Modelling fish growth with imperfect data: The case of Trachurus picturatus

• Published in: Fishes
• Main Authors: Neves, Ana, Vieira, Ana Rita, Sequeira, Vera, Silva, Elisabete, Silva, Frederica, Duarte, Ana Marta, Mendes, Susana, Ganhão, Rui, Assis, Carlos, Rebelo, Rui, Magalhães, Maria Filomena, Gil, Maria Manuel, Gordo, Leonel Serrano
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI 2022
• Subjects: Bayesian; Carangidae; Fisheries; Mortality; Northeast Atlantic
• Online Access: http://hdl.handle.net/10400.8/7566; https://doi.org/10.3390/fishes7010052

Funding: This publication was funded by the European Maritime and Fisheries Fund MAR2020 project “VALOREJET: Valorização de espécies rejeitadas e de baixo valor comercial”, MAR-01.03.01-FEAMP-0003 and by Fundação para a Ciência e Tecnologia through research contracts attributed to Vera Sequeira (CEECIND/02705/2017) and Ana Rita Vieira (CEECIND/01528/2017) and strategic project UIBD/04292/2020. Growth modelling is essential to inform fisheries management but is often hampered by sampling biases and imperfect data. Additional methods such as interpolating data through backcalculation may be used to account for sampling bias but are often complex and time-consuming. Here, we present an approach to improve plausibility in growth estimates when small individuals are under-sampled, based on Bayesian fitting growth models using Markov Chain Monte Carlo (MCMC) with informative priors on growth parameters. Focusing on the blue jack mackerel, Trachurus picturatus, which is an important commercial fish in the southern northeast Atlantic, this Bayesian approach was evaluated in relation to standard growth model fitting methods, using both direct readings and back-calculation data. Matched growth parameter estimates were obtained with the von Bertalanffy growth function applied to back-calculated length at age and the Bayesian fitting, using MCMC to direct age readings, with both outperforming all other methods assessed. These results indicate that Bayesian inference may be a powerful addition in growth modelling using imperfect data and should be considered further in age and growth studies, provided relevant biological information can be gathered and included in the analyses. info:eu-repo/semantics/publishedVersion