Selective sweeps on novel and introgressed variation shape mimicry loci in a butterfly adaptive radiation
Bibliographic record
Abstract
Natural selection leaves distinct signatures in the genome that can reveal the targets and history of adaptive evolution. By analysing high-coverage genome sequence data from 4 major colour pattern loci sampled from nearly 600 individuals in 53 populations, we show pervasive selection on wing patterns in the Heliconius adaptive radiation. The strongest signatures correspond to loci with the greatest phenotypic effects, consistent with visual selection by predators, and are found in colour patterns with geographically restricted distributions. These recent sweeps are similar between co-mimics and indicate colour pattern turn-over events despite strong stabilising selection. Using simulations, we compare sweep signatures expected under classic hard sweeps with those resulting from adaptive introgression, an important aspect of mimicry evolution in Heliconius butterflies. Simulated recipient populations show a distinct 'volcano' pattern with peaks of increased genetic diversity around the selected target, characteristic of sweeps of introgressed variation and consistent with diversity patterns found in some populations. Our genomic data reveal a surprisingly dynamic history of colour pattern selection and co-evolution in this adaptive radiation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".