Introduction
Soil contains the largest ecosystem carbon (C) stock (Batjes, 1996).
Widespread land degradation (Dregne, 2002), including land use change and soil
and vegetation degradation, has resulted in severe soil C and nitrogen (N)
loss (Wang et al., 2009; Parras-Alcántara et al., 2013), which is
estimated to be 19–29 Pg C worldwide (Lal, 2001). Restoration of the
degraded ecosystems, therefore, has a great potential to sequestrate C from
the atmosphere (Lal, 2004) at an annual rate of 0.9–1.9 Pg C for a 25- to
50-year period in drylands (Lal, 2001).
Grassland stores about 15.2 % of the terrestrial ecosystem vegetation and
soil C stock (Ajtay, 1979). Either the aboveground vegetation (Fan et al.,
2007) or the top 1 m of soil and root C stock (Yang et al., 2008) in an
alpine meadow in the Qinghai–Tibet Plateau (QTP) account for a large
proportion of those in grassland ecosystems in China (Ni, 2002). However, over
one-third of the grassland in the QTP has been severely degraded due to climate
change, grazing and road constructing since the 1990s (Ma et al., 1999), which
has led to 1.8 Gg C loss in aboveground C stock from 1986 to 2000 (Wang et
al., 2008). In addition to the vegetation C loss, land degradation could also
result in decline in soil C and N (Wang et al., 2008; Wen et al., 2013), and
consequently might alter net C balance in the alpine meadow (Li et al.,
2011).
The primary factor causing “black soil type” degradation over the QTP is
rodent grazing and burrowing (Ma et al., 1999). Rodent grazing activities
trigger decline in biomass; lead to change in belowground biomass
distribution, soil structure and microclimate; cause soil erosion and
nutrient loss; and finally affect the ecosystem C balance (Li et al., 2011).
Current studies about ecosystem C balance in alpine meadows focus on net
ecosystem exchange (NEE) (Kato et al., 2004), inter-annual variation in NEE
(Kato et al., 2006), soil respiration (Rs) and ecosystem
respiration (ER) responses to experimental warming (Peng et al., 2014b; Luo
et al., 2010; Lin et al., 2011). Effects of rodent-induced land degradation
on ecosystem CO2 fluxes have rarely been investigated. Studies only
examining the responses of Rs to land degradation (Zhang et al.,
2010; J. Wang et al., 2007) cannot provide solid evidence for determining the
response of ecosystem C balance. No field experiment has been conducted in
the permafrost region of the QTP to investigate the effect of land
degradation on NEE, a direct measure of the ecosystem C balance, and on its
components: ER and gross ecosystem production (GEP). We conducted a field
study to investigate (1) how the NEE and its components respond to
rodent-induced land degradation, and (2) how biotic and abiotic factors
affect those CO2 fluxes with land degradation processes in a
Kobresia pygmaea-dominated alpine meadow in a permafrost area of the
QTP.
Location of the study area.
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Materials and methods
Site description
The study site is situated in the source
region of the Yangtze River and in the middle of the QTP (Fig. 1,
92∘56′ E, 34∘49′ N) with a mean altitude of
4635 m a.s.l. and a typical alpine climate. Mean annual temperature is
-3.8 ∘C (2000–2010) with minimum mean monthly temperature of
-27.9 ∘C in January and maximum mean of 19.2 ∘C in July.
Mean annual precipitation is 290.9 mm with 95 % falling from May to
October. Mean annual potential evaporation is 1316.9 mm, mean annual
relative humidity is 57 % and mean annual wind velocity is
4.1 m s-1 (Lu et al., 2006). The study site is a winter-grazed range,
dominated by alpine meadow vegetation: Kobresia capillifolia,
Kobresia pygmaea and Carex moorcroftii, with a mean plant
height of 5 cm. Plant roots are mainly within the 0–20 cm soil layer, with
average soil organic carbon (SOC) of 1.5 %. Soil development is weak and
is alpine meadow soil (soil taxonomy in China, and Cryosols in World
Reference Base (WRB) taxonomy; IUSS, 2006) with a mattic epipedon at
approximately 0–10 cm depth and an organic-rich layer at 20–30 cm
(G. Wang et al., 2007). The parent soil material is of fluvio-glacial origin,
and sand (> 0.05 mm) content is about 95 %. Permafrost thickness
observed near the experimental site is 30–70 m, and the depth of the active
layer is 1.5–3.5 m (Wu and Liu, 2004). However, the thickness of the active
layer has been increasing at a rate of 3.1 cm yr-1 since 1995 due to
climatic warming (Wu and Liu, 2004).
Features of different habitats, which are represented by different
number of rodents holes (NRHs, deep and shallow), coverage, plant height
(H) and major plant species.
DD
NRHs
NRHs
Coverage
H ± SE
Major
(deep)
(shallow)
(%)
(cm)
species
D1
19
7
18
9.5 ± 1.2
Carex moorcroftii
D2
5
13
35
7 ± 0.7
Kobresia humilis, Kobresia pygmaea
D3
0
3
80
6.5 ± 0.2
Kobresia pygmaea
D4
12
15
42
8 ± 0.4
Carex moorcroftii, Kobresia pygmaea
D5
17
13
30
7.5 ± 0.5
Carex moorcroftii, Kobresia pygmaea
D6
2
0
60
12 ± 0.3
Carex moorcroftii
Major devices, measuring procedure, specific feature of methods and
equipments to conduct the measurement of soil chemical properties and
ecosystem CO2 fluxes.
Items
Devices or procedure
Specific feature
Literature
T
6000-09TC, Li-Cor, Utah, USA
A thermo-probe
Rs
6400-09, Li-Cor, Utah, USA
A collar 5 cm in depth
Zhou et al. (2007)
ER
6400XT, Li-Cor, Utah, USA
A collar 50 cm in depth
Steduo et al. (2002), Niu et al. (2008)
NEE
6400XT, Li-Cor, Utah, USA
A transparent chamber CO2 gradient
Steduo et al. (2002), Niu et al. (2008)
AGB
A frame and a ruler
Linear regression
Xu et al. (2015)
RB
An auger
Xu et al. (2015)
SOC
Walkley–Black method
Walkley et al. (1947)
TN
Kjeldahl nitrogen method
NH4+, NO3-
Spectrometer
LC
Spectrometer
Blair et al. (1995)
T is the soil temperature at 5cm depth; Rs, soil
respiration; ER, ecosystem respiration; NEE, net ecosystem exchange; AGB,
aboveground biomass; RB, root biomass; SOC, soil organic carbon; TN, total
nitrogen; NH4+ and NO3-, ammonia and nitrate nitrogen,
respectively; LC, labile carbon.
Experimental design and measurement protocol
Experimental design
We selected six habitats with different number of rodent holes (NRHs) and
community coverage in a mountain slope based on our filed observation. The
habitats were sequenced D1–D6 from east to northeast. The distance between
each habitat was about 200–300 m. Two subplots (2 m × 4 m) were set
up in each habitat. The NRHs, coverage, plant height and major species in
D1–D6 were shown in Table 1.
Measurement protocol
Soil temperature
Soil temperature at the depth of 5 cm was monitored by a thermo-probe
attached to a Li-Cor 6400 (Lincoln, NE, USA) when measurements of
Rs, NEE and ER were conducted.
CO2 fluxes: a PVC collar (80 cm2 in area
and 5 cm in height) was inserted 2–3 cm into soil permanently at the
centre of each plot for measuring Rs. The measuring procedure of
Rs was similar to that reported in former studies (Peng et al.,
2014b; Zhou et al., 2007). Ecosystem respiration and NEE were measured with a
transparent chamber (0.5 × 0.5 × 0.5 m) attached to an
infrared gas analyser (Li-Cor 6400, Lincoln, NE, USA). The method used was
similar to that reported by Steduto et al. (2002) and Niu et al. (2008).
Gross ecosystem production was the calculated as the sum of NEE and ER.
Ecosystem CO2 fluxes were measured once a month from June to September
in each plot.
Soil sampling
One soil sample was collected at the soil depth of 0–30 cm in each plot in
June 2012.
AGB and RB
Aboveground biomass (AGB) was obtained from a step-wise linear regression, with AGB
as the dependent variable, and coverage and plant height as independent
variables (Peng et al., 2014b; Xu et al., 2015). Coverage of each plot was
measured using a 10 cm × 10 cm frame in four diagonally divided
subplots replicated eight times in D1–D6 in June 2012. Plant height was
measured 40 times by a ruler and averaged for each plot. Root biomass (RB) was
obtained from soil samples that were air-dried for 1 week and passed
through a sieve (Φ=2 mm) to remove large particles. Roots were
separated from the soil by washing, and fine roots was retrieved by sieve
(Φ=0.25 mm). Living roots were separated from dead roots by their
colour and consistency (Yang et al., 2007), and the separated roots were dried
at 75 ∘C for 48 h.
Chemical analysis
Soil organic carbon was analysed using the Walkley–Black method (Walkley,
1947). Total nitrogen was measured via the Kjeldahl method. Ammonia and
nitrate N were measured colorimetrically through a spectrometer. Labile soil
carbon (LC) measurement was carried out by the procedure by Moscatelli et
al. (2007). Devices or procedure used in measuring above parameters, specific
feature of the measurement were inclued in Table 2.
Data analysis
The statistical differences of soil temperature, Rs, NEE, ER,
GEP, SOC, total nitrogen (TN), inorganic N, C : N and biomass in D1–D6
were tested by the one-way ANOVA analysis at the P<0.05, and the Tukey test
was used in doing the post hoc analysis for SOC, TN, inorganic N, C : N and
biomass. Previously to conduct the ANOVA, the Kolmogorov–Smirnov test and
Levene test were used to test the normality and homogeneity of variance of
the parameters. Monthly data of soil temperature, Rs, NEE and GEP
measured in each subplot from June to September were used in the analysis.
Plots in D3 and D6 were ranked as group I, and those in D1, D2, D4 and D5
were considered group II because the total NRHs in D3 and D6 was much lower
than that in D1, D2, D4 and D5. The statistical significance of CO2
fluxes between the two groups was also tested by one-way ANOVA analysis. The
monthly differences in CO2 fluxes were analysed by repeated ANOVA.
Relationships of Rs, NEE and ER with soil temperature, ABG, RB
and TN or inorganic N were analysed by linear regression analyses. Pearson
correlation analysis was used to investigate the relationships of NRHs with
soil chemical properties and biomass. The linear regression and Pearson
correlation were considered significant with P<0.05. Soil respiration, NEE
and ER data were the averages of 4 months in D1–D6 when conducting the
correlation analysis. All the analyses were conducted in SPSS 16.0 for
Windows.
Soil organic carbon (SOC), labile soil carbon (LC), total
nitrogen (TN), inorganic nitrogen (NH4+-N and NO3--N),
aboveground biomass (AGB) and root biomass (RB) in different sites (D1–D6)
and results (F values) of one-way ANOVA analysis.
DD
SOC
LC
TN
NH4+_N
NO3-_N
AGB
RB
C : N
(g kg-1)
(g kg-1)
(mg kg-1)
(mg kg-1)
(mg kg-1)
(g m-2)
(kg m-2)
D1
4.91 ± 0.13b
1.21 ± 0.13b
0.44 ± 0.02b
8.21 ± 0.32b
4.16 ± 0.62a
149
3.8 ± 0.06c
11.2 ± 0.7ab
D2
8.70 ± 1.19a
2.12 ± 0.31a
0.75 ± 0.10a
13.11 ± 1.23a
3.81 ± 0.51ab
145
13.8 ± 3.5a
11.5 ± 0.2ab
D3
5.02 ± 1.01b
1.23 ± 0.29b
0.46 ± 0.11ab
8.54 ± 1.00b
2.31 ± 0.38bc
272
11.3 ± 1.3a
10.9 ± 0.3a
D4
3.95 ± 0.62b
1.28 ± 0.34b
0.36 ± 0.05ab
7.56 ± 1.39b
2.62 ± 0.24bc
189
6.0 ± 1.5b
10.8 ± 0.3b
D5
3.77 ± 0.32b
0.9 ± 0.09b
0.38 ± 0.03b
9.38 ± 1.33b
1.98 ± 0.21c
141
6.1 ± 0.9b
10.1 ± 0.2b
D6
3.41 ± 0.35b
0.83 ± 0.04b
0.34 ± 0.02b
8.08 ± 0.76b
2.64 ± 0.10bc
336
5.7 ± 0.3b
9.9 ± 0.4b
F, P value
F=11.05, P<0.01
F=7.9, P<0.01
F=5.9, P<0.01
F=5.5, P<0.01
F=7.5, P<0.01
F=15.7, P<0.01
F=4.8, P=0.012
The values in the table were the average and standard error of soil samples
at each site. Different letters in each column stands for significant difference of at P<0.05 level, n=4.
Results (F values) of ANOVA on the effect of land degradation on
soil respiration (Rs), ER (ecosystem respiration), NEE (net
ecosystem exchange) and GEP (gross ecosystem respiration).
D1–D6
Group I and group II
Rs
ER
NEE
GEP
Rs
ER
NEE
GEP
F
1.69
2.64
1.35
2.27
7.41
8.21
1.59
6.01
P
0.12
0.04
0.26
0.06
0.01
0.006
0.21
0.02
Group I includes D3 and D6, while group II
includes D1, D2, D4 and D5. Numbers in bold stand for the statistical
significance at the P<0.05 level.
Results
Soil temperature
Soil temperature at 5 cm depth maximized
in July (Fig. 2) and average soil temperature of the 4 months had no
significant difference (P>0.05) among treatments. The monthly average soil
temperature was about 9.6–12.4 ∘C from D1 to D6. Soil temperature
also had no significant difference between group I and II.
Soil chemical properties and biomass
Soil organic carbon, LC,
TN, ammonia N and RB were higher in D2 than in other habitats (Table 3).
AGB was higher in D3 and D6 than in others. Soil organic carbon, LC, TN and
inorganic N (NH4+-N and NO3--N) had no obvious trend with the
increasing NRHs, whereas AGB was negatively correlated with the NRHs
(r=-0.89, P<0.05).
Soil respiration, net ecosystem exchange, ecosystem respiration and
gross ecosystem production
Repeated one-way ANOVA showed the significant seasonal change in
Rs (P<0.01), ER (P<0.05), NEE (P<0.01) and GEP (P<0.01). The maximum Rs and ER were in July (Fig. 3a, b), whereas
the maximum NEE and GEP were in June (Fig. 3c, d). Growing season average
Rs and NEE had no significant difference in D1–D6, while ER and
GEP were marginally higher in D3 and D6 than in others (Table 4). Soil
respiration, ER and GEP were significantly higher in group I than in group II
(P<0.05).
Relationship of <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mi mathvariant="normal">s</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, ER and NEE with soil temperature, soil nitrogen and
biomass
Ecosystem CO2 fluxes had no obvious relationship with soil temperature
(Fig. 4a), soil inorganic N (Fig. 4b) and RB (Fig. 4d), while they correlated
positively with AGB, with the steepest regression slope in Rs,
followed by ER and NEE (Fig. 4c).
Soil temperature in each degradation level (D1–D6) from June to
September. Error bars represent the standard error for D1–D6 in each month.
![]()
Monthly soil respiration (Rs, a), ecosystem
respiration (ER, b), net ecosystem exchange (NEE, c) and
gross ecosystem production (GEP, d) among different degradation
levels from June to September. Values in the bars were the average of four
replicates (two replicates in two subplots), and error bars are standard
errors.
![]()
Linear regressions of CO2 fluxes (soil respiration,
Rs; ecosystem respiration, ER; net ecosystem exchange, NEE) with
soil temperature (a), inorganic nitrogen (b), aboveground
biomass (c) and root biomass (d). Rs, ER and
NEE data were the average of four measurements from June to September within
two subplots; inorganic nitrogen and root biomass (0–30 cm) were derived
from soil samples at 0–30 cm depth in June.
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Discussion
C and N loss
Soil organic carbon, LC, TN and inorganic N were only significantly higher in
D2 in our study (Table 3). The results indicate that C and N loss induced by
rodent activities were different, with nutrient loss associated with
desertification and wind erosion in temperate grassland (Zhang et al., 2010)
or in the alpine meadow ecosystem (Xue et al., 2009). Soil C and N loss can
occur in degraded land by (1) reducing vegetative growth and exposing the
soil surface to wind and water erosion, and by (2) reducing the return of
litter to soil (Nunes et al., 2012). Higher AGB in D3 and D6 (Table 3)
suggests more litter returning to the soil, but more ecosystem CO2
emission from soil in terms of higher Rs could be the reason for
lower SOC, LC and TN in D3 and D6. Positive correlation between AGB and
Rs indicates that decomposition of fresh litter from AGB might be
the major component of Rs in the alpine meadow. The highest RB in
D2 in spite of lower AGB compared with that in D3 and D6 (Table 3) provides
evidence that RB is the major source of soil C and N in the alpine meadow
ecosystem. The results was similar to a study where soil C and N storage
increases were positively correlated with the increase of belowground biomass
allocation with grazing pressure (Li et al., 2011).
CO<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> fluxes
Soil temperature explains most of the temporal variation (Peng et al.,
2014a), but RB determines the spatial variation in Rs over the
QTP (Geng et al., 2012). The lack of an obvious relationship between
Rs and soil temperature (Fig. 4a) suggests other factors might be
involved in controlling the temporal variation in Rs in degraded
land. Significant reduction of Rs only appears on the severely
degraded alpine meadow level (Zhang et al., 2010). Rs being lower
in group I than in group II (1) supports the above finding because community
coverage in D1, D2, D4 and D5 (Table 1) conforms to the standard of the
severely degraded alpine meadow (Xue et al., 2009) and (2) indicates the
controlling effect of biomass on Rs in degraded land induced by
rodent activities. Soil respiration is composed of autotrophic respiration
from plant roots and their symbionts, and heterotrophic respiration from
litter and SOC decomposition (Hanson et al., 2000). Aboveground biomass and
dead roots are the major sources of alpine meadow litter (Sun and Wang,
2008), and SOC abates due to the decreasing litter input into soil as a
result of lower AGB and plant detritus (Wang et al., 2009; Wen et al., 2013).
RB, SOC and LC being lower yet AGB being higher in D3 and D6 than in D1 and
D2 (Table 3) implies that AGB is the major controlling factor of
Rs with the development of land degradation, which is proved by
the positive correlation between Rs and AGB (Fig. 4c). In
disturbed ecosystems, competition among microorganisms induces the microbes
to use more C energy for cell integrity and maintenance (Moscatelli et al.,
2007), and the consequently higher respiration quotient (Nunes et al., 2012)
could contribute to the insignificant change in Rs with
development of land degradation.
Ecosystem respiration comprises of respiration of AGB and Rs
(Zhang et al., 2009). Higher Rs therefore could be one reason
for the higher ER in D3 and D6. Lower relative difference in Rs
(38.2 %) than in ER (44.5 %) between the two groups suggests the
influence of other factors like AGB on ER difference, which is supported by
the positive correlation between ER and AGB (Fig. 4c).
The highest net photosynthesis in June in the alpine meadow ecosystem (Yi et al.,
2000) justifies the maximum GEP in June (Fig. 3d). Sedge percentage will
decrease and forb percentage increase with the development of land
degradation (Liu et al., 2008). The relatively higher net photosynthetic rate
of forb species (Polygonum viviparum Linn.) than that of sedge
species (Carex atrofusca Schkuhr, unpublished data) and higher AGB
might compensate for the effect of species composition change on GEP due to
the positive correlation between GEP and AGB (r=0.84, P<0.05) in the
current study.
The maximum NEE in June is a result of the highest GEP and lower ER in this
time (Fig. 3). Positive average NEE (Fig. 3c) indicates alpine meadow is weak
C sink in the growing season. The insignificant difference of NEE in the two
groups (Table 4) might be the result of the corresponding change of ER
(44.5 % higher in group I than in group II) and GEP (46.5 % higher in
group I than in group II).
Implication of the soil C dynamics
The insignificant difference in NEE among different degradation levels
suggests that SOC loss (in D1, D4 and D5) with land degradation is not a
direct result of changes in net C uptake and emission. The higher SOC, LC and
TN in D2 with more NRHs, and the positive correlation between RB and SOC
suggest that other dynamics associated with land degradation, like species
composition (W. Li et al., 2011) and C allocation between AGB and RB change
(G. Li et al., 2011), might be involved in the soil C and N dynamics in
degraded land in the alpine meadow ecosystem (Zhang et al., 2010).