Spatial variation in scent emission within flowers
Bibliographic record
Abstract
Floral scent is considered an important long‐distance signal that attracts pollinators, but also has been suggested to function at shorter distances such as within‐flower nectar guides or as a defense against antagonists. Indeed, in some species floral scent production and emission show spatial patterns of variation within flowers, as certain compounds are exclusively emitted from specific floral tissues. In other species, the different volatile organic compounds that constitute the floral bouquet are emitted evenly from the entire flower. Here, we summarize the current evidence on floral scent variation within flowers by combining a literature review of published data on tissue‐level floral scent variation (41 species) with floral scent dissections (17 species). For each species, we recorded the total number of volatile compounds separately and grouped in major chemical classes. To facilitate comparisons across diverse species, we compared volatiles emitted by 1) the whole flower, 2) the visual floral tissues (i.e. petals and colored structures), 3) non‐visual floral tissues (i.e. green parts and reproductive structures), as well as 4) the compounds emitted by both visual and non‐visual tissues. Results show that floral scent variation is frequent, but by no means ubiquitous, occurring in species from distantly related groups. We discuss the two main functional hypotheses promoting floral scent variation within flowers, i.e. as a pollinator attractant at short‐distances or a defensive function against antagonists, together with non‐functional hypotheses (e.g. pleiotropic effects, ecological costs). We point out further directions on this topic and suggest experimental approaches testing the attractiveness of compounds emitted by different floral parts alone and in combination with other floral signals. Our synthesis provides a foundation for future studies on the functional ecology of floral scent and reinforces the idea of high complexity in floral chemical signals.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".