Cyclic dynamics drive summer movement ecology of snowshoe hares (Lepus americanus)
Bibliographic record
Abstract
Animals exhibit dynamic movement and activity in response to environmental variation including changes in reproductive opportunities, predation risk, or food availability. Yet, it remains unclear which factors are primary in affecting animal movement, and whether the relative importance of these factors are consistent through time. We tracked snowshoe hares (Lepus americanus) using GPS telemetry during eight summers spanning a hare population cycle (2015–2022) in southwestern Yukon, Canada, to determine associations between environmental variation and hare movement and home range size. Hare density varied 25-fold during the study and home range size increased markedly during low hare density, especially for males. Both sexes retained similar core space use and linearity of movements, but at low densities males had greater and more variable movement rates and time spent travelling. Trail cameras revealed that annual changes in hare movement were also correlated with relative abundance of lynx (Lynx canadensis) and coyotes (Canis latrans). However, hare detection rates within a season were not closely associated with seasonal variation in predator detection. Observed differences between male and female hares in some metrics highlighted that different life histories and reproductive behavior are likely the main drivers of hare movement dynamics. Therefore, fitness rewards associated with successful mate search and reproduction appear to outweigh risks associated with increased movement, even in highly variable environments where costs of prioritizing reproduction-related activities are notably high and variable.
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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 | 0.000 |
| 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) | 0.001 | 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".