Climate based multi-year predictions of the Barents Sea cod stock

Predicting fish stock variations on interannual to decadal time scales is one of the major issues in fisheries science and management. Although the field of marine ecological predictions is still in its infancy, it is understood that a major source of multi-year predictability resides in the ocean....

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Published in:PLOS ONE
Main Authors: Årthun, Marius, Bogstad, Bjarte, Daewel, Ute, Keenlyside, Noel S., Sandø, Anne Britt, Schrum, Corinna, Ottersen, Geir
Format: Text
Language:English
Published: Public Library of Science 2018
Subjects:
Online Access:http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6200261/
http://www.ncbi.nlm.nih.gov/pubmed/30356300
https://doi.org/10.1371/journal.pone.0206319
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spelling ftpubmed:oai:pubmedcentral.nih.gov:6200261 2023-05-15T15:38:30+02:00 Climate based multi-year predictions of the Barents Sea cod stock Årthun, Marius Bogstad, Bjarte Daewel, Ute Keenlyside, Noel S. Sandø, Anne Britt Schrum, Corinna Ottersen, Geir 2018-10-24 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6200261/ http://www.ncbi.nlm.nih.gov/pubmed/30356300 https://doi.org/10.1371/journal.pone.0206319 en eng Public Library of Science http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6200261/ http://www.ncbi.nlm.nih.gov/pubmed/30356300 http://dx.doi.org/10.1371/journal.pone.0206319 © 2018 Årthun et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. CC-BY Research Article Text 2018 ftpubmed https://doi.org/10.1371/journal.pone.0206319 2018-11-25T01:11:49Z Predicting fish stock variations on interannual to decadal time scales is one of the major issues in fisheries science and management. Although the field of marine ecological predictions is still in its infancy, it is understood that a major source of multi-year predictability resides in the ocean. Here we show the first highly skilful long-term predictions of the commercially valuable Barents Sea cod stock. The 7-year predictions are based on the propagation of ocean temperature anomalies from the subpolar North Atlantic toward the Barents Sea, and the strong co-variability between these temperature anomalies and the cod stock. Retrospective predictions for the period 1957–2017 capture well multi-year to decadal variations in cod stock biomass, with cross-validated explained variance of over 60%. For lead times longer than one year the statistical long-term predictions show more skill than operational short-term predictions used in fisheries management and lagged persistence forecasts. Our results thus demonstrate the potential for ecosystem-based fisheries management, which could enable strategic planning on longer time scales. Future predictions show a gradual decline in the cod stock towards 2024. Text Barents Sea North Atlantic PubMed Central (PMC) Barents Sea PLOS ONE 13 10 e0206319
institution Open Polar
collection PubMed Central (PMC)
op_collection_id ftpubmed
language English
topic Research Article
spellingShingle Research Article
Årthun, Marius
Bogstad, Bjarte
Daewel, Ute
Keenlyside, Noel S.
Sandø, Anne Britt
Schrum, Corinna
Ottersen, Geir
Climate based multi-year predictions of the Barents Sea cod stock
topic_facet Research Article
description Predicting fish stock variations on interannual to decadal time scales is one of the major issues in fisheries science and management. Although the field of marine ecological predictions is still in its infancy, it is understood that a major source of multi-year predictability resides in the ocean. Here we show the first highly skilful long-term predictions of the commercially valuable Barents Sea cod stock. The 7-year predictions are based on the propagation of ocean temperature anomalies from the subpolar North Atlantic toward the Barents Sea, and the strong co-variability between these temperature anomalies and the cod stock. Retrospective predictions for the period 1957–2017 capture well multi-year to decadal variations in cod stock biomass, with cross-validated explained variance of over 60%. For lead times longer than one year the statistical long-term predictions show more skill than operational short-term predictions used in fisheries management and lagged persistence forecasts. Our results thus demonstrate the potential for ecosystem-based fisheries management, which could enable strategic planning on longer time scales. Future predictions show a gradual decline in the cod stock towards 2024.
format Text
author Årthun, Marius
Bogstad, Bjarte
Daewel, Ute
Keenlyside, Noel S.
Sandø, Anne Britt
Schrum, Corinna
Ottersen, Geir
author_facet Årthun, Marius
Bogstad, Bjarte
Daewel, Ute
Keenlyside, Noel S.
Sandø, Anne Britt
Schrum, Corinna
Ottersen, Geir
author_sort Årthun, Marius
title Climate based multi-year predictions of the Barents Sea cod stock
title_short Climate based multi-year predictions of the Barents Sea cod stock
title_full Climate based multi-year predictions of the Barents Sea cod stock
title_fullStr Climate based multi-year predictions of the Barents Sea cod stock
title_full_unstemmed Climate based multi-year predictions of the Barents Sea cod stock
title_sort climate based multi-year predictions of the barents sea cod stock
publisher Public Library of Science
publishDate 2018
url http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6200261/
http://www.ncbi.nlm.nih.gov/pubmed/30356300
https://doi.org/10.1371/journal.pone.0206319
geographic Barents Sea
geographic_facet Barents Sea
genre Barents Sea
North Atlantic
genre_facet Barents Sea
North Atlantic
op_relation http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6200261/
http://www.ncbi.nlm.nih.gov/pubmed/30356300
http://dx.doi.org/10.1371/journal.pone.0206319
op_rights © 2018 Årthun et al
http://creativecommons.org/licenses/by/4.0/
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
op_rightsnorm CC-BY
op_doi https://doi.org/10.1371/journal.pone.0206319
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