Many eyes or many ewes: vigilance tactics in female bighorn sheep <i>Ovis canadensis</i> vary according to reproductive status
Bibliographic record
Abstract
In gregarious animals, there is usually a negative relationship between individual vigilance and group size. This effect of group size is generally explained by increasing probability of predator detection (the many‐eyes hypothesis) and by the dilution of risk occurring in larger groups. Few studies have attempted to examine the specific implications of either hypothesis on the expected vigilance pattern of an animal. Here we examine whether reproductive status affects vigilance patterns in bighorn sheep Ovis canadensis ewes. We also test whether the observed vigilance patterns are consistent with predictions from dilution or detection models of vigilance. Although vigilance decreased with increasing group size, vigilance tactics differed between barren and lactating females. Lactating ewes relied solely on predator detection. In contrast, barren ewes benefited from both detection and dilution effects when group size increased and adjusted vigilance effort according to the proportion of lactating ewes in their group. It is generally assumed that gregariousness increases safety. Here we further show that reproductive status influenced how animals reduce predation risk and that some individuals take advantage of the vigilance effort provided by others.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".