Chin J Plan Ecolo ›› 2007, Vol. 31 ›› Issue (5): 976-981.doi: 10.17521/cjpe.2007.0124

• Research Articles • Previous Articles     Next Articles

STUDYING BETA DIVERSITY: ECOLOGICAL VARIATION PARTITIONING BY MULTIPLE REGRESSION AND CANONICAL ANALYSIS

Pierre Legendre   

  1. Département de sciences biologiques, Université de Montréal, C.P. 6128, succursale Centre-ville, Montréal, Québec, Canada H3C 3J7
  • Online:2007-09-30 Published:2007-09-30
  • Contact: Pierre Legendre

Abstract:

Aims Beta diversity is the variation in species composition among sites in a geographic region. Beta diversity is a key concept for understanding the functioning of ecosystems, for the conservation of biodiversity, and for ecosystem management. This paper describes how to analyze it from community composition and associated environmental and spatial data tables.
Methods Beta diversity can be studied by computing diversity indices for each site and testing hypotheses about the factors that may explain the variation among sites. Or, one can carry out a direct analysis of the community composition data table over the study sites, as a function of sets of environmental and spatial variables. These analyses are carried out by the statistical method of partitioning the variation of the diversity indices or the community composition data t able with respect to environmental and spatial variables. Variation partitioning is briefly described in this paper. 
Important findings Variation partitioning is a method of choice for the interpretation of beta diversity using tables of environmental and spatial variables. Beta diversity is an interesting “currency" for ecologists to compare either different sampling areas, or different ecological communities co-occurring in an area. Partitioning must be based upon unbiased estimates of the variation of the community composition data table that is explained by the various tables of explanatory variables. The adjusted coefficient of determination provides such an unbiased estimate in both multiple regression and canonical redundancy analysis. After partitioning, one can test the significance of the fractions of interest and plot maps of the fitted values corresponding to these fractions.

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