Machine learning methods to discover patterns in microbially driven soil carbon sequestration

Thompson, Jaron, author
Munsky, Brian, advisor
Metcalf, Jessica, committee member
Chan, Joshua, committee member
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Understanding how microbiomes function is a major area of research, as microorganisms play a significant role in environments spanning nearly every corner of the earth. Recent advances in DNA sequencing technology have made it possible to profile microbial communities, yet noise and sparsity in microbiome data makes it difficult to identify consistent patterns in microbial community behavior. In this thesis, we apply a host of machine learning methods to elucidate the role of the soil microbiome in mediating soil carbon sequestration. We demonstrate that broad characteristics of the soil microbiome such as richness and biomass can be used to forecast abundance of dissolved organic carbon (DOC) in soil. We also show that feature selection analysis using a host of machine learning and standard statistical techniques identifies a consensus set of significant taxa that predict DOC abundance. Finally, we demonstrate how these feature selection techniques can be used to explore more advanced probabilistic models that assign accurate estimates of prediction confidence. The methods proposed in this thesis could be used to design optimized microbial communities that combat climate change by promoting increased levels of carbon storage in soil.
2020 Spring.
Includes bibliographical references.
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climate change
machine learning
carbon sequestration
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