DOI: 10.5281/zenodo.14909646
title: Leveraging Remote Sensing and Crowd-Sourced Biodiversity Data for Enhanced Plant Functional Trait Mapping

description: Abstract

High-resolution maps of plant functional traits are essential for understanding terrestrial ecosystem processes, yet their integration into ecosystem models has been hindered by uncertainties and a lack of spatially detailed data. To address this, we developed an approach combining remotely sensed data, global crowd-sourced biodiversity records, and plant trait databases to estimate community-weighted mean values and additional statistical descriptors—standard deviation, skewness, and kurtosis—at a global 1 km resolution. Comparisons with plot-level trait estimates from thousands of sites revealed strong correlations (r  > 0.5) and low relative errors (rME <6% and rRMSE < $11%) for traits including Specific Leaf Area (SLA), Leaf Nitrogen Concentration (LNC), and Leaf Phosphorus Concentration (LPC). Notably, our results reveal a non-Gaussian structure in community trait distributions over large areas, suggesting potential biases in previous estimates. By providing spatially explicit distributions and their higher-order moments, our findings deliver unprecedented detail for understanding plant functional diversity, improving predictions of biodiversity patterns, species coexistence, and ecosystem functioning. 

 
description: Technical Description of Global Plant Functional Trait Maps (SLA, LNC, LPC)

This dataset provides high-resolution (1 km spatial resolution) global maps of three essential plant functional traits: Specific Leaf Area (SLA), Leaf Nitrogen Concentration (LNC), and Leaf Phosphorus Concentration (LPC). Each trait map includes detailed spatial distributions and statistical metrics derived from remotely sensed data, global biodiversity records (GBIF), and plant trait data (TRY database).

Traits and Measurement Units:





Specific Leaf Area (SLA): Ratio of leaf area to leaf dry mass (mm² mg⁻¹).




Leaf Nitrogen Concentration (LNC): Ratio of leaf nitrogen mass to leaf dry mass (mg g⁻¹).




Leaf Phosphorus Concentration (LPC): Ratio of leaf phosphorus mass to leaf dry mass (mg g⁻¹).



Data Layers Provided:





Community-Weighted Mean (CWM): Represents the mean trait values weighted by species abundances within each 1 km grid cell.




Log transformed Standard Deviation (SD): Captures within-grid trait variability.




Skewness: Describes the asymmetry of trait distribution within grid cells.




Excess Kurtosis (kurtosis - 3): Indicates the peakedness and tail distribution of traits.



Methodology:

The trait maps were generated by integrating:





Geolocated biodiversity occurrences from the Global Biodiversity Information Facility (GBIF).




Plant trait observations from the TRY database.




Remote sensing-derived fractional Plant Functional Type (PFT) data.



An adaptive multiresolution sampling method was applied to estimate species abundances, correcting sampling biases in biodiversity data. Community-weighted statistical moments were calculated iteratively, combining species abundance and PFT fractions.

Applications:

This dataset enables improved modeling and ecological assessments, including biodiversity predictions, ecosystem functioning, species coexistence analysis, and responses to global environmental changes.

Spatial Coverage:

Global

Temporal Coverage:

Trait data represent current ecological conditions based on the most recent biodiversity and remote sensing datasets.

Resolution:

Spatial: 1 km

Data Format:

Raster (GeoTIFF)