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Statistical methods for temporal and space-time analysis of community composition data.

Pierre Legendre1, Olivier Gauthier

  • 1Département de Sciences Biologiques, Université de Montréal, , C.P. 6128, Succursale Centre-ville, Montréal, Québec, Canada , H3C 3J7, Laboratoire des Sciences de l'Environnement Marin (LEMAR), UMR CNRS 6539, Institut Universitaire Européen de la Mer, Université de Bretagne Occidentale, , rue Dumont d'Urville, Plouzané 29280, France.

Proceedings. Biological Sciences
|January 17, 2014
PubMed
Summary
This summary is machine-generated.

This review explores temporal beta diversity, the change in species over time, using advanced statistical methods. It analyzes ecological data to understand community dynamics and environmental influences.

Keywords:
Chesapeake BayMoran's eigenvector mapsasymmetric eigenvector mapslocal contributions to beta diversityspatial eigenfunctionstemporal eigenfunctions

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Area of Science:

  • Ecology
  • Environmental Science
  • Statistical Ecology

Background:

  • Temporal beta diversity quantifies changes in community composition over time.
  • Understanding these temporal dynamics is crucial for ecological research and conservation.
  • Existing methods for analyzing temporal variation are diverse, necessitating a comprehensive review.

Purpose of the Study:

  • To review and synthesize methods for analyzing temporal beta diversity.
  • To introduce novel temporal eigenfunction analysis techniques for multiscale variation.
  • To illustrate these methods using long-term ecological data.

Main Methods:

  • Analysis of temporal beta diversity using classical and recent statistical approaches.
  • Application of temporal eigenfunction analysis, distance-based Moran's eigenvector maps, and asymmetric eigenvector maps.
  • Utilizing variation partitioning, scalogram, multivariate correlogram, multivariate regression tree, and two-way MANOVA.

Main Results:

  • Demonstration of temporal and space-time variability in benthic communities.
  • Computation and graphical analysis of local (temporal) contributions to beta diversity (LCBD) indices.
  • Correlation of LCBD values with environmental variables and analysis of species' roles.

Conclusions:

  • The study provides a comprehensive overview of temporal beta diversity analysis methods.
  • Ecological data from Chesapeake Bay illustrate the practical application of these techniques.
  • The findings offer insights into community dynamics and species interactions over time.