Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction and data manipulation
- 2 Experimental design
- 3 Basics of gradient analysis
- 4 Using the Canoco for Windows 4.5 package
- 5 Constrained ordination and permutation tests
- 6 Similarity measures
- 7 Classification methods
- 8 Regression methods
- 9 Advanced use of ordination
- 10 Visualizing multivariate data
- 11 Case study 1: Variation in forest bird assemblages
- 12 Case study 2: Search for community composition patterns and their environmental correlates: vegetation of spring meadows
- 13 Case study 3: Separating the effects of explanatory variables
- 14 Case study 4: Evaluation of experiments in randomized complete blocks
- 15 Case study 5: Analysis of repeated observations of species composition from a factorial experiment
- 16 Case study 6: Hierarchical analysis of crayfish community variation
- 17 Case study 7: Differentiating two species and their hybrids with discriminant analysis
- Appendix A Sample datasets and projects
- Appendix B Vocabulary
- Appendix C Overview of available software
- References
- Index
3 - Basics of gradient analysis
Published online by Cambridge University Press: 09 February 2010
- Frontmatter
- Contents
- Preface
- 1 Introduction and data manipulation
- 2 Experimental design
- 3 Basics of gradient analysis
- 4 Using the Canoco for Windows 4.5 package
- 5 Constrained ordination and permutation tests
- 6 Similarity measures
- 7 Classification methods
- 8 Regression methods
- 9 Advanced use of ordination
- 10 Visualizing multivariate data
- 11 Case study 1: Variation in forest bird assemblages
- 12 Case study 2: Search for community composition patterns and their environmental correlates: vegetation of spring meadows
- 13 Case study 3: Separating the effects of explanatory variables
- 14 Case study 4: Evaluation of experiments in randomized complete blocks
- 15 Case study 5: Analysis of repeated observations of species composition from a factorial experiment
- 16 Case study 6: Hierarchical analysis of crayfish community variation
- 17 Case study 7: Differentiating two species and their hybrids with discriminant analysis
- Appendix A Sample datasets and projects
- Appendix B Vocabulary
- Appendix C Overview of available software
- References
- Index
Summary
The methods for analysing species composition are usually divided into gradient analysis and classification. The term gradient analysis is used here in the broad sense, for any method attempting to relate species composition to the (measured or hypothetical) environmental gradients.
Traditionally, the classification methods, when used in plant community ecology, were connected with the discontinuum approach (or vegetation unit approach) or sometimes even with the Clementsian superorganismal approach, whereas the methods of gradient analysis were connected with the continuum concept or with the Gleasonian individualistic concept of (plant) communities (Whittaker 1975). While this might reflect the history of the methods, this distinction is no longer valid. The methods are complementary and their choice depends mainly on the purpose of a study. For example, in vegetation mapping some classification is necessary. Even if there are no distinct boundaries between adjacent vegetation types, we have to cut the continuum and create distinct vegetation units for mapping purposes. Ordination methods can help find repeatable vegetation patterns and discontinuities in species composition, and show any transitional types, etc. These methods are now accepted even in phytosociology. Also, the methods are no longer restricted to plant community ecology. They became widespread in most studies of ecological communities with major emphasis on species composition and its relationship with the underlying factors. In fact, it seems to us that the advanced applications of gradient analysis are nowadays found outside the vegetation sciences, for example in hydrobiological studies (see the bibliographies by Birks et al. 1996, 1998).
- Type
- Chapter
- Information
- Multivariate Analysis of Ecological Data using CANOCO , pp. 25 - 42Publisher: Cambridge University PressPrint publication year: 2003