Better Targeting Water Quality Remediation Using Bacterial Source Tracking

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1 Better Targeting Water Quality Remediation Using Bacterial Source Tracking 2015 UCOWR/NIWR/CUAHSI Conference Kevin Wagner, George DiGiovanni, Terry Gentry, Daren Harmel, Lucas Gregory

2 Bacteria/Pathogens The #1 Cause of River/Stream Impairment in U.S. Rank Rivers/Streams Lakes/Reservoirs Bays/Estuaries 1 Pathogens (16%) Mercury (43%) Mercury (33%) 2 Sediment (12%) Nutrients (18%) PCBs (23%) 3 Nutrients () PCBs (16%) Pathogens (21%) 4 Organic enrichment / Oxygen Depletion (9%) 5 PCBs (8%) Turbidity (8%) Organic enrichment / Oxygen Depletion (8%) Organic enrichment / Oxygen Depletion (17%) Dioxins (14%)

3 Where did the Bacteria (E. coli) Come From? Potential sources Humans Domesticated animals Wildlife Methods for determining sources Source survey Modeling Bacterial source tracking (BST)

4 PREMISE BEHIND BST Different guts Different adaptations Different E. coli strains Genetic Differences Phenotypic Differences

5 Classifications of BST Methods

6 Establishment of Texas BST Program (2007) Two DNA fingerprinting methods selected: Enterobacterial repetitive intergenic consensus sequence-polymerase chain reaction (ERIC-PCR) RiboPrinting (RP) Required BST Library Development

7 Development of Texas E. coli BST Library Sources Isolate E. coli DNA Fingerprint Add to Library

8 Texas E. coli BST Library Contains 1,669 E. coli isolates From 1,455 different fecal samples Representing >50 animal subclasses Collected from 13 watersheds (& growing) across Texas Human 25% Domestic Animals 34% Wildlife 41%

9 Use of Texas E. coli BST Library for Identifying Water Isolates Isolate E. coli DNA Fingerprint Compare to Library Source ID

10 Texas E. coli BST Library composition & rates of correct classification (RCC) Source Class Number of Isolates Number of Samples Library Composition and Expected Random Rate of Correct Classification Calculated Rate of Correct Classification (RCC) RCC to Random Ratio *** Left Unidentified (unique patterns) HUMAN % DOMESTIC ANIMALS % Pets % Cattle % Avian Livestock % Other Non-Avian Livestock % WILDLIFE % Avian Wildlife % Non-Avian Wildlife % Overall way = 100% 7-way = 92% 20%

11 Texas BST Studies To Date Typical Landuse in 11 BST Watersheds

12 3-Way Split (averages based on findings in 11 watersheds) 5-Way Split (averages based on findings in 10 watersheds) Wildlife 51% Non-Avian Wildlife 32% Avian Wildlife 18% Unidentified 12% Domestic Animals 27% Human Unidentified 11% Human All Livestock 24% Pets 5% 7-Way Split (averages based on findings in 7 watersheds) Non-Avian Wildlife 32% Avian Wildlife 18% Unidentified 12% Human Cattle 13% Pets 5% Other Non- Avian Avian Livestock Livestock 5% 5%

13 Relation of Landuse to BST Results Developed vs Pet & Human Contributions % of isolates from pets 18% 16% 14% 12% 8% 6% 4% 2% 0% R² = % 5% 15% 20% 25% 30% % of watershed developed % of isolates from human 18% 16% 14% 12% 8% 6% 4% 2% 0% R² = % 5% 15% 20% 25% 30% % of watershed developed Significant correlation between % of watershed developed and % of isolates from pets No correlation between % of watershed developed and % of isolates from human

14 Relation of Landuse to BST Results Cattle % of isolates cattle 25% 20% 15% 5% R² = % of isolates cattle 25% 20% 15% 5% R² = 2E-06 0% 0% 20% 40% 60% 80% % of watershed pasture/range No correlation between watershed landuse and % of isolates from cattle % of isolates cattle 0% 0% 20% 40% 60% 80% % of watershed pasture 25% R² = % 15% 5% 0% 0% 20% 40% 60% 80% % of watershed range

15 Relation of Landuse to BST Results Wildlife % of isolates wildlife 70% 60% 50% 40% 30% 20% Only one significant correlation observed: R² = % 80% 85% 90% 95% 100% % of watershed pasture/forest/range Btwn % of watershed as pasture/range/forest & % of isolates as non-avian wildlife % of isolates avian wildlife % of isolates non-avian wildlife 70% 60% 50% 40% 30% 20% R² = % 80% 85% 90% 95% 100% % of watershed pasture/forest/range 70% 60% 50% 40% R² = % 20% 0% 80% 85% 90% 95% 100% % of watershed pasture/forest/range

16 Grazing Evaluation (Wagner et al. 2012) Objective Evaluate effects of grazing management on bacteria runoff from rangeland and improved pasture 3 Treatments Tested (7 total sites) Heavily stocked (2 x recommended rate) 1 location Moderately stocked (at recommended rates) 3 locations No grazing 3 locations Methods Edge-of-field runoff collected over 3 yrs E. coli - EPA Method 1603 BoBac & AllBac (Layton et al., 2006)

17 Mean Background Levels in Runoff Site Fecal Coliform (#/100 ml) E. coli (cfu/100 ml) Reference Ungrazed pasture 6,200-11,000 Wagner et al Control plots 6,800 Guzman et al Ungrazed pasture 6,600 Doran et al Ungrazed pasture 10,000 Robbins et al. 1972

18 Impacts of Migratory Wildlife E. coli Concentration (cfu/100 ml) E. coli concentrations at ungrazed site BB1 ( ) 300, , , , ,000 50,000 - Mar-09 Apr-09 May-09 Jun-09 Jul-09 Aug-09 Sep-09 Oct-09 Nov-09 Dec-09 Jan-10 Feb-10 Mar-10 Apr-10 May-10 Date BB1 BB2 BB3 3/13/ /25/09 1,200 3/26/09 1,000 7,200 3/27/09 2,000 4/17/09 1, /18/09 4,400 2,225 2,100 4/28/09 7,600 12,200 24,000 10/4/09 57,000 5,114 3,065 10/9/09 36,000 24,043 15,000 10/13/09 42,851 23,826 5,591 10/22/09 172,500 10/26/09 261, ,000 45,000 >80% of E. coli loading from wildlife at 3 sites in 2009

19 BoBac/AllBac % 50% 40% Estimated percentage from cattle 30% 20% 0% BoBac/AllBac %

20 Conclusions BST tremendously helpful in identifying significant bacteria sources Wildlife is source of 50% of isolates in predominately rural watersheds Generally no correlations between landuse and isolate source (i.e. LULC may not be good predictor of bacteria sources) Edge of field monitoring confirms significance of background sources & impacts

21 Questions? Kevin Wagner TWRI Assoc. Director George Di Giovanni Professor, UT School of Public Health El Paso Terry Gentry Assoc. Professor, Texas A&M AgriLife Research