Scientia Marina, Vol 74, No 2 (2010)

Consideration of uncertainty in the design and use of harvest control rules


https://doi.org/10.3989/scimar.2010.74n2371

Yan Jiao
Department of Fisheries and Wildlife Sciences, Virginia Polytechnic Institute and State University , United States

Kevin Reid
Ontario Commercial Fisheries Association , Canada

Tom Nudds
Department of Integrative Biology, University of Guelph , Canada

Abstract


Harvest control rules are widely used by management agencies for decision-making and for promoting public awareness of the status of marine and freshwater fisheries. Many current control rules combine fishing mortality and biomass-based biological reference points. Control rules were introduced as a precaution against the influence of uncertainty and to decrease the risk of overfishing, but are compromised if the uncertainties of the biological reference points are not explicitly considered. Uncertainty has been widely acknowledged but has not been incorporated into control rule design and application. In this paper, we used a Bayesian statistical catch-at-age model to estimate uncertainties in the indicators of fishing mortality, population size, and biological reference points. We apply this model to the Lake Erie walleye (Sander vitreus) fishery, and by fully considering the uncertainty of the indicators, the risk of overfishing and the risk of the population being overfished can be explicitly estimated in the control rules. We suggest short and long-term approaches to incorporate uncertainty in the design of control rules. We also suggest that control rules for specific fisheries should be designed with explicit consideration of the uncertainty of the biological reference points, based on a risk level that the management agency and stakeholders agree upon.

Keywords


harvest control rule; fishery status evaluation; uncertainty; decision-making; Bayesian analysis

Full Text:


PDF

References


ASMFC (Atlantic States Marine fisheries Commission). − 2002. Amendment 4 to the Interstate Fishery Management Plan for Atlantic Weakfish. Fish. Manag. Rep. No. 39: 1-101pp.

Brodziak, J. and C.M Legault. – 2005. Model averaging to estimate rebuilding targets for overfished stocks. Can. J. Fish. Aquat. Sci., 62(3): 544-562. doi:10.1139/f04-199

Butterworth, D.S. and P.B. Best. – 1994. The origins of the choice of 54% of carrying capacity as the protection level for baleen whale stocks, and the implications thereof for management procedures. Rep. Int. Whal. Commn., 44: 491-497.

Caddy, J.F. and R. Mahon. – 1995. Reference points for fisheries management. FAO Fish. Tech. Pap., 347.

Chen, Y. and C. Wilson. – 2002. A simulation study to evaluate impact of uncertainty on the assessment of American lobster fishery in the Gulf of Maine. Can. J. Fish. Aquat. Sci., 59(8): 1394-1403. doi:10.1139/f02-102

Chen, Y., Y. Jiao and L. Chen. – 2003. Developing robust frequentist and Bayesian fish stock assessment methods. Fish Fish., 4(2): 105-120. doi:10.1046/j.1467-2979.2003.00111.x

de Valpine, P. and A. Hasting. – 2002. Fitting population models incorporating process noise and observation error. Ecol. Monogr., 72(1): 57-76. doi:10.1890/0012-9615(2002)072[0057:FPMIPN]2.0.CO;2

FAO. – 1995. Precautionary Approach to Fisheries. Part 1: Guidelines on the precautionary approach to capture fisheries and species introductions, United Nations. FAO Fish. Tech. Pap,. 350/1.

Francis, R.I.C.C. and R. Shotton. – 1997. “Risk” in fisheries management: a review. Can. J. Fish. Aquat. Sci., 54(8): 1699-1715. doi:10.1139/cjfas-54-8-1699

Garcia S.M. – 1996. The precautionary approach to fisheries and its implications for fishery research, technology and management: An updated review. FAO Fish. Tech. Paper, 350/2.

Gilks, W.R. – 1996. Full conditional distributions. In: W.R. Gilks, S. Richardson and D.J. Spiegelhalter (eds.), Markov Chain Monte Carlo in Practice, pp. 75-78. Chapman and Hall, London, U.K.

Helser, T.E., T. Sharov and D.M Kahn. – 2001. A stochastic decision-based approach to assessing the Delaware Bay blue crab (Callinectes sapidus) stock. In: J.M. Berkson, L.L. Kline and D.J. Orth (eds.), Incorporating uncertainty into fishery models, pp. 63-82. American Fisheries Society Publication, 27. Bethesda, Md.

Hilborn R. – 2002. The dark side of reference points. Bull. Mar. Sci., 70(2): 403–408.

Hilborn, R. – 2006. Fisheries success and failure: the case of the Bristol Bay salmon fishery. Bull. Mar. Sci., 78(3): 487-498.

Hilborn, R. and C. Walters. – 1992. Quantitative Fisheries Stock Assessment: Choice, Dynamics, and Uncertainty. Chapman and Hall, New York.

Hoeting, J.A., D. Madigan, A.E. Raftery and C.T. Volinsky. – 1999. Bayesian model averaging: a tutorial. Stat. Sci., 14(4): 382-417.

Jiao, Y., Y. Chen and J. Wroblewski. – 2005. An application of the composite risk assessment method in assessing fisheries stock status. Fish. Res., 72(2-3): 173-183. doi:10.1016/j.fishres.2004.11.003

Katsukawa, T. – 2004. Numerical investigation of the optimal control rule for decision-making in fisheries management. Fish. Sci., 70(1): 123-131. doi:10.1111/j.1444-2906.2003.00780.x

Koeller, P. – 2003. The lighter side of reference points. Fish. Res., 62(1): 1-6. doi:10.1016/S0165-7836(02)00241-2

Lake Erie Walleye Task Group (LEWTG). – 2004. Report of the walleye task group to the standing technical committee. Lake Erie Committee of the Great Lakes Fishery Commission.

Lake Erie Walleye Task Group (LEWTG). – 2008. Report of the walleye task group to the standing technical committee. Lake Erie Committee of the Great Lakes Fishery Commission.

May, R.M., J.R. Beddington, C.W. Clark, S.J. Holt and R.M. Laws. – 1979. Management of multispecies fisheries. Science, 205(4403): 267-277. doi:10.1126/science.205.4403.267 PMid:17747032

McAllister, M.K. and J.N. Ianelli. – 1997. Bayesian stock assessment using catch-age data and the sampling-importance-resampling algorithm. Can. J. Fish. Aquat. Sci., 54(2): 284-300. doi:10.1139/cjfas-54-2-284

Millar, R.B. and R. Meyer. – 2000. Non-linear state-space modeling of fisheries biomass dynamics by using Metropolis-Hastings within-Gibbs sampling. Appl. Stat., 49(3): 327-342. doi:10.1111/1467-9876.00195

Munawar, M., I.F. Munawar, R. Dermott, H. Niblock and S. Carou.– 2002. Is Lake Erie a resilient ecosystem? Aquat. Ecosyst. Health Manage., 5(1): 79-93. doi:10.1080/14634980260199981

Patterson, K., R. Cook, C. Darby, S. Gavaris, L. Kell, P. Lewy, B. Mesnil, A. Punt, V. Restrepo, D.W. Skagen and G. Stefansson. – 2001. Estimating uncertainty in fish stock assessment and forecasting. Fish Fish., 2(2): 125-157. doi:10.1046/j.1467-2960.2001.00042.x

Punt, A.E. and R. Hilborn. – 1997. Fisheries stock assessment and decision analysis: the Bayesian approach. Rev. Fish. Biol. Fish., 7(1): 35-63. doi:10.1023/A:1018419207494

Quinn, T. and R.B. Deriso. – 1999. Quantitative fish dynamics. Oxford University Press, Oxford, UK.

Restrepo, V.R., and J.E. Powers. – 1999. Precautionary control rules in US fisheries management: specification and performance. ICES J. Mar. Sci., 56(6): 846-852. doi:10.1006/jmsc.1999.0546

Restrepo, V.R., G.G. Thompson, P.M. Mace, W.L. Gabriel, L.L. Wow, A.D. MacCall, R.D. Methot, J.E Powers, B.L. Taylor, P.R. Wade and J.F. Witzig. – 1998. Technical guidance on the use of precautionary approaches to implementing National Standard 1 of the Magnuson–Stevens Fishery Conservation and Management Act. NOAA Tech. Memora. NMFS–F/SPO– 31.

Roughgarden, J. and F. Smith. –1996. Why fisheries collapse and what to do about it? Proc. Natl. Acad. Sci. USA., 93(10): 5078-5083. doi:10.1073/pnas.93.10.5078

Rubin, D.B. – 1988. Using the SIR algorithm to simulate posterior distributions. In: J. Bernardo, M. DeGroot, D.V. Lindley and A.F.M. Smith (eds.), Bayesian Statistics, 3, pp. 395-402. Oxford University Press, Oxford.

SAFMC (South Atlantic Fisheries Management Council). – 2005. SEDAR stock assessment for Black Sea Bass. Beaufort, North Carolina.

Shepherd, J.G. – 1982. A versatile new stock-recruitment relationship for fisheries, and the construction of sustainable yield curves. I. Cons. Int. Explor. Mer., 40(1): 67-75.

Smith, A.F.M. and A.E. Gelfand. – 1992. Bayesian statistics without tears: A sampling-resampling perspective. Amer. Stat., 46(2): 84-88. doi:10.2307/2684170

Spiegelhalter, D.J., A. Thomas, N. Best and D. Lunn. – 2004. WinBUGS user manual (version 1.4.1). MRC Biostatistics Unit, Cambriage, U.K.

Sunstein, C.R. – 2005. Cost-benefit analysis and the environment? Ethics, 115(2): 351-385. doi:10.1086/426308

Thompson, G.G. – 1993. A proposal for a threshold stock size and maximum fishing mortality rate. Can. Spec. Publ. Fish. Aquat. Sci., 120: 303-320.




Copyright (c) 2010 Consejo Superior de Investigaciones Científicas (CSIC)

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.


Contact us scimar@icm.csic.es

Technical support soporte.tecnico.revistas@csic.es