Long‐Term Alpine Plant Responses to Global Change Drivers Depend on Functional Traits
Bibliographic record
Abstract
Forecasting plant responses under global change is a critical but challenging endeavour. Despite seemingly idiosyncratic responses of species to global change, greater generalisation of 'winners' and 'losers' may emerge from considering how species functional traits influence responses and how these responses scale to the community level. Here, we synthesised six long-term global change experiments combined with locally measured functional traits. We quantified the change in abundance and probability of establishment through time for 70 alpine plant species and then assessed if leaf and stature traits were predictive of species and community responses across nitrogen addition, snow addition and warming treatments. Overall, we found that plants with more resource-acquisitive trait strategies increased in abundance but each global change factor was related to different functional strategies. Nitrogen addition favoured species with lower leaf nitrogen, snow addition favoured species with cheaply constructed leaves and warming showed few consistent trends. Community-weighted mean changes in trait values in response to nitrogen addition, snow addition and warming were often different from species-specific trait effects on abundance and establishment, reflecting in part the responses and traits of dominant species. Together, these results highlight that the effects of traits can differ by scale and response of interest.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.010 |
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; both teacher heads agree on what is shown here.
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".