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Model-Based Distance Sampling

Author

Listed:
  • S. T. Buckland

    (University of St Andrews)

  • C. S. Oedekoven

    (University of St Andrews)

  • D. L. Borchers

    (University of St Andrews)

Abstract

Conventional distance sampling adopts a mixed approach, using model-based methods for the detection process, and design-based methods to estimate animal abundance in the study region, given estimated probabilities of detection. In recent years, there has been increasing interest in fully model-based methods. Model-based methods are less robust for estimating animal abundance than conventional methods, but offer several advantages: they allow the analyst to explore how animal density varies by habitat or topography; abundance can be estimated for any sub-region of interest; they provide tools for analysing data from designed distance sampling experiments, to assess treatment effects. We develop a common framework for model-based distance sampling, and show how the various model-based methods that have been proposed fit within this framework.

Suggested Citation

  • S. T. Buckland & C. S. Oedekoven & D. L. Borchers, 2016. "Model-Based Distance Sampling," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(1), pages 58-75, March.
    • Handle: RePEc:spr:jagbes:v:21:y:2016:i:1:d:10.1007_s13253-015-0220-7
    DOI: 10.1007/s13253-015-0220-7
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    References listed on IDEAS

    as
    1. Fernanda F. C. Marques & Stephen T. Buckland, 2003. "Incorporating Covariates into Standard Line Transect Analyses," Biometrics, The International Biometric Society, vol. 59(4), pages 924-935, December.
    2. Tiago Marques & Stephen Buckland & Regina Bispo & Brett Howland, 2013. "Accounting for animal density gradients using independent information in distance sampling surveys," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(1), pages 67-80, March.
    3. Stephen T. Buckland & Jeffrey L. Laake & David L. Borchers, 2010. "Double-Observer Line Transect Methods: Levels of Independence," Biometrics, The International Biometric Society, vol. 66(1), pages 169-177, March.
    4. D. L. Borchers & J. L. Laake & C. Southwell & C. G. M. Paxton, 2006. "Accommodating Unmodeled Heterogeneity in Double-Observer Distance Sampling Surveys," Biometrics, The International Biometric Society, vol. 62(2), pages 372-378, June.
    5. Devin S. Johnson & Jeffrey L. Laake & Jay M. Ver Hoef, 2010. "A Model-Based Approach for Making Ecological Inference from Distance Sampling Data," Biometrics, The International Biometric Society, vol. 66(1), pages 310-318, March.
    6. T. A. Marques & S. T. Buckland & D. L. Borchers & D. Tosh & R. A. McDonald, 2010. "Point Transect Sampling Along Linear Features," Biometrics, The International Biometric Society, vol. 66(4), pages 1247-1255, December.
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    Cited by:

    1. David L. Borchers & Tiago A. Marques, 2017. "From distance sampling to spatial capture–recapture," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(4), pages 475-494, October.

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