Challenge accepted: Evolutionary lineages versus taxonomic classification of North American shrub willows (<i>Salix</i>)
Bibliographic record
Abstract
PREMISE: The huge diversity of Salix subgenus Chamaetia/Vetrix clade in North America and the lack of phylogenetic resolution within this clade has presented a difficult but fascinating challenge for taxonomists to resolve. Here we tested the existing taxonomic classification with molecular tools. METHODS: In this study, 132 samples representing 46 species from 22 described sections of shrub willows from the United States and Canada were analyzed and combined with 67 samples from Eurasia. The ploidy levels of the samples were determined using flow cytometry and nQuire. Sequences were produced using a RAD sequencing approach and subsequently analyzed with ipyrad, then used for phylogenetic reconstructions (RAxML, SplitsTree), dating analyses (BEAST, SNAPPER), and character evolution analyses of 14 selected morphological traits (Mesquite). RESULTS: The RAD sequencing approach allowed the production of a well-resolved phylogeny of shrub willows. The resulting tree showed an exclusively North American (NA) clade in sister position to a Eurasian clade, which included some North American endemics. The NA clade began to diversify in the Miocene. Polyploid species appeared in each observed clade. Character evolution analyses revealed that adaptive traits such as habit and adaxial nectaries evolved multiple times independently. CONCLUSIONS: The diversity in shrub willows was shaped by an evolutionary radiation in North America. Most species were monophyletic, but the existing sectional classification could not be supported by molecular data. Nevertheless, monophyletic lineages share several morphological characters, which might be useful in the revision of the taxonomic classification of shrub willows.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".