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General strategyfor extracting vegetation classificationfrom
large phytosociological databases
Milan Chytr
Dept. of BotanyMasaryk UniversityBrno, Czech Republic
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Step 1: Establishment of the database Example: Czech National
Phytosociological Database Started in 1996 Current state: 55,000
phytosociological relevs Sampled in 19222002 Made by 332 authors
1.3 Million individual plant records
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Step 2: Relev selection Deletion of extreme plot sizes
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Step 3: Geographical stratification(Chytry & Tichy 2003,
Folia Fac. Sci. Univ. Masar. Brun. 108, in press; Kuzelova &
Tichy, talk at this Symposium)
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Step 3: Geographical stratification(Chytry & Tichy 2003,
Folia Fac. Sci. Univ. Masar. Brun. 108, in press; Kuzelova &
Tichy, talk at this Symposium)
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Step 4: Identification of major gradients and groups in the data
set
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Step 4: Identification of gradients and groups in the data
set(Bruelheide & Chytry 2000, J. Veg. Sci. 11: 295306)
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An alternative approach? Delimitation of vegetation units by
formal definitions
(Bruelheide & Chytry 2000, J. Veg. Sci. 11: 295306)
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Step 5: Evaluation of expert-based phytosociological
classification Calculation of diagnostic capacity of species for
traditional phytosociological units(Chytry et al. 2002, J. Veg.
Sci. 13: 7990)
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Step 5: Evaluation of expert-based phytosociological
classification Calculation of diagnostic capacity of species for
traditional phytosociological units(Chytry et al. 2002, J. Veg.
Sci. 13: 7990)
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Step 6: Reproduction of traditional syntaxa by formal
definitions Only well-defined syntaxa are reproduced Cocktail
method, applied to a large database (Bruelheide 2000, J. Veg. Sci.
11: 167178) Species co-occurring together are combined into
sociological groups Sociological species groups are combined by
logical operators to form definitions of vegetation units
Example of association definition: (Caltha palustris Group AND
Cirsium rivulare Group) AND NOT (Carex echinata Group)
Example with cover: Filipendula ulmaria cover > 25 % AND
Chaerophyllum hirsutum Group
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Step 6: Reproduction of traditional syntaxa by formal
definitions
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Step 7: Fixing overlaps and unassigned relevs by similarity
criterion(Koci et al. 2003, J. Veg. Sci. 14, in press;Tichy, poster
at this Symposium)
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Step 8: Parametrization of formally defined vegetation units
Diagnostic species statistical comparisons of species occurrences
in the relevs of the vegetation unit and in the rest of the
database Constant and dominant species Means and variances of
measured vegetation and environmental variables
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Step 8: Parametrization of formally defined vegetation units
Diagnostic species statistical comparisons of species occurrences
in the relevs of the vegetation unit and in the rest of the
database Constant and dominant species Means and variances of
measured vegetation and environmental variables Ellenberg indicator
values
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Step 8: Parametrization of formally defined vegetation units
Diagnostic species statistical comparisons of species occurrences
in the relevs of the vegetation unit and in the rest of the
database Constant and dominant species Means and variances of
measured vegetation and environmental variables Ellenberg indicator
values GIS overlays
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Step 9: Predictive distribution modeling Coincidence maps of
diagnostic species GIS-based models
