Data analysis in vegetation ecology
Evolving from years of teaching experience by one of the top experts in vegetation ecology, Data Analysis in Vegetation Ecology aims to explain the background and basics of mathematical (mainly multivariate) analysis of vegetation data.
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| Main Author | |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Chichester, West Sussex, UK
Wiley-Blackwell
2010
Hoboken, NJ John Wiley & Sons, Incorporated |
| Edition | 1 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 0470661011 9780470661024 9780470661017 047066102X 9780470664971 0470664975 |
| DOI | 10.1002/9780470664971 |
Cover
Table of Contents:
- Intro -- Data Analysis in Vegetation Ecology -- Contents -- Preface -- List of Figures -- List of Tables -- 1 Introduction -- 2 Patterns in Vegetation Ecology -- 2.1 Pattern recognition -- 2.2 Interpretation of patterns -- 2.3 Sampling for pattern recognition -- 2.3.1 Getting a sample -- 2.3.2 Organizing the data -- 3 Transformation -- 3.1 Data types -- 3.2 Scalar transformation and the species enigma -- 3.3 Vector transformation -- 3.4 Example: Transformation of plant cover data -- 4 Multivariate Comparison -- 4.1 Resemblance in multivariate space -- 4.2 Geometric approach -- 4.3 Contingency testing -- 4.4 Product moments -- 4.5 The resemblance matrix -- 4.6 Assessing the quality of classifications -- 5 Ordination -- 5.1 Why ordination? -- 5.2 Principal component analysis (PCA) -- 5.3 Principal coordinates analysis (PCOA) -- 5.4 Correspondence analysis (CA) -- 5.5 The horseshoe or arch effect -- 5.5.1 Origin and remedies -- 5.5.2 Comparing DCA, FSPA and NMDS -- 5.6 Ranking by orthogonal components -- 5.6.1 Method -- 5.6.2 A numerical example -- 5.6.3 A sampling design based on RANK (example) -- 6 Classification -- 6.1 Group structures -- 6.2 Linkage clustering -- 6.3 Minimum-variance clustering -- 6.4 Average-linkage clustering: UPGMA, WPGMA, UPGMC and WPGMC -- 6.5 Forming groups -- 6.6 Structured synoptic tables -- 6.6.1 The aim of ordering tables -- 6.6.2 Steps involved -- 6.6.3 Example: Ordering Ellenberg's data -- 7 Joining Ecological Patterns -- 7.1 Pattern and ecological response -- 7.2 Analysis of variance -- 7.2.1 Variance testing -- 7.2.2 Variance ranking -- 7.2.3 How to weight cover abundance (example) -- 7.3 Correlating resemblance matrices -- 7.3.1 The Mantel test -- 7.3.2 Correlograms: Moran's I -- 7.3.3 Spatial dependence: Schlaenggli data revisited -- 7.4 Contingency tables -- 7.5 Constrained ordination
- 8 Static Explanatory Modelling -- 8.1 Predictive or explanatory? -- 8.2 The Bayes probability model -- 8.2.1 The discrete model -- 8.2.2 The continuous model -- 8.3 Predicting wetland vegetation (example) -- 9 Assessing Vegetation Change in Time -- 9.1 Coping with time -- 9.2 Rate of change and trend -- 9.3 Markov models -- 9.4 Space-for-time substitution -- 9.4.1 Principle and method -- 9.4.2 The Swiss National Park succession (example) -- 9.5 Dynamics in pollen diagrams (example) -- 10 Dynamic Modelling -- 10.1 Simulating time processes -- 10.2 Including space processes -- 10.3 Processes in the Swiss National Park (SNP) -- 10.3.1 The temporal model -- 10.3.2 The spatial model -- 10.3.3 Simulation results -- 11 Large Data Sets: Wetland Patterns -- 11.1 Large data sets differ -- 11.2 Phytosociology revisited -- 11.3 Suppressing outliers -- 11.4 Replacing species with new attributes -- 11.5 Large synoptic tables? -- 12 Swiss Forests: A Case Study -- 12.1 Aim of the study -- 12.2 Structure of the data set -- 12.3 Methods -- 12.4 Selected questions -- 12.4.1 Is the similarity pattern discrete or continuous? -- 12.4.2 Is there a scale effect from plot size? -- 12.4.3 Does the vegetation pattern reflect the environmental conditions? -- 12.4.4 Is tree species distribution man-made? -- 12.4.5 Is the tree species pattern expected to change? -- 12.5 Conclusions -- Appendix A On Using Software -- A.1 Spreadsheets -- A.2 Databases -- A.3 Software for multivariate analysis -- Appendix B Data Sets Used -- References -- Index