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Parameterization and Sensitivity Analysis of a Honey Bee Colony Dynamics Model for Neonicoinoid Exposure Events Using MCMC Methods
Citation:
Minucci, J., R. Curry, G. DeGrandi-Hoffman, K. Garber, R. Johnson, A. Kanarek, C. Lin, AND Tom Purucker. Parameterization and Sensitivity Analysis of a Honey Bee Colony Dynamics Model for Neonicoinoid Exposure Events Using MCMC Methods. 2018 International Congress on Environmental Modelling and Software, Fort Collins, CO, June 24 - 28, 2018.
Impact/Purpose:
Oral presentation at International Environmental Modelling and Software Society. June 2018.
Description:
Honey bee colony losses have increased in recent decades in both Europe and North America, resulting in a potential risk to pollinator-dependent crops and wild plants. While multiple stressors to honey bee colonies appear to be driving this decline, exposure to neonicotinoid insecticides has been identified as a key factor leading to increased bee mortality. Neonicotinoid insecticides, such as clothianidin and thiamethoxam, are applied as seed treatments to as much as 90% of the corn planted in the United States, and residues from these treatments can contaminate wild pollen sources utilized by honey bees. The simulation model VarroaPop+Pesticide, developed by the USDA, is currently being modified to predict honey bee hive dynamics in response to pesticide exposure. However, applying this model to neonicotinoids is complicated by a lack of parameterization information from the supporting literature for many variables, especially those related to in-hive pesticide dynamics. Here, we utilize data from a field study which measured neonicotinoid loads in pollen and tracked population dynamics of exposed hives to improve our estimation of VarroaPop parameters. We used Markov Chain Monte Carlo (MCMC) methods to sample the probability distribution of VarroaPop parameters and examined the likelihood of each parameter combination, given the field-derived population data. Through this procedure, we obtained posterior distributions which represent the most likely parameter values given a realistic neonicotinoid exposure scenario. Future work will use these neonicotinoid-optimized parameter distributions for global sensitivity analysis to determine what factors are most important in driving hive success or failure following exposure events. By improving the parameterization of VarroaPop for neonicotinoid exposure, we allow for this model to be more confidently used to evaluate the impacts of these compounds on honey bee populations.
