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Galton Galton (1886)
Regression toward mediocrity in hereditary statusJ. R. Anthropol. Inst., 15
Witte Witte, Asmuth Asmuth (2003)
Do we really need phytosociological classes to calibrate Ellenberg indicatorVeg. Sci., 14
Hill Hill, Roy Roy, Mountford Mountford, Bunce Bunce (2000)
Extending Ellenberg's indicator values to a new area: an algorithmic approachJ. Appl. Ecol., 37
Smart Smart, Scott Scott (2004)
Bias in indicator values — problems with the detection of the effect of vegetation typeJ. Veg. Sci., 15
Wamelink Wamelink, Dobben Dobben, Berendse Berendse (2002)
Validity of Ellenberg indicator values judged from physico‐chemical field measurementsJ. Veg. Sci., 13
Diekmann Diekmann (2003)
Species indicator values as an important tool in applied ecology — a reviewBasic Appl. Ecol., 4
Ellenberg Ellenberg, Weber Weber, Dull Dull, Wirth Wirth, Werner Werner, Paulißen Paulißen (1991)
Zeigerwerte von Pflanzen in MitteleuropaScripta Geobot., 18
Wamelink Wamelink, Dobben Dobben, Berendse Berendse (2003)
Apparently we do need phytosociological classes to calibrate Ellenberg indicator values!J. Veg. Sci., 14
Smart & Scott (2004, this is sue) 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.
Journal of Vegetation Science – Wiley
Published: Dec 1, 2004
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