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Crowdsourced biodiversity monitoring fills gaps in global plant trait mapping

Daniel Lusk,Sophie Wolf,Daria Svidzinska,Carsten F. Dormann,J. Kattge,Helge Bruelheide,F. Sabatini,Gabriella Damasceno,Álvaro Moreno Martínez,C. Violle,D. Hending,Georg J. A. Hähn,S. Tabeni,S. Phartyal,Fernando Gonçalves,H. Kreft,Marco Schmidt,Han Y. H. Chen,B. Güler,J. Doležal,R. Pielech,Anaclara Guido,Ciara Dwyer,Francesca Napoleone,J. Willie,André Luís de Gasper,M. Macía,Milan Chytrý,J. Lenoir,Dinesh Thakur,J. Dengler,Sebastian Świerszcz,J. Altman,L. Mucina,A. N. Nerlekar,Kaoru Kakinuma,Pravin Rawat,Z. Stančić,R. Testolin,Mohamed Z. Hatim,Flávio Rodrigues,J. Homeier,Márcia C. M. Marques,James K. McCarthy,M. A. El‐Sheikh,Kirill Korznikov,Kilian Gerberding,Teja Kattenborn

Published 2026 in Nature Communications

ABSTRACT

Plant functional traits are fundamental to ecosystem dynamics and Earth system processes, but their global characterization is limited by available field surveys and trait measurements. Recent expansions in biodiversity data aggregation—including vegetation surveys, citizen science observations, and trait measurements—offer new opportunities to overcome these constraints. Here we demonstrate that combining these diverse data sources with high-resolution Earth observation data enables accurate modeling of key plant traits at up to 1 km2 resolution. Our approach achieves correlations up to 0.63 (15 of 31 traits exceeding 0.50) and improved spatial transferability, effectively bridging gaps in under-sampled regions. By capturing a broad range of traits with high spatial coverage, these maps can enhance understanding of plant community properties and ecosystem functioning, while serving as tools for modeling global biogeochemical processes and informing conservation efforts. Our framework highlights the power of crowdsourced biodiversity data in addressing longstanding extrapolation challenges in global plant trait modeling, with continued advancements in data collection and remote sensing poised to further refine trait-based understanding of the biosphere. Plant traits drive ecosystem dynamics yet are challenging to map globally due to sparse measurements. Here, the authors combine crowdsourced biodiversity observations with Earth observation data to accurately map 31 plant traits at 1 km2 resolution.

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