Elevation gradient modulates the effects of herbaceous encroachment on the long-term growth trends and climate sensitivity of <i>Rhododendron aureum</i> in alpine tundra
Bibliographic record
Abstract
Abstract As global warming drives plant upward migration, the alpine tundra of Changbai Mountain is experiencing encroachment by Deyeuxia angustifolia (Komarov) Y. L. Chang, a low-elevation herb. However, its impact on native shrubs such as Rhododendron aureum Georgi remains unclear. Here, we analyzed the radial growth trends and climate sensitivity of R. aureum across elevations and encroachment gradients using linear and mixed-effects model methods, and explored the mediating roles of soil properties and plant traits. Our study revealed that R. aureum exhibited stronger positive long-term growth trend at higher elevations compared to lower elevations. Mild and moderate encroachment of D. angustifolia enhanced the positive growth trend of R. aureum, especially at the low elevations. Moreover, R. aureum showed weak climate sensitivity at mid-elevation but stronger responses to winter temperatures at low elevation and to spring–summer temperatures and precipitation at high elevation. D. angustifolia encroachment further intensified this sensitivity, characterized by stronger negative responses to spring, autumn and winter temperatures but positive responses to summer temperatures and autumn precipitation. Overall, elevation primarily influenced R. aureum growth and its sensitivity to precipitation through soil conditions and plant size traits, while soil conditions and leaf economic traits influence temperature sensitivity. These findings advance understanding of alpine vegetation dynamics and contribute to ecosystem conservation under climate change.
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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".