Experiences in Bayesian Inference in Baltic Salmon Management

Research output: Contribution to journalArticleScientificpeer-review

Abstract

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.
Original languageEnglish
JournalStatistical Science
Volume29
Issue number1
Pages (from-to)42-49
ISSN0883-4237
DOIs
Publication statusPublished - May 2014
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 112 Statistics and probability

Cite this

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title = "Experiences in Bayesian Inference in Baltic Salmon Management",
abstract = "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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author = "Sakari Kuikka and Jarno Vanhatalo and Samu M{\"a}ntyniemi and Henni Pulkkinen and Jukka Corander",
year = "2014",
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Experiences in Bayesian Inference in Baltic Salmon Management. / Kuikka, Sakari; Vanhatalo, Jarno; Mäntyniemi, Samu; Pulkkinen, Henni; Corander, Jukka.

In: Statistical Science, Vol. 29, No. 1, 05.2014, p. 42-49.

Research output: Contribution to journalArticleScientificpeer-review

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AU - Vanhatalo, Jarno

AU - Mäntyniemi, Samu

AU - Pulkkinen, Henni

AU - Corander, Jukka

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AB - 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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U2 - 10.1214/13-STS431

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