Northwest Fisheries Science Center

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Document Type: Journal Article
Center: NWFSC
Document ID: 7405
Title: Relative magnitude of cohort, age, and year effects on size at age of exploited marine fishes
Author: James T. Thorson, Carolina Minte-Vera
Publication Year: 2016
Journal: Fisheries Research
Volume: 180
Pages: 45-53
DOI: doi:10.1016/j.fishres.2014.11.016
Keywords: time-varying growth, random effect, hierarchical model, weight at age, biphasic growth, von Bertlanffy growth function, empirical weight at age,

Variation in individual growth rates contributes to changes over time in compensatory population growth and surplus production for marine fishes. However, there is little evidence regarding the prevalence and magnitude of time-varying growth for exploited marine fishes in general, whether it is best approximated using changes in length-at-age or weight-at-length parameters, or how it can be represented parsimoniously. We therefore use a database of average weight in each year and age for 91 marine fish stocks from 25 species, and fit models with random variation in length and weight parameters by year, age, or cohort (birth-year). Results show that year effects are more parsimonious than age or cohort effects and that variation in length and weight parameters provide roughly similar fit to average weight-at-age data, although length parameters show a greater magnitude of variability than weight parameters. Finally, the saturated model can explain nearly 2/3 of total variability, while a single time-varying factor can explain nearly 1/2 of variability in weight-at-age data. We conclude that time-varying growth can often be estimated parsimoniously using a single time-varying factor, either internally or prior to including ‘empirical’ weight at age in population dynamics models.

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Theme: Recovery and rebuilding of marine and coastal species
Foci: Develop methods to use physiological, biological and behavioral information to predict population-level processes.
Characterize the population biology of species, and develop and improve methods for predicting the status of populations.