Abstract. The relationship between mean Ellenberg indicator values (IV) per vegetation relevé and environmental parameters measured in the field usually shows a large variation. We tested the hypothesis that this variation is caused by bias dependent on the phytosociological class. For this purpose we collected data containing vegetation relevés and measured soil pH (3631 records) or mean spring groundwater level (MSL, 1600 records). The relevés were assigned to vegetation types by an automated procedure. Regression of the mean indicator values for acidity on soil pH and the mean indicator values for moisture on MSL gave percentages explained variance similar to values that were reported earlier in literature. When the phytosociological class was added as an explanatory factor the explained variance increased considerably. Regression lines per vegetation type were estimated, many of which were significantly different from each other. In most cases the intercepts were different, but in some cases their slopes differed as well. The results show that Ellenberg indicator values for acidity and moisture appear to be biased towards the values that experts expect for the various phytosociological classes. On the basis of the results, we advise to use Ellenberg IVs only for comparison within the same vegetation type.

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Since its establishment around 1990, the Ecological Conditions Database (EC; GIVD ID EU-00-006) has been accumulating vegetation relevés from the Netherlands, each accompanied by at least one abiotic soil measurement (e.g. pH or nutrient availability). On 1-1-2010, the database contained 8,229 relevés, covering the period from 1936 to 2009, and representing contributions from 110 authors. The most frequently measured soil parameter is pH, with well over 5,000 entries. All the data in the database are subjected to ISO 9001 quality control. The database can be used as the starting point for estimating plant species responses to a range of abiotic variables, such as pH, groundwater table, or nitrate concentration, and for vegetation modelling (model parameterisation and validation).