Experimental manipulation of perceived predation risk alters survival, cause of death, and demographic patterns in juvenile snowshoe hares
Bibliographic record
Abstract
Perceived predation risk alters prey behaviour and physiology, but few studies have examined downstream consequences on prey demography in wild populations. Perceived predation risk could alter adult reproductive performance via reduced investment in offspring quality and post-birth care. We manipulated perceived predation risk in snowshoe hare ( Lepus americanus Erxleben, 1777) by exposing pregnant mothers to chases by a domestic dog ( Canis familiaris Linnaeus, 1758). Litter size was comparable between risk-augmented and control groups, but treated females had more stillbirths and gave birth to leverets of lower body condition. Leverets from risk-augmented females had 88% higher 40-day mortality rate. Maternally preventable causes of death like starvation or predation by red squirrel ( Tamiasciurus hudsonicus (Erxleben, 1777)) caused this difference, particularly during the nursing period. Risk-augmented mothers were always more active than controls, but the difference was greatest during the nursing period. We found that perceived predation risk reduces maternal productivity pre- and post-partum, implying downstream consequences to populations. Because our treatment ended before parturition, we can link offspring performance such as survival and behaviour specifically to maternal life-history trade-offs, which has not been shown in a wild mammal.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".