Hostname: page-component-77c89778f8-7drxs Total loading time: 0 Render date: 2024-07-18T20:08:02.196Z Has data issue: false hasContentIssue false

Combining Bayesian and simulation approaches to compare the efficiency of two types of tags used in tropical tuna fisheries

Published online by Cambridge University Press:  15 June 2004

Daniel Gaertner
Affiliation:
IRD (UR 109), Centre de Recherche Halieutique Méditerranéenne et Tropicale, BP 171, 34203 Sète, France
Jean-Pierre Hallier
Affiliation:
IRD (UR 109), Centre de Recherche Halieutique Méditerranéenne et Tropicale, BP 171, 34203 Sète, France
Get access

Abstract

Conventional “spaghetti” tags and tags originally designed for “sport fishing” and called Betyp tags, were used during a tuna tagging program conducted on board Dakar baitboats in 1999. With the aim of comparing the recapture rate of both types of tags, additional information obtained from previous tagging trips are used in a Bayesian context to set up an informative prior for conventional tags. We show in this study (1) how to account for the sampling uncertainty in the construction of the Beta prior with a likelihood method, and (2) how a simulation-based alternative can be useful for performing the probability density function of the difference between two posterior recapture rates. On the light of the resulting simulated difference we found that Betyp tags have a very strong negative effect on the return rate of bigeye tuna (−19.6% on average). For skipjack the strength of evidence concerning the decrease in recapture rate due to the implementation of Betyp tags (−3.2%) was supported only at a 10% level. The Bayesian approach is compared with the conventional “frequentist” approach and with a likelihood method allowing for the integration of previous information. The results obtained from different approaches indicate that the choice of the method, as well as the choice of the prior, does not modify the conclusion of the study. Potential causes for explaining the lowest efficiency of Betyp tags are discussed.

Type
Research Article
Copyright
© EDP Sciences, IFREMER, IRD, 2004

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Agresti A., 1990, Categorical data analysis. Wiley Series in Probability and Mathematical Statistics. Wiley-Interscience Publication, New York.
Bayliff W., Holland K., 1986, Materials and methods for tagging tuna and billfishes, recovering the tags and handling the recapture data. FAO Fish. Tech. Pap. 279.
Berger, J.O., Berry, D.A., 1988, Statistical analysis and the illusion of objectivity. Am. Scient. 76, 159-165.
Bernier J., Parent E., Boreux J.-J., 2000, Statistique pour l'environnement. Traitement bayésien de l'incertitude. Ed. Tec & Doc., Paris.
Chen, Y., Breen, P.A., Andrew, N.L., 2000, Impacts of outliers and mis-specification of priors on Bayesian fisheries-stock assessment. Can. J. Fish. Aquat. Sci. 57, 2293-2305. CrossRef
Fonteneau, A., Diouf, T., 1994, An efficient way of bait fishing for tunas recently developed in Senegal. Aquat. Living Resour. 7, 139-151. CrossRef
Fried, S.M., Hilborn, R., 1988, Inseason forecasting of Bristol Bay, Alaska, sockeye salmon (Oncorhynchus nerka) abundance using Bayesian probability theory. Can. J. Fish. Aquat. Sci. 45, 850-855. CrossRef
Hallier J.P., Delgado de Molina A., 2000, Baitboat as a tuna aggregating device. Le canneur : un dispositif de concentration des thons. In: Le Gall J.-Y., Cayré P., Taquet M. (Eds.), Pêches thonières et dispositifs de concentration de poissons. Edn. Ifremer, Actes Colloq. 28, pp. 553-578.
Hallier J.P., Diouf T., Hervé A., Peignon C., 2001, Le Programme MAC : État des opérations et des analyses. Doc. multigraphié, CRODT, IRD, CNROP.
Hallier, J.-P., Gaertner, D., 2002, Comparative efficiency between Betyp tags and Conventional tags. ICCAT Coll. Vol. Sci. Pap. 54, 17-32.
Hilborn, R., Liermann, M., 1998, Standing on the shoulders of giants: learning from experience in fisheries. Rev. Fish. Biol. Fish. 8, 273-283. CrossRef
Hoenig J.M., Warren W.G., Stocker M. 1994, Bayesian and related approaches in fitting surplus production models. Can. J. Fish. Aquat. Sci. 51, 1823-1831.
Holbert, D., Johnson, J.C., 1989, Using prior information in fisheries management: A comparison of classical and Bayesian methods for estimating population parameters. Coast. Manage. 17, 333-347. CrossRef
Kearney R.E. 1982, Methods used by the South Pacific Commission for the survey and assessment of skipjack and baitfish resources. SPC, Tech. Rep. 7, 21-43.
Leonard T., Hsu J.S.J., 1999, Bayesian Methods. An analysis for statisticians and interdisciplinary researchers. Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, New York.
Maunder, M.N., 2001, A general framework for integrating the standardization of catch per unit of effort into stock assessment models. Can. J. Fish. Aquat. Sci. 58, 795-803. CrossRef
Maunder, M.N., 2003, Paradigm shifts in fisheries stock assessment: from integrated analysis to Bayesian analysis and back again. Nat. Resour. Model. 16, 465-475. CrossRef
McAllister, M.K., Kirkwood, G.P., 1998, Bayesian stock assessment: a review and example application using the logistic model. ICES J. Mar. Sci. 55, 1031-1060. CrossRef
Meyer, R., Millar, R.B., 1999, BUGS in Bayesian stock assessments. Can. J. Fish. Aquat. Sci. 56, 1078-1086. CrossRef
Millar, R.B., 2002, Reference priors for Bayesian fisheries models. Can. J. Fish. Aquat. Sci. 59, 1492-1502. CrossRef
Prince, E.D., Ortiz, M., Venizelos, A., Rosenthal, D.S., 2002, In-water conventional tagging techniques developed by the Cooperative Tagging Center for large, highly migratory species. Am. Fish. Symp. 30, 155-171.
Punt A.E., Hilborn R., 1997, Fisheries stock assessment and decision analysis: the Bayesian approach. Rev. Fish. Biol. Fish. 35-63.
Rivot, E., Prevost, E., 2002, Hierarchical Bayesian analysis of capture-mark-recapture data. Can. J. Fish. Aquat. Sci. 59, 1768-1784. CrossRef
Schweder, T., 1998, Fisherian or Bayesian methods of integrating diverse statistical information? Fish. Res. 37, 61-75.
Vose D., 2001, Risk analysis. A quantitative guide. 2nd edition. John Wiley & Sons, New York, USA.
Walters, C.J., Ludwig, D., 1994, Calculation of Bayes posterior probability distributions for key population parameters. Can. J. Fish. Aquat. Sci. 51, 713-722. CrossRef