Data Analysis in Vegetation Ecology

by Wildi, Otto

• ISBN13: 9780470661024
• ISBN10: 047066102X
• Format: Paperback
• Copyright: 2010-05-17
• Publisher: WILEY

Summary

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.The book lays out the basic operations involved in the analysis, the underlying hypotheses, aims and points of views. It conveys the message that each step in the calculations has a specific, straightforward meaning and that patterns and processes known by ecologists often find their counterpart in mathematical operations 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 data sets. Showing how to find patterns in time series, how to generate simple dynamic models, how to reveal spatial patterns and related occurrence probability maps.

Table of Contents

Preface	p. xi
List of Figures	p. xv
List of Tables	p. xxi
Introduction	p. 1
Patterns in Vegetation Ecology	p. 5
Pattern recognition	p. 5
Interpretation of patterns	p. 9
Sampling for pattern recognition	p. 11
Getting a sample	p. 11
Organizing the data	p. 14
Transformation	p. 17
Data types	p. 17
Scalar transformation and the species enigma	p. 19
Vector transformation	p. 21
Example: Transformation of plant cover data	p. 23
Multivariate Comparison	p. 25
Resemblance in multivariate space	p. 25
Geometric approach	p. 27
Contingency testing	p. 29
Product moments	p. 30
The resemblance matrix	p. 32
Assessing the quality of classifications	p. 33
Ordination	p. 35
Why ordination?	p. 35
Principal component analysis (PCA)	p. 37
Principal coordinates analysis (PCOA)	p. 41
Correspondence analysis (CA)	p. 43
The horseshoe or arch effect	p. 47
Origin and remedies	p. 47
Comparing DCA, FSPA and NMDS	p. 49
Ranking by orthogonal components	p. 51
Method	p. 51
A numerical example	p. 53
A sampling design based on RANK (example)	p. 55
Classification	p. 59
Group structures	p. 59
Linkage clustering	p. 62
Minimum-variance clustering	p. 64
Average-linkage clustering: UPGMA, WPGMA, UPGMC and WPGMC	p. 66
Forming groups	p. 67
Structured synoptic tables	p. 69
The aim of ordering tables	p. 69
Steps involved	p. 70
Example: Ordering Ellenberg's data	p. 72
Joining Ecological Patterns	p. 75
Pattern and ecological response	p. 75
Analysis of variance	p. 77
Variance testing	p. 77
Variance ranking	p. 79
How to weight cover abundance (example)	p. 80
Correlating resemblance matrices	p. 84
The Mantel test	p. 84
Correlograms: Moran's I	p. 86
Spatial dependence: Schlaenggli data revisited	p. 89
Contingency tables	p. 92
Constrained ordination	p. 96
Static Explanatory Modelling	p. 101
Predictive or explanatory?	p. 101
The Bayes probability model	p. 102
The discrete model	p. 104
The continuous model	p. 105
Predicting wetland vegetation (example)	p. 106
Assessing Vegetation Change in Time	p. 111
Coping with time	p. 111
Rate of change and trend	p. 112
Markov models	p. 115
Space-for-time substitution	p. 122
Principle and method	p. 122
The Swiss National Park succession (example)	p. 125
Dynamics in pollen diagrams (example)	p. 127
Dynamic Modelling	p. 133
Simulating time processes	p. 135
Including space processes	p. 141
Processes in the Swiss National Park (SNP)	p. 142
The temporal model	p. 142
The spatial model	p. 145
Simulation results	p. 146
Large Data Sets: Wetland Patterns	p. 151
Large data sets differ	p. 151
Phytosociology revisited	p. 153
Suppressing outliers	p. 156
Replacing species with new attributes	p. 158
Large synoptic tables?	p. 162
Swiss Forests: A Case Study	p. 169
Aim of the study	p. 169
Structure of the data set	p. 170
Methods	p. 172
Selected questions	p. 175
Is the similarity pattern discrete or continuous?	p. 175
Is there a scale effect from plot size?	p. 176
Does the vegetation pattern reflect the environmental conditions?	p. 177
Is tree species distribution man-made?	p. 178
Is the tree species pattern expected to change?	p. 184
Conclusions	p. 184
On Using Software	p. 189
Spreadsheets	p. 189
Databases	p. 190
Software for multivariate analysis	p. 191
Data Sets Used	p. 193
References	p. 195
Index	p. 205
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