globalchange  > 气候变化与战略
CSCD记录号: CSCD:5753984
论文题名:
应用最大熵模型模拟预测大尺度范围油松毛虫灾害
其他题名: Application of the Maximum Entropy Model ( MaxEnt) to Simulation and Forecast of Large Scale Outbreaks of Dendrolimus tabulaeformis ( Lepidoptera: Lasiocampidae)
作者: 宋雄刚1; 王鸿斌1; 张真1; 孔祥波1; 苗振旺2; 刘随存3; 李永福4
刊名: 林业科学
ISSN: 1001-7488
出版年: 2016
卷: 52, 期:6, 页码:22-39
语种: 中文
中文关键词: 油松毛虫 ; 物候因子 ; 气候变化 ; 灾害
英文关键词: MaxEnt ; Dendrolimus tabulaeformis ; MaxEnt ; bio-climatic variables ; climate change ; outbreak
WOS学科分类: FORESTRY
WOS研究方向: Forestry
中文摘要: 【目的】探讨利用最大熵模型MaxEnt,基于油松毛虫暴发的历史灾情数据和相应的气象数据,对未来大尺度范围油松毛虫暴发区进行模拟和预测的可行性。【方法】以山西省20022011年的油松毛虫灾情数据和山西省20022011年的地面气象数据为基础,结合油松毛虫完成生活史不同发育阶段对不同气候因子的响应衍生出与油松毛虫灾害发生潜在相关的物候因子80个,运用主成分分析和逐步回归法从中筛选出与油松毛虫灾害发生相关性最高的前8个物候因子,即X_(29) ( 10月份日均温< 5 ℃的天数) 、X_(43) ( 7月平均湿度> 75%的天数) 、X_(54) ( 3月平均风速) 、 X_(55) ( 4,5 ,6月分平均风速) 、X_(56) ( 7,8月均风速) 、X_(62) ( 10月日均风速> 10 m·s~(- 1)的天数) 、X63 ( 9月最大日均风速) 、X_(67) ( 4,5 ,6月总降雨量) 。【结果】利用筛选出的8个物候因子,运用最大熵模型MaxEnt代入实际灾害发生数据进行训练模拟,并应用刀切法分析确立气象因子X_(43) ,X_(54)和X_(55)为灾害模型应用的最佳因子,其接受者操作特性曲线( ROC)检验结果即曲线下面积( AUC)值为0. 820,标准差为0. 019;最终利用该模型参数,对气候变化背景不同外排模式下未来油松毛虫灾害趋势进行预测, 2050年2种外排模式( RCP4. 5与RCP6. 0)下灾害发生趋势变化不同,其中RCP4. 5模式灾害发生将集中于北京、河北及河北与内蒙古交界处,RCP6. 0模式在山西中南部灾害将会加强。【结论】MaxEnt模型对未来气候变化条件下油松毛虫害虫暴发区的准确模拟与预测具有潜在应用价值。
英文摘要: 【Obiective】The Chinese pine caterpillar,Dendrolimus tabulaeformis,is a serious native pine defoliator with frequent outbreaks in northern China. The MaxEnt model is one of the most effective software packages available for modeling species'distributions. The main objective of the current study was to test and determine the possibility of using MaxEnt to simulate and predict future large-scale outbreaks of D. tabulaeformis based on county-level historical outbreak records ( 2002 - 2011) ,and daily meteorological data from 19 weather stations in Shanxi province. 【Method】Using Principal Component Analysis and Step-wise Regression methods with actual pest outbreak data,the 8 most relevant factors were chosen from 80 outbreak-related bio-climate factors potentially affecting development of the insect. The key factors were X_(29) ( days with mean temperature < 5 ℃ in October) ,X_(43) ( days with humidity > 75% in July) ,X_(54) ( mean monthly wind speed in March) ,X_(55) ( mean monthly wind speed in April,May and June) ,X_(56) ( mean monthly wind speed in July and August) ,X_(62) ( days with wind speed > 10 m·s~(- 1) in October) ,X63 ( maximum daily wind speed in September) ,X_(67) ( precipitations in April,May and June) . 【Result】With the 8 screened phenological factors,the MaxEnt model was used to make the training simulation with the actual disaster data. The Jackknife test showed that X_(43) ,X_(54) and X_(55) were the three principle climatic factors that best simulated historical outbreaks using the MaxEnt model,and ROC ( recevier operating characteristic curve) test showed an AUC ( area uner the ROC curve) value of 0. 82 with a STD( standard deviation) of 0. 019. Based on data from the WorldClim database for future climate scenarios,pine caterpillar outbreak distribution maps for 2050 were generated via the MaxEnt model under RCP( representative concentration pathway) 4. 5 and RCP6. 0. According to these maps,in the 2050s,Beijing and Hebei province,plus the southern border area of Inner Mongolia Autonomous Region with Hebei,will have a high risk of outbreaks under RCP4. 5,while more serious outbreak area will be the south central region of Shanxi province under RCP6. 0. 【Conclusion】MaxEnt model is potentially useful for forecasting future pine caterpillar outbreaks under climate change.
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/151086
Appears in Collections:气候变化与战略

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作者单位: 1.中国林业科学研究院森林生态环境与保护研究所, 国家林业局森林保护学重点实验室, 北京 100091, 中国
2.山西省森林病虫害防治检疫站, 太原, 山西 030012, 中国
3.山西省林业科学研究院, 太原, 山西 030012, 中国
4.山西省大同市灵丘县森林病虫害防治检疫站, 灵丘, 034400

Recommended Citation:
宋雄刚,王鸿斌,张真,等. 应用最大熵模型模拟预测大尺度范围油松毛虫灾害[J]. 林业科学,2016-01-01,52(6):22-39
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