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Nature Ecology & Evolution
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Citizen science plant observations encode global trait patterns

Nature Ecology & Evolutionvolume 6, pages1850–1859 (2022)Cite this article

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Abstract

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 athttps://doi.org/10.5281/zenodo.6671891 (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 (https://doi.org/10.5281/zenodo.6671891) 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.

References

  1. Sakschewski, B. et al. Leaf and stem economics spectra drive diversity of functional plant traits in a dynamic global vegetation model.Glob. Change Biol.21, 2711–2725 (2015).

    Article  Google Scholar 

  2. Berzaghi, F. et al. Towards a new generation of trait-flexible vegetation models.Trends Ecol. Evol.35, 191–205 (2020).

    Article PubMed  Google Scholar 

  3. Bruelheide, H. et al. Global trait–environment relationships of plant communities.Nat. Ecol. Evol.2, 1906–1917 (2018).

    Article PubMed  Google Scholar 

  4. Joswig, J. S. et al. Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation.Nat. Ecol. Evol.6, 36–50 (2021).

    Article PubMed PubMed Central  Google Scholar 

  5. van Bodegom, P. M., Douma, J. C. & Verheijen, L. M. A fully traits-based approach to modeling global vegetation distribution.Proc. Natl Acad. Sci. USA111, 13733–13738 (2014).

    PubMed PubMed Central  Google Scholar 

  6. Moreno Martínez, A. et al. A methodology to derive global maps of leaf traits using remote sensing and climate data.Remote Sens. Environ.218, 69–88 (2018).

    Article  Google Scholar 

  7. Pérez-Harguindeguy, N. et al. New handbook for standardized measurment of plant functional traits worldwide.Aust. J. Bot.23, 167–234 (2013).

    Article  Google Scholar 

  8. Kattge, J. et al. TRY—a global database of plant traits.Glob. Change Biol.17, 2905–2935 (2011).

    Article  Google Scholar 

  9. Kattge, J. et al. TRY plant trait database-enhanced coverage and open access.Glob. Change Biol.26, 119–188 (2020).

    Article  Google Scholar 

  10. Jetz, W. et al. Monitoring plant functional diversity from space.Nat. Plants2, 16024 (2016).

    Article PubMed  Google Scholar 

  11. Butler, E. E. et al. Mapping local and global variability in plant trait distributions.Proc. Natl Acad. Sci. USA114, E10937–E10946 (2017).

    Article PubMed PubMed Central  Google Scholar 

  12. Boonman, C. C. et al. Assessing the reliability of predicted plant trait distributions at the global scale.Glob. Ecol. Biogeogr.29, 1034–1051 (2020).

    Article PubMed PubMed Central  Google Scholar 

  13. Madani, N. et al. Future global productivity will be affected by plant trait response to climate.Sci. Rep.8, 2870 (2018).

  14. Vallicrosa, H. et al. Global distribution and drivers of forest biome foliar nitrogen to phosphorus ratios (N:P).Glob. Ecol. Biogeogr.31, 861–871 (2022).

    Article  Google Scholar 

  15. Meyer, H. & Pebesma, E. Predicting into unknown space? Estimating the area of applicability of spatial prediction models.Methods Ecol. Evol.12, 1620–1633 (2021).

    Article  Google Scholar 

  16. Schiller, C. et al. Deep learning and citizen science enable automated plant trait predictions from photographs.Sci. Rep.11, 16395 (2021).

  17. Aguirre-Gutiérrez, J. et al. Pantropical modelling of canopy functional traits using sentinel-2 remote sensing data.Remote Sens. Environ.252, 112–122 (2021).

    Article  Google Scholar 

  18. Homolova, L. et al. Review of optical-based remote sensing for plant trait mapping.Ecol. Complex.15, 1–16 (2013).

    Article  Google Scholar 

  19. Van Cleemput, E. et al. The functional characterization of grass-and-shrubland ecosystems using hyperspectral remote sensing: trends, accuracy and moderating variables.Remote Sens. Environ.209, 747–763 (2018).

    Article  Google Scholar 

  20. Kattenborn, T., Fassnacht, F. E. & Schmidtlein, S. Differentiating plant functional types using reflectance: which traits make the difference?Remote Sens. Ecol. Conserv.5, 5–19 (2019).

    Article  Google Scholar 

  21. Hauser, L. T. et al. Explaining discrepancies between spectral and in-situ plant diversity in multispectral satellite earth observation.Remote Sens. Environ.265, 112684 (2021).

    Article  Google Scholar 

  22. Wäldchen, J. & Mäder, P. Plant species identification using computer vision techniques: a systematic literature review.Arch. Comput. Methods Eng.25, 507–543 (2018).

    Article PubMed  Google Scholar 

  23. Jones, H. G. What plant is that? Tests of automated image recognition apps for plant identification on plants from the British flora.AoB Plants12, plaa052 (2020).

    Article PubMed PubMed Central  Google Scholar 

  24. Hampton, S. E. et al. Big data and the future of ecology.Front. Ecol. Environ.11, 156–162 (2013).

    Article  Google Scholar 

  25. WÜest, R. O. et al. Macroecology in the age of big data—where to go from here?J. Biogeogr.47, 1–12 (2020).

    Article  Google Scholar 

  26. Mäder, P. et al. The Flora Incognita app—interactive plant species identification.Methods Ecol. Evol.12, 1335–1342 (2021).

    Article  Google Scholar 

  27. Di Cecco, G. J. et al. Observing the observers: how participants contribute data to iNaturalist and implications for biodiversity science.BioScience71, 1179–1188 (2021).

    Article  Google Scholar 

  28. Mahecha, M. D. et al. Crowd-sourced plant occurrence data provide a reliable description of macroecological gradients.Ecography44, 1131–1142 (2021).

    Article  Google Scholar 

  29. Botella, C. et al. Jointly estimating spatial sampling effort and habitat suitability for multiple species from opportunistic presence-only data.Methods Ecol. Evol.12, 933–945 (2021).

    Article  Google Scholar 

  30. iNaturalist Research-Grade Observations (GBIF, accessed 5 January 2022);https://www.gbif.org/dataset/50c9509d-22c7-4a22-a47d-8c48425ef4a7

  31. Callaghan, C. T. et al. Three frontiers for the future of biodiversity research using citizen science data.BioScience71, 55–63 (2020).

    Google Scholar 

  32. Dickinson, J. L., Zuckerberg, B. & Bonter, D. N. Citizen science as an ecological research tool: challenges and benefits.Ann. Rev. Ecol. Evol. Syst.41, 149–172 (2010).

    Article  Google Scholar 

  33. Kosmala, M. et al. Assessing data quality in citizen science.Front. Ecol. Environ.14, 551–560 (2016).

    Article  Google Scholar 

  34. Boakes, E. H. et al. Patterns of contribution to citizen science biodiversity projects increase understanding of volunteers’ recording behaviour.Sci. Rep.6, 33051 (2016).

    Article CAS PubMed PubMed Central  Google Scholar 

  35. Bowler, D.E. et al. Temporal trends in the spatial bias of species occurrence records.Ecography 2022, e06219 (2022).https://doi.org/10.1111/ecog.06219

  36. GBIF Occurrence Download (GBIF, 4 January 2022);https://doi.org/10.15468/dl.34tjre

  37. Bruelheide, H. et al. sPlot—a new tool for global vegetation analyses. journal of vegetation science.J. Veg. Sci.30, 161–186 (2019).

    Article  Google Scholar 

  38. Sabatini, F. et al. sPlotOpen—an environmentally balanced, open access, global dataset of vegetation plots.Glob. Ecol. Biogeogr.30, 1740–1764 (2021).

    Article  Google Scholar 

  39. Whittaker, R.H. et al.Communities and Ecosystems (Macmillan/Collier Macmillan, 1970).

  40. Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity.BioScience51, 933–938 (2001).

    Article  Google Scholar 

  41. Joswig, J., Wirth, C. & Schuman, M. Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation.Nat. Ecol. Evol.6, 36–50 (2022).

    Article PubMed  Google Scholar 

  42. Díaz, S. et al. The global spectrum of plant form and function.Nature529, 167–171 (2016).

    Article PubMed  Google Scholar 

  43. Ploton, P. et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models.Nat. Commun.11, 4540 (2020).

    Article CAS PubMed PubMed Central  Google Scholar 

  44. Meyer, H. & Pebesma, E. Spatial validation reveals poor predictive performance of large-scale ecological mapping models.Methods Ecol. Evol.12, 1620–1633 (2021).

    Article  Google Scholar 

  45. Schrodt, F. et al. Bhpmf—a hierarchical Bayesian approach to gap filling and trait prediction for macroecology and functional biogeography.Glob. Ecol. Biogeogr.24, 1510–1521 (2015).

    Article  Google Scholar 

  46. Kuppler, J. et al. Global gradients in intraspecific variation in vegetative and floral traits are partially associated with climate and species richness.Glob. Ecol. Biogeogr.29, 992–1007 (2020).

    Article  Google Scholar 

  47. Scheiter, S., Langan, L. & Higgins, S. I. Next-generation dynamic global vegetation models: learning from community ecology.New Phytol.198, 957–969 (2013).

    Article PubMed  Google Scholar 

  48. Taubert, F. et al. Confronting an individual-based simulation model with empirical community patterns of grasslands.PLoS ONE15, e0236546 (2020).

  49. Roger, E. & Klistorner, S. (2016) Bioblitzes help science communicators engage local communities in environmental research.J. Sci. Commun.https://doi.org/10.22323/2.15030206 (2016).

  50. Legendre, P. & Legendre, L.Numerical Ecology 3rd edn (Elsevier, 2012).

  51. Warton, D. I. et al. Smatr 3—an R package for estimation and inference about allometric lines.Methods Ecol Evol3, 257–259 (2012).

    Article  Google Scholar 

  52. Wolf, S. et al. iNaturalist_traits: iNaturalist trait maps version 1 (January 5, 2022)Zenodohttps://doi.org/10.5281/zenodo.6671891 (2022).

Download references

Acknowledgements

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 (http://www.try-db.org) and the sPlot consortium (http://www.idiv.de/splot). 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.

Author information

Authors and Affiliations

  1. Remote Sensing Centre for Earth System Research, Leipzig University, Leipzig, Germany

    Sophie Wolf, Miguel D. Mahecha, Karin Mora & Teja Kattenborn

  2. Remote Sensing Centre for Earth System Research, Helmholtz Centre for Environmental Research, UFZ, Leipzig, Germany

    Miguel D. Mahecha

  3. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

    Miguel D. Mahecha, Francesco Maria Sabatini, Christian Wirth, Helge Bruelheide, Jens Kattge, Karin Mora & Teja Kattenborn

  4. BIOME Lab, Department of Biological, Geological and Environmental Sciences (BiGeA), Alma Mater Studiorum University of Bologna, Bologna, Italy

    Francesco Maria Sabatini

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

    Francesco Maria Sabatini & Helge Bruelheide

  6. Institute of Systematic Botany and Functional Biodiversity, Leipzig University, Leipzig, Germany

    Christian Wirth

  7. Max Planck Institute for Biogeochemistry, Jena, Germany

    Christian Wirth & Jens Kattge

  8. Image Processing Laboratory, Universitat de Valéncia, Valéncia, Spain

    Álvaro Moreno Martínez

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Contributions

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.

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Correspondence toSophie 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 databasen = 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 Table1 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 ofr and buffer radius (buffer-based approach).

Relationship ofr 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 sizen = 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 TableS1.

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 withn = 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 traitsa) Leaf N per mass [ln mg/g],b) Leaf N per area [ln g/m2], andc) 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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Supplementary Information

Supplementary Tables 1 and 2 and Methods.

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Wolf, S., Mahecha, M.D., Sabatini, F.M.et al. Citizen science plant observations encode global trait patterns.Nat Ecol Evol6, 1850–1859 (2022). https://doi.org/10.1038/s41559-022-01904-x

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