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

Abstract

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

CLC Number:

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.

Figures/Tables 12

Table 1

Fig.1

Table 2

Independent variablesof risk model for H.cunea"

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

Table 2

Fig.2

Table 3

Table 4

Table 5

Fig.3

Table 6

Fig.4

Fig.5

Fig.6

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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

Risk Pattern Analysis of Hyphantria cunea Based on Spatial Matrix Model and 0-1 Measure

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