Foraging decisions of snowshoe hares in response to experimentally induced coat-colour mismatch
Bibliographic record
Abstract
ABSTRACT Animals may exhibit various strategies to mitigate the adverse effects of phenological mismatch. In species experiencing coat colour mismatch, the effects of lost camouflage on the susceptibility to predation may be compensated for with other antipredator traits, such as altered foraging decisions, and may further depend on the intensity of risk. We artificially simulated coat colour mismatch and predation risk in wild-caught snowshoe hares and measured their forage intake rate of black spruce browse, intraspecific selection for forage quality, i.e., % nitrogen of browse, and resulting body mass loss across different risk levels, simulated by cover or lack thereof. We found that hares did not adjust their intake rate in response to mismatch, but hares in our high-risk treatment ate significantly more than hares in our low-risk treatment. Mismatched brown hares, however, selected for more nitrogen-rich forage than their matched brown counterparts. Mismatched white hares lost 4.55% more body mass than their matched white counterparts, despite not reducing their intake rate. Hares in our high-risk treatment lost 1.29% more body mass than those in covered enclosures. We suggest that the increased selection for nitrogen-rich forage observed in brown mismatched hares may occur to mitigate the body mass loss consequences of mismatch. Similarly, the increased intake rate of hares in clear roof enclosures relative to those in opaque roof enclosures may be a compensatory behavioural response to increased body mass loss. Our results highlight the potential indirect effects of coat colour mismatch on snowshoe hares, but also the corresponding behavioural mechanisms that may partially mitigate these effects.
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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 | 0.001 |
| 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".