The effects of grazing on foliar trait diversity and niche differentiation in Tibetan alpine meadows
Bibliographic record
Abstract
Niche differentiation arising in functional trait diversity is expected to increase the potential for species coexistence, but empirical evidence for these relationships is sparse. We test whether grazing increases the functional diversity of leaf traits and niche differentiation in phosphorus limited Tibetan alpine meadows. We measured five traits in the leaf economic spectrum (LES; LC, leaf carbon concentration; LN, leaf nitrogen concentration; LP, leaf phosphorus concentration; SLA, specific leaf area; and LDMC, leaf dry matter content) for all species occurring in grazed and ungrazed plots at each of five sites. By comparing indicators of the fundamental and realized niches of co‐occurring plants in both grazed and ungrazed plots, we quantified a grazing‐mediated competitive effect on trait divergence and convergence. This trait response reflects the relative importance of niche differentiation and competitive exclusion in response to grazing. We found that while grazing induced LP divergence, both LC and LN tended to converge under grazing. Grazing had no effect on either SLA or LDMC diversity. When all five traits are considered together as a functionally integrated suite (LES hypervolume), there is no evidence for either divergence or convergence in response to grazing. Although grazing promotes functionally relevant diversity in LP that enables niche differentiation in competition for scarce soil available P, these results suggest that coordinated shifts in other LES traits sustain effective overall foliar function despite shifts in LP.
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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 | 0.001 | 0.001 |
| 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".