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....
Published in: | PLOS ONE |
---|---|
Main Authors: | , , , , , , |
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 |
id |
ftpubmed:oai:pubmedcentral.nih.gov:6200261 |
---|---|
record_format |
openpolar |
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 |
container_title |
PLOS ONE |
container_volume |
13 |
container_issue |
10 |
container_start_page |
e0206319 |
_version_ |
1766369465773064192 |