Microbial Pathogen Microbial Pathogen Monitoring and Monitoring and Modeling at the Modeling at the

Cooperative Oxford Cooperative Oxford LaboratoryLaboratory

NCCOS Cooperative Oxford LaboratoryNCCOS Cooperative Oxford Laboratory

Use of Molecular Techniques Use of Molecular Techniques to Detect Emerging Pathogens to Detect Emerging Pathogens

in Coastal Ecosystemsin Coastal EcosystemsEarl J. LewisEarl J. Lewis11, A.K. Leight, A.K. Leight11, Ron Fayer, Ron Fayer22, Jim , Jim

TroutTrout22, Monica Santin, Monica Santin22, and Lihua Xiao, and Lihua Xiao33

11NOAA Cooperative Oxford Laboratory, NOAA Cooperative Oxford Laboratory, 22USDA Beltsville, USDA Beltsville, 33CDC AtlantaCDC Atlanta

East and Gulf Coast Oyster Results- 2002East and Gulf Coast Oyster Results- 2002

# Pos.# Pos. Prev.Prev.

NortheastNortheast 18/47518/475 3.8%3.8%

Mid AtlanticMid Atlantic 14/22514/225 6.2%6.2%

SoutheastSoutheast 3/2253/225 1.3%1.3%

GulfGulf 10/30010/300 3.3%3.3%

Cryptosporidium detection by PCR

Bacterial pathogen abundance in relation to land use and Bacterial pathogen abundance in relation to land use and water quality in the Coastal Bays watershedwater quality in the Coastal Bays watershed

18 sites sampled monthly August 18 sites sampled monthly August 2005 – November 2006 with NPS 2005 – November 2006 with NPS water quality monitoring program water quality monitoring program (n = 216).(n = 216).

Rapid, quantitative, molecular Rapid, quantitative, molecular methods for methods for Vv, VpVv, Vp, and , and Mycobacterium spp.Mycobacterium spp.

Examining linkages between water Examining linkages between water quality, land-use, and pathogen quality, land-use, and pathogen abundance.abundance.

Land Use Water Quality Pathogens

J. Jacobs, M. Rhodes, B. Wood -NOAA/NOS/Cooperative Oxford Lab, B. Sturgis - National Park Service, Assateague Island National SeashoreA. Depaola, J. Nordstrom, G. Blackstone – USFDA, Dauphin Island, AL

Chesapeake Chesapeake Bay MonitoringBay Monitoring

Collaboration with Collaboration with MDNR Water Quality MDNR Water Quality Monitoring ProgramMonitoring Program

Quarterly sampling of all Quarterly sampling of all tidal fixed stations (n = tidal fixed stations (n = 120) since August 2006 120) since August 2006

Testing models Testing models developed for Coastal developed for Coastal Bays on large scale.Bays on large scale.

Land Use Water Quality Pathogens

Indicator Bacteria Monitoring and Source Tracking

-Endpoints:

Total Fecal Coliforms

Total E.coli

Total Enterococcus

-Matrices:

Surface Waters

Bottom Waters

Shellfish

Sediment

Indicator Bacteria Monitoring and Source Tracking

Source Tracking Techniques:

-Antibiotic Resistance Analysis

-esp Gene Detection

-Ribotyping

-Coliphage Typing

-Optical Brighteners

-Human Polyomavirus

Shellfish Harvest Area Closure Decision Making Using Predictive Models

1R. Heath Kelsey

2,3Porter, D.E., 3,4Scott, G.I., 5Newell, C.E., 6D.L. White1. NOAA Center for Coastal Environmental Health and Biomolecular Research, Oxford, MD (JHT Inc)

2. Baruch Institute for Marine and Coastal Sciences, University of South Carolina3. Arnold School of Public Health, University of South Carolina

4. NOAA Center for Coastal Environmental Health and Biomolecular Research, Charleston, SC5. Shellfish Sanitation Program, South Carolina Department of Health and Environmental Control

6. NOAA NOS Hollings Marine Lab, Charleston, SC

I. Background

Harvest area classification:

Approved, Conditionally Approved, Restricted, Prohibited.

Management for public health risk

Sanitary Surveys: 3-year geometric mean fecal coliform density, potential pollution sources

Harvest area classification:

Approved, Conditionally Approved, Restricted, Prohibited.

Management for public health risk

Sanitary Surveys: 3-year geometric mean fecal coliform density, potential pollution sources

II. Project Goals and ApproachFactors affecting fecal coliform bacteria loading and survival

I. BackgroundMedian log density = f (24-Hour Raingauge Precipitation)

R2 = 0.00 (p=0.67)

Predicted median log(FCD)

Ob

serv

ed

me

dia

n lo

g(F

CD

)

0.0 0.5 1.0 1.5 2.0

0.0

0.5

1.0

1.5

2.0

2.5

Murrells Inlet Precipitation Model

I. Background

• NEXRAD precipitation estimate

• Previous research identified potential improvements to rain gauge precipitation

• NEXRAD precipitation estimate

• Previous research identified potential improvements to rain gauge precipitation

NEXRAD RADAR Precipitation

I. Background

• NEXRAD precipitation extraction

• NEXRAD precipitation extraction

24-hour Rain Gauge Precipitation (in)

24

-ho

ur

NE

XR

AD

Pre

cip

itatio

n (

in)

0.0 0.2 0.4 0.6

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Pearson p = .49Spearman p = .83

I. Background

Raingauge and NEXRAD correlation

II. Project Goals and Approach

Potential improvements through modeling:

• Alternative data to raingauge precipitation

• Area-weighted precipitation estimates

• Increased predictability of fecal coliform bacteria density

Selected four trial estuaries

• Murrell’s Inlet (Area 4)

• Pawley’s Island (Area 4)

• Mt Pleasant (Area 9A)

• May and New Rivers (Area 19)

At each estuary:

• Model median fecal coliform density on sampling dates

• Trial closure criteria: ½ of stations exceed 43 cfu/100 ml

II. Project Goals and Approach• Murrell’s Inlet and Pawley’s Island (both in Area 4): High salinity, highly

urbanized, lagoonal

• Mt Pleasant (Area 9A): Urbanized, estuary is part of ICW

• May and New Rivers (Area 19): Riverine, lower salinity, less urbanized

II. Project Goals and Approach

Grouped by sample dateSpearman p = .01Pearson p = .04

Gauge Precipitation (in)

Fe

cal C

olif

orm

me

dia

n (

log

(FC

D))

0.0 0.2 0.4 0.6

01

23

4

Murrells Inlet Fecal Coliform Density by Raingauge Precipitation

II. Project Goals and Approach

Grouped by sample dateSpearman p = .001Pearson p = 4.8 E-6

NEXRAD Precipitation (in)

Fe

cal C

olif

orm

me

dia

n (

log

(FC

D))

0.0 0.2 0.4 0.6 0.8 1.0 1.2

01

23

4

Murrells Inlet Fecal Coliform Density by NEXRAD Precipitation

II. Project Goals and Approach

Grouped by sample dateSpearman p = .0009Pearson p = .0027

Water Temperature (C)

Fe

cal C

olif

orm

me

dia

n(l

og

(FC

D))

10 15 20 25

01

23

4

Murrells Inlet Fecal Coliform Density by Water Temperature

II. Project Goals and Approach

Grouped by sample dateSpearman p = .03Pearson p = 3.3 E-7

Salinity, (ppt)

Fe

cal C

olif

orm

me

dia

n (

log

(FC

D))

24 26 28 30 32 34 36

01

23

4

Murrells Inlet Fecal Coliform Density by Salinity

II. Project Goals and ApproachEmphasis on simple models focusing on differences between days

• Evaluate rain gauge and NEXRAD precipitation• Evaluate importance of salinity as predictor• Evaluate tree models, regression models• Four years model development data, additional year

validation data

III. Model Results Simple models effective

• Salinity and temperature explain ~50% of variability• Salinity most important predictor• NEXRAD precipitation data more useful as a predictor • More complex models higher R2 but more sensitive• Regression tree models had lower classification of criterion

exceedence

III. Model ResultsComplex models:

Median Log Density = f (Water Temperature + Salinity + NEXRAD + Tide + Wind)

Model

Predicted/Observed

<43

Predicted/Observed

>43 ARCC (%)Missed

ClosuresUnnecessary

Closures

Regression 42/43 4/4 98% 0 1

Regression No Salinity 43/44 1/4 92% 3 1

Regression Tree 39/44 2/4 85% 2 5

Regression Tree No Salinity 42/44 2/4 92% 2 1

NEXRAD Data

Model

Predicted/Observed

<43

Predicted/Observed

>43 ARCC (%)Missed

ClosuresUnnecessary

Closures

Regression 36/43 4/4 85% 0 5

Regression No Salinity 41/44 1/4 88% 3 2

Regression Tree 39/44 1/4 83% 2 5

Regression Tree No Salinity 44/44 0/4 92% 4 0

Rain Gauge Data

Predicted median log(FCD)

Ob

serv

ed

me

dia

n lo

g(F

CD

)

0 1 2 3 4

0.0

0.5

1.0

1.5

2.0

2.5

Murrells Inlet Complex Model

III. Model Results

Under Predicted

Median Log Density = f ( Water Temperature + Salinity + NEXRAD + Tide)R2 = 0.79

43 cfu/100ml

43 c

fu/1

00m

l

Over Predicted

Predicted median log(FCD)

Ob

serv

ed

me

dia

n lo

g(F

CD

)

0 1 2 3

0.0

0.5

1.0

1.5

2.0

2.5

Murrells Inlet Limited Model

III. Model Results

Under Predicted

Over Predicted

Median Log Density = f ( Water Temperature + Salinity) R2 = 0.48

43 cfu/100ml

43 c

fu/1

00m

l

Under Predicted

Over Predicted

IV. ConclusionsImplications for Management

• Models based on raingauge precipitation not overly predictive in all areas

• Move to models based on salinity and water temperature

Under Predicted

Over Predicted

Predicted median log(FCD)

Ob

serv

ed

me

dia

n lo

g(F

CD

)

0.0 0.5 1.0 1.5 2.0

0.0

0.5

1.0

1.5

2.0

2.5

Murrells Inlet Precipitation Model

Predicted median log(FCD)

Ob

serv

ed

me

dia

n lo

g(F

CD

)

0 1 2 3

0.0

0.5

1.0

1.5

2.0

2.5

Murrells Inlet Limited Model

IV. ConclusionsContinued Research

• Problem with data availability for salinity• Potential predictors for simple salinity model – single station?• Continue model development and validation• Analyze by watershed unit, subset stations• Long Term: Incorporate real time data for ‘Now-Cast’ and forecast

data for ‘Forecast’ of closure conditions• Also potential to apply this approach to beach closures

Acknowledgments

Mr. Chuck Gorman, South Carolina Dept. of Health and Env. ControlMr. Mike Pearson, South Carolina Dept. of Health and Env. ControlDr. Don Edwards, University of South Carolina Dept of StatisticsDr. Roumen Vesselinov , University of South Carolina Dept of StatisticsMr. Matthew Neet, University of South Carolina Baruch Institute for Marine and Coastal SciencesMr. Sam Walker, University of South Carolina Baruch Institute for Marine and Coastal SciencesMs. Jackie Whitlock, University of South Carolina Baruch Institute for Marine and Coastal SciencesMr. Wayne Dodgens, University of South Carolina Baruch Institute for Marine and Coastal SciencesMr. Ed Yu, University of South Carolina Mr. Randy Shelley, University of South CarolinaDr. Howard Townsend, NOAA Chesapeake Bay OfficeDr. Hongguang Ma, NOAA Chesapeake Bay Office (Versar)Dr. Xinsheng Zhang, NOAA NOS NCCOS (JHT, Inc) Ms. Caroline Wicks, University of Maryland Center for Env. ScienceMs. Kate Boicourt, University of Maryland Center for Env. ScienceMs. Maddy Sigrist, NOAA Chesapeake Bay Office (CRC)

Baruch Institute for Marine and Coastal Sciences, University of South Carolina

Arnold School of Public Health, University of South Carolina

The Urbanization and Southeast Estuarine Systems Project

South Carolina Sea Grant

NOAA Center for Coastal Environmental Health and Biomolecular Research, Charleston, SC, and Oxford, MD

Contacts

Cooperative Oxford LabCooperative Oxford Lab904 S. Morris St.904 S. Morris St.Oxford, MD 21654Oxford, MD 21654410.226.5193410.226.5193

Jay Lewis Jay Lewis Jay.Lewis@noaa.gov

AK LeightAK LeightAK.Leight@noaa.gov

John JacobsJohn JacobsJohn.Jacobs@noaa.gov

R. Heath KelseyR. Heath Kelseyheath.kelsey@noaa.gov