Leveraging Crowd-sourced Biodiversity Data for an Enhanced Plant Functional Trait Mapping

Abstract

Plant functional traits play a crucial role in determining how terrestrial ecosystems function. However, most Earth system models (ESMs) oversimplify this information, representing it with a limited number of static, empirically fixed values assigned to a selection of plant functional types (PFTs). This results in a reduction of the diversity of plant communities into a relatively small number of categories and the loss of key variability within individual PFTs. As a result, local processes occurring within ESM grid cells are not well represented, leading to uncertainties in predicting ecosystem functions.The TRY global plant traits database is home to the most extensive collection of in-situ trait observations for a broad spectrum of species across the globe. Nonetheless, despite the numerous species and samples included in TRY, it still falls short compared to the overall richness and diversity of species and ecosystem functions worldwide. As a result, various initiatives have emerged to create global maps of plant traits. In this study, we created maps of essential plant traits, such as specific leaf area (SLA), leaf nitrogen content (LNC), and leaf phosphorus content (LPC), at a spatial resolution of 1 km. We took an innovative approach by leveraging the use of biodiversity, trait databases, and remote sensing data as primary sources of information. Additionally, we provide ancillary data layers that indicate regions where data gaps currently exist and where more samples are needed to improve trait representation in TRY.We compared our results to plot-level estimates for thousands of sites globally. The comparison demonstrated strong correlations (r > 0.5) and low relative errors (rME < 6% and rRMSE < 11%) for all considered traits despite the challenges in scaling up from local to global scales. Our results reveal the non-Gaussian nature of trait distributions at a global scale when computing community representative mean trait values and further statistical descriptors, including standard deviation, skewness, and kurtosis estimations. These higher-order moments provide a more detailed and nuanced view of plant functional diversity and distribution. Using these new data to parameterize global ecological models could lead to more accurate predictions and a better understanding of the main drivers of different ecosystem processes.

Authors 19

1. Álvaro Moreno‐Martínez corresponding

   Universitat de València

   Affiliation as printed

   University of Valencia, Image Processing Laboratory (IPL), Image Signal Processing Group, Paterna, Spain (alvaro.moreno@uv.es)

2. Jordi Muñoz-Marı́

   Universitat de València

   Affiliation as printed

   University of Valencia, Image Processing Laboratory (IPL), Image Signal Processing Group, Paterna, Spain (alvaro.moreno@uv.es)

3. José E. Adsuara corresponding

   Universitat de València

   Affiliation as printed

   University of Valencia, Image Processing Laboratory (IPL), Image Signal Processing Group, Paterna, Spain (alvaro.moreno@uv.es)

4. Benjamin Dechant corresponding

   German Centre for Integrative Biodiversity Research

   Affiliation as printed

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

5. Jens Kattge corresponding

   Max Planck Institute for Biogeochemistry

   Affiliation as printed

   Max Planck Institute for Biogeochemistry, Jena, Germany

6. Teja Kattenborn corresponding

   University of Freiburg

   Affiliation as printed

   Department for Sensor-based Geoinformatics, Faculty of Environment and Natural Resources, University of Freiburg, Germany

7. Francesco María Sabatini

   University of Bologna

   Affiliation as printed

   BIOME Lab, Department of Biological, Geological and Environmental Sciences (BiGeA), University of Bologna, Italy

8. Ethan E. Butler

   University of Minnesota

   Affiliation as printed

   Department of Forest Resources, University of Minnesota, USA

9. Peter M. van Bodegom corresponding Aachen Institute of Environmental Sciences (CML)

   Leiden University

   Affiliation as printed

   Institute of Environmental Sciences, Leiden University, the Netherlands

10. Fabian Schneider

    Jet Propulsion Laboratory

    Affiliation as printed

    Jet Propulsion Laboratory, California Institute of Technology, USA

11. Miguel D. Mahecha corresponding

    Leipzig University

    Affiliation as printed

    Leipzig University, Leipzig, Germany

12. Josep Peñuelas corresponding

    Centre for Research on Ecology and Forestry Applications

    Affiliation as printed

    CREAF, Cerdanyola del Vallès, Catalonia, Spain

13. Philip A. Townsend

    University of Wisconsin–Madison

    Affiliation as printed

    University of Wisconsin, Madison, WI, USA

14. Gerhard Boenisch

    Max Planck Institute for Biogeochemistry

    Affiliation as printed

    Max Planck Institute for Biogeochemistry, Jena, Germany

15. Emma Izquierdo‐Verdiguier corresponding

    BOKU University

    Affiliation as printed

    Institute of Geomatics, BOKU, Vienna, Austria

16. Nuno Carvalhais corresponding

    Max Planck Institute for Biogeochemistry

    Affiliation as printed

    Max Planck Institute for Biogeochemistry, Jena, Germany

17. Grégory Duveiller corresponding

    Max Planck Institute for Biogeochemistry

    Affiliation as printed

    Max Planck Institute for Biogeochemistry, Jena, Germany

18. Daniel Lusk corresponding

    University of Freiburg

    Affiliation as printed

    Department for Sensor-based Geoinformatics, Faculty of Environment and Natural Resources, University of Freiburg, Germany

19. Gustau Camps‐Valls corresponding

    Universitat de València

    Affiliation as printed

    University of Valencia, Image Processing Laboratory (IPL), Image Signal Processing Group, Paterna, Spain (alvaro.moreno@uv.es)