Intraspecific variation in Artiodactyla social organisation: A Bayesian phylogenetic multilevel analysis of detailed population-level data
Bibliographic record
Abstract
Abstract Understanding inter -specific variation in social systems is a major goal of behavioural ecology. Previous comparative studies of mammalian social organisation produced inconsistent results, possibly because they ignored intra -specific variation in social organisation (IVSO). The Artiodactyla have been the focus of many comparative studies as they occupy a wide diversity of habitats and exhibit large variation in life history patterns as well as other potential correlates of social organisation. Here we present the first systematic data on IVSO among Artiodactyla, infer their ancestral social organisation, and test whether habitat, sexual dimorphism, seasonal breeding, and body size predict inter- and intraspecific variation in social organisation. We found data on social organisation for 110 of 226 artiodactyl species, of which 74.5% showed IVSO. Using Bayesian phylogenetic multilevel models, the ancestral artiodactyl population was predicted to have a variable social organisation with significantly higher probability (0.77, 95% CI 0.29-1.00) than any non-variable form (i.e. solitary, pair-living, group-living). Greater sexual dimorphism and smaller body size both predicted more IVSO; smaller body size also predicted a higher likelihood of pair-living. Our results challenge the long-held assumption that ancestral Artiodactyla were pair-living and strongly imply that taking IVSO into account is crucial for understanding mammalian social evolution.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".