Distribution of Suitable Habitats for Soft Corals (Alcyonacea) Based on Machine Learning
The soft coral order Alcyonacea is a common coral found in the deep sea and plays a crucial role in the deep-sea ecosystem. This study aims to predict the distribution of Alcyonacea in the western Pacific Ocean using four machine learning-based species distribution models. The performance of these m...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/2/242 |
| _version_ | 1797297843644399616 |
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| author | Minxing Dong Jichao Yang Yushan Fu Tengfei Fu Qing Zhao Xuelei Zhang Qinzeng Xu Wenquan Zhang |
| author_facet | Minxing Dong Jichao Yang Yushan Fu Tengfei Fu Qing Zhao Xuelei Zhang Qinzeng Xu Wenquan Zhang |
| author_sort | Minxing Dong |
| collection | DOAJ |
| description | The soft coral order Alcyonacea is a common coral found in the deep sea and plays a crucial role in the deep-sea ecosystem. This study aims to predict the distribution of Alcyonacea in the western Pacific Ocean using four machine learning-based species distribution models. The performance of these models is also evaluated. The results indicate a high consistency among the prediction results of the different models. The soft coral order is primarily distributed in the Thousand Islands Basin, Japan Trench, and Thousand Islands Trench. Water depth and silicate content are identified as important environmental factors influencing the distribution of Alcyonacea. The RF, Maxent, and XGBoost models demonstrate high accuracies, with the RF model exhibiting the highest prediction accuracy. However, the Maxent model outperforms the other three models in data processing. Developing a high-resolution, high-accuracy, and high-precision habitat suitability model for soft corals can provide a scientific basis and reference for China’s exploration and research in the deep sea field and aid in the planning of protected areas in the high seas. |
| first_indexed | 2024-03-07T22:26:16Z |
| format | Article |
| id | doaj.art-fdcc73128a8f4902bff6f3d1de9c553a |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-07T22:26:16Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-fdcc73128a8f4902bff6f3d1de9c553a2024-02-23T15:23:05ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-01-0112224210.3390/jmse12020242Distribution of Suitable Habitats for Soft Corals (Alcyonacea) Based on Machine LearningMinxing Dong0Jichao Yang1Yushan Fu2Tengfei Fu3Qing Zhao4Xuelei Zhang5Qinzeng Xu6Wenquan Zhang7College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaKey Laboratory of Marine Geology and Metallogeny, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, ChinaKey Laboratory of Marine Geology and Metallogeny, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, ChinaCollege of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaKey Laboratory of Marine Eco-Environmental Science and Technology, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, ChinaKey Laboratory of Marine Eco-Environmental Science and Technology, First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, ChinaNational Deep Sea Center, Ministry of Natural Resources, Qingdao 266237, ChinaThe soft coral order Alcyonacea is a common coral found in the deep sea and plays a crucial role in the deep-sea ecosystem. This study aims to predict the distribution of Alcyonacea in the western Pacific Ocean using four machine learning-based species distribution models. The performance of these models is also evaluated. The results indicate a high consistency among the prediction results of the different models. The soft coral order is primarily distributed in the Thousand Islands Basin, Japan Trench, and Thousand Islands Trench. Water depth and silicate content are identified as important environmental factors influencing the distribution of Alcyonacea. The RF, Maxent, and XGBoost models demonstrate high accuracies, with the RF model exhibiting the highest prediction accuracy. However, the Maxent model outperforms the other three models in data processing. Developing a high-resolution, high-accuracy, and high-precision habitat suitability model for soft corals can provide a scientific basis and reference for China’s exploration and research in the deep sea field and aid in the planning of protected areas in the high seas.https://www.mdpi.com/2077-1312/12/2/242species distribution modelsmachine learningmaximum entropy modelrandom forestAlcyonacea |
| spellingShingle | Minxing Dong Jichao Yang Yushan Fu Tengfei Fu Qing Zhao Xuelei Zhang Qinzeng Xu Wenquan Zhang Distribution of Suitable Habitats for Soft Corals (Alcyonacea) Based on Machine Learning Journal of Marine Science and Engineering species distribution models machine learning maximum entropy model random forest Alcyonacea |
| title | Distribution of Suitable Habitats for Soft Corals (Alcyonacea) Based on Machine Learning |
| title_full | Distribution of Suitable Habitats for Soft Corals (Alcyonacea) Based on Machine Learning |
| title_fullStr | Distribution of Suitable Habitats for Soft Corals (Alcyonacea) Based on Machine Learning |
| title_full_unstemmed | Distribution of Suitable Habitats for Soft Corals (Alcyonacea) Based on Machine Learning |
| title_short | Distribution of Suitable Habitats for Soft Corals (Alcyonacea) Based on Machine Learning |
| title_sort | distribution of suitable habitats for soft corals alcyonacea based on machine learning |
| topic | species distribution models machine learning maximum entropy model random forest Alcyonacea |
| url | https://www.mdpi.com/2077-1312/12/2/242 |
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