Improved estimation of age composition by accounting for spatiotemporal variability in somatic growth
Age composition is defined as the proportion of a fish population belonging to each age class and is an informative input to stock assessment models. Variations in somatic growth rates may lead to larger errors in age composition estimates. To reduce this source of error, we compared the performance...
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ftunivtoronto:oai:localhost:1807/102374 2023-05-15T15:43:52+02:00 Improved estimation of age composition by accounting for spatiotemporal variability in somatic growth Correa, Giancarlo M. Ciannelli, Lorenzo Barnett, Lewis A.K. Kotwicki, Stan Fuentes, Claudio 2020-08-03 http://hdl.handle.net/1807/102374 http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2020-0166 unknown NRC Research Press (a division of Canadian Science Publishing) 0706-652X http://hdl.handle.net/1807/102374 http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2020-0166 Article Article Post-Print 2020 ftunivtoronto 2020-10-26T07:21:50Z Age composition is defined as the proportion of a fish population belonging to each age class and is an informative input to stock assessment models. Variations in somatic growth rates may lead to larger errors in age composition estimates. To reduce this source of error, we compared the performance of four methods for estimating age compositions of a simulated fish population: two methods based on age–length keys (ALK, pooled and annual) and two model-based approaches (generalized additive models (GAMs) and continuation ratio logits (CRLs)). CRL was the most robust and precise method, followed by annual ALKs, particularly when significant growth variability was present. We applied these methods to survey age subsample data for Pacific cod (Gadus macrocephalus) in the eastern Bering Sea, estimating age compositions that were then incorporated in its stock assessment model. The model that included age compositions estimated by CRL displayed the highest consistency with other data in the model. CRL approach has utility for estimating age compositions employed in stock assessment models, especially when substantial variation in somatic growth is present. The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author. Article in Journal/Newspaper Bering Sea University of Toronto: Research Repository T-Space Bering Sea Pacific |
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University of Toronto: Research Repository T-Space |
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ftunivtoronto |
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description |
Age composition is defined as the proportion of a fish population belonging to each age class and is an informative input to stock assessment models. Variations in somatic growth rates may lead to larger errors in age composition estimates. To reduce this source of error, we compared the performance of four methods for estimating age compositions of a simulated fish population: two methods based on age–length keys (ALK, pooled and annual) and two model-based approaches (generalized additive models (GAMs) and continuation ratio logits (CRLs)). CRL was the most robust and precise method, followed by annual ALKs, particularly when significant growth variability was present. We applied these methods to survey age subsample data for Pacific cod (Gadus macrocephalus) in the eastern Bering Sea, estimating age compositions that were then incorporated in its stock assessment model. The model that included age compositions estimated by CRL displayed the highest consistency with other data in the model. CRL approach has utility for estimating age compositions employed in stock assessment models, especially when substantial variation in somatic growth is present. The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author. |
format |
Article in Journal/Newspaper |
author |
Correa, Giancarlo M. Ciannelli, Lorenzo Barnett, Lewis A.K. Kotwicki, Stan Fuentes, Claudio |
spellingShingle |
Correa, Giancarlo M. Ciannelli, Lorenzo Barnett, Lewis A.K. Kotwicki, Stan Fuentes, Claudio Improved estimation of age composition by accounting for spatiotemporal variability in somatic growth |
author_facet |
Correa, Giancarlo M. Ciannelli, Lorenzo Barnett, Lewis A.K. Kotwicki, Stan Fuentes, Claudio |
author_sort |
Correa, Giancarlo M. |
title |
Improved estimation of age composition by accounting for spatiotemporal variability in somatic growth |
title_short |
Improved estimation of age composition by accounting for spatiotemporal variability in somatic growth |
title_full |
Improved estimation of age composition by accounting for spatiotemporal variability in somatic growth |
title_fullStr |
Improved estimation of age composition by accounting for spatiotemporal variability in somatic growth |
title_full_unstemmed |
Improved estimation of age composition by accounting for spatiotemporal variability in somatic growth |
title_sort |
improved estimation of age composition by accounting for spatiotemporal variability in somatic growth |
publisher |
NRC Research Press (a division of Canadian Science Publishing) |
publishDate |
2020 |
url |
http://hdl.handle.net/1807/102374 http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2020-0166 |
geographic |
Bering Sea Pacific |
geographic_facet |
Bering Sea Pacific |
genre |
Bering Sea |
genre_facet |
Bering Sea |
op_relation |
0706-652X http://hdl.handle.net/1807/102374 http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2020-0166 |
_version_ |
1766378086245335040 |