Modeling of honey bee and varroa mite population dynamics

Abstract EN

The life history of the honey bee Apis mellifera and the bee mite Farroa Jacobson and their interactions were modeled using a commercial software package known as Stella II.

Stella II provides a no mathematically intensive modeling technique that allows the user to generate a series of differential equations that can track a population through time with a minimum set or parameters.

The model generates population statistics at regular intervals throughout a designated time period and outputs diagrams, a series of equations, tables and graphs.

Mite parameters included in the model were : initial mite population, number of female offspring per mother mite in both worker and drone brood, number of reproductive cycles, fertility, percent mite preference for drone versus worker brood, phonetic period, mortality, and removal of infested brood cells by .hygienic worker bees.

Interaction between the bee and mite population were also modeled.

For example, the impact of various mite infestation levels on honey bee colony dynamics was modeled using three different mites initial infestations rates (5, 10 and d 20 mites).

The first year infestation rate resulted in different maximum mite population’s levels, and the estimate was 690, 1,339 and 2,521 for the three rates, respectively.

In the second year, the mite population maximum grew to 11,000, 17,000 and 25,000, respectively.

However, the colony collapse was inversely proportional to the initial infestation rate.

For example, the colony with an initial rate of 20 mites collapsed first.

It is also possible to change other parameters of the model such as post capping period, fertility and fecundity rate, or to introduce chemical and biological control mechanisms.

The results from these model simulations may help beekeepers alter their control strategies for the mites.

The objective of this modeling project is to develop a tool will both predict bee and mite populations under specific conditions and allow scientists to vary and check the influence of single factors on the mite population development.

In addition, it should help in the development of research hypotheses to test under field conditions.

It also was the hope of this modeling project that by integrating past research results it would help identify research gaps that need further investigation in an effort to find better ways to control mite populations in bee colonies.