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Trends in Growth Modeling in Fisheries Science

Department of Oceanography and Coastal Sciences, College of the Coast and Environment, Louisiana State University, Baton Rouge, LA 70803, USA
Quantitative Fisheries Center, Department of Fisheries and Wildlife, College of Agriculture and Natural Resources, Michigan State University, East Lansing, MI 48824, USA
Author to whom correspondence should be addressed.
Received: 30 October 2020 / Revised: 21 December 2020 / Accepted: 7 January 2021 / Published: 19 January 2021
Growth models estimate life history parameters (e.g., growth rates and asymptotic size) that are used in the management of fisheries stocks. Traditionally in fisheries science, it was common to fit one growth model—the von Bertalanffy growth model—to size-at-age data. However, in recent years, fisheries science has seen an increase in the number of growth models available and the evaluation of multiple growth models for a given species or study. We reviewed n = 196 peer-reviewed age and growth studies and n = 50 NOAA (National Oceanic and Atmospheric Administration) regional stock assessments to examine trends in the use of growth models and model selection in fisheries over time. Our results indicate that the total number of age and growth studies increased annually since 1988 with a slight proportional increase in the use of multi-model frameworks. Information theoretic approaches are replacing goodness-of-fit and a priori model selection in fisheries studies; however, this trend is not reflected in NOAA stock assessments, which almost exclusively rely on the von Bertalanffy growth model. Covariates such as system (e.g., marine or fresh), location of study, diet, family, maximum age, and range of age data used in model fitting did not contribute to which model was ultimately the best fitting, suggesting that there are no large-scale patterns of specific growth models being applied to species with common life histories or other attributes. Given the importance and ubiquity of growth modeling to fisheries science, a historical and contemporary understanding of the practice is critical to evaluate improvements that have been made and future challenges. View Full-Text
Keywords: Akaike’s information criteria (AIC); fish growth; model selection; multi-model inference; stock assessment; von Bertalanffy Akaike’s information criteria (AIC); fish growth; model selection; multi-model inference; stock assessment; von Bertalanffy
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MDPI and ACS Style

Flinn, S.A.; Midway, S.R. Trends in Growth Modeling in Fisheries Science. Fishes 2021, 6, 1.

AMA Style

Flinn SA, Midway SR. Trends in Growth Modeling in Fisheries Science. Fishes. 2021; 6(1):1.

Chicago/Turabian Style

Flinn, Shane A., and Stephen R. Midway 2021. "Trends in Growth Modeling in Fisheries Science" Fishes 6, no. 1: 1.

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