This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: 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 paper is published in the journal eLife. 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 to altering pollinator behavior 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 the chemical structures of odorants and previously recorded behavioral responses of bees.
They refined the model with new behavioral data the team obtained in the lab from honey bees and Drosophila (fruit flies), allowing for more accurate predictions of insect olfactory responses. Once optimized, the system screened more than 50 million compounds, ultimately identifying about 130 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 avoided the candidates, closely matching the model's predictions. Subsequent field experiments with freely foraging bees confirmed that all seven compounds tested reliably repelled bees from honeycombs 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." Unintended exposure to pesticides is likely a contributing factor to bee population decline and colony collapse. While some pesticides harmful to bees have been banned or restricted, Ray said using repellents alongside pesticides could further reduce the risk of bee contact.
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