BGBiogeosciencesBGBiogeosciences1726-4189Copernicus PublicationsGöttingen, Germany10.5194/bg-14-99-2017Soil CO2 efflux from two mountain forests in the eastern
Himalayas, Bhutan: components and controlsWangdiNorbunorwangs@gmail.comhttps://orcid.org/0000-0002-9175-5614MayerMathiashttps://orcid.org/0000-0003-4366-9188NirolaMani PrasadZangmoNorbuOrongKarmaAhmedIftekhar Uddinhttps://orcid.org/0000-0002-2747-1561DarabantAndrasJandlRobertGratzerGeorgSchindlbacherAndreasInstitute of Forest Ecology, University of Natural
Resources and Life Sciences, 1180 Peter Jordan Strasse,
Vienna, AustriaUgyen Wangchuck Institute for Conservation and Environment, Department of Forests and Park Services, Lamai Goempa, Bumthang, BhutanFederal Research and Training Centre for Forests,
Natural Hazards and Landscape – BFW, A-1131 Vienna,
AustriaNational Biodiversity Center, Ministry of Agriculture
and Forests, Thimphu, BhutanThese authors contributed equally to this work.Norbu Wangdi (norwangs@gmail.com)10January20171419911013July20168August201614December201614December2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://bg.copernicus.org/articles/14/99/2017/bg-14-99-2017.htmlThe full text article is available as a PDF file from https://bg.copernicus.org/articles/14/99/2017/bg-14-99-2017.pdf
The biogeochemistry of mountain forests in the Hindu Kush Himalaya range is
poorly studied, although climate change is expected to disproportionally
affect the region. We measured the soil CO2 efflux (Rs) at a
high-elevation (3260 m) mixed forest and a lower-elevation (2460 m) broadleaf
forest in Bhutan, eastern Himalayas, during 2015. Trenching was applied to
estimate the contribution of autotrophic (Ra) and heterotrophic (Rh) soil
respiration. The temperature (Q10) and the moisture sensitivities of Rh
were determined under controlled laboratory conditions and were used to model
Rh in the field. The higher-elevation mixed forest had a higher standing tree
stock, reflected in higher soil C stocks and basal soil respiration. Annual
Rs was similar between the two forest sites
(14.5 ± 1.2 t C ha-1 for broadleaf;
12.8 ± 1.0 t C ha-1 for mixed). Modelled annual contribution of Rh
was ∼ 65 % of Rs at both sites with a higher heterotrophic
contribution during winter and lower contribution during the monsoon season.
Rh, estimated from trenching, was in the range of modelled Rh but showed
higher temporal variability. The measured temperature sensitivity of Rh was
similar at the mixed and broadleaf forest sites (Q10 2.2–2.3) under
intermediate soil moisture but decreased (Q10 1.5 at both sites) in dry
soil. Rs closely followed the annual course of field soil temperature at both
sites. Covariation between soil temperature and moisture (cold dry winters
and warm wet summers) was likely the main cause for this close relationship.
Under the prevailing weather conditions, a simple temperature-driven model
was able to explain more than 90 % of the temporal variation in Rs.
A longer time series and/or experimental climate manipulations are required to
understand the effects of eventually occurring climate extremes such as
monsoon failures.
Introduction
Carbon dioxide (CO2) efflux from soil (soil respiration; Rs) is one
of the major fluxes in the global C cycle, affects atmospheric CO2
concentrations and potentially provides feedback on global climate change
(Reichstein et al., 2003; Frey et al., 2013; Wang et al., 2014; Hashimoto et
al., 2015). Counteracting to C uptake via photosynthesis, Rs primarily
determines whether forest ecosystems serve as C sinks or sources to the
atmosphere (Bolstad et al., 2004; Dixon et al., 1994; Schlesinger and
Andrews, 2000). The current function of forests as a global C sink (Stocker,
2014; Janssens et al., 2003) could weaken and they could even become a source if
climate change disproportionally accelerates respiratory processes such as
Rs (Cox et al., 2000). Rs consists of an autotrophic component (Ra; root and
rhizosphere respiration), which is closely linked to C gain by
photosynthesis, and a heterotrophic component (Rh), which is the respiratory
product of soil organic matter (SOM) decomposition. While the source of Ra
is recently assimilated CO2, Rh can release stored soil C to the
atmosphere. For a better prediction of the response of forest C cycling to
climate change, it is crucial to understand how Rs and its components are
affected by changing environmental parameters such as temperature and
moisture (Davidson and Janssens, 2006; Sierra et al., 2015). Rates and
climate sensitivity of Rs, Ra and Rh can vary among forest ecosystem type
and climatic region (Hashimoto et al., 2015). So far, research has focused
on the temperate and boreal areas of the Northern Hemisphere whereas remote
forested areas are still largely uninvestigated (Bond-Lamberty and Thomson,
2010).
The Hindu Kush Himalaya range represents a region where research on forest
biogeochemistry is gaining momentum (Pandey et al., 2010; Sundarapandian and
Dar, 2013; Sharma et al., 2010b; Dorji et al., 2014b; Ohsawa,
1991; Wangda and Ohsawa, 2006a; Tashi
et al., 2016; Verma et al., 2012). It extends over 4.3 million km2
across eight countries with an average forest cover of approximately 20 %
(Schild, 2008), ranging from lowland tropical forest to high-altitude
forests of up to ∼ 4900 m (Liang et al., 2016; Schickhoff, 2005).
Situated in the eastern Himalayas, Bhutan has a forest cover of 70 %
(DoFPS, 2011). Most forests in Bhutan
are natural old growth (Ohsawa, 1987), store high amounts of C in biomass and soil (Dorji et al., 2014a;
Sharma and Rai, 2007) and serve as an important regional C sink (FAO, 2010).
As climate change is expected to intensify in the Himalayan region (Shrestha
et al., 2012; Singh, 2011; Xu and Grumbine, 2014; Tsering et al., 2010; Xu et
al., 2009), the effects on forest C cycling could have implications not only
regionally, but also on a global scale.
With the objective of a better understanding of soil C cycling of mountain
forest ecosystems, we studied Rs and its components (Ra, Rh), as well as the
effects of environmental drivers such as temperature and moisture at a
moderately high-altitude cool temperate mixed forest and a lower-altitude
cool temperate broadleaf forest in Bhutan. These forest types cover large
areas of the eastern Himalayas.
Materials and methodsSite description
Two representative forest ecosystems for the eastern Himalayas
(Wikramanayake, 2002), a cool temperate conifer-dominated mixed forest and a
cool temperate broadleaf forest, were studied at Thimphu and
Wangduephodrang districts, Bhutan. The cool temperate mixed forest (Grierson
and Long, 1983) was situated on a south-east-facing slope close to the top
of a mountain ridge (elevation 3260 m a.s.l). The cool temperate broadleaf
forest was situated on an east-facing gentle slope along the same mountain
ridge ∼ 11 km eastwards (elevation 2640 m a.s.l.). The sites will
be referred to as “mixed forest” and “broadleaf forest” hereafter. The mixed forest was dominated by Tsuga dumosa along with Picea spinulosa, Quercus semecarpifolia, Abies densa, and Taxus baccata. The
broadleaf forest was dominated by Quercus lanata and Quercus griffithii. Soils at the mixed forest were
Cambisols. Soils at the broadleaf forest were Luvisols. A detailed site
and soil description and the comparison are given in Table 1. The current
study was aligned within a larger-scale throughfall manipulation experiment,
which consisted of controlled and temporarily roofed areas within each forest
type. For this study, we randomly distributed all our plots within the
control areas (∼ 1500 m2 each) of the throughfall
manipulation experiment.
All stand and soil parameters are expressed as the mean ± standard
error.
Field measurements
Basic climate parameters were measured using automatic weather stations
located at a distance of approx. 1 km from the sites at the same
elevation. Data were recorded at 15 min intervals on a Decagon-EM50 data
logger (Decagon Devices Inc., Pullman, WA, USA). The automatic weather
stations recorded precipitation with an ECRN-100 rain gauge (Decagon Devices
Inc., Pullman, WA, USA), and air temperature and relative humidity with a
VP-3 vapour pressure, temperature and relative humidity sensor (Decagon
Devices Inc., Pullman, WA, USA).
Stand and soil inventories were carried out in March and April 2014 at both
sites covering an area of ∼ 1500 m2 each. The location, height
and the diameter at breast height of all trees with a
dbh > 10 cm were assessed. The basal area was calculated for
each tree species. Standing volume was estimated based on species-specific
volume equations developed by Laumans (1994), Forest Survey of India (FSI) (1996) and Department of
Forests and Park Services (FRD) (2005). Aboveground litter fall was collected
monthly using mesh traps (n=10) at each site, with an area of 1.0 m2
(100×100 cm). Litter was dried at 80 ∘C and the C content
was assumed to be 50 % of the dry weight (de Wit et al., 2006). Soil
samples were collected from the 0–10, 10–20 and 20–30 cm mineral soil
layers from four locations at both sites in May 2014. Soil samples were sieved
(2 mm) and dried (105 ∘C, 48 h). Soil organic C (SOC) of a ground
(Pulverisette 5, Fritsch, Germany) 0.1 g subsample was measured by means of
the dry combustion technique using a CN analyser
(TruSpec® CN, LECO Inc., Michigan, USA). Soil
organic C stocks (t ha-1) were calculated for each horizon by
multiplying the SOC concentration (%) by the bulk density (g cm -3)
and the depth of the horizon (cm). Fine root (≤ 2 mm) biomass was
estimated using the soil core method (Makkonen and Helmisaari, 1999) in spring
2014 at both sites. We used a cylindrical soil corer (7.5 cm diameter) for
sampling. The extracted samples were divided into three depth sections of
0–10, 10–20 and 20–30 cm. After washing and sorting (live roots and
necromass), roots were dried at 70 ∘C to constant mass before
weighing dry biomass. The contribution of fine root C was estimated at 50 %
of the plant tissue.
Rs was measured at both sites once every 3 weeks from April 2015 to
December 2015 at 10 randomly chosen plots (n=10) at each. To cover the
within-plot variability, Rs was measured at four positions within each plot
(total 40 positions per site). We used a portable infrared gas analyser
(EGM-4, PP-Systems, Amesbury, USA) with an attached soil respiration chamber
(SRC-1, PP-Systems, Amesbury, USA) for Rs measurements. Prior to
measurements (March 2015), we installed permanent collars (total height 5 cm, 2–3 cm inserted into the soil, diameter 10 cm) at each plot which served
as a base for Rs measurements. Rs was estimated by applying a linear fit to the
increasing headspace CO2 concentration over time (chamber closure time
90 seconds). A soil respiration measurement campaign lasted for
∼ 5 h at each site. Measurement order among plots and collars
was fully random to avoid bias from temporal variations in Rs.
We installed two trenching plots at each site in April 2014 (1 year prior soil
efflux CO2 measurements) to estimate the relative contributions of Ra
and Rh. Trenches (1.5×1.5 m) were dug to ∼ 1 m depth, and
all roots within the trenches were cut. The trenches were sealed with double
layered plastic foil in order to restrict tree root ingrowth. Adjoined to
each trenched plot was a corresponding control plot of the same size. Each trenched and control plot hosted three collars for Rs
measurements. We measured soil CO2 efflux at trenched and corresponding
control plots after finishing regular Rs measurements (same day).
Volumetric soil water content (0–20 cm soil depth; (vol. %)) was measured
in the centre of each plot (Rs plots, trenched plots, control plots) using a
portable Field Scout TDR meter (Spectrum Technologies, Inc. Aurora, USA)
during Rs measurements. Soil temperature at 5 cm soil depth was measured
with a handheld thermometer probe (Hanna Instruments, Germany) at each Rs
measurement location. Soil temperature and soil moisture were measured
continuously at soil profile pits (two pits per site) with five combined
soil temperature–moisture sensors (TM-5; Decagon Devices, Inc., Pullman, WA,
USA) at soil depths ranging from 5 to 120 cm. Data were recorded at 15 min
intervals on Decagon-EM50 data loggers (Decagon Devices, Inc., Pullman, WA,
USA).
Laboratory incubation
About 500 g of mineral soil (0–10 cm depth) and approximately 250 g of
forest floor litter were sampled at six random locations (n=6) at each
site in mid-September 2015. The mineral soil was homogenised and sieved
(2 mm mesh) and stored at 4 ∘C at field moisture for 1 week prior
to transport from Bhutan to Austria for further processing. Forest floor
litter was not sieved. Upon arrival in Austria, mineral soil samples were
further divided into three subsamples to account for potential soil
heterogeneity at individual sampling locations. Samples were filled into
200 cm3 stainless steel cylinders at approximate field bulk density
(∼ 0.5 g dry weight cm-3 for mineral soil; ∼ 0.1 g dry
weight cm-3 for forest floor). In total, we incubated 36 subsamples
(cylinders) for mineral soil and 12 subsamples for the forest floor litter.
Filled cylinders were kept at 4 ∘C for 5 days for equilibration
before incubation. Soil CO2 efflux (=Rh) was measured using a fully
automated incubation system. During incubation, samples were put into 2 L
containers and their CO2 efflux was determined by a dynamic closed
chamber system (Pumpanen et al., 2009). For CO2 measurements, containers
were sequentially connected to an infrared gas analyser (SBA-4, PP Systems
International Inc., Amesbury, MA, USA) by means of a tubing system. Meanwhile, disconnected containers were ventilated by means of an air pump in
order to prevent internal CO2 enrichment. Wet tissues were put into
containers to prevent samples from drying out during incubations;
moisture loss was thereby negligible (< 2 vol. %). CO2
concentration within connected containers was measured for 6 min with a
recording interval of 10 s. Rates of CO2 efflux were calculated from
the headspace CO2 increase during 2–6 min, after Pumpanen et
al. (2009).
Incubations proceeded in two steps. We first incubated at different soil
temperatures to assess the temperature sensitivity of Rh. In a second step,
we incubated under different soil moisture contents to assess the
sensitivity of Rh to changes in soil moisture. In addition, we repeated the
temperature runs with wet (140 % gravimetric water content (grav. %))
and dry (30 grav. %) soil in order to test for effects of soil moisture
on the temperature sensitivity of Rh. Between incubations, soil cores
were stored in a cold room (+4 ∘C). During storage, soil
moisture was kept constant by periodical water addition.
Temperature incubation started with mineral soil. Soil temperature was
increased from 5 to 25 ∘C in 5 ∘C
steps, with each temperature step lasting for 6 h. At each temperature step,
efflux measurements were repeated three times for each cylinder. To account
for a warm-up period between the individual temperature steps only a
calculated mean value of the latter two measurements was used for further
analysis. After finishing the temperature run, we remeasured Rh at 10 ∘C to assess and correct for potential effects of labile C loss
during the ∼ 30 h incubation. The forest floor litter was
incubated under the same procedure as mineral soil.
After the temperature incubation, we set soil moisture of all mineral soil
subsamples to 80 grav. %, incubated at constant 15 ∘C for 6 h
and measured Rh as described above. Afterwards, the three subsamples from
each sampling location were split into (i) a subsample that was kept at
constant soil moisture (80 grav. %), (ii) a subsample that was allowed
to dry out (60 to 15 grav. %), and (iii) a subsample that was
progressively watered (100 to 160 grav. %). Between repeated
incubations (all at 15 ∘C for 6 h) cylinders were taken out from
incubation containers and were stored at 4 ∘C. The whole
moisture incubation procedure lasted for 10 weeks with ∼ 2-week intervals between incubations (time-limiting step was soil
drying). We used Rh from the subsamples which had been kept at constant
moisture to correct for potential decreases in Rh due to a loss in labile C
throughout the experiment. After finishing all incubations, samples were
dried and actual bulk density, as well as actual gravimetric (grav. %)
and volumetric soil moisture (vol. %) of each subsample (cylinder), was
calculated and their total C content was determined (TruSpec®
CN, LECO Inc., Michigan, USA). Rh rates were expressed as µmol
CO2 kg C-1 s-1.
Data analysis
The effects of field Rs, soil temperature and moisture on the sites were tested by
means of repeated ANOVA measurements with a mixed-effects model structure
(Pinheiro and Bates, 2000). The significance level for this and all other
analyses was set at P<0.05. The relationship between soil
temperature and Rs was fitted by an exponential function (Buchmann, 2000):
R=β0⋅e(β1⋅T),
where R (µmol CO2 m-2 s-1) is the measured Rs, T
(∘C) is the soil temperature at 5 cm depth, and bi is the model
parameters. Equation (1) was fitted to the daily averages of each site as
well as to the individual plot data. Basal respiration rates at
10 ∘C soil temperature (Rs10) were subsequently calculated
(using Eq. 1) for each site. One sampling date (16 July 2015) was excluded
from this analysis because heavy rain occurred during the measurements. The
relationship between Rs and soil moisture was tested by fitting a polynomial
function obtained from lab incubation (see further below). Cumulative annual
Rs of both sites and both years were calculated by linear interpolation of
field Rs between measurement dates of each individual plot (the area beneath
the curves in Fig. 1d). In addition, model parameters of Eq. (1), together
with daily field soil temperatures at 5 cm depth were used to calculate a
projected daily field Rs. To account for a spatial variation in soil
temperature, continuously measured data were adjusted to discontinuously
measured plot data by linear modelling. Cumulative annual Rs rates were
calculated by averaging the summed-up daily plot Rs values.
Seasonal course of air temperature and precipitation (a),
soil temperature (b), volumetric soil water content (c) and soil
respiration (d) measured at a mixed and a broadleaf forest in the Bhutan
Himalayas in 2015. Circles represent daily mean values of manual
measurements. Solid lines (a, b, c) represent daily mean values of
continuous measurements. Error bars indicate standard error of the mean.
(a) Relationship between soil CO2 efflux
(Rs) and soil temperature, and (b)Rs and soil
moisture (vol. %) at a broadleaf and a mixed forest in the Bhutan Himalayas.
(c) Relationship between heterotrophic soil respiration (Rh) and
soil temperature and (d)Rh and soil moisture (vol. %) as
determined during a laboratory incubation. A temperature response was fitted
with an exponential function (Eq. 1) and a moisture response was fitted
with a polynomial function (Eq. 3). Error bars represent standard error of
the mean (SE). Basal respiration rates at 10 ∘C (Rs10,
Rh10) and temperature sensitivity of Rh (Q10) are given (mean ± SE).
Average Rh rates from laboratory incubations were calculated for each site,
soil horizon (mineral soil, forest floor litter) and temperature step (5–25 ∘C), respectively. Equation (1) was fitted to the
temperature incubation data separately for each site and soil horizon. Basal
heterotrophic respiration rates at 10 ∘C (Rh10) were
calculated for each site. Temperature sensitivity (Q10) of Rh was
calculated as follows:
Q10=e(10⋅β1),
where Q10 is the factor by which Rh changes at a temperature change of
10 ∘C, and β1 is the model parameter derived from Eq. (1). To determine the relationship between soil moisture and Rh, we fitted a
polynomial function to the moisture incubation data:
R=β0+β1⋅VWC+β2⋅VWC2,
where R is the measured CO2 efflux from soil samples (Rh), βi is the model parameters and VWC is the volumetric water content of the
samples. Effects of soil moisture on Q10 values were tested by means of
one-way ANOVA and Tukey's post-hoc tests.
We followed two approaches to estimate the contribution of Ra and Rh in the
field. In a first approach, we used the trenching data, assuming that the
CO2 efflux from the trenched plots solely represented Rh, while the
CO2 efflux from adjacent control plots represented Rs and, accordingly,
the difference between trenched and control plot CO2 efflux represented
Ra. As trenched plots lack water uptake by tree roots, they were regularly
wetter than control plots. We accounted for that by correcting the soil
CO2 efflux for the difference in soil moisture by using Eq. (3) (see
Schindlbacher et al., 2009 for details).
Basal respiration rates (Rh10) and temperature
sensitivity (Q10) of litter and mineral soil (0–10 cm depth) samples
derived from laboratory incubations. Incubations took place initially after
sampling (Incubation 1) using a set of three samples per plot (six plots per
site). Subsequently, sets were split and the moisture sensitivity of Rh was
tested (Fig. 2d). Subsequent to moisture incubations, the different subsets
(dry, intermediate, wet) were re-incubated to test temperature sensitivities at
different moisture contents (Incubation 2). The time lag between Incubation
1 and Incubation 2 was approximately 10 weeks. Different letters indicate
significant differences in Q10 between soil moisture levels of the
mineral soil samples.
Contribution of autotrophic soil respiration (Ra) to
total soil CO2 efflux (Rs) at a (a) broadleaf and
(b) mixed forest in Bhutan Himalayas. Autotrophic contribution was
derived from the differences between Rs measured at control and trenched
plots (bars) and from the differences between modelled Rs and heterotrophic
soil respiration rates (lines), respectively.
In a second approach, we applied the response functions of Rh derived during
laboratory incubation together with field soil C stocks and field climate
data. This allowed an alternative way of estimating the contribution of Rh in
the field (Gough et al., 2007; Kutsch et al., 2010). Model parameters
derived from Eq. (1), together with continuously measured temperature data
from 5 cm soil depth, were used to model daily Rh from the litter and from
the mineral soil at depths of 0–10 cm. Model parameters for
mineral soils, together with continuous measurements of soil temperature at
20 cm depth, were further used to model daily Rh from the mineral soil at 10–30 cm depth. Predicted Rh rates (µmol CO2 kg C-1) were
multiplied by the C stocks (kg C m-2) of the respective soil layer. We
used the litter Q10, together with continuous temperature at 5 cm soil
depth, to model daily Rh from the litter layer. In order to scale to field
fluxes, we used the annual litter input (Table 1) as a proxy for field
litter C stocks. A first rough litter assessment in March 2015 showed that
litter stocks were in a similar range to the annual litter input at both
sites. This procedure enabled us to upscale Rh to the whole soil profile in
the field (Kutsch et al., 2010). To account for a moisture response as well,
predicted Rh rates were also corrected for soil moisture conditions in the
field. For that, model parameters derived from Eq. (3) were used to
calculate Rh rates at actual moisture conditions in the field (from
continuous moisture data) and at initial moisture conditions of the soil
samples during incubation (mixed forest: 33 vol. %, broadleaf forest:
35 vol. %, litter: 46 vol. %); their relative difference was
subsequently used to correct Rh rates predicted with Eq. (1). Since litter
soil moisture was not regularly measured in the field, we applied the same
moisture parameters and continuous soil moisture records as for mineral soil
(0–10 cm). The R code of the empirical model is provided in the Supplement.
Results
Air and soil temperatures were ∼ 4 ∘C higher at the
lower-elevation broadleaf forest (Table 1) with a stable trend throughout
both study years (Fig. 1a). Air temperatures reached maximums of 29.6 and 22.6 ∘C at the broadleaf and mixed forests,
respectively. Winter air temperatures dropped slightly below freezing at the
mixed forest, which showed ephemeral snow cover. Soil temperatures remained
above freezing at both sites during the full study period (Fig. 1b). Annual
precipitation in 2015 was similar at the sites (1167 mm at the mixed forest,
1120 mm at the broadleaf forest). Both sites received maximum rainfall (60–75 % of annual
precipitation) during the peak monsoon months (June, July and August). Soil
moisture was significantly higher at the broadleaf forest during summer
(Fig. 1c). During the dry season (November–April), manually measured soil
moisture decreased to < 20 vol. % at both sites. Continuous soil
moisture records indicated accelerated drying at the broadleaf forest
(Fig. 1c).
Aboveground and belowground C stocks were markedly higher in the mixed
forest (Table 1). Standing volumes were 1066 and 464 m3 ha-1, at the
mixed and broadleaf forests. Mineral soil organic C stocks
down to 30 cm soil depth were 142 and 90 t ha-1 and leaf litter inputs
(2015) were 3.5 and 3.4 t C ha-1 at the mixed and broadleaf forests. Fine root biomass (0–30 cm mineral soil) was lower at the
mixed forest (2.3 t C ha-1) compared to the broadleaf forest
(3.2 t C ha-1).
Rs did not differ significantly between the two sites (mean Rs broadleaf:
4.2 ± 0.7 µmol CO2-C m-2 s-1, mixed: 4.0±0.6µmol CO2-C m-2 s-1) but basal respiration rates
(Rs10) were higher at the mixed forest (Fig. 2a). Cumulative annual Rs
were 14.3 ± 0.5 t C ha-1 for the broadleaf forest and
13.0 ± 0.5 t C ha-1 for the mixed forest when calculated by linear interpolation
between measurement dates. These values were very close to the ones obtained
by the modelling approach (Eq. 1): 14.5 ± 1.2 t C ha-1 for
broadleaf and 12.8 ± 1.0 t C ha-1 for mixed. Rs showed a higher
spatial variability at the mixed forest (21–87 % coefficient of
variation, CV) than at the broadleaf forest (23–46 % CV). Between 89
and 96 % of the annual temporal variation in measured Rs was explained by
field soil temperature (Eq. 1, Fig. 2a). Rs showed a weak relationship
with soil moisture at the broadleaf forest site, whereas there was no
significant correlation between Rs and soil moisture at the mixed forest
site (Fig. 2b).
Laboratory incubations showed a strong positive, exponential relationship
between soil temperature and Rh (Fig. 2c). Temperature sensitivity of
mineral soil Rh was similar between sites (mixed Q10=2.2,
broadleaf Q10=2.3; Fig. 2c, Table 2) and slightly lower for forest
floor litter (mixed Q10=1.9; broadleaf Q10=2.2; Table 2).
Q10 values of dry soil (mixed Q10=1.6;
broadleaf Q10=1.5) were significantly lower than Q10from the
soil which remained at intermediate moisture content (P<0.05,
Table 2). Q10 values obtained from dry and wet soil did not differ
significantly (Table 2). Rh and soil moisture showed a unimodal relationship
with the highest rates of Rh at intermediate soil moisture (40–50 vol. %)
and decreasing rates at lower and higher moisture levels (Fig. 2d). Overall, soil
from both sites responded similarly to changes in soil moisture.
Mixed forest soil showed a slightly sharper decrease in Rh at lower and
higher soil moisture (Fig. 2d).
Trenching plots indicated average autotrophic and heterotrophic
contributions of 29 and 27 % and 71 and 73 % at the mixed and
broadleaf forest sites during the whole 2015 season, respectively (Fig. 3). The contribution of Ra and Rh to Rs, obtained by trenching, showed high
temporal variability and strong fluctuations between individual measurement
dates at the mixed forest site (Fig. 3).
The modelling approach yielded annual heterotrophic contributions of 67 %
in mixed forest and 63 % in broadleaf forest. Modelled cumulative
annual Rh and Ra were 8.6 and 4.2 t C ha-1 at the mixed forest and 9.5 and
5.0 t C ha-1 at the broadleaf forest. Modelled Rh was in the
range of field Rs during the cold season (Fig. 3). The gap between Rh and Rs
became larger during the growing season, implying the highest contribution of Ra
during the warm monsoon months at both sites (Figs. 3 and 4). The strong
temporal fluctuation in sources (Ra, Rh) which was obtained from trenching
was not confirmed by Rh model output (Fig. 3).
Seasonal course of modelled soil CO2 efflux (Rs) and
heterotrophic soil respiration rates (Rh) at a broadleaf and mixed forest
in Bhutan Himalayas in 2015. Open circles are measured Rs rates. Error bars
and shaded areas represent standard error of the daily mean. Dashed and
dotted lines indicate the CO2 contributions of litter and mineral soil
layers to Rh. The area between the full line (total Rh) and the dashed line
represents the contribution from litter, the area between dashed and dotted
line represents the contribution of the topsoil (0–10 cm), and the area
below the dotted line represents the contribution from the 10–30 cm mineral
soil layer.
Discussion
Annual Rs of both forest sites (12.8–14.5 t C ha-1) was in the range
of values reported for similar ecosystems (10.1–13 t C ha-1, Dar et
al., 2015; 10–12 t C ha-1, Li et al., 2008; 13.7 t C ha-1,
Yang et al., 2007 and 14.7 t C ha-1, Wang et al., 2010). The higher-altitude mixed forest had a double tree basal area and standing stock,
indicating that this specific site is exceptionally productive (Singh et al.,
1994; Sharma et al., 2010a; Tashi et al., 2016; Wangda and Ohsawa, 2006b).
Soil C stocks of ∼ 140 t ha-1 (0–30 cm depth mineral soil)
indicate that these mixed forests are likely among the ecosystems with the
highest C storage capacity in the eastern Himalayas (Wangda and Ohsawa,
2006a; Sheikh et al., 2009; Dorji et al., 2014a; Tashi et al., 2016). High
soil C contents and stocks were reflected in generally higher basal
respiration (Rs10) at the mixed forest, explaining the comparatively high
annual Rs rates at this cooler, higher-altitude site. Soil C input via
aboveground litter fall was almost identical between sites
(∼ 3.5 t C ha-1), although the tree basal area was substantially
lower at the broadleaf forest. This can be attributed to a generally higher
leaf litter production in broadleaf ecosystems (Tiwari and Joshi, 2015;
Bisht et al., 2014). Fine root stocks at both sites fall within the upper
range of estimates from other surveys in the Himalayan region (Adhikari et
al., 1995; Usman et al., 1999; Garkoti, 2008; Rana et al., 2015), especially
if it is considered that fine root contents in this study were estimated
solely for 0–30 cm mineral soil depth.
At both forests, Rs tightly followed the seasonal course of soil temperature
because soil temperature and soil moisture covaried with dry and cold
winters and optimal soil moisture during the warm summer months (Figs. 1b, c;
2a, b). Rs can also be affected by labile C allocation to soil (Gu et al.,
2004). During the growing season, trees tend to allocate higher amounts of
labile C below ground, thereby potentially increasing the contribution of Ra
and simultaneously accelerating SOM decomposition by increased availability
of labile C and rhizosphere priming (Kuzyakov, 2010; Bader and Cheng, 2007;
Bengtson et al., 2012; Dijkstra and Cheng, 2007; Schindlbacher et al., 2009).
Such processes would further increase Rs and Ra during the warm summer
months. Our modelled Rh and Ra data suggest that this was also likely the
case in the studied forests (significant increase in Ra contribution during
the summer months; Fig. 3).
Our model-generated wintertime Rh fluxes were in the range of, or
slightly below, Rs fluxes (Fig. 4). During frost periods, downward C-flux
from the tree canopy is limited and the contribution of Ra to Rs is
considered low during winter (Rey et al., 2002; Hanson et al., 2000). Our
modelled wintertime (and overall) Rh therefore lay in a realistic range.
However, there is evidence that the contribution of Ra can be significant
even during cold winters (Schindlbacher et al., 2007; Tucker et al., 2014).
Roots in deeper and warmer soil layers can remain active and add to the soil
CO2 efflux. Accordingly, modelled Rh rather represents the upper edge
of potential Rh at our site. Our modelling approach was based on a
relatively simple set of soil C stocks combined with temperature and
moisture sensitivities, and holds corresponding uncertainty with regard to
quantity of Rh and its temporal dynamics. C stocks from deeper soil layers
(> 30 cm depth) were not accounted for and a single Q10
(obtained from 0–10 cm depth) was used for the whole mineral soil layer.
Stabilisation of SOC usually increases with soil depth (Fontaine et al.,
2007). Our Rh predictions for deeper layers (10–30 cm) might therefore
overestimate the real rate. Using annual litter input as proxy for litter C
stocks is a further source of uncertainty. Litter input has temporal
patterns and therefore affects litter decomposition dynamics. Such temporal
patterns in litter input/decomposition were not reflected in our model. The
modelled contribution of the litter layer to total soil Rh was, however,
small (Fig. 4) and, therefore, the uncertainty related to temporal litter
layer dynamics can also be considered small. We further used a constant
Q10 throughout the year, although the Q10 may vary with season due
to changes in substrate supply and quality (Davidson and Janssens, 2006; Gu
et al., 2004) and/or interactions with soil moisture (Sierra et al., 2015).
We showed that soil moisture affected the temperature sensitivity of Rh by
significantly lowering Q10 under dry conditions (lab incubation, Table 2).
Such dry conditions were, however, not observed in the field. We therefore
assume that ignoring potential moisture effects on Q10 in our model only had
minimal effects on the Rh estimate. Rhizosphere priming could have
affected Rh dynamics as well, but we were not able to account for that in
our model. Moreover, soil sieving could have positively affected Rh rates
during incubation by releasing physically protected SOM and/or providing
additional C sources via disrupted fungal hyphae and fine root fragments
(Datta et al., 2014). Nevertheless, the modelled annual ∼ 65 % contribution of Rh falls well within estimates from similar forests
(Lee et al., 2010). Even if we overestimated the real contribution of Rh, we
are confident that the model relatively robustly reflected the temporal
dynamics of Rh/Ra throughout the year.
In contrast to the modelling approach, trenching was applied as an attempt
to estimate Ra in situ. The trenching method, although highly invasive, was shown
to provide reasonable estimates of Ra for several forest types (Hanson et
al., 2000; Subke et al., 2006). Trenching suggested slightly higher
contributions of Rh at both sites (average 72 % both sites) but showed
much stronger temporal variations in Rh/Ra, especially at the mixed forest
(Fig. 3). Trenching has several drawbacks. Soil moisture is usually higher
in trenched plots because water uptake by roots is interrupted. This bias
was accounted for as we used the moisture response function (Eq. 3) for
correction. However, trenched fine roots can maintain respiration for a
comparatively long time after cutting (Lee et al., 2003) and, when fine roots
finally die, their decomposition can add to the soil CO2 efflux from
the trenched plots (Hanson et al., 2000). Assuming a dead fine root mass
loss of roughly one-third during the second year after trenching
(Díaz-Pinés et al., 2010) and accounting for the corresponding
effects on soil CO2 efflux (additional efflux ∼ 1 t C ha-1), the estimated annual contribution of Rh decreases to
∼ 65 % of Rs, which is in the range of our modelling
results. Potential effects of root decomposition, however, do not explain
the atypically strong temporal variation in Ra at the mixed forest site.
Soil CO2 efflux from trenched plots was similar or even higher than
from corresponding control plots, suggesting a steep decrease in Ra between
July and September (Fig. 3). We do not have a straightforward explanation
for this pattern. We probably did not trench deep enough and missed a larger
proportion of roots, which added to the summertime CO2 efflux from
trenched plots. A further explanation could be an altered availability of
nutrients to decomposers in the trenched plots. In trenched plot soil, roots do not
compete for nutrients, potentially increasing nutrient availability
to decomposers. This could accelerate SOM decomposition and soil CO2
efflux. In summary, trenching showed a less clear outcome at the two study
sites when compared to other forests. Therefore, other methods, such as
girdling or isotope labelling might be alternatively applied to the studied forest
types.
Our simple empirical temperature-driven Rs model explained most of the
temporal variation in Rs under the typical monsoon weather patterns during
2015. However, monsoon failures and drought periods have occurred in the
past and may even increase in frequency and/or severity of climate change
(Schewe and Levermann, 2012; Menon et al., 2013; Cook et al., 2010; Sharmila
et al., 2015). To model drought effects, it is necessary to further develop
the model by integrating potential soil moisture response of Rs. To do so,
longer Rs time series, including dry years and/or data from artificial
drought experiments, are needed for model parameterisation and testing.
Conclusions
The monsoon climate allows for highly productive mountain forests in the
eastern Himalayas. Such forests can store high amounts of C in plant biomass
and soil, which was particularly evident in the high-altitude mixed forest
in our study. At both forests, a simple temperature-driven model was
sufficient to explain most of the temporal variation in Rs during the study
year. The sites experienced a typical monsoon climate with dry and cold
winters and monsoon rain during the warm season. Further research and model
development is, however, warranted to better understand how
infrequent/extreme events such as monsoon failure and drought affect
soil/ecosystem C cycling and Rs in these forest ecosystems.
Data availability
All relevant soil respiration, soil moisture and soil temperature data from
the field and the laboratory incubations are freely available from the open
source figshare repository (https://figshare.com) via
10.6084/m9.figshare.4239122.
The Supplement related to this article is available online at doi:10.5194/bg-14-99-2017-supplement.
Norbu Wangdi carried out the field research, analysed data and drafted the
manuscript. Mathias Mayer performed modelling and contributed to writing the
manuscript. Mani Prasad Nirola carried out the incubation experiment and analysed
the data. Norbu Zangmo and Karma Orong collected the data and continuously
monitored the research sites. Iftekhar Uddin Ahmed carried out the root and soil
analyses. Georg Gratzer designed the larger-scale throughfall manipulation
experiment. Robert Jandl, Georg Gratzer and Andras Darabant designed this study and
provided feedback on the manuscript. Andreas Schindlbacher supervised the overall
work, designed the experiment and critically revised the manuscript.
The views and opinions expressed in this article are those of the authors
and do not necessarily reflect the views of any institutions of the Royal
Government of Bhutan or the Government of Austria.
Acknowledgements
We are highly grateful to the management and staff of the Ugyen Wangchuck
Institute for Conservation and Environment, Bumthang for supporting the
study. This study was part of the work package I of the BC-CAP project
(Climate Change Adaptation potentials of forests in Bhutan – Building human
capacities and knowledge base) jointly implemented by the Department of
Forest and Park Services, Bhutan and University of Natural Resources and
Life Sciences (BOKU), Austria with funding by the Austrian Ministry of
Agriculture, Forestry, Environment and Water Management.
Edited by: J.-A. Subke
Reviewed by: three anonymous referees
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