Patterns of vegetation change in Yukon: recent findings and future research in dynamic subarctic ecosystems
Bibliographic record
Abstract
In Yukon, Canada, average air temperature has increased by 2 °C over the past 50 years and, by the end of the century up to 6.9 °C of further warming is predicted, along with increased climate variability. As a result of these and other changes, vegetation communities are predicted to shift in space and composition. Changes to the vegetation assemblages across multiple ecological units or bioclimate zones will impact carbon and nutrient cycling, animal habitat, biodiversity levels, and other ecosystem processes. Yukon has a wide variety of vegetation communities, and paleoecological evidence indicates that significant vegetation changes have occurred throughout the territory in the past. No documented synthesis of changes to vegetation assemblages exists, restricting predictions of their future likelihood, abundance, and influence. Here, we review the literature of documented examples of vegetation change throughout Yukon that have occurred ( i) in different vegetation communities due to the persistent press of climate change and ( ii) after natural disturbances. Future research into all vegetation responses under ongoing climate change is warranted. We identify critical research gaps for each vegetation community and disturbance type that should be addressed to produce a more encompassing understanding of the response of Yukon bioclimate zones and vegetation communities to future warming and disturbances.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".