Predicting the species composition of Nardus stricta communities by logistic regression modelling

Question: Predictive models in plant ecology usually deal with single species or community types. Little effort has so far been made to predict the species composition of a community explicitly. The modelling approach presented here provides a conceptual framework on how to achieve this by combining habitat models for a large number of species to an additive community model. Our approach is exemplified by Nardus stricta communities (acidophilous, low-productive grassland). Location: Large areas of Germany, 0-2040 m a.s.l. Methods: Logistic regression is applied for individual species models which are subsequently combined for an explicit prediction of species composition. Several parameters reflecting soil, management and climatic conditions serve as predictor variables. For validation, bootstrap and jackknife resampling procedures are used as well as ordination techniques (DCA, CCA). Results: We calculated significant models for 138 individual species. The predictions of species composition and species richness yield good agreements with the observed data. DCA and CCA results show that the community model preserves the main patterns in floristic space. Conclusions: Our approach of predicting species composition is an effective tool that can be applied in nature conservation, e.g. to assess the effects of different site conditions and alternative management scenarios on species composition and richness.