Does predation risk cause snowshoe hares to modify their diets?
Bibliographic record
Abstract
Snowshoe hares (Lepus americanus) undergo a 10-year population cycle with several years of low densities. Several authors have suggested that snowshoe hares modify their foraging behaviour to reduce predation risk during the low phase, resulting in protein-poor diets and poor body condition. We test that idea by using a factorial manipulation of food supplementation and predator reduction and by examining the species composition, browse size, and nutritional quality of snowshoe hare diets during 3 years of low snowshoe hare abundance in southwestern Yukon. Our results negate the hypothesis that snowshoe hares change their diets in response to mammalian predators during the cyclic low phase. Snowshoe hares on the different treatments had diets that differed in species composition and twig sizes, but protected hares did not have higher protein diets than unprotected hares. Snowshoe hares with access to supplemental food ate more fibrous and lower protein natural browse than unfed hares, showing that they did not choose diets primarily for protein content. Instead, snowshoe hares converted a wide range of forage availabilities into similar intakes of protein and fibre, despite variation in predator presence. Our results suggest that snowshoe hares select their diets to balance the protein and fibre contents. Although sublethal effects of predators may influence cyclic dynamics, our results show that such a feedback does not occur via a nutritional mechanism, counter to previous suggestions.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".