Methodologically induced differences in oak site classifications in a homogeneous tree-ring network

Abstract

Spatiotemporal variations in tree growth are induced by varying environmental conditions. Different methods like variants of the principal component analysis and the hierarchical cluster analysis are commonly applied in dendroecology to separate subsets of growth patterns within large tree-ring datasets. To seek for methodological differences in classification techniques and their specific characteristics, we compared three standard methods using a homogeneous oak ( Quercus spp.) network from temperate forests in Central-West Germany. Classifications of the original dataset consisting of 46 oak ring-width sites, carried out with the varimax rotated principle component analysis, Ward's method and the average linkage method, reveal differences in the classification of approximately 20% of the sites. Analyses with modified datasets are calculated to evaluate effects of dataset extension, different time periods and different tree-ring detrendings. The application of the principal component analysis generally leads to the most stable site classifications, whereas the most sensitive response to changes in the dataset is obtained by Ward's method. The average linkage method separates single sites in the classification and thus emphasises outliers within the tree-ring network.