Support for Integrated Ecosystem Assessments of 

NOAA’s National Estuarine Research Reserves 

System (NERRS), Volume I: 

The Impacts of Coastal Development on the Ecology 

and Human Well-being of Tidal Creek Ecosystems of 

the US Southeast

NOAA Technical Memorandum NOS NCCOS 82 

NC 

NERRS 

Sapelo Island 

ACE Basin 

N. Inlet-Winyah Bay 
This report has been reviewed by the National Ocean Service of the National 

Oceanic and Atmospheric Administration (NOAA) and approved for publication. 

Mention of trade names or commercial products does not constitute endorsement 

or recommendation for their use by the United States government.

Citation for this Report 

Sanger, D., A. Blair, G. DiDonato, T. Washburn, S. Jones, R. Chapman, D. Bergquist, G. 

Riekerk, E. Wirth, J. Stewart, D. White, L. Vandiver, S. White, D. Whitall. 2008. Support for 

Integrated Ecosystem Assessments of NOAA’s National Estuarine Research Reserves System 

(NERRS), Volume I: The Impacts of Coastal Development on the Ecology and Human Wellbeing of Tidal Creek Ecosystems of the US Southeast. 

NOAA Technical Memorandum NOS NCCOS 82. 76 pp. 
Support for Integrated Ecosystem Assessments of NOAA’s National Estuarine 

Research Reserves System (NERRS), Volume I: The Impacts of Coastal 

Development on the Ecology and Human Well-being of Tidal Creek 

Ecosystems of the US Southeast 

D. Sangera, A. Blairb, G. DiDonatob, T. Washburnc, S. Jonesc, R. Chapmand, D. Bergquistd, G. 

Riekerkd, E. Wirthe, J. Stewarte, D. Whiteb, L. Vandiverf, S. Whiteb, D. Whitallg

a

South Carolina Sea Grant Consortium, 287 Meeting Street, Charleston, SC 29401, USA 

b

NOAA, Center for Human Health Risk, Hollings Marine Laboratory, 331 Fort Johnston Road, 

Charleston, SC, 29412, USA 

c

Graduate Program in Marine Biology, College of Charleston, 205 Fort Johnson Road, 

Charleston, SC, 29412, USA 

d

Marine Resources Research Institute, South Carolina Department of Natural Resources, 217 

Fort Johnson Road, Charleston SC, 29412, USA 

e

NOAA, Center for Coastal Environmental Health and Biomolecular Research, 219 Fort Johnson 

Road, Charleston, SC, 29412, USA 

f

Arnold School of Public Health, University of South Carolina, 800 Sumter Street, Columbia, SC 

29208, USA 

g

NOAA, Center for Coastal Monitoring and Assessment, 1305 East-West Highway, Silver 

Spring, MD, 20910, USA

NOAA Technical Memorandum NOS NCCOS 82 

October 2008 

United States Department of National Oceanic and National Ocean Service 

Commerce Atmospheric Administration 

Carlos M. Gutierrez Conrad C. Lautenbacher, Jr. John (Jack) H. Dunnigan 

 Secretary Administrator Assistant 

Table of Contents 

Acknowledgements ......................................................................................................................iii

List of Tables ................................................................................................................................ iv

List of Figures................................................................................................................................ v

Abstract.......................................................................................................................................... 1

1. Introduction.............................................................................................................................. 3

2. Methods..................................................................................................................................... 5 

2.1 Study Sites.................................................................................................................... 5 

2.2 Land Use and Watershed Determinations ................................................................ 6 

2.3 Stormwater Runoff Determinations .......................................................................... 8 

2.4 Sample Design.............................................................................................................. 9 

2.5 Laboratory Processing Methods .............................................................................. 11 

2.5.1 Basic Water Quality ......................................................................................... 11 

2.5.2 Nutrients and Phytoplankton .......................................................................... 11 

2.5.3 Pathogen Indicators......................................................................................... 11 

2.5.4 Chemical Contaminants .................................................................................. 11 

2.5.5 Macrobenthic Community ............................................................................... 13 

2.5.6 Nekton Community .......................................................................................... 13 

2.5.7 Oyster Tissue Genomics................................................................................... 13 

2.6 Data Summary and Statistical Analyses ................................................................. 14

3. Results ..................................................................................................................................... 15 

3.1 Stressors ..................................................................................................................... 15 

3.2 Exposures ................................................................................................................... 19 

3.2.1 Stormwater Runoff........................................................................................... 19 

3.2.2 Basic Water Quality ......................................................................................... 21 

3.2.3 Nutrients and Phytoplankton .......................................................................... 23 

3.2.4 Pathogens......................................................................................................... 28 

3.2.5 Sediment Quality.............................................................................................. 30 

3.3 Ecological Response .................................................................................................. 36 

3.3.1 Macrobenthic Community ............................................................................... 36 

3.3.2 Nekton Community .......................................................................................... 42 

3.4 Human Responses...................................................................................................... 45 

3.4.1 Oyster Tissue Pathogens.................................................................................. 45 

3.4.2 Oyster Tissue Contaminants............................................................................. 47 

3.4.3 Oyster Tissue Genomics................................................................................... 50

Research Highlight – Related Research in Sapelo Island NERR........................................... 53

i 
4. Discussion................................................................................................................................ 56

4.1 Sentinel Habitats........................................................................................................ 56 

4.2 Conceptual Model...................................................................................................... 58 

4.3 NERRs as Regional References................................................................................ 60 

4.4 Forecasting ................................................................................................................. 61 

4.5 Summary .................................................................................................................... 62

5. Overall Project Summary and Conclustions....................................................................... 63 

5.1 Summary Points from Volume I............................................................64 

5.2 Summary Points from Volume II............................................................................. 64 

5.3 Coastal Management Applications and Opportunities.......................................... 65

References.................................................................................................................................... 67

Appendix A. Expected range of method detection limits for the sediment and oyster 

 tissue contaminant analyses...............................................................72 

Appendix B. The 24 analytes used to calculate the mean ERM quotient...................... 76 

ii 
Acknowledgements 

The breadth of this research project has resulted in a large number of individuals and 

organizations to thank for their efforts. 

We are grateful to our National Estuarine Research Reserve partners for field work and support: 

R. Ellin, J. Fear, P. Murray, H. Wells (North Carolina) and D. Hurley, B. Sullivan (Sapelo 

Island). We appreciate the field assistance and laboratory space provided by P. Christian and K. 

Gates, University of Georgia Marine Extension Office, Brunswick. 

We wish to thank, for their dedication and hard work, the many individuals who assisted in 

sample collection: P. Biondo, C. Buzzelli, A. Coghill, A. Colton, C. Cooksey, D. Couillard, S. 

Drescher, M. Dunlap, J. Felber, R. Garner, A. Hilton, S. Lovelace, E. McDonald, M. 

Messersmith, S. Mitchell, C. Rathburn, J. Reeves, J. Richardson, A. Rourk, K. Seals, J. Siewicki, 

and M. Tibbett. 

We appreciate laboratory work provided by the following. Barry A. Vittor & Associates and the 

South Carolina Department of Natural Resouces – Marine Resources Research Institute 

laboratory staff including P. Biondo, S. Burns, J. Felber, L. Forbes, and A. Rourk processed 

benthic, nekton and sediment samples. Coastal Environmental Health and Biomolecular 

Research laboratory staff including J. Gregory, C. Johnston, B. Robinson, B. Thompson, and L. 

Webster analyzed water and oyster samples for pathogens. Center for Human Health Risk / 

Hollings Marine Laboratory (CHHR/HML) staff including D. Liebert, Y. Sapozhnikova, B. 

Shaddix, L. Thorsell analyzed sediment and oyster samples for contaminants, and CHHR/HML 

staff including M. Beal and A. Mancia performed oyster genomic analysis. University of 

Maryland Center for Environmental Science – Chesapeake Biological Laboratory's Nutrient 

Analytical Services Lab staff analyzed water samples for nutrients and phytoplankton. 

We thank the individuals who provided insightful peer-reviews of the report: John Fear, Dorset 

Hurley, and Gunnar Lauenstein. And we thank Len Balthis, Mike Fulton, Fred Holland, and Bob 

Van Dolah for providing comments enhancing the quality of this document. 

iii 
List of Tables 

Table 1. Orders sampled, date sampled, latitude, and longitude for each study creek network by 

state. 

Table 2. Creek system, land use class, watershed area, and watershed impervious cover for 

each creek segment. 

Table 3. Results of 2-way ANOVA on averages and selected ranges of water quality indicator 

variables sampled in summer, 2005 and 2006. 

Table 4. Characteristics and contaminant concentrations in tidal creek sediments for selected 

parameters. 

Table 5. The number of ERL exceedences (as defined by Long et al. 1995) for each 

contaminant class at each sampling site. 

Table 6. Results of 2-way ANOVA on sediment characteristics and calculated mean Effects 

Range Median Quotient (mERMQ) values. 

Table 7. Average number of individuals per meter squared and the percent of samples in which 

the major taxonomic classes were collected in summer, 2005 and 2006. 

Table 8. Average number of individuals per meter squared and percent of samples in which the 

twenty most abundant species were collected in summer, 2005 and 2006. 

Table 9. Results of 2-way ANCOVA on average densities (except percent composition for 

Oligochaeta and Polychaeta) of macrobenthic taxa and diversity metrics sampled in 

summer, 2005 and 2006. 

Table 10. Average abundance per area of the top ten most abundant nekton species collected by 

land use class (Forested, Marsh, Suburban, Urban) and by creek order (intertidal, 

subtidal). 

Table 11. Results of 1-way ANOVA examining differences in average abundance in intertidal 

and subtidal creeks separately by land use class. 

Table 12. Results of 2-way ANOVA examining differences in concentrations of selected 

pathogen indicators measured in tissue from oysters collected in study creeks. 

Table 13. Contaminant wet weight concentrations in tidal creek oysters. 

Table 14. Oyster tissue analysis. 

iv 
List of Figures 

Figure 1. Conceptual model developed by Holland et al. (2004) identifying linkages between 

development of the upland and the ecological response of South Carolina tidal creeks. 

Figure 2. North Carolina, South Carolina, and Georgia sampling sites. 

Figure 3. Area of intertidal and subtidal watersheds examined within this study. 

Figure 4. Proportional land cover categories (from NLCD 2001) within intertidal (upper) and 

subtidal (lower) watersheds examined within this study. 

Figure 5. Relationship between population density and impervious cover within watersheds. 

Figure 6. Relationship between forested land cover and impervious cover within watersheds 

Figure 7. Stormwater runoff volume for intertidal watersheds grouped by land use class. 

Figure 8. Stormwater runoff volume for intertidal watersheds grouped by land use class. 

Figure 9. Rainfall percent that converts to stormwater runoff in intertidal watersheds. 

Figure 10. Relationship between stormwater runoff volume and impervious cover for intertidal 

watersheds. 

Figure 11. Salinity averages and ranges (maximum minus minimum) for intertidal creeks. 

Figure 12. Relationship between salinity range and impervious cover for the study watersheds. 

Figure 13. Intertidal nutrient and phytoplankton levels for individual creeks. 

Figure 14. Nutrient and phytoplankton levels by land use class and longitudinal gradient. 

Figure 15. Relationship between nutrient concentrations and impervious cover for the study 

watersheds. 

Figure 16. Percentage of creeks classified into TDN, TDP, and Chl-a qualitative categories 

based upon guidelines developed by NOAA (Bricker et al. 1999) for coastal waters. 

Figure 17. Intertidal F+ and F- coliphage levels for individual creeks. 

Figure 18. Bacterial and viral indicator levels by land use class and longitudinal gradient. 

Figure 19. Relationship between pathogen indicators and impervious cover for the study 

watersheds. 

v 
vi 

Figure 20. Characteristics and contaminant concentrations in tidal creek sediments. 

Figure 21. Relationship between contaminant quotients and impervious cover for the study 

watersheds. 

Figure 22. Concentration of polybrominated diphenyl ethers (PBDEs) in intertidal creek 

sediments. 

Figure 23. Average taxa abundances and the Carolinian Province Index of Biological Integrity 

(CP-IBI) by land use class and longitudinal gradient. 

Figure 24. Monopylephorus rubroniveus and Streblospio benedicti (the two most abundant 

species) abundances by land use class and longitudinal gradient. 

Figure 25. Relationship between pollution species and impervious cover for intertidal 

watersheds. 

Figure 26. Palaemontes spp. and Callinectes sapidus abundances in the intertidal sections. 

Figure 27. Relationship between Palaemonetes spp. abundance and creek latitude by land use 

class. 

Figure 28. Relationship between pathogen indicator levels in oyster tissues and impervious 

cover for the study watersheds. 

Figure 29. Biplots of pathogen indicators comparing water column concentrations with oyster 

tissue concentrations. 

Figure 30. Lipid level percentage in oyster tissues for intertidal and subtidal creeks. 

Figure 31. DDT levels in oyster tissues for intertidal and subtidal creeks. 

Figure 32. Ability of gene expression profiles (hepatopancreas tissues) to discriminate creek 

types based upon levels of impervious cover. 

Figure 33. Ability of gene expression profiles (hepatopancreas tissues) to discriminate creek 

types based upon land use class. 

Figure 34. Hydrographs of modeled stormwater runoff from Guerin Creek, a forested study 

watershed. 

Figure 35. Conceptual model identifying linkages between development of the upland and 

ecological and human well-being of southeastern U.S. tidal creeks. 

Figure 36. Graphical presentation forecasting the impacts of land use change on tidal creek 

environmental quality. 
Abstract 

A study was conducted, in association with the Sapelo Island and North Carolina National 

Estuarine Research Reserves (NERRs), to evaluate the impacts of coastal development on 

sentinel habitats (e.g., tidal creek ecosystems), including potential impacts to human health and 

well-being. Uplands associated with southeastern tidal creeks and the salt marshes they drain are 

popular locations for building homes, resorts, and recreational facilities because of the high 

quality of life and mild climate associated with these environments. Tidal creeks form part of 

the estuarine ecosystem characterized by high biological productivity, great ecological value, 

complex environmental gradients, and numerous interconnected processes. This research 

combined a watershed-level study integrating ecological, public health and human dimension 

attributes with watershed-level land use data. The approach used for this research was based 

upon a comparative watershed and ecosystem approach that sampled tidal creek networks 

draining developed watersheds (e.g., suburban, urban, and industrial) as well as undeveloped 

sites. The primary objective of this work was to clearly define the relationships between coastal 

development with its concomitant land use changes and non-point source pollution loading and 

the ecological and human health and well-being status of tidal creek ecosystems. 

Nineteen tidal creek systems, located along the southeastern United States coast from southern 

North Carolina to southern Georgia, were sampled during summer (June-August), 2005 and 

2006. Within each system, creeks were divided into two primary segments based upon tidal 

zoning: intertidal (i.e., shallow, narrow headwater sections) and subtidal (i.e., deeper and wider 

sections), and watersheds were delineated for each segment. In total, we report findings on 24 

intertidal and 19 subtidal creeks. Indicators sampled throughout each creek included water 

quality (e.g., dissolved oxygen concentration, salinity, nutrients, chlorophyll-a levels), sediment 

quality (e.g., characteristics, contaminants levels including emerging contaminants), pathogen 

and viral indicators, and abundance and genetic responses of biological resources (e.g., 

macrobenthic and nektonic communities, shellfish tissue contaminants, oyster microarray 

responses). 

For many indicators, the intertidally-dominated or headwater portions of tidal creeks were found 

to respond differently than the subtidally-dominated or larger and deeper portions of tidal creeks. 

Study results indicate that the integrity and productivity of headwater tidal creeks were impaired 

by land use changes and associated non-point source pollution, suggesting these habitats are 

valuable early warning sentinels of ensuing ecological impacts and potential public health 

threats. For these headwater creeks, this research has assisted the validation of a previously 

developed conceptual model for the southeastern US region. This conceptual model identified 

adverse changes that generally occurred in the physical and chemical environment (e.g., water 

quality indicators such as indicator bacteria for sewage pollution or sediment chemical 

contamination) when impervious cover levels in the watershed reach 10-20%. Ecological 

characteristics responded and were generally impaired when impervious cover levels exceed 20-

30%. Estimates of impervious cover levels defining where human uses are impaired are 

currently being determined, but it appears that shellfish bed closures and the flooding 

vulnerability of headwater regions become a concern when impervious cover values exceed 10-

30%. This information can be used to forecast the impacts of changing land use patterns on tidal 

creek environmental quality as well as associated human health and well-being. In addition, this 

1 
study applied tools and technologies that are adaptable, transferable, and repeatable among the 

high quality NERRS sites as comparable reference entities to other nearby developed coastal 

watersheds. The findings herein will be of value in addressing local, regional and national needs 

for understanding multiple stressor (anthropogenic and human impacts) effects upon estuarine 

ecosystems and response trends in ecosystem condition with changing coastal impacts (i.e., 

development, climate change). 

Key Words: National Estuarine Research Reserve System (NERRS), North Carolina NERR, 

Sapelo Island NERR, tidal creek, sentinel habitat, conceptual model, impervious cover, land use, 

urbanization, sediment and tissue contaminants, water quality, pathogens, nekton, oysters, 

macrobenthos, physical and chemical environment. 

2 
1. Introduction 

This project, “Support for Integrated Ecosystem Assessments of NOAA’s National Estuarine 

Research Reserves System (NERRS)”, initiated in 2006, was completed as part of an emerging 

research partnership between the National Centers for Coastal Ocean Science (NCCOS) and the 

National Estuarine Research Reserve System (NERRS) to develop approaches for characterizing 

the ecosystem condition and public-health status of NERRS sites as compared to nearby, similar, 

more developed watersheds. This collaboration also provides a framework for continued 

monitoring (including the transfer of techniques and technologies) and prediction of future 

conditions in these important protected estuarine systems. 

There are two components of the overall study: (1) a sentinel habitat component conducted in 

tidal creeks at the NERRS sites in Georgia (Sapelo Island) and North Carolina (Masonboro 

Island); and (2) a subtidal probabilistic-sampling component conducted at all four North Carolina 

NERR sites (Currituck Banks, Rachel Carson, Masonboro Island, Zeke’s Island). This effort 

involves four NCCOS Centers (Hollings Marine Laboratory, Center for Coastal Environmental 

Health and Biomolecular Research, Center for Coastal Monitoring and Assessment, Center for 

Coastal Fisheries and Habitat Research) working in close collaboration with the NERRS 

program to address common research and coastal management goals. NCCOS’s mission is to 

provide coastal managers with scientific information and tools needed to balance society’s 

environmental, social, and economic goals (NCCOS 2004). The mission of NERRS is to 

practice and promote coastal and estuarine stewardship through innovative research and 

education activities (NERRS 2005). Through this collaboration, a number of high priority U.S. 

coastal management issues are being addressed such as assessing impacts of changing watershed 

land use, coastal habitat change, nutrient runoff on coastal rivers and bays, and providing tools 

and information for modeling the effects of sea level change with coastal watersheds (Pew Ocean 

Commission 2003, Coastal States Organization 2004). 

This NERRS-NCCOS partnership for this project resulted in solid contributions by the NERRS 

in terms of planning, field support and logistics, and data interpretation. The NERRS local 

information assisted greatly in the identification of tidal creek sampling sites and any nearby 

watershed influences on water quality (i.e., land use change and development) that supported 

improved watershed and historical data interpretation. Reserve access, both on land and on the 

water, was facilitated through NERRS and enabled improved access to sampling locations. 

Together, the two project components demonstrate the utility of two complementary assessment 

tools, (1) a sentinel habitats study designed to evaluate the impacts of development on tidal creek 

ecosystems, including potential impacts to human health and well-being; and (2) a probabilistic 

study providing a means for assessing the spatial extent of condition throughout a targeted 

resource category (i.e., sub-tidal estuarine waters of a reserve) and how the relative proportions 

of healthy vs. degraded areas may be changing with time. An associated objective of the project 

is to provide a prototype framework of assessment strategies (including tools and technology 

transfer) that can be applied systematically across other reserves, to support national and regional 

comparisons. This collaboration is a crucial step towards determining if healthy coastal 

ecosystems are associated with healthy people and healthy economies as well as assessing trends 

in ecosystem characteristics over time. 

3 
The sentinel habitats portion of this effort was also conducted as part of the Hollings Marine 

Laboratory’s (HML) Center of Excellence in Oceans and Human Health (OHH). The Center 

brings basic, applied, and medical researchers together to identify and understand factors that 

affect the health of coastal ecosystems and the humans who live in or visit the coastal zone. The 

science focus of the HML Center is to develop biotechnology that identifies and evaluates 

linkages between coastal development, the condition of the marine ecosystems, and public health 

and well-being. 

As human land use in coastal areas has shifted from industry to residential development, point 

source industrial discharge has been replaced by non-point source pollution as the primary threat 

to estuarine ecological health. The amount, timing, and quality of stormwater runoff from rapid, 

relatively unplanned development directly affects the introduction of freshwater, sediments, 

chemicals, bacteria, viruses, and other pollutants into tidal creeks and salt marshes. These 

pollutants often come from non-point sources (NPS), which can be difficult to identify, and can 

substantially degrade environmental quality. Pathogens and chemical contaminants can 

accumulate to such high levels in the water, sediments, and organisms that seafood products 

become unsafe to eat and water becomes unsafe for contact recreation. Thus, human activities, 

depending on the extent, may impair these valuable resources and result in negative feedback to 

human health. 

Tidal creeks provide a useful sentinel habitat for coastal systems. Tidal creeks form part of the 

estuarine ecosystem characterized by high biological productivity, great ecological value, 

complex environmental gradients, and numerous interconnected processes. River and tidal creek 

networks form the primary hydrologic link between estuaries and land based activities. Finally, 

these networks are critical feeding grounds, spawning areas, and nursery habitats for many 

species of fish, shellfish, birds, waterfowl, and mammals. Thus, the significant economic and 

ecological value of tidal creek habitats and the degree of human interaction with these habitats 

are disproportionate to their spatial area. Recent research has demonstrated that the ecological 

condition of intertidal headwater areas of southeastern tidal creek ecosystems is a sensitive 

indicator of land use changes within the local watershed (Holland et al. 2004, Sanger et al. 2004, 

DiDonato et al.in press). These headwater reaches are the first zone of impact for non-point 

source pollution runoff, and as a result, the levels of microbial and chemical contamination in 

headwaters are frequently an order of magnitude greater than levels reported for their contiguous 

deeper open-water environments (Holland and Sanger Unpublished, DiDonato et al. in press). 

As such, tidal creek ecosystems serve as sentinel habitats for assessing the impact of watershed 

development on ecosystem condition and public health risk. They also provide a reliable tool for 

identifying pollution sources and serve as sentinels for assessment of the effectiveness of 

corrective actions aimed at recovering ecosystem-scaled health. 

 A conceptual model linking watershed development (stressors), the associated physical and 

chemical exposures, and ecological responses has been developed for South Carolina tidal creeks 

(Holland et al. 2004, Figure 1). Changes in the rate and volume of stormwater runoff resulting 

from increases in impervious cover were a predominant factor driving ecological impairment. 

Adverse changes in the physical and chemical environment occurred when impervious cover 

exceeded 10-20%. Ecological processes responded when impervious cover exceeded 20-30%. 

4 
The overall goals of the sentinel habitat study were: 1) to evaluate the applicability of the current 

tidal creek classification framework and conceptual model linking urban and suburban growth to 

tidal creek ecological condition and potential impacts on ecosystem function, including human 

health and well-being, in the southeast region through collaborations with Sapelo Island NERR 

and North Carolina NERR; and 2) to support the transfer of this information/data to appropriate 

coastal managers to improve decision making related to land use/development issues and human 

health concerns in the Southeast. Results of this study are presented here in Volume I of a twovolume report for the overall NERRS project. Results of the probabilistic survey of ecological 

condition throughout subtidal waters of the four North Carolina NERR sites are presented in the 

companion Volume II. 

2. Methods 

2.1 Study Sites 

Nineteen tidal creek networks located from New Hanover County, NC, to Glynn County, GA, 

were sampled during summer (June-August) periods in 2005 and 2006 (Figure 2). Twelve 

networks were sampled in SC in 2005, and four networks were resampled in 2006. In 2006, four 

creek networks were sampled in GA, and three networks were sampled in NC. The GA and NC 

sites were sampled in association with the NERRS sites (Table 1). 

Figure 1. Conceptual model developed by Holland et al. (2004) identifying linkages between 

development of the upland and the ecological response of South Carolina tidal creeks. 

Ranges of impervious cover percent denoting transition from one model phase to the next are 

shown at the bottom of the model.

10-20% Impervious 

Cover

Altered Land Cover

Increased Runoff

Increased Impervious Cover

Altered Hydrography

Change in Salinity

Altered Sediment 

Characteristics

Increased Chemical 

Contaminants

Physical-Chemical

Environment

Reduced Shrimp Abundance

Few Stress-Sensitive Taxa

Altered Food Webs

Shellfish Bed Closures

Stressor Exposure Response

Living Resources Human Population 

Density

20-30% Impervious 

Cover

Increased Bacterial Load

10-20% Impervious 

Cover

Altered Land Cover

Increased Runoff

Increased Impervious Cover

Altered Hydrography

Change in Salinity

Altered Sediment 

Characteristics

Increased Chemical 

Contaminants

Physical-Chemical

Environment

Reduced Shrimp Abundance

Few Stress-Sensitive Taxa

Altered Food Webs

Shellfish Bed Closures

Stressor Exposure Response

Living Resources Human Population 

Density

20-30% Impervious 

Cover

Increased Bacterial Load

5 
A longitudinal gradient was 

identified by applying a 

freshwater stream classification 

model (Horton, 1945; Strahler, 

1957) to tidal creek systems. The 

first order, or headwater, of each 

creek directly drained coastal 

uplands or salt marsh habitat and 

was characterized by its narrow 

width and predominately 

intertidal habitat. These first 

order sections will be referred to 

as intertidal in the remaining text. 

The second order of each creek 

was formed by the confluence of 

two or more first order creeks. 

Second order systems were wider 

and had subtidally-dominated 

habitats. The third order of each 

creek was formed by the 

confluence of two or more second order creeks. Third order systems were large creeks with a 

proportionally small amount of intertidal habitat and substantial subtidal habitat. For simplicity, 

the second and third order systems will be collectively referred to as subtidal throughout the 

remaining text. While combining second and third orders resulted in the loss of some 

information about the tidal creek longitudinal gradient, two points support their pooling in this 

study: (1) in SC, the differences between second and third orders were small, and (2) only one 

third order creek could be sampled outside of SC, making regional evaluation of that order 

impossible. Each order was divided into three equidistant reaches using ArcGIS 9 (ESRI, 

Redlands, CA); by convention, the first reach within an order was the furthest upstream, while 

the second reach was the middle, and the third reach was the furthest downstream section 

sampled. Within any reach of any creek order, stations were randomly located for sample 

collection. The specific sampling activities within each reach are detailed below. 

Figure 2. North Carolina, South Carolina, and Georgia sampling 

sites. 

2.2 Land Use and Watershed Determinations 

Watersheds and subwatersheds were identified using ArcGIS 9 to evaluate the land use and 

impervious cover of each creek and order. Watersheds and their sub-watersheds were delineated 

based on elevation contours. Elevation Derivatives for National Applications (EDNA) data were 

downloaded from the United States Geological Survey (USGS, http://edna.usgs.gov/). The 

EDNA watersheds have a resolution of 30 meters that corresponds to a scale of 1:24,000. Single 

watershed boundaries were identified for the respective research area and overlaid with digital 

elevation model (DEM) and USGS topographic data. Visual confirmation of the delineation of 

the EDNA watersheds using the topographic maps was conducted, and subwatersheds were 

delineated for each creek order. In general, the EDNA data were found to represent the expected 

watershed boundaries with only slight modifications needed to reflect specific elevation 

6 
gradients or other attributes such as roads that might impede surface runoff. These changes were 

made by hand digitization.

Table 1. Orders sampled, date sampled, latitude, and longitude for each study creek network 

by state. NOC = North Carolina NERR, NIWB = North Inlet-Winyah Bay NERR, ACE =

Ashepoo, Combahee and Edisto NERR, SAP=Sapelo Island NERR. 

 Date Sampled 

Creek System Orders Sampled 2005 2006 Latitude Longitude 

North Carolina 

Hewlitts 1, 1, 2 7-Aug-06 34.189 -77.857 

NOC-Masonboro 1 9-Aug-06 34.152 -77.849 

Whiskey Creek 1, 2 9-Aug-06 34.161 -77.865 

South Carolina 

Albergottie 1, 2, 3 17-Aug-05 32.448 -80.720 

Bulls 1, 2, 3 29-Jun-05 32.825 -80.027 

Guerin 1, 1, 2, 2, 3 5-Jul-05 20-Jun-06 32.944 -79.766 

James Island 1, 1, 2, 2, 3 1-Aug-05 17-Aug-06 32.744 -79.974 

Murrells Inlet 2, 3 22-Jun-05 33.564 -79.025 

New Market 1 8-Aug-05 24-Jul-06 32.806 -79.940 

NIWB-Town 1, 1, 2, 2, 3 20-Jun-05 33.339 -79.189 

Okatee 1, 2, 3 18-Jul-05 32.287 -80.929 

Orangegrove 1, 1, 2, 3 20-Jul-05 32.812 -79.978 

Parrot 1, 1, 2, 3 7-Jul-05 32.733 -79.910 

Shem 1, 2 29-Aug-05 32.801 -79.869 

ACE-Village 1, 2, 3 3-Aug-05 5-Jul-06 32.419 -80.522 

Georgia 

Burnett 1, 2 19-Jul-06 31.234 -81.538 

SAP-Duplin 1, 2, 3 11-Jul-06 31.145 -81.285 

SAP-Oakdale 1 11-Jul-06 31.481 -81.272 

Postell 1 19-Jul-06 31.417 -81.375 

National Land Cover Data (NLCD 2001, Homer et al. 2004) were downloaded for selected 

regions in SC, NC and GA using the MRLC web tool 

(http://gisdata.usgs.net/website/MRLC/viewer.php). The land cover and impervious cover data 

were matched to the watershed and subwatershed boundary data. Land cover data were 

determined from this layer and summed to obtain simplified categories of land cover. The 

impervious cover data were further modified by removing data that represented marsh and open 

water using National Wetlands Inventory (NWI). Specifically, three attribute classes from the 

NWI were selected and saved as a unique Shapefile® including Bay/Estuary, Non-Forested 

Wetland, and Open Water and then all attribute classes were merged to create one unique 

attribute class. This was used to separate marsh and open water (i.e., undevelopable areas) from 

the impervious cover data. All editing functions were performed in ArcEditor. Impervious cover 

levels were then calculated directly from the NLCD for all sub-watersheds and watersheds. 

These NLCD-derived impervious cover estimates were compared to values published in Holland 

et al. (2004). Both of these data sets used aerial photography from similar time frames (1999-

7 
2001) to estimate impervious cover, but the NLCD impervious cover values underestimated the 

ground-truthed data reported in Holland et al. (2004). A similar underestimation has also been 

reported by Jarnagin et al. (2006). A quadratic relationship was developed (y = 2.9301 + 

2.16789x – 0.01611x2

 where y is the adjusted impervious cover percent and x is NLCD-derived 

impervious cover, White et al. in prep), and used to adjust the NLCD-derived impervious cover 

percentage. Adjusted values are reported and used in this study. 

Creek watersheds were classified at the largest order level into the following land use categories 

based on impervious cover as modified from Holland et al. (2004): (1) forested (< 10% 

impervious cover); (2) suburban (≥ 10% but < 35% impervious cover); (3) urban (≥ 35% 

impervious cover); and (4) salt marsh (emergent marsh instead of upland as the dominant land 

cover class). There were two exceptions to this classification. The Orangegrove watershed was 

estimated to have 37.3% impervious cover; however, since this was primarily light residential 

development and a small amount of upland (127 ha) relative to the total watershed size (322 ha), 

we categorized this as a suburban watershed. The Burnett watershed was estimated to have 

11.8% impervious cover; however, since this was a superfund site designated by the United 

States Environmental Protection Agency (USEPA), it was categorized as an urban watershed. 

The creek networks varied in regards to their size (i.e., number of orders and watershed size) and 

also with respect to the surrounding land use. It is important to note that several systems had 

upland creeks showing various levels of human development but also had creek segments that 

were dominated by salt marsh. Specifically, within the North Inlet, Guerin, Parrot, and 

Orangegrove networks, we sampled both upland and salt marsh creeks and they are treated 

separately in statistical analyses. 

2.3 Stormwater Runoff Determinations 

The flow curve number method developed by the U.S. Department of Agriculture (USDA) 

National Resources Conservation Service (NRCS) was used to estimate stormwater runoff 

volume for the 19 intertidal subwatersheds of upland creeks. The method is based upon the 

relationship between rainfall, runoff, and retention (rain not converted to runoff), and the 

hypothesis is that the ratio of actual retention to the potential maximum retention is similar to the 

ratio of actual direct runoff to potential maximum runoff (i.e., total rainfall. The flow curve 

number (CN) is a representation of the potential maximum retention and reflects the drainage 

characteristics of a watershed’s soil and land cover. CN is determined by identifying the 

proportional composition of land cover categories and hydrologic soil groups within a watershed. 

 

USDA-NRCS provides listings of numerous land cover categories, with each having 4 CN 

values based upon soil classification from ‘A’ (most pervious – sands) to ‘D’ (most impervious – 

clays). NLCD land cover categories were combined to match the CN method’s applicable 

categories. For each land cover category, the USDA-provided CN number was modified by the 

proportion of hydrologic soil groups in the watershed, as determined by spatial soil data layers 

provided by USDA. The calculated CN was modified in the developed land cover categories by 

increasing the imperviousness by two grades to reflect soil compaction (Lim et al. 2006). 

Once the CN was determined, watershed runoff volume was solved as follows (NRCS 2004): 

8 
 

Two additional equations were used based on our modification of the Ia : S ratio from 0.2 to 0.05 

(Woodward et al. 2003). 

The solved value for runoff volume was converted from acre-feet to m3

 by applying a factor of 

1233.48. Disparities in watershed areas were normalized by determining volume per unit area 

(km2). For consistency in this report, all volumes were based on precipitation of 4.5 inches 

which is the rainfall designated by the National Weather Service as the two-year storm event for 

the general geographical location of the study watersheds. 

The data calculated by applying the modified NRCS-CN method were used to examine the 

relationship between differences in the amount of runoff and in the amount of developed land 

cover among watersheds and to compare the mean and median differences among land use 

classes. 

2.4 Sample Design 

Each creek was sampled during the ebbing tide, approximately 2-3 hours prior to low tide over 

two consecutive days. The first order was sampled by foot, while the second and third orders 

were sampled by boat. Sampling was generally conducted in an upstream direction to minimize 

habitat disturbance. Within each creek order, samples were collected to quantify water quality, 

water column nutrients, pathogen indicators, macrobenthic infauna, resident nekton, sediment 

contaminants, oyster transcriptome profiles, and oyster pathogen and contaminant body burdens. 

Sampling stations were selected using a stratified random method. The number of samples 

collected in each order varied by sample type. 

12

AQ Q dd

vol

  Qvol = volume of runoff, acre-feet (af) 

 Qd = depth of runoff, inches 

 Ad = drainage area, acres 

 12 = conversion factor for inches to feet 

S )I-(P

)I-(P Q

a

2

a

d   Qd = depth of runoff, inches 

 P = depth of rainfall (inches) 

 Ia = initial abstraction (rainfall lost to infiltration and 

 surface depressions before runoff occurs (in.) 

1.15

0.05 0.20  )S1.33(S 1.879     11CN100

100 CN 15.1

0.20

0.05

 

10

CN

1000 S  S = maximum potential retention after runoff begins 

9 
Water quality data (temperature, dissolved oxygen, salinity, pH, turbidity, chlorophyll-a) were 

collected in bottom waters (0.3 m above bottom) using a YSI 6600 data logger. A logger was 

deployed in the second reach of each creek order and collected data at 15 minute intervals for up 

to 2 full tidal cycles (25 hrs). Water samples for pathogen indicators were collected in the 

second reach in sterile 2 L polypropylene bottles. In addition, a water sample was collected in an 

acid-washed 500 mL polyethylene bottle within each reach of each creek order for nutrient 

determinations. All water grab samples were collected approximately 0.3 meters below surface, 

or mid-water column in water less than 0.3 m in depth. In addition, all water grab samples were 

collected by hand and in an upstream direction to prevent contamination. Bottle caps were 

removed immediately prior to sampling, and bottles were inverted until at sample depth. 

Macrobenthic infauna were sampled using two different field methods. In intertidal creeks, the 

benthos was sampled approximately 1 m below mean high water (MHW) using a 0.0044 m2

 core 

sampler. A total of 9 cores (3 from each reach) were collected at randomly located stations to 

ensure sampling of the benthic fauna along the entire headwater habitat. A small scoop of mud 

was collected next to each core sample for sediment analysis [% sand, % silt, % clay, total 

organic carbon (TOC)). Additionally, 2 small cores (0.0009 m2

) were collected at each site and 

composited across all sites within each reach to quantify porewater ammonium (NH4

+

). In 

subtidal creeks, the infauna were sampled using a 0.04 m2

 modified Van Veen grab sampler. 

One grab sample was collected for benthos in each reach. Sediment samples for grain size 

analysis and porewater ammonia determination were taken from the top 2 centimeters of a 

second intact grab from each site. 

Sediments were sampled for chemical contaminants only once in each creek order. In the second 

reach of intertidal creeks, the top 2 cm of sediment were carefully scraped off the surface of mud 

exposed at low tide and homogenized in a stainless steel bowl. In the second reach of subtidal 

creeks, the top 2 cm of a successful Van Veen grab were homogenized for chemical analysis. 

The homogenate was apportioned to appropriate pre-cleaned sample jars (i.e., metals in plastic 

and organics in glass) and placed on ice as soon as possible. 

Nekton, predominantly fish and epibenthic crustaceans, were sampled using different field 

methods for different creek orders. In intertidal creeks, the nekton was sampled using a 1⁄4 inch 

mesh seine net. One seine was pulled in each reach in an upstream direction for up to 25 meters. 

Every effort was made to stretch the net from bank to bank. In cases where this was not possible 

the seined width was estimated to the nearest meter. Water width and depth were measured at 

both the starting and end points in the seine to calculate the area and volume of the creek swept. 

In subtidal creeks, nekton were sampled using a 4-seam trawl (5.5 m foot rope, 4.6 m head rope, 

and 1.9 cm bar mesh throughout) pulled at a constant speed in the downstream direction for 250 

m. 

In 2006 only, oysters (Crassostrea virginica) were hand-collected in every creek order when 

present. If possible oysters were collected near the site where the data logger was deployed and 

sediment samples collected in the second reach. After collection, oysters were divided up for 

genomic transcriptome analyses (25 oysters), pathogen determination (~ 20 oysters), and 

chemical contaminant body burdens (~12 oysters). 

10 
2.5 Laboratory Processing Methods 

2.5.1 Basic Water Quality 

Basic water quality data (i.e., temperature, pH, DO, salinity, turbidity, depth, and chlorophyll-a) 

were downloaded from the data loggers and examined to remove data resulting from exposure at 

low tide (common in first order creeks). Data loggers were calibrated prior to deployment and a 

post-calibration check was conducted after retrieval to ensure the logger was functioning 

properly. Summary data (e.g., mean, maximum, minimum, range) for the measured parameters 

were calculated. 

2.5.2 Nutrients and Phytoplankton 

Both whole and filtered water samples were used for nutrient analyses. Whole water samples 

were analyzed for total nitrogen (TN) and total phosphorus (TP) using the persulfate digestion 

method (D’Elia et al. 1977). Additional samples were filtered through a 47 mm GF/F 

(Whatman) to quantify dissolved constituents (i.e., ammonium (NH4

+

), nitrite+nitrate (NO2/3), 

total dissolved nitrogen (TDN), ortho-phosphate (PO4

3-), total dissolved phosphorus (TDP), and 

silicate (DSi)). Ammonium was analyzed via the Berthelot Reaction using a Technicon 

AutoAnalyzer (Technicon Industrial Systems, 1986), and silicate was measured using the 

“molybdenum blue” method on the same AutoAnalyzer (Technicon Industrial Systems, 1986). 

Both ortho-phosphate and nitrate+nitrite were analyzed using standard methods (EPA methods 

365.1 and 365.2, respectively, in USEPA, 1979). The material remaining on the filter paper was 

extracted in acetone and analyzed for chlorophyll-a (Chl-a) and phaeophytin (Phaeo) using 

fluorometric techniques (Welschmeyer 1994). 

2.5.3 Pathogen Indicators 

Water collected for pathogen indicators was analyzed for both bacterial and viral indicators 

within 24 hours. Fecal coliforms (FC) and enterococci (Ent) were enumerated by membrane 

filtration according to standard methods (APHA 1998). Coliphages were enumerated and 

characterized as described in Stewart et al. (2006). Both male-specific (F+) and somatic (F-) 

coliphages were enumerated by the single agar layer method, adapted from USEPA Method 

1602 (USEPA 2001). 

Oysters to be tested for pathogen body burdens were first homogenized and composited for each 

collection site to obtain at least 100 g (wet weight) tissue. The tissue liquor was tested for the 

microbial indicators FC, Ent, and also the viral indicators F+, F- coliphages using similar 

methods to water samples. 

2.5.4 Chemical Contaminants 

Sediments and oyster tissues were analyzed for a suite of 22 trace metals, 22 pesticides, 25 

polycyclic aromatic hydrocarbons (PAHs), 79 polychlorinated biphenyls (PCBs), and 13 

polybrominated diphenyl ethers (PBDEs) (Appendix A). PBDEs are used as flame retardants 

and considered to be an emerging contaminant of concern. Data quality was assured using a 

series of spikes, blanks, and standard reference materials (NIST 1944 for sediments, and NIST 

1566b for tissues). Sediment samples were kept frozen at approximately - 40 oC until analyzed. 

To thaw, samples were left in closed containers in a + 4 oC cooler for approximately 24 hours. 

Samples were thoroughly homogenized using a ProScientific handheld homogenizer prior to any 

11 
sample extraction. Tissues from multiple oysters (maximum of 12 oysters) were composited to 

obtain 15 g of wet weight and then frozen and stored at - 40 oC until analysis. Oyster tissues 

were removed from the freezer and stored overnight at 4 oC and allowed to partially thaw. The 

oyster tissue was well homogenized using a ProScientific homogenizer in 500 mL Teflon 

containers. The homogenized tissue sample was split into an organic (pre-cleaned glass 

container) and inorganic (pre-cleaned polypropylene container) sample and stored at - 40 oC until 

extraction or digestion. A percent dry-weight determination was made gravimetrically on an 

aliquot of each wet sediment and tissue sample. 

Inorganic sample digestion and analysis consisted of the following steps. Dried sediment was 

ground with a mortar and pestle and transferred to a 20 mL plastic screw-top container. A 0.25 g 

sub-sample of the ground material was transferred to a Teflon-lined digestion vessel and digested 

in 5 mL of concentrated nitric acid using microwave digestion. The sample was brought to a 

fixed volume of 50 mL in a volumetric flask with deionized water and stored in a 50 mL 

polypropylene centrifuge tube until instrumental analysis of Li, Be, Al, Fe, Mg, Ni, Cu, Zn, Cd, 

and Ag. A second 0.25 g sub-sample was transferred to a Teflon-lined digestion vessel and 

digested in 5 mL of concentrated nitric acid and 1 mL of concentrated hydrofluoric acid in a 

microwave digestion unit. The sample was then evaporated on a hotplate at 225 °C to near 

dryness, and 1 mL of nitric acid was added. The sample was brought to a fixed volume of 50 mL 

in a volumetric flask with deionized water and stored in a 50 mL polypropylene centrifuge tube 

until instrumental analysis for V, Cr, Co, As, Sn, Sb, Ba, Tl, Pb, and U. Selenium was analyzed 

by hotplate digestion using a 0.25 g sub-sample and 5 mL of concentrated nitric acid. Each 

sample was brought to a fixed volume of 50 mL in a volumetric flask with deionized water and 

stored in a 50 mL polypropylene centrifuge tube until instrumental analysis. Additionally, two to 

three grams wet tissue were microwave digested in Teflon-lined digestion vessels using 10 mL 

of concentrated nitric acid along with 2 mL of hydrogen peroxide. Digested samples were 

brought to a fixed volume with deionized water in graduated polypropylene centrifuge tubes and 

stored until analysis. 

A separate inorganic aliquot was used for mercury analysis. Approximately 0.5 g of wet 

sediment or tissue was analyzed on a Milestone DMA-80 Direct Mercury Analyzer. All 

remaining elemental analysis was performed using an Inductively Coupled Plasma Mass 

Spectrometry (ICP-MS) except for silver, which was determined using Graphite Furnace Atomic 

Absorption (GFAA) spectroscopy. Data quality was controlled by using a series of blanks, 

spiked solutions, and standard reference materials including NRC MESS-3 (Marine Sediments) 

and NIST 1566b (freeze dried mussel tissue). 

Organic extraction and analysis consisted of the following steps. An aliquot (10 g sediment or 5 

g tissue wet weight) was extracted with anhydrous sodium sulfate using Accelerated Solvent 

Extraction (ASE) in either 1:1 methylene chloride:acetone for sediments or 100% 

Dichloromethane for tissues (Schantz 1997). Following extraction, samples were dried and 

cleaned using Gel Permeation Chromatography and Solid Phase Extraction to remove lipids and 

then solvent-exchanged into hexane for analysis. Samples were analyzed for PAHs, PBDEs, 

PCBs, and a suite of chlorinated pesticides using appropriate Gas Chromatograph and Mass 

Spectrometer (GC-MS) technology. Data quality was ensured by assessing a spiked blank, a 

12 
reagent blank, and appropriate standard reference materials with each set of samples to ensure 

the integrity of the analytical method. 

2.5.5 Macrobenthic Community 

Benthic samples collected in the field were sieved through a 0.5 mm standard sieve, and the 

material retained on the screen was transferred to a polyethylene bottle and preserved in 10% 

formalin containing Rose Bengal. Samples were capped and stored until benthic 

macroinvertebrates could be sorted out from the detritus, identified down to the lowest practical 

taxonomic unit, and counted. Quality control / quality assurance (QA/QC) procedures were 

followed. One out of every 10 samples was re-sorted to ensure 90% sorting efficiency. If 10% 

of the organisms remained in the sample after sorting, then all 10 samples were resorted. The 

samples were then identified to the lowest taxonomic level via dissecting and compound 

microscopes. One out of every ten samples was re-identified by a taxonomist for QA/QC 

purposes. If 10% of the dominant organisms or 25% of the rare organisms were misidentified, 

then all 10 samples were re-identified. 

2.5.6 Nekton Community 

Nekton collected from seine (0.635 cm bar mesh) sampling were rinsed carefully, and retained 

fish and crustaceans were preserved in 10% formalin in seawater. Preserved organisms were 

sorted in the laboratory and identified to the lowest practical taxonomic level (usually species). 

Animals collected in trawls (1.9 cm bar mesh) were identified and counted in the field; one 

sample each day was returned to the laboratory for QA/QC. Procedures followed were 90% 

sorting accuracy and a 90% or 25% identification accuracy for abundant and rare taxa, 

respectively. 

2.5.7 Oyster Tissue Genomics 

For genomic analysis, 25 oysters were shucked, weighed, and dissected; samples of the gill and 

hepatopancreas tissues were excised, preserved in buffer (RNA later®, Ambion), and kept on 

ice. Oyster tissue samples were hard-frozen in liquid nitrogen as soon as possible and stored 

until genomic analysis. Total RNA was isolated from RNAlater® (Ambion Inc, Austin, TX) 

stabilized oyster tissues using the RNeasy® Mini kits (Qiagen, Valencia, CA) with an on-column 

DNase (Qiagen, Valencia, CA) treatment. Extracted RNA was quantified by absorbance at 

260nm using a NanoDrop® ND-1000 Spectrophotometer (NanoDrop Technologies, 

Wilmington, DE). One microgram of total RNA was used to produce Cy3-labeled aminoallyl 

RNA (Cy3-aRNA) probe using the Amino Allyl MessageAmpTM II aRNA Amplification Kit 

(Ambion Inc, Austin, TX), according to the manufacturer's instructions. Ten micrograms of the 

subsequently produced Cy3-aRNA was diluted (1:3) in hybridization buffer (50% formamide, 

2.4% SDS, 4X SSPE, 2.5X Denhardt's solution, and 2 l of blocking solution [1 g Cot-1 DNA 

and 1 g poly dA]). The probe was then boiled for 1 min and incubated in the dark for 1 h at 

50 °C prior to hybridization. 

The oyster cDNA microarray (Jenny et al. 2007) slides were prewashed with 0.2% SDS for 2 

min, just-boiled Milli-Q water for 2 min, rinsed in 70% ethanol for 2 min, and then dried. Slides 

were pre-hybridized in a hybridization oven with a pre-hybridization buffer (33.3% formamide, 

1.6% SDS, 2.X SSPE, 1.6X Denhardt’s solution, and 0.1 M salmon sperm DNA) in the dark for 

1 h at 50 °C. Slides were hybridized with Cy-3-aRNA in the dark for 16 h at 50 °C. After the 

13 
hybridization, slides were rinsed in 2X SSC, 0.1% SDS and soaked in 0.2X SSC, 0.1% SDS for 

15 min in the dark at room temperature, followed by a rinse in 0.2X SSC, soaking in 0.2X SSC 

for 15 min, 0.1X SSC for 15 min and finally Milli-Q water for 5 min in the dark in order to 

remove carryover SDS. The microarrays were then dried and scanned with ScanArrayTM 

Express and SpotArray software at 70 V photomultiplier tube (PMT) gain and analyzed with 

QuantArray software (Perkin Elmer, Boston, MA). 

2.6 Data Summary and Statistical Analyses 

Resulting data (e.g., land cover, nutrient concentrations, infaunal abundances, pathogen 

abundances) were entered into a relational database for storage. This database, a PostgreSQL 

database, was accessed by individual users via Microsoft Access frontends (White et al. 2008). 

Through the frontends, individual users could query the database for relevant summaries and 

datasets for statistical analysis. Data are available upon request. 

Tidal creek data from both 2005 and 2006 summer sampling periods have been summarized into 

one data set; no attempt was made to examine year-to-year variability. The main unit of 

statistical inference is the creek order, and the resulting data set comprises 43 observations (24 

from intertidal systems, 19 from subtidal systems). In cases involving multiple measures per 

order, data were first averaged within each order to obtain one value for each indicator. Creek 

data were further summarized by averaging across the second and third orders to get one value 

representing the larger subtidally-dominated habitats. Lastly, for creeks that were sampled 

across both years (i.e., Guerin, James Island School, New Market, and Village; Table 1), data 

were averaged across years resulting in a single value for each intertidal system and each 

subtidal system for any particular parameter. 

Statistical analyses were designed to address two research questions: 1) Do measured parameters 

vary across the sampled land use classes? and 2) Do measured parameters vary along the creek 

longitudinal gradient? To address these research questions, we employed Analysis of Variance 

(ANOVA), Analysis of Covariance (ANCOVA), and regression. The basic ANOVA model was 

a two-way, fixed factor model, with Land Use Class Type (salt marsh, forested, suburban, urban) 

and Creek Order (intertidal, subtidal) as the main effects. The interaction term was included in 

all models tested and excluded if nonsignificant (p ≥ 0.05). Pairwise differences were examined 

by comparing least square means (using PDIFF in SAS). For the macrobenthic community, 

ANCOVAs were used to account for the effects of sediment type and salinity levels prior to 

testing for differences among land use classes and between orders. Lastly, individual response 

variables were regressed against impervious cover by creek order to document significant 

predictive relationships. Regressions were considered significant at p < 0.05. The regressions 

were performed with the forested, suburban, and urban creeks. The salt marsh creeks were 

excluded from this analysis because they had no developable upland. If data were found to be 

non-normal or heteroscedastic, basic transformations (log, square root, arcsine) were attempted. 

If those transformations did not improve the distribution of the data, data were rank transformed. 

Analyses were performed using SAS 9.1 (SAS Institute Inc., Cary, NC) or Systat 11 (Systat 

Software, Inc., San Jose, CA). 

To summarize sediment contaminant data, the values less than the method detection limits 

(MDL) were set to 0 before analysis. Total PAH, total PCB, and total PBDE concentration were 

14 
determined by summing the 25, 79, and 13 individual analytes, respectively, measured for each 

group of contaminants (Appendix A). The concentrations of trace metals, PAHs, PCBs, and 

pesticides from the study creeks were compared to sediment quality guidelines. Long et al. 

(1995) developed sediment quality guidelines by summarizing the published literature on the 

effects of a suite of sediment contaminants on a wide range of marine biota and derived two 

threshold values, an effects range-low (ERL) and an effects range-median (ERM) for individual 

analytes. An ERL was defined as the sediment concentration of a given contaminant where 10% 

of all published studies have reported an adverse effect, and an ERM was defined as the 

sediment concentration where 50% of all published studies have reported an adverse effect. 

Values below the ERL would rarely be expected to be associated with measurable biological 

effects; values between the ERL and ERM represent a range in which there are possible 

biological effects for a wide range of organisms. Values above the ERM represent a range above 

which there are probable biological effects. 

The mean ERM quotient (mERMQ) was calculated for all contaminants (Total mERMQ) and for 

each major class of contaminant (i.e., trace metals, PAHs, PCBs, and pesticides) using the 24 

analytes outlined by Long et al. (1995, Appendix B). Calculations were made by (1) dividing the 

concentration of each analyte or analyte group by the published ERM value, (2) summing the 

ratios of analytes within each contaminant class (e.g., trace metals), and (3) dividing by the 

number of contaminants in that class. In addition, total mERMQ values, which encompassed all 

four contaminant classes, were calculated for each sample. These values were calculated in the 

same fashion except that analytes were not combined within contaminant classes, instead the 

ratios of all 24 analytes were summed, and the total was divided by 24 (Long et al. 1998). The 

use of these quotients provides a way to compare potential cumulative effects of contaminants 

after weighting them on a toxicological basis. In analyses, mERMQs were used. 

3. Results 

3.1 Stressors 

The 19 tidal creek systems surveyed for this study consisted of one to five watersheds depending 

upon the number of intertidal and subtidal creek segments sampled (Table 2). In addition, each 

system had one or two land use designations depending upon the land surrounding the sampled 

creeks. Watersheds for creeks draining salt marsh were classified as marsh, and watersheds for 

creeks draining upland areas were classified as either forested, suburban, or urban. 

Intertidal watersheds ranged in size from 28 ha (Parrot, marsh) to greater than 2400 ha (Burnett, 

urban and Okatee, suburban; Figure 3) and impervious cover ranged from 0% (salt marsh 

watersheds) up to about 70% (New Market, urban). Subtidal watersheds included the intertidal 

area and ranged in size from 59 ha (Orangegrove, marsh) to 5501 ha (Okatee, suburban) and 

impervious cover in these watersheds ranged from 0 to 47.7% (Shem, urban). 

Land cover in each watershed was determined using NLCD categories of developed-high, 

developed-low, agricultural, bare land, forested, forested wetland, marsh, or water. The percent 

composition of land cover shows a progressive change along the salt marsh-forested-suburbanurban gradient (Figure 4). Creek watersheds classified as salt marsh were primarily marsh land 

cover, while the forested systems were primarily forested land cover. For suburban and urban 

15 
watersheds, there was a general increase in urban land cover (both developed-low and 

developed-high) and a concomitant decrease in forested land cover. 

Table 2. Creek system, land use class, watershed area, and watershed impervious cover for 

each creek segment. Subtidal watershed area includes the related intertidal area. 

Creek Land Use Creek Area Impervious 

System Class Watersheds Order Segment (ha) Cover (%)

North Carolina

Hewlitts Suburban Hewlitts-S 1 Intertidal 614 40.9

Hewlitts-N 1 Intertidal 459 34.5

Hewlitts 2 Subtidal 2782 33.4

Masonboro Marsh NOC-Masonboro 1 Intertidal 29 2.9

Whiskey Creek Suburban Whiskey 1 Intertidal 482 34.7

Whiskey 2 Subtidal 712 32.4

South Carolina

Albergottie Suburban Albergottie 1 Intertidal 558 8.1

Albergottie 2 & 3 Subtidal 2096 23.9

Bulls Urban Bulls 1 Intertidal 369 40.5

Bulls 2 & 3 Subtidal 510 38.1

Guerin Marsh Guerin 1 Intertidal 25 0.0

Guerin 2 Subtidal 342 0.0

Forested Guerin 1 Intertidal 219 3.0

Guerin 2 & 3 Subtidal 3427 3.0

James Island Suburban James Island-N 1 Intertidal 296 30.0

James Island-N 2 Subtidal 773 29.1

Suburban James Island-S 1 Intertidal 144 41.3

James Island-S 2 & 3 Subtidal 1820 29.5

Murrells Inlet Urban Murrells 2 & 3 Subtidal 1297 40.3

New Market Urban New Market 1 Intertidal 199 70.4

North Inlet Marsh NIWB-Clambank 1 Intertidal 55 0.0

NIWB-Clambank 2 Subtidal 102 0.0

Forested NIWB-Crabhaul 1 Intertidal 184 2.9

NIWB-Town 2 & 3 Subtidal 1860 2.9

Orangegrove Marsh Orangegrove 1 Intertidal 18 0.0

Orangegrove 2 Subtidal 59 0.0

Suburban Orangegrove 1 Intertidal 61 39.2

Orangegrove 2 & 3 Subtidal 322 37.3

Parrot Marsh Parrot 1 Intertidal 28 0.0

Suburban Parrot 1 Intertidal 62 21.2

Parrot 2 & 3 Subtidal 501 17.7

Okatee Suburban Okatee 1 Intertidal 2415 17.9

Okatee 2 & 3 Subtidal 5501 13.3

Shem Urban Shem 1 Intertidal 456 49.4

Shem 2 Subtidal 1269 47.7

Village Forested Village 1 Intertidal 630 3.6

Village 2 & 3 Subtidal 2016 4.0

Georgia

Burnett Urban Burnett 1 Intertidal 2425 11.2

Burnett 2 Subtidal 2589 11.8

Duplin Forested SAP-Duplin 1 Intertidal 385 3.0

SAP-Duplin 2 & 3 Subtidal 1480 3.0

Oakdale Forested SAP-Oakdale 1 Intertidal 286 3.1

Postell Urban Postell 1 Intertidal 218 39.8

16 
0

2000

4000

6000

Guerin,

SC

NIWB-Clambank, SC

Orangegrove,

SC

Parrot, SC

NOC-Masonboro, NC

NIWB-Crabhaul, SC

SAP-Duplin,

GA

Guerin,

SC

SAP-Oakdale, GA

Village, SC

Albergottie, SC

Okatee, SC

Parrot, SC

James Island-N, SC

Hewlitts-N, NC

Whiskey, NC

Orangegrove,

SC

Hewlitts-S, NC

James Island-S, SC

Burnett, GA

Postell, GA

Bulls, SC

Shem, SC

New

Market, SC

Marsh Forested Suburban Urban

Intertidal Watershed Area (ha) .

0

2000

4000

6000

Guerin,

SC

NIWB-Clambank, SC

Orangegrove,

SC

NIWB-Town,

SC

SAP-Duplin,

GA

Guerin,

SC

ACE-Village,

SC

Okatee, SC

Parrot, SC

Albergottie, SC

JamesIsland-N, SC

James Island-S, SC

Whiskey, NC

Hewlitts, NC

Orangegrove,

SC

Burnett, GA

Bulls, SC

Murrells Inlet, SC

Shem, SC

Marsh Forested Suburban Urban

Subtidal Watershed Area (ha) . Figure 3. Area of intertidal (upper) and subtidal (lower) watersheds examined within this 

study. The subtidal watersheds include the associated intertidal land areas. Land use class 

is marked by color. 

17 
0.0

0.2

0.4

0.6

0.8

1.0

Guerin, SC

NIWB-Clambank, SC

Orangegrove, SC

Parrot, SC

NOC-Masonboro, NC

NIWB-Crabhaul, SC

SAP-Duplin, GA

Guerin, SC

SAP-Oakdale, GA

Village, SC

Albergottie, SC

Okatee, SC

Parrot, SC

James Island-N, SC

Hewlitts-N, NC

Whiskey, NC

Orangegrove, SC

Hewlitts-S, NC

James Island-S, SC

Burnett, GA

Postell, GA

Bulls, SC

Shem, SC

New Market, SC

Marsh Forested Suburban Urban

Intertidal Watershed

Land Cover (proportional) .

Water

Bare Land

Marsh

Forested Wetland

Forested

Agricultural

Developed Low

Developed High

0.0

0.2

0.4

0.6

0.8

1.0

Guerin, SC

NIWB-Clambank, SC

Orangegrove, SC

NIWB-Town, SC

SAP-Duplin, GA

Guerin, SC

ACE-Village, SC

Okatee, SC

Parrot, SC

Albergottie, SC

James Island-N, SC

James Island-S, SC

Whiskey, NC

Hewlitts, NC

Orangegrove, SC

Burnett, GA

Bulls, SC

Murrells Inlet, SC

Shem, SC

Marsh Forested Suburban Urban

Subtidal Watershed. 

Land Cover (proportional) .

Water

Bare Land

Marsh

Forested Wetland

Forested

Agricultural

Developed Low

Developed High

Figure 4. Proportional land cover categories (from NLCD 2001) within intertidal (upper) and 

subtidal (lower) watersheds examined within this study. 

 

18 
Land use classes were primarily determined using watershed impervious cover, and as expected, 

the amounts of impervious cover within each watershed class were significantly different from 

each other (ANOVA, p<0.0001). Across land use classes, there was no significant difference 

between the intertidal and subtidal impervious cover amounts. 

Classifying watersheds based on impervious cover provides a useful framework for analyzing 

and interpreting study results. Similarly, impervious cover is a valuable direct indicator variable 

to describe the physical conditions and attributes of these study watersheds. For intertidal and 

subtidal creek watersheds, 

human population density 

(individuals ha-1) is linearly 

related to the impervious cover 

(%) and explains 88% and 75%, 

respectively, of the total 

variability (Figure 5). 

Impervious cover is also strongly 

related to aspects of urbanization 

(i.e., the decrease in nondeveloped land cover classes and 

the increase in classes of 

developed land). For intertidal 

and subtidal creek watersheds, 

forested land cover was 

negatively related to impervious 

cover (Figure 6). Impervious 

cover appears to be a strong predictor of the overall watershed human population density and 

land cover attributes. 

r

2

 = 0.88*

r

2

 = 0.75*

0

4

8

12

16

0 10 20 30 40 50 60 70 80

Impervious cover (%)

Population Density (ind. ha-1

)

Intertidal Subtidal

Figure 5. Relationship between population density and 

impervious cover within watersheds. Model r2 is shown for 

each regression with asterisk (*) indicating significance (p 

< 0.05). Marsh watersheds are excluded owing to lack of 

impervious cover. 

Figure 6. Relationship between 

forested land cover and 

impervious cover within 

watersheds. Impervious cover 

was regressed against forested 

land cover. Model r2 is shown 

for each regression with asterisk 

(*) indicating significance (p < 

0.05). Marsh watersheds are 

excluded owing to lack of 

impervious cover. 

r

2

 = 0.64*

r

2

 = 0.75*

0

20

40

60

80

100

0 10 20 30 40 50 60 70 80

Impervious cover (%)

Forested Land Cover (%) .

3.2 Exposures 

3.2.1 Stormwater Runoff 

Urbanization alters the hydrologic cycle or water budgets of watersheds. As land becomes 

covered with surfaces impervious to rain, water is redirected from groundwater recharge and 

19 
evapotranspiration to stormwater runoff. This is a critical issue considering that nonpoint source 

(NPS) pollution is the leading cause of water quality degradation, and stormwater runoff 

accounts for most NPS pollution (USEPA 2002). 

Stormwater runoff amounts for 4.5 in. rainfall were calculated for the 19 watersheds draining 

intertidal creek segments. In general, the runoff volume increased along the gradient of forestedsuburban-urban land use classes 

(Figure 7). Average amounts from 

suburban and urban watersheds were 

2.5 and 4 times greater, respectively, 

than from forested watersheds. 

Comparisons of medians among land 

use classes provide even larger 

differences, with runoff volume 

medians for suburban and urban 

watershed medians exceeding 

forested by factors of 6 and 8.4, 

respectively (Figure 8). More than 

50% of runoff volumes from 

suburban and urban watersheds fell 

within a range of 28,000 to 45,000 

0

20000

40000

60000

Stormwater Runoff (m 3 km-2) .

 m3

 km-2 while 50% of the forested 

watershed volumes were between 

5,000 and 16,000 m3

 km-2. 

Another way to consider stormwater 

runoff is in terms of the percent of the total rainfall that is converted to runoff for each 

watershed. Runoff percent generally increased with increasing impervious cover and ranged 

from 4.6% (SAP-Duplin and SAP-Oakdale, forested) to 63.3% (New Market, urban; Figure 9). 

A number of creeks did not follow this pattern due to naturally occurring variability, differences 

Figure 7. Stormwater runoff volume for intertidal 

watersheds grouped by land use class. Bars represent 

average runoff volumes. Error bars are  1 standard 

error. Volumes are standardized by watershed area and

based on rainfall of 4.5 in. 

Forested Suburban Urban

 80000

 

Figure 8. Stormwater 

runoff volume for 

intertidal watersheds 

grouped by land use 

class. The center 

horizontal line marks 

the median, and the 

length of each box 

shows the range within 

which the central 50% 

of the values fall. 

Calculated volumes are 

standardized by 

watershed area and 

based on precipitation of 

4.5 inches. 0 

20000

40000

60000

Runoff Volume (m3 km-2) 

Forested Suburban Urban

20 
0%

20%

40%

60%

80%

NIWB-Crabhaul, SC

SAP-Duplin, GA

Guerin, SC

SAP-Oakdale, GA

ACE-Village, SC

Albergottie, SC

Okatee, SC

Parrot, SC

James Island-N, SC

Hewlitts-N, NC

Whiskey, NC

Orangegrove, SC

Hewlitts-S, NC

James Island-S, SC

Burnett, GA

Postell, GA

Bulls, SC

Shem, SC

New Market, SC

Forested Suburban Urban

Stormwater Runoff

Figure 9. Rainfall 

percent that converts 

to stormwater runoff 

in intertidal 

watersheds. Land 

use class is marked 

by color. 

Percentages are 

standardized by 

watershed area and 

based on rainfall of 

4.5 in. 

in hydrologic soil groups, and land use considerations. Guerin and NIWB-Crabhaul (both 

forested systems) had runoff percentages greater than several watersheds classified as developed 

because both had a greater proportion of more impervious soils. Burnett (urban) had a runoff 

percentage (9.1%) lower than all but 3 forested watersheds because the watershed was classified 

as urban although impervious cover is only 11.2% based on its industrial history (creosote wood 

preserving). 

Runoff volume calculated by the modified NRCS-CN method was significantly associated with 

percent of impervious cover in the respective watersheds (Figure 10). This close relationship 

suggests that relative differences in runoff volumes for watersheds can be predicted based upon 

percent of impervious cover. Greatest 

variation occurs in the forested 

watersheds with low impervious cover 

levels. 

3.2.2 Basic Water Quality 

Basic water quality metrics were 

sampled including temperature, pH, 

salinity, and dissolved oxygen (DO). 

Temperature affects the rate of 

chemical reactions, and organisms 

have differing physiological 

tolerances to temperature. Extreme 

values of pH can occur when acids or 

caustic materials enter creek waters 

indicating the presence of pollutants. 

Salinity levels influence the 

distribution and diversity of many 

invertebrates and fish species and can 

r

2

 = 0.87*

0

20000

40000

60000

80000

0 10 20 30 40 50 60 70 80

Impervious cover (%)

Runoff volume (m3 km-2 )

Figure 10. Relationship between stormwater runoff volume 

and impervious cover for intertidal watersheds. Calculated 

volumes are standardized by watershed area and based on 

precipitation of 4.5 inches. Model r2 is shown with asterisk 

(*) indicating significance (p < 0.05). 

21 
be stressful to many organisms when large variations occur over short time periods. Low DO 

levels can limit distribution or survival of most biota, especially if conditions persist for extended 

periods (Van Dolah et al. 2004). 

Averages for creek water temperature values were observed from 25.0° C (NIWB-Crabhaul, 

subtidal, forested) to 32.6° C (Albergottie, intertidal, suburban). Average pH values were 

observed from 6.52 (Okatee, intertidal, suburban) to 7.97 (NOC-Masonboro, intertidal, marsh, 

Figure 11). Average salinity values were observed from 0.51 ppt (Bulls, intertidal, urban) to 

35.1 ppt (NIWB-Clambank, intertidal, marsh). Average DO values were observed to from 2.68 

mg L-1 (James Island-S, intertidal, suburban) to 6.89 mg L-1 (Hewlitts-N, subtidal, suburban). 

Temperature ranges (maximum minus minimum) were observed from 1.24° C (Orangegrove, 

subtidal, suburban) to 13.18° C (Orangegrove, intertidal, suburban). pH ranges were observed 

from 0.18 (Bulls, intertidal, urban) to 1.53 (Hewlitts-N, intertidal, suburban). Salinity ranges 

were observed from 0.8 ppt (SAP-Duplin, subtidal, forested) to 31.3 ppt (Hewlitts-S, intertidal, 

suburban). DO ranges were observed from 2.68 mg L-1 (James Island-S, intertidal, suburban) to 

15.02 mg L-1 (Hewlitts-N, intertidal, suburban). 

In general, basic water quality averages and ranges for NOC-Masonboro, SAP-Duplin, and SAPOakdale were similar to the other creeks in marsh and forested watersheds. Salinity levels were 

high in NOC-Masonboro, SAP-Duplin, and SAP-Oakdale, similar to the other creeks with a high 

oceanic influence (Figure 11). Salinity ranges were also high in the Georgia and North Carolina 

intertidal creeks draining developed watersheds. 

Average water quality levels were not significantly affected by land use class or longitudinal 

gradient. Land use had a significant effect on salinity range with the urban and suburban creeks 

having significantly larger ranges than the marsh and forested creeks (Table 3). In addition, 

salinity range as well as temperature range and DO range responded to the longitudinal spatial 

gradient sampled with the intertidal creeks having significantly larger ranges compared to the 

subtidal creeks. 

Intertidal salinity ranges showed a significant relationship with the amount of impervious cover 

in the watersheds (Figure 12). This pattern is similar to previous research in SC intertidal creeks 

and has been attributed to flashier runoff from developed watersheds due to increased 

impervious cover (Lerberg et al. 2000, Holland et al. 2004). None of the other basic water 

quality metrics had statistically significant regressions. 

22 
0

10

20

30

40

Salinity average (ppt)

 

3.2.3 Nutrients and Phytoplankton 

Sampled nutrients and phytoplankton metrics comprised ammonium (NH4

+

), nitrate plus nitrite 

(NO2/3), total dissolved nitrogen (TDN), total nitrogen (TN), orthophosphate (PO4

3-), total 

dissolved phosphate (TDP), total phosphate (TP), silicate (DSi), chlorophyll-a (Chl-a), and 

phaeophytin (Phaeo). Nutrient concentrations can be related to anthropogenic influences and 

adverse impacts on creek biota. In particular, stormwater runoff from developed land carries 

NO2/3 and PO4

3- from fertilizer applications into creek waters. High NO2/3 concentrations 

indicate possible creek eutrophication which can lead to large algal blooms resulting in low and 

fluctuating dissolved oxygen levels that can adversely impact creek biota. Chl-a is a measure of 

phytoplankton biomass, and high concentrations can indicate the presence of excessive algal 

blooms from eutrophication. 

Concentration levels for nutrients ranged from one to three orders of magnitude among the 

creeks sampled. TN ranged from 0.33 mg L-1 (Hewlitts, subtidal, suburban) to 4.65 mg L-1

(Orangegrove, intertidal, marsh). NO2/3 ranged from 0.003 mg L-1 (SAP-Duplin, intertidal, 

forested) to 0.397 (Parrot, intertidal, suburban). Chl-a ranged from 0.57 μg L-1 (Orangegrove, 

intertidal, suburban) to 174.15 μg L-1 (Orangegrove, intertidal, marsh). From an individual creek 

standpoint, Orangegrove (intertidal, marsh) had far higher levels of phosphorous (PO4

3-, TDP, 

0

10

20

30

40

Guerin,

SC

NIWB-Clambank, SC

Orangegrove, SC

Parrot, SC

NOC-Masonboro, NC

NIWB-Crabhaul, SC

SAP-Duplin, GA

Guerin,

SC

SAP-Oakdale,

GA

ACE-Village, SC

Albergottie, SC

Okatee, SC

Parrot, SC

James

Island-N, SC

Hewlitts-N, NC

Whiskey, NC

Orangegrove, SC

Hewlitts-S, NC

James

Island-S, SC

Burnett, GA

Postell, GA

Bulls,

SC

Shem, SC

New Market, SC

Marsh Forested Suburban Urba

Salinity range (ppt) .

n

Figure 11. Salinity averages and ranges (maximum minus minimum) for 

intertidal creeks. Land use class is marked by color. Ranges were 

calculated from semi-continuous (every 15 minutes) sampling over a 24-

hour period. 

23 
TP) than any other creek. This could reflect the historic land use in the area of phosphate 

mining. Phaeophytin levels also were far higher in the Orangegrove marsh watershed creek than 

in other creeks (Figure 13). 

Nutrient and phytoplankton concentrations for NOC-Masonboro (intertidal, marsh) were 

consistently low, similar to the other marsh watershed creeks that had no forests adjacent to the 

watershed (i.e., NIWB-Clambank, Parrot). Concentrations for SAP-Duplin and SAP-Oakdale 

creeks were similar to or higher than the other forested watershed creeks. SAP-Duplin 

(intertidal, forested) had the highest DSi concentrations (7.87 mg L-1) of all creeks, developed 

and undeveloped. NOC-Masonboro (intertidal, marsh) had the lowest DSI concentrations (0.41 

mg L-1). Concentrations of the three phosphorous species were consistently low in all North 

Carolina creeks (marsh and suburban). 

Figure 32. Ability of gene expression profiles (hepatopancreas tissues) to discriminate creek types 

based upon levels of impervious cover. Percent of correct classifications are shown for each 

comparison of levels. Error bars are  1 standard error

Table 13. Oyster tissue analysis. Assignment of individual creeks to impervious cover and lan

use classes based upon watershed characteristics. 

Table 3. Results of 2-way ANOVA on averages and selected ranges of water quality indicator d 

variables sampled in summer, 2005 and 2006. Land use class factors are Marsh (M), Forested (F), 

Suburban (S), Urban (U). Order factors are Intertidal (I) and Subtidal (S). Post hoc multiple 

comparisons were performed using least squared means; model factors (arranged from low to high) 

with different superscripts are statistically different. 

Parameter 

Model 

p-value r

2

Land Use 

p-value 

Order 

p-value 

Interaction Land Use 

LS means 

Order 

LS means 

Basic water quality – range (sonde data) 

Temperature <0.001 0.609 0.111 <0.001 ns I

a

 Sb

Salinity 0.001 0.366 <0.05 <0.05 ns Fa

 Ma Sb Ub Sa Ib

Dissolved oxygen (DO) <0.001 0.436 0.303 <0.001 ns Sa

 Ib

pH 0.059 0.208 0.057 0.183 ns 

Basic water quality – average (sonde data) 

Temperature 0.144 0.161 0.085 0.599 ns 

Salinity 0.061 0.206 0.071 0.120 ns 

Dissolved oxygen (DO) 0.500 0.082 0.774 0.121 ns 

pH 0.450 0.090 0.855 0.092 ns 

Nutrients / Phytoplankton (grab samples) 

Ammonium (NH4

+

) 0.003 0.332 0.169 0.001 ns Sa Ib

Nitrate+nitrite (NO2/3) 0.001 0.399 0.003 0.329 ns Fa

 Ma Sb Ub 

Total dissolved nitrogen (TDN) 0.057 0.210 0.663 0.006 ns Sa

 Ib

Total nitrogen (TN) <0.001 0.417 0.687 <0.001 ns Sa

 Ib

Ortho-phosphate (PO4

3-) 0.062 0.205 0.084 0.092 ns 

Total dissolved phosphorous (TDP) 0.059 0.208 0.111 0.056 ns 

Total phosphorous (TP) 0.003 0.338 0.120 0.001 ns Sa

 Ib

Silicate (DSi) 0.149 0.159 0.871 0.014 ns Sa

 Ib

Chlorophyll-a (Chl-a) 0.341 0.234 0.858 0.002 ns Sa

 Ib

Phaeophytin (Phaeo) 0.004 0.330 0.677 <0.001 ns Sa

 Ib

Pathogen Indicators (grab samples) 

Enterococcus (ENT) 0.001 0.367 0.015 0.002 ns Ma

 Fa,b Sb Ub Sa Ib

Fecal coliform (FC) <0.001 0.616 <0.001 <0.001 ns Ma

 Fa Sb Ub Sa Ib

F- coliphage (F-) <0.001 0.493 <0.001 0.001 ns Ma

 Fa Sb Ub Sa Ib

F+ coliphage (F+) 0.007 0.301 0.004 0.300 ns Ma

 Fa Sa Ub 

 

24 
r

2

 = 0.25*

r

2

 = 0.08

0

7

14

21

28

35

0 10 20 30 40 50 60 70 80

Impervious cover (%)

Salinity range (ppt) .

Intertidal Subtidal

Figure 23. Average taxa

abundances and the 

Carolinian Province 

Index of Biological 

Integrity (CP-IBI) by 

land use class and 

longitudinal gradient. 

Error bars are  1 

standard error.

Figure 12. Relationship 

between salinity range and 

impervious cover for the 

study watersheds. Model r2

is shown for each regression 

with asterisk (*) indicating 

significance (p < 0.05). 

0.0

0.6

1.2

1.8

PO43- (mg L-1

) Figure 13. Intertidal nutrient and phytoplankton levels for individual creeks. 

Land use class marked by color. Bars represent average concentrations. 

PO4

3-=orthophosphate, Phaeo=phaeophytin. 

0.0

1.0

2.0

3.0

Guerin,

SC

NIWB-Clambank, SC

Orangegrove, SC

Parrot, SC

NOC-Masonboro, NC

NIWB-Crabhaul, SC

SAP-Duplin, GA

Guerin, SC

SAP-Oakdale, GA

ACE-Village, SC

Albergottie, SC

Okatee, SC

Parrot, SC

James 

Island-N, SC

Hewlitts-N, NC

Whiskey, NC

Orangegrove, SC

Hewlitts-S, NC

James 

Island-S, SC

Burnett, GA

Postell, GA

Bulls, SC

Shem, SC

New Market, SC

Marsh Forested Suburban Urban

Phaeo (

μg L-1

)

25 
The type of land use surrounding the tidal creeks had little effect on most nutrient and 

phytoplankton concentrations, whereas the longitudinal spatial gradient sampled showed a more 

consistent significant effect (Table 3). Land use class did have a significant effect on NO2/3

levels, and concentrations for creeks in marsh and forested watershed classes were significantly 

lower than in developed watershed classes. The results for NO2/3 was probably related to the 

combined increase of fertilizer use and stormwater runoff in developed watersheds. All nutrient 

concentrations, with the exception of NO2/3, exhibited similar spatial gradients, with intertidal 

creeks having significantly (or trending toward significance, p < 0.10) higher levels than subtidal 

creeks (Figure 14). 

Intertidal concentrations of NH4

+

 and NO2/3 and subtidal levels of NO2/3 increased significantly 

with increasing levels of impervious cover in the watersheds (Figure 15). None of the other 

nutrients or phytoplankton measures were significantly related to impervious cover. 

Figure 14. Nutrients and phytoplankton levels by land use class and longitudinal 

gradient. Bars represent average concentrations. Error bars are  1 standard error. 

0.00

0.04

0.08

0.12

NO2/3 (mg L-1

)

0.0

0.2

0.4

0.6

0.8

PO43- (mg L-1

)

0

20

40

60

Chl-a (

μg L-1) 

Marsh Forested Suburban Urban Marsh Forested Suburban Urban

0.00

0.10

0.20

0.30

0.40

NH4

+ (mg L-1

)

0.0

0.2

0.4

0.6

0.8

Intertidal Subtidal

TDP (mg L-1

)

0.00

0.40

0.80

1.20

Intertidal Subtidal

TDN (mg L-1

)

26 
r

2

 = 0.31*

r

2

 = 0.06

0.0

0.2

0.4

0.6

NH4

+ (mg L-1

)

Intertidal Subtidal

Figure 15. Relationship 

between nutrient concentrations

and impervious cover for the 

study watersheds. Impervious 

cover was regressed against 

ammonium (NH4

+

; upper) and 

against nitrate plus nitrite 

(NO2/3; lower). Model r2 is 

shown for each regression with 

asterisk (*) indicating 

significance (p < 0.05). 

r

2

 = 0.30*

r

2

 = 0.25

0.0

0.1

0.2

0 10 20 30 40 50 60 70 80

Impervious cover (%)

NO2/3 (mg L-1

)

Based on categorical guidelines developed for coastal waters by NOAA (Bricker et al. 1999), 

concentrations found in this study for TDN and TDP ranged from medium to high, and Chl-a

from low to hypereutrophic. In general, intertidal creek concentrations were classified into the 

higher categories compared to the subtidal creeks (Figure 16). TDN concentrations for intertidal 

creeks draining suburban and urban watersheds were classed as medium for North Carolina 

study sites and either medium or high for South Carolina and Georgia study sites. TDN 

concentrations were classified as medium for all subtidal creeks and for intertidal creeks draining 

forested and marsh watersheds with one exception (Orangegrove, marsh, intertidal: high). 

TDN

75%

25%

Low Medium High

Intertidal

100%

Subtidal

TDP

62%

38%

Low Medium High

Intertidal

37%

63%

Subtidal

Chl-a

33%

21%

46%

Low Medium High Hyper

Intertidal

Table 6. Average number

of individuals per meter 

squared and the perc

samples in which the m

taxonomic classes were 

collected in summ 16%

79%

5%

Subtidal

ent of 

ajor 

er, 2005 

and 2006. For creeks 

sampled in both years

*NOC-Masonboro is a mars

creek classified as forested for 

the genomics analysis. 

Figure 16. Percentage of h 

creeks classified into TDN, 

TDP, and Chl-a qualitative 

categories based upon 

guidelines developed by 

NOAA (Bricker et al. 1999) 

for coastal waters. 

27 
TDP concentrations in South Carolina and Georgia generally were classified as high for 

intertidal creeks and as high and medium for subtidal creeks. All of the North Carolina sites 

were classified as medium. Intertidally the Chl-a concentrations ranged from medium to 

hypereutrophic across all of the land use classes. Subtidal creek concentrations were generally 

classified as medium. 

3.2.4 Pathogens 

Fecal coliform (FC) and Enterococcus (ENT) are bacteria that have been used extensively as 

indicators of fecal pollution and enteric pathogens. However, they may be inadequate indicators 

for all pathogens that are associated with fecal pollution, particularly enteric viruses. 

Coliphages, viruses that infect Escherichia coli, are being investigated to determine if they are a 

more appropriate indicator for water borne pathogens. F+ and F- are the two main types of 

coliphages. 

FC concentrations ranged from <1 to 91,000 colony forming units (CFU) 100 ml-1 while ENT 

concentrations ranged from 3 to 21,000 CFU 100 ml-1. Levels of measured viruses tended to be 

lower than those of the bacteria, ranging from 0 to 450 plaque forming units (PFU) 100 ml-1 and 

<1 to 1,200 PFU 100 ml-1 for F+ and F- coliphages, respectively. 

In general, results for the National Estuarine Research Reserves' (NERR) creeks were similar to 

the other forested and marsh 

watershed creeks, with 

concentrations lower than 

developed creeks. However, 

concentrations of F+ and Fcoliphages in the intertidal 

section of the SAP-Oakdale, 

(forested) were far higher than 

all other forested sites, and 

were similar to results from 

sampled creeks draining 

suburban watersheds (Figure 

17). Of the creeks in the 

forested watersheds, intertidal 

SAP-Oakdale also had the 

highest levels of FC. ENT and 

FC concentrations for NOCMasonboro (marsh) were low, 

similar to the other marsh 

watershed creeks that did not 

have forests adjacent to the 

watershed. 

0

100

200

300

400

F+ (PFU ml-1

)

0

200

400

600

800

Guerin, SC

NIWB-Clambank, SC

Orangegrove, SC

Parrot, SC

NOC-Masonboro, NC

NIWB-Crabhaul, SC

SAP-Duplin, GA

Guerin, SC

SAP-Oakdale, GA

ACE-Village, SC

Albergottie, SC

Okatee, SC

Parrot, SC

James Island-N, SC

Hewlitts-N, NC

Whiskey, NC

Orangegrove, SC

Hewlitts-S, NC

James Island-S, SC

Burnett, GA

Postell, GA

Bulls, SC

Shem, SC

New Market, SC

Marsh Forested Suburban Urban

F- (PFU ml-1

) Figure 17. Intertidal F+ and F- coliphage levels for individual 

creeks. Land use class is marked by color. Bars represent average 

concentrations in plaque forming units (PFU) 100ml-1.

The type of land use 

surrounding the tidal creeks and 

the spatial gradient sampled 

28 
were found to affect both bacterial and viral pathogen indicator densities measured in this study. 

Concentrations generally were lowest in salt marsh and forested watershed classes and highest in 

the urban watershed classes, a pattern that is most apparent in the intertidal creek sections. FC 

and F- coliphage levels differed significantly for the salt marsh and forested creeks compared to 

the developed (suburban and urban) watershed classes (Table 3). ENT concentrations were 

significantly higher in the suburban and urban classes compared to the salt marsh class, with the 

forested class similar to all of the other classes. F+ coliphage levels were <1 PFU 100 mL-1 in 

all salt marsh creeks, and the F+ concentrations in the marsh, forested and suburban classes were 

significantly lower than concentrations in the urban class. ENT, FC, and F- coliphage 

concentrations exhibited similar spatial gradients, with intertidal creeks having significantly 

higher densities of pathogen indicators than subtidal creeks. The F+ coliphage concentrations 

showed a similar trend but was not statistically significant (Figure 18, Table 3). 

Concentrations of pathogen indicators increased with increasing levels of impervious cover in 

the watersheds (except ENT in subtidal areas), especially in the intertidal systems (Figure 19). 

In intertidal creeks, significant relationships were found between all of the pathogen indicators 

and the amount of impervious cover in the watershed. In the subtidal creeks, only the F+ 

coliphage showed a significant relationship with impervious cover in the watershed. 

0

2

4

6

Ent (log CFU 100 ml-1

)

Marsh Forested Suburban Urban

0

2

4

6

Intertidal Subtidal

FC (log CFU 100 ml-1

)

0

1

2

3

Intertidal Subtidal

F- (log PFU ml-1

) 0

1

2

3

F+ (log PFU ml-1

)

Marsh Forested Suburban Urban

Figure 18. Bacterial and viral indicator levels by land use class and longitudinal gradient. 

Bars represent average concentrations and error bars are  1 standard error. 

Ent = Enterococcus, FC = Fecal Coliform, F+ and F- = coliphages. Log transformation is 

x +1. 

29 
3.2.5 Sediment Quality

Tidal creek sediments are often characterized as pluff mud which is a soft and mucky sediment 

rich in organic matter and high in clay content. These types of sediments are often repositories 

for chemical contaminants entering the tidal creeks from runoff and recreational use of the 

waters. Characterizing the sediments is an important aspect of interpreting sediment 

contaminant data. For example, trace metal contaminants bind to clay particles while organic 

contaminants bind to organic carbon (Beeftink et al. 1982, Boehm and Farrington 1984, Barrick 

and Prahl 1987). Therefore, higher clay and total organic carbon (TOC) levels will often be 

associated with higher contaminant loadings. Ammonious nitrogen (TAN) is also an important 

indicator of sediment quality and can cause toxicity to benthic organisms. 

3.2.5.1 Sediment Composition 

Sediment characteristics (e.g., % sand, % clay, % silt, and TOC) were evaluated using a 2-way 

ANOVA. The coarse grain composition (% sand) was not significantly related to either the 

surrounding land use or creek order. With respect to the finer particulates (% clay), sediment 

clay concentration ranged from 1.6% (NIWB-Clambank, subtidal, marsh) to 74.0% 

(Orangegrove, subtidal, marsh); these variable data did not show a significant relationship with 

either land use class or creek order (Tables 4, 5, 6). In comparison, the TOC ranged from 0.09% 

(Hewlitts, subtidal, suburban) to 10.7% (Hewlitts-S, intertidal, suburban) with significantly 

higher TOC concentrations in the intertidal creeks compared to the subtidal creeks (Tables 4, 5, 

6). Sediment TOC was significantly related to creek order, with higher organic carbon 

concentrations in the intertidal creeks. There was no measurable effect of land use class. 

Figure 19. Relationship between pathogen indicators and impervious cover for the study 

watersheds. Model r2 is shown for each regression with asterisk (*) indicating significance (p < 

0.05). Marsh watersheds are excluded owing to lack of impervious cover. Ent = Enterococcus, 

FC = Fecal Coliform, F+ and F- = coliphages. Log transformation is x + 1. 

r

2

 = 0.35*

r

2

 = 0.02

0

2

4

6

Ent (log CFU 100 ml-1

)

Intertidal Subtidal

i

r

2

 = 0.39*

r

2

 = 0.25*

0

1

2

3

F+ (log PFU 100 ml-1

)

Intertidal Subtidal

r

2

 = 0.63*

r

2

 = 0.05

0

2

4

6

0 10 20 30 40 50 60 70 80

Impervious cover (%)

FC (log CFU 11 ml-1

)

r

2

 = 0.32*

r

2

 = 0.25

0

1

2

3

0 10 20 30 40 50 60 70 80

Impervious cover (%)

F- (log PFU 100 ml-1

) 30 
Sediment TAN was significantly lower in forested creeks compared to suburban creeks (Table 

6). Urban and marsh creeks were in between forested and suburban ones, but not significantly 

different from each other. Most creek types showed higher concentrations of sediment TAN in 

the intertidal habitats, although on average, there was no difference between intertidal and 

subtidal creeks with respect to sediment TAN. 

Table 4. Characteristics and contaminant concentrations in tidal creek sediments for selected parameters. 

Total PAH is the sum of 23 analytes and total PCB is the sum of 79 analytes. Italicized numbers indicate 

concentrations that exceeded the ERL (as defined by Long et al. 1995) for that parameter. 

Land Use Creek

Clay 

(%)

TOC 

(%)

Total 

PAHs

Total 

PCBs

Total 

DDTs As Cr Cu Hg Pb Zn

Intertidal

Salt Marsh Guerin 47.40 5.10 114.7 0.40 0.00 9.83 61.0 33.5 0.081 23.0 65.2

NOC-Masonboro 9.99 0.39 8.3 0.18 0.00 4.14 17.1 0.0 0.006 7.9 0.0

NIWB-Clambank 60.77 4.31 112.5 0.00 0.00 20.45 59.6 16.1 0.048 22.1 68.6

Orangegrove 64.68 8.26 1276.5 1.20 1.71 16.50 92.6 35.7 0.144 37.9 125.0

Parrot 67.43 4.40 181.3 0.00 0.00 23.60 69.9 19.5 0.067 24.1 79.8

Forested SAP-Duplin 40.94 6.33 15.4 1.02 0.00 12.10 33.4 9.6 0.037 13.2 42.9

Guerin 51.87 4.15 45.4 0.19 0.00 6.41 55.8 8.2 0.058 20.6 43.5

NIWB-Crabhaul 12.59 1.00 33.1 0.00 0.00 7.62 16.0 0.0 0.020 7.8 16.8

SAP-Oakdale 18.54 1.66 45.1 0.64 0.00 5.91 22.0 0.0 0.022 10.5 31.4

ACE-Village 4.32 0.18 9.7 0.34 0.00 0.96 14.0 0.0 0.004 7.8 0.0

Suburban Albergottie 15.21 1.14 105.2 0.00 2.03 2.65 26.7 0.0 0.029 11.1 26.0

Hewlitts-N 12.35 3.56 1749.4 1.48 7.05 4.66 24.9 23.6 0.047 18.2 44.3

Hewlitts-S 18.67 10.68 555.9 0.36 2.35 4.50 23.1 0.0 0.039 13.0 41.0

James Island-N 28.42 3.31 384.5 1.34 0.95 7.45 46.4 14.0 0.046 23.3 58.6

James Island-S 22.16 2.93 1646.8 0.40 0.94 7.51 36.0 12.2 0.036 18.1 46.5

Okatee 24.52 1.48 82.3 5.93 0.00 3.76 31.6 0.0 0.025 14.4 30.4

Orangegrove 29.52 2.65 697.2 3.78 2.24 6.58 43.0 12.8 0.056 17.0 45.6

Parrot 67.25 4.87 286.0 4.45 3.54 19.60 67.1 25.7 0.102 30.0 97.4

Whiskey 20.57 3.43 482.8 2.10 2.27 7.02 27.6 15.8 0.044 13.6 57.0

Urban Bulls 36.52 3.67 2000.9 0.79 1.18 7.79 56.0 19.2 0.081 28.2 84.0

Burnett 15.82 2.28 2290.1 60.87 0.00 3.12 29.0 61.5 0.124 13.9 36.9

New Market 35.38 6.69 10237.7 107.26 23.28 14.18 93.9 51.9 0.366 92.9 197.0

Postell 5.33 0.30 223.2 2.62 0.87 5.67 23.3 0.0 0.022 14.4 37.2

She 58.01 7.42 8081.4

3.2.5.2 Sediment Contamination

Polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), pesticides, and 

polybrominated diphenyl ethers (PBDEs) were measured in this study. PAHs are a major 

component of lubricating oils and fossil fuels which are released into the environment when 

m 1.30 3.34 20.57 73.9 41.5 0.133 32.9 144.0

Subtidal

Salt Marsh Guerin 3.47 0.35 18.4 0.74 0.00 4.34 21.8 0.0 0.011 9.2 23.1

NIWB-Clambank 1.58 0.10 13.2 0.00 0.00 2.41 15.5 0.0 0.003 5.7 0.0

Orangegrove 74.02 5.04 1095.9 0.70 1.36 18.50 88.0 35.4 0.137 36.0 122.5

Forested SAP-Duplin 51.85 2.68 49.3 0.35 0.00 15.40 49.1 8.3 0.022 16.3 54.0

Guerin 14.79 0.30 32.6 0.15 0.00 5.15 25.4 1.7 0.017 12.2 17.3

NIWB-Town 1.79 0.12 12.4 0.00 0.00 2.48 11.3 0.0 0.003 4.8 0.0

ACE-Village 11.69 0.33 104.0 5.29 2.63 2.69 19.0 1.6 0.009 7.4 8.5

Suburban Albergottie 10.13 0.47 59.9 0.33 0.27 3.02 21.4 0.0 0.009 7.7 0.0

Hewlitts 2.44 0.09 0.0 0.16 0.00 2.24 15.8 0.0 0.002 6.9 0.0

James Island-N 6.07 0.35 292.4 0.16 0.46 2.75 15.4 0.0 0.012 8.4 21.1

James Island-S 29.84 2.10 526.0 0.36 0.60 8.54 50.0 12.0 0.040 17.6 49.9

Okatee 39.47 0.56 41.3 0.36 0.00 8.08 42.0 6.1 0.027 15.1 40.4

Orangegrove 71.29 2.67 1956.8 0.74 1.72 16.05 77.5 31.0 0.125 33.3 107.0

Parrot 22.48 2.09 117.9 0.00 0.00 6.83 33.2 3.7 0.022 13.2 30.7

Whiskey 69.64 5.30 552.9 0.83 0.88 15.80 35.4 26.1 0.067 19.6 76.3

Urban Bulls 37.78 2.98 941.1 1.00 0.93 8.62 53.3 17.9 0.077 25.1 74.2

Burnett 21.59 2.77 39.9 2.71 0.00 0.56 6.3 0.0 0.009 3.9 0.0

Murrells Inlet 6.06 0.28 198.4 0.00 0.00 4.28 30.3 0.0 0.006 11.5 10.9

Shem 1.59 0.15 531.8 0.68 0.00 1.28 20.3 0.0 0.004 8.0 0.0

Sediment Trace Metals

31 
these products are spilled or 

combusted. Potential sources 

are runoff from highways and 

parking lots, street dust, fuel 

spills, marinas and recreational 

boating activities, and 

atmospheric fallout (reviewed by 

Weinstein 1996). In addition, 

natural sources of PAHs include 

forest fires. PCB production was 

banned in the 1970s, but PCBs 

have been reported to 

accumulate in estuarine 

environments (reviewed by 

Weinstein 1996). Most of the 

pesticides measured for this 

study are historical pesticides 

such as DDT, mirex, and 

chlordane (this group was 

banned in the 1970s). Currentuse pesticides were not measured 

because they are water soluble 

and do not accumulate in 

sediments. Trace metals are 

naturally occurring elements and 

are influenced by natural 

weathering of basement rock 

(Williams et al. 1994). Several 

are anthropogenically enhanced 

from industrial and urban 

associated uses (e.g., lead, 

chromium, copper, cadmium, 

zinc, and mercury). Due to the 

large number of analytes 

measured, only the totals and a 

few of the anthropogenically 

introduced metals will be 

discussed (Tables 4, 5). PBDEs 

are contaminants of emerging 

concern that are used as flame 

retardants in furniture, plastics, 

and clothing. 

T

a

m t, 

r

he units for sediment metals and organics are μg g-1 dry weight 

nd ng g-1 dry weight, respectively. The units for oyster tissue 

etals and organics are μg g-1 dry weight and ng g-1 dry weigh

espectively. 

PAH PCB Pesticides Trace Metals

Land Use Creek # >ERL # >ERL # >ERL # >ERL

Intertidal

Salt Marsh Guerin 0 0 0 1

NOC-Masonboro 0 0 0 0

NIWB-Clambank 0 0 0 1

Orangegrove 1 0 1 3

Parrot 0 0 0 1

Forested SAP-Duplin 0 0 0 1

Guerin 0 0 0 0

NIWB-Crabhaul 0 0 0 0

SAP-Oakdale 0 0 0 0

ACE-Village 0 0 0 0

Suburban Albergottie 0 0 1 0

Hewlitts-N 1 0 2 0

Hewlitts-S 0 0 1 0

James Island-N 0 0 0 0

James Island-S 0 0 0 0

Okatee 0 0 0 0

Orangegrove 0 0 1 0

Parrot 0 0 2 1

Whiskey 0 0 1 0

Urban Bulls 0 0 0 0

Burnett 4 1 0 1

New Market 13 1 2 6

Postell 0 0 0 0

Shem 9 0 2 2

Subtidal

Salt Marsh Guerin 0 0 0 0

NIWB-Clambank 0 0 0 0

Orangegrove 1 0 0 3

Forested SAP-Duplin 0 0 0 1

Guerin 0 0 0 0

NIWB-Town 0 0 0 0

ACE-Village 0 0 1 0

Suburban Albergottie 0 0 0 0

Hewlitts 0 0 0 0

James Island-N 0 0 0 0

James Island-S 0 0 0 1

Okatee 0 0 0 0

Orangegrove 2 0 1 1

Parrot 0 0 0 0

Whiskey 0 0 0 1

Urban Bulls 1 0 0 1

Burnett 0 0 0 0

Murrells Inlet 0 0 0 0

Shem 0 0 0 0

Table 5. The number of ERL exceedences (as defined by Long 

et al. 1995) for each contaminant class at each sampling site. 

There are 13, 1, 2, and 8 individual parameters for the PAHs, 

PCBs, pesticides, and metals, respectively.

Table 5. The number of ERL exceedences (as defined by Long 

et al. 1995) for each contaminant class at each sampling site. 

There are 13, 1, 2, and 8 individual parameters for the PAHs, 

PCBs, pesticides, and metals, respectively. 

32 
Table 6. Results of 2-way ANOVAs performed on sediment characteristics and calculated Effects 

Range Median Quotient (ERMQ) values. ERMQ was calculated as the Total ERMQ, including 

PAHs, PCBs, Pesticides, and Trace metals; in addition, an ERMQ value was calculated for some of 

the subcomponents (PAHs, Metals, PCBs). See text for details. Land use factors are Marsh (M), 

Forested (F), Suburban (S), Urban (U). Order factors are Intertidal (I) and Subtidal (S). Post hoc 

multiple comparisons were done by comparing least squared means; model factors (arranged from 

low to high) with same superscripts are not statistically different. 

Parameter 

Model 

p-value r2

Land Use 

p-value 

Order pvalue Interaction Land Use 

LS Means 

Order 

LS means 

Sediment TAN 0.061 0.21 0.052 0.207 ns Fa Uab Mab Sb 

Sediment % Clay 0.291 0.12 0.776 0.051 ns 

Sediment TOC 0.014 0.27 0.533 0.001 ns Sa I

b 

Total ERMQ 0.009 0.29 0.055 0.009 ns Fa Mab Sb Ub Sa Ib

PCB ERMQ 0.043 0.22 0.046 0.149 ns Fa

 Ma Sa Ub 

Metal ERMQ 0.061 0.21 0.361 0.016 ns Sa

 Ib

PAH ERMQ 0.001 0.49 0.007 0.040 0.016 Fa

 Ma Sa Ub 

Sediments collected in intertidal systems generally showed increasing concentrations of PAHs, 

PCBs and pesticides going from forested and salt marsh to suburban and urban creeks (Table 4). 

In particular, the intertidal portions of the most urbanized creeks including New Market, Shem, 

and Burnett creeks had a number of exceedences of the ERL for PAHs, PCBs and pesticides 

(Table 5). Burnett Creek is a current Superfund site that has a legacy of previous contamination 

from Brunswick Wood Preserving. A major spill of diesel oil and pentachlorophenol (PCP) into 

the creek occurred in 1989, and extensive contamination of the groundwater has occurred. Cu 

levels in the intertidal component of Burnett Creek were the highest of any sampled for this 

study, but Cu was undetectable in the subtidal component of the creek. This indicates that the 

Cu source was in the headwater area of this creek. With respect to the trace metals, though, 

creeks that exceeded the ERL level were more interspersed throughout all four land use classes. 

In particular, levels of As, a naturally occurring metal in these systems, were found to be the 

primary metal that exceeded the ERL. None of the sediment contaminant levels exceeded the 

ERM level for any parameter. 

In subtidal systems, sediment contaminant levels were often below the established ERL (Table 

5). Three creeks (SAP-Duplin, forested; ACE-Village, forested; Orangegrove, marsh) had 

elevated levels of Total DDT, suggesting a historical use of these pesticides in these areas. SAPDuplin and ACE-Village creeks have past agricultural activities in their watersheds, while 

Orangegrove creek is located on the Ashley River, a river with a legacy of industrial 

contamination. With respect to the metals, at least one creek from each class exceeded the As 

ERL, and Orangegrove (marsh) had elevated Cr and Cu levels as well. 

Sediment contaminant data suggest that the Sapelo Island NERR and North Carolina NERR sites 

were some of the least impacted of all the creeks studied. NOC-Masonboro had very low 

concentrations of PAHs (total PAH = 8.3 ng/g dry weight) and other analytes with no 

exceedences of the ERL (Tables 4, 5). Similarly, both SAP-Oakdale and SAP-Duplin creeks 

demonstrated very low levels of sediment contamination. None of the 24 analytes exceeded the 

33 
ERL level in SAP-Oakdale Creek. Arsenic consistently exceeded the ERL in both the upper 

portion and lower reaches of SAP-Duplin Creek. Arsenic often occurs naturally in these 

systems, and the arsenic ERL level was often exceeded in forested or otherwise relatively 

unimpacted salt marsh creeks. 

The Total mERMQ value ranged from 0.0043 to 0.342 and 0.0041 to 0.085 in intertidal and 

subtidal creeks, respectively. When analyzed in a 2-way ANOVA, forested creeks had 

significantly lower Total mERMQ values than both suburban and urban creeks, and marsh creeks 

were between the forested and the suburban/urban creeks (Table 6, Figure 20). The generally 

low values in subtidal creeks in comparison to the intertidal creeks probably resulted in fewer 

statistical differences among land use classes. The intertidal creeks showed a clear trend of 

increasing values from forested to suburban to urban creeks. Salt marsh creeks were often 

similar to the suburban creeks which may be due to the high levels of TOC in these creeks. The 

Pesticide mERMQ and Metal mERMQ values were similar across land use classes, while the 

PAH mERMQ and PCB mERMQ values were significantly higher in the urban land use class 

compared to the other classes (Table 6, Figure 20). The intertidal creeks had significantly higher 

concentrations of overall contamination (Total mERMQ) as well as pesticide and metal 

contamination. The PAH mERMQ and PCB mERMQ values were found to be in similar 

concentrations down the length of the creek. 

0.00

0.08

0.16

0.24

Total mERMQ . 

0.00

0.08

0.16

0.24

Pesticide mERMQ . 

Hyland et al. (1999) provided evidence that mERMQ values greater than 0.020 represent a 

moderate risk of observing a degraded benthic community and values > 0.058 represent a high 

risk of observing a degraded benthic community. It should be noted that these estimates were 

derived from large, subtidal areas (orders 2 and 3 as well as open estuarine habitats) and may not 

Figure 20. Characteristics and contaminant concentrations in tidal creek sediments. From top, 

the Total mean Effects Range Median Quotient (mERMQ), PAH mERMQ, Pesticide 

mERMQ, and Metals mERMQ for each land use and creek order class. Bars represent 

average concentrations. Error bars are  1 standard error. 

Marsh Forested Suburban Urban Marsh Forested Suburban Urban

0.00

0.08

0.16

0.24

Intertidal Subtidal

PAH mERMQ . 

0.00

0.08

0.16

0.24

Intertidal Subtidal

Metals mERMQ . 

 

34 
accurately reflect the tolerances of the macrobenthic community in shallow, intertidal headwater 

creeks to sediment contaminants. In the intertidal creeks, 4 of the 5 salt marsh creeks exceeded 

the 0.020 threshold with one of those exceeding the 0.058 threshold; 2 of the 5 forested creeks 

exceeded 0.020; 8 of the 9 suburban creeks exceeded 0.020 with 2 of those exceeding 0.058; and 

5 of the 5 urban creeks exceeded 0.020 with 4 of those exceeding 0.058. In the subtidal creeks, 1 

of the 3 salt marsh creeks exceeded the 0.058 threshold; 1 (SAP-Duplin Creek) of the 4 forested 

creeks exceeded 0.020; 4 of the 8 suburban creeks exceeded 0.020 with 1 of those exceeding 

0.058; and 1 of the 4 urban creeks exceeded 0.020. 

Regression analysis demonstrated that mERMQ generally increased with increasing levels of 

impervious cover (Figure 21). Regressions of total mERMQ, metals mERMQ, and PAH 

mERMQ versus impervious cover were statistically significant in the intertidal creeks. In 

addition, PAH mERMQ was statistically significant in the subtidal creeks. 

r

2

 = 0.46*

r

2

 = 0.15

0.0

0.1

0.2

0.3

0.4

Total mERMQ .

Intertidal Subtidal

Figure 21. Relationship 

between contaminant 

quotients and impervious 

cover for the study 

watersheds. Impervious 

cover was regressed against 

Total mean Effects Range 

Median Quotient (mERMQ; 

upper) and against PAH 

mERMQ (lower). Model r2 is 

shown for each regression 

with asterisk (*) indicating 

significance (p < 0.05). 

r

2

 = 0.46*

r

2

 = 0.30*

0.0

0.1

0.2

0.3

0.4

0 10 20 30 40 50 60 70 80

Impervious cover (%)

PAH mERMQ .

PDBEs were only detected in the intertidal areas of the more developed creeks: James Island and 

Orangegrove (suburban) and Bulls, Shem, and New Market (urban, Figure 22). It appears, then, 

that these intertidal creeks are potentially valuable sentinels for detecting more of the emerging 

contaminants of concern as analytical methods are developed. 

35 
0.0

0.5

1.0

1.5

2.0

2.5

3.0

Guerin, SC

NIWB-Clambank, SC

Orangegrove, SC

Parrot, SC

NOC-Masonboro, NC

NIWB-Town, SC

SAP-Duplin, GA

Guerin, SC

SAP-Oakdale, GA

ACE-Village, SC

Okatee, SC

Parrot, SC

Albergottie, SC

James Island-N, SC

James Island-S, SC

Whiskey, NC

Hewlitts, NC

Orangegrove, SC

Burnett, GA

Bulls, SC

Postell, GA

Murrells Inlet, SC

Shem, SC

New Market, SC

Marsh Forested Suburban Urban

PBDE (ng g-1 dry wt). Figure 22. Concentration of polybrominated diphenyl ethers (PBDEs) in intertidal creek 

sediments. Land use class is marked by color. Bars represent measured concentrations. 

3.3 Ecological Response 

3.3.1 Macrobenthic Community 

The macrobenthic community was sampled to examine how different levels of development 

affect abundances and distributions of these organisms. A total of 18,296 organisms 

representing 279 taxa were collected in 303 samples. Sampling methods varied between 

intertidally-dominated (intertidal) and subtidally-dominated (subtidal) components of the system, 

as did volume of sediment collected. Because samples collected in subtidal systems were ten 

times larger than those collected in intertidal systems, abundances were converted to density 

(expressed as ind. m-2) prior to analysis. 

Overall, oligochaetes composed 60% of the organisms sampled while polychaetes composed 

35% of the organisms sampled. In the intertidal systems, oligochaetes composed 70% of the 

macrobenthic community while polychaetes comprised 28% of the community with the 

frequency of occurrence of each sample similar. Conversely, in the subtidal systems, 

polychaetes composed 65% of the community while oligochaetes composed only 10% with the 

frequency of occurrence higher for polychaetes than for oligochaetes (Table 7). Many other 

studies have found oligochaetes and polychaetes to be the numerically dominant organisms in 

southeastern tidal creeks (Sanger 1998, Lerberg et al. 2000, Holland et al. 2004, Gillett et al. 

2007). The remaining 5% of the community consisted of crustaceans, mollusks, and nemerteans 

which will not be discussed further due to the relative minimum numbers of organisms obtained 

within the overall samples (Figure 23). 

36 
Intertidal Subtidal 

Major Taxonomic Group 

Average 

(Ind. m-2) 

Frequency 

(%) 

Average 

(Ind. m-2) 

Frequency 

(%) 

Polychaeta 1660.3 72.2 1535.0 95.4 

Oligochaeta 4064.5 78.7 235.1 78.2 

Amphipoda 15.8 3.7 264.1 58.6 

Other 31.1 12.0 83.5 72.4 

Molluska 39.3 9.3 127.2 64.4 

Other Crustacea 15.3 4.6 82.5 51.7 

Decapoda 1.0 0.5 15.4 36.8 

Total 5827.4 2342.8 

Table 7. Average 

number of ind. m-2 and 

the percent of samples in 

which the major 

taxonomic classes were 

collected in summer, 

2005 and 2006. For 

creeks sampled in both 

years, average values 

were calculated. 

Marsh Forested Suburban Urban Marsh Forested Suburban Urban

0

2000

4000

6000

8000

10000

Oligochaeta (ind. m-2

)

0

300

600

900

1200

Amphipoda (ind. m-2

)

0

1000

2000

3000

4000

5000

Polychaeta (ind. m-2

)

0

10

20

30

40

50

Decapoda (ind. m-2

)

0

1

2

3

4

Intertidal Subtidal

CP-IBI

0

100

200

300

400

Intertidal Subtidal

Mollusca (ind. m-2

) Figure 23. Average taxa abundances and the Carolinian Province Index of 

Biological Integrity (CP-IBI) by land use class and longitudinal gradient. Error bars 

are  1 standard error. 

37 
The ten most numerically abundant species and genera (5 oligochaetes and 5 polychaetes) 

composed over 80% of the individuals sampled (Table 8). The most abundant species was the 

oligochaete Monopylephorus rubroniveus which comprised 47% of the individuals collected. 

This species was found in 44% of the total samples collected; however, frequencies were much 

higher in intertidal systems relative to subtidal systems. The second most abundant species was 

the polychaete Streblospio benedicti which comprised 6.5% of the community and was found in 

41% of the samples collected. Most of M. rubroniveus were found intertidally where they 

composed 65% of the community while most S. benedicti were found subtidally and composed 

30% of the community (Table 8). These species have also been found to be the dominant 

benthic organisms in several other tidal creek studies (Sanger 1998, Lerberg et al. 2000, Holland 

et al. 2004, Gillett et al. 2007). 

Table 8. Average number of ind. m-2 and percent of samples in which the twenty most 

abundant species were collected in the summer, 2005 and 2006. All species were in 

one of two classes of annelids: Oligochaeta (O), Polychaeta (P). 

Intertidal Subtidal 

 

 

Scientific Name 

Average 

(Ind. m-2) 

Frequency 

(%) 

Average 

(Ind. m-2) 

Frequency 

(%) 

Monopylephorus rubroniveus (O) 3220.01 59.72 7.71 4.60 

Streblospio benedicti (P) 264.97 26.85 459.25 77.01 

Tubificoides heterochaetus (O) 359.93 23.15 9.54 9.20 

Laeonereis culveri (P) 349.72 30.56 1.04 4.60 

Cirriformia sp. (P) 269.57 6.48 35.79 17.24 

Tubificidae (O) 244.55 24.07 59.95 31.03 

Capitella capitata (P) 206.77 28.24 16.20 10.34 

Tubificoides brownae (O) 123.04 13.89 118.21 45.98 

Heteromastus filiformis (P) 143.46 20.37 35.53 36.78 

Tubificoides wasselli (O) 101.60 6.02 29.26 20.69 

Nereis succinea (P) 77.60 14.35 70.40 44.83 

Spionidae (P) 71.48 15.28 56.43 24.14 

Mediomastus sp. (P) 2.04 0.93 208.99 45.98 

Polycirrus sp. (P) 0.00 0.00 174.76 11.49 

Fabricia sp. (P) 51.57 3.24 0.00 0.00 

Leitoscoloplos fragilis (P) 42.89 6.94 5.09 16.09 

Nemertea (P) 31.14 12.04 30.69 55.17 

Scoletoma tenuis (P) 0.00 0.00 105.93 34.48 

Spiophanes bombyx (P) 0.51 0.46 94.83 17.24 

Capitomastus aciculatus (P) 35.74 7.41 0.26 1.15 

Analyses were performed on five individual species as well as larger taxonomic groups. The 

four most abundant species were chosen for analyses and included the oligochaetes 

Monopylephorus rubroniveus and Tubificoides heterochaetus and the polychaetes Laeonereis 

culveri and Streblospio benedicti. The next two most abundant taxa were Cirriformia sp. and 

Tubificidae, but they were not analyzed because Cirriformia sp. was only found in large 

abundances in North Inlet, and Tubificidae is a grouping of possibly many species that could not 

38 
be identified to a lower taxonomic class. Thus the fifth species analyzed was Capitella capitata

which was the 7th most abundant organism and is common in tidal creek systems. It should be 

noted that these species were the most abundant when examining total abundances as well as 

abundances in the intertidal systems. The species compositions subtidally were very different 

than intertidal systems (Table 8). 

The macrobenthic community in NOC-Masonboro (marsh) was similar to the other salt marsh 

creeks sampled. Abundances of M. rubroniveus were approximately 1.5 times higher (~1500 

ind. m-2) in NOC-Masonboro than the other four salt marsh creeks studied; however, the 

proportion of oligochaetes and polychaetes were similar in all salt marsh creeks. In comparison, 

SAP-Oakdale (forested) had the lowest abundances of all five species for all forested creeks. In 

particular, M. rubroniveus had an average abundance in forested creeks (2000 ind. m-2) nearly 

two orders of magnitude greater than the abundance found in SAP-Oakdale (25 ind. m-2). 

Finally, SAP-Duplin (forested) appeared similar to other forested creeks in the subtidal systems 

except for having higher abundances of S. benedicti than any other forested creek. Conversely, 

the intertidal SAP-Duplin had over twice as many organisms (~10,000 ind. m-2) as all other 

intertidal forested creeks sampled except NIWB-Crabhaul (~7,000 ind. m-2). However, of the 9 

samples, two in each creek drove this abundance. One sample collected contained nearly 80% 

(60,000 ind. m-2) of the oligochaetes collected in SAP-Duplin intertidally while another sample 

had over 70% (9,000 ind. m-2) of the polychaetes collected illustrating the patchiness of these 

systems. 

Two-way ANCOVAs were performed on the selected species, as discussed above. Factors were 

land use class and order, and covariates were salinity and sediment composition. Salinity and 

sediment composition have often been shown to greatly influence benthic communities (Seys et 

al. 1999, Lerberg et al. 2000, Gillett et al. 2007). Salinity and sediment composition were 

significant covariates for C. capitata, salinity was significant for T. heterochaetus and S. 

benedicti, and neither covariate was significant for M. rubroniveus and L. culveri. Order 

differences were significant for all species except S. benedicti with higher densities observed in 

the intertidal creeks than the subtidal creeks. Streblospio benedicti had an opposite trend with 

higher abundances of organisms subtidally (Table 9, Figure 23). Intertidal creeks are often 

considered more stressful than subtidal creeks because they are subjected to wider ranges of 

environmental factors as well as higher levels of chemical contaminants due to their proximity to 

the uplands (Lerberg et al. 2000, Mallin et al. 2000, Holland et al. 2004). Monopylephorus

rubroniveus and S. benedicti were not significantly different among land use classes. However, 

more than twice the average number of M. rubroniveus was found in suburban and urban creeks 

(~4500 ind. m-2) compared to forested creeks and this was more than five times the average of 

individuals found in salt marsh creeks. In contrast, abundances of S. benedicti were most 

abundant in forested creeks (625 ind. m-2) which had 2 to 3 times higher abundances than salt 

marsh, suburban, or urban systems (Figure 24). Capitella capitata was found to be significantly 

higher in forested creeks relative to suburban and urban creeks. Tubificoides heterochaetus and 

L. culveri had higher abundances in the suburban and urban creeks relative to salt marsh creeks 

with differing significance among classes for the two species (Table 9). Lerberg et al. (2000) 

also found M. rubroniveus and L. culveri to be more abundant in degraded systems and S. 

benedicti to be more abundant in less degraded systems.

39 
Analyses were performed on taxonomic groups to examine higher-taxa changes in the 

macrobenthic communities as well. For these analyses, sums of oligochaete and polychaete 

species in each sample were used. Both taxa had higher abundances intertidally but only 

oligochaetes were significantly different (p < 0.0001). Neither group showed significant 

differences between land use classes; however, oligochaetes generally had higher abundances in 

the suburban and urban systems while polychaetes generally had higher abundances in the salt 

marsh and forested systems (Table 9, Figure 23). 

The abundances of each dominant organism were regressed against the level of impervious cover 

for each creek order. The r2

 values were generally low (0.05-0.15), but most taxa showed an 

increase or decrease in abundance as impervious cover increased. Both oligochaete species 

Table 9. Results of 2-way ANCOVA on average densities (except percent composition for 

Oligochaeta and Polychaeta) of macrobenthic taxa and diversity metrics sampled in summer, 2005 and 

2006. Land use (LU) factors are Marsh (M), Forested (F), Suburban (S), Urban (U). Order factors are 

Intertidal (I) and Subtidal (S). Covariates are percent mud and salinity. Post hoc multiple 

comparisons were performed using least squared means; model factors (arranged from low to high) 

with different superscripts are statistically different. 

Parameter 

Model 

p-value r2

Land Use 

p-value 

Order 

p-value 

% Mud 

p-value 

Salinity 

p-value Interactions 

Land Use 

LS Means 

Order 

LS means 

Total Organisms 0.003 0.44 0.044 0.001 ns ns LU*order Sa Uab Mab Fb Sa Ib

M. rubroniveus <0.001 0.84 0.502 <0.001 ns ns LU*order Sa Ib

S. benedicti 0.013 0.32 0.700 0.180 ns 0.009 ns 

T. heterochaetus <0.001 0.61 0.109 0.012 ns <0.001 LU*sal Ma Fab Sab Ub Sa Ib

L. culveri <0.001 0.61 0.007 <0.001 ns ns ns Ma Fab Sbc Uc Sa Ib

C. capitata <0.001 0.65 0.018 <0.001 0.067 0.074 ns Ma Sab Ub Fb Sa Ib

Oligochaeta <0.001 0.41 0.180 <0.001 ns ns ns Sa

 Ib

Polychaeta 0.006 0.34 0.209 0.126 ns 0.011 ns Sa

 Ua Ma Fb

H' <0.001 0.64 0.194 0.533 ns 0.025 order*sal I

a

 Sb

J' 0.020 0.26 0.085 0.014 ns ns ns Ua

 Sab Fb Mb I

a

 Sb

Mao-Tao <0.001 0.58 0.039 <0.001 0.019 ns order*mud Sa Uab Mb Fb I

a

 Sb

0

300

600

900

1200

1500

Intertidal Subtidal

S. benedicti (ind. m-2

)

Marsh Forested Suburban Urban

0

2000

4000

6000

8000

Intertidal Subtidal

M. rubroniveus (ind. m-2

)

Marsh Forested Suburban Urban

Figure 24. Monopylephorus rubroniveus and Streblospio benedicti (the two most abundant 

species) abundances by land use class and longitudinal gradient. Bars represent average 

concentrations. Error bars are  1 standard error. 

40 
examined, M. rubroniveus and T. heterochaetus, as well as the polychaete L. culveri increased in 

abundance as impervious cover increased, but only intertidally. On the other hand the other two 

polychaete species, S. benedicti and C. capitata had decreasing abundances with increasing 

impervious cover; however, the regression of subtidal S. benedicti was the only one that was 

significant. 

Species diversity was also examined in the creek systems. The 9 intertidal cores were summed 

and the 3 subtidal cores were averaged to evaluate species diversity because of the differences in 

sample size (i.e., core is ~1/10 the grab). Three measures were used for most analyses which 

included Shannon-Wiener diversity (H'), evenness (J'), and Mao-Tau. Mao-Tau uses analytical 

formulas to compute expected species accumulation curves to calculate expected species 

richness (Colwell et al. 2004, Mao et al. 2005). All measures were significantly higher in the 

subtidal systems compared to intertidal systems. H' and J' detected no significant differences 

between land use classes, and the Mao-Tau measure of diversity found significant differences. 

All measures had higher numbers in forested creeks relative to urban and/or suburban systems. 

Both J’ and Mao-Tau also indicated significantly higher diversities in the salt marsh systems 

relative to urban or suburban creeks, respectively (Table 9). 

The Benthic Index of Biological Integrity developed for the Carolinian Province (Cape Henry, 

VA - St. Lucie Inlet, FL; CP-IBI) as described in Van Dolah et al. (1999) was also used to 

examine the ecological status of the benthos. The CP-IBI is a measure of the health of a 

community, and higher numbers represent less impaired communities. A significant difference 

between orders was found with higher CP-IBI scores in the subtidal systems (Figure 23). There 

was also a significant difference between land use classes with forested and marsh systems 

having higher CP-IBI scored than suburban and urban systems. This index was developed to 

measure benthic communities in open water systems, and species composition in intertidal 

creeks differs from that of open water. Therefore, significant differences among land use classes 

in intertidal systems may not signify differences in community health. According to the CP-IBI, 

all intertidal creek systems are degraded regardless of land use class. This result is probably 

driven by the fact that the metric was developed for subtidal waters and not for the more stressed 

headwater creeks. 

Indices for intertidal communities were developed by Lerberg et al. (2000) for pollution indicator 

and pollution sensitive species. Pollution indicator species included M. rubroniveus, L. culveri

and Tubificoides brownae which are species often associated with stressful environments while 

pollution sensitive species included T. heterochaetus, S. benedicti, Nemertinea, Heteromastus 

filiformis and Tharyx acutus. Two-way ANOVAs found significant differences among land use 

classes in pollution tolerant species with a significantly higher proportion of pollution tolerant 

organisms being found in suburban and urban systems. Pollution indicator species increased 

significantly in abundance as creek watersheds increased in impervious cover, and pollution 

sensitive species decreased significantly in abundance as impervious cover increased (Figure 25). 

41 
r

2

 = 0.27*

0

20

40

60

80

100

Pollution Sensitive Species (%) . .

3.3.2 Nekton Community 

The nekton assemblages in intertidally-dominated (intertidal) and subtidally-dominated 

(subtidal) portions of a system differed substantially (Table 10). The two different components 

of these systems differ in water quality and volume. This reflects in part the distinct habitat 

structure in each creek segment type. Sampling methods could also add to the differences; 

intertidal assemblages were sampled by seine and subtidal by trawl. Fifty-nine intertidal and 

fifty-nine subtidal species were identified; however, only species occurring most commonly 

across the creeks were analyzed statistically and discussed in this section. For the intertidal 

systems, abundances are reported as individuals per square meter (ind. m-2), and the species 

occurring most commonly were Callinectes sapidus (blue crabs), Fundulus heteroclitus

(mummichog), Leiostomus xanthurus (spot), Palaemonetes spp. (grass shrimp), Litopenaeus 

setiferus (white shrimp) and Farfantepenaeus aztecus (brown shrimp). For the subtidal systems, 

units are individuals per hectare (ind. ha-1), seven species occurred most commonly: Bairdiella 

chrysoura (silver perch), Callinectes sapidus, Lagodon rhomboids (pinfish), Leiostomus 

xanthurus, Lolliguncula brevis (brief squid), Farfantepenaeus aztecus (brown shrimp), and 

Litopenaeus setiferus (white shrimp). 

In the intertidal creeks, a resident creek species, Palaemonetes spp., was the most abundant, and 

densities ranged from 0.01 ind. m-2 (NIWB-Clambank, marsh) to 24.04 ind. m-2 (SAP-Duplin, 

forested). Transient Penaeidae (Litopenaeus setiferus and Farfantepenaeus aztecus) ranked 

second in abundance with densities ranging from 0 ind. m-2 (Okatee, suburban) to 6.58 ind. m-2

(SAP-Oakdale, forested). SAP-Duplin (forested) and SAP-Oakdale had higher abundances 

r

2

 = 0.31*

0

20

40

60

80

100

0 10 20 30 40 50 60 70 8

Figure 25. Relationship between 

pollution species and impervious 

cover for intertidal watersheds. 

Impervious cover was regressed 

against pollution sensitive 

species (upper) and against 

pollution indicator species 

(lower). Model r2

 is shown for 

each regression with asterisk (*) 

indicating significance (p < 

0.05). 

Impervious cover (%)

Pollution Indicator Species (%) .. 

0

42 
 of the two Penaeid shrimp, 

Palaemonetes spp. and F. 

heteroclitus compared to other 

creeks draining forested 

watersheds, and C. sapidus

densities were four times 

greater in SAP-Oakdale (0.94 

ind. m-2) than the next highest 

density (0.21 ind. m-2, Parrot, 

suburban) observed in the study 

(Figure 26). NOC-Masonboro 

(marsh) densities generally 

were low compared to the other 

creeks draining marsh 

watersheds. These differences 

may have been driven by the 

geographical location of NOCMasonboro at the northern 

range of the sampling area, 

especially since Cape Hatteras, 

North Carolina is a dividing 

line for many coastal fish 

assemblages. 

Species Marsh Forested Suburban Urban 

Intertidal (ind. m-2) 

Bairdiella chrysoura 0.59 0.16 0.01 0.08 

Callinectes sapidus 0.02 0.14 0.06 0.05 

Fundulus heteroclitus 0.45 0.42 0.61 0.65 

Gambusia holbrooki 0.00 0.03 0.07 0.18 

Gerreidae 0.04 0.14 0.00 0.02 

Lagodon rhomboides 0.10 0.00 0.01 0.01 

Leiostomus xanthurus 0.07 0.03 0.23 0.04 

Palaemonetes sp. 2.27 6.28 1.45 4.44 

Penaeidae 0.62 2.09 1.20 2.40 

Poecilia latipinna 0.00 0.08 0.14 0.93 

Subtidal (ind. ha-1) 

Anchoa mitchilli 27.29 23.75 126.88 4.83 

In the subtidal creeks, L. 

setiferus was most abundant, 

ranging from 0 ind. ha-1

(NIWB-Clambank, marsh) to 

3,368 ind. ha-1 (Okatee, 

suburban). Second in 

abundance, L. xanthurus density ranged from 0 ind. ha-1 (Hewlitts, suburban) to 2,576 ind. ha-1

(Orangegrove, marsh). SAP-Duplin was the only creek draining a marsh or forested watershed 

with no presence of L. setiferus. Four creeks within developed watersheds had no presence of L. 

setiferus, including three located in NC and GA. SAP-Duplin and three creeks in developed 

watersheds in NC and GA also had low or no densities for L. xanthurus. 

Bairdiella chrysoura 528.90 18.12 599.49 4.03 

Callinectes sapidus 22.72 10.06 27.07 26.81 

Lagodon rhomboides 278.31 13.69 47.92 143.99 

Leiostomus xanthurus 1283.95 49.08 330.38 313.24 

Lolliguncula brevis 35.43 101.05 59.19 104.67 

Micropogonias undulatus 0.00 10.87 3.34 38.82 

Farfantepenaeus aztecus 10.29 101.02 121.85 213.90 

Litopenaeus setiferus 1741.01 48.71 1130.69 137.68 

Stellifer lanceolatus 82.13 6.84 16.72 1.61 

Table 10. Average abundance per area of the top ten most abundant 

nekton species collected for each land use class (Forested, Marsh, 

Suburban, Urban) and creek order (intertidal, subtidal). Intertidal 

creek abundances are individuals per square meter, and subtidal 

abundances are individuals per hectare. 

In the intertidal creeks, Palaemonetes spp. trended toward a significant land use class effect with 

forested creeks having the highest abundance while suburban creeks were significantly lower 

than the other land use classes (Table 11). Several factors appear to be important in explaining 

the variation of the grass shrimp, including geographic location, although the patterns were not 

statistically significant (Figure 27). 

43 
0

5

10

15

20

25

30

Palaemonetes spp. (ind. m-2)

0.0

0.2

0.4

0.6

0.8

1.0

Guerin, SC

NIWB-Clambank, SC

Orangegrove, SC

Parrot, SC

NOC-Masonboro, NC

NIWB-Crabhaul, SC

SAP-Duplin, GA

Guerin, SC

SAP-Oakdale, GA

ACE-Village, SC

Albergottie, SC

Okatee, SC

Parrot, SC

James Island-N, SC

Hewlitts-N, NC

Whiskey, NC

Orangegrove, SC

Hewlitts-S, NC

James Island-S, SC

Burnett, GA

Postell, GA

Bulls, SC

Shem, SC

New

Market, SC

Marsh Forested Suburban Urban

Callinectes sapidus (ind. m-2)

Figure 26. Palaemontes spp. and Callinectes sapidus abundances in the 

intertidal sections. Land use class is marked by color. Bars represent average 

abundances. 

In the subtidal creeks, L. rhomboides was the only species with a significant land use class effect 

(Table 11). The forested and suburban classes had significantly lower abundances of this species 

compared to the marsh class, and the the urban class was similar to the other classes. The 

general trend regarding land use class was that the abundances of individual species in marsh 

creeks were different from the abundances in the other three land use classes. 

Surveys previously conducted in intertidal creeks in South Carolina found reduced numbers of 

Penaeidae in watersheds with higher impervious cover levels (Holland et al. 2004). None of the 

regression analyses of the nekton species for intertidal or subtidal creeks was significant. 

44 
0

31 32 33 34 35

Latitude

Marsh Forested Suburban Urban

5

10

15

20

25

Palaemonetes spp. (ind. m-2

).

Figure 27. Relationship 

between Palaemonetes

spp. abundance and creek 

latitude by land use class. 

Regression results were 

not significant. 

Parameter 

Model 

p-value r2 

Land Use 

LS means 

Intertidal 

F. heteroclitus 0.884 0.03 Table 11. Results of 1-way ANOVA 

examining differences in average abundance in 

intertidal and subtidal creeks separately by land 

use class. Land use class factors are Marsh 

(M), Forested (F), Suburban (S), Urban (U). 

Post hoc multiple comparisons were performed 

using least squared means; model factors 

(arranged from low to high) with different 

superscripts are statistically different. 

C. sapidus 0.297 0.17 

L. xanthurus 0.539 0.10 

Palaemonetes spp. 0.064 0.30 Sa

 Mab Ub Fb

Penaeidae 0.233 0.19 

Subtidal 

B. chrysoura 0.422 0.17 

C. sapidus 0.304 0.21 

L. rhomboides 0.044 0.41 Fa

 Sa Uab Mb

L. xanthurus 0.179 0.27 Fa

 Uab Sab Mb

L. brevis 0.462 0.15 

P. aztecus 0.851 0.05 

P. setiferus 0.416 0.17 

Penaeidae 0.806 0.06 

3.4 Human Responses 

3.4.1 Oyster Tissue Pathogens 

Oysters were only collected during the 2006 sampling period from 11 tidal creek systems: SAPDuplin, SAP-Oakdale, Guerin, Burnett, Postell, New Market, James Island, ACE-Village, 

Hewlitts, Whiskey, and NOC-Masonboro. Tissues were examined for pathogen body burdens, 

and the same pathogen indicators measured in the water column were measured in the oyster 

tissues. 

Pathogen concentrations for any particular parameter varied over 3-4 orders of magnitude. FC 

ranged from 23 CFU 100 g-1 tissue weight in ACE-Village (subtidal, forested) to 220,000 CFU 

100 g-1 tissue weight in New Market (interidal, urban). ENT concentrations varied from 3,240 

CFU 100 g-1 tissue weight to 320,000 CFU 100 g-1 tissue weight in James Island (subtidal, 

suburban) and SAP-Duplin (subtidal, forested), respectively. F- coliphages in a few cases were 

45 
not detected, but reached 6,136 PFU 100 g-1 tissue weight in New Market Creek (urban). F+ 

coliphages were only detected in 4 samples; the highest concentrations were observed in SAPOakdale (intertidal, forested; 4,816 PFU 100 g-1 wet tissue weight). 

Samples collected from NERR locations were difficult to classify with respect to the quality of 

the resource. For instance, FC, F+, and F- measured in NOC-Masonboro oysters were some of 

the lowest concentrations observed; yet, the ENT concentration (60,000 CFU 100 g-1 tissue 

weight) was in the middle of the observed values for all creeks. SAP-Oakdale showed 

concentrations higher than the median concentration for FC, F-, and F+ indicators; ENT 

concentrations (6,000 CFU 100 g-1 tissue weight) were fairly low compared to the other sites. 

The subtidal portion of SAP-Duplin (forested) had the highest ENT concentrations (320,000 

CFU 100 g-1 tissue weight) observed in any of the samples. 

The overall sample size was small, as oysters were only collected from a subset of the sampled 

systems. Nonetheless, ANOVA results indicated a significant land use class effect in Fcoliphages, with concentrations in oysters collected from forested watersheds being lower than 

those collected from either suburban or urban watersheds (Table 12). 

Table 12. Results of 2-way ANOVA examining differences in concentrations of selected 

pathogen indicators measured in tissue from oysters collected in study creeks. Land use factors 

are Forested (F), Suburban (S), Urban (U). The one sample from a Marsh creek 

(NOC_Masonboro) was excluded from these analyses. Order refers to creek order (intertidal, 

subtidal). Post hoc multiple comparisons were performed using least squared means; model 

factors (arranged from low to high) with different superscripts are statistically different. 

Parameter 

 Model 

p-value r2 

Land Use 

p-value

Order 

p-value

Interaction Land Use 

LS means 

Order 

LS means 

Enterococcus 0.563 0.15 0.375 0.774 ns 

Fecal Coliform 0.181 0.32 0.419 0.078 ns 

F- Coliphage 0.005 0.65 0.004 0.076 ns Fa

 Ub Sb 

F+ Coliphage 0.352 0.23 0.677 0.153 ns 

Regression analysis showed that there was a significant (p < 0.05) positive relationship between 

watershed impervious cover and FC concentrations in oysters collected in intertidal creeks 

(Figure 28). For F- coliphages, there were significant relationships in both creek orders. There 

were no other significant regressions. 

For exploratory purposes, the indicator concentrations from oyster tissue were plotted versus 

water concentrations for each system. Oysters are presumed to be bioconcentrators of water 

column materials, and the hypothesis that concentrations in oyster tissues would be higher than 

in the water was evaluated with biplots. Results indicated that oysters collected in these systems 

were consistently above the 1:1 line (assuming that conc g-1 and conc ml-1 are equivalent) for 

ENT (Figure 29). For FC and F-, the concentrations were more likely to fall on the 1:1 line, 

although oysters seem to be concentrating FC in water that had low concentrations. Data were 

scarce for F+ coliphages, but 4 of the 5 oyster samples where F+ coliphages were found had 

much higher concentrations than in the water. 

46 
3.4.2 Oyster Tissue Contaminants 

Oyster tissues were collected from the 11 tidal creek systems sampled in 2006. Samples 

represent all orders in the 12 intertidal and 7 subtidal creek segments. Tissues were analyzed for 

lipids and contaminants including PAHs, PCBs, PBDEs, pesticides, and metals. Statistical 

analyses were not performed on these data due to the high number of non detects and low sample 

size. 

Oyster tissue lipid concentrations ranged from 5.7% (SAP-Duplin, subtidal, forested) to 38% 

(ACE-Village, subtidal, forested). In general, higher values were observed in the forested creeks 

compared to the marsh and developed creeks (Figure 30). Exceptions were SAP-Duplin 

(intertidal and subtidal, forested) with levels similar to the developed systems, and James Island 

(intertidal, suburban) with levels similar to the forested systems. Overall lipid concentrations 

were higher than those observed in the May River when it was listed as an Outstanding Resource 

Waterway by South Carolina Department of Health and Environmental Control (Van Dolah et al. 

2004). In addition, the lipid concentrations tended to be a little lower in the subtidal creeks 

 compared to the intertidal creeks. 

Total PAH tissue concentrations ranged from 0 (Guerin, intertidal, forested; Hewlitts, subtidal, 

suburban) to 2,161 ng g-1 dry weight (Burnett, intertidal, urban). Naphthalene, a low molecular 

weight PAH, was most commonly detected and was found in marsh, forested and developed 

systems. The other PAHs detected included acenaphthylene, anthracene, benzo(a)anthracene, 

benzo(b)fluoranthene, benzo(g,h,i)perylene, fluoranthene, and pyrene. All except 

acenaphthylene are high molecular weight PAHs typical of pyrogenic sources, and were found in 

developed systems, except for one forested system (SAP-Duplin, subtidal). SAP-Duplin 

(subtidal) had high levels of benzo(g,h,i)perylene (488 ng g-1 dry weight). 

Total PCB concentrations ranged from 0 to 244.3 ng g-1 dry weight (Burnett, intertidal, urban). 

Concentrations above the detection limit were found in the three urban intertidal creeks and in 

Figure 28. Relationship between pathogen indicator levels in oyster tissues and impervious cover for 

the study watersheds. Impervious cover was regressed against fecal coliform (FC; left) and against Fcoliphage (right). Model r2 is shown for each regression with asterisk (*) indicating significance (p < 

0.05). Log transformation is x +1. 

r

2

 = 0.96*

r

2

 = 0.70*

0

2

4

6

0 10 20 30 40 50 60 70 80

Impervious cover (%)

F- col (log PFU 100 g -1 )

Intertidal Subtidal

r

2

 = 0.39

r

2

 = 0.43*

0

2

4

6

0 10 20 30 40 50 60 70 80

Impervious cover (%)

FC (log CFU 100 g -1 )

Intertidal Subtidal

47 
1

100

10000

1000000

Tissue (CFU 100 g -1) . .

ENT

1

100

10000

1000000

Tissue (CFU 100 g -1) .

FC

1

100

10000

1000000

Tissue (CFU 100 g -1) .

F+

the one urban subtidal creek; low concentrations (<2 ng g-1 dry weight) were found in Guerin 

(subtidal, forested) and Whiskey (subtidal, 

suburban). PBDEs, flame retardants, were 

detected in oyster tissue at only one site, 

New Market (intertidal, urban). Pesticide 

concentrations in oyster tissues were 

dominated by DDT and its derivatives. 

Total DDT concentrations ranged from 2.53 

(Guerin, intertidal, forested) to 20.54 ng g-1

dry weight (New Market, intertidal, urban) 

(Figure 31). In general, total DDT tissue 

concentrations were higher in the developed 

systems compared to the forested and marsh 

systems. The only other detectable 

pesticide contaminants were mirex (3.49 ng 

g-1 dry weight) in Guerin (intertidal, 

forested), endosulfan I (2.38 ng g-1 dry 

weight) in ACE-Village (intertidal, 

forested), and dieldrin (3.82 ng g-1 dry 

weight) in Postell, (intertidal, urban). 

1

100

10000

1000000

1 10 100 1000 10000 100000

Water (CFU 100 ml-1)

Tissue (CFU 100 g -1) . .

FFigure 29. Biplots of pathogen indicators 

comparing water column concentrations with 

oyster tissue concentrations. Ent = Enterococcus, 

FC = Fecal Coliform, F+ and F- = coliphages. 

To evaluate the potential for 

bioaccumulation of PCBs, PCB congeners 

were grouped by number of chlorines and 

then compared to sediment PCBs. PCBs 

with 7 or more chlorines have an increased 

potential for transfer up the food web 

(Oliver and Niimi 1988). The sediment 

total PCB concentration in New Market 

(intertidal, urban) was the highest of all 

creeks (107 ng g-1 dry weight) and consisted 

primarily of lower chlorinated compounds 

(hexa- and tetrachlorobiphenyls). New 

Market's oyster tissue concentrations were 

comparatively low (approximately 52 ng g-1

dry weight). This difference corresponds 

with conclusions that lower chlorinated compounds found in the sediments are not 

bioaccumulating in tissues. In comparison, Burnett (urban, intertidal) had the second highest 

total PCB concentration in sediments (61 ng g-1 dry weight) and consisted primarily of higher 

chlorinated compounds. Oyster tissue concentrations were high in Burnett (244 ng g-1 dry 

weight), reflecting high bioaccumulation in this system. 

48 
Concentration data for only a few metals (As, Cd, Cr, Cu, Pb, Hg) are discussed here. Except 

for arsenic, these are the metals which are often elevated from anthropogenic sources. Lead 

concentrations were similar across the different land use classes and were generally low (< 0.7 

μg g-1 dry weight) except for New Market (intertidal, urban: 1.58 μg g-1 dry weight). Mercury 

concentrations were similar across land use classes and were generally low (< 0.19 μg g-1 dry 

weight) except for Burnett (urban) in both the intertidal (0.35 μg g-1 dry weight) and subtidal 

(0.42 μg g-1 dry weight) creek segments. The highest concentrations of arsenic were found in 

the North Carolina creeks, similar to the fish tissue contamination findings of Cooksey et al. 

(2008). The cadmium, copper, and chromium concentrations were generally higher in the 

forested and suburban creeks compared to the marsh and urban creeks. In particular, Guerin, a 

forested creek in the Francis Marion National Forest, had some of the highest concentrations of 

these metals. 

0

5

10

15

20

25

NOC-Masonboro, NC

SAP-Duplin, GA

Guerin, SC

SAP-Oakdale, GA

ACE-Village, SC

Hewlitts-N, NC

Whiskey, NC

Hewlitts-S, NC

James Island-S, SC

Burnett, GA

Postell, GA

New Market, SC

Marsh Forested Suburban Urban

Total DDT (ng g-1 dry weight).

0

5

10

15

20

25

SAP-Duplin, GA

Guerin, SC

ACE-Village, SC

James Island-S, SC

Whiskey, NC

Hewlitts, NC

Burnett, GA

Forested Suburban Urban

Total DDT (ng g-1 dry weight) .

Figure 31. DDT levels in oyster tissues for intertidal (left) and subtidal (right) creeks. 

 Land use class is marked by color. 

0

10

20

30

40

NOC-Masonboro, NC

SAP-Duplin, GA

Guerin, SC

SAP-Oakdale, GA

ACE-Village, SC

Hewlitts-N, NC

Whiskey, NC

Hewlitts-S, NC

James Island-S, SC

Burnett, GA

Postell, GA

New Market, SC

Marsh Forested Suburban Urban

Oyster Tissue Lipids (%) . 

0

10

20

30

40

SAP-Duplin, GA

Guerin, SC

ACE-Village, SC

James Island-S, SC

Whiskey, NC

Hewlitts, NC

Burnett, GA

Forested Suburban Urban

Oyster Tissue Lipids (%) .

Figure 30. Lipid level percentage in oyster tissues for intertidal (left) and subtidal (right) 

creeks. Land use class is marked by color. 

49 
In addition, the oyster tissue concentrations on wet weight basis were compared to Food and 

Drug Administration (FDA, FDA 2001) environmental chemical contaminant action levels and 

the USEPA (2000) human-health consumption limits for cancer and non-cancer endpoints (Table 

13). The FDA action levels are simply threshold values for comparison against tissue 

concentrations (non-consumption based). None of the concentrations observed in oyster tissue 

exceeded the molluscan bivalve or any fish actions levels for As, Cd, Cr, Pb, Ni, methyl 

mercury, PCBs, DDT, heptachlor epoxide, or mirex. The USEPA values are based on a 

consumption rate of four 8-ounce meals of fish per month for an adult population. It should be 

noted that we are comparing oyster tissue to fish tissue values; however, the comparison 

represents a level of potential risk. It should also be noted that for a number of these systems the 

shellfish are closed for harvest. Inorganic arsenic (estimated as 2% of total arsenic (USEPA 

2004) and total DDT values exceeded the cancer endpoint for all sites sampled for oyster tissue. 

Dieldrin values exceeded the cancer endpoint at one site (Postell, intertidal, urban). Total PCB 

values exceeded the cancer endpoint at three sites (New Market, intertidal, urban; Whiskey, 

subtidal, suburban; Burnett, subtidal, urban) with one site exceeding the non-cancer endpoint 

(Burnett, intertidal, urban). 

Table 13. Contaminant wet weight concentrations in tidal creek oysters. Concentrations given in 

ng g-1 for PCB and DDT, and in μg g-1 for metals (As, Cd, Hg, and Se). Inorganic Arsenic 

calculated as 2% of total arsenic. Methyl mercury calculated as 10% of total mercury. Italicized 

numbers indicate concentrations that exceeded the EPA cancer-endpoint consumption limits for 

fish, based on four 8-ounce meals per month. Bold and italicized numbers indicate concentrations 

that exceed non-cancer endpoints. No measurements were found to exceed FDA levels of concern 

for shellfish. 

Land Use Creek

Total 

PCBs

Total 

DDTs

Total 

As

Estimated 

Inorganic As

Total 

Cd

Total 

Hg

Total 

Methyl Hg

Total 

Se

Intertidal

Marsh Masonboro NERR 0.00 0.34 3.15 0.06 0.10 0.01 0.001 1.08

Forested Duplin Creek 0.00 0.30 0.79 0.02 0.07 0.01 0.001 1.09

Forested Guerin Creek 0.00 0.28 0.63 0.01 0.33 0.02 0.002 0.95

Forested Oakdale Creek 0.00 0.32 1.07 0.02 0.08 0.01 0.001 1.42

Forested Village Creek 0.00 1.04 1.01 0.02 0.10 0.01 0.001 1.21

Suburban Hewlitts Creek S 0.00 1.31 1.43 0.03 0.08 0.01 0.001 0.74

Suburban Hewlitts Creek N 0.00 1.14 1.23 0.02 0.08 0.01 0.001 0.56

Suburban James Island Creek 0.00 0.98 0.85 0.02 0.09 0.01 0.001 1.22

Suburban Whiskey Creek 0.00 0.93 1.57 0.03 0.06 0.01 0.001 1.07

Urban Burnett Creek 29.93 1.04 0.57 0.01 0.12 0.04 0.004 0.76

Urban New Market Creek 3.81 1.52 0.90 0.02 0.11 0.01 0.001 0.65

Urban Postell Creek 0.00 0.30 0.67 0.01 0.08 0.01 0.001 0.64

Subtidal

Forested Duplin Creek 0.00 0.29 1.02 0.02 0.12 0.01 0.001 1.30

Forested Guerin Creek 0.00 0.36 0.86 0.02 0.28 0.02 0.002 1.05

Forested Village Creek 0.00 0.30 1.47 0.03 0.24 0.01 0.001 1.65

Suburban Hewlitts Creek 0.00 1.02 4.15 0.08 0.10 0.01 0.001 1.10

Suburban James Island Creek 0.00 0.94 1.16 0.02 0.15 0.01 0.001 1.27

Suburban Whiskey Creek 0.19 0.96 2.55 0.05 0.05 0.01 0.001 1.53

Urban Burnett Creek 20.14 0.18 0.70 0.01 0.13 0.05 0.005 1.03

3.4.3 Oyster Tissue Genomics 

Modern biotechnological tools such as DNA micro-arrays provide highly sensitive means for 

monitoring human health. These techniques also appear to have great potential as tools for 

50 
detecting responses of keystone organisms to environmental stresses associated with 

anthropogenic or climatic perturbations. Unfortunately, there is presently little basis for 

interpreting these genomic cues in terms of their ecological consequences at relevant scales of 

natural populations, communities and ecosystems. To integrate these innovative tools into future 

environmental monitoring, assessment and management programs, it is essential that we gain an 

understanding of the ecosystem context for these genomic measurements. This is particularly 

important under present federal initiatives that seek to use “ecosystem-based” approaches for 

managing coastal waters, including key habitats and exploited animal populations. As a proof of 

concept, we evaluated the utility of an oyster microarray developed as a part of the NOAA 

Oceans and Human Health Initiative to discriminate between first order creeks with differing 

land use classes and levels of impervious cover. 

There are several motivating questions. Do genome-scale changes in gene expression exhibit 

clear responses to different types and levels of stress? Are patterns of DNA micro-array gene 

expressions related in qualitative and quantitative terms to ecological signals observed at higher 

levels of biological organization such as populations and ecosystems? What is the nature of these 

relationships across biological scales, and how do they change under different regimes of 

environmental stress? 

Preliminary analysis (neural networks followed by receiver operating characteristic (ROC) 

curve) of gene expression profiles of the 12 creeks (2006 only) classified by the amount of 

impervious cover in the surrounding watershed indicated that unique expressed sequence tags 

(EST; 215 in gill, 202 in hepatopancreas) were important contributors to discrimination in more 

than 20% of the pairwise comparisons. The weakest discrimination (between <10% and 30-

50% impervious cover in hepatopancreas tissues) was still significantly different from random, 

and this must be viewed as a significant deviation (p < 0.01, Figure 32). The lower level of 

discrimination in this comparison probably stems from combining creeks into the 30-50% 

category which have significant differences in contaminant loads. This combination should 

result in high variance in expression profiles within the group, leading to a lower discriminatory 

power (Table 14). 

51 
52 

0

20

40

60

80

100

 <10 vs

10-30

 <10 vs

30-50

<10 vs

>50

10-30 vs

30-50

10-30 vs

>50

>30-50 vs

>50

Impervious cover (%) levels comparison

Correct classification (%) 

Figure 32. Ability of gene 

expression profiles 

(hepatopancreas tissues) to 

discriminate creek types 

based upon levels of 

impervious cover. Percent of 

correct classifications are 

shown for each comparison of 

levels. Error bars are  1 

standard error.

The discriminatory power gene 

expression profiles based upon the 

classification scheme of Holland 

et al. (2004) is illustrated in 

Figure 33. Both the gill and 

hepatopancreas comparisons were 

significantly greater than random 

expectations. In three of the six 

total comparisons, however, the 

gill data was significantly better 

than hepatopancreas in 

discriminating between land use 

classes. The weakest 

discrimination was between 

forested sites and the single 

industrial site and this may stem 

from the limited number of 

samples examined from Burnett 

Creek (n=12).

Table 14. Oyster tissue analysis. Assignment of individual 

creeks to impervious cover and land use classes based upon 

watershed characteristics. 

Creek 

Impervious 

Surface Land Use 

Sample 

Size 

Guerin <10% Forested 8 

ACE-Village <10% Forested 9 

SAP-Duplin <10% Forested 14 

SAP-Oakdale <10% Forested 24 

Postell >30%-50% Urban 24 

Burnett 10%-20% Industrial 12 

New Market 50% Urban 25 

Hewletts 30%-50% Suburban 17 

NOC-Masonboro* <10% Forested 25 

Whiskey 30%-50% Suburban 13 

James Island-S 30%-50% Suburban 9 

*NOC-Masonboro is a marsh creek classified as forested for the 

 genomics analysis.

0

20

40

60

80

100

Forested vs

Suburban

Forested vs

Urban

Forested vs

Industrial

Urban vs

Suburban

Suburban vs

Industrial

Urban vs

Industrial

Correct classification (%) .

Figure 33. Ability of gene 

expression profiles 

(hepatopancreas tissues) to 

discriminate creek types 

based upon land use class. 

Percent of correct 

classifications are shown for 

each land use class 

comparison. Error bars are 

 1 standard error. 
Research Highlight – Related Research in Sapelo Island NERR 

Preliminary Results from Bottlenose Dolphin Studies in Sapelo Island National Estuarine 

Research Reserve and the Turtle/Brunswick River Estuary 

An effort to examine chemical contaminant exposure for bottlenose dolphins which utilize the 

Sapelo Island, Georgia NERR was initiated in August 2007. This effort was coordinated with the 

Integrative Ecological Assessment efforts presented in this report in order to provide 

supplemental information on a top level predator in the NERRS. Dolphin exposure to persistent 

organochlorine contaminants (POCs) is of particular interest due to the magnification of these 

compounds through the food chain, and the dolphins’ propensity to accumulate persistent 

contaminants in their lipid-rich blubber. Because of these exposures and the fact that many 

dolphin populations show a high degree of site-fidelity to a localized region, dolphins represent 

good integrative indicators of estuarine and coastal ecosystem health. Contaminant levels 

measured in dolphin tissues reflect the contamination of the habitat in which they reside. 

This study is examining chemical contaminant exposure in bottlenose dolphins in the 

Turtle/Brunswick River estuary (TBRE), a highly polluted site approximately 20 miles southwest 

of the Sapelo Island NERR, and dolphins residing within and near the Sapelo Island NERR. 

Samples of dolphin skin and blubber are being obtained by remote biopsy sampling. 

Initial results indicate extremely high concentrations of PCBs in dolphins sampled from both the 

TBRE and the Sapelo NERR (Figure A). Geometric mean PCB concentrations from dolphins 

sampled within and near the Sapelo NERR measured to date are comparable to concentrations 

measured from dolphins in northern Biscayne Bay, previously reported as some of the highest in 

US estuaries (Litz et al. 2007). Geometric mean concentrations measured from dolphins in the 

TBRE were over 2-fold higher than those measured in northern Biscayne Bay. The maximum 

concentration measured from the TBRE (2822 g/g lipid) exceeded the prior worldwide 

maximum concentration (2100 g/g lipid) for bottlenose dolphins that had been measured in a 

stranded dolphin from a highly polluted area of the Mediterranean Sea (Corsolini et al. 1995, 

Aguilar et al. 2002). 

The geometric mean PCB concentration measured from dolphins near the Sapelo NERR (156.7 

g/g lipid) was approximately 3-4 times higher than concentrations reported from dolphins 

sampled in other southeast U.S. estuaries: Charleston, South Carolina (42.1 g/g lipid for male 

& juvenile) and Beaufort, North Carolina (31.7 g/g lipid for male & juvenile). Dolphins 

sampled in the TBRE (401 g/g lipid) were nearly 10-fold higher than those reported for 

Charleston and Beaufort. 

PCB congener profiles from both TBRE and Sapelo were indicative of an Aroclor 1268 signature, 

with a high prevalence of octa- and nonachlorobiphenyls. Signatures with these highly 

chlorinated homologs have been described in prior studies of sediments and biota and linked to 

the LCP Chemicals Superfund site near Brunswick, GA (Kannan et al. 1997, Kannan et al. 1998). 

Our preliminary results suggest a strong Aroclor 1268 signature for dolphins sampled in the 

TBRE, with 59% of the total PCBs being contributed by octa- and nonachlorobiphenyls. The 

proportion of these same homologs was slightly less in the Sapelo dolphins (41%), but still in 

stark contrast to other southeast sites where octa- and nonachlorobiphenyls comprise a small 

percentage (~9%) of the total PCBs. 

53 
Concentrations of PBDEs and the most prevalent pesticide compounds were several times lower 

than total PCBs and were comparable to values measured in other southeast U.S. sites (Figure 

B). Total PBDE concentrations were comparable to those measured from northern Biscayne 

Bay. Measured DDT concentrations were high (20.1 and 17.7 g/g lipid for males from Sapelo 

and TBRE, respectively), but were comparable to other nearby sites in the Carolinas. 

0

100

200

300

400

500

600

PCBs g/g lipid

N=7 a N=12 a

N=17

N=37

N=9 a

N=16 b

N=15 b

Beaufort, NC 

Charleston, SC 

Sapelo NERRS, GA 

TBRE, GA 

IRL, FL 

N Biscayne Bay, FL 

S Biscayne Bay, FL 

Figure A. Comparison of PCB concentrations measured in male/juvenile bottlenose dolphins sampled 

from southeast U.S. estuaries. Bars represent geometric means calculated for this study or reported in 

published literature, whiskers represent 95% CIs around the geometric mean calculated for this study. 

Data sources: a=Hansen et al. 2004; b= Litz et al. 2007. Lightly shaded overlay for Beaufort, NC 

represents geometric mean for 27 PCB congeners reported by Hansen et al. 2004; darker shaded area 

represents an estimated increase in total if 66 congeners were to be included (unpublished data, Beaufort, 

NC).

Significance: Threshold tissue concentrations to elicit physiological effects have been proposed 

for PCBs in marine mammal blubber: 14-17 g/g lipid (Kannan et al. 2000, Schwacke et al. 

2002). For both male and female dolphins in the TBRE, 100% of samples exceeded these 

thresholds (minimum concentration = 19.8 and 374.4 g/g lipid for females and males, 

respectively). In fact, all of the male samples from TBRE exceeded the threshold by more than an 

order of magnitude and the highest male sample measured (2822 g/g lipid total PCBs) was 100-

fold greater than proposed toxic thresholds. Even more alarming is that dolphins that utilize the 

Sapelo NERRS, a protected area dedicated for the preservation of wildlife habitat, are also being 

exposed to high levels of the same PCBs. All of the males and all but one of the females sampled 

in Sapelo exceeded the toxic threshold for PCBs. The minimum concentration (76.5 g/g lipid) 

measured in male samples from Sapelo was more than 4-fold higher and the maximum 

concentration (332 g/g lipid) was nearly 20-fold higher than proposed toxic thresholds.

54 
55 

At this point, it is unclear whether the dolphins sampled in Sapelo are exposed to high PCB levels 

because they range into the TBRE or whether their high PCB concentrations result from a 

movement of contaminated prey biota. 

0

5

10

15

20

25

30

35

40

Concentration (g/g lipid)

Project Status & Plans: Research on dolphins in these areas is continuing: (1) evaluation of 

mercury levels from skin samples is currently being performed, and (2) photo-identification 

studies have been initiated to elucidate dolphin distribution and movements. A final report 

summarizing biopsy and photo-identification efforts in the TBRE and Sapelo NERR is scheduled 

to be available in December 2009. 

Acknowledgments: The bottlenose dolphin study within the Sapelo NERR and TBRE is a 

collaborative effort of the NOAA Cooperative Center for Marine Animal Health. Collaborators 

include NOAA/NOS/NCCOS, NOAA Fisheries OPR and SEFSC, NIST, NOAA/NERRS, and GA 

DNR. Student funding for the photo-identification effort is also being provided the Chicago 

Zoological Society (CZS). For further information, contact Lori Schwacke 

(Lori.Schwacke@noaa.gov). 

Figure B. Comparison of  DDT,  Chlordane and  PBDE concentrations measured in male/juvenile 

bottlenose dolphins sampled from southeast U.S. estuaries. Bars represent geometric means calculated 

for this study or reported in published literature, whiskers represent 95% CIs around the geometric 

mean. Data sources: a=Hansen et al. 2004; b= Litz et al. 2007. 

 DDT

 Chlordane

 PBDE

N=12 a

N=7 a,c

N=17

N=37

N=9 a,c

N=16 b

N=15 b

Beaufort, NC 

Charleston, SC 

Sapelo NERRS, GA 

TBRE, GA 

IRL, FL 

N Biscayne Bay, FL 

S Biscayne Bay, FL 
4. Discussion 

4.1 Sentinel Habitats 

Recent comprehensive reports of the condition of the Nation’s coastal resources have noted 

measurably diminished ecosystem condition and potential human health risks (USEPA 2001, 

NMFS 2002, Pew 2003). Major sources of impairment across all regions and habitats included 

chemical and microbial contamination, increased “flashiness” in freshwater inflows, nutrient 

over-enrichment and hypoxia, increased frequency of harmful algal blooms, habitat modification 

and degradation, wetland loss, increased abundance of non-native species, over-harvesting of 

fisheries, and impaired biological communities. Most of these reports conclude that the major 

environmental threats to coastal resources are from diffuse or non-point sources of pollution. In 

addition, the cumulative effects of multiple stressors, including the interactions among them, 

have been identified as a major contributor to diminished resources. Most of these assessments 

were based on broad scale coastal sampling using standard ecosystem condition indicators that 

reliably document existing conditions. These monitoring networks are, however, insensitive to 

many stressors and do not provide early warning of ensuing harm to the ecosystems or the 

humans that rely on and use them. Further, these existing environmental monitoring networks 

also provide little reliable information about impacts on public health and other human resources 

or the specific source(s) of the stress. 

The lack of early warning of degradation is related to choices made in monitoring open water 

habitats; however, sampling sentinel habitats in nearshore environments can provide an early 

warning of any ensuing harm to the larger coastal ecosystem. There are several principal 

attributes of sentinel habitats. They should be key structural components of ecosystems that 

have important roles in sustaining overall ecosystem function (e.g., nursery habitat, feeding 

grounds, mineral cycling, biological productivity). The amount and complexity of the sentinel 

habitat has a major influence on the condition of the ecosystems in which it exists. This 

complexity and the impacts on the habitat are at least partially understood by scientists and 

recognized by the public. They generally receive high exposure to stressors (e.g., shallow water 

in environments with limited capacity to dilute pollutants or modulate environmental change) 

and are sensitive to changes in environmental conditions, particularly multiple stressors. 

Sentinel habitats should also be easily observed to ensure changes can be measured. Close 

proximity to land and shallow depth are other useful attributes. 

Tidal creeks and salt marshes are the interface between the landscape and estuaries, resulting in 

dynamic environments that are renowned for their ecological complexity and seafood 

production. In the Southeast US, watersheds containing tidal creeks are among the most rapidly 

developing in the Nation. The headwater regions of these creeks are the receiving waterbody for 

pollution released into the environment. The integrity and productivity of headwater portions of 

tidal creek environments are impaired by land use changes and the related non-point source 

pollution years to decades in advance of similar signals occurring in deeper open estuarine 

waters, suggesting these habitats are valuable sentinels of ensuing degradation, including a 

myriad of potential public health threats. 

Previous research in tidal creek ecosystems has demonstrated the environmental quality of these 

systems, particularly the intertidally-dominated portions or headwaters, to be sensitive to land 

56 
use change within the watershed (Sanger et al. 1999a, 1999b, Lerberg et al. 2000, Mallin et al. 

2000, Holland et al. 2004). Because they are sensitive to local changes, these systems provide an 

early warning of the degradation from upland land use well before changes would be detected in 

larger coastal waters (e.g., tidal rivers, estuaries). Tidal creeks are therefore useful sentinels for 

monitoring the impacts of human activities on coastal habitats. The current study demonstrates 

that the sensitivity of tidal creeks to changes in these small coastal watersheds diminishes down 

their length (i.e., from small headwater creeks to larger tidal creeks). This spatial variability 

must be recognized before assessing the environmental quality of these habitats. 

For many of the measured parameters, the intertidally-dominated or headwater portions of tidal 

creeks were found to respond differently than the subtidally-dominated or larger portions of tidal 

creeks. For example, water quality parameters (including basic water quality, nutrients, and 

pathogen indicators) showed many differences between the intertidal and subtidal segments of 

these creek networks. The smaller intertidal creeks generally had higher concentrations of 

nonpoint source pollutants, which is likely an indication of an upland runoff component as well 

as an estuarine dilution influence (i.e., tidal flushing) in the larger creeks. Biological parameters 

measured (e.g., nekton and benthos) also demonstrate significant variability along the headwaters 

to tidal river gradient. There is a marked shift in the benthic fauna, from one dominated by 

oligochaetes in the headwaters to one dominated by polychaetes in the larger water bodies. The 

nekton also appears to shift along this gradient, from more resident and nursery species in the 

headwaters to larger transient organisms in deeper creeks. Recognizing this spatial variability 

not only allows valid comparisons to be made across similar creek classes (with respect to 

surrounding land use for example) but also may provide information about which tools or indices 

may be useful for specific faunas. The environmental quality in intertidally-dominated portions 

of tidal creeks was also found to have stronger relationships with land cover changes than the 

subtidally-dominated habitats. These relationships may serve as the basis for future predictive 

models, and the significant relationships common in these habitats (both subtidal and intertidal, 

although mostly intertidal) will serve a valuable function in developing predictive models of land 

use impacts on these valuable coastal habitats. 

In addition to accounting for the spatial variability down the length of a creek, the type of land 

cover in the watershed is also an important factor to consider when comparing creeks. The 

creeks draining watersheds with only salt marsh land cover were found to respond differently 

than creeks draining watersheds with forested upland land cover. The input of pollution is very 

different between these two systems. Creeks draining only salt marsh primarily receive 

contaminants from downstream sources. In comparison, creeks draining terrestrial areas receive 

significant freshwater input and contaminants from the upstream and upland areas. The input of 

freshwater is a critical factor to consider when assessing the impacts of land use change. 

Freshwater input in the form of stormwater runoff increases with increasing levels of impervious 

cover, carrying increased pollutants from the surrounding watershed into tidal creeks (Figure 34). 

57 
< 1%

30%

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80

Time 

50%

Stormwater Runoff (cfs) .

24-hour 4.5" Storm Event 

Impervious Runoff Runoff Peak 

 Cover Volume Percent Rate 

 50% 128 af 63% 90 cfs 

 30% 95 af 47% 56 cfs 

 <1% 52 af 26% 24 cfs 

(hrs)

Figure 34. Hydrographs of modeled stormwater runoff from Guerin Creek, a forested study 

watershed. Runoff is shown at the current level of impervious cover (<1%) and also at levels of 

30% and 50%. cfs = cubic feet per second, af.= acre-foot. 

4.2 Conceptual Model 

As noted above, tidal creek networks are the primary hydrologic link between estuaries and land 

based activities. As the first zone of impact for non-point source pollution runoff, the potential 

for microbial and chemical contamination in tidal creek habitats is great. Developing a 

conceptual model is a critical step for identifying and evaluating monitoring and management 

strategies including what parameters to measure and when and where measurements should be 

taken (Saila 1979, NRC 1990, Barnthouse and Brown 1994). Holland et al. (2004) developed a 

conceptual model to describe the source-receptor links between the origin of an environmental 

problem (e.g., human activity, extreme natural event, linkages between ocean processes) and 

anticipated impacts on ecosystems. The model was based on the EPA Ecological Risk 

Assessment paradigm with stressors leading to changes in the physical-chemical environment 

(i.e., exposures) which in turn leads to a biological response. 

The Holland et al. (2004) conceptual model for intertidally-dominated tidal creeks in South 

Carolina did not include a number of new indicators (e.g., nutrients, emerging contaminants of 

concern) or show the relationship the environment has on the health and welfare of humans. 

Historically, scientists have only looked at human impacts on the environment but are now 

beginning to recognize how environmental changes impact humans. Based on the data collected 

by this study, the conceptual source-receptor model has been expanded (Figure 35). This model 

provides an overview of the linkages between coastal development and associated human 

activities, “key” changes in the physical-chemical environment, and anticipated responses of 

tidal creek ecosystems and human systems. This model continues to be updated and revised as 

new data and information become available. This model was developed using an integrated 

weight of evidence approach based on the information collected. The stressor or coastal 

development activities in the surrounding watersheds found in this study include changes in the 

land cover and increases in the population density and impervious cover as development occurs. 

58 
The exposure or changes in the physical chemical environment associated with increasing 

development include increases in the salinity range, levels of NO2/3 and NH4+, the amount of 

stormwater runoff, bacterial and viral pathogen indicators, and chemical contamination of the 

sediment including some emerging chemicals of concern with increasing development. The 

biological response or impacts on the living resources identified in this study include impacts on 

the macrobenthic communities such as changes in the species composition, number of organisms 

and diversity with increasing levels of development and impaired oyster health as evidenced by 

the changes in gene expression. Finally, the societal response of impacts to the health and wellbeing observed in this study include increased flooding from increases in development and 

increased public health risk from bacteria in the water and shellfish. 

The tidal creek conceptual model identified adverse changes generally occurred in the physical 

and chemical environment (e.g., water quality indicators such as indicator bacteria for sewage 

pollution or sediment chemical contamination) when impervious cover levels in the watershed 

reach 10-20%. Ecological processes responded to and were generally impaired when impervious 

cover levels exceeded 20-30% in suburban and urban watersheds (Figure 35). There is an 

emerging consensus that patterns of coastal development are associated with evidence of 

increasing fecal pollution in tidal creeks, estuaries, and bathing beaches (this study, Mallin et al. 

2000, Karn and Harada 2001, Holland et al. 2004, Mallin 2006). From a human health 

perspective, the accumulation of pathogens in the water, sediments, and organisms may render 

Figure 35. Conceptual model identifying linkages between development 

of the upland and ecological and human well-being of southeastern U.S. 

tidal creeks. Ranges of impervious cover percent denoting transition from 

one model phase to the next are shown toward the bottom of the model. 

59 
seafood products unsafe to eat and water unsafe for contact recreation. Flooding vulnerability, 

public health risk, and economic impacts are also being considered. Estimates of impervious 

cover levels defining where human uses are impaired are currently being determined, but it 

generally appears that shellfish bed closures and the flooding vulnerability of headwater regions 

become a concern when impervious cover values exceed 10-30%. This research project has 

validated the model for the southeastern U.S. 

The tidal creek conceptual model also provides a framework for defining system feedback loops 

and identifying which level of government is responsible for ensuring appropriate actions are 

taken to remediate and restore impaired systems (Figure 35). County and municipal 

governments are responsible for regulating land use activities and making most zoning decisions 

and are the agencies controlling impervious cover levels. State and federal governments mainly 

influence physical-chemical exposures (water and sediment quality) but also play a large role in 

enforcement and permitting activities related to near-marsh development. 

A pamphlet was created to summarize this tidal creek research for decision-makers including 

resource managers and land use planners. The NERRS has strong education and stewardship 

programs through its Coastal Training Program (CTP) and Stewardship Coordinators. 

Therefore, an opportunity exists to work with the North Carolina and Sapelo Island NERRs to 

provide this information to the public and decision makers through presentations and the 

pamphlet. The information has been presented to numerous audiences to encourage wise 

decision making as well as encourage the general public, elected officials, and county/city 

administrators to become stewards of the environment. 

4.3 NERRs as Regional References 

Much of our understanding of the impacts of anthropogenic activities on natural environments 

relies heavily on the concept of reference sites or the reference condition (Stoddard et al. 2006). 

For stream ecosystems, for example, biological criteria used to characterize the quality of a 

stream segment are developed by comparison to a population of reference sites (e.g., Hughes 

1995, Barbour et al. 1995). No such effort has been made to document appropriate coastal 

reference sites to evaluate the impacts of human activity in the coastal zone and adjacent marine 

waters. This study, and the partnership with the NERRS, was undertaken in part to explore tidal 

creeks as sentinel habitats and to evaluate whether this network of protected coastal habitats 

could serve as reference sites for future research. 

The two NERRs, North Carolina and Sapelo Island, that were sampled as part of this study were 

found to be reasonable regional references; for many of the sampled parameters, data showed 

that creeks within (or adjacent to) these NERRs differed from the typical condition of other 

suburban or urban creeks. However, in some cases, the land use (both current and historical) 

within and around these areas appears to impact the reserves and may diminish their capacity as 

regional references. For example, Oakdale Creek within the Sapelo Island NERR (accessible 

only by boat) had high levels of a number of the pathogen indicators in both water and oyster 

tissues. The sources of these pathogens are unidentified; however, there are many possibilities. 

Free ranging cattle and high densities of whitetail deer roam the island, a large stork and egret 

rookery is located in the immediate sampling vicinity, a small Gullah-Geechee (descendants of 

West African slaves) community is located upland of the sampling site, and the island is also 

60 
occupied by research and management communities and visitors. Determining the sources of 

these pathogens is important to develop future management decisions (e.g., excluding cattle from 

salt marshes/tidal creeks, septic maintenance programs) regarding these habitats and their 

watersheds. In addition, land use decisions outside of the NERR boundaries can also influence 

the habitats within the Reserves. For example, the suburban development adjacent to the North 

Carolina Masonboro site has the potential to impact the reserve. Educating decision-makers on 

land use change is an important activity to ensure that the potential impacts of the land use 

decisions are considered. 

The dolphin contaminant study highlighted in this report also identifies other considerations in 

using the NERRs as regional references. Dolphin tissue levels of PCBs around the Sapelo Island 

NERR were found to be high compared to most other areas previously studied (Hansen et al. 

2004, Litz et al. 2007); however, it is unknown if these levels are due to dolphin or prey 

movements between contaminated sites (e.g., Turtle River/Brunswick) and the Sapelo Island 

NERR. The research on dolphin tissue levels and migration patterns in these areas is continuing. 

4.4 Forecasting 

The relationship between watershed development and the ecological condition of the headwater 

areas of tidal creeks in SC is fairly well-understood (Sanger et al. 1999a, 1999b, Lerberg et al. 

2000, Holland et al. 2004), but spatial and temporal variability and patterns in ecological 

condition along tidal creek networks are poorly characterized. Effective monitoring, assessment, 

and prediction of the effects of coastal urbanization on tidal creeks and estuaries require that this 

variability be understood. Stratification of tidal creek networks into units that represent 

relatively homogenous environments or creek classes is one tool for characterizing and 

understanding the variability within tidal creek networks. This stratification is crucial for 

understanding at what scale land use impacts can be observed. Classifying watersheds that drain 

into specific creek networks based on the degree and type of development that has occurred is a 

tool and requirement for understanding variability among creek networks and forecasting the 

impacts of development. 

In addition to using a classification system, other metrics must also be added to provide 

forecasting ability. One particularly important component is modeling the volume and rate of 

runoff leaving the upland. Smith (2005) found that runoff into creeks with developed watersheds 

occurred over hours immediately following the rain event compared to a reference creek with an 

undeveloped watershed where runoff occurred more slowly over a period of a day. However, 

measuring the volume and rate of runoff entering into tidal creeks is an expensive and time 

consuming activity. Therefore, other opportunities such as modeling the volume and rate of 

runoff need to be explored to determine the potential impact of the volume and rate of water 

entering into tidal creek ecosystems. Existing models such as the Natural Resources 

Conservation Service's flow curve number and dimensionless unit hydrograph methods have 

been modified for the southeastern US region to estimate the runoff volume and rate for a variety 

of rainfall events and under different development scenarios (i.e., changes in the impervious 

cover) (Figure 34). This modeling of stormwater runoff entering headwater tidal creeks will 

allow for future predictions of the runoff and pollutant loadings as watershed impervious cover 

levels are increased. The runoff modeling used in this study is being validated and future work 

61 
includes the development of a tool that resource managers can use to evaluate the impact of 

development on a tidal creek. 

Most importantly, the tidal creek study and conceptual model provide a framework for 

forecasting the changes in ecological and public health indicators based on watershed attributes 

and impervious cover associated with coastal development. Ecological forecasting has begun to 

emerge as an organizing principle in many research efforts (Clark et al. 2001). For example, 

with freshwater resources stretched thin in many areas of the world, the effects of urbanization 

on stream ecosystems is a critical area for research on forecasting. The opportunities are great, 

but integrating traditionally disparate disciplines to develop appropriate forecasting models is a 

challenge (Nilsson et al. 2003). The tidal creek research described here provides a roadmap for 

forecasting. Information on watersheds and the impervious surface associated with coastal 

development (Figure 36a) relates strongly to hydrographic characteristics of creeks (Figure 36b) 

and ultimately on indicators of water quality and ecosystem condition such as bacterial indicators 

(Figure 36c). Based on these relationships, it is possible to use simple linear relationships to 

forecast the likely effects that increasing impervious cover may have on critical indicators of the 

ecosystem and public health and well-being (Figure 36d). The forecast models can be used by 

planners, developers, and managers to understand the potential impacts of a development before 

building instead of years after (Clark et al. 2001). Imper. cover (%) 

4.5 Summary 

In the Southeastern US, coastal uplands adjacent to tidal creeks and salt marshes are increasingly 

popular locations for building new homes, resorts, retirement destinations, and recreational 

0

20

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Forested Suburban Urban

(a) Watershed Attributes

14

Runoff (cms)

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(b) Hydrographic 

Characteristics

Fecals (log (x+1)

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(c) Bacterial Indicators

Imper. cover (%) 

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(a) Watershed Attributes

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(a) Watershed Attributes

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(d) Prediction

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Runoff (cfs mi -2)

Suburban

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Suburban

Figure 36. Graphical presentation forecasting the impacts of land use change on tidal creek 

environmental quality. 

62 
facilities. These tidal creek networks are also critical feeding grounds, spawning areas, and 

nursery habitats for many species of fish, shellfish, birds, and mammals. Tidal creeks also form 

the primary hydrologic link between estuaries and land-based activities and, as such, reflect the 

impacts of coastal development earlier than larger coastal waterbodies. Nonpoint source 

pollution (e.g., stormwater runoff) carries sediments, chemicals, bacteria, viruses, and other 

pollutants into tidal creeks and salt marshes and degrades water quality. The relationships 

between development levels and the environmental quality and public health and well-being 

indicators evaluated were strongest in the shallow, intertidally-dominated headwater creeks. 

The scale of our tidal creek study watersheds (100s to 1000s ha) is also the spatial scale at which 

coastal land use decisions and remediation actions typically occur. If creeks draining watersheds 

this size, especially the headwater portions of those watersheds, are valuable indicators of 

impacts from land use activities and urbanization, managers or land use planners are afforded a 

valuable tool to predict the impacts of developments on pathogen indicator concentrations in 

nearby tidal creeks and thereby inform the decision-making process. 

5. Overall Project Summary and Conclusions 

The present report is part of a two-volume set summarizing results of a collaborative NCCOSNERRS effort to assess the status of ecosystem conditions and potential stressor impacts at 

NERRS sites in the southeastern U.S., and to provide this information as a basis for monitoring 

future conditions in these same areas or in other NERRS locations. There are two 

complementary components of this overall initial effort: (1) a sentinel habitats study designed to 

evaluate the impacts of development on tidal creek ecosystems, including potential impacts to 

human health and well-being; and (2) a probabilistic monitoring component to assess the spatial 

extent of ecological condition throughout sub-tidal estuarine waters, based on the status of 

various measured ecological indicators relative to specific management thresholds. The tidal 

creek component, discussed in the present Volume I, was conducted on NERRS sites at Sapelo 

Island, Georgia and Masonboro Island, North Carolina and was coordinated with results of 

related tidal creek work in South Carolina. The sub-tidal probabilistic component, discussed in 

Volume II (Cooksey et al. 2008), was conducted throughout all four North Carolina NERR 

locations (Currituck Sound, Rachel Carson, Masonboro Island, and Zeke’s Island). Together, the 

two project components are intended to provide a demonstration of the utility of the 

complementary assessment tools, one serving as a sentinel of environmental signals in areas of 

estuaries where signals are likely to occur (i.e., tidal creeks close to pollutant sources), and the 

other providing a quantitative basis for assessing the relative proportions of degraded vs. nondegraded conditions throughout a targeted resource category (i.e., sub-tidal estuarine waters of a 

reserve) relative to the various measured indicators and associated management thresholds. 

While providing new information on the status of ecological condition and human health risks in 

several southeastern U.S. NERRS locations, the results also are intended to serve as a useful 

framework of assessment strategies that could be applied systematically across other reserves to 

support broader regional and national comparisons. 

63 
5.1 Summary Points from Volume I: The Impacts of Coastal Development on the Ecology 

and Human Well-being of Tidal Creek Ecosystems of the US Southeast (Present Volume) 

Southeastern tidal creeks are sensitive to coastal development and provide an early warning of 

potential degradation from upland land uses well before adverse conditions would be detected in 

larger coastal waters (e.g., tidal rivers, bays). Accounting for the spatial variability in 

hydrological and other watershed attributes among individual tidal creek systems is an important 

factor to consider in assessing the environmental quality of these habitats. Thus, the application 

of an appropriate tidal creek classification scheme in the sampling design process to 

accommodate such variability was a critical aspect of the research described in Volume I. One 

important finding of this research was that the sensitivity of tidal creeks to changes in the 

environmental quality of the surrounding watersheds diminishes downstream toward the mouths 

of the creeks. Smaller intertidal creeks generally had higher concentrations for indicators of nonpoint source pollutants (e.g., water quality, nutrients, and pathogen indicators), which likely 

reflect both the greater upland runoff component and estuarine dilution influence (i.e., tidal 

flushing) associated with larger creeks. Additionally, indicators of deteriorating environmental 

quality were found to vary directly with increasing levels of impervious cover, the latter allowing 

greater inputs of runoff and associated pollutants from the surrounding watershed into tidal 

creeks, particularly in headwater regions. The integrity and productivity of headwater portions 

of tidal creek environments are often impaired by land use changes and associated non-point 

source pollution, suggesting that these habitats serve as valuable early-warning sentinels of 

ensuing stress including ecological and potential public health threats (e.g., seafood consumption 

advisories, swimming advisories). A conceptual model of these linkages was validated and 

expanded for the southeastern US. Lastly, the tidal creek study and its associated conceptual 

model provide a useful framework for forecasting potential changes in ecological and human 

health indicators within these systems in relation to varying watershed attributes and land use 

patterns. 

5.2 Summary Points from Volume II: Assessing Ecological Condition and Stressor Impacts 

in Subtidal Waters of the North Carolina NERRS (from Cooksey et al. 2008.) 

This component of the project was aimed at assessing the status of ecological condition and 

stressor impacts in subtidal estuarine waters throughout the four North Carolina NERR locations 

(Currituck Sound, Rachel Carson, Masonboro Island, and Zeke’s Island). Sampling incorporated 

multiple indicators of ecosystem condition including measures of water quality, sediment 

quality, biological condition, and potential threats to human health and well-being (e.g., fishtissue contaminant levels relative to human health consumption limits, various aesthetic 

properties). A probabilistic sampling design permitted statistical estimation of the spatial extent 

of degraded versus non-degraded condition across these estuaries relative to specified threshold 

levels of the various indicators (where possible). With some exceptions, the status of this reserve 

appeared to be in relatively good to fair condition overall, with the majority of the area (about 

54%) having various water quality, sediment quality, and biological (benthic) condition 

indicators rated in the healthy to intermediate range of corresponding guideline thresholds. Only 

three of the 30 stations sampled, representing 10.5% of the area, had one or more of these 

indicators rated as poor/degraded in all three categories. However, although co-occurrences of 

adverse biological and abiotic environmental conditions were limited spatially, at least one 

indicator of ecological condition rated in the poor/degraded range was observed over a broader 

area (35.5% represented by 11 stations). In addition, fish-tissue contaminant data were not 

64 
included in these overall spatial estimates; however, the majority of samples (77% of fish that 

were analyzed, from 79%, of stations where fish were caught) contained inorganic arsenic above 

the consumption limits for human cancer risks, though most likely derived from natural sources. 

Such symptoms reflect a growing realization that North Carolina estuaries are under multiple 

pressures from a variety of natural and human influences. These data also suggest that, while the 

current status of overall ecological condition appears to be good to fair, long-term monitoring is 

warranted to track potential changes in the future. This study establishes an important baseline 

of overall ecological condition within the NC NERR that can be used to evaluate any such future 

changes and to trigger appropriate management actions in this rapidly evolving coastal 

environment. 

5.3 Coastal Management Applications and Opportunities 

NCCOS’s mission, as defined in its FY05-09 Strategic Plan (NCCOS 2004), is to provide coastal 

managers with scientific information and tools needed to balance society’s environmental, social, 

and economic goals. The NCCOS Strategic Plan also calls for baseline assessments of 

ecological resources and potential stressor impacts in NERRS and other NOAA protected areas. 

The NERRS’ mission is to practice and promote coastal and estuarine stewardship through 

innovative research and education activities focused on the NERRS (NERRS 2005). The present 

collaborative studies sought to address common research and management goals supportive of 

both program missions. The two studies provide new information on the current status of the 

ecological condition and human health risks at NERRS sites and neighboring waters in Georgia, 

South Carolina, and North Carolina as well as a set of complementary assessment tools for 

monitoring future conditions in these same areas or in other NERRS locations. The tidal creek 

study and its associated conceptual model (Volume I) provide a framework for forecasting 

potential changes from coastal development on the ecological and human health and well-being 

within these systems. The tidal creek study also exemplifies the utility of these habitats as a 

sentinel of environmental signals in areas of estuaries (e.g., in upper reaches close to pollutant 

sources) where signals are most likely to occur. The probabilistic sampling approach used in the 

subtidal assessment (Volume II) provides an additional unbiased statistical basis for quantifying 

the spatial extent of condition relative to the various measured indicators and desired 

management thresholds, throughout a targeted resource category, and thus a quantitative baseline 

for monitoring how the relative proportions of healthy vs. degraded areas may be changing with 

time. Thus, in addition to providing new information on the status of ecosystem conditions in 

specific southeastern U.S. NERRS locations, the results also are intended to serve as a 

framework of assessment strategies that could be applied systematically across other reserves to 

support national comparisons. Such ecological assessment tools would also complement systemwide, water-quality monitoring program (SWMP) and other site-specific research activities 

currently underway in the NERRS program. 

Such baseline assessments of the status of ecosystem conditions and stressor impacts also 

provide the first steps in the implementation of Integrated Ecosystem Assessments (IEAs) of 

NERRS sites. An IEA is a synthesis and quantitative analysis of information on relevant natural 

and socio-economic factors in relation to specified ecosystem management goals (NOAA 2007, 

Murawski and Menashes 2007). The NERRS, as a system of protected areas, offers an ideal 

series of place-based sites for an IEA, which begins with the assessment of baseline conditions 

defining current ecosystem status, as well as the assessment of stressor impacts and their links to 

65 
source drivers and pressures (as was the focus here). NOAA has placed an emphasis on 

conducting IEAs to support improved ecosystem approaches to management (EAM) within its 

protected coastal resources in order to offer coastal managers a more comprehensive framework 

to their coastal decision making. Such an approach requires increased understanding of these 

complex systems and improved integration and collaboration in their management. 

Related to the above point, as a NOAA protected resource, the NERRS offers an ideal 

opportunity to become a suite of place-based reference sites across the nation for documenting 

status and trends in coastal ecosystem conditions among reserves and in comparison to other 

non-protected areas. Results of the present two studies have provided new information on the 

status of conditions at NERRS sites in the southeastern U.S. These areas were found to be 

reasonable regional references, with the caveat that some symptoms of stress were detectable and 

thus the realization that such areas and surrounding watersheds are under multiple pressures from 

a variety of natural and human influences. Long-term monitoring is warranted to track potential 

changes in the future. The data and assessment strategies provided here can be applied in any 

such efforts for these same areas, as well as in any future surveys in other NERRS sites to 

support broader-scale regional and national comparisons. 

Another underlying project goal is to make the present information and assessment tools readily 

available for meeting NERRS research and management needs. There is a tremendous 

opportunity to achieve this goal through the educational and outreach resources of NERRS, 

including their strong education programs, Coastal Training Programs (CTP), and stewardship 

coordinators. Any related efforts to inform the public and coastal management community 

through targeted presentations and products derived from these studies should help to expand 

their utility toward addressing important coastal management and human health concerns. 

66 
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Appendix A. The expected range of method detection limits (MDLs) based on extracted sample 

mass for the sediment and oyster tissue contaminant analyses. 

The units for sediment metals and organics are μg g-1 dry weight 

and ng g-1 dry weight, respectively. The units for oyster tissue 

metals and organics are μg g-1 dry weight and ng g-1 dry weight, 

respectively. 

Analyte Sediment 

MDL Range

Tissue 

MDL Range

Aluminum 0.008 - 0.090 5.35 - 10

Antimony 0.786 - 0.804 0.196 - 0.368

Arsenic 0.091 - 0.933 0.023 - 0.124

Barium 0.108 - 1.1 0.026 - 0.050

Beryllium 0.159 - 0.167 0.019 - 0.037

Cadmium 0.0036 - 0.0038 0.002 - 0.004

Chromium 0.161 - 1.64 0.201 - 0.376

Cobalt 0.079 - 0.082 0.049 - 0.093

Copper 6.26 - 6.56 0.779 - 6.49

Iron 0.051 - 0.532 31.6 - 59.1

Lead 0.051 - 0.518 0.012 - 0.024

Lithium 0.652 - 0.683 0.081 - 0.152

Manganese 4.96 - 5.2 0.617 - 1.16

Mercury 0.0006 - 0.008 0.006 - 0.014

Nickel 1.1 - 1.16 0.027 - 0.051

Selenium 0.161 - 0.165 0.103 - 0.194

Silver 0.269 - 0.282 0.18 - 1.12

Thallium 0.030 - 0.032 0.019 - 0.036

Tin 0.141 - 0.144 0.035 - 0.066

Uranium 0.047 - 0.048 0.029 - 0.055

Vanadium 0.06 - 0.061 0.015 - 0.028

Zinc 16 - 16.8 99.5 - 186

2,4'-DDD 0.315 - 1.32 5.11 - 8.98

2,4'-DDE 0.0787 - 0.33 1.28 - 2.25

2,4'-DDT 0.355 - 1.49 5.76 - 10.1

4,4'-DDD 0.265 - 1.11 4.29 - 7.55

4,4'-DDE 0.263 - 1.1 4.26 - 7.49

4,4'-DDT 0.789 - 3.31 12.8 - 22.5

Aldrin 0.527 - 2.21 8.56 - 15

Hexachlorobenzene 0.214 - 0.897 3.47 - 6.1

Chlorpyrifos 0.0447 - 0.187 0.724 - 1.27

cis-Chlordane 1.39 - 5.85 22.6 - 39.8

Dieldrin 0.116 - 0.486 1.88 - 3.31

Endosulfan I 0.127 - 0.531 2.05 - 3.61

Endosulfan II 0.26 - 1.09 4.23 - 7.43

Endosulfan sulfate 0.38 - 1.59 6.16 - 10.8

Heptachlor 0.205 - 0.861 3.33 - 5.86

Heptachlor epoxide 0.585 - 2.45 9.49 - 16.7

Gamma-HCH (g-BHC, lindane) 0.087 - 0.366 1.41 - 2.49

Mirex 0.178 - 0.745 2.88 - 5.07

Trans-nonachlor 1.43 - 6 23.2 - 40.8

PBDE 100 0.124 - 0.522 2.02 - 3.55

PBDE 138 0.372 - 1.56 6.04 - 10.6

72 
Appendix A (cont.)

Analyte Sediment 

MDL Range

Tissue 

MDL Range

PBDE 153 0.119 - 0.5 1.93 - 3.4

PBDE 154 0.685 - 2.87 11.1 - 19.5

PBDE 17 0.106 - 0.446 1.72 - 3.03

PBDE 183 0.176 - 0.741 2.86 - 5.04

PBDE 190 0.735 - 3.08 11.9 - 21

PBDE 28 0.158 - 0.665 2.57 - 4.52

PBDE 47 0.142 - 0.598 2.31 - 4.07

PBDE 66 0.134 - 0.562 2.17 - 3.82

PBDE 71 0.14 - 0.589 2.28 - 4

PBDE 85 0.732 - 3.07 11.9 - 20.9

PBDE 99 0.134 - 0.562 2.17 - 3.82

PCB 101 0.157 - 0.66 2.55 - 4.49

PCB 103 0.148 - 0.62 2.4 - 4.22

PCB 104 0.128 - 0.535 2.07 - 3.64

PCB 105 0.14 - 0.589 2.28 - 4

PCB 107/108 0.272 - 1.14 4.42 - 7.77

PCB 110 0.197 - 0.825 3.19 - 5.61

PCB 114 0.231 - 0.968 3.74 - 6.58

PCB 118 0.485 - 2.03 7.87 - 13.8

PCB 119 0.234 - 0.981 3.79 - 6.67

PCB 12 0.189 - 0.794 3.07 - 5.4

PCB 123 0.926 - 3.89 15 - 26.4

PCB 126 0.164 - 0.687 2.66 - 4.67

PCB 128 0.095 - 0.401 1.55 - 2.73

PCB 130 0.174 - 0.732 2.83 - 4.98

PCB 132/168 0.161 - 0.674 2.6 - 4.58

PCB 138 0.089 - 0.375 1.45 - 2.55

PCB 141 0.125 - 0.526 2.04 - 3.58

PCB 146 0.473 - 1.99 7.68 - 13.5

PCB 149 0.319 - 1.34 5.17 - 9.1

PCB 15 0.165 - 0.691 2.67 - 4.7

PCB 151 0.151 - 0.633 2.45 - 4.31

PCB 153 0.394 - 1.66 6.4 - 11.3

PCB 154 0.14 - 0.589 2.28 - 4

PCB 156 0.12 - 0.504 1.95 - 3.43

PCB 157 0.11 - 0.459 1.78 - 3.13

PCB 158 0.183 - 0.767 2.97 - 5.22

PCB 159 0.090 - 0.379 1.47 - 2.58

PCB 169 0.119 - 0.5 1.93 - 3.4

PCB 170 0.14 - 0.589 2.28 - 4

PCB 172 0.13 - 0.544 2.1 - 3.7

PCB 174 0.154 - 0.647 2.5 - 4.4

PCB 177 0.169 - 0.709 2.74 - 4.82

PCB 18 0.422 - 1.77 6.85 - 12

PCB 180 0.115 - 0.482 1.86 - 3.28

PCB 183 0.149 - 0.625 2.41 - 4.25

PCB 184 0.102 - 0.428 1.66 - 2.91

73 
Appendix A (cont.) 

Analyte Sediment 

MDL Range

Tissue 

MDL Range

PCB 187 0.086 - 0.361 1.4 - 2.46

PCB 188 0.092 - 0.388 1.5 - 2.64

PCB 189 0.129 - 0.54 2.09 - 3.67

PCB 193 0.191 - 0.803 3.1 - 5.46

PCB 194 0.087 - 0.366 1.41 - 2.49

PCB 195 0.254 - 1.07 4.12 - 7.25

PCB 198 0.173 - 0.727 2.81 - 4.95

PCB 2 0.146 - 0.611 2.36 - 4.16

PCB 20 0.134 - 0.562 2.17 - 3.82

PCB 201 0.117 - 0.491 1.9 - 3.34

PCB 202 0.115 - 0.482 1.86 - 3.28

PCB 206 0.103 - 0.433 1.67 - 2.94

PCB 207 0.296 - 1.24 4.79 - 8.43

PCB 209 0.116 - 0.486 1.88 - 3.31

PCB 26 0.125 - 0.526 2.04 - 3.58

PCB 28 0.383 - 1.61 6.21 - 10.9

PCB 29 0.224 - 0.941 3.64 - 6.4

PCB 3 0.098 - 0.415 1.6 - 2.82

PCB 31 0.325 - 1.37 5.28 - 9.28

PCB 37 0.184 - 0.772 2.98 - 5.25

PCB 44 0.116 - 0.486 1.88 - 3.31

PCB 45 0.231 - 0.968 3.74 - 6.58

PCB 48 0.107 - 0.451 1.74 - 3.06

PCB 50 0.21 - 0.883 3.42 - 6.01

PCB 52 0.146 - 0.611 2.36 - 4.16

PCB 56/60 0.216 - 0.906 3.5 - 6.16

PCB 61/74 0.285 - 1.2 4.62 - 8.13

PCB 63 0.218 - 0.915 3.54 - 6.22

PCB 66 0.215 - 0.901 3.48 - 6.13

PCB 69 0.372 - 1.56 6.04 - 10.6

PCB 70 0.44 - 1.85 7.14 - 12.6

PCB 76 0.292 - 1.23 4.74 - 8.34

PCB 77 0.156 - 0.656 2.54 - 4.46

PCB 8 0.367 - 1.54 5.95 - 10.5

PCB 81 0.188 - 0.79 3.05 - 5.37

PCB 82 0.215 - 0.901 3.48 - 6.13

PCB 84 0.342 - 1.44 5.55 - 9.77

PCB 87 0.203 - 0.852 3.29 - 5.8

PCB 88 0.219 - 0.919 3.55 - 6.25

PCB 9 0.403 - 1.69 6.54 - 11.5

PCB 92 0.134 - 0.562 2.17 - 3.82

PCB 95 0.102 - 0.428 1.66 - 2.91

PCB 99 0.182 - 0.763 2.95 - 5.19

Acenaphthene 0.048 - 21.97 117.76 - 207.16

Acenaphthylene 0.064 - 7.63 40.88 - 71.91

Anthracene 0.063 - 8.63 46.25 - 81.36

Benzo(b)fluoranthene 0.063 - 4.6 24.68 - 43.41

Benzo(a)anthracene 0.065 - 5.42 29.05 - 51.1

74 
75 

Appendix A (cont.) 

Analyte Sediment 

MDL Range

Tissue 

MDL Range

Benzo(a)pyrene 0.054 - 13.61 72.97 - 128.36

Benzo(e)pyrene 0.054 - 16.72 89.64 - 157.68

Benzo(g,h,i)perylene 0.051 - 9.16 49.07 - 86.32

Benzo(k+j)fluoranthene 0.133 - 11.87 63.62 - 111.91

Biphenyl 3.49 - 8.61 46.15 - 81.18

Chrysene+Triphenylene 0.047 - 14.07 75.39 - 132.63

Dibenz(a,h)anthracene 0.158 - 17.94 96.17 - 169.17

Dibenzothiophene 0.043 - 8.27 44.34 - 77.99

Fluoranthene 24.62 - 60.71 325.39 - 572.4

Fluorene 0.052 - 22.97 123.11 - 216.56

Naphthalene 0.037 - 4.64 24.88 - 43.77

1,6,7 Trimethylnaphthalene 0.123 - 32.6 174.73 - 307.36

1-Methylnaphthalene 0.036 - 17.35 93 - 163.6

2,6 Dimethylnaphthalene 0.03 - 18.7 100.23 - 176.31

2-Methylnaphthalene 0.059 - 27.06 145.02 - 255.1

Indeno(1,2,3-cd)pyrene 0.167 - 22.72 121.78 - 214.22

Perylene 2.33 - 5.75 30.84 - 54.25

Phenanthrene 4.06 - 10.02 53.7 - 94.46

1-Methylphenanthrene 0.037 - 11.12 59.59 - 104.83

Pyrene 4.62 - 11.38 61.01 - 107.32
Appendix B. The 24 analytes used to calculate the mean ERM quotient. For total PCB and DDT, 

refer to Appendix A for the individual analytes used. 

Analyte

Arsenic

Cadmium

Chromium

Copper

Lead

Mercury

Silver

Zinc

4,4'-DDE

Total DDT

Acenaphthene

Acenaphthylene

Anthracene

Benzo(a)anthracene

Benzo(a)pyrene

Chrysene+Triphenylene

Dibenz(a,h)anthracene

Fluoranthene

Fluorene

Naphthalene

2-Methylnaphthalene

Phenanthrene

Pyrene

Total PCB

76 

United States Department of Commerce 

Carlos M. Gutierrez 

Secretary 

National Oceanic and Atmospheric Administration 

Vice Admiral Conrad C. Lautenbacher, Jr. USN (Ret.) 

Under Secretary of Commerce for Oceans and Atmospheres 

National Ocean Service 

John (Jack) H. Dunnigan 

 Assistant Administrator for Ocean Service and Coastal Zone Management