Our results highlight important contributors to honey bee exposure and risk, identify in-hive parameters that may require additional research, inform the calibration of sensitive parameters, avoid overparameterization, and assess the relative importance of model submethods of hive population dynamics.īy 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 dynamics. We use linear approaches to assess first-order parameter sensitivities which allows us to determine how variance in the output is attributed to each of the input variables across different exposure scenarios in addition, the daily resolution of the model allows us to conditionally identify sensitivity metrics. foliar application, seed treatment and soil application) and a baseline scenario. We use a modified version of Varroapop, Varroapop+Pesticide, to predict population growth and behavior temporally in three pesticide exposure scenarios (i.e.
COLONY SURVIVAL EXITS TO MAIN MENU REGISTRATION
We evaluate the Varroapop colony model, proposed by the US EPA for pesticide registration evaluations, by simulating hive cohort dynamics with Monte Carlo simulations and sensitivity analysis techniques. Regulatory agencies assess risks to honey bees from pesticides through a tiered process that includes predictive modeling with empirical toxicity and chemical data of pesticides as a line of evidence.