Monitoring biodiversity and understanding its consequences for ecosystem and global processes are critical challenges in the face of rapid global change. Efforts to remotely monitor plant biodiversity using airborne imaging spectroscopy require understanding how optical data can be used to predict multiple dimensions of biodiversity at the spatial scale at which species turn over. Biodiversity experiments can play an important role. Here we use the Cedar Creek prairie grassland biodiversity experiment (BioDIV) to test the feasibility of using remotely sensed optical diversity and functional variation to predict plant diversity aboveground (using multiple diversity metrics), as well as soil and microbial processes belowground. It has been well-documented within the Cedar Creek BioDIV experiment that diversity begets productivity. We thus used several statistical procedures (partial CCA, PLSR, MLM) to tease apart remotely detected productivity measures from those of biodiversity, a challenge given the spatial resolution of airborne data (1 m^2) relative to the size of herbaceous plants. We measured a suite of functional traits potentially linked to belowground ecosystem processes using airborne and leaf level spectra, and measured soil microbial diversity, microbial biomass, soil enzyme activity and decomposition.
After accounting for variation in productivity, we are still able to predict biodiversity at the plot level using remotely sensed airborne imaging spectroscopy. Phylogenetic and functional evenness are detected with greater accuracy than species richness. We find that remotely sensed functional traits at the plot level predict microbial processes, but aboveground plant diversity does not predict belowground microbial diversity.