Soil Biology & Biochemistry 42 (2010) 1728e1734
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Soil Biology & Biochemistry
journal homepage: www.elsevier.com/locate/soilbio
Are ecological gradients in seasonal Q10 of soil respiration explained
by climate or by vegetation seasonality?
Xuhui Wang a, Shilong Piao a, *, Philippe Ciais b, Ivan A. Janssens c, Markus Reichstein d,
Shushi Peng a, Tao Wang a, b
a
Department of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education,
Peking University, Beijing 100871, China
Laboratoire des Sciences du Climat et de l’Environnement, CEA CNRS UVSQ, 91191 Gif sur Yvette, France
c
Department of Biology, University of Antwerpen, Universiteitsplein 1, 2610 Wilrijk, Belgium
d
Max Planck Institute for Biogeochemistry, PO Box 100164, 07701 Jena, Germany
b
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 20 March 2010
Received in revised form
1 June 2010
Accepted 10 June 2010
Available online 25 June 2010
Soil respiration (SR) is highly sensitive to future climate change, and particularly to global warming.
However, considerable uncertainties remain associated with the temperature sensitivity of SR and its
controlling processes. Using 384 field measurement data from 114 published papers and one book, this
study quantifies the variation in the seasonal Q10 values of soil respiration, the multiplier by which
respiration rates increase for a 10 C increase in temperature, and its drivers across different sites. No
significant correlation between Q10 and mean annual temperature or mean annual precipitation is found
when statistically controlling seasonal changes in vegetation activity, deduced from satellite vegetation
greenness index observations (normalized difference vegetation index, or NDVI). In contrast, the seasonal
amplitude of NDVI is significantly and positively correlated with the apparent Q10 of SR. This result
indicates that the variations of seasonal vegetation activity exert dominant control over the variations of
the apparent Q10 of SR across different sites, highlighting the ecological linkage between plant physiological processes and soil processes. It further implies that the seasonal variation of vegetation activity
may thus dominate the apparent seasonal temperature sensitivity. We conclude that the apparent Q10
value of SR estimated from field measurements is generally larger than the intrinsic temperature
sensitivity of soil organic matter decomposition, and thus cautions should be taken when applying
apparent Q10 values directly in ecosystem models. Our regression analysis further shows that when the
amplitude of NDVI variation approximates 0 (and thus when the seasonality in vegetation activity is
marginal), the residual Q10 of SR for soil temperature measured at 5 cm depth is about 1.5.
Ó 2010 Elsevier Ltd. All rights reserved.
Keywords:
Carbon cycle
Climate
Q10
Soil respiration
Temperature sensitivity
Vegetation activity
1. Introduction
Over the last decades, soil respiration (SR) has attracted
considerable research interest, mainly because SR not only is an
essential process in the global carbon cycle, but also is very sensitive to temperature that has dramatically increased during the last
three decades, and will continue to increase in this century (IPCC,
2007). Supposing, for instance, that warming would lead to a 1%
increase of global SR per year, this extra CO2 source would outweigh
the projected annual increase in fossil fuel emissions (Schimel,
1995). Obviously, the response of SR to global warming is sensitive to any change in the current relationship between SR and
* Corresponding author. Tel.: þ86 10 6276 5578; fax: þ86 10 6275 6560.
E-mail address: [email protected] (S. Piao).
0038-0717/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved.
doi:10.1016/j.soilbio.2010.06.008
temperature. Therefore, better understanding of the temperature
sensitivity of SR is strongly needed to reduce uncertainties in the
current global and regional carbon balance and in predictions of
future feedbacks in the coupled carbon-climate system (Cox et al.,
2000; Raich et al., 2002; Piao et al., 2008). Estimates of Q10, the
factor by which respiration is multiplied when temperature
increases by 10 degrees, have been performed at many sites around
the world. However, although there is abundant evidence showing
that the Q10 of soil respiration declines from the boreal to the
temperate zone, the multitude of large scale syntheses of these data
(e.g. Schleser, 1982; Raich and Schlesinger, 1992; Kirschbaum, 1995;
Janssens et al., 2003; Reichstein et al., 2003; Chen and Tian, 2005;
Bahn et al., 2008; Peng et al., 2009) have so far not succeeded at
establishing which environmental variable determines the spatial
variation of the apparent Q10 of SR. In this study, we compile and
harmonize field measurements of the sensitivity of SR to seasonal
X. Wang et al. / Soil Biology & Biochemistry 42 (2010) 1728e1734
variations in temperature (from here on noted seasonal Q10), from
384 samples reported by 114 peer-reviewed publications and one
published book. Using this new database, we then analyze the
relationship between seasonal Q10 and climatic drivers (temperature and precipitation) or biological drivers, such as satellitederived Normalized Difference Vegetation Index (NDVI) data.
SR is comprised of two dominant fluxes, controlled by distinct
processes: plant root respiration (autotrophic respiration; Ra) and
soil organic carbon (SOC) decomposition by soil fauna and soil
microbes during which CO2 is released (heterotrophic respiration;
Rh) (Hanson et al., 2000). Accordingly, the response of SR to
temperature is determined by the effects of temperature on root
respiration, SOC decomposition and their interactions, which are
also affected by soil moisture, nutrient cycling, substrate availability and plant species traits (Cornwell et al., 2008). A number of
recent studies have suggested that seasonal vegetation activity
plays an important role in the seasonal variation of SR. For example,
Boone et al. (1998) found that variations in SR through the growing
season in a temperate forest are determined mainly by variations of
root respiration in response to seasonal temperature changes.
Curiel Yuste et al. (2004) showed that variations in plant phenology
significantly contributed to the variations in calculated seasonal Q10
values of SR in a mixed temperate forest, which was later attributed
directly to seasonal differences in ecosystem photosynthesis
(Sampson et al., 2007). Moreover, Bader and Cheng (2007) reported
that the seasonal Q10 of SOC decomposition is obscured by seasonal
changes in rhizosphere priming effects of Populus fremontii, via
which the presence of live roots may accelerate SOC decomposition.
Because of this link between the seasonality of vegetation activity
and the seasonal changes in SR, the main hypothesis tested in this
study is that the different seasonal Q10 values of SR observed at
different sites can be better explained by differences in the seasonal
amplitude of vegetation activity than by climatic differences.
2. Materials and methods
2.1. Data sets
In this study, we assembled literature data on seasonal Q10
values of SR estimated from chamber-based SR measurements in
the field. To avoid confusion in explaining variations of Q10, data
derived from measurements in manipulated environment or
laboratory incubations are not included in our database. In total, we
compiled Q10 values of SR for 384 samples from 114 studies and one
book (see Fig. 1 and Table S2), at various sites distributing from
19 N to 74 N. However, we only included the Q10 values derived
from longer than six months of measurements (256 samples). The
samples cover almost all major ecosystem types of the Northern
Hemisphere including deciduous broadleaved forest (DBF; 34
Fig. 1. Spatial distribution of sites included in the soil respiration Q10 database.
1729
samples), deciduous coniferous forest (DCF; 5 samples), evergreen
broadleaved forest (EBF; 34 samples), evergreen coniferous forest
(ECF; 94 samples), mixed forest (MF; 24 samples), shrubland
(11 samples), wetland (1 sample), grassland (29 samples), desert
(4 samples), plantation (4 samples) and cropland (16 samples). In
addition to Q10, we also compiled several physical and environmental variables at these sites, including latitude and longitude,
elevation, mean annual temperature (MAT), mean annual precipitation (MAP), vegetation type, and the methods applied to measure
SR and to measure soil temperature.
Normalized Difference Vegetation Index (NDVI) defined as the
ratio of the difference between near-infrared reflectance and red
visible reflectance to their sum, is an indicator of vegetation
productivity (Tucker et al., 2005). NDVI data used in this study are
produced by the Global Inventory Monitoring and Modeling
Studies (GIMMS) group using the AVHRR/NOAA series satellites at
a spatial resolution of 8 8 km2 and 15-day interval, for the period
from 1982 to 2002 (Tucker et al., 2005). Information about seasonal
changes in temperature and precipitation for each site is derived
from the monthly mean temperature and precipitation dataset
during the period of 1982e2002 from the Climatic Research Unit
(CRU), School of Environmental Sciences, University of East Anglia,
UK (Mitchell and Jones, 2005). Temperature and precipitation of
the CRU dataset fit well with the corresponding observation data
(Fig. S1).
2.2. Analyses
Since the amplitude of seasonal soil temperature generally
varies with soil depth, Q10 depends on the depth of the soil
temperature measurements (Janssens et al., 2003; Pavelka et al.,
2007; Reichstein and Beer, 2008). In order to test this in our dataset, we compared Q10 inferred by using temperature from five
distinct depths: soil surface (ST0), 2 cm (ST2), 5 cm (ST5), 10 cm
(ST10) and 15 cm (ST15). Here, we employ a non-parametric test,
the Wilcoxon’s Signed Rank Test (Wilcoxon, 1945; Siegel, 1956)
instead of the classic Student’s t-test. This is not only because our
database contains a limited number of sites where at least two Q10
values based on soil temperature at different depths can be calculated, but also because the Q10 distribution at these sites does not
follow a normal distribution (e.g. for ST10, KeS test, P < 0.01),
which is an essential requirement for the validity of t-test. In the
basic structure of the Wilcoxon’s Signed Rank Test, the difference
between two corresponding values in the two groups (e.g. the
difference between Q10 derived with soil temperature measurement at 5 cm in site A and that at 10 cm in site A) are ranked
regardless of the sign, the smallest absolute difference is ranked 1.
After the ranking process, the original sign is assigned to the corresponding rank. Both sum of the positive ranks and sum of the
negative ranks are then calculated, of which the smaller is applied
in calculating the probability assuming the null hypothesis.
In order to attribute the variations in Q10 to climatic drivers or to
seasonality in plant activity, we had to exclude that differences in
methodology or differences in vegetation type did not confound
our analysis. Our database includes SR data obtained with different
methods (closed static chamber (CSC), closed dynamic chamber
(CDC), open dynamic chamber (ODC), and other methods (OTM)).
In order to test if Q10 values differed among these four SR
measurement methods, one-way analysis of variance (ANOVA) was
conducted for Q10 values derived from temperature measurement
at 5 cm depth. Different measurement methods did not differ
statistically significantly in Q10 (P > 0.05), confirming earlier work
by Janssens et al. (2000) who found that four different techniques
gave similar estimates of seasonal Q10 of SR in a pine forest
(although estimates of the basal respiration rate differed widely).
X. Wang et al. / Soil Biology & Biochemistry 42 (2010) 1728e1734
3. Results
a
35%
31.6%
ST0
30%
26.3%
Frequency
25%
19.3%
20%
15%
12.3%
8.8%
10%
5%
1.8%
0%
<1.5
1.5-2.0
2.0-2.5
2.5-3.0
3.0-3.5
>3.5
Q10
b
40%
35%
ST5
33.8%
30%
Frequency
ANOVA was also applied to test whether Q10 differed among
ecosystems. Again, this was not the case, so from here on we
ignored the potential confounding effects of measurement method
and ecosystem type in our analysis.
In order to investigate the factors controlling variations in Q10,
nine different variables are correlated with Q10: MAT, MMT (mean
air temperature during the measurement period), MAP, MMP
(mean precipitation during the measurement period), MANDVI
(mean annual NDVI), MMNDVI (mean NDVI during the measurement period), seasonal amplitude of temperature (TS), seasonal
amplitude of precipitation (PS) and seasonal amplitude of NDVI
(NDVIS). MMT is calculated by two steps. First, mean monthly air
temperature during the SR measurement months were calculated
for each year from 1982 to 2002; Then, we average the value of the
21 years. Similarly, MMP is defined as the sum of mean monthly
precipitation during the SR measurement period from 1982 to
2002. MMNDVI (or MANDVI) is the average of mean biweekly NDVI
during the SR measurement period (or during the whole year) from
1982 to 2002. TS is calculated at each site as the average difference
between maximum and minimum monthly temperature during
the SR measurement period from 1982 to 2002. We similarly
calculated PS and NDVIS. It should be noted that in a Mediterranean
site (Viterbo in Central Italy), monthly precipitation is negatively
correlated with temperature, while seasonal Q10 is larger than 1,
implying that the seasonal change in soil respiration synchronizes
with temperature, but not with precipitation. Furthermore,
a previous study has suggested that soil moisture is also one of the
limited factors of SR at the site (Tedeschi et al., 2006). Therefore, PS
was calculated as the average difference between minimum and
maximum monthly precipitation during the SR measurement
period at the Viterbo site in Central Italy.
In order to determine whether seasonal Q10 values of SR
observed at different sites are primarily determined by the seasonal
amplitude of vegetation activity or by climatic differences, we used
partial correlation analysis. Partial correlation is the correlation of
two variables while statistically controlling for a third or more
other variables. If the partial correlation approaches 0, the inference is that the original correlation is spurious and there is no
direct causal link between the two original variables.
25%
20.8%
20%
15%
11.7%
14.3%
10.4%
10%
9.1%
5%
0%
<1.5
1.5-2.0
2.0-2.5
2.5-3.0
3.0-3.5
>3.5
Q10
c
30%
27.9% 27.9%
ST10
25%
Frequency
1730
20%
15%
10%
3.1. Effects of soil temperature measurement depth on Q10 value
5%
Our results show that across all sites, Q10 values are larger than 1,
suggesting that SR is generally increased with seasonal increase in
temperature. The average value SD (standard deviation) of Q10
estimated by using AT (air temperature), ST0 (soil surface temperature), ST2 (soil temperature at 2 cm), ST5 (soil temperature at
5 cm), ST10 (soil temperature at 10 cm), ST15 (soil temperature at
15 cm), ST20 (soil temperature at 20 cm), and other depths are
2.2 1.0 (17 samples), 2.2 0.6 (57 samples), 2.8 1.0
(16 samples), 2.4 0.8 (77 samples), 3.1 1.0 (46 samples),
2.7 0.7 (10 samples), 2.6 2.7 (5 samples), and 2.6 1.2
(28 samples), respectively. The frequency distribution of Q10 at the
three different depth (ST0, ST5 and ST10), on which most SR
measurements were based, is shown in Fig. 2. In order to test the
significance of the depth effect, we selected sites where at least two
different depths were available to calculate Q10, and performed the
Wilcoxon’s Signed Rank Test. The results indicate that Q10 is
significantly higher when temperature data from deeper soil layers
are used, except between ST10 and ST15 (Table S1), which may be
related to the increasing time lag that creates artifacts (Janssens
et al., 2003). This result supports former conclusions that the
depth at which temperature is measured influences the inferred
0%
11.6%
11.6%
9.3%
9.3%
7.0%
2.3%
<1.5 1.5-2.0 2.0-2.5 2.5-3.0 3.0-3.5 3.5-4.0 4.0-4.5 >4.5
Q10
Fig. 2. Histogram of Q10 derived from (a) soil temperature at the surface (ST0), (b) soil
temperature at 5 cm (ST5) and (c) soil temperature at 10 cm (ST10).
Q10 of SR (Pavelka et al., 2007; Reichstein and Beer, 2008; Peng
et al., 2009) and implies that harmonized temperature measurement depths are required in any data-mining exercise to assess
the temperature dependence of SR. In the rest of this study, we
therefore used Q10 values estimated from ST5 measurements, since
published Q10 values are most commonly based on soil temperature
at 5 cm depth (n ¼ 77 out of 256).
3.2. Relationships between Q10 value and climatic factors and NDVI
Fig. 3 illustrates the relationships between Q10 and environmental
drivers, MAT, MMT, TS, MAP, MMP, PS, MANDVI, MMNDVI, and NDVIS
(See “Materials and methods” section for detailed descriptions). As
X. Wang et al. / Soil Biology & Biochemistry 42 (2010) 1728e1734
7
a
7
R =-0.51 P<0.001
1
7
0
d
5
10 15 20 25
R =-0.34 P=0.004
5
1
10 15 20 25
0
e
R =-0.32 P=0.009
1000
MAP(mm)
3
0
R =-0.11 P=0.373
1000
MMP(mm)
h
2000
3
1
R =-0.38 P=0.002
3
-100
R =-0.02 P=0.896
5
Q10
5
7
f
40
1
7
0
i
100 200 300 400
Ps(mm)
R =0.60 P<0.001
5
Q10
g
2000
7
20
Ts(oC)
5
1
0
R =-0.05 P=0.713
3
MMT(oC)
Q10
Q10
Q10
7
0
5
3
c
1
-5
MAT(oC)
5
Q10
3
1
-5
7
5
Q10
3
7
R =-0.49 P<0.001
5
Q10
Q10
5
b
1731
3
1
3
1
0.1 0.2 0.3 0.4 0.5 0.6 0.7
0.1 0.2 0.3 0.4 0.5 0.6 0.7
0.1 0.2 0.3 0.4 0.5 0.6 0.7
MANDVI
MMNDVI
NDVIs
Fig. 3. Relationships of soil respiration Q10 value based on temperature at the soil depth of 5 cm with (a) mean annual temperature (MAT), (b) mean air temperature of
measurement period (MMT), (c) amplitude of seasonal air temperature during measurement periods (Ts), (d) mean annual precipitation (MAP), (e) mean precipitation of
measurement period (MMP), (f) amplitude of seasonal precipitation during measurement periods (Ps), (g) mean annual NDVI (MANDVI), (h) mean NDVI of measurement period
(MMNDVI), and (i) amplitude of seasonal NDVI (NDVIs) during measurement periods. Correlation coefficient between two variables (R) and its statistical significance level (P) are
provided on the upper right corner of each sub-figure.
shown in Fig. 3, the correlation of Q10 with NDVIS (R ¼ 0.60 P < 0.001)
is stronger than those with any other drivers. Statistically significant
negative correlations were also found between Q10 and MAT and
between Q10 and MMT (R ¼ 0.51, P < 0.001; R ¼ 0.49 P < 0.001).
However, partial correlation analysis suggests that Q10 is insignificantly correlated with both MAT and MMT when statistically
removing the confounding effects of MAP, MMP, PS and NDVIS on Q10
(RQ10-MAT. MMT, MAP, MMP, Ps, NDVIs ¼ 0.10, P ¼ 0.476; RQ10-MMT. MAT, MAP,
MMP, Ps, NDVIs ¼ 0.02, P ¼ 0.875), suggesting that the significant
correlation between Q10 and MAT shown in Fig. 4a should thus be
regarded as a response to the combined effects of other climatic and
biotic factors rather than as temperature “acclimation” of SR. Similarly, the correlations between Q10 and MAP, Q10 and MMP and Q10
and PS are insignificant when the confounding effects of other factors
are removed (RQ10-MAP. MAT, MMT, MMP, Ps, NDVIs ¼ 0.05, P ¼ 0.732; RQ10MMP. MAT, MMT, MAP, Ps, NDVIs ¼ 0.15, P ¼ 0.289; RQ10-Ps. MAT, MMT, MAP, MMP,
NDVIs ¼ 0.26, P ¼ 0.060), respectively. In contrast, co-variation with
MAT, MAP and PS does not affect the conclusion that NDVIS is
significantly and positively correlated with Q10 (RQ10-NDVIs. MAT, MMT,
MAP, MMP, Ps ¼ 0.33, P ¼ 0.017). Therefore, our results support the
hypothesis that seasonal amplitude of vegetation activity, better than
climate, explains the observed difference of seasonal Q10 of SR at
different sites.
Larionova et al., 2007). Schleser (1982) argued that Q10 takes higher
values in cold regions than in warm regions, and thus temperature
increases would have greater impacts on SR in northern latitudes
than in southern latitudes. However, a number of studies have
suggested that the temperature sensitivity of SR declines at higher
temperature due to ecosystem self-adjustment on a long time scale
(Kirschbaum, 1995, 2000; Parton et al., 1995; Davidson and
Janssens, 2006).
4. Discussion
4.1. The dependence of Q10 on temperature
How temperature impacts SR is one of the key knowledge gaps
that limit predictions of the future carbon cycle (Kirschbaum, 2006;
Fig. 4. Uncertainty of intercepts of the regression between soil respiration Q10 value
based on temperature at the soil depth of 5 cm and NDVIs. A normal distribution curve
was fitted.
1732
X. Wang et al. / Soil Biology & Biochemistry 42 (2010) 1728e1734
Our analysis of Q10 suggests that there is a significant negative
correlation between Q10 and MAT across the studied sites
(R ¼ 0.51, P < 0.001) (Fig. 3a), which is consistent with the results
of previous studies (Schleser, 1982; Kirschbaum, 1995; Janssens
et al., 2003; Chen and Tian, 2005; Peng et al., 2009). For example,
Chen and Tian (2005) reported that a 1 C increase in MAT will
reduce Q10 by 0.06e0.08 at global scale. However, after removing
the contribution explained by the seasonal amplitude of vegetation
activity (NDVIS) and other climatic variables (MMT, MAP, MMP, Ps),
our study reveals that Q10 is not significantly correlated with MAT
(P ¼ 0.476). This result not only implies that NDVIs is a better
variable to use for generating maps of Q10 of SR at global scale, but
also suggests that increased temperature alone may not necessarily
decrease Q10.
It should be noted, however, that our results still can not answer
whether or not soil respiration “acclimation” occurs across the
temperature gradient, since the relative abundance of various soil
carbon substrates was neglected in our analysis due to lack of
information. It has been suggested that different SOC pools should
have different intrinsic temperature sensitivities (Knorr et al., 2005;
Davidson and Janssens, 2006; Feng and Simpson, 2008; Hartley
et al., 2008), and thus that changes in the relative abundance of
different soil carbon substrates across sites may lead to differences
in the temperature sensitivity of overall SOC decomposition (Knorr
et al., 2005; Fierer et al., 2006). So far the evidence for this is
ambiguous; some studies were able to detect different Q10s for
different substrates (Knorr et al., 2005; Leifeld and Fuhrer, 2005;
Fierer et al., 2006), whereas others did not or obtained inconsistent results (Fang et al., 2005; Reichstein et al., 2005; Conen et al.,
2006, 2008). Further research is needed to quantify the effects of
different relative abundance of various soil carbon substrates on
the different SR sensitivity to temperature observed at different
sites.
4.2. The dependence of Q10 on vegetation activity
The importance of vegetation activity in controlling the
apparent temperature response of SR has increasingly been
recognized (Reichstein et al., 2003; Curiel Yuste et al., 2004;
Högberg and Read, 2006; Sampson et al., 2007). Several previous
studies have found a strong positive correlation between basal rate
of SR (SR at 10 C) and vegetation productivity across different
forest sites (Sampson et al., 2007; Irvine et al., 2008). Furthermore,
Sampson et al. (2007) also suggested that variation in the Q10 of SR
among studies may be related to seasonal differences in photosynthesis, which is supported by the finding of this study. Our
results show that there is no significant relationship between
MANDVI (or MMNDVI) and Q10 (P > 0.05), but NDVIS is significantly
positively correlated with Q10 across all sites (RQ10-NDVIs. MAT, MMT,
MAP, MMP, Ps ¼ 0.33, P ¼ 0.017). This implies that the seasonal
amplitude of vegetation activity may be a good indicator of the
distribution of Q10 of SR at global scale.
In general, the seasonal Q10 of SR can be stimulated by seasonal
vegetation activity through several (at least two) mechanisms. First,
vegetation activity has a direct effect on SR via root and rhizosphere
respiration. The root and rhizosphere component of SR is tightly
coupled with aboveground photosynthesis (Högberg et al., 2001;
Kuzyakov and Cheng, 2001; Tang and Baldocchi, 2005) and is
largely controlled by allocation of recent photosynthates to roots
and root exudates. This process provides more substrate to root and
rhizosphere respiration during the growing season than during the
dormant season, and therefore enlarges the amplitude of seasonal
SR. In virtually all field measurements, the estimated Q10 value is
based on the regression of seasonal change in observed SR against
contemporary seasonal changes in soil temperature. At more than
95% of the sites used in this study, we found a positive correlation
between monthly temperature and monthly NDVI. Thus, a more
pronounced seasonality of vegetation activity (NDVIS) is logically
expected to produce a higher “calculated” seasonal Q10 value of SR.
Second, vegetation activity indirectly affects microbial decomposition of SOC via the so-called rhizosphere priming effect, in which
interactions between roots and soil may accelerate microbial
decomposition of native SOC (Kuzyakov and Cheng, 2001; Dijkstra
and Cheng, 2007). Higher vegetation productivity brings more
substrates into the soil (Wardle et al., 2004; Bardgett et al., 2005;
Fontaine et al., 2007; Schaefer et al., 2009). These fresh substrates
may carry enough energy for soil fungi and bacteria to decompose
SOC more efficiently (Fontaine et al., 2007). Evidence has shown
that these photosynthetic carbon input may account for substantial
increases in SOC decomposition (Curiel Yuste et al., 2007). Therefore, a higher seasonality of vegetation activity (NDVIS) would also
lead to a higher seasonality of substrate input to the soil and
a stronger rhizosphere priming effect, and, hence, a higher
“calculated” seasonal Q10 value of SOC decomposition. It should be
noted that, however, it is not clear how much either of the above
two processes contributes to the significant relationship between
seasonal vegetation activity and seasonal Q10 of SR observed in our
study.
4.3. Implications for modeling SR
It is important to better understand the temperature sensitivity
of SR. An assumption made in most SR measurements and some
models is that the temperature sensitivity of SR equals the value of
Q10 estimated from the exponential regression analysis between
seasonal variations in temperature and SR. As mentioned earlier,
however, seasonally-derived Q10 values of SR generally reflect
variables other than soil temperature (e.g. plant phenology, root
growth dynamics, substrate input), and thus the ‘intrinsic’
temperature sensitivity of SR should be calculated after removing
the contribution of other factors on the seasonal change in SR. It has
been suggested that the field measurement derived seasonal Q10 of
SR will generally be larger than the intrinsic temperature sensitivity
of SR (Curiel Yuste et al., 2004). In other words, it should be avoided
to use seasonal Q10 of SR as SR sensitivity on temperature change in
the global carbon cycle models. In addition, the impacts of vegetation activity on soil respiration are at least partially included in
most carbon cycle models through dividing SR into four different
components (heterotrophic respiration, microbial growth respiration, root maintenance respiration, and root growth respiration). It
should be noted that these components of SR may respond to
warming in different ways (Hartley et al., 2007; Zhou et al., 2007).
Temperature generally directly influences heterotrophic respiration and root maintenance respiration, while indirectly affects root
growth respiration mainly through influencing root biomass and
resource allocation. Separating temperature sensitivities of these
processes is necessary for accurately assessing how environmental
changes may alter the balance of CO2 flux from belowground
ecosystems. However, very little is known about this, since current
techniques separating autotrophic and heterotrophic components
in the field have various limitations and resulting inaccurateness
(Subke et al., 2006; Maseyk et al., 2008; Bronson et al., 2008).
Overall, how to quantify the “intrinsic” temperature sensitivity of
SR and all of its components still remains a big challenge.
The regression analysis between Q10 estimated from ST5
measurements and NDVIS across all sites (Fig. 3i) further suggests
that Q10 approximates 1.5 when NDVIS is extrapolated down to zero
(the intercept of regression). In other words, without seasonal
changes in vegetation activity, the corresponding Q10 based on soil
temperature at 5 cm would be about 1.5, which is only 60% of mean
X. Wang et al. / Soil Biology & Biochemistry 42 (2010) 1728e1734
field measurement estimated Q10 values based on soil temperature
at 5 cm. This is close to a global scale optimized temperature
sensitivity of SOC decomposition (Q10 w ¼ 1.4, Ise and Moorcroft,
2006). In order to further analyze the uncertainty of this value,
we performed regression analysis between Q10 value from ST5
measurements and NDVIS for 1000 times. For each time, we
randomly selected only 80% of samples from the sites. Fig. 4 shows
histogram of the regression-derived intercepts of Q10 values. As
shown in Fig. 4, the intercepts of Q10 value vary from 1.4 to 1.6 in
around 75% of regression performances, while only 19 performances show intercepts larger than 1.7.
4.4. Conclusions
The results presented in this paper suggest that the explanation
of variations in seasonal amplitude of vegetation activity on the
variation of seasonally-derived temperature sensitivity of SR is
higher than that of climatic variables such as temperature and
precipitation. This result highlights the linkage between plant
physiological processes and soil processes, but also implies that the
vegetation driven processes may substantially stimulates seasonal
Q10 of SR. Thus, the value of seasonally-derived Q10 value of SR from
field measurements is necessarily larger than the ‘intrinsic’
temperature sensitivity of SR due to the confounding effects of
seasonal variations in plant phenology, root growth and substrate
input to the soil. Therefore, one can not directly apply the seasonally-derived values of Q10 from field measurements to the current
biogeochemical models. How to accurately estimate the ‘intrinsic’
temperature sensitivity of SR from field measurements is a big
challenge for quantifying future carbon cycle evolution, and further
studies are strongly needed.
Acknowledgements
We thank Zhu Biao for useful comments. This study was supported by China Educational Foundation for Students of Sciences
(No. J0630531), the National Natural Science Foundation of China
(grants 30970511 and 30721140306), and the European Community’s Seventh Framework Programme ([FP7/2007-2013]) under
grant agreement No. 24316. IAJ acknowledges support from the
Flemish National Science Foundation.
Appendix. Supplementary data
Supplementary data associated with this article can be found in
the online version, at doi:10.1016/j.soilbio.2010.06.008.
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