Data Analysis in Community and Landscape Ecology.

Abstract

Bibliographical notes 26 2.9 Exercise 2.10 Solution to the exercise 27 /f 5 .~\Ordination 91 < \5Jr--/ Introduction 91 §-l-J) Aim and usage 91 \T^Z Data approximation and response models in ordination 93 5.1.3Outline of Chapter 5 5.2 Correspondence analysis (CA) and detrended correspondence analysis (DCA) 95 5.2.1 From weighted averaging to correspondence analysis 95 5.2.2 Two-way weighted averaging algorithm 5.2.3 Diagonal structures: properties and faults of correspondence analysis 103 5,2.4/Detrended correspondence analysis (DCA) 5727$/ Joint plot of species and sites 108 5.2.6 Block structures and sensitivity to rare species 109 5.2.7 Gaussian ordination and its relation with CA and DCA 110 5.3_ Principal components analysis (PCA) QJJJ) From least-squares regression to principal components analysis 116 5.3.2Two-way weighted summation algorithm 5 J,3 Best lines and planes in m-dimensional space 125 ^5.3^) Biplot of species and site scores 127 5.3.5 Data transformation 130 5.3.6 R-mode and Q-mode algorithms ( §A) Interpretation of ordination with external data 132 5.5 Canonical ordination 136 5.5.1 Introduction 136 5.5.2Canonical correspondence analysis (CCA) 5.5.3Redundancy analysis (RDA) 144 5.5.4Canonical correlation analysis (COR) 5.5.5 Canonical variate analysis (CVA) 5.5.6 Interpreting canonical axes 150 5.5.7 Data transformation 151 5.6 Multidimensional scaling 151 5.7 Evaluation of direct gradient and indirect gradient analysis techniques 153 5.8 Bibliographic notes 156 5.9Ordination methods in terms of matrix algebra 158 5.9.1 Principal components analysis (PCA) 5.9.2 Correspondence analysis (CA) 160 5.9.3 Canonical correllation analysis (COR) 5.9.4 Redundancy analysis (RDA) 163 5.9.5 Canonical correspondence analysis (CCA) 5.10 Exercises 165 5.11 Solutions to exercises 169 6 Cluster analysis