Electron acceptors for anaerobic oxidation of methane drive microbial community structure and diversity in mud volcanoes
Bibliographic record
Abstract
Mud volcanoes (MVs) emit globally significant quantities of methane into the atmosphere, however, methane cycling in such environments is not yet fully understood, as the roles of microbes and their associated biogeochemical processes have been largely overlooked. Here, we used data from high-throughput sequencing of microbial 16S rRNA gene amplicons from six MVs in the Junggar Basin in northwest China to quantify patterns of diversity and characterize the community structure of archaea and bacteria. We found anaerobic methanotrophs and diverse sulfate- and iron-reducing microbes in all of the samples, and the diversity of both archaeal and bacterial communities was strongly linked to the concentrations of sulfate, iron and nitrate, which could act as electron acceptors in anaerobic oxidation of methane (AOM). The impacts of sulfate/iron/nitrate on AOM in the MVs were verified by microcosm experiments. Further, two representative MVs were selected to explore the microbial interactions based on phylogenetic molecular ecological networks. The sites showed distinct network structures, key species and microbial interactions, with more complex and numerous linkages between methane-cycling microbes and their partners being observed in the iron/sulfate-rich MV. These findings suggest that electron acceptors are important factors driving the structure of microbial communities in these methane-rich environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".