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朱梦婕, 缪佳, 赵雪利. 基于最大熵模型的狸尾豆属植物在中国的潜在分布区模拟[J]. 植物科学学报, 2020, 38(4): 476-482. DOI: 10.11913/PSJ.2095-0837.2020.40476
引用本文: 朱梦婕, 缪佳, 赵雪利. 基于最大熵模型的狸尾豆属植物在中国的潜在分布区模拟[J]. 植物科学学报, 2020, 38(4): 476-482. DOI: 10.11913/PSJ.2095-0837.2020.40476
Zhu Meng-Jie, Miao Jia, Zhao Xue-Li. Simulation of potential distribution of Uraria in China based on maximum entropy model[J]. Plant Science Journal, 2020, 38(4): 476-482. DOI: 10.11913/PSJ.2095-0837.2020.40476
Citation: Zhu Meng-Jie, Miao Jia, Zhao Xue-Li. Simulation of potential distribution of Uraria in China based on maximum entropy model[J]. Plant Science Journal, 2020, 38(4): 476-482. DOI: 10.11913/PSJ.2095-0837.2020.40476

基于最大熵模型的狸尾豆属植物在中国的潜在分布区模拟

Simulation of potential distribution of Uraria in China based on maximum entropy model

  • 摘要: 基于19个气候因子和203条狸尾豆属(Uraria)植物地理分布记录,采用最大熵模型(MaxEnt)对植物当前分布点的气候变量进行分析,推断其在末次盛冰期(LGM)、当前和未来气候(2070s)情景下的潜在分布;采用受试者工作曲线和刀切法对模型的准确性进行检验并探明影响该属在中国分布的气候因子。结果显示:最大熵模型模拟结果极准确,测试集和训练集假阳性值(AUC)分别达到0.934和0.936;影响该属植物分布的主要气候因子是最暖季节降水和最冷月份最低温度;广西、广东及台湾地区为该属在中国的起源中心。在全球气候变暖背景下,狸尾豆属植物的适生环境将向中国北部及东部沿海地区推移,且面积逐渐增加。

     

    Abstract: This study predicted the potential distribution of the genus Uraria under the Last Glacial Maximum (LGM) and current and future (2070s) climate scenarios using the maximum entropy model (MaxEnt). Based on 19 climatic factors and 203 distribution records, dominant factors were chosen using the Jackknife test and receiver operating characteristic (ROC) curves were used to evaluate the simulations. Results revealed that the accuracy of the prediction was "Excellent". The climatic factors with the greatest impact on the distribution of the genus were precipitation in the warmest quarter and minimum temperature of the coldest month. Guangxi, Guangdong, and Taiwan were determined to be the origin centers of the genus in China. With global warming, the suitable geographical distribution of Uraria will shift to the northern and eastern coastal areas of China.

     

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