Question: How useful are Ellenberg N-values for predicting the herbage yield of Central European grasslands in comparison to approaches based on ordination scores of plant species composition or on soil parameters?
Location: Central Germany (11°00′-11°37′ E, 50°21′-50°34′ N, 500 - 840 m a.s.l.).
Methods: Based on data from a field survey in 2001, the following models were constructed for predicting herbage yield in montane Central European grasslands: (1) Linear regression of mean Ellenberg N-, R- and F-values; (2) Linear regression of ordination scores derived from Non-metric Multidimensional Scaling (NMDS) of vegetation data; and (3) Multiple linear regression (MLR) of soil variables. Models were evaluated by cross-validation and validation with additional data collected in 2002.
Results: Best predictions were obtained with models based on species composition. Ellenberg N-values and NMDS scores performed equally well and better than models based on Ellenberg R- or F-values. Predictions based on soil variables were least accurate. When tested with data from 2002, models based on Ellenberg N-values or on NMDS scores accurately predicted productivity rank order of sites, but not the actual herbage yield of particular sites.
Conclusions: Mean Ellenberg N-values, which are easy to calculate, are as accurate as ordination scores in predicting herbage yield from plant species composition. In contrast, models based on soil variables may be useful for generating hypotheses about the factors limiting herbage yield, but not for prediction. We support the view that Ellenberg N-values should be called productivity values rather than nitrogen values.
Nomenclature: Wisskirchen & Haeupler (1998).
Abbreviations: CV = Coefficient of variation; ICP-AES = Inductively coupled plasma-atomic emission spectrometry; MLR = Multiple linear regression; NMDS = Non-metric Multidimensional Scaling