Research Article Divergent Selection and Then What Not: The Conundrum of Missing Reproductive Isolation in Misty Lake and Stream Stickleback
Bibliographic record
Abstract
Copyright © 2012 Katja Räsänen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In ecological speciation, reproductive isolation evolves as a consequence of adaptation to different selective environments. A frequent contributor to this process is the evolution of positive assortativemate choice between ecotypes.We tested this expectation for lake and inlet stream threespine stickleback (Gasterosteus aculeatus) from the Misty system (Vancouver Island, Canada), which show strong genetically based adaptive divergence and little genetic exchange in nature. This, and work on other stickleback systems, led us to expect positive assortative mating. Yet, our standard “no-choice ” laboratory experiment on common-garden fish revealed no evidence for this—despite divergence in traits typically mediating assortative mating in stickleback. These results remind us that divergent natural selectionmay not inevitably lead to the evolution of positive assortative mate choice. The apparent lack of strong and symmetric reproductive barriers in this system presents a conundrum: why are such barriers not evident despite strong adaptive divergence and low gene flow in nature? 1.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".