Rodent odour bait: A new bumble bee conservation tool to enhance nest box occupancy
Bibliographic record
Abstract
Abstract Bumble bee conservation focuses on supplementing floral resources. Yet, nesting site availability is linked to bumble bee abundance. As a supplement to natural nest sites, nest boxes could be deployed and baited with synthetic lures. As queen bumble bees reportedly establish colonies in abandoned rodent burrows, we hypothesized (1) that queen bumble bees sense, and behaviourally respond to, rodent odour, and (2) that lures of synthetic rodent odour can guide spring queens to nest boxes. We collected headspace odorants from bedding soiled with urine and faeces of house mice, Mus musculus , and identified the 10 odorants that elicited responses from queen antennae. To field‐test attraction of queens to mouse excreta odorants, we tree‐mounted paired nest boxes in florally rich locations, and assigned clean and soiled bedding, respectively, to one box in each pair. Queens established colonies in 17 mouse‐scented boxes and in six unscented boxes. This 43% occupancy rate of mouse‐scented boxes represents a significant improvement over the 10% occupancy rate common for unscented boxes. In a further field experiment, we baited one box in each pair with a synthetic mouse odour lure and found that queens established colonies in 13 baited boxes and in six unbaited control boxes. Specifically, Bombus mixtus established seven colonies in baited boxes and only one colony in an unbaited box. With this proof‐of‐concept that synthetic lures can guide queens to nest boxes, we anticipate that bumble bee conservation programs will soon be able to offer both expanded floral resources and baited nest boxes readily detectable by queens.
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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 | 0.000 |
| 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) | 0.003 | 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".