Heredity

Over the past decade there has been increasing interest in the use of microhaplotype markers in autopolyploid taxa. This has been driven by theoretical and observed improvements in signals of allelic dosage, linkage, and heritability. Yet, to date there has been little investigation into the suitability of microhaplotype markers for estimating kinship. Here, we develop the theory of kinship estimation from microhaplotypes, introduce the MCHap microhaplotype caller for autopolyploid populations, and apply these methods to a highly diverse germplasm population of mixed-ploidy Actinidia (kiwifruit and relatives). We find that microhaplotype-based kinship estimates are generally superior to equivalent single nucleotide variant based estimates. This is because microhaplotypes minimize the coalescent signal among alleles which may bias estimates within the context of a recent reference population. Hence, kinship estimates from microhaplotypes more accurately capture the recent demographic history of a population. These findings are supported by both coalescent simulations and the analysis of real data. Our findings are relevant to organisms of any ploidy, but most actionable in highly heterozygous taxa such as Actinidia.

Demographic inference based on sequentially Markovian coalescent (SMC) model is widely used for disentangling the evolutionary history of species and populations. However, the genetic effects of self-fertilization, including the expected reduction of population-level genetic diversity and hence effective population size (Ne), are often ignored in demographic inference, despite the fact that selfing is a common reproductive mode in both invertebrates and plants. To investigate how self-fertilization could bias demographic inference, we apply eSMC2, a selfing-aware demographic inference method, to Hawaiian populations of Caenorhabditis elegans, a predominantly self-fertilizing nematode. Using an expanded dataset that contains more Hawaiian strains than in previous studies, we recovered previously reported genetic groups, including one largely consisting of strains from the Big Island. Population substructure is also detected among these Big Island subpopulations, highlighting the importance of identifying subgroups to avoid misinterpretations of demography. For different Big Island subpopulations, we estimate high selfing rates compatible with previous studies and infer consistent population declines. Population sizes inferred using eSMC2 are smaller than those inferred using PSMC', which does not account for selfing. However, rescaling PSMC' parameters to account for self-fertilization produces a similar demographic trajectory as eSMC2 inference. Our study thus demonstrates how one can account for effects of selfing when using SMC-based methods. It also indicates that the Hawaiian group shows population declines similar to those previously found outside Hawaii.

The linkage disequilibrium (LD) or correlation of alleles at different loci is a fundamental statistic in population genetics. This work presents a new estimator for the standardised LD measure r^2 between a pair of loci. The Random Probe (RP) estimator involves generating a set of random dummy loci, before calculating the r^2 between each synthetic locus and the two focal loci using one of the existing estimators (even it is known to be biased). The correlation between the two vectors of r^2 is the LD estimate. Computer simulations show promising results, with the RP estimator at least as unbiased as the current Ragsdale and Gravel estimator in most common scenarios. In the more challenging scenarios with skewed allele frequencies and small sample size, RP is preferred by having notably reduced bias, and that the bias is less sensitive to sample size and underlying LD, while maintaining mean squared error comparable to existing methods. The new estimator will most benefit applications relying on accurate measures of LD. Beyond LD, this study stimulates further discussions on whether RP estimator can be generalised to other genetic summary statistics or measures of relatedness.

The Linkage Disequilibrium (LD) of unlinked loci can be used to estimate contemporary effective population size (Ne) of one to a few generations ago. In genomic datasets loci on different chromosomes are considered unlinked, but there are many more pairs of unlinked loci than there are independent pairs of chromosomes, resulting to confidence intervals (C.I.) being too narrow if the non-independence is not taken into account. Simulations were run to investigate the correlation structure among LD of unlinked loci, which can be expressed by the LD of loci along the same chromosomes, based on a discovery of a novel Random Probe LD estimator. We classify the correlation into two categories: overlapping of loci and disjoint pairs. The former is induced from the same locus being considered twice and is the stronger form of correlation. These correlations feed into {rho}, a parameter to quantify the degree of pseudo-replication in a dataset, and further a correction formula from which C.I. can be properly inferred. We demonstrate the use of our method via an analysis of genomic data from the malaria-transmitting Anopheles gambiae s.s mosquitoes. Apart from the point and C.I. estimates, we find that Var((r^2 ) ) is inflated by about 550 times due to pseudo-replication, highlighting the danger of not handling genetic correlation properly.

Arctic ecosystems are undergoing rapid climate warming, providing an opportunity to examine molecular evolution under environmental stress. Here, we used whole-genome resequencing to assess population structure, genetic diversity, and recent demographic history in the snowy owl (Bubo scandiacus), a highly mobile Arctic apex predator classified as Vulnerable by the IUCN. We analysed genome-wide data from individuals sampled across the species' circumpolar range using a chromosome-level reference genome. Population genomic analyses revealed an absence of detectable population structure, indicating extensive gene flow and near-panmixia across continents. Genome-wide nucleotide diversity was low ({pi}= 2.47 x 10^(-4)), while runs of homozygosity were dominated by short tracts, indicating limited standing genetic variation despite little evidence for recent close inbreeding. Our demographic inference revealed a sustained decline in effective population size over the past ~1,600 years, a pronounced reduction coinciding with the Medieval Warm Period followed by a partial recovery during the subsequent cooler interval. This direct, measurable effect of past climate warming on effective population size raises concerns for the long-term survival of the snowy owl. Our results demonstrate that high connectivity and low levels of recent inbreeding have not necessarily buffered this species against climate-driven demographic erosion of genetic diversity. This study highlights the value of using whole-genome population genomics to inform genomic monitoring and conservation decision-making in rapidly changing environments.

Amongst measures of linkage disequilibrium, the correlation measure r2, due originally to Sewall Wright, has several advantages. Although basically a haploid statistic, it can readily be estimated using covariance and correlation from diploid data. Its population expectation can also be found from probability methods. We show that the probability of linked identity by descent, LIBD, directly estimates the expectation for r2 for Wright-Fisher models. The simplest such model for two loci is the haploid model, where each haplotype is chosen independently from haplotypes of the previous generation. This gives an equilibrium between recombination (recombination frequency c) and drift (population size N) where the expected value of r2 is approximately 1/(1+4Nc). For diploid models, the expectation is similar for low values of c, but contains a term in c2 and rises to double the haploid value for the limiting value of c = 0.5. An increase in r2 is also expected for the model of monogamous mating, as opposed to random mating for each offspring. The expectation for r2 is unaffected by the use of diploid rather than haploid data, an exception being the case of monogamous mating. Computer simulation shows close agreement of r2 with probability expectations, with minor discrepancies observed for unlinked loci. Using the probabilistic definition, r2 can be intuitively understood as a parameter shaping the effective sample size in genome-wide association studies.

The two-spotted spider mite (Tetranychus urticae) is an extremely polyphagous pest, yet the genetic basis of this adaptive potential remains to be fully elucidated. Since expression quantitative trait loci (eQTLs) provide the genetic basis of numerous phenotypes, we aimed to identify trans-eQTL hotspots underlying T. urticae fitness upon transfer from a common (bean) to a challenging (tomato) host plant. Nonetheless, the identification of trans-eQTLs is complex and often constrained by methodological challenges and high costs. Therefore, we employed a multiparental mapping strategy driven by RNA-seq, enabling us to leverage extensive genetic variation in a cost-efficient manner. A randomly mating population was generated on bean from a small number of genetically diverse, often heterozygous parents, and subsequently transferred to tomato prior to RNA-seq. Upon whole genome sequencing of the parents, RNA-seq of the mapping population individuals was sufficient to reconstruct their genomes as a combination of parental haploblocks. Subsequent eQTL mapping identified 23 distinct trans-eQTL hotspot regions associated with the expression of numerous target genes. The most prominent hotspot on chromosome 3 was associated with approximately 900 genes and showed enrichment for functions related to detoxification and digestion. Furthermore, 4 of these trans-eQTL hotspot genotypes explained significant variation in mite fitness on the challenging host plant. This contrasted a traditional QTL mapping approach, where these genotypes could not be detected due to multiple testing correction. Our study hence offers a powerful strategy for trans-eQTL hotspot screening and the discovery of trait-associated loci in complex, multiparental genetic backgrounds.

Chinese bahaba (Bahaba taipingensis) is a critically endangered marine fish that has been commercially extinct in the wild. Conservation-oriented captive breeding has become an effective strategy for rescuing such endangered species, but concerns about inbreeding depression often remain. Here, after merely two generations of closed breeding from 18 wild founders, Chinese bahaba exhibited severe growth depression, with body length and weight diverging by more than three-fold. To further investigate the early genomic consequences of captive breeding, we analyzed whole-genome sequences from 348 F2 individuals together with 19 wild individuals from the same marine region. While genome-wide genetic diversity was overall preserved, closed breeding rapidly partitioned the F2 population into eight distinct genetic lineages. A significant elevation in genomic inbreeding was observed to facilitate the homozygous exposure of recessive deleterious mutations, which was strongly correlated with growth decline. An integrative framework combining GWAS, gene-level burden test, and ROH hotspots was applied to dissect the genetic architecture underlying growth decline, which converged predominantly on skeletal scaffolding and immune regulation. This was evidenced by candidate genes related to skeleton development and homeostasis (e.g. fam20b, c-fos, trpv5, and il23r), the GH/IGF axis (e.g. socs1a), and immune/inflammatory pathways (e.g. il23r, nlrp7, cd163, il12rb2), as well as calcium ion transport pathway. Together, our findings provide novel insights into the genomic consequences and genetic architecture of growth decline under captive conditions in Chinese bahaba, and offer critical guidance for genetic management in captive conservation programs of endangered species.

Parental ageing can influence offspring through non-genetic mechanisms. The contribution of epigenetic inheritance parental effects still remains poorly understood. DNA methylation is a widespread regulator of gene expression that changes during development and ageing and can also act as a mediator of intergenerational effects. We tested whether age-related changes in parental DNA methylation are transmitted to offspring in the short-lived turquoise killifish (Nothobranchius furzeri, Cyprinodontiformes). Using reduced-representation sequencing, we quantified genome-wide DNA methylation and examined methylation dynamics at individual loci. The overall proportion of methylated CpG sites increased during early ageing but declined at later ages, revealing a non-linear trajectory with substantial among-individual variation. Despite these age-related changes, we found no evidence that parental methylation patterns were transmitted to offspring, either at the genome-wide level or at individual loci. Our findings indicate that although DNA methylation undergoes pronounced age-dependent remodelling in adult killifish, these changes are not detectably inherited by the next generation. We discuss these results in the context of epigenetic inheritance and ageing in vertebrate model systems.

Anthropogenic copper (Cu) contamination from agriculture, mining, and industrial runoff creates environmental gradients affecting physiology and behavior in wild populations. While Cu toxicity in Drosophila melanogaster is well characterized, it remains unclear whether Cu resistance is one integrated trait or several independently evolving components. Using a subset of recombinant inbred lines (RILs) from the Drosophila Synthetic Population Resource (DSPR), we measured three components of Cu response: feeding avoidance, oviposition avoidance, and physiological tolerance (median lethal time, LT50) under sustained Cu exposure. All three traits showed substantial phenotypic variation among RILs. Feeding and oviposition avoidance were both highly heritable (H 2 ~ 0.88), and RIL identity accounted for 49.5% of the variance in LT50. However, the three traits showed no significant correlation across RILs, indicating distinct genetic architecture. We identified a single male specific quantitative trait locus (QTL) on chromosome 2R that explained 17.7% of the variation in feeding preference; the interval included candidate detoxification genes Jheh1, Jheh2, Jheh3 and sano, the latter of which is associated with olfactory behavior. No significant QTL were detected for oviposition preference, suggesting a highly polygenic structure that may difficult to detect with our limited panel size. Together, these results indicate that Cu resistance in D. melanogaster is genetically modular. Behavioral avoidance during feeding, oviposition, and physiological tolerance are heritable but architecturally distinct components, each with potential to respond to selection independently.

Parental qualities can influence the development and fitness of their offspring via genetic and non-genetic effects. Although these effects are often linked to parental phenotypes, the effect of parental genetic variation linked with relevant phenotypes is less well understood. We performed full factorial crosses based on parental genotypes for an age-at-maturity-related gene, vgll3, to investigate how the parental genotypes influence Atlantic salmon (Salmo salar) offspring survival, growth, and development in their early life. Beyond the connection with age at maturity, the additional association between vgll3 and body condition in Atlantic salmon offers a potential pathway by which the maternal vgll3 genotype could influence offspring early life fitness. Combined with measurements of maternal phenotype and egg characteristics, the crossing design therefore allowed us to disentangle the maternal and paternal genetic and non-genetic contributions to variation in offspring survival and phenotypic traits. The phenotypic traits measured were hatching length and yolk sac area, growth, and yolk sac consumption and conversion efficiency. Parental vgll3 genotype did not influence the majority of our measured egg traits or alevin traits except for a genetic effect of paternal vgll3 genotype on offspring survival, whereby the paternal late maturation allele was associated with higher survival. Maternal effects were strongest for survival and for traits associated with hatching and weaker for alevin growth and yolk sac usage. Paternal effects on the measured alevin traits were negligible. The results from our study demonstrate that both maternal and paternal effects have the potential to influence offspring early life fitness traits.

Risk tolerance is influenced by both environmental and genetic factors and may be shaped by local selective pressures in hazardous environments. In Northern Senegal, fishermen from the village of Guet Ndar are chronically exposed to high occupational mortality risk, making this population a potential model for local adaptation to risky environments. In a previous study, we showed that men from this fishing community were less risk-tolerant than men from a nearby farming village, although this difference could not be explained by variation at the DRD4 locus. Here, we investigated whether genetic variants previously associated with general risk tolerance in large genome-wide association studies (GWAS) exhibit signals of local adaptation in these Senegalese populations. We genotyped 44 candidate SNPs in 373 individuals sampled from the risky fishing area and the non-risky farming area. Population genetic analyses revealed extremely low and non-significant differentiation between the two populations (mean FST = 0.0003, p = 0.33), indicating that the two groups are genetically indistinguishable at these loci. Furthermore, none of the candidate SNPs showed a significant association with experimentally measured risk tolerance after correction for multiple testing. These results provide no evidence that local adaptation for risky behavior has occurred, several explanations may account for this negative result.

Demographic history can profoundly shape genomic variation by altering levels of inbreeding, the strength of genetic drift, and the efficacy of purifying selection, thereby influencing both the accumulation and expression of deleterious variation. Yet the extent to which these genomic consequences translate into organismal performance remains poorly understood. Here, we investigated how contrasting demographic histories shape genomic diversity, inbreeding, genetic load, and performance across mainland and insular populations of the Italian wall lizard, Podarcis siculus. Using whole-genome sequence data from 263 wild-caught individuals and field-based performance measurements from 134 lizards, we characterised population structure and demographic history, quantified genomic diversity, runs of homozygosity, and deleterious variation, and tested associations between genomic inbreeding and performance traits. Island populations showed reduced genetic diversity, increased autozygosity, and elevated genetic load relative to mainland populations, consistent with stronger genetic drift and reduced efficacy of purifying selection. Patterns of deleterious variation were consistent with purging of strongly, but not mildly, deleterious variants. Greater genomic autozygosity was associated with reduced bite force in island lizards, a pattern consistent with inbreeding depression, whereas sprint speed, sperm quality, immunocompetence, and spatial cognition showed no significant association with inbreeding. Genome-wide mapping of runs of homozygosity further identified multiple genomic regions associated with reduced bite performance. Together, these results show how demographic history shapes genomic diversity, homozygosity, and deleterious variation in wild vertebrate populations, while revealing that phenotypic consequences can be trait-specific, a pattern that may reflect differences in genetic architecture and the extent of environmental influence on trait variation.

Extreme climatic events (ECEs) could potentially induce episodes of rapid adaptive evolution, altering the evolutionary trajectories and viability of wild populations experiencing increasingly stochastic environments. Adaptive potential stems substantially from additive genetic variance (V) in survival. However, the degree to which ECEs impact this variance, and the resulting evolutionary implications, have not been fully conceptualised or quantified in nature. These aims are highly challenging because underlying genetic and environmental variances have fundamentally non-linear effects on the dichotomous phenotypic outcome of survival versus death, while substantial data are required for estimation. We present a conceptual and analytical framework that formalises biological hypotheses for how V depends on environmental severity, how such dependences are reshaped by non-linear genotype-environment-phenotype relationships and demographic structure, and the implications for evolutionary responses. We then use 25 years of survival and pedigree data from European shags (Gulosus aristotelis) to decompose variance in adult survival across diverse environmental conditions including ECEs. Using a capture-recapture animal model, we demonstrate non-negligible V underlying adult survival across all years combined, implying notable adaptive potential. Yet, partitioning by environmental severity revealed substantial V in good years with high survival but negligible V in bad years with ECE-induced low survival, implying a genotype-by-environment interaction that constrains adaptive potential under severe conditions. However, scale transformations showed how the non-linear genotype-environment-phenotype relationship can distort or even reverse such patterns, by reducing the extent to which V is phenotypically expressed in good conditions with high mean survival and amplifying it in more severe conditions with lower mean survival. Evolutionary responses to severe conditions could consequently be non-negligible even given small underlying V. We thereby demonstrate how ECEs can reshape adaptive potential, highlighting the need to integrate impacts of environmental severity on genetic variation in fitness components into predictions of population resilience under climate change.

Understanding the demographic processes shaping population connectivity is essential for conservation. Yet disentangling the effects of dispersal and local effective density remains challenging because traditional genetic approaches integrate signals over evolutionary timescales. Identity-by-descent (IBD) segments provide opportunities to reconstruct recent demographic connectivity over timescales relevant to contemporary conservation challenges, but their reliability under realistic ecological conditions remains poorly evaluated. Here, we assess the ability of two promising spatiall IBD-based methods, IBD-Analysis and MAPS, to estimate effective density and dispersal variance. Using a simulation-inference framework combining spatially explicit simulations, IBD segment extraction, and demographic inference, we evaluated both methods across a wide range of demographic, ecological and methodological scenarios. Both approaches reliably recovered demographic parameters under homogeneous landscapes, with IBD-Analysis providing accurate estimates of global effective density and dispersal across a broad range of conditions. Spatial heterogeneity emerged as the main driver of inference performance. When demographic parameters varied across space, IBD-Analysis integrated local variation into biased global estimates, whereas MAPS captured spatial patterns but showed substantial variability and generated spurious spatial structure in homogeneous landscapes. Methodological factors had comparatively weaker effects than ecological complexity, highlighting the importance of accounting for landscape structure when applying IBD-based demographic inference. These results highlight the potential of IBD-based approaches to move beyond descriptive patterns of genetic connectivity toward mechanistic inference of demographic processes. By distinguishing the contributions of dispersal and density, these methods can improve conservation decisions by linking genomic patterns to key demographic parameters relevant for conservation.

Inbreeding depression (the reduction in fitness associated with inbreeding) has been demonstrated in a wide range of animals, but despite its ubiquity, is not an inevitable consequence of inbreeding. As a result, there is uncertainty about the extent to which inbreeding depression poses an ongoing risk to endangered species currently experiencing significant demographic recovery. Quantifying inbreeding depression will be critical if we want to understand these risks. A comprehensive quantification of the fitness costs of inbreeding requires detailed individual-based longitudinal data so lifetime impacts can be assessed. Here, we use an extraordinarily detailed long-term dataset on Mauritius kestrels (Falco punctatus) to explore inbreeding depression in a population currently experiencing significant demographic recovery. To do so, we constructed a social pedigree of 1,758 individuals and combined this with 1,240 nest records and 1,411 individual resighting histories to explore lifetime fitness effects over a 30-year period. Inbreeding increased significantly over time as the population recovered before stabilising. Inbred eggs were less likely to survive to fledging. Inbred adult male and female birds had significantly lower annual reproductive success than outbred individuals because of a lower annual egg-to-fledgling survival probability. This resulted in significantly lower lifetime reproductive success in inbred females but not males, which showed a negative trend. Population growth was negative and extinction risk increased slightly at current levels of inbreeding. These impacts will become more severe should inbreeding levels increase in the future, which is highly likely given ongoing genomic erosion. Taken together, our results demonstrate significant fitness costs associated with inbreeding in Mauritius kestrels, which pose an ongoing risk to population viability. This suggests that monitoring and managing inbreeding risks in endangered species will likely be required even in populations that are showing significant demographic recovery in response to conservation interventions.

Genotype-environment association (GEA) analyses are widely used to identify loci underlying local adaptation by examining correlations between allele frequencies and environmental variables across a species' range. A major challenge for this approach is distinguishing true adaptive signals from spurious associations arising from population structure. Several methods have been developed to account for population structure, but these methods can suffer from reduced statistical power or increased false positives under some conditions. To address this, we introduce a new GEA method, termed SimGEA. In essence, SimGEA infers a neutral evolutionary model that reproduces the population structure observed in empirical data and uses this model to simulate neutral alleles. By applying the same GEA statistic to both the empirical and simulated data, SimGEA evaluates the significance of observed associations against neutral expectations that account for population structure. We compared the performance of SimGEA with that of existing GEA methods, including LFMM2 and BayPass, using simulations of local adaptation in two-dimensional space. We found that SimGEA consistently controlled the false discovery rate without substantially sacrificing statistical power across the scenarios examined. These results suggest that calibrating statistics using neutral simulations provides a robust and flexible approach for accounting for population structure in GEA analyses.

Many questions in population genetics are concerned with reconstructing evolutionary history through time, such as inferring how population structure has changed throughout the past. Yet, many existing approaches have only an implicit temporal component, using quantities such as allele frequency or haplotype length as rough proxies for age. Recent advances in the inference of Ancestral Recombination Graphs (ARGs) have made it possible to estimate the entire sequence of local genealogies along the genome. These genealogies explicitly encode how samples are related to each other at different time points in the past, enabling the inference of how population structure has changed over time. To this end, recent work has used ARGs to define time-stratified versions of widely-used population genetics summary statistics in an attempt to capture the population structure present within a particular time window. Here, we show that naive approaches result in statistics that cannot be interpreted solely in terms of the population structure present within the time window they are targeting. To address this problem, we introduce a framework of coalescent-based time-stratified statistics, which use coalescence probabilities to partition classical summary statistics into interval-specific contributions. Using coalescent simulations, we demonstrate that these statistics accurately isolate population structure at different temporal depths and avoid spurious signals. Our results highlight the necessity of integrating coalescent theory into ARG-based temporal analyses and provide a principled and practical foundation for studying the dynamics of population structure through time.

Understanding patterns of evolution and divergence in populations is important for defining conservation units and informing taxonomy. However, when populations have low genetic diversity, or are closely related, genetic differentiation can be difficult to detect, particularly when relying on small numbers of genetic markers. Whole-genome sequencing allows for genome-wide identification of both neutral and outlier variation, improving the resolution of subtle population structure and providing insight into evolutionary processes. Here, we investigated genomic differentiation in a recently diverged and highly threatened albatross complex, the Antipodean (D. antipodensis antipodensis) and Gibson's albatross (D. a. gibsoni), using genome-wide neutral and outlier datasets. Whole-genome resequencing of 86 individuals sampled across Antipodes Island (D. a. antipodensis) and the Auckland Islands (D. a. gibsoni) identified 381,176 neutrally evolving and 57 independently segregating outlier (putatively adaptive) SNPs. Analyses of both datasets revealed significant genetic differentiation between the Antipodean and Gibson's albatross, no evidence of contemporary gene flow and evidence of selective sweeps suggesting local adaptation. Within-population structure was also identified for the Gibson's albatross, with genetic differentiation among sample sites from different islands (Adams Island and Disappointment Island). Patterns of heterogenous differentiation across the genome suggest the taxa are on different evolutionary trajectories. Together with existing morphological and behavioural evidence, these genomic results support reassessment of their conservation and taxonomic status. More broadly, this study demonstrates the value of combining neutral and putatively adaptive genomic variation to resolve subtle population structure in recently diverged taxa.

A central goal when assessing patterns of population structure for conservation is to identify populations with unique genetic compositions. The use of genomic sequencing to identify distinct populations has become an increasingly popular method of delineating conservation units. Reduced costs associated with sequencing make it possible to generate larger, more informative datasets to assess genetic diversity within and among populations. In species that exhibit nest-site philopatry, genetic population structure can emerge on much finer scales, particularly in maternally inherited mitochondrial genomes. We demonstrate the feasibility and insight gained by using whole mitochondrial genome sequencing for evaluating population genetic structure and comparing to previous single marker studies in a vulnerable turtle. We used whole mitochondrial genome sequences from diamond-backed terrapin (Malaclemys terrapin) to evaluate whether nest-site philopatry generates fine-scale genetic structure among M. terrapin nesting beaches in western Mobile Bay (Alabama, USA). We then compared haplotype diversity between the Alabama population and M. terrapin populations from the Atlantic and Gulf coasts and evaluated the utility of using whole mitochondrial genomes rather than a subset of loci to characterize unique haplotypic diversity. We found no genetic structure associated with nest-site philopatry within Alabama, but none of the haplotypes in this region were shared with other Gulf Coast sites. This genetic structure is consistent with strong female natal philopatry within western Mobile Bay relative to the Gulf of Mexico and suggests that the Mobile Bay population is genetically unique relative to other M. terrapin populations and merits a unique conservation and management plan.