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The drivers of plant richness at fine spatial scales in steppe ecosystems are still not sufficiently understood. Our main research questions were: (i) How rich in plant species are the natural steppes of Southern Siberia compared to natural and semi-natural grasslands in other regions of the Palaearctic? (ii) What are the main environmental drivers of the diversity patterns in these steppes? (iii) What are the diversity–environment relationships and do they vary between spatial scales and among different taxonomic groups? We sampled the steppe vegetation (vascular plants, bryophytes and lichens) in Khakassia (Russia) with 39 nested-plot series (0.0001–100-m2 plot size) and 54 additional 10-m2 quadrats across the regional range of steppe types and measured various environmental variables. We measured β-diversity using z-values of power-law species–area relationships. GLM analyses were performed to assess the importance of environmental variables as predictors of species richness and z-value. Khakassian steppes showed both high α- and β-diversity. We found significant scale dependence for the z-values, which had their highest values at small spatial scales and then decreased exponentially. Total species richness was controlled predominantly by heat load index, mean annual precipitation, humus content and soil skeleton content. The positive role of soil pH was evident only for vascular plant species richness. Similar to other studies, we found that the importance of environmental factors strongly differed among taxonomic groups and across spatial scales, thus highlighting the need to study more than one taxon and more than one plot size to get a reliable picture

Fig. 2. Spatial coverage of GrassPlot data from Morocco to Japan. Currently, the majority comes from sub-Mediterranean to hemiboreal Europe (black = multi-scale plots, grey = other plots). Current content v. 1.00 (January 2018) • 126 datasets • 198 data owners • 36 countries • 168,997 plots, among them 14,064 with data also for non-vascular plants • 66,000 0.01-m² plots, 17,206 1-m² plots, 5,520 10-(or 9-) m² plots, 2,545 100-m² plots • 2,797 nested-plot series (with at least 4 grain sizes)