Altered microbial community structure and organic matter composition under elevated CO<sub>2</sub>and N fertilization in the duke forest
Bibliographic record
Abstract
Abstract The dynamics and fate of terrestrial organic matter (OM) under elevated atmospheric CO2and nitrogen (N) fertilization are important aspects of long‐term carbon sequestration. Despite numerous studies, questions still remain as to whether the chemical composition of OM may alter with these environmental changes. In this study, we employed molecular‐level methods to investigate the composition and degradation of various OM components in the forest floor (O horizon) and mineral soil (0–15 cm) from the Duke forest free air CO2enrichment (FACE) experiment. We measured microbial responses to elevated CO2and N fertilization in the mineral soil using phospholipid fatty acid (PLFA) profiles. Increased fresh carbon inputs into the forest floor under elevated CO2were observed at the molecular‐level by two degradation parameters of plant‐derived steroids and cutin‐derived compounds. The ratios of fungal to bacterial PLFAs and Gram‐negative to Gram‐positive bacterial PLFAs decreased in the mineral soil with N fertilization, indicating an altered soil microbial community composition. Moreover, the acid to aldehyde ratios of lignin‐derived phenols increased with N fertilization, suggesting enhanced lignin degradation in the mineral soil.1H nuclear magnetic resonance (NMR) spectra of soil humic substances revealed an enrichment of leaf‐derived alkyl structures with both elevated CO2and N fertilization. We suggest that microbial decomposition of SOM constituents such as lignin and hydrolysable lipids was promoted under both elevated CO2and N fertilization, which led to the enrichment of plant‐derived recalcitrant structures (such as alkyl carbon) in the soil.
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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 | 0.001 | 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".