Information on spatial distribution of syntaxa is often summarized in vegetation maps. Maps of actual vegetation show the current distribution of vegetation types in a given area, usually in small areas of particular interest, such as nature reserves. Fine-scale mapping of actual vegetation requires operational definitions of syntaxon boundaries and their differential floristic and structural features, which are laid down in detailed mapping keys.
For mapping larger areas, the concept of potential natural vegetation (PNV) is often used. PNV is hypothetical vegetation that would exist at certain sites under current site conditions and current climate, provided the vegetation is not disturbed by humans and is allowed to develop into equilibrium with the prevailing site conditions. Being based on the knowledge of the relationship between habitat and natural vegetation, PNV maps implicitly or explicitly rely on models, which can take different forms. Traditional phytosociology establishes the correspondence between actual (e.g., certain meadow or weed communities) and natural vegetation (e.g., certain forest types), and maps PNV units by interpreting actual vegetation. More modern PNV models are calibrated from joint descriptions of vegetation and site conditions of remnant natural stands, and use combinations of site conditions to extrapolate natural vegetation for any point in the landscape. Process-based models predict the outcome of competition between the dominant plant species, but have so far rarely been used to construct PNV maps.
While maps of actual or potential vegetation provide full coverage of a study area and its vegetation units, selective maps show the distribution of certain syntaxa, based on the available releves. They can be presented as dot maps of exact plot positions or as grid maps, indicating presence or absence of the syntaxon in grid cells. As, however, information on distribution of syntaxa is often less comprehensive than on plant species, the potential range of a syntaxon can be modeled by superimposing distribution maps of its diagnostic species. The more the number of these co-occur in a certain area, the higher the probability to find the respective community type there. Models of potential syntaxon ranges can be based on outline or grid maps and on simple or weighted sums of species, but the prediction value is best for high-resolution grid maps where the contribution of diagnostic species to the prediction of a syntaxon is weighted by their fidelity to the latter.
Spatial models of syntaxon distribution can also be based on the knowledge of the relationships between environmental variables (including land use) and syntaxon occurrence. If digital maps of environmental factors and landscape structures relevant to plant distribution are available, the model can be made with the probability of syntaxon occurrence as a response variable and a set of landscape variables as predictors. The relevant environmental maps are then overlaid in a GIS and the probabilities of syntaxon occurrence predicted by the model are mapped.
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