ABSTRACT
Log-Gaussian Cox processes (LGCPs) are typically used to model phenomena that are environmentally driven. A common method for inference in LGCP models is to approximate the latent Gaussian field by means of a gridding approach. The LGCP model can be expressed within the generalized linear mixed model framework. While the previous approach is a common method for inference in LGCP models, the results obtained depend on the construction of a fine regular grid that cannot be locally refined. An alternative computationally efficient method to perform computational inference on LGCP is presented in Simpson et al. Species distribution models allow people to understand spatial patterns, and assess the influence of factors on species occurrence. These models are crucial for the development of appropriate strategies that help protect species and the environments where they live.
