Foraging costs of vigilance in large mammalian herbivores
Bibliographic record
Abstract
Vigilance has been assumed to reduce food intake by taking away time from food processing. Such foraging costs of vigilance have been predicted to have profound effects on the structure of communities. Recently, however, it has been argued that mammalian herbivores might be capable of maintaining their rate of food intake despite being vigilant, because of their ability to scan the environment while chewing vegetation. We conducted behavioral observations to evaluate whether vigilance decreases the bite rate of free‐ranging female bison ( Bison bison ) in Prince Albert National Park and elk ( Cervus canadensis ) in Yellowstone National Park. Modeling of foraging processes indicated that chewing time exceeded the time that bison and elk spent searching for food, interacting with conspecifics, and scanning. Consequently, bison and elk might have been capable of maintaining their rate of food intake despite vigilance. The maintenance of intake rate would have required bison and elk to match scanning events closely with chewing bouts, but we did not detect a positive correlation between the duration of scanning bouts and the number of consecutive bites taken just before vigilance events. As a result, vigilance was costly, and as it increased, bite rate declined for both herbivore species. Scanning still overlapped partially with food handling. Indeed, we estimated that 31% of feeding time being vigilant decreased bite rate by 20% for bison and 26% for elk, whereas total absence of overlap between chewing and scanning should have reduced bite rate by 31%. While we observed that vigilance induced foraging costs, these costs were less important than traditionally assumed.
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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 | 0.002 |
| 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 | 0.001 | 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".