Search for: All records

Pairwise geographic distances (m) between mire-wide plots in Stordalen Mire, northern Sweden. Distances are in the file Mirewide_Plots_distances-m.csv. This file was generated with the script Mirewide_Plots_Distances.R, using Mirewide_Plots_GPS.csv as input, and geosphere package version 1.5-10. Details of the plots, including latitude, longitude, and vegetation cover, are in the dataset "Stordalen Mire mire-wide survey: Vegetation cover" (https://doi.org/10.5281/zenodo.15048198). The latitude & longitude provided in that dataset represent more precise versions of the coordinates in Mirewide_Plots_GPS.csv (which also omits plot 8); the coordinates are otherwise identical in both datasets. FUNDING: National Aeronautics and Space Administration, Interdisciplinary Science program: From Archaea to the Atmosphere (award # NNX17AK10G). National Science Foundation, Biology Integration Institutes Program: EMERGE Biology Integration Institute (award # 2022070). United States Department of Energy Office of Biological and Environmental Research, Genomic Science Program: The IsoGenie Project (grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440). We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164. 

Stordalen Mire sample metadata from a mire-wide survey (2015) and co-analyzed autochamber site samples (2014-2015). These samples were analyzed by 16S rRNA amplicon sequencing, and the 16S data is available under NCBI BioProject PRJNA1236848. Column descriptions for this metadata file: The first 4 columns (sample_name, SRA library_ID, SRA accession, BioSample) include sample & library names and accessions in NCBI. The sample_name column also matches the SampleID__ attribute in the EMERGE Database (EMERGE-DB; https://emerge-db.asc.ohio-state.edu/). The next 7 columns (SampleID, Habitat, Depth, Description, Source, Site, Origin) are the metadata used for the 16S data analysis (results available at https://doi.org/10.5281/zenodo.15047596 and https://doi.org/10.5281/zenodo.15047715). The final 9 columns (Latitude, Longitude, Date, Full Site Name, Core #, DepthMin (cm), DepthMax (cm), DepthAvg (cm), pH_porewater) provide other metadata, including latitude/longitude, sampling dates, full site and core names, depths, and porewater pH, standardized to match the nomenclature in the EMERGE-DB. FUNDING: National Aeronautics and Space Administration, Interdisciplinary Science program: From Archaea to the Atmosphere (award # NNX17AK10G). National Science Foundation, Biology Integration Institutes Program: EMERGE Biology Integration Institute (award # 2022070). United States Department of Energy Office of Biological and Environmental Research, Genomic Science Program: The IsoGenie Project (grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440). Sequencing was performed using startup funding from the University of Arizona to Virginia Rich. We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164. 

This dataset provides summaries of temperature (T) and water table depth (WTD) conditions prior to the collection of peat samples from Stordalen Mire, Sweden, in July of 2011-2017. These summaries include the following files: t_wtd_summaries_July2011-2017samplings.csv This file gives summary statistics over various time intervals for the following environmental measurements: AirTemperature: Mean daily air temperature (°C), obtained from automatic sensors at the nearby Abisko Scientific Research Station (ANS) (station ID 188790; the source file [ANS_Daily_Wx_Jul84_Dec17.txt] is not included due to sharing restrictions). WTD: Water table depths (cm), obtained from Manual active layer and and water table depth measurements from the autochamber sites at Stordalen Mire, northern Sweden (2003-2017) (from Patrick Crill et al.). The time intervals for these summaries are defined relative to the peat sampling date at each site (see EMERGE Sample Metadata Sheet for Samples with Microbiomes), which varies by site and year. The specific intervals are defined as follows: 7d: 7 days prior to the sampling date, plus the sampling date itself. 14d: 14 days prior to the sampling date, plus the sampling date itself. 21d: 21 days prior to the sampling date, plus the sampling date itself. 28d: 28 days prior to the sampling date, plus the sampling date itself. growing: Time from beginning of growing season (defined as June 1) until (and including) the sampling date. all_growing: Entire growing season (June 1 – Sept. 30). For clarity, the start and end dates for each time interval (inclusive) are also given under the columns Start_Date and End_Date, where End_Date=Sampling_Date for all intervals except all_growing. Summary statistics for each interval include: measurement count (n), median (median), mean (mean), and standard deviation (sd), and are given under the column names beginning with these statistic labels. IMPORTANT NOTE:  For temperature, these statistics are calculated based on the average temperature measured on each day, meaning that the standard deviations do NOT account for within-day temperature variation. To provide short-term (1 day) temperature variation context for each sampling date, the within-day mean, minimum, and maximum air temperatures for the sampling date only (taken directly from the corresponding row & columns in the source ANS data file) are provided in the columns samplingdate_mean_AirTemperature, samplingdate_min_AirTemperature, and samplingdate_max_AirTemperature. wtd_summaries_July2011-2017samples.csv This file gives the percentage of time that each peat sample's depth midpoint (DepthAvg__) was at or below the water table depth (WTD), over each of the longer time intervals (≥21 days) defined above for the temperature & WTD summaries. (Intervals <21 days are not included due to the lower frequency of WTD measurements, which results in low n for shorter intervals.) The first few columns are taken directly from the EMERGE Sample Metadata Sheet for Samples with Microbiomes, for the samples collected in July of 2011-2017 from the MainAutochamber sites. The last set of columns include the following, with the time interval labels (defined as in the above temperature summaries) appended at the end of each column name: n_WTD_*: Number of WTD measurements used in the calculation. pct_time_below_WTD_*: Fraction (relative to 1) of measured WTDs over the given time interval that were at or above the DepthAvg__ for each sample, which equates to the fraction of measurement timepoints during which the given sample was at or below the WTD. This is the same method used for calculating "% Time below water table" in Figure 6 of Singleton et al. (2018). For palsa sites, this value is automatically set to 0 based on the lack of a water table at all timepoints in the analysis.) As above, the WTD values used for these calculations were obtained from Manual active layer and and water table depth measurements from the autochamber sites at Stordalen Mire, northern Sweden (2003-2017) (Patrick Crill et al.). Funding acknowledgments This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070. This research was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440. The temperature summary has been made possible by data provided by Abisko Scientific Research Station and the Swedish Infrastructure for Ecosystem Science (SITES). We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164. 

This is a pre-release of this MAG set, which is now published in ENA under BioProject PRJNA386568.FILES: mags_emerge_20230110.tar.gz - Archive containing MAG files (.fna). metadata_MAGs_EMERGE.tsv - Table containing MIMAG(5.0)-formatted sample attributes, genome information, and other metadata for the MAGs. This table also includes JGI or NCBI genome accession #s for some additional MAGs that are not part of the .tar.gz archive.  NEW in Version 1.0.0: Added source metagenome accessions, including SRA runs (derived_from) and BioSamples (metaG_biosample), for all MAGs where this info was available. Added other metadata (including SampleID__, assembly methods, and sequencing technology) that was previously absent for the externally-cited MAGs.   FUNDING: This research is a contribution of the EMERGE Biology Integration Institute (https://emerge-bii.github.io/), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070. This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632. DE-SC0010580. and DE-SC0016440. We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164. Data collected at the Joint Genome Institute was generated under the following awards: The majority of sequencing at JGI was supported by BER Support Science Proposal 503530 (DOI: 10.46936/10.25585/60001148), conducted by the U.S. Department of Energy Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Sequencing of SIP samples was performed under the Facilities Integrating Collaborations for User Science (FICUS) initiative (proposal 503547; award DOI: 10.46936/fics.proj.2017.49950/60006215) and used resources at the DOE Joint Genome Institute (https://ror.org/04xm1d337) and the Environmental Molecular Sciences Laboratory (https://ror.org/04rc0xn13), which are DOE Office of Science User Facilities. Both facilities are sponsored by the Office of Biological and Environmental Research and operated under Contract Nos. DE-AC02-05CH11231 (JGI) and DE-AC05-76RL01830 (EMSL). 

This release (MAGs v2) is a major new version of this metagenome-assembled genome (MAG) set. All previous releases on this page (which only differ in the metadata) are designated "MAGs v1." The current release (MAGs v2) uses CheckM2 v1.0.2 filtering (≥70% completeness, ≤10% contamination) to expand this dataset to include 36,419 MAGs, with the following subcategories: Cronin_v1:  Manually-curated subset of the "Field" category from MAGs v1. Cronin_v2:  MAGs from raw bin filtering on the same assemblies used to generate Cronin_v1. Woodcroft_v2:  MAGs from raw bin filtering on the same assemblies used to generate the MAGs reported in Woodcroft & Singleton et al. (2018). SIPS:  Updated genomes from samples originating from a stable isotope probing (SIP) incubation experiment by Moira Hough et al. ("SIP" in MAGs v1), re-analyzed due to read truncation and sample linkage issues in MAGs v1. JGI:  Expanded set of genomes from the Joint Genome Institute's metagenome annotation pipeline.   FILES: Emerge_MAGs_v2.tar.gz - Archive containing the MAG files (.fna). metadata_MAGs_v2_EMERGE.tsv - Table containing source sample names and accessions, GTDB taxonomy information, CheckM2 quality reports, NCBI GenomeBatch- and MIMAG(6.0)-formatted sample attributes and other metadata for the MAGs.    FUNDING: This research is a contribution of the EMERGE Biology Integration Institute (https://emerge-bii.github.io/), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070. This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632. DE-SC0010580. and DE-SC0016440. We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164. Data collected at the Joint Genome Institute was generated under the following awards: The majority of sequencing at JGI was supported by BER Support Science Proposal 503530 (DOI: 10.46936/10.25585/60001148), conducted by the U.S. Department of Energy Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Sequencing of SIP samples was performed under the Facilities Integrating Collaborations for User Science (FICUS) initiative (proposal 503547; award DOI: 10.46936/fics.proj.2017.49950/60006215) and used resources at the DOE Joint Genome Institute (https://ror.org/04xm1d337) and the Environmental Molecular Sciences Laboratory (https://ror.org/04rc0xn13), which are DOE Office of Science User Facilities. Both facilities are sponsored by the Office of Biological and Environmental Research and operated under Contract Nos. DE-AC02-05CH11231 (JGI) and DE-AC05-76RL01830 (EMSL). 

Abstract The dynamics of methane (CH₄) cycling in high-latitude peatlands through different pathways of methanogenesis and methanotrophy are still poorly understood due to the spatiotemporal complexity of microbial activities and biogeochemical processes. Additionally, long-termin situmeasurements within soil columns are limited and associated with large uncertainties in microbial substrates (e.g. dissolved organic carbon, acetate, hydrogen). To better understand CH₄cycling dynamics, we first applied an advanced biogeochemical model,ecosys, to explicitly simulate methanogenesis, methanotrophy, and CH₄transport in a high-latitude fen (within the Stordalen Mire, northern Sweden). Next, to explore the vertical heterogeneity in CH₄cycling, we applied the PCMCI/PCMCI+ causal detection framework with a bootstrap aggregation method to the modeling results, characterizing causal relationships among regulating factors (e.g. temperature, microbial biomass, soil substrate concentrations) through acetoclastic methanogenesis, hydrogenotrophic methanogenesis, and methanotrophy, across three depth intervals (0–10 cm, 10–20 cm, 20–30 cm). Our results indicate that temperature, microbial biomass, and methanogenesis and methanotrophy substrates exhibit significant vertical variations within the soil column. Soil temperature demonstrates strong causal relationships with both biomass and substrate concentrations at the shallower depth (0–10 cm), while these causal relationships decrease significantly at the deeper depth within the two methanogenesis pathways. In contrast, soil substrate concentrations show significantly greater causal relationships with depth, suggesting the substantial influence of substrates on CH₄cycling. CH₄production is found to peak in August, while CH₄oxidation peaks predominantly in October, showing a lag response between production and oxidation. Overall, this research provides important insights into the causal mechanisms modulating CH₄cycling across different depths, which will improve carbon cycling predictions, and guide the future field measurement strategies. 

Abstract Northern peatlands are a globally significant source of methane (CH₄), and emissions are projected to increase due to warming and permafrost loss. Understanding the microbial mechanisms behind patterns in CH₄production in peatlands will be key to predicting annual emissions changes, with stable carbon isotopes (δ¹³C‐CH₄) being a powerful tool for characterizing these drivers. Given that δ¹³C‐CH₄is used in top‐down atmospheric inversion models to partition sources, our ability to model CH₄production pathways and associated δ¹³C‐CH₄values is critical. We sought to characterize the role of environmental conditions, including hydrologic and vegetation patterns associated with permafrost thaw, on δ¹³C‐CH₄values from high‐latitude peatlands. We measured porewater and emitted CH₄stable isotopes, pH, and vegetation composition from five boreal‐Arctic peatlands. Porewater δ¹³C‐CH₄was strongly associated with peatland type, with δ¹³C enriched values obtained from more minerotrophic fens (−61.2 ± 9.1‰) compared to permafrost‐free bogs (−74.1 ± 9.4‰) and raised permafrost bogs (−81.6 ± 11.5‰). Variation in porewater δ¹³C‐CH₄was best explained by sedge cover, CH₄concentration, and the interactive effect of peatland type and pH (r² = 0.50,p < 0.001). Emitted δ¹³C‐CH₄varied greatly but was positively correlated with porewater δ¹³C‐CH₄. We calculated a mixed atmospheric δ¹³C‐CH₄value for northern peatlands of −65.3 ± 7‰ and show that this value is more sensitive to landscape drying than wetting under permafrost thaw scenarios. Our results suggest northern peatland δ¹³C‐CH₄values are likely to shift in the future which has important implications for source partitioning in atmospheric inversion models. 

Abstract Quantifying the temperature sensitivity of methane (CH₄) production is crucial for predicting how wetland ecosystems will respond to climate warming. Typically, the temperature sensitivity (often quantified as a Q₁₀value) is derived from laboratory incubation studies and then used in biogeochemical models. However, studies report wide variation in incubation-inferred Q₁₀values, with a large portion of this variation remaining unexplained. Here we applied observations in a thawing permafrost peatland (Stordalen Mire) and a well-tested process-rich model (ecosys) to interpret incubation observations and investigate controls on inferred CH₄production temperature sensitivity. We developed a field-storage-incubation modeling approach to mimic the full incubation sequence, including field sampling at a particular time in the growing season, refrigerated storage, and laboratory incubation, followed by model evaluation. We found that CH₄production rates during incubation are regulated by substrate availability and active microbial biomass of key microbial functional groups, which are affected by soil storage duration and temperature. Seasonal variation in substrate availability and active microbial biomass of key microbial functional groups led to strong time-of-sampling impacts on CH₄production. CH₄production is higher with less perturbation post-sampling, i.e. shorter storage duration and lower storage temperature. We found a wide range of inferred Q₁₀values (1.2–3.5), which we attribute to incubation temperatures, incubation duration, storage duration, and sampling time. We also show that Q₁₀values of CH₄production are controlled by interacting biological, biochemical, and physical processes, which cause the inferred Q₁₀values to differ substantially from those of the component processes. Terrestrial ecosystem models that use a constant Q₁₀value to represent temperature responses may therefore predict biased soil carbon cycling under future climate scenarios.