Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

Can we project changes in fish abundance and distribution in response to climate?

Large-scale and long-term changes in fish abundance and distribution in response to climate change have been simulated using both statistical and process-based models. However, national and regional fisheries management requires also shorter term projections on smaller spatial scales, and these need to be validated against fisheries data. A 26-year time series of fish surveys with high spatial resolution in the North-East Atlantic provides a unique opportunity to assess the ability of models to correctly simulate the changes in fish distribution and abundance that occurred in response to climate variability and change. We use a dynamic bioclimate envelope model forced by physical-biogeochemical output from eight ocean models to simulate changes in fish abundance and distribution at scales down to a spatial resolution of 0.5°. When comparing with these simulations with annual fish survey data, we found the largest differences at the 0.5° scale. Differences between fishery model runs driven by different biogeochemical models decrease dramatically when results are aggregated to larger scales (e.g. the whole North Sea), to total catches rather than individual species or when the ensemble mean instead of individual simulations are used. Recent improvements in the fidelity of biogeochemical models translate into lower error rates in the fisheries simulations. However, predictions based on different biogeochemical models are often more similar to each other than they are to the survey data, except for some pelagic species. We conclude that model results can be used to guide fisheries management at larger spatial scales, but more caution is needed at smaller scales.

Determining the causes behind the collapse of a small pelagic fishery using Bayesian population modeling

Small pelagic fish species present complex dynamics that challenge population biologists and prevent effective management. Huge fluctuations in abundance have traditionally been associated with external environmental forcing on recruitment, exempting other processes from contributing to fisheries collapse. On the other hand, theory predicts that density dependence and overexploitation can increase the likelihood of population oscillations. Here, we combined nonlinear population modeling with Bayesian analysis to examine the importance of different regulatory mechanisms on the collapse of European anchovy (Engraulis encrasicolus) in the Bay of Biscay. The approach relied on detailed population data and in a careful characterization of changes in the environment experienced by anchovy early stages based mainly on satellite remote sensing. Alternative hypotheses about external forcing on recruitment determined prediction skill and provided alternative interpretations of the causes behind the collapse. Density dependence was weak and unable to generate huge oscillations. Instead, models considering changes in phytoplankton phenology or in larval drift presented the best prediction skill. Nevertheless, an extensive surrogate analysis showed that environmental fluctuations alone barely explain anchovy collapse without considering the impact of fishing. Our results highlight the effectiveness of a Bayesian approach to analyze the dynamics and collapse of managed populations.