Published April 28, 2022 | Version v1
Dataset Open

Methane fluxes from four elevation zones in a St. Lawrence Estuary salt marsh

  • 1. University of Birmingham, University of McGill
  • 2. University of Birmingham
  • 3. McGill University

Description

Dataset used in Spartina alterniflora has the highest methane emissions in a St. Lawrence estuary salt marsh - IOPscience.

The dataset contains methane fluxes calculated from gas measurements taken over a 40 or 60 minute period using a dark static chamber method. Methane fluxes were measured at six locations in four elevation zones of a northern salt marsh on the St. Lawrence River estuary at La Pocatière, Quebec (47°22'24.7"N 70°03'26.3"W). Additional environmental data was collected including carbon dioxide fluxes, extractable soil nitrate, extractable soil ammonium, extractable soil dissolved organic carbon, extractable soil total dissolved nitrogen, salinity, temperature, water table depth, soil total organic carbon, soil total nitrogen, soil organic carbon to nitrogen ratio and bulk density. Soil cores were collected from 0-15 cm and used for extractable nutrient analysis, bulk density and soil organic carbon and nitrogen analysis. The work was carried out with funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie Grant Agreement 838296, a NSERC Discovery Grant and a Natural Environment Research Council grant number (NE/T012323/1). This dataset is used in a publication entitled Spartina alterniflora has the highest methane emissions in a St. Lawrence Estuary salt marsh in Environmental Research: Ecology (https://doi.org/10.1088/2752- 664X/ac706a), which also contains more details on fieldsite and methodology.

Gas samples were collected from dark, static chambers (18L, 26 cm diameter), which were placed onto pre-inserted collars in the vegetated zones (inserted to 2.5 cm, 3 days prior to sampling) or placed directly onto the mudflat. The chambers were insulated and fitted with fans and venting tubes. Gas samples were collected on the 23rd August 2020 from all sites, soil cores were collected between the 24-25th August 2020 and the 19-20th September 2020. Soil samples were collected at 0-15 cm using a 2.5 cm diameter dutch gouge corer.

Soil temperature was measured at 10 cm depth using a soil thermometer, (°C, DeltaTrak 11050, Pleasanton, USA), salinity was measured in the laboratory using a portable ATC refractometer. Water table depth was measured using a PVC piezometer, a plastic pipe with tubing was inserted into the piezometer and blown into to determine water table depth through bubbling sound (cm). Soil cores were dried at 60 °C to constant weight and the dry weight over core volume used to calculate bulk density (g cm-3), soil was finely ground and analysed for total organic carbon and total nitrogen (%) using an Elemental Analyser (ThermoFinnigan Flash EA 1112 CN analyser, Carlo Erba, Milan, Italy) with an accuracy of ±5 % for N and ±1 % for C, and a limit of 171 detection of 0.05 % for both N and C. Extractable nitrate+nitrite (assumed to be nitrate) were analysed in soil extractant (2M KCl, 5:1 of extractant to soil) using a microplate reader and methods in Sims et al., 1995 (https://doi.org/10.1080/00103629509369298) with a limit of detection of 0.1 ppm and accuracy of ±5%. Extractable dissolved organic carbon and total dissolved nitrogen were analysed in soil extractant (ultrapure water 18.2 MΩ, 5:1 of extractant to soil) on a TOC/TDN analyser (TOC VCSn + TMN-1, Shimadzu, Kyoto, Japan), with a 50 mg C l -1 standard resulting in an accuracy and precision of 3.0 and ±4.4 mg l-1, respectively. CH4 and CO2 concentrations were measured in the gas samples using a gas chromatograph (GC-14, Shimadzu, Kyoto, Japan) fitted with a flame ionisation detector, CO2 was methanised to CH4 before analysis. Standards of CH4 (5.1 ppm) and CO2 (5000 ppm) resulted in an accuracy and precision of 6.6±1.5 and 0.4 ppm, and 5324±324 and 78 ppm, respectively, for CH4 and CO2. Changes in gas concentration over time were converted to fluxes using a linear regression of the linear portion fo the flux and if fluxes were below the minimum detectable concentration difference (see https://doi.org/10.1002/2017JG003783), they were set to zero. Results from the experiments were entered into an Excel spreadsheet for ingestion into the Zenodo data repository.

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Additional details

Funding

UK Research and Innovation
Large Area Distributed Real Time Soil (DiRTS) Monitoring NE/T012323/1
European Commission
MarshFlux - The effect of future global climate and land-use change on greenhouse gas fluxes and microbial processes in salt marshes 838296