Can plant functional traits explain shifts in community composition in a changing Arctic?
Bibliographic record
Abstract
The Arctic is warming twice as fast as the rest of the globe. Graminoid, deciduous shrub, and evergreen shrub cover has increased in some regions, but not others. To better understand why plant responses vary across regions, we compared change in plant cover over time with nine functional traits of 12 dominant species in three regions of northern Alaska (Utqiaġvik, Atqasuk, and Toolik Lake). Cover was measured three times from 2008 to 2018. Repeated-measures analysis of variance (ANOVA) found that one species — Carex aquatilis — showed significant change in cover over time, increasing by 12.7% at Atqasuk. Canonical correspondence analysis suggested a relationship between shifts in species cover and traits, but Pearson and Spearman rank correlations did not find a significant trend for any trait when analyzed individually. Investigation of community-weighted means (CWMs) for each trait revealed no significant changes over time for any trait in any region. By comparison, estimated ecosystem values for several traits important to ecosystem functioning showed consistent increases over time in two regions (Utqiaġvik and Atqasuk). Our results indicate that vascular plant community composition and function have remained consistent over time; however, documented increases in total plant cover have important implications for ecosystem functioning.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 teacher head, 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".