Artificial neural networks to forecast biomass of Pacific sardine and its environment

We tested the forecasting performance of artificial neural networks (ANNs) using several time series of environmental and biotic data pertaining to the California Current (CC) neritic ecosystem. ANNs performed well predicting CC monthly 10-m depth temperature up to nine years in advance, using temperature recorded at Scripps Institution of Oceanography pier. Annual spawning biomass of Pacific sardine (Sardinops sagax caeruleus) was forecasted reasonably well one year in advance using time series of water temperature, wind speed cubed, egg and larval abundance, commercial catch, and spawning biomass of northern anchovy (Engraulis mordax) and Pacific sardine as predictors. We discuss our results and focus on the philosophy and potential problems faced during ANN modelling.