应用最大熵模型预测不同气候情景下绿竹的潜在适生区OA
Prediction of Potential Suitable Habitats for Bambusa oldhamii under Different Climate Scenarios Using the Max-Ent Model
为应对气候变化对绿竹(Bambusa oldhamii)资源的影响,明确其适生区变化.根据 71 个绿竹分布点与 28 个环境变量(生物气候、土壤、辐射及海拔因子),应用最大熵(MaxEnt)模型预测了绿竹在当前及未来(2041-2060 年、2061-2080 年)3 种气候情景(SSP126、SSP245、SSP585)下的潜在适生区.研究结果表明:①模型预测精度高,AUC 值为 0.967;②当前适生区主要分布于华南、华东及西南的亚热带和暖温带地区;③未来情景下,SSP126 和 SSP245 情景的适生区面积呈"先增后减"趋势,分布先北扩后南缩,SSP585 情景下适生区面积呈显著减少趋势;④最冷季节降水量、海拔、最干燥季节平均气温和最湿润季节降水量是影响绿竹分布的关键因子,其中,最冷季节降水量贡献率最高(71.5%),海拔的置换重要性最高(37.8%).
To address the impacts of climate change on Bambusa oldhamii resources and clarify the dynamics of its suitable habi-tats,the MaxEnt model was applied to predict the potential suitable habitats of B.oldhamii under current and future(2041-2060,2061-2080)climate scenarios(SSP126,SSP245,SSP585),based on 71 occurrence records and 28 en-vironmental variables including bioclimatic,soil,radiation and elevation factors.The results showed that:(1)The model achieved high prediction accuracy with an AUC value of 0.967.(2)At present,suitable habitats are mainly distributed in subtropical and warm-temperate regions of South China,East China and Southwest China.(3)Under future scenarios,the suitable habitat area showed an initial increase followed by a decrease under SSP126 and SSP245,with a northward expan-sion followed by a southward contraction,while a significant decreasing trend was observed under SSP585.(4)Precipita-tion of the coldest quarter,elevation,mean temperature of the driest quarter and precipitation of the wettest quarter were the key factors affecting the distribution of B.oldhamii.Among them,precipitation of the coldest quarter contributed the most(71.5%),and elevation had the highest permutation importance(37.8%).
曾奎;辉朝茂;刘蔚漪;郑蓉
西南林业大学林学院(亚太林学院),昆明,650224西南林业大学林学院(亚太林学院),昆明,650224西南林业大学林学院(亚太林学院),昆明,650224福建省林业科学研究院,福州,350012
农业科技
最大熵模型潜在适生区气候变化环境因子
MaxEnt modelPotential suitable habitatClimate changeEnvironmental factors
《东北林业大学学报》 2026 (9)
48-59,12
"十四五"国家重点研发计划项目(2023YFD2201201)云南省基础研究项目(202201AT070053)云南省农业联合专项项目(202301BD070001-123).
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