Data from: Female preference for novel males constrains contemporary evolution of assortative mating in guppies
Bibliographic record
Abstract
Progress toward local adaptation is expected to be enhanced when divergent selection is multi-dimensional, because many simultaneous sources of selection can increase the total strength of selection and enhance the number of independent traits under selection. Yet, whether local adaptation ensues from multi-dimensional selection also depends on its potential to cause the build-up of reproductive barriers such as sexual signals and preference for these signals. We used replicate experimental introductions of guppies (Poecilia reticulata) in nature to test whether an abrupt and dramatic shift in multiple important ecological dimensions (at a minimum: parasitism, predation, and diet/resources) promoted the contemporary evolution of assortative mating. After 8-12 post-introduction guppy generations in the wild, we bred descendants of each population in a common-garden laboratory environment for two generations, after which we recorded the preferences of females from each population for males from each population. We found contemporary evolution of male traits (size, body condition, colour) that should influence mate choice, but no evidence for the occurrence of positive assortative preferences. That is, females in a given evolving population did not prefer males from that population over males from other populations. Instead, females tended to prefer novel males (i.e. disassortative mating), which likely acts as a mechanism preventing the evolution of reproductive isolation. Preferences for novelty may explain why many cases of local adaptation do not lead to the evolution of reproductive barriers and ecological speciation.
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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) | 0.006 | 0.001 |
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".