QUANTIFYING THE CONSTRAINING INFLUENCE OF GENE FLOW ON ADAPTIVE DIVERGENCE IN THE LAKE-STREAM THREESPINE STICKLEBACK SYSTEM
Bibliographic record
Abstract
The constraining effect of gene flow on adaptive divergence is often inferred but rarely quantified. We illustrate ways of doing so using stream populations of threespine stickleback (Gasterosteus aculeatus) that experience different levels of gene flow from a parapatric lake population. In the Misty Lake watershed (British Columbia, Canada), the inlet stream population is morphologically divergent from the lake population, and presumably experiences little gene flow from the lake. The outlet stream population, however, shows an intermediate phenotype and may experience more gene flow from the lake. We first used microsatellite data to demonstrate that gene flow from the lake is low into the inlet but high into the outlet, and that gene flow from the lake remains relatively constant with distance along the outlet. We next combined gene flow data with morphological and habitat data to quantify the effect of gene flow on morphological divergence. In one approach, we assumed that inlet stickleback manifest well-adapted phenotypic trait values not constrained by gene flow. We then calculated the deviation between the observed and expected phenotypes for a given habitat in the outlet. In a second approach, we parameterized a quantitative genetic model of adaptive divergence. Both approaches suggest a large impact of gene flow, constraining adaptation by 80-86% in the outlet (i.e., only 14-20% of the expected morphological divergence in the absence of gene flow was observed). Such approaches may be useful in other taxa to estimate how important gene flow is in constraining adaptive divergence 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".