While it is widely recognized that microorganisms are intimately linked with every biogeochemical cycle in all ecosystems, it is not clear how and when microbial dynamics constrain ecosystem processes. As a result, it is know clear how to apply the value of increasingly detailed characterization of microbial properties to our understanding of ecosystem ecology. Several recent papers have demonstrated how information about microbial dynamics can be incorporated into ecosystem models (Allison et al. 2010, McGuire and Treseder 2010, Todd - Brown et al. 2011a), but it is generally not clear what types of microbial data are most useful in explaining variation in biogeochemical processes and ecosystem functioning, especially in the face of global change. There is a clear need to quantitatively evaluate which microbial data are best suited to improve our ability to predict ecosystem processes, and to direct future sampling efforts toward emerging approaches that are most likely to advance our understanding of ecosystem functioning. The USGS has a storied legacy of collecting important metrics for quantifying and describing our nation’s resources. The potential for microbial processes to provide further insight into the controls of ecosystem function has spurred the development of a growing group of USGS scientists conducting research on environmental microorganisms. In line with these efforts, we propose to use existing datasets describing microbial properties and ecosystem function to address which microbial processes are most likely to enhance our understanding of ecosystem processes and their projections in a changing world. Our overall goal is to identify key microbial indicators of fundamental ecological processes, which will help to focus future monitoring and research efforts from the USGS and the broader scientific community.
Bier, R.L., Bernhardt, E.S., Boot, C.M., Graham, E.B., Hall, E.K., Lennon, J.T., Nemergut, D., Osborne, B.B., Ruiz-Gonzalez, C., Schimel, J.P., Waldrop, M.P., Wallenstein, M.D., (2015) Linking microbial community structure and microbial processes: an empirical and conceptual overview. FEMS Microbiology Ecology. DOI: http://dx.doi.org/10.1093/femsec/fiv113
Rocca, J.D., Hall, E.K., Lennon, J.T., Evans, S.E., Waldrop, M.P., Cotner, J.B., Nemergut, D.R., Graham, E.B., & Wallenstein, M.D. (2014). Relationships between protein-encoding gene abundance and corresponding process are commonly assumed yet rarely observed. ISME J, doi:10.1038/ismej.2014.252
Hall EK, Bernhardt ES, Bier RL, Bradford MA, Boot CM, Cotner JB, del Giorgio PA, Evans SE, Graham EB, Jones SE, Lennon JT. Understanding how microbiomes influence the systems they inhabit. Nature microbiology. 2018 Sep;3(9):977.
Edward K Hall (Fort Collins Science Center)
Matthew Wallenstein (Colorado State University)
Jay Lennon (Indiana University Bloomington)
Claudia Boot (Colorado State University)
Emily Bernhardt (Duke University)
Jim Cotner (University of Minnesota)
Josh Schimel (University of California Santa Barbara)
Mark Bradford (Yale School of Forestry and Environmental Studies)
Mark P Waldrop (USGS Volcano Science Center)
Paul Del Giorgio (University of Quebec at Montreal)
Sarah Evans (University of California, Irvine)
Stuart Jones (University of Notre Dame)
Jenny Rocca (Colorado State University)
Raven Bier (Duke University)
Diana Nemergut (Duke University)
Emily Graham (University of Colorado Boulder)
Ken Locey (Indiana University Bloomington)
Clara Ruiz Gonzalez (University of Quebec at Montreal)
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