Evolutionary walks through flower colour space driven by gene expression in<i>Petunia</i>and allies (Petunieae)
Bibliographic record
Abstract
The structure and function of biochemical and developmental pathways determine the range of accessible phenotypes, which are the substrate for evolutionary change. Accordingly, we expect that observed phenotypic variation across species is strongly influenced by pathway structure, with different phenotypes arising due to changes in activity along pathway branches. Here, we use flower colour as a model to investigate how the structure of pigment pathways shapes the evolution of phenotypic diversity. We focus on the phenotypically diverse Petunieae clade in the nightshade family, which containsca180 species ofPetuniaand related genera, as a model to understand how flavonoid pathway gene expression maps onto pigment production. We use multivariate comparative methods to estimate co-expression relationships between pathway enzymes and transcriptional regulators, and then assess how expression of these genes relates to the major axes of variation in floral pigmentation. Our results indicate that coordinated shifts in gene expression predict transitions in both total anthocyanin levels and pigment type, which, in turn, incur trade-offs with the production of UV-absorbing flavonol compounds. These findings demonstrate that the intrinsic structure of the flavonoid pathway and its regulatory architecture underlies the accessibility of pigment phenotypes and shapes evolutionary outcomes for floral pigment production.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".