EFFECTS OF SPECIES PARTITION ON EXPLANATORY VARIABLES IN DIRECT GRADIENT ANALYSIS - A CASE-STUDY FROM SENEGAL

Species-environment data from Senegal, West Africa, are used to study the effects of partition of a large species data set into subsets corr esponding to rare and common species respectively. The original data s et contains 129 woody plant species from 909 plots and 60 explanatory variables. By applying Canonical Correspondence Analysis to data subse ts, marked differences in the forward-selected variables were detected . The highest resemblance was found between the complete species set a nd the common species subset. Only one of eight selected variables was common to all species and the rare species groups. These findings wer e tested with partial ordination, applying the selected variables from the original species group (Vb), as variables and covariables to the analyses of common and rare species. For the common species this appli cation resulted in a constrained ordination with higher eigenvalues as compared to the set of variables selected with reference to the commo n species group. Using the rare species group, the application of Vb g ave a much lower sum of eigenvalues than did the ordination with selec ted variables based on the rare species group only. Evidently, the set of variables selected on the basis of the rare species data were more significant. Hence, the resulting gradients depend on the frequency o f the species. Gradient analysis is apparently only valid for species groups with closely resembling characteristics. This implies that diff erent functional types of species, with different distributions and ab undances, respond individually to environmental variation. Extrapolati ng deduced gradients from one species group to another maybe risky, pa rticularly when used in vegetation modelling.