Abstract• Existing growth and yield plots of pure and mixed stands of Norway spruce (Picea abies (L.) H. Karst.) and European beech (Fagus sylvatica L.) were aggregated in order to unify the somewhat scattered sources of information currently available, as well as to develop a sound working hypothesis about mixing effects. The database contains information from 23 long-term plots, covering an ecological gradient from nutrient poor and dry to nutrient rich and moist sites throughout Central Europe.• An empirically formed interaction model showed, that depending on the site conditions, dry mass growth in mixed stands can range from −46% to +138 % of the growth yielded by a scaled combination of pure stands at equal mixing proportions.• Drawing from the interaction model, overyielding of the mixed stands appears to be triggered by two separate mechanisms. On poor sites, where significant overyielding is commonly found, facilitation by beech offsets nutrient-related growth limitations in spruce. In contrast, overyielding of mixed stands occurs less frequently on rich sites, and appears to be based on an admixture effect, with spruce reducing the severe intra-specific competition common in pure beech stands.• It was concluded that silviculture can accelerate growth of spruce by beech admixtures on poor sites, while growth of beech can be promoted by admixture of spruce, particularly on excellent sites.

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Based on individual tree damage data dating back to the gale “Lothar” (winter 1999) in Baden-Württemberg, Germany, a statistical model was developed to estimate the risk of storm damage for individual trees. The data were compiled from the National German Forest Inventory. The model attempts to separate the effects of tree-specific variables, topography, site conditions and flow field related effects on damage probability. The crucial problem of missing information on the actual flow field parameters was solved by applying a generalized additive model that enables the simultaneous fit of a spatial trend function. The geographical location of risk hotspots as predicted by the model correspond well to the actual distribution pattern of storm damage as assessed by the forest service. Tree height proved to be one of the most important factors affecting the level of damage, while height to diameter at breast height ratio influences damage probability to a much lesser extent. The Norway spruce ( Picea abies (L.) Karst.) group has the highest potential to be damaged followed by the silver fir ( Abies alba Miller) – Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco) group and the Scots pine ( Pinus sylvestris L.) – larches ( Larix spp.) group. Predicted probabilities for deciduous trees are generally lower than those of conifers. West- to south-exposed locations bear a considerably higher damage risk and waterlogged soils show an increased predicted probability compared with slightly or not waterlogged soils.