A Bayesian hierarchical analysis of stockrecruit data: quantifying structural and parameter uncertainties
Stockrecruit functions are important in fisheries stock assessment, but there is often uncertainty surrounding the appropriate stockrecruit model and its parameter values. Combining different stockrecruit data sets of related species through Bayesian hierarchical analysis can decrease these uncer...
Published in: | Canadian Journal of Fisheries and Aquatic Sciences |
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Main Authors: | , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Canadian Science Publishing
2004
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Subjects: | |
Online Access: | http://dx.doi.org/10.1139/f04-048 http://www.nrcresearchpress.com/doi/pdf/10.1139/f04-048 |
Summary: | Stockrecruit functions are important in fisheries stock assessment, but there is often uncertainty surrounding the appropriate stockrecruit model and its parameter values. Combining different stockrecruit data sets of related species through Bayesian hierarchical analysis can decrease these uncertainties and help to characterize appropriate stockrecruit forms and ranges of plausible parameter values. Two different stockrecruit functions (BevertonHolt and Ricker) have been parameterized in terms of the steepness, which is a parameter that is comparable between populations. In the hierarchical analysis, the prior probability distribution of parameters for the cross-population variation in steepness is determined through a concise model structure. By calculating the Bayes' posteriors for alternative model forms, model uncertainty is accounted for. This methodology has been applied to Atlantic salmon (Salmo salar) stockrecruit data to provide predictions for the steepness of the stockrecruit function for Baltic salmon for which no stockrecruit data exist. |
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