Not so scary after all? Decoding the landscape of fear through hormonal responses to risky times and places
Bibliographic record
Abstract
Abstract Prey must balance the energetic benefits of foraging with avoiding predation risk. This risk-reward trade-off, a cornerstone of behavioural ecology, hinges not only on realized predation risk but also on how prey perceive that risk. We often assume energetically rewarding habitats must be inherently risky because prey often increase their vigilance in these habitats or avoid them altogether. However, our assumption that these antipredator behaviours reflect perceived risk frequently goes untested. We used non-behavioural data to test our assumptions about which habitats prey perceive as risky by pairing observations of habitat use of elk ( Cervus canadensis ) with their physiological responses measured from faecal hormones: glucocorticoids (GC), which reflect stress from perceived risk and hunger, and triiodothyronine (T3), which increases with energy intake. Elk had lower GC and T3 in the forest, a putatively safer and poorer foraging habitat than cropland, where they produced more T3, indicating foraging. Surprisingly, GC levels were consistent in cropland, even during the daytime when human activity—and putative risk—peaked. This lack of risk responsiveness highlights that physiological responses are a nuanced integration of perceived risk and reward rather than a guaranteed outcome of habitat use. Our study challenges the assumption that high-reward habitats are inherently risky, and that safer habitats limit energy intake, revealing that the assumptions we make about habitats from a behavioural lens may not always be the reality for prey.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".