[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2170":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"view_count":32,"doi":33,"paper":34,"created_at":62},2170,"Predicting the suitable habitat distribution of greater amberjack using the MaxEnt model with fishery and remote sensing data in the surrounding waters of Taiwan","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-60453-6","Abstract 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 \u003C 35 PSU, SSH between 0.41 and 0.64 m, CHL > 0.22 mg\u002Fm 3 , MLD is between 10 and 17 m, and EKE \u003C 0.007 m 2 \u002Fs 2 . 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.","摘要 卫星遥感海洋学数据的日益普及，为评估海洋学现象驱动的栖息地适宜性变化以及为科学管理策略提供依据带来了巨大潜力，尤其是与最大熵（MaxEnt）方法相结合时。本研究收集了2014—2019年期间的海洋学变量，包括海表温度（SST）、海表盐度（SSS）、海表高度（SSH）、海表叶绿素-a浓度（CHL）、混合层深度（MLD）和涡动能（EKE），以及来自台湾渔船的渔业数据。MaxEnt模型是一种广泛使用的方法，通过分析物种出现数据与环境变量之间的关系来预测物种的地理分布。我们的结果表明，栖息地适宜性的年际响应曲线受到环境因素的显著影响。具体而言，高栖息地适宜性的最优环境条件为：SST > 24 °C，SSS \u003C 35 PSU，SSH在0.41至0.64 m之间，CHL > 0.22 mg\u002Fm³，MLD在10至17 m之间，EKE \u003C 0.007 m²\u002Fs²。MaxEnt模型在所有季节及总体上均表现出较强的预测性能，受试者工作特征曲线（AUC）值超过0.8，真实技巧统计量（TSS）值高于0.7，从而证实了其预测高体鰤出现的准确性，为未来的保护和管理优先事项提供了参考。",null,"Scientific Reports","2026-09-09T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,14,8,1,"利用遥感与渔业数据构建MaxEnt栖息地模型，方法扎实、结论具体，对渔业资源管理与海洋生态保护有参考价值，但属区域性与学科细分研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"海洋牧场","渔业遥感","栖息地预测","渔业资源管理","MaxEnt模型",0,"10.1038\u002Fs41598-026-60453-6",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":54,"card":55,"direction":59,"ingested_from":61},"W7212047677",[37,40,43,46,48,51],{"name":38,"orcid":39},"Mubarak Mammel","https:\u002F\u002Forcid.org\u002F0000-0002-5546-2531",{"name":41,"orcid":42},"Baker Matovu","https:\u002F\u002Forcid.org\u002F0000-0002-1814-5705",{"name":44,"orcid":45},"Sajna Beegum","https:\u002F\u002Forcid.org\u002F0009-0009-7526-5015",{"name":47,"orcid":9},"Abdul Azeez Pokkathappada",{"name":49,"orcid":50},"Ming‐An Lee","https:\u002F\u002Forcid.org\u002F0000-0001-6970-7643",{"name":52,"orcid":53},"Li-Chi Cheng","https:\u002F\u002Forcid.org\u002F0000-0002-4624-994X","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-60453-6.pdf",{"tldr":56,"method":57,"finding":58,"direction":59,"opportunity":60},"用MaxEnt结合遥感与渔业数据预测台湾周边高体鰤适宜栖息地分布。","MaxEnt模型，2014–2019年SST、SSS、SSH、CHL、MLD、E","高适宜区条件为SST>24°C、SSS\u003C35、SSH 0.41–0.64m、CHL>0.22mg\u002Fm","农业遥感与作物表型","可将该遥感+MaxEnt框架迁移至近海养殖选址与渔业资源管理，并引入气候变化情景预测栖息地迁移。","openalex","2026-09-11T23:30:31.565418Z"]