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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.
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Reframing trait trade-offs in marine microbes
Abstract The oceans sequester a vast amount of carbon thus playing a central role in the global carbon cycle. Assessing how carbon cycling will be impacted by climate change requires an improved understanding of microbial dynamics, which are responsible for most carbon transformations in the oceans. Current numerical models used for predicting future states represent simplified microbial phenotypes and thus may not produce robust predictions of microbial communities. We propose reframing approaches for studying microbial trait change to allow for selection on multi-trait phenotypes. Integrating statistical approaches and trait-based models will allow for the incorporation of evolution into carbon cycle predictions.
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- Award ID(s):
- 2044852
- PAR ID:
- 10502167
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Communications Earth & Environment
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2662-4435
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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