Morphology delimits more species than molecular genetic clusters of invasive <i>Pilosella</i>
Bibliographic record
Abstract
UNLABELLED: • PREMISE OF THE STUDY: Accurate assessments of biodiversity are paramount for understanding ecosystem processes and adaptation to change. Invasive species often contribute substantially to local biodiversity; correctly identifying and distinguishing invaders is thus necessary to assess their potential impacts. We compared the reliability of morphology and molecular sequences to discriminate six putative species of invasive Pilosella hawkweeds (syn. Hieracium, Asteraceae), known for unreliable identifications and historical introgression. We asked (1) which morphological traits dependably discriminate putative species, (2) if genetic clusters supported morphological species, and (3) if novel hybridizations occur in the invaded range.• METHODS: We assessed 33 morphometric characters for their discriminatory power using the randomForest classifier and, using AFLPs, evaluated genetic clustering with the program structure and subsequently with an AMOVA. The strength of the association between morphological and genotypic dissimilarity was assessed with a Mantel test.• KEY RESULTS: Morphometric analyses delimited six species while genetic analyses defined only four clusters. Specifically, we found (1) eight morphological traits could reliably distinguish species, (2) structure suggested strong genetic differentiation but for only four putative species clusters, and (3) genetic data suggest both novel hybridizations and multiple introductions have occurred.• CONCLUSIONS: (1) Traditional floristic techniques may resolve more species than molecular analyses in taxonomic groups subject to introgression. (2) Even within complexes of closely related species, relatively few but highly discerning morphological characters can reliably discriminate species. (3) By clarifying patterns of morphological and genotypic variation of invasive Pilosella, we lay foundations for further ecological study and mitigation.
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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) | 0.001 | 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".