Statistical models for spatially explicit biological data

Rogers, David J. and Sedda, Luigi (2012) Statistical models for spatially explicit biological data. Parasitology, 139 (14). pp. 1852-1869. ISSN 0031-1820

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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.

Item Type:
Journal Article
Journal or Publication Title:
Parasitology
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2400/2405
Subjects:
ID Code:
76333
Deposited By:
Deposited On:
23 Oct 2015 08:54
Refereed?:
Yes
Published?:
Published
Last Modified:
07 Jan 2020 04:37