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Canonical correlation analysis as an ordination technique

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Summary

Canonical correlation analysis seeks linear axes that reveal the joint structure of two matrices. Potentially, CCA could be a valuable technique for ordination and analysis of dual matrices of community and environmental measurements. Performance of CCA was tested with simulated and real vegetational data. CCA was found to have stringent requirements for linearity, and consequently to have little value for ordination. Indirect ordination of community data by reciprocal averaging, followed by interpretation of environmental relationships of the axes, should generally be more effective.

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Author notes

  1. T. R. Wentworth

    Present address: Department of Botany, North Carolina State University, 27607, Raleigh, North Carolina, U.S.A.

Authors and Affiliations

  1. Ecology and Systematics, Cornell University, 14853, Ithaca, New York, U.S.A.

    H. G. Gauch Jr. & T. R. Wentworth

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  1. H. G. Gauch Jr.
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  2. T. R. Wentworth
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Additional information

This research was supported by a grant from the National Science Foundation. We appreciate preparation of the manuscript and aid with computations from S.B. Conley, C.E. French, Jr. and B.D. Lanyon.

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Gauch, H.G., Wentworth, T.R. Canonical correlation analysis as an ordination technique. Vegetatio 33, 17–22 (1976). https://doi.org/10.1007/BF00055295

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