Data Analysis in Vegetation Ecology 2nd Edition by Otto Wildi 1118562542 9781118562543

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ISBN 10: 1118562542 

ISBN 13: 9781118562543

Author: Otto Wildi

 

The first edition of Data Analysis in Vegetation Ecology provided an accessible and thorough resource for evaluating plant ecology data, based on the author’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 ‘dave’, 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 ‘R’
• 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:

“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.”

Data Analysis in Vegetation Ecology 2nd Table of contents:

1. Introduction

2. Patterns in Vegetation Ecology

Pattern recognition

Interpretation of patterns

Sampling for pattern recognition

Getting a sample

Organizing the data

Pattern recognition in R

3. Transformation

Data types

Scalar transformation and the species enigma

Vector transformation

Example: Transformation of plant cover data

4. Multivariate Comparison

Resemblance in multivariate space

Geometric approach

Contingency measures

Product moments

The resemblance matrix

Assessing the quality of classifications

5. Classification

Group structures

Linkage clustering

Average linkage clustering

Minimum-variance clustering

Forming groups

Silhouette plot and fuzzy representation

6. Ordination

Why ordination?

Principal component analysis

Principal coordinates analysis

Correspondence analysis

Heuristic ordination

The horseshoe or arch effect

Flexible shortest path adjustment

Nonmetric multidimensional scaling

Detrended correspondence analysis

How to interpret ordinations

Ranking by orthogonal components

RANK method

A sampling design based on RANK (example)

7. Ecological Patterns

Pattern and ecological response

Evaluating groups

Variance testing

Variance ranking

Ranking by indicator values

Contingency tables

Correlating spaces

The Mantel test

Correlograms

More trends: ‘Schlaenggli’ data revisited

Multivariate linear models

Constrained ordination

Nonparametric multiple analysis of variance

Synoptic vegetation tables

The aim of ordering tables

Steps involved in sorting tables

Example: ordering Ellenberg’s data

8. Static Predictive Modelling

Predictive or explanatory?

Evaluating environmental predictors

Generalized linear models

Generalized additive models

Classification and regression trees

Building scenarios

Modelling vegetation types

Expected wetland vegetation (example)

9. Vegetation Change in Time

Coping with time

Temporal autocorrelation

Rate of change and trend

Markov models

Space-for-time substitution

Principle and method

The Swiss National Park succession (example)

Dynamics in pollen diagrams (example)

10. Dynamic Modelling

Simulating time processes

Simulating space processes

Processes in the Swiss National Park

The temporal model

The spatial model

11. Large Data Sets: Wetland Patterns

Large data sets differ

Phytosociology revisited

Suppressing outliers

Replacing species with new attributes

Large synoptic tables?

12. Swiss Forests: A Case Study

Aim of the study

Structure of the data set

Selected questions

Is the similarity pattern discrete or continuous?

Is there a scale effect from plot size?

Does the vegetation pattern reflect environmental conditions?

Is tree species distribution man-made?

Is the tree species pattern expected to change?

Conclusions

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