Conservation genomics reveals pesticide and pathogen exposure in the declining bumble bee <i>Bombus terricola</i>
Bibliographic record
Abstract
In recent years, many pollinators have experienced large population declines, which threaten food security and the stability of natural ecosystems. Bumble bees are particularly important because their ability to "buzz" pollinate and tolerate cooler conditions make them critical pollinators for certain plants and regions. Here, we apply a conservation genomics approach to study the vulnerable Bombus terricola. We sequenced RNA from 30 worker abdomens, 18 of which were collected from agricultural sites and 12 of which were collected from nonagricultural sites. We found transcriptional signatures associated with exposure to insecticides, with gene expression patterns suggesting that bumble bees were exposed to neonicotinoids and/or fipronil-two compounds known to negatively impact bees. We also found transcriptional signatures associated with pathogen infections. In addition to the transcriptomic analysis, we carried out a metatranscriptomic analysis and detected five pathogens in the abdomens of workers, three of which are common in managed honey bee and bumble bee colonies. Our conservation genomics study provides functional support for the role of pesticides and pathogen spillover in the decline of B. terricola. We demonstrate that conservation genomics is an invaluable tool which allows researchers to quantify the effects of multiple stressors that impact pollinator populations in the wild.
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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 | 0.001 | 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".