Integrating commercial fisheries and scientific survey data: Advances, new tools and applications to model the fish and fishery dynamics

Abstract

The present thesis aims to support the ecosystem-based fisheries management (EBFM) approach with state-of-the-art statistical and bio-economic tools to model both fish population and fishery dynamics, with insights derived from the Danish fisheries in the Baltic Sea. The central pillar of the thesis is the development of a flexible spatiotemporal statistical model (log-Gaussian Negative Binomial process, hereafter simply LGNB) that integrates both fishery-dependent and -independent data simultaneously, while accounting for their relative bias contributions in the abundance estimator of commercially exploited fishery resources. As initially expected, our findings revealed that the integrated LGNB model increased considerably the precision of the estimated abundance fields, and each of the data sources provided complementary information in the spatiotemporal age-specific abundance indices. Accordingly, this makes our model eligible to support fish stock assessments, as well as applications related to sampling optimization, identification of essential fish habitats, and calibration of fisheries bio-economic models, all aspects addressed in this thesis. The first application, thus, focused on issues related to sampling optimization and the cost-effectiveness of fisheries monitoring programs, an aspect that is typically difficult to assess. To do so, we used the LGNB model to develop a comprehensive analytical framework to explore the trade-offs between a set of performance metrics for estimating fish abundance, sampling size and costs of both fishery-dependent and -independent monitoring programs, while considering risk and statistical robustness. Overall, our results suggested that, if sampling costs have to be reduced, reliable abundance indices can still be achieved by complementing the reduced fishery-independent data with information from fishery-dependent data. These results, nevertheless, have also shown to be sensitive to the evaluated species and year. In the second application, we applied the LGNB model on two inter-related contexts. First, we used the model to identify persistent nursery and spawning grounds for a selected fish species in order to test them as potential fishing closures. Secondly, we calibrated the population dynamics model of the fisheries management strategy evaluation (MSE) tool DISPLACE with the integrated LGNB model. As such, we could explore not only the possible benefits of coupling a more refined abundance predictive model into DISPLACE, as well as evaluate the effect of fishing closures on the fish and fishery dynamics. Our general findings revealed that the LGNB model accounted more realistically for the stochastic nature of the fishery resource dynamics in the MSE framework, and yielded more conservative estimates in the biological and socio-economic indicators (e.g., spawning stock biomass, revenue, and income inequality). Most importantly, our results highlighted the importance of including the social and behavioral aspects in the fisheries management process, as the establishment of fishing closures have shown to induce fishermen to increase their catch rates of other species to compensate for the economic loss on the target species.