Research on Spatial Pattern of Monochamus alternatus's Occurrence Rate Based on Meteorological Factors

WANG Qing; BI Meng; MA Si-jia; SHI Lei

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Research on Spatial Pattern of Monochamus alternatus's Occurrence Rate Based on Meteorological Factors

1.
Research Institute of Resource Insects, Chinese Academy of Forestry, Kunming 650224, Yunnan, China
2.
Southwest Forestry University, Kunming 650224, Yunnan, China

Received Date: 2014-08-21

Abstract

According to the data of China's Monochamus alternatus Hope occurrence in the period of 2002 to 2012, and taking the average occurrence rate of prefectures affected by the insect pest as a predictor, the meteorological data in those prefectures were calculated using a climate simulation software called ClimateChina v 4.40. By means of partial least squares regression, the regression equation about the average occurrence rate and meteorological factor, i.e. the spatial pattern model of average occurrence rate, was obtained to predict the future trend of potential changes on M. alternatus in China combined with the geospatial data and attribute data. The results showed that the spatial pattern model of M. alternatus' average occurrence rate built by 12 selected meteorological factors had high reliability. The prediction accuracy of the spatial pattern model was 83.14%. Based on the model, the spatial pattern of M. alternatus' average occurrence rate was predicted. The prediction results of the occurrence rate in 2020s, 2050s, and 2080s, showed that compared with the data of 2002-2012, the area with moderate or severe insect pest would be larger in eastern Sichuan, central Guizhou, eastern Hunan, western Jiangxi and western Zhejiang. The severe occurrence area in southeast Shaanxi would be less, while the mild occurrence area would decrease obviously in eastern Shandong and central Anhui.
- meteorological factor,
- Monochamus alternatus,
- insect occurrence,
- partial least squares,
- spatial pattern

References

[1]	萧刚柔.中国森林昆虫[M].北京:中国林业出版社,1992.
[2]	石雷.基于3S技术的云南省松材线虫病风险评估[M].北京:中国林业出版社,2010.
[3]	张新平,焦淑萍,张静文,等.松墨天牛入侵新疆的风险分析[J].西北林学院学报,2013,28(1):110-113.
[4]	张星耀.森林病理学研究的生态数学方法[M].北京:中国林业出版社,1999:76-98.
[5]	张星耀,骆有庆.中国森林重大生物灾害[M].北京:中国林业出版社,2003.
[6]	常晓娜,高慧璟,陈法君,等.环境温湿度和降雨对昆虫的影响[J].生态学杂志,2008,27(4):619-625.
[7]	孔维娜,王慧,李捷,等.温湿度对松墨天牛越冬幼虫寿命的影响[J].山西农业大学学报,2006,26(3):294-295.
[8]	泽桑梓,闫争亮,赵涛,等.我国松墨天牛防治及引诱技术研究的现状及前景分析[J].西部林业科学,2010,39(3):93-96.
[9]	杨忠武, 黄吉平, 杨春生,等. 桂林市松墨天牛生物学特性研究[J].广西林业科学,2010,39(1):5-7.
[10]	王文琦,张伟,陶热,等松墨天牛成虫野外空间格局[J].中国农学通报,2010,26(17):256-259.
[11]	王玲萍,陈顺立,武福华,等.松墨天牛幼虫空间格局的研究[J].福建林学院学报,2002,22(1):1-3.
[12]	周书永,陈绘画,徐卫民.松墨天牛成虫发生期预测[J].东北林业大学学报,2013,41(7):100-103.
[13]	周樟庭,徐真旺,戴黎瑶,等.冰雪冻灾后松墨天牛成虫种群数量动态研究[J].中国森林病虫,2011,30(3):18-24.
[14]	徐正会,段艳,史胜利,等.云南省松墨天牛地理分布及危害程度调查[J].西南林学院学报,2010,30(2):37-42.
[15]	毕猛,杜婷,石雷,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.
[16]	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.
[17]	Hamann A,Wang T L. Models of climate normals for genecology and climate change studies in British Columbia[J]. Agricultural and Forest Meteorology,2005,128(3-4):211-221 .
[18]	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.
[19]	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.
[20]	张雷,刘世荣,孙鹏森,等.气候变化对马尾松潜在分布影响预估的多模型比较[J].植物生态学报,2011,35(11):1091-1105.
[21]	高惠璇.两个多重相关变量组的统计分析(3)(偏最小二乘回归与PLS过程)[J].数理统计与管理,2002,21(2):58-64 .
[22]	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.
[23]	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.
[24]	汤剑平,陈星,赵鸣,等.IPCC A2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.
[25]	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.

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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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Research on Spatial Pattern of Monochamus alternatus's Occurrence Rate Based on Meteorological Factors

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

2. Southwest Forestry University, Kunming 650224, Yunnan, China

Received Date: 2014-08-21

Abstract: According to the data of China's Monochamus alternatus Hope occurrence in the period of 2002 to 2012, and taking the average occurrence rate of prefectures affected by the insect pest as a predictor, the meteorological data in those prefectures were calculated using a climate simulation software called ClimateChina v 4.40. By means of partial least squares regression, the regression equation about the average occurrence rate and meteorological factor, i.e. the spatial pattern model of average occurrence rate, was obtained to predict the future trend of potential changes on M. alternatus in China combined with the geospatial data and attribute data. The results showed that the spatial pattern model of M. alternatus' average occurrence rate built by 12 selected meteorological factors had high reliability. The prediction accuracy of the spatial pattern model was 83.14%. Based on the model, the spatial pattern of M. alternatus' average occurrence rate was predicted. The prediction results of the occurrence rate in 2020s, 2050s, and 2080s, showed that compared with the data of 2002-2012, the area with moderate or severe insect pest would be larger in eastern Sichuan, central Guizhou, eastern Hunan, western Jiangxi and western Zhejiang. The severe occurrence area in southeast Shaanxi would be less, while the mild occurrence area would decrease obviously in eastern Shandong and central Anhui.

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Reference (25)

[1]	萧刚柔.中国森林昆虫[M].北京:中国林业出版社,1992.
[2]	石雷.基于3S技术的云南省松材线虫病风险评估[M].北京:中国林业出版社,2010.
[3]	张新平,焦淑萍,张静文,等.松墨天牛入侵新疆的风险分析[J].西北林学院学报,2013,28(1):110-113.
[4]	张星耀.森林病理学研究的生态数学方法[M].北京:中国林业出版社,1999:76-98.
[5]	张星耀,骆有庆.中国森林重大生物灾害[M].北京:中国林业出版社,2003.
[6]	常晓娜,高慧璟,陈法君,等.环境温湿度和降雨对昆虫的影响[J].生态学杂志,2008,27(4):619-625.
[7]	孔维娜,王慧,李捷,等.温湿度对松墨天牛越冬幼虫寿命的影响[J].山西农业大学学报,2006,26(3):294-295.
[8]	泽桑梓,闫争亮,赵涛,等.我国松墨天牛防治及引诱技术研究的现状及前景分析[J].西部林业科学,2010,39(3):93-96.
[9]	杨忠武, 黄吉平, 杨春生,等. 桂林市松墨天牛生物学特性研究[J].广西林业科学,2010,39(1):5-7.
[10]	王文琦,张伟,陶热,等松墨天牛成虫野外空间格局[J].中国农学通报,2010,26(17):256-259.
[11]	王玲萍,陈顺立,武福华,等.松墨天牛幼虫空间格局的研究[J].福建林学院学报,2002,22(1):1-3.
[12]	周书永,陈绘画,徐卫民.松墨天牛成虫发生期预测[J].东北林业大学学报,2013,41(7):100-103.
[13]	周樟庭,徐真旺,戴黎瑶,等.冰雪冻灾后松墨天牛成虫种群数量动态研究[J].中国森林病虫,2011,30(3):18-24.
[14]	徐正会,段艳,史胜利,等.云南省松墨天牛地理分布及危害程度调查[J].西南林学院学报,2010,30(2):37-42.
[15]	毕猛,杜婷,石雷,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.
[16]	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.
[17]	Hamann A,Wang T L. Models of climate normals for genecology and climate change studies in British Columbia[J]. Agricultural and Forest Meteorology,2005,128(3-4):211-221 .
[18]	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.
[19]	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.
[20]	张雷,刘世荣,孙鹏森,等.气候变化对马尾松潜在分布影响预估的多模型比较[J].植物生态学报,2011,35(11):1091-1105.
[21]	高惠璇.两个多重相关变量组的统计分析(3)(偏最小二乘回归与PLS过程)[J].数理统计与管理,2002,21(2):58-64 .
[22]	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.
[23]	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.
[24]	汤剑平,陈星,赵鸣,等.IPCC A2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.
[25]	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.

Research on Spatial Pattern of Monochamus alternatus's Occurrence Rate Based on Meteorological Factors

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通讯作者: 陈斌, bchen63@163.com

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Research on Spatial Pattern of Monochamus alternatus's Occurrence Rate Based on Meteorological Factors

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