Fecal sampling protocol to assess bumble bee health in conservation research
Bibliographic record
Abstract
An increasing number of wild bee species are declining or threatened with extinction worldwide. Decline has been proposed to be caused by a combination of threats, including increasing wild bee disease prevalence and pathogen spillover from managed bees that can reduce health of wild bees. Most approaches aiming at characterizing bee health, 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 present a non-destructive protocol to collect bumble bee faeces and assess parasite loads of wild-caught individuals. The standard protocol consists of net-capturing individual bumble bees and placing them in a 10 cm (diameter) petri dish to collect faeces. This fecal screening approach is frequently used in laboratory settings, but much less in the field, which can impair conservation research. When placing bumble bees in a previously refrigerated cooler, we successfully collected faeces for 86% individuals, while the standard protocol, as used in laboratory settings, yielded 76% success in collecting faeces. We also identified cells and spores of two common gut parasites Crithidia spp. and Vairimorpha spp. in faecal samples. The faecal sampling presented here opens future avenues to assess bee pathogen loads using molecular techniques, while collected faeces could also be used to assess bee health more broadly, including bee microbiota and bee diet.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".