Location: Honey Bee Research
Project Number: 2022-21000-018-14-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Sep 8, 2015
End Date: Sep 30, 2019
1) Add impact of resource availability and changing forager population sizes to colony population dynamics. 2) Add user interface to enable input related to forager activity and pesticide exposure. 3) Add new logic for adult worker bee aging with user specified forager population sizes. 4) Add new logic for adult worker bee longevity and mortality from Varroa based on user specified forager population sizes. 5) Revise logic for mite infestation of drone cells and make appropriate changes in program code. 6) Add R wrapper functionality into the model for all forager dynamics. 7) Allow user to specify multiple pesticide applications. 8) Improve interoperability with multiple weather file formats and weather file addressing. 9) Support collaboration and validation activities.
The BEEPOP/VARROAPOP program developed by ARS (Gloria DeGrandi-Hoffman and Robert Curry) was modified to include mortality from pesticides. Computer code was written to include equations predicting mortality at each lifestage based on exposure rate and toxicity of the pesticide. A user interface was designed and additional computer code was written so that pesticide information can be included in simulations, and the program can read weather data formatted for EPA. Those components are now fully functional. Additional programming is needed to increase the sensitivity to the amounts of incoming contaminated nectar and pollen based on user-defined forager population, and for storage and use of contaminated resources. Determining the effects that changes in forager populations have on Varroa population dynamics, worker longevity and ultimately colony mortality from the combination of sublethal pesticide exposure and Varroa infestation also requires additional programming and modifications to the user interface. Sensitivity analysis will require numerous simulations with the model based on random distributions of input variables. To aid in this analysis, code will be written to enter input variables based on Monte Carlo distributions. Output will be saved to files for further analysis.