Scented nectar and the challenge of measuring honest signals in pollination
Bibliographic record
Abstract
Abstract Nectar scents are thought to function as honest signals of reward used by pollinators, but this hypothesis has rarely been tested. Using Penstemon digitalis, we examined honest signalling of the nectar volatile (S)‐(+)‐linalool and pollinator responses to linalool in both field and laboratory settings. Because our previous work showed that linalool emission was associated with higher female fitness and that nectar is scented with linalool, we hypothesized that linalool was an honest signal of nectar reward. To assess honesty, we measured linalool–nectar associations including nectar volume, sugar amount, concentration and production rate for inflorescences and flowers in several populations. We also assessed whether Bombus impatiens, the main pollinator of P. digitalis at our sites, can use linalool as a foraging signal. We supplemented real or artificial flowers in the field and laboratory with varying linalool–nectar combinations to measure pollinator behavioural responses. We found that an inflorescence's linalool emissions could be used to predict nectar rewards in P. digitalis, but this was driven by indirect associations with display size rather than directly advertising more profitable flowers. For flowers within inflorescences there was also no evidence for an association between signal and reward. Field tests of bumblebee behaviour were inconclusive. However, in laboratory assays, bumblebees generally used variation in linalool emissions to choose more profitable flowers, demonstrating they can detect differences in linalool emitted by P. digitalis and associate them with reward profitability. These results suggest experiments that decouple display size, scent and reward are necessary to assess whether (and when) bees prefer higher linalool emissions. Bees preferred nectars with lower linalool concentrations when linalool flavoured the nectar solution, suggesting the potential for conflicting pressures on scent emission in the field. Synthesis. Our results highlight the challenges of assessing function for traits important to fitness and suggest that the perception of floral signalling honesty may depend on whether pollinators use inflorescences or flowers within inflorescences when making foraging decisions. We conclude that future research on honest signalling in flowering plants, as well as its connection to phenotypic selection, should explicitly define honesty, in theoretical and experimental contexts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".