Prediction and analysis of the global suitability of Lymantria dispar based on MaxEnt
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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。 Abstract:Objective Lymantria dispar (Linnaeus) is a leaf-eating international quarantine pest, which has caused serious economic losses to many countries and regions in the world. In this study, environmental variables limiting the distribution of Lymantria dispar were screened out, and MaxEnt software was used to predict the global adaptation range of Lymantria dispar under current and future climatic conditions, so as to clarify the adaptation range of Lymantria dispar under different climatic conditions. Method We used ArcGIS software to set up buffer zones to screen the global distribution data of Lymantria dispar. We used MaxEnt, SPSS and ArcGIS software to screen four kinds of environmental variables, i.e. biological climate variable, monthly total precipitation, monthly mean maximum temperature and monthly mean minimum temperature, according to the contribution rate of environmental variables, knife cutting method and variable correlation analysis. We used R software to calculate the regularization multiplier and feature classes and other factors to adjust the parameters of MaxEnt model. We used MaxEnt model to predict the global distribution range of Lymantria dispar under current and future climatic conditions. Result The distribution data of 734 Lymantria dispar were obtained by buffer screening. In the MaxEnt model results, the test omission rate was in high agreement with the theoretical omission rate, and the AUC value of the model was 0.940. MaxEnt model predicted that Lymantria dispar was mainly distributed in most parts of Europe, central and eastern North America, western and eastern Asia, and less in Africa, Oceania and South America under current conditions. In addition, Lymantria dispar boundaries in the northern hemisphere will shift to the north under future climatic conditions, and the areas of high and moderate adaptability in North America and Eurasia will expand significantly. Conclusion The distribution of Lymantria dispar is influenced by a variety of environmental variables and temperature and precipitation are consistent with the specific development stage of Lymantria dispar. The predicted results of MaxEnt model can reflect the distribution characteristics of Lymantria dispar in the world. This study can provide theoretical basis for prevention and control of Lymantria dispar and establishment of relevant quarantine measures. -
Key words:
- Lymantria dispar /
- MaxEnt /
- adaptive area prediction /
- CMIP6
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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 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 percentage0 25 重复训练次数
Number of repetitions1 10 最大重复次数
Maximum number of repetitions500 5000 
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
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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
YearAUC值
AUC value当前
Current当前 Current 0.940 低强迫情景 ssp126
Low compulsion scenario ssp1262021—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 ssp2452021—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 ssp3702021—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 ssp5852021—2040 0.945 2041—2060 0.941 2061—2080 0.944 2081—2100 0.943
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