Microsatellite DNA Suggests that Group Size Affects Sex‐Biased Dispersal Patterns in Red Colobus Monkeys
Bibliographic record
Abstract
Dispersal is a major life history trait of social organisms influencing the behavioral and genetic structure of their groups. Unfortunately, primate dispersal is difficult to quantify, because of the rarity of these events and our inability to ascertain if individuals dispersed or died when they disappear. Socioecological models have been partially developed to understand the ecological causes of different dispersal systems and their social consequences. However, these models have yielded confusing results when applied to folivores. The folivorous red colobus monkey (Procolobus rufomitratus) in Kibale National Park, Uganda is thought to exhibit female-biased dispersal, although both sexes have been observed to disperse and there remains considerable debate over the selective pressures favoring the transfers of males and females and the causes of variation in the proportion of each sex to leave the natal group. We circumvent this problem by using microsatellite DNA data to investigate the prediction that female dispersal will be more frequent in larger groups as compared to smaller ones. The rationale for this prediction is that red colobus exhibit increased within-group competition in bigger groups, which should favor higher female dispersal rates and ultimately lower female relatedness. Genetic data from two unequally sized neighboring groups of red colobus demonstrate increased female relatedness within the smaller group, suggesting females are less likely to disperse when there is less within-group competition. We suggest that the dispersal system is mediated to some degree by scramble competition and group size. Since red colobus group sizes have increased throughout Kibale by over 50% in the last decade, these changes may have major implications for the genetic structure and ultimately the population viability of this endangered primate.
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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 | 0.000 | 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".