Strategies to Assess and Manange Soil Microbial Communities for Ecosystem Services.

Managing for soil ecosystem services requires an ecological understanding of the organisms involved, such as WHO the important ecosystem service providers are, WHAT abiotic and biotic factors regulate their abundance, diversity and activity, and WHEN and WHERE ecosystem service providers operate across spatial and temporal scales. For example, the presence of specific microorganisms determines a soil’s genetic potential to function, but the actual manifestation of microbial function is likely regulated by land use, biological interactions, soil physicochemical properties, and other abiotic factors that are spatially and temporally heterogeneous. Unfortunately, knowledge gaps exist in our understanding of microbial communities, thereby limiting our ability to effectively manage them for ecosystem services. In this presentation I will outline a discover-and-manage approach to assess the diversity and abundance of soil microbial ecosystem service providers (with traditional, physiological and metagenomic methods) and identify important abiotic and biotic factors associated with microbial community structure and function in space and time (through geostatistics and development of predictive models). As proof of concept, I will discuss how this approach was applied to a specific group of soil bacteria that degrade atrazine and therefore contribute to contaminant degradation (positive service) but also loss of weed control (negative service).  Qualitative PCR was employed to determine the spatial distribution of a gene (atzC) encoding for atrazine degradation across northeastern Colorado soils that demonstrated a range in atrazine degradation activity. We collected field management history data and analyzed soil properties to identify factors that likely regulated the expression of atzC. We then utilized classification and regression tree analysis to develop a simple model that predicted microbial activity (i.e., rate of atrazine degradation) based on management practice (atrazine use history), soil pH, organic matter content, but not presence of atzC. Growers are able to apply this model to predict whether a particular field soil will degrade atrazine rapidly, and therefore can modify their management practices to prevent loss of weed control.