Adaptive divergence and the evolution of hybrid trait mismatch in threespine stickleback
Bibliographic record
Abstract
Abstract Selection against mismatched traits in hybrids is the phenotypic analogue of intrinsic hybrid incompatibilities. Mismatch occurs when hybrids resemble one parent population for some phenotypic traits and the other parent population for other traits, and is caused by dominance in opposing directions or from segregation of alleles in recombinant hybrids. In this study, we used threespine stickleback fish (Gasterosteus aculeatus L.) to test the theoretical prediction that trait mismatch in hybrids should increase with the magnitude of phenotypic divergence between parent populations. We measured morphological traits in parents and hybrids in crosses between a marine population representing the ancestral form and twelve freshwater populations that have diverged from this ancestral state to varying degrees according to their environments. We found that trait mismatch was greater in more divergent crosses for both F1 and F2 hybrids. In the F1, the divergence–mismatch relationship was caused by traits having dominance in different directions, whereas it was caused by increasing segregating phenotypic variation in the F2. Our results imply that extrinsic hybrid incompatibilities accumulate as phenotypic divergence proceeds.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".