has been developed, such as deer or chicken. Finally, other studies have highlighted
the potential for community analysis to partition assemblages within microbial
communities to known sources and quantify the contribution of those sources to
the total sample [93]. Refinement of this approach for fecal sources would be a
powerful MST tool.
It is important to note that the use of community analysis for MST is in its
infancy so it is necessary to be aware of method limitations. For example, the
typical approach is to characterize microbial community structure in known sources
and then compare these to unknown samples. This approach is very similar to
earlier library-dependent MST methods and therefore has similar limitations such
as the need to collect, analyze, and maintain data on known sources, as well as
dealing with temporal and geographical variability. In addition, the methods are
expensive (although costs are dropping rapidly) and require considerable expertise
to analyze and interpret. Often, this expertise will not be locally accessible and will
require collaboration with a research laboratory. Community analysis simultaneously characterizes hundreds to thousands of markers; thus, it is not particularly
sensitive for detecting individual markers and is not suitable for detecting very low
levels of a particular source [94].
Newer methods (e.g., next-generation sequencing) may potentially overcome
this limitation and improve sensitivity, but they also require the highest level of
expertise for managing and analyzing large, complex data sets. As a result of these
limitations, community analysis should be considered a method of last resort, only
to be employed when it is suspected that information that can be gained could not be
gathered from simpler and more cost-effective approaches. It is likely best suited to
large TMDL projects or waters of high economic value where potential benefits
justify the required resources. In these situations, community analysis can be a
potentially powerful tool for discerning sources of fecal contamination. As DNA
sequencing and other culture-independent methods continue to evolve, community
analysis will likely continue to become a more universally accessible tool for MST.
5 The Tiered Approach for Microbial Source Tracking
When deciding to use MST, it is important to recognize that there are still deficiencies with every type of MST approach [63]. First, no single DNA-based marker
accounts for FIB, human waste, and pathogens; if such exists, it has not yet been
discovered. Second, most assays are insufficiently tested to know their absolute
specificity to host fecal material, especially in geographical areas different from
coastal California, the region included in the SIPP study [95]; third, the environmental fate, mainly degradation over time, of most DNA‐based fecal markers is
unknown, so linking quantities (e.g., via fate and transport modeling) to far‐
upstream sources or predicting the health consequences is not feasible at this
point [96, 97]. These deficiencies indicate that DNA‐based assays for determining
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B. Badgley and C. Hagedorn
the potential for community analysis to partition assemblages within microbial
communities to known sources and quantify the contribution of those sources to
the total sample [93]. Refinement of this approach for fecal sources would be a
powerful MST tool.
It is important to note that the use of community analysis for MST is in its
infancy so it is necessary to be aware of method limitations. For example, the
typical approach is to characterize microbial community structure in known sources
and then compare these to unknown samples. This approach is very similar to
earlier library-dependent MST methods and therefore has similar limitations such
as the need to collect, analyze, and maintain data on known sources, as well as
dealing with temporal and geographical variability. In addition, the methods are
expensive (although costs are dropping rapidly) and require considerable expertise
to analyze and interpret. Often, this expertise will not be locally accessible and will
require collaboration with a research laboratory. Community analysis simultaneously characterizes hundreds to thousands of markers; thus, it is not particularly
sensitive for detecting individual markers and is not suitable for detecting very low
levels of a particular source [94].
Newer methods (e.g., next-generation sequencing) may potentially overcome
this limitation and improve sensitivity, but they also require the highest level of
expertise for managing and analyzing large, complex data sets. As a result of these
limitations, community analysis should be considered a method of last resort, only
to be employed when it is suspected that information that can be gained could not be
gathered from simpler and more cost-effective approaches. It is likely best suited to
large TMDL projects or waters of high economic value where potential benefits
justify the required resources. In these situations, community analysis can be a
potentially powerful tool for discerning sources of fecal contamination. As DNA
sequencing and other culture-independent methods continue to evolve, community
analysis will likely continue to become a more universally accessible tool for MST.
5 The Tiered Approach for Microbial Source Tracking
When deciding to use MST, it is important to recognize that there are still deficiencies with every type of MST approach [63]. First, no single DNA-based marker
accounts for FIB, human waste, and pathogens; if such exists, it has not yet been
discovered. Second, most assays are insufficiently tested to know their absolute
specificity to host fecal material, especially in geographical areas different from
coastal California, the region included in the SIPP study [95]; third, the environmental fate, mainly degradation over time, of most DNA‐based fecal markers is
unknown, so linking quantities (e.g., via fate and transport modeling) to far‐
upstream sources or predicting the health consequences is not feasible at this
point [96, 97]. These deficiencies indicate that DNA‐based assays for determining
278
B. Badgley and C. Hagedorn