Ellenberg's indicator values have been suggested as useful method of estimating site conditions using plants. We examined whether Ellenberg's R values are suitable for indicating soil reaction and if calibration to physical pH measurements can improve bioindication in oligotrophic and mesotrophic submontane broad-leaved forests in Slovakia. Vegetation relevés and pH-H2O and pH-CaCl2 soil reaction were recorded for this purpose. Ellenberg's R values (Re) were compared to Jurko's indicator values (Rj) and a set of species R values and tolerances (T), which were calibrated with physical pH data using the weighted averaging (Rw, Tw) and Huisman-Olff-Fresco modelling (R h , T h ). Original Re values were then recalibrated with measured pH data to establish new, adjusted set of scores (Rc, Tc) at Ellenberg's scale. The Re values are significantly correlated with the other R values, and they demonstrate similar frequency distribution to Rj and Rw values for the studied species pool. The frequency distribution becomes similar across all the R values when indifferent species were excluded. The performance of all the indicator values in terms of bioindication was tested. Relevé means of the R values were regressed on the field pH measurements. The performance of bioindication varied from 36% to 49% of the explained variance for pH-CaCl2, with the Re and Rc values yielding 46% and 49% respectively. The bioindication slightly improved for all calibrated methods (Rw, R h and Rc) when species were weighted inversely with their tolerances -the performance varied from 42% to 51%, and the Rc values performed most effectively. We concluded that Ellenberg's R values represent a powerful system for bioindicating soil acidity when compared to the other alternatives, with pH-CaCl2 showing better results than pH-H2O. Recalibration of Ellenberg's values to the measured data improved the indicator system.
A fine-scaled approach for predicting soil acidity using plant species in a spatially limited area (Čepúšky Nature Reserve, Slovakia) is presented here. This approach copes with some specific limitations: i) a limited pool of vegetation data may make the predictions too sensitive to the lack of species information, and ii) the predictions may be sensitive to the narrow pH gradient. Vegetation relevés and soil reaction (pH-H 2 O and pH-CaCl 2 ) were systematically recorded. A set of species indicator values and amplitudes was calibrated with physical pH data using the Weighted Averaging (WA), HOF modelling and Non-Metric Multidimensional Scaling (NMDS) methods, along with Ellenberg indicator values. Two prediction methods were tested: i) WA and ii) Amplitude Overlap (AO). WA prediction with Ellenberg's and WA-calibrated species indicator values were the most powerful technique (R 2 =68.4-68.7% and 53.4-59.1% for pH-CaCl 2 and pH-H 2 O, respectively). WA-prediction with HOF-based indicator values was less effective (R 2 =61.7% and 50.7%) due to the decrease in species' information because with HOF modelling many species are assumed indifferent or too rare. The NMDS method does not bring any significant gain to the calibration, though it avoids the lack of species information. The AO method was proven to be less powerful under studied circumstances, because it is sensitive both to the lack of species' information and to the truncation of species responses. The results prove that a spatially explicit approach can provide significant indices to estimate changes in soil acidity -pHCaCl 2 better than pH-H 2 O.
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