Construction and Comparison of Different Models to Forecast Central Fishing Grounds for Trawl Fishery Targeting Argentine Shortfin Squid (Illex argentinus) in the Southwest Atlantic

Abstract

The abundant Argentine shortfin squid resource plays a key role in the Patagonian Large Marine Ecosystem, the Polar Frontal Zone Ecosystem, and the South Atlantic Subtropical Gyre Ecosystem. In this article, we analyzed the annual and monthly changes in catch per unit effort (CPUE) of Argentine shortfin squid with a spatial resolution of 0.25° × 0.25° and constructed three ensemble learning and two deep learning fishing grounds forecasting models using catch information and spatial–temporal and marine environmental data. The results of the study were as follows: 1. From 2016 to 2021, Argentine shortfin squid in the Southwest Atlantic experienced notable interannual fluctuations, with the resource showing an increase and then remaining stable from 2016 to 2018, a decline in 2019, and a substantial increase from 2020 to 2021. Seasonally, CPUE was low from November to January, rose significantly from February to May, and declined in June; 2. The XGBoost model had the best overall performance among the three tree models, achieving an average of 68.86% accuracy, 70.19% F1-score; 3. In the 2021 actual production data validation, the Fusion ResNet18 model achieved an average production data accuracy of 74.47%, F1-score of 73.85%; the Fusion 3DResNet18 model achieved an average production data accuracy of 81.27%, F1-score of 82.43%. This indicates that convolutional neural networks, particularly 3D versions, are more suitable than decision tree-based ensemble models for predicting Argentine shortfin squid fishing grounds. Highly accurate fishing grounds forecasts help enterprises save production costs while providing some reference for the sustainable development of fishery resources.

Bibliographic Information