Differences in visitation of honeybees and bumblebees to ornamental plant varieties can be explained by floral traits
Bibliographic record
Abstract
Global bee populations are rapidly declining. One way of supporting bee populations is by enhancing urban green spaces with plants attractive to bees. Plant breeding has introduced a high degree of variability in floral traits, which can affect the attractiveness and usefulness of ornamental plants to bees. In this study, we investigated how variations in floral traits, including nectar sugar content, corolla tube depth, flower colour, UV-presence and the number of flowers, affected the attractiveness of 119 cultivars from eight ornamental plant genera (Salvia nemorosa, Gaillardia aristata, Delosperma cooperi, Lavandula angustifolia, Lavandula stoechas, Sedum telephium, Perovskia atriplicifolia and Agastache hybrida) to honeybees and bumblebees. Our results show that differences in bee visitation rate among cultivars were directly related to variation in floral traits. For most plant genera, cultivars of the same species varied significantly in attractiveness. Honeybees and bumblebees generally did not find the same cultivars and plant genera attractive. Nectar sugar content and flower colour were important for cultivar attractiveness to both honeybees and bumblebees, with corolla tube depth also being an important factor for honeybees. We found that flower colour was often related to the favourability of other floral traits that promote more rewarding or easily accessible flowers. However, most cultivars were considered unattractive and only a small number of cultivars were highly attractive to honeybees (6%) and bumblebees (10%). Overall, our study gives valuable insights for plant breeders, emphasising how different floral traits affect the attractiveness of ornamental plants which helps to select for floral traits that result in more attractive ornamental plants for bees.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".