Chapter 12 What does “unconstrained” mean?

Unconstrained ordination methods are multivariate techniques used to visualize and explore relationships among variables or observations in a dataset without imposing any specific constraints on the relationships. These methods are called “unconstrained” because they do not require prior knowledge or assumptions about the structure of the data or the relationships among variables.

Unconstrained ordination methods work by representing the variables or observations in a lower-dimensional space, such as a two-dimensional or three-dimensional plot, while preserving the overall structure of the data.

Some common unconstrained ordination methods include principal component analysis (PCA), correspondence analysis (CA), and multidimensional scaling (MDS). These methods can be applied to a wide range of ecological data, including species abundance data, environmental data, and community similarity matrices.

Overall, unconstrained ordination methods are widely used in ecology to explore patterns and relationships among biological communities and environmental variables. These methods are valuable tools for identifying key factors that influence community composition and structure, and for developing hypotheses about the underlying ecological processes that drive these patterns.