Multivariate methods are no longer restricted to the exploration of data and to the generation of new hypotheses. In particular, constrained ordination is a powerful tool for analysing data from manipulative experiments. In this chapter, we review the basic types of experimental design, with an emphasis on manipulative field experiments. Generally, we expect that the aim of the experiment is to compare the response of studied objects (e.g. an ecological community) to several treatments (treatment levels). Note that one of the treatment levels is usually a control treatment (although in real ecological studies, it might be difficult to decide what is the control; for example, when we compare several types of grassland management, which of the management types is the control one?). Detailed treatment of the topics handled in this chapter can be found for example in Underwood (1997).

If the response is univariate (e.g. number of species, total biomass), then the most common analytical tools are ANOVA, general linear models (which include both ANOVA, linear regression and their combinations), or generalized linear models. Generalized linear models are an extension of general linear models for the cases where the distribution of the response variable cannot be approximated by the normal distribution.

Completely randomized design

The simplest design is the completely randomized one (Figure 2–1). We first select the plots, and then randomly assign treatment levels to individual plots. This design is correct, but not always the best, as it does not control for environmental heterogeneity. This heterogeneity is always present as an unexplained variability. If the heterogeneity is large, use of this design might decrease the power of the tests.