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Aims Understanding fine‐grain diversity patterns across large spatial extents is fundamental for macroecological research and biodiversity conservation. Using the GrassPlot database, we provide benchmarks of fine‐grain richness values of Palaearctic open habitats for vascular plants, bryophytes, lichens and complete vegetation (i.e., the sum of the former three groups). Location Palaearctic biogeographic realm. Methods We used 126,524 plots of eight standard grain sizes from the GrassPlot database: 0.0001, 0.001, 0.01, 0.1, 1, 10, 100 and 1,000 m2 and calculated the mean richness and standard deviations, as well as maximum, minimum, median, and first and third quartiles for each combination of grain size, taxonomic group, biome, region, vegetation type and phytosociological class. Results Patterns of plant diversity in vegetation types and biomes differ across grain sizes and taxonomic groups. Overall, secondary (mostly semi‐natural) grasslands and natural grasslands are the richest vegetation type. The open‐access file ”GrassPlot Diversity Benchmarks” and the web tool “GrassPlot Diversity Explorer” are now available online (https://edgg.org/databases/GrasslandDiversityExplorer) and provide more insights into species richness patterns in the Palaearctic open habitats. Conclusions The GrassPlot Diversity Benchmarks provide high‐quality data on species richness in open habitat types across the Palaearctic. These benchmark data can be used in vegetation ecology, macroecology, biodiversity conservation and data quality checking. While the amount of data in the underlying GrassPlot database and their spatial coverage are smaller than in other extensive vegetation‐plot databases, species recordings in GrassPlot are on average more complete, making it a valuable complementary data source in macroecology.

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Assessing the ecological requirements of species coexisting within a community is an essential requisite for developing sound conservation action. A particularly interesting question is what mechanisms govern the stable coexistence of cryptic species within a community, i.e. species that are almost impossible to distinguish. Resource partitioning theory predicts that cryptic species, like other sympatric taxa, will occupy distinct ecological niches. This prediction is widely inferred from eco-morphological studies. A new cryptic long-eared bat species, Plecotus macrobullaris, has been recently discovered in the complex of two other species present in the European Alps, with even evidence for a few mixed colonies. This discovery poses challenges to bat ecologists concerned with planning conservation measures beyond roost protection. We therefore tested whether foraging habitat segregation occurred among the three cryptic Plecotus bat species in Switzerland by radiotracking 24 breeding female bats (8 of each species). We compared habitat features at locations visited by a bat versus random locations within individual home ranges, applying mixed effects logistic regression. Distinct, species-specific habitat preferences were revealed.

The detection and evaluation of changes in vegetation patterns is a prerequisite for monitoring programs. The Swiss mire monitoring program aims to assess the changes in mire vegetation in order to examine the efficiency of the management measures. A promising way to explore and detect vegetation structure and vegetation change is the application of predictive vegetation mapping that combines image classification and predictive habitat distribution models. These models deal with predictor variables derived from remotely sensed spectral data and from environmental variables such as a digital surface model (DSM). Low accuracy of environmental data to predict vegetation at the local scale is due to the difficulties to capture dominant fine-scale enironmental gradients. Using high resolution spectral and topographical data sets of 50 cm pixel size and below, the study presented here aims to improve the simulation of local-scale vegetation properties. The spectral data for fine-scale modelling are based on CIR orthoimages with a ground resolution of 32 cm. Various spectral variables and spectral-textural variables were derived for the modelling process. A new method to reduce the number of predictor variables, the composite modelling is presented in this paper. In comparison to existing methods, composite modelling has the advantage of being independent of the scale of the predictor variables, and at the same time being transferable among various data sets. Mean indicator values for moisture, nutrients and light derived from vegetation data are used as response variables. Results show that the topographical variables based on relief features are less powerful predictors than the spectral variables but that combining them enhances the overall predictive power. Stratification of the data according to the tree layer and the shadow areas increases the accuracy of the model.