http://lod.bco-dmo.org/id/dataset/986587
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)
Diversity and distribution of sediment bacteria across an ecological and trophic gradient from 2018 to 2019 (Cyanos Great Lakes project)
2025-10-13
publication
2025-10-13
revision
Woods Hole Oceanographic Institution (WHOI BCO-DMO)
2026-02-09
publication
https://doi.org/10.26008/1912/bco-dmo.986587.1
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) Diversity and distribution of sediment bacteria across an ecological and trophic gradient from 2018 to 2019 (Cyanos Great Lakes project). Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 1) Version Date 2025-10-13 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.986587.1 [access date]
Diversity and distribution of sediment bacteria across an ecological and trophic gradient Dataset Description: Methods and Sampling: <p><strong>Site description&nbsp;</strong></p>
<p>For this study, we selected twenty lakes within Minnesota’s Sentinel Lakes in a Changing Environment (SLICE) program. SLICE is a collaborative research initiative that provides long-term data on a representative sub-sampling of Minnesota’s lakes spanning the diverse geographic, land-use, and climatic gradients present in Minnesota (Fig. 1 in Sauer et al., 2022). The lakes span four of the seven Environmental Protection Agency/Commission for Environmental Cooperation (Level III) ecological regions. These regions are characterized by differences in underlying geology, soils, vegetation, and land use (Table S1 in Sauer et al., 2022). This is the first comprehensive sediment bacterial survey of these lakes.</p>
<p>&nbsp;</p>
<p><strong>Water Sample Collection &amp; Analysis</strong></p>
<p>At each site, we collected water profile measurements of temperature, pH, conductivity, turbidity, and dissolved oxygen using a YSI EXO2 multi-parameter sonde (YSI, Inc.). We also collected an integrated epilimnetic water sample (0–2 m) and a hypolimnetic water sample (maximum lake depth – 1 m) when thermal stratification was present. All samples were stored on ice in the field and at either 4°C or −20°C in the laboratory, depending on methodology, until processed.</p>
<p>Samples for soluble reactive phosphorus (SRP), dissolved organic carbon (DOC), and dissolved inorganic carbon (DIC) were filtered, processed, and analyzed within 36 hours of sampling using standard methods for SRP (4500-P) on a SmartChem 170 (Unity Scientific, Inc.) and for DIC/DOC (Method 5310-C) using a Torch Combustion TOC Analyzer (Teledyne Tekmar, Inc.) (American Public Health Association, 2012). Samples for total nitrogen (TN) and total phosphorus (TP) were frozen and analyzed using standard methods for TN (4500-N) and TP (4500-P). Samples for ammonia (NH₃) and nitrate (NO₃) were filtered and frozen prior to analysis following methods 4500-NH₃ and 4500-NO₃. All TP, TN, NH₃, and NO₃ samples were analyzed within six months of sampling on a SmartChem 170 (Unity Scientific, Inc.) discrete analyzer (APHA, 2012).</p>
<p>Additionally, samples for chlorophyll-a were filtered, frozen, and analyzed via fluorometry following EPA Method 445.0 (Arar et al., 1997). A complete summary of aqueous chemistry results, including sampling dates, is provided in Table S2 (Sauer et al., 2022).</p>
<p><strong>Sediment Sample Collection &amp; DNA Isolation&nbsp;</strong></p>
<p>Sediment cores were collected from July 2018 through June 2019 using a rod-driven piston corer with a 7 cm diameter polycarbonate tube (Wright, 1997). Coring locations (i.e., flat areas near the deepest basin) were determined using publicly available bathymetric maps (<a class="decorated-link" href="https://www.dnr.state.mn.us/lakefind/index.html" rel="noopener" target="_new">https://www.dnr.state.mn.us/lakefind/index.html</a>), while avoiding steep-sided “holes” where sediment focusing may be high. Following retrieval, core tops were stabilized in the field using a gelling agent (e.g., Zorbitrol), and intact cores were returned to the laboratory, where they were stored vertically at 4°C for no more than seven days prior to processing. In cases where the upper sediments were extremely flocculent, the uppermost sections (~0–30 cm) were immediately sectioned in the field to prevent mixing during transport.</p>
<p>Cores were vertically extruded in the laboratory at 1–2 cm intervals, depending on lake productivity, and subsamples from two intervals were collected for DNA analysis. Subsamples were collected from the 0–2 cm interval (hereafter referred to as <em>shallow</em>) and from either the 3–4 cm or 4–6 cm interval (hereafter referred to as <em>deep</em>). Subsamples were frozen under nitrogen for up to three months prior to DNA extraction (Table S3 in Sauer et al., 2022).</p>
<p>DNA was extracted from 0.25 g of wet sediment from each subsample using a PowerSoil DNA Isolation Kit (Qiagen, Inc.) following the manufacturer’s protocols. Negative controls were performed by carrying out extractions on blanks containing only reagents and no sample. Final bulk DNA concentrations were determined using a Qubit™ dsDNA HS Assay Kit (Molecular Probes, Eugene, OR, USA) and a Qubit™ Fluorometer (Invitrogen, Carlsbad, CA, USA). The detection limit of the Qubit™ dsDNA HS Assay Kit is 10 pg μL⁻¹. All samples that yielded detectable amounts of DNA were submitted for sequencing (Table S3 in Sauer et al., 2022). Although DNA was not detected in negative controls, these samples were submitted for sequencing; they failed quality control performed by the University of Minnesota Genomics Center (UMGC), and no sequencing data were obtained.</p>
<p><strong>Nucleic acid preparation, amplification, and sequencing</strong></p>
<p>DNA samples were submitted to the University of Minnesota Genomics Center (UMGC), where library preparation for Illumina high-throughput sequencing was performed using a Nextera XT workflow with 2 × 300 bp chemistry. This workflow utilizes transposome-based shearing, which fragments DNA and adds adapter sequences in a single step. DNA was amplified and dual-indexed with adapter sequences through PCR using primers 515F (5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGGTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGGACTACHVGGGTWTCTAAT-3′), targeting the V4 hypervariable region of the bacterial 16S SSU rRNA gene.</p>
<p>The amplicon library preparation methods developed and employed by the UMGC have been shown to be more quantitatively accurate and qualitatively complete, detecting taxonomic groups that often go undetected with existing methods (Gohl et al., 2016). Indexed samples were sequenced once using an Illumina MiSeq at the UMGC. A total of 3.29 million (3,290,170) raw reads were obtained from 40 samples.</p>
<p><strong>Temporal bounds within the dataset</strong></p>
<p>The date range associated with this dataset represents the sediment core collection dates.&nbsp;</p>
<p><strong>Data Availability in SRA</strong></p>
<p>All relevant data are reported in the results paper, Sauer et al. (2022). All 16S rRNA amplicon data are available from the Sequence Read Archive (SRA) under BioProject accession PRJNA763898.</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
message
sample_name
sample_title
organism
host
isolation_source
collection_date
geo_loc_name
lat
lon
ref_biomaterial
samp_collect_device
samp_size
country
state
lake_name
sample_depth_range_min
sample_depth_range_max
Illumina MiSeq (Illimuna)
SmartChem 170 (Unity Scientific, Inc.)
Torch Combustion TOC Analyzer (Teledyne Tekmar, Inc.)
YSI XO2 multi-parameter sonde (YSI, Inc.)
theme
None, User defined
accession number
status
sample identification
sample type
sequence
No BCO-DMO term
sample description
date
latitude
longitude
seq_descrip
instrument
sample_volume
depth core
featureType
BCO-DMO Standard Parameters
Automated DNA Sequencer
Discrete Analyzer
Total Organic Carbon Analyzer
YSI EXO multiparameter water quality sondes
instrument
BCO-DMO Standard Instruments
otherRestrictions
otherRestrictions
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
-96.339307
-90.16812
43.90347
47.98746
2018-07-31
2019-07-25
Lake Superior and Lake Erie
0
BCO-DMO catalogue of parameters from Diversity and distribution of sediment bacteria across an ecological and trophic gradient from 2018 to 2019 (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/993314.rdf
Name: accession
Units: unitless
Description: <p>NCBI Sequence Read Archive (SRA) run accession identifying a unique sequencing run.</p>
http://lod.bco-dmo.org/id/dataset-parameter/993315.rdf
Name: message
Units: unitless
Description: <p>Message from NCBI - should indicate successfully loaded.</p>
http://lod.bco-dmo.org/id/dataset-parameter/993316.rdf
Name: sample_name
Units: unitless
Description: <p>Unique name of the environmental or biological sample.</p>
http://lod.bco-dmo.org/id/dataset-parameter/993317.rdf
Name: sample_title
Units: unitless
Description: <p>Sample title (this is an optional field within the dataset and may not apply to every sample).</p>
http://lod.bco-dmo.org/id/dataset-parameter/993318.rdf
Name: organism
Units: unitless
Description: <p>DNA source - metagenome for environmental sample.</p>
http://lod.bco-dmo.org/id/dataset-parameter/993319.rdf
Name: host
Units: unitless
Description: <p>Host organism (if relevant, can be left blank).</p>
http://lod.bco-dmo.org/id/dataset-parameter/993320.rdf
Name: isolation_source
Units: unitless
Description: <p>Type of substrate DNA was extracted from (e.g. sediment, soil, tissue).</p>
http://lod.bco-dmo.org/id/dataset-parameter/993321.rdf
Name: collection_date
Units: unitless
Description: <p>Date of sample collection.</p>
http://lod.bco-dmo.org/id/dataset-parameter/993322.rdf
Name: geo_loc_name
Units: unitless
Description: <p>County, State, Country of sample collection.</p>
http://lod.bco-dmo.org/id/dataset-parameter/993323.rdf
Name: lat
Units: decimal degrees
Description: <p>Latitude of sample collection location in decimal degrees; a positive value indicates a northern coordinate.</p>
http://lod.bco-dmo.org/id/dataset-parameter/993324.rdf
Name: lon
Units: decimal degrees
Description: <p>Longitude of sample collection location in decimal degrees; a negative value indicates a western coordinate.</p>
http://lod.bco-dmo.org/id/dataset-parameter/993325.rdf
Name: ref_biomaterial
Units: unitless
Description: <p>DNA source.</p>
http://lod.bco-dmo.org/id/dataset-parameter/993326.rdf
Name: samp_collect_device
Units: units
Description: <p>Device used for sample collection (core, pump, etc).</p>
http://lod.bco-dmo.org/id/dataset-parameter/993327.rdf
Name: samp_size
Units: mL
Description: <p>Size of sample collected.</p>
http://lod.bco-dmo.org/id/dataset-parameter/993328.rdf
Name: country
Units: unitless
Description: <p>Country of sample collection derived from geo_loc_name.</p>
http://lod.bco-dmo.org/id/dataset-parameter/993329.rdf
Name: state
Units: unitless
Description: <p>State of sample collected derived from geo_loc_name.</p>
http://lod.bco-dmo.org/id/dataset-parameter/993330.rdf
Name: lake_name
Units: unitless
Description: <p>Name of lake where sample was collected derived from geo_loc_name.</p>
http://lod.bco-dmo.org/id/dataset-parameter/993331.rdf
Name: sample_depth_range_min
Units: cm
Description: <p>Minimum depth range of sediment core derived from geo_loc_name.</p>
http://lod.bco-dmo.org/id/dataset-parameter/993335.rdf
Name: sample_depth_range_max
Units: cm
Description: <p>Maximum depth range of sediment core derived from geo_loc_name.</p>
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.986587.1
download
onLine
dataset
<p><strong>Site description&nbsp;</strong></p>
<p>For this study, we selected twenty lakes within Minnesota’s Sentinel Lakes in a Changing Environment (SLICE) program. SLICE is a collaborative research initiative that provides long-term data on a representative sub-sampling of Minnesota’s lakes spanning the diverse geographic, land-use, and climatic gradients present in Minnesota (Fig. 1 in Sauer et al., 2022). The lakes span four of the seven Environmental Protection Agency/Commission for Environmental Cooperation (Level III) ecological regions. These regions are characterized by differences in underlying geology, soils, vegetation, and land use (Table S1 in Sauer et al., 2022). This is the first comprehensive sediment bacterial survey of these lakes.</p>
<p>&nbsp;</p>
<p><strong>Water Sample Collection &amp; Analysis</strong></p>
<p>At each site, we collected water profile measurements of temperature, pH, conductivity, turbidity, and dissolved oxygen using a YSI EXO2 multi-parameter sonde (YSI, Inc.). We also collected an integrated epilimnetic water sample (0–2 m) and a hypolimnetic water sample (maximum lake depth – 1 m) when thermal stratification was present. All samples were stored on ice in the field and at either 4°C or −20°C in the laboratory, depending on methodology, until processed.</p>
<p>Samples for soluble reactive phosphorus (SRP), dissolved organic carbon (DOC), and dissolved inorganic carbon (DIC) were filtered, processed, and analyzed within 36 hours of sampling using standard methods for SRP (4500-P) on a SmartChem 170 (Unity Scientific, Inc.) and for DIC/DOC (Method 5310-C) using a Torch Combustion TOC Analyzer (Teledyne Tekmar, Inc.) (American Public Health Association, 2012). Samples for total nitrogen (TN) and total phosphorus (TP) were frozen and analyzed using standard methods for TN (4500-N) and TP (4500-P). Samples for ammonia (NH₃) and nitrate (NO₃) were filtered and frozen prior to analysis following methods 4500-NH₃ and 4500-NO₃. All TP, TN, NH₃, and NO₃ samples were analyzed within six months of sampling on a SmartChem 170 (Unity Scientific, Inc.) discrete analyzer (APHA, 2012).</p>
<p>Additionally, samples for chlorophyll-a were filtered, frozen, and analyzed via fluorometry following EPA Method 445.0 (Arar et al., 1997). A complete summary of aqueous chemistry results, including sampling dates, is provided in Table S2 (Sauer et al., 2022).</p>
<p><strong>Sediment Sample Collection &amp; DNA Isolation&nbsp;</strong></p>
<p>Sediment cores were collected from July 2018 through June 2019 using a rod-driven piston corer with a 7 cm diameter polycarbonate tube (Wright, 1997). Coring locations (i.e., flat areas near the deepest basin) were determined using publicly available bathymetric maps (<a class="decorated-link" href="https://www.dnr.state.mn.us/lakefind/index.html" rel="noopener" target="_new">https://www.dnr.state.mn.us/lakefind/index.html</a>), while avoiding steep-sided “holes” where sediment focusing may be high. Following retrieval, core tops were stabilized in the field using a gelling agent (e.g., Zorbitrol), and intact cores were returned to the laboratory, where they were stored vertically at 4°C for no more than seven days prior to processing. In cases where the upper sediments were extremely flocculent, the uppermost sections (~0–30 cm) were immediately sectioned in the field to prevent mixing during transport.</p>
<p>Cores were vertically extruded in the laboratory at 1–2 cm intervals, depending on lake productivity, and subsamples from two intervals were collected for DNA analysis. Subsamples were collected from the 0–2 cm interval (hereafter referred to as <em>shallow</em>) and from either the 3–4 cm or 4–6 cm interval (hereafter referred to as <em>deep</em>). Subsamples were frozen under nitrogen for up to three months prior to DNA extraction (Table S3 in Sauer et al., 2022).</p>
<p>DNA was extracted from 0.25 g of wet sediment from each subsample using a PowerSoil DNA Isolation Kit (Qiagen, Inc.) following the manufacturer’s protocols. Negative controls were performed by carrying out extractions on blanks containing only reagents and no sample. Final bulk DNA concentrations were determined using a Qubit™ dsDNA HS Assay Kit (Molecular Probes, Eugene, OR, USA) and a Qubit™ Fluorometer (Invitrogen, Carlsbad, CA, USA). The detection limit of the Qubit™ dsDNA HS Assay Kit is 10 pg μL⁻¹. All samples that yielded detectable amounts of DNA were submitted for sequencing (Table S3 in Sauer et al., 2022). Although DNA was not detected in negative controls, these samples were submitted for sequencing; they failed quality control performed by the University of Minnesota Genomics Center (UMGC), and no sequencing data were obtained.</p>
<p><strong>Nucleic acid preparation, amplification, and sequencing</strong></p>
<p>DNA samples were submitted to the University of Minnesota Genomics Center (UMGC), where library preparation for Illumina high-throughput sequencing was performed using a Nextera XT workflow with 2 × 300 bp chemistry. This workflow utilizes transposome-based shearing, which fragments DNA and adds adapter sequences in a single step. DNA was amplified and dual-indexed with adapter sequences through PCR using primers 515F (5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGGTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGGACTACHVGGGTWTCTAAT-3′), targeting the V4 hypervariable region of the bacterial 16S SSU rRNA gene.</p>
<p>The amplicon library preparation methods developed and employed by the UMGC have been shown to be more quantitatively accurate and qualitatively complete, detecting taxonomic groups that often go undetected with existing methods (Gohl et al., 2016). Indexed samples were sequenced once using an Illumina MiSeq at the UMGC. A total of 3.29 million (3,290,170) raw reads were obtained from 40 samples.</p>
<p><strong>Temporal bounds within the dataset</strong></p>
<p>The date range associated with this dataset represents the sediment core collection dates.&nbsp;</p>
<p><strong>Data Availability in SRA</strong></p>
<p>All relevant data are reported in the results paper, Sauer et al. (2022). All 16S rRNA amplicon data are available from the Sequence Read Archive (SRA) under BioProject accession PRJNA763898.</p>
Specified by the Principal Investigator(s)
<p>Following sequencing and initial quality control performed by the University of Minnesota Genomics Center, we conducted all downstream sequence processing and analyses using established bioinformatic and statistical workflows.</p>
<p>We conducted post-sequence processing in Mothur (v1.43.0) following the MiSeq SOP (Schloss et al., 2009; Kozich et al., 2013). Briefly, we merged forward and reverse reads and screened, trimmed, and removed ambiguous bases. We aligned reads to references in the SILVA database (v.132) and identified and removed chimeras using vsearch (v2.13.3) (Quast et al., 2013; Edgar et al., 2011). Finally, given the nature of the study (i.e., broad-scale patterns of diversity), we classified sequences as operational taxonomic units (OTUs) using a 97% similarity threshold and assigned taxonomy using the SILVA database (Stackebrand et al., 1994; Glassman et al., 2018).</p>
<p>Unless otherwise stated, all statistical analyses were conducted in R (v4.0.0) (R Core Team, 2018; Wickham et al., 2019). Both environmental and community data were loaded into R using Phyloseq (v1.32.0) (McMurdie et al., 2013), and reads classified as mitochondrial or chloroplast were removed. The final dataset after all post-processing contained 2,181,132 reads assigned to 53,854 taxa across 40 samples (two sediment depths per lake).</p>
<p>&nbsp;</p>
<p><em>Alpha diversity</em></p>
<p>All singletons (OTUs observed only once across all 40 samples) were removed prior to calculating alpha diversity statistics. Given the observed correlation between richness and sample read depth across sequencing batches (Fig. S1 in Sauer et al., 2022), the data were rarefied to 90% of the read depth of the lowest-depth samples (15,771 reads; Fig. S2 and Table S3 in Sauer et al., 2022). The final dataset used for alpha diversity analyses included 630,840 read counts representing 25,563 taxa across 40 samples.</p>
<p>Alpha diversity metrics were calculated using the Phyloseq package in R (Fig. S3 and Table S4 in Sauer et al., 2022) (McMurdie et al., 2013). Richness (observed number of OTUs) and evenness (Shannon index) were compared between sediment depths (shallow n = 20, deep n = 19) using a Wilcoxon test, and among trophic status categories (hypereutrophic n = 4, eutrophic n = 16, mesotrophic n = 16, oligotrophic n = 3) and ecological regions (Western Cornbelt Plains n = 12, North Central Hardwood Forests n = 14, Northern Lakes &amp; Forests n = 8, Canadian Shield n = 5) using Kruskal–Wallis tests with Dunn post hoc tests and Bonferroni correction. In all analyses, one outlying sample (Trout, Deep) was excluded due to uncharacteristically low diversity.</p>
<p>The predictive relationships between environmental parameters measured at the time of sampling (Table S2 in Sauer et al., 2022) and alpha diversity metrics were evaluated using multiple regression. The significance and variance explained by each predictor were assessed using the <em>relimpo</em> (v.2.2.3) and <em>vegan</em> (v.2.5–6) packages in R (Groemping et al., 2006; Oksanen et al., 2009). Final models for richness (observed) and evenness (Shannon) were selected based on AIC scores.</p>
<p>&nbsp;</p>
<p><em>Beta diversity</em></p>
<p>Prior to beta diversity analyses, OTUs were filtered by removing those with fewer than two total counts and occurring in fewer than 10% of samples. Following filtering, the average number of reads per sample was 47,605, with a minimum read depth of 15,150 and a maximum read depth of 99,561. Because OTU count data exhibit strong positive skew, a variance-stabilizing transformation (VST) was applied to reduce heteroscedasticity (Love et al., 2020). Log-like transformations such as VST have been shown to transform count data toward near-normal distributions and produce larger eigengap values, resulting in more consistent correlation estimates that influence downstream analyses (Badri et al., 2020). After filtering and transformation, the final dataset for beta diversity analyses included 5,512 taxa across 40 samples.</p>
<p>Sample dissimilarity was visualized using principal component analysis (PCA) with the <em>ordinate</em> function in Phyloseq (McMurdie et al., 2013). Differences in community composition among ecological regions were assessed using permutational analysis of variance (PERMANOVA) with the <em>adonis</em> function in <em>vegan</em> (Oksanen et al., 2009), based on Bray–Curtis dissimilarity. Dispersion within groups was evaluated using permutation tests with the <em>betadisp</em> and <em>permutest</em> functions in <em>vegan</em>. Prior to calculating the dissimilarity matrix, negative VST values were converted to zero, as these values likely represent zero or near-zero counts and were considered negligible for distance calculations and hypothesis testing. Cluster analysis was performed using Ward’s (D2) method with the same dissimilarity matrix used for PERMANOVA.</p>
<p>&nbsp;</p>
Specified by the Principal Investigator(s)
* The primary data file of this dataset has been converted from its original format (.tsv) to csv.
* Within the primary data file (filename: 986587_v1_sediment_bacteria_in_MN_lakes.csv), lat and lon values have been split into two separate columns. Originally, both values were provided in a column named "lat_lon." The published file has both a "lat" column and a "lon" column.
* Unit values (grams represented by "g"s) have been removed from the "sample_size" column so the data within this column can be rendered accurately as numeric values.
* Country, state, lake name and sample depth range values have been parsed from the column "geo_loc_name" into individual columns. The original "geo_loc_name" column has been retained within the data file. Core ranges have been further split into seperate columns, so there are sample_depth_range_min and sample_depth_range max columns within the published primary data file.
Specified by BCO-DMO Data Managers
asNeeded
7.x-1.1
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
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Illumina MiSeq (Illimuna)
Illumina MiSeq (Illimuna)
PI Supplied Instrument Name: Illumina MiSeq (Illimuna) PI Supplied Instrument Description:Indexed amplicon libraries were sequenced using an Illumina MiSeq platform at the University of Minnesota Genomics Center (UMGC). The MiSeq is a benchtop, high-throughput sequencing system that generates paired-end reads using sequencing-by-synthesis chemistry. In this study, a single sequencing run produced a total of 3,290,170 raw reads across 40 samples, which were subsequently processed using established bioinformatics workflows. 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.
SmartChem 170 (Unity Scientific, Inc.)
SmartChem 170 (Unity Scientific, Inc.)
PI Supplied Instrument Name: SmartChem 170 (Unity Scientific, Inc.) PI Supplied Instrument Description:The SmartChem 170 (Unity Scientific, Inc.) is an automated discrete analyzer used for colorimetric determination of nutrient concentrations in water samples following American Public Health Association (APHA, 2012) standard methods. In this study, the instrument was used to analyze soluble reactive phosphorus (SRP; Method 4500-P), total phosphorus (TP; Method 4500-P), total nitrogen (TN; Method 4500-N), ammonia (NH₃; Method 4500-NH₃), and nitrate (NO₃; Method 4500-NO₃) in filtered or frozen water samples within the prescribed holding times. Instrument Name: Discrete Analyzer Instrument Short Name:Discrete Analyzer Instrument Description: Discrete analyzers utilize discrete reaction wells to mix and develop the colorimetric reaction, allowing for a wide variety of assays to be performed from one sample. These instruments are ideal for drinking water, wastewater, soil testing, environmental and university or research applications where multiple assays and high throughput are required.
Torch Combustion TOC Analyzer (Teledyne Tekmar, Inc.)
Torch Combustion TOC Analyzer (Teledyne Tekmar, Inc.)
PI Supplied Instrument Name: Torch Combustion TOC Analyzer (Teledyne Tekmar, Inc.) PI Supplied Instrument Description:Dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) were analyzed using a Torch Combustion TOC Analyzer (Teledyne Tekmar, Inc.) following APHA Method 5310-C (American Public Health Association, 2012). The analyzer quantifies carbon concentrations in filtered water samples via high-temperature combustion and detection of the resulting carbon dioxide. Samples were processed within 36 hours of collection to ensure compliance with standard holding time requirements. Instrument Name: Total Organic Carbon Analyzer Instrument Short Name:TOC analyzer Instrument Description: A unit that accurately determines the carbon concentrations of organic compounds typically by detecting and measuring its combustion product (CO2). See description document at: http://bcodata.whoi.edu/LaurentianGreatLakes_Chemistry/bs116.pdf Community Standard Description: http://vocab.nerc.ac.uk/collection/L05/current/LAB04/
YSI XO2 multi-parameter sonde (YSI, Inc.)
YSI XO2 multi-parameter sonde (YSI, Inc.)
PI Supplied Instrument Name: YSI XO2 multi-parameter sonde (YSI, Inc.) PI Supplied Instrument Description:At each site, we collected vertical water column profiles of temperature, pH, conductivity, turbidity, and dissolved oxygen using a YSI EXO2 multi-parameter sonde (YSI, Inc.), a field-deployable instrument designed for simultaneous in situ measurement of multiple physicochemical parameters. The sonde was lowered through the water column to obtain depth-resolved measurements characterizing thermal structure, oxygen conditions, and general water chemistry at the time of sampling. Instrument Name: YSI EXO multiparameter water quality sondes Instrument Short Name:YSI EXO Instrument Description: Comprehensive multi-parameter, water-quality monitoring sondes designed for long-term monitoring, profiling and spot sampling. The EXO sondes are split into several categories: EXO1 Sonde, EXO2 Sonde, EXO3 Sonde. Each category has a slightly different design purpose with the EXO2 and EXO3 containing more sensor ports than the EXO1. Data are collected using up to four user-replaceable sensors and an integral pressure transducer. Users communicate with the sonde via a field cable to an EXO Handheld, via Bluetooth wireless connection to a PC, or a USB connection to a PC. Typical parameter specifications for relevant sensors include dissolved oxygen with ranges of 0-50 mg/l, with a resolution of +/- 0.1 mg/l, an accuracy of 1 percent of reading for values between 0-20 mg/l and an accuracy of +/- 5 percent of reading for values 20-50 mg/l. Temp ranges are from-5 to +50 degC, with an accuracy of +/- 0.001 degC. Conductivity has a range of 0-200 mS/cm, with an accuracy of +/-0.5 percent of reading + 0.001 mS/cm and a resolution of 0.0001 - 0.01 mS/cm.