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E-raamat: Community Ecology: Analytical Methods Using R and Excel

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  • Formaat: 425 pages
  • Sari: Data in the Wild
  • Ilmumisaeg: 01-Feb-2014
  • Kirjastus: Pelagic Publishing
  • Keel: eng
  • ISBN-13: 9781907807657
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Community Ecology: Analytical Methods Using R and ExcelSuurem pilt
  • Formaat: 425 pages
  • Sari: Data in the Wild
  • Ilmumisaeg: 01-Feb-2014
  • Kirjastus: Pelagic Publishing
  • Keel: eng
  • ISBN-13: 9781907807657
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Covers many of the mainstays of community analysis including: diversity, similarity and cluster analysis, ordination and multivariate analyses. Aimed at undergraduate and postgraduate students and researchers seeking a step-by-step methodology for analysing plant and animal communities using R and Excel.



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.

Arvustused

Following an intuitive thread from data entry through to analysis and interpretation, this is intended as a comprehensive course in the main methods of community analysis, both traditional and current. The intimidating length can largely be attributed to the numerous worked examples with full output. Some techniques are demonstrated in both Excel and R, which seems superfluous, since the latter is almost invariably superior. I would have liked more on GREP, an invaluable tool for checking and formatting data, and a notable weakness of Excel. Overall this is a useful resource for postgraduate students, but it could have been more concise and selective. -- Markus Eichhorn * Frontiers of Biogeography * Without a doubt there is a challenge here since Gardener seeks to enlighten the reader about both community ecology as a topic (although he admits in the foreword that this is not exhaustive) and the analytical techniques needed to successfully study it. This is a feat which I felt that he managed reasonably well. I find his style easy-going and he does well at not assuming the reader has expert knowledge. The book follows a logical path and is packed with reassuring screen shots and coding advice. The fact that it is written by an ecologist makes the data relevant to biologists and it all seems easy to follow, specimen data are again provided on the website. Gardener offers alternative analyses for each type of data, explains clearly when he thinks a particular analysis is most useful and then encourages the reader to have a go. -- Mark Edwards * EcoBlogging *

Introduction viii
1 Starting to look at communities
1(7)
1.1 A scientific approach
1(1)
1.2 The topics of community ecology
2(2)
1.3 Getting data -- using a spreadsheet
4(1)
1.4 Aims and hypotheses
5(1)
1.5 Summary
5(2)
1.6 Exercises
7(1)
2 Software tools for community ecology
8(8)
2.1 Excel
8(1)
2.2 Other spreadsheets
9(1)
2.3 The R program
10(5)
2.4 Summary
15(1)
2.5 Exercises
15(1)
3 Recording your data
16(4)
3.1 Biological data
16(2)
3.2 Arranging your data
18(1)
3.3 Summary
19(1)
3.4 Exercises
19(1)
4 Beginning data exploration: using software tools
20(44)
4.1 Beginning to use R
20(8)
4.2 Manipulating data in a spreadsheet
28(32)
4.3 Getting data from Excel into R
60(2)
4.4 Summary
62(1)
4.5 Exercises
63(1)
5 Exploring data: choosing your analytical method
64(11)
5.1 Categories of study
64(2)
5.2 How `classic' hypothesis testing can be used in community studies
66(4)
5.3 Analytical methods for community studies
70(3)
5.4 Summary
73(1)
5.5 Exercises
74(1)
6 Exploring data: getting insights
75(31)
6.1 Error checking
75(3)
6.2 Adding extra information
78(2)
6.3 Getting an overview of your data
80(24)
6.4 Summary
104(1)
6.5 Exercises
105(1)
7 Diversity: species richness
106(45)
7.1 Comparing species richness
108(11)
7.2 Correlating species richness over time or against an environmental variable
119(4)
7.3 Species richness and sampling effort
123(25)
7.4 Summary
148(1)
7.5 Exercises
149(2)
8 Diversity: indices
151(45)
8.1 Simpson's index
151(9)
8.2 Shannon index
160(8)
8.3 Other diversity indices
168(26)
8.4 Summary
194(1)
8.5 Exercises
195(1)
9 Diversity: comparing
196(76)
9.1 Graphical comparison of diversity profiles
197(2)
9.2 A test for differences in diversity based on the t-test
199(13)
9.3 Graphical summary of the t-test for Shannon and Simpson indices
212(15)
9.4 Bootstrap comparisons for unreplicated samples
227(25)
9.5 Comparisons using replicated samples
252(17)
9.6 Summary
269(1)
9.7 Exercises
270(2)
10 Diversity: sampling scale
272(62)
10.1 Calculating beta diversity
272(27)
10.2 Additive diversity partitioning
299(4)
10.3 Hierarchical partitioning
303(3)
10.4 Group dispersion
306(3)
10.5 Permutation methods
309(6)
10.6 Overlap and similarity
315(10)
10.7 Beta diversity using alternative dissimilarity measures
325(2)
10.8 Beta diversity compared to other variables
327(4)
10.9 Summary
331(2)
10.10 Exercises
333(1)
11 Rank abundance or dominance models
334(32)
11.1 Dominance models
334(24)
11.2 Fisher's log-series
358(2)
11.3 Preston's lognormal model
360(3)
11.4 Summary
363(2)
11.5 Exercises
365(1)
12 Similarity and cluster analysis
366(53)
12.1 Similarity and dissimilarity
366(16)
12.2 Cluster analysis
382(34)
12.3 Summary
416(2)
12.4 Exercises
418(1)
13 Association analysis: identifying communities
419(27)
13.1 Area approach to identifying communities
420(8)
13.2 Transect approach to identifying communities
428(3)
13.3 Using alternative dissimilarity measures for identifying communities
431(5)
13.4 Indicator species
436(8)
13.5 Summary
444(1)
13.6 Exercises
445(1)
14 Ordination
446(78)
14.1 Methods of ordination
447(2)
14.2 Indirect gradient analysis
449(41)
14.3 Direct gradient analysis
490(15)
14.4 Using ordination results
505(15)
14.5 Summary
520(2)
14.6 Exercises
522(2)
Appendices 524(18)
Bibliography 542(5)
Index 547
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 word (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, is the most complicated and ill-behaved and is 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 organizations. Mark lives in rural Devon with his wife Christine, a biochemist who consequently has little need of statistics.
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