Gene Flow Constrains and Facilitates Genetically Based Divergence in Quantitative Traits
Bibliographic record
Abstract
Theory predicts that gene flow will decrease phenotypic differences among populations. Correlational studies have in some cases documented constraining effects of gene flow on phenotypic divergence and/or have also provided evidence for local differentiation despite high gene flow. However, correlative studies are unable to evaluate how gene flow affects genetically based phenotypic divergence or the extent to which gene flow constrains adaptive divergence. Translocation experiments using Trinidadian guppies provided an opportunity to test the effects of new gene flow on quantitative traits in native recipient populations. We measured a suite of traits in guppies reared in common garden environments before and multiple generations following gene flow from guppies that originated from a different environment. We interpreted our results in light of a priori predictions based on evolutionary theory and extensive background information about guppies and our focal populations. Although we could not include a spatiotemporal control that would allow us to be certain that the observed changes were directly caused by gene flow, we found that post-gene flow populations showed genetically based shifts in most traits. Whether traits shifted in predicted adaptive directions or whether they became more or less similar to the source population depended on the trait and initial conditions of the population. Our study provided a rare opportunity to test how recent gene flow affects genetically based changes in traits with known adaptive significance, and our results attest to the complex interactions between gene flow and selection.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".