Stark sexual display divergence among jumping spider populations in the face of gene flow
Bibliographic record
Abstract
Gene flow can inhibit evolutionary divergence by eroding genetic differences between populations. A current aim in speciation research is to identify conditions in which selection overcomes this process. We focused on a state of limited differentiation, asking whether selection enables divergence with gene flow in a set of Habronattus americanus jumping spider populations that exhibit three distinct male sexual display morphs. We found that each population is at high frequency or fixed for a single morph. These strong phenotypic differences contrast with low divergence at 210 AFLP markers, suggesting selection has driven or maintains morph divergence. Coinciding patterns of isolation by distance and 'isolation by phenotype' (i.e. increased genetic divergence among phenotypically contrasting populations) across the study area support several alternative demographic hypotheses for display divergence, each of which entails gene flow. Display-associated structure appears broadly distributed across the genome and the markers producing this pattern do not stand out from background levels of differentiation. Overall, the results suggest selection can promote stark sexual display divergence in the face of gene flow among closely related populations.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".