Statistical Science

Experiences in Bayesian Inference in Baltic Salmon Management

Sakari Kuikka, Jarno Vanhatalo, Henni Pulkkinen, Samu Mäntyniemi, and Jukka Corander

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We review a success story regarding Bayesian inference in fisheries management in the Baltic Sea. The management of salmon fisheries is currently based on the results of a complex Bayesian population dynamic model, and managers and stakeholders use the probabilities in their discussions. We also discuss the technical and human challenges in using Bayesian modeling to give practical advice to the public and to government officials and suggest future areas in which it can be applied. In particular, large databases in fisheries science offer flexible ways to use hierarchical models to learn the population dynamics parameters for those by-catch species that do not have similar large stock-specific data sets like those that exist for many target species. This information is required if we are to understand the future ecosystem risks of fisheries.

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Statist. Sci., Volume 29, Number 1 (2014), 42-49.

First available in Project Euclid: 9 May 2014

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Bayesian inference Baltic salmon risk analysis fishery management decision analysis


Kuikka, Sakari; Vanhatalo, Jarno; Pulkkinen, Henni; Mäntyniemi, Samu; Corander, Jukka. Experiences in Bayesian Inference in Baltic Salmon Management. Statist. Sci. 29 (2014), no. 1, 42--49. doi:10.1214/13-STS431.

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