Data Analysis in Vegetation Ecology (2^nd Ed.)

The first edition of Data Analysis in Vegetation Ecology provided an accessible and thorough resource for evaluating plant ecology data, based on the author&rsquo,s extensive experience of research and analysis in this field. Now, the Second Edition expands on this by not only describing how to analyse data, but also enabling readers to follow the step–by–step case studies themselves using the freely available statistical package R.  , , , The addition of R in this new edition has allowed coverage of additional methods for classification and ordination, and also logistic regression, GLMs, GAMs, regression trees as well as multinomial regression to simulate vegetation types. A package of statistical functions, specifically written for the book, covers topics not found elsewhere, such as analysis and plot routines for handling synoptic tables. All data sets presented in the book are now also part of the R package &lsquo,dave&rsquo,, which is freely available online at the R Archive webpage. , The book and data analysis tools combined provide a complete and comprehensive guide to carrying out data analysis students, researchers and practitioners in vegetation science and plant ecology. Summary: A completely revised and updated edition of this popular introduction to data analysis in vegetation ecology Now includes practical examples using the freely available statistical package &lsquo,R&rsquo, Written by a world renowned expert in the field Complex concepts and operations are explained using clear illustrations and case studies relating to real world phenomena Highlights both the potential and limitations of the methods used, and the final interpretations Gives suggestions on the use of the most widely used statistical software in vegetation ecology and how to start analysing data Praise for the first edition:  ,&ldquo,This book will be a valuable addition to the shelves of early postgraduate candidates and postdoctoral researchers. Through the excellent background material and use of real world examples, Wildi has taken the fear out of trying to understand these much needed data analysis techniques in vegetation ecology.&rdquo, Austral Ecology

Preface to the second edition xi Preface to the first edition xv List of figures xix List of tables xxv About the companion website xxvii 1 Introduction 1 2 Patterns in vegetation ecology 5 2.1 Pattern recognition 5 2.2 Interpretation of patterns 9 2.3 Sampling for pattern recognition 12 2.3.1 Getting a sample 12 2.3.2 Organizing the data 14 2.4 Pattern recognition in R 17 3 Transformation 23 3.1 Data types 23 3.2 Scalar transformation and the species enigma 26 3.3 Vector transformation 30 3.4 Example: Transformation of plant cover data 33 4 Multivariate comparison 37 4.1 Resemblance in multivariate space 37 4.2 Geometric approach 38 4.3 Contingency measures 43 4.4 Product moments 45 4.5 The resemblance matrix 48 4.6 Assessing the quality of classifications 50 5 Classification 53 5.1 Group structures 53 5.2 Linkage clustering 56 5.3 Average linkage clustering 59 5.4 Minimum–variance clustering 61 5.5 Forming groups 63 5.6 Silhouette plot and fuzzy representation 66 6 Ordination 71 6.1 Why ordination? 71 6.2 Principal component analysis 75 6.3 Principal coordinates analysis 82 6.4 Correspondence analysis 86 6.5 Heuristic ordination 89 6.5.1 The horseshoe or arch effect 89 6.5.2 Flexible shortest path adjustment 91 6.5.3 Nonmetric multidimensional scaling 93 6.5.4 Detrended correspondence analysis 95 6.6 How to interpret ordinations 96 6.7 Ranking by orthogonal components 100 6.7.1 RANK method 100 6.7.2 A sampling design based on RANK (example) 104 7 Ecological patterns 109 7.1 Pattern and ecological response 109 7.2 Evaluating groups 111 7.2.1 Variance testing 111 7.2.2 Variance ranking 115 7.2.3 Ranking by indicator values 117 7.2.4 Contingency tables 120 7.3 Correlating spaces 124 7.3.1 The Mantel test 124 7.3.2 Correlograms 127 7.3.3 More trends: &lsquo,Schlaenggli&rsquo, data revisited 130 7.4 Multivariate linear models 134 7.4.1 Constrained ordination 134 7.4.2 Nonparametric multiple analysis of variance 141 7.5 Synoptic vegetation tables 146 7.5.1 The aim of ordering tables 146 7.5.2 Steps involved in sorting tables 147 7.5.3 Example: ordering Ellenberg&rsquo,s data 151 8 Static predictive modelling 155 8.1 Predictive or explanatory? 155 8.2 Evaluating environmental predictors 156 8.3 Generalized linear models 159 8.4 Generalized additive models 164 8.5 Classification and regression trees 166 8.6 Building scenarios 169 8.7 Modelling vegetation types 171 8.8 Expected wetland vegetation (example) 176 9 Vegetation change in time 185 9.1 Coping with time 185 9.2 Temporal autocorrelation 186 9.3 Rate of change and trend 188 9.4 Markov models 192 9.5 Space–for–time substitution 199 9.5.1 Principle and method 199 9.5.2 The Swiss National Park succession (example) 203 9.6 Dynamics in pollen diagrams (example) 207 10 Dynamic modelling 213 10.1 Simulating time processes 214 10.2 Simulating space processes 222 10.3 Processes in the Swiss National Park 223 10.3.1 The temporal model 223 10.3.2 The spatial model 228 11 Large data sets: wetland patterns 233 11.1 Large data sets differ 233 11.2 Phytosociology revisited 235 11.3 Suppressing outliers 239 11.4 Replacing species with new attributes 241 11.5 Large synoptic tables? 245 12 Swiss forests: a case study 255 12.1 Aim of the study 255 12.2 Structure of the data set 256 12.3 Selected questions 258 12.3.1 Is the similarity pattern discrete or continuous? 258 12.3.2 Is there a scale effect from plot size? 262 12.3.3 Does the vegetation pattern reflect environmental conditions? 266 12.3.4 Is tree species distribution man–made? 270 12.3.5 Is the tree species pattern expected to change? 276 12.4 Conclusions 278 Bibliography 281 Appendix A Functions in package dave 293 Appendix B Data sets used 295 Index 297