Response of carbon utilization and enzymatic activities to nitrogen deposition in three forests of subtropical China
Bibliographic record
Abstract
Nitrogen (N) deposition has been shown to affect soil carbon (C) and N cycling in subtropical forests; however, the underlying microbial mechanisms are poorly understood. We used patterns of community-level physiological profiles and enzyme activities to assess the relative effects of the addition of four N levels (0, 50, 100, and 150 kg·ha−1·year−1) on the soil microbial community in three forest stands (pine, mixed, and broadleaf forests) in southern China, where the forests have been experimentally manipulated for over 8 years. In pine forests, N50 addition significantly increased microbial biomass carbon (MBC) concentration but decreased soil pH levels. N100 addition significantly increased soil peroxidase activity but decreased soil β-1,4-glucosidase activity. In broadleaf forests, N addition increased soil dissolved organic carbon (DOC) concentration and polyphenol oxidase activity but decreased soil MBC concentration and soil pH levels. N addition also significantly increased soil microbial metabolism activity (expressed as average well color development) in pine forest and broadleaf forest soils. However, the mixed forests responded slowly to N additions and exhibited no significant response of C-utilization profiles and soil enzyme activities. Principal component analysis of C-utilization data separated microbial communities with respect to N addition and forest successional stage. In addition, microbial C utilization was driven by soil pH levels. Although enzyme activities were correlated with soil MBC and microbial biomass nitrogen concentrations, stepwise regression results indicated that soil total carbon contents that were integrated with soil pH levels were key integrators of soil enzyme activities. Our results suggest that soil acidification due to N addition increased soil bacterial C utilization and enzyme activities.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".