Multivariate Analysis of Ecological Data using CANOCO

Transcription

1 Multivariate Analysis of Ecological Data using CANOCO JAN LEPS University of South Bohemia, and Czech Academy of Sciences, Czech Republic Universitats- uric! Lanttesbibiiothek Darmstadt Bibliothek Biologie PETR SMILAUER University of South Bohemia, Czech Republic CAMBRIDGE UNIVERSITY PRESS

2 Contents Preface page ix 1. Introduction and data manipulation Why ordination? Terminology Types of analyses Response variables Explanatory variables 7,.,, 1.6. Handling missing values in data Importing data from spreadsheets - WCanoImp program Transformation of species data Transformation of explanatory variables Experimental design Completely randomized design Randomized complete blocks Latin square design Most frequent errors - pseudoreplications Combining more than one factor Following the development of objects in time - repeated observations Experimental and observational data Basics of gradient analysis Techniques of gradient analysis Models of species response to environmental gradients Estimating species optima by the weighted averaging method Calibration 32

3 vi Contents 3.5. Ordination Constrained ordination Basic ordination techniques Ordination diagrams Two approaches Testing significance of the relation with environmental variables Monte Carlo permutation tests for the significance of regression Using the Canoco for Windows 4.5 package Overview of the package Typical flow-chart of data analysis with Canoco for Windows Deciding on the ordination method: unimodal or linear? PCA or RDA ordination: centring and standardizing DCA ordination: detrending Scaling of ordination scores Running CanoDraw for Windows New analyses providing new views of our data sets Constrained ordination and permutation tests Linear multiple regression model Constrained ordination model RDA: constrained PCA Monte Carlo permutation test: an introduction Null hypothesis model Test statistics Spatial and temporal constraints Split-plot constraints Stepwise selection of the model Variance partitioning procedure Similarity measures Similarity measures for presence-absence data Similarity measures for quantitative data Similarity of samples versus similarity of communities Principal coordinates analysis Non-metric multidimensional scaling Constrained principal coordinates analysis (db-rda) Mantel test Classification methods Sample data set Non-hierarchical classification (K-means clustering) Hierarchical classifications TWINSPAN 108

4 Contents vii 8. Regression methods Regression models in general General linear model: terms Generalized linear models (GLM) Loess smoother Generalized additive models (GAM) Classification and regression trees Modelling species response curves with CanoDraw Advanced use of ordination Testing the significance of individual constrained ordination axes Hierarchical analysis of community variation Principal response curves (PRC) method Linear discriminant analysis Visualizing multivariate data What we can infer from ordination diagrams: linear methods What we can infer from ordination diagrams: unimodal methods Visualizing ordination results with statistical models Ordination diagnostics t-value biplot interpretation Case study 1: Variation in forest bird assemblages Data manipulation Deciding between linear and unimodal ordination 169 ' Indirect analysis: portraying variation in bird community Direct gradient analysis: effect of altitude Direct gradient analysis: additional effect of other habitat characteristics Case study 2: Search for community composition patterns and their environmental correlates: vegetation of spring meadows The unconstrained ordination Constrained ordinations Classification Suggestions for additional analyses 193

5 viii Contents 13. Case study 3: Separating the effects of explanatory variables Introduction Data Data analysis Case study 4: Evaluation of experiments in randomized complete blocks Introduction Data Data analysis Case study 5: Analysis of repeated observations of species composition from a factorial experiment Introduction Experimental design Sampling Data analysis Univariate analyses Constrained ordinations Further use of ordination results Principal response curves Case study 6: Hierarchical analysis of crayfish community variation Data and design Differences among sampling locations Hierarchical decomposition'of community variation Case study 7: Differentiating two species and their hybrids with discriminant analysis Data Stepwise selection of discriminating variables Adj usting the discriminating variables "Displaying results 250 Appendix A: Sample datasets andprojects 253 Appendix B: Vocabulary 254 Appendix C: Overview ofavailable software 25 8 References 262 Index 267