Demersal distribution and fisheries independent trends of beaked and golden redfish in the Barents and Norwegian Seas

Abstract Monitoring stock status using fisheries-independent methods is essential for sustainable marine resource management. These methods help identify biases in fisheries-dependent data but often require comprehensive, long-term surveys. Recent advancements in spatiotemporal models enable the integration of fisheries-independent data from multiple sources, enhancing stock status estimates. We compiled bottom trawl survey data (over 43 000 trawl hauls from 1981 to 2024) for beaked (Sebastes mentella) and golden redfish (Sebastes norvegicus) from the Institute of Marine Research databases. Using spatiotemporal generalized linear mixed-effects models, we estimated trends and spatial distributions of these species in the Barents and Norwegian Seas. Model selection, conducted through 10-fold cross-validation, highlighted depth, spatial, and spatiotemporal random fields as crucial components for estimating survey indices. Beaked redfish had the highest biomass densities in the Central Barents Sea, while golden redfish dominated along the Norwegian Coast. Nursery areas for both species were primarily located in the Barents Sea, with beaked redfish juveniles distributed further north. We also detected distribution shifts: the Central Barents Sea has gained beaked redfish biomass, while the Southern Barents Sea and the close shore Norwegian coast are increasingly important for golden redfish. Additionally, we observed reductions in golden redfish biomass on the large banks south of Lofoten and in the Western and Central Barents Sea. Biomass indices indicated a positive trend for beaked redfish and a stable trend for golden redfish. Biomass changes in beaked redfish were correlated with rising bottom temperatures; however, golden redfish were only weakly correlated. The survey indices for both species correlated with commercial catches and total stock biomass estimates from current assessments. This study demonstrates the capability to track changes in species distribution over time, improving stock assessments. The methodology presented here is applicable for monitoring and managing marine species globally.