Friends because of foes: synchronous movement within predator–prey domains
Bibliographic record
Abstract
For prey, movement synchrony represents a potent antipredator strategy. Prey, however, must balance the costs and benefits of using conspecifics to mediate risk. Thus, the emergent patterns of risk-driven sociality depend on variation in space and in the predators and prey themselves. We applied the concept of predator-prey habitat domain, the space in which animals acquire food resources, to test the conditions under which individuals synchronize their movements relative to predator and prey habitat domains. We tested the response of movement synchrony of prey to predator-prey domains in two populations of ungulates that vary in their gregariousness and predator community: (i) elk, which are preyed on by wolves; and (ii) caribou, which are preyed on by coyotes and black bears. Prey in both communities responded to cursorial predators by increasing synchrony during seasons of greater predation pressure. Elk moved more synchronously in the wolf habitat domain during winter and caribou moved more synchronously in the coyote habitat domains during spring. In the winter, caribou increased movement synchrony when coyote and caribou domains overlapped. By integrating habitat domains with movement ecology, we provide a compelling argument for social behaviours and collective movement as an antipredator response. This article is part of the theme issue 'The spatial-social interface: A theoretical and empirical integration'.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".