Management strategies for short lived species: The case of Australia's Northern Prawn Fishery: 3. Factors affecting management and estimation performance |
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| Affiliation: | 1. CSIRO, P.O. Box 76, Cleveland, Qld. 4163, Australia;2. School of Aquatic and Fishery Sciences, Box 355020, University of Washington, Seattle, WA 98195-5020, USA;3. CSIRO, GPO Box 1538, Hobart, Tasmania 7001, Australia;4. MRL/TAFI, University of Tasmania, Private Bag 49, Hobart, Tasmania 7001, Australia;1. National Marine Fisheries Service, Southeast Fisheries Science Center, 4700 Avenue U, Galveston, TX 77550, United States;2. Texas A&M University, Department of Wildlife and Fisheries, College Station, TX 77843, United States;1. University of Maryland Center for Environmental Science, Chesapeake Biological Laboratory, PO Box 38, Solomons, MD, 20688, USA;2. Maryland Department of Natural Resources, 580 Taylor Ave, Annapolis, MD, 21401, USA;1. Divisão de Modelação e Gestão de Recursos da Pesca, Instituto Português do Mar e da Atmosfera (IPMA), Av. Brasília, 1449-006 Lisboa, Portugal;2. ArtesanalPesca—Organização de Produtores de Pesca, C.R.L., Porto de Abrigo, 2970-000 Cotovia, Portugal;3. Direcção Geral de Recursos Naturais, Segurança e Serviços Marítimos, Av. Brasília, 1449-030 Lisboa, Portugal;1. Department of Environmental, Social and Spatial Change, Roskilde University, P.O. Box 260, DK 4000, Roskilde, Denmark;2. DHI, Agern Alle 5, DK 2970, Hoersholm, Denmark |
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| Abstract: | Management strategies for tiger prawns, Penaeus semisulcatus and P. esculentus, in Australia's Northern Prawn Fishery (NPF), are evaluated in terms of conservation- and economic-related performance measures. A two-stage process is used to determine the factors to which these performance measures are most sensitive. The first stage involves identifying the possible factors and their interactions, constructing a partial factorial design to allow the impact of first- and second-order interactions on the performance measures to be identified, and analysing the resultant performance measures using generalised linear models. The second stage entails an experiment based on a balanced design of the possible combinations of the key factors. The factors found to have the greatest impact on the performance measures are: (a) how fishing efficiency has changed over time and whether or not the assessment is based on the correct trend in fishing efficiency, (b) the catchability coefficient used to convert from fishing effort to fishing mortality, (c) the difference between the intended fishing effort and the actual fishing effort expended (implementation error), and (d) whether recruitment is spatially correlated among stocks or not. |
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