A Phylogenetic Study of Evolutionary Transitions in Sexual Systems in Australasian<i>Wurmbea</i>(Colchicaceae)
Bibliographic record
Abstract
Using phylogenies to make sound inferences about character evolution depends on a variety of factors, including tree uncertainty, taxon sampling, and the degree of evolutionary lability in the character of interest. We explore the effect of these and other sources of ambiguity for maximum likelihood (ML)–based inferences of sexual‐system evolution in Wurmbea, a small genus of geophytic monocots from the Southern Hemisphere. We reconstructed Wurmbea phylogeny using four noncontiguous regions (ca. 5.5 kb) of the plastid genome across a broad sampling of taxa, and we confirm that the genus is divided into two well‐supported clades, each defined by its geography (Africa vs. Australasia) and variation in sexual system (i.e., uniformly monomorphic vs. sexually variable, respectively). We demonstrate that the predominantly Australian clade includes the sexually monomorphic species Iphigenia novae‐zelandiae. We observe treewide uncertainty in the state of all ancestral nodes, and therefore all state transitions, when all taxa in Wurmbea are considered. We demonstrate that this is primarily a consequence of interspersion of terminals with gender dimorphism vs. monomorphism throughout the Australasian clade, rather than tree uncertainty or the presence of very short internal branches. We accounted for tree uncertainty by randomly sampling alternative resolutions of branches that are poorly supported by ML bootstrap analysis, effectively interpreting these as soft polytomies. Under the assumption that well‐supported aspects of our gene tree accurately depict organismal phylogeny, there is a marked evolutionary lability in the sexual systems of Australasian Wurmbea. A more problematic issue is that our results contradict the monophyly of two sexually polymorphic Australian species, Wurmbea dioica and Wurmbea biglandulosa. If this reflects paraphyly at the species level, lateral gene transfer, or failed coalescence, then the interpretations of character transitions will need to be adjusted. Our analysis provides an example of the impediments to linking macroevolutionary pattern with microevolutionary processes for evolutionarily labile traits in recently evolved plant groups that possess a high degree of variation in sexual characters.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".