Community Ecology: Analytical Methods Using R and Excel

452 Pages·2014·16.658 MB·English

by  Mark Gardener

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Published by Pelagic Publishing
www.pelagicpublishing.com
PO Box 725, Exeter, EX1 9QU
Community Ecology
®
Analytical Methods Using R and Excel
ISBN 978–1–907807–61–9 (Pbk)
ISBN 978–1–907807–62–6 (Hbk)
ISBN 978–1–907807–63–3 (ePub)
ISBN 978–1–907807–65–7 (PDF)
ISBN 978–1–907807–64–0 (Mobi)
Copyright © 2014 Mark Gardener
All rights reserved. No part of this document may be produced, stored in a retrieval system, or transmitted in any form or by any means,
electronic, mechanical, photocopying, recording or otherwise without prior permission from the publisher.
While every effort has been made in the preparation of this book to ensure the accuracy of the information presented, the information contained
in this book is sold without warranty, either express or implied. Neither the author, nor Pelagic Publishing, its agents and distributors will be
held liable for any damage or loss caused or alleged to be caused directly or indirectly by this book.
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British Library Cataloguing in Publication Data
A catalogue record for this book is available from the British Library.
Cover image: Over under water picture, showing Fairy Basslets (Pseudanthias tuka) amongst Cabbage Coral (Turbinaria reniformis) and
tropical island in the background. Indo Pacific. © David Fleetham/OceanwideImages.com
Typeset by Swales & Willis Ltd, Exeter, Devon, UK
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About the author
Mark Gardener (www.gardenersown.co.uk) is an ecologist, lecturer and writer working in the UK. His
primary area of research was in pollination ecology and he has worked in the UK and around the world
(principally Australia and the United States). Since his doctorate he has worked in many areas of ecology,
often as a teacher and supervisor. He believes that ecological data, especially community data, are the most
complicated and ill-behaved and are consequently the most fun to work with. He was introduced to R by a
like-minded pedant whilst working in Australia during his doctorate. Learning R was not only fun but opened
up a new avenue, making the study of community ecology a whole lot easier. He is currently self-employed
and runs courses in ecology, data analysis and R for a variety of organisations. Mark lives in rural Devon with
his wife Christine, a biochemist who consequently has little need of statistics.
Acknowledgements
There are so many people to thank that it is hard to know where to begin. I am sure that I will leave some
people out, so I apologise in advance. Thanks to Richard Rowe (James Cook University) for inspiring me to
use R. Data were contributed from various sources, especially from MSc students doing Biological
Recording; thanks especially to Robin Cure, Jessie MacKay, Mark Latham, John Handley and Hing Kin Lee
for your hard-won data. The MSc programme helped me to see the potential of ‘proper’ biological records and
I thank Sarah Whild for giving me the opportunity to undertake some teaching on the course. Thanks also to
the Field Studies Council in general: many data examples have arisen from field courses I’ve been involved
with.
Software used
®
Several versions of Microsoft’s Excel spreadsheet were used in the preparation of this book. Most of the
®
examples presented show version 2007 for Microsoft Windows although other versions may also be
illustrated.
The main version of the R program used was 2.12.1 for Macintosh: The R Foundation for Statistical
Computing, Vienna, Austria, ISBN 3-900051-07-0, http://www.R-project.org/. Other versions were used in
testing code.
Support material
Free support material is available on the Community Ecology companion website, which can be accessed via
the book’s resources page: http://www.pelagicpublishing.com/community-ecology-resources.html
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Contents
Introduction
1. Starting to look at communities
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
1.6 Exercises
2. Software tools for community 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: choosing your analytical method
5.1 Categories of study
5.2 How ‘classic’ hypothesis testing can be used in community studies
5.3 Analytical methods for community studies 70
5.4 Summary
5.5 Exercises
6. Exploring data: getting insights
6.1 Error checking
6.2 Adding extra information
6.3 Getting an overview of your data
6.4 Summary
6.5 Exercises
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7. Diversity: species richness
7.1 Comparing species richness
7.2 Correlating species richness over time or against an environmental 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 and Simpson 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 or dominance 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
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13.2 Transect approach to identifying communities
13.3 Using alternative dissimilarity measures for identifying 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
Bibliography
Index
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Introduction
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 planet’s
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 free. Novel analytical methods are being
added constantly to the already comprehensive suite of tools available in R.
What you will learn in this book
This book is intended to give you some insights into some of the analytical methods employed by ecologists
in the study of communities. The book is not intended to be a mathematical or theoretical treatise but
inevitably there is some maths! I’ve tried to keep this in the background and to focus on how to undertake the
appropriate analysis at the right time. There are many published works concerning ecological theory; this
book is intended to support them by providing a framework for learning how to analyse your data.
The book does not cover every aspect of community ecology. There are a few minor omissions – I hope to
cover some of these in later works.
How this book is arranged
There are four main strands to scientific study: planning, recording, analysis and reporting. The first few
chapters deal with the planning and recording aspects of study. You will see how to use the main software
tools, Excel and R, to help you arrange and begin to make sense of your data. Later chapters deal more
explicitly with the grand themes of community ecology, which are:
• Diversity – the study of diversity is split into several chapters covering species richness, diversity
indices, beta diversity and dominance–diversity models.
• Similarity and clustering – this is contained in one chapter covering similarity, hierarchical clustering
and clustering by partitioning.
• Association analysis – this shows how you can identify which species belong to which community by
studying the associations between species. The study of associations leads into the identification of
indicator species.
• Ordination – there is a wide range of methods of ordination and they all have similar aims; to represent
complicated species community data in a more simplified form.
The reporting element is not covered explicitly; however the presentation of results is shown throughout the
book. A more dedicated coverage of statistical and scientific reporting can be found in my previous work,
Statistics for Ecologists Using R and Excel.
Throughout the book you will see example exercises that are intended for you to try out. In fact they are
expressly aimed at helping you on a practical level – reading how to do something is fine but you need to do it
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for yourself to learn it properly. The Have a Go exercises are hard to miss.
Have a Go: Learn something by doing it
The Have a Go exercises are intended to give you practical experience at various analytical methods. Many
will refer to supplementary data, which you can get from the companion website. Some data are intended to
be used in Excel and others are for using with R.
Most of the Have a Go exercises utilise data that is available on the companion website. The material on the
website includes various spreadsheets, some containing data and some allowing analytical processes. The
CERE.RData file is the most helpful – this is an R file, which contains data and custom R commands. You
can use the data for the exercises (and for practice) and the custom commands to help you carry out a variety
of analytical processes. The custom commands are mentioned throughout the book and the website contains a
complete directory.
You will also see tips and notes, which will stand out from the main text. These are ‘useful’ items of detail
pertaining to the text but which I felt were important to highlight.
Tips and Notes: Useful additional information
The companion website contains supplementary data, which you can use for the exercises. There are also
spreadsheets and useful custom R commands that you can use for your own analyses.
At the end of each chapter there is a summary table to help give you an overview of the material in that
chapter. There are also some self-assessment exercises for you to try out. The answers are in Appendix 1.
Support files
The companion website (see resources page: http://www.pelagicpublishing.com/community-ecology-
resources.html) contains support material that includes spreadsheet calculations and data in Excel and CSV
(comma separated values) format. There is also an R data file, which contains custom R commands and
datasets. Instructions on how to load the R data into your copy of R are on the website. In brief you need to
use the load() command, for Windows or Mac you can type the following:
load(file.choose())
This will open a browser window and you can select the CERE.RData file. On Linux machines you’ll need to
replace the file.choose() part with the exact filename in quotes, see the website for more details.
I hope that you will find this book helpful, useful and interesting. Above all, I hope that it helps you to
discover that analysis of community ecology is not the ‘boring maths’ at the end of your fieldwork but an
enjoyable and enlightening experience.
Mark Gardener, Devon 2013
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