Emergent Rainy Winter Warm Spells May Promote Boreal Predator Expansion into the Arctic
Bibliographic record
Abstract
Climate change has been characterized as the most serious threat to Arctic biodiversity. In addition to gradual changes such as climate warming, extreme weather events, such as melting temperatures in winter and rain on snow, can have profound consequences for ecosystems. Rain-on-snow events lead to the formation of ice layers in the snow pack, which can restrict access to forage plants and cause crashes of herbivore populations. These direct impacts can have cascading effects on other ecosystem components, often mediated by trophic interactions. Here we document how heavy rain in early winter, leading to the formation of a thick layer of ice, was associated with dramatic mortality of domestic reindeer on Yamal Peninsula, Russia. In the subsequent summer, breeding of two boreal generalist predators, red fox and Hooded Crow, was recorded for the first time in a monitoring area in the Low Arctic tundra of this region. We suggest that the resource pulse created by the abnormally high reindeer mortality and abundance of carrion may have facilitated these breeding events north of the known breeding range of the two species. Our observations provide an example of how specific emergent weather events may indirectly pave the way for more abrupt, although possibly temporary, species range changes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".