An assessment of artificial nests for cavity-nesting bees (Hymenoptera: Megachilidae) in lowbush blueberry (Ericaceae)
Bibliographic record
Abstract
Abstract Fluctuating bee (Hymenoptera: Apoidea) populations jeopardise pollination services. Nesting habitat for solitary bees is potentially limited in many agroecosystems, but the provision of artificial nests could augment bee communities and the pollination services they provide. We investigated whether cavity-nesting bees (Hymenoptera: Megachilidae) in lowbush blueberry (Vaccinium angustifoliumAiton (Ericaceae)) fields would use artificial trap nests. Different nest designs were compared, as was nesting occupancy between fruit-bearing and vegetative fields. Milk carton nests had significantly more uptake by and emergence ofOsmiaPanzer andMegachileLatreille than wooden nests. Only 3% of wooden nests had at least one occupied nesting tube versus 73% of milk carton nests, with a total of 34% nesting tubes occupied. Bee emergence was significantly higher in nesting tubes from fruit-bearing fields than vegetative fields.OsmiaandMegachileemergence was low from milk carton nests, with bees emerging from less than 10% of occupied nesting tubes, in large part due to parasitism. Overturned clay lids were tested as potential nesting sites forOsmia inermisZetterstedt, but only 3% of lids had nesting evidence. Our results suggest that certain artificial nests have potential for encouraging communities of cavity-nesting bees, but further study on nest design and handling protocols is needed.
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 | 0.001 |
| 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) | 0.001 | 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".