Group structure and kinship in beluga whale societies
Bibliographic record
Abstract
Evolutionary explanations for mammalian sociality typically center on inclusive-fitness benefits of associating and cooperating with close kin, or close maternal kin as in some whale societies, including killer and sperm whales. Their matrilineal structure has strongly influenced the thinking about social structure in less well-studied cetaceans, including beluga whales. In a cross-sectional study of group structure and kinship we found that belugas formed a limited number of distinct group types, consistently observed across populations and habitats. Certain behaviours were associated with group type, but group membership was often dynamic. MtDNA-microsatellite profiling combined with relatedness and network analysis revealed, contrary to predictions, that most social groupings were not predominantly organized around close maternal relatives. They comprised both kin and non-kin, many group members were paternal rather than maternal relatives, and unrelated adult males often traveled together. The evolutionary mechanisms that shape beluga societies are likely complex; fitness benefits may be achieved through reciprocity, mutualism and kin selection. At the largest scales these societies are communities comprising all ages and both sexes where multiple social learning pathways involving kin and non-kin can foster the emergence of cultures. We explore the implications of these findings for species management and the evolution of menopause.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".