Aim: Understanding the variation in community composition and species abundances (i.e., β‐diversity) is at the heart of community ecology. A common approach to examine β‐diversity is to evaluate directional variation in community composition by measuring the decay in the similarity among pairs of communities along spatial or environmental distance. We provide the first global synthesis of taxonomic and functional distance decay along spatial and environmental distance by analysing 148 datasets comprising different types of organisms and environments.Location: Global.Time period: 1990 to present.Major taxa studied: From diatoms to mammals.Method: We measured the strength of the decay using ranked Mantel tests (Mantel r) and the rate of distance decay as the slope of an exponential fit using generalized linear models. We used null models to test whether functional similarity decays faster or slower than expected given the taxonomic decay along the spatial and environmental distance. We also unveiled the factors driving the rate of decay across the datasets, including latitude, spatial extent, realm and organismal features.Results: Taxonomic distance decay was stronger than functional distance decay along both spatial and environmental distance. Functional distance decay was random given the taxonomic distance decay. The rate of taxonomic and functional spatial distance decay was fastest in the datasets from mid‐latitudes. Overall, datasets covering larger spatial extents showed a lower rate of decay along spatial distance but a higher rate of decay along environmental distance. Marine ecosystems had the slowest rate of decay along environmental distances.Main conclusions: In general, taxonomic distance decay is a useful tool for biogeographical research because it reflects dispersal‐related factors in addition to species responses to climatic and environmental variables. Moreover, functional distance decay might be a cost‐effective option for investigating community changes in heterogeneous environments.

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.

Aim: Species–area relationships (SARs) are fundamental scaling laws in ecology although their shape is still disputed. At larger areas, power laws best represent SARs. Yet, it remains unclear whether SARs follow other shapes at finer spatial grains in continuous vegetation. We asked which function describes SARs best at small grains and explored how sampling methodology or the environment influence SAR shape.Location: Palaearctic grasslands and other non‐forested habitats.Taxa: Vascular plants, bryophytes and lichens.Methods: We used the GrassPlot database, containing standardized vegetation‐plot data from vascular plants, bryophytes and lichens spanning a wide range of grassland types throughout the Palaearctic and including 2,057 nested‐plot series with at least seven grain sizes ranging from 1 cm² to 1,024 m². Using nonlinear regression, we assessed the appropriateness of different SAR functions (power, power quadratic, power breakpoint, logarithmic, Michaelis–Menten). Based on AICc, we tested whether the ranking of functions differed among taxonomic groups, methodological settings, biomes or vegetation types.Results: The power function was the most suitable function across the studied taxonomic groups. The superiority of this function increased from lichens to bryophytes to vascular plants to all three taxonomic groups together. The sampling method was highly influential as rooted presence sampling decreased the performance of the power function. By contrast, biome and vegetation type had practically no influence on the superiority of the power law.Main conclusions: We conclude that SARs of sessile organisms at smaller spatial grains are best approximated by a power function. This coincides with several other comprehensive studies of SARs at different grain sizes and for different taxa, thus supporting the general appropriateness of the power function for modelling species diversity over a wide range of grain sizes. The poor performance of the Michaelis–Menten function demonstrates that richness within plant communities generally does not approach any saturation, thus calling into question the concept of minimal area.

Aims: To clarify the syntaxonomic position of the grasslands in Navarre, with special focus on the dry grasslands, and to characterise the resulting syntaxonomic units in terms of diagnostic species and ecological conditions. Study area: Navarre (northern Spain). Methods: We sampled 119 plots of 10 m2 following the standardised EDGG methodology and analysed them together with 839 plots of similar size recorded in the 1990. For the classification, we used the modified TWINSPAN algorithm, complemented by the determination of diagnostic species with phi coefficients of association, which led to the creation of an expert system. We conducted these steps in a hierarchical manner for each syntaxonomic rank. We visualised the position of the syntaxa along environmental gradients by means of NMDS. Species richness, and structural and ecological characteristics of the syntaxa were compared by ANOVAs. Results: We could clearly identify five phytosociological classes: Lygeo-Stipetea, Festuco-Brometea, Molinio-Arrhenatheretea, Nardetea strictae, and Elyno-Seslerietea. Within the Festuco-Brometea a xeric and a meso-xeric order could be distinguished, with two alliances each, and eight associations in total: Thymelaeo-Aphyllanthetum, Jurineo-Festucetum, Helianthemo-Koelerietum, Prunello-Plantaginetum, Carduncello-Brachypodietum, Helictotricho-Seslerietum, Calamintho-Seselietum and Carici-Teucrietum. Conclusions: The combination of numerical methods allowed a consistent and more objective classification of grassland types in Navarre than previous approaches. At the association level, we could largely reproduce the units previously described with traditional phytosociological methods. By contrast, at higher syntaxonomic level, our analyses suggest significant modifications. Most importantly, a major part of the units traditionally included in the Festuco-Ononidetea seem to fall within the Festuco-Brometea. We could show that bryophytes and lichens are core elements of these grasslands and particularly the Mediterranean ones of Lygeo-Stipetea, both in terms of biodiversity and of diagnostic species. We conclude that the combination of our different numerical methods is promising for deriving more objective and reproducible delimitations of syntaxa in a hierarchical manner. Taxonomic references: Euro+Med (2006–2021) for vascular plants, Hodges et al. (2020) for bryophytes and The British Lichen Society (2021) for lichens, except for Endocarpon loscosii, Heppia lutosa, Psora saviczii and P. vallesiaca, which follow Nimis and Martellos (2021), and Buellia zoharyi, Fulgensia poeltii, Lichenochora clauzadei and Toninia massata, which follow Llimona et al. (2001). Syntaxonomic reference: Mucina et al. (2016), except for those syntaxa specifically treated here and given with authorities. Abbreviations: ANOVA = analysis of variance; EDGG = Eurasian Dry Grassland Group; NMDS: non-metric multidimensional scaling; TWINSPAN = Two-Way Indicator Species Analysis.