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Determining E. coli burden on pasture in a headwater catchment: Combined field and modelling approach
Environment International 43 (2012) 6–12
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Environment International journal homepage: www.elsevier.com/locate/envint
Determining E. coli burden on pasture in a headwater catchment: Combined field and modelling approach David M. Oliver a,⁎, Trevor Page b, Ting Zhang b, A. Louise Heathwaite b, Keith Beven b, Heather Carter b, Gareth McShane b, Patrick O. Keenan b, Philip M. Haygarth b a b
Biological & Environmental Sciences, School of Natural Sciences, University of Stirling, Stirling, FK9 4LA, UK Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
a r t i c l e
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Article history: Received 29 July 2011 Accepted 22 February 2012 Available online 22 March 2012 Keywords: Burden Critical source area Delivery E. coli Headwater catchment Source Water quality
a b s t r a c t Empirical monitoring studies of catchment-scale Escherichia coli burden to land from agriculture are scarce. This is not surprising given the complexity associated with the temporal and spatial heterogeneity in the excretion of livestock faecal deposits and variability in microbial content of faeces. However, such information is needed to appreciate better how land management and landscape features impact on water quality draining agricultural landscapes. The aim of this study was to develop and test a field-based protocol for determining the burden of E. coli in a small headwater catchment in the UK. Predictions of E. coli burden using an empirical model based on previous best estimates of excretion and shedding rates were also evaluated against observed data. The results indicated that an empirical model utilising key parameters was able to satisfactorily predict E. coli burden on pasture most of the time, with 89% of observed values falling within the minimum and maximum range of predicted values. In particular, the overall temporal pattern of E. coli burden on pasture is captured by the model. The observed and predicted values recorded a disagreement of > 1 order of magnitude on only one of the nine sampling dates throughout an annual period. While a first approximation of E. coli burden to land, this field-based protocol represents one of the first comprehensive approaches for providing a real estimate of a dynamic microbial reservoir at the headwater catchment scale and highlights the utility of a simple dynamic empirical model for a more economical prediction of catchment-scale E. coli burden. © 2012 Elsevier Ltd. All rights reserved.
1. Introduction The management of livestock and their manure within agricultural catchments can have a significant impact on the microbial quality of drainage water (Chadwick et al., 2008; Muirhead and Monaghan, 2012). The microbial risk attributed to receiving waters is indexed via faecal indicator organisms (FIOs). These indicator bacteria, the most common of which is Escherichia coli, serve as a surrogate of infection risk to humans from faecally contaminated water and are used as regulatory parameters throughout the world in order to monitor the hygienic status of designated water bodies. The contribution of diffuse sources of FIOs to water bodies is of particular importance in catchments dominated by agriculture (Hampson et al., 2010; Winter et al., 2011) with storm event driven transfers known to contribute high loadings of FIOs to stream networks and thus coastal waters (Hunter et al., 1992; Kay et al., 2010). The importance of microbial pollutants in catchments has been reinforced within the EU Water Framework Directive (WFD) through the designation of ⁎ Corresponding author at: Biological & Environmental Sciences, School of Natural Sciences, University of Stirling, Scotland, UK. Tel.: + 44 1786 467846. E-mail address: [email protected] (D.M. Oliver). 0160-4120/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.envint.2012.02.006
‘protected areas’, which are considered to be particularly sensitive to pollution or have particular economic, social or environmental importance. Bathing and shellfish harvesting waters, routinely monitored for FIOs, represent two types of ‘protected areas’ because of the potential for adverse effects on human health and the economy if contaminated with FIOs. Diffuse water pollution from agriculture can be conceptualised using a source–mobilisation–delivery–impact model for a range of pollutants (see Haygarth et al., 2005). Each stage within this ‘transfer continuum’ approach offers considerable scope for research across a range of complementary scales. For example, sources of FIOs in catchments can be variable and exact magnitudes of the contribution of different sources (e.g. livestock versus wildlife) are sometimes difficult to interpret. The transfer of FIOs from these various sources occurs via a number of different hydrological pathways (Oliver et al., 2010a) and first requires the mobilisation of FIOs from organic material via detachment processes (e.g. physical disruption following raindrop impact). Mobilised FIOs may transfer as freely suspended cells in suspension or attached to manure or soil particles (Soupir and Mostaghimi, 2011; Soupir et al., 2010). Delivery of pollutants is particularly complex because it can represent a stepwise process that is difficult to quantify (Beven et al., 2005). To understand the
D.M. Oliver et al. / Environment International 43 (2012) 6–12
magnitude of pollutant delivery from different catchment types it is helpful to think in terms of delivery coefficients which describe the efficiency by which a contaminant is transported from the soil source, through the landscape to the catchment outlet (Beven et al., 2005; Defra, 2006). In simple terms, an FIO delivery coefficient is the ratio of the FIO source loading (burden) on pasture to the amount of FIOs that are delivered to the water body for a given time period. Delivery coefficients are useful in contrast to absolute delivery loads or concentrations because they allow the amount of contaminant being delivered to a receiving water to be considered in context of the original landscape burden. The burden of FIOs to land in agricultural systems is largely derived from inputs from grazing livestock. Other sources include the application of solid and liquid manures. Wildlife also contributes to catchment FIO burden but such sources are difficult to determine (Oliver et al., 2009a; Pachepsky et al., 2006). Studies that consider FIO burden in catchments have tended, in the past, to use ‘back of the envelope’ calculations based on typical livestock excretion rates and FIO contents per gram of faeces. However, these calculations are inherently uncertain because of variability in FIO content of livestock faeces (Davies-Colley et al., 2008; Donnison et al., 2008). The reason such budget type approaches are used is due to the complexity, and time and cost constraints of trying to capture the FIO content of faeces distributed across the landscape via field-based sampling and subsequent analysis. Plot scale approaches for trying to deal with such problems have been published based on an areal system of FIO budgeting (e.g. Muirhead, 2009). Likewise, dynamic empirical models accounting for FIO deposition, die-off and regrowth have been developed for plot scale studies (Oliver et al., 2010b) which may prove useful for future catchment investigation if they can be tested against appropriate field data and deemed ‘fit for purpose’. Thus, there is a need for comparative field and modelling-based assessments of FIO burden within agricultural systems not only to gain a better understanding of dynamic microbial reservoirs at catchment scales, but to also develop and evaluate empirical models to aid prediction of FIO catchment burden where field assessment is not possible. Accurate determination of FIO burden allows FIO delivery coefficients to be derived for different catchment typologies, where water quality monitoring is undertaken, which will help provide policy guidance for farm management options to reduce FIO burden and subsequent delivery from land to water. The aim of this study was to: (i) establish a field protocol for determining the observed catchment-based burden of FIOs (principally E. coli) in a small agricultural headwater catchment, and; (ii) evaluate the closeness-of-fit of E. coli burden determined using the field-based protocol against that predicted by a published empirical model. The study focused on E. coli because it has been shown to be the most practical classifier of pathogen presence (Wilkes et al., 2009) and because the current evidence base for alternative indicators such as intestinal enterococci is not yet sufficient to develop a field-relevant predictive model. 2. Materials and methods 2.1. Field site A headwater catchment (Farm D1) in Lancashire, approximately 6 miles from the University of Lancaster, was identified and selected to test the field-based protocol for determining E. coli burden to land (see Fig. 1). The catchment area is 0.40 km 2. Land attributed to three farming enterprises fell within the catchment area and necessitated the co-operation of all landowners in order to obtain access to pasture. The land use within the catchment included rough grazing (40%) and improved pasture (60%). A large section of the riparian zone forms a waterlogged marshland (considered rough grazing) for much of the year and is shown in Fig. 1.
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2.2. Field study The catchment was delineated into two distinct areas: those considered to be critical source areas (CSAs) for contributing FIOs to water and those that were not. A CSA is defined as an area where high source potential and high transport potential for a given substance intersect (e.g. see Gburek and Sharpley, 1998; Maas et al., 1985; Pionke et al., 2000). In this study the CSA delineation was based predominantly on the transport potential (hydrology): i.e. it was assumed that diffuse inputs of FIOs would be distributed relatively homogeneously across the catchment, since it can be difficult to quantify all the processes leading to spatial heterogeneity of input rates (e.g. Page et al., 2005). A number of methods were combined to provide the most appropriate representation of the CSA and an assumption was made that such areas remained static in time to simplify the sampling stratification. The methods used included a visual assessment (via catchment walk and mapping under wet weather conditions) and the generation of connectivity indices using the SCIMAP model (Lane et al., 2009). The CSA covered a total area of 0.01 km 2 and is shown as the area marked by the solid line boundary in Fig. 1. 2.3. Sampling approach A workshop was held ahead of the field sampling campaign to draw on advice and knowledge of eight international experts in the field of FIO catchment dynamics. This helped to refine the sampling strategy proposed by the project team. Field sampling of E. coli burden in the catchment was undertaken nine times throughout an annual period (June 2010–June 2011) with increased frequency throughout May–September because of increased livestock activity and co-incidence with the operational period of the EU Bathing Waters Directive. On each occasion 20 random GPS co-ordinates were generated for both CSA and non-CSA zones in the catchment. These co-ordinates indicated the sampling point at which two 4 m diameter sampling circles (of area 12.6 m 2) were randomly designated; in each sampling circle all faecal material was collected. Sheep and dairy faeces were differentiated and bulked independently of one another to keep the faecal sources distinct for microbial analysis. Only faecal material was collected and any associated soil and vegetation were discarded. Best efforts were made to differentiate between faecal material and the soil interface, and to separate faeces that had been trounced into the root mat (i.e. the interface between the soil surface and perennial grass roots that form complex mats to hold the soil in place) but ultimately this was based on the discretion of the sampling team. If no faecal material was found in a sampling circle a “zero faeces” policy for sample collection was used whereby an additional two sampling circles were assessed for faeces, and this was repeated once more if still no faecal material was found. If no faecal material was located within six of the sampling circles attributed to a GPS coordinate then it was confirmed that no faecal material was present. The “zero faeces” sampling policy increased the sampling area in lower loading areas (or sites where faecal distribution was likely to be concentrated in specific spatial zones e.g. livestock tracks dissecting marshland) to ensure accurate coverage. Faecal collection was repeated at all 40+ sampling points within the CSA and non-CSA resulting in four bulked faecal batches for microbial analysis including: (i) dairy faeces from the CSA; (ii) dairy faeces from the nonCSA; (iii) sheep faeces from the CSA; and (iv) sheep faeces from the non-CSA. Bulked samples were used to try and obtain the best estimate of the average E. coli concentration given analytical constraints. The bulked samples provide an arithmetic mean E. coli concentration which converges on the true average as the sample size increases (assuming adequate homogenisation of the samples). The minimum total area sampled within the CSA and non-CSA on each date was 1008 m 2 (80 × 12.6 m 2) and accounted for 0.3% of the total catchment
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Fig. 1. Boundary and UK location of Farm D1 headwater catchment. Large red marker indicates catchment outlet, smaller markers show GPS positions of the Critical Source Area delimitation.
area, and 5.0% (504 m 2 sampled from 10,000 m 2) and 0.1% (504 m 2 sampled from 390,000 m 2) of the CSA and non-CSA, respectively. In order to include some representation of the degree of error associated with our observed burden values we undertook replicate data collection (i.e. repeated burden sampling) using an extended field team on one of the sampling dates. Prior to transfer to the laboratory for microbiological analysis the bulked faecal material was mixed with sterile water to form a semiliquid consistency. This allowed for subsequent homogenization of the faecal matrix by vortex mixing using an electric drill and sterilised mixing attachment. The mass of the resulting dairy and sheep homogenised mixes collected from the CSA and non-CSA was recorded to allow for estimates of (dry weight) faecal deposition across the entire catchment area. Next, 20 subsamples were collected and bulked from each of the four faecal matrices using a spatula (sterilised by flaming) and stored in sterile centrifuge tubes within a cool box. This provided a more manageable faecal volume (~50 g) associated with each of the four faecal matrices for transfer to the laboratory. Concentrations of E. coli and faecal coliforms (FC) in the faecal material were analysed in the laboratory within 24 h of sample collection. 2.4. Microbiological analysis For each sampling date the composite faecal sample for the delineated CSA and non-CSA was analysed for E. coli and FC. A mass of 0.5 g of faecal material was transferred to a MacCartney bottle containing 9 mL of sterile water to create an initial dilution. The remaining faecal material was used to determine the gravimetric water content by drying at 105 °C for 24 h. The MacCartney bottles containing the faecal material were then shaken using an orbital shaker (60 rpm) for 30 min to allow for cell dispersion. Next, 1 mL of the eluent was aseptically transferred to 9 mL of sterile water and appropriate serial 10-fold dilutions were subsequently made. Standard UK Environment Agency methods of membrane filtration were then used to determine
bacterial concentrations (EA, 2009) and the results were determined on a dry weight basis. Briefly, samples were washed through a filtration unit (Sartorius, Germany) with 20 mL of sterile water. Membrane filters of 0.45 micron pore size (Sartorius, Germany) were aseptically transferred to Membrane Lactose Glucuronide Agar (Oxoid) and incubated in an inverted position, at 37 °C (±0.2 °C) for 18–24 h for the determination of presumptive E. coli and FC colonies. 2.5. Modelled predictions of E. coli burden at Farm D1 An empirical model was used to predict E. coli burden to land in the catchment based on livestock numbers and manure applications specific to the catchment location, using E. coli concentrations provided by the field component of this present study. The original model is described in detail elsewhere (Oliver et al., 2009b, 2010b). Briefly, this empirical model was constructed using biological parameters of die-off, faecal excretion and E. coli shedding rate and was informed by our field data (parameter values for daily E. coli shedding by dairy cows, sheep and lambs were modified to 8.99× 1010, 5.42 × 109 and 1.01 × 1010 CFU day− 1, respectively, from values originally published in Oliver et al., 2009b to reflect local herd conditions) and also experimentation reported in the literature. For E. coli die-off rates linked to bovine and ovine faeces we incorporated a +/−33% degree of error associated with this parameter and applied a sine wave approximation of seasonal die-off fluctuations throughout an annual cycle. Thus, for bovine faeces, die-off rates spanned 0.0606 to 0.0909 day− 1, and for ovine faeces spanned 0.0640 to 0.0920 day− 1. The E. coli shedding rates were associated with a degree of error of +/−1 log10 CFU g− 1 dry weight. To help constrain our estimates of the range of error associated with the model parameters we undertook an assessment of the distribution of measured values from a range of existing literature values (as described in Oliver et al., 2010b). The model accounts dynamically for the accumulation and depletion of FIO burden to land at daily time-steps. The quantity of E. coli on pasture was calculated as the sum of two terms (i) the daily fresh
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input of E. coli by all livestock; and (ii) the E. coli burden deposited on previous days and now declining as a result of first-order die-off: −bx
EðxÞ ¼ EinðxÞ þ Eðx−1Þ e
ð1Þ
where Ex is the magnitude of the E. coli store on day x, Ein is the E. coli input of fresh deposits, e is a mathematical constant (base of natural log), and b is the exponential die-off constant. Specifically, daily E. coli loading was calculated by multiplying the number of livestock by both the daily dry matter excreted per livestock type and a typical value for E. coli per gram of dry faeces associated with each livestock type (as described above). Forward uncertainty propagation was implemented using 2000 model simulations using randomly chosen parameter sets from the error ranges listed in Oliver et al. (2010b). Each scenario was given a different weighting, based upon its deviation from the nominal parameter values and each scenario weighting was calculated using triangular fuzzy membership functions for each parameter, summed to give an overall weighting (e.g. see the approach of Page et al., 2004). A modification for sampling the E. coli concentration distributions was made to the original model (Oliver et al., 2010b) whereby the initial E. coli concentration shed by sheep, lambs or cattle was chosen randomly from the acceptable range specified and then sampled randomly within a ‘window’ of error for each additional time step which was set to +/−5% of the entire range of error but was not allowed to deviate from the maximum allowable multiplier range. This approach was used because it is unlikely that cattle excrete exactly the same number of cells each day owing to biological variability and fluctuations reported in the literature (Donnison et al., 2008). This allowed a general ‘drift’ in shedding rate, but did not allow large, unrealistic short-term fluctuations. 3. Results An estimate of the total mass per hectare of dry weight faecal material distributed across the different catchment areas on each sampling date and differentiated by livestock type is shown in Table 1. These values were scaled from the mass collected within the sampling frames according to the appropriate coverage of CSA, non-CSA and total catchment area. The total mass of faecal material distributed across the entire non-CSA was always greater than that attributed to the smaller CSA. As an example, for dairy faeces there was 1635 times more dry weight faecal material in the nonCSA than the CSA in June 2010 reducing to only 27 times more in April 2011. The difference in mass apportioned to CSA and non-CSA was more consistent for sheep faeces with between 29 and 96 times more faecal material in the non-CSA than the CSA through the monitoring period. The typical concentration of E. coli (and FC) within bulked cattle and sheep faeces on each sampling date is shown in Table 2. Highest concentrations of E. coli in both faecal types coincided with warmer months (e.g. May, June and July). February tended to record the lowest levels of E. coli in faecal material. For dairy faeces there was almost four orders of magnitude difference between the lowest (4.01 log10 CFU g− 1; February 2011) and the highest (7.99 log10 CFU g− 1; June 2011) concentrations of E. coli detected. In contrast, sheep faeces showed only three orders of magnitude difference in recorded concentrations over the annual sampling cycle (8.93 log10 CFU g− 1 in June 2011 versus 5.69 log10 CFU g− 1 in February 2011).
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To calculate the E. coli burden for the entire catchment area (CSA and non-CSA) the concentration data (Table 2) were combined with the projected faecal loading to pasture based on data shown in Table 1. A time-series of the observed E. coli burden throughout the annual period of monitoring (June 2010–June 2011) is presented in Fig. 2A along with predicted E. coli levels on pasture for the period January 2010– June 2011 using the model described above. The error bar shown on the date of the repeated sampling event depicts a first approximation of variance associated with the observed values. Tables 1 and 2 also show the data from the repeated sampling events (r1 and r2) to demonstrate the precision of the methodology with regard to faecal matter acquisition (Table 1) and concentrations of FIO in faecal samples (Table 2). The upper limit of the error bar in Fig. 2A was determined by applying the difference between the highest and lowest replicates collected on the repeated sampling date to the mean of the observed values, and this was repeated for the lower limit too. Weekly rainfall distribution throughout this period is also shown. The modelled plots show the 50th percentile and minimum and maximum values of predicted dynamic E. coli burden through time. For reference, the first phase of modelled predictions show: (i) the contributions of E. coli from 35 sheep prior to their removal at lambing (resulting in the 1st order burden decline); and (ii) contributions of E. coli following the reintroduction of sheep 3 weeks later at numbers of 25 increasing steadily to 65 combined with subsequent increase following the onset of the cattle grazing period at Julian day 138 (34 dairy cows). The first field-based assessment of burden occurred shortly afterwards in June (Julian Day 167). Considering predictions based on the 50th percentile of model runs then it is apparent that at this point in time the empirical model under-predicted the true E. coli burden on pasture. The margin of difference between predicted and observed data was reduced at the second sampling date (July) and in August the predicted values over-predict the observed values. However, the field-sampling methodology used in this study derived values within the bounds of uncertainty (minimum and maximum model predictions) identified by the model on eight out of nine sampling dates. Using first-order die-off coefficients the maximum median potential E. coli reservoir during the first grazing season was predicted to be approximately 8.50× 1013 E. coli on day 209 (July 28th 2010), with a maximum observed E. coli burden of 1.57 × 1014 E. coli recorded on June 16th 2010. A similar pattern was repeated 365 days later. The observed and predicted values differed by >1 order of magnitude on only one of the nine sampling dates throughout an annual monitoring period. Day 410 (February 14th 2011) is a clear anomaly in the mismatch between observed and predicted data. The observed burden registered as 5.52× 1011 CFU, whereas the predicted burden using the empirical model was over one order of magnitude greater at 2.00 × 1013 CFU. A replicated burden sample was undertaken on one occasion and the mean value was used as the actual observed E. coli burden on this date. This repeated sampling event was used to demonstrate the precision of the methodology with data presented in Tables 1 and 2. Perhaps as expected the fluctuation in predicted (and observed) burden looks to be directly related to the temporal fluctuation in livestock units (LUs) distributed across the catchment throughout the monitoring period (see Fig. 2B). Fig. 2B is useful in that it also depicts the shifts in the magnitude of dry weight faecal material distributed across the catchment area through time and appears to be related not only to LUs but also rainfall. The E. coli burden attributed to different livestock types and catchment areas (CSA versus non-CSA) is plotted as a time series in Fig. 2C. The pattern for all faecal types and catchment areas is generally one of decline from July through to April, with occasional increases in observed burden relative to the previous months' sampling. This trend looks set to reverse from May 2011 onwards with the reintroduction of sheep following the second lambing period and the beginning of the dairy grazing period for 2011.
4. Discussion Determining the burden of E. coli on pasture is important for catchment scale studies of diffuse microbial pollution and associated risk assessment linked to the protection of water resources
Table 1 Mass of dry weight faecal material attributed to different livestock types and catchment areas. Month
Total mass of dry weight faeces (kg ha− 1) Dairy
June ‘10 July ‘10 Aug ‘10 Oct ‘10 Dec ‘10 Feb ‘11 April ‘11 (r1) April ‘11 (r2) May ‘11 June ‘11
Sheep
Total (dairy and sheep)
CSA
Non-CSA
Combined
CSA
Non-CSA
Combined
CSA
Non-CSA
Catchment
3.1 37.7 11.2 2.4 12.3 2.6 5.0 0 0 0
117.1 66.0 20.0 17.7 21.5 36.8 2.1 1.3 9.1 6.8
114.2 65.3 19.8 17.3 21.2 36.0 2.2 1.3 8.9 6.6
33.3 50.3 10.0 5.9 17.6 11.0 12.9 8.7 4.6 11.2
30.5 46.3 17.7 6.7 13.2 27.1 9.9 8.8 8.2 21.6
30.6 46.4 17.6 6.7 13.3 26.7 10.0 8.8 8.1 21.3
36.4 88.0 21.2 8.3 29.9 13.6 17.9 8.7 4.6 11.2
147.6 112.3 37.7 24.4 34.7 64.0 12.0 10.1 17.3 28.3
144.8 111.7 37.3 24.0 34.5 62.7 12.1 10.1 17.0 27.9
April data shown for 2 replicate burden samples (r1 and r2) to demonstrate precision of the methodology.
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Table 2 Concentrations of E. coli and faecal coliforms (FC) in dairy and sheep faeces sampled in critical source areas (CSAs) and non-CSAs throughout the sampling period.
X: no faecal material present. Livestock present?: column identifies sampling dates when the livestock were not present so that only aged faecal material was sampled. D = dairy; Sh = sheep; solid shading = livestock present; no shading = livestock not present. April data shown for 2 replicate burden samples (r1 and r2) to demonstrate accuracy of the methodology. ** Missing data.
(Ferguson et al., 2009). This study reports on the first determination of landscape-scale E. coli burden via field observation using a pragmatic field-based protocol. In turn this has demonstrated the utility of a simple empirical model to predict E. coli burden within acceptable limits of error for a headwater catchment. Previous field-based studies have, at best, only accounted for E. coli burden at experimental plot scales (Muirhead, 2009). The main reason for this is that physical collection of a substantial sample of faecal material from across hillslopes and catchments is a time consuming and labour intensive process (Tate et al., 2000). However, fundamental understanding of faecal loading of contaminants on pasture remains a pivotal first step in beginning to understand landscape derived contamination risks arising from grazing activity. In the past, ‘back of the envelope’ budgeting approaches that couple livestock excretion rates with typical E. coli concentrations of faeces have provided one means of estimating faecal contributions of bacteria to pasture. Other approaches do exist. For example, Tate et al. (2000) modified an herbage yield methodology (using quadrats and transects) for estimating cattle faecal loading to rangelands, but such an approach accommodates an element of subjectivity based on assessor judgements of faecal deposit weights and typologies and fails to measure any form of contaminant contained within the faecal load. Elsewhere, the distribution of livestock through pasture has received attention in animal behavioural studies (Marion et al., 2008) and others have tried to map the distribution of livestock faecal deposition (Auerswald et al., 2010). However, analysing the faeces for specific contaminants goes one step further and adds significant complexity to any study as well as incurring additional and considerable time and cost implications. The difference in the mass of dry weight faecal material observed in CSA versus non-CSA in this headwater catchment is not surprising. The CSA occupies a much smaller area (0.01 km 2) compared to the non-CSA (0.39 km 2) and so when the mass of sampled faeces is scaled up from the sampling frames to the corresponding area those differences are unavoidable. The disproportionate mass of faecal matter often seen in the non-CSA relative to the CSA is most likely a function of the marshland features of much of the CSA which impacts on the distribution of faeces within this area (see later discussion). Likewise, the fluctuations in E. coli concentrations in dairy and sheep faeces through time are not unexpected. The values are typical of others reported in the literature (e.g. Moriarty et al., 2011; Soupir et al., 2008; Van Kessel et al., 2007) and peak concentrations linked to warmer summer months have been reported in New Zealand
(Sinton et al., 2007). It may be that the warmer ambient temperatures are promoting cell growth in the faecal matrix (Oliver et al., 2010b) and this may explain the under prediction of the model in the summer months of June and July. For dairy cattle, the decline in E. coli concentrations in faeces (Table 2) from October onwards coincides with the removal of cattle from pasture. Thus, there is no replenishment of E. coli from the cattle reservoir until the reintroduction of cattle to pasture the following year and so the dairy-derived E. coli population is likely to ‘die-off’ through time. In contrast the concentration of E. coli in sheep faeces does not fluctuate to the same extent through the monitoring period. This is most likely the result of continuous sheep grazing on pasture in this particular catchment throughout the course of the entire year (except for a brief spell during the spring lambing period) and so fresh faecal material is continually deposited on pasture to keep E. coli levels relatively stable. The introduction of lambs into the catchment area following the lambing periods may also have contributed to the higher observed E. coli levels in June relative to the model predictions. Younger animals such as lambs can shed higher concentrations of faecal microorganisms than adult sheep but they also excrete less faecal material per day in total than ewes or rams (Dorner et al., 2004). In balance, the E. coli contribution from lambs and sheep is therefore similar in our burden model and others have also determined sheep and lamb E. coli daily production to be of the same order of magnitude (Lewis and Post, 2003: used with Vinten et al., 2004). However, for the burden model to account fully for the contribution of E. coli from lambs over time we would require the use of a specific die-off coefficient associated with E. coli persistence in lamb faeces. Unfortunately there is a lack of published data for E. coli persistence in lamb faeces and this hinders our ability to include a lamb/sheep distinction in die-off rates within the model. Instead an ‘ovine’ die-off rate was used and applied to all ovine faecal materials. While differentiating between young and adult livestock would be preferable it is problematic because there is no empirical data available to defend this level of complexity in the model at current time. On first inspection it appears strange that both faecal and E. coli burdens decrease through the summer when livestock are still grazing. However, this is probably a consequence of wash-out of faecal material during an exceptionally wet July in Lancashire. The shift to cooler weather could also be linked to lower excretion of faecal material by livestock. White et al. (2001) found that the number of defecations by livestock can decline in cooler weather and observed a 15% reduction in faecal loading to pasture in contrasting warm and cool seasons. The declining E. coli burden observed in this study may be a result of more rapid erosion and disruption of the faecal pats during the wetter weather and Fig. 2B confirms a reduction in the total dry weight faeces in summer even when LUs remain constant (e.g. between the June and July sampling dates). Weekly rainfall totals for three of the weeks between those sampling dates include 39, 22 and 74 mm and are likely to have reduced the retention of faecal material on pasture. Others note that the rate of decomposition of dung pats changes with season (Dickinson et al., 1981) and at the D1 catchment the role of rainfall linked to different seasons is probably the strongest driver of that decomposition process. The reduction of E. coli burden with declining dry weight faeces is reassuring but it could be that there is dispersal of faecal material into the root mat which then causes problems for acquisition of faecal material during sample collection. This could be particularly true for sheep faeces whose properties differ considerably from that of dairy faecal deposits. Sheep faeces are typically of a ‘pellet-like’ form and can become integrated with the root mat more easily than dung pats. This sampling bias is potentially higher in winter (with high dispersion) and during those months without a livestock presence (or fresh contribution of faeces). Of course, any faecal material dispersed into the root mat should have a relatively low count due to it being both older and less dense. Future work should explore the role of the root mat in sustaining E. coli populations on pasture.
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Fig. 2. A: Total E. coli burden per hectare (observed versus predicted) on pasture in the headwater catchment relative to weekly rainfall profile over the sampling period. Predicted E. coli burden (median, minimum and maximum) shown by solid and dashed lines respectively, and observed E. coli burden shown by black circles (with error shown by vertical bars). Red vertical band indicates extremely cold winter conditions (including 7 days during which temperatures failed to rise above 0 °C). B: temporal change in dry weight of faecal burden across the catchment area coupled with changes in livestock units; C: time-series of E. coli burden from dairy and sheep (including lamb) contributions to critical source areas and non critical source areas at Farm D1 headwater catchment.
The sampling date in February (sample 6) provides interesting data because the measured E. coli burden declined by an order of magnitude and yet the mass of dry weight faeces increased following several months at a stable LU stocking rate. This may be related to a prolonged period of extremely cold weather and one of the coldest Decembers ever recorded in the UK (MET Office, 2011). The red vertical band shown in Fig. 2A identifies the timing of this extreme cold spell that occurred between the collection of samples 5 and 6. Sub-freezing temperatures over a period of several weeks (including 7 days during which temperatures did not reach above 0 °C for any part of the day) potentially led to non-conducive conditions for sustaining E. coli in faeces and thus a more rapid population decline (or increase in the proportion of cells entering a viable-but-non-culturable-state) than otherwise expected (Habteselassie et al., 2008). The random sampling approach used in this study makes an assumption that livestock faeces are distributed uniformly across the landscape. In contrast, other studies have determined livestock excretions to be
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associated with water troughs, slope position and percentage and hydrologic position among other factors (Auerswald et al., 2010; Nash et al., 2009; Tate et al., 2003; White et al., 2001). It may be that a more targeted sampling strategy would reduce the error between observed and predicted E. coli burden values. However, such strategies would be unique for every catchment and unpredictable to a certain degree. The use of a “zero faeces” sampling policy in the D1 catchment did ensure that a higher sampling density was used in the marshland CSA zone because of the uneven distribution of faecal deposits (e.g. sheep faeces were confined to animal tracks that are used routinely by livestock and small patches of short grass). The delineation of CSA and non-CSA zones provided an interesting comparison in this study. However, for generic application to other catchments where farmer cooperation may not be possible then such an approach would prove difficult because of a lack of data on livestock movements within different CSA/non-CSA zones. This zoning aspect of the protocol would only prove fruitful if a specified catchment area (or field) was easily distinguished as a CSA or nonCSA and livestock information was readily available. It could be argued that the importance of burden deposited in CSA should be weighted more heavily than non-CSA burden because of it being more connected to the watercourse and therefore more likely to contribute to microbial watercourse pollution. Our use of a catchment average based on a sum of CSA and non-CSA was not implicitly weighted towards the CSA but we had more confidence in CSA values because of the higher sampling density. Future considerations could be to weight the CSA more heavily than non-CSA and move to sampling only the CSA on the basis of cost and time constraints. However, the results of our study demonstrated that the CSA does not appear to attract greater loading of faecal material and therefore greater E. coli burden. It is interesting to note that a greater faecal burden derived from sheep in the CSA was observed during drier months though this observation was relatively short lived. The CSA within the D1 catchment is a particularly wet area of marshland and during summer months this probably becomes more accessible for sheep grazing. The contribution of dairy faeces to the CSA is consistently low and this was dictated, in part, by field boundaries that restrict cattle access from the majority of the CSA. Others have observed that wetland areas receive less faecal contamination by cattle and attributed this to livestock avoiding these boggy areas (Collins, 2004). The determination of E. coli delivery coefficients linked to different catchment typologies will play an important role in helping identify suitable ‘programmes of measures’ to protect water quality from microbial impairment. Understanding behavioural characteristics of pollutants in the environment, in this case the dynamic E. coli burden response with time and livestock management, will underpin the derivation of such catchment-based delivery metrics (Oliver et al., 2010c). While this study has identified the temporal patterns of E. coli burden on pasture it has not been able to inform on the proportion of the E. coli burden that is potentially mobile. Laboratory scale studies have reported the mobility of FIOs from different faecal matrices (Hodgson et al., 2009) and could perhaps inform the ‘riskiness’ of different faecal contributions to pasture thus offering potential for coupling to land based assessments of E. coli burden as demonstrated in this study. Through improved and tested confidence in model predictions and by demonstrating the potential of a field-based protocol to determine E. coli burden on pasture we can open up new opportunities for determining reliable catchment scale ‘delivery coefficients’ for microbial contaminants sourced from agricultural land. 5. Conclusions This study represents the first dynamic accounting of E. coli burden in a small headwater catchment via direct field-based observation and complementary modelling. While field-based studies are difficult to undertake they are essential so that we can ground-truth
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existing model predictions and assess whether they are in fact ‘fit for purpose’. Deriving field-based values is of course inherently uncertain and our approach marks a first approximation evaluated against predictions from an empirical model that has proved to be of reasonable fit to the observed data for this particular catchment. This is encouraging because it suggests that time-consuming and labour-intensive field sampling strategies are not always necessary to inform on approximate catchment scale levels of E. coli burden. However, E. coli burden will not only vary with LUs but can also encounter significant shifts linked to extreme temperature conditions and rainfall drivers that can lead to a loss of these bacteria, which are surrogates of infection risk to humans, from the catchment either through enhanced rates of die-off or increased wash-off from the soil surface. Understanding how these extreme environmental drivers impact on catchment scale E. coli burden now needs investigation in order to refine empirical modelling approaches further for public health and water quality protection. In addition, future research should explore the role of rough grazing versus improved pasture as differential sources of faecal material linked to grazing behaviour. Finally, this study reports on one particular catchment and further testing and evaluation of the methodology are needed in catchments with differing landscape and management characteristics. Acknowledgements This study was funded under contract to the UK Department for Environment, Food and Rural Affairs (Defra) as part fulfilment of project WQ0129. The authors would like to acknowledge the valuable and insightful contribution of the participating farmers and are particularly grateful for land access permissions without which the project could not have been undertaken. In addition, contributions from David Kay, Andy Vinten, Richard Muirhead, Davey Jones, Roger Pickup, Chris Hodgson, Rob Fish and Luke Spadavecchia at the PEDAL2 FIO expert workshop at Lancaster University were instrumental in helping to identify a suitable sampling strategy for this study. Finally, the critique and recommendations of the two anonymous reviewers significantly improved the final version of this paper. References Auerswald K, Mayer F, Schnyder H. Coupling of spatial and temporal pattern of cattle excreta patches on a low intensity pasture. Nutr Cycl Agroecosyst 2010;88:275–88. Beven K, Heathwaite AL, Haygarth PM, Walling D, Brazier R, Withers P. On the concept of delivery of sediments and nutrients to stream channels. Hydro Proc 2005;19:551–6. Chadwick D, Fish R, Oliver DM, Heathwaite L, Hodgson C, Winter M. Management of livestock and their manure to reduce the risk of microbial transfers to water: the case for an interdisciplinary approach. Trends Food Sci Tech 2008;19:240–7. Collins R. Fecal contamination of pastoral wetlands. J Environ Qual 2004;33:1912–8. Davies-Colley R, Lydiard E, Nagels J. Stormflow-dominated loads of faecal pollution from an intensively dairy-farmed catchment. Water Sci Tech 2008;57:1519–23. Defra. Phosphorus export and delivery in agricultural landscapes: the PEDAL project. Final Report 2006. (pp31), available online at: http://randd.defra.gov.uk/Document. aspx?Document=PE0113_7347_FRP.pdf. (accessed 7th June 2011). Dickinson CH, Underhay VSH, Ross V. Effect of season, soil fauna and water content on the decomposition of cattle dung pats. New Phytol 1981;88:129–41. Donnison A, Ross C, Clark D. Escherichia coli shedding by dairy cows. New Zeal J Agr Res 2008;51:273–8. Dorner SM, Huck PM, Slawson RM. Estimating potential environmental loadings of Cryptosporidium spp. and Campylobacter spp. from livestock in the Grand River Watershed, Ontario, Canada. Environ Sci Technol 2004;38:3370–80. Environment Agency. The microbiology of drinking water 2009 part 4 – methods for the isolation and enumeration of coliform bacteria and Escherichia coli (including E. coli O157:H7). Bristol: Standing Committee of Analysts 2009, The Environment Agency; 2009. 104 pp. Ferguson CM, Charles K, Deere DA. Quantification of microbial sources in drinkingwater catchments. Crit Rev Environ Sci Tech 2009;39:1-40. Gburek WJ, Sharpley AN. Hydrologic controls on phosphorus loss from upland agricultural watersheds. J Environ Qual 1998;27:267–77. Habteselassie M, Bischoff M, Blume E, Applegate B, Reuhs B, Brouder S, Turco RF. Environmental controls on the fate of Escherichia coli in soil. Water Air Soil Pollut 2008;190:143–55. Hampson D, Crowther J, Bateman I, Kay D, Posen P, Stapleton C, Wyer M, Fezzi C, Jones P, Tzanopoulos J. Predicting microbial pollution concentrations in UK rivers in response to land use change. Water Res 2010;44:4748–59.
Haygarth PM, Condron LM, Heathwaite AL, Turner BL, Harris GP. The phosphorus transfer continuum: linking source to impact with an interdisciplinary and multi-scaled approach. Sci Total Environ 2005;344:5-14. Hodgson CJ, Bulmer N, Chadwick DR, Oliver DM, Heathwaite AL, Fish RD, Winter M. Establishing relative release kinetics of faecal indicator organisms from different faecal matrices. Lett Appl Microbiol 2009;49:124–30. Hunter C, McDonald A, Beven KJ. Input of fecal coliform bacteria to an upland stream channel in the Yorkshire Dales. Water Resour Res 1992;28:1869–76. Kay D, Anthony S, Crowther J, Chambers BJ, Nicholson FA, Chadwick DR, Stapleton CM, Wyer MD. Microbial watercourse pollution: a screening tool for initial catchmentscale assessment and source apportionment. Sci Total Environ 2010;23:5649–56. Lane SN, Reaney S, Heathwaite AL. Representation of landscape hydrological connectivity using a topographically driven surface flow index. Water Resour Res 2009;45:W08423. Lewis DR, Post B. PAMIMO-C. A catchment scale model of faecal indicator organism (FIO) transport from agricultural land to surface waters. IWA DipCon 2003 conference on diffuse pollution and basin management, University College Dublin, August 2003; 2003. Maas RP, Smolen MD, Dressing SA. Selecting critical areas for nonpoint-source pollution control. J Soil Wat Cons 1985;40:68–71. Marion G, Smith LA, Swain DL, Davidson RS, Hutchings MR. Agent-based modelling of foraging behaviour: the impact of spatial heterogeneity on disease risks from faeces in grazing systems. J Agric Sci 2008;146:507–20. MET Office. http://www.metoffice.gov.uk/news/releases/archive/2011/cold-dec 2011. (Accessed 7th June 2011). Moriarty EM, Mackenzie ML, Karki N, Sinton LW. Survival of Escherichia coli, enterococci and Campylobacter spp. in sheep feces on pastures. Appl Environ Microbiol 2011;77:1797–803. Muirhead RW. Soil and faecal material reservoirs of Escherichia coli in a grazed pasture. New Zeal J Agr Res 2009;52:1–8. Muirhead RW, Monaghan RM. A two reservoir model to predict Escherichia coli losses to water from pastures grazed by dairy cows. Environ Int 2012;40:8-14. Nash MS, Heggem DT, Ebert D, Wade TG, Hall RK. Multi-scale landscape factors influencing stream water quality in the state of Oregon. Environ Monit Assess 2009;156:343–60. Oliver DM, Heathwaite AL, Fish RD, Chadwick DR, Hodgson CJ, Winter M, Butler AJ. Scale appropriate modelling of diffuse microbial pollution from agriculture. Progr Phys Geogr 2009a;33:358–77. Oliver DM, Fish RD, Hodgson CJ, Heathwaite AL, Chadwick DR, Winter M. A crossdisciplinary toolkit to assess the risk of faecal indicator loss from grassland farm systems to surface waters. Agric Ecosyst Environ 2009b;129:401–12. Oliver DM, Page T, Hodgson CJ, Heathwaite AL, Chadwick DR, Fish RD, Winter M. Development and testing of a risk indexing framework to determine field scale critical source areas of faecal bacteria on grassland. Environ Model Software 2010a;25:503–12. Oliver DM, Page T, Heathwaite AL, Haygarth PM. Re-shaping models of E. coli population dynamics in livestock faeces: increased bacterial risk to humans? Environ Int 2010b;36:1–7. Oliver DM, Heathwaite AL, Haygarth PM. A ‘culture’ change in catchment microbiology? Hydrol Process 2010c;24:2973–6. Pachepsky YA, Sadeghi AM, Bradford SA, Shelton DR, Guber AK, Dao T. Transport and fate of manure-borne pathogens: modelling perspective. Agric Water Manage 2006;86: 81–92. Page T, Beven KJ, Whyatt JD. Predictive capability in estimating changes in water quality: long-term responses to atmospheric deposition. Water Air Soil Pollut 2004;151:215–44. Page T, Haygarth PM, Beven KJ, Joynes A, Butler P, Keeler C, Freer J, Owens P, Woods G. Spatial variability of soil phosphorus in relation to the topographic index and critical source areas: sampling for assessing risk to water quality. J Environ Qual 2005;34:2263–77. Pionke HB, Gburek WJ, Sharpley AN. Critical source area controls on water quality in an agricultural watershed located in the Chesapeake basin. Ecol Eng 2000;14:325–35. Sinton LW, Braithwaite RR, Hall CH, Mackenzie ML. Survival of indicator bacteria in bovine feces on pasture. Appl Environ Microbiol 2007;73:7917–25. Soupir ML, Mostaghimi S. Escherichia coli and enterococci attachment to particles in runoff from highly and sparsely vegetated grassland. Water Air Soil Pollut 2011;216:167–78. Soupir ML, Mostaghimi S, Lou J. Die-off of E. coli and enterococci in dairy cowpats. Trans ASABE 2008;51:1987–96. Soupir ML, Mostaghimi S, Dillaha T. Attachment of Escherichia coli and enterococci to particles in runoff. J Environ Qual 2010;39:1019–27. Tate KW, Atwill ER, McDougald NK, George MR, Witt D. A method for estimating cattle fecal loading on rangeland watersheds. J Range Manage 2000;53:506–10. Tate KW, Atwill ER, McDougald NK, George MR. Spatial and temporal patterns of cattle feces deposition on rangeland. J Range Manage 2003;56:432–8. Van Kessel JS, Pachepsky YA, Shelton DR, Karns JS. Survival of Escherichia coli in cowpats in pasture and in laboratory conditions. J Appl Microbiol 2007;103:1122–7. Vinten AJA, Lewis DR, McGechan M, Duncan A, Aitken M, Hill C, Crawford C. Predicting the effect of livestock inputs of E. coli on microbiological compliance of bathing water. Water Res 2004;38:3214–24. White SL, Sheffield RE, Washburn SP, King LD, Green JT. Spatial and time distribution of dairy cattle excreta in an intensive pasture system. J Environ Qual 2001;30:2180–7. Wilkes G, Edge T, Gannon V, Jokinen C, Lyautey E, Medeiros D, Neumann N, Ruecker N, Topp E, Lapen DR. Seasonal relationships among indicator bacteria, pathogenic bacteria, Cryptosporidium oocysts, Giardia cysts, and hydrological indices for surface waters within an agricultural landscape. Water Res 2009;43:2209–23. Winter M, Oliver DM, Fish R, Heathwaite AL, Chadwick D, Hodgson C. Catchments, subcatchments and private spaces: scale and process in managing microbial pollution from source to sea. Environ Sci Policy 2011;14:315–26.
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Environment International journal homepage: www.elsevier.com/locate/envint
Determining E. coli burden on pasture in a headwater catchment: Combined field and modelling approach David M. Oliver a,⁎, Trevor Page b, Ting Zhang b, A. Louise Heathwaite b, Keith Beven b, Heather Carter b, Gareth McShane b, Patrick O. Keenan b, Philip M. Haygarth b a b
Biological & Environmental Sciences, School of Natural Sciences, University of Stirling, Stirling, FK9 4LA, UK Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
a r t i c l e
i n f o
Article history: Received 29 July 2011 Accepted 22 February 2012 Available online 22 March 2012 Keywords: Burden Critical source area Delivery E. coli Headwater catchment Source Water quality
a b s t r a c t Empirical monitoring studies of catchment-scale Escherichia coli burden to land from agriculture are scarce. This is not surprising given the complexity associated with the temporal and spatial heterogeneity in the excretion of livestock faecal deposits and variability in microbial content of faeces. However, such information is needed to appreciate better how land management and landscape features impact on water quality draining agricultural landscapes. The aim of this study was to develop and test a field-based protocol for determining the burden of E. coli in a small headwater catchment in the UK. Predictions of E. coli burden using an empirical model based on previous best estimates of excretion and shedding rates were also evaluated against observed data. The results indicated that an empirical model utilising key parameters was able to satisfactorily predict E. coli burden on pasture most of the time, with 89% of observed values falling within the minimum and maximum range of predicted values. In particular, the overall temporal pattern of E. coli burden on pasture is captured by the model. The observed and predicted values recorded a disagreement of > 1 order of magnitude on only one of the nine sampling dates throughout an annual period. While a first approximation of E. coli burden to land, this field-based protocol represents one of the first comprehensive approaches for providing a real estimate of a dynamic microbial reservoir at the headwater catchment scale and highlights the utility of a simple dynamic empirical model for a more economical prediction of catchment-scale E. coli burden. © 2012 Elsevier Ltd. All rights reserved.
1. Introduction The management of livestock and their manure within agricultural catchments can have a significant impact on the microbial quality of drainage water (Chadwick et al., 2008; Muirhead and Monaghan, 2012). The microbial risk attributed to receiving waters is indexed via faecal indicator organisms (FIOs). These indicator bacteria, the most common of which is Escherichia coli, serve as a surrogate of infection risk to humans from faecally contaminated water and are used as regulatory parameters throughout the world in order to monitor the hygienic status of designated water bodies. The contribution of diffuse sources of FIOs to water bodies is of particular importance in catchments dominated by agriculture (Hampson et al., 2010; Winter et al., 2011) with storm event driven transfers known to contribute high loadings of FIOs to stream networks and thus coastal waters (Hunter et al., 1992; Kay et al., 2010). The importance of microbial pollutants in catchments has been reinforced within the EU Water Framework Directive (WFD) through the designation of ⁎ Corresponding author at: Biological & Environmental Sciences, School of Natural Sciences, University of Stirling, Scotland, UK. Tel.: + 44 1786 467846. E-mail address: [email protected] (D.M. Oliver). 0160-4120/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.envint.2012.02.006
‘protected areas’, which are considered to be particularly sensitive to pollution or have particular economic, social or environmental importance. Bathing and shellfish harvesting waters, routinely monitored for FIOs, represent two types of ‘protected areas’ because of the potential for adverse effects on human health and the economy if contaminated with FIOs. Diffuse water pollution from agriculture can be conceptualised using a source–mobilisation–delivery–impact model for a range of pollutants (see Haygarth et al., 2005). Each stage within this ‘transfer continuum’ approach offers considerable scope for research across a range of complementary scales. For example, sources of FIOs in catchments can be variable and exact magnitudes of the contribution of different sources (e.g. livestock versus wildlife) are sometimes difficult to interpret. The transfer of FIOs from these various sources occurs via a number of different hydrological pathways (Oliver et al., 2010a) and first requires the mobilisation of FIOs from organic material via detachment processes (e.g. physical disruption following raindrop impact). Mobilised FIOs may transfer as freely suspended cells in suspension or attached to manure or soil particles (Soupir and Mostaghimi, 2011; Soupir et al., 2010). Delivery of pollutants is particularly complex because it can represent a stepwise process that is difficult to quantify (Beven et al., 2005). To understand the
D.M. Oliver et al. / Environment International 43 (2012) 6–12
magnitude of pollutant delivery from different catchment types it is helpful to think in terms of delivery coefficients which describe the efficiency by which a contaminant is transported from the soil source, through the landscape to the catchment outlet (Beven et al., 2005; Defra, 2006). In simple terms, an FIO delivery coefficient is the ratio of the FIO source loading (burden) on pasture to the amount of FIOs that are delivered to the water body for a given time period. Delivery coefficients are useful in contrast to absolute delivery loads or concentrations because they allow the amount of contaminant being delivered to a receiving water to be considered in context of the original landscape burden. The burden of FIOs to land in agricultural systems is largely derived from inputs from grazing livestock. Other sources include the application of solid and liquid manures. Wildlife also contributes to catchment FIO burden but such sources are difficult to determine (Oliver et al., 2009a; Pachepsky et al., 2006). Studies that consider FIO burden in catchments have tended, in the past, to use ‘back of the envelope’ calculations based on typical livestock excretion rates and FIO contents per gram of faeces. However, these calculations are inherently uncertain because of variability in FIO content of livestock faeces (Davies-Colley et al., 2008; Donnison et al., 2008). The reason such budget type approaches are used is due to the complexity, and time and cost constraints of trying to capture the FIO content of faeces distributed across the landscape via field-based sampling and subsequent analysis. Plot scale approaches for trying to deal with such problems have been published based on an areal system of FIO budgeting (e.g. Muirhead, 2009). Likewise, dynamic empirical models accounting for FIO deposition, die-off and regrowth have been developed for plot scale studies (Oliver et al., 2010b) which may prove useful for future catchment investigation if they can be tested against appropriate field data and deemed ‘fit for purpose’. Thus, there is a need for comparative field and modelling-based assessments of FIO burden within agricultural systems not only to gain a better understanding of dynamic microbial reservoirs at catchment scales, but to also develop and evaluate empirical models to aid prediction of FIO catchment burden where field assessment is not possible. Accurate determination of FIO burden allows FIO delivery coefficients to be derived for different catchment typologies, where water quality monitoring is undertaken, which will help provide policy guidance for farm management options to reduce FIO burden and subsequent delivery from land to water. The aim of this study was to: (i) establish a field protocol for determining the observed catchment-based burden of FIOs (principally E. coli) in a small agricultural headwater catchment, and; (ii) evaluate the closeness-of-fit of E. coli burden determined using the field-based protocol against that predicted by a published empirical model. The study focused on E. coli because it has been shown to be the most practical classifier of pathogen presence (Wilkes et al., 2009) and because the current evidence base for alternative indicators such as intestinal enterococci is not yet sufficient to develop a field-relevant predictive model. 2. Materials and methods 2.1. Field site A headwater catchment (Farm D1) in Lancashire, approximately 6 miles from the University of Lancaster, was identified and selected to test the field-based protocol for determining E. coli burden to land (see Fig. 1). The catchment area is 0.40 km 2. Land attributed to three farming enterprises fell within the catchment area and necessitated the co-operation of all landowners in order to obtain access to pasture. The land use within the catchment included rough grazing (40%) and improved pasture (60%). A large section of the riparian zone forms a waterlogged marshland (considered rough grazing) for much of the year and is shown in Fig. 1.
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2.2. Field study The catchment was delineated into two distinct areas: those considered to be critical source areas (CSAs) for contributing FIOs to water and those that were not. A CSA is defined as an area where high source potential and high transport potential for a given substance intersect (e.g. see Gburek and Sharpley, 1998; Maas et al., 1985; Pionke et al., 2000). In this study the CSA delineation was based predominantly on the transport potential (hydrology): i.e. it was assumed that diffuse inputs of FIOs would be distributed relatively homogeneously across the catchment, since it can be difficult to quantify all the processes leading to spatial heterogeneity of input rates (e.g. Page et al., 2005). A number of methods were combined to provide the most appropriate representation of the CSA and an assumption was made that such areas remained static in time to simplify the sampling stratification. The methods used included a visual assessment (via catchment walk and mapping under wet weather conditions) and the generation of connectivity indices using the SCIMAP model (Lane et al., 2009). The CSA covered a total area of 0.01 km 2 and is shown as the area marked by the solid line boundary in Fig. 1. 2.3. Sampling approach A workshop was held ahead of the field sampling campaign to draw on advice and knowledge of eight international experts in the field of FIO catchment dynamics. This helped to refine the sampling strategy proposed by the project team. Field sampling of E. coli burden in the catchment was undertaken nine times throughout an annual period (June 2010–June 2011) with increased frequency throughout May–September because of increased livestock activity and co-incidence with the operational period of the EU Bathing Waters Directive. On each occasion 20 random GPS co-ordinates were generated for both CSA and non-CSA zones in the catchment. These co-ordinates indicated the sampling point at which two 4 m diameter sampling circles (of area 12.6 m 2) were randomly designated; in each sampling circle all faecal material was collected. Sheep and dairy faeces were differentiated and bulked independently of one another to keep the faecal sources distinct for microbial analysis. Only faecal material was collected and any associated soil and vegetation were discarded. Best efforts were made to differentiate between faecal material and the soil interface, and to separate faeces that had been trounced into the root mat (i.e. the interface between the soil surface and perennial grass roots that form complex mats to hold the soil in place) but ultimately this was based on the discretion of the sampling team. If no faecal material was found in a sampling circle a “zero faeces” policy for sample collection was used whereby an additional two sampling circles were assessed for faeces, and this was repeated once more if still no faecal material was found. If no faecal material was located within six of the sampling circles attributed to a GPS coordinate then it was confirmed that no faecal material was present. The “zero faeces” sampling policy increased the sampling area in lower loading areas (or sites where faecal distribution was likely to be concentrated in specific spatial zones e.g. livestock tracks dissecting marshland) to ensure accurate coverage. Faecal collection was repeated at all 40+ sampling points within the CSA and non-CSA resulting in four bulked faecal batches for microbial analysis including: (i) dairy faeces from the CSA; (ii) dairy faeces from the nonCSA; (iii) sheep faeces from the CSA; and (iv) sheep faeces from the non-CSA. Bulked samples were used to try and obtain the best estimate of the average E. coli concentration given analytical constraints. The bulked samples provide an arithmetic mean E. coli concentration which converges on the true average as the sample size increases (assuming adequate homogenisation of the samples). The minimum total area sampled within the CSA and non-CSA on each date was 1008 m 2 (80 × 12.6 m 2) and accounted for 0.3% of the total catchment
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Fig. 1. Boundary and UK location of Farm D1 headwater catchment. Large red marker indicates catchment outlet, smaller markers show GPS positions of the Critical Source Area delimitation.
area, and 5.0% (504 m 2 sampled from 10,000 m 2) and 0.1% (504 m 2 sampled from 390,000 m 2) of the CSA and non-CSA, respectively. In order to include some representation of the degree of error associated with our observed burden values we undertook replicate data collection (i.e. repeated burden sampling) using an extended field team on one of the sampling dates. Prior to transfer to the laboratory for microbiological analysis the bulked faecal material was mixed with sterile water to form a semiliquid consistency. This allowed for subsequent homogenization of the faecal matrix by vortex mixing using an electric drill and sterilised mixing attachment. The mass of the resulting dairy and sheep homogenised mixes collected from the CSA and non-CSA was recorded to allow for estimates of (dry weight) faecal deposition across the entire catchment area. Next, 20 subsamples were collected and bulked from each of the four faecal matrices using a spatula (sterilised by flaming) and stored in sterile centrifuge tubes within a cool box. This provided a more manageable faecal volume (~50 g) associated with each of the four faecal matrices for transfer to the laboratory. Concentrations of E. coli and faecal coliforms (FC) in the faecal material were analysed in the laboratory within 24 h of sample collection. 2.4. Microbiological analysis For each sampling date the composite faecal sample for the delineated CSA and non-CSA was analysed for E. coli and FC. A mass of 0.5 g of faecal material was transferred to a MacCartney bottle containing 9 mL of sterile water to create an initial dilution. The remaining faecal material was used to determine the gravimetric water content by drying at 105 °C for 24 h. The MacCartney bottles containing the faecal material were then shaken using an orbital shaker (60 rpm) for 30 min to allow for cell dispersion. Next, 1 mL of the eluent was aseptically transferred to 9 mL of sterile water and appropriate serial 10-fold dilutions were subsequently made. Standard UK Environment Agency methods of membrane filtration were then used to determine
bacterial concentrations (EA, 2009) and the results were determined on a dry weight basis. Briefly, samples were washed through a filtration unit (Sartorius, Germany) with 20 mL of sterile water. Membrane filters of 0.45 micron pore size (Sartorius, Germany) were aseptically transferred to Membrane Lactose Glucuronide Agar (Oxoid) and incubated in an inverted position, at 37 °C (±0.2 °C) for 18–24 h for the determination of presumptive E. coli and FC colonies. 2.5. Modelled predictions of E. coli burden at Farm D1 An empirical model was used to predict E. coli burden to land in the catchment based on livestock numbers and manure applications specific to the catchment location, using E. coli concentrations provided by the field component of this present study. The original model is described in detail elsewhere (Oliver et al., 2009b, 2010b). Briefly, this empirical model was constructed using biological parameters of die-off, faecal excretion and E. coli shedding rate and was informed by our field data (parameter values for daily E. coli shedding by dairy cows, sheep and lambs were modified to 8.99× 1010, 5.42 × 109 and 1.01 × 1010 CFU day− 1, respectively, from values originally published in Oliver et al., 2009b to reflect local herd conditions) and also experimentation reported in the literature. For E. coli die-off rates linked to bovine and ovine faeces we incorporated a +/−33% degree of error associated with this parameter and applied a sine wave approximation of seasonal die-off fluctuations throughout an annual cycle. Thus, for bovine faeces, die-off rates spanned 0.0606 to 0.0909 day− 1, and for ovine faeces spanned 0.0640 to 0.0920 day− 1. The E. coli shedding rates were associated with a degree of error of +/−1 log10 CFU g− 1 dry weight. To help constrain our estimates of the range of error associated with the model parameters we undertook an assessment of the distribution of measured values from a range of existing literature values (as described in Oliver et al., 2010b). The model accounts dynamically for the accumulation and depletion of FIO burden to land at daily time-steps. The quantity of E. coli on pasture was calculated as the sum of two terms (i) the daily fresh
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input of E. coli by all livestock; and (ii) the E. coli burden deposited on previous days and now declining as a result of first-order die-off: −bx
EðxÞ ¼ EinðxÞ þ Eðx−1Þ e
ð1Þ
where Ex is the magnitude of the E. coli store on day x, Ein is the E. coli input of fresh deposits, e is a mathematical constant (base of natural log), and b is the exponential die-off constant. Specifically, daily E. coli loading was calculated by multiplying the number of livestock by both the daily dry matter excreted per livestock type and a typical value for E. coli per gram of dry faeces associated with each livestock type (as described above). Forward uncertainty propagation was implemented using 2000 model simulations using randomly chosen parameter sets from the error ranges listed in Oliver et al. (2010b). Each scenario was given a different weighting, based upon its deviation from the nominal parameter values and each scenario weighting was calculated using triangular fuzzy membership functions for each parameter, summed to give an overall weighting (e.g. see the approach of Page et al., 2004). A modification for sampling the E. coli concentration distributions was made to the original model (Oliver et al., 2010b) whereby the initial E. coli concentration shed by sheep, lambs or cattle was chosen randomly from the acceptable range specified and then sampled randomly within a ‘window’ of error for each additional time step which was set to +/−5% of the entire range of error but was not allowed to deviate from the maximum allowable multiplier range. This approach was used because it is unlikely that cattle excrete exactly the same number of cells each day owing to biological variability and fluctuations reported in the literature (Donnison et al., 2008). This allowed a general ‘drift’ in shedding rate, but did not allow large, unrealistic short-term fluctuations. 3. Results An estimate of the total mass per hectare of dry weight faecal material distributed across the different catchment areas on each sampling date and differentiated by livestock type is shown in Table 1. These values were scaled from the mass collected within the sampling frames according to the appropriate coverage of CSA, non-CSA and total catchment area. The total mass of faecal material distributed across the entire non-CSA was always greater than that attributed to the smaller CSA. As an example, for dairy faeces there was 1635 times more dry weight faecal material in the nonCSA than the CSA in June 2010 reducing to only 27 times more in April 2011. The difference in mass apportioned to CSA and non-CSA was more consistent for sheep faeces with between 29 and 96 times more faecal material in the non-CSA than the CSA through the monitoring period. The typical concentration of E. coli (and FC) within bulked cattle and sheep faeces on each sampling date is shown in Table 2. Highest concentrations of E. coli in both faecal types coincided with warmer months (e.g. May, June and July). February tended to record the lowest levels of E. coli in faecal material. For dairy faeces there was almost four orders of magnitude difference between the lowest (4.01 log10 CFU g− 1; February 2011) and the highest (7.99 log10 CFU g− 1; June 2011) concentrations of E. coli detected. In contrast, sheep faeces showed only three orders of magnitude difference in recorded concentrations over the annual sampling cycle (8.93 log10 CFU g− 1 in June 2011 versus 5.69 log10 CFU g− 1 in February 2011).
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To calculate the E. coli burden for the entire catchment area (CSA and non-CSA) the concentration data (Table 2) were combined with the projected faecal loading to pasture based on data shown in Table 1. A time-series of the observed E. coli burden throughout the annual period of monitoring (June 2010–June 2011) is presented in Fig. 2A along with predicted E. coli levels on pasture for the period January 2010– June 2011 using the model described above. The error bar shown on the date of the repeated sampling event depicts a first approximation of variance associated with the observed values. Tables 1 and 2 also show the data from the repeated sampling events (r1 and r2) to demonstrate the precision of the methodology with regard to faecal matter acquisition (Table 1) and concentrations of FIO in faecal samples (Table 2). The upper limit of the error bar in Fig. 2A was determined by applying the difference between the highest and lowest replicates collected on the repeated sampling date to the mean of the observed values, and this was repeated for the lower limit too. Weekly rainfall distribution throughout this period is also shown. The modelled plots show the 50th percentile and minimum and maximum values of predicted dynamic E. coli burden through time. For reference, the first phase of modelled predictions show: (i) the contributions of E. coli from 35 sheep prior to their removal at lambing (resulting in the 1st order burden decline); and (ii) contributions of E. coli following the reintroduction of sheep 3 weeks later at numbers of 25 increasing steadily to 65 combined with subsequent increase following the onset of the cattle grazing period at Julian day 138 (34 dairy cows). The first field-based assessment of burden occurred shortly afterwards in June (Julian Day 167). Considering predictions based on the 50th percentile of model runs then it is apparent that at this point in time the empirical model under-predicted the true E. coli burden on pasture. The margin of difference between predicted and observed data was reduced at the second sampling date (July) and in August the predicted values over-predict the observed values. However, the field-sampling methodology used in this study derived values within the bounds of uncertainty (minimum and maximum model predictions) identified by the model on eight out of nine sampling dates. Using first-order die-off coefficients the maximum median potential E. coli reservoir during the first grazing season was predicted to be approximately 8.50× 1013 E. coli on day 209 (July 28th 2010), with a maximum observed E. coli burden of 1.57 × 1014 E. coli recorded on June 16th 2010. A similar pattern was repeated 365 days later. The observed and predicted values differed by >1 order of magnitude on only one of the nine sampling dates throughout an annual monitoring period. Day 410 (February 14th 2011) is a clear anomaly in the mismatch between observed and predicted data. The observed burden registered as 5.52× 1011 CFU, whereas the predicted burden using the empirical model was over one order of magnitude greater at 2.00 × 1013 CFU. A replicated burden sample was undertaken on one occasion and the mean value was used as the actual observed E. coli burden on this date. This repeated sampling event was used to demonstrate the precision of the methodology with data presented in Tables 1 and 2. Perhaps as expected the fluctuation in predicted (and observed) burden looks to be directly related to the temporal fluctuation in livestock units (LUs) distributed across the catchment throughout the monitoring period (see Fig. 2B). Fig. 2B is useful in that it also depicts the shifts in the magnitude of dry weight faecal material distributed across the catchment area through time and appears to be related not only to LUs but also rainfall. The E. coli burden attributed to different livestock types and catchment areas (CSA versus non-CSA) is plotted as a time series in Fig. 2C. The pattern for all faecal types and catchment areas is generally one of decline from July through to April, with occasional increases in observed burden relative to the previous months' sampling. This trend looks set to reverse from May 2011 onwards with the reintroduction of sheep following the second lambing period and the beginning of the dairy grazing period for 2011.
4. Discussion Determining the burden of E. coli on pasture is important for catchment scale studies of diffuse microbial pollution and associated risk assessment linked to the protection of water resources
Table 1 Mass of dry weight faecal material attributed to different livestock types and catchment areas. Month
Total mass of dry weight faeces (kg ha− 1) Dairy
June ‘10 July ‘10 Aug ‘10 Oct ‘10 Dec ‘10 Feb ‘11 April ‘11 (r1) April ‘11 (r2) May ‘11 June ‘11
Sheep
Total (dairy and sheep)
CSA
Non-CSA
Combined
CSA
Non-CSA
Combined
CSA
Non-CSA
Catchment
3.1 37.7 11.2 2.4 12.3 2.6 5.0 0 0 0
117.1 66.0 20.0 17.7 21.5 36.8 2.1 1.3 9.1 6.8
114.2 65.3 19.8 17.3 21.2 36.0 2.2 1.3 8.9 6.6
33.3 50.3 10.0 5.9 17.6 11.0 12.9 8.7 4.6 11.2
30.5 46.3 17.7 6.7 13.2 27.1 9.9 8.8 8.2 21.6
30.6 46.4 17.6 6.7 13.3 26.7 10.0 8.8 8.1 21.3
36.4 88.0 21.2 8.3 29.9 13.6 17.9 8.7 4.6 11.2
147.6 112.3 37.7 24.4 34.7 64.0 12.0 10.1 17.3 28.3
144.8 111.7 37.3 24.0 34.5 62.7 12.1 10.1 17.0 27.9
April data shown for 2 replicate burden samples (r1 and r2) to demonstrate precision of the methodology.
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D.M. Oliver et al. / Environment International 43 (2012) 6–12
Table 2 Concentrations of E. coli and faecal coliforms (FC) in dairy and sheep faeces sampled in critical source areas (CSAs) and non-CSAs throughout the sampling period.
X: no faecal material present. Livestock present?: column identifies sampling dates when the livestock were not present so that only aged faecal material was sampled. D = dairy; Sh = sheep; solid shading = livestock present; no shading = livestock not present. April data shown for 2 replicate burden samples (r1 and r2) to demonstrate accuracy of the methodology. ** Missing data.
(Ferguson et al., 2009). This study reports on the first determination of landscape-scale E. coli burden via field observation using a pragmatic field-based protocol. In turn this has demonstrated the utility of a simple empirical model to predict E. coli burden within acceptable limits of error for a headwater catchment. Previous field-based studies have, at best, only accounted for E. coli burden at experimental plot scales (Muirhead, 2009). The main reason for this is that physical collection of a substantial sample of faecal material from across hillslopes and catchments is a time consuming and labour intensive process (Tate et al., 2000). However, fundamental understanding of faecal loading of contaminants on pasture remains a pivotal first step in beginning to understand landscape derived contamination risks arising from grazing activity. In the past, ‘back of the envelope’ budgeting approaches that couple livestock excretion rates with typical E. coli concentrations of faeces have provided one means of estimating faecal contributions of bacteria to pasture. Other approaches do exist. For example, Tate et al. (2000) modified an herbage yield methodology (using quadrats and transects) for estimating cattle faecal loading to rangelands, but such an approach accommodates an element of subjectivity based on assessor judgements of faecal deposit weights and typologies and fails to measure any form of contaminant contained within the faecal load. Elsewhere, the distribution of livestock through pasture has received attention in animal behavioural studies (Marion et al., 2008) and others have tried to map the distribution of livestock faecal deposition (Auerswald et al., 2010). However, analysing the faeces for specific contaminants goes one step further and adds significant complexity to any study as well as incurring additional and considerable time and cost implications. The difference in the mass of dry weight faecal material observed in CSA versus non-CSA in this headwater catchment is not surprising. The CSA occupies a much smaller area (0.01 km 2) compared to the non-CSA (0.39 km 2) and so when the mass of sampled faeces is scaled up from the sampling frames to the corresponding area those differences are unavoidable. The disproportionate mass of faecal matter often seen in the non-CSA relative to the CSA is most likely a function of the marshland features of much of the CSA which impacts on the distribution of faeces within this area (see later discussion). Likewise, the fluctuations in E. coli concentrations in dairy and sheep faeces through time are not unexpected. The values are typical of others reported in the literature (e.g. Moriarty et al., 2011; Soupir et al., 2008; Van Kessel et al., 2007) and peak concentrations linked to warmer summer months have been reported in New Zealand
(Sinton et al., 2007). It may be that the warmer ambient temperatures are promoting cell growth in the faecal matrix (Oliver et al., 2010b) and this may explain the under prediction of the model in the summer months of June and July. For dairy cattle, the decline in E. coli concentrations in faeces (Table 2) from October onwards coincides with the removal of cattle from pasture. Thus, there is no replenishment of E. coli from the cattle reservoir until the reintroduction of cattle to pasture the following year and so the dairy-derived E. coli population is likely to ‘die-off’ through time. In contrast the concentration of E. coli in sheep faeces does not fluctuate to the same extent through the monitoring period. This is most likely the result of continuous sheep grazing on pasture in this particular catchment throughout the course of the entire year (except for a brief spell during the spring lambing period) and so fresh faecal material is continually deposited on pasture to keep E. coli levels relatively stable. The introduction of lambs into the catchment area following the lambing periods may also have contributed to the higher observed E. coli levels in June relative to the model predictions. Younger animals such as lambs can shed higher concentrations of faecal microorganisms than adult sheep but they also excrete less faecal material per day in total than ewes or rams (Dorner et al., 2004). In balance, the E. coli contribution from lambs and sheep is therefore similar in our burden model and others have also determined sheep and lamb E. coli daily production to be of the same order of magnitude (Lewis and Post, 2003: used with Vinten et al., 2004). However, for the burden model to account fully for the contribution of E. coli from lambs over time we would require the use of a specific die-off coefficient associated with E. coli persistence in lamb faeces. Unfortunately there is a lack of published data for E. coli persistence in lamb faeces and this hinders our ability to include a lamb/sheep distinction in die-off rates within the model. Instead an ‘ovine’ die-off rate was used and applied to all ovine faecal materials. While differentiating between young and adult livestock would be preferable it is problematic because there is no empirical data available to defend this level of complexity in the model at current time. On first inspection it appears strange that both faecal and E. coli burdens decrease through the summer when livestock are still grazing. However, this is probably a consequence of wash-out of faecal material during an exceptionally wet July in Lancashire. The shift to cooler weather could also be linked to lower excretion of faecal material by livestock. White et al. (2001) found that the number of defecations by livestock can decline in cooler weather and observed a 15% reduction in faecal loading to pasture in contrasting warm and cool seasons. The declining E. coli burden observed in this study may be a result of more rapid erosion and disruption of the faecal pats during the wetter weather and Fig. 2B confirms a reduction in the total dry weight faeces in summer even when LUs remain constant (e.g. between the June and July sampling dates). Weekly rainfall totals for three of the weeks between those sampling dates include 39, 22 and 74 mm and are likely to have reduced the retention of faecal material on pasture. Others note that the rate of decomposition of dung pats changes with season (Dickinson et al., 1981) and at the D1 catchment the role of rainfall linked to different seasons is probably the strongest driver of that decomposition process. The reduction of E. coli burden with declining dry weight faeces is reassuring but it could be that there is dispersal of faecal material into the root mat which then causes problems for acquisition of faecal material during sample collection. This could be particularly true for sheep faeces whose properties differ considerably from that of dairy faecal deposits. Sheep faeces are typically of a ‘pellet-like’ form and can become integrated with the root mat more easily than dung pats. This sampling bias is potentially higher in winter (with high dispersion) and during those months without a livestock presence (or fresh contribution of faeces). Of course, any faecal material dispersed into the root mat should have a relatively low count due to it being both older and less dense. Future work should explore the role of the root mat in sustaining E. coli populations on pasture.
D.M. Oliver et al. / Environment International 43 (2012) 6–12
Fig. 2. A: Total E. coli burden per hectare (observed versus predicted) on pasture in the headwater catchment relative to weekly rainfall profile over the sampling period. Predicted E. coli burden (median, minimum and maximum) shown by solid and dashed lines respectively, and observed E. coli burden shown by black circles (with error shown by vertical bars). Red vertical band indicates extremely cold winter conditions (including 7 days during which temperatures failed to rise above 0 °C). B: temporal change in dry weight of faecal burden across the catchment area coupled with changes in livestock units; C: time-series of E. coli burden from dairy and sheep (including lamb) contributions to critical source areas and non critical source areas at Farm D1 headwater catchment.
The sampling date in February (sample 6) provides interesting data because the measured E. coli burden declined by an order of magnitude and yet the mass of dry weight faeces increased following several months at a stable LU stocking rate. This may be related to a prolonged period of extremely cold weather and one of the coldest Decembers ever recorded in the UK (MET Office, 2011). The red vertical band shown in Fig. 2A identifies the timing of this extreme cold spell that occurred between the collection of samples 5 and 6. Sub-freezing temperatures over a period of several weeks (including 7 days during which temperatures did not reach above 0 °C for any part of the day) potentially led to non-conducive conditions for sustaining E. coli in faeces and thus a more rapid population decline (or increase in the proportion of cells entering a viable-but-non-culturable-state) than otherwise expected (Habteselassie et al., 2008). The random sampling approach used in this study makes an assumption that livestock faeces are distributed uniformly across the landscape. In contrast, other studies have determined livestock excretions to be
11
associated with water troughs, slope position and percentage and hydrologic position among other factors (Auerswald et al., 2010; Nash et al., 2009; Tate et al., 2003; White et al., 2001). It may be that a more targeted sampling strategy would reduce the error between observed and predicted E. coli burden values. However, such strategies would be unique for every catchment and unpredictable to a certain degree. The use of a “zero faeces” sampling policy in the D1 catchment did ensure that a higher sampling density was used in the marshland CSA zone because of the uneven distribution of faecal deposits (e.g. sheep faeces were confined to animal tracks that are used routinely by livestock and small patches of short grass). The delineation of CSA and non-CSA zones provided an interesting comparison in this study. However, for generic application to other catchments where farmer cooperation may not be possible then such an approach would prove difficult because of a lack of data on livestock movements within different CSA/non-CSA zones. This zoning aspect of the protocol would only prove fruitful if a specified catchment area (or field) was easily distinguished as a CSA or nonCSA and livestock information was readily available. It could be argued that the importance of burden deposited in CSA should be weighted more heavily than non-CSA burden because of it being more connected to the watercourse and therefore more likely to contribute to microbial watercourse pollution. Our use of a catchment average based on a sum of CSA and non-CSA was not implicitly weighted towards the CSA but we had more confidence in CSA values because of the higher sampling density. Future considerations could be to weight the CSA more heavily than non-CSA and move to sampling only the CSA on the basis of cost and time constraints. However, the results of our study demonstrated that the CSA does not appear to attract greater loading of faecal material and therefore greater E. coli burden. It is interesting to note that a greater faecal burden derived from sheep in the CSA was observed during drier months though this observation was relatively short lived. The CSA within the D1 catchment is a particularly wet area of marshland and during summer months this probably becomes more accessible for sheep grazing. The contribution of dairy faeces to the CSA is consistently low and this was dictated, in part, by field boundaries that restrict cattle access from the majority of the CSA. Others have observed that wetland areas receive less faecal contamination by cattle and attributed this to livestock avoiding these boggy areas (Collins, 2004). The determination of E. coli delivery coefficients linked to different catchment typologies will play an important role in helping identify suitable ‘programmes of measures’ to protect water quality from microbial impairment. Understanding behavioural characteristics of pollutants in the environment, in this case the dynamic E. coli burden response with time and livestock management, will underpin the derivation of such catchment-based delivery metrics (Oliver et al., 2010c). While this study has identified the temporal patterns of E. coli burden on pasture it has not been able to inform on the proportion of the E. coli burden that is potentially mobile. Laboratory scale studies have reported the mobility of FIOs from different faecal matrices (Hodgson et al., 2009) and could perhaps inform the ‘riskiness’ of different faecal contributions to pasture thus offering potential for coupling to land based assessments of E. coli burden as demonstrated in this study. Through improved and tested confidence in model predictions and by demonstrating the potential of a field-based protocol to determine E. coli burden on pasture we can open up new opportunities for determining reliable catchment scale ‘delivery coefficients’ for microbial contaminants sourced from agricultural land. 5. Conclusions This study represents the first dynamic accounting of E. coli burden in a small headwater catchment via direct field-based observation and complementary modelling. While field-based studies are difficult to undertake they are essential so that we can ground-truth
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D.M. Oliver et al. / Environment International 43 (2012) 6–12
existing model predictions and assess whether they are in fact ‘fit for purpose’. Deriving field-based values is of course inherently uncertain and our approach marks a first approximation evaluated against predictions from an empirical model that has proved to be of reasonable fit to the observed data for this particular catchment. This is encouraging because it suggests that time-consuming and labour-intensive field sampling strategies are not always necessary to inform on approximate catchment scale levels of E. coli burden. However, E. coli burden will not only vary with LUs but can also encounter significant shifts linked to extreme temperature conditions and rainfall drivers that can lead to a loss of these bacteria, which are surrogates of infection risk to humans, from the catchment either through enhanced rates of die-off or increased wash-off from the soil surface. Understanding how these extreme environmental drivers impact on catchment scale E. coli burden now needs investigation in order to refine empirical modelling approaches further for public health and water quality protection. In addition, future research should explore the role of rough grazing versus improved pasture as differential sources of faecal material linked to grazing behaviour. Finally, this study reports on one particular catchment and further testing and evaluation of the methodology are needed in catchments with differing landscape and management characteristics. Acknowledgements This study was funded under contract to the UK Department for Environment, Food and Rural Affairs (Defra) as part fulfilment of project WQ0129. The authors would like to acknowledge the valuable and insightful contribution of the participating farmers and are particularly grateful for land access permissions without which the project could not have been undertaken. In addition, contributions from David Kay, Andy Vinten, Richard Muirhead, Davey Jones, Roger Pickup, Chris Hodgson, Rob Fish and Luke Spadavecchia at the PEDAL2 FIO expert workshop at Lancaster University were instrumental in helping to identify a suitable sampling strategy for this study. Finally, the critique and recommendations of the two anonymous reviewers significantly improved the final version of this paper. References Auerswald K, Mayer F, Schnyder H. Coupling of spatial and temporal pattern of cattle excreta patches on a low intensity pasture. Nutr Cycl Agroecosyst 2010;88:275–88. Beven K, Heathwaite AL, Haygarth PM, Walling D, Brazier R, Withers P. On the concept of delivery of sediments and nutrients to stream channels. Hydro Proc 2005;19:551–6. Chadwick D, Fish R, Oliver DM, Heathwaite L, Hodgson C, Winter M. Management of livestock and their manure to reduce the risk of microbial transfers to water: the case for an interdisciplinary approach. Trends Food Sci Tech 2008;19:240–7. Collins R. Fecal contamination of pastoral wetlands. J Environ Qual 2004;33:1912–8. Davies-Colley R, Lydiard E, Nagels J. Stormflow-dominated loads of faecal pollution from an intensively dairy-farmed catchment. Water Sci Tech 2008;57:1519–23. Defra. Phosphorus export and delivery in agricultural landscapes: the PEDAL project. Final Report 2006. (pp31), available online at: http://randd.defra.gov.uk/Document. aspx?Document=PE0113_7347_FRP.pdf. (accessed 7th June 2011). Dickinson CH, Underhay VSH, Ross V. Effect of season, soil fauna and water content on the decomposition of cattle dung pats. New Phytol 1981;88:129–41. Donnison A, Ross C, Clark D. Escherichia coli shedding by dairy cows. New Zeal J Agr Res 2008;51:273–8. Dorner SM, Huck PM, Slawson RM. Estimating potential environmental loadings of Cryptosporidium spp. and Campylobacter spp. from livestock in the Grand River Watershed, Ontario, Canada. Environ Sci Technol 2004;38:3370–80. Environment Agency. The microbiology of drinking water 2009 part 4 – methods for the isolation and enumeration of coliform bacteria and Escherichia coli (including E. coli O157:H7). Bristol: Standing Committee of Analysts 2009, The Environment Agency; 2009. 104 pp. Ferguson CM, Charles K, Deere DA. Quantification of microbial sources in drinkingwater catchments. Crit Rev Environ Sci Tech 2009;39:1-40. Gburek WJ, Sharpley AN. Hydrologic controls on phosphorus loss from upland agricultural watersheds. J Environ Qual 1998;27:267–77. Habteselassie M, Bischoff M, Blume E, Applegate B, Reuhs B, Brouder S, Turco RF. Environmental controls on the fate of Escherichia coli in soil. Water Air Soil Pollut 2008;190:143–55. Hampson D, Crowther J, Bateman I, Kay D, Posen P, Stapleton C, Wyer M, Fezzi C, Jones P, Tzanopoulos J. Predicting microbial pollution concentrations in UK rivers in response to land use change. Water Res 2010;44:4748–59.
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