[Spatial point pattern analysis of pine wilt disease occurrence and its influence factors]. / æ¾æçº¿è™«ç— å‘ç”Ÿç‚¹æ ¼å±€åŠå½±å“å› ç´ .

Pine wilt disease has caused huge losses in ecological and economic values in China, especially in the southern parts. Analyzing the spatial distribution of pine wilt disease and quantifying the impact of environmental factors on its occurrence were of great significance for its prevention and control. In this study, we examined the spatial pattern of pine wilt disease occurrence and its response to environmental variables in Nankang District, Ganzhou, Jiangxi Province, using kernel-smoothing density, Ripley's K function, and point process model. The results showed that the occurrence of pine wilt disease in the study region was not randomly distributed, but was obviously clustered at some areas. Terrain, vegetation, and human activity were the main factors affecting the heterogeneous distribution of pine wilt disease. Spatial point pattern analysis showed that altitude, slope, distance to the nearest road, road density, distance to nearest settlement, canopy closure, and vegetation type had significant effects on the occurrence of pine wilt disease. In addition to strengthening the control of disease transmission caused by human activities, we should also consider the effects of terrain and vegetation types for early warning and monitoring in forest disease management.

[Relationship between surface dead fuel loadings and environmental factors in southern Jiangxi, China]. / èµ£å—åœ°åŒºæ£®æž—åœ°è¡¨æ­»å¯ç‡ƒç‰©è½½é‡ä¸ŽçŽ¯å¢ƒå› å­çš„å ³ç³».

The distribution pattern of forest fuel loading is driven by the interaction of environmental factors, such as terrain and vegetation. Based on field sampling data of surface dead fuels of seven main forest types in southern Jiangxi Province, and according to the classification standard of different time-lags, we constructed structural equation models to explore the relationship between surface fuel loadings and environmental factors such as terrain and vegetation etc. We analyzed the influence path of each factor and its direct, indirect, and total influence. The results showed that the coniferous and broad-leaved mixed forest had the highest loadings and the Phyllostachys heterocycla pure forest had the lowest loadings for all the 1, 10, and 100 h time-lag fuels. The influencing coefficient of environmental factors for 1 h time-lag fuels were ranked as: slope (0.40) > crown height (0.07) > tree species (-0.03) > canopy closure (0.01). For the 10 h time-lag fuels, the environmental factors were ranked as: diameter at breast height (0.15) >tree species (-0.09) > aspect (-0.08) > canopy closure (-0.06). For the 100 h time-lag fuels, the environmental factors were ranked as: aspect (0.25) > diameter at breast height (0.19) > canopy closure (-0.08) > tree species (0.02). The influencing coefficient of environmental factors for the total fuels were ranked as: slope (0.22) > tree species (-0.04), canopy closure (-0.04) > crown height (-0.01).