The multidimensional data on community composition, properties of individual populations, or properties of environment are the bread and butter of an ecologist's life. They need to be analysed with taking their multidimensionality into account. A reductionist approach of looking at the properties of each variable separately does not work in most cases. The methods for statistical analysis of such data sets fit under the umbrella of ‘multivariate statistical methods’.

In this book, we present a hopefully consistent set of approaches to answering many of the questions that an ecologist might have about the studied systems. Nevertheless, we admit that our views are biased to some extent, and we pay limited attention to other less parametric methods, such as the family of non-metric multidimensional scaling (NMDS) algorithms or the group of methods similar to the Mantel test or the ANOSIM method. We do not want to fuel the controversy between proponents of various approaches to analysing multivariate data. We simply claim that the solutions presented are not the only ones possible, but they work for us, as well as many others.

We also give greater emphasis to ordination methods compared to classification approaches, but we do not imply that the classification methods are not useful. Our description of multivariate methods is extended by a short overview of regression analysis, including some of the more recent developments such as generalized additive models.

Our intention is to provide the reader with both the basic understanding of principles of multivariate methods and the skills needed to use those methods in his/her own work.