Data Analysis in Vegetation Ecology
Published on 9. September 2011
234 pages
978-1-119-96563-3 (ISBN)
Description
Evolving from years of teaching experience by one of the topexperts in vegetation ecology, Data Analysis in VegetationEcology aims to explain the background and basics ofmathematical (mainly multivariate) analysis of vegetation data.
The book lays out the basic operations involved in the analysis,the underlying hypotheses, aims and points of views. It conveys themessage that each step in the calculations has a specific,straightforward meaning and that patterns and processes known byecologists often find their counterpart in mathematical operationsand functions. The first chapters introduce the elementary conceptsand operations and relate them to real-world phenomena andproblems. Later chapters concentrate on combinations of methods toreveal surprising features in data sets. Showing how to findpatterns in time series, how to generate simple dynamic models, howto reveal spatial patterns and related occurrence probabilitymaps.
Reviews / Votes
"All students sampling vegetation data should be aware that thisis a really nice book summarizing the approaches used in vegetationecology . . . I enjoyed reading this book and can recommend it forstudents of botany or general ecology. It is also well suited teachingmaterial." (Folia Geobot, 1 December2011) "This book will be a valuable addition to the shelves ofearly postgraduate candidates and postdoctoralresearchers." (Austral Ecology, 1November 2012)More details
Other editions
Additional editions
O. Wildi
Data Analysis in Vegetation Ecology
Software
07/2010
Wiley
€107.57
No shipping information available
Otto Wildi
Data Analysis in Vegetation Ecology
Book
04/2010
1st Edition
Wiley
€57.90
Article exhausted; check for reprint
Otto Wildi
Data Analysis in Vegetation Ecology
Book
04/2010
1st Edition
Wiley
€119.00
Article exhausted; check for reprint
Person
Dr Otto Wildi, Head of Research Unit Community Ecology, Switzerland.
Content
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.
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.
5.6 Ranking by orthogonal components.
6 Classification.
6.1 Group structures.
6.2 Linkage clustering.
6.3 Minimum-variance clustering.
6.4 Average-linkage clustering: UPGMA, WPGMA, UPGMC andWPGMC.
6.5 Forming groups.
6.6 Structured synoptic tables.
7 Joining Ecological Patterns.
7.1 Pattern and ecological response.
7.2 Analysis of variance.
7.3 Correlating resemblance matrices.
7.4 Contingency tables.
7.5 Constrained ordination.
8 Static Explanatory Modelling.
8.1 Predictive or explanatory?
8.2 The Bayes probability 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.
9.5 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).
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.
12.5 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.
