The Limited Contribution of Reciprocal Gene Loss to Increased Speciation Rates Following Whole-Genome Duplication
Bibliographic record
Abstract
Hybrid incompatibilities contribute to reproductive isolation between species, allowing them to follow independent evolutionary trajectories. Since hybrid incompatibilities are by definition deleterious, they cannot be selected for directly and must arise as a by-product of evolutionary divergence. Divergent resolution of duplicate genes, a special case of Dobzhansky-Muller incompatibilities, is one mechanism by which hybrid incompatibility can evolve. Following whole-genome duplication, loss of gene copies could possibly increase the opportunity for divergent resolution and, hence, the evolution of hybrid incompatibilities. However, divergent resolution can take place only when populations are isolated in allopatry; genes lost within a species cannot contribute to future speciation. Furthermore, nearly complete allopatry is necessary for passive divergent resolution. Using mathematical models, we demonstrate that these two factors severely impede the ability of divergent resolution alone to increase speciation rates, except under very particular conditions. Instead, we find that the population dynamics of diverging lineages dominate this process, leading to a larger role for ecology relative to genetics in the origin of new species, even by passive mechanisms. Divergent resolution of duplicate genes might increase speciation rates in some clades at some times, but our results indicate that it alone is unlikely to account for the macroevolutionary success of polyploid clades.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".