Catching the thief: Nectar robbing behaviour by bumblebees on naturalised Fuchsia magellanica in Ireland
Bibliographic record
Abstract
Fuchsia magellanica (Ongaraceae) is a plant with a traditionally ornithopholous pollination system, pollinated primarily by hummingbirds in its native range. As a naturalised alien plant in Ireland, F. magellanica is visited largely by bumblebees, with evidence for nectar robbing behaviour of the long-tubed flowers. We aimed to investigate nectar robbing behaviour of bumblebees on F. magellanica, and in particular whether floral and pollinator traits (size) determined likelihood of nectar robbing. While F. magellanica was visited by a number of bumblebee species, only two with shorter tongue lengths were observed to rob nectar from flowers. Although there was no observed relationship between intra-specific bee body size and nectar robbing behaviour, nectar robbing was observed most frequently in the site with the highest number of bees. Proportions of robbed flowers were low overall and varied between populations, but there was a significant relationship between flower size and whether it was nectar robbed with larger flowers robbed more often. Our work suggests that floral size determines whether a flower-visitor will choose to nectar rob or not in this system. Nectar robbing may also be related to bee density which could suggest this behaviour is driven by competition for resources, or that it is learnt by observing other bees.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".