Using fish to understand how cities affect sexual selection before and after mating
Bibliographic record
Abstract
Urbanization transforms natural and agricultural areas into built landscapes. Such profound habitat alteration imposes strong pressure on phenotypic trait changes through processes related to natural and/or sexual selection. Evidence of how natural selection drives changes to traits in urban biota is increasing, but little is known about the role of sexual selection. In this study, we assessed the effect of urbanization on the expression and interaction of males' pre-mating traits (body size and color) and a post-mating trait (sperm load). We used a widespread invasive species, the guppy (Poecilia reticulata), which is a wellknown model for studying sexual selection, but have never been studied in urban systems for this purpose. We found that urbanization did not affect mean body size or condition, but it resulted in size-dependent reductions in the expression of orange and iridescent colors, as well as sperm load. The orange color was reduced in small urban guppies, while the iridescent colors were reduced in large urban guppies compared to non-urban guppies. The difference in sperm load was only found in large males, with lower sperm load in urban guppies. The relationship between orange color and sperm load was positive in urban guppies but negative in non-urban guppies, while the association between iridescent color and sperm load followed the opposite pattern. Our findings suggest that sexual selection on pre- and post-mating traits is weaker in urban than in non-urban systems and that interactions between such traits are context dependent. These responses can be related to the pollution and altered visual environment of urban systems and provide an opportunity to advance our understanding of the mechanisms determining adaptation in cities.
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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 | 0.000 | 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".