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Assessing the impact of different management options using ISIS-Fish: the French Merluccius merlucciusNephrops norvegicus mixed fishery of the Bay of Biscay*

Published online by Cambridge University Press:  01 April 2006

Hilaire Drouineau
Affiliation:
IFREMER, Dép. Écologie et modèles pour l'halieutique, BP 21105, 44311 Nantes Cedex 03, France
Stéphanie Mahévas
Affiliation:
IFREMER, Dép. Écologie et modèles pour l'halieutique, BP 21105, 44311 Nantes Cedex 03, France
Dominique Pelletier
Affiliation:
IFREMER, Dép. Écologie et modèles pour l'halieutique, BP 21105, 44311 Nantes Cedex 03, France
Benoît Beliaeff
Affiliation:
IFREMER, Dép. Dynamiques de l'environnement côtier, BP 21105, 44311 Nantes Cedex 03, France
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Abstract

In this paper, we present an approach to compare the impact of different management options on the dynamics of a mixed fishery. We used ISIS-Fish, a simulation tool aimed at evaluating the impact of spatial and seasonal management measures on the dynamics of mixed fisheries. The French Nephrops norvegicus (Norway lobster) – Merluccius merluccius (hake) mixed fishery of the Bay of Biscay was chosen as a study case. First, we parameterised the population and exploitation models. We then selected several management measures, including marine protected areas (MPAs) and total allowable catches (TAC), and parameterised fishermen's reaction to each measure. Then, a sensitivity analysis was performed according to a fractional factorial experimental design. Management scenarios were assessed and compared using a statistical simulation design. The sensitivity analysis showed the large influence of some parameters, such as natural mortality, N. norvegicus fecundity, and catchability on both abundance and catches. Given model parameters, an improvement of trawl selectivity and several MPA designs (differing in size, seasonality and location) were found to result in a significant increase in abundance over 10 years, especially for N. norvegicus. This study illustrates the need for a pluri-specific approach to fisheries assessment and management.

Type
Research Article
Copyright
© EDP Sciences, IFREMER, IRD, 2006

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Footnotes

*

http://www.ifremer.fr/isis-fish

References

Abbes R., 1991, Atlas des ressources et des pêches françaises dans les mers européennes, Rennes, Editions Ouest-France.
Botsford, L., Castilla, J., Peterson, C., 1997, The management of fisheries and marine ecosystems. Science 277, 509-515. CrossRef
Charuau A., Biseau A., 1989, Étude d'une gestion optimale des pêcheries de langoustines et de poissons démersaux en mer Celtique. Rapp. interne, DRV-89.009 - Tome 2, IFREMER/RH.
Commission of the European Communities, 2001a, Règlement (CE) No. 2602/2001 de la Commission du 27 décembre 2001 instituant des mesures visant à reconstituer le stock de merlu dans les sous-zones CIEM III, IV, V, VI et VII et les divisions CIEM VIII a, b, d et e. J. off. Communautés européennes L345, 49-51.
Commission of the European Communities, 2001b, Règlement (CE) No. 1162/2001 de la Commission du 14 juin 2001 instituant des mesures visant à reconstituer le stock de merlu dans les sous-zones CIEM III, IV, V, VI et VII et les divisions CIEM VIII a, b, d et e ainsi que les conditions associées pour le contrôle des activités des navires de pêche. J. off. Communautés européennes L159, 4-9.
Commission of the European Communities, 2001c, Selectivity and gear technology for Northern hake stock. SEC (2001) 1195, CE.
Commission of the European Communities, 2002, Règlement (CE) No. 494/2002 de la Commission du 19 mars 2002 instituant des mesures techniques supplémentaires visant à reconstituer le stock de merlu dans les sous-zones CIEM III, IV, V, VI et VII et les sous-divisions CIEM VIII a, b, d et e. J. off. Communautés européennes 8-10.
Commission of the European Communities, 2004, Règlement (CE) No. 811/2004 du Conseil du 21 avril 2004 instituant des mesures de reconstitution du stock de merlu du nord. J. off. Communautés européennes L150, 1-86.
De Castro, L.A.B., Petrer, M. Jr., Commune, A.E., 2001, Sensitivity of the BEAM4 fisheries bio-economic model to the main biological input parameters. Ecol. Model. 141, 53-66. CrossRef
De Pontual, H., Bertignac, M., Battaglia, A., Bavouzet, G., Moguedet, P., Groison, A.L., 2003, A pilot tagging experiment on European hake (Merluccius merluccius): methodology and preliminary results. ICES J. Mar. Sci. 60, 1318-1327. CrossRef
Direction des Pêches maritimes / OFIMER, 2003, Bilan annuel de production 2002 des Pêches et de l'Aquaculture. Rapp. Div. Observatoire économique et Entreprise, Paris, DPM / OFIMER.
Droesbeke J.J., Fine J., Saporta G., 1997, Plans d'expériences: applications à l'entreprise, Paris, Editions Technip.
Drouineau H., 2004, Paramétrage et développement d'un plan de simulation de l'outil de simulation ISIS-Fish pour évaluer l'impact de différentes réglementations de l'activité de pêche de la pêcherie mixte merlu-langoustine de la Grande Vasière. Mémoire DAA, Halieutique, Agrocampus Rennes, p. 62.
Elkalay, K., Frangoulis, C., Skliris, N., Goffart, A., Gobert, S., Lepoint, G., Hecq, J.H., 2003, A model of the seasonal dynamics of biomass and production of the seagrass Posidonia oceanica in the Bay of Calvi (Northwestern Mediterranean). Ecol. Model. 167, 1-18. CrossRef
FAO, 2000, Fisheries Department, Fishery Information, Data and Statistics Unit. Fishstat Plus: Universal software for statistical time series, Version 2.3.
Guichet R., 1996, Le merlu européen (Merluccius merluccius L.). Rapp. interne DRV. 96-04, Direction des Ressources Vivantes, Nantes, IFREMER.
Gulland J.A., 1983, Fish stock assessment: A manual of basic methods. New York, John Wiley.
Guyon G., Rahni N., 1997, Validation of a building thermal model in CLIM2000 simulation software using full-scale experimental data, sensitivity analysis and uncertainty analysis. In: Spitler J.D., Hensen J. (Eds.) Proc. 5th Int. IBPSA Building Simulation'97 Conf. Sept. 8-10, Prague, Vol. 2, pp. 47-54.
Henderson-Sellers, B., Henderson-Sellers, A., 1996, Sensitivity evaluation of environmental model using fractional factorial experimentation. Ecol. Model. 86, 291-295. CrossRef
Hilborn R., Walters C.J., 1992, Quantitative Fisheries Stock Assessment. New York, Chapman & Hall.
Holland, D.S., 2000, A bioeconomic model of marine sanctuaries on Georges Bank. Can. J. Fish. Aquat. Sci. 57, 1307-1319. CrossRef
Holland, D.S., 2003, Integrating spatial management measures into traditional fishery management systems: the case of the Georges Bank multispecies groundfish fishery. ICES J. Mar. Sci. 60, 915-929. CrossRef
ICES, 1992, Report of the Working Group on the fisheries units in sub areas VII and VIII. ICES Doc. CM 1992/G:15.
ICES, 2003a, Report of the Study Group on precautionary reference points for advice on fishery management. ICES CM 2003/ACFM:15.
ICES, 2003b, Report of the Working Group on Nephrops stocks. ICES CM 2003/ACFM:18.
ICES, 2003c, Report of the Working Group on the assessment of southern shelf stocks of hake, monk and megrim. ICES CM 2004/ACFM:02.
ICES, 2004, Report of the ICES Advisory Committee on fishery management and advisory committee on ecosystems, 2004, ICES Advice Vol., No 2.
Ivanova T., Malone L., Mollaghasemi M., 1999, Comparison of a two-stage group-screening design to a standard 2k-p design for a whole-line semiconductor manufacturing simulation model. In: Farrington P.A., Nembhard H.B., Sturrock D.T., Evans G.W. (Eds.), Proc. 1999 Winter Simulation Conf., Phoenix AZ, IEEE, 1, pp. 640-646.
Jamieson, G.S., Levings, C.O., 2001, Marine protected areas in Canada – implications for both conservation and fisheries management. Can. J. Fish. Aquat. Sci. 58, 138-156.
Kleijnen J.P.C., 1987, Statistical tools for simulation practitioners. New York, Marcel Dekker Inc. Publ.
Kleijnen J.P.C., 1998, Experimental design for sensitivity analysis, optimization, and validation of simulations model. In: Banks J. (Eds.), Handbook of simulation: principles, methodology, advances, applications, and practice. New York, Wiley. Engineering & Management Press, pp. 173-224.
Mahévas, S., Pelletier, D., 2004, ISIS-Fish, a generic and spatially explicit simulation tool for evaluating the impact of management measures on fisheries dynamics. Ecol. Model. 171, 65-84. CrossRef
Matsushita, B., Xu, M., Chen, J., Kameyama, S., Tamura, M., 2004, Estimation of regional net primary productivity (NPP) using a process-based ecosystem model: How important is the accuracy of climate data? Ecol. Model. 178, 371-388. CrossRef
Maury, O., Gascuel, D., 1999, SHADYS (“Simulateur HAlieutique de DYnamiques Spatiales”), a GIS based numerical model of fisheries. Example application: the study of a marine protected area. Aquat. Living Resour. 12, 77-88.
Morizur, Y., Conan, G., Guénolé, A., Omnes, M.H., 1981, Fécondité de Nephrops norvegicus dans le golfe de Gasgogne. Mar. Biol. 63, 319-324. CrossRef
Murawski, S.A., Stewart, P., 1996, Report of the symposium on gear selectivity and technical interactions in mixed species fisheries. J. Northw. Atl. Fish. Sci. 19, 7-10. CrossRef
Murua, H., Lucio, P., 1998, Reproductive modality and batch fecundity of the European hake (Merluccius merluccius L.) in the Bay of Biscay. Calif. Coop. Ocean. Fish. Investig. Rep. 39, 196-203.
NIST/SEMATECH, 2004, e-Handbook of Statistical Methods. Available on line at http://www.itl.nist.gov/div898/ handbook/.
Pelletier, D., 1990, Sensitivity and variance estimators for virtual population analysis and the equilibrium yield-per-recruit model. Aquat. Living Resour. 3, 1-12. CrossRef
Pelletier, D., Gros, P., 1991, Assessing the impact of sampling error on model-based management advice: comparison of equilibrium yield-per-recruit variance estimators. Can. J. Fish. Aquat. Sci. 49, 2129-2139. CrossRef
Pelletier, D., Laurec, A., 1992, Management under uncertainty: defining strategies for reducing overexploitation. ICES J. Mar. Sci. 49, 389-401. CrossRef
Pelletier, D., Magal, P., 1996, Dynamics of a migratory population under different fishing effort allocation schemes in time and space. Can J. Fish. Aquat. Sci. 53, 1186-1199. CrossRef
Pelletier D., Mahévas S., Poussin B., Bayon J., André P., Royer J.C., 2001, A conceptual model for evaluating the impact of spatial management measures on the dynamics of a mixed fishery. In: Kruse G.H., Bez N., Booth A., Dorn M.W., Hills S., Lipcius R.N., Pelletier D., Roy C., Smith S.J., Witherell D. (Eds.), Spatial Processes and Management of Marine Populations, Anchorage, University of Alaska Sea Grant, AK-SG-01-02, pp. 53-66.
Pet, J.S., Machiels, M.A.M., Densen, W.L.T.V., 1996, A size-structured simulation model for evaluating management strategies in gillnet fisheries exploiting spatially differentiated populations. Ecol. Model. 88, 195-214. CrossRef
Quéro J.C., Vayne J.J., 1997, Les poissons de mer des pêches françaises, Paris, Delachaux et Niestlé.
Quéro J.C., Vayne J.J., 1998, Les fruits de la mer et plantes marines des pêches françaises, Paris, Delachaux et Niestlé.
Ruget, F., Brisson, N., Delécolle, R., Faivre, R., 2002, Sensitivity analysis of a crop simulation model, STICS, in order to choose the main parameters to be estimated. Agronomie 22, 133-158. CrossRef
Safina, C., 1995, The world's imperiled fish. Sci. Am. 273, 46-53. CrossRef
Sumaila, U.R., Guénette, S., Alder, J., Chuenpadgee, R., 2000, Addressing ecosystem effects of fishing using marine protected areas. ICES J. Mar. Sci. 57, 752-760. CrossRef
Trenkel, V.M., Lorance, P., Mahévas, S., 2004, Do visual transects provide true population density estimates for deepwater fish? ICES J. Mar. Sci. 61, 1050-1056.
Verdoit, M., Pelletier, D., Talidec, C., 1999, A growth model that incorporates individual variability for the Norway lobster population (Nephrops norvegicus L., 1758) of the Bay of Biscay. ICES J. Mar. Sci. 56, 734-745. CrossRef