Spatial Modeling in Ecology: The Flexibility of Eigenfunction Spatial Analyses

Recently, analytical approaches based on the eigenfunctions of spatial configuration matrices have been proposed in order to consider explicitly spatial predictors. The present study demonstrates the usefulness of eigenfunctions in spatial modeling applied to ecological problems and shows equivalenc...

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Bibliographic Details
Published in:Ecology (Durham) 2006-10, Vol.87 (10), p.2603-2613
Main Authors: Griffith, Daniel A., Peres-Neto, Pedro R.
Format: Article
Language:English
Subjects:
Animal and plant ecology
Animal, plant and microbial ecology
Animals
Autocorrelation
Biological and medical sciences
ecological community
Ecological modeling
Ecology - methods
Ecosystems
Eigenfunctions
eigenvalue
eigenvector
Eigenvectors
Flexibility
Fundamental and applied biological sciences. Psychology
General aspects
General aspects. Techniques
Geography - methods
Marine ecology
Methods and techniques (sampling, tagging, trapping, modelling...)
Mites
Modeling
Models, Biological
Moran coefficient
principal coordinates of neighbor matrices
Regression analysis
spatial autocorrelation
spatial filter
spatial model
Spatial models
spatial structure
Species
Studies
Topology
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Summary:Recently, analytical approaches based on the eigenfunctions of spatial configuration matrices have been proposed in order to consider explicitly spatial predictors. The present study demonstrates the usefulness of eigenfunctions in spatial modeling applied to ecological problems and shows equivalencies of and differences between the two current implementations of this methodology. The two approaches in this category are the distance-based (DB) eigenvector maps proposed by P. Legendre and his colleagues, and spatial filtering based upon geographic connectivity matrices (i.e., topology-based; CB) developed by D. A. Griffith and his colleagues. In both cases, the goal is to create spatial predictors that can be easily incorporated into conventional regression models. One important advantage of these two approaches over any other spatial approach is that they provide a flexible tool that allows the full range of general and generalized linear modeling theory to be applied to ecological and geographical problems in the presence of nonzero spatial autocorrelation.
ISSN:0012-9658
1939-9170
DOI:10.1890/0012-9658(2006)87[2603:SMIETF]2.0.CO;2