Modelling daily weight variation in honey bee hives

A quantitative understanding of the dynamics of bee colonies is important to support global efforts to improve bee health and enhance pollination services. Traditional approaches focus either on theoretical models or data-centred statistical analyses. Here we argue that the combination of these two approaches is essential to obtain interpretable information on the state of bee colonies and show how this can be achieved in the case of time series of intra-day weight variation. We model how the foraging and food processing activities of bees affect global hive weight through a set of ordinary differential equations and show how to estimate the parameters of this model from measurements on a single day. Our analysis of 10 hives at different times shows that the estimation of crucial indicators of the health of honey bee colonies are statistically reliable and fall in ranges compatible with previously reported results. The crucial indicators, which include the amount of food collected (foraging success) and the number of active foragers, may be used to develop early warning indicators of colony failure. Author summaryHoney bees are under threat and dying at an alarming rate due to pesticides, parasites, and other stressors. Obtaining information about the health of bee colonies is essential to understand how this happens and to identify measures that can prevent this from happening. Herein, we built a mathematical model of the daily dynamics of hives that allows such information to be extracted without detailed and expensive measurements. Based only on measurements of how the weight of the hive changes during the day, our model can be used to estimate how many bees are collecting food, how successful they are, and how much time they spend outside the hive. Due to its simplicity, the model presented here can be applied to a wide range of hive scale systems and help beekeepers track how healthy and productive their bees are.

Automated summary

A quantitative understanding of bee colony dynamics is crucial for improving bee health and pollination services. This study combines theoretical modeling and statistical analysis to interpret the state of bee colonies using intra-day weight variation data. The results show that crucial indicators of colony health can be estimated, providing early warning indicators of colony failure.