Metagenomic insight into shifts of methane-cycling communities following pasture conversion in the Amazon basin
Bibliographic record
Abstract
Abstract Background: Deforestation threatens the integrity of the Amazon biome and the ecosystem services it provides. Most of this deforested land is converted to pastures for cattle raising. Early studies revealed that forest-to-pasture conversion alters the flux of methane gas (CH4) in Amazonian soils, driving a switch from acting as a sink to a source of atmospheric CH4. We sought to better understand this phenomenon by investigating the soil microbial metagenomes, focusing on the taxonomic and functional structure of methane-cycling communities. Results: Metagenomic data were combined with in situ measurements of CH4 fluxes, and measurements of soil edaphic factors. We found a significantly higher abundance of methanogens and reduced methanotrophs in pasture soils. As inferred by co-occurrence networks, these microorganisms seem to be less interconnected within the soil microbiota in pasture soils. Metabolic traits were also different between land uses, with increased hydrogenotrophic and methylotrophic pathways of methanogenesis in pasture soils. Similarly, methanotrophs harboring the soluble form of methane monooxygenase enzyme (sMMO) were depleted in pasture soils. Redundancy analysis and multimodel inference revealed that the shift in methane-cycling communities in pasture soils was associated with higher soil compaction, pH, organic matter, and micronutrients. Conclusions: These results provide new knowledge about the effect of forest-to-pasture conversion on the community traits of methane-cycling microorganisms in the Amazon region, resulting in increased abundance and functional potential of methanogens in pastures, and thus increasing the emission of CH4 in deforested soils. Keywords: land-use change, methanogens, methanotrophs, soil properties, multimodel inference
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".