Tracking human impact on current tree species distribution using plant communities

Questions Can we adequately model beech distribution based on accompanying vegetation, and how does this compare with a climate model? To what extent does accompanying vegetation predict larger extension of beech at its SW margin compared with current distribution? Do human factors explain the discrepancy between current and potential distribution? Location Lowland forests at the SW European range margin of beech. Methods We built a logistic regression model of beech presence based on accompanying understorey vegetation using 66 976 plots in the French National Forest Inventory (NFI) network. Explanatory variables were obtained by multivariate reduction of 252 species in NFI plots. The model was calibrated in regions where human impact on beech has been comparatively low. The probability of beech presence was estimated at its SW margin and compared with current distribution. We tested whether forest management factors could explain beech absence in locations where the flora predicted its presence. Performance of this model was compared with a classical climatic-envelope model. Results Modelled potential distribution of beech along its SW margin was larger than observed distribution, suggesting area shrinkage under man's influence. Sites where beech was predicted as present but was currently absent were significantly less common in high forest stands, and more common in coppices, plantations and private forests. Characteristics of species replacing beech (early-successional, plantation or exotic species) confirmed the role of forest management and disturbance in beech disappearance. The floristic model provided a finer resolution distribution map than the climatic-envelope model. Conclusions Plant communities, together with precise tree species maps, allowed us to estimate manipulation of beech by man, and main silvicultural causes of its disappearance. The improvement in quality of current distribution models has important implications for modelling of niches under future climate scenarios