Soil trace gas oxidizers divergently respond to short- and long-term warming
Bibliographic record
Abstract
Abstract The upland soil microbiome is dominated by aerobic bacteria that oxidize atmospheric trace gases, including CO, H 2 , and CH 4 . As a result, soils are the largest biological sink for these climate-active gases. Whether global warming will enhance or suppress these processes remains unclear. Here, we studied the warming responses of soil trace gas oxidizers by profiling natural geothermal gradients in a subarctic grassland with over 60 years of field warming at +6°C. We integrate field flux measurements, ex situ biogeochemical assays, metagenomics, and metatranscriptomics to determine ecosystem and cellular-level responses. Our results show that the oxidation of atmospheric CO and H 2 , but not CH 4 , increased with long-term warming due to higher cell numbers. However, at the cellular level, trace gas oxidizers, especially methanotrophs, tended to reduce gas consumption and transcription of gas-metabolizing enzymes in response to long-term warming. Our findings suggest that soils may remain a robust sink for trace gases despite lower per-cell activity. This work establishes a framework for interpreting the relationships between temperature and microbial trace gas oxidation on timescales relevant to Earth’s climate system.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".