Data from: Genetic coupling of female mate choice with polygenic ecological divergence facilitates stickleback speciation
Bibliographic record
Abstract
Ecological speciation with gene flow is widespread in nature, but it presents a conundrum: how are associations between traits under divergent natural selection and traits that contribute to assortative mating maintained? Theoretical models suggest that genetic mechanisms inhibiting free recombination between loci underlying these two types of traits (hereafter, “genetic coupling”) can facilitate speciation. Here, we perform a direct test for genetic coupling by mapping both divergent traits and female mate choice in a classic model of ecological speciation: sympatric benthic and limnetic threespine stickleback (Gasterosteus aculeatus). By measuring mate choice in F2 hybrid females, we allowed for recombination between loci underlying assortative mating and those under divergent ecological selection. In semi-natural mating arenas in which females had access to both benthic and limnetic males, we found that F2 females mated with males similar to themselves in body size and shape. In addition, we found two quantitative trait loci (QTLs) associated with female mate choice that also predicted female morphology along the benthic-limnetic trait axis. Furthermore, a polygenic genetic model that explains adaptation to contrasting benthic and limnetic feeding niches also predicted F2 female mate choice. Together, these results provide empirical evidence that genetic coupling of assortative mating with traits under divergent ecological selection helps maintain species in the face of gene flow, despite a polygenic basis for adaptation to divergent environments.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.113 | 0.021 |
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".