Skip to main content
SpringerLink
Log in

Description of Species Structures

  • Chapter
  • First Online:
Multivariate Analysis of Ecological Data with ade4

Abstract

Several simple data analysis methods can be used to analyse species data tables, i.e., tables having sites as rows and species as columns. Like in the previous chapter, simple means that these methods are adapted to the analysis of only one table. Three particular data analysis methods will be studied here: Correspondence Analysis (CA), centred Principal Component Analysis (cPCA), and Principal Coordinate Analysis (PCoA).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 99.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Austin MP (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol Model 157:101–118

    Article  Google Scholar 

  • Austin M, Noy-Meir I (1971) The problem of non-linearity in ordination: experiments with two-gradient models. J Ecol 59:763–773

    Article  Google Scholar 

  • Beals EW (1973) Ordination: mathematical elegance and ecological naivete. J Ecol 61:23–35

    Article  Google Scholar 

  • Benzécri JP (1969) Statistical analysis as a tool to make patterns emerge from data. In: Watanabe S (ed) Methodologies of pattern recognition. Academic, New York, pp 35–60

    Chapter  Google Scholar 

  • Borcard D, Gillet F, Legendre P (2011) Numerical Ecology with R. Springer, New York

    Book  Google Scholar 

  • ter Braak CJF (1985) Correspondence analysis of incidence and abundance data: properties in terms of a unimodal reponse model. Biometrics 41:859–873

    Article  Google Scholar 

  • ter Braak CJF, Looman CWN (1986) Weighted averaging, logistic regression and the Gaussian response model. Vegetatio 65:3–11

    Article  Google Scholar 

  • ter Braak CJF, Verdonschot PFM (1995) Canonical correspondence analysis and related multivariate methods in aquatic ecology. Aquat Sci 57:255–289

    Article  Google Scholar 

  • Curtis JT, McIntosh RP (1951) An upland forest continuum in the prairie-forest border region of Wisconsin. Ecology 32(3):476–496

    Article  Google Scholar 

  • Gleason HA (1926) The individualistic concept of the plant association. Bull Torrey Bot Club 53(1):7–26

    Article  Google Scholar 

  • Goodall DW (1954) Objective methods for the classification of vegetation. III. An essay on the use of factor analysis. Aust J Bot 2:304–324

    Article  Google Scholar 

  • Gower JC (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53:325–338

    Article  MathSciNet  Google Scholar 

  • Gower JC (1984) Distance matrices and their euclidean approximation. In: Diday E, Jambu M, Lebart L, Pagès J, Tomassone R (eds) Data analysis and informatics III. Elsevier, Amsterdam, pp 3–21

    Google Scholar 

  • Gower J, Legendre P (1986) Metric and euclidean properties of dissimilarity coefficients. J Classif 3:5–48

    Article  MathSciNet  Google Scholar 

  • Greenacre M (1984) Theory and applications of correspondence analysis. Academic, London

    MATH  Google Scholar 

  • Hill M (1973) Reciprocal averaging: an eigenvector method of ordination. J Ecol 61:237–249

    Article  Google Scholar 

  • Hill MO (1974) Correspondence Analysis: a neglected multivariate method. J R Stat Soc Ser C (Appl Stat) 23:340–354

    MathSciNet  Google Scholar 

  • Jarraud S, Mougel C, Thioulouse J, Lina G, Meugnier H, Forey F, Nesme X, Etienne J, Vandenesch F (2002) Relationships between staphylococcus aureus genetic background, virulence factors, agr type (alleles), and human disease type. Infect Immun 70:631–641

    Article  Google Scholar 

  • Lebreton JD, Chessel D, Prodon R, Yoccoz NG (1988a) L’analyse des relations espèces-milieu par l’analyse canonique des correspondances. I. Variables de milieu quantitatives. Acta Oecol Oecol Generalis 9(1):53–67

    Google Scholar 

  • Legendre P, Anderson M (1999) Distance-based redundancy analysis: testing multi-species responses in multi-factorial ecological experiments. Ecol Monogr 69:1–24

    Article  Google Scholar 

  • Legendre P, Legendre L (1998) Numerical ecology. Elsevier, Amsterdam

    MATH  Google Scholar 

  • McIntosh RP (1978) Matrix and plexus techniques. In: Whittaker RH (ed) Ordination of plant communities. Springer, Netherlands, pp 151–184

    Chapter  Google Scholar 

  • Nishisato S (1980) Analysis of categorical data: dual scaling and its applications. University of Toronto Press, Toronto

    MATH  Google Scholar 

  • Oksanen J (1987) Problems of joint display of species and site scores in correspondence analysis. Vegetatio 72:51–57

    Article  Google Scholar 

  • Oksanen J, Minchin PR (2002) Continuum theory revisited: what shape are species responses along ecological gradients? Ecol Model 157:119–129

    Article  Google Scholar 

  • Swan J (1970) An examination of some ordination problems by use of simulated vegetational data. Ecology 51:89–102

    Article  Google Scholar 

  • Thioulouse J, Chessel D (1992) A method for reciprocal scaling of species tolerance and sample diversity. Ecology 73(2):670–680

    Article  Google Scholar 

  • Verneaux J (1973) Cours d’eau de Franche-Comté (Massif du Jura). Recherches écologiques sur le réseau hydrographique du Doubs. Essai de biotypologie. Thèse de Doctorat d’Etat, Besançon

    Google Scholar 

  • Whittaker R (1956) Vegetation of the great smoky mountains. Ecol Monogr 26(1):1–80

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratoire de Biométrie et Biologie Evolutive, CNRS UMR 5558 – Université de Lyon, Villeurbanne, France

    Jean Thioulouse, Stéphane Dray, Anne-Béatrice Dufour & Aurélie Siberchicot

  2. Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK

    Thibaut Jombart

  3. Centre d’Ecologie et des Sciences de la Conservation (CESCO), Muséum national déHistoire naturelle, CNRS, Sorbonne Université, Paris, France

    Sandrine Pavoine

Authors
  1. Jean Thioulouse
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stéphane Dray
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anne-Béatrice Dufour
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aurélie Siberchicot
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Thibaut Jombart
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sandrine Pavoine
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Thioulouse, J., Dray, S., Dufour, AB., Siberchicot, A., Jombart, T., Pavoine, S. (2018). Description of Species Structures. In: Multivariate Analysis of Ecological Data with ade4. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-8850-1_6

Download citation

Publish with us

Policies and ethics

Search

Navigation