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Understanding Zooplankton Long Term Variability through Genetic Programming

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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7246))

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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Authors and Affiliations

  1. ISMAR - Marine Sciences Institute in La Spezia, CNR - National Research Council of Italy, Forte Santa Teresa, Loc. Pozzuolo, 19032, Lerici, SP, Italy

    Simone Marini & Alessandra Conversi

  2. Marine Institute, University of Plymouth, Plymouth, PL4 8AA, United Kingdom

    Alessandra Conversi

Authors
  1. Simone Marini
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  2. Alessandra Conversi
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Editor information

Editors and Affiliations

  1. Department of Animal Production Epidemiology and Ecology, University of Torino, Via Leonardo da Vinci 44, 10095, Grugliasco, (TO), Italy

    Mario Giacobini

  2. Universidade Nove de Lisboa, ISEGI, 1070-312 Lisboa, Portugal and University of Milano-Bicocca, D.I.S.Co., Viale Sarca 336, 20126 Milan, Italy

    Leonardo Vanneschi

  3. Center for Human Genetics Research, Department of Biomedical Informatics, Vanderbilt University, 519 Light Hall, 37232, Nashville, TN, USA

    William S. Bush

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© 2012 Springer-Verlag Berlin Heidelberg

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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

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29065-7

  • Online ISBN: 978-3-642-29066-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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