Crowdsourced biodiversity monitoring fills gaps in global plant trait mapping

D Daniel Lusk S Sophie Wolf D Daria Svidzinska C Carsten F. Dormann J Jens Kattge H Helge Bruelheide (Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg) F Francesco Maria Sabatini G Gabriella Damasceno Álvaro Moreno Martínez C Cyrille Violle (CEFE, University of Montpellier, CNRS, EPHE-PSL University, IRD, Montpellier, France.) D Daniel Hending G Georg J. A. Hähn S Solana Tabeni S Shyam Phartyal F Fernando Gonçalves (Section for Molecular Ecology and Evolution, Globe Institute, University of Copenhagen) H Holger Kreft (Department of Biodiversity, Macroecology and Biogeography, University of Göttingen) M Marco Schmidt H Han Chen (GBRCE for Functional Molecular Engineering, LIFM, IGCME, School of Chemistry) B Behlül Güler J Jiri Dolezal R Remigiusz Pielech A Anaclara Guido C Ciara Dwyer (Centre for Environmental and Climate Science, Lund University) F Francesca Napoleone J Jacob Willie A André Luís Gasper M Manuel J. Macía M Milan Chytrý J Jonathan Lenoir D Dinesh Thakur J Jürgen Dengler S Sebastian Świerszcz J Jan Altman L Ladislav Mucina A Ashish N. Nerlekar K Kaoru Kakinuma P Pravin Rawat Z Zvjezdana Stančić R Riccardo Testolin M Mohamed Z. Hatim F Flávio Rodrigues J Jürgen Homeier M Marcia C. M. Marques J James K. McCarthy M M. A. El-Sheikh K Kirill Korznikov K Kilian Gerberding T Teja Kattenborn (Chair of Sensor-based Geoinformatics)

Abstract

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.

Article Details

Volume / Issue Vol. 17, Issue 1
Published January 30, 2026
ISSN 2041-1723
Publisher Nature Portfolio

Journal Info

Nature Communications

Nature Portfolio

ISSN: 2041-1723 Open Access Life Sciences

Authors (48)

D

Daniel Lusk

S

Sophie Wolf

D

Daria Svidzinska

C

Carsten F. Dormann

J

Jens Kattge

H

Helge Bruelheide

Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg

F

Francesco Maria Sabatini

G

Gabriella Damasceno

Álvaro Moreno Martínez

C

Cyrille Violle

CEFE, University of Montpellier, CNRS, EPHE-PSL University, IRD, Montpellier, France.

D

Daniel Hending

G

Georg J. A. Hähn

S

Solana Tabeni

S

Shyam Phartyal

F

Fernando Gonçalves

Section for Molecular Ecology and Evolution, Globe Institute, University of Copenhagen

H

Holger Kreft

Department of Biodiversity, Macroecology and Biogeography, University of Göttingen

M

Marco Schmidt

H

Han Chen

GBRCE for Functional Molecular Engineering, LIFM, IGCME, School of Chemistry

B

Behlül Güler

J

Jiri Dolezal

R

Remigiusz Pielech

A

Anaclara Guido

C

Ciara Dwyer

Centre for Environmental and Climate Science, Lund University

F

Francesca Napoleone

J

Jacob Willie

A

André Luís Gasper

M

Manuel J. Macía

M

Milan Chytrý

J

Jonathan Lenoir

D

Dinesh Thakur

J

Jürgen Dengler

S

Sebastian Świerszcz

J

Jan Altman

L

Ladislav Mucina

A

Ashish N. Nerlekar

K

Kaoru Kakinuma

P

Pravin Rawat

Z

Zvjezdana Stančić

R

Riccardo Testolin

M

Mohamed Z. Hatim

F

Flávio Rodrigues

J

Jürgen Homeier

M

Marcia C. M. Marques

J

James K. McCarthy

M

M. A. El-Sheikh

K

Kirill Korznikov

K

Kilian Gerberding

T

Teja Kattenborn

Chair of Sensor-based Geoinformatics