Conserved islands of divergence associated with adaptive variation in sockeye salmon are maintained by multiple mechanisms
Bibliographic record
Abstract
Local adaptation is facilitated by loci clustered in relatively few regions of the genome, termed genomic islands of divergence. The mechanisms that create and maintain these islands and how they contribute to adaptive divergence is an active research topic. Here, we use sockeye salmon as a model to investigate both the mechanisms responsible for creating islands of divergence and the patterns of differentiation at these islands. Previous research suggested that multiple islands contributed to adaptive radiation of sockeye salmon. However, the low-density genomic methods used by these studies made it difficult to fully elucidate the mechanisms responsible for islands and connect genotypes to adaptive variation. We used whole genome resequencing to genotype millions of loci to investigate patterns of genetic variation at islands and the mechanisms that potentially created them. We discovered 64 islands, including 16 clustered in four genomic regions shared between two isolated populations. Characterisation of these four regions suggested that three were likely created by structural variation, while one was created by processes not involving structural variation. All four regions were small (< 600 kb), suggesting low recombination regions do not have to span megabases to be important for adaptive divergence. Differentiation at islands was not consistently associated with established population attributes. In sum, the landscape of adaptive divergence and the mechanisms that create it are complex; this complexity likely helps to facilitate fine-scale local adaptation unique to each population.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".