Demographic response of tundra small mammals to a snow fencing experiment
Bibliographic record
Abstract
Snow cover is a key environmental component for tundra wildlife that will be affected by climate change. Change to the snow cover may affect the population dynamics of high‐latitude small mammals, which are active throughout the winter and reproduce under the snow. We experimentally tested the hypotheses that a deeper snow cover would enhance the densities and winter reproductive rates of small mammals, but that predation by mustelids could be higher in areas of increased small mammal density. We enhanced snow cover by setting out snow fences at three sites in the Canadian Arctic (Bylot Island, Nunavut, and Herschel Island and Komakuk Beach, Yukon) over periods ranging from one to four years. Densities of winter nests were higher where snow depth was increased but spring lemming densities did not increase on the experimental areas. Lemmings probably moved from areas of deep snow, their preferred winter habitat, to summer habitat during snow melt once the advantages associated with deep snow were gone. Our treatment had no effect on signs of reproduction in winter nests, proportion of lactating females in spring, or the proportion of juveniles caught in spring, which suggests that deep snow did not enhance reproduction. Results on predation were inconsistent across sites as predation by weasels was higher on the experimental area at one site but lower at two others and was not higher in areas of winter nest aggregations. Although this experiment provided us with several new insights about the impact of snow cover on the population dynamics of tundra small mammals, it also illustrates the challenges and difficulties associated with large‐scale experiments aimed at manipulating a critical climatic factor.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".