How individual, social, and ecological conditions influence dispersal decisions in male vervet monkeys
Bibliographic record
Abstract
Dispersal between social groups reduces the risk of inbreeding and can improve individuals' reproductive opportunities. However, this movement has costs, such as increased risk of predation and starvation, loss of allies and kin support, and increased aggression associated with entering the new group. Dispersal strategies, such as the timing of movement and decisions on whether to transfer alone or in parallel with a peer, involve different costs and benefits. We used demographic, behavioral, hormonal, and ecological data to examine the causes and consequences of 36 dispersal events from 29 male vervet monkeys (Chlorocebus pygerythrus) at Lake Nabugabo, Uganda. Adult males' secondary dispersal coincided with the conception season in females, and males improved their potential access to females by moving to groups with higher female-to-male sex ratios and/or by increasing their dominance rank. Males that dispersed with a peer had lower fecal glucocorticoid and androgen metabolite levels than lone dispersers. Subadult males were not more likely to engage in parallel dispersals compared to adult males. Dispersal was also used as a mechanism to avoid inbreeding, but changes in hormone levels did not seem to be a trigger of dispersal in our population. Our findings illustrate the complex individual strategies used during dispersal, how many factors can influence movement decisions, as well as the value of dominance and hormone analyses for understanding these strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".