Disparate social structures are underpinned by distinct social rules across a primate radiation
Bibliographic record
Abstract
ABSTRACT Over six decades of research on wild baboons and their close relatives (collectively, the African papionins) have uncovered substantial variation in their behavior and social systems. While most papionins form discrete social groups (single-level societies), a few others form small social units that are nested within larger supergroups (multi-level societies). These two systems are generally thought to be qualitatively distinct, but data from wild populations increasingly suggest that there may be areas of overlap. To quantify this potential gradient in social structure, a more systematic, comparative analysis is needed. Here, we constructed a database of behavioral and demographic records spanning 135 group-years, 28 social groups, 13 long-term field studies, and 11 species to quantify variation in grooming network structure, and identify the individual and dyadic properties (e.g., kinship and social status effects) that underlie this variation. Consistent with accumulating observations in the field, the single-level species could be divided into two categories: cohesive and cliquish . Cohesive single-level networks were dense, kin-biased, and moderately rank-structured, while cliquish single-level networks were more differentiated, slightly more kin-biased, and strongly rank-structured. As expected, multi-level networks were very modular and shaped by females’ ties to specific dominant males but varied in their kin biases. Taken together, these data suggest that (i) kin and rank biases are widespread but vary in their strength; (ii) male-centered subgroups are exclusive to multi-level systems; and (iii) increases in network modularity can emerge in response to heightened nepotism and male-centered clustering. SIGNIFICANCE STATEMENT What forces explain variation in primate societies? While kinship and dominance shape the social lives of many of our close relatives, it is unclear how their effects differ across species. Using a new database comprising decades of field research, we found that baboons and their close relatives fell into three general patterns: one in which groups were cohesive, kin-biased, and moderately rank-biased, another in which groups were more cliquish and nepotistic, and a third in which groups were divided into clusters centered on dominant males. Distinct primate societies may thus reflect differences in the strength of females’ nepotistic biases and the degree of males’ social influence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".