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42 results for “mire”
Summaries of temperature and water table depth prior to peat sampling in Stordalen Mire, 2011-2017
<div> <p>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:</p> <h2><strong>t_wtd_summaries_July2011-2017samplings.csv</strong></h2> </div> <p>This file gives summary statistics over various time intervals for the following environmental measurements:</p> <ul> <li><strong>AirTemperature</strong>: 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).</li> <li><strong>WTD</strong>: Water table depths (cm), obtained from <a href="https://doi.org/10.5281/zenodo.10420396">Manual active layer and and water table depth measurements from the autochamber sites at Stordalen Mire, northern Sweden (2003-2017)</a> (from Patrick Crill et al.).</li> </ul> <p>The time intervals for these summaries are defined relative to the peat sampling date at each site (see <a href="https://doi.org/10.5281/zenodo.12827096">EMERGE Sample Metadata Sheet for Samples with Microbiomes</a>), which varies by site and year. The specific intervals are defined as follows:</p> <ul> <li><strong>7d</strong>: 7 days prior to the sampling date, plus the sampling date itself.</li> <li><strong>14d</strong>: 14 days prior to the sampling date, plus the sampling date itself.</li> <li><strong>21d</strong>: 21 days prior to the sampling date, plus the sampling date itself.</li> <li><strong>28d</strong>: 28 days prior to the sampling date, plus the sampling date itself.</li> <li><strong>growing</strong>: Time from beginning of growing season (defined as June 1) until (and including) the sampling date.</li> <li><strong>all_growing</strong>: Entire growing season (June 1 – Sept. 30).</li> </ul> <p>For clarity, the start and end dates for each time interval (inclusive) are also given under the columns <strong>Start_Date</strong> and <strong>End_Date</strong>, where End_Date=<strong>Sampling_Date</strong> for all intervals except all_growing.</p> <p>Summary statistics for each interval include: measurement count (<strong>n</strong>), median (<strong>median</strong>), mean (<strong>mean</strong>), and standard deviation (<strong>sd</strong>), and are given under the column names beginning with these statistic labels.</p> <p><em>IMPORTANT NOTE: </em>For temperature, these statistics are calculated based on the average temperature measured on each day, meaning that<strong> </strong><em>the standard deviations do NOT account for within-day temperature variation.</em> 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 <strong>samplingdate_mean_AirTemperature</strong>, <strong>samplingdate_min_AirTemperature</strong>, and <strong>samplingdate_max_AirTemperature</strong>.</p> <div> <div> <h2><strong>wtd_summaries_July2011-2017samples.csv</strong></h2> </div> <p>This file gives the percentage of time that each peat sample's depth midpoint (<strong>DepthAvg__</strong>) 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 <em>n</em> for shorter intervals.)</p> <p>The first few columns are taken directly from the <a href="https://doi.org/10.5281/zenodo.12827096">EMERGE Sample Metadata Sheet for Samples with Microbiomes</a>, 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:</p> <ul> <li><strong>n_WTD_*</strong>: Number of WTD measurements used in the calculation.</li> <li><strong>pct_time_below_WTD_*</strong>: 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 <a href="https://doi.org/10.1038/s41396-018-0065-5">Singleton et al. (2018)</a>. For palsa sites, this value is automatically set to 0 based on the lack of a water table at all timepoints in the analysis.)</li> </ul> <p>As above, the WTD values used for these calculations were obtained from <a href="https://doi.org/10.5281/zenodo.10420396">Manual active layer and and water table depth measurements from the autochamber sites at Stordalen Mire, northern Sweden (2003-2017)</a> (Patrick Crill et al.).</p> <h1>Funding acknowledgments</h1> <p>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.</p> <p>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.</p> <p>The temperature summary has been made possible by data provided by Abisko Scientific Research Station and the Swedish Infrastructure for Ecosystem Science (SITES).</p> <p>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.</p> </div>
Autochamber CH4 Fluxes and δ13C Values at Stordalen Mire
<p>Autochamber-based CH<sub>4</sub> fluxes and δ<sup>13</sup>C values measured with a Tunable Infrared Laser Direct Absorption Spectrometer (TILDAS, Aerodyne Research Inc.); and ancillary data, including CO<sub>2</sub> fluxes (measured with a LGR Greenhouse Gas Analyzer), temperatures, atmospheric pressure, and photosynthetically active radiation (PAR).</p> <p>In addition to the data published here, data from 2011 is also available in the supplementary files to <a href="https://doi.org/10.1038/nature13798"><strong>McCalley et al. (2014)</strong></a> under the <strong><a href="https://static-content.springer.com/esm/art%3A10.1038%2Fnature13798/MediaObjects/41586_2014_BFnature13798_MOESM61_ESM.xlsx">Source data to Fig. 1</a></strong> link.</p> <p> </p> <p>METHODS:</p> <p>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 m<sup>2</sup> (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<sup>-1</sup>. 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.</p> <p>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 CO<sub>2</sub> and total hydrocarbons, using a linear regression of changing headspace CH<sub>4</sub> 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 r<sup>2</sup>) 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.</p> <p><em>References:</em></p> <p>Bäckstrand, K., Crill, P. M., Mastepanov, M., Christensen, T. R. & Bastviken, D. Total hydrocarbon flux dynamics at a subarctic mire in northern Sweden. <em>Journal of Geophysical Research</em> <strong>113</strong>, (2008).</p> <p>Bubier, J. L., Crill, P. M., Mosedale, A., Frolking, S. & Linder, E. Peatland responses to varying interannual moisture conditions as measured by automatic CO<sub>2</sub> chambers. <em>Global Biogeochemical Cycles</em> <strong>17</strong>, (2003).</p> <p>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, <em>Nature</em>, 514:478-481, doi:10.1038/nature13798.</p> <p> </p> <p>FILES:</p> <p>Files are named with the year or date range, followed by a suffix indicating data resolution:</p> <ul> <li>*<strong>_CH4output_clean_ckm.txt</strong> - Individual measurements of CH<sub>4</sub> fluxes (CH4Flux), CO<sub>2</sub> fluxes (CO2flux; for select years), and δ<sup>13</sup>C signature of emitted CH<sub>4</sub> (Flux13CH4) for each chamber closure. CH4FluxRsq is the R<sup>2</sup> value of the linear fit used to calculate CH<sub>4</sub> flux, CO2Rsq is the R<sup>2</sup> value of the linear fit used to calculate CO<sub>2</sub> flux, and Flux13CH4_stdev is the standard deviation of the δ<sup>13</sup>C signature (standard deviation of the intercept of the Keeling plot).</li> <li>*<strong>_DailyCH4output_ckm.txt</strong> - Daily average CH<sub>4</sub> fluxes (CH4Flux) and δ<sup>13</sup>C 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 δ<sup>13</sup>C 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.</li> </ul> <p>MEASUREMENT UNITS (same for both file types):</p> <ul> <li>CH<sub>4</sub> flux: mg CH<sub>4</sub> m<sup>−2</sup> hr<sup>−1</sup></li> <li>CO<sub>2</sub> flux: mg C m<sup>−2</sup> h<sup>−1</sup></li> <li>δ<sup>13</sup>C: ‰</li> <li>Temperature: °C</li> <li>Air pressure: mbar</li> <li>PAR: µmol photons m<sup>−2</sup> s<sup>−1</sup></li> </ul> <p> </p> <p>FUNDING:</p> <p>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br>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.<br>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.<br>Autochamber measurements between 2013 and 2017 were supported by a grant from the US National Science Foundation MacroSystems program (NSF EF 1241037, PI Varner).</p>
Metagenome-assembled genomes from Stordalen Mire, Sweden (MAGs v2)
<p><strong>This release (MAGs v2) is a major new version of this metagenome-assembled genome (MAG) set.</strong> All previous releases on this page (which only differ in the metadata) are designated "MAGs v1." The current release (MAGs v2) uses<strong> </strong>CheckM2 v1.0.2 filtering (≥70% completeness, ≤10% contamination) to expand this dataset to include <strong>36,419 MAGs</strong>, with the following subcategories:</p> <ul> <li>Cronin_v1: Manually-curated subset of the "Field" category from MAGs v1.</li> <li>Cronin_v2: MAGs from raw bin filtering on the same assemblies used to generate Cronin_v1.</li> <li>Woodcroft_v2: MAGs from raw bin filtering on the same assemblies used to generate the MAGs reported in <a href="https://doi.org/10.1038/s41586-018-0338-1">Woodcroft & Singleton et al. (2018)</a>.</li> <li>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.</li> <li>JGI: Expanded set of genomes from the Joint Genome Institute's metagenome annotation pipeline.</li> </ul> <p> </p> <p>FILES:</p> <ul> <li><strong>Emerge_MAGs_v2.tar.gz</strong> - Archive containing the MAG files (.fna).</li> <li><strong>metadata_MAGs_v2_EMERGE.tsv</strong> - 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. </li> </ul> <p> </p> <p>FUNDING:</p> <p>This research is a contribution of the EMERGE Biology Integration Institute (<a href="https://emerge-bii.github.io">https://emerge-bii.github.io/</a>), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.</p> <p>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.</p> <p>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.</p> <p>Data collected at the Joint Genome Institute was generated under the following awards:</p> <ul> <li>The majority of sequencing at JGI was supported by BER Support Science Proposal 503530 (DOI: <a href="https://doi.org/10.46936/10.25585/60001148">10.46936/10.25585/60001148</a>), conducted by the U.S. Department of Energy Joint Genome Institute (<a href="https://ror.org/04xm1d337">https://ror.org/04xm1d337</a>), 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.</li> <li>Sequencing of SIP samples was performed under the Facilities Integrating Collaborations for User Science (FICUS) initiative (proposal 503547; award DOI: <a href="https://doi.org/10.46936/fics.proj.2017.49950/60006215">10.46936/fics.proj.2017.49950/60006215</a>) and used resources at the DOE Joint Genome Institute (<a href="https://ror.org/04xm1d337">https://ror.org/04xm1d337</a>) and the Environmental Molecular Sciences Laboratory (<a href="https://ror.org/04rc0xn13">https://ror.org/04rc0xn13</a>), 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).</li> </ul>
Autochamber CH4 Fluxes at Stordalen Mire, 2014 (from LGR)
<p>METHODS:</p> <p>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 m<sup>2</sup> (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.</p> <p>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<sup>-1</sup>. 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).</p> <p><em>References:</em></p> <p>Bäckstrand, K., Crill, P. M., Mastepanov, M., Christensen, T. R. & Bastviken, D. Total hydrocarbon flux dynamics at a subarctic mire in northern Sweden. <em>Journal of Geophysical Research</em> <strong>113</strong>, (2008).</p> <p>Bubier, J. L., Crill, P. M., Mosedale, A., Frolking, S. & Linder, E. Peatland responses to varying interannual moisture conditions as measured by automatic CO<sub>2</sub> chambers. <em>Global Biogeochemical Cycles</em> <strong>17</strong>, (2003).</p> <div> <div>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. <em>Global Biogeochemical Cycles</em>, 36, e2021GB007113, doi:10.1029/2021GB007113.</div> </div> <p>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, <em>Nature</em>, 514:478-481, doi:10.1038/nature13798.</p> <p> </p> <p>FUNDING:</p> <p>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br>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.<br>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.<br>These autochamber measurements were also supported by a grant from the US National Science Foundation MacroSystems program (NSF EF 1241037, PI Varner).</p>
Code and data for Mire microclimate: groundwater buffers temperature in waterlogged versus dry soils.
<p>Version of record of the manuscript's code and data, as accepted by the International Journal of Climatology.</p>
Metagenome-assembled genomes from Stordalen Mire, Sweden (2019) (MAGs from long-read, short-read, & hybrid assemblies)
<p>METHODS:</p> <p>Soil samples (6 total) were collected at the Stordalen Mire site in 2019 from two depths (1-5 & 20-24 cm below ground) across three habitats (Palsa, Bog, and Fen). DNA was extracted based on the protocol described by <a href="http://dx.doi.org/10.17504/protocols.io.yxmvm244bg3p/v1">Li et al. (2024)</a>. For short reads, libraries were prepared at the Joint Genome Institute (JGI) with the KAPA Hyperprep kit, and sequenced with Illumina NovaSeq 6000. For long reads, libraries were prepared with the SMRTbell Express Template Prep Kit 2.0 (PacBio), then sequenced using PacBio Sequel IIe at JGI. PacBio data was processed at JGI to form filtered CCS (Circular Consensus Sequencing) reads. </p> <p>Assemblies were generated with short-only, long-only, and hybrid read sources: <strong>Short-only</strong> was assembled with <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411777/">metaSPAdes</a> (v3.15.4) using <a href="https://zenodo.org/records/10806928">Aviary</a> (v0.5.3) with default parameters. <strong>Long-only</strong> was assembled with <a href="https://www.nature.com/articles/s41592-020-00971-x">metaFlye</a> (v2.9-b1768) using <a href="https://zenodo.org/records/10806928">Aviary</a> (v0.5.3) with default parameters. <strong>Hybrid</strong> assembly was performed using <a href="https://zenodo.org/records/10806928">Aviary</a> v0.5.3 with default parameters. This involved a step-down procedure with long-read assembly through <a href="https://www.nature.com/articles/s41592-020-00971-x">metaFlye</a> (v2.9-b1768), followed by short-read polishing by <a href="https://genome.cshlp.org/content/27/5/737">Racon</a> (v1.4.3), <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0112963">Pilon</a> (v1.24) and then Racon again. Next, reads that didn't map to high-quality metaFlye contigs were hybrid assembled with <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411777/">SPAdes (--meta option)</a> and binned out with <a href="https://peerj.com/articles/7359/">MetaBAT2</a> (v2.1.5). For each bin, the reads within the bin were hybrid assembled using <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005595">Unicycler</a> (v0.4.8). The high-coverage metaFlye contigs and Unicycler contigs were then combined to form the assembly fasta file. Genome recovery was performed using <a href="https://zenodo.org/records/10806928">Aviary</a> v0.5.3 with samples chosen for differential abundance binning by <a href="https://zenodo.org/records/10939393">Bin Chicken</a> (v0.4.2) using <a href="https://zenodo.org/records/7130825">SingleM metapackage S3.0.5</a>. This involved initial read mapping through <a href="https://zenodo.org/records/10531254">CoverM</a> (v0.6.1) using <a href="https://academic.oup.com/bioinformatics/article/34/18/3094/4994778">minimap2</a> (v2.18) and binning by <a href="https://peerj.com/articles/1165/">MetaBAT</a>, <a href="https://peerj.com/articles/7359/">MetaBAT2</a> (v2.1.5), <a href="https://www.nature.com/articles/s41587-020-00777-4">VAMB</a> (v3.0.2), <a href="http://doi.org/10.1038/s41467-022-29843-y">SemiBin</a> (v1.3.1), <a href="https://zenodo.org/records/10460259">Rosella</a> (v0.4.2), <a href="https://www.nature.com/articles/nmeth.3103">CONCOCT</a> (v1.1.0) and <a href="https://academic.oup.com/bioinformatics/article/32/4/605/1744462">MaxBin2</a> (v2.2.7). Genomes were analyzed using <a href="https://www.nature.com/articles/s41592-023-01940-w">CheckM2</a> (v1.0.2) and clustered at 95% ANI using <a href="https://zenodo.org/records/10526086">Galah</a> (v0.4.0).</p> <p> </p> <p>FILES:</p> <ul> <li><strong>EMERGE_MAGs_2019_long-short-hybrid.tar.gz</strong> - Archive containing the MAG files (.fna).</li> <li><strong>metadata_MAGs_2019_EMERGE.tsv</strong> - Table containing source sample names and accessions, GTDB classifications, CheckM2 quality information, NCBI GenomeBatch- and MIMAG(6.0)-formatted attributes, and other metadata for the MAGs.</li> </ul> <p> </p> <p>FUNDING:</p> <p>This research is a contribution of the EMERGE Biology Integration Institute (<a href="https://emerge-bii.github.io/">https://emerge-bii.github.io/</a>), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.</p> <p>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.</p> <p>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.</p> <p>Data from the Joint Genome Institute (JGI) was collected under BER Support Science Proposal 503530 (DOI: <a href="https://doi.org/10.46936/10.25585/60001148">10.46936/10.25585/60001148</a>), conducted by the U.S. Department of Energy Joint Genome Institute (<a href="https://ror.org/04xm1d337">https://ror.org/04xm1d337</a>), 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.</p>
A grid-based spatial database of current and potential mires in Estonia (EstMire)
<p>The EstMire dataset <span>includes </span><span>11,394,461 points (</span>center points of a <span>25</span><span>×</span><span>25 m regular grid</span><span>)</span> <span>covering</span> the<span> <span>known (as of 2022) and potential mires in Estonia. It was compiled and modified from multiple data sources for running a spatial simulation model, SooSim. The database includes the areas mapped by the Estonian Fund for Nature (1997–2021), wetland polygons from Estonian Topographic Database (2023), and the completed mire restoration projects carried out by the State Forest Management Centre </span></span>(2013<span>–2022</span>). Added to these sources are the<span> remaining Histosols areas from Estonian soil map, which were screened for being either so far unmapped mires or potential areas (mostly drained forests) that could develop into mires once restored. </span>Natural open- or semi-open (wooded) mires, peatland forests and areas with the recovery potential was separated by assessing tree canopy height and density based on the Lidar data provided by Estonian Land Board, and by combining this with land use data to remove regenerating clear-cuts or otherwise human modified areas. Each current or potential mire point includes its coordinates and 10 variables describing its woody cover and restoration potential, mire site type, the surrounding ditch length, and (if recently subjected to ditch renovation or restoration) the year of those interventions. The dataset consists of two tables, the current mire points (“Estmire_current.csv”) and other peatland points (“Estmire_potential.csv”).</p>
Spatial predictions of the morpho-ecological state of Finnish palsa mires
<p>Spatial predictions of the probability of a good morpho-ecological state are provided for Finnish palsa mires as a TIFF file with a 10 m resolution, using the EUREF FIN TM35FIN coordinate system.</p> <p>These predictions were produced through spatial modeling that combined classified point data on the state of Finnish palsa mires (Ruuhijärvi et al., 2022) with high-resolution (10 m) environmental datasets. The predictions were computed for the extent of palsa mires (Tammilehto et al., 2024). Modelling was conducted in mgcv package (version 1.9.0; Wood, 2011) in R (version 4.3.2; R Core Team 2023). The predictions were developed during the preparation of the manuscript: <em>"The morpho-ecological state of palsa mires in sub-arctic Fennoscandia: insights from high-resolution spatial modelling"</em> (Leppiniemi et al., 2024, in-review).</p> <p> </p> <p>References:</p> <p>Leppiniemi, O., Karjalainen, O., Aalto, J., Yletyinen., E., Luoto, M., & Hjort, J. 2024. The morpho-ecological state of palsa mires in sub-arctic Fennoscandia: insights from high-resolution spatial modelling. (In-review).</p> <p>R Core Team (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (accessed 14 October 2024).</p> <p>Ruuhijärvi, R., Salminen, P., & Tuominen, S., 2022. Distribution range, morphological types, and state of palsa mires in Finland in the 2010s. <em>Suo</em> 73, 1–32. (In Finnish with English summary).</p> <p>Tammilehto, A., Härmä, P., Kallio, M., Törmä, M., Saikkonen, A., Tuominen, S., Impiö, M., Heikkinen, M., Kervinen, M., Jussila, T., Böttcher, K., Pääkkö, E., Kokko, A., Mäkelä, K., & Anttila, S., 2024. Ylä-Lapin luonnon kaukokartoitus – Projektin loppuraportti osa 1 – Aineistot ja menetelmät. Vantaa. (In Finnish).</p> <p>Wood, S.N., 2011. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Series B Stat. Methodol. 73, 3–36. https://doi.org/10.1111/J.1467-9868.2010.00749.X</p> <p> </p>
Virus operational taxonomic units (vOTUs) from Stordalen Mire, Sweden
<p>This file is a collection of 5,051 virus operational taxonomy units (vOTUs) that were identified from samples collected from Stordalen Mire, a long-term ecological research site in Abisko, Sweden. Peat soil was sampled from the active layer across 3 different thaw stages (palsa, bog, and fen) between 2010-2017. From 379 bulk metagenomes, virus sequences were identified <em>in silico</em> using VirSorter1 (with additional stringent filtering criteria). Virus sequences above 5kb in length were then clustered into vOTUs (using a threshold of 95% identity and 80% coverage). This vOTU dataset is the basis of a research paper, entitled 'Virus ecology and 7-year temporal dynamics across a permafrost thaw gradient,' (Sun and Pratama, <em>et al.</em>, <em>in prep.</em>), which provides insights into permafrost viruses and their role in terrestrial carbon cycling.</p> <p> </p> <p>FUNDING:</p> <p>This research is a contribution of the EMERGE Biology Integration Institute (<a href="https://emerge-bii.github.io">https://emerge-bii.github.io/</a>), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br> 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.<br> 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.<br> A portion of this research was performed under the Facilities Integrating Collaborations for User Science (FICUS) program (proposal: 10.46936/10.25585/60001148) and used resources at the DOE Joint Genome Institute (<a href="https://www.google.com/url?q=https://ror.org/04xm1d337&sa=D&source=docs&ust=1674859614742521&usg=AOvVaw2XgXYw9eI4JIXRMKn3S9Se">https://ror.org/04xm1d337</a>) and the Environmental Molecular Sciences Laboratory (<a href="https://www.google.com/url?q=https://ror.org/04rc0xn13&sa=D&source=docs&ust=1674859614742655&usg=AOvVaw3UXdoHIFmVjc-mXUhDXYQt">https://ror.org/04rc0xn13</a>), which are DOE Office of Science User Facilities operated under Contract Nos. DE-AC02-05CH11231 (JGI) and DE-AC05-76RL01830 (EMSL).</p>
Manual active layer and and water table depth measurements from the autochamber sites at Stordalen Mire, northern Sweden (2003-2017)
<p>Files:</p> <ul> <li><strong>Active_Layer_Water_Table_03-17.xlsx</strong> - Data file, with main data in the "DATA" tab.</li> <li><strong>IsoGenieSite_AL_WTD_MapsVisualNotes_200310.pdf</strong> - Visual notes on the measurement locations.</li> </ul> <p>The following site labels (with chamber numbers in parentheses) correspond to the main autochamber sites:</p> <ul> <li>Dry (1,3,5) = Palsa Autochamber Site</li> <li>Mesic (2,4,6) = Sphagnum Autochamber Site</li> <li>Wet (7,8) = Eriophorum Autochamber Site</li> </ul> <p>Water table depth (W D) was measured in wells.</p> <p>Active layer depth (A L) was measured by inserting a metal rod into the surface. The original instruction page is included in page 3 of the pdf.</p> <p>All depths are in centimeters (cm) below peat surface (i.e. peat or <em>Sphagnum</em> spp. vegetation surface = 0), with negative values indicating depth below the surface and positive values (for water table) indicating height of standing water above the surface. Blank data in the Palsa or water table column means no water table observed.</p> <p>Staff gauge was added July 2006 at the edge of a small pond in the fen visible from the shack, with measurements reported in meters. All other measures are in cm.</p> <p> </p> <p>FUNDING:</p> <p>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.</p> <p>This research is a contribution of the EMERGE Biology Integration Institute, funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070. The IsoGenie Project (which funded much of the work at these sites during the measurement period) was 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.</p>
Redox potential and related ancillary measurements from Lakkasuo raised mire in 2014-2016
<p>The dataset contains redox potential and related ancillary measurements from the Lakkasuo raised mire complex from 2014 to 2016. </p>
Quantifying wetness variability in aapa mires with Sentinel-2: towards improved monitoring of an EU priority habitat [Dataset]
<p>This repository contains data used in Jussila et al. 2023 paper "Quantifying wetness variability in aapa mires with Sentinel-2: towards improved monitoring of an EU priority habitat" (submitted). The files uploaded in the repository include (i) spatial polygon data of flark mires located in Finnish aapa mire occurrence zone, (ii) polygon subset of the flark mires observed in the study, focused on mires belonging to Natura 2000 network (ii) monthly information for April-September period in 2017-2020 of climatic water balance and Sentinel-2 -derived wetness metrics, as average values per mire, and (iii) training point data used to train the decision tree model which was used in the study to detect wet flark surfaces in the studied aapa mires. Retrieving the metrics from Sentinel-2 satellite imagery for the analysis of variability in wetness was executed with Sentinelhub Batch statistical API, and the process is documented in project GitHub repository: <a href="https://github.com/sykefi/feo-aapa">https://github.com/sykefi/feo-aapa</a>. </p> <p>Additional information of data is provided in the README file.</p>
Exposure of boreal aapa mires to climate change
<p>This repository contains four zipped data files which contain (i) the spatial distribution of aapa mire complexes (‘aapa mires’) and their wettest flark-dominated parts (‘wet aapa mires’) situated in the aapa mire and palsa mire zones of Finland, as selected for the study by Heikkinen et al. (in review), (ii) values for the six bioclimatic variables (growing degree days, mean January and July temperature, annual precipitation, and May and July water balance) averaged for the years 1981–2010, and developed for the studied aapa mires and wet aapa mires using a 50 x 50 m lattice system, and (iii) values for the same six bioclimatic variables developed for future climates and the two types of study mires, based on the global climate models for 2040–2069 and two Representative Concentration Pathways (RCP4.5 and RCP8.5), and (iv) values of climate velocity metrics calculated for the six bioclimatic variables and the two types of study mires. These data provide the essential data employed in conducting the analysis in the following work:</p> <p>Risto K. Heikkinen<sup>1</sup>, Kaisu Aapala<sup>1</sup>, Niko Leikola<sup>1</sup> and Juha Aalto<sup>2</sup>: Exposure of boreal aapa mires to climate change, in review.</p> <p><sup>1</sup> Biodiversity Centre, Finnish Environment Institute, Latokartanonkaari 11, FI-00790 Helsinki, Finland</p> <p><sup>2 </sup>Finnish Meteorological Institute, Weather and climate change impact research, Helsinki, Finland</p> <p>The data files are embedded in four compressed zip files (one of them including a geodatabase folder with files) which include several ArcGIS compatible tiff-raster or shape files. The names and contents of the four zipped files are as follows: (1) mires.zip – includes shape files describing the location and spatial configuration of the aapa mires (‘Aapa_mires.shp’) and the wet aapa mires (‘Wet_aapa_mires.shp’) included in the study, and the borders of different mire zones in Finland (‘Mire_zones.shp’); (2) climate_data_aapa_mires.zip – includes 18 tiff raster files showing the values of the six bioclimatic variables in the studied aapa mires within the 50 x 50 m resolution grid. The data in this zipped file include climate data averaged for the years 1981 – 2010 and for the future time slice of 2040–2069 and two Representative Concentration Pathways (RCP4.5 and RCP8.5); (3) climate_data_wet_aapa_mires.zip – includes 18 tiff raster files showing the values of the six bioclimatic variables in the studied wet aapa mires within the 50 x 50 m resolution grid. Similarly as in (2), the data in this zipped file include climate data averaged for the years 1981 – 2010 and for the future time slice of 2040–2069 and two Representative Concentration Pathways (RCP4.5 and RCP8.5); (4) velocity_data_for_mires.zip – includes zipped geodatabase folder velocity_open_mires.gdb which, in turn, includes spatial ArcGIS surfaces for the climate change velocity metric calculated for all the six bioclimatic variables, and the two types of mires and the two RCPs.</p> <p>In the zipped files (2) and (3), first part of the names of the included files refer to one of the six bioclimatic variables as follows: GDD5 – growing degree days, PREC – annual precipitation, TEMP_Jan – mean January temperature, TEMP_July – mean July temperature, WAB_May – May water balance, WAB_July – July water balance; and the remaining part of the name indicates the time period, type of the RCP and that of the mire. </p> <p>It should be noted that these data are embargoed until the end of the SUMI project for which they were developed, i.e. 1.1.2023. The coordinate system for the data files is: ETRS-TM35FIN (EPSG: 3067) (or YKJ Finland/Finnish Uniform Coordinate System (EPSG: 2393)).</p> <p>Summarization of the key settings of the study is provided below. A detailed treatment is included in the manuscript Heikkinen et al. (in review). Once the manuscript is accepted for publication an updated link will be provided.</p> <p><strong>Study system:</strong> Aapa mires are waterlogged, peat-accumulating EU Habitats Directive priority habitats whose ecological conditions and biodiversity values may be jeopardized by climate change. Aapa mires depend on the surface water flows from the surroundings which makes them sensitive to hydrological alterations and falling water tables caused by land use (ditching for peatland drainage) as well as climate change (Gong et al. 2012, Sallinen et al. 2019). This sensitivity of aapa mires and their biodiversity to increasing temperatures and decreasing water balance and precipitation can be of particular concern as they occur in northern hemisphere, in areas where the largest climatic changes are projected to take place (AMAP 2017, Väliranta et al. 2017. Kolari et al. 2021). In the study by Heikkinen et al. (in review), we assess the climate exposure of these habitats by developing velocity metrics for both the aapa mire complexes (‘aapa mires’) and their wettest flark-dominated parts (‘wet aapa mires’) in Finland.</p> <p><strong>Aapa mire data: </strong>Occurrences of aapa mires were identified from the CORINE CLC2018 land cover data which is available in Finland as a 20 x 20 m resolution raster data, by focusing on the CORINE category 4121 (‘Peatbogs’) which includes various open mires occurring in aapa mire and palsa mire zones, as well as in raised bogs zones. We excluded open mires occurring in the raised bogs zone but included CORINE Peatbog occurrences both from the aapa mire and palsa mire zones. This opted for this decision because open mires in aapa and palsa mire zones share several matching ecological features, and because palsa mires may provide suitable habitats for aapa mire species under warming climate.</p> <p>The adjacent peatbog 20-m pixels in the aapa and palsa mire zones were merged and converted into contiguous peatland polygons. From these, polygons smaller than 10 ha in size were excluded because typically they show only limited number of ecological elements central to the representative aapa mires. These selected ≥10 ha peatland polygons formed the first study mire dataset, aapa mire complexes, or ‘aapa mires’ in short (i.e., the whole aapa mire ecosystem containing all embedded mire habitats therein). The second study mire dataset was constrained to include only the wettest parts of aapa mire complexes characterized by flarks, i.e., open water pools, referred here simply as ‘wet aapa mires’. These wet aapa mire occurrences are typically smaller than the whole aapa mire complexes and occur more sparsely in the landscape. Thus, the climatic exposure of wet aapa mires can be expected to be greater than that of aapa mire complexes. This will very likely cause elevated climate change adaptation challenges for habitat specialist species that require open water or permanently wet environments. The spatial data for the wet aapa mires were determined with the help of the topographic database developed by the National Land Survey of Finland (NLS), and the land cover class ‘Swamps classified as difficult, dangerous and impossible to cross’ therein.</p> <p><strong>Climate data: </strong>In the first phase, monthly average air temperature data for 1981–2010 were constructed at the 50 x 50 m spatial resolution across Finland, as described in Aalto et al. (2017) and Heikkinen et al. (2020, 2021). This was done by modelling the weather station data from 313 Fennoscandian stations together with variables of geographical location, local topography and water cover. Monthly precipitation data were developed by fitting kriging interpolation method to the data on 343 rain gauges, and the data on geographical location, topography and proximity to the sea. Based on the monthly temperature and precipitation data, six bioclimatic variables describing key ecological winter- and summer-time conditions for aapa mire ecosystems were calculated (cf. Parviainen and Luoto 2007, Ruuhijärvi 1988, Rydin and Jeglum 2006): (1) annual temperature sum above the base temperature of 5 °C (growing degree days, GDD5), (2) mean January temperature, (3) mean July temperature, (4) monthly climatic water balance calculated for May and (5) for July, and (6) annual precipitation sum. The two climatic water balance variables were calculated as the difference between the May - or July - total precipitation sum and the potential evapotranspiration (PET) in the corresponding month following Skov and Svenning (2004).</p> <p>In the second step, the data based on an ensemble of 23 global climate models from the Coupled Model Intercomparison Project (CMIP5) archives (Taylor et al. 2012) were employed to develop future climate surfaces averaged for the years 2040–2069 and the two Representative Concentration Pathways (RCP4.5 and RCP8.5). The monthly air temperature and precipitation data in these climate surfaces were interpolated to match the 50 × 50 m grid, then the change predicted by the GCMs was added to the 1981–2010 climate data, and finally, the values for the six bioclimatic variables were recalculated for the 50-m resolution grid across the whole Finland.</p> <p>In the third step, all the developed climate surface datasets were intersected by the spatial datasets of the two differently delimited aapa mire networks, i.e. ‘aapa mires’ and ‘wet aapa mires’. This allowed calculation of the climate change velocity metrics separately for the two types of aapa mires, namely, for both mire datasets by measuring the distance between climatically similar 50-m grid cells in the present and future climates by considering only locations with either (i) aapa mires, or (ii) wet aapa mires. Thus, matrix areas providing unsuitable habitat for aapa mire biodiversity were excluded and for both types of mires the distance from the present-day mire cell was linked to the nearest corresponding mire cell with similar future climatic conditions.</p> <p>The climate data for the years 1981 – 2010 and the future time slice of 2040–2069 and the two Representative Concentration Pathways (RCP4.5 and RCP8.5), clipped to the networks of the two types of aapa mires for all the six bioclimatic variables are included in the following two zipped files: ‘climate_data_aapa_mires.zip’ and ‘climate_data_wet_aapa_mires.zip’.</p> <p><strong>Climate change velocity metrics: </strong>The climate velocities for the six bioclimatic variables, developed separately for the two types of aapa mires and the two RCPs, were calculated with climate-analog method (see Brito-Morales et al. 2018). For these calculations, both the present-day and future climate data from the two RCP scenarios were converted from continuous values into categorical climate surfaces following Hamann et al. (2015). During these conversion processes, following categories and within-class ranges were used: GDD5, within-class range 50 °C; January and July temperatures, within-class range 0.5 °C; water balance of May and July, within-class range 2.5 mm; and annual precipitation, within-class range 25 mm.</p> <p>In the conversion process, the climate surfaces in each of the 50-m grid cells were reclassified into one of the 29 GDD5, 27 January temperature, 22 July temperature, 21 May water balance, 22 July water balance, and 19 annual precipitation categories. Using the reclassified climate surfaces, the minimum distances between mire grid cells with similar present-day and future climates for the six variables were determined with the Euclidean distance function in ArcGIS. In the final step of calculating the velocity metrics, the mire-to-mire distances were divided by the number of years between the two points in time (see Brito-Morales et al., 2018; Heikkinen et al., 2020).</p> <p>The derived velocity metrics for the six bioclimatic variables yielded six individual estimates of climate exposure for the two types of study mires, illustrating the magnitude of climate displacement that the local mire species communities are projected to experience (Hamann et al. 2015, Brito-Morales et al., 2018). In our study, for each contiguous aapa mire and wet aapa mire, the mean velocity value for the climate variables were calculated as the average of the 50-m grid cells included in it.</p> <p>The data on the 50-m resolution velocities for the six bioclimatic variables and the two types of aapa mires and the two RCPs are included in the zip file ‘velocity_data_for_mires.zip’.</p> <p><strong>References</strong></p> <p>Aalto, J., Riihimäki, H., Meineri, E., Hylander, K., Luoto, M. (2017) Revealing topoclimatic heterogeneity using meteorological station data. International Journal of Climatology 37, 544-556.</p> <p>AMAP (2017) Snow, Water, Ice and Permafrost in the Arctic (SWIPA) 2017. Arctic Monitoring and Assessment Programme (AMAP), Oslo, Norway.</p> <p>Brito-Morales, I., García Molinos, J., Schoeman, D.S., Burrows, M.T., Poloczanska, E.S., Brown, C.J., Ferrier, S., Harwood, T.D., Klein, C.J., McDonald-Madden, E., Moore, P.J., Pandolfi, J.M., Watson, J.E.M., Wenger, A.S., Richardson, A.J. (2018) Climate Velocity Can Inform Conservation in a Warming World. Trends in Ecology & Evolution 33, 441-457.</p> <p>Gong, J., Wang, K., Kellomäki, S., Zhang, C., Martikainen, P.J., Shurpali, N. (2012) Modeling water table changes in boreal peatlands of Finland under changing climate conditions. Ecological Modelling 244, 65-78.</p> <p>Hamann, A., Roberts, D.R., Barber, Q.E., Carroll, C., Nielsen, S.E. (2015) Velocity of climate change algorithms for guiding conservation and management. Global Change Biology 21, 997-1004.</p> <p>Heikkinen, R.K., Kartano, L., Leikola, N., Aalto, J., Aapala, K., Kuusela, S., Virkkala, R. (2021) High-latitude EU Habitats Directive species at risk due to climate change and land use. Global Ecology and Conservation 28, e01664.</p> <p>Heikkinen, R.K., Leikola, N., Aalto, J., Aapala, K., Kuusela, S., Luoto, M., Virkkala, R. (2020) Fine-grained climate velocities reveal vulnerability of protected areas to climate change. Scientific Reports 10.</p> <p>Kolari, T.H.M., Sallinen, A., Wolff, F., Kumpula, T., Tolonen, K., Tahvanainen, T. (2021) Ongoing Fen–Bog Transition in a Boreal Aapa Mire Inferred from Repeated Field Sampling, Aerial Images, and Landsat Data. Ecosystems.</p> <p>Parviainen, M., Luoto, M. (2007) Climate envelopes of mire complex types in fennoscandia. Geografiska Annaler: Series A, Physical Geography 89, 137-151.</p> <p>Ruuhijärvi, R., (1988) Mire vegetation. Atlas of Finland 141-143. Biogeography, nature conservation. . National Board of Survey and Geographical Society of Finland, Helsinki, pp. 2-4.</p> <p>Rydin, H., Jeglum, J. (2006) The biology of peatlands. Oxford University Press, Oxford.</p> <p>Sallinen, A., Tuominen, S., Kumpula, T., Tahvanainen, T. (2019) Undrained peatland areas disturbed by surrounding drainage: a large scale GIS analysis in Finland with a special focus on aapa mires. Mires and Peat 24, 1-22.</p> <p>Skov, F., Svenning, J.-C. (2004) Potential impact of climatic change on the distribution of forest herbs in Europe. Ecography 27, 366-380.</p> <p>Taylor, K.E., Stouffer, R.J., Meehl, G.A. (2012) An Overview of CMIP5 and the Experiment Design. Bulletin of the American meteorological Society 93, 485-498.</p> <p>Väliranta, M., Salojärvi, N., Vuorsalo, A., Juutinen, S., Korhola, A., Luoto, M., Tuittila, E.-S. (2017) Holocene fen–bog transitions, current status in Finland and future perspectives. The Holocene 27, 752-764.</p> <p> </p>
Stordalen Mire July 2016 Metatranscriptome Data from McGivern et al.
<p>Tabs</p> <p>MetaT = information for the 27 metatranscriptomes</p> <p>metaT_genes = gene level mapping data</p> <p>metaT_MAGs = MAG level mapping data</p> <p> </p> <p>FUNDING:<br> 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.<br> 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.<br> 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.<br> A portion of this research was performed under the Facilities Integrating Collaborations for User Science (FICUS) program (proposal: 10.46936/fics.proj.2017.49950/60006215 and 10.46936/10.25585/60001148) and used resources at the DOE Joint Genome Institute (<a href="https://www.google.com/url?q=https://ror.org/04xm1d337&sa=D&source=docs&ust=1674859614742521&usg=AOvVaw2XgXYw9eI4JIXRMKn3S9Se">https://ror.org/04xm1d337</a>) and the Environmental Molecular Sciences Laboratory (<a href="https://www.google.com/url?q=https://ror.org/04rc0xn13&sa=D&source=docs&ust=1674859614742655&usg=AOvVaw3UXdoHIFmVjc-mXUhDXYQt">https://ror.org/04rc0xn13</a>), which are DOE Office of Science User Facilities operated under Contract Nos. DE-AC02-05CH11231 (JGI) and DE-AC05-76RL01830 (EMSL).</p>
Data from: Bryophyte community composition and diversity are indicators of hydrochemical and ecological gradients in temperate kettle hole mires in Ohio, USA
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The carbon balance of a rewetted minerogenic peatland does not immediately resemble that of natural mires in boreal Sweden
Open the record for dataset details and reuse information.
Variations in ecosystem-scale methane fluxes across a boreal mire complex assessed by a network of flux towers
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FIGURE 3 in Descriptions of two new and one newly recorded enchytraeid species (Clitellata, Enchytraeidae) from the Ozegahara Mire, a heavy snowfall highmoor in Central Japan
FIGURE 3. Globulidrilus helgei Christensen & Dózsa-Farkas, 2012 from Ozegahara-mire, Japan (NSMT-An 474–477), chaetae figures from fixed specimens, other figures from living specimens. A. Anterior body region (12 segments) of a mature specimen, from whole mount, body interior, dorsal view. B. Coelomocytes. C. Spermatheca. D. Ventral chaetae. E. Lateral chaetae.
FIGURE 2 in Descriptions of two new and one newly recorded enchytraeid species (Clitellata, Enchytraeidae) from the Ozegahara Mire, a heavy snowfall highmoor in Central Japan
FIGURE 2. Chamaedrilus ozensis sp. nov. from Ozegahara-mire, Japan (NSMT-An 468–473), chaetae figures from fixed specimens, other figures from living specimens. A. Anterior body region (7 segments) of a mature specimen, from whole mount, body interior, dorsal view, coelomocytes and vessel systems omitted. B. Clitellum dosally, with granulocytes (dotted) and hyalocytes (interspaces). C. Brain. D. Nephridia. E. Spermatheca. F. Sperm funnel. G. Coelomocytes. H. Ventral chaetae. I. Lateral chaetae.
FIGURE 1 in Descriptions of two new and one newly recorded enchytraeid species (Clitellata, Enchytraeidae) from the Ozegahara Mire, a heavy snowfall highmoor in Central Japan
FIGURE 1. Mesenchytraeus nivalis sp. nov. from Ozegahara-mire, Japan (NSMT-An 463), chaetae figures from fixed specimens, other figures from living specimens. A. Anterior body region (14 segments) of a mature specimen, body interior, dorsal view, coelomocytes omitted. B. Brain. C. Coelomocyte. D. Spermatheca (sperm omitted). E. Sperm funnel. F. Nephridium. G. Vas deferens with a fusiform atrium lying outside the penial bulb. H. Male pores and bursal slits. I. Sperm bundles. J. Ventral chaetae. K. Lateral chaetae.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.