Genome divergence during evolutionary diversification as revealed in replicate lake–stream stickleback population pairs
Bibliographic record
Abstract
Evolutionary diversification is often initiated by adaptive divergence between populations occupying ecologically distinct environments while still exchanging genes. The genetic foundations of this divergence process are largely unknown and are here explored through genome scans in multiple independent lake-stream population pairs of threespine stickleback. We find that across the pairs, overall genomic divergence is associated with the magnitude of divergence in phenotypes known to be under divergent selection. Along this same axis of increasing diversification, genomic divergence becomes increasingly biased towards the centre of chromosomes as opposed to the peripheries. We explain this pattern by within-chromosome variation in the physical extent of hitchhiking, as recombination is greatly reduced in chromosome centres. Correcting for this effect suggests that a great number of genes distributed widely across the genome are involved in the divergence into lake vs. stream habitats. Analyzing additional allopatric population pairs, however, reveals that strong divergence in some genomic regions has been driven by selection unrelated to lake-stream ecology. Our study highlights a major contribution of large-scale variation in recombination rate to generating heterogeneous genomic divergence and indicates that elucidating the genetic basis of adaptive divergence might be more challenging than currently recognized.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".