Liming pasture soils in the Amazon region promotes low-affinity methane oxidation by type I and II methanotrophs
Bibliographic record
Abstract
Abstract In the Amazon Forest region, cattle pastures are the main land use subsequent to deforestation. This land-use change affects the soil microbial community and methane fluxes, shifting the soil from a methane sink to a source. Soil physical and chemical attributes are changed due to slash-and-burn processes, including an increased soil pH after forest-to-pasture conversion. Without amendments, the pasture soils can become acidic again resulting in many cases in soil degradation. Liming is a standard management practice to increase soil pH while decreasing Al 3+ availability. Liming is important to recover these degraded lands and increase soil fertility, but its impact on soil methane cycling in tropical soils is unknown. Here we investigated the role of soil pH on methane uptake under high concentrations of the gas. The top layer of forest (pH 4.1) and adjacent pasture soils (pH 4.8) from the Eastern Amazon were subjected to liming treatment (final pH 5.8) and incubated with ∼10,000 ppm of 13 CH 4 for 24 days to label DNA with 13 C. Soil DNA was evaluated with Stable Isotopic Probing (SIP-DNA), methanotrophic abundance was quantified ( pmoA gene), and high throughput sequencing of 16S rRNA was performed. Liming increased the methane uptake in both forest (∼10%) and pasture (∼25%) soils. Methanotrophs Methylocaldum sp . (type I) and Beijerinckaceae (type II) were identified to actively incorporate carbon from methane in limed pasture soils. In limed forest soils, Nitrososphaeraceae , Lysobacter sp., and Acidothermus sp . were identified as 13 C-enriched taxa. The enrichment of the archaeal family Nitrososphaeraceae , known as ammonia oxidizers, is correlated with an increase of ammonia monooxygenase genes, which code for an enzyme complex with wide substrate specificity that can also perform methane oxidation. In conclusion, liming Amazonian pasture soils not only contributes to the fertility and recovery of degraded areas but also has the potential to improve the oxidation of methane at high concentrations of this gas.
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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 | 0.000 | 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".