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Using Bayesian Network Inference to Examine Zooplankton Ecology and Herring Interactions in the Irish Sea

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Bayesian network inference algorithms can provide a robust framework to help understand biological systems. In this study we apply these algorithms to food web modeling, for the zooplankton community in the Irish Sea. We test the viability of extending this approach to include modeling herring stocks in the Irish Sea. For this reason particular attention is paid to the dietary preferences of herring and historical variations in zooplankton abundance (using CPR data).

We consider networks containing individual species, and networks grouped by herring food consumption, and find that the latter provides a robust method for inferring causal relations between the zooplankton groups and environmental data. Using naïve Bayes classifiers we show a change in the prey preference of herring during the summer months of April to September. We consider the differences between networks from 1970-85 and 1985-2000 for data grouped by herring food consumption and the changes of herring prey preference during this time. We find that the influence of copepods and decapod larvae decreases, while the influence of alternative food sources increase. We show that herring is more abundant at higher temperatures, which corresponds to yearly peaks in the summer. The period 1985-2000 has greater probabilities of higher abundances than 1970-1985, with the exception of medium low temperatures for which abundance decreases.

This talk is part of the British Antarctic Survey series.

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