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.