Climatic Change
DOI 10.1007/s10584-010-9942-2
Modeling soil respiration and variations in source
components using a multi-factor global climate
change experiment
Xiongwen Chen · Wilfred M. Post ·
Richard J. Norby · Aimée T. Classen
Received: 26 February 2008 / Accepted: 5 October 2010
© Springer Science+Business Media B.V. 2010
Abstract Soil respiration is an important component of the global carbon cycle and
is highly responsive to changes in soil temperature and moisture. Accurate prediction
of soil respiration and its changes under future climatic conditions requires a clear understanding of the processes involved. Most current empirical soil respiration models
incorporate just few of the underlying mechanisms that may influence its response.
In this study, a new partially process-based component model that separately treated
several source components of soil respiration was tested with data from a climate
change experiment that manipulated atmospheric [CO2 ], air temperature and soil
moisture. Results from this model were compared to results from other widely used
models with the parameters fitted using experimental data. Using the component
model, we were able to estimate the relative proportions of heterotrophic and
autotrophic respiration in total soil respiration for each of the different treatments.
The value of the Q10 parameters for temperature response component of all of the
models showed sensitivity to soil moisture. Estimated Q10 parameters were higher
for wet treatments and lower for dry treatments compared to the values estimated
using either the data from all treatments or from only the control treatments. Our
results suggest that process-based models provide a better understanding of soil
respiration dynamics under changing environmental conditions, but the extent and
contribution of different source components need to be included in mechanistic and
process-based soil respiration models at corresponding scales.
X. Chen (B)
Program of Forestry, Ecology & Wildlife, Alabama A & M University,
PO Box 1927, Normal, AL 35762, USA
e-mail: [email protected]
W. M. Post · R. J. Norby · A. T. Classen
Environmental Sciences Division, Oak Ridge National Laboratory,
Oak Ridge, TN 37831, USA
A. T. Classen
Department of Ecology and Evolutionary Biology, University of Tennessee,
Knoxville, TN 37996, USA
Climatic Change
Abbreviations
ACAT Ambient atmospheric CO2 concentration and ambient temperature
ACET Ambient atmospheric CO2 concentration and elevated temperature
ECAT Elevated atmospheric CO2 concentration and ambient temperature
ECET Elevated atmospheric CO2 concentration and elevated temperature
D
Dry split-plot
W
Wet split-plot
1 Introduction
Soil respiration is estimated to contribute around 75 × 1015 g C year−1 to the global
carbon (C) budget annually and is second only to oceans in the magnitude of the
gross CO2 flux to the atmosphere (Schlesinger and Andrews 2000). Because soil
respiration is a major C flux between the biosphere and the atmosphere (Raich
and Schlesinger 1992; Townsend et al. 1992; Davidson et al. 2006), small changes
in its rate may alter the annual C sink of terrestrial ecosystems. For example, if
soil CO2 efflux is greater than plant production, soil respiration will significantly
alter atmospheric CO2 concentrations (Cox et al. 2000). Soil respiration has recently
received considerable attention in the literature, because it is not only a factor that
influences net ecosystem C budgets, but also an important component of global
change (Ryan and Law 2005; Trumbore 2006). Researchers need to accurately model
soil respiration to understand changes in ecosystem C storage or changes in net
fluxes of C to the atmosphere under climatic change. Workshops and synthesis
work have indicated the need to develop and test models of soil respiration in close
collaboration with experimental results (Ryan and Law 2005; Hibbard et al. 2004,
2005), but this has rarely been done.
Bulk soil respiration can be partitioned into two component fluxes—heterotrophic
respiration (Rh ) resulting from the microbial breakdown of organic matter and autotrophic respiration (Ra ) from plant root production and root-associated organisms,
such as mycorrhizae. Both of these components are highly responsive to changes
in environmental conditions, such as (1) soil temperature (Lloyd and Taylor 1994;
Boone et al. 1998; Rustad et al. 2001), (2) soil water content (Davidson et al.
2000), (3) soil nutrient availability (Raich and Tufekcioglu 2000), and (4) plant
photosynthetic rates (Högberg et al. 2001). Elevated air temperature may increase
soil respiration rates and consequently alter the soil C sink or source strength under
climate change resulting from increasing atmospheric CO2 . However, the expected
increase in soil respiration with temperature is not always detected by measurements
because other factors may limit respiration (Davidson et al. 1998; Reichstein et al.
2003; Hibbard et al. 2005). The CENTURY model (Parton et al. 1987) and the
Rothamsted model (Jenkinson and Rayner 1977) both use temperature functions to
model the decomposition of soil organic matter (SOM). Some soil respiration models
also use a temperature function with a single value of Q10 (defined as the factor by
which the rate of a chemical reaction increases when temperature increases by 10◦ C)
for estimating soil respiration under different environment conditions. However, Q10
values for soil respiration often vary depending on the nominal soil temperature
range (Lomander et al. 1998; Holland et al. 2000; Xu and Qi 2001).
Climatic Change
Soil water may inhibit microbial decay of SOM at excessively high and low
water contents. Many experimental studies have shown that drying of soils can limit
heterotrophic respiration when water availability drops below a certain threshold
level (Orchard and Cook 1983; Skopp et al. 1990; Howard and Howard 1993). While
respiration by living roots and microbes within the rhizosphere can be affected by
low soil water content, the direct relationship between soil moisture and root and rhizosphere respiration can be decoupled due to water uptake by roots from deeper soil
layers, thereby maintaining root-rhizosphere functioning (hydraulic redistribution).
Plant root respiration is also dependent on the supply of carbohydrate from
photosynthesis to roots (Poorter et al. 1991; Millenaar et al. 2000). Up to 52% of
the carbohydrate fixed in photosynthesis may be used for root respiration daily (van
der Werf et al. 1988; Poorter et al. 1991; Lambers et al. 1996). Boone et al. (1998)
indicated that respiration from roots and rhizosphere produce a large portion of total
soil respiration in a mixed temperate forest, which could limit C sequestration by
increasing allocation of photosynthate to roots under an increased CO2 environment.
Fine roots contributed about 28% to the variability in maximum soil respiration
across different biomes (Hibbard et al. 2005). Respiration rates are often linearly
related to relative growth rates in roots of many plants (Lambers 1979). In some
studies, high CO2 around roots has been shown to inhibit or alter the total root
respiration rate (Burton et al. 1997) and maintenance respiration rate (Qi et al. 1994;
McDowell et al. 1999). The influence of CO2 enrichment on root respiration has
yielded variable results (Davey et al. 2004).
Currently, experimental partitioning of soil respiration has been attempted but
with varying results using different methods. The partitioning under experimental
treatments is further complicated by differential responses of the components to
environmental factors related to climate change (Kirschbaum 1995; Trumbore et al.
1996; Giardina and Ryan 2000), and strong covariation among factors (Davidson
et al. 1998). Thus, accurate partitioning of soil respiration remains to be developed.
The few manipulative field experiments that investigate how climate change factors
interact with one another to alter soil respiration (e.g., Wan et al. 2007) have not been
able to separate soil respiration into its components without significantly disrupting
the soil (Hanson et al. 2000).
Quantifying or modeling changes in soil respiration under different environmental
settings is critical for further exploring the mechanisms underlying change in soil
respiration due to climate change. Soil environments are complex, thus to date,
most studies rely on empirical models (e.g., Subke et al. 2006), which are based
on the strong correlations between temperature (some including soil moisture) and
soil respiration (Janssens and Pilegaard 2003). However, process-based models are
needed to advance our quantitative understanding of soil respiration by taking into
account additional factors, such as soil water content and root growth (e.g., Ryan and
Law 2005; Hibbard et al. 2005).
We developed and tested a component model that separates soil respiration into
an autotrophic component and a heterotrophic component using data collected
from a multifactor climate change experiment conducted in a constructed oldfield ecosystem. Old-fields are common along the east coast of the US and are
often made up of a combination of grasses, forbs, and N-fixing plants. The Oldfield Community Climate and Atmospheric Manipulation experiment (OCCAM)
manipulated atmospheric [CO2 ](ambient, ambient + 350 ppm), air temperature
Climatic Change
(ambient, ambient + 3.5◦ C) and soil moisture (wet, dry) in a randomized, complete
block, split plot design (see Garten et al. 2008). Data from this experiment provided
us with the opportunity to test the validity of our model as well as how it compared to
seven other models that are commonly used. Specifically, the aims of this study were
to (1) test our component model; (2) quantify the variations of soil respiration from
different components under climate change treatments; (3) test other empirical soil
respiration models under climate change treatments; and (4) to test the widespreadhypothesis that Q10 values are altered under different environmental conditions and
that temperature is the major contributing factor to variation in soil respiration under
climate change.
2 Materials and methods
2.1 Site description
The measurements were made at the OCCAM experiment on the Oak Ridge
National Environmental Research Park in Oak Ridge, Tennessee, USA (35◦ 54
12 N, 84◦ 20 22 W). OCCAM was designed to test the interactive effects of elevated
atmospheric [CO2 ], atmospheric warming, and soil water content on the functioning
of a constructed old-field ecosystem. The soil type at the study site is Captina silt
loam Typic Fragiudult (Soil Conservation Service 1967; Edwards and Norby 1999).
In this area, precipitation is generally distributed evenly over the year with an annual
mean of 1,322 mm. The mean July maximum temperature is 31.2◦ C, and the mean
January minimum temperature is −2.7◦ C.
2.2 Experimental design, treatment application and monitoring
Atmospheric CO2 concentration, air temperature, and soil moisture were controlled
through the use of open-top chambers (OTCs) surrounding 12 circular wholeplots (4 m diameter) arranged in a randomized block split-plot design. Split-plots
were assigned to either ‘wet’ or ‘dry’ soil water treatments. In August 2002, plots
were planted with seven plant species common to old-field communities in the
southeastern United States. These species are Plantago lanceolata L., Andropogon
virginicus L., Festuca pretense L. syn F. elatior L., Dactylis glomerata L., Trifolium
pretense L., Solidago canadensis, and Lespedeza cuneata (Dum. Cours.). Plots were
watered and weeded to ensure seedling establishment until treatment initiation in
May 2003 (Engel et al. 2009; Kardol et al. 2010a, b).
Mean air temperatures over the observation time period were 15.9 ± 0.1◦ C in
ambient-temperature chambers and 18.5 ± 0.3◦ C in elevated temperature chambers.
CO2 was introduced into the plenum to achieve a daytime CO2 concentration around
695.8 ± 10.0 ppm in elevated CO2 chambers compared to 395.6 ± 2.8 ppm in
ambient CO2 concentration chambers. Precipitation was excluded over each OTC,
but collected rainwater was used to irrigate the plots weekly with 2 or 25 mm of
water for ‘dry’ and ‘wet’ treatments, respectively (Dermody et al. 2007). Detailed
information about the experimental setup as well as plant and soil responses can be
found in Wan et al. (2007), Garten et al. (2008, 2009), Engel et al. (2009), Castro et al.
(2010), and Kardol et al. (2010a, b).
Climatic Change
2.3 Measurements of soil respiration, soil water content, roots, and total N content
For this study, we used monthly measurements of soil respiration, soil temperature
(0–15 cm), soil water content (0–15 cm), and total root length and root production
from March 2005 to April 2006. Nitrogen (N) concentrations of roots collected from
soil cores were measured once within each treatment, and these data were used in
models to set the possible range of root N concentrations in the experiment (see
Garten et al. 2008, for more details).
Soil respiration was measured monthly using a LI6200 infrared gas analyzer (LiCor Inc., Lincoln, Nebraska, USA) with attached chamber. Respiration from the
aboveground plants was excluded by removing living plants inside the collars once
a week and the dead plant material was removed from the collars (Wan et al.
2007). Thus, soil respiration measured here did not include aboveground respiration
from living plants and respiration from aboveground litter located on the soil
surface. Soil temperature (at 15 cm) was measured at the time as soil respiration
measurement. Soil volumetric water content at each plot was monitored using timedomain reflectometry (see Dermody et al. 2007, for more details).
Root production was measured using a minirhizotron tube (Bartz Technology
Corporation, Santa Barbara, CA) installed in each split plot. Digital images were
captured in the field by the I-CAP system (Bartz Technology Corp, Santa Barbara,
CA) and analyzed by RooTracker software (Duke University, Durham, NC). Length
and width of each root segment were measured and the incremental growth or
disappearance (mortality) recorded. Fine-root production (mm) for a time period
was calculated as the total length of new roots and segments of new growth on
existing roots for that date. The detailed information on how soil respiration and
root length were measured can be found in Wan et al. (2007). These data were used
in the component model.
2.4 Models
We used eight models to model soil respiration at the OCCAM site. Some are
partial process based models and others are simple empirical models. Each model
considers a different set of processes and factors. Because all of these models
utilized the same dataset collected from the climate change treatments at OCCAM,
it is possible to compare these models’ performance at the ambient conditions
as well as the manipulated conditions. In addition each of these models included
different environmental variables (e.g., temperature and soil water), which allow us
to compare their relative importance. The eight models compared in this paper are
outlined below.
(1) The Component Model
The component model is defined as soil respiration (Rs ) = Rs = a × Rh + Ra , where
Rh is heterotrophic soil respiration, Ra is autotrophic soil respiration, and a is a
coefficient. Rh was estimated using the model of Del Grosso et al. (2005). Ra =
b × rm + c × r g , where rm is root maintenance respiration and r g is root growth
respiration, b and c are coefficients for respiration from a unit (gram) root biomass.
rm = (0.058N + 0.622M) e0.098T , where N is the root nitrogen concentration (g kg−1 ),
M is the soil matric water potential (MPa), and T is the soil temperature (◦ C at 15 cm
Climatic Change
depth) (Burton et al. 1998; Pregitzer et al. 1998). Since relative soil water content was
measured, we estimated the soil matric water potential from the relative soil water
content using soil texture (Saxton et al. 1986). Soil texture across the experimental
site is silt loam and is composed of 20% sand and 15% clay. Fine root biomass
production and average root biomass per unit root length were estimated from field
root length measurements using the average value of 0.03338 g m−1 . The a values,
relative proportions of heterotrophic respiration (Rh ) and autotropic respiration
(Ra ), were estimated using optimization for each treatment and for all treatments
combined.
(2) The heterotrophic model (Del Grosso et al. 2005)
Del Grosso et al. (2005) considered heterotrophic soil respiration to be a function
of soil temperature (F(t)) and soil water content (F(w)). The heterotrophic model
is defined as Rh = d × M × F(t) × F(w), where Rh is the heterotrophic soil respiration, d is a coefficient and M is the maximal soil respiration provided by Del Grosso
et al. (2005) for different biomes, and F(t) and F(w) are temperature and water
limitation functions, respectively.
F(t) and F(w) are calculated as:
F (t) = 0.56 + (1.46 × (arctan (π × 0.0309) × (t − 15.7)) /π ) ,
and
F (w) = 5 × (0.287 + (arctan (π × 0.009 × (w − 17.47))) /π ) ,
t is temperature in ◦ C, and w is relative soil water content (%).
(3) The Linear Temperature Model (T)
The linear temperature model is defined as Rs = f × T, where T is temperature in
◦
C, and f is a coefficient.
(4) The Exponential Temperature Model (e.g., Nakane 1980; Silvola et al. 1985)
The exponential temperature model is defined as Rs = λeβt , where λ is defined as
soil respiration at temperature of 0◦ C (μmol CO2 m−2 s−1 ) and it is estimated about
0.9506 in this study, and β is a coefficient. Based on this model, the Q10 parameter
can be derived as Q10 = eβ×10 .
(5) The Soil Water Content Model
The soil water content model is defined as Rs = g × RSW, were RSW is the relative
soil water content and g is a coefficient.
(6) The Linear Combined Temperature and Water model
The linear combined temperature and water model is defined as Rs = i × T + j ×
RSW, where T is temperature, RSW is relative soil water content, and i and j are
coefficients.
Climatic Change
(7) The Exponential Model with Temperature and Water
The exponential model with temperature and water is defined as Rs = λek×t+l×w ,
where λ is defined as soil respiration at a temperature of 0◦ C (same as model 4), t is
temperature, w is relative soil water content, and k and l are coefficients.
(8) The Seasonal Pattern Model (Hanson et al. 1993)
The seasonal pattern model was developed to integrate seasonal patterns of soil
respiration. This model has been used to estimate soil respiration components
of net ecosystem exchange (Wilson et al. 2001). The seasonal pattern model is
defined as
Rs = Mresp + Gresp ,
where
Mresp =
(T/10)
Rb Q10
ψmax − ψ35
CF
1−
100
ψmax
and
Gresp = RG × Gcos t
Where
Mresp
Gresp
Rb
Q10
T
CF
ψ max
ψ 35
RG
Gcost
the maintenance respiration rate of roots and soil microbes.
the cost of growing new roots
the maintenance respiration of roots and soil microbes when temperatures
approach 0◦ C.
the rate of change in respiration for a 10◦ C increase in soil temperature.
the soil temperature at a depth of 10–15 cm (in ◦ C)
the percent coarse fraction of the soil. In this study this value is 0.
the soil matric potential (Mpa) corresponding to the complete inhibition of
soil respiration.
the observed soil matric potential of the upper 35 cm of soil (Mpa). Here the
observed soil matric potential of the upper 15 cm of soil is used.
the rate of root growth (g dry matter as C) on a ground-area basis. In
this study the biomass of 1 m fine roots is about 0.03338 g based on field
measurements.
the carbon cost for growing roots.
2.5 Parameters estimation, optimization and statistical analyses
For each soil respiration model, we estimated optimal parameters using data across
all the treatments simultaneously as well as for each specific treatment separately.
The parameter optimization was accomplished using a cost function that quantifies
the model-data mismatch and employing the simplex method contained in the add-on
software, PopTools (Hood 2004) to adjust the model parameters until the mismatch
is minimized. This method has been previously used for soil C and soil respiration
model parameter identification (e.g., Del Grosso et al. 2005; McLauchlan et al.
Climatic Change
2006). ANOVA (SAS Institute Inc., Cary, NC, USA) was used to examine the
treatment effects on soil respiration and for the auto- and heterotrophic components.
To compare the results among models, in addition to a mean correlation coefficient
R2 , the root mean squared error (RMSE) for penalized likelihood was calculated
(Burnham and Anderson 2002).
3 Results
3.1 Comparing model results
Parameters for eight models using data from all treatments considered together were
estimated (Table 1). The Linear Temperature Model (T), the Exponential Temperature Model, and the Exponential Model with Temperature and Water have relatively
low RMSE (493.29, 503.32, and 602.84 respectively); the Component model and Del
Grosso et al. (2005) model had slightly higher RMSE (e.g., 799.22 and 806.00); and
the Linear Combined Temperature and Water model and Seasonal Pattern Model
had a higher RMSE (e.g., 926.20 and 968.24); and the Soil Water Content Model had
the highest RMSE (1733.29). For all eight models with parameters estimated using
data across all treatments, the highest RMSE are for the ACET-W, ECAT-D and
ECAT-W treatments (Table 1).
There were several features of the data that even the models with the lowest
RMSE did not capture across all the treatments. To examine whether the models
could capture some of the detailed features we optimized model-data fits for each
treatments separately (Table 2). These parameters are different from the modeldata optimizations over all treatments. Out of the eight models, the component
model performed relatively well describing soil respiration across different climate
change treatments, with mean R2 values ranging between 0.66 and 0.86 and mean
RMSE from 0.2397 to 1.0651 (Table 2). The optimal parameter values for this
model varied among the treatments and were different than the parameter values
when all treatments were considered together. With the exception of the Soil Water
Model, which always had a higher RMSE, other models performed well under
ambient temperature and ambient CO2 concentrations (ACAT). However, some
models, for example, the Del Grosso et al. (2005) model, did not perform well
when environmental conditions changed (warming and elevated CO2 ), as indicated
by low R2 and higher RMSE (Table 2). In contrast, temperature related models (Linear Temperature Model (T) and Exponential Model with Temperature)
consistently worked well under both ambient and altered conditions. The parameter for Linear Temperature Model (T) varied from 0.11 to 0.26. The exponent
coefficient (β) of the Exponential Temperature Model ranged from 0.04 to 0.97,
when λ was fixed at 0.9506, a nominal value for all the treatments in the OCCAM
experiment.
When temperature and soil water content are both considered in linear or
exponential models for soil respiration (Linear Combined Temperature and Water
Model and Exponential Model with Temperature and Water), values of i and k
were always positive and values of j and l were always negative. These results
indicate that increasing temperature contributes positively to soil respiration, while
increasing soil water content contributes negatively to soil respiration. It appears that
493.29
0.5586
27.29
0.8226
45.89
0.6598
53.57
0.7074
73.27
0.7864
49.02
0.5067
118.06
0.4028
42.81
0.5535
83.40
0.6238
0.167
T
503.32
0.5643
33.15
0.8387
51.54
0.6743
74.46
0.7121
61.88
0.8234
53.73
0.4794
109.83
0.4353
44.87
0.5248
73.85
0.6235
0.065
λeβt
1733.29
0.1901
194.88
0.3218
178.04
0.3690
85.31
0.2995
357.42
0.5619
140.34
0.0958
263.54
0.5595
222.40
0.2008
291.39
0.3041
3.70
Water
926.20
0.1623
79.86
0.1046
89.04
0.2647
26.95
0.2366
193.53
0.1482
77.51
0.4516
181.05
0.0666
103.35
0.1448
174.91
0.2556
k1 = 0.075
k2 = 1.875
T& water
602.84
0.5654
40.72
0.8321
57.67
0.6783
32.56
0.7160
111.18
0.7941
56.59
0.5237
136.05
0.3663
56.03
0.5279
112.04
0.6247
k = 0.05
l = 0.25
λek×t+l×w
Rb = 0.75
Q = 2.00
RG = 3.20
968.24
0.1192
39.51
0.5801
91.07
0.1243
73.35
0.1966
166.32
0.1243
108.22
0.1027
241.07
0.0054
87.62
0.2458
161.10
0.1117
Seasonal pattern model
Seasonal Pattern Model means the model by Hanson et al. (1993)
AC ambient [CO2 ], EC elevated [CO2 ], AT ambient temperature, ET elevated temperature, D dry, W wet, T the Linear Temperature Model, Water the Linear Soil
Water Content Model, T& water the Linear Combined Temperature and Water model
806.00
0.3240
62.00
0.4887
71.07
0.4083
47.07
0.1770
111.76
0.3702
142.81
0.6414
217.23
0.6835
61.51
0.4076
93.06
0.4487
0.65
a = 0.49
b = 0.007
c = 0.02
799.22
0.3337
44.56
0.5451
75.48
0.4644
38.01
0.2220
89.18
0.4992
134.27
0.1131
271.41
0.6929
68.13
0.3189
78.17
0.4944
Parameters
Overall RMSE
Overall R2
RMSE at ACAT-D
R2 at ACAT-D
RMSE at ACAT-W
R2 at ACAT-W
RMSE at ACET-D
R2 at ACET-D
RMSE at ACET-W
R2 at ACET-W
RMSE at ECAT-D
R2 at ECAT-D
RMSE at ECAT-W
R2 at ECAT-W
RMSE at ECET-D
R2 at ECET-D
RMSE at ECET-W
R2 at ECET-W
Del Grosso et al.
Component model
Treatments
Table 1 Parameters of general fitting models across different treatments and root mean square error (RMSE) and correlation coefficient (R2 ) between the modeled
Rs and observed Rs for overall treatments and each treatment individually
Climatic Change
Mean R2
Mean RMSE
Parameters
Mean R2
Mean RMSE
Parameters
Mean R2
Mean RMSE
Parameters
ACAT-D
ACET-W
ACET-D
Mean R2
Mean RMSE
1.2693
a = 0.212
b = 0.027
c = 0.268
0.7010
0.5750
a = 0.186
b = 0.012
c = 5.198
0.858
0.4378
a = 0.0547
b = 0.1551
c = 0.0014
0.7553
0.6250
a = 0.0072
b = 0.35
c = 14
0.661
0.4469
Parameters
ACAT-W
0.7193
0.4146
0.4413
1.6104
1.0107
0.858
0.4381
0.7129
0.7838
0.5770
1.3105
Del Grosso et al.
Component model
Treatments
0.7848
0.5432
0.8781
0.7602
0.1115
0.8599
0.9012
0.1829
0.8057
0.7481
0.1363
0.1181
T
0.7891
0.6651
0.8767
0.7524
0.0419
0.8744
0.9091
0.0788
0.8280
0.8873
0.0584
0.0465
λeβt
0.4821
1.5293
0.7278
2.9761
3.2694
0.4273
1.5293
4.2114
0.7072
2.4884
2.5975
3.323
Water
0.7332
0.4947
0.8928
0.5247
i = 0.1187
j = −0.7554
0.8367
0.7786
i = 0.2182
j = −1.4457
0.7833
0.9256
i = 0.1537
j = −0.8526
i = 0.2569
j = −2.1359
T& water
0.7387
0.4898
0.8626
0.8460
k = 0.0554
l = −1.2702
0.7914
0.4898
k = 0.0756
l = −0.5262
0.7724
0.6929
k = 0.07618
l = −0.7448
k = 0.1618
l = −1.456
λek×t+l×w
Table 2 Comparison of estimated model parameters based on field measurements of soil respiration under each environmental setting and the mean correlation
coefficient (R2 ) and root mean square error (RMSE) between the modeled Rs and observed Rs
Climatic Change
Mean R2
Mean RMSE
Parameters
Mean R2
Mean RMSE
Parameters
Mean R2
Mean RMSE
Parameters
ECAT-D
ECET-W
ECET-D
a = 0.9675
b = 0.4942
c = 0.0641
0.821
0.7022
a = 1.0449
b = 0.2512
c = 0.3832
0.868
0.2397
a = 0.3361
b = 0.0364
c = 0.0003
0.892
1.0651
a = 0.2290
b = 0.0717
c = 2.7510
0.727
0.7496
0.6094
0.8054
0.6496
1.2871
0.5465
0.8624
0.5147
0.4945
0.7817
0.7078
1.4229
1.6656
0.7027
0.9643
0.7493
1.3733
0.1793
0.7318
0.7381
0.1910
0.715
1.5088
0.1721
0.2626
0.7051
0.9484
0.8550
0.8717
0.0670
0.7597
0.7199
0.0794
0.8156
1.0937
0.0647
0.0913
0.489
2.8185
0.5096
2.7479
4.9445
0.2415
1.8632
6.6390
0.7918
1.4918
4.1116
2.1844
0.6702
0.9136
0.8616
0.7051
i = 0.2019
j = −0.9394
0.7123
0.7219
i = 0.2904
j = −1.8893
0.7014
1.3674
i = 0.1856
j = −0.5051
i = 0.3455
j = −1.6647
0.676
0.9146
0.8521
0.7319
k = 0.0728
l = −0.3326
0.7350
0.6941
k = 0.0830
l = −0.1136
0.8268
1.0707
k = 0.0713
l = −0.3315
k = 0.1033
l = −1.2266
AC ambient [CO2 ], EC elevated [CO2 ], AT ambient temperature, ET elevated temperature, D dry, W wet, T the Linear Temperature Model, Water the Linear
Soil Water Content Model, T& water the Linear Combined Temperature and Water model
Mean R2
Mean RMSE
Parameters
ECAT-W
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Fig. 1 Soil respiration patterns (μmol m−2 s−1 )
modeled by different models with higher R2 at
the dry condition of elevated CO2 concentration and ambient temperature (ECAT-D subplot 7). a Component model; b Del Grosso
et al. model; and c T model. Observed observed
total soil respiration; autotrophic modeled autotrophic soil respiration, Modeled total modeled total soil respiration
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adding soil water content to the original temperature related models does not always
improve R2 but does always decrease RSME values. In contrast, in some cases soil
water slightly decreased the model fit as measured by R2 . Moreover, comparing the
behavior of some models that had higher R2 and lower RMSE, we could see which
model best matches the observed soil respiration. For example, under dry conditions
with elevated [CO2 ] and ambient temperature (ECAT-D subplot number 7), the
component model came closest to fitting all the peaks (Fig. 1). The Seasonal Pattern
Model worked well at the dry ambient [CO2 ] and ambient temperature conditions
(ACAT-D). However, under changed environmental conditions, it did not fit the
data well unless the Q10 values were well out of the acceptable range 1.5∼3.0 (Lloyd
and Taylor 1994). Based on the biological knowledge, Q10 values can not be close to 0
or very large (e.g., 312 and 1093). The higher RMSE values showed similar patterns
to the models across all treatments, where the parameters were within reasonable
ranges. Additional information is required to constrain the optimization process for
the Seasonal Pattern Model.
3.2 Contribution of Rh and Ra to total soil respiration
Based on the parameter of the component model within each treatment (Table 2),
the proportions of Rh in total soil respiration were estimated to vary from 0.33 to 0.85
across treatments (Fig. 2). The proportion of Rh was significantly higher ( p = 0.0458)
under ECAT (0.75 ± 0.18) than under ACAT (0.51 ± 0.11). In the dry split plots, the
estimated proportion of Rh was significantly higher ( p = 0.03) at ECAT treatment
(0.86 ± 0.12) than at ECET treatment (0.57 ± 0.07). Soil water manipulations did
not cause significant changes in the proportion of Rh except under ECAT, where the
proportion of Rh was significantly higher ( p = 0.045) under dry conditions (0.86 ±
0.12) relative to wet condition (0.58 ± 0.06). There was no significant difference in the
proportion of Rh in total soil respiration between ambient and elevated temperature
treatments or between ambient and elevated [CO2 ] treatments.
There were large differences in the estimated ratios of root growth to root
maintenance respiration across treatments, and there was high within treatment
variation through time in the ratio of root growth respiration to root maintenance
respiration. The average ratio was lower under ECAT (0.06 ± 0.08) than under ACAT (0.56 ± 0.97) ( p = 0.01), ACET (0.32 ± 0.56) ( p = 0.03) and ECET
(0.85 ± 0.97) ( p < 0.01). There were significant differences between ratios under
ACET and ECET ( p = 0.01). Within treatments, the ratio of root growth respiration
to root maintenance respiration was significantly higher under dry relative to wet
conditions with the exception of ECET treatments.
3.3 Q10 values
Based on the β values in Exponential Model with Temperature (Table 2), Q10 values
were estimated for the different treatments. The Q10 value was significantly higher
( p = 0.03) in wet treatments, Q10 = 1.983 ± 0.26, relative to dry treatments, Q10 =
1.753 ± 0.21. Q10 values did not significantly differ between elevated CO2 (1.95 ±
0.25) and ambient CO2 (1.79 ± 0.25) treatments nor between elevated temperature
(1.87 ± 0.25) and ambient temperature (1.86 ± 0.27) treatments.
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a
b
Fig. 2 a The relative proportion of Rh in total Rs in different climate change treatments based on
models at each treatment (wet and dry sub-treatments are mixed); and b the relative proportion of
Rh in total Rs at dry sub-treatments. Dif ferent letters indicate significant different ( p < 0.05), while
same letter means no significant difference ( p > 0.05)
4 Discussion
4.1 Testing the utility of the component model
When data from all treatments were considered together the Linear Temperature
Model (T), the Exponential Temperature Model, and the Exponential Model with
Temperature and Water had relatively small values of RMSE. This suggests that
Climatic Change
soil respiration, from a cumulative perspective over all processes, may be considered
to be largely determined by soil temperature. This result confirms numerous
field observations that soil respiration is highly correlated with temperature (e.g.,
Rochette et al. 1991; Alvarez et al. 1995; Luo et al. 2001). However, when individual
treatments are examined separately, these simple models do not perform as well
unless their parameters are modified. The disadvantage of these models is that they
could not provide the detailed information about possible changes in underlying
processes or the tradeoff among different source components of soil respiration
under altered environments. So while many of the processes that influence soil
respiration are themselves temperature sensitive, temperature alone is not sufficient
for characterizing the response of soil respiration to a combination of environmental
changes.
In this study, the inclusion of other factors in a model (e.g., soil water content)
did not always improve model fit (such as R2 ), and sometimes even decreased the
overall model fit. Hibbard et al. (2005) reported an analysis of soil respiration across
northern hemisphere temperate ecosystem and found that including soil moisture
could improve correlation but not significantly. Zhou et al. (2007) indicated that the
combined function of soil temperature and moisture did not fit the data well under
severe water stress. Jassal et al. (2008) indicated that in an 18-year-old temperate
Douglas-fir stand soil respiration was positively correlated to soil temperature at
the 2 cm depth if soil water content at the 4 cm depth >0.11 m3 m−3 . If soil
water content was below this value, soil respiration was largely decoupled from soil
temperature.
This suggests that under changed environmental conditions, the appropriate representation of some specific soil respiration processes may be altered. For example,
elevated CO2 can reduce transpiration and result in higher soil water content
(Pendall et al. 2003). Results such as these can increase respiration in 2 ways
completely unrelated to temperature, through (1) higher soil moisture content, and
(2) through the production of a larger amount of organic matter inputs that then
fuel respiration. In studies with a high frequency soil respiration measurements it is
possible to find a large fraction of soil respiration that is directly related to the magnitude of photosynthesis, which not only has a temperature component different than
soil temperature, but also has a component related the amount of photosynthetically
active radiation, that are independent of soil environmental factors (Davidson et al.
2006; Liu et al. 2006; Gu et al. 2008). These analyses show that the photosynthetic
influence reproduced a typical seasonal pattern of apparent temperature sensitivity
reported in the literature: higher sensitivity in winter (dormant season) and lower
sensitivity in summer (growing season). Such pattern has been incorrectly interpreted
as an indication of temperature acclimation of soil respiration by previous studies.
Extending the application of simple correlative models from ambient to changed
climate conditions is of limited utility.
Comparing the models with their parameters estimated independently for each
specific treatment, the component model, which represents many of the possible
components of soil respiration separately, worked relatively well under ambient
temperature and CO2 conditions, and it also worked well under changed conditions
with appropriate changes of the model parameters (Table 2). This indicates that there
are still additional processes need to be explicitly represented in order to capture all
the impacts of changed climate conditions.
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4.2 Quantifying the variation of soil respiration from Rh and Ra
under climate change treatments
Under each specific treatment the component model partitions soil respiration into
autotrophic and heterotrophic components. Unfortunately, data are not available
for directly addressing this result, because we were unable to measure autotrophic
and heterotrophic respiration at the experimental site (excavation would have
compromised the experiment and plant species were short-stature with intermingled
root systems that had a high turnover rate). In addition excavation may not have
given us reliable data to use in our models for a number of reasons. For example,
measuring the respiration of roots in situ directly after removing the associated soil,
or by digging trenches around small areas to exclude roots, results in significant soil
disturbance likely increasing soil CO2 flux (Hanson et al. 2000). Högberg et al. (2001)
applied large scale girdling, in which the phloem was cut away killing roots to estimate root contribution to total soil respiration, but this decreases labile carbon inputs
into the soil, thus altering microbial activity. More recently, isotopic approaches
have been developed (e.g., Trumbore et al. 1996), but these methods require an
isotopic tracer and analysis but these analyses were not done in this experiment.
Experimental studies are intrinsically limited with either the sampling methodology
or by changes in soil conditions (Hanson et al. 2000). These pitfalls emphasize
the need of reliable process-based component models to estimate autotrophic and
heterotrophic soil respiration in order to understand how these components may
respond to climate changes differently.
Using our component model, we could estimate the relative proportion of Rh in
total soil respiration across CO2 , temperature, and soil water treatments under each
specific setting. Hanson et al. (2000) showed the mean contribution in non-forest
ecosystems is 63%. The magnitude of the estimated Rh at the ambient condition in
this study is slightly lower than the range of 60–88% in grasslands and croplands
(Buyanovsky et al. 1987; Buyanovsky and Wagner 1995; Raich and Tufekcioglu
2000). Also, root respiration (Ra ) in trees has been found to peak in late spring,
coinciding with high temperature and leaf flush, and to peak again in autumn prior
to litterfall due to the change in root physiology and phenology (e.g., Dickmann
et al. 1996). The component model was able to represent these detailed changes by
accounting for root growth respiration.
4.3 Limitations of soil respiration models under climate change treatments
It is well established that tree root elongation declines when soil water potential
decreases (e.g., Larson 1980; Teskey and Hinckley 1981). Because of their limited
root system, grasses and forbs may be more impacted by low soil water potential.
This may be why root maintenance and growth contributed little to soil respiration
in this study. When root maintenance and growth contributed to soil respiration,
they contributed less to overall respiration than heterotrophic respiration, and as a
result the estimates of component model were similar to the Del Grosso et al. (2005)
model.
Another limit to model predictions is the difficulty in obtaining accurate field
measurements of root activity and respiration. Young, new roots can respire four
Climatic Change
times more C than old roots, but it is difficult to determine the age of roots or their
individual contribution to soil respiration in the field (Lipp and Andersen 2003).
Another challenge is in measuring root nutrient uptake. High soil respiration is
associated with new root nutrient uptake (Veen 1980; van der Werf et al. 1988).
Accurate measurements of root nitrogen, a proxy for the amount of metabolically
active tissue, are challenging without severely disturbing the root system, and thus
soil respiration. Due to limited measurements of root nitrogen concentration in this
study, we approximated root nitrogen concentration using the model with a basic
biological constraint (e.g., Ra ≥ 0 or R ≥ 0 and N ≥ 0). All these estimated values
were in the range of measured values.
The relationship between the time lag of soil water change and soil respiration
presents a further challenge. It is often assumed that the soil respiration is close
to zero when there is severe water stress, but in our experiment, there was still
soil respiratory activity happening under very dry conditions. During dry periods,
soil respiration may be under-predicted by models because (1) the estimation of
soil water potential (or matric potential) during severe stress may be miscalculated
due to high spatial heterogeneity of water distribution, or (2) as the result of
hydrological redistribution by roots (Caldwell and Richards 1989). This suggests that
the current models for soil respiration, especially mechanistic models, may have a
high uncertainty under severe water stress.
Root physiology and phenology are not considered in the non-component models. Dickmann et al. (1996) found root respiration to peak in late spring, which
coincides with rising temperatures and leaf flush, and peaks again in autumn prior
to litterfall. Although the temperature related models (T and exponential model)
can provide approximations, the fits cannot capture these respiration excursions
due to the gradual change of temperature or soil water content. However, the
component model can accurately provide this information through the use of root
growth and maintenance respiration estimates. The temperature sensitivity of soil
respiration may not directly support the hypothesis that higher temperatures deplete
soil C pools due to increased soil respiration (e.g., Jenkinson et al. 1991; Raich
and Schlesinger 1992; Amundson 2001), because higher soil respiration may not
necessarily indicate faster decomposition of soil organic matter (Raich and Mora
2005). Instead, it may represent increased rates of root and rhizosphere respiration
(Kirschbaum 2000; Andrews and Schlesinger 2001). Hibbard et al. (2005) reported
that lowest respiration rates were observed at the highest summer temperature due to
drought.
Our study found that modeling soil respiration as a function only of soil moisture
was of limited utility. This indicates that soil moisture contributes little to soil
respiration until it reaches a threshold or it has to be considered with other factors,
such as temperature and root growth. The Seasonal Pattern Model (Hanson et al.
1993) did not work well in this study even with the parameter optimization. Possible
reasons might be that (1) the water potentials at 15 cm were used instead of
35 cm; and (2) the root maintenance respiration and partial heterotrophic respiration were not included separately in the model. This study indicates that both
Rb and Q10 may change under changed environments. In fact, Rb can be sitespecific and a function of a variety of characteristics, such as quantity of soil organic
matter, root density, nutrient status, microbial population size and activity (Hanson
et al. 1993).
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4.4 Testing the hypothesis that Q10 values are altered and that temperature
is the major contributing factor to variation in soil respiration
under climate change
Soil respiration models generally apply a fixed Q10 coefficient for the exponential
function between soil respiration and temperature. However, in this study, irrespective of CO2 and T treatments, the fitted optimal Q10 values in wet treatments
were larger than in dry treatments. Davidson et al. (1998) also showed larger values
for the wetter sites than for dry sites. However, Zhou et al. (2007) indicated that
warming did not significantly change Q10 values, and even decreased this value in
grass ecosystems (e.g., McHale et al. 1998; Luo et al. 2001). Other studies indicated
that Q10 varies among ecosystems and across temperature ranges, in part because
the various components of soil respiration have different temperature sensitivities
(Kirschbaum 1995; Trumbore et al. 1996). Boone et al. (1998) suggested different
temperature sensitivities for live roots, associated mycorrhizae, and the oxidation
of plant detritus (dead roots, leaves and wood input), root exudates, and humified
organic matter. Several have indicated that increased atmospheric [CO2 ] would lead
to larger Q10 values by increasing below-ground C allocation (Canadell et al. 1997;
Cardon 1997; Hungate et al. 1997). However, we did not find significant changes
in Q10 values at treatments of elevated temperature or CO2 concentration when
components of respiration are considered individually.
5 Conclusions
Global climate change will alter bulk soil respiration. However, it is difficult to
predict the direction and magnitude of changes in this important part of the global C
cycle without accounting for how the heterotrophic and autotrophic components will
respond independent of each other. In this study, based on short-term intensive observations, a partially process-based component model was tested and compared with
other models across CO2 , temperature, and soil water treatments. It appears that a
process-based model had relatively high accuracy and could provide more biological
details. The further developing of this model requires additional algorithms based
on a better understanding of the mechanisms that alter soil respiration across spatial
and temporal scales, such as the interactions among factors (e.g., soil temperature
and water content), especially near thresholds values. Some soil respiration models
that predict ambient condition or general models that work well across all different
treatments may not accurately predict soil respiration under future climate regimes
due to changes in ecological processes and feedbacks. Changes in parameters (such
as those in component model) or including more processes at corresponding spatial
and temporal scales may be necessary to apply these models under projected future
environments. Our results show that controlled experiments to quantify the effects
of single or combined factors (such as temperature, soil moisture, plant roots and
microbial activity) on soil respiration under multiple environmental conditions are
necessary to accurately define the mechanisms that alter soil respiration. Since these
mechanisms include dynamic autotrophic processes including diurnal and seasonal
patterns of carbohydrate supply, phenology of root growth, active redistribution of
soil moisture, as well as heterotrophic processes, soil respiration cannot be accurately
Climatic Change
modeled independently from aboveground carbon cycle processes. Considering the
entire terrestrial ecosystem as a unified system will be required to predict soil
respiration responses in the future using models.
Acknowledgements This research was supported in part by a research appointment to the Oak
Ridge National Laboratory/Oak Ridge Associated Universities Historically Black Colleges and
Universities and Minority Education Institutions Summer Faculty Research Program.
The OCCAM experiment was sponsored by the U.S. Department of Energy, Office of Science,
Biological and Environmental Research under contract DE-AC05-00OR22725 with Oak Ridge
National Laboratory (ORNL), managed by UT-Battelle. This study was also partially supported
by USDA (Evan-Allen and Mc-Stennis) and Department of Energy under cooperative agreement
No. DE-FC26-06NT43029-005. Jake Weltzin was integral in establishing the OCCAM experiment
and Katherine Sides, Joanne Childs, and Courtney Campany assisted with data collection. We thank
Paul Kardol, George Byrd, and Lara Souza and anonymous reviewers for their helpful suggestions
and comments on earlier drafts.
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