Vegetation degradation impacts soil nutrients and enzyme activities in wet meadow on the Qinghai-Tibet Plateau
Bibliographic record
Abstract
Vegetation degradation, due to climate change and human activities, changes the biomass, vegetation species composition, and soil nutrient input sources and thus affects soil nutrient cycling and enzyme activities. However, few studies have focused on the responses of soil nutrients and enzymes to vegetation degradation in high-altitude wet meadows. In this study, we examined the effects of vegetation degradation on soil nutrients (soil organic carbon, SOC; total nitrogen, TN; total phosphorus, TP) and enzyme activities (i.e., urease, catalase, amylase) in an alpine meadow in the eastern margin of the Qinghai-Tibet Plateau. Four different levels of degradation were defined in terms of vegetation density and composition: primary wet meadow (CK), lightly degraded (LD), moderately degraded (MD), and heavily degraded (HD). Soil samples were collected at depth intervals of 0-10, 10-20, 20-40, 40-60, 60-80, and 80-100 cm to determine soil nutrient levels and enzyme activities. The results showed that SOC, TN, catalase and amylase significantly decreased with degradation level, while TP and urease increased with degradation level (P < 0.05). Soil nutrient and enzyme activity significantly decreased with soil depth (P < 0.05), and the soil nutrient and enzyme activity exhibited obvious "surface aggregation". The activities of soil urease and catalase were strongest in spring and weakest in winter. The content of TN in spring, summer, and autumn was significantly higher than observed in winter (P < 0.05). The soil TP content increased in winter. Soil amylase activity was significantly higher in summerm than in spring, autumn, and winter (P < 0.05). TP was the main limiting factor for plant growth in the Gahai wet meadow. Values of SOC and TN were positively and significantly correlated with amylase and catalase (P < 0.05), but negatively correlated with urease (P < 0.05). These results suggest the significant role that vegetation degradation and seasonal freeze-thaw cycle play in regulating enzyme activities and nutrient availability in wet meadow soil.
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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 | 0.001 | 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".