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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.


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