Snow physical properties may be a significant determinant of lemming population dynamics in the high Arctic
Bibliographic record
Abstract
Cyclic population fluctuations are common in boreal and Arctic species but the causes of these cycles are still debated today. Among these species, lemmings are Arctic rodents that live and reproduce under the snow and whose large cyclical population fluctuations in the high Arctic impact the whole tundra food web. We explore, using lemming population data and snow modeling, whether the hardness of the basal layer of the snowpack, determined by rain-on-snow events (ROS) and wind storms in autumn, can affect brown lemming population dynamics in the Canadian high Arctic. Using a 7-year dataset collected on Bylot Island, Nunavut, Canada over the period 2003–2014, we demonstrate that liquid water input to snow is strongly inversely related with winter population growth (R2 ≥ 0.62) and to a lesser extent to lemming summer densities and winter nest densities (R2 = 0.29–0.39). ROS in autumn can therefore influence the amplitude of brown lemming population fluctuations. Increase in ROS events with climate warming should strongly impact the populations of lemmings and consequently those of the many predators that depend upon them. Snow conditions may be a key factor influencing the cyclic dynamics of Arctic animal populations.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".