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

Our understanding of a biological population can be greatly enhanced by modelling their distribution in space and as a function of environmental covariates.
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 offer a comparison of recent advances in the field and consider the likely directions of future research. 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 an \textsf{R} package developed by the authors and are largely implemented in the popular Windows package Distance (or are soon to be incorporated).
Density surface modelling enables applied ecologists to reliably estimate abundances and create maps of animal/plant distribution. Such models can also be used to investigate the relationships between distribution and environmental covariates.