Living in fear: How experience shapes caribou responses to predation risk
Bibliographic record
Abstract
Abstract Wild prey can reduce predation risk by avoiding areas used by their predators. As they get older, individuals should be able to fine‐tune this avoidance based on their increased experience with predation risk. Such learning mechanisms are expected to play a key role in how individuals cope with risk during their life, particularly in altered landscapes where human disturbances have created habitat conditions distinct from those of the past. We studied the role of experience on the avoidance of risky areas by boreal caribou ( Rangifer tarandus caribou ) in a system where they are under high predation pressure from gray wolves ( Canis lupus ) and black bears ( Ursus americanus ). Using telemetry data collected on 28 caribou, 31 wolves, and 12 bears, we investigated whether caribou adjusted their level of predator avoidance with passing monitoring years, a proxy of increasing experience. We observed an increase in the avoidance of areas suitable to wolves (during two study periods) and bears (during all study periods) with passing years. Periods during which caribou did not adjust their behavior toward wolves (winter and calving) were characterized by persistent—potentially innate—avoidance. Our results suggest that, in most circumstances, caribou became more efficient at avoiding areas selected by their predators as they gained experience. Future work should attempt to demonstrate whether such tactics are heritable; if so, our results would suggest that, given time, caribou living in disturbed environments would have the potential to adapt to changing levels of risk. This would give hope for the conservation of caribou, a species at risk in Canada, provided levels of risk do not surpass the limits of their behavioral plasticity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".