Phylogeographic inferences from chloroplast DNA: quantifying the effects of mutations in repetitive and non‐repetitive sequences
Bibliographic record
Abstract
Phylogeographic inference can be a powerful tool in reconstructing species' evolutionary histories; however, although inferred phylogeographic patterns should depend in part on the underlying types and rates of mutations, the effects of different types of mutations have seldom been quantified. In this study we identified two chloroplast minisatellites in the common reed Phragmites australis, and showed that these are more variable than chloroplast microsatellites. We then recreated parsimony networks of the global phylogeography of P. australis based on data that either included or excluded repetitive sequences (minisatellites and microsatellites), thereby illustrating the influence that these repetitive sequences can have on large-scale phylogeographic inference. The resulting networks differed in the numbers of mutational steps, degrees of uncertainty, and total numbers of haplotypes. In addition, the suggested ancestor-descendant relationships among lineages changed substantially depending on whether repetitive sequences were included. We therefore caution against the inclusion of repetitive sequences in large-scale networks because of their high potential for homoplasy. Nevertheless, we advocate the inclusion of repetitive sequences in other analyses: specifically, we show that the ratio of mutations in repetitive vs. non-repetitive regions can provide insight into the relative ages of lineages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".