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Canonical correspondence analysis and related multivariate methods in aquatic ecology

Canonical correspondence analysis and related multivariate methods in aquatic ecology Canonical correspondence analysis (CCA) is a multivariate method to elucidate the relationships between biological assemblages of species and their environment. The method is designed to extract synthetic environmental gradients from ecological data-sets. The gradients are the basis for succinctly describing and visualizing the differential habitat preferences (niches) of taxavia an ordination diagram. Linear multivariate methods for relating two set of variables, such as two-block Partial Least Squares (PLS2), canonical correlation analysis and redundancy analysis, are less suited for this purpose because habitat preferences are often unimodal functions of habitat variables. After pointing out the key assumptions underlying CCA, the paper focuses on the interpretation of CCA ordination diagrams. Subsequently, some advanced uses, such as ranking environmental variables in importance and the statistical testing of effects are illustrated on a typical macroinvertebrate data-set. The paper closes with comparisons with correspondence analysis, discriminant analysis, PLS2 and co-inertia analysis. In an appendix a new method, named CCA-PLS, is proposed that combines the strong features of CCA and PLS2. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Aquatic Sciences Springer Journals

Canonical correspondence analysis and related multivariate methods in aquatic ecology

Aquatic Sciences , Volume 57 (3) – Dec 6, 2004

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References (138)

Publisher
Springer Journals
Copyright
Copyright © 1995 by Birkhäuser Verlag
Subject
Life Sciences; Freshwater & Marine Ecology; Marine & Freshwater Sciences; Oceanography; Ecology; Life Sciences, general
ISSN
1015-1621
eISSN
1420-9055
DOI
10.1007/BF00877430
Publisher site
See Article on Publisher Site

Abstract

Canonical correspondence analysis (CCA) is a multivariate method to elucidate the relationships between biological assemblages of species and their environment. The method is designed to extract synthetic environmental gradients from ecological data-sets. The gradients are the basis for succinctly describing and visualizing the differential habitat preferences (niches) of taxavia an ordination diagram. Linear multivariate methods for relating two set of variables, such as two-block Partial Least Squares (PLS2), canonical correlation analysis and redundancy analysis, are less suited for this purpose because habitat preferences are often unimodal functions of habitat variables. After pointing out the key assumptions underlying CCA, the paper focuses on the interpretation of CCA ordination diagrams. Subsequently, some advanced uses, such as ranking environmental variables in importance and the statistical testing of effects are illustrated on a typical macroinvertebrate data-set. The paper closes with comparisons with correspondence analysis, discriminant analysis, PLS2 and co-inertia analysis. In an appendix a new method, named CCA-PLS, is proposed that combines the strong features of CCA and PLS2.

Journal

Aquatic SciencesSpringer Journals

Published: Dec 6, 2004

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