Social factors influencing natal dispersal in male white‐faced capuchins (<i>Cebus capucinus</i>)
Bibliographic record
Abstract
White-faced capuchin males disperse from their natal group at around 4.5 years of age, but there is much variation in dispersal timing: our youngest confirmed disperser was 19 months and the oldest 11 years old. In this study, we investigate possible factors influencing dispersal decisions in this species. Between 1983 and 2010, 64 males were born into three study groups in Santa Rosa National Park, Area de Conservación Guanacaste, and Costa Rica. As of August 2010, 21 died or were presumed dead (<14 months), 13 remained natal residents, and 30 were presumed dispersers. We used backward logistic regression to identify proximate factors that predict the occurrence of male natal dispersal. The occurrence of a takeover (significant positive association) and group size (nonsignificant negative association) were included in the model. Male age, number of maternal brothers, and number of adult males were not significant predictors of natal dispersal. The resultant model correctly classified 97% of dispersed and 89% of resident natal males, for an overall success rate of 95%. The occurrence of a group takeover was the strongest predictor of male dispersal, with natal males being 18.7 times more likely to disperse in the context of a group takeover than during peaceful times. A linear regression model showed that the tenure length of a male's probable father influences the age of natal dispersal, explaining 15% of the observed variation in age. However, when our oldest disperser was removed (an outlier) this effect disappeared. Collectively, these results indicate that group instability, as evidenced by the occurrence of a takeover, shorter tenure length of a natal male's father, and smaller group size, triggers natal dispersal in this species while the converse leads to a delay. These data add to our growing evidence of the enormous impact that takeovers have on the behavioral ecology of this species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".