Supplemental nesting habitat increases bee abundance in apple orchards
Bibliographic record
Abstract
Abstract Identifying the resources that limit bee populations is essential for both bee conservation and pollination management in agroecosystems. Land‐use change typically leads to decreased habitat availability for wild pollinators, including loss of nesting habitat, which is an essential but often‐overlooked resource for wild bees. Cavity‐nesting bees, such as many Osmia spp. (Hymenoptera: Megachilidae), occupy holes in wood or reeds to build their nests; due to their nesting habits, they are frequently scarce in agricultural settings. Nevertheless, under the right circumstances, these bees can be ideal pollinators of apple and other orchard crops. Artificial nesting structures (‘bee hotels’ or ‘trap‐nests’) are often used to study cavity‐nesting bees and have been proposed as tools for bee conservation. To evaluate the effects of additional nesting habitat on the local abundance of pollinators, we selected 24 sites in apple orchards in eastern Canada in 2021 and 2022. Each site comprised two study plots: one in which we installed artificial nesting structures for cavity‐nesting bees, and one without added nesting habitat (control). Pollinator surveys were conducted in both plot types to measure pollinator visits to apple blossoms and, after apple bloom, to flowers in the undergrowth and/or in shrubs. Numbers and size of developing fruit were also recorded. Both Osmia bees and overall pollinator numbers were significantly higher in the treatment with trap‐nests (respectively, 33% and 22% higher during the 2021 growing season, and 113% and 30% higher during apple bloom across the 2 years of the study); however, there was no consistent difference in apple size or fruit set between the two treatments. Thus, trap‐nests locally increased the numbers of cavity‐nesting bees as well as total pollinator numbers, but they had little effect on apple yield, likely because apple production was not pollinator limited in the years of this study. Synthesis and applications. These findings suggest that bee populations in apple orchards can be limited by nesting resources such that addition of constructed nesting habitat could be an effective means of increasing pollinator numbers in orchards, and potentially, supporting bee conservation.
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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 | 0.001 | 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".