Increased heterosis in selfing populations of a perennial forb
Bibliographic record
Abstract
Quantifying the importance of random genetic drift in natural populations is central to understanding the potential limits to natural selection. One approach is to estimate the magnitude of heterosis, the increased fitness of progeny derived from crosses between populations relative to crosses within populations caused by the heterozygous masking of deleterious recessive or nearly recessive alleles that have been fixed by drift within populations. Self-fertilization is expected to reduce the effective population size by half relative to outcrossing, and population bottlenecks may be common during the transition to selfing. Therefore, chance fixation of deleterious alleles due to drift in selfing populations should increase heterosis between populations. Increased homozygosity due to fixation or loss of alleles should also decrease inbreeding depression within populations. Most populations of the perennial herb Arabidopsis lyrata ssp. lyrata are self-incompatible (SI), but several have evolved self-compatibility and are highly selfing. We quantified heterosis and inbreeding depression in two predominantly self-compatible (SC) and seven SI populations in a field common garden experiment within the species' native range and examined the correlation between these metrics to gauge the similarity in their genetic basis. We measured proportion germination in the lab, and survival and fecundity (flower and seed production) for 2 years in the field, and calculated estimates of cumulative fitness. We found 7.2-fold greater heterosis in SC compared with SI populations, despite substantial heterosis in SI populations (56 %). Inbreeding depression was >61 %, and not significantly different between SC and SI populations. There was no correlation between population estimates of heterosis and inbreeding depression, suggesting that they have somewhat different genetic bases. Combined with other sources of information, our results suggest a history of bottlenecks in all of these populations. The bottlenecks in SC populations may have been severe, but their strong inbreeding depression remains enigmatic.
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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 | 0.000 |
| 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 | 0.000 | 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 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".