Hierarchy of fear: experimentally testing ungulate reactions to lion, African wild dog and cheetah
Bibliographic record
Abstract
Abstract Experiments have begun demonstrating that the fear (antipredator behavioral responses) large carnivores inspire in ungulates can shape ecosystem structure and function. Most such experiments have focused on the impacts of either just one large carnivore, or all as a whole, rather than the different impacts different large carnivores may have in intact multi-predator-prey systems. Experimentally testing the relative fearfulness ungulates demonstrate toward different large carnivores is a necessary first step in addressing these likely differing impacts. We tested the fearfulness ungulates demonstrated to playbacks of lion (Panthera leo), African wild dog (Lycaon pictus), cheetah (Acinonyx jubatus) or non-predator control (bird) vocalizations, in Greater Kruger National Park, South Africa. Ungulates ran most to lions, then wild dogs, and then cheetahs, demonstrating a very clear hierarchy of fear. Those that did not run looked toward the sound more on hearing large carnivores than controls, looking most on hearing lions. Notably, prey species-specific population level kill rates by each predator did not predict the patterns observed. Our results demonstrate that different large carnivores inspire different levels of fear in their ungulate prey, pointing to differing community-level impacts, which we discuss in relation to the ongoing worldwide decline and loss of large carnivores.
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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) | 0.001 | 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 | 0.001 |
| 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".