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Assessing opportunity and relocation costs of marine protected areas using a behavioural model of longline fleet dynamics
Authors:Natalie A Dowling  Chris Wilcox  Marc Mangel  Sean Pascoe
Affiliation:1. Pelagic Fisheries and Ecosystems, CSIRO Marine and Atmospheric Research, Castray Esplanade, Hobart 7000, Tasmania, Australia;2. Center for Stock Assessment Research, Department of Applied Mathematics and Statistics MS E‐2, University of California, Santa Cruz, CA 95064, USA;3. Department of Biology, University of Bergen, Bergen, Norway;4. CSIRO Marine and Atmospheric Research, Ecosciences Precinct, 41 Boggo Road, Dutton Park 4102, Queensland, Australia
Abstract:Increasing use of spatial management tools in fisheries requires an understanding of fleet response, and in particular to where displaced fishing effort is likely to move. We develop a state‐dependent decision‐making model to address the spatial allocation of effort in an Australian tuna longline fishery. We assume that fishers have an economic objective in deciding where to fish, but that decisions in any period are also influenced by the remaining quota held at the time of the decision. Key features of the model include endogenous price dynamics, a moving stock and a competitive pool of different vessel types operating from different port locations. We utilize this model to illustrate fleet responses to marine reserves and limits on fishing effort. The results illustrate that the model framework provides advantages over statistically based models in that decisions made in response to the imposition of a reserve are not consistent with a proportional reallocation of effort. Rather, the stochastic dynamic model yielded an overall profit level of ~4% higher relative to scenarios with no reserve. Incorporating the opportunity cost of a quota into the model resulted in an optimal utilization of effort, in which effort was concentrated in time periods and locations yielding maximized profit. Under a low level of effort relative to the season length, the model indicated an overall profit level 43% greater than the highest obtained when the same level of effort was applied solely within any given quarter of the season.
Keywords:Fishery fleet dynamics  location choice model  spatial fisheries management  state‐dependent model  stochastic dynamic programming  tuna longline fishery
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