| Zusammenfassung: | Interspecific associations detected in phytosociological data sets sampled in local areas can reflect locally specific combinations of environmental factors and may thus differ from the interspecific associations existing on a regional scale. As a result, vegetation units derived from numerical classifications of local data sets can accurately reflect local environmental gradients, but their boundaries or spectra of diagnostic species must be frequently adjusted when transferred to the regional scale. Local vegetation classifications can be useful for some purposes, but regional classifications are superior, as they facilitate communication among the researchers from different areas. We demonstrated changes in interspecific associations between regional and local scale, using a data set of 14 589 relevés of herbaceous vegetation of the Czech Republic, and 16 local subsets of this national data set. We focused on sociological species groups, derived statistically in the national data set. Changes in coherence of these groups when applied to the local data sets were described on the basis of statistical association between the relevés containing some species of these groups and the species belonging vs. not belonging to these groups. The results were summarized using the principal components analysis (PCA). In addition, relevé data sets were compared with respect to presence/absence of sociological species groups, using the principal coordinate analysis (PCoA). The results of PCA and PCoA were compared by Procrustean analysis. Local data sets differed from the national data set to different extent. The national data set was more remote to the local data sets if the analysis focused on the coherence of species group rather than on presence/absence. The species groups from the national data set retained most of their coherence in low-altitude hilly landscapes with thermophilous flora, i.e., the most diverse landscape type of the Czech Republic. On the other hand, many species groups from the national data set could not be recognized in mountainous areas or flat lowlands. These results suggest that interspecific associations existing on regional scale are best reproduced in those local areas which have a high habitat heterogeneity or which have a central position along the major gradients existing on regional scale.
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