Spatial Point Processes for Modelling Plant Communities in the Presence of Interaction Uncertainty

Glenna Evans, Janine B. Illian and Ruth King

Universities of St. Andrews and Edinburgh


Current ecological research seeks to understand the mechanisms that sustain biodiversity and allow a large number of species to coexist. Coexistence concerns inter-individual interactions. Consequently, there is an interest in identifying and quantifying interactions within and between species as reflected in the spatial pattern formed by the individuals. This study analyses the spatial pattern formed by the locations of plants in a community with high biodiversity from Western Australia. We fit a pairwise interaction Gibbs marked point process to the data using a Bayesian approach and quantify the inhibitory interactions within and between the two species. We select the most suitable model through Bayes Factors obtained from a Reversible Jump Markov Chain Monte Carlo algorithm. The results provide evidence that the intra-specific interactions for the two species studied are generally lower than those between the two species.


Multivariate spatial point patterns; Gibbs point processes; Reversible Jump Markov Chain Monte Carlo.