台州市松材线虫病受害程度的时空变化特征及其影响因素OA
Spatiotemporal variations and influencing factors of pine wilt disease damage in Taizhou
探究松材线虫病疫情的时空动态特征及其影响因素,可为松材线虫病的区域差异化防控提供参考.采用Mann-Kendall趋势检验、Moran's I指数和LISA指数分析2021-2024年台州市松材线虫病的时空分布格局.基于海拔、夏季均温、降水量、湿地面积、人口密度等变量分别构建OLS、GWR、GTWR模型,探讨变量与松材线虫病受害程度的时空异质性,并比较模型性能.台州市北部、西部和东南部受害程度较高,东部较低.2021-2024年全市平均受害程度分别为17.76%、16.96%、13.53%和8.72%,其中60个乡镇/街道受害程度显著下降,7个显著上升.Moran's I指数均显著大于0.GTWR模型的拟合效果优于OLS和GWR,R2为0.543,AICc为4093.23,RSS为75995.8.各变量回归系数依次为:海拔0.019,降水量0.060,夏季均温1.880,湿地面积0.30,人口密度0.003.这表明,高感染区域主要分布于山地丘陵多、林木密度高的地区,低感染区域集中于地势平坦、人口聚集的主城区,并呈现"高-高"与"低-低"集聚特征.2021-2024年松材线虫病受害面积总体下降,且下降幅度逐年增大,空间集聚特征相对稳定.GTWR模型可更精细度量变量与受害程度的时空非平稳关系.夏季均温和海拔对松材线虫病发生起正向作用,而降水量和湿地面积的影响具有波动性和复杂性.
Clarifying the spatiotemporal variations and influencing factors of pine wilt disease epidemic provides a reference for the spatiotemporal dynamic monitoring and regionally differentiated prevention and the control of pine wilt disease.The Mann-Kendall trend test,Moran's I index,and LISA index were employed to analyze the spatio-temporal distribution of pine wilt disease from 2021 to 2024.OLS,GWR,and GTWR models were constructed based on variables such as elevation,summer mean temperature,precipitation,wetland area,and population den-sity to explore the spatiotemporal heterogeneity of their relationships with pine wilt disease severity and to compare model performance.The results showed that the northern,western,and southeastern regions of Taizhou experienced higher infection severity,while the eastern region showed lower severity.The average infection severity for Taizhou from 2021 to 2024 was 17.76%,16.96%,13.53%,and 8.72%,respectively.Over this period,60 towns/subdis-tricts showed a significant decline in infection severity,while 7 towns showed a significant increase.Moran's I in-dex was significantly greater than 0,indicating spatial clustering.The GTWR model outperformed the OLS and GWR models,with an R2 of 0.543,an AICc of 4093.23,and an RSS of 75995.8.The regression coefficients for each variable were as follows:elevation(0.019),precipitation(0.060),summer mean temperature(1.880),wetland area(0.30),and population density(0.003).These findings suggest that high-infection areas are mainly distributed in mountainous and hilly regions with high-density forests while low-infection areas are concentrated in flat,densely populated urban centers,showing a"high-high"and"low-low"clustering pattern.From 2021 to 2024,the overall infected area showed a declining trend,with an increasing rate of decrease each year,while the spatial clustering pattern remained relatively stable.The GTWR model more accurately captures the spatiotemporal non-stationary relationships between influencing factors and pine wilt disease severity.Summer mean temperature and elevation positively contribute to pine wilt disease outbreaks,whereas the effects of precipitation and wetland area exhibit fluctuations and complexity.
董恩义;陈超;王松;申奥;吴松;赵萍
浙江省地矿勘察院有限公司,杭州 310000浙江省地矿勘察院有限公司,杭州 310000台州市林业技术推广总站,浙江台州 318000合肥工业大学资源与环境工程学院,合肥 230000合肥工业大学资源与环境工程学院,合肥 230000合肥工业大学资源与环境工程学院,合肥 230000
GTWR模型松材线虫病时空异质性台州市
GTWR modelpine wilt diseasespatiotemporal heterogeneityTaizhou
《生态学杂志》 2026 (5)
1751-1760,10
国家自然科学基金(42401413)和浙江省地矿勘察院有限公司委托项目(W2024JSFW0430)资助.
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