LAUSR

Understanding drivers of forest-cover change is essential for a broad range of ecosystem properties. In this work, we assessed changes in forest cover using physical, climatic and socio-economic drivers, including forest neighbourhood effects representing spatial interactions within and between two time periods (1880–1940; 1940–2010), for a mountainous study area located in eastern Switzerland. The robust assessment relied on an ensemble modelling approach that combined projections of six statistical models (GAM, CART, ANN, RF, GBM, GLM). A generic neighbourhood variable (distance to forest edge) explained most of the forest-cover change (variable importance of >90%) for both forest gain and loss, whereas socio-economic, physical and climatic variables were of least importance. The performance of models of forest loss was consistently higher and less varied (TSS of model ensembles 0.65–0.82) compared to models of forest gain (TSS of model ensembles 0.2–0.62) independent of the time period considered. We concluded that (a) the relative importance of drivers for the simulated processes is independent of the time period considered; (b) ensemble modelling proves a powerful tool to assess projection robustness by considering a suite of models rather than a single model type and (c) the inclusion of generic neighbourhood variables such as distance to forest edge improves model performance significantly.