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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Published on: June 26, 2013

Variation partitioning involving orthogonal spatial eigenfunction submodels.

Pierre Legendre1, Daniel Borcard, David W Roberts

  • 1Département de sciences biologiques, Université de Montréal, C.P. 6128, succursale Centre-ville, Montréal, Québec H3C 3J7, Canada. Pierre.Legendre@umontreal.ca

Ecology
|July 7, 2012
PubMed
Summary
This summary is machine-generated.

Spatial modeling of ecological data faces challenges with partitioning variation. This study proposes solutions to accurately interpret results from spatial eigenfunctions and environmental variables across multiple scales.

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Area of Science:

  • Ecology
  • Spatial Statistics
  • Environmental Science

Background:

  • Partitioning variation in ecological data using spatial eigenfunctions (e.g., Moran's eigenvector maps, MEM) can lead to intersection artifacts.
  • Existing methods based on adjusted R2 statistics produce nonzero overlap between orthogonal spatial submodels.

Purpose of the Study:

  • To address the issue of intersection artifacts in spatial variation partitioning.
  • To propose and describe new methods for accurate interpretation of spatial modeling results.

Main Methods:

  • Describing the phenomenon of intersection artifacts in spatial variation partitioning.
  • Proposing two solutions: proportional apportionment of intersection fractions and hierarchical partitioning.
  • Developing new partitioning equations and providing R functions for implementation.

Main Results:

  • The proposed methods resolve nonzero intersection values between orthogonal spatial submodels.
  • New partitioning equations are derived to accurately apportion variation.
  • R functions are available for practical application in ecological studies.

Conclusions:

  • Accurate partitioning of variation is crucial for interpreting spatial models.
  • The developed methods improve the understanding of relationships between environmental variables and spatial patterns.
  • This work is essential for ecological research involving multiple spatial scales.