Fire-induced rapid suppression of microbial necromass accumulation in a subtropical <i>Cunninghamia lanceolata</i> forest
Bibliographic record
Abstract
Microbial necromass carbon (MNC) is crucial for soil organic carbon (SOC) stabilization. However, how wild fire influence MNC accumulation remains to be elucidated. Here, we investigate the MNC dynamic and related soil and microbial traits on the 10th, 30th, and 90th days following a wildfire in a subtropical forest. Wildfire significantly decreased total MNC, primarily driven by reductions in fungal necromass (FNC), while bacterial necromass remained unaffected. The immediate impact (day 10) featured a sharp FNC decline, largely attributed to direct combustion, pyrolysis, and volatilization of existing necromass by high temperatures. While microbial biomass carbon (MBC) was not significantly reduced on day 10, an overall decrease was observed during the study period. This sustained FNC reduction and the overall lower MBC levels (indicating reduced MNC formation) were linked to intensified post-fire carbon limitation (due to reduced fine root input), suppression of specific fungal groups, and increased microbial investment in extracellular enzyme activities over growth. Concurrently, increased N-acetylglucosaminidase activity, likely stimulated by a shift from phosphorus to carbon limitation and altered microbial network complexity, indicated accelerated FNC decomposition. Despite substantial MNC losses, its proportional contribution to SOC remained unchanged within 3 months, suggesting concurrent losses of other SOC components. These findings evaluated the direct fire-induced necromass destruction offering crucial insights into short-term MNC fate and SOC dynamics in fire-disturbed subtropical forests.
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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".