Spatial models for distance sampling data: recent developments and future directions

Abstract

SummaryOur understanding of a biological population can be greatly enhanced by modelling their distribution in space and as a function of environmental covariates. Such models can be used to investigate the relationships between distribution and environmental covariates as well as reliably estimate abundances and create maps of animal/plant distribution.Density surface models consist of a spatial model of the abundance of a biological population which has been corrected for uncertain detection via distance sampling methods.We review recent developments in the field and consider the likely directions of future research before focussing on a popular approach based on generalized additive models. In particular, we consider spatial modelling techniques that may be advantageous to applied ecologists such as quantification of uncertainty in a two‐stage model and smoothing in areas with complex boundaries.The methods discussed are available in anRpackage developed by the authors (dsm) and are largely implemented in the popular Windows software Distance.