Community Ecology: Analytical Methods Using R and Excel (Data in the Wild)

✍ Scribed by Mark Gardener

• Publisher: Pelagic Publishing
• Year: 2014
• Tongue: English
• Leaves: 567
• Category: Library

✦ Synopsis

Interactions between species are of fundamental importance to all living systems and the framework we have for studying these interactions is community ecology. This is important to our understanding of the planets biological diversity and how species interactions relate to the functioning of ecosystems at all scales. Species do not live in isolation and the study of community ecology is of practical application in a wide range of conservation issues.

The study of ecological community data involves many methods of analysis. In this book you will learn many of the mainstays of community analysis including: diversity, similarity and cluster analysis, ordination and multivariate analyses. This book is for undergraduate and postgraduate students and researchers seeking a step-by-step methodology for analysing plant and animal communities using R and Excel.

Microsoft's Excel spreadsheet is virtually ubiquitous and familiar to most computer users. It is a robust program that makes an excellent storage and manipulation system for many kinds of data, including community data. The R program is a powerful and flexible analytical system able to conduct a huge variety of analytical methods, which means that the user only has to learn one program to address many research questions. Its other advantage is that it is open source and therefore completely free. Novel analytical methods are being added constantly to the already comprehensive suite of tools available in R.

Mark Gardener is both an ecologist and an analyst. He has worked in a range of ecosystems around the world and has been involved in research across a spectrum of community types. His knowledge of R is largely self-taught and this gives him insight into the needs of students learning to use R for complicated analyses.

✦ Table of Contents

About the author
Acknowledgements
Software used
Support material
Reader feedback
Publish with Pelagic Publishing
Contents
Introduction
What you will learn in this book
How this book is arranged
Support files
1. Starting to look atcommunities
1.1 A scientific approach
1.2 The topics of community ecology
1.3 Getting data – using a spreadsheet
1.4 Aims and hypotheses
1.5 Summary
2. Software tools forcommunity ecology
2.1 Excel
2.2 Other spreadsheets
2.3 The R program
2.4 Summary
2.5 Exercises
3. Recording your data
3.1 Biological data
3.2 Arranging your data
3.3 Summary
3.4 Exercises
4. Beginning data exploration:using software tools
4.1 Beginning to use R
4.2 Manipulating data in a spreadsheet
4.3 Getting data from Excel into R
4.4 Summary
4.5 Exercises
5. Exploring data: choosingyour analytical method
5.1 Categories of study
5.2 How ‘classic’ hypothesis testing can be usedin community studies
5.3 Analytical methods for community studies
5.4 Summary
5.5 Exercises
6. Exploring data: gettinginsights
6.1 Error checking
6.2 Adding extra information
6.3 Getting an overview of your data
6.4 Summary
6.5 Exercises
7. Diversity: species richness
7.1 Comparing species richness
7.2 Correlating species richness over time or against anenvironmental variable
7.3 Species richness and sampling effort
7.4 Summary
7.5 Exercises
8. Diversity: indices
8.1 Simpson’s index
8.2 Shannon index
8.3 Other diversity indices
8.4 Summary
8.5 Exercises
9. Diversity: comparing
9.1 Graphical comparison of diversity profiles
9.2 A test for differences in diversity based on the t-test
9.3 Graphical summary of the t-test for Shannon andSimpson indices
9.4 Bootstrap comparisons for unreplicated samples
9.5 Comparisons using replicated samples
9.6 Summary
9.7 Exercises
10. Diversity: sampling scale
10.1 Calculating beta diversity
10.2 Additive diversity partitioning
10.3 Hierarchical partitioning
10.4 Group dispersion
10.5 Permutation methods
10.6 Overlap and similarity
10.7 Beta diversity using alternative dissimilarity measures
10.8 Beta diversity compared to other variables
10.9 Summary
10.10 Exercises
11. Rank abundance ordominance models
11.1 Dominance models
11.2 Fisher’s log-series
11.3 Preston’s lognormal model
11.4 Summary
11.5 Exercises
12. Similarity and cluster analysis
12.1 Similarity and dissimilarity
12.2 Cluster analysis
12.3 Summary
12.4 Exercises
13. Association analysis:identifying communities
13.1 Area approach to identifying communities
13.2 Transect approach to identifying communities
13.3 Using alternative dissimilarity measures foridentifying communities
13.4 Indicator species
13.5 Summary
13.6 Exercises
14. Ordination
14.1 Methods of ordination
14.2 Indirect gradient analysis
14.3 Direct gradient analysis
14.4 Using ordination results
14.5 Summary
14.6 Exercises
Appendices
Appendix 1: Answers to exercises
Appendix 2 Custom R commands in this book
Bibliography
Index