Geospatial Data and Fishery Management: Innovative Modelling Approaches

Type Thesis
Date 2018
Language English
Other localization
Author(s) Depalle Maxime1
University University of California Davis
Discipline Phylosophy
Thesis supervisor James N Sanchirico
Thesis co-supervisor Thébaud Olivier
Note ISBN: 978-0-438-62819-9
French abstract

L'objectif de cette thèse est d'examiner de quelles manières les progrès récents en matière de disponibilité de données géospatiales peuvent être mis à profit afin d'améliorer la gestion des ressources naturelles. Prenant comme cas d'application des données issues de systèmes de surveillance des navires par satellite (VMS) pour la gestion de pêcheries dans le Golfe du Mexique et en Atlantique Nord, ce travail de recherche développe des approches innovantes permettant de mieux modéliser les comportements de navires de pêche commerciale et d'analyser les politiques de gestion.

Keyword(s) Brexit, Discrete-choice model, Hidden-markov model, Spatial modelling, VMS data, Vessel Monitoring Syste, United Kingdom, European Union

This dissertation investigates how recent advances in the availability of geospatial data can be utilized to improve natural resources management. Taking Vessel Monitoring System (VMS) data and fisheries from the Gulf of Mexico and the North East Atlantic region as empirical settings, it develops novel approaches to better model commercial fishing behavior and to analyze policy interventions. In Chapter I, I focus on discrete choice models and spatial aggregation issues. Combining simulated geospatial data from Monte Carlo experiments with real VMS data from fishing vessels in the Gulf of Mexico, I show how models’ results depend on the choice of the spatial scale of analysis and on data spatial heterogeneity. I illustrate the implications for policy analysis exposing the potential biases when assessing the welfare impact of the implementation of a hypothetical marine protected area. In Chapter II, I employ VMS data to explore a topical policy issue, examining the possible impacts of Brexit on the French commercial fishing fleet. I consider two spatial closure scenarios which could be implemented as a consequence of the United Kingdom leaving the European Union. Taking advantage of data high level of resolution, I draw a comprehensive picture of the dependency of French fisheries to UK waters. Focusing on five key fleet segments, I build on the methodological and modelling framework developed in Chapter I to anticipate fishing effort reallocation patterns and to assess the welfare impact resulting from the closure of UK waters to French fishers. -v- In Chapter III, I develop an innovative framework based on Hidden-Markov Models to analyze the behavior of fishers at sea. I apply it using VMS data and I show how this approach can enhance our understanding of fishers’ response to new management measures. Taking the example of the implementation of Individual Fishing Quotas in the Bottom Longline fishery in the Gulf of Mexico, I am able to uncover the heterogeneity of fishers’ behavioral reaction to the new policy. Overall, this dissertation takes a pluralistic approach to examine the value of geospatial data for resource management. Building upon a diversity of case studies and policy questions, it brings forward the scientific basis for decision-making in fisheries policy and more broadly for the sustainable management of natural resources.

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