OOS 3-5
Generating a continental scale, systems model to explore scalar interactions within and between NEON terrestrial observation sites

Monday, August 5, 2013: 2:50 PM
101C, Minneapolis Convention Center
Jack A. Gilbert, Earth Microbiome Project (http://www.earthmicrobiome.org), University of Chicago, Argonne National Laboratories, Chicago
Peter Larsen, Biosciences, Argonne National Laboratory, IL
Jacob Parnel, National Earth Observation Network
Noah Fierer, Ecology and Evolutionary Biology and CIRES, University of Colorado, Boulder, CO
Rob Knight, Chemistry and Biochemistry, University of Colorado, Boulder, CO
Janet K. Jansson, Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA
Beth Drewniak, Argonne National Laboratory
Rob Jacob, Argonne National Laboratory

Understanding the impact of terrestrial microbiology on Earth’s climate and ecology requires multi-scale observations of the biosphere. However, to acquire and process physical samples of soil that comprise the appropriate spatial and temporal resolution to capture the immense variation in microbial dynamics, would require a herculean effort and immense financial resources dwarfing even the most ambitious projects to date. To overcome this hurdle we are combing the crowd-sourced Earth Microbiome Project’s database of soil-based microbial variation across the US with high-temporal and spatial resolution analysis of microbial variation at the 20 proposed fixed-sites that form the core of the National Earth Observation Network strategy for placed-based genomic observatories. One of the key goals of this strategy is to map the multi-year spatiotemporal variability of microbial communities at each location, and correlate these with detailed observed physical, chemical and biological measurements about the environment. Combined with the EMPs ~3000 soil microbial diversity profiles from across the US, these site-based data will capture the changes in important microbially-driven processes that need to be appropriately expressed in models to provide reliable forecasts of ecosystem phenotype across our changing continent. This is essential if we are to develop economically sound strategies to be good stewards of our country.


We recognize that environments are comprised of complex sets of interdependent parameters and that the development of useful predictive computational models of both terrestrial and atmospheric systems requires recognition and accommodation of sources of uncertainty. Here we present models that combine the EMP, NEON and the Community Earth System Model to predict the microbial diversity, and functional carbon and nitrogen metabolism for the continental USA, enabling forecasted predictions up to the year 2100.