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Citizen science plant observations encode global trait patterns


Global maps of plant functional traits are essential for studying the dynamics of the terrestrial biosphere, yet the spatial distribution of trait measurements remains sparse. With the increasing popularity of species identification apps, citizen scientists contribute to growing vegetation data collections. The question emerges whether such opportunistic citizen science data can help map plant functional traits globally. Here we show that we can map global trait patterns by complementing vascular plant observations from the global citizen science project iNaturalist with measurements from the plant trait database TRY. We evaluate these maps using sPlotOpen, a global collection of vegetation plot data. Our results show high correlations between the iNaturalist- and sPlotOpen-based maps of up to 0.69 (r) and higher correlations than to previously published trait maps. As citizen science data collections continue to grow, we can expect them to play a significant role in further improving maps of plant functional traits.

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Fig. 1: Density and distribution of iNaturalist and sPlotOpen datasets.
Fig. 2: Example of a global trait map.
Fig. 3: Pixel-by-pixel correlation of iNaturalist and sPlotOpen global trait maps.
Fig. 4: Difference between sPlotOpen cwm grid cell averages and iNaturalist observation averages per grid cell for each terrestrial biome.

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Data availability

The trait maps in GeoTiff format for both the iNaturalist and sPlotOpen maps are openly available at (ref. 52). All data used to create and analyse these maps are openly accessible (consult workflow for information on how to download the data).

Code availability

We provide a fully reproducible workflow ( of all analyses presented here and a script that can be used readily and without much effort to create updated global trait maps using the latest data, as citizen science data continue to grow52.


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This study was funded by the National Research Data Infrastructure Germany for Biodiversity, NFDI4Biodiversity, a project by the German Research Foundation (DFG), project no. 442032008. The study is supported by the TRY initiative on plant traits ( and the sPlot consortium ( The TRY initiative and database are hosted, developed and maintained by J.K. and G. Boenisch (Max Planck Institute for Biogeochemistry, Jena, Germany), currently supported by Future Earth/bioDISCOVERY and the German Centre for Integrative Biodiversity Research Halle-Jena-Leipzig (iDiv, DFG-FZT 118, 202548816). The sPlot is a strategic project of iDiv and is supported by the German Research Foundation (DFG-FZT 118, 202548816). F.M.S. gratefully acknowledges the support of the Italian Ministry of University and Research, under the Maria Levi Montalcini programme. K.M. is funded by iDiv via a FLEXPOOL project, funded by DFG (DFG-FZT 118, 202548816). We thank the vegetation scientists, who sampled vegetation plots in the field, digitized them or made them available in databases. We thank the many iNaturalist citizen scientists, who volunteer their time and expertise to build the research-grade dataset. We thank our reviewers for their helpful comments and suggestions, which substantially strengthened the message and scientific relevance of our manuscript.

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Authors and Affiliations



S.W. performed the analyses and wrote the manuscript. T.K. conceived and supervised the study. M.D.M. and C.W. supervised the project. F.M.S. and H.B. provided insight into the sPlotOpen data. J.K. contributed insight into the TRY data. Á.M.M. and K.M. contributed knowledge of opportunistic citizen science data. All authors contributed ideas and were involved in writing and editing the manuscript.

Corresponding author

Correspondence to Sophie Wolf.

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Nature Ecology & Evolution thanks Angela Moles, Michael Belitz and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 iNaturalist data growth.

Number of iNaturalist vascular plant observations added to "research-grade observations" every year since its foundation in 2008. More observations for 2021 are expected to come in, as the validation process takes time.

Extended Data Fig. 2 Density of iNaturalist observations before linking to TRY.

Density of iNaturalist vascular plant observations before linking to TRY database n = 14, 019, 405 observations; 2° resolution, or 221 km grid size. Colour corresponds to number of observations per cell.

Extended Data Fig. 3 iNaturalist trait maps.

Global trait maps (trait values ln-transformed) using iNaturalist observations linked to TRY, displayed here at 2° resolution. Sample sizes for each trait see Table 1 in main text. For maps in GeoTiff format, refer to the Data availability statement.

Extended Data Fig. 4 Correlation scatter plots of iNaturalist and sPlotOpen trait maps.

Scatter plots of sPlotOpen map pixel values plotted against the respective iNaturalist map pixel values for all 18 traits at a 2° spatial resolution. Correlation quantified using a weighted correlation coefficient (r weighted by grid cell area). Trait values are ln-transformed, 1:1 line is displayed in dotted grey, and SMA regression slope in red. For clarity, the secondary y axis on the right shows the raw trait values marked on a log scale, which correspond to the ln-transformed values on the left. Plot extents are the 0.01 and 0.99 quantiles of the data.

Extended Data Fig. 5 Relationship of r and buffer radius (buffer-based approach).

Relationship of r and buffer radius (1 km to 256 km) for alternative approach: aggregation of iNaturalist observations in buffer radius around each vegetation plot. The lines connecting the points solely enhance readability.

Extended Data Fig. 6 Correlation density plots of iNaturalist and sPlotOpen using the buffer-based approach.

Density plots (KDE plots) of the correlation of each community-weighted mean value in sPlotOpen plot with average trait value of all iNaturalist observations in its vicinity, using the alternative approach with buffers, here using a 64 km radius buffer. r is the Pearson correlation coefficient.

Extended Data Fig. 7 Differences between biomes, forests not aggregated.

Difference between sPlotOpen and iNaturalist maps for each WWF terrestrial biome. All traits were scaled by range [ − 1, 1] using the 0.05 and 0.95 quantiles. The bounds of the box are defined by the first and third quartile, the centre lines are the medians, the whiskers mark the 1.5 interquartile range (IQR), outliers are not shown. The red step-graph shows the sample size n = the number of iNaturalist map pixels that overlap the respective sPlotOpen map per biome and trait. The blue step-graph marks the mean density of iNaturalist vascular plant observations per km2 in each biome. For exact sample sizes per biome and trait, see Supplementary Information Table S1.

Extended Data Fig. 8 Differences between iNaturalist and sPlotOpen maps in relationship to iNaturalist observation density.

Differences between iNaturalist and sPlotOpen maps in relationship to the number of iNaturalist observations: Comparing number of observations per grid cell to the scaled absolute difference of iNaturalist and sPlotOpen means per respective grid cell at 2° resolution, over all traits with n = 17024, or 1037 pixel pairs over 18 traits. a) Distribution of absolute scaled differences of iNaturalist and sPlotOpen map pixels (scaled using 0.01 and 0.99 quantiles); x-axis range cropped to (0,2). b) Distribution of iNaturalist observation counts per grid cell, for grid cells that overlap with sPlotOpen map, n = 17024. The bounds of the box are defined by the first and third quartile, the centre lines are the medians, the whiskers mark the 1.5 interquartile range (IQR), outliers are not shown. c) Distribution of the absolute scaled difference of iNaturalist - sPlotOpen pixels in each bin. The bins are based on 0.25 quantiles of the number of iNaturalist observations within each grid cell, all 18 trait maps combined: each bin has sample size of 4256. The bounds of the box, centre line, and whiskers are defined as in b).

Extended Data Fig. 9 Growth forms coverage.

Correlation of growth forms coverage (tree coverage, shrub coverage, and herb coverage) in iNaturalist and sPlotOpen grid maps in each WWF biomes.

Extended Data Fig. 10 Comparison of iNaturalist maps with the Schiller trait maps.

Comparison of iNaturalist maps with the Schiller et al (2021) trait maps, which are based on estimating traits from iNaturalist photos. From left to right for each of the three traits a) Leaf N per mass [ln mg/g], b) Leaf N per area [ln g/m2], and c) SLA [ln m2/kg] : 1. Scatter plot of each corresponding pixel in the two maps, r weighted by grid cell area, black 1:1 line, 2. Scatter plot of the difference of each respective pixel to the sPlotOpen map, 3. Frequency distributions of trait values for iNaturalist maps, the Schiller maps, and the sPlotOpen maps.

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Wolf, S., Mahecha, M.D., Sabatini, F.M. et al. Citizen science plant observations encode global trait patterns. Nat Ecol Evol 6, 1850–1859 (2022).

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