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

Coline C.F. Boonman*, Ana Benítez-López, Aafke M. Schipper, Wilfried Thuiller, Madhur Anand, Bruno E.L. Cerabolini, Johannes H.C. Cornelissen, Andres Gonzalez-Melo, Wesley N. Hattingh, Pedro Higuchi, Daniel C. Laughlin, Vladimir G. Onipchenko, Josep Peñuelas, Lourens Poorter, Nadejda A. Soudzilovskaia, Mark A.J. Huijbregts, Luca Santini

*Corresponding author for this work

    Research output: Contribution to JournalArticleAcademicpeer-review


    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 accuracy of the models, 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 predictive performance of the models, the plausibility of predicted trait combinations, the influence of data quality, and the uncertainty 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 predictive 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 performance 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 respond to large-scale environmental factors. We recommend applying ensemble forecasting to account for model uncertainty, using representative trait data, and more routinely assessing the reliability of trait predictions.

    Original languageEnglish
    Pages (from-to)1034-1051
    Number of pages18
    JournalGlobal Ecology and Biogeography
    Issue number6
    Early online date20 Mar 2020
    Publication statusPublished - Jun 2020


    • ensemble forecasting
    • environmental filtering
    • intraspecific trait variation
    • leaf nitrogen concentration
    • plant height
    • specific leaf area
    • trait model
    • trait–environment relationships
    • wood density


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