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Soil microbiomes are heterogeneous, complex microbial communities. Metagenomic analysis is generating vast amounts of data, creating immense challenges in sequence assembly and analysis. Although advances in technology have resulted in the ability to easily collect large amounts of sequence data, soil samples containing thousands of unique taxa are often poorly characterized. These challenges reduce the usefulness of genome-resolved metagenomic (GRM) analysis seen in other fields of microbiology, such as the creation of high quality metagenomic assembled genomes and the adoption of genome scale modeling approaches. The absence of these resources restricts the scale of future research, limiting hypothesis generation and the predictive modeling of microbial communities. Creating publicly available databases of soil MAGs, similar to databases produced for other microbiomes, has the potential to transform scientific insights about soil microbiomes without requiring the computational resources and domain expertise for assembly and binning. 

Inferring linkages between microbial metabolism and dissolved organic matter (DOM) across environmental gradients is a promising avenue to improve biogeochemical predictions at large spatial scales. Despite decades of metagenomic studies identifying microbial functional trait-environment patterns at small spatial scales, general patterns at continental or global scales that may improve large-scale models remain unresolved. Recent influx of multi-omics datasets that represent diverse environmental conditions has enabled scalable analyses linking microbial metabolic niche breadths with key environmental processes, such as carbon and nutrient transformations.Here, we leveraged publicly available microbial metagenome assembled genomes (MAGs) derived from the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) data paired with metabolomic (FTICR-MS) and sediment chemistry data to link microbial metabolic potential with organic chemistry. We annotated 1,384 MAGs representing 65 sites using the R tool microTrait, which categorizes functional traits under the YAS (growth yield-resource acquisition-stress tolerance) framework. Following Hutchinsonian niche theory, we modeled microbial trait combinations as n-dimensional hypervolumes and observed trait-DOM patterns at the continental scale, showing microbial functional tradeoffs along gradients of organic carbon. We expect that at the continental scale, microbial trait profiles will be distinct across climatic regions, and that niche breadth (i.e. the size of individual hypervolumes in trait space) will correlate with DOM/metabolite diversity. The results of this work will distill generalizable patterns of microbe-DOM availability and diversity at large spatial scales, thus identifying information to improve current biogeochemical models.