Chapter 3 Why “advanced multivariate methods”?

The previous workshop presented the basics of multivariate analyses:

  • How to choose appropriate distance metrics and transformations
  • Hierarchical clustering
  • Unconstrained ordinations
    • Principal component analysis
    • Principal coordinate Analysis
    • Correspondence analysis
    • Nonmetric multidimensional scaling

The present workshop builds on this knowledge, and will focus on constrained analyses. All the methods overviewed during Workshop 9 allowed us to find patterns in the community composition data or in the descriptors, but not to explore how environmental variables could be driving these patterns. With constrained analyses, such as redundancy analysis (RDA), linear discriminant analysis (LDA) and multivariate regression tree (MRT), one can describe and predict relationships between community composition data and environmental variables.