| Author |
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, Georg J. A. Hähn, Solana Tabeni, Shyam 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 Gasper, Manuel J. Macía, Milan Chytry, 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 C. M. Marques, James K. McCarthy, M. A. El-Sheikh, Teja Kattenborn
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| Abstract |
Plant functional traits are fundamental to ecosystem dynamics and Earth system processes, but their global characterization is limited by the availability of field surveys and trait measurements. Recent expansions in biodiversity data aggregation, including large collections of 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 resolution. Our approach achieves high predictive power, reaching 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 our understanding of plant community properties and ecosystem functioning globally, and can serve as useful tools in modeling global biogeochemical processes and informing worldwide conservation efforts. Ultimately, our framework highlights the power and necessity of crowdsourced biodiversity data in high-resolution plant trait modeling. We anticipate that advancements in biodiversity data collection and remote sensing capabilities will further refine global trait mapping, fostering a dynamic trait-based understanding of the biosphere.
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