Honey Bee Pathophysiology as a Predictive Measure of Overwinter Colony Loss

View the project final report

Commodities

• Animals: bees

Practices

• Animal Production: animal protection and health
• Crop Production: pollinator health

At the forefront of apicultural research, scientists are looking for ways to mitigate the high levels of colony loss, both over winter and annually. Though there are many ways to approach the main drivers of these losses (poor nutrition, pesticides, and parasites), few studies have investigated how tissue level changes relate to mortality in honey bees or their colonies. Here I propose to enhance an existing project designed to use pathophysiology as a tool for predicting overwinter colony mortality. This will be achieved by building on previous work where pathophysiology was used to successfully identify symptoms in bees and colonies diagnosed with Colony Collapse Disorder. To date, I have performed over 2,100 autopsies as part of a doctoral thesis and determined that pathophysiology in September is most predictive of overwinter mortality. Using archived samples from an existing honey bee health monitoring program, I propose to increase our sample size in September with the aim of enhancing our predictive model, which employs a machine learning method developed and used to predict the malignancy status of breast tumors with over 95% accuracy. Given that an average of 2 hours is required to autopsy and manage data for one sample of bees, the focus of this proposal is on the manual labor needed for its execution. The result of this project will be a predictive model that may eventually allow beekeepers to intervene prior to colony mortality, thus reducing the financial loss associated with colony replacement.

1. Perform a retrospective case control study on pathophysiology traits of bees collected from colonies in September which survived the winter or did not.

2. Further refine and validate a colony survival predictive model.