Seasonal dynamics of small mammal populations: resource availability and cold exposure interact to govern abundance
Bibliographic record
Abstract
Organisms in seasonal environments respond to both resources in the summer and environmental conditions in winter. Small mammals, in particular, respond quickly to changes in their environment, with many species reliant on the thermal refuge of the subnivium in the winter. However, there has been little research exploring how resources and cold exposure drive the seasonal dynamics of small mammal populations. We studied the populations of three subnivium-specialist small mammal species in seasonally snow-covered forests in Wisconsin, USA across 5 years during summer and winter. In summer, mast availability and canopy cover governed white-footed mouse ( Peromyscus leucopus Rafinesque, 1818) populations, coarse woody debris drove short-tailed shrew ( Blarina brevicauda Say, 1823) populations, and rainfall influenced red-backed vole ( Myodes gapperi (Vigors, 1830)) abundance. Dietary analysis via stable isotopes revealed that shrews primarily consumed arthropods, and mice predominately consumed hard mast despite interannual changes in availability. In winter, white-footed mice and red-backed vole abundances were negatively related to cold exposure. Short-tailed shrew winter population was positively related to their abundances the previous summer. These small mammals responded to species-specific drivers during the growing and snow seasons. Consequently, shorter snow seasons from climate change appear to be restructuring communities by creating a less hospitable environment for winter-adapted species, likely contributing to their regional declines.
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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 | 0.001 | 0.000 |
| 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".