Abstract
Engraulis ringens (E. ringens) is a small pelagic fish of which the geographic and bathymetric distribution is conditioned by fluctuations in oceanographic conditions at different time scales (daily, weekly, monthly, annually, supra-annually, and longer) and by fishing. Understanding the organism−environment interactions and predicting the spatial distribution of its schools can improve conservation actions and fishery management, along with the operation of the fleets targeting E. ringens. There is an important fishery of E. ringens in Northern Chile (18°21′ S–26°00′ S), which provides about 80% of the purse seine catch. To identify and predict potential fishing grounds for E. ringens in this system, we implemented a predictive model of fishing grounds based on neural networks, which was trained with the georeferenced data of daily catches by industrial purse sein ships from 2003 to 2020 and information on oceanographic variables (sea surface temperature, salinity, depth of the mixed layer, sea height, and currents) obtained from the Copernicus Marine Enviroment Monitoring Service (CMEMS program). The neural network model had a very good performance (86%). Longitude (23%) was the most relevant variable for identifying potential fishing grounds, followed by the mixed layer depth (18%), latitude (15%), sea surface temperature (12%), month (12%), sea height (9%), salinity (9%), and the zonal and meridional components of the current velocity (2%). The neural network model classified correctly the majority of the areas with and without fishing potential; thus, its use is recommended to predict fishing grounds for E. ringens in the study area. Its application could increase by 88% of the probability of capture anchovy by the purse seine fleet of Northern Chile.