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Statistical models for spatially explicit biological data

Published online by Cambridge University Press:  19 October 2012

DAVID J. ROGERS  and
LUIGI SEDDA
DAVID J. ROGERS*
Affiliation:
University of Oxford, Department of Zoology, South Parks Rd., Oxford OX1 3PS, UK
LUIGI SEDDA
Affiliation:
University of Oxford, Department of Zoology, South Parks Rd., Oxford OX1 3PS, UK
*
*Corresponding author: David J RogersUniversity of Oxford, Department of Zoology South Parks Rd., Oxford OX1 3PS Tel.: 01865 271240 Fax: 01865 310447 Email: [email protected]
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Summary

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 modelskrigingcokrigingvariogramsbluetongue
Type
Research Article
Information
Parasitology , Volume 139 , Issue 14: Dynamics of parasite distributions: modern analytical approaches , December 2012 , pp. 1852 - 1869
Copyright
Copyright © Cambridge University Press 2012

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