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Phytosociology

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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
ª 2008 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 (phytoco enosis),
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 pro tocol
for sampling plant species co-occurrences at the stand
scale. Being derived from the vast body of releve
´
data,
syntaxonomy provides a compreh ensive 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 avenue s for exploring lar ge-
scale ecological patterns and processes, and provide spa-
tially explicit information necessary for enviro nmental
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 compos ed 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 3 Paris quadrifolia þ
Polypodium vulgare þ
T2 Picea abies 1 Prenanthes purpurea þ
Primula elatior þ
S Picea abies 1 Ranunculus lanuginosus 1
Rumex alpestris þ
H Acer pseudoplatanus þ Salvia glutinosa 1
Aconitum vulparia þ Sanicula europaea þ
Adenostyles alliariae 1 Saxifraga rotundifolia 1
Adoxa moschatellina þ Senecio fuchsii 1
Athyrium filix-femina þ Stellaria nemorum 2
Cardamine flexuosa þ Thelypteris limbosperma þ
Chaerophyllum hirsutum þ Veronica urticifolia þ
Chrysosplenium alternifolium þ Viola biflora þ
Cicerbita alpina þ
Deschampsia cespitosa þ C Atrichum undulatum 1
Dryopteris dilatata þ Brachythecium rutabulum þ
Dryopteris filix-mas þ Conocephalum conicum þ
Epilobium montanum þ Ctenidium molluscum þ
Galeopsis tetrahit þ Dicranella heteromalla þ
Galium odoratum þ Dicranum scoparium þ
Geranium robertianum þ Fissidens taxifolius þ
Gymnocarpium dryopteris
þ Mnium spinosum þ
Impatiens noli-tangere þ Plagiochila porelloides þ
Lamiastrum montanum 1 Plagiomnium undulatum þ
Luzula sylvatica subsp. sieberi þ Plagiothecium curvifolium þ
Lysimachia nemorum 1 Polytrichum formosum þ
Mercurialis perennis þ Rhizomnium punctatum þ
Mycelis muralis 1 Thuidium 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 importan ce 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
þ 2–5 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 mislea ding 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 stringen t 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 softw are
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, an d 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 recove red and
stored with as much original information as possible,
including meta-data on sampling desig n 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 dif ferent 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 i s 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 ‘Phyt osociological
ranks and nomenclature’).
Within the Braun-Blanquet approac h, the concept of
character and differential species is important for the
recognition of previously defined syntaxa. Differential
species are those that positively diffe rentiate, 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 dif ferential and character species com-
bined are called diagnostic species. The val idity 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 respec t 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 spe cies may be incorpo rated
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 Approac hes
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 spec ies 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 secti on
entitled ‘Phytosociological tables’). The classifications of
the same data sets produced by agglomarative clustering
and TWINSPAN us ually 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 cru cially
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, G statistic, or phi
coefficient of association. Some fidelity measures have
also been propose d to deal with cover-abundances, for
example, the Dufre
ˆ
ne–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 eithe r 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-b ased classifications, which often
use unclear classification criteria, numerical classifica-
tion meth ods 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 problem s, 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 w hich 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 Internati onal 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
1234567891011121314151617181920212223242526272829n ¼ 13 n ¼ 10 n ¼ 6
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 ..þ 1...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-Nardet um strictae
Nardus stricta .......... . ......1.2.þ 1344445. 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 .......... . . . 21þ 11þ 2. þ 2. þ 1r . . . 90 50
Bistorta major ......þ ... . . . 1þ 1 þþþ1. 1þþ. þþ..8 90 50
Agrostis rupestris .......... . ...1...þ . 11. . þ ..... 40 17
Carex bigelowii .......... . . . 4þ .3þ . 111. 1þ . þ . þ .7067
Hieracium alpinum agg. ......1... þ ...þ 2. þþ121þþ1 þþþ.15 80 83
Companion species
Avenella flexuosa þþþ111211þ 1111þ 13þ . 31224
þ 32. 2100 90 83
Vaccinium vitis-idaea . 212. 2. 121 þ 1211. . . þ .....þþþ2 . 77 30 67
Cetraria islandica (C) . þ 1r . 1. þ 2r 2 . r . þ 2. þ ..23þ ......69 60 .
Festuca supina ...1....1. 1. . 2231. 3. 2þ ...þ .1þ 23 70 50
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Solidago virgaurea . þþ......þþ......þ r þ ..1þ ..þ . r 31 40 50
Homogyne alpina ..r......r.....þ . þ ...þ 1 þ . . 2 1 . 15 40 50
Huperzia selago . þ ....1þ .......þ . þþ..1þ ......23 50 .
Calamagrostis villosa ....þ ..... þþ....þ ........þ .rþ 23 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 ........ . . . . þ 1. 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 Class ification
Synusial ap proaches
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 app roaches
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 phytoso ciological 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
ˇ
marda 1950
Class: Juncetea trifidi Hadac
ˇ
in Klika et Hadac
ˇ
1944
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 gradi ents (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 possibl e occurrence of each vegetation type in ecolo-
gical space. The overall signific ance 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 vegetati on patterns, from which the
principal ecological gradients are extracted. While
separate species group systems and indicator value
systems hav e 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 ran ges 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 relation ships 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 uni ts 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 spec ies 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 eco system processes.
In Europe, phytosociological units were imp ortant 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 hab itat 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 endange rment. 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¨llR,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
ª 2008 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 poo r 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 be drock 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 consider able; 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.).
General Ecology
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Pioneer Species 2779
... The Braun-Blanquet approach is a classification system based on physiognomy, floristics (species composition), and ecology (or biogeography) (Dengler et al. 2008). This classification is usually based on plot observations (also known as phytosociological relevés), and classification schemes have a hierarchical structure, where the association is the fundamental unit and class is the highest rank (Westhoff and van der Maarel 1978;Dengler et al. 2008). ...
... The Braun-Blanquet approach is a classification system based on physiognomy, floristics (species composition), and ecology (or biogeography) (Dengler et al. 2008). This classification is usually based on plot observations (also known as phytosociological relevés), and classification schemes have a hierarchical structure, where the association is the fundamental unit and class is the highest rank (Westhoff and van der Maarel 1978;Dengler et al. 2008). Despite criticisms regarding the common preferential sampling strategy and the heterogeneity in the use of plot sizes and abundance scales (Dengler et al. 2008;De Cáceres et al. 2015), current development of classification methods including the implementation of expert systems (Tichý et al. 2019;Bruelheide et al. 2021) and the proliferation of initiatives compiling and sharing records in vegetation-plot databases (Dengler et al. 2011;Alvarez et al. 2012), enhance the potential application of the Braun-Blanquet approach as a reference system for the summary of vegetation diversity at a national level. ...
... This classification is usually based on plot observations (also known as phytosociological relevés), and classification schemes have a hierarchical structure, where the association is the fundamental unit and class is the highest rank (Westhoff and van der Maarel 1978;Dengler et al. 2008). Despite criticisms regarding the common preferential sampling strategy and the heterogeneity in the use of plot sizes and abundance scales (Dengler et al. 2008;De Cáceres et al. 2015), current development of classification methods including the implementation of expert systems (Tichý et al. 2019;Bruelheide et al. 2021) and the proliferation of initiatives compiling and sharing records in vegetation-plot databases (Dengler et al. 2011;Alvarez et al. 2012), enhance the potential application of the Braun-Blanquet approach as a reference system for the summary of vegetation diversity at a national level. ...
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Aims : The Braun-Blanquet approach has been widely implemented to generate classification schemes at the country level and Chile is not an exception. In spite of numerous studies, a revised system for the whole country is still missing and most of the current surveys are restricted to a small set of vegetation groups or specific study sites. To fill this gap, we established a vegetation-plot database and updated the classification into a single syntaxonomic scheme. We also performed a comparison of this scheme with the formation system following the EcoVeg approach. Study area : Continental Chile. Methods : We compiled a database of 1,582 plot observations, which are classified into 29 classes, 43 orders, 65 alliances, and 162 associations according to the Braun-Blanquet approach. Results : These observations were assigned to 7 formation classes, 10 subclasses and 19 formations in the EcoVeg approach. There are several mismatches between phytosociological classes and EcoVeg formations, which indicates some inconsistencies in the current stage of syntaxonomy in Chile. Besides a big contrast on bioclimatic conditions within the country’s territory, the occurrence of intrazonal vegetation may explain the high diversity of phytosociological associations recorded in this database. Conclusions : This work may constitute the basis for the implementation of the EcoVeg classification at the levels of alliance and association and can be extended for other countries in the South American sub-continent.
... Management type was defined as traditional (natural succession, uneven stand age, single-tree cutting) or regular (planted, even age stands with regular thinning). Vegetation was sampled using the Barkman et al. (1964) [53] scale, with cover the measure of species abundance, due to the preference for pure cover scales in ecological studies [53,54]. The terrain slope angle was measured with an inclinometer. ...
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Understanding the relationship between disturbance and forest community dynamics is a key factor in sustainable forest management and conservation planning. The study aimed to determine the main factors driving unusual differentiation of forest vegetation into four communities, all coexisting on the same geological substrate. The fieldwork, conducted on the fluvioglacial sand area in Central Poland, consisted of vegetation sampling, together with soil identification and sampling, up to depths of 150 cm. Additional soil parameters were measured in the laboratory. A Geographical Information System was applied to assess variables related to topography and forest continuity. Vegetation was classified and forest communities identified. Canonical Correspondence Analysis indicated significant effects of organic horizon thickness, forest continuity, soil disturbance and soil organic matter content on vegetation composition. We found that the coexistence of four forest communities, including two Natura 2000 habitats, a Cladonia-Scots pine forest and an acidophilous oak forest (codes–91T0 and 9190 respectively), resulted from former agricultural use of the land followed by secondary succession. The lowest soil-disturbance level was observed within late-successional acidophilous oak forest patches. Nearly complete soil erosion was found within the early-successional Cladonia-Scots pine forest. We propose that both protected habitat types may belong to the same successional sere, and discuss the possibility of replacement of the early- and late-successional forest habitat types in the context of sustainable forest management and conservation.
... La figura 1 muestra la zona trabajada y la ubicación de los censos de vegetación. Los relevamientos se realizaron en rodales fisonómica, florística y ecológicamente homogéneos, evitando zonas ecotonales hacia bosques u otra formación vegetal, de acuerdo a los requisitos de muestreo planteados por la fitosociología (Dengler et al. 2008). La nomenclatura sigue a Zuloaga et al. (2008) y Rodríguez y Marticorena (2019). ...
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The peat bogs of Capitán Prat Province in Aisén Region in Chilean Patagonia were studied, raising 204 vegetation samples with plant sociological methodology, with which an initial table with 106 species of flora in its first column was built. With traditional methodology, this table was ordered using differential species and with multivariate classification and ordination statistics, all the possible ecological information contained in it was extracted. Four plant communities of the peat bog formation were determined: Gaultherio-Sphagnetum magellanici (Sphagnum peat bog), Schoeno andinus-Lepidothamnetum fonkii (ciprés enano peat bog), Cortaderio egmontianae-Schoenetum andinus (gramineous peat bog) and Drosero uniflorae-Donatietum fascicularis (pulvinate peat bog). The third Cortaderio egmontianae-Schoenetum andinus is proposed as a new plant association. The floristic similarity among them was high, even though they can be easily physiognomically differentiated. Multivariate ordination suggests that waterlogging and temperature, conditioned by relief and altitude, are important factors in the differentiation of peat bog communities. It is concluded that of these four associations, two-the Sphagnum peat bog and Lepidothamnus fonkii peat bog-correspond to the region of the evergreen Magellanic Forest, while the other two are northern outposts of the Magellanic Tundra Region, typical of the Southern Hemisphere, especially in Chile and New Zealand. RESUMEN Se estudió la vegetación turbosa de la provincia de Capitán Prat en la Región de Aisén, Patagonia chilena, levantando 204 censos de vegetación con metodología fitosociológica, con ellos se construyó una tabla inicial con 106 especies vegetales en su primera columna. Con metodología tradicional se ordenó esta tabla utilizando especies diferenciales y con estadística multivariada de clasificación y ordenación, se extrajo toda la información ecológica encerrada en ella. Se determinaron cuatro asociaciones vegetales turbosas: Gaultherio-Sphagnetum magellanici (turbera esfagnosa), Schoeno andinus-Lepidothamnetum fonkii (turbera esfagnosa de ciprés enano), Cortaderio egmontianae-Schoenetum andinus (turbera graminosa) y Drosero uniflorae-Donatietum fascicularis (turbera pulvinada). La tercera, Cortaderio egmontianae-Schoenetum andinus se propone como una asociación vegetal nueva. La similitud florística entre ellas fue alta, aun cuando pueden diferenciarse fácilmente por su fisonomía. El análisis multivariado de ordenación, sugiere que la disponibilidad hídrica y la temperatura, condicionados por el relieve y la altitud, son factores importantes en la diferenciación de las comunidades. Se concluye que de estas cuatro asociaciones dos, las turberas esfagnosa y de ciprés enano, corresponden a la región de los bosques magallánicos perennifolios, mientras que las otras dos, son avanzadas septentrionales de la Región de la Tundra magallánica, propia del hemisferio Sur, especialmente de Chile y Nueva Zelanda.
... Specifically, the approach deals with plant species co-occurrences, or, in other words, species compositional patterns and gradients at the scale of the plant community. It works with empirical, plot-based data and techniques to compare floristic composition among communities and relates these patterns to environmental factors (Westhoff and Van der Maarel 1973;Ewald 2003;Dengler et al. 2008). It organizes vegetation types in a hierarchical system based on floristic composition and similarity. ...
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Aims : To link the Braun-Blanquet units of the EuroVegChecklist (EVC) with the upper levels of the International Vegetation Classification (IVC), and to propose a division level classification for Europe. Study area : Europe. Methods : We established a tabular linkage between EVC classes and IVC formations and identified mismatches between these two levels. We then proposed IVC division level units to organize EVC classes. Results : We organized the EVC classes into 21 formations and 30 divisions. We flagged classes that did not fit comfortably within an existing formation, either because its content corresponded to more than one formation or because it did not fit any formation description. In a few cases, we split EVC classes because they seemed too heterogenous to be assigned to a single formation. Conclusions : The IVC approach adds a set of physiognomic and ecological criteria that effectively organizes the EVC classes, which are already being increasingly informed by physiognomy. Therefore, the formation concepts are relatively natural extensions of concepts already embedded in the classes. However, physiognomic placement of Braun-Blanquet classes can be difficult when the sampling of the vegetation is at finer grain than usual in the respective formation (tall-scrub, annual pioneer communities). Some EVC classes seem too heterogenous to fit into the IVC formation system. Delimitation of these classes has often been a matter of debate for many decades, and the IVC perspective might help to solve these intricate issues. In other cases, mismatches between phytosociological classes and IVC formations might better be solved by emending the current formation concepts. Abbreviations : BB = Braun-Blanquet; EVC = EuroVegChecklist; IVC = International Vegetation Classification.
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The aim of this study is to outline syntaxonomical diversity of yew (Taxus baccata L.) in the eastern of Hyrcanian forests, Jahan Nama Protected Area (JNPA), and to identify their main environmental gradients. Vegetation units were classified using modified TWINSPAN (two‐way indicator species analysis) and were translated into syntaxonomic system. Syntaxa were determined by re‐arrangement of each relevé based on diagnostic species. Syntaxa were finally evaluated by diagnostic species and environmental parameters according to phi‐values and analysis of variance, respectively. Detrended correspondence analysis was used to visualize the dissimilarity of syntaxa and their relationships with the environmental factors. The classification of JNPA yew forests resulted in six vegetation units. These patterns were translated into four associations (Asso.), two sub‐associations (Subasso.) and two variants (Var.). (Asso.1) Fago orientalis‐Taxetum baccatae is found in northern aspects with lower slopes and higher soil depth; (Asso.2) Aceri velutini‐Taxetum baccatae is occurring in the moderate but rocky slopes. Asso.1 and Asso.2 are the same in altitude and involving Carpinus betulus as a co‐dominant. (Asso.3) Carpino betuli‐Carpino orientalis‐Taxetum baccatae developed in the intermediate slopes. (Asso.4) Carpino orientalis‐Taxetum baccatae appeared in the highest slope of northeast and northwest aspects with shallow soil depth. The main factors determining the species composition of the JNPA syntaxa are slope, eastness, elevation, and clay content. Finally, we concluded that yew could be associated by different plant species with different ecological desirability in the eastern of Hyrcanian forests. This study also provided the specific pattern of forest community type between C. betulus and C. orientalis in the JNPA using species combination concept. The aim of this study is to outline syntaxonomical diversity of yew (Taxus baccata L.) in the eastern of Hyrcanian forests, Jahan Nama Protected Area (JNPA), and to identify their main environmental gradients. We also used species combination concept for determining diagnostic species in the second association.
Chapter
Despite the abundant evidence of benefits for conservation, climate mitigation, and human livelihoods, wetlands globally remain under pressure of loss and degradation. Such pressures are particularly prevalent in the tropics, where institutional capacity for management and protection can be limited. This chapter outlines some key features of tropical wetlands with a focus on vegetated marshes and their connection with floodplains. It provides an overview of the character of tropical marshes and the importance of ecology for their characterization and connectivity within catchments. The delineation of tropical wetlands and floodplains has benefited greatly from developments in Earth Observations, but basic description of ecological form and function, and the response of that to seasonal changes in hydrology and, increasingly, human pressures is an ongoing requirement throughout the tropics. While good evidence-based guidance exists to support the management of tropical wetlands, and new methods are being developed and applied, it is also very necessary to adopt a pragmatic approach to how best to conserve and manage sites when resources are lacking. We outline different levels of possible management, using case studies from central Africa.
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Aims : To quantify how fine-grain (within-plot) beta diversity differs among biomes and vegetation types. Study area : Palaearctic biogeographic realm. Methods : We extracted 4,654 nested-plot series with at least four different grain sizes between 0.0001 m² and 1,024 m² from the GrassPlot database spanning broad geographic and ecological gradients. Next, we calculated the slope parameter ( z -value) of the power-law species–area relationship (SAR) to use as a measure of multiplicative beta diversity. We did this separately for vascular plants, bryophytes and lichens and for the three groups combined (complete vegetation). We then tested whether z -values differed between biomes, ecological-physiognomic vegetation types at coarse and fine levels and phytosociological classes. Results : We found that z -values varied significantly among biomes and vegetation types. The explanatory power of area for species richness was highest for vascular plants, followed by complete vegetation, bryophytes and lichens. Within each species group, the explained variance increased with typological resolution. In vascular plants, adjusted R ² was 0.14 for biomes, but reached 0.50 for phytosociological classes. Among the biomes, mean z -values were particularly high in the Subtropics with winter rain (Mediterranean biome) and the Dry tropics and subtropics. Natural grasslands had higher z -values than secondary grasslands. Alpine and Mediterranean vegetation types had particularly high z -values whereas managed grasslands with benign soil and climate conditions and saline communities were characterised by particularly low z -values. Conclusions : In this study relating fine-grain beta diversity to typological units, we found distinct patterns. As we explain in a conceptual figure, these can be related to ultimate drivers, such as productivity, stress and disturbance, which can influence z -values via multiple pathways. The provided means, medians and quantiles of z -values for a wide range of typological entities provide benchmarks for local to continental studies, while calling for additional data from under-represented units. Syntaxonomic references : Mucina et al. (2016) for classes occurring in Europe; Ermakov (2012) for classes restricted to Asia. Abbreviations : ANOVA = analysis of variance; EDGG = Eurasian Dry Grassland Group; SAR = species-area relationship.
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The project "Die Pflanzengesellschaften Mecklenburg-Vorpommerns und ihre Gefährdung" (The plant communities of Mecklenburg-Vorpommern and their vulnerability) is published as a two-volume series consisting of a table volume (2001) and a text volume (2004). Based on a huge vegetation-plot database and a consistent and well-documented methodology, the complete vegetation of this federal state in NE Germany was phytociolociologally classified de novo. The result are 34 phytosociological classes, 12 subclasses, 70 orders, 6 suborders, 125 alliances and 284 associations. This text volume contains detailed descriptions, nomenclatural revisions, diagnostic species and conservation assessments of all syntaxa of Mecklenburg-Vorpommern. The Introductory section provides detailed insights into the natural history of the study region as well as the methodology of the project. The concluding Conservation section summarizes the outcomes of the unique conservation assessment methodology applied in the project. An Introduction and summary for English-speaking readers (M. Isermann & J. Dengler; pp. 16–21 of the text volume 2004) makes most of the content of both volumes accessible to persons not comprehending German.
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Development and evaluation of new approaches in phytosociology with special regard to vegetation classification Phytosociology had its starting point in the beginning of the 20th century as one of several schools which deal with vegetation from a scientific point of view. Josias Braun-Blanquet, who-se textbook ‘Pflanzensoziologie’ had been published in its first edition in 1928, could be seen as the founder of this school. Basic ideas of the Braun-Blanquet approach of vegetation science are the following: • Plant stands on plots are documented in a standardised manner, the so-called vegetation relevés, which form an adequate mean between being too superficial and being too lavish. They comprise information on location, site conditions and vegetation structure as well as a complete list of the occurring plant species each of those quantified by means of a combined cover-abundance scale. • Concrete plant stands (phytocoenoses) are thought to be assignable to abstract plant communities (phytocoena), which are characterised and separated one from another with regard to their whole species combination (floristic-coenological method). Since each of its constituent species has a certain ecological optimum and a specific geographical range it should be expected that they as a whole result in a clearly limited ecological space and synareal in which the community is distributed. Therefore it is not necessary to use the accordance in site conditions and geographical range as additional classification criteria. • Phytocoena are arranged into a hierarchical system according to their floristic similarity. The principal ranks of this system are bottom to top association, alliance, order and class. Those so-called syntaxa are given scientific names deriving from one or two species names. • As means for recognition and distinction of syntaxa diagnostic species are derived from the phytosociological data: Species which are restricted to a large extent to one such syntaxon are called character species, whereas differential species only differentiate one side only. Phytosociology has undergone an enormous impetus within the last century and has integrated most of the other former schools of vegetation science step by step. By means of the Braun-Blanquet approach, millions of relevés from all continents have been collected until now. For several countries comprehensive overviews of the plant communities occurring on their territories have been published. Since the 1960s numerous attempts have been made to automate the classification process by means of computer programs (numerical syntaxonomy). Phytosociology also found its way into legislation in the field of nature conservancy as protected habitats defined by the occurrence of certain syntaxa. In spite of this quantitative success phytosociology was always exposed to intense criticism, going as far as to blame it for not being scientific. Indeed, it must be stated that consistent and generally accepted methods are missing in syntaxonomy as one of the core subjects of phytosociology – even though a huge amount of phytosociological papers have been published and also numerous textbooks. Therefore the major aim of this Ph.D thesis is to develop such methods and to back them up from a theoretical point of view. As a first step, the three major purposes of vegetation classifications are determined: (1) Naming of the research object to enable communication about them. (2) Reduction of data and making them representable. This means that instead of determining and describing the distribution of numerous species doing the same only for several plant communities will be suitable generally. (3) Framework for the ‘inductive generalisation’: The floristic-coenological delimitation of phytocoena results into a far reaching accordance of the phytocoenoses belonging to them with regard to species combination and site conditions. Phytocoena therefore are suitable reference entities especially for ecological research and nature conservation. Moreover they can be used for bioindication. These purposes lead to the requests to be made on vegetation classifications which preferably should be meat. Most important are both the inner coherence of the separated units with respect to their major properties and their simple and clear discernibility. As further criteria the following should be mentioned: (1) completeness of the system. (2) Stability of the classification. (3) Tolerance against heterogeneous data. (4) Supra-regional applicability. (5) Applicability for different purposes. (6) Hierarchical structure. (7) Equivalence of syntaxa of the same rank. (8) Adequate number of discerned entities. As a whole these criteria are already fulfilled in the ‘classical’ form of the Braun-Blanquet approach. Nevertheless some weak points remain: • Until now precise and operational definitions for major concepts are missing. Syntaxonomy therefore is often difficult to comprehend for laymen. Instead of clear and verifiable criteria for and against a certain classification authors of phytosociological papers often refer to their own ‘expert knowledge’ or quote the opinion of a renowned phytosociologist. • Even though it has been well known for a long time, that numerous phytocoenoses and phytocoena are lacking character species, there is no generally accepted and methodically sound way to include them into the phytosociological system. As a result such plant stands frequently are not documented by relevés or at least not taken into account in the final syntaxonomic treatment. • Among phytosociologists belief in minimal areas is widely distributed. These should be relevé areas, beginning with which the species number does not increase substantially any more with further enlargement of the area. But it is well known for a long time by numerous empirical data as well as via theoretical arguments that every increase in area will cause an increase in the mean species number. The only problem ist that this increase is hardly recognizable when plotting the two axes of a species-area-curve in a linear scaling. This is because the function more or less follows a power function. The assumption of the existence of minimal areas lead many phytosociologists not to draw their major attention to relevé areas in syntaxonomy as long as they approximately correspond to the assumed mimimal areas. Relevé sizes for different vegetation types suggested by various textbooks differ by 150,000. Due to the dependence of species numbers from area it is not permissible to compare relevés of different area sizes. Most of the synthetic properties of plant communities as well are influenced by area size. This especially holds true for constancy which is the percentage of occurrences of a certain species within a set of relevés. By means of a conceptual model and empirical data a function is derived which approximately describes this dependence: St (A) = 1 – (1 – St0)(A / A0)^0,42, where St is the constancy and A the area size. The classification method put forward is formulated as an axiomatic system comprising twelve suggestions of definitions: 1. Phytocoenoses are defined in a pure operational manner as the plant individuals of different species growing in a time-space unit of a certain dimension. Neither discreteness nor integration are demanded a priori. This definition includes expressively all synusiae thriving in that time-space unit as for example epiphytes. 2. The so-called ‘basic syntaxonomic axiom’ states that every phytocoenosis belongs – within a syntaxonomic system – to exactly one syntaxon of a certain rank. 3. The classical definition of fidelity degrees by SZAFER & PAWŁOWSKI (1927) is both contradictory and impractical. Therefore a new differential species criterion is presented in which it says that a species can be called differential of one syntaxon versus another syntaxon of the same rank if its constancy is at least twice as high and this difference in commonness most probably is not due to chance. This formulation goes back to BERGMEIER & al. (1990) but uses constancy percentages instead of constancy classes as otherwise there would be unreasonable changes in the minimum requirements for differential species below and above the class borders. 4. Whereas constancy at association level and below is defined as percentage of relevés in which the species occurs, a constancy reference value (short: constancy) of a higher syntaxon should be calculated as a mean of the constancy values in all associations belonging to it. This me-thod of calculation is due to the fact that associations are considered the basic units of the system and prevents the results from being influenced from different examination intensities in different associations. 5. A differential species below the vegetation class must fulfil the differential species criterion against all other syntaxa of the same rank within the syntaxon of the next higher rank. To a-void both taxa from being named differential species which nearly do not contribute to the discernibility of the syntaxa and effects due to chance a restriction has to be appended: Only those taxa should be given the status of differential species which have at least 20 % constancy within the particular syntaxon and not more than 20 % in the compared syntaxa. 6. As common differential species of a class are those taxa named which are a nowhere seen character species within the particular structural type, but fulfil the differential criterion of two or three classes against all other classes of this type. 7. Character species of a syntaxon are those taxa which meet the differential species criterion compared with all other syntaxa of the same rank within a structural type. The function of character species is twofold: (1) In the classification process the principal demand of the existence of character taxa ascertains the approximate equivalence of syntaxa of one rank, which especially holds true for associations. (2) When a classification is done on the basis of the complete species combination, character species as those taxa which have a clear sociological optimum in a particular syntaxon can be used best as a means for recognition and discrimination of the entities. If the next higher syntaxa occur nowhere together, as an exception from the general rule, one taxon could be named character species in two independent syntaxa. The major reason for restricting the character species to structural types is the dependency of constancy from area size. Since the customary relevé sizes widely differ between different vegetation types, which seems to be sensible at least to some extent, it is not acceptable to classify all of them within one system. It seems to be appropriate to discern two (vegetation types with and without phanerophytes) or three (vegetation types with phanerophytes, without phanerophytes, but with other vascular plants and those built up solely by one layer of mosses, lichens and algae) floristically defined structural types a priori. Some problems related to the practical implementation of this rule are discussed and a pragmatic approach is recommended. 8. Species which meet the character species criterion within more than one syntaxon fitted into one another, which is often the case, are called transgressive character species. 9. With regard to syntaxa not possessing character species of their own it is recommended to apply the central syntaxon concept of DIERSCHKE (1981) at all syntaxonomic levels below the class. Accordingly, at a maximum one central syntaxon not or not sufficiently characterised by character species of its own can be distinguished within a syntaxon of the next higher rank. This is named in the same manner as all other syntaxa. As compared to the ‘deductive meth-ode’ developed by KOPECKÝ & HEJNÝ (1971) and other approaches to deal with negatively characterised syntaxa the suggested method has several theoretical and practical advantages. These are discussed in detail. Most important is probably the fact that the presented approach avoids the introduction of different ways of naming syntaxa, which unavoidably gives the impression that there would be an ecological difference – which in fact does not exist. 10. Syntaxa from association to class level either must be sufficiently characterised by character species of their own or be the central syntaxon of the next higher entity. ‘Sufficiently’ in this case means a constancy sum of all character species of at least 100%. 11. The association is the lowest syntaxon that could be characterised by character species of its own and not divided further in such syntaxa or otherwise it can be regarded as the central syntaxon of a (sub-) alliance. 12. The class is the highest syntaxon characterised well by character species within one structural type. To measure the ‘quality’ of a syntaxon the sum of the constancy values of its character species seems to be appropriate. An adequate classification then would be one in which the constancy sums of all classes are as high as possible. Both the minimum and the mean of those should be maximised. Some further recommendations for syntaxonomic work are presented which do not form part of the above axiomatic system: • ‘Graduated’ hierarchies are – from an information theoretical point of view – favourable to ‘flat’ ones. In many cases they reflect the structure of syntaxonomic data better. • Below association level it seems inappropriate to prolong the linear-monohierarchical classification from above. Here a multidimensional-polyhierarchical approach in which different complexes of differentiating factors stand aside with equal importance has its advantages. • Formal syntaxonomic classification above class level is to be rejected since the class is ac-cording to the suggested definition 12 the uppermost syntaxon. The syntaxonomic concept leads to requests upon the drawing up of relevés, the most important of which are the following: • Since – as has been shown – sound syntaxonomic classification is only possible on the basis of relevés of the same (or at least a similar) size, standard sizes for relevés in different structural types are proposed. • Due to the above definition of phytocoenoses and to the often high diagnostic value of non-vascular plants it is requested to document in relevés of phytocoenoses (holocoenoses) in principle all macroscopically visual photoautotroph organisms, including all synusiae such as epiphytes. The practical application of the presented syntaxonomic concept could be understood as a problem of optimisation which has to be treated in an iterative process: The most similar relevés on a floristic basis are to be combined together until the emerging units either possess character species of their own or can be regarded as the central association of a higher unit which evolves from the further joining of these basic units (associations). The treatment continues with analyses on the question which quotients and differences of percentage constancy values accord to statistical significant differences in commonness. They lead to the recommendation to complete – in case of low numbers of relevés – the requested constancy quotient q > 2 from the differential species criterion by an minimal constancy difference of Δ = (2 ⋅ n1)-1/2, where n1 is the number of relevés in the particular syntaxon. Additionally, some recommendations concerning the adequate presentation of syntaxonomic data in form of tables arise from the presented syntaxonomic concept: • It is essential always to give the (mean) sizes of the relevé areas, also in the case of constancy tables. • Constancy values should be presented as percentages and not in form of constancy classes to allow the application of the differential species criterion. • To make the results transparent, it is advisable, to give columns for all syntaxonomic levels in constancy tables and not only for associations and their subdivisions. Nomenclature is the second indispensable part of syntaxonomy besides the classification. It is ruled by the ‘International Code of Phytosociological Nomenclature’ (ICPN), which has been published in its third edition by WEBER & al. (2000). The basic function of the ICPN is to guarantee unambiguity and stability of the scientific names of syntaxa. To achieve this, two major principles are employed, that of priority and that of nomenclatural types. Although this construction and the Code as a whole have proven to be useful, some possibilities for improvement remain, of which the following should render prominent: • It seems necessary to clearly separate the system of phytocoena and that of synusiae. It is a logical contradiction if one entity at the same time is regarded as syntaxon and synusia as it is common practice at the time being. It is therefore recommended to put the synusial system on a clear nomenclatural basis, by reserving separate endings for its ranks within the ICPN. • The demands for original diagnoses of associations should be increased, to avoid the further accumulation of nomenclatural ‘ballast’ in literature. It should be pointed out in the ICPN that the requested full species list for a description of an association has to include bryophytes and lichens as well. • The ruling of subassociations by the ICPN should be omitted, especially due to the fact that it is incompatible with multidimensional subdivisions. How the presented ideas work when practically applied is shown in detail by several examples, most of which are taken from the comprehensive treatment of the plant communities in the German state Mecklenburg-Vorpommern by BERG & al. (2001b), where they have been use in a large scale survey for the first time. In particular, it is shown how more equivalent classes could be achieved in the system and how central syntaxa could solve classificatory problems about which has been debated at length but to no avail. Distribution maps of syntaxa have been published rarely so far. One possibility to generate such synchorological maps is the superimposition of distribution maps of their diagnostic species. This results in maps of ‘potential synareals’. This method is both applicable to outline and lattice maps. How this could be done is described in detail. Examples of either case are shown and their interpretation is discussed. Finally, the presented syntaxonomic concept is examined as a whole: Due to the axiomatic construction it is consistent. It meets the above given requests for vegetation classifications better than any other known approach. It has proven its practical suitability in BERG & al. (2001b), where it enabled the establishment of a syntaxonomic classification of the extant vegetation types within a region, based on a large databank. A major strength of the approach could be seen in the fact, that clear criteria are given to evaluate different classifications with respect to which of those is conform with the method at all and – if there being more – which is the best of them. Likewise high importance has the applicability of the presented method both in manual table work and as basis for an implementation in a numerical classification algorithm. The latter will be a necessary property of any syntaxonomic approach with which the data of one of the large national and supranational vegetation-plot databases arising can be analysed one day.
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The first objective of this paper is to define a new measure of fidelity of a species to a vegetation unit, called u. The value of u is derived from the approximation of the binomial or the hypergeometric distribution by the normal distribution. It is shown that the properties of u meet the requirements for a fidelity measure in vegetation science, i.e. (1) to reflect differences of a species’relative frequency inside a certain vegetation unit and its relative frequency in the remainder of the data set; (2) to increase with increasing size of the data set. Additionally (3), u has the property to be dependent on the proportion of the vegetation unit's size to the size of the whole data set.The second objective is to present a method of how to use the value of u for finding species groups in large data bases and for defining vegetation units. A species group is defined by possession of species that show the highest value of u among all species in the data set with regard to the vegetation unit defined by this species group. The vegetation unit is defined as comprising all relevés that include a minimum number of the species in the species group. This minimum number is derived statistically in such a way that fewer relevés always belong to a species group than would be expected if the differential species were distributed randomly among the relevés. An iterative algorithm is described for detecting species groups in data bases. Starting with an initial species group, species composition of this group and the vegetation unit defined by this group are mutually optimized. With this algorithm species groups are formed in a data set independently of each other. Subsequently, these species groups can be combined in such a way that they are suited to define commonly known syntaxa a posteriori.