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Interfacing Vegetation Databases with ecological theory and
practical analysis.Mike Austin, Margaret Cawsey and
Andre Zerger
CSIRO Sustainable EcosystemsCanberra Australia
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Examples of Current Vegetation Databases
• Purpose:Vegetation classification– TurboVeg: Phytosociological relevees– Vegbank: General vegetation classification
• Purpose: Vegetation Analysis– Minimalist: minimum data set– Biograd: Regional prediction and mapping
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Purposeand
Product
Statistical methods
model
Ecological theory model
DataMeasurement
model
RelationalDatabase
GeographicInformation
System(GIS)
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Topics
• Interface between vegetation databases theory and analysis
• Interface between data and practical applications for conservation evaluation
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Biograd Database
• Grew from minimalist database– Location, plot data, co-occurrence of canopy species,
slope, aspect, elevation.– Current size 10027 plots.
• Used software packages and GIS to derive environmental variables– Temperature, rainfall, radiation, soil properties.
• Predicted potential vegetation from species environmental models
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Application to Theory
• Pattern of Species Density in relation to climate.
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Plot Tree Species Density in response to Temperature
0
5
10
15
20
25
30
0 2 4 6 8 10 12 14 16 18 20
Annual Mean Temperature
Plot
Spe
cies
Den
sity
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Mean Species Density in response to Mean Annual
Temperature in one degree classes
0
1
2
34
5
6
7
8
2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.
5
11.5 12.
5
13.
5
14.
5
15.
5
16.
5
17.
5
18.
5
19.
5
Mean Annual Temperature Classes
Mea
n Spe
cies
Den
sity
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Number of plots in each temperature class
0
200
400
600
800
1000
1200
1400
1600
1800
2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5
Temperature class (midpoint)
Num
ber
of p
lots
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Species Density and Mean Annual Temperature by Lithology
0
2
4
6
8
10
12
14
16
2.5 4.5 6.5 8.5 10.5 12.5 14.5 16.5 18.5 20.5
Mean Annual Temperature classes
Sp
ecie
s d
ensi
ty Volcanics
Hard Seds
Soft Seds
Granites
Other liths
Quat Seds
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Species Density and Mean Annual Temperature by Topographic position on Soft Sediments
0
2
4
6
8
10
12
0 5 10 15 20
Mean annual Temperature
Spe
cies
Den
sity ridge
slope
lowslope
gully
flat
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Questions•What is a suitable statistical method for species/environment modelling
•What environmental variables predict species density?
•What is their relative importance?
•Does their importance vary with mean annual temperature?
•What does this say about models of species density determinants?
•What are the Database requirements for this type of analysis?
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Some Suggested Answers
• Statistical modelling using Generalized Additive Modelling (GAM)
• Predictors: use both climatic and local variables ( 7 variables used)
• Importance: GAM gives relative measure
• Hypothesis: Behaviour of tree species density differs above and below 12ºC :- split data.
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Mean annual temperature
Mean annual rainfall
slope
Mean annual temperature
Mean annual rainfall
slope
>=12 degrees<12 degrees
Species density responses to environmental predictors for two models <12 and >12 degrees
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topography
aspect
>=12 degrees<12 degrees
Species density responses to environmental predictors for two models <12 and >12 degrees
topography
aspect
4=gully1=ridge
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lithology
relative heat load
>=12 degrees<12 degrees
Species density responses to environmental predictors for two models <12 and >12 degrees
relative heat load and lithology are not
included in this model
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>=12 degrees model <12 degrees model
Relative contribution of environmental predictors
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Purposeand
Product
Statistical methods
model
Ecological theory model
DataMeasurement
model
RelationalDatabase
GeographicInformation
System(GIS)
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Application to conservation evaluation
• Problem of aggregating data into classes for inclusion in a data base
• How many soil types should be recognised?
• What are the implications for predicting species distribution?
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Predicting Spatial Distribution of Acacia pendula
• Acacia pendula occurs on floodplain soils under low rainfall conditions (<600mm mean annual rainfall) in the Central Lachlan region of New South Wales, Australia.
• GAM models of 135 tree and shrub species including A. pendula were used to predict potential vegetation on cleared areas in the region.
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Condobolin
Tullamore
Parkes
Forbes
GrenfellCowra
147 º 148 º 150 º-32.5 º
-33 º
-33.5 º
-34 º
Selected study area
The central Lachlan region
Study area
1:100,000 mapsheet boundary
NSW
...
..
.
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Relational Database
Geographical InformationSystems (GIS) data
DigitalElevation
Model (DEM)
Data Collectionand Management
SurveyClassificationand Mapping
Products
Soil landscapedata frommanuals
EnvironmentalStratification
Survey
DigitalTerrain
Models (DTM)
Climaticattributes
Soil landscapes
Multivariatepattern analysis
Statisticalmodelling of
individual species
SpeciesPrediction
SpeciesPrediction
SpeciesPrediction
SpeciesPrediction
SpeciesPredictions
Spatial allocation tovegetation communities
PredictedVegetation
Vegetationplot dataPlant species
data
Plot location &environmental
data
Plotvegetation
data
An integrated approach to vegetation mapping
Drainage
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Individual species predictionsMean
Temperature
Geology
TopographicPosition
ArcViewGrasp script
Great Soil Group
Soil Depth
Soil pH
Soil Fertility
TemperatureSeasonality
RainfallSeasonality
Annual MeanRainfall
S-PlusGrasp
Species LookupTables
Species LookupTables
Species LookupTables
Species LookupTables
Species LookupTables
PlotData
SpeciesModels
SpeciesPrediction
SpeciesPrediction
SpeciesPrediction
SpeciesPrediction
SpeciesPredictions
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Spatial Prediction of Acacia pendula using original Great Soil Groups
Masked mean annual rainfall > 568mm
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Spatial Prediction of Acacia pendula using reaggregated Great Soil Groups
Masked mean annual rainfall >568mm
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Spatial Prediction of Acacia pendula
Difference between model predictions
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Conclusions
• Small changes in attribute classification can have a marked impact on outcomes
• Attributes in a database should be kept at as disaggregated a level as possible
• How cost-effective are databases where numerous attributes are kept which may not be used?
• Is this best done with “in-house” or commercial software
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Parkes
Forbes
Cowra
Grenfell
Condobolin
Location map of central Lachlan region
Predicted vegetation map for the central Lachlan region
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Parkes
Forbes
Cowra
Grenfell
Condobolin
Location map of central Lachlan region
Current remnant distribution of predicted vegetation communities
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Remaining area for different communities(based on M305 mapping of woody vegetation)
Red < 10 % remaining Green > 30 % remaining
Alliance Community Potential woodedarea (km2)
Area remaining(%)
1 E. melliodora / E. microcarpa 1552 3 2 E. melliodora 22 8 3 E. camaldulensis / E. melliodora 260 10
Eucalyptus melliodora
4 E. albens / E. melliodora 262 4 6 E. goniocalyx / E. blakelyi / E. melliodora 755 92 Eucalyptus melliodora /
E. blakelyi 7 E. bridgesian / E. blakelyi / E. melliodora 172 88 E. microcarpa / Callitris glaucophylla 547 2110 Allocasuarina luehmanii / E. microcarpa 59 3
Eucalyptus microcarpa
13 E. microcarpa / Casuarina cristata 277 2 Callitris glaucophylla 15 Callitris glaucophylla / E. albens 67 2 Eucalyptus populnea 18 E. populnea / Callitris glaucophylla 5202 7
23 Callitris endlicheri / E. sideroxylon 1557 20 Callitris endlicheri24 E. dealbata/C. endlicheri/A. doratoxylon 92 25
Eucalyptus blakelyi / E. macrorhyncha
28 E. blakelyi / Callitris endlicheri 381 5
32 Callitris endlicheri / E. macrorhyncha 393 5733 Calytrix tetragona / C. endlicheri / E. macrorhyncha
1232 49 Callitris endlicheri / E. macrorhyncha
34 C.endlicheri / Baeckea cunninghamiana / E. sideroxylon
1063 26
36 E. macrorhyncha / E. goniocalyx 76 48 E. macrorhyncha37 E. polyanthemos / E. macrorhyncha / E. albens
75 13
43 E. viminalis / Acacia melanoxylon 107 23 E. pauciflora/E. viminalis44 E. pauciflora / Acacia dealbata 35 73
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Purposeand
Product
Statistical methods
model
Ecological theory model
DataMeasurement
model
RelationalDatabase
GeographicInformation
System(GIS)
Final
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Vegetation plots in “good” condition
(Good condition is defined as greater than 50% native plant cover in the lower vegetation layer)
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Community Arearemaining
(%)
Number ofplots
surveyed
Proportion > 50 %
native cover
Area with“modest”
condition (%) 1 E. melliodora / E. microcarpa 3 30 0.17 0.5 2 E. melliodora 8 39 0.03 0.2 3 E. camaldulensis / E. melliodora 10 12 0.08 0.8 4 E. albens / E. melliodora 4 57 0.05 0.2 6 E. goniocalyx / E. blakelyi / E. melliodora 9 47 0.08 0.7 7 E. bridgesian / E. blakelyi / E. melliodora 8 31 0.03 0.2 8 E. microcarpa / Callitris glaucophylla 21 63 0.38 8.010 Allocasuarina luehmanii / E. microcarpa 3 18 0.39 1.218 E. populnea / Callitris glaucophylla 7 105 0.31 2.223 Callitris endlicheri / E. sideroxylon 20 30 0.30 6.024 E. dealbata/C. endlicheri/A. doratoxylon 25 14 0.86 12.028 E. blakelyi / Callitris endlicheri 5 20 0.15 0.834 C.endlicheri / Baeckea cunninghamiana / E. sideroxylon
26 12 0.75 19.5
36 E. macrorhyncha / E. goniocalyx 48 18 0.06 2.948 E. sideroxylon / E. dwyeri 34 23 0.30 10.249 E. sideroxylon / E. microcarpa 13 19 0.47 6.162 E. dwyeri /Callitris endlicheri / A. doratoxylon
68 38 0.42 28.6
71 E. camaldulensis 10 82 0.16 1.675 E. albens / E. microcarpa 6 103 0.01 0.6
Area and condition estimates for communities
Red < 10 % in “modest” condition
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COMMUNITY AS AN AREAL CONCEPT
RECOGNITION OF COMMUNITIES DEPENDS ON THE FREQUENCY OF ENVIRONMENTAL COMBINATIONS IN THE LANDSCAPE
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Topographic distribution of “communities” as indicated in previous slide
Altered topographic distribution of “communities” with the lowest bench at 170m and the highest bench at 430m
Frequency of species co-occurrences as a function of landscape