1992—2022年我国林业有害生物发生时空特征及影响因素分析

doi:10.11707/j.1001-7488.LYKX20250014

Abstract

Abstract:

Objective: This study aims to analyze the spatial and temporal characteristics of forestry pest occurrence in China (1992—2022), and examine the multiscale impacts of natural climatic and socioeconomic factors on pest dynamics, providing a theoretical basis for prevention and control strategies. Method: Based on the occurrence area of forest pests in 31 provinces of mainland China from 1992 to 2022, STEM clustering analysis was used to classify the occurrence characteristics of the provinces. Combined with natural climatic factors and socioeconomic factors, the random forest, Pearson correlation, and coupling coordination degree models were employed to systematically analyze the influencing factors of forestry pest occurrence at multiple scales. Result: 1) From 1992 to 2022, forestry pest occurrence area in China showed an overall increasing trend. Temporally, plant diseases exhibited the most rapid growth after 2018, and spatially, the 31 provinces demonstrated 3 categories comprising 11 distinct growth models. 2) At the national scale, railway density, expressway density, temperature, and seedling production showed statistically significant importance (P<0.05) in driving forestry pest incidence changes. Temporally, all seven factors exhibited significant correlations with forestry pest incidence (P<0.05). Spatially, however, only railway density, expressway density and per capita GDP were significantly correlated with forestry pest incidence (P<0.05). 3) At the provincial scale, the coupling coordination degree between temperature and precipitation and forestry pest incidence was relatively low in Guangdong, Guangxi, and Hainan, while significantly higher in Shandong, Tianjin, and Shanghai. The coupling coordination degree between railway density, expressway density and forest pest occurrence was significantly higher in eastern regions including Shandong, Tianjin, and Shanghai compared to western regions such as Yunnan, Qinghai, and Xizang. 4) Further analysis was conducted to characterize the differential impacts of natural climatic and socioeconomic factors on native and invasive forestry pest incidence. Native forestry pest incidence showed higher prevalence in northern regions, demonstrating significant negative correlation with precipitation (P<0.05) but positive correlation with railway density (P<0.05). Invasive forestry pest incidence was higher in coastal areas, exhibiting highly significant positive correlations with both railway density (P<0.001) and expressway density (P<0.01). Conclusion: From 1992 to 2022, forestry pest occurrence area in China has an overall increasing trend temporally and displays multiple variation models spatially. At national and provincial scales, there are significant differences in the relative importance, correlation, and coupling coordination degree of natural climatic and socioeconomic factors on forestry pest incidence. There are significant differences in the spatial distribution of the incidence of native and invasive forestry pests and their correlation characteristics with various factors. Based on these multi-scale analysis results, a multi-level prevention and control system should be constructed, including factor-level prioritization, spatiotemporal synergy controls, regionalized prevention, and differentiated strategies for native versus invasive forest pests.

Key words: forestry pests, spatial and temporal characteristics, natural climatic factors, economic and social factors, invasive forestry pests, native forestry pests

CLC Number:

S763

Yugao Sun,Yingchao Ji,Dehui Wang,Shuikun Li,Yongming Zheng,Shaohua He,Bin Zhang,Yanfen Zhang. Spatial and Temporal Analyses of Pest Occurrence in Forestry in China from 1992 to 2022 and the Influencing Factors[J]. Scientia Silvae Sinicae, 2026, 62(3): 122-132.

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References 0

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类群 Clusters	文件 Profiles	省份 Provinces
1	14	北京 Beijing 天津Tianjin 内蒙古 Neimenggu 辽宁 Liaoning 吉林Jilin 安徽Anhui 江西Jiangxi 广西Guangxi 海南Hainan 重庆Chongqing 四川Sichuan 贵州 Guizhou 云南Yunnan 陕西Shaanxi 宁夏 Ningxia
2	43	山西 Shanxi 上海 Shanghai 江苏 Jiangsu 青海 Qinghai
3	29	福建Fujian 广东Guangdong
	38	浙江 Zhejiang 湖北 Hubei
	48	黑龙江 Heilongjiang 河南 Henan
	16	河北 Hebei
	19	甘肃 Gansu
	34	新疆Xinjiang
	37	湖南 Hunan
	41	西藏 Xizang
	42	山东 Shandong

因素 Factors	时间尺度上与林业有害生物发生率的拟合关系 Temporal goodness-of-fit for forestry pest incidence	空间尺度上与林业有害生物发生率的拟合关系 Spatial goodness-of-fit for forestry pest incidence
气温 Temperature	r = 0.552 1, P < 0.01 Y = 0.463 9X?1.942, R² = 0.304 8	r = 0.100 8, P > 0.05
降水 Precipitation	r =?0.641 9, P < 0.000 1 Y =?2.457X + 6.232, R² = 0.412 0	r =?0.319 3, P > 0.05
铁路里程率 Railway density	r = 0.760 3, P < 0.000 1 Y = 241.5X + 1.535, R² = 0.578 1	r = 0.549 7, P < 0.01 Y = 178.8X + 2.206, R² = 0.302 1
高速公路里程率 Expressway density	r = 0.713 7, P < 0.000 1 Y = 40.21X + 3.255, R² = 0.509 3	r = 0.646 0, P < 0.000 1 Y = 218.2X + 2.019, R² = 0.417 3
苗木产量 Seedling production	r = 0.523 7, P < 0.01 Y = 0.000 001 491X + 3.484, R² = 0.274 2	r = 0.082 1, P > 0.05
进出口总额 Total imports and exports	r = 0.721 1, P < 0.001 Y = 0.000 355 8X + 3.286, R² = 0.520 0	r = 0.205 2, P > 0.05
人均GDP Per capita GDP	r = 0.650 4, P < 0.000 1 Y = 0.000 008 546X + 3.279, R² = 0.423 0	r = 0.380 6, P < 0.05 Y = 0.000 142X + 1.593, R² = 0.144 9

因素 Factors	本土林业有害生物发生率 Incidence of native forestry pests	入侵林业有害生物发生率 Incidence of invasive forestry pests
气温 Temperature	r =?0.073 1, P > 0.05	r = 0.266 5, P > 0.05
降水 Precipitation	r =?0.444 1, P < 0.05 Y =?0.030 2X + 0.067 9, R² = 0.197	r =?0.008 7, P > 0.05
铁路里程率 Railway density	r = 0.370 8, P < 0.05 Y = 0.522 2X + 0.019 7, R² = 0.137 5	r = 0.592 8, P < 0.001 Y = 0.681 4X + 0.006 7, R² = 0.351 4
高速公路里程率 Expressway density	r = 0.351 5, P > 0.05	r = 0.498 8, P < 0.01 Y = 0.453 8X?0.002 5, R² = 0.248 8
苗木产量 Seedling production	r =?0.178 5, P > 0.05	r = 0.089 8, P > 0.05
进出口总额 Total imports and exports	r =?0.109 4, P > 0.05	r = 0.347 8, P > 0.05
人均GDP GDP per capita	r = 0.152 4, P > 0.05	r = 0.228 1, P > 0.05

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Spatial and Temporal Analyses of Pest Occurrence in Forestry in China from 1992 to 2022 and the Influencing Factors

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