Evidence for asymmetric migration load in a pair of ecologically divergent stickleback populations
Bibliographic record
Abstract
Gene flow between ecologically divergent populations can prevent local adaptation, resulting in lower mean fitness and directional selection within a population. Such maladaptation should tend to be stronger in populations receiving a relatively larger fraction of immigrants. We test this expectation by comparing the strength of selection in a pair of three-spine stickleback populations in adjoining but unequal-sized lake basins in British Columbia. A larger deeper basin is connected to a smaller shallower basin by a short channel that allows extensive migration between populations. The two basins contain distinct habitats and prey communities, and stickleback stomach contents and isotope ratios differ accordingly. Trophic morphology is correlated with diet, so we would expect these ecological differences to be accompanied by morphological divergence. However, high gene flow appears to constrain adaptive divergence: microsatellites indicate that the two basins represent a single panmictic gene pool, and phenotypic divergence is very subtle. As a result, fish in the smaller lake basin are subject to persistent directional selection towards a more benthic phenotype, whereas the larger population exhibits no significant selection. The results illustrate the potentially asymmetrical effect of migration-selection balance, and its effect on fitness within populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".