Spatial models for distance sampling data: recent developments and future directions
Article first published online: 27 AUG 2013
© 2013 The Authors. Methods in Ecology and Evolution © 2013 British Ecological Society
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Methods in Ecology and Evolution
Volume 4, Issue 11, pages 1001–1010, November 2013
How to Cite
Miller, D. L., Burt, M. L., Rexstad, E. A., Thomas, L. (2013), Spatial models for distance sampling data: recent developments and future directions. Methods in Ecology and Evolution, 4: 1001–1010. doi: 10.1111/2041-210X.12105
- Issue published online: 6 NOV 2013
- Article first published online: 27 AUG 2013
- Accepted manuscript online: 29 JUL 2013 10:10AM EST
- Manuscript Accepted: 19 JUL 2013
- Manuscript Received: 13 MAR 2013
- US Navy, Chief of Naval Operations. Grant Number: N00244-10-1-0057
- abundance estimation;
- Distance software;
- generalized additive models;
- line transect sampling;
- point transect sampling;
- population density;
- spatial modelling;
- wildlife surveys
- Our 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 an R package developed by the authors (dsm) and are largely implemented in the popular Windows software Distance.