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-We assessed the effect of chemical soil properties and acidifying depositions (sulphur and nitrogen) on the occurrence of storm damage during the storms "Lothar" and "Martin" (December 1999). Data from 969 sites in France, southern Germany and Switzerland was analysed with multiple logistic regression models. Variables found to be significantly related to storm damage, which was mainly scattered damage in our study, were "country", "soil pH", "proportion of coniferous trees", "slope", "humus type", "stand height", and "altitude". Wind speed was not significantly related to storm damage in the global model, but only in the model for France. Soil pH was one of the most significant factors with a lower pH on damaged plots. Atmospheric deposition rates were significantly associated with soil pH, but not directly with storm damage. Even though the mechanisms involved in the relationship between soil acidity and storm damage are still poorly understood, soil acidity should be considered a significant risk factor. Moreover, this large-scale study confirms that increasing the proportion of deciduous trees would reduce the susceptibility of forests to storm damage. deposition / logistic regression / soil pH / wind damage / wind speed Résumé -Les forêts au sol acide sont plus souvent endommagées par les tempêtes. Nous avons étudié l'effet des propriétés chimiques des sols et des dépôts acidifiants (soufre et azote) sur les dommages dus aux tempêtes durant les passages de « Lothar » et de « Martin » en décembre 1999. Les données de 969 sites en France, au sud de l'Allemagne et en Suisse ont été analysées à l'aide de modèles de régression logistique multiple. Les variables liées de manière significative aux dommages dus aux tempêtes étaient les suivantes : le pays, le pH du sol, la proportion de conifères, la déclivité du terrain, le type d'humus, la hauteur des arbres et l'altitude. Dans la plupart des sites, les dommages n'étaient que partiels. La vitesse du vent n'était pas liée de manière significative aux dommages dans le modèle global, mais dans un modèle utilisant uniquement les données de France. Le pH du sol, qui s'avère être l'un des principaux facteurs, était plus bas dans les forêts endommagées. Les taux de dépôts atmosphériques étaient étroitement liés à l'acidité des sols, mais pas directement aux dommages dus à la tempête. Même si les mécanismes provoquant l'interdépendance de l'acidité du sol et des dommages dus aux tempêtes ne sont pas clairement élucidés, l'acidité du sol devrait être considérée comme un facteur risque de grande importance. En outre, cette étude réalisée à large échelle confirme qu'une plus grande proportion d'arbres à feuilles caduques réduirait la sensibilité des forêts aux dommages dus aux tempêtes. dépôts atmosphériques / régression logistique / pH du sol / dommages dus aux tempêtes / vitesse du vent

Soil bulk density (ρ) is an important physical property, but its measurement is frequently lacking in soil surveys due to the time‐consuming nature of making the measurement. As a result pedotransfer functions (PTFs) have been developed to predict ρ from other more easily available soil properties. These functions are generally derived from regression methods that aim to fit a single model. In this study, we use a technique called Generalized Boosted Regression Modelling (GBM; Ridgeway, 2006) which combines two algorithms: regression trees and boosting. We built two models and compared their predictive performance with published PTFs. All the functions were fitted based on the French forest soil dataset for the European demonstration Biosoil project. The two GBM models were Model G3 which involved the three most frequent quantitative predictors used to estimate soil bulk density (organic carbon, clay and silt), and Model G10, which included ten qualitative and quantitative input variables such as parent material or tree species. Based on the full dataset, Models G3 and G10 gave R2 values of 0.45 and 0.86, respectively. Model G3 did not significantly outperform the best published model. Even when fitted from an external dataset, it explained only 29% of the variation of ρ with a root mean square error of 0.244 g/cm3. In contrast, the more complex Model G10 outperformed the other models during external validation, with a R2 of 0.67 and a predictive deviation of ±0.168 g/cm3. The variation in forest soil bulk densities was mainly explained by five input variables: organic carbon content, tree species, the coarse fragment content, parent material and sampling depth.

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