The blue shark Prionace glauca is a top predator with one of the widest geographic distributions of any shark species, yet classified as critically endangered in the Mediterranean Sea, and Near Threatened globally. Previous genetic studies did not reject the null hypothesis of a single global population across the worldwide species range. Blue shark situation was proposed as a possible archetype of the ‘grey zone of population differentiation’, coined to designate cases where population structure may be too recent or too faint to be detected using a limited set of markers. Here, blue shark samples collected throughout its global range were sequenced using a specific ddRAD method (DArTseq; Georges et al. 2018), which recovered 37,655 genome-wide single nucleotide polymorphisms (SNPs). Two main groups emerged, with Mediterranean Sea and Northern Atlantic samples significantly differentiated from the Indo-west Pacific samples. Significant pairwise FST values indicated further genetic differentiation within the Atlantic Ocean, and between the Atlantic Ocean and the Mediterranean Sea. Reconstruction of recent demographic history suggested the divergence between northern and southern oceanic populations emerged about 500 generations ago and revealed a drastic reduction in effective population size from a large ancestral population. Our results illustrate the power of high-density genome scans to detect population structure and reconstruct demographic history in highly migratory marine species. As the management of the blue shark fishery, either as target or as bycatch, does not account for this delineation, we strongly recommend that the results presented here be considered in future stock assessment and management plans.

The use of NGS datasets has increased dramatically over the last decade, however, there have been few systematic analyses quantifying the accuracy of the commonly used variant caller programs. Here we used a familial design consisting of diploid tissue from a single Pinus contorta parent and the maternally derived haploid tissue from 106 full-sibling offspring, where mismatches could only arise due to mutation or bioinformatic error. Given the rarity of mutation, we used the rate of mismatches between parent and offspring genotype calls to infer the SNP genotyping error rates of FreeBayes, HaplotypeCaller, SAMtools, UnifiedGenotyper, and VarScan. With baseline filtering HaplotypeCaller and UnifiedGenotyper yielded one to two orders of magnitude larger numbers of SNPs and error rates, whereas FreeBayes, SAMtools and VarScan yielded lower numbers of SNPs and more modest error rates. To facilitate comparison between variant callers we standardized each SNP set to the same number of SNPs using additional filtering, where UnifiedGenotyper consistently produced the smallest proportion of genotype errors, followed by HaplotypeCaller, VarScan, SAMtools, and FreeBayes. Additionally, we found that error rates were minimized for SNPs called by more than one variant caller. Finally, we evaluated the performance of various commonly used filtering metrics on SNP calling. Our analysis provides a quantitative assessment of the accuracy of five widely used variant calling programs and offers valuable insights into both the choice of variant caller program and the choice of filtering metrics, especially for researchers using non-model study systems.