Taller and larger: shifts in Arctic tundra leaf traits after 16 years of experimental warming
Bibliographic record
Abstract
Understanding plant trait responses to elevated temperatures in the Arctic is critical in light of recent and continuing climate change, especially because these traits act as key mechanisms in climate-vegetation feedbacks. Since 1992, we have artificially warmed three plant communities at Alexandra Fiord, Nunavut, Canada (791N). In each of the communities, we used open-top chambers (OTCs) to passively warm vegetation by 1‐21C. In the summer of 2008, we investigated the intraspecific trait responses of five key species to 16 years of continuous warming. We examined eight traits that quantify different aspects of plant performance: leaf size, specific leaf area (SLA), leaf dry matter content (LDMC), plant height, leaf carbon concentration, leaf nitrogen concentration, leaf carbon isotope discrimination (LCID), and leaf d 15 N. Long-term artificial warming affected five traits, including at least one trait in every species studied. The evergreen shrub Cassiope tetragona responded most frequently (increased leaf size and plant height/ decreased SLA, leaf carbon concentration, and LCID), followed by the deciduous shrub Salix arctica (increased leaf size and plant height/decreased SLA) and the evergreen shrub Dryas integrifolia (increased leaf size and plant height/ decreased LCID), the forb Oxyria digyna (increased leaf size and plant height), and the sedge Eriophorum angustifolium spp. triste (decreased leaf carbon concentration). Warming did not affect d 15 N, leaf nitrogen concentration, or LDMC. Overall, growth traits were more sensitive to warming than leaf chemistry traits. Notably, we found that responses to warming were sustained, even after many years of treatment. Our work suggests that tundra plants in the High Arctic will show a multifaceted response to warming, often including taller shoots with larger leaves.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".