Additive negative effects of decadal warming and nitrogen addition on grassland community stability
Bibliographic record
Abstract
1. Much recent research has explored how global warming and increased nitrogen (N) deposition, two important components of global environmental changes, influence the structure and functioning of natural ecosystems. However, how ecosystem dynamics respond to the combination of long-term warming and N enrichment remains largely unexplored. 2. We investigated the impact of warming and N addition on the temporal stability of plant communities in a decade-long field experiment, conducted in a desert steppe in northern China, using a split-plot design with warming as the main-plot factor and N addition as the split-plot factor. 3. Long-term warming and N addition had additive, negative effects on plant community stability. A warming-induced decrease in species richness was not a significant driver of decreased community stability, which was instead driven by the decreased stability of dominant species under warming. On the other hand, a N-induced decrease in community stability was ascribed to both decreased stability of dominant and common species and decreased asynchronous population dynamics under N addition. 4. Synthesis. Our results suggest that ongoing anthropogenic environmental changes may have appreciable consequences for the stability of natural grassland functions and services while also highlighting the different mechanisms associated with the similar effects of climate warming and increased N deposition on grassland community stability.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".