Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction and data types
- 2 Using Canoco 5
- 3 Experimental design
- 4 Basics of gradient analysis
- 5 Permutation tests and variation partitioning
- 6 Similarity measures and distance-based methods
- 7 Classification methods
- 8 Regression methods
- 9 Interpreting community composition with functional traits
- 10 Advanced use of ordination
- 11 Visualising multivariate data
- 12 Case study 1: Variation in forest bird assemblages
- 13 Case study 2: Search for community composition patterns and their environmental correlates: vegetation of spring meadows
- 14 Case study 3: Separating the effects of explanatory variables
- 15 Case study 4: Evaluation of experiments in randomised complete blocks
- 16 Case study 5: Analysis of repeated observations of species composition from a factorial experiment
- 17 Case study 6: Hierarchical analysis of crayfish community variation
- 18 Case study 7: Analysis of taxonomic data with discriminant analysis and distance-based ordination
- 19 Case study 8: Separating effects of space and environment on oribatid community with PCNM
- 20 Case study 9: Performing linear regression with redundancy analysis
- Appendix A Glossary
- Appendix B Sample data sets and projects
- Appendix C Access to Canoco and overview of other software
- Appendix D Working with R
- References
- Index to useful tasks in Canoco 5
- Subject index
16 - Case study 5: Analysis of repeated observations of species composition from a factorial experiment
Published online by Cambridge University Press: 05 May 2014
- Frontmatter
- Contents
- Preface
- 1 Introduction and data types
- 2 Using Canoco 5
- 3 Experimental design
- 4 Basics of gradient analysis
- 5 Permutation tests and variation partitioning
- 6 Similarity measures and distance-based methods
- 7 Classification methods
- 8 Regression methods
- 9 Interpreting community composition with functional traits
- 10 Advanced use of ordination
- 11 Visualising multivariate data
- 12 Case study 1: Variation in forest bird assemblages
- 13 Case study 2: Search for community composition patterns and their environmental correlates: vegetation of spring meadows
- 14 Case study 3: Separating the effects of explanatory variables
- 15 Case study 4: Evaluation of experiments in randomised complete blocks
- 16 Case study 5: Analysis of repeated observations of species composition from a factorial experiment
- 17 Case study 6: Hierarchical analysis of crayfish community variation
- 18 Case study 7: Analysis of taxonomic data with discriminant analysis and distance-based ordination
- 19 Case study 8: Separating effects of space and environment on oribatid community with PCNM
- 20 Case study 9: Performing linear regression with redundancy analysis
- Appendix A Glossary
- Appendix B Sample data sets and projects
- Appendix C Access to Canoco and overview of other software
- Appendix D Working with R
- References
- Index to useful tasks in Canoco 5
- Subject index
Summary
Introduction
Repeated observations of experimental units are frequently used in many areas of ecological research. A special case is the replicated BACI (before after control impact) design, in which the units (plots) are sampled first before the experimental treatment is imposed on some of them. In this way, you obtain ‘baseline’ data, i.e. the data where differences between the sampling units are caused solely by random variability. After the treatment is imposed, the units are sampled once or several times to reveal the difference in the development (dynamics) of manipulated and control units.
To analyse a univariate response (e.g. number of species, or total biomass) in this design, you can usually apply the repeated measurements model of ANOVA. There are in fact two possibilities for analysing such data. You can use a split-plot ANOVA with time, i.e. the repeated measures factor, being the ‘within plot’ factor or you can analyse the data using MANOVA. Although the theoretical distinction between those two approaches is complicated, the first option (often called a ‘univariate repeated measurements ANOVA’) is usually adopted, because it provides a stronger test. But it also has stronger assumptions for its validity (see e.g. von Ende 1993; Lindsey 1993), which are not always fulfilled. The interaction between time and the treatment reflects the difference in the development of the units between treatments.
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- Information
- Multivariate Analysis of Ecological Data using CANOCO 5 , pp. 267 - 300Publisher: Cambridge University PressPrint publication year: 2014