The demography and population genomics of evolutionary transitions to self-fertilization in plants
Bibliographic record
Abstract
The evolution of self-fertilization from outcrossing has occurred on numerous occasions in flowering plants. This shift in mating system profoundly influences the morphology, ecology, genetics and evolution of selfing lineages. As a result, there has been sustained interest in understanding the mechanisms driving the evolution of selfing and its environmental context. Recently, patterns of molecular variation have been used to make inferences about the selective mechanisms associated with mating system transitions. However, these inferences can be complicated by the action of linked selection following the transition. Here, using multilocus simulations and comparative molecular data from related selfers and outcrossers, we demonstrate that there is little evidence for strong bottlenecks associated with initial transitions to selfing, and our simulation results cast doubt on whether it is possible to infer the role of bottlenecks associated with reproductive assurance in the evolution of selfing. They indicate that the effects of background selection on the loss of diversity and efficacy of selection occur rapidly following the shift to high selfing. Future comparative studies that integrate explicit ecological and genomic details are necessary for quantifying the independent and joint effects of selection and demography on transitions to selfing and the loss of genetic diversity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.001 | 0.001 |
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".