Electronic supplementary material 1
Predicting canopy cover change in Tasmanian eucalypt forests using dynamically
downscaled regional climate projections.
GRANT J. WILLIAMSON1*, LYNDA D. PRIOR1, MICHAEL R. GROSE2,3, REBECCA M.
B. HARRIS2 AND DAVID M.J.S. BOWMAN1
1
School of Plant Science, University of Tasmania, Private Bag 55, Hobart, Tasmania 7001
(grant.williamson@utas.edu.au)
2
Antarctic Climate and Ecosystems Cooperative Research Centre (ACE CRC), Private Bag
80 Hobart TAS 7000 Australia
3. CSIRO Marine and Atmospheric Research, 107 - 121 Station Street, Aspendale VIC 3195
*
Corresponding author
Appendix 1
Figure S1. The distribution of a range of Tasmanian climate variables (black), in comparison to
the distribution across the rest of south-eastern Australia (white).
Appendix 2. Preliminary modelling used to identify the best-supported variables for the
Random Forest model.
Preliminary modelling based on multi-model inference, generalized linear modelling and AIC
model selection (Burnham and Anderson 2002) was used to select a limited, biologicallyrelevant suite of explanatory variables to explain canopy cover of eucalypt forest, including
annual averages and the average July minimum (winter) and average January (summer)
maximum temperatures to capture seasonality. Preliminary analyses show that these months
are the modal for minimum and maximum temperatures respectively, for both current and
projected future climates. Additional variables to capture seasonal shifts in temperature and
precipitation were based on the “bioclim” variable suite. We included Bio18 (precipitation of
warmest quarter, intended to capture any shift towards or away from summer rainfall), Bio15
(precipitation seasonality), and Bio4 (temperature seasonality). The first suite of models
aimed to identify the best suite of climate variables to explain canopy cover percentage using
AIC model selection; the second identified the best topographic variable in combination with
the best climatic variables; and the third the best water balance variables. Finally, a suite of
models was constructed combining variables from the best climatic, topographic and water
balance models, and the best combined model was selected. Generalized linear modelling was
performed in R 2.15.1 (R Development Core Team 2012), with a Gaussian distribution and an
identity link function.
This showed the best-supported set of climatic variables to explain Eucalyptus cover
in south-eastern Australia was Mean Annual Precipitation + Mean Maximum January
Temperature. There was stronger statistical support for adding the topographic variable Slope
than Elevation to the climatic model, and the best-supported water balance variable was
Annual Water Balance (Mean Annual Precipitation – Annual Pan Evaporation) (Table S1).
The best overall model was the one that combined all four explanatory variables (Table S1).
Table S1 Best generalized linear models to explain canopy cover for each model suite, and
corresponding AIC values and percent deviance explained. Lowest AIC values indicate the
greatest statistical support.
Model Type
Model Variables
AIC
Deviance
Explained (%)
Climate
Rain_Yr + MaxT_Jan
83548.4
67.8
Climate +
Rain_Yr + MaxT_Jan + Slope
82996.7
69.3
Water Balance
WB_Yr
82971.5
69.9
Combined
Rain_YR + MaxT_Jan + Slope
82588.9
71.0
Topographic
+ WB_Yr
Appendix 3. Model evaluation
Fig. S2. Actual versus predicted canopy cover (%), based on the Random Forest model (R2 =
0.806, n = 1,041,030; P < 0.0001).
Fig. S3. Maps showing actual (satellite) tree canopy cover, and predicted cover based
on the Random Forest model. There was good spatial agreement between the Random Forest
model predictions and satellite measurements of existing canopy cover for Tasmanian
eucalypt forest, although with some over-prediction around the boundary of the agricultural
Midlands region
Appendix 4
Monthly mean maximum and minimum temperatures were used in constructing the eucalypt
cover change models, rather than 3-month seasonal climate variables. However, monthly
values were highly correlated with the equivalent seasonal mean, as demonstrated in figures
S3 and S4.
.
Figure S4. Correlation between July minimum temperature and winter minimum temperature.
Figure S5. Correlation between January maximum temperature and summer maximum
temperature.
Appendix 5
Fig S6. Interim Biogeographic Regionalisation for Australia bioregions for Tasmania.