PREDICTIVE UNDERSTANDING OF SUBSURFACE BIOGEOCHEMICAL FUNCTIONING: USING GENOMES TO INFORM WATERSHED-SCALE MODELS

Shallow subsurface environments represent a complex component of the Earth System. They are shaped by interactions among plants, animals, microorganisms, minerals, migrating fluids, and dissolved constituents that occur within a heterogeneous framework and across a range of temporal and spatial scales. Biogeochemical cycling in these environments is relevant to terrestrial carbon budgets, contaminant mobility and biofuel crop sustainability, among other topics, and yet our ability to predict it remains an unmet challenge. A new project, ‘Sustainable Systems 2.0’, sponsored by the U.S. Department of Energy, Biological and Environmental Research (BER), aims to develop an understanding of how the subsurface microbiome affects biogeochemical watershed functioning, how watershed-scale processes affect microbial functioning, and how these interactions co-evolve with climate and land-use stresses. A key facet of this research is to understand how subsurface microbial metabolic processes in biogeochemically active areas such as floodplains respond to water table fluctuations and other system perturbations. An initial perturbation experiment simulating the influence of migrating fluids containing nitrate into a bioreduced zone in the subsurface has demonstrated the power of linking metagenomes, metatranscriptomes and biogeochemistry in the context of field experiments. In addition to revealing extraordinary microbial diversity in sediments prior to the perturbation, metagenomic and metatranscriptomic data show the emergence of biogeochemical processes related to N cycling (e.g., nitrite reduction, anaerobic ammonia oxidation) and S and Fe oxidation. Results from these “omics” data are informing EcoTrait models coupled to reactive transport models that are capable of predicting competitive advantage in a microbial community based on their fitness landscape. The result will be a new subsurface science community simulation tool, the Genome-Enabled Watershed Simulation Capability (GEWaSC), establishing a predictive framework for exploring how genomic information stored in a subsurface microbiome affects biogeochemical cycling.