Managed honeybees affect the foraging behaviour of bumblebees in Geranium sylvaticum
Bibliographic record
Abstract
Due to shared evolutionary history, native pollinator diversity and coexistence is promoted by niche partitioning and behavioural differences between species. Introduced insect species, however, have potential to compete with wild pollinators and negatively affect native insect populations. Honeybee (Apis mellifera) is an introduced pollinator species in northern Europe and may affect native pollinator populations negatively. Diversity in plant communities also promotes variation in the associated pollinator communities. Diversity of a community encompasses not only species diversity but includes within species variation as well. Within species, genetic diversity could promote insect coexistence and affect competitive interactions between pollinator insects. In this study, we measured floral visitation rates in female and hermaphrodite Geranium sylvaticum genotypes in the presence and absence of a beehive (Apis mellifera) in an experimental field located in Central Finland. We show that competition with honeybees reduced visitation rates by bumblebees, but not by other native pollinator groups. Furthermore, bumblebees preferred some plant genotypes in the absence of the honeybees, but not in the presence of honeybees. Overall, bumblebees preferred females over hermaphrodite plants, but honeybees showed no such preference. Our study links the native pollinators and genetically diverse plant populations, and sheds light on the competition between pollinator insects.
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 | 0.000 |
| 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".