Floral specialization for beetle pollination and its implications for pollen dispersal in an African orchid
Bibliographic record
Abstract
PREMISE: Pollination by beetles is relatively rare in orchids, and this has been attributed to the clumsy behavior of beetles being unsuitable for the precise pollen transfer mechanisms that characterize the orchid family. We investigated floral specialization for beetle pollination in the rare fire-dependent South African orchid Disa elegans and explored its implications for the efficiency and spatial pattern of pollen dispersal. METHODS: We observed flower visitors and identified their pollen loads. We studied floral traits, including spectral reflectance patterns, nectar secretion, and scent chemistry. We tracked the dispersal of color-labeled pollen. RESULTS: Disa elegans was found to be pollinated by large scarab beetles. Apparent floral adaptations for beetle pollination include the platform-like corymbose inflorescence of upward-facing, bowl-shaped flowers, secretion of very dilute nectar on exposed surfaces of the petals, and fruity floral scent dominated by the monoterpene alcohol R-(-)-β-linalool and benzenoid ester methyl benzoate. Beetles carry large loads of pollinaria and transfer ~13% of the pollen they remove from anthers to stigmas. We found a classic leptokurtic kernel of pollen dispersal with an average distance from donors to recipients of 6.7 m. Self-pollen made up ~30% of all pollen deposited on stigmas by beetles. These pollen dispersal patterns are similar to those obtained in plants pollinated by other insect groups, such as bees. CONCLUSIONS: These results provide evidence of floral specialization for beetle pollination in an orchid species and show that beetles can be effective agents of pollen dispersal in orchid populations.
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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 | 0.000 | 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) | 0.001 | 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".