Abstract
High-resolution maps of plant functional traits are crucial for understanding terrestrial ecosystem processes; however, their integration into ecosystem models has been hindered by uncertainties and a lack of spatially detailed data. Here we combine optical remote sensing, global crowd-sourced biodiversity records and plant trait databases to map community trait distributions worldwide at 1-km resolution, estimating community-weighted means (CWMs) and higher-order moments (standard deviation, skewness, and kurtosis) for specific leaf area (SLA), leaf nitrogen (LNC) and leaf phosphorus (LPC) concentrations. Benchmarking against sPlotOpen plot-level CWMs shows low explained variance (R2 = 0.10–0.27 across traits), indicating limited plot-scale predictive skill under current limited open global benchmarks and scale mismatches. Agreement increases when using a canopy-weighted comparator (TWM; R2 = 0.22–0.38; relative RMSE ≈ 12–18%), consistent with the top-of-canopy sensitivity of optical sensors. By providing spatially explicit trait distributions and their higher-order moments, our findings deliver improved detail for understanding biodiversity patterns and ecosystem functioning and provide landscape-scale insights into trait-mediated coexistence. This work enhances ecological modeling and offers a foundation for assessing the impacts of global environmental changes, advancing our understanding of plant functional diversity’s role in ecosystem resilience and sustainability.
Data availability
The plant functional trait maps and higher-order statistical moments generated in this study have been deposited in the Zenodo repository under accession code 18832368. An interactive visualization is available via Google Earth Engine (https://ee-almoma153.projects.earthengine.app/view/gbiftraitdescription) and through the Google Earth Engine Community Catalog (https://gee-community-catalog.org/). The TRY and sPlotOpen databases used in this study are openly accessible at their respective repositories: https://www.try-db.org, and https://www.idiv.de/splot. The biodiversity occurrence data used in this study were retrieved from GBIF.org; the data are available via the dataset citation provided in the References58, https://doi.org/10.15468/dl.an9g68, https://doi.org/10.15468/dl.q65cat, and https://doi.org/10.15468/dl.75a8qk.
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Acknowledgements
A.M.M., J.E.A., and G.C.V. were supported by the European Research Council (ERC) under the ERC Synergy Grant USMILE (grant agreement 855187) and the European Union’s Horizon Europe research and innovation programme under grant agreement no. 101137682 (AI4PEX—Artificial Intelligence and Machine Learning for Enhanced Representation of Processes and Extremes in ESMs). J.K. acknowledges support from the TRY initiative on plant traits (http://www.try-db.org). A.M.M. and E.I.V. were additionally supported by the Google Award “BenchFlux: Scale-aware Benchmarks for Nature-based Climate Solutions Using Flux Measurements and Earth Observations” and A.M.M. by the Spanish AEI Ramón y Cajal Grant [RYC2023-044930-I]. The authors also appreciate the support of the German Research Foundation for funding sPlot, as well as the sPlot consortium (http://www.idiv.de/splot), one of the iDiv (DFG FZT 118, 202548816) research platforms. T.K. acknowledges funding from the German Research Foundation (DFG) under the project BigPlantSens (Assessing the Synergies of Big Data and Deep Learning for the Remote Sensing of Plant Species; Project number 444524904) and PANOPS (Revealing Earth’s plant functional diversity with citizen science; project number 504978936). B.D. was supported by sDiv, the Synthesis Center of iDiv (DFG FZT 118, 202548816). We also acknowledge input from the sDiv working group sTRAITS: Integrating in-situ, upscaled, and air- and spaceborne observations of plant traits (https://www.idiv.de/straits), which included organizing several workshops on global foliar trait mapping and sharing foliar trait data. The research carried out by FDS at the Jet Propulsion Laboratory, California Institute of Technology, was under a contract with the National Aeronautics and Space Administration (80NM0018D0004). Government sponsorship is acknowledged. F.M.S. gratefully acknowledges financial support from the Rita-Levi Montalcini (2019) program, funded by the Italian Ministry of University.
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Á.M.M. conceived the study, designed the methodology, and led the writing of the manuscript. Á.M.M., J.M.M., and J.E.A. performed the formal analyses, with J.M.M. and J.E.A. also contributing to the study design. F.M.S. and J.K. provided expert insights regarding the sPlotOpen and TRY datasets, respectively. G.C.V., B.D., F.D.S., E.E.B., P.M.V., T.K., J.K., and M.D.M. made substantial contributions to drafting the manuscript. Finally, J.M.M, J.E.A., B.D., J.K., F.D.S., G.D., P.M.V., E.E.B., M.D.M., E.I.V., T.K., J.P., G.B., R.P.P., P.A.T., N.C., D.L., and G.C.V. contributed conceptual ideas and critically reviewed and edited the manuscript.
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Moreno-Martínez, Á., Muñoz-Marí, J., Adsuara, J.E. et al. Leveraging remote sensing and crowd-sourced biodiversity data for enhanced plant functional trait mapping. Nat Commun (2026). https://doi.org/10.1038/s41467-026-72111-6
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DOI: https://doi.org/10.1038/s41467-026-72111-6
