Geoderma 189–190 (2012) 65–71
Contents lists available at SciVerse ScienceDirect
Geoderma
journal homepage: www.elsevier.com/locate/geoderma
Effects of simulated acid rain on soil CO2 emission in a secondary forest in
subtropical China
Shutao Chen a, b,⁎, Xiaoshuai Shen a, Zhenghua Hu a, Haishan Chen c, Yanshu Shi a, Yan Liu a
a
b
c
School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China
a r t i c l e
i n f o
Article history:
Received 20 October 2011
Received in revised form 24 March 2012
Accepted 2 May 2012
Available online xxxx
Keywords:
Simulated acid rain (SAR)
Soil respiration (Rs)
Heterotrophic respiration (Rh)
Soil temperature
Soil moisture
Subtropical forest
a b s t r a c t
Acid rain, which is caused mainly by dissolution of sulfur dioxide (SO2) and nitrogen oxides (NOx) in the atmosphere, has been reported to have negative effects on ecosystems. However, few investigations have focused on the impacts of acid rain on soil CO2 emission in forest. In this study, the effects of simulated acid
rain (SAR) on soil respiration (Rs) and its heterotrophic component (Rh) in a secondary forest in subtropical
China were investigated. Soil CO2 efflux was measured by using a Li-8100 infrared gas analyzer with attached chamber. Measurements were generally made once a week from 21 March 2010 to 16 May 2011
in order to investigate the seasonal variations of Rs and Rh under different SAR treatments. Soil temperature
and moisture at the depth of 5 cm were measured at the time of soil CO2 efflux measurements. Results indicated that different SAR treatments exhibited similar seasonal patterns of Rs and Rh. Seasonal mean Rs
rates for the CK (deionized water), A1 (pH 4.0), A2 (pH 3.0) and A3 (pH 2.0) treatments were 2.63, 1.92,
1.89 and 2.16 μmol m − 2 s − 1, respectively, while mean Rh rates for the four treatments were 1.80, 1.64,
1.76 and 1.79 μmol m − 2 s − 1, respectively. Two-factor analysis (respiration components and SAR) of variance implied that SAR had significant (p = 0.031) effects on soil CO2 emissions, but this was contingent
on the specific respiration components. SAR showed significant inhibition effects on Rs (autotrophic +
heterotrophic components) rather than Rh. The ratio of Rh to Rs was significantly higher in the CK than in the
acid rain treatments (A1, A2 and A3). Soil temperature and moisture were two controlling factors regulating
the seasonal patterns of Rs and Rh for each of the SAR treatment. Soil temperature and moisture accounted for
more than 80% of the seasonal variations observed in Rs and Rh. This work highlights that the effects of SAR
are important to consider in assessing the annual soil CO2 emission, particularly under the scenario of increasing
acid rain pollution.
© 2012 Elsevier B.V. All rights reserved.
1. Introduction
Acid rain is one of the foremost examples of regional air pollution
and has received worldwide attention because acidification damages
are often the result of atmospheric transport of sulfur and nitrogen
emissions across state and/or national boundaries (Menz and Seip,
2004). Regions that have been most affected by acidic deposition include Europe, eastern North America, and Southeast Asia, especially
central and southern China (Kuylenstierna et al., 2001; Menz and
Seip, 2004). Acid rain is caused mainly by dissolution of sulfur dioxide
(SO2) and nitrogen oxides (NOx) in the atmosphere. These pollutants
mainly originate from human activity such as the combustion of fossil
fuels within thermal power plants and automobiles (Francisco
Sant'Anna-Santos et al., 2006; Kita et al., 2004; Zhang et al., 2007).
⁎ Corresponding author at: School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China. Tel./
fax: +86 25 58731090.
E-mail address: [email protected] (S. Chen).
0016-7061/$ – see front matter © 2012 Elsevier B.V. All rights reserved.
doi:10.1016/j.geoderma.2012.05.002
Because of the difficulty and cost in disposing these gases in many
countries, they are often emitted into the atmosphere with no effective treatments. The nationwide monitoring data provided by China
Meteorological Administration in 2007 demonstrate that most areas
in southern China were registered acid rain with pH values below
5.0 (Hou and Zhao, 2009). Zhang and Jiang (2012) reviewed that
most ecosystems in southern China had received large quantities of
acidic inputs.
Second to gross photosynthesis, CO2 emissions from soils (i.e., soil respiration) exceed all other terrestrial–atmospheric carbon exchanges
(Raich and Schlesinger, 1992). Over two thirds of terrestrial carbon is
stored belowground and a significant amount of the atmospheric CO2 assimilated by plants is respired by roots and microbes in terrestrial soils
(Hibbard et al., 2005). Soil respiration is therefore a key process that underlies our understanding of the terrestrial carbon cycle (Davidson et al.,
2006). Increases in soil CO2 emissions have the potential to exacerbate increasing atmospheric CO2 levels and to provide a positive feedback to
global warming (Raich and Tufekcioglu, 2000). Generally, soil respiration
(Rs) is separated into two components: root (autotrophic) respiration
66
S. Chen et al. / Geoderma 189–190 (2012) 65–71
(Ra) and microorganism (heterotrophic) respiration (Rh) (Kuzyakov,
2006; Kuzyakov and Larionova, 2005).
Impacts of acid rain on forest ecosystem are an increasing environmental concern. Reich et al. (1988) and Wright et al. (1990) found
that simulated acid rain (SAR) application changed foliar N content.
Some investigations suggest that SAR can accelerate leaching of nutrients from plant foliage and soil (Reddy et al., 1991; Turner and
Tingey, 1990; Zhang et al., 2007). Acid rain is thought to be responsible for elevated levels of toxic aluminum in soil, leaching of plant nutrients (particularly magnesium) from soils, or reduced availability of
phosphorus (Menz and Seip, 2004). Schaedle et al. (1989) found that
acid rain increased Al 3+ in soil solution, which is toxic to fine roots.
Fan and Wang (2000) reported that high H + load in SAR inhibited
seedling growth. Since acid rain application changes roots and soil
conditions which are thought to influence CO2 emissions from roots
and organic matter decomposition (Boone et al., 1998; Kuzyakov,
2006; Kuzyakov and Larionova, 2005), acid rain has the potential to
affect Rs. Fritze (1992), for example, has found that acidic loads applied during a short time period, sometimes even in a single load,
demonstrated the toxic effects of acid on Rs.
Many efforts have been devoted to study the impacts of acid rain
on plant and soil traits in forest in southern China (e.g. Fan and
Wang, 2000; Wang et al., 2009; Zhang et al., 2007). Unfortunately,
few investigations have focused on the impacts of acid rain on soil
CO2 emission in forest; particularly lacking, to our knowledge, is the
long-term in situ measurements. Also, the information about the dependences of soil respiration on temperature and moisture under different SAR pH levels is also rare.
Because the H + in the SAR is toxic to soil microorganisms and
roots, it was assumed that soil respiration components might be affected by acid rain. In order to examine the potential effects of SAR
on soil CO2 emission, a field experiment with different SAR levels
has been conducted. The specific questions addressed here were the
following: (1) whether and how does SAR affect Rs and Rh in the subtropical forest? (2) How do soil temperature and moisture affect the
variability of soil CO2 emission under different SAR treatments?
2. Materials and methods
2.1. Site description
In 2010, experiments were performed at Longwang Mountain
(32.20°N, 118.72°E) near Nanjing City, Jiangsu province, China. This
study site is located on the north shore of lower reaches of Yangze
River. The Longwang Mountain has a monsoon climate and falls into
the northern edge of the humid subtropical climate zone. Annual average temperature of the experimental site is 15.6 °C and annual rainfall averages 1 100 mm. The broad leaf and needle leaf mixed
hardwood forest is dominated by hackberry (Celtis sinensis L.),
sweet gum (Liquidambar formosana L.), hardleaf oatchestnut
(Castanopsis sclerophylla L.) and masson pine (Pinus massoniana L.),
with some stands of Chinese pistache (Pistacia chinensis L.) and
crape myrtle (Lagerstroemia indica L.). The stand has a density of
2000 trees ha − 1, which reaches canopy closure of 0.8 and presents
rare herbaceous vegetations. The soil collected from the experimental
site is classified as yellow-brown soil in Chinese taxonomy or Typic
Paleudults in soil taxonomy. The soil in this experimental site is shallow; its depth varies from 35 to 50 cm and is underlain by quartzite
bedrock. Soil properties of C and N content and pH are shown in
Table 1.
2.2. SAR treatment
In February 2010, the experiment was arranged in a split-plot design, which was indicated in Fig. 1. There were four main blocks; each
block was split into Rs and Rh treatments. Four simulated acid rain
Table 1
Soil C and N content and pH in the soil profile.
Depth (cm)
Soil C content (g kg− 1)
Soil N content (g kg− 1)
pH
0–4
4–10
10–20
20–30
30–40
40–50
62.7 ± 0.8
62.1 ± 1.1
60.2 ± 2.0
58.5 ± 2.5
53.8 ± 3.1
49.6 ± 3.7
3.0 ± 0.2
2.7 ± 0.2
2.6 ± 0.2
2.4 ± 0.2
2.2 ± 0.2
2.0 ± 0.2
6.8 ± 0.0
6.7 ± 0.1
6.6 ± 0.1
6.5 ± 0.1
6.5 ± 0.3
6.6 ± 0.2
(SAR) treatments, which were CK (control), A1 (pH 4.0), A2 (pH
3.0) and A3 (pH 2.0), respectively, were randomly assigned in each
of the Rs and Rh treatments. There were 32 micro-plots assigned in
the field experiments, with each micro-plot area measuring
1 m × 1 m. Comparing soil CO2 effluxes in small, trenched plots,
where trenching excludes roots and nearby undisturbed locations, is
one method for partitioning field-based estimates of annual Rs into
its autotrophic and heterotrophic components (Bowden et al., 1993;
Hanson et al., 2000; Kelting et al., 1998). The plots in Rh treatment
were trenched with care to minimize soil disturbance. Trenches
were cut (>30 cm) into the soil to sever roots entering the plot
(Lavigne et al., 2003; Mäkiranta et al., 2010). To exclude also any C
input from subcanopy herbs, where present, these were generally removed in trenched plots (Lavigne et al., 2003). In un-trenched plots
where Rs was measured, only vegetation within the soil collar for
measuring CO2 efflux was eradicated by hand. The vegetation was removed about a month before the commencement of respiration measurement. Soil CO2 effluxes measured in trenched and un-trenched
plots were Rh and Rs, respectively.
The rain applied in the CK (control) treatment contained only deionized water with pH 6.7. In order to have the SAR reflecting the real
mole ratio of H:S:N according to previous acid rain records (Hou and
Zhao, 2009; Zhang et al., 2007), acidic solutions were prepared by
adding a mixture of H2SO4 and HNO3 (4.5:1 mole ratio) to deionized
water (Zhang et al., 2007). SAR events were applied bi-weekly and
the amount applied to each micro-plot was 1.25 L m − 2 per application event. The simulated rainfall was applied to the treatments by
means of a simulation apparatus capable of delivering droplet sizes
in the range of 1.0 to 1.2 mm diameter. This study simulated an
over 14-month exposure experiment in acid rain intensity. There is
a several-day interval between SAR addition and soil CO2 efflux measurements in order to avoid pulse CO2 emission due to water
sprinkling.
2.3. Soil CO2 efflux measurement
Soil CO2 efflux was measured by using a Li-8100 infrared gas analyzer (Li-Cor Inc., Lincoln, NE, USA) with attached chamber. The PVC
soil collar (20 cm in diameter) was permanently installed (3 cm)
into the soil in each SAR treatment for soil CO2 efflux measurements.
There was one collar in each plot. Aboveground vegetation within the
soil collar was eradicated by hand prior to chamber placement and
measurements to avoid canopy CO2 exchange. Therefore, for soil
CO2 efflux measured, we did not include aboveground respiration
from living plants. Measurements were generally made once a week
from 21 March 2010 to 16 May 2011, in order to investigate the seasonal variations of Rs and Rh under different SAR treatments. During
respiration measurements, a double-sealed gasket system seals the
chamber both inside and outside of the soil collar to minimize CO2
leaks and wind effects. Air flow generated by a rotary pump inside
the analyzer control unit of Li-8100 provides a steady flow of air to
the 20 cm chamber, with minimal pulsations. The analyzer optical
bench measures CO2 concentration; the concentration is then used
to calculate flux rate.
S. Chen et al. / Geoderma 189–190 (2012) 65–71
Rh
Rs
67
Rs
Rs
A1
CK
CK
A3
A1
A3
A1
A2
A3
A2
A1
A2
CK
A2
CK
A3
A3
A2
A2
A3
CK
A3
A2
CK
CK
A1
A1
CK
A2
A1
A1
A3
Rh
Rh
Rs
Rh
Fig. 1. Schematic drawing of field split-plot experiment.
2.4. Statistical analyses
Repeated measures of ANOVA with a split-plot design were conducted to test the effects of trenching, SAR and their interactions on
soil CO2 emission. Simple and multiple regression analyses were
used to examine dependences of Rs (or Rh) to soil temperature and
moisture. Analysis of covariance (ANCOVA) was used to investigate
the SAR treatment and trenching interaction effects on soil respiration. In other words, in this study, ANCOVA tested whether SAR had
an effect on the dependency of Rh on Rs. All statistical analyses were
performed with Excel 2003 (Microsoft Inc., Seattle, WA, USA) and
SYSTAT 10 (SAS Inc., Cary, NC, USA) softwares.
3. Results
decreased sharply during autumn 2010. Seasonal mean Rs rates for
the CK, A1, A2 and A3 treatments were 2.63, 1.92, 1.89 and
2.16 μmol m − 2 s − 1, respectively (Fig. 4a), while mean Rh rates for
the four treatments were 1.80, 1.64, 1.76 and 1.79 μmol m − 2 s − 1
(Fig. 4b), respectively.
Two-factor analysis (trenching and SAR) of variance implied that
SAR had significant (p = 0.031) effects on soil CO2 emission
(Table 2). As shown in Table 2, the effects of trenching (respiration
component) (p b 0.001) and SAR treatment (p = 0.031) were significant. Although, generally, SAR had inhibition effects on soil CO2 emission, this was contingent on the specific respiration components (Fig.
4a and b). Repeated measures of ANOVA showed that SAR significantly (p = 0.03) reduced Rs. In comparison with CK, A1, A2 and A3 reduced Rs by 27%, 28% and 18%, respectively (Fig. 4a), and no
significant (p > 0.1) difference among the three SAR treatments (A1,
A2 and A3) was observed. However, there were no significant
(p > 0.05) differences in Rh rates among the CK, A1, A2 and A3 treatments, suggesting that SAR had no significant effects on Rh.
Rh was significantly and positively correlated to Rs for each of the
SAR treatment (Fig. 5). As shown in Fig. 5, the relationship between
40
Ts_ Rs (°C)
The Li-8100 chamber was put approximately 2 min on a PVC collar
to measure soil CO2 efflux at each plot and then moved to the next
plot. On each measurement date, the whole measurement started at
08:30 and lasted for about 60 min. According to a report by Tang et
al. (2006), the diurnal studies demonstrated that CO2 fluxes measured at this time were close to daily means. Soil temperature (°C)
and moisture (volumetric water content, %) at the depth of 5 cm
were monitored adjacent to each PVC collar using a probe connected
to the Li-8100 at the time of soil CO2 efflux measurements. Root biomass in the 0–7 cm soil profile was sampled on 3 March 2011, dried
at 75 °C for several days and weighed.
30
40%
Ws_ Rs
A1
A2
A3
10
b
20%
0%
c
6
-2 -1
Rs (µmol m s )
The seasonal changes of soil temperature and moisture were
shown in Fig. 2a and b and Fig. 3a and b. Soil temperature showed
clear seasonal patterns that followed local seasonal variation in air
temperature, while soil moisture did not show such patterns. The
highest soil temperature (~30 °C) appeared in August 2010. Both
soil temperature and soil moisture were not significantly (p > 0.05)
different among different SAR treatments. This experimental site experienced a serious drought from November 2010 to January 2011,
with the lowest soil moisture of ~ 4% (Figs. 2b and 3b).
Rs and Rh exhibited some clear seasonal patterns. The seasonal
variation in Rs and Rh developed with a similar pattern under different SAR treatments during the experimental period (Figs. 2c and
3c), generally associated with the seasonal pattern of soil temperature (Figs. 2a and 3a). From March 2010 to May 2011, Rs and Rh increased during spring and summer (March 2010–August 2010) but
CK
20
0
3.1. Effects of SAR on respiration
a
3
0
05-Mar-10 04-May-10 03-Jul-10 01-Sep-10 31-Oct-10 30-Dec-10 28-Feb-11 29-Apr-11
Date
Fig. 2. Seasonal variations of soil temperature (Ts_Rs) (a), soil moisture (Ws_Rs) (b) and
soil respiration (Rs) (c) in un-trenched plots.
68
S. Chen et al. / Geoderma 189–190 (2012) 65–71
Ts_ Rh (°C)
40
a
CK
30
A1
A2
A3
20
10
0
b
Ws_ Rh
40%
Table 2
ANOVA for the split-plot experiments. SS: sum of squares; MS: mean of squares.
Source
SS
df
MS
F-ratio
p
Respiration components (RS and Rh)
SAR
Respiration components SAR
Error
1.301
0.944
0.542
2.170
1
3
3
24
1.301
0.315
0.181
0.090
14.397
3.482
1.999
b 0.001
0.031
0.141
20%
0%
c
3.2. Temperature and moisture relationships of respiration
-2 -1
Rh (µmol m s )
6
3
0
05-Mar-10 04-May-10 03-Jul-10
01-Sep-10 31-Oct-10 30-Dec-10 28-Feb-11 29-Apr-11
Date
Fig. 3. Seasonal variations of soil temperature (Ts_Rh) (a), soil moisture (Ws_Rh) (b)
and heterotrophic respiration (Rh) (c) in trenched plots.
Rh and Rs can be strongly explained by a linear function model, implying that Rh occurs only if Rs occurs in the soil. However, covariance
analysis (Table 3) showed that there was a significant (p = 0.004) difference of the slope among different SAR treatments, with a lower
slope for the CK treatment (Fig. 5).
When the entire experimental period was considered, seasonal
variability in Rs and Rh was mainly controlled by soil temperature
(Figs. 6 and 7). Soil temperature itself can explain more than 68%
(R 2 > 0.68) seasonal variations in respiration rates (Figs. 6 and 7). By
using the exponential model, the apparent Q10 values of Rs against
soil temperature were 2.85, 3.11, 2.78 and 3.04 for CK, A1, A2 and
A3 treatments, respectively, while those of Rh were 2.48, 2.38, 2.43
and 2.60, respectively (Table 4). Although the calculated R 2 values
for the Arrhenius model were less than the exponential model, both
Arrhenius-based and exponential-based Q10 values showed that, generally, Rs had the higher temperature sensitivity than Rh.
Soil moisture was another important factor influencing the seasonal variations in soil CO2 effluxes. Combining the effects of soil temperature and moisture on respiration rates yielded the exponential
function modeling the temporal variations of Rs and Rh (Table 5).
Soil temperature and moisture accounted for more than 80% of the
seasonal variations observed in Rs and Rh (Table 5). Overall, seasonal
variations in Rs and Rh largely attributed to the combination effects of
soil temperature and moisture.
4. Discussion
3.5
a
Rs (µmol m-2 s-1)
3.0
4.1. SAR and soil respiration
Effects of SAR application have been observed on the soil CO2
emission but the results are variable and also partly contradictory
(Vanhala, 2002; Vanhala et al., 1996). Following SAR application,
soil respiration was reported as unaffected, decreased, or increased.
2.5
2.0
1.5
9
a
1.0
b
CK
y = 0.789x
y = 0.687x
0.5
6
A1
2
2
R = 0.897
R = 0.941
0.0
CK
A1
A2
A3
Treatments
3.5
b
Rh (µmol m-2 s-1)
3.0
2.5
Rh (µmol m-2 s-1)
3
0
c
y = 0.875x
6
2.0
d
A2
A3
y = 0.802x
2
2
R = 0.941
R = 0.889
1.5
3
1.0
0.5
0
0.0
CK
A1
A2
A3
0
2
4
6
0
2
4
6
8
Rs (µmol m-2 s-1)
Treatments
Fig. 4. Mean soil respiration (Rs) (a) and heterotrophic respiration (Rh) (b) rates for different SAR treatments.
Fig. 5. Relationship between heterotrophic respiration (Rh) and soil respiration (Rs).
(a), (b), (c) and (d) represent CK, A1, A2 and A3, respectively. All p values for the regression lines in (a), (b), (c) and (d) are less than 0.001.
S. Chen et al. / Geoderma 189–190 (2012) 65–71
9
Table 3
Covariance analysis of the effects of SAR treatment on the relationship between Rh and
Rs. SS: sum of squares; MS: mean of squares. The relationship between Rh and Rs was
significant (p b 0.001), but this relationship was significantly different (p = 0.004)
among different SAR treatments.
SS
df
MS
F
p
Corrected model
Intercept
SAR
Rs
Error
Total
Corrected total
269.747
2.777
3.116
257.605
35.063
1004.642
304.810
4
1
3
1
155
160
159
67.437
2.777
1.039
257.605
0.226
298.107
12.274
4.591
1139.000
b 0.001
b 0.001
0.004
b 0.001
a
9
CK
a
y = 0.380e
b
0.1049 x
y = 0.296e
2
6
A1
0.1134 x
0.091 x
A1
y = 0.405e
0.0866 x
2
R = 0.716
R = 0.726
3
0
c
d
A2
y = 0.392e
0.0889 x
A3
y = 0.359e
2
0.0954 x
2
R = 0.745
R = 0.684
3
0
0
10
20
0
10
20
30
Ts_Rh (°C)
Fig. 7. Relationship between heterotrophic respiration (Rh) and soil temperature
(Ts_Rh). (a), (b), (c) and (d) represent CK, A1, A2 and A3, respectively. All p values
for the regression lines in (a), (b), (c) and (d) are less than 0.001.
Before this SAR experiment, we assumed that acid rain would inhibit both autotrophic and heterotrophic components of Rs, because
the H + in the SAR might be toxic to soil microorganisms and roots.
However, we found the significant inhibition effects of SAR on Rs rather than on Rh, indicating that SAR only significantly reduced root respiration during our experimental period. This was supported not only
by the ratios of Rh to Rs (Fig. 5) but also by topsoil (0–7 cm) root density (root mass per gram soil) measured in different SAR treatments
(Fig. 8). SAR treatments resulted in a decrease in topsoil root density
in comparison with CK (Fig. 8), which would probably reduce CO2
emission from roots (Kuzyakov, 2006). In addition, as shown in
Fig. 2c, we have observed several times of abnormally high Rs rates
(mainly for A3) from July to September 2010. If these abnormally
high CO2 fluxes were not considered, the inhibition effects of SAR
on Rs would be more significant (data are not shown).
4.2. Soil temperature, moisture and respiration
2
R = 0.757
R = 0.680
Soil temperature and moisture are generally considered two basic
factors in controlling soil CO2 emission processes. Temperature controls the rate of biological reactions through its influence on enzyme
kinetics (Davidson and Janssens, 2006; Davidson et al., 2006; Lloyd
3
Rs (µmol m-2 s-1)
b
2
6
Will et al. (1986), for example, reported that soil CO2 efflux was not
affected by acid rain. A field study conducted in a weakly developed
acidic podzolic soil indicated that SAR decreased soil CO2 emission
(Zelles et al., 1987). The reduction of soil CO2 efflux might be due to
the toxicity of high H + loads which may inhibit soil microorganism
activity. Salonius (1990) found that a low level of SAR application
appeared to increase soil CO2 efflux. In our study, Rs was significantly
inhibited by SAR (Table 2, Fig. 4), but Rh was not affected by SAR.
There are several reasons contributing to these inconsistent results. Firstly, the length of time in which the SAR is applied may influence the SAR effects on soil CO2 efflux. Some previous results
obtained varied across different lengths of time of SAR application
(Salonius, 1990; Vanhala et al., 1996; Will et al., 1986). Secondly,
the effects of SAR depend on the H + loads. Vanhala et al. (1996), for
example, suggested that the application of 2.92 kmol H +
ha − 1 year − 1 did not affect soil CO2 emission, whereas the application
of 14.9 kmol H + ha − 1 year − 1 decreased soil CO2 emission by about
20%. A decrease in the soil CO2 emission was observed only at extremely high acid loads. Thirdly, the effects of SAR vary across different biomes and soil micro-environments (Vanhala et al., 1996).
Vanhala et al. (1996) pointed out that the microflora in subarctic
soil was more sensitive to acidic precipitation than the microflora in
temperate soil. They also found that soil CO2 emission in dry
nutrient-poor forests tended to be more sensitive to acidic deposition
than that in medium and mesic forests.
CK
y = 0.326e
6
Rh (µmol m-2 s-1)
Source
69
0
c
d
A2
y = 0.352e
0.1024 x
y = 0.332e
2
2
R = 0.690
6
Table 4
Calculated Q10 and R2 values by using both exponential and Arrhenius equations (Lloyd
and Taylor, 1994). The fitted exponential and Arrhenius equations between respiration
and temperature are R = aebT and R = αe− E0/(T − T0), respectively. In the equations, R
(μmol m− 2 s− 1) is soil respiration (Rs) or heterotrophic respiration (Rh); T is soil temperature and T0 is a temperature parameter, both having units of K; a, b, α and E0 are
parameters. R2 is the determinant coefficient. Q10 values for single degree C intervals
were calculated from the fitted temperature models using the formula Q10 = (R2/
R1)(10/(T2 − T1)), where both T1 and T2 are temperature; Q10 is the temperature coefficient; R1 and R2 are soil respiration rates measured at T1 and T2, respectively.
A3
0.111 x
R = 0.740
3
Equation
0
0
10
20
0
10
20
30
40
Ts_Rs (°C)
Fig. 6. Relationship between soil respiration (Rs) and soil temperature (Ts_Rs). (a), (b),
(c) and (d) represent CK, A1, A2 and A3, respectively. All p values for the regression
lines in (a), (b), (c) and (d) are less than 0.001.
Respiration Q10
components
CK
Exponential Rs
Rh
Arrhenius
Rs
Rh
2.85
2.48
2.03
2.32
R2
A1
A2
A3
CK
A1
A2
A3
3.11
2.38
2.61
1.98
2.78
2.43
2.51
2.35
3.04
2.60
2.77
2.58
0.757
0.726
0.580
0.569
0.680
0.716
0.618
0.543
0.690
0.745
0.580
0.601
0.740
0.684
0.533
0.573
70
S. Chen et al. / Geoderma 189–190 (2012) 65–71
Table 5
Statistical modeling of the effects of soil temperature and moisture on the seasonal variations in respiration. Parameters a, b and c are three coefficients in the regression function
R = ae(bT + cW), where R (μmol m− 2 s− 1) is soil respiration (Rs) or heterotrophic respiration (Rh), T is soil temperature (°C), and W is soil moisture (V/V). R2 is the determinant coefficient, and p is the statistical significance of the regression function. CIa (95%), CIb (95%) and CIc (95%) are confidence intervals (95%) for a, b and c, respectively. Standard errors
are estimated by bootstrap method.
Rs or Rh
SAR
a
b
c
R2
CIa (95%)
CIb (95%)
CIc (95%)
p
Rs
CK
A1
A2
A3
CK
A1
A2
A3
0.398 ± 0.076
0.403 ± 0.069
0.369 ± 0.061
0.225 ± 0.046
0.322 ± 0.080
0.370 ± 0.062
0.424 ± 0.070
0.379 ± 0.061
0.083 ± 0.006
0.085 ± 0.006
0.076 ± 0.006
0.099 ± 0.007
0.069 ± 0.006
0.072 ± 0.006
0.073 ± 0.006
0.072 ± 0.006
1.754 ± 0.593
1.256 ± 0.604
2.142 ± 0.524
2.926 ± 0.489
2.115 ± 1.026
1.822 ± 0.444
1.290 ± 0.622
1.977 ± 0.406
0.812
0.842
0.836
0.886
0.829
0.822
0.813
0.805
0.245–0.551
0.256–0.540
0.247–0.492
0.134–0.317
0.162–0.483
0.247–0.493
0.285–0.581
0.258–0.500
0.071–0.095
0.072–0.098
0.063–0.088
0.085–0.112
0.057–0.081
0.060–0.084
0.062–0.084
0.060–0.084
0.567–2.940
0.046–2.465
1.094–3.191
1.947–3.905
0.062–4.168
0.934–2.711
0.045–2.534
1.165–2.789
b0.001
b0.001
b0.001
b0.001
b0.001
b0.001
b0.001
b0.001
Rh
and Taylor, 1994). Our results exhibited a strongly exponential correlation of Rs and Rh with soil temperature for each SAR treatment,
which was in agreement with previous studies (Davidson et al.,
1998; Kang et al., 2003; Wang et al., 2002), especially in subtropical
and temperate forests where a majority of biological processes coincides with temperature dynamics (Janssens and Pilegaard, 2003).
Temperature not only directly affects the capacity of both soluble
and membrane-bound enzymes and the affinity of the enzyme for
substrates (Davidson et al., 2006), but also indirectly influences substrate availability by regulating daily metabolism and seasonal carbon
distributions (Campbell and Law, 2005; Wang et al., 2006).
Although soil temperature is the main driver of seasonal variations in Rs and Rh, soil water availability probably determines the responses of Rs and Rh to temperature (Conant et al., 2004; Wan et al.,
2007). In wet soils when water content remains high throughout
the year, soil CO2 emission tends to be much less responsive to soil
moisture (Hanson et al., 1993; Keith et al., 1997). In dry soil, microbial
and root activity would be reduced because of water stress. If a wet
site dries substantially or a dry site wet substantially, then a large variability of soil CO2 emission may occur (Davidson et al., 1998; Jassal
et al., 2008; Lee et al., 2004). Our experimental region experienced
great variation of soil moisture during the experimental period
(Figs. 2b and 3b), which contributed substantially to the significant
effects of soil moisture on soil CO2 emission.
4.3. Measurements of Rs and Rh
Although respiration chamber is widely used in field soil CO2 flux
measurements (Bond-Lamberty and Thomson, 2010a,b), potential errors from this implementation still remain. There are two possible
sources for this error. One is the disturbance resulting from the
Root density (mg g-1)
2.0
1.5
1.0
0.5
0.0
CK
A1
A2
A3
Treatments
Fig. 8. Topsoil (0–7 cm) root density (root mass per gram soil) for different SAR treatments in un-trenched plots.
chamber installation (Muhr and Borken, 2009). Wang et al. (2005) argued that root cut by collar insertion increased with insertion depth
and this could significantly affect the subsequent soil respiration
measurement. The other source of error is related to the property
(flat or not) of forest floor. Because chamber volume is an important
variable in the calculation of CO2 exchange rates in a closed gasexchange configuration (Long and Hallgren, 1985), non-planar forest
floor surfaces (a common phenomenon) can introduce an unavoidable source of error to the measurements (Hanson et al., 1993). In
this study, we attempted to ensure that observations were conducted
on forest floor locations that approximated a flat surface. Also, we
inserted the 3‐cm chambers into the soil; this depth was adopted to
prevent gas leakage and to minimize root dissection (Zhao et al.,
2011).
One of the main environmental effects of acid rain will be on the
metabolic activity of vascular plants. In our un-trenched plots where
Rs was measured, only vegetation within the soil collar was removed.
Herbs in these plots remained undisturbed and were assigned to SAR
application, but there were no trees in our experiment plots. Previous
investigations have indicated that the damage to the stomata due to
SAR impaired the plant growth and yield because it could result in
photosynthesis and transpiration alterations (Francisco Sant'AnnaSantos et al., 2006; Neufeld et al., 1985). The occurrence of cells
with abnormal quantities of starch has also been found, which was
thought to be related to the inhibiting effect of SAR on the translocation of carbohydrates from the leaves to the roots (Rennenberg et al.,
1996). Therefore, it is important for us to quantify the effect of SAR on
Rs in the context of coupling of underground processes and aboveground trees, because Rs is related to leaf photosynthesis (Kuzyakov
and Gavrichkova, 2010). Future work should concentrate on investigating the effects of SAR on soil–plant system or ecosystem.
The use of trenching to subdivide Rs may overestimate Rh because
of inherent limitations of the method and the way we applied it
(Hanson et al., 2000). One possibility is that some roots existed
below 30 cm depth, and consequently some autotrophic respiration
which occurred in trenched plots was assigned to Rh (Lavigne et al.,
2003). However, the extent of this error with our trenching depth
was probably small. There are two reasons for this. Firstly, most respiratory CO2 is produced near the surface of forest soil, because the
large majority of fine-root biomass and recent detritus are found in
the upper 5–10 cm, and so the contribution of CO2 from deep soil
was most likely small (Muhr and Borken, 2009). For example, Wang
et al. (2005) have reported that the fine root density in soil at
0–10 cm was 1.4, 1.6, 3.7, and 6.9 times greater than that at 10–20,
20–30, 30–40, and 40–50 cm, respectively. We also observe rare roots
below 30 cm depth at one soil pit. Moreover, the soil in our experiment
site is shallow and develops from quartzite bedrock. Trenches were cut
(>30 cm) into the soil to obtain a uniform depth for all trenching plots.
Our inferences and methods might be partly verified by a previous Rs
partitioning study by Lavigne et al. (2003).
S. Chen et al. / Geoderma 189–190 (2012) 65–71
5. Conclusions
It can be concluded that the different SAR treatments showed similar temporal patterns of Rs and Rh. Annual mean Rs (autotrophic +
heterotrophic components) rates were significantly inhibited by
SAR treatments. SAR had no significant effects on Rh. The ratio of Rh
to Rs was significantly higher in the CK than in the acid rain added
treatments. In the subtropical secondary forest, soil temperature
and moisture were two controlling factors regulating the seasonal
patterns of Rs and Rh for each of the SAR treatment.
Acknowledgments
This study was financially supported in part by the National Natural
Science Foundation of China (NSFC 41005088 and 41175136) and the
Project by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (10KJB610006). We thank Han Zhang, Yong
Zhang, Qingzi Meng and Jingquan Ren for their help in the field work.
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