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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]

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]

The Bay of Biscay is being affected by increasing level of marine litter, which is causing a wide variety of adverse environmental, social, public health, safety and economic impacts. The term "beach littering" has been coined to refer to the marine litter that is deposited on beaches. This litter may come from the sea and through land-based pathways, either from remote or adjacent areas. Dirty beaches can derive in loss of aesthetical value, beach cleaning cost, environmental harm or tourism revenue reduction among others. Therefore, local authorities have started to search for cost-effective approaches to understand and reduce litter accumulation in their beaches. A model is presented in this paper, which is based on Bayesian Networks and enables the forecasting of marine litter beaching at seven beaches located on the south-eastern coast of the Bay of Biscay. The model uses 9.5 years of metocean, environmental and beach cleaning data. The class to predict was defined as a variable with two possible values: Low and High accumulation of beach litter. The obtained models reached an average accuracy of 65.3 ± 6.4%, being the river flow, precipitation, wind and wave the most significant predictors and likely drivers of litter accumulation in beaches. These models may provide some insight to local authorities on the drivers affecting the litter beaching and may help to define their strategies for its reduction. • A model for beach litter accumulation understanding and forecasting designed. • The model used beach litter and metocean data to forecast the amount of litter. • River flow, precipitation, wave and wind were the top predictors of beach littering. • Predictive models had a competitive performance of approx. 65% success rate. [ABSTRACT FROM AUTHOR]