2024
DOI: 10.1016/j.fishres.2023.106894
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Inclusion of ageing error and growth variability using a bootstrap estimation of age composition and conditional age-at-length input sample size for fisheries stock assessment models

Peter-John F. Hulson,
Benjamin C. Williams
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Cited by 3 publications
(2 citation statements)
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“…( 4) summed across strata. Further details on the specific methods the AFSC uses to expand length and age samples to abundance are shown in Hulson et al (2023). The stocks selected for this analysis (Table 1) are all assessed by the AFSC with statistical catch-at-age models and have corresponding expanded age and (or) length composition estimates from the respective bottom trawl surveys.…”
Section: Computing Length and Age Composition From Bottom Trawl Surve...mentioning
confidence: 99%
“…( 4) summed across strata. Further details on the specific methods the AFSC uses to expand length and age samples to abundance are shown in Hulson et al (2023). The stocks selected for this analysis (Table 1) are all assessed by the AFSC with statistical catch-at-age models and have corresponding expanded age and (or) length composition estimates from the respective bottom trawl surveys.…”
Section: Computing Length and Age Composition From Bottom Trawl Surve...mentioning
confidence: 99%
“…It is often important for demographic modelling to account for the uncertainty in the age estimates, especially when sampling probabilities depend on the age of individuals (i.e., when there is 'selectivity'), which is fundamental to fishing (Vasilakopoulos et al, 2020). Correctly accounting for ageing error is therefore still an active part of fisheries research (e.g., Hulson & Williams, 2024). Fournier and Archibald (1982) showed how ageing error in catchat-age data can be accounted for as long as the ageing error is known.…”
Section: Introductionmentioning
confidence: 99%