Sociality and movement change through space and time: implications for anti-predator strategies in caribou
Bibliographic record
Abstract
Risk could vary temporally with predator activity levels or as animals age. While risk could vary spatially as a function of habitat complexity, where open habitats are risky because prey are more visible to predators. Prey, such as caribou ( Rangifer tarandus (Linnaeus, 1758)), attenuate predator risk by altering behaviour through space and time. We tested the “risky times” and “risky places” hypotheses to investigate how sociality can mitigate putative risk during risky times and in risky places. We predicted that caribou are social in risky times (e.g., night and calving) because their predators are primarily nocturnal and newborn calves are vulnerable. We also predicted that sociality is higher in open habitats at nighttime relative to closed habitats at nighttime to mitigate risk during a riskier time and place. We used location data collected from global positioning system collared caribou in Newfoundland to generate social networks to estimate sociality. In winter, caribou were more social at night than during the day. During the night, caribou were more social in open habitat than in forested habitat, indicating a potential trade-off between selecting open foraging habitat and the risk of predation. We illustrate that sociality mitigates risky times and risky places.
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 | 0.001 | 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) | 0.001 | 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".