Partitioning carbon fluxes is key to understanding the process underlying
ecosystem response to change. This study used soil and canopy fluxes with
stable isotopes (13C) and radiocarbon (14C) measurements in an 18 km2, 50-year-old, dry (287 mm mean annual precipitation; nonirrigated)
Pinus halepensis forest plantation in Israel to partition the net ecosystem's CO2 flux
into gross primary productivity (GPP) and ecosystem respiration (Re) and
(with the aid of isotopic measurements) soil respiration flux (Rs) into
autotrophic (Rsa), heterotrophic (Rh), and inorganic (Ri) components. On an
annual scale, GPP and Re were 655 and 488 g C m-2, respectively, with a
net primary productivity (NPP) of 282 g C m-2 and carbon-use efficiency
(CUE = NPP / GPP) of 0.43. Rs made up 60 % of the Re and comprised 24±4 %Rsa, 23±4 %Rh, and 13±1 %Ri. The contribution of root and microbial respiration to Re
increased during high productivity periods, and inorganic sources were more
significant components when the soil water content was low. Comparing the
ratio of the respiration components to Re of our mean 2016 values to those
of 2003 (mean for 2001–2006) at the same site indicated a decrease in the
autotrophic components (roots, foliage, and wood) by about -13 % and an
increase in the heterotrophic component (Rh/Re) by about +18 %, with
similar trends for soil respiration (Rsa/Rs decreasing by -19 % and Rh/Rs
increasing by +8 %, respectively). The soil respiration sensitivity to
temperature (Q10) decreased across the same observation period by 36 % and 9 % in the wet and dry periods, respectively. Low rates of soil
carbon loss combined with relatively high belowground carbon allocation
(i.e., 38 % of canopy CO2 uptake) and low sensitivity to temperature
help explain the high soil organic carbon accumulation and the relatively
high ecosystem CUE of the dry forest.
Introduction
The annual net storage of carbon in the land biosphere, known as net
ecosystem production (NEP), is the balance between carbon uptake during
gross primary productivity (GPP) and carbon loss during growth, maintenance
respiration by plants (i.e., autotrophic respiration, Ra), and decomposition
of litter and soil organic matter (i.e., heterotrophic respiration, Rh;
Bonan, 2008). The difference between GPP and Ra expresses the net primary
production (NPP) and is the net carbon uptake by plants that can be used for
new biomass production. Measurements from a range of ecosystems have shown
that total plant respiration can be as large as 50 % of GPP (e.g., Etzold
et al., 2011) and together with Rh comprises total ecosystem respiration
(Re; Re=Ra+Rh). The partitioning of the ecosystem carbon fluxes can
therefore be summarized as
GPP=NPP+Ra=NEP+Rh+Ra.
Earlier campaign-based measurements carried out by Maseyk et al. (2008a) and
Grünzweig et al. (2009) in the semiarid Pinus halepensis (Aleppo pine) Yatir Forest
indicated that GPP at this site was lower than among temperate coniferous
forests (1000–1900 g C m-2 yr-1) but within the range estimated
for Mediterranean evergreen needleleaf and boreal coniferous forests (Falge
et al., 2002; Flechard et al., 2019b) and had a high carbon-use efficiency (CUE)
of 0.4 (CUE = NPP / GPP; DeLucia et al., 2007). The total flux of CO2
released from the ecosystem (Re) can be partitioned into aboveground
autotrophic respiration (i.e., foliage and sapwood, Rf) and soil CO2
flux (Rs). Rs, in turn, is a combination of three principal components and
can be further partitioned into the components originating from roots or
rhizospheres and mycorrhizas (i.e., Rsa), from
carbon respired during the decomposition of dead organic matter by soil
microorganisms and macrofauna (Rh; Bahn et al.,
2010; Kuzyakov, 2006), and from pedogenic or anthropogenic acidification of soils
containing CaCO3 (Ri; Joseph et al., 2019; Kuzyakov, 2006), which is
expressed as
Re=Rs+Rf=Rsa+Rh+Ri+Rf.
Previously published results show that the contribution of Rsa and Rh to Rs
ranges from 24 % to 65 % and from 29 % to 74 %, respectively, in forest
soils in different biomes and ecosystems (Binkley et al., 2006; Chen et al.,
2010; Flechard et al., 2019a; Frey et al., 2006; Hogberg et al., 2009; Subke
et al., 2011). Some studies reported significant proportions of abiotic
contribution to Rs, ranging between 10 % and 60 % (Martí-Roura et al.,
2019; Ramnarine et al., 2012; Joseph et al., 2019). However, most of these
experiments were performed in boreal, temperate, or subtropical forests, and
there is a general lack of information on water-limited environments, such
as dry Mediterranean ecosystems. Using both 13C and CO2/O2
ratios also showed that abiotic processes, such as CO2 storage,
transport, and interactions with sediments, can influence Rs measurements at
such sites (Angert et al., 2015; Carmi et al., 2013). Furthermore,
root-respired CO2 can also be dissolved in the xylem water and carried
upward with the transpiration stream (Etzold et al., 2013).
Rates of the soil–atmosphere CO2 flux (Rs) have been altered owing to
global climatic change, particularly through changes in soil temperature
(Ts) and soil moisture (SWC; Bond-Lamberty and Thomson, 2010; Buchmann,
2000; Carvalhais et al., 2014; Hagedorn et al., 2016; Zhou et al., 2009),
which could account for 65 %–92 % of the variability of Rs in a mixed
deciduous forest (Peterjohn et al., 1994). Soil moisture impacts on Rs have
been observed in arid and Mediterranean ecosystems, where Ts and SWC are
negatively correlated (e.g., Grünzweig et al., 2009). CO2 efflux
generally increases with increasing soil temperatures (Frank et al., 2002),
which can produce positive feedback on climate warming (Conant et al.,
1998), converting the biosphere from a net carbon sink to a carbon source
(IPCC, 2014). A range of empirical models have been developed to relate
Rs rate and temperature (Balogh et al., 2011; Lellei-Kovács et al.,
2011), and the most widely used models rely on the Q10 approach
(Bond-Lamberty and Thomson, 2010), which quantifies the sensitivity of Rs to
temperature and can integrate it with physical processes, such as the rate
of O2 diffusion into and CO2 diffusion out of soils and the
intrinsic temperature dependency of enzymatic processes (Davidson and
Janssens, 2006). Soil moisture (SWC) may be of greater importance than
temperature in influencing Rs in water-limited ecosystems (Hagedorn et al.,
2016; Grünzweig et al., 2009; Shen et al., 2008). In general, the Rs
rate increases with the increase of SWC at low levels but decreases at high
levels of SWC (Deng et al., 2012; Hui and Luo, 2004; Jiang et al., 2013).
Several studies highlight the sensitivity of carbon fluxes in semiarid
Mediterranean ecosystems to the irregular seasonal and interannual
distribution of rain events (Poulter et al., 2014; Ross et al., 2012). While
Rs is generally constrained by low SWC during summer months, abrupt and
large soil CO2 pulses have been observed after rewetting the dry soil
(Matteucci et al., 2015).
The objectives were twofold: first, to obtain detail on partitioning of the
carbon fluxes in a semiarid pine forest to help explain the high
productivity and carbon use efficiency recently reported for this ecosystem
(Qubaja et al., 2019) and provide process-based information to assess the
carbon sequestration potential of such a semiarid afforestation system; and
second, to combine this 2016 study with the results of a similar one at the
same site in 2003 (mean values for 2001–2006; Grünzweig et al., 2007,
2009) to obtain a long-term perspective across 13 years on soil respiration
and its partitioning. We hypothesized that the high carbon-use efficiency of
the dry-forest ecosystem is associated with high belowground carbon
allocation and relatively low decomposition rates and that the long-term
trend associated with warming may be suppressed by the dry conditions.
Materials and methodsSite description
The Yatir Forest (31∘20′49′′ N, 35∘03′07′′ E; 650 m a.s.l.) is situated in the transition zone between subhumid and arid
Mediterranean climates (Fig. S1 in the Supplement) on the edge of the Hebron mountain ridge.
The ecosystem is a semiarid pine afforested area established in the 1960s and
covering approximately 18 km2. The average air temperatures for January
and July are 10.0 and 25.8 ∘C, respectively. Mean
annual potential evapotranspiration (ET) is 1600 mm, and mean annual
precipitation is 287 mm. Only winter (December to March) precipitation
occurs in this region, creating a distinctive wet season, while summer (June
to October) is an extended dry season. There are short transition periods
between seasons, with a wetting season (i.e., fall) and a drying season
(i.e., spring). The forest is dominated by Aleppo pine (Pinus halepensis Mill.), with
smaller proportions of other pine species and cypress and little understory
vegetation. Tree density in 2007 was 300 trees ha-1; mean tree height
was 10.0 m; diameter at breast height (DBH) was ∼15.9 cm, and
the leaf area index (LAI) was ∼1.5. The native background
vegetation was sparse shrubland, which is dominated by the dwarf shrub
Sarcopoterium spinosum (L.) Spach, with patches of herbaceous annuals and perennials reaching a
total vegetation height of 0.30–0.50 m (Grünzweig et al., 2003, 2007).
The root density range is 30–80 roots m-2 at the upper 0.1 m soil
depth, falling to the minimum value (∼0 roots m-2) at
0.7 m soil depth (Preisler et al., 2019). Biological soil crust (BSC) is
evident in the forest but is less than in the surrounding shrub by
∼40 % (Gelfand et al., 2012).
The soil at the research site is shallow (20–40 cm), reaching only 0.7–1.0 m; the stoniness fraction for the soil depth (0–1.2 m) is 15 %–60 %, and
the rock cover of the surface ranges between 9 % and 37 %, as recently
described in detail (Preisler et al., 2019); the soil is eolian-origin loess
with a clay–loam texture (31 % sand, 41 % silt, and 28 % clay;
density is 1.65±0.14 g cm-3) overlying chalk and limestone
bedrock. Deeper soils (up to 1.5 m) are sporadically located at topographic
hollows. While the natural rocky hill slopes in the region are known to
create flash floods, the forested plantation reduces runoff dramatically to
less than 5 % of annual rainfall (Shachnovich et al., 2008). Groundwater
is deep (> 300 m), reducing the possibility of groundwater
recharge due to negative hydraulic conductivity or of water uptake by trees
from the groundwater.
Flux and meteorological measurements
An instrumented eddy covariance (EC) tower was erected in the geographical
center of Yatir Forest, following the EUROFLUX methodology (Aubinet et al.,
2000). The system uses a three-dimensional (3-D) sonic anemometer
(Omnidirectional R3, Gill Instruments, Lymington, UK) and a closed path
LI-7000 CO2/H2O gas analyzer (LI-COR Inc., Lincoln, NE, USA)
to measure the evapotranspiration flux (ET) and net CO2 flux (NEE). EC
flux measurements were used to estimate the annual scale of NEP by
integrating half-hourly NEE values. The long-term operation of our EC
measurement site (since 2000; see Rotenberg and Yakir, 2010) provides
continuous flux and meteorological data with about 80 % coverage, which
are subjected to U* nighttime correction and quality control, and gap
filling is based on the extent of the missing data, as recently described in
more detail in Tatarinov et al. (2016). A site-specific algorithm was used
for flux partitioning into Re and GPP. Daytime ecosystem respiration (Re-d;
in µmol m-2 s-1) was estimated based on measured nighttime
values (Re-n; i.e., when the global radiation was < 5 W m-2),
averaged for the first 3 half hours of each night. The daytime
respiration for each half hour was calculated according to Eq. (3) (Maseyk et
al., 2008a; Tatarinov et al., 2016):
Re-d=Re-nα1βsdTs+α2βwdTa+α3βfdTa,
where βs, βw, and βf are coefficients that correspond
to soil, wood, and foliage, respectively; dTs and dTa are soil and air
temperature deviations from the values at the beginning of the night; and
α1, α2, and α3 are partitioning coefficients fixed
at 0.5, 0.1, and 0.4, respectively. The βs, βw, and βf
coefficients were calculated as follows: βs values were based on
Q10 from the Grünzweig et al. (2009) study at the same site, where
βs= 2.45 for wet soil (i.e., SWC in the upper 30 cm above 20 % vol.); βs= 1.18 for dry soil (i.e., SWC in the upper 30 cm equal to
or below 20 % vol.); βf=3.15–0.036 Ta; and βw=1.34+0.46exp(-0.5((DoY-162)/66.1)2), where DoY is the day of the
hydrological year starting from 1 October. Finally, GPP was calculated as
GPP = NEE–Re. Negative values of NEE and GPP indicated that the
ecosystem was a CO2 sink.
Half-hourly auxiliary measurements used in this study included photosynthetic
activity radiation (PAR; mol m-2 s-1), vapor pressure deficit
(VPD; kPa), wind speed (m s-1), and relative humidity (RH; %), with
additional measurements as described elsewhere (Tatarinov et al., 2016).
Furthermore, the soil microclimatology half-hourly measurements were measured
and calculated with soil chamber measurements, using the LI-8150-203
(LI-COR, Lincoln, NE), as described below, namely air temperature (Ta;
∘C) and relative humidity at 20 cm above the soil
surface and soil temperature (Ts; ∘C) at a 5 cm soil depth using
a soil temperature probe, as well as volumetric soil water content
(SWC0-10; m3 m-3) in the upper 10 cm of the soil near the
chambers, using the ThetaProbe model ML2x (Delta-T Devices Ltd., Cambridge,
UK), which was calibrated to the soil composition based on the
manufacturer's equations.
Soil CO2 fluxes
Soil CO2 fluxes (Rs) were measured with automated non-steady-state
systems, using 20 cm diameter opaque chambers and a multiplexer to allow for
simultaneous control of several chambers (LI-8150, -8100-101, -8100-104;
LI-COR, Lincoln, NE). The precision of CO2 measurements in the
chambers' air is ±1.5 % of the measurements' range (0–20 000 ppm). The chambers were closed on preinstalled PVC collars of 20 cm diameter,
allowing for a short measurement time (i.e., 2 min), and positioned away from
the collars for the rest of the time. Data were collected using a system in
which air from the chambers was circulated (2.5 L min-1) through an
infrared gas analyzer (IRGA) to record CO2 (µmol CO2 mol-1
air) and H2O (mmol H2O mol-1 air) concentrations in the system
logger (1 s-1). Gap filling of missing data due to technical problems
(i.e., 27 % of the data across the study period between November 2015 and
October 2016) was based on the average diurnal cycle of each month.
The rates of soil CO2 flux, Rs (µmol CO2 m-2 s-1), were calculated from chamber data using a linear fit of change in the
water-corrected CO2 mole fraction using Eq. (4) as
follows:
Rs=dCdt⋅vPsTaR,
where dC/dt is the rate of change in the water-corrected CO2 mol
fraction (µmol CO2 mol-1 air s-1), v is the system
volume (m3), P is the chamber pressure (Pa), s is the soil surface area
within the collar (m2), Ta is the chamber air temperature (K), and R is
the gas constant (J mol-1 K-1). A measurement period of 2 min
was used, based on preliminary tests to obtain the most linear increase of
CO2 in the chambers with the highest R2.
Soil CO2 fluxes in the experimental plot were measured between November 2015 and October 2016 by means of three measurement chambers using 21 collars grouped in seven sites in the forest stand, with three locations
(i.e., three collars) per site, based on different distances from the
nearest tree (Dt). The collars were inserted 5 cm into the soil. Data were
recorded on a half-hourly basis (48 daily records). The three chambers were
rotated between the seven sites every 1–2 weeks to cover all sites and to
assess spatial and temporal variations.
Upscaling of the collar measurements to plot-scale soil CO2 flux was
carried out by grouping collars based on three locations (i.e., under trees
(< 1 m from nearest tree; UT), in gaps between trees (1–2.3 m; BT),
and in open areas (> 2.3 m; OA)), with one chamber taking
measurements at each location, and estimating the fractional areas (∅) of
the three locations based on mapping the sites according to the distances
noted above, as previously done by Raz-Yaseef et al. (2010):
5Rs=RsOA⋅∅OA+RsBT⋅∅BT+RsUT⋅∅UT,6∅OA+∅BT+∅UT=1.
The annual scale of Rs was derived from the upscaled chamber measurements
(Eq. 5) based on daily records (48 half-hourly values) of spatially upscaled
Rs.
Estimating the temperature sensitivity of Rs (Q10) was performed as
described by Davidson and Janssens (2006) using a first-order exponential
equation (see also Xu et al., 2015):
Rs=aebTs,
where Rs represents the half-hourly spatially upscaled time series of soil
respiration flux (µmol m-2 s-1), Ts (∘C) is soil
temperature at a 5 cm depth (upscaled spatially and temporally using the
same method as for Rs), and a and b are fitted parameters. The b values were
used to calculate the Q10 value according to the following equation:
Q10=e10b.
Soil CO2 flux partitioning
The determination of different sources of soil CO2 efflux was based on
linear mixing models (Lin et al., 1999) to estimate proportions of three
main sources (autotrophic, heterotrophic, and abiotic), using isotopic
analysis of soil CO2 profiles and soil incubation data from eight
campaigns (January to September) during 2016, according to Eqs. (9)–(11).
Partitioning of the monthly Rs values into components was done using a
three-end-member triangular model for interpreting the δ13C and
Δ14C values of CO2 flux; the three-end-member triangular
corners are the autotrophic (Rsa), heterotrophic (Rh), and abiotic (Ri)
sources of Rs. The δ13C and Δ14C isotope
signatures of monthly Rs locate it inside the triangle (Fig. S2):
9δ13CRs=fsa⋅δ13Csa+fh⋅δ13Ch+fi⋅δ13Ci,10Δ14CRs=fsa⋅Δ14Csa+fh⋅Δ14Ch+fi⋅Δ14Ci,111=fsa+fh+fi,
where f indicates the fraction of total soil flux (e.g.,
fh=Rh/Rs), while subscripts sa, h, and i indicate
autotrophic, heterotrophic, and inorganic components, respectively. The
three-equations system was used to solve the three unknown f fractions of
the total soil flux based on empirical estimates of the isotopic end-members.
Additionally, δ13C and Δ14C are the stable and
radioactive carbon isotopic ratios, where δ13C= [([13C/12C]sample/ [13C/12C]reference) -1] ⋅1000 ‰,
and the reference is the Vienna international standard (VPDB). Radiocarbon
data are expressed as Δ14C in parts per thousand or per mil
(‰), which is the deviation of a sample
14C/12C ratio relative to the OxI standard in 1950 (see Taylor et
al., 2015), that is, Δ14C= [([14C/12C]sample/ (0.95⋅ [14C/12C]reference⋅exp [(y-1950)/8267])) -1] ⋅1000 ‰, where y is the year of sample measurements.
The δ13CRs was estimated monthly using the Keeling plot
approach (Figs. S3 and S4; Pataki et al., 2003; Taneva and Gonzalez-Meler,
2011). Soil air was sampled using closed-end stainless-steel tubes (6 mm
diameter) perforated near the tube bottom at four depths (30, 60, 90, and
120 cm). Samples of soil air were collected in pre-evacuated 150 mL glass
flasks with high-vacuum valves, the dead volume in the tubing and flask
necks having been purged with soil air using a plastic syringe equipped with
a three-way valve.
Note that the Keeling plot approach is based on the two-end-member mixing
model (see Review of Pataki et al., 2003), which often does not hold in
soils because of variations in the δ13C values of source
material with depth (see a recent example in Joseph et al., 2019). However,
probably because of the very dry conditions at our study site, no change in
δ13C with depths in the root zone is observed (±0.1 ‰ across the 35 cm depth profiles; Fig. S5), providing
an opportunity to avoid this caveat; we must also conclude of course that
the variations among the contributions of Rsa, Rh, and Ri do not change
significantly with depth, permitting the use of the single set of
isotopic signatures in Table 2. The soil CO2 samplings carried out
therefore represented predominantly the mixing of atmospheric CO2 with
a single integrated soil source signal, consistent with the Keeling plot
approach.
The autotrophic (δ13Csa) end-member was estimated based on
incubations during the sampling periods of excised roots, following Carbone
et al. (2008). Fine roots (< 2 mm diameter) were collected, rinsed
with deionized water, and incubated for 3 h in 10 mL glass flasks
connected with Swagelok Ultra-Torr tee fittings to 330 mL glass flasks
equipped with Louwers high-vacuum valves. The flasks were flushed with
CO2-free air at room temperature close to field conditions. The
CO2 was allowed to accumulate to at least 2000 ppm (∼2 h).
The heterotrophic (δ13Ch) end-member was estimated as in
Taylor et al. (2015), and, similar to the root-incubation experiment, soil
samples from the top 5 cm of the litter layer or 10 cm below the soil
surface were collected, and roots were carefully removed to isolate
heterotrophic components. Root-free soils were placed in 10 mL glass flasks
and allowed to incubate for 24 h before being transferred to evacuated
330 mL glass flasks. The inorganic source (δ13Ci)
end-member was estimated using 1 g of dry soil (ground to pass through a
0.5 mm mesh) placed in a 10 mL tube with a septum cap; then, 12 mL of 1 M HCl
was added to dissolve the carbonate fraction, and the fumigated CO2
withdrawn from each tube was collected using a 10 mL syringe and injected
into a 330 mL evacuated flask for isotopic analysis.
Radiocarbon estimates were based on the work of Carmi et al. (2013) at the
same site, adjusted to the measured atmospheric 14C values during the
study period (49.5 ‰; Carmi et al., 2013). The Δ14Csa and Δ14Ch end-members were estimated based
on the assumption that they carry the 14C signatures of 4 and 8.5 years, respectively, older than the 14C signature of the atmosphere at
the time of sampling, based on mean ages previously estimated (Graven et
al., 2012; Levin et al., 2010; Taylor et al., 2015). The ratio Δ14Ci
was obtained from Carmi et al. (2013). Monthly values of Δ14CRs were obtained using the linear equation of the regression
line of the measured δ13C values of Rsa, Rsh, and Ri and the
corresponding estimated Δ14C values (Fig. S2) and monthly
δ13C values of Rs.
Isotopic analysis
Isotopic analysis followed the methodology described in Hemming et al. (2005). The δ13C of CO2 in the air was analyzed using a
continuous-flow mass spectrometer connected to a 15-flask automatic manifold
system. An aliquot of 1.5 mL of air was expanded from each flask into a
sampling loop on a 15-position valve (Valco, Houston, TX, USA). CO2 was
cryogenically trapped from the air samples using helium as a carrier gas; it
was then separated from N2O with a Carbosieve G (Sigma-Aldrich) packed
column at 70 ∘C and analyzed on a Europa 20-20 isotope ratio mass
spectrometer (IRMS; Sercon, Crewe, UK). The δ13C results were quoted in parts per
thousand (‰) relative to the VPDB international
standard. The analytical precision was 0.1 ‰. To measure
[CO2], an additional 40.0 mL subsample of air from each flask was
expanded into mechanical bellows and then passed through an infrared gas
analyzer (LI-6262; LI-COR, Lincoln, NE, USA) in an automated system. The
precision of these measurements was 0.1 ppm. Flasks filled with calibrated
standard air were measured with each batch of 10 sample flasks; five
standards were measured per 10 samples for δ13C analyses and
four standards per 10 samples for [CO2] analyses.
Organic matter samples were dried at 60 ∘C and milled using a
Wiley mill fitted with a size 40 mesh, and soil samples were ground in a
pestle and mortar. Soils containing carbonates were treated with 1 M
hydrochloric acid. Between 0.2 and 0.4 mg of each dry sample was weighed
into tin capsules (Elemental Microanalysis Ltd., Okehampton, UK), and the
δ13C of each was determined using an elemental analyzer linked
to a Micromass Optima IRMS (Manchester, UK). Three replicates of each sample
were analyzed, and two samples of a laboratory working standard cellulose
were measured for every 12 samples. Four samples of the acetanilide
(Elemental Microanalysis Ltd.) international standard were used to calibrate
each run, and a correction was applied to account for the influence of a
blank cup. The precision was 0.1 ‰.
Total belowground carbon allocation (TBCA)
TBCA (g C m-2 yr-1) was calculated following Giardina and Ryan (2002) for the study year (November 2015–October 2016) as follows:
TBCA=Rs-Ralp+ΔCsoil,
where Ralp is the annual aboveground litter production between November 2014 and October 2015, and ΔCsoil is the annual change in
belowground total soil organic C. Litter production, not measured during the
present study, was estimated based on values obtained by Masyk et al. (2008) for 2000–2006 (56 g C m-2 yr-1) and assumed to have
increased in the study period (2014–2015) proportionally to the measured
increase in leaf area index (LAI; 1.31 to 1.94; i.e., Ralp= [(1.94⋅56)/1.31] = 83 g C m-2 yr-1). For herbaceous litter
production, three plots of 25 m2 were randomly selected in 2002 and
harvested at the end of the growing season, total fresh biomass was weighed,
and subsamples were used to determine dry weight and C content.
Grünzweig et al. (2007) found that herbaceous litter production was
close to the average rainfall for the specific year; this method was adapted
in the current study for the period between November 2014 and October 2015.
Since aboveground litter (Ralp; the sum of tree litter and herbaceous
litter production) of a given year was mainly produced during that year but
decayed during the following hydrological year, TBCA was based on the current
year's Rs (2015–2016) and the previous year's Ralp (2014–2015).
ΔCsoil was set constant as the average annual belowground
carbon increase since afforestation (Qubaja et al., 2019).
Statistical analyses
Two-way ANOVA tests were performed at a significance level set at p=0.001
to detect significant effects of locations (OA, BT, and UT), sites, and
their interactions on Rs and metrological parameters. Pearson correlation
analysis (r) was used to detect the correlation between Rs and meteorological
parameters. To quantify spatiotemporal variability in Rs, the coefficient
of variation (CV %) was calculated as [(STDEV / Mean) ⋅ 100 %].
Heterogeneity was considered weak if CV % ≤ 10 %, moderate if 10 % < CV % ≤ 100 %, and strong if CV % > 100 %. All the analyses were performed using MATLAB software, Version R2017b
(MathWorks, Inc., MA, USA).
Annual mean of half-hourly values across locations (OA, open area;
BT, between trees; UT, under tree) in seven sites in the forest during the
study period of soil respiration flux rates (Rs) together with the soil
water content at 10 cm depth (SWC), minimum distances from nearby tree (Dt),
soil temperature at 5 cm depth (Ts), and air temperature (Ta) and relative
humidity (RH) at the soil surface (numbers in parentheses indicate ±SE).
* Correlation is significant at the 0.05 level (two-tailed). ** Correlation is significant at the 0.01 level (two-tailed).
ResultsSpatial variations
The spatial variations in Rs across locations (distance from nearest tree)
and sites (across the study area) are reported in Table 1, together with
other measured variables. The results indicated an overall mean Rs value of
0.8±0.1µmol m-2 s-1, with distinct values for the
three locations. Rs was greater at UT locations than at the BT and OA
locations by a factor of ∼2. The spatial variability among
the locations was also apparent in the Rs daily cycle (Fig. 1), with clear
differences between the wet season (November to April), when the UT showed
consistently higher Rs values than at other locations by a factor of about
1.6, and the dry season, when the equivalent values differed by a factor of approximately 2.6. Note that the daily
peak in Rs remained at midday in both the wet and dry seasons. Overall, the
21 collars showed moderate variations (CV = 55 %; Table 1); Rs was
negatively correlated with distance from trees (Dt; r=-0.62; p<0.01) and with soil and air temperatures (Ts and Ta; r=-0.45; p<0.05) and positively correlated with soil water content and relative
humidity (SWC and RH; r=0.50; p<0.05). The inverse correlation
between Rs and distance from the nearest tree could be useful in considering
the expected decline in stand density due to thinning and mortality (e.g.,
associated with a drying climate). For a first approximation, the results
indicate that decreasing from the present stand density of 300 to 100 trees ha-1 and the resulting increase in mean distance
among trees could result in decreasing ecosystem Rs by 11 %.
Representative diurnal cycles of soil respiration (Rs; using soil
chambers across locations: open area, OA; between trees, BT; under trees,
UT) and sites in panels (a) and (b), of net ecosystem exchange (NEE; canopy-scale eddy covariance) and gross primary production (GPP) and ecosystem
respiration (Re) and its partitioning to soil respiration (Rs) and
aboveground tree respiration (Rf) in panels (c) and (d), during the wet
(November–April) and dry (May–October) periods. Based on half-hourly values over the
diurnal cycle; shaded areas indicate ±SE; Rf was estimated as the
residual as Rf=Re-Rs and is presented as a dashed line.
Temporal dynamics
On the diurnal timescale, CO2 fluxes showed typical daily cycles (Fig. 1). As expected, on average, all CO2 fluxes were higher during the wet
period compared to the dry season by a factor of ∼2. However,
Rs and Re peaked around midday in both the wet and dry seasons, while the
more physiologically controlled NEE and GPP showed a shift from midday
(around 11:00–14:00 LT) to early morning (08:00–11:00 LT) in the dry season,
with a midday depression and a secondary afternoon peak (Fig. 1d).
The δ13C and Δ14C signature of soil
respiration (Rs) and its partitioning into autotrophic (Rsa), heterotrophic
(Rh), and abiotic (Ri), together with the relative contribution of each to
the soil and ecosystem respiration for Yatir Forest during eight campaigns
of measurements from January to September 2016 (numbers in parentheses
indicate ±SE) in comparison to results obtained previously in the
same forest (2001–2006 mean values). The monthly contribution of Rsa, Rh,
and Ri to Rs or Re is presented in Fig. 3a and b, respectively.
1 Measured in the present study. 2 Measured by Carmi et al. (2013). 3 Calculated based on the measured atmospheric value by Carmi
et al. (2013). 4 Calculated based on the best-fit regression
equation in Fig. S2.
The temporal variations across the seasonal cycle are reported in Fig. 2,
based on monthly mean values and exhibiting sharp differences between the wet
and dry seasons. As previously observed in this semiarid site, all CO2
fluxes peak in early spring between March and April. The corresponding
high-resolution data are reported in Fig. S6, which show also that the
high winter (February) Rs rates were associated with clear days when
photosynthetic active radiation (PAR) increased with air temperature, Ta.
These data also show that, following rainy days, daily Rs values could reach
6.1 µmol m-2 s-1 (i.e., in the UT microsite; data not
shown), although the average was 1.1±0.2µmol m-2 s-1 during the wet period, which diminished by ∼55 %
in the dry season to mean daily values of 0.5±0.1µmol m-2 s-1. In spring (April), all CO2 fluxes peaked during the
crossover trends of decreasing soil moisture content and increasing
temperature and PAR (Fig. S6).
Seasonal trends of monthly mean values during the research period
of (a) the fluxes of net ecosystem exchange (NEE), gross primary production
(GPP), and ecosystem respiration (Re) and its components, soil respiration
(Rs) and aboveground tree respiration (Rf), and monthly mean of precipitation (P), and monthly mean of key
environmental parameters; (b) soil water content at the top 10 cm
(SWC0-10) and soil temperature at 5 cm (Ts); and (c) vapor pressure
deficit (VPD) and photosynthetic activity radiation (PAR). Rf is obtained
from Re-Rs. Vertical dotted lines indicate the winter, spring, summer,
and fall seasons.
The temporal variations in the half-hourly values of Rs reflected changes in
soil moisture at 0–5 cm depth and PAR (r=0.5 and 0.2, respectively; p<0.01) and negative correlations with Ts and RH (r=0.2 and 0.1,
respectively; p<0.01). The variations in the integrated Rs showed a
CV of 71 %, with the temporal variations dominated strongly by PAR (CV > 100 %), moderately by SWC (CV ∼85 %), and
weakly by RH (CV ∼40 %; correlations and CV values were not
included in figures and tables). Repeating the models applied by
Grünzweig et al. (2009), the potential climatic factors that best
predicted daily Rs shifted from SWC and PAR in the dry season to Ts and PAR
in the wet season (Table S2). These equations explained 43 % and 70 % of the variation in Rs in the dry and wet seasons, respectively (Table S2). A reasonable forecast of the temporal variations in Rs (µmol m-2 s-1) at half-hourly values (R2=0.60, p<0.0001)
was obtained based on SWC0-10 and Ts values across the entire seasonal
cycle, based on
Rs=0.05126⋅exp(0.04274⋅Ts+28.51⋅SWC-74.44⋅SWC2).
At the ecosystem scale, Re was characterized by high fluxes in the wet
season and peak values of ∼2.4µmol m-2 s-1
in February to April (Fig. 2; Table S1). Re fluxes rapidly decreased after
the cessation of rain and reached the lowest values in the fall (September
to October), with mean dry-period values of 0.5±0.1µmol m-2 s-1 (Fig. 2, Table S1). GPP had a mean value of -1.8±0.4µmol m-2 s-1, and daily NEE had a mean value of -0.5±0.3µmol m-2 s-1 (Table S1 and Fig. S6), with
the same seasonality for both (Fig. 2).
Figure 3 (see also Table 2) summarizes the seasonal variations in Rs and Re
partitioning. The monthly Rsa and Rh were not significantly different but
were significantly different from Ri (p<0.05). The Rsa/Rs ratios
ranged from 0.32 to 0.46, the largest contribution occurring in early spring
from February to April. The Rh/Rs fraction ranged between 0.33 and 0.45,
being the highest during the wet season. The Ri/Rs fraction – the fraction of
inorganic sources from the total soil respiration – ranged from 0.09 to
0.35, the largest contribution being in the driest period. The mean relative
contributions of these components to Rs over the sampling campaigns are
presented in Fig. 3a, but, on average, soil biotic fluxes were higher than
abiotic fluxes by a factor of ∼4. Re partitioning showed an
average increase in Rf/Re from 25 % in the wet season to 54 % in the
dry season and a decline in Rs/Re from 75 % to 46 % on average from the
wet to the dry season, respectively, which reflected a seasonal change of
Rf in the wet season to peak values in the dry season (Fig. 3b). Both the
highest and lowest Rs fractions (∼0.74 and nearly 0.34) along
the seasonal cycle were associated with low total Re fluxes, that is, in the
fall before the Rf peak in the spring and in the summer, when physiological
controls limited water loss (Fig. 2).
(a) Seasonal variations in the relative contribution of soil
autotrophic (Rsa), heterotrophic (Rh), and abiotic (Ri) components to Rs,
and (b) seasonal variations in the relative contribution of soil autotrophic
(Rsa), heterotrophic (Rh), abiotic (Ri), and foliage and stem respiration
(Rf is obtained from Re-Rs) components to ecosystem respiration (Re)
during eight campaigns (January–September) in 2016. The contributions were estimated
with linear mixing models using δ13C and Δ14C of
soil respiration (Rs) and a soil CO2 profile method at 0 to 120 cm
soil depth. Vertical dotted lines indicate the winter, spring, summer, and
fall seasons. These results confirmed earlier estimates of Grünzweig
et al. (2009) and Maseyk et al. (2008a).
Mean annual values of ecosystem respiration (Re), its components
and associated ratios, net ecosystem exchange (NEE; from eddy covariance),
net primary productivity (NPP), gross primary productivity (GPP), carbon-use
efficiency (CUE), leaf area index (LAI), and ratio of total belowground
carbon allocation (TBCA) to GPP (TBCA / GPP) in the present study (mean of November 2015 to October 2016) and in comparison to results obtained previously in the
same forest (2001–2006 mean values). Ri, Rh, Rsa, Rs, Rl and Rw denote
abiotic, heterotrophic, soil autotrophic, soil, foliage, and wood CO2
flux, respectively. Q10 is derived during the two studies for the wet
and dry seasons.
StudyRsRhRsaRlRwRiReNEENPPGPP(g m-2 yr-1) Mean (2001–2006)4061472032607056735-211-358-880x/Rs0.360.500.14x/Re0.550.200.280.350.100.07Mean (2015–2016)2951151191553961488-167-282-655x/Rs0.390.400.21x/Re0.600.230.240.320.080.13Ratio of (x/Rs)2016/(x/Rs)20031.080.811.50Ratio of (x/Re)2016/(x/Re)20031.091.180.880.900.841.64StudyQ10CUE TBCA / GPP3LAI SWC1SWC2(m2 m-2) Mean (2001–2006)2.5 1.2 0.40 0.41 1.3 Mean (2015–2016)1.6 1.1 0.43 0.38 2.1 Ratio of x2016/x20030.64 0.92 1.06 0.93 1.62
1 SWC ≥0.2 (m3 m-3). 2 SWC < 0.2
[m3 m-3]. 3 The mean of GPP used by Grünzweig et al. (2009) to estimate the TBCA / GPP ratio was 834 g m-2 yr-1.
Annual scale
On an annual timescale, estimates of CO2 flux components based on EC
measurements resulted in annual values of GPP, NPP, Re, and NEP of 655, 282,
488, and 167 g C m-2 yr-1, respectively (Tables 3 and S1). On
average across the measurement period, Rs was the main CO2 flux to
atmosphere, making up 60±6 % of Re (295±4 g C m-2 yr-1; Tables 3 and S1), and Rf was another significant component
accounting for 40±6 % of Re (Fig. 3b), which reflected the low-density (300 trees ha-1) nature of the semiarid forest. As indicated
above, Re partitioning showed a decrease in Rs/Re and an increase in Rf/Re
from winter to summer, which is clearly apparent in Fig. 3b. On an annual
scale, during the study period, estimates of Rf, Rsa, Rh, and Ri values were
194±36, 119±21, 115±20, and 61±6 g C m-2 yr-1, respectively. These rates of respiration fluxes
translated at the ecosystem scale to Re/ GPP of ∼75 %,
lower than observed in other ecosystems (Table S3) and leading, in turn,
to high ecosystem CUE of 0.43.
Using the site records of nearly 20 years, long-term trends in GPP, NPP, Re,
and NEP were examined. Soil respiration and its partitioning could not be
similarly monitored continuously, but combining the present results with the
2001–2006 values obtained by Grünzweig et al. (2009) and Maseyk et al. (2008a) provided a basis for estimating the long-term trends in soil
respiration. Notably, no clear or significant trend over time was observed
in any of the canopy-scale continuously monitored fluxes, but, because of
relatively large interannual variations, associated mainly with those in
precipitation (see Qubaja et al., 2020), it is likely that the relative
contributions of the different fluxes, expressed as ratios in Table 3,
provide a more robust perspective of the long-term temporal changes in the
ecosystem functioning. The results presented in Table 3 reflect the
long-term growth of the forest, with a relatively large increase in LAI, but
the TBCA remained around 40 %. The results also indicated little change
in the total soil respiration, Rs, component, (as a fraction of Re or GPP)
but a general shift from the autotrophic components to the heterotrophic
component (i.e., Rh). This was reflected in the decreasing ratio of the
autotrophic components (i.e., Rsa, Rl, and Rw) and the increasing
ratio of Rh to Re (Table 3) across the 13-year observation period (2003 to 2016).
Discussion
Partitioning ecosystem carbon fluxes and long-term observational studies are
key to understanding ecosystem carbon dynamics and their response to change.
Overall, the results support our research hypothesis that the observed high
CUE at our site is at least partly due to the characteristics of the carbon
flux partitioning that can be associated with the semiarid conditions.
Compared to other sites and climates (see comparative compilation in
Table S3 in the Supplement), the results reflect several key points: (1) relatively high
belowground allocation; (2) low soil respiration in general and low
heterotrophic respiration in particular; (3) combining the results for 2016
and those of our earlier study offered a long-term perspective across 13 years, indicating that the low Rs in this ecosystem is robust and exhibits
reduced sensitivity to temperature; and (4) there is a general long-term
shift from autotrophic to heterotrophic respiration.
Comparing CO2 fluxes in this forest with fluxes in a range of European
forests showed that mean NEP in the semiarid forest (167 g C m-2 yr-1) is similar to the mean NEP in other European forests (150 g C m-2 yr-1; FLUXNET).
Carbon partitioning belowground (TBCA / GPP) was relatively high
(∼38 %), with little change across the long-term
observation period. It is, however, within the range of mean values for
forests in different biomes (Litton et al., 2007). High belowground
allocation helps explain the high rate of SOC (soil organic carbon) accumulation observed over the
period since afforestation (Grünzweig et al., 2007; Qubaja et al.,
2019). Note that, irrespective of the soil carbon accumulation, the abiotic
component to the CO2 flux seems to be significant in dry environments
(Table 3) and in particular in the dry seasons, when biological activities
drastically decrease (Kowalski et al., 2008; Lopez-Ballesteros et al., 2017;
Serrano-Ortiz et al., 2010; Martí-Roura et al., 2019). The results show
that considering the abiotic effects on estimating soil respiration and, in
turn, on estimating the carbon budget in dry calcareous soils can play an
important part in estimating soil and ecosystem respiration fluxes (Angert
et al., 2015; Roland et al., 2012).
The soil CO2 efflux in the semiarid forest (295 g C m-2 yr-1)
is at the low end of Rs values across the range of climatic regions, from 50
to 2750 g C m-2 yr-1 (Adachi et al., 2017; Chen et al., 2014;
Grünzweig et al., 2009; Hashimoto et al., 2015). This is clearly lower
than the mean Rs value for global evergreen needle forests, which is
estimated at 690 g C m-2 yr-1 (Chen et al., 2014), and between
estimates for desert scrub and Mediterranean woodland (224–713 g C m-2 yr-1; Raich and Schlesinger, 1992) or for Mediterranean forests
(561–1,015 g C m-2 yr-1; Casals et al., 2011; Luyssaert
et al.,
2007; Matteucci et al., 2015; Misson et al., 2010; Rey et al., 2002;
Rodeghiero and Cescatti, 2005). The mean instantaneous rate of Rs, 0.8 µmol m-2 s-1, is also in the range reported for unmanaged
forest and grassland in the dry Mediterranean region (0.5 and 2.1 µmol m-2 s-1; Correia et al., 2012).
The observed low Rs values were associated with a relatively high fraction
of autotrophic and a lower fraction of heterotrophic respiration. The mean
annual-scale Rsa/Rs ratio of 0.40 was at the high end of the global range of
0.09 to 0.49 (Chen et al., 2014; Hashimoto et al., 2015). In contrast,
heterotrophic respiration showed an annual-scale Rh/Rs ratio of 0.39±0.02 (Table 2 and Fig. 3), which is lower than the estimated global mean
Rh/Rs value of 0.56 (Hashimoto et al., 2015) but within the range of
Mediterranean region forest, which varies between 0.29 and 0.77 (Casals et
al., 2011; Luyssaert et al., 2007; Matteucci et al., 2015; Misson et al.,
2010; Rey et al., 2002; Rodeghiero and Cescatti, 2005). The relatively low
annual scale of the heterotrophic respiration to Rs is consistent with the
dry soil over much of the year in this forest (Figs. 2 and S6) and the
observed low decomposability of plant detritus and the high mean SOC
accumulation rate (Grünzweig et al., 2007).
The long-term perspective from the 13-year observation period indicates
emerging trends that can be a basis for assessing the effects of forest age
and the evident increase in LAI (Table 3) and changes in environmental
conditions (generally warming and drying; see, e.g., Lelieveld et al.,
2012). Here, because comparing the noncontinuous data from the present (2016) and earlier (2001–2006) studies is sensitive to the large
interannual variations in the ecosystem activities and fluxes (Qubaja et
al., 2019), we focused on the more robust changes in the ratio of the
respiration components to the overall fluxes (Re; Table 3). This shows a
shifting trend from the autotrophic components to the heterotrophic, with
little change in the contribution of Rs to the overall efflux. The ratios of
Rsa, Rl, and Rw to Re tended to decrease by about 13 %, while that of Rh
increased by about 18 %; similar trends were seen in soil respiration,
with Rsa/Rs decreasing by -19 % and Rh/Rs increasing by +8 % (Table 3). The relatively low Rs under conditions of high temperature in the
semiarid ecosystem implies reduced sensitivity of respiration to
temperature. This is partly imposed by low SWC conditions during extended
parts of the year (Grünzweig et al., 2009; cf. Rey et al., 2002; Xu and
Qi, 2001). Accordingly, Rs showed greater sensitivity to Ts in the wet
period, but, during the 8–9 months of the year when SWC was below
∼0.2 m3 m-3, Rs varied predominantly with water
availability. The long-term perspective reported in Table 3 indicates a
further decrease in temperature sensitivity, with mean Q10 values in
the dry season decreasing from 1.6 to 1.1. These estimated Q10 values
are generally consistent with published values for different ecosystems (1.4
to 2.0; Hashimoto et al., 2015; Zhou et al., 2009) and with low values under
low SWC (Reichstein et al., 2003; Tang et al., 2005). This is also
consistent with soil warming experiments by 0.76 ∘C in
Mediterranean ecosystems, which decreased the Rs by 16 % and Q10 by
14 % (Wang et al., 2014). Note also that the low temperature sensitivity
in the dry season is likely to be related to reduced microbial activity but
may also involve downregulation of plant activity (Maseyk et al., 2008a) and
drought-induced dormancy of shallow roots (Schiller, 2000). Finally, we also
note that the greater importance of moisture availability in influencing
respiration is clearly apparent from the observed relationships of Rs and Rh
to mean annual precipitation (MAP) in European evergreen needle forests (Fig. S8; see also Grünzweig et al., 2007), which are not observed with
respect to mean annual temperature.
Data availability
The data used in this study are archived and available from the
corresponding author upon request (dan.yakir@weizmann.ac.il).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-17-699-2020-supplement.
Author contributions
RQ and DY designed the study; RQ, FT, ER, and DY performed the experiments.
RQ and DY analyzed the data. RQ and DY wrote the paper, with discussions and
contributions to interpretations of the results from all the coauthors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This long-term study was funded by the Forestry Department of
Keren Kayemeth LeIsrael (KKL) and the German Research Foundation (DFG) as
part of the project “Climate feedbacks and benefits of semi-arid forests”
(CliFF), by the Israel Science Foundation (ISF; grant no. 1976/17), and by the Israel Science Foundation and the National Natural Science Foundation of China (ISF–NSFC) joint research program (grant no. 2579/16). The authors thank Efrat Schwartz for assistance with lab
work. The long-term operation of the Yatir Forest Research Field Site is
supported by the Cathy Wills and Robert Lewis Program in Environmental
Science. We thank the entire Yatir team for technical support and the local
KKL personnel for their cooperation.
Financial support
This research has been supported by the Forestry Department of
Keren Kayemeth LeIsrael (KKL) and the German Research Foundation (DFG) as
part of the project “Climate feedbacks and benefits of semi-arid forests”
(CliFF), the Israel Science Foundation (ISF; grant no. 1976/17), and the National Natural Science Foundation of China (ISF–NSFC) joint research program (grant no. 2579/16).
Review statement
This paper was edited by Frank Hagedorn and reviewed by two anonymous referees.
ReferencesAdachi, M., Ito, A., Yonemura, S., and Takeuchi, W.: Estimation of global
soil respiration by accounting for land-use changes derived from remote
sensing data, J. Environ. Manage., 200, 97–104,
10.1016/j.jenvman.2017.05.076, 2017.Angert, A., Yakir, D., Rodeghiero, M., Preisler, Y., Davidson, E. A., and Weiner, T.: Using O2 to study the relationships between soil CO2 efflux and soil respiration, Biogeosciences, 12, 2089–2099, 10.5194/bg-12-2089-2015, 2015.
Aubinet, M., Grelle, A., Ibrom, A., Rannik, U., Moncrieff, J., Foken, T.,
Kowalski, A. S., Martin, P. H., Berbigier, P., Bernhofer, C., Clement, R.,
Elbers, J., Granier, A., Grunwald, T., Morgenstern, K., Pilegaard, K.,
Rebmann, C., Snijders, W., Valentini, R., and Vesala, T.: Estimates of the
annual net carbon and water exchange of forests: The EUROFLUX methodology,
Adv. Ecol. Res., 30, 113–175, 2000.Bahn, M., Janssens, I. A., Reichstein, M., Smith, P., and Trumbore, S. E.:
Soil respiration across scales: towards an integration of patterns and
processes, New Phytol., 186, 292–296, 10.1111/j.1469-8137.2010.03237.x,
2010.Balogh, J., Pinter, K., Foti, S., Cserhalmi, D., Papp, M., and Nagy, Z.:
Dependence of soil respiration on soil moisture, clay content, soil organic
matter, and CO2 uptake in dry grasslands, Soil Biol. Biochem.,
43, 1006–1013, 10.1016/j.soilbio.2011.01.017, 2011.Binkley, D., Stape, J. L., Takahashi, E. N., and Ryan, M. G.: Tree-girdling
to separate root and heterotrophic respiration in two Eucalyptus stands in
Brazil, Oecologia, 148, 447–454, 10.1007/s00442-006-0383-6, 2006.
Bonan, G. B.: Ecological climatology: concepts and applications, 2nd Edn., Cambridge: Cambridge University Press, Cambridge, 28–37, 2008.Bond-Lamberty, B. and Thomson, A.: Temperature-associated increases in the
global soil respiration record, Nature, 464, 579–582, 10.1038/nature08930,
2010.Buchmann, N.: Biotic and abiotic factors controlling soil respiration rates
in Picea abies stands, Soil Biol. Biochem., 32, 1625–1635,
10.1016/s0038-0717(00)00077-8, 2000.Carbone, M. S., Winston, G. C., and Trumbore, S. E.: Soil respiration in
perennial grass and shrub ecosystems: Linking environmental controls with
plant and microbial sources on seasonal and diel timescales, J.
Geophys. Res.-Biogeo., 113, G02022, 10.1029/2007jg000611, 2008.Carmi, I., Yakir, D., Yechieli, Y., Kronfeld, J., and Stiller, M.:
Variations in soil CO2 concentrations and isotopic values in a semi-arid
region due to biotic and abiotic processes in the unsaturated zone,
Radiocarbon, 55, 932–942, 2013.Carvalhais, N., Forkel, M., Khomik, M., Bellarby, J., Jung, M., Migliavacca,
M., Mu, M. Q., Saatchi, S., Santoro, M., Thurner, M., Weber, U., Ahrens, B.,
Beer, C., Cescatti, A., Randerson, J. T., and Reichstein, M.: Global
covariation of carbon turnover times with climate in terrestrial ecosystems,
Nature, 514, 213–217, 10.1038/nature13731, 2014.Casals, P., Lopez-Sangil, L., Carrara, A., Gimeno, C., and Nogues, S.:
Autotrophic and heterotrophic contributions to short-term soil CO2 efflux
following simulated summer precipitation pulses in a Mediterranean dehesa,
Global Biogeochem. Cy., 25, GB3012, 10.1029/2010gb003973, 2011.Chen, D., Zhang, Y., Lin, Y., Zhu, W., and Fu, S.: Changes in belowground
carbon in Acacia crassicarpa and Eucalyptus urophylla plantations after tree
girdling, Plant Soil, 326, 123–135, 10.1007/s11104-009-9986-0, 2010.Chen, S. T., Zou, J. W., Hu, Z. H., Chen, H. S., and Lu, Y. Y.: Global
annual soil respiration in relation to climate, soil properties and
vegetation characteristics: Summary of available data, Agr.
Forest Meteorol., 198, 335–346, 10.1016/j.agrformet.2014.08.020, 2014.Conant, R. T., Klopatek, J. M., Malin, R. C., and Klopatek, C. C.: Carbon
pools and fluxes along an environmental gradient in northern Arizona,
Biogeochemistry, 43, 43–61, 10.1023/a:1006004110637, 1998.Correia, A. C., Minunno, F., Caldeira, M. C., Banza, J., Mateus, J.,
Carneiro, M., Wingate, L., Shvaleva, A., Ramos, A., Jongen, M., Bugalho, M.
N., Nogueira, C., Lecomte, X., and Pereira, J. S.: Soil water availability
strongly modulates soil CO2 efflux in different Mediterranean ecosystems:
Model calibration using the Bayesian approach, Agr. Ecosyst.
Environ., 161, 88–100, 10.1016/j.agee.2012.07.025, 2012.Davidson, E. A. and Janssens, I. A.: Temperature sensitivity of soil carbon
decomposition and feedbacks to climate change, Nature, 440, 165–173,
10.1038/nature04514, 2006.DeLucia, E. H., Drake, J. E., Thomas, R. B., and Gonzalez-Meler, M.: Forest
carbon use efficiency: is respiration a constant fraction of gross primary
production?, Glob. Change Biol., 13, 1157–1167,
10.1111/j.1365-2486.2007.01365.x, 2007.Deng, Q., Hui, D., Zhang, D., Zhou, G., Liu, J., Liu, S., Chu, G., and Li,
J.: Effects of Precipitation Increase on Soil Respiration: A Three-Year
Field Experiment in Subtropical Forests in China, Plos One, 7, e41493,
10.1371/journal.pone.0041493, 2012.Etzold, S., Ruehr, N. K., Zweifel, R., Dobbertin, M., Zingg, A., Pluess, P.,
Hasler, R., Eugster, W., and Buchmann, N.: The Carbon Balance of Two
Contrasting Mountain Forest Ecosystems in Switzerland: Similar Annual
Trends, but Seasonal Differences, Ecosystems, 14, 1289–1309,
10.1007/s10021-011-9481-3, 2011.Etzold, S., Zweifel, R., Ruehr, N. K., Eugster, W., and Buchmann, N.:
Long-term stem CO2 concentration measurements in Norway spruce in relation
to biotic and abiotic factors, New Phytol., 197, 1173–1184,
10.1111/nph.12115, 2013.Falge, E., Baldocchi, D., Tenhunen, J., Aubinet, M., Bakwin, P., Berbigier,
P., Bernhofer, C., Burba, G., Clement, R., Davis, K. J., Elbers, J. A.,
Goldstein, A. H., Grelle, A., Granier, A., Guomundsson, J., Hollinger, D.,
Kowalski, A. S., Katul, G., Law, B. E., Malhi, Y., Meyers, T., Monson, R.
K., Munger, J. W., Oechel, W., Paw, K. T., Pilegaard, K., Rannik, U.,
Rebmann, C., Suyker, A., Valentini, R., Wilson, K., and Wofsy, S.:
Seasonality of ecosystem respiration and gross primary production as derived
from FLUXNET measurements, Agr. Forest Meteorol., 113, 53–74,
10.1016/s0168-1923(02)00102-8, 2002.Flechard, C. R., Ibrom, A., Skiba, U. M., de Vries, W., van Oijen, M.,
Cameron, D. R., Dise, N. B., Korhonen, J. F. J., Buchmann, N., Legout, A.,
Simpson, D., Sanz, M. J., Aubinet, M., Loustau, D., Montagnani, L.,
Neirynck, J., Janssens, I. A., Pihlatie, M., Kiese, R., Siemens, J.,
Francez, A.-J., Augustin, J., Varlagin, A., Olejnik, J., Juszczak, R.,
Aurela, M., Chojnicki, B. H., Dämmgen, U., Djuricic, V., Drewer, J.,
Eugster, W., Fauvel, Y., Fowler, D., Frumau, A., Granier, A., Gross, P.,
Hamon, Y., Helfter, C., Hensen, A., Horváth, L., Kitzler, B., Kruijt,
B., Kutsch, W. L., Lobo-do-Vale, R., Lohila, A., Longdoz, B., Marek, M. V.,
Matteucci, G., Mitosinkova, M., Moreaux, V., Neftel, A., Ourcival, J.-M.,
Pilegaard, K., Pita, G., Sanz, F., Schjoerring, J. K., Sebastià, M.-T.,
Tang, Y. S., Uggerud, H., Urbaniak, M., van Dijk, N., Vesala, T., Vidic, S.,
Vincke, C., Weidinger, T., Zechmeister-Boltenstern, S., Butterbach-Bahl, K.,
Nemitz, E., and Sutton, M. A.: Carbon / nitrogen interactions in European forests and semi-natural vegetation. Part I: Fluxes and budgets of carbon, nitrogen and greenhouse gases from ecosystem monitoring and modelling, Biogeosciences Discuss., 10.5194/bg-2019-333, in review, 2019a.Flechard, C. R., van Oijen, M., Cameron, D. R., de Vries, W., Ibrom, A.,
Buchmann, N., Dise, N. B., Janssens, I. A., Neirynck, J., Montagnani, L.,
Varlagin, A., Loustau, D., Legout, A., Ziemblińska, K., Aubinet, M.,
Aurela, M., Chojnicki, B. H., Drewer, J., Eugster, W., Francez, A.-J.,
Juszczak, R., Kitzler, B., Kutsch, W. L., Lohila, A., Longdoz, B.,
Matteucci, G., Moreaux, V., Neftel, A., Olejnik, J., Sanz, M. J., Siemens,
J., Vesala, T., Vincke, C., Nemitz, E., Zechmeister-Boltenstern, S.,
Butterbach-Bahl, K., Skiba, U. M., and Sutton, M. A.: Carbon / nitrogen
interactions in European forests and semi-natural vegetation. Part II:
Untangling climatic, edaphic, management and nitrogen deposition effects on
carbon sequestration potentials, Biogeosciences Discuss.,
10.5194/bg-2019-335, in review, 2019b.Frank, A. B., Liebig, M. A., and Hanson, J. D.: Soil carbon dioxide fluxes
in northern semiarid grasslands, Soil Biol. Biochem., 34,
1235–1241, 10.1016/s0038-0717(02)00062-7, 2002.Frey, B., Hagedorn, F., and Giudici, F.: Effect of girdling on soil
respiration and root composition in a sweet chestnut forest, Forest Ecol.
Manage., 225, 271–277, 10.1016/j.foreco.2006.01.003, 2006.Gelfand, I., Grünzweig, J. M., and Yakir, D.: Slowing of nitrogen
cycling and increasing nitrogen use efficiency following afforestation of
semi-arid shrubland, Oecologia, 168, 563–575, 10.1007/s00442-011-2111-0,
2012.Giardina, C. P. and Ryan, M. G.: Total belowground carbon allocation in a
fast-growing Eucalyptus plantation estimated using a carbon balance
approach, Ecosystems, 5, 487–499, 10.1007/s10021-002-0130-8, 2002.Graven, H. D., Guilderson, T. P., and Keeling, R. F.: Observations of
radiocarbon in CO2 at La Jolla, California, USA 1992–2007: Analysis of the
long-term trend, J. Geophys. Res.-Atmos., 117, D02302,
10.1029/2011jd016533, 2012.Grünzweig, J. M., Gelfand, I., Fried, Y., and Yakir, D.: Biogeochemical factors contributing to enhanced carbon storage following afforestation of a semi-arid shrubland, Biogeosciences, 4, 891–904, 10.5194/bg-4-891-2007, 2007.Grünzweig, J. M., Hemming, D., Maseyk, K., Lin, T., Rotenberg, E.,
Raz-Yaseef, N., Falloon, P. D., and Yakir, D.: Water limitation to soil CO2
efflux in a pine forest at the semiarid “timberline”, J. Geophys.
Res.-Biogeo., 114, G03008, 10.1029/2008jg000874, 2009.Grünzweig, J. M., Lin, T., Rotenberg, E., Schwartz, A., and Yakir, D.:
Carbon sequestration in arid-land forest, Glob. Change Biol., 9, 791–799,
10.1046/j.1365-2486.2003.00612.x, 2003.Hagedorn, F., Joseph, J., Peter, M., Luster, J., Pritsch, K., Geppert, U.,
Kerner, R., Molinier, V., Egli, S., Schaub, M., Liu, J. F., Li, M. H.,
Sever, K., Weiler, M., Siegwolf, R. T. W., Gessler, A., and Arend, M.:
Recovery of trees from drought depends on belowground sink control, Nat.
Plants, 2, 1–5, 10.1038/nplants.2016.111, 2016.Hashimoto, S., Carvalhais, N., Ito, A., Migliavacca, M., Nishina, K., and
Reichstein, M.: Global spatiotemporal distribution of soil respiration
modeled using a global database, Biogeosciences, 12, 4121–4132, 10.5194/bg-12-4121-2015, 2015.Hemming, D., Yakir, D., Ambus, P., Aurela, M., Besson, C., Black, K.,
Buchmann, N., Burlett, R., Cescatti, A., Clement, R., Gross, P., Granier,
A., Grunwald, T., Havrankova, K., Janous, D., Janssens, I. A., Knohl, A.,
Ostner, B. K., Kowalski, A., Laurila, T., Mata, C., Marcolla, B., Matteucci,
G., Moncrieff, J., Moors, E. J., Osborne, B., Pereira, J. S., Pihlatie, M.,
Pilegaard, K., Ponti, F., Rosova, Z., Rossi, F., Scartazza, A., and Vesala,
T.: Pan-European delta C-13 values of air and organic matter from forest
ecosystems, Glob. Change Biol., 11, 1065–1093,
10.1111/j.1365-2486.2005.00971.x, 2005.Hogberg, P., Bhupinderpal, S., Lofvenius, M. O., and Nordgren, A.:
Partitioning of soil respiration into its autotrophic and heterotrophic
components by means of tree-girdling in old boreal spruce forest, Forest
Ecol. Manag., 257, 1764–1767, 10.1016/j.foreco.2009.01.036, 2009.Hui, D. F. and Luo, Y. Q.: Evaluation of soil CO2 production and transport
in Duke Forest using a process-based modeling approach, Global
Biogeochem. Cy., 18, GB4029, 10.1029/2004gb002297, 2004.
IPCC: Climate Change 2014: Mitigation of Climate Change, Contribution of
Working Group III to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Edenhofer, O., PichsMadruga, R., Sokona, Y., Minx, J. C., Farahani, E., Kadner, S., Seyboth, K., Adler, A., Baum, I., Brunner, S., Eickemeier, P., Kriemann, B., Savolainen, J., Schlomer, S., VonStechow, C., and Zwickel, T., Cambridge University
Press, Cambridge and New York, 843–846, 2014.Jiang, H., Deng, Q., Zhou, G., Hui, D., Zhang, D., Liu, S., Chu, G., and Li,
J.: Responses of soil respiration and its temperature/moisture sensitivity
to precipitation in three subtropical forests in southern China,
Biogeosciences, 10, 3963–3982, 10.5194/bg-10-3963-2013, 2013.Joseph, J., Kulls, C., Arend, M., Schaub, M., Hagedorn, F., Gessler, A., and
Weiler, M.: Application of a laser-based spectrometer for continuous in situ
measurements of stable isotopes of soil CO2 in calcareous and acidic soils,
Soil, 5, 49–62, 10.5194/soil-5-49-2019, 2019.Kowalski, A. S., Serrano-Ortiz, P., Janssens, I. A., Sanchez-Moral, S.,
Cuezva, S., Domingo, F., Were, A., and Alados-Arboledas, L.: Can flux tower
research neglect geochemical CO2 exchange?, Agr. Forest
Meteorol., 148, 1045–1054, 10.1016/j.agrformet.2008.02.004, 2008.Kuzyakov, Y.: Sources of CO2 efflux from soil and review of partitioning
methods, Soil Biol. Biochem., 38, 425–448,
10.1016/j.soilbio.2005.08.020, 2006.Lelieveld, J., Hadjinicolaou, P., Kostopoulou, E., Chenoweth, J., El Maayar,
M., Giannakopoulos, C., Hannides, C., Lange, M. A., Tanarhte, M., Tyrlis,
E., and Xoplaki, E.: Climate change and impacts in the Eastern Mediterranean
and the Middle East, Climatic Change, 114, 667–687,
10.1007/s10584-012-0418-4, 2012.Lellei-Kovacs, E., Kovacs-Lang, E., Botta-Dukat, Z., Kalapos, T., Emmett,
B., and Beier, C.: Thresholds and interactive effects of soil moisture on
the temperature response of soil respiration, Eur. J. Soil
Biol., 47, 247–255, 10.1016/j.ejsobi.2011.05.004, 2011.Levin, I., Naegler, T., Kromer, B., Diehl, M., Francey, R. J., Gomez-Pelaez,
A. J., Steele, L. P., Wagenbach, D., Weller, R., and Worthy, D. E.:
Observations and modelling of the global distribution and long-term trend of
atmospheric (CO2)-C-14, Tellus B, 62, 207–207, 10.1111/j.1600-0889.2010.00456.x, 2010.Lin, G. H., Ehleringer, J. R., Rygiewicz, P. T., Johnson, M. G., and Tingey,
D. T.: Elevated CO2 and temperature impacts on different components of soil
CO2 efflux in Douglas-fir terracosms, Glob. Change Biol., 5, 157–168,
10.1046/j.1365-2486.1999.00211.x, 1999.Litton, C. M., Raich, J. W., and Ryan, M. G.: Carbon allocation in forest
ecosystems, Glob. Change Biol., 13, 2089–2109,
10.1111/j.1365-2486.2007.01420.x, 2007.Lopez-Ballesteros, A., Serrano-Ortiz, P., Kowalski, A. S., Sanchez-Canete,
E. P., Scott, R. L., and Domingo, F.: Subterranean ventilation of
allochthonous CO2 governs net CO2 exchange in a semiarid Mediterranean
grassland, Agr. Forest Meteorol., 234, 115–126,
10.1016/j.agrformet.2016.12.021, 2017.Luyssaert, S., Inglima, I., Jung, M., Richardson, A. D., Reichstein, M.,
Papale, D., Piao, S. L., Schulzes, E. D., Wingate, L., Matteucci, G.,
Aragao, L., Aubinet, M., Beers, C., Bernhofer, C., Black, K. G., Bonal, D.,
Bonnefond, J. M., Chambers, J., Ciais, P., Cook, B., Davis, K. J., Dolman,
A. J., Gielen, B., Goulden, M., Grace, J., Granier, A., Grelle, A., Griffis,
T., Grunwald, T., Guidolotti, G., Hanson, P. J., Harding, R., Hollinger, D.
Y., Hutyra, L. R., Kolar, P., Kruijt, B., Kutsch, W., Lagergren, F.,
Laurila, T., Law, B. E., Le Maire, G., Lindroth, A., Loustau, D., Malhi, Y.,
Mateus, J., Migliavacca, M., Misson, L., Montagnani, L., Moncrieff, J.,
Moors, E., Munger, J. W., Nikinmaa, E., Ollinger, S. V., Pita, G., Rebmann,
C., Roupsard, O., Saigusa, N., Sanz, M. J., Seufert, G., Sierra, C., Smith,
M. L., Tang, J., Valentini, R., Vesala, T., and Janssens, I. A.: CO2 balance
of boreal, temperate, and tropical forests derived from a global database,
Glob. Change Biol., 13, 2509–2537, 10.1111/j.1365-2486.2007.01439.x,
2007.Marti-Roura, M., Hagedorn, F., Rovira, P., and Romanya, J.: Effect of land use and carbonates on organic matter stabilization and microbial communities in Mediterranean soils, Geoderma, 351, 103–115, 10.1016/j.geoderma.2019.05.021, 2019.Maseyk, K., Grünzweig, J. M., Rotenberg, E., and Yakir, D.: Respiration
acclimation contributes to high carbon-use efficiency in a seasonally dry
pine forest, Glob. Change Biol., 14, 1553–1567,
10.1111/j.1365-2486.2008.01604.x, 2008.Matteucci, M., Gruening, C., Ballarin, I. G., Seufert, G., and Cescatti, A.:
Components, drivers and temporal dynamics of ecosystem respiration in a
Mediterranean pine forest, Soil Biol. Biochem., 88, 224–235,
10.1016/j.soilbio.2015.05.017, 2015.Misson, L., Rocheteau, A., Rambal, S., Ourcival, J. M., Limousin, J. M., and
Rodriguez, R.: Functional changes in the control of carbon fluxes after 3
years of increased drought in a Mediterranean evergreen forest?, Glob.
Change Biol., 16, 2461–2475, 10.1111/j.1365-2486.2009.02121.x, 2010.Pataki, D. E., Ehleringer, J. R., Flanagan, L. B., Yakir, D., Bowling, D.
R., Still, C. J., Buchmann, N., Kaplan, J. O., and Berry, J. A.: The
application and interpretation of Keeling plots in terrestrial carbon cycle
research, Global Biogeochem. Cy., 17, 1022, 10.1029/2001gb001850, 2003.Peterjohn, W. T., Melillo, J. M., Steudler, P. A., Newkirk, K. M., Bowles,
F. P., and Aber, J. D.: Responses of trace gas fluxes and n availability to
experimentally elevated soil temperatures, Ecol. Appl., 4,
617–625, 10.2307/1941962, 1994.Poulter, B., Frank, D., Ciais, P., Myneni, R. B., Andela, N., Bi, J.,
Broquet, G., Canadell, J. G., Chevallier, F., Liu, Y. Y., Running, S. W.,
Sitch, S., and van der Werf, G. R.: Contribution of semi-arid ecosystems to
interannual variability of the global carbon cycle, Nature, 509, 600–603,
10.1038/nature13376, 2014.Preisler, Y., Tatarinov, F., Grunzweig, J. M., Bert, D., Ogee, J., Wingate,
L., Rotenberg, E., Rohatyn, S., Her, N., Moshe, I., Klein, T., and Yakir,
D.: Mortality versus survival in drought-affected Aleppo pine forest depends
on the extent of rock cover and soil stoniness, Funct. Ecol., 33,
901–912, 10.1111/1365-2435.13302, 2019.Qubaja, R., Grünzweig, J., Rotenberg, E., and Yakir, D.: Evidence for
large carbon sink and long residence time in semiarid forests based on 15
year flux and inventory records, Glob. Change Biol., 2019, 1–12, 10.1111/gcb.14927,
2019.Qubaja, R., Amer, M., Tatrinov, F., Rotenberg, E., Preisler, Y., Sprintsin,
M., and Yakir, D.: Partitioning evapotranspiration and its long-term
evolution in a dry pine forest using measurement-based estimates of soil
evaporation, Agr. Forest Meteorol., 281, 107831,
10.1016/j.agrformet.2019.107831, 2020.Raich, J. W. and Schlesinger, W. H.: The global carbon-dioxide flux in soil
respiration and its relationship to vegetation and climate, Tellus
B, 44, 81–99,
10.1034/j.1600-0889.1992.t01-1-00001.x, 1992.Ramnarine, R., Wagner-Riddle, C., Dunfield, K. E., and Voroney, R. P.:
Contributions of carbonates to soil CO2 emissions, Can. J. Soil
Sci., 92, 599–607, 10.4141/cjss2011-025, 2012.Raz-Yaseef, N., Rotenberg, E., and Yakir, D.: Effects of spatial variations
in soil evaporation caused by tree shading on water flux partitioning in a
semi-arid pine forest, Agr. Forest Meteorol., 150, 454–462,
10.1016/j.agrformet.2010.01.010, 2010.Reichstein, M., Rey, A., Freibauer, A., Tenhunen, J., Valentini, R., Banza,
J., Casals, P., Cheng, Y. F., Grünzweig, J. M., Irvine, J., Joffre, R.,
Law, B. E., Loustau, D., Miglietta, F., Oechel, W., Ourcival, J. M.,
Pereira, J. S., Peressotti, A., Ponti, F., Qi, Y., Rambal, S., Rayment, M.,
Romanya, J., Rossi, F., Tedeschi, V., Tirone, G., Xu, M., and Yakir, D.:
Modeling temporal and large-scale spatial variability of soil respiration
from soil water availability, temperature and vegetation productivity
indices, Global Biogeochem. Cy., 17, 1104, 10.1029/2003gb002035, 2003.Rey, A., Pegoraro, E., Tedeschi, V., De Parri, I., Jarvis, P. G., and
Valentini, R.: Annual variation in soil respiration and its components in a
coppice oak forest in Central Italy, Glob. Change Biol., 8, 851–866,
10.1046/j.1365-2486.2002.00521.x, 2002.Rodeghiero, M. and Cescatti, A.: Main determinants of forest soil
respiration along an elevation/temperature gradient in the Italian Alps,
Glob. Change Biol., 11, 1024–1041, 10.1111/j.1365-2486.2005.00963.x,
2005.
Roland, M.: Contributions of carbonate weathering to the net ecosystem
carbon balance of a mediterranean forest, Ph.D. thesis, Antwerpen
University, Antwerpen, Belgium, 117–142, 2012.Ross, I., Misson, L., Rambal, S., Arneth, A., Scott, R. L., Carrara, A.,
Cescatti, A., and Genesio, L.: How do variations in the temporal
distribution of rainfall events affect ecosystem fluxes in seasonally
water-limited Northern Hemisphere shrublands and forests?, Biogeosciences,
9, 1007–1024, 10.5194/bg-9-1007-2012, 2012.Rotenberg, E. and Yakir, D.: Contribution of Semi-Arid Forests to the
Climate System, Science, 327, 451–454, 10.1126/science.1179998, 2010.
Schiller, G.: Ecophysiology of Pinus halepensis Mill. and P. brutia Ten, in:
Ecology, Biogeography and Management of Pinus halepensis and P. brutia
Forest Ecosystems in the Mediterranean Basin, edited by: Ne'eman, G.
and
Trabaud, L., Backhuys, Leiden, The Netherlands, 51–65, 2000.Serrano-Ortiz, P., Roland, M., Sanchez-Moral, S., Janssens, I. A., Domingo,
F., Godderis, Y., and Kowalski, A. S.: Hidden, abiotic CO2 flows and gaseous
reservoirs in the terrestrial carbon cycle: Review and perspectives,
Agr. Forest Meteorol., 150, 321–329,
10.1016/j.agrformet.2010.01.002, 2010.Shachnovich, Y., Berliner, P. R., and Bar, P.: Rainfall interception and
spatial distribution of throughfall in a pine forest planted in an arid
zone, J. Hydrol., 349, 168–177, 10.1016/j.jhydrol.2007.10.051,
2008.Shen, W. J., Jenerette, G. D., Hui, D. F., Phillips, R. P., and Ren, H.:
Effects of changing precipitation regimes on dryland soil respiration and C
pool dynamics at rainfall event, seasonal and interannual scales, J.
Geophys. Res.-Biogeo., 113, G03024, 10.1029/2008jg000685, 2008.Subke, J.-A., Voke, N. R., Leronni, V., Garnett, M. H., and Ineson, P.:
Dynamics and pathways of autotrophic and heterotrophic soil CO2 efflux
revealed by forest girdling, J. Ecol., 99, 186–193,
10.1111/j.1365-2745.2010.01740.x, 2011.Taneva, L. and Gonzalez-Meler, M. A.: Distinct patterns in the diurnal and seasonal variability in four components of soil respiration in a temperate forest under free-air CO2 enrichment, Biogeosciences, 8, 3077–3092, 10.5194/bg-8-3077-2011, 2011.Tang, J. W., Baldocchi, D. D., and Xu, L.: Tree photosynthesis modulates
soil respiration on a diurnal time scale, Glob. Change Biol., 11,
1298–1304, 10.1111/j.1365-2486.2005.00987.x, 2005.
Tatarinov, F., Rotenberg, E., Maseyk, K., Ogee, J., Klein, T., and Yakir,
D.: Resilience to seasonal heat wave episodes in a Mediterranean pine
forest, New Phytol., 210, 485–496, 10.1111/nph.13791, 2016.Taylor, A. J., Lai, C. T., Hopkins, F. M., Wharton, S., Bible, K., Xu, X.
M., Phillips, C., Bush, S., and Ehleringer, J. R.: Radiocarbon-Based
Partitioning of Soil Respiration in an Old-Growth Coniferous Forest,
Ecosystems, 18, 459–470, 10.1007/s10021-014-9839-4, 2015.Wang, X., Liu, L. L., Piao, S. L., Janssens, I. A., Tang, J. W., Liu, W. X.,
Chi, Y. G., Wang, J., and Xu, S.: Soil respiration under climate warming:
differential response of heterotrophic and autotrophic respiration, Glob.
Change Biol., 20, 3229–3237, 10.1111/gcb.12620, 2014b.Xu, M. and Qi, Y.: Soil-surface CO2 efflux and its spatial and temporal
variations in a young ponderosa pine plantation in northern California,
Glob. Change Biol., 7, 667–677, 10.1046/j.1354-1013.2001.00435.x, 2001.Xu, Z. F., Tang, S. S., Xiong, L., Yang, W. Q., Yin, H. J., Tu, L. H., Wu,
F. Z., Chen, L. H., and Tan, B.: Temperature sensitivity of soil respiration
in China's forest ecosystems: Patterns and controls, Appl. Soil Ecol.,
93, 105–110, 10.1016/j.apsoil.2015.04.008, 2015.Zhou, T., Shi, P. J., Hui, D. F., and Luo, Y. Q.: Global pattern of
temperature sensitivity of soil heterotrophic respiration (Q10) and its
implications for carbon-climate feedback, J. Geophys.
Res.-Biogeo., 114, G02016, 10.1029/2008jg000850, 2009.