In mountainous systems with seasonal snow-cover, interactions between vegetation, microorganisms and geology are key to the retention of water, nutrients and other elements. Snowpack dynamics, including snow depth and timing of snowmelt, shape these interactions. The timing of snowmelt is changing, and with it plant and microbial activity that control watershed exports. Using remote sensing, metagenomics and machine learning, we are building new ways to predict of how plant and microbial metabolism interact to influence biogeochemistry across watersheds in the headwaters of the Colorado River.
Proposer: Eoin Brodie, Lawrence Berkeley National Laboratory
Proposal: Scaling microbial traits from genomes to watersheds through combined airborne hyperspectral imaging, soil biogeochemistry, and metagenome assembled genomes