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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Main Authors: Correa, Giancarlo M., Ciannelli, Lorenzo, Barnett, Lewis A.K., Kotwicki, Stan, Fuentes, Claudio
Format: Article in Journal/Newspaper
Language:unknown
Published: NRC Research Press (a division of Canadian Science Publishing) 2020
Subjects:
Online Access:http://hdl.handle.net/1807/102374
http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2020-0166
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spelling 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
institution Open Polar
collection University of Toronto: Research Repository T-Space
op_collection_id ftunivtoronto
language unknown
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
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