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Andonegi, E., Fernandes, J. A., Quincoces, I., Irigoien, X., Uriarte, A., Pérez, A., Howell, D., and Stefánsson, G. 2011. 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. – ICES Journal of Marine Science, 68: 1257–1269.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. [ABSTRACT FROM PUBLISHER]

In this study, we analysed the factors explaining the distribution and betadiversity of mesozooplankton species in shelf and coastal areas in the Bay of Biscay. Nonparametric multiplicative regression models showed that for 35% of the 26 species analysed, the geographical position was the main factor explaining distribution. The variation partitioning results of the betadiversity analysis indicate that niche adaptation (12–25%) and dispersal limitation (14–15%) influence the distribution of mesozooplankton species equally. Therefore, most of the variance (57–66%) remains unexplained suggesting there are other explaining factors not controlled or that a part of the variability is purely stochastic. A log-normal distribution fitted the species rank-abundance better than Hubbell's neutral model, which also indicates that the mesozooplankton distribution is not the result of pure neutral communities colonizing randomly from a large species pool. Overall, the combination of results suggests that mesozooplankton species have distribution centres that are often associated with geographical features, and from which they disperse randomly. [ABSTRACT FROM AUTHOR]