Predicting Optimal Targeting Strategies in Multispecies Fisheries: A Portfolio Approach
AbstractWhen regulations are species specific but the species are part of a multispecies fishery, studies show that harvest rates are correlated such that net revenues attributed to each species are also correlated. This correlation suggests that portfolio theory is well suited for multispecies fisheries that exhibit joint productive characteristics. This paper uses a portfolio approach to model the behavior of fishermen faced with multiple targeting options in a random harvest fishery. The approach draws from the expected utility hypothesis and financial portfolio theory to predict optimal targeting strategies. The methodology is applied to the pelagic longline fleet operating in the U.S. Atlantic Ocean, Caribbean, and Gulf of Mexico. The model provides evidence that area closures aimed at reducing juvenile swordfish mortality will be more effective in certain regions. Efficient risk-return frontiers are also generated for use in predicting targeting behavior in lieu of a closure.
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Bibliographic InfoArticle provided by Marine Resources Foundation in its journal Marine Resource Economics.
Volume (Year): 20 (2005)
Issue (Month): 1 ()
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Web page: http://www.uri.edu/cels/enre/mre/mre.htm
fisheries economics; fisheries management; highly migratory species; multispecies fisheries; portfolio theory; swordfish; targeting strategies; Resource /Energy Economics and Policy; Q22; G11; D81; C61;
Find related papers by JEL classification:
- Q22 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Fishery
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
- D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
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