Assessing the long-term species composition predicted by PrognAus

Tree growth models are supposed to contain stand growth laws as so called “emergent properties” which derive from interactions of individual-tree growth and mortality functions. This study investigates whether the evolving tree species composition in a long term simulation by the distance-independent tree growth model PrognAus matches the species composition of the potential natural vegetation type which is expected to occur if one refrains from further management interventions and major disturbances, climate change, and changes in site conditions can be excluded. For this purpose the development of 6933 sample plots of the Austrian National Forest Inventory was predicted for 2500 years. The resulting species proportions, derived from volume per hectare of 15 tree species or species groups, were used to classify every sample plot according to potential natural forest types, following a classification scheme based on expert knowledge. These simulated potential natural vegetation types were compared with expert reconstructions of the sample plots of the Austrian National Forest Inventory. A total of 5789 plots were actually classified with the scheme; in 33% of the cases the classification on the basis of the PrognAus-simulations was identical with the classification by the Austrian National Forest Inventory. A predominantly correct classification was achieved for the subalpine Picea abies-type and the Fagus sylvatica-type although PrognAus showed a tendency to overestimate the proportion of F. sylvatica and P. abies. Weaknesses in the ability to simulate forest types dominated by Quercus spp., Acer spp., and Pinus sylvestris were identified. This shortcoming might be caused by the mortality model which allows a larger diameter at breast height for F. sylvatica or by the ingrowth model whose terms for the consideration of inter-specific competition may lead to a disadvantage of Quercus spp., P. sylvestris, and Abies alba. Moreover, the ingrowth model might be influenced by management effects and the effect of selective browsing.