A new multivariate analysis technique, developed to relate community composition to known variation in the environment, is described. The technique is an extension of correspondence analysis (reciprocal averaging), a popular ordination technique that extracts continuous axes of variation from species occurrence or abundance data. Such ordination axes are typically interpreted with the help of external knowledge and data on environmental variables; this two—step approach (ordination followed by environmental gradient identification) is termed indirect gradient analysis. In the new technique, called canonical correspondence analysis, ordination axes are chosen in the light of known environmental variables by imposing the extra restriction that the axes be linear combinations of environmental variables. In this way community variation can be directly related to environmental variation. The environmental variables may be quantitative or nominal. As many axes can be extracted as there are environmental variables. The method of detrending can be incorporated in the technique to remove arch effects. (Detrended) canonical correspondence analysis is an efficient ordination technique when species have bell—shaped response curves or surfaces with respect to environmental gradients, and is therefore more appropriate for analyzing data on community composition and environmental variables than canonical correlation analysis. The new technique leads to an ordination diagram in which points represent species and sites, and vectors represent environmental variables. Such a diagram shows the patterns of variation in community composition that can be explained best by the environmental variables and also visualizes approximately the "centers" of the species distributions along each of the environmental variables. Such diagrams effectively summarized relationships between community and environment for data sets on hunting spiders, dyke vegetation, and algae along a pollution gradient.
[1]
C. Braak,et al.
Principal Components Biplots and Alpha and Beta Diversity
,
1983
.
[2]
Nellie Smeenk-Enserink,et al.
Correlations Between Distributions of Hunting Spiders (Lycosidae, Ctenidae) and Environmental Characteristics in a Dune Area
,
1974
.
[3]
Hugh G. Gauch,et al.
Noise Reduction By Eigenvector Ordinations
,
1982
.
[4]
K. Gabriel,et al.
The biplot graphic display of matrices with application to principal component analysis
,
1971
.
[5]
P. Greig-Smith,et al.
An application of principal components analysis to vegetation change in permanent plots
,
1980
.
[6]
O. Loucks,et al.
Ordinating Forest Communities by Means of Environmental Scalars and Phytosociological Indices
,
1962
.
[7]
Hugh G. Gauch,et al.
Multivariate analysis in community ecology
,
1984
.
[8]
H. Gauch,et al.
Vegetation and Soil Pattern in a Mesophytic Forest at Ithaca, New York
,
1979
.
[9]
T. Carleton.
Residual Ordination Analysis: A Method for Exploring Vegetation‐Environment Relationships
,
1984
.
[10]
C. Braak.
Correspondence Analysis of Incidence and Abundance Data:Properties in Terms of a Unimodal Response Model
,
1985
.
[11]
K Ruben Gabriel,et al.
Biplot Display of Multivariate Matrices for Inspection of Data and Diagnosis.
,
1980
.
[12]
A. Mercer.
Introduction to linear regression analysis: Douglas C. MONTGOMERY and Elizabeth A. PECK Wiley, New York, 1982, xiii + 504 pages, £25.75
,
1983
.