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.
Original languageEnglish
Pages (from-to)58-75
Number of pages18
JournalJournal of Agricultural, Biological and Environmental Statistics
Issue number1
Early online date3 Sept 2015
Publication statusPublished - Mar 2016


  • Distance sampling
  • Line transect sampling
  • Model-based inference
  • Point transect sampling


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