The cold shoulder: free-ranging snowshoe hares maintain a low cost of living in cold climates
Bibliographic record
Abstract
The hypothesis that cold air temperatures (Ta) constrain the metabolic diversity of high-latitude endotherms is based on the observation among birds and mammals that mean field metabolic rate (FMR) increases, whereas the variability of FMR decreases, from the warm tropics to the cold poles. However, there is a paucity of FMR measurements from above 60° latitude and below 0 °C. We measured the daily energy expenditure of a high-latitude population of free-ranging snowshoe hares ( Lepus americanus Erxleben, 1777) in Yukon, Canada, in winter (Ta-mean= –16.4 °C) and in autumn (Ta-mean = 0.5 °C). Doubly labelled water measures of FMR were approximately 20% lower in winter than in autumn, and were a similar, low multiple of resting metabolic rate in both seasons (2.04 and 1.94, respectively). The mass-corrected FMR of snowshoe hares in winter was only half the value predicted by extrapolating the relationship between FMR and Ta > 0 to –16.4 °C. These results contribute to an emerging pattern of a reversal in the relationship between FMR and Tain free-ranging mammals from negative above 0 °C to positive below 0 °C. We refer to the positive, low Taportion of this relationship as the cold shoulder, and suggest that it may reflect the general necessity for free-ranging mammals to use behavioural and (or) physiological means to conserve energy during long winters when cold conditions coincide with resource scarcity.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".