Snow hardness impacts intranivean locomotion of arctic small mammals
Bibliographic record
Abstract
Abstract Fossorial locomotion is often considered as the most energetically costly of all terrestrial locomotion. Small arctic rodents, such as lemmings, dig tunnels not only in the soil but also through the snowpack, which is present for over 8 months of the year. Lemmings typically dig in the softest snow layer called the depth hoar but with climate change, melt‐freeze and rain‐on‐snow (ROS) events are expected to increase in the Arctic, leading to a higher frequency of hardened snowpacks. We assessed the impacts of snow hardness on the locomotion of two lemming species showing different morphological adaptations for digging. We hypothesized that an increase in snow hardness would (1) decrease lemming performance and (2) increase their effort while digging, but those responses would differ between lemming species. We exposed four brown lemmings ( Lemmus trimucronatus ) and three collared lemmings ( Dicrostonyx groenlandicus ) to snow of different hardness (soft, hard, and ROS) during 30‐min trials ( n = 63 trials) in a cold room and filmed their behavior. We found that the digging speed and tunnel length of both species decreased with snow hardness and density, underlining the critical role of snow properties in affecting lemming digging performance. During the ROS trials, time spent digging by lemmings increased considerably and they also started using their incisors to help break the hard snow, validating our second hypothesis. Overall, digging performance was higher in collared lemmings, the species showing more morphological adaptations to digging, than in brown lemmings. We conclude that the digging performance of lemming is highly dependent on snowpack hardness and that the anticipated increase in ROS events may pose a critical energetic challenge for arctic rodent populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.027 | 0.001 |
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; both teacher heads agree on what is shown here.
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".