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A dynamic model of the Bay of Biscay pelagic fleet simulating fishing trip choice: the response to the closure of the European anchovy (Engraulis encrasicolus) fishery in 2005

Author

Listed:
  • Youen Vermard

    (HMMN - Unité Halieutique Manche Mer du Nord - IFREMER - Institut Français de Recherche pour l'Exploitation de la Mer, EMH - Unité Écologie et Modèles pour l'Halieutique - IFREMER - Institut Français de Recherche pour l'Exploitation de la Mer)

  • Paul Marchal

    (LRHLR - Laboratoire Ressources Halieutiques La Rochelle-L'Houmeau - HGS - Unité Halieutique Gascogne Sud - IFREMER - Institut Français de Recherche pour l'Exploitation de la Mer)

  • Stéphanie Mahévas

    (EMH - Unité Écologie et Modèles pour l'Halieutique - IFREMER - Institut Français de Recherche pour l'Exploitation de la Mer)

  • Olivier Thébaud

    (EM - Unité d'Economie Maritime - IFREMER - Institut Français de Recherche pour l'Exploitation de la Mer)

Abstract

The scope of this paper is to describe, evaluate, and forecast fishing trip choices of the Bay of Biscay pelagic fleet using random utility modeling (RUM). First, alternative fishing trip choices of this fleet were identified using multivariate statistical methods based on species landings weighted by value and defined as distinct fishing activity or fisheries (termed métiers). A RUM was specified, which included trip components as attributes during the period 2001-2004 (a lagged percentage of the value per unit of effort of the main species caught, total value per unit of effort, and inertia in terms of changes from one métier to another). For the main métiers, the proportion of correct effort allocation is 90% during the calibration period of 2001-2004. The results from the RUM are used to parameterize a simulation model of trip choice. The model is used to predict trip choices in 2005, throughout most of which fishing was constrained by the closure of the European anchovy (Engraulis encrasicolus) fishery. Simulation results are compared with observed trip choices following the fishing ban: 80% of observed trip choices are correctly predicted by the model. The capacity of the behavioral model to predict responses to the closure is then discussed.

Suggested Citation

  • Youen Vermard & Paul Marchal & Stéphanie Mahévas & Olivier Thébaud, 2008. "A dynamic model of the Bay of Biscay pelagic fleet simulating fishing trip choice: the response to the closure of the European anchovy (Engraulis encrasicolus) fishery in 2005," Post-Print hal-00368317, HAL.
    • Handle: RePEc:hal:journl:hal-00368317
    DOI: 10.1139/F08-147
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    Citations

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    Cited by:

    1. Grafton, R. Quentin & Akter, Sonia & Kompas, Tom, 2009. "Guide to the Ex-Ante Socio-Economic Evaluation of Marine Protected Areas," Research Reports 94827, Australian National University, Environmental Economics Research Hub.
    2. Cabral, Reniel B. & Geronimo, Rollan C. & Lim, May T. & Aliño, Porfirio M., 2010. "Effect of variable fishing strategy on fisheries under changing effort and pressure: An agent-based model application," Ecological Modelling, Elsevier, vol. 221(2), pages 362-369.
    3. David A Carozza & Daniele Bianchi & Eric D Galbraith, 2017. "Formulation, General Features and Global Calibration of a Bioenergetically-Constrained Fishery Model," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-28, January.
    4. Le Floc'h, Pascal & Merzéréaud, Mathieu & Beckensteiner, Jennifer & Alban, Frédérique & Duhamel, Erwan & Thébaud, Olivier & Wilson, James, 2023. "Explaining technical change and its impacts over the very long term: The case of the Atlantic sardine fishery in France from 1900 to 2017," Research Policy, Elsevier, vol. 52(9).
    5. Vermard, Youen & Rivot, Etienne & Mahévas, Stéphanie & Marchal, Paul & Gascuel, Didier, 2010. "Identifying fishing trip behaviour and estimating fishing effort from VMS data using Bayesian Hidden Markov Models," Ecological Modelling, Elsevier, vol. 221(15), pages 1757-1769.
    6. Manuel Mendoza-Carranza & Elisabet Ejarque & Leopold A J Nagelkerke, 2018. "Disentangling the complexity of tropical small-scale fisheries dynamics using supervised Self-Organizing Maps," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-28, May.

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