Indicating Soil Acidity Using Vegetation Releves in Spatially Limited Areas - Case Study from the Povazsky Inovec, Slovakia

A fine-scaled approach for predicting soil acidity using plant species in a spatially limited area (epA(0)ky 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 relev,s and soil reaction (pH-H2O and pH-CaCl2) 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-CaCl2 and pH-H2O, 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 - pH-CaCl2 better than pH-H2O.