Crowdsourced biodiversity monitoring fills gaps in global plant trait mapping
Jazyk angličtina Země Anglie, Velká Británie Médium electronic
Typ dokumentu časopisecké články
Grantová podpora
444524904
Deutsche Forschungsgemeinschaft (German Research Foundation)
504978936
Deutsche Forschungsgemeinschaft (German Research Foundation)
442032008
Deutsche Forschungsgemeinschaft (German Research Foundation)
459819582
Deutsche Forschungsgemeinschaft (German Research Foundation)
EXC 3127 Future Forests
Deutsche Forschungsgemeinschaft (German Research Foundation)
202548816
Deutsche Forschungsgemeinschaft (German Research Foundation)
202548816
Deutsche Forschungsgemeinschaft (German Research Foundation)
202548816
Deutsche Forschungsgemeinschaft (German Research Foundation)
FORTRACK
European Space Agency (ESA)
TMPFP2_217531
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation)
88887.974741/2024-00
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazilian Federal Agency for the Support and Evaluation of Graduate Education)
PubMed
41617716
PubMed Central
PMC12858941
DOI
10.1038/s41467-026-68996-y
PII: 10.1038/s41467-026-68996-y
Knihovny.cz E-zdroje
- MeSH
- biodiverzita * MeSH
- crowdsourcing * metody MeSH
- ekosystém MeSH
- rostliny * genetika MeSH
- Publikační typ
- časopisecké články MeSH
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.
Biodiversity Macroecology and Biogeography University of Göttingen Göttingen Germany
Biology Education Dokuz Eylül University Buca Izmir Turkey
Botany and Microbiology Department College of Science King Saud University Riyadh Saudi Arabia
CEFE CNRS EPHE IRD University of Montpellier Montpellier France
Centre for Environmental and Climate Science Lund University Lund Sweden
Centre for Research and Conservation Royal Zoological Society of Antwerp Antwerp Belgium
Centro de Investigación en Biodiversidad y Cambio Global Universidad Autónoma de Madrid Madrid Spain
Chair of Sensor based Geoinformatics University of Freiburg Freiburg Germany
College of Grassland Science Inner Mongolia Agricultural University Hohhot China
Departamento de Biología Universidad Autónoma de Madrid Madrid Spain
Departamento de Botânica SCB Universidade Federal do Paraná Curitiba Brazil
Department of Biology University of Oxford Oxford UK
Department of Biometry and Environmental System Analysis University of Freiburg Freiburg Germany
Department of Botany and Zoology Faculty of Science Masaryk University Brno Czechia
Department of Evolutionary Biology and Environmental Studies University of Zurich Zurich Switzerland
Department of Forestry Mizoram University Aizawl India
Department of Geography and Environmental Studies Stellenbosch University Stellenbosch South Africa
Department of Plant Biology Michigan State University East Lansing MI USA
Faculty of Forestry and Wood Sciences Czech University of Life Sciences Prague Prague Czechia
Faculty of Geotechnical Engineering University of Zagreb Zagreb Croatia
Faculty of Resource Management HAWK University of Applied Sciences and Arts Göttingen Germany
Faculty of Science University of South Bohemia České Budějovice Czechia
German Centre for Integrative Biodiversity Research Halle Jena Leipzig Leipzig Germany
ICFRE Himalayan Forest Research Institute Shimla Himachal Pradesh India
Image Signal Processing Group Image Processing Laboratory University of Valencia Paterna Spain
Institute of Botany Faculty of Biology Jagiellonian University Kraków Poland
Institute of Botany of the Czech Academy of Sciences Trebon Czechia
Institute of Natural Resource Sciences Wädenswil Switzerland
Instituto Argentino de Investigaciones de las Zonas Áridas CONICET Mendoza Argentina
Korea Advanced Institute of Science and Technology Daejeon South Korea
Manaaki Whenua Landcare Research Lincoln New Zealand
Max Planck Institute for Biogeochemistry Jena Germany
Palmengarten der Stadt Frankfurt am Main Frankfurt am Main Germany
Plant Ecology Laboratory UNEMAT Nova Xavantina MT Brazil
Program in Ecology Evolution and Behavior Michigan State University East Lansing MI USA
Remote Sensing Centre for Earth System Research Leipzig University Leipzig Germany
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