Seeing a Ghost? Vigilance and Its Drivers in a Predator‐free World
Bibliographic record
Abstract
Abstract Vigilance is a key to the early detection of predators, but may be costly if it impairs foraging efficiency. Hence, we would expect vigilance to be suppressed and/or counter‐selected in predator‐free environments, although this might depend on the environmental drivers influencing perceived predation risk. We studied vigilance in two populations of Sitka black‐tailed deer ( Odocoileus hemionus sitkensis ) on Haida Gwaii (Canada) which have not been exposed to predators since they colonized the study islands approx. 60 yr ago. In this context, anti‐predator behavior should not have any obvious current benefit. Moreover, its maintenance should be particularly costly in our study populations because these deer have depleted their food resources and, thus, anti‐predator behaviors should interfere with time spent searching for scarce resources. We used bait stations equipped with camera traps to assess vigilance under standardized feeding conditions. We expected to observe lower vigilance levels than those observed elsewhere in locations with predators. We investigated how vigilance varied in relation to the amount of bait, the level of visibility, and between day and night. During the day, deer spent, on average, 14% of their time in overt vigilance during foraging bouts, a level similar to, although in the lower range of, values reported at sites where predators are present. Levels of vigilance were lower at night, and decreased with increasing visibility, but not during the day. Deer were less vigilant when bait availability was high, but only when visibility was also high. We discuss why the maintenance of vigilance is here best explained by the ghosts of predators past, and how, at the temporal scale of a few generations, the ecological factors driving vigilance levels might override the absence of significant risk from large predators.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".