Context-dependence of diagnostic species:: A case study of the Central European spruce forests

In the phytosociological literature, there are numerous different approaches to the designation of diagnostic species. Frequently, this results in discrepancies between the lists of diagnostic species published for one and the same community. We examined different approaches to determining diagnostic species using as an example Picea abies forests within the broader context of all Central European forests. Diagnostic species of spruce forests were determined from a data set of 20,164 phytosociological releves of forests from the Eastern Alps, Western Carpathians, and the Bohemian Massif, which included 3,569 releves of spruce forests. Phi coefficient of association was used to measure species fidelity, and species with the highest fidelities were considered as diagnostic. Diagnostic species were determined in four ways, including (A) comparison of spruce forests among the three mountain ranges, (B) comparison between spruce forests and other forests, performed separately in each of the mountain ranges, (C) simultaneous comparison of spruce forests of each of the mountain ranges with spruce forests of the other two ranges and with the other forests of all ranges, (D) comparison of spruce forests with the other forests, using pooled data sets from the three mountain ranges. The sets of diagnostic species of spruce forests yielded in comparisons A and B were sharply different; the set resulting from comparison C was intermediate between the first two and comparison D resulted in similar diagnostic species as comparison B. In comparison A, spruce forests of the Eastern Alps had a number of diagnostic species, while the spruce forests of the other two mountain ranges had only few diagnostic species. In comparison B, by contrast, the number and quality of diagnostic species decreased from the Bohemian Massif to the Eastern Alps. This exercise points out that lists of diagnostic species published in phytosociological literature are dependent on the context, i.e. the underlying data sets and comparisons: some of these lists are useful for identification of vegetation units at a local scale, some others for distinguishing units within a narrowly delimited community type over a large area. The thoughtless application of published lists of diagnostic species outside of the context for which they were intended should therefore be avoided.