Bridging the gap between commercial fisheries and survey data to model the spatiotemporal dynamics of marine species.

Monitoring and assessment of natural resources often require inputs from multiple data sources. In fisheries science, for example, the inference of a species' abundance distribution relies on two main data sources, namely commercial fisheries and scientific survey data. Despite efforts to combine these data into an integrated statistical model, their coupling is frequently hampered due to differences in their sampling designs, which imposes distinct bias sources in the estimator of the abundance distribution. We developed a flexible species distribution model (SDM) that can integrate both data sources while filtering out their relative bias contributions. We applied the model on three different age groups of the western Baltic cod stock. For each age group, we tested the model on (1) survey data and (2) integrated data (survey + commercial) as a means to compare their differences and investigate how the inclusion of commercial fisheries data improved the spatiotemporal abundance estimator and parameter estimates. Moreover, we proposed a novel validation approach to evaluate whether the inclusion of commercial fisheries data in the integrated model is not in direct contradiction with the survey data. Following our approach, the results indicated that the use of commercial fisheries data is suitable for the integrated model. Across all age groups, our results demonstrated how commercial fisheries supplied additional information on cod's spatiotemporal abundance dynamics, highlighting sometimes abundance hot spots that were not detected by the survey model alone. Additionally, the integrated model provided a reduction of up to 20% and 10% in the uncertainty (SE) of the predicted abundance fields and fixed-effect parameters, respectively. The proposed model represents thus a valuable benchmark for evaluating spatiotemporal dynamics of fish, and strengthens the science-based advice for marine policymakers.

Searching for a compromise between biological and economic demands to protect vulnerable habitats.

Identifying vulnerable habitats is necessary to designing and prioritizing efficient marine protected areas (MPAs) to sustain the renewal of living marine resources. However, vulnerable habitats rarely become MPAs due to conflicting interests such as fishing. We propose a spatial framework to help researchers and managers determine optimal conservation areas in a multi-species fishery, while also considering the economic relevance these species may have in a given society, even in data poor situations. We first set different ecological criteria (i.e. species resilience, vulnerability and trophic level) to identify optimal areas for conservation and restoration efforts, which was based on a traditional conservationist approach. We then identified the most economically relevant sites, where the bulk of fishery profits come from. We overlapped the ecologically and economically relevant areas using different thresholds. By ranking the level of overlap between the sites, representing different levels of conflicts between traditional conservation and fishing interests, we suggest alternatives that could increase fishers' acceptance of protected areas. The introduction of some flexibility in the way conservation targets are established could contribute to reaching a middle ground where biological concerns are integrated with economic demands from the fishing sector.