Predicting the suitable habitat distribution of greater amberjack using the MaxEnt model with fishery and remote sensing data in the surrounding waters of Taiwan

Scientific Reports

卷:16

Article

Mubarak Mammel¹  Baker Matovu^1,2,3  Sajna Beegum⁴  Abdul Azeez Pokkathappada⁵  Ming-An Lee^1,6,7,8  Li-Chi Cheng⁹ 

[1] Doctoral Degree Program in Ocean Resource and Environmental Changes, National Taiwan Ocean University, No.2, Beining Rd., Zhongzheng Dist, 20224, Keelung City, Taiwan

[2] Department of Sustainable Development, Blue Economy, and Ocean Futures, Africa International Policy Research Center, Nairobi, Kenya

[3] Coastal and Offshore Fishery Research Center, Fisheries Research Institute, Ministry of Agriculture, No. 6 Yugang N. 3rd Rd., Qianzhen Dist, 806043, Kaohsiung City, Taiwan

[4] Future Earth Coasts (FEC) Fellow, Kampala, Uganda

[5] Center of Excellence for Oceans, National Taiwan Ocean University, No.2, Beining Rd., Zhongzheng Dist, 20224, Keelung City, Taiwan

[6] Center for Humanities and Social Sciences in Governance, National Taiwan Ocean University, No.2, Beining Rd, Zhongzheng Dist, 20224, Keelung City, Taiwan

[7] Department of Environmental Biology and Fisheries Science, National Taiwan Ocean University, No.2, Beining Rd., Zhongzheng Dist, 20224, Keelung City, Taiwan

[8] Government College of Education, Affiliated with Pondicherry University, Kadmat Island, Lakshadweep, India

[9] ICAR-Central Marine Fisheries Research Institute, 682018, Kochi, Kerala, India

关键词: Habitat suitability index;    Maximum entropy model;    Satellite data;    Oceanographic variable;    Habitat selection;   

DOI  :  10.1038/s41598-026-60453-6

 received in 2025-11-13, accepted in 2026-06-29,  发布年份 2026

来源: Springer

【 摘 要 】

The increasing availability of satellite-derived remotely sensed oceanographic data offers significant potential for evaluating changes in habitat suitability driven by oceanographic phenomena and for informing scientific management strategies, particularly when coupled with maximum entropy (MaxEnt) methods. Oceanographic variables, including sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), sea surface chlorophyll-a concentration (CHL), mixed layer depth (MLD), and eddy kinetic energy (EKE), alongside fishery data from Taiwanese fishing vessels, were collected for the period 2014–2019. The MaxEnt model is a widely employed method for predicting species’ geographical distributions by analyzing species occurrence data in relation to environmental variables. Our results indicated that the annual response curves of habitat suitability were significantly influenced by environmental factors. Specifically, optimal environmental conditions for high habitat suitability were identified as SST > 24 °C, SSS < 35 PSU, SSH between 0.41 and 0.64 m, CHL > 0.22 mg/m3, MLD is between 10 and 17 m, and EKE < 0.007 m2/s2. The MaxEnt models exhibited strong predictive performance across all seasons and overall, as evidenced by receiver operating characteristic curve (AUC) values exceeding 0.8 and true skill statistics (TSS) values above 0.7, thereby confirming their accuracy in predicting greater amberjack presence, a reference for future conservation and management priorities.

【 授权许可】

CC BY   
© The Author(s) 2026

【 图 表 】

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Figure 3. Preparation of copper (Cu) plates and assay setup.

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