Abstract. Separate logistic regression models were developed to predict the distribution and large‐scale spatial patterns of dominant graminoid species and communities in alpine grasslands. The models are driven by four bioclimatic parameters: degree‐days of growing season (basis 0 °C), a moisture index for July, potential direct solar radiation for March, and a continentality index. Geology and slope angle were used as a surrogate for nutrient availability and soil water capacity. The bioclimatic parameters were derived from monthly mean temperature, precipitation, cloudiness and potential direct solar radiation. The environmental parameters were interpolated using a digital elevation model with a resolution of 50 m. The vegetation data for model calibration originate from field surveys and literature. An independent test data set with samples from three different climatic zones was used to test the model. The degree of coincidence between simulated and observed patterns was similar for species and communities, but the κ‐values for communities were generally higher (κ= 0.539) than for species (mean individual κ= 0.201). Information on land use was detected as a major factor that could significantly improve both the species and the community model. Nevertheless, the climatic factors used to drive the model explained a major part of the observed patterns.

Abstract:We developed a quantitative chironomid-July air temperature inference model based on surface sediments

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Abiotic factors such as climate and soil determine the species fundamental niche, which is further constrained by biotic interactions such as interspecific competition. To parameterize this realized niche, species distribution models (SDMs) most often relate species occurrence data to abiotic variables, but few SDM studies include biotic predictors to help explain species distributions. Therefore, most predictions of species distributions under future climates assume implicitly that biotic interactions remain constant or exert only minor influence on large-scale spatial distributions, which is also largely expected for species with high competitive ability. We examined the extent to which variance explained by SDMs can be attributed to abiotic or biotic predictors and how this depends on species traits. We fit generalized linear models for 11 common tree species in Switzerland using three different sets of predictor variables: biotic, abiotic, and the combination of both sets. We used variance partitioning to estimate the proportion of the variance explained by biotic and abiotic predictors, jointly and independently. Inclusion of biotic predictors improved the SDMs substantially. The joint contribution of biotic and abiotic predictors to explained deviance was relatively small (Â9%) compared to the contribution of each predictor set individually (Â20% each), indicating that the additional information on the realized niche brought by adding other species as predictors was largely independent of the abiotic (topo-climatic) predictors. The influence of biotic predictors was relatively high for species preferably growing under low disturbance and low abiotic stress, species with long seed dispersal distances, species with high shade tolerance as juveniles and adults, and species that occur frequently and are dominant across the landscape. The influence of biotic variables on SDM performance indicates that community composition and other local biotic factors or abiotic processes not included in the abiotic predictors strongly influence prediction of species distributions. Improved prediction of species' potential distributions in future climates and communities may assist strategies for sustainable forest management.

The relationships between the distribution of alpine species and selected environmental variables are investigated by using two types of generalized linear models (GLMs) in a limited study area in the Valais region (Switzerland). The empirical relationships are used in a predictive sense to mimic the potential abundances of alpine species over a regular grid. Here, we present the results for the alpine sedge Carex curvula ssp. curvula. The modelling approach consists of (1) a binomial GLM, including only the mean annual temperature as explanatory variable, which is adjusted to species presence/ absence data in the entire study area; (2) a logistic model restricted to stands occurring within the a priori defined temperature range for the species -which allows ordinal abundance data to be adjusted; (3) the two species-response functions combined in a GIS to generate a map of the species' potential abundance in the study area; (4) model predictions filtered by the classes of the qualitative variables under which the species never occur.Such a stratified approach used to better fit the variability within the optimal altitudinal zone for the species. Removing stand descriptions from altitudes too high or too low, where the species is unlikely to occur, enhances the global modelling performance by allowing the identification of important environmental variables only retained in the second model.The model evaluation is finally carried out with the γmeasure of association in an ordinal contingency table. It shows that abundance is satisfactorily predicted for C. curvula.