Free Standard AU & NZ Shipping For All Book Orders Over $80!
Register      Login
Marine and Freshwater Research Marine and Freshwater Research Society
Advances in the aquatic sciences
Shopping Cart: ( empty )
You are here: Home > Journals > MF > MF07126
RESEARCH ARTICLE
Contents Vol 59(2)

Impacts of prior mis-specification on Bayesian fisheries stock assessment

Yong Chen A D , Chi-Lu Sun B and Minoru Kanaiwa C
+ Author Affiliations
- Author Affiliations

A School of Marine Sciences, University of Maine, Orono, ME 04469, USA.

B Institute of Oceanography, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan.

C Tokyo University of Agriculture, Department of Aqua-Bioscience and Industry 196 Yasaka, Abashiri, Hokkai Japan 099-2493.

D Corresponding author. Email: ychen@maine.edu

Marine and Freshwater Research 59(2) 145-156 https://doi.org/10.1071/MF07126
Submitted: 3 July 2007  Accepted: 7 January 2008   Published: 27 February 2008

Abstract

One of the key features of a Bayesian stock assessment is that the modeller needs to provide knowledge on model parameters. Priors summarise modellers’ understanding of model parameters and are often defined by a probability distribution function. Priors are often mis-specified with arbitrary and unrealistic accuracy and precision in perceiving the state of nature for the parameters as a result of our limited understanding of fisheries ecosystems. Commonly used probability functions such as normal distribution functions tend to be sensitive to prior mis-specification, resulting in large uncertainty and/or errors in Bayesian stock assessment. Fat-tailed functions such as the Cauchy distribution function have been found to be robust to prior mis-specification. Using the Maine sea urchin fishery as an example, we evaluated the impacts of mis-specification in defining the prior distributions on Bayesian stock assessment. The present study suggests that the quantification of priors with a Cauchy distribution tends to be robust to the prior mis-specification. Given our limited understanding of fisheries a function such as the Cauchy distribution function that is robust to prior mis-specification tends to be more desirable. Future studies should explore the use of other fat-tailed distribution functions for quantifying priors in fisheries stock assessment.

Key words: Bayesian stock assessment, Cauchy distribution, prior, prior mis-specification, robust, uncertainty.


Acknowledgement

Financial support of the present study was partially provided by the National Science Council of Taiwan (NSC95-2811-B-002-024), National Taiwan University, Maine Sea Grant and Maine Department of Marine Resources. We would like to thank Dr Paul Breen and Dr Neil Andrew for discussing the robust priors many years ago.


References

Adkison, M. D. , and Peterman, R. M. (1996). Results of Bayesian methods depending on details of implementation: an example of estimating salmon escapements. Fisheries Research 25, 155–170.
| Crossref | GoogleScholarGoogle Scholar | Berger J. O. (1985). ‘Statistical Decision Theory and Bayesian Analysis.’ (Springer-Verlag: New York.)

Berger, J. O. (1994). An overview of robust Bayesian analysis. Test 3, 5–124.
| Crossref | GoogleScholarGoogle Scholar | Box G., and Tiao G. (1992). ‘Bayesian Inference in Statistical Analysis.’ (Addison-Wesley: Reading, MA.)

Chen, Y. , and Hunter, M. (2003). Assessing the green sea urchin (Strongylocentrotus droebachiensis) stock in Maine, USA. Fisheries Research 60, 527–537.
| Crossref | GoogleScholarGoogle Scholar | Cox D. R., and Hinkley D. V. (1974). ‘Theoretical Statistics.’ (Chapman and Hall: London.)

Dennis, B. (1996). Discussion: should ecologists become Bayesians? Ecological Applications 6, 1095–1103.
| Crossref | GoogleScholarGoogle Scholar | Fournier D. A. (1996). ‘AUTODIFF. A C++ array language extension with automatic differentiation for use in nonlinear modeling and statistics.’ (Otter Res. Ltd.: Nanaimo, BC, Canada.)

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.
Hilborn R., and Walters C. J. (1992). ‘Quantitative Fisheries Stock Assessment: Choice, Dynamics, & Uncertainty.’ (Chapman and Hall: New York.)

Kanaiwa, M. , Chen, Y. , and Hunter, M. (2005). An evaluation of a complex length-based fisheries stock assessment model for the green sea urchin fishery in Maine, USA. Fisheries Research 74, 96–115.
| Crossref | GoogleScholarGoogle Scholar | NRC (National Research Council) (1997). ‘Improving Fish Stock Assessments.’ (National Academic Press: Washington D.C.)

NRC (National Research Council) (1999). ‘Sustaining Marine Fisheries.’ (National Academic Press: Washington D.C.)

Punt, A. E. , and Hilborn, R. (1997). Fisheries stock assessment and decision analysis: the Bayesian approach. Reviews in Fish Biology and Fisheries 7, 35–63.
| Crossref | GoogleScholarGoogle Scholar | Quinn T. J.II, and Deriso R. B. 1999. ‘Quantitative Fish Dynamics.’ (Oxford University Press: New York.)

Russell, M. , and Meredith, R. (2000). Natural growth lines in echinoid ossicles are not reliable indicators of age: a test using Strongylocentrotus droebachiensis. Invertebrate Biology 119, 410–420.
Smith S. J., Hunt J. J., and Rivard D. (1993). Risk evaluation and biological reference points for fisheries management. Canadian Special Publication of Fisheries and Aquatic Sciences, 120.

Taylor, B. L. , Wade, P. R. , Stehn, R. A. , and Cochrane, J. E. (1996). A Bayesian approach for classification criteria for Spectacled Eiders. Ecological Applications 6, 1077–1089.
| Crossref | GoogleScholarGoogle Scholar |

Walters, C. J. (1998). Evaluation of quota management policies for developing fisheries. Canadian Journal of Fisheries and Aquatic Sciences 55, 2691–2705.
| Crossref | GoogleScholarGoogle Scholar |

Walters, C. J. , and Ludwig, D. (1994). Calculation of Bayes posterior probability distribution for key population parameters:a simplified approach. Canadian Journal of Fisheries and Aquatic Sciences 51, 713–722.


Zellner, A. (1976). Bayesian and non-Bayesian analysis of regression models with multivariate student-t error terms. Journal of the American Statistical Association 71, 400–405.
| Crossref | GoogleScholarGoogle Scholar |