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Crowdsourced biodiversity monitoring fills gaps in global plant trait mapping

Lusk, Daniel ; Wolf, Sophie ; Svidzinska, Daria ; Dormann, Carsten F. ; Kattge, Jens ; Bruelheide, Helge ; Sabatini, Francesco Maria ; Damasceno, Gabriella ; Moreno Martínez, Álvaro and Violle, Cyrille , et al. (2026) In Nature Communications 17(1).
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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Nature Communications
volume
17
issue
1
article number
1203
publisher
Nature Publishing Group
external identifiers
  • pmid:41617716
  • scopus:105028970177
ISSN
2041-1723
DOI
10.1038/s41467-026-68996-y
language
English
LU publication?
yes
id
6533c627-2262-41b0-b281-6ab597951c88
date added to LUP
2026-02-17 11:13:20
date last changed
2026-02-18 03:00:09
@article{6533c627-2262-41b0-b281-6ab597951c88,
  abstract     = {{<p>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<sup>2</sup> 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.</p>}},
  author       = {{Lusk, Daniel and Wolf, Sophie and Svidzinska, Daria and Dormann, Carsten F. and Kattge, Jens and Bruelheide, Helge and Sabatini, Francesco Maria and Damasceno, Gabriella and Moreno Martínez, Álvaro and Violle, Cyrille and Hending, Daniel and Hähn, Georg J.A. and Tabeni, Solana and Phartyal, Shyam and Gonçalves, Fernando and Kreft, Holger and Schmidt, Marco and Chen, Han and Güler, Behlül and Dolezal, Jiri and Pielech, Remigiusz and Guido, Anaclara and Dwyer, Ciara and Napoleone, Francesca and Willie, Jacob and Gasper, André Luís and Macía, Manuel J. and Chytry, Milan and Lenoir, Jonathan and Thakur, Dinesh and Dengler, Jürgen and Świerszcz, Sebastian and Altman, Jan and Mucina, Ladislav and Nerlekar, Ashish N. and Kakinuma, Kaoru and Rawat, Pravin and Stančić, Zvjezdana and Testolin, Riccardo and Hatim, Mohamed Z. and Rodrigues, Flávio and Homeier, Jürgen and Marques, Marcia C.M. and McCarthy, James K. and El-Sheikh, M. A. and Korznikov, Kirill and Gerberding, Kilian and Kattenborn, Teja}},
  issn         = {{2041-1723}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature Communications}},
  title        = {{Crowdsourced biodiversity monitoring fills gaps in global plant trait mapping}},
  url          = {{http://dx.doi.org/10.1038/s41467-026-68996-y}},
  doi          = {{10.1038/s41467-026-68996-y}},
  volume       = {{17}},
  year         = {{2026}},
}