Skip to main content
ARS Home » Pacific West Area » Tucson, Arizona » Carl Hayden Bee Research Center » Research » Publications at this Location » Publication #340242
Title: Sensitivity analyses for simulating pesticide impacts on honey bee colonies

Author
item KUAN, A. CARMEN - Oak Ridge Institute For Science And Education (ORISE)
item DeGrandi-Hoffman, Gloria
item CURRY, ROBERT - Crystal River Consulting Llc
item GARBER, KRIS - Environmental Protection Agency (EPA)
item KANAREK, ANDREW - Environmental Protection Agency (EPA)
item SNYDER, MARCIA - Environmental Protection Agency (EPA)
item WOLFE, KURT - Environmental Protection Agency (EPA)
item PURUCKER, S. THOMAS - Environmental Protection Agency (EPA)

Submitted to: Ecological Modelling
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/20/2018
Publication Date: 3/17/2018
Citation: Kuan, A., Hoffman, G.D., Curry, R., Garber, K., Kanarek, A., Snyder, M., Wolfe, K., Purucker, S. 2018. Sensitivity analyses for simulating pesticide impacts on honey bee colonies. Ecological Modelling. 376:15-27. https://doi.org/10.1016/j.ecolmodel.2018.02.010.
DOI: https://doi.org/10.1016/j.ecolmodel.2018.02.010

Interpretive Summary: A factor frequently associated with the loss of honey bee colonies is sub-lethal exposure to pesticides. Often mortality is the product of a combination of stress factors experienced by the colony that include poor queen quality, nutritional stress and the effects on worker production and longevity, and levels of Varroa infestation. To assess the impact that pesticides might have on colony survival, we conducted simulations with the VARROAPOP model that includes mortality from pesticide exposure (VarroaPop+Pesticide). VARROAPOP predicts colony growth based on numerous biotic and environmental factors. By including exposure to pesticides into the model, the interactions among factors that affect colony population growth could be simultaneously considered with pesticide exposure. Simulations with VarroaPop+Pesticide indicate that queen strength and worker lifespan are consistent, critical factors affecting colony growth and survival with and without pesticide exposure. A pesticide’s adult contact toxicity, the application rate and concentration of the pesticide in nectar loads also are critical parameters for colony dynamics. Daily sensitivity analysis reveals that the relative importance of these parameters fluctuates throughout the simulation period according to the status of other inputs such as season when the colony was exposed to the pesticide and condition of the colony at the time of exposure. Implementing the approach used during the development of this regulatory model can provide a defendable tool for assessing risks to pollinators from pesticide exposure and reduce uncertainty commonly associated with predictions of complex ecological processes for environmental risk assessment.

Technical Abstract: We employ Monte Carlo simulation and sensitivity analysis techniques to describe the population dynamics of pesticide exposure to a honey bee colony using the VarroaPop+Pesticide model. Simulations are performed of hive population trajectories with and without pesticide exposure to determine the effects of weather, queen strength, foraging activity, colony resources, and Varroa populations on colony growth and survival. The daily resolution of the model allows us to conditionally identify sensitivity metrics. We use linear approaches to assess first-order parameter sensitivities within VarroaPop+Pesticide, allowing us to determine how variance in the output is attributed to each of the input variables across different exposure scenarios. Simulations with VarroaPop+Pesticide model indicate queen strength and forager lifespan are consistent, critical inputs for colony dynamics in both the control and exposed conditions. Adult contact toxicity, application rate and nectar load become critical parameters for colony dynamics within exposed simulations. Daily sensitivity analysis also reveals that the relative importance of these parameters fluctuates throughout the simulation period according to the status of other inputs. By examining partial correlation coefficients from day to day, we were able to identify conditional model variability and attribute these sensitivities to seasonal and life history influences. Implementing this approach during the development of this regulatory model can better inform calibrations of the VarroaPop+Pesticide simulation model, provide a defendable model for assessing risks to pollinators from pesticide exposure and reduce uncertainty associated with using predictions of complex ecological processes for environmental risk assessment.