Sex ratio rather than population size affects genetic diversity in <i>Antennaria dioica</i>
Bibliographic record
Abstract
Habitat fragmentation and small population size can lead to genetic erosion in threatened plant populations. Classical theory implies that dioecy can counteract genetic erosion as it decreases the magnitude of inbreeding and genetic drift due to obligate outcrossing. However, in small populations, sex ratios may be strongly male- or female-biased, leading to substantial reductions in effective population size. This may theoretically result in a unimodal relationship between sex ratios and genetic diversity; yet, empirical studies on this relationship are scarce. Using AFLP markers, we studied genetic diversity, structure and differentiation in 14 highly fragmented Antennaria dioica populations from the Central European lowlands. Our analyses focused on the relationship between sex ratio, population size and genetic diversity. Although most populations were small (mean: 35.5 patches), genetic diversity was moderately high. We found evidence for isolation-by-distance, but overall differentiation of the populations was rather weak. Females dominated 11 populations, which overall resulted in a slightly female-biased sex ratio (61.5%). There was no significant relationship between population size and genetic diversity. The proportion of females was not unimodally but positively linearly related to genetic diversity. The high genetic diversity and low genetic differentiation suggest that A. dioica has been widely distributed in the Central European lowlands in the past, while fragmentation occurred only in the last decades. Sex ratio has more immediate consequences on genetic diversity than population size. An increasing proportion of females can increase genetic diversity in dioecious plants, probably due to a higher amount of sexual reproduction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".