[Current editors: Miquel De Cáceres]
Vegetation can be classified on the basis of different attributes of plant communities. The following is a brief review of different approaches with respect to the kind of attributes that are considered relevant (modified from Vigo 2005):
Physiognomic classification are based on the physiognomy (i.e. the set of functional and morphological atributes) of the dominant plants in the community. In order to follow this approach, it is necessary to choose which morphological or functional plant attributes are relevant. Physiognomic classifications are useful to describe the vegetation of large areas. The abstract units in physiognomic classifications are called formations, which can be arranged in a hierarchical system. In order to characterize formations it is sometimes important the vertical (i.e. stratification) and horizontal (i.e. open- or closed-canopy) structure of the plant community. An example of physiognomic classifications is Dansereau (1957).
Environmental factors, specially those related to climate and soil conditions, have an important effect on the resulting structure and composition of plant communities. Several proposals have been made to classify plant communities on the sole basis of environmental factors, although these have not been specially successful.
Given that physiognomy already implies an adaptation to environmental conditions, pure physiognomic classification are already informative with respect to environmental conditions. Nevertheless, there are classification systems that make this relationship explicit. They are not essentially different from pure physiognomic classifications, and abstract units are also called formations. They combine the physiognomy of plant communities with their ecology (mainly climate, soil and biogeography). An example of physiognomic-environmental classification of vegetation is that adopted by UNESCO in 1973.
This kind of vegetation classifications are based on previous determination of socio-ecological groups, defined as groups of plants that have similar ecological requirements. Each socio-ecological group indicates specific environmental conditions, or a range of environmental conditions. Then, a plant community type is defined as the combination of socio-ecological groups. An example of socio-ecological classification can be found in Duvigneaud (1974).
Floristic classifications take, as the basis for defining community types, the taxonomic identity of the plants in the community. Unlike, physiognomic classifications, which are only related to the structure and general environmental conditions of the community, floristic classifications bear historical and biogeographical information also. This happens because each plant species has its own geographic distribution and particular population and metapopulation history. Floristic classification, thus, can convey more detail and are particularly useful to describe habitats for conservation purposes. Floristic classifications arise from the vegetation data obtained using vegetation plots. Each plot record typically includes, among other information, the list of (vascular) plant species found in the community, along with an estimation of abundance. There are two main kinds of floristic classifications, depending on whether the plant community is divided vertically or not:
Continua of independent species distributions revealed in gradient analyses have generally been interpreted as evidence for Gleason's concept of individualistic species assemblages (Gleason 1926) and this concept has been organized into the individualistic-continuum theory (Goodall 1963). However, while the continuum model grew out of Gleason's essays on the individualistic distribution of species they should not be considered synonymous. The individualistic hypothesis is a species-scale phenomenon involving the tolerance of individuals of different species to local environmental conditions, which may include interspecific interactions. In contrast, the continuum model is a community-level construct of the collective distributions and abundance of species along environmental gradients. It is therefore possible, that individualistic distribution of species gives rise to discrete communities as well as to continuum (Collins et al. 1993).
Although most ecologists and vegetation scientists now accept the continuum model to be correct, the debate concerning the validity of these models still continues (Callaway 1997). Westman (1983) suggested the debate endures because empirical evidence exists that supports both points of view. On the other hand, Shipley & Keddy (1987) determined that neither model applied to species distributions along complex environmental gradients in wetlands. Roberts (1987) suggested that both the community-unit and continuum models were consistent with a mechanistic view of vegetation development. From a hierarchical perspective, the two models are not competitive; rather, they reflect differences in scale of perception. Palmer & White (1994) adopt a more pragmatic attitude and suggest that community should be defined in as “the living organisms present within a space-time unit of any magnitude”.
Mucina (1997) argued against the common believe of vegetation scientists that plant communities are 'natural' units. He writes: "It is often believed that only temporally stable plant communities can be ‘real’ or ‘true’ communities or, in terms of habitat characteristics, only those plant assemblages supported by stable environments are worthy of a classification approach".... "Another source of error concerning the nature of plant communities is the uncritical belief in that the structure and dynamics of vegetation are only the result of habitat conditions, mainly soil and climate". Other authors have also stressed the conventional character of vegetation units. Mirkin (1989) writes "The basis of syntaxonomy is entirely conventional owing to the continuous nature of vegetation. As a result, the scope of the association concept varies with different national schools, and the hierarchy in syntaxonomy is of a pragmatical, at best of an ecological nature". The conventional nature of vegetation units indicates that adopting one method or the other is a decision based on conventions. Moreover, adopting formalized procedures is an imperative in order to follow conventions in a consistent way.
Traditional phytosociology attempted to build a classification system (preferably hierarchical) of plant communities. Each system, however, is a reflection of the geographical and temporal extent of the plot observations used to define it (Mucina 1997). Practical experience indicates that new vegetation types are defined (or existing ones are modified) as new areas are surveyed. Mucina (1997) argues that stability in a classification system is an illusion, because it neglects several facets controlling the pathways of vegetation dynamics. Including more data does not necessarily lead to a more stable classification system. As Feoli & Lausi (1981) put it: “the aim of syntaxonomy is not to create stable syntaxonomic systems but stable classificatory structures of the available data on which to base the biological discussions and interpretations”.
Early methods for vegetation classification, such as those of the Braun-Blanquet school, were based on sorting floristic data tables by hand, and were often collectively described as 'subjective'. After the advent of computers, various numerical methods were devised, and these were described as 'objective'. However, the words 'objective' and 'subjective' need to be used with care in the context of vegetation classification. The issue is nicely explained in Kent (2012), from which we largely edited this subsection. Numerical methods are defined as 'objective' only in the sense of repeatability. For one set of data, any researcher using the same numerical method should obtain the same result, thus removing the element of subjectivity in the classification process. However, different numerical methods give varying results for the same data set, depending on the mathematical properties of each technique. As the user does not normally know all the subtle differences among methods and their alternative parameterizations, the subjective element is not completely removed from the analysis, and there is no unique solution or single classification of a set of data. The idea of user satisfaction is still very important with numerical classification. Many users expected numerical approaches to yield ‘more objective’ results. Making choices among many data transformations, resemblance measures and classification or ordination algorithms is far from objective. Thus it is not the question of being ‘more objective’, but rather being ‘more formal’ in terms of exactness, repeatability and liability to experiment on the data (Mucina 1997).