Tree species composition in European pristine forests:: Comparison of stand data to model predictions

The degree of general applicability across Europe currently achieved with several forest succession models is assessed, data needs and steps for further model development are identified and the role physiology based models can play in this process is evaluated. To this end, six forest succession models (DISCFORM, ForClim, FORSKA-M, GUESS, PICUS v1.2, SIERRA) are applied to simulate stand structure and species composition at 5 European pristine forest sites in different climatic regions. The models are initialized with site-specific soil information and driven with climate data from nearby weather stations. Predicted species composition and stand structure are compared to inventory data. Similarity and dissimilarity in the model results under current climatic conditions as well as the predicted responses to six climate change scenarios are discussed. All models produce good results in the prediction of the right tree functional types. In about half the cases, the dominating species are predicted correctly under the current climate. Where deviations occur, they often represent a shift of the species spectrum towards more drought tolerant species. Results for climate change scenarios indicate temperature driven changes in the alpine elevational vegetation belts at humid sites and a high sensitivity of forest composition and biomass of boreal and temperate deciduous forests to changes in precipitation as mediated by summer drought. Restricted generality of the models is found insofar as models originally developed for alpine conditions clearly perform better at alpine sites than at boreal sites, and vice versa. We conclude that both the models and the input data need to be improved before the models can be used for a robust evaluation of forest dynamics under climate change scenarios across Europe. Recommendations for model improvements, further model testing and the use of physiology based succession models are made.