Microbial community-level features linked to divergent carbon flows during early litter decomposition in a constant environment
Johansen, R.; Albright, M.; Lopez, D.; Gallegos-Graves, L. V.; Runde, A.; Mueller, R.; Washburne, A.; Yoshida, T.; Dunbar, J.
Show abstract
During plant litter decomposition in soils, carbon has two general fates: return to the atmosphere via microbial respiration or transport into soil where long-term storage may occur. Discovering microbial community features that drive carbon fate from litter decomposition may improve modeling and management of soil carbon. This concept assumes there are features (or underlying processes) that are widespread among disparate communities, and therefore amenable to modeling. We tested this assumption using an epidemiological approach in which two contrasting patterns of carbon flow in laboratory microcosms were delineated as functional states and diverse microbial communities representing each state were compared to discover shared features linked to carbon fate. Microbial communities from 206 soil samples from the southwestern United States were inoculated on plant litter in microcosms, and carbon flow was measured as cumulative carbon dioxide (CO2) and dissolved organic carbon (DOC) after 44 days. Carbon flow varied widely among the microcosms, with a 2-fold range in cumulative CO2 efflux and a 5-fold range in DOC quantity. Bacteria, not fungi, were the strongest drivers of DOC variation. The most significant community-level feature linked to DOC abundance was bacterial richness--the same feature linked to carbon fate in human-gut microbiome studies. This proof-of-principle study under controlled conditions suggests common features driving carbon flow in disparate microbial communities can be identified, motivating further exploration of underlying mechanisms that may influence carbon fate in natural ecosystems.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Enhancement of nitrous oxide emissions in soil microbial consortia via copper competition between proteobacterial methanotrophs and denitrifiers 96%
- Variation in root exudate composition influences soil microbiome membership and function 95%
- High arsenic levels increase activity rather than diversity or abundance of arsenic metabolism genes in paddy soils 95%
Similar papers in this journal
- Rapid response of nitrogen cycling gene transcription to labile carbon amendments in a soil microbial community 97%
- Evaluation of the effects of library preparation procedure and sample characteristics on the accuracy of metagenomic profiles 95%
- Time-series RNA metabarcoding of the active Populus tremuloides root microbiome reveals hidden temporal dynamics and dormant core members 95%
Similar papers in this journal
- Response of total (DNA) and metabolically active (RNA) microbial communities in Miscanthus x giganteus cultivated soil to different nitrogen fertilization rates 97%
- Whats under the Christmas tree? Soil acidification alters fir tree rhizosphere bacterial and eukaryotic communities, their interactions, and functional traits 97%
- Exploring the prokaryote-eukaryote interplay in microbial mats from an Andean athalassohaline wetland 95%
Similar papers in this journal
- Experimental evidence for the impact of soil viruses on carbon cycling during surface plant litter decomposition 97%
- Distinct Microbiomes Underlie Divergent Responses of Methane Emissions from Diverse Wetland soils to Oxygen Shifts 96%
- Establishment of a transparent soil system to study Bacillus subtilis chemical ecology 95%
Similar papers in this journal
- Novel oil-associated bacteria in Arctic seawater exposed to different nutrient biostimulation regimes 95%
- Bacterial community dynamics explain carbon mineralization and assimilation in soils of different land-use history 95%
- Assembly of the amphibian microbiome is influenced by the effects of land-use change on environmental reservoirs 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.