Identification and Forecast of Potential Fishing Grounds for Anchovy (Engraulis ringens) in Northern Chile Using Neural Networks Modeling

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.

Neural network approach for detecting spatial changes in catch probability of Engraulis ringens during El Niño‐Southern Oscillation events in northern Chile

“…This study aimed to understand the spatial changes of E. ringens during El Niño, La Niña, and Neutral event; these have direct influence in the probable fishing areas and the landings of the industrial fleet, which has been decreasing in recent years (Figure 2). We used a neural network model which has proven it efficacy in representing nonlinear processes (Armas et al, 2022;Tan & Beklioglu, 2006) such as those of the relation between oceanographic variables and the distribution of species in the ocean (Wang et al, 2015). This approach, requiring few assumptions about fishery and environmental data, outperforms conventional statistical models such as generalized linear models (GLM) and generalized additive models (GAM) (Mateo et al, 2011;Suryanarayana et al, 2008;Tan & Beklioglu, 2006;Wang et al, 2015).…”

“…This study goes a step further than Armas et al (2022), adopting a longer-term temporal perspective at a monthly and quarterly scale, exploring changes in the spatio-temporal distribution of E. ringens in response to extreme El Niño and La Niña events. This approach provides a robust foundation for medium-term analysis and decisionmaking in fisheries management, expanding the utility of the model beyond short-term predictions.…”

“…During these events, the schools of E. ringens are more disperse, even if there is greater biomass (Gutiérrez et al, 2007). (Armas et al, 2022;Böhm et al, 2017). The National Oceanic and Atmospheric Administration (NOAA) declared that a La Niña event began to be observed in June-September 2020, with an anomaly of À0.5 C below the historical mean ("Climate Prediction Center -ONI", 2022, "October 2020 La Niña updatejNOAA Climate.gov", 2020); it lasted until December 2022.…”

“…Recently, Armas et al (2022) demonstrated the efficacy of neural network models to identify areas with high CP of E. ringens by the industrial seine fleet in northern Chile in the short term (1-3 days).…”

Fishing Area Prediction Using Scene-Based Ensemble Models

“…However, recently, the world fishery area is facing increased pressure from a range of factors, including overfishing, climate change, pollution, and habitat degradation [6]. This problem held great importance due to the connection between the support received by the fishing industry and the overexploitation of marine resources [7,8].…”