In a win for pollinator conservation, researchers at the University of California, Riverside, have developed a machine learning-based method to identify chemical compounds that can safely repel honey bees from pesticide-treated crops. The research offers a potential solution to one of agriculture’s most urgent ecological challenges: the ongoing decline in honey bee populations, driven in part by pesticide exposure.
The interdisciplinary team led by Anandasankar Ray, a professor of molecular, cell and systems biology and an expert on insect olfactory behavior, tackled a central obstacle in altering behavior of pollinators to protect them: the complexity of the honey bee olfactory system. With more than 200 odor receptors capable of detecting a vast array of volatile compounds, finding scents that repel bees was a major challenge — until now.
“Bees rely heavily on their sense of smell to forage, but that sensitivity makes it tough to find odors that push them away instead of drawing them in,” Ray said. “Our goal was to flip that script and find a way to use scent as a deterrent — safely and effectively.”
To do this, Ray’s team collaborated with honey bee researchers in the lab of Boris Baer, a professor of entomology. The researchers created a machine-learning model trained on both the chemical structures of odorants and previously recorded behavioral responses of bees. The model was refined with new behavioral data the team obtained in the lab from honey bees and Drosophila (fruit flies), allowing for a more accurate prediction of insect olfactory responses. Once optimized, the system screened more than 50 million compounds, ultimately identifying about 130 that were predicted to have strong potential as bee repellents.
“It is generally thought you need abundant data to do any kind of machine learning, but that’s not true for olfaction,” Ray said. “You simply need good-quality data and iterative improvement steps.”
The team reports in the journal eLife how they put the top-performing candidates to the test. In the lab, honey bees exhibited clear avoidance behaviors when exposed to these candidates, aligning closely with the model’s predictions. Subsequent field experiments with freely foraging bees confirmed that all seven compounds tested reliably repelled bees from honey combs without harming them.
“This is a powerful demonstration of how machine learning can help solve real-world ecological problems,” Ray said. “By keeping bees away from harmful pesticides, we can potentially reduce their risk of exposure without compromising the protection of crops.”
A contributing factor to bee population decline and colony collapse likely is unintended exposure to pesticides. While some pesticides that are harmful to bees have been banned or restricted, Ray said there is potential to further lower risks by using the pesticides with repellents that can minimize bee contact.
The bee-repelling compounds have other applications.
“In certain public environments — hospitals, office buildings, and residential areas, for example — reducing the formation of beehives can help avoid human-bee conflicts,” he said. “Also, some agricultural practices want to avoid pollination altogether, particularly with seedless fruit varieties.”
According to Ray, the research is a step toward developing bee-friendly pesticide formulations — products that safeguard pollinators while still meeting the needs of modern agriculture.
“Protecting pollinators doesn’t have to come at the expense of food security,” Ray said. “With the right tools, we can strike a balance — and this model helps us get there. We believe our work will help guide further exploration of machine learning-guided solutions in environmental protection and sustainable farming practices.”
Ray and Baer were joined in the research by Joel Kowalewski, Barbara Baer-Imhoof, Tom Guda, Matthew Luy, and Payton DePalma.
Ray is founder and president of Sensorygen and Remote Epigenetics and has equity in both companies. Kowalewski has equity in Sensorygen. Ray, Kowalewski, Baer-Imhoof, Guda, Luy and Baer are inventors in a patent application on the compounds discussed in this article.
The research was funded by a grant from the California Research Alliance by BASF.
The research paper is titled “Machine learning of honey bee olfactory behavior identifies repellent odorants in free flying bees in the field.”
Header image credit: Yu Fang/UCR.
