Modelling drivers of trawl fisheries discards using Bayesian spatio-temporal models

Effective spatial fisheries management requires a proper understanding of the spatial distribution of both target species and discards. Also, spatial modelling of fishery-dependent data is an effective tool to capture uncertainties in data-limited situations. This study analyses the drivers behind d...

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Detalles Bibliográficos
Autores: Soto-Ruiz, María, Fernández-Peralta, Lourdes, Rey-Sanz, Javier, Czerwinski, Ivone A., García-Cancela, Ramón, Llope, Marcos, Liébana, María, Pennino, Maria Grazia
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2023
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/344330
Acceso en línea:http://hdl.handle.net/10261/344330
https://api.elsevier.com/content/abstract/scopus_id/85169038459
Access Level:acceso abierto
Palabra clave:Bayesian hierarchical modelling
Discards
Ecosystem-based fisheries management
Spatial management tool
INLA
Mauritania
Random forest
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spelling Modelling drivers of trawl fisheries discards using Bayesian spatio-temporal modelsSoto-Ruiz, MaríaFernández-Peralta, LourdesRey-Sanz, JavierCzerwinski, Ivone A.García-Cancela, RamónLlope, MarcosLiébana, MaríaPennino, Maria GraziaBayesian hierarchical modellingDiscardsEcosystem-based fisheries managementSpatial management toolINLAMauritaniaRandom forestEffective spatial fisheries management requires a proper understanding of the spatial distribution of both target species and discards. Also, spatial modelling of fishery-dependent data is an effective tool to capture uncertainties in data-limited situations. This study analyses the drivers behind discarding by comparing the standardising properties of three different components: Total Discards, Discards Per Unit of Effort and Total Discard Ratio. These metrics were analysed by means of Bayesian hierarchical spatio-temporal Gamma regression models to correctly to identify areas with high discards values that are characterized as discards hotspots. Our results showed that Total Discards is the component which better quantified the aggregated ecological impact of discarding practices, whereas Total Discard Ratio and Discards Per Unit of Effort identify complementary issues of benefits versus loss of biomass. Spatial maps obtained by combining these three approaches are a powerful tool for the spatial management of discards.MGP would like to thank projects financed by the European Union-NextGenerationEU. Componente 3. Inversión 7. Convenio entre el ministerio de agricultura, pesca, y alimentación y la agencia estatal Consejo Superior De Investigaciones Científicas—a través del instituto español de oceanografía—para impulsar la investigación pesquera como base para la gestión pesquera sostenible; Eje4, FishClim: Conocimiento científico para la adaptación al cambio climático del sector pesquero español; and Eje6, Math4Fish: Nuevas herramientas para el modelado matemático en el asesoramiento científico de pesquerías españolas.Peer reviewedElsevierMinisterio de Agricultura, Pesca y Alimentación (España)Consejo Superior de Investigaciones Científicas (España)CSIC - Instituto Español de Oceanografía (IEO)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242023info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionhttp://hdl.handle.net/10261/344330https://api.elsevier.com/content/abstract/scopus_id/85169038459reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésFisheries Researchhttps://doi.org/10.1016/j.fishres.2023.106830Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3443302026-05-22T06:33:51Z
dc.title.none.fl_str_mv Modelling drivers of trawl fisheries discards using Bayesian spatio-temporal models
title Modelling drivers of trawl fisheries discards using Bayesian spatio-temporal models
spellingShingle Modelling drivers of trawl fisheries discards using Bayesian spatio-temporal models
Soto-Ruiz, María
Bayesian hierarchical modelling
Discards
Ecosystem-based fisheries management
Spatial management tool
INLA
Mauritania
Random forest
title_short Modelling drivers of trawl fisheries discards using Bayesian spatio-temporal models
title_full Modelling drivers of trawl fisheries discards using Bayesian spatio-temporal models
title_fullStr Modelling drivers of trawl fisheries discards using Bayesian spatio-temporal models
title_full_unstemmed Modelling drivers of trawl fisheries discards using Bayesian spatio-temporal models
title_sort Modelling drivers of trawl fisheries discards using Bayesian spatio-temporal models
dc.creator.none.fl_str_mv Soto-Ruiz, María
Fernández-Peralta, Lourdes
Rey-Sanz, Javier
Czerwinski, Ivone A.
García-Cancela, Ramón
Llope, Marcos
Liébana, María
Pennino, Maria Grazia
author Soto-Ruiz, María
author_facet Soto-Ruiz, María
Fernández-Peralta, Lourdes
Rey-Sanz, Javier
Czerwinski, Ivone A.
García-Cancela, Ramón
Llope, Marcos
Liébana, María
Pennino, Maria Grazia
author_role author
author2 Fernández-Peralta, Lourdes
Rey-Sanz, Javier
Czerwinski, Ivone A.
García-Cancela, Ramón
Llope, Marcos
Liébana, María
Pennino, Maria Grazia
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Agricultura, Pesca y Alimentación (España)
Consejo Superior de Investigaciones Científicas (España)
CSIC - Instituto Español de Oceanografía (IEO)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Bayesian hierarchical modelling
Discards
Ecosystem-based fisheries management
Spatial management tool
INLA
Mauritania
Random forest
topic Bayesian hierarchical modelling
Discards
Ecosystem-based fisheries management
Spatial management tool
INLA
Mauritania
Random forest
description Effective spatial fisheries management requires a proper understanding of the spatial distribution of both target species and discards. Also, spatial modelling of fishery-dependent data is an effective tool to capture uncertainties in data-limited situations. This study analyses the drivers behind discarding by comparing the standardising properties of three different components: Total Discards, Discards Per Unit of Effort and Total Discard Ratio. These metrics were analysed by means of Bayesian hierarchical spatio-temporal Gamma regression models to correctly to identify areas with high discards values that are characterized as discards hotspots. Our results showed that Total Discards is the component which better quantified the aggregated ecological impact of discarding practices, whereas Total Discard Ratio and Discards Per Unit of Effort identify complementary issues of benefits versus loss of biomass. Spatial maps obtained by combining these three approaches are a powerful tool for the spatial management of discards.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
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/10261/344330
https://api.elsevier.com/content/abstract/scopus_id/85169038459
url http://hdl.handle.net/10261/344330
https://api.elsevier.com/content/abstract/scopus_id/85169038459
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Fisheries Research
https://doi.org/10.1016/j.fishres.2023.106830
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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 15,811543

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