Enhanced Release and Reactivity of Soil Water-Extractable Organic Matter Following Wildfire in a Subtropical Forest
Bibliographic record
Abstract
Climate-driven increases in wildfire frequency may disrupt soil carbon dynamics, potentially creating positive feedback within global carbon cycle. However, the release and lability of soil carbon following wildfire remain unclear, limiting our ability to predict fire impacts on carbon cycling. Here, we investigated chemical alterations in soil water-extractable organic matter (WEOM) following a subtropical forest wildfire by comparing burned soils to an adjacent unburned site. The consensus is that fire-altered DOM is aromatic and less reactive. However, we found that 10 months postfire, burned soils contained nearly three times more water-extractable organic carbon (WEOC) than the control site. Reactomics analysis further revealed an overall 8-fold increase in potential reactivity of this carbon, identified by higher abundances of molecular formulas involved in identified microbial reaction pathways. Specifically, burned soils exhibited elevated potential oxidative enzyme reactions, linked to a higher nominal oxidation state of carbon (NOSC) in WEOM. Metagenomic analysis revealed an enrichment of microbial taxa specialized in degrading aromatic compounds in burned areas, supporting the occurrence of potential microbial reaction pathways acting on WEOM in postfire soils. These findings highlight that wildfires may accelerate soil carbon loss through reactive WEOM mobilization and microbial response, with implications for long-term carbon-climate projections.
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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) | 0.000 | 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".