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Updated: Aug 24, 2025

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
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Citizen science plant observations encode global trait patterns.

Sophie Wolf1, Miguel D Mahecha2,3,4, Francesco Maria Sabatini4,5,6

  • 1Remote Sensing Centre for Earth System Research, Leipzig University, Leipzig, Germany. sophie.wolf@uni-leipzig.de.

Nature Ecology & Evolution
|October 20, 2022
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Summary
This summary is machine-generated.

Citizen science data from iNaturalist, combined with TRY trait data, can effectively map global plant functional traits. These new maps show high correlations with existing vegetation plot data.

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Area of Science:

  • Ecology
  • Botany
  • Biodiversity

Background:

  • Global maps of plant functional traits are crucial for understanding the terrestrial biosphere.
  • Current trait measurement data is spatially limited.
  • Citizen science initiatives are generating vast amounts of vegetation data.

Purpose of the Study:

  • To investigate the utility of citizen science data for global plant functional trait mapping.
  • To create novel global trait maps by integrating diverse data sources.

Main Methods:

  • Combined vascular plant observations from iNaturalist (citizen science) with trait measurements from the TRY database.
  • Validated the generated trait maps against sPlotOpen, a global vegetation plot dataset.

Main Results:

  • Achieved high correlations (up to r=0.69) between iNaturalist- and sPlotOpen-based trait maps.
  • The new maps demonstrated higher accuracy compared to previously published trait maps.
  • Demonstrated the potential of citizen science data to enhance global ecological datasets.

Conclusions:

  • Citizen science data, particularly from iNaturalist, is a valuable resource for improving global plant functional trait maps.
  • Integrating citizen science data offers a scalable approach to overcome data sparsity in ecological research.
  • Future growth in citizen science data collections will further enhance global biodiversity and ecological modeling efforts.