Effects of different fidelity measures and contexts on the determination of diagnostic species

摘要

How different are lists of diagnostic species of vegetation units, derived using various fidelity measures, in different contexts and with presence/absence versus cover data?Six different fidelity measures were calculated for vegetation units of two classified data sets covering contrasting types of Central European vegetation (beech forest and dwarf shrub vegetation). Both statistical and non-statistical fidelity measures were used, and either species presence/absence or cover was considered. Each measure was calculated on four hierarchical levels and within two different contexts, either within the whole data set or within the next higher level of hierarchical classification. Average similarities of the diagnostic species lists derived from various combinations of fidelity measures and contexts were calculated and visualized using principal coordinate analysis (PCoA).The correlations between fidelity values derived from non-statistical and statistical measures were rather weak. Nevertheless, diagnostic species lists calculated for the same syntaxon by different measures usually had several species in common. Average similarity between pairs of fidelity measures or contexts (based on the Sorensen similarity index) ranged from 0.21 to 0.92. PCoA clustered individual combinations of fidelity measures and contexts mainly according to the context and the use of presence/absence versus cover data, rather than according to the fidelity measures.The strongest impact on the lists of diagnostic species was not the fidelity measure itself but the context of its application and the use of presence/absence or cover data. Despite the weak correlation between individual fidelity values, traditional (non-statistical) and statistical measures produce quite similar lists of diagnostic species, provided that the context of the analysis is the same. Both approaches have their advantages and disadvantages, and the choice of the appropriate algorithm should depend on the focus of the study.