Modelling the Spatial Dependence of Multi-Species Point Patterns.

The study of the spatial point patterns in ecology, such as the records of the observed locations of trees, shrubs, nests, burrows, or documented animal presence, relies on multivariate point process models. This study aims to compare the efficacy and applicability of two prominent multivariate point process models, the multivariate log Gaussian Cox process (MLGCP), and the saturated pairwise interaction Gibbs point process model (SPIGPP), highlighting their respective strengths and weaknesses when prior knowledge of the underlying mechanisms driving the patterns is lacking. Using synthetic and real datasets, we assessed both models based on their predictive accuracy of the empirical K function.