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

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.