[Current editors: Miquel De Cáceres]
Classifications of vegetation provide a useful way of summarizing our knowledge of vegetation patterns. They are a necessary tool for nature conservation, landscape mapping and land-use planning. The development of numerical classification methods and the recent availability of vegetation plot record databases have made it possible to derive formalized, repeatable vegetation classifications. Moreover, these technical advances have opened an avenue for the development of expert systems for vegetation classification.
Vegetation is often chosen as the basis for the classification of terrestrial ecosystems because it generally integrates the ecological processes acting on a site or landscape more measurably than any other factor or set of factors. Vegetation is a critical component of energy flow in ecosystems and provides habitat for many organisms. In addition, vegetation is often used to infer soil and climate patterns. For these reasons, a classification of terrestrial ecological communities based on vegetation can serve to describe many facets of ecological patterns across the landscape.Since the basis of vegetation classification is mainly conventional, many methodological options are possible, whether formalized or not. Nevertheless, there is a strong need to standardize classification methods in order to achieve consistency among classifications. Important unresolved issues still hamper the consistency of numerical vegetation classifications based on floristic composition. One of these issues is deciding an appropriate sampling design for classification purposes, and another is the difficulty in validating the cluster patterns obtained in a vegetation sample. More importantly, there is not yet a widely accepted definition of vegetation type that can be unambiguously related to a specific numerical classification method. Different numerical clustering methods often produce different, but equally sound, classification results.
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).
In this comparative framework a distinction is made between procedural and structural elements of plot-based classification of vegetation (see table below). Two structural elements, vegetation-plot record and vegetation type, are well known to vegetation scientists. The most comprehensive structural element is the classification system, which is defined as an organized set of vegetation types used to describe the variation of vegetation within given spatial, temporal and ecological scopes. Classification systems are often hierarchical, meaning that vegetation types are organized in hierarchical classification levels and qualified using ranks (e.g., association or alliance). In addition, hierarchical systems usually include nested relationships between vegetation types of different ranks.
Broad-scale classification systems often involve sets of vegetation types defined based on varying classification criteria. To account for this variation explicitly, this framework introduces a new concept called consistent classification section (CCS) and defines it as a subset of a classification system where vegetation types are defined using the same criteria and procedures (i.e., using the same classification protocol; see below). For example, the vegetation types of a CCS may broadly summarize the woody vegetation of a given area on the basis of physiognomy, whereas another may classify the same vegetation based on detailed floristic composition; in this example, the set of vegetation types of each CCS might be placed at different hierarchical levels within the same classification system (e.g., CCSs A and B in the figure below). Classification systems may allow vegetation types of the same hierarchical level, but corresponding to very different kinds of vegetation, to be defined using different criteria. For example, a classification system may allow forest associations to be defined based on the dominant species of the tree layer and species composition of the herb layer, while aquatic associations are defined focusing on the dominant species and its position in the water column; these will represent different CCSs of the same hierarchical level (e.g., CCSs B and C).
Now we turn our attention to procedural elements. A classification protocol is defined as the set of criteria and procedures that underlie the creation or modification of a consistent classification section. For example, the protocol for a set of floristically-based vegetation types may include specifications of field sampling design, plot size, taxonomic resolution, taxon abundance measure, plot resemblance measure, clustering algorithm, etc. Although the focus of this framework is on plot-based classification, not all vegetation types are required to be defined directly as groups of plot records. Vegetation types of a given hierarchical rank may be explicitly defined as groups of vegetation types of a lower rank (e.g., CCS A). For example, one may define floristically-based alliances after grouping the constancy columns of a synoptic table of associations. Classification protocols of this kind are be qualified as type-based, whereas those dealing with plot records directly will be qualified as plot-based. The CCSs and vegetation types resulting from the application of classification protocols will also be qualified as type-based or plot-based, accordingly. The term classification exercise is used to denote the application of a classification protocol to a particular subset of the vegetation continuum. Finally, classification approach is defined as the set of concepts, criteria and procedures that underlie the creation or modification of a classification system. Analogously to classification exercises, the term classification project is used to denote the application of a classification approach to a particular subset of the vegetation continuum, an activity that creates or modifies a classification system.
The comparison framework includes definitions for the properties of structural and procedural elements. These properties, shown in the table below, are meant to organize the comparison of classification approaches and classification systems.
The primary vegetation attributes of a plot-based classification protocol are the attributes consistently used to determine whether plot records are members of the same or different vegetation types. Analogously, the primary vegetation attributes of a type-based protocol are the attributes consistently used to determine which vegetation types of a lower rank are grouped to form a vegetation type of a higher rank. In both cases, these are attributes of the vegetation itself and not of its environment. Vegetation classifications are often required to describe, reflect or indicate other vegetation characteristics not included in the set of primary attributes, or external factors, such as climatic or edaphic conditions, anthropogenic disturbance regime or biogeographic history. The term secondary attributes is used to collectively refer to all those attributes (whether of vegetation or not) that are not primary vegetation attributes. A special situation arises when a subset of secondary attributes, without being explicitly used to determine membership, are used to constrain the definition of vegetation types. The framework refers to these as constraining attributes of the classification protocol. For example, although ‘classes’ of the Braun-Blanquet approach are defined using floristic composition, a specific subset of plant taxa may be selected as primary attributes in order to make classes distinct in terms of environmental conditions and biogeographic context. The presence or absence of those taxa is the only information needed to consistently determine membership, but climatic and biogeographic factors have indirectly influenced the definition of vegetation types.
The extensive class definition of a plot-based vegetation type is a list of the plot records that belong to it. This list will be enlarged every time new plot records are assigned to the type. Analogously, the extensive class definition of a type-based vegetation type is a list of the vegetation types of the lower rank that belong to it. The intensive class definition of a vegetation type is a statement about the values of primary vegetation attributes that are required to be a member (either plots or vegetation types of a lower rank). A broader property of a vegetation type is its primary characterization (or description), which includes all statements about primary vegetation attributes. Whereas intensive definitions impose limits to plot membership for a single vegetation type, they are often not sufficient to unambiguously determine the membership of a plot record among the set of vegetation types that constitute a CCS. The framework refers to the formal procedures used to determine the membership of new plot records to the predefined vegetation types of a CCS as assignment rules. Because different sets of assignment rules can produce different plot memberships, the definition of a CCS should include a preferred set of assignment rules. To preserve consistency, such set of rules should be able to reproduce the extensive class definition of vegetation types when applied to the original plot records. These are named consistent assignment rules. Additional sets of rules of a CCS are referred to as complementary assignment rules. While the attributes used in the consistent rules must be primary vegetation attributes, the attributes used in complementary rules may be either primary or secondary.