Bumble-bee learning selects for both early and long flowering in food-deceptive plants
Bibliographic record
Abstract
Most rewardless orchids engage in generalized food-deception, exhibiting floral traits typical of rewarding species and exploiting the instinctive foraging of pollinators. Generalized food-deceptive (GFD) orchids compete poorly with rewarding species for pollinator services, which may be overcome by flowering early in the growing season when relatively more pollinators are naive and fewer competing plant species are flowering, and/or flowering for extended periods to enhance the chance of pollinator visits. We tested these hypotheses by manipulating flowering time and duration in a natural population of Calypso bulbosa and quantifying pollinator visitation based on pollen removal. Both early and long flowering increased bumble-bee visitation compared with late and brief flowering, respectively. To identify the cause of reduced visitation during late flowering, we tested whether negative experience with C. bulbosa (avoidance learning) and positive experience with a rewarding species, Arctostaphylos uva-ursi, (associative learning) by captive bumble-bees could reduce C. bulbosa's competitiveness. Avoidance learning explained the higher visitation of early- compared with late-flowering C. bulbosa. The resulting pollinator-mediated selection for early flowering may commonly affect GFD orchids, explaining their tendency to flower earlier than rewarding orchids. For dissimilar deceptive and rewarding sympatric species, associative learning may additionally favour early flowering by GFD species.
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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) | 0.002 | 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".