2023 Modelling daily weight variation in honey bee hives

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In our manuscript, we address the challenge of determining the health state of bee hives by analysing the time series of hive weights (readily available data). We have assembled a multidisciplinary team of mathematicians and honeybee biologists to build a meaningful mechanistic model that connects foraging activities with the fluctuations in the weight of a honeybee hive. Additionally, we apply a Bayesian inference framework that allows us to infer key parameters about the state of the hive from the time series of hive weight. Unlike traditional approaches to data analysis, which rely on standard fitting techniques and phenomenological considerations, our approach enables us to estimate interpretable parameters of the evaluated hives—such as the number of active foragers and their foraging success—using rigorous statistical analysis.

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Description: Graphical representation of the solution of our model, where the weight is described as a function of time, W(t). Source: Plos-Computational Biology, Research article, Modelling daily weight variation in honey bee hives