Soil nitrogen cycling potential at watershed scales inferred from soil metagenomes and aboveground proxies detected by remote sensing
Abstract
Genome-centric microbiome analyses should illuminate the ecological roles of microbial taxa in high-altitude mountainous environments that are sensitive to climate change. Yet long-standing research questions remain. For example, how does soil microbial functional potential - occurring over relatively small spatial scales - affect watershed processes (e.g. riverine nitrogen export) that operate over relatively broad spatial scales. If microbial genome features co-vary with aboveground landscape or plant features, then it may be possible to scale microbial functional potential using remote sensing characterization of aboveground features. In this study, we sequenced 250 soil metagenomes as part of the Watershed Function Scientific Focus Area (SFA), by sampling soils from 12 sites across four catchments in the upper East River, Colorado Watershed. This ground sampling campaign was performed concurrently with a NEON Airborne Observatory Platform (AOP) imaging flight which quantified plant traits (e.g., leaf C:N ratio, leaf mass area, leaf water content) based on hyperspectral profiles collected at 1-meter spatial resolution over the entire study area. Soil conductivity in the topsoil (down to approximately 15 cm below surface) was also extensively quantified at all 12 locations using electromagnetic induction (EM) geophysical imaging. Microbial functional trait profiles for each metagenome were produced using the microTrait bioinformatic tool. Plant foliar traits and species identities greatly affected soil nitrogen cycling metabolic potential. For example, soil metagenomes sampled from beneath Salix spp. (Willow) canopies had the most significant potential for nitrogen retention via the dissimilatory nitrate reduction to ammonia pathway. We use landscape features detected by remote sensing and machine learning to map nitrogen cycling trait distributions across the East River watershed. Our workflow provides a scalable means to quantify the controls on microbial trait distributions at watershed scales, predict locations (i.e., hotspots) with potentially high nitrogen cycle activity, and design lab and field experiments to validate such predictions.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.B56A..02S