A New Approach for Detecting Sublethal Effects of Neonicotinoids on Bumblebees Using Optical Sensor Technology

SIMPLE SUMMARY: Several lab studies have shown that pesticides are causing sublethal effects on bees; however, little is known about their impact in actual field conditions. Continuous surveillance of the bees in the field will help monitor their pesticide exposure and better understand their impact on bee health. This study investigated the potential use of a new optical sensor, coupled with machine learning, to automatically identify flying insects, as an approach to monitoring the sublethal effects of pesticides on insects. To do so, bumblebees, Bombus terrestris, were exposed to field-realistic doses of a neonicotinoid pesticide, and their flight activity was recorded by the sensor. Through the data acquired, the investigation focused on whether the machine learning algorithm could distinguish the flight events that were created by the healthy bumblebees and the bumblebees that had been exposed to sublethal doses of the pesticide. The results showed that the algorithm could differentiate flight events, indicating the promise of the sensor as a continuous monitoring tool for bee health in the field. ABSTRACT: Among insects, bees are important pollinators, providing many vital ecosystem services. The recent pollinator decline is threatening both their diversity and abundance. One of the main drivers of this decline is the extensive use of pesticides. Neonicotinoids, one of the most popular groups of pesticides, can be toxic to bees. In fact, numerous studies have found that neonicotinoids can cause sublethal effects, which can impair the biology, physiology, and colony survival of the bees. Yet, there are still knowledge gaps, and more research is needed to better understand the interaction between neonicotinoids and bees, especially in the field. A new optical sensor, which can automatically identify flying insects using machine learning, has been created to continuously monitor insect activity in the field. This study investigated the potential use of this sensor as a tool for monitoring the sublethal effects of pesticides on bumblebees. Bombus terrestris workers were orally exposed to field-realistic doses of imidacloprid. Two types of exposures were tested: acute and chronic. The flight activity of pesticide-exposed and non-exposed bumblebees was recorded, and the events of the insect flights recorded by the sensor were used in two ways: to extract the values of the wingbeat frequency and to train machine learning models. The results showed that the trained model was able to recognize differences between the events created by pesticide-exposed bumblebees and the control bumblebees. This study demonstrates the possibility of the optical sensor for use as a tool to monitor bees that have been exposed to sublethal doses of pesticides. The optical sensor can provide data that could be helpful in managing and, ideally, mitigating the decline of pollinators from one of their most major threats, pesticides.