High-resolution maps of plant functional traits are crucial for understanding terrestrial ecosystem processes; however, their integration into ecosystem models has been hindered by uncertainties and a lack of spatially detailed data. Here we combine optical remote sensing, global crowd-sourced biodiversity records and plant trait databases to map community trait distributions worldwide at 1-km resolution, estimating community-weighted means (CWMs) and higher-order moments (standard deviation, skewness, and kurtosis) for specific leaf area (SLA), leaf nitrogen (LNC) and leaf phosphorus (LPC) concentrations. Benchmarking against sPlotOpen plot-level CWMs shows low explained variance (R2 = 0.10–0.27 across traits), indicating limited plot-scale predictive skill under current limited open global benchmarks and scale mismatches. Agreement increases when using a canopy-weighted comparator (TWM; R2 = 0.22–0.38; relative RMSE ≈ 12–18%), consistent with the top-of-canopy sensitivity of optical sensors. By providing spatially explicit trait distributions and their higher-order moments, our findings deliver improved detail for understanding biodiversity patterns and ecosystem functioning and provide landscape-scale insights into trait-mediated coexistence. This work enhances ecological modeling and offers a foundation for assessing the impacts of global environmental changes, advancing our understanding of plant functional diversity’s role in ecosystem resilience and sustainability. Plant functional traits govern ecosystem processes but lack spatially detailed global representations. Here, the authors combine remote sensing and crowd-sourced biodiversity data to map leaf trait distributions and their higher-order moments worldwide.
Moreno-Martínez et al. (Tue,) studied this question.