Abundant carbon substrates drive extremely high sulfate reduction rates and methane fluxes in Prairie Pothole Wetlands

Abstract

Inland waters are increasingly recognized as critical sites of methane emissions to the atmosphere, but the biogeochemical reactions driving such fluxes are less well understood. The Prairie Pothole Region (PPR) of North America is one of the largest wetland complexes in the world, containing millions of small, shallow wetlands. The sediment pore waters of PPR wetlands contain some of the highest concentrations of dissolved organic carbon (DOC) and sulfur species ever recorded in terrestrial aquatic environments. Using a suite of geochemical and microbiological analyses, we measured the impact of sedimentary carbon and sulfur transformations in these wetlands on methane fluxes to the atmosphere. This research represents the first study of coupled geochemistry and microbiology within the PPR and demonstrates how the conversion of abundant labile DOC pools into methane results in some of the highest fluxes of this greenhouse gas to the atmosphere ever reported. Abundant DOC and sulfate additionally supported some of the highest sulfate reduction rates ever measured in terrestrial aquatic environments, which we infer to account for a large fraction of carbon mineralization in this system. Methane accumulations in zones of active sulfate reduction may be due to either the transport of free methane gas from deeper locations or the co-occurrence of methanogenesis and sulfate reduction. If both respiratory processes are concurrent, any competitive inhibition of methanogenesis by sulfate-reducing bacteria may be lessened by the presence of large labile DOC pools that yield noncompetitive substrates such as methanol. Our results reveal some of the underlying mechanisms that make PPR wetlands biogeochemical hotspots, which ultimately leads to their critical, but poorly recognized role in regional greenhouse gas emissions.

Dissolved Organic Matter Chemistry and Transport Along an Arctic Tundra Hillslope

“…Candidate metabolites were identified by matching the chemical shift, intensity information, and J coupling to metabolite libraries available in the Chenomx library, and these 1-D 1 H spectra were collected as previously described (Dalcin Martins et al, 2017;Daly et al, 2016). The 1-D spectra were processed using Chenomx NMR Suite 8.3 software, with peak quantification assigned relative to the DSS internal standard.…”

“…Global Biogeochemical Cycles assigned relative to the DSS internal standard. Candidate metabolites were identified by matching the chemical shift, intensity information, and J coupling to metabolite libraries available in the Chenomx library, and these 1-D 1 H spectra were collected as previously described (Dalcin Martins et al, 2017;Daly et al, 2016). For reference, the detection and concentration of annotated metabolites is provided for representative lysimeters in Table S2.We procured the InChI code for each annotated metabolite from PubChem and used ClassyFire to identify its chemical taxonomy (Feunang et al, 2016).…”

Hydrologic Lag Effects on Wetland Greenhouse Gas Fluxes

“…Wetland systems often are distinguished by highly dynamic GHG fluxes that display substantial temporal and spatial variability. This variability has been associated with a myriad of factors including abiotic conditions (e.g., soil moisture and temperature), biotic communities (e.g., microbial, vegetation), emission pathway (diffusive flux, ebullition, and plant-mediated flux), wetland type (e.g., peatland, mineral-soil, tidal), and weather [4,[6][7][8][9][10][11]. Wetland hydrology, specifically inundation or ponding, is a primary factor that directly and indirectly influences the GHG and carbon balance of wetland systems [4,12].…”

Untargeted metabolomic profiling of Sphagnum fallax reveals novel antimicrobial metabolites

“…A list of the average chemical shifts per identified metabolite is included in Table . NMR spectra were collected as described previously (Dalcin Martins et al, ), and the detection limit of the method was ~1 µM. Only metabolites above 1 µM were included in this study.…”

“…The one-dimensional (1D) 1 H NMR spectrum of the Sphagnum sample was collected in accordance with standard Chenomx sample preparation and data collection guidelines (Weljie, Newton, Mercier, Carlson, & Slupsky, 2006 Table S1. NMR spectra were collected as described previously (Dalcin Martins et al, 2017), and the detection limit of the method was ~1 µM. Only metabolites above 1 µM were included in this study.…”