Effects of Warming, Nitrogen and Grazing on Plant Functional Traits Differ Between Alpine and Sub‐Alpine Grasslands
Bibliographic record
Abstract
ABSTRACT Questions Alpine grasslands are affected by a range of global change drivers, including land‐use change, climate warming and pollution. How these drivers interact and affect plant functional communities is poorly understood. We used plant functional traits to test the single and interactive effects of warming, nitrogen addition and grazing on alpine grassland communities and assessed the importance of intraspecific trait variation. Location Alpine and sub‐alpine grasslands in western Norway. Methods For three years, we applied global change treatments to test the effects of warming with nitrogen addition, and warming with grazing at an alpine and sub‐alpine plant community. We measured six plant functional traits related to plant size and leaf economics, including intraspecific trait variation. Results Our results show that warming and nitrogen addition shifted size‐related traits in plant communities towards taller plants with larger leaves, and more strongly in the alpine than in the sub‐alpine plant community. Warming also affected leaf economic traits, promoting faster traits in the alpine and slower traits in the sub‐alpine plant community. Grazing shifted communities to faster leaves (grazing tolerant ) in the sub‐alpine community and slower leaves (grazing avoidance ) in the alpine community. There were no interactive effects between the global change drivers. The relative contributions of species turnover and intraspecific trait variation to overall trait variation differed between origins of the two plant communities. Conclusions We show that these global change drivers shift alpine and sub‐alpine plant communities in different directions, likely due to differences in resource availability. Our results support the need for site‐specific management strategies in these systems.
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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) | 0.001 | 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 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".