Model-Based Distance Sampling View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-03

AUTHORS

S. T. Buckland, C. S. Oedekoven, D. L. Borchers

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. More... »

PAGES

58-75

References to SciGraph publications

  • 2002. Estimating Animal Abundance, Closed Populations in NONE
  • 2014-06. Bayesian Methods for Hierarchical Distance Sampling Models in JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS
  • 2009-12. Analyzing designed experiments in distance sampling in JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS
  • 2015-12. Distance sampling with a random scale detection function in ENVIRONMENTAL AND ECOLOGICAL STATISTICS
  • 2010-09. Estimating Distance Sampling Detection Functions When Distances Are Measured With Errors in JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS
  • 2013-03. Accounting for animal density gradients using independent information in distance sampling surveys in STATISTICAL METHODS & APPLICATIONS
  • 2004-06. Spatial models for line transect sampling in JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS
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    http://scigraph.springernature.com/pub.10.1007/s13253-015-0220-7

    DOI

    http://dx.doi.org/10.1007/s13253-015-0220-7

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