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Received Date : 17-Mar-2014 Revised Date : 24-Apr-2014 Accepted Date : 09-May-2014 Article type
: Original Article
Runoff Studies Demonstrate Parallel Transport Behavior for a Marker of Poultry Fecal Contamination and Staphylococcus aureus
Jennifer Weidhaas *, West Virginia University, Civil and Environmental Engineering, PO Box 6103, Morgantown, WV 26506, USA, PH: 304-293-9952, E: [email protected], Emily Garner, West Virginia University, Civil and Environmental Engineering, Morgantown, WV 26506, USA Tom Basden, West Virginia University Extension Service, Agriculture and Natural Resources, Morgantown, WV 26506, USA Valerie J. Harwood, University of South Florida, Department of Integrative Biology, Tampa, FL, 33620
* corresponding author
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as an 'Accepted Article', doi: 10.1111/jam.12543 This article is protected by copyright. All rights reserved.
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KEYWORDS: Microbial source tracking; poultry feces; Brevibacterium; qPCR; nonpoint source pollution; runoff; vegetated filter strips, water quality.
RUNNING HEADING: Fecal bacteria in simulated runoff
ABSTRACT Aim: To determine if poultry litter marker gene LA35 is correlated with pathogens and fecal indicator bacteria (FIB) in runoff from poultry litter amended plots. Methods and Results: A rainfall simulator with various vegetative filter strip lengths was employed to evaluate the correlation of a microbial source tracking (MST) marker for poultry feces/litter (the 16S rRNA gene of Brevibacterium sp. LA35 [LA35] measured by quantitative PCR) with pathogens and fecal indicator bacteria in runoff. LA35 was correlated with Staphylococcus aureus, Escherichia coli, Enterococcus spp. and Bacteroidales levels. Salmonella was present at low concentration in litter, but became undetectable by qPCR in runoff. E. coli, LA35 and S. aureus exhibited mass based first flush behavior in the runoff. Conclusion: Correlation of LA35 with FIB and pathogens in runoff from poultry litter amended fields suggest comparable transport mechanisms and that LA35 is a useful tracer for harmful bacteria in the environment released from poultry litter.
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Significance and Impact of the Study: In order to protect human health, an effective marker for poultry fecal contamination should exhibit similar fate and transport characteristics compared to pathogens. This study is among the first to demonstrate such a relationship in runoff for a MST marker.
INTRODUCTION Poultry litter (poultry feces, spilled feed, feathers and bedding materials) is widely used as an agricultural fertilizer. Estimated application rates of poultry litter as fertilizer in the United States in 2008 were upwards of 1.6 billion kg (USEPA 2012). Poultry litter can contain pathogens such as Yersinia enterocolitica, Listeria monocytogenes, Salmonella enterica, Staphylococcus. aureus, Pseudomonas aeruginosa, Escherichia coli O157:H7, Campylobacter jejuni, and Clostridium perfringens, and fecal indicator bacteria (FIB) such as E. coli and Enterococcus spp. (McCaskey and Anthony 1979; Kelley et al. 1995). Studies have shown that these microorganisms can run off of agricultural fields on which litter has been applied with stormwater and impact environmental waters as nonpoint source pollution (McMurry et al. 1998; Moore et al. 1998; Smith et al. 2007; Brooks et al. 2009; Weidhaas et al. 2010; McLaughlin et al. 2011; Weidhaas and Lipscomb 2013). Pollutants transported in runoff from urban and rural surfaces can be more concentrated at the beginning of a storm event compared to the end of the event. This phenomenon has been described as the “first flush.” The term first flush has been used to describe the disproportionally high concentration of pollutants or pathogens during the initial period of runoff after initiation of a storm event (Sansalone and Cristina 2004). Microorganisms such as E. coli, Enterococcus spp.
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and fecal coliforms (Soupir et al. 2006) and nutrients such as phosphorus (Faucette et al. 2004) originating from poultry litter have been shown to exhibit first flush behavior from poultry litteramended fields. Methods commonly used to definitively identify the occurrence of a first flush event include both mass based first flush (MBFF) and concentration based first flush. These methods have been shown to be conceptually and mathematically equivalent (Sansalone and Cristina 2004). The absence of a concentration or mass based first flush indicates that the pollutant concentration is proportional to the volume of water in the runoff. Vegetated areas along receiving water bodies generally improve the quality of storm
water runoff, thus reducing nonpoint source pollution (Chaubey et al. 1994; Coyne et al. 1998; Abu-Zreig et al. 2004). Mechanisms shown to improve the water quality of overland or channelized water passing through vegetated areas include 1) reduction in water velocity (Lee et al. 1989) which increases water infiltration and particle sedimentation rates (Abu-Zreig et al. 2004) and 2) increased filtration through vegetation and leaf matter (Cardoso et al. 2012). Vegetative filter strips are Stormwater best management practices situated between pollutant source areas and surface waters that receive runoff. The effectiveness of vegetated filter strips (VFS) for improving cropland runoff water quality has been reported previously (Chaubey et al. 1994; Patty et al. 1997; Sharpley 1997; Lim et al. 1998; Entry et al. 2000; Atwill et al. 2002). For example, VFS are have been show to remove 70.0 to 97.6 % of incoming suspended solids (Lim et al. 1998), 67.0 to 93.6 % of phosphorus, 64.9 to 95.3 percent of nitrogen (Chaubey et al. 1994; Lim et al. 1998) and 44 to 100 % of pesticides such as atrazine (Patty et al. 1997). In runoff from agricultural fields amended with animal manure, VFS have been shown to remove greater than 99.9% of Cryptosporidium parvum oocysts (Atwill et al. 2002), 70 to 95% of E.
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coli, 72 to 94% of Salmonella (Cardoso et al. 2012) and up to 100% of fecal coliforms (Coyne et al. 1998; Lim et al. 1998). While numerous methods are available for detection of pathogens, or more commonly
FIB originating from agricultural waste, these tests do not reveal the source of the contamination (e.g., poultry, swine, or cattle), as multiple fecal and other sources may contain these microorganisms. Additionally, the paradigm of FIB correlating with risk of waterborne disease (USEPA 1986) is increasingly less accepted as FIB have been shown to be relatively poor predictors of pathogens’ presence under some conditions (Harwood et al. 2005; Hellein et al. 2011). Microbial source tracking (MST) is an alternative to FIB based methods for detecting
fecal pollution. MST methods focus on the detection of organisms that are unique to the feces of one particular animal (e.g., chickens) or groups of animals (e.g., ruminants). These MST methods aid in identifying nonpoint source of fecal pollution by identifying host specific species in the polluted water, thus contributing to a weight of evidence argument (when combined with field observation and physical-chemical measurements) regarding fecal pollution sources. Certain microorganisms have been found to be associated with chicken feces (Lu et al. 2007; Kobayashi et al. 2012) or their soiled litter (Weidhaas et al. 2010; Ryu et al. 2013) and may be appropriate MST targets. Recently, we reported on a 16S rRNA marker gene of Brevibacterium sp. LA35 (hereafter LA35 marker gene), which is similar to Brevibacterium avium. B. avium was first isolated from lesions in diseased poultry (Pascual and Collins 1999); however, Brevibacterium sp. LA35 is highly concentrated in soiled poultry litter, is found in the feces of healthy animals and is rarely found in the feces of other animals (Weidhaas et al. 2010), making it a very strong candidate for an MST tool. We also reported on a SYBR based quantitative polymerase chain reaction (qPCR) assay (Weidhaas et al. 2011) and a Taqman ® qPCR assay
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(Weidhaas and Lipscomb 2013) for the LA35 marker gene that we developed to quantify this marker gene in environmental samples and its use in two poultry litter-impacted watersheds. Use of marker genes specific to host organisms for source tracking is preferred to other fecal indicator bacteria tracking such as Staphylococcus spp., E. coli, Enterococcus spp. as these other organisms have natural reservoirs in the environment (Fujioka et al. 1999; Berg et al. 2005; Byappanahalli et al. 2006). The objectives of this study were to 1) evaluate the release of the LA35 marker gene, FIB and pathogens from soiled poultry litter to runoff during rainfall events, 2) to compare the effect of VFS length on the removal of sediments and microorganisms originating from poultry litter and 3) to determine the correlation of the poultry litter-associated LA35 marker gene with FIB and pathogens in runoff from VFSs. To achieve these objectives a simulated rainfall runoff study on poultry litter-amended plots was conducted and the concentration of total suspended soils (TSS) and various microorganisms originating from poultry litter were quantified in runoff. Specific microorganisms evaluated in this study by quantitative polymerase chain reaction (qPCR) included the poultry litter specific microorganism Brevibacterium sp. LA35, the pathogens Salmonella spp. and S. aureus and the fecal indicator bacteria E. coli, Enterococcus spp. and Bacteroidales.
MATERIALS AND METHODS Runoff Plots A simulated runoff study was conducted at the West Virginia University (WVU) Agronomy Farm in Morgantown, WV. Soils in the study area were classified as Dormont and Guernsey Silt
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Loams with moderate water holding capacities (7 to 9 inches) (NRCS 2013). Soil samples from the study area were analyzed by the WVU Soil Testing Laboratory for soil pH (5.6), K2O (0.14
meq per 100 gram soil), Ca2+ (2.8 meq per 100 gram soil), Mg2+ (0.04 meq per 100 gram soil)
and total cation exchange capacity (8 meq per 100 gram soil). Native vegetation at each plot consisted of predominately fescue with a mixed sward of white clover and other broadleaf forbs. The vegetation was clipped to no more than 4 inches in height during the course of the study. Plots were selected to obtain uniform slope and well-established uniform grass cover.
Each field experiment included two adjacent 1 m (measured perpendicular to slope) plots
to allow for data collection in duplicate (Figure 1). The simulated runoff plots were designed based on previously published systems by the US Department of Agriculture, Agricultural Research Service (Sharpley and Kleinman 2003). The four treatments utilized in this study included 1) a no litter applied, no VFS (a.k.a. control plot), 2) a 0 m long VFS (i.e., litter was applied over the entire study area and runoff did not travel through a VFS prior to sample collection), 3) a 4.9 m long VFS and 4) a 12.2 m long VFS. Metal barriers, inserted to a depth of 8 cm below the ground surface were used to isolate the plots and prevent runoff from entering or exiting the upslope and side edges. Hydrated bentonite clay was placed along the interface between the ground surface and the metal barrier to prevent infiltration of the runoff along the metal barriers making the edges of the plots. The plots were finished with a collection gutter placed flush with the soil surface at the downslope end of each plot (perpendicular to the slope) and sealed with hydrated bentonite clay so that all runoff exiting the plot enters the gutter. A cover was installed over each gutter to prevent rainfall from entering the gutter directly. Hoses attached to each gutter allowed for collection of the runoff water.
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Rainfall simulator The rainfall simulator consisted of a cubical aluminum frame approximately 3 m on all sides. Nozzles (#50, Spraying Systems Co., Glendale Heights, IL) were used to achieve a simulated rainfall and were positioned 3 m above the ground. A 760 L tank, continuously filled with chlorinated municipal tap water, was used along with a gasoline powered pump to supply water to the simulator. A flowmeter and pressure gauge were installed in order to monitor and control the volume of water applied to each experiment and maintain a consistent pressure at each nozzle Heavy tarps enclosed the simulator to prevent wind impacts on the uniformity of the nozzle spray. For the 4.9 and 12.2 m VFS plots, the rainfall simulator was altered, using timber to extend the frame, tarps to enclose the frame, and PVC pipes to extend the water distribution system. Additional nozzles were installed every 3 m (Figure 1).
The uniformity of rainfall application in each plot was measured by placing up to ten
cups randomly throughout each plot. Rainfall was simulated for 10 minutes and the weight of each cup was measured. The uniformity coefficient (UC) was determined by the methods of Christiansen (1942) as shown in equation 1: (Equation 1)
Where Xi is the measured application depth (cm), m is the mean application depth (cm), and n is the number of observations (Christiansen 1942). All plots were saturated immediately before each experiment by applying simulated rainfall for a minimum of 30 minutes to all plots or until the water volume flowing from the plots was at steady state.
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Experimental Design One scoop of poultry litter was aseptically collected from each of 19 pens at the WVU Animal Sciences Farm poultry house as well as from five buckets of litter recently removed from pens. The litter was composited and thoroughly homogenized using a concrete mixer. Litter was sampled before each simulation and LA35 marker gene concentrations were measured. Plots 1 &2 acted as control plots, with rainfall simulation and sample collection completed once without litter application, then again with 0.75 kg of litter applied to each replicate (i.e., No VFS plots #1 and #2). For the 4.9 m and 12.2 m VFS plots, 0.52 kg of litter were applied to each replicate plot, consistent with agronomic use loading rates for poultry litter in WV. Litter was applied only to the upper 2.4 m of each plot by hand and spread evenly over the application area to simulate broadcast application methods.
Sample Collection and Handling Runoff samples were collected from the gutter of each duplicate plot in 5 minute increments after runoff began. Samples were collected in 19-L buckets placed on a scale and total weight in each increment was recorded. The samples were then mixed to homogenize sediments in the solution and a composite grab sample of 1-L was collected aseptically in a sterile 1-L highdensity polyethylene bottle. After rainfall ceased, all runoff was collected and a final composite sample was collected. Samples were stored at 4 ºC upon return to the lab and were filtered within 48 hours of collection. Four soil samples were collected from the study site prior to litter application. For each sample, one scoop (c. 25 mls) was aseptically taken from below the vegetation cover. Source water used to simulate rainfall, which was supplied by municipal drinking water, was collected in sterile 1-L
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high-density polyethylene bottles from the site of the study, The DNA was extracted from these control samples as described below.
Sample Analysis Total suspended solids in each sample collected was determined by filtering between 25 and 290 mL through a sterile 0.45 µm pore, size 47 mm nitrocellulose water testing membrane filters (Thermo Fisher Scientific, Inc., Waltham, MA, USA) and drying for 1 h at 105 oC per Standard
Methods (APHA 2005). Water samples were prepared for DNA extraction by filtration through sterile 0.45 µm pore size 47 mm nitrocellulose water testing membrane filters. Between 20 and 100 mL of each runoff sample and 2 L for each source water sample were filtered and filters were frozen at -80 °C. DNA was extracted from the stored filters, as well as all soil and litter samples as described previously (Weidhaas et al. 2011). DNA was extracted from the frozen filters within 1 week of sample collection, while the litter and soil DNA was extracted within 6 hours of sample collection. Microbial concentrations or presence/absence detections in runoff samples were determined by qPCR or nested qPCR targeting 1) the pathogens Salmonella spp. invA gene and Staphylococcus
aureus sec gene (Lee et al. 2006), 2) the fecal indicator bacteria E. coli uidA gene (Lee et al.
2006), Enterococcus spp. 23S rRNA gene (Ludwig and Schleifer 2000), and Bacteroidales 16S rRNA gene (Dick and Field 2004; Siefring et al. 2008), and 3) the poultry litter associated Brevibacterium sp. LA35 16S rRNA gene (Weidhaas and Lipscomb 2013) (Table 1). Each 25 µL qPCR reaction included 1X of the TaqMan® Fast Advanced Master Mix (Applied Biosystems, Carlsbad, CA, USA), previously published optimal primer (Integrated DNA Technologies, Inc., Coralville, IA) and probe concentrations (Biosearch Technologies, Inc,
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Novato, CA, USA) (Table 1), 1 µL of template DNA sample and molecular-grade water (Ambion, Carlsbad, CA, USA). Amplification was performed using a 7300 Real Time PCR System (Applied Biosystems, Carlsbad, CA, USA) according to previously published methods (see Table 1 references) with the exception of the Master Mix used. Control samples for each qPCR run included qPCR negative controls (e.g., DNA-free water instead of template), qPCR positive controls (i.e., LA35 marker gene inserted into a plasmid [pLA35]), linear DNA or DNA from pure cultures instead of template) and matrix spiked samples (i.e., pLA35 or pure culture DNA added to DNA template). For construction of standard curves, a 7-fold dilution series averaged approximately 10.4 to 1.04 * 107 target genomes containing LA35 (y = -0.3054 x +
11.531, efficiency = 102 %), Bacteroidales (y = -0.3006 x + 11.47, efficiency = 100 %), E. coli (y = -0.3257x + 12.243, efficiency = 110 %), Enterococcus spp. (y = -0.3055 x + 11.129,
efficiency = 103 %), S. aureus (y = -0.3001 x + 12.506, efficiency = 99 %), and Salmonella spp.
(y = -0.3642 x +14.415, efficiency = 130 %) target genes were assayed by qPCR. Samples in which Salmonella invA was not detected, nested qPCR was conducted by performing two rounds of qPCR analysis using the invA primers and taking 1 μl of the amplified products from the first qPCR assay as the template for the second round of qPCR.
Runoff Calculations and Statistical Analysis Effectiveness of the VFS for microbial and solids reduction in runoff was computed according to the methods of Chaubey, Edwards, et al., (1994) as shown in equation 2: Eij = 100 [(M0,j-Mi,j)/M0,j]
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(Equation 2)
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where, Ei,j is the effectiveness of VFS length i for removal of parameter j (%), Mi,j is the mass of parameter j transported past VFS length i (mg TSS, gene copies), and M0,j is the mass of
parameter j transported past the no VFS treatment (mg TSS, gene copies) (Chaubey et al. 1994). To account for variable applied bacterial loads to each treatment plot, the data were volumeweighted by the volume of water leaving each plot as shown in equation 3 (Leecaster et al. 2002): n
M i, j = ∑ C j *V j
(Equation 3)
j =1
where Cj is the concentration of bacteria estimated by qPCR or TSS in each successive
subsampling event (gene copies L-1, or mg TSS L-1) and Vj is the volume of runoff water in each successive subsampling event (L). These volume-weighted peak values were used to determine VFS removal effectiveness.
To evaluate mass-based first flush (MBFF), two dimensionless parameters were calculated as shown in equations 4 and 5 (Sansalone and Cristina 2004): k
M (t ) =
∑C
j
*V j
i −0 n
∑C
(Equation 4) j
*V j
i −0
k
V (t ) =
∑ *V
j
i −0 n
∑ *V
(Equation 5) j
i −0
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where, V(t) is the dimensionless ratio of the total volume of runoff observed at any time k to the total runoff volume observed for the event; k is any time between the initiation of the runoff and the time coinciding with the cessation of runoff (n); M(t) is the dimensionless ratio of constituent mass delivered at any time k to the total mass of the constituent delivered throughout the event. A MBFF is defined as any period when M(t) > V(t). Analysis of variance among the load normalized runoff concentration of microorganisms
and TSS were determined with the subroutine PROC ANOVA and the Student’s t-test (SAS 9.4, SAS Institute Inc., Cary, NC, USA) with alpha at 0.05. Pearson’s correlations and one way ANOVA on ranks using Dunn’s pairwise multiple comparison between log concentrations of load normalized runoff concentrations of microorganisms over time were determined using SigmaPlot (V 11.0, Systat Software, Inc., Chicago,, IL, USA). The average of replicate VFS, load normalized concentrations of microorganisms in the runoff were used for the correlation analysis to remove bias associated with variable applied litter loads between experimental VFS. Box plots and other graphs were generated using SigmaPlot.
RESULTS Site soil, tap water and litter microbiological characterization The concentrations of microorganisms in applied litter, in the soils at the study site outside the runoff study area and in tap water used for the simulated runoff studies are shown in Table 2. All organisms under study were detected in the litter samples prior to field application, with Bacteroidales, Enterococcus spp., LA35, and E. coli being present in high concentrations, while S. aureus were detected at significantly lower concentrations. Salmonella spp. were detected in This article is protected by copyright. All rights reserved.
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only the first litter sample tested at relatively low concentrations. As the litter was stored in a 19 L bucket for approximately 1 week in the shade and covered with black plastic between applications, there was likely a die-off of E. coli and Salmonella spp. during litter storage which is reflected in the decreased concentration in the second litter sample. In site soils and tap water neither LA35 nor Salmonella spp. were detected by qPCR. Low concentrations of Bacteroidales,
Enterococcus spp., and E. coli were detected in site soils and tap water (collected from an agricultural barn exterior faucet). The no-template controls did not amplify nucleic acids in this study and the matrix spike samples showed that the qPCR assays were not inhibited.
Simulated runoff study results A summary of the rainfall simulator study events, plot characteristics, microorganism loading rates, water application and runoff rates as well as the rainfall uniformity coefficient is presented in Table 3. Approximately 24%, 12%, 28% and 15% of the applied rainfall in the no amendment 0 m control plot, 0 m, 4.9 m and 12.2 m VFS, respectively was collected as runoff from the treatment plots. Uniform rainfall distribution over the treatment plots is indicated by the greater than 96% rainfall uniformity coefficient in all treatments. Concentrations of FIB and poultry litter marker in the runoff from the treatment plots are
shown in Figure 2A through 2F. Salmonella spp. were not detected in the runoff from any of the treatment plots, even when a nested qPCR approach was used (i.e., two rounds of amplification with the invA primers). In all cases the maximum concentration of FIB (Enterococcus spp. and Bacteroidales) in runoff from the unamended control plot was several orders of magnitude lower than that observed in the litter-amended treatment plots (i.e., 4.1, 3.5, and 2.6 log lower in the 0
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m, 4.9 m and 12.2 m VFS plots, respectively). Specifically, in the runoff from the unamended control plot, Enterococcus spp. concentrations (log gene copies L-1) were 3.6 ± 0.5 (average ±
standard deviation) and Bacteroidales were 4.1 ± 0.9. E. coli and S. aureus were not detected in
runoff from the unamended control plot. As shown in Figure 2 (C, D, E and F), peak concentrations of LA35 and S. aureus occurred between 10 and 30 minutes after runoff initiation in the replicate 4.9 and 12.2 m plots. The runoff behavior of LA35 and S. aureus therefore exhibited first flush behavior (peak discharge and tailing), indicative of release of these organisms solely from the poultry litter. By comparison, the FIB showed an increase in concentrations in the runoff, but did not exhibit strong peak discharge and tailing behavior based on absolute concentrations of microorganisms (i.e., log gene copies L-1).
Because variable flow rates among the treatment plots could mask obvious first flush
behavior, the occurrence of MBFF events was assessed in each of the treatment plots, where M(t) was plotted versus V(t) in Figure 3. Each of the plots A through F in Figure 3 correspond to the plots in Figure 2 for comparison. An MBFF is defined as an event where M(t) exceeds V(t) indicating that a disproportionately high mass of pollutants has been delivered by a given volume of flow. A MBFF was observed in the 0 m VFS (Figure 3A) and 1 replicate of the 4.9 m (Figure 3C) and 12.2 m VFS (Figure 3E) treatments. In general the higher the runoff rate from the plots, the more likely a MBFF was observed as shown in Figure 3C and E versus Figure 3D and F. The average runoff rates for the 4.9 m (i.e., 6.4 L min-1) and 12.2 m VFS (i.e., 6.8 L min-1) replicates in which MBFF occurred (Figure 3C and E, respectively) were greater than in their replicate treatments (i.e., 4.8 and 1.9 L min-1, respectively) but only statistically significantly in
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the 12.2 m VFS (Student’s t, P < 0.001). When observed, the MBFF was greatest for E. coli, S. aureus and LA35. In contrast a MBFF was not observed for TSS in any treatment. To evaluate if levels of FIB, poultry litter marker gene, pathogens and suspended solids
were correlated in the runoff over the course of the simulation, Pearson’s correlation coefficients were determined (Table 4). FIB (Bacteroidales, E. coli, and Enterococcus spp.) concentrations were correlated, regardless of the VFS length. LA35 was also correlated with FIB in runoff from litter-amended treatments. S. aureus was strongly correlated with the FIB and LA35 when VFS were present, but less strongly correlated with VFS absent. Total suspended solids were correlated with Bacteroidales and Enterococcus spp. in control plots, and with E. coli, Enterococcus spp. and LA35 in the litter-amended, no VFS plot, but not in plots with VFS.
Effectiveness of VFS for microbial and solids removal The range in concentration of microorganisms and TSS in runoff from all plots are shown in Figure 4. In all cases the longer the VFS the lower the concentration of microorganisms in the runoff from the plots, e.g., Enterococcus spp., E. coli, and LA35 were significantly higher in runoff from the litter-amended plots without VFS compared to those with VFS (Fisher’s least significant difference, α = 0.05). The ranges of runoff rates were higher from the 4.9 and 12.2 m plots than the plots without VFS, as rainfall was applied along the length of the VFS (Figure 1) to ensure even distribution of rainfall along the VFS length. Despite the higher runoff rate in the treatments with VFS, both the absolute concentrations (gene copies L-1) and volume-weighted
concentrations (by equation 3) of Enterococcus spp., E. coli, and LA35 concentrations were found to be significantly lower in runoff from the VFS plots.
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To evaluate the effectiveness of the VFS for removing microorganisms and solids originating from poultry litter in runoff, the removal effectiveness was calculated for the 4.9 and 12.2 m VFS compared to the no VFS treatment (Table 5) based on the volume-weighted peak concentrations of microorganisms and solids in runoff using equations 2 and 3. Microorganism removal efficiency varied from 23.3 % to 99.9 % and overall was significantly lower for Bacteroidales in the 4.9 m VFS and slightly lower for S. aureus in both VFS treatments (Fisher’s least significant difference, α = 0.05) compared to the other organisms under study. There was a reduction in removal efficiency in TSS with increasing length of the VFS, likely due to increased sediment entrainment in the runoff from the longer plot.
DISCUSSION State and federal agencies recommend identification of potential fecal sources and installation of targeted best management practices to reduce NPS pollution and achieve total maximum daily load (TMDL) goals. MST has been shown to be an effective method for identifying potential fecal sources to watersheds, which allows targeted investigation of specific sources, thereby increasing accuracy of source identification and effectiveness of remediation (Stoeckel and Harwood 2007; Harwood et al. 2014). A United States Environmental Protection Agency guidance document has identified the characteristics of an ideal source identifier or MST marker (USEPA 2005). These characteristics include, among others; host specificity, distribution in all host waste, no geographical variability in marker/host association, and abundance in primary and secondary habitat. Finally, the EPA suggests that ideal markers can be used to regulate water quality due to marker presence or abundance correlating to human health risk (USEPA 2005).
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A series of studies have been conducted to determine the suitability of LA35 as a marker of poultry litter contamination in the environment following the recommended EPA MST guidance. Recent studies have shown the specificity of LA35 to poultry litter or feces, of both chickens and turkeys, as well as the qPCR assay sensitivity (Weidhaas et al. 2010; Ryu et al. 2013; Weidhaas and Lipscomb 2013). The poultry litter marker gene has been shown to be geographically stabile as it has been found in poultry litter and chicken feces from Arkansas, Delaware, Florida, Georgia, Minnesota, Ohio, Oklahoma, Puerto Rico, Utah, and West Virginia. Field evaluations have been conducted to show the distribution of the marker in the environment in areas of intensive poultry rearing and poultry litter spreading. Specifically, recent studies (Weidhaas et al. 2011; Weidhaas and Lipscomb 2013) have found decreasing concentrations of the poultry litter marker with distance from the source of the poultry litter in two watersheds where significant poultry rearing activities occur, namely the Illinois River Watershed in Arkansas and Oklahoma and the Potomac River headwaters in West Virginia. The previously reported mean concentrations of the poultry litter marker in edge of field runoff from poultry litter amended plots was 6.9 ± 7.2 log gene copies L-1 (Weidhaas et al. 2010), in the same range as concentrations of the LA35 marker gene observed in the runoff from the 4.9 m VFS in this study. However, ten to 100-fold higher concentrations of the LA35 marker gene were found in the runoff from the no VFS control plots (i.e. 7.9 to 8.8 log gene copies L-1) as compared to the
previously reported field studies. The higher concentrations of the LA35 marker gene in the no VFS control plots in this study is likely due to the lack of filter strip and the use of freshly collected poultry litter that had not undergone environmental exposure and thus microbial dieoff.
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The last ideal criterion recommended by the EPA, the marker presence or abundance should be correlated to human health risk, is difficult to measure directly as it requires expensive, logistically difficult epidemiological studies. Correlation of marker behavior with that of pathogens is one proxy for human health risk, and correlation with FIBs links marker levels with general fecal contamination and current regulatory tools. In an ideal world, both persistence and transport characteristics of pathogens, FIB, and MST markers like LA35 would all be similar; and while we found some evidence to support this paradigm, there were exceptions. The correlation of the LA35 marker gene with both FIB and S. aureus suggests the marker gene may be valuable for monitoring water for human health risk. However it is important to note that the LA35 marker gene and the S. aureus sec gene exhibited differential transport characteristics from the FIB in this study. While the FIB increased with time after runoff initiation, they did not exhibit peaking and tailing behavior similar to S. aureus and the LA35 marker gene. The difference in transport characteristics may explain others observations that the LA35 marker gene was found in only approximately 30% of stream samples in poultry litter impacted areas where elevated FIB levels were observed (Ryu et al. 2013). Others have reported that E. coli and fecal coliforms originating from poultry litter do not exhibit characteristic first flush behavior in simulated runoff studies (Eldridge et al. 2009). The behavior of FIB in response to rain events may be driven by the presence of “naturalized” (persistent) populations in environmental habitats (Ishii et al. 2006; Badgley et al. 2011a; Badgley et al. 2011b; Byappanahalli et al. 2012), a problem that does not appear to be an issue in the case of Brevibacterium sp. LA35. In this study, no correlation was found between TSS and the various microorganisms in
the runoff from the 4.9 and 12.2 m VFS. Additionally, the TSS did not exhibit MBFF behavior in any of the treatments evaluated, while E. coli, LA35 and S. aureus did. The lack of correlation
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between TSS and the various microorganisms suggest, as we found previously in edge of field runoff from poultry litter applied fields (Weidhaas et al. 2011), that the microorganisms may not be adsorbed onto particles or solids or adsorbed to particles less than 0.45 µm in size. Rather the microorganisms in the runoff from the VFS this study may be free cells unattached to solids, as has been suggested previously as an overland transport mechanism for pathogens (Tyrrel and Quinton 2003) or they are attached to colloids or small particles passing through the 0.45 µm
filters. There are conflicting reports in the literature as to whether fecal indicator organisms will be attached to particles during runoff from manure amended soils. For example, it has been reported that E. coli eroded from cowpats and transported in runoff are generally unattached (Muirhead et al. 2005), or are associated with particles near the same size or smaller than the E. coli cells (Muirhead et al. 2006). Others have reported that a majority of E. coli and enterococci are attached to manure associated colloids in the 8 to 62 µm particle size category (Soupir et al. 2010).
The poultry litter associated LA35 marker gene is shown here to be correlated to S. aureus and the FIB E. coli, Enterococcus spp. and Bacteroidales in runoff from poultry litter amended fields, and that VFS diminish bacterial concentrations in runoff by several orders of magnitude. These studies illustrate the potential for VFS as short as 4.9 m to function as one of a suite of best management practices to limit the concentration of microorganisms and solids in runoff from fields where poultry litter is land applied. The microorganisms originating in applied poultry litter appear not to be associated with particles > 0.45 µm in size in the runoff. Finally, the studies presented herein further suggest that the LA35 marker gene is a viable microbial source tracking marker gene for poultry litter in the environment.
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ACKNOWLEDGEMENTS Funding for this project was partially obtained from a Summer Undergraduate Research Fellowship from the WVU Department of Civil and Environmental Engineering, as well as National Science Foundation (NSF) Environmental Engineering program funding to Jennifer Weidhaas (Grant no. 1234366). The authors appreciate the insightful comments and constructive criticism of the independent peer reviewers and the editorial board, which strengthened this manuscript.
Conflict of Interest No conflict of interest declared.
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Sharpley, A. and Kleinman, P. (2003) Effect of rainfall simulator and plot scale on overland flow and phosphorus transport. J Enviro Qual 32, 2172-2179.
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LIST OF TABLES Table 1. qPCR primers and probes used in this study
Table 2. Initial concentration of microorganisms in applied litter, in site soils (pre-application) and in applied water.
Table 3. Runoff simulator plot characteristics and rainfall simulation characteristics
Table 4. Correlation of microbial targets and suspended solids in the simulated runoff study.
Table 5. Overall removal effectiveness of VFS
LIST OF FIGURES Figure 1. Rainfall simulator plot orientation for the control plots (A), 4.9 m VFS (B) and the 12 m VFS (C).
Figure 2. Representative concentrations of E. coli, S. aureus, Enterococcus spp., Bacteroidales, and LA35 (gene copies L-1) in runoff from the no litter amendment control plot, and the litter
applied 0 m VFS, 4.9 m VFS and 12.2 m VFS plots.
Figure 3. Mass based first flush plots for the control plot (A), no litter added 0 m VFS (B), 4.9 m VFS plots (C and D), 12.2 m VFS (E and F). Lines above the M(t) = V(t) line indicate a mass based first flush occurred.
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Figure 4. Box plots of 5th and 95th percentile and outliers of log gene copies L-1 runoff, total suspended solids and runoff rate in the duplicate control plots (CP), no VFS (0 m), and VFS treatments (4.9 m and 12.2 m). Means with the same letter are not significantly different (Student’s t, P < 0.05).
Table 1. qPCR primers and probes used in this study Organism
(gene targeted)
Enterococcus spp. (23S rRNA)
Primer and probe sequences (5’ to 3’)
Size (bp)
Reference
F: GAGAAATTCCAAACGAACTTG R: CAGTGCTCTACCTCCATCATT 86 P: TGGTTCTCTCCGAAATAGCTTTAGGGCTA
Bacteroidales
F: GGGGTTCTGAGAGGAAGGT
(16S rRNA)
R: CCGTCATCCTTCACGCTACT
Ludwig et al, 2000
Dick & Field 2004 129
P: CAATATTCCTCACTGCTGCCTCCCGTA
E. coli
F: GTCCAAAGCGGCGATTTG
(uidA)
R: CAGGCCAGAAGTTCTTTTTCCA
Siefring, et al., 2008
58
Lee, et al., 2006
66
Lee, et al., 2006
P: ACGGCAGAGAAGGTA
Salmonella spp.
F: CGTTTCCTGCGGTACTGTTAATT
(invA)
R: AGACGGCTGGTACTGATCGATAA P: CCACGCTCTTTCGTCT
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Brevibacterium sp. LA35 (16S rRNA)
F: ACCGGATACGACCATCTGC R: TCCCCAGTGTCAGTCACAGC P: CAGCAGGGAAGAAGCCTTCGGGTGACGGTA
Staphylococcus aureus (sec)
Weidhaas, et al., 2010 571
F: CGTATTAGCAGAGAGCCAACCA R: GTGAATTTACTCGCTTTGTGCAA
62
Weidhaas & Lipscomb, 2013 Lee, et al., 2006
P: ACCCTACGCCAGATGA
Table 2. Initial concentration of microorganisms in applied litter, in site soils (pre-application) and in applied water. Mean concentration ± standard deviation (log gene copies per g or L) Litter 1 a
Litter 2 b
Soil (%+) c
Water (%+) d
Bacteroidales
8.2
8.1
2.5 ± 0.5 (100)
1.4 ± 0.7 (100)
E. coli
6.1
2.8
-0.01 (25) g
0.9 (50) g
Enterococci
7.7
8.2
3.1 ± 0.4 (100)
2.1 ± 0.1 (100)
Brevibacterium LA35
7.6
6.6
Not detected
Not detected
Target
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Salmonella S. aureus a
3.4
Not detected
Not detected
Not detected
4.6
4.6
Not detected
Not detected
applied to no VFS and 12.2 m VFS treatment plots; b applied to 4.9 m VFS treatment
plots; c4 Percent of replicates positive out of 4; d Percent of replicates positive out of 2; g
no standard deviation calculable as microorganism was only detected in one sample
Table 3. Runoff simulator plot characteristics and rainfall simulation characteristics Applied microorganism load
Total water applied
(log gene copies m-2) Slope
Treatment
(%)
Control Plot #1
11
Control Plot #2
Litter applied (kg) Baca
E. coli
Staph Ent
b
LA35
c
d
(cm rain)
Rainfall Rainfall Total study Uniformity runoff duration Coefficient (%) (L) (min) 97
74.3
40
10.6
96
64.6
40
No VFS #1
11
100
16.5
52
No VFS #2
10.6
100
31.9
52
4.9 m VFS #1
7.1
99
311.2
48
4.9 m VFS #2
7.1
98
90.9
49
0
0.74
0.52
0
0
0
2494 1859 2333
1729
598
1755
0
2316
1405
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0
1387
990
6.6
4.7
5.1
10.0
12.2 m VFS #2
10.2
Accepted Article
12.2 m VFS #1
0.52
1753 1306 1639
1627
975
97
230.0
59
97
186.9
57
10.7
a.
Bac = Bacteroidales; b. Ent = Enterococcus spp.; c. LA35 = Brevibacterium sp. LA35; d. Staph = Staphylococcus aureus
Table 4. Correlation of microbial targets and suspended solids in the simulated runoff study.
Target
Bace
Correlation a E. coli
Entb
LA35c
Staphd
TSS
Control plot ND f
E. coli
0.999**
ND g
ND
0.998**
ND
ND
ND
ND
ND
ND
0.997**
ND
ND
Ent LA35 Staph
Bac
ND 0 m VFS 0.979** h
E. coli
0.992**
0.997**
0.557*
NS i
0.992**
0.992**
0.553*
0.681**
0.999**
0.608*
0.693**
0.594*
0.673**
Ent LA35 Staph
Bac
NS 4.9 m VFS 0.959**
0.997**
0.993**
0.985**
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NS
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E. coli
0.974**
Ent
0.986**
0.957**
NS
0.993**
0.988**
NS
NS
NS
LA35 Staph
Bac E. coli Ent LA35
NS 12.2 m VFS 0.929**
0.964**
0.994**
0.884**
NS
0.807**
0.973**
0.764**
NS
0.995**
0.902**
NS
0.924**
NS
Staph a
NS
All data are applied load normalized. Comparisons between data were performed using Pearson’s correlation coefficient and are reported as r. Only correlations in which P values were
Received Date : 17-Mar-2014 Revised Date : 24-Apr-2014 Accepted Date : 09-May-2014 Article type
: Original Article
Runoff Studies Demonstrate Parallel Transport Behavior for a Marker of Poultry Fecal Contamination and Staphylococcus aureus
Jennifer Weidhaas *, West Virginia University, Civil and Environmental Engineering, PO Box 6103, Morgantown, WV 26506, USA, PH: 304-293-9952, E: [email protected], Emily Garner, West Virginia University, Civil and Environmental Engineering, Morgantown, WV 26506, USA Tom Basden, West Virginia University Extension Service, Agriculture and Natural Resources, Morgantown, WV 26506, USA Valerie J. Harwood, University of South Florida, Department of Integrative Biology, Tampa, FL, 33620
* corresponding author
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as an 'Accepted Article', doi: 10.1111/jam.12543 This article is protected by copyright. All rights reserved.
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KEYWORDS: Microbial source tracking; poultry feces; Brevibacterium; qPCR; nonpoint source pollution; runoff; vegetated filter strips, water quality.
RUNNING HEADING: Fecal bacteria in simulated runoff
ABSTRACT Aim: To determine if poultry litter marker gene LA35 is correlated with pathogens and fecal indicator bacteria (FIB) in runoff from poultry litter amended plots. Methods and Results: A rainfall simulator with various vegetative filter strip lengths was employed to evaluate the correlation of a microbial source tracking (MST) marker for poultry feces/litter (the 16S rRNA gene of Brevibacterium sp. LA35 [LA35] measured by quantitative PCR) with pathogens and fecal indicator bacteria in runoff. LA35 was correlated with Staphylococcus aureus, Escherichia coli, Enterococcus spp. and Bacteroidales levels. Salmonella was present at low concentration in litter, but became undetectable by qPCR in runoff. E. coli, LA35 and S. aureus exhibited mass based first flush behavior in the runoff. Conclusion: Correlation of LA35 with FIB and pathogens in runoff from poultry litter amended fields suggest comparable transport mechanisms and that LA35 is a useful tracer for harmful bacteria in the environment released from poultry litter.
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Significance and Impact of the Study: In order to protect human health, an effective marker for poultry fecal contamination should exhibit similar fate and transport characteristics compared to pathogens. This study is among the first to demonstrate such a relationship in runoff for a MST marker.
INTRODUCTION Poultry litter (poultry feces, spilled feed, feathers and bedding materials) is widely used as an agricultural fertilizer. Estimated application rates of poultry litter as fertilizer in the United States in 2008 were upwards of 1.6 billion kg (USEPA 2012). Poultry litter can contain pathogens such as Yersinia enterocolitica, Listeria monocytogenes, Salmonella enterica, Staphylococcus. aureus, Pseudomonas aeruginosa, Escherichia coli O157:H7, Campylobacter jejuni, and Clostridium perfringens, and fecal indicator bacteria (FIB) such as E. coli and Enterococcus spp. (McCaskey and Anthony 1979; Kelley et al. 1995). Studies have shown that these microorganisms can run off of agricultural fields on which litter has been applied with stormwater and impact environmental waters as nonpoint source pollution (McMurry et al. 1998; Moore et al. 1998; Smith et al. 2007; Brooks et al. 2009; Weidhaas et al. 2010; McLaughlin et al. 2011; Weidhaas and Lipscomb 2013). Pollutants transported in runoff from urban and rural surfaces can be more concentrated at the beginning of a storm event compared to the end of the event. This phenomenon has been described as the “first flush.” The term first flush has been used to describe the disproportionally high concentration of pollutants or pathogens during the initial period of runoff after initiation of a storm event (Sansalone and Cristina 2004). Microorganisms such as E. coli, Enterococcus spp.
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and fecal coliforms (Soupir et al. 2006) and nutrients such as phosphorus (Faucette et al. 2004) originating from poultry litter have been shown to exhibit first flush behavior from poultry litteramended fields. Methods commonly used to definitively identify the occurrence of a first flush event include both mass based first flush (MBFF) and concentration based first flush. These methods have been shown to be conceptually and mathematically equivalent (Sansalone and Cristina 2004). The absence of a concentration or mass based first flush indicates that the pollutant concentration is proportional to the volume of water in the runoff. Vegetated areas along receiving water bodies generally improve the quality of storm
water runoff, thus reducing nonpoint source pollution (Chaubey et al. 1994; Coyne et al. 1998; Abu-Zreig et al. 2004). Mechanisms shown to improve the water quality of overland or channelized water passing through vegetated areas include 1) reduction in water velocity (Lee et al. 1989) which increases water infiltration and particle sedimentation rates (Abu-Zreig et al. 2004) and 2) increased filtration through vegetation and leaf matter (Cardoso et al. 2012). Vegetative filter strips are Stormwater best management practices situated between pollutant source areas and surface waters that receive runoff. The effectiveness of vegetated filter strips (VFS) for improving cropland runoff water quality has been reported previously (Chaubey et al. 1994; Patty et al. 1997; Sharpley 1997; Lim et al. 1998; Entry et al. 2000; Atwill et al. 2002). For example, VFS are have been show to remove 70.0 to 97.6 % of incoming suspended solids (Lim et al. 1998), 67.0 to 93.6 % of phosphorus, 64.9 to 95.3 percent of nitrogen (Chaubey et al. 1994; Lim et al. 1998) and 44 to 100 % of pesticides such as atrazine (Patty et al. 1997). In runoff from agricultural fields amended with animal manure, VFS have been shown to remove greater than 99.9% of Cryptosporidium parvum oocysts (Atwill et al. 2002), 70 to 95% of E.
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coli, 72 to 94% of Salmonella (Cardoso et al. 2012) and up to 100% of fecal coliforms (Coyne et al. 1998; Lim et al. 1998). While numerous methods are available for detection of pathogens, or more commonly
FIB originating from agricultural waste, these tests do not reveal the source of the contamination (e.g., poultry, swine, or cattle), as multiple fecal and other sources may contain these microorganisms. Additionally, the paradigm of FIB correlating with risk of waterborne disease (USEPA 1986) is increasingly less accepted as FIB have been shown to be relatively poor predictors of pathogens’ presence under some conditions (Harwood et al. 2005; Hellein et al. 2011). Microbial source tracking (MST) is an alternative to FIB based methods for detecting
fecal pollution. MST methods focus on the detection of organisms that are unique to the feces of one particular animal (e.g., chickens) or groups of animals (e.g., ruminants). These MST methods aid in identifying nonpoint source of fecal pollution by identifying host specific species in the polluted water, thus contributing to a weight of evidence argument (when combined with field observation and physical-chemical measurements) regarding fecal pollution sources. Certain microorganisms have been found to be associated with chicken feces (Lu et al. 2007; Kobayashi et al. 2012) or their soiled litter (Weidhaas et al. 2010; Ryu et al. 2013) and may be appropriate MST targets. Recently, we reported on a 16S rRNA marker gene of Brevibacterium sp. LA35 (hereafter LA35 marker gene), which is similar to Brevibacterium avium. B. avium was first isolated from lesions in diseased poultry (Pascual and Collins 1999); however, Brevibacterium sp. LA35 is highly concentrated in soiled poultry litter, is found in the feces of healthy animals and is rarely found in the feces of other animals (Weidhaas et al. 2010), making it a very strong candidate for an MST tool. We also reported on a SYBR based quantitative polymerase chain reaction (qPCR) assay (Weidhaas et al. 2011) and a Taqman ® qPCR assay
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(Weidhaas and Lipscomb 2013) for the LA35 marker gene that we developed to quantify this marker gene in environmental samples and its use in two poultry litter-impacted watersheds. Use of marker genes specific to host organisms for source tracking is preferred to other fecal indicator bacteria tracking such as Staphylococcus spp., E. coli, Enterococcus spp. as these other organisms have natural reservoirs in the environment (Fujioka et al. 1999; Berg et al. 2005; Byappanahalli et al. 2006). The objectives of this study were to 1) evaluate the release of the LA35 marker gene, FIB and pathogens from soiled poultry litter to runoff during rainfall events, 2) to compare the effect of VFS length on the removal of sediments and microorganisms originating from poultry litter and 3) to determine the correlation of the poultry litter-associated LA35 marker gene with FIB and pathogens in runoff from VFSs. To achieve these objectives a simulated rainfall runoff study on poultry litter-amended plots was conducted and the concentration of total suspended soils (TSS) and various microorganisms originating from poultry litter were quantified in runoff. Specific microorganisms evaluated in this study by quantitative polymerase chain reaction (qPCR) included the poultry litter specific microorganism Brevibacterium sp. LA35, the pathogens Salmonella spp. and S. aureus and the fecal indicator bacteria E. coli, Enterococcus spp. and Bacteroidales.
MATERIALS AND METHODS Runoff Plots A simulated runoff study was conducted at the West Virginia University (WVU) Agronomy Farm in Morgantown, WV. Soils in the study area were classified as Dormont and Guernsey Silt
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Loams with moderate water holding capacities (7 to 9 inches) (NRCS 2013). Soil samples from the study area were analyzed by the WVU Soil Testing Laboratory for soil pH (5.6), K2O (0.14
meq per 100 gram soil), Ca2+ (2.8 meq per 100 gram soil), Mg2+ (0.04 meq per 100 gram soil)
and total cation exchange capacity (8 meq per 100 gram soil). Native vegetation at each plot consisted of predominately fescue with a mixed sward of white clover and other broadleaf forbs. The vegetation was clipped to no more than 4 inches in height during the course of the study. Plots were selected to obtain uniform slope and well-established uniform grass cover.
Each field experiment included two adjacent 1 m (measured perpendicular to slope) plots
to allow for data collection in duplicate (Figure 1). The simulated runoff plots were designed based on previously published systems by the US Department of Agriculture, Agricultural Research Service (Sharpley and Kleinman 2003). The four treatments utilized in this study included 1) a no litter applied, no VFS (a.k.a. control plot), 2) a 0 m long VFS (i.e., litter was applied over the entire study area and runoff did not travel through a VFS prior to sample collection), 3) a 4.9 m long VFS and 4) a 12.2 m long VFS. Metal barriers, inserted to a depth of 8 cm below the ground surface were used to isolate the plots and prevent runoff from entering or exiting the upslope and side edges. Hydrated bentonite clay was placed along the interface between the ground surface and the metal barrier to prevent infiltration of the runoff along the metal barriers making the edges of the plots. The plots were finished with a collection gutter placed flush with the soil surface at the downslope end of each plot (perpendicular to the slope) and sealed with hydrated bentonite clay so that all runoff exiting the plot enters the gutter. A cover was installed over each gutter to prevent rainfall from entering the gutter directly. Hoses attached to each gutter allowed for collection of the runoff water.
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Rainfall simulator The rainfall simulator consisted of a cubical aluminum frame approximately 3 m on all sides. Nozzles (#50, Spraying Systems Co., Glendale Heights, IL) were used to achieve a simulated rainfall and were positioned 3 m above the ground. A 760 L tank, continuously filled with chlorinated municipal tap water, was used along with a gasoline powered pump to supply water to the simulator. A flowmeter and pressure gauge were installed in order to monitor and control the volume of water applied to each experiment and maintain a consistent pressure at each nozzle Heavy tarps enclosed the simulator to prevent wind impacts on the uniformity of the nozzle spray. For the 4.9 and 12.2 m VFS plots, the rainfall simulator was altered, using timber to extend the frame, tarps to enclose the frame, and PVC pipes to extend the water distribution system. Additional nozzles were installed every 3 m (Figure 1).
The uniformity of rainfall application in each plot was measured by placing up to ten
cups randomly throughout each plot. Rainfall was simulated for 10 minutes and the weight of each cup was measured. The uniformity coefficient (UC) was determined by the methods of Christiansen (1942) as shown in equation 1: (Equation 1)
Where Xi is the measured application depth (cm), m is the mean application depth (cm), and n is the number of observations (Christiansen 1942). All plots were saturated immediately before each experiment by applying simulated rainfall for a minimum of 30 minutes to all plots or until the water volume flowing from the plots was at steady state.
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Experimental Design One scoop of poultry litter was aseptically collected from each of 19 pens at the WVU Animal Sciences Farm poultry house as well as from five buckets of litter recently removed from pens. The litter was composited and thoroughly homogenized using a concrete mixer. Litter was sampled before each simulation and LA35 marker gene concentrations were measured. Plots 1 &2 acted as control plots, with rainfall simulation and sample collection completed once without litter application, then again with 0.75 kg of litter applied to each replicate (i.e., No VFS plots #1 and #2). For the 4.9 m and 12.2 m VFS plots, 0.52 kg of litter were applied to each replicate plot, consistent with agronomic use loading rates for poultry litter in WV. Litter was applied only to the upper 2.4 m of each plot by hand and spread evenly over the application area to simulate broadcast application methods.
Sample Collection and Handling Runoff samples were collected from the gutter of each duplicate plot in 5 minute increments after runoff began. Samples were collected in 19-L buckets placed on a scale and total weight in each increment was recorded. The samples were then mixed to homogenize sediments in the solution and a composite grab sample of 1-L was collected aseptically in a sterile 1-L highdensity polyethylene bottle. After rainfall ceased, all runoff was collected and a final composite sample was collected. Samples were stored at 4 ºC upon return to the lab and were filtered within 48 hours of collection. Four soil samples were collected from the study site prior to litter application. For each sample, one scoop (c. 25 mls) was aseptically taken from below the vegetation cover. Source water used to simulate rainfall, which was supplied by municipal drinking water, was collected in sterile 1-L
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high-density polyethylene bottles from the site of the study, The DNA was extracted from these control samples as described below.
Sample Analysis Total suspended solids in each sample collected was determined by filtering between 25 and 290 mL through a sterile 0.45 µm pore, size 47 mm nitrocellulose water testing membrane filters (Thermo Fisher Scientific, Inc., Waltham, MA, USA) and drying for 1 h at 105 oC per Standard
Methods (APHA 2005). Water samples were prepared for DNA extraction by filtration through sterile 0.45 µm pore size 47 mm nitrocellulose water testing membrane filters. Between 20 and 100 mL of each runoff sample and 2 L for each source water sample were filtered and filters were frozen at -80 °C. DNA was extracted from the stored filters, as well as all soil and litter samples as described previously (Weidhaas et al. 2011). DNA was extracted from the frozen filters within 1 week of sample collection, while the litter and soil DNA was extracted within 6 hours of sample collection. Microbial concentrations or presence/absence detections in runoff samples were determined by qPCR or nested qPCR targeting 1) the pathogens Salmonella spp. invA gene and Staphylococcus
aureus sec gene (Lee et al. 2006), 2) the fecal indicator bacteria E. coli uidA gene (Lee et al.
2006), Enterococcus spp. 23S rRNA gene (Ludwig and Schleifer 2000), and Bacteroidales 16S rRNA gene (Dick and Field 2004; Siefring et al. 2008), and 3) the poultry litter associated Brevibacterium sp. LA35 16S rRNA gene (Weidhaas and Lipscomb 2013) (Table 1). Each 25 µL qPCR reaction included 1X of the TaqMan® Fast Advanced Master Mix (Applied Biosystems, Carlsbad, CA, USA), previously published optimal primer (Integrated DNA Technologies, Inc., Coralville, IA) and probe concentrations (Biosearch Technologies, Inc,
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Novato, CA, USA) (Table 1), 1 µL of template DNA sample and molecular-grade water (Ambion, Carlsbad, CA, USA). Amplification was performed using a 7300 Real Time PCR System (Applied Biosystems, Carlsbad, CA, USA) according to previously published methods (see Table 1 references) with the exception of the Master Mix used. Control samples for each qPCR run included qPCR negative controls (e.g., DNA-free water instead of template), qPCR positive controls (i.e., LA35 marker gene inserted into a plasmid [pLA35]), linear DNA or DNA from pure cultures instead of template) and matrix spiked samples (i.e., pLA35 or pure culture DNA added to DNA template). For construction of standard curves, a 7-fold dilution series averaged approximately 10.4 to 1.04 * 107 target genomes containing LA35 (y = -0.3054 x +
11.531, efficiency = 102 %), Bacteroidales (y = -0.3006 x + 11.47, efficiency = 100 %), E. coli (y = -0.3257x + 12.243, efficiency = 110 %), Enterococcus spp. (y = -0.3055 x + 11.129,
efficiency = 103 %), S. aureus (y = -0.3001 x + 12.506, efficiency = 99 %), and Salmonella spp.
(y = -0.3642 x +14.415, efficiency = 130 %) target genes were assayed by qPCR. Samples in which Salmonella invA was not detected, nested qPCR was conducted by performing two rounds of qPCR analysis using the invA primers and taking 1 μl of the amplified products from the first qPCR assay as the template for the second round of qPCR.
Runoff Calculations and Statistical Analysis Effectiveness of the VFS for microbial and solids reduction in runoff was computed according to the methods of Chaubey, Edwards, et al., (1994) as shown in equation 2: Eij = 100 [(M0,j-Mi,j)/M0,j]
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(Equation 2)
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where, Ei,j is the effectiveness of VFS length i for removal of parameter j (%), Mi,j is the mass of parameter j transported past VFS length i (mg TSS, gene copies), and M0,j is the mass of
parameter j transported past the no VFS treatment (mg TSS, gene copies) (Chaubey et al. 1994). To account for variable applied bacterial loads to each treatment plot, the data were volumeweighted by the volume of water leaving each plot as shown in equation 3 (Leecaster et al. 2002): n
M i, j = ∑ C j *V j
(Equation 3)
j =1
where Cj is the concentration of bacteria estimated by qPCR or TSS in each successive
subsampling event (gene copies L-1, or mg TSS L-1) and Vj is the volume of runoff water in each successive subsampling event (L). These volume-weighted peak values were used to determine VFS removal effectiveness.
To evaluate mass-based first flush (MBFF), two dimensionless parameters were calculated as shown in equations 4 and 5 (Sansalone and Cristina 2004): k
M (t ) =
∑C
j
*V j
i −0 n
∑C
(Equation 4) j
*V j
i −0
k
V (t ) =
∑ *V
j
i −0 n
∑ *V
(Equation 5) j
i −0
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where, V(t) is the dimensionless ratio of the total volume of runoff observed at any time k to the total runoff volume observed for the event; k is any time between the initiation of the runoff and the time coinciding with the cessation of runoff (n); M(t) is the dimensionless ratio of constituent mass delivered at any time k to the total mass of the constituent delivered throughout the event. A MBFF is defined as any period when M(t) > V(t). Analysis of variance among the load normalized runoff concentration of microorganisms
and TSS were determined with the subroutine PROC ANOVA and the Student’s t-test (SAS 9.4, SAS Institute Inc., Cary, NC, USA) with alpha at 0.05. Pearson’s correlations and one way ANOVA on ranks using Dunn’s pairwise multiple comparison between log concentrations of load normalized runoff concentrations of microorganisms over time were determined using SigmaPlot (V 11.0, Systat Software, Inc., Chicago,, IL, USA). The average of replicate VFS, load normalized concentrations of microorganisms in the runoff were used for the correlation analysis to remove bias associated with variable applied litter loads between experimental VFS. Box plots and other graphs were generated using SigmaPlot.
RESULTS Site soil, tap water and litter microbiological characterization The concentrations of microorganisms in applied litter, in the soils at the study site outside the runoff study area and in tap water used for the simulated runoff studies are shown in Table 2. All organisms under study were detected in the litter samples prior to field application, with Bacteroidales, Enterococcus spp., LA35, and E. coli being present in high concentrations, while S. aureus were detected at significantly lower concentrations. Salmonella spp. were detected in This article is protected by copyright. All rights reserved.
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only the first litter sample tested at relatively low concentrations. As the litter was stored in a 19 L bucket for approximately 1 week in the shade and covered with black plastic between applications, there was likely a die-off of E. coli and Salmonella spp. during litter storage which is reflected in the decreased concentration in the second litter sample. In site soils and tap water neither LA35 nor Salmonella spp. were detected by qPCR. Low concentrations of Bacteroidales,
Enterococcus spp., and E. coli were detected in site soils and tap water (collected from an agricultural barn exterior faucet). The no-template controls did not amplify nucleic acids in this study and the matrix spike samples showed that the qPCR assays were not inhibited.
Simulated runoff study results A summary of the rainfall simulator study events, plot characteristics, microorganism loading rates, water application and runoff rates as well as the rainfall uniformity coefficient is presented in Table 3. Approximately 24%, 12%, 28% and 15% of the applied rainfall in the no amendment 0 m control plot, 0 m, 4.9 m and 12.2 m VFS, respectively was collected as runoff from the treatment plots. Uniform rainfall distribution over the treatment plots is indicated by the greater than 96% rainfall uniformity coefficient in all treatments. Concentrations of FIB and poultry litter marker in the runoff from the treatment plots are
shown in Figure 2A through 2F. Salmonella spp. were not detected in the runoff from any of the treatment plots, even when a nested qPCR approach was used (i.e., two rounds of amplification with the invA primers). In all cases the maximum concentration of FIB (Enterococcus spp. and Bacteroidales) in runoff from the unamended control plot was several orders of magnitude lower than that observed in the litter-amended treatment plots (i.e., 4.1, 3.5, and 2.6 log lower in the 0
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m, 4.9 m and 12.2 m VFS plots, respectively). Specifically, in the runoff from the unamended control plot, Enterococcus spp. concentrations (log gene copies L-1) were 3.6 ± 0.5 (average ±
standard deviation) and Bacteroidales were 4.1 ± 0.9. E. coli and S. aureus were not detected in
runoff from the unamended control plot. As shown in Figure 2 (C, D, E and F), peak concentrations of LA35 and S. aureus occurred between 10 and 30 minutes after runoff initiation in the replicate 4.9 and 12.2 m plots. The runoff behavior of LA35 and S. aureus therefore exhibited first flush behavior (peak discharge and tailing), indicative of release of these organisms solely from the poultry litter. By comparison, the FIB showed an increase in concentrations in the runoff, but did not exhibit strong peak discharge and tailing behavior based on absolute concentrations of microorganisms (i.e., log gene copies L-1).
Because variable flow rates among the treatment plots could mask obvious first flush
behavior, the occurrence of MBFF events was assessed in each of the treatment plots, where M(t) was plotted versus V(t) in Figure 3. Each of the plots A through F in Figure 3 correspond to the plots in Figure 2 for comparison. An MBFF is defined as an event where M(t) exceeds V(t) indicating that a disproportionately high mass of pollutants has been delivered by a given volume of flow. A MBFF was observed in the 0 m VFS (Figure 3A) and 1 replicate of the 4.9 m (Figure 3C) and 12.2 m VFS (Figure 3E) treatments. In general the higher the runoff rate from the plots, the more likely a MBFF was observed as shown in Figure 3C and E versus Figure 3D and F. The average runoff rates for the 4.9 m (i.e., 6.4 L min-1) and 12.2 m VFS (i.e., 6.8 L min-1) replicates in which MBFF occurred (Figure 3C and E, respectively) were greater than in their replicate treatments (i.e., 4.8 and 1.9 L min-1, respectively) but only statistically significantly in
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the 12.2 m VFS (Student’s t, P < 0.001). When observed, the MBFF was greatest for E. coli, S. aureus and LA35. In contrast a MBFF was not observed for TSS in any treatment. To evaluate if levels of FIB, poultry litter marker gene, pathogens and suspended solids
were correlated in the runoff over the course of the simulation, Pearson’s correlation coefficients were determined (Table 4). FIB (Bacteroidales, E. coli, and Enterococcus spp.) concentrations were correlated, regardless of the VFS length. LA35 was also correlated with FIB in runoff from litter-amended treatments. S. aureus was strongly correlated with the FIB and LA35 when VFS were present, but less strongly correlated with VFS absent. Total suspended solids were correlated with Bacteroidales and Enterococcus spp. in control plots, and with E. coli, Enterococcus spp. and LA35 in the litter-amended, no VFS plot, but not in plots with VFS.
Effectiveness of VFS for microbial and solids removal The range in concentration of microorganisms and TSS in runoff from all plots are shown in Figure 4. In all cases the longer the VFS the lower the concentration of microorganisms in the runoff from the plots, e.g., Enterococcus spp., E. coli, and LA35 were significantly higher in runoff from the litter-amended plots without VFS compared to those with VFS (Fisher’s least significant difference, α = 0.05). The ranges of runoff rates were higher from the 4.9 and 12.2 m plots than the plots without VFS, as rainfall was applied along the length of the VFS (Figure 1) to ensure even distribution of rainfall along the VFS length. Despite the higher runoff rate in the treatments with VFS, both the absolute concentrations (gene copies L-1) and volume-weighted
concentrations (by equation 3) of Enterococcus spp., E. coli, and LA35 concentrations were found to be significantly lower in runoff from the VFS plots.
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To evaluate the effectiveness of the VFS for removing microorganisms and solids originating from poultry litter in runoff, the removal effectiveness was calculated for the 4.9 and 12.2 m VFS compared to the no VFS treatment (Table 5) based on the volume-weighted peak concentrations of microorganisms and solids in runoff using equations 2 and 3. Microorganism removal efficiency varied from 23.3 % to 99.9 % and overall was significantly lower for Bacteroidales in the 4.9 m VFS and slightly lower for S. aureus in both VFS treatments (Fisher’s least significant difference, α = 0.05) compared to the other organisms under study. There was a reduction in removal efficiency in TSS with increasing length of the VFS, likely due to increased sediment entrainment in the runoff from the longer plot.
DISCUSSION State and federal agencies recommend identification of potential fecal sources and installation of targeted best management practices to reduce NPS pollution and achieve total maximum daily load (TMDL) goals. MST has been shown to be an effective method for identifying potential fecal sources to watersheds, which allows targeted investigation of specific sources, thereby increasing accuracy of source identification and effectiveness of remediation (Stoeckel and Harwood 2007; Harwood et al. 2014). A United States Environmental Protection Agency guidance document has identified the characteristics of an ideal source identifier or MST marker (USEPA 2005). These characteristics include, among others; host specificity, distribution in all host waste, no geographical variability in marker/host association, and abundance in primary and secondary habitat. Finally, the EPA suggests that ideal markers can be used to regulate water quality due to marker presence or abundance correlating to human health risk (USEPA 2005).
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A series of studies have been conducted to determine the suitability of LA35 as a marker of poultry litter contamination in the environment following the recommended EPA MST guidance. Recent studies have shown the specificity of LA35 to poultry litter or feces, of both chickens and turkeys, as well as the qPCR assay sensitivity (Weidhaas et al. 2010; Ryu et al. 2013; Weidhaas and Lipscomb 2013). The poultry litter marker gene has been shown to be geographically stabile as it has been found in poultry litter and chicken feces from Arkansas, Delaware, Florida, Georgia, Minnesota, Ohio, Oklahoma, Puerto Rico, Utah, and West Virginia. Field evaluations have been conducted to show the distribution of the marker in the environment in areas of intensive poultry rearing and poultry litter spreading. Specifically, recent studies (Weidhaas et al. 2011; Weidhaas and Lipscomb 2013) have found decreasing concentrations of the poultry litter marker with distance from the source of the poultry litter in two watersheds where significant poultry rearing activities occur, namely the Illinois River Watershed in Arkansas and Oklahoma and the Potomac River headwaters in West Virginia. The previously reported mean concentrations of the poultry litter marker in edge of field runoff from poultry litter amended plots was 6.9 ± 7.2 log gene copies L-1 (Weidhaas et al. 2010), in the same range as concentrations of the LA35 marker gene observed in the runoff from the 4.9 m VFS in this study. However, ten to 100-fold higher concentrations of the LA35 marker gene were found in the runoff from the no VFS control plots (i.e. 7.9 to 8.8 log gene copies L-1) as compared to the
previously reported field studies. The higher concentrations of the LA35 marker gene in the no VFS control plots in this study is likely due to the lack of filter strip and the use of freshly collected poultry litter that had not undergone environmental exposure and thus microbial dieoff.
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The last ideal criterion recommended by the EPA, the marker presence or abundance should be correlated to human health risk, is difficult to measure directly as it requires expensive, logistically difficult epidemiological studies. Correlation of marker behavior with that of pathogens is one proxy for human health risk, and correlation with FIBs links marker levels with general fecal contamination and current regulatory tools. In an ideal world, both persistence and transport characteristics of pathogens, FIB, and MST markers like LA35 would all be similar; and while we found some evidence to support this paradigm, there were exceptions. The correlation of the LA35 marker gene with both FIB and S. aureus suggests the marker gene may be valuable for monitoring water for human health risk. However it is important to note that the LA35 marker gene and the S. aureus sec gene exhibited differential transport characteristics from the FIB in this study. While the FIB increased with time after runoff initiation, they did not exhibit peaking and tailing behavior similar to S. aureus and the LA35 marker gene. The difference in transport characteristics may explain others observations that the LA35 marker gene was found in only approximately 30% of stream samples in poultry litter impacted areas where elevated FIB levels were observed (Ryu et al. 2013). Others have reported that E. coli and fecal coliforms originating from poultry litter do not exhibit characteristic first flush behavior in simulated runoff studies (Eldridge et al. 2009). The behavior of FIB in response to rain events may be driven by the presence of “naturalized” (persistent) populations in environmental habitats (Ishii et al. 2006; Badgley et al. 2011a; Badgley et al. 2011b; Byappanahalli et al. 2012), a problem that does not appear to be an issue in the case of Brevibacterium sp. LA35. In this study, no correlation was found between TSS and the various microorganisms in
the runoff from the 4.9 and 12.2 m VFS. Additionally, the TSS did not exhibit MBFF behavior in any of the treatments evaluated, while E. coli, LA35 and S. aureus did. The lack of correlation
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between TSS and the various microorganisms suggest, as we found previously in edge of field runoff from poultry litter applied fields (Weidhaas et al. 2011), that the microorganisms may not be adsorbed onto particles or solids or adsorbed to particles less than 0.45 µm in size. Rather the microorganisms in the runoff from the VFS this study may be free cells unattached to solids, as has been suggested previously as an overland transport mechanism for pathogens (Tyrrel and Quinton 2003) or they are attached to colloids or small particles passing through the 0.45 µm
filters. There are conflicting reports in the literature as to whether fecal indicator organisms will be attached to particles during runoff from manure amended soils. For example, it has been reported that E. coli eroded from cowpats and transported in runoff are generally unattached (Muirhead et al. 2005), or are associated with particles near the same size or smaller than the E. coli cells (Muirhead et al. 2006). Others have reported that a majority of E. coli and enterococci are attached to manure associated colloids in the 8 to 62 µm particle size category (Soupir et al. 2010).
The poultry litter associated LA35 marker gene is shown here to be correlated to S. aureus and the FIB E. coli, Enterococcus spp. and Bacteroidales in runoff from poultry litter amended fields, and that VFS diminish bacterial concentrations in runoff by several orders of magnitude. These studies illustrate the potential for VFS as short as 4.9 m to function as one of a suite of best management practices to limit the concentration of microorganisms and solids in runoff from fields where poultry litter is land applied. The microorganisms originating in applied poultry litter appear not to be associated with particles > 0.45 µm in size in the runoff. Finally, the studies presented herein further suggest that the LA35 marker gene is a viable microbial source tracking marker gene for poultry litter in the environment.
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ACKNOWLEDGEMENTS Funding for this project was partially obtained from a Summer Undergraduate Research Fellowship from the WVU Department of Civil and Environmental Engineering, as well as National Science Foundation (NSF) Environmental Engineering program funding to Jennifer Weidhaas (Grant no. 1234366). The authors appreciate the insightful comments and constructive criticism of the independent peer reviewers and the editorial board, which strengthened this manuscript.
Conflict of Interest No conflict of interest declared.
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LIST OF TABLES Table 1. qPCR primers and probes used in this study
Table 2. Initial concentration of microorganisms in applied litter, in site soils (pre-application) and in applied water.
Table 3. Runoff simulator plot characteristics and rainfall simulation characteristics
Table 4. Correlation of microbial targets and suspended solids in the simulated runoff study.
Table 5. Overall removal effectiveness of VFS
LIST OF FIGURES Figure 1. Rainfall simulator plot orientation for the control plots (A), 4.9 m VFS (B) and the 12 m VFS (C).
Figure 2. Representative concentrations of E. coli, S. aureus, Enterococcus spp., Bacteroidales, and LA35 (gene copies L-1) in runoff from the no litter amendment control plot, and the litter
applied 0 m VFS, 4.9 m VFS and 12.2 m VFS plots.
Figure 3. Mass based first flush plots for the control plot (A), no litter added 0 m VFS (B), 4.9 m VFS plots (C and D), 12.2 m VFS (E and F). Lines above the M(t) = V(t) line indicate a mass based first flush occurred.
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Figure 4. Box plots of 5th and 95th percentile and outliers of log gene copies L-1 runoff, total suspended solids and runoff rate in the duplicate control plots (CP), no VFS (0 m), and VFS treatments (4.9 m and 12.2 m). Means with the same letter are not significantly different (Student’s t, P < 0.05).
Table 1. qPCR primers and probes used in this study Organism
(gene targeted)
Enterococcus spp. (23S rRNA)
Primer and probe sequences (5’ to 3’)
Size (bp)
Reference
F: GAGAAATTCCAAACGAACTTG R: CAGTGCTCTACCTCCATCATT 86 P: TGGTTCTCTCCGAAATAGCTTTAGGGCTA
Bacteroidales
F: GGGGTTCTGAGAGGAAGGT
(16S rRNA)
R: CCGTCATCCTTCACGCTACT
Ludwig et al, 2000
Dick & Field 2004 129
P: CAATATTCCTCACTGCTGCCTCCCGTA
E. coli
F: GTCCAAAGCGGCGATTTG
(uidA)
R: CAGGCCAGAAGTTCTTTTTCCA
Siefring, et al., 2008
58
Lee, et al., 2006
66
Lee, et al., 2006
P: ACGGCAGAGAAGGTA
Salmonella spp.
F: CGTTTCCTGCGGTACTGTTAATT
(invA)
R: AGACGGCTGGTACTGATCGATAA P: CCACGCTCTTTCGTCT
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Brevibacterium sp. LA35 (16S rRNA)
F: ACCGGATACGACCATCTGC R: TCCCCAGTGTCAGTCACAGC P: CAGCAGGGAAGAAGCCTTCGGGTGACGGTA
Staphylococcus aureus (sec)
Weidhaas, et al., 2010 571
F: CGTATTAGCAGAGAGCCAACCA R: GTGAATTTACTCGCTTTGTGCAA
62
Weidhaas & Lipscomb, 2013 Lee, et al., 2006
P: ACCCTACGCCAGATGA
Table 2. Initial concentration of microorganisms in applied litter, in site soils (pre-application) and in applied water. Mean concentration ± standard deviation (log gene copies per g or L) Litter 1 a
Litter 2 b
Soil (%+) c
Water (%+) d
Bacteroidales
8.2
8.1
2.5 ± 0.5 (100)
1.4 ± 0.7 (100)
E. coli
6.1
2.8
-0.01 (25) g
0.9 (50) g
Enterococci
7.7
8.2
3.1 ± 0.4 (100)
2.1 ± 0.1 (100)
Brevibacterium LA35
7.6
6.6
Not detected
Not detected
Target
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Salmonella S. aureus a
3.4
Not detected
Not detected
Not detected
4.6
4.6
Not detected
Not detected
applied to no VFS and 12.2 m VFS treatment plots; b applied to 4.9 m VFS treatment
plots; c4 Percent of replicates positive out of 4; d Percent of replicates positive out of 2; g
no standard deviation calculable as microorganism was only detected in one sample
Table 3. Runoff simulator plot characteristics and rainfall simulation characteristics Applied microorganism load
Total water applied
(log gene copies m-2) Slope
Treatment
(%)
Control Plot #1
11
Control Plot #2
Litter applied (kg) Baca
E. coli
Staph Ent
b
LA35
c
d
(cm rain)
Rainfall Rainfall Total study Uniformity runoff duration Coefficient (%) (L) (min) 97
74.3
40
10.6
96
64.6
40
No VFS #1
11
100
16.5
52
No VFS #2
10.6
100
31.9
52
4.9 m VFS #1
7.1
99
311.2
48
4.9 m VFS #2
7.1
98
90.9
49
0
0.74
0.52
0
0
0
2494 1859 2333
1729
598
1755
0
2316
1405
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0
1387
990
6.6
4.7
5.1
10.0
12.2 m VFS #2
10.2
Accepted Article
12.2 m VFS #1
0.52
1753 1306 1639
1627
975
97
230.0
59
97
186.9
57
10.7
a.
Bac = Bacteroidales; b. Ent = Enterococcus spp.; c. LA35 = Brevibacterium sp. LA35; d. Staph = Staphylococcus aureus
Table 4. Correlation of microbial targets and suspended solids in the simulated runoff study.
Target
Bace
Correlation a E. coli
Entb
LA35c
Staphd
TSS
Control plot ND f
E. coli
0.999**
ND g
ND
0.998**
ND
ND
ND
ND
ND
ND
0.997**
ND
ND
Ent LA35 Staph
Bac
ND 0 m VFS 0.979** h
E. coli
0.992**
0.997**
0.557*
NS i
0.992**
0.992**
0.553*
0.681**
0.999**
0.608*
0.693**
0.594*
0.673**
Ent LA35 Staph
Bac
NS 4.9 m VFS 0.959**
0.997**
0.993**
0.985**
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NS
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E. coli
0.974**
Ent
0.986**
0.957**
NS
0.993**
0.988**
NS
NS
NS
LA35 Staph
Bac E. coli Ent LA35
NS 12.2 m VFS 0.929**
0.964**
0.994**
0.884**
NS
0.807**
0.973**
0.764**
NS
0.995**
0.902**
NS
0.924**
NS
Staph a
NS
All data are applied load normalized. Comparisons between data were performed using Pearson’s correlation coefficient and are reported as r. Only correlations in which P values were
