Quantifying the Inhibitory Impact of Soluble Phenolics on Carbon Mineralization from Sphagnum-rich Peatlands

The mechanisms controlling the extraordinarily slow carbon (C) mineralization rates characteristic of Sphagnum-rich peatlands (“bogs”) remain somewhat elusive, despite decades of research on this topic. Soluble phenolic compounds have been invoked as potentially significant contributors to bog peat recalcitrance due to their affinity to slow microbial metabolism and cell growth. Despite this potentially significant role, the effects of soluble phenolic compounds on bog peat C mineralization remain unclear. We analyzed this effect by manipulating the concentration of free soluble phenolics in anaerobic bog peat incubations using water-soluble polyvinylpyrrolidone (PVP), a compound that binds with and inactivates phenolics, preventing phenolic-enzyme interactions. CO2 and CH4 production rates (end-products of C mineralization) correlated positively with PVP concentration following Michaelis-Menten (M.M.) saturation functions. Using M.M. parameters, we determined that soluble phenolics inhibit, at minimum, 57 ± 16% of total C (CO2+CH4) mineralization in the anaerobic incubation conditions studied. These findings are consistent with other studies that have indicated that soluble phenolics play a significant role in regulating bog peat stability in the face of decomposition.

soluble phenolics likely persist [44] and (2) of these persisting soluble phenolics, some could 120 feasibly continue interacting with enzymes, leading to continued inhibition of C mineralization. hydrologic regimes. For an in-depth description of these habitats, see [45][46][47]. Peat samples for 137 this study were collected from a bog site within the Mire, which falls mid-stage along the 138 permafrost thaw progression. The bog site is perched above the regional water table separated by 139 a layer of permafrost. As such, its water inputs are limited to rainfall ("ombrotrophic").

143
Field Collection 144 Peat for the laboratory incubation experiments was obtained in July 2018 using an 145 Eijenkamp perforated stainless steel corer. We sectioned the core using a razor blade and set 146 aside peat from 9-19 cm depth for incubation analysis. The peat was stored at -20°C from the 147 point of collection up until the experiment start date.

148
Experimental Design 149 We prepared six treatments, each with three replicates, totaling 21 vials. Treatment 1 was 150 an untreated control. Treatments 2-6 spanned a polyvinylpyrrolidone (PVP) concentration range 151 of 0.001-0.064 g/mL (Table 1) with N 2 gas. We repeated this process until headspace CO 2 concentrations measured less than 162 0.1% (see "Gas Analysis"), which was two magnitudes lower than the CO 2 production measured 163 during the experiment. The above-gauge headspace pressure was ~3.5 psi immediately following 164 headspace flushing. We stored the vials in total darkness at room temperature (20-22°C) and 165 allowed them to sit for a 25-day pre-incubation phase. production per g dry peat (see "Statistical Analysis").

188
We performed all CO 2 and CH 4 concentration analyses via Flame-Ionization-Detector

189
Gas Chromatography (GC-FID) using methods established by [46]. We used a gas-tight syringe 190 for injection of all samples and standards. The GC flow rate was 30 mL/min, and the 191 temperatures were 140, 160, and 380℃ for the column, detector, and methanizer, respectively.

192
On each sampling day, we created a linear calibration curve for both CO 2 and CH 4 using 193 standards of known concentrations. Before sample analyses, we shook the vials vigorously to 194 liberate gases trapped in the peat pore-spaces. We also recorded headspace pressures to calculate 195 partial pressures of CO 2 and CH 4 (which were essential for statistical analysis).

197
We calculated average production rates (µmoles × g dry peat -1 × day -1 ) for CO 2 and CH 4 198 using the steps outlined below. We determined total C mineralization rates ("C tot ") by taking the 199 sum of CO 2 and CH 4 production rates.
To calculate gas production rates, we first determined the quantity of gas injected into the 201 GC-n gas(inj) -by inputting sample peak amplitudes into our standard calibration curve (eqn. 1). 202 We used the ideal gas law to determine the total moles injected-n tot(inj) (eqn. 2). We calculated 203 the gas fraction-F gas -using eqn. 3, which we used to calculate headspace partial pressures-204 P gas (eqn. 4). We then applied this value to the ideal gas law to quantify the headspace moles-205 n gas(HS) (eqn. 5). Using Henry's Law (eqn. 6), we calculated the dissolved gas concentration-206 C gas(aq.) -which we used to determine the moles in the aqueous phase-n gas(aq) (eqn. 7). We 207 determined the moles per vial-n gas × vial -1 -using eqn. 8. Our final daily production values-208 n gas × g -1 -were obtained by eqn. 9 (where g=dry peat weight). Production rate time series were best approximated using linear regression equations. We 219 determined average production rates (n gas × g -1 × d -1 ) using the slopes of these equations. We The relationship between gas production rate and PVP concentration was best 225 approximated using Michaelis-Menten equations. Since production rates were expected to be 226 nonzero in controls (where PVP=0 g × mL -1 ) we appended a y-intercept to the Michaelis-Menten Pandas Python data packages. To determine the fraction of observed variance explained by the

241
When production rates of all three of CO 2 , CH 4 , and C tot (CO 2 +CH 4 ) follow similar 242 trends, we will refer to them collectively as GHG c production. GHG c production rates increased 243 with PVP concentration across the entire PVP concentration range studied (Fig 2, Table 2).

244
GHG c production was linear with time for all incubations. Average production rates and R 2 245 values are included in Table 2. CO 2 :CH 4 production ratios were significantly (p<0.05) elevated 246 relative to median values between days 1-10-a phenomenon that we attributed primarily to 247 increasing CH 4 production rates. After this period, CO 2 :CH 4 production ratios stabilized (as did 248 CH 4 production rates). We include only data collected after the stabilization period ended (day 249 10) in our calculations of average CO 2 :CH 4 production ratios.  Table 3). PVP-saturated production rates (Prod sat ; calculated using Eqn. 11, Methods) were significantly (p<0.05) higher than control production rates (Prod 0 ; calculating by averaging 268 control production rates), amounting to a 1.9, 2.3, and 2.4-fold increase in CO 2 , CH 4 , and C tot 269 production, respectively. CO 2 :CH 4 production ratios ranged from 2. Production rates (µmoles × g -1 × d × -1 ) were derived from best-fit linear regression lines.  We hypothesized that increasing PVP concentration would yield increasing GHG c 281 production rates and that this relationship would follow an amended Michaelis-Menten (saturation) function (Fig 1). We aimed to quantitatively constrain the extent to which soluble 283 phenolics inhibit C mineralization by cross-analyzing GHG c production rates in control (Prod 0 ) 284 vs. PVP-saturated peat (Prod sat , calculated using Eqn. 11, Methods). Table 3. Michaelis-Menten parameters for CO 2 , CH 4 , and C tot (Fig 2a-c). Michaelis-Menten equations relate PVP concentration (g × mL -1 ) to CO 2 , CH 4 , and C tot 289 (CO 2 +CH 4 ) production rate (in µmoles × g -1 × d -1 ). k m and v max were calculated using the  a Prod 0 (µmoles × g -1 × d -1 ) refers to the average control production rate (where PVP=0 g ⋅ mL -1 ) 294 and is equivalent to the y-intercept for the Michaelis-Menten curves.  305 We calculated the extent to which phenolics apparently inhibited GHG c production in our 306 incubated bog peat using the following equation: Inhibition.

316
Using this equation, we determined that phenolics significantly (p<0.05) inhibited C tot , 317 CO 2 , and CH 4 production by 57 ± 16, 64 ± 24, and 46 ± 16%, respectively ( Quantifying differences between the soluble phenolic content in our incubations vs. field 324 pore-water is beyond the scope of the study, which prevents us from applying %Phenolic 325 Inhibition calculations directly to the field. To quantify the extent of this influence, it is 326 necessary to identify potential differences between the abundance and speciation of soluble 327 phenolics in incubation vs. field pore-water.

328
It is feasible that %Phenolic Inhibition in peat bogs varies significantly by site and depth,  in peat chemistry associated with permafrost thaw increase greenhouse gas production.