Abstract
Hierarchical models have been widely deployed for the modelling of species distribution and abundance, because they enable one to separately model the actual quantity of interest (e.g., presence/absence or abundance) from measurement errors commonly found in ecological data sets. When modelling species distributions/abundance across large spatial domains and/or using a large number of observed locations, accommodating spatial autocorrelation becomes increasingly important. Failing to account for measurement errors and/or residual spatial autocorrelation (i.e., remaining spatial autocorrelation after accounting for environmental covariates) can lead to biased and overly precise estimates, potentially jeopardizing scientific conclusions and management decisions based on such data sets. In this workshop, we present highly scalable approaches for hierarchical Bayesian spatial modelling of species distributions and abundance. This course focuses on practical implementation of hierarchical spatial models using the spOccupancy and spAbundance R packages.
