Comparative Analysis of Prediction Models for Trawling Grounds of the Argentine Shortfin Squid <i>Illex argentinus</i> in the Southwest Atlantic High Seas Based on Vessel Position and Fishing Log Data
To evaluate and compare the effectiveness of prediction models for Argentine squid <i>Illex argentinus</i> trawling grounds in the Southwest Atlantic high seas based on vessel position and fishing log data, this study used AIS datasets and fishing log datasets from fishing seasons spanni...
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2025-01-01
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| author | Delong Xiang Yuyan Sun Hanji Zhu Jianhua Wang Sisi Huang Shengmao Zhang Famou Zhang Heng Zhang |
| author_facet | Delong Xiang Yuyan Sun Hanji Zhu Jianhua Wang Sisi Huang Shengmao Zhang Famou Zhang Heng Zhang |
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| description | To evaluate and compare the effectiveness of prediction models for Argentine squid <i>Illex argentinus</i> trawling grounds in the Southwest Atlantic high seas based on vessel position and fishing log data, this study used AIS datasets and fishing log datasets from fishing seasons spanning 2019–2024 (December to June each year). Using a spatial resolution of 0.1° × 0.1° and a monthly temporal resolution, we constructed two datasets—one based on vessel positions and the other on fishing logs. Fishing ground levels were defined according to the density of fishing locations, and combined with oceanographic data (sea surface temperature, 50 m water temperature, sea surface salinity, sea surface height, and mixed layer depth). A CNN-Attention deep learning model was applied to each dataset to develop <i>Illex argentinus</i> trawling ground prediction models. Model accuracy was then compared and potential causes for differences were analyzed. Results showed that the vessel position-based model had a higher accuracy (Accuracy = 0.813) and lower loss rate (Loss = 0.407) than the fishing log-based model (Accuracy = 0.727, Loss = 0.513). The vessel-based model achieved a prediction accuracy of 0.763 on the 2024 test set, while the fishing log-based model reached an accuracy of 0.712, slightly lower than the former, indicating the high accuracy and unique advantages of the vessel position-based model in predicting fishing grounds. Using CPUE from fishing logs as a reference, we found that the vessel position-based model performed well from January to April, whereas the CPUE-based model consistently maintained good accuracy across all months. The 2024 fishing season predictions indicated the formation of primary fishing grounds as early as January 2023, initially near the 46° S line of the Argentine Exclusive Economic Zone, with grounds shifting southeastward from March onward and reaching around 42° S by May and June. This study confirms the reliability of vessel position data in identifying fishing ground information and levels, with higher accuracy in some months compared to the fishing log-based model, thereby reducing the data lag associated with fishing logs, which are typically available a year later. Additionally, national-level fishing log data are often confidential, limiting the ability to fully consider fishing activities across the entire fishing ground region, a limitation effectively addressed by AIS vessel position data. While vessel data reflects daily catch volumes across vessels without distinguishing CPUE by species, log data provide a detailed daily CPUE breakdown by species (e.g., <i>Illex argentinus</i>). This distinction resulted in lower accuracy for vessel-based predictions in December 2023 and May–June 2024, suggesting the need to incorporate fishing log data for more precise assessments of fishing ground levels or resource abundance during those months. Given the near-real-time nature of vessel position data, fishing ground dynamics can be monitored in near real time. The successful development of vessel position-based prediction models aids enterprises in reducing fuel and time costs associated with indiscriminate squid searches, enhancing trawling efficiency. Additionally, such models support quota management in global fisheries by optimizing resource use, reducing fishing time, and consequently lowering carbon emissions and environmental impact, while promoting marine environmental protection in the Southwest Atlantic high seas. |
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| spelling | doaj-art-a12dd7b7d63e474c940d8182ef3ed1262025-01-24T13:23:23ZengMDPI AGBiology2079-77372025-01-011413510.3390/biology14010035Comparative Analysis of Prediction Models for Trawling Grounds of the Argentine Shortfin Squid <i>Illex argentinus</i> in the Southwest Atlantic High Seas Based on Vessel Position and Fishing Log DataDelong Xiang0Yuyan Sun1Hanji Zhu2Jianhua Wang3Sisi Huang4Shengmao Zhang5Famou Zhang6Heng Zhang7Key Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, ChinaKey Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, ChinaKey Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, ChinaKey Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, ChinaKey Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, ChinaKey Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, ChinaKey Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, ChinaKey Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, ChinaTo evaluate and compare the effectiveness of prediction models for Argentine squid <i>Illex argentinus</i> trawling grounds in the Southwest Atlantic high seas based on vessel position and fishing log data, this study used AIS datasets and fishing log datasets from fishing seasons spanning 2019–2024 (December to June each year). Using a spatial resolution of 0.1° × 0.1° and a monthly temporal resolution, we constructed two datasets—one based on vessel positions and the other on fishing logs. Fishing ground levels were defined according to the density of fishing locations, and combined with oceanographic data (sea surface temperature, 50 m water temperature, sea surface salinity, sea surface height, and mixed layer depth). A CNN-Attention deep learning model was applied to each dataset to develop <i>Illex argentinus</i> trawling ground prediction models. Model accuracy was then compared and potential causes for differences were analyzed. Results showed that the vessel position-based model had a higher accuracy (Accuracy = 0.813) and lower loss rate (Loss = 0.407) than the fishing log-based model (Accuracy = 0.727, Loss = 0.513). The vessel-based model achieved a prediction accuracy of 0.763 on the 2024 test set, while the fishing log-based model reached an accuracy of 0.712, slightly lower than the former, indicating the high accuracy and unique advantages of the vessel position-based model in predicting fishing grounds. Using CPUE from fishing logs as a reference, we found that the vessel position-based model performed well from January to April, whereas the CPUE-based model consistently maintained good accuracy across all months. The 2024 fishing season predictions indicated the formation of primary fishing grounds as early as January 2023, initially near the 46° S line of the Argentine Exclusive Economic Zone, with grounds shifting southeastward from March onward and reaching around 42° S by May and June. This study confirms the reliability of vessel position data in identifying fishing ground information and levels, with higher accuracy in some months compared to the fishing log-based model, thereby reducing the data lag associated with fishing logs, which are typically available a year later. Additionally, national-level fishing log data are often confidential, limiting the ability to fully consider fishing activities across the entire fishing ground region, a limitation effectively addressed by AIS vessel position data. While vessel data reflects daily catch volumes across vessels without distinguishing CPUE by species, log data provide a detailed daily CPUE breakdown by species (e.g., <i>Illex argentinus</i>). This distinction resulted in lower accuracy for vessel-based predictions in December 2023 and May–June 2024, suggesting the need to incorporate fishing log data for more precise assessments of fishing ground levels or resource abundance during those months. Given the near-real-time nature of vessel position data, fishing ground dynamics can be monitored in near real time. The successful development of vessel position-based prediction models aids enterprises in reducing fuel and time costs associated with indiscriminate squid searches, enhancing trawling efficiency. Additionally, such models support quota management in global fisheries by optimizing resource use, reducing fishing time, and consequently lowering carbon emissions and environmental impact, while promoting marine environmental protection in the Southwest Atlantic high seas.https://www.mdpi.com/2079-7737/14/1/35Southwest Atlantic<i>Illex argentinus</i>AISdeep learningfishing ground prediction |
| spellingShingle | Delong Xiang Yuyan Sun Hanji Zhu Jianhua Wang Sisi Huang Shengmao Zhang Famou Zhang Heng Zhang Comparative Analysis of Prediction Models for Trawling Grounds of the Argentine Shortfin Squid <i>Illex argentinus</i> in the Southwest Atlantic High Seas Based on Vessel Position and Fishing Log Data Biology Southwest Atlantic <i>Illex argentinus</i> AIS deep learning fishing ground prediction |
| title | Comparative Analysis of Prediction Models for Trawling Grounds of the Argentine Shortfin Squid <i>Illex argentinus</i> in the Southwest Atlantic High Seas Based on Vessel Position and Fishing Log Data |
| title_full | Comparative Analysis of Prediction Models for Trawling Grounds of the Argentine Shortfin Squid <i>Illex argentinus</i> in the Southwest Atlantic High Seas Based on Vessel Position and Fishing Log Data |
| title_fullStr | Comparative Analysis of Prediction Models for Trawling Grounds of the Argentine Shortfin Squid <i>Illex argentinus</i> in the Southwest Atlantic High Seas Based on Vessel Position and Fishing Log Data |
| title_full_unstemmed | Comparative Analysis of Prediction Models for Trawling Grounds of the Argentine Shortfin Squid <i>Illex argentinus</i> in the Southwest Atlantic High Seas Based on Vessel Position and Fishing Log Data |
| title_short | Comparative Analysis of Prediction Models for Trawling Grounds of the Argentine Shortfin Squid <i>Illex argentinus</i> in the Southwest Atlantic High Seas Based on Vessel Position and Fishing Log Data |
| title_sort | comparative analysis of prediction models for trawling grounds of the argentine shortfin squid i illex argentinus i in the southwest atlantic high seas based on vessel position and fishing log data |
| topic | Southwest Atlantic <i>Illex argentinus</i> AIS deep learning fishing ground prediction |
| url | https://www.mdpi.com/2079-7737/14/1/35 |
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