One panel to rule them all: DArTcap genotyping for population structure, historical demography, and kinship analyses, and its application to a threatened shark. (13th July 2020) - Feutry, Pierre; Devloo‐Delva, Floriaan; Tran Lu Y, Adrien; Mona, Stefano; Gunasekera, Rasanthi M.; Johnson, Grant; Pillans, Richard D.; Jaccoud, Damian; Kilian, Andrzej; Morgan, David L.; Saunders, Thor; Bax, Nicholas J.; et al.
Abstract: With recent advances in sequencing technology, genomic data are changing how important conservation management decisions are made. Applications such as Close‐Kin Mark‐Recapture demand large amounts of data to estimate population size and structure, and their full potential can only be realised through ongoing improvements in genotyping strategies. Here we introduce DArTcap, a cost‐efficient method that combines DArTseq and sequence capture, and illustrate its use in a high resolution population analysis of Glyphis garricki, a rare, poorly known and threatened euryhaline shark. Clustering analyses and spatial distribution of kin pairs from four different regions across northern Australia and one in Papua New Guinea, representing its entire known range, revealed that each region hosts at least one distinct population. Further structuring is likely within Van Diemen Gulf, the region that included the most rivers sampled, suggesting additional population structuring would be found if other rivers were sampled. Coalescent analyses and spatially explicit modelling suggest that G. garricki experienced a recent range expansion during the opening of the Gulf of Carpentaria following the conclusion of the Last Glacial Maximum. The low migration rates between neighbouring populations of a species that is found only in restricted coastal and riverine habitats show the importance of managing each population separately, including careful monitoring of local and remote anthropogenic activities that may affect their environments. Overall we demonstrated how a carefully chosen SNP panel combined with DArTcap can provide highly accurate kinship inference and also support population structure and historical demography analyses, therefore maximising cost‐effectiveness.