HOW MUCH OF THE VARIATION IN ADAPTIVE DIVERGENCE CAN BE EXPLAINED BY GENE FLOW? AN EVALUATION USING LAKE-STREAM STICKLEBACK PAIRS
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Bibliographic record
Abstract
How much of the variation in adaptive divergence can be explained by gene flow? The answer to this question should objectively reveal whether gene flow generally places a substantial constraint on evolutionary diversification. We studied multiple independent lake-stream population pairs of threespine stickleback (Gasterosteus aculeatus). For each pair, we quantified adaptive divergence based on morphological traits that have a genetic basis and are subject to divergent selection. We then estimated gene flow based on variation at five unlinked microsatellite loci. We found a consistent and significant pattern for morphological divergence to be positively correlated with genetic divergence and negatively correlated with gene flow. Statistical significance and the amount of variation explained varied within and among traits: 36.1-74.1% for body depth and 11.8-51.7% for gill raker number. Variation within each trait was the result of differences among methods for estimating genetic divergence and gene flow. Variation among traits likely reflects different strengths of divergent selection. We conclude that gene flow has a substantial effect on adaptive divergence in nature but that the magnitude of this effect varies among traits. An alternative explanation is that cause and effect are reversed: adaptive divergence is instead constraining gene flow. This effect seems relatively unimportant for our system because genetic divergence and gene flow were not correlated with ecologically relevant habitat features of lakes (surface area) or streams (width, depth, flow, canopy openness).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it