Long-term monitoring reveals topographical features and vegetation that explain winter habitat use of an Arctic rodent
Bibliographic record
Abstract
The quality of wintering habitats, such as depth of snow cover, plays a key role in sustaining population dynamics of Arctic lemmings. However, few studies so far investigated habitat use during the Arctic winter. Here, we used a unique long-term time series to test whether lemmings are associated with topographical and vegetational habitat features for their wintering sites. We examined yearly numbers and distribution of 22 769 winter nests of the collared lemming Dicrostonyx groenlandicus (Traill, 1823) from an ongoing long-term research on Traill Island, Northeast Greenland, collected between 1989 and 2019, and correlated this information with data on dominant vegetation types, elevation, and slope. We found that the number of lemming nests was highest in areas with a high proportion of Dryas heath, but was also correlated with other vegetation types, suggesting some flexibility in resource use of wintering lemmings. Furthermore, lemmings showed a higher use for sloped terrain, probably as it enhances the formation of deep snow drifts, which increases the insulative characteristics of the snowpack and protection from predators. With global warming, prime lemming winter habitats may become scarce through alteration of snow physical properties, potentially resulting in negative consequence for the whole community of terrestrial vertebrates.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".