Distance Sampling
Modelling
          Presented by
StatMathComp
Consulting
by Schwarz, Inc
          Carl James Schwarz  
       
Fellow of the American Statistical Association
        Accredited Professional Statistician
           P.Stat. (Statistical Society of Canada),
           PStat® (American Statistical Association)

Distance Sampling Modelling

Quck links: A complete zip file of this material is available here.

What is distance sampling modelling?

The term "distance sampling" covers a range of methods for assessing wildlife abundance: The course will concentrate primarily on line and point transect sampling but will deal with the more advanced topics of Multiple Covariate Distance Sampling (MCDS) and Mark-recapture Distance Sampling (MRDS).

The concepts of distance sampling will be explained and the assumptions of the methods discussed. Although the basic theory will be covered, the focus of the course will be on practical application of the methods.

The course will start with an introduction to wildlife population assessment methods, and demonstration of how line and point transect methods are generalizations of sample count methods (strip counts and point counts respectively). The underlying theory and assumptions of both line and point transect sampling will be covered, and the relative merits of the two approaches in different circumstances discussed.

More complex issues will then be covered. Special methods are required when animals occur in groups or "clusters". For example, size bias can occur - large clusters have a higher probability of detection than small clusters, so that population size is overestimated. Methods for adjusting for this bias will be given. Another issue is stratification, which is used to improve the precision of estimates when animal abundance, detection probability or clustering varies over time or space. Good survey design is an essential ingredient of a successful survey so design issues and field methods will be covered in detail. Some specialized applications of distance sampling such as cue counting, trapping webs, and indirect counts (e.g., dung or nests) will be mentioned.

A nice introduction is found at this article in Significance magazine


Software

Distance program. An R-package is also available and likely "better" for most usages because it works on all platforms (Mac and Windoze), it is more flexibile in data formats etc, and you have the ability to save scripts across analyses. Contact me for details.

DISTANCE 6.0
This program is available at no charge from from http://www.ruwpa.st-and.ac.uk/distance/. If you want to make use of the density surface modelling (DSM-not covered in this course) or mark-recapture distance sampling (MRDS) analysis engines, you need to have the free statistics software R installed on your computer. Consult the DISTANCE web site for details.
Mac users - The program DISTANCE only runs on Windows platforms. So Mac users need to install a virtualization program (e.g. VirtualBox or BootCamp), install Windows on the virtual machine, and then the Mac looks like a Windows machine. (I'm an avid Mac user; ask me if you run into problems!)


Courses

I offer a three day course on Distance Sampling IModelling covering the following topics. I can also do this or other courses onsite at your organization. Please contact me (Carl Schwarz) for information.

Slides from the course are available in this directory.

Additional documentation available here

A typical course outline

  1. Introduction and brief statistical review
  2. Line Transect Distance Sampling.
  3. Point Transect Distance sampling.
  4. Multiple covariate line and point distance sampling (MCDS)
  5. Dealing with multipliers such as animal signs.
  6. Planning a distance/point transect survey - some sample size guidelines.
  7. Mark-Recapture Distance Sampling (MRDS)
Additonal course covering more advance topics in Distance Sampling modelling (e.g. bayesian methods, spatial methods, multi-method, multi-scale) can also be arranged. Please contact me for details.

Sample data, code, and analyses

An alphabetical list of all the data is available.

Directory of sample data


Email comments or suggestions to cschwarz.stat.sfu.ca@gmail.com
Last updated 2025-11-01