Influence of immediate predation risk by lions on the vigilance of prey of different body size
Bibliographic record
Abstract
The effects on vigilance behavior of environmental cues that affect perceived risk of predation have been widely measured in gregarious herbivores. How extrinsic (e.g., predator activity within certain habitats) and intrinsic (e.g., within-group competition) cues interact depends on the biology of the prey species. However, very little is known about the impact of the actual presence of the predator in the vicinity on fine scale prey vigilance behavior. For this study, we monitored the vigilance of plains zebra (Equus quagga) and impala (Aepyceros melampus) in and around Hwange National Park, Zimbabwe. We assessed how the presence of radio-collared lions (Panthera leo) affected the vigilance of their prey. To evaluate the factors affecting vigilance behavior, we measured routine and intense vigilance. Routine vigilance can be conducted while chewing, although during intense vigilance chewing is halted and thus imposes foraging costs as food processing is delayed. As the most acute form of vigilance, we predicted that the presence of lions would lead to an increase in intense vigilance in both species. We found this to be the case for zebra, a key prey species for lions, while impala adjusted their intense vigilance to risk cues less specific to the presence of lions. Potential predation risk posed by lions in the immediate vicinity differs not only between species but also for a given species in different contexts. Our results also reveal how other environmental risk indicators influence the structure of vigilance behavior of large prey species in a manner that reflects their respective ecologies.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".