陕西省森林蓄积量空间分布特征及气候多因子耦合分析OA

As a core ecological barrier in China's north-south transition zone,the spatial heterogeneity of forest stock volume in Shaanxi Province directly impacts the maintenance of regional carbon sinks and the construc-tion of ecological security patterns.This study integrated field data from 3,613 fixed plots and meteorological data from 2014 in Shaanxi Province to characterize the spatial distribution characteristics of forest stock vol-ume and its multi-factor coupling with climate.The results show that the spatial pattern of forest stock vol-ume exhibits a distinct geographic gradient:"higher in the south and lower in the north",with clustering in mountainous areas and dispersion in plains.Spatial autocorrelation analysis identified five major forest regions(Qinling and Bashan Mountains)as high-value clusters(HH clusters,mean 138 m3·hm-2).In contrast,continuous cold spots(LL clusters,mean 18.63 m3·hm-2)have formed in the gully region of the Loess Plateau in the northern Shaanxi and the low mountain-hill region in the southern Shaanxi due to natural condi-tions or human activities.The study reveals that hydrothermal synergy is the dominant factor driving stock volume distribution,with a pronounced threshold effect.The Qinba Mountains demonstrate optimal produc-tivity when accumulated temperature(1 600～3 400℃)and precipitation(800～1 200 mm)are well-matched.Two machine learning models confirm the contribution hierarchy:growing degree days(GDD)＞precipitation(Precip)＞aridity index(AI).This study demonstrates how machine learning and traditional spatial statistics can be synergistically integrated to uncover the threshold contributions of climatic drivers.