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Published online 17 July 2007
Published in J Environ Qual 36:1324-1330 (2007)
DOI: 10.2134/jeq2006.0496
© 2007 American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America
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TECHNICAL REPORTS

Surface Water Quality

Efficacy of Bacteroides Measurements for Reducing the Statistical Uncertainty Associated with Hydrologic Flow and Fecal Loads in a Mixed Use Watershed

Randall W. Gentrya,d,*, Alice C. Laytond, Larry D. McKayc, John F. McCarthyc,d, Dan E. Williamsd, Shesh R. Koiralab and Gary S. Saylerd

a Inst. for a Secure and Sustainable Environment, The Univ. of Tennessee, 311 Conference Center Building, Knoxville, TN 37996-4134
b Civil & Environmental Engineering, The Univ. of Tennessee, 62 Perkins Hall, Knoxville, TN 37996-2010
c Dep. of Earth and Planetary Sciences, The Univ. of Tennessee, Knoxville, TN 37996
d Center for Environmental Biotechnology, The Univ. of Tennessee, Knoxville, TN 37996

* Corresponding author (rgentry{at}utk.edu).

Received for publication November 9, 2006.

   ABSTRACT
TOP
 NOTES
 ABSTRACT
INTRODUCTION
 Materials and Methods
 Results and Discussion
 Conclusions
 REFERENCES
 
This paper presents an analysis of the occurrence and uncertainty of source-specific Bacteroides and Escherichia coli in a stream in a mixed land-use watershed with human, cattle, and wildlife fecal inputs located in a karstic geologic region during baseflow conditions. The objectives of the study were to evaluate the occurrence, hydrologic significance, and source of fecal mass in the stream using assays for total Bacteroides (AllBac) and bovine-specific Bacteroides (BoBac), and then to compare these measurements with E. coli densities and loads. Samples were collected during baseflow conditions over several months at seven different main channel sites in the Stock Creek watershed, a 49.3 km2 basin located in Knoxville, TN (USA). We determined instantaneous loads for total fecal loads, bovine fecal loads, and E. coli from measured flow rates and the representative Bacteroides fecal masses and/or E. coli densities. The study indicated a strong correlation between total fecal load (kg d–1), bovine fecal load (kg d–1), E. coli load rate (CFU d–1), 7-d antecedent precipitation, and turbidity. The various datasets were used to establish parameter correlations and spatial dependencies throughout the watershed. The data analysis demonstrated two prevalent patterns throughout the watershed: (i) a runoff-dominated transport and occurrence; and (ii) potential groundwater-dominated transport and occurrence.

Abbreviations: AllBac, total fecal mass • BoBac, bovine-associated fecal mass • RT-PCR, real time polymerase chain reaction


   INTRODUCTION
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
Materials and Methods
 Results and Discussion
 Conclusions
 REFERENCES
 
ONE OF THE MOST critical problems associated with managing impacted watersheds is the uncertainty with how fecal loads are delivered to streams (National Research Council, 2001; USEPA, 2002). Gentry et al. (2006) presented a study on the spatial variability and erratic nature of Escherichia coli at baseflow in a mixed-use watershed. Recently, researchers have demonstrated the need to differentiate between soil-delivered and fecal-delivered sources of E. coli, which is further complicated by complex hydrological driven fluxes (Byappanahalli et al., 2006; Whitman et al., 2006). Recent research has sought to more robustly relate the influence of watershed scale processes, such as flow, sediment transport, and precipitation to fecal indicators in streams (Mallin et al., 2001; Kistemann et al., 2002; Byappanahalli et al., 2003; Tyrrel and Quinton, 2003; George et al., 2004; Reeves et al., 2004; Gentry et al., 2006). Some researchers have sought to develop better modeling methodologies for forecasting bacterial loads using E. coli (Olyphant et al., 2003; Reeves et al., 2004) and Bacteroidales (Shanks et al., 2006). A review of the traditional methodologies for modeling microbial pollution at the watershed scale was presented by Jamieson et al. (2004). Whereas most microbial pollution modeling has been performed using E. coli, the focus of this research was to explore the efficacy of Bacteroides as a less variable, relative to E. coli, fecal load indicator within a mixed-use watershed, thus reducing the uncertainty associated with representing fecal loads in watersheds.

Total coliforms and/or E. coli have been used as an indicator of potential fecal contamination for almost 100 yr (Feng et al., 2002), the primary assumption being that if bacteria commonly associated with mammalian intestinal tracts are present in a water system then this water may contain pathogenic bacteria or viruses. Epidemiologic evidence correlating the presence of total coliforms and E. coli in recreational waters with higher incidence of gastrointestinal disease formed the basis for regulatory limits on permissible pathogen indicator levels to reduce disease risk (Dufour 1984). More recently, studies have demonstrated the need to use alternative fecal indicators for water quality when considering health risks (Colford et al., 2007). Bacteria belonging to the genus Bacteroides have been suggested as alternative fecal indicators to E. coli or fecal coliforms (Fiksdal et al., 1985; Kreader, 1995) because they make up a significant portion of the fecal bacteria (Madigan et al., 2003), have little potential for growth in the environment (Fiksdal et al., 1985; Kreader, 1995), and have a high degree of species specificity which likely reflects differences in host animal digestive systems (Dick et al., 2005). Lamendella et al. (2007) have identified the spatial/temporal dynamics of human- and ruminant-specific Bacteroidetes and have identified the need to continue investigations as to how these markers relate to water quality standards/regulations. Layton et al. (2006) developed real time (RT) polymerase chain reaction (PCR) assays for total fecal mass (AllBac) and bovine-associated fecal mass (BoBac) using Bacteroides 16S rRNA genes.

The spatial and temporal variability of fecal indicator bacteria in streams have been related to sources of baseflow such as the presence of discrete seeps or springs, rather than diffuse discharge, as well as precipitation events which can mobilize both pathogens and sediment (Dussart-Baptista et al., 2003) and other hydrologic processes. For example, Mallin et al. (2001) alluded to the possible importance of sediment transport of fecal indicator bacteria and found that both turbidity and rainfall in the previous 24 h strongly correlated with fecal coliforms. George et al. (2004) found that fecal coliform bacteria were linked to particles in small streams and that the fraction increased with suspended sediment content. Dussart-Baptista et al. (2003) correlated turbidity and sessile (attached) and planktonic (non-attached) bacteria and also concluded that intrakarstic storage and resuspension played an important role, in agreement with Massei et al. (2002, 2003) who noted that groundwater transfer through a chalk karstic aquifer induced a large decrease in the concentration of planktonic bacteria but no reduction in the sessile population. In a small mixed-use watershed, Gentry et al. (2006) discovered that E. coli had a spatial dependency throughout the watershed, correlating with antecedent rainfall in the upstream reaches, and with turbidity in the downstream reaches. Due to the linkage of the AllBac and BoBac RT-PCR assays with fecal mass, these assays may help elucidate source fecal loadings tied to hydrologic processes with a higher degree of certainty.

The goal of our study was to evaluate the spatial variations of total fecal loads (subsequently referred to as AllBac), bovine fecal loads (subsequently referred to as BoBac), and E. coli in a mixed land use watershed at baseflow. The overall hypothesis of this study was that Bacteroides can provide a more reliable measure of fecal loads and hydrologic transport in a mixed-use watershed at baseflow or near baseflow conditions. The primary objective of this study was to test the hypothesis by systematically evaluating the relative statistical relationship (using covariance and correlation coefficients, and K-means cluster analyses) between AllBac, BoBac, and E. coli loading rates with other direct (flow and precipitation) and indirect (turbidity) hydrologic variables at baseflow or near baseflow conditions within a karst watershed study site.


   Materials and Methods
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 Materials and Methods
Results and Discussion
 Conclusions
 REFERENCES
 
Study Site Description
The site chosen for the field investigations is the Stock Creek watershed located a few kilometers south of Knoxville in eastern Tennessee, USA. The watershed is a 49.3 km2 basin which drains into the Tennessee River (Fig. 1), located in the valley and ridge physiographic region on the western flanks of the Appalachian Mountains and is characterized by alternating northeast-southwest trending ridges of Paleozoic sedimentary rocks. The approximate elevation of the outlet of the watershed is 250 m NGVD (national geodetic vertical datum) and the headwater ridges have an elevation of approximately 394 m NGVD (see Fig. 2). Average annual precipitation in Knox County is 122 cm with an average of 128 wet days during the year. The Stock Creek watershed is a natural flow system, with no dams or impoundments throughout the study site, although at the flow exit of the watershed some localized embayment conditions were experienced due to backwater from the Tennessee River. The Stock Creek watershed is underlain mainly by karstic carbonate rock which provides conditions favorable to rapid transport of pathogen-contaminated groundwater either to drinking water wells or back to surface water through seeps, springs, and fractures.


Figure 1
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  		Fig. 1. Location map for Knox County, Tennessee, USA and Stock Creek.

 

Figure 2
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  		Fig. 2. Stock Creek digital elevation model (DEM) showing subbasins and associated sample designations.

 
From April 2003 to February 2004, we collected 84 individual grab samples from the water surface at seven locations along the main channel of Stock Creek. Stream flow was measured at many main channel locations during each sampling event. The main channel sites were designated as SC-1 through SC-7, and the representative subbasins are identified in Fig. 2. The subbasins were defined based on the specific area of drainage represented by each sampling location along the main channel, as shown in Fig. 2.

Synoptic evaluation of AllBac, BoBac, E. coli, and water quality parameters throughout the Stock Creek watershed was performed throughout the year which included dry and wet seasons. We monitored seven sites (SC-1 to SC-7) at baseflow or near baseflow conditions. Details on the water chemistry have been provided by Gentry et al. (2006). Site SC-1 was not included in the study due to backflow or embayment conditions that exist near the watershed outlet which discharges to a river system whose level is managed by the Tennessee Valley Authority (TVA). The reach length, drainage area, and identified number of sinkholes associated with each sampling site are summarized in Table 1 (Gentry et al., 2006). In general, the reach lengths associated with the sample sites ranged from 1.00 to 2.60 km (x = 1.81 km, {sigma} = 0.65 km). The drainage area associated with each sampling site is more variable (x = 7.05 km2, {sigma} = 0.65 km2) with values ranging from 2.40 to 21.14 km2. However, the ratio of reach length to drainage area is less variable (x = 0.34 km km–2, {sigma} = 0.13 km km–2) with values ranging from 0.11 to 0.47. The largest drainage area was associated with site SC-2, located toward the discharge end of the watershed (see Fig. 2). The number of sinkholes per basin ranged from 1 to 23 (TVA, unpublished data, 2004 ), thus the sinkhole densities (count/km2 of drainage area) ranged from 0.42 to 2.26 per km2 (x = 1.32 per km2, {sigma} = 0.66 per km2).


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  		Table 1. Drainage areas associated with main channel sampling locations (Gentry et al., 2006).

 
Flow Measurements and Field Parameters
Flow and stage measurements were taken along the main channel. Flow measurements were performed at sites SC-2 through SC-7 using the velocity-area method. Stream velocity measurements were collected using a Marsh-McBirney Model 2000 Flow Mate current meter. The flow meter is calibrated for zero velocity by submerging the device in a stagnant pool of water.

Individual grab samples from the water surface for turbidity were collected and delivered within 3 h to the Tennessee Department of Health's Knoxville Regional Lab. Sample analyses were performed using a nephelometric turbidimeter, and the sample results were reported as nephelometric turbidity units (NTU).

E. coli Analyses
Eighty-four water samples were collected for E. coli analysis from the main channel sites. Samples were placed on ice, and delivered to the Tennessee Department of Health's Knoxville Regional Lab within 3 h of collection. The lab used the Colilert method to analyze samples, in accordance with standard method 9223 (Eaton et al., 1998). The method uses a defined substrate technology where E. coli uses ß-glucuronidase to metabolize the nutrient-indicator 4-methylumbelliferyl-ß-D-glucuronide to create fluorescence. Sample densities are reported as colony forming units per liter (CFU L–1). Instantaneous load rates in the stream were calculated by multiplying the flow rate by the E. coli density and are reported as CFU per day (CFU d–1).

Bacteroides (Total Fecal Mass and Bovine-Associated Fecal Mass) Analyses
The methods for developing the AllBac and BoBac RT-PCR assays were previously reported by Layton et al. (2006). Direct quantitative RT-PCR without DNA extraction (Fode-Vaughan et al., 2001) was performed on 2.5 µL creek water samples. The 25 µL PCR mixes contained 12.5 µL Quantitect master mix (Qiagen Inc, Valencia CA), and primer and probe concentrations reported previously by Layton et al. (2006). Filter-sterilized high performance liquid chromatography (HPLC) grade water was used as negative controls. Each assay plate contained a plasmid dilution standard curve run in triplicate. Copies per liter of total Bacteroides in the AllBac assay was converted to milligrams per liter assuming 2 x 1010 copies per gram. Also, copies per liter of bovine Bacteroides in the BoBac assay was converted to milligrams per liter assuming 5 x 109 copies per gram. For each assay the fecal concentration was determined using triplicate 2.5 µL creek water samples.

Statistical Methods and K-means Cluster Analyses
Two datasets were used for the statistical analyses. The first set of data was the global dataset, representing the entire sample data collected throughout the watershed. The second dataset included the individual sample sites SC-2 through SC-7. The experimental design established global scale and individual spatial scale relationships that were important for understanding processes involved with fecal loading magnitudes in the watershed. In general, multivariate statistical techniques were used to determine the correlation coefficients between key parameters using data covariances and standard deviations. In addition, key hydrologic parameters and bacterial parameters (7-d antecedent precipitation, flow, turbidity, AllBac, and BoBac) were resolved into principal components and evaluated using K-means cluster analysis (Webb, 2005). The principal components analyses and K-means cluster analyses were performed using the JMP 6.0 software package (SAS Institute, 2005). Principal components were normalized by the mean, and eigenvalues and vectors were calculated using the correlation matrix due to the differing parameter units. In recent years, many types of clustering approaches have been coupled with water quality data to discern hydrologic behavior (Reghunath et al., 2002; Batelaan et al., 2003; Kim et al., 2003; Carrera et al., 2004; Guler and Thyne, 2004a, 2004b; Hussein, 2004; Lambrakis et al., 2004; Thyne et al., 2004; Kim et al., 2005). The coupling of the hydrologic and water quality data in cluster analysis may provide a way to discern behavior characteristics unique to the dataset.


   Results and Discussion
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 Materials and Methods
 Results and Discussion
Conclusions
 REFERENCES
 
Flow, turbidity, and bacterial data were summarized using the geometric mean for each sampling station. A summary of these statistics is provided in Table 2 for all of the sampling dates. In addition to the geometric means, the range of values measured will be presented in the following paragraphs.


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  		Table 2. Geometric means for bacterial and hydrologic data.

 
Stock Creek generally gained flow from SC-7 to SC-2, within the range of sensitivity of the flow measurement. Flow rates ranged from 1.48 to 12.40 m3 s–1 at SC-2, compared to flow rates of 0.21 to 3.18 m3 s–1 at SC-7 in the headwater portion of the watershed. The geometric means for flows at each of the sites ranged from 0.60 to 4.84 m3 s–1. Two National Weather Service (NWS) gage stations (40–4946 and 40–4950) were used to calculate the average 7-d antecedent precipitation associated with each sampling event. The 7-d antecedent precipitation ranged from 0.00 to 66.29 mm for sampling events in October and July of 2003, respectively.

The calculated E. coli loading rates ranged from 0.04 x 1010 to 77.41 x 1010 CFU d–1. The highest loading rates were observed toward the outlet of the watershed at site SC-2 which had geometric means of 3480 CFU L–1 and 14.58 x 1010 CFU d–1, respectively. AllBac loads ranged from 17.07 to 3769.57 kg d–1 with the highest load occurring at SC-2. BoBac loads ranged from 0.62 to 801.12 kg d–1 with the highest occurring at SC-3.

A scatter plot matrix with corresponding correlation coefficients for each of the bivariate combinations on the whole watershed dataset is shown in Fig. 3. Bivariate 95% density ellipses, the area encompassing 95% of the dataset, are also shown in Fig. 3 to demonstrate the spread of the data. The more rounded ellipses indicate poor correlation, whereas elongated ellipses are indicative of stronger correlation. The global analysis demonstrates that of the variables analyzed, BoBac (kg d–1) was most strongly correlated ({rho}xy = 0.83) with flow and with E. coli loads ({rho}xy = 0.71) throughout the watershed. Of the three bacterial measures, BoBac had the best correlation ({rho}xy = 0.63) with 7-d antecedent precipitation, but was less correlated with turbidity ({rho}xy = 0.50) than either E. coli loads or AllBac loads. AllBac (kg d–1) loads were more strongly correlated with BoBac loads ({rho}xy = 0.77), E. coli loads ({rho}xy = 0.79), and turbidity ({rho}xy = 0.70), than with flow ({rho}xy = 0.63) or with 7-d antecedent precipitation ({rho}xy = 0.47).


Figure 3
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  		Fig. 3. Scatter plot matrix and correlation coefficients for water quality, hydrologic data, and bacterial load data for all sites.

 
Spatial dependencies were evaluated by calculating the degree of correlation for all bivariate combinations at individual main channel sites, as was done with the global dataset. The analysis indicates an interesting pattern throughout the watershed for E. coli, AllBac, and BoBac load rates with turbidity, flow, and antecedent precipitation, which are summarized in Table 3 for each main channel site. The highest correlations for BoBac at all sites included antecedent precipitation (0.59 ≤ {rho}xy ≤ 0.93). In addition, BoBac correlated highly with AllBac (0.48 ≤ {rho}xy ≤ 0.96), although there tended to be a spatial dependency with poor correlation at site SC-3. At SC-3, BoBac was most highly correlated with flow ({rho}xy = 0.92), indicating a possible groundwater response at this site. Also, SC-3 has the highest sinkhole density (2.26 per km2) of all subbasin sampling sites. This indicates that the karst drainage response may be responsible for the persistence of bacteria at baseflow in this area. BoBac correlated well with turbidity at SC-2 ({rho}xy = 0.71), but was generally poor upstream (0.16 ≤ {rho}xy ≤ 0.55). AllBac tended to correlate strongly with E. coli loads (0.60 ≤ {rho}xy ≤ 0.95) and turbidity (0.60 ≤ {rho}xy ≤ 0.91) in the tailwater portion of the watershed, SC-2 to SC-4. In the headwater portion of the watershed, SC-5 to SC-7, AllBac correlated best with E. coli (0.61 ≤ {rho}xy ≤ 0.80) loads and 7-d antecedent precipitation (0.48 ≤ {rho}xy ≤ 0.96). A similar relationship between parasite, bacterial loads, and turbidity has been identified in the literature by Kistemann et al. (2002). However, in this study we see spatial dependencies in the relationship between turbidity and AllBac and BoBac loads. In general, there tends to be a higher correlation to 7-d antecedent precipitation in the headwater portion of the watershed, and to turbidity in the tailwater portion of the watershed. A similar study by Shanks et al. (2006) has also shown the benefit of basin-wide studies of source-specific markers in point and nonpoint source analysis.


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  		Table 3. Degree of correlation between hydrologic and bacterial parameters spatially.{dagger}

 
As previously reported by Gentry et al. (2006), the spatial dependencies observed throughout the watershed are likely connected to the drainage-storage relationships (surface and subsurface) of each subbasin. As shown in Fig. 4, the results of the cluster analyses for E. coli, AllBac, and BoBac demonstrate a prominent cluster, cluster 2, with similar 7-d antecedent precipitation, flow, and turbidity. The remaining clusters represent more variable data, with E. coli and AllBac being most similar. Cluster 1 and 3 for each dataset show a spreading along two directions on the principal components graphs. BoBac appears less variable with a somewhat different cluster configuration, where the cluster points are more tightly located with less variability. For each case the principal component data extend along similar patterns. The data spread along the portion of the graph with higher 7-d antecedent precipitation, turbidity, and bacterial loads, whereas the alternate portion of the data extend along higher 7-d antecedent precipitation and bacterial loads, but with lower turbidity. The most likely reason for the alternate patterns in the cluster analysis is the representation of differing hydrologic regimes. The lower turbidity pattern likely represents elevated baseflow or groundwater response with bacterial persistence from the karst system interacting with the stream (Gentry et al., 2006). These findings may indicate the same pattern noted by Dussart-Baptista et al. (2003). The conceptual model is presented in Fig. 5, using the BoBac data as an example. This was also confirmed by the global dataset (Fig. 3), where at sample location SC-3 BoBac correlates more strongly with flow ({rho}xy = 0.83) and less with 7-d antecedent precipitation ({rho}xy = 0.63) and turbidity ({rho}xy = 0.50), indicating that while there is a runoff component, baseflow or karst delivery to the system may represent a strong influence on the persistence of BoBac loads in groundwater-dominated areas of the main channel.


Figure 4
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  		Fig. 4. K-means clusters using 7-d antecedent precipitation, flow, turbidity, and bacterial parameters for all sites.

 

Figure 5
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  		Fig. 5. Conceptual model for bacterial impacted hydrologic regimes.

 

   Conclusions
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 Materials and Methods
 Results and Discussion
 Conclusions
REFERENCES
 
Our study demonstrates that source-specific bacterial loads can be used to better understand hydrologic influences on bacterial delivery and persistence in the system. AllBac and E. coli load rates are highly correlated in the Stock Creek watershed. BoBac seems to be more strongly correlated to flow than AllBac at all sites, indicating that groundwater drainage from the sinkholes in the system may play an important role in bovine bacteria persistence at baseflow. Using K-means cluster analyses with a combination of the AllBac and BoBac assay data improves the uncertainty associated with the E. coli data and provides a conceptual model for separating pure runoff-dominated persistence versus potential groundwater-dominated responses. These new datasets may help provide better management throughout a watershed with complex hydrologic interactions.


   ACKNOWLEDGMENTS
 
This research was funded by the Tennessee Dep. of Environment and Conservation (TDEC) under the direction of Dr. Sherry Wang and Mr. Jonathon Burr with co-funding from the Water Resources Research Inst. Program (Project Number: 2003TN7b) and the Univ. of Tennessee Center for Environmental Biotechnology. We would like to thank Mr. Burr for additional collaboration in the selection of field sampling sites and for coordination of field equipment and sample analysis by Tennessee Dep. of Health's Knoxville Regional Lab.


   NOTES
TOP
 NOTES
ABSTRACT
 INTRODUCTION
 Materials and Methods
 Results and Discussion
 Conclusions
 REFERENCES
 
All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.


   REFERENCES
TOP
 NOTES
 ABSTRACT
 INTRODUCTION
 Materials and Methods
 Results and Discussion
 Conclusions
 REFERENCES
 




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