Spatial Pattern of Dendrolimus punctatus Incidence Rate Based on Meteorological Factors

WANG Qing; BI Meng; DU Ting; LIAO Huai-jian; SHI Lei

Volume 29 Issue 2

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Spatial Pattern of Dendrolimus punctatus Incidence Rate Based on Meteorological Factors

1.
Research Institute of Resources Insects, Chinese Academy of Forestry, Kunming 650224, Yunnan, China

Received Date: 2015-09-10

Abstract

[Objective]Taking the average incidence rate based on China's nationwide data from 2002-2012 as indicator to predict the potential trend of Dendrolimus punctatus incidence rate. [Method]By means of partial least squares regression, the regression equation about average incidence rate and the related meteorological factors was obtained. Combined with the geographic spatial data and future meteorological data, the spatial pattern model of the average incidence rate of D. punctatus was established. [Result]The spatial pattern model of D. punctatus' average incidence rate built by 12 selected meteorological factors has the prediction accuracy of 86.98%. Based on this model, the spatial pattern models for 2020s, 2050s, and 2080s were established. It was predicted that compared with 2002-2012, the area of moderate and severe insect pests in East and Central China would significantly increase, and there would be a trend of spreading. The mild incidence area would decrease in East China, while the mild incidence area would has a trend of amplification in parts of Southern China. [Conclusion]The spatial pattern model obtained by partial least squares regression method can be used to predict the potential changes of the average incidence rate of D. punctatus in China.
- Dendrolimus punctatus Walker,
- meteorological factor,
- insect incidence rate,
- partial least squares,
- spatial pattern

References

[1]	刘兴平,温小遂.马尾松毛虫成灾原因及其综合治理[J].江西植保,2007,30(2):61-64.
[2]	曾菊平,戈峰,苏建伟,等.我国林业重大害虫松毛虫的灾害研究进展[J].昆虫知识,2010,47(3):451-459.
[3]	刘志明,晏明,张旭东,等.气象因子对落叶松毛虫的影响分析[J].灾害学,2002,17(4):32-37.
[4]	李振华.雨日对马尾松毛虫诱发作用的初探[J].江西林业科技,1997(5):32-33.
[5]	何忠,韩瑞东,刘向辉,等.环境温度对马尾松毛虫发育与存活的影响[J].应用生态学报,2006,17(3):483-488.
[6]	李兆麟,何忠,贾凤友.马尾松毛虫幼虫发育的光周效应[J].林业科学研究,1993,6(3):276-281.
[7]	侯陶谦.中国主要害虫--松毛虫的综合防治[M].北京:中国林业出版社,1979.
[8]	王学锋,周德丽,童青,等.思茅林区松毛虫害发生与气象因子的关系[J].云南林业科技,2003(3):77-82.
[9]	何忠,席瑞华,王序英,等.环境湿度对马尾松毛虫卵和低龄幼虫发育与成活的影响[C]//李典谟,康乐,吴钜文,等.昆虫学创新与发展,北京:中国科学技术出版社,2002:508-513.
[10]	孙思清,邵守英,刘鸿岩.台风暴雨对赤松毛虫虫口密度的影响[J].山东林业科技,1997(6):22-23.
[11]	余会康,黄岩彬.古田水口林区松毛虫发生的气候成因分析[J].四川林业科技,2005,26(6):83-85.
[12]	厉悦,刘敏,张合平,等.基于多元统计分析的影响马尾松毛虫灾害发生的生态环境因素研究[J].江苏农业科学,2012,40(5):90-92.
[13]	田万银,徐华潮.浙江沿海防护林马尾松毛虫的预测预报模型[J].环境昆虫学报,2012,34(4):401-406.
[14]	陈绘画,朱寿燕,崔相富,等.基于人工神经网络的马尾松毛虫发生量预测模型的研究[J].林业科学研究,2003,16(2):159-165.
[15]	高倩,安英博,吴凤祥.基于Logistic回归的落叶松毛虫预测模型研究[J].河北农业大学学报,2011,34(6):108-110.
[16]	向昌盛.基于地统计学定阶的松毛虫发生面积组合预测[J].计算机应用研究,2012,29(3):984-987.
[17]	Wold S, Albano C. Modeling Data Tables by Principal Component and PLS: class Patterns and Quantitative Predictive Relations[J].Analysis,1984,12:477-485.
[18]	HÖskuldson A. PLS regression methods[J].Journal of Chemometrics,1988,2:221-228.
[19]	高惠璇.两个多重相关变量组的统计分析(偏最小二乘回归与PLS过程)[J].数理统计与管理,2002,21(2):58-64.
[20]	王惠文,吴载斌,孟浩.偏最小二乘回归的线性与非线性方法[M].北京:国防工业出版社,2006.
[21]	Wang T, Hamann A, Spittlehouse D L, et al. Development of scale-free climate data for western Canada for use in resource management[J].International Journal of Climatology,2006,26(3):383-397.
[22]	Hamann A, Wang T L. Models of climate normal for genecology and climate change studies in British Columbia[J].Agricultural and Forest Meteorology, 2005,128(3-4):211-221.
[23]	Daly C, Gibson W P, Taylor G H, et al. A knowledge-based approach to the statistical mapping of climate [J].Climate Research, 2002,22(2):99-113.
[24]	Mitchell T D, Jones P D. An improved method of constructing a database of monthly climate observations and associated high-resolution grids [J]. International Journal of Climatology,2005,25(6):693-712.
[25]	毕猛,杜婷,马思佳,等.基于气象因子的杨树溃疡病发生率空间格局研究[J].林业科学研究,2014,27(1):24-30.
[26]	王庆,毕猛,马思佳,等. 基于气象因子的松墨天牛发生率空间格局研究[J].林业科学研究,2015,28(1):61-66.
[27]	Chong I G,Jun C H. Performance of some variable selection methods when multicollinearity is present[J].Chemometrics and Intelligent Laboratory Systems,2005,78(1-2):103-112.
[28]	汤剑平,陈星,赵鸣,等.IPCCA2情景下中国区域气候变化的数值模拟[J].气象学报,2008,66(1):13-25.
[29]	江志红,张霞,王冀.IPCC-AR4模式对中国21世纪气候变化的情景预估[J].地理研究,2008,27(4):787-799.
[30]	王冀.中国地区极端气温变化的模拟评估及其未来情景预估[D].南京:南京信息工程大学,2008.
[31]	陈南祥,曹连海,李梅,等.偏最小二乘回归神经网络的矿坑涌水量预测[J].吉林大学学报,2005,35(6):765-770.

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通讯作者: 陈斌, bchen63@163.com

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沈阳化工大学材料科学与工程学院沈阳 110142

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Spatial Pattern of Dendrolimus punctatus Incidence Rate Based on Meteorological Factors

1. Research Institute of Resources Insects, Chinese Academy of Forestry, Kunming 650224, Yunnan, China

Received Date: 2015-09-10

Abstract: [Objective]Taking the average incidence rate based on China's nationwide data from 2002-2012 as indicator to predict the potential trend of Dendrolimus punctatus incidence rate. [Method]By means of partial least squares regression, the regression equation about average incidence rate and the related meteorological factors was obtained. Combined with the geographic spatial data and future meteorological data, the spatial pattern model of the average incidence rate of D. punctatus was established. [Result]The spatial pattern model of D. punctatus' average incidence rate built by 12 selected meteorological factors has the prediction accuracy of 86.98%. Based on this model, the spatial pattern models for 2020s, 2050s, and 2080s were established. It was predicted that compared with 2002-2012, the area of moderate and severe insect pests in East and Central China would significantly increase, and there would be a trend of spreading. The mild incidence area would decrease in East China, while the mild incidence area would has a trend of amplification in parts of Southern China. [Conclusion]The spatial pattern model obtained by partial least squares regression method can be used to predict the potential changes of the average incidence rate of D. punctatus in China.

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Reference (31)

[1]	刘兴平,温小遂.马尾松毛虫成灾原因及其综合治理[J].江西植保,2007,30(2):61-64.
[2]	曾菊平,戈峰,苏建伟,等.我国林业重大害虫松毛虫的灾害研究进展[J].昆虫知识,2010,47(3):451-459.
[3]	刘志明,晏明,张旭东,等.气象因子对落叶松毛虫的影响分析[J].灾害学,2002,17(4):32-37.
[4]	李振华.雨日对马尾松毛虫诱发作用的初探[J].江西林业科技,1997(5):32-33.
[5]	何忠,韩瑞东,刘向辉,等.环境温度对马尾松毛虫发育与存活的影响[J].应用生态学报,2006,17(3):483-488.
[6]	李兆麟,何忠,贾凤友.马尾松毛虫幼虫发育的光周效应[J].林业科学研究,1993,6(3):276-281.
[7]	侯陶谦.中国主要害虫--松毛虫的综合防治[M].北京:中国林业出版社,1979.
[8]	王学锋,周德丽,童青,等.思茅林区松毛虫害发生与气象因子的关系[J].云南林业科技,2003(3):77-82.
[9]	何忠,席瑞华,王序英,等.环境湿度对马尾松毛虫卵和低龄幼虫发育与成活的影响[C]//李典谟,康乐,吴钜文,等.昆虫学创新与发展,北京:中国科学技术出版社,2002:508-513.
[10]	孙思清,邵守英,刘鸿岩.台风暴雨对赤松毛虫虫口密度的影响[J].山东林业科技,1997(6):22-23.
[11]	余会康,黄岩彬.古田水口林区松毛虫发生的气候成因分析[J].四川林业科技,2005,26(6):83-85.
[12]	厉悦,刘敏,张合平,等.基于多元统计分析的影响马尾松毛虫灾害发生的生态环境因素研究[J].江苏农业科学,2012,40(5):90-92.
[13]	田万银,徐华潮.浙江沿海防护林马尾松毛虫的预测预报模型[J].环境昆虫学报,2012,34(4):401-406.
[14]	陈绘画,朱寿燕,崔相富,等.基于人工神经网络的马尾松毛虫发生量预测模型的研究[J].林业科学研究,2003,16(2):159-165.
[15]	高倩,安英博,吴凤祥.基于Logistic回归的落叶松毛虫预测模型研究[J].河北农业大学学报,2011,34(6):108-110.
[16]	向昌盛.基于地统计学定阶的松毛虫发生面积组合预测[J].计算机应用研究,2012,29(3):984-987.
[17]	Wold S, Albano C. Modeling Data Tables by Principal Component and PLS: class Patterns and Quantitative Predictive Relations[J].Analysis,1984,12:477-485.
[18]	HÖskuldson A. PLS regression methods[J].Journal of Chemometrics,1988,2:221-228.
[19]	高惠璇.两个多重相关变量组的统计分析(偏最小二乘回归与PLS过程)[J].数理统计与管理,2002,21(2):58-64.
[20]	王惠文,吴载斌,孟浩.偏最小二乘回归的线性与非线性方法[M].北京:国防工业出版社,2006.
[21]	Wang T, Hamann A, Spittlehouse D L, et al. Development of scale-free climate data for western Canada for use in resource management[J].International Journal of Climatology,2006,26(3):383-397.
[22]	Hamann A, Wang T L. Models of climate normal for genecology and climate change studies in British Columbia[J].Agricultural and Forest Meteorology, 2005,128(3-4):211-221.
[23]	Daly C, Gibson W P, Taylor G H, et al. A knowledge-based approach to the statistical mapping of climate [J].Climate Research, 2002,22(2):99-113.
[24]	Mitchell T D, Jones P D. An improved method of constructing a database of monthly climate observations and associated high-resolution grids [J]. International Journal of Climatology,2005,25(6):693-712.
[25]	毕猛,杜婷,马思佳,等.基于气象因子的杨树溃疡病发生率空间格局研究[J].林业科学研究,2014,27(1):24-30.
[26]	王庆,毕猛,马思佳,等. 基于气象因子的松墨天牛发生率空间格局研究[J].林业科学研究,2015,28(1):61-66.
[27]	Chong I G,Jun C H. Performance of some variable selection methods when multicollinearity is present[J].Chemometrics and Intelligent Laboratory Systems,2005,78(1-2):103-112.
[28]	汤剑平,陈星,赵鸣,等.IPCCA2情景下中国区域气候变化的数值模拟[J].气象学报,2008,66(1):13-25.
[29]	江志红,张霞,王冀.IPCC-AR4模式对中国21世纪气候变化的情景预估[J].地理研究,2008,27(4):787-799.
[30]	王冀.中国地区极端气温变化的模拟评估及其未来情景预估[D].南京:南京信息工程大学,2008.
[31]	陈南祥,曹连海,李梅,等.偏最小二乘回归神经网络的矿坑涌水量预测[J].吉林大学学报,2005,35(6):765-770.

Spatial Pattern of Dendrolimus punctatus Incidence Rate Based on Meteorological Factors

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通讯作者: 陈斌, bchen63@163.com

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Spatial Pattern of Dendrolimus punctatus Incidence Rate Based on Meteorological Factors

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