Publication details

Semi-dry grasslands along a climatic gradient across Central Europe: Vegetation classification with validation

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Year of publication 2007
Type Article in Periodical
Magazine / Source Journal of Vegetation Science
MU Faculty or unit

Faculty of Science

Field Botany
Keywords Austria; Bromion; Cirsio-Brachypodion; Czech Republic; Germany; Hungary; Phytosociology; Romania; Slovakia; Training and test data sets; Vegetation database
Description Question: What is the variation in species composition of Central European semi-dry grasslands? Can we apply a training-and-test validation approach for identifying phytosociological associations which are floristically well defined in a broad geographic comparison; can we separate them from earlier described associations with only a local validity? Location: A 1200 km long transect running along a gradient of increasing continentality from central Germany via Czech Republic, Slovakia, NE Austria, Hungary to NW Romania. Methods: Relevés with > 25% cover of Brachypodium pinnatum and/or Bromus erectus were geographically selected from a larger database. They were randomly split into two data sets, TRAINING and TEST, each with 422 relevés. Cluster analysis was performed for each data set on scores from significant principal coordinates. Different partitions of the TRAINING data set were validated on the TEST data set, using a new method based on the comparison of % frequencies of species occurrence in clusters. Clusters were characterized by statistically defined groups of diagnostic species and values of climatic variables. Results: Species composition changed along the NW-SE gradient and valid clusters were geographically well separated. Optimal partition level was at 11 clusters, six being valid: two clusters Germany and the Czech Republic corresponded to the Bromion erecti; two clusters from the Czech Republic and Hungary to the Cirsio-Brachypodion, and two clusters were transitional between these two alliances. Conclusion: The training-and-test validation method used in this paper proved to be efficient for discriminating between robust clusters, which are appropriate candidates for inclusion in the national or regional syntaxonomic overviews, and weak clusters, which are specific to the particular classification of the given data set.
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