Gulf of Mexico Tidal Creeks Serve as Sentinel 

Habitats for Assessing the Impact of Coastal 

Development on Ecosystem Health 

NOAA Technical Memorandum NOS NCCOS 136 
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., D. Bergquist, A. Blair, G. Riekerk, E. Wirth, L. Webster, J. Felber, T. Washburn, G. 

DiDonato, A.F. Holland. 2011. Gulf of Mexico Tidal Creeks Serve as Sentinel Habitats for 

Assessing the Impact of Coastal Development on Ecosystem Health. NOAA Technical 

Memorandum NOS NCCOS 136. 64 pp. 
Gulf of Mexico Tidal Creeks Serve as Sentinel Habitats for Assessing the 

Impact of Coastal Development on Ecosystem Health 

D. Sangera, D. Bergquistb , A. Blairc, G. Riekerkb, E. Wirthd, L. Websterd, J. Felberb, T. 

Washburnd

, G. DiDonatoc, A.F. Hollandc

a

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

b

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

Fort Johnson Road, Charleston SC, 29412, USA 

c

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

Charleston, SC, 29412, USA 

d

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

Road, Charleston, SC, 29412, USA 

NOAA Technical Memorandum NOS NCCOS 136 

September 2011 

United States Department of National Oceanic and National Ocean Service 

Commerce Atmospheric Administration 

 Rebecca Blank Jane Lubchenco David Kennedy 

 Acting Secretary Administrator Assistant Administrator
i 
i 

Table of Contents 

Acknowledgements ....................................................................................................................... ii

List of Tables ................................................................................................................................ iii

List of Figures ............................................................................................................................... iv

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

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

2. Methods ...................................................................................................................................... 7

2.1 Study Sites ...................................................................................................................... 7 

2.2 Watershed Determinations and Land Cover Characterizations ..................................... 9 

2.3 Sample Design ................................................................................................................ 9 

2.4 Laboratory Processing Methods ................................................................................... 11 

2.4.1 Basic Water Quality ..................................................................................... 11 

2.4.2 Nutrients and Phytoplankton........................................................................ 11 

2.4.3 Pathogen Indicators ...................................................................................... 11 

2.4.4 Chemical Contaminants ............................................................................... 12 

2.4.5 Macrobenthic Community ........................................................................... 13 

2.4.6 Nekton Community ...................................................................................... 14 

2.5 Data Summary and Statistical Analyses ......................................................... 14 

3. Results ...................................................................................................................................... 17

3.1 Stressors ........................................................................................................................ 17 

3.2 Exposures ..................................................................................................................... 18 

3.2.1 Basic Water Quality ..................................................................................... 18 

3.2.2 Nutrients and Phytoplankton........................................................................ 21 

3.2.3 Pathogens ..................................................................................................... 23 

3.2.4 Sediment Quality ......................................................................................... 25 

3.2.4.1 Sediment Composition .............................................................................. 25 

3.2.4.2 Sediment Contamination ........................................................................... 26 

3.3 Ecological Response ..................................................................................................... 30 

3.3.1 Macrobenthic Community ........................................................................... 30 

3.3.2 Nekton Community ...................................................................................... 33 

3.4 Human Responses ........................................................................................................ 37

3.4.1 Oyster Tissue Pathogens .............................................................................. 37 

3.4.2 Bivalve Tissue Contaminants ...................................................................... 38 

4. Discussion................................................................................................................................. 42

4.1 Regional Comparisons ................................................................................................. 42 

4.2 Classification Framework............................................................................................. 44 

4.3 Responses to Coastal Development ............................................................................. 45 

4.3 NERRs as Regional References ................................................................................... 47 

4.5 Summary ...................................................................................................................... 47 

References .................................................................................................................................... 49

Appendix A .................................................................................................................................. 55

Appendix B .................................................................................................................................. 59

Appendix C .................................................................................................................................. 60

Appendix D .................................................................................................................................. 62
ii 

Acknowledgements 

Without the help of a large number of individuals and organizations, a research project of this 

breadth would not have been possible. 

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

M. Woodrey, G. Grammer, and D. Rupple (Grand Bay); S. Phipps and L.G. Adams (Weeks 

Bay); R. Ellin, J. Fear, P. Murray, and H. Wells (North Carolina); and D. Hurley and 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: S. Jones, S. Schoepner, L. Vandiver, P. Biondo, C. Buzzelli, A. Coghill, A. 

Colton, C. Cooksey, D. Couillard, S. Drescher, M. Dunlap, 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 Resources – Marine Resources Research Institute 

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

nekton and sediment samples. Center for Coastal Environmental Health and Biomolecular 

Research (CCEHBR) laboratory staff including J. Gregory, C. Johnston, B. Robinson, B. 

Thompson, and L. Webster analyzed water and oyster samples for pathogens. We would also 

like to recognize the Gulf Coast Research Laboratory at University of Southern Mississippi 

including J. Grimes, D. Rebarchik, J. McDonald, and M. Fiello for both running fecal indicator 

bacterial samples as well as providing laboratory space to CCEHBR staff. Center for Human 

Health Risk / Hollings Marine Laboratory (CHHR/HML) staff including D. Liebert, Y. 

Sapozhnikova, B. Shaddix, and L. Thorsell analyzed sediment and oyster samples for 

contaminants. University of Maryland Center for Environmental Science – Chesapeake 

Biological Laboratory's Nutrient Analytical Services Lab staff analyzed water samples for 

nutrients and phytoplankton. 

We also appreciate the input of three peer-reviewers: Scott Phipps (Weeks Bay NERR), Len 

Balthis (NOAA), and Lisa Vandiver (NOAA). Their input enhanced the final product. 
iii 

List of Tables 

Table 2-1. Creek orders sampled, date sampled, latitude, and longitude for each tidal creek 

network sampled by state. Latitude and longitude are average values for each creek. 

Table 3-1. Creek system, land use class, watershed area, and watershed impervious cover for 

each creek segment. 

Table 3-2. Characteristics and contaminant concentrations in tidal creek sediments for selected 

parameters. 

Table 3-3. Results of SIMPER analysis for the macrobenthic community for the SE and GoM 

tidal creeks in the intertidal and subtidal systems with the difference between them. 

Table 3-4. Results of SIMPER analysis of land use differences in macrobenthic community 

structure in southeast creeks. 

Table 3-5. Results of SIMPER analysis of land use differences in macrobenthic community 

structure in Gulf of Mexico creeks. 

Table 3-6. Results of SIMPER analysis for the nekton community for the SE and GoM tidal 

creeks in the intertidal and subtidal systems with the difference between them. 

Table 3-7. Results of SIMPER analysis of land use differences in nekton community structure 

in southeast and Gulf of Mexico intertidal creeks. 

Table 3.8. Contaminant wet weight concentrations in tidal creek bivalves (oyster for SE and MS 

and mussel for AL). 
iv 

List of Figures 

Figure 1-1. Conceptual model identifying linkages between development of the upland and the 

ecological response of South Carolina tidal creeks. 

Figure 2-1. Map of the Gulf of Mexico and southeastern sampling sites with insets for the two 

regions. 

Figure 3-1. Area of intertidal and subtidal watersheds examined within this study. 

Figure 3-2. Proportional land cover categories (from NLCD 2001) within intertidal and subtidal 

watersheds examined within this study. 

Figure 3-3. Relationship between population density and impervious cover within watersheds.

Figure 3-4. Average basic continuous water quality levels by land use, region, and longitudinal 

gradient. 

Figure 3-5. Range of basic continuous water quality levels by land use, region, and longitudinal 

gradient. 

Figure 3-6. Relationship between basic continuous water quality and impervious cover for the 

study watersheds. 

Figure 3.7. Nutrients and phytoplankton levels by land use, region, and longitudinal gradient. 

Figure 3.8. Relationship between nutrient concentrations and impervious cover for the study 

watersheds.

Figure 3-9. Pathogen indicator levels by land use, region, and longitudinal gradient. 

Figure 3.10. Relationship between pathogen indicators and impervious cover for the study 

watersheds. 

Figure 3-11. Sediment characteristic levels by land use, region, and longitudinal gradient. 

Figure 3-12. mERMQ levels by land use, region, and longitudinal gradient. 

Figure 3-13. Relationship between mERMQ ratios and impervious cover for the study 

watersheds. 

Figure 3-14. Concentrations of polybrominated diphenyl ethers (PBDEs) in creek sediments. 

Figure 3-15. Macrobenthic community number of species and total abundance by land use, 

region, and longitudinal gradient. 
v 

Figure 3-16. Relationship between macrobenthic number of species and density versus 

impervious cover for the study watersheds. 

Figure 3-17. Multidimensional scaling (MDS) ordination of benthic communities with SE and 

GoM regions combined and each region separately. 

Figure 3-18. Nekton community number of species and total abundance by land use and region 

for only the intertidal systems. 

Figure 3-19. Relationship between nekton number of species and density versus impervious 

cover for the study watersheds. 

Figure 3-20. Multidimensional scaling (MDS) ordination of nekton communities with both 

regions combined, SE region, and GoM region. 

Figure 3-21. Relationship between pathogen indicator levels in oyster tissues and impervious 

cover for the study watersheds. 

Figure 3-22. Concentrations of total PAHs and total PCBs in creek sediments for the intertidal 

and subtidal systems. 
1 
1 

Abstract 

A study was conducted, in association with the Alabama and Mississippi National Estuarine 

Research Reserves (NERRs) in the Gulf of Mexico (GoM) as well as the Georgia, South 

Carolina, and North Carolina NERRs in the Southeast (SE), to evaluate the impacts of coastal 

development on tidal creek sentinel habitats, including potential impacts to human health and 

well-being. Uplands associated with Southeast and Gulf of Mexico 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 cover 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 (Holland et al. 2004, Sanger et al. 2008). The primary objective of this work 

was to define the relationships between coastal development with its concomitant land cover 

changes, and non-point source pollution loading and the ecological and human health and wellbeing status of tidal creek ecosystems. 

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

North Carolina to southern Georgia, and five Gulf of Mexico systems from Alabama and 

Mississippi were sampled during summer (June-August) 2005, 2006 (SE) and 2008 (GoM). 

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 29 intertidal 

and 24 subtidal creeks. Indicators sampled throughout each creek included water quality (e.g., 

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

contaminant levels including emerging contaminants), pathogen and viral indicators (e.g., fecal 

coliform, enterococci, F+ coliphages, F- coliphages), and abundance and tissue contamination of 

biological resources (e.g., macrobenthic and nektonic communities, shellfish tissue 

contaminants). 

Tidal creeks have been identified as a sentinel habitat to assess the impacts of coastal 

development on estuarine areas in the southeastern US. A conceptual model for tidal creeks in 

the southeastern US identifies that human alterations (stressors) of upland in a watershed such as 

increased impervious cover will lead to changes in the physical and chemical environment such 

as microbial and nutrient pollution (exposures), of a receiving water body which then lead to 

changes in the living resources (responses). The overall objective of this study is to evaluate the 

applicability of the current tidal creek classification framework and conceptual model linking 

tidal creek ecological condition to potential impacts of development and urban growth on 

ecosystem value and function in the Gulf of Mexico US in collaboration with Gulf of Mexico 

NERR sites. The conceptual model was validated for the Gulf of Mexico US tidal creeks. The 

tidal creek classification system developed for the southeastern US could be applied to the Gulf 

of Mexico tidal creeks; however, some differences were found that warrant further examination. 

In particular, pollutants appeared to translate further downstream in the Gulf of Mexico US 
2 

compared to the southeastern US. These differences are likely the result of the morphological 

and oceanographic differences between the two regions. Tidal creeks appear to serve as sentinel 

habitats to provide an early warning of the ensuing harm to the larger ecosystem in both the 

Southeastern and Gulf of Mexico US tidal creeks. 

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

Sapelo Island NERR, Weeks Bay NERR, Grand Bay NERR, tidal creek, sentinel habitat, 

conceptual model, impervious cover, land cover, urbanization, sediment and tissue contaminants, 

water quality, pathogens, nekton, oysters, macrobenthos, physical and chemical environment. 
3 

1. Introduction 

The coastal United States hosts abundant natural resources that contribute hundreds of billions of 

dollars to the US economy annually (Colgan 2003). In addition, these resources provide many 

free ecological services, including waste processing, clean air and water, and scenic vistas worth 

untold billions of dollars (Costanza et al. 1997). Approximately 17% of the US land area 

(excluding Alaska) and >50% of the population are located along the US coasts (Crossett et al. 

2004). Coastal ocean-based tourism is the fastest growing component of the coastal economy, 

with hundreds of millions of Americans and international guests visiting our coasts annually 

(Colgan 2003). Not surprisingly, coastal population densities are 2-5 times higher than in the 

rest of the nation (Beach 2002). Along our coasts, land is being consumed for urban 

development 3-6 times faster than the rate of population growth (Beach 2002), resulting in major 

and permanent alterations to coastal ecosystems. These trends appear to be accelerating, with 

potentially serious impacts on the long term health of coastal ecosystems and the quality of life 

of the people who live, work, and recreate there (Cohen et al. 1997, Vitousek et al. 1997). 

Recent reports have noted the measurably diminished condition of our nation’s coastal natural 

resources (USEPA 2001a, NMFS 2002, Pew 2003). Major sources of impairment across all 

regions and habitats include chemical and microbial contamination, increased “flashiness” in 

freshwater inflows, nutrient over-enrichment, 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 the major contributor to 

diminished resources. 

New approaches and collaborations are required to understand and resolve the complex, 

regional-scale environmental issues including cumulative stress from multiple sources facing our 

coasts. Existing observational systems do not provide early warning and have failed to link 

degraded ecosystem condition and human health and quality of life. Public health and wellbeing and ecosystem science can no longer be viewed as separate domains but are interconnected 

and linked disciplines. It is obvious that the health of people, wildlife, and ecosystems are linked 

in the web of life. A paradigm of one health (human, wildlife, and ecosystem) is crucial to 

sustain the critical ecological services and quality of life that currently exists in the coastal zone. 

One of the earliest symptoms of broad scale coastal ecosystem impairment has historically been 

declines in the amount and condition of rare and critical habitats that are sensitive to localized 

and relatively small changes in environmental conditions. Notable examples include sea grass 

beds, oyster reefs, kelp forests, coral reefs, and wetlands. These “sentinel habitats” or “first 

responders” generally decline in extent and condition years-to-decades before system wide 

impairment is documented by routine environmental quality monitoring activities. For example, 

the extent of sea grass beds in the Chesapeake Bay exhibited declines as early as the 1960s. The 

greatest declines occurred in the headwaters of major tributaries of the Bay well before non-point 

source nutrient and sediment inputs were recognized as a Bay-wide problem (Bayley et al. 1978). 

Coral reefs in Florida Bay also exhibited symptoms of disease, bleaching, and impairment years 
4 

before the impacts of alterations in pollution loading and changes in freshwater flow were 

identified as major issues for the region (Dustan and Halas 1987). Unfortunately, the scientific 

knowledge needed to understand the warning signals provided by sentinel habitats has only 

recently become available (Kemp et al. 1983, Hoegh-Guldberg 1999, Porter and Tougas 2001, 

Turgeon et al. 2002). 

In estuarine environments of the southeastern US (SE), salt marsh habitats and their associated 

tidal creek networks serve a range of important functions including nursery habitats, storm 

buffers, and pollutant filters. Tidal creeks and salt marshes also serve as the interface between 

the upland landscape and estuaries where freshwater from the land mixes with saline water from 

the ocean, resulting in dynamic environments that are renowned for their ecological complexity, 

biological productivity, and seafood production (Kneib 1997, Sanger et al. 1999a, b, Lerberg et 

al. 2000, Mallin et al. 2000b, Holland et al. 2004). Salt marshes are bisected by tidal creeks 

which facilitate water moving into and out of the system. In the SE, the watersheds associated 

with headwater tidal creeks are among the most rapidly developing in the nation. Because tidal 

creek networks form the primary hydrologic link between estuaries and land-based activities, 

small intertidal portions of tidal creek networks represent the first zone of impact for non-point 

source pollution runoff from upland areas. The potential for the microbial and chemical 

contamination in these tidal creek habitats is great. Consequently, tidal creeks provide a 

potential sentinel habitat for impacts from human landscape alterations in coastal areas. 

Holland et al. (2004) developed a conceptual model for SC, further refined by Sanger et al. 

(2008) for the SE, of 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 these tidal creek ecosystems. The model mirrored the US Environmental Protection 

Agency (USEPA) Ecological Risk Assessment model with stressors leading to changes in the 

physical-chemical environment (i.e., exposures) which in turn leads to biological responses. The 

conceptual model for tidal creeks describes that human alterations of upland in a watershed such 

as increased impervious cover (stressors) will lead to changes in the physical and chemical 

environment such as microbial and nutrient pollution (exposures) of a receiving water body 

which will then lead to changes in the living resources (responses) (Figure 1-1). Holland et al. 

(2004) and Sanger et al. (2008) predicted that adverse changes in the physical and chemical 

environment (e.g., water quality indicators such as indicator bacteria for sewage pollution or 

sediment chemical contamination) generally occurred when impervious cover levels in the 

watershed exceed 10-20% and that ecological processes responded and were generally impaired 

when impervious cover levels exceeded 20-30% in suburban and urban watersheds (Figure 1-1). 

The original conceptual model did not include the linkages between the ecosystem condition and 

human health and well-being. There is an emerging consensus that current patterns of coastal 

development are associated with increasing fecal pollution in tidal creeks, estuaries, and bathing 

beaches (Mallin et al. 2000a, 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 seafood products unsafe to eat and water unsafe for primary contact recreation. 

Current patterns of coastal development may also affect flooding vulnerability, public health 

risk, and the economic impacts. In order to begin addressing impacts on human health and 

welfare, the conceptual model has been updated to include societal responses, including human 
5 

health and well-being (Figure 1-1; Sanger et al. 2008). Impervious cover levels defining where 

human uses are impaired are currently being determined, but it generally appears that shellfish 

bed closures increase and the flooding vulnerability of headwater regions become a concern 

when impervious cover values exceed 10-30%. 

To assist in understanding the complexity and variability associated with freshwater and wetland 

ecosystems, classification frameworks have been developed that integrate the ecological 

attributes of these systems within their biogeography, hydrology, and short- and long-term 

ecological history (e.g., Horton 1945, Cowardin et al. 1979, Frissell et al. 1986). Classification 

approaches have made, however, only limited contributions to the understanding of spatial and 

temporal variability and scale issues for estuarine ecosystems (e.g., Anderson et al. 1976, Odum 

1984). To partition and account for the variability and complexity of SE tidal creek networks, 

we developed a geographically-independent, hierarchical classification framework for tidal 

creeks that is patterned after the freshwater stream classification system (Horton 1945, Strahler 

1957). This preliminary tidal creek classification framework enhanced the characterization and 

understanding of natural and human induced environmental gradients and spatial variability for 

SE tidal creeks (Sanger et al. 2008) and is being evaluated for GoM tidal creeks as a part of this 

effort. 

Figure 1-1. Conceptual model identifying linkages between 

development of the upland and the ecological responses of South 

Carolina tidal creeks, expanded to include societal responses and 

associated feedback loops. 
6 

Although the conceptual model and tidal creeks classification scheme were developed initially 

for South Carolina, they have been validated as applicable to the SE (South Carolina, North 

Carolina, and Georgia; Sanger et al. 2008). It is currently unknown if similar tidal creek 

networks can also be used as early warning systems for regions outside the SE. The overall 

objective of the current study is to evaluate the applicability of the current tidal creek 

classification framework and conceptual model linking tidal creek ecological condition to 

potential impacts of development and urban growth on ecosystem value and function in the GoM 

through collaborations with GoM NERR sites. The study also provides the two GoM NERRs 

with data to assess the health of their NERR and surrounding sites. 
7 

2. Methods 

2.1 Study Sites 

A total of twenty-three tidal creek networks were sampled along the southeastern (SE) and Gulf 

of Mexico (GoM) coasts during the summer (June-August) (Table 2-1, Figure 2-1). In the SE, 

nineteen tidal creek networks from New Hanover County, NC, to Glynn County, GA, were 

sampled in 2005 and 2006. Twelve tidal creek networks were sampled in SC in 2005. Four of 

these networks were re-sampled in 2006. In 2006, four creek networks were sampled in GA, and 

two were sampled in NC. In the GoM, five tidal creek networks from Mobile Bay, AL (n=2) to 

Pascagoula, MS (n=3), were sampled in 2008. The GA, NC, AL, and MS sites were sampled in 

association with the staff of the adjacent National Estuarine Research Reserves. 

A longitudinal gradient 

was defined for each tidal 

creek network by 

applying a freshwater 

stream classification 

model (Horton 1945, 

Strahler 1957). The first 

order, or headwater, of 

each creek was defined as 

directly draining coastal 

uplands or salt marsh 

habitat and was 

characterized by narrow 

(<10 m) width with 

predominately intertidal 

habitat. These first order 

sections will be referred 

to as intertidal sections in 

the remaining text. The 

second order of each 

creek was formed by the 

confluence of two or 

more first order creeks. 

Second order creeks were 

wider (usually >10 m but 

<30 m) and had abundant 

subtidal habitats. The 

third order of each creek 

was formed by the 

confluence of two or 

more second order creeks. 

Third order systems were 

large, wide (>30 m) 

Table 2-1. Creek order sampled, date sampled, latitude, and longitude 

for each tidal creek network sampled by state. Latitude and longitude are 

average values for each creek. NIWB = North Inlet-Winyah Bay NERR, 

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

NERR. 

System

Orders 

Sampled Dates Sampled Latitude Longitude

Alabama

Weeks Bay 1, 2 7/16/2008 30.415 -87.830

Bear Creek 1, 2 7/15/2008 30.273 -87.716

Mississippi

Bayou Heron 1, 2, 3 7/28/2008 30.410 -88.406

Bayou Chico 1, 2, 3 7/29/2008 30.345 -88.517

Bayou Pattassa 1, 2 7/30/2008 30.377 -88.546

North Carolina

Hewlitts 1, 1, 2 8/7/2006 34.189 -77.857

Whiskey Creek 1, 2 8/9/2006 34.161 -77.865

South Carolina

Albergottie 1, 2, 3 8/17/2005 32.448 -80.720

Bulls 1, 2, 3 6/29/2005 32.825 -80.027

Guerin 1, 2, 3 7/5/2005, 6/20/2006 32.944 -79.766

James Island 1, 1, 2, 2, 3 8/1/2005, 8/17/2006 32.744 -79.974

Murrells Inlet 2, 3 6/22/2005 33.564 -79.025

New Market 1 8/8/2005, 7/24/2006 32.806 -79.940

NIWB-Town 1, 2, 3 6/20/2005 33.340 -79.177

Okatee 1, 2, 3 7/18/2005 32.287 -80.929

Orangegrove 1, 2, 3 7/20/2005 32.812 -79.978

Parrot 1, 2, 3 7/7/2005 32.733 -79.910

Shem 1, 2 8/29/2005 32.801 -79.869

ACE-Village 1, 2, 3 8/3/2005, 7/5/2006 32.419 -80.522

Georgia

Burnett 1, 2 7/19/2006 31.234 -81.538

SAP-Duplin 1, 2, 3 7/19/2006 31.147 -81.375

SAP-Oakdale 1 7/11/2006 31.481 -81.272

Postell 1 7/11/2006 31.415 -81.285
8 

creeks composed mainly of subtidal habitat and a small amount of intertidal habitat in the creek. 

For simplicity, the second and third order systems will be collectively referred to as subtidal 

sections throughout the remaining text. While combining second and third orders results 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 in a wide range of parameters between second 

and third orders were small (Sanger et al. 2008), and (2) only a limited number of third order 

creeks were sampled outside of SC, making a regional evaluation of that order impossible. 

Each creek 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. Within any reach 

of any creek order, stations were randomly located for sample collection. The specific sampling 

activities that occurred within each reach are detailed below. 

Figure 2-1. Map of the Gulf of Mexico and southeastern sampling sites with insets for 

the two regions. NERRs sampled were NC = North Carolina, NI-WB = North Inlet-Winyah 

Bay, ACE = Ashepoo, Combahee, and Edisto, SAP = Sapelo Island, GB = Grand Bay, WB 

= Weeks Bay. 
9 

2.2 Watershed Determinations and Land Cover Characterizations 

Watersheds and sub-watersheds 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. Sub-watersheds 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 

gradients or other attributes such as roads that might impede surface runoff. Modifications to 

EDNA watershed boundaries were made by hand digitization.

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

the five states using the MRLC web tool (http://gisdata.usgs.net/website/MRLC/viewer.php). 

Land cover data were derived from the 2001 NLCD dataset. The land cover and impervious 

cover data were matched to the watershed and sub-watershed boundary data. Land cover 

estimates were determined from this layer and summed to obtain simplified categories of land 

cover. The impervious cover data were further modified by removing data for marsh and open 

water categories. All editing functions were performed in ArcView. Impervious cover levels 

were then calculated from the NLCD for all watersheds and sub-watersheds. These NLCDderived 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 (1999-2001) to estimate 

impervious cover, but the NLCD impervious cover values underestimated the ground-truthed 

data reported in Holland et al. (2004). A similar underestimation of NLCD-derived impervious 

cover data 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 NLCDderived impervious cover percentage. 

Creek watersheds were classified at the highest 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); and (3) urban (≥35% 

impervious cover). There were two exceptions to this classification. The Orangegrove Creek 

(SC) watershed was estimated to have 37.3% impervious cover; however, since this was 

primarily light residential development on a small amount of upland (127 ha) relative to the total 

watershed size (322 ha), we categorized this site as a suburban watershed. The Burnett Creek 

(GA) watershed was estimated to have 11.8% impervious cover; however, since this was a 

superfund site designated by the USEPA, it was categorized as an urban watershed. 

2.3 Sample Design 

Each creek was sampled during the ebbing tide over a two or three day period. Southeast creeks 

(semi-diurnal tides) were sampled approximately 2-3 hours prior to low tide and GoM creeks 

(diurnal tides) were sampled approximately 4-5 hours prior to low tide. First order was sampled 

by walking to designated sample sites, and second and third order creeks were sampled from a 
10 

small boat. Sampling was generally conducted by moving in an upstream direction to minimize 

effects of physical disturbance on subsequently sampled stations. Within each creek order, 

samples were collected to characterize water quality, water column nutrients, pathogen 

indicators, macrobenthic infauna, resident nekton, sediment contaminants, and oyster pathogen 

and contaminant body burdens. Sampling stations were selected using a stratified random 

method with each order divided into 3 equal reaches. The first reach was furthest upstream, the 

second reach was in the middle, and the third reach was located furthest downstream. The 

number of samples collected in each order varied by sample type. 

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

collected in bottom waters (0.3 m above bottom) using Yellow Springs Instrument (YSI) 6600 

data loggers. A logger was deployed in the second reach of each creek order and collected data 

at 15 minute intervals for at least 2 full tidal cycles (25 hrs in the SE and 50 hrs in the GoM). 

One water sample for pathogen indicators was collected in the second reach of each creek order 

in sterile 2 L polypropylene bottles. In addition, one water sample was collected in an acidwashed 500 mL polyethylene bottle within each reach of each creek order for nutrient 

quantifications. All water samples were collected approximately 0.3 m below the surface of the 

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

collected in an upstream direction to prevent contamination. Bottle caps were removed 

immediately prior to sampling, and bottles were inverted at the appropriate sample depth. 

Macrobenthic infauna were collected using two field methods. In intertidal creeks, the benthos 

was sampled approximately 1 m below mean high water (MHW), primarily when sediment was 

exposed, using a 0.0044 m2

 core sampler to a depth of 15 cm. 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 intertidal habitat. A small scoop of mud (upper 2 cm) was collected next to each core 

sample for sediment analysis (% sand, % silt, % clay, total organic carbon [TOC]). Additionally, 

2 small cores (upper 2 cm, 0.0009 m2) were collected at each site and composited across all sites 

within each reach to quantify porewater ammonium (NH4

+

). In the subtidal reaches, the infauna 

were sampled using a 0.04 m2

 modified Van Veen grab sampler in the SE and a 0.023 m2 Petit 

Ponar grab sampler in the GoM. 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 cm of a second intact grab from each site. 

Sediments were sampled for chemical contaminants once in each creek order. At a randomly 

selected station 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 minimum of 3 successful grab samples 

were homogenized for chemical analysis. A stainless steel spatula was used to remove sediment 

samples from the sediment surface and place them in the appropriate container. 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 crustaceans, were sampled in first order creeks. Nekton was 

sampled using a 0.635 mm mesh seine net. One seine was pulled in each reach in an upstream 

direction over a distance of 25 m. Every effort was made to stretch the net from bank to bank. 
11 

When 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 2006, oysters (Crassostrea virginica) were hand-collected in every creek order when present. 

In 2008, oysters (Crassostrea virginica) in MS and ribbed mussels (Geukensia demissa) in AL 

were hand-collected in every creek order when present. When possible oysters or mussels were 

collected in the second reach near the site where the data logger was deployed and sediment 

samples were collected. After collection, oysters and mussels were separated for determination 

of pathogen loads (~ 20 individuals, oysters only), and chemical contaminant body burdens (~12 

individuals). 

2.4 Laboratory Processing Methods 

2.4.1 Basic Water Quality 

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

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

tide (common in first order creeks). In the SE, 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 (i.e., mean, maximum, minimum, range) for the measured parameters 

were calculated. In the GoM, data loggers were calibrated prior to deployment, but a postcalibration check was not conducted after retrieval. Data were reviewed extensively to 

determine if each logger was functioning properly. Any data that did not meet pre-established 

criteria were removed. 

2.4.2 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), and silicate was measured using the 

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

2.4.3 Pathogen Indicators 

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

within 24 hours of collection. 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 2001b). 
12 

Actual values were not obtained for FC and ENT at three of the GoM sites because the 

concentrations at these sites were not sufficiently diluted such that they exceeded the level of 

quantification (600 colony forming units [CFU]) for that dilution set. One site was located in the 

intertidal section of Bayou Chico and the other two sites were located in the intertidal and 

subtidal sections of Bayou Pattassa. This lack of realistic values resulted in our removing the 

ENT data at sites with greater than quantification levels recorded and developing estimates of the 

FC water concentration at two of the sites based on FC oyster concentrations. A regression of 

oyster tissue concentrations to water concentrations from the 2006 SE sites was performed (y = 

1.0257x - 0.3858 on log transformed data, R2 = 0.58) and used to estimate the water 

concentrations for two of the three sites in the GoM. 

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

collection site to obtain approximately 100 g (wet weight) tissue. The tissue homogenate was 

tested for the microbial indicators FC and ENT were enumerated most probable number 

according to standard methods (APHA 1998). 

In 2008, water and oyster samples were also analyzed for norovirus and enterovirus using viral 

RNA detection protocols. Each water sample was passed through a 0.45 μm filter membrane 

until the filter clogged. A subsample of shellfish homogenate was archived at –80 oC until RNA 

isolation was performed. RNA was extracted from each matrix using an RNeasy kit as described 

in Noble et al. (2006). The norovirus reverse transcription-quantitative polymerase chain 

reaction (RT-qPCR) assay was performed as in Gregory et al. (2011). Samples were similarly 

analyzed for enterovirus according to Gregory et al. (2006). 

2.4.4 Chemical Contaminants 

Sediments and tissues (oyster or mussel) 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 are considered to be an emerging contaminant of concern. Data quality was 

assured using a series of spikes, blanks, and standard reference materials (NIST 1944 and NRC 

MESS-3 for sediments; and NIST 1566b, 1974b, and NRC DOLT-3 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. Sediment samples were 

thoroughly homogenized by hand with a stainless steel spatula prior to extraction. Tissues from 

multiple oysters (maximum of 12 individuals) were composited to obtain 15 g of wet weight. 

The 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. Tissues were removed from the freezer and stored overnight at 4 oC and 

allowed to partially thaw. 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 
13 

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 oC 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, 2-3 g 

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 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 et al. 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 

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

the integrity of the analytical method. 

2.4.5 Macrobenthic Community 

Benthic samples 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% buffered formalin 

containing Rose Bengal. Samples were capped and stored until macrobenthic invertebrates 

could be removed from the detritus, identified to the lowest practical taxonomic unit, and 

counted. The following quality control/quality assurance (QA/QC) procedures were used. 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 re-sorted. The samples were 

identified to the lowest taxonomic level using dissecting and compound microscopes. One out of 

every ten samples was re-identified by a taxonomist for QA/QC purposes. If 10% of the 
14 

dominant organisms or 25% of the rare organisms were misidentified, then all 10 samples were 

re-identified. Densities of all organisms and number of taxa were calculated for each sample. 

2.4.6 Nekton Community 

Fish and crustaceans collected by seine net (0.635 cm bar mesh) were rinsed carefully and 

preserved in 10% formalin in seawater. Preserved organisms were sorted in the laboratory and 

identified to the lowest practical taxonomic level (usually species). A 90% identification 

efficiency was ensured using similar methods to the macrobenthic samples. 

2.5 Data Summary and Statistical Analyses 

Only a limited number of tidal creek systems could be sampled for this study in the GoM which 

resulted in a focus on tidal creeks in or of interest to the Grand Bay National Estuarine Research 

Reserve (NERR), MS and Weeks Bay NERR, AL. The NERRs provided regional reference sites 

and local knowledge of the tidal creek systems and creeks near their sites. We combined the data 

for GoM creeks with previous data collected for SE creeks for three reasons: (1) to determine the 

similarities and differences in tidal creek ecosystems between the two regions; (2) to evaluate if 

the responses of tidal creek ecosystems to coastal development were similar for the two regions; 

and (3) to increase the power of statistical tests. In general, relationships between the degree of 

coastal development and the amount of impairment increased as a result of combining the two 

data sets. 

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

abundances) were stored in a relational database. This database, initially managed in a 

PostgreSQL database and then migrated to a Microsoft SQL Server, was accessed by individual 

users via a Microsoft Access interface (White et al. 2008). This interface allows individual users 

to query the database to develop summaries and datasets for statistical analysis. Data are 

available upon request by contacting the senior author. 

Tidal creek data from 2005, 2006, and 2008 summer sampling periods have been compiled into 

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

statistical inference was creek order, and the resulting data set was comprised of 43 observations 

(29 from intertidal systems, 24 from subtidal systems). In cases involving multiple measures per 

order, data were averaged within each order to obtain a single value for each indicator. Data 

were also averaged across the second and third orders to obtain a single value representing the 

subtidally-dominated sections. Lastly, for creeks that were sampled in both 2005 and 2006 (i.e., 

Guerin, James Island School, New Market, and Village; Table 2-1), data were averaged across 

years (2005 and 2006) resulting in a single value for each intertidal system and each subtidal 

system for each parameter. 

Statistical analyses were designed to address three questions: 1) Do measured parameters vary 

across the two geographic regions?, (2) Do measured parameters vary across the sampled land 

use classes?, and 3) Do measured parameters vary along the creek longitudinal gradient? To 

address these questions, we employed Analysis of Variance (ANOVA) and regression analyses. 

The basic ANOVA model was a three-way, fixed factor model, with Region (GoM, SE), Land 

Use Class Type (forested, suburban, urban), and Creek Order (intertidal, subtidal) as the main 

effects. The interaction terms were included in all models and excluded if determined to be 
15 

nonsignificant (p ≥ 0.05). Type III sums of squares were used for evaluating the significance of 

the factors. Pairwise differences were examined by comparing least square means (using PDIFF 

in SAS). Lastly, individual response variables were regressed against impervious cover by creek 

order to identify predictive relationships. Regression analysis included both the SE and GoM 

data sets. Regressions were considered significant at p < 0.05. If data were found to be nonnormal or heteroscedastic, appropriate basic transformations (log, square root, arcsine) were 

used. Analyses were performed using SAS 9.1 (SAS Institute Inc., Cary, NC). 

In addition, the macrobenthic invertebrate and nekton community data were analyzed by 

multivariate analyses. Multidimensional scaling (MDS) ordination was employed using Primer 

v6 statistical package (Clarke and Gorley 2006). The samples collected were averaged for each 

creek and habitat type. All data were fourth-root transformed while salinity and sediment 

composition were log transformed. Similarities among land use class were then examined. The 

analysis of similarity (ANOSIM) function was used to test for differences in biological 

communities. These analyses compared creek order between and within region. 

To summarize sediment contaminant data, values less than method detection limits (MDL) were 

set to 0 before analysis. Total PAH, total PCB, total pesticides, and total PBDE concentrations 

were determined by summing the values for the 25, 79, 22, and 13 individual analytes measured 

for each group of contaminants, respectively (Appendix A). 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 reported an adverse effect, and an ERM was 

defined as the sediment concentration where 50% of all published studies 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 identified in 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 and MacDonald 1998). 

mERMQs provide a way to compare potential cumulative effects of contaminants after 

weighting them on a toxicological basis and were used in subsequent analyses. 

Bivalve tissue chemical contamination was not analyzed using statistical methods due to the low 

sample size. Bivalve tissue concentrations on wet weight basis were compared to Food and Drug 

Administration (FDA) environmental chemical contaminant action levels (FDA 2001) and the 
16 

USEPA (2000) human-health consumption limits for cancer and non-cancer endpoints. The 

FDA action levels are simply threshold values for comparison against tissue concentrations (nonconsumption based). 
17 

3. Results 

3.1 Stressors 

The five Gulf of Mexico (GoM) tidal creek systems surveyed for this study each consisted of 2 

or 3 sub-watersheds depending upon the number of intertidal and subtidal creek segments 

sampled (Table 3-1). All systems included at least some upland areas and were classified as 

either forested, suburban, or urban based upon their land use designation. 

In the GoM, intertidal watersheds ranged in size from 12 ha (Bayou Heron South, forested) to 

136 ha (Bayou Chico, urban; Table 3-1) and impervious cover ranged from 2.9% (Weeks Bay, 

forested) to 48.8% (Bayou Chico, urban). Subtidal watersheds included the intertidal area and 

ranged from 68 ha (Bayou Pattassa, urban) to 2402 ha (Bayou Heron, forested) in size. 

Impervious cover in these watersheds ranged from 2.9% (Weeks Bay) to 58.4% (Bayou Chico, 

urban). The GoM intertidal watersheds examined here were smaller than the average SE 

intertidal watershed (Figure 3-1) but still fell within the range of SE watershed sizes (18 - 2,425 

ha). This same pattern generally held for subtidal watersheds with the exception of the rather 

large Bayou Heron watershed (Figure 3-1). However, all GoM watersheds fell within the range 

of SE subtidal watersheds (59 - 5,501 ha). The GoM watersheds also had a similar range of 

impervious cover levels to SE intertidal (2.9 - 70.4%) and subtidal (0.0 – 47.7%) watersheds. 

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

developed-low, agricultural, forest, scrub, palustrine wetland (swamp, floodplain, etc.), marsh 

(primarily salt marsh), or water. The percent composition of land cover shows a progressive 

change from scrub, palustrine wetland, and salt marsh to low and high development along the 

forested-suburban-urban gradient (Figure 3-2). GoM creek watersheds classified as forested 

were primarily scrub, marsh, and palustrine land cover, as compared to the primarily forest land 

cover of SE creeks. For suburban and urban watersheds, there was a general increase in urban 

land cover (both developed-low and developed-high) with the two urban watersheds having 

Table 3-1. Creek system, land use class, watershed area, impervious cover, population density 

(individual hectare-1), and median income for each creek segment. Subtidal watershed area includes the 

related intertidal area. 

Creek Land Use Station Watershed Impervious Population Median 

System Class Watersheds Order Type Area (ha) Cover (%) Density Income

Alabama

Weeks Bay Forested Weeks Bay 1 Intertidal 33 2.9 0.12 $50,208

Weeks Bay 2 Subtidal 141 2.9 0.12 $50,208

Bear Creek Suburban Bear Creek 1 Intertidal 54 16.0 0.60 $41,250

Bear Creek 2 Subtidal 222 11.6 0.60 $41,250

Mississippi

Bayou Heron Forested Bayou Heron S 1 Intertidal 12 6.3 0.05 $29,773

Bayou Heron 2 & 3 Subtidal 2402 3.5 0.11 $33,349

Bayou Chico Urban Bayou Chico 1 Intertidal 136 48.8 10.36 $34,996

Bayou Chico 2 & 3 Subtidal 654 58.4 11.61 $32,235

Bayou Pattassa Urban Bayou Pattassa 1 Intertidal 53 55.0 6.99 $22,107

Bayou Pattassa 2 Subtidal 68 52.0 6.39 $31,685
18 

greater than 90% total developed 

land cover (a percentage greater 

than SE urban watersheds 

previously examined). Subtidal 

impervious cover was generally 

higher than the intertidal 

impervious cover in urban 

watersheds in the GoM, opposite 

the overall pattern seen in the SE. 

Classifying watersheds based on 

impervious cover provides a 

useful framework for analyzing 

and interpreting study results. 

Similarly, impervious cover is a 

valuable indicator of the general 

level of development of 

watersheds that describes more 

complex conditions and 

attributes than other metrics such 

as the level of development or 

population density. For GoM 

intertidal and subtidal creek 

watersheds, human population 

density (individuals ha-1) is 

linearly related to the impervious 

cover (%) which explains 90% 

and 82%, respectively, of the 

total variability (Figure 3-3). 

The slopes of the regression lines describing the relationship between total population density 

and impervious cover in the GoM are almost identical to those for the SE, indicating this is a 

very consistent predictor of human population density in coastal watersheds. 

3.2 Exposures 

3.2.1 Basic Water Quality 

The basic water quality metrics that were sampled included 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 thus this measure may indicate the presence of pollutants. 

pH also changes in response to photosynthesis and respiration by algae and to salinity through 

tidal fluctuations and rainfall. Salinity levels influence the distribution and diversity of many 

invertebrates and fish species and can be stressful to many estuarine 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). 

Figure 3-1. Area of intertidal (upper) and subtidal (lower) 

watersheds examined within this study. The subtidal 

watersheds include the associated intertidal areas. Land use 

class is marked by color.

0

200

400

600

800

1000

Intertidal Watershed Area (ha)

0

500

1000

1500

2000

2500

3000

3500

Weeks Bay

Creek

Bayou Heron

Bear Creek

Bayou Pattassa

Bayou Chico

Forest

Suburban

Urban

Subtidal Watershed Area (ha)

Southeast Average
19 

Among GoM creeks during the 

study period, average water 

temperature values varied from 

28.7 o

C (Weeks Bay, intertidal, 

forested) to 32.4 o

C (Bayou 

Heron, intertidal, forested), and 

average pH values varied from 

7.08 (Bayou Heron, intertidal, 

forested) to 8.11 (Bayou Chico, 

intertidal, urban) (No pH data 

were available for AL creeks). 

Average salinity values varied 

from 6.13 ppt (Weeks Bay, 

intertidal, forested) to 24.0 ppt 

(Bayou Chico, subtidal, urban), 

and average DO values ranged 

from 3.1 mg L-1 (Bayou Pattassa, 

subtidal, urban) to 10.5 mg L-1

(Weeks Bay, subtidal, forested). 

Temperature ranges (maximum 

minus minimum) varied from 2.3 

o

C (Bear Creek, intertidal, 

suburban) to 8.4° C (Bayou 

Pattassa, intertidal, urban), and 

pH ranges varied from 0.49 

(Bayou Heron, subtidal, forested) 

to 1.03 (Bayou Chico, intertidal, 

urban). Salinity ranges varied 

from 3.74 ppt (Bayou Heron, 

subtidal, forested) to 22.0 ppt 

(Bayou Chico, intertidal, urban), 

and DO ranges varied from 3.02 

mg L-1 (Bayou Pattassa, subtidal, 

urban) to 9.36 mg L-1 (Weeks 

Bay, intertidal, forested). 

Average water quality values 

were not different in the GoM 

compared to the SE except for the 

average salinity values which 

were lower in the GoM 

(Appendix C). Average DO, pH 

and salinity values were 

significantly lower in the 

intertidal areas compared to the 

subtidal areas. Average DO 

Figure 3-2. Proportional land cover categories (from NLCD 

2001) within intertidal (upper) and subtidal (lower) 

watersheds examined within this study.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Intertidal Land Cover (%)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Weeks Bay Creek

Bayou Heron

Bear Creek

Bayou Pattassa

Bayou Chico

Forested

Suburban

Urban

Subtidal Land Cover (%)

High Development Low Development Agricultural Forest

Scrub Palustrine Salt Marsh Open Water

Southeast Average

Figure 3-3. Relationship between population density and 

impervious cover within watersheds. Model R2 is shown for 

each regression with asterisk (*) indicating significance (p < 

0.05). Open markers represent GoM sites.

0

4

8

12

16

0 10 20 30 40 50 60 70 80

Population Density (ind hec-1)

Impervious Cover (%)

Intertidal

Subtidal

R2 = 0.82*

R2 = 0.90*
20 

levels were the only water quality parameter which showed a land use class effect with the 

forested creeks having significantly higher DO levels than the urban creeks (Figure 3-4). 

Only the DO and pH ranges (max-min) were significantly different between regions with ranges 

being higher in GoM creeks than in SE creeks (Appendix C). Salinity range and pH range varied 

significantly with land use. The salinity ranges in GoM urban and suburban creeks were 

significantly larger than in forested creeks (Figure 3-5). Unlike salinity range, pH range was 

significantly greater in the suburban 

creeks than in urban and forested creeks. 

In addition, ranges of water quality 

metrics varied along the longitudinal 

spatial gradient, with intertidal creeks 

having significantly larger ranges for 

salinity, temperature, DO, and pH 

compared to the subtidal creeks. 

Intertidal and subtidal salinity ranges 

showed a significant positive 

relationship with the amount of 

impervious cover in the watersheds 

(Figure 3-6, Appendix D). This pattern 

in the intertidal areas is similar to that 

found by previous research in SC that 

attributed the pattern to flashier runoff 

(i.e., larger volumes and faster rates) 

from developed watersheds due to 

increased impervious cover (Lerberg et 

al. 2000, Holland et al. 2004). The 

Figure 3-5. Range of basic continuous water quality 

levels by land use, region, and longitudinal gradient. 

Bars represent average concentrations. Error bars are 

+/- 1 standard error.

0

1

2

3

4

5

6

7

8

9

10

DO Ramge (mg L-1)

Forested

Suburban

Urban

0

5

10

15

20

25

Salinity Range (ppt)

Intertidal

Gulf of Mexico Southeast Gulf of Mexico Southeast

Subtidal

Figure 3-4. Average basic continuous water quality levels by land use, region, and longitudinal 

gradient. Bars represent average concentrations. Error bars are +/- 1 standard error.

0

2

4

6

8

10

12

DO Average (mg L-1)

Forested

Suburban

Urban

0

5

10

15

20

25

30

35

Salinity Average (ppt)

24

26

28

30

32

34

Temperature Average (C)

Intertidal

Gulf of Mexico Southeast Gulf of Mexico Southeast

Subtidal

0

1

2

3

4

5

Depth Average (m)

Gulf of Mexico Southeast Gulf of Mexico Southeast

Intertidal Subtidal
21 

strength of the pattern in the subtidal areas (a pattern not observed in SE tidal creeks) may be due 

to the lower tidal range and presumably reduced oceanic exchange in creeks of the northern 

GoM. In addition, intertidal average DO % saturation (but not mg L-1) showed a significant 

relationship decreasing with increasing amounts of impervious cover in the watersheds. This 

pattern has not been observed previously in the SE. The other basic water quality metrics had no 

statistically significant relationships with impervious cover. 

 3.2.2 Nutrients and Phytoplankton 

Sampled nutrient and phytoplankton metrics included 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). Enriched nutrient levels can be related to anthropogenic influences and 

may cause 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 algal blooms and enhanced organic 

matter deposition. This may result in increased respiration and low and fluctuating DO 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 standing stocks from eutrophication. 

Nutrient and phytoplankton measures generally were not significantly different between the 

GoM and SE regions with the only exception being significantly higher TSS in the SE 

(Appendix C). Similarly, there were no significant interactions between region and land use 

class (forested, suburban, urban) or station type (intertidal, subtidal). The lack of interactions 

indicates that the model developed in the SE is directly applicable to the GoM in terms of 

nutrients and phytoplankton, and thus the two regions can be pooled and analyzed 

simultaneously for other effects (e.g., land use class, order). 

All nutrient and phytoplankton measures were significantly higher in intertidal compared to 

subtidal creeks (with the exception of Chl-a, which was marginally significant – p<0.10). These 

Figure 3-6. Relationship between basic continuous water quality and impervious cover for the 

study watersheds. Model R2

 is shown for each regression with asterisk (*) indicating significance 

(p<0.05). Open markers represent GoM sites. 

0

3

6

9

12

0 10 20 30 40 50 60 70 80

DO Average (mg L-1)

Impervious Cover (%)

Intertidal Subtidal

R2 = 0.09

R2 = 0.11

0

10

20

30

40

0 10 20 30 40 50 60 70 80

Salinity Range (ppt)

Impervious Cover (%)

R2 = 0.36*

R2 = 0.34*
22 

measures were also generally higher in 

watersheds classified as urban with various 

measures of phosphorous (TP, TDP, and 

PO4

3-) and inorganic nitrogen (NO2/3 and 

NH4) being significantly different across 

land classes (Figure 3-7, Appendix C). 

Although not significant, the phytoplankton 

pigments (Chl-a and Phaeo) were the only 

measures that were lower in urban creeks 

than in forested or suburban creeks. The 

results for phosphorus may reflect a 

combination of fertilizer use and stormwater 

runoff in developed watersheds. They may 

also reflect human land disturbance 

(including historical mining activities) 

interacting with natural phosphate deposits. 

In the GoM, land use class differences were 

primarily driven by elevated phosphorus and 

NO2/3 levels in the intertidal and subtidal 

sections of both urban systems sampled, 

Bayou Chico and, to a lesser extent, Bayou 

Pattassa. 

Similar but generally weaker relationships 

emerged when analyzing nutrients and 

phytoplankton measures as a function of 

impervious cover (Appendix D). NO2/3

increased significantly in both intertidal and 

subtidal areas; measures of NH4 in intertidal 

areas and phosphorous in both intertidal and 

subtidal areas increased marginally (p<0.10) 

with increasing impervious cover (Figure 3-

8). 

Overall, the incorporation of the GoM data 

into the larger SE dataset resulted in the 

nutrient and phytoplankton measures having 

stronger (higher R2 and lower p-values) 

relationships among land classes and creek 

station types. This suggests that data across 

a larger geographic scale and a broader 

range of conditions supported our conceptual 

model and increased the predictive power 

accordingly. Figure 3-7. Nutrients and phytoplankton levels by 

land use, region, and longitudinal gradient. Bars 

represent average concentrations. Error bars are +/- 

1 standard error.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

NH4 (mg L-1)

Forested

Suburban

Urban

0

10

20

30

40

50

60

Chl-a(ug L-1)

Intertidal

Gulf of Mexico Southeast Gulf of Mexico Southeast

Subtidal

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

NO 2/3 (mg L-1)

0.0

0.3

0.6

0.9

1.2

1.5

1.8

TN (mg L-1)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

PO4 (mg L-1)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

TP (mg L-1)
23 

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. The only TDN concentrations (n=5) classified 

as high were for intertidal creeks draining suburban and urban watersheds in the SE. All other 

sites had TDN concentrations classified as medium. Twenty sites had TDP concentrations 

classified as high with the majority of the sites being located in the SE in intertidal areas of 

suburban and urban watersheds. All other sites had TDP concentrations classified as medium. 

Chl-a concentrations in two intertidal sites from suburban and urban watersheds in the SE were 

classified as hypereutrophic. In an additional thirteen sites, Chl-a concentrations were classified 

as medium, primarily from intertidal areas. Only five sites were classified as low based on Chl-a

concentrations. 

3.2.3 Pathogens 

Fecal coliform (FC) and Enterococcus (ENT) bacteria 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. F+ and Fcoliphages are viruses that infect Escherichia coli; these are being investigated to determine if 

Figure 3-8. Relationship between nutrient concentrations and impervious cover for the study 

watersheds. Model R2

 is shown for each regression with asterisk (*) indicating significance (p<0.05). 

Open markers represent GoM sites. The regressions for NO2/3, and TP were performed on log 

transformed data but are shown here as quadratic on non-transformed data scale.

0.0

0.2

0.4

0.6

NH4 (mg L-1)

Intertidal Subtidal

R2 = 0.17*

R2 = 0.09

0.00

0.05

0.10

0.15

0.20

0 10 20 30 40 50 60 70 80

NO2/3 (mg L-1)

Impervious Cover (%)

R2 = 0.39*

R2 = 0.51*

0.0

0.2

0.4

0.6

0.8

PO4 (mg L-1)

R2 = 0.15

R2 = 0.18

0.0

0.2

0.4

0.6

0.8

0 10 20 30 40 50 60 70 80

TP (mg L-1)

Impervious Cover (%)

R2 = 0.11

R2 = 0.14
24 

they are a more appropriate indicator for water-borne pathogens (IAWPRC 1991, Havelaar et al. 

1993). 

Fecal coliform concentrations ranged from <1 to 91,000 colony forming units (CFU) 100 ml-1 in 

the SE and 2 to 89,550 CFU 100 ml-1 (estimated based on oyster tissue concentrations) in the 

GoM. Enterococci concentrations ranged from 3 to 21,000 CFU 100 ml-1 in the SE and 0 to 

>600 CFU 100 ml-1 in the GoM. Levels of indicator viruses tended to be lower than those of the 

bacteria. F+ coliphages ranged from 0 to 453 plaque forming units (PFU) 100 ml-1 in the SE and 

0 to 14 PFU 100 ml-1 in the GoM. F- coliphages ranged from <1 to 2,615 PFU 100 ml-1 in the 

SE and 0 to 253 PFU 100 ml-1 in the GoM. 

The land use class of the watershed 

surrounding the tidal creeks, the spatial 

longitudinal gradient sampled, and the 

geographic region were found to affect 

both bacterial (FC and ENT) and viral 

pathogen (F+ and F-) indicator densities 

(Appendix C, Figure 3-9). Levels of 

ENT and F- were found to be 

significantly higher in the SE compared 

to the GoM; however, FC and F+ were 

similar between the two geographic 

regions. Levels of FC, ENT, and F+ 

showed a similar pattern of increasing 

values from forested to suburban to 

urban watershed classes in both regions. 

For FC and F+, the urban watershed 

classes were significantly higher than 

the forested and suburban classes which 

were similar. For ENT, the urban 

watershed class was significantly higher 

than the forested class but both were 

similar to the suburban class. FC, ENT, 

and F- concentrations were significantly 

higher in the intertidal compared to the 

subtidal areas. The F+ coliphage 

concentration showed a similar trend 

but this trend was not statistically 

significant (Appendix C). 

Concentrations of pathogen indicators 

significantly increased with increasing 

levels of impervious cover in the 

watersheds (except ENT in subtidal 

areas) (Appendix D). The relationships 

were stronger in the intertidal systems Figure 3-9. Pathogen indicator levels by land use, region, 

and longitudinal gradient. Bars represent average 

concentrations. Error bars are +/- 1 standard error.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

ENT (log CFU 100 ml-1)

Forested

Suburban

Urban

0.0

0.5

1.0

1.5

2.0

2.5

3.0

F- (log PFU 100 ml-1)

Intertidal

Gulf of Mexico Southeast Gulf of Mexico Southeast

Subtidal

0 0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

FC (log CFU 100 ml-1) 0.0

0.3

0.6

0.9

1.2

1.5

1.8

F+ (log PFU 100 ml-1)

00 00
25 

compared to the subtidal systems (Figure 3-10). Enterovirus and norovirus were measured in the 

GoM only, but no detectable concentrations were found in any of the creeks sampled. 

3.2.4 Sediment Quality 

Tidal creek sediments are often characterized as pluff mud which is a soft sediment rich in 

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

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

waters. 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 levels. Ammonious nitrogen (TAN) is also an important indicator of 

sediment quality, and some components of TAN may be toxic to benthic organisms. 

3.2.4.1 Sediment Composition 

The coarse grain composition (% sand) and the clay content (% clay) were not significantly 

related to region, surrounding land use or station type (Appendix C, Figure 3-11). The silt 

content of the sediment was significantly affected by the region, surrounding land use, and 

station type with significant interaction terms for region by land use class and region by station 

type. This indicates that the patterns were more complex and varied depending on the two 

regions for this sediment component. Sediment characteristics were not associated with the 

levels of impervious cover in the surrounding watershed for the intertidal or subtidal habitats 

(Appendix D). 

Figure 3-10. 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). Log 

transformation is x + 1 for ENT, F+ and F-. Open markers represent GoM sites.

0.0

2.0

4.0

6.0

ENT (log CFU 100 ml-1)

Intertidal Subtidal

R2 = 0.33*

R2 = 0.03

0.0

1.0

2.0

3.0

F+ (log PFU 100 ml-1)

R2 = 0.40*

R2 = 0.26*

0.0

1.0

2.0

3.0

4.0

0 10 20 30 40 50 60 70 80

F- (log PFU 100 ml-1)

Impervious Cover (%)

R2 = 0.27*

R2 = 0.31*

0.0

2.0

4.0

6.0

0 10 20 30 40 50 60 70 80

FC (log CFU 100 ml-1)

Impervious Cover (%)

R2 = 0.24*

R2 = 0.64*
26 

Porewater TAN was not significantly 

related to region, land use class or 

station type (Appendix C). 

Porewater TAN was not significantly 

associated with the impervious cover 

level of the surrounding watersheds 

for either the intertidal or subtidal 

habitats (Appendix D). 

3.2.4.2 Sediment 

Contamination 

Polycyclic aromatic hydrocarbons 

(PAHs), polychlorinated biphenyls 

(PCBs), pesticides, and 

polybrominated diphenyl ethers 

(PBDEs) were measured during this 

study. PAHs are a major component 

of lubricating oils and fossil fuels 

which are released into the 

environment when these products are 

spilled or combusted. Potential 

sources include runoff from 

highways and parking lots, street 

dust, fuel spills, marinas and 

recreational boating activities, and 

atmospheric fallout (reviewed by 

Weinstein 1996). In addition, forest 

fires provide a natural source of 

many PAH compounds. The 

production of PCBs was banned in 

the 1970s, but these compounds have 

been reported to accumulate and 

persist 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 also banned in the 1970s). 

Current-use pesticides were not 

measured because they are generally more water soluble and do not accumulate as readily in 

sediments. The PBDEs are contaminants of emerging concern that are used as flame retardants 

in furniture, plastics, and clothing. Although trace metals are naturally occurring elements with 

concentrations influenced by natural weathering of basement rock (Williams et al. 1994), several 

are also anthropogenically enhanced from industrial and urban uses (e.g., lead, chromium, 

Figure 3-11. Sediment characteristic levels by land use, 

region, and longitudinal gradient. Mud is the sum of 

clay and silt which were not distinguished for every 

sample. Bars represent average concentrations. Error 

bars are +/- 1 standard error.

0

20

40

60

80

100

Clay (%)

Forested

Suburban

Urban

0.0

2.0

4.0

6.0

8.0

TOC (%)

Intertidal

Gulf of Mexico Southeast Gulf of Mexico Southeast

Subtidal

0

20

40

60

80

100

Sand (%)

0

20

40

60

80

100

Mud (%)
27 

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. 

The intertidal portions of the most urbanized creeks in the SE including New Market, Shem, and 

Burnett creeks had a number of exceedences of the ERL for PAHs, PCBs, and pesticides. 

Similarly, Bayou Pattassa, one of the two urban creeks in the GoM, had ERL exceedences for 4 

individual PAHs and total PAH, total DDT, and cis-Chlordane (Table 3-2). Weeks Bay Creek 

also exceeded the ERL for total DDT and 4,4-DDE. Elevated concentrations of DDT have also 

been observed in other forested and developed creeks (Sanger et al. 1999a, Sanger et al. 2008). 

No metal concentrations exceeded the effects range low (ERL) or effects range median (ERM) in 

the intertidal habitats of any of the GoM creeks sampled (Table 3-2). In contrast, metal 

contamination in the SE intertidal creeks was not uncommon. In particular, arsenic 

concentrations, a naturally elevated metal throughout the SE, were found to commonly exceed 

the ERL. None of the sediment contaminant levels exceeded the ERM level for any parameter. 

The subtidal areas in the GoM creeks had more chemical contaminant exceedences than the 

intertidal areas which is in contrast to the spatial pattern observed in the SE where exceedences 

were more common in the intertidal areas. This occurred in the GoM for select metals and total 

DDT (Table 3-2). The subtidal habitat in Bayou Pattassa, one of the two urban creeks in the 

GoM, had ERL exceedences for 7 individual PAHs, total PAH, 4,4-DDE, and total DDT 

concentrations. The ERM was exceeded for Dibenz(a,h)anthracene and cis-Chlordane. Total 

PCBs, lead, and zinc concentrations were also found to exceed the ERL in the subtidal area of 

Bayou Pattassa. Bayou Chico, the other urban GoM creek, exceeded the ERL for total DDT and 

zinc in the subtidal area. The subtidal portion of Weeks Bay Creek, an AL NERR forested site, 

exceeded the ERL for total DDT and 4,4-DDE (Table 3-2). 

Table 3-2. 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. Organics are 

ng g-1 dry weight and metals are μg g-1 dry weight. Italicized numbers indicate concentrations that 

exceed the ERL (as defined by Long et al. 1995) for that parameter.

* paired site data not available - average of the reach is provided 

** paired site data not available – average of the station type is provided

Organics

Land Use Creek

Clay 

(%)

Mud 

(%)

TOC 

(%)

Total 

PAHs

Total 

PCBs

Total 

DDTs As Cr Cu Hg Pb Zn

Intertidal

Forested Weeks Bay Creek 57.34 92.61 6.87* 216 0 2.69 13.5 46.0 11.8 0.093 28.2 106

Bayou Heron 10.75 34.48 2.1* 30 0 0 1.7 16.8 1.8 0.016 10.3 11

Suburban Bear Creek 3.87 7.13 0.51* 7 0 0 2.1 14.3 1.3 0.007 4.3 6

Urban Bayou Chico 2.39 3.98 0.36* 41 0 0 0.5 6.7 1.6 0.005 5.2 28

Bayou Pattassa 3.63 8.19 3.27 5072 11.8 6.34 1.6 11.2 7.0 0.026 27.2 88

Subtidal

Forested Weeks Bay Creek 156 0 1.93 13.9 62.4 12.2 0.090 27.2 109

Bayou Heron 29.90 85.04 1.96** 161 0.15 0 5.6 33.0 9.5 0.049 22.2 64

Suburban Bear Creek 20.68 40.99 1.03** 68 0 0 6.8 29.4 6.1 0.031 10.1 41

Urban Bayou Chico 30.20 63.86 2.38** 849 10.4 1.73 7.6 41.9 25.9 0.082 46.0 225

Bayou Pattassa 53.49 5.41 7245 66.2 16.21 5.8 28.9 26.9 0.121 69.2 302

ERL 4022 22.7 1.58 8.2 81 34 0.15 46.7 150

ERM 44792 180 46.1 70 370 270 0.71 218 410

Sediment Trace Metals
28 

The total mean effects range median 

quotient (mERMQ) values ranged from 

0.005 to 0.257 in GoM creeks and 

0.0036 to 0.375 and SE creeks. When 

mERMQ values were analyzed in a 3-

way ANOVA, region and station type 

were not significant. The total mERMQ 

levels were similar in the forested and 

suburban land use classes and both were 

significantly lower than total mERMQ 

levels in the urban land class (Appendix 

C, Figure 3-12). The region by station 

type interaction was significant due to a 

significantly higher total mERMQ levels 

in the intertidal compared to the subtidal 

habitats in the SE versus higher total 

mERMQ levels (although not 

significantly different) in the subtidal 

compared to the intertidal habitats in the 

GoM. The metals mERMQ values were 

similar across region, land use class, and 

station type. The PAH mERMQ and 

PCB mERMQ values were significantly 

higher in the urban land use class 

compared to the other classes (Appendix 

C, Figure 3-12). The pesticide mERMQ 

levels were significantly higher in the 

urban class compared to the forested 

class, and both were similar to the 

suburban class. 

Regression analysis demonstrated that 

mERMQs increased with increasing 

levels of impervious cover (Appendix D, 

Figure 3-13). Regressions of all 

quotients versus impervious cover were 

statistically significant in the intertidal 

creeks. Regressions of all quotients 

except metals ERMQ versus impervious 

cover were statistically significant in the 

subtidal creeks. The relationships were 

stronger in the intertidal creeks 

compared to the subtidal creeks for all of 

the quotients except PCB mERMQ. 

Figure 3-12. mERMQ levels by land use, region, and 

longitudinal gradient. Bars represent average 

concentrations. Error bars are +/- 1 standard error.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

mERMQ (ratio)

Forested

Suburban

Urban

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Pesticide mERMQ (ratio)

Intertidal

Gulf of Mexico Southeast Gulf of Mexico Southeast

Subtidal

0.00

0.05

0.10

0.15

0.20

0.25

Metal mERMQ (ratio)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

PAH mERMQ (ratio)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

PCB mERMQ (ratio)
29 

PDBEs were only detected in the more developed creeks: Bayou Chico and Bayou Pattassa 

(urban) in the GoM, and James Island and Orangegrove (suburban) and Bulls, Shem, and New 

Market (urban) in the SE (Figure 3-14). In the SE, PBDE levels above the detection limit were 

only found in the intertidal creeks; however, in the GoM detectable concentrations were found in 

both the intertidal and subtidal areas. Intertidal creeks as well as subtidal creeks in GoM 

appeared to be potentially valuable sentinels for detecting emerging contaminants of concern. 

Figure 3-13. Relationship between mERMQ (log+0.01) and impervious cover for the study 

watersheds. Model R2

 is show for each regression with asterisk (*) indicating significance (p<0.05). 

Open markers represent GoM sites.

-2.0

-1.5

-1.0

-0.5

0.0

Total mERMQ (log)

Intertidal Subtidal

R2

 = 0.26*

-2.0

-1.5

-1.0

-0.5

0.0

0 10 20 30 40 50 60 70 80

Impervious Cover (%)

Metals mERMQ (log)

R2 = 0.19*

R2

 = 0.11

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

PAH mERMQ (log)

R2 = 0.47*

R2

 = 0.53*

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0 10 20 30 40 50 60 70 80

Impervious Cover (%)

PCB mERMQ (log)

R2 = 0.21*

R2

 = 0.23*

R2

 = 0.39*

Figure 3-14. Concentrations of polybrominated diphenyl ethers (PBDEs) in creek sediments. Only 

the sites with detectable levels are shown. I indicates an intertidal area and S indicates a subtidal area. 

Land use class is marked by color. Bar represents measured concentrations.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

PBDE (ng g-1 dry wt.)

Suburban Urban

Gulf of Mexico Southeast 
30 

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. In the GoM, a total of 2,530 organisms 

representing 40 taxa were collected in 66 samples. In the SE, a total of 18,296 organisms 

representing 279 taxa were collected in 303 samples. Sampling methods varied between 

intertidal and subtidal sections of the creeks system, as did volume of sediment collected. 

Because of these differences, abundances were converted to density (expressed as ind. m-2) prior 

to analysis. 

In the GoM, intertidal creek macrobenthic communities had higher densities (668,222 ind. m-2

and 27 taxa) than subtidal creeks (37,957 ind. m-2 and 29 taxa) but similar number of taxa. 

Within individual GoM creeks, the largest numbers of macroinvertebrate taxa were collected 

from Bear Creek and Bayou Heron (16-20 taxa depending on creek and station type). Eight or 

fewer taxa were collected from all other GoM creek station type. The amphipod Corophium 

simile and tanaid Paratanais sp. numerically dominated the intertidal macrobenthic invertebrate 

community with most of those being found in Bear Creek. Other numerically abundant species 

found in the intertidal GoM creeks included the polychaete Ceratocephale oculata, various 

tubificid worms and Grandidierella amphipods. Among subtidal creeks, C. oculata was the 

numerically most abundant taxon in subtidal collections, primarily from Bayou Pattassa. 

Nemerteans were the next most abundant taxon, and these were most abundant in the forested 

creeks, Weeks Bay and Bayou Heron. 

Average macrobenthic invertebrate 

abundances and number of species 

per sample were not significantly 

different between the GoM and SE in 

either intertidal or subtidal creeks 

(Appendix C, Figure 3-15). Both 

measures varied significantly with 

land use class in intertidal creeks but 

not in subtidal creeks (Appendix C). 

Overall, densities and numbers of 

species were significantly higher in 

GoM suburban intertidal creeks than 

in forested or urban creeks. This was 

due to particularly high infaunal 

densities in Bear Creek, the only 

suburban system sampled in the 

GoM (Figure 3-15). Macrobenthic 

invertebrate densities were not 

significantly different between 

forested and urban creeks in this 

region. The significant difference in 

number of species among intertidal 

Figure 3-15. Macrobenthic community number of 

species and total abundance by land use, region, and 

longitudinal gradient. Bars represent average 

concentrations. Error bars are +/- 1 standard error.

0

10

20

30

40

50

Benthic Num Spp

Forested

Suburban

Urban

0

5000

10000

15000

20000

25000

30000

Benthic Density (ind. m -2)

Intertidal

 Gulf of Mexico Southeast Gulf of Mexico Southeast

Subtidal
31 

creeks representing different land use classes was due to a significant difference between 

suburban and urban intertidal creeks; all other comparisons were not significant (Appendix C). 

Macrobenthic invertebrate abundance was not significantly related to impervious cover in either 

intertidal or subtidal creeks (Figure 3-16). The relationship between number of species and 

impervious cover was negative and marginally significant (p = 0.067) in intertidal creeks and 

negative and not significant in subtidal creeks (Figure 3-16), except for a single large 

macrobenthic invertebrate density in Bear Creek, AL. 

When examined using multivariate analyses (i.e., MDS), the composition of the macrobenthic 

communities, the GoM communities were significantly different from those of the SE in both 

intertidal (ANOSIM: R = 1.000, p < 0.001) and subtidal creeks (ANOSIM: R = 0.462, p = 

0.011). Most of these differences were due to the identities of the dominant fauna differing 

between the two biogeographic regions. Intertidal communities within each region clearly 

clustered together, and the communities of the two regions did not overlap (Figure 3-17A). 

Eight of the ten taxa most responsible for the dissimilarity between intertidal creeks of the GoM 

and SE were present in only one region (Table 3-3). The subtidal macrobenthic community of 

the SE had far more taxa than the intertidal communities. In addition, the subtidal communities 

in the two regions shared a larger proportion of taxa compared to the intertidal communities. As 

a result, the clusters representing subtidal communities from the GoM and SE regions did not 

diverge as strongly as they did for intertidal habitats (Figure 3-17B). Five of the ten taxa most 

responsible for the dissimilarity between the subtidal creeks of the two regions were, however, 

present in only one region (Table 3-3). Of the taxa most responsible for the differences between 

the two regions in both intertidal and subtidal creeks, most were either polychaetes or 

oligochaetes. Exceptions included the amphipod Corophium simile, the gastropod Ilyannasa 

obsoleta and Nemerteans. 

Figure 3-16. Relationship between macrobenthic number of species and density versus impervious 

cover for the study watersheds. Model R2

 is show for each regression with asterisk (*) indicating 

significance (p<0.05). Open markers represent GoM sites.

R2 = 0.16

R2 = 0.05

0

10

20

30

40

50

60

70

80

0 10 20 30 40 50 60 70 80

Impervious Cover (%)

Benthic Num. Sp.

Intertidal Subtidal

R2 = 0.00

R2 = 0.08

0

5000

10000

15000

20000

25000

30000

0 10 20 30 40 50 60 70 80

Impervious Cover (%)

Benthic Den. (ind m -2)
32 

Due to the dissimilarity of the benthic communities between the SE and GoM, the relationships 

with land use class were analyzed for each region separately. In SE creeks, benthic community 

composition varied significantly with land use class for intertidal creeks (ANOSIM: R = 0.229, p 

= 0.012), but no relationship with land use class was found for subtidal creeks (ANOSIM: R = 

0.098, p = 0.208). Intertidal macrobenthic community composition in suburban creeks was 

significantly different than in forested creeks (R = 0.349, p = 0.008) and forested creeks were 

marginally significantly different than urban creeks (R = 0.212, p = 0.071). The macrobenthic 

community inhabiting suburban and urban creeks were not different (R = -0.060, p = 0.634). In 

Figure 3-17. Multidimensional scaling (MDS) ordination of benthic communities with SE and 

GoM regions combined (A and B), just the SE region (C and D) and just the GoM region (E and 

F). Boxes = SE region creeks, circles = GoM region creeks. Green = forested creeks, yellow = 

suburban creeks and red = urban creeks.

Intertidal Subtidal

SE and GOM SE GOM

A B

C D

E F

2D Stress: 0

2D Stress: 0

2D Stress: 0

2D Stress: 0.12

2D Stress: 0.21

2D Stress: 0.15
33 

intertidal creeks, the species responsible for the differences among land use classes were 

primarily annelids. An unidentified gastropod and the nemerteans were notable exceptions 

(Table 3-4). In subtidal creeks, annelids were also most important in detecting differences 

among land use classes, but non-annelid taxa played a more important role here and included the 

amphipods Ampelisca abdita and C. simile, the gastropod I. obsolete, and the bivalve Tellina 

agilis (Table 3-4). 

Due to a small sample size in the GoM, differences among land use classes were not analyzed 

using ANOSIM. Although not statistically analyzed, macrobenthic communities of urban creeks 

clearly separated from those of suburban and forested creeks. In GoM intertidal creeks as in the 

SE intertidal creeks, annelids were most responsible for differentiating among land use classes, 

but two amphipods C. simile and Grandidierella sp., an unidentified pelecypod bivalve, and the 

tanaid Hargeria rapax were also important (Table 3-5). In subtidal creeks, annelids also 

dominated the taxa most responsible for the differences among land use types with three 

amphipods A. abdita, C. simile, and Ericthonius brasiliensis, an unidentified gastropod, an 

unidentified pelecypod bivalve and nemerteans also being important (Table 3-5). 

3.3.2 Nekton Community 

Nekton, primarily larger 

crustaceans and fish, were only 

collected from four intertidal 

creeks in the GoM with two 

representing forested and two 

representing urban land use (Bear 

Creek was not sampled). Of the 

intertidal creeks examined, the 

forested Bayou Heron hosted the 

highest average densities (9,170 

ind. ha-1) and average numbers (8 

taxa) of nekton taxa, followed by 

the urban Bayou Chico (5,288 

individuals ha-1, 5.7 taxa) and the 

forested Weeks Bay Creek (3,593 

ind. ha-1, 4.0 taxa). The urban 

creek Bayou Pattassa hosted the 

lowest densities (1,970 ind. ha-1) 

and number (3.3 taxa) of nekton 

taxa. The most abundant nekton 

taxon collected in the GoM was 

the unidentified menhaden 

Brevoortia sp. (average density 

1,212 ind. ha-1), but it was only 

caught in Bayou Heron at a density of 4,847 ind. ha-1. This was followed by unidentified 

juvenile shrimp in the genus Penaeus (average density 1,032 ind. ha-1) which was found in all 

four creeks, the molly Poecilia latipinna (725 . ha-1) found primarily in urban creeks, and the 

unidentified anchovy Anchoa sp. (477 ind. ha-1) found only in the forested creeks. 

Table 3-4. Results of SIMPER analysis of land use differences 

in macrobenthic community structure in southeast creeks.

Taxon For Sub Urb For vs. Sub For vs. Urb Sub vs. Urb

Intertidal

 Tubificoides brownae 4.69 1.35 3.61 8.15 6.2 9.62

 Tubificoides heterochaetus 1.57 3.66 4.37 7.82 9.12 9.25

 Laeonereis culveri 3.67 5.34 6.7 7.06 --

 Heteromastus filiformis 3.29 1.38 2.18 6.31 5.94 5.67

 Monopylephorus 5.89 8.16 7.16 6.01 6.05 6.14

 Fabricia sp. 0 2.8 0.55 5.67 -- 7.58

 Streblospio benedicti 4.77 2.96 3.48 5.29 6.6 8.53

 Nereis succinea 3.34 2.42 3.3 5.25 -- 6.61

 Spionidae 2.83 1.58 5.14 -- --

 Gastropoda 2.08 0 0.78 4.48 -- 4.77

 Nemertea 3.84 2.67 0.62 -- 6.96 7.01

 Sabellidae 0.93 2.03 -- -- 6.28

 Capitomastus aciculatus 2.11 1.12 -- 4.8 --

Subtidal

 Polydora cornuta 2.46 1.68 2.21 2.27 2.04 --

 Mediomastus sp. 5.29 2.98 2.71 2.27 2.95 4.01

 Nereis succinea 4.05 2.49 3.19 2.22 1.82 2.93

 Ampelisca abdita 2.69 1.28 2.11 -- --

 Tubificoides brownae 4.38 3.55 2.21 2.04 2.7 4.27

 Tharyx acutus 1.92 0 0 2.02 1.8 --

 Scoletoma tenuis 2.75 2.2 2.68 1.98 2.05 2.88

 Streblospio benedicti 6.16 4.94 1.97 -- --

 Scoloplos rubra 2.56 1.43 1.91 1.94 1.9 --

 Corophium simile 2.51 0 1.94 -- --

 Tubificoides wasselli 2.47 2.28 1.4 -- 2.01 3.56

 Ilyanassa obsoleta 2.23 1.68 1.5 -- 2.11 3.54

 Tubificoides heterochaetus 0.31 1.76 -- -- 3.33

 Tellina agilis 0.23 2.75 -- -- 3.23

 Spionidae 1.87 2.57 -- -- 2.99

 Heteromastus filiformis 3.08 2.31 1.24 -- 2.15 2.88

Abundance in % Contribution to Difference
34 

Using a main effects two-way 

ANOVA, average nekton 

densities and numbers of taxa 

were significantly different in the 

GoM compared to the SE with the 

GoM region having lower values 

for both measures (Figure 3-18, 

Appendix C). Neither measure 

varied significantly with land use 

class (Figure 3-18). Because 

nekton data were unavailable for 

suburban creeks in the GoM, a 

full interaction ANOVA was not 

possible; however, a second 

analysis was performed in which 

SE suburban creeks were 

excluded and a full two-way 

ANOVA was performed with 

only forested and urban creeks. 

This analysis confirmed the 

results of the main effects model 

tested above. SE creeks 

supported higher densities and 

number of taxa than did GoM 

creeks, and neither measure 

varied significantly with land use 

(Appendix C). 

Nekton density was negatively although not significantly (p = 0.819) related to impervious cover 

in intertidal creeks across both the SE and GoM regions (Figure 3-19, Appendix D). Similarly, 

the relationship between number of species and impervious cover was negative and not 

significant in intertidal creeks (Figure 3-19, Appendix D). 

When examined using multivariate analyses (nMDS), the nekton communities of intertidal GoM 

creeks were significantly different from those of the southeastern US (ANOSIM: R = 1.000, p = 

0.002). Nekton communities within each region clearly clustered together, and the clusters 

representing the communities of the two regions did not overlap (Figure 3-20A). Four of the ten 

taxa most responsible for the dissimilarity between intertidal creeks of the GoM and SE were 

present in only one region (Table 3-6). The species most responsible for the difference between 

regions were primarily small fish such as mummichogs (Fundulus heteroclitus and Fundulus

Table 3-5: Results of SIMPER analysis of land use differences 

in macrobenthic community structure in GoM creeks.

Taxon For Sub Urb For vs. Sub For vs. Urb Sub vs. Urb

Intertidal

 Paratanais sp. 1.96 8.7 0 11.18 -- 12.98

 Corophium simile 3.42 9.67 0 9.48 10.48 14.43

 Hargeria rapax 0 5.41 0 8.7 -- 8.07

 Grandidierella sp. 2.7 4.58 0 8.3 5.39 11.3

 Heteromastus filiformis 0 4.6 0 7.41 -- 6.87

 Pelecypoda 1.62 5.77 0 6.96 -- 8.61

 Capitella capitata 0 4.32 3.25 6.95 7.14 --

 Amphicteis gunneri 2.92 6.82 1.62 6.76 6.48 7.93

 Ceratocephale oculata 3.71 7.4 6.18 6.59 10.55 --

 Eteone heteropoda 1.62 5.51 0 6.54 -- 8.22

 Mediomastus sp. 6.46 5 0 -- 16.44 7.47

 Mediomastus californiensis 2.8 0 -- 8.59 --

 Streblospio benedicti 6.52 3.25 -- 8.13 --

 Ceratonereis longicirrata 2.41 3.58 -- 6.77 --

 Tubificoides sp. 4.85 5.69 -- 5.77 --

Subtidal

 Eteone heteropoda 0 4.08 0 12.49 -- 11.17

 Ampelisca macrocephala 1.05 4.76 0 11.66 -- 13.05

 Amphicteis gunneri 1.9 4.76 1.37 9.31 7.41 9.42

 Corophium simile 0 2.74 0 8.4 -- 7.51

 Streblospio benedicti 4.78 7.44 5.95 8.21 -- 4.12

 Capitella capitata 2.95 5.32 0 7.01 12.54 14.57

 Cossura soyeri 2.41 0 1.05 6.71 9.17 --

 Gastropoda 1.97 2.74 0 5.49 6.99 --

 Leitoscoloplos fragilis 1.05 5.24 0 5.46 -- 7.51

 Heteromastus filiformis 4 0 2.24 5.45 12.21 8.04

 Ceratocephale oculata 1.56 2.74 3.66 -- 14.53 9.95

 Nemertea 4.87 3.07 -- 7.76 --

 Pelecypoda 1.9 0 -- 6.72 --

 Ericthonius brasiliensis 0 1.37 -- 5.36 --

 Mediomastus sp. 6.26 7.83 5.2 -- 5.04 7.16

Abundance in % Contribution to Difference
35 

 grandis), silversides (an unidentified species of Menidia), mollies (Poecilia latipinna), and 

mosquitofish (Gambusia holbrooki) as well as shrimp in the genera Palaemonetes and Penaeus 

(Litopanaeus, Farfantepanaeus). 

Due to the dissimilarity of the nekton communities between the SE and GoM, the impact of land 

use class was analyzed for each region separately. Nekton community composition did not vary 

significantly with land use class for intertidal creeks in the SE (ANOSIM: R = 0.027, p = 0.339; 

Figure 3-20B). In the SE, nekton communities did not differ significantly between forested and 

urban (R = -0.06, p =0.706) or urban and suburban (R = -0.082, p = 0.755) creeks and only 

differed marginally significantly between forested and suburban creeks (R = 0.18, p = 0.08). 

Due to the small sample size, the GoM creek nekton communities could not be analyzed alone, 

but the forested and urban creeks communities clearly separated on the MDS ordination (Figure 

3-20C). In the SE, the differences between creeks representing forested, suburban, and urban 

land use types were primarily due to differences in the abundance of various fish species (Table 

Figure 3-19. Relationship between nekton number of species and density versus impervious cover 

for the study watersheds. Model R2

 is show for each regression with asterisk (*) indicating 

significance (p<0.05). Open markers represent GoM sites.

R2 = 0.06

0

50000

100000

150000

200000

250000

300000

350000

0 10 20 30 40 50 60 70 80

Impervious Cover (%)

Nekton Den. (ind m -2)

R2 = 0.01

0

2

4

6

8

10

12

14

0 10 20 30 40 50 60 70 80

Impervious Cover (%)

Nekton Num. Spp.

Inte rtidal

Figure 3-18. Nekton community number of species and total abundance by land use and region for 

only the intertidal systems. Bars represent average concentrations. Error bars are +/- 1 standard 

error.

0

3

6

9

12

Nekton Num Spp.

Forested

Suburban

Urban

 Gulf of Mexico Southeast 

0

25000

50000

75000

100000

125000

150000

175000

200000

Nekton Density (ind. hec -2)

 Gulf of Mexico Southeast 
36 

3-7). In the GoM region, the differences between forested and urban creeks were also dominated 

by differences in fish species, but decapod crustaceans (Panaeus setiferus and Uca minax) also 

contributed (Table 3-7). 

Table 3-6. Results of SIMPER analysis for nekton 

community for the SE and GoM tidal creeks in the intertidal 

and subtidal systems.

% Contribution

Taxon Southeast Gulf of Mexico to Difference

Palaemonetes sp. 9.99 1.25 9.09

Fundulus heteroclitus 8.2 0 8.75

Panaeus sp. 8.5 2.93 6.15

Panaeus setiferus 0 4.46 4.71

Menidia sp. 0.23 3.99 4.1

Poecilia latipinna 4.93 4.3 3.86

Gambusia holbrooki 3.97 2.98 3.62

Bairdiella chrysoura 3.4 0 3.41

Lagondon rhomboids 1.73 3.58 3.22

Fundulus grandis 0 2.95 3.2

Average Abundance in

Table 3-7. Results of SIMPER analysis of land use differences in nekton community 

structure in southeast and GoM intertidal creeks.

Taxon For Sub Urb For vs. Sub For vs. Urb Sub vs. Urb

Southeast

 Poecilia latipinna 2.98 5.65 5.87 6.05 5.94 4.81

 Bairdiella chrysoura 4.7 2.01 4.07 5.36 4.79 5.87

 Gambusia holbrooki 2.45 4.36 4.95 5.23 4.89 5.07

 Gerreidae 4.04 0.54 2.44 5.17 4.72 3.85

 Leiostomus xanthurus 3.28 4.88 5.82 5.16 4.08 3.96

 Mugil cephalus 1.31 3.42 4.66 4.55 5.18 4.64

 Cynoscion nebulosus 9.58 7.46 2.15 4.17 3.59 --

 Brevoortia tyrannus 2.5 0.88 0.92 3.46 3.44 --

 Fundulus majalis 2.68 0 1.38 3.43 -- --

 Symphurus plagiusa 1.66 2.31 0.76 3.31 -- --

 Cyprinodon variegatus 0 1.4 2.75 -- -- 4.35

 Fundulus luciae 1.04 1.58 2.84 -- 3.41 4.27

 Penaeus sp. 9.58 7.46 8.87 -- -- 4.24

 Lagodon rhomboides 1.5 1.43 2.36 -- 3.4 4.11

Gulf of Mexico

 Gambusia holbrooki 0 -- 5.97 -- 7.97 --

 Penaeus setiferus 7.27 -- 1.66 -- 7.92 --

 Poecilia latipinna 1.49 -- 7.12 -- 7.87 --

 Anchoa sp. 6.13 -- 0 -- 7.74 --

 Fundulus grandis 0 -- 5.9 -- 7.69 --

 Menidia sp. 1.74 -- 6.24 -- 6.45 --

 Cyprinodon variegatus 0 -- 3.76 -- 4.94 --

 Uca minax 2.98 -- 0 -- 4.9 --

 Lagodon rhomboides 5.17 -- 2 -- 4.43 --

 Brevoortia sp. 4.24 -- 0 -- 4.33 --

Abundance in % Contribution to Difference
37 

3.4 Human Responses 

3.4.1 Oyster Tissue Pathogens 

Oysters were only collected in the three 

Mississippi creeks in the GoM in 2008: 

Bayou Heron and Bayou Chico 

(intertidal) and Bayou Pattassa (subtidal); 

and along the entire spatial gradient in 

ten tidal creeks in the SE in 2006: Sapelo 

Island NERR-Duplin, Sapelo Island 

NERR -Oakdale, Guerin, Burnett, 

Postell, New Market, James Island, ACE 

Basin NERR-Village, Hewlitts, and 

Whiskey. Tissues were examined for FC 

and ENT body burdens only. 

Pathogen concentrations for FC and ENT 

body burdens varied over 3-4 orders of 

magnitude. FC ranged from 23 to 

220,000 CFU 100 g-1 tissue weight in the 

SE and from 480 to 160,000 CFU 100 g-1

tissue weight in the GoM. ENT 

concentrations varied from 3,240 to 

320,000 CFU 100 g-1 tissue weight in the 

SE and from 4,800 to 10,000 CFU 100 g1

 tissue weight in the GoM. 

Because the overall sample size was 

small, as oysters were only collected 

from a subset of the sampled systems, 

ANOVAs were not performed on these 

data. Regression analysis showed that 

there was a significant positive 

relationship between watershed 

impervious cover and FC concentrations 

in oysters collected in intertidal and 

subtidal creeks (Figure 3-21). No 

significant relationship was found for 

ENT body burdens and impervious 

cover. 

Figure 3-20. Multidimensional scaling (MDS) 

ordination of nekton with SE and GoM regions 

combined (A), just the SE region (B) and just the GOM 

region (C). Boxes = SE region creeks, circles = GoM 

region creeks. Green = forested creeks, yellow = 

suburban creeks and red = urban creeks. SE and GOM SE GOM

A

B

C

Bayou Chico

Weeks Bay Bayou Heron

Bayou Pattassa

Bayou Chico

Weeks Bay

Bayou Heron

2D Stress: 0.14

2D Stress: 0

2D Stress: 0.18

Bayou Pattassa
38 

3.4.2 Bivalve Tissue Contaminants 

Bivalve tissues were collected from the 11 SE tidal creek systems sampled in 2006 and 3 GoM 

tidal creek systems sampled in 2008. In addition, mussel tissue was collected from 3 GoM tidal 

creek systems sampled in 2008. 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 small sample size. Oyster and mussel tissues are 

summarized as a group. 

Oyster and mussel tissue lipid concentrations ranged from 5.7% to 38% (average = 21.4%) in the 

SE and 4.7% to 11.2% in the GoM (average = 7.4%). 

Total PAH tissue concentrations ranged from 0 to 2,161 ng g-1 dry weight (average = 297) in the 

SE and 28.4 to 1,947 ng g-1 dry weight (average = 648) in GoM (Figure 3-22). In the SE, PAHs 

detected included acenaphthylene, anthracene, benzo(a)anthracene, benzo(b)fluoranthene, 

benzo(g,h,i)perylene, fluoranthene, and pyrene. All except acenaphthylene and naphthalene are 

high molecular weight PAHs typical of pyrogenic sources, and were predominantly found in 

developed systems. The GoM oyster and mussel tissues had detectable PAH concentrations for 

all but one of the analytes measured (dibenz(a,h)anthracene). Thirteen of the 24 detectable 

analytes were found in all six samples. Urban creeks in both the GoM and SE consistently had 

higher PAH concentrations particularly higher molecular weight PAHs from pyrogenic sources 

(Figure 3-22). 

Total PCB concentrations ranged from 0 to 244 ng g-1 dry weight (average = 21.2) in the SE and 

10.6 to 105.5 ng g-1 dry weight (average = 46.3) in the GoM (Figure 3-22). In the SE, PCB 

concentrations above the detection limit were found in the two urban intertidal creeks and in one 

urban and one suburban subtidal creek. In the GoM, all six samples collected from the forested, 

suburban and urban creeks had detectable concentrations of PCBs. The two urban GoM creek 

samples contained lower concentrations of PCBs than the samples from Burnett Creek (a 

superfund site in GA for PCB contamination) but were almost double the values observed in 

oysters from the next highest site in the SE, New Market Creek (urban, SC). 

Figure 3-21. Relationship between pathogen indicator levels in oyster tissues and impervious cover 

for the study watersheds. Impervious cover was regressed against Enterococcus (ENT; upper) and 

fecal coliform (FC; lower). Model R2 is shown for each regression with asterisk (*) indicating 

significance (p < 0.05). Log transformation is x + 1 for ENT. Open markers represent GoM sites.

R2 = 0.58*

R2 = 0.67*

0

1

2

3

4

5

6

0 10 20 30 40 50 60 70 80

Impervious Cover (%)

FC (log CFU 100 g-1)

R2 = 0.02

R2 = 0.11

2

3

4

5

6

0 10 20 30 40 50 60 70 80

Impervious Cover (%)

ENT (log CFU 100 g-1)

Intertidal Subtidal
39 

PBDEs, flame retardants, were detected in oyster tissues at New Market (SE, intertidal, urban), 

Guerin (SE, intertidal, forested), Bayou Chico (GoM, intertidal, urban), and Bayou Pattassa 

(GoM, subtidal, urban). Total PBDE concentrations ranged from 4.04 to 37.9 ng g-1 dry weight. 

PBDE 47 was detected in the three urban systems, PBDE 99 was detected in Bayou Chico and 

Guerin Creek, and PBDE 100 and 17 were only detected in Bayou Chico. The three urban 

creeks had detectable PBDE levels in the sediments; however, detectable levels were not 

observed in the forested creek sediments. 

Pesticide concentrations in 

oyster tissues were dominated 

by DDT and its derivatives. 

Total DDT concentrations 

ranged from 2.53 to 20.84 ng 

g-1 dry weight (average = 

7.31) in the SE and 3.86 to 

119.9 ng g-1 dry weight 

(average = 34.7) in the GoM. 

In general, total DDT tissue 

concentrations were higher in 

the developed systems 

compared to the forested 

systems in the SE. This trend 

was similar in MS but not in 

AL. The highest 

concentration observed was 

for the Weeks Bay subtidal 

mussels. In addition, Bear 

Creek, a low density suburban 

system, had the next highest 

concentrations in the intertidal 

and subtidal areas. Detectable 

pesticide contaminants 

included mirex, endosulfan I 

and s, dieldrin, chlorpyrifos, 

and trans-nonachlor. Dieldrin 

was detected in all six tissue 

samples in the GoM and in 

only one tissue sample in the 

SE. 

Concentration data for metals (Cd, Cr, Cu, Pb, Hg) that are often elevated as a result of 

anthropogenic uses plus arsenic (As) are discussed below. Lead concentrations in bivalve tissues 

were similar across the two regions and three land use classes and were generally low (< 0.8 μg 

g

-1 dry weight) except for three heavily urbanized creeks: New Market (SE, 1.58 μg g-1 dry 

weight), Bayou Chico (GoM, 1.84 μg g

-1 dry weight) and Bayou Pattassa (GoM, 1.64 μg g-1 dry 

Figure 3-22. Concentrations of total PAHs and total PCBs in creek 

sediments for the intertidal and subtidal systems. Land class is 

marked by color. 

0

500

1000

1500

2000

2500

Bayou Heron

Bear Creek

Bayou Chico

Duplin Creek

Guerin Creek

Oakdale Creek

Village Creek

Hewlitts Creek

James Island Creek

Whiskey Creek

Burnett Creek

New Market Creek

Postell Creek

Weeks Bay Creek

Bear Creek

Bayou Pattassa

Duplin Creek

Guerin Creek

Village Creek

Hewlitts Creek

James Island Creek

Whiskey Creek

Burnett Creek

Tissue Total PAH (ng g -1 dry wt.)

Forested

Suburban

Urban

0

50

100

150

200

250

Bayou Heron

Bear Creek

Bayou Chico

Duplin Creek

Guerin Creek

Oakdale Creek

Village Creek

Hewlitts Creek

James Island Creek

Whiskey Creek

Burnett Creek

New Market Creek

Postell Creek

Weeks Bay Creek

Bear Creek

Bayou Pattassa

Duplin Creek

Guerin Creek

Village Creek

Hewlitts Creek

James Island Creek

Whiskey Creek

Burnett Creek

Tissue Total PCB (ng g -1 dry wt.)

Intertidal

 Gulf of Mex. Southeast Gulf of Mex. Southeast

Subtidal
40 

weight). Mercury concentrations were similar across regions and land use classes and were 

generally low (< 0.19 μg g

-1 dry weight) except for Burnett (SE, 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 (ranged from 14.4 to 33.4 μg g

-1 dry weight) were found in the North 

Carolina creeks, similar to the fish tissue contamination findings of Cooksey et al. (2008). In 

general, the SC and GA creeks were similar to the As concentrations in the GoM creeks with 

concentrations <14.3 μg g

-1 dry weight. The cadmium, copper, and chromium concentrations 

were approximately double in the SE creeks compared to the GoM creeks. These values were 

also generally higher in the forested and suburban creeks compared to the urban creeks in the SE 

but this pattern was not apparent in the GoM creeks. In particular, Guerin, a forested creek in the 

Francis Marion National Forest, had some of the highest concentrations of metals. 

In addition, the bivalve tissue chemical concentrations were compared to Food and Drug 

Administration (FDA; FDA 2011) environmental chemical contaminant action levels and the 

USEPA (2000) human-health consumption limits for cancer and non-cancer endpoints (Table 3-

8). The FDA action levels are simply threshold values for comparison against tissue 

concentrations (non-consumption based). None of the concentrations observed in oyster tissue 

on a wet weight basis exceeded the molluscan bivalve or fish action 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, and that 

comparing oyster tissue to fish tissue values represents a level of potential risk. It should also be 

noted that for many of the systems studied, shellfish grounds are closed for harvest, thus 

reducing risks associated with human consumption. Inorganic arsenic (estimated as 2% of total 

arsenic [USEPA 2004]) values exceeded the lower cancer endpoint for all sites sampled for 

Table 3-8. Contaminant wet weight concentrations in tidal creek bivalves (oyster for SE and MS and 

mussel for AL). Concentrations given in ng g-1 wet weight for PAH, PCB and DDT, and in μg g-1 wet 

weight for metals (As and Hg). Inorganic Arsenic calculated as 2% of total arsenic. Methyl mercury 

calculated as 100% 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.

Creek Name

Station 

Type Hg Total As

Estimated 

Inorganic As Total DDT Total PAH Total PCB

Weeks Bay Creek Subtidal 0.012 1.24 0.02474 15.3 94.0 3.9

Bayou Heron Intertidal 0.012 0.79 0.01588 0.5 27.2 1.3

Bear Creek Intertidal 0.022 1.24 0.02473 4.1 46.2 2.1

Bear Creek Subtidal 0.008 1.03 0.02055 1.7 2.5 1.5

Bayou Chico Intertidal 0.004 0.51 0.01012 1.1 35.8 6.9

Bayou Pattassa Subtidal 0.012 0.58 0.01162 0.9 138.7 6.5

FDA 1 86 5000

EPA noncancer >0.12-0.23 >0.35-0.7 >59-120 >23-47

EPA cancer >0.0078-0.016 >35-69 >1.6-3.2 >5.9-12
41 

oyster tissue in the SE and GoM. Total PCB values exceeded the lower EPA cancer endpoint at 

two sites (Bayou Chico, intertidal, urban; Bayou Pattassa, subtidal, urban). This is in 

comparison to only one creek, Burnett Creek (Sanger et al. 2008), exceeded the upper cancer 

endpoint at both the intertidal and subtidal sites with the interidal site also exceeded the lower 

non-cancer endpoint. Burnett is an urban creek that was listed and cleaned up as a Superfund 

site for PCB and Hg contamination. Total PAH values exceeded the lower and sometimes upper 

cancer endpoint at all SE and GoM sites except for Guerin (interidal, forested) and Hewlitts 

(subtidal, suburban) both of which are in the SE (Sanger et al. 2008). No other published EPA 

values were exceeded. 
42 

4. Discussion 

Tidal creeks have been used as a sentinel habitat to provide early warning and assess the impacts 

of pollution from coastal development on estuarine areas in the southeastern US (e.g., Mallin et 

al. 2000a, Holland et al. 2004, Van Dolah et al. 2007, Sanger et al. 2008) and Tampa Bay, FL 

(Greenwood et al. 2008a, Greenwood et al. 2008b, Sherwood et al. 2008). Most of the previous 

studies have shown that the amount of impervious cover (e.g., roads, parking lots and other 

paved areas, roofs) is a reliable indicator of the degree of coastal watershed development and the 

amount of pollution released into tidal creeks. Changes in the volume and rate (or flashiness) of 

stormwater runoff associated with increases in watershed imperviousness (e.g., percent 

impervious cover) is the major mechanism through which coastal development impairs water 

quality, public health, and the quality of coastal living (Holland et al. 2004, Holland and Sanger 

2008). Impervious surfaces impede rain from penetrating the soils and returning to groundwater. 

Coastal development also removes natural forests decreasing the amount of rain that is returned 

to the atmosphere through evapotranspiration. In forested settings, only about 10 to 20 percent 

of the rain that falls on a coastal watershed is transferred to tidal creeks as runoff. In suburban 

and urban areas, 15 to 75 percent of rainfall is discharged to tidal creeks as runoff. When the 

amount of impervious cover in a tidal creek watershed exceeds 10 to 20 percent of the area, 

measurable increases in the volume and rate of freshwater runoff generally occur. Increased 

runoff contains non-point source pollution which impairs water quality, introduces excessive 

amounts of anthropogenic chemicals into sediments, and increases the amount of pathogens in 

water and shellfish. Similar relationships have been observed between the degree of watershed 

imperviousness and the quality of freshwater stream habitats, particularly intermittent and lower 

order streams (e.g., Schueler 1994, Yoder and Rankin 1999, Davies et al. 2010, Coleman et al. 

2011). 

4.1 Regional Comparisons 

The Gulf of Mexico (GoM) and the southeastern (SE) US coasts share features of their coastlines 

which include abundant and productive tidal creek and salt marsh habitats. These habitats are 

important nursery and feeding areas for numerous recreational, commercial, and ecological 

resources including birds, finfish, crustaceans, and their food sources. Both the GoM and SE are 

characterized by low topographic relief. The most significant difference between these two 

regions is the magnitude of tidal forcing. Southeast tidal creeks are characterized by semidiurnal tides that have a tidal range of 0.5 m to 2 m from North Carolina to Georgia. GoM tidal 

creeks experience diurnal tides that have an average tidal range of about 0.5 m in the Alabama 

and Mississippi focus areas of this study. Tidal range in GoM marshes and tidal creeks is more 

strongly influenced by wind than in the SE. Differences in tidal range influence the amount of 

intertidal habitat that occurs within a creek. Tidal creeks in areas with larger tidal range have a 

greater area of intertidal habitat and broader, deeper headwaters. Changes in the amount of 

intertidal habitat influence the capacity of headwaters to dilute land-based stormwater runoff 

without impairment of ecosystem services. In addition, the flushing rate and residence time of 

pollutants may also be different in the GoM versus the SE. 

The GoM and SE also differ in the overall environmental cond ition of their estuarine and coastal 

resources as defined by the US EPA Coastal Condition Report III (USEPA 2008). In this report, 

the GoM had an overall rating of fair to poor with a score of 2.2 in comparison to the SE overall 
43 

rating of fair with a score of 3.6. The water quality ratings of these two regions were similar but 

sediment quality, benthic condition, and cumulative coastal habitat condition are higher in the 

SE. This finding suggests that the estuarine systems in the SE generally experience less 

impairment from chemical contaminants associated with sediments than similar habitats in the 

GoM. Tidal creek habitats were not sampled for the Coastal Condition Report because they were 

too shallow during low tides for the methods used. These shallow tidal creek habitats represent 

from about 30-60% of area of SE estuaries (Nummedal et al. 1977). The findings of this report 

suggest the ecological condition of tidal creeks in the SE generally experienced less impairment 

than similar tidal creek habitat in the GoM which is supported by the findings of the Coastal 

Condition Report. 

Potential regional differences were examined and accounted for in the statistical analyses 

(ANOVAs) by using least squares means for comparisons in order to determine whether GoM 

tidal creeks can be used as sentinel habitats with similar patterns for the classification scheme 

and conceptual model. A variety of stressors were evaluated for this study including the amount 

of impervious cover, population density, and land cover characteristics. In general, tidal creek 

watershed sizes were within the same range for the two regions. Land cover varied between the 

GoM and SE primarily in the composition of salt marsh (less), palustrine (more), and forested 

(less) land covers in the GoM compared to the SE. In addition, the GoM urban creeks sampled 

had higher proportions of developed (low and high) land compared to the SE creeks. The range 

in impervious cover was similar, but only one urban subtidal watershed was sampled in the SE. 

In general, the physical-chemical responses of GoM creeks to coastal development were similar 

to that found for the SE (e.g., increased variability in salinity, increased abundances of pathogen 

indicators, and increased amounts of chemically contaminated sediments). As was the case in 

the SE, we hypothesize that these differences are due to increases in the rate and volume of 

stormwater runoff that occur as coastal watersheds are developed. These changes in hydrology 

are mainly due to increases in the amount of impervious cover and decreases in the abundance of 

vegetation that are associated with development. A few interesting differences in physicalchemical responses were, however, observed between the GoM and SE. The average salinity 

was lower in the GoM compared to the SE (probably an artifact of creeks sampled), and the 

range in DO and pH was higher in the GoM compared to the SE. This probably resulted because 

the GoM creeks were generally more eutrophic than the SE creeks which is in agreement with 

the Coastal Condition Report (USEPA 2008). The significantly higher amounts of suspended 

sediment in the SE compared to the GoM may be related to the larger areas of salt marsh that 

were associated with SE creeks. Nutrient concentration patterns were generally similar, 

however, nitrate/nitrite levels in the GoM urban intertidal systems were extremely high 

compared to similar systems in the SE or to GoM suburban and forested systems. 

Orthophosphate and total phosphorus concentrations were also exceptionally high in the 

intertidal area of Bayou Pattassa. High nitrate/nitrite and orthophosphate concentrations in tidal 

creeks are indicative of commercial fertilizer input from adjacent upland sources. The two GoM 

urban creeks sampled were in downtown Pascagoula, MS with limited fringing marsh areas and 

abundant lawns and bulkheaded suburban properties adjacent to creek banks. 

Enterococcus and F- coliphage, two of the four pathogen indicators, were higher in the SE 

compared to GoM. We are unsure of the mechanism causing these differences. GoM sediments 
44 

were more contaminated with pesticides than SE sediments. In addition, higher levels of 

chemical contamination of sediments were generally found further downstream in subtidal 

portions of GoM creeks. This was likely because the tidal range in GoM creeks was small 

compared to the SE creek resulting in substantially less intertidal area. As a result, it appears 

that sediment deposition areas occur in the subtidal portion of GoM creeks and in intertidal 

portions of SE creeks. 

Comparison of biological response between the GoM and SE was focused on a limited number 

of metrics for infaunal benthic and shallow water nekton communities. Macrobenthic 

community total abundance and number of species were similar between the GoM and SE in 

both the intertidal and subtidal areas. Only the intertidal areas were sampled for the nekton 

community. Nekton total abundance and number of species were significantly higher in the SE 

compared to the GoM. Because the amount of intertidal habitat is limited in the GoM compared 

to the SE, the subtidal regions of tidal creeks may represent the most expansive and functional 

nursery habitats in the GoM. 

The societal responses measured in the SE and GoM were focused on oyster tissue 

contamination with pathogens and chemicals. Oyster tissue fecal indicator concentrations were 

generally similar in the GoM and SE. Organic chemical (PCBs, DDT, PAHs) shellfish tissue 

levels tended to be higher in the GoM than in the SE; however, lipid concentrations in the SE 

were higher than in the GoM. All five GoM sites where shellfish tissues were collected 

exceeded the EPA cancer endpoint for total PAHs and inorganic arsenic, and two of the sites for 

total PCBs. Similar patterns were found by the NOAA Mussel Watch Program when the data for 

NC, SC, GA, MS, and AL were compared (Kimbrough et al. 2008). 

4.2 Classification Framework 

Classification frameworks have been developed and extensively used to integrate the ecological 

attributes of freshwater ecosystems in the context of their biogeography, hydrology, and shortand long-term ecological history (e.g., Horton 1945, Frissell et al. 1986). These classification 

systems assist in understanding the complexity and natural variability associated with freshwater 

ecosystems. In estuarine ecosystems, classification approaches have made only a limited 

contribution to understanding the complexity and explaining spatial and temporal variability 

(e.g., Anderson et al. 1976, Odum 1984). The reasons estuarine ecologists have not embraced 

classification as a means of partitioning and understanding complexity may include: (1) 

standardized approaches for resolving scale, spatial, temporal, and location differences within 

and among habitats and estuaries have not been broadly applied; (2) natural factors vary on 

multiple temporal and spatial scales (e.g., tidal, diel, extreme events, seasonal, year-to-year, 

climatic, geological); and (3) much of the ecosystem and habitat scale science for estuaries is 

based on indirect evidence from relatively few places. 

The tidal creek classification system we developed in the SE and applied to the GoM facilitated 

the integration of the findings and enhanced the understanding of the complexity and variability 

in tidal creek habitats. Further study should, however, be undertaken to fully explain the 

differences observed in the characteristics (e.g., length, depth, area) of creek orders between the 

GoM and SE. In the SE, the intertidal portion was a repository for many of the pollutants 

discharged with stormwater runoff to estuarine systems as evidenced by the higher pollutant 
45 

concentrations in the intertidal regions. This resulted in intertidal or first order tidal creeks 

having substantially stronger relationships between coastal development patterns and 

environmental quality than subtidal creek orders. This pattern is not as clearly defined in the 

GoM. The land use relationship in the intertidal and subtidal systems in the GoM was not 

analyzed separately due to the low number of creeks sampled (n=5); however, very few 

interaction terms were significant. This either indicates that even though there was a difference 

between the two regions regarding the creek classification (i.e., intertidal and subtidal) the 

overall relationship with land use patterns were similar or that our sample size was too small to 

detect significant interactions. 

The reason for the potential difference in the location or place where land-derived pollutants 

accumulated in tidal creeks may be related to the substantially reduced tidal flushing that occurs 

in the GoM compared to the SE tidal creeks. GoM tidal creeks are characterized by small tides 

(~0.5 m) which occur once a day and are strongly influenced by rain and wind. Tides in the SE 

are diel, much larger (1-2 m), and less influenced by rain events and wind. In the SE, stormwater 

runoff and pollutants appear to enter the system and dissipate in the shallow intertidal or 

headwater areas before being transported downstream to the deeper systems. As a result, the 

headwaters of SE tidal creeks appear to function as a repository for sediments and the pollutants 

associated with them. This suggests the differences between intertidal and subtidal areas may 

not be as clearly defined in the GoM as in the SE. 

4.3 Responses to Coastal Development 

In general, the relationships between indicators of tidal creek environmental quality and the 

degree of coastal development in the associated watershed as indicated by the amount of 

impervious cover were similar in the GoM and SE. This was consistent with previous research 

(e.g., Mallin et al. 2000a, Lerberg et al. 2000, Holland et al. 2004) that has found relationships 

with a wide range of exposure (physical-chemical) indicators including salinity range, sediment 

contamination, nutrient enrichment, and water borne pathogens and the imperviousness of the 

drainage area or watershed. 

The salinity range (one of the significant SE indicators of runoff rate and volume or flashiness) 

showed a similar relationship with significantly higher salinity ranges for the urban and suburban 

creeks compared to the forested creeks. A significant relationship was also observed between 

the salinity range and impervious cover for both the intertidal and subtidal sections. This 

indicates that the runoff entering both GoM and SE tidal creeks in developed watersheds is 

flashier (increased volume and rate of runoff) than in forested watersheds. Coleman et al. (2011) 

found similar relationships between urbanization and hydrological changes. 

Our previous research in the SE did not find average DO concentration to be a significant 

indicator of coastal developmental effects; however, with the addition of the GoM data the 

average DO concentration was found to be significantly lower in the urban creeks compared to 

the forested systems. This may indicate that DO concentration may be an additional indicator of 

land use impacts in the GoM if confirmed with further testing. The only nutrient and 

phytoplankton indicator in the previous SE research that was found to increase with increasing 

levels of development was nitrate/nitrite, a nutrient indicative of fertilizer input. This parameter 

was also found to be a potential indicator for the combined GoM and SE datasets. Several other 
46 

nutrients were also found to be significantly different in ANOVA analyses but not with the 

regression analysis indicating there was not a clear pattern with increasing development. In 

combination, these findings agree with other research in microtidal and macrotidal areas that 

indicate microtidal systems are generally more eutrophic than macrotidal systems (Monbet 

1992). 

The waterborne pathogen indicators found to be important in the SE appear to be important in 

the GoM; however, laboratory detection limits were a concern. Water column fecal coliform 

concentrations in GoM creeks were estimated at several sites using oyster tissue concentrations 

(R2=0.58). Despite the detection limit concerns, it is clear that fecal coliform, enterococcus, F+, 

coliphages, and F-coliphages are all potential indicators of the levels of development in both the 

SE and GoM tidal creeks. This is in agreement with other research in SE tidal creeks which has 

also found strong relationships between pathogen indicators and the level of urbanization (Mallin 

et al. 2000a, Line et al. 2008). Stewart et al. (2008) highlighted the use of sentinel habitats for 

evaluating pathogen levels to provide early warning of ecosystem health impacts. 

In general, the sediment contaminant indicators (total mERMQ, PAHs mERMQ, PCBs 

mERMQ, and pesticides mERMQ) were found to be good indicators of the level of development 

in the watershed with the forested and suburban systems having similar concentrations and the 

urban systems being significantly higher. This was similar to what was observed with just SE 

research (Sanger et al. 1999a, 1999b, Sanger et al. 2008) with the highest sediment chemical 

contaminant concentrations in the heavily developed areas and the low to moderately developed 

areas not showing significant increases in chemical contaminant levels. 

The biological response with urbanization was not as clear in the GoM as previously observed in 

the SE (Lerberg et al. 2000, Holland et al. 2004, Sanger et al. 2008). The total abundance and 

number of species patterns for the macrobenthic and nekton communities regarding land use 

were complex. In general, very few differences were found in the GoM (note the two regions 

were analyzed separately due to species composition). The limited sample size in the GoM 

probably resulted in a lack of any relationships between urbanization and the macrobenthic or 

nekton communities. Very few relationships have been found in other studies with regard to 

anthropogenic alterations and nekton communities in estuarine and coastal environments 

(Holland et al. 2004, Van Dolah et al. 2007, Bilcovic 2011). This is in contrast to freshwater 

studies which have found nekton community parameters to be indicators of urbanization (e.g., 

Coleman et al. 2011). One of the few relationships observed in a previous study was a negative 

relationship between impervious cover and abundance of Panaeus sp. shrimp (Holland et al. 

2004); however, this relationship has only been observed in a small geographic area (SC) and 

with a large sample size (n>30 creeks). Further studies evaluating the macrobenthic and nekton 

communities in the GoM are necessary to determine if any patterns exist with regard to coastal 

development. 

The societal responses measured in both the SE and GoM were focused on shellfish tissue 

contamination with pathogens and chemicals. The oyster tissue fecal coliform levels were found 

to increase with increasing levels of development for the intertidal and subtidal habitats. In 

contrast, enterococci levels in oyster tissues were not found to be related to the level of 

development in the surrounding watershed. The shellfish tissue chemical contaminant levels 
47 

observed in the GoM were complex. In general, the concentrations of organic contaminants 

were higher in the more developed tidal creeks systems; however, the differences were not as 

apparent as those observed in the SE. 

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 

and estuarine waters. This study, and the partnership with the NERRS, was undertaken in part to 

explore tidal creeks as sentinel habitats and to evaluate whether the NERRs network of protected 

coastal habitats could serve as reference sites for future research. 

Over the last decade tidal creeks have been sampled in four SE NERRs (North Carolina; North 

Inlet-Winyah Bay, SC; ACE Basin, SC; Sapelo Island, GA) and two GoM NERRs (Grand Bay, 

MS; Weeks Bay, AL). For most of the indicators of tidal creek environmental quality the 

NERRs were found to be reasonable regional references with environmental quality of tidal 

creeks found to be higher within the boundaries of the NERRs than adjacent creeks draining 

developed watersheds. However, current and historical land use and management practices 

within and around the NERRs appears to impact the reserves and may diminish their capacity as 

regional references. 

4.5 Summary 

This study demonstrates the value of subdividing a creek network into orders in the SE but 

further research is needed before the SE tidal creek classification systems can be validated for 

creek networks in the GoM. The SE creek classification provided a hydrologic and 

biogeographic context for understanding scale and spatial patterns characteristic of SE tidal creek 

networks. The SE data consistently suggested that for most environmental quality and public 

health indicators, the signal of land use effects on tidal creek ecosystems and human risk was 

strongest in shallow first order creeks. The degree of impact and risk of environmental harm 

decreased in second and third orders. Thus, application of the classification system clearly 

demonstrated that failure to sample first order creeks may result in a Type II error or false 

negative relative to land use impact on tidal creek ecosystems. That is, a conclusion may be 

reached that there is no impact to the tidal creek network from stormwater runoff and the 

associated land-based pollution when in fact there is significant impact but it is confined to the 

first order or intertidal sections which serve as a repository for sequestering pollution inputs. 

However, in the GoM the strong difference in land use impacts between intertidally and 

subtidally dominated habitats is not as apparent. The flushing rate and residence times of 

pollutants in tidal creeks are likely important in understanding the differences in spatial 

distributions; however, little information exists on these for GoM or SE tidal creeks. These 

factors reinforce the importance of spatial scale at which research and monitoring activities occur 

and proximity to pollution sources are very important for identifying the impacts of land use 

changes on ecosystems. 
48 

Developing a conceptual model is a critical step to identifying and evaluating monitoring and 

management strategies including defining what parameters to measure and when and where 

measurements should be taken (NRC 1990). The conceptual model detailing the effects of 

coastal development on tidal creeks, a sentinel habitat, which was developed for the SE was 

found to be applicable to GoM tidal creeks (Figure 1-1). In general, the addition of the GoM 

data strengthened our understanding of the complexity and spatial patterns observed in the SE. 

The coastal development relationships were strongest for the physical-chemical exposure 

indicators and weaker for the biological response indicators. This hierarchy of response patterns 

as you move from the left side of the model to the right has also been observed in other studies 

(e.g., Limburg et al. 2005). 

The societal response component of the conceptual model was not studied as thoroughly in the 

GoM compared to the SE. Further research in this area is warranted. 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 (Mallin et al. 2000a, Karn and Harada 

2001, Holland et al. 2004, Mallin 2006) which was also evidenced by this study. From a human 

health perspective, the accumulation of pathogens in the water, sediments, and organisms may 

render seafood products unsafe to eat and water unsafe for body contact recreation. Flooding 

vulnerability, public health risk, and economic impacts are metrics just being added to the SE 

model which will require additional effort in the GoM to fully understand if these linkages are 

also applicable to the GoM. This is highlighted in two research studies from other areas that 

documented decreased property values from impaired water quality (Leggett and Bockstael 

2000) and decreased ecosystem services values from increased urbanization (Zang et al. 2010) 

illustrating the continued importance for assessing these impacts. 

Tidal creek networks are the primary hydrologic link between estuaries and land based activities. 

Therefore, tidal creeks serve as sentinel habitats that provide early warning of the ensuing harm 

that is likely to occur to seaward habitats and ecosystems in both the SE and GoM. As the first 

zone of impact for non-point source pollution runoff, the potential for alterations to freshwater 

inputs, increased levels of microbial and chemical contamination, and adverse effects on 

biological communities in tidal creek habitats is great. This is particularly evident given the high 

rate of coastal population growth which has the potential to lead to further impairment of natural 

resources as well as impairment of the free ecosystem services tidal creeks provide if current 

land use decisions continue. Educating decision-makers on the impact of existing land use 

practices is an important activity to ensure that the potential impacts of current and future land 

use decisions are considered. 
49 

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55 

Appendix A 

The expected range of method detection limits (MDLs) based on extracted sample mass for the 

sediment and oyster tissue contaminant analyses. Organics are ng g-1 dry weight and metals are 

μg g-1 dry weight.

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
56 

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
57 

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
58 

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
59 

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
60 

Appendix C 

Results of 3-way or 2-way ANOVAs on indicator variables sampled in summer in the SE and 

GoM. Region factors are southeast (SE) and Gulf of Mexico (GoM). Land use class factors are 

Forested (F), Suburban (S), and Urban (U). Station type 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. $--interaction 

term not calculated due to lack of suburban systems in GoM for these variables. 

Parameter 

Model 

p-value R2

Region 

p-value 

Land 

Use pvalue 

Station 

Type 

p-value 

Interactions Region 

LS Means 

Land Use 

LS Means 

Station 

Type 

LS 

Means 

Water Quality Average 

DO (mg/l) 0.0062 0.39 0.0827 0.0188 0.0201 r*l SEa GoMa Ua Sab Fb Ia Sb

pH 0.1519 0.18 0.2687 0.7949 0.0397 SEa

 GoMa Sa Fa Ua Ia Sb

Salinity 0.0088 0.30 0.0107 0.1560 0.0172 GoMa

 SEb Sa Ua Fa Ia Sb

Temperature 0.1218 0.17 0.0515 0.0811 0.8867 SEa

 GoMa Ua Fa Sa Sa Ia

Turbidity 0.2449 0.13 0.2686 0.9477 0.0502 GoMa

 SEa Ua Sa Fa Sa Ia

 

Water Quality Range 

DO (mg/l) 0.0004 0.42 0.0214 0.3502 <0.0001 SEa

 GoMb Fa Ua Sa Sa Ib

pH 0.0126 0.31 0.0115 0.0391 0.0359 SEa

 GoMb Fa Ua Sb Sa Ib

Salinity <0.0001 0.50 0.6097 <0.0001 0.0014 SEa

 GoMa Fa Sb Ub Sa Ib

Temperature <0.0001 0.62 0.2769 0.7039 0.0007 r*l, r*s SEa

 GoMa Sa Fa Ua Sa Ib

Turbidity 0.1003 0.19 0.0610 0.8871 0.0612 GoMa

 SEa Fa Ua Sa Sa Ia

 

Water Column Nutrients and Phytoplankton 

Chl-a 0.4105 0.10 0.9907 0.8363 0.0627 SEa

 GoMa Ua Sa Fa Sa Ia

Phaeophytin 0.0072 0.31 0.4946 0.9404 0.0003 GoMa

 SEa Ua Fa Sa Sa Ib

NH4 0.0008 0.39 0.8866 0.0428 0.0002 SEa

 GoMa Fa Sab Ub Sa Ib

NO2/3 <0.0001 0.69 0.9881 <0.0001 0.0013 r*l*s SEa

 GoMa Fa Sb Uc Sa Ib

TDN 0.0003 0.42 0.2080 0.1134 <0.0001 GoMa

 SEa Fa Sab Ub Sa Ib

TN <0.0001 0.51 0.6841 0.2319 <0.0001 GoMa

 SEa Sa Fa Ua Sa Ib

PO4 0.0133 0.28 0.8036 0.0253 0.0164 GoMa

 SEa Fa Sab Ub Sa Ib

TDP 0.0016 0.37 0.0937 0.0032 0.0307 GoMa

 SEa Fa Sa Ub Sa Ib

TP 0.0008 0.39 0.1619 0.0083 0.0024 GoMa

 SEa Fa Sa Ub Sa Ib

TSS 0.0019 0.36 0.0140 0.1833 0.0017 GoMa

 SEb Sa Fa Ua Sa Ib

 

Water Column Pathogens 

FC <0.0001 0.74 0.7054 <0.0001 <0.0001 r*l GoMa

 SEa Fa Sa Ub Sa Ib

ENT <0.0001 0.63 <0.0001 0.0431 <0.0001 GoMa

 SEb Fa Sab Ub Sa Ib

F+ 0.0025 0.35 0.3639 0.0007 0.1833 GoMa

 SEa Fa Sa Ub Sa Ia

F- <0.0001 0.61 0.0112 <0.0001 0.0002 r*l GoMa

 SEb Sa Fa Ub Sa Ib

 
61 

Parameter 

Model 

p-value R2

Region 

p-value 

Land 

Use pvalue 

Station 

Type 

p-value 

Interactions Region 

LS Means 

Land Use 

LS Means 

Station 

Type 

LS 

Means 

Sediment Composition 

Sand 0.8814 0.03 0.5082 0.8440 0.5461 GoMa SEa Fa Sa Ua Ia Sa

Mud 0.8765 0.03 0.5252 0.8485 0.5194 SEa

 GoMa Ua Sa Fa Sa Ia

Clay 0.8315 0.04 0.4435 0.7207 0.9752 GoMa

 SEa Ua Fa Sa Ia Sa

Silt <0.0001 0.72 <0.0001 0.0009 0.0005 r*l, r*s SEa

 GoMb Sa Ua Fb Ia Sb

TOC 0.1409 0.17 0.9035 0.8130 0.0111 SEa

 GoMa Fa Ua Sa Sa Ib

Porewat. TAN 0.2680 0.13 0.3648 0.1133 0.5347 SEa

 GoMa Fa Uab Sb Sa Ia

 

Sediment Contamination 

Total mERMQ 0.0071 0.35 0.4728 0.0104 0.6057 r*s SEa

 GoMa Fa Sa Ub Ia Sa

mERMQ Metal 0.0545 0.25 0.4289 0.3576 0.2821 r*s SEa

 GoMa Fa Sa Ua Ia Sa

mERMQ PAH 0.0005 0.41 0.8004 <.0001 0.2812 SEa

 GoMa Fa Sa Ub Sa Ia

mERMQ PCB 0.0111 0.29 0.9006 0.0028 0.3111 SEa

 GoMa Fa Sa Ub Sa Ia

mERMQ Pest. 0.0998 0.19 0.3670 0.0619 0.1806 SEa

 GoMa Fa Sab Ub Sa Ia

 

Macrobenthos 

Intertidal 

Density (#/m2

) 0.30 0.5220 0.0120 r*l GoMa SEa Sa Fb Ub 

# of Species 0.27 0.2690 0.0250 r*l SEa

 GoMa Sa Fab Ub 

Subtidal 

Density (#/m2

) 0.23 0.8820 0.5270 SEa GoMa Sa Fa Ua 

# of Species 0.16 0.0860 0.4140 GoMa

 SEa Sa Fa Ua 

 

Nekton 

Density (#/hec) 0.46 <0.001 0.1050 $ SEa

 GoMb Fa Ua Sa 

# of Species 0.25 0.0090 0.2410 $ SEa

 GoMb Fa Ua Sa 
62 

Appendix D 

Regression results for various parameters versus impervious cover for intertidal and subtidal 

systems separately. Bolded values are significant at p < 0.05. 

Parameter

Transformation Station Type R2Model pvalue Slope Intercept

Water Quality Average 

Depth (m) log10 Intertidal 0.11 0.1285 -17.84 19.65

Depth (m) log10 Subtidal 0.09 0.2049 -15.90 25.98

DO (% sat) Intertidal 0.20 0.0372 -0.85 75.28

DO (% sat) Subtidal 0.09 0.1955 -0.257 41.73

DO (mg/l) Intertidal 0.11 0.1242 -9.20 62.45

DO (mg/l) Subtidal 0.09 0.1944 -3.46 40.29

pH Intertidal 0.06 0.3126 15.94 -88.82

pH Subtidal 0.05 0.3967 13.821 -77.97

Salinity (ppt) Intertidal 0.11 0.1304 -0.78 38.43

Salinity (ppt) Subtidal 0.00 0.8249 0.12 20.63

Temperature (deg C) Intertidal 0.00 0.8137 -0.65 44.44

Temperature (deg C) Subtidal 0.00 0.8802 -0.36 34.19

Turbidity (NTU) Intertidal 0.10 0.1541 -0.38 37.38

Turbidity (NTU) Subtidal 0.01 0.7327 0.10 21.26

 

Water Quality Range 

Depth (m) log10 Intertidal 0.05 0.2988 -20.59 26.61

Depth (m) log10 Subtidal 0.04 0.4157 -10.90 25.18

DO (% sat) log10 Intertidal 0.00 0.9522 1.48 22.23

DO (% sat) log10 Subtidal 0.00 0.9520 1.61 20.51

DO (mg/l) log10 Intertidal 0.01 0.6515 11.64 15.90

DO (mg/l) log10 Subtidal 0.00 0.9794 0.69 22.95

pH Intertidal 0.06 0.2806 18.84 14.25

pH Subtidal 0.17 0.0922 47.82 0.10

Salinity (ppt) Intertidal 0.34 0.0041 1.26 8.43

Salinity (ppt) Subtidal 0.36 0.0055 2.61 5.15

Temperature (deg C) log10 Intertidal 0.02 0.5390 -13.50 35.50

Temperature (deg C) log10 Subtidal 0.09 0.1878 28.96 10.46

Turbidity (NTU) log10 Intertidal 0.01 0.6472 -5.50 35.66

Turbidity (NTU) log10 Subtidal 0.01 0.6562 4.84 15.23

 
63 

Parameter

Transformation Station Type R2Model pvalue Slope Intercept

Water Column Nutrients and Phytoplankton 

Chl a log10 Intertidal 0.03 0.4513 -9.12 36.97

Chl a log10 Subtidal 0.09 0.1950 -19.64 44.70

Phaeo Intertidal 0.02 0.5475 -0.57 31.18

Phaeo Subtidal 0.06 0.2927 -1.95 34.27

NH4 log10 Intertidal 0.17 0.0506 13.49 41.27

NH4 log10 Subtidal 0.09 0.2025 8.48 40.19

NO2/3 log10 Intertidal 0.51 0.0002 27.73 62.89

NO2/3 log10 Subtidal 0.39 0.0034 27.15 69.57

PO4 Intertidal 0.15 0.0766 46.40 17.71

PO4 Subtidal 0.18 0.0618 117.05 16.08

Si log10 Intertidal 0.00 0.9606 -0.86 25.57

Si log10 Subtidal 0.08 0.2141 -20.72 29.07

TDN log10 Intertidal 0.10 0.1506 23.44 5.87

TDN log10 Subtidal 0.00 0.7770 7.57 19.53

TDP log10 Intertidal 0.14 0.0817 15.67 39.88

TDP log10 Subtidal 0.17 0.0740 20.04 47.74

TN log10 Intertidal 0.03 0.4203 28.17 22.91

TN log10 Subtidal 0.00 0.9297 3.46 23.86

TP log10 Intertidal 0.11 0.1323 18.01 36.31

TP log10 Subtidal 0.14 0.1058 26.21 47.55

TSS log10 Intertidal 0.00 0.7998 -3.50 30.75

TSS log10 Subtidal 0.07 0.2550 -24.75 55.93

 

Water Column Pathogens 

FC - est./no >600 log10 Intertidal 0.64 <0.0001 18.96 -37.88

FC - est./no >600 log10 Subtidal 0.24 0.0283 9.02 6.16

ENT - no >600 log10 Intertidal 0.33 0.0079 15.46 -23.85

ENT - no >600 log10 Subtidal 0.03 0.5140 3.12 15.59

F+ log10 Intertidal 0.40 0.0016 17.33 16.49

F+ log10 Subtidal 0.26 0.0219 19.12 18.52

F- log10 Intertidal 0.31 0.0067 14.39 -0.33

F- log10 Subtidal 0.27 0.0200 15.12 7.41

 

 
64 

Parameter

Transformation Station Type R2Model pvalue Slope Intercept

Sediment Composition 

Sand (%) Intertidal 0.01 0.7212 0.07 20.95

Sand (%) Subtidal 0.07 0.2580 -0.21 37.62

Mud (%) Intertidal 0.01 0.7268 -0.07 28.16

Mud (%) Subtidal 0.07 0.2781 0.20 16.96

Clay (%) Intertidal 0.00 0.8753 -0.04 26.10

Clay (%) Subtidal 0.01 0.7199 0.07 20.94

Silt (%) Intertidal 0.00 0.9438 0.04 24.70

Silt (%) Subtidal 0.00 0.8484 -0.06 23.62

TOC (%) Intertidal 0.05 0.3209 2.08 18.20

TOC (%) Subtidal 0.17 0.0791 5.41 15.07

Porewater TAN log10 Intertidal 0.18 0.0512 35.53 9.69

Porewater TAN log10 Subtidal 0.09 0.1871 13.58 18.07

 

Sediment Contamination

ERMQ log10 Intertidal 0.39 0.0020 35.98 74.41

ERMQ log10 Subtidal 0.26 0.0229 28.96 65.64

ERMQ Metals log10 Intertidal 0.19 0.0428 32.09 62.91

ERMQ Metals log10 Subtidal 0.11 0.1562 18.83 46.38

ERMQ PAHs log10 Intertidal 0.53 0.0001 32.46 76.98

ERMQ PAHs log10 Subtidal 0.47 0.0008 34.97 83.32

ERMQ PCB log10 Intertidal 0.21 0.0330 19.44 57.17

ERMQ PCB log10 Subtidal 0.23 0.0309 22.97 64.18

ERMQ Pesticides log10 Intertidal 0.47 0.0004 27.92 67.24

ERMQ Pesticides log10 Subtidal 0.23 0.0343 20.98 58.82

 

Oyster Tissue Pathogens 

ENT log10 Intertidal 0.02 0.7063 7.00 -1.54

ENT log10 Subtidal 0.11 0.4617 -8.28 57.85

FC log10 Intertidal 0.58 0.0061 16.41 -29.47

FC log10 Subtidal 0.67 0.0234 14.67 -17.11

 

Macrobenthos

Density (ind/m2) 1/3 root Intertidal 0.00 0.9502 -0.0001 25.46

Density (ind/m2

) 1/3 root Subtidal 0.08 0.2359 -0.008 29.17

Num of Species log10 Intertidal 0.16 0.0623 -50.53 73.09

Num of Species log10 Subtidal 0.05 0.3330 -11.87 37.82

 

Nekton 

Density (ind/hec) log10 Intertidal 0.01 0.6179 -3.83 27.31

Num of Species Intertidal 0.06 0.2869 -2.42 43.453
65 
66 

United States Department of Commerce 

Rebecca Blank 

Acting Secretary 

National Oceanic and Atmospheric Administration 

Jane Lubchenco 

Under Secretary of Commerce for Oceans and Atmospheres 

National Ocean Service 

David Kennedy 

 Assistant Administrator for Ocean Service and Coastal Zone Management