Adaptive Species Differentiation and Population Uniformity in Viola Species Sharing Similar Geographical Distribution but Differing Habitat Preferences
Bibliographic record
Abstract
Selection of favorable alleles is sufficient to maintain species cohesion even with low levels of gene flow. Here, in the study of two sister species growing in contrasting restricted ecological areas, we assess whether their divergent traits have been subject to uniform selection among populations. Two different analyses were performed to evaluate the relative importance of selection and drift. First, we compared FST and PST indexes, analogous to QST, in eleven populations each of Viola eizanensis and V. chaerophylloides. PST was computed for two reproductive traits, two leaf traits and two allocation traits. Second, we compared the observed PST with the expected distribution of PST under neutrality generated by phylogeny-based simulation assuming the Brownian motion model for trait evolution. These analyses indicated that spring leaf mass per area and seed number per capsule were divergent between species and uniform among populations. We suggest that these candidate traits are associated with ecological speciation. We also show that uniform, not divergent, selection among populations is probably common in both species. This result suggests that uniform selection maintaining similarity of traits among populations is a more dominant force than divergent selection in species growing in restricted ecological areas.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".