Spatial modeling in ecology: The flexibility of eigenfunction spatial analyses

  • Griffith D
  • Peres-Neto P
  • 742


    Mendeley users who have this article in their library.
  • 322


    Citations of this article.


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.

Author-supplied keywords

  • Ecological community
  • Eigenvalue
  • Eigenvector
  • Moran coefficient
  • Principal coordinates of neighbor matrices
  • Spatial autocorrelation
  • Spatial filter
  • Spatial model
  • Spatial structure

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document


Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free