Response of fungal composition and diversity to simulated nitrogen deposition and manipulation of precipitation in soils of an Inner Mongolia desert steppe of Northern China
Bibliographic record
Abstract
Nitrogen (N) deposition and precipitation changes can strongly influence soil microbial properties in arid and semiarid regions. Here, we examined these effects on soil samples from the Inner Mongolia desert steppe of northern China after 7 yr of consecutive simulated N deposition by adding NH4NO3 and manipulation of precipitation, using a dilution plate method, PCR analysis, and 18S rRNA sequencing. The experimental treatments were as follows: control (CK), N addition (+N), N and water addition (+N+W), and N addition plus water reduction (+N−W). In this study, 14 genera and 32 fungal species were isolated, and Penicillium was determined to be the dominant fungal group. Treatment +N−W significantly increased (by 94.8%) the number of cultivable fungi as compared with CK. Compared with the CK community, fungal communities exposed to the three treatments, especially +N+W and +N−W, showed shifts in the relative abundances of cultivable fungi. Treatment +N−W significantly enhanced species richness compared with +N at the 0–2 cm soil depth. However, N addition and manipulation of precipitation did not influence species richness, the Shannon–Weiner index, or evenness at the 0–30 cm soil depth. This study can provide insight into how fungal composition and diversity respond to climate change scenarios.
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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".