Selfing, adaptation and background selection in finite populations
Bibliographic record
Abstract
Classic deterministic genetic models of the evolution of selfing predict species should be either completely outcrossing or completely selfing. However, even species considered high selfers outcross to a small degree (e.g. Arabidopsis thaliana and Caenorhabditis elegans). This discrepancy between theory and data may exist because the classic models ignore the effects of drift interacting with selection, that is, Hill-Robertson effects. High selfing rates make the effective rate of recombination near zero, which is expected to cause the build-up of negative disequilibria in finite populations. Despite the transmission advantage associated with complete selfing, low levels of outcrossing may be favoured because of the benefits of increasing the effective rate of recombination to dissipate negative disequilibria. Using multilocus simulations, we confirm that selfing reduces effective population size through background selection and causes negative disequilibria between selected sites. Consequently, the rate of adaptation is substantially reduced in strong selfers. When selfing rate is allowed to evolve, populations evolve to be either strong outcrossers or strong selfers, depending on the parameter values. Amongst selfers, low, but nonzero, levels of outcrossing can be maintained by selection even when all mutations are deleterious; more outcrossing is maintained with higher rates of deleterious mutation. The addition of beneficial mutations can (i) lead to a quantitative increase in the degree of outcrossing amongst stronger selfers but (ii) may cause outcrossing species to evolve into stronger selfers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".