Genetic structure and signatures of selection in grey reef sharks (Carcharhinus amblyrhynchos)

Abstract

With overfishing reducing the abundance of marine predators in multiple marine ecosystems, knowledge of genetic structure and local adaptation may provide valuable information to assist sustainable management. Despite recent technological advances, most studies on sharks have used small sets of neutral markers to describe their genetic structure. We used 5517 nuclear single-nucleotide polymorphisms (SNPs) and a mitochondrial DNA (mtDNA) gene to characterize patterns of genetic structure and detect signatures of selection in grey reef sharks (Carcharhinus amblyrhynchos). Using samples from Australia, Indonesia and oceanic reefs in the Indian Ocean, we established that large oceanic distances represent barriers to gene flow, whereas genetic differentiation on continental shelves follows an isolation by distance model. In Australia and Indonesia differentiation at nuclear SNPs was weak, with coral reefs acting as stepping stones maintaining connectivity across large distances. Differentiation of mtDNA was stronger, and more pronounced in females, suggesting sex-biased dispersal. Four independent tests identified a set of loci putatively under selection, indicating that grey reef sharks in eastern Australia are likely under different selective pressures to those in western Australia and Indonesia. Genetic distances averaged across all loci were uncorrelated with genetic distances calculated from outlier loci, supporting the conclusion that different processes underpin genetic divergence in these two data sets. This pattern of heterogeneous genomic differentiation, suggestive of local adaptation, has implications for the conservation of grey reef sharks; furthermore, it highlights that marine species showing little genetic differentiation at neutral loci may exhibit patterns of cryptic genetic structure driven by local selection.

Figure 1

Map showing the sampling locations. In brackets are the numbers of individuals used for the mtDNA analyses and the SNP analyses, respectively. Numbers in italics represent samples from which data were retrieved from Momigliano et al. (2015a). Numbers in bold represent samples for which genetic data were generated in this study.

Figure 2

Minimum spanning network obtained from 247 individual partial ND4 sequences comprising 26 distinct haplotypes.

Figure 3

(a) Outlier loci identified as potentially under divergent selection by Arlequin and BAYESCAN. The dashed line represents the false discovery rate of 0.05, black open circles to the right of the dashed line represent the outlier loci that were identified by both methods and black filled circles represent the outliers identified by at least three independent methods. (b) Results from FLK. Black lines define the 99% probability envelope of the neutral distribution of FLK statistics, black open circles show outliers identified by FLK at a critical P-value of 0.001 and filled black circles represent the eight outliers that were identified by at least three outlier tests. (c) F_ST distribution of neutral loci estimated using the OutFLANK method. (d) Right tail of the neutral F_ST distribution, showing the three outliers identified by OutFLANK (that were also identified by all other approaches). (e, f) Relationship between ‘neutral’ F_ST (estimated from the core distribution of F_ST) and average F_ST of the eight outliers that were identified by at least three outlier tests (e) and the three outliers identified by the OutFLANK approach (f). There was no significant correlation between ‘neutral’ F_ST and F_ST estimated from outlier loci when all locations were included (e, f). When all locations from eastern Australia (filled black circles) are excluded from the analyses, there is a positive linear relationship between neutral F_ST and F_ST estimated from outlier loci ((e): P=6 × 10⁻⁸, R²=0.83; (f) P=9 × 10⁻⁹, R²=0.87).

Figure 4

Estimates of pairwise genetic differentiation across all sampled location estimated from SNP data (F_ST) and mtDNA data (Φ_ST). Comparisons are arranged on the x axis in ascending order of F_ST values. The error bars represent 95% confidence intervals estimated by bootstrapping individuals within locations (only for F_ST). Symbols are color coded based on whether the comparisons are within or among distinct regions and whether they are or are not statistically significant.

Figure 5

Results from the fastSTRUCTURE and DAPC analyses. (a, b) Results from fastSTRUCTURE using logistic prior, K=2 and K=3, respectively. (c, d) Results from DAPC analysis performed on SNP data from all locations (c) and only from locations in Australia and Indonesia (d). The x and y axes represent the first and second discriminant functions, respectively and ellipses represent 95% confidence inertia ellipses.

Figure 6

Patterns of isolation by distance (a, b) using F_ST (a) estimated from SNP data from the ‘core’ of the F_ST distribution and Φ_ST (b, mtDNA data). R² and slope of the fitted linear model were estimated by major axes regression, whereas the significance of the correlation between genetic and geographic distance (P) was estimated via Mantel test using 30 000 randomizations. Open circles represent statistically nonsignificant pairwise distances and filled circles represent significant pairwise comparisons. (c, d) Estimates of spatial autocorrelation (r) across different distance classes (within the same reef and within 500, 1000 and 1500 km, respectively) using samples from eastern Australia (c) and western Australia (d). Error bars represent 95% confidence intervals estimated from 999 bootstraps, and the dotted lines represent the 95% confidence intervals of the null model of no spatial autocorrelation.