An approximate Bayesian computation approach for estimating parameters of complex environmental processes in a cellular automata
Modelling an environmental process involves creating a model structure and parameterising the model with appropriate values to accurately represent the process. Determining accurate parameter values for environmental systems can be challenging. Existing methods for parameter estimation typically make assumptions regarding the form of the Likelihood, and will often ignore any uncertainty around estimated values. This can be problematic, however, particularly in complex problems where Likelihoods may be intractable. In this paper we demonstrate an Approximate Bayesian Computational method for the estimation of parameters of a stochastic CA. We use as an example a CA constructed to simulate a range expansion such as might occur after a biological invasion, making parameter estimates using only count data such as could be gathered from field observations. We demonstrate ABC is a highly usefulmethod for parameter estimation, with accurate estimates of parameters that are importantfor the management of invasive species such as the intrinsic rate of increase and the point in a landscapewhere a species has invaded. We also show that the method is capable of estimatingthe probabilityof long distance dispersal, a characteristic of biological invasions that isveryinfluentialin determining spread rates but has until now proved difficult to estimate accurately.
approximate bayesian computations cellular automata range expansion population dynamics