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

Bacteria The #1 Cause of Water Quality Impairment

in Texas

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)

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

Development of TexasE. coli BST Library

Sources

Isolate

E. coli

DNA

Fingerprint

Add to

Library

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

Wildlife41%

Domestic Animals

34%

Human25%

Use of Texas E. coli BST Library for Identifying Water Isolates

Isolate

E. coli

DNA

Fingerprint

Compare

to Library

Source ID

Texas BST Studies To Date

Typical Landuse in 11 BST Watersheds

Wildlife51%

Human10%

Domestic An-imals27%

Unidentified12%

3-Way Split (n=11)

Non-Avian Wildlife

32% Avian Wildlife18%

Pets5%

All Livestock24%

Human10%

Unidentified12%

5-Way Split (n=10)

Non-Avian Wildlife

32%

Avian Wildlife18%

Pets5%Other Non-

Avian Livestock5%

Avian Livestock5%

Cattle13%

Human10%

Unidentified12%

7-Way Split (n=7)

Typical landuse in BST watershedsRelation of Landuse to BST ResultsDeveloped vs Pet & Human Contributions

Significant correlation between % of watershed developed and % of isolates from petsNo correlation between % of watershed developed and % of isolates from human

0% 5% 10% 15% 20% 25% 30%-2%0%2%4%6%8%

10%12%14%16%18%

R² = 0.576711679898713

% of watershed developed

% o

f iso

late

s fr

om p

ets

0% 5% 10% 15% 20% 25% 30%0%2%4%6%8%

10%12%14%16%18%

R² = 0.113328300080188

% of watershed developed

% o

f iso

late

s fr

om h

uman

Typical landuse in BST watershedsRelation of Landuse to BST ResultsCattle

No correlation between watershed landuse and % of isolates from cattle

10% 20% 30% 40% 50% 60% 70% 80%0%

5%

10%

15%

20%

25%

R² = 0.404892816738837

% of watershed pasture/range

% o

f iso

late

s ca

ttle

0% 10% 20% 30% 40% 50% 60% 70% 80%0%

5%

10%

15%

20%

25%

R² = 2.38335975988324E-06

% of watershed pasture

% o

f iso

late

s ca

ttle

0% 10% 20% 30% 40% 50% 60% 70% 80%0%

5%

10%

15%

20%

25%

R² = 0.208320974149374

% of watershed range

% o

f iso

late

s ca

ttle

Typical landuse in BST watershedsRelation of Landuse to BST ResultsWildlife

Only one significant correla-tion observed: Btwn % of watershed as pasture/range/forest & % of isolates as non-avian wildlife

80% 82% 84% 86% 88% 90% 92% 94% 96% 98% 100%0%

10%

20%

30%

40%

50%

60%

70%

R² = 0.00673601610141417

% of watershed pasture/forest/range

% o

f iso

late

s w

ildlif

e

80% 82% 84% 86% 88% 90% 92% 94% 96% 98% 100%0%

10%

20%

30%

40%

50%

60%

70%

R² = 0.498558153563506

% of watershed pasture/forest/range

% o

f iso

late

s no

n-av

ian

wild

life

80% 82% 84% 86% 88% 90% 92% 94% 96% 98% 100%0%

10%

20%

30%

40%

50%

60%

70%

R² = 0.207255593935729

% of watershed pasture/forest/range

% o

f iso

late

s av

ian

wild

life

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)

Comparison of E. coli Levelsof Stocked & Destocked Sites

Cultivated Cultivated with grazed pasture

Grazed pasture

E. coli

(cfu p

er 100 m

L)

1

10

100

1000

10000

100000

Outlier 90th 75th Median Mean 25th 10th

Stocked

Destocked

Comparison of E. coli Levels of Destocked & Ungrazed Sites

Cultivated Cultivated with grazed pasture

Grazed pasture

E. coli

(cfu p

er 100 m

L)

1

10

100

1000

10000

100000

Outlier 90th 75th Median Mean 25th 10th

Mean Background Levels in Runoff

Site

Fecal Coliform

(#/100 mL)

E. coli(cfu/100

mL) Reference

Ungrazed pasture 10,000Robbins et al.

1972

Ungrazed pasture 6,600Doran et al.

1981

Control plots 6,800Guzman et al.

2010Pasture destocked >2 mos. 103 to 104

Collins et al. 2005

Ungrazed pasture6,200-11,000

Wagner et al. 2012

Pasture destocked >2 wks.

2,200-6,000

Wagner et al. 2012

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

Mar

-09

Apr-0

9

May

-09

Jun-

09

Jul-0

9

Aug-0

9

Sep-

09

Oct-0

9

Nov-0

9

Dec-0

9

Jan-

10

Mar

-10

Apr-1

0

May

-10

Jun-

10 -

50,000

100,000

150,000

200,000

250,000

300,000

E. coli concentrations at ungrazed site BB1 (2009-2010)

E.

coli C

once

ntr

ati

on (

cfu/1

00 m

L)

>80% of E. coli loading from wildlife at 3 sites in 2009

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

Implications/QuestionsImplications: • 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?

“In wine there is wisdom, in beer there is strength, in water there is bacteria”

German Proverb