1. Microbial Pathogen Monitoring and Modeling at the Cooperative Oxford Laboratory

2. Use of Molecular Techniques to Detect Emerging Pathogens in Coastal Ecosystems Earl J. Lewis 1, A.K. Leight 1, Ron Fayer 2, Jim Trout 2, Monica Santin 2, and Lihua Xiao 3 1 NOAA Cooperative Oxford Laboratory, 2 USDA Beltsville, 3 CDC Atlanta

3. East and Gulf Coast Oyster Results- 2002 # Pos. # Pos.Prev.Northeast18/475 3.8% 3.8% Mid Atlantic 14/225 6.2% 6.2% Southeast 3/225 3/225 1.3% 1.3% Gulf10/300 3.3% 3.3% Cryptosporidium detection by PCR

4. Bacterial pathogen abundance in relation to land use and water quality in the Coastal Bays watershed  18 sites sampled monthly August 2005 – November 2006 with NPS water quality monitoring program (n = 216).  Rapid, quantitative, molecular methods for Vv, Vp, and Mycobacterium spp.  Examining linkages between water quality, land-use, and pathogen abundance. Land UseWater QualityPathogens J. Jacobs, M. Rhodes, B. Wood -NOAA/NOS/Cooperative Oxford Lab, B. Sturgis - National Park Service, Assateague Island National Seashore A. Depaola, J. Nordstrom, G. Blackstone – USFDA, Dauphin Island, AL

5. Chesapeake Bay Monitoring  Collaboration with MDNR Water Quality Monitoring Program  Quarterly sampling of all tidal fixed stations (n = 120) since August 2006  Testing models developed for Coastal Bays on large scale. Land UseWater QualityPathogens

6. Indicator Bacteria Monitoring and Source Tracking -Endpoints: Total Fecal Coliforms Total E.coli Total Enterococcus -Matrices: Surface Waters Bottom Waters Shellfish Sediment

7. Indicator Bacteria Monitoring and Source Tracking Source Tracking Techniques: -Antibiotic Resistance Analysis -esp Gene Detection -Ribotyping -Coliphage Typing -Optical Brighteners -Human Polyomavirus

8. Shellfish Harvest Area Closure Decision Making Using Predictive Models 1 R. Heath Kelsey 2,3 Porter, D.E., 3,4 Scott, G.I., 5 Newell, C.E., 6 D.L. White 1.NOAA Center for Coastal Environmental Health and Biomolecular Research, Oxford, MD (JHT Inc) 2.Baruch Institute for Marine and Coastal Sciences, University of South Carolina 3.Arnold School of Public Health, University of South Carolina 4.NOAA Center for Coastal Environmental Health and Biomolecular Research, Charleston, SC 5.Shellfish Sanitation Program, South Carolina Department of Health and Environmental Control 6.NOAA NOS Hollings Marine Lab, Charleston, SC

9. 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

10. II.Project Goals and Approach Factors affecting fecal coliform bacteria loading and survival

11. I.Background Median log density = f (24-Hour Raingauge Precipitation) R 2 = 0.00 (p=0.67)

12. 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

13. I.Background NEXRAD precipitation extraction

14. Pearson p =.49 Spearman p =.83 I.Background Raingauge and NEXRAD correlation

15. 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

16. 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

17. II.Project Goals and Approach Grouped by sample date Spearman p =.01 Pearson p =.04

18. II.Project Goals and Approach Grouped by sample date Spearman p =.001 Pearson p = 4.8 E-6

19. II.Project Goals and Approach Grouped by sample date Spearman p =.0009 Pearson p =.0027

20. II.Project Goals and Approach Grouped by sample date Spearman p =.03 Pearson p = 3.3 E-7

21. II.Project Goals and Approach Emphasis 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

22. 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 R 2 but more sensitive Regression tree models had lower classification of criterion exceedence

23. III.Model Results Complex models: Median Log Density = f (Water Temperature + Salinity + NEXRAD + Tide + Wind)

24. III.Model Results Under Predicted Median Log Density = f ( Water Temperature + Salinity + NEXRAD + Tide) R 2 = 0.79 43 cfu/100ml Over Predicted

25. III.Model Results Under Predicted Over Predicted Median Log Density = f ( Water Temperature + Salinity) R 2 = 0.48 43 cfu/100ml Under Predicted Over Predicted

26. IV.Conclusions Implications 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

27. IV.Conclusions Continued 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

28. Acknowledgments Mr. Chuck Gorman, South Carolina Dept. of Health and Env. Control Mr. Mike Pearson, South Carolina Dept. of Health and Env. Control Dr. Don Edwards, University of South Carolina Dept of Statistics Dr. Roumen Vesselinov, University of South Carolina Dept of Statistics Mr. Matthew Neet, University of South Carolina Baruch Institute for Marine and Coastal Sciences Mr. Sam Walker, University of South Carolina Baruch Institute for Marine and Coastal Sciences Ms. Jackie Whitlock, University of South Carolina Baruch Institute for Marine and Coastal Sciences Mr. Wayne Dodgens, University of South Carolina Baruch Institute for Marine and Coastal Sciences Mr. Ed Yu, University of South Carolina Mr. Randy Shelley, University of South Carolina Dr. Howard Townsend, NOAA Chesapeake Bay Office Dr. Hongguang Ma, NOAA Chesapeake Bay Office (Versar) Dr. Xinsheng Zhang, NOAA NOS NCCOS (JHT, Inc) Ms. Caroline Wicks, University of Maryland Center for Env. Science Ms. Kate Boicourt, University of Maryland Center for Env. Science Ms. 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

29. Contacts Cooperative Oxford Lab 904 S. Morris St. Oxford, MD 21654 410.226.5193 Jay Lewis Jay.Lewis@noaa.gov AK Leight AK.Leight@noaa.gov John Jacobs John.Jacobs@noaa.gov R. Heath Kelsey heath.kelsey@noaa.gov