Transcript PowerPoint - Carolina Vegetation Survey

Interfacing Vegetation Databases
with ecological theory and
practical analysis.
Mike Austin, Margaret Cawsey and
Andre Zerger
CSIRO Sustainable Ecosystems
Canberra Australia
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
Purpose
and
Product
Ecological
theory model
Relational
Database
Data
Measurement
model
Statistical
methods
model
Geographic
Information
System
(GIS)
Topics
• Interface between vegetation databases
theory and analysis
• Interface between data and practical
applications for conservation evaluation
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
Application to Theory
• Pattern of Species Density in relation to
climate.
Plot Tree Species Density in response to Temperature
Plot Species Density
30
25
20
15
10
5
0
0
2
4
6
8
10
12
Annual Mean Temperature
14
16
18
20
Mean Species Density in response to Mean Annual
Mean Species Density
Temperature in one degree classes
8
7
6
5
4
3
2
1
0
2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10. 11.5 12. 13. 14. 15. 16. 17. 18. 19.
5
5
5
5
5
Mean Annual Temperature Classes
5
5
5
5
Number of plots in each temperature class
1800
Number of plots
1600
1400
1200
1000
800
600
400
200
0
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)
Species Density and Mean Annual Temperature by
Lithology
16
Species density
14
12
Volcanics
Hard Seds
Soft Seds
Granites
Other liths
Quat Seds
10
8
6
4
2
0
2.5
4.5
6.5
8.5 10.5 12.5 14.5 16.5 18.5 20.5
Mean Annual Temperature classes
Species Density and Mean Annual Temperature by
Topographic position on Soft Sediments
Species Density
12
10
ridge
slope
lowslope
gully
flat
8
6
4
2
0
0
5
10
15
Mean annual Temperature
20
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?
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.
Species density responses to environmental
predictors for two models <12 and >12 degrees
<12 degrees
Mean annual temperature
Mean annual rainfall
slope
>=12 degrees
Mean annual temperature
Mean annual rainfall
slope
Species density responses to environmental
predictors for two models <12 and >12 degrees
<12 degrees
topography
aspect
>=12 degrees
1=ridge
4=gully
topography
aspect
Species density responses to environmental
predictors for two models <12 and >12 degrees
<12 degrees
relative heat load and
lithology are not
included in this model
>=12 degrees
relative heat load
lithology
Relative contribution of environmental
predictors
<12 degrees model
>=12 degrees model
Purpose
and
Product
Ecological
theory model
Relational
Database
Data
Measurement
model
Statistical
methods
model
Geographic
Information
System
(GIS)
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?
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.
The central Lachlan region
147 º
-32.5 º
.
148 º
150 º
Tullamore
-33 º
-33.5 º
.
NSW
Condobolin
.
.
Parkes
Forbes
.
Grenfell
-34 º
.
Selected study area
Cowra
Study area
1:100,000 mapsheet
boundary
An integrated approach to vegetation mapping
Data Collection
and Management
Classification
and Mapping
Survey
Multivariate
pattern analysis
Relational Database
Plot location &
environmental
data
Soil landscape
data from
manuals
Plot
vegetation
data
Plant species
data
Vegetation
plot data
Survey
Geographical Information
Systems (GIS) data
Digital
Elevation
Model (DEM)
Soil landscapes
Drainage
Climatic
attributes
Digital
Terrain
Models (DTM)
Products
Statistical
modelling of
individual species
Species
Species
Species
Species
Prediction
Species
Prediction
Prediction
Prediction
Predictions
Spatial allocation to
vegetation communities
Environmental
Stratification
Predicted
Vegetation
Individual species predictions
Mean
Temperature
Plot
Data
Temperature
Seasonality
Annual Mean
Rainfall
Species
Models
S-Plus
Grasp
Rainfall
Seasonality
Topographic
Position
ArcView
Grasp script
Species
Lookup
Species
Lookup
Species
Lookup
Species
Lookup
Tables
Species
Lookup
Tables
Tables
Tables
Tables
Geology
Great Soil Group
Soil Depth
Soil pH
Soil Fertility
Species
Species
Species
Species
Species
Prediction
Prediction
Prediction
Prediction
Predictions
Spatial Prediction of Acacia pendula using
original Great Soil Groups
Masked mean annual
rainfall > 568mm
Spatial Prediction of Acacia pendula using
reaggregated Great Soil Groups
Masked mean annual
rainfall >568mm
Spatial Prediction of Acacia pendula
Difference between model predictions
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
Predicted vegetation map for the central Lachlan region
Location map of central Lachlan region
Condobolin
Parkes
Forbes
Grenfell
Cowra
Current remnant distribution of predicted vegetation communities
Location map of central Lachlan region
Condobolin
Parkes
Forbes
Grenfell
Cowra
Remaining area for different communities
(based on M305 mapping of woody vegetation)
Alliance
Community
Eucalyptus melliodora
1 E. melliodora / E. microcarpa
2 E. melliodora
3 E. camaldulensis / E. melliodora
4 E. albens / E. melliodora
2 Eucalyptus melliodora / 6 E. goniocalyx / E. blakelyi / E. melliodora
E. blakelyi
7 E. bridgesian / E. blakelyi / E. melliodora
Eucalyptus microcarpa 8 E. microcarpa / Callitris glaucophylla
10 Allocasuarina luehmanii / E. microcarpa
13 E. microcarpa / Casuarina cristata
Callitris glaucophylla
15 Callitris glaucophylla / E. albens
Eucalyptus populnea
18 E. populnea / Callitris glaucophylla
Callitris endlicheri
23 Callitris endlicheri / E. sideroxylon
24 E. dealbata/C. endlicheri/A. doratoxylon
Eucalyptus blakelyi /
28 E. blakelyi / Callitris endlicheri
E. macrorhyncha
Callitris endlicheri /
32 Callitris endlicheri / E. macrorhyncha
E. macrorhyncha
33 Calytrix tetragona / C. endlicheri /
E. macrorhyncha
34 C.endlicheri / Baeckea cunninghamiana /
E. sideroxylon
E. macrorhyncha
36 E. macrorhyncha / E. goniocalyx
37 E. polyanthemos / E. macrorhyncha /
E. albens
E. pauciflora/E. viminalis 43 E. viminalis / Acacia melanoxylon
44 E. pauciflora / Acacia dealbata
Red < 10 % remaining
Potential wooded Area remaining
area (km2)
(%)
1552
3
22
8
260
10
262
4
755
9
172
8
547
21
59
3
277
2
67
2
5202
7
1557
20
92
25
381
5
393
1232
57
49
1063
26
76
75
48
13
107
35
23
73
Green > 30 % remaining
Final
Purpose
and
Product
Ecological
theory model
Relational
Database
Data
Measurement
model
Statistical
methods
model
Geographic
Information
System
(GIS)
Vegetation plots in “good” condition
(Good condition is defined as greater than 50% native plant cover in the lower vegetation layer)
Area and condition estimates for communities
Community
1
2
3
4
6
7
8
10
18
23
24
28
34
36
48
49
62
71
75
E. melliodora / E. microcarpa
E. melliodora
E. camaldulensis / E. melliodora
E. albens / E. melliodora
E. goniocalyx / E. blakelyi / E. melliodora
E. bridgesian / E. blakelyi / E. melliodora
E. microcarpa / Callitris glaucophylla
Allocasuarina luehmanii / E. microcarpa
E. populnea / Callitris glaucophylla
Callitris endlicheri / E. sideroxylon
E. dealbata/C. endlicheri/A. doratoxylon
E. blakelyi / Callitris endlicheri
C.endlicheri / Baeckea cunninghamiana /
E. sideroxylon
E. macrorhyncha / E. goniocalyx
E. sideroxylon / E. dwyeri
E. sideroxylon / E. microcarpa
E. dwyeri /Callitris endlicheri /
A. doratoxylon
E. camaldulensis
E. albens / E. microcarpa
Area
remaining
(%)
3
8
10
4
9
8
21
3
7
20
25
5
26
Number of
plots
surveyed
30
39
12
57
47
31
63
18
105
30
14
20
12
Proportion
> 50 %
native cover
0.17
0.03
0.08
0.05
0.08
0.03
0.38
0.39
0.31
0.30
0.86
0.15
0.75
Area with
“modest”
condition (%)
0.5
0.2
0.8
0.2
0.7
0.2
8.0
1.2
2.2
6.0
12.0
0.8
19.5
48
34
13
68
18
23
19
38
0.06
0.30
0.47
0.42
2.9
10.2
6.1
28.6
10
6
82
103
0.16
0.01
1.6
0.6
Red < 10 % in “modest” condition
COMMUNITY AS AN AREAL CONCEPT
RECOGNITION OF COMMUNITIES DEPENDS ON THE FREQUENCY OF
ENVIRONMENTAL COMBINATIONS IN THE LANDSCAPE
Frequency of species co-occurrences as a
function of landscape
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