The aim of classification is to obtain groups of objects (samples, species) that are internally homogeneous and distinct from the other groups. When the species are classified, the homogeneity can be interpreted as their similar ecological behaviour, as reflected by the similarity of their distributions. The classification methods are usually categorized as in Figure 7–1.

Historically, numerical classifications were considered an objective alternative to subjective classifications (such as the Zürich-Montpellier phytosociological system, Mueller-Dombois & Ellenberg 1974). They are indeed ‘objective’ by their reproducibility, i.e. getting identical results with identical inputs (in classical phytosociology, the classification methods obviously provide varying results). But also in numerical classifications, the methodological choices are highly subjective and affect the final result.

Sample data set

The various possibilities of data classification will be demonstrated using vegetation data of 14 relevés from an altitudinal transect in Nízké Tatry Mts, Slovakia. Relevé 1 was recorded at an altitude of 1200 metres above sea level (m a.s.l.), relevé 14 at 1830ma.s.l. Relevés were recorded using the Braun–Blanquet scale (r, +, 1–5, see Mueller-Dombois & Ellenberg 1974). For calculations, the scale was converted into numbers 1 to 7 (ordinal transformation, Van der Maarel 1979). Data were entered as a classical vegetation data table (file tatry.xls) and further imported (using the WCanoImp program) into the condensed Cornell (CANOCO) format (file tatry.spe), to enable use of the CANOCO and TWINSPAN programs. The data were also imported into a Statistica file (file tatry.sta).