Including climate into the assessment of future fish recruitment, using multiple regression models ...
No abstracts are to be cited without prior reference to the author.Climate variability has generally not been included in the assessment of fish stocks in the Barents Sea and Norwegian Sea. However, in recent years there has been a focus on implementing climate variability in the assessment for seve...
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ftdatacite:10.17895/ices.pub.25350346 2024-04-28T08:05:27+00:00 Including climate into the assessment of future fish recruitment, using multiple regression models ... Stiansen, Jan Erik Aglen, Asgeir Bogstad, Bjarte Loeng, Harald Mehl, Sigbjørn Nakken, Odd Ottersen, Geir Svendsen, Einar 2024 https://dx.doi.org/10.17895/ices.pub.25350346 https://ices-library.figshare.com/articles/conference_contribution/Including_climate_into_the_assessment_of_future_fish_recruitment_using_multiple_regression_models/25350346 unknown ASC 2005 - O - Theme session https://ices-library.figshare.com/ICES-ASC-2005/groups ICES Custom Licence https://www.ices.dk/Pages/library_policies.aspx Fisheries and aquaculture Ecosystem observation, processes and dynamics Pressures, impacts, conservation, and management article Other CreativeWork Conference contribution 2024 ftdatacite https://doi.org/10.17895/ices.pub.25350346 2024-04-02T12:00:32Z No abstracts are to be cited without prior reference to the author.Climate variability has generally not been included in the assessment of fish stocks in the Barents Sea and Norwegian Sea. However, in recent years there has been a focus on implementing climate variability in the assessment for several stocks in both areas. A promising approach, using linear multiple regression models, has been applied for short time projections of recruitment of Northeast Arctic cod, Norwegian spring spawning herring and Barents Sea capelin. Environmental factors influence the fish throughout their life history. Time lagged climate variables can be used in combination with stock abundance at younger ages to make models with predictive power 1-3 years ahead. The presented models describe 65-85 % of the variance in the recruitment data. The choice of variables in these kinds of models will always be a trade-off between best possible fit, the presences of a time lag and the possibility of updating the regressions/prognosis as ... Article in Journal/Newspaper Arctic cod Arctic Barents Sea Northeast Arctic cod Norwegian Sea DataCite Metadata Store (German National Library of Science and Technology) |
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Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
Fisheries and aquaculture Ecosystem observation, processes and dynamics Pressures, impacts, conservation, and management |
spellingShingle |
Fisheries and aquaculture Ecosystem observation, processes and dynamics Pressures, impacts, conservation, and management Stiansen, Jan Erik Aglen, Asgeir Bogstad, Bjarte Loeng, Harald Mehl, Sigbjørn Nakken, Odd Ottersen, Geir Svendsen, Einar Including climate into the assessment of future fish recruitment, using multiple regression models ... |
topic_facet |
Fisheries and aquaculture Ecosystem observation, processes and dynamics Pressures, impacts, conservation, and management |
description |
No abstracts are to be cited without prior reference to the author.Climate variability has generally not been included in the assessment of fish stocks in the Barents Sea and Norwegian Sea. However, in recent years there has been a focus on implementing climate variability in the assessment for several stocks in both areas. A promising approach, using linear multiple regression models, has been applied for short time projections of recruitment of Northeast Arctic cod, Norwegian spring spawning herring and Barents Sea capelin. Environmental factors influence the fish throughout their life history. Time lagged climate variables can be used in combination with stock abundance at younger ages to make models with predictive power 1-3 years ahead. The presented models describe 65-85 % of the variance in the recruitment data. The choice of variables in these kinds of models will always be a trade-off between best possible fit, the presences of a time lag and the possibility of updating the regressions/prognosis as ... |
format |
Article in Journal/Newspaper |
author |
Stiansen, Jan Erik Aglen, Asgeir Bogstad, Bjarte Loeng, Harald Mehl, Sigbjørn Nakken, Odd Ottersen, Geir Svendsen, Einar |
author_facet |
Stiansen, Jan Erik Aglen, Asgeir Bogstad, Bjarte Loeng, Harald Mehl, Sigbjørn Nakken, Odd Ottersen, Geir Svendsen, Einar |
author_sort |
Stiansen, Jan Erik |
title |
Including climate into the assessment of future fish recruitment, using multiple regression models ... |
title_short |
Including climate into the assessment of future fish recruitment, using multiple regression models ... |
title_full |
Including climate into the assessment of future fish recruitment, using multiple regression models ... |
title_fullStr |
Including climate into the assessment of future fish recruitment, using multiple regression models ... |
title_full_unstemmed |
Including climate into the assessment of future fish recruitment, using multiple regression models ... |
title_sort |
including climate into the assessment of future fish recruitment, using multiple regression models ... |
publisher |
ASC 2005 - O - Theme session |
publishDate |
2024 |
url |
https://dx.doi.org/10.17895/ices.pub.25350346 https://ices-library.figshare.com/articles/conference_contribution/Including_climate_into_the_assessment_of_future_fish_recruitment_using_multiple_regression_models/25350346 |
genre |
Arctic cod Arctic Barents Sea Northeast Arctic cod Norwegian Sea |
genre_facet |
Arctic cod Arctic Barents Sea Northeast Arctic cod Norwegian Sea |
op_relation |
https://ices-library.figshare.com/ICES-ASC-2005/groups |
op_rights |
ICES Custom Licence https://www.ices.dk/Pages/library_policies.aspx |
op_doi |
https://doi.org/10.17895/ices.pub.25350346 |
_version_ |
1797575484726312960 |