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Data Analysis in Vegetation Ecology [NOOK Book]

Overview

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 explains the background and basics of mathematical (mainly multivariate) analysis of vegetation data.

The book describes the basic processes involved in the analysis, the underlying hypotheses, aims and viewpoints within the subject. It conveys the message that each step in the ...

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Data Analysis in Vegetation Ecology

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Overview

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 explains the background and basics of mathematical (mainly multivariate) analysis of vegetation data.

The book describes the basic processes involved in the analysis, the underlying hypotheses, aims and viewpoints within the subject. It conveys the message that each step in the calculations has a specific, straightforward meaning and that patterns and processes know by ecologists often find their counterpart in mathematical processes and functions. The first chapters introduce the elementary concepts and operations and relate them to real-world phenomena and problems. Later chapters concentrate on combinations of methods to reveal surprising features in datasets. These include how to find patterns in time series, how to generate simple dynamic models, how to reveal spatial patterns and related occurrence probability maps.

  • An accessible and straightforward introduction to data analysis in vegetation ecology
  • 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
  • Includes suggestions for the use of the most widely used statistical software in vegetation ecology and how to start analysing data
  • Accompanying website available at www.wiley.com/go/wildi
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Editorial Reviews

From the Publisher
"All students sampling vegetation data should be aware that this is a really nice book summarizing the approaches used in vegetation ecology . . . I enjoyed reading this book and can recommend it for students of botany or general ecology. It is also well suited teaching material.” (Folia Geobot, 1 December 2011)

“This book will be a valuable addition to the shelves of early postgraduate candidates and postdoctoral researchers.” (Austral Ecology, 1 November 2012)

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Product Details

  • ISBN-13: 9781119965633
  • Publisher: Wiley
  • Publication date: 9/9/2011
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 1
  • Pages: 234
  • File size: 11 MB
  • Note: This product may take a few minutes to download.

Meet the Author

Otto Wildi is from the WSL Swiss Federal Institute for Forest, Snow and Landscape Research, Birmensdorf, Switzerland.

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Table of 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.

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 and WPGMC.

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.

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