Assessing the relationship between female aggression and male phenotype in two sister <i>Malurus</i> fairywren species
Bibliographic record
Abstract
Abstract In a large and ever-growing number of animal species, it is now appreciated that females use colors as a visual signal in a range of social interactions, including both courtship and territorial aggression. Yet, it remains unclear whether female color phenotypes and/or aggressive behaviors are correlated with any attributes of their mate’s phenotype. For example, we might expect species in which males contribute more to parental care or territorial defense to have more colorful or aggressive females. On the other hand, within species, we might expect those females mated to higher quality males to be more colorful or aggressive than those mated to lower quality males. To begin to address these possibilities, we conducted a preliminary study in two sister taxa of fairywren (Maluridae) with distinct life-history strategies and plumage dichromatism: white-shouldered fairywrens ( Malurus alboscapulatus moretoni ) in tropical Papua New Guinea, a species in which both males and females have ornamented plumage and jointly defend territories year-round, and red-backed fairywrens ( M. melanocephalus melanocephalus ) in temperate Australia, a sexually dichromatic species with ornamented males and unornamented females. At the between species level, we predicted white-shouldered fairywren females would be more aggressive in same-sex interactions than red-backed fairywrens, as both white-shouldered males contribute to year-round territorial defense, whereas territories break-down during non-breeding in red-backed fairywrens. Further, we predicted that, within species, females mated to males of higher quality would be more aggressive in simulated same-sex encounters. Between species, female white-shouldered fairywrens were more aggressive on average than female red-backed fairywrens as predicted. Within both species, indices of male quality were not related to female aggression (although there was a non-significant tendency for more aggressive female white-shouldered fairywren to have heavier mates with longer tails). These results point to a need for additional research exploring relationships between life history, female plumage, and female aggressive behaviors in a wider range of 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".