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

林业科学 ›› 2026, Vol. 62 ›› Issue (3): 122-132.doi: 10.11707/j.1001-7488.LYKX20250014

1992—2022年我国林业有害生物发生时空特征及影响因素分析

孙钰高^1,^2,³(),季英超⁵,王德辉⁶,李水坤⁷,郑永明⁸,何少华⁹,张宾^3,*(),张艳芬^1,^2,^4,*()

1. 中国科学院动物研究所　动物多样性保护与有害动物防控全国重点实验室　北京?100101
2. 中国科学院大学　中国科学院生物互作卓越创新中心　北京?100049
3. 河北大学生命科学学院　保定?071002
4. 山西汾河平原农田防护林生态系统定位观测研究站　晋中?030801
5. 山东农业大学植物保护学院　泰安?271018
6. 建昌县林业和草原发展保障中心　葫芦岛?125300
7. 建德市林业生态服务中心　杭州?311600
8. 临安区植物检疫站　杭州?311300
9. 麻城市森林病虫防治检疫站　黄冈?438300

收稿日期:2025-01-11 修回日期:2025-11-05 出版日期:2026-03-15 发布日期:2026-03-12
通讯作者: 张宾,张艳芬 E-mail:sunyg@ioz.ac.cn;binzhang@hbu.edu.cn;yanfenzhang@ioz.ac.cn
基金资助:
国家自然科学基金重点项目（32230066）；国家重点研发计划项目（2025YFC2609102）；国家自然科学基金联合项目（U24A20432）；国家林业和草原局揭榜挂帅项目（202401-10）；中国科学院动物研究所自主部署项目（2023IOZ0103，2023IOZ0203，2023IOZ0204）。

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

Yugao Sun^1,^2,³(),Yingchao Ji⁵,Dehui Wang⁶,Shuikun Li⁷,Yongming Zheng⁸,Shaohua He⁹,Bin Zhang^3,*(),Yanfen Zhang^1,^2,^4,*()

1. State Key Laboratory of Animal Biodiversity Conservation and Integrated Pest Management　Institute of Zoology, Chinese Academy of Sciences　Beijing?100101
2. Chinese Academy of Sciences Center for Excellence in Biotic Interactions　University of Chinese Academy of Sciences　Beijing?100049
3. College of Life Sciences, Hebei University　Baoding?071002
4. Shanxi Fenhe Plain Farmland Shelterbelt Ecosystem Research Station for Long-Term Observation　Jinzhong?030801
5. College of Plant Protection, Shandong Agricultural University　Tai’an?271018
6. Jianchang County Forestry and Grassland Development and Protection Center　Huludao?125300
7. Jiande City Forestry and Ecological Service Center　Hangzhou?311600
8. Lin’an District Plant Quarantine Station　Hangzhou?311300
9. Macheng City Forestry Pest Control and Quarantine Station　Huanggang?438300

Received:2025-01-11 Revised:2025-11-05 Online:2026-03-15 Published:2026-03-12
Contact: Bin Zhang,Yanfen Zhang E-mail:sunyg@ioz.ac.cn;binzhang@hbu.edu.cn;yanfenzhang@ioz.ac.cn

摘要/Abstract

摘要：

目的: 分析1992—2022年我国林业有害生物发生的时空特征，从多尺度探究自然气候因素、经济社会因素对其的影响，为我国林业有害生物防控决策提供理论依据。方法: 基于1992—2022年我国31个省份的林业有害生物发生面积，运用STEM聚类分析对省份发生特征进行分类；结合自然气候因素和经济社会因素，采用随机森林、皮尔逊相关及耦合协调度模型，从多尺度解析林业有害生物发生的影响因素。结果: 1） 1992—2022年，我国林业有害生物发生面积总体呈上升趋势，在时间上2018年后病害增速最快，在空间上31个省份呈现为3类11种增长模型。2）全国尺度上，铁路里程率、高速公路里程率、气温、苗木产量对林业有害生物发生率变化的重要性显著（P < 0.05）。在时间维度，气温、降水、铁路里程率、高速公路里程率、苗木产量、人均GDP、进出口总额7个因素均与林业有害生物发生率显著相关（P < 0.05）；在空间维度，仅铁路里程率、高速公路里程率和人均GDP与林业有害生物发生率显著相关（P < 0.05）。3）省份尺度上，气温、降水与林业有害生物发生率的耦合协调度在广东、广西、海南较低，在山东、天津、上海较高；铁路里程率、高速公路里程率与林业有害生物发生率的耦合协调度在山东、天津、上海等东部地区较高，在云南、青海、西藏等西部地区较低。4）进一步分析自然气候因素、经济社会因素对本土和入侵林业有害生物发生率的差异影响特征，本土林业有害生物发生率在北方地区较高，与降水呈显著负相关（P < 0.05），与铁路里程率呈显著正相关（P < 0.05）；入侵林业有害生物发生率在沿海地区较高，与铁路里程率（P < 0.001）、高速公路里程率（P < 0.01）呈极显著正相关。结论: 1992—2022年我国林业有害生物发生面积在时间上总体呈上升趋势，在空间上呈现多种变化模型。全国、省份尺度上，自然气候因素、经济社会因素对林业有害生物发生率的相对重要性、相关性、耦合协调度存在显著差异。本土和入侵林业有害生物发生率的空间分布及与各因素的相关性特征存在显著差异。基于多尺度分析结果，应构建因素分级管理、时空协同防控、分区防控、本土和入侵林业有害生物差异防控的多级防控体系。

关键词: 林业有害生物, 时空特征, 自然气候因素, 经济社会因素, 本土林业有害生物, 入侵林业有害生物

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

中图分类号:

S763

孙钰高,季英超,王德辉,李水坤,郑永明,何少华,张宾,张艳芬. 1992—2022年我国林业有害生物发生时空特征及影响因素分析[J]. 林业科学, 2026, 62(3): 122-132.

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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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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1992—2022年我国林业有害生物发生时空特征及影响因素分析

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

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