Background/Question/Methods Although microbes (bacteria and fungi) play key roles in many of the processes or phenomena that constitute NEON's “grand challenges”, it has only been recently that planning has matured to ensure that an adequate suite of observations can be made to help address specific roles of microbes within and among domains, and to determine how those roles change through time and space as a result of climate and land use changes. Developing a suitable set of observations for microbes remains a challenge, however, as it is essential to identify the most critical and cost-effective variables that can be measured, and to do so across spatial and temporal scales that will generate data that can be integrated with biogeochemical, flora and faunal observations obtained at local to regional scales. To optimize the process of selecting the best microbial variables to observe given its various constraints, NEON, Inc. has elected to develop a pilot study, the goals of which are to initiate a set of gene sequence-based analyses at multiple time points on a large sampling grid in 4 of NEON's designated domains including Southeast (Domain 3), Great Basin (Domain 15), Taiga (Domain 19), and Pacific Tropical (Domain 20). Results/Conclusions The study design includes analyses of 16S and 18S/ITS rRNA genes to identify soil bacterial, archaeal and fungal diversity and community structure using a pyrosequencing approach to generate approximately 1500 reads per sample. A parallel effort will also explore an important functional gene in the soil nitrogen cycle, nifH (for nitrogenase). Based on initial results, a metagenomic analysis will be undertaken at 1-2 sites chosen selected for their patterns of diversity. Phylogenetic and nifH analyses will be conducted at up to 5 time points within a year to capture short-term variability and variability within a growing season; spatial variability will be assessed using random samples obtained from a grid within the expected sampling area of the NEON automated microclimate and soil sensors. The resulting data will help constrain the extent of spatial and temporal sampling necessary to capture long-term trends in microbial communities, including both community composition and dynamics of a key functional group. The results will also be used to develop and assess “workloads” for data analysis that will facilitate rapid sequence identification and incorporation into data products readily interpretable by a broad range of user groups.