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Spatial models for distance sampling data: recent developments and future directions

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Spatial models for distance sampling data : recent developments and future directions. / Miller, David Lawrence; Burt, M Louise; Rexstad, Eric; Thomas, Len.

In: Methods in Ecology and Evolution, Vol. 4, No. 11, 11.2013, p. 1001-1010.

Research output: Contribution to journalArticle

Harvard

Miller, DL, Burt, ML, Rexstad, E & Thomas, L 2013, 'Spatial models for distance sampling data: recent developments and future directions' Methods in Ecology and Evolution, vol. 4, no. 11, pp. 1001-1010. https://doi.org/10.1111/2041-210X.12105

APA

Miller, D. L., Burt, M. L., Rexstad, E., & Thomas, L. (2013). Spatial models for distance sampling data: recent developments and future directions. Methods in Ecology and Evolution, 4(11), 1001-1010. https://doi.org/10.1111/2041-210X.12105

Vancouver

Miller DL, Burt ML, Rexstad E, Thomas L. Spatial models for distance sampling data: recent developments and future directions. Methods in Ecology and Evolution. 2013 Nov;4(11):1001-1010. https://doi.org/10.1111/2041-210X.12105

Author

Miller, David Lawrence ; Burt, M Louise ; Rexstad, Eric ; Thomas, Len. / Spatial models for distance sampling data : recent developments and future directions. In: Methods in Ecology and Evolution. 2013 ; Vol. 4, No. 11. pp. 1001-1010.

Bibtex - Download

@article{8cd9b7c05b0e40579a4bf5f6c59ccd85,
title = "Spatial models for distance sampling data: recent developments and future directions",
abstract = "Our understanding of a biological population can be greatly enhanced by modelling their distribution in space and as a function of environmental covariates.Density surface models consist of a spatial model of the abundance of a biological population which has been corrected for uncertain detection via distance sampling methods.We offer a comparison of recent advances in the field and consider the likely directions of future research. In particular we consider spatial modelling techniques that may be advantageous to applied ecologists such as quantification of uncertainty in a two-stage model and smoothing in areas with complex boundaries.The methods discussed are available in an \textsf{R} package developed by the authors and are largely implemented in the popular Windows package Distance (or are soon to be incorporated).Density surface modelling enables applied ecologists to reliably estimate abundances and create maps of animal/plant distribution. Such models can also be used to investigate the relationships between distribution and environmental covariates.",
keywords = "Abundance estimation, Distance software, Generalized additive models, Line transect sampling, Point transect sampling, Population density, Spatial modelling, Wildlife surveys",
author = "Miller, {David Lawrence} and Burt, {M Louise} and Eric Rexstad and Len Thomas",
year = "2013",
month = "11",
doi = "10.1111/2041-210X.12105",
language = "English",
volume = "4",
pages = "1001--1010",
journal = "Methods in Ecology and Evolution",
issn = "2041-210X",
publisher = "John Wiley & Sons, Ltd (10.1111)",
number = "11",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Spatial models for distance sampling data

T2 - Methods in Ecology and Evolution

AU - Miller, David Lawrence

AU - Burt, M Louise

AU - Rexstad, Eric

AU - Thomas, Len

PY - 2013/11

Y1 - 2013/11

N2 - Our understanding of a biological population can be greatly enhanced by modelling their distribution in space and as a function of environmental covariates.Density surface models consist of a spatial model of the abundance of a biological population which has been corrected for uncertain detection via distance sampling methods.We offer a comparison of recent advances in the field and consider the likely directions of future research. In particular we consider spatial modelling techniques that may be advantageous to applied ecologists such as quantification of uncertainty in a two-stage model and smoothing in areas with complex boundaries.The methods discussed are available in an \textsf{R} package developed by the authors and are largely implemented in the popular Windows package Distance (or are soon to be incorporated).Density surface modelling enables applied ecologists to reliably estimate abundances and create maps of animal/plant distribution. Such models can also be used to investigate the relationships between distribution and environmental covariates.

AB - Our understanding of a biological population can be greatly enhanced by modelling their distribution in space and as a function of environmental covariates.Density surface models consist of a spatial model of the abundance of a biological population which has been corrected for uncertain detection via distance sampling methods.We offer a comparison of recent advances in the field and consider the likely directions of future research. In particular we consider spatial modelling techniques that may be advantageous to applied ecologists such as quantification of uncertainty in a two-stage model and smoothing in areas with complex boundaries.The methods discussed are available in an \textsf{R} package developed by the authors and are largely implemented in the popular Windows package Distance (or are soon to be incorporated).Density surface modelling enables applied ecologists to reliably estimate abundances and create maps of animal/plant distribution. Such models can also be used to investigate the relationships between distribution and environmental covariates.

KW - Abundance estimation

KW - Distance software

KW - Generalized additive models

KW - Line transect sampling

KW - Point transect sampling

KW - Population density

KW - Spatial modelling

KW - Wildlife surveys

U2 - 10.1111/2041-210X.12105

DO - 10.1111/2041-210X.12105

M3 - Article

VL - 4

SP - 1001

EP - 1010

JO - Methods in Ecology and Evolution

JF - Methods in Ecology and Evolution

SN - 2041-210X

IS - 11

ER -

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ID: 47731379