| 摘要: |
| 【目的】探究森林病虫害时空动态变化特征及关键因素驱动力的空间分布，为森林病虫害防控与治理提供参考。【方法】采用空间联系局部指标（LISA）和Mann-Kendall(M-K)趋势检验，分析2008-2018年福建省县域森林病虫害发生率时空变化特征，利用夏季均温、冬季均温、月均降水量、夜间灯光均值等变量构建时空加权回归（STWR）模型，分析各变量与森林病虫害发生率之间的时空异质性。【结果】①2008-2018年福建省有16个县域森林病虫害发生率呈下降趋势，空间分布以“低 低”聚集的空间聚集类型为主，空间分布范围先缩小后扩张。不同变量对森林病虫害发生率的影响程度从强到弱依次为夜间灯光均值、月均降水量、夏季均温、冬季均温。②4种不同变量的影响机制有明显的时空分异性，其中夜间灯光均值、夏季均温、冬季均温对森林病虫害发生有正向促进作用，月均降水量对森林病虫害发生有负向抑制作用。③在森林病虫害高发生率县域中，夜间灯光均值为最大的负向影响因素，月均降水量也通常为负向影响因素。【结论】STWR模型的拟合性能优于地理加权回归（GWR）模型与普通线性回归（OLS）模型，且STWR模型预测能力比GWR模型更佳，能更精细地分析4个变量对县域森林病虫害发生率影响的时空变化过程。 |
| 关键词: 时空异质性 森林病虫害 空间统计 时空加权回归（STWR）模型 福建省 |
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| 基金项目:福建省自然科学基金(2021J05030)；2021年福建省省级科技创新重点项目(2021G02007)；中央引导地方科技发展专项(2021L3033，2020L3006)；福建农林大学科技创新专项(CXZX2020149C，KCX21F33A） |
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| Driving factors of forest diseases and insect pests based on STWR model |
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SU Shaoqiang,QUE Xiang,YAN Xuanhui,et al
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| Abstract: |
| 【Objective】The temporal and spatial dynamic characteristics of forest diseases and pests and the spatial distribution of driving forces were investigated to provide references for forest pest control and management.【Method】The local indicators of spatial association (LISA) and Mann Kendall (M-K) trend tests were used to analyze the spatiotemporal variation characteristics of forest diseases and pests incidence rates at county level in Fujian from 2008 to 2018.The spatiotemporal weighted regression (STWR) model was constructed by variables including summer average temperature,winter average temperature,monthly average precipitation,and average value of nighttime light.Then,it was used to analyze the temporal and spatial heterogeneity between variables and forest diseases and pests incidence rates.【Result】① From 2008 to 2018,forest diseases and pests incidence rates showed a downward trend in 16 counties in Fujian,mainly in the “low-low” aggregation type.The spatial distribution range first decreased and then expanded.The influences of factors were in the decreasing order of nighttime light,monthly average precipitation,summer average temperature,and winter average temperature.② Nighttime light,summer average temperature, and winter average temperature had positive effects on the occurrence of forest diseases and pests,while monthly average precipitation had negative effects.③ Nighttime light was the largest negative influencing factor and monthly average precipitation was usually negative in counties with high incidences of forest diseases and insect pests.【Conclusion】The fitting performance of the STWR model was better than geographically weighted regression (GWR) and global least squares regression (OLS),and the predictive ability of the STWR model was better than that of the GWR model.Thus,the STWR model can be used for analyzing effects of different factors on spatiotemporal variations of incidence of forest diseases and pests. |
| Key words: spatiotemporal heterogeneity forest diseases and insect pests spatial statistics spatiotemporal weighted regression (STWR) model Fujian Province |
