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Thirty years of fleet dynamics modelling using discrete-choice models: what have we learned?

Girardin, R, Hamon, KG, Pinnegar, J, Poos, JJ, Thebaud, O, Tidd, A ORCID: 0000-0001-8165-9699, Vermard, Y and Marchal, P 2017 , 'Thirty years of fleet dynamics modelling using discrete-choice models: what have we learned?' , Fish and Fisheries, vol. 18, no. 4 , pp. 638-655 , doi: 10.1111/faf.12194.

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Abstract

Anticipating fisher behaviour is necessary for successful fisheries management. Ofthe different concepts that have been developed to understand individual fisherbehaviour, random utility models (RUMs) have attracted considerable attention inthe past three decades, and more particularly so since the 2000s. This study aimedat summarizing and analysing the information gathered from RUMs used duringthe last three decades around the globe. A methodology has been developed tostandardize information across different studies and compare RUM results. Thestudies selected focused on fishing effort allocation. Six types of fisher behaviourdrivers were considered: the presence of other vessels in the same fishing area, tradition,expected revenue, species targeting, costs, and risk-taking. Analyses wereperformed using three separate linear modelling approaches to assess the extent towhich these different drivers impacted fisher behaviour in three fleet types: fleetsfishing for demersal species using active gears, fleets fishing for demersal speciesusing passive gears and fleets fishing for pelagic species. Fishers are attracted byhigher expected revenue, tradition, species targeting and presence of others, butavoid choices involving large costs. Results also suggest that fishers fishing fordemersal species using active gears are generally more influenced by past seasonal(long-term) patterns than by the most recent (short-term) information. Finally, thecomparison of expected revenue with other fisher behaviour drivers highlights thatdemersal fishing vessels are risk-averse and that tradition and species targetinginfluence fisher decisions more than expected revenue.

Item Type: Article
Authors/Creators:Girardin, R and Hamon, KG and Pinnegar, J and Poos, JJ and Thebaud, O and Tidd, A and Vermard, Y and Marchal, P
Keywords: fisher behaviour, meta-analysis, random utility model
Journal or Publication Title: Fish and Fisheries
Publisher: Blackwell Publishing Ltd
ISSN: 1467-2960
DOI / ID Number: 10.1111/faf.12194
Copyright Information:

Copyright 2016 John Wiley & Sons Ltd.

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