Recruitment forecasting of yellowfin tuna in the eastern Pacific Ocean with artificial neuronal networks

Abstract The recruitment of yellowfin tuna in the eastern Pacific Ocean is modeled based on oceanographic as well as biological parameters, using two nonlinear autoregressive network models with exogenous inputs (NARX). In the first model (Model 1) the quarterly recruitment is modeled considering eastern Pacific global oceanographic conditions: the Southern Oscillation Index (SOI), the Pacific Decadal Oscillation (PDO), and spawners biomass. In Model 2, recruitment is predicted based on sea surface temperature, wind magnitude, and oceanic current magnitude of a smaller area within the eastern Pacific Ocean, considered as relevant for spawning and recruitment, and total spawners biomass. The correlation coefficient between the ANN recruitment estimate and the “real” recruitment is r > 0.80 in both models. Series of sensitivity analysis suggest that the SOI and the sea surface temperature are the most important variables for the recruitment in Model 1 and Model 2 also show that warm sea surface favors recruitment. A forecasting model under different climatological scenarios indicates that the recruitment of yellowfin tuna could be higher in the period 2015–2020 compared to the ones registered in the period 2009–2013.