A non-destructive method to detect pathogens in bumble bees for conservation research
Bibliographic record
Abstract
Abstract Wild bees are declining worldwide, and many species are now threatened with extinction. Decline is caused by a combination of threats, including disease spillover from managed bees that reduces health of wild bees. An increasing number of studies thus aim to characterize bee health. The common approaches, however, require sacrificing tens to hundreds of individual bees per site or species, with reports of several thousand individuals collected per study. Considering the widespread need to assess bee health, this sampling approach is not sustainable, especially for endangered populations or species. Here, we propose a non-destructive method to assess parasite loads of wild-caught bumble bees. The standard protocol consists of net-capturing individual bumble bees and placing them in a 10 cm (diameter) petri dish to collect faeces. Although this approach is frequently used in laboratory settings, it is not in the field, because of the low success in collecting faeces. Placing bumble bees in a previously refrigerated cooler, we significantly improved faecal collection in the field from 76% with the standard protocol to 86% with the cooler protocol. We also successfully identified spores and cells of two common gut parasites Crithidia spp. and Vairimorpha spp. in faecal samples. The efficacy of the cooler protocol, combined to the low-cost and widespread availability of the equipment should promote its use in field studies. Implication for insect conservation: As there are calls to reduce destructive sampling methods in bee research, using the updated cooler protocol will contribute to achieving this goal. This opens future avenues of combining this non-destructive approach to assess bee health with molecular tools.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".