A two-stage biomass dynamic model for Bay of Biscay anchovy: a Bayesian approach

A two-stage biomass-based state-space model with stochastic recruitment processes and deterministic dynamics was developed for the Bay of Biscay anchovy population. It is fitted in a Bayesian context with posterior computations carried out using Markov chain Monte Carlo techniques. The model is test...

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Detalles Bibliográficos
Autores: Ibaibarriaga, Leire, Fernández-Llana, Carmen, Uriarte, Andrés, Roel, Beatriz
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2008
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/328487
Acceso en línea:http://hdl.handle.net/10261/328487
Access Level:acceso abierto
Palabra clave:Centro Oceanográfico de Vigo
Pesquerías
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oai_identifier_str oai:digital.csic.es:10261/328487
network_acronym_str ES
network_name_str España
repository_id_str
spelling A two-stage biomass dynamic model for Bay of Biscay anchovy: a Bayesian approachIbaibarriaga, LeireFernández-Llana, CarmenUriarte, AndrésRoel, BeatrizCentro Oceanográfico de VigoPesqueríasA two-stage biomass-based state-space model with stochastic recruitment processes and deterministic dynamics was developed for the Bay of Biscay anchovy population. It is fitted in a Bayesian context with posterior computations carried out using Markov chain Monte Carlo techniques. The model is tested first on a simulated dataset and the effects of different modelling assumptions and of missing values evaluated. Then, it is applied to a real historical series of commercial catch and survey data from 1987 to 2006. Results are compared with those obtained by the standard assessment model for this stock, integrated catch-at-age analysis (ICA). From the posterior distribution of biomass in the latest year (2006), the distribution of unexploited biomass in 2007 can be derived assuming the distribution of recruitment in 2007 to be a mixture of the posterior distributions of past series recruitment. Hence, the effect of different catch options on future biomass levels can be quantified in probabilistic terms. Finally, directions for possible further improvements are indicated.Peer reviewedConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202320232008info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttp://hdl.handle.net/10261/328487reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésCentro Oceanográfico de Vigohttps://doi.org/10.1093/icesjms/fsn002Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3284872026-05-22T06:33:51Z
dc.title.none.fl_str_mv A two-stage biomass dynamic model for Bay of Biscay anchovy: a Bayesian approach
title A two-stage biomass dynamic model for Bay of Biscay anchovy: a Bayesian approach
spellingShingle A two-stage biomass dynamic model for Bay of Biscay anchovy: a Bayesian approach
Ibaibarriaga, Leire
Centro Oceanográfico de Vigo
Pesquerías
title_short A two-stage biomass dynamic model for Bay of Biscay anchovy: a Bayesian approach
title_full A two-stage biomass dynamic model for Bay of Biscay anchovy: a Bayesian approach
title_fullStr A two-stage biomass dynamic model for Bay of Biscay anchovy: a Bayesian approach
title_full_unstemmed A two-stage biomass dynamic model for Bay of Biscay anchovy: a Bayesian approach
title_sort A two-stage biomass dynamic model for Bay of Biscay anchovy: a Bayesian approach
dc.creator.none.fl_str_mv Ibaibarriaga, Leire
Fernández-Llana, Carmen
Uriarte, Andrés
Roel, Beatriz
author Ibaibarriaga, Leire
author_facet Ibaibarriaga, Leire
Fernández-Llana, Carmen
Uriarte, Andrés
Roel, Beatriz
author_role author
author2 Fernández-Llana, Carmen
Uriarte, Andrés
Roel, Beatriz
author2_role author
author
author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Centro Oceanográfico de Vigo
Pesquerías
topic Centro Oceanográfico de Vigo
Pesquerías
description A two-stage biomass-based state-space model with stochastic recruitment processes and deterministic dynamics was developed for the Bay of Biscay anchovy population. It is fitted in a Bayesian context with posterior computations carried out using Markov chain Monte Carlo techniques. The model is tested first on a simulated dataset and the effects of different modelling assumptions and of missing values evaluated. Then, it is applied to a real historical series of commercial catch and survey data from 1987 to 2006. Results are compared with those obtained by the standard assessment model for this stock, integrated catch-at-age analysis (ICA). From the posterior distribution of biomass in the latest year (2006), the distribution of unexploited biomass in 2007 can be derived assuming the distribution of recruitment in 2007 to be a mixture of the posterior distributions of past series recruitment. Hence, the effect of different catch options on future biomass levels can be quantified in probabilistic terms. Finally, directions for possible further improvements are indicated.
publishDate 2008
dc.date.none.fl_str_mv 2008
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/328487
url http://hdl.handle.net/10261/328487
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Centro Oceanográfico de Vigo
https://doi.org/10.1093/icesjms/fsn002
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,228081

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