Can we project changes in fish abundance and distribution in response to climate?
arge‐scale and long‐term changes in fish abundance and distribution in response to climate change have been simulated using both statistical and process‐based models. However, national and regional fisheries management requires also shorter term projections on smaller spatial scales, and these need...
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Online Access: | https://doi.org/10.1111/gcb.15081 |
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ftzenodo:oai:zenodo.org:4545998 2024-09-15T18:24:45+00:00 Can we project changes in fish abundance and distribution in response to climate? Jose A. Fernandes Louise Rutterford Stephen D. Simpson Momme Butenschön Thomas L. Frölicher Andrew Yool William W. L. Cheung Alastair Grant 2020-05-07 https://doi.org/10.1111/gcb.15081 unknown Zenodo https://zenodo.org/communities/eu https://doi.org/10.1111/gcb.15081 oai:zenodo.org:4545998 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Global Change Biology, 26(7), 3891-3905, (2020-05-07) info:eu-repo/semantics/article 2020 ftzenodo https://doi.org/10.1111/gcb.15081 2024-07-26T05:46:12Z arge‐scale and long‐term changes in fish abundance and distribution in response to climate change have been simulated using both statistical and process‐based models. However, national and regional fisheries management requires also shorter term projections on smaller spatial scales, and these need to be validated against fisheries data. A 26‐year time series of fish surveys with high spatial resolution in the North‐East Atlantic provides a unique opportunity to assess the ability of models to correctly simulate the changes in fish distribution and abundance that occurred in response to climate variability and change. We use a dynamic bioclimate envelope model forced by physical–biogeochemical output from eight ocean models to simulate changes in fish abundance and distribution at scales down to a spatial resolution of 0.5°. When comparing with these simulations with annual fish survey data, we found the largest differences at the 0.5° scale. Differences between fishery model runs driven by different biogeochemical models decrease dramatically when results are aggregated to larger scales (e.g. the whole North Sea), to total catches rather than individual species or when the ensemble mean instead of individual simulations are used. Recent improvements in the fidelity of biogeochemical models translate into lower error rates in the fisheries simulations. However, predictions based on different biogeochemical models are often more similar to each other than they are to the survey data, except for some pelagic species. We conclude that model results can be used to guide fisheries management at larger spatial scales, but more caution is needed at smaller scales. Article in Journal/Newspaper North East Atlantic Zenodo Global Change Biology 26 7 3891 3905 |
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arge‐scale and long‐term changes in fish abundance and distribution in response to climate change have been simulated using both statistical and process‐based models. However, national and regional fisheries management requires also shorter term projections on smaller spatial scales, and these need to be validated against fisheries data. A 26‐year time series of fish surveys with high spatial resolution in the North‐East Atlantic provides a unique opportunity to assess the ability of models to correctly simulate the changes in fish distribution and abundance that occurred in response to climate variability and change. We use a dynamic bioclimate envelope model forced by physical–biogeochemical output from eight ocean models to simulate changes in fish abundance and distribution at scales down to a spatial resolution of 0.5°. When comparing with these simulations with annual fish survey data, we found the largest differences at the 0.5° scale. Differences between fishery model runs driven by different biogeochemical models decrease dramatically when results are aggregated to larger scales (e.g. the whole North Sea), to total catches rather than individual species or when the ensemble mean instead of individual simulations are used. Recent improvements in the fidelity of biogeochemical models translate into lower error rates in the fisheries simulations. However, predictions based on different biogeochemical models are often more similar to each other than they are to the survey data, except for some pelagic species. We conclude that model results can be used to guide fisheries management at larger spatial scales, but more caution is needed at smaller scales. |
format |
Article in Journal/Newspaper |
author |
Jose A. Fernandes Louise Rutterford Stephen D. Simpson Momme Butenschön Thomas L. Frölicher Andrew Yool William W. L. Cheung Alastair Grant |
spellingShingle |
Jose A. Fernandes Louise Rutterford Stephen D. Simpson Momme Butenschön Thomas L. Frölicher Andrew Yool William W. L. Cheung Alastair Grant Can we project changes in fish abundance and distribution in response to climate? |
author_facet |
Jose A. Fernandes Louise Rutterford Stephen D. Simpson Momme Butenschön Thomas L. Frölicher Andrew Yool William W. L. Cheung Alastair Grant |
author_sort |
Jose A. Fernandes |
title |
Can we project changes in fish abundance and distribution in response to climate? |
title_short |
Can we project changes in fish abundance and distribution in response to climate? |
title_full |
Can we project changes in fish abundance and distribution in response to climate? |
title_fullStr |
Can we project changes in fish abundance and distribution in response to climate? |
title_full_unstemmed |
Can we project changes in fish abundance and distribution in response to climate? |
title_sort |
can we project changes in fish abundance and distribution in response to climate? |
publisher |
Zenodo |
publishDate |
2020 |
url |
https://doi.org/10.1111/gcb.15081 |
genre |
North East Atlantic |
genre_facet |
North East Atlantic |
op_source |
Global Change Biology, 26(7), 3891-3905, (2020-05-07) |
op_relation |
https://zenodo.org/communities/eu https://doi.org/10.1111/gcb.15081 oai:zenodo.org:4545998 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.1111/gcb.15081 |
container_title |
Global Change Biology |
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26 |
container_issue |
7 |
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3891 |
op_container_end_page |
3905 |
_version_ |
1810465163181031424 |