The potential (and limitations) of global plant trait databases: Lessons from TRY

Background/Question/Methods

Plant traits determine how primary producers respond to environmental factors, affect other trophic levels, influence ecosystem processes and services, and provide a link from species richness to ecosystem functional diversity. Plant traits thus are a key to understand and predict the adaptation of ecosystems to environmental changes. At the same time plant trait data are dispersed over a wide range of databases, many of these not publicly available. To overcome this deficiency we have developed a worldwide communal plant trait database, called TRY. Its goal is the construction of a standard resource of plant trait data for the ecological community and for the development of global vegetation models, while at the same time respecting intellectual data property. With this approach, TRY is a novel undertaking from both the scientific and sociological points of view. 

Results/Conclusions

So far the TRY initiative has united a wide range of the plant trait research community worldwide and gained an unprecedented buy-in of trait data: about 100 trait databases have been contributed and the data repository currently contains almost three million trait entries for about 70,000 out of the world's 400,000 plant species. The database includes data for 750 traits, characterizing the vegetative and regeneration stages of the plant life cycle, including growth, dispersal, establishment and persistence. A first data analysis shows that most plant traits are approximately log-normally distributed, with widely differing ranges of variation across traits. Most trait variation is between species (interspecific), but significant intraspecific variation is also documented. Simple a-priori plant functional types (PFTs), as commonly used in vegetation models, capture a substantial fraction of the observed variation - but for several traits up to 70% of variation occurs within PFTs. Due to its origin from several independent databases the joint dataset is still a sparse data matrix. This sparsity might be overcome using advanced methods currently being developed in applied statistics and machine learning. On the long term the improved availability of plant trait data in the unified global database is expected to support a paradigm shift from species to trait- and trait syndrome-based ecology, offer new opportunities for synthetic plant trait research and enable a more realistic and empirically grounded representation of terrestrial vegetation in Earth system models.