Application of SourceTracker for Accurate Identification of Fecal

Mar 5, 2018 - The efficacy of SourceTracker software to attribute contamination from a variety of fecal sources spiked into ambient freshwater samples...
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Environmental Measurements Methods

Application of SourceTracker for Accurate Identification of Fecal Pollution in Recreational Freshwater: A Double-Blinded Study Christopher Staley, Thomas Kaiser, Aldo Lobos, Warish Ahmed, Valerie J. Harwood, Clairessa M. Brown, and Michael J. Sadowsky Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b05401 • Publication Date (Web): 05 Mar 2018 Downloaded from http://pubs.acs.org on March 6, 2018

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Application of SourceTracker for Accurate Identification of Fecal Pollution in Recreational Freshwater: A Double-Blinded Study Christopher Staley1, Thomas Kaiser1, Aldo Lobos2, Warish Ahmed3, Valerie J. Harwood2, Clairessa M. Brown1, and Michael J. Sadowsky1,4,* 1

BioTechnology Institute, University of Minnesota, 1479 Gortner Ave, St. Paul, MN 55108; 2Department of Integrative Biology, SCA 110, University of South Florida, 4202 East Fowler Ave, Tampa, Florida 33620; 3CSIRO Land and Water, Ecosciences Precinct, 41 Boggo Road, Qld 4102, Australia; 4Department of Soil, Water, and Climate, University of Minnesota, 1991 Upper Buford Cir, St. Paul, MN 55108

*

Corresponding Author: Michael J. Sadowsky, BioTechnology Institute, University of Minnesota, 140 Gortner Lab, 1479 Gortner Ave, Saint Paul, MN 55108; Phone: (612)624-2706, Email: [email protected]

Running title: SourceTracker validation blinded study

Keywords: microbial community / microbial source tracking / next-generation sequencing / SourceTracker / water quality

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ABSTRACT

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The efficacy of SourceTracker software to attribute contamination from a variety of fecal

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sources spiked into ambient freshwater samples was investigated. Double-blinded

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samples spiked with ≤ 5 different sources (0.025-10% vol/vol) were evaluated against

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fecal taxon libraries characterized by next-generation amplicon sequencing. Three

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libraries, including an initial library (17 non-local sources), a blinded source library (5

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local sources), and a composite library (local and non-local sources) were used with

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SourceTracker. SourceTracker’s predictions of fecal compositions in samples were

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made, in part, based on distributions of taxa within abundant genera identified as

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discriminatory by discriminant analyses, but also using a large percentage of low

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abundance taxa. The initial library showed poor ability to characterize blinded samples,

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but, using local sources, SourceTracker showed 91% accuracy (31/34) at identifying the

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presence of source contamination, with two false positives for sewage and one for

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horse. Furthermore, sink predictions of source contamination were positively correlated

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(Spearman’s ρ ≥ 0.88, P < 0.001) with spiked source volumes. Using the composite

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library did not significantly affect sink predictions (P > 0.79) compared to those made

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using the local sources alone. Results of this study indicate that geographically

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associated fecal samples are required for SourceTracker to assign host sources

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accurately.

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1. INTRODUCTION Fecal pollution of water is a significant global health issue due to the likely

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presence of waterborne pathogens. Therefore, identification of the source(s) of fecal

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pollution is critical for implementing appropriate remediation strategies and protecting

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human health risks associated with water use and reuse. Fecal pollution of

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environmental waters has been historically assessed by enumerating fecal indicator

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bacteria (FIB), such as Escherichia coli, Enterococcus spp., and Clostridium perfringens

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using culture-based methods1. However, monitoring FIB in environmental waters does

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not provide information on the source of pollution, e.g., human or animal feces2, or

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naturalized FIB in the environment3,4, necessitating the use of microbial source tracking

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(MST) methodologies. Early MST tools were library-dependent and required isolation

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and typing of hundreds-to-thousands of FIB from human and animal feces to generate

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source-associated libraries5–7. Conversely, library-independent methods target a gene

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fragment from a taxonomic group that typically co-evolved, or is otherwise associated

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(e.g. by infection), with a specific host, providing a host-associated marker typically

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enumerated by quantitative PCR (qPCR)2.

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Either type of MST method requires extensive validation prior to its application in

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environmental studies8. In a previous multi-laboratory study, 22 laboratories used 12

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different library-dependent and –independent methods to determine sources in blinded

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water samples spiked with one to three fecal sources9. Results indicated that library-

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dependent methods were prone to false positive detection, while library-independent

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methods tended to produce false negative results. Despite drawbacks of both types of

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methods, library-independent methods have been predominantly used over the last

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decade2, due to decreased labor and supply costs as well as reduced spatiotemporal

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variability compared to that associated with library-dependent methods8. More recently,

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several studies have suggested the use of next-generation sequencing (NGS) to

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characterize bacterial contamination from multiple sources including recreational

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waters, hospital environments, and other ecosystems10–13. In a similar strategy to that

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used in previous MST method comparisons, a blinded study was performed in which

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three community-based methods were evaluated, including terminal restriction fragment

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length polymorphism, phylogenetic microarray, and Illumina NGS using community

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dissimilarity indices14. Sixty-four blinded samples, spiked with single or dual sources,

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were generated from 12 host groups. While all three methods were able to correctly

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identify the dominant sources for 95% of the blinded samples, detection of the second,

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minor sources was less accurate.

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To accurately quantify multiple sources present at low abundances in sink

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communities (communities impacted by contaminated from a source, e.g., recreational

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waters receiving fecal contamination), the Bayesian algorithm SourceTracker was

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proposed10. The allowance of unknown sources using this method was hypothesized to

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improve accuracy in overall source assignments. The SourceTracker program has been

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evaluated in field studies and validated against hydrodynamic modeling of source

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contamination15 as well as in vitro constructed source mixtures16. The program

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performed with high accuracy, sensitivity, and specificity using default parameters.

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However, studies have also noted that source assignments with high relative standard

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deviations (i.e., greater variability in quantitation across technical replicates) had lower

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confidence16,17, and these more variable sources were detected at very low abundances

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(≤ 10%). However, inconsistencies were noted when results were compared to those

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obtained using qPCR assays for the well-established, human-associated HF183

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marker18, as well as markers for avian and cattle fecal contamination19.

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Results of some validation studies tended to support the use of SourceTracker to

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characterize multiple sources of fecal pollution, at least in a toolbox approach. However,

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the extent to which factors that typically encumber library-dependent methods, such as

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library size and spatiotemporal variability, have yet to be assessed in a systematic way.

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Moreover, while NGS offers the promise of characterizing members of the rare

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biosphere20, a variety of technical limitations and causes of error exist21. Furthermore,

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the discrepancy between qPCR assays, which target taxa typically abundant in source

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communities, e.g., Bacteroidales2, compared to SourceTracker results suggests a

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disconnect between more traditional microbiological interrogation of source

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communities compared to the more highly technical machine learning and Bayesian

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approaches applied to evaluate NGS datasets.

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The primary aim of this study was to clarify the inconsistent results obtained

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between traditional statistical and Bayesian approaches to determine differences in

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community composition between source categories, as well as address the feasibility of

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using non-local source libraries to identify local fecal source contamination. Source

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communities from previously published amplicon-based NGS datasets were

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characterized using non-parametric, multivariate statistics (principle coordinate analysis

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and linear discriminant analysis of effect sizes) to identify which genera in these

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communities were presumed to be the most informative. These genera were then

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compared against operational taxonomic units (OTUs) selected by the SourceTracker

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algorithm, which assumes a Dirichlet distribution among source (i.e., host animal

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bacterial communities) and sink (i.e., spiked freshwater) communities22 and employs a

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Bayesian machine learning approach10. The inter-laboratory transmissibility of the

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source library, using SourceTracker, was then challenged against blinded freshwater

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samples spiked with source fecal material from a geographic region not represented in

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the initial library. Finally, an amplicon-based library was constructed using the local,

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spiked sources to determine which combination of sources and community features

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(genera) afforded the best prediction of the true composition of the blinded samples.

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Results of this study provide a basis for interpreting the more highly technical results

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obtained from these machine learning, Bayesian analyses as well as provide insight as

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to the most biologically robust features to include and consider when building NGS

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libraries for community-based MST.

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2. METHODS

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2.1.

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Initial Taxon Library Assembly The initial fecal taxon library was comprised of previously published amplicon-

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based source communities done using Illumina NGS of the V5+V6 hypervariable

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regions of the 16S rRNA gene. Bacterial communities in primary-treated influent from

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wastewater treatment plants (WWTPs) came from various cities throughout

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Australia19,23 as well as previously unpublished data from California, USA. Fecal

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samples from birds (including plover, wood duckling, noisy miner, Pacific black duckling,

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blue-faced honeyeater, magpie, crow, ibis, seagull, and topknot pigeon), cats, dogs,

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horses, kangaroos, and possums were also included and were obtained from

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throughout Queensland, Australia19,24. Fecal samples from beavers, Canada geese,

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cats, cattle (beef and dairy, as separate sources), chickens, deer, dogs, gulls, rabbits,

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swine, and turkeys were obtained throughout Minnesota, USA17. Previously unpublished

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data from dogs and gulls collected in California were also included. In total, the initial

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library was comprised of beavers (n = 19), beef cattle (10), birds (13), Canada geese

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(25), cats (27), chickens (15), dairy cattle (21), deer (19), dogs (42), gulls (28), horses

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(14), kangaroos (14), possums (18), rabbits (18), swine (18), turkeys (18), and influent

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from WWTPs (79). Sources used to create blinded samples for validation (described

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below) were not included in the initial library.

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2.2.

Sample preparation and DNA extraction For the double-blinded study, 20 L of freshwater was collected from Hillsborough

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River (28.0549° N, 82.3635° W, Tampa, FL, USA). Ten individual fresh fecal samples

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were collected from each of five animal hosts, including cow, horse, cat, and dog.

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Animal fecal samples were obtained around the Tampa Bay area. In addition,

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approximately 1 g of feces (wet weight) from each animal fecal sample was measured

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and mixed to form a single-host-species composite fecal sample (10 individual animals

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represented per composite) to spike into blinded samples (see below) and to serve as

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positive controls for identification of blinded sources. A primary-treated wastewater

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sample was collected from a WWTP in Tampa, and, in triplicate, 10 ml sewage was

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filtered through 0.45 µm mixed cellulose esters filter membranes (Thermo Fisher

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Scientific, Waltham, MA, USA). The DNeasy PowerSoil DNA extraction kit (QIAGEN,

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Hilden, Germany) was used to extract DNA from 250 mg (wet weight) of feces from all

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individual animals, composites, and sewage filters, with a holding time of no more than

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6 hours prior to extraction. Forty-three fecal source samples and four composite source

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samples were generated for NGS sequencing.

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Composite animal fecal samples were diluted with 300 mL of phosphate buffered

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saline (pH 8.0) to make fecal slurries for each animal host. Ambient river water samples

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(300 ml) were spiked with fecal slurries and sewage in various combinations (ranging

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from 0.025 to 10% vol/vol per source; Table 1). Spiked river water samples were filtered

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through 0.45 µm filter membranes and the PowerSoil kit was used to extract DNA

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directly from the membrane. All DNA samples were stored at -80ºC and shipped to the

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analytical laboratory, blinded, on dry ice.

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2.3.

PCR Amplification and Sequencing The V5+V6 regions of the 16S rRNA gene were amplified using the

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BSF1064/784 primer set, described previously25. Amplification and sequencing was

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done using the dual index method by the University of Minnesota Genomics Center

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(UMGC, Minneapolis, MN, USA)26. Paired-end sequencing was done on the Illumina

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MiSeq platform (Illumina, Inc., San Diego, CA) at a read length of 300 nucleotides (nt).

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Previously unpublished sequencing data are available under BioProject accession

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number SRP118701 in the Sequence Read Archive at the National Center for

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Biotechnology Information.

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2.4.

Bioinformatics and SourceTracker Analyses

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Sequence processing and analysis was done using mothur ver. 1.35.127.

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Sequences were trimmed to 150 nt, to remove low quality regions at the 3’ ends while

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still allowing for an overlap of approximately 20 nt, and paired-end joined using fastq-

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join28. Quality trimming was performed as described previously29. Samples were aligned

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against the SILVA database ver. 12330 and subjected to a 2% pre-clustering step31.

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Chimeras were identified and removed using UCHIME ver. 4.2.4032. Operational

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taxonomic units were assigned at 97% similarity using complete-linkage clustering and

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taxonomic classification was performed using the Ribosomal Database Project release

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ver. 1433. Samples source libraries were rarefied to 10,000 sequence reads per sample

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for comparison34. Blinded sink samples were similarly normalized to 10,000 sequence

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reads, where possible, for consistency. Blinded sink samples SW06, SW07, SW12,

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SW14, SW25 had fewer sequence reads (5616, 8599, 6414, 1769, and 271 reads,

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respectively) but were included in the SourceTracker analyses since lower numbers of

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reads among sink samples would not influence source assignments.

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SourceTracker analysis was performed using SourceTracker ver. 0.9.810 and

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default parameters. To determine the extent to which SourceTracker could differentiate

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individual sources from geographic regions among the preliminary dataset, samples

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were binned by broad host category, which included domesticated animals (cats and

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dogs), livestock (cattle, horses, and swine), avians (birds, Canada geese, chickens,

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gulls, and turkeys), wildlife (beavers, deer, kangaroos, possums, and rabbits), and

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WWTP samples. Due to compositional similarity in communities of some members of

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the avian group (i.e., the general bird group from Queensland and gulls from California)

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with WWTP communities, these avian groups were included in an additional grouping

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with WWTP samples. Among WWTP samples, geographic location was specified as

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accurately as possible based on prior publications19,23. The Queensland WWTP

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samples reflect samples collected from a broader geographic region and at a different

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sampling time than Brisbane WWTP samples, which were collected from a single

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WWTP.

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Approximately 50% of the individuals in each source category (host and

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geographic location) were randomly grouped to SourceTracker source or sink

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categories in order to determine the library’s ability to classify sink samples not included

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in the library. Taxonomic fingerprints for each source category were determined by

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genus-level classification of OTUs that contributed to the sink assignment of that

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category, normalized to 100% of the total sink prediction. To evaluate blind samples,

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three source libraries were used: 1) the initial library without sources from Tampa, FL, 2)

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the FL sources alone, and 3) all sources (initial + FL). Geographic distinction was not

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included in source distinctions to evaluate blind samples, and false positives were

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determined to be samples in which SourceTracker identified ≥ 1.0% of a source that did

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not correspond to a spike. To assess potential mischaracterization of samples when a

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source was not included in the library, the FL library alone was used to analyze samples

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with all samples related to a single spiked source designated a SourceTracker sink (five

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separate runs, one for each spiked source). All results reflect those of one

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SourceTracker run per library configuration using default parameters.

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2.5.

Statistical Analyses Differences between bacterial communities (beta diversity) were determined

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using analysis of similarity (ANOSIM)35 calculated using Bray-Curtis dissimilarity

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matrices36. Similarly, ordination was performed by principal coordinate analysis

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(PCoA)37 using Bray-Curtis matrices. Significance of sample clustering on ordination

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plots was evaluated by analysis of molecular variance (AMOVA)38. To determine which

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genera were significantly correlated with ordination position, OTUs were classified to

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genera and total genera abundances were related using corr.axes analysis for

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Spearman correlations in mothur. For clarity, only the five most abundant genera among

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the broader grouping were plotted. Linear discriminant analysis (LDA) of effect sizes

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(LEfSe)39 was used to identify highly differential, source-associated OTUs (LDA score ≥

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4.0), which were then classified to genera. Spearman correlations relating

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SourceTracker sink predictions to source material spiked (% volume) and ANOVA

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analysis comparing sink predictions between the FL blinded and full source libraries

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were calculated using XLSTAT ver. 17.06 (Addinsoft, Belmont, MA, USA). All statistics

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were evaluated at α = 0.05, with Bonferroni correction for multiple comparisons.

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3. RESULTS

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3.1. Community Composition of the Initial Library

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Among feces from all hosts and sewage represented in the initial library, the

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majority of communities were comprised predominantly of members of the families

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Lachnospiraceae and Ruminococcaceae, within the Firmicutes phylum, and

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Bacteroidaceae and Prevotellaceae, within the Bacteroidetes phylum (Supplementary

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Figure S1). Lower abundances of relatively less abundant families were observed

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among domesticated animals than the other host source groups. Moreover,

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communities within the avian group tended to harbor greater relative abundances of

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Enterobacteriaceae and Pseudomonadaceae (phylum Proteobacteria), and

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Lactobacillaceae (Firmicutes).

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Among all sources, communities generally differed significantly from each other

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by both host species and geography, within a host species, as evaluated by ANOSIM (r

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= 0.879, P < 0.001; Figure 1). Some similarity was observed among the Brisbane,

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Perth, and Queensland WWTP communities, as well as between Hobart and

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Melbourne, and these communities did not differ significantly at a Bonferroni-corrected α

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= 0.0002. Queensland bird communities were also not significantly different from the

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Hobart and Perth WWTPs (r = 0.365 and 0.263, P = 0.001 and 0.061). When grouped

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into broader categories (e.g., domesticated animals, as described in Methods), samples

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were also clustered independently following ordination by PCoA (AMOVA P < 0.001;

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Figure 1), although similarities observed by ANOSIM were maintained on ordination and

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were not significantly separated by AMOVA.

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The OTUs that were identified as highly discriminant among source categories by

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LEfSe (Figure 2) were typically classified among the genera that were significantly

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associated with ordination position by Spearman correlation (P < 0.05; Figure 1).

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Furthermore, these genera tended to belong to the most abundant families found in

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each host group (Figure S1). For example, the genus Bacteroides was correlated with

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ordination position for Queensland cats and dogs in the “domestic” source category

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(Figure 1), and was included among the discriminatory genera in LEfSe analysis (Figure

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2). However, among livestock sources, the most abundant genera that correlated with

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ordination position were not discretely associated with specific source categories, as

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noted by positioning of most of these away from source communities, with the exception

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of Xylanibacter, (Figure 1). Correspondingly, LEfSe identified OTUs primarily within less

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abundant genera as source-associated but did not identify OTUs within Xylanibacter to

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differentiate livestock, although Rikenella, Alistipes and Bacteroides were identified

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(Figure 2).

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3.2.

SourceTracker Results for the Initial Library

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Overlap of bacterial communities among host sources was evaluated by

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assigning all samples in a single host group in the library, irrespective of geography, as

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a source and all remaining samples in the initial library as sinks (Supplementary Table

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S1). A moderate-to-high (26.2 – 99.5%) predicted community similarity was observed

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among host samples within the broader avian group. Cat and dog communities also

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showed a greater degree of overlap, with mean similarity of 77.5 and 89.4%. Livestock

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and wildlife showed much less overlap in community composition (5.2 – 23.6% and 2.1

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– 44.2% predicted similarity, respectively). In contrast, beef and dairy cattle showed

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much greater overlap as 77.3 and 89.9% of the communities were predicted to be in

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common. Inter-group similarity was generally much less than intra-group similarity, with

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the exception that a moderate-to-high degree of similarity was predicted between avian

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sources and those from WWTPs (mean 43.1 ± 25.6% community similarity).

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To assess the accuracy of SourceTracker software to discriminate among closely related sources and different geographic regions, and to classify OTUs from fecal

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samples not in the library, the initial library was divided approximately in half (i.e., half of

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the individuals in a host category were assigned as source and half were sinks) and

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evaluated amongst the broader host categories. Overall, sink predictions were accurate,

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with ≥ 80% of sink community taxa identified as the corresponding source category, with

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specificity to geography (Figure 3). Sinks within the avian category were identified less

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accurately, with approximately 60% of chicken communities correctly assigned, while

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bird communities from Queensland were poorly identified as a mixture of MN Canada

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geese and CA gulls, in addition to the correct source assignment. Bacterial communities

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from WWTPs were also less accurately identified compared to other animal sources,

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with overlap in assignments among the more highly similar WWTPs. Furthermore, the

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bacterial communities in WWTP influent that were poorly assigned were not well

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represented in the source library (n < 5).

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Informative OTUs utilized by the SourceTracker software to perform source

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assignments in sink samples varied among specific host groups within broader source

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categories (Figure 4). Many of the OTUs were classified within abundant genera that

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were associated with specific source categories, e.g., a large proportion of the

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SourceTracker fingerprint for Queensland dogs was attributed to OTUs within the genus

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Xylanibacter (Figure 4A), which was highly correlated with their ordination position

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(Figure 1A). Livestock and wildlife sources were predominantly defined by OTUs within

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less abundant genera (Figures 4B and 4D), while genera at greater abundances were

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poorly associated with hosts (Figures 1B and 1D). The avian sources, which had more

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distinct community compositions than did other categories (Figure S1), could be

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identified using OTUs within a fewer number of genera than most of the other broader

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categories, particularly the California birds in which Pseudomonas was a dominant

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driver of classification, and less abundant genera comprised less than 5% of the

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taxonomic fingerprint (Figure 4C). Furthermore, profiles for communities represented at

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lower abundances in the source libraries (e.g., Hobart and Melbourne WWTPs) were

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defined almost entirely by OTUs within less prominent genera (Figure 4E-F).

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3.3.

Evaluation of Blind Samples Using the Initial Library Source 05 was identified as the FL WWTP source based on the number of

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samples (n = 3), since only triplicates of this source were processed. The remaining

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blinded source samples were evaluated against the initial library using SourceTracker,

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but could not be unambiguously assigned to specific sources by this method (mean sink

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prediction ≤ 6.9% to any source, irrespective of geography). Furthermore, community

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compositions in all blinded sources were significantly different from all other sources

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represented in the preliminary library (ANOSIM P < 0.001). Therefore, results from

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PCoA and LEfSe analyses among the initial library sources were used to narrow down

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possible sources based on predominant taxonomic composition (Supplementary Figure

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S2). Sources 01 and 02 were determined to belong to livestock or wildlife groups based

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on the abundance of Rikenella (Figures 1B and 1D). Sources 03 and 04 were

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preliminarily identified as domestic animal sources based on abundances of Blautia

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(Figure 1A) and Catenibacterium (Figure 2A). Sources were further interrogated by

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PCoA within these broader host source categories (Supplementary Figure S3). On the

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basis of these observations, sources were identified as coming from cows, horses, cats,

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and dogs, respectively, which were then confirmed (with those who prepared the double

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blinded samples) to be correct. Composite source samples were evaluated as positive

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controls against the FL library alone and were correctly classified by SourceTracker at

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>86% community similarity.

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Blinded samples were interrogated against the unblinded FL sources (Table 1),

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which comprised the local library. Source identifications in samples, as determined by

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SourceTracker, were 91% (31/34) accurate based on presence/absence sample

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composition. In two samples, SW25 and SW32, SourceTracker identified a weak

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sewage signature representing 1.2 and 2.3% of the community, respectively, which was

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not consistent with a source spike. Similarly, in sample SW31, which was spiked with

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only cow fecal material, SourceTracker identified a low signature for horse, as well

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(6.7% of the community). When data from single FL source categories (e.g., all cattle)

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were removed from the library, individual fecal samples were incorrectly assigned,

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generally as the most closely related host. Cow samples were misidentified as horse

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(mean 13.6 ± 2.4% of the community), horse samples were misidentified as cow (5.3 ±

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1.4%), cat samples were misidentified as a mixture of dog and sewage (61.3 ± 21.7%

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and 15.8 ± 22.3%, respectively), dog samples were misidentified as cat (90.5 ± 9.4%),

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and WWTP samples were misidentified as cat (5.5 ± 1.8%). With allowance for these

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misclassifications (e.g., ignoring classification of a cattle spike as horse when cattle

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samples were excluded from the source library), the presence/absence results among

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blinded samples did not change when a single source was omitted from the library.

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To evaluate the relative quantitative accuracy of SourceTracker, Spearman

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correlations were performed relating sink predictions (as %) with sample volumes

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spiked (0.025 – 10%). Strong and significant positive correlations were observed

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between SourceTracker sink predictions and volumes spiked (ρ = 0.974, 0.924, 0.887,

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0.884, 0.953, with respect to cow, horse, cat, dog, and sewage sources, P < 0.0001 for

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all sources).

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When evaluated against the initial library alone, sources were poorly identified in

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blinded sink samples (sink predictions ≤ 12.8% to specific hosts, irrespective of

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geography; Supplementary Table S2), similar to the blinded source samples.

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Furthermore, combining the initial and FL source libraries did not significantly affect sink

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predictions (ANOVA P = 0.787 – 0.997 for each source, individually; Table S2) from

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those observed when using the local FL library alone.

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4. DISCUSSION Results of this study indicate that identification of fecal source contamination in

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recreational freshwater using SourceTracker is dependent on the inclusion of

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geographically associated source samples present in the source library. Using an initial

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library with geographically divergent sources, but no representation from local sources,

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blinded source samples could not be unambiguously defined, with mean similarities of

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blinded source communities < 7% to sources in the initial fecal library. Furthermore,

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despite a great overlap in community composition among certain host species, e.g.,

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avian species, the algorithm was generally able to assign >80% of the sink community

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to the correct source and geography. These results suggest that, despite taxonomic

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similarity in the fecal microbial community among closely related sources40, individuals

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vary by geographic region and specific species compositions (here assessed as OTUs),

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as has been well documented among humans41,42.

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We specifically sought to investigate potential discrepancies between source

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identification using SourceTracker and amplicon sequencing data from conventional

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qPCR assays. Multivariate statistical analyses generally identified more highly abundant

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genera and OTUs within these genera as potentially discriminatory (Figure 2), and in

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most cases, these genera contributed, in part, to sink predictions as determined by

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SourceTracker (Figure 4). However, in many cases, OTUs within less abundant genera

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accounted for ≥ 50% of the source fingerprints, and we suggest this is due to the over-

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dispersed nature of bacterial communities22. Thus, while qPCR assays target specific

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taxa that are shared within most individuals of a host species2, SourceTracker is able to

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use some of these as well as to capitalize on lower abundance species to more

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accurately discriminate among closely related hosts. This may indicate a trade-off in

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methodologies, where qPCR has greater sensitivity to detect low levels of source

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contamination, while SourceTracker offers highly specific source identification.

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However, it is important to note that the SourceTracker algorithm correctly identified as

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little as 0.025% of spiked source, by volume (Table 1), and, although this result does

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not represent a truly quantitative measurement of bacterial contamination (i.e., numbers

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of cells), it is highly suggestive that the algorithm has appropriate sensitivity to detect

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biologically relevant contamination events. Future work will be necessary to provide a

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more accurate quantitative assessment of this observation and place it in the context of

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current water quality monitoring standards.

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Library size has also historically been an important consideration for library-

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dependent MST methods8. We previously reported that a minimum library size of 13

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individuals was necessary to inform a powered analysis of statistically significant

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differences in community composition17, but previous studies evaluating SourceTracker

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have sometimes relied on only one or two individuals and achieved results that

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corresponded with the expected source composition of their samples16,43. Here, 10

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individuals were sufficient for accurate and, perhaps, relatively quantitative identification

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of fecal contamination among blinded spikes. Furthermore, analysis of the initial library

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suggests that, in practice,