A Geodetector Approach to Quantifying Key Drivers of Understory Plant Diversity in Pinus tabuliformis Plantation

Understanding the drivers of understory vegetation diversity in plantation forests is critical for ecosystem management, yet traditional analytical methods are often constrained by assumptions of normality, linearity, and independence among variables. This study used the geographical detector (GeoDetector) method to quantify the independent and interactive effects of environmental factors on understory plant diversity in Pinus tabuliformis plantations. We established 36 standard plots at the Shihe Forest Farm in the Zhongtiao Mountains of Shanxi Province, China. A total of 25 environmental factors, encompassing stand structure, topography, soil physical properties, and soil chemical properties, were examined as potential drivers of shrub-layer and herb-layer diversity. The results identified distinct key drivers for different vegetation layers. Shrub-layer diversity was primarily influenced by regeneration potential, aspect, soil non-capillary porosity, and total soil nitrogen. In contrast, herb-layer diversity was mainly driven by forest type, slope, soil non-capillary porosity, and the soil nitrogen-to-phosphorus ratio. Factor interactions were widespread, with nonlinear enhancement and bivariate enhancement being the dominant types. The combined effect of interacting factors was consistently stronger than that of any single factor alone. Compared to conventional statistical methods, GeoDetector does not rely on linear assumptions and is unaffected by multicollinearity. This allows for more effective identification of drivers that have low independent explanatory power yet high ecological importance, as well as their interactive effects. This study demonstrates that vegetation diversity in P. tabuliformis plantations results from the synergistic effects of multiple factors. The findings provide a theoretical basis for managing and enhancing understory biodiversity in plantation ecosystems. Furthermore, they offer a novel and effective analytical framework for investigating the environmental driving mechanisms of understory vegetation diversity.