• Open Access

Distance software: design and analysis of distance sampling surveys for estimating population size

Authors

  • Len Thomas,

    Corresponding author
    1. Research Unit for Wildlife Population Assessment, Centre for Research into Ecological and Environmental Modelling, University of St. Andrews, St. Andrews KY16 9LZ, UK
      *Correspondence author. E-mail: len@mcs.st-and.ac.uk
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  • Stephen T. Buckland,

    1. Centre for Research into Ecological and Environmental Modelling, University of St. Andrews, St. Andrews KY16 9LZ, UK
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  • Eric A. Rexstad,

    1. Research Unit for Wildlife Population Assessment, Centre for Research into Ecological and Environmental Modelling, University of St. Andrews, St. Andrews KY16 9LZ, UK
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  • Jeff L. Laake,

    1. National Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, 7600 Sand Point Way NE F/AKC3, Seattle, WA 98115 6349, USA
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  • Samantha Strindberg,

    1. Wildlife Conservation Society, 2300 Southern Boulevard, Bronx, NY 10460, USA
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  • Sharon L. Hedley,

    1. Centre for Research into Ecological and Environmental Modelling, University of St. Andrews, St. Andrews KY16 9LZ, UK
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  • Jon R.B. Bishop,

    1. Research Unit for Wildlife Population Assessment, Centre for Research into Ecological and Environmental Modelling, University of St. Andrews, St. Andrews KY16 9LZ, UK
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  • Tiago A. Marques,

    1. Research Unit for Wildlife Population Assessment, Centre for Research into Ecological and Environmental Modelling, University of St. Andrews, St. Andrews KY16 9LZ, UK
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  • Kenneth P. Burnham

    1. Colorado Cooperative Fish and Wildlife Research Unit, Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, CO 80523, USA
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*Correspondence author. E-mail: len@mcs.st-and.ac.uk

Summary

1. Distance sampling is a widely used technique for estimating the size or density of biological populations. Many distance sampling designs and most analyses use the software Distance.

2. We briefly review distance sampling and its assumptions, outline the history, structure and capabilities of Distance, and provide hints on its use.

3. Good survey design is a crucial prerequisite for obtaining reliable results. Distance has a survey design engine, with a built-in geographic information system, that allows properties of different proposed designs to be examined via simulation, and survey plans to be generated.

4. A first step in analysis of distance sampling data is modelling the probability of detection. Distance contains three increasingly sophisticated analysis engines for this: conventional distance sampling, which models detection probability as a function of distance from the transect and assumes all objects at zero distance are detected; multiple-covariate distance sampling, which allows covariates in addition to distance; and mark–recapture distance sampling, which relaxes the assumption of certain detection at zero distance.

5. All three engines allow estimation of density or abundance, stratified if required, with associated measures of precision calculated either analytically or via the bootstrap.

6. Advanced analysis topics covered include the use of multipliers to allow analysis of indirect surveys (such as dung or nest surveys), the density surface modelling analysis engine for spatial and habitat modelling, and information about accessing the analysis engines directly from other software.

7.Synthesis and applications. Distance sampling is a key method for producing abundance and density estimates in challenging field conditions. The theory underlying the methods continues to expand to cope with realistic estimation situations. In step with theoretical developments, state-of-the-art software that implements these methods is described that makes the methods accessible to practising ecologists.

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