Bumble bee forager abundance on lowland heaths is predicated by specific floral availability rather than the presence of honey bee foragers: evidence for forage resource partitioning
Bibliographic record
Abstract
Honey bees are being scrutinized for their potential impact upon wild bees. In lowland heath mosaics, a simple but resource rich habitat for pollinators, there is a higher probability of niche overlap for bumble bees and honey bees due to the requirement of similar resources and limited floral diversity. This study assesses i) if there is any evidence of forage competition between bumble bees and honey bees and ii) asks to what extent the number of bumble bee foragers in a lowland heath mosaic over the summer months is affected by floral resource availability in different heath types (wet/dry). Bumble bee and honey bee counts were conducted at 30 wet heath and 30 dry heath 20 m × 20 m sites, in the Poole Basin, UK. The relationships between bumble bee and honey bee forager observations and ericaceous forage availability throughout the summer were evaluated using GLMMs considering presence and abundance of honey bees and specific floral availability as factors.Only weak correlations of honey bee forager abundance on bumble bee forager abundance were detected. Instead, the most important factors relating to bumble bee numbers were the abundance of specific floral resources within the heath type (wet/dry). Bumble bees and honey bees showed resource use consistent with resource partitioning with bumble bees predominantly using wet heaths and honey bees using dry heaths. These findings provide evidence of the importance of maintaining complex habitat mosaics within broader habitats to promote coexistence between bumble bees and honey 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 | 0.001 |
| 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".