http://lod.bco-dmo.org/id/dataset/986580
eng; USA
utf8
dataset
Highest level of data collection, from a common set of sensors or instrumentation, usually within the same research project
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
2025-10-13
ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
Impacts of Terrestrial Organic Matter on Methanogenic Archaea in Littoral and Pelagic Sediments of the Mississippi River Headwaters from June to September 2020 (Cyanos Great Lakes Project)
2026-01-30
publication
2026-01-30
revision
Woods Hole Oceanographic Institution (WHOI BCO-DMO)
2026-02-09
publication
https://doi.org/10.26008/1912/bco-dmo.986580.1
Trinity Hamilton
University of Minnesota
principalInvestigator
Hailey Sauer
University of Minnesota
principalInvestigator
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
publisher
Cite this dataset as: Hamilton, T., Sauer, H. (2026) Impacts of Terrestrial Organic Matter on Methanogenic Archaea in Littoral and Pelagic Sediments of the Mississippi River Headwaters from June to September 2020 (Cyanos Great Lakes Project). Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2026-01-30 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.986580.1 [access date]
Impacts of Terrestrial Organic Matter on Methanogenic Archaea in Littoral and Pelagic Sediments of the Mississippi River Headwaters Dataset Description: Methods and Sampling: <p><strong>Sample Collection</strong></p>
<p>In June 2020, duplicate gravity cores were collected from five sampling locations within the Mississippi River headwaters (Fig. 1A; Fig. S1 in Sauer et al., 2024). Of these sites, two were pelagic, two were littoral, and one was riverine. The surrounding watershed lies within Itasca State Park (Minnesota, USA) and is dominated by mature mixed deciduous–coniferous forest. We performed loss-on-ignition (LOI) analyses on homogenized 0–15 cm subsamples from one core per site to determine sediment organic matter content (Table S1 in Sauer et al., 2024). The remaining core from each site was used to establish microcosms representing three treatment groups: high, low, and no terrestrial organic matter (tOM) addition (i.e., leaf litter).</p>
<p><strong>Microcosm Set-up</strong></p>
<p>We used the second core from each of the five sites for microcosms – following a 3x3 design with three tOM (dried leaf litter) treatments (0% tOM addition, 5% tOM addition, 15% tOM addition), each with three replicates. We extruded and homogenized the top 15cm of each core under micro-oxic conditions (using an inflatable anaerobic chamber filled with N<sub>2</sub>) and added the homogenized mixture to 125mL serum vials. We filled each vial to a depth of 2cm. For each core, we filled the first three vials for the 0% tOM addition treatment and calculated the average sediment weight of the three replicates. We used this value and the starting sediment organic fraction percentage to determine the mass of dried leaf litter needed to increase the organic fraction by 5% and 15%. For tOM additions, we collected and homogenized a dried (105°C for 12h; sieved through No. 5 mesh) mixture of leaves and needles from <em>Acer</em>, <em>Quercus</em>, and <em>Pinus </em>species. We assumed the leaf litter was 100% organic. While there was a statistically significant difference in our tOM addition percentages between treatment (t-test; p&lt;0.001) there was overlap across the percentages and sites, as a result instead of referring to these as firm percentage additions we will refer to them as “low tOM spike” (target 5% tOM) and “high tOM spike” (target 15% tOM). To maintain anoxic conditions, we flushed all the vials with N<sub>2</sub> before adding sediment and prior to sealing with a gas-tight butyl-rubber stopper and aluminum crimp.&nbsp; Finally, we covered the vials in tinfoil and stored them at 4°C in the dark for the duration of the experiment – 180 days. While we recognize that these conditions do not reflect the <em>in situ</em> conditions, our aim was to study methane production dynamics under consistent growth conditions, irrespective of the starting conditions of the initial sediment and abiotic forces.&nbsp;</p>
<p><strong>Methane Concentrations</strong></p>
<p>We collected 10mL from the headspace of each vial using a gas-tight syringe and injected the sample into Labco Exetainers that were pre-flushed with helium. Exetainers were stored inverted at 4°C until they could be further processed. We replaced the microcosm headspace after every gas pull with 11mL N<sub>2</sub> gas to maintain headspace pressure and avoid methanogenesis inhibition caused by the accumulation of CH<sub>4</sub> or CO<sub>2 </sub>(Grasset et al., 2018). We measured CH<sub>4</sub> concentrations using a GC-2014 gas chromatograph equipped with a flame ionization detector and an 80/100 Porapak N column (6ft x 1/8in x 2.1mm SS). The carrier gas was argon run at a 25mL min<sup>-1</sup> flow rate, and the calibration standards ranged from 200 to 60,000ppm. We then converted these ppm concentrations to molar concentration using the ideal gas law and Henry’s law. All production rates reported in the text were normalized per gram of C in the microcosm and reported as µmol CH<sub>4</sub> gC<sup>-1 </sup>d<sup>-1</sup>. We used the values obtained from our total organic carbon analysis to normalize production rates by gram carbon in the supplemental materials. To calculate production rates per gram organic C OUT, we assumed the litter was 80% organic matter with a C:N ratio of 20 (Hornbach et al., 2021). Due to product backorders and shipping delays during the pandemic, we did not have enough Exetainers and were unable to pull gas samples from the Mississippi River site for days 4 and 7.</p>
<p><strong>DNA Isolation, Sequencing, and Post-processing&nbsp;</strong></p>
<p>During the initial microcosm set up, we subsampled ~2g of sediment from the 0-15cm slurry and ~6g of leaf litter and stored these at -20°C until extraction. At the end of the experiment, we combined and homogenized the sediment from the three replicates for each treatment. Again, we collected a ~2g from the pooled and homogenized sediment and stored it at -20°C until extraction. We extracted triplicate DNA for each sample using ~0.25g of sediment or leaf litter using a Qiagen Dneasy PowerSoil Pro kit following manufacturer protocols including 4°C incubation steps. We performed negative controls by carrying out extractions on blanks, using only reagents with no sample. We determined the final bulk DNA concentrations using a Qubit dsDNA HS Assay kit and Qubit Fluorometer. We did not detect any DNA in our blanks (detection limit for the assay kit is 10pg/µL). We then pooled 10µL of each DNA yielding replicate and recalculated bulk DNA for the pooled sample. We sent all DNA yielding samples and blanks to the University of Minnesota Genomic Center (UMGC) for sequencing.&nbsp;</p>
<p>The UMGC prepared libraries for the samples for Illumina sequencing using a Nextera XT workflow and 2x300bp chemistry. They targeted the V3-V4 hypervariable region of the bacterial and archaeal 16S SSU rRNA gene using primers 341F (5’- CCTAYGGGRBGCASCAG-3’) and 806R (5’- GGACTACNNGGGTATCTAAT-3’) (Yu et al., 2005). The amplicon preparation performed at the UMGC have been shown to be quantitatively more accurate and qualitatively complete than existing methods (Gohl et al., 2016). We recovered a total of 352,403 raw reads from 24 samples, including blanks and leaf litter. We processed these reads using Mothur (v.1.48.0) following the MiSeq SOP (Kozich et al., 2013; Schloss et al., 2009). We aligned our reads using the SILVA database (v.138) and removed chimeras with vsearch (v2.17.1) (Edgar et al., 2011; Quast et al., 2013). Finally, we classified the sequences as operational taxonomic units (OTUs) using a 97% similarity threshold and assigned taxonomy using the SILVA database (Glassman &amp; Martiny, 2018; Stackebrandt &amp; Goebel, 1994). After processing we had 273,151 reads across the 24 samples.&nbsp;</p>
<p>All further analyses were conducted in R (v4.3.2) using the following packages: tidyverse, phyloseq, vegan, DESeq2, pheatmap, MicrobiomeStat, topicmodels, and ldatuning (Grün &amp; Hornik, 2011; Kolde, 2019; Love et al., 2020; McMurdie &amp; Holmes, 2013; Nikita, 2020; Oksanen et al., 2009; R Core Team, 2018; C. Zhang, 2022). Prior to analyzing the community composition of these sites, we filtered the data by removing any OTU that did not have 2 or more counts in at least 5% of samples. We also removed the nine OTUs which had reads in blank samples, each OTU having a single read. After filtering, the average number of reads per sample was 13,762 and the minimum and maximum read depths were 8,858 and 20,302, respectively. OTU data have a strong positive skew due to many zero counts. To diminish these effects, we used a variance stabilizing transformation (Love et al., 2020). Log-like transformations such as this bring count data to near-normal distributions, produce larger eigengap values, and lead to more consistent correlation estimates all of which influence downstream analysis (Badri et al., n.d.). To compare microbial community structure before and after the microcosm experiment, we conducted a principal component analysis (PCA) of the entire microbial community and the methanogen populations. From both PCAs we pulled the scores of the top two PCs and used those as variables for difference in composition when determining if sediment community composition influences CH<sub>4</sub>&nbsp;production. We determined methanogens based on taxonomy and compared the methanogen composition based on energy conservation strategies (i.e., those with or without cytochromes) (Buan, 2018; Ou et al., 2022; Thauer et al., 2008). Finally, we took two approaches to compare the change in populations from initial sediments to post-microcosm. First, we calculated the percent change in each OTU (after adding 10% the lowest observed pre-and-post microcosm abundance to any 0 values). We then selected the top 100 OTUs that had the greatest percent change from initial sediment to post-microcosm ).&nbsp;</p>
<p>Second, we used Latent Dirichlet Allocation (LDA) or topic modeling to examine the structural differences across microcosm treatments. LDA is a mixture model which correlates microbial communities with relevant environmental factors of interest. The advantages of using LDA over other mixed model approaches is that LDA allows fractional membership allowing samples to be composed of multiple sub-communities. The application of LDA models to microbiome datasets has been described in detail by Sankaran and Holmes 2019 (Sankaran &amp; Holmes, 2019). Briefly, we determined the relevant number of topics or sub-communities (k=34) using the FindTopicsNumber function in ldatuning using a Gibbs sampling method and CaoJuan 2009, Arun 2010 metrics, and Deveaud2014. Then we conducted the LDA model again using Gibbs sampling and the topicsmodels package. We then converted our LDA model back to a phyloseq object to further assess the differential abundance in the 34 sub-communities across select parameters (e.g., treatment addition, treatment quantity, and sample site). For this, we use the linda function in MicrobiomeStat with an alpha of 0.01, winsorization of outliers at 3%, and zero count data handling set to imputation – where in zero counts are given values with respect to sequencing depth (Zhou et al., 2022). From both approaches, we aggregated the list of 100 most changed OTUs and those with &gt;1% OUT-sub-community probabilities in the significantly different sub-communities (n=3; 46 OTUs) and evaluated how the abundances of those OTUs explained methane production rates.&nbsp;</p>
Funding provided by NSF Division of Ocean Sciences (NSF OCE) Award Number: OCE-1948058 Award URL: https://www.nsf.gov/awardsearch/show-award?AWD_ID=1948058
completed
Trinity Hamilton
University of Minnesota
St. Paul
MN
55108
USA
trinityh@umn.edu
pointOfContact
Hailey Sauer
University of Minnesota
hsauer@smm.org
pointOfContact
asNeeded
Dataset Version: 1
Unknown
accession
study
object_status
bioproject_accession
biosample_accession
sample_name
library_ID
title
library_strategy
library_source
library_selection
library_layout
platform
instrument_model
design_description
filetype
fasta_file
assembly
filename
filename2
Illumina MiSeq
Conflow IV open split interface
GC-2014 Gas Chromatograph with Flame Ionization Detector (Shimadzu)
Delta V Advantage IRMS (Thermo Fisher Scientific)
Qubit Fluorometer (Thermo Fisher Scientific)
theme
None, User defined
accession number
NCBI SRA study accession (SRP)
status
NCBI BioProject accession
NCBI BioSample accession
sample identification
No BCO-DMO term
project
seq_descrip
instrument model
sampling_method
file_name
featureType
BCO-DMO Standard Parameters
Automated DNA Sequencer
Continuous Flow Interface for Mass Spectrometers
Gas Chromatograph
Isotope-ratio Mass Spectrometer
Qubit fluorometer
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BCO-DMO Standard Instruments
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Access Constraints: none. Use Constraints: Please follow guidelines at: http://www.bco-dmo.org/terms-use Distribution liability: Under no circumstances shall BCO-DMO be liable for any direct, incidental, special, consequential, indirect, or punitive damages that result from the use of, or the inability to use, the materials in this data submission. If you are dissatisfied with any materials in this data submission your sole and exclusive remedy is to discontinue use.
Collaborative Research: Cyanobacteria, Nitrogen Cycling, and Export Production in the Laurentian Great Lakes
https://osprey.bco-dmo.org/project/891875
Collaborative Research: Cyanobacteria, Nitrogen Cycling, and Export Production in the Laurentian Great Lakes
<p><em>NSF Award Abstract:</em><br />
The Great Lakes hold about 20% of the freshwater on Earth and have been increasingly impacted by human activities in recent decades. Lake Erie suffers from large, annually recurring, toxic cyanobacterial blooms in summer, whereas Lake Superior experiences smaller, localized cyanobacterial blooms after storm events. Cyanobacterial blooms have harmful ecological, human health, and economic implications. These blooms are a global phenomenon, observed in lakes and oceans, and can lead to low oxygen conditions and the production of toxins, both of which can be harmful for ecosystems. Understanding how different types of cyanobacteria influence nutrient cycling remains a major knowledge gap. This project aims to provide a deeper understanding of the long-term state of the Great Lakes ecosystem. The research approach combines new and established methods. Project results and implications will be shared with local and regional water interests in partnership with the Pittsburgh Collaboratory for Water Research, Education, and Outreach, the Great Lakes Commission Harmful Algal Blooms Collaborative, and the Lake Erie Area Research Network. Education is a central part of this project and training opportunities target next generation of scientists, including postdoctoral, graduate, and undergraduate students. The students and postdoc will receive state-of-the-art training in the rapidly developing fields of biogeochemistry and geomicrobiology, while working with an interdisciplinary team of scientists.</p>
<p>This study will examine nitrogen cycling, phytoplankton community composition, and the nitrogen isotopic composition of chloropigments in order to evaluate cyanobacterial productivity in the modern Laurentian Great Lakes as well as the historical record of cyanobacterial blooms over the past several hundred years. The nitrogen isotope composition of chloropigments is expected to provide a powerful new proxy for understanding primary productivity and the relative importance of cyanobacteria to export production and nitrogen cycling. This proxy would be valuable not only for management of modern systems but has important implications for increasing our understanding of the role of cyanobacteria throughout Earth history. This project would test this molecular isotopic proxy in contemporary aquatic ecosystems to assess its efficacy for: (1) determining the relative contributions of cyanobacteria vs eukaryotic algae (e.g., diatoms) to primary production; (2) evaluating export production of cyanobacterial productivity (including blooms); and (3) constraining historical cyanobacteria productivity in the sedimentary record. Comparison of a system characterized by eutrophication and seasonal cyanobacterial blooms (Lake Erie) with one characterized by picocyanobacteria productivity, but the near-absence of large-scale cyanobacterial blooms (Lake Superior), will provide information about the range of impacts that cyanobacteria can have on carbon and nitrogen cycling. Further information regarding nitrogen cycling will be derived from analysis of solid and dissolved nitrogen species throughout the annual cycle, as well as seasonal studies of sediment processes to measure associated sediment nitrogen removal rates through different processes.</p>
<p>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.</p>
Cyanos Great Lakes
largerWorkCitation
project
eng; USA
oceans
-95.12355
-95.12355
47.239854
47.239854
2020-06-01
2020-09-30
Lake Superior and Lake Erie
0
BCO-DMO catalogue of parameters from Impacts of Terrestrial Organic Matter on Methanogenic Archaea in Littoral and Pelagic Sediments of the Mississippi River Headwaters from June to September 2020 (Cyanos Great Lakes Project)
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
http://lod.bco-dmo.org/id/dataset-parameter/993625.rdf
Name: accession
Units: unitless
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http://lod.bco-dmo.org/id/dataset-parameter/993626.rdf
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Description: <p>Links to the BioProject database, which provides a central location for related projects.</p>
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GB/NERC/BODC > British Oceanographic Data Centre, Natural Environment Research Council, United Kingdom
Biological and Chemical Oceanography Data Management Office (BCO-DMO)
Unavailable
508-289-2009
WHOI MS#36
Woods Hole
MA
02543
USA
info@bco-dmo.org
http://www.bco-dmo.org
Monday - Friday 8:00am - 5:00pm
For questions regarding this resource, please contact BCO-DMO via the email address provided.
pointOfContact
https://doi.org/10.26008/1912/bco-dmo.986580.1
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dataset
<p><strong>Sample Collection</strong></p>
<p>In June 2020, duplicate gravity cores were collected from five sampling locations within the Mississippi River headwaters (Fig. 1A; Fig. S1 in Sauer et al., 2024). Of these sites, two were pelagic, two were littoral, and one was riverine. The surrounding watershed lies within Itasca State Park (Minnesota, USA) and is dominated by mature mixed deciduous–coniferous forest. We performed loss-on-ignition (LOI) analyses on homogenized 0–15 cm subsamples from one core per site to determine sediment organic matter content (Table S1 in Sauer et al., 2024). The remaining core from each site was used to establish microcosms representing three treatment groups: high, low, and no terrestrial organic matter (tOM) addition (i.e., leaf litter).</p>
<p><strong>Microcosm Set-up</strong></p>
<p>We used the second core from each of the five sites for microcosms – following a 3x3 design with three tOM (dried leaf litter) treatments (0% tOM addition, 5% tOM addition, 15% tOM addition), each with three replicates. We extruded and homogenized the top 15cm of each core under micro-oxic conditions (using an inflatable anaerobic chamber filled with N<sub>2</sub>) and added the homogenized mixture to 125mL serum vials. We filled each vial to a depth of 2cm. For each core, we filled the first three vials for the 0% tOM addition treatment and calculated the average sediment weight of the three replicates. We used this value and the starting sediment organic fraction percentage to determine the mass of dried leaf litter needed to increase the organic fraction by 5% and 15%. For tOM additions, we collected and homogenized a dried (105°C for 12h; sieved through No. 5 mesh) mixture of leaves and needles from <em>Acer</em>, <em>Quercus</em>, and <em>Pinus </em>species. We assumed the leaf litter was 100% organic. While there was a statistically significant difference in our tOM addition percentages between treatment (t-test; p&lt;0.001) there was overlap across the percentages and sites, as a result instead of referring to these as firm percentage additions we will refer to them as “low tOM spike” (target 5% tOM) and “high tOM spike” (target 15% tOM). To maintain anoxic conditions, we flushed all the vials with N<sub>2</sub> before adding sediment and prior to sealing with a gas-tight butyl-rubber stopper and aluminum crimp.&nbsp; Finally, we covered the vials in tinfoil and stored them at 4°C in the dark for the duration of the experiment – 180 days. While we recognize that these conditions do not reflect the <em>in situ</em> conditions, our aim was to study methane production dynamics under consistent growth conditions, irrespective of the starting conditions of the initial sediment and abiotic forces.&nbsp;</p>
<p><strong>Methane Concentrations</strong></p>
<p>We collected 10mL from the headspace of each vial using a gas-tight syringe and injected the sample into Labco Exetainers that were pre-flushed with helium. Exetainers were stored inverted at 4°C until they could be further processed. We replaced the microcosm headspace after every gas pull with 11mL N<sub>2</sub> gas to maintain headspace pressure and avoid methanogenesis inhibition caused by the accumulation of CH<sub>4</sub> or CO<sub>2 </sub>(Grasset et al., 2018). We measured CH<sub>4</sub> concentrations using a GC-2014 gas chromatograph equipped with a flame ionization detector and an 80/100 Porapak N column (6ft x 1/8in x 2.1mm SS). The carrier gas was argon run at a 25mL min<sup>-1</sup> flow rate, and the calibration standards ranged from 200 to 60,000ppm. We then converted these ppm concentrations to molar concentration using the ideal gas law and Henry’s law. All production rates reported in the text were normalized per gram of C in the microcosm and reported as µmol CH<sub>4</sub> gC<sup>-1 </sup>d<sup>-1</sup>. We used the values obtained from our total organic carbon analysis to normalize production rates by gram carbon in the supplemental materials. To calculate production rates per gram organic C OUT, we assumed the litter was 80% organic matter with a C:N ratio of 20 (Hornbach et al., 2021). Due to product backorders and shipping delays during the pandemic, we did not have enough Exetainers and were unable to pull gas samples from the Mississippi River site for days 4 and 7.</p>
<p><strong>DNA Isolation, Sequencing, and Post-processing&nbsp;</strong></p>
<p>During the initial microcosm set up, we subsampled ~2g of sediment from the 0-15cm slurry and ~6g of leaf litter and stored these at -20°C until extraction. At the end of the experiment, we combined and homogenized the sediment from the three replicates for each treatment. Again, we collected a ~2g from the pooled and homogenized sediment and stored it at -20°C until extraction. We extracted triplicate DNA for each sample using ~0.25g of sediment or leaf litter using a Qiagen Dneasy PowerSoil Pro kit following manufacturer protocols including 4°C incubation steps. We performed negative controls by carrying out extractions on blanks, using only reagents with no sample. We determined the final bulk DNA concentrations using a Qubit dsDNA HS Assay kit and Qubit Fluorometer. We did not detect any DNA in our blanks (detection limit for the assay kit is 10pg/µL). We then pooled 10µL of each DNA yielding replicate and recalculated bulk DNA for the pooled sample. We sent all DNA yielding samples and blanks to the University of Minnesota Genomic Center (UMGC) for sequencing.&nbsp;</p>
<p>The UMGC prepared libraries for the samples for Illumina sequencing using a Nextera XT workflow and 2x300bp chemistry. They targeted the V3-V4 hypervariable region of the bacterial and archaeal 16S SSU rRNA gene using primers 341F (5’- CCTAYGGGRBGCASCAG-3’) and 806R (5’- GGACTACNNGGGTATCTAAT-3’) (Yu et al., 2005). The amplicon preparation performed at the UMGC have been shown to be quantitatively more accurate and qualitatively complete than existing methods (Gohl et al., 2016). We recovered a total of 352,403 raw reads from 24 samples, including blanks and leaf litter. We processed these reads using Mothur (v.1.48.0) following the MiSeq SOP (Kozich et al., 2013; Schloss et al., 2009). We aligned our reads using the SILVA database (v.138) and removed chimeras with vsearch (v2.17.1) (Edgar et al., 2011; Quast et al., 2013). Finally, we classified the sequences as operational taxonomic units (OTUs) using a 97% similarity threshold and assigned taxonomy using the SILVA database (Glassman &amp; Martiny, 2018; Stackebrandt &amp; Goebel, 1994). After processing we had 273,151 reads across the 24 samples.&nbsp;</p>
<p>All further analyses were conducted in R (v4.3.2) using the following packages: tidyverse, phyloseq, vegan, DESeq2, pheatmap, MicrobiomeStat, topicmodels, and ldatuning (Grün &amp; Hornik, 2011; Kolde, 2019; Love et al., 2020; McMurdie &amp; Holmes, 2013; Nikita, 2020; Oksanen et al., 2009; R Core Team, 2018; C. Zhang, 2022). Prior to analyzing the community composition of these sites, we filtered the data by removing any OTU that did not have 2 or more counts in at least 5% of samples. We also removed the nine OTUs which had reads in blank samples, each OTU having a single read. After filtering, the average number of reads per sample was 13,762 and the minimum and maximum read depths were 8,858 and 20,302, respectively. OTU data have a strong positive skew due to many zero counts. To diminish these effects, we used a variance stabilizing transformation (Love et al., 2020). Log-like transformations such as this bring count data to near-normal distributions, produce larger eigengap values, and lead to more consistent correlation estimates all of which influence downstream analysis (Badri et al., n.d.). To compare microbial community structure before and after the microcosm experiment, we conducted a principal component analysis (PCA) of the entire microbial community and the methanogen populations. From both PCAs we pulled the scores of the top two PCs and used those as variables for difference in composition when determining if sediment community composition influences CH<sub>4</sub>&nbsp;production. We determined methanogens based on taxonomy and compared the methanogen composition based on energy conservation strategies (i.e., those with or without cytochromes) (Buan, 2018; Ou et al., 2022; Thauer et al., 2008). Finally, we took two approaches to compare the change in populations from initial sediments to post-microcosm. First, we calculated the percent change in each OTU (after adding 10% the lowest observed pre-and-post microcosm abundance to any 0 values). We then selected the top 100 OTUs that had the greatest percent change from initial sediment to post-microcosm ).&nbsp;</p>
<p>Second, we used Latent Dirichlet Allocation (LDA) or topic modeling to examine the structural differences across microcosm treatments. LDA is a mixture model which correlates microbial communities with relevant environmental factors of interest. The advantages of using LDA over other mixed model approaches is that LDA allows fractional membership allowing samples to be composed of multiple sub-communities. The application of LDA models to microbiome datasets has been described in detail by Sankaran and Holmes 2019 (Sankaran &amp; Holmes, 2019). Briefly, we determined the relevant number of topics or sub-communities (k=34) using the FindTopicsNumber function in ldatuning using a Gibbs sampling method and CaoJuan 2009, Arun 2010 metrics, and Deveaud2014. Then we conducted the LDA model again using Gibbs sampling and the topicsmodels package. We then converted our LDA model back to a phyloseq object to further assess the differential abundance in the 34 sub-communities across select parameters (e.g., treatment addition, treatment quantity, and sample site). For this, we use the linda function in MicrobiomeStat with an alpha of 0.01, winsorization of outliers at 3%, and zero count data handling set to imputation – where in zero counts are given values with respect to sequencing depth (Zhou et al., 2022). From both approaches, we aggregated the list of 100 most changed OTUs and those with &gt;1% OUT-sub-community probabilities in the significantly different sub-communities (n=3; 46 OTUs) and evaluated how the abundances of those OTUs explained methane production rates.&nbsp;</p>
Specified by the Principal Investigator(s)
<p>After DNA was sequenced at the University of Minnesota Genomic Center for Sequencing,&nbsp;the resulting reads were processed&nbsp;using Mothur (v.1.48.0) following the MiSeq SOP (Kozich et al., 2013; Schloss et al., 2009) and aligned our reads using the SILVA database (v.138) (Quast et al., 2013).</p>
<p>We performed all subsequent analyses in R, including Latent Dirichlet Allocation (LDA), which correlates microbial communities with relevant environmental factors. A detailed description of LDA&nbsp;for microbiomes is provided in Sankaran and Holmes (2019) and also outlined in our SI methods (Sankaran &amp; Holmes, 2019).</p>
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* The primary data file of this dataset has been converted from its original format (.tsv) to csv.
* Redundant and empty file name columns have been removed from the primary data file.
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Illumina MiSeq
Illumina MiSeq
PI Supplied Instrument Name: Illumina MiSeq PI Supplied Instrument Description:The Illumina MiSeq platform was used for high-throughput sequencing of 16S rRNA gene amplicons targeting the V3–V4 hypervariable region. Libraries were prepared using a Nextera XT workflow and sequenced using 2×300 bp paired-end chemistry, generating raw reads for microbial community analysis. Instrument Name: Automated DNA Sequencer Instrument Short Name:Automated Sequencer Instrument Description: A DNA sequencer is an instrument that determines the order of deoxynucleotides in deoxyribonucleic acid sequences.
Conflow IV open split interface
Conflow IV open split interface
PI Supplied Instrument Name: Conflow IV open split interface PI Supplied Instrument Description:The Conflow IV open split interface was used to introduce combustion-derived gases into the isotope ratio mass spectrometer (IRMS) for δ¹³C and δ¹⁵N analysis. The interface regulates sample and reference gas flow to ensure stable isotopic measurements. Instrument Name: Continuous Flow Interface for Mass Spectrometers Instrument Short Name: Instrument Description: A Continuous Flow Interface connects solid and liquid sample preparation devices to instruments that measure isotopic composition. It allows the introduction of the sample and also reference and carrier gases.
Examples: Finnigan MATConFlo II, ThermoScientific ConFlo IV, and Picarro Caddy.
Note: This is NOT an analyzer
GC-2014 Gas Chromatograph with Flame Ionization Detector (Shimadzu)
GC-2014 Gas Chromatograph with Flame Ionization Detector (Shimadzu)
PI Supplied Instrument Name: GC-2014 Gas Chromatograph with Flame Ionization Detector (Shimadzu) PI Supplied Instrument Description:A Shimadzu GC-2014 gas chromatograph equipped with a flame ionization detector (FID) and an 80/100 Porapak N column (6 ft × 1/8 in × 2.1 mm stainless steel) was used to quantify methane (CH₄) concentrations in microcosm headspace samples. Methane concentrations were determined from calibration standards and converted to molar concentrations for production rate calculations. Instrument Name: Gas Chromatograph Instrument Short Name:Gas Chromatograph Instrument Description: Instrument separating gases, volatile substances, or substances dissolved in a volatile solvent by transporting an inert gas through a column packed with a sorbent to a detector for assay. (from SeaDataNet, BODC) Community Standard Description: http://vocab.nerc.ac.uk/collection/L05/current/LAB02/
Delta V Advantage IRMS (Thermo Fisher Scientific)
Delta V Advantage IRMS (Thermo Fisher Scientific)
PI Supplied Instrument Name: Delta V Advantage IRMS (Thermo Fisher Scientific) PI Supplied Instrument Description:The Delta V Advantage isotope ratio mass spectrometer (IRMS) was used to measure total organic carbon (C), total organic nitrogen (N), and stable isotopic ratios (δ¹³C, δ¹⁵N) in acidified sediment samples. The instrument determines isotopic composition by measuring relative abundances of stable isotopes in CO₂ and N₂ gases generated from sample combustion. Instrument Name: Isotope-ratio Mass Spectrometer Instrument Short Name:IR Mass Spec; IRMS Instrument Description: The Isotope-ratio Mass Spectrometer is a particular type of mass spectrometer used to measure the relative abundance of isotopes in a given sample (e.g. VG Prism II Isotope Ratio Mass-Spectrometer). Community Standard Description: http://vocab.nerc.ac.uk/collection/L05/current/LAB16/
Qubit Fluorometer (Thermo Fisher Scientific)
Qubit Fluorometer (Thermo Fisher Scientific)
PI Supplied Instrument Name: Qubit Fluorometer (Thermo Fisher Scientific) PI Supplied Instrument Description:A Qubit Fluorometer was used to quantify double-stranded DNA concentrations in extracted sediment and leaf litter samples using the Qubit dsDNA High Sensitivity assay. The instrument provides fluorescence-based measurement of DNA concentration prior to sequencing. Instrument Name: Qubit fluorometer Instrument Short Name: Instrument Description: Benchtop fluorometer. The Invitrogen Qubit Fluorometer accurately and quickly measures the concentration of DNA, RNA, or protein in a single sample. It can also be used to assess RNA integrity and quality.
Manufactured by Invitrogen, Carlsbad, CA, USA (Invitrogen is one of several brands under the Thermo Fisher Scientific corporation.)