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基于MaxEnt模型对舞毒蛾全球适生区的预测及分析

王艳君 高泰 石娟

王艳君, 高泰, 石娟. 基于MaxEnt模型对舞毒蛾全球适生区的预测及分析[J]. 北京林业大学学报, 2021, 43(9): 59-69. doi: 10.12171/j.1000-1522.20200416
引用本文: 王艳君, 高泰, 石娟. 基于MaxEnt模型对舞毒蛾全球适生区的预测及分析[J]. 北京林业大学学报, 2021, 43(9): 59-69. doi: 10.12171/j.1000-1522.20200416
Wang Yanjun, Gao Tai, Shi Juan. Prediction and analysis of the global suitability of Lymantria dispar based on MaxEnt[J]. Journal of Beijing Forestry University, 2021, 43(9): 59-69. doi: 10.12171/j.1000-1522.20200416
Citation: Wang Yanjun, Gao Tai, Shi Juan. Prediction and analysis of the global suitability of Lymantria dispar based on MaxEnt[J]. Journal of Beijing Forestry University, 2021, 43(9): 59-69. doi: 10.12171/j.1000-1522.20200416

基于MaxEnt模型对舞毒蛾全球适生区的预测及分析

doi: 10.12171/j.1000-1522.20200416
基金项目: 国家自然科学基金项目(31770687)
详细信息
    作者简介:

    王艳君。主要研究方向:植物检疫。Email:yjunwang@foxmail.com 地址:100083 北京市海淀区清华东路35号北京林业大学林学院

    责任作者:

    石娟,教授,博士生导师。主要研究方向:植物检疫。Email:shi_juan@263.net 地址:同上

  • 中图分类号: S763

Prediction and analysis of the global suitability of Lymantria dispar based on MaxEnt

  • 摘要:   目的  舞毒蛾是一种食叶性的国际性检疫害虫,给世界上许多国家和地区造成了严重的经济损失。该研究筛选出限制舞毒蛾分布的环境变量,利用MaxEnt软件预测舞毒蛾当前及未来气候条件下的全球适生区范围,明确舞毒蛾在不同气候条件下的适生区变化。  方法  利用ArcGIS软件设置缓冲区筛选舞毒蛾在全球的分布点数据;利用MaxEnt、SPSS和ArcGIS软件根据环境变量贡献率、刀切法和变量相关性分析对生物气候变量、月总降水量、月平均最高温度和月平均最低温度4种环境变量进行筛选;利用R软件计算调控倍频和特征组合等因子调整MaxEnt模型参数;利用MaxEnt模型预测当前和未来不同情境条件下舞毒蛾全球适生区的分布范围。  结果  经过缓冲区筛选得到734个舞毒蛾的分布点数据;MaxEnt模型结果中,测试遗漏率与理论遗漏率吻合度高,而且模型AUC值为0.940;MaxEnt模型预测当前条件下舞毒蛾在全球的高、中度适生区主要集中在欧洲的大部分地区,北美洲中东部,亚洲的东西部,而非洲、大洋洲和南美洲分布较少。此外,舞毒蛾在未来气候条件下北半球适生区的边界向北偏移,北美洲以及欧亚大陆的高、中度适生区的面积扩增明显。  结论  舞毒蛾的分布受多种环境变量影响,并且温度和降水与舞毒蛾的特定发育阶段相吻合。MaxEnt模型的预测结果能够反映舞毒蛾在全球的分布特征。该研究可为防治舞毒蛾和制定相关检疫措施提供理论依据。

     

  • 图  1  R包ENMeval结果

    L. 线性;Q. 二次;H. 片段化;P. 乘积型;T. 阈值。L、LQ、LQP、QHP、LQH、LQHP、QHPT、LQHPT为不同的特征组合。L, linear; Q, quadratic; H, hinge; P, product; T, threshold. L, LQ, LQP, QHP, LQH, LQHP, QHPT, LQHPT are different characteristic combinations.

    Figure  1.  Results of ENMeval of R package

    图  2  遗漏率

    Figure  2.  Omission rates

    图  3  环境变量对预测舞毒蛾分布的重要度

    Figure  3.  Importance of environmental variables for predicting the distribution of Lymantria dispar

    图  4  存在概率与环境变量的响应曲线

    红色代表均值,蓝色代表标准差。Red represents mean value, blue represents SD.

    Figure  4.  Response curves between probability of presence and environmental variables

    图  5  当前气候条件下舞毒蛾在全球的分布点(A)及潜在分布(B)

    Figure  5.  Global distribution (A) and potential distribution (B) of Lymantria dispar under current climatic conditions

    图  6  ssp126气候模式下舞毒蛾在全球的潜在分布

    Figure  6.  Future species distribution models of Lymantria dispar on global scale under ssp126 climate scenarios predicted by MaxEnt

    图  7  ssp245气候模式下舞毒蛾在全球的潜在分布

    Figure  7.  Future species distribution models of Lymantria dispar on global scale under ssp245 climate scenarios predicted by MaxEnt

    图  8  ssp370气候模式下舞毒蛾在全球的潜在分布

    Figure  8.  Future species distribution models of Lymantria dispar on global scale under ssp370 climate scenarios predicted by MaxEnt

    图  9  ssp585气候模式下舞毒蛾在全球的潜在分布

    Figure  9.  Future species distribution models of Lymantria dispar on global scale under ssp585 climate scenarios predicted by MaxEnt

    图  10  多种气候排放场景及时间段下MaxEnt模型预测舞毒蛾在全球的适生区面积

    Figure  10.  MaxEnt models predicting suitability areas of Lymantria dispar on global scale under climate scenarios and time periods

    表  1  MaxEnt参数设置值

    Table  1.   Parameter setting values of MaxEnt

    选项
    Option
    参数 Parameter
    默认值
    Default value
    设置值
    Setting value
    随机选取测试集比例
    Random text percentage
    025
    重复训练次数
    Number of repetitions
    110
    最大重复次数
    Maximum number of repetitions
    5005000
    下载: 导出CSV

    表  2  影响舞毒蛾分布的环境变量及其贡献率

    Table  2.   Environmental variables affecting Lymantria dispar distribution and their contribution rates

    编号 No.气候变量 Climatic variable贡献率 Contribution rate/%
    Bio2 温差月均值 Monthly mean temperature difference 3.7
    Bio9 最干季度平均温度 Mean temperature of driest quarter 1.2
    Bio10 最暖季度平均温度 Mean temperature of warmest quarter 37.9
    Bio14 最干月降水量 Precipitation of driest month 39.9
    Bio16 最湿季度降水量 Precipitation of wettest quarter 3.8
    Prec3 3月平均降水量 Average precipitation in March 13.5
    下载: 导出CSV

    表  3  多种气候情景的AUC值

    Table  3.   AUC values under various climatic scenarios

    气候情景
    Climate change scenario
    年份
    Year
    AUC值
    AUC value
    当前
    Current
    当前 Current 0.940
    低强迫情景 ssp126
    Low compulsion scenario ssp126
    2021—2040 0.940
    2041—2060 0.944
    2061—2080 0.942
    2081—2100 0.943
    中等强迫情景ssp245
    Moderate compulsion scenario ssp245
    2021—2040 0.943
    2041—2060 0.943
    2061—2080 0.944
    2081—2100 0.944
    中等至高等强迫情景ssp370
    Moderate to high compulsive scenario ssp370
    2021—2040 0.942
    2041—2060 0.941
    2061—2080 0.942
    2081—2100 0.943
    高等强迫情景ssp585
    High compulsion scenario ssp585
    2021—2040 0.945
    2041—2060 0.941
    2061—2080 0.944
    2081—2100 0.943
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-12-24
  • 修回日期:  2021-01-28
  • 网络出版日期:  2021-07-06
  • 刊出日期:  2021-10-15

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