Reproductive skew and female trait elaboration in a cooperatively breeding rail
Bibliographic record
Abstract
Intrasexual competition for reproduction is thought to be an important factor in the evolution of ornaments and weapons in males. However, the evolution of morphologically similar traits in females is often explained through other mechanisms, and the role of intrasexual competition in female trait elaboration has received little attention. Here, we explore the factors associated with female trait elaboration in the cooperatively breeding Pukeko (the New Zealand race of the Purple Swamphen Porphyrio porphyrio melanotus) by comparing sexual dimorphism in an ornament across two populations. Importantly, the two populations considered differ in several social factors that could affect the degree of female–female competition, and could thereby produce differential selection on elaborate female traits. Recent studies have suggested that high reproductive skew (i.e. monopolization of reproduction by dominant individuals) could influence the intensity of intrasexual competition and select for female elaboration. However, we found that sexual dimorphism was diminished and Pukeko females had more elaborate ornaments under conditions of low reproductive skew. We discuss alternative factors that could influence the degree of female–female competition, and show that reproductive skew may not always provide an accurate estimate of the scope for intrasexual competition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".