Machine learning of honey bee olfactory behavior identifies repellent odorants in free-flying bees in the field
Preventing beneficial insects like honey bees ( Apis mellifera ) from contacting pesticides on crops using odorants could counter current pollinator declines. However, the discovery of behaviorally aversive odorants is impeded by the complexity of the honey bee olfactory system where >170 olfactory receptors detect volatiles and generate valence. To solve this systems-level challenge, we…
Researchers have developed a machine-learning model to identify repellent odorants that could help combat pollinator declines caused by pesticide exposure. Their approach addresses the complexity of honey bee olfactory systems, which feature 170 olfactory receptors detecting volatile compounds.
The team first created a predictive model using published data on honey bee behavioral responses to various chemicals. By generating species-level behavioral data for honey bees and fruit flies, they refined the model to screen a vast chemical space of 50 million compounds. This screening process identified 130 potential repellent candidates.
To validate their findings, the researchers conducted laboratory behavioral tests on honey bees, confirming a high success rate in predicting repellency. The top seven candidates were then tested in a field assay involving freely foraging honey bees. These field tests revealed strong repellency, suggesting these compounds could effectively deter foraging bees from pesticide-treated crops.
This machine learning approach, combined with iterative testing and modeling, offers a promising method for discovering aversive volatiles to control insects, particularly when limited data is available.
Written by urgent.news from eLife's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
