Ecology and Neurobiology of Fear in Free-Living Wildlife
Bibliographic record
Abstract
The ecology of fear concerns the population-, community-, and ecosystem-level consequences of the behavioral interactions between predators and prey, i.e., the aggregate impacts of individual responses to life-threatening events. We review new experiments demonstrating that fear itself is powerful enough to affect the population growth rate in free-living wild birds and mammals, and fear of large carnivores—or the human super predator—can cause trophic cascades affecting plant and invertebrate abundance. Life-threatening events like escaping a predator can have enduring, even lifelong, effects on the brain, and new interdisciplinary research on the neurobiology of fear in wild animals is both providing insights into post-traumatic stress (PTSD) and reinforcing the likely commonality of population- and community-level effects of fear in nature. Failing to consider fear thus risks dramatically underestimating the total impact predators can have on prey populations and the critical role predator-prey interactions can play in shaping ecosystems.
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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 | 0.001 | 0.001 |
| 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".