Inferring the strength of directional selection on armor plates in Lake Washington stickleback while accounting for migration and drift
Bibliographic record
Abstract
Contemporary evolution allows us to investigate how natural selection drives phenotypic and genotypic evolution in nature. Recent advances in molecular genetics have identified causative genes underlying adaptive traits, enabling estimation of selection coefficients at these loci. However, estimating selection is challenging when populations receive migrants from genetically and phenotypically distinct populations. With genome-wide data now allowing estimation of migration rates and effective population sizes, these demographic parameters can be integrated into models for measuring selection. In Lake Washington, USA, the frequency of the completely plated morph of the threespine stickleback (Gasterosteus aculeatus) increased from 1957 to 2005, plausibly due to increased trout predation pressure caused by enhanced water clarity. Here, we estimated the selection coefficient at a major locus responsible for the plate morph using historical data, taking migration and genetic drift into consideration. Model-based predictions of present allele frequencies were tested with samples collected in 2022. Consistent with directional selection, the completely plated morphs and the underlying allele have increased since 2005, but to higher frequencies than predicted, suggesting a recent increase in selection. Thus, integrating molecular genetics, population genomics, and simulations enables the estimation of selection strength while considering migration and drift, to reveal directional selection in nature.
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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 | 0.001 |
| 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) | 0.000 | 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 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".