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Supporting Material
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S1 - Experiment design
In this study we use a climate-biosphere model (Sections S2 and S3) to assess
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the deforestation impact on the Amazon bioclimatic equilibrium. We generated 20
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scenarios of deforestation that cover a wide range of possible land use scenarios for
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Amazon rainforest and Cerrado. The simulations have five ensembles for each
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deforestation scenario, and a duration of 50 years (equivalent to the period of 1951 to
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2000), totaling 5,250 years of simulation. The first 10 years are left for the model to
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approach an equilibrium state, specifically with respect to soil moisture, while the last
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40 years of the five ensembles are used to define the average climate and to perform the
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bioclimatic analysis according to Malhi et al. (2009). The atmospheric CO2
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concentration is fixed at 380 ppm and Sea Surface Temperature (SST) is the observed
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for the 1951-2000 period. We used five different simulation startups (initialization at
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17, 18, 19, 20 and 21 January 1951) to reduce dependence on initial conditions and
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obtain a more realistic representation of the climate for the period. Two sets of
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simulations were evaluated (Table S1). Group I simulations consider only the Amazon
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deforestation, i.e. Cerrado remains intact. The only difference among the Group I
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simulations is the total Amazon deforested area, ranging from 10% to 100%. Group II
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simulations consider the combined effect of Amazon and Cerrado deforestation. The
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only difference among the Group II simulations is the total Amazon deforested area,
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ranging from 10% to 100%, and the total Cerrado deforested area, ranging from 60%
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(value closest to the current deforested amount in Cerrado) to 100%. This experiment
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elucidates under what levels of deforestation Amazon rainforest bioclimate is
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compromised. It also clarifies whether the Cerrado deforestation has influence on the
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Amazon bioclimatic equilibrium or not.
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Table S1 - Fraction of deforested land for each deforestation scenario. The indices i and
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j of each deforestation scenario FiCj represent, respectively, the percentage of
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deforested Amazon and deforested Cerrado.
Group I
Group II
Deforestation
Scenario
Forest
deforested
area (%)
Cerrado
deforested Deforestation
area (%)
Scenario
Forest
deforested
area (%)
Cerrado
deforested
area (%)
F0C0 (Control)
0
0
-
-
-
F10C0
10
0
F10C60
10
60
F20C0
20
0
F20C60*
20
60
F30C0
30
0
F30C65
30
65
F40C0
40
0
F40C70
40
70
F50C0
50
0
F50C75
50
75
F60C0
60
0
F60C80
60
80
F70C0
70
0
F70C85
70
85
F80C0
80
0
F80C90
80
90
F90C0
90
0
F90C95
90
95
F100C0
100
0
F100C100
100
100
(*) Closest to current state of land-use
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S2 - Model description
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This is essentially the same climate-biosphere model evaluated by Senna et al.
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(2009) and used by Costa e Pires (2010) and Costa et al. (2007). We use the National
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Center for Atmospheric Research (NCAR) Community Climate Model version 3
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(CCM3, Kiehl et al., 1998) coupled with an updated version of the Integrated Biosphere
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Simulator (IBIS) of Foley et al. (1996), and we refer to this coupled model as CCM3-
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IBIS. CCM3 is an atmospheric general circulation model with spectral representation of
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the horizontal fields. In this study, to allow the longer runs and several required
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simulations, we choose to operate the model at a resolution of T42L18 (the spectral
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representation of the horizontal fields is truncated at the 42nd wave number using a
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triangular truncation; horizontal fields are converted to a grid 2.81° × 2.81° on average;
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18 levels in the vertical), with a 20-min time step. This resolution allows for a
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reasonable representation of the major climate features of the region, and most of the
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large and synoptic scale processes, although it is not sufficient to represent sub-synoptic
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or mesoscale phenomena. The global terrestrial biosphere model IBIS (version 2.6) is a
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comprehensive model of terrestrial biospheric processes, representing two vegetation
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layers (i.e. trees and short vegetation) and simulates land surface physics, canopy
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physiology, and plant phenology. Although IBIS also includes a dynamic vegetation
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component, in this study it is not used, and vegetation cover is fixed through the
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duration of the experiment. Land surface physics and canopy physiology are calculated
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with the time step used by the atmospheric model. The plant phenology algorithm has a
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daily time step. In these simulations, IBIS operates on the same T42 spatial grid as the
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CCM3 atmospheric model. We have updated the rainforest representation in IBIS with a
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new calibration against flux data from four different Amazonia flux tower sites (Senna
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et al. 2009), using data from the Large-Scale Biosphere Experiment in Amazonia
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(LBA).
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S3 - Validation of the control run
For effective model validation we use five precipitation estimates:
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(i) CMAP (CPC Merged Analysis of Precipitation - Xie and Arkin, (1997),
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(ii) CRU (Climatic Research Unit, University of East Anglia – New et al, 1999),
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(iii) TRMM (Tropical Rainfall Measuring Mission - Kummerow et al., 1998),
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(iv) Leemans & Cramer (1990),
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(v) and Legates &Willmott (1990).
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For total forest region (as defined in Figure 2-a), the datasets annual
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precipitation vary from 5.33 to 6.43 mm/day (Table S2) (dataset average 6.02 mm/day).
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The CCM3-IBIS average annual precipitation (6.18 mm/day) overestimates the dataset
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average by 2.6%. For the arc-of-deforestation (as defined in Figure 2-b), the
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precipitation estimates vary from 4.87 to 5.80 mm/day (dataset average of 5.26
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mm/day). The precipitation simulated by CCM3-IBIS (5.32 mm/day) overestimates the
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dataset average by 1.1%.
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The seasonality of precipitation is well represented by the model, but CCM3-
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IBIS is advanced in time by 1 month (Figure S1). This fact does not influence the
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results because MCWD is calculated for P <100 mm/month (according to Malhi et al.,
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2009 methodology), and not for fixed months. The seasonal amplitude is also accurately
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represented by CCM3-IBIS.
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Figure S2 illustrates the spatial distribution of mean annual precipitation for
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northern South America simulated by the CCM3-IBIS (Figure S2-a) and estimated by
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the same datasets used in the previous analysis (Figures S2 b - f). Figures S2 (g) - (k)
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show the areas where the CCM3-IBIS overestimates (positive values) or underestimates
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(negative values) precipitation according to CRU, CMAP, Leemans and Cramer and
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Legates and Willmott, respectively. In northeastern Amazon, CCM3-IBIS overestimates
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the precipitation by 2 mm.day-1 or more according to CRU, CMAP, Legates &Willmott
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and Leemans & Cramer, but not according to TRMM. The first four databases are
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derived from rainfall stations data, so the low density of gauges and the convective
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nature of precipitation in the region may lead to poor representation of the spatial
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patterns of precipitation. The TRMM product has a high spatial and temporal resolution,
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and should overcome this limitation. CCM3-IBIS underestimates the rainfall over the
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Guyanas, Amapá, Marajó Island and the Peru-Ecuador border. The climate of the arc of
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deforestation region (represented by the polygon marked in Figure S2) is well
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represented by the model.
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Figure S3 illustrates the spatial distribution of mean dry season (June to
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September) precipitation for northern South America simulated by the CCM3-IBIS
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(Figure S3-a) and estimated by the same datasets used in the previous analysis (Figures
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S3 b - f). ). Figures S3 (g) - (k) show the areas where the CCM3-IBIS overestimates
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(positive values) or underestimates (negative values) precipitation according to the five
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datasets. CCM3-IBIS overestimates dry season precipitation over the northwestern
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Amazonia (Guyanas, northern Amazonas and northern Para states) according to all the
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datasets, by 2 mm/day or more, but the dry season climate of the arc of deforestation is
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well represented by the model.
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Table S2 - Average annual precipitation for the Amazon rainforest and the arc of
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deforestation according to five databases and the CCM3-IBIS.
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Average precipitation
(mm/day)
e(%)(*)
CMAP
5.33
+ 15.9
CRU
6.07
+ 1.9
TRMM
6.43
- 3.9
Legates and Willmot
6.21
- 0.5
Leemans and Cramer
6.06
+ 2.0
Dataset average
6.02
+ 2.6
CCM3-IBIS
6.18
-
CMAP
4.87
+ 9.3
CRU
4.91
+ 8.3
TRMM
5.80
- 8.3
Legates and Willmot
5.18
+ 2.7
Leemans and Cramer
5.56
- 4.3
Dataset average
5.26
+ 1.1
Precipitation dataset
Amazon forest (Figure 2a)
Arc-of-deforestation (Figure 2b)
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CCM3-IBIS
5.32
* e (%) is calculated using each dataset as reference (Control/dataset – 1).
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Figure S1 - Model validation against five datasets for (a) the rainforest region and (b)
the arc-of-deforestation.
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Figure S2 - Annual mean precipitation climatology for northern South America
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(mm/day) (a) simulated by CCM3-IBIS in the control run, and estimated by the
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following datasets: (b) CRU, (c) CMAP, (d) TRMM, (e) Leemans and Cramer and (f)
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Legates and Willmott. Differences between the control run and each dataset are also
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shown: (g) control-CRU, (h) control-CMAP, (i) control-TRMM, (j) control-
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Leemans&Cramer, (k) control-Legates&Willmott.
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Figure S3 – Dry season (June to Semptember) mean precipitation for northern South
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America (mm/day) (a) simulated by CCM3-IBIS in the control run, and estimated by
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the following datasets: (b) CRU, (c) CMAP, (d) TRMM, (e) Leemans and Cramer and
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(f) Legates and Willmott. Differences between the control run and each dataset are also
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shown: (g) control-CRU, (h) control-CMAP, (i) control-TRMM, (j) control-
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Leemans&Cramer, (k) control-Legates&Willmott.
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Section S4 - Uniform Bioclimatic Tendency Sub-region (UBTS) computational
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algorithm
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The low resolution of CCM3-IBIS could be insufficient to represent
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satisfactorily the climate of too small regions. Considering that, the calculations to
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check the possible sub-regional bioclimatic transition after progressive deforestation
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were not done pixel by pixel. We use a set of 8 to 10 contiguous pixels to define the
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‘sub-region unity’ to proceed with the calculations. Thus, the procedure considers the
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‘sub-region unity’ as a vector with 10 positions, and each position is filled by a pixel of
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region A (Figure 2), in such a way that the ‘sub-region unity’ is formed solely by
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contiguous pixels. The next step is to calculate annual mean precipitation (AP, mm/yr)
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and MCWD (mm) of the ‘sub-region unity’, for the control simulation and for all of the
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different deforestation levels (scenarios) we considered. The AP and MCWD calculated
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values are then compared to the limits represented in the bioclimatic diagrams (Figure 4,
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redrawn from Malhi et al., 2009) to check if there is any bioclimatic transition (from
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forest to savanna or from forest to seasonal forest), from the control simulation to any
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deforestation level. In case there is a transition, the contiguous pixels of the ‘sub-region
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unity’ and the transition type found are stored. The ‘sub-region unity’ is updated in the
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next step considering at least 1 different pixel from the previous step, so that a new set
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of pixels is evaluated. This procedure is repeated until the full sweep of region A
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(Figure 2). By the end of the entire procedure, each one of the 84 pixels that constitute
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Amazonia in CCM3-IBIS is swept and analyzed at least 10 times. We consider that a
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pixel crosses a tipping point only if it shows the same type of transition in at least 90%
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of all times it is accessed in the ‘sub-region unity’; these pixels are represented in Figure
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2-c.
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As a result of the identification done by this procedure, forest areas located in
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border regions show tendency to undergo transitions, either to savannization or to
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seasonalization, while forest portions at the core of the original forest area remain in the
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rainforest bioclimatic envelope (Figure 2-c). The bioclimatic tendencies are shown in
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the bioclimatic diagrams in Figure 4.
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