Coevolution of social network structure and life history in toothed whales
Bibliographic record
Abstract
Toothed whales offer a 34 million year-long natural experiment for the evolution of complex mammalian societies. However, quantitative comparative analyses of social structure in these species are lacking. Here, we draw on existing social network analyses to compare social structure across toothed whales. We consider published measures of two social network traits across all toothed whales: modularity (Q), which captures divisions or “cliques” in a social community, and social differentiation (S), which estimates how much whales vary in their relationships – whether they associate equally with others or form special bonds with specific individuals. Combining these with a recently published database of life history traits, we applied phylogenetic multilevel models with the objective of exploring the origins of social network structure in toothed whales. We identified 98 measures of modularity and 89 measures of social differentiation from 23 toothed whale species. Social network structure was more similar among closely related species (i.e., showed strong phylogenetic signal), despite substantial intraspecific variability. Toothed whales with longer lifespans and larger bodies tended to form more modular social networks, as did those where males were proportionally larger than females. Similar, but weaker patterns were found between life history traits and social differentiation. Results from causal coevolutionary models provide preliminary evidence that social structure has been both a cause and consequence of changes in life history. Our findings reveal the correlated evolution of social network structure and life history traits in toothed whales, shedding new light on the origins of social relationships.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".