A new modelling framework for predator-prey interactions: A case study of an aphid-ladybeetle system

The significance of the relationship between data and models in ecology cannot be overstated. Over the past few decades, numerous techniques have been developed and applied in the study of ecological systems. One such technique is the Bayesian calibration of model parameters, which draws from Bayes' theorem to incorporate prior knowledge in estimating parameter values and quantifying their uncertainty. In this study, we establish a Bayesian framework for analysing ecological time series and apply it to a specific case of aphid-ladybeetle predation. To accomplish this, we construct and assess six mathematical models consisting of ordinary differential equations. These models encompass three phenomenological models and three data-driven models. To gain insights into the behaviour of the models concerning a specific quantity of interest, namely aphid abundance, we conduct a sensitivity analysis of the parameters. Subsequently, we compare the outputs of the models by employing model selection techniques. Based on our analysis, the best-performing model is a phenomenological one that incorporates a Holling's type II functional response. We believe that this framework has the potential for application and extension to other ecological systems, thereby enhancing our understanding of these intricate systems.