Phylogeographic structure and population connectivity of a small benthic fish (Tripterygion tripteronotum) in the Adriatic Sea

Abstract

AbstractAimPopulation connectivity of benthic marine organisms depends strongly on planktonic larval dispersal and is controlled by geographic distance and oceanographic structure. We examine isolation by distance versus resistance to barriers (ocean current boundaries) against a background of post‐glacial habitat expansion in a small benthic fish of the Adriatic Sea.LocationAdriatic Sea, Eastern Mediterranean.TaxonTripterygion tripteronotum.MethodsWe performed population genetic analyses using mitochondrial control region sequences of 550 individuals from 25 locations sampled along the Eastern Adriatic coast. Investigations of population structure included differentiation tests, cluster analyses and distance‐based redundancy analysis. We then ran Lagrangian simulations of passive larval drift to examine correlations among population structure, geographic distance and the Adriatic gyre system. To test for signatures of a post‐glacial range expansion, we modelled the demographic history of the populations and examined the geographic distribution of genetic diversity.ResultsGenetic population structure corresponded to the Adriatic gyres without additional effect of geographic distance. Inference of northward‐biased gene flow between the northern and the Istrian gyre was consistent with simulated trajectories of passive drift, whereas the phylogeographic break coinciding with the boundary between the Central and the Northern Adriatic gyre was stronger than predicted by drift simulations. Genetic connectivity of populations within gyres was high. Genetic signatures of population expansion were consistent with a rapid post‐glacial recolonization of the northern Adriatic.Main conclusionsThe combination of dense sampling and passive drift simulation allowed us to distinguish among effects of geographic distance, oceanographic features and palaeoenvironmental changes on current population structure. Comparisons between realized and potential connectivity illustrate the value of integrating different data sources to understand population structure and inform conservation planning.