Leveraging Remote-Sensing of Plant Traits to Infer Soil Biogeochemical Properties and Microbial Metabolic Potential at Watershed Scale.
Abstract
Using aboveground features of plants as an indicator of soil microbiome function is an attractive approach to scaling soil biogeochemical processes because plant traits can be mapped across the landscape and quantified using remote-sensing of the plant canopy. However, whether plant canopy properties are effective proxies of belowground processes is not well tested, in part, because until recently there have not been high resolution methods to connect aboveground plant canopy properties to soil microbiome function belowground. The Watershed Function Scientific Focus Area (SFA) is a high-altitude community watershed observatory and is working to build a predictive understanding of the responses of mountainous watersheds to environmental perturbations. As part of the Watershed Function SFA, we sampled more than 400 soils in June 2018 from 12 locations across four catchments in the upper East River watershed which varied by hydrological and geological properties, as well as dominant plant species and community types. This ground sampling campaign was performed concurrently with a NEON Airborne Observatory Platform imaging flight which quantified plant traits (e.g. leaf CN ratio, leaf mass area, leaf water content) based on hyperspectral profiles collected at 1-meter spatial resolution over the entire study area. We have quantified microbial biomass stoichiometry, extractable soil nitrogen, as well bacterial and fungal community composition via rRNA gene sequencing. In addition, we sequenced soil metagenomes from 250 samples that were representative of variation in hydrology, topographic properties, and plant communities. We used hierarchical clustering based on the variance in microbial biomass and extractable soil N stocks to classify soils into similar soil clusters which exhibited considerable nestedness invariant of sampling site, elevation, or landscape position. Plant traits such as leaf water content and leaf mass area were stronger predictors of the variance in microbial biomass stoichiometry or soil nitrogen stocks compared to hydrologic or topographic predictors. Our ongoing work is testing the dependence of microbial community assembly patterns (e.g. selection versus dispersal limitation) and soil nitrogen cycling metabolic potential on plant traits and plant species identities with the aim of using machine learning to map nitrogen cycling trait distributions across the watershed.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2021
- Bibcode:
- 2021AGUFM.B21D..07S