基于空间矩阵模型及0~1测度的美国白蛾风险格局分析

doi:10.11707/j.1001-7488.20210115

摘要/Abstract

摘要：

目的: 分析美国白蛾在中国的扩散风险格局，为其防治和风险管理提供精准的空间位置和技术支持。方法: 收集全国2011年至2016年美国白蛾的乡镇检疫数据，进行寄主、气象要素、地理环境、人为影响变量的空间模拟。将影响有害生物发生发展的因子及风险描述为空间栅格点上的连续变量，利用GIS (地理信息系统)的空间矩阵模型表达变量，并借助SPSS筛选和建立0~1测度的多变量Logistic概率模型，以250 m为最小空间栅格，描述全国任意一个空间地理单元发生扩散的风险概率。结果: 除生物气候变量外，人流物流变量的影响显著，高风险位于东部农区、建设用地及人工植被区域，主要集中在辽宁、北京、天津、上海、河北、山东、河南、安徽、湖北、江苏、陕西等省市，并有扩散到吉林、内蒙、湖南、江西、新疆、宁夏等省区的趋势。结论: 利用矩阵模型及0~1概率化测度描述有害生物发生及扩散风险，对基层开展检疫和防治具明显的指导作用。引入人流物流扩散影响模拟变量，可有效提高测报预警精度。人为活动密集区既是疫区，又是传播通道。山地森林系统的自然植被对传播扩散具有阻隔作用，加强高风险区的近自然林修复和建设，加大重要通道的检疫，对防控具有重要意义。

关键词: 空间矩阵, 风险分析, 美国白蛾, 人流物流, 概率化测报

Abstract:

Objective: This study aimed at providing spatial guidance for prevention and control of risk management, and the factors and risks affecting the occurrence and spread of pests were described as continuous variables on the spatial grid points to improve the precision of measurement and warning. The raster model of GIS (geographic information system) was used to express the variables in digital matrix and spatial unit, and the multivariate logistic probability model with 0-1 measure was established by SPSS to present the risk of pest occurrence. The flowchart was illustrated by taking Hyphantria cunea as an example. Method: Based on the national quarantine data in township of Hyphantria cunea during 2011 and 2016, simulation of host, meteorological factors, biogeographic environment, and main factors impact variables was carried out to analyze the risk pattern of occurrence and spread of Hyphantria cunea. Taking 250 m as the minimum spatial grid, the risk probability of any spatial geographic unit is described. Result: Besides bioclimates, the impact of human mobility and logistics variables is significant in risk assessment. The high risk is located in the eastern farmland and construction land of man-made vegetation regions, mainly in Liaoning, Beijing, Tianjin, Shanghai, Hebei, Shandong, Henan, Anhui, Hubei, Jiangsu, Shanxi and other provinces and cities, and has the trend of spreading to Jilin, Inner Mongolia, Hunan, Jiangxi, Xinjiang, Ningxia and other provinces. Conclusion: The matrix model and 0-1 probabilistic measure are used to describe the risk of occurrence and diffusion of pests. The simulation of human impacts spread can effectively improve the accuracy of measurement and warning. The human activity intensive region is not only the epidemic area, but also the transmission path. The natural vegetation of the mountain forest system has a blocking effect on the propagation and diffusion. It is of great significance for the prevention and control to strengthen the restoration and construction of near-natural forests in high-risk areas and to increase the quarantine of important passageways.

Key words: spatial matrix, risk analysis, Hyphantria cunea, human mobility and logistics, probabilistic prediction and warning

中图分类号:

叶江霞,王敬文,张明莎,周汝良,石雷. 基于空间矩阵模型及0~1测度的美国白蛾风险格局分析[J]. 林业科学, 2021, 57(1): 140-152.

Jiangxia Ye,Jingwen Wang,Mingsha Zhang,Ruliang Zhou,Lei Shi. Risk Pattern Analysis of Hyphantria cunea Based on Spatial Matrix Model and 0-1 Measure[J]. Scientia Silvae Sinicae, 2021, 57(1): 140-152.

图/表 12

表1

图1

表2

美国白蛾风险模型自变量表①"

变量类别 Variable classification	变量名 Variable name	变量释义 Variable explanation	变量类别 Variable classification	变量名 Variable name	变量释义 Variable explanation
生物气候变量 Bioclimatic variables	Bio1	年平均气温 Annual mean temperature	生物气候变量 Bioclimatic variables	Bio16	最湿季降水 Precipitation of the wettest quarter
	Bio2	平均气温日较差 Mean diurnal temperature range		Bio17	最干季降水 Precipitation of the driest quarter
	Bio3	等温性 Isothermality		Bio18	最暖季降水 Precipitation of the warmest quarter
	Bio4	温度季节性变化 Temperature seasonality		Bio19	最冷季降水 Precipitation of the coldest quarter
	Bio5	最暖月的最高温 Max temperature of the warmest month	生物类因子 Biological factors	Hst	寄主分布 Host distribution
	Bio6	最冷月的最低温 Min temperature of the coldest month	生物类因子 Biological factors	VCF	植被覆盖 Vegetation cover
	Bio7	气温年变幅 Temperature annual range	地理环境因子 Geographical Environment factors	Ele	海拔 Elevation
	Bio8	最湿季均温 Mean temperature of the wettest quarter		Slp	坡度 Slope
	Bio9	最干季均温 Mean temperature of the driest quarter		Asp	坡向指数 Aspect index
	Bio10	最暖季均温Mean temperature of the warmest quarter	人为扩散因子 Human spread factors	Citypct	地级城市影响力 Impacts of prefecture city
	Bio11	最冷季均温Mean temperature of the coldest quarter		Cntypct	县级城市影响力 Impacts of county city
	Bio12	年降水 Annual precipitation		Rawpct	铁路影响力 Impacts of railway
	Bio13	最湿月降水 Precipitation of the wettest month		Hwpct	高速公路影响力 Impacts of highway
	Bio14	最干月降水 Precipitation of the driest month		Nrpct	国道影响力 Impacts of national roads
	Bio15	降水季节性变化 Precipitation seasonality		Pvrpct	省道影响力 Impacts of provincial roads

表2

图2

表3

表4

表5

图3

表6

图4

图5

图6

参考文献 0

	曹江峰, 林常松, 常治. 承德市美国白蛾发生特点及治理对策. 河北林业科技, 2011, (2): 64- 65. doi: 10.3969/j.issn.1002-3356.2011.02.028
	Cao J F , Lin C S , Chang Z . Occurrence characteristics and control countermeasures of Hyphantria cunea in Chengde city. Hebei Forestry Sci-Tech, 2011, (2): 64- 65. doi: 10.3969/j.issn.1002-3356.2011.02.028
	曹铭昌, 周广胜, 翁恩生. 广义模型及分类回归树在物种分布模拟中的应用与比较. 生态学报, 2005, 25 (8): 2031- 2040. doi: 10.3321/j.issn:1000-0933.2005.08.029
	Cao M C , Zhou G S , Weng E S . Application and comparison of generalized models and classification and regression tree in simulating tree species distribution. Acta Ecologica Sinica, 2005, 25 (8): 2031- 2040. doi: 10.3321/j.issn:1000-0933.2005.08.029
	曹向锋. 2010. 外来入侵植物黄顶菊在中国潜在适生区预测及其风险评估. 南京: 南京农业大学硕士学位论文.
	Cao X F. 2010. The prediction of potential suitable distribution and risk assessment of the alien invasive plant Flaveria Bidentis(L.)in China. Nanjing: MS thesis of Nanjing Agricultural University.[in Chinese]
	陈景芸. 2013. 苏州外来有害生物美国白蛾检疫监测与防控技术的研究. 苏州: 苏州大学硕士学位论文.
	Chen J Y.2013. Studies on phytosanitary and comprehensive control of Hyphantria cunea(Drury). Suzhou: MS thesis of Suzhou University.[in Chinese]
	陈仲梅, 张生芳, 李玉璠, 等. 美国白蛾——一种新传进我国的危险性害虫. 植物保护, 1980, 6 (3): 37- 38.
	Chen Z M , Zhang S F , Li Y P , et al. Hyphantria cunea-a new and dangerous pest to our country. Plant Protection, 1980, 6 (3): 37- 38.
	程冬兵, 赵元凌, 张平仓, 等. 基于Logistic模型的江西省崩岗侵蚀风险评估. 中国水土保持科学, 2017, 15 (6): 106- 116.
	Cheng D B , Zhao Y L , Zhang P C , et al. On the risk assessment of collapse gully erosion in Jiangxi province based on Logistic model. Science of Soil and Water Conservation, 2017, 15 (6): 106- 116.
	崔亚琴, 郭思维, 葛雪贞, 等. 气候变化条件下悬铃木方翅网蝽在中国的适生性分析. 植物保护, 2019, 45 (5): 171- 177.
	Cui Y Q , Guo S W , Ge X Z , et al. Prediction of potential geographical distributions of Corythucha ciliate (Hemiptera: Tingidae) in China under climate change condition. Plant Protection, 2019, 45 (5): 171- 177.
	杜娟, 南宫自艳, 周国娜, 等. 美国白蛾SSR反应体系的优化与初步应用. 林业科学, 2010, 46 (3): 173- 177.
	Du J , Nangong Z Y , Zhou G N , et al. Optiimization and preliminary application of SSR analysis system in Hyphantria cunea. Scientia Silvae Sinicae, 2010, 46 (3): 173- 177.
	高崇省. 2006. 进境黑麦草籽实有害生物风险分析. 西安: 西北农林科技大学硕士学位论文.
	Gao C S. 2006. Pest risk analysis of importation of Ryegrass seeds. Xi'an: MS thesis of Northwest A&F University.[in Chinese]
	纪烨琳, 苏喜友, 于治军. 基于随机森林模型的美国白蛾在中国的潜在生境预测. 南京林业大学学报: 自然科学版, 2019, 43 (6): 121- 128.
	Ji Y L , Su X Y , Yu Z J . Potential habitat prediction of Hyphantria cunea based on random forest model in China. Journal of Nanjing Forestry University: Natural Sciences Edition, 2019, 43 (6): 121- 128.
	季荣, 谢宝瑜, 李欣海, 等. 外来入侵种——美国白蛾的研究进展. 应用昆虫学报, 2003, 40 (1): 13- 18. doi: 10.3969/j.issn.0452-8255.2003.01.004
	Ji R , Xie B Y , Li X H , et al. Research progress on the invasive species, Hyphantria cunea. Chinese Journal of Applied Entomology, 2003, 40 (1): 13- 18. doi: 10.3969/j.issn.0452-8255.2003.01.004
	蒋星华, 童爱珍, 池友军. 金华市美国白蛾风险分析. 现代农业科技, 2008, (2): 89- 90, 99. doi: 10.3969/j.issn.1007-5739.2008.02.060
	Jiang X H , Tong A Z , Chi Y J . Risk analysis of Hyphantria cunea in Jinhua city. Modern Agricultural Science and Technology, 2008, (2): 89- 90, 99. doi: 10.3969/j.issn.1007-5739.2008.02.060
	鞠珍. 2007. 美国白蛾在不同树种上的生物学特性及抗寒性的研究. 济南: 山东农业大学硕士学位论文.
	Ju Zhen. 2007. Biological characteristics and cold resistance of American white moth on different tree species. Jinan: MS thesis of Shandong Agricultural University.[in Chinese]
	孔雪华. 2010. 极端温度对美国白蛾生长发育和存活的影响. 济南: 山东农业大学硕士学位论文.
	Kong X H. 2010. Effect of extreme temperatures on survival, development and growth of Hyphantria cunea. Jinan: MS thesis of Shandong Agricultural University.[in Chinese]
	孔雪华, 夏江宝, 吴玉新, 等. 低温对美国白蛾越冬蛹的影响研究. 应用昆虫学报, 2012, 49 (5): 1263- 1267.
	Kong X H , Xia J B , Wu Y X , et al. The effect of low temperatures on the over-wintering pupae of Hyphantria cunea. Chinese Journal of Applied Entomology, 2012, 49 (5): 1263- 1267.
	李淑贤, 高宝嘉, 张东风, 等. 美国白蛾在中国的危险性评估研究. 中国农学通报, 2008, 25 (10): 202- 206.
	Li S X , Gao B J , Zhang D F , et al. Risk assessment research of Hyphantria cunea in China. Chinese Agricultural Science Bullbetin, 2008, 25 (10): 202- 206.
	李涛, 李泽华, 余仲东, 等. 四川省美国白蛾适生性分析及风险评估. 西北农林科技大学学报: 自然科学版, 2018, 46 (1): 60- 67.
	Li T , Li Z H , Yu Z D . Adaptability analysis and risk assessment of Hyphantria cunea in Sichuan. Journal of Northwest A&F University: Nat Sci Ed, 2018, 46 (1): 60- 67.
	李志红, 秦誉嘉. 有害生物风险分析定量评估模型及其比较. 植物保护, 2018, 44 (5): 139- 150.
	Li Z H , Qin Y J . Review on the quantitative assessment models for pest risk analysis and their comparison. Plant Protection, 2018, 44 (5): 139- 150.
	林伟. 1991. 美国白蛾在中国适生性的初步研究. 北京: 北京农业大学硕士学位论文.
	Lin W. 1991. A preliminary study on the suitability of Hyphantria cunea in China. Beijing: MS thesis of Beijing Agriculture University.[in Chinese]
	刘海军. 2003. 北京地区林木外来重大有害生物风险分析. 北京: 北京林业大学硕士学位论文.
	Liu H J, 2003. Pest risk analysis on serious non-indigenous pests to Beijing area. Beijing: MS thesis of Beijing Forestry University.[in Chinese]
	刘晓彤, 袁泉, 倪健. 中国植物分布模拟研究现状. 植物生态学报, 2019, 43 (4): 273- 283.
	Liu X T , Yuan Q , Ni J . Research status of plant distribution simulation in China. Chinese Journal of Plant Ecology, 2019, 43 (4): 273- 283.
	刘晓燕. 北京市丰台区美国白蛾发生特点与防治技术研究. 安徽农学通报, 2011, (24): 85- 89. doi: 10.3969/j.issn.1007-7731.2011.24.048
	Liu X Y . Study on occurrence characteristics and control technology of Hyphantria cunea in Fengtai District, Beijing. Anhui Agricultural Science Bulletin, 2011, (24): 85- 89. doi: 10.3969/j.issn.1007-7731.2011.24.048
	陆霞, 杜新民, 安建梅. 基于GARP的美国白蛾在中国的适生区预测. 农业与技术, 2016, 36 (19): 46- 50.
	Lu X , Du X M , An J M . Prediction of Hyphantria cunea habitat in China based on GARP. Agriculture and Technology, 2016, 36 (19): 46- 50.
	沈安平, 石雷, 周汝良. 云南省松材线虫病风险评估GIS平台开发. 云南地理环境研究, 2011, 23 (1): 38- 41, 46. doi: 10.3969/j.issn.1001-7852.2011.01.008
	Shen A P , Shi L , Zhou R L . Development of GIS platform on risk assessment of Pine wood nematode disease in Yunnan province. Yunnan Geographic Environment Research, 2011, 23 (1): 38- 41, 46. doi: 10.3969/j.issn.1001-7852.2011.01.008
	申卫星. 2012. 泰山松材线虫病、美国白蛾风险分析评估与预警技术研究. 济南: 山东农业大学博士学位论文.
	Shen W X. 2012. Risk assessment and early warning research on Bursaphelenchus xylophilus and Hyphantria cunea in MT. Tai. Jinan: PhD thesis of Shandong Agricultural University.[in Chinese]
	申卫星, 郭慧玲, 迟元凯, 等. 美国白蛾在泰山的适生性分析. 林业科学, 2012, 48 (6): 165- 169.
	Shen W X , Guo H L , Chi Y K , et al. Analysis of the suitability of Hyphantria cunea in Mount Tai. Scientia Silvae Sinicae, 2012, 48 (6): 165- 169.
	石雷. 2008. 基于3S技术的云南省松材线虫病风险评估研究. 北京: 中国林业科学研究院博士学位论文.
	Shi L. 2008. The research on Pine wilt disease risk assessment in Yunnan province on 3S technology. Beijing: PhD thesis of Chinese Academy of Forestry.[in Chinese]
	史永善. 美国白蛾的天敌——日本追寄蝇. 昆虫学报, 1981, (3): 108.
	Shi Y S . Exorista japonica: A natural enemy of the fall webworm Hyphantria cunea Drury. Acta Entomologica Sinica, 1981, (3): 108.
	宋红敏, 张清芬, 韩雪梅, 等. CLIMEX: 预测物种分布区的软件. 昆虫知识, 2004, (4): 379- 387.
	Song H M , Zhang Q F , Han X M , et al. CLIMEX: professional biological software for predicting potential distribution of species. Entomological Knowledge, 2004, (4): 379- 387.
	苏茂文, 张钟宁. 外来有害生物美国白蛾入侵、危害和治理. 生物学通报, 2008, 43 (12): 5- 6. doi: 10.3969/j.issn.0006-3193.2008.12.006
	Shu M W , Zhang Z N . Alien pest invasion, harm and control of Hyphantria cunea. Bulletin of Biology, 2008, 43 (12): 5- 6. doi: 10.3969/j.issn.0006-3193.2008.12.006
	肖东升, 杨松. 基于夜间灯光数据的人口空间分布研究综述. 国土资源遥感, 2019, 31 (3): 10- 19.
	Xiao D S , Yang S . A review of population spatial distribution based on nighttime light data. Remote Sensing for Land Resources, 2019, 31 (3): 10- 19.
	徐海峰, 刑真诣. 长春市美国白蛾发生风险分析. 吉林林业科技, 2019, 48 (3): 33- 34.
	Xu H F , Xing Z Y . Risk analysis of Hyphantria cunea in Changchun. Journal of Jilin Forestry Science and Technology, 2019, 48 (3): 33- 34.
	杨明琪. 2013. 不同气候情景下美国白蛾在我国的适生区预测. 北京: 中国林业科学研究院硕士学位论文.
	Yang M Q. 2013. Prediction of Fall Webworm's potential suitable geographic distribution in different weather conditions in China. Beijing: MS thesis of Chinese Academy of Forestry.[in Chinese]
	杨忠岐. 利用天敌昆虫控制我国重大林木害虫研究进展. 中国生物防治, 2004, (4): 221- 227.
	Yang Z Q . Advance in bio-control researches of the important forest insect pests with natural enemies in China. Chinese Journal Biological Control, 2004, (4): 221- 227.
	叶江霞, 周汝良, 吴明山, 等. 云南省松墨天牛适生性空间模拟. 林业科学研究, 2013, 26 (4): 420- 425. doi: 10.3969/j.issn.1001-1498.2013.04.005
	Ye J X , Zhou R L , Wu M S , et al. Spatial simulation of the adaptability of Monochamus alternatus Hope in Yunnan province. Forest Research, 2013, 26 (4): 420- 425. doi: 10.3969/j.issn.1001-1498.2013.04.005
	叶明福. 舒城县美国白蛾入侵风险评估及防控对策. 安徽林业科技, 2019, 45 (1): 57- 60.
	Ye M F . Risk assessment on and targeted control measures for Hyphantria cunea invasion to Shucheng country. Anhui Forestry Science and Technology, 2019, 45 (1): 57- 60.
	赵铁珍, 高岚, 柯水发. 2004年全国美国白蛾疫区的非经济损失评估. 林业经济问题, 2006, 26 (4): 321- 326. doi: 10.3969/j.issn.1005-9709.2006.04.008
	Zhao T Z , Gao L , Ke S F . Evaluation of Hyphantria cunea's loss in China's epidemic stricken area in 2004. Problems of Forestry Economics, 2006, 26 (4): 321- 326. doi: 10.3969/j.issn.1005-9709.2006.04.008
	Andersen M C , Adams H , Hope B , et al. Risk assessment for invasive species. Risk Analysis, 2004, 24 (4): 787- 793. doi: 10.1111/j.0272-4332.2004.00478.x
	Baker R, Eyre D, Brunel S, et al. 2015. Mapping endangered areas for pest risk analysis. Pest risk modelling and mapping for invasive alien species. Wallingford: CABI, 18-34.
	Carranza E J M , Hale M . Geologically constrained probabilistic mapping of gold potential, Baguio district, Philippines. Natural Resources Research, 2000, 9 (3): 237- 253. doi: 10.1023/A:1010147818806
	Gilbert M , Grégoire J C , Freise J , et al. Long-distance dispersal and human population density allow the prediction of invasive patterns in the horse chestnut leafminer Cameraria ohridella. Journal of Animal Ecology, 2004, 73 (3): 459- 468. doi: 10.1111/j.0021-8790.2004.00820.x
	Guo F T , Selvaraj S L , Lin F F , et al. Geospatial information on geographical and human factors improved anthropogenic fire occurrence modeling in the Chinese boreal forest. Canadian Journal of Forest Research, 2016, 46 (4): 582- 594. doi: 10.1139/cjfr-2015-0373
	Huang Q , X Yang , B Gao , et al. Application of DMSP/OLS nighttime light images: A meta-analysis and a systematic literature review. Remote Sensing, 2014, 6 (8): 6844- 6866.
	Hudak A T , Crookston N L , Evans J S , et al. Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data. Remote Sensing of Environment, 2008, 112 (5): 2232- 2245.
	Hansen M C , Defries R S , Townshend J R G , et al. The MODIS 500 meter global vegetation field products. Analysis of Multi-Temporal Remote Sensing Images, 2004, 3 (1): 295- 301.
	IPPC(The international plant protection convention secretariat). 2017. ISPM No.11: Pest risk analysis for quarantine pests (originally adopted in 1995, revised in 2013). FAO: 2017.
	Kehlenbeck H , Robinet C , van der Werf W , et al. Modelling and mapping spread in pest risk analysis: a generic approach. EPPO Bulletin, 2012, 42 (1): 74- 80.
	Koch F H , Denys Y , Haack R A , et al. Using a network model to assess risk of forest pest spread via recreational travel. Plos One, 2014, 9 (7): e102105.
	Lippitt C D , Rogan J , Toledano J , et al. Incorporating anthropogenic variables into a species distribution model to map gypsy moth risk. Ecological Modelling, 2008, 210 (3): 339- 350.
	Muirhead J R , Leung B , Overdijk C V , et al. Modelling local and long-distance dispersal of invasive emerald ash borer Agrilus planipennis(Coleoptera) in North America. Diversity and Distributions, 2006, 12 (1): 71- 79.
	Pan S , Su X . Study on geographic distribution of fall webworm based on maximum entropy model. Nature Environment & Pollution Technology, 2017, 16 (3): 737- 744.
	Song W G , Wang J , Satoh K Y . Three types of power-law distribution of forest five in Japan. Ecological Modelling, 2006, 196 (1): 527- 532.
	Rosenkrantz R D. 1989. Where do we stand on maximum entropy? Springer Netherlands.
	Fick S E , Hijman R J . Worldclim 2:new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 2017, 37 (12): 4302- 4315.
	Wu Y Y , Xu L T , Chang L , et al. Bacillus thuringiensis(Bt) cry1C expression from the plastid genome of poplar leads to high mortality of leaf eating caterpillars. Tree Physiology, 2019, 39 (9): 1525- 1532.
	Ye J X , Wu M S , Deng Z J , et al. Modeling the spatial patterns of human wildfire ignition in Yunnan province, China. Applied Geography, 2017, 89 (1): 150- 162.

序号 No.	植被类型 Vegetation type	量化 Quantification
1	水稻Oryza sativa、大豆Glycine max、小麦Triticum aestivum等农作物Agriculture vegetation；梧桐Firmiana platanifolia、桑Morus alba、榆Ulmus pumila、白蜡槭Acer negundo等园林绿化人工植被Landscape and artificial vegetation	100
2	香椿Toona sinensis、泡桐Paulownia fortunei、枫杨Pterocarya stenoptera、栎Quercus acutissima、垂柳Salix babylonica、旱柳Salix matsudana、毛白杨Populus tomentosa、赤杨Alniphyllum fortunei、大叶黄杨Buxus megistophylla等阔叶人工植被Broad-leaved afforested vegetation	80
3	茶Camellia sinensis、油茶Camellia oleifera、杨梅Myrica rubra、柑桔Citrus reiculata、枇杷Eriobotrya japonica等经济作物Non-timber products forest；杂草类Weed；山毛榉Fagus longipetiolata、栲Castanopsis fargesii、樟科Lauraceae、红树Rhizophora apiculata、竹林及其他阔叶树等人工植被Bamboo and other broad-leaved afforested vegetation	60
4	稀树灌草丛、其他草地等自然植被Savanna and other grassland natural vegetation	40
5	灌丛或矮林Bush or shrubs natural vegetation	30
6	针阔混交林、荒漠植被Coniferous and broad-leaved mixed forest, desert vegetation	20
7	针叶林及其他自然植被Coniferous forest and other natural vegetation	10
8	其他非植被类型Other non-vegetation type	0

步骤 Step	-2对数似然 -2 Log likelihood	考克斯-斯奈尔R² Cox & Snell R²	内戈尔科R² Nagelkerke R²
1	3 335.387	0.296	0.395
2	3 178.509	0.329	0.439
3	2 913.663	0.382	0.510
4	2 863.679	0.392	0.523
5	2 822.589	0.399	0.533
6	2 795.262	0.404	0.540
7	2 610.950	0.438	0.584
8	2 611.036	0.438	0.584
9	2 586.684	0.442	0.589
10	2 553.291	0.448	0.597
11	2 553.882	0.447	0.597
12	2 503.451	0.456	0.608
13	2 442.154	0.466	0.622

观测值Observed			预测值Forecast Y		正确百分比 Correct percentage(%)	总体百分比 Overall percentage(%)
观测值Observed			0	1	正确百分比 Correct percentage(%)	总体百分比 Overall percentage(%)
步骤Step 13	Y	0	1 334	347	79.4	83
步骤Step 13	Y	1	202	1 344	86.9	83

变量 Variable	回归系数B Coefficient B	标准误差 SE	瓦尔德 Wald	自由度 df	显著性 Sig.	回归系数的指数 Exp(B)
Bio1	-6.695	0.712	88.432	1	0.000	0.001
Bio12	-3.394	0.249	185.919	1	0.000	0.034
Bio13	1.741	0.179	94.271	1	0.000	5.703
Bio2	1.986	0.202	97.075	1	0.000	7.283
Hwpct	0.776	0.145	28.509	1	0.000	2.173
Pvrpct	0.194	0.090	4.636	1	0.031	1.214
Ele	-2.343	0.271	74.641	1	0.000	0.096
Bio15	-1.450	0.118	152.053	1	0.000	0.235
Bio6	9.854	1.316	56.058	1	0.000	19 027.165
Bio7	2.615	0.800	10.698	1	0.001	13.672
常数 Constant	-0.662	0.071	85.918	1	0.000	0.516

观测Y Observed Y	预测Y Forecast Y		正确百分比 Correct percentage(%)	总体百分比 Overall percentage(%)
观测Y Observed Y	0	1	正确百分比 Correct percentage(%)	总体百分比 Overall percentage(%)
0	555	170	76.55	75.68
1	169	494	74.81	75.68