Global Plant Functional Traits

[dluks]

Contact Details

daniel.lusk@geosense.uni-freiburg.de

Dataset description

Global plant functional trait maps

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. This dataset provides wall-to-wall global maps of 37 plant functional traits as defined in the TRY Plant Trait Database at 1 km resolution.

Key features

• Resolution: 1 km
• Extent: Global
• Traits: 37 plant functional traits (15 traits with correlation > 0.50)
• Plant functional types: Two versions available - Shrub/Tree/Grass combined, and Shrub/Tree only

Methodology

The maps are extrapolations by ensemble models trained on:

• ~40 million citizen science species observations from the Global Biodiversity Information Facility (GBIF)
• Vegetation surveys from the sPlot database
• TRY trait measurements
• Global Earth observation datasets

Bands

Each trait raster contains three bands:

• B1: Trait values (scaled, see metadata for offset/scale)
• B2: Coefficient of variation (model uncertainty indicator)
• B3: Area of applicability mask (indicates extrapolation beyond training data, see Meyer and Pebesma, 2021)

Available traits

Includes: SLA, Leaf N, Leaf C, Leaf P, LDMC, Plant height, Seed mass, Stem diameter, Wood fiber length, Root diameter, and many more (see metadata for full list with TRY IDs).

Links

• Paper: https://www.nature.com/articles/s41467-026-68996-y
• Dataset download: https://zenodo.org/records/14646322
• Interactive app: https://global-traits.projects.earthengine.app/view/global-traits
• Project website: https://planttraits.earth

Funding

This dataset was created in the framework of the PANOPS project, funded by the German Research Foundation (DFG).

Citation (please cite both)

Paper

Lusk, D., Wolf, S., Svidzinska, D. et al. Crowdsourced biodiversity monitoring fills gaps in global plant trait mapping. Nat Commun 17, 1203 (2026). https://doi.org/10.1038/s41467-026-68996-y

Dataset

Lusk, D., Wolf, S., Svidzinska, D., & Kattenborn, T. (2026). Global plant trait maps based on crowdsourced biodiversity monitoring and Earth observation - 1 km - All PFTs [Data set]. In Nature Communications (1.0.0, Vol. 17, Number 1203). Zenodo. https://doi.org/10.5281/zenodo.14646322

Earth Engine Snippet if dataset already in GEE

Basic usage

// Load a trait (e.g., SLA - Specific Leaf Area)
var trait = ee.Image("projects/global-traits/assets/Shrub_Tree_Grass/X3117_mean_Shrub_Tree_Grass_1km");

// Band 1: Trait values (rescale using metadata)
var traitValues = trait.select('b1')
  .multiply(ee.Number(trait.get('trait_scale')))
  .add(ee.Number(trait.get('trait_offset')));

// Band 2: Coefficient of variation (rescale using metadata)
var covValues = trait.select('b2')
  .multiply(ee.Number(trait.get('cov_scale')))
  .add(ee.Number(trait.get('cov_offset')));

// Band 3: Area of applicability mask
var aoa = trait.select('b3').updateMask(trait.select('b3').neq(0));

// Visualize
Map.addLayer(traitValues, {min: 5, max: 30, palette: ['#2e1e3b', '#413d7b', '#37659e', '#348fa7', '#40b7ad', '#8bdab2']}, 'SLA');
Map.addLayer(covValues, {min: 0, max: 0.5, palette: ['#000004', '#3b0f70', '#8c2981', '#de4968', '#fe9f6d', '#fcfdbf']}, 'Coefficient of variation', false);
Map.addLayer(aoa, {palette: ['#d7b509']}, 'Outside AOA', false);

Enter license information

CC-BY-4.0

Keywords

plant traits, functional traits, SLA, leaf nitrogen, LDMC, specific leaf area, TRY database, biodiversity, vegetation, citizen science, GBIF, sPlot, remote sensing, machine learning, ecosystem, biogeography, global, 1km, leaf traits, wood traits, seed traits, plant height, leaf area, carbon, nitrogen, phosphorus, trait mapping, community ecology, Earth observation

Code of Conduct

• I agree to follow this project's Code of Conduct