Trade-offs between sight lines and escape habitat determine spatial strategies of risk management by a keystone herbivore
Bibliographic record
Abstract
Prey individuals possess four basic strategies to manage predation risk while foraging: time allocation, space use, apprehension, and foraging tenacity. But there are no direct tests of theory detailing how spatial strategies change and covary from fine to coarse scales of environmental variability. We address this shortcoming with experiments that estimated space use and vigilance of snowshoe hares while we measured foraging tenacity in artificial resource patches placed in risky open versus safe alder habitat. Hares employed only two of eight a priori options to manage risk. Hares increased vigilance and reduced foraging in open areas as the distance from cover increased. Hares did not differentiate between open and alder habitats, increase vigilance at the coarse-grained scale, or reduce vigilance and foraging tenacity under supplemental cover. Hares were more vigilant in the putatively safe alder than in the purportedly risky open habitat. These apparently paradoxical results appear to reflect a trade-off between the benefit of alder as escape habitat and the cost of obscured sight lines that reduce predator detection. The trade-off also appears to equalize safety between habitats at small scales and suggests that common-sense predictions detailing how prey reduce risk may make no sense at all.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".