Community Ecology

Mark Gardener

Description

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.

1. Starting to look at communities

2. Software tools for community ecology

3. Recording your data

4. Beginning data exploration: using software tools

5. Exploring data: choosing your analytical method

6. Exploring data: getting insights

7. Diversity: species richness

8. Diversity: indices

9. Diversity: comparing

10. Diversity: sampling scale

11. Rank abundance or dominance models

12. Similarity and cluster analysis

13. Association analysis: identifying communities

14. Ordination

Appendices

Bibliography

Index

Sujets

• Sciences & Techniques
• Statistiques
• Statistiques
• Sciences de la nature
• Sciences de l'environnement
• Probabilités et statistiques
• Life Sciences
• Sciences et techniques
• Excel (desambiguación)
• Biology
• Excel
• General officer
• Probability and statistics
• Ecology
• General
• 454 Life Sciences
• Microsoft Excel
• Science

Informations

• Publié par: Pelagic Publishing
• Date de parution: 01 février 2014
• Nombre de lectures: 1
• EAN13: 9781907807657
• Langue: English
• Poids de l'ouvrage: 37 Mo

Extrait

Community Ecology
Analytical Methods Using
®R and Excel
Mark Gardener
DATA IN THE WILD SERIES
Pelagic Publishing | www.pelagicpublishing.comPublished 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.
Windows, Excel and Word and are trademarks of the Microsoft Corporation. For more
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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, UKAbout 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.htmlReader feedback
We welcome feedback from readers – please email us at [e-mail] and
tell us what you thought about this book. Please include the book title in the subject line
of your email.
Publish with Pelagic Publishing
We publish scientific books to the highest editorial standards in all life science disciplines,
with a particular focus on ecology, conservation and environment. Pelagic Publishing
produces books that set new benchmarks, share advances in research methods and encourage
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If you are interested in publishing with Pelagic please contact
[e-mail] with a synopsis of your book, a brief history of your previous written work and a
statement describing the impact you would like your book to have on readers.Contents
Introduction viii
1. Starting to look at communities 1
1.1 A scientific approach 1
1.2 The topics of community ecology 2
1.3 Getting data – using a spreadsheet 4
1.4 Aims and hypotheses 5
1.5 Summary 5 1.6 Exercises 7
2. Software tools for community ecology 8
2.1 Excel 8
2.2 Other spreadsheets 9
2.3 The R program 10
2.4 Summary 15 2.5 Exercises 15
3. Recording your data 16
3.1 Biological data 16
3.2 Arranging your data 18
3.3 Summary 19 3.4 Exercises 19
4. Beginning data exploration: using software tools 20
4.1 Beginning to use R 20
4.2 Manipulating data in a spreadsheet 28
4.3 Getting data from Excel into R 60
4.4 Summary 62 4.5 Exercises 63
5. Exploring data: choosing your analytical method 64
5.1 Categories of study 64
5.2 How ‘classic’ hypothesis testing can be used in community studies 66|vi Contents
5.3 Analytical methods for community studies 70
5.4 Summary 73 5.5 Exercises 74
6. Exploring data: getting insights 75
6.1 Error checking 75
6.2 Adding extra information 78
6.3 Getting an overview of your data 80
6.4 Summary 104 6.5 Exercises 105
7. Diversity: species richness 106
7.1 Comparing species richness 108
7.2 Correlating species richness over time or against an
environmental variable 119
7.3 Species richness and sampling effort 123
7.4 Summary 148 7.5 Exercises 149
8. Diversity: indices 151
8.1 Simpson’s index 151 8.2 Shannon 160
8.3 Other diversity indices 168
8.4 Summary 194 8.5 Exercises 195
9. Diversity: comparing 196
9.1 Graphical comparison of diversity profiles 197
9.2 A test for differences in diversity based on the t-test 199
9.3 Graphical summary of the t-test for Shannon and Simpson indices 212
9.4 Bootstrap comparisons for unreplicated samples 227
9.5 Comparisons using replicated samples 252
9.6 Summary 269 9.7 Exercises 270
10. Diversity: sampling scale 272
10.1 Calculating beta diversity 272
10.2 Additive diversity partitioning 299
10.3 Hierarchical partitioning 30310.4 Group dispersion 306
10.5 Permutation methods 309
10.6 Overlap and similarity 315
10.7 Beta diversity using alternative dissimilarity measures 32510.8 Beta diversity compared to other variables 327
10.9 Summary 33110.10 Exercises 333|Contents vii
11. Rank abundance or dominance models 334
11.1 Dominance models 33411.2 Fisher’s log-series 358
11.3 Preston’s lognormal model 360
11.4 Summary 36311.5 Exercises 365
12. Similarity and cluster analysis 366
12.1 Similarity and dissimilarity 366
12.2 Cluster analysis 38212.3 Summary 416
12.4 Exercises 418
13. Association analysis: identifying communities 419
13.1 Area approach to identifying communities 420
13.2 Transect approach to identifying communities 428
13.3 Using alternative dissimilarity measures for identifying communities 431
13.4 Indicator species 43613.5 Summary 444
13.6 Exercises 445
14. Ordination 446
14.1 Methods of ordination 447
14.2 Indirect gradient analysis 449
14.3 Direct gradient analysis 490
14.4 Using ordination results 505
14.5 Summary 52014.6 Exercises 522
Appendices 524
Bibliography 542
Index 547Introduction
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