An evaluation of alternative explanations for widespread cytonuclear discordance in annual sunflowers (<i>Helianthus</i>)
Bibliographic record
Abstract
Cytonuclear discordance is commonly observed in phylogenetic studies, yet few studies have tested whether these patterns reflect incomplete lineage sorting or organellar introgression. Here, we used whole-chloroplast sequence data in combination with over 1000 nuclear single-nucleotide polymorphisms to clarify the extent of cytonuclear discordance in wild annual sunflowers (Helianthus), and to test alternative explanations for such discordance. Our phylogenetic analyses indicate that cytonuclear discordance is widespread within this group, both in terms of the relationships among species and among individuals within species. Simulations of chloroplast evolution show that incomplete lineage sorting cannot explain these patterns in most cases. Instead, most of the observed discordance is better explained by cytoplasmic introgression. Molecular tests of evolution further indicate that selection may have played a role in driving patterns of plastid variation - although additional experimental work is needed to fully evaluate the importance of selection on organellar variants in different parts of the geographic range. Overall, this study represents one of the most comprehensive tests of the drivers of cytonuclear discordance and highlights the potential for gene flow to lead to extensive organellar introgression in hybridizing taxa.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".