Individual dispersal delays in a cooperative breeder: Ecological constraints, the benefits of philopatry and the social queue for dominance
Bibliographic record
Abstract
Delayed dispersal is a key step in the evolution of familial animal societies and cooperative breeding. However, no consensus has been reached on the ecological and social circumstances driving delayed dispersal. Here, we test predictions from the ecological constraints and benefits of philopatry hypotheses as well as the recently proposed dual benefits hypothesis to better understand the evolution of group-living and cooperative breeding. Furthermore, we consider how individual social circumstances within groups affect dispersal decisions. We examine 11 years of life-history information on a wild population of cooperatively breeding southern pied babblers Turdoides bicolor. We investigate the effects of ecological conditions, natal-group membership and individual social context on male and female dispersal delays, disperser survival and acquisition of dominance. Female dispersal decisions are generally unconstrained by ecological or social circumstances. In contrast, males disperse in response to relaxed ecological constraints, decreases in nepotistic tolerance or when low social rank in the queue for dominance decreases their likelihood of gaining a dominant breeding position. Early dispersal by end-of-queue males often leads to a head-of-queue subordinate position in a non-natal group, thereby increasing access to dominant breeding positions. However, males and females remaining in natal groups gain benefits of philopatry via increased survival and, for head-of-queue males, very high likelihood of acquisition of a breeding position. Overall, predictions from the dual benefits hypothesis best describe these results, while some predictions from each of the ecological constraints and benefits of philopatry hypotheses were supported. The benefits of living and working together (collective action benefits) in large stable groups are of central importance in shaping dispersal delays in southern pied babbler societies. In addition, position in the subordinate social queue for dominance is the key in determining access to reproduction, particularly for males. This research highlights the importance of considering the costs and benefits of individual social circumstances in dispersal decisions and illustrates how the dual benefits hypothesis offers new perspectives in understanding delayed dispersal.
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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 | 0.002 |
| 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) | 0.001 | 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".