Mating system impacts the genetic architecture of adaptation to heterogeneous environments
Bibliographic record
Abstract
Summary Self‐fertilisation has consequences for variation across the genome as it reduces effective population size, effect recombination rates and pollen flow, with implications for local adaptation. We conducted simulations of divergent stabilising selection on a quantitative trait with drift, pollen flow, mutation, recombination and different outcrossing rates. We quantified trait divergence and the genetic architecture of adaptation. We conducted an F ST outlier analysis to identify candidate loci and quantified the impact of mating system on detectability. Selfing promoted trait divergence mainly through reductions in pollen flow. Moreover, trait architecture became more diffuse with selfing. Average effect size of trait loci was lower, while the number of loci, and their clustering distance increased. The genetic architecture of selfers was also more diffuse than outcrossers for equivalent migration rates. However, when deleterious alleles were included, architectures became more concentrated in selfers, likely to be because of reductions in population size caused by mutational meltdown and impacts of background selection on N e . Our simulations demonstrate that mating system has important impacts on adaptive divergence of traits and the genetic landscape underlying that divergence. Selfing has a significant effect on detectability of regions of the genome important for adaptation because of neutral divergence and diffuse trait architecture.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".