Linking spectral, phylogenetic and functional diversity of wetland plant communities

Given the global threats 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 in 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 floating hydrophyte and helophyte communities sampled from different sites. We used non-linear parametric and machine learning models. The results show that all three functional diversity metrics can be estimated from spectral features using machine learning models (random forest regression; R² = 0.90-0.92), whereas parametric models show inferior performance (generalised additive models; R² = 0.40-0.79), especially for the estimation of community evenness. The integration of phylogenetic and spectral features improves the predictive ability of machine learning models for functional richness and divergence (R² = 0.95-0.96), although this benefit is significant only for the estimation of community evenness when parametric models are employed. The combination of imaging spectroscopy and phylogenetic analysis provides a quantitative means of capturing the diversity observed in plant communities across scales and gradients, which is valuable to ecologists involved in the study and monitoring of biodiversity and related processes.