Divergent Responses of Leaf Area Index to Abiotic Drivers Across Abies Forest Types in China

The Leaf Area Index (LAI) is a fundamental biophysical parameter quantifying forest canopy structure and regulating water–energy exchange. While Abies Mill. forests constitute a vital component of China’s alpine ecosystems, the spatial heterogeneity of their LAI and its sensitivity to environmental filtering remain underexplored. This study employed Random Forest (RF) and Structural Equation Modeling (SEM) to disentangle the direct and interactive effects of climate, soil, topography, and human footprint (HFP) on LAI across 17 distinct Abies forest types. The results revealed that temperature was the dominant positive driver for the overall Abies forests (Total effect = 2.197), whereas Elevation (DEM) exerted the strongest negative regulation (Total effect = −0.335). However, driver dominance varied substantially among forest types: climatic water availability was the primary constraint for Abies georgei var. smithii (Viguié & Gaussen) W.C.Cheng & L.K.Fu forest (Type 55), while DEM determined LAI in Abies fargesii Franch. forest (Type 49). Notably, we found that HFP could exert positive effects on LAI in specific communities (e.g., Abies densa Griff. forest, Type 58), likely due to understory compensation under moderate disturbance. These findings highlight the necessity of type-specific management strategies and provide a theoretical basis for predicting alpine forest dynamics under changing environments.