ADAPTIVE DIVERGENCE AND THE BALANCE BETWEEN SELECTION AND GENE FLOW: LAKE AND STREAM STICKLEBACK IN THE MISTY SYSTEM
Bibliographic record
Abstract
We investigated the interplay between natural selection and gene flow in the adaptive divergence of threespine stickleback (Gasterosteus aculeatus) that reside parapatrically in lakes and streams. Within the Misty Lake system (Vancouver Island, British Columbia), stickleback from the inlet stream (flowing into the lake) have fewer gill rakers and deeper bodies than stickleback from the lake--differences thought to facilitate foraging (benthic macroinvertebrates in the stream vs. zooplankton in the open water of the lake). Common-garden experiments demonstrated that these differences have a genetic basis. Reciprocal transplant enclosure experiments showed that lake and inlet stickleback grow best in their home environments (although differences were subtle and often not significant). Release-recapture experiments in the inlet showed that lake fish are less well-suited than inlet fish for life in the stream (higher mortality or emigration in lake fish). Morphological divergence in the wild and under common rearing was greater between the lake and the inlet than between the lake and the outlet. Genetic divergence (mitochondrial DNA and microsatellites) was greatest between the lake and the upper inlet (1.8 km upstream from the lake), intermediate between the lake and the lower inlet (0.9 km upstream), and least between the lake and the outlet stream (1.2 km downstream). Relative levels of gene flow estimated from genetic data showed the inverse pattern. The negative association between morphological divergence and gene flow is consistent with the expectation that gene flow can constrain adaptation. Estimated absolute levels of gene flow also implied a constraint on adaptation in the outlet but not the inlet. Our results suggest that natural selection promotes the adaptive divergence of lake and stream stickleback. but that the magnitude of divergence can be constrained by gene flow.
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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 | 0.000 | 0.001 |
| 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".