Longitudinal analyses of catch-at-age data for reconstructing year-class strength, with an application to lake trout ( Salvelinus namaycush ) in the main basin of Lake Huron
We investigated using longitudinal models to reconstruct year-class strength (YCS) from catch-at-age data, with an example application to lake trout ( Salvelinus namaycush) in the main basin of Lake Huron. The best model structure depended on the age range used for model implementation. The YCS traj...
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Published in | Canadian journal of fisheries and aquatic sciences Vol. 80; no. 1; pp. 183 - 194 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Ottawa
NRC Research Press
01.01.2023
Canadian Science Publishing NRC Research Press |
Subjects | |
Online Access | Get full text |
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Summary: | We investigated using longitudinal models to reconstruct year-class strength (YCS) from catch-at-age data, with an example application to lake trout ( Salvelinus namaycush) in the main basin of Lake Huron. The best model structure depended on the age range used for model implementation. The YCS trajectory from the full age range (3–30 years) was similar to the trajectory from a narrow age range that approximated the age of recruitment to the fishing gears (5–7 years), but YCS estimates from the full age range included additional variations due to time-dependent selectivity and mortality. When using ages younger or older than the likely ages of recruitment, YCS estimates did not represent recruitment abundances and were also biased by trends in age-specific selectivity and mortality across years. Longitudinal YCS estimates are likely more robust than single-age recruitment indices, which are often subject to interannual changes in catchability and selectivity. Our findings provide guidance for future applications of the longitudinal YCS reconstruction that in turn may inform and supplement more comprehensive research and management programs for understanding fish recruitment dynamics. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 0706-652X 1205-7533 |
DOI: | 10.1139/cjfas-2022-0140 |