Assessing fish–fishery dynamics from a spatially explicit metapopulation perspective reveals winners and losers in fisheries management

Sustainable management of living resources must reconcile biodiversity conservation and socioeconomic viability of human activities. In the case of fisheries, sustainable management design is made challenging by the complex spatiotemporal interactions between fish and fisheries. We develop a comprehensive metapopulation framework integrating data on species life-history traits, connectivity and habitat distribution to identify priority areas for fishing regulation and assess how management impacts are spatially distributed. We trial this approach on European hake fisheries in the north-western Mediterranean, where we assess area-based management scenarios in terms of stock status and fishery productivity to prioritize areas for protection. Model simulations show that local fishery closures have the potential to enhance both spawning stock biomass and landings on a regional scale compared to a status quo scenario, but that improving protection is easier than increasing productivity. Moreover, the interaction between metapopulation dynamics and the redistribution of fishing effort following local closures implies that benefits and drawbacks are heterogeneously distributed in space, the former being concentrated in the proximity of the protected site. A network analysis shows that priority areas for protection are those with the highest connectivity (as expressed by network metrics) if the objective is to improve the spawning stock, while no significant relationship emerges between connectivity and potential for increased landings. Synthesis and applications. Our framework provides a tool for (1) assessing area-based management measures aimed at improving fisheries outcomes in terms of both conservation and socioeconomic viability and (2) describing the spatial distribution of costs and benefits, which can help guide effective management and gain stakeholder support. Adult dispersal remains the main source of uncertainty that needs to be investigated to effectively apply our model to fisheries regulation.