Inlabru: an R package for Bayesian spatial modelling from ecological survey data

Abstract

Abstract Spatial processes are central to many ecological processes, but fitting models that incorporate spatial correlation to data from ecological surveys is computationally challenging. This is particularly true of point pattern data (in which the primary data are the locations at which target species are found), but also true of gridded data, and of georeferenced samples from continuous spatial fields. We describe here the R package inlabru that builds on the widely used RINLA package to provide easier access to Bayesian inference from spatial point process, spatial count, gridded, and georeferenced data, using integrated nested Laplace approximation (INLA, Rue et al., 2009). The package provides methods for fitting spatial density surfaces and estimating abundance, as well as for plotting and prediction. It accommodates data that are points, counts, georeferenced samples, or distance sampling data. This paper describes the main features of the package, illustrated by fitting models to the gorilla nest data contained in the package spatstat (Baddeley, & Turner, 2005), a line transect survey dataset contained in the package dsm (Miller, Rexstad, Burt, Bravington, & Hedley, 2018), and to a georeferenced sample from a simulated continuous spatial field.