How Does Selfing Affect the Pace and Process of Speciation?
Bibliographic record
Abstract
Surprisingly little attention has been given to the impact of selfing on speciation, even though selfing reduces gene flow between populations and affects other key population genetics parameters.Here we review recent theoretical work and compile empirical data from crossing experiments and genomic and phylogenetic studies to assess the effect of mating systems on the speciation process.In accordance with theoretical predictions, we find that accumulation of hybrid incompatibilities seems to be accelerated in selfers, but there is so far limited empirical support for a predicted bias toward underdominant loci.Phylogenetic evidence is scarce and contradictory, including studies suggesting that selfing either promotes or hampers speciation rate.Further studies are therefore required, which in addition to measures of reproductive barrier strength and selfing rate should routinely include estimates of demographic history and genetic divergence as a proxy for divergence time.S peciation rates vary considerably among lin- eages, but the sources of this variation are still poorly known (Coughlan and Matute 2020).One factor that may affect the pace as well as the process of speciation is mating system, which ranges from obligately outcrossing to predominantly selfing in many groups, including plants and animals (Igic and Kohn 2006;Jarne and Auld 2006;
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".