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Targets and Limits for Management of Fisheries: A Simple Probability-Based Approach

2003; Wiley; Volume: 23; Issue: 2 Linguagem: Inglês

10.1577/1548-8675(2003)023 2.0.co;2

1548-8675

Michael H. Prager, Clay E. Porch, Kyle W. Shertzer, John F. Caddy,

Bayesian Modeling and Causal Inference

North American Journal of Fisheries ManagementVolume 23, Issue 2 p. 349-361 Article Targets and Limits for Management of Fisheries: A Simple Probability-Based Approach Michael H. Prager, Corresponding Author Michael H. Prager mike.prager@noaa.gov Population Dynamics Team, Center for Coastal Fisheries and Habitat Research, National Oceanic and Atmospheric Administration, 101 Pivers Island Road, Beaufort, North Carolina, 28516 USA Corresponding author: mike.prager@noaa.gov E-mail address for this author: caddy.j@tiscali.itSearch for more papers by this authorClay E. Porch, Clay E. Porch Southeast Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 75 Virginia Beach Drive, Miami, Florida, 33149 USASearch for more papers by this authorKyle W. Shertzer, Kyle W. Shertzer Population Dynamics Team, Center for Coastal Fisheries and Habitat Research, National Oceanic and Atmospheric Administration, 101 Pivers Island Road, Beaufort, North Carolina, 28516 USASearch for more papers by this authorJohn F. Caddy, John F. Caddy caddy.j@tiscali.it Department of Environmental Science and Technology, University of London, Prince Consort Road, London, SW7 2BP UK Departamento de Recursos del Mar, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mérida, MexicoSearch for more papers by this author Michael H. Prager, Corresponding Author Michael H. Prager mike.prager@noaa.gov Population Dynamics Team, Center for Coastal Fisheries and Habitat Research, National Oceanic and Atmospheric Administration, 101 Pivers Island Road, Beaufort, North Carolina, 28516 USA Corresponding author: mike.prager@noaa.gov E-mail address for this author: caddy.j@tiscali.itSearch for more papers by this authorClay E. Porch, Clay E. Porch Southeast Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 75 Virginia Beach Drive, Miami, Florida, 33149 USASearch for more papers by this authorKyle W. Shertzer, Kyle W. Shertzer Population Dynamics Team, Center for Coastal Fisheries and Habitat Research, National Oceanic and Atmospheric Administration, 101 Pivers Island Road, Beaufort, North Carolina, 28516 USASearch for more papers by this authorJohn F. Caddy, John F. Caddy caddy.j@tiscali.it Department of Environmental Science and Technology, University of London, Prince Consort Road, London, SW7 2BP UK Departamento de Recursos del Mar, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Mérida, MexicoSearch for more papers by this author First published: 08 January 2011 https://doi.org/10.1577/1548-8675(2003)023 2.0.CO;2Citations: 47Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract Precautionary fishery management requires that a distinction be made between target and limit reference points. We present a simple probability framework for deriving a target reference point for the fishing mortality rate (F) or biomass (B) from the corresponding limit reference point. Our framework is a generalization of one devised previously by Caddy and McGarvey (1996). Both methods require an a priori management decision on the allowable probability of exceeding the limit reference point; our method removes a major assumption by accounting for the uncertainty in the limit reference point. We present the theory underlying the method, an algorithm for solution, and examples of its application. The new procedure, like the old, requires an estimate of the implementation uncertainty expected in the following year's management, an estimate that might be obtained by a review of the effectiveness of past management actions. Either method can be implemented easily on a modern desktop computer. Our generalized framework is more complete, and we believe that it has wide applicability in the use of fishery reference points or, for that matter, in other conservation applications that strive for resource sustainability. Citing Literature Volume23, Issue2May 2003Pages 349-361 RelatedInformation