Big bees do a better job: intraspecific size variation influences pollination effectiveness.
Bibliographic record
Abstract
Bumblebees (Bombus spp.) are efficient pollinators of many flowering plants, yet the pollen deposition performance of individual bees has not been investigated. Worker bumblebees exhibit large intraspecific and intra-nest size variation, in contrast with other eusocial bees; and their size influences collection and deposition of pollen grains.Laboratory studies with B. terrestris workers and Vinca minor flowers showed that pollen grains deposited on stigmas in single visits (SVD) were significantly positively related to bee size; larger bees deposited more grains, while the smallest individuals, with proportionally shorter tongues, were unable to collect or deposit pollen in these flowers. Individuals did not increase their pollen deposition over time, so handling experience does not influence SVD in Vinca minor.Field studies using Geranium sanguineum and Echium vulgare, and multiple visiting species, confirmed that individual size affects SVD. All bumblebee species showed size effects, though even the smallest individuals did deposit pollen, whereas there was no detectable effect with Apis with its limited size variation. Two abundant hoverfly species also showed size effects, particularly when feeding for nectar.Mean size of foragers also varied diurnally, with larger individuals active earlier and later, so that pollination effectiveness varies through a day; flowers routinely pollinated by bees may best be served by early morning dehiscence and visits from larger individuals.Thus, while there are well-documented species-level variations in pollination effectiveness, the fine-scale individual differences between foragers should also be taken into account when assessing the reproductive outputs of biotically-pollinated plants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".