U.S. Department of Energy

Pacific Northwest National Laboratory

Towards Better Models of Subsurface Microbial Communities

Preparing soil samples in the field.

The Science                                

Microbial communities within subsurface aquifers are metabolically influenced by the mineral mixes that bind them. Communities like this also exchange gases on such a vast scale that they influence the composition of the Earth’s atmosphere. Data about microbial activity, even underwater or in the transition zones between water and land, inform predictive climate models. But subsurface microbial communities are hard to access and hard to study, so modelers need proxy variables to predict their likely spatial distribution.

The Impact

In a recent paper, a team of researchers led by James C. Stegen and Jim K. Fredrickson at Pacific Northwest National Laboratory describe an unexploited opportunity for modeling the distribution of subsurface microbial communities. If the mineralogy and other characteristics of sediments influence subsurface microbial communities, the researchers reasoned, then spatial distributions of sediment characteristics – which are easier to describe – can be used to predict the spatial distribution of biogeochemically relevant microbial communities.

Previous papers have used hydrogeological properties as proxies for microbial activity. But none (until now) have leveraged biogeochemical facies in order to spatially project microbial biomass. Such an approach, the authors said, will generate fundamental knowledge about the spatial distributions of key properties within microbial communities. It will also provide important constraints to hydro-biogeochemical models in both initial and dynamic conditions. 

Summary

Along a stretch of the Columbia River near Richland, Wash., Stegen and his team worked at the Hanford Site 300 Area, where the subsurface geochemical and biogeochemical processes that influence the transport of contaminants have already been widely identified. They also took advantage of 35 extant boreholes and the strongly vertical (and weakly horizontal) structure of the area’s fine-grained Ringold geologic formation.

The researchers set out to evaluate their hypotheses: that the richness of a microbial community will most strongly be related to redox state, which influences how much energy     is available to microbial cells. And that mixing complementary electron donors and acceptors will create a biogeochemical “hot spot” in the transition zone, resulting in elevated microbial biomass. From there, the researchers coupled variations in microbial biomass with spatial distributions of biogeochemical facies to predict microbial biomass across a 3-dimensional spatial domain.

To arrive at a 3-D map of microbial biomass, the researchers used geologist well logs from the 35 boreholes in order to define elevations of the redox transition zones, where they expected to find the highest microbial biomass. They used these data to generate a 3-D reconstruction of biogeochemical facies, combining that with facies-specific microbial biomass estimates. In turn, the researchers were able to generate a 3-D map of microbial biomass, based on the more easily measured proxy variable of redox state. 

Although PNNL researchers called for this approach of using proxy variables decades ago, it has received limited attention. The researchers say it has great potential for creating multi-scale models that have field-scale predictive value for biogeochemical function in the Earth’s climate-critical transition zones between water and land. 

Funding

Office of Biological and Environmental Research (BER) as part of Subsurface Biogeochemistry Research Program’s Scientific Focus Area (SFA) at the Pacific Northwest National Laboratory (PNNL).

Publication

Stegen JC, A Konopka, JP McKinley, C Murray, X Lin, MD Miller, DW Kennedy, EA Miller, CT Resch, and JK Fredrickson. 2016. “Coupling among Microbial Communities, Biogeochemistry, and Mineralogy across Biogeochemical Facies.” Scientific Reports6. DOI 10: 1038/srep30553.

Date: 
July 2016
| Pacific Northwest National Laboratory