egetation dynamics involves processes operating at widely different spatial and temporal scales, from stomatal opening and closing (minutes to days, at the leaf level) to biome shifts (decades to centuries, across entire continents). Tremendous research efforts have been devoted to understanding and predicting how plant processes and functional traits of individuals combine to determine the structure, function and dynamics of vegetation on larger scales. To integrate process understanding from different disciplines, dynamic vegetation models (DVMs) have been developed that combine elements from plant biogeography, biogeochemistry, plant physiology, forest ecology and micrometeorology. The best-known DVMs, dynamic global vegetation models (DGVMs), have found a wide field of application, including assessments of land-atmosphere carbon, water and trace gas exchanges; water resources; impacts of environmental change on plants and ecosystems; land management; and feedbacks from vegetation changes to regional and global climates 1,2. DVMs have also been applied on local scales for testing of ecological hypotheses and to answer practical questions in forest management and agriculture. All DVMs are based on the assumption of universally valid processes, which, in principle, enable them to make predictions under conditions outside the range of observations used for model development.