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J Dengler, M Chytry
´, and J Ewald. Phytosociology. In Sven Erik Jørgensen and Brian D. Fath
(Editor-in-Chief), General Ecology. Vol. [4] of Encyclopedia of Ecology,
5 vols. pp. [2767-2779] Oxford: Elsevier.
Author's personal copy
Phytosociology
J Dengler, University of Hamburg, Hamburg, Germany
M Chytry
´, Masaryk University, Brno, Czech Republic
J Ewald, University of Applied Sciences Weihenstephan, Freising, Germany
ª2008 Elsevier B.V. All rights reserved.
Introduction
Phytosociological Data
Classification of Vegetation
Applied Phytosociology
Further Reading
Introduction
Phytosociology is a subset of vegetation science, in which
it stands out by focusing on extant (vs. fossil), taxonomic
(vs. physiognomic or functional) plant assemblages at the
scale of vegetation stands (vs. landscapes or biomes). Its
principal goal is the definition and functional character-
ization of vegetation types based on the total floristic
composition of stands. Phytosociology distinguishes
between concrete vegetation stand (phytocoenosis),
which can be represented by a plot record (releve
´), and
abstract vegetation type (syntaxon), representing a group
of all stands sharing certain attributes. The classification
framework (syntaxonomy) is designed in close analogy to
plant taxonomy, with association as the basic unit.
The fundamental concepts of phytosociology were
developed by Josias Braun-Blanquet in the 1920s. He
combined a standardized protocol for plot sampling,
sorting of species-by-plot matrices, demarcation of com-
munity types, and their hierarchical ordering into a
practical and efficient framework for the study of vegeta-
tion. In this article, we use the term phytosociology for
the Braun-Blanquet approach and its modern extensions.
Phytosociology is the mainstream vegetation classifi-
cation scheme in Europe, as well as in several countries
outside Europe, and has become increasingly popular
worldwide from the 1990s onward. Within modern ecol-
ogy, phytosociology represents the most comprehensive
and consistent methodology for vegetation classification.
Releve
´s are the most widely used standardized protocol
for sampling plant species co-occurrences at the stand
scale. Being derived from the vast body of releve
´data,
syntaxonomy provides a comprehensive yet open system
of vegetation types, which are indispensable in land-use
management and nature conservation. Consisting of
abundance data on individual plant species, releve
´s and
vegetation types organized in large phytosociological
databases are an enormous source of fine-scale biodiver-
sity information. If linked to the growing body of plant
trait or indicator value data or environmental information
in geographical information systems (GISs), phyto-
sociological data open new avenues for exploring large-
scale ecological patterns and processes, and provide spa-
tially explicit information necessary for environmental
management.
Phytosociological Data
Data Records
In phytosociology, the data of a single plot are called a
releve
´(French for record, see Table 1), which consists of
‘header’ and species data. The ‘header’ comprises plot
identification, methodological information, and metric,
ordinal, or categorical data on geographic position, envir-
onmental conditions, and overall vegetation structure.
Some of these data are essential, others optional, depend-
ing on the purpose and resources of a project (Table 2).
The species data are composed of a list of plant taxa
(species and infraspecific taxa; further referred to as ‘spe-
cies’) and their attributes. A full releve
´lists all plant
species occurring in the plot and growing on soil, includ-
ing bryophytes, lichens, and macroalgae. Additional
recording of species growing on substrata other than
soil, such as on living plants (epiphytes), rocks (saxicolous
plants), or dead wood (lignicolous plants), is desirable, but
not standard in phytosociology. Every species observation
is assigned to a vertical stratum (e.g., tree layer, shrub
layer, herb layer, and cryptogam layer). Woody species
occurring in different layers are recorded separately for
each layer. For each species observation in a layer, an
importance value is estimated and usually expressed on a
simplified scale of abundance (number of individuals/
ramets) and/or cover (area of the vertical projection of
all aerial parts of a species relative to the total plot area)
(Table 3). As mixed cover-abundance scales pose pro-
blems in data analysis, pure cover scales are preferred
when precise quantitative estimates are required, for
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Table 1 Example of a forest releve´ with five vegetation layers distinguished: upper tree layer (T1), lower tree layer (T2), shrub layer (S),
herb layer (H), and cryptogam layer (C)
Plot ID/methodology
Field number 291
Author J Ewald
Plot size (m
2
) 144
Plot shape square
Sampling date 3 June 1997
Preliminary syntaxon Galio-Fagetum adenostyletosum
Geographic data
UTM coordinates 32 U 4434393 E – 5272800 N
Locality Ettaler Manndl, Ho¨ llenstein, 3 km W from
Eschenlohe,
Garmisch-Partenkirchen, Bavaria, Germany
Environmental data
Elevation (m a.s.l.) 1300
Slope aspect ()35
Slope inclination ()32
Soil type Cambisol
Parent material Cretaceous sandstone
Management Protective forest
Stand age (year) 140
Structural data
Height upper tree layer (m) 30
Height lower tree layer (m) 6
Height shrub layer (m) 3
Cover upper tree layer (%) 75
Cover lower tree layer (%) 3
Cover shrub layer (%) 1
Cover herb layer (%) 20
Cover cryptogam layer (%) 3
Layer Species Importance Layer Species Importance
T1 Fagus sylvatica 3HOxalis acetosella 2
Picea abies 3Paris quadrifolia þ
Polypodium vulgare þ
T2 Picea abies 1Prenanthes purpurea þ
Primula elatior þ
SPicea abies 1Ranunculus lanuginosus 1
Rumex alpestris þ
HAcer pseudoplatanus þSalvia glutinosa 1
Aconitum vulparia þSanicula europaea þ
Adenostyles alliariae 1Saxifraga rotundifolia 1
Adoxa moschatellina þSenecio fuchsii 1
Athyrium filix-femina þStellaria nemorum 2
Cardamine flexuosa þThelypteris limbosperma þ
Chaerophyllum hirsutum þVeronica urticifolia þ
Chrysosplenium alternifolium þViola biflora þ
Cicerbita alpina þ
Deschampsia cespitosa þCAtrichum undulatum 1
Dryopteris dilatata þBrachythecium rutabulum þ
Dryopteris filix-mas þConocephalum conicum þ
Epilobium montanum þCtenidium molluscum þ
Galeopsis tetrahit þDicranella heteromalla þ
Galium odoratum þDicranum scoparium þ
Geranium robertianum þFissidens taxifolius þ
Gymnocarpium dryopteris þMnium spinosum þ
Impatiens noli-tangere þPlagiochila porelloides þ
Lamiastrum montanum 1Plagiomnium undulatum þ
Luzula sylvatica subsp. sieberi þPlagiothecium curvifolium þ
Lysimachia nemorum 1Polytrichum formosum þ
Mercurialis perennis þRhizomnium punctatum þ
Mycelis muralis 1Thuidium tamariscinum þ
Myosotis sylvatica þ
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example, in studies of vegetation change in permanent
plots. Sometimes, additional characteristics of the
species – such as sociability (degree of clustering of the
individuals), vitality, fertility, age class (e.g., seedling or
juvenile), and phenological status – are recorded, but
these are of little or no importance for standard analyses.
Selection and Size of Plots
Plot sites in the field are positioned in vegetation stands
that are relatively homogeneous in terms of structure,
species composition, and environment, so that variation
is minimized within and maximized between plots.
The traditional sampling strategy in phytosociology,
preferential sampling, in which the researcher selects
stands that are considered as representative of some
vegetation units, has several disadvantages: it is not repea-
table by other researchers, tends to neglect some
vegetation types and oversample others, and produces a
nonrepresentative sample of vegetation diversity in the
study area. In spite of these disadvantages, probabilistic
sampling strategies, such as random or systematic sam-
pling, have never received wider acceptance in
phytosociology. While providing reliable estimates of
vegetation attributes, probabilistic sampling is less suited
to phytosociology’s goal of representing maximum varia-
tion in vegetation diversity across a study area, as it tends
to undersample or even miss rare types. GIS and global
positioning system (GPS) technology have made strati-
fied-random sampling schemes increasingly popular in
phytosociology. Based on the overlay of digital maps in
a GIS, the study area can be stratified into patches with
certain combinations of land-cover types and environ-
mental variables that are supposed to correlate with
plant distribution. Within each of these strata, plot posi-
tions are randomly placed and subsequently found in the
field with a GPS receiver. A related sampling strategy is a
gradient-oriented transect or gradsect, which establishes
plot sites along a landscape transect that runs parallel to
an important environmental gradient.
Phytosociological plots are usually squares or rectan-
gles, which, as a rule of thumb, are roughly as large in
square meters as the vegetation is high in decimeters (e.g.,
200 m
2
for a forest of 20 m height). Despite this rule and
other suggestions in textbooks, actual plot sizes used may
Table 2 Essential (
) and selected optional data to be included in the ‘header’ of a phytosociological releve´
Group Data Comment
ID/methodology Field number
Author(s)
Plot size
Plot shape
Sampling date
Preliminary assignment to a syntaxon
Geographic data Geographic coordinates
Locality in textual form
For example, Greenwich coordinates, UTM
including political and/or natural geographic units
Environmental data Elevation (m a.s.l.)
Slope aspect
Inclination
Soil For example, type, texture, depth, pH, humus form, humus
content, C/N ratio
Geology (parent material)
Management
Structural data Height of vegetation layers (m) For example, tree layer, shrub layer, herb layer, cryptogam layer
Cover of vegetation layers (%)
Cover of each layer and total cover
Cover of other surfaces (%) For example, bare soil, litter, woody debris, rocks, open water
Table 3 Customary version of an extended Braun-Blanquet
cover-abundance scale with ordinal values, which are often used
for numerical interpretation. In the original Braun-Blanquet scale,
2m, 2a, and 2b were joined under the symbol ‘2’
Symbol
Abundance (number of
individuals/ramets)
Cover
interval
(%)
Ordinal
value
r 1 0–5 1
þ2–5 0–5 2
1 6–50 0–5 3
2m More than 50 0–5 4
2a Any 5–12.5 5
2b Any 12.5–25 6
3 Any 25–50 7
4 Any 50–75 8
5 Any 75–100 9
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span more than one order of magnitude within the same
vegetation type. Standardization of plot sizes is hindered
by the vague and misleading concept of ‘minimal area’,
which is thought to be a certain plot size specific for each
vegetation type, beyond which any further enlargement
has negligible effects on species richness and composition.
However, plot size strongly influences estimates of spe-
cies richness and other vegetation parameters. Joint use of
differently sized releve
´s in a single analysis may thus
produce artifacts in classification, ordination, and calcula-
tion of fidelity of species to vegetation units. To safeguard
data compatibility, standard plot sizes have been
proposed for use within certain structural formations,
for example, 200 m
2
in forest vegetation; 50 m
2
in scrub
vegetation; 16 m
2
in grassland, heathland, and other her-
baceous vegetation; and 4 m
2
in aquatic and low-growing
herbaceous vegetation.
Vegetation Databanks
Phytosociology has a long tradition of publishing, archiv-
ing, and re-analyzing releve
´s as its basic primary data.
Many phytosociological journals print full tables includ-
ing all relevant releve
´s, thus making data accessible for
future compilation and analysis, which was traditionally
performed as synoptic tables on paper. The limitations of
manual data management were overcome by using table
editing and databank software, which allows seizing, stor-
ing, managing, filtering, and analyzing releve
´data in
multiple ways.
Compilation in a databank requires that all informa-
tion obeys stringent formal and technical rules laid down
in reference lists, meta-data and data models. Databanks
of different formats and complexity were established,
ranging from simple spreadsheets to relational and
object-based data models that allow flexible definitions
and comprehensive documentation of meta-data. Simple
databanks are able to exchange data freely if the same
standards, database formats, definitions, and reference
lists are used. The success of phytosociological databanks
is so far due to rather simple management software
packages such as TURBOVEG, which is currently the
most widespread program in Europe and beyond, distrib-
uted free of charge or at small cost along with taxonomic
reference lists and tools to create, edit, and analyze phyto-
sociological tables.
While early databank development revolved around
fixing standards for data types and references for plant
taxon concepts and names, modern ecoinformatics pro-
vides tools to exchange data of different formats and
taxonomic reference and, ultimately, link up databanks
of any format in networks. Rather than enforcing standard
formats, these systems require that data are recovered and
stored with as much original information as possible,
including meta-data on sampling design and methods,
cover-abundance scales, definition of layers, taxonomic
references, and original data sources.
Classification of Vegetation
Aims and Criteria
Vegetation classifications are performed with three
fundamental goals: (1) delimiting and naming parts
of the vegetation continuum to enable communication
about them; (2) predicting a multitude of ecosystem attri-
butes (e.g., species composition, site conditions, and
ecological processes) from the assignment of a particu-
lar stand to a vegetation unit; and (3) making multi-
species co-occurrence patterns representable by verbal
descriptions, tables, diagrams, and maps. Floristically
defined vegetation types are thus suitable reference enti-
ties for ecological research, bioindication, and nature
conservation.
Reaching these aims requires of the classification
approach:
1. coherence of units with respect to major ecosystem
properties;
2. simple and clear discernability of units;
3. completeness of the system (i.e., coverage of all vege-
tation types of the given area);
4. robustness (i.e., minor changes of the data should not
considerably change the classification);
5. tolerance against varying data quality;
6. supra-regional applicability;
7. applicability for a range of different purposes;
8. hierarchical structure, allowing for different degrees
of generalization;
9. equivalence of units of the same hierarchical level; and
10. adequate number of units with respect to practical
use.
As no single classification can ideally meet all of these
criteria at the same time, and their relative importance
depends on the purposes, competing classifications of the
same objects and data are a reality. Thus, the interpreta-
tion of local data will change with scaling up from local to
regional and supra-regional context. However, there
is also a practical requirement to have a unified supra-
regional classification to enable communication among
scientists, managers, and authorities between regions.
Braun-Blanquet Approach
The ‘Braun-Blanquet approach’ provides a methodologi-
cal framework for vegetation classification that seeks an
optimal combination of the above criteria and that recon-
ciles conflicting requirements of different scales and
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purposes. However, it is not an unambiguous and uni-
form set of recipes, and it has been subject to diverse
modifications. Despite the variety of different versions,
practitioners agree on certain fundamentals, which distin-
guish the Braun-Blanquet approach from most other ways
of vegetation classification: (1) The classification is based
on the (total) species composition of the sample plots
(floristic–sociological method), whereas structural or
environmental criteria play a subordinate role. (2) The
classification units called syntaxa (singular: syntaxon) are
arranged into a hierarchical system according to their
floristic similarity. The principal ranks of this system are,
from bottom up, association, alliance, order, and class.
(3) There are generally accepted rules for the scientific
naming of syntaxa (see the section entitled ‘Phytosociological
ranks and nomenclature’).
Within the Braun-Blanquet approach, the concept of
character and differential species is important for the
recognition of previously defined syntaxa. Differential
species are those that positively differentiate, by their
occurrence, the target syntaxon from other syntaxa.
Character species are a special case of differential species:
they positively differentiate the target syntaxon from all
other syntaxa. The differential and character species com-
bined are called diagnostic species. The validity of
diagnostic species may be restricted to comparisons
within the syntaxon of the next higher rank or within a
physiognomic vegetation type. Diagnostic species are
based on the concept of fidelity, that is, concentration of
their occurrence or abundance within the given syntaxon.
Traditionally, arbitrary measures of fidelity were used,
such as constancy in the target syntaxon had to be at least
twice as high as in any other syntaxon. Nowadays, statis-
tical fidelity measures are increasingly used (see the
section entitled ‘Numerical approaches’). However, in
spite of several attempts at a formal definition of differ-
ential and character species, no widely accepted
agreement in this respect has been reached so far.
Phytosociology faces difficulties in the classification of
vegetation types that lack species of narrow ecological
amplitude which could be used as character species of the
respective syntaxa. This problem led early practitioners
to avoid stands without specialist species as ‘atypical’ and
‘fragmentary’ and oversample those containing presumed
character species. Even when sampled and recognized,
such poorly characterized vegetation types were often
excluded from the syntaxonomic system. Vegetation
types poor in diagnostic species may be incorporated
into the system in several ways, for example:
(1) Deductive classification affiliates such units as so-
called basal or derivative communities to higher syntaxa
of the system, from which their formal names are derived
(e.g., Elymus repens [Artemisietea vulgaris] derivative com-
munity). (2) According to the concept of central syntaxon,
there can be one negatively differentiated syntaxon
within the next superior syntaxon of the hierarchy;
central syntaxa have the same ranks (e.g., association)
and nomenclature as normal syntaxa.
This diversity of approaches within the Braun-Blanquet
system must be unified where all vegetation types of a large
area are to be placed in a single coherent system, such as in
modern projects of national vegetation classifications.
These projects have usually developed consistent systems
of standardized and operational methodology of vegetation
classification based on the Braun-Blanquet approach.
Numerical Approaches
Traditional phytosociological work was based on the
subjective delimitation of vegetation units, made either
already during the field reconnaissance and sampling or
in the process of manual sorting of releve
´s and species
within tables. The need for more formal, transparent,
efficient, and repeatable classification procedures led to
the introduction of numerical classification methods in
phytosociology since the 1960s. They can be either
agglomerative or divisive. Agglomerative methods start
with linking individual releve
´s based on the similarity of
their species composition, forming releve
´clusters and
subsequently linking these clusters to form a hierarchical
classification, usually presented as a dendrogram. Divisive
methods start with dividing the set of releve
´s into subsets,
which are further divided into subsets on a lower hier-
archical level, thus eventually proceeding to the single
releve
´s. The most popular divisive method is two-way
indicator species analysis (TWINSPAN), which uses the
ordination method of correspondence analysis to divide
the releve
´s into subsets. Simultaneously with the classifi-
cation of releve
´s, TWINSPAN classifies species, and
produces an ordered species-by-releve
´table similar to
that used in traditional phytosociology (see the section
entitled ‘Phytosociological tables’). The classifications of
the same data sets produced by agglomarative clustering
and TWINSPAN usually roughly correspond but differ
in details. Agglomerative clustering is the method of
choice when cluster homogeneity is the principal goal,
while TWINSPAN better reflects the main gradients in
species composition of the input data set. An important
choice in any numerical procedure is the transformation
of cover-abundance data, which determines to what
degree species cover-abundance will be accounted for in
the analysis.
In addition to numerical classification, phytosociology
frequently uses various ordination methods, such as cor-
respondence analysis (CA), detrended correspondence
analysis (DCA), or principal components analysis
(PCA). Sometimes, ordination and classification are per-
ceived as antagonististic approaches, representing the
Gleasonian continuum concept and the Clementsian concept
of superorganism, respectively. However, phytosociologists
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never engaged in that ideological debate, and nowadays
both approaches seem to be reconciled: classification stu-
dies often use ordination to visualize the position of
vegetation units along gradients, and ordination patterns
are used to propose the delimitation of releve
´groups for
certain purposes.
Applicability of an established classification crucially
depends on finding those species that are typical of releve
´
groups (vegetation units) and make them recognizable by
simple floristic criteria. Such species may include the
most frequent species, dominant species, or diagnostic
species. The former two groups of species can be easily
defined by setting some threshold of constancy or cover-
abundance values that a species must exceed to be con-
sidered as frequent (constant) or dominant, respectively.
Diagnostic species are determined based on the concept
of fidelity, which quantifies the degree of concentration of
a species’ occurrence or abundance in the releve
´s of the
target vegetation unit. If a species occurs mainly in the
releve
´s of the target vegetation unit while it is largely
absent elsewhere, it is considered as faithful to this vege-
tation unit. Fidelity can be quantified by various statistical
measures. If it is based on species presence/absence, var-
ious measures of association between categorical variables
can be used, for example, chi-square, Gstatistic, or phi
coefficient of association. Some fidelity measures have
also been proposed to deal with cover-abundances, for
example, the Dufre
ˆne–Legendre indicator value. The
properties of different fidelity measures vary slightly, for
example, with respect to the weight given to rare or
common species. Statistical significance of fidelity can
be either derived directly from the values of some of
these measures or determined by a separate procedure
such as permutation test. Apart from the selection of the
appropriate fidelity measure, fidelity can be measured in
two different ways. First, species occurrence in the target
group of releve
´s can be compared with all the releve
´sin
the data set that do not belong to the target group,
irrespective of the divisions of the rest of the data set.
Second, species frequency in that group of releve
´s
where it is most common is compared with its frequency
in the group where this species is the second most com-
mon. In both cases, some arbitrary threshold fidelity value
is selected and species that exceed this value are consid-
ered as diagnostic. The first approach is not affected by
the divisions of the data set outside the target vegetation
unit, thus yielding a more general result, whereas the
latter approach is only valid in the context of a given
table or classification, but it provides a clearer separation
of vegetation units through diagnostic species within this
table or classification. The results of both approaches
depend on the geographical extent, sampling design, and
delimitation of the available set of releve
´s, the ‘universe of
investigation’.
Integrating the Different Approaches
While having the basic aims in common, the traditional
Braun-Blanquet approach and numerical approaches
differ in some respects. Indeed, no approach produces
an objective or ‘the correct’ classification. In spite of
the high degree of formalization involved in numerical
classification, the numerous choices concerning the
data set composition, cover-abundance transformation,
numerical coefficients, classification algorithms, or num-
ber of vegetation units to be accepted result in the fact
that numerical methods, like the traditional expert-based
approaches, may suggest many different partitions of the
same data.
Unlike the expert-based classifications, which often
use unclear classification criteria, numerical classifica-
tion methods consistently use explicit information on
species occurrence and cover-abundance and apply it
consistently across vegetation types. However, while
experts often implicitly incorporate in the classification
process knowledge of species behavior in a broad geo-
graphical and environmental range, numerical methods
only use information contained in the particular data
set, which often results in rather idiosyncratic classifi-
cations. It is therefore difficult to combine different
numerical classifications into a single system of syntaxa,
which would be valid over large areas and different
habitats, without relying on expert judgment.
To avoid these problems, supervised classification
methods have gained importance recently. They take
traditional syntaxa that are widely recognized by phyto-
sociologists as given and assign new releve
´s to these
syntaxa by numerical procedures. Such an approach sup-
ports both the stability of the traditional phytosociological
system, which has already received wide acceptance, and
the application of formal, unequivocal classification pro-
cedures. A simple approach is to calculate an index of
similarity of species composition between new releve
´s
and constancy columns of synoptic tables (see the section
entitled ‘Phytosociological tables’) that summarize the
traditional classification and subsequent matching of
each new releve
´to the vegetation unit to which it has
the highest similarity. More sophisticated methods of
supervised classification include quadratic discriminant
analysis, multinomial log-linear regression, classification
trees, and artificial neural networks. The latter, for exam-
ple, can establish a classifier based on the previous
knowledge of what the releve
´s belonging to a certain
vegetation unit look like. When new releve
´s are sub-
mitted, the classifier assigns them, with some degree of
uncertainty, to the correct vegetation unit.
Another method of supervised classification is
COCKTAIL, which was specifically designed to imitate
traditional Braun-Blanquet classification. It uses the
external information on species behavior, extracted from
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large phytosociological databases, and forms sociological
groups of species with statistical tendency of co-occur-
rence in the releve
´s of the database. Then, unequivocal
definitions of syntaxa are created that involve decision
rules, postulating which of the sociological species groups
must be present or absent for a particular releve
´to be
assigned to the target syntaxon. COCKTAIL definitions
can be created to fit the meaning of the syntaxa of tradi-
tional phytosociology. In such a way, traditional syntaxa
can be defined formally and applied in the computer
expert systems, which automatically assign newly
encountered releve
´s to syntaxa.
Phytosociological Tables
In phytosociology, original data and classification
results are presented as tables of species by releve
´sor
community types. There are two types of phytosocio-
logical tables, releve
´tables (Table 4(a)) and synoptic
tables (Table 4(b)). In both cases, species are listed in
the lines and releve
´s (in releve
´tables) or combined
groups of releve
´s (in synoptic tables) in the columns.
Both types of tables are normally presented in a struc-
tured manner. Lines and columns are arranged in such a
way that ‘species blocks’ (i.e., groups of nonempty table
cells) form more or less a diagonal from the top left to
the bottom right. Therefore, the diagnostic species corre-
sponding to the syntaxa ordered from left to right are
to be found from top downward (except for the
negatively differentiated syntaxa). In tables representing
multi-layered woody vegetation, plant species of upper
layers are normally listed at the top of the table to give an
impression of stand structure. At the bottom of the table,
those species are listed that have no diagnostic value
within the respective table. These may be diagnostic
species of superior syntaxa or ‘companions’, that is, spe-
cies that have no diagnostic value for any syntaxon
included in the table. Within blocks, species are sorted
by decreasing constancy or decreasing fidelity. Species
blocks or individual diagnostic species can be highlighted
by frames or shadings in the tables; the criteria for doing
so are related to species fidelity to syntaxa and should be
clearly defined in particular studies.
In synoptic tables, all releve
´s assigned to the same
vegetation unit are represented by a single column with
constancy values (i.e., the percentage proportion of
releve
´s in which the species is present). Constancy values
are often presented as classes indicated by Roman numer-
als (I: 1–20%; II: 21–40%; ...; V: 81–100%), but the use of
percentages has several advantages, for example, it does
allow the application of modern fidelity concepts and
merging of different synoptic tables without loss of accu-
racy. In addition to the constancy values, medians or
ranges of the cover-abundance values or fidelity levels
may be indicated. It is important to note (though
long-neglected in phytosociology) that the calculation
and comparison of constancy values does only make
sense for plots of the same or similar size, because con-
stancy values are strongly influenced by plot size.
Phytosociological Ranks and Nomenclature
Abstract vegetation units defined by floristic–sociological
criteria are termed syntaxa. They are positioned in a
hierarchy of different ranks (Table 5), which is meant to
make the multitude of units manageable and offers the
opportunity to vary the conceptual resolution of analysis,
maps, and graphs. The association is considered as the
basic unit, comparable to species in taxonomy. Ranks
below the association level are often used to express
edaphic (subassociations and variants), climatic (altitudi-
nal forms), geographic (vicariants or races), structural
(facies of dominant species), and successional variation
(phases).
Like other fields of biological systematics, syntax-
onomy is an open-ended process that is carried out by a
large community of independent researchers and requires
unequivocal rules for naming classification units.
Therefore, the Nomenclature Commission of the
International Association for Vegetation Science (IAVS)
and the Fe
´deration Internationale de Phytosociologie
(FIP) have established the International Code of
Phytosociological Nomenclature (ICPN), similar to the
nomenclature codes used in botanical and zoological
taxonomy.
The ICPN regulates the scientific nomenclature of four
principal and four supplementary ranks of syntaxa. Neither
synusial nor symphytosociological units (see the section
entitled ‘Symphytosociological approaches’), nor infor-
mally named syntaxa (e.g., Elymus repens community) fall
under the ICPN. The ICPN provides precise instructions
for the formation of syntaxon names, their valid publica-
tion, and the decision about which of several available
names from the earlier literature to apply. According to
the ICPN, every syntaxon of a certain circumscription and
rank has only one correct name. However, the ICPN only
regulates the nomenclature and does not define rules for
proper delimitation and classification of syntaxa. Aiming to
provide unambiguity and stability of syntaxon names, the
ICPN is based on two major principles: (1) among several
names for a syntaxon, the oldest valid (published) name is
the correct one (priority); (2) each syntaxon name is con-
nected to a nomenclatural type (a single releve
´for
associations, a validly described lower-rank syntaxon for
higher syntaxa), which determines the usage of the name
when this syntaxon is split off, merged with others, or
otherwise changed in its delimitation.
Syntaxon names are formed of the scientific names of
one or two (in the case of subassociations, up to three)
plant species or infraspecific taxa, which usually are, but
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Table 4 (a) Worked example (I): Releve´ table containing three associations of three alliances and two classes of the subalpine heathland and grassland vegetation of the Czech Republic (see Table 6 for their position in the
syntaxonomic hierarchy). Species of the cryptogam layer are marked with ‘C’, the other species belong to the herb layer. Blocks of diagnostic species are shaded. Within blocks, diagnostic species are ranked by decreasing
fidelity to the given syntaxon. Fidelity was measured with the phi coefficient of association and was based on the comparison of species occurrences within the syntaxa of this table only; species with > 0.25 were
considered as diagnostic. As each association belongs to a different alliance, diagnostic species of the associations can be partly considered as diagnostic of the alliances. Companion species are ranked by decreasing
constancy within the entire table. Data were taken from the Czech National Phytosociological Database. Species occurring in a single releve´ are not shown. (b) Worked example (II): Synoptic table based on the same data as
Table 4a. The numbers in the table are percentage constancies
(a)Releve
´table (b)Synoptic table
Association Junco trifidi-Empetretum hermaphroditi Cetrario-Festucetum supinae Carici-Nardetum J-E C-F C-N
Releve
´number 1234567891011121314151617181920212223242526272829n¼13 n¼10 n¼6
Diagnostic species of the association Junco trifidi-Empetretum hermaphroditi
Empetrum hermaphroditum 5333543333 4 4 4 . . . . þ...........100 10 .
Hylocomium splendens (C) ..1r.2..1...r................ 38 . .
Vaccinium myrtillus 1122121122 1 2 2 1 þ..þ1...þ..þþþ. 100 50 50
Melampyrum sylvaticum ..þ..þ...þ..þ................ 31 . .
Pleurozium schreberi (C) ..1r.2..3r..r.............þ. . 46 . 17
Polytrichum piliferum (C) 2 þ....2þ..2.....1............ 38 10 .
Diagnostic species of the association Cetrario-Festucetum supinae
Cladonia bellidiflora (C) .......... . ......þþ..þ........ 30 .
Thamnolia vermicularis (C) .......þ......þ..þþ...þ...... 8 40 .
Diagnostic species of the association Carici bigelowii-Nardetum strictae
Nardus stricta .......... . ......1.2.þ1344445. 40 100
Galium saxatile .......... . ............2.þ.2.. . 50
Anthoxanthum alpinum .......... . ........1....r.12.. 10 50
Deschampsia cespitosa .......... . .............þ.1... . 33
Festuca rubra agg. .......... . ...............1þ.. . 33
Luzula campestris agg. .......... . ...............1þ.. . 33
Potentilla erecta .......... . ...............1þ.. . 33
Diagnostic species of the class Juncetea trifidi
Calluna vulgaris .......... . . . 21þ11þ2. þ2. þ1r . . . 90 50
Bistorta major ......þ... . . . 1þ1þþþ1. 1þþ.þþ..8 90 50
Agrostis rupestris .......... . ...1...þ. 11. . þ..... 40 17
Carex bigelowii .......... . . . 4 þ.3þ. 111. 1þ.þ.þ.7067
Hieracium alpinum agg. ......1... þ...þ2. þþ121þþ1þþþ.15 80 83
Companion species
Avenella flexuosa þþþ111211þ1111þ13þ. 31224þ32. 2100 90 83
Vaccinium vitis-idaea . 212. 2. 121 þ1211. . . þ.....þþþ2 . 77 30 67
Cetraria islandica (C) . þ1r . 1. þ2r 2 . r . þ2. þ..23þ......69 60 .
Festuca supina ...1....1. 1. . 2 23 1 . 3. 2 þ...þ.1þ23 70 50
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Solidago virgaurea .þþ......þþ......þrþ..1þ..þ. r 31 40 50
Homogyne alpina ..r......r.....þ.þ...þ1þ. . 2 1 . 15 40 50
Huperzia selago .þ....1þ.......þ.þþ..1þ......23 50 .
Calamagrostis villosa ....þ..... þþ....þ........þ.rþ23 10 50
Juncus trifidus 12.....3......2...1..........23 20 .
Trientalis europaea ..þ......þþþ..............þ..31 . 17
Vaccinium uliginosum ...þ.þ..þ...þ....þ...........31 10 .
Racomitrium lanuginosum (C) .2.....2......þ..þ...........15 20 .
Cladonia arbuscula (C) .1.............2.þ...þ.......8 30 .
Cladonia rangiferina (C) . þ........ . . . . þ1. þ...........8 30 .
Dicranum fuscescens (C) .1....21.....................23 . .
Polytrichastrum alpinum (C) . þ.....1.............1.......15 10 .
Alectoria ochroleuca (C) .......þ............2.þ......8 20 .
Campanula bohemica .......... . ....þ.....þ....1... 20 17
Pulsatilla alpina
ssp. austriaca
.......... . ......þ....þ....1.. 20 17
Pohlia nutans (C) . þ.....................þ.....8 . 17
Polytrichum strictum (C) ..2......r...................15 . .
Silene vulgaris .......... . . . þ............þ... 10 17
Cetraria cucullata (C) .......... . . . . þ..þ............ 20 .
Diphasiastrum alpinum .......... . ......þ....þ....... 20 .
Polytrichum juniperinum (C) .......... . ......þ....þ....... 20 .
Cladonia
pyxidata (C)
.......... . .......þ.....þ..... 10 17
Veratrum album
subsp. lobelianum
.......... . ........r....þ..... 10 17
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need not be, characteristic in the respective vegetation
type. The formation of the scientific syntaxon names
involves connecting vowels, the declination of the taxon
epithets, and addition of terminations indicating syntaxo-
nomic rank (Table 5). An ‘author citation’ (i.e., the
author(s) and year of the first valid publication) also
forms part of the complete syntaxon name (see Table 6).
Other Levels of Classification
Synusial approaches
While phytosociological classifications are usually based on
all plant species occurring in vegetation stands, for some
purposes sampling may be restricted to certain taxonomic,
functional, or structural parts of these. Abstract types of such
partial communities are called synusiae (singular: synusia)
in order to differentiate them from normal community types
(syntaxa) (Table 7). Synusiae include plant assemblages
of horizontally differentiated microhabitats within larger
vegetation stands, of vertical vegetation layers, and
of seasonally separated phenological phases. Epiphytic
cryptogams inhabiting tree bark are a typical example of a
synusia,whichisrecordedinsmallplotswithcoverprojec-
tion estimated perpendicular to the substrate surface.
Synusiae should be placed in a separate hierarchical system
with ranks of their own and the union as its basic unit.
However, many studies of partial communities place their
units in the system of syntaxa, leading to the ambiguous
situation that the same name can refer to both a synusia and
asyntaxon.
Symphytosociological approaches
While plant communities and partial communities are
assemblages of plant species and their individuals,
symphytosociological units are assembled of synusiae or
syntaxa (Table 7) and represent a coarser view of com-
munity diversity. Sampling and classification basically
follow the phytosociological method, but use synusiae or
syntaxa (fine-scale vegetation types) instead of plant spe-
cies as objects of observation. Two major concepts fall in
this category and may be combined: (1) ‘Integrated synu-
sial phytosociology’ of some French authors classifies
separate ‘associations’ for tree, shrub, herb, and cryptogam
layers, which in the normal terminology would be synu-
siae. These ‘associations’ are recorded in releve
´s of entire
Table 5 Syntaxonomic ranks whose names are regulated by the International Code of Phytosociological Nomenclature (ICPN)
Rank Termination Example (without author citation)
Class
a
-etea Koelerio-Corynephoretea
Subclass
b
-enea Koelerio-Corynephorenea
Order
a
-etalia Phragmitetalia australis
Suborder
b
-enalia Oenanthenalia aquaticae
Alliance
a
-ion Fagion sylvaticae
Suballiance
b
-enion Cephalanthero-Fagenion
Association
a
-etum Corniculario aculeatae-Corynephoretum canescentis
Subassociation
b
-etosum or ‘typicum’or‘inops’Corniculario aculeatae-Corynephoretum canescentis cladonietosum
a
Principal rank (obligatory).
b
Supplementary rank (optional).
Table 6 Worked example (III): Syntaxonomic hierarchy
including all principal ranks and full syntaxon names with author
citations for the syntaxa presented in Table 4
Class: Loiseleurio-Vaccinietea Eggler ex Schubert 1960
Order: Rhododendro-Vaccinietalia Braun-Blanquet in Braun-
Blanquet et Jenny 1926
Alliance: Loiseleurio procumbentis-Vaccinion Braun-
Blanquet in Braun-Blanquet et Jenny 1926
Association: Junco trifidi-Empetretum hermaphroditi
S
ˇmarda 1950
Class: Juncetea trifidi Hadac
ˇin Klika et Hadac
ˇ1944
Order: Caricetalia curvulae Braun-Blanquet in Braun-Blanquet
et Jenny 1926
Alliance: Juncion trifidi Krajina 1933
Association: Cetrario-Festucetum supinae Jenı´k 1961
Alliance: Nardo strictae-Caricion bigelowii Nordhagen 1943
Association: Carici bigelowii-Nardetum strictae (Zlatnı´k
1928) Jenı´k 1961
Table 7 Levels of classification from synusial phytosociology
to sigmasociology
Concrete object
Elements
recorded in
releve
´s Abstract type
Partial vegetation stand
(e.g., layer)
Species Synusia
Vegetation stand
(phytocoenosis)
Species Syntaxon
Vegetation stand
(phytocoenosis)
Synusiae Coenotaxon
Vegetation mosaic (tesela) Syntaxa or
coenotaxa
Sigmataxon
Landscape mosaic (catena) Sigmataxa Geosigmataxon
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stands, analyzed like species in normal phytosociological
tables, and such releve
´s are then classified to form so-
called coenotaxa. (2) Sigmasociology records syntaxa (or
coenotaxa) in large releve
´s of uniform macrotopography,
substrate, and climate (tesela), which are tabulated and
classified to form sigmataxa, which at a yet coarser scale
(catena) become the elements of landscape units called
geosigmataxa.
Applied Phytosociology
Ecological Assessment
The study of species–environment and community–
environment relations is the key to the functional inter-
pretation of plant communities and to applications of
phytosociology in bioindication and predictive modeling.
Since the time of Braun-Blanquet, many releve
´s have
been made in conjunction with measurements of soil,
topographic, and climate variables. If releve
´coordinates
are known, environmental variables can also be post hoc
read from maps or modeled from geodata. Environmental
data are of multivariate nature, which requires conden-
sing their information content and choosing the most
meaningful variables. As in species-by-releve
´matrices,
the dimensions of environmental variation can be reduced
by extracting continuous gradients (ecological factors) or
by forming clusters (site types). Relationships with the
environment can be established for community types or
species.
While often restricted to verbal descriptions and sim-
ple outlines of schematic correspondences (e.g.,
vegetation type–soil type) in early phytosociology, vege-
tation type–environment relationships are nowadays
studied based on measured variables. These data enable
to establish environmental envelopes, which define
the possible occurrence of each vegetation type in ecolo-
gical space. The overall significance of environmental
differentiation between types can be tested, for example,
by nonparametric permutation procedures (MRPPs).
There is a long tradition in phytosociology of defining
ecological groups of species that exhibit similar behavior
along gradients and represent species of similar realized
niche (‘ecological amplitude’) rather than fundamental
niche (‘physiological amplitude’). Such groups are mainly
based on expert knowledge and are only partly calibrated
on independent measurements of environmental vari-
ables. Also, the derivation of ecological indicator values
of plant species strongly relies on phytosociological
descriptions of vegetation patterns, from which the
principal ecological gradients are extracted. While
separate species group systems and indicator value
systems have been devised for vegetation of arable
fields, grasslands, and forests, Heinz Ellenberg created a
general, semiquantitative system of indicator values for
the central European flora, in which most species of
vascular plants, bryophytes, and lichens are assigned a
value on an ordinal scale, ranging from 1 to 9 and repre-
senting the estimated ecological optimum with respect to
the principal factors light, temperature, continentality,
moisture, soil reaction, nutrient availability, and salinity.
Being unique in summarizing the niches of an entire flora,
Ellenberg values are widely used for calibrating ecologi-
cal conditions based on plant communities. The concept
of plant indicator values has been recently adapted for use
beyond central Europe.
Vegetation Maps
Information on spatial distribution of syntaxa is often
summarized in vegetation maps. Maps of actual vegeta-
tion 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 hypothe-
tical 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 condi-
tions. Being based on the knowledge of the relationship
between habitat and natural vegetation, PNV maps impli-
citly or explicitly rely on models, which can take different
forms. Traditional phytosociology establishes the corre-
spondence between actual (e.g., certain meadow or weed
communities) and natural vegetation (e.g., certain forest
types), and maps PNV units by interpreting actual vegeta-
tion. 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 land-
scape. 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 releve
´s. They can be presented as
dot maps of exact plot positions or as grid maps, indicat-
ing 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
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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 environmen-
tal variables (including land use) and syntaxon occurrence. If
digital maps of environmental factors and landscape struc-
tures 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 pre-
dictors. The relevant environmental maps are then overlaid
in a GIS and the probabilities of syntaxon occurrence pre-
dicted by the model are mapped.
Monitoring Temporal Change
As spatially and temporally explicit, detailed representa-
tions of vegetation, phytosociological releve
´s and maps are
appropriate tools for monitoring change in plant species
composition and the underlying environmental conditions.
Thus, fine-scale monitoring systems in agriculture, for-
estry, nature conservation, and civil engineering have
used repeated phytosociological releve
´s at permanently
marked locations over many decades, which allow us to
analyze trends in diversity of species and species groups
(such as Ellenberg indicators or plant functional types).
Besides detecting gradual changes, phytosociology
expresses succession as a change of community types.
Where many permanent plots conform to the same rules,
succession can be generalized into temporal gradients and/
or sequences of community types (seres). However, many
phytosociological succession models have been based on
comparative observation (space-for-time substitution)
rather than real time series. Larger groups of old releve
´s
without permanent marking are sometimes used to detect
succesional trends by making new releve
´s in the supposed
old positions (‘quasi-permanent plots’) and by detecting
systematic differences between old and new data.
Repeated mapping may reveal changes in the spatial
delimitation of vegetation units and allow representation
of succession in a transition matrix. However, its validity
crucially depends on fully operational mapping keys that
unequivocally define the criteria for drawing boundaries
between types.
Nature Conservation
The conservation of species depends on the maintenance
of their habitats. Habitat classifications can be founded on
structural or abiotic features, but they are often based on
syntaxa, conveying a summary of ecosystem properties
that are difficult to measure or model. Preserving the
diversity of extant plant communities is thought to
safeguard the survival of typical species not only of plants,
but also of animals, fungi, and microorganisms, and the
maintenance of current ecosystem processes.
In Europe, phytosociological units were important in
defining habitats (biotopes) in the CORINE and EUNIS
systems, which contain a comprehensive classification of
European habitats. The CORINE classification provided
the basis for inclusion of habitat types under the Habitats
Directive of the European Union, the most powerful
legislative instrument for nature conservation in Europe.
In the Union-wide conservation network Natura 2000,
phytosociologically defined habitat types are crucial for
the delimitation, inventory, monitoring, and management
of protected areas.
In landscape planning and policy making, phytosocio-
logical units are used to underpin normative judgments
and set conservation priorities by evaluating their natur-
alness and endangerment. Naturalness, or its reciprocal
concept, hemeroby, ranks communities by the strength of
human influence and consequent alterations of species
composition, structure, and ecological processes.
Methodologies range from assigning community types
to classes of naturalness to complex evaluation schemes
taking detailed account of community features.
Reporting the degree of threat to the habitats of a
region, red lists of plant communities are another poten-
tially powerful policy tool in nature conservation.
Compilation of red lists presupposes a comprehensive
and well-established phytosociological classification for
the target region, including detailed knowledge about
distribution, commonness, and temporal trends of syn-
taxa. With the advent of phytosociological databanks
and GIS, red list compilation is moving from pure expert
judgment to a process driven by releve
´data and
rule-based decisions on the vulnerability and conserva-
tion value of plant communities. While vulnerability
considers current distribution, quantitative development
in the past, and foreseeable threats in the future, conser-
vation value may be based on the frequency and status of
component red-listed plant species, naturalness of the
inhabited sites, and responsibility of the target region for
the global preservation of a syntaxon. The combination of
vulnerability and conservation value may be used to set
reasonable priorities for conservation measures.
See also: Application of Ecological Informatics; Artificial
Neural Networks: Temporal Networks; Association;
Biodiversity; Biotopes; Community; Dominance;
Ecological Niche; Ecosystem Ecology; Ecosystems;
Environmental Protection and Ecology; History of
Ecology; Intertidal Zonation; Ordination; Plant
Demography; Plant Ecology; Principal Components
Analysis; Scale; Seasonality; Spatial Models and
Geographic Information Systems; Statistical Prediction;
Succession; Synecology.
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Further Reading
Barkman JJ (1990) Controversies and perspectives in plant ecology and
vegetation science. Phytocoenologia 18: 565–589.
Berg C, Dengler J, Abdank A, and Isermann M (eds.) (2001–04) Die
Pflanzengesellschaften Mecklenburg-Vorpommerns und ihre
Gefa¨ hrdung, 2 vols. Jena: Weissdorn.
Braun-Blanquet J (1964) Pflanzensoziologie – Grundzu¨geder
Vegetationskunde, 3rd edn. Vienna: Springer.
Bruelheide H (2000) A new measure of fidelity and its application to defining
species groups. Journal of Vegetation Science 11: 167–178.
Chytry
´M and Oty
´pkova´ Z (2003) Plot sizes used for phytosociological
sampling of European vegetation. Journal of Vegetation Science
14: 563–570.
Chytry
´M, Tichy
´L, Holt J, and Botta-Duka´ t Z (2002) Determination of
diagnostic species with statistical fidelity measures. Journal of
Vegetation Science 13: 79–90.
Dengler J (2003) Archiv naturwissenschaftlicher Dissertationen 14:
Entwicklung und Bewertung neuer Ansa¨ tze in der Pflanzensoziologie
unter besonderer Beru¨ cksichtigung der Vegetationsklassifikation.
Nu¨ mbrecht: Galunder.
Dierschke H (1994) Pflanzensoziologie – Grundlagen und Methoden.
Stuttgart: Ulmer.
Ellenberg H, Weber HE, Du¨ll R, et al. (1992) Scripta Geobotanica 18:
Zeigerwerte von Pflanzen in Mitteleuropa, 2nd edn. Go¨ ttingen: Goltze.
Ewald J (2001) Der Beitrag pflanzensoziologischer Datenbanken zur
vegetationso¨ kologischen Forschung. Berichte der Reinhold-Tu¨xen-
Gesellschaft 13: 53–69.
Gillet F and Gallandat J-D (1996) Integrated synusial phytosociology:
Some notes on a new, multiscalar approach to vegetation analysis.
Journal of Vegetation Science 7: 13–18.
Mucina L, Schamine´ e JHJ, and Rodwell JS (2000) Common data
standards for recording releve´ s in field survey for vegetation
classification. Journal of Vegetation Science 11: 769–772.
Rodwell JS, Schamine´ e JHJ, Mucina L, et al. (2002) Rapport EC-LNV
2002/054: The Diversity of European Vegetation – An Overview of
Phytosociological Alliances and Their Relationships to EUNIS
Habitats. Wageningen: National Reference Centre for Agriculture,
Nature and Fisheries.
Weber HE, Moravec J, and Theurillat J-P (2000) International code of
phytosociological nomenclature, 3rd edn. Journal of Vegetation
Science 11: 739–768.
Whittaker RH (ed.) (1973) Handbook of Vegetation Science 5:
Ordination and Classification of Communities. The Hague: Junk.
Pioneer Species
J W Dalling, University of Illinois Urbana-Champaign, Urbana, IL, USA
ª2008 Elsevier B.V. All rights reserved.
Introduction
Pioneers in Primary Succession
Pioneers in Secondary Succession
Further Reading
Introduction
In early ecological literature, the term pioneer was used to
describe those plant species that initiate community devel-
opment on bare substrate (primary succession). More
recently, usage of the term has included microbial and
invertebrate taxa, and describes the first colonists of sites
affected by less extreme disturbance which undergo sec-
ondary succession. Pioneers of primary and secondary
successions share some traits; in both cases colonization of
new habitat depends on effective dispersal, which generally
selects for high reproductive output and small propagule
size. However, differences in resource availability between
these habitat types result in different opportunities for
growth and reproduction. Few species can be successful
on both primary and secondary successions.
Pioneers in Primary Succession
Primary succession occurs when extreme disturbances,
such as landslides and volcanic eruptions, create new
habitats by removing or covering existing vegetation
and soil. Pioneers that initiate primary succession must
be able to establish and grow on substrates that are
nutrient poor and that often have unfavorable moisture
conditions. The most extreme sites are exposed unweath-
ered rock surfaces. Here, colonization may be limited to
cyanobacteria (‘blue-green algae’), lichens, and bryo-
phytes, with no further vegetation development.
Somewhat more nutrient-rich conditions associated with
weathered or fragmented bedrock surfaces, such as the
scree slopes of landslides, are often dominated by tree
species. Sites still richer in mineral nutrients, which may
contain some residual organic soil, such as the deposi-
tional zones of glacial moraines, in turn are often
colonized by herbaceous species and grasses with faster
growth rates (Figure 1).
For pioneers in primary successions, nitrogen is often
the most limiting resource. Unlike other mineral nutrients
that can be released through weathering of underlying
rock, nitrogen must either be transported to primary
successions through leaching and deposition, or fixed
in situ. Some of the most inconspicuous pioneers on
exposed rock faces are nitrogen-fixing cyanobacteria.
Rates of nitrogen fixation by cyanobacterial ‘biofilms’ on
rock surfaces may be considerable; thus, nitrogen-rich
leachate from these surfaces may affect community devel-
opment at down-slope sites. Cyanobacteria may also form
symbiotic associations with lichens (e.g., Stereocaulon spp.).
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