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Statistical models for spatially explicit biological data

Statistical models for spatially explicit biological data
Statistical models for spatially explicit biological data
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.
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
0031-1820
1852-1869
Rogers, D. J.
658f04d4-bdb9-4ab2-bebb-539a94f85e99
Sedda, L.
ae6a74e0-ff67-4678-aefc-9976179294f6
Rogers, D. J.
658f04d4-bdb9-4ab2-bebb-539a94f85e99
Sedda, L.
ae6a74e0-ff67-4678-aefc-9976179294f6

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

Record type: Article

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.

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More information

Published date: 2012
Additional Information: 048IP Times Cited:2 Cited References Count:80
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
Organisations: Geography & Environment

Identifiers

Local EPrints ID: 361776
URI: http://eprints.soton.ac.uk/id/eprint/361776
ISSN: 0031-1820
PURE UUID: df90e2f9-3e08-4f69-82eb-548582b69954

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Date deposited: 06 Feb 2014 11:19
Last modified: 14 Mar 2024 15:56

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Contributors

Author: D. J. Rogers
Author: L. Sedda

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