Assessing the reliability of predicted plant trait distributions at the global scale

Aim: Predictions of plant traits over space and time are increasingly used to improve our understanding of plant community responses to global environmental change. A necessary step forward is to assess the reliability of global trait predictions. In this study, we predict community mean plant traits at the global scale, and present a systematic evaluation of their reliability in terms of the model's accuracy, ecological realism, and various sources of uncertainty.Location: Global.Time period: Present.Major taxa studied: Vascular plants.Methods: We predicted global distributions of community mean specific leaf area, leaf nitrogen concentration, plant height, and wood density with an ensemble modelling approach based on georeferenced, locally measured trait data representative of the plant community. We assessed the models' predictive performance, plausibility of predicted trait combinations, influence of data quality, and uncertainty across geographic space due to spatial extrapolation and diverging models' predictions.Results: Individual modelling techniques varied greatly in predictive performance and lead to divergent predictions mostly in African deserts and the Arctic. The ensemble predictions of community mean plant height, specific leaf area, and wood density showed ecologically plausible trait-environment relationships and trait-trait combinations. Leaf nitrogen content could, however, not reliably be predicted. High data quality, i.e. including intra-specific trait variation and using a sample of species representative of the community increased model performance by 28% compared to models based on species-average trait values and a random sample of species. Predictions were mainly extrapolated for the Arctic and deserts. Main conclusions: Plant community traits can be reliably predicted at the global scale when data quality is high, but prediction accuracy differs among traits, models, and geographic regions. We recommend applying ensemble forecasting to account for model uncertainty, using representative trait data, and more routinely assessing the reliability of trait predictions.