A novel statistical approach to deal with spatial bias in maturity ogive estimation

Abstract

The proportion of mature fish at length is one of the most important population attributes when evaluating reproductive potential for fish stock assessment purposes. Bias in maturity ogive parameters can lead to fishery management decisions based on misspecified biological reference points. These parameters can vary spatially and temporally, and this variability should be understood and included in the assessment models. However, integrating this variability becomes challenging when specific spatial-dependent ogives cannot be used in the stock assessment model. Hence, this study proposes a novel use of a multivariate response Bayesian regression model, employing an integrated nested Laplace approximation to estimate a single global maturity ogive using data from various spatial areas. This model explicitly accounts for differences in the sampling process and combines information from different areas to estimate shared maturity ogive parameters using joint-likelihood procedures. The model is applied to the European hake stock in ICES (International Council for the Exploration of the Sea) Divisions 27.8.c and 27.9.a, serving as a practical guide. In this model, we have considered different predictors to handle the relationship between the probability of being mature and the length and year covariates. Our results suggest that the logistic formulation correctly captures the relationship between the probability of being mature and length. For year variability, including a year factor covariate or year random effect in the predictor model produces similar values of goodness of fit measures.