A functional classification for predicting the dynamics of landscapes

Functional classifications have been derived for various purposes using subjective, objective and deductive approaches. Most of the classifications were derived to describe a static state of a region or landscape rather than to predict the dynamics of the system. Here, we suggest a simple, but comprehensive functional classification based on life history parameters that can predict the dynamics of plant communities subject to recurrent disturbances. The predicted dynamics are described in terms of survival and local extinction of the functional groups. The groups derived item the classification are probably largely independent of functional groupings that may be derived for other aspects of community composition (e.g. structure, phenology) and community interactions (roughness, albedo etc.). We emphasize that functional classification is context-dependent and we should not expect to find a useful, universal classification into functional groups. Soft ware has been developed to help classify the species into functional groups, to derive successional sequences and to predict community composition under different disturbance regimes both in point and landscape models.