Measurement errors and regression to the mean cannot explain bias in average Ellenberg indicator values

Smart & Scott (2004, this issue) criticized our paper (Wamelink et al. 2002) about the bias in average Ellenberg indicator values. Their main criticism concerns the method we used, regression analysis. They state the bias can be mimicked by the construction of an artificial data set and that regression analysis is not a suited tool to investigate underlying phenomena. Moreover they claim that the present bias is caused by the distribution of Ellenberg indicator values between syntaxa, instead of a bias in average Ellenberg indicator values per species. We show that their criticism of the use of regression analysis does not hold. We selected average Ellenberg values per vegetation group for several pH classes and applied an F-test to determine whether or not the vegetation groups within each pH class differed significantly from each other. This was the case for all tested classes (P <0.001). Moreover we simulated an artificial data set, of which the F-test for varying measurement error could not explain the magnitude of the F-value we found earlier. This indicates that the bias we found in average Ellenberg indicator values cannot be explained by measurement errors or by regression to the mean. In the end, Smart & Scott, as we did, come to the conclusion that there is a bias present and that separate regression lines per vegetation type are necessary, but the debate remains open on whether or not this is caused by the bias in Ellenberg indicator values per species