Species groups can be transferred across different scales.

Abstract

Aim To test whether species groups (i.e. assemblages of species co‐occurring in nature) that are statistically derived at one scale (broad, medium, or fine scale) can be transferred to another scale, and to identify the driving forces that determine species groups at the various scales. Location Northern Bohemia (Czech Republic, central Europe) in the Ještědský hřbet mountain range and its neighbourhood. Methods Three data sets were sampled: a floristic data set at the broad scale, another floristic data set at the intermediate scale, and a vegetation data set at the habitat scale. First, in each data set, species groups were produced by the COCKTAIL algorithm, which ensures maximized joint occurrence in the data set using a fidelity coefficient. Corresponding species groups were produced in the individual data sets by employing the same species for starting the algorithm. Second, the species groups formed in one data set, i.e. at a particular scale, were applied crosswise to the other data sets, i.e. to the other scales. Correspondence of a species group formed at a particular scale with a species group at another scale was determined. Third, to highlight the driving factors for the distribution of the plant species groups at each scale, canonical correspondence analysis was carried out. Results Twelve species groups were used to analyse the transferability of the groups across the three scales, but only six of them were found to be common to all scales. Correspondence of species groups derived from the finest scale with those derived at the broadest scale was, on average, higher than in the opposite direction. Forest (tree layer) cover, altitude and bedrock type explained most of the variability in canonical correspondence analysis across all scales. Main conclusions Transferability of species groups distinguished at a fine scale to broader scales is better than it is in the opposite direction. Therefore, a possible application of the results is to use species groups to predict the potential occurrence of missing species in broad‐scale floristic surveys from fine‐scale vegetation‐plot data.