Biodiversity measures of a grassland plant-pollinator community are resilient to the introduction of honey bees (Apis mellifera)
Bibliographic record
Abstract
The prairies of Canada support a diversity of insect pollinators that contribute pollination services to flowering crops and wild plants. Habitat loss and use of managed pollinators has increased conservation concerns for wild pollinators, as mounting evidence suggests that honey bees (Apis mellifera) may reduce their diversity and abundance. Plant-pollinator community analyses often omit non-bee pollinators, which can be valuable contributors to pollination services. Here, we experimentally introduced honey bees to examine how their abundance affects the species richness, diversity, abundance, species composition, interaction richness, and interaction diversity of all wild pollinators, and of four higher taxa separately. We identified all insect pollinators and analyzed how honey bee abundance affected the above biodiversity metrics, controlling for flower abundance and flower species richness. Even with high honey bee densities, there was no change to any of these variables, except that beetle species diversity increased. All other taxa had no significant relationship to honey bee abundance. Considering the widespread use of managed honey bees, the effect they have on wild pollinators should be firmly established. Our results suggest that honey bees have little to no short-term impact on the wild pollinator community or its interactions with plants in this native grassland.
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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 | 0.000 |
| 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".