Data from: Quantitative analysis of connectivity in populations of a semi-aquatic mammal using kinship categories and network assortativity
Escoda, Lídia; Fernández-González, Angel; Castresana, Jose (2018), Data from: Quantitative analysis of connectivity in populations of a semi-aquatic mammal using kinship categories and network assortativity, Dryad, Dataset, https://doi.org/10.5061/dryad.rq583
Analyzing the impact of anthropogenic and natural river barriers on the dispersal of aquatic and semi-aquatic species may be critical for their conservation, but no adequate genetic methods have been developed for quantifying the effect of specific barriers on current connectivity. Knowledge of kinship relationships between individuals and reconstructions of pedigrees obtained using genomic data can be extremely useful, not only for studying the social organization of animals, but also inferring how the last few generations of offspring have dispersed. In this study, we used kinship data to analyze connectivity patterns in a small semi-aquatic mammal, the Pyrenean desman, in an area comprising two river systems with close headwaters and dams of various sizes. Using a large SNP dataset from 70 specimens, we obtained kinship categories and reconstructed pedigrees. To quantify the barrier effect of specific obstacles, we constructed kinship networks and devised a method based on the assortativity coefficient, which measures the proportion between observed and expected kinship relationships across a barrier. The estimation of this parameter enabled us to infer that the most important barrier in the area was the watershed divide between the rivers, followed by a dam on one of the rivers. Other barriers did not significantly reduce the expected number of kinship relationships across them. This strategy and the information obtained with it may be crucial in determining the most important connectivity problems in an area and help develop conservation plans aimed at improving genetic exchange between populations of threatened species.