Differential Effects of Nitrogenous Fertilizers on Methane-Consuming Microbes in Rice Field and Forest Soils
Bibliographic record
Abstract
The impact of environmental perturbation (e.g., nitrogenous fertilizers) on the dynamics of methane fluxes from soils and wetland systems is poorly understood. Results of fertilizer studies are often contradictory, even within similar ecosystems. In the present study the hypothesis of whether these contradictory results may be explained by the composition of the methane-consuming microbial community and hence whether methanotrophic diversity affects methane fluxes was investigated. To this end, rice field and forest soils were incubated in microcosms and supplemented with different nitrogenous fertilizers and methane concentrations. By labeling the methane with 13C, diversity and function could be coupled by analyses of phospholipid-derived fatty acids (PLFA) extracted from the soils at different time points during incubation. In both rice field and forest soils, the activity as well as the growth rate of methane-consuming bacteria was affected differentially. For type I methanotrophs, fertilizer application stimulated the consumption of methane and the subsequent growth, while type II methanotrophs were generally inhibited. Terminal restriction fragment length polymorphism analyses of the pmoA gene supported the PLFA results. Multivariate analyses of stable-isotope-probing PLFA profiles indicated that in forest and rice field soils, Methylocystis (type II) species were affected by fertilization. The type I methanotrophs active in forest soils (Methylomicrobium/Methylosarcina related) differed from the active species in rice field soils (Methylobacter/Methylomonas related). Our results provide a case example showing that microbial community structure indeed matters, especially when assessing and predicting the impact of environmental change on biodiversity loss and ecosystem functioning.
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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) | 0.001 | 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) | 0.000 | 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".