A pan-Arctic synthesis of potential CH4 and CO2 production under saturated
conditions
Treat et al.
Supplemental materials
Introduction
The relative amounts of CH4 and anaerobic CO2 produced are a function of
methanogenic pathways that can generate both CH4 and CO2 and other anaerobic
pathways that generate CO2 (e.g., denitrification, sulfate reduction). Here we present a
brief overview of the drivers of variation in CH4 and CO2 production via methanogenic
pathways.
Methane production is a terminal decomposition process that can occur through
two pathways: acetoclastic methanogenesis of acetate or autotrophic reduction of CO2
and hydrogen (H2). The relative importance of each pathway varies with depth
(Hornibrook et al. 1997; Nilsson and Oquist 2009), temperature (Bergman et al. 1999;
Nilsson and Oquist 2009; Updegraff et al. 1995; Yavitt et al. 1987) and pH (Bergman et
al. 1999; Nilsson and Oquist 2009; Yavitt et al. 1987). There is also substantial variation
among vegetation types (Hines et al. 2008) and sites. Acetoclastic methanogenesis is less
common in Alaskan peatlands (Hines et al. 2001; Rooney-Varga et al. 2007) than other
peatlands and results in more CO2 production relative to CH4 production. Furthermore,
changes in vegetation following permafrost thaw may shift the dynamics of CH4
production pathways, thus altering relative amounts of CO2 and CH4 production
(Hodgkins et al. 2014; McCalley et al. 2014).
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Methane production within soils is directly controlled by several factors:
environmental conditions, availability of substrate, and methanogen presence and
viability. Methanogens are obligate anaerobes, so favorable conditions for CH4
production necessitate anoxia. Furthermore, CH4 production has a low thermodynamic
energy yield and is inhibited by the presence of more energetically favorable alternate
electron acceptors such as sulfate, iron, nitrogen, and manganese (Duddleston et al. 2002;
Knorr and Blodau 2009). Water table fluctuations can result in the oxidation and
regeneration of electron acceptors due to exposure to oxygen, which results in the
inhibition of CH4 production for extended periods of time even after soils become
anaerobic again, until the more favorable alternate electron acceptors have been reduced.
Temperature is positively related to CH4 production (Lupascu et al. 2012; Yavitt et al.
2006), although the temperature response is mediated by substrate quality (Lupascu et al.
2012), similar to aerobic decomposition (Davidson and Janssens 2006). CH4 production is
also dependent on pH (Bergman et al. 1998; Valentine et al. 1994, Dunfield et al. 1993).
If favorable anaerobic conditions for CH4 production are created in permafrost or panarctic soils, we do not yet know whether all soils will produce CH4, which likely depends
on substrate availability and methanogens.
Substrate availability controls CH4 production in several ways. First, there is the
abundance of substrates for methanogenesis (Bergman et al. 1998; Chanton et al. 1995;
Duddleston et al. 2002; Hines et al. 2008). Substrates for methanogenesis are diverse
fermentation products such as acetate, H2, CO2, formate, methanol, and methylamines.
The fermentation reactions that produce substrates for methanogens are
thermodynamically driven by the consumption of end products due to the low-energy
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yields of these microbially-mitigated processes (c.f. Conrad 1999). Overall, the substrate
availability for methanogenesis and fermentation reactions are controlled by the amount,
type, and quality of organic matter inputs into anoxic zone. Substrate quality for
anaerobic decomposition differs by plant type, extent of organic matter degradation prior
to anoxic conditions, and the form of organic matter inputs—i.e., roots, leaf litter, or
exudates (Bergman et al. 1998; Hines et al. 2008; Kuder and Kruge 2001; Nilsson and
Oquist 2009). Using a modeling framework, Nilsson and Öquist (2009) determined that
the sources of organic matter in the anoxic zone were mainly derived from root litter and,
to a lesser extent, root exudates. CH4 production and field fluxes are also related to plant
species composition (Bergman et al. 1998; Bubier 1995; Hines et al. 2008; Olefeldt et al.
2012) and rates of ecosystem productivity (Olefeldt et al. 2012; Whiting and Chanton
1993) through the decomposition of recently-fixed organic matter that is allocated to
plant roots (Chanton et al. 1995; Coles and Yavitt 2004; Prater et al. 2007; Strom et al.
2003).
Finally, CH4 production is a microbially mediated process that depends on the
presence and viability of microbial communities. A syntrophy between fermenter and
methanogen communities exists because methanogens utilize the byproducts of
fermentation and allow these low-energy yield fermentation reactions to proceed (Schink
1997). Disruption of the syntrophic associations can result in the accumulation of
fermentation products or the inhibition of methanogenesis by substrate depletion (Beer
and Blodau 2007; Conrad 1999; Nilsson and Oquist 2009; Schink 1997). A recent study
using advanced microbial techniques has found that methanogen abundance increases
with depth in the top 20 cm (Tveit et al. 2013). This suggests that CH4 production may be
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limited by methanogen abundance at the surface, where dry conditions are not favorable
for CH4 production; in other arctic soils, methanogens were only found in soils that were
saturated for most of the growing season (Hoj et al. 2006). Methanogen communities can
be dynamic over time due to permafrost thaw; in thawing permafrost, an increase in
methanogen relative abundance occurred immediately after thaw (Mackelprang et al.
2011). Additionally, presence of a specific methanogen, Candidatus ‘Methanoflorens
stordalenmirensis, was found to be the most significant predictor of CH4 fluxes among
sites in a permafrost thaw gradient (McCalley et al. 2014). However, seasonal changes in
methanogen community abundance and composition have not been detected in
permafrost ecosystems (Juottonen et al. 2008). Additionally, methanogen community
composition may differ between depths (Galand et al. 2002), vegetation types (RooneyVarga et al. 2007) and ecosystem types (Basiliko et al. 2003; Juottonen et al. 2005).
Finally, due to the low energy yields provided by anaerobic decomposition, methanogen
communities grow slowly; the low abundance of active methanogens may limit potential
CH4 production in incubations, especially with short incubation times (Yavitt et al. 2006).
Permafrost ecosystems contain large amounts of old but potentially labile soil
organic matter (SOM) that has been thermally protected from decomposition (Dutta et al.
2006; Schuur et al. 2009; Zimov et al. 2006). We do not yet know whether all the SOM
is vulnerable to decomposition under anaerobic conditions, nor the relative partitioning
between anaerobic CO2 and CH4 production, which will ultimately affect net radiative
forcing from permafrost thaw. Furthermore, we expect vegetation communities to shift
with permafrost thaw and between anaerobic and aerobic conditions (Johansson et al.
2006), which will alter pathways of anaerobic decomposition (Hines et al. 2008). Carbon
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mineralization rates during anaerobic incubations are high from some sites, but not others,
due to differences in permafrost history and organic matter quality among sites (Lee et al.
2012; Treat et al. 2014). It is likely that this results from an interaction between favorable
conditions for CH4 production, substrate availability, and methanogen communities.
However, we lack a widespread understanding of these interactions due to the difficulties
of conducting incubations to account for interactions between all potential factors; here
we use existing incubation studies to assess how anaerobic decomposition varies within
and between sites.
Methods
Aggregate dataset vs. full dataset
The dataset of anaerobic CH4 and CO2 production included incubation studies that varied
in terms of incubation length, incubation temperature, and sample depth. The magnitude
of CH4 production varied widely among samples and over the duration of the
experiments (Figure S1), whereas CO2 production declined with time as labile substrate
was depleted (Figure S2). We observed significant variation in lag times before
maximum CH4 production rates among samples (Figure S3; Table 2), which was likely
due to different environmental conditions and microbial communities, as well as differing
incubation lengths and number of production measurements. This necessitated using
maximum CH4 production rates instead of mean CH4 production in the aggregated data
set (Figures S1, S3). CH4 production varied predictably with depth (Figure S4); due to
this co-linearity and our interest in trends in CH4 production with depth, we accounted for
depth in the mixed model analysis as a fixed effect. We did not standardize production
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measurements by temperature because most studies did not incubate at multiple
temperatures and for those that did, Q10 values among studies were highly variable (0.77
to 2.18 for CO2; 0.43 to 316 for CH4), so selecting a single Q10 to apply to all studies
would likely introduce large error in the temperature standardized estimates. Instead, we
included incubation temperature as a fixed effect in our mixed model analysis. We
calculated both mean daily CO2:CH4 production (in main text) and the instantaneous
CO2:CH4 production on the day of maximum CH4 production, and data trends were
similar (Table 2, Table S1).
Breakdown of ancillary data
We collected ancillary measurements for samples including categorical site descriptions,
environmental conditions, vegetation descriptions, and other physical soil properties. The
number of measurements included in each biome, landscape position, and vegetation
category as well as the number of measurements reporting ancillary variables are reported
for the aggregated dataset (Table S2).
To estimate missing ancillary data (% C, bulk density), we developed linear
regressions to predict % C and bulk density using depth and soil type (Table S3; Figure
S5). The relationship used to predict % C for organic soils is limited to depths < 1m. Bulk
density predictions were significantly improved by using the categorical depth predictor
(3 depth increments: 0 – 20 cm, 20 – 100 cm, and > 100 cm) compared to the continuous
depth prediction (F1,95 = 5.03, P = 0.03; AICcat = 46.72, AICcont = 49.87).
Multi-dimensional analysis
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We performed multivariate ordination to graphically explore the sources of
variation in the CH4 flux data and their degree of similarity between the individual study
sites and best predictor variables obtained from step-wise regression (Table 4). We
conducted Non-metric Multidimensional Scaling (NMDS; Kruskal, 1964) using the
vegan package (Version 2.0-2) in R v.2.8.1(R Development Core Team, Vienna, Austria).
We chose small number of axes (k=3) a priori, and the data were fit to those dimensions.
NMDS is a numerical technique that iteratively seeks a solution and stops after finding an
acceptable solution. We pre-specified 25 such iterations after which convergence of
solutions was attained. Unlike other ordination methods, NMDS does not assume linear
or modal relationships; this was the best method of ordination for our dataset.
Response variables used for the NMDS analysis were: molar ratio of CH4:CO2,
maximum CH4 production (µg CH4-C gC-1 hr-1), and mean CO2 production (µg CO2-C
gC-1 hr-1). We performed deterministic imputation using linear regression predictions to
estimate missing values for molar ratio of CH4:CO2 and CO2 production (µg CO2-C gC-1
hr-1) before proceeding with the ordination analysis. The imputation procedure was
conducted using the mice package (Version 2.21) in R. The data were subsequently
square-root transformed and then submitted to Wisconsin double standardization using
the default routine of metaMDS function in vegan.
Ordination scores from NMDS were derived from pH, depth (active layer and
permafrost), incubation temperature and percent C loss as CO2 and CH4. NMDS scores
for the predictive variables were obtained using Bray-Curtis distance matrix (k = 2)
generated previously with metaMDS function of vegan. The envfit function was used to
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visualize relationships between CH4 flux data and NMDS scores, which were correlated
with the predictor variables and mapped to the multi-dimensional space.
Results
Carbon emissions during anaerobic decomposition were correlated with the % C
in the soil, pH and incubation temperature. The amount of CO2 and CH4 produced during
anaerobic incubations was significantly, but weakly positively correlated with the % C in
the soil (Figure S6). Both relationships were significant (P < 0.0001) but % C explained
little variability among production rates (15% and 18% for CH4 and CO2, respectively).
Maximum CH4 production during anaerobic incubations was significantly correlated with
an interaction between soil pH and incubation temperature (Figure S7).
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Table S1. Median CO2:CH4 production on
the day of maximum CH4 production,
excluding samples in which maximum CH4
production occurred after day 150 (n = 193).
Category
Median CO2:CH4
Temperature
<10°C
35
10-20°C
48
>20°C
100
Relative water table position
Dry
98
Fluctuating
56
Inundated
32
Depth
0 - 20 cm
16
20 – 100 cm
136
> 100 cm
1014
Vascular vegetation
Graminoids
34
Shrubs
31
Trees
2410
Moss type
Sphagnum
28
Other mosses
221
No moss
36
Landscape position
Drained Lake
34
Flood plain
353
Lowland
2410
Upland
31
Wetland
19
Biome
Boreal
1288
Tundra
15
Soil type
Organic
42
Mineral
59
Permafrost
Active layer
32
Permafrost
1068
No permafrost
80
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Table S2. Number of measurements in the
aggregated dataset for each category, and number
of studies that reported ancillary data (e.g., pH,
%C, %N). The number of measurements for
temperature, depth, water table position and
landscape position can be found in Table 2.
Variable
# of Samples
Biome
Boreal
Tundra
Dominant vascular plant
Graminoid
Shrub
Tree
Moss type
None
Sphagnum
Other moss types
Permafrost present/absent
Present
Absent
Permafrost/active layer
Active layer
Permafrost
Soil type
Mineral
Organic
Cryoturbated O/M
pH
%C
%N
Bulk density
Water content
303
122
181
256
150
70
36
284
58
171
55
303
219
84
219
175
44
303
71
230
2
243
188
239
100
126
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Table S3. Linear models used to estimate missing % C and bulk density values.
Predictor
Estimate
Error
P predictor
F-values (df) r2
%C
Intercept
7.4868
1.3294
< 0.0001
F3,203 = 200
0.75
Organic Soil
35.1313
1.6772
< 0.0001
Depth (cm)
-0.0029
0.0026
0.28
Organic soil x depth
-0.1292
0.0281
< 0.0001
Bulk density (g cm-3)
Mineral: 0 – 20 cm
Mineral: 20 -100 cm
Organic: 0 – 20 cm
Organic: 20 – 100 cm
0.812
0.910
0.090
0.263
0.102
0.185
0.012
0.028
< 0.0001
< 0.0001
0.624
< 0.0001
F5,95 = 70.5
0.79
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Figure S1. Rates of CH4 production for all incubations measurements included in the full
dataset by sample day. The aggregated dataset included maximum CH4 production for
each sample.
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Figure S2. Rates of CO2 production for all incubations measurements included in the full
dataset over time.
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Figure S3. Using the daily CH4 production to maximum CH4 production ratio, we
calculated the lag time before maximum CH4 production, which occurs on Day 0 in this
figure, during each incubation experiment. The exponential decay of CH4 production
rates (k) occurs following maximum CH4 production on Day 0. A comparison of lag
times and decay rates are shown for differing relative water table positions (dry,
fluctuating, and saturated samples).
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Figure S4. For all depths included in study (a) Maximum CH4 production rates, (b) mean
CO2 production, and (c) and CO2:CH4 production for organic (dark circles) and mineral
(gray triangles) soil types. Note log-scale for CO2:CH4 production.
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Figure S5. Relationship between %C and depth for top 250 cm of soils. Also plotted are
the regression lines for the empirical relationship developed between % C, depth, and soil
type. Regression coefficients are found in Table S3.
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Figure S6. Relationships between % C in soils and CH4 production (top) and CO2
production (bottom) in anaerobic incubations. Both relationships were significant (P <
0.0001) but % C explained little variability among production rates (15% and 18% for
CH4 and CO2, respectively).
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Figure S7. The relationship between pH and maximum CH4 production was dependent
on incubation temperature (Chi2 = 35, df = 4, P < 0.001). Note log scale for maximum
CH4 production rates.
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Figure S8. Non-metric Multidimensional Scaling of CH4:CO2 production, mean CH4
production (µg CH4-C gC-1 hr-1), maximum CH4 production (µg CH4-C gC-1 hr-1), and
CO2 production (µg CO2-C gC-1 hr-1) from combined dataset of measurements and
imputation. (a) Data points represent ordination scores grouped by water table position
(Dry, Fluctuating and Inundated) based on Bray-Curtis dissimilarity matrix (left). (b)
Ordination scores indexed by number of observations show clustering by incubation
temperature bin (black: High, > 20 oC; dark gray: Mid, 10 – 20 oC; and light gray: Low, <
10 oC).
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Figure S9. Fit showing vectors of continuous predictor variables: pH and %C loss; and
centroids of levels of the class variable, depth (Active Layer, AL and Permafrost, PF).
Data points represent ordination scores grouped by water table position (Dry, Fluctuating
and Inundated) based on Bray-Curtis dissimilarity matrix. Arrows show direction of the
increasing gradient, and length of the arrow is proportional to the correlation between
predictor variable and the ordination. Text labels for ordination scores are shown based
on priority assigned to points representing highest CH4 flux.
S20
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