Generalized additive modelling and zero inflated count data

This paper describes a flexible method for modelling zero inflated count data which are typically found when trying to model and predict species distributions. Zero inflated data are defined as data that has a larger proportion of zeros than expected from pure count (Poisson) data. The standard methodology is to model the data in two steps, first modelling the association between the presence and absence of a species and the available covariates and second, modelling the relationship between abundance and the covariates, conditional on the organism being present. The approach in this paper extends previous work to incorporate the use of Generalized Additive Models (GAM) in the modelling steps. The paper develops the link and variance functions needed for the use of GAM with zero inflated data. It then demonstrates the performance of the models using data on stem counts of Eucalyptus mannifera in a region of South East Australia.