Full proceedings at: http://www.extension.org/72882 According to the 305(b) report, the majority of waterbodies in the US do not meet established water quality standards. Over half (51%) of river miles assessed in 2010 were impaired. Pathogens are the leading cause of water quality impairment in rivers and streams of the US, impairing 16% of river/stream miles assessed. Computer models used to assess bacteria sources and loads in impaired watersheds are generally able to attribute loadings to specific land uses or in some cases specific animal categories based on known or estimated animal population numbers and fecal production rates. To provide better data on the predominant animal sources of bacterial impairments, Texas initiated a bacterial source tracking (BST) program in 2003.

1. What’re the sources of bacteria
in your watershed? They may not
be what you expect
2015 Waste to Worth Conference
March 31, 2015
Kevin Wagner, Terry Gentry, Daren Harmel,
George Di Giovanni, Lucas Gregory, Elizabeth
Casarez, R. Karthikeyan

2. Bacteria
The #1 Cause of Water Quality Impairment in Texas

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. 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

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

6. Texas E. coli BST Library
• Contains
• 1,632 E. coli isolates
• From 1,423 different
fecal samples
• Representing >50
animal subclasses
• Collected from 13
watersheds (& growing)
across Texas
Wildlife
41%
Domestic
Animals
34%
Human
25%

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

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

9. Wildlife
51%
Human
10%
Domestic
Animals
27%
Unidentified
12%
3-Way Split (n=11)
Non-Avian
Wildlife
32% Avian Wildlife
18%
Pets
5%
All Livestock
24%Human
10%
Unidentified
11%
5-Way Split (n=10)
Non-Avian
Wildlife
32%
Avian Wildlife
18%
Pets
5%
Other Non-
Avian
Livestock
5%
Avian
Livestock
5%
Cattle
13%
Human
10%
Unidentified
12%
7-Way Split (n=7)

10. Relation of Landuse to BST Results
Developed vs Pet & Human Contributions
• Significant correlation between % of watershed
developed and % of isolates from pets
• No correlation between % of watershed
developed and % of isolates from human
R² = 0.5767
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
0% 5% 10% 15% 20% 25% 30%
%ofisolatesfrompets
% of watershed developed
R² = 0.1133
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
0% 5% 10% 15% 20% 25% 30%
%ofisolatesfromhuman
% of watershed developed

11. Relation of Landuse to BST Results
Cattle
• No correlation
between watershed
landuse and % of
isolates from cattle
R² = 0.4049
0%
5%
10%
15%
20%
25%
0% 20% 40% 60% 80%
%ofisolatescattle
% of watershed pasture/range
R² = 2E-06
0%
5%
10%
15%
20%
25%
0% 20% 40% 60% 80%
%ofisolatescattle
% of watershed pasture
R² = 0.2083
0%
5%
10%
15%
20%
25%
0% 20% 40% 60% 80%
%ofisolatescattle
% of watershed range

12. Relation of Landuse to BST Results
Wildlife
• Only one significant
correlation observed:
– Btwn % of watershed as
pasture/range/forest & % of
isolates as non-avian wildlife
R² = 0.0067
0%
10%
20%
30%
40%
50%
60%
70%
80% 85% 90% 95% 100%
%ofisolateswildlife
% of watershed pasture/forest/range
R² = 0.4986
0%
10%
20%
30%
40%
50%
60%
70%
80% 85% 90% 95% 100%
%ofisolatesnon-avianwildlife
% of watershed pasture/forest/range
R² = 0.2073
0%
10%
20%
30%
40%
50%
60%
70%
80% 85% 90% 95% 100%
%ofisolatesavianwildlife
% of watershed pasture/forest/range

13. 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)

14. Comparison of E. coli Levels
of Stocked & Destocked Sites
Cultivated Cultivated with
grazed pasture
Grazed
pasture
E.coli(cfuper100mL)
1
10
100
1000
10000
100000
Outlier
90th
75th
Median
Mean
25th
10th
Stocked
Destocked

15. Comparison of E. coli Levels of
Destocked & Ungrazed Sites
Cultivated Cultivated with
grazed pasture
Grazed
pasture
E.coli(cfuper100mL)
1
10
100
1000
10000
100000
Outlier
90th
75th
Median
Mean
25th
10th

16. Mean Background Levels in Runoff
Site
Fecal
Coliform
(#/100 mL)
E. coli
(cfu/100 mL) Reference
Ungrazed pasture 10,000
Robbins et al.
1972
Ungrazed pasture 6,600 Doran et al. 1981
Control plots 6,800
Guzman et al.
2010
Pasture destocked >2
mos. 103 to 104 Collins et al. 2005
Ungrazed pasture 6,200-11,000
Wagner et al.
2012
Pasture destocked >2
wks. 2,200-6,000
Wagner et al.
2012

17. Impacts of Migratory Wildlife
Date BB1 BB2 BB3
3/13/09 140
3/25/09 1,200
3/26/09 1,000 7,200
3/27/09 2,000
4/17/09 1,155 980 450
4/18/09 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
10/26/09 261,000 181,000 45,000
-
50,000
100,000
150,000
200,000
250,000
300,000
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
E.coliConcentration(cfu/100mL)
E. coli concentrations at ungrazed site BB1
(2009-2010)
>80% of E. coli loading from wildlife at 3 sites in 2009

18. 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
• Edge of field monitoring confirms significance
of background sources & impacts

19. Implications/Questions
Implications:
• Background/wildlife loadings need to be considered when:
– Applying water quality standards
– Developing tmdls and watershed based plans
• Ignoring background concentrations may lead to:
– Nonattainment of water quality standards
– Inaccurate load allocations and reductions
Questions remain including:
• How do we better integrate background/wildlife loadings into
water quality management?
• What can/should we do to address wildlife loads?
• What are sources of “unidentified” isolates?

20. “In wine there is wisdom, in beer there
is strength, in water there is bacteria”
German Proverb