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
9 - Interpreting community composition with functional traits
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 composition of ecological communities changes along environmental gradients and such change is often predictable and repeatable across various biogeographical areas when expressed in terms of prevailing species trait composition (Garnier et al. 2004). We can often predict over various biogeographical regions what will be the traits of dominant species in a certain environment and which traits will determine species response to an environmental change. Functional groups (or functional types) defined in various ways are used also in various simulation models predicting e.g. the dynamics of vegetation facing environmental or land use changes, or in the description of these changes.
Functional types used in large-scale models of vegetation dynamics are usually broad categories of species based on their morphological and physiological traits or on large phylogenetical groups, or these two types of criteria can be combined. For example in temperate grasslands, four groups are often used – grasses (further distinguished into C3 and C4 groups in North America), sedges (and rushes), legumes, and (non-legume) forbs. In large-scale dynamic simulation models, the overwhelming diversity of species does not allow for modelling directly the changes in species composition, and so we need some broader groups of similarly functioning species; nevertheless, it is clear that rough classifications such as the one mentioned above are often not sufficient for this purpose. Detailed (and usually spatially limited) studies, where we can evaluate the species composition response to environmental gradients and we also have the data on traits of individual species available, provide a good lead for designing functional classification of species that can be subsequently used in large-scale modelling.
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- Multivariate Analysis of Ecological Data using CANOCO 5 , pp. 151 - 166Publisher: Cambridge University PressPrint publication year: 2014