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Recruitment prediction with genetic algorithms with application to the Pacific Herring fishery

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  • Dreyfus-León, Michel
  • Chen, D.G.

Abstract

Recruitment prediction is a key element for management decisions in many fisheries. Nevertheless, accuracy of predictions is very low. Generally, recruitment is related to spawning biomass and other factors, but with relative low success (i.e. low correlations). We developed a new approach using genetic algorithms (GA) as a tool to produce a formula to predict very high, high, medium, low, and very low levels of recruitment in the Pacific Herring (Clupea pallassi) fishery stock of the west coast of Vancouver Island, British Columbia, Canada. With spawning biomass, sea surface temperature, salinity, and pacific hake biomass data from 1948 to 1989, the corresponding prediction formula is searched through a population of 100 individuals and 100,000 generations with 5% mutation rate. The formula produced 61.9% correct predictions within the time series available. Spawning biomass seems an insignificant factor to establish a recruitment level. Recruitment seems to be marked rather by the environment.

Suggested Citation

  • Dreyfus-León, Michel & Chen, D.G., 2007. "Recruitment prediction with genetic algorithms with application to the Pacific Herring fishery," Ecological Modelling, Elsevier, vol. 203(1), pages 141-146.
    • Handle: RePEc:eee:ecomod:v:203:y:2007:i:1:p:141-146
    DOI: 10.1016/j.ecolmodel.2005.09.016
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    Cited by:

    1. Fernandes, Jose A. & Irigoien, Xabier & Goikoetxea, Nerea & Lozano, Jose A. & Inza, Iñaki & Pérez, Aritz & Bode, Antonio, 2010. "Fish recruitment prediction, using robust supervised classification methods," Ecological Modelling, Elsevier, vol. 221(2), pages 338-352.
    2. Rojin Akbari & Saeid Pourmanafi & Ali Reza Soffianian & Saman Galalizadeh & Loghman Khodakarami, 2024. "Enhancing ecotourism site suitability assessment using multi-criteria evaluation and NSGA-II," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(11), pages 28663-28698, November.
    3. Parolo, Gilberto & Ferrarini, Alessandro & Rossi, Graziano, 2009. "Optimization of tourism impacts within protected areas by means of genetic algorithms," Ecological Modelling, Elsevier, vol. 220(8), pages 1138-1147.

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