A Bayesian spatially explicit estimation of daily egg production: application to anchovy in the Bay of Biscay

Abstract

Biomass estimates of fish resources by the daily egg production method (DEPM) are sensitive to the high variability of the daily egg production (P0) and egg mortality (Z) in space. This work presents a Bayesian approach to estimate these parameters. A prior distribution of Z based on literature serves to overcome the biologically implausible Z estimates that can result from frequentist approaches. In addition to the classical estimation of a single P0 over the spawning area, the Bayesian framework allows also the modelling of egg densities in space, by including either spatial random effects, smoothing functions, or kriging like models, providing insights into the spatial variability of P0. The Bayesian approach was applied to the Bay of Biscay anchovy DEPM surveys. Results showed that this Bayesian approximation solved the implausible Z problem resulting in tighter credible intervals of both P0 and Z. Overall, spatial models outperformed the non-spatial model in terms of goodness of fit and resulted in slightly different total production estimates across models for each year, with a moderate decrease on uncertainty estimates.