Multivariate statistics as a tool for model-based prediction of floodplain vegetation and fauna

Robust prediction models for the spatial distribution of grassland vegetation, molluscs and carabids in a study area at the Middle Elbe (Germany) had to be generated by means of multivariate statistical methods. An appropriate study design has been developed. Data of all three taxons as well as numerous parameters of the abiotic environment were ascertained. Canonical correspondence analysis (CCA) detected clear dependencies between the occurrence of biotic objects and mainly hydrological parameters. By use of Arc/Info applications CANOGEN and CANORES, it was possible to extrapolate the models from the sample plots to the whole study area. The predicted spatial distribution of nearly all in the field studies examined species could be depicted with these instruments. Comparison between investigated and predicted distribution of species showed high correspondence. ness of the models was proved by interchanging model parameters for different study years and also in applying models at a second study area located 40 km upstream of the original study area. econd method for further investigation, logistic regression was used to build generalised linear models (GLM) for potential indicator species in the study area.