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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.

Question: While it is well known that species richness depends on plot size, it is not generally recognised that the same must be true for constancy. Accordingly, many authors use varying plot sizes when classifying vegetation based on the comparison of constancies between groups of plots. We ask whether the constancy‐area relationship follows a general rule, how strong the effect of plot sizes is on constancies, and if it is possible to correct constancies for area. Location: For empirical evaluation, we use data from plant communities in the Czech Republic, Sweden and Russia. Methods: To assess the potential influence of differences in plot size on constancies, we develop a mathematical model. Then, we use series of nested plot species richness data from a wide range of community types (herbaceous and forest) to determine the parameters of the derived function and to test how much the shape of the constancy‐area relationship depends on taxa or vegetation types. Results: Generally, the constancy‐area relationship can be described by C (A)=1−(1−C0)(A/A0)^d, with C being constancy, A area, C0 known constancy on a specific area A0, and d a damping parameter accounting for spatial autocorrelation. As predicted by this function, constancies in plant communities always varied from values near 0% to near 100% if plot sizes were changed sufficiently. For the studied vegetation types, a two‐ to fourfold increase in plot size resulted in a change of conventional constancy classes, i.e. an increase of constancy by 20% or more. Conclusions: Vegetation classification, which largely relies on constancy values, irrespective of whether traditional or modern fidelity definitions are used, is strongly prone to distorting scale effects when relevés of different plot sizes are combined in studies. The constancy‐area functions presented allow an approximate transformation of constancies to other plot sizes but are flawed by idiosyncrasies in taxa and vegetation types. Thus, we conclude that the best solution for future surveys is to apply uniform plot sizes within a few a priori delimited formations and to determine diagnostic species only within these formations. Finally, we suggest that more detailed analyses of constancy‐area relationships can contribute to a better understanding of species‐area relationships because the latter are the summation of the first for all species.

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