The diverse effects of phenotypic dominance on hybrid fitness
Bibliographic record
Abstract
Abstract When divergent populations interbreed, their alleles are brought together in hybrids. These hybrids may express novel phenotypes, not previously exposed to selection. In the initial F1 cross, most divergent alleles are present as heterozygotes. Therefore, F1 fitness can be influenced by dominance effects that first appear together in the hybrids, and so could not have been selected to function well together. We present a systematic study of these F1 dominance effects by introducing variable phenotypic dominance into Fisher’s geometric model. We show that dominance often reduces hybrid fitness, which can lead to patterns of optimal outbreeding and a steady decline in F1 fitness at high levels of divergence. We also show that “lucky” beneficial effects sometimes arise by chance, which might be especially important when hybrids can access novel environments. We then explore the interaction of phenotypic dominance with uniparental inheritance, showing that dominance can lead to violations of Haldane’s Rule (reduced fitness of the heterogametic sex) while strengthening Darwin’s Corollary (fitness differences between cross directions). Taken together, our results show that dominance could play an important role in the outcomes of hybridisation after secondary contact, and thus to the maintenance or collapse of isolating barriers. Nevertheless, the telltale signs of dominance are relatively few and subtle. Results also suggest that dominance effects are smaller than the cost of segregation variance, implying that simple additive models may still give good predictions for later-generation recombinant hybrids, even when dominance qualitatively alters outcomes for the F1.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".