Avoiding and escaping predators: Movement tortuosity of snowshoe hares in risky habitats
Bibliographic record
Abstract
Prey animals use different strategies to avoid detection by predators and to flee once detected. Key issues are what aspects of movement prey change in response to predation risk and how differences in habitat affect escape movements. We answer these questions for snowshoe hares (Lepus americanus) in Montana, using an experimental manipulation and habitats for which annual mortality rates varied more than fourfold. We examine a) whether the mortality risk of a habitat affects movement tortuosity and speed of foraging snowshoe hares and b) whether tortuosity and speed of hares fleeing from a predator (a leashed dog, Canis familiaris) differ among these forest stands. Snowshoe hares did not differ in tortuosity or speed while foraging in these stands, suggesting that other anti-predator behaviours were used. Hares fleeing from the leashed dog showed much faster and straighter movements than foraging hares, but escape trajectories were similar in all forest stands, suggesting a relatively inflexible response while fleeing. Varying tortuosity and speed are clearly part of the snowshoe hare's behavioural repertoire for escaping predation, but these attributes of movement were insensitive to the annual mortality rates in each forest stand.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".