- Semi-dry grasslands along a climatic gradient across Central Europe - 835
Journal of Vegetation Science 18: 835-846, 2007
© IAVS; Opulus Press Uppsala.
Abstract
Question: What is the variation in species composition of
Central European semi-dry grasslands? Can we apply a training-
and-test validation approach for identifying phytosociological
associations which are loristically well deined in a broad
geographic comparison; can we separate them from earlier
described associations with only a local validity?
Location: A 1200 km long transect running along a gradient
of increasing continentality from central Germany via Czech
Republic, Slovakia, NE Austria, Hungary to NW Romania.
Methods: Relevés with > 25% cover of Brachypodium pin-
natum and/or Bromus erectus were geographically selected
from a larger database. They were randomly split into two
data sets, TRAINING and TEST, each with 422 relevés.
Cluster analysis was performed for each data set on scores
from signiicant principal coordinates. Different partitions of
the TRAINING data set were validated on the TEST data set,
using a new method based on the comparison of % frequencies
of species occurrence in clusters. Clusters were characterized
by statistically deined groups of diagnostic species and values
of climatic variables.
Results: Species composition changed along the NW-SE gra-
dient and valid clusters were geographically well separated.
Optimal partition level was at 11 clusters, six being valid: two
clusters Germany and the Czech Republic corresponded to
the Bromion erecti; two clusters from the Czech Republic and
Hungary to the Cirsio-Brachypodion, and two clusters were
transitional between these two alliances.
Conclusion: The training-and-test validation method used in
this paper proved to be eficient for discriminating between
robust clusters, which are appropriate candidates for inclusion
in the national or regional syntaxonomic overviews, and weak
clusters, which are speciic to the particular classiication of
the given data set.
Keywords: Austria; Bromion; Cirsio-Brachypodion; Czech
Republic; Germany; Hungary; Phytosociology; Romania;
Slovakia; Training and test data sets; Vegetation database.
Nomenclature: Ehrendorfer (1973).
Semi-dry grasslands along a climatic gradient across
Central Europe: Vegetation classiication with validation
Illyés, Eszter
1*
; Chytrý, Milan
2,6
; Botta-Dukát, Zoltán
1,7,
; Jandt, Ute
3
; Škodová, Iveta
4,8
;
Janišová, Monika
4,9
; Willner, Wolfgang
5
& Hájek, Ondřej
2,10
1
Institute of Ecology and Botany, Hungarian Academy of Sciences, H-2163 Vácrátót, Hungary;
2
Department of Botany
and Zoology, Masaryk University, Kotláská 2, CZ-611 37 Brno, Czech Republic;
3
Institute of Geobotany and Botanical
Garden, Am Kirchtor 1, D-06108 Halle, Germany; E-mail ute.jandt@botanik.uni-halle.de;
4
Institute of Botany, Slovak
Academy of Sciences, Dúbravská cesta 14, SK-845 23 Bratislava, Slovakia;
5
VINCA – Vienna Institute for Nature Conser-
vation and Analyses, Giessergasse 6/7, A-1090 Vienna, Austria; E-mail wolfgang.willner@vinca.at;
6
E-mail chytry@sci.
muni.cz;
7
E-mail bdz@botanika.hu;
8
E-mail iveta.skodova@savba.sk; E-mail
9
monika.janisova@savba.sk;
10
E-mail ohajek@sci.muni.cz;
*
Corresponding author; Fax +36 28360110; E-mail illyese@botanika.hu
Introduction
The past decade has witnessed a rapid development
of electronic phytosociological databases (Ewald 2001;
Hennekens & Schaminée 2001), which can be used to
create vegetation classiication schemes valid over large
areas and across national boundaries. In Europe, this of-
fers a unique opportunity for international harmonization
of vegetation classiication, habitat typologies and the
subsequent planning of conservation strategies.
However, vegetation units based on numerical
classiications of data from selected areas or selected
vegetation types are often not appropriate for direct inclu-
sion in large-scale vegetation overviews, because such
classiications are highly idiosyncratic. They accurately
relect the structure of the input data set but do not use
any external information; therefore some clusters are
often speciic to the particular classiication but are rarely
found in the classiications of other data sets from the
same vegetation type. National or international systems
of vegetation classiication, however, should be more
robust and include only those vegetation units which have
been recognized in several independent classiications.
Large-scale vegetation classiication projects would be
greatly improved if the case studies involving numeri-
cal vegetation classiication clearly separated clusters
with a more general validity from clusters speciic to
the particular data set. One approach to achieve this is
simple validation with a training and test data set (Duda
et al. 2001), i.e. making a classiication on a training data
set, then applying the same classiication method to a
different (test) data set, comparing the classiications of
the training and test data sets, and inally identifying the
corresponding clusters (vegetation types) revealed in both
data sets. So far, validation has been rarely used in studies
describing vegetation patterns across landscapes, mainly
due to the limited amounts of available data, which rarely