Context-dependent group size: effects of population density, habitat, and season
Bibliographic record
Abstract
Abstract Group size can vary in relation to population density, habitat, and season. Habitat and season may also interact with population density and affect group size through varying foraging benefits of social aggregation in different ecological contexts. We tested the hypothesis that group size varies across ecological contexts, including population density, habitat type, and season, for woodland caribou (Rangifer tarandus) in ten herds over 25 years in Newfoundland, Canada. We predicted that group size would increase as a function of population density. Based on the foraging benefits of social aggregation, we predicted larger groups as habitat openness increased because open areas tend to have higher quality foraging resources. We predicted larger groups during winter when foraging resources are covered in snow because caribou and other social animals exploit social information about the location of foraging resources. In contrast to our prediction, group size decreased as a function of population density. In support of our prediction, group size was larger in winter than calving and summer, and we found that group size increased with habitat openness in some, but not all, cases. Patterns of animal grouping are context-dependent and the additive effect of different ecological contexts on variation in group size informs our understanding of the implicit trade-offs between competition, predation risk, and profitability of forage.
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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".