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Title: The effect of multiple uncertainties on the performance of bioeconomic models for fishery management
Author: Hoshino, Eriko
ISNI:       0000 0004 2696 3753
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2011
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An approach known as management strategy evaluation (MSE) provides a framework for identifying robust management strategies in the presence of multiple management objectives and system uncertainties, and has been increasingly used as a practical fisheries management framework in recent years. However, few examples exist that incorporate economics in MSEs. Meanwhile, there has been increased attention given to economic instruments for sustainable management of fishery resources, including the use of bioeconomic target reference points (RPs). However, investigation of the causes of errors and bias in the estimates of bioeconomic parameters is scarcely documented compared to their biological counterparts, and the implications of simplified assumptions concerning both the biological and economic parts of the bioeconomic models have not been adequately investigated. In this thesis, I used three case study fisheries to illustrate how economics can be explicitly integrated within the MSE framework, and demonstrated the usefulness of this flexible approach as a rigorous tool for the evaluation of the effect of uncertainties in key parameter estimates from bioeconomic fisheries models, as well as highlighting the merits of including economics in MSE in general. The interaction between life history characteristics, fisheries variables and economic systems strongly affect the behaviour and robustness of the bioeconomic target RPs for the case studies fisheries. It was clear from these examples that the MSE approach has the potential to radically improve the robust estimation of bioeconomic RPs as well as the construction, evaluation, and implementation of economically-oriented harvest strategies.
Supervisor: Miler-Gulland, E. J. ; Agnew, David ; Bjørndal, Trond Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral