Spatial and Temporal Variability in Bacterial Communities in Surface and Groundwater at Lake Erie Beaches with Varying Water Quality

Spatial and Temporal Variability in Bacterial Communities in Surface and Groundwater at Lake Erie Beaches with Varying

Water Quality

Brandon Durant

August 9^th, 2018

A thesis submitted to the Faculty of the Graduate School of

the University at Buffalo, State University of New York in partial fulfillment of the requirements for the

degree of

Master of Science

Department of Civil, Structural, and Environmental Engineering

I. Abstract

Microbial pollution is a crucial issue in the Laurentian Great Lakes. Recreational water quality is assessed by culturing Fecal Indicator Bacteria (FIB), specifically E. coli and Enterococcus spp. If the concentration is above US EPA set criteria, the water body is closed to recreational use.

However, these conventional water quality monitoring methods are limited, varying on temporal and spatial scales and providing no information about the fecal pollution source. Researchers have proposed microbial community analysis using Next Generation Sequencing (NGS) to identify sources of fecal pollution and their relative contributions in coastal surface waters. More research is needed on how to interpret NGS data and to better understand microbial community dynamics at beaches with impaired water quality. In addition, little to no research has

investigated the microbial community in groundwater and how that influences coastal water quality. The present study focuses on Woodlawn Beach, a beach along eastern Lake Erie that is often closed over 50% of the swimming season. Microbial community analysis is used to (1) investigate the spatial and temporal variability in microbial communities at Woodlawn Beach and three nearby beaches, (2) examine the microbial communities in surface waters and groundwater at Woodlawn Beach to determine the dominant influence, and (3) investigate the influence of sewage at all sites using the microbial community signature of influent and effluent.

Among the four beaches, microbial communities were statistically different between locations and sampling dates, however the microbial communities were not statistically different between the three swimming beaches. Beach samples were found to be minimally impacted by sewage with sewage associated amplicon sequence variants (ASVs) representing <l % of total ASVs at each beach suggesting little to no sewage influence. Microbial communities in Woodlawn Beach

different between surface waters, groundwaters and Lake Erie, but did not differ by sampling date. Woodlawn Beach surface and groundwater communities were also minimally impacted by sewage associated ASV s suggesting sewage pollution cannot explain the high E. coli

concentrations at Woodlawn Beach. Results from this study suggest that the microbial community in Lake Erie at Woodlawn Beach is influenced more by surface waters than groundwater. This study demonstrates a potentially useful application ofNGS as a tool for analyzing microbial community dynamics in surface waters and groundwaters over spatial and temporal scales at an impaired recreational beach.

Table of Contents

I. Abstract .... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ii II. Introduction.... ... ... ... ... ... ... ... ... ... ... ... ... ... ... .... ... ... ... ... ... ... ... 1 III. Materials and Methods .... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... .. 4 IV. Results .... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 9 V. Discussion.... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 24 VI. References ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 31 VII. Supplementary Information .... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... .... 34

II. Introduction

Increasing urbanization of coastal shorelines impacts water quality and coastal ecosystems.

In 2010, 123.3 million people, or 39% of the entire United States population, lived in coastal counties despite coastal counties representing less than 10% of United States land [1]. Population growth along the coast results in increased microbial and chemical (e.g., nutrients) pollution [2, 3]. Pollutants are introduced to recreational waters via point sources such as sewage spills or broken pipes and nonpoint sources such as storm water, agricultural and urban runoff. Among these pollutants, fecal pollution causes the most concern due to its association with increased pathogen concentrations that pose a threat to human and ecosystem health. Every year approximately 850 billion gallons of untreated wastewater and storm water are released as Combined Sewer Overflows (CSOs) and Sanitary Sewer Overflows (SSOs) in the United States, creating significant public health concerns [ 4]. CSOs and SSOs are estimated to have caused between 3,448 and 5,576 illnesses each year at recognized beaches [4]. While the human health risk is clear, how long the health risk persists and the impact it has on the ecosystem, specifically the microbial community at a given location, is not well understood.

Conventional methods of assessing water quality and human health risks in coastal recreational waters include enumeration of culturable Fecal Indicator Bacteria (FIB).

Epidemiological studies conducted in both marine and freshwater environments have shown a positive correlation between the incidence of human illness and FIB concentrations, specifically E. coli and Enterococcus spp. [5-8]. Based on these epidemiological studies, the US

Environmental Protection Agency (EPA) sets recreational water quality criteria based on E. coli or Enterococcus spp. and beaches are closed to protect public health when the concentration

exceeds the criteria. Unfortunately, studies have shown FIB can be temporally and spatially variable due to environmental variables such as sunlight. High variability calls into question the usefulness of culture-based FIB enumeration in a single sample collected at a single time of day for assessing water quality and health risks. In addition, FIB cannot be used to determine the source (e.g. human, sewage or animal) of fecal pollution [9]. Identifying the source(s) of fecal pollution is critical to mitigation efforts and assessments of the human health risks associated with fecal pollution in recreational waters [ 10, 11].

To address the limitations of conventional culture-based methods, molecular techniques are being developed for Microbial Source Tracking (MST). Molecular assays have been developed to quantify genetic markers associated with specific fecal hosts ( e.g. human, cow, gull, etc.) using quantitative polymerase chain reaction (qPCR) [12-15]. Despite successful use of host associated markers in MST studies [16, 17], limitations do exist. Cross reactivity of markers, changes in geographic location, marker specificity and sensitivity, and differential distribution of the genes containing the markers in host populations all contribute to the

complexity of MST using single markers, especially when a water body is impacted by multiple sources of microbial pollution [ 17].

Microbial community analysis using Next Generation Sequencing (NGS) has the potential to augment fecal source identification in coastal waters. Having the capacity to sequence millions of amplicons in parallel, this library-dependent method is able to overcome the limitations of single host associated fecal markers [ 17]. With a more comprehensive view of the microbial community, researchers have been able to identify multiple fecal sources and their relative contribution in numerous impacted environmental waters such as the Y eongsan river basin in

on the potential ofNGS to identify sources of fecal pollution, especially at low levels. In addition, few studies have examined the microbial community in both surface water and

groundwater to understand their interactions and relative contribution to impaired coastal waters.

Groundwater is an important drinking water resource and a potential transport route for

microbial pollution flowing into coastal waters important for recreational use and downstream uses. Consideration and characterization of the microbial communities in both surface water and groundwater is critical to better understanding water quality impaired beaches.

Coastal microbial pollution is a crucial issue in the Laurentian Great Lakes region.

Nationally, the Great Lakes region has the highest beach closure rate (13% in 2013) [20]. Part of the Great Lakes watershed, Lake Erie functions as a multi-purpose resource used for recreation, as an important habitat for native migratory fish, and as the drinking water source for over 11 million people. It also acts as the largest fishery of the four Great Lakes, supporting 10,000 jobs per year and boosting the economies by over $1 billion annually [ 17]. The research presented here focuses on Woodlawn Beach State Park along eastern Lake Erie in Western New York.

According to NYS Office oflnformation Tech. Services, Woodlawn Beach State Park hosts 146,000 visitors annually but is frequently closed during the summer seasons (often over 50% of the summer season) due to high E. coli concentrations or the predictive model currently being used. With no clear point source of microbial pollution and economic and social losses due to frequent beach closures, Woodlawn Beach is an ideal site to apply microbial community analysis to elucidate potential sources and transport routes of microbial pollution to the coast.

The present study focuses on spatial and temporal variability of microbial community diversity and composition at 4 beaches along eastern Lake Erie and among surface waters and

groundwater at Woodlawn Beach. The specific objectives of the research were to (1) compare microbial communities at Woodlawn Beach to 3 nearby recreational beaches to elucidate

similarities and differences that might provide insight into the water quality at Woodlawn Beach, (2) examine the temporal and spatial variability in surface waters and groundwater flowing into Lake Erie at Woodlawn Beach, and (3) determine if microbial community analysis is a useful tool to investigate surface water and groundwater interactions and the potential influence of sewage pollution. To our knowledge, this is the first study to incorporate microbial community analysis of groundwater into microbial source tracking efforts at an impaired beach. This study informs future research at Woodlawn Beach and provides an example of how microbial

community analysis can be used to better understand microbial pollution at chronically closed recreational beaches.

III. Materials and Methods

Sampling Sites and Procedure

Water was collected at Woodlawn Beach, Hamburg Beach, Bennett Beach, and Gallagher Beach along eastern Lake Erie in July and August 2016 (Fig. 1). The focus of the study was Woodlawn Beach which is a popular beach with a history of high and variable E. coli concentrations and frequent closures based on both E. coli concentrations and water quality model predictions.

Gallagher Beach is approximately 6.2 km north of Woodlawn Beach and is not a swimming beach. Hamburg and Bennett Beaches are both popular swimming beaches approximately 5. 8 km and 23 km south of Woodlawn Beach. At Woodlawn Beach, surface water was collected from two sites in Lake Erie (LE2 and LE3) as well as Rush Creek (Rup) and Blasdell Creek (BD)

were collected from monitoring wells 2, 3 and 7 (MW2, MW3, MW7) installed by Nature's Way Environmental, Inc. (Alden, NY) to 12, 16, and 12 feet deep, respectively. Water samples were collected from each of these sites on 7/14/2016, 7/19/2016, 7/26/2016 and 8/2/2016. Sewage influent and effluent was collected from a local utility on 9/18/17. In total, 28 surface water samples and 9 groundwater samples were collected.

Figure 1. Map showing location ofBuffalo, NY and Lake Erie (Top}, sampling sites from eastern Lake Erie beaches (Lower Left), and Woodlawn Beach swface and ground waters (Lower Right).

Sample collection took place between 8:00 AM and 10:50 AM in each given day to minimize the effect of photo-inactivation on bacterial concentrations. Surface water samples were collected on an incoming wave or from upstream at the water's surface. Groundwater samples were collected

using a peristaltic pump (Masterflex E/S Portable Sampler, 115 VAC). Water was collected in polypropylene bottles (Nalgene 2105 series) sterilized with 10% hydrochloric acid. All

collection bottles were rinsed with the sample three times before sample collection. All samples were stored on ice until analysis within 6 hours. At the laboratory, water was processed for culture-based enumeration ofE. coli and Enterococcus spp. and filtered for molecular assays.

Culture-based enumeration of E. coli.

E. coli were enumerated by IDEXX Colilert-18 following manufacturer instructions.

Filtration and DNA Extraction

At the laboratory, 200-300 mL of sample was filtered through 0.4-µm-polycarbonate-pore-size filters (Millipore). Filtration blanks were made with molecular grade water with every set of filters. Filters were then stored at -80°C until DNA extraction.

DNA was extracted from filters using the Qiagen DNeasy Power Water Kit following the manufacturer's instructions. An extraction blank was done with each set of extractions

performed. DNA was eluted in 100 µL warmed Elution Buffer (EB) and stored at -20°C until analysis. During analysis, the impact of ASVs that appeared in the extraction blank was analyzed using the two approaches outlined in Nguyen et al., 2014 [21]. The sequences found in the extraction blank were represented at less than 1 % in all the samples and assumed to be negligible (Supplementary Information Table S 1 ).

16S rRNA PCR amplification and lllumina MiSeq sequencing

The bacterial community in the samples was characterized using high throughput sequencing on

the Earth Microbiome Project protocol [22]. The 16S rRNA sequences used to target the V4 region were 515F (GTGYCAGCMGCCGCGGTAA) and 806R

(GGACTACNVGGGTWTCTAAT). Barcoded primer pairs 515FB and 806RB were used for amplification of 46 samples each utilizing different 12-bp-error-correcting Golay barcodes. This included a Mock Community sample and a Pooled Extraction Blank sample as positive and negative controls, respectively. All PCR reactions were prepared in a UV sterilized hood. One­

hundred-twenty-five-microliter reaction mixtures were prepared and 25 µL was aliquoted into 5 PCRs with the template and a non-template control (NTC). Two, three, and five microliters of template was used in PCR reactions for extraction yields greater than 5 ng/µL, between 1 ng/µL and 5 ng/µL, and less than 1 ng/µL respectively. Reaction mixtures consisted of 0.8 X Platinum Hot Start PCR Buffer, 0.2 µM forward and reverse primers, and template DNA. Forward and reverse primers contained Illumina adapters. Additionally, each forward primer contained a Golay barcode. PCR cycling parameters included an initial pre-denaturation step at 94 °C for 3 min, followed by of 94°C for 45 s, 50°C for 60 s, and 72°C for 90 s, and a final extension at 72°C for 10 min. The 5 PCRs were pooled, and 10 µL was run on a 1.5% agarose gel for 70 mins at 50 V. All PCRs and gels were run in a separate room from DNA extractions to prevent

contamination. Ten microliters ofNTC was also run in the gel to check for contamination. After confirmation of target amplicon in the gel, the pooled amplicons were then cleaned using the QIAquick PCR Purification Kit according to manufacturer instructions (Qiagen Sciences). Two­

hundred-forty nano-grams of DNA from each sample was then pooled into a single, sterile tube for sequencing. In total, 46 samples were sent out for sequencing. Illumina 2 x 300 paired end sequencing runs were performed at the UB Genomics and Bioinformatics Core in Buffalo, NY using the Illumina MiSeq.

Sequence processing

Raw sequences were processed using the Divisive Amplicon Denoising Algorithm (DADA2) pipeline in the open-source language R. DADA2 infers exact amplicon sequence variants

(ASVs) from Illumina-sequenced amplicon reads using a model-based approach that corrects for amplicon errors. Sequences are not binned into operational taxonomic units (OTUs) based on 3%

similarity nor is the dataset rarefied [23]. ASVs can be different by a single nucleotide and thus provide finer resolutions for biological inferences [24]. The DADA2 workflow includes filtering for quality, dereplication, denoising, removal of chimeric sequences, and merging of paired end reads [25]. ASVs are assigned taxonomic designations using the GreenGenes (GG, version 13_8) reference database through the DADA2 R package. Further specifics regarding DADA2 filtering and trimming parameters can be found in the Supplementary Information.

Statistical analyses

Statistical analyses in R were conducted using the phyloseq package [1.22.3] and the vegan package [2.4.6] [26, 27]. The phyloseq and vegan packages were utilized for calculation of the Shannon-Weaver and Simpson diversity indices and Bray-Curtis dissimilarity analyses,

specifically nonmetric multidimensional scaling (nMDS) plots and analysis of similarity

(ANOSIM) tests. ANOSIMs were performed to determine if there were significant dissimilarities in community composition on the basis of sampling date and sampling location. Dissimilarities were visualized using a nMDS plot using Bray-Curtis dissimilarities. Relative abundances were calculated by dividing the number of sequences for a given ASV by the total number of

sequences in the sample set.

IV. Results

Culture-based enumeration of E. coli. E. coli was quantifiable in all surface water samples but only 3 of 12 groundwater samples (Table 1).

Table 1. E.coli concentrations quantified by IDEXX Colilert-18 (MPN/100 mL). Concentrations in bold mark days when the beach would be closed for recreational use based on the EPA criteria

of 235 CFU/100 mL.

7/14/16 7/19/16 7/26/16 8/2/16 Beach Sites E.coli concentrations (MPN/100 mL)

Gallagher Beach 258.9 59.8 62 45.7

Woodlawn Beach 480 1732.9 3076 201

Hamburg Beach 67 80.9 934 1732.9

Bennett Beach 5.2 35 246 36.9

Woodlawn Beach Sites E.coli concentrations (MPN/100 mL)

Woodlawn Beach (LE2) 480 1732.9 3076 201

Woodlawn Beach (LE3) 857 648.6 9804 253

Rush Creek (Rup) 344.8 1226.2 7270 465

Blasdell Creek (BD) 25.6 517.2 17329 120

Monitoring Well 2 (MW2) <l <l 30.9 30.4 Monitoring Well 3 (MW3) <l <l <l <l

Monitoring Well 7 (MW7) <l <l <l 2

NGS Data. After removing low quality reads and chimeras, the data set contained 9,206,487 total reads representing 38,386 unique ASVs. Samples had between 183,836 reads (MW7) and 378,258 reads (HAM) with an average of 248,824 reads. Despite using primers targeting bacterial species, the data set contained 239,461 archaeal reads composing 2.59% of the total reads. Archaeal reads were most abundant in Woodlawn Beach groundwater samples, composing 4.25%, 20.18%, and 8.01 % ofreads within MW2, MW3, and MW7 samples, respectively.

Bacterial and archaeal relative abundances by sampling location are displayed in Table S2 of the Supplementary Information.

Quality Control. No DNA was quantified in any of the DNA extraction blanks. Despite yielding

no quantifiable DNA, five of the extraction blank eluates were pooled and prepared for

sequencing as negative controls to identify sequences that were the result of contamination in the sequence preparation and sequencing steps. Sequencing of the extraction blank resulted in 23,569 reads or 0.25% of the total reads that passed quality control and filtering and contained

149 unique ASVs (Supplementary Information Table S3).

Mock Community. A 20 Strain Even Mix Genomic Material (ATCC ^® MSA-1002™) Mock Community was sequenced to verify the accuracy of Illumina sequencing, determine potential contamination from sequencing preparation and sequencing, and investigate PCR bias. The lowest taxonomic assignments identified through the pipeline used and the Greengenes database (GG, version 13_8) for Mock Community sequencing is displayed in Table S4 of the

Supplementary Information. Of the 20 species sequenced in the original Mock Community, 11 were identified to the genus level, and 9 were identified at the species level. Of the 9 identified past the genus level, 2 were incorrectly identified at the species level despite being correctly identified at the genus level. Even though species level classification wasn't achieved in all cases, sequencing results were consistent with the known Mock Community composition.

Seventeen additional ASVs totaling 10,585 reads (3.24% of total mock community reads) were identified in the Mock Community at relative abundances of 1.1 % and lower.

Sewage Community. We defined sewage ASVs to be ASVs with a relative abundance of 1 % or

greater in either the influent or the effluent from a nearby wastewater utility. The lowest

taxonomic assignments for the sewage ASVs and their relative abundances are shown in Tables

2 and 3 for influent and effluent ASVs, respectively. The combined sewage ASV community is displayed in Table S5 of the Supplementary Information.

Table 2. Influent Sewage ASVs SewageASV

Arcobacter cryaerophilus (species) Acinetobacter johnsonii (species) Pseudomonas alcaligenes (species) Klebsiella (genus)

Arcobacter cryaerophilus (species) Arcobacter (genus)

Streptococcus (genus) Enhydrobacter (genus) Limnohabitans (genus) Aeromonadaceae (family) Tolumonas (genus)

Cloacibacterium (genus) Leptotrichia (genus) Blautia (genus)

Acinetobacter johnsonii (species) Roseburia (genus)

Bacteroides (genus) Betaproteobacteria (class) Acinetobacter (genus)

Table 3. Sewage Effluent ASVs SewageASV

Mycobacterium (genus) Acinetobacter (genus) Mycobacterium (genus) Betaproteobacteria (class) SC3 (class)

Limnohabitans (genus) Geothrix (genus)

Arcobacter cryaerophilus (species) Sediminibacterium (genus)

Dechloromonas (genus)

Pseudomonas alcaligenes (species)

Relative abundance (%) 13.5

5.4 4.7 3.5 3.4 3.4 3.3 2.8 2.4 2.1 1.6 1.4 1.3 1.3 1.3 1.3 1.1 1.1 1.1

Relative abundance (%) 23.1

4.8 3.2 3.1 1.8 1.7 1.7 1.4 1.3 1.3 1.2

Environmental Conditions. The four dates sampled in this study ranged in environmental conditions. July 14^th was rainy, cloudy, windy (15.93 kph windspeed, SW) and had high waves (33.02 cm. wave height). The average water temperature was 24.5°C and the water turbidity was 33.7 NTU. July 19^th revealed much calmer conditions than July 14^th. It was partly cloudy, with calm wind (wind speed 2.57 kph, SE), and low waves (5.08 cm. wave height). The average water temperature was 21 °C and the water turbidity was 45.85 NTU. Additionally, there was 0.1 cm of rainfall within the 24 hours prior to July 19^th• It rained on July 25^th, one day before the sampling on July 26^th . Within the 24 hours prior to July 26^th, there was 2.06 cm of rainfall. A breezy wind was recorded at 12.07 kph (NW) with a water temperature of 22°C and a wave height of 20.32 cm. Finally, the water turbidity was 39.75 NTU. August 2^nd was sunny with calm winds

(windspeed 1.61 kph, W) and had no waves (0 cm wave height). The average water temperature was 23°C and the water turbidity was 1.455 NTU. Finally, there was no rainfall within the 24 hours prior to August 2^nd. On all collection days, the presence of algae on the lake was observed.

Environmental conditions are available in Table S6 of the Supplementary Information.

Spatial variability in microbial community diversity and composition at four beaches along the eastern Lake Erie shoreline. We compared the amplicon sequences of 16 samples taken over a four-week period from Hamburg Beach, Woodlawn Beach, Gallagher Beach, and Bennett Beach, all located on the eastern Lake Erie shoreline to investigate spatial variation within the microbial community. Two alpha diversity indices, Shannon-Weaver and Simpson, were used to compare the alpha diversity at the beaches. The Shannon-Weaver index varied from 4.2 to 5.9 and the Simpson index varied from 0.945 to 0.985 (Supplementary Information, Fig. SI).

Differences in microbial community composition among eastern Lake Erie beaches were

visualized using a non-parametric multidimensional scaling (nMDS) ordination plot constructed using Bray-Curtis dissimilarities (Fig. 2). Among eastern Lake Erie beaches, microbial

communities were significantly different between sampling locations (ANOSIM, R ⁼ 0.1606, p ⁼ 0.042). However, when Gallagher (a non-swimming beach) is removed from the analysis,

microbial communities are not significantly different between beaches (Woodlawn, Hamburg and Bennett beaches) (ANOSIM, R ⁼ 0.02778, p ⁼ 0.35) (Supplementary Information, Fig. S2).

Bray Curtis nMDS (Beaches by Site)

♦

0.5 ♦

•

Site

♦ _{■ Bennett}

♦ Gallagher

0.0

-0.5

♦

• _•

■

■

■ • • _••

A .

Hamburg Woodlawn

■

•0.5 0.0 0.5 1.0

Figure 2. Bray Curtis non-metric multidimensional scaling plot (Stress= 0.112) indicating differences in community composition between Bennett Beach (square), Gallagher Beach (diamond), Hamburg Beach (triangle), and Woodlawn Beach

(circle).

Among ASVs present in beach samples, the most abundant families (sum 2: 75% total sequences) belong to the Unclassified (26.43%), Pelagibacteraceae (10.78%), ACK-Ml (9.67%),

Comamonadaceae (8.41 %), Cryomorphaceae (4.40%), Chitinophagaceae (4.00%),

Cyclobacteriaceae (3.76%), Cytophagaceae (2.84%), Cerasicoccaceae (2.28%), Mitochondria (2.13%), and NA (2.11 %) families. Most abundant phyla are shown in Fig. S3 of the

Supplementary Information.

Shared ASVs between eastern Lake Erie beaches. To determine the extent of overlap between

microbial communities among different sampling locations, shared ASV s between samples originating from different beaches across all sampling dates was tabulated (Table 4).

Table 4. Shared ASVs across all sampling dates between eastern Lake Erie beaches.

Gallagher Woodlawn Hamburg Bennett

Gallagher

---

Woodlawn 1382

---

Hamburg 1278 3077

---

Bennett 1174 1892 1808

Profiling sewage taxa within eastern Lake Erie beach samples ordered by site.

To determine the relative contribution of sewage influent and effluent in the beaches' microbial communities, the abundance (meads) of taxa identified as sewage (both effluent and influent) were plotted against the abundance (meads) of all taxa within beach samples (Fig. 3). Sewage reads contributed minimally towards total beach taxa, comprising 0.86%, 0.49%, 0.29% and 0.27% of Woodlawn Beach, Hamburg Beach, Gallagher Beach, and Bennett Beach samples across all dates, respectively.

Relative Abundance of Sewage Taxa (Genus) in Beaches by Site

Woodlawn

Genus g_

g_ Adnetobacter g_ Atcobacter g_Bacteroidos g_ Blauba

Hamburg ^g_ Cloadbactenum

g_Oechloromonas g_Enhydrobacter g_ Geothrli

u5 2 ^g_ Kklbslella

g_Leptotnchla g_ Llmnohabltans

Gallagher ^g_ Mycobacteoum

g_Pseudomonas g_Roseburia g_ Sedim1n1bac1erlum g_ Streptococcus g_Totumonas NA Bennett

0 2 3 4 5

Percentage of Sequences

Figure 3. Relative abundance ofsewage taxa (genus) within eastern Lake Erie beach samples by sampling location.

Temporal variability in microbial community diversity and composition at four beaches along the eastern Lake Erie shoreline. Microbial communities at the four beaches were analyzed for differences in community composition associated with time, specifically collection date, over a four-week period. Differences in community composition were visualized using an nMDS ordination plot constructed using Bray-Curtis dissimilarities (Fig. 4). Among the four sample collection dates, sampling date was found to have a significant impact on community composition (ANOSIM, R = 0.2318, p = 0.017). When Gallagher (a non-swimming beach) is

• •

•

removed from the analysis, sample collection date is still found to have a significant impact on community composition (ANOSIM, R ⁼ 0.3765, p ⁼ 0.02, Supplementary Information Fig. S4).

Bray Curtis nMDS (Beaches by Date)

■

0.5

■

Date

♦ ■ ⁷¹⁴¹⁶

■

.A.72616

• ^■

0.0 • 80216

♦ .A.

♦

••

-0.5

•0.5 0.0 0.5 1.0

Figure 4. Bray Curtis non-metric multidimensional scaling plot (Stress= 0.112) indicating differences in community composition across all beaches on July 14¹h, 2016 (square), July 19¹h, 2016 (diamond), July 26¹\2016 (triangle), and August 2^nd,

2016 (circle).

Profiling sewage taxa within eastern Lake Erie beach samples by date.

To determine the relative contribution of sewage towards eastern Lake Erie beach communities on different dates, the abundance (meads) oftaxa identified as sewage (both effluent and influent) were plotted against the abundance (meads) of all taxa on different dates (Fig. 5).

Sewage reads contributed minimally towards total beach taxa on each sampling date, comprising

sites, respectively. Among sewage ASVs present in beach samples, the most abundant (sum 2:

90% total sewage sequences) belong to the Rhodocyclaceae (37.30%), Comamonadaceae (18.15%),Aeromonadaceae (17.89%), Chitinophagaceae (11.12%), and Weeksellaceae (6.97%) families.

Relative Abundance of Sewage Taxa (Genus) in Beaches by Date

80216

Genus g_

g_Acinetobacter g_ Atcobacter

g_ Bacte,oldes g_ Blauba

72616 g_Cloacibactenum

g_Oechloromonas g_ Enhydrobacter

2 ^{g_ Geothrlx}

ro g_Klobslella

0

g_Leptotrichla

g_ Umnohabltans

71916 ^g_ Mycobacteoum

g_Pseudomonas g_Rosebuna g_ Sed1m1f\lbac1erlum g_ Strnptococcus g_ Totumonas NA

71416

0 2 3 4 5

Percentage of Sequences

Figure 5. Relative abundance ofsewage taxa (genus) within eastern Lake Erie beach samples by sampling date.

Surface water and groundwater influence on Woodlawn Beach community diversity and composition. We compared the amplicon sequences of 16 samples taken from Blasdell Creek (BD), Woodlawn beach (LE2), Rush Creek (Rup), and the intersection of Rush Creek, Blasdell

• •

and Lake Erie (LE3), surface waters located in Woodlawn Beach, to investigate spatial variation between microbial communities. Shannon-Weaver and Simpson alpha diversity indices were calculated for each sample to investigate changes in diversity associated with spatial separation.

The Shannon-Weaver index varied from 4.5 to 6.4 and the Simpson index varied from 0.95 to 0.99 (Supplementary Information, Fig. S5).

Differences in community composition among Woodlawn beach surface waters were visualized using a non-parametric multidimensional scaling (nMDS) ordination plot constructed using Bray-Curtis dissimilarity coefficients (Fig. 6). Among Woodlawn Beach surface waters, sample location was found to have a significant impact on community composition (ANOSIM, R = 0.5764, p = 0.001).

Bray Curtis nMDS (Woodlawn Surface Waters by Site)

• •

0.4

♦

Sile

■ so

00

1

. Rup

■ ■

-04

■ ■

♦

-0.5 00 05 1 0

Among ASVs present in surface water samples, the most abundant (sum 2: 75% total sequences) belong to the Unclassified (24.13%), Comamonadaceae (12.63%), ACK-Ml (6.38%),

Pelagibacteraceae (4. 78%), Cytophagaceae ( 4.34% ), Cryomorphaceae ( 4.25% ),

Oxalobacteraceae (3.73%), NA (2.91 %), Chitinophagaceae (2.76%), Flavobacteriaceae

(2.62%), Porphyromonadaceae (2.59%), Moraxellaceae (2.28%), and Methylophilaceae (2.09%) families. Most abundant phyla are shown in Fig. S6 of the Supplementary Information.

Shared ASVs among surface waters. To determine the extent of overlap between microbial

communities among different sampling locations, shared ASV s between samples originating from Woodlawn Beach surface waters was tabulated (Table 5).

Table 5. Shared ASVs between Woodlawn Beach surface waters.

Lake Erie Lake Erie Rush Creek Blasdell Creek

(LE2) (LE3) (Rup) (BD)

Lake Erie (LE2)

---

Lake Erie (LE3) 2879

---

Rush Creek (Rup) 1153 1624

---

Blasdell Creek (BD) 1321 1976 1562

To determine the influence of groundwater at Woodlawn Beach, 9 additional groundwater

samples from 3 separate groundwater monitoring wells (MW2, MW3, MW7) were sequenced. In total, 25 samples including both surface and ground water samples within Woodlawn beach were used for comparison of surface and ground waters. Shannon-Weaver and Simpson alpha

diversity indices were calculated for each sample to investigate changes in diversity associated with spatial separation. The Shannon-Weaver index varied from 4 to 8 and the Simpson index varied from 0.92 to 0.999 (Supplementary Information, Fig. S7).

Differences in community composition among Woodlawn Beach surface and ground waters were visualized using a non-parametric multidimensional scaling (nMDS) ordination plot constructed using Bray-Curtis dissimilarity coefficients (Fig. 7). Among Woodlawn Beach surface and ground waters, sample location was found to have a significant impact on community composition (ANOSIM, R = 0.8463, p = 0.001). Specifically, the surface water samples grouped and were distinct from the groundwater samples (Fig. 7).

Bray Curtis nMDS (Woodlawn Surface and Ground Waters by Site)

. ..

OS

0.0

. ..

..

♦

•• _•

Site

BD LE2 LE3

I

. MW3

,0

"··

•l.0

·S..__...---.---~---~

Figure 7. Bray Curtis non-metric multidimensional scaling plot (Stress= 0.08) indicating differences in community composition between Woodlawn Beach surface and ground waters.

Among ASVs present in groundwater samples, the most abundant (sum 2: 75% total sequences) belong to the Unclassified ( 43.31 %), NA (10.52%), Rhodocyclaceae (3.20%),

Campylobacteraceae (2.60%), Geobacteraceae (2.50%), Comamonadaceae (2.39%), Rhodospirillaceae (2.36%), Helicobacteraceae (2.26%), Pseudomonadaceae (2.07%), Oxalobacteraceae (1.98%), Desulfobulbaceae (1.59%), and Dehalococcoidaceae (1.46%) families. Most abundant phyla are shown in Fig. S8 of the Supplementary Information.

Shared ASVs among surface and ground waters. To determine the extent of overlap between

microbial communities among different sampling locations, shared ASV s between samples originating from Woodlawn Beach surface and ground waters was tabulated (Table 6).

Table 6. Shared ASV s between Woodlawn Beach surface and ground waters.

Lake Erie Lake Erie Rush Creek Blasdell Creek Groundwater

(LE2) (LE3) (Rup) (BD) (GW)

Lake Erie

(LE2)

~

Lake Erie

(LE3) 2879

~

Rush Creek

(Rup) 1153 1624

~

Blasdell Creek

(BD) 1321 1976 1562

~

Groundwater

(GW) 600 585 671 659

Profiling sewage taxa within Woodlawn Beach surface and ground water samples.

To determine the relative contribution of sewage to Woodlawn Beach surface water and groundwater communities, the abundance (meads) of taxa identified as sewage (both effluent and influent) were plotted against the abundance (meads) of all taxa (sewage and non-sewage)

within Woodlawn Beach surface and ground water samples across all dates, respectively (Fig. 8).

Sewage reads comprised 0.42%, 1.13%, 2.75%, 4.48%, 2.01%, 0.86% and 0.29% ofRup, MW7, MW3, MW2, LE3, LE2 and BD samples, respectively. Among sewage ASVs present in surface water samples, the most abundant (sum 2: 90% total sewage sequences) belong to the

Rhodocyclaceae (45.67%), Weeksellaceae (14.66%), Aeromonadaceae (12.17%),

Comamonadaceae (8.99%), and Chitinophagaceae (8.60%) families. Among sewage ASVs present in groundwater samples, the most abundant (sum 2: 90% total sewage sequences) belong to the Rhodocyclaceae (60.49%), Aeromonadaceae (27.12%), and Comamonadaceae (8.45%) families.

Relative Abundance of Sewage Taxa (Genus) in Woodlawn Surface and Ground Waters

Rup

Genus MW7

MW3

2 MW2

i:i5

LE3

LE2

BD

0 2 3 4

Percentage of Sequences

Temporal changes in community composition in surface waters and ground water at

Woodlawn Beach. The effect of sample collection date on Woodlawn Beach surface and ground waters was investigated. Differences in community composition among Woodlawn Beach

surface and ground waters were visualized using a non-parametric multidimensional scaling (nMDS) ordination plot constructed using Bray-Curtis dissimilarity coefficients (Fig. 9). Among all Woodlawn beach waters, collection time was found to not have a significant impact on community composition (ANOSIM, R ⁼ -0.05126, p ⁼ 0.762).

Bray Curtis nMDS (Woodlawn Surface and Ground Waters by Date)

1.0 ♦

0.5

~

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••

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72616

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•1.0

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Figure 9. Bray Curtis non-metric multidimensional scaling plot (Stress= 0.08023) indicating differences in community composition between Woodlawn Beach swface and ground waters by sampling date.

V. Discussion

Increased urbanization of coastal shorelines has led to increased pollution resulting in impaired water quality. Coastal microbial pollution and resulting beach closures represent important public health risks and economic losses to individuals and coastal communities. Identifying pollution sources and the associated health risks using conventional methods of water quality monitoring relying on FIB enumeration are limited. To inform efforts to protect public health and mitigate pollution sources, spatial and temporal information is needed about the sources and fate of microbial pollution. Microbial community analysis using NGS has the potential to identify microbial pollution sources by their microbial community signature and assess their impact over time [17, 18]. Studies applying microbial community analysis thus far have focused on sewage impacted surface waters. Less is known about microbial community dynamics at beaches impacted by non-point sources of microbial pollution and, to our knowledge, no studies have compared microbial communities in surface waters and groundwater at a recreational beach with impaired water quality. This paper addresses these knowledge gaps and informs future uses ofNGS to better understand coastal microbial community dynamics in surface waters and groundwater.

Spatial separation along eastern Lake Erie beaches results in separate microbial

communities. Previous studies performed on microbial communities have determined spatial variation among sampling locations to produce unique microbial signatures [ 18, 28]. This is consistent with the results presented in this paper showing significant grouping by beach site despite close proximity (Gallagher and Woodlawn as well as Woodlawn and Hamburg beaches

excluded from the analysis however, sampling location is no longer a significant grouping variable suggesting that the microbial communities at Woodlawn, Hamburg, and Bennett

beaches are similar. Gallagher Beach is behind a break-wall and is likely influenced by different environmental variables. While the microbial community at Woodlawn Beach differs from Gallagher Beach, the fact that it is similar to Hamburg and Bennett Beaches implies that the microbial community composition itself cannot explain the consistently higher E. coli concentrations measured at Woodlawn Beach compared to the other beaches.

Analysis of shared ASVs reveal partial overlaps of microbial communities among beach

samples, which is expected given their proximity along eastern Lake Erie. Among ASV s present in beach samples, the most abundant (>.5%) belong to the phyla Proteobacteria (35.39%), Bacteroidetes (27.78%), Cyanobacteria (13.92%), Actinobacteria (11.89%), and

Verrucomicrobia (6.13%). These results are consistent with findings reported in Newton et. al.

(YEAR) which reviews and analyses freshwater lake bacteria from 69 published papers [29]. Of the 21 phyla recovered from lake epilimnia in the study, 5 phyla (Proteobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria, Verrucomicrobia) were recovered commonly and most

abundantly [29].

The impact of sewage at each beach was considered by examining the presence of sewage ASVs in the beach samples. The relative abundance of sewage ASVs was <l % for all beaches

suggesting little to no significant sewage-derived contamination in the beach surface water samples. These results are consistent with findings reported in other source tracking studies in which sewage ASVs regularly represented <0.5% of the surface water microbial communities during dry weather [ 17]. Future research is needed to assess potential health risks associated with

the presence of sewage associated taxa and determine the criteria for classifying surface waters as sewage-impacted.

Microbial communities at eastern Lake Erie beaches vary temporally. In addition to spatial microbial community distinctions, we also observed significant differences in microbial

community composition by sampling date over the 4 weeks of sampling with and without including Gallagher Beach (a non-swimming beach). Microbial communities are affected by both biotic (viral lysis, predation, and/or competition) and abiotic (sunlight exposure,

temperature, etc.) variables [30]. Previous studies have identified variables that significantly impact coastal microbial composition including temperature and nutrient concentration, rainfall, sunlight, wind speed and direction, and lake height [30-32]. The 4 dates sampled for this study included a range of environmental conditions ranging from one day with rainfall, high turbidity (33.7 NTU), wind, and waves (July 14^th), to calmer conditions (45.85 NTU and 39.75 NTU) (July 19^th and July 26^th) to a high temperature day with no waves and low turbidity (1 .455 NTU) (August 2^nd). Based on E.coli concentrations, Woodlawn Beach would have been closed to recreational activity on July 14^th, 19^th and 26^th while Gallagher Beach would have only been closed on July 14^th, Hamburg Beach on July 26^th and August 2^nd and Bennett Beach only on July 26^th• The highest E. coli concentrations across beaches were observed on July 26^th which was likely related to 0.81 inches of rainfall the day before samples were collected.

While the percent sewage ASV s found at each beach was less than 1 %, more sewage associated ASV s were detected after rainfall events and when turbidity levels were higher. The temporal persistence of microbial pollution in coastal waters and the environmental variables affecting

persistence are not well understood [33, 34]. More research is needed to determine the impact and persistence of sewage pollution.

Surface water and groundwater microbial communities at Woodlawn Beach are distinct.

The interaction between groundwater, surface waters, and coastal waters is important to consider as groundwater can easily be contaminated by leaking pipes and contaminated surface waters, and can transport microbial pollutants to the coastal environment. At Woodlawn Beach, the microbial communities in the groundwater samples were statistically different (p = 0.001) than the communities in the surface water samples (Rush Creek and Blasdell Creek) as well as in Lake Erie (LE3 and LE2). In addition, Lake Erie samples shared more ASVs with surface water than groundwater samples. These results suggest the surface water microbial community has a greater influence on the Lake Erie microbial community at Woodlawn Beach than groundwater.

Among ASVs present in surface water samples, the most abundant (>.5%) belong to the Proteobacteria (40.69%), Bacteroidetes (25.66%), Cyanobacteria (12.63%), Actinobacteria (9.17%), and Verrucomicrobia (3.72%) phyla. These results are consistent with findings reported in a previous study performed on freshwater microbial communities within the Great Lakes region [29]. Among the groundwater samples, the most abundant (>.5%) ASVs belong to the Bacteroidetes (18.34%), Firmicutes (4.03%), OP3 (13.22%), and Proteobacteria (38.84%) phyla.

The impact of sewage on Woodlawn Beach microbial communities was considered by

examining the presence of influent and effluent sewage ASV s in Woodlawn Beach surface and ground water samples. The relative abundance of sewage ASV s within Woodlawn Beach surface waters ranged from 0.29% (BD) to 2.01 % (LE3), suggesting a lack of significant sewage derived

contamination in surface water samples. In groundwater samples however, the relative abundance of sewage ASVs ranged from 1.13% (MW7) to 4.48% (MW2), warranting further analysis. Dechloromonas (genus), Aeromonadaceae (family), and Limnohabitans (genus), ASVs known to be prevalent in freshwater, were the most abundant sewage ASVs represented within groundwater samples comprising 60.5%, 26.5%, and 8.45% of ASVs respectively [35-37]. The shared taxa between surface waters and the sewage associated taxa group suggest this data should be interpreted carefully. On July 14th (rainy day), there appeared to be an influx of these 3 taxa within groundwater samples suggesting interactions between surface water and

groundwater or potential sewage contamination of the groundwater through leaking pipes or surface runoff infiltration. More research on groundwater microbial communities and the influence of surface waters on groundwater is warranted.

Microbial communities in surface waters and groundwater at Woodlawn beach did not vary temporally. Despite determining collection date to be significant towards microbial

community composition among the four Lake Erie beaches sampled, the same was not true when applied to Woodlawn Beach surface waters. Microbial communities in Woodlawn Beach surface water samples were not different by collection date which is expected since all surface waters at Woodlawn Beach were affected by the same environmental variables on a given day (e.g. runoff, wind direction and speed, rainfall, etc.). After including groundwater in the analysis, sample collection date remained an insignificant determinant of microbial community composition.

Study Limitations. One of the limitations of the present study was the frequency of sampling performed over the limited study period. Year-round sampling of additional sampling sites along

multiple watersheds over seasonal changes, allowing for a more in-depth study of microbial community dynamics in the Lake. In addition, sampling of additional potential sources of microbial pollution including bacterial reservoirs such as sand and lake bottom sediments could provide crucial information needed to better understand microbial dynamics and potential reasons for high E. coli concentrations at Woodlawn Beach.

Methodological limitations should be considered during interpretation and implementation of NGS results. It is important to note that NGS only provides relative abundance information as opposed to quantitative concentrations. During sequencing, bacterial proportions may be altered through the introduction of PCR bias, specifically PCR drift, during amplification [38]. PCR drift is the result of random events occurring in the early cycles of the PCR reaction and is

predominantly responsible for changes in relative abundances between bacteria during amplification [39]. Use of a mock community as a positive control helps provide insight into how relative abundances may have been influenced during PCR [21]. Sequencing of an

extraction blank helped us conclude the presence of a negligible amount of contamination across all samples (<1 %), allowing us to confidently proceed with the analysis.

Implications for the use of NGS in fecal pollution source tracking. Results from the present

research show that microbial community analysis using NGS can provide valuable insight into surface water and groundwater interactions and their influence on coastal water quality. Results also show the potential for NGS data to be used to detect sewage associated taxa at potentially low levels. However, careful analysis of the influence of sewage should be done when taxonomic assignments are given above the genus or species level. Other studies utilizing NGS for

comprehensive looks at microbial communities have determined NGS and the information it provides to be extremely useful analytical tools towards informing the management of microbial

contamination [17, 40]. Decreasing costs coupled with the rapid development ofNGS technology has resulted in many opportunities to better understand microbial community dynamics in complex systems which will inform effective monitoring practices and mitigation strategies [ 41].

VI. References

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VII. Supplementary Information

Figure S1. Shannon-Weaver and Simpson Diversity Indices generated using eastern Lake Erie beach samples. The Shannon-Weaver index varied from 4.2 to 5.9 and the Simpson index varied from 0.945 to 0.985

Shannon Simpson

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swimming beaches (Bennett, Hamburg, Woodlawn) were visualized using a non-parametric multidimensional scaling (nMDS) ordination plot constructed using Bray-Curtis dissimilarity coefficients (Stress= 0.0444). Among eastern Lake Erie swimming beaches, sample location does not significantly impact community composition (ANOSIM, R = 0.02778, p = 0.35).

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Phylum

I

Bennett

2 p_

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Actinobacleria Bacteroidetes Cyanobacteria Proteobacleria Verrucomicrobia

Figure S4. Differences in microbial community composition among eastern Lake Erie

swimming beaches (Bennett, Hamburg, Woodlawn) by sampling date were visualized using a non-parametric multidimensional scaling (nMDS) ordination plot constructed using Bray-Curtis dissimilarity coefficients (Stress= 0.0444). Among eastern Lake Erie swimming beaches,

sampling date is found to significantly impact community composition (ANOSIM, R = 0.3765, p

= 0.02).

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Figure S5. Shannon-Weaver and Simpson Diversity Indices generated using Woodlawn Beach

surface water samples. The Shannon-Weaver index varied from 4.5 to 6.4 and the Simpson index varied from 0.95 to 0.99

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