Introduction
During the 2016 Convention of the Parties (COP21) of the United Nations Framework
Convention on Climate Change (UNFCCC) in Paris, the goal of increasing global
soil organic carbon (SOC) stocks by 0.4 % per year was set, with the
aim of mitigating global anthropogenic greenhouse gas emissions (Minasny et
al., 2017). This ambitious target was set based on the concept that the SOC
in the top soil layer is sensitive and responsive to management changes and
may offer opportunities to mitigate the current increases in atmospheric
CO2 concentration (McConkey et al., 2007). Of the carbon (C) that
enters into ecosystems via photosynthesis, a fraction is directly respired by
the roots and above-ground plant parts (autotrophic respiration) to produce
energy (i.e., adenosine-5′-triphosphate), with the other fraction synthesized
into organic molecules. Some of these C-containing compounds are harvested or
consumed by herbivores and the remainder is added to the soil as plant
residues (Janzen et al., 1998). Subsequently, a portion of these fresh
organic compounds is respired by organisms (heterotrophic respiration) and
the other portion is converted into SOC by the genesis of soil organic matter
(SOM) (Janzen, 2006; Lal, 2005). If the amount of new organic residues added
to the soil is greater than the C lost by SOC decomposition, SOC content
increases (Ellert and Bettany, 1995).
Typically, many years (up to decades) are needed to assess SOC stock changes
over time in order to evaluate which management practices are beneficial for
SOC sequestration (Harmon et al., 2011; Wood et al., 2012). This timeframe is
impractical for policymakers to evaluate the mitigation potential of
different land management practices, in particular with the pressing need of
the UNFCCC goal of increasing the global SOC stocks by 0.4 % per year.
An alternative approach that allows a more rapid evaluation of these long-term impacts is to combine the SOC stock change procedure (e.g., VandenBygaart
et al., 2008) with the soil C efflux balance approach (i.e., Hergoualc'h and
Verchot, 2011), which although demanding and with some uncertainties can
provide results on soil dynamics on an annual basis. The soil C efflux
balance approach involves calculating the rate of C entry and exit in the
soil. However, the total CO2 efflux (Rs) from soil does not provide
the necessary information to estimate whether the soil is a net source or net
sink for atmospheric CO2 (Kuzyakov and Larionova, 2005). Total soil
efflux is a combination of root-based respiration (autotrophic (Ra)) and
heterotrophic respiration (Rh). Autotrophic respiration does not contribute
to net C losses to the atmosphere as it is cycled within the ecosystem,
whereas Rh represents net C losses. However, the boundary between Ra and Rh is
not easy to distinguish (i.e., the rhizomicrobial respiration is linked to
both) and realistic Rh assessments are difficult to produce (Braig and Tupek,
2010).
Reviews of Rh–Rs segregation methods have been made (e.g., Kuzyakov, 2006) but
no site-specific study has been made analyzing several different partition
techniques simultaneously. The goal of our study was to compare partitioning
methods to separate CO2 efflux into its Rs and Rh components in a
subtropical secondary forest in Hong Kong. Five methods were selected based
on their suitability in the studied ecosystem. Three methods were traditional
techniques (i.e., regression between root biomass and CO2 efflux,
root exclusion bags with hand-sorted roots and soil
δ13C–CO2 natural abundance) and two were innovative
variations of existing methods (i.e., root exclusion bags with intact soil
blocks and lab incubations with minimally disturbed soil microcosm cores).
The influence of soil moisture and temperature on CO2 efflux was
also analyzed.
Methodology
The research was conducted in a subtropical secondary forest of Hong Kong
(Tai Po Kau Nature Reserve; 22∘27′ N, 114∘11′ E). The landscape is typical of the escarpment of the Tai
Mo Shan, the system formed by volcanic activities in the Late
Jurassic epoch (Langford et al., 1989). The rocks are mainly rhyodacite to
rhyolite from the Tsuen Wan Volcanic Group (Davis et al., 1997). The study
site was approximatively 600 m above sea level and the slope surfaces were
stable and vegetated. The forest was approximatively 50 years old and was
covered with continuous canopy. More than 100 plant species were registered
in the nature reserve. The following genera were found in the study area:
Machilus sp., Meliosma sp., Garcinia sp., Engelhardia sp., Psychotria sp.,
Ilex sp., Eurya sp. and Lithocarpus sp. The mean annual temperature was
23.3 ∘C and annual precipitation was 2400 mm, with a hot, humid
season (April–September) and a cool, dry season (October–March) (Hong Kong
Observatory). The study area was 1 ha and was located inside a long-term
research site belonging to the Chinese University of Hong Kong. The canopy
was closed in an area with an average solar radiation of 13.8 Wm-2 at 2 m height (unpublished data).
Soil general characterization
Four soil profiles were dug in the study area, characterizing the different
landforms (i.e., back slope and foot slope) present at the site. Morphological
description was done according to Jahn et al. (2006) and the soil was classified
with the World Reference Base (IUSS-Working-Group-WRB, 2014). Soil pH was
determined with a glass–calomel electrode pH meter (McLean, 1982). Rainfall
and air temperature were recorded hourly with a HOBO Weather Station (rain
gauge, S-RGB-M002; air temperature/RH, sensor S-THB-M008, Onset Computer
Corp., USA). Water-holding capacity was assessed by saturating the soils,
allowing them to freely drain for 24 h and determining gravimetric water
content after oven-drying at 105 ∘C following Arcand et al. (2016).
Root biomass was measured by collecting soil cores (inner diameter 5 cm, height 5 cm)
and determined using the approach of Tufekcioglu et al. (1999). The soil was dried, finely ground and subsequently analyzed for
total C and N content using a CNS Analyzer System (Perkin Elmer 2400 Series II
CHNS/O Analyzer, USA) at the Earth System Science Laboratory of the
Chinese University of Hong Kong (ESSL-CUHK).
Partitioning soil respiration
To produce estimates of % Rh, five different approaches were designated
based on their suitability in the study area. We decided to use three
customary methods often employed in this type of ecosystem (i.e., regression
between root biomass and CO2 efflux, root exclusion bags with
hand-sorted roots and soil δ13C–CO2 natural abundance)
and two innovative variations of existing techniques (i.e., root exclusion bags
with intact soil blocks and lab incubations with minimally disturbed soil
microcosm cores).
Regression between root biomass and CO2 efflux
method
The root biomass regression technique is based on the relationship between
the CO2 emitted by the root rhizosphere and root biomass and the
CO2 efflux slope fitted from SOM decomposition (i.e., Rh),
corresponding to the intercept of the linear regression line (Kucera and
Kirkham, 1971). This method was followed according to Farmer (2013) with 22 sampling
spots. Each spot was a square of 20×20 cm randomly distributed in the study
area. In each spot, Rs was determined per triplicate using a portable IRGA as
described above. Concurrently with CO2 efflux measurements, air and
soil (10 cm depth) temperatures and soil volumetric moisture content were
measured at each sampling spot. Immediately after the Rs measurement, the 20×20 cm squares were excavated to 25 cm depth. All the visible roots
(diameter larger than 0.1 cm) from the excavated soil were collected. In the
lab, the roots were washed and then oven-dried at 60 ∘C until
a steady dry weight was attained, which was then recorded. A linear
regression analysis between root quantity and CO2 efflux was
performed using the program R Foundation for Statistical Computing version 2.8.1 (R Development Core Team, 2008).
Field sampling for the lab incubations: a stratified random design
in the 1 ha study area.
Lab incubations
For the lab incubations, undisturbed soil cores with a volume of
98 cm3
(inner diameter 5 cm, height 5 cm) were collected using a stainless-steel
core soil sampler from the upper part of the soil profile (0–5 cm). In the
study area, four groups of four soil cores (stratified random design) were
collected, then pooled per group and brought to the lab (Fig. 1).
Subsequently all visible roots were removed but with special care to not
destroy the micro-aggregates. The soil was then repacked to original bulk
density in minimally disturbed soil microcosm cores of 45 cm3 (inner
diameter 3.5 cm, height 5 cm). The soil cores were separated in four groups
of different volumetric moisture content (i.e., 15, 25, 35 and 45). These
moisture levels corresponded to the natural annual fluctuation in the field
(i.e., from dry to moist season) (Cui and Lai, 2016). After moisturizing the
samples with distilled water, each individual soil core was placed into a
hermetically sealed 2.9 dm3 plastic container and left to stabilize in
the dark for 2 weeks at 25 ∘C. After that, the experiment lasted
4 weeks and had four different incubation temperature levels (one per
week: 14, 20, 26 and
32 ∘C) corresponding to the minimum, intermediate and maximum
soil temperature values in the field based on preliminary studies (Cui and
Lai, 2016). At the beginning of each week, the soil cores were pre-incubated
in their incubation box to their corresponding weekly temperature (i.e., week no. 1, 14 ∘C … week no. 4, 32 ∘C)
for 3 days and then opened and vented for 1 min. From all the boxes gas
samples were collected (20 mL) with an airtight syringe (t=0, 24, 72 h)
after box closure. The CO2 concentrations were analyzed
within 48 h with a gas chromatograph (GC system 7890A, Agilent
Technologies). The GC system was equipped with a flame ionization detector
and an electron capture detector to quantify CO2 concentration.
Between each measurement session, the boxes were opened to vent and the
moisture of the soil cores was re-adjusted if needed.
A Gaussian 3-D regression fitted curve was derived as shown in Eq. (1),
using SigmaPlot version 10.0 (Systat Software, San Jose, CA).
fx,y=a×exp-0.5×x-x0b2+y-y0c2,
where f(x,y) is the CO2 efflux function; a, b and c are
constant coefficients; x is the soil temperature (∘C); y is the
soil moisture content (%); x0 is the average temperature; y0 is
the average soil moisture.
Root exclusion bag methods
To partition the CO2 efflux in situ into Rs and Rh using mesh bags,
two different approaches were followed: (1) the traditional dug soil with
hand-sorted root removal and refilling method (HS) (Fenn et al., 2010;
Hinko-Najera, 2015) and (2) a variant of it with intact soil blocks (IB). The
HS method consisted of digging a pit for each bag with a size matching the
bag dimensions (20×20 cm, depth: 25 cm) where the soil was
excavated in layers (to maintain soil horizons) and visible roots were
removed before repacking the bag inside the pit with the removed soil. The IB
variant of this technique consisted of extracting a cube as intact as
possible from the soil (20×20 cm, depth: 25 cm). This was then
tightly placed into the micromesh bag and inserted back into its original
pit. For both methods, the same type of micromesh bags (38 µm nylon
mesh), closed at the bottom but open at the top, was used. This mesh size was
used to impede roots from entering inside the bags, but allowed mycorrhiza to
penetrate (Moyano et al., 2007). In all the pits excavated, no root below 25 cm
depth was observed. Collars measuring 10 cm diameter were installed on the
soil in the center of each bag to a depth of 8 cm, for heterotrophic
emissions sampling.
Seven plots were randomly distributed inside the study area. In each plot,
an IB bag was paired with a HS bag with a space of 150 cm between them. The
root exclusion bags were installed during the month of October 2016 and were
allowed to stabilize for 3 months. At 1 m distance from each root exclusion
bag, a collar was inserted into non-disturbed soil to measure Rs. To assess
Rs and Rh without the influence of litterfall decomposition, the collars
were cleared of leaves and flowers on a weekly basis.
From 3 February to 19 April 2017 the collars were measured
weekly with an IRGA (Environmental Gas Monitor, EGM-4, PP Systems, UK)
attached to a soil respiration chamber (SRC-1, PP Systems, UK). Soil
temperature and soil moisture were measured in the area located between the
collar and the edge of the bag (to 10 cm depth, HH2, Delta-T Devices,
Cambridge, England). At the end of the study all the root exclusion bags
were removed from the soil and inspected to ensure that no root had
penetrated inside. The soil inside the measurement collars was then
collected to assess bulk density (van Reeuwijk, 1992). Mathematical
calculation and descriptive statistical analyses were done with Microsoft
Excel XP®.
δ13C natural abundance method
Millard et al. (2010) have demonstrated that the natural abundance
δ13C (‰) of Rs falls between the
δ13C values of the Rh and Ra. The δ13C of Rs and Rh
respiration was determined following Lin et al. (1999) and Millard et al. (2010).
The isotopic partitioning experiment assessed values of the
δ13C of the Rs, Ra and Rh. The sampling took place on 15 March 2017.
A closed chamber (10 cm diameter, 10 cm high) was positioned
on each emissions measurement collar (n=7 as described in Sect. 2.2.3).
The chambers were flushed for 2 min with CO2-free air to remove
all the atmospheric air trapped within the headspace. Chambers were left to
incubate for 40 min to ensure the concentration of the chamber sample
reached above 400 ppm of CO2 from which a duplicate sample of the
gas in the chamber headspace was extracted into evacuated vials to give the
δ13C of the Rs. Subsequently, the soil under the chamber was dug
and immediately brought to the lab (ESSL-CUHK, less than 30 min travel)
where the soil and the roots were carefully separated. The roots were gently
washed with water to remove adhered soil aggregates and slightly dried
afterward with paper towels. Samples of 5 g of root and 10 g of root-free
soil per chamber were incubated in CO2-free air in 250 mL airtight
glass bottles to give the δ13C of the Ra and Rh, respectively. The
bottles were left to incubate for 90 min before duplicate extraction into
evacuated vials. As recommended by Midwood and Millard (2011), before gas sample
extraction, the butyl rubber septa used to seal the vials were heated at
105 ∘C for 12 h. The C isotope ratio of the CO2 in
all samples was analyzed using the GasBench II connected to a DELTAplus
Advantage isotope ratio mass spectrometer (both Thermo Finnigan, Bremen,
Germany) at the James Hutton Institute Scotland UK. The δ13C
ratios, all expressed relative to Vienna Pee Dee Belemnite (VPDB), was
calculated with respect to CO2 reference gases injected with every
sample and traceable to International Atomic Energy Agency reference material
NBS 19 TS-Limestone. Measurement of the individual signatures of the natural
abundance δ13C of the Rs, Rh and Ra allowed partitioning between
the different sources using the mass balance mixing model (Lin et al., 1999;
Millard et al., 2010):
%Rh=1-δRs-δRhδRa-δRh×100,
where % Rh is the proportion of Rh from Rs, and δRs, δRh and δRa are the
δ13C isotopic signatures.
Qualitative comparison of segregation methods
While there is much debate in the literature, and methods are still being
developed, isotopic partitioning methods are increasingly being recognized as
a more accurate approach to segregation of Rs than non-isotopic techniques
(Paterson et al., 2009; Kuzyakov, 2006). Thus, for comparison purposes we
used the soil δ13C natural abundance method as a reference point
for segregation relative accuracy. Partition methods that had Rh %
< 10, 10–20 and > 20 lower or larger than the
δ13C–CO2 natural abundance were categorized as high,
intermediate and low relative accuracy, respectively. The level of precision
of the segregation methods was determined with the statistical variance
associated with the Rh / Rs ratio averages. High, intermediate and low
precision levels were attributed to Rh % standard errors of < 10, 10–20 and
> 20, respectively. The level of complexity was evaluated with the number
of steps required to complete each method. For example, the hand-sorted root
exclusion bag technique was judged as a four-step method (pit excavation,
root removal, bag/pit refiling and CO2 efflux measurements).
Methods with five steps or fewer were deemed simple and six steps or more
deemed as complex. The time needed to set up the experiment was assessed by
counting the number of working hours (8 h equal 1 day) required
prior to the start of the measurements. The time needed to produce seasonal
trends was the number of months of measurements (in the field or in the lab)
required to characterize the Rh at the different temperature and moisture
levels of the year.
Morphological description of the soil profiles at the study site.
Horizon depth
Color
Color
Field
Structureb
Rock fragments
Roots mg root cm-3
pH
pH
WHC
Soil organic C;
(cm)
(dry)
(moist)
texturea
volume %
(H2O)
(KCl)
(gH2Ogsoil-1)
N (%)
A 0–10
10 YR 6/2
10 YR 3/2
SL
gr
0
9.3 ± 3.4
4.2
3.3
0.50 ± 0.01
3.2 ± 0.2; 0.24 ± 0.02
AB 10–25
10 YR 6/3
10 YR 4/2
SL
gr
0
1.6 ± 0.6
4.5
3.9
–
–
Bt 25–60
10 YR 8/8
7.5 YR 6/8
CL
sbk
10–20
0.5 ± 0.2
4.7
4.0
–
–
C 60–100
10 YR 8/8
10 YR 7/8
CL
sbk
> 60
0
4.7
4.0
–
–
a SL: sandy loam; CL: clay loam.b gr: granular; sbk: subangular blocky.WHC: water-holding capacity; C: carbon; N: nitrogen.
Results
Soil characteristics
According to their morphology and diagnostic properties, the soil was
classified as Alic Umbrisol (Nechic) and Haplic Alisol (Nechic)
(IUSS-Working-Group-WRB, 2014). The difference between the two soil groups
was the thickness of humus-containing horizon (between 20 and 30 cm for the
Umbrisol, while it was 10 to 20 cm for the Alisol). The A horizon had high organic
C content (3.2 ± 0.2 %) and high acidity (pHH2O 4.2) (Table 1).
The subsurface soil was represented by clayey yellow-colored profiles
with an argic horizon. Soil texture was heavier in the argic horizon than in
the topsoil and parent material. The structure in all the soil profiles was
predominantly granular in the upper horizons, whereas the argic horizon was
characterized by subangular blocky structure (Table 1). The argic horizon was
deemed to be of high-activity clays and low cation base status based on
previous results in the area, along with soil acidity, type of parent
material and level of mineralization of the bedrock in the soil pits.
Comparison of environmental parameters inside and outside the root
exclusion bags.
Method
Soil temperature
Soil moisture
Bulk density
(∘C)
(vol. %)
(gcm-3)
Inside hand-sorted root exclusion bags (HS)
22.4 (0.2) α
20.5 (1.2) β
1.16 (0.04) α
Inside intact root exclusion bags (IB)
22.6 (0.3) α
25.5 (1.4) α
1.13 (0.05) α
Outside root exclusion bags (Rs)
22.4 (0.2) α
24.8 (0.8) α
1.14 (0.03) α
Values are means and standard error. Values in the same column followed by a
different Greek letter (α, β) are significantly different
from each other at α=0.05.
Parameter values of the Gaussian 3-D regression fitted curve
(Eq. 1).
Efflux
Parameter a
Parameter x0
Parameter y0
Parameter b
Parameter c
(Mgha-1yr-1)
Rh lab incubation
5.0***
49.2***
34.7***
15.7***
19.2***
Rs field
10.3**
24.9**
18.3 NS
9.6 NS
15.8 NS
Rh IB
5.7 NS
21.93 NS
21.4 NS
4.8 NS
13.4 NS
Rh HS
8.1 NS
21.7 NS
9.5 NS
5.1 NS
14.2 NS
Rh lab incubation: heterotrophic respiration from the soil cores
incubation.Rs field: total soil respiration from outside of the root exclusion
bags.Rh IB: heterotrophic respiration from the intact root exclusion
bags.Rh HS: heterotrophic respiration from the hand-sorted root exclusion
bags.** and *** denote values that are significant at p<0.01 and p<0.05,
respectively; NS non-significant.Parameters are from Eq. (1). Parameter a corresponds to the height of the
maximum high of the curve (gCO2m-2h-1), x0 is the
peak soil temperature point (∘C) in the curve, y0 is the
peak soil moisture (%) point in the curve and b and c are the
Gaussian root mean squared widths of the soil temperature and soil moisture,
respectively.
Average δ13C–CO2 results.
Method
δ13C–CO2 (‰)
Rsa
-18.21 (0.53) αβ
Rh HSb
-16.65 (0.44) β
Rh IBc
-16.52 (1.07) β
Rh labd
-16.75 (0.54) β
Ra labe
-20.44 (0.65) α
a Rs: gas samples collected from the field total soil respiration
collars.b Rh HS: gas samples collected from the field hand-sorted root
exclusion bag collars.c Rh IB: gas samples collected from the field intact blocks root
exclusion bag collars.d Rh lab: gas samples collected from lab incubations of soil with
freshly removed roots.e Ra lab: gas samples collected from lab incubations of the roots
extracted in Rh lab.Values are means and standard error; n=14 for Rs and Ra and n=7 for
HS, IB and Rh lab.Values followed by a different Greek letter (α, β) are
significantly different from each other at α=0.05.
Comparison of heterotrophic respiration assessment methods.
Method
Rh effluxa
Rs effluxb
Rh / Rs
MgCO2–Cha-1yr-1
%
Root biomass regression
6.0 (2.4)
11.1 (1.0)
54 (41)
Soil cores incubation
0.4–1.9c
–
8–17d
Hand-sorted root exclusion bags (HS)
4.8 (0.3)
6.1 (0.3)
79 (3)
Intact root exclusion bags (IB)
3.0 (0.3)
6.1 (0.3)
49 (7)
Soil δ13C–CO2 natural abundance
–
–
61 (39)
Values are means and standard error; n=22 for the root biomass
regression, n=47 for soil incubation,
n=28 for both root exclusion bag techniques.a Rh: heterotrophic respiration.b Rs: total soil efflux taken alongside the Rh
efflux.c Efflux range at temperature between 14 ∘C and
26 ∘C.d Calculated as Rh from incubation at 14 and
26 ∘C divided by average field Rs at 14 and
26 ∘C, respectively.
Regression between root biomass and CO2 efflux
The 22 quadrats used for the root biomass regression assessment yielded an
average Rs of 11.1 ± 1.0 MgCO2–Cha1yr-1. The
regression of the CO2 efflux against root biomass produced a
statistically significant slope correlation of 0.08 ± 0.04 gCO2m2h-1 per mgrootcm-3 (p=0.03), and set the
intercept at 0.25 ± 0.10 gCO2m2h-1 (p=0.02),
which represented the basal efflux in the absence of roots, i.e., the Rh (Fig. 3).
The Rh measured (i.e., slope intercept) when the root biomass regression
technique was performed (October 2016) was
6.0 ± 2.4 MgCha-1yr-1, equivalent to 54 % of
the Rs (Table 5).
(a) Soil
and air temperature and daily rainfall over the study
period; (b) total soil CO2 efflux (Rs), heterotrophic CO2
efflux (Rh) from hand-sorted root exclusion bags (HS) and Rh from intact
block root exclusion bags (IB).
Linear regression between root biomass and CO2 efflux.
Results from the lab incubation: regression between incubation
temperature, moisture and CO2 efflux.
Lab incubation
During the incubation with minimally disturbed soil microcosms, the average
(moisture levels combined) CO2 efflux at 14, 20, 26 and
32 ∘C was 0.36 ± 0.50, 0.67 ± 0.38,
1.40 ± 0.91 and 2.24 ± 1.39 MgCO2–Cha1yr-1,
respectively (Fig. 4). The exponential relationship between
CO2 efflux, soil temperature and moisture is presented in Table 3.
Root exclusion bag methods
During the root exclusion bag measurement period (February–April 2017), the
average air temperature was 16 ∘C and the total rainfall 107 mm.
During that period the Rs averaged 6.1 MgCha-1yr-1
(Fig. 2). One of the requirements for the suitability of root exclusion bag
methods to estimate Rh is that soil bulk density, soil temperature and
moisture are statistically equal inside and outside of the bags. In this
experiment, no significant differences were detected regarding the bulk
density and soil temperature (p=0.87 and p=0.15, respectively) but the
volumetric soil moisture in the HS bags was on average 17 % lower than
outside the root exclusion bags (p=0.04) (Table 2). As would be expected,
all Rh IB and Rh HS efflux rates were lower than the Rs efflux at each
measurement date. Throughout the experiment, the Rh IB was consistently lower
than the Rh HS, except on 31 March (Fig. 2b).
Soil δ13C–CO2 natural abundance
On a landscape basis, the δ13C–CO2 natural abundance
method reasonably segregated the three respiration components (Table 4). The
δ13C–CO2 values of the Rh HS, Rh IB and Rh lab were in a very
close range (i.e., -16.52 to -16.75), indicating that the efflux measured in
the root exclusion bags were not contaminated with root respiration. Based on
the δ13C–CO2 of the Rs (-18.21 ± 0.53), the Rh lab
(-16.75 ± 0.54) and the Ra lab (-20.44 ± 0.65), the average
percentage of heterotrophic respiration was 61 ± 39 % (Table 5).
Using the δ13C–CO2 method as a baseline, the
increase/decrease of the Rh from root biomass regression, lab incubation,
hand-sorted and intact block (IB) root exclusion techniques were -11,
-72–87, +30 and -20 %, respectively (Table 5).
Qualitative evaluation of the partition methods.
Segregation method
Relative
Precisionb
Complexity of
Time needed to
Time needed to produce
accuracya
proceduresc
set up experimentd
seasonal trends
Root biomass regression
High
Low
Simple
2–3 days
6 months to 1 year
Soil cores incubation
Low
High
Complex
5–7 days
< 1 to 2 months
Hand-sorted root exclusion bags (HS)
Intermediate
High
Simple
2–3 days
6 months to 1 year
Intact root exclusion bags (IB)
Intermediate
High
Simple
2–3 days
6 months to 1 year
Soil δ13C–CO2 natural abundance
–
Low
Complex
1–2 days
6 months to 1 year
a Partition methods that had Rh % < 10, 10–20 and > 20 lower
or larger than the δ13C–CO2 natural abundance were
categorized as high, intermediate and low accuracy, respectively.b High, intermediate and low precision values were attributed to Rh %
standard errors of < 10, 10–20 and > 20, respectively.c Methods with five steps or fewer were deemed simple and six steps or
more deemed as more complex.d The time needed to set up experiment was assessed with the number of
working hours required prior to be able to start the measurements.e The time needed to produce seasonal trends was the number of months
of measurements required to characterize the Rh at the different temperature
and moisture levels of the year.
Discussion
Regression between root biomass and CO2 efflux
technique
As demonstrated by Gupta and Singh (1981) the intercept of the regression
line between the independent variable (i.e., root biomass) and the dependent
variable (i.e., Rs) corresponds to soil respiration in the absence of
roots
(i.e.,
Rh) (Fig. 3). In this study the regression had 10 points (45 %) outside
the confidence interval but the intercept and slope were still statistically
significant. This uncertainty in the regression fit was likely caused in
large part by the older roots which are bulkier but respire less than fine
and young roots (Behera et al., 1990). However, this method had the closest
average Rh / Rs ratio to the δ13C natural abundance technique.
Consequently the root biomass regression technique was assessed as high
relative accuracy and low precision (Table 6). Previous studies also
highlighted large variation of CO2 efflux and root biomass which
causes relatively low coefficient of determinations (Behera et al., 1990;
Farmer, 2013). In accordance to Kuzyakov (2002), this method was
comparatively simple (Table 6).
Lab incubation method
Interpreting soil respiration processes in response to seasonal changes is
generally challenging because soil temperature and moisture regularly co-vary
(Carbone et al., 2011; Davidson et al., 1998). The lab incubation technique
was the only method capable of dividing the effect of soil temperature and
moisture on Rh and producing a significant Gaussian regression fit (Table 3).
However, the microcosm incubation produced Rh values notably lower than
the other techniques (Table 5). This may be due to three different causes:
first, the fact that the soil column in the incubation microcosms was
5 cm
thick while the A horizon in the field (i.e., where the Rh assessments from
the other techniques were made) was 10 cm (Table 1). Second, to prevent
potential shifts in the microbial community during the incubations (i.e.,
adapting to lower resource availability), prior to the beginning of the
experiment, the microcosms were left to stabilize for two weeks. Accordingly,
the fresh and labile organic residues that would, in the other segregation
methods, contribute to the soil respiration had already decomposed before the
beginning of the incubations. Third, the low Rh of the lab incubation method
could also be attributed in part to the fact that this technique did not
contain any rhizomicrobial respiration and its priming effect (Kuzyakov et
al., 2000); that is, this method produced Rh from basal microbial
respiration which is considered to be from stabilized SOM with slow turnover
rates (Kuzyakov, 2006; Neff et al., 2002). In view of that, with additional
field and lab method development, it would be possible to further segregate
Rh assessments into percentage of rhizomicrobial respiration, decomposition
of plant residues and basal decomposition of SOM. Overall, the lab
incubation technique was slightly more complex than the non-isotopic field
Rh assessment methods but allowed a prompt determination of Rh whilst
simulating year-round field conditions (Table 6). Further studies should
test the effect of microcosm height on Rh in relation to field measurements
and determine microbial C use efficiency by isothermal microcalorimetry
during the incubations.
Root exclusion bag methods
The HS and IB methods had % Rh of 79 ± 3 and 49 ± 7 %,
respectively. The variances around the means were the lowest of all the field
segregation methods tested (Table 5). Comparing the % Rh of the HS and IB
with the δ13C natural abundance technique, they resulted 18 %
above and 12 % below, respectively. Thus the root exclusion bag methods
were judged to be of intermediate relative accuracy and high precision. Also, the
HS and IB methods were fast and simple to set up (Table 6).
The micromesh size used in the root exclusion bags was 38 µm, which was
reported to impede root penetration but to allow arbuscular mycorrhiza to
spread inside the bags (Moyano et al., 2007; Rühr and Buchmann, 2010). In
turn, Fenn et al. (2010) stated that in the mycorrhizal structures the
arbuscules exist within roots, and therefore, the CO2 efflux from
these bags could contain some portions of the roots' respiration. Contrary to
this, the IB and HS air samples analyzed for δ13C had an isotopic
signature close to and not statistically different from the gas samples
collected in the lab airtight glass bottle of fresh soil without roots. This
indicates that the root exclusion bags (both IB and HS) did not comprise
traces of root respiration that had a significantly larger δ13C–CO2 signature (Table 4). After the 3-month period of
soil stabilization, both bag methods for partitioning total soil respiration
and root-free soil respiration components successfully produced Rs>Rh on
every sampling day, indicating that efflux rates within the bags had reached
an apparent post-disturbance state (Fig. 2). Also, in both IB and HS, soil
temperature and bulk density were statistically equal to the surrounding soil
(i.e., Rs) (Table 2). This indicates that the environmental conditions inside
and outside of the bags were similar in respect to these two parameters.
However, the soil moisture of the IB was statistically equal to the
surrounding soil but for HS it was significantly lower. This was likely
caused by the breakdown of the original soil structure at the moment of root
removal that increased the drainage inside the HS bags. Moyano et al. (2007)
also found that soil moisture can be affected by the hand-sorted root
exclusion bag method. Overall, HS had a moisture level 20 % lower and an
Rh efflux 60 % larger than IB (Table 5). The larger HS Rh efflux compared
with IB Rh could be due in part to the lower soil moisture in the former.
This likelihood is supported by the fact that in the regression fit the
maximum Rh was when moisture content was relatively low (i.e., y0, Table 3).
In addition, the breakdown of numerous soil aggregates during the root
removal likely allowed the soil microorganisms to thrive in previously
encrusted SOM domains of the HS soil. It has been shown that the part of the
SOM that is located in the interior of the soil aggregates is hardly
accessible to microorganisms, and thus not easily mineralized unless the
aggregates are shattered (Goebel et al., 2005).
Soil δ13C natural abundance method
The three respiration components of this method (i.e., δ13C–CO2 from Rs, Rh and Ra) had large standard errors (Table 4)
that produced a high uncertainty value in the Rh / Rs ratio assessment
(61 ± 39 %, Table 5). This method was accordingly deemed to be of low
precision (Table 6). This, in turn, impeded our ability to produce an Rh / Rs
ratio assessment in the individual collars. The low precision of this method
indicates that some biases in the assessment of relative accuracy could
potentially have been generated. This large δ13C–CO2
variance was likely caused by variability of δ13C in soil and
plants residues and also due to 13C discrimination by plants that is
affected by moisture content and nitrogen availability (Hogh-Jensen and
Schjoerring, 1997). In addition, other studies reported the variability of
δ13C in soil or plants of at least 1–2 ‰,
which in some cases can limit the capacity to produce soil respiration
segregation assessments (Accoe et al., 2002; Cheng, 1996; Farquhar et al.,
1989). Because soils are porous mediums, excluding any atmospheric
CO2 that has a different isotopic composition (i.e., δ13C
-7.5 to -8.5 ‰) to that of the Rs efflux is
challenging, and potential air contaminations have to be considered when analyzing the
results (Millard et al., 2010). In our study, the Rh δ13C was
measured in the field (IB and HS; potentially air-contaminated) and from
airtight containers in lab incubations of root-free soil (Rs lab; not
potentially air-contaminated). Both ways produced δ13C in a close
range and without statistical differences between them (Table 4). This
indicates that the chamber system used in the field to collect the
δ13C efflux samples was adequately effective to prevent air
contamination. Overall, the soil δ13C natural abundance method was
fast to set up but was relatively complex to perform with a field and lab
component to be accomplished within a short period of time (Table 6). Further
studies should use a large number of sampling points to attempt to reduce the
respiration components standard errors.
Comparison of methods and recommendations
The analysis of the five different Rs partitioning methods examined in this
study shows that none of them were fully satisfactory; that is, each technique
had strengths and weaknesses (Table 6). Using δ13C–CO2 is acknowledged as the preeminent way to segregate Rs
(Cheng, 1996; Kuzyakov, 2006); and accordingly the relative accuracy of the
other methods was defined by their difference with this method. However, we
recognize this was a precarious approach because the δ13C–CO2 method had a large variation. The root biomass
regression, which is also recognized in the literature as a reliable method
(Kuzyakov, 2006), gave a similar % Rh estimate. However, we found several
other shortcomings with the δ13C–CO2 method. First, the
conjunction of field and lab procedures makes it difficult to complete this
method in 1 day as needed. Second, the air flushing with CO2-free
gas in the field (to prevent ambient δ13CO2
contamination) makes that technique more complex than the other methods to
assess Rh %. Third, the ability to perform this technique in remote areas
is limited because the δ13C–CO2 needs to be quickly
assessed with a calibrated and accurate spectrometer (Midwood et al., 2006).
Fourth, the large variation in δ13C–CO2 of the
respiration components (i.e., Ra, Rh and Rs) impeded the assessment of
Rh % per individual collar. Accordingly, further studies should analyze
the spatial relationships of δ13C–CO2 with soil
properties and root characteristics. As a stand-alone method, the
δ13C–CO2 technique was unable to produce an assessment of
soil CO2 efflux, and thus it needed to be performed in conjunction
with field Rs measurements. In this regard, the δ13C–CO2
complemented the root exclusion bag methods well because it allowed
us to determine whether the buried bags had been torn and been invaded by roots and
to standardize Rh % determination.
The root biomass regression method had the advantage of being simple, and
produced an average Rh % close to the δ13C–CO2
natural abundance. However it had the disadvantage of requiring a high number
of replicates due to the low coefficient of determination between the CO2
efflux and the root biomass. Another disadvantage of the root biomass regression
technique is that in order to produce seasonal trends, the labor-intensive
procedures (i.e., pit digging, CO2 measurements and root counting)
need to be reinitiated several times during the year. This shortcoming can be
particularly impractical in small plot experiments. Complementary studies
should assess thresholds of root size to be included in the regression fit in
order to optimize the correlation fit and use the δ13C–CO2 natural abundance method to determine the effect of
root size on the isotopic signature.
The root exclusion bag methods (i.e., HS and IB) had the advantage of being
easy to monitor throughout the year, capturing temporal variability of the
% of Rh. The bag methods can be considered as a miniaturization of the
traditional soil trenching method. However, contrasting with large trenches
(e.g., Comeau et al., 2016; Fisher and Gosz, 1986) the root exclusion bags had
the advantage of being simpler to establish and allowed mycorrhiza
development inside the mesh bags (Moyano et al., 2007). Conversely, due to
the relatively small bag sizes, root webs on the outside edge could
potentially contaminate Rh assessment. In this study, the δ13C–CO2 determination made with the collars located in the
center of the bags showed no isotopic signature of root respiration. Similar
to a trenching method, the root exclusion bag method had the disadvantage of
requiring several months of soil stabilization before CO2 efflux
measurements could begin. Compared with the δ13C–CO2
natural abundance method, the HS and IB overestimated and underestimated
% Rh by 18 and 12 %, respectively. The divergences were likely caused
by soil disturbances, alteration in root demise dynamic and a lack of root
exudates. Correspondingly, Carbone et al. (2016) found an 11 % difference in
Rh % assessment between an isotopic partition method and the trenching
technique. Comparing the HS and IB, the former created more soil disturbances
but the latter would not be suitable for soil with a high amount of sand,
gravel or rock because the intact blocks would collapse.
The lab incubations of the minimally disturbed microcosms was the only method
that had absolutely no influence of roots or mycorrhiza. Thus the results from
this method exclusively represented the CO2 efflux originating from
the mineralization of the slow turnover SOC pool (i.e., basal soil
respiration) (Pell et al., 2006). Assessment of basal soil respiration in
relationship with the total Rh is of great importance in evaluating the
dynamic of the stabilized SOC. In this study, the Rh % from the lab
incubation was 8–17 %, while the δ13C–CO2 natural
abundance had an average of 61 % Rh. Thus, as the soil incubation results
were not affected by the height of the soil columns (as discussed above),
basal respiration represented approximately one-fifth of the Rh. Because
stabilized SOC is a key indicator of soil quality and health (Creamer et al.,
2014), further research should study the relationship between basal soil
respiration and rhizosphere-derived Rh. Also, future studies on soil
CO2 efflux segregation should include other partitioning techniques
like the trenching method and radiocarbon-based assessments (Chiti et al.,
2016). Overall, results from field experiments exhibited a range of potential
Rh between 2.5 and 6.0 MgCO2-Cha-1yr-1. With the
publication of the total annual live biomass growth (i.e., including root and
above-grown biomass) at the study site (Tai Po Kau Nature Reserve), assessment
of the net ecosystem C balance would then be possible.