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Model-based distance sampling

Research output: Contribution to journalArticle

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Model-based distance sampling. / Buckland, Stephen Terrence; Oedekoven, Cornelia Sabrina; Borchers, David Louis.

In: Journal of Agricultural, Biological and Environmental Statistics, Vol. 21, No. 1, 03.2016, p. 58-75.

Research output: Contribution to journalArticle

Harvard

Buckland, ST, Oedekoven, CS & Borchers, DL 2016, 'Model-based distance sampling' Journal of Agricultural, Biological and Environmental Statistics, vol. 21, no. 1, pp. 58-75. https://doi.org/10.1007/s13253-015-0220-7

APA

Buckland, S. T., Oedekoven, C. S., & Borchers, D. L. (2016). Model-based distance sampling. Journal of Agricultural, Biological and Environmental Statistics, 21(1), 58-75. https://doi.org/10.1007/s13253-015-0220-7

Vancouver

Buckland ST, Oedekoven CS, Borchers DL. Model-based distance sampling. Journal of Agricultural, Biological and Environmental Statistics. 2016 Mar;21(1):58-75. https://doi.org/10.1007/s13253-015-0220-7

Author

Buckland, Stephen Terrence ; Oedekoven, Cornelia Sabrina ; Borchers, David Louis. / Model-based distance sampling. In: Journal of Agricultural, Biological and Environmental Statistics. 2016 ; Vol. 21, No. 1. pp. 58-75.

Bibtex - Download

@article{a38ed61e78734b42994f9f13b1acbe2d,
title = "Model-based distance sampling",
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",
author = "Buckland, {Stephen Terrence} and Oedekoven, {Cornelia Sabrina} and Borchers, {David Louis}",
note = "CSO was part-funded by EPSRC/NERC Grant EP/1000917/1.",
year = "2016",
month = "3",
doi = "10.1007/s13253-015-0220-7",
language = "English",
volume = "21",
pages = "58--75",
journal = "Journal of Agricultural, Biological and Environmental Statistics",
issn = "1085-7117",
publisher = "Springer",
number = "1",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Model-based distance sampling

AU - Buckland, Stephen Terrence

AU - Oedekoven, Cornelia Sabrina

AU - Borchers, David Louis

N1 - CSO was part-funded by EPSRC/NERC Grant EP/1000917/1.

PY - 2016/3

Y1 - 2016/3

N2 - 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.

AB - 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.

KW - Distance sampling

KW - Line transect sampling

KW - Model-based inference

KW - Point transect sampling

U2 - 10.1007/s13253-015-0220-7

DO - 10.1007/s13253-015-0220-7

M3 - Article

VL - 21

SP - 58

EP - 75

JO - Journal of Agricultural, Biological and Environmental Statistics

T2 - Journal of Agricultural, Biological and Environmental Statistics

JF - Journal of Agricultural, Biological and Environmental Statistics

SN - 1085-7117

IS - 1

ER -

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ID: 214296895