Revealing the effects of environmental and spatio-temporal variables on changes in Japanese sardine (Sardinops melanostictus) high abundance fishing grounds based on interpretable machine learning approach
The construction of accurate and interpretable predictive model for high abundance fishing ground is conducive to better sustainable fisheries production and carbon reduction. This article used refined statistical maps to visualize the spatial and temporal patterns of catch changes based on the 2014...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2024.1503292/full |
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| author | Yongchuang Shi Lei Yan Shengmao Zhang Fenghua Tang Shenglong Yang Wei Fan Haibin Han Yang Dai |
| author_facet | Yongchuang Shi Lei Yan Shengmao Zhang Fenghua Tang Shenglong Yang Wei Fan Haibin Han Yang Dai |
| author_sort | Yongchuang Shi |
| collection | DOAJ |
| description | The construction of accurate and interpretable predictive model for high abundance fishing ground is conducive to better sustainable fisheries production and carbon reduction. This article used refined statistical maps to visualize the spatial and temporal patterns of catch changes based on the 2014-2021 fishery statistics of the Japanese sardine Sardinops melanostictus fishery in the Northwest Pacific Ocean. Three models (XGBoost, LightGBM, and CatBoost) and two variable importance visualization methods (model built-in (split) and SHAP methods) were used for comparative analysis to determine the optimal modeling and visualization strategies. Results: 1) From 2014 to 2021, the annual catch showed an overall increasing trend and peaked at 220,009.063 tons in 2021; the total monthly catch increased and then decreased, with a peak of 76, 033.4944 tons (July), and the catch was mainly concentrated in the regions of 39.5°-43°N and 146.75°-155.75°E; 2) Catboost model predicted better than LightGBM and XGBoost models, with the highest values of accuracy and F1-score, 73.8% and 75.31%, respectively; 3) the overall importance ranking of the model’s built-in method differed significantly from that in the SHAP method, and the overall importance ranking of the spatial variables in the SHAP method increased. Compared to the built-in method, the SHAP method informs the magnitude and direction of the influence of each variable at the global and local levels. The results of the research help us to select the optimal model and the optimal visualization method to construct a prediction model for the Japanese sardine fishing grounds in the Northwest Pacific Ocean, which will provide a scientific basis for the Japanese sardine fishery to achieve environmental and economically sustainable fishery development. |
| format | Article |
| id | doaj-art-87e889fca89349c69c787b99d4cab228 |
| institution | Kabale University |
| issn | 2296-7745 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj-art-87e889fca89349c69c787b99d4cab2282025-01-13T12:23:03ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452025-01-011110.3389/fmars.2024.15032921503292Revealing the effects of environmental and spatio-temporal variables on changes in Japanese sardine (Sardinops melanostictus) high abundance fishing grounds based on interpretable machine learning approachYongchuang Shi0Lei Yan1Shengmao Zhang2Fenghua Tang3Shenglong Yang4Wei Fan5Haibin Han6Yang Dai7Key Laboratory of Fisheries Remote Sensing, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, ChinaKey Laboratory of Sustainable Utilization of Open-sea Fishery, Ministry of Agriculture and Rural Affairs, South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou, ChinaKey Laboratory of Fisheries Remote Sensing, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, ChinaKey Laboratory of Fisheries Remote Sensing, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, ChinaKey Laboratory of Fisheries Remote Sensing, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, ChinaKey Laboratory of Fisheries Remote Sensing, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, ChinaKey Laboratory of Fisheries Remote Sensing, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, ChinaKey Laboratory of Fisheries Remote Sensing, Ministry of Agriculture and Rural Affairs, East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai, ChinaThe construction of accurate and interpretable predictive model for high abundance fishing ground is conducive to better sustainable fisheries production and carbon reduction. This article used refined statistical maps to visualize the spatial and temporal patterns of catch changes based on the 2014-2021 fishery statistics of the Japanese sardine Sardinops melanostictus fishery in the Northwest Pacific Ocean. Three models (XGBoost, LightGBM, and CatBoost) and two variable importance visualization methods (model built-in (split) and SHAP methods) were used for comparative analysis to determine the optimal modeling and visualization strategies. Results: 1) From 2014 to 2021, the annual catch showed an overall increasing trend and peaked at 220,009.063 tons in 2021; the total monthly catch increased and then decreased, with a peak of 76, 033.4944 tons (July), and the catch was mainly concentrated in the regions of 39.5°-43°N and 146.75°-155.75°E; 2) Catboost model predicted better than LightGBM and XGBoost models, with the highest values of accuracy and F1-score, 73.8% and 75.31%, respectively; 3) the overall importance ranking of the model’s built-in method differed significantly from that in the SHAP method, and the overall importance ranking of the spatial variables in the SHAP method increased. Compared to the built-in method, the SHAP method informs the magnitude and direction of the influence of each variable at the global and local levels. The results of the research help us to select the optimal model and the optimal visualization method to construct a prediction model for the Japanese sardine fishing grounds in the Northwest Pacific Ocean, which will provide a scientific basis for the Japanese sardine fishery to achieve environmental and economically sustainable fishery development.https://www.frontiersin.org/articles/10.3389/fmars.2024.1503292/fullSardinops melanostictusmodel prediction performanceSHAP visualizationfishery managementNorthwest Pacific Ocean |
| spellingShingle | Yongchuang Shi Lei Yan Shengmao Zhang Fenghua Tang Shenglong Yang Wei Fan Haibin Han Yang Dai Revealing the effects of environmental and spatio-temporal variables on changes in Japanese sardine (Sardinops melanostictus) high abundance fishing grounds based on interpretable machine learning approach Frontiers in Marine Science Sardinops melanostictus model prediction performance SHAP visualization fishery management Northwest Pacific Ocean |
| title | Revealing the effects of environmental and spatio-temporal variables on changes in Japanese sardine (Sardinops melanostictus) high abundance fishing grounds based on interpretable machine learning approach |
| title_full | Revealing the effects of environmental and spatio-temporal variables on changes in Japanese sardine (Sardinops melanostictus) high abundance fishing grounds based on interpretable machine learning approach |
| title_fullStr | Revealing the effects of environmental and spatio-temporal variables on changes in Japanese sardine (Sardinops melanostictus) high abundance fishing grounds based on interpretable machine learning approach |
| title_full_unstemmed | Revealing the effects of environmental and spatio-temporal variables on changes in Japanese sardine (Sardinops melanostictus) high abundance fishing grounds based on interpretable machine learning approach |
| title_short | Revealing the effects of environmental and spatio-temporal variables on changes in Japanese sardine (Sardinops melanostictus) high abundance fishing grounds based on interpretable machine learning approach |
| title_sort | revealing the effects of environmental and spatio temporal variables on changes in japanese sardine sardinops melanostictus high abundance fishing grounds based on interpretable machine learning approach |
| topic | Sardinops melanostictus model prediction performance SHAP visualization fishery management Northwest Pacific Ocean |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2024.1503292/full |
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