Honey bees as biomonitors of environmental contaminants, pathogens, and climate change
Bibliographic record
Abstract
Monitoring the environment for pollution, pesticides, and pathogens is crucial for protecting human, agriculture, and overall ecosystem health. Diverse strategies ranging from physical sensors to sentinel species have been used for environmental monitoring. The European honey bee, Apis mellifera, is a globally managed pollinator that can serve as a continuous biomonitoring species. During foraging, honey bees are exposed to contaminants and pathogens and carry them to their hives where they can be detected and quantified. Although individual bees are vulnerable to environmental stressors, the honey bee colony as a whole is more resilient and can accumulate contaminants or respond to them without collapsing. This allows for long-term monitoring of the colony to map contaminants in a geographical area and study ecotoxicology gradients over space and time. In this paper, we review demonstrated and proposed uses of honey bees for environmental monitoring. We focus our discussion on heavy metals, air pollutants, pesticides, and plant pathogens that can be detected in bees and their hive materials including honey, wax, and stored pollen. We present the use of gene expression, microbiome profiling, and other high-throughput methodologies to study dose-dependent exposure and increase detection sensitivity; for example, stored pollen analysis with next generation sequencing can reveal the presence of plant viruses, fungi, and invasive species earlier than traditional detection methods. Finally, we discuss opportunities for using honey bees to monitor emerging threats such as climate change and antimicrobial resistance. This narrative review highlights the versatility and potential utility of the European honey bee as a biomonitoring species for ecosystem health.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".