A computational analysis of honeybee colony collapse prediction model and simulations

Abstract

Honeybees play a critical role as natural pollinator and are essential to global food production. Monitoring honeybee population densities can provide valuable insights into the environmental status of a given region, although effectively carrying out such monitoring is challenging. To address this issue, this study focused on the development of a mathematical model to predict population density and detect potential colony collapse. The model utilized a set of effective arrays of differential equations that consider crucial parameters. Analyzing actual data using the model revealed that regions with higher flower densities experienced reduced vulnerability to unnatural deaths or diseases, while those with lower flower densities tended to have smaller populations. Furthermore, numerical simulations showed that unnatural death rates exerted the most significant impact on the model. In adverse environmental conditions, forager populations decline first, leading to decreased food availability and potential colony collapse. This model, as a highly practical tool, holds immense value for environmentalists seeking precise predictions of honeybee population density within their respective regions.