GENETIC SIGNATURE OF ADAPTIVE PEAK SHIFT IN THREESPINE STICKLEBACK
Bibliographic record
Abstract
Transition of an evolving population to a new adaptive optimum is predicted to leave a signature in the distribution of effect sizes of fixed mutations. If they affect many traits (are pleiotropic), large effect mutations should contribute more when a population evolves to a farther adaptive peak than to a nearer peak. We tested this prediction in wild threespine stickleback fish (Gasterosteus aculeatus) by comparing the estimated frequency of large effect genetic changes underlying evolution as the same ancestor adapted to two lake types since the end of the ice age. A higher frequency of large effect genetic changes (quantitative trait loci) contributed to adaptive evolution in populations that adapted to lakes representing a more distant optimum than to lakes in which the optimum phenotype was nearer to the ancestral state. Our results also indicate that pleiotropy, not just optimum overshoot, contributes to this difference. These results suggest that a series of adaptive improvements to a new environment leaves a detectable mark in the genome of wild populations. Although not all assumptions of the theory are likely met in natural systems, the prediction may be robust enough to the complexities of natural environments to be useful when forecasting adaptive responses to large environmental changes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".