Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
  • BCO-DMO Processing Description
• Parameters
• Project Information
• Funding

Site description 

For this study, we selected twenty lakes within Minnesota’s Sentinel Lakes in a Changing Environment (SLICE) program. SLICE is a collaborative research initiative providing long-term data on a representative sub-sampling of Minnesota’s lakes that span 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’s (level III) ecological regions. These regions can be characterized by their underlying geology, soils, vegetation, and land use (S1 Table in Sauer et al., 2022). This is the first comprehensive sediment bacterial survey of these lakes.

Water Sample Collection & Analysis

From each site we collected water profile measures for temperature, pH, conductivity, turbidity, and dissolved oxygen using a YSI XO2 multi-parameter sonde (YSI, Inc.). We also collected an integrated (0-2m) epilimnetic water sample, and a hypolimnetic (maximum lake depth–1m) water sample when thermal stratification was present. All samples were stored on ice in the field and at 4°C or -20°C in the laboratory, depending on methodology, until processed. 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 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 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 NH3 (4500-NH3) and NO3 (4500-NO3). All TP, TN, NH3, NO3 samples were analyzed within six months of sampling on a SmartChem 170 (Unity Scientific, Inc.) discrete analyzer (APHA 2012). Additionally, we filtered, froze, and analyzed samples for chlorophyll-a concentrations via fluorometry following the EPA method 445.0 (Arar et al., 1997). We provided a complete summary of aqueous chemistry results, including sampling dates, in the S2 Table (Table S2 in Sauer et al., 2022).

Sediment Sample Collection & DNA Isolation 

We collected sediment cores from July 2018 through June 2019 using a rod-driven piston corer with a 7cm diameter polycarbonate tube (Wright 1997). We determined coring locations (i.e., flat areas near the deepest basin) using publicly available bathymetric maps (https://www.dnr.state.mn.us/lakefind/index.html), avoiding steep-sided “holes” where sediment-focusing may be high. After sediment core retrieval, we stabilized core tops in the field using a gelling agent (e.g., Zorbitrol) and returned intact cores to the laboratory where we stored them vertically at 4°C for no more than seven days until processing. In cases where the upper sediments were extremely flocculent, we immediately sectioned the upper most sections (~0–30 cm) in the field to prevent mixing during transport.

We vertically extruded the cores in the lab in 1 to 2 cm intervals, depending on lake productivity, and took subsamples from two intervals for DNA analysis. The subsamples collected were from the 0-2cm (hereafter referred to as shallow) and either the 3-4cm or 4-6cm interval (hereafter referred to as deep). Subsamples were frozen under nitrogen for up to three months before DNA was extracted (S3 Table in Sauer et al., 2022). We extracted DNA from 0.25g of wet sediment from each subsample using a PowerSoil DNA Isolation Kit (Qiagen, Inc.) following the manufacturer’s protocols. We performed negative controls by carrying out extractions on blanks, using only reagents without sample. We determined final bulk DNA concentrations using a Qubit™ dsDNA HS Assay kit (Molecular Probes, Eugene, OR, USA) and Qubit™Fluorometer (Invitrogen, Carlsbad, CA, USA). The detection limit for the Qubit™ dsDNA HS Assay Kit is 10 pg/μL. All samples that yielded detectable amounts of DNA were sent for sequencing (S3 Table in Sauer et al., 2022). Despite not detecting DNA in our negative controls, these were submitted for sequencing where they failed to pass quality control performed by the University of Minnesota Genomic Center (UMGC) and no sequencing information was obtained.

Temporal bounds within the dataset

The date range associated with this dataset represents the sediment core collection dates. 

Data Availability in SRA

All relevant data are in Sauer et al., 2022. All 16S rRNA amplicon data are available from the SRA database at BioProject PRJNA763898.

Nucleic acid preparation, amplification, and sequencing

We submitted the DNA samples to the UMGC where they performed library preparation for Illumina high-throughput sequencing using a Nextera XT workflow and a 2x300 bp chemistry. The workflow utilizes transposome-based shearing which fragments the DNA and adds adapter sequences in one step. The DNA was amplified and dual-indexed with adapter sequences through PCR, using primers 515F (5′-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGGTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGGACTACHVGGGTWTCTAAT-3′) to target the V4 hypervariable region of bacterial 16S SSU rRNA gene sequences. The amplicon library preparation methods created 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). The indexed samples were then 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.

Data processing

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 then aligned the 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 the 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).

Community analysis & statistics

Unless otherwise stated, we conducted all statistical analyses in R (v4.0.0) (R Core Team, 2018; Wickham et al., 2019). We loaded both the environmental and community data into R using Phyloseq (v1.32.0) (McMurdie et al., 2013) and removed any reads classified as mitochondrial or chloroplast. Our final dataset after all post-processing contained 2,181,132 reads assigned to 53,854 taxa across 40 (two sediment depths/lake) samples.

Alpha diversity

We removed all singletons (OTUs observed only once across all 40 samples) from the data before calculating alpha diversity statistics. Given the observed correlation of richness based on sample read depth across sequencing batches (S1 Fig in Sauer et al., 2022), we chose to rarefy the data to 90% the read depth of the lowest samples (15,771 reads; S2 Fig in Sauer et al., 2022 and S3 Table in Sauer et al., 2022). Our final dataset for alpha diversity included 630,840 read counts of 25,563 taxa across 40 samples. We calculated alpha diversity measures using the Phyloseq package in R (S3 Fig in Sauer et al., 2022 and S4 Table in Sauer et al., 2022) (McMurdie et al., 2013). We compared the richness (observed number of OTUs) and evenness (Shannon) of the samples based on sample depth (shallow n = 20, deep n = 19) using a Wilcox test, and trophic status (hypereutrophic n = 4, eutrophic n = 16, mesotrophic n = 16, and oligotrophic n = 3) and ecological status (Western Cornbelt Plains n = 12, North Central Hardwood Forests n = 14, Northern Lakes & Forests n = 8, Canadian Shield n = 5) using a Kruskal-Wallis test with a Dunn Post Hoc test and Bonferroni correction. In all tests, one outlying sample (Trout, Deep) was removed due to uncharacteristically low diversity. Finally, we assessed the predictive capabilities of the environmental parameters, collected at the time of sampling (S2 Table in Sauer et al., 2022), on the alpha diversity of the sample using multiple regression and determined the significance and variance partitioned by each regressor using the relaimpo (v.2.2.3) and vegan (v.2.5–6) packages in R (Groemping et al., 2006; Oksanen et al., 2009). We selected the final models based on AIC scores for both richness (observed) and evenness (Shannon).

Beta diversity

Prior to beta diversity analysis we filtered the samples by removing any OTU that did not have 2 or more counts and occur in at least 10% of the samples. Post filtering, the average number of reads per sample was reduced to 47,605, the minimum read depth was 15,150, and the maximum read depth was 99,561. Since OTU data have a strong positive skew, we attempted to diminish the effects using a variance stabilizing transformation (VST) (Love et al., 2020). Log-like transformations, like VST, have been shown to transform count data to near-normal distributions and produce larger eigengap values, ultimately leading to more consistent correlation estimates which influence downstream analyses (Badri et al., 2020). After filtering and transformation, the final dataset for beta diversity analysis included 5,512 taxa across 40 samples.

We visualized the sample dissimilarity using principal component analysis (PCA) and the ordinate function in Phyloseq (McMurdie et al., 2013). After ordination, we further analyzed the distribution of taxa based on the ecological regions using permutational analysis of variance (PERMANOVA) and the “adonis” function in vegan (Oksanen et al., 2009). We used a Bray-Curtis dissimilarity to test for group differences and assessed dispersion within groups using permutations and vegan’s “betadisp” and “permutest” functions. Prior to creating the dissimilarity matrix, we converted negative VST values to zero because negative values after transformation likely represent zero counts or very few counts and for the distances and hypothesis in future tests these values would be negligible. We performed a cluster analysis using Ward’s (D2) method and the same dissimilarity matrix generated for the PERMANOVA analysis.

Data Availability in SRA

All relevant data are in Sauer et al., 2022. All 16S rRNA amplicon data are available from the SRA database at BioProject PRJNA763898.

* 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." Currently, the 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.

NSF Award Abstract:
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.

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.

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.