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

Abstract

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.

Keywords: fishery-dependent data; fishery-independent data; hierarchical model; integrated analysis; species distribution model (SDM); template model builder (TMB).