Journal of Vegetation Science && (2011) Too good to be true: pitfalls of using mean Ellenberg indicator values in vegetation analyses David Zeleny ´ & Andre ´ P. Schaffers Keywords Bio-indication; Circularity of reasoning; Compositional similarity; Environmental calibration; Null models; Ordination analysis; Randomization; Species richness; Weighted averaging Abbreviations ANOVA = analysis of variance; CA = correspondence analysis; CCA = canonical correspondence analysis; DCA = detrended correspondence analysis; EIVs = Ellenberg indicator values; TWINSPAN = two-way indicator species analysis Received 2 September 2010 Accepted 14 October 2011 Co-ordinating Editor: Michael Palmer Zeleny ´ , D. (corresponding author, zeleny@sci.muni.cz): Department of Botany and Zoology, Masaryk University, Kotla ´r ˇska ´ 2, CZ-611 37, Brno, Czech Republic Schaffers, A.P. (Andre.Schaffers@wur.nl): Department of Environmental Sciences, Nature Conservation and Plant Ecology Group, Wageningen University, PO BOX 47, 6700 AA, Wageningen, The Netherlands Abstract Question: Mean Ellenberg indicator values (EIVs) inherit information about compositional similarity, because during their calculation species abundances (or presence–absences) are used as weights. Can this similarity issue actually be demonstrated, does it bias results of vegetation analyses correlating mean EIVs with other aspects of species composition and how often are biased studies published? Methods: In order to separate information on compositional similarity possibly present in mean EIVs, a new variable was introduced, calculated as a weighted average of randomized species EIVs. The performance of these mean randomized EIVs was compared with that of the mean real EIVs on the one hand and random values (randomized mean EIVs) on the other. To demonstrate the similarity issue, differences between samples were correlated with dissimilarity matrices based on various indices. Next, the three mean EIV variables were tested in canonical correspondence analysis (CCA), detrended correspondence analysis (DCA), analysis of variance (ANOVA) between vegetation clusters, and in regression on species richness. Subsequently, a modified permutation test of significance was proposed, taking the similarity issue into account. In addition, an inventory was made of studies published in the Journal of Vegetation Science and Applied Vegeta- tion Science between 2000 and 2010 likely reporting biased results due to the simi- larity issue. Results: Using mean randomized EIVs, it is shown that compositional similarity is inherited into mean EIVs and most resembles the inter-sample distances in cor- respondence analysis, which itself is based on iterative weighted averaging. The use of mean EIVs produced biased results in all four analysis types examined: unrealistic (too high) explained variances in CCA, too many significant correla- tions with ordination axes in DCA, too many significant differences between cluster analysis groups and too high coefficients of determination in regressions on species richness. Modified permutation tests provided ecologically better interpretable results. From 95 studies using Ellenberg indicator values, 36 reported potentially biased results. Conclusions: No statistical inferences should be made in analyses relating mean EIVs with other variables derived from the species composition as this can pro- duce highly biased results, leading to misinterpretation. Alternatively, a modi- fied permutation test using mean randomized EIVs can sometimes be used. Introduction Ellenberg indicator values (EIVs; Ellenberg et al. 1992) and their geographic alternatives (e.g. Landolt 1977; Borhidi 1995; Hill et al. 1999; Pignatti 2005; Lawesson et al. 2009) are frequently used by European vegetation scientists as surrogates for measured environmental variables. Several studies have demonstrated that the calculated mean of EIVs for species present in the vegetation sample are often a good estimate of real environmental conditions, even if this relationship may be limited to certain parts of a given gradient or to a particular vegetation type (for a detailed Journal of Vegetation Science Doi: 10.1111/j.1654-1103.2011.01366.x © 2011 International Association for Vegetation Science 1