Analyzing designed experiments in distance sampling

  • Stephen T. Buckland
  • Robin E. Russell
  • Brett G. Dickson
  • Victoria A. Saab
  • Donal N. Gorman
  • William M. Block
Article

DOI: 10.1198/jabes.2009.08030

Cite this article as:
Buckland, S.T., Russell, R.E., Dickson, B.G. et al. JABES (2009) 14: 432. doi:10.1198/jabes.2009.08030

Abstract

Distance sampling is a survey technique for estimating the abundance or density of wild animal populations. Detection probabilities of animals inherently differ by species, age class, habitats, or sex. By incorporating the change in an observer’s ability to detect a particular class of animals as a function of distance, distance sampling leads to density estimates that are comparable across different species, ages, habitats, sexes, and so on. Increasing interest in evaluating the effects of management practices on animal populations in an experimental context has led to a need for suitable methods of analyzing distance sampling data. We outline a two-stage approach for analyzing distance sampling data from designed experiments, in which a two-step bootstrap is used to quantify precision and identify treatment effects. We illustrate this approach using data from a before—after control-impact experiment designed to assess the effects of large-scale prescribed fire treatments on bird densities in ponderosa pine forests of the southwestern United States.

Key Words

BACI designBootstrapDistance samplingPoint transect sampling

Copyright information

© International Biometric Society 2009

Authors and Affiliations

  • Stephen T. Buckland
    • 1
  • Robin E. Russell
    • 2
  • Brett G. Dickson
    • 3
  • Victoria A. Saab
    • 2
  • Donal N. Gorman
    • 4
  • William M. Block
    • 5
  1. 1.Centre for Research into Ecological and Environmental ModellingUniversity of St. AndrewsSt. AndrewsScotland
  2. 2.U.S. Forest Service, Rocky Mountain Research StationMontana State University CampusBozeman
  3. 3.Center for Sustainable EnvironmentsNorthern Arizona UniversityFlagstaff
  4. 4.School of Mathematics and StatisticsUniversity of St. AndrewsSt. AndrewsUK
  5. 5.U.S. Forest Service, Rocky Mountain Research StationSouthwest Forest Science ComplexFlagstaff