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The goal of the New Hampshire Soil Sensor Network is to examine spatial and temporal changes in soil properties and processes as the climate changes. Data collected can also calibrate and validate models that examine how ecosystems may respond to changing climate and land use. To determine how soil processes are affected by climate change and land management, this soil sensor network measures snow depth, air temperature, soil temperature, soil volumetric water content, and soil electrical conductivity, as well as soil CO2 fluxes. This data package includes air temperature, soil temperature at 5 cm, and soil volumetric water content at 5 cm, and soil CO2 flux at the time of sampling, as well as gap-filled soil CO2 fluxes using non-linear least squares regression. Data were collected at the following sites: BRT = Bartlett Experimental Forest, Bartlett, NH; BDF = Burley-Demmerit Farm, Lee, NH; DCF = Dowst Cate Forest, Deerfield, NH; HUB = Hubbard Brook Experimental Forest, Woodstock, NH; SBM = Saddleback Mountain, Deerfield, NH; THF = Thompson Farm, Durham, NH; and Trout Pond Brook, Strafford, NH. 

Hourly water table depth (WTD) measurements at 36 gauge locations, from June 9, 2014 (4pm) until October 8, 2014 (1pm). FILES: water-level_2014_logger-metadata.tsv - Water level logger sensor descriptions, locations, and elevations. water-level_2014_measurements.tsv - Water level measurements. Water table depths are recorded in millimeters (mm), where negative values indicate a water level above ground. NA indicates when no measurements were taken. Note that these sensors were not regularly checked due to lack of staff at the site, so there may be some data quality issues. 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. 

RGB mosaic of 2500 images extracted from video captured with a Lecia camera system aboard a Mavic 2 Pro UAV. Images were captured at solar noon at approximately 80 m above the ground. Spatial resolution is 3 cm. 

METHODS: Fluxes were measured using a system of 8 automatic gas-sampling chambers made of transparent Lexan (n=3 each in the palsa and bog habitats, and n=2 in the fen habitat). Chambers were initially installed in the three habitat types at Stordalen Mire in 2001 (Bäckstrand et al., 2008) and the chamber lids were replaced in 2011 with the current design, similar to that described by Bubier et al 2003. Chambers cover an area of 0.2 m2 (45 cm x 45 cm), with a height ranging from 15-75 cm depending on habitat vegetation. At the Palsa and bog site the chamber base is flush with the ground and the chamber lid (15 cm in height) lifts clear of the base between closures. At the fen site the chamber base is raised 50–60 cm on lexon skirts to accommodate large stature vegetation. The chambers are connected to the gas analysis system, located in an adjacent temperature-controlled cabin, by 3/8” Dekoron tubing through which air is circulated at approximately 2.5 L min-1. Each chamber lid is closed once every 3 hours for a period of 8 min, with a 5 min flush period before and after lid closure. Gas concentration in the chamber headspace was measured with a Los Gatos Research (LGR) Fast Greenhouse Gas Analyzer, with timing control and data acquisition using a Campbell CR10x (Holmes et al., 2022). References: Bäckstrand, K., Crill, P. M., Mastepanov, M., Christensen, T. R. & Bastviken, D. Total hydrocarbon flux dynamics at a subarctic mire in northern Sweden. Journal of Geophysical Research 113, (2008). Bubier, J. L., Crill, P. M., Mosedale, A., Frolking, S. & Linder, E. Peatland responses to varying interannual moisture conditions as measured by automatic CO2 chambers. Global Biogeochemical Cycles 17, (2003). Holmes, M. E., Crill, P. M., Burnett, W. C., McCalley, C. K., Wilson, R. M., Frolking, S., Chang, K. ‐Y., Riley, W. J., Varner, R. K., Hodgkins, S. B., IsoGenie Project Coordinators, IsoGenie Field Team, McNichol, A. P., Saleska, S. R., Rich, V. I., Chanton, J. P. (2022). Carbon accumulation, flux, and fate in Stordalen Mire, a permafrost peatland in transition. Global Biogeochemical Cycles, 36, e2021GB007113, doi:10.1029/2021GB007113. McCalley, C.K., B.J. Woodcroft, S.B. Hodgkins, R.A. Wehr, E-H. Kim, R. Mondav, P.M. Crill, J.P. Chanton, V.I. Rich, G.W. Tyson, S.R. Saleska (2014), Methane dynamics regulated by microbial community response to permafrost thaw, Nature, 514:478-481, doi:10.1038/nature13798.   FUNDING: This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.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 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.These autochamber measurements were also supported by a grant from the US National Science Foundation MacroSystems program (NSF EF 1241037, PI Varner). 

Autochamber-based CH4 fluxes and δ13C values measured with a Tunable Infrared Laser Direct Absorption Spectrometer (TILDAS, Aerodyne Research Inc.); and ancillary data, including CO2 fluxes (measured with a LGR Greenhouse Gas Analyzer), temperatures, atmospheric pressure, and photosynthetically active radiation (PAR). In addition to the data published here, data from 2011 is also available in the supplementary files to McCalley et al. (2014) under the Source data to Fig. 1 link.   METHODS: Methane fluxes were measured using a system of 8 automatic gas-sampling chambers made of transparent Lexan (n=3 each in the palsa and bog habitats, and n=2 in the fen habitat). Chambers were initially installed in the three habitat types at Stordalen Mire in 2001 (Bäckstrand et al., 2008) and the chamber lids were replaced in 2011 with the current design, similar to that described by Bubier et al 2003. Chambers cover an area of 0.2 m2 (45 cm x 45 cm), with a height ranging from 15-75 cm depending on habitat vegetation. At the Palsa and bog site the chamber base is flush with the ground and the chamber lid (15 cm in height) lifts clear of the base between closures. At the fen site the chamber base is raised 50–60 cm on lexon skirts to accommodate large stature vegetation. The chambers are instrumented with thermocouples measuring air and surface ground temperature, and water table depth and thaw depth are measured manually 3–5 times per week. The chambers are connected to the gas analysis system, located in an adjacent temperature-controlled cabin, by 3/8” Dekoron tubing through which air is circulated at approximately 2.5 L min-1. Each chamber lid is closed once every 3 hours for a period of 8 min, with a 5 min flush period before and after lid closure. We measured methane concentration using a Tunable Infrared Laser Direct Absorption Spectrometers (TILDAS, Aerodyne Research Inc.) connected to the main chamber circulation using ¼” Dekoron tubing (McCalley et al 2014). Calibrations were done every 90 min using 3 calibration gases spanning the observed concentration range (1.8–10 ppm). For each autochamber closure we calculated flux using a method consistent with that detailed by Bäckstrand et al 2008 for CO2 and total hydrocarbons, using a linear regression of changing headspace CH4 concentration over a period of 2.5 min. Eight 2.5 min regressions were calculated, staggered by 15 sec, and the most linear fit (highest r2) was then used to calculate flux. Daily average flux for each chamber was used to calculate daily flux and standard error for each cover type. References: Bäckstrand, K., Crill, P. M., Mastepanov, M., Christensen, T. R. & Bastviken, D. Total hydrocarbon flux dynamics at a subarctic mire in northern Sweden. Journal of Geophysical Research 113, (2008). Bubier, J. L., Crill, P. M., Mosedale, A., Frolking, S. & Linder, E. Peatland responses to varying interannual moisture conditions as measured by automatic CO2 chambers. Global Biogeochemical Cycles 17, (2003). McCalley, C.K., B.J. Woodcroft, S.B. Hodgkins, R.A. Wehr, E-H. Kim, R. Mondav, P.M. Crill, J.P. Chanton, V.I. Rich, G.W. Tyson, S.R. Saleska (2014), Methane dynamics regulated by microbial community response to permafrost thaw, Nature, 514:478-481, doi:10.1038/nature13798.   FILES: Files are named with the year or date range, followed by a suffix indicating data resolution: *_CH4output_clean_ckm.txt - Individual measurements of CH4 fluxes (CH4Flux), CO2 fluxes (CO2flux; for select years), and δ13C signature of emitted CH4 (Flux13CH4) for each chamber closure. CH4FluxRsq is the R2 value of the linear fit used to calculate CH4 flux, CO2Rsq is the R2 value of the linear fit used to calculate CO2 flux, and Flux13CH4_stdev is the standard deviation of the δ13C signature (standard deviation of the intercept of the Keeling plot). *_DailyCH4output_ckm.txt - Daily average CH4 fluxes (CH4Flux) and δ13C values (13CH4), grouped by site: Palsa, Bog, Fen, and Chamber 9 (bog/fen transition); along with standard deviations (stdev) and standard errors (se) of the flux or δ13C for each site type. For the Palsa, Bog, and Fen sites, these averages are calculated by chamber (n=3 for Palsa and Bog, n=2 for Fen), so each chamber's daily average is calculated, and then a daily average for that site is calculated as the average of the chambers. For Chamber 9 (bog/fen intermediate; n=1 chamber), averages are calculated by day as there are no chamber replicates. MEASUREMENT UNITS (same for both file types): CH4 flux: mg CH4 m−2 hr−1 CO2 flux: mg C m−2 h−1 δ13C: ‰ Temperature: °C Air pressure: mbar PAR: µmol photons m−2 s−1   FUNDING: This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.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 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.Autochamber measurements between 2013 and 2017 were supported by a grant from the US National Science Foundation MacroSystems program (NSF EF 1241037, PI Varner). 

Methane (CH4) emissions in Stordalen Mire (northern Sweden), estimated via two different approaches: "Paint by number" (field ch4_modified_prj.tif): CH4 emission across the landscape calculated via “paint-by-number” approach, using 2014 autochamber-based flux measurements (https://doi.org/10.5281/zenodo.14052690) mapped to landcover classifications (https://doi.org/10.5281/zenodo.15042233). DNDC-modeled (Modeled CH4.tif): CH4 emission across the landscape modeled via Wetland-DNDC (https://www.dndc.sr.unh.edu/), driven by remotely sensed landcover classifications (https://doi.org/10.5281/zenodo.15042233), water table depth (https://doi.org/10.5281/zenodo.15092752), climate data (provided by the Abisko Scientific Research Station), and soil parameters (defined as in Deng et al. 2014, 2017). The DNDC model was run on vegetation and water table clusters (determined by k-means clustering), and model output was spatially assigned to each map pixel. Modeled CH4 emissions account for CH4 production from DOC (Randomforest_stack_epsg32634_extent_kmeansclass10_CH4 prod from DOC.tif) and from CO2 (Randomforest_stack_epsg32634_extent_kmeansclass10_CH4 prod from CO2.tif), minus oxidation (Randomforest_stack_epsg32634_extent_kmeansclass10_CH4 oxid.tif). The model also outputs a map of CH4 isotopic composition (δ13C-CH4) of emissions (Randomforest_stack_epsg32634_extent_kmeansclass10_Delta CH4 flux.tif). The difference between these approaches is provided as a difference map (CH4diff.tif), calculated as the "paint-by-number" (PBN) emissions (field ch4_modified_prj.tif) minus the Wetland-DNDC modeled emissions (Modeled CH4.tif). These images are GeoTIFFs with embedded georeferencing information. 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). National Science Foundation, MacroSystems program (grant # EF-1241037). 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. 

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. 

From our climate to the air we breathe, the ocean influences the world around us. Scientists are always looking for new ways to explore and study the ocean. One way we do this is by going on specially designed ships that allow us to study the deep sea, far from land. On our latest expedition aboard the Research Vessel Sally Ride, we went out 300 miles into the North Pacific Ocean for a week. We used some of the most important ocean science tools to catch tiny marine animals, collect water from some of the deepest depths, uncover mysteries of oceans past, and study how desert dust feeds marine animals today. 

ABSTRACT While wetlands are major sources of biogenic methane (CH₄), our understanding of resident microbial metabolism is incomplete, which compromises the prediction of CH₄emissions under ongoing climate change. Here, we employed genome-resolved multi-omics to expand our understanding of methanogenesis in the thawing permafrost peatland of Stordalen Mire in Arctic Sweden. In quadrupling the genomic representation of the site’s methanogens and examining their encoded metabolism, we revealed that nearly 20% of the metagenome-assembled genomes (MAGs) encoded the potential for methylotrophic methanogenesis. Further, 27% of the transcriptionally active methanogens expressed methylotrophic genes; forMethanosarcinalesandMethanobacterialesMAGs, these data indicated the use of methylated oxygen compounds (e.g., methanol), while forMethanomassiliicoccales, they primarily implicated methyl sulfides and methylamines. In addition to methanogenic methylotrophy, >1,700 bacterial MAGs across 19 phyla encoded anaerobic methylotrophic potential, with expression across 12 phyla. Metabolomic analyses revealed the presence of diverse methylated compounds in the Mire, including some known methylotrophic substrates. Active methylotrophy was observed across all stages of a permafrost thaw gradient in Stordalen, with the most frozen non-methanogenic palsa found to host bacterial methylotrophy and the partially thawed bog and fully thawed fen seen to house both methanogenic and bacterial methylotrophic activities. Methanogenesis across increasing permafrost thaw is thus revised from the sole dominance of hydrogenotrophic production and the appearance of acetoclastic at full thaw to consider the co-occurrence of methylotrophy throughout. Collectively, these findings indicate that methanogenic and bacterial methylotrophy may be an important and previously underappreciated component of carbon cycling and emissions in these rapidly changing wetland habitats. IMPORTANCEWetlands are the biggest natural source of atmospheric methane (CH₄) emissions, yet we have an incomplete understanding of the suite of microbial metabolism that results in CH₄formation. Specifically, methanogenesis from methylated compounds is excluded from all ecosystem models used to predict wetland contributions to the global CH₄budget. Though recent studies have shown methylotrophic methanogenesis to be active across wetlands, the broad climatic importance of the metabolism remains critically understudied. Further, some methylotrophic bacteria are known to produce methanogenic by-products like acetate, increasing the complexity of the microbial methylotrophic metabolic network. Prior studies of Stordalen Mire have suggested that methylotrophic methanogenesis is irrelevantin situand have not emphasized the bacterial capacity for metabolism, both of which we countered in this study. The importance of our findings lies in the significant advancement toward unraveling the broader impact of methylotrophs in wetland methanogenesis and, consequently, their contribution to the terrestrial global carbon cycle. 

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.