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Distance Sampling

Ecological Statistics

  1. Len Thomas1,
  2. Stephen T. Buckland1,
  3. Kenneth P. Burnham2,
  4. David R. Anderson2,
  5. Jeffrey L. Laake3,
  6. David L. Borchers1,
  7. Samantha Strindberg1

Published Online: 15 SEP 2006

DOI: 10.1002/9780470057339.vad033

Encyclopedia of Environmetrics

Encyclopedia of Environmetrics

How to Cite

Thomas, L., Buckland, S. T., Burnham, K. P., Anderson, D. R., Laake, J. L., Borchers, D. L. and Strindberg, S. 2006. Distance Sampling. Encyclopedia of Environmetrics. 1.

Author Information

  1. 1

    University of St Andrews, UK

  2. 2

    Colorado State University, CO, USA

  3. 3

    National Marine Mammal Laboratory, WA, USA

Publication History

  1. Published Online: 15 SEP 2006

This is not the most recent version of the article. View current version (15 JAN 2013)

Abstract

Distance sampling is a widely-used group of closely related methods for estimating the density and/or abundance of biological populations. The main methods are line transects and point transects (also called variable circular plots). These have been used successfully in a very diverse array of taxa, including trees, shrubs and herbs, insects, amphibians, reptiles, birds, fish, marine and land mammals. In both cases, the basic idea is the same. The observer(s) perform a standardized survey along a series of lines or points, searching for objects of interest (usually animals or clusters of animals). For each object detected, they record the distance from the line or point to the object. Not all the objects that the observers pass will be detected, but a fundamental assumption of the basic methods is that all objects that are actually on the line or point are detected. Intuitively, one would expect that objects become harder to detect with increasing distance from the line or point, resulting in fewer detections with increasing distance. The key to distance sampling analyses is to fit a detection function to the observed distances, and use this fitted function to estimate the proportion of objects missed by the survey. From here one can readily obtain point and interval estimates for the density and abundance of objects in the survey area.

Keywords:

  • line transects;
  • strip transects;
  • interval estimation;
  • cluster size;
  • bootstrap