The potential use of a Gadget model to predict stock responses to climate change in combination with Bayesian networks: the case of Bay of Biscay anchovy

Abstract

The European anchovy (Engraulis encrasicolus) is a short-lived pelagic species distributed in Atlantic European waters, with the Bay of Biscay being one of the main centres of abundance. Because it is a short-lived species, the state of the stock is determined largely by incoming recruitment. Recruitment is highly variable and depends on a variety of factors, such as the size of the spawning stock and environmental conditions in the area. The use of a coupled model that could serve to predict the evolution of the anchovy stock in the short, medium, and long term under several fishing-pressure scenarios and given climate scenarios is demonstrated. This coupled model consists of a Gadget (Globally Applicable Disaggregated General Ecosystem Toolbox) model that was used to analyse the status of the Bay of Biscay anchovy population and to simulate future scenarios based on the estimated recruitment levels, combined with a probabilistic Bayesian network model for recruitment estimation based on machine-learning methods and using climatic indices as potential forecasting factors. The results indicate that certain combinations of medium to high fishing pressure and adverse environmental conditions could force the stock outside its biological reference boundaries.