Spring fever: collared lemmings warm up above the snow despite predation risk
Bibliographic record
Abstract
Small mammals inhabiting cold climates face high heat losses and thus, high energy demands for body temperature regulation. However, behavioral adaptations, such as seeking refuge from extreme cold in the subnivean space, can reduce the energetic cost of thermoregulation. Using automated cameras, we monitored collared lemmings (Dicrostonyx groenlandicus) at the northern limit of their range, as they surfaced from their snow burrows in spring, to assess the effect of weather variables on the occurrence of this behavior. We hypothesized that lemmings reduce the energetic cost of thermoregulation when they come to the surface of the snow in spring. As expected, the frequency of surface activity increased with air temperature, but decreased with cloud cover and wind speed. In addition, the operative temperature was higher above the snow than below, while snow profiles showed the absence of liquid water in the snowpack. These findings support that lemming surface activity in spring is a behavioral thermoregulation strategy. However, observations of several predators in the study area, combined with vigilance behavior observed in lemmings at the snow surface, stress the risks associated with such exposure on the snow. We therefore suggest that lemmings may face a trade-off between thermoregulation and predation risk. Given that data on lemming winter ecology are scarce, we also exploited this behavior to gather valuable knowledge on molting phenology and reproduction. Overall, our results provide insight into the complex trade-off between thermoregulation and other needs in small mammals inhabiting cold climates, and highlight some potential implications for arctic ecosystem dynamics based on predator-prey interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".