Introduction
Grassland ecosystems respond forcefully to drought events via substantial
reduction of their primary production
(GPP;
Hoover et al., 2014; Parton et al., 2012; Reichstein et al., 2013). In
contrast, belowground respiration is not so strongly affected
(van
der Molen et al., 2011; Yang and Zhou, 2013) but tends to be reduced as well
under drought (Balogh et al., 2011; Suseela and
Dukes, 2013). Soil respiration is the second largest component of carbon
cycling in grasslands and returns as much as 50–90 % of annual GPP back
to the atmosphere
(Bahn
et al., 2008). Thus, the magnitude of soil respiration can turn the carbon
budget from a net sink into a net source in dry years
(Nagy et al., 2007). Here we address the
question whether under drought this is primarily a function of autotrophic
respiration declining along with the soil drying while heterotrophic
respiration remains less affected.
According to climate change scenarios the frequency of droughts is expected
to increase in central Europe (Prudhomme
et al., 2014), where dry grassland ecosystems represent one of the major land
use types. It is well known that there is a need for better mechanistic
models to address the effects of climatic extremes on carbon fluxes (e.g.,
Blagodatsky and Smith, 2012). However, progress has so far
been limited due to the high complexity of responses given by the different
ecosystem respiration components to the climatic drivers.
Soil organic matter (SOM) and litter-derived respiration is considered to
belong to the heterotrophic soil respiration component
(Moyano et al., 2009). Their decomposition is attributed
mainly to soil bacteria and fungi and has about 50 % share in the total
soil respiration in dry grasslands
(Bao et al., 2010;
Chen et al., 2009; Gomez-Casanovas et al., 2012). In contrast, some of
the soil fungi relying on recent photosynthetic assimilates contribute
to the autotrophic respiration component. Arbuscular mycorrhizal fungi (AMF)
are obligatory symbiont soil fungi, forming intimate mutualistic
associations in 70–90 % of the plant species in grasslands
(Hiiesalu et al., 2014). About 10–20 % of the
assimilated C may be attributed to AMF in exchange for acquiring water and
essential nutrients for plant productivity (van der Heijden
et al., 2015). Therefore soil respiration includes components of an
autotrophic–heterotrophic continuum from roots through the root-associated
fungi (rhizospheric and mycorrhizal) to non-root-associated (heterotrophic)
microbial components.
Belowground CO2 production by the autotrophic and heterotrophic
components show large diel and seasonal variability
(Fassbinder et al., 2011;
Moyes et al., 2010). The drivers behind all this are not fully revealed and
the role of soil microbes in the process is still not clear mainly due to
the diversity of soil biota (Bardgett et al.,
2008). Moreover, drivers of CO2 production frequently interact with
each other (Balogh et al., 2015; Vargas et
al., 2010), hampering the partitioning of the total CO2 efflux into
components. Studies found a stronger effect of photosynthesis than that of
temperature on root respiration
(Gomez-Casanovas
et al., 2012; Heinemeyer et al., 2012; Hopkins et al., 2013). Both
autotrophic and heterotrophic components were shown to be sensitive to water
shortages (Carbone et al., 2011;
Moyano et al., 2013). The autotrophic component was found to be dominant
over the heterotrophic one during drought periods in a Mediterranean
woodland ecosystem (Casals et al., 2011), but we have limited
information about grasslands of shallow rooted herb species regarding the
dominant source of carbon during drought periods.
Measured and estimated CO2 effluxes and isotopic signals in
this study.
CO2 efflux
Isotopic signals
Measured
Reco, Rsoil, Rre, Rrme
δ13CReco, δ13CRsoil, δ13CRre, δ13CRrme
Estimated
Rrhizo, Rmyc, Rhet
δ13CRmycrhizδ13CRrhizo, δ13CRmyc
The widely used separation techniques (trenching and girdling) are not
considered suitable for grasslands (Epron, 2009); thus the
physical separation of the soil CO2 efflux components via root
exclusion is hardly feasible without seriously disturbing the soil structure
and the root system. A viable option, however, is the use of stable isotopic
signatures (δ13C) of soil respiration to estimate the relative
contributions of the main components
(Carbone et al., 2011;
Hopkins et al., 2013). Although diel patterns in δ13C may also
be subject to biases in the measuring methods
(Fassbinder et al., 2011;
Midwood and Millard, 2011), seasonal changes are expected to reflect the
changes in the contributions of source components rather than the changes in
the isotopic signals of the component itself
(Knohl et
al., 2005). However, SOM δ13C can also change during the year
with fresh plant material being more depleted in 13C than the older SOM
components (Bowling et al., 2002); therefore fresh litter may
contribute to the decreasing δ13C of the heterotrophic
component. Drying of the surface layers can also modify δ13CO2 since heterotrophic respiration could be restricted to the
deeper layers of the soil (Moyes et al.,
2010). Drying of the soil can also change the amount of CO2 produced in
the topsoil layer (Balogh et al., 2015) by allowing greater
atmospheric invasion and thereby enriching soil air in 13C
(Phillips and Nickerson, 2010). The disequilibrium between
the measured isotopic composition and the isotopic composition of the
respiratory source could be significant especially in tracer experiments
(Gamnitzer et al., 2011) but it is assumed to be less
pronounced in open-chamber measurements due to the steady-state diffusion
(Nickerson et al., 2013).
Uncertainties in estimating the contributions of soil respiration components
could be reduced by a combination of different methodologies
(Risk et al., 2012). The question we are asking is,
which of the investigated soil respiration components (autotrophic –
including rhizospheric and mycorrhizal fungi – and heterotrophic components)
of the dry grasslands dominates during drought? Our hypothesis was that
autotrophic respiration would be reduced linearly with photosynthesis,
whereas heterotrophic respiration might not be affected as strongly,
resulting in a net loss of C from the soil carbon reservoir. In order to
achieve our goals we used an experimental approach based on the physical
separation of soil respiration components combined with measurements of soil
CO2 efflux and its isotopic (13C) signal.
Methods
Site description
The vegetation at the Bugac site (46.69∘ N, 19.6∘ E; 114 m above sea level) is a dry
sandy grassland dominated by Festuca pseudovina, Carex stenophylla and Cynodon dactylon and it was
under extensive grazing for 20 years prior to our study. Ten-year mean
annual precipitation (2004–2013) was 575 mm and the mean annual temperature
reached 10.4 ∘C. The soil is a chernozem-type sandy soil with
high organic carbon content (Balogh et al., 2015).
Spatial separation of soil CO2 efflux components
In 2010 10 soil cores (160 mm in diameter and 800 mm in depth, one of them
600 mm in diameter) were excavated. The roots were removed and the root-free
soil was packed back – layer by layer – into PVC tubes with the same
dimensions. Four tubes were used to exclude both roots and mycorrhiza. Walls
of another six tubes were partially removed and replaced by inox mesh (40 µm mesh size) to exclude roots while ensuring that the mycorrhiza
filaments can grow into the tubes (Moyano et al., 2007).
These root-free and root- and mycorrhiza-free soil cores were settled at a
distance of 6 m from the eddy covariance (EC) tower to the south direction (Supplement Fig. S1). The distance between the soil cores/tubes was 50 cm.
Soil CO2 efflux and its isotopic signal were measured in
plots:
with undisturbed soil (various positions, 36 positions in total within a
∼ 4 m2 plot): total soil respiration, Rsoil,
δ13CRsoil;
without roots and AMF (four spatial replications): heterotrophic component only, Rrme,
δ13CRrme;
with root-excluded soil (six spatial replications): without roots, but
with AMF, Rre, δ13CRre.
Gas exchange measuring systems
Three different gas exchange systems were used in our study: EC
system, automated soil respiration measuring system (SRS) connected to
an isotopic CO2 analyzer (cavity ring-down spectroscopy system, CRDS).
The experimental area was in the EC footprint (Fig. S1) but the size of the EC flux footprint area was larger by several orders
of magnitude than the area covered by the SRS. Care was taken during the
establishment of the experiment to select a plot with the same average soil
characteristics and vegetation cover as found in the EC footprint area.
Hence, the net ecosystem exchange (NEE) and evapotranspiration (ET)
estimates obtained in this way can be considered representative also for the
small-scale SRS and isotope measurements.
Data from 15 May to 12 November 2013 (182 days) were
analyzed in the present study.
Eddy covariance setup
The EC system at the Bugac site measured the CO2 and H2O fluxes
continuously from 2002. In dry years the grassland can turn into a net
carbon source (Nagy et al., 2007) but the
longer-term annual sums of NEE showed it to be a net sink, ranging from
-171 to +106 g C m-2 yr-1
(Pintér et al., 2010) with a
-100 g C m-2 yr-1 average.
The EC system consists of a CSAT3 sonic anemometer (Campbell Scientific,
USA) and a Li-7500 (Licor Inc, USA) open-path infrared gas analyzer (IRGA),
both connected to a CR5000 data logger (Campbell Scientific, USA) via an SDM
(synchronous device for measurement) interface. Additional measurements used
in this study were air temperature and relative humidity (HMP35AC; Vaisala,
Finland), precipitation (ARG 100 rain gauge; Campbell, UK), global radiation
(dual pyranometer; Schenk, Austria), incoming and reflected
photosynthetically active radiation (SKP215; Campbell, UK), volumetric soil
moisture content (CS616; Campbell, UK) and soil temperature (105T; Campbell,
UK). These measurements were performed as described by
Nagy et al. (2007) and
Pintér et al. (2010).
Fluxes of sensible and latent heat and CO2 were processed using an IDL
program after Barcza et al. (2003) adopting the CarboEurope
IP methodology. For a detailed description of data processing and
gap-filling see Nagy et al. (2007) and
Farkas et al. (2011).
Soil respiration system
The 10-chamber automated soil respiration system was set up in July 2011.
The system is an open dynamic one, consisting of an SBA-4 infrared gas
analyzer (PPSystems, UK), pumps, flow meters (D6F-01A1-110, Omron Co.,
Japan), electro-magnetic valves and PVC/metal soil chambers (Fig. S3). The
chambers were 10.4 cm high with a diameter of 5 cm, covering a soil surface
area of 19.6 cm2. The flow rate through the chambers was 300 mL min-1, replacing the air in the chamber in 40 s. The PVC chambers
were enclosed in a white metal cylinder with 2 mm airspace in between to
stabilize the chamber and to prevent warming by direct radiation. Four vent
holes with a total area of 0.95 cm2 were drilled on the top of the
chambers. Vent holes also served to allow precipitation to drip into the
chambers. Chamber walls exceeded the chamber top by 3 mm directing
precipitation to the vent holes. The system caused minor disturbances in the
soil structure and the spatial structure of the vegetation. It was applied
without cutting the leaves/shoots of the plants, so it did not disturb
transport processes taking place inside the plant stems and roots. It was
suitable for continuous, long-term unattended measurements of soil CO2
efflux and was also used in previous experiments
(Balogh et al., 2015; Nagy et al., 2011). The soil
respiration chambers contained no standing aboveground plant material.
Before the study the system was tested on a calibration tank (CzechGlobe,
Brno, Czech Republic) against known fluxes (FSRS= 0.98 × Fcalibration_tank, R2= 0.92, n= 86) and it was
also compared to a LI-6400 system at the study site (FSRS= 0.92 × FLI6400, R2=0.92, n= 36).
Other studies (Nickerson et al., 2013; Risk et
al., 2011) also used this chamber size, arguing that these chambers could be
placed between the plants in grasslands, while larger chambers might create
a non-representative surface due to the cutting necessary for placing the
chambers on the ground (Risk et al., 2011).
Rsoil was measured by six SRS chambers, while Rrme and Rre were measured by two SRS chambers, respectively.
Isotopic (13CO2) measurements
A Picarro G1101-i gas analyzer (CRDS; Picarro Inc., CA, USA) was attached to
the soil respiration system from May to November 2013. This CRDS system
measured the isotopic composition inside the chambers and in the reference
air. Reference air was sampled 10 cm above the surface in the foliage of
plants. The SRS sequentially measured each of the 10 chambers for 3 min.
Every second chamber was additionally probed for isotopic signature
measurements by the CRDS (3 min), followed by reference air measurements
for another 3 min. Thus, the isotopic measurements of five chambers took
30 min in a single cycle. The CRDS integration time was set at 10 s, and
thus the CRDS provided 18 measurement points per chamber per cycle. Although
the system response of the CRDS was clearly slower than the response of the
SRS, the 3 min duration was long enough to obtain robust results. Since
CRDS followed the 3 min intervals of SRS measurements no additional grace
time had to be considered for the isotopic measurements.
Although this sampling scheme provides very good temporal coverage
(replication in time), it is not perfectly addressing spatial variability
and hence the position of each of the chambers was moved 11 times to
randomly selected locations during the study period (i.e., every 2–3 weeks)
to obtain sequential spatial replications for each plot type (undisturbed,
root-excluded, root- and mycorrhizal-fungi-excluded; see Figs. S1 and S2). More precisely, δ13CRsoil was
measured by three chambers at 36 (three chambers × 12 positions) randomly selected
positions within the experimental area (undisturbed soil,
Fig. S1). δ13CRre was measured by one chamber which
was moved to positions 1, 3, 5, 6, 8 and 9 during the study period
(Fig. S2). δ13CRrme was measured by
one chamber which was moved to positions 2, 4, 7 and 10 during the study period
(Fig. S2).
Since contributions by the different soil CO2 efflux components were
estimated for five different periods within the study period distinguished
by NEE, soil water content (SWC) values and isotopic signals (see Results), data for each
estimation originated from two to three spatial replications.
Data processing and modeling
Data processing and statistical analysis were done in R (R Core
Team, 2014). Before calculating daily averages of δ13C values a
filtering method was applied to each dataset. Out of each 180-second-long
measurement on a certain chamber, the first 70 s (to measure a steady-state
signal) and the last 20 s were cut and the remaining values were used for
further calculations. As reference and chamber air were measured,
sequentially reference values during chamber measurements were estimated by
linear interpolation between the neighboring reference sequences.
After the interpolation, δ13C values of the soil CO2 efflux were calculated using the isotopic mass balance approach in each
plot:
δ13CR=δ13Cout×cout-δ13Cin×cincout-cin,
where δ13Cout and δ13Cin are the
isotopic signature of the outgoing and incoming air of the chamber and
cout and cin are the CO2 concentrations of the outgoing and
incoming air of the chamber, respectively.
δ13C=RsampleRstandard-1,
where R stands for the 13C : 12C isotope ratio of the sample and the
international VPDB standard (0.011182), respectively.
Individual measurements were filtered out by using a moving-window procedure
if the investigated value (at the window center) was outside the range of
the mean ± median absolute deviation of the values in a 10-day moving
window. This filtering procedure left an overall data availability of
68–70 %. Daily averages were calculated using the remaining data.
To determine the isotopic signature of the ecosystem respiration
(Reco), Keeling plots were constructed by plotting the nighttime
δ13C values measured 10 cm over the surface against the inverse
of the CO2 concentration. The extrapolated y intercept of the linear
regression was used as δ13CReco values.
Total soil CO2 efflux was separated isotopically into its components.
We defined the components following the terminology presented by
Moyano et al. (2009):
Heterotrophic respiration is microbial respiration from litter and SOM
decomposition.
Autotrophic respiration is mycorrhizospheric respiration including
rhizospheric and mycorrhizal fungi components.
Rhizospheric respiration is respiration of roots and root-associated
microorganisms in the rhizosphere, not including mycorrhizal fungi.
Two-source mixing models were used to estimate the fraction (a) of the
rhizospheric and (b) mycorrhizospheric components based on the measured
isotopic signals:
δ13CRsoil=a×δ13CRrhizo+1-a×δ13CRre,δ13CRsoil=b×δ13CRmycrhiz+1-b×δ13CRrme,
where δ13CRsoil is the δ13C of the total soil
CO2 efflux, δ13CRre is the δ13C of the
root-excluded soil, δ13CRrme is the δ13C of
the root- and mycorrhiza-excluded soil (heterotrophic respiration), a is the
fraction of the rhizospheric component (Rrhizo) and b is the fraction of
the mycorrhizospheric component (Rmycrhiz) to the total soil efflux.
According to these equations 1-b represents the ratio of heterotrophic
respiration component to the total soil efflux and b-a represents the ratio of
mycorrhizal fungi component.
δ13CRrhizo value was estimated by plotting δ13CRsoil values against the Rre / Rsoil ratio
(Fig. S3b). Since Rre / Rsoil is
hypothetically zero when only rhizospheric respiration is present,
y intercept of the linear regression was assumed as δ13CRrhizo. δ13CRmycrhiz was estimated using
the same approach (Fig. S3a), δ13CRsoil values were plotted against the Rrme / Rsoil
ratio and y intercept of the linear regression was assumed as δ13CRmycrhiz. Similarly, δ13CRre values were
plotted against the Rrme / Rre ratio and y intercept of the linear
regression was assumed as δ13CRmyc (Fig. S4c) but this value was not used in further calculations.
Contributions of rhizospheric, mycorrhizal fungi and heterotrophic
respirations to total soil respiration were calculated by the mixing models
applied on subsets (periods) of the dataset of the total study period.
Estimated values of rhizospheric (Rrhizo), mycorrhizal fungi
(Rmyc) and heterotrophic respiration (Rhet) were calculated by
multiplying the measured Rsoil rates (total soil respiration) with the
estimated fractional contributions (F) of each component as follows:
Rhet=Rsoil×Fhet,Rrhizo=Rsoil×Frhizo,Rmyc=Rsoil×Fmyc,
where Fhet, Frhizo and Fmyc are the fractions of the
heterotrophic, rhizospheric and mycorrhizal respiration in total soil
respiration, respectively.
Microbial investigations
Soil samples for the microbial investigations were taken after the gas
exchange measurements in May 2014 to avoid the disturbance of the
measurements by sampling the soil. Sampling date was chosen considering the
maximum of the carbon sequestration capacity of the investigated grassland
(Nagy et al., 2007). Soil samples were taken from five soil
layers (0–10, 10–20, 20–30, 30–40 and 40–50 cm) in
each plot.
Determination of AM fungal hyphal length in the soil was based on the
methods of Bååth and Söderström (1979) using
separation by wet sieving and centrifugation. The separated fungal hyphae
were stained using agar solution (0.75 %) containing trypan blue
(0.05 %), then dried for 24 h at 70 ∘C. The hyphal length was
measured in the dried agar film by the intersection method
(Tennant, 1975) under a binocular microscope.
The fluorescein diacetate (FDA) hydrolysis assay was used to estimate the
total microbial activity in soil samples and expressed as mg fluorescein-released kg-1 dry soil (Adam and Duncan, 2001).
Uncertainty assessment
Isotopic signals of soil-respired CO2 were studied extensively but
several uncertainties related to the different methods were also revealed.
Steady-state methods were found to provide more robust estimates than static
chambers but still charged with biases (e.g., diffusive fractionation; Nickerson and Risk, 2009). Open systems have the advantage of
unattended automatic measurement collecting large amount of data but are
less sensitive to small isotopic differences
(Midwood and Millard, 2011).
(a) Daily averages of soil temperature (Ts), soil water content
(SWC) at 5 cm depth and daily sum of precipitation; (b) daily minimum
half-hourly NEE (NEEmin) and maximum half-hourly ET (maxET); (c) daily
averages of CO2 efflux in undisturbed soil (Rsoil), root-excluded
soil (Rre) and root- and mycorrhizal-fungi-excluded soil
(Rrme);
(d) daily averages of δ13C of soil CO2 efflux in
undisturbed soil (δ13CRsoil), root-excluded soil (δ13CRre) and
root- and mycorrhizal-fungi-excluded soil (δ13CRrme); and (e) daily averages of δ13C of ecosystem
respiration (δ13CReco) during the study period in 2013, at
Bugac site. Arrows indicate the positions changes of the soil chambers. Gray
horizontal lines show y major values.
In our study δ13CReco estimates were independent of
chamber-related biases, using nighttime δ13CO2 and
CO2 concentration data of the free air over the surface for the
calculation (Keeling plot approach). This approach gave similar results to
the chamber-based measurements, providing also partial verification of the
latter ones. Moreover, isotopic measurements were independent of soil
CO2 efflux measurements, since IRGA and CRDS systems took different air
samples from the same soil chambers. Isotopic data together with CO2
efflux rates were collected during 1980 measurement cycles on 182 days in
order to have robust estimates of isotopic signals.
A C4 grass (Cynodon dactylon) was also present in the study site potentially modifying the
δ13C of the respired CO2. Its cover was about 10 % in
the pasture (Koncz et al., 2014) but it was less
frequent (i.e., less than 5 %) in the experimental area. Calculated
uncertainties of the relative contributions of each components
(rhizospheric, mycorrhizal fungi and heterotrophic) contain the uncertainty
due to a possible 5 % contribution by the C4 grass. The isotopic signal of
CO2 efflux by the C4 plant was supposed to be -14 ‰.
In order to estimate the uncertainty of the measurements and estimated
contributions by the different components to the total soil respiration
random errors of each factor (CO2 concentrations, isotopic
compositions, model fit errors and possible C4 contribution) were propagated
by Gaussian error propagation (Lo, 2005).
Results
Meteorological conditions, NEE, ET, soil CO2 efflux and δ13C
of CO2 efflux
The end of May to the beginning of June was the most productive period in
the year due to ample water availability with the lowest NEE (strongest
carbon sink activity) and highest ET values being
measured in this period (Fig. 1a). It rained only a few times from the end
of June to 19 August (total precipitation: 10 mm) and the accompanying high
temperature resulted in drought. Daily minimum NEE was around zero at the
end of July and in August. Rain events after the drought period had
significant effects on soil CO2 effluxes (Fig. 1c). There was a second
active period following autumn rains but CO2 uptake and ET were smaller
than in May or June.
Mean measured (undisturbed soil and tubes) and estimated
(heterotrophic, mycorrhizal fungi and rhizospheric) respiration rates for
the different periods (µmol CO2 m-2 s-1) with propagated
uncertainties.
Measured (mean ±SD)
Estimated (mean ±SE)
Period
Rsoil
Rre
Rrme
Rhet
Rmyc
Rrhizo
Active
7.7 ± 1.6
5.1 ± 1.5
3.9 ± 1.1
1.7 ± 1.1
1.0 ± 1.2
5.1 ± 1.1
Drying
5.7 ± 2.0
3.8 ± 1.5
2.9 ± 0.6
2.9 ± 0.7
0.6 ± 0.9
2.1 ± 0.8
Drought
3.2 ± 1.1
2.3 ± 0.4
1.9 ± 0.4
1.7 ± 0.5
0.3 ± 0.6
1.1 ± 0.5
Wetting
4.8 ± 1.7
4.3 ± 1.5
2.6 ± 1.2
1.5 ± 0.6
1.0 ± 0.7
2.3 ± 0.7
Re-greening
3.8 ± 1.0
2.4 ± 0.8
2.0 ± 1.1
1.1 ± 0.4
0.3 ± 0.5
2.4 ± 0.4
Total study period
5.0 ± 2.1
3.8 ± 1.6
2.6 ± 1.2
1.8 ± 0.6
0.7 ± 0.8
2.6 ± 0.7
Rsoil was the highest among the soil CO2 effluxes, while Rrme
was the lowest; the average CO2 effluxes in the whole study period were
5.0 ± 2.1, 3.8 ± 1.6 and 2.6 ± 1.2 µmol CO2 m-2 s-1
(mean ±SD) in Rsoil, Rre and Rrme,
respectively (Table 2). Rre was sometimes higher than Rsoil,
especially shortly after rain events. The lowest daily average total soil
CO2 efflux was measured on 15 August
(2.22 µmol CO2 m-2 s-1), while the lowest daily average Rre and Rrme values
were observed on 2 October (1.25 µmol CO2 m-2 s-1) and 2 November (1.04 µmol CO2 m-2 s-1),
respectively. The highest values of soil CO2 effluxes were
measured in May in all treatments (Rsoil, Rre and Rrme).
Sudden increases in Rre and Rrme were observed shortly after rain
events but Rsoil showed slower (but more persistent) response to
precipitation (Fig. 1c).
Measured (Reco, Rsoil, Rre, Rrme) and estimated
(Rmycrhiz, Rrhizo, Rmyc) δ13C values of the
respiration components. Horizontal black lines in boxes show medians and
dashed whiskers show data extremes. Open circles and solid whiskers show
mean ± propagated standard errors. Gray horizontal lines show y major
values.
Isotopic signature of Reco was the lowest in May and June, increased in
July and August and decreased again in October and November (Fig. 1e).
δ13CReco showed clear responses to precipitation pulses
with sudden declines being observed during the rain events. Chamber-based
δ13CRsoil showed similar changes during the study period.
δ13CRrme and δ13CRre showed large
scatter during the whole study period with no clear and detectable trends
(Fig. 1d). Differences between δ13CRsoil and δ13CRrme were the largest in the active period and the smallest
under drought conditions.
According to the NEE, SWC values and isotopic signals we distinguished five
periods within the study period: an active period from 15 May to
20 June, a drying (stress development) period from 21 June to
22 July, a drought period from 23 July to 19 August, a
wetting (stress release) period from 20 August to 16 September
and a re-greening (recovery) period from 17 September to the end of
the study period (11 November; Fig. 1).
δ13C of the respiration components
Figure 2 shows the measured and estimated δ13C values of the
different soil CO2 efflux components. δ13CRrme was
the highest, while δ13CRsoil was the lowest, suggesting
that it was the rhizospheric respiration that was the most substantially
depleted, while heterotrophic respiration was the least depleted in
13C. Mean values of δ13CReco, δ13CRsoil, δ13CRre
and δ13CRrme were -27.9 ± 0.5, -26.8 ± 1.3, -26.4 ± 1.8 and
-25.7 ± 2 ‰ (mean ±SE), respectively. The
estimated isotopic signals of the respiration of mycorrhizospheric (δ13CRmycrhiz), rhizospheric (δ13CRrhizo) and
mycorrhizal fungi components (δ13CRmyc) were -28.6 ± 1.6, -28.9 ± 1.7 and
-27.2 ± 2.3 ‰ (estimate ±SE), respectively
(Fig. 2).
Relative contributions made by rhizospheric, mycorrhizal fungi and
heterotrophic components to the total soil respiration in the different
parts of the vegetation period (15 May 2013–12 November 2013) at Bugac site.
Propagated uncertainties of each estimate are shown in the lower panel. Gray
horizontal lines show y major values.
While 36 % of the variation in δ13CRsoil was explained by
SWC (δ13CRsoil=-0.1267×SWC-25.537, R2= 0.36,
P < 0.0001), only 3 % of the variation of δ13CRrme was explained by SWC and there was no correlation between
δ13CRre and SWC. Similar results were obtained between
Ts and the isotopic signals but the correlation was weaker (δ13CRsoil= 0.1056 × Ts-28.588,
R2= 0.11, P < 0.0001). Daily minimum NEE (NEEmin, Fig. 1b) explained 29 % of the
variation in δ13CRsoil
(δ13CRsoil= 0.0941 × NEEmin -26.245, R2= 0.29, P < 0.0001) but no
correlation was found between NEEmin and δ13CRrme and
between NEEmin and δ13CRre.
Fraction of the different components in total soil respiration during the
vegetation period
Two end-member mixing models (Eqs. 3 and 4) were used to estimate the
relative contributions of rhizospheric, mycorrhizal fungi and heterotrophic
components to total soil respiration during the study period. The estimated
contributions by the different components were 50 ± 6, 13 ± 8 and 37 ± 6 % (mean ±SE) for the rhizospheric, mycorrhizal
fungi and heterotrophic components, respectively. The autotrophic component
(mycorrhizospheric component) of soil respiration showed significant
decrease during the drying and drought periods. Rhizospheric component was
the most sensitive to drying and drought. Average contributions by the
rhizospheric component to total soil CO2 efflux decreased from
66 ± 7 % (mean ±SE) in the active period to 35 ± 13 %
during the drought period (Fig. 3). After drought rhizospheric contributions
increased again and become dominant during the re-greening period in autumn
63 ± 7 % (mean ± SE). During the transient (drying and wetting)
periods the rhizospheric contributions to the total soil CO2 efflux
were 38 ± 11 and 46 ± 8 %, respectively. Relative
mycorrhizal contributions were between 8 and 21 % during the whole study
period, with the highest contribution (21 ± 11 %; mean ±SE)
during the wetting period. Heterotrophic contributions to soil respiration
were the lowest in the active period (21 ± 7 %) and the highest under
drought (54 ± 13 %; Fig. 3).
Changes in soil CO2 effluxes showed similar responses to drying and
drought conditions as isotopic signals. Average Rsoil decreased by
60 % (referenced to the average during the active period) as a response to
drought, while Rre and Rrme showed declines of 56 and 52 %
respectively, suggesting that declines in root respiration were
substantially larger than those in Rsoil (60 %).
(a) Mean hyphal length (m g-1 dry soil) and (b) mean microbial
activity expressed as fluorescein released (mg kg-1 dry soil) in the
undisturbed soil, root exclusion and root and mycorrhiza exclusion in
different soil depths. Asterisks denote significant differences from
undisturbed soil determined by the Tukey honest significant difference test.
The estimated rates of rhizospheric, mycorrhizal fungi and heterotrophic
components (Eqs. 5–7) are shown in Table 2. Pearson correlation coefficients
pairing the estimated respiration rates and their possible driving variables
(NEE, Ts, SWC) showed significant negative correlation between Rrhiz
and NEE (R=-0.94, p < 0.05) and a significant positive correlation
between Rrhiz and SWC (R= 0.82, p < 0.0.5). Rhet changed
with Ts but the correlation was not significant.
Microbial biomass and activity
Hyphal length (on dry soil weight basis) was significantly lower in the
upper layers of root- and mycorrhiza-excluded soil than in undisturbed soil,
while it was significantly higher in root-excluded plots at 10–20 cm depth.
Hyphal length in the root-excluded soil was similar to undisturbed soil in
the other soil layers. Fluorescein values were significantly lower in all
soil layers in the root- and mycorrhiza-excluded plots than in the
undisturbed soil. Fluorescein values in the root-excluded plots were also
lower than in undisturbed soil but this difference was not significant (Fig. 4).
Discussion
Our approach combined the root- and root- and mycorrhiza-exclusion
treatments with isotopic measurements. The aim of this combination was to
assess the contributions made by the heterotrophic and autotrophic
components in soil CO2 efflux of the undisturbed soil. Although the
root and mycorrhiza exclusion caused large disturbances in soil structure
by inserting the tubes into the soil, we used these treatments only for
identifying the isotopic signals of the investigated components. All of the
estimated contributions to soil CO2 efflux by rhizospheric, mycorrhizal
fungi and heterotrophic components were applied for the undisturbed soil.
Estimated contributions made by the different components to the total soil
CO2 efflux and effect of drought on CO2 effluxes and δ13C values
While the percentages of the autotrophic component in the total soil
CO2 efflux were 63 ± 6 % on average (rhizospheric and
mycorrhizal fungi components: 50 ± 6 and 13 ± 8 %,
respectively), thus much higher than the average percentage of the
heterotrophic (37 ± 6 %) component, the contributions by the
different components showed significant changes during the growing season.
In other studies conducted in grassland ecosystems the estimated yearly
average ratio of the autotrophic component was found to be lower accounting
for 38–52 % of the total soil respiration
(Bao et al., 2010; Heinemeyer et al., 2012),
while reaching 74 % during the growing season in a prairie grassland
(Gomez-Casanovas et al., 2012) and
60–74 % in an arid perennial grassland (Carbone et al.,
2008). Our study was conducted from May to the beginning of November;
therefore we can assume considering the lower vegetation activity in the
dormant season (Nagy et al., 2007) that the
contribution of the autotrophic component could be lower while that of the
heterotrophic component higher for the whole year than the estimations for
the growing season.
Soil CO2 effluxes decreased in all treatments (Rsoil, Rre,
Rrme) under dry conditions, the largest decline being observed in total
soil respiration (Rsoil); therefore a strong response of the
autotrophic component to drought could be assumed. The measured isotopic
signals also showed decreasing autotrophic contributions to CO2 efflux
during soil drying. δ13CRsoil showed negative responses
to SWC and was more enriched when SWC was low, while δ13C of
the root- and mycorrhiza-excluded respiration (Rrme) showed no
response. Since δ13CReco was the lowest of the measured
isotopic signals it can be assumed that the isotopic signals of the
aboveground respiration could be the most depleted δ13C.
Therefore, the observed increase in δ13CReco and δ13CRsoil values during the drying period and during the drought
also showed the decline of both the above- and belowground autotrophic
components. The same phenomenon was shown by the modeling results with the
smallest contribution made by the rhizospheric component estimated for the
drought period (35 ± 13 %; mean ±SE), while the highest for the
active period (66 ± 7 %; mean ±SE). Fractions of the
heterotrophic respiration were the highest during drought (54 ± 13 %
mean ±SE) and the mycorrhizal fungi respiration showed only a small
decrease during drought compared to the active period (from 13 ± 10 to 11 ± 18 %;), suggesting that the non-root-associated
microbes and mycorrhizal filaments were less sensitive to water shortages
than the rhizosphere. Soil aggregates are expected to provide microhabitats
for soil organisms that should be moist enough for those organisms to thrive
even under drought (Davidson et al., 2012). Since there
was an absence in plant photosynthetic supply during drought period,
mycorrhizal fungi component is expected to use stored carbon for respiration
(van der Heijden et al., 2008).
Low δ13CRsoil and δ13CReco values were
measured in the wetting and re-greening periods due to the drought-induced
fall of the fresh litter to the surface as fresh plant material could be
more depleted than the old litter (Bowling et al., 2002). The
declines in δ13CRsoil and δ13CReco
immediately after the rain events during drying and drought periods could
also be explained by the wetting of the litter layer, exposing relatively
fresh substrate to degradation for short periods. This phenomenon could also
cause an overestimation in contributions made by the depleted components
(rhizospheric) during rain events. Since the rhizospheric contribution
estimated for the re-greening period was high it is assumed that this result
was obtained partly due to the increased amount of fresh litter. Similar
results were obtained in a tallgrass prairie by
Gomez-Casanovas et al. (2012), where
the autotrophic components were more sensitive to soil drying than the
heterotrophic ones. In contrast, Carbone et al. (2008)
found more sensitive response by the heterotrophic component in an arid
(< 150 mm annual precipitation) perennial grassland. Fractions of
autotrophic components were reported to increase in response to drought in a
woodland ecosystem, supposing that the signature of the recent
photosynthetic supply became enriched during drought and that could also
explain the increase in the soil-respired CO2 (Casals et
al., 2011). A drought-induced increase in δ13C of root
respiration of trees was also assumed in a recent study
(Risk et al., 2012), suggesting that the isotopic
signals of the assimilates, and thus the signals of the autotrophic
component,
might also increase. In our study, Rrme / Rsoil showed significant
positive correlation with δ13CRsoil (the regression was
used to estimate δ13CRmycrhiz,
Fig. S4), so δ13CRsoil was high if the fraction of
heterotrophic CO2 efflux to the total soil CO2 efflux was found to
be high. Moreover, NEEmin values were close to zero during drought (average
daily minimum NEE was -0.91 µmol CO2 m-2 s-1), showing
the lack of the photosynthetic supply in this period. Photosynthetic
CO2 uptake of this vegetation was found to be sensitive to drought
conditions (Nagy et al., 2007) and it can act
as a driver of the soil CO2 production and efflux (Balogh
et al., 2015). The observed strong correlation between the estimated
rhizospheric respiration and NEE can also be explained by the interacting
effects of drought and photosynthetic supply of respiration. These findings
support the concept that in the grasslands under study the autotrophic respiration
component was more sensitive to soil drying and its activity determined the
isotopic signals of the total soil respiration during the study period.
According to these studies and to our results we can assume that the
different vegetation types may respond differently to drought: woodlands may
increase their autotrophic contribution while grasslands may decrease it
(Casals et al.,
2011; Gomez-Casanovas et al., 2012; Risk et al., 2012). Plants with
different rooting habits have different water availability during dry
periods (van
der Molen et al., 2011), which could explain the differences between the
different ecosystems in their response to drought.
Measured and estimated isotopic signals of the soil respiration
components
Measured and calculated δ13C values of the different
respiration components showed differences similar to the ones reviewed by
Bowling et al. (2008). δ13CReco (containing also the signal from aboveground green biomass) was the most depleted, while δ13CRrme (heterotrophic components only) was the least depleted.
δ13C of the root- and mycorrhiza-excluded respiration was
similar to SOM δ13C measured in a previous study
(Denef et al., 2013):
-25 and -26 ‰ in the topsoil layers
(without the litter layer). CO2 effluxes from mycorrhizal fungi were
expected to be more enriched in 13C relative to the total soil
respiration (about +3 ‰;
Bowling et al., 2008).
Estimated δ13C of mycorrhizal fungi component was -27.2 ± 2.3 ‰ (estimate ±SE), which is
1.7 ‰ higher than the rhizospheric component
(-28.9 ± 1.7 ‰; estimate ±SE).
In our study neither δ13CRrme values (heterotrophic
respiration) nor δ13CRre values
(heterotrophic + mycorrhizal fungi respiration) showed correlation with SWC
but δ13CRsoil (total soil respiration) showed significant
negative correlation with SWC. We can assume that δ13C of
heterotrophic respiration was not influenced by SWC changes during the
growing season as it was found also by other studies
(Phillips and Nickerson, 2010; Risk et al.,
2012). Furthermore, the lack of correlation with the present study also
suggests that soil-moisture-induced changes in diffusivity (disequilibrium
effect due to changing soil moisture) were not large enough to affect the
measured δ13C values.
Microbial investigations
High hyphal density was maintained in Rre plots and low but still
significant microbial activities (SOM decomposition) were detected in
Rrme plots; therefore the measured δ13C values
characterized the sources of the root-free (δ13CRre) and
root- and mycorrhiza-free (δ13CRrme) soils. The fact that
very high amounts of hyphae were found in the root-excluded soil in the
10–20 cm layer proved that mycorrhizal fungi filaments were able to
penetrate through the inox mesh and supported significant microbial
activity. Grasses have extensive fibrous root systems with moderate to high
levels of mycorrhizal colonization (van der Heijden et al.,
2015). The range of AM hyphal lengths found in this study (1.9–8.8 m g-1 soil) was similar to that reported in the literature
(e.g.,
Mummey and Rillig, 2008). The higher
hyphal densities found in root-free soil might have been related to the
higher availability of SOM-derived nutrients and to more space without the
roots (i.e., lack of competition). According to our results, a significant
amount of CO2 was respired from mycorrhizal filaments in the
undisturbed soil, with a 12–31 % share in the respiration carried out by
the autotrophic component.
Values of fluorescein in root-excluded plots were similar to those measured
in the undisturbed soil probably because hyphae of AM fungi provide an
increased area for interaction with other microorganisms
(hyphosphere; Andrade et al.,
1997) but were much lower in root- and mycorrhiza-excluded soil. These
results support the component estimations showing the significant activities
of root-associated microorganisms.