Modelling Fish Growth with Imperfect Data: The Case of Trachurus picturatus

Growth modelling is essential to inform fisheries management but often hampered by sampling biases and imperfect data. Additional methods such as interpolating data through back-calculation may be used account for bias are complex time-consuming. Here, we present an approach improve plausibility in growth estimates when small individuals under-sampled, based on Bayesian fitting models using Markov Chain Monte Carlo (MCMC) with informative priors parameters. Focusing the blue jack mackerel, Trachurus picturatus, which important commercial fish southern northeast Atlantic, this was evaluated relation standard model methods, both direct readings Matched parameter were obtained von Bertalanffy function applied back-calculated length at age fitting, MCMC readings, outperforming all other assessed. These results indicate that inference a powerful addition should considered further studies, provided relevant biological information can gathered included analyses.