Crowdsourced biodiversity monitoring fills gaps in global plant trait mapping

作者：Daniel Lusk, Sophie Wolf, Daria Svidzinska, Carsten F. Dormann, Jens Kattge, Helge Bruelheide, Francesco Maria Sabatini, Gabriella Damasceno, Álvaro Moreno Martínez, Cyrille Violle, Daniel Hending, G. Hahn, Solana Tabeni, Shyam S. Phartyal, Fernando Gonçalves, Holger Kreft, Marco Schmidt, Han Chen, Behlül Güler, Jiri Dolezal, Remigiusz Pielech, Anaclara Guido, Ciara Dwyer, Francesca Napoleone, Jacob Willie, André Luís de Gasper, Manuel J. Macía, Milan Chytrý, Jonathan Lenoir, Dinesh Thakur, Jürgen Dengler, Sebastian Świerszcz, Jan Altman, Ladislav Mucina, Ashish N. Nerlekar, Kaoru Kakinuma, Pravin Rawat, Zvjezdana Stančić, Riccardo Testolin, Mohamed Z. Hatim, Flávio Rodrigues, Jürgen Homeier, Marcia o Marques, James K. McCarthy, M. A. El-Sheikh, Kirill Korznikov, Kilian Gerberding, Teja Kattenborn · 发表于：Nature Communications · 年份：2026 · DOI：10.1038/s41467-026-68996-y · 被引用次数：6 · 研究领域：Species Distribution and Climate Change、Remote Sensing in Agriculture、Ecology and Vegetation Dynamics Studies

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 km 2 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.