Cavender-Bares, JeannineSchweiger, Anna K.Gamon, JohnGholizadeh, HamedKimberly, HelzerLapadat, CathleenMadritch, MichaelTownsend, Philip A.Wang, ZhihuiHobbie, Sarah E.2021-06-082021-06-082021-06-08https://hdl.handle.net/11299/220311Foliar traits, plant taxonomic and phylogenetic-functional group data, soil microbial and chemical data and airborne spectroscopic data from the BioDIV Cedar Creek Ecosystem Science Reserve biodiversity experiment in Minnesota and the Wood River Nature Conservancy biodiversity experiment in Nebraska and R scripts for analyses of spectral reflectance data and structural equation models.Imaging spectroscopy provides the opportunity to incorporate leaf and canopy optical data into ecological studies, but the extent to which remote sensing of vegetation can enhance the study of belowground processes is not well understood. In terrestrial systems, aboveground and belowground vegetation quantity and quality are coupled, and both influence belowground microbial processes and nutrient cycling, providing a potential link between remote sensing and belowground processes. We hypothesized that ecosystem productivity, and the chemical, structural and phylogenetic-functional composition of plant communities would be detectable with remote sensing and could be used to predict belowground plant and soil processes in two grassland biodiversity experiments—the BioDIV experiment at Cedar Creek Ecosystem Science Reserve in Minnesota and the Wood River Nature Conservancy experiment in Nebraska. Specifically, we tested whether aboveground vegetation chemistry and productivity, as detected from airborne sensors, predict soil properties, microbial processes and community composition. Imaging spectroscopy data were used to map aboveground biomass and green vegetation cover, functional traits and phylogenetic-functional community composition of vegetation. We examined the relationships between the image-derived variables and soil carbon and nitrogen concentration, microbial community composition, biomass and extracellular enzyme activity, and soil processes, including net nitrogen mineralization. In the BioDIV experiment—which has low overall diversity and productivity despite high variation in each—belowground processes were driven mainly by variation in the amount of organic matter inputs to soils. As a consequence, soil respiration, microbial biomass and enzyme activity, and fungal and bacterial composition and diversity were significantly predicted by remotely sensed vegetation cover and biomass. In contrast, at Wood River—where plant diversity and productivity were consistently higher—remotely sensed functional, chemical and phylogenetic composition of vegetation predicted belowground extracellular enzyme activity, microbial biomass, and net nitrogen mineralization rates. Aboveground biomass (or cover) did not predict these belowground attributes. The strong, contrasting associations between the quantity and chemistry of aboveground inputs with belowground soil processes and properties provide a basis for using imaging spectroscopy to understand belowground processes across productivity gradients in grassland systems. However, a mechanistic understanding of how above and belowground components interact among different ecosystems remains critical to extending these results broadly.Attribution-NonCommercial-NoDerivs 3.0 United Stateshttp://creativecommons.org/licenses/by-nc-nd/3.0/us/biodiversitysoil enzyme activityhyperspectral datamicrobial biomasssoil processesplant traitsremote sensingvegetation chemistryDatasethttps://doi.org/10.13020/fvdj-zv57