Formalized reproduction of an expert-based phytosociological classification

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Kočí, M. et al.

researchers often take a conservative approach and rather

stick to the units of the traditional expert-based classifi-

cation. An increasingly common practice seems to be

combining numerical classification with subsequent in-

terpretation of clusters selected subjectively across dif-

ferent hierarchical levels, merging selected clusters so

that they better correspond to traditional units, or even

manually re-assigning selected relevés among clusters

(Hennekens et al. 1995; Bergmeier 2002; Krestov &

Nakamura 2002; Willner 2002). This practice of post

hoc  manual re-arrangement of the numerical classifi-

cation results indicates that formalization of the tradi-

tional expert-based classification by cluster analysis and

related unsupervised methods has mostly failed so far.

An alternative to the commonly used numerical clas-

sification algorithms is the Cocktail method proposed

by Bruelheide (1995, 2000). This method produces for-

malized definitions of vegetation units by providing

unequivocal criteria for assignment of relevés to these

units. At the same time it appears to be able to delimit

vegetation units in a similar way to the traditional ex-

pert-based classification (Bruelheide & Chytrý 2000),

but with the elimination of the latter’s inherent incon-

sistencies. An important difference between the Cock-

tail method and the numerical classification algorithms

is that the Cocktail method does not assign all the

relevés in the data set to vegetation units. It preferably

defines vegetation units in those parts of the vegetation

continuum, where several species with rather narrow

ecological or geographical ranges meet, while those

parts of the vegetation continuum which contain only

widespread generalist species are often not assigned to

any vegetation unit by the Cocktail method.

However, non-assignment of some relevés to the

vegetation units may become a problem in some appli-

cations of vegetation classification, notably in vegeta-

tion mapping. Therefore it seems to be advantageous to

apply the Cocktail method in combination with numeri-

cal procedures that assign relevés to vegetation units by

calculating similarity between the relevés and constancy

columns of vegetation tables (Hill 1989; Dodd et al.

1994). If these procedures are run in large phyto-

sociological data sets, diagnostic species of vegetation

units can be formally defined (Chytrý et al. 2002) and

performance of the similarity calculations can be en-

hanced by positive weighting of diagnostic species.

The aim of the present paper is to test the ability of

the Cocktail method, combined with a newly designed

procedure of similarity-based assignment of relevés to

vegetation units, to reproduce an expert-based vegeta-

tion classification in a formal way. As a test data set, we

use the subalpine tall-forb and deciduous scrub vegeta-

tion of the Czech Republic, previously classified at the

level of associations by expert knowledge.

Material and Methods

Material

We took the classification of the subalpine tall-forb

and deciduous scrub vegetation of the class Mulgedio-

Aconitetea  in the Czech Republic (Kočí 2001) as an

example of the traditional expert-based classification.

The data set used for creating this classification con-

sisted of 718 relevés of subalpine tall-forb vegetation,

with species cover estimated on the Braun-Blanquet or

Domin scale (Westhoff & van der Maarel 1978). This

classification was largely based on expert knowledge,

being a compromise between the field experience of the

author and different local classifications published in

earlier literature. The classification was aided by nu-

merical divisive algorithm of the TWINSPAN program

(Hill 1979), which was used in several successive runs.

Several TWINSPAN end-groups were either merged or

further divided according to the subjective opinion of

the author. Assignment of each of the relevés to the end-

groups was checked manually and some relevés were

eventually moved to groups other than those suggested

by TWINSPAN. In the end, each of the 718 relevés was

assigned to one of 16 recognized associations.

The Cocktail classification, which was used to for-

mally reproduce the expert-based classification by Kočí

(2001), was performed with a data set of 21 794 relevés,

containing all vegetation types of the Czech Republic.

This data set was taken from the Czech National Phyto-

sociological Database (Chytrý & Rafajová 2003), us-

ing a geographically stratified selection that made it

possible to avoid a great influence of the over-sampled

areas on the results. In this selection, we took only one

relevé of each syntaxon per grid square of 1.25 longi-

tudinal 

× 0.75 latitudinal minute (ca. 1.5 km × 1.4 km).

The assignment to syntaxa at the level of association

(or alliance) was according to the original assignments

by the relevé authors. If two or more relevés of the

same association were encountered in the same grid

square, selection priority was given to the relevés with

recorded cryptogams and to newer relevés. If this

selection still yielded more than one relevé in the grid

square, one of them was selected at random. Due to the

stratified selection, several relevés contained in the

above-mentioned data set of 718 relevés were not

included in this data set.

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