Linking spectral, phylogenetic and functional diversity of wetland plant communities

Considering the global threat to freshwater ecosystems, the conservation of aquatic plant diversity has emerged as a priority area of concern. In the last decade, remote sensing has facilitated the measurement of biodiversity, particularly across terrestrial biomes. The combination of spectral features with additional information derived from community phylogeny can further advance the accurate characterisation of plant functional diversity across scales. In this study, we investigated the potential of using spectral features extracted from centimetre-resolution hyperspectral imagery collected by a drone in conjunction with phylogenetic features derived from a fully resolved supertree to estimate functional diversity (richness, divergence, and evenness) in communities of floating hydrophytes and helophytes sampled from different sites. To this end, we employed non-linear parametric and machine learning models. The results demonstrate that all three functional diversity metrics can be estimated from spectral features using machine learning models (random forest; R² = 0.90–0.92), whereas parametric models exhibit inferior performance (generalised additive models; R² = 0.40–0.79), particularly in the estimation of community evenness. The integration of phylogenetic and spectral features enhances the predictive capacity of machine learning models for functional richness and divergence (R²=0.95-0.96), although this benefit is significant for estimating only community evenness when parametric models are employed. The conjunction of imaging spectroscopy and phylogenetic analysis offers a quantitative means of capturing the diversity observed in plant communities across scales and gradients, which is valuable to ecologists engaged in the study and monitoring of biodiversity and associated processes.