Random and non-random variation in flower color along an urban-rural gradient in the introduced mustard <i>Hesperis matronalis</i>
Bibliographic record
Abstract
ABSTRACT Premise Urbanization can alter the interplay of stochastic genetic drift and natural selection but these effects will depend on the biology and history of a species. To explore the influences of drift and selection we investigated patterns of flower color variation among populations of the introduced ornamental mustard, Hesperis matronalis, along an urban-rural gradient in eastern Ontario Canada. Methods We surveyed 136 naturalized stands of H. matronalis over three generations, and for each stand estimated the diversity of the three color morphs (white, pink, purple), the number of reproductive plants, and the degree of urbanization based on night sky brightness. Key Results Flower color morph diversity increased with both stand size and urbanization which is consistent with effects of genetic drift during colonization combined with multiple introductions of this horticultural plant in urban areas. However, the frequency and fixation of the purple morph systematically increased towards the rural end of the gradient. Although lifetime seed production did not vary among morphs, pre-dispersal seed predation by a recently adventive weevil was higher in the purple morph, particularly in rural areas. Estimated seed production in the absence of predation suggests a previous fitness advantage for the purple and pink morphs in rural areas and for the white morph in urban areas. Conclusions Random variation in flower color diversity may be influenced by stochastic processes and colonization history, while systematic variation in color morph frequencies may reflect past fitness differences among morphs that have been recently erased by seed predation.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".