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Statistical models for spatially explicit biological data

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Statistical models for spatially explicit biological data. / Rogers, David J.; Sedda, Luigi.
In: Parasitology, Vol. 139, No. 14, 12.2012, p. 1852-1869.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Rogers, DJ & Sedda, L 2012, 'Statistical models for spatially explicit biological data', Parasitology, vol. 139, no. 14, pp. 1852-1869. https://doi.org/10.1017/S0031182012001345

APA

Rogers, D. J., & Sedda, L. (2012). Statistical models for spatially explicit biological data. Parasitology, 139(14), 1852-1869. https://doi.org/10.1017/S0031182012001345

Vancouver

Rogers DJ, Sedda L. Statistical models for spatially explicit biological data. Parasitology. 2012 Dec;139(14):1852-1869. Epub 2012 Oct 19. doi: 10.1017/S0031182012001345

Author

Rogers, David J. ; Sedda, Luigi. / Statistical models for spatially explicit biological data. In: Parasitology. 2012 ; Vol. 139, No. 14. pp. 1852-1869.

Bibtex

@article{2f8696b0924f401d9645068d5aeaa4f2,
title = "Statistical models for spatially explicit biological data",
abstract = "Existing algorithms for predicting species' distributions sit on a continuum between purely statistical and purely biological approaches. Most of the existing algorithms are aspatial because they do not consider the spatial context, the occurrence of the species or conditions conducive to the species' existence, in neighbouring areas. The geostatistical techniques of kriging and cokriging are presented in an attempt to encourage biologists more frequently to consider them. Unlike deterministic spatial techniques they provide estimates of prediction errors. The assumptions and applications of common geostatistical techniques are presented with worked examples drawn from a dataset of the bluetongue outbreak in northwest Europe in 2006. Emphasis is placed on the importance and interpretation of weights in geostatistical calculations. Covarying environmental data may be used to improve predictions of species' distributions, but only if their sampling frequency is greater than that of the species' or disease data. Cokriging techniques are unable to determine the biological significance or importance of such environmental data, because they are not designed to do so.",
keywords = "Species' distribution models, kriging, cokriging, variograms, bluetongue, SPECIES DISTRIBUTION MODELS, BLUETONGUE VIRUS SEROTYPE-8, NORTH-WESTERN EUROPE, GEOSTATISTICAL APPROACH, DISEASE, DISTRIBUTIONS, VARIOGRAM, ACCURACY, EPIDEMIC, ECOLOGY",
author = "Rogers, {David J.} and Luigi Sedda",
year = "2012",
month = dec,
doi = "10.1017/S0031182012001345",
language = "English",
volume = "139",
pages = "1852--1869",
journal = "Parasitology",
issn = "0031-1820",
publisher = "Cambridge University Press",
number = "14",

}

RIS

TY - JOUR

T1 - Statistical models for spatially explicit biological data

AU - Rogers, David J.

AU - Sedda, Luigi

PY - 2012/12

Y1 - 2012/12

N2 - Existing algorithms for predicting species' distributions sit on a continuum between purely statistical and purely biological approaches. Most of the existing algorithms are aspatial because they do not consider the spatial context, the occurrence of the species or conditions conducive to the species' existence, in neighbouring areas. The geostatistical techniques of kriging and cokriging are presented in an attempt to encourage biologists more frequently to consider them. Unlike deterministic spatial techniques they provide estimates of prediction errors. The assumptions and applications of common geostatistical techniques are presented with worked examples drawn from a dataset of the bluetongue outbreak in northwest Europe in 2006. Emphasis is placed on the importance and interpretation of weights in geostatistical calculations. Covarying environmental data may be used to improve predictions of species' distributions, but only if their sampling frequency is greater than that of the species' or disease data. Cokriging techniques are unable to determine the biological significance or importance of such environmental data, because they are not designed to do so.

AB - Existing algorithms for predicting species' distributions sit on a continuum between purely statistical and purely biological approaches. Most of the existing algorithms are aspatial because they do not consider the spatial context, the occurrence of the species or conditions conducive to the species' existence, in neighbouring areas. The geostatistical techniques of kriging and cokriging are presented in an attempt to encourage biologists more frequently to consider them. Unlike deterministic spatial techniques they provide estimates of prediction errors. The assumptions and applications of common geostatistical techniques are presented with worked examples drawn from a dataset of the bluetongue outbreak in northwest Europe in 2006. Emphasis is placed on the importance and interpretation of weights in geostatistical calculations. Covarying environmental data may be used to improve predictions of species' distributions, but only if their sampling frequency is greater than that of the species' or disease data. Cokriging techniques are unable to determine the biological significance or importance of such environmental data, because they are not designed to do so.

KW - Species' distribution models

KW - kriging

KW - cokriging

KW - variograms

KW - bluetongue

KW - SPECIES DISTRIBUTION MODELS

KW - BLUETONGUE VIRUS SEROTYPE-8

KW - NORTH-WESTERN EUROPE

KW - GEOSTATISTICAL APPROACH

KW - DISEASE

KW - DISTRIBUTIONS

KW - VARIOGRAM

KW - ACCURACY

KW - EPIDEMIC

KW - ECOLOGY

U2 - 10.1017/S0031182012001345

DO - 10.1017/S0031182012001345

M3 - Journal article

VL - 139

SP - 1852

EP - 1869

JO - Parasitology

JF - Parasitology

SN - 0031-1820

IS - 14

ER -