Decoding Growth Parameters of Small Pelagics: A Critical Examination of Model Effectiveness with a Focus on the European Anchovy

Margarita María María Rincón Hidalgo ^1,2* MariAngeles Gamaza Márquez ^1,2 María José Zúñiga Basualto ^1,2 Fernando Ramos ^1,2 Jorge Tornero ^1,2

• ¹ Oceanographic Center of Cádiz,Spanish Institute of Oceanography (IEO), Cádiz, Spain
• ² Spanish National Research Council (CSIC), Madrid, Madrid, Spain

Traditionally, parameters defining life history traits, such as growth, were solely determined through length or age-length databases and then included as fixed in integrated stock assessment models. In current practice, growth parameters are usually estimated within these models (“inside”) and fitted to other datasets. However, for short-lived and small pelagic species, challenges may arise, particularly when there is a high variability in the age-length data or sampling biases are inadequately identified or addressed by these models.To test model effectiveness in capturing the growth dynamics of these species, we propose a comparative analysis following recommended practices for incorporating age-length data into integrated stock assessment models for the specific case of anchovy (Engraulis encrasicolus) stock in the Gulf of Cadiz. The reason is twofold: its significant ecological and economic importance, and the need to improve the accuracy of growth parameter estimates used to inform Total Allowable Catch (TAC) scientific advice.The overarching goal of this analysis is to identify the optimal model configuration that provides accurate growth parameter estimates. Our approach shows that random effects can effectively estimate growth in species with high age-length variability. Furthermore, using the obtained estimates as fixed in the stock assessment model, reduces computational time and enhances the goodness of fit, resulting in a more efficient model. The results address a significant gap in existing integrated models used for scientific advice, which often do not have the “random effects on parameters” feature. Notably, this framework is widely applicable to other short-lived small pelagic species that typically exhibit a high data variability, offering a valuable solution for improving efficiency and robustness in fisheries management decision-making.