Selection and Application of Microbial

Source Tracking Tools For Water-Quality

Investigations

Chapter 3 of

Section A, Biological Science, of

Book 2, Collection of Environmental Data

U.S. Department of the Interior

U.S. Geological Survey

Techniques and Methods 2–A3

Selection and Application of Microbial 

Source Tracking Tools For Water-Quality 

Investigations

By Donald M. Stoeckel

Techniques and Methods Book 2, Chapter A3

U.S. Department of the Interior

U.S. Geological Survey

Chapter 3 of

Book 2, Collection of Environmental Data,

Section A, Biological Science
U.S. Department of the Interior

Gale A. Norton, Secretary

U.S. Geological Survey

P. Patrick Leahy, Acting Director

U.S. Geological Survey, Reston, Virginia: 2005

For more information about the USGS and its products:

Telephone: 1-888-ASK-USGS

World Wide Web: http://www.usgs.gov/

Any use of trade, product, or firm names in this publication is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Citation:

Stoeckel, D.M., 2005, Selection and application of microbial source tracking tools for water-quality investigations: 

U.S. Geological Survey Techniques and Methods Book 2, Chapter A3, 43 p.
iii

Contents

Abstract...........................................................................................................................................................1

1. Introduction—What is Microbial Source Tracking? ..........................................................................1

2. Description of MST Protocols ................................................................................................................3

2.1 Source Identifiers..........................................................................................................................3

2.2 Detection Methods........................................................................................................................4

2.3 Analytical Approaches .................................................................................................................4

2.3.1 Empirical Matching Approach ........................................................................................4

2.3.2 Population Biology Approach..........................................................................................4

2.3.3 Approach Based on Detection Rates.............................................................................5

2.4 Methods-Comparison Studies.....................................................................................................5

3. Matching MST Protocols to Study Objectives ....................................................................................7

3.1 Single Household Scale—Ohio Septic Leach Field Study .....................................................7

3.2 Local Area, Small Population—”Possible Sources of Nitrate in Ground Water 

at Swine Licensed-Managed Feeding Operations in Oklahoma, 2001”.........................8

3.3 Medium Area, Medium Population—”Patterns and Sources of Fecal Coliform 

Bacteria in Three Virginia Streams” ....................................................................................9

3.4 Medium Area, Transient Population—”Tiered Approach for Identification of 

a Human Fecal Population Source at a Recreational Beach: Case Study 

at Avalon Bay, Catalina Island, California” .........................................................................8

3.5 Large Area, Large Population—”Sharing of Ribotype Patterns of Escherichia coli

Isolates During Baseflow and Stormflow Conditions” ...................................................10

4. Assumptions Behind Choice of MST Protocol ..................................................................................11

4.1 Host Specificity............................................................................................................................12

4.2 Even Distribution Within Host Population ...............................................................................12

4.3 Stability of Source Identifiers....................................................................................................14

4.3.1 Temporal Stability ............................................................................................................14

4.3.2 Spatial Stability ................................................................................................................14

4.4 Consistent Decay Rates in the Environment...........................................................................14

4.5 Relevance to Study Objectives .................................................................................................15

5. References Cited ....................................................................................................................................16

6. Glossary....................................................................................................................................................24

Appendix A: Source Identifiers ................................................................................................................26

A.1 Regulatory Source Identifiers...................................................................................................26

A.2 Nonregulatory Source Identifiers ............................................................................................28

Appendix B: Detection Methods..............................................................................................................29

B.1 Conventional Typing Methods ..................................................................................................31

B.2 Phenotypic Methods ..................................................................................................................31

B.3 Genotypic Methods ....................................................................................................................32
iv

Figure

1. Decision points in the process of microbial source tracking application, 

and basic information and decisions that lead to effective use of MST 

protocols, sampling designs, and interpretation ....................................................................2

Tables

1. General sources of microbial source tracking information...................................................2

2. Summary of how various microbial source tracking (MST) source 

identifiers meet generalized assumptions of host specificity, distribution 

among the host population, stability in individual animal hosts, stability 

in the environment, and relevance to public health regulations or public 

health risk ....................................................................................................................................37

Conversion Factors and Abbreviated Water-Quality Units

Multiply By To obtain

foot (ft) 0.3048 meter (m)

square mile (mi2) 259.0 hectare (ha)

square mile (mi2) 2.590 square kilometer (km2)

gallons per day (gal/d) 0.003785 cubic meter per day (m3/d) 

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F=(1.8×°C)+32

Concentrations of chemical constituents in water are given either in milligrams per liter (mg/L) 

or micrograms per liter (μg/L).

Concentrations of bacteria in water are reported in colony-forming units per 100 mililiters 

(CFU/100 mL), colony-forming units per gram of sampled medium (CFU/g), or cells per gram of 

sampled medium (cells/g).

Concentrations of phage viruses and enterovirus are reported in plaque-forming units per 100 

mililiters (PFU/100 mL).
Selection and Application of Microbial 

Source Tracking Tools for Water-Quality 

Investigations

By Donald M. Stoeckel

Abstract 

Microbial source tracking (MST) is a complex process that includes many decision-making steps. Once a contamination problem has been defined, the potential user of MST tools 

must thoroughly consider study objectives before deciding upon a source identifier, a detection method, and an analytical approach to apply to the problem. Regardless of which MST 

protocol is chosen, underlying assumptions can affect the results and interpretation. It is crucial to incorporate tests of those assumptions in the study quality-control plan to help validate 

results and facilitate interpretation. 

Detailed descriptions of MST objectives, protocols, and assumptions are provided in this 

report to assist in selection and application of MST tools for water-quality investigations. Several case studies illustrate real-world applications of MST protocols over a range of settings, 

spatial scales, and types of contamination. Technical details of many available source identifiers and detection methods are included as appendixes. By use of this information, researchers should be able to formulate realistic expectations for the information that MST tools can 

provide and, where possible, successfully execute investigations to characterize sources of 

fecal contamination to resource waters.

1. Introduction — What is Microbial Source Tracking?

Many of our Nation’s resource waters fail to achieve water-quality standards for designated uses, as required by the Clean Water Act, even after implementation of the National 

Point-source Discharge Elimination System (NPDES; U.S. Environmental Protection Agency, 

2000b). Therefore, contemporary (2005) state water-management plans emphasize control of 

nonpoint sources of contamination. Difficulty in allocating contributions of various nonpoint 

sources, however, poses a substantial obstacle to control of contaminant loads. The emerging 

field of microbial source tracking (MST) may help identify nonpoint sources of fecal contamination. This document describes the science and application of MST to water-quality investigations and identifies current technical and theoretical limitations associated with a variety of 

MST protocols.

The term “microbial source tracking” refers to a group of analytical protocols used to 

determine the source of fecal contamination. As evidence, MST uses host-associated characteristics of various microorganisms present in feces; that is, physiological differences in hosts 

are expected to select for specific characteristics (such as adhesion factors, antibiotic resistance, temperature optima, and other metabolic traits) in associated enteric microorganisms. 

The MST approach can be thought of as having several key common components and decision 

points, as illustrated in figure 1. In particular, the protocol used for MST (source identifier, 

detection method, and analytical approach) must be applicable to the scale and specific objectives of the study.
   Selection and Application of Microbial Source Tracking Tools for Water-Quality Inverstigations

Table 1. General sources of microbial source tracking information (MST), by decision point in the MST process.

Topic References

1. Problem definition Ground Water Rule (U.S Environmental Protection Agency, 2000c); Beach guidance, Appendix G 

(U.S. Environmental Protection Agency, 2002)

2. Formulation of objectives This document

3. Choice of protocol This document; Scott and others, 2002; Simpson and others, 2002; Sinton and others, 1998

4. Design of sampling strategy Protocol for developing pathogen TMDLs (U.S. Environmental Protection Agency, 2001)

5. Data colection and analysis Albert and others, 2003; Ritter and others, 2003

6. Interpretation Hyer and Moyer, 2004; Long and Plummer, 2004

Figure 1. Decision points in the process of microbial source tracking application, and 

basic information and decisions that lead to effective use of MST protocols, sampling 

designs, and interpretation. General sources of information for each decision point can be 

found in table 1.

1. Define Problem

Sanitary survey

Targeted sampling

Historic monitoring data

2. Formulate Objectives

Categorization

Unknown source,

need information

Suspected source(s),

desire confirmatory evidence

Quantification

Quantification of

source loadings

Application

Must be linked to

regulatory indicators

Must be linked to

public health risk

Any indication of fecal sources

is acceptable

Must meet high legal

standard of veracity

Presence/absence

evidence of suspected source

Fine-resolution source

categories (species level)

Coarse-resolution source

categories (human/nonhuman)

3. Choose MST protocol

4. Define area of relevance and design sampling strategy

5. Collect and analyze data

6. Interpret data
2. Description of MST Protocols 

For the purposes of this report, a MST protocol is defined by a combination of source 

identifier, detection method, and analytical approach. Source identifiers are microbiological 

markers that can be used to discriminate feces from various sources. Examples are Escherichia coli (E. coli) genomic fingerprints, coliphage serotypes, and markers within a 16S 

rRNA gene carried by Bacteroidetes. Detection methods are laboratory methods used to 

detect or distinguish among source identifiers. Examples are antibiotic resistance analysis 

and ribotyping to distinguish E. coli types, traditional serotyping and line-blot hybridization 

to distinguish coliphage types, and length heterogeneity polymerase chain reaction (PCR) or 

terminal restriction fragment length polymorphism analysis to detect host-associated Bacteroidetes markers. Analytical approaches are strategies used to classify contamination in 

test samples by comparison to known-source fecal reference materials. Examples are direct 

matching of subtypes and clustering by fecal source.

Many MST protocols are available for research or practical application. None of the 

protocols, however, is completely accurate, nor is any one protocol universally applicable to 

all objectives. This report describes several prior applications of MST and presents information intended to assist in selection of appropriate MST protocols to meet design objectives 

of water-quality investigations. Information about formulation of objectives and choice of 

protocol is presented in the context of the framework portrayed in figure 1, but the other MST 

decision points from figure 1 are not discussed at length in this report. Other sources of information about each MST decision point are listed in table 1.

2. Description of MST Protocols 

MST protocols are made up of three interrelated parts—a source identifier, a detection 

method, and an analytical approach:

•	 Source identifiers are the microbiological targets (that is, viruses, bacteria, or protozoa) 

on which the MST study is based. Examples are genotypes or phenotypes of E. coli, 

genotypes of F-specific RNA-based coliphage, and host-specific markers found in 

Bacteroides/Prevotella bacteria. (See Appendix A for detailed descriptions.) 

•	 Detection methods are the techniques (generation of genotypic or phenotypic profiles, 

detection of molecular markers) used to recognize source identifiers from various 

sources. Examples are ribotyping, antibiotic resistance analysis, length-heterogeneity 

polymerase chain reaction (LH-PCR), or hybridization with host-specific sequences. 

(See appendix B for detailed descriptions.) 

•	 Analytical approaches are the conceptual frameworks used to link results from water 

samples to fecal sources. Examples are library-dependent approaches (epidemiological matching, population biology clustering) and library-independent approaches 

(based on detection of host-specific markers). A library, in the context of MST, is an 

extensive collection of source identifiers (often bacterial isolates) that represents fecal 

material from a source.

2.1 Source Identifiers

The choice of source identifier depends, in part, on the objectives of the work. For example, MST in support of recreational water-quality objectives (such as total maximum daily 

load (TMDL) plans) frequently uses regulated indicator microorganisms as source identifiers. 

The U.S. Environmental Protection Agency (USEPA) has promulgated regulatory criteria for 

E. coli and enterococci concentrations in recreational waters (Dufour, 1984), and for total 

coliform and coliphage (proposed) presence as a measure of drinking-water quality (U.S. 

Environmental Protection Agency, 2000c). Similarly, the U.S. Food and Drug Administration uses fecal coliform concentrations as a regulatory measure of shellfish-harvesting water 

quality (U.S. Food and Drug Administration, 2003). Nonregulatory source identifiers, such 
 Selection and Application of Microbial Source Tracking Tools for Water-Quality Inverstigations

as Bacteroidetes, are used sometimes to confirm fecal sources when a sanitary survey is ambiguous. Alternately, nonregulatory source identifiers sometimes are used on the basis that any 

reduction in feces from detected sources (based on evidence from nonregulatory source identifiers) will reduce concentrations of regulatory fecal-indicator microorganisms. Descriptions of 

many available source identifiers are provided in Appendix A.

2.2 Detection Methods

Choice of detection method depends on the source identifier chosen, the objectives of the 

study, and the availability of analytical capabilities. Detection methods that have been developed and used for MST range from the conventional (based on host-associated species such as 

Enterococcus faecalis; Wheeler and others, 2002) through various typing methods of individual 

isolates (phenotypic or genotypic profiling). A new generation of MST methods based on 

detection of host-specific gene sequences from whole-water extracted DNA is under development (Field and others, 2003). Descriptions of many available detection methods are provided in 

Appendix B.

2.3 Analytical Approaches

A basic division in analytical approaches for library-dependent MST is between epidemiology and population biology. Epidemiology hypothesizes that specific microbial subtypes are 

present predominantly in one host category, whereas population biology hypothesizes that a 

more general combination of traits is common to source identifiers from a host category (such 

as antibiotic resistance patterns among the human population). A large number of cultivated 

reference isolates must be collected in the same time and space as the test samples to support 

classification by library-dependent approaches. 

In contrast to library-dependent MST, inferred presence of a fecal source by library-independent MST is based on detection of (generally) a single host-associated marker. Presence 

of a single trait is anticipated among all or many individuals in a host category, and the trait 

is expected to be absent in individuals from other host categories. In the library-independent 

approach, reference feces samples must be collected to develop and validate the method, but the 

reference sample requirement from the same time and space as the test samples is dictated solely 

by quality-control needs.

2.3.1 Empirical Matching Approach

The empirical matching approach is often referred to as one-to-one matching within 

library-dependent MST. In this approach, each individual source identifier isolate is assigned to 

a type. Most types are expected to be exclusive to broad or narrow host categories, regardless 

of a functional basis describing why that type would be specific to a host category. A functional 

basis, which may indicate a host-microbe interaction that would support the hypothesis of host 

specificity, may be used to support the host specificity implied by empirical pattern matching, 

but it is not necessary: simple detection is sufficient. Epidemiologists use a wide variety of typing methods in an attempt to define useful microbial types in applications related to MST. (See 

Appendix B for details.) The most discriminating typing methods can separate even closely 

related lineages into defined types. Types are defined and tested for specificity to a host to allow 

association of unknown isolates with sources (Hyer and Moyer, 2003; Samadpour and Chechowitz, 1995).

2.3.2 Population Biology Approach

The population-biology-based approach can also be library dependent; users of this 

approach assume an underlying distribution of host-specific characteristics in enteric microorganisms. These characteristics could arise, for instance, during coevolution with specific 
2. Description of MST Protocols 

hosts (Gordon, 2001; Souza and others, 1999) and result in competitive advantage for specific 

strains in their coevolved hosts. For source tracking by use of a population biology model, it is 

assumed that functional characteristics such as phenotypes based on carbon utilization capability 

(Hagedorn and others, 2003) or sensitivity to antibiotics (Whitlock and others, 2002; Wiggins, 

1996) can be used to generate rules to associate bacterial isolates with hosts. Genetic similarity measures, when used in library-dependent approaches, generally are not linked to a specific 

functional gene. In some experiments, however, functional-gene sequences have been used to 

define genotypes (Ram and others, 2004; Guan and others, 2002).

Because bacteria reproduce asexually yet often share genetic material, microbial population 

geneticists look at bacterial genes as a continuum of information in which “species” are nodes 

that represent stable combinations of genes. Within a species, finer-scale characteristics that 

may be advantageous within specific hosts are passed down through clonal succession. Statistical group separation methods, such as discriminant analysis, are used to identify combinations 

of characteristics that are found more commonly in one host over another (Harwood and others, 

2000; Parveen and others, 1999; Wiggins, 1996).

2.3.3 Approach Based on Detection Rates

Interpretation of results from library-independent approaches is similar to the matching-type library-dependent approaches—a characteristic is either detected or not detected in a 

DNA extract. Methodological advances with quantitative PCR are showing some promise for 

quantitation of host-specific markers (Dick and Field, 2004; Seurinck and others, 2005); otherwise, frequency of detection or most-probable-numbers style analysis are the only ways to infer 

that feces from a particular host are dominant or not dominant. This approach is also different 

from the cultivation-based library-dependent approaches because (1) DNA contained in a large 

volume of water often has to be efficiently extracted because most of these methods are cultivation-independent, (2) extracted DNA has to be purified so that PCR-based detection can be done 

effectively, and (3) none of the source identifiers used in library-independent protocols have been 

directly correlated with concentrations of regulated fecal indicator microorganisms.

2.4 Methods-Comparison Studies

Many protocols are available for MST research. (See Appendixes A and B.) Two efforts to 

compare various protocols and approaches have been completed. A USGS-led study compared 

analytical protocols, all of which utilized a library-dependent, E. coli-based approach (Stoeckel 

and others, 2004). A USEPA-funded study led by the Southern California Coastal Water 

Research Project (SCCWRP) compared protocols utilizing a variety of approaches. The results 

of the USEPA study were published as a series of reports (see Griffith and others, 2003, for summary and other references). 

In both studies, various researchers used MST protocols to determine whether they could 

correctly associate challenge known-source material with the correct host. In the USGS study, 

the same 630-isolate library of E. coli collected from animals in Berkeley County, W.Va., was 

distributed to each researcher for evaluation. The known-source library was used to classify isolates from a separate challenge collection of 200 isolates. The challenge isolates were collected 

from the same area but from different individual animals. 

In the USEPA study, a collection of 36 reference feces samples (rather than isolates) and 

8 reference composite waste samples was distributed to each researcher for characterization. 

Twelve challenge slurries were made from the reference materials, representing one to three 

sources. The USEPA study tested a broader range of MST protocols than the USGS study and, 

unlike the USGS study, full representation of the source population was achieved because the 

very same fecal reference materials added to test slurries were previously characterized by each 

researcher. Because feces—not isolates—were used as reference material, a variety of library-
 Selection and Application of Microbial Source Tracking Tools for Water-Quality Inverstigations

dependent and library-independent methods could be compared with one another (Griffith and 

others, 2003). 

The USGS study pointed out some limitations of protocols used in library-dependent, E. 

coli-based approaches to MST (Stoeckel and others, 2004). The evaluated protocols represented 

techniques often used for this type of MST at the time of the study (2001 to 2002):

•	 Antibiotic resistance analysis (ARA)

•	 Carbon utilization profiles

•	 Repetitive DNA element polymerase chain reaction (rep-PCR):

• REP primer pair

• Box A1R primer 

•	 Ribotyping:

• HindIII restriction enzyme

• EcoR1 and PvuII restriction enzymes

•	 Macrorestriction using NotI restriction enzyme, separated by pulsed-field gel electropho- resis

The seven protocols were evaluated for their ability to

•	 reproducibly generate data for a given E. coli isolate and

•	 accurately classify E. coli isolates to their hosts of origin:

•	 eight-way classification to host species,

•	 three-way classification to management categories of human, domestic animals, and 

wildlife, and

•	 two-way classification to human or nonhuman.

Of the seven protocols evaluated, only pulsed-field gel electrophoresis was able to correctly 

classify all replicates, a group of challenge isolates subcultured from the known-source library. 

The other protocols classified fewer than 60 percent of replicates to the correct host species. 

Failure to correctly classify replicates indicated that either E. coli isolates in the known-source 

library lacked host specificity or tested protocols lacked precision. 

When accuracy was evaluated, a clear tradeoff between correctness and completeness of 

classification was evident. One protocol correctly classified 90 percent (to species) of the 6 

percent attempted; typical of other results was the protocol that correctly classified 27 percent 

(to species) of the 100 percent attempted. For the two-way classification, four protocols were 

63–100 percent accurate when attempting classification of 6–100 percent of the isolates, but 

accuracy for three protocols was not significantly different from random classification to two 

sources (50 percent). These accuracy results indicated that tracking isolates to source categories 

may fail when, as in this study, feces from only a few individuals are used to characterize a much 

larger population (in this study, 20 humans from a population of 80,000). Failure was evident as 

both false classification and inability to classify many challenge isolates.

A fundamental conclusion of the USEPA study was that all current approaches to MST have 

both strengths and weaknesses. Library-dependent methods had low false-positive detection 

rates, indicating that they would best be applied in settings when large populations of individuals are expected to contribute fecal contamination (Field and others, 2003). One weakness of 

library-independent methods, however, is that the source identifier may not be present in the 

feces of most individuals (for example, viruses and E. coli toxin genes; see Appendix A for 

descriptions). This absence of the source identifier would lead to false-negative results. When 

applied in a setting with large populations, these methods would be expected to have a lower 

false-negative rate. Library-dependent methods, on the other hand, had low false-negative detection rates, in part because they tended to detect all sources in all samples. This tendency led to 

high false-positive rates that could be reduced by establishing “minimum detectable threshold” 

criteria to aid interpretation (Harwood and others, 2003; Myoda and others, 2003; Ritter and 

others, 2003). Setting a minimum detectable threshold involves estimation of classification 

error, then rejecting detection rates that are less than that classification error. This allows the 
3. Matching MST Protocols to Study Objectives 

researcher to report only sources that are more certain to have contributed fecal contamination to 

the sample and to reject the uncertain ones.

Although every researcher had samples of all materials used to make the test samples, no 

method was completely accurate for all sources. Griffith and others (2003) concluded that length 

heterogeneity PCR, using markers for Bacteroidetes (see Appendix A, sec. A.2.4.1 and Appendix 

B, sec. B.3.3.2), had the lowest error rates of all protocols tested. This protocol is limited in the 

number of sources for which markers have been developed (human and ruminant) and the current 

lack of ability to quantify the detected sources. Library-dependent methods, though more prone 

to error, may be able to detect the presence of each source and to estimate relative contribution by each source. The results of the USEPA study, however, indicated that library-dependent 

methods as currently applied often assigned a large proportion of isolates to an “unknown” category, and there was no reliable relation between proportion of bacteria associated with each host 

and the known composition of the samples (Ritter and others, 2003).

3. Matching MST Protocols to Study Objectives

MST can be used to address a variety of objectives related to impacts of feces released to 

the environment. The following examples illustrate just a few of the ways in which MST protocols have been applied at various scales. This discussion is not intended to be a comprehensive 

review of past application of MST protocols, nor should these examples necessarily be taken as 

models of how MST should be applied. Following the description of each study is a brief discussion of alternative protocols that might have been applied to the problem.

3.1 Single-Household Scale—Ohio Septic Leach Field Study 

Ohio’s state guidelines for siting leach lines for household sewage-treatment system (septic 

systems) leach lines are based on the assumption that treatment of pathogens occurs within the 

first 2 ft of soil and treatment of nutrients occurs within the first 4 ft of soil (Duncan and others, 

1994; Mancl and Slater, 2002). Curtain drains, where constructed to lower the local water table 

underlying a leach field, often are installed 10 ft from the leach line in Ohio. The hypothesis 

evaluated in this study was that septic systems installed in Ohio soils with a shallow depth to a 

limiting condition (the limiting condition often is a high water table or bedrock) provide sufficient treatment of waste. This hypothesis was tested by analyzing near-surface ground water collected by curtain drains and in piezometers at various distances from selected septic leach fields. 

E. coli were detected at horizontal distances of 4–20 ft from the septic leach line at a shallow 

depth range (4–8 ft), but not in deeper soil water. E. coli also were detected in one of two tested 

curtain-drain effluents.

The area of inference and source populations in this study were both strictly limited; thus, 

the approach used in this study was to detect direct matches between E. coli cultivated from 

potential sources and E. coli cultivated from contaminated water. There was no underlying 

assumption that E. coli genotypes were host-specific; rather, it was hypothesized (and tested) 

that human and alternate host populations did not carry the same genotypes. MST data were 

supplemented in this study by collection of nitrogen and oxygen stable-isotopes ratios, wastewater-organic-compound concentrations, and chloride concentrations (Dumouchelle and Stoeckel, 

2005).

The objective of MST in this study was to determine whether E. coli were present in nearsurface ground water and curtain-drain effluent. When present, E. coli were tested to determine 

whether types detected in ground water were the same as those detected in a particular septic 

system. The physical proximity of the sample water to the septic system makes support of this 

hypothesis likely, but E. coli could also come from droppings on the land surface (pets) or from 

widespread contamination of offsite, near-surface soil water. Populations of likely contributing 

hosts were small, consisting of a single family for the septic system and a few pets or wildlife for 

surface deposits. Contributing hosts were fully represented (household) or heavily represented 

(local outdoor pets and wildlife) in the known-source library. Widespread contamination of off-
 Selection and Application of Microbial Source Tracking Tools for Water-Quality Inverstigations

site, near-surface soil water would be detected by observation of E. coli in water samples distant 

from the septic system. E. coli isolates were characterized by use of a rep-PCR protocol (see 

Appendix B, sec. B.3.1.3) similar to that used by Dombek and others (2000). 

By use of MST tools (rep-PCR typing of cultivated E. coli), not only were E. coli detected 

in near-surface ground water, but the probable source of the contamination was indicated. Without the use of E. coli typing, it would have been possible to claim that the E. coli found in curtain 

drain effluent and other near-surface ground water samples came from surface scat, not from the 

septic system. Indeed, in one case where E. coli were detected at great distance (20 ft) from the 

leach field, E. coli typing indicated that the probable source of those E. coli was dog feces at the 

land surface rather than the septic leach field. 

Most, if not all, available E. coli typing methods probably would have served the same 

purpose as rep-PCR in this case (van Belkum and others, 2001). Typing of enterococci probably also would have been sufficient to accomplish the objectives of this study. The low diversity 

of F-specific coliphage (only four types) would have limited its utility for this specific purpose; 

however, if found, F-specific coliphage and other library-independent markers may have been 

useful to indicate whether fecal contamination was of human or nonhuman origin. Dye tracers and wastewater organics would be sufficient in this setting to demonstrate a hydrologic link 

but could not demonstrate subsurface transport of particulates such as E. coli nor exclude other 

origins of E. coli detected at a distance from the septic leach field.

3.2 Local Area, Small Population—“Possible Sources of Nitrate in Ground 

Water at Swine Licensed-Managed Feeding Operations in Oklahoma, 

2001” 

The Oklahoma Department of Agriculture, Food, and Forestry collected ground-water 

samples underlying swine confined-animal feeding operations (CAFOs). Nitrate concentrations exceeding 10 mg/L as N were detected in 79 wells near 35 CAFOs. The nitrate Maximum 

Contaminant Level (MCL) for drinking source water is 10 mg/L (U.S. Environmental Protection 

Agency, 1996). Waste from the CAFOs was treated in waste-stabilization lagoons, and each 

lagoon contained high concentrations of nitrogen. Nitrate concentrations have been measured as 

high as 300 mg/L in swine waste-stabilization lagoons (cited in Becker and others, 2003). The 

hypothesis tested in this study was that nitrate-enriched water leaks from waste-stabilization 

lagoons and may contribute nitrate to nearby ground water. Support of this hypothesis required 

some test for subsurface flow between the lagoon and contaminated ground water through the 

lagoon liner.

MST was applied in this study to determine whether the types of E. coli in monitoring wells 

were the same as those in the adjacent waste-stabilization lagoon. Detection of the same types 

would provide evidence that E. coli traveled from the lagoon to the monitoring well and would 

support the hypothesis of a hydrologic link between the monitoring well and the lagoon. The 

objective was addressed by a design in which genotypes of E. coli in monitoring wells were 

compared with genotypes of E. coli in the associated swine lagoons. MST data were supplemented in this study by collection of data on surrounding land use, nitrogen stable-isotope ratios, 

and organic wastewater compound concentrations. E. coli collected in this study were analyzed 

by ribotyping at the Institute for Environmental Health by the protocol reported in Hyer and 

Moyer (2003; see Appendix B, sec. B.2.3.1). Selected isolates also were analyzed at the USGS 

Ohio Water Microbiology Laboratory (Columbus, Ohio) with a rep-PCR protocol similar to that 

reported by Dombek and others (2000; see also Appendix B, sec. B.3.1.3).

Fecal coliform concentrations in the lagoons were highly variable (16 to 210,000 CFU/100 

mL), and E. coli were detected in 6 of 79 monitoring wells. Of the six wells where E. coli were 

detected, ribotyping characteristics indicated a swine source in five. Additional sources (human, 

avian, deer) were indicated in two of the five. The sixth had indications of deer as the primary 

source of fecal contamination (Becker and others, 2003). Additional evidence was collected 

by an alternate method (rep-PCR) for a well at which E. coli were detected1 and its associated 

lagoon (data not reported in Becker and others, 2003). The well sample contained E. coli that 

1

 Referred to as well 360618098024501 in Becker and others (2003).
3. Matching MST Protocols to Study Objectives 

were indistinguishable, by rep-PCR banding patterns, from E. coli in the lagoon. This evidence 

supported the interpretation based on ribotyping that swine were a source of E. coli to that well. 

The well also yielded water with a nitrate concentration (37 mg/L) above the MCL of 10 mg/L 

but with no detections of animal-waste-associated wastewater compounds (Becker and others, 

2003).

As with the first study (section 3.1), this study required that enough different types of E. 

coli could be detected that a match with a type from the swine lagoon was strong evidence that 

the swine lagoon was the source of the fecal contamination. Many typing protocols could have 

been used to accomplish this purpose (see Appendix B); alternatively, enterococci could have 

been used as the indicator of fecal contamination (see Appendix A). An additional alternative 

approach would have been to search for swine-specific markers (such as the swine-specific toxin 

gene of E. coli; see appendix B, sec. B.3.3.2) in the monitoring wells, or to establish a hydrologic 

link between the lagoon and the monitoring well by use of dyes, detection of swine-associated 

fecal compounds, or other strategies.

3.3 Medium Area, Medium Population—“Patterns and Sources of Fecal 

Coliform Bacteria in Three Virginia Streams” 

The objective of the study was to demonstrate field applicability of a MST approach and to 

identify the sources of fecal coliforms in the study streams (Hyer and Moyer, 2003). In general, 

the potential sources of fecal contamination in each watershed were considered to be humans, 

pets, domestic livestock and poultry, migratory birds, or other wildlife. Increased effort toward 

control of any one of the listed management categories by resource managers, without appropriate supporting data, likely would be contentious to the local government, the agricultural community, or the general public.

This study illustrates a different approach from the Ohio septic system study or the repPCR part of the Oklahoma swine lagoon study. Reseachers in the Ohio and Oklahoma studies 

attempted to compare a source population of E. coli (septic tank and swine lagoon, respectively) with the E. coli found in contaminated water. The specific identification of host was not 

attempted in those studies. Specific identification of host, as was done in the Virginia study, goes 

beyond “this was found both here and there, so there may be a link” to “wherever it is found, this 

E. coli type is associated with a specific host.” The assumptions implicit in this approach are the 

main points of discussion in the chapter on assumptions underlying application of MST (chapter 

4).

Many streams in Virginia do not meet applicable designated-use criteria because of microbiological impairment, despite control of known point sources of fecal contamination. Three 

streams were chosen for evaluation in the reported study: Accotink Creek, drainage area 25 mi2, 

human population greater than 110,000, primarily urban; Blacks Run, drainage area 20 mi2, 

human population about 34,700, mixed urban and agricultural; Christians Creek, drainage area 

107 mi2, human population about 12,000, primarily agricultural. Extensive base-flow, eventflow, and synoptic sampling was done in each watershed over 20 months. MST by ribotyping 

(Appendix B, sec. B.3.2.1) was done on E. coli isolates collected at a state-determined waterquality compliance point for each watershed. The known-source E. coli reference collection of 

Dr. Mansour Samadpour (Institute for Environmental Health, Seattle, Wash.; more than 50,000 

isolates at the time) was used and supplemented by known-source acquisitions in the studied 

watersheds. The hypothesis in this study was that ribotypes (strains) of E. coli are specific to 

host species; therefore, any stream-isolated E. coli with a ribotype that matched a known-source 

isolate could be assigned to that host species as the source. 

Multiple lines of evidence were used to evaluate expectations for MST findings in these 

study streams. The authors began by evaluating populations and distributions of fecal sources in 

each watershed. Synoptic sampling was done to evaluate longitudinal trends in fecal-indicator 

concentrations in the main stem, in tributaries, and in effluents discharged to the main stem. Seasonal and flow-related trends in fecal-indicator concentrations also were evaluated. These data 

were interpreted in the context of transport pathways and animal distributions in the watersheds 

to indicate expected sources of fecal-indicator bacteria. 
10 Selection and Application of Microbial Source Tracking Tools for Water-Quality Inverstigations

The MST results were a combination of the expected and the unexpected. Ribotype matching of fecal-indicator bacteria in Accotink Creek, the urban setting, indicated contributions by 

human and pet feces, as expected, but also a strong influence by waterfowl. Blacks Run fecalindicator bacteria were of mixed sources (human, pet, and livestock) as expected. Fecal-indicator concentrations in Christians Creek had a larger human and pet component than expected for 

a primarily agricultural watershed (about 25 percent of isolates), compared with livestock and 

poultry (about 50 percent). A further unexpected finding in all three watersheds was that relative contributions from each major source were about the same during base-flow and stormflow 

periods, despite the expectation that different transport pathways would dramatically change relative contributions from different sources. Lastly, the study documented seasonal patterns in the 

contributions of bacteria from cattle and poultry sources in Blacks Run and Christians Creek; this 

seasonal pattern was consistent with the land-management strategies used in each watershed. 

Several supporting elements were included to help interpret MST data in this study. The 

unexpectedly high contribution by waterfowl in the urban Accotink Creek watershed was supported by the results of a prior study in a neighboring urban watershed, Four Mile Run (Simmons 

and others, 2000). Contributions of bacteria from human sources were independently supported 

by detection of organic wastewater compounds. (Detectable concentrations of caffeine and cotinine were present in all three streams.) Detection of seasonal variability of poultry waste inputs 

to Christians Creek was supported by trends in total arsenic data collected by Hancock and others 

(2000). The poultry-feed amendment Roxarsone contains arsenic that is generally excreted by 

the birds, and the poultry litter eventually is applied to the surrounding agricultural fields. Total 

arsenic concentrations increased during storm runoff, supporting the hypothesis that field-applied 

poultry litter was flushed into streams.

Ribotyping of E. coli is one of several currently (2005) available approaches that could 

address the objectives of this study. Because of the size of the watershed (and the large contributing populations of sources), use of any library-dependent method would require a very large 

library of known-source bacteria (Wiggins and others, 2003; Yang and others, 2004). Because 

the data were used for development of TMDL plans, numerical approximations of contribution 

by each source was required; thus, library-independent methods would have been less appropriate to meet the goals of this study. One labor-intensive alternative would have been to conduct 

more intensive sampling of both flow and concentration in the streams and to calculate the load 

from each source on the basis of known population contributing to many small subdrainages.

3.4 Medium Area, Transient Population—“Tiered Approach for 

Identification of a Human Fecal Pollution Source at a Recreational Beach: 

Case Study at Avalon Bay, Catalina Island, California”

Avalon Bay is a tourist destination on Catalina Island, Calif. Boehm and others (2003) 

report that 17,500 tourists arrive on a typical summer-season day at the town of Avalon, population 3,500. At the time of the study, the beach was closed if any one of seven water-quality 

criteria was exceeded (single-sample or geometric-mean concentrations of total coliforms, fecal 

coliforms, or enterococci, plus the ratio of total coliform to fecal coliform concentrations). The 

authors report that in the summers of 2000 and 2001, the beach was commonly closed because 

of single-sample enterococci concentrations. The source of enterococci was not obvious. The 

community is sewered with trunk lines parallel to the beach at a distance of 20 m from the shoreline, and the wastewater-treatment plant outfall (2,200 m3

/d) is southeast of Avalon Bay, 100 m 

from the shore and 65 m deep. Other potential sources of fecal contamination include “nuisance 

water” from street cleaning and seabirds. The sewage collection systems of the 400 or so boats 

in the harbor are controlled by a dye program that limits the potential for undetected direct discharge by these vessels.

The authors of this study (Boehm and others, 2003) proposed an approach that combines 

intensive monitoring of fecal-indicator bacteria concentrations, observation of local hydrology 

and potential sources, and qualitative testing for presence of fecal material from specific sources 

to understand a particular contamination problem. This is one of the few published reports in 

which a library-independent, cultivation-independent protocol was applied to an environmental 
3. Matching MST Protocols to Study Objectives 11

setting. The researchers in this study used a combination of intensive sampling (similar to the 

targeted sampling approach proposed in Kuntz and others, 2003), a comprehensive sanitary survey that included sampling potential sources for fecal-indicator bacteria concentrations, and testing for human-specific genetic markers contained in Bacteroidetes (Bernhard and Field, 2000b) 

and human-specific enterovirus (Monpoeho and others, 2000).

The objective of MST in this study was to evaluate whether human feces contributed to 

water-quality-standard exceedances in Avalon Bay. The tiered approach described in the report 

was composed of three parts. First was a survey of contamination patterns, which, in conjunction with local hydrology, showed that contamination came from a localized nearshore source 

(effectively ruling out the wastewater-treatment-plant discharge as a source). Second was a survey of potential sources, in which a broken pipe that released fecal-indicator bacteria was found 

and subsurface beach water, nuisance water from street cleaning, and seabird droppings all were 

shown to carry high concentrations of fecal-indicator bacteria. A nonpermitted discharge pipe 

and boat cooling-water discharge were shown to carry negligible concentrations of fecal-indicator bacteria. The third and final step was to apply two library-independent tests (Bacteroidetes

human-specific marker and human-specific enterovirus) to various water samples. Though 

human-specific markers were detected in both subsurface beach water and Avalon Bay water, 

the incidence of human-specific marker detection was not related to exceedance of the waterquality standards in the bay. Thus, although presence of human fecal material was detected in 

one source of high fecal-indicator bacteria, the relative contribution of that source and other 

sources of fecal-indicator bacteria to water-quality-standard exceedances in Avalon Bay remains 

unknown.

The objective of the reported study was to detect whether human-origin fecal material was 

present in Avalon Bay. By use of the approach described, the authors were able to detect fecal 

material of human origin in Avalon Bay—also indicated were that (1) one hydrologic pathway 

for that fecal material was by way of subsurface transport under the beach (recall that the sewage 

system trunk line runs 20 m from the shoreline), and that (2) the wastewater-treatment-plant 

outfall probably did not contribute fecal material of human origins to Avalon Bay. Had quantification of contributions been a requirement, the researchers may have chosen more expensive 

alternatives of calculating loads from each source to support a model for bay water quality or 

applying a library-dependent method in an attempt to classify the source of each colony-forming 

unit of fecal-indicator bacteria. Library-dependent methods would, however, be almost certain 

to fail in this setting because of the transience of the tourist population (17,500 different people 

every day).

3.5 Large Area, Large Population—“Sharing of Ribotype Patterns of 

Escherichia coli Isolates During Baseflow and Stormflow Conditions”

The upper Chattahoochee River flows through the city of Atlanta, Ga. Many reaches in the 

watershed in and around Atlanta do not meet water-quality standards for fecal-coliform bacteria 

concentrations. A study was designed to evaluate differences in E. coli detected during base 

flow and stormflow as a means of indicating management strategies to control fecal-coliform 

contamination in the study area. The hypothesis tested in the study was that distinct E. coli 

genotypes could be detected at sample sites during base flow in contrast to those detected during 

stormflow.

The objective of the study was to test the spatial and flow-related variability in E. coli

genotypes. E. coli were differentiated by ribotyping by the protocol described in Hartel and 

others (2004), which is very similar to that used in the Oklahoma (Becker and others, 2003) and 

Virginia studies (Hyer and Moyer, 2003) described in sections 3.2 and 3.3. (See Appendix B, 

sec. B.3.2.1.)

This study demonstrated high diversity in E. coli ribotypes found in a watershed with many 

tributary streams and outfalls, a large catchment area, and large populations of people and other 

animals in an urban setting. From a collection of 262 isolates ribotyped, 163 distinct patterns 

were detected. Only 21 isolates (8 percent) were multiple detections of the same ribotype (6 

ribotypes were detected multiple times). Because there were so many singleton detections 
12 Selection and Application of Microbial Source Tracking Tools for Water-Quality Inverstigations

of ribotypes (only detected once during the study), the opportunity to characterize patterns in 

ribotype pattern occurrence with flow regime was very limited. It was apparent from this study 

that enormous sampling effort, particularly for a known-source library, would be necessary to 

do library-dependent MST in a system as complex as the Chattahoochee River watershed. An 

alternative approach in this setting might be to narrow the scope of the study area by use of 

targeted sampling (Kuntz and others, 2003) or to use some of the emerging library-independent 

PCR-based markers in a most-probable-numbers dilution series format to estimate the relative 

contributions by various sources in the watershed. (See appendix B, sec. B.3.3.)

4. Assumptions Behind Choice of MST Protocol

A comprehensive objective of MST studies is quantitative assessment of all fecal or 

fecal-indicator loads from multiple source categories affecting regulatory or other public health 

standards for water quality at a sample site. Reliable attainment of this objective is constrained 

by the following assumptions:

• Host specificity. The source identifier(s) is associated exclusively with a single host species or other grouping. There is no alternate source of the source identifier(s).

• Even distribution within host population. The source identifier(s) is found in every individual of the host population at the same concentration.

• Stability of source identifiers. The source identifier(s) is invariable on the spatial and 

temporal scale of the study’s area of inference.

• Consistent decay rate in the environment. The source identifier(s) for each host category 

decays at the same rate and does not replicate outside of the host.

• Relevance to study objectives. The chosen source identifier(s) is amenable to specific 

objectives of the investigation, such as reduction of regulatory fecal-indicator bacteria 

concentrations or verifiable reduction in public health risk.

No MST protocol meets all of these assumptions (table 2 at back of report), nor do the 

objectives of every MST study require that each assumption be met. Choice of MST protocol is 

driven, in part, by the importance of each assumption to meeting the objective of the study and 

the degree to which the MST protocol meets each assumption.

4.1 Host Specificity

Host specificity is expected to be highest for microorganisms that coevolved with their 

hosts. The textbook examples of this type of host-specific association are symbioses, like the 

relationship in which the bacterium Vibrio fischeri produces light in a special organ under the 

eyes of the flashlight fish (Photoblepharon palpebratus) and the relationship in which bacteria 

of the species Rhizobium nodulate the roots of leguminous plants for nitrogen fixation (Atlas and 

Bartha, 1993). Examples of host-specific relationships more relevant to MST include pathogenic 

viruses, which must be able to recognize the correct host cell for infection. There is no expectation of strong host specificity for a microorganism that does not have a functional interaction 

with its host. 

The regulatory fecal indicators E. coli, total and fecal coliforms, coliphage, and enterococci 

are examples of MST source identifiers that make up a small proportion of the enteric biota 

and have little or no functional interaction with their hosts. The widely studied species E. coli

is considered to be an opportunist that has limited specificity for its enteric environment and, in 

warmblooded hosts, can be found in other body systems (such as urethra, causing urinary tract 

infections; udder, causing mastitis; and eyes, causing ocular conjunctivitis or pinkeye). E. coli is 

not strictly limited to warmblooded hosts: the species has been observed in the feces of reptiles 
4. Assumptions Behind Choice of MST Protocol 13

(Souza and others, 1999) and the water cups of bromeliads (Rivera and others, 1988), and it is 

suspected to sustain viable populations in warm, nutrient-rich environments such as beach sand 

(Whitman and Nevers, 2003) and beach wrack (Whitman and others, 2003). These observations 

about host specificity extend also to the total coliforms and fecal coliforms, of which E. coli is 

a member. Similarly, enterococci have been cultivated from a variety of warmblooded animals (Wheeler and others, 2002), cold-blooded animals (Ott, 2001), and plant surfaces (Kanoe 

and Abe, 1988). Though perhaps rare, these exceptions to the assumption that fecal-indicator 

microbes are exclusively associated with the feces of warmblooded animals illustrate that absolute specificity of fecal-indicator bacteria subtypes to species is unlikely.

Souza and others (2002) proposed that hosts act as selective sieves in which only the best 

adapted subtypes will succeed. The concept of selection does not, however, imply exclusivity. Regulatory fecal-indicator bacteria have been reported to survive and reproduce in a variety 

of environments, and none of the regulatory fecal-indicator microorganisms fully exhibits host 

specificity (table 2). Host specificity for regulated fecal-indicator microorganisms might be 

best described as “differential distribution.” Accuracy of MST is limited, in part, by the extent 

to which host specificity is absolute. For example, detection of a subtype of E. coli that is most 

often found in cattle might be considered circumstantial evidence that cattle are a source of 

E. coli to that water. Human-specific adenoviruses and enteroviruses, in contrast, have been 

observed only in human hosts (Noble and others, 2003). Detection of these human-specific 

viruses provides strong evidence that humans were a source of fecal contamination to the water 

sample, but no evidence of how dominant the human source was to overall contamination. None 

of the alternative indicators of fecal contamination is nearly as well characterized as E. coli or 

enterococci (see Appendix A, sec. A.2), so there is the risk that limitations of alternative indicators will arise as they are studied more closely.

4.2 Even Distribution Within Host Population

Few comprehensive studies have been done to evaluate the distribution of source identifiers 

in their hosts. Perhaps not surprisingly, many of the studies that have been done have emphasized regulatory fecal-indicator microorganisms and pathogens of human health or economic 

importance. One such organism is E. coli. Gordon and Cowling (2003) measured the distribution of enteric coliform bacteria (of which E. coli is one species) among Australian mammals 

and found them to be only sporadically detected in component of gut populations (detection 

threshold >1 percent of bacteria capable of growth on MacConkey agar). In other studies, fecal 

coliform concentrations (of which E. coli is a major component) have been found to vary by 

orders of magnitude in feces coming from the same species (Geldreich, 1976). Use of E. coli

concentrations to make interpretations about actual feces loads to the study area might lead to 

false conclusions. Where information is available, indications are that the other proposed source 

identifiers also tend to violate the assumption of presence at consistent concentrations in feces of 

their hosts (table 2). 
14  Selection and Application of Microbial Source Tracking Tools for Water-Quality Inverstigations

4.3 Stability of Source Identifiers

4.3.1 Temporal Stability

Microorganisms are susceptible to genetic shuffling (genome plasticity). Genome plasticity occurs over timeframes relevant to this discussion because some microorganisms go through 

many generations in a matter of days (Savageau, 1983). Because bacterial reproduction is 

asexual, mechanisms for genetic rearrangement are generally within a single generation rather 

than during the reproductive process. Mechanisms include transfer of genetic material within 

and between species (conjugation), uptake and incorporation of naked DNA in the environment (transformation), incorporation of virus DNA (lysogeny), and transfer of mobile genetic 

elements (plasmids; Smith-Keary, 1989). Genome plasticity may affect subtypes defined by 

genetic fingerprints (pulsed-field gel electrophoresis (PFGE), ribotypes, rep-PCR and others) 

and phenotypic characteristics partially defined by plasmid-based traits (ARA) more than those 

defined by a single locus or other target sequence (length heterogeneity PCR, toxin genes, 

coliphage genotyping). Virus reproduction, through host-mediated replication, is subject to the 

same random genetic shuffling mechanisms as the host during replication. As with bacteria, 

some viruses are known to be more prone to genetic recombination and mutation than others. 

The mechanisms of genome plasticity have been defined and observed; however, actual rates of 

transformation and recombination in the environment are not well defined in most cases. 

 In addition to genome plasticity, there are constant shifts in dominance among the enteric 

microorganisms. Caugant and others (1981) reported finding few “resident” E. coli types and 

many ”transient” types in a human individual over about a year of repeated sampling. Overall, 

though, 62 percent of E. coli isolated from this individual were of the two resident types; thus, 

at any given time it would seem that a host is most likely to shed the resident type(s). Functional genetic markers may prove to be more temporally stable source indicators. Examples of 

functional genes include those encoding housekeeping genes (Maynard Smith and others, 2000) 

and adhesion proteins (Scott and others, 2005). Function-based markers have the dual benefit 

of conferring host specificity on the carrier organism and limiting fitness of alternates, thereby 

reducing the chance that individuals not carrying the marker will be shed persistently by the 

host. 

4.3.2 Spatial Stability

There may be a geographic area to which each source identifier is limited. Studies of E. 

coli (Hartel and others, 2002; Scott and others, 2003) and enterococci (Wiggins and others, 

2003) from known-sources indicate that libraries may be useful across watersheds within a 

region but not necessarily between regions. The dynamics of population distribution include not 

only suitability of habitat (host specificity, influenced by local diet and environment) but also 

transfer and colonization among geographically isolated host populations. An isolated population of host animals may contain a dominant marker, bacteria subtype, or other source identifier 

that, though suitable for colonization of other host populations, does not get the chance to do so 

because of geographic separation.

4.4 Consistent Decay Rates in the Environment

The failure of the fecal coliform/fecal streptococcus (FC/FS) ratio for use in source tracking was a lesson to heed in current pursuit of MST. This lesson is particularly relevant when 

attempting to quantify source contributions, which is the objective of many TMDL evaluations, 

and for less ambitious attempts to designate detected hosts as “major” or “minor” contributors. 

In the case of the FC/FS ratio, it was initially established that the distribution in fresh human 

feces is weighted toward fecal coliforms; thus, concentration ratios lower than 0.8 indicated predominantly nonhuman sources, whereas ratios greater than 4 indicated human sources of fecal 

contamination (Geldreich, 1976). Unfortunately, the relevance of the interpretations was found 

to be limited to recent contamination events because fecal coliform concentrations decay faster 
4. Assumptions Behind Choice of MST Protocol 15

than fecal streptococci concentrations; over the course of time, the FC/FS ratio shifts to high 

numbers (indicative of human sources) regardless of the source of fecal contamination (Clesceri 

and others, 1998).

Instability of source-indicating ratios (such as FC/FS), or shifts in the relative concentrations 

of source indicators, can occur through differential die-off rates or processes that change a source 

identifier’s type (such as mutation, exchange of genetic material, or changes in gene expression 

because of environmental conditions). Selective pressures in the secondary (non-enteric) habitat 

may very well undermine interpretations based on observations of source identifier characteristics in the primary (enteric) habitat (Savageau, 1983; Gordon and others, 2002). This is a very 

important potential limitation to development of known-source isolate libraries by use of fresh 

feces as reference material and use of other microbial ratios to indicate fecal sources.

4.5 Relevance to Study Objectives

Study objectives may require direct correlation with regulated fecal-indicator bacteria, direct 

correlation with human or other health risk, or both. Research that indicates whether each source 

identifier meets these criteria is described in table 2.
16  Selection and Application of Microbial Source Tracking Tools for Water-Quality Inverstigations

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24  Selection and Application of Microbial Source Tracking Tools for Water-Quality Inverstigations

Empirical type matching, one-to-one matching – The 

strategy of assigning each isolate to a type, such that isolates 

of the same type appear the same by use of the typing method. 

Isolates of unknown source are typed and assigned to a source 

only if every isolate of the same type came from the same 

source. If the type is detected in multiple sources, the isolate 

of unknown origin is classified as cosmopolitan. (See also

cosmopolitan.)

Fecal indicator, regulatory fecal-indicator microorganism

Microorganisms that are typically found only in feces. Fecal 

indicators generally are not pathogenic themselves, but they 

indicate the potential presence of fecal pathogens. Regulatory fecal indicators are those that are written into legislation 

designed to establish acceptable levels of fecal contamination.

Functional gene – A gene found in a microorganism’s genetic 

material that codes for some expressed characteristic mediating a host-microbe interaction in the preferred host but not 

in other hosts. An example functional gene used as a source 

identifier in microbial source tracking is the esp gene, encoding an adhesion factor that allows carrier strains of Enterococcus faecium to stick in the human intestine.

Genotype, genotypic profile – Characteristics that are specifically defined by the genetic sequence of the target organism, 

such as presence of a genetic marker or possession of identical 

DNA-fragment profiles.

Host category, fecal source – The category to which microbial source tracking protocols attempt to classify fecal contamination. Commonly used host classification schemes are 

by host species or by management category (human, wildlife, 

domestic livestock, or pets; human, wildlife, or domestic animals; human or nonhuman).

Inhibitor, PCR inhibitor – Chemical constituents, either from 

the environment (humic acids) or from the process of laboratory manipulation (ethanol) that stop the PCR from amplifying 

a source identifier genetic sequence and result in a false-negative result. PCR inhibitors are controlled for in laboratory 

analysis by adding a known PCR target (positive control) to 

an aliquot of the sample—if the known target is not amplified, 

then PCR inhibitors are present and a negative result for the 

source identifier does not mean that the source identifier is not 

present in that sample.

Isolate – A laboratory-generated culture of identical microorganisms that originated from a single cell or, in the case of 

conglomerates, from a colony- or plaque-forming unit.

Library, known-source library – An extensive collection 

of source identifiers (often bacterial isolates) used in librarydependent microbial source tracking approaches to represent 

fecal material from a source.

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Chen, N., Lee, S.H., Calderon, R.L., and Beach, M.J., 2004, 

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Zelles, L., Bai, Q.Y., Beck, T., and Beese, F., 1992, Signature 

fatty acids in phospholipids and lipopolysaccharides as 

indicators of microbial biomass and community structure in 

agricultural soils: Soil Biology and Biochemistry, v. 24, 

no. 4, p. 317–323. 

6. Glossary

The following are definitions of some terms used in 

this document. Definitions presented are not intended to be 

universally applicable—several terms are defined in a document-specific sense.

Approach, MST approach – The choice of library-dependent or library-independent strategies to characterize sources 

of fecal contamination. Within library-dependent MST, the 

choice of population-based classification or one-to-one matching to assign individual isolates to a host species or other 

source category. (See also library and associated definitions, 

empirical type matching, population biology-based matching.)

Challenge isolates, challenge feces – Quality-control 

samples composed of cultivated source identifiers or knownsource feces. The source of the challenge isolates or feces is 

known to the compiler of the challenge but not to the analytical laboratory. Challenge materials are used to measure 

the internal accuracy of MST detection methods and can be 

used, for instance, to calculate the rate of correct classification for known-source libraries. Challenge isolates or feces 

differ from spikes in that they are in the same condition as the 

known-source library or reference material collection: the 

environmental matrix is excluded.

Clone – Individuals arising from a common lineage through 

asexual reproduction of mother cell to two daughter cells. 

Clones are genetically identical and, therefore, inseparable by 

various genetic typing methods (though they may demonstrate 

phenotypic differences depending on conditions).

Cosmopolitan, cosmopolitan source identifier or type – A 

source identifier that is found in individuals belonging to multiple host categories. Cosmopolitan source identifiers or types 

violate the MST assumption of host specificity.

Detection method, MST detection method – The laboratory 

methods used to detect or distinguish among source identifiers 

as part of a microbial source tracking protocol.
6. Glossary 25

Reference material, known-source reference feces – Feces, 

either fresh (primary habitat) or treated (secondary habitat), 

for which the host of origin is known. Reference material is 

used to build known-source libraries, to validate library-independent methods, and to estimate classification error in MST.

Resident, resident source identifier or type – A source identifier that is consistently found in an individual over time.

Sanitary survey – The critical first step in a MST evaluation, 

in which the contributing area to a contaminated water body 

is inspected for sources of the contamination. A thorough 

sanitary survey is necessary to determine which sources need 

to be included or can be excluded from a MST evaluation. In 

some cases, the results of the sanitary survey may be convincing enough that the MST evaluation does not need to be done.

Secondary habitat – The environment outside of the host gut; 

the habitat which the source identifier must survive in order 

to be reintroduced to a primary habitat. The secondary habitat 

can be either a source of fecal contamination (such as a septic 

field or waste lagoon) or the resource water that is contaminated. (See also tertiary habitat.)

Source identifier – Microbiological marker used as part of a 

microbial source tracking protocol to discriminate feces from 

various sources.

Speciation – Classification of microorganisms, particularly 

bacteria, to species as a microbial source tracking detection 

method. Some species (such as Enterococcus gallinarum and 

Rhodococcus coprophilus) were named for the habitats in 

which they were commonly found.

Spike sample, challenge spike – A sample submitted to the 

analytical laboratory in which feces or isolates from known 

sources are mixed into an environmental matrix (such as 

autoclaved river water). The source of the feces or isolates is 

not made known to the analytical laboratory. Spike samples 

are used to estimate the accuracy of microbial source tracking 

protocols to classify isolates to host categories after passage 

from the primary habitat (source environment) to the secondary habitat (resource water).

Tertiary habitat – A term sometimes used in conjunction 

with primary and secondary habitat. When the primary habitat 

is the host gut and the secondary habitat is a waste-control 

facility, then the resource water which is contaminated is 

sometimes referred to as the tertiary habitat to distinguish it 

from the secondary-habitat source of fecal contamination.

Total Maximum Daily Load – A provision of the Clean 

Water Act under which non-point sources of contamination 

to a water body must be identified and controlled if the water 

body fails to meet regulatory standards even after all point 

sources of contamination have been controlled.

Transient, transient source identifier or type – A source 

identifier that is not consistently found in an individual over 

time.

Library dependent, library-dependent MST approach

MST that requires an extensive collection of reference source 

identifiers from the same time and geographic location as the 

test samples for effective classification of test isolates.

Library independent, library-independent MST approach

MST that does not require further collection of reference fecal 

material after the host-specificity of the source identifier has 

been established. The library-independent approach does, 

however, necessitate quality-control reference fecal material to 

be evaluated to ensure that the source identifier is relevant to 

the time and geographic location of the study setting.

Marker, host-associated or host-specific marker – A single 

characteristic, generally genotypic but sometimes phenotypic, 

used to classify results. Examples are presence of human-specific Bacteroidetes sequence and the presence of human-associated serotypes of coliphage.

Operational taxonomic unit – A measure of genetic similarity that represents some pragmatic separation of isolates but 

does not necessarily have phylogenetic or taxonomic meaning. In one-to-one matching, an operational taxonomic unit 

includes all isolates that are genetically similar at or above the 

level of the typing method’s precision.

Phenotype, phenotypic profile – Characteristics that are specifically defined by the physiological expression of traits, such 

as metabolism (carbon utilization) and resistance to antibiotics.

Polymerase chain reaction, PCR – The molecular-biology method that allows amplification (reproduction) of very 

specific sequences of genetic material. Use of the PCR allows 

detection of genes or other genetic characteristics that would 

otherwise be undetectable because of low concentrations. The 

PCR is used to amplify target DNA to the point where it can 

be detected by other laboratory techniques.

Population-biology-based matching – A MST approach 

based on the hypothesis that competition in hosts with various metabolisms, basal temperatures, and gut physiologies 

will select for a subset of microorganisms that share some 

characteristics. A common example of this hypothesis is 

antibiotic resistance, for which only those animals that have 

been exposed to antibiotics (humans and domestic animals) 

are expected to shed gut bacteria that are resistant to particular 

antibiotics.

Primary habitat – The host gut; the major environment to 

which the source identifier is adapted to live and reproduce.

Protocol, MST protocol – A combination of source identifier, 

detection method, and analytical approach chosen to accomplish microbial source tracking.
26  Selection and Application of Microbial Source Tracking Tools for Water-Quality Inverstigations

Appendix A: Source Identifiers

A.1 Regulatory Source Identifiers

A.1.1 Escherichia coli

E. coli is a member of the normal intestinal flora for most warmblooded animals (Gordon 

and Cowling, 2003). These bacteria constitute approximately 0.1 percent of total bacteria within 

an adult’s intestines (on a western diet; Tannock, 1995). Detection of E. coli is a confirmatory 

step in the total coliform rule for ground water, and criteria for surface water designated use 

attainment in many states are based, in whole or in part, on E. coli concentrations. The USEPA 

required that all states initiate E. coli or enterococci-concentration-based monitoring for recreational waters by April 2004 (U.S. Congress, 2000). Though they have been recommended by 

the USEPA since 1984 for recreational water-quality assessment (for freshwaters), E. coli are 

bacteria; therefore, they may not adequately represent transport and survival characteristics of 

protozoan and viral pathogens.

E. coli are gram-negative, facultatively anaerobic bacteria that ferment lactose and grow at 

temperatures up to 44.5 ̊C. They are commonly detected by colony growth on the media mTEC, 

modified mTEC, MI, or the combination of mENDO and NA-MUG agars after membrane filtration. E. coli also can be detected in a most-probable-number format by use of, for instance, the 

commercial Colilert and Colitag systems. Immunological and PCR-based tests also have been 

proposed for E. coli detection and quantification. Confirmation of E. coli is typically achieved 

through biochemical tests or enzymatic assays. In clinical applications, the most commonly 

used confirmatory tests are collectively known as IMViC—indole production, methyl red reaction, Voges-Proskauer test, and failure to grow on citrate-minimal media. In research labs, it is 

common to use the Analytical Profile Index (API) biotyping system (22 biochemical tests) or 

Biolog GN plates (95 sole-source substrates) or simply to test for MUG (4-methylumbelliferyl-ßD-glucuronide) hydrolysis (a test for ß-glucuronidase activity). For recreational waters, tests for 

indole production, gas formation on lactose, failure to express urease, failure to express oxidase, 

and failure to grow on citrate-minimal media are the recommended confirmation tests (U.S. 

Environmental Protection Agency, 2000a). There is a need for standardization of E. coli detection and confirmation methods among MST researchers because each of the listed media and 

confirmation tests excludes some portion of the entire E. coli population.

A.1.2 Enterococci

Like E. coli, enterococci are members of the normal intestinal flora for most, if not all, 

warmblooded animals. The enterococci are non motile, gram-positive cocci that are found in 

mammals, birds (Wheeler and others, 2002), plants (Ott and others, 2001), fish, and insects 

(Kanoe and Abe, 1988). They predominate in the gastrointestinal tract and occur less commonly in the genitourinary tract and the oral cavity of animals. Enterococci survive longer in 

marine environments (Cabelli and others, 1982) and through water treatment processes (Miescier 

and Cabelli, 1982) than coliforms do. Numbers in recreational waters correlate with the risk of 

gastrointestinal illness better than E. coli (Cabelli and others, 1982; Turbow and others, 2003). 

Though they have been recommended by the USEPA since 1984 for recreational water-quality 

assessment (for both fresh and marine waters) enteroccocci are bacteria; therefore, they may not 

adequately represent transport and survival characteristics of protozoan and viral pathogens.

Enterococci are differentiated from other fecal streptococci by their ability to grow in 6.5 

percent NaCl, at pH 9.6, and at 10 ̊C and 45 ̊C; they fail to express catalase, and they create a 

black precipitate on esculin iron agar (EIA agar; U.S. Environmental Protection Agency, 2000a) 

or exhibit ß-D glucosidase activity (mEI agar; Rhodes and Kator, 1997; U.S. Environmental 

Protection Agency, 2000a). The enterococci were previously classified as Group D streptococci, 
Appendix A 27

because they contain a group-D-specific cell wall carbohydrate (glycerol teichoic acid) linked to 

the cytoplasmic membrane. The group includes as members the species Enterococcus faecalis, 

E. faecium, E. avium, E. gallinarum, and E. durans.

Enterococci are commonly detected by colony growth after membrane filtration using mEI 

agar or the combination of mE and EIA agars. Enterococci also can be detected in a most-probable-number format by use of, for example, the commercial Enterolert system. Standard maintenance growth conditions include incubation on brain heart infusion (BHI) or similar broth or agar 

at 35–37 ̊C without aeration (U.S. Environmental Protection Agency, 2000a).

A.1.3 Coliphage

Coliphage are bacteriophages (viruses) that infect E. coli and, to a lesser extent, other coliform bacteria (Furuse, 1987). Because their presence is directly related to the presence of E. coli

(Borrego and others, 1987), coliphage have been proposed as superior fecal-indicator microorganisms. Coliphage are generally divided into somatic and F-specific types, based on their site 

of infection on the E. coli cell. F-specific RNA coliphage serve as model indicators of enteric 

viruses in freshwater (Havelaar, 1993), an area where E. coli is deficient as a model organism (Borchardt and others, 2003). Coliphage may be promulgated formally as regulatory fecal 

indicators for ground water in the United States under the proposed Ground Water Rule (U.S. 

Environmental Protection Agency 2000c). 

Coliphage is an operational taxonomic unit that includes distinct bacteriophage of multiple 

taxa. As noted above, the taxa correspond to somatic and F-specific coliphage. Somatic coliphage (at least four families, all DNA based) use a lipopolysaccharide on the E. coli cell outer 

surface as a recognition factor to initiate infection. F-specific coliphage use antigens on the 

F-pilus (used for sexual exchange of genetic material between E. coli cells) to initiate infection. 

F-specific coliphage include those that use DNA as their genetic material (Inoviridae) and those 

that use single-stranded RNA as their genetic material (Leviveridae; Vinje and others, 2004). 

F-specific RNA-based coliphage have been subdivided into four groups based on serotyping 

(Furuse, 1987) and genotyping (Beekwilder and others, 1996; Hsu and others, 1995; Vinje and 

others, 2004). Groups I and IV are most commonly found in nonhuman hosts, and groups II and 

III are most commonly found in human hosts (Cole and others, 2003).

Coliphage are detected by the presence of plaques (areas of no growth) on a lawn of E. 

coli host bacteria. Different host strains of E. coli are used for somatic and F-specific coliphage. 

Detection methods typically follow USEPA method 1601 (presence-absence) or method 1602 

(quantification). In method 1601, host E. coli are added to a water sample to allow coliphage 

reproduction, and the resulting enrichment is spotted onto a plate previously inoculated with the 

same host E. coli. Formation of a plaque around the spot indicates the presence of coliphage in 

the sample. In method 1602, the water sample is combined with host E. coli and molten nutrient agar. During incubation, a plaque forms in the E. coli lawn where an infectious coliphage 

particle was embedded in the agar gel, leading to a count of plaque-forming units.

A.1.4 Fecal Coliforms

Fecal coliform is an operational taxonomic unit that includes E. coli. Many states historically used fecal coliform concentrations as their primary indicator of bacterial water quality. As 

“thermotolerant coliforms,” the fecal coliform group was established as an alternative to total 

coliforms to detect organisms exclusively of fecal origin. The group includes species other than 

E. coli, including some that have environmental origins and some with a potential for regrowth in 

the environment. In a landmark epidemiology study, USEPA (Dufour, 1984) did not find a good 

relation between health risk and fecal coliform concentrations; therefore, since 1986, USEPA has 

recommended the use of other indicator organisms to monitor for bacteriological water quality 

(U.S. Environmental Protection Agency, 1986). Fecal coliforms are all gram-negative, facultatively anaerobic bacteria that ferment lactose with gas production and grow at temperatures up 

to 44.5 ̊C. They are commonly detected by colony growth after membrane filtration using mFC 

agar. 
28  Selection and Application of Microbial Source Tracking Tools for Water-Quality Inverstigations

A.1.5 Total Coliforms

Total coliform is an operational taxonomic unit that includes E. coli and fecal coliforms. 

Total coliform concentrations have been used to monitor drinking-water sources for almost 100 

years because they are seen as a more conservative indication of fecal contamination than E. coli

or fecal coliforms. The total coliform group encompasses 19 genera (including Escherichia, 

Klebsiella, Enterobacter, and Citrobacter) and 80 species (Leclerc and others, 2001). Coliforms 

reside in the intestinal tract of humans and animals, but also occur naturally in soil, surface water, 

fish, mollusks, leafy plants, insects, and rodents. This is one reason that total coliforms are 

generally considered poor indicators of fecal contamination and health risk (Borrego and others, 

1987). Total coliforms are used in the Ground Water Rule as an initial indication of fecal contamination (U.S. Environmental Protection Agency, 2000c). Total coliforms are gram-negative, 

aerobic or facultatively anaerobic, nonsporulating bacteria that ferment lactose at 35 ̊C within 48 

hours. Total coliforms are commonly detected by colony growth after membrane filtration using 

mENDO or MI agar. Total coliforms also can be detected in a most-probable-number format by 

use of, for example, the commercial Colilert system.

A.2 Nonregulatory Source Identifiers

A.2.1 Ratios of Microbes

One of the earliest approaches to MST involved use of the ratio of fecal coliforms to fecal 

streptococci (Geldreich and Kenner, 1969). Use of these ratios eventually was deemed unreliable because of the variable survival rates of the bacterial species involved (Clesceri and others, 

1998). Refer to section 4.4 for further details.

A.2.2 Host-Specific Bacteria

Several source identifier bacterial species currently are used for MST. Enterococcus 

faecalis may have a host range limited to humans and a few other animals (dogs and chickens; 

Pourcher and others, 1991). Wheeler and others (2002) concluded that combining speciation of 

Ent. faecalis with ribotyping was a useful approach for identifying human-source contamination. 

Rhodococcus coprophilus is found primarily in wheat straw and, therefore, is most commonly 

detected in the feces of grazing animals such as cattle (Sinton and others, 1998). Two Bifodobacterium species, B. adolescentis and B. dentium, have a narrow host range that is dominated by 

humans (Bonjoch and others, 2004).

A.2.3 Host-Specific Viruses

Adenoviruses have been proposed as an index of human viruses (Jiang and others, 2001; 

Piña and others, 1998). Enteroviruses have been found in activated sludge, sewage outfalls, 

and fresh and marine waters associated with human fecal contamination (Kopecka and others, 

1993; Reynolds and others, 1998; Griffin and others, 1999; Noble and Fuhrman, 2001; Jiang and 

others, 2001). Both adenoviruses and enteroviruses have been evaluated and recommended for 

MST; however, it appears that they are limited to identifying human contamination originating 

from populations, rather than from individuals (Noble and others, 2003).

Bovine enteroviruses (Ley and others, 2002) and bovine and porcine adenoviruses (Maluquer de Motes and others, 2004) also have been proposed to detect animal-source fecal contamination. Similarly, teschoviruses have been used as an indicator of porcine fecal contamination 

(Jiménez-Clavero and others, 2003). 
Appendix A 29

A.2.4 Host-Specific Genetic Markers

Genetic markers are sometimes used to detect host-specific microorganisms or types. 

These markers are often genes or other fragments of DNA that can be multiplied from within a 

mix with other DNA by the polymerase chain reaction. Once the marker has been multiplied, it 

can be detected by gel electrophoresis or with labeled probes. Many references describe these 

and other molecular techniques. The reader is directed to Persing and others (1993) as one such 

reference.

A.2.4.1 DNA Coding16S Ribosome of Bacteroides/Prevotella Group Anaerobes

Source-specific Bacteroidetes sequences have been identified by means of terminal restriction fragment length polymorphism (t-RFLP), length heterogeneity PCR, or subtractive hybridization, and used to design PCR primers for MST (Bernhard and Field, 2000a,b). The length 

heterogeneity PCR primer pairs have been field tested (Bernhard and others, 2003) and they 

produced relatively accurate results for identification of human and ruminant contamination in a 

methods comparison study (Field and others, 2003). Primer pairs for additional host species are 

under development (Dick and others, 2002).

A.2.4.2 Toxin Genes of Pathogenic Escherichia coli

PCR assays have been developed that target the heat-stable enterotoxin STIb to identify 

human fecal contamination (Oshiro and Olson, 1995), the heat-labile enterotoxin LTIIa to identify cattle fecal contamination (Khatib and others, 2002), and the heat-stable enterotoxin STII to 

identify swine fecal contamination (Khatib and others, 2003).

Appendix B: Detection Methods

B.1 Conventional Typing Methods

B.1.1 Speciation

Method

Microbiologists first divided bacteria into species on the basis of phenotypic information 

such as cell morphology and physiology. Suites of biochemical tests that facilitate classification 

of bacteria to species are described in “Bergey’s Manual of Systematic Bacteriology” (Atlas, 

1988). Classical approaches to bacterial systematics have been challenged by the advent of phylogenetics, which reveals an evolutionary basis for species definition (Stackebrandt, 2003). Still, 

the guiding principle for species definition is 70 percent DNA-DNA homology, a threshold that 

leaves considerable room for variability within species (Maynard Smith and others, 2000).

Viruses, on the other hand, were referred to until recently simply by their common names 

(for example, poliovirus). Formal classification is now based on nucleic acid type, particle size, 

shape and structure, and mode of replication (Atlas, 1988). Classification of protozoa follows 

more closely the model of macrobiology: as eukaryotes, protozoa fit within the traditional definition of species.

Information Content

Speciation has a long history of use in classification. Host specificity has influenced speciation with effects ranging from incidental in the case of commensals to integral in the case of 

some pathogens.
30 Selection and Application of Microbial Source Tracking Tools for Water-Quality Inverstigations

Speciation is significant to the many forms of MST that start with a basic taxonomic unit 

(often the species) as a means of separating out extraneous information. Reports indicate that 

Bacteroides fragilis bacteriophage (Tartera and others, 1989), at least one species within Enterococcus (Wheeler and others, 2002), and Cryptosporidium species (Ong and others, 2002) are 

specific to host sources.

B.1.2 Serotyping

Method

Serotyping was among the first subtyping methods developed for identification of pathogenic strains. Serotyping has been applied extensively to organisms that are now used for source 

tracking, including E. coli. In serotype analysis, antibodies to cell-surface antigens are added to 

a suspension. If the antibody matches the target antigen, the suspension agglutinates (clumps), 

and the reaction is positive. For many gram-negative bacteria, series of antibodies have been 

defined against cell-surface lipopolysaccharides (O antigens) and flagellar proteins (H antigens). 

These results often are reported in conjunction, as in E. coli O157:H7. In addition to the O and 

H antigens, capsular (K antigens) sometimes are used in serotyping. The method for serotyping 

coliphage differs in mechanism but not principle (Hsu and others, 1995).

Information Content

The information gained from serotyping is limited by the finite number of tested antigens 

and the reactions expressed by that isolate against the suite of antigen tests. Expression of cellsurface antigens is a stable characteristic. The O:H scheme of serotyping E. coli includes 170 O 

antigens and 55 H antigens, with more than 9,000 possible serotypes (Bettelheim and Thomas, 

2004). Not all strains can be serotyped.

Surface antigens are an important component of host-microbe interactions. As such, there 

may be a functional link between surface antigen expression and adaptation to succeed in a host 

(Toivanen and others, 2001). Enterotoxigenic E. coli associated with specific hosts have been 

defined by serotype (O:H) or serogroup (O only) conventions (Bettelheim and Thomas, 2004). 

Early studies of the stability of E. coli lineages in the human gastrointestinal tract also made use 

of serotyping (Sears and Brownlee, 1952). See the review by Scott and others (2002) and the 

investigation by Parveen and others (2001) for descriptions of bacterial serotyping as used in 

source tracking investigations. Serotypes of the viral fecal-indicator F+ RNA coliphage also have 

been evaluated for use in source tracking (Furuse, 1987).

B.1.3 Multilocus Enzyme Electrophoresis

Method

Multilocus enzyme electrophoresis (MLEE) has been used extensively in ecological studies 

to track populations of organisms. To perform MLEE, pure cultures of organisms are grown out, 

and enzymes are separated by electrophoretic mobility. Isolates demonstrating different enzyme 

mobilities are termed electrophoretic types (ETs).

Information Content

The enzymes used in MLEE are generally housekeeping enzymes, those essential for cell 

function. Substitutions of amino acids in the primary enzyme structure may not affect enzyme 

function but can result in altered electrophoretic mobility. Because primary enzyme structure is 

directly related to genetic information, MLEE data often are reported as representing genotypic 

rather than phenotypic information.

MLEE may be useful for MST using either the 1:1 matching strategy or the population 

genetics strategy because there is a known basis for similarity among ETs. MLEE data have 
Appendix B  31

been used to evaluate E. coli in studies related to source tracking (Caugant and others, 1984; 

Gordon, 2001; Gordon and others, 2002; and Souza and others, 1999) 

B.2 Phenotypic Methods

B.2.1 Carbon Utilization Profiles

Method

To generate carbon utilization profiles, pure cultures of a strain are inoculated into test solutions containing a single carbon source. Growth of the test strain causes some observable change 

in the medium, which is scored as presence or absence of substrate utilization.

Information content

Presence or absence of carbon utilization ability has been criticized as an unstable or variable characteristic in bacterial strains (Tenover and others, 1997). Commercial substrate suites 

were developed to discriminate among species but may not have ecological significance, hindering data interpretation. Carbon utilization profiles using pure cultures of indicator organisms 

have been proposed as a viable approach for MST (Hagedorn and others, 2003).

B.2.2 Membrane Fatty Acids

Method

The membranes of bacteria contain phospholipids that are crucial factors controlling cell 

permeability. To analyze the phospholipids, cell cultures are first saponified, then resultant fatty 

acids are extracted into an organic phase. Free fatty acids are then methylated, purified, and 

concentrated. Concentrated extracts are analyzed by gas chromatography. This variant of phospholipid fatty acid analysis is called gas chromatography fatty acid methyl ester (GC-FAME) 

analysis.

Information Content

Phospholipid composition information has been applied to the evaluation of microbial 

community composition in agricultural soils and other settings. Signature fatty acids have been 

proposed that indicate the presence of specific groups of microorganisms (Zelles and others, 

1992) on the basis of fatty acid function in the cell membrane.

Parveen and others (2001) included GC-FAME analysis in their continuing characterization 

of a collection of E. coli used for source tracking and found no relation between FAME profile 

and isolate source. No other published reports have been found in the literature.

B.2.3 Antibiotic Resistance

Method

Bacteria routinely are characterized by their sensitivity or resistance to antibiotics in clinical use. Many permutations of clinical antibiotic resistance methods have been developed in 

research laboratories. In clinical use, two standardized methods are the Kirby-Bauer test and 

multi-antibiotic resistance profiling using the National Committee for Clinical Laboratory Standards (NCCLS) to define clinical resistance. 

 The Kirby-Bauer test is done by placing a panel of paper disks impregnated with different 

antibiotics on the surface of a plate seeded with a test strain. Results are recorded as diameter of 
32 Selection and Application of Microbial Source Tracking Tools for Water-Quality Inverstigations

growth inhibition after incubation for a standardized amount of time at a specified temperature. 

The diameter of growth inhibition is related to the degree of antibiotic sensitivity because of a 

diffusion-induced concentration gradient of antibiotic in the agar.

Clinical profiling for multiple antibiotic resistance is done by use of Mueller-Hinton agar 

fortified with known concentrations of chosen antibiotics. Each concentration of each antibiotic 

in the test panel is represented in a separate agar plate. Standardized inocula of test isolates are 

placed on each plate, usually with a replica plater. 

Information Content

Information from antibiotic-resistance-based methods has logical value when source 

categories are exposed to different antibiotics. An example might be where differentiation was 

needed between feces from a population of cattle, which had been exposed to one antibiotic, and 

a population of swine, which had been exposed to a different antibiotic.

Many researchers have reported the ability of experimental antibiotic resistance methods to 

separate isolates obtained from animal sources (Graves and others, 2002; Hagedorn and others, 

1999; Harwood and others, 2000; Parveen and others, 1997; Wiggins and others, 1999). Two 

experimental variants on antibiotic sensitivity testing have been used extensively in pursuit of 

source tracking. Multiple antibiotic resistance analysis (MAR; Parveen and others, 1997) tests 

each isolate against a single concentration of each antibiotic in the panel. The antibiotics and 

concentrations in the panel were developed to reflect antibiotic use in humans and domestic animals. Antibiotic resistance analysis (ARA; Wiggins, 1996) challenges each isolate against multiple concentrations of antibiotics, the concentrations of which are chosen on the basis of ability 

to separate isolates from different hosts rather than the NCCLS standards. Information generated 

by the Kirby-Bauer method has been demonstrated in one study to be ineffective for MST using 

E. coli (Hagedorn, 1999); however, its use continues to be investigated (J. Mott, Texas A&M 

University, Corpus Christi, Tex., oral commun., November 2004). A modification of ARA, in 

which multiple concentrations of antibiotic are used to calculate an antibiotic resistance index 

representative of a fecal-source population, also has been used for MST (D.S. Francy, U.S. Geological Survey, Columbus, Ohio, oral commun., December 2004).

B.3 Genotypic Methods

Genotypic methods are represented by multiple approaches in three major categories: 

evaluation of similarity based on the whole genome, evaluation of similarity based on genes 

present in all target organisms, and direct evaluation of source based on presence or absence of a 

DNA sequence. Within these three major categories exist a wide array of sometimes overlapping 

protocols. Where possible, each is discussed separately.

Many of the genotypic methods were developed in the context of epidemiology or systematics. Several reviews present in-depth discussion of these methods, including those by Louws 

and others (1999), Olive and Bean (1999), Struelens (1998), Tenover and others (1997), and van 

Belkum and others (2001).

B.3.1 Similarity Based on Whole Genome Analysis

B.3.1.1 Macrorestriction Digest Pulsed-Field Gel Electrophoresis (PFGE)

Method

In PFGE, extracted chromosomal DNA is purified and digested by an infrequently cutting 

restriction enzyme, resulting in some 10 to 30 large fragments. The sum of all fragment lengths 

can be used to estimate the size of the chromosome. Fragments are separated by PFGE in a 

specialized rig. Banding patterns are visualized by staining, and fragment sizes are estimated by 

comparison with a ladder of known molecular weight DNA standards.
Appendix B  33

Information Content

PFGE is the standard against which other methods are compared in many epidemiological 

investigations. The method is described as “superior to most other methods for biochemical and 

molecular typing” (Olive and Bean, 1999). Interpretation of similarity is possible because each 

change in banding pattern can be related to a genetic event.

Parveen and others (2001) evaluated PFGE data by means of discriminant analysis and 

found no relation between fragment pattern and source when the restriction enzyme SfiI was used 

to digest. Simmons and others (2000), however, reported success when applying PFGE typing to 

MST using 1:1 association of unknown patterns with source patterns. Current usage is to restrict 

with NotI following Simmons and others (2000) or XbaI following the CDC PulseNet protocol 

for PFGE (Centers for Disease Control and Prevention, 1996).

B.3.1.2 Amplified Fragment Length Polymorphism Analysis (AFLP)

Method

AFLP is a partial-genome analogue to PFGE. Whole chromosomal DNA is digested by 

both a frequently cutting and an infrequently cutting restriction enzyme. An adapter is ligated 

onto the sticky end left by the infrequently cutting restriction enzyme, and PCR is done using a 

primer complementary to the adapter sequence. If fewer bands are desired, the primer can be 

designed to overhang the cut site by a single base, effectively reducing amplification to one in 

four possible products. PCR products are separated by agarose gel electrophoresis and visualized 

by staining (Savelkoul and others, 1999).

Information Content

The information generated by AFLP may represent the whole-genome similarity of two 

strains but may not be indicative of phylogenetic relatedness. Use of this method has not been 

frequently reported in the source-tracking literature. One report on AFLP utility for MST studies 

(Guan and others, 2002) is available in the literature, and further work is ongoing.

B.3.1.3 Repetitive Element PCR (Rep-PCR)

Method

Repetitive elements have been observed and described in many bacteria. Some of these 

repeated elements flank transposable elements (insertion sequences; Smith-Keary, 1989), and 

others are present at high frequency but have unknown function (Stern and others, 1984). The 

rep-PCR method uses primers for these repetitive sequences to generate PCR products representing DNA between proximal, facing repetitive sequences. Besides the insertion sequences, 

primers for three conserved motifs in bacterial repetitive elements are used for rep-PCR; namely, 

REP (repetitive extragenic palindromic sequence), ERIC (enterobacterial repeating intragenic 

consensus sequence), and BOX (named after the boxes drawn around candidate sequences in 

early research).

Information Content

Bands generated by rep-PCR represent cryptic DNA fragments—the only thing that is 

known is that each band has the repetitive element at each end. Changes in banding patterns 

could be caused by a variety of genetic events, hindering interpretation of band pattern similarity. 

DNA amplification products represent a small fraction of the genome.

Variants of the rep-PCR method should be applicable to any microorganism having repeated 

sequences in its genome. In the past, the methods have been used to type plant pathogens (Rademaker and de Bruijn, 1997). Olive and Bean (1999) reported that rep-PCR was quickly becom-
34  Selection and Application of Microbial Source Tracking Tools for Water-Quality Inverstigations

ing a widely used typing protocol because of its discriminatory power, ease of application, and 

low cost.

Dombek and others (2000) reported that rep-PCR using the BOXA1R primer had utility for 

source tracking. Carson and others (2003) reported performance of rep-PCR using the BOXA1R 

primer that was equal or superior to the performance of ribotyping by use of the HindIII restriction enzyme.

B.3.2 Similarity Based on Analysis of Specific Genes

Methods similar to the whole-genome-based methods are sometimes used with defined 

genes in an attempt to generate more readily interpreted similarity data. The genes chosen tend 

to have functional relevance, such as ribosomal RNA operons and toxin genes.

B.3.2.1 Ribotyping and Other Restriction Fragment Length Polymorphism (RFLP)-Type 

Methods

Method

The ribotyping procedure begins by extracting and purifying whole chromosomal DNA 

from a pure culture. Purified DNA is digested by a frequently cutting restriction enzyme. 

Digested DNA fragments are separated by agarose gel electrophoresis. If the DNA is visualized at this stage, it appears as a smear of DNA fragments on the gel. Digested, separated DNA 

fragments are blotted from the gel onto a nylon membrane (the Southern blot procedure). The 

DNA is fixed in space on the nylon membrane by UV cross-linking. Fragments containing DNA 

encoding 5S, 16S, and/or 23S rRNA are detected by hybridization with radiolabeled universal 

rDNA probes or digoxigenin-labeled probes. Ribotype exposures can be inspected visually or 

scanned into image analysis software (Olive and Bean, 1999).

Other variants of the RFLP protocol are accomplished similarly with probes for other genes 

of interest. For more information about alternate RFLP protocols, refer to Olive and Bean (1999) 

and Tenover and others (1997).

Information Content

Some of the information generated by ribotyping is based on differences within the rRNA 

operon and is therefore conserved in related strains. The terminal fragments, however, extend 

outside of the operon into the nonconserved region of the genome. As such, similarity of ribotypes does not necessarily relate directly to whole-genome or phylogenetic similarity.

Ribotyping protocols using E. coli are among the most common means of accomplishing 

source tracking (Carson and others, 2001; Jenkins and others, 2003; Parveen and others, 1999; 

Samadpour and Chechowitz, 1995; Scott and others, 2003). In past MST studies, the enzyme 

used generally has been HindIII for the single digest technique (for example, Parveen and others, 1999) or EcoRI and PvuII for the double digest technique (for example, Samadpour and 

Chechowitz, 1995). 

B.3.2.2 Amplified Ribosomal DNA Restriction Analysis (ARDRA) and Other PCR-RFLP 

Methods

Method

In PCR-RFLP methods, variation in a specific gene is detected by gene amplification using 

PCR followed by restriction of the amplicon with a frequently-cutting restriction enzyme. Minor 

variations in the sequences of functional genes can be detected by PCR-RFLP. The ARDRA 

analysis is the variant of PCR-RFLP based on amplification of the rRNA operon. (Olive and 

Bean, 1999; Vaneechoutte and Heyndrickx, 2001).
Appendix B  35

Information Content

Interpretation of differences in the amplified gene is facilitated by the presumed or known 

function of the gene sequence evaluated. Though theoretically suitable, use of ARDRA and 

other PCR-RFLP methods is not commonly reported in the MST literature. 

B.3.2.3 Terminal Restriction Fragment Length Polymorphism (t-RFLP)

Method

The t-RFLP method can be thought of as a further specialized application of PCR-RFLP. 

As in PCR-RFLP, a gene sequence of presumed or known function is amplified by PCR. In this 

case, however, one primer is labeled such that only a terminal restriction fragment is visualized, 

resulting in only one bit of information for each gene copy within each strain evaluated. The 

terminal fragment can be sequenced, or the pattern of terminal fragments can be observed by 

capillary electrophoresis in the sequencer. This method more often is used to characterize communities of microorganisms rather than individual strains (Bernhard and Field, 2000a).

Information Content

Data are generated by t-RFLP without the need for cultivation of isolates; therefore, this 

method has found utility for community comparisons. Fragment size information generated by 

t-RFLP does not have functional or phylogenetic significance, though sequencing information 

may have significance.

Bernhard and Field (2000b) sequenced signature t-RFLP peaks prevalent in host sources 

to enable their design of host-specific PCR primers against Bacteroides-Prevotella. Ongoing 

research by others in the field investigates the use of Bacteroides-Prevotella marker profiles to 

indicate the origin of fecal contamination (Seurinck and others 2005).

B.3.3 Typing by Marker Sequence Presence or Absence

B.3.3.1 Coliphage Genotyping and Other Dot Blot Analyses

Method

To genotype coliphage, RNA is fixed to a nylon membrane and challenged against each of 

four labeled probes. The probes could be labeled either radioactively or with digoxigenin for 

chemiluminescent detection. Successful hybridization with one of the four probes indicates the 

type of the coliphage (Hsu and others, 1995). Comparable probes could be developed for other 

applications. A reverse line blot hybridization method, in which the probes are fixed to the nylon 

membrane and tested against each isolate, uses essentially the same principle and promises to 

have higher throughput than traditional dot blot hybridization (Vinje and others, 2004)

Information Content

A great deal of prior knowledge is required to ensure that hybridization with a probe 

adequately represents the type. In the example of coliphage, types were defined on the basis of 

serological reactions, and the probes developed are surrogates for that information. Genotyping 

coliphages was reported to be a useful method of categorizing coliphage into the groups used for 

source tracking (Calci and others, 1998; Schaper, Duran, and Jofre, 2002; Schaper, Jofre, and 

others, 2002).
36  Selection and Application of Microbial Source Tracking Tools for Water-Quality Inverstigations

B.3.3.2 Length-Heterogeneity PCR (LH-PCR) for Escherichia coli Toxin Genes and 

Bacteroides-Prevotella Group Markers

Method

LH-PCR is a method whereby a sequence of interest is amplified by PCR and resolved by 

agarose gel electrophoresis. Prior knowledge about specificity of the primer and expected amplicon size is required for interpretation of amplified product (Simpson and others, 2002).

Information content

Production of the desired product using a specific primer or primer set indicates the presence of the target sequence. Confirmation can be done by sequencing or probing the product by 

dot blot analysis. 

This method should be applicable to a wide range of source identifiers provided that sufficient prior knowledge of products is available. LH-PCR provides an alternative to dot blotting 

to detect specific gene sequences from mixed DNA. Primers have been developed to detect 

Bacteroides-Prevotella types associated with different hosts (Bernhard and Field, 2000b) and to 

detect the presence of host-specific toxin genes in E. coli (Khatib and others, 2002). 
Table 2  37

Table 2: Summary of how various microbial source tracking (MST) source identifiers meet generalized 

assumptions for application. 

[More extensive details are presented in the text]

Source 

identifier Host specificity Distribution Stability in host Stability (survival) in environment

Relevance to regulations, health risk

----------------------------------------- Virus-based source identifiers -----------------------------------------

Adenoviruses

Porcine and bovine adenovirus not detected in 12 

human sewage samples 

or pooled samples 

(3–5 individuals) from 

other alternate hosts 

(Maluquer de Motes 

and others, 2004).

Human adenovirus detected in 16 of 18 treated 

and untreated human 

sewage samples and 4 

of 17 slaughterhouse 

sewage samples (3 pig 

and 1 chicken; Piña 

and others, 1998).

Human adenovirus not detected in 3 of 3 samples 

seeded with nonhuman 

feces (Noble and others, 2003).

Porcine and bovine-specific 

viruses detected in about 

70% of pooled samples 

(3–5 individuals; 

Maluquer de Motes and 

others, 2004).

Human adenovirus detected in 16 of 18 human 

sewage samples, but 

concentrations were not 

determined (Piña and 

others, 1998).

Human adenovirus detected 

in 3 of 4 samples seeded 

with sewage and 1 of 

4 samples seeded with 

individual feces (Noble 

and others, 2003).

No report of the 

timespan over 

which adenoviruses occur or 

variation in concentration within 

individual hosts 

was found.

More stable in the 

environment than 

enteroviruses (in 

Piña and others, 

1998).

Most resistant to UV 

irradiation of any 

pathogenic enteric 

virus tested (Gerba 

and others, 2002).

Frequently asymptomatic 

but may cause disease 

(Maluquer de Motes 

and others, 2004), 

particularly childhood gastroenteritis 

(in Noble and others, 

2003).

Detected with greater 

frequency than pathogenic enterovirus and 

hepatitis A virus in 

human waste streams; 

detected in 25 of the 

30 samples in which 

1 of the other viruses 

was detected. Not 

correlated with fecal 

coliform concentrations (Piña and others, 

1998).

Bacteroides 

fragilis 

phage

Specificity dependent 

upon the B. fragilis

strain used to recover 

phage. One strain 

(HSP40) only recovered human-associated phage, whereas 5 

others recovered phage 

from other animal 

hosts (Puig and others, 

1999).

HSP40 phage strongly 

associated with 

human-origin fecal 

contamination (Tartera 

and others, 1989).

Detected in 12 of 12 urban 

sewage streams. Concentrations ranged from 

3,200 to 19,000 PFU/100 

mL (Puig and others, 

1999).

Concentrations ranged 

from 210 to 46,000 

PFU/100 mL in 20 sewage samples (Tartera and 

others, 1989).

Concentrations ranged from 

2,300 to 20,900 PFU/100 

mL in 18 samples of 

treated or untreated 

sewage (Piña and others, 

1998).

No report of the 

timespan over 

which B. fragilis

phage occur or 

variation in concentration within 

individual hosts 

was found.

B. fragilis phages 

generally survive 

as long as do 

coliphages and 

for longer than do 

fecal coliforms and 

enterococci (Sinton 

and others, 1999).

Generally more resistant to treatment and 

inactivation than 

coliphages (Jofre 

and others, 1995).

Cannot reproduce in 

natural waters and 

sediments (Tartera 

and others, 1989).

B. fragilis phages may 

be a more conservative indicator of fecal 

contamination than 

enterococci because 

of their lower dieoff 

rate in the environment 

(Tartera and others, 

1989).

B. fragilis phages strongly correlated with 

human-pathogenic 

viruses concentrations 

in raw urban sewage 

(Piña and others, 

1998).
38  Selection and Application of Microbial Source Tracking Tools for Water-Quality Inverstigations

Source 

identifier Host specificity Distribution Stability in host Stability (survival) in environment

Relevance to regulations, health risk

Coliphage

(genotypes)

Types reported to 

indicate human (II, 

III) and nonhuman (I, 

IV) sources of fecal 

contamination (Furuse, 

1987).

Types differentially 

distributed among 

hosts (Cole and others, 

2003).

Human types II and III 

found in 16 of 16 

sewage samples and 

17 of 29 pig samples. 

Nonhuman types I, 

IV found in 0 of 16 

sewage samples and 

74 of 91 chicken and 

pig samples (Hsu and 

others, 1995).

Human type III coliphages detected (23 of 

31 isolates were type 

III) in composited gull 

feces (Noble and others, 2003) and poultry 

feces (Schaper, Jofre, 

and others, 2002).

Human type II coliphages 

were detected in human, cattle, poultry, 

and pig feces, though 

nonhuman type I and 

IV were not detected in 

human feces (Schaper, 

Jofre, and others, 

2002).

Concentration and distribution both vary by host; 

where detected, concentration varies by 2 orders 

of magnitude (Calci and 

others, 1998).

Detected in 5 of 14 hosts 

tested (human, swine, 

cattle, gull, goose); 

where detected, concentration varies by 3 orders 

of magnitude (Cole and 

others, 2003).

Never detected in feces of 

nine hosts, though present in local water (Griffin and others, 2000).

Isolated from about 10% of 

individual humans and at 

generally higher rates (to 

70%) from nonhumans 

(Schaper, Jofre, and others, 2002).

Human types II and III detected in 5 of 7 samples 

seeded with sewage 

but only 1 of 9 samples 

seeded with individual 

feces (Noble and others, 

2003).

No report of the 

timespan over 

which coliphages 

occur or variation 

in concentration 

within individual 

hosts was found.

Type I more resistant 

to inactivation than 

Type II; Types III 

and IV more susceptible to inactivation 

than types I and II 

(Schaper, Duran, 

and Jofre, 2002).

F+ coliphages generally survive longer 

in the environment than do fecal 

coliforms and 

enterococci (Sinton 

and others, 1999).

The proposed Ground 

Water Rule uses detection of any coliphages 

to indicate fecal 

contamination, not 

specifically F+ RNA 

coliphages. The incidence or concentration 

of F+ RNA coliphage 

was not consistently 

related to the concentration of coliphage in 

general in the supporting information for the 

Ground Water Rule 

(U.S. Environmental 

Protection Agency, 

2000c).

Table 2: Summary of how various microbial source tracking (MST) source identifiers meet generalized assumptions for application.—

Continued 

[More extensive details are presented in the text]
Table 2  39

Source 

identifier Host specificity Distribution Stability in host Stability (survival) in environment

Relevance to regulations, health risk

Enterovirus

Human enterovirus not 

detected in 4 of 4 

samples seeded with 

nonhuman feces (Noble 

and others, 2003).

Bovine enterovirus found 

in significant proportions of cattle, Canada 

geese, and deer but 

other animals (including humans) not tested 

(Ley and others, 2002).

Human enterovirus detected 

in 3 of 4 samples seeded 

with sewage and 0 of 

4 samples seeded with 

individual feces (Noble 

and others, 2003).

In 1 sample, enterovirus 

were present at 70 

PFU/100 mL in human 

sewage (Piña and others, 

1998).

Bovine enterovirus found 

in 76% of cattle, 1 of 3 

Canada geese, and 38% 

of white-tailed deer individuals (Ley and others, 

2002). 

No report of the 

timespan over 

which enterovirus 

occur or variation 

in concentration 

within individual 

hosts was found.

Less resistant to UV 

than adenovirus 

(Gerba and others, 

2002).

Less stable in the 

environment than 

adenovirus (in Piña 

and others, 1998).

No correlation between 

regulated fecal indicators and pathogenic 

enterovirus (Blackwood and others, 

2003; Piña and others, 

1998).

Bovine enteroviruses are 

generally asymptomatic (Ley and others, 

2002).

Teschovirus

Putative specificity to 

swine, not found in 

feces of cattle, sheep, 

goats (Jiménez-Clavero 

and others, 2003).

No extensive evaluation of 

teschovirus distribution 

and/or concentration in 

various hosts was found

No report of the 

timespan over 

which teschovirus occur or 

variation in concentration within 

individual hosts 

was found.

Detectable 3 km (1.8 

mi) downstream 

from a known 

source of swine 

feces (JiménezClavero and others, 

2003).

No study of the correlation between teschovirus concentration and 

regulated fecal indicators was found.

No report of human 

health risk from teschovirus was found.

--------------------------------------- Bacteria-based source identifiers ---------------------------------------

Bacteroides/

Prevotella 

(markers)

Of 4 probes by 2 researchers, sequences detected 

in slurries made from 

other hosts in 3 of 18 

cases (4% false detect; 

Field and others, 2003).

Ribotypes of Bacteroides/

Prevotella are differentially distributed among 

various hosts (Wood 

and others, 1998).

Present at concentrations of 

1–10 x 1010 cells/g dry 

human feces (Franks and 

others, 1998), or 8 x 1010

cells/g wet human feces 

(Marteau and others, 

2001).

Present in all individual 

cows and humans tested 

(Bernhard and Field, 

2000b).

No report of the 

timespan over 

which these fecal 

anaerobes persist 

or variation in 

concentration 

within individual 

hosts was found.

Survival in the environment is limited 

to days (Kreader, 

1998).

No description of the 

relation between 

Bacteroides/Prevotella

incidence or concentration with regulatory fecal-indicator 

microbe incidence 

or concentration was 

found.

Table 2: Summary of how various microbial source tracking (MST) source identifiers meet generalized assumptions for application.—

Continued 

[More extensive details are presented in the text]
40 Selection and Application of Microbial Source Tracking Tools for Water-Quality Inverstigations

Source 

identifier Host specificity Distribution Stability in host Stability (survival) in environment

Relevance to regulations, health risk

Bifidobacterium

species

Twelve species are known 

to inhabit the human 

gastrointestinal tract 

(in Satokari and others, 

2001).

B. dentium, B. adolescentis, and B. longum

found exclusively in 

human-origin sewage; 

5 other species differentially distributed. 

Limited (3 of 19) 

detections in slaughterhouse waste streams 

(Bonjoch and others, 

2004).

Sorbitol-fermenting types 

found only in humans 

and pigs of 11 hosts 

tested (Mara and Oragui, 1983).

Present at concentrations of 

0.3–1.5 x 1010 cells/g dry 

human feces (Franks and 

others, 1998) or 8 x 1010

cells/g wet human feces 

(Marteau and others, 

2001).

Present in all 20 animals 

tested; B. dentium and B. 

adolescentis found in 12 

of 12 humans and 22 of 

22 human waste streams 

tested, and B. longum

found in 7 of 12 humans 

tested (Bonjoch and others, 2004).

Sorbitol-fermenting Bifidobacterium concentrations 

range across 3 orders of 

magnitude, where present (Mara and Oragui, 

1983).

Stable populations 

indicated over 4 

weeks of monitoring 

(Satokari and 

others, 2001).

Short survival time 

in the environment 

and very sensitive 

to chlorination (in 

Mara and Oragui, 

1983).

No description of the 

relation between 

Bifidobacterium spp.

incidence or concentration with regulatory fecal-indicator 

microbe incidence 

or concentration was 

found.

Enterococcus

(adhesin 

genes)

Only found in humanassociated sewage 

sources (Scott and 

others, 2005).

Found in 94% of human-associated sewage sources 

(Scott and others, 2005).

See entry for 

Enterococcus

genotypes and 

phenotypes.

See entry for Enterococcus genotypes 

and phenotypes. 

May be relevant to 

enterococci-concentration-based regulations.

Enterococcus

(genotypes 

and phenotypes)

Differentially distributed 

among hosts (Aarestrup 

and others, 2000).

Human feces contain 

105–108 CFU/g fecal 

streptococci (of which 

enterococci are a subset). 

Other mammals contain 

approximately 106 CFU/

g (Geldreich, 1976).

No report of the 

time span over 

which various 

Enterococcus

types persist or 

variation in concentration within 

individual hosts 

was found.

No report of the 

relative survival of 

various Enterococcus genotypes and 

phenotypes was 

found.

Directly relevant to 

enterococci-concentration-based regulations.

Enterococcus

(species)

Differentially distributed among hosts 

(Aarestrup and others, 

2000; Delahoya and 

others, 2002).

Of 61 E. faecalis, 30 were 

from humans and the 

remainder from dogs 

and chickens (Wheeler 

and others, 2002). 

Note that the number 

of individuals sampled 

was 2–3 per host.

One of 4 human, 1 of 2 dog, 

and 2 of 3 chicken individuals sampled hosted 

E. faecalis (Wheeler and 

others, 2002).

No report of the 

timespan over 

which various 

Enterococcus

species persist or 

variation in concentration within 

individual hosts 

was found.

No report of the relative survival of various Enterococcus

species was found.

Indirectly relevant to 

enterococci-concentration-based regulations.

Table 2: Summary of how various microbial source tracking (MST) source identifiers meet generalized assumptions for application.—

Continued 

[More extensive details are presented in the text]
Table 2  41

Source 

identifier Host specificity Distribution Stability in host Stability (survival) in environment

Relevance to regulations, health risk

Escherichia 

coli

(genotypes 

and phenotypes)

Genotype distribution 

minimally (6%) attributable to host species (Gordon, 2001); 

however, “geographic 

effects and host taxonomic group accounted 

for most of the genetic 

differentiation (of E. 

coli)” (Souza and others, 1999).

Same genotype found in 

multiple hosts (Gordon, 

2001; McLellan and 

others, 2003; Scott 

and others, 2003); 

about 10–20% of types 

(40–65% of isolates) 

shared among hosts 

(Hartel and others, 

2002).

Gene transfer has been 

demonstrated among 

E. coli between host 

species (Oppegaard and 

others, 2001).

Prevalent in mammals, 

less prevalent in birds, 

and uncommon in fish, 

amphibians, and reptiles 

(>1% of cultivable 

aerobic enteric bacteria; 

Gordon and Cowling, 

2003). Found in areas 

with no fecal contamination expected (Carrillo 

and others, 1985).

Genotype distribution is 

minimally (1–10%) 

attributable to spatial 

separation (Gordon, 

2001).

Concentrations can vary 

by as much as 6 orders 

of magnitude in specific 

hosts (1.9x103 – 6.3x109

CFU/g wet wt poultry 

dropping); geometric 

mean concentrations 

range from 1.5x103 in 

horses to 2.1x109 CFU/g 

wet feces in dogs (Mara 

and Oragui, 1983).

Genotypes are 

temporally 

unstable in the 

host (Cooke and 

others, 1969; 

Gordon, 2001).

About half of E. coli

genotypes are 

resident (stable) 

and the remainder are transient 

(unstable) in an 

individual (Caugant and others, 

1981; Whittam, 

1989).

Different genotypes 

detected in bird 

feces and underlying 

(aged) litter (Whittam, 1989).

Not considered to have 

survival strategies in 

the environment as a 

component of its life 

cycle (Winfield and 

Groisman, 2003).

Known to be stable in 

tropical environments, though 

thought to be unable 

to reproduce in temperate environments 

(Rivera and others, 

1988; Winfield and 

Groisman, 2003); 

however, there exist 

habitats in temperate 

climates in which E. 

coli can survive and 

perhaps reproduce 

(Whitman and Nevers, 2003; Whitman 

and others, 2003).

One of two recommended 

fecal indicator bacteria 

for coastal recreational 

waters (U.S. Congress, 

2000).

E. coli

(glucuronidase gene 

uidA)

Several alleles are host 

specific; however, the 

majority are carried 

by more than one 

host (Ram and others, 

2004).

Approximately 95% of E. 

coli have uidA–coded 

glucuronidase (Brenner 

and others, 1993).

Diversity of uidA alleles 

has been demonstrated 

(Farnleitner and others, 

2000).

See entries for E. 

coli genotypes 

and phenotypes.

See entries for E. 

coli genotypes and 

phenotypes.

Should be directly relevant to E. coli-based 

regulations because 

the gene is nearly 

ubiquitous among E. 

coli.

Table 2: Summary of how various microbial source tracking (MST) source identifiers meet generalized assumptions for application.—

Continued 

[More extensive details are presented in the text]
42 Selection and Application of Microbial Source Tracking Tools for Water-Quality Inverstigations

Source 

identifier Host specificity Distribution Stability in host Stability (survival) in environment

Relevance to regulations, health risk

E. coli

(toxin genes)

The gene lt-IIa has been 

found exclusively in 

cattle (Pickett and 

others, 1987); the gene 

st-II (pig associated) is 

differentially distributed among several hosts 

(Chern and others, 

2004); the gene slt-IIe 

has been found in pigs 

and humans (Franke 

and others, 1995); the 

gene st-Ib (human-associated) was detected 

in 8% of non-human 

individuals and 9% 

of human individuals 

(100% of composite 

human sources and 

25% of composite nonhuman sources; Field 

and others, 2003)

Of three probes used to 

detect toxin genes, detection rate in alternate 

sources was 2 of 9 (7% 

false detect; Field and 

others, 2003)

Fewer than 1% of E. coli 

carry toxin genes in most 

waste streams (Chern 

and others, 2004). 

Pathogenic E. coli are 

absent from most healthy 

individuals (Chern and 

others, 2004; Gordon, 

2001). 

No report of the 

timespan over 

which pathogenic 

E. coli persist or 

variation in concentration within 

individual hosts 

was found.

See entries for E. coli 

genotypes and phenotypes.

Indirectly relevant to E. 

coli-based regulations. 

Directly related to 

health risk for susceptible animals

Table 2: Summary of how various microbial source tracking (MST) source identifiers meet generalized assumptions for application.—

Continued 

[More extensive details are presented in the text]
Table 2  43

Source 

identifier Host specificity Distribution Stability in host Stability (survival) in environment

Relevance to regulations, health risk

Salmonella 

enterica

serovar Typhimurium

Avian types more similar 

to one another than 

to those from other 

hosts. Avian types can 

colonize other hosts 

and, specifically, cause 

disease in humans 

(Refsum and others, 

2002).

No report of the distribution among various 

hosts was found.

No report of the timespan 

over which Salmonella enterica serovar 

Typhimurium persists or 

variation in concentration within individual 

hosts was found.

Considered to have survival 

strategies in the environment as a component 

of its life cycle, leading 

to long survival under 

adverse conditions (t50 up 

to 1 year in soil; Winfield 

and Groisman, 2003).

Human outbreaks of 

salmonellosis (in 

Refsum and others, 

2002).

------------------------------------ Protozoan-based source identifier ------------------------------------

Cryptosporidium

(species)

C. parvum genotype I is 

exclusive to humans, 

type II readily transfers 

between humans and 

other mammals, and 

various other hostassociated types occasionally are detected 

in humans (in Ong and 

others, 2002).

Nine species other than C. 

parvum are host associated but not exclusive 

(Fayer and others, 

2000).

Only five genotypes of 

Cryptosporidium spp. 

are known to infect 

humans (Xiao and others, 2001).

Some populations appear 

to be host-specific, but 

others move between 

host species (Mallon 

and others, 2003).

Incidence in fresh 

livestock waste ranged 

from 5% (cattle) to 

29% (sheep; Hutchison 

and others, 2004).

C. parvum detected in 

1% of wildlife (6 of 19 

species), 5% of sewage 

influent samples, and 

4% of agricultural 

waste (Heitman and 

others, 2002).

Detected in 4% of cattle 

(Atwill and others, 

1999).

C. parvum detected in 

13–22% of rodents; 

C. muris detected in 

2–10% of rodents 

(Chalmers and others, 

1997).

C. parvum detected in 

12% of wild mammals 

(Sturdee and others, 

1999).

Over 100 species have 

been reported infected 

with C. parvum and 

C. parvum-like strains 

(Fayer and others, 

2000).

Known to pass through and 

be transmitted by various 

hosts (Fayer and others, 

2000).

Can remain viable for 

greater than 6 months 

at 20 degrees Celsius in 

water (Fayer and others, 

2000.

In three environmental fresh 

waters, 0.1% survival 

over 10 weeks at 25 degrees and higher rates 

of survival at 4 degrees 

(Simmons and Sobsey, 

2002).

Survival and infectivity in 

freshwater detected after 

more than 12 weeks (Olson and others 1999).

Pathogenic agent of 

cryptosporidiosis, 

caused 50% of reported gastroenteritis 

cases due to consumption of treated water 

in 2001–02 (Yoder 

and others, 2004).

Table 2: Summary of how various microbial source tracking (MST) source identifiers meet generalized assumptions for application.—

Continued 

[More extensive details are presented in the text]