Model-Based Distance Sampling

S. T. Buckland (), C. S. Oedekoven and D. L. Borchers
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S. T. Buckland: University of St Andrews
C. S. Oedekoven: University of St Andrews
D. L. Borchers: University of St Andrews

Journal of Agricultural, Biological and Environmental Statistics, 2016, vol. 21, issue 1, No 4, 58-75

Abstract: 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.

Keywords: Distance sampling; Line transect sampling; Model-based inference; Point transect sampling (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13253-015-0220-7

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