Gene flow and selection interact to promote adaptive divergence in regions of low recombination
Bibliographic record
Abstract
Abstract Adaptation to new environments often occurs in the face of gene flow. Under these conditions, gene flow and recombination can impede adaptation by breaking down linkage disequilibrium between locally adapted alleles. Theory predicts that this decay can be halted or slowed if adaptive alleles are tightly linked in regions of low recombination, potentially favouring divergence and adaptive evolution in these regions over others. Here, we compiled a global genomic data set of over 1,300 individual threespine stickleback from 52 populations and compared the tendency for adaptive alleles to occur in regions of low recombination between populations that diverged with or without gene flow. In support of theory, we found that putatively adaptive alleles ( F ST and d XY outliers) tend to occur more often in regions of low recombination in populations where divergent selection and gene flow have jointly occurred. This result remained significant when we employed different genomic window sizes, controlled for the effects of mutation rate and gene density, controlled for overall genetic differentiation, varied the genetic map used to estimate recombination and used a continuous (rather than discrete) measure of geographic distance as proxy for gene flow/shared ancestry. We argue that our study provides the first statistical evidence that the interaction of gene flow and selection biases divergence toward regions of low recombination.
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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 | 0.002 |
| 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 | 0.001 | 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".