Foraging in groups allows collective predator detection in a mammal species without alarm calls
Bibliographic record
Abstract
Although collective detection plays a key role in determining individual survival, few studies have carefully examined the collective process of detection. We investigated collective detection in the eastern grey kangaroo (Macropus giganteus), a species that forages in temporary groups and rarely produces auditory alarm signals on detection. In experimental trials, we exposed one group member to a predation threat (a python, Morelia spilota) that its companions could only detect indirectly by observing the reaction of the detector. We videotaped these and control trials in which individuals were not exposed to the python and focal females simply used vigilance routinely. Our aims were to 1) examine whether collective detection occurred and, if so, 2) investigate the temporal pattern of the information transfer among individuals. The latencies between the focal females’ first scans and those of their 4 neighbors were shorter in the exposed than those in the control groups. The latencies between successive individuals’ scans were on average shorter in the exposed groups and at the beginning of the reaction chain, and interindividual distances acted to constrain information transfer. More individuals became vigilant in the exposed than in the control groups. Thus, detection of the snake by focal females provided information about a potential threat to other close group members and reactions to this initial detection proceeded sequentially like a domino effect. Collective detection thus is not restricted to social species that exhibit conspicuous alarm signals.
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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".