Multi-output Gaussian processes for species distribution modelling

Abstract

Species distribution modelling is an active area of research in ecology. In recent years, interest has grown in modelling multiple species simultaneously, partly due to the ability to ‘borrow strength’ from similar species to improve predictions. Mixed and hierarchical models allow this but typically assume a (generalised) linear relationship between covariates and species presence and absence. On the other hand, popular machine learning techniques such as random forests and boosted regression trees are able to model complex nonlinear relationships but consider only one species at a time. We apply multi‐output Gaussian processes (MOGPs) to the problem of species distribution modelling. MOGPs..