Coherence among Different Microbial Source Tracking Markers in a Small Agricultural Stream with or...

MST marker and pathogen coherence in an intermittent stream

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ENVIRONMENTAL MICROBIOLOGY 1

Coherence among different microbial source tracking markers in a small 2

agricultural stream with and without livestock exclusion practices 3

Running Title: MST marker and pathogen coherence in an intermittent stream 4

KEYWORDS. Beneficial management practice; cattle; riparian zone; source; Cryptosporidium; 5

Bacteroidales; Viruses; Coliphage; Salmonella; serovar; pathogen 6

7

GRAHAM WILKES1, JULIE BRASSARD2, TOM EDGE3, VICTOR GANNON4, 8

CASSANDRA JOKINEN4, TINEKE H. JONES5, ROMAIN MARTI6, NORMAN 9

NEUMANN7, NORMA RUECKER8, MARK SUNOHARA1, EDWARD TOPP6, DAVID R. 10

LAPEN#1, 11

Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada K1A 0C61; Agriculture and Agri-12

Food Canada, Saint-Hyacinthe, Québec, Canada J2S 8E32; Water Science & Technology, 13

National Water Research Institute, Environment Canada, Burlington, Ontario, Canada L7R 14

4A63; Laboratory for Foodborne Zoonoses, Public Health Agency of Canada, Lethbridge, 15

Alberta, Canada T1J 3Z44; Agriculture and Agri-Food Canada, Lacombe, Alberta, Canada T4L 16

1W15; Agriculture and Agri-Food Canada, London, Ontario, Canada N5V 4T36; School of 17

Public Health, University of Alberta, Edmonton, Alberta, Canada T6G 1C97; Department of 18

Microbiology and Infectious Diseases, University of Calgary, Calgary, Alberta, Canada T2N 19

4N18; 20

#Corresponding author. Mailing address: Agriculture and Agri-Food Canada, 960 Carling Ave., 21

Ottawa, Ontario, Canada K1A 0C6. Phone: (613) 759-1537. Fax: (613) 759-1515. E-mail: 22

[email protected] 23

Copyright © 2013, American Society for Microbiology. All Rights Reserved.Appl. Environ. Microbiol. doi:10.1128/AEM.01626-13 AEM Accepts, published online ahead of print on 2 August 2013


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ABSTRACT 24

Over 1400 bi-weekly water samples were collected over 6 years from an intermittent 25

stream protected from pasturing cattle, and unprotected from cattle. Host specific Bacteroidales 26

markers, Crytosporidium species/genotypes, viruses and coliphages associated with humans or 27

animals, and bacterial zoonotic pathogens were monitored. Ruminant Bacteroidales markers did 28

not increase downstream of the restricted pasture, relative to upstream monitoring sites; whereas 29

the ruminant Bacteroidales marker increased significantly in the unrestricted cattle access reach. 30

Human Bacteroidales markers significantly increased downstream of homes where septic issues 31

were documented. Wildlife Bacteroidales markers were detected downstream of the protected 32

pasture where stream and riparian habitat was protected, but detections decreased after the 33

unrestricted pasture. A great number of human virus detections were shown to increase 34

downstream of homes, and similar trends were observed for the human Bacteroidales marker. 35

There was considerable interplay among biomarkers with stream flow, season, and localized 36

upstream land uses. There were no, to very weak, associations with Bacteroidales markers and 37

bacterial, viral, and parasitic pathogens. Overall, discrete sample by sample coherence among 38

the different MST markers that expressed a similar microbial source was minimal, but spatial 39

trends were physically meaningful in terms of land use (e.g., beneficial management practice) 40

effects on sources of fecal pollution. 41

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INTRODUCTION 43

Microbial source tracking (MST) can help reveal the sources of fecal contamination in 44

water resources (1-5). There are a suite of MST tools that have been used in this capacity (6); 45

some of these include antimicrobial resistance patterns of target bacteria (7, 8), bacterial markers 46

(9) such as Bacteroidales (2), host specificity associated with parasitic organisms like 47

Cryptosporidium (10, 11), mitochondrial DNA methods (12, 13), and virus host specificity (14-48

16). There is potential for these tools to help identify how different land uses can impact the 49

sources of fecal pollution in open surface water systems like agricultural watersheds; systems 50

typically prone to variable inputs of human, livestock, and wildlife feces. While these tools have 51

potential utility for such assessments, they are different in their limits of detection, source 52

discrimination power, spatial and temporal stability, persistence in environmental matrices, and 53

what they express biophysically in water samples; and as such, results from one method may not 54

be necessarily coherent with another method (5, 17-18). But under some circumstances, 55

coherency, or lack of it, can reveal what biomarkers are appropriate for the use at hand, and MST 56

coherence with the occurrence of other fecal indicator organisms and pathogens can be viewed as 57

an important factor for helping to identify mitigation measures that will reduce public health 58

risks (19-22). 59

Many agricultural beneficial management practices (BMPs) are designed to be protective 60

of water quality, yet such interventions in open systems like watersheds, could impact variably 61

the degree and source of fecal contamination in a receiving water body (23, 24). In this context, 62

MST information could provide a more accurate assessment of how fecal pollution mechanisms 63

associated with the target organism of the BMP (e.g., livestock), truly respond to that BMP (e.g., 64

cattle exclusion fencing along a stream). Clearly, for systems impacted by multiple sources of 65

fecal contamination, mitigation benefits of a BMP could be potentially offset or clouded by other 66

fecal pollution sources. 67

The objectives of this research are to: i) examine the spatial and temporal consistency 68

among a broad suite of different MST markers (specifically aimed at: human, ruminant, rodent, 69

avian, pig) detected along an intermittent stream affected variably by humans, wildlife, and 70


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pasturing cattle (22, 24); ii) examine relationships among MST marker detections and the 71

occurrence of pathogens, and iii) in relation to a pasture where cattle can interact directly with 72

the stream, determine if there are MST signature differences and/or changes that result from 73

intervention of a BMP that excludes cattle from stream and protects the riparian zone. 74

MATERIAL AND METHODS 75

General study site description. The study site is detailed in Sunohara et al. (24) and 76

Wilkes et al. (33). Briefly, the experimental site is located on a small intermittent stream located 77

in the South Nation River basin in eastern Ontario Canada (Fig. 1). The stream runs through a 78

pasture where cattle are restricted from the water course by exclusion fencing (~3-5m buffer 79

from stream channel) (RCA), and a pasture where cattle have unrestricted access to the water 80

course (URCA). Land use immediately surrounding the pastures consists of cultivated crop land 81

and several residential homes located close to the south bank of the stream. There is also a home 82

located on the north bank of the stream immediately upstream of the RCA. The experimental 83

site was designed following an upstream-downstream methodology (25). Livestock (Holstein 84

cows) densities were maintained at 2.5 animals ha -1 for both the RCA and URCA starting from 85

2005 forward. Livestock pastured from May or June to typically November, after which they 86

were trucked off site. 87

Stream monitoring and microbiological analysis. Over 1400 water samples were 88

collected by hand using sterile gloves and sterile water collection implements from April-May to 89

stream freeze–up (~mid to late November) between 2004 and 2010. Samples were collected at 90

RCAin (serving as a baseline of water quality input into the restricted cattle access experimental 91

region), RCAout\URCAin (representing the water quality output from the RCA experimental area, 92

and also serving as a baseline of water quality input into the unrestricted cattle access area), at a 93

midpoint of the URCA area (URCAmid), and finally at the output of the URCA (URCAout) 94

(Figure 1). The URCAmid site was introduced into the study in late 2005. Water quality data 95

collected at these sample sites were divided into two groups: 96


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1) Temporally Concurrent Data: data collected concurrently (synchronously) from 2005-2010 at 97

RCAin; RCAout\URCAin; and URCAout, and data collected from 2005-2010 concurrently at 98

RCAin and RCAout for parasites (due to concurrent sample limitations at RCAout\URCAin this 99

data was excluded in this analysis (analysis 1)). Temporally concurrent data were used 100

exclusively for evaluating pasture treatment effects because of consistent data support among 101

upstream and downstream monitoring sites; and 102

2) All Available Data: all data collected at each monitoring site from 2004-2010 including 103

URCAmid, irrespective of temporal concurrence of sampling (i.e., one site may have more data 104

support than another). 105

Sampled water was shipped to Agriculture and Agri-Food Canada (AAFC) (London, ON) 106

for Bacteroidales analysis (21), the Alberta Provincial Laboratory for Public Health (Calgary, 107

AB) for Cryptosporidium oocyst and Giardia cyst quantification and genotyping (26), the Public 108

Health Agency of Canada’s (PHAC) Laboratory for Foodborne Zoonoses (Lethbridge, AB) for 109

pathogenic bacteria detection (27), AAFC (Lacombe, AB) for F-RNA and F-DNA coliphage 110

detection, quantification and source attribution (described below and in Sunohara et al. (24) and 111

Wilkes et al. (22)), and AAFC (Saint-Hyacinthe, QC) for pathogen and host marker virus 112

detection and attribution (described in Wilkes et al. (22), and below). 113

Quantitative PCR (QPCR) analyses were performed as follows. Between 25 and 300 mL 114

of water were filtered through 0.45 μm Nuclepore membrane filters (Whatman, Thermo Fisher 115

Scientific, Ottawa, Ontario). Filters were placed in a 15 mL falcon tube containing 0.5 mL of 116

GITC buffer (5 M guanidine isothiocyanate, 100 mM EDTA [pH 8.0], 0.5% Sarkosyl) and 117

frozen at −80 °C until extraction. DNA was extracted following manufacturer instructions using 118

the DNeasy tissue kit (Qiagen, Mississauga, Ontario), except that the proteinase K step was 119

omitted. The elution volume was 100 μL. PCR amplification was performed using a Bio-Rad 120

CFX96 qPCR instrument with Bio-Rad CFX Manager software, version 2.0. Primer and probe 121

sequences used in this study are presented in Marti et al. (21; Table S1) targeting total 122

Bacteroidales, markers HF183 (human), BacR (ruminant), Pig-2-Bac (pig), CGOF1-Bac 123

(Canada goose), with the exception of marker MuBa (muskrat) which is presented in Marti et al. 124


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(2). All markers used Taqman® chemistry except for the human marker which used SYBR 125

Green® chemistry. All primers and probes were synthesized by SIGMA (Sigma-Aldrich, 126

Toronto, ON). The mix reaction was performed with the Brilliant II QPCR Master Mix (Agilent, 127

Toronto) for Taqman® PCR and the Brilliant II SYBR Green® Low ROX QPCR Master Mix 128

(Agilent) for SYBR Green® PCR. Two microliters of template DNA were added, and deionised 129

water was used to reach a final volume of 25 μL. Negative controls (no template DNA) were 130

performed in triplicate for each run. Each reaction was run in triplicate with the following cycle 131

conditions: 1 cycle at 95 °C for 10 min followed by 40 cycles of 95 °C for 15 s and annealing 132

temperature for 40 s. For the SYBR Green® assay, a melting curve step was added in order to 133

check the purity of the PCR product. This step consisted of a ramp temperature from 65 to 95 °C, 134

with an increment of 0.5 °C and holding for 5 s for each step. The presence/absence of PCR 135

inhibitors was verified on ten times template DNA dilution by using TaqMan® Exogenous 136

Internal Positive Control kit (Applied Biosystems, Toronto, ON) following the manufacturer 137

instructions, if inhibitors were present, a 100 times dilution of the DNA template proceeded. 138

Serotyping of all Salmonella isolates was performed by the PHAC, Laboratory for 139

Foodborne Zoonoses, at the Office International des Épizooties Salmonella Reference 140

Laboratory in Guelph, Ontario. Somatic and flagellar antigens of all Salmonella isolates were 141

determined using slide agglutination (28), and test tube precipitation with microtitre plates (29). 142

The antigenic formulae of Grimont and Weill were used to identify and name the serovars (30). 143

For virus analysis, 500 mL samples were processed in accordance with the OPFLP 04 144

standard method for the recovery and concentration of viruses in artificially and naturally 145

contaminated water in Health Canada’s compendium of analytical methods (31, 32). Negative 146

controls were processed in tandem with the water sample. Viral nucleic acids were extracted 147

using the QIAamp RNA Viral Mini Kit (Qiagen, Mississauga, ON, Canada). Real-time TaqMan 148

RT-PCR and PCR assays for the detection of viruses (some of which are host attributed viruses) 149

norovirus GI, GII, GIII and GIV, hepatitis A virus, hepatitis E virus, Adenovirus 40/41, human 150

Adenoviruses, Astrovirus, Sapovirus, Torque teno virus, Torque teno sus virus, Rotavirus and 151

feline Calicivirus (as a sample process control) were performed in 25 µl with the 1 step Brillant 152


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II QRT PCR core reagent kit for RNA viruses and with the Brillant I QPCR core kit for DNA 153

viruses (Agilent Technologies Canada, Mississauga, ON, Canada) (33-43). Standard curves 154

were generated using 10-fold serial dilution of standard cDNA plasmids containing PCR 155

product of each virus (108 to 100 genomic equivalents) for viral copy quantification (detection 156

limit of those assays was between 1 x101 to 1 x 100 genomic equivalent copies). RNA viruses 157

Hepatitis A, Astrovirus, Norovirus GI and GIV and Sapovirus are commonly associated with 158

human hosts, while Norovirus GIII is commonly associated with bovine hosts. DNA viruses 159

Adenoviruses (General and types 40/41) and Torque teno virus are associated with human hosts 160

(44), while Torque teno sus virus is associated with swine. 161

The RNA from confirmed F-RNA coliphage isolates (isolated as described by Sunohara 162

et al. (24)) was obtained from F-RNA coliphage suspensions that were thawed and diluted 1:50 163

in DNase/RNase free water, boiled for 5 min, held on ice for 2 min and then centrifuged at 164

14,000 g for 10 s. The RNA extracts of F-RNA coliphages were genotyped into genogroups I-IV 165

by real time RT-PCR. Real time RT-PCR reactions were carried out with a Quantitect ® 166

Multiplex no ROX RT-PCR kit (Qiagen Inc., Missisauga, ON, Canada) on a Stratagene 167

MX3005P QPCR thermocycler (Agilent) using conditions as described in Jones et al. (45). 168

Genogroups I and IV were detected by a duplex assay using LV1 and GIV primers and probes as 169

described by Jones et al. (45), and genogroups II and III were detected in individual reactions 170

using the primers and probes as described by Wolf et al. (46). Each 25 ul reaction contained 200 171

nM of each forward and reverse primers and probe, 0.25 ul of Quantitect Multiplex RT Mix, 0.03 172

uM of ROX reference dye and 2.5 ul of RNA extract in 1 x Quantitect Multiplex RT-PCR 173

Mastermix. RNA extracts that were positive for GI or GIV were assigned to F-RNA coliphage of 174

animal origin and those that were positive for GII or GIII were assigned to F-RNA coliphage of 175

human origin. Detection limits for viable phages were 5 L-1 and genome copies by RT-PCR were 176

120 L-1. 177

Crytosporidium were sequenced and genotyped by phylogenetic analyses, and assigned 178

to broad known host classes as described in Ruecker et al. (26) and Wilkes et al. (11). 179


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Statistical analyses. Site comparisons for Bacteroidales and source specific pathogen 180

occurrence (‘microbial source tracking (MST) endpoints') amongst the sites, amongst the sites 181

seasonally, and under differing flow regimes (high, low, no flow), were made using Fisher’s 182

exact tests. Bacteroidales source marker (number of copies per day-1 as determined by 183

multiplying noon instantaneous stream flow at time of sampling by Bacteroidales copies vol-1 of 184

the sampled water, and density copies volume-1) distribution amongst the sites, amongst the sites 185

seasonally, and amongst the sites under differing flow regimes were examined using Kruskal–186

Wallis and Mann-Whitney U tests. Both the Kruskal–Wallis and Mann-Whitney methods rank 187

continuous data, for Mann-Whitney U test results we calculate mean rank sum for groups that are 188

significantly different to show higher value direction between groups. The previous site 189

comparisons were performed on both Temporally Concurrent Data and All Available Data 190

independently. The detection and non-detection of host specific markers in pathogen positive 191

samples vs. pathogen negative samples and marker positive samples vs. marker negative samples 192

were examined using Fisher’s exact tests and odds ratio (OR) estimates amongst all site data 193

combined. Qualitative occurrences within bi-weekly groupings (within a 10 day window to 194

capture bi-weekly samplings) of biomarkers were also examined using a heat map. Lastly, 195

interactions among site, season, and flow in terms of MST endpoint presence/absence were 196

examined using CART (Classification and Regression Tree Analysis) (V. 6.6, Salford Systems, 197

CA) following Wilkes et al. (22). 198

RESULTS 199

Microbial source tracking markers by cattle exclusion BMP, season, and flow. 200

Ruminant Bacteroidales marker detection rates for all Temporally Concurrent Data (Figure 2) 201

decreased by 3%, and increased by 17% (p<0.05) from the RCAin to RCAout\URCAin and 202

RCAout\URCAin to URCAout, respectively. At low flows, the URCAout had significantly higher 203

detections (33%) than the RCAout\URCAin (7%) suggesting impact of cattle access to stream. In 204

summer, prevalence of the pig Bacteroidales marker was significantly higher at the RCAin, 205

relative to no detections at sites RCAout\URCAin and URCAout (Figure 2); likely as a result of 206

agricultural drainage from fields that had received swine manure applications (highly transient 207


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drainage that occurred from fields out of the watershed proper, was channeled via a road side 208

ditch that drained into the stream locally near RCAin). 209

Regarding wildlife generalized source classes, detection of muskrat Bacteroidales 210

markers was significantly higher at RCAout\URCAin (12%) relative to RCAin (4%) and URCAout 211

(1%) (Figure 2). This suggests an impact of muskrat habitat protection in the RCA. In spring, 212

this trend also held true, where rates of muskrat Bacteroidales marker detection were 14% in the 213

RCAout\URCAin, relative to no detections in the RCAin and URCAout (Figure 2). For all Canada 214

goose (C. goose) Bacteroidales marker data, detection trends were similar to that of the muskrat 215

Bacteroidales marker, where the highest C. goose marker detections were observed at 216

RCAout\URCAin (13%), relative to 1% at both RCAin and URCAout (Figure 2). Similar 217

statistically significant trends for C. goose Bacteroidales marker data were observed for high 218

flow, and spring data sets, respectively. 219

Of note for All Available Data (Figure 3), all animal TTV virus detections were 220

significantly different amongst some sites and at low flows, where detections were significantly 221

higher at RCAout\URCAin (15%) and URCAmid (17%) relative to RCAin (0%) (Figure 3). At low 222

flows, Animal TTV virus (23%) was highest at RCAout\URCAin, relative to RCAin (0%), 223

URCAmid (11%) and URCAout (0%). 224

Detection of human Bacteroidales marker was significantly higher at RCAout\URCAin 225

(19%), relative to RCAin (4%) for all data (Figure 2). Similar, statistically significant trends in 226

the human marker were observed in summer and at high flows as well. Support for the trends 227

observed for Figures 2-3 are given in Supplementary (Supp.) Table 1A-B. The site trends are 228

believed to correspond to downstream impact from homes located immediately adjacent to the 229

pasture treatments. 230

To assess the impact of the BMP on marker trends, taking into account small differences 231

in stream flow at each sample site that could impact dilution and mass transport downstream 232

along the pasture system, Bacteroidales copies day-1 were compared. Differences in the 233

distribution of the Bacteroidales copies day-1 in stream water for the livestock (i.e., ruminant 234

Bacteroidales) marker were observed between RCAout\URCAin and URCAout for Temporally 235


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Concurrent Data (Table 1), where lower average rank sums (i.e., lower ranked values, rank data 236

not shown) were observed at RCAout\URCAin vs. URCAout (average rank sum of 59 & 70, 237

respectively). At low flow average rank sums of ruminant Bacteroidales marker (copies day-1) 238

were lower at RCAout\URCAin (24) vs. URCAout (31) (Temporally Concurrent Data). 239

Additionally, Bacteroidales ruminant marker mean (copies day-1) were consistently higher at 240

URCAout relative to RCAout\URCAin (Table 2); similar trends were observed for All Available 241

Data (Tables 3-4). Significant differences in the distribution of wildlife related Bacteroidales 242

markers (copies day-1), namely the muskrat marker, were observed between RCAout\URCAin and 243

URCAout (Table 1 and 3). Average rank sum values for muskrat Bacteroidales marker (copies 244

day-1) for Temporally Concurrent Data were higher at RCAout\URCAin (71) vs. URCAout (64) 245

overall, and in spring, average rank sums were 32 and 28, respectively. And, average rank sums 246

of Bacteroidales muskrat marker (copies day-1) for Temporally Concurrent Data were greater at 247

RCAout\URCAin (32) vs. RCAin (28) in spring. Muskrat Bacteroidales marker mean (copies day-248

1) were higher at RCAout\URCAin relative to URCAout (Table 4) for All Available Data. Average 249

rank sums of C. goose Bacteroidales marker (copies day-1) increased from RCAin (64) to 250

RCAout\URCAin (71) for Temporally Concurrent Data (‘all’ data), complimenting the muskrat 251

marker ‘wildlife’ trends. All average rank sums for the (copies day-1) of human markers that 252

were significantly different (Table 1) amongst the Temporally Concurrent Data exhibited higher 253

rank sums in the downstream direction (rank sums were higher for RCAout\URCAin & URCAout 254

vs. RCAin). The above copies day-1 results mimicked very closely the Bacteroidales marker 255

copies 100 mL-1 trends observed in Supplementary Tables 2A-3B, due to the relatively short 256

distance between sampling sites and limited hydrologic contributing area. 257

The most commonly occurring serovar of Salmonella was I:4,5,12:b:- (n=22), followed 258

by Kentucky (n=8), Mbandaka (n=3) and Bovismorbificans (n=3) (Supp. Table 1C). Serovar 259

I:4,5,12:b:- was found most often RCAout\URCAin (n=9), URCAmid (n=5), and URCAout (n=8). 260

Cattle exclusion BMP, season and flow interactions among Bacteroidales source markers 261

and pathogens. The occurrence of Cryptosporidium associated with (a/w) livestock (36% of the 262


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22 samples contained Cryptosporidium a/w livestock, 27% of these 22 samples were C. 263

andersoni or 6 of the 8 livestock samples in this grouping) and Cryptosporidium a/w wildlife 264

(68% of the 22 samples contained Crytosporidium a/w wildlife) was greatest in the fall at high 265

flow for Temporally Concurrent Data as identified by the CART analysis. Whereas 266

Cryptosporidium a/w muskrat genotype (consisting of Muskrat genotypes I & II) and 267

Cryptosporidium a/w unknown source occurrence was greatest in summer and fall (35% of the 268

62 samples contained Cryptosporidium a/w muskrat genotype), and fall only (25% of the 24 269

samples contained Cryptosporidium a/w unknown sources), respectively (Supp. Table 4). 270

In contrast, the effects of site (pasture treatment) were more notable on Bacteroidales 271

source markers. Greatest rates of detection were observed at RCA & under (no flow) conditions 272

and at RCA & under (high flow) & (low flow) conditions for ruminant markers and muskrat 273

markers, respectively (32% and 15% occurrence in samples for these groupings). Most (82% of 274

the C. goose detections) of the C. goose markers were observed at RCAout\URCAin and most pig 275

markers (71% of pig marker detections) were observed at RCAin, whereas human marker 276

observations were evident in URCA in summer and fall (22% of 76 samples) (Supp. Table 4). 277

Norovirus GI (associated with human) and GIII (associated with bovine) occurred in spring and 278

summer and high and low flows (24% & 20% occurrence in 45 samples), respectively. Pasture 279

treatment effects for human and animal Torque teno virus were observed, namely in the URCA 280

area in spring & summer (22% & 17% of 18 & 36 samples, respectively). Greatest occurrence 281

of F-RNA coliphage associated with animal occurred in the fall (53% of 30 samples). 282

Co-occurrence of host specific markers and viruses. The detection of host specific 283

Bacteroidales markers, generalized host classes of Cryptosporidium, and host specific viruses 284

were examined qualitatively for all sites through time (10 day windows throughout the course of 285

the study) using the heat map approach (Supp. Figure 1). For the human markers and pathogens, 286

there were no co-occurrences between human Cryptosporidium and human Bacteroidales 287

marker. There were a total of 53 human virus observations. This generated 23 human virus 288


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events within the heat map, and 9 co-occurrences of the human Bacteroidales marker (39%) for 289

these 23 events (Supp. Figure 1). 290

For the ruminants (livestock), there were 4 out of 20 (20%) occurrences of livestock 291

associated Cryptosporidium (C. andersoni, C. parvum or C. ubiquitum) when ruminant 292

Bacteroidales marker was detected (Odds ratio (OR)=1.7, 95% confidence interval (CI) range 293

0.3-7.6), 2 out of 9 (22%) occurrences of ruminant Bacteroidales marker when Norovirus GIII 294

a/w (bovine) was present (OR=0.9, 95% CI range 0.1-5), and 9 out of 41 (~23%) occurrences of 295

ruminant Bacteroidales marker when F-RNA coliphage (animal) was present (OR=0.8, 95% CI 296

range 0.3-2.1) in samples collected at the same site and time (Supp. Table 5A-B), but here, there 297

were no significance difference in occurrences (Supp. Table 5B-C). For pig, there were a total of 298

10 occurrences for animal Torque teno virus a/w swine, but no co-occurrences in these samples 299

for pig Bacteroidales marker. 300

There were 2 observations of avian associated Cryptosporidium and no co-occurrence 301

with C. goose Bacteroidales marker (Supp. Table 5A). Of some 42 F-RNA coliphage animal 302

detections, there were only 3 concurrent C. goose Bacteroidales marker observations (7%). For 303

muskrat/vole associated Cryptosporidium (Muskrat genotypes I & II), there were 17 observations 304

and no co-occurrence with the Bacteroidales muskrat marker. Of some 41 F-RNA coliphage 305

animal observations, there were only 4 co-occurrences of the muskrat marker (10%). 306

The cross tabulations and contingency table analyses performed to assess overall co-307

occurrence and non-occurrence of host specific markers quantitatively using Fisher’s exact tests 308

and odds ratio estimate analyses using All Available Data indicated the following. There were 309

only significant differences on co-occurrences for: Cryptosporidium, for all generalized 310

Cryptosporidium host classes except for human; for Bacteroidales C. goose marker and 311

Bacteroidales ruminant marker (negative relationship), Norovirus GIV a/w human and 312

Astrovirus a/w human (positive relationship), Norovirus GIV a/w human and Norovirus GIII 313

(bovine) (positive relationship), and for F-RNA coliphage animal and Norovirus GIII a/w bovine 314

(positive relationship). 315


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Associations among pathogens and Bacteroidales and other source tracking 316

markers. There were two notable significant associations between the markers and pathogens, 317

and these were between Bacteroidales ruminant marker and Cryptosporidium (Supp. Figure 2); 318

where there were significantly higher detections of ruminant marker in the presence of 319

Cryptosporidium, with an associated OR estimate (~4.7) and confidence intervals above unity 320

(Table 5). 321

DISCUSSION 322

Comparison of ruminant Bacteroidales marker occurrence, Bacteroidales copies day-1 323

(‘load’), and marker copies 100 mL-1 in stream water within the fenced and unfenced pasture 324

region, indicates that the reach of stream protected from pasturing cattle did not increase 325

ruminant marker occurrences and ‘loading’ from upstream monitoring sites, but as expected, 326

where cattle were allowed to interact with the stream, ruminant marker occurrence and copies 327

day-1 increased significantly. This was at a consequence of increased direct fecal release in and 328

around the stream, and lack of filtering vegetation in the riparian zone to buffer contaminated 329

drainage from adjacent land where cattle pastured. While there were no significant associations 330

of the Bacteroidales ruminant marker amongst the suite of pathogens examined here 331

(Campylobacter spp., Salmonella spp., E. coli O157:H7, Cryptosporidium, Giardia, Hepatitis E, 332

Norovirus GII, Rotavirus), except for Cryptosporidium spp. (of which 10% of these samples had 333

the human pathogenic C. parvum), the marker data points to the potential for increased risk of 334

cattle fecal contamination for stream/riparian systems that are not protected from pasturing 335

cattle, even for those with small pasturing densities. By extension, while the pathogens 336

examined were not overall, statistically coherent with the ruminant marker, as per other studies 337

(1, 21), the increase in the marker for the pasture where stream access was unrestricted, points to 338

elevated potential for livestock derived pathogens to enter the water course. Wilkes et al. (11, 339

22) found that E. coli O157:H7 occurred most frequently in the unrestricted cattle access area, in 340

relation to other surface water quality monitoring sites in the region. In Wilkes et al. (22), 341

Salmonella spp. occurred more often in the URCA region; here, we observed a variety of 342


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Salmonella serovars in the pasturing corridor, including serovar I:4,5,12:b:- which predominated 343

in the livestock impacted site (61% of the overall serovars observed). Hence, in the assessment 344

of BMP interventions that are subjected to multiple sources of fecal contamination, 345

Bacteroidales source data is useful for gauging BMP effectiveness and/or impact of land use 346

change on sources of fecal inputs. Whether or not Bacteroidales source markers have more 347

utility in this capacity relative to fecal indicator bacteria (27), for example, is the product of 348

future research. 349

Certainly protecting the riparian zone is important for many reasons including enhancing 350

wildlife habitat and reducing surface water pollution (47). In many agriculturally intensive 351

watersheds, wildlife habitat is becoming increasingly constrained, and riparian corridors are 352

important areas to maintain and conserve for purposes of biodiversity, as well as for reducing 353

water pollution and enhancing ecological goods and services (48, 49). Sunohara et al. found that 354

the cattle exclusion fencing promoted greater numbers and types of plant species and notably 355

greater degrees of wildlife (e.g., muskrats, voles, turtles, bird) in the study site described here 356

(21). The wildlife Bacteroidales markers utilized in this study, muskrat and C. goose, 357

demonstrated that indeed protecting habitat could increase inputs of wildlife fecal material 358

significantly, yet where cattle have open access to a stream (where they eat plants, trample soil 359

and plants etc.), the wildlife Bacteroidales markers were significantly reduced in relation to 360

protected upstream sites, and daily loads of ruminant Bacteroidales copies and occurrence of 361

ruminant Bacteroidales markers increased relative to protected upstream sites. While there were 362

increases in these wildlife markers in the protected riparian zone, there were no significant 363

increases in pathogen occurrence. In fact, Wilkes et al. found that Campylobacter spp. actually 364

decreased from the RCAin to the URCAout, altogether (22). The wildlife (muskrat, C. goose) 365

associated Bacteroidales markers served as excellent indicators of the impact of land use change 366

on wildlife activity; where the trends were highly dissimilar to those generated by the 367

Bacteroidales ruminant markers, yet coherent in the context of how degrading the stream and 368

riparian zone can reduce wildlife fecal material, and by extension, wildlife proliferation. Green 369

et al. (50) have observed that wildlife markers (i.e., targeting avian) have been distributed in 370


15

surface water of a geographically wide range of watersheds, and so too has Fremaux et al. (51), 371

who observed a large proportion of wildlife (i.e., C. goose) detections in surface water. 372

As for human Bacteroidales markers, the signature significantly increased downstream of 373

the RCAin at monitoring sites immediately downstream of homes where some small septic issues 374

were documented. These trends were significantly more pronounced at high flows and in 375

summer as a result of intense rain events promoting impacted drainage to the adjacent stream. 376

Although not significant statistically, there were noteworthy spatial trends that were similar to 377

those of human Bacteroidales marker among Astrovirus a/w humans, Norovirus GI a/w humans, 378

Human TTV, and to a lesser extent, Cryptosporidium a/w humans. These trends are all 379

promising in terms of identifying how human sources of fecal pollution behave in a system 380

designed to promote reductions in livestock fecal pollution (open systems). McQuaig et al. have 381

observed human marker (human polyomaviruses and Bacteroides human markers) concurrence 382

consistently in septic waste (52), and Peed et al. (53) have observed human markers and septic 383

system contamination of a low order stream during rainfall events: trends similar to those 384

observed in this work. 385

Overall, there was promising consistency in downstream occurrence of the Bacteroidales 386

human markers and human viruses among the water sampling sites, even over this relatively 387

small intermittent stream reach. All markers behaved in accordance with expected hypothetical 388

spatial trends and therefore Bacteroidales are an enormously powerful tool to gauge land use, 389

and land use change effects on fecal pollution (19-21, 53, 54). This can have significant potential 390

when assessing how a BMP behaves, or doesn’t behave, in open watershed systems where 391

multiple sources of fecal inputs will occur (23, 55, 56). 392

While the overall spatial trends above made sense from a biophysical standpoint, there 393

was no significant systematic ‘sample by sample’ or temporal window based coherence among 394

similar source tracking endpoints nor any strong associated links with pathogens. Fremaux et al. 395

(1) and Marti et al. (21) did not find strong associations among Bacteroidales markers and 396

selected waterborne pathogens. Generalized lack of coherence among similar marker endpoints 397


16

(and markers and pathogens) are no doubt multifactorial, however, some factors could be related 398

to: i) not all water samples that had Bacteroidales source markers (density differences of various 399

markers and pathogens) contained pathogens (of those monitored), ii) variable dissipation among 400

the Bacteroidales source markers and the pathogens and host specific pathogens (1, 21, 54, 55), 401

iii) size of organism or microbial source tracking end point resulting in differential transport 402

behavior (57, 58), iv) detection limits and analytical constraints such as those discussed in Marti 403

et al. (21), including extraction of DNA/RNA (or nucleic acids) from larger water volumes and 404

impact of PCR inhibitors (59), and v) variability in the degree of host specificity among the 405

source tracking endpoints. 406

Several noteworthy deductions can be drawn from this study: 407

i. Changes in the prevalence of Bacteroidales markers can occur over short distances as a 408

result of land use changes (with the intervention of cattle exclusion BMPs that protect the 409

stream and riparian zone). Bacteroidales ruminant and the bovine associated virus marker 410

did not increase significantly from the pasture treatment input into areas where pasturing 411

cattle were restricted from the stream, suggesting a positive BMP impact, yet 412

Bacteroidales ruminant markers did increase from the output of the restricted area to 413

areas where cattle access was unrestricted. There were significant positive associations 414

among ruminant markers and Cryptosporidium, suggesting targeting livestock exclusion 415

BMPs to reduce pathogen risk associated with ruminants is prudent (11). 416

ii. Pig Bacteroidales markers were more often observed in close proximity to drainage 417

inputs from fields associated with land applications of swine manure, but not elsewhere. 418

iii. Select wildlife Bacteroidales (e.g., muskrat and C. goose) marker detections were 419

observed more frequently downstream of the protected riparian zone. Wildlife markers 420

were significantly reduced where cattle had access to the stream as a result of ruminant 421


17

activity (24). There were no significant associations between pathogens and detection of 422

these wildlife Bacteroidales markers, and Wilkes et al. (22) did not observe a significant 423

increase in pathogen detection in the stream associated with the protected riparian zone, 424

relative to stream input into to that system. 425

iv. Human Bacteroidales markers, found to be significantly higher downstream of homes 426

located along the experimental pastures, likely occurred as a result of septic system 427

leakages. There were no significant associations with this marker and other pathogens. 428

v. There was considerable interplay in MST marker results with stream flow, season, and 429

localized upstream land uses. Interactions were highly variable depending on the marker. 430

vi. Cryptosporidium exhibited consistent seasonal loading over all sites for livestock and 431

wildlife at high flow (with overall greatest occurrences in fall perhaps resulting from fall 432

flow events flushing oocysts in the system that accumulated over spring and summer, 433

downstream). Cryptosporidium associated with muskrat/vole occurrence was higher in 434

summer during the biologically active period of year. There were no significant 435

associations with Cryptosporidium generalized host classes and MST markers other than 436

those with ruminant Bacteroidales markers (positive prevalence relationships). 437

vii. Few viruses and coliphages attributable to a particular source demonstrated site specific 438

affinities (there were few statistically significant differences amongst sites for host 439

specific viruses or coliphages). But a vast majority of human viruses were shown to 440

increase from the stream input in to the experimental area, to sampling sites downstream 441

as per human Bacteroidales marker data. 442

viii. Overall, discrete sample by sample (or sampling window) coherence among the different 443

MST markers that expressed a similar microbial source or source class was minimal. 444

Reasons for this lack of coherence are no doubt multifactorial, but persistence, variable 445


18

density factors, and transport disposition of the various microorganisms would seem to be 446

at least, a contributing factor. 447


19

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21. 634


27

FIGURES AND FIGURE CAPTIONS 635

Figure. 1. (A) General location of study site in Canada, and (B) simplified graphical depiction of 636

the layout of the experimental site setup and design (note: graphic not to scale and is simplified 637

for clarity, see Sunohara et al. (24) for a detailed map and study site description). The restricted 638

cattle access (RCA) area is 1.8 ha with a flow length of ~356 m and gradient of 0.002 m m-1. The 639

unrestricted cattle access (URCA) area is 2.2 ha of bounded land and 348 m of length, and 640

gradient of 0.004 m m-1. 641

Figure. 2. The percentage (%) of samples under specific site, season, and flow conditions 642

positive for microbial source tracking markers. Temporally Concurrent Data were used for: the 643

RCAin, RCAout\URCAin, and URCAout for source markers and source viruses, and 644

Cryptosporidium associated source were summarized in a temporally concurrent manner at sites 645

RCAin and URCAout. See Figure 3 for parasite source occurrence at RCAout\URCAin. High flow 646

≥ 0.018 m3 s-1; low flow ≥ 0.002 m3 s-1 and < 0.018 m3 s-1, and no flow < 0.002 m3 s-1. A/w, 647

associated with. 648

Figure. 3. The percentage (%) of samples under specific site, season, and flow conditions 649

positive for microbial source tracking markers. Data represented here were based on All 650

Available Data. See Fig. 2 for flow regime limits. A/w, associated with. 651

Table 1. Median (ME) and mean absolute deviation (MAD) of source Bacteroidales (copies day-1) for Temporally Concurrent Data under seasonal and flow conditions with accompanying significant Kruskal–Wallis (KW) and Mann-Whitney U (W) tests amongst sites and site pairs, respectively. Significant differences between sites (identified by site number) are indicated in median column and are bolded (p<0.05). High flow ≥ 0.018 m3 s-

1; low flow ≥ 0.002 m3 s-1 and < 0.018 m3 s-1; and no flow < 0.002 m3 s-1.

All data spring summer fall high low no flow

Microbial source tracking endpoint Site (#) MAD ME MAD ME MAD ME MAD ME MAD ME MAD ME MAD ME

Bacteroidales ruminant marker RCAin (18) 3.37E+10 0KW(15W) 6.82E+10 0 1.23E+10 0 6.31E+09 0KW

(15W) 7.62E+10 0 6.85E+09 0 7.78E+08 0 Bacteroidales ruminant marker RCAout\URCAin(22) 1.79E+10 0KW

(15W) 9.85E+09 0 3.28E+10 0 9.25E+09 0KW(15W) 9.25E+09 0 3.23E+09 0(15W) 2.06E+09 0

Bacteroidales ruminant marker URCAout (15) 1.35E+11 0KW(18W;22W) 3.49E+10 0 3.13E+11 0 4.52E+10 0KW

(18W;22W) 6.28E+10 0 2.57E+11 0(22W) 1.66E+10 0 Bacteroidales muskrat marker RCAin (18) 1.62E+08 0KW 0 0KW

(22W) 2.38E+08 0 3.33E+08 0 0 0KW 3.67E+08 0 2.06E+07 0 Bacteroidales muskrat marker RCAout\URCAin(22) 9.60E+09 0KW

(15W) 2.95E+09 0KW(18W;15W) 1.61E+10 0 1.26E+10 0 1.86E+10 0KW 4.64E+09 0 0 0

Bacteroidales muskrat marker URCAout (15) 9.63E+08 0KW(22W) 0 0KW

(22W) 2.72E+09 0 0 0 0 0KW 2.26E+09 0 0 0 Bacteroidales C. goose marker RCAin (18) 2.97E+09 0KW

(22W) 0 0KW(22W) 8.40E+09 0 0 0 0 0KW

(22W) 6.95E+09 0 0 0 Bacteroidales C. goose marker RCAout\URCAin(22) 1.37E+11 0KW

(18W;15W) 2.79E+11 0KW(18W) 1.81E+10 0 2.11E+10 0 2.93E+11 0KW

(18W) 3.38E+10 0(15W) 0 0 Bacteroidales C. goose marker URCAout (15) 7.20E+10 0KW

(22W) 1.52E+11 0KW 0 0 0 0 1.62E+11 0KW 0 0(22W) 0 0 Bacteroidales pig marker RCAin (18) 7.93E+10 0 9.01E+09 0 1.65E+11 0KW 7.54E+10 0 1.78E+11 0(15W) 9.60E+08 0 0 0 Bacteroidales pig marker RCAout\URCAin(22) 3.29E+10 0 7.46E+10 0 0 0KW 0 0 7.99E+10 0 0 0 0 0 Bacteroidales pig marker URCAout (15) 2.12E+09 0 0 0 0 0KW 8.99E+09 0 0 0(18W) 4.98E+09 0 0 0 Bacteroidales human marker RCAin (18) 4.37E+09 0KW

(22W;15W) 9.69E+09 0 0 0 KW(22W;15W) 0 0 0 0KW

(22W;15W) 1.00E+10 0 0 0 Bacteroidales human marker RCAout\URCAin(22) 1.30E+12 0KW

(18W) 1.20E+12 0 9.56E+11 0 KW(18W) 1.96E+12 0 1.75E+12 0KW

(18W) 1.27E+12 0 2.29E+09 0 Bacteroidales human marker URCAout (15) 6.40E+11 0KW

(18W) 1.29E+12 0 2.88E+10 0 KW(18W) 2.87E+11 0 1.48E+12 0KW

(18W) 3.85E+10 0 1.20E+10 0

Table 2. Mean (M) source Bacteroidales (copies day-1), number of samples (N), and standard deviation (SD) for Temporally Concurrent Data under different season and flow conditions. See Suppl. 2A for flow regime definition.


Microbial source tracking endpoint

Site N M SD N M SD N M SD N M SD N M SD N M SD N M SD

Bacteroidales ruminant marker RCAin 64 1.87E+10 1.17E+11 26 3.79E+10 1.81E+11 23 6.87E+09 3.08E+10 15 3.64E+09 1.21E+10 26 4.13E+10 1.83E+11 27 4.41E+09 1.10E+10 11 4.79E+08 1.26E+09

Bacteroidales ruminant marker RCAout\URCAin 64 9.97E+09 5.17E+10 26 5.51E+09 2.53E+10 23 1.80E+10 8.17E+10 15 5.34E+09 1.50E+10 26 2.21E+10 8.02E+10 27 1.75E+09 6.33E+09 11 1.44E+09 3.11E+09

Bacteroidales ruminant marker URCAout 64 8.30E+10 4.40E+11 26 2.02E+10 6.69E+10 23 1.84E+11 7.28E+11 15 3.76E+10 6.19E+10 26 4.48E+10 1.07E+11 27 1.50E+11 6.70E+11 11 9.28E+09 3.03E+10

Bacteroidales muskrat marker RCAin 67 8.46E+07 4.75E+08 29 0 0 23 1.30E+08 5.95E+08 15 1.78E+08 6.91E+08 27 0 0 28 1.98E+08 7.26E+08 12 1.12E+07 3.90E+07

Bacteroidales muskrat marker RCAout\URCAin 67 5.40E+09 2.35E+10 29 1.71E+09 5.23E+09 23 8.84E+09 3.66E+10 15 7.24E+09 1.96E+10 27 1.05E+10 3.60E+10 28 2.81E+09 7.36E+09 12 0 0

Bacteroidales muskrat marker URCAout 67 4.89E+08 4.00E+09 29 0 0 23 1.42E+09 6.83E+09 15 0 0 27 0 0 28 1.17E+09 6.19E+09 12 0 0

Bacteroidales C. goose marker RCAin 67 1.51E+09 1.23E+10 29 0 0 23 4.39E+09 2.11E+10 15 0 0 27 0 0 28 3.61E+09 1.91E+10 12 0 0

Bacteroidales C. goose marker RCAout\URCAin 67 7.88E+10 4.26E+11 29 1.68E+11 6.42E+11 23 9.89E+09 3.46E+10 15 1.13E+10 4.38E+10 27 1.75E+11 6.65E+11 28 1.97E+10 5.50E+10 12 0 0

Bacteroidales C. goose marker URCAout 67 3.81E+10 2.94E+11 29 7.85E+10 4.23E+11 23 0 0 15 0 0 27 8.44E+10 4.38E+11 28 0 0 12 0 0

Bacteroidales pig marker RCAin 67 4.24E+10 1.95E+11 29 4.67E+09 2.51E+10 23 9.13E+10 3.05E+11 15 4.04E+10 1.56E+11 27 1.05E+11 2.99E+11 28 4.98E+08 2.63E+09 12 0 0

Bacteroidales pig marker RCAout\URCAin 67 1.67E+10 1.37E+11 29 3.86E+10 2.08E+11 23 0 0 15 0 0 27 4.15E+10 2.16E+11 28 0 0 12 0 0

Bacteroidales pig marker URCAout 67 1.08E+09 8.83E+09 29 0 0 23 0 0 15 4.82E+09 1.87E+10 27 0 0 28 2.58E+09 1.37E+10 12 0 0

Bacteroidales human marker RCAin 67 2.25E+09 1.30E+10 29 5.20E+09 1.95E+10 23 0 0 15 0 0 27 0 0 28 5.39E+09 1.98E+10 12 0 0

Bacteroidales human marker RCAout\URCAin 67 7.53E+11 2.46E+12 29 6.43E+11 2.69E+12 23 5.83E+11 1.56E+12 15 1.22E+12 3.16E+12 27 1.13E+12 2.65E+12 28 7.14E+11 2.77E+12 12 1.25E+09 4.33E+09

Bacteroidales human marker URCAout 67 3.48E+11 2.20E+12 29 7.04E+11 3.31E+12 23 1.65E+10 7.10E+10 15 1.66E+11 5.96E+11 27 8.37E+11 3.44E+12 28 2.10E+10 7.74E+10 12 8.01E+09 1.90E+10

Table 3. Median (ME) and mean absolute deviation (MAD) of source Bacteroidales (copies day-1) for All Available Data under seasonal and flow conditions with accompanying significant Kruskal–Wallis (KW) and Mann-Whitney U (W) tests amongst sites and site pairs, respectively. Significant differences between sites (identified by site number) are indicated in median column and are bolded (p<0.05). See Suppl.2A for flow regime definition.


Microbial source tracking end point

Site (Site #) MAD ME MAD ME MAD ME MAD ME MAD ME MAD ME MAD ME

Bacteroidales ruminant marker RCAin (18) 2.65E+10 0KW(15W) 6.14E+10 0 8.44E+09 0KW

(23W;15W) 4.91E+09 0KW(15W) 6.68E+10 0KW

(15W) 6.51E+09 0KW 4.75E+08 0

Bacteroidales ruminant marker RCAout\URCAin(22) 1.76E+10 0KW(15W) 1.12E+10 0 3.04E+10 0KW

(23W) 9.25E+09 0KW(15W) 3.75E+10 0KW

(15W) 3.03E+09 0KW(23W;15W) 2.06E+09 0

Bacteroidales ruminant marker URCAmid (23) 1.64E+11 0KW 4.03E+10 0 3.73E+11 0KW(18W;22W;15W)) 0 0KW

(15W) 2.35E+11 0KW 1.15E+11 0KW

(22W) 5.39E+09 0

Bacteroidales ruminant marker URCAout (15) 1.27E+11 0KW(18W;22W) 3.18E+10 0 2.76E+11 0KW

(18W;23W) 6.01E+10 0KW(18W;22W;23W)) 9.40E+10 0KW

(18W;22W) 2.05E+11 0KW(22W) 1.60E+10 0

Bacteroidales muskrat marker RCAin (18) 1.27E+08 0KW(22W) 0 0KW

(22W) 1.66E+08 0 2.54E+08 0 0 0 3.44E+08 0 1.04E+07 0

Bacteroidales muskrat marker RCAout\URCAin(22) 9.12E+09 0KW(18W;15W) 2.79E+09 0KW

(18W;15W) 1.50E+10 0 1.26E+10 0 1.75E+10 0 4.38E+09 0 0 0

Bacteroidales muskrat marker URCAmid (23) 6.02E+09 0KW 1.55E+09 0KW 0 0 2.17E+10 0(15W) 1.00E+10 0 3.34E+09 0 0 0

Bacteroidales muskrat marker URCAout (15) 7.80E+08 0KW(22W) 0 0KW

(22W) 2.11E+09 0 0 0(23W) 0 0 1.82E+09 0 0 0

Bacteroidales C. goose marker RCAin (18) 2.32E+09 0KW(22W) 3.68E+07 0KW

(22W) 5.76E+09 0 0 0 0 0KW(22W) 6.51E+09 0KW 4.67E+07 0

Bacteroidales C. goose marker RCAout\URCAin(22) 1.30E+11 0KW(18W;23W;15W)E 2.64E+11 0KW

(18W;23W) 1.67E+10 0 2.11E+10 0 2.76E+11 0KW(18W;23W) 3.19E+10 0KW

(15W) 0 0

Bacteroidales C. goose marker URCAmid (23) 0 0KW(22W) 0 0KW

(22W) 0 0 0 0 0 0KW(22W) 0 0KW 0 0

Bacteroidales C. goose marker URCAout (15) 5.42E+10 0KW(22W) 1.52E+11 0KW 0 0 0 0 1.34E+11 0KW 0 0KW

(22W) 0 0

Bacteroidales pig marker RCAin (18) 6.27E+10 0 8.19E+09 0 1.15E+11 0 5.75E+10 0 1.59E+11 0 8.98E+08 0 0 0

Bacteroidales pig marker RCAout\URCAin(22) 3.11E+10 0 6.99E+10 0 0 0 0 0 7.46E+10 0 0 0 0 0

Bacteroidales pig marker URCAmid (23) 4.67E+09 0 9.51E+09 0 0 0 0 0 1.04E+10 0 0 0 0 0

Bacteroidales pig marker URCAout (15) 4.63E+09 0 0 0 0 0 1.51E+10 0 7.34E+09 0 4.01E+09 0 0 0

Bacteroidales human marker RCAin (18) 3.43E+09 0KW(22W;15W) 8.84E+09 0 0 0KW

(22W;15W) 0 0(22W) 0 0KW(22W;15W) 9.39E+09 0 0 0(22W;15W)

Bacteroidales human marker RCAout\URCAin(22) 1.24E+12 0KW(18W;23W) 1.13E+12 0 8.95E+11 0KW

(18W;23W) 1.96E+12 0(18W) 1.66E+12 0KW(18W;23W) 1.19E+12 0(23W) 2.29E+09 0(18W)

Bacteroidales human marker URCAmid (23) 0 0KW(22W;15W) 0 0 0 0KW

(22W) 0 0 0 0KW(22W) 0 0(22W) 0 0

Bacteroidales human marker URCAout (15) 5.25E+11 0KW(18W;23W) 1.29E+12 0 2.26E+10 0KW

(18W) 2.04E+11 0 1.24E+12 0KW(18W) 4.93E+10 0 1.03E+10 0(18W)

Table 4. Mean (M) source Bacteroidales (copies day-1) and number of samples (N) and standard deviation (SD) for All Available Data under season and flow conditions. See Suppl.2A for flow regime definition.


Microbial source tracking endpoint Site N M SD N M SD N M SD N M SD N M SD N M SD N M SD

Bacteroidales ruminant marker RCAin 83 1.44E+10 1.03E+11 29 3.39E+10 1.71E+11 34 4.69E+09 2.53E+10 20 2.73E+09 1.05E+10 30 3.58E+10 1.70E+11 29 4.10E+09 1.07E+10 24 2.73E+08 8.93E+08Bacteroidales ruminant marker RCAout\URCAin 68 9.93E+09 5.03E+10 28 6.44E+09 2.51E+10 25 1.66E+10 7.84E+10 15 5.34E+09 1.50E+10 28 2.19E+10 7.73E+10 29 1.63E+09 6.12E+09 11 1.44E+09 3.11E+09Bacteroidales ruminant marker URCAmid 48 9.89E+10 2.87E+11 23 2.21E+10 8.14E+10 15 2.83E+11 4.61E+11 10 0 0 21 1.45E+11 3.75E+11 24 7.02E+10 2.04E+11 3 4.04E+09 7.00E+09Bacteroidales ruminant marker URCAout 83 8.29E+10 3.93E+11 29 1.81E+10 6.35E+10 30 1.64E+11 6.45E+11 24 5.93E+10 7.42E+10 33 7.14E+10 1.48E+11 35 1.25E+11 5.89E+11 15 1.01E+10 2.66E+10Bacteroidales muskrat marker RCAin 86 6.59E+07 4.20E+08 32 0 0 34 8.80E+07 4.90E+08 20 1.34E+08 5.99E+08 31 0 0 30 1.84E+08 7.02E+08 25 5.40E+06 2.70E+07Bacteroidales muskrat marker RCAout\URCAin 71 5.09E+09 2.28E+10 31 1.60E+09 5.07E+09 25 8.14E+09 3.51E+10 15 7.24E+09 1.96E+10 29 9.76E+09 3.48E+10 30 2.62E+09 7.14E+09 12 0 0 Bacteroidales muskrat marker URCAmid 48 3.21E+09 1.64E+10 23 8.09E+08 3.88E+09 15 0 0 10 1.36E+10 3.49E+10 21 5.26E+09 2.41E+10 24 1.82E+09 6.25E+09 3 0 0 Bacteroidales muskrat marker URCAout 83 3.95E+08 3.60E+09 29 0 0 30 1.09E+09 5.98E+09 24 0 0 33 0 0 35 9.36E+08 5.54E+09 15 0 0 Bacteroidales C. goose marker RCAin 86 1.18E+09 1.09E+10 32 1.90E+07 1.07E+08 34 2.97E+09 1.73E+10 20 0 0 31 0 0 30 3.37E+09 1.84E+10 25 2.44E+07 1.22E+08Bacteroidales C. goose marker RCAout\URCAin 71 7.44E+10 4.14E+11 31 1.58E+11 6.21E+11 25 9.10E+09 3.33E+10 15 1.13E+10 4.38E+10 29 1.63E+11 6.42E+11 30 1.84E+10 5.33E+10 12 0 0 Bacteroidales C. goose marker URCAmid 48 0 0 23 0 0 15 0 0 10 0 0 21 0 0 24 0 0 3 0 0 Bacteroidales C. goose marker URCAout 83 2.74E+10 2.50E+11 29 7.85E+10 4.23E+11 30 0 0 24 0 0 33 6.90E+10 3.97E+11 35 0 0 15 0 0 Bacteroidales pig marker RCAin 86 3.30E+10 1.72E+11 32 4.23E+09 2.39E+10 34 6.17E+10 2.52E+11 20 3.03E+10 1.35E+11 31 9.12E+10 2.80E+11 30 4.64E+08 2.54E+09 25 0 0 Bacteroidales pig marker RCAout\URCAin 71 1.58E+10 1.33E+11 31 3.61E+10 2.01E+11 25 0 0 15 0 0 29 3.86E+10 2.08E+11 30 0 0 12 0 0 Bacteroidales pig marker URCAmid 48 2.38E+09 1.65E+10 23 4.97E+09 2.39E+10 15 0 0 10 0 0 21 5.45E+09 2.50E+10 24 0 0 3 0 0 Bacteroidales pig marker URCAout 83 2.37E+09 1.57E+10 29 0 0 30 0 0 24 8.21E+09 2.89E+10 33 3.78E+09 2.17E+10 35 2.06E+09 1.22E+10 15 0 0 Bacteroidales human marker RCAin 86 1.75E+09 1.15E+10 32 4.72E+09 1.86E+10 34 0 0 20 0 0 31 0 0 30 5.03E+09 1.92E+10 25 0 0 Bacteroidales human marker RCAout\URCAin 71 7.10E+11 2.40E+12 31 6.01E+11 2.61E+12 25 5.37E+11 1.50E+12 15 1.22E+12 3.16E+12 29 1.05E+12 2.57E+12 30 6.66E+11 2.68E+12 12 0 4.33E+09Bacteroidales human marker URCAmid 48 0 0 23 0 0 15 0 0 10 0 0 21 0 0 24 0 0 3 0 0 Bacteroidales human marker URCAout 83 2.85E+11 1.98E+12 29 7.04E+11 3.31E+12 30 1.26E+10 6.23E+10 24 1.19E+11 4.75E+11 33 6.85E+11 3.12E+12 35 2.71E+10 9.07E+10 15 6.41E+09 1.72E+10

Table 5: Odds ratio estimates for microbial source tracking end points versus pathogen occurrence in water with associated confidence intervals estimates for odds ratios for All Available Data. Significant results bolded. a/w, associated with.

PathogensMicrobial Source

Tracking Endpoint Estimate

Campylobacter spp.

Salmonella spp.

E. coli O157:H7

Cryptosporidium Giardia Hepatitis E Norovirus GII Rotavirus

Crytosporidium a/w livestock

Lower 95% CI odds ratio estimate 0.25 0.29 0.00 6.76 0.24 0.00 0.00 0.00Odds ratio estimate 1.27 3.62 0.00 ∞ 1.13 0.00 0.00 0.00

Upper 95% CI odds ratio estimate 5.35 29.35 ∞ ∞ 4.25 ∞ 11.81 ∞

Crytosporidium a/w wildlife



Crytosporidium a/w avian

Lower 95% CI odds ratio estimate 0.06 0.00 0.00 0.32 0.03 0.00 0.00 0.00Odds ratio estimate ∞ 0.00 0.00 ∞ 1.83 0.00 0.00 0.00

Upper 95% CI odds ratio estimate ∞ 541.39 ∞ ∞ 36.67 ∞ ∞ ∞

Crytosporidium a/w humans


Upper 95% CI odds ratio estimate 13.21 79.75 ∞ ∞ 588.77 ∞ ∞ ∞

Crytosporidium a/w muskrat/voles


Upper 95% CI odds ratio estimate 6.35 27.26 ∞ ∞ 2.21 ∞ ∞ ∞

Crytosporidium a/w unknown sources



Bacteroidales ruminant marker

Lower 95% CI odds ratio estimate 0.28 0.01 0.33 1.33 0.41 0.08 0.67 0.03Odds ratio estimate 0.59 0.39 6.50 4.74 1.79 ∞ 2.68 1.54

Upper 95% CI odds ratio estimate 1.19 3.00 389.00 21.66 6.89 ∞ 10.17 30.79

Bacteroidales muskrat marker

Lower 95% CI odds ratio estimate 0.12 0.00 0.00 0.01 0.04 0.00 0.02 0.00Odds ratio estimate 0.55 0.00 0.00 0.42 2.36 0.00 0.88 0.00

Upper 95% CI odds ratio estimate 1.97 7.85 36.75 8.46 48.35 450.19 7.85 19.46

Bacteroidales C. goose marker


Upper 95% CI odds ratio estimate 6.43 ∞ 53.70 106.91 48.35 1249.15 0.83 71.37

Bacteroidales pig marker



Bacteroidales human marker



Bacteroidales unknown marker

Lower 95% CI odds ratio estimate 0.16 0.09 0.04 0.06 0.08 0.01 0.37 0.08Odds ratio estimate 0.47 0.78 ∞ 0.76 0.86 ∞ 2.84 ∞

Upper 95% CI odds ratio estimate 1.35 36.24 ∞ 7.01 45.42 ∞ 129.81 ∞

Hepatitis A a/w human

Lower 95% CI odds ratio estimate 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00Odds ratio estimate ∞ 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Upper 95% CI odds ratio estimate ∞ 483.82 ∞ ∞ ∞ 3917.62 240.89 998.20

Astrovirus a/w human Lower 95% CI odds ratio estimate 0.41 0.02 0.00 0.01 0.00 0.00 0.27 0.00

Odds ratio estimate 1.44 0.88 0.00 ∞ 0.00 0.00 1.68 0.00Upper 95% CI odds ratio estimate 5.81 7.76 ∞ ∞ ∞ 240.89 7.61 9.77

Norovirus GI a/w human


Upper 95% CI odds ratio estimate 15.10 17.53 ∞ ∞ ∞ 283.74 9.69 11.65

Norovirus GIII a/w bovine


Upper 95% CI odds ratio estimate 38.00 4.93 ∞ ∞ ∞ ∞ 7.07 38.29

Norovirus GIV a/w human


Upper 95% CI odds ratio estimate 22.89 8.21 ∞ ∞ ∞ ∞ 14.21 20.95

Sapovirus a/w human Lower 95% CI odds ratio estimate 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Odds ratio estimate 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Upper 95% CI odds ratio estimate ∞ ∞ ∞ ∞ ∞ ∞ ∞ ∞

Human Torque teno virus



Torque teno sus virus a/w swine



Adenovirus 40/41 a/w human



General Adenovirus a/w human



F-RNA coliphage human


Upper 95% CI odds ratio estimate 7.27 11.70 52.81 ∞ ∞ 530.43 17.55 23.41

F-RNA coliphage animal


Upper 95% CI odds ratio estimate 4.62 1.12 5.57 48.21 11.80 ∞ 2.57 6.41

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