Crop and landscape factors affecting variation in composition and behaviour of the pollinator community in field bean crops
Bibliographic record
Abstract
The global decline in pollinators is likely to negatively affect the yield of insect pollinated crops such as field beans (Vicia faba). To understand the impact of such declines it is important to investigate how the abundance, composition and behaviour of the pollinator community relates to crop yield. We observed pollinators (specifically Bombus spp. and Apis mellifera) foraging in field bean fields. Some bees actively pollinated flowers whereas others robbed the nectar without actively pollinating the flowers. As legitimate foraging visits are more likely to pollinate the crop, we explored infield and landscape scale factors affecting this variation in behaviour. Infield factors included sowing time (winter/spring) and plant density. Landscape factors were the area of flower-rich habitat, and of mass-flowering crops within 1 km of the bean field. We also explored how the abundance and behaviour of different functional groups (short-tongued bumblebees, long-tongued bumblebees or honeybees) responded to these factors. Finally, we assessed how the abundance and behaviour of these pollinators affected field bean yield. Pollinators were more abundant in bean crops that were spring sown and where there was a smaller area of mass-flowering crops in the landscape. Functional groups varied in their predominant foraging behaviour and in how their behaviour was influenced by external factors. There was no relationship between pollinator abundance and bean yield however the proportion of pollinators legitimately foraging was negatively related to yield. Our findings align with previous research in suggesting that the benefits of insect pollinators for field beans are context specific. Additional work is required to develop pollinator conservation measures that will facilitate crop pollination.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".