Field observations and experimental data of effects of nitrogen (N) deposition oil plant species diversity have been Used to derive empirical critical N loads for various ecosystems. The great advantage of such an approach is the inclusion of field evidence, but there are also restrictions, such as the absence of explicit criteria regarding significant effects on the vegetation, and the impossibility to predict future impacts when N deposition changes. Model approaches Call account for this. In this paper, we review the possibilities of static and dynamic multispecies models in combination with dynamic soil-vegetation models to (1) predict plant species composition as a function of atmospheric N deposition and (2) calculate critical N loads in relation to a prescribed protection level of the species composition. The similarities between the models are presented, but also several important differences, including the Use of different indicators for N and acidity and the prediction of individual plant species VS. Plant communities. A summary of the strengths and weaknesses of the various Models, including their validation status, is given. Furthermore,. examples are given of critical load calculations with the model chains and their comparison with empirical critical N loads. We show that linked biogeochemistry-biodiversity models for N have potential for applications to support European policy to reduce N input, but the definition of damage thresholds for terrestrial biodiversity represents a major challenge. There is also a clear need for further testing and validation of the models against long-term monitoring or long-term experimental data sets and against large-scale survey data. This requires a focused data collection ill Europe. combing vegetation descriptions with variables affecting the species diversity. such as Soil acidity, nutrient status and water availability. Finally there is a need for adaptation and upscaling of the models beyond the regions for which dose-response relationships have been parameterized, to make them generally applicable.