Developing robust frequentist and Bayesian fish stock assessment methods

Fish and Fisheries

Volumn 4, Issue 2, 2003, Pages 105-120

  Chen, Yong ^a   Jiao, Yan ^b   Chen, Liqiao ^c  

^a UNIVERSITY OF MAINE   (United States)

^b MEMORIAL UNIVERSITY OF NEWFOUNDLAND   (Canada)

^c EAST CHINA NORMAL UNIVERSITY   (China)

Author keywords

Fisheries; Outlier; Robust Bayesian method; Robust frequentist method; Stock assessment

Indexed keywords

BAYESIAN ANALYSIS; ERROR ANALYSIS; FISHERY MANAGEMENT; FISHERY MODELING; STOCK ASSESSMENT;

EID: 0041972671     PISSN: 14672960     EISSN: None     Source Type: Journal    
DOI: 10.1046/j.1467-2979.2003.00111.x     Document Type: Article

References (39)

1. Andrew, N. (1999) Stock Assessment of Paua in PAU 5 and 7. Progress Report for Ministry of Fisheries Research Project PAU9701. National Institute of Water and Atmospheric Research. PO Box 14901. Wellington. New Zealand, 35 pp.
2. Berger, J.O. (1994) An overview of robust Bayesian analysis. Test, 3: 5-124. Published by Sociedad Espanola de Estodistica Investigation Operativa, Madrid. Spain, 608 pp.
3. Box, G. and Tiao, G. (1973) Bayesian Inference in Statistical Analysis. Addison-Wesley, Reading, MA.
4. Chen, Y. and Andrew, N. (1998) Parameter estimation in modelling the dynamics of fish stock biomass: are currently used observation-error estimator reliable? Canadian Journal of Fisheries and Aquatic Sciences 55, 749-760.
5. Chen, Y., Breen, P.A. and Andrew, N.L., (2000) Impacts of outliers and mis-specification of priors on Bayesian fisheries stock assessment. Canadian Journal of Fisheries and Aquatic Sciences 57, 2293-2305.
6. Chen, Y. and Fournier, D. (1999) Impacts of atypical data on Bayesian inference and robust Bayesian approach in fisheries. Canadian Journal of Fisheries and Aquatic Sciences 56, 1525-1533.
7. Chen, Y., Jackson, D.A. and Paloheimo, J.E. (1994) Robust regression analysis of fisheries data. Canadian Journal of Fisheries and Aquatic Sciences 51, 1420-1429.
8. Chen, Y. and Montgomery, S. (1999) Modelling the dynamics of rock lobster. Jasus verreauxi, stock in New South Wales, Australia. Fishery Bulletin 97, 25-38.
9. Chen, Y. and Paloheimo, J.E. (1998) Can a more realistic model error structure improve parameter estimation in modelling the dynamics of fish populations? Fisheries Research 38, 9-17.
10. Collie, S.J. and Sissenwine, M.P. (1983) Estimating population size from relative abundance data measured with error. Canadian Journal of Fisheries and Aquatic Sciences 40, 1871-1879.
11. Fournier, D.A. (1996) AUTODIFF: a C++ Array Language Extension with Automatic Differentiation for Use in Non-linear Modelling and Statistics. Otter Research Ltd, Nanaimo, BC, Canada. 98 pp.
12. Fournier, D.A. and Archibald, C. (1982) A general theory for analyzing catch at age data. Canadian Journal of Fisheries and Aquatic Sciences 39, 1195-1207.
13. Fournier, D.A., Hampton, J. and Sibert, J.R. (1998) MULTIFAN-CL: a length-based, age-structured model for fisheries stock assessment, with application to South Pacific albacore, Thunnus alalunga. Canadian Journal of Fisheries and Aquatic Sciences 55, 2105-2116.
14. Fournier, D.A., Sibert, J.R.J., Majkowski, J. and Hampton, J. (1990) MULTIFAN: a likelihood-based method for estimating growth parameters and age-composition from a multiple length frequency data set illustrated using data for southern bluelin tuna (Thunnus maccoyii). Canadian Journal of Fisheries and Aquatic Sciences 47, 301-317.
15. Geweke, J. (1994). Priors for macroeconomic time series and their application. Econometric Theory 10, 609-632.
16. Helser, T., Sharov, A. and Kahn, D. (2001) A stochastic decision-based approach to assessing the Delaware Bay blue crab (Callinectes sapidus) stock. In: Incorporating Uncertainty into Fishery Models (eds J.M. Berkson, L.L. Kline and D.J. Orth). American Fisheries Society Publication. Bethesday, MD.
17. Hilborn, R. and Walters, C. (1992) Quantitative Fisheries Stock Assessment: Choice, Dynamics, and Uncertainty Chapman & Hall, New York.
18. Hinrichsen, R.A. (2001) The importance of influence diagnostics: examples from Snake River Chinook salmon spawner-recruit models. Canadian Journal of Fisheries and Aquatic Sciences 58, 551-559.
19. Lawrence, K.D. and Arthur, J.L. (1990) Robust Regression: Analysis and Applications. Marcel Dekker, New York.
20. Megrey, B.A. (1989) Review and comparison of age-structured stock assessment models from theoretical and applied points of view. In: Mathematical Analysis of Fish Stock Dynamics (eds E.F. Edwards and B.A. Megrey). American Fisheries Society Publication. Bethesda, Maryland, pp. 8-48.
21. NRC (National Research Council) (1997) Improving Fish Stock Assessments. National Academy Press. Washington, DC. 184 pp.
22. NRC (National Research Council) (1999) Sustaining Marine Fisheries. National Academy Press. Washington, DC.
23. Polacheck, T., Hilborn, R. and Punt, A.E. (1993) Fitting surplus production models: comparing methods and measuring uncertainty. Canadian Journal of Fisheries and Aquatic Sciences 50, 2597-2607.
24. Punt, A.E. (1988) Model selection for the dynamics of Southern African hake resources. Report Benguela Ecology Program of South Africa. No. 15, 395 pp.
25. Punt, A.E. and Hilborn, R. (1997) Fisheries stock assessment and decision analysis: the Bayesian approach. Reviews of Fish Biology and Fisheries 7, 35-63.
26. Quinn, T.J. II and Deriso, R.B. (1999) Quantitative Fish Dynamics. Oxford University Press, New York.
27. Restrepo, V.R. (ed.) (1999) Providing scientific advice to implement the precautionary approach under the Magnuson-Stevens fishery conservation and management act. Proceedings of the Fifth National NMFS Stock Assessment Workshop. NOAA Technical Memorandum, NMFSF/SPO-40, 161 pp.
28. Restrepo, V.R. and Powers, J.E. (1997) Application of high-breakdown robust regression to tuned stock assessment models. Fishery Bulletin 95, 149-160.
29. Rosenberg, A.A. and Restrepo, V.R. (1994) Uncertainty and risk evaluation in stock assessment advice for US Marine fisheries. Canadian Journal of Fisheries and Aquatic Sciences 51, 2715-2720.
30. Rousseeuw, P.J. and Leroy, A.M. (1987) Robust Regression and Outlier Detection, Wiley. New York.
31. Rubin, D.B. (1988) Using the SIR algorithm to simulate posterior distributions. In: Bayesian Statistics 3 (eds J.M. Bernardo, M.H. de Groot, D.V. Lindley and A. Smith). Clarendon Press, Oxford, UK, pp. 395-402.
32. Schnute, J. (1989) The influence of statistical error on stock assessment: illustrations from Schaefer's model. In: Effects of Ocean Variability on Recruitment and an Evaluation of Parameters Used in Stock Assessment Models (eds R.J. Beamish and G.A. McFarlane). Canadian Special Publication of Fisheries and Aquatic Sciences 108. National Research Council Canada, Ottawa, Canada, pp. 101-109.
33. Schnute, J.T. and Hilborn, R. (1993) Analysis of contradictory data sources in fish stock assessments. Canadian Journal of Fisheries and Aquatic Sciences 50, 1916-1923.
34. Schnute, J.T. and Richards, L.J. (1995) The influence of error on population estimates from catch-age models. Canadian Journal of Fisheries and Aquatic Sciences 52. 2063-2077.
35. Schnute, J.T. and Richards, L.J. (2001) Use and abuse of fishery models. Canadian Journal of Fisheries and Aquatic Sciences 58, 10-17.
36. Shepherd, J.G. (1988) Fish stock assessment and their data requirements. In: Fish Population Dynamics (ed. J.A. Gulland). John Wiley & Sons Ltd. Chichester. pp. 35-62.
37. Shertzer, K. and Prager, M. (2002) Least median of squares: a suitable objective function for stock assessment models? Canadian Journal of Fisheries and Aquatic Sciences 59, 1474-1481.
38. Walters, C.J. (1998) Evaluation of quota management policies for developing fisheries. Canadian Journal of Fisheries and Aquatic Sciences 55, 2691-2705.
39. Walters, C.J. and Ludwig, D. (1981) Effects of measurement errors on the assessment of stock-recruitment relationships. Canadian Journal of Fisheries and Aquatic Sciences 38, 704-710.