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Multivariate Analysis of Ecological Data using CANOCO / Edition 1

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Overview

Multivariate statistical methods are described in this study and advice is given on how best to apply these methods using CANOCO software. Data sets and program files for the case studies are provided on a supporting website. The book is useful to anyone studying complex ecological problems, such as the variation of biotic communities with environmental conditions or their response to experimental manipulations. It is primarily targeted at ecologists who need to analyze data resulting from field observations and experiments.
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Editorial Reviews

From the Publisher
"The book is helpful for users interested in analyses of distributions of qualitative variables." Ole Wendroth, University of Kentucky, Vadose Zone Journal

"I sincerely recommend the book to all graduate students and scientists working with ecological data." Écoscience

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Product Details

  • ISBN-13: 9780521891080
  • Publisher: Cambridge University Press
  • Publication date: 6/1/2003
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 284
  • Product dimensions: 6.85 (w) x 9.72 (h) x 0.59 (d)

Meet the Author

Dr Jan Leps is Professor of Ecology in the Department of Botany at the University of South Bohemia, Czech Republic.

Dr Petr Smilauer is Lecturer in Multivariate Statistics at the University of South Bohemia, Czech Republic.

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Table of Contents

1. Introduction and data manipulation; 2. Experimental design; 3. Basics of gradient analysis; 4. Using Canoco; 5. Constrained ordination and permutation tests; 6. Similarity measures; 7. Classification methods; 8. Regression methods; 9. Advanced use of ordination; 10. Visualising multivariate data; 11. Case study 1: variation in forest bird assemblages; 12. Case study 2: search for community composition patterns and their environmental correlates: vegetation of spring meadows; 13. Case study 3: separating the effects of explanatory variables; 14. Case study 4: evaluation of experiments in randomised complete blocks; 15. Case study 5: analysis of repeated observations of species composition from factorial experiment; 16. Case study 6: hierarchical analysis of crayfish community variation; 17. Case study 7: differentiating two species and their hybrids with discriminant analysis.
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