Regional modelling of vegetation distributions in … · Regional modelling of vegetation...
Transcript of Regional modelling of vegetation distributions in … · Regional modelling of vegetation...
Regional modelling of vegetation distributions in Australia Regional modelling of vegetation distributions in Australia
David HilbertCSIRO Sustainable EcosystemsTopical Forest Research CentreAtherton, Queensland
CSIRO Veg modelling Canberra 07
Prerequisites to success
• A “proper” vegetation classification for the objectives• An appropriate modelling method for the objectives• Careful data selection for training/fitting including an
appropriate range of environments and veg. types• Careful verification and awareness of limitations• Creative use of the model incorporating all ecological
knowledge
‘It s better to have the right combination of ecological expertise, local knowledge and statistical skills in the team than to be using the current “best method”’. Austin et al. 1995
CSIRO Veg modelling Canberra 07
Empirical modelling method
spatial distributions of vegetation classes, functional types or species
spatial distributions of environmental variables
frequency distributions of vegetation in environmental space
100
50
Probability (%)
Mean T
empe
raturePrecipitation
modelling
generalized suitability of environments for each veg. type
vegetation map
CSIRO Veg modelling Canberra 07
Common modelling approaches
Generalised Linear Models (GLM), GeneralisedAdditive Models (GAM), Classification andRegression Trees (CART), and Artificial NeuralNetworks (ANNs)
Uses –pre-clearing vegetationregional climate change impact assessmentpalaeoecology
CSIRO Veg modelling Canberra 07
2530 plots, 223 species
1. Vegetation classified into 75 “communities” 2. Statistical modelling – used GAM to predict
% cover of 135 species based on climate, topography, geology and soil variables
3. Predicted species distributions4. Used the predicted distributions and the
classification to map the vegetation communities
GAMs
Austin et al. 2000. Predicted Vegetation Cover in the Central Lachlan Region
Objective: pre-clearing vegetation map
CSIRO Veg modelling Canberra 07
Wet Tropics of NE Queensland Artificial Neural Network example
• Challenges – why an empirical (black box) approach?• Value of a structural vegetation classification• The Neural Net Classifier• Testing• Using all the information – various indices• Applications/further testing
CSIRO Veg modelling Canberra 07
The Wet Tropics Bioregion
AustraliaQueensland
c. 2 million hectares
Wet TropicsBioregion
CSIRO Veg modelling Canberra 07
Wet Tropics Bioregion The Wet Tropics Bioregion has:high relief (0-1600m)recent volcanismrich soilsvery high rainfallvery high biodiversity
CSIRO Veg modelling Canberra 07
Modelling difficulties in rainforests
• > 800 species of tree species in c. 80 families• Not all described and named• Limited and often biased biogeographic information• Very poor autecological knowledge even for the best
studied, economically important species• No useful plant functional type classification in
rainforests• Lack of predictive theoretical models• => Mechanistic approaches are impossible -
empirical approaches, focused on vegetation structure might be useful – used artificial neural network model (classifier)
CSIRO Veg modelling Canberra 07
“Proper” classifications of vegetation is essential
• Len Webb’s physiognomic/structural classification of RF, Specht’s classification for sclerophyll forests
• Physiognomic and structural characteristics of forests in the Wet Tropics are controlled by local environments (climate, topography, soils)
• Largely, these features are not determined by floristics
• => useful in recent past and near future (-18kyr to +50yr)
CSIRO Veg modelling Canberra 07
ANN Structure parametric and nonlinear
GIS
INPUTS
23 InputNodes
15 OutputNodes
Environmentalsuitability for
each of 15 forestclasses
0.9830.0000.0000.0000.0000.0030.0000.0060.0000.0030.0000.0000.0000.0000.000
HiddenLayer
(80 nodes)climategeologyterrain
Hilbert & van den Muysenberg 1999
CSIRO Veg modelling Canberra 07
Advantages on ANNs
• Advantages = Disadvantages
• Not analytical, no hypothesis testing, no assumptions, very good predictors with sufficient data
CSIRO Veg modelling Canberra 07
Data sampling is a key element Supervised Training
Training Set 75 000 pixels, 4.3% total foreststratified random areaproportional to
fA
This equalized error among vegetation types with orders of magnitude differences in area
CSIRO Veg modelling Canberra 07
75%at 1 hectareresolution
Acc
urac
y
Window Size (hectares)5004003002001000
0.74
0.76
0.78
0.80
0.82
0.84
Accuracy increases at
coarser scales
Modeled
Mapped
Accuracy
CSIRO Veg modelling Canberra 07
Apred = 10 028+ 0.913 AmappedR2 = 0.987
80060040020000
200
400
600
800
Mapped Area (103 ha)
Pred
icte
d A
rea
(103
ha)
Total areas of each forest class are predicted very well
CSIRO Veg modelling Canberra 07
MLW MVF
Mean annual rainfall (mm)
ANNoutput
2000 4000 6000 8000
1.0
0.8
0.6
0.4
0.2
0.0
ANN Responses
0
nonlinear, “well behaved”
SNSMVF
datarange
CSIRO Veg modelling Canberra 07
Additional, derived indices are useful
• Dissimilarity – index of stress• Probability of occurrence • Conversion sensitivity – considering climate change uncertainty
CSIRO Veg modelling Canberra 07
the index of dissimilarity is γ divided by π/2 to normalize the index to the range [0,1]
Index of Dissimilarity
ideal vector(1,0,0)
ANNoutput vector(.45,.60,.20)
γ
FT1
FT2
FT3
How “ideal” is the local environment for the forest type that is mapped there?
Hilbert & van den Muysenberg 199
CSIRO Veg modelling Canberra 07
Spatial estimates of the probability of finding a forest class
0
10
20
30
40
50
60
70
80
90
100
0.00 0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95 1.00
Certainty Index Ranges
Prob
abili
ty o
f Tal
l Ope
n Fo
rest
(%)P(TOF) = 9.12+77.431*C(PFANN) R2 = 0.98P(TOF) = 4.87+80.735*C(FANN) R2 = 0.95
For a range of certainty values - number of pixels that are Tall Open Forest/total number of pixels
Certainty Index = ANN output TOF node x output TOF nodesum all output nodes
CSIRO Veg modelling Canberra 07
Applications
• Pre-clearing forest distributions• Palaeo-distributions• Landscape sensitivity – refugia• Future climate change• Linking with Cellular Automata
CSIRO Veg modelling Canberra 07
Pre-clearing
Cairns
Atherton
Herberton
Daintree
Mossman
Innisfail88% accuracy whentested with independent mapping of forest fragments
Hilbert (in press) 2007
CSIRO Veg modelling Canberra 07
Potential Today18000 BP
colddry
7000 BP
coolwet
5000 BP
warmwet
Past distributions of rainforest
Hilbert et al. 2007
CSIRO Veg modelling Canberra 07
SNSMHighlandRainforests
CNVFUplandRainforests
MVFLowlandRainforests
Mean stability of 9x9 window
Landscape sensitivity to climate – refugia
Inter-glacialrefugia
Glacial refugia
Hilbert et al. 2007
CSIRO Veg modelling Canberra 07
Future climate change
Comparing potential todaywith +1 oC, -10% rainfall
HighlandRainforests
Potential loss of the rich, endemic flora and fauna in the highlands
Hilbert et al. 2001
CSIRO Veg modelling Canberra 07
Lowland rainforest environments (MVF) will increase even if rainfall declines somewhat
Cha
nge
in A
rea
(%)
-80
-60
-40
-20
0
20
40
60
80
-15 -10 -5 0 5 10 15 20 25
Precipitation Change (%)
+1oC
Hilbert et al. 2001
CSIRO Veg modelling Canberra 07
Highland rainforest environments will decline greatly with global warming, irrespective of changes in rainfall
Cha
nge
in A
rea
(%)
-80-60-40
-200
20406080
-15 -10 -5 0 5 10 15 20 25
Precipitation Change (%)
+1oC warming
Potentially devastating to high altitude fauna
Hilbert et al. 2004Williams & Hilbert 2006
Hilbert et al. 2001
CSIRO Veg modelling Canberra 07
Highland rainforests will be highly stressed by global warming, irrespective of changes in rainfall
Dis
sim
ilarit
y (in
dex
of s
tres
s)
0
0.2
0.4
0.6
0.8
-15 -10 -5 0 5 10 15 20 25
Today
Precipitation Change (%)
+1oC warming
Hilbert et al. 2001
CSIRO Veg modelling Canberra 07
Spatial sensitivity to climate change Windsor Tableland
MVFMVFPSDMVFCNVFNVFSNSMDMVTVFAETOFTWMOFWMLWCCMRPAVFNSEF
0 1 2 3 4 5
+1oC warming5 rainfall scenarios
SNSM
Counts of change
boundaries &certain forest typesare most sensitive
Hilbert et al. 2001
CSIRO Veg modelling Canberra 07
Moving forward
Can static, empirical approaches contribute to dynamics?
CSIRO Veg modelling Canberra 07
Linking with Cellular AutomataCombining the output of the ANN with
spatial and ecological Information
Future environ- mental conditions
Future environmental suitability for individual
forest types
ANNModel
EcologicalConstraints
Neighborhood information
(updated during model iterations) Future
Vegetation
Ostendorf et al. 2001
CSIRO Veg modelling Canberra 07
Differences between the ANN and CA/ANN model at equilibrium for +1 oC and - 10% precipitation scenario
Spatial Constraints OnlySpatial Constraints Only Spatial and Ecological Constraints
Spatial and Ecological Constraints
CSIRO Veg modelling Canberra 07
Conclusions
• Regional modelling of vegetation distributions is usually empirical – often by necessity (scale issues and limited basic understanding).
• The specific modelling method is less important than the range of skills and data that are available.
• Careful empirical modelling, coupled with local ecological knowledge, good biogeographic data and creative analyses can be very informative, both scientifically and for management and policy development.
• “Black box” approaches have great value when coupled with ecological insight and are essential in the many circumstances where mechanistic models are not yet capable of addressing very important regional issues about climate change.
This research has had very significant influence on regional, state and national policy
Questions or Comments ?
David W. HilbertCSIRO Sustainable EcosystemsTropical Forest Research CentreAtherton, Queensland
Phone: +61 7 4091 8835Email: [email protected]
Contact Us Phone: 1300 363 400 or +61 3 9545 2176
Email: [email protected] Web: www.csiro.au