Fungal necromass is reduced by intensive drought in subsoil but not in topsoil
Bibliographic record
Abstract
The frequency and intensity of droughts worldwide are challenging the conservation of soil organic carbon (SOC) pool. Microbial necromass is a key component of SOC, but how it responds to drought at specific soil depths remains largely unknown. Here, we conducted a 3-year field experiment in a forest plantation to investigate the impacts of drought intensities under three treatments (ambient control [CK], moderate drought [30% throughfall removal], and intensive drought [50% throughfall removal]) on soil microbial necromass pools (i.e., bacterial necromass carbon, fungal necromass carbon, and total microbial necromass carbon). We showed that the effects of drought on microbial necromass depended on microbial groups, soil depth, and drought intensity. While moderate drought increased total (+9.1% ± 3.3%) and fungal (+13.5% ± 4.9%) necromass carbon in the topsoil layer (0-15 cm), intensive drought reduced total (-31.6% ± 3.7%) and fungal (-43.6% ± 4.0%) necromass in the subsoil layer (15-30 cm). In contrast, both drought treatments significantly increased the BNC in the topsoil and subsoil. Our results suggested that the effects of drought on the microbial necromass of the subsoil were more pronounced than those of the topsoil. This study highlights the complex responses of microbial necromass to drought events depending on microbial community structure, drought intensity and soil depth with global implications when forecasting carbon cycling under climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".