Spatial model predicts bumblebee exposure to pesticide use

Agricultural pesticides have long been known to pose one of the greatest threats to bees and other essential pollinators. What farmers lack is an understanding of how different pesticides, applied at different times to various crops, affect the exposure risk of bees living near fields.

Researchers used real-world data to try to fill this gap, developing and testing a spatial model to predict pesticide exposure in bumblebees. The work is published in Total Environmental Science and focuses on the interactions of the yellow-faced bumblebee (Bombus vosnesenskii) with crops in California.

“We were able to explain almost 75% of the spatial variation in pesticide exposure among bumble bee hives using our model,” says Eric Lonsdorf, first author of the study and assistant professor in the Department of Health Sciences. Emory environment.

Relatively simple models proved more effective in preventing exposures than researchers expected.

“Our results suggest that simple data on where and when a pesticide was sprayed is enough to make a good prediction of the threat to neighboring hives,” says Lonsdorf.

Including data on how long a particular chemical persisted in the landscape or how attractive a particular crop’s flowers were to bees did not make a significant difference in the predictive power of the model.

“We found that even if a crop is not very attractive to bees, the chemicals from that crop still end up in their pollen,” Lonsdorf says. “Bees can pick up the chemical due to the pesticide drifting onto nearby weeds where they forage.”

Providing tools for conservation

Lonsdorf studies natural capital, or nature’s contributions to humans. It translates ecological principles and knowledge into predictive models that enable industry leaders and policy makers to better manage natural resources.

He is currently using the models he developed to help the U.S. Fish and Wildlife Service identify priority areas for bee conservation in the United States.

Lonsdorf says more research is needed to determine whether the bumblebee risk prediction model will extend to different landscapes and different bee species. The current study also did not examine how the amount of a particular pesticide found in pollen translated into toxicity to bees.

Rely on fine-scale data

The researchers began with experiments conducted among diverse cultures in Yolo County in northern California. Fourteen pairs of yellow-faced bumblebee colonies were placed around the agricultural landscape. This bumblebee species is native to the West Coast and is the most abundant wild bee species in this range, found in both urban and agricultural areas.

Pollen collected by bees from each hive was sampled at six different times during the growing season. The pollen samples were then assessed for exposure to 52 different active ingredients encompassing a range of pesticides.

Data from these experiments were combined with field data from the California Department of Pesticide Regulation on pesticides sprayed and spray days.

“California is unique in providing public data at such a precise scale,” Lonsdorf says. “In most parts of the United States, information on pesticide sprays is only collected at the county level and summarized on an annual basis.”

The detailed data allowed the researchers to consider a range of factors in their predictive model to identify the factors with the highest predictive power.

“Our risk prediction model marks another step toward assessing pollinator conservation issues to help guide policies related to pollinator landscapes,” says Lonsdorf. “The next step is to conduct a field toxicity assessment to better understand how pesticides affect bee health.”

He and his colleagues are currently conducting such a study on bees, he adds.