Global Biogeochemical Cycles
Supporting Information for
A comparison of plot-based, satellite and Earth system model estimates of tropical
forest net primary production
Cory C. Cleveland,1* Philip Taylor,2 K. Dana Chadwick,3 Kyla Dahlin, 4 Christopher E.
Doughty,5 Yadvinder Malhi,5 W. Kolby Smith,6 Benjamin W. Sullivan,7 William R. Wieder,8
and Alan R. Townsend9
1
Department of Ecosystem and Conservation Sciences, University of Montana, Missoula, MT, USA,
Institute of Arctic and Alpine Research and Department of Ecology and Evolutionary Biology, University of
Colorado, Boulder, CO 80309 USA and Nicholas School of the Environment, Duke Universtity, Durham, NC,
USA, 3Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA, 4Michigan State
University Department of Geography, East Lansing, MI USA, 5Environmental Change Institute, School of
Geography and the Environment, University of Oxford University, Oxford, UK, 6Department of Ecosystem
and Conservation Sciences, University of Montana, Missoula, MT, USA, 7Department of Natural Resources
and Environmental Sciences, University of Nevada, Reno, NV, USA, 8Climate and Global Dynamics Division,
National Center for Atmospheric Research, Boulder, CO USA, 9Nicholas School of the Environment, Duke
University, Durham, NC, USA
2
Contents of this file
Figure S1
Figure S2
Table S1
Table S2
Introduction
This supporting information contains two figures and two tables that are referenced and
described in the main text.
Figure S1. Field measurements of aboveground net primary production using plot-based
inventory techniques as a function of mean annual precipitation. We used a nonlinear
model optimized by minimizing root mean square error, which is a less biased approach
to predicting ANPP with rainfall-based extrapolation than maximizing the correlation
coefficient (i.e., r2). The model (ANPP = A* MAPB/exp(C * MAP) has been applied in prior
empirical modeling studies, and was optimized using least-squares, where A = 0.559991,
B = 0.416652, and C = 0.0000308789 [Del Grosso et al. 2008].
Figure S2. Amazonian ecoregions sampled from MODIS and CLM results 2009-2010 and
compared with RAINFOR-GEM observations from the Southern (Hacienda Kenia in
Guarayos Province, Santa Cruz, Bolivia), Western (Tambopata-Candamo Reserve in the
Madre de Dios region of Peru), and Eastern Amazon (Caxiuanã National Forest Reserve).
Color bars show the percentage of each gridcell covered by evergreen tropical forests in
CLM that are <60% forest cover.
Table S1. Spatial (2000-2010 average) correlation coefficients (Pearson’s R) between
drivers of MODIS- and CLM-based NPP. Bold text indicates statistical significance at P <
0.05.
Method
Variable
Correlation
MODIS
Fraction of PAR absorbed
Photosynthetically active radiation (W m-2)
Leaf area index
CLM
0.26
- 0.24
0.22
Minimum temperature (°C)
- 0.66
Vapor pressure deficit (kPa)
0.26
Rain (mm d-1)
0.38
Water stress
0.57
Evapotranspiration (mm d-1)
0.06
Specific humidity
Soil moisture (kg m-2)
- 0.23
0.19
Atmospheric temperature (°K)
- 0.45
Vegetation temperature (°K)
- 0.45
Incoming radiation (W m-2)
0.007
Incoming – reflected radiation (W m-2)
0.44
N limitation of GPP
0.65
Table S2. Correlations between climate and energy variables and 2009-2010 fieldMODIS- and CLM-based NPP estimates. Field data are from the RAINFOR GEM plots.
TMP = average monthly temperature; PCP = precipitation (mm mo-1); RAD = radiation
(W m-2); VPD = vapor pressure deficit (kPa); PAR = photosynthetically active radiation (W
m-2); PAR = photosynthetically active radiation (W m-2); Tmin = average monthly
minimum temperature (°C); fPAR = fraction of PAR; TBOT = temperature at the bottom
of the atmosphere (°K); Rain = precipitation (mm d-1).
Method
Field
MODIS
CLM
Variable
Region of Amazon
Western
Eastern
Southern
TMP
0.66***
-0.31
0.39*
PCP
0.31
0.64***
0.29
RAD
0.41*
-0.53**
0.10
VPD
0.29
-0.32
0.02
Tmin
-0.29
-0.14
-0.24
fPAR
0.77***
0.52**
0.40*
PAR
0.94***
0.78***
0.23
VPD
0.82***
0.35*
-0.30
TBOT
-0.19
-0.42*
0.13
Rain
0.19
0.55**
0.85***
PAR
0.33
-0.45*
-0.17
*P < 0.1, **P < 0.01, ***P < 0.001.