Mowing increased community stability in semiarid grasslands more than either fencing or grazing
Bibliographic record
Abstract
A substantial body of empirical evidence suggests that anthropogenic disturbance can affect the structure and function of grassland ecosystems. Despite this, few studies have elucidated the mechanisms through which grazing and mowing, the two most widespread land management practices, affect the stability of natural grassland communities. In this study, we draw upon 9 years of field data from natural grasslands in northern China to investigate the effects of gazing and mowing on community stability, specifically focusing on community aboveground net primary productivity (ANPP) and dominance, which are two major biodiversity mechanisms known to characterize community fluctuations. We found that both grazing and mowing reduced ANPP in comparison to areas enclosed by fencing. Grazing reduced community stability by increasing the likelihood of single-species dominance and decreasing the relative proportion of nondominant species. In contrast, mowing reduced the productivity of the dominant species but increased the productivity of nondominant species. As a consequence, mowing improved the overall community stability by increasing the stability of nondominant species. Our study provides novel insight into understanding of the relationship between community species fluctuation-stability, with implications for ecological research and ecosystem management in natural grasslands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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) | 0.001 | 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 teacher head, 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".