Scenario Barents Sea A Tool for Evaluating Fisheries Management Regimes
The issues of scenario modelling in general are discussed. A scenario model for the Barents Sea fisheries is presented, with VPA based management for cod and herring, and capelin management based on the CapTool procedure.
Probing scenarios are used to investigate specific questions, and simulations describing selected issues connected to the management procedures are presented.
Uncertainty scenarios are used to evaluate one management procedure, or to compare several, and issues of robustness and efficiency are central to the analysis of simulation results. An uncertainty experiment, with scenarios chosen purely for demonstration, is simulated, and the main results are presented. With mean yearly catches as the only response, the best management strategy is found to be a combination of F high for cod, a spawning target of 500 000 tonnes for capelin, and a constant nominal fishing mortality rate of F=0.20 for herring. When considering biomass variables as additional performance measures, it is found that the 500 000 tonnes spawning target still is the best for capelin, but the cod procedure should rather be F med than F high and the herring procedure should be the yield-per-recruit based strategy F 0.3.
KeywordsManagement Regime Natural Mortality Fishing Mortality Total Catch Stock Size
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- von Bertalanffy, L. 1938. A quantitative theory of organic growth. Hum.Biol. (10) 2: 181–213.Google Scholar
- Bogstad, B., H. Gjøsæther & S. Tjelmeland 1992. CapTool — a versatile aid in Barents Sea capelin catch quota options calculation. Working document to the 1992 meeting of the ICES Atlanto-Scandian Herring and Capelin Working Group.Google Scholar
- Bogstad, B. & S. Tjelmeland 1993. A method for estimating predation mortalities on capelin using a cod-capelin model for the Barents Sea. Unpublished manuscript.Google Scholar
- Box, G. E.; W. G. Hunter & S. Hunter 1978. Statistics for Experimenters. J. Wiley& Sons, New York.Google Scholar
- Chambers, J. M. & T. J. Hastic (eds.) 1992. Statistical models. In S. Wadsworth & Brooks /Advanced Books and Software, Pacific Grove, California.Google Scholar
- Cooke, J.G. 1995. The International Whaling Commission’s Revised Management Procedure as an example of a new approach to fishery management. Pp. 647–657 in: Blix, A. S.; L. Walløe & Ø. Ulltang (eds.). Whales, seals, fish and man. Elsevier Science B.V.Google Scholar
- Hagen, G.; E. Hatlebakk & T. Schweder 1994. SCENARIO BARENTS SEA. A tool for evaluating Fisheries Management regimes. NR-note STAT/07/1994.Google Scholar
- Hagen, G.; E. Hatlebakk & T. Schweder 1994.SCENARIO BARENTSHAV: Simuleringsresultater (1) (in Norwegian). NR-note STAT/08/1994.Google Scholar
- Hagen, G; E. Hatlebakk & T. Schweder 1995. SCENARIO BARENTSHAV, Predasjonsmodell (in norwegian). NR-note STAT/02/1995.Google Scholar
- Hylen, A., O. Nakken & K. Sunnanå 1986. The use of acoustic and bottom trawl surveys in the assessment of north-east arctic cod and haddoc stocks. Alton, M. (ed). A workshop on comparative biology, assessment and management of gadids from the North Pacific and Atlantic oceans. Northwest and Alaska Fisheries Center, Seattle, p. 473–497.Google Scholar
- ICES. 1993. Report of the Arctic Fisheries Working Group Copenhagen 1993.Google Scholar
- IWC. 1993. Report of the Scientific Committee, Rep. Int. Whal. Commn 43, 57–64Google Scholar
- Laurec, A. & J. G. Shepherd 1983. On the analysis of catch an effort data. J. Cons. int. Explor. Mer. 41:81–84.Google Scholar
- Pope, J. G. & J. G. Shepherd 1982. A simple method for the consistent interpretation of catch-at-age data. J. Cons. int. Explor. Mer. 40:176–184.Google Scholar
- Pope, J. G. & J. G. Shepherd. 1985. A comparison of the performance of various methods for tuning VPAs using effort data. J. Cons. int. Explor. Mer. 42:129–151.Google Scholar