Sex differences in the drivers of reproductive skew in a cooperative breeder
Bibliographic record
Abstract
Many cooperatively breeding societies are characterized by high reproductive skew, such that some socially dominant individuals breed, while socially subordinate individuals provide help. Inbreeding avoidance serves as a source of reproductive skew in many high-skew societies, but few empirical studies have examined sources of skew operating alongside inbreeding avoidance or compared individual attempts to reproduce (reproductive competition) with individual reproductive success. Here, we use long-term genetic and observational data to examine factors affecting reproductive skew in the high-skew cooperatively breeding southern pied babbler (Turdoides bicolor). When subordinates can breed, skew remains high, suggesting factors additional to inbreeding avoidance drive skew. Subordinate females are more likely to compete to breed when older or when ecological constraints on dispersal are high, but heavy subordinate females are more likely to successfully breed. Subordinate males are more likely to compete when they are older, during high ecological constraints, or when they are related to the dominant male, but only the presence of within-group unrelated subordinate females predicts subordinate male breeding success. Reproductive skew is not driven by reproductive effort, but by forces such as intrinsic physical limitations and intrasexual conflict (for females) or female mate choice, male mate-guarding and potentially reproductive restraint (for males). Ecological conditions or "outside options" affect the occurrence of reproductive conflict, supporting predictions of recent synthetic skew models. Inbreeding avoidance together with competition for access to reproduction may generate high skew in animal societies, and disparate processes may be operating to maintain male vs. female reproductive skew in the same species.
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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) | 0.002 | 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".