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CANOCO5.0教程.pdf
Cover
Half title
Title
Copyright
Contents
Preface
1 Introduction and data types
1.1 Why ordination?
1.2 Data types
1.3 Data transformation and standardisation
1.3.1 Transformation
1.3.2 Standardisation
1.4 Missing values
1.5 Types of analyses
2 Using Canoco 5
2.1 Philosophy of Canoco 5
2.2 Data import and editing
2.3 Defining analyses
2.4 Visualising results
2.5 Beware, CANOCO 4.x users!
3 Experimental design
3.1 Completely randomised design
3.2 Randomised complete blocks
3.3 Latin square design
3.4 Pseudoreplicates
3.5 Combining more than one factor
3.5.1 Factorial designs
3.5.2 Hierarchical designs
3.6 Following the development of objects in time: repeated observations
3.7 Experimental and observational data
4 Basics of gradient analysis
4.1 Techniques of gradient analysis
4.2 Models of response to gradients
4.3 Estimating species optima by weighted averaging
4.4 Calibration
4.5 Unconstrained ordination
4.6 Constrained ordination
4.7 Basic ordination techniques
4.8 Ordination axes as optimal predictors
4.9 Ordination diagrams
4.10 Two approaches
4.11 Testing significance of the relation with explanatory variables
4.12 Monte Carlo permutation tests for the significance of regression
4.13 Relating two biotic communities
4.14 Community composition as a cause: using reverse analysis
5 Permutation tests and variation partitioning
5.1 Permutation tests: the philosophy
5.2 Pseudo-F statistics and significance
5.3 Testing individual constrained axes
5.4 Tests with spatial or temporal constraints
5.5 Tests with hierarchical constraints
5.6 Simple versus conditional effects and stepwise selection
5.7 Variation partitioning
5.8 Significance adjustment for multiple tests
6 Similarity measures and distance-based methods
6.1 Similarity measures for presence-absence data
6.2 Similarity measures for quantitative data
6.2.1 Measuring and transforming the quantity
6.2.2 Similarity or distance between cases
6.2.3 Similarity between species
6.3 Similarity of cases versus similarity of communities
6.4 Similarity between species in trait values
6.5 Principal coordinates analysis
6.6 Constrained principal coordinates analysis (dbRDA)
6.7 Non-metric multidimensional scaling
6.8 Mantel test
7 Classification methods
7.1 Example data set properties
7.2 Non-hierarchical classification (K-means clustering)
7.3 Hierarchical classification
7.3.1 Agglomerative classification (cluster analysis)
7.3.2 Divisive classification
7.4 TWINSPAN
7.4.1 TWINSPAN analysis of the Tatry data
8 Regression methods
8.1 Regression models in general
8.2 General linear model: terms
8.3 Generalized linear models (GLM)
8.4 Loess smoother
8.5 Generalized additive models (GAM)
8.6 Mixed-effect models (LMM, GLMM and GAMM)
8.7 Classification and regression trees (CART)
8.8 Modelling species response curves with Canoco
9 Interpreting community composition with functional traits
9.1 Required data
9.2 Two approaches in traits - environment studies
9.3 Community-based approach
9.4 Species-based approach
9.4.1 Selection of species
9.4.2 Predicting response to each environmental variable separately
10 Advanced use of ordination
10.1 Principal response curves (PRC)
10.2 Separating spatial variation
10.3 Linear discriminant analysis
10.4 Hierarchical analysis of community variation
Total variation
Variation among ranges
Variation among ridges
Variation among peaks
Residual variation
10.5 Partitioning diversity indices into alpha and beta components
10.6 Predicting community composition
11 Visualising multivariate data
11.1 Reading ordination diagrams of linear methods
11.2 Reading ordination diagrams of unimodal methods
11.3 Attribute plots
11.4 Visualising classification, groups, and sequences
11.5 T-value biplot
12 Case study 1: Variation in forest bird assemblages
12.1 Unconstrained ordination: portraying variation in bird community
12.2 Simple constrained ordination: the effect of altitude on bird community
12.3 Partial constrained ordination: additional effect of other habitat characteristics
12.4 Separating and testing alpha and beta diversity
13 Case study 2: Search for community composition patterns and their environmental correlates: vegetation of spring meadows
13.1 Unconstrained ordination
13.2 Constrained ordination
13.3 Classification
13.4 Suggestions for additional analyses
13.5 Comparing two communities
14 Case study 3: Separating the effects of explanatory variables
14.1 Introduction
14.2 Data
14.3 Changes in species richness and composition
14.4 Changes in species traits
15 Case study 4: Evaluation of experiments in randomised complete blocks
15.1 Introduction
15.2 Data
15.3 Analysis
15.4 Calculating ANOVA using constrained ordination
16 Case study 5: Analysis of repeated observations of species composition from a factorial experiment
16.1 Introduction
16.2 Experimental design
16.3 Data coding and use
16.4 Univariate analyses
16.5 Constrained ordinations
16.6 Principal response curves
16.7 Temporal changes across treatments
16.8 Changes in composition of functional traits
16.8.1 Response of community weighted means of traits to treatments
16.8.2 Species-based approaches
17 Case study 6: Hierarchical analysis of crayfish community variation
17.1 Data and design
17.2 Differences among sampling locations
17.3 Hierarchical decomposition of community variation
18 Case study 7: Analysis of taxonomic data with discriminant analysis and distance-based ordination
18.1 Data
18.2 Summarising morphological data with PCA
18.3 Linear discriminant analysis of morphological data
18.4 Principal coordinates analysis of AFLP data
18.5 Testing taxon differences in AFLP data using db-RDA
18.6 Taking populations into account
19 Case study 8: Separating effects of space and environment on oribatid community with PCNM
19.1 Ignoring the space
19.2 Detecting spatial trends
19.3 All-scale spatial variation of community and environment
19.4 Variation partitioning with spatial predictors
19.5 Visualising spatial variation
20 Case study 9: Performing linear regression with redundancy analysis
20.1 Data
20.2 Linear regression using program R
20.3 Linear regression with redundancy analysis
20.4 Fitting generalized linear models in Canoco
Appendix A Glossary
Appendix B Sample data sets and projects
Appendix C Access to Canoco and overview of other software
Appendix D Working with R
References
Index to useful tasks in Canoco 5
Subject index
Multivariate Analysis of Ecological Data using Canoco 5
This revised and updated edition focuses on constrained ordination (RDA, CCA), vari-
ation partitioning and the use of permutation tests of statistical hypotheses about mul-
tivariate data. Both classification and modern regression methods (GLM, GAM, loess)
are reviewed and species functional traits and spatial structures are analysed.
Nine case studies of varying difficulty help to illustrate the suggested analytical meth-
ods, using the latest version of Canoco 5. All studies utilise descriptive and manipulative
approaches, and are supported by data sets and project files available from the book
website: http://regent.prf.jcu.cz/maed2/.
Written primarily for community ecologists needing to analyse data resulting from
field observations and experiments, this book is a valuable resource for students and
researchers dealing with both simple and complex ecological problems, such as the
variation of biotic communities with environmental conditions or their response to
experimental manipulation.
Petr ˇSmilauer is Associate Professor of Ecology in the Department of Ecosystem Biology,
at the University of South Bohemia. His main research interests are: multivariate statis-
tical analysis, modern regression methods, as well as the role of arbuscular mycorrhizal
symbiosis in plant communities. He is co-author of the multivariate analysis software
Canoco 5, CANOCO for Windows 4.5, CanoDraw, and TWINSPAN for Windows.
Jan Lepˇs is Professor of Ecology in the Department of Botany, at the University of
South Bohemia, and in the Institute of Entomology at the Czech Academy of Sciences.
His main research interests include: plant community biology, statistical analysis in the
field of ecology, as well as the studies of species diversity, and the role of functional
traits in plant community ecology and ecology of hemiparasitic plants. Together with
P. ˇSmilauer, he regularly offers international courses on multivariate statistics.
Multivariate Analysis of
Ecological Data using Canoco 5
Second Edition
PETR ˇSMILAUER
University of South Bohemia, Czech Republic
JAN LEP ˇS
University of South Bohemia, Czech Republic
University Printing House, Cambridge CB2 8BS, United Kingdom
Published in the United States of America by Cambridge University Press, New York
Cambridge University Press is part of the University of Cambridge.
It furthers the University’s mission by disseminating knowledge in the pursuit of
education, learning and research at the highest international levels of excellence.
www.cambridge.org
Information on this title: www.cambridge.org/9781107694408
C Cambridge University Press 2014
This publication is in copyright. Subject to statutory exception
and to the provisions of relevant collective licensing agreements,
no reproduction of any part may take place without the written
permission of Cambridge University Press.
First published 2003
Second Edition 2014
Printed in the United Kingdom by TJ International Ltd. Padstow Cornwall
A catalogue record for this publication is available from the British Library
Library of Congress Cataloguing in Publication data
ˇSmilauer, Petr, 1967–
Multivariate analysis of ecological data using CANOCO 5 / Petr ˇSmilauer, University of South Bohemia,
Czech Republic, Jan Lepˇs, University of South Bohemia, Czech Republic. – Second edition.
pages cm
Includes bibliographical references and index.
ISBN 978-1-107-69440-8 (pbk.)
1. Ecology – Statistical methods.
studies.
QH541.15.S72S63
577.01
5195 – dc23
4. Multivariate analysis – Case studies.
2013048954
2014
2. Multivariate analysis.
3. Ecology – Statistical methods – Case
I. Lepˇs, Jan, 1953– II. Title.
ISBN 978-1-107-69440-8 Paperback
Additional resources for this publication at http://regent.prf.jcu.cz/maed2/
Cambridge University Press has no responsibility for the persistence or accuracy of
URLs for external or third-party internet websites referred to in this publication,
and does not guarantee that any content on such websites is, or will remain,
accurate or appropriate.
Contents
Preface
Introduction and data types
1.1 Why ordination?
1.2 Data types
1.3 Data transformation and standardisation
1.4 Missing values
1.5 Types of analyses
Using Canoco 5
2.1 Philosophy of Canoco 5
2.2 Data import and editing
2.3 Defining analyses
2.4 Visualising results
2.5 Beware, CANOCO 4.x users!
Experimental design
1
2
3
3.1 Completely randomised design
3.2 Randomised complete blocks
3.3 Latin square design
3.4 Pseudoreplicates
3.5 Combining more than one factor
3.6 Following the development of objects in time: repeated observations
3.7 Experimental and observational data
4
Basics of gradient analysis
4.1 Techniques of gradient analysis
4.2 Models of response to gradients
4.3 Estimating species optima by weighted averaging
4.4 Calibration
4.5 Unconstrained ordination
page x
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vi
Contents
4.6 Constrained ordination
4.7 Basic ordination techniques
4.8 Ordination axes as optimal predictors
4.9 Ordination diagrams
4.10 Two approaches
4.11 Testing significance of the relation with explanatory variables
4.12 Monte Carlo permutation tests for the significance of regression
4.13 Relating two biotic communities
4.14 Community composition as a cause: using reverse analysis
5
Permutation tests and variation partitioning
5.1 Permutation tests: the philosophy
5.2 Pseudo-F statistics and significance
5.3 Testing individual constrained axes
5.4 Tests with spatial or temporal constraints
5.5 Tests with hierarchical constraints
5.6 Simple versus conditional effects and stepwise selection
5.7 Variation partitioning
5.8 Significance adjustment for multiple tests
6
Similarity measures and distance-based methods
6.1 Similarity measures for presence–absence data
6.2 Similarity measures for quantitative data
6.3 Similarity of cases versus similarity of communities
6.4 Similarity between species in trait values
6.5 Principal coordinates analysis
6.6 Constrained principal coordinates analysis (db–RDA)
6.7 Non-metric multidimensional scaling
6.8 Mantel test
Classification methods
7.1 Example data set properties
7.2 Non-hierarchical classification (K-means clustering)
7.3 Hierarchical classification
7.4 TWINSPAN
Regression methods
8.1 Regression models in general
8.2 General linear model: terms
8.3 Generalized linear models (GLM)
8.4 Loess smoother
7
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