Understanding Beech Predominance Through Sapling and Adult Demography Along Environmental Gradients

Understanding how species abundances are driven by biotic interactions along environmental gradients is a fundamental ecological question. In Central European forests, a classical theory by Ellenberg predicts that beech (Fagus sylvatica L.) outcompetes other tree species within a mesic range of soil pH and water levels, while other species prevail under less favorable conditions. While the theory is generally accepted in ecology, only certain aspects of it have been substantiated by empirical evidence. Moreover, the demographic drivers of the turnover from beech to other tree species along the soil gradients remain unexplained. To address this, we inversely calibrated a parsimonious forest model with a sapling stage and interacting populations using short time series of tree abundances from the German national forest inventory. By modeling how demographic rates for beech and the other species-grouped into an average species-vary along pH and soil water gradients in a disturbed system, we tested the prediction that beech predominates only under favorable soil conditions at equilibrium. Moreover, we tested with simulations how environmentally responsive demographic rates explain beech's changing abundance along the two gradients. Our results largely confirm that beech outcompetes the grouped other species in a central environmental range. Change of beech's relative abundance along environmental gradients is primarily explained by variation in its net basal area increment, followed by its competition response at both overstory and sapling stage. Although sapling tolerance to shading is the primary mechanism for beech predominance, its variability is secondary for change in its relative abundance along environmental gradients. For the first time, we substantiate Ellenberg's theory by elucidating how species turnover along environmental gradients is based in demography. Our approach of calibrating demographic rates that respond to the environment can be utilized to predict distributions of interacting species and explain their dynamics across environmental gradients.