Is structural variation necessary to create islands of divergence in moderate gene flow species? A case study in sockeye salmon
Bibliographic record
Abstract
Local adaptation is often facilitated by loci clustered in relatively few regions of the genome, termed genomic islands of divergence. However, the mechanisms that create, mold, and maintain these islands are poorly understood. Here, we use sockeye salmon as a model species to investigate the mechanisms responsible for creating islands of divergence linked to adaptive variation. Previous research suggests that multiple islands are involved in adaptive radiation of sockeye salmon. However, these studies were based on low-density genomic methods that genotyped tens to thousands of loci, making it difficult to elucidate the mechanisms responsible for islands. We used whole genome resequencing to genotype millions of loci to investigate these mechanisms. We discovered 64 islands, 16 of which were shared between two isolated populations; these 16 islands were clustered in four genomic regions. Characterization of the shared regions suggested that three of four were likely created by chromosomal inversions, while the other was created by processes not involving structural variation. Additionally, all four regions were relatively small (< 600 kb), suggesting inversions and other low recombination regions do not have to span megabases to be important for adaptive divergence. In sum, our study demonstrates that heterogeneous selection can lead to a mosaic of islands created by different mechanisms within the same genome. Future studies should continue to investigate how gene flow, selection, and the architecture of genetic traits interact to influence the genomic landscape of adaptive divergence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".