Data Analysis in Vegetation Ecology, Second Edition
β Scribed by Otto Wildi(auth.)
- Publisher
- Wiley-Blackwell
- Year
- 2013
- Tongue
- English
- Leaves
- 314
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
The first edition of Data Analysis in Vegetation Ecology provided an accessible and thorough resource for evaluating plant ecology data, based on the authorβs 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. Β Β Β
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 βdaveβ, which is freely available online at the R Archive webpage.Β
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 βRβ
- 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:Β βThis 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.β Austral EcologyContent:
Chapter 1 Introduction (pages 1β4):
Chapter 2 Patterns in Vegetation Ecology (pages 5β21):
Chapter 3 Transformation (pages 23β35):
Chapter 4 Multivariate Comparison (pages 37β52):
Chapter 5 Classification (pages 53β69):
Chapter 6 Ordination (pages 71β107):
Chapter 7 Ecological Patterns (pages 109β154):
Chapter 8 Static Predictive Modelling (pages 155β183):
Chapter 9 Vegetation Change in Time (pages 185β212):
Chapter 10 Dynamic Modelling (pages 213β231):
Chapter 11 Large Data Sets: Wetland Patterns (pages 233β253):
Chapter 12 Swiss Forests: A Case Study (pages 255β279):
π SIMILAR VOLUMES
Content: <br>Chapter 1 Vegetation Ecology: Historical Notes and Outline (pages 1β27): Eddy van der Maarel and Janet Franklin<br>Chapter 2 Classification of Natural and Semi?natural Vegetation (pages 28β70): Robert K. Peet and David W. Roberts<br>Chapter 3 Vegetation and Environment: Discontinuities
<strong>Key features:</strong><br /><br /><br />Unique in its combination of serving as an introduction to spatial statistics and to modeling agricultural and ecological data using R Provides exercises in each chapter to facilitate the book's use as a course textbook or for self-study Adds new mater
<i>Amstat News</i> asked three review editors to rate their top five favorite books in the September 2003 issue. <i>Categorical Data Analysis</i> was among those chosen. </p><p xmlns="http://www.w3.org/1999/xhtml" class="c1">A valuable new edition of a standard reference </p><p xmlns="http://www.w3.
The new edition of this important text has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving and important area of biostatistics. Two new chapters have been added on fully parametric models for discrete repeated measures d