil (TI; −20.50, −29.82; Bjorndal et al., 2006); and Rocas Atoll, Brazil (RA; −3.87, −33.82; Bjorndal et al., 2006; Encalada et al., 1996).

308

E. Naro-Maciel et al. / Journal of Experimental Marine Biology and Ecology 461 (2014) 306–316

of inheritance, specifically maternally inherited mitochondrial DNA and biparentally inherited microsatellites, have revealed complex and often conflicting patterns of genetic structure. Analysis of mitochondrial control region sequences tends to indicate strong differentiation among rookeries, supporting the natal homing hypothesis of female fidelity to natal regions with rare departures to colonize new habitats (Bowen et al., 1992) (Fig. 1 and references therein). These studies could not address male philopatry, and initial research based on few nuclear markers suggested that male-mediated gene flow could be occurring because significant structure among rookeries went largely undetected (Roberts et al., 2004). An alternative hypothesis posited that nuclear genetic mixing occurred when philopatric green turtles of both genders mated during spatially and temporally overlapping migrations (FitzSimmons et al., 1997a, 1997b). Recent work using nuclear markers has begun to detect low but significant structure among rookeries at nuclear loci as well, thus supporting philopatry of both genders in leatherback (Dutton et al., 2013) and loggerhead (Carreras et al., 2011) turtles around the world, as well as green turtles of the Mediterranean (Bagda et al., 2012) and Pacific (Roden et al., 2013). Further, the historical processes affecting green turtle phylogeography are insufficiently understood. The existence of at least two Pleistocene glacial refugia, initially thought to have been located in the Caribbean and the western South Atlantic, was suggested in mitochondrial work that revealed two divergent lineages in the Atlantic and Mediterranean combined (Fig. 1; Supplementary Fig. 1; Encalada et al., 1996; Reece et al., 2005). Henceforth these are referred to as the “northern lineage” and the “southern lineage”, respectively. Estimates of the most recent population expansions from these refugia range from 10 to 18 kya when temperatures warmed, glaciers melted, and sea levels rose after the Last Glacial Maximum (LGM) (Encalada et al., 1996; Naro-Maciel et al., 2010), to the previous glacial period (Formia et al., 2006; Ruiz-Urquiola et al., 2010). However almost all published research focuses on mitochondrial markers and thus may portray a biased reflection of evolutionary history (DiBattista et al., 2012; Karl et al., 2012). Since these initial studies were carried out, over a thousand new control region sequences (Fig. 1; Supplementary Table 1) and new analytical techniques have been published, which, along with the novel nuclear DNA data provided in this study, greatly expand historical biogeographic research possibilities. Here we examine Atlantic/Mediterranean green turtle population structure and historical biogeography utilizing a comprehensive multilocus dataset. We characterize the distribution of nuclear and mitochondrial genetic variability using newly acquired microsatellite data from six western Atlantic rookeries, and published mitochondrial control region haplotypes. To investigate the potential processes and historical events contributing to these patterns, we also estimate migration rates and historical demography from genetic data. In doing so, we aim to provide a more complete enumeration of spatial patterns of genetic diversity and valuable historic context for conservation of this sensitive species.

2. Materials and methods 2.1. Nuclear microsatellites 2.1.1. Molecular techniques Analysis was carried out using previously extracted DNA from six rookeries (n = 301) distributed throughout the species' western Atlantic range (Table 1; Fig. 1). Fifteen polymorphic microsatellite loci were genotyped. Markers available in the published literature when lab work was initiated, and known to be reliable for green turtle amplification, were selected (Table 2). We avoided known problems with standardizing microsatellite results obtained in different laboratories by genotyping all samples for each marker in the same lab. Initially, eight microsatellite markers were analyzed for all samples at the Sackler Institute for Comparative Genomics, American Museum of Natural History (NY, USA) using individually tagged forward primers with 5′ fluorescent labels and previously described methods (Naro-Maciel, 2006). To ensure consistency among runs, duplicate multiplex samples were included on all plates and checked. Subsequently, to increase the number of markers, an additional seven loci were genotyped at the University of Georgia, where these markers had been originally developed and optimized (Shamblin et al., 2007, 2009). 2.1.2. Genetic diversity All microsatellite data were screened using MICRO-CHECKER (Van Oosterhout et al., 2004) and MSTOOLKIT (Park, 2001). The latter was used also to calculate the number of alleles per population and locus and size ranges. Allelic richness was calculated using FSTAT (Goudet, 2002). Heterozygosity — observed (Ho), expected (He), and Nei's (1987) unbiased expected (UHe) under Hardy–Weinberg equilibrium (HWE), and private alleles, were calculated using GENALEX v6.5 (Peakall and Smouse, 2006). Pearson's linear correlation tests were carried out using STATPLUS v2009 (AnalystSoft Inc., 2009). Deviations from HWE within and across loci for each locality and globally were tested using the score test in GENEPOP v4.0.10 (Rousset, 2008). The global test for heterozygote deficit was also applied to population clusters. Exact p-values were calculated using a Markov-chain randomization procedure (Guo and Thompson, 1992) with 10,000 dememorization steps, 500 batches, and 5000 iterations per batch. Pairwise linkage disequilibrium (LD) among loci was investigated using GENEPOP, with significance tested as above. Sequential Bonferroni corrections were applied to correct for Type I errors from multiple comparisons (Rice, 1989). 2.1.3. Genetic structure We used a number of techniques to investigate the population structure of West Atlantic green turtles, both for individual rookeries and at the regional level. Pairwise comparisons among rookeries were carried out using the estimated D statistic recommended in cases of high allelic diversity (Dest; Jost, 2008) and calculated by SMOGD (Crawford, 2010). Analysis of Molecular Variance (AMOVA) and pairwise comparisons based on FST were carried out in ARLEQUIN with significance assessed

Table 1 Microsatellite analysis details including rookery location, sample size (N), genetic diversity measures, estimated number of nesting females, and reference for the latter. Genetic diversity measures include: mean number of different alleles per locus (A), allelic richness (AR) and observed (Ho), expected (He), and Nei's (1987) unbiased expected (UHe) heterozygosity measures. Rookery

N

A

Florida, USA Tortuguero, Costa Rica Matapica, Surinam Aves Island, Venezuela Rocas Atoll, Brazil Trindade Island, Brazil

49 58 49 33 30 82

13.67 13.93 13.33 10.47 11.07 13.47

AR ± ± ± ± ± ±

8.25 7.42 6.07 4.69 4.50 6.64

11.93 12.01 11.91 10.17 10.98 10.82

He

Ho ± ± ± ± ± ±

6.40 5.84 4.89 4.50 4.46 4.85

0.792 0.818 0.835 0.788 0.804 0.787

± ± ± ± ± ±

0.015 0.013 0.014 0.018 0.019 0.012

0.808 0.815 0.822 0.767 0.783 0.780

UHe ± ± ± ± ± ±

0.029 0.027 0.024 0.036 0.031 0.036

0.816 0.823 0.831 0.779 0.797 0.785

± ± ± ± ± ±

0.029 0.027 0.024 0.037 0.032 0.036

Females

Reference

779 27,023 1814 850 115 900

Seminoff (2004) Seminoff (2004) Seminoff (2004) Penaloza (2000) Bellini et al. (1995) Almeida et al. (2011)

The samples from Florida were collected from clutches laid during a single 10-day window in 2007 along several Atlantic coast beaches, and the samples from Surinam were collected in 1999. Sampling at Aves Island was described by Bowen et al. (1992), at Tortuguero by Bjorndal et al. (2005; sampling years 2001 and 2002), and at Rocas (from 2000 to 2001) and Trindade (1998–1999) by Bjorndal et al. (2006).

E. Naro-Maciel et al. / Journal of Experimental Marine Biology and Ecology 461 (2014) 306–316 Table 2 Genetic diversity and details of microsatellite loci analyzed in this study. The locus name is given with the literature reference, the kind of repeat, number of alleles (A), allelic richness (AR), and size range. Locus name

Reference

# repeats

A

AR

Range

Klk314 Cc117 Or7 Cc10

Kichler et al. (1999) FitzSimmons et al. (1995) Aggarwal et al. (2004) Monzón-Argüello et al. (2008) FitzSimmons et al. (1995) FitzSimmons et al. (1995) FitzSimmons et al. (1995) FitzSimmons (1998) Shamblin et al. (2007) Shamblin et al. (2007) Shamblin et al. (2007) Shamblin et al. (2007) Shamblin et al. (2007) Shamblin et al. (2009) Shamblin et al. (2009)

Dinucleotide Dinucleotide Dinucleotide Dinucleotide

13 18 8 15

7.2 12.1 5.9 10.0

90 225 219 399

– – – –

122 261 247 431

Dinucleotide Dinucleotide Dinucleotide Dinucleotide Tetranucleotide Tetranucleotide Tetranucleotide Tetranucleotide Tetranucleotide Tetranucleotide Tetranucleotide

19 14 39 25 17 18 39 13 10 21 18

10.4 10.3 25.5 13.6 13.2 13.0 22.7 11.1 6.3 16.4 8.8

154 123 220 162 251 218 293 214 269 169 261

– – – – – – – – – – –

208 149 304 224 309 294 393 262 325 257 321

Cm3 Cm58 Cm72 Cc7 Cc1G02 Cc5H07 Cc2H12 Cc7B07 Cc7E11 CcP7E05 CcP8D06

over 10,000 permutations. Principal Component Analyses (PCA) were implemented using GENALEX based on these statistics. To evaluate regional population structure without a priori location information, a Bayesian clustering algorithm was employed using STRUCTURE v2.3.1 (Pritchard et al., 2000). Different numbers of clusters (K = 1 to 8) were tested, and the algorithm estimated the log-likelihood of the data for the pre-defined K values and assigned membership probabilities to each cluster. The admixture option was used, and 10 independent long runs (105 burn-in steps and 106 total steps) were performed for each value of K. Average and standard deviation (SD) were calculated for the maximum log-likelihood (lnL) values from all runs corresponding to each K. The number of population clusters that best explained the data was derived from the K with the highest average maximum lnL and the smaller SD. The ΔK statistic, which is based on the rate of change in the log probability of data between successive K values and found by Evanno et al. (2005) to be an accurate reflection of population number, was also examined. Hierarchical sub-structure was investigated by independently reevaluating each sample subset obtained in this initial analysis following the same procedures. We used the BARRIER v2.2 program (Manni et al., 2004) to identify and visualize barriers to gene flow. To estimate recent migration rates between the principal population clusters identified by STRUCTURE, we employed the Bayesian method BAYESASS v3 (Wilson and Rannala, 2003). Ten million MCMC iterations were performed in BAYESASS, with the first 10% discarded as burn-in, and the chain was sampled every 100 iterations. 2.1.4. Evolutionary history We used the coalescent-based program MIGRATE-N v3.2.6 (Beerli, 2009; Beerli and Felsenstein, 2001) to estimate historical effective population size (Ne) and gene flow between the principal clusters revealed by STRUCTURE. The parameters M (=m/μ, where m is the proportion of new immigrants per generation and μ is the mutation rate) and Ne were estimated between clusters using a maximum likelihood (ML) approach and asymmetric population sizes and migration rates. An average μ of 5.5 × 10−5 per generation was used (Ellegren, 2004). Ten replicates were run, each one with one initial chain (105 burn-in steps) and one long final chain (5 × 106 steps). FST was specified as a starting value for estimating M, and every 100th parameter estimation was sampled once the first 100,000 genealogies on each chain were discarded. Tests were additionally carried out to determine whether any of the rookeries or principal clusters identified by STRUCTURE went through a recent bottleneck. Two tests were run using the BOTTLENECK software (Cornuet and Luikart, 1996; Piry et al., 1999): 1) a mode-shift test; and 2) a Wilcoxon-signed rank test that detects significant heterozygote

309

excess with respect to allele number. A two-phase model (TPM) that may best explain marine turtle microsatellite evolution (Roberts et al., 2004), with a variance of 3 (see Rodríguez-Zárate et al., 2013), was run under two scenarios: A) 90% stepwise and 10% infinite allele mutations, parameters widely used and recommended for microsatellite loci (Garza and Williamson, 2001; Luikart et al., 1998; Piry et al., 1999); and B) 73% stepwise and 27% infinite allele mutations, the parameters reported for olive ridley turtles (Hoekert et al., 2002). Estimates were based on 10,000 replications. We also calculated M-ratios (Garza and Williamson, 2001) for each rookery and cluster. Critical values for M were determined for the above TPM models under a range of parameters (after Rodríguez-Zárate et al., 2013) using the program Critical_M (Garza and Williamson, 2001). To span the cluster and rookery sample sizes (n) used in this study (Table 1), the analysis was run for three values of 2n: 60, 150, and 400. Individual rookeries were tested for the first two values only (2n = 60, 150), which span the rookery sample sizes. Clusters were tested for the last two values only (2n = 150, 400), which span the cluster sample sizes (Table 1). To test for population expansion, we used the statistics k (which is more sensitive to recent expansion) and g (which is more sensitive to older expansions; Reich et al., 1999). Both statistics were calculated using a Microsoft Excel macro (KGTESTS; Bilgin, 2007). We identified the number of loci exhibiting negative k statistics (indicative of population expansion) and determined whether this was significantly more than expected for each rookery or cluster. A conservative cutoff value of 0.3 was used to determine significance of the interlocus g statistic, based on simulations by Reich et al. (1999). 2.2. Mitochondrial control region sequences 2.2.1. Evolutionary history To gain a better understanding of the historical demographic processes that contribute to current patterns of genetic diversity in Atlantic green turtles, we investigated matrilineal population structure and the relative timing of lineage expansion using published region-wide mitochondrial control region rookery sequences (~ 480 bp, references in Fig. 1). Analyses were carried out considering: 1) all rookeries combined, 2) northern and southern lineages separately, as they could have distinct histories (following Naro-Maciel et al., 2011; Viñas et al., 2004), and 3) differentiated rookeries. When considering individual rookeries containing small percentages of haplotypes from a different lineage (Supplementary Table 1), simulations were run both with and without these haplotypes for comparison. The program GENEIOUS v6.1 (Biomatters Inc.) was used to construct a haplotype tree through a Bayesian approach implemented by MR. BAYES v3.2.1 (Ronquist et al., 2011) using the HKY85 model (chain length = 1,100,000, 4 heated chains, unconstrained branch lengths). ARLEQUIN v3.5.1.2 (Excoffier and Lischer, 2010) was used to calculate haplotype diversity (Nei, 1987; input file available from the Dryad Digital Repository: http://doi. org/10.5061/dryad.q1kf0). Fu's FS test (Fu, 1997) was used with significance (p b 0.02; Excoffier and Lischer, 2010) tested over 10,000 coalescent simulations in DNASP v5 (Librado and Rozas, 2009). To detect contractions suggested by an excess of old mutations, Fu and Li's D* (Fu and Li, 1993) was calculated with significance tested over 10,000 simulations using DNASP (input files available from the Dryad Digital Repository: http://doi.org/10. 5061/dryad.q1kf0). These tests, however, do not offer information on the timing and magnitude of demographic change. Rather, coalescent methods employing Bayesian skyline plots (BSP) have been used to infer demographic history and the effects of glaciation events from mitochondrial control region data in a wide variety of systems (Amato et al., 2008; Guiher and Burbrink, 2008; Ruzzante et al., 2008; Shapiro et al., 2004). We created Bayesian skyline plots in BEAST v1.6.2 (Drummond and Rambaut, 2007). To prevent overrepresentation of heavily sampled rookeries, analyses of all rookeries combined included 30 samples

310

E. Naro-Maciel et al. / Journal of Experimental Marine Biology and Ecology 461 (2014) 306–316

randomly selected per rookery, while those of each lineage used 50 samples. As all genetic samples were collected relatively recently (within one Chelonia generation) we did not specify individual tip dates. The mutation rate was fixed at 1.75 × 10−8 substitutions/site/year (Formia et al., 2006), and the Tamura-Nei mutation model without site heterogeneity was used. Chain length for each run was gradually increased (from 10 million to up to 100 million iterations) until proper mixing and convergence was achieved. The effective sample size estimator was used to diagnose convergence. The median of the posterior distribution of dates for the most recent common ancestor (MRCA) was used as an estimator of the time of origin for each lineage or rookery. The medians of the population size distributions at either the median MRCA or at year zero were used as estimators of ancestral (NeA) and recent (NeR) effective population sizes, respectively. The 97.5% and 2.5% quantiles for posterior distributions were used to delineate CIs. 3. Results 3.1. Nuclear microsatellites 3.1.1. Genetic diversity Polymorphism varied by locus, and the number of alleles per marker ranged from 8 in OR7 to 39 at Cm6 and Cm72 (Table 2; the microsatellite data set is available from the Dryad Digital Repository: http://doi. org/10.5061/dryad.q1kf0). Duplicate green turtle samples had highly consistent results (Naro-Maciel, 2006), and negligible genotyping error was generated from duplicate genotyping of 15% of loggerhead turtle samples of similar yields (0.243%, Shamblin et al., 2007). Although these markers were developed for various marine turtle species, no cross-species marker issues were evident in MICRO-CHECKER or MSTOOLKIT, nor have they been reported in the literature. Further, major results detected in the current data set were consistent with the initial analysis of 8 markers (Naro-Maciel, 2006). Average number of alleles per rookery ranged from 10.5 to 13.9 and was highest in Costa Rica, although diversity measures were similar among rookeries (Table 1). No significant correlation was revealed between Ho and sample size (R = − 0.099, p = 0.852) or number of nesting females (R = 0.386, p = 0.450). There was no evidence of LD in the western Atlantic overall or in pairwise comparisons following sequential Bonferroni corrections, and the frequency of private alleles per rookery per locus was low (b0.05). Significant heterozygote deficit was not found in any population (p N 0.076) or locus following sequential Bonferroni corrections. When the principal population clusters identified by STRUCTURE were examined separately, there was almost no variation in p values nor was there a significant heterozygote deficit (p = 0.10). However, global estimates across loci and populations of the six rookeries overall revealed a significant heterozygote deficit (p = 0.041). 3.1.2. Genetic structure Overall low, but significant, genetic structure was observed in the western Atlantic (FST = 0.033, p b 0.001, Table 3). Pairwise FST and Dest values were generally highest in comparisons between the

Table 3 Genetic differentiation among western Atlantic rookery pairs as revealed by microsatellite analysis. Dest values are shown above the diagonal. Fst values are shown below the diagonal, and all pairwise Fst comparisons were highly significant (p b 0.001). Rookery

Trindade Rocas Tortuguero Aves Florida Surinam

Trindade Island, Brazil

Rocas Atoll, Brazil

Tortuguero, Costa Rica

Aves Island, Venezuela

Florida, USA

Matapica, Surinam

– 0.011 0.036 0.037 0.038 0.031

0.025 – 0.028 0.035 0.027 0.019

0.106 0.085 – 0.024 0.008 0.009

0.107 0.106 0.073 – 0.020 0.022

0.138 0.085 0.012 0.058 – 0.006

0.115 0.063 0.031 0.064 0.009 –

southern (Atol das Rocas and Trindade, Brazil) versus northern hemisphere groups (Table 3). Aves Island, Venezuela had higher levels of differentiation from the remaining three northern rookeries (Florida, Costa Rica and Surinam) than these had among themselves (Table 3). The PCAs based on Dest and FST (data not shown) were very similar and revealed 2 (northern, southern) or 3 (northern, southern, and Aves) possible clusters (Fig. 2). The AMOVA revealed that most of the genetic structure was within populations (N96.69%) regardless of whether two (northern vs. southern) or three (northern, southern, Aves) population groups were defined. Bayesian clustering using the STRUCTURE program revealed highest lnL values for K = 2, although K = 3 was a close alternate possibility (Fig. 3). Similar results were found when the ΔK statistic (Evanno et al., 2005) was examined. STRUCTURE revealed that the two clusters corresponded to a northern and a southern group. Analyses within these groups revealed no substructure within the southern group (highest lnL values were for K = 1), but possible substructure within the northern cluster. However, even when the northern group was analyzed separately, this structure was not clearly correlated with any spatial location when K = 2. Similarly Aves Island, Venezuela appeared to separate out when K = 3, but the other populations did not form spatially meaningful clusters (Supplementary Fig. 2). The BARRIER program identified 5 barriers to dispersal, with the most significant being between northern and southern hemisphere rookeries. A secondary barrier to dispersal isolating Aves was revealed, along with three other less significant barriers (Supplementary Fig. 3). Proportional estimates of recent migration rates per generation between clusters from BAYESASS were roughly symmetrical (southern to northern: 1.8%; northern to southern: 1.6%). 3.1.3. Evolutionary history MIGRATE-N revealed the historical proportion of immigrants per generation per cluster as highest from south to north (southern to northern: m = 0.013%, CI: 1.27–1.92 × 10−4; northern to southern: m = 0.0084%, CI: 5.88–9.9 × 10− 5). The ML historical Ne estimates were 31,645 for the southern cluster (CI: 30,668–40,081) and 20,350 for the northern cluster (CI: 16,136–20,909). The BOTTLENECK tests failed to reveal evidence of recent contractions. All mode-shift analyses resulted in normal L-shaped distributions, and heterozygote excess with respect to allele number was not detected in either the whole data (TPM73% p = 0.976; TPM90% p = 0.992), northern or southern clusters (TPM73% p N 0.953; TPM90% p N 0.979), or at individual rookeries (TPM73% p N 0.489; TPM90% p N 0.738). M-ratios were similar among rookeries and clusters (mean M = 0.789; range = 0.748–0.837; Supplementary Table 2). M-values fell below the critical threshold only for two rookeries (Surinam and Aves, Venezuela) when the lowest level of pre-bottleneck genetic diversity (θ = 2) was assumed (Supplementary Table 2). However, evidence of recent population expansions was found. The majority of loci exhibited negative k statistics for any given cluster or rookery (Supplementary Table 2). The number of loci with negative k statistics was significantly greater than expected for both the northern and southern clusters (Supplementary Table 2). For individual rookeries, however, the number of loci with negative k statistics was significantly greater than expected only for Surinam and Costa Rica. In contrast, the value of the g statistic was well above the cutoff value of 0.3 for all rookeries and clusters (Supplementary Table 2), indicating little evidence for more ancient population expansions. 3.2. Mitochondrial control region sequences 3.2.1. Evolutionary history Neutrality tests of the data as a whole (13 rookeries, n = 1427) failed to detect population size changes (Table 4), but when the lineages (Supplementary Fig. 1) were analyzed separately, significant negative Fu's FS and Fu and Li's D* statistics indicated population expansion and

E. Naro-Maciel et al. / Journal of Experimental Marine Biology and Ecology 461 (2014) 306–316

311

Fig. 2. Principal component analysis of Dest values. The percentage of variation explained by each coordinate is shown in parentheses. Abbreviations as in Fig. 1.

contraction in the northern lineage, and expansion in the southern lineage (Table 4). BSP analyses of each lineage indicated recent expansions in both, beginning and peaking earlier in the northern lineage, with peaks after the LGM (Fig. 4). A similar expansion was detected for both lineages combined. Median estimates of coalescence dates were 416,770 years ago for the whole data set (CI: 185,530–703,610), 93,934 years ago for the northern lineage (CI: 16,134–223,410), and 60,811 years ago for the southern lineage (CI: 12,269–247,150). The northern lineage NeR was 16,173 (CI: 1741–130,268), while Median NeA was 1390 (CI: 479–3960). The southern lineage NeR was 13,922 (CI: 1183–155,909), while Median NeA was 1262 (CI: 229–5891). Results for individual rookeries are shown in Table 4 and Fig. 4. 4. Discussion 4.1. Genetic structure Our new findings of highly significant structure among individual Atlantic rookeries at microsatellite loci are consistent with male philopatry, indicating that male-mediated gene flow may be less frequent than previously inferred. This possibility was reported early on for Australian green turtles based on genetic and mark-recapture data (FitzSimmons et al., 1997a, 1997b; Limpus, 2008; Limpus and Reed, 1985; Limpus et al., 1994), and more recently around the world for various species (Bagda et al., 2012; Carreras et al., 2011; Dutton et al., 2013; Roden et al., 2013), including at the Rocas Atoll green turtle breeding and courtship area (Naro-Maciel et al., 2012). Although significant, our estimates of rookery differentiation in the western Atlantic calculated from microsatellites (FST = 0.033) are about 20 times lower than those based on control region data (Fig. 1 and references therein), as reported previously (FitzSimmons et al., 1997a, 1997b; Roberts et al., 2004; but see Bagda et al., 2012). Lower levels of population structure in

nuclear compared to mitochondrial markers could conceivably result from differences in mode of inheritance (leading to larger effective population size and slower genetic drift in diploid versus haploid markers) or from variation in molecular evolution (i.e. higher mutation rates and increased chances of homoplasy) in microsatellites compared to mitochondrial sequences (DiBattista et al., 2012; Dutton et al., 2013; Karl et al., 2012), as well as from gene flow. While male-biased gene flow is still a possibility, the Australian research, substantiated by decades of extensive mark-recapture data (see above), emphasized that males and females are equally philopatric. Those studies attributed the lower levels of nuclear differentiation when compared to mtDNA primarily to genetic mixing during spatially and temporally overlapping migrations, such as occurs along the Great Barrier Reef (FitzSimmons et al., 1997a, 1997b, and references therein). In the Mediterranean, green turtle males have also been satellite tracked visiting several breeding areas en route to the final destination (Wright et al., 2012). Overlapping migrations are possible in parts of the western Atlantic, although corroborating data on observed mixed pairings have not been reported. The southern hemisphere nesting seasons overlap in the austral summer from December to April (Hirth, 1997), and adult green turtles foraging along the South American coast are known from mark-recapture and genetic data to primarily utilize their original breeding grounds in Brazil (Trindade or Rocas Atoll), Ascension, Surinam, or relatively rarely, other rookeries (Naro-Maciel et al., 2007, 2012; Pritchard, 1976; Schulz, 1975). The clustering of Rocas Atoll and Trindade could result from gene flow along the coast when both genders are reproductively active. This, however, is likely counterbalanced by the remote oceanic island nature of southern hemisphere rookeries (Fig. 1), precluding widespread overlap during oceanic migrations and leading to the low but significant difference detected among Brazilian nesting areas.

Fig. 3. Results of population structure evaluation using a Bayesian clustering algorithm implemented by the STRUCTURE program. Abbreviations as in Fig. 1. On the left is a graph displaying the average and standard deviation (SD) for the maximum log-likelihood values from all runs corresponding to each K tested (LnP(K)). The highest average with the minimum SD is K = 2. The graph on the right shows the average probability of each individual to be assigned to population one (in gray) or population two (in black). The width of the columns is based on number of individuals sampled.

312

E. Naro-Maciel et al. / Journal of Experimental Marine Biology and Ecology 461 (2014) 306–316

Table 4 Mitochondrial control region diversity measures (n = sample size, A = number of haplotypes, h = haplotype diversity) and neutrality test results for the whole data set, northern and southern lineages, and individual rookeries. The kind of change is also shown (E = expansion, C = contraction), along with numbers of nesting females and reference. Analyses for rookeries containing haplotypes of both lineages were carried out with (before /) and without (after /) haplotypes from the minority lineage. Region

Parameter n A h Neutrality tests Fu's Fs Fu's Fs p Fu and Li's D* Fu and Li's D* p Kind of change Nesting females Reference

Region

Parameter n A h Neutrality tests Fu's Fs Fu's Fs p Fu and Li's D* Fu and Li's D* p Kind of change Nesting females Reference

Mediterranean

Northwest Atlantic/Caribbean

Total

Northern lineage

Southern lineage

Cyprus and Turkey

Quintana Roo, Mexico

Tortuguero, Costa Rica

Florida, USA

Cuba

1427 39 –

752 15 –

675 24 –

248 6 0.056

20/19 7/6 0.816

433/396 5/2 0.163

60/56 6/5 0.624

28 7 0.648

−3.954 0.135 −1.5503 0.067 − 29,589

−10.991 0.001 −2.1805 0.042 E, C NA

−17.215 0.000 −0.811 0.222 E NA

−10.840 0.021 −3.711 0.002 E, C 350 Broderick et al. (2002)

−0.565/−0.799 0.393/0.315 −1.751/0.070 0.058/0.506 − 1587 Seminoff (2004)

3.715/−3.392 0.904/0.247 0.618/−2.354 0.687/0.018 −/C 27,023 Seminoff (2004)

1.047/−0.932 0.720/0.308 −0.088/−1.856 0.435/0.076 – 779 Seminoff (2004)

−2.638 0.017 0.959 0.832 E 200 Ruiz-Urquiola et al. (2010)

Central Atlantic

South/East Atlantic

Aves Island, Venezuela

Matapica, Surinam

Rocas Atoll, Brazil

Trindade Island, Brazil

Ascension Island, UK

Poilao, Guinea Bissau

Sao Tome

Bioko, Eq. Guinea

55/51 2/1 0.137

46/45 3/2 0.167

53 7 0.520

99 7 0.505

245 13 0.303

70 1 0.000

20 7 0.584

50 2 0.184

5.305/NA 0.975/NA 1.396/NA 0.979/NA – 850 Penaloza (2000)

0.667/−0.791 0.660/0.426 −4.017/0.553 0.000/0.732 C/– 1814 Seminoff (2004)

−2.144 0.108 −0.853 0.241 – 115 Bellini et al. (1995)

−3.156 0.059 −1.746 0.093 – 900 Almeida et al. (2011)

−13.554 0.000 −2.665 0.010 E, C 3709 Seminoff (2004)

NA NA NA NA NA 2523 Seminoff (2004)

−2.281 0.049 −6.865 0.237 – 90 Formia et al. (2006)

0.199 0.291 0.543 0.883 – 407 Seminoff (2004)

The temporally distinct southern and northern hemisphere summer breeding seasons potentially limit overlap of turtles from different clusters during reproductive migrations. Although Surinam nesters have long been known to forage in northeastern Brazil (Lima and Troeng, 2001; Lima et al., 2003, 2008; Schulz, 1975), nesting there peaks in April and May largely subsequent to the Brazilian breeding season (Hirth, 1997). Further, Surinam breeders move north and follow different migratory paths than those headed for oceanic rookeries. Gene flow could occur during the temporally overlapping nesting seasons in Florida, Costa Rica, and Aves Island (June–September), although the peaks at Aves occur later in August and September, and the Aves turtles

would likely follow distinct migratory routes. To better understand these patterns in a historical context, as well as to interpret the strong discord observed between nuclear and mitochondrial markers with respect to the Aves Island and Surinam rookeries, we must also consider the past. 4.2. Evolutionary history Our Bayesian analyses support the hypothesis that ancient climatic changes led to historical isolation of green turtles in glacial refugia. Both nuclear and mitochondrial markers cluster into two main groups

Fig. 4. Bayesian skyline plots showing median and 95% CIs for population size of the northern and southern mitochondrial lineages, as well as individual rookeries, over time and with respect to the Last Glacial Maximum (LGM, black rectangle). Ne calculations assume a generation time of 47 years (Formia et al., 2006) and are shown on a log scale. The estimated population trajectory is shown from the median estimate of coalescence date to the present (year zero).

E. Naro-Maciel et al. / Journal of Experimental Marine Biology and Ecology 461 (2014) 306–316

containing substructure, suggesting that there were at least two refugia. The MIGRATE results show that only ~0.01% of each cluster detected by the STRUCTURE and BARRIER analyses migrated over time between northern and southern areas, revealing limited gene flow as expected in isolated refugia (Maggs et al., 2008; Provan and Bennett, 2008). Such population structure can cause a heterozygote deficit in Hardy– Weinberg tests (Raymond and Rousset, 1995), as detected when the Western Atlantic rookeries were analyzed as one unit in this study. The north–south spatial arrangement of the mitochondrial control region lineages (Fig. 1) coincides with that of mitochondrial cytochrome oxidase I (COI) haplotypes (Naro-Maciel et al., 2010). In combination with the microsatellite structure, these data support the previously proposed hypothesis of distinct Pleistocene Caribbean and southern refugia (Encalada et al., 1996; Reece et al., 2005). The Wisconsin glacial period (~ 100–20 kya; Petit et al., 1999) spans our estimated time of origin for both extant mitochondrial lineages (northern lineage: ~ 93,934 years ago; southern lineage: ~ 60,811 years ago). During this time green turtles were apparently constrained to relatively small refugial populations. Although bottlenecks and pronounced contractions were not consistently detected in our analyses, both northern and southern refugial NeA (ancient) estimates from BSP were about nine times lower than NeR (recent), and demographic expansion peaked after the LGM (Fig. 4). Our historical demographic analyses of both markers revealed population growth in the northern and southern lineages/clusters that is consistent with expansion from glacial refugia. The Bayesian skyline plots indicated a rapid and recent increase for the northern lineage (occurring after the LGM), while the inferred expansion in the southern lineage was more gradual (Fig. 4). During the Holocene interglacial, green turtle populations likely expanded as temperatures warmed, glaciers melted, sea levels rose, and niches opened up, as reported in other marine organisms discussed above. Non-Bayesian methods largely agreed with ours that Atlantic green turtles colonized the Mediterranean Sea after the LGM (Fig. 4; Encalada et al., 1996; Bagda et al., 2012). In contrast, the Mediterranean served as a refugium for more temperate marine species including loggerhead turtles (Clusa et al., 2013), as did the Atlantic coast of the Iberian Peninsula (Maggs et al., 2008). Despite challenges in marine paleoecological research (Provan and Bennett, 2008), a multi-taxa marine refugium has been identified in the North Atlantic south of Florida and into the Gulf of Mexico (Maggs et al., 2008), although the extent of overlap between this location and the hypothesized Caribbean green turtle refugium (Encalada et al., 1996; Reece et al., 2005; Ruiz-Urquiola et al., 2010; this study) remains unknown. Although some estimates of Atlantic/Caribbean rookery expansion are somewhat older than our LGM dates, the discrepancy may be related to use of less complex methods available at the time, or different criteria for identifying the start of expansions (Formia et al., 2006; Ruiz-Urquiola et al., 2010). Neutrality tests such as Fu's Fs are affected by restricted migration among demes (Ray et al., 2003) as occurs in philopatric green turtle females, which may explain why demographic expansion was detected at fewer rookeries by those tests than with BSP (Table 4; Fig. 4). Time-dependence of mutation rates can also hinder accurate estimation of the timing of population expansions. Specifically, using older vicariant events to determine the molecular clock rate often leads to overestimation of the expansion date (Karl et al., 2012). In green turtles, the closure of the Panama seaway about 3 million years ago has traditionally been used to estimate mutation rate, and unfortunately no more recent calibration dates are available. Expansion dates derived using this mutation rate should therefore be considered maximums. As such, it is likely that more rookeries expanded after the LGM, or later in the Holocene, than indicated by the BSP analyses presented here. During interglacial periods, spatial expansion led to potentially repeated secondary contact between groups historically isolated in refugia. Higher estimates of recent migration rates between the northern and

313

southern clusters derived from BAYESASS compared to long-term estimates from MIGRATE lend support to ongoing shifts in the magnitude and direction of gene flow as opposed to a stable pattern of gene flow over time (Chiucchi and Gibbs, 2010). However, we note that simulation studies indicate that BAYESASS may overestimate recent migration rates, especially when differentiation among populations is low (Wang, 2014). Contrasting nuclear and mitochondrial clusters at Aves and Surinam, which belong to the southern mitochondrial lineage but group with the north at nuclear markers, indicate mixing of the two refugial populations occurred in these central areas. In green turtles, departures from female philopatry are necessary for colonization of new nesting habitats and have occurred in all ocean basins (Bourjea et al., 2007; Dethmers et al., 2006; Encalada et al., 1996; Poloczanska et al., 2010; Reece et al., 2005). The new rookeries, which may have undergone recent bottlenecks consistent with founder events (Supplementary Table 2), were likely colonized by breeding females from the southeast Atlantic. As noted by Shamblin et al. (2012) based on haplotype distributions, Aves and Surinam are dominated by the CMA5 haplotype, a southern lineage sequence only reported among southern rookeries at Sao Tome, where it occurs at ~5% frequency (Supplementary Table 1). This pattern is consistent with a stepping stone scenario where female founders from the southeastern Atlantic, the likely location of the refugium, bore the CMA5 haplotype to central areas (Surinam/Aves). There these females likely mixed with northern males, as suggested by the limited nuclear gene flow between Aves and Surinam in the north, versus the southern rookeries (Supplementary Fig. 3; Table 3). This would rapidly obscure any nuclear genetic heritage from the south (through intraspecies “genetic swamping”) while mitochondrial markers would continue to reflect the South Atlantic origin of these rookeries due to natal homing to the newly established rookeries. The lack of northern haplotypes in the south, but presence of southern haplotypes in the north (Supplementary Table 1), as well as historic Ne estimates of a larger southern population and estimated migration rates, substantiate the idea that more turtles moved northward than southward historically. 4.3. Conservation applications The Atlantic refugial populations have transformed into the largely genetically distinct rookeries of today, many of which are endangered (Fig. 1 and references therein; IUCN, 2014). This study provides a comprehensive and long-term context for considering recovery targets, setting conservation priorities, and identifying regional management units (Wallace et al., 2010). The finding that significant microsatellite structure among rookeries aligns with mitochondrial structure facilitates stock designation, as noted by Dutton et al. (2013). Many rookeries can now be considered separate management units (MUs) based on nuclear as well as mitochondrial markers. This improves on earlier studies in which the lack of nuclear differentiation led to inconsistent MU delineations depending on the marker. For example, designating MUs was initially challenging because most pairwise comparisons of Atlantic rookeries were not significant at nuclear markers (Roberts et al., 2004), but were significant at mitochondrial loci (Bowen and Karl, 2007). Because of the wide distribution of marine turtles there was also a need to designate RMUs above the rookery but below the species, that might be developing on independent evolutionary trajectories (Wallace et al., 2010). Although our nuclear data set was incomplete, highlighting the need for profiles of Ascension and Africa, the principal clusters that we found were suggestive of independent trajectories and consistent with RMU designations (Wallace et al., 2010). The RMUs incorporate the discord between microsatellite and mitochondrial genetic structure by grouping Surinam and Aves with the South Caribbean cluster, which overlaps spatially to a certain extent with the Northwest, Southwest, and South Central Atlantic RMUs.

314

E. Naro-Maciel et al. / Journal of Experimental Marine Biology and Ecology 461 (2014) 306–316

Despite known recent declines in green turtle populations, we found scant evidence for a very recent population bottleneck in our nuclear microsatellite data. Genetic signatures of recent population bottlenecks have been reported for olive ridley sea turtles (Lepidochelys olivacea) in the Pacific, although these results were not consistent across different testing methods (Rodríguez-Zárate et al., 2013). The bottleneck testing methods most commonly used with microsatellite data are either highly sensitive to assumptions regarding microsatellite mutation and prebottleneck genetic diversity, or have generally low power to detect less extreme bottlenecks (post-bottleneck Ne N 25; Peery et al., 2012). Given these caveats and the declines known to have occurred in these populations, a failure to detect a bottleneck in this case should in no way be taken as evidence for a lack of an effect of human activity on green turtle populations. Green turtle historical biogeography offers cautious promise for conservation efforts. Because populations have decreased in the past and bounced back, they have the potential to recover from recent declines, as has been observed in key Atlantic areas providing an “encouraging outlook for recovery” (Chaloupka et al., 2008). Further, ecosystemlevel changes such as loss of habitat are potentially reversible and can be incorporated into conservation strategies such as by establishing more protected areas. Effective responses to climate change may depend on a flexible conservation strategy not limited to current protected areas but also encompassing projected future range changes. However, colonization of new areas may be hindered by threats such as continued harvest, development and pollution. Even if turtles depart from philopatry they can only colonize suitable nesting habitat, which may not be available due to development and other threats (Poloczanska et al., 2010). Changes such as food web alteration due to climate are potentially less reversible, and achieving ecological functionality may require larger population sizes as occurred in the past. The historical perspective emphasizes the vast extent of colony loss or reduction, highlighting the importance of protecting diverse populations throughout the range, and tempering the encouraging outlook with a strong note of caution. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.jembe.2014.08.020. Acknowledgments We gratefully acknowledge our funders, the Regina Bauer Frankenberg Foundation and the Telljohann family. We thank Brian Bowen and Stephen Karl for rookery samples, as well as Luciano Soares and Martin Mendez for assistance. Computationally intensive analyses were carried out through the High Performance Computing Core, College of Staten Island, City University of New York. [SS] References Aggarwal, R.K., Velavan, T.P., Udaykumar, D., Hendre, P.S., Shanker, K., Choudhury, B.C., Singh, L., 2004. Development and characterization of novel microsatellite markers from the olive ridley sea turtle (Lepidochelys olivacea). Mol. Ecol. Notes 4, 77–79. Almeida, A.P., Moreira, L.M.P., Bruno, S.C., Thome, J.C.A., Martins, A.S., Bolten, A.B., Bjorndal, K.A., 2011. Green turtle nesting on Trindade Island, Brazil: abundance, trends, and biometrics. Endanger. Species Res. 14, 193–201. Amato, M.L., Brooks, R.J., Fu, J., 2008. A phylogeographic analysis of populations of the wood turtle (Glyptemys insculpta) throughout its range. Mol. Ecol. 17, 570–581. AnalystSoft Inc., 2009. StatPlus:mac (Statistical Analysis program for MacOS). http://www. analystsoft.com/en/ (Accessed 13 September 2013.). Bagda, E., Bardakci, F., Turkozan, O., 2012. Lower genetic structuring in mitochondrial DNA than nuclear DNA among the nesting colonies of green turtles (Chelonia mydas) in the Mediterranean. Biochem. Syst. Ecol. 43, 192–199. Beerli, P., 2009. How to use MIGRATE or why are Markov chain Monte Carlo programs difficult to use? In: Bertorelle, G., Bruford, M.W., Hauffe, H.C., et al. (Eds.), Population Genetics for Animal Conservation. Cambridge University Press, Cambridge, pp. 42–79. Beerli, P., Felsenstein, J., 2001. Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach. Proc. Natl. Acad. Sci. U. S. A. 98, 4563–4568. Bellini, C., Marcovaldi, M.A., Sanches, T.M., Grossman, A., Sales, G., 1995. Atol das Rocas biological reserve: second largest Chelonia rookery in Brazil. Mar. Turt. Newsl. 72, 1–2.

Bilgin, R., 2007. Kgtests: a simple Excel Macro program to detect signatures of population expansion. Mol. Ecol. Notes 7, 416–417. Bjorndal, K.A., Jackson, J.B., 2003. Roles of sea turtles in marine ecosystems: reconstructing the past. In: Lutz, P.L., Musick, J.A., Wyneken, J. (Eds.), The Biology of Sea Turtles vol.2. CRC Press, Boca Raton, pp. 259–274. Bjorndal, K.A., Bolten, A.B., Troëng, S., 2005. Population structure and genetic diversity in green turtles nesting at Tortuguero, Costa Rica, based on mitochondrial DNA control region sequences. Mar. Biol. 147, 1449–1457. Bjorndal, K.A., Bolten, A.B., Moreira, L.M., Bellini, C.B., 2006. Population structure and diversity of Brazilian green turtle rookeries based on mitochondrial DNA sequences. Chel. Cons. Biol. 5, 262–268. Bolker, B.M., Okuyama, T., Bjorndal, K.A., Bolten, A.B., 2007. Incorporating multiple mixed stocks in mixed stock analysis: “many-to-many” analyses. Mol. Ecol. 16, 685–695. Bourjea, J., Lapegue, S., Gagnevin, L., Broderick, D., Mortimer, J.A., Ciccione, S., Roos, D., Taquet, C., Grizel, H., 2007. Phylogeography of the green turtle, Chelonia mydas, in the Southwest Indian Ocean. Mol. Ecol. 16, 175–186. Bowen, B.W., Karl, S.A., 2007. Population genetics and phylogeography of sea turtles. Mol. Ecol. 16, 4886–4907. Bowen, B.W., Meylan, A.B., Ross, J.P., Limpus, C.J., Balazs, G.H., Avise, J.C., 1992. Global population structure and natural history of the green turtle (Chelonia mydas) in terms of matriarchal phylogeny. Evolution (NY) 46, 865–881. Broderick, A.C., Glen, F., Godley, B.J., Hays, G.C., 2002. Estimating the number of green and loggerhead turtles nesting annually in the Mediterranean. Oryx 36, 227–235. Broderick, A.C., Frauenstein, R., Glen, F., Hays, G.C., Jackson, A.L., Pelembe, T., Ruxton, G.D., Godley, B.J., 2006. Are green turtles globally endangered? Glob. Ecol. Biogeogr. 15, 21–26. Carr, A.F., 1967. So Excellent a Fishe: A Natural History of Sea Turtles. Natural History Press, Garden City. Carreras, C., Pascual, M., Cardona, L., Marco, A., Bellido, J.J., Castillo, J.J., Tomás, J., Raga, J.A., Sanfélix, M., Fernández, G., Aguilar, A., 2011. Living together but remaining apart: Atlantic and Mediterranean loggerhead sea turtles (Caretta caretta) in shared feeding grounds. J. Hered. 102, 666–677. Chaloupka, M., Bjorndal, K.A., Balazs, G.H., Bolten, A.B., Ehrhart, L.M., Limpus, C.J., Suganuma, H., Troëng, S., Yamaguchi, M., 2008. Encouraging outlook for recovery of a once severely exploited marine megaherbivore. Glob. Ecol. Biogeogr. 17, 297–304. Chiucchi, J.E., Gibbs, H.L., 2010. Similarity of contemporary and historical gene flow among highly fragmented populations of an endangered rattlesnake. Mol. Ecol. 19, 5345–5358. Clusa, M., Carreras, C., Pascual, M., Demetropoulos, A., Margaritoulis, D., Rees, A.F., Hamza, A.A., Khalil, M., Aureggi, M., Levy, Y., Türkozan, O., Marco, A., Aguilar, A., Cardona, L., 2013. Mitochondrial DNA reveals Pleistocenic colonisation of the Mediterranean by loggerhead turtles (Caretta caretta). J. Exp. Mar. Biol. Ecol. 439, 15–24. Cornuet, J.M., Luikart, G., 1996. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144, 2001–2014. Cowen, R.K., Gawarkiewicz, G., Pineda, J., Thorrold, S.R., Werner, F.E., 2007. Population connectivity in marine systems: an overview. Oceanography 20, 14–21. Crawford, N.G., 2010. SMOGD: software for the measurement of genetic diversity. Mol. Ecol. Resour. 10, 556–557. Dethmers, K.E.M., Broderick, D., Moritz, C., Fitzsimmons, N.N., Limpus, C.J., Lavery, S., Whiting, S., et al., 2006. The genetic structure of Australasian green turtles (Chelonia mydas): exploring the geographical scale of genetic exchange. Mol. Ecol. 15, 3931–3946. DiBattista, J.D., Rocha, L.A., Craig, M.T., Feldheim, K.A., Bowen, B.W., 2012. Phylogeography of two closely related Indo-Pacific butterfly fishes reveals divergent evolutionary histories and discordant results from mtDNA and microsatellites. J. Hered. 103, 617–629. Drummond, A.J., Rambaut, A., 2007. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214. Dutton, P.H., Roden, S.E., Stewart, K.R., LaCasella, E., Tiwari, M., Formia, A., Thomé, J.C., Livingstone, S.R., Eckert, S., Chacon-Chaverri, D., Rivalan, P., Allman, P., 2013. Population stock structure of leatherback turtles (Dermochelys coriacea) in the Atlantic revealed using mtDNA and microsatellite markers. Cons. Genet. 14, 625–636. Ellegren, H., 2004. Microsatellites: simple sequences with complex evolution. Nat. Rev. Genet. 5, 435–445. Encalada, S.E., Lahanas, P.N., Bjorndal, K.A., Bolten, A.B., 1996. Phylogeography and population structure of the Atlantic and Mediterranean green turtle Chelonia mydas: a mitochondrial DNA control region sequence assessment. Mol. Ecol. 5, 473–483. Evanno, G., Regnaut, S., Goudet, J., 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620. Excoffier, L., Lischer, H.E.L., 2010. Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567. FitzSimmons, N.N., 1998. Single paternity of clutches and sperm storage in the promiscuous green turtle (Chelonia mydas). Mol. Ecol. 7, 575–584. FitzSimmons, N.N., Moritz, C., Moore, S.S., 1995. Conservation and dynamics of microsatellite loci over 300 million years of marine turtle evolution. Mol. Biol. Evol. 12, 432–440. FitzSimmons, N.N., Limpus, C.J., Norman, J.A., Goldizen, A.R., Miller, J.D., Moritz, C., 1997a. Philopatry of male marine turtles inferred from mitochondrial DNA markers. Proc. Natl. Acad. Sci. U. S. A. 94, 8912–8917. FitzSimmons, N.N., Moritz, C., Limpus, C.J., Pope, L., Prince, R., 1997b. Geographic structure of mitochondrial and nuclear gene polymorphisms in Australian green turtle populations and male-biased gene flow. Genetics 147, 1843–1854. Formia, A., Godley, B.J., Dontaine, J.-F., Bruford, M.W., 2006. Mitochondrial DNA diversity and phylogeography of endangered green turtle (Chelonia mydas) populations in Africa. Conserv. Genet. 7, 353–369. Fu, Y.X., 1997. Statistical tests of neutrality against population growth, hitchhiking and background selection. Genetics 147, 915–925.

E. Naro-Maciel et al. / Journal of Experimental Marine Biology and Ecology 461 (2014) 306–316 Fu, Y.X., Li, W.H., 1993. Statistical tests of neutrality of mutations. Genetics 133, 693–709. Garza, J.C., Williamson, E.G., 2001. Detection of reduction in population size using data from microsatellite loci. Mol. Ecol. 10, 305–318. Goudet, J., 2002. FSTAT, a program to estimate and test gene diversities and fixation indices. http://www2.unil.ch/popgen/softwares/fstat.htm (Accessed 13 September 2013). Guiher, T.J., Burbrink, F.T., 2008. Demographic and phylogeographic histories of two venomous North American snakes of the genus Agkistrodon. Mol. Phylogenet. Evol. 48, 543–553. Guo, S., Thompson, E., 1992. Performing the exact test of Hardy–Weinberg proportion for multiple alleles. Biometrics 48, 361–372. Hirth, H.F., 1997. Synopsis of the Biological Data on the Green Turtle Chelonia mydas (Linnaeus 1758). Fish and Wildlife Service, U.S. Department of the Interior, Washington DC. Hoekert, W.E.J., Neuféglise, H., Schouten, A.D., Menken, S.B.J., 2002. Multiple paternity and female-biased mutation at a microsatellite locus in the olive ridley sea turtle(Lepidochelys olivacea). Heredity 89, 107–113. IUCN, 2014. IUCN Red List of Threatened Species. http://www.iucnredlist.org/details/ 4615/0 (Accessed 13 August 2014). Jost, L., 2008. GST and its relatives do not measure differentiation. Mol. Ecol. 17, 4015–4026. Karl, S.A., Toonen, R.J., Grant, W.S., Bowen, B.W., 2012. Common misconceptions in molecular ecology: echoes of the modern synthesis. Mol. Ecol. 21, 4171–4189. Kaska, Y., 2000. Genetic structure of Mediterranean sea turtle populations. Turk. J. Zool. 24, 191–197. Kichler, K., Holder, M.T., Davis, S.K., Marquez, R., Owens, D.W., 1999. Detection of multiple paternity in the Kemp's ridley sea turtle with limited sampling. Mol. Ecol. 8, 819–830. Lambeck, K., Esat, T.M., Potter, E.-K., 2002. Links between climate and sea levels for the past three million years. Nature 419, 199–205. Librado, P., Rozas, J., 2009. DnaSP v5: a software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25, 1451–1452. Lima, E.H.S.M., Troeng, S., 2001. Link between green turtles foraging in Brazil and nesting in Costa Rica? Mar. Turt. Newsl. 94, 9. Lima, E.H.S.M., Lagueux, C.J., Barata, P.C.R., Marcovaldi, M.A., 2003. Second record of a green turtle (Chelonia mydas) tagged in Brazil and captured in Nicaragua. Mar. Turt. Newsl. 101, 27. Lima, E.H.S.M., Melo, M.T.D., Severo, M.M., Barata, P.C.R., 2008. Green turtle tag recovery further links northern Brazil to the Caribbean region. Mar. Turt. Newsl. 119, 14–15. Limpus, C.J., 1993. The green turtle, Chelonia mydas, in Queensland: breeding males in the southern Great Barrier Reef. Wildl. Res. 20, 513–523. Limpus, C.J., 2008. A Biological Review of Australian Marine Turtles. 2. Green Turtle Chelonia mydas (Linnaeus). Queensland Environmental Protection Agency, Brisbane. Limpus, C.J., Reed, P.C., 1985. The green turtle, Chelonia mydas, in Queensland: population structure in a coral reef feeding area. In: Grigg, G., Shine, R., Ehmann, H. (Eds.), Biology of Australian Frogs and Reptiles. Surrey Beatty and Sons, Sydney, pp. 47–52. Limpus, C.J., Couper, P.J., Read, M.A., 1994. The green turtle, Chelonia mydas, in Queensland: population structure in a warm temperate feeding area. Mem. Queensland Mus. 35, 139–154. Luikart, G., Allendorf, F.W., Cornuet, J.M., Sherwin, W.B., 1998. Distortion of allele frequency distributions provides a test for recent population bottlenecks. J. Hered. 89, 238–247. Maggs, C.A., Castilho, R., Foltz, D., Henzler, C., Jolly, M.T., Kelly, J., Olsen, J., Perez, K.E., Stam, W., Väinölä, R., Viard, F., Wares, J., 2008. Evaluating signatures of glacial refugia for North Atlantic benthic marine taxa. Ecology 89, 108–122. Manni, F., Guerard, E., Heyer, E., 2004. Geographic patterns of (genetic, morphologic, linguistic) variation: how barriers can be detected by using Monmonier's algorithm. Hum. Biol. 76, 173–190. McClenachan, L., Jackson, J.B.C., Newman, M.J.H., 2006. Conservation implications of historic sea turtle nesting beach loss. Front. Ecol. Environ. 4, 290–296. McMillen-Jackson, A., Bert, T.M., 2004. Genetic diversity in the mtDNA control region and population structure in the pink shrimp Farfantepenaeus duorarum. J. Crustac. Biol. 24, 101–109. Monzón-Argüello, C., Munoz, J., Marco, A., Lopez-Jurado, L.F., Rico, C., 2008. Twelve new polymorphic microsatellite markers from the loggerhead sea turtle (Caretta caretta) and cross-species amplification on other marine turtle species. Conserv. Genet. 9, 1045–1049. Naro-Maciel, E., 2006. Connectivity and structure of Atlantic green sea turtles (Chelonia mydas): a genetic perspective. (PhD thesis). Columbia University, New York, USA. Naro-Maciel, E., Becker, J.H., Lima, E.H.S.M., Marcovaldi, M.A., DeSalle, R., 2007. Testing dispersal hypotheses in foraging green sea turtles (Chelonia mydas) of Brazil. J. Hered. 98, 29–39. Naro-Maciel, E., Reid, B., Fitzsimmons, N.N., Le, M., Desalle, R., Amato, G., 2010. DNA barcodes for globally threatened marine turtles: a registry approach to documenting biodiversity. Mol. Ecol. Resour. 10, 252–263. Naro-Maciel, E., Reid, B., Holmes, K.E., Brumbaugh, D.R., Martin, M., DeSalle, R., 2011. Mitochondrial DNA sequence variation in spiny lobsters: population expansion, panmixia, and divergence. Mar. Biol. 158, 2027–2041. Naro-Maciel, E., Bondioli, A.C.V., Martin, M., Almeida, A.P., Baptistotte, C., Bellini, C., Marcovaldi, M.A., Santos, A.J.B., Amato, G., 2012. The interplay of homing and dispersal in green turtles: a focus on the southwestern Atlantic. J. Hered. 103, 792–805. Naro-Maciel, E., Gaughran, S.J., Putman, N.F., Amato, G., Arengo, F., Dutton, P.H., McFadden, K.W., Vintinner, E.C., Sterling, E.J., 2014. Predicting connectivitiy of green turtles at Palmyra Atoll, central Pacific: a focus on mtDNA and dispersal modelling. J. R. Soc. Interface 11, 20130888. Nei, M., 1987. Molecular Evolutionary Genetics. Columbia University Press, New York. Park, S.D.E., 2001. Trypanotolerance in West African cattle and the population genetic effects of selection. (PhD thesis). University of Dublin, Dublin, Ireland.

315

Peakall, R., Smouse, P.E., 2006. Genalex 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295. Peery, M.Z., Kirby, R., Reid, B.N., Stoelting, R., Doucet-Bëer, E., Robinson, S., VásquezCarrillo, C., Pauli, J.N., Palsbøll, P.J., 2012. Reliability of genetic bottleneck tests for detecting recent population declines. Mol. Ecol. 21, 3403–3418. Penaloza, C.L., 2000. Demografia y viabilidad de la poblacion de tortuga verde, Chelonia mydas, en Isla de Aves; Informe Final del Proyecto de Grado para optar al título de Licenciado en Biología. (BS thesis). Universidad Simón Bolívar, Caracas, Venezuela. Petit, J.R., Raynaud, D., Basile, I., Chappellaz, J., Ritz, C., Delmotte, M., Legrand, M., Lorius, C., Pe, L., 1999. Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica. Nature 399, 429–436. Piry, S., Luikart, G., Cornuet, J.-M., 1999. Computer note. BOTTLENECK: a computer program for detecting recent reductions in the effective size using allele frequency data. J. Hered. 90, 502–503. Poloczanska, E.S., Limpus, C.J., Hays, G.C., 2010. Vulnerability of marine turtles to climate change. Adv. Mar. Biol. 56, 151–211. Pritchard, P.C.H., 1976. Post nesting movements of marine turtles (Cheloniidae and Dermochelyidae) tagged in the Guianas. Copeia 4, 749–754. Pritchard, J.K., Stephens, M., Donnelly, P., 2000. Inference of population structure using multilocus genotype data. Genetics 155, 945–959. Provan, J., Bennett, K.D., 2008. Phylogeographic insights into cryptic glacial refugia. Tree 23, 564–571. Putman, N.F., Naro-Maciel, E., 2013. Finding the “lost years” in green turtles: insights from ocean circulation models and genetic analysis. Proc. R. Soc. Lond. B 280, 20131468. Ray, N., Currat, M., Excoffier, L., 2003. Intra-deme molecular diversity in spatially expanding populations. Mol. Biol. Evol. 20, 76–86. Reece, J.S., Castoe, T.A., Parkinson, C.L., 2005. Historical perspectives on population genetics and conservation of three marine turtle species. Conserv. Genet. 6, 235–251. Reich, D.E., Feldman, M.W., Goldstein, D.B., 1999. Statistical properties of two tests that use multilocus data sets to detect population expansions. Mol. Biol. Evol. 16, 453–466. Reich, K.J., Bjorndal, K.A., Bolten, A.B., 2007. The “lost years” of green turtles: using stable isotopes to study cryptic lifestages. Biol. Lett. 3, 712–714. Rice, W.R., 1989. Analyzing tables of statistical tests. Evolution (NY) 43, 223–225. Roberts, M.A., Schwartz, T.S., Karl, S.A., 2004. Global population genetic structure and male-mediated gene flow in the green sea turtle (Chelonia mydas): analysis of microsatellite loci. Genetics 166, 1857–1870. Roden, S.E., Morin, P.A., Frey, A., Balazs, G.H., Zarate, P., Cheng, I.-J., Dutton, P.H., 2013. Green turtle population structure in the Pacific: new insights from SNPs and microsatellites. Endanger. Species Res. 20227–20234. Rodríguez-Zárate, C.J., Rocha-Olivares, A., Behergaray, L.B., 2013. Genetic signature of a recent metapopulation bottleneck in the olive ridley turtle (Lepidochelys olivacea) after intensive commercial exploitation in Mexico. Biol. Cons. 168, 10–18. Ronquist, F., Teslenko, M., van der Mark, P., Ayres, D., Darling, A., Hohna, S., Larget, B., Liu, L., Suchard, M.A., Huelsenbeck, J.P., 2011. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. http://dx.doi.org/ 10.1093/sysbio/sys029. Rousset, F., 2008. Genepop'007: a complete reimplementation of the Genepop software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106. Rousset, F., Raymond, M., 1995. Testing heterozygote excess and deficiency. Genetics 140, 1413–1419. Ruiz-Urquiola, A., Riverón-Giró, F.B., Pérez-Bermúdez, E., Abreu-Grobois, F.A., 2010. Population genetic structure of greater Caribbean green turtles (Chelonia mydas) based on mitochondrial dna sequences, with an emphasis on rookeries from southwestern Cuba. Rev. Investig. Mar. 31, 33–52. Ruzzante, D.E., Walde, S.J., Gosse, J.C., Cussac, V.E., Habit, E., Zemlak, T.S., Adams, E.D., 2008. Climate control on ancestral population dynamics: insight from Patagonian fish phylogeography. Mol. Ecol. 17, 2234–2244. Schulz, J.P., 1975. Sea turtles nesting in Surinam. Zool. Verh. 143, 3–172. Seminoff, J.A., 2004. IUCN red list global status assessment of the green turtle (Chelonia mydas). IUCN, Gland. Shamblin, B.M., Faircloth, B.C., Dodd, M., Wood-Jones, A., Castleberry, S.B., Carroll, J.P., Nairn, C.J., 2007. Tetranucleotide microsatellites from the loggerhead sea turtle (Caretta caretta). Mol. Ecol. Notes 7, 784–787. Shamblin, B.M., Faircloth, B.C., Dodd, M.G., Bagley, D. a, Ehrhart, L.M., Dutton, P.H., Frey, A., Nairn, C.J., 2009. Tetranucleotide markers from the loggerhead sea turtle (Caretta caretta) and their cross-amplification in other marine turtle species. Conserv. Genet. 10, 577–580. Shamblin, B.M., Bjorndal, K.A., Bolten, A.B., Hillis–Starr, Z.M., Lundgren, I., Naro–Maciel, E., Nairn, C.J., 2012. Mitogenomic sequences better resolve stock structure of southern Greater Caribbean green turtle rookeries. Mol. Ecol. 21, 2330–2340. http://dx.doi. org/10.1111/j.1365–294X.2012.05530.x. Shapiro, B., Drummond, A.J., Rambaut, A., Wilson, M.C., Matheus, P.E., Sher, A.V., Pybus, O.G., Gilber, M.T.P., Barnes, I., Binladen, J., Willerslev, E., Hansen, A.J., Baryshnikov, G.F., Burns, J.A., Bavydow, S., Driver, J.C., Froese, D.G., Harington, C.R., Keddie, G., Kosintsev, P., Kunz, M.L., Martin, L.D., Stephenson, R.O., Tedford, R., Zimov, S., Cooper, A., 2004. Rise and fall of the Beringian steppe bison. Science 306, 1561–1565. Tolley, K., Groeneveld, J., Gopal, K., Matthee, C., 2005. Mitochondrial DNA panmixia in spiny lobster Palinurus gilchristi suggests a population expansion. Mar. Ecol. Prog. Ser. 297, 225–231. Van Oosterhout, C., Hutchinson, W.F., Wills, D.P.M., Shipley, P., 2004. Micro-Checker: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538. Viñas, J., Bremer, J.A., Pla, C., 2004. Phylogeography of the Atlantic bonito (Sarda sarda) in the northern Mediterranean: the combined effects of historical vicariance, population

316

E. Naro-Maciel et al. / Journal of Experimental Marine Biology and Ecology 461 (2014) 306–316

expansion, secondary invasion, and isolation by distance. Mol. Phylogenet. Evol. 33, 32–42. Wallace, B.P., DiMatteo, A.D., Hurley, B.J., Finkbeiner, E.M., Bolten, A.B., Chaloupka, M.Y., Hutchinson, B.J., Abreu-Grobois, F.A., Amorocho, D., Bjorndal, K.A., Bourjea, J., Bowen, B.W., Dueñas, R.B., Casale, P., Choudhury, B.C., Costa, A., Dutton, P.H., Fallabrino, A., Girard, A., Girondot, M., Godfrey, M.H., Hamann, M., LópezMendilaharsu, M., Marcovaldi, M.A., Mortimer, J.A., Musick, J.A., Nel, R., Pilcher, N.J., Seminoff, J.A., Troëng, S., Witherington, B., Mast, R.B., 2010. Regional management units for marine turtles: a novel framework for prioritizing conservation and research across multiple scales. PLoS One 5, e15465. Wallace, B.P., DiMatteo, A.D., Bolten, A.B., Chaloupka, M.Y., Hutchinson, B.J., AbreuGrobois, F.A., Mortimer, J.A., Seminoff, J.A., Amorocho, D., Bjorndal, K.A., Bourjea, J.,

Bowen, B.W., Briseño Dueñas, R., Casale, P., Choudhury, B.C., Costa, A., Dutton, P.H., Fallabrino, A., Finkbeiner, E.M., Girard, A., Girondot, M., Hamann, M., Hurley, B.J., López-Mendilaharsu, M., Marcovaldi, M.A., Musick, J.A., Nel, R., Pilcher, N.J., Troëng, S., Witherington, B., Mast, R.B., 2011. Global conservation priorities for marine turtles. PLoS One 6, e24510. Wang, J., 2014. Estimates of migration rates from marker-based parentage analysis. Mol. Ecol. 23, 3191–3213. Wilson, G.A., Rannala, B., 2003. Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163, 1177–1191. Wright, L.I., Stokes, K.L., Fuller, W.J., Godley, B.J., McGowan, A., Snape, R., Tregenza, T., Broderick, A.C., 2012. Turtle mating patterns buffer against disruptive effects of climate change. Proc. R. Soc. B 279, 2122–2127.