Fear of large carnivores is tied to ungulate habitat use: evidence from a bifactorial experiment
Bibliographic record
Abstract
The fear large carnivores inspire in large ungulates has been argued to have cascading effects down food webs. However, a direct link between ungulate habitat use and their fear of large carnivores has not been experimentally tested. To fill this critical gap, we conducted a bi-factorial experiment in an African savanna. We removed shrub cover and broadcast large carnivore vocalizations (leopard, hyena, dog) or non-threatening control vocalizations in both experimentally cleared and shrubby control sites. We recorded the proactive (frequency of visitation) and reactive (fleeing or vigilance) responses of multiple prey (impala, warthog, nyala and bushbuck). Critically, we found a significant proactive-reactive interaction. Ungulates were 47% more likely to run after hearing a predator vocalization in shrubby control sites than experimental clearings, demonstrating that ungulates perceived less fear from large carnivores in open habitat (clearings). Consistent with this finding, ungulates visited clearings 2.4 times more often than shrubby control sites and visited shrubby control sites less often at night, when large carnivores are most active. Combined with results from previous experiments demonstrating that the disproportionate use of available habitats by large ungulates can alter ecosystem properties, our experiment provides critical evidence that the fear large carnivores inspire in large ungulates can cause trophic cascades.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".