Type of Document Master's Thesis Author Susko, Emily Clare URN etd-03022012-012029 Title The effects of life history strategy and uncertainty on a probability-based approach to managing the risk of overfishing Degree Master of Science Department Fisheries and Wildlife Sciences Advisory Committee
Advisor Name Title Berkson, James M. Committee Chair Conn, Paul Committee Member Jiao, Yan Committee Member Orth, Donald J. Committee Member Keywords
- gulf menhaden
- simulation model
- life history
- sandbar shark
- fisheries management
- vermilion snapper
- stock assessment
Date of Defense 2012-02-17 Availability unrestricted AbstractRecent U.S. legislation applies a precautionary approach to setting catch regulations in federal fisheries management. A transparent approach to complying with federal guidelines involves calculating the catch recommendation that corresponds to a specified probability, P*, of exceeding the “true” overfishing limit (OFL) located within an estimated distribution.
The P* methodology aims to manage the risk of overfishing explicitly, but choice of P* alone does not provide sufficient information on all of the risks associated with a control rule—both the probability of overfishing and the severity of overfishing. Rather, the ramifications of P* choices depend on the amount of uncertainty in the stock assessment and on the life history of the species in question. To evaluate these effects on the risks associated with P* rules, my study simulated fishing three example species under three levels of uncertainty.
Trends identified among example species were consistent with predictions from life history. Periodic strategists, which have highly variable recruitment, experienced probabilities of overfishing which exceeded P* and which increased in time. Equilibrium strategists showed more predictable risks of overfishing but may have less capacity to recover from depleted biomass levels. Differences in the size of the OFL distribution—representing differences in levels of uncertainty—led to mixed results depending on whether the distribution was biased or whether uncertainty was fully characterized. Lastly, because OFL distributions are themselves estimates and subject to uncertainty in their shape and size, lower P* values closer to the tails of the estimated distribution produced more variable resulting risks.
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