Mapping functional diversity using individual tree-based morphological and physiological traits in a subtropical forest

Functional diversity (FD) provides a link between biodiversity and ecosystem functioning, summarizing inter- intra-specific variation of functional traits. However, quantifying plant traits FD consistently cost-effectively across large heterogeneous forest areas is challenging with traditional field sampling. Airborne light detection ranging (LiDAR) imaging spectroscopy provide spatially explicit data, which allow mapping selected at different spatial scales. We develop an individual tree-based method to measure from tree neighborhoods whole forests, demonstrate the approach by over one million trees in subtropical China. retrieved canopy morphological (95th quantile height, leaf area index foliage height diversity) physiological (proxies nitrogen, carotenoids specific area) for each crown LiDAR respectively. Based on multivariate trait space spanned six axes filled measured individuals, we mapped as richness, divergence evenness, explored patterns well FD–area FD–tree number relationships. The results show that LiDAR-derived spectral indices are consistent measurements weak correlations other level. Morphological richness follows hump-shaped pattern along elevational gradient 984–1805 m, maximum values elevations around 1450 while high occurs medium elevations. At scale 30 × increases continuously density, but decreases again very densities. Moreover, shows logarithmic relationship increasing or trees, local convergence predominant our study area. ability quantify using remote sensing, pathway conduct individual-level trait-based ecology wall-to-wall data.