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

• Published in: Global Ecology and Biogeography
• Main Authors: Boonman, Coline, C.F., Benítez-López, Ana, Schipper, Aafke, M., Thuiller, Wilfried, Anand, Madhur, Cerabolini, Bruno E.L., Cornelissen, Johannes Hc, González-Melo, Andrés, Hattingh, Wesley, Higuchi, Pedro, Laughlin, Daniel C., Onipchenko, Vladimir G., Penuelas, Joseph, Poorter, Lourens, Soudzilovskaia, Nadejda A., Huijbregts, Mark A.J., Santini, Luca
• Other Authors: Laboratoire d'Ecologie Alpine (LECA), Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (Fédération OSUG)-Université Grenoble Alpes (UGA)
• Format: Article in Journal/Newspaper
• Language: English
• Published: CCSD 2020
• Subjects: ensemble forecasting; environmental filtering; intraspecific trait variation; leaf nitrogen concentration; plant height; specific leaf area; trait–environment relationships; trait model; wood density; [SDE]Environmental Sciences; [SDV]Life Sciences [q-bio]; Arctic
• Online Access: https://cnrs.hal.science/hal-02960113; https://cnrs.hal.science/hal-02960113v1/document; https://cnrs.hal.science/hal-02960113v1/file/geb.13086-2.pdf; https://doi.org/10.1111/geb.13086

International audience AbstractAim: 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 sys- tematic evaluation of their reliability in terms of the accuracy of the models, ecologi- cal 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 model- ling approach based on georeferenced, locally measured trait data representative of the plant community. We assessed the predictive performance of the models, the plausibility of predicted trait combinations, the influence of data quality, and the un- certainty across geographical space attributed to spatial extrapolation and diverging model predictions.Results: Ensemble predictions of community mean plant height, specific leaf area and wood density resulted in ecologically plausible trait–environment relationships and trait–trait combinations. Leaf nitrogen concentration, however, could not be predicted reliably. The ensemble approach was better at predicting community trait means than any of the individual modelling techniques, which varied greatly in pre- dictive performance and led to divergent predictions, mostly in African deserts and the Arctic, where predictions were also extrapolated. High data quality (i.e., including intraspecific variability and a representative species sample) increased model perfor- mance by 28%.Main conclusions: Plant community traits can be predicted reliably at the global scale when using an ensemble approach and high-quality data for traits that mostly re- spond to large-scale environmental factors. We recommend applying ensemble fore- casting ...