Geographic variation in winter adaptations of snowshoe hares (<i>Lepus</i> <i>americanus</i>)
Bibliographic record
Abstract
Understanding adaptations of nonhibernating northern endotherms to cope with extreme cold is important because climate-induced changes in winter temperatures and snow cover are predicted to impact these species the most. We compared winter pelage characteristics and heat production of snowshoe hares (Lepus americanus Erxleben, 1777) on the southern edge of their range, in Pennsylvania (USA), to a northern population, in the Yukon (Canada), to investigate how hares might respond to changing environmental conditions. We also investigated how hares in Pennsylvania altered movement rates and resting spot selection to cope with variable winter temperatures. Hares from Pennsylvania had shorter, less dense, and less white winter coats than their northern counterparts, suggesting lower coat insulation. Hares in the southern population had lower pelage temperatures, indicating that they produced less heat than those in the northern population. In addition, hares in Pennsylvania did not select for resting spots that offered thermal advantages, but selected locations offering visual obstruction from predators. Movement rates were associated with ambient temperature, with the smallest movements occurring at the lower and upper range of observed ambient temperatures. Our results indicate that snowshoe hares may be able to adapt to future climate conditions via changes in pelage characteristics, metabolism, and behavior.
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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 | 0.000 |
| 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 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".