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.

Questions: Which environmental factors influence fine‐grain beta diversity of vegetation and do they vary among taxonomic groups?Location: Palaearctic biogeographic realm.Methods: We extracted 4,654 nested‐plot series with at least four different grain sizes between 0.0001 m² and 1,024 m² from the GrassPlot database, covering a wide range of different grassland and other open habitat types. We derived extensive environmental and structural information for these series. For each series and four taxonomic groups (vascular plants, bryophytes, lichens, all), we calculated the slope parameter (z‐value) of the power law species–area relationship (SAR), as a beta diversity measure. We tested whether z‐values differed among taxonomic groups and with respect to biogeographic gradients (latitude, elevation, macroclimate), ecological (site) characteristics (several stress–productivity, disturbance and heterogeneity measures, including land use) and alpha diversity (c‐value of the power law SAR).Results: Mean z‐values were highest for lichens, intermediate for vascular plants and lowest for bryophytes. Bivariate regressions of z‐values against environmental variables had rather low predictive power (mean R² = 0.07 for vascular plants, less for other taxa). For vascular plants, the strongest predictors of z‐values were herb layer cover (negative), elevation (positive), rock and stone cover (positive) and the c‐value (U‐shaped). All tested metrics related to land use (fertilization, livestock grazing, mowing, burning, decrease in naturalness) led to a decrease in z‐values. Other predictors had little or no impact on z‐values. The patterns for bryophytes, lichens and all taxa combined were similar but weaker than those for vascular plants.Conclusions: We conclude that productivity has negative and heterogeneity positive effects on z‐values, while the effect of disturbance varies depending on type and intensity. These patterns and the differences among taxonomic groups can be explained via the effects of these drivers on the mean occupancy of species, which is mathematically linked to beta diversity.

Questions: Species–area relationships (SARs) are fundamental for understanding biodiversity patterns and are generally well described by a power law with a constant exponent z. However, z‐values sometimes vary across spatial scales. We asked whether there is a general scale dependence of z‐values at fine spatial grains and which potential drivers influence it.Location: Palaearctic biogeographic realm.Methods: We used 6,696 nested‐plot series of vascular plants, bryophytes and lichens from the GrassPlot database with two or more grain sizes, ranging from 0.0001 m² to 1,024 m² and covering diverse open habitats. The plots were recorded with two widespread sampling approaches (rooted presence = species “rooting” inside the plot; shoot presence = species with aerial parts inside). Using Generalized Additive Models, we tested for scale dependence of z‐values by evaluating if the z‐values differ with gran size and tested for differences between the sampling approaches. The response shapes of z‐values to grain were classified by fitting Generalized Linear Models with logit link to each series. We tested whether the grain size where the maximum z‐value occurred is driven by taxonomic group, biogeographic or ecological variables.Results: For rooted presence, we found a strong monotonous increase of z‐values with grain sizes for all grain sizes below 1 m². For shoot presence, the scale dependence was much weaker, with hump‐shaped curves prevailing. Among the environmental variables studied, latitude, vegetation type, naturalness and land use had strong effects, with z‐values of secondary peaking at smaller grain sizes.Conclusions: The overall weak scale dependence of z‐values underlines that the power function generally is appropriate to describe SARs within the studied grain sizes in continuous open vegetation, if recorded with the shoot presence method. When clear peaks of z‐values occur, this can be seen as an expression of granularity of species composition, partly driven by abiotic environment.