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Prediction of the Relative Resource Abundance of the Argentine Shortfin Squid Illex argentinus in the High Sea in the Southwest Atlantic Based on a Deep Learning Model

文献类型： 外文期刊

作者： Xiang, Delong 1 ; Sun, Yuyan 1 ; Zhu, Hanji 1 ; Wang, Jianhua 1 ; Huang, Sisi 1 ; Han, Haibin 1 ; Zhang, Shengmao 1 ; Shang, Chen 1 ; Zhang, Heng 1 ;

作者机构： 1.Chinese Acad Fishery Sci, East China Sea Fisheries Res Inst, Key Lab Ocean & Polar Fisheries, Minist Agr & Rural Affairs, Shanghai 200090, Peoples R China

2.Qingdao Marine Sci & Technol Ctr, Laoshan Lab, Qingdao 266104, Peoples R China

3.Shanghai Ocean Univ, Coll Marine Living Resource Sci & Management, Shanghai 201308, Peoples R China

4.Dalian Ocean Univ, Coll Nav & Ship Engn, Dalian 116023, Peoples R China

关键词： Illex argentinus; southwest Atlantic; ensemble learning; deep learning; relative resource abundance

期刊名称：ANIMALS （ 影响因子：2.7； 五年影响因子：3.2 ）

ISSN： 2076-2615

年卷期： 2024 年 14 卷 21 期

页码：

收录情况： SCI

摘要： To analyze the impact of the marine environment on the relative abundance of Illex argentinus (high and low categories) in the southwest Atlantic, this study collected logbook data from Chinese pelagic trawlers from December 2014 to June 2024, including vessel position data and oceanographic variables such as sea surface temperature, 50 m and 100 m water temperature, sea surface salinity, sea surface height, chlorophyll-a concentration, and mixed layer depth. Vessel positions were used to enhance the logbook data quality, allowing an analysis of the annual trends in the resource center of this squid at a spatial resolution of 0.1 degrees x 0.1 degrees and a temporal resolution of ten days. The findings showed that the resource center is primarily located around 42 degrees S in the north and between 45 degrees S and 47 degrees S in the south, with a trend of northward movement during the study period. Additionally, we constructed two ensemble learning models based on decision trees-AdaBoost and PSO-RF-aiming to identify the most critical environmental factors that affect its resource abundance; we found that the optimal model was the PSO-RF model with max_depth of 5 and n_estimators of 46. The importance analysis revealed that sea surface temperature, mixed layer depth, sea surface height, sea surface salinity, and 50 m water temperature are critical environmental factors affecting this species' resources. Given that deep learning models generally have shorter running times and higher accuracy than other models, we developed a CNN-Attention model based on the five most important input factors. This model achieved an accuracy of 73.6% in forecasting this squid for 2024, predicting that the population would first appear near the Argentine exclusive economic zone around mid-December 2023 and gradually move east and south thereafter. The predictions of the model, validated through log data, maintained over 70% accuracy during most periods at a time scale of ten days. The successful construction of the resource abundance forecasting model and its accuracy improvements can help enterprises save fuel and time costs associated with blind searches for target species. Moreover, this research contributes to improving resource utilization efficiency and reducing fishing duration, thereby aiding in lowering carbon emissions from pelagic trawling activities, offering valuable insights for the sustainable development of this species' resources.

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