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
7 - Classification methods
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
The aim of classification is to obtain groups of objects (cases, variables) that are internally homogeneous and distinct from the other groups. When the variables (such as biological species) are classified, the homogeneity can be interpreted as their positive correlation, implying for species similar ecological behaviour, as reflected by the similarity of their distributions. The classification methods are usually categorised as in Figure 7–1.
Historically, numerical classifications were considered an objective alternative to subjective classifications, such as the classification of vegetation types by the Zürich–Montpellier phytosociological system (Mueller-Dombois & Ellenberg 1974; van der Maarel & Franklin 2013). It should be noted, however, that the results of numerical classifications are objective just in the sense that the same method gives the same results. Nevertheless, the results of all numerical classifications depend on the methodological choices, as we discuss in Section 7.3.1.
Example data set properties
The various possibilities of data classificationwill be demonstrated using vegetation data of 14 cases (‘relevés’) from Nízké Tatry Mts, already introduced in Section 6.5. Data were imported from the Excel file into a Canoco 5 project (TatryDCA.c5p). The primary data table was then exported into the condensed Cornell format (file tatry.dta) used by earlier versions of Canoco, to enable use of the TWINSPAN for Windows program. The data table present in the Excel file was also imported into the R software as a data frame called tatry, using the read.delim function.
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- Multivariate Analysis of Ecological Data using CANOCO 5 , pp. 112 - 128Publisher: Cambridge University PressPrint publication year: 2014