Abstract
Zooplankton are considered good indicators for understanding how oceans are affected by climate change. While climate influence on zooplankton abundance variability is currently accepted, its mechanisms are not understood, and prediction is not yet possible. This paper utilizes the Genetic Programming approach to identify which environmental variables, and at which extent, can be used to express zooplankton abundance dynamics. The zooplankton copepod long term (since 1988) time series from the L4 station in the Western English Channel, has been used as test case together with local environmental parameters and large scale climate indices. The performed simulations identify a set of relevant ecological drivers and highlight the non linear dynamics of the Copepod variability. These results indicate GP to be a promising approach for understanding the long term variability of marine populations.
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Marini, S., Conversi, A. (2012). Understanding Zooplankton Long Term Variability through Genetic Programming. In: Giacobini, M., Vanneschi, L., Bush, W.S. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2012. Lecture Notes in Computer Science, vol 7246. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29066-4_5
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DOI: https://doi.org/10.1007/978-3-642-29066-4_5
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