A NOVEL THEORETICAL AND QUANTITATIVE FRAMEWORK TO UNDERSTAND AND PREDICT SOIL CARBON STORAGE AND NITROGEN RECYCLING

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Soils play an important role in the global carbon cycle. Carbon that enters the soil as leaf litter or woody debris can lie dormant for hundreds to thousands of years, or it can be released to the atmosphere almost immediately as a byproduct of decomposition by microbes. The residence time of carbon in the soil, which impacts the availability of nutrients essential to plants, is influenced by the physical and chemical structure of the soil, and by moisture and temperature. Through laboratory experiments using soils harvested from different habitats, and a comparison with observational data collected from a range of biomes, this project will infer which factors are most important in determining how carbon moves into and out of soil, and how their relative importance changes across environmental gradients. The researchers will use newly collected and analyzed data to refine models of soil carbon dynamics, with the aim of advancing our understanding of how interactions between environmental conditions, vegetation, and soil characteristics drive carbon and nitrogen cycling across different ecosystems. The project will engage undergraduates and K-12 teachers to develop and sustain a Soil Inquiry project for the local School District, including a recurring public event on World Soil Day.The researchers will develop an open-access ecosystem model (MEMS 2.0) based a comprehensive conceptual framework for soil organic matter dynamics. They will evaluate the conceptual framework through a combination of controlled experiments, data synthesis, statistical analyses, and process-based modeling. They will perform experiments manipulating drivers of soil C and N cycling to evaluate the effects of different edaphic characteristics on soil C storage and N loss. Leveraging soil organic matter and elemental and isotopic analyses of soil samples from all the National Ecological Observatory Network sites and other existing datasets, they will develop a relational database and test their hypotheses using traditional multivariate statistics and structural equation modeling. They will then calibrate the MEMS 2.0 model using data form across the US. Finally, MEMS 2.0 will be used to forecast C and N dynamics under different temperature and precipitation regimes. This project will help to bridge the historical gap between modelers and experimentalists and train a new generation of scientists to effectively span these domains.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.