Skip to main content
The Eizaguirre Lab

New paper

Integrating population genomics and biophysical models towards evolutionary-based fisheries management. 

This opinion/review/perspective paper comes from a collaboration with a former PhD student of our group Miguel Baltazar-Soares (now Post-doc at Bournemouth University, UK) and Hans-Harald Hinrichsen (Research Associate at GEOMAR, Germany). We discuss the need to integrate evolutionary biology into fisheries management. We propose that combining molecular ecology with ocean modelling may offer solutions to facilitate the detection of management stocks and determine the adaptive potential of exploited species. The manuscript is in ICES Journal of Marine Science.



Overfishing and rapid environmental shifts pose severe challenges to the resilience and viability of marine fish populations. To develop and implement measures that enhance species’ adaptive potential to cope with those pressures while, at the same time, ensuring sustainable exploitation rates is part of the central goal of fisheries management. Here, we argue that a combination of biophysical modelling and population genomic assessments offer ideal management tools to define stocks, their physical connectivity and ultimately, their short-term adaptive potential. To date, biophysical modelling has often been confined to fisheries ecology whereas evolutionary hypotheses remain rarely considered. When identified, connectivity patterns are seldom explored to understand the evolution and distribution of adaptive genetic variation, a proxy for species’ evolutionary potential. Here, we describe a framework that expands on the conventional seascape genetics approach by using biophysical modelling and population genomics. The goals are to identify connectivity patterns and selective pressures, as well as putative adaptive variants directly responding to the selective pressures and, ultimately, link both to define testable hypotheses over species response to shifting ecological conditions and overexploitation.



Back to top