the use of large data sets and information systems in nowadays vegetation research joop schaminée...
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The use of large data sets and information systems in nowadays vegetation research
Joop Schaminée & Stephan Hennekens
Brest, 4 November 2010, Colloque international Centenaire de la Phytosociologie
The history of vegetation data
1915 Oldest relevés according to Braun-Blanquet
1992 First version of Turboveg (DOS-version)
1994 First questionnaire on overview of European vegetation data
1996 Turboveg for Windows
2008 Preslia: overview of European vegetation data
2010 Global Index of Vegetation-plot Databases (GIVD)
Content of presentation
National vegetation databases
Scientific research
Information systems
National vegetation databases
Overview vegetation data Europe
Country Total Computerized Turboveg
Germany 1,600,000 90,000 65,000
Netherlands 700,000 630,000 630,000
France 350,000 310,000 0
Poland 180,000 15 ,000 0
Spain 165,000 77,000 15,000
Italy 150,000 20,000 1,000
United Kingdom
132,000 107,000 26,000
Roumenia 70,000 0 0
Belgium 58,000 45,000 33,000
Russia 57,000 37,000 27,000
Ireland 23,000 10,000 10,000
Luxemburg 15,000 14,000 14,000
Total number of plots - absolute numbers
Dutch national vegetation databank
Dutch national vegetation databank
560,000 relevés
10,000,000 geo-referenced species records
1920-2010
Information systems
Biological information systems are
• Data base driven
• Operational on different levels
• Incorporating a GIS platform
• Acting as electronic encyclopedia
• Generating new information by combining data sets
• Important for nature planning and policy making
The input and management of vegetation data
Electronic encyclopedia
Electronic encyclopedia
Electronic encyclopedia
Electronic encyclopedia
Combining data bases
Combining data bases
Combining data bases
GIS application
GIS application
Scientific research
Scientific research
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0 2 4 6 8 10 12 0
2
4
6
8
10
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Ozinga et al. 2008
DCA axis 1 ( moisture r = 0.92 )
DC
A a
xis
2(
Sal
inity
= 0
.75)
Indirect ordination plant communities
Scientific research
Ordination axes 1 2 3
EigenvaluesLengths of gradient
0.729.594
0.699.307
0.528.441
Species-environment correlations:- Total (multiple correlation)- Moisture- Productivity- Light availability- Base saturation- Salinity- Temperature
0.973-0.93-0.040.230.180.070.42
0.9230.280.85
-0.030.730.220.64
0.917-0.170.33
-0.780.04
-0.54-0.11
Scientific research
Ozinga et al. (2005) Oikos
Potential for LDD
Adult longevity
Seed longevity low high low high low high low high
low high
low high low high
0
2
4
6
8
10
12
14
26 76 70 80 23 99 110 109
Exp
lain
ed
varia
nce
(%)
N:
Exp
lain
ed v
aria
nce
***
***
*GLM
Scientific research
Ozinga et al. (2005) Oikos
Potential for LDD
Adult longevity
Seed longevity low high low high low high low high
low high
low high low high
0
2
4
6
8
10
12
14
26 76 70 80 23 99 110 109
Exp
lain
ed
varia
nce
(%)
N:
Exp
lain
ed v
aria
nce
***
***
*GLM
Typha latifolia
Scientific research
Ozinga et al. (2005) Oikos
Potential for LDD
Adult longevity
Seed longevity low high low high low high low high
low high
low high low high
0
2
4
6
8
10
12
14
26 76 70 80 23 99 110 109
Exp
lain
ed
varia
nce
(%)
N:
Exp
lain
ed v
aria
nce
***
***
*GLM
Pedicularis sylvatica
Conclusion
The eco-informatics approach opens fascinating and new ways in vegetation research by integrating large databases on different biological levels (species, plant communities, landscape) and by using a GIS platform for the vizualisation of spatial information. The new perspectives can be reached optimal by an open and free exchange of data, in other words by data-sharing and collaboration.
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