Wild bumblebee pollinators have declined in abundance, diversity, and geographic distribution at an alarming rate over recent years. Chemical pesticides are a likely contributor to decline. Neonicotinoids in particular have been shown to cause behavioral problems at sublethal doses that persist in the environment. However, because wild pollinators are difficult to study in the field, it has
been challenging to determine whether these behavioral defects in individual pollinators translate into widespread population declines.
We have developed a data-driven agent-based computational model to test this hypothesis. The bum-blebee and plant agents in the model are programmed to follow behavioral rules based on both field observations as ground-truth and data from laboratory experiments. Our initial results show that when we introduce behavioral defects in the virtual bees that are known to occur in real bees from chronic neon-icotionoid exposure, the data-driven model predicts significant population declines of both bees and plants, and loss of ecosystem biodiversity (Fig H). Further development of the model can help policy makers better understand and predict the long-term effects of chemical toxins on the environment and make the case for better management of our natural resources along with introduction of appropriate policies to avoid ecological catastrophe.
Researchers: Dr. Liz Ryder, Biology & Biotechnology, & Data Science faculty, TBD.
We have developed a data-driven agent-based computational model to test this hypothesis. The bum-blebee and plant agents in the model are programmed to follow behavioral rules based on both field observations as ground-truth and data from laboratory experiments. Our initial results show that when we introduce behavioral defects in the virtual bees that are known to occur in real bees from chronic neon-icotionoid exposure, the data-driven model predicts significant population declines of both bees and plants, and loss of ecosystem biodiversity (Fig H). Further development of the model can help policy makers better understand and predict the long-term effects of chemical toxins on the environment and make the case for better management of our natural resources along with introduction of appropriate policies to avoid ecological catastrophe.
Researchers: Dr. Liz Ryder, Biology & Biotechnology, & Data Science faculty, TBD.