The release of long‐stored carbon from thawed permafrost could fuel increased methanogenesis in northern lakes, but it remains unclear whether old carbon substrates released from permafrost are metabolized as rapidly by methanogenic microbial communities as recently produced organic carbon. Here, we apply methane (CH₄) clumped isotope (Δ₁₈) and ¹⁴C measurements to test whether rates of methanogenesis are related to carbon substrate age. Results from culture experiments indicate that Δ₁₈ values are negatively correlated with CH₄ production rate. Measurements of ebullition samples from thermokarst lakes in Alaska and glacial lakes in Sweden indicate strong negative correlations between CH₄ Δ₁₈ and the fraction modern carbon. These correlations imply that CH₄ derived from older carbon substrates is produced relatively slowly. Relative rates of methanogenesis, as inferred from Δ₁₈ values, are not positively correlated with CH₄ flux estimates, highlighting the likely importance of environmental variables other than CH₄ production rates in controlling ebullition fluxes

Anthony, Katey M. Walter

Crill, Patrick M.

Dawson, Katherine S.

Douglas, Peter M. J.

Eiler, John M.

Lloyd, Max K.

Moguel, Regina Gonzalez

Sessions, Alex L.

Smith, Derek A.

Stolper, Daniel A.

Wik, Martin

Yanay, Ella

English

Caltech Authors - Main

Clumped isotopes link older carbon substrates with slower rates of methanogenesis 1 
in northern lakes 2 
 3 
Authors: Peter M. J. Douglas1,2, Regina Gonzalez Moguel1, Katey M. Walter Anthony3, 4 
Martin Wik4, Patrick M. Crill4, Katherine S. Dawson2,5, Derek A. Smith2,6, Ella Yanay2, 5 
Max K. Lloyd2,7, Daniel A. Stolper7, John M. Eiler2, Alex L. Sessions2 6 
 7 
1- Department of Earth and Planetary Sciences, McGill University, Montreal, QC, 8 
Canada  9 
2- Division of Geological and Planetary Sciences, California Institute of Technology, 10 
Pasadena, CA, USA 11 
3- International Arctic Research Center, University of Alaska-Fairbanks, Fairbanks, AK, 12 
USA 13 
4- Department of Geological Sciences and Bolin Center for Climate Research, Stockholm 14 
University, Stockholm, Sweden 15 
5- Department of Environmental Sciences, Rutgers University, New Brunswick, NJ, USA 16 
6- Department of Biology, Case Western Reserve University, Cleveland, OH, USA 17 
7- Department of Earth and Planetary Sciences ,University of California-Berkeley, 18 
Berkeley, CA, USA 19 
 20 
Supplementary Materials 21 
Supplementary Text and Figures: 22 
ST1- Details on culture experiment sterile media:  23 
The sterile media used in the pure and enrichment culture experiments contained (g/L): 24 
NaCl (23.4), MgSO4•7 H2O (9.44), NaHCO3 (5.0), KCl (0.8), NH4Cl (1.0), Na2HPO4 25 
(0.6), CaCl2•2 H2O (0.14), cysteine-HCl (0.25), with the addition of 10 mL DSM 141 26 
Trace Element solution, 10 mL of DSM 141 Vitamin solution, and 2.5 mL of a 50 mM 27 
H2S- solution. 28 
ST2- Details on Δ13CH3D and Δ12CH2D2 Measurements 29 
CH4 samples from the Batch 3 culture experiments were measured for δD, δ13C, 30 
Δ13CH3D, and for three samples additionally Δ12CH2D2. For these measurements the 31 
relevant equations are:  32 
Δ13CH3D = (13CH3DR/13CH3DR* –1)       (6) 33 
Δ12CH2D2 = (12CH2D2R/12CH2D2R* –1)       (7) 34 
Where the measured and expected random isotope ratios are defined as: 35 
13CH3DR = ([13CH3D] /[12CH4]        (8) 36 
12CH2D2R = ([12CH2D2] /[12CH4]       (9) 37 
13CH3DR* = 4× 2R × 13R( )         (10) 38 
and
 39 
12CH 2D2R* = 6× 2R⎡⎣ ⎤⎦
2⎛
⎝
⎜
⎞
⎠
⎟
        (11)
 40 
A complete description of the analytical methods used for this measurement is found in  41 
Eldridge et al. [2019]. Samples were measured against a working reference gas with 42 
calibrated Δ13CH3D and Δ12CH2D2 values of 2.59 ± 0.14 ‰ and 5.86 ± 0.60 ‰, 43 
respectively Eldridge et al. [2019]. External reproducibility of δD, δ13C, Δ13CH3D, and 44 
Δ12CH2D2 measurements, as determined from repeated measurements of laboratory 45 
standards, was 0.17, 0.02, 0.38 and 1.52 ‰ (1σ standard deviations; n=16), respectively. 46 
ST3- Rationale for assumptions in model of kinetic isotope effects and comparison to 47 
data 48 
 We make three assumptions to model the relationship between CH4 production 49 
rate and Δ18 or Δ13CH3D values, and to compare these model results to empirical data 50 
from culture experiments.  First, we assume a constant rate of reverse methanogenesis 51 
(rrev). This implies that reverse methanogenesis is a zero-order reaction. There are few 52 
constraints on the rate of reverse methanogenesis and its variability. However, anaerobic 53 
oxidation of methane (AOM) is thought to proceed using a reverse methanogenesis 54 
pathway [Timmers et al., 2017], and previous studies have inferred zero-order kinetics for 55 
AOM at high CH4 concentrations [Vavilin, 2013; Vavilin and Rytov, 2013], which would 56 
be applicable to our culture experiments.  57 
 Second, to compare empirical results from culture experiments and model 58 
predictions, we express rrev and rnet normalized to culture media volume. This comparison 59 
requires an assumption of uniform cell density between the culture experiments. This 60 
assumption is an oversimplification and could be responsible for some of the scatter in 61 
our data-model comparison (See ST-5). Accounting for cell density would be valuable in 62 
future studies relating Δ18 and CH4 production rates.  63 
 Third, the model was developed for hydrogenotrophic methanogenesis [Stolper et 64 
al., 2015], but we are comparing it to CH4 produced using four different carbon 65 
substrates (Figure 1). However, the model specifically describes kinetic isotope effects 66 
associated with the addition of a hydrogen atom to methyl-coenzyme-M. This step is 67 
common to all of the CH4 production pathways studied here, and therefore kinetic isotope 68 
effects for clumped isotopologues can be reasonably assumed to be similar for these 69 
different methanogenesis pathways, as discussed by Gruen et al. [2018]. 70 
 To compare the range of CH4 production rates from thermokarst lake incubation 71 
experiments [Heslop et al., 2015] with the modeled relationship between Δ18 and rnet, we 72 
took the full range of reported CH4 production potentials (0.02 to 8.08 µg C-CH4 g dry 73 
sediment-1 day-1) and converted this to mg CH4 ml pore water-1 hour-1 using mean values 74 
of the gravimetric water content for the two sample types (organic rich mud and thawed 75 
permafrost) with the highest and lowest CH4 production rates. 76 
 77 
 78 
ST-4 Details on enrichment culture experiment results 79 
The enrichment culture experiments are clearly differentiated from the pure culture 80 
experiments in Figure 1. With this limited dataset we cannot clearly identify the cause of 81 
this difference. One possibility is a higher rate of the reverse reactions of 82 
methanogenesis. If rrev is increased by a factor of 10 (to 5x10-3 mg CH4 hr-1 ml-1) the 83 
kinetic isotope effect model provides a relatively good fit to these data (Supplementary 84 
Figure 1). This is plausible given that with the diverse consortium of microbes potentially 85 
present in these experiments there would have been the possibility of sulfate reduction or 86 
other bacterial metabolisms acting as an electron acceptor for AOM, which could have 87 
increased the rates of reverse methanogenesis and increased Δ18 values [Ash et al., 2019]. 88 
We note that sulfate was present in the culture media, and there was anecdotal evidence 89 
(i.e. odor) of the presence of hydrogen sulfide during gas sampling of these cultures. 90 
 Alternately, if the methanogen cell density in these experiments was substantially 91 
higher than in the pure culture experiments, it is possible that the relatively high net CH4 92 
production rate is not an accurate reflection of the cell-specific CH4 production rate 93 
relative to the pure cultures. However, it is unlikely that the enrichment culture 94 
methanogen community would have reached the large turbid densities of the pure culture 95 
experiments.  96 
 97 
 98 
 99 
 100 
 101 
 102 
Supplementary Figure 1: Plot of log CH4 production rate vs. Δ18 and Δ13CH3D in a 103 
subset of pure and enrichment culture experiments. Solid and dashed lines indicate 104 
predicted values based on a model of methanogenesis kinetic isotope effects[Stolper et 105 
al., 2015], assuming a constant value of rrev of 4x10-4 mg CH4 hr-1 ml-1. Gray solid and 106 
dashed lines indicate predicted values with an rrev value of 5x10-3 mg CH4 hr-1 ml-1. Gray 107 
box as in Figure 1. Analytical errors for Δ18 or Δ13CH3D measurements (1σ) are smaller 108 
than the symbols. MeOH, methanol; TMA, Trimethylamine. 109 
 110 
 111 
ST-5 Clumped isotope variability in pure culture experiment results and model sensitivity 112 
to fractionation factors 113 
The model of kinetic isotope effects predicts the general trend of the pure culture results 114 
shown in Figure 1, but there is clearly variability in these results that is not accounted for 115 
by the model. It is beyond the scope of this study to thoroughly assess the causes of this 116 
variability. However, we observe that varying the value of κf-13CH3D (as defined in the 117 
Methods) from 1.929 to 1.936 generates model curves that encompass the variability in 118 
the pure culture data (Supplementary Figure 2). It is important to note that differences in 119 
κf-13CH3D would have a much smaller effect at the lower rates of methanogenesis that are 120 
generally found in natural environments. 121 
 The largest differences from the model predicted values occur in the Batch 3 122 
experiments (Table 2). These experiments were all conducted using methanol as a 123 
substrate, suggesting that differences between methanogenesis pathways are not 124 
producing the observed variability. Additionally, these experiments were conducted in 125 
smaller batch experiments with less overall substrate provided, and the observed 126 
variability may be partly caused by closed-system Rayleigh fractionation effects that 127 
would potentially be more pronounced in smaller batch experiments. We observe 128 
substantial enrichment of the δ13C and δD of CH4 in our experiments (Supplementary 129 
Table 2) that would be consistent with substrate depletion during closed-system Rayleigh 130 
fractionation [Whiticar, 1999; Hayes, 2001].  131 
 Three measurements of the Batch 3 samples included both Δ13CH3D and 132 
Δ12CH2D2 values, and therefore afford an opportunity to examine whether the model 133 
predictions are consistent with measurements of two distinct clumped isotopologues. 134 
These measurements are consistent with the modeled relationship between rnet and 135 
Δ12CH2D2 across a relatively narrow range of κf-12CH2D2 values (1.62 to 1.64) 136 
(Supplementary Figure 3A). Likewise, the measurements are consistent with the model 137 
predicted relationship between Δ13CH3D and Δ12CH2D2, applying the same range of κf-138 
13CH3D as applied in Supplementary Figure 2 (Supplementary Figure 3B). Currently 139 
there are few available measurements of Δ12CH2D2  in the literature, but as this 140 
measurement becomes more widespread it will provide additional constraints that can be 141 
used to test hypothesized relationships between clumped isotope values and rates of 142 
methanogenesis.  143 
 144 
Supplementary Figure 2: Plot of log CH4 production rate vs. Δ18 and Δ13CH3D in a 145 
subset of pure and enrichment culture experiments. Dashed lines indicate predicted 146 
values based on a model of methanogenesis kinetic isotope effects [Stolper et al., 2015], 147 
with varying values of κf-13CH3D (see Methods), assuming a constant value of rrev of 148 
4x10-4 mg CH4 hr-1 ml-1. Analytical errors for Δ18 or Δ13CH3D measurements (1σ) are 149 
smaller than the symbols. MeOH, methanol; TMA, Trimethylamine. 150 
 151 
 152 
-14 -12 -10 -8 -6 -4 -2-8
-6
-4
-2
0
2
4
6
CO2 + H2
Acetate
MeOH
TMA
Enrichment
log Methane production rate per volume of culture media
(ln [mg CH4 hr
-1 ml-1])
Δ
18
 (‰
; F
ill
ed
)  
or
  Δ
13
C
H
3D
 (‰
, U
nf
ill
ed
)
Modeled Kinetic 
Fractionation
(Δ13CH3D)
κf = 1.934
κf = 1.929
κf = 1.936
   153 
Supplementary Figure 3:  Δ12CH2D2 results from the Batch 3 pure culture experiments 154 
plotted against (A) the natural log of CH4 production rate and (B) Δ13CH3D. Dashed lines 155 
indicate predicted values based on a model of methanogenesis kinetic isotope effects 156 
[Stolper et al., 2015], with varying values of κf-12CH2D2 (A) and κf-13CH3D (B), assuming a 157 
constant value of rrev of 4x10-4 mg CH4 hr-1 ml-1. Analytical errors for Δ12CH2D2 and 158 
Δ13CH3D measurements (1σ) are smaller than the symbols. Gray line in (B) indicates the 159 
predicted values for equilibrium fractionation [Young et al., 2017].  160 
 161 
ST-6: Comparison of CH4 concentration and stable carbon and hydrogen isotopic 162 
composition with Fm values  163 
 In both lake types we do not observe a significant correlation (p < 0.05) between 164 
CH4 concentration or δ13C values and CH4 Fm values (Supplementary Figure 4). For the 165 
Alaskan dataset there are also measurements of CO2 δ13C, and we do not observe a 166 
significant correlation between 13αCO2-CH4 and Fm values. We do observe a significant 167 
negative correlation between CH4 δD and Fm in the Stordalen dataset, although this 168 
correlation is weaker than that between Δ18 and Fm. We do not observe a similar 169 
correlation in the Alaskan dataset.   170 
   In general, a greater proportion of CH4 derived from acetoclastic 171 
methanogenesis is associated with higher δ13C values and lower δD values [Whiticar, 172 
1999]. In the Alaskan dataset we do not observe a clear correlation between Fm and these 173 
variables. In the Stordalen dataset we observe correlations that are consistent with higher 174 
Fm with more acetoclastic methanogenesis. However, culture experiments do not show a 175 
significant difference between these pathways for Δ18 if CH4 is produced at the same rate 176 
(Figure 1). Furthermore, neither δD nor δ13C is as strong a predictor of Fm as is Δ18. 177 
Given the inconsistent and relatively weak correlations between δD or δ13C and Fm, we 178 
conclude that rates of methanogenesis, as opposed to methanogenic pathway, is a more 179 
important variable for understanding variability in Fm in these lakes. 180 
 Some studies have inferred that variable degrees of anaerobic or aerobic oxidation 181 
can influence Δ18 values [Wang et al., 2016; Young et al., 2017; Giunta et al., 2019], 182 
although the direction of this effect is different for aerobic or anaerobic oxidation, with 183 
predicted lower values for aerobic oxidation and higher values for anaerobic oxidation. 184 
Greater CH4 oxidation will generally lead to higher δD and δ13C values in CH4, and 185 
smaller values of 13αCO2-CH4 [Whiticar, 1999].  We do not observe a pattern that is 186 
consistent with oxidation being linked to Fm in either lake type. As discussed above, at 187 
Stordalen higher Fm is correlated with higher δ13C but with lower δD values, which is 188 
not consistent with an oxidation effect. In Alaska there is no apparent correlation between 189 
Fm and δ13C or 13αCO2-CH4.  190 
 Substrate depletion in a closed system is expected to lead to enrichment of δ13C in 191 
CH4 [Whiticar et al., 1999]. Currently the effects of substrate depletion on Δ18 have not 192 
been explicitly studied. We suggest that the absence of a significant correlation between 193 
CH4 δ13C and Fm in either lake system means that this process is not strongly influencing 194 
the correlation between Δ18 and Fm. As discussed above (ST-5), closed-system substrate 195 
depletion may have influenced our pure culture results, but any substrate depletion effects 196 
appear to be secondary to the effect of net CH4 production rate (Supplementary Figure 2).  197 
     198 
 199 
 200 
Supplementary Figure 4: Scatter plots of CH4 14C age vs. different isotopic or chemical 201 
parameters for the Alaskan and Stordalen datasets. (A) and (B): δ13C of CH4; (C) and (D) 202 
δD of CH4; (E) and (F) CH4 concentration (% by volume); (G) 13αCO2-CH4 (Alaskan dataset 203 
only). R2 and p values are shown for each correlation. In (D) the solid line shows the 204 
best-fit regression curve, and the dotted lines show its 95% confidence interval. 205 


Clumped Isotopes Link Older Carbon Substrates With Slower Rates of Methanogenesis in Northern Lakes

https://authors.library.caltech.edu/102400/2/grl60364-sup-0001-2019gl086756-si.pdf

Clumped Isotopes Link Older Carbon Substrates With Slower Rates of Methanogenesis in Northern Lakes

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