Multivariate Analysis of Ecological Data using CANOCO
$47.91 ( ) USD
- Jan Lepš, University of South Bohemia, Czech Republic
- Petr Šmilauer, University of South Bohemia, Czech Republic
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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.Read more
- Written for ecologists - no background in statistics necessary
- Contains hands-on tutorials
- Datasets are provided
Reviews & endorsements
"The book is helpful for users interested in analyses of distributions of qualitative variables." Ole Wendroth, University of Kentucky, Vadose Zone JournalSee more reviews
"I sincerely recommend the book to all graduate students and scientists working with ecological data." Écoscience
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- Date Published: May 2005
- format: Adobe eBook Reader
- isbn: 9780511075070
- contains: 110 b/w illus.
- availability: This ISBN is for an eBook version which is distributed on our behalf by a third party.
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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