Community ecology: analytical methods using R and excel / Mark Gardener

Incluye bibliografía: páginas 542-546 e índice: páginas 547-556

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.. 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.. 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.. 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

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. eng