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基于MaxEnt对桉树枝瘿姬小蜂在中国发生趋势的预测

黄梦伊 赵佳强 石娟

黄梦伊, 赵佳强, 石娟. 基于MaxEnt对桉树枝瘿姬小蜂在中国发生趋势的预测[J]. 北京林业大学学报, 2020, 42(11): 64-71. doi: 10.12171/j.1000-1522.20190053
引用本文: 黄梦伊, 赵佳强, 石娟. 基于MaxEnt对桉树枝瘿姬小蜂在中国发生趋势的预测[J]. 北京林业大学学报, 2020, 42(11): 64-71. doi: 10.12171/j.1000-1522.20190053
Huang Mengyi, Zhao Jiaqiang, Shi Juan. Predicting occurrence tendency of Leptocybe invasa in China based on MaxEnt[J]. Journal of Beijing Forestry University, 2020, 42(11): 64-71. doi: 10.12171/j.1000-1522.20190053
Citation: Huang Mengyi, Zhao Jiaqiang, Shi Juan. Predicting occurrence tendency of Leptocybe invasa in China based on MaxEnt[J]. Journal of Beijing Forestry University, 2020, 42(11): 64-71. doi: 10.12171/j.1000-1522.20190053

基于MaxEnt对桉树枝瘿姬小蜂在中国发生趋势的预测

doi: 10.12171/j.1000-1522.20190053
基金项目: 国家重点研发计划(2016YFC1202102)
详细信息
    作者简介:

    黄梦伊。主要研究方向:植物检疫。Email:hmyiyouxiang@163.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

Predicting occurrence tendency of Leptocybe invasa in China based on MaxEnt

  • 摘要:   目的  桉树枝瘿姬小蜂是我国一种入侵性小蜂,自2007年传入我国广西省,在短短几年时间内危害了我国南部的桉树种植地,造成严重经济损失。本研究利用MaxEnt模型对桉树枝瘿姬小蜂在中国现在和未来的适生区进行预测,以了解桉树枝瘿姬小蜂在温度变化的影响下其适生地范围的变化。  方法  采用MaxEnt预测模型—最大熵模型,通过收集桉树枝瘿姬小蜂在中国报道的分布地数据、调查最新发生地危害轻重程度对该蜂在中国的适生区现在以及未来RCP8.5气候情景下的适生区进行模拟并检测模拟结果的精准度。  结果  模型模拟结果的测试遗漏率与理论遗漏率基本吻合。AUC 值为0.898,标准差为0.022,表明所使用的数据无空间自相关,模型筛选的参数结果可靠且准确度高。MaxEnt模型预测得到桉树枝瘿姬小蜂在中国的最佳适生区主要分布在长江以南的福建、广东、广西、海南等地,在RCP8.5气候情景下其未来中度适生区面积有小范围的下降,但整体适生区范围显著增加。  结论  通过适生地范围预测结果的分析,本研究对桉树枝瘿姬小蜂的监测以及潜在危险入侵地区制定有效的防治手段具有重要的理论指导意义。

     

  • 图  1  气候因子相关性分析和聚类分析

    bio1. 年平均温度;bio2. 温差月均值;bio3. 等温性;bio4. 温度季节变化;bio5. 最热月最高温;bio6. 最冷月最低温;bio7. 年温差;bio8. 最湿季平均温度;bio9. 最干季平均温度;bio10. 最热季平均温度;bio11. 最冷季平均温度;bio12. 年降水量;bio13. 最湿月降水量;bio14. 最干月降水量;bio15. 降水量季节性变动系数;bio16. 最湿季降水量;bio17. 最干季降水量;bio18. 最热季降水量;bio19. 最冷季降水量。下同。bio1, annual mean temperature; bio2, mean diurnal range; bio3, isothermality; bio4, temperature seasonality; bio5, maximum temperature of the warmest month; bio6, minimum temperature of coldest month; bio7, temperature annual range; bio8, mean temperature of the wettest quarter; bio9, mean temperature of the driest quarter; bio10, mean temperature of the warmest quarter; bio11, mean temperature of the coldest quarter; bio12, annual precipitation; bio13, precipitation of wettest month; bio14, precipitation of the driest month; bio15, precipitation seasonality (coefficient of variation); bio16, precipitation of the wettest quarter; bio17, precipitation of the driest quarter; bio18, precipitation of the warmest quarter; bio19, precipitation of the coldest quarter. The same below.

    Figure  1.  Correlation analysis and cluster analysis of climatic variables

    图  2  模型预测检验

    Figure  2.  Validation charts of model predicting

    图  3  筛选后的气候因子对MaxEnt模型的贡献值

    Figure  3.  Contribution values of selected climate factors to MaxEnt model

    图  4  主导气候因子响应曲线

    Figure  4.  Response curves of dominant climatic factors

    图  5  桉树枝瘿姬小蜂在现在气候条件下的全国预测分布图

    Figure  5.  Predicted potential distribution of Leptocybe invasa in China under current climate conditions

    图  6  桉树枝瘿姬小蜂在未来气候条件下的全国预测分布图

    Figure  6.  Predicted potential distribution of L. invasa in China under future climate conditions

    图  7  桉树枝瘿姬小蜂在当前和未来气候条件下不同适生范围占全国面积的比例

    Figure  7.  Area proportion of different ranges for L. invasa under current and future climate conditions

    图  8  寄主分布区与预测分布区叠加后的分布图

    Figure  8.  Superposition diagram of host distribution area and predicted distribution area

    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 Randomly selected test set percentage025
    正则化乘数 Regularization multiplier12
    重复迭代次数 Number of iterations repeated110
    最大重复次数 Maximum number of repetitions5005 000
    应用阈值规则 Applying threshold rules无 None10%训练值验证 10% training value verification
    下载: 导出CSV

    表  2  筛选的12个气候因子及其贡献率

    Table  2.   Screening 12 climatic variables and their contribution rates

    序号 No.数据简称 Data abbreviation气候因子 Climatic factor贡献率 Contribution rate/%
    1 bio11 最冷季平均温度 Mean temperature of the coldest quarter 38.9
    2 bio4 温度季节变化 Temperature seasonality 5.4
    3 bio10 最热季平均温度 Mean temperature of the warmest quarter 3.9
    4 bio12 年降水量 Annual precipitation 0.8
    5 bio3 等温性 Isothermality 2.5
    6 bio2 温差月均值 Monthly mean temperature difference 13.4
    7 bio15 降水量季节性变动系数 Precipitation seasonality (coefficient of variation) 9.8
    8 bio8 最湿季平均温度 Mean temperature of the wettest quarter 1.0
    9 bio7 年温差 Temperature annual range 4.1
    10 bio19 最冷季降水量 Precipitation of the coldest quarter 4.8
    11 bio13 最湿月降水量 Precipitation of the wettest month 11.7
    12 bio18 最热季降水量 Precipitation of the warmest quarter 3.7
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-01-20
  • 修回日期:  2020-04-07
  • 网络出版日期:  2020-11-21
  • 刊出日期:  2020-12-14

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