NOAA Status and Trends Mussel Watch Project

[From the U.S. Government Printing Office, www.gpo.gov]

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year 6
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1992)

NOAA STATUS AND TRENDS
Mussel Watch Project
Year 6 Technical Report

The Geochemical and
Environmental Research Group
Texas A&M Research Foundation

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December 1992

NOAA
STATUS AND TRENDS

Mussel Watch Project

Year 6 Technical Report

r
Property of CSC Library

Prepared by

The Geochemical and Environmental Research
Group
Texas A&M University
833 Graham Road
College Station, Texas 77845

Submitted to

jiU.S. Department of Commerce
National Oceanic & Atmospheric Administration
Ocean Assessment Division
6001 Executive Blvd., Rin 323
Rockville, Maryland 20852

December 1992 U . S . DEPARTMENT 01F COMMERCE NOAA
COASTAL SERVICES CENTER
2234 SOUTH HOBSON AVENUE
CHARLESTON , SC 29405-2413

TABLE oF CoNTENTs

1.0 EXECUTIVE SUMMARY ............................................................................ 1-1

REPRINT 1: Processes Controlling Temporal Trends in
Gutf of Mexico Oyster Health and Contaminant
Concentrations ........................................................................................ 1-9

REPRINT 2: International Mussel Watch: the Initial
Implementation Phase ...................................................................... 1-17

2.0 INTRODUCMON ........................................................................................... 2-1

2.1 Project Relevance and Direction ............................................... 2-1
2.2 Study Objectives ............................................................................... 2-4

3.0 POLYNUCLEAR AROMATIC HYDROCARBON RESULTS ............... 3-1

REPRINT 3: Trace Organic Contamination in Galveston
Bay: Results from the NOAA National Status and Trends
Mussel Watch Program ..................................................................... 3-33

REPRINT 4: Toxic Contamination of Aquatic Organisms in
Galveston Bay ..................................................... . ................................. 3-37

REPRINT 5: Transplanted Oysters as Sentinel Organisms
in Monitoring Studies ....................................................................... 3-41

REPRINT 6: The Effects of the Apex Barge Oil Spill on the
F1.sh ofGah@esbn Bay ......................................................................... 3-43

PREPRINT 1: Polynuclear Aromatic Hydrocarbon
Contaminants in Oysters from the Gutf of Mexico (1986-
1990) ....................................................................................................... 3-47

PREPRINT 2: Sources of Local Variation in Polynuclear
Aromatic Hydrocarbon and Pesticide Body Burden .............. 3-85

4.0 CBLOR1XATED 11YDROCARBONS .......................................................... 4-1

REPRINT 5: Chlorinated Hydrocarbons in Gu!f of Mexico
Oysters: Overview of the FIrst Four Years of the NOAA's
National Status and Trends Mussel Watch Program
(1986-1989) ......................................................................................... 4-21

PREPRINT 3: National Status and Trends Mussel Watch
Program: Chlordane-Related Compounds in Gutf of
Mexico Oysters, 1986-1989 ........................................................... 4-39

PREPRINT 4: Concurrent Chemical and Historical
Analyses: Are They Compatible? .................................................. 4-61

PREPRINT 5: Environmental Significance of the Uptake
and Depuration of Planar PCB Congeners by the
American Oyster (Crassostrea virginica) .................................... 4-61

5.0 TRACE METALS RESULTS ...................................................................... 5-1

5. 1 Laboratory Intercalibration Results .............................................. 5-1
5.2 Muce Metals in Year 6 Oysters ...................................................... 5-1
5.3 Summary and Conclusions from Six Years of Trace
Metals in Oysters Data ....................................I ................................. 5-6

REPRINT 8: T@-ace Metals in Galveston Bay Oysters .................. 5-21

6.0 BUTYLTIN RESULTS .................................................................................. 6-1

PREPRINT 6:, Butyltin Concentrations on Oysters from the
Gulf of Mexko during 1989-1991 ................................................... 6-5

7.0 BIOLOGICAL RESULTS .............................................................................. 7-1

7.1 Condition Index/Shell Length ................................................. 7-1
7.2 Gonadal/Somatic Index .............................................................. 7-1
7.3 Long-Term Changes ..................................................................... 7-3

PREPRINT 7: Spatial and Temporal Distributions of
Contaminant Body Burden and Disease in Guff of Mexico
Oyster Populations: The Role of Local and Large-Scale
Climatic Conbuls ................................................................................ 7-21

1.0 Executive Summary

The purpose of the Mussel Watch Project is to determine the
long-term temporal and spatial trends of selected environmental
contaminant concentrations in bays and estuaries. The key questions
in this regard are: (1) What is the current condition of the nation's
coastal zone?; and (2) Are these conditions getting better or worse?
This report contains the first six years of data from a multi-year
project. The first question has been addressed in detail as evidenced
by the scientific papers and reports (Table 1.1) that have resulted
from the Geochemical and Environmental Research Group's (GERG)
interpretations of the Gulf Coast data. Publications not included in
GERG's Year 4 or 5 Technical Report are appended to the appropriate
sections.

This report is an estimate of the current condition of the Gulf of
Mexico coastal zone, based on results from Years I thru 6 of the NOAA
Mussel Watch Project. Following is a brief sampling survey of these
years:

Year I - 51 sites (153 stations) - sediments and oysters
Year 2 - 49 sites (147 stations) - sediments and oysters
Year 3 - 65 sites - oysters. Sediments at new sites only.
Year 4 - 67 sites - oysters. Sediments at new sites only.
(the 67 sites.were sampled in 26 days.)
Year 5 - 71 sites - oysters. Sediments at new sites only.
Year 6 - 64 sites - oysters. Sediments at new sites only.

Year 6 sites included the original list of sites sampled in Years 1
and 2, some sites first sampled in Year 3, seven new sites'sampled for
the first time in Year 4, four new sites sampled in Year 5 and two new
sites in year 6. Sediments and oysters were collected from all but one
of the new sites for Years 4, 5 and 6 sites. Eleven sites were deleted
from those sampled in previous years. Three of the Texas sites, which
had no oysters in Year 3 due to the freshwater-induced die-off, again
had no oysters for collection in Year 4. These sites were in San
Antonio and Espiritu Santo Bays (SAPP, SAMP, ESSP). Extensive effort
was made to sample the sites but only a few spat, too small and too few
for collection, were found.

Although a new site for Year 4 was designated in the lower
Laguna Madre at Arroyo Colorado, no oysters were found. Further
surveys around Port Mansfield also yielded no oysters. Thus, no
samples were collected at the new site designated in the lower Laguna
Madre.

Oysters from three stations were collected at all sites where
there were oysters except at the Pass A Loutre site on the Mississippi
River (MRPL). Two and a half hours of dredging did not provide
sufficient samples for three replicate sites of twenty individuals per
site. This low productivity, adverse and worsening weather, and one
total engine failure combined to result in a short site (not enough
replicates for all analyses). In Year 5, twelve sites were eliminated
from and five sites were added to the sampling project. In year 6, two
new sites were sampled. Details of the sample collection and location
of field sampling sites are contained in a separate report titled "Fleld
Sampling and Logistics".

The oyster and sediment samples were analyzed for contaminant
concentrations [trace metals, polynuclear aromatic hydrocarbons
(PAH), pesticides and PCBsJ, disease incidence and other parameters
that aid in the interpretation of contaminant distributions (grain size,
oyster size, lipid content, etc.). The analytical procedures used and
the QA/QC Project Plan are detailed in a separate report titled
"Analytical Methods". The data that was produced from the sample
analyses for Year 6 is found in a separate report titled "Analytical Data".

A complete and comprehensive interpretation of the data from
the Status and Trends Project for oyster data coupled. with the
sediment data is an ongoing process. We have begun and are
continuing that process as evidenced by this report and the scientific
manuscripts that we have published or submitted for publication
(Table 1.1). As part of the data interpretation and dissemination, over
forty presentations of the NOAA NS&T Gulf Coast Mussel Watch
Project were given at national as well as international meetings. With
six years of data, the question of temporal trends of contaminant
concentrations can be addressed. Detailed examinations of this
question are presented in individual sections of this report. A general
conclusion that is found for most contaminants measured is that the
concentrations have remained relatively constant over the six-year
sampling period (Reprint 1). This general trend, however, is not
observed at all sites. Some sites show significant changes (both
increases and decreases) between years (Reprint 1). Continued
sampling will be required to determine the frequency and rates of
these changes.

Exceptions to this general trend are found for DDTs and TBT.
When historical data for DDT in bivalves is compared to current NS&T
data, a decrease in concentration is apparent. Also based on TBT data
collected as part of the NOAA NS&T Mussel Watch Project, a decline
in TBT concentration in oysters is apparent. Both declines may be in
response to regulatory actions.

1-2

During Year 3 of this project, sixteen additional sites were
sampled. These sites were chosen to be closer to urban areas, and
therefore to the sources of contaminant inputs. These sixteen new
sites were not, however. located near any known point sources of
contaminant input. These sites were added to better represent the
current status of contaminant concentrations in the Gulf of Mexico.
Generally the mean contaminant concentration at these new sites was
higher than the other 48 or 49 Gulf of Mexico sites.

While sampling sites for this project were specifically chosen to
avoid known point sources of contaminant input, the detection of
coprostanol in sediment from all sites indicates that the products of
man's activities have reached all of the sites sampled. However, when
compared to known point sources of contamination, all of the
contaminant concentrations reported are, in most cases, many orders
of magnitude lower than obviously contaminated areas. The lower
concentrations in Gulf of Mexico samples most -likely reflect the fact
that the sites are far removed from point sources of inputs, a condition
which is harder to achieve in East and West Coast estuaries. In fact,
new sites added in Years 3. 4, 5 and 6 to be closer to urban areas
generally had higher contaminant concentrations. An important
conclusion derived from the extensive NS&T data set is that
contamination levels in Gulf Coast Near Shore areas remain the same
or are getting better, and most areas removed from point sources are
not severely contaminated.

In addition to analyzing and synthesizing data from the Status
and Trends portion of the "International Mussel Watch," GERG was
also involved in its initiation. The objectives of that program, funded
by NOAA through UNEP/IOC, have been summarized in Reprint 2.

This document represents one of four report products as part of
Year 6 of the NS&T Gulf of Mexico projects. The other three reports
are entitled:

� Analytical Methods
9 Analytical Data
� Field Sampling and Logistics

1-3

Table 1.1 GERG/NOAA NS&T PUBLICATIONS Included in
Year Report

Wade, T.L., B. Garcia-Romero and J.M. Brooks (1988)
Tributyltin Contamination of Bivalves from U.S.
Coastal Estuaries. Environmental Science and 01
Technology, 22:1488-1493. IV

Wade, T.L., E.L. Atlas, J.M. Brooks, M.C. Kennicutt H, R.G.
Fox, J. Sericano, B. Garcia-Romero and D. DeFreitas
(1988) NOAA Gulf of Mexico Status and Trends
Program: Trace Organic Contaminant Distribution
in Sediments and Oysters. Estuaries 11:171-179. IV 01

Wade, T.L., B. Garcia-Romero and J.M. Brooks (1988)
Tributyltin analyses in association with NOAA's r
National Status and Trends Mussel Watch
Program. OCEANS '88 Conference Proceedings,
Baltimore, MD, 31 Oct. - 2 Nov. 1988, pp 1198-1201. IV

Wade, T.L., M.C. Kennicutt, H and J.M. Brooks (1989) Gulf of
Mexico Hydrocarbon Seep Communities: III:
Aromatic Hydrocarbon Burdens of Organisms
from Oil Seep Ecosystems. Marine Environmental
Research, 27:19-30. IV

Wade, T.L. and J.L. Sericano (1989) Trends in Organic 01
Contaminant Distributions in Oysters from the
Gulf of Mexico. Proceedings, Oceans '89 Conference,
Seattle, WA, 585-589. IV -

Wade, T.L. and B. Garcia-Romero (1989) Status and Trends
of Tributyltin Contamination of Oysters and
Sediments from the Gulf of Mexico. Proceedings,
Oceans '89 Conference, Seattle, WA, 550-553. IV

Wade, T.L. and C.S. Giam (1989) Organic contaminants in
the Gulf of Mexico. In: Proceedings, 22nd Water for
Texas Conference, Oct. 19-21, 1988, South Shore Harbour
Resort and Conference Center, Uague City, TX (R. Jensen
and C. Dunagan, Eds.), pp. 25-30. V

Craig, A., E.N. Powell, R.R. Fay and J.M. Brooks (1989)
Distribution of Perkinsus marinus in Gulf coast
oyster populations. Estuaries, 12:82-91. IV

Presley, B.J., R.J. Taylor and P.N. Boothe (1990) Trace
Metals in Gulf of Mexico Oysters. The Science of the
Total Environment, 97/98, pp. 551-553. IV

1-4

Sericano, J.L., E.L. Atlas, T.L. Wade and J_M. Brooks.(1990)
NOAA's Status and Trends Mussel Watch
Program: Chlorinated Pesticides and PCBs in
Oysters (Crassostrea virginica) and Sediments
from the Gulf of Mexico, 1986-1987Marine
Environmental Research 29:161-203. IV

Wade, T.L., B. Garcia-Romero, and J.M. Brooks (1990)
Butyltins in Sediments and Bivalves from U.S.
CoastalAreas. Chemosphere 20:647-662. IV

Brooks, J.M., M.C. Kennicutt II, T.L. Wade, A.D. Hart, G.J.
Denoux and T.J. McDonald (1990) Hydrocarbon
Distributions Around a Shallow Water Multimell
Platform. Environmental Science and Technology,
24:1079-1085. IV

Sericano, J.L., T.L. Wade, E.L. Atlas and J.M. Brooks (1990).
Historical Perspective on the Environmental
Bioavailability of DDT and Its Derivatives to Gulf
of Mexico Oysters. Environmental Science and
Technology, 24:1541-1548. IV

Wade, T.L., J.L. Sericano, B. Garcia-Romero, J.M. Brooks, and
B.J. Presley (1990) Gulf Coast NOAA National
Status & Trends Mussel Watch: The First Four
Years, MTS'90 Conference Proceedings, Washington,
D.C., 26-28 September 1990, pp. 274-280. IV, V

Brooks, J.M., T.L. Wade, B.J. Presley, J.L. Sericano, T.J.
McDonald, T.J. Jackson, D.L. Wilkinson and T.F. Davis
(1991) Toxic Contamination of Aquatic Organisms
in Galveston Bay, Proceedings Galveston Bay
Characterization Workshop, February 21-23, 1991, pp. 65-
67. VI

Wade, T.L. J.M. Brooks, J.L. Sericano, T.J. McDonald, B.
Garcia-Romero, R.R. Fay, and D.L. Wilkinson (1991)
Trace Organic Contamination in Galveston Bay:
Results from the NOAA National Status and
Trends Mussel Watch Program, Proceedings
Galveston Bay Characterization Workshop, February 21-23,
1991, pp. 68-70. VI

Presley, B.J., R.J. Taylor and P.N. Boothe (1991) Trace
Metals in Galveston Bay Oysters, Proceedings
Galveston Bay Characterization Workshop, February 21-23,
1991, pp- 71-73. VI

Sericano, J.L., T.L. Wade and J.M. Brooks (1991)
Transplanted Oysters as Sentinel Organisms in
Monitoring Studies, Proceedings Galveston Bay
Characterization Workshop, February 21-23, 1991, pp. 74-
75. VI

McDonald, S.J., J.M. Brooks, D. Wilkinson, T.L. Wade, and T.J.
McDonald (1991) The Effects of the Apex Barge Oil
Spill on the Fish of Galveston Bay, Proceedings
Galveston Bay Characterization Workshop, February 21-23,
1991, pp. 85-86. VI

Wade, T.L., J.M. Brooks, M.C. Kennicutt 11, T.J. McDonald,
G.J. Denoux and T.J. Jackson (1991) Oysters as
Biomonitors of Oil in the Ocean. Proceedings 23rd
Annual Offshore Technology Conference (6529) (Houston,
TX, May 6-9, 199 1), p. 275-280. V

Brooks, J.M., M.A. Champ, T.L. Wade, and S.J. McDonald
(1991) GEARS: Response Strategy for Oil and
Hazardous Spills. Sea Technology, April 1991, pp. 25-
32. V

Sericano, J.L., T. L. Wade and J.M. Brooks (1991) Chlorinated
hydrocarbons in Gulf of Mexico oysters:
Overview of the first four years of the NOAA's
National Status and Trends Mussel Watch
Program (1986-1989) In: Water Pollution: Modelling,
Measuring and Pre&ction. Wrobel, L.C. and Brebbia, C.A_
(Eds.) Computational Mechanics Publications,
Southampton, and Elsevier Applied Science, London. pp.
665-681. V'VI

Wade, T.L., B. Garcia-Romero, and J.M. Brooks (1991)
Bioavailability of butyltins. In: Organic Geochemistry
- Advances and applications in the natural environment.
Manning, D.A.C. (Ed.). Manchester University Press,
Manchester. pp. 571-573. V

Wilson, E.A., EX Powell, M.A. Craig, T.L. Wade and J.M.
Brooks (1991) The distribution of Perkinsus
marinus in Gulf coast oysters: Its relationship
with temperature, reproduction and pollutant body
burden. Int. Reuve der Gesantan Hydrobioligie, 75:533-
550. IV

Sericano, J.L., A.M. El-Husseini, and T.L. Wade (1991)
Isolation of planar polychlorinated biphenyls by
carbon column chromatography. Chemosphere,
23(7):915-924. V, VI

Wade, T.L., B. Garcia-Romero, and J.M. Brooks (1991)
Oysters as Biomonitors of Butyltins in the Gulf of
Mexico. Marine Environmental Research, 32:233-24 1. IV

1-6

Powell, W.N., J.D. Gauthier, E.A. Wilson, A. Nelson, R.R. Fay,
and J.M. Brooks (1992) Oyster disease and climate
change. Are yearly changes in Perkinsus marinus
parasitism in oysters (Crassostrea virginica)
controlled by climatic cycles in the Gulf of
Mexico? PSZNI: Mar. EcoL (In Press). IV

Sericano, J.L., T.L.'Wade, and J.M. Brooks (1993) The
Usefulness of Transplanted Oysters in
Biomonitoring Studies. Proceedings of The Coastal
Society Twelfth International Conference, Oct. 21-24, 1990,
San Antonio, TX (In Press). V

Wade, T.L., J.L. Sericano, J_M. Brooks, and B.J. Presley (1993)
Overview of the First Four Years of the NOAA
National Status and Trends Mussel Watch
Program. Proceedings of The Coastal Society Twelfth
International Conference, Oct. 21-24, 1990 (San Antonio,
TX) (In Press). V

Sericano, J.L., T.L. Wade, B. Garcia-Romero, and J.M. Brooks,
Environmental Accumulation and Depuration of
Tributyltin by the American Oyster, Crassostrea
virginica. Marine Biology (Submitted). IV

Sedcano, J.L., T.L. Wade, E.N. Powell and J.M. Brooks,
Concurrent Chemical and Histological Analyses:
Are They Compatible? Chemistry and Ecology (in
press). V'V1

Wilson, E.A., E.N. Powell, T.L. Wade, R.J. Taylor, B.J. Presley,
and J.M. Brooks, Spatial and Temporal Distributions
of Contaminant Body Burden and Disease in Gulf
of Mexico Oyster Populations: The Role of Local
and Large-Scale Climatic Controls. Helgol.
Meeresunters (in press). V

Sericano, J.L., T.L. Wade, J.M. Brooks, E.L. Atlas, R.R. Fay and
D.L. Wilkinson. National Status and Trends Mussel
Watch Program: Chlordane-related compounds in
Gulf of Mexico oysters: 1986-1990. Environmental
Pollution (in press). V'V1

Hofmann, E.E., J.M. Klinck, E.N. Powell, S. Boyles, M. Ellis.
Modeling oyster populations 11. Adult size and
reproductive effort. (in preparation). V

Hofmann, E.E., E.N. Powell, J.M. Klinck, E.A. Wilson.
Modeling oyster populations 111. Critical feeding
periods, growth and reproduction. (in preparation). V

1-7

Jackson, T.J., T.L. Wade, T.J. McDonald, D.L. Wilkinson and
J.M. Brooks. Polynuclear Aromatic Hydrocarbon OF
Contaminants in Oysters from the Gulf of Mexico
(1986-1990) Environmental Pollution (in press). vi

01
Sericano, J.L., T.L. Wade, A.M. El-Husseini and J.M. Brooks.
Environmental significance of the uptake and
depuration, of planar PCB congeners by the
American oyster (Crassostrea virginica). Marine
Pollution Bulletin (inpress). VI

Wade, T.L., E.N. Powell, T.J. Jackson and J.M. Brooks (1992)
Processes Controlling Temporal Trends in Gulf of
Mexico Oyster Health and Contaminant
Concentrations. Proceedings MTS '92, Marine
Technology Society, Oct. 19-21, 1992, Washington, D.C.
223-229. VI

Ellis, M.S., K.-S. Choi, T.L. Wade, E-N. Powell, T.J. Jackson
and D.H. Lewis. Sources of local variation in
polynuclear aromatic hydrocarbon and pesticide
body burden in oysters (Crassostrea virginica)
from Galveston Bay, Texas. Estuaries (Submitted). V1

Tripp, B.W., J.W. Farrington, E-D. Goldberg, and J.L. Sericano
(1992) International Mussel Watch: the initial
implementation phase. Marine Pollution Bulletin,
24:371-373. VI

Garcia-Romero, B., T.L. Wade, G.G. Salata, and J.M. Brooks.
Butyltin concentrations in oysters from the Gulf
of Mexico during 1989-1991. Environmental Pollution
(in press). VI

1-8

Reprint 1

Processes Controlling Temporal Trends in Gulf of Mexico Oyster
Health and Contaminant Concentrations

Terry L. Wade, Eric N. Powell, Thomas J. Jackson, and James M. Brooks

PROCESSES CONTROLLING TEMPORAL TRENDS IN GULF OF
ME)GCO OYSTER HEALTH AND CONTAMINANT CONCENTRATioNs

Terry L. Wade. Eric N. Powell, Thomas J_ Jackson and James M. Brooks
Geochemical and Environmental Research Group
Texas A&M University
833 Graham Road
College Station, TX 77845 USA

recently summarized in a worldwide
ABSTRACT mussel watch literature survey (1). The
NS&T program has already provided a
The concentrations of PAIL DDT, PCB, good description of the current status
dieldrin and chlordane in Gulf of of selected contaminants in bivalves
Mexico oysters as cumulative % for five and sediments (2.3,4,5,6) from U. S.
consecutive years are discussed. Gulf- coastal areas.
wide changes in contaminant The continued collection of data will
concentrations are observed. PAH and allow for the resolution of possible
DDT co-vary while PCB has a different temporal trends in the contaminant
distribution. The body burden of PAH concentrations. The objective of this
and pesticides in oysters is correlated paper is to examine polynuclear
with latitude. Available evidence-
suggest a linkage between oyster aromatic hydrocarbon (PAH), selected
health '(infection intensity), pesticides, and polychlorinated
reproductive effort and contaminant biphenyl (PCB) data for the first five
body burden. Analyses of oyster years of the NS&T Gulf of Mexico
gonadal material confirms the program for temporal trends and to
reproductive process can purge summarize correlations between oyster
contaminants from oysters, which health and contaminant concentra-
confounds interpretation o f tions. These trends will continue to be
contaminant body burden data. reassessed as additionial years of data
INTRODUCTION become available.
METHODS
The National Status and Trends
(NS&T) Mussel Watch Program was The collection and analytical
instituted in 1986/87 by the techniques used have been described
National Oceanic and Atmospheric elsewhere (3,6) and will only be briefly
Administration (NOAA). The purpose described here. Homogenized oyster
of this program is to determine the tissue is extracted with CH2C12 in the
current status and long-term trends of presence of NA2SO4 (to remove
selected contaminants in U.S. coastal water). The pesticide/ PCB/ PAH are
waters using bivalves as sentinel isolated from other organic materials
organisms. This approach has been using silica gel/alumina column
used successfully in the past as chromatography and high performance

1-10

liquid chromatography with phenogel The distributions of the PAH/PCB and
columns. The purified samples are pesticide concentrati6ns are described
then. analyzed by gas chromatography by a lognormal distribution, Le. the
with a mass selective detection for PAH distribution of data is skewed to low
and an electron capture detector for concentrations and has a fraction
pesticides/ PCBs. The accuracy and which extends to high concentrations.
precision of these methods have been The lognormal distribution. typical of
established by several intercalibration environmental data. has been used (2)
exercises overseen by the U.S. National to define "high" concentrations as
Institute of Standards and Technology. those with logarithmic values more
These intercalibrations document the than the mean plus one standard
comparability of the data between deviation of the logarithms for all
sampling years and between partici- concentrations.
pating analytical laboratories. Distribution functions are useful
measures of environmental quality
RESULTS AND DISCUSSION data in that changes with time can be
determined without being influenced
The geographical distributions 'of by "outliers". For the cumulative
PAH/ PCB /pesticides for the most distribution plot, the data -are -sorted
southern Texas site and continuing to from lowest value to highest value.
the most southern Florida site have similar to rank transformation (10).
been reported (3,4,5,6). . In general, Each observation is 1/n fraction of the
no consistent temporal trends in data set, where n ds the number of
concentration have been observed for samples in the data set.. Te sum of
most contaminants measured as part the fraction of samples less than the
of the NS&T program (4). with the concentration is plotted against- -the
exception of tributyltin which has concentration. From this plot the
decreased from 1989 to the present median can be determined, since it is
(7). defined as the 50th percentile. The
interquatrile range (IQR) is usedas a
Bar graphs (5) or crossplots (4) of data measure of variability@ The IQR is the
comparing one year's data versus 75th percentile minus the 25th
another have been used to display the percentile and equals 1.35 times the
general trend for PAH/PCB and- standard deviation for a -normal
pesticide data (4,5,8). The variations distribution (11).
in concentration for a particular site
are easily visualized using these data The cumulative % distribution for
presentations. However, a cumulative DDTs is shown in Figure 1. Similar
frequency function can be used distribution plots were made for all the
to examine the heterogeneous contaminants measured, but their
distribution of contaminants in Gulf of distributions will only be summarized
Mexico oysters (9). This approach has here. All of the DDT plots are smooth
the advantage of examining the Gulf of "S" shaped curves, indicating the log
Mexico as a single environmental data is a normal distribution. There is
system, determining the percentage of a slight decrease in DDT in Year 2 at
sites exposed to a particular threshold lower concentrations compared to Year
concentration, and providing informa- 1. but almost identical distributions
tion for environmental evaluation. at higher concentrations. The

YFAR I YEAR2
120--- 120-

100--- 100-

so- ---------------- 80-

60- 60

- - ---------------
40- 40 FE

20 20

0 0
1 10 100 1000 10000 1 10 100 1000 10000
TOTAL DDrS (ng/gl TOTAL DDrS (Wgo,

MAR 3 YEAR 4

120- 120-

100- 100-

80- so--
60- ff

IF I
40- 40--

20 20--

0
0
.1 10 100 1000 10000 1 10 100 1000 10000
TOTAL DDrS (ndgi TOTAL DDrS (W&J

YEAR 5 YEAR 4 AND 5
120- 120

100- 100
10 .40
so- so-

60- 60-
Ae
40-- :D 40-

10 100 1000 10000
1 10 100 1000 10000
TOTAL DDTS (ag/gI TOTAL DDrS (agl&j

Figure 1. Cumulative percent of total DDT (ng/g).
row

1-12

distribution for Years 3 and 4 indicates YEAR
increased concentrations throughout 0.0 1.0 2.0 13.0 4@O
the entire Gulf of Menxico. In Year 5 a 10001:_
return to a similar distribution to that
seen in Years I and 2 was observed. It _t@b
I easier to see changes between years
when the plots can be superimposed. 13 PAH
100
The last graph in Figure 1 shows DDTs 0
for Years 4 and 5 on the same plot- It PCB
is easy to see that both are normal
DDT
distributions, but Year 4 (farthest from Z
W
the right) had higher concentrations 0 10
:;_1 CHLO .LNE
throughout the entire Gulf. 0
DIE@@RIN
Because of space limitations. not all of
the distributions are presented here. 1
However, the geometric median of the
distributions for PAH, PCB. DDT, Figure 2. Median vs. year.
chlordane and dieldrin are plotted vs.
sampling year in Figure 2. This figure
summarizes the distributions as seen
from comparison of the DDT
distribution in Figure I to the DDT
plot in Figure 2. In Figure 2 the The NS&T program was designed to
geometric mean concentration for DDT address the question of temporal
decreases slightly between Years I and trends in the contaminant loadings of
2, increases in Years 3 and 4 and is U. S. coastal waters. Examination of
back to concentrations similar to Years the first five years of data indicates
I and 2 in Year 5. This was expected. that for the entire Gulf of Mexico
based on Figure 1. The distribution contaminant concentrations appear to
for the total of the 18 PAHs measured be remaining relatively constant.
as part of the NS&T program shows However, there are yearly fluctuations
the same distribution as the DDT above and below this "normal
(Figure 2). The PAH distribution has concentration". These concordant
been described in detail (12).. The Gulf-wide fluctuations suggest that
other contaminant classes displayed climatic factors exert a strong
different distributions. Total PCBs influence on contaminant body
distribution for the Gulf of Mexico had burdens and on biological attributes of
a slight decrease between Year I and oysters (13).
Year 2, then a steady increase from
Year 2 to Year 5. It therefore appears The NS&T program was not designed
that the PCBs have different source to answer the question of what effect
functions than the DDTs and PAHs. the contaminant loading has on the
The dieldrin and chlordane concen- health of oysters. The program does,
trations change little between the however, measure several indicators of
years, except for the possibility of a oyster health, including condition
slight decrease over the five years of index. disease incidence and
sampling. reproductive stage. Since the NS&T

1-13

study was not designed to answer reduce reproductive 'effort (24). In
these. questions. they can not be oysters, both reproductive rate and
answered rigorously with the data disease are significantly effected by
available, but can be partially temperature and salinity (25) and
answered. thus could serve as important
intermediaries between climate change
Biological and environmental factors and contaminant body burden.
may effect the rate and extent of
bioaccumulation. These biological Recently, newly developed techniques
factors include differential growth rate have enabled us to determine t "he
(14,15). reproductive stage (14,16,17), concentration of organic contaminants
stress and disease (18,19.20). These in oyster gonadal tissues (26). These
biological factors make spatial and analyses revealed that eggs and sperm
temporal comparisons designed to are enriched in PAH and PCB
evaluate the status and trends of compared to somatic tissue. Eggs. but
contaminant loading more difficult. not sperm, were enriched in DI)Ts and
chlordane. Dieldrin was not detected
Analysis of the first four years of NS&T in these oyster samples. This evidence
data has shown that the body burden indicates that the frequency of
of polynticlear aromatic hydrocarbons spawning and timing of oyster
(PAHs) and pesticides in oysters is collection during their spawning cycle
correlated with latitude in the Gulf of may effect body burden of
Mexico. Wilson et al. (21) suggested contaminants. These processes may
that the latitudinal temperature explain the latitudinal gradient in PAH
gradient in the Gulf produced variation and pesticide body burdens observed
in reproductive effort and that this for the Gulf -of Mexico (2 1) and the
variation in reproductive effort effected relationship of PAH body burden and.
PAH body. burden sufficiently to climate change.
override the effect of local variation in
contaminant loading in many cases. These complexities as oysters . makes
Wilson et al. (13). in a more thorough interpretation of NS&T data - a
analysis, showed that PAH body challenge. The more we can learn
burden responds to climate change about the NS&T sentinel organisms,
and that biological factors. climate's C.virginim the more likely we will be
effect on temperature, and freshwater able to meet that challenge..
inflow may effect the final body burden
of PAHs.
Likely biological factors are ACKNOWLEDGMENTS
reproduction and disease (Perkinsus This research was supported by the
marinus) infection intensity. National Oceanic and Atmospheric
Reproduction has frequently been Administration (NOAA), U.S.
suggested as an important route of Department of Commerce, Grants
depuration (22) because lipid loss 50-DGNC-5-00262 and 50-DGNC-0-
peaks at this time. Parasites and 00047 from the Office of Ocean
pathogens are less frequently Resources Conservation and
implicated (23). but parasites and Assessment.
pathogens should have an effect: if for
no other reason, they frequently

1-14

REFERENCES Gulf of Me-xico. t Marine Envtron.
Res., 32.1991. pp. 233-241.
1. Cantillo, A. Y. Mussel Watch
worldwide literature survey. 8- Wade. T. L_ Brooks. J. M..
NOAA Technical Memorandum Kennicutt 11. M. C.. Denoux, G.J.
NOS ORCA 63, 1991, 136 p. and Jackson. T. J. Oysters as
Biomonitors of Oil in the Ocean.
2. O'Connor, T. P. Coastal Proceedings of the 23rd Annual
Environmental Quality in the Offshore Technology Conference,
United States. 1990. Chemical OTC 6529. 199 1. pp. 275-280.
Contamination in Sediment and
Tissues. A Special NOAA 20th 9. Mackay, D. and Paterson. S.
Anniversary Report, 1990. 34 p. Spatial concentration distri-
butions. Environ. Sci. TechnoL,
3. Wade, T. L., Atlas, E. L.. Brooks. 18, 1984, pp. 207A-214A_
J. M., Kennicutt 11, M. C.. Fox, R.
G., Sericano. J., Garcia-Romero. 10. Conover. W. J. and Iman. R_ L.
B. and Defreitas, D. A. NOAA Gulf Rank Transformations as a Bridge
of Mexico Status and Trends between Parametric and Non-
Program: Trace organic contam- parametric Statistics. Arneri.
inant distribution in sediments Statistic@ 35, 1981.pp. 124-129.
and oysters. Estuaries, 11. 1988.
pp. 171-179. 11. Hensel, D. R_ Less than obvious.
Statistical treatment of data below
4. Wade, T.L. @ and Sericano. J_ the detection limit. Environ. ScL
Trends in Organic Contaminant TechnoL.24,1990, pp. 1766-1774.
Distributions in Oysters from the
Gulf of Mexico. Oceans '89 12. Jackson. T.J., Wade. T. L..
Proceedings, 1989. pp. 585-589. McDonald. T.J., Wilkinson, D. L.
and Brooks. J. M. Polynuclear
5. Wade, T. L., -Sericano, J. L., Aromatic Hydrocarbon
Garcia-Romero, B., Brooks, J. M. Contaminants in Oysters from the
and Presley, B. J. Gulf Coast Gulf of Mexico (1986-1990).
NOAA National Status & Trends Environmental Pollution (in press).
Mussel Watch: The, First Four
Years. Proc. Mar. TeclL Soc., 1990, 13. Wilson, E.A.. Powell, E.N., Wade.
pp. 274-280. T. L_ Taylor, R. J., Presley, B.J.
and Brooks, J.M. Spatial and
6. Sericano, J. L., Wade, T. L., Atlas. temporal distributions of
E. L. and Brooks, J. M. Historical contaminant body burden and
Perspective on the Environmental disease in Gulf of Mexico oyster
Bioavailability of DDT and Its populations: The role of local and
Derivatives to Gulf of Mexico large-scale climatic controls.
Oysters. EnvLron. ScL Technol.. Helgot. Meeresunters. (submitted).
77,1990. pp. 1541-1548. 14. Cunningham, P.A. and Tripp,
7. Wade, T. L., Garcia-Romero. B., M.R. Factors affecting the
and Brooks, J. M. Oysters as accumulation and removal of
Biomonitors of Butyltins in the mercury from tissues of the

American oyster Crassostrea marinus in Gulf Coast oysters: its
qirginica. Mar. Biol. (Berl.). 3 1. relationship with temperature.
1.975, pp. 311-319. reproduction, and pollutant body
burden. Int. Rev. Gesamten
15. Boyden. C.R- Effect of size upon Hydrobiol.. 75. 1990, pp. 533-550.
metal content of shellfish. J. Mar.
Biol. Assoc. U.K. 57. 1977, pp. 22. Cossa. D. A review of the use of
675-714. Mytilus spp. as quantitative
indicators of cadmium and
16. Frazier, J.M. The dynamics of mercury contamination in coastal
metals in the American oyster, waters. Oceanol. Acta, 12. 1989.
Crassostrea virginica. 1. Seasonal pp. 417-432.
effects. Chesapeake Sci.. 16.
1975, pp. 162-171. 23. Khan. R-A. Effects of chronic
exposure t o petroleum
17. Martinicic.- D., Ndrnberg. H.W., hydrocarbons on two species of
Stoeppler, M., and Branica, M. marine fish infected with a
Bioaccumulation of heavy metals hernoprotozoan. Trypanosoma
by bivalves from Lim ]Fjord (North munnanensis. Cam J. - Zool.. 65,
Adriatic Sea). Mar. Biol. (Berl.), 1987, pp. 2703-2709.
81, 1984, pp. 177-188. 24. Barber. B-J.. Ford, S.E. and
18. Shuster, Jr., C.N. and Pringle, Haskin, H.H. Effects of the
B.H. Trace metal accumulation parasite MSX (Haplosporidium
by the American eastern oyster, nelsoni) on oyster (Crassostrea
Crassostrea 6irginica. Rroc. NatL virginica) energy metabolism. 1.
Shell IL Assoc., 59, 1969, pp. 9 1 - Condition index.. and relative
Yls
103. fecundity. J. Sheltftsh Res., 7.
1988, pp. 25-3 1.
19. Sindermann, C.J. An examination
of some relationships between 25. Soniat, T.M. and Gauthier. J.D.
pollution and disease. Rapp. P-V. The prevalence and intensity of
R6un. Cons. Int. Explor. Mer., 182, Perkinsus marinus from the mid
1983, pp. 37-43. northern Gulf of Mexico. with
comments or! the relationship of
20. Moore, M.N., Livingstone, D.R. the oyster parasite to temperature
and Widdows, J. Hydrocarbons in and salinity@ Tulane Stu& Zool.
marine mollusks: Biological Bot., 27. 1989. pp. 21-27.
effects and ecological
consequences. In: U. Varanasi 26. Ellis, M.S., Choi, MS.. Wade, T.L.,
(ed.). Metabolism of polycyclic Powell, E. N.. Jackson, T.J. and
aromatic hydrocarbons . in the Lewis. D. H. Sources of local
aquatic environment. CRC Press, variation in.polynuclear aromatic
Boca Raton, FL, 1989, pp. 291- hydrocarbon and pesticide body
329. burden in oysters (Crassostrea
virginica) from Galveston Bay.
21. Wilson, E.A., Powell, E.N., Craig, Texas. Estuaries (submitted).
M.A., Wade, T.L. and Brooks, J.M.
The distribution of Perkinsus

1-16

Reprint 2

International Mussel Watch: the Initial Implementation Phase

Bruce W. Tripp, John W. Farrington, Edward D. Goldberg, and- Jos6 Sericano

International Mussel Watch: the TABLE I
Members of the Intemational Mussel Watch Committee.
initial implementation phase Members EX Officio
As a consequence of increasing population and inten- Edward D. Goldberg, Chairman Bruce NV. Tripp,
sifying industrial development on a global scale, the Scripps Institution of Executive Officer
Oceanography, USA CRC/Woods Hole Oceanographic
world's coastal waters will continue to receive societal Institution, USA
John W Farrington,
waste. The goals of the International Mussel Watch vice Chairman Josi Sericano,
Project are to assess the extent and severity of contarm- Woods Hole Oceanographic Field Scientific Officer
nation of the coastal waters of the world with respect to Institution, USA GERG/Tcxas A&M University,
selected chemicals, and to develop an international Roger Dawson USA
Chesapeake Biological Anthony H. Knap,
infrastructure of cooperating scientists and laboratories Laboratory, USA UNESCO-GEMS[ Liaison
for research and monitoring of contaminants in coastal Arne 13. jlmelo, Bermuda Biological Station for
waters worldwide in the future. Water & Air Pollution Research Research, Bermuda
The International Oceanographic Commission of Laboratory, Sweden
UNESCO (IOC), in collaboration with the United Laurence D_ Mee
Intemational Atomic Energy
Nations Environment Program (UNEP) and the US Aeency, Monaco
National Oceanographic and Atmospheric Administra- Eric Schneider
tion (NOAA) are jointly funding -the International Nationil Oceanic and
Mussel Watch Program and have initiated a monitoring Atmospheric Administration,
programme in Central and South America in 1991-92. USA
The programme is being directed by the International
Mussel Watch Committee (Table 1) and administered by , The need for an International Mussel Watch project
the Project Secretariat office based at the Woods Hole was recognized in 1975, when Professor Edward Gold-
Oceanographic Institution, Woods Hole, Massachusetts, berg in his Marine Pollution Bulletin editorial, called for
02543, USA- a global marine monitoring programme to serve as a

1-18

Marine Pollution Bulletin

-springboard for actiore. He outlined a fiscally reason- meeting. Communication'at the international level was
Z,
able, global scale monitoring programme based on the continued at a second meeting held in Hawaii in
sentinel organisms concept--Mis monitoring programme November of 1983. Participants at the Hawaii meeting
must be capable of detecting spatial and temporal trends examined the conceptual approaches used by the Mussel
in concentrations of several important chemical con- Watch programmes and assessed the potential for
taminants. Since the late 1960s, scientists have been expansion of this approach to a global scale and
usinQ bivalve filter-feeding molluscs to monitor for especially to the southern hemisphere- The need for the
selected chemical contaminants in coastal marine waters International Mussel Watch Project was reaffirmed at
and an extensive'mussel watch' literature has developed the Hawaii meeting. Planning momentum was main-
from that work. Such contamination of coastal waters tained by the International Mussel Watch Committee
mi ht result in changes that are deleterious, over the tong during the next few years.
9 C,
tenn. to both the integrity of the coastal environment The International Mussel Watch Project is being
and to human health. Because of their sedentary habits implemented initially in the Central-South America and
and their ability to bioconcentrate the pollutants of Caribbean region and will focus on organochlorine
interest, mussels and other bivalve species appear to be biocide contaminants and PCBs (Table 2). Plans are
appropriate sentinel organisms even considering com-
plexities such as age, season, organism health and inter- TABLE2
species differences. The mussel watch approach has Chlorinated hydrocarbons to be analysed in collected tissue samples.
been adopted as one of several coastal environmental We envisage that about 70-80 sites will be sampled for indigenous
quality monitoring strategies by several national pro- bivalves and tissue samples will be analysed for a variety of chlorinated
grammes and by UN programmes. The International pesticides and -,elected chlorinated biphenyls- -
Mussel Watch Project will build on this cumulative Aldrin Heptachlor
Fmdrin Reptachlor epoxide
experience. A world-wide literature search has recently Dieldrin Rex;ichlorobemene (HCB)
been completed by the US NOAA Status and Trends chlodane, m-Hexachlorocycloherane (cc-HCH)
Program and is available as a special reporL fl-Hexachlorocyclohexane (fl-HCH)
Particulaily important among the monitoring
., pro- op@-DDD undarte (rHCH)
grammes that were established during the 1970s were pV-DDD Trans-nonachlor
p%DDE Methoxychlor
those of the International Council for the Exploration of o' '
pp -DDE
the Sea (ICES@ The United Nations Environment o,p'-DDrr
Program has also created its Regional Seas Program p4)'-DDT
which has placed a major emphasis on the development NOTES: I-firex and Kelthane may be added to the suite of
of host country capabilities for measuring the levels of contaminants analysed if funding for analysis becomes available-
contaminants in coastal and marine environments. The A common set of individual chlorobiphenyls (PCEks) %%ill be chosen
for analysis followirig the assessment of the results of the first round of
IOC sponsored the formation of a Task Team on Marine intercalibration exercises Qf lOC/lCES/JMG. TOW PCBs will be
Pollution Research and Monitoring in the West Pacific estimated from these dam
region- National governments in many countries have
initiated their own coastal monitoring, progg th tami
.,rammes to being made to expand the programme to o er con
provide technica *I information that can be used to protect nants and to other regions so that all countries that wish
coastal resources from the deleterious effects of to participate, may do so. We invite anyone interested in
chemical contamination. In the United States, the participating in subsequent phases. of International
'Mussel Watch' Program was begun by the US EPA in Mussel Watch to contact the Project Secretariat in
the mid 1970s and involved academic scientists from Woods Hole. Currently available funding does not
several academic research institutions. This programme permit a global-scale programme or a Western Hemi-
used mussels and oysters as indicators of the local levels sphere program that includes all types of chemical
of four classes of pollutants in US coastal waters, includ- contaminants. This initial implementation phase will
ing synthetic organics, fossil fuel compounds and their focus on organochlorine biocides because of their
derivatives, several metals, and the transuranic radio- continued used in agricultural and public health applica-
active elements produced in the nuclear fuel cycle and tions in several tropical and sub-tropical areas and
by fallout from nuclear weapons tests. Mussel Watch because we know very little about production and use or
became an operational contaminant monitorin
g pro- about the resulting coastal contamination. The experi-
gramme in the United States in 1986 and is presently enced gained from this initial phase vill be useful in
directed by US NOAA as a component of the Status and implementing an expansion of the programme.
Trends Program- In May, 1991 members of the International Mussel
In a 1978 workshop in Barcelona, the members of the Watch Committee and representatives of three regional
US Mussel Watch Program joined with scientists of monitoring programmes met at the University of Costa
other countries to assess the methodologies employed Rica to finalize the initial implementation phase of Inter-
for the detection and measurement of pollutants in national Mussel Watch. At that meeting, sampling sites
coastal zones throu-h the sentinel organism approach. and participating national scienti@ts were selected. The
The participants at the Barcelona workshop decided Project Secretariat coordinates the work of two central
that continuing international collaboration and com- analytical facilities. International Laboratory for Marine
munication would be worthwhile, and elected a com- Radioactivity (ILMR) in Monaco and Geochemical and
mittee charged uith the task of planning for A f Environmental Research Group (GERG) at Texas

Volume 24/NumbLr7/July 1992

A&M University, will anaIN se the collected samples for country scientists is being organized for early 1993. For
ori!anochlorine contaminants. Tissue samples and those scientists with analytical expertise, tissue samples
extracts will be archived for later analysis of other will be available for in-country analysis and inter-
contaminants if funding is available. ILMR will also laboratory comparison. Host-country scientists will be
supervise the Field Scientist responsible for sample asked to assemble production and use data as well, from
collection. The International Mussel Watch Project vill available sources in their respective countries.
complement, regional monitoring programmes where This initial implementation phase will: L generate
they are established, thus linking the existing pro- high quality data on chlorinated pesticides and estimate
grammes and increasing their effectiveness. These exist- PCB concentrations in the Central-South America-
ino regional programmes provide a base on which to Caribbean coastal region, 2 serve as a 'field-test' of a
build an international prop
,ramme and their support and large-scale international marine monitoring programme
collaboration is critical to the success of the inter- for chemical contaminants, 3 create an international
national programme. In the initial implementation network of coastal environmental scientists, 4. provide a
0
phase, samples will be collected throughout the region forum for training and for discussion of analytical
with the assistance of host-country scientists. These results, and 5. create the institutional structure for a
scientists will form the nucleus of an internationar global scale coastal monitorina . programme.
marine monitoring network, through which the results of Continuation (and expansi on) of this project will be
the project will be disseminated. considered when the programme is assessed at the
Host-country scientists and IMW sampling sites will conclusion of the initial implementation phase. Host-
be coordinated by the Woods Hole-based Project countries and the entire UN family will benefit from the
Secretariat, working with the Field Scientific Officer. AU scientific results generated during this initial phase and
sampling and sample logistics will be supervised by the will have an opportunity to expand local monitoring
Field Scientific Officer and the host-country scientists activities with technical support from the Project as well
will work directly. with him. The field sampling is as to integrate these activities into regional and global-
currently underway, and collection have already been scale programmes.
completed in much of Central America and South
America. The Project Secretariat and the Field Scien- BRUCE W. TRIPP
Woods Hole Oceanographic Instifution, Woods Hole,
tific Officer will provide technical support to host- 02543 SA
MA U _ I
country scientists as resources permit. The International JOHN W. FARRINGTON
Mussel Watch Committee, in concert with the Project Woods Hole Oceanographic Institution, Woods Hole,
Secretariat, the Field Scientific Officer, and the contract MA 02-543, USA.
EDWARD D. GOLDBERG
laboratories will provide data interpretation, taking into Scripps Instilwion of0ceanography, La Jolla, C4 92093, USA.
account comments from host-country scientists..'An JOSE SERICANO
international meeting, involving participating host- internationai Laboratotyfor Marine Radioac&4, Monaco.

1-20

2.0 Introduction

This document is one volume of the Sixth Annual Report
prepared by the Geochemical and Environmental Research Group
(GERG), in the College of Geosciences and Maritime Studies at Texas
A&M University, for the U.S. Department of Commerce National
Oceanic and Atmospheric Administration's Mussel Watch Project for
the Gulf of Mexico. This section discusses the background and
relevance of the proposed project and reviews the study objectives.

The overall goal of the national Mussel Watch Project is to assess
and document the status and long-term changes in the environmental
quality of coastal and estuarine environments along the East and West
coasts of the United States and the Gulf of Mexico coast. In order to
meet this goal, a series of systematic observations of selected chemical
contaminants (e.g., trace metals, PAHs, PCBs, and pesticides) in
representative samples of bivalves and sediments has been
undertaken. GERG's portion of the project deals with U.S. Gulf of
Mexico coastal sites. This document presents the results obtained
during the first six years of the project. Three other documents as
part of the sixth year study include:

0 Analytical Methods
0 Analytical Data
0 Field Sampling and Logistics

2.1 Project Relevance and Direction

Over the last several decades, problems associated with
chemical contamination of the marine environment have received
increasing attention. Numerous studies have been undertaken to
identify the inputs, transport, and effects of a variety of elements and
compounds. Among the major contaminants studied are petroleum
hydrocarbons, halogenated organic compounds, and a suite of trace
metals including cadmium (Cd), lead (Pb), zinc (Zn), mercury (Hg),
and others. Particular attention has focused on the coastal zone and
estuaries near large population centers. These areas potentially
experience the largest impact from chemical contamination and may
be most sensitive to the accumulation of toxic compounds.

One approach for monitoring the status of coastal and estuarine
pollution on a national scale has been the concept of "sentinel
organisms" or "bioindicators". The National Mussel Watch Project
initially sponsored by the Environmental Protection Agency was an
application of this concept. The project used bivalves to monitor the
"health" of marine ecosystems and identify "hot spots" of chemical
contamination along the nation's coastline. Some of the results of this

2-1

project have been summarized (Farrington et al., 1983; NAS, 1980)
and are discussed later in this report.

Farrington et al. (1983) summarized the rationale for using
common mussels (Mytilus sp.), various oyster species (Crassostrea and
Ostrea) and other bivalves as "sentinel" organisms:

1. Bivalves are cosmopolitan (widely distributed
geographically). This characteristic minimizes the
problems inherent in comparing data for markedly different
species with different life histories and relationships within
their habitat.

2. They are sedentary and are thus better than mobile species
as integrators of chemical pollution at a given area.

3. They concentrate many chemicals by factors of 102 to 105
compared to seawater concentrations in their habitat.
Trace constituent measurements are easier to accomplish in
tissues than in seawater.

4. Inasmuch as the chemicals are measured in the bivalves, an
assessment of biological availability of chemicals is obtained.

5. In comparison to fish and crustacea, bivalves exhibit low or
undetectable activity of those enzyme systems that
metabolize many xenobiotics such as aromatic hydrocarbons
and polychlorinated biphenyls (PCBs). Thus, a more
accurate assessment of the magnitude of xenobiotic
contamination in the habitat of the bivalves can be made.

6. They have many relatively stable local populations extensive
enough to be sampled repeatedly, providing data on short-
and long-term temporal changes in concentrations of
pollutant chemicals.

7. They survive under conditions of pollution that often
severely reduce or eliminate other species.

8. They can be successfully transplanted and maintained on
subtidal moorings or on intertidal shore areas where normal
populations do not grow due to a lack of suitable substrate.

9. They are a commercially valuable seafood species on a
worldwide basis. Therefore, measurement of chemical
contamination is of interest for public health considerations.

2-2

An international workshop. "Mussel Watch 11", convened to
reassess the "Mussel Watch" concept and to evaluate the
accomplishments and deficiencies of the EPA Mussel Watch Project
that was implemented. Some of the conclusions are summarized
below:

Accomplishments:

� An extensive data base of radionuclides in mussels and oysters
was obtained. These data allowed the detection of several
minor leakages from nuclear reactors.

0 The Mussel Watch data base of trace metals has permitted an
assessment of the perturbations in the biogeochemical cycles
of metals in coastal waters induced by their mobilization by
man and by waste discharges.

� Measurements of PCB and DDT compounds established a data
base against which future changes -can be measured.

� Data from the Mussel Watch Project provided conclusive
evidence that' polynuclear aromatic hydrocarbons produced
from combustion products are not generally accumulated in
food webs.

Deficiencies:

� Analytical limitations in trace organic analysis prevented a
wider spectrum of organic compounds from being measured.
Subsequent analyses have revealed other compounds such as
hexachlorobenzene, mirex, and others.

� Data management was inadequate, and consequently data was
not promptly available.

� Some samples from the Gulf Coast were never analyzed.

0 Statistical design was not established prior to the sampling
and analytical project.

� Mussels (or oysters) are unsatisfactory for the identification of
new pollutant compounds, and they do not readily accumulate
potentially important compounds or compound groups.

The consensus opinion was that a modified, more specifically
defined approach to using marine organisms as environmental
indicators would be a valuable tool in assessing estuarine and coastal
contamination.

2-3

2.2 Study Objectives

Reliable and continuous information regarding the status and
trends of environmental quality in the nation's coastal and estuarine
regions is necessary to make informed decisions involving the use and
allocation of resources. The National Status and Trends Project for
Marine Environmental Quality was initiated in 1984 by the Ocean
Assessment Division of NOAA to provide this environmental quality
information. Based on the experience gained during the EPA Mussel
Watch Project and on recommendations from a workshop report, the
chemical measurements segment of the National Status and Trends
Project was developed. During the workshop on chemical
measurements, the working hypothesis of the project was worded as
follows:

"Chemical measurements of t@xic contaminant levels in
environmental samples serve as leading indicators of trends
in environmental quality and can reflect trends in inputs of
these chemicals into marine systems. Significant
correlations have been demonstrated between contaminant
levels in marine samples and the health of marine biological
components."

Implicit in such a statement is that the chemical measurements are of
the highest quality obtainable, are directly comparable between all
sites and samples, and have a known statistical variability. Simply
stated, the objective of this project is to provide such measurements
in sediments and bivalves and to provide sufficient ancillary data to
allow meaningful interpretation of the measurements.

There are four stated objectives for the National Status and
Trends Project:

1. The primary objective is to establish a national data base
using state-of-the-art sampling, preservation, and analysis
methodologies which are consistently applied and subject to
rigorous quality control and assurance.

2. Use the information in the data base to estimate
environmental quality, to establish a statistical basis for
detecting spatial and temporal change, and to identify areas
of the nation that might benefit from more intensive study.

3. Seek and validate additional measurement techniques,
especially those that describe a biological response to the
presence of contaminants.

2-4

4. Create a cryogenic, archival specimen bank containing
environmental samples collected and preserved through
techniques that will permit reliable analysis over a period of
decades.

In a general sense, scientific objectives relating to the goals of
our Mussel Watch portion of the National Status and Trends Project
include:

� What is the geographic distribution of contaminant
concentration in oysters and sediments at selected sites
along the Gulf of Me2dco coast?

� Are there "problem" areas?

� Are particular compounds or classes of compounds significant
contaminants in broad regions of the Gulf.?

� What is the relationship between contaminant concentration
in sediments and in oysters?

� What is the relationship between the concentration of
specific metals and organic compounds?

� What is the variability in contaminant concentrations VAthin
sites and between sites?

� What portion of that variability can be removed by normalizing
chemical measurements to other parameters (e.-g. to TOC,
lipid weight, etc.)?

� Are there unidentified contaminants present in significant
quantities in the samples?

� What is the relationship between the "health" of oysters and
the concentration of contaminants9

� Are contaminant concentrations increasing or decreasing
with time?

The long-term project is designed to examine the above
problems in a rigorous manner. Some of these objectives are being
pursued as is evidenced by publications that have resulted from this
program (Table 1.1). It can be expected, too, that other problems and
questions will arise during the course of the project.

Researchers at GERG are pursuing the answers to some of the
above questions through our association with this NOAA NS&T Project
as well as other programs (i.e. EPA Galveston Bay National Estuary

2-5

Program, EPA - EA4AP-NQ and through unfunded student thesis and
dissertation research. Some of the ongoing research at GERG involves
oyster transplant studies in an attempt to better calibrate oysters as
detectors of environmental contaminants. GERG has developed
techniques to analyze alkylated PAH, PAH metabolites and planer
PCBs, and is currently developing methods for dioxins and
dibenzofurans. All of these research projects may be of value to the
NS&T Project in the future.

2-6

3.0 Polynuclear Aromatic Hydrocarbon Results

Polynuclear Aromatic Hydrocarbon (PAH) concentrations are of
concern because many of these compounds are known or suspected
carcinogens and/or mutagens. The sources of PAH in the environment
are petroleum, petroleum products, and combustion of fossil fuels and
organic materials (i.e. forest fires). /,@n@estuarine systems PAH inputs
may come from natural seepage,/oil production, oil transportation,
atmospheric deposition, combustion products (i.e. creosote),
municipal waste, industrial wastq and runoff. Although large spills get
most of the publicity in the popular press, they account for less than
15% of the total PAH entering the marine environment.
The concentration of 24 POI y1nuclear Aromatic Hydrocarbons
(PAH) are measured as part of the NS&T project in oyster and
sediment samples from the Gulf of Me@dco. The NS&T data can ' be
used to provide some indication of the relative importance of
petroleum verses combustion sources, but analyses of additional
alkylated PAH is even more definitive (Preprint 1).

One purpose of the NS&T project is to determine the
environmental quality of the nation's coastal zone. This has been fairly
well addressed, as described in the recent publications and reports
that have resulted from this project (Tablel.1 and reprints 3.4.5. and
6). Another purpose of the NS&T project is to determine if the
environmental conditions of the U.S. coastal zone are getting better or
worse. The continued collection of data is necessary in order to
address this last question.

The NS&T sites are chosen to avoid "hot spots" or known point
sources of contaminant inputs. The sites are sampled once a year in
the winter in an attempt to eliminate seasonal variability. Samples are
collected from three stations at each site and analyzed individually.
The geographical distribution of Gulf Coast oyster PAHs are shown in
Figures 3.1 to 3.29. The total of the PAH measured in all years (Figure
3. 1) indicates concentration ranges from below the detection limit
(-20 ng/g) to concentration of over 12 mg/g. Based on the total of
measured PAH, little change is obvious for the first six years in
geographic PAH distribution, when within-site variability is
considered. Most sites have lower total measured PAH concentration
in Year 6 when- compared to the mean of Years I to 5. Only three sites
had higher concentrations in Year 6. The 2 and 3 ring lower
molecular weight PAHs (Figure 3.25) show a similar distribution with
only three sites higher in year 6. The high molecular weight 4 and 5
ring PAHs (Figure 3.26) had higher concentrations at only eight sites
in Year 6 compared with the mean of the first five years. The 4 and 5
ring PAHs represent the major percentage of PAH present in these
samples (Figures 3.27 and 3.28). The total of all 24 PAHs measured

3-1

since year 2 (Figure 3.29) shows the same trend as the total of the 18
PAHs.

The concentrations of PAH at most sites did not change when
the concentrations for Year 6 are compared to the mean
concentrations for Years I to 5. The predominant PAHs detected
were pyrene, fluoranthene, chrysene and napthalenes. In general, the
4 and 5 member rings predominated, however, there were
considerable amounts of 2 and 3 ring aromatics at some sites. -The 01
decrease at some sites in total aromatics for Years 1-4 vs. Year 5 was
due to a decrease in the 2 and 3 ring aromatics (i.e., BBSD, CBJB and
SAV%TB, Figure 3.25). The presence of the 4 and 5 ring aromatics is
indicative of PAH from combustion sources. However, as discussed
above, alkylated PAH provide additional resolution of sources (Preprint
U.

There are generally higher PAH concentrations in bay systems
that are adjacent to large urban areas with the associated high levels of
industrial activities. An example of this is Galveston Bay, Texas. The
closer the site is to the urban area, the higher the PAH concentration
(Reprint 4).

The general overall conclusion from the PAH data is there is no
significant change in PAH concentrations at most of the sites sampled
over the six year period. The PAHs found in higher concentrations
(pyrene, fluoranthene) are mainly derived from combustion sources.
This input should be relatively constant with time and reflect the
consistency in PAH concentrations between sampling years. The sites
that show large increases in a given sampling year usually show
decreases in subsequent years. This indicates that episodic inputs of
PAH, possibly from oil spills, account for these increases. Then when
the input stops, the ecosystem starts to recover.

We are continuing to look for temporal trends in the PAH data.
The data set for the six years of NS&T program is large. Therefore,
trends analyses require the use of various techniques including
statistical ones. Other complications with data interpretation are
caused by the nature of oysters. They can accumulate and depurate
contaminants. There is currently only limited data on these
processes. GERG is developing more data through several
independant resarch projects (Preprint 2 and Reprint 5). We are
currently looking at the data in an attempt to detect any gulf-wide
temporal trends.

3-2

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b. LA w IA

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80-

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Figure 3.7 Average acenaphthylene concentrations in oysters from
each NS&T Mussel Watch Gulf of Mexico sampling site
for Years 1-5 and Year 6.
3-9

00 06

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171

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.....................

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Reprint 3

Trace Organic Contamination in Galveston Bay:
Results from the NOAA National Status and Trends
Mussel Watch Program

Terry L. Wade, James M. Brooks, Josil, L. Sericano, Thomas J. McDonald,
Bernardo Garcia-Romero, Roger R. Fay, and Dan L. Wilkinson

Trace Organic Contamination in Galveston Bay:
Results &onx the NOAA National Status and Trends
Mussel Watch Program

Terry L. Wade, James M. Brooks, Josd L. Sericano, Thomas J. McDonald,
Bernardo Garcia-Romero, Roger R. Fay, and Dan L. Wilkinson
Geochemical and Environmental Research Group, Texas A&M University

In order to determine the current status and long- term trends for selected
environmental contaminants in U.S. coastal areas, the National Oceanic and
Atmospheric Administration (NOAA) established the National Status and Trends
(NS&T) Mussel Watch Program. As part of the NS&T Program, sediment and
oyster samples have been collected and analyzed from over 70 estuarine sites in
the Gulf of Mexico representing all major Gulf Coast estuaries. Sampling sites
were located in areas not influenced by known point sources of inputs.

Oysters have been employed as sentinel organisms because they are
cosmopolitan, sedentary, known to bioaccumulate contaminants of interest, able
to provide an assessment of bioavailability, not readily capable of metabolizing
contaminants, able to survive pollution loading, readily found as locally stable
populations, transplantable, and commercially valuable. Oysters are, therefore,
excellent biomonitors for contamination in estuarine areas.

The Galveston Bay system is one of the largest and most economically important
estuaries along the Texas Gulf Coast. This area has been the recipient of various
contaminant inputs because of an aggressively growing urban. and industrial
region. Houston, Deer Park, Baytown, Texas City and Galveston, surrounding
Galveston Bay to the north and west, are some of the most heavily industrialized
areas in Texas. Hundreds of industrial plants bordering the Galveston Bay
estuarine system, including petrochemical complexes and refineries, as well as
runoff, are--likely to introduce significant amounts of organic contaminants into
the bay. In general, ecological studies have suggested that the waters of
Galveston Bay contained contaminants in sublethal amounts which caused stress
to organisms resulting in significant changes in the, estuarine community
structure.

Samples were collected at six locations in Galveston Bay (Fig. 1). Sampling was
conducted each winter and began January of 1986 at four sites (15-18), and in
December of 1987 at two other sites (58-59). Additional samples were collected at
some of these sites to provide information on seasonal trends in contaminant
concentrations. Sediments (top 1 cm) and oysters (20) were collected at three
stations at each site and analyzed for polynuclear aromatic hydrocarbons (PAHs),
polychlorinated biphenyls (PCBs), chlorinated pesticides (e.g DDT, chlordane) and
tributyltin. All sample analyses were performed using Standard Operating
Procedures to provide high quality, precise, accurate and reproducible data. Data
quality was further assured by participation in NOAA/NIST intercalibration
exercises. This allows for direct comparison of NS&T Gulf Coast data with NS&T
data for the East and West Coasts.

3-33

rRINlry BAY

16 CASf
*17
GALVESTON 8AY

too
Of

-SAN LUIS 01 11 1. 1 1 1 '"i"
-AS$

Figure 1. Galveston Bay sampling sites included the Ship Channel (59), Yacht Club (15), Todd's
Dump (16), Hanna Reef (17), Offatt's Bayou (58) and Confiederate Reef (18).

3-34

Total PAH average concentrations ranged from 54 to'2400 ng/g. Th highe
C r
concentrations were measured in oysters from the upper portion of Galveston Bay
(i.e., stations 15 and 59) and near the City of Galveston (i.e., stations 18 and 58).
Oyster samples from areas farther away from urban centers (i.e., stations 16 and
171) had average concentrations one to two orders of magnitude lower. In general,
these concentrations are in good agreement with those previously encountered
during temporal studies in Galveston Bay. Two PAHs, pyrene and fluoranthene,
generally accounted for >25% of the total PAHs measured. The predominance of
these compounds would suggest that the major source of PAHs in the Galveston
Bay area is combustion products.

Average total PCB and DDT concentrations in Galveston Bay oysters were in the
48-1100 and 12-240 ng(g ranges, respectively. Most of the DDT residue is present
as metabolites, DDE and DDD. hi general, less than 10% of the total contaminant
load in oysters is the parent compound, DDT. Samples from stations 15 and 59
were the most contaminated, while oysters from Station 17 had the lowest residue
concentrations. These concentrations agree with the ranges reported earlier for
Galveston Bay bivalves.

Contaminant concentration patterns were similar for most contaminants. The
upper bay sites (15; 59) had higher concentrations than the mid-bay sites (16, 17)
for DDT, PAH, PCB and butyltins. Sites from the lower bay (18, 58) had
intermediate concentrations. This most likely results from proximity to large
urban areas and runoff inputs. The lower contaminant loading in the mid-bay
region probably results from dilution effects. The concentrations found in
Galveston Bay are similarto the range found throughout the Gulf of Mexico for
the NS&T Program. The concentrations in the upper bay are above average for
the Gulf of Mexico, mid-bay concentrations are below, and lower bay
concentrations are close to the average Gulf of Mexico concentrations. Most of the
sites show no consistent temporal trend for the organic contaminants. However,
there is a general decrease in concentrations over time at Station 15 for PAH, PCB
and DDT. '" Sample collections at other times of the year indicate some seasonal
variability of contamination concentrations.

3-35

Reprint 4

Toxic Contamination of Aquatic Organisms in Galveston Bay

James M. Brooks, Terry L. Wade, Bobby J. Presley, Jos6 L. Sericano, Thomas
J. McDonald, Thomas J. Jackson, Dan L. Wilkinson, and Tamara F. Davis

Toidc Contamination ofAquatic Organisnis in "veston B:4y

James M. Brooks, Terry L_ Wade, Bobby J. Presley, Jos,,5 L. Sericano, Thomas J. r
McDonald, Thomas J. Jackson, Dan L_ Wilkinson and Tamara F. Davis
Geochemical and Environmental Research Group, Texas A&M University
01

Little information regarding historical trends and concentrations of heavy
metals, hydrocarbons, pesticides and PCBs in aquatic organisms in Galveston
Bay has been available to guide decision makers and regulators. Each year
millions of pounds of fish and shellfish are caught by commercial and sport
fishermen in Galveston Bay and consumed as nutritional seafood. However, little
or no testing of edible tissues for toxic contamination by heavy metals,
hydrocarbons, pesticides and PCBs has been conducted to assure public health
and safety.' For this reason, the Galveston Bay National Estuary Program
(GBNEP), funded by.the U.S. Environmental Protection Agency (EPA) and the
State of Texas, undertook this study to characterize cont.-iminatibn in selected
aquatic organisms in Galveston Bay.

The 'Sampling design called for the analysis of trace contamin a*nts in five species
from four sites in Galveston Bay. Five species of marine organisms were targeted
for collection and analyzed as follows: two macroinvertebrates, Crassostrea
virginica, the oyster, and Callinectes sapidus, the blue crabl' ind three vertebrate
marine fishes, Cynoscion nebulosus, the spotted sea trout, Pogonias cromis, the
black drum, and Paralichthys lethostigma, the southern flounder. The goal of the
program was to collect. ten specimens of each target organism of legal market size
from each collection site. Standard fisheries data were recorrded for all
collections. The collection sites for these target species (Fig. 1) were Morgan's
Point, at the mouth of the Galveston Ship Channel, Eagle Point off San Leon,
Carancahua Reef in West Bay, and Hanna Reef in East Bay.

Four samplings of aquatic organisms have been undertaken for: the GBNEP. The
first sampling May 23-25 collected oyster and crab samples; however, trawling for
fish was not very successful as a result of low salinity water due to Trinity River
flooding. A second sampling was undertaken June 6-8 that involved gill netting
at the four sites. This sampling had some success in collecting black drum, sea
catfish (Arius felis), spotted sea trout and southern flounder from some of the
sites, although not in sufficient quantities for most analyses. Most fish samples
were collected from a sampling from July 30 to August 3 after the bay had
returned to a somewhat normal salinity regime. However, late July sampling
was complicated by the-Apex Barge oil spill that occurred on July 28. Because of
this spill, few fish were collected near Eagle Point (close to the oit spill site). A
final sampling trip on September 4-6 completed the remaining sampling at Eagle
Point.

The analytical program called for the analyses of ten individual specimens of the
five target organisms from each site (200 edible muscle tissue samples). Fifty
liver samples were composited for analyses from the approximately 120 fishes-
Trace contaminants measured included heavy metals, polynuclear aromatic
hydrocarbons (PAHs), pesticides and PCBs and a GC-MS scan for'other EPA

3-37

TRINI r Y BA Y
R N'S
IN

. ..... EAGLE OAy
NT
EAS HANN
GALVESMV BAY ROUOVER PASS)

of
kf

0 1 2 5 5 6 OWWS
S4A1 LUIS
PASS

Figure 1. Collection sites for tissue samples.

3-38

organic priority pollutants. Trace elements of interest in this study were those on
the EPA Priority Pollutant List (PPL) which included: arsenic (As), cadmium
(Cd), chromium (Cr), copper (Cu), lead (Pb), mercury (Hg), nickel (Ni), selenium
(Se), silver (Ag), and zinc (Zn). PAHs determined by GC/MS/SIMs included 39
two- to five-ring aromatics and selected alkylated homologs. Pesticides and PCBs
were determined by gas chromatography with election capture detection (ECD).
Selected chlorinated pesticides (aldrin, chlordane, dieldrin, endrin, heptachlor,
BHC, heptachlor epoxide, hexachlorobenzene, lindane, mirex, trans-nonachlor,
toxaphene, DDTs, DDDs and DDEs) and individual PCB congeners were
quantitated. Analytical methods for trace organic analyses. followed those of the
NOAA National Status and Trends Mussel Watch Program.

None of the average concentrations of trace metals or trace organic contaminants
in fish tissue, oysters, or crabs collected in this study pose a risk to human health
associated with consumption of seafood based'on the U.S. EPA (,1989) guidance
manual for assessing human health risks for chemically contaminated fish and
shellfish. In general, trace contaminants were higher in oyster and crab tissues
than fish tissue. This was especially true for trace organics and certain trace
metals such as zinc, lead, nickel, copper, cadmium and silver. Mercury showed
the opposite trend with higher concentrations in fish tissue. Most PAHs in
Galveston Bay seem to originate from combustion sources (atmospheric
deposition or runoff) and not from petroleum inputs based -on the distribution of
PAHs and their alkylated homologs. The chlorinated hydrocarbons were
represented by low levels of DDT and its metabolites (DDD and DDE). As expected,
higher contaminant levels were generally found in the upper portion of Galveston
Bay.(Morgan's Point) near the Houston Ship Channel.

3-39

Reprint 5

Transplanted Oysters as Sentinel Organisms in Monitoring
Studies

Jos6 L. Scricano, Terry L. Wade, and James M. Brooks

Tranvlanted Oysters as Sentinel Organisms in Monitonng Stuches

Jos4 L_ Sericano, Terry L. Wade and Jarnes M. Brooks
Geochemical and Environmental Research Group, Texas A&M University

01
Coastal marine environment contamination by a number of organic compounds
of synthetic or natural origin has received increasing attention over the last
several years. Biomonitoring of these compounds in the aquatic environment is
well established and bivalves are generally preferred for this purpose. The
rationale for the "Mussel Watch" approach using different bivalves, e.g., mussel,
oysters and/or clams, has been summarized by different authors and its concept
has been applied to many monitoring programs during the last decade.

The National Oceanic and Atmospheric Administration (NOAA). National Status
and Trends (NS&T) Program, for example, is designed to monitor the current
status and long-term effects of selected organic and inorganic contaminants of
environmental concern, i.e., polynuclear aromatic hydrocarbons (PAHs),
chlorinated pesticides, polychlorinated biphenyls (PCBs), and trace metals.
Concentrations of these contaminants in bivalves are measured along the coasts
of the U.S.A. over several years. During the first five years of this program (1986-
1990) the objective was to sample all the locations prescribed by NOAA; however,
this goal was compromised by locations depleted of living oysters because of
diseases, predators, excessive freshwater runoff, harvesting or dredge material
burying entire reefs. Therefore, in some instances, it was not possible to obtain
samples. At the end of the first five years of the NS&T program, nearly 20% of the
original locations presented some of the above mentioned sampling problems that
left the data base withmissing values. Transplantation of bivalves to areas where
indigenous individuals were not originally present or have been lost because of
natural or man-induced actions could be a potentially useful tool in monitoring
environmental pollution.

The present study was designed to examine the uptake and depuration of selected
organic contaminants, PAHs and PCBs in oysters (Crassostrea virginica)
through transplantation experiments in two -locations in Galveston Bay, Texas.

Approximately 250 oysters of similar dimensions (e.g., 6-8 cm) were collected
from a relatively uncontaminated area in Galveston Bay, Hanna Reef, and
transplanted in 24x7O cm net bags, containing 25-30 individuals per bag, to a new
location near the Houston Ship Channel (HSC) in the upper part of the bay.
Composite samples of 20 transplanted and 15 indigenous oysters were collected at
zero, three, seven, 17, 30, and 48 days during the first phase of the transplantation
experiment. The remaining Hanna Reef oysters were then back- transplanted to
their original location in Galveston Bay. At the same time, approximately 150
indigenous oysters from the HSC site were also transplanted to the Hanna Reef
area. Composite samples of 20 oysters from each population were collected at
three, six, 18, 30, and 50 days after transplantation.

The concentrations of most organic contaminants in oysters transplanted from
Hanna Reef to the HSC increased dramatically during the seven-week exposure

3-41

period. Comparatively, concentrations of individual PAHs and PCBs in
indigenous oysters during the first phase of this experiment were fairly constant.
The analyte concentrations in native oysters represent the time-integrated
contaminant concentrations available to the oysters in solution, adsorbed onto
particles and incorporated into food.

Initial concentrations of total PAHs in transplanted oysters increased from 290
ng/g to a final value of 4360 ng/g. Two- and three-ring PAHs were detected in low
concentrations in transplanted and indigenous oysters. Four- and five-ring
compounds were accumulated to the highest concentrations in Hanna Reef
oysters. By the end of the first 48 days, transplanted oysters accumulated these
PAHs to levels that were not statistically differentiable from the concentrations
measured in native individuals. The PAHs accumulated to the highest
concentrations were: pyrene > fluoranthene > chrysene > benzo(e)pyrene >
benzo(b)anthraceni;

Hanna Reef and HSC oysters showed statistically significant depluration (p < 0.05)
of four- and five-ring PAHs after relocation to the Hanna Reef area. Depurations
of these aromatic compounds by both groups of oysters were approximately
exponential. The half-lives ranged from 10.4 and 12.4 days for pyrene to 25.6 and
38.5 days for fluoranthene in Hanna Reef and HSC oysters, respectively. Most of
the values were, however, between ten and 16 days.

PCB concentrations in transplanted oysters increased from 30 ng/g to 850 ng/g
after the 48-days exposure period. Pentachlorobiphenyls were the compounds
accumulated to the highest concentrations in transplanted and native oysters. In
comparison, practically no octa-, nona- or decachlorobiphenyls were detected in
either oyster group. Unlike the PAHs, not all the PCB homologs, measured in
transplanted oysters reached the concentration encountered in indigenous
individuals by the end of the first phase of this experiment. While there were no
statistically significant differences in the tri- and tetra chlorobiphenyl
concentrations measured in transplanted and native oysters, significant
differences were observed in the total concentrations of penta- and
hexachlorobiphenyls. It is evident that a longer exposure period is needed for the
higher molecular weight PCB to reach a steady state concentration.

Hanna Reef and HSC oysters showed statistically significant depuration (p < 0.05)
of low molecular weight PCBs when relocated to the Hanna Reef area. Originally
uncontaminated oysters depurated PCBs at a faster rate than chronically
contaminated oysters. The clearance rates of high molecular weight PCBs were
significantly slower in both oyster populations Biological half-lives for these PCBs
in Hanna Reef and HSC oysters ranged from 21 to 129 days and from 20 days to >
year, respectively-

Transplanted oysters can be considered valuable bioindicators of environmental
contamination by PAHs and PCBs in areas lacking indigenous oysters. However,
in order to avoid misleading interpretations of environmental data collected using
transplanted bivalves, it is imperative to understand that some trace organic
compounds need extremely long time, i.e., several months, to reach equilibrium
concentrations.

3-42

Reprint 6

The Effects of the Apex Barge Oil Spill
on the Fish of Galveston Bay

Susanne J. McDonald, James M. Brooks, Dan Wilkinson,
Terry L. Wade, and Thomas J. McDonald

The Effects of ffie Apex Bmge OR SpiR on ffie Msh of Galveston Bay

Susanne J. McDonald, James M. Brooks, Dan Wilkinson,
Terry L. Wade, and Thomas J. McDonald
Geochemical and Environin .ental Research Group, Texas A&M University

On July 28, 1990 the Greek tanker, Shinoussa, collided with three barges in the
Houston Ship Channel in Galveston Bay, Texas. Over 700,000 gallons of
petroleum product.were released into the bay from one of the Apex barges. The
spilled petroleum was a processed product known as a vacuum reformate that
contained unusually high concentrations of the toxic polynuclear aromatic
hydrocarbon (PAH), benzo(alpyrene (BaP). The purpose of this study was to
assess the effects of the spilled petroleum on the fish of Galveston Bay and to
compare results obtained from more typical monitoring methods (i.e., PAH tissue
residue concentrations) *and a recently developed technique for detecting PAH
metabolites in fish bile. Measuring the concentrations of biliary PAH metabolites
in fish is a sensitive method that can provide an improved estimation of PAH
exposure, early indications of habitat deterioration, and a clear association
between pollutant source and resultant exposure. Data of this nature is beneficial
for informed management and regulatory decisions.

Field crews were on Galveston Bay collecting fish as part o f the Galveston Bay
National Estuary Program (GBNEP) monitoring study the week following the oil
spill. One of the designated stations in this study, Todd's Dump (or Eagle Point),
is located within two miles of the Apex barge oil spill and was sampled on August
3, 1990, one week after the spill. Fish were collected using gill nets at the
north/northwest end of Redfish Island and over the oyster reef at Todds Dump. A
prominent oil slick was observed in waters surrounding Redfish Island; whereas,
no obvious slick was evident in waters over the oyster reef. Additionally,. follow up
studies resampled the Todd's Dump area for fish approximately four and sixteen
weeks after the spill. The fish captured were analyzed for PAH metabolites in bile
and PAH residue in liver and muscle tissues. The PAH metabolites analyzed
were naphthalenes, phenanthrenes, and BaPs.

Fish rapidly metabolize lipophillic PAH to more polar and excretable metabolites.
A number of polar metabolites formed by the enzymatic transformation of PAH in
fish livers are excreted into bile and urine. Biliary PAH metabolites were
analyzed using a non-radiometric technique employing HPLC and fluorescence
detection. The advantages of this techniq .ue include the ease with which samples
are collected and stored, the minimal sample preparation required, its sensitivity,
its low cost, and that it is an in vivo measurement. Field studies have
documented elevated concentrations of PAH metabolites in the bile of fish collected
near hydrocarbon contaminated sediments and downstream from an oil spill. An
increased incidence of idiopathic hepatic lesions and reduced ovarian maturation
has been correlated with high concentrations of biliary PAH metabolites in fish.

The analysis of fish collected near Redfish Island, one week after the spill,
revealed the highest biliary concentrations of PAH metabolites ever reported for
fish. The. mean concentration of naphthalene, phenanthrene and benzo[alpyrene

3-44

metabolites was 4,200,000, 1,900,000, and 11,000 ng/g wet weight, respectively.
Fish captured over the oyster reef, in waters that were not obviously oiled, had
metabolite concentrations of 1,100,000 (naphthalene), 540,000 (phenanthrene), and
3,900 (BaP) ng/g. The. high concentration of BaP metabolites is of particular
concern since many of these compounds are highly carcinogenic and reflect the
high concentration of BaP in the spilled petroleum. The mean biliary
concentrations of PAH metabolites in fish captured four weeks after the spill were
lower than those observed one week after the spill, but were still elevated
(naphthalene = 900,000, phenanthrene = 290,000, and BaP = 2400 ng/g); whereas,
fish collected sixteen weeks after the spill had significantly lower concentrations
of PAH metabolites in their bile (naphthalene = 240,000, phenanthiene = 70,000,
and BaP = 630 ng/g).

In contrast to results of the bilary- analysis, the concentration of PAH residues in
the tissues of fish captured one week after the spill are low to nondetected.
Significant concentrations of PAH are seldom detected in fish, even when the
adjacent environment contains high concentrations of PAH, because fish rapidly
metabolize PAH to derivatives not detected by routine analytical techniques for
monitoring hydrocarbon exposure. Evaluating the effects of the Apex oil spill only
on the concentration of PAH residue in fish tissues would suggest no significant
evidence of -exposure. However, metabolite data indicates that the fish near
Todd's Dump were exposed to high concentrations of PAH.

-A r.

Preprint I

Polynuclear Aromatic Hydrocarbon Contaminants in Oysters
from the Gulf of Mexico (1986-1990)

Thomas J. Jackson, Terry L. Wade, Thosmas J. McDonald, Dan L. Wilkinson.
and James M. Brooks

manuscript 00646

Polynuclear Aromatic Hydrocarbon Contaminants
in Oysters from the Gulf of Mexico (1986-1990)

Thomas J. Jackson, Terry L. Wade, Thomas J. McDonald,
Dan L. Wilkinson and James M. Brooks

Geochemical and Environmental Research Group,
College of Geosciences and Maritime Studies,
Texas A & M University,
College Station, Texas, U.S.A. 77845

ABSTRACT

Polynuclear aromatic hydrocarbon (PAH) contaminant

concentrations in 870 composite oyster samples from coastal

and estuarine areas of the Gulf of Mexico analyzed as part

of National Oceanic and Atmospheric Administration's

(NOAA's). National Status-and Trends-(NS&T) Mussel Watch

Program exhibit a lognormal distribution. There are two

major populations in the data. The cumulative frequency

function was used to deconvolute the data distribution into

two probability density functions and calculate summary

statistics for each population. The first population

consists of sites with lower PAH concentration probably due

to background contamination (i.e., stormwater runoff,
atmospheric deposition). The secoi@d population are sites

with higher concentrations of PAHs associated with local
point sources of PAH input (i.e., small oil-- spills, etc-.).

The temporal pattern for the mean concentration of the
populations from the Gulf of Mexico is consistent with
large-scale climatic factors such as the El Nii@o cycles

which affect the precipitation regime.

3-47

2
Manuscript 00646

INTRODUCTION

Oysters and other bivalve molluscs have been used for

monitoring contaminants in the environment (Farrington, et

al., 1983). Oysters are sentinel organisms which

concentrate contaminants from the marine environment, yet do

not readily metabolize contaminants such as polynuclear

aromatic hydrocarbons (PAHs) (Farrington and Quinn, 1973).

PAHs enter the near-coastal environment through a number of.

mechanisms (e.g. runoff, discharge of industrial waste or

sewage, natural or industrial combustion processes, natural

oil seepages, and spills of petroleum or petroleum

products).

The contaminants found in oysters reflect the current

contaminant burden of an ecosystem. The concentration of a

contaminant in an oyster is the difference between uptake

and excretion of that contaminant. Galveston Bay oysters

transplanted from a "high" level site to a "low." level site

and vice versa come to a new equilibrium concentration, for

trace organic contaminants such as PAHs, within,
approximately one month (Sericano qnd Wade, unpublished

data).

To assess the spatial and temporal va'r`iation of

contaminant levels of coastal and estuarine environments,

the National Oceanic and Atmospheric Administration (NOAA)

instituted the National Status and Trends (NS&T) Mussel

Watch Program under its Program for Marine Environmental

3-48

3
Manuscript 00646

Quality (O'Connor, 1990). The sample sites were selected to

characterize the overall concentration of contaminants in

coastal and estuarine ecosystems away from known point-

sources of contamination.

The focus of this paper is to examine the distribution

of the PAH contaminant concentrations in oysters collected

from the Gulf-of Mexico as part of NOAA's NS&T Mussel Watch

Program, and determine the environmental factors controlling

tile concentration of PAHs.

METHODS

Sample Collection

Oysters (Crassostrea virginica) were collected from

three stations at each site, during the winter of each year

(1986 - 1990). The number of sites per year varied from 48

to 68. In some years not all sites had three stations due

to the low abundance of oysters at a specific site

(Table 1). Sample sites give coverage of the Gulf of Mexico

coastal and estuarine areas from southern-most Texas to

southern-most Florida (Figure 1).. Individual stations at

each site are generally from 100 to 1,000 meters apart. An

analysis at each station represents a composite of twenty

individual oysters. Each year, the field sampling returned

to as many sites as possible. In some instances it was

necessary to relocate or abandon an established oyster site

3-49

4
Manuscript 00646

due to lack of suitable size bivalves (Wilkinson, et al.,

1991). The locations and designators for the oyster sites

are found in Wilkinson, et al. (1991), Sericano et al.

(1990) and Wade et al. (1990).

Tissue Extraction

The tissue extraction used was adapted from a method

developed by MacLeod, et al. (1985). Approximately 15 grams

of wet tissue were used.for the PAH analysis. After the

addition of internal standards (surrogates) and 50 grams of

anhydrous Na2SO4, the tissue was extracted three times with

dichloromethane using 4 tissuemizer. A 20 ml sample was

removed from the total solvent volume and concentrated to

one ml for lipid percentage determination. The 280 ml of

remaining solvent was concentrated to approximately 20 ml in

a flat-bottomed flask equipped with a three-ball Synder

column condenser. The tissue extract was then transferred

to a Kuderna-Danish tubes heated in a water bath (600C) to

concentrate the extracts to a final volume of two

milliliters. During concentration, the dichIoromethane was

exchanged for hexane.

The tissue extracts were fractionated--,-by alumina:silica
(80-100 mesh) open column chromatography. The silica gel

was activated at 1700C for 12 hours and partially

deactivated with 3% distilled water (v/w). Twenty grams of

silica gel were slurry-packed in dichloromethane over ten

3-50

5
manuscript 00646

grams of alumina. Alumina was activated at 4000C for four

hours and partially deactivated with 1% distilled water

(V/W). The dichloromethane was replaced with pentane by
elution. The extract was then applied to the top of the

column. The extract was sequentially eluted from the column

with 50 ml of pentane (aliphatic fraction) and 200 ml of 1:1

pentane:dichloronethane (aromatic fraction). The aromatic

fraction was further purified by HPLC to remove the lipids.

The lipids were removed by size exclusion using

dichloromethane as an isocratic mobile phase (7 ml/min) and

two 22.5 x 250 mm Phenogel 100 columns (Krahn, et al.,

1988). The purified aromatic fraction was collected from

1.5 minutes prior to the elution of 4,41-dibromofluoro-

biphenyl to 2 minutes after the elution of perylene. The

retention times of the two marker peaks were checked prior

to the beginning and at the end of a set of ten samples.

The purified aromatic fraction was concentrated to 1 ml

using Kuderna-Danish tubes heated in a water bath at 600C.

Quality assurance for each set of ten samples included

a procedural blank, matrix spike, duplicate, and tissue

standard reference material (NIST-SRM 1974) which were

carried through the entire analytical scheme. Internal

standards (surrogates) were added to the samples prior to

extraction and were used for quantitation. The surrogates

were d8-naphthalene, djo-acenaphthene, djo-phenanthrene,

d12-chrysene, and d12-perylene. Surrogates were added at a

3-51

Manuscript 00646

concentration similar to that expected for the analytes of

interest. To monitor the recovery of the surrogates,

chromatography internal standards djo-fluorene and

dl2-benzo(a)pyrene were added just prior to GC-MS analysis.

Gas Chromatography-Mass Spectrometry (GC-MS)

PAHs were separated and quantified by GC-XS (HP5980-GC

interfaced to a HP5970-MSD). The samples were injected in

the splitless mode on to a 30 m X 0.25 mm (0.32 um film

thickness) DB-5 fused silica capillary column (J&W

Scientific Inc.) at an initial temperature of 600C and

temperature programmed at 120C/min to .3000C and held at the

final temperature for 6 minutes. The mass spectral data

were acquired using selected ions for each of the PAH

analytes. The GC-MS was calibrated and linearity determined

by injection of a standard containing all analytes at five

concentrations ranging from 0.01 ng/ul to 1 ng/ul. Sample

component concentrations were calculated from the average

response factor for each analyte. Analyte identifications

were based on correct retention time of the quantitation ion

(molecular ion) for the specific analyte and confirmed by

the ratio of quantitation ion to confirmation ion.

Calibration check samples were run with each set of

samples (beginning, middle, and end), with no more than six

hours between calibration checks. The calibration check

must maintain an average response factor within 10% for all

3-52

7
Manuscript 00646

analytes, with no one analyte greater than +25% of the known

concentration. A laboratory reference sample (oil spiked

solution) was also analyzed with each set of samples to

confirm GC-MS system performance and calibration.

RESULTS and DISCUSSION

oyster site variations

During the first five years of this study a total of

870 composited-oyster samples have been analyzed for PAHs.

The tPAH (total NS&T PAHs) is the sum of the eighteen

aromatic hydrocarbon analytes, as measured in Year.I, with

concentrations greater than 20 ng/g dry weight (Table 2);

this was the reporting limit for Year I data (Wade, et al.,

1988). The median PAH concentration at a site is used as a

measure of the best indicator of the concentration. The

median is a more stable (or "resistant") estimator of the

typical value than the mean for data which may contain

outliers (Hensel, 1990).

The data in Table 3 presents t
.@ie spatial and temporal

variation for the median tPAH concentration in the coastal
and estuarine areas of the Gulf of Mexico., The sites are
separated into Bay groups (Wilson, et al., 1992) for data
comparison. The variability for each Bay group is the
standard deviation as computed from the interquatrile range

(IQR) for the five years of data (Hensel, 1990). In Texas,

3-53

8
Manuscript 00646

Corpus Christi (CCBH, CCNB, CCIC & ABHI) and Galveston Bays

(GBCR, GBOB, GBTD, GBYC, GBSC & GBHR) are near industrial

and population centers and exhibit high median

concentrations of tPAH and large variability in

concentration compared to Matagorda (ESBD, MBGP, MBLR, MBCB,

MBTP & MBDI) and Aransas Bays (ABLR, CBCR & MBAR) which

exhibit low median concentrations of tPAH and small

variability in concentration. The highest median tPAH

concentration for a Bay Group in Texas is the Brazos River.

(BRCL & BRFS), which carries the runoff from agriculture and

wastewater discharge from industrial point-sources (NOAA,

1985). For the entire coastal and estuarine area of the

Gulf of Mexico (Table 3), the highest median tPAH

concentration for a Bay Group is near Panama 4City, Florida

(PCLO' PCMP & SAWB), which is close to-a paper mill (NOAA,

1985; Wilkinson et al., .1991).

There are fifteen sites (LMSB, ABLR, CBCR, MBAR, SAPP,

ESSP, ESBD, MBGP, MBCB, MBTP, CLCL, LBMP, TBCB, CBBI & RBHC)

with low concentration of tPAH ( < 100 ng/g) and little

variation in the observed values (e.g., Figure 2). There

are also six sites (GBSC, BBMB, MSBB, CBJB, PCMP & SAWB), of
the seventy-eight different sites, where high concentrations

of tPAH ( > 1000 ng/g) are observed. Four sites (CCIC,

PBPH, PBIB & PCMP) exhibited a decrease in the tPAH each
year during the first five years of this study. Many sites
exhibited a cyclic variation with time. At Choctawhatchee

3-54

9
Manuscript 00646

Bay off Santa Rosa (CBSR, Figure 3), the order of magnitude

increase in concentration of tPAH in Years II and III is

probably due to relocation of the collection site to an area

containing wood pilings, which if treated with creosote are

a source of PARs. The decrease in Years IV and V probably

reflects relocation of the collection stations to an oyster

reef away from wood pilings. Due to prolohged freshwater

conditions in San Antonio Bay during 1988 and 1989 (Years

III and IV), the oyster..reefs experienced a die-off

resulting in no oysters being taken from SAPP, SAMP and 01

ESSP.

cumulative Frequency Model

Bar Graphs (Wade, et al., 1990), or crossplots (Wade

and Sericano, 1989) of data comparing one year's data versus

another have been used to display the general trend for tPAH

data (Wade and Sericano, 1989; Wade, et al., 1990; Wade, et

al., 1991). These data presentations easily visualize the

variation in concentration for a particular site. In this

report the cumulative frequency function is used to examine
the heterogeneous distribution of PAHs in Gulf of Mexico

oysters (Mackay and Paterson, 1984). This approach has the

advantage of examining the Gulf of Mexico ds a single
environmental system, determining the percentage of sites
exposed to a particular threshold concentration, and
providing information for environmental evaluation.

3-55

10
Manuscript 00646

The distribution of the PAH data in Table 3 is best

described by a lognormal distribution; i.e. the distribution

of data is skewed to low concentrations and has a fraction

which extends to high concentrations (Figure 4). O'Connor

(1990) used the lognormal distribution, typical of

environmental data, to def ine "high" concentrations as those

whose logarithmic value is more than the mean plus one

standard deviation of the logarithms for all concentrations.

The tPAH data in Figure 4 is further skewed in that analytes

with concentrations less than 20 ng/g are not included in

the sum of eighteen 2 - 5 ring aromatic hydrocarbon analytes

in Table 2, i.e., the data has been censored. For Years I -

III, only censored data was available, whereas for Years IV

and V both censored and uncensored data was available. A

regression analysis of the censored (tPAH) data versus

uncensored data for the sum of all analytes (T-PAH) in Table

2 from Years IV and V yields the best fit line as y = 153.0
+ 0.9834 x (r2=0.9989) ; where y uncensored data, and x

censored data. Using the best fit line from the Year IV and

V data, the censored data for the cumulative -fr .equency data
was corrected to,be the same as theN uncensored cumulative

frequency data.

Distribution functions are useful measures of

environmental quality data in that changes with time can be

ascertained without being influenced by "outliers". For the

cumulative distribution plot, the data is sorted from the

3-56

Manuscript 00646

lowest value to the highest, similar to rank transformation

(Conover and Iman, 1981). Each observation is 1/n fraction

of the data set, where n is the number of samples in the

data set. The sum of the fraction of samples less than the

concentration is plotted against the concentration. From

this plot the median can be determined, since it is defined

as the 50th percentile. The interquatrile range (IQR) is

used as a measure of variability. The IQR is the 75th

percentile minus the 25th percentile and equals 1.35 times

the standard deviation for a normal distribution (Hensel,

1990).

To begin the examination of the distribution of the PAH

concentration data, the logarithm of the sum of all PAH

analytes (T-PAH) for Year V data was plotted as a cumulative

frequency distribution. The 50th percentile was 250 ppb and

the standard deviation as determined from the IRQ was 218.

The log of the data versus fraction of the samples was

plotted and compared with a lognormal distribution

(Figure 5). The shape of the cumulative frequency curve
(i.e., the positive deviation from :% the lognormal model) for
the T-PAH data suggests two overlapping lognormal
distributions. Making the assumption that--there is a 2.5%
overlap for the two distributions, the mean and standard

deviation were computed for each data set, or population
(Table 4). The cumulative frequency distribution from the
two population model data compare well with the actual T-PAH

3-57

12
Manuscript 00646

data (Figure 6). Other increments of overlap were computed,

but did not compare as well with the actual data for Year V.

The implication of the two populations in the data is

that there are two primary mechanisms accounting for the

distribution of T-PAH concentration in the Year V data. The

sites with lower concentration PAHs are probably due to low

level background inputs from stormwater runoff, atmospheric

deposition and sewage effluents, etc. (NOAA, 1985). The

sites with higher concentration PAHs are probably due to

local point-sources of PAH contamination (i.e., small

spills). From the lognormal cumulative frequency function

two probability density functions were derived, the relative

proportion of the two populations were estimated to be 0.9

for population one and 0.25 for population two. Comparison

of the cumulative frequency distribution derived from the

sum of the two probability density functions, in the above

proportions, with the actual data for the cumulative

frequency distribution (Figure 7) indicates a good

correlation.

since historical NS&T data (TAble 3) is censored data

(Wade, et al., 1988; Wade and Sericano, 1989; Wade, et al.,

1990), the cumulative frequency distribution of this

censored (tPAH) data was corrected using the best-fit-line

from the data for Years IV and V. Data below the reporting

limit were extrapolated (Hensel, 1990; Mackay and Paterson,
1984). The summary statistics for the corrected data using

3-58

13
Manuscript 00646

the two population model for Years I to Year V data

(Table 5) were calculated using the data from 0-80% for the

original cumulative frequency distribution for Population 1

and from 77.5-100% of the original cumulative frequency

distribution for population 2 (Table 6).

The summary statistics for the first five years of

measuring PAH contaminants in the Gulf of Mexico for NOAA's

NS&T Mussel Watch Program (Table 5) show variation in the

means for both populations, indicating temporal change in

the total Gulf of Mexico data, with the highest values found

in Years III and IV. The higher mean concentrations of PAHs

in Years III and IV and the lower abundance in Years 1, 11

and V pattern is probably related to large-scale climatic
factors such as the El Kino cycles (Philander, 1989) which

affects the precipitation regime (Wilson, et al., 1992).

Examination of the PAH data for individual sites, as

discussed above,. does not show this pattern.

The cumulative frequency data for Years I to V gives

the percentage of sites whose PAH concentration is less than
a particular'concentration (Table @). As an example, using

1,000 ppb as an arbitrary concentration, 89% of the sites

for Years I and II are less than this conc6ntration, while

Year III had 80%, Year IV 83% and Year V had 87%.

Alternatively, the cumulative frequency data can be used to
calculate the percentage of sites exposed to a concentration

in excess of a particular threshold.

3-59

14
Manuscript 00646

The cumulative frequency distribution was used in this

study.as an environmental evaluation tool to examine the

heterogeneous distribution of total PAH contaminants in Gulf

of Mexico oysters from coastal and estuarine areas collected

during the winters of 1986 - 1990. The PAH concentration

exhibits a lognormal distribution with two major populations

in the data for each year. The two populations were

deconvoluted into probability density functions and summary

statistics for each population were calculated. The lower

PAH concentrations are probably related to chronic inputs.

Many of these low PAH concentration sites show little

variability from year to year, supporting the contention

that the PAH contamination is on a continual basis. The

higher concentration PAHs are probably associated with local

point-sources of PAH contamination or spills. Most of the

high concentration sites ( > 1000 ng/g dry tissue) show

large variability from year to year, supporting the

contention that PAH contamination for these sites is on an

episodic basis. In additiont 20% of Gulf of Mexico sites in

Year III were exposed to a PAH threshold concentration of
greater than 1000 ng/g of dry oyste-r tissue. whereas, in

Years I and II only 11% of the Gulf of Mexico sites had

concentrations greater than 1000 ng/g of t6tal NS&T PAHs.

The changes in the mean concentration of the two populations

between years display a cyclic pattern which is probably due
to large-scale climatic factors such as the El Ni@o cycles

which affects the precipitation regime (Wilson, et al.,

3-60

15
Manuscript 00646

1992). The cyclic pattern was obtained by examining the

Gulf of Mexico as a single heterogeneous system, since the

PAH concentration data for individual sites does not clearly

show this pattern.

ACKNOWLEGEMENTS

Funding for this research was supported by the National

Oceanic and Atmospheric Administration, contract number

50-DGNC-5-00262 (National Status and Trends Mussel Watch

Program)., through the Texas A & M Research Foundati on,

Texas A & M University.

3-61

manuscript 00646 16

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in sediments and oysters. Estuaries, 11, 171-179.

Wade, T.L. & Sericano, J.L. (1989). Trends in organic

Contaminant Distribution in oysters form the Gulf Of

Mexico. Oceansf89 Proceedings, pp. 585-589.

Wade, T.L., Sericano, J.L., Garcia-Romero, B., Brooks, J.M.

& Presley, B.J. (1990). Gulf Coast NOAA National

Status & Trends Mussel Watch: The-First Four Years.

Proc. Mar. Tech. Soc., 274-280.

Wade, T.L., Brooks, J.M., Kennicutt II, M.C., Denoux, G.J. &

Jackson, T.J. (1991). Oysters as Biomonitors of Oil in

the Ocean. Proceedings of the 23rd Annual Offshore

Technology Conference, OTC 6529, pp. 275-280.

Wilkinson, D.L., Brooks, J.M. & Fay, R.R. (1991). NOAA

Status and Trends: Mussel Watch Program- Field
Sampling and Logistics Report'- Year VI. GERG

Technical Report 91-046, U.S. Department of Commerce,

National Oceanic & Atmospheric Administration, Ocean

Assessment Division.

3-64

19
Manuscript 00646

Wilson, E.A., Powell, E.N., Wade, T.L., Taylor, R.J.

Presley, B.J., and Brooks, J.M. (1992). Spatial and

temporal distributions of body burden and disease in

Gulf of Mexico oyster populations: The role of local

and large-scale climatic controls. Helgol.

Meeresunters. (in press).

3-65

20
Manuscript 00646

Table 1: National Status and Trends oysters
Gulf of Mexico Sampling Program - Summary of Sampling
Year 1986 1987 1988 1989 1990
I II III IV V

Number of Sites 49 48 65 62 68

Number of Samples 142 144 195 186 203

3-66

21
Manuscript 00646

Table 2: National Status and Trends Oysters
Polynuclear Aromatic Hydrocarbon Analytes OF

Analytes used in tPAH summation

Low Molecular Weight Aromatic Hydrocarbons

Biphenyl * Acenaphthene
Naphthalene Acenaphthylene
1-methylnaphthalene * Fluorene
2-methylnaphthalene * Phenanthrene
2,6-dimethylnaphthalene * Anthracene,
1,6,7-trimethylnaphthalene * 1-methylphenanthrene

High Molecular Weight Aromatic Hydrocarbons

Fluoranthene * Benzo(a)pyrene
Pyrene * Benzo(e)pyrene
Benz(a)anthracene * Perylene
Chrysene * Dibenz(a,h]anthracene
Indeno[1,2,3-cd]pyrene Benzo(g,h,i)perylene

3-67

Manuscript 00 646 22

Table 3a: Total NS&T PAH concentration in oysters
(Texas)
Median concentration in ng/g of tPAH

Site V IV III II I Bay Group
Code 1990 1989 1988 1987 1986 Median

1 LMSB 22 20 30 20 25
52 LMPI 3380 30 + 58
78 LMAC 120

53 CCBH 1530 1600
2 CCNB 161 264 598 434 45 565 + 725
3 CCIC 137 430 848 1140
54 ABHI 1870

4 ABLR 20 20 20 21 20
5 CBCR 88 20 20 22 20 + I
6 MBAR 20 20 2-0 20 21

7 SAPP 26 51 45
8 SAMP 49 93 25 + 23
9 ESSP 20 21 20

10 ESBD 21 70 21
12 MBGP 20 86 56 20
11 MBLR 96 348 59 90 45 + 48
56 MBCB 20 56
13 MBTP 20 20 56 20 20
55 MBDI 53

14 MBEM 201 200 23 22 78 138 + 119

72 BRCL 761 60
57 BRFS 955 1670 682 792 + 792

18 GBCR 370 1170 525 478 1070
58 GBOB 315 593 543
16 GBTD 25 44 20 112 149 259 + 606
15 GBYC 247 132 207 56-8 1030
59 GBSC 1290 1350 3100
17 GBHR 20 119 34 20 31

3-68

23
Manuscript 00646

Table 3b: Total NS&T PAH concentration in Oysters
(Louisiana, Mississippi, Alabama)
Median concentration in ng/g of tPAH

site V IV III II I Bay Group
Code 1990 1989 1988 1987 1986 Median

Louisiana

19 SLBB 108 154 169 26 247 154 + 72

20 CLSJ 180 228 102 57 :376 220 + 218
60 CLLC 404 726 20

21 JHJH 88 72 20 84 43 44 + 50

22 VBSP 189 31 20 118 79 79 + 108

24 ABOB 20 28 192 115 32 22 + 42
25 CLCL 20 54 20 20 20

26 TBLB 20 49 306 37 20 40 + 162
27 TBLF 101 50 83 20 25

61 BBTB 20
28 BBSD 963 5480 44 25 57 963 + 1020
29 BBMB 1080 @1380 1460 1150 822

65 MRTP 212 310 1410 391 + 582
64 MRPL 403 330 695

31 BSSI 185 71 484 68 177 181 + 134
30 BSBG 45 202 213 118 265

32 LBMP 20 84 89 26 20 39 + 59
62 LBNO 81

Mississippi

33 MSPC 103 300 175 31-9 99
34 MSBB 1210 893 1500 4310 1600 322 + 654
35 MSPB 59 306 776 300 246

Alabama

36 MBCP 20 90 288 137 31
66 MBHI 767 554 1110 295 + 740
79 MBDR 1520

3-69

24
Manuscript 00646

Table 3c: Total NS&T PAH concentration in Oysters
(Florida)
Median concentration in ng/g of tPAH

Site V IV III II I Bay Group
Code 1990 1989 1988 1987 1986 Median

67 PBPH 168 369 842
37 PBIB 21 204 250 406 197 + 198
80 PBSP 130

73 CBJB 1680 8590.
39 CBSP 225 355 703 543 428 429 + 1140
38 CBSR 69 21 2540 2470 208

74 PCLO 98 229
68 PCMP 1210 2690 4750 1800 + 1590
40 SAWB 1150 2090 1990 1970 11800

41 APDB 20 24 2800 20 20 57 + 530
42 APCP 269 1110 740 20 109

75 AESP 33 74 64 + 103

69 SRWP 119
43 CKBP 20 74 24 68 22 46 + 103

76 TBNP 269 394
47 TB14K 101 170 20 49 372
44 TBPB 20 217 286 68 95
70 TBOT 112 357 212 126 + 165
77 TBKA 252 834
45 TBHB 552 2150 460
46 TBCB 20 65 94 22 20

48 CBBI 20 83 31 43 20 51 + 180
71 CBFM 69 546 272

49 NBNB 87 203 253 108 228 72 + 129
50 RBHC 20 77 67 20 47

51 EVFU 47 68 257 20 112 68 + 125

3-70

25
Manuscript 00646

Table 4: Two Population Lognormal Distribution Model

Year V - T-PAH data (2.5% overlap)

Percentile STD= STD of
Set 25% 50% 75% IRQ/1.35 Log-mean Log-data

1 135 214 320 137 2.3308 0.2783

2 801 1210 1530 544 3.0810 0.2093

3-71

26
manuscript 00646

Table 5: Two Population Lognormal Distribution Model

Corrected tPAH data - ng/g dry weight

Median Population 1 Population 2
Year Total Mean STD Mean STD
Data (LOG) (LOG) (LOG) (LOG)

1 229 197 108 1075 714
(2.2945) (0.2298) (3.0314) (0.2772)

11 208 186 87 1150 1100
(2.2695) (0.1967) (3.0599) (0.3811)

111 345 259 216 1910 1190
(2.4133) (0.3435) (3.2808) (0.2618)

IV 352 269 174 1350 1190
(2.4298) (0.2500) (3.1316) (0.3039)

V 270 212 131 1170 637
(2.3263) (0.2639) (3.0689) (0.2435)

3-72

27
Manuscript 00646

Table 6: NS&T Concentration Distribution Data
(Cumulative Frequency)

Corrected tPAH data - ng/g dry weight

1990 1989 1988 1987 1986
Year V Year IV Year III Year II Year I

10% 110 171 110 110 110
20% 140 200 153 140 140
30-9a 164 226 206 162 169
40% 212 269 259 186 197
50% 270 352 345 208* 229
60-0o 318 435 445 258 286
70% 397 519 832 370 378
80% 597 .869 1030 480 557
9016 1290 1440 2090 1300 1180
95% 1670 2840 3020 2300 1750
98% 1920 5630 4550 3740 2450

3-73

28
Manuscript 00646

Figure Captions

Figure 1: Location of NS&T Mussel Watch Sites in the Gulf

of Mexico (Sericano, et al., 1990).

Figure 2: Total NS&T PAH concentration distribution during

the first five years for all three stations; Caillou Lake in

Louisiana (Site 25 - clcl).

Figure 3: Total NS&T PAH concentration distribution during

the first five years for all three stations; Choctawatchee

Bay off Santa Rosa (Site 38 - CBSR).

Figure 4: Frequency distribution of the median total NS&T

PAH (tPAH) concentration in the Gulf of Mexico during the

first five years of the program.

Figure 5: Plot of the cumulative frequency distribution for

Year V total NS&T PAH (tPAH) concentration, compared to the

gaussian curve and its cumulative frequency distribution

generated from a lognormal model with a mean of 250 ppb and

standard deviation of 218.

Figure 6: Plot of the cumulative frequency distribution for

Year V NS&T PAH (tPAH) concentration, compared to the

gaussian curves and their cumulative frequency distributions

generated from a two population lognormal model with a mean

of 214 ppb for Population 1 and a mean of 1205 ppb for

Population 2.

3-74

29
Manuscript 00646

Figure 7: Comparison of the cumulative frequency

distributions for the actual Year V total NS&T PAH (tPAH)

concentration data and the cumulative frequency distribution

generated from the two population model.

3-75

Mao m MM M m

MISSISSIPPI ALABAMA

TEXAS LOUISIANA
Gulf 0
T
0 Baton Rbuge 0 39 Panam&Clty
67
62 140, 313 34 35 16 36
Houston
300- 0 59 Now Orleans
32 30
20 0 6 2.4 31 42
2`1 22 23 0 41
29*- 1? V)3, @@ 57 Alveston 24 26 2 26 0 -64
14
1
5- 910
280- 2-6%47416(46
.1 #1 @4
51 3

270-

GIF OF hJDW
52
IV. S. 0-1
260---, 1
MEX

250-
MSSSS
A
N @n.
a.
-w Or' 32

23 2@
27 26

240-

970 960 950 940 930 920 910 900 890 880 870 860 850 84

NS&T PAH Data - Years I to V
500

V
m
IV

400-

300-

U)
'r
<

200-
C-5
C/)
z

100-

I I WMA W
0-
CLCL-1 CLCL-2 CLCL-3
Site and Station
ML

MINI 11111111011 mile 101 1 1 1

NS&T PAH Data - Years I to V
4500-

V
m
4000- IV

3500

-0 3000-

u) 2500-

CL
00 2000-
06
U) 1500-
z

1000-

500-

01 ------
CBSR-l CBSR-2 CBSR-3
Site and Station

NS&T PAH Data - Years I to V

70-

60-

50-

U) 40-
4-
0

(D
E 30-
=3
z

20-

10-

0-
<20 30 100 300 1000 3000 10000 30000
Median of Site - NS&T PAH (ppb)
IIM.

Sim

Year V lognormal MODEL
Mean 250 STD =218

0.9- -0.9 Model
--i-
0.8- -0.8 Actual

0
0.7- -0.7

'0
22 0.6- 0.6 r-
4- =3
a)
> 0.5- Gaussian -0.5
Cz
Function
0.4- 0.4
E -0.3
0.3-
L)
0.2- 0.2

-0.1

01 Ili 0
10 100 1000 10000
Total NS&T PAHs (ppb)

Year V lognormal MODEL-2 populations
Mean 1 2 14 Mean 2 12 0 5

1 - 0.9

Model
0.9- -0.8
0.8- -0.7 Actual
'C' 0.7-
-0.6 Cz
c" 0.6-
-0.5 5
0 0.5- .0
Cz
-0.4 (D
0.4- Gaussian >
Function +-A
E -0.3 Cz
0.3- a)
C) 0.2- -0.2 cc
OAT -0.1

0- 1 T-r-r- 10
10 100 1000, 10000
Total NS&T PAHs (ppb)
ian

0
_@Fua@nucstsin

Year V-lognormal MODEL 2 populations
Mean 1 =214 Mean2= 1205

0.9- Actual
Model
0.8-
C:
0 0.7-
T 0.6-

> 0.5-

0.4-
E Model is
0.3- Sum 0.90 x Popl + 0.25 x Pop2

0,2-

0.11

0-
10 100 1000 10000
Total NS&T PAHs (ppb)

Pre rint 2

Sources of Local Variation in Polynuclear Aromatic
Hydrocarbon and Pesticide Body Burden

Matthew S. Ellis, Kwang-Sik Choi, Terry L. Wade, Eric N. Powell, Thomas J.
Jackson, Donald H. Lewis

Ellis 2

ABSTRACT

The sources of local (intrapopulation) variation in PAH body burden

among adjacent oysters on a reef in Galveston Bay were examined. Both eggs

and sperm contain significantly more PAH than somatic tissue. The quantity of

gonadal material was the most important correlate of PAH body burden. Sex was

an important secondary determinant. Body burden of males was correlated with

general indicators of health such as digestive gland atrophy; body burden of

females was not. The evidence suggests that the most important factor

determining variation in PAH body burden within an oyster population during

any single sampling period is the frequency of spawning and how soon

collection occurred after the most recent spawn. Analysis of eggs and sperm

for PAHs and pesticides revealed that eggs and sperm were enriched in all PAHs

relative to somatic tissue. Eggs, but not sperm, were enriched in chlorinated

compounds (e.g. chlordane, DDE, DDD). Both eggs and sperm were enriched in

total PCBs relative to somatic tissue. oysters may lose 50% or more of their

total body burden of certain PAHs and pesticides in a single spawn.

3-85

Ellis 3

INTRODUCTION

Bivalve molluscs have frequently been used as indicator organisms in

studies monitoring levels of contaminants in the environment. These organisms

are utilized because of their ability to accumulate and concentrate both metal

and organic contaminants enabling them to serve as long-term integrators of

their environment (Phillips, 1977). One such program is the NOAA Status and

Trends (NS&T) Program ("Mussel Watch") designed to monitor changes in

environmental quality along the Atlantic, Pacific, and Gulf coasts of the

United States by measuring levels of chemical contaminants in fish, bivalves,

and sediments and identifying biological responses to those contaminants (e.g.

Wilson et al., in press, 1990; Sericano et al., 1990; Presley et al., 1990).

Unfortunately, many biological and environmental fact6rs affect the rate

and extent of bioaccumulation besides contaminant availability. Biological

factors include differential growth rate (Cunningham and Tripp, 1975; Boyden,

1977), reproductive stage (Cunningham and Tripp, 1975; Frazier, 1975;

Martinci6 et al., 1984), stress and disease (Shuster and Pringle, 1969;

Sindermann, 1983; Moore et al., 1989). These biological factors make spatial

and temporal comparisons designed to evaluate the status and trends of

contaminant loading more difficult. The NOAA Status and Trends Program has

proven to be no exception.

In the Gulf of Mexico, the mollusc used for monitoring by NOAA is the

oyster Crassostreg virginic . Analysis of the first 4 yr of NS&T data has

shown that the body burden of polynuclear aromatic hydrocarbons (PAHs) and

pesticides in oysters is correlated with latitude in the Gulf of Mexico.

Contaminant body burdens average higher at higher latitudes. Wilson et al.

(1990) suggested that the latitudinal temperature gradient in the Gulf

produced variation in reproductive effort and that this variation in

3-86

Ellis 4

reproductive effort affected PAH body burden sufficiently to override the

effect of local variation in contaminant loading. Wilson et al. (in press),

in a more thorough analysis, showed that PAH body burden responds to climate

change and that biological factors are the likely intemediaries between

climate's effect on temperature and freshwater inflow and the final body

burden of PAHs.

Two likely intermediaries are spawning and disease. Spawning has

frequently been forwarded as an important route of depuration Marcus and

Stokes, 1985; Jovanovich and Marion, 1987; Cossa, 1989) because lipid loss

peaks at this time (Chu et al., 1990). Parasites and pathogens are less

frequently implicated (Khan, 1987), but parasites and pathogens should have an

.effect; if for no other reason, they frequently reduce spawning frequency or

the numbenof gametes per spawn (Akberali and Trueman, 1985; Ford and

Figueras, 1988; Barber et al., 1988). In oysters, both spawning frequency and

disease are significantly affected by temperature and salinity (Hofmann et

al., in press, submitted; Soniat and Gauthier, 1989) and thus could serve as

important intermediaries by which variation in climate might affect

contaminant body burden.

Climate exerts its influence over large geographic scales. Biological

parameters capable of responding to climate change and, thus, affecting

contaminant body burden on a large geographic scale should certainly do so as

well on a local scale. Accordingly, spawning frequency and disease should be

important sources of local (within population) variability in contaminant body

burden. Monitoring programs typically sample infrequently (NS&T samples once

per year) so that the basis for within-sample variability is an important

consideration. Accordingly, the primary purpose of this study was to examine

sources of local variability in PAH body burden at any sampling period. Some

analyses of pesticides were also conducted.
3-87

Ellis 5

Unfortunately, the variables likely of most importance in determining

local variability in body burden, spawning frequency and the time since the

last spawn, are variables that cannot be readily measured even in a

temporally-intensive sampling program because continuous (or dribble) spawning

is a frequent condition at latitudes south of Chesapeake Bay, including the

entire Gulf of 118xico (Hofmann et al., in press). Consequently, more readily
measured variables must be used as surrogates for the more desirable @/
variables. Thus, we examined a series of indices related to reproductive

state, including stage of reproduction and the quantity of gonadal material

present, and a series of indices related to health, namely digestive gland

atrophy, condition and P marinu infection intensity. P-,. marinu , an

endoparasitic protozoan, is responsible for high mortality (typically > 50%)

in market-sized oysters in the Gulf each year (Hofstetter, 1977; Osburn et

al., 1985; Ray, 1987) and is known to delay reproduction (White et al., 1988;

Wilson et al., 1988). Digestive gland atrophy is a putatively pathogenic

condition (e.g. Marig6mez et al., 1990; Moore et al., 1989) common in Gulf

coast oysters (Gauthier et al., 1990).

METHODS

Within-population differences in body burden,

Oysters were collected in September from Confederate Reef in the West

Bay extension of Galveston Bay. Confederate Reef oysters normally have a

relatively high PAH body burden in comparison to the Gulf-wide mean (Sericano

et al., 1990; Wade et al., 1988). September is near the end of the spawning

season; most individuals should have spawned at least twice over the 4

previous months. The oysters were placed on ice and returned to the

laboratory. Maximum length and wet weight were determined. The condition of

each meat was rated on a semiquantitative scale from 1, very good, to 9, very

3-88

Ellis 6

poor, according to Quick and Mackin (1971). A small section of gonadal tissue

was taken and fixed in Davidson's fixative (Fig. 28 in NOAA, 1983). A small

section of mantle tissue was removed for determination of P. marinus infection

following Ray (1966). The remaining tissue was placed in a precombusted mason

jar with a teflon-lined screw cap and frozen for PAH analyses.

P
, marinus infection intensity was rated on the 0 (uninfected) to 5

(highly infected) point scale of Mackin (1962) as modified by Craig et al.

(1989). Tissue samples were embedded in paraffin, sectioned at 6 Pm and

stained in Harris' hematoxylin and picro/Navy eosin (Preece, 1972).

Reproductive stage was rated on a scale of 1 (sexually undifferentiated) to 8

(spawned out) slightly expanded from Ford and Figueras (1988) by GERG (1990)

(Table 1). Digestive gland atrophy was rated semiquantitatively from 0 (no

atrophy) to 4 (extreme atrophy) as described by Gauthier et al.., (1990) (Table

2).

The analytical procedures used for PAHs and pesticides were based on the

NOAA's NS&T techniques for organic compounds (MacLeod et al. 1985) with some

modification by Wade et al. (1988). These methods have been detailed

elsewhere (Wade et al., 1988; Wade and Sericano, 1989; Sericano et al., 1990;

GERG, 1990) and only a brief overview will be given here.

Samples were extracted with methylene chloride after drying with Na 2so 4*

The samples were then purified by silica/alumina column chromatography. In

order to remove lipids, a high-performance liquid chromatography separation

was performed. Purified extracts were then analyzed by gas chromatography

with a mass spectrometry detector, GC/MS/SIM for PAHs and GC-ECD for

pesticides. All concentrations are reported as ng of analyte per gram dry

weigh t of sample, or ppb. Concentrations in the procedural blanks were, in

all cases, below reporting levels for each individual analyte. The accuracy

3-89

Ellis 7

and precision of these methods have been established by several

intercalibration exercises overseen by the U.S. National Institute of

Standards and Technology.

Oyster gonadal tissue surrounds much of the body mass and, thus, is

difficult to excise cleanly and weigh (Kennedy and Battle, 1964; Morales-Alamo

and Mann, 1989). Thus, a quantitative gonadal index based on gonad weight, as

is frequently used in invertebrates and fish, is not available. Accordingly,

a polyclonal rabbit anti-oyster egg antibody was used to quantify the amount

of egg protein present (Choi et al., in press). A single radial

immunodiffusion assay (14ancini et al., 1965; Garvey et al., 1977) was

performed to quantitate egg protein using 1.5% agarose in barbitone buffer

(0.01 M sodium barbital, 0.0022 M barbital, 0.01% sodium azide as

preservative, pH 8.6). Two ml of the rabbit serum containing anti-oyster

antibody was mixed in 18 ml of the agarose gel and cast on a 10 X 10 cm glass

plate. Four mm diameter wells were made on the plate using a gel puncher and

20 pl of oyster egg standard (0.05 mg ml- to 3.2 mg ml- or the sample were

placed in the wells and incubated in a humid chamber for 48 hr at room

temoeriture. After incubation, the plate was pr essed, dried, stained with

0.5% (w/v) Coomassie Brilliant Blue, and destained with 50% EtOH and 10%

acetic acid. Diameters of the precipitation rings were measured to the

nearest 0.1 mm. A standard curve was constructed by plotting concentration of

the egg standard against the diameter squared of the precipitation rings and

the concentration of each sample Was read from the curve.

Removal of the body section for histological analysis biases both the

total PAH concentration and the gonadal quantity as measured by us. Sericano

et al. (in pre33 b) showed that the effect of this bias on PAH content is an

exoected 10 to 201. reduction in measured body burden. For gonadal quantity,

3-90

Ellis 8

the percent reduction can be expected to be considerably higher. Readers are OF
cautioned not to accept the reported measures of gonadal quantity as true

measures of completely intact oysters. However, as most oysters were similar

in size, the bias introduced in both measures would be equivalent over all

samples and thus not compromise the data analysis.

Bpdy_burden of elz s and SDerM.'

In July, 1991 additional oysters were obtained from Galveston Bay for

examining the relative PAH and pesticide content.of eggs, sperm and the

remaining body tissues. Most oysters were. 7 to 12 cm long and exhibited

fully-developed gonads. Oysters were shucked and their sex determined by

microscoDe slide smear.

The contaminant content of the gametes, which, is the only tissue

com,Donen.t lost during spawning, *may be dissimilar from the remaining gonadal

tissue. Therefore, the eggs and sperm were isolated from the remaining

gonadal and somatic mass. The body of each oyster was separated from other

somatic tissues. The remainder including gill, mantle, adductor muscle, and

labial palps were stored at -20*C for PAH and Desticide analysis. Gonads

containing eggs or sperm were excised from the visceral mass using scissors

and forceps. Gonads were placed on a petri dish and phosphate buffered saline

(0.15 M NaCl, 0.003 M KCI, 0.01 M phosphate buffer, pH 7.4) (PBS) was added.

Eggs or sperm were extracted by squeezing the gonads with a rubber-headed

syringe piston. The egg extract was then filtered through a 100 PM nylon mesh

screen; the sperm extract was filtered through a 30 pm nylon mesh screen.

Oyster egg filtrates were washed 4 times by resuspending the filtrates

into 30 ml of PBS and centrifuging at 700 xg for 10 min. During each washing,

tissue debris and other impurities sedimented on the egg pellets were removed

by pasteur pipette. After the final washing, the egg pellets were resuspended

3-91

Ellis 9

into an equal volume of PBS. Five m.1 of the resuspension was transferred to a

15-ml. centrifuge tube, 7 ml PBS added to resuspend the eggs, and the

suspension centrifuged at 500 xg for 15 min. Any remaining tissue debris

layered on the egg pellet was removed using a pasteur pipette. Egg pellets

from 10 to 20 oysters were pooled in a 50-ml centrifuge tube and sedimented by

centrifugation (700 xg for 15 min). Oyster egg pellets were then resuspended

into an equal volume of PBS. A 60% Percoll solution (4:6 PBS/100% Percoll)

(100% Percoll is 9:1 Percoll stock: 1OX PBS) was prepared. Five ml egg

suspension was mixed with 35 mi 60 % Percoll. and centrifuged at 900 xg for 20

min. Oyster eggs formed an aggregate at the top of the centrifuge tube after

centrifugation, Purified eggs were harvested from the tube and washed twice

by centrifuging at 700 xg for 10 min.

Oyster sperm filtrates were washed 4 times with PBS by centrifuging 700

xg for 15 min. Tissue debris found at the top of the oyster sperm pellet was

removed using a pasteur pipette during each washing step. After the final

washing, the sperm extracts were resuspended into an equal volume of PBS. 70%

Percoll. was prepared and 35 ml. 70% Percoll was mixed with 5 ml. sperm

suspension and centrifuged at 900 xg for 20 min. Oyster sperm was found at

the bottom of the centrifuge tube and other impurities found at the top of the

Percoll as a float. Purified oyster sperm was pooled from 20 to 30 oysters

and washed twice with PBS by centrifuging at 800 xg for 15 min.

Because an involved procedure of this sort could lend to significant

contamination, each solution was subjected to PAH analysis. No solutions were

found to be significantly contaminated.

3-92

Ellis 10

RESULTS

Within-population differences in PAH body burden.

Forty oysters were analyzed, 30 females and 10 males. We present the

means and ranges of the variables measured in Table 3. The mean length for

the group was 8.0 cm, wet weight 9.61 g, condition code 4.3 (fair plus), F,

marinus infection intensity 1.33 (light plus), and digestive gland atrophy 2.1

(about half atrophied). The sample contained individuals covering nearly the

entire range of condition codes, two-thirds of the range of possible P'_

marinus infection intensities, six of eight possible gonadal states and all

stages of digestive gland atrophy. The variability in this data set is

typical of single collections of oysters in the Gulf of Mexico region (Wilson

et al., 1990).

By sex, the lengths of females and males were fairly close (7.9 cm vs.

8.1 cm), however females were heavier than males (9.9 vs. 8.6 g). The weight

difference is considerable since females are actually 0.2 am shorter on

average. Condition code for both sexes was also fairly close, 4.6 for males

vs. 4.2 for females, as was digestive gland.atrophy, 1.9 for males and 2.2 for

females. P. marinus infection intensity differed substantially with males at

0.77 and females at 1.67. Most animals were nearly ready to spawn or

spawning. Reproductive stage was-similar: 5.3 and 5.6 for males and females,

respectively. When measured quantitatively, the 30 females averaged 6.29 mg
eggs per female (equivalent to about 4 .8 x 105 fully.-developed eggs per

female). As a section of gonad was removed for histology, these values

underestimate female fecundity.

Although we explored the entire suite of PAHs per NOAA's Status and

Trends protocol (GERG, 1990), we only report data for the 5 most important

PAHs: fluoranthene, phenanthrene, pyrene, naphthalene, and chrysene. Males

3-93

Ellis 11

and females had similar body burdens except for rluorantheno where females had
about one-third more. Means for both sexes ranged from 12.0 ng g-dry wt -1 for
phenanthrene to 49.0 ng g dry wt-1 for fluoranthene.

A Spearman's rank analysis showed that many of the biological variables

were correlated as might be expected. Accordingly, prior to considering their

relationship with the PAHs, the relationships among the biological variables

themselves must be understood. Because of the many significant correlations

among them, we chose to identify the best 3-variable model explaining

variation for each of the important biological variables, as detailed in

Tables 4 to 6. Because gonadal quantity was measured in only 30 of the 40

individuals and only in females, we examined the data with and without this

variable included. The variables examined were length, wet weight, F,, marinus

infection intensity, digestive gland atrophy, sex, condition code, gonadal

stage and gonadal quantity.

The important correlations were ones (a) between sex and T,_ marinus

infection intensity, males had lighter infections, and (b) between gonadal

stage, condition code and digestive gland atrophy. Among the females, only

the relationship between gonadal-stage and condition code remained

significant. Among the males, digestive gland atrophy was correlated with L.-

marinus infection intensity. Inasmuch as the two sexes were distinctive in

the relationships among biological attributes, we will consider the sexes

separately in most of the remaining analyzes.

Considering both sexes together, condition code and sex were the most

important variables correlating with the PAHs (Table 7). Among the females,

gonadal quantity had a significant effect in 3 of 5 cases (Table 8):

fluoranthene, pyrene and chrysene. Each of the contaminant's concentrations

was higher in females having more eggs. Digestive gland atrophy was also a

3-94

Ellis 12

significant correlate of chrysene. Female oysters having a higher degree of

atrophy had more chrysene. If gonadal quantity was removed, few significant

correlations remained. Among the males, digestive gland atrophy was

significantly correlated in 3 of 5 cases (Table 9). PAH concentration was

lower in male oysters characterized by a greater degree of digestive gland

atrophy. Condition code was significant in 2 of 5 cases; higher condition

code (less healthy) occurred with higher PAH concentration.

Body burden of eggs and sperm.

Samples of pure eggs and sperm, collected from oysters taken earlier in

the spawning season than those supporting the previous data$' had significantly

higher PAH levels than somatic tissue for all 5 PAHs (Table 10). A factor of

5 difference was typical. Total PCBs were concentrated in eggs and sperm by a

factor of about 5 over the somatic tissue. The chlorinated compounds like

lindane, chlordane, dieldrin and DDT (plus breakdown products) were

concentrated in eggs by about 4 times, but tended to be equivalent to or lower

than the somatic tissue in sperm.

DISCUSSION

Spawning as a route of devuration.

Our data suggest that reproduction is an important depuration route for

oysters; the frequency of reproduction is the most important determinant of

body burden, under equivalent exposure levels. Sex and health are important

secondary determinants of body burden because both affect reproductive state

and the frequency of reproduction. The three following observations support

these two conclusions.

(1) Both eggs and sperm contain significantly more PAH and pesticide

than somatic tissue. The concentration factor is sufficient to conclude that

3-95

Ellis 13

over half of the PAH body burden, and somewhat less of the pesticide body

burden, could be in gonadal tissue prior to spawning. Eggs and sperm had PAH

concentrations 5 times higher than somatic tissue, 3 to 4 times higher for

pesticides, and the gonadal tissue can account for 25% of animal dry weight

prior to spawning (Choi et al.9 in press; Klinck et al., 1992).

(2) The quantity of gonadal material was the most important correlate of
PAH body burden and much more important than, for example, gonadal stage.

Less gonadal material indicates recent spawning since these oysters were

collected well into the spawning season; all had certainly spawned at least

once prior to collection.

(3) Sex was an important determinant of body burden. Not only did PAH

concentrations differ in some casess and dramatically so for some pesticides,

but the factors correlating with body burden also differed among the sexes.

Health-related factors were much more important in males. Factors decreasing

health probably also decrease spawning frequency. The most important

correlate occurred with digestive gland atrophy; however in males, digestive

gland atrophy was highly inversely correlated with E.,_ marinus infection

intensity, so the two parameters behaved similarly in explaining the variation

in PAH body burden among oysters taken from the same site. PAHs were lower

with lower L, marinus infection intensity and E, marinus is known to slow

reproduction in oysters (Wilson et al., 1988; White et al., 1988).

Reproduction, he;alth-and body burden.

The importance of reproduction in molluscs in controlling or affecting

body burden is open to disagreement. Mix et al. (1982) and DiSalvo et al.

(1975) found PAHs no more concentrated in Mytilus edulis gonadal material than

somatic tissue (purified eggs were not measured), but noticed a significant

drop in body burden during the spawnJ*,ng,,,season. Sericano et al. (i n press)

Ellis 14

found that the central body region including the gonad contained

proportionately more PAH in oysters. Lee et al. (1972), Fortner and Sick

(1985) and Solbakken et al. (1982), as examples, found the hepatopancreas to

be an important depot for PAHs in bivalves, however gonadal material, and in

particular, gametes, were not separately measured. In scallops where gonads

can be separated from the somatic tissue by dissection, Friocourt et al.

(1985) found gonadal material enriched in PAHs over muscle but not digestive

gland tissue. Rossi and Anderson (1977) observed spawning to be an important

depuration route in a polychaete Neanthe arenaceodentata.

If spawning is an important route of depuration, then factors affecting

spawning frequency and how recently the last spawn occurred prior to

collection will affect body burden. The biological variables measured as

surrogates of spawning frequency Are gonadal quantity and gonadal stage,

marinus infection intensity, and some general indicators of health. Few of

these were correlated among themselves, so that most serve as separate,

somewhat unique, indicators of the many factors that might affect spawning

frequency and how recently the last spawn occurred prior to collection. Each

has its own history, in some cases not necessarily related to spawning

frequency, so that each is only a poor surrogate for the desired variable, but

we emphasize that these are variables that can normally be easily measured in

oyster individuals whereas spawning time and frequency cannot. Nevertheless,

under these conditions, only the strongest relationships might be expected to

generate a signal of sufficient intensity to be observed as a significant

correlation.

Correlations were found, indicating the importance of reproductive state

and health on body burden. The amount of variation explained among

individuals in their PAR body burdens was generally low; however, this

3-97

Ellis 15

probably emphasizes the previous point, that each of the measured variables

are themselves relatively poor indictors of how recently and how frequently

each animal had spawned. Stegeman and Teal (1973) emphasized the importance

of the total exposure history of any individual organism in determining body

burden. One aspect of this exposure history is the time since the last

significant depuration event due to spawning.

Hydrocarbons can be taken up by feeding as well as in the dissolved

phase (e.g. McElroy et al., 1989) and can affect filtration rate (Axiak et

al., 1988; Barszcz et al., 1978). PAHs can also affect the digestive gland

(Nott and Moore, 1987). Theoretically, digestive gland atrophy should be

related to nutritional state. Digestive gland atrophy was correlated weakly

with higher PAHs in females and more strongly with lower PAHs in males. One

possible explanation for these divergent results is the strong correlation of

digestive gland atrophy and T-, marinu infection intensity in males. In any

case, no unambiguous effect of digestive gland atrophy could be discerned.

Our data clearly support the importance of reproduction, at least in

oysters, during the summer and fall. We suggest that the weak evidence for

the importance of reproduction in most time series of contaminant body burden

generally stems from 3 factors: collect ion of animals out of spawning season

when little gonadal material is present, failure to analyze purified gametes

which are the primary vehicle of depuration during spawning, and the poor

understanding of the dynamics of uptake after spawning. We suggest that the

timing of the last spawning event prior to animals recover their

body burden within a month or less after a depuration event (Sericano et al.,

in press a) - and the degree of gonadal development (e.g. Hofmann et al., in

press) are important variables affecting PAR body burden in oysters.

3-98

Ellis 16

Lowe and Pipe (1987) and Moore et al. (1989) observed gonadal resorption

at high PAH concentrations. We observed no such effect in our analyses,

however body burdens were lower.

Variation between COMRounds.

Fluoranthene, pyrene and chrysene were very similar in their response to

the biological variables; naphthalene and phenanthrene formed a second group

quite different from the other three. Certainly, uptake, storage and

depuration must be relatively similar within these two groups but different

between them. Phenanthrene and naphthalene are lower molecular weight, more

water soluble compounds and equilibrate faster with the environment (Pruell et

al., 1986, Sericano et al., in prep.). They might lose the signal imposed by

spawning events faster than the larger three PAHs examined. Phenanthrene and

naphthalene,supported fewer significant correlations, none with reproduction,

despite their enriahment in eggs-and sperm, but were correlated with general MN

measures of health, like condition. Possibly such general measures include

factors controlling the equilibrium state of these PAHs. These analyses again

suggest that an important variable controlling PAH body burden is the time

between the most recent spawning and collection.

Nasci and Fossato (1982) noted that female gonadal material was enriched

in total DDTs but male gonadal material was not in Mytilus galloprovincialis.

Total PCBs were enriched in both female and male gonadal tissue. We observed

the same phenomenon in oysters. Unlike PAHs and PCBs, sperm do not

concentrate DDTs. The biochemical basis for this observation remains unclear.

Reproduction and the latitudinal gradient in body burden,

The data suggest one explanation for the latitudinal gradient in PAH and

pesticide body burden observed in the Gulf of Mexico (Wilson et al., 1990) and

3-99

Ellis 17

the relationship of PAH body burden and climate change (Wilson et al., in

press). Slight variations in temperature, as affected by climate change, or

varying average temperature across latitudes will vary the reproductive

season, the annual reproductive effort, and the frequency of spawning in

oysters (Hofmann et al., in press, submitted). Small changes in temperature

produce large changes in reproductive effort. As a result, body burdens will

vary even under similar exposure levels and this variability may be

considerable if a substantial fraction of the body burden is lost in spawning.

Wilsqn et al. (1990) found the latitudinal gradient in PAH body burden

to be stronger than the latitudinal iradient in pesticide body burden. We

found gonadal material concentrated much more highly in PA113 than pesticides

and some pesticides are not concentrated in male gonadal material at all. Our

data would suggest that temperature, and therefore latitude, should have a

much grlaater impact on PAHs through reproduction than on pesticides, in

agreement with the findings of Wilson et al. (1990). Taken together, our data

and those of Wilson et al. (1990, in press) suggest that interpretation of the

results of monitoring studies such as the Status and Trends program using

bivalves requires that close attention be paid to the reproductive state and

health of the sampled populations.

ACKNOWLEDGMENTS

This research was supported by a grant from the Center for Energy and

Minerals Resources, Texas A&M University (TAMU), an institutional grant NA89-

AA-D-SG139 to TAMU bythe National Sea Grant College Program, National Oceanic

and Atmospheric Administration (NOAA), U.S. Department of Commerce, grant 50-

DGNC-5-00262 from the U.S. Department of Commerce, NOAA, Ocean Assessments

Division, and computer funds from the TAMU College of Geosciences and Maritime

Studies Research Development Fund. We appreciate this support.
3-100

Ellis 18 OF

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LEGENDS TO TABLES 01
Table 1 The scale used for the analysis of gonadal stage

(after GERG, 1990).

Table 2 The scale used for digestive gland atrophy.

Table 3 Means and ranges of PAH concentration and the

biological parameters measured. ND, not determined.

Table 4 Best 3-variable model for each biological variable for

all oysters combined (i.e. both sexes combined) and
the amount of variation explained . (R 2). Significant

partial correlations are shown by asterisks: *, 0.05 <

P < 0.1; **, 0.025 < P < 0.05; ***, 0.01 < P < 0.025;

0.001 < P < 0.01; 0.0001 < P < 0.001.

Table 5 Best 3-variable model for each biological variable for

female oysters and the amount of variation. explained
(R2 Analyses were conducted with and without

gonadal quantity included. Signif icant partial

correlations are shown by asterisks, as defined in

Table 4.

Table 6 Best 3-variable model for each biological variable for

male oysters and the amount of variation explained
(R2 Significant partial correlations are shown by

asterisks, as defined in Table 4.

Table 7 Best 3-variable model for each PAH for all oysters
combined and the amount of variation explained (R 2 ).

Significant partial correlations are shown by

asterisks, as defined in Table 4.

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Ellis 27

Tabl e 8 Best 3-variable model for each PAH for female oysters
and the amount of variation explained (W). Analyses

were conducted with and without gonadal quantity

included. Significant partial correlations are shown

by asterisks, as defined in Table 4.

Table 9 Best 3-variable model for each- PAH for male oysters
and the amount of variation explained (R2

Significant partial correlations are shown by

asterisks, as defined in Table 4.

Table 10 PAH concentrations in pooled samples of purified

oyster eggs, purified sperm and somatic tissue (in

ppb).

Table 11 Pesticide concentrations in pooled samples of purified

oyster eggs, purified sperm and somatic tissue (in

ppb).

3-110

Ellis 28

Tabl e 1

Assigned

Numerical

Developmental St"g Valur, Description

Sexually

Undifferentiated 1 Little or no gonadal tissue visible

Early Development 2 Follicles beginning to expand

Mid-Development 3 Follicles expanded and beginning to

coalesce; no mature gametes present
Late Development 4 Follicles greatly expanded, coalesced, but 01

considerable connective tissue remaining;

some mature gametes present

Fully Developed 5 Mc>st gametes mature; little connective

tissue remaining

Spawning 6 Gametes visible in gonoducts

Spawned 7 Reduced number of gametes; some mature

gametes still remaining; evidence of

renewed reproductive activity

Spawned 8 Few or no gametes visible, gonadal tissue

atrophying

3-111

Ellis 29

Table 2

Assigned

Numerical

Val ue Description

0 normal

1 less than one-half atrophied

2 about one-half atrophied

3 greater than one-half atrophied

4 completely atrophied.

3-112

Ellis 30

Tabl e 3

Condi- -L maring Wet Digestive Gonad Fluor- Phenan- Napth-

Length tion Infection Weight Gonadal Gland Quantity anethene threne alene Pyrene Chrysene

(cm) Code Intensity (g) Stage Atrophy(mg dry wt.)(ppb) (ppb) (ppb) (ppb) (ppb)

Mean 8.0 4.3 1.45 9.6 5.5 2.1 ND 48.95 11.98 24.60 26.01 21.70

Range 4.8-10.5 2-6 0.-3.33 5.9-20.9 2-7 o-4 ND 13.-104.85.2-52.0 14.1-83.8 B.-56.35.7-41.1

Mean
Yale 8.1 4.6 0.77 8.6 5.3 1.8 ND 38.47 14.08 28.84 21.34 21.59

Mean
Femalb 7.9 4.2 1.67 9.9 5.6 2.2 6.29 52.44 11.28 23.19 27.57 21-74

Ellis 31

Table 4

Variable B2 Explanatory Variable (N=39)

Perkinsus marinus infection .18 Condition code

intensity Wet weight

Sex***

Digestive gland atrophy .14 Length

Condition code

Gonadal stage **

Sex .21 Length

Condition code

P. marinus infection intensty ***

Gonadal stage .34 Condition code *

Wet weight ****

Digestive gland atrophy *

Condition code .15 Gonadal stage *

Wet weight **

Digestive gland atrophy

3-114

Ellis 32

Tabl. e 5

With Gonadal Quantity (N=23) Without Gonadal Quantity (N=29)

Variabl 12 Explanatory Variabl 32 Explanatory Variabl

Gonadal stage .54 Length .47 Condition code

Condition code** Wet weight

Wet weight*** Digestive gland atrophy

Condition code .23 Length .11 Gonadal stage

Gonadal state Wet weight

Wet weight Digestive gland atrophy

Perkinsus marinu infection .22 Length o6 Condition code

intensity Gonadal stage Gonadal stage

Digestive gland atrophy Digestive gland atrophy
Digestive gland atrophy .16 Per@insus marinu infection .11 Condition code

intensity Wet weight

Length Gonadal stage

Wet weight

Gonadal quantity .07 Perkinsu marinu infection

intensity

Wet weight

Digestive gland atrophy

I= Ism I I

Ellis 33

Table 6

Variable B2 Explanatory Variable (N=10)

Gonadal stage .70 Length

Wet weight

Perkinsus marinus infection

intensity***

Condition code .20 Length

Wet weight

Perkinsus marinus infection intensity

Perkinsus marinus infection .74 Length*

intensity Wet weight**

Digestive gland atrophy****

Digestive gland atrophy .80 Perkinsus marinus infection

intensity****

Length**

Wet weight***

3-116

Ellis 34

Table 7

Variable _B2 Explanatory Variabl

Fluoranthene .20 Length

Perkinsu marinu infection

intensity

Phenanthrene .11 Sex**

Condition code

Gonadal stage

Naphthalene .20 Sex

Condition code***

Gonadal stage

Pyrene .19 Sex

Length

Perkinsu marinu infection

Chrysene .14. intensity

Sex**

-Perkinsu marinu infection

intensity

Wet Weight

Sex

3-117

Ellis 35

Tabl e 8

With Gonadal Quantity Without Gonadal Quantity

Variabl 12 Explanatory Variable B2 Explanatory Variable

Fluoranthene .37 Condition code .18 Length

Wet Weight Condition code

Gonadal quantity*** Perkinsu. marinu.

infection intensity

Phenanthrene .18 Perkinsu marinu .16 Condition code

infection intensity Perkinsu marinu

Digestive gland atrophy infection intensity

Gonadal quantity Digestive gland atroohy

Naphthalene .21 Length .27 Length***

Digestive gland atrophy Perkinsu marinu

Gonadal quantity infection intensity

Digestive gland atrophy*

Pyrene .31 Gonadal quantity** .20 Length

Digestive glandatrophy Condition code

Gonadal stage Perkinsu marinu

infection intensity

Chrysene .51 Length*** .25 Perkinsu marinu

Digestive gland atrophy** infection intensity*

Gonadal quantity***** Wet weight*

Digestive gland atrophy

3-118

Ellis 36
Mariabl Tabl e 9
B2 Explanatory Variabl

Fluoranthene .49 Length

Gonadal stage

Digestive gland atrophy*

Phenanthrene .67 Condition code**

Gonadal stage

Digestive gland atrophy

Naphthalene .73 Condition code***

Gonadal stage*

Digestive gland atrophy

Pyrene .68 Length

Gonadal stage

Digestive gland atrophy***

Chrysene .59 Condition code

Gonadal stage

Digestive gland atrophy*

3-119

Ellis 37

Table 10

Group A Group B Group C Group D Group E

Eggs Tissue Eggs Tissue Eggs Tissue Sperm Tissue Sperm Tissue

Naphthalene 45.1 9.0 51.9 8.9 42.5 5.9 64.8 12.3 70.5 12.3

Phenanthrene 23.5 2.9 26.9 4.1 29.0 3.4 26.1 5.6 29.9 5.6

Fluoranthene 16.1 2.9 15.8 3.0 17.7 3.2 11.6 3.3 17.6 3.3

Pyrene 20.7 3.7 18.4 3.7 18.2 3.8 13.1 4.0 18.1 4.0

Chrysene 11.5 2.4 12.5 2.0 10.9 2.2 7.2 2.4 16.6 2.4

3-120

Ellis 38

Table 11

Group A Group B Group C Group D Group E

Eggs Tissue Eggs Tissue Eggs Tissue Sperm Tissue Sperm Tissue

Lindane 9.4 2.06 5.5 2.2 8.2 1.8 <1.0 2.2 0.0 2.2

Total BHCs 14.7 5.0 9.5 5.2 14.0 3.9 0.0 5.2 2.4 5.2

a-chlordane 6.5 3.8 5.0 3.8 5.1 2.4 0.0 4.5 [email protected] 4.5

Dieldrin 6.3 2.2 6.1 1.9 5.8 1.7 0.0 1.8 1.7 1.8

4,41 DDE 32.1 9.1 26.o 8.2 26.7 7.5 4.1 11.9 6.6 11.9

4,4T DDD 12.3 3.7 11.7 3.2 12.5 3.1 <1.0 3.6 3.5 3.6

Total PCBs 132.6 36.5 147.8 33.5 113.0 29.6 114.2 53.8 102.3 53.8

3-121

4.0 Chlorinated Hydrocarbons

The concentration of selected chlorinated hydrocarbons has
been measured for six years (1986-1991) in oyster samples from 50 to
71 Gulf of Mexico sites as part of the NOAA National Status and Trends
(NS&T) Mussel Watch project. The results for pesticides and PCBs as
the mean of years I to 5 verses year 6 are plotted in Figures 4.1 to
4.19. Oysters are valuable sentinel organisms that reflect
contamination of an ecosystem on time scale of months. These sites,
removed from known point-sources of contamination, give coverage of
U.S. Gulf of Mexico coastal areas from southern-most Texas to
southern-most Florida. General overviews of the results of the NOAA!s
NS&T Program pesticide and PCB data have already been reported
(Table 1. 1 and Reprint 7).

Total DDT (sum of o-p'DDE + p-p'DDE + o-p'DDD + p-p'DDD + o-
p'DDT + p-p'DDT) data for oysters collected along the U.S. Gulf of
Mexico coast between 1986 and 1989 is shown in Figure 4.10. Total
DDT is the most abundant chlorinated pesticide found in Gulf of
Mexico oysters. Most of the DDT is present as metabolites, DDE and
DDD. Less than 10% of the total contaminant load in oysters is the
parent compound, DDT. The highest total DDT concentrations were
encountered in samples near the Brazos River mouth (BRFS) and
Galveston Bay (GBSC) in Texas, Mississippi River (MRPL and LPGO) in
Louisiana, Mobile Bay (MBHI) in Alabama, and Choctawhatchee (CBSP.
and PCLO) and St. Andrews Bays (SAWB) in Florida. With few
exceptions, total DDT concentrations were consistently low in samples
from southern Texas, Louisiana sites to the west of the Mississippi
River, and southernmost Florida. The general distribution of total DDT
concentrations encountered during 1991 in the Gulf of Mexico was
very similar to the distribution for the 1986-1990 'sampling period.
Total DDT concentrations measured during Year 6 were lower or the
same as those encountered in the average of the previous five years of
this study at all except five sites. The incorporation of new sampling
sites, located closer to supposed contaminating centers, during 1989,
1990, and 1991 added more details to the overall DDT distribution in
the northern Gulf of Mexico, but did not greatly affect the regional
distribution. In general, the average concentrations measured at those
sites were in good agreement with the concentrations previously
reported for the surrounding geographical area. A greater fraction of
DDT is found as DDE in the general region south of Galveston Bay along
the Texas coast. This may be related to an "older" source of DDTs in
the South Texas area, but additional data is required to examine this
hypothesis.

The concentrations of hexachlorobenzene (HCB, Figure 4.1)
were generally low (maximum was 2.10 ng/g). These values are very
close to the method detection limit and analytical difficulties make

4-1

interpretation of this data problematic. This same problem is found
when attempting to interpret the data for lindane, heptachlor, aldrin,
heptachlor epoxide and mirex (Figures 4.2, 4.3, 4.4. 4.5 and 4.9).

Chlordane and its breakdown products are represented by a-
chlordane (Figure 4.6) and trans-nonachlor (Figure 4.7). The
chlordane distribution is similar to the DDT distribution with low
concentrations in Southern Texas, highs in Galveston Bay, near the
Mississippi River, and in Florida Bays. Details of the chlodane
distribution have been reported (Preprint 3). Dieldrin (Figure 4.8) has
a distribution similar to chlodane, with the exception that the Florida
sites are not as high relative to the Galveston Bay and Mississippi River
sites.

Endrin was measured in NS&T oysters for the first time in Year
5. The concentrations ranged from below detection (-I ng/g) to 30.6
ng/g. The concentrations are lower in southernmost Texas and
Florida, with higher concentrations in the northern regions of the Gulf
Coast. Endrin concentrations were in general lower in year 6
compared to year 5. Endrin does not appear to have a simlar
distribution to other chlorinated hydrocarbons.

The general trend of chlorinated hydrocarbon concentrations is
relatively constant at most sites with episodic increases and decreases
at selected sites. These episodic changes are probably due to site
specific input events. However, Gulf-wide -temporal changes have been
reported (Reprint I ). I
PCBs proved to be ubiquitous contaminants in Gulf of Mexico
oysters. PCB congeners were detected in all NS&T samples analyzed
(Figure 4.18). PCB concentrations were higher at 18 sites in year 6
compared to the mean of years 1 to 5. As in the case of DDTs, the
addition of new locations to the sampling project did not greatly
modify the general distribution of average PCB concentrations in Gulf
of Mexico oysters described for the first three years of the NS&T
project. A possible exception could be Tampa Bay (TBKA), which had
average PCB concentrations clearly higher than the surrounding sites.
The new site for year 6 (LPGO) has the highest concentration
reported.

GERG has recently taken a closer look at the consequence of
removing part of the oyster sample that was collected for organic
analyses to use in biological testing (Preprint 4). While it would not
affect the overall interpretation of NS&T data, it does add a bias into
the data. More details are available in the attached Preprint 3. GERG
has also recently developed methods for analyses of the most toxic
planar PCB congeners and applied these techniques to selected NS&T
samples (Preprint 5).

4-2

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SRWP
MD. MR7?
MBGP m CKBP
MRPL
0 A) TBNP
@:r MBLR
0 NIBCB BSSI TBMK
0 M13TP BSBG TRPB
NMDI LBNT 7130T
0 M13FM LBNO TBKA
BRCL LPGO LA nHB
BRFS ................. ........ ncB
MSPC
GBCR
CBBI
MSBB ms
GBOB CBFM
GBTD MSPB
................................................................. NBNB
0 GBYC MBCP RBHC
GBSC ALi: RVFU
CD (D GBHR M13DR BHKF
SUR

C'D

tA LA LA
.,A Z > LMSB ............... CLSI PBPH
@< I-wi CLLC' PBM
1. 11 LMAC PBSP
cn '-3 p IHJH CBM
cc
CCN13 VBSP CBSR
Ul ND ccic ABOD CBSP
@ cun) ABLR CLCL PCLO
A1319. TBLJ3 PCMP
TBLF
C13CR SAWH
w MBAR APDB
C-tl
9) 00 SAPP APCP
@7. 0 BBSD
@Z SAMP AESP
ESSP B B INVM SRWP
ESBD MRI?
CKBP
MBGP
0 p MRPL TBNP
MBLR
BSSI TBNT
0 NMCB
0 13SBG TBPB
cn MB7?
*.. TBOT
:z @MDI MA
0 MBEM L13NO
r LA TBHB
4..@ BRCL LPGO .................................................................... TnBCB
C'n, BRFS
tv MSPC
GBCR CBBI
MSBR
ms
GBOB CBFM
0 GBID MSP9 ................................................. NBNB
Co. GBYC MBCP AL' RB14C
Gasc NMM
RVFU
m

m
IM

'N' GBHR MBDR =Room= BHKF
SLBB 44,98-

LJ tj W
tA %A t.A LA -.A

LMSB CLS1 PBPH
LMPI PBM
(D CO CLLC
9
LWMMAC PBSP
CCBH CBJB
VBSP
CCNH
ABOB
ccic
A B LLUR CLCL PCLO
A1B IMU 7BLJB3 PCMP
CBCR
7TILF SAWB
MBAR Bffm APD3
SAPP
:7. 0 SAMP BBSD APCP
z AESP
ESSP BRNM
SRWP
00 ESBD MRTP CKBP
MBGP
0 PO MRPL
a MBLR TBNP
0 MBCB BSSI TRUK
0 MJB3Tp BSBG TBPH
NMDl LBMP TBOT
0 0 MEEM L33NO TTIKA
Cl) BRCL LPGO V1 LA T13BB
BRFS mspc ............ ............................. TBCB
GBCR CBBI
MSBR ms
GBOB CBFM
GEM MSPB
................................................................. NBNB
Gayc MBCP RBHC
GBSC
GBHR MBIT AL EVFU
SLIBB MHDR HHKF
.................

In III

tA kA tA tA
I-MSB m . CLSI PBPH
LMPI' CLLC PBIMB
LMAC PBSP
jHJH
CCBH CBM
CCNB VBSP 30.60 CBSR
ccic ABOB CBSP
CL ABLR CLCL PCLO
0 ABM 7BLB PCMp
cn CBCR SAWB
7TIB3 L F
MBAR B IMMI 3 APDB
SAPP APCP
cr) SAMP BBSD AESP
Essp BBMB
S WP
cn. cn FSBD'Wo MRTP
CKBP
MBGP MRPL ITMB NTP
0
MBLR
NMCB BSSI TBMX
BSBG IBPB
MBTP
:5- NMDI LHNT TBOT
z MBEM L2NO TBKA
cn BRC LPGO LA TBHB
vi Ro ...................................................
@-i BRFS MSPC T13CB
GBCR CBBI
0.4 MSHB ms
GBOB CHFM
MSPB
cn GBM ......................................................... NBNB
GBYC MBCP 111111C
9) GBSC NMIR EVFU
MR MBDR BffKF
SLBB

LMSB CLSI PBPH
LMPI MIS
CLLC'
L.MAC PBSP
CCBH CBJB
z All I VBsp
cn 0 cm CBSR
Ro e+ ccic ABOB
HO CBSP
e-+
ABLR CLCL PCLO
ABIMR IBLE PCMP
0
CBCR TBLF SAWB
MBAR
Go SAPP BBTB APDB
SAMP 13BSD APCP
4 r+. AESP
0 F-SSP BBNIB
ESBD MRTP SRWP
MBGP CKBP
MRPL
MBLR TBNP
OSSI TBMK
0 baCB
BSBG
MBIP LA TBPB
MBDI LBNV TBOT
r+
0 MBEM TBKA
LBNO
1212.40
BRCL LPGO T13HB
BRFS ...........................
GBCR MSPC .... TBCB
CBS
GBOR MSBB ms CBFM
GBM MSPB
otk (D . .................................... ............................ NBNB
GBYC MBCP RBHC
GESC wtu AIL EVFU
GBRR 1115.00
SL.BB MBDR BHKF
113

"M k 1 11 1 1 1

Reprint 7

Chlorinated Hydrocarbons in Gulf of Mexico Oysters:
Overview of the First Four Years of the NOAA!s National Status
and Trends Mussel Watch Program (1986-1989)

J.L. Sericano, T.L. Wade, and J.M. Brooks

Chlorinated Hydrocarbons in Gulf of
Mexico Oysters: Overview of the First Four
Years of the NOAA's National Status and
Trends Mussel Watch Program (1986-1989)
J.L. Sericano, T.L. Wade, J.M. Brooks
Geochemical and Environmental Research Group,
College of Geosciences., Texas A&M University,
833 Graham Road, College Station, Texas 77845,
U.S.A.

ABSTRACT

During the first four years of the NOAA's National Status and
Trends Mussel Watch program selected chlorinated hydrocarbons
were analyzed in more than 660 oyster samples from the northern
coast of the Gulf of Mexico. Chlordane-related compounds, DDT
and its metabolites and PCB congeners were detected at all the
locations monitored.- Concentrations ranged over two to three
orders of magnitude. Alpha-chlordane and trans-nonachlor
comprised more than 90% of the total load of chlordane-related
compound in the samples. The bulk of the total DW burden in
oysters corresponded to the degradation products, DDE and DDD.
while DDT isomers only accounted for a small fraction of the total
load. PCB congeners corresponded mainly to the four-, five- and
six-chlorine homologs. After the first four yean of this progTam,
the concentration distributions in oysters from the northem Gulf
of Mexico is well defined. Temporal trends are not apparent at
most sites.

INTRODUCTION

The National Oceanic and Atmospheric Administration's
National Status and Trends Mussel Watch (NS&T) Program has
been designed to monitor the current status and long-term trends
of selected organic and inorganic environmental contaminant@;
e.g. chlorinated pesticides, polychlorinated biphenyls (PCBs),
polynuclear aromatic hydrocarbons (PAHs) and trace metals,
along the coasts of the U.S.A. by measuring the concentrations of
these contaminants in bivalves and sediments over several years-
The rationale for the "Mussel Watch" approach using bivalves;
e.g. mussels, oysters and clams, has been summarized by

4-22

666 Watcr Pollution

different authors [1-4), and -its concept has been applied to several
monitoring programs during the last decade (5-10).

An over-view of the concentrations of the selected chlorinated
hydrocarbons analyzed in oyster samples collected during the first
four yearis of the NOAA's NS&T program in the Guif of Mexico
are presented here. The ultimate goals of this program are to
define the geographical distributions of contaminants and
determine trends in their concentrations.

MATERIALS AND METHODS

Sampling
Originally, the NS&T sampling program contemplated tile
collection of bivalve samples from three stations at 51 sites from
Gulf of Mexico coastal areas. Distances between stations within
each site varied from 100 to 1000 meters- Oyster samples were
collected over two- to three-month periods starting in late
December or early January. Depending on the water depth,
oysters were collected by hand, tongs or dredge. Twenty oysters
per site were pooled in precombusted jars and frozen until
analysis. During 1986 and 1987, oyster samplings were completed
at 49 and 48 sites with totals of 147 and 143 samples, respectively.
These sites provided a good coverage of the northem Gulf of
Mexico coast from the U.S.A_-Mex:ico border to southernmost
Florida, with an ample variety of different environmental
conditions- The sites were selected to avoid known-point source of
contaminants. Starting in 1988, new sites were added to the
sampling program to obtain more information in areas located-
closer to suspected sources of contaminants. During 1988 and*
1989, oyster samples were collected from 63 and 62 different
locations with totals of 189 and 186 samples, respectively. By the
end of the fourth sampling period, 76 sites have been visited, 41.of
them in all four years (Figure 1 and Table 1).
Table 1. Sampling site locations in the Gulf of Mexico for the:
NOA_A7s Status and Trends Mussel Watch Program, 1986-1989.

-----------------------------------------------
Site General Specific state

1 LMSB Laguna Madre South Bay TX
2 CCNB Corpus Christi Nueces Bay Tx
52 LMPI Laguna Madre Port Isabel TX
53 CCBH Corpus Christi Boat Harbor Tx
3 CCIC Corpus Christi Ingleside Cove TX
54 ABHI Aransas Bay Harbor Island TX
4 ABLR Aransas Bay Long Reef T*X

-----------------------------------------------

4-23

miss
ISSIPPI ALABAMA GEORGIA
...........

LOUISIANA ......
I'll, 14.60
Jo@ No. 32333433 36
TEXAS 2 21 -30 FLORIDA
22 -31 42 43"
-14
29, 11,1356
12 1 24 21 29

10 10
G U L F OF ME XICO Pa -
A4 -4S
41- 41

41@
31
U, S. A.
49'
MIXIC0 so
31

t". 9% a). *1- 9,0- toy. 1,4. 1r

Fig. I Gulf of Mexico sampling site locations. Shown are the original sites (0) and the sites add
sampling program (N)since 1988. See Table 1 for a complete site identification.

66.1; Walcj-

Table 1. (Continued)

------------------------------------- : -----------
Site General Specific State

6 CBCR Copano Bay Copano Reef Tx
6 MBAR Mesquite Bay Ayres Reef Tx
7 SAPP San Antonio Bay Panther Pt- Reef Tx
8 SAMP San Antonio Bay Mosquito Point Tx
9 ESSP Espiritu Santo South Past Reef Tx
10 ESBD Espiritu Santo Bill Days Reef TX
11 MBLR Matagorda Bay Layaca. River Tx
12 MBGP Matagorda Bay Galliniper Point Tx
56 MBCB Matagorda Bay Carancahua Bay TX,
13 MBTP Matagorda Bay Tres Palacios Bay Tx
55 MBDI Matagorda Bay Dog Island TX
14 MBEM Matagorda Bay East Matagorda TX
57 BRFS Brazos River Freeport Surfside TX
72 BRCL Brazos River Cedar Lakes Tx
15 GBYC Galveston Bay Yacht Club TX
59 GBSC Galveston Bay Ship Channel TX
58 GBOB Galveston Bay Offats; Bayou TX
16 GBTD Galveston Bay Todd's Dump TX
17 GBHR Galveston Bay Hanna Reef TX
18 GBCR Galveston Bay Confederate Reef TX
19 SLBB Sabine Lake Blue Buck Point TX
20 CLSJ Calcasieu Lake St. John!s Island La
60 CLLC Calcasieu Lake Lake Charles La..
21 JHJH Joseph Harbor Bayou Joseph Harbor Bay La.
22 VBSP Vermillion Bay Southwest Pass La%
23 ECSP East Cote Blanche South Point La.'
24 ABOB Atchafalaya Bay OysterBayou La
25 CLCL Caillou Lake Caillou Lake La
26 TBLB Terreborme Bay Lake Barre La
Z7 TBLF Terreborme Bay Lake Felicity La.:
61 BBTB Barataria Bay Turtle Bay La
2B BBSD Barataria Bay Bayou SL Denis La:
29 BBMB Barataria Bay Middle Bank LA.
65 MRTP Mississippi River Tiger Pass La
64 MRPL Mississippi River Pass a Loutre La
30 BSBG Breton Sound Bay Gardeme La:'
31 BSSI Breton Sound Sable Island La
32 LBMP Lake Borgne Malheureux Point La
62 LBNO Lake Borgne New Orleans La"
33 MSPC Mississippi Sound Pass Christian MS
34 MSBB Mississippi Sound Bilo3d Bay Ms ,
35 MSPB Mississippi Sound Pascagoula Bay Ms
36 MBCP Mobile Bay Cedar Point Reef Al
66 MBHI Mobile Bay Hollingers Is. Ch- A!
------------------------------------------------

4-25

XV;itcr PoIlLition

Table L (Continued)

-----------------------------------------------
Site General Specific State

67 PBPH Pensacola Bay Public Harbor F1
37 PRIB Pensacola Bay Indian Bayou F1
38 CBSR Choctawhatchee Bay Off Santa Rosa F1
39 CBSP Choctawhatchee Bay Shirk Point F1
73 CBJB Choctawhatchee Bay Joe7s Bayou F1
68 PCMP Panama City Municipal Pier F1
74 PCLO Panama City Little Oyster Bay F1
40 SAVVB St. Andrew Bay Watson Bayou F1
41 APDB Apalachicola Bay Dry Bar F1
42 A_PCP Apalachicola Bay Cat Point Bar F1
75 AESP. Apalachee Bay Spring Creek -F1
69 SRWP Suwannee River West Pass Fl
43 CKBP Cedar Key Black Point F1
44 TBFB Tampa Bay Papys Bayou F1
70 TBOT Tampa Bay Old Tampa Bay F1
45 TBBB Tampa Bay Hillsborough Bay Fl
46 TBCB Tampa Bay Cockroach Bay F1
76 TBNP Tampa Bay Narvaez Park F1
77 TBKA Tampa Bay P. O'Knight Airport. Fl
47 TBAM Tampallay Mullet Key Bayou F1
48 CBBI Charlotte Harbor Bird Island Fl
71 CBFM Charlotte Harbor Fort Meyers F1
49 NBNB Naples Bay Naples Bay F1
50 RBHC Rookery Bay Henderson Creek Fl
51 EVFU Everglades Faka Union Bay F1

------- - --------------------------------------

Analytical -Procedure
The analytical procedure was adapted from a method developed by
MacLeod et al. (111 and has been described in more details
elsewhere (9,101. Briefly, approximately 15 g of wet tissue were
extracted 3 times with methylehe chloride using a homogenizer
(Tekmar Tissumizer). Before extraction 4:4 dibromooctafluoro-
biphenyl (DBOFB) and two PCBs, 1UPAC numbers 103 and 198,
were added to all samples, blanks and reference materials as
internal standards. Tissue extracts were fractionated into two
fractions by silica-alumina column chromatography. Pentane
and pentane:methylene chloride (50:50) were used as elutants for
the first (aliphatic hydrocarbons) and second (chlorinated and
polynuclear aromatic hydrocarbons) fractions, respectively. A
further clean-up of the second fraction was performed by either
Sephadex LH-20 column chromatography (121 (years I, II and IM
or high performance liquid chromatography (HPLQ (year IV).
Both techniques have produced comparable results. Sample
extracts were finally concentrated to a volume of 0.5 nil, in

4-26

670 \Vticr Polltition

hexane, for gas chromatographic analysis- Each set of eight to
ten samples was accompanied by a complete system blank and
spiked blank or,reference material, carried through the entire
analytical procedure-

Gas chromatography
Chlorinated hydrocarbon concentrations were determined by gas
chromatography with an electron capture detector (GC-ECD,
63Ni) using a 30 in DB-5 fused silica capillary column (0-25 um
film thickness, 0.25 min i.d.) as previously described (9,1o].
Chlorinated hydrocarbon were quantitated against authentic
standards injected at four different concentrations.

Quality control/Quality assurance activities, that included
several laboratory inter-calibration exercises with repeated,
routine analyses of homogenated samples supplied by the
National Institute of Standards and Technology (NIST), formerly
National Bureau of Standards (NBS), have been undertaken to
ensure that the data produced during the NS&T program is
reproducible, accurate and analyst independent. Interim
reference materials as well as spiked blanks are also part of our
ongoing laboratory QA/QC activity.

RESULTS AND DISCUSSION

Over 660 oyster samples from 76 different sites on the riorthern
Gulf of Mexico coast have been analyzed for selected chlorinated
hydrocarbons during the first four years of the NS&T program-
In the following sections, the average concentrations of total
chlordane-related compounds, i.e., sum of alpha- chlo rdane,
trans-nonachlor, heptachlor and heptachlor epoxide, total DDT,
i.e. the sum of o-p@-DDE, p-p'@-DDE, o-p-DDD, p-p@--DDD, o-p-DDT
and p-p-DDT, and total PCBs will be discussed- During 1986 and
1987, total PCB, concentrations represented the sum of all the
measurable PCB congeners in the samples. Starting in 1988, total
PCB concentrations in oyster samples were calculated by a
regression equation relating the sum of 18 selected PCB congener
data, generally the major components of commercial PCB
mixtures and among the most commonly observed congeners in
environmental samples, and total PCB congener data from the
previous years. Analyte mean concentrations for each site
represent the average of the mean concentrations encountered
during each sampling period-

Chlordane-related cQmyounds
Technical chlordane is a complex mixture formed by more than
140 different components. Recently, 120 of these compounds have
been identified (131. The most abundant constituents of technical
chlordane are alpha-chlordane, gamma-chlordane, heptachlor

4-27

Water Pollution 671

and tran-nonachlor. Because of the toxicity, potential
carcinogenicity and environmental persistence of ist components,
technical chlordane sales and/or applications in the U.S.A. were
suspended after April 15, 1988. The NS&T program included
three chlordane-related compounds: alpha-chlordane, heptachlor
and trans-nonachlor. Heptachlor epoxide, a metabloite of
heptachlor, was also monitored.

Chlordane-related compounds were gernerally present in low
ng g-1 concentrations. Average concentrations, plus 1 standard
deviation, ranged from 3.5+0.44 to 120+70 ng g-1 (Figure 2).

Fig. 2. Average concentrations, in ng g-1, of total chlordane-
related compounds in oysters from the Gulf of Mexico.

4-28

672 Water Pollution

The highest average concentrations in oyster samples were
encountered in samples from sites in Galveston Bay (GBYC, 852q�54
ng g-1; GBSC, 87�73 ng g-1; GBOB, 93�7-1 ng g-1), near the mouth
of the Mississippi River (MRPL, 64�4-0 ng g-1), Biloxi Bay (MSBB.
82�13 ng g-1), and Choctawhatchee (CBSP, 114�120 ng g-1), Tarapa
(TBPB, 75�19 ng g-1; TBCB, 88�36 ng g-1; TBNP, 120�66 ng g-1;
TBKA, 110�51 ng g-1) and Charlotte Bays (CBFM, 120�70 ng g-1).
With the exception of the sites in the Galveston Bay area, average
concentrations were lower to the west of the Mississippi River-

Approximately 90% of the total load of chlordane-related
compounds encountered in oysters corresponded to the sum Of
alpha chlordane (45�2.5%) and trans-nonachlor (43�4.2%), two of
the most important constituents of technical chlordane (131
(Figure 3). The fact that beptachlor epoxide is dominant over its
parent compound indicates environmental degradation of
heptachlor.

Fig. 3. Average composition of total chlordane-related compoun6q6i
in oyster samples from the Gulf of Mexico.

A comparison of average concentrations measured in 1989
with the concentrations encountered during the first year of the
N32qS32q&T program, i.e. 1986, is presented in Figure 4. On this scatter
plot, sites with the same concentrations during both sampling
years fall on the center line of the graph (intercept = 0; slope = 1).:
Sites that plot above or below the center line show an increase or a
decrease in concentration, respectively, between 1986 and 1989.
Also shown are lines that represent a 2-fold increase or decrease

4-29

Water Pollution 673

10000
Chlordane-related compounds

l000

100

1 10 100 IODO 10000

1986 Average Concentrations (ng/g)

Fig. 4 Oyster total chlordane-related compound concentrations in
1986 versus 1989.

average concentrations encountered in 1986 when compared to
data collected four years later. Over 80% of the sites showed a
decrease in average concentrations; nearly 30% of the sites had a
decrease larger than a 2-fold change. Only 15% of these locations
revealed slight increases in their average concentrations.

DDT and metabolites
In spite of its ban in 1972, DDT and its metabolites, DDE and DDD,
are still present, in significant concentrations, in the near-shore
environments of the U.S.A. DDT and/or its derivatives were
detected in every oyster sample analyzed. Figure 5 summarizes
the average total DDT concentrations, plus 1 standard deviation,
encountered in oyster samples from the Gulf of Mexico. Total
DDT concentrations ranged from 6.92�1.9 to 890�440 ng g-1. With
the exception of some samples collected from sites in Galveston
Bay (GBSC, 170�81 ng g-1) and near the mouth of the Brazos River
(BRFS, 220�47 ng g-1), in Texas, and from locations in Tampa
(TBKA, 160�32 ng g-1) and Charlotte (CBFK 170�85 ng g-1) Bays,
in Florida, the highest concentrations were encountered in
samples collected to the east of the Mississippi River between its
mouth and St Andrew Bay, Florida, e.g., MRPL, 280�54 ng g-1;
MBCP. 200�64 ng g-1; MBHI, 830�270 ng g-1; CBSP, 890�440 ng g-1,
and PCLO, 520�730 ng g-1. With the exceptions mentioned above,
the lowest concentrations were generally found along the Texas
a0nd southern Florida coasts.

4-30

6-74 Waizr Pollution

2S-URSO
2-CCNB 29-BBIAM
IN
52AA 65-AIRTP
53-CCBH 64-bUWL
3-CCIC 30-BSBG
54-ABHI 31-BSSI
4-AID31LAR 32-LBNIIP
5-CBCR 62-LENO
6-AIBAR 33-MSPC
7-SAPP 34-MSBB
S-SAMP 35-MSPB
*-MP 36-1kf8CP
10-ESSD 6G@MBM
U-MBLR 67-PEPH
MZ*IBGP 37-PBM
56-DIBCB 38-CBSR
1341IMP 33k'BSP
55-NMZDI 73-(MJB
14-TUBEA1 68-PCCWMP
57-BRFS 74-FPICLWO
72,BRCL 40-SAWB
15-GRYC 41-APDR
5%GBSC 4@2-APCP
58-GBOR 75AESP
1S-GBTD G9-SRW
17-GBEIR 43-CEMP
18-GBCR 44-TEPR
O-SLBB 70-TBOT
20-(X.SJ 45-TEUB
60-CLLC 4G@TBCB
21-JHJH 76-TBNTP
2Z-VBSP 77-TBEA
23-ECSP 47-TBbIK
24-ABOB 48-03BI
25-CLCL
49
50-=
27-TBLIF C
61-BBTB 51-EVIFU

0 200 400 600 800 0 200 400 6W 80

Fig. 5 Average concentrations, in ng g-1, of total DDT in oyster
samples &om the Gulf of Meidco.

Isomers of the DDT accounted for a small fraction (5.2+-3.0%)
of the total DDT burden in oysters during this study (Figure 6).
Isomers of the DDD and DDE'contributed with 50�2.1 and 44+-2-3%
of the total amount, respectively. Technical DDT contained
appro)dmately 75% p-p-DDT, 15% a-p-DDT, 5% p-p'-DDE, <05%
o-p'-DDE, <0.5%. p-p'-DDD, <0.5% o-p-DDD and <5% unidentified
compounds. It is generally accepted that increasing percentages
of DDE and/or DDD, which were found as impurities in the

4-31

Water Polluilon 675

commercial DDT mixture, indicate a decreasing exposure to new
inputs of DDT-

DDE

DDD

DDT

0 10 2() 39 4D 5D 6)

Percentages

Fig. 6 Average composition of the DDT burden i n oysters from the
Gulf of Mexico

t4 10000-
DDT and Metabolites
b

0 10W 7

AN
10
4W

100 1" 10"
1286 Average Concenft-altions (ng/g)

Fig. 7 Oyster total DDT concentrations in 1986 versus 1989.

The average total DDT concentrations encountered at the
different sites during 1986 and 1989 are compared on Pigure 7. In

4-32

670 W.QCr P01111,1011

general, concentrations were very similar at most of the sites.
Only a few sites showed differences in concentrations greater
than a 2-fold change. Although it is not possible to visualize a
trend in average total DDT concentrations with a four-year data
base, mainly due to the long environmental persistency of these
compounds, it has been shown that the total DDT concentrations
in Gulf of Mexico oysters have decreased since 1969 [101-

Polyclilorinated Biphenyls
PCBs have proven to be ubiquitous contaminants in.the Gulf of
Me)dco coastal environment. Average total PCB concentrations,
plus 1 standard deviation, are presented in Figure S.

I-LMS13 28-BBSD
2,-CXCNt.B 2:9-laMM
52-LMPI GS-NUUP
S3-(=H &I-NaIPL
3-(=C 30-USBG
54-AMM 31-BSSI
4-A)MR 32-LWIP
54CBM 62-LENO
C-DIMB"AR 33-MSM
7-qAM 34-WSBB
B-SAW 35-WISPB
S-ESSP S&NMCP
10-ESSD er@BIBM
MAZUR V-P13PH
M?4WP 37-PBM
5&xas= 38-CBM
13-Itfln? 39-CWP
55-MDI 73-CBJB
14-INMEM 6s-PCbI:P
57-BIPJS 74-PC7WO
72-BRCL 40--SAWR
IS4WC 41-A.PDB
59-GBSC 42-APCP
584MOB- 75-AESP
16-CBM 69-SRWP
17-GBIEIR 43-CKSP
18-GUM 44-TBPB
194SLBB 70-TBOT
20-CLSJ 45-TREEB
60-CLLC 46@T13M
21-JELJH 76-TBNP
22-NVFBSP 77-TBKA
23-ECSP 47-TBAIK
24-ABOB 48-CBBI
25-CLCL 71-CBF"NI
2&TBLB 49-NBrM
27-TBLF 50-RBHC
61-BBTE -4 51-EVFU
0 300 600 900 1200 0 300 600 900 1200

Fig. 8 Average total PCB concentrations in oyster samples from
the Gulf of Mexico

4-33

XV.itcr Pollulioti 077

PCB congeners were detected in all the oyster samples
collected between 1986 and 1989 with average concentrations
--ranging from 26�17 to 1000�730 ng g-1. The highest
concentrations were encountered in samples collected from sites
in Galveston Bay (GBYC, 1000�730 ng g-1; GBSC, 910�81 ng g-1),
near the mouth of the Mississippi River (MRPL, 620�210 ng g-1),
Mobile Bay (MBHI, 520�150 ng g-1) and Pensacola (PBIB, 590�130
ng g-1), St Andrew (SAWB, 620�190 ng g-1) and Tampa (TBKA,
490�110 ng g-1) Bays. Similar to total DDT concentrations, the
high. concentrations measured in samples from Galveston Bay
were the exception to the west of the Mississippi river.

The average composition of PCBs in oysters collected during
1986 and 1987 was largely dominated by pentachlorobiphenyls
(46.8%), with some hexa- (22.3%) and tetrachlorobiphenyls
(21.0%), and were almost depleted in di- (0.6%), octa- (0.5%) and
nonachlorobiphenyls (0.2%) (Figure 9). The same general
distribution is found in organisms from different PCB-
contaminated locations (14-16]. In general, differential partition
of PCB congeners between aqueous and lipids phases as well as
stereochemistry appear to significantly affect bioaccumulation
(14,17-19]. Maximum PCB uptake by organisms is observed with
isomers having four to six chlorine atoms. Congeners with a
lower number of chlorines have higher water solubilities and, as
a consequence less favorable partition coefficients, while
congeners with more than six chlorines-have unfavorable steric
configurations.

Di-CB

Tri-CB

Tetra-CB

Penta-CB

Hexa-CE

Hepta-CB

Octa-CB

Nona-CB;

0 10 20 30 40 50

Permnta@

Fig. 9 Fractional composition of PCB homologs in oyster samples
from the Gulf of Mexico.

4-34

67S Walcr Politit

Total PCB concentrations measured in oyster samples in 1989
did not differ significantly from the levels encountered four years
earlier (Figure 10). With few exceptions, most of the sites had
PCB concentrations in 1986-1989 that fell within the factor-of-two
lines.

Polychlorinated Biphenyls

0 Iwo 7

U 100 Ole* 19.0-'

Cz 10

1 100W

1986 Average Concentrations (XW9)

Fig. 10 Oyster total PCB concentrations in 1986 versus 1989.

CONCLUSIONS

After the first four years, the objectives of the NS&T program in
the Gulf of Mexico have been partially accomplished.
Distributions of chlordane-related compounds, DDT and its
metabolites, and PCBs in oysters are weU established- Oysters
from some locations have consistently had high concentrations of
these analytes, e.g. sites located in Galveston and Tampa Bays,
near the mouth of the Mississippi River and in different sites
along the Florida coastline between the Mississippi River and St
Andrew Bay. Temporal trends are not readily apparent for all the
contaminants. Continued sampling will help to further establish
temporal trends for various analytes at the different sites.

ACKNOWLEDGEMENTS

This work was supported by the National Oceanic and
Atmospheric Administration, contract No. 50-DGNC-5-00262,
through the Texas A&M Research Foundation, Texas A&M
University.

4-35

Waier Pollutioti 679

REFERENCES

1. Goldberg, E.D.. Bowen, V.T., Farrington, J.W., Harvey, G.,
Martin, J.H., Parker. P.L., Risebrough, W., Schneider, E. and
Gamble, E. The Mussel Watch, Environmental Conservation,
Vol 5,101-125,1978.

2. Farrington, J.W-, Albaiges, J, Burns, K.A., Dunn, B.P.,
Eaton, P., Laseter, J.L., Parker, P.L. and Wise, S- The
International Mussel Watch: Report of a Workshop Sponsored
by the Environmental Studies Board Commission on Natural
Resources, pp 7-77, National Research Council, Washington
DC, 1980.

3. Phillips, D.J.H. Quantitative Biological Indicator, Their Use
to Monitor Trace Metals and Organochlorine Pollution,
Applied Science, London, 1980.

4- Risebrough, R.W., DeLappe, B.W., Walker II, W., Simoneit,
B.T., Grimalt, J., Albaijes, J. and Regueiro, J.A.G.
Application of the Mussel Watch Concept in Studies of the
Distribution of Hydrocarbons in the Coastal Zone of the Ebro
Delta. Marine Pollution Bulletin, Vol. 14, pp. 181-187,1983.

5. F@@gton, J.W., Goldberg, E.D-, Risebrough, R-W., Martin,
J.H. and Bowen, V.T. U.S- 'Mussel Watch' 1976-1978: An
Overview of the Trace Metal, DDE, PCB, Hydrocarbons and
Artificial Radionuclide Data. Environmental Science and
Technology, Vol. 17, pp. 490-496,1983.

6. Martin, M. State Mussel Watch: Toxic Surveillance in
California, Marine Pollution Bulletin, Vol. 16, pp.140-146,
1985.

7. Tavares, T.M., Rocha, V.C., Porte, C., Barceld, D. and
Albaig6s, J. Application of the Mussel Watch Concept in
Studies of Hydrocarbons, PCBs and DDT in the Brazilian Bay
of Todos os Santos (Bahia), Marine Pollution Bulletin, Vol. 19,
pp. 575-578,1988.

8. Wade, T.L-, Atlas, E.L., Brooks, J.M., Kennicutt II, M.C.,
Fox, R-G., Sericano, J.L., Garcia-Romeroj B., DeFreitas, D.
NOAA Gulf of Mexico Status and Trends Program: Trace
Organic Contaminant Distribution in Sediments and Oysters,
Estuaries, Vol. 11, pp. 171-179,1988.

9. Sericano, J.L, Atlas, E-L., Wade, T.L and Brooks, J.M.
NOAA*s Status and Trends Mussel Watch Program:
Chlorinated Pesticides and PCBs in Oysters (Crassostrea

4-36

6SO walet- Pollutioll

virginica) and Sediments from the Gulf of Mexico, 19,96-1987.
Marine Environmental Research, Vol- 29, pp- 161-203, 1990.

10. Sericano, J.L., Wade, T-L., Atlas. E.L. and Brooks, J_14.
Historical Perspective on the Environmental Bioavailability of
DDT and its Derivatives to Gulf of Mexico Oysters.
Environmental Science and Technology, Vol. 24, pp. 1541-1548,
1990.

11- MacLeod, W.D., Brown, D-W., Friedman, A.J., Burrows,
D.G., Maynes, 0., Pearce, R-W-, Wigren, C-A- and Bogar,
R.G- Standard Analytical Procedures of the NOAA National
Analytical Facility, 1985-1986.. Extractable Toxic Organic
Compounds (2nd Edition), U.S- Department of Commerce,
NOAA Technical Memorandum, NMFS F/NWC-92,1985-

12. Ramos, L and Prohaska, P- Sephadex LH-20 Chromatography
of Extracts of Marine Sediments and Biological Samples for
the Isolation of Polynuclear Aromatic Hydrocarbons. Journal
of Chromatography, Vol- 211, pp- 284-289,1981-

13. Dearth, ACA- and Hites, R-A. Complete Analysis of Technical
Chlordane Using Negative Ionization Mass Spectrometry
Environmental Science and Technology, Vol. 25, pp. 245-254,
1991.

14. Shaw, G.R. and Connell, D.W. Relationships Between Stearic
Factors and Bioaccumulation of Polychlorinated Biphenyls
(PCBs) by the Sea Mullet (Mugil cephalus Linnaeus),
Chemosphere, Vol. 9, pp. 731-743, 1980.

15- Duinker, J.C., Hillebrand, M-T.J. and Boon, J-P., Organo-
chlorines in Benthic Invertebrates and Sediments from the
Dutch Wadden Sea; Identification of Individual PCB
Components, Netherlands Journal of Sea Research, Vol 17,
pp. 19-38,1983.

16. Boon, J.P., Van Zantvoort, NLB-, Govaert, M.J.M.A. and
Duinker, J.C. Organochlorines in Benthic Polychaetes
(Nephtys spp.) and Sediments from the Southern North Sea.
Identification of Individual PCB Components, Netherlands
Journal of Sea Research, Vol 19, pp. 93-109, 1985.

17. Matsuo, M- A Thermodynamic Interpretation of
bioaccumulation of Aroclor 1254 (PCB) in Fish, Chemosphere,
Vol. 9, pp. 671-675,1980.

18. Shaw, G-R. and Connell, D-W. Physicochemical Properties
Controlling Polychlorinated Biphenyls (PCB) Concentrations'
in Aquatic Organisms. Environmental Science and
Technology, Vol- 8, pp.18-23,1984-

19. Samuel Ian, J. and O*Connor, J.M- Structure-a ctivity
Relationship and Accumulation of PCB Congeners in
Estuarine Fishes: A Field Study (Abstract only), Estuaries,
Vol. 8. p- 83A. 19854 _37

Preprint 3

National Status and Trends Mussel Watch Program:
Chlordane-Related Compounds in Gulf of Mexico Oysters,
1986-1990

Josd L. Sericano, Terry L. Wade, James M. Brooks,
Elliot L. Atlas. Roger R. Fay, and Dan L. WiUdnson

National Status and Trends Mussel Watch Program:

Chlordane-Related Compounds in Gulf of Mexico Oysters, 1986-1990

Jos6 L. Sericano+*. Terry L. Wade+. James M. Brooks+. Elliot L AtlasA.

Roger RL Fay' and Dan L Wffldmon+

+ Geochemical and Environmental Research Group,

Department of Oceanography. Texas A&M University.

833 Graham Road. College Station. Texas 77845. U.SA-

ANational Center for Atrnospheric Research
P.O. Box 3000, Boulder@ Colorado 80307, U.S-k or

e
Abstract

The National Oceanic and Atmospheric Administration's National Status and

Trends (NS&T) Program has been monitoring the chemical contamination in bivalve

tissues from the U.S. coastal waters since 1986. Alpha-chlordane, trans-nonachlor,

heptachlor and heptachlor epoxide. components of technical chlordane. are among the
chloriiiiied- pesticides measured. The geographical distribution of these chlordane

compounds in the.oyster samples from the U.S. Gulf of Mexico is well established. For.

example, highest residue levels, predominantly alpha-chlordane and trans-nonachlor,

were encountered in samples. collected near heavily populated areas in contrast with

the concentrations measured in predominantly agricultural areas. Data collected

during five years of bivalve sampling are used to evaluate temporal trends in residue

concentrations at most NS&T sites. Minor decreases can be observed in the

concentrations of alpha-chlordane and trans-nonachlor. Heptachlor and its epoxide

concentratIons. in contrast, have been increasing since 1987.

4-39

Introduction

In 1986. the National Oceanic and Atmospheric Administration (NOAA) initiated

the National Status and Trends (NS" Mussel Watch Program to assess the extent of

coastal marine contamination in the U.S. by measuring selected organic and inorganic

contaminants, e.g.. chlorinated pesticides, polychlorinated biphenyls (PCBs).

polynuclear aromatic hydrocarbons (PAHs) and trace metals, and to identify trends in

their concentratioiis with time. The NS&-T Program is an ongoing project which has

been collecting bivalve samples, on an annual basis. from over 150 sites along the East.

Gulf and West coasts. including the Hawaiian islands. Overviews of the initial NS&T

results have been published (1-7).

This report focuses on the chlordane-related compounds. i.e.. alpha-chlordane.

trans-nonachlor, heptachlor and heptachlor epoxide. included in the suite of
chlorinated pesticides measured for the NS&r program- Technical grade chlordane is a

complex mixture of more than 140 different components. ReceAtly, 120 of these

compounds have been resolved and identified by high-resolution. gas chromatography

combined. with negative ionization mass spectrometry. Alpha-chlordane, gamma-

chlordane, heptachlor and b-ans-nonachlor are the dominant constituents (8). Since

1946, the total production of chlordane by Velsicol Chern.. Co.. the major producer in the

U.S..1;'estimated to .be over 70,000 tons (8). Because of the toxicity. potential

carcinogenecity @Lnd environmental persistence of their components and/or

metabolites, e.g.. heptachlor hepoxide and oxychlordane, the use of chlordane is under

federal regulations (9). In 1974, the EPA proposed the cancellation of all uses of

chlordane. Ayear later, the EPA suspended the production of heptachlor and@chlordane

and limited their uses on most food crops. Its use was pennitted only where no other

alternative existed and in all home and garden application with the exception of

underground termite control. In 1987. Velsicol Chem. Co.. in an agreement with the

4-40

EPA, voluntarily reduced the sales and distribution of chlordane and all sales and/or

uses in the U.S. were suspended after April 15. 1988.

The. extensive use of technical chlordane during the last decades. together with

their natural persistence. caused a worldwide environmental. This paper examines the

geographical distribution and trends in concentrations of chlordane in oyster samples

collected from the northern coast of the Gulf of Mejdco between 1986 and 1990.

Methods

Sampling. Oyster samples were collected each year over a two- to three-month

period starting In late December or early January. Depending on water depth. oysters

(twenty per station) were collected by hand, tongs or dredge, pooled in precombusted jars

and frozen until analysis.

Bivalve samples were collected from three stations at approximately 50 sites

located on the U.S: Gulf of Meidco. Distances between stations within each site varied

from 100 to 1000 meters. During 1986 and 1987, oysters *collection was completed at 49

and 48 sites with a total of 147 and 143 samples. rvespectively. - Although these sites

provided a good coverage of a broad range of different environmental conditions from

the U.S.A_-Mexico border. to southern Florida, they were specifically selected to avoid

known sources of contaminant Inputs. Starting in 1988 .. new sites were added to the
sampfirl@:prograrri in order to obtain more information from areas located closer to but

not at suspected sources of contaminants. During 1988. 1989 and 1990. oyster samples

were collected from 63. 62 and 68 sites with totals of 189. 186 and 203 samples.

respectively.

Thus. by the end of the fifth year of the NS&T program in the Gulf of Mexico.

eighty different sites had been sampled (Figure 1); thirty nine of them in all five years.

Sites numbered I through 51 are the original locations, sites numbered 52 through 80

are the new sites added to the sampling program since 1988.

4-41

Extraction and separation. The analytical procedure used was adapted from a

method developed by MacLeod et aL(10). Briefly, approximately 15 g of wet tissue were

extracted with methylene chloride using a homogenizer (Tekmar Tissurnizer). Each set

of eight to ten samples was accompanied by a complete system blank and spiked blank

or reference material that were carried through the entire analytical procedure. Before

extraction. 4:4 dibromooetafluorobiphenyl (DBOFB) and two polychlorinated

biphenyls UUPAC No 103 and 198) were added to all samples. blanks and reference

material as interrial standards. Tissue extracts were fractionated by silica:alumina

column chromatography. The sample extracts were eluted from the column using

pentane (fl=aliphatic hydrocarbons) and pentane:methylene chloride (1:1)

(f2=chlorinated hydrocarbons and PAHs). The second fractions were further purified to

remove lipids by either Sephadex LH-20 column chromatography eluted with a mixture

of cyclohexane:raethanol:methylene chloride (6:4:3) (11) or high-performance liquid

chromatography (112). Finally. the samples extracts were concentrated to a volume of

0.5 to 1 mL, in hexane, for gas chromatographic analysis.

Gas chromatography. Alpha-chlordane, trans-nonachlor. heptachlor and its

epwdde were determined by gas chromatography with an electron capture detector (GC-
ECD. 63Ni) using a 30 m DB-5 fused silica capillary column (0.25 mm film thickness.

0.25 nii m--' @d.. J&W Scientific). as previously described (6.7). Quantitation was achieved

using authentic standards injected at four different concentrations. The detection limit

for each of these compounds, calculated on the basis of 2 g dry weight of oyster tissue
sample sizes and 0.2% by volurne'of the extract injected into the GC-ECD, is 0.25 ng g- 1.

guality Control/guality Assurance (!gA/gC). These activities included several

laboratory intercalibration exercises with repeated. routine analysis of homogenated

natural samples. supplied by the National Institute of Standards and Technology

(NIST), to ensure that the data produced during this program is reproducible, accurate,

4-42

and analyst independent. Interim reference material and spiked blanks. analyzed

along with each set of samples, are also part of an ongoing laboratory QA/QC program.

Data analysis. For summaxy and statistical purposes, the reported mean total

analyte concentrations include contributions equal to the analytical detection limits

for those analytes that were below the limit of detection. Trends In concentrations with

time at those locations that were sampled at least four of the five years were

statistically evaluated. at alpha=0.05. by linear regression.

Results and discussion

For the last five years. alpha-chlordane. trans-nonachlor. heptachlor and Its

metabolite heptachlor epoxide were analyzed in more than 860 oyster samples collected

from 80 different sites along the northern coast of the Gulf of Mexico as part of the
NS&T Program (Fijure 1). A summary of the median and average concentrations as

well as ranges and distribution frequencies for each compound and t6tal chlordane, i.e.

summed individual analytes. corresponding to the original locations are given in Table

1. Table H summarizes the complete data set. Le. all sites included. from 1988 to .1990.

Except for a few samples collected in .1990, these analytes were detected in every

sample analyzed since 1986. Concentrations varied over I to 3 orders of magnitude
(Table--I).:: In 1986, concentrations for total chlordane ranged from 2.00 to 175 ng g- 1
with a mean value. of 24.1�30.3 ng g- 1. During 1987, the overall average concentration
for the Gulf of Mexico (29.5�58.5 ng g- 1. range 2.12-590 ng g- 1) was higher than the

average concentration encountered in 1986. As previously discussed for DDT and Its

metabolites (7), this increase was a consequence of high residue concentrations
encountered at site 39 (Choctawhatchee Bay; 288�256 ng g-1). In 1988, the total

chlordane average concentration was sirnflar to the concentration measured during the
first sampling year (21.7�22.8 ng g- 1. range 1.29-132 ng g- 1). Further decreases in mean

4-43

concentrations were observed in 1989 (16_3�25.0 ng g- 1. range 1.37-159 ng g- 1) and in
1990 (15.3�14.0 ng g-1, range <1.00-69.4 ng g-1)_

The addition of sites closer to suspected sources of contaminants to the sampling

program resulted In higher average concentrations and larger ranges (Table 11).

However, these changes were not as dramatic as expected for sites located near suspected

sources of contaminants. In general. the concentrations encountered at the new sites

compared well with the existing information obtained from the original 51 sites.

Geographical distribution. At this point of the NS&T program, the distribution of

chlordane concentrations in oysters from the U.S. Gulf of Mexico is well established.

Overall average concentration in the entire area for the five-year period was 24.1�25.2
ng g- 1. The highest residue concentrattons were encountered in oysters collected near

highly populated urban areas. For example, mean concentrations higher than three

times the overall -average for the Gulf of Mexico were measured in samples from

Galveston Bay. Texas, Mississippi Sound. Mississippi and Chocta*h@tchee. Tampa and

Charlotte Bays. Florida (Figure 2). The c3dstence of higher residue concentrations in

these areas. compared to predominantly agricultural coastal areas, is in good

agreement with the regulations that have ruled chlordane usage for the last 15 years.

After 1975, most of the chlordane use In the U.S. was limited to structural underground

termite .- control. therefore., the higher the population. e.g. more houses, the higher the

chlordane concentrations. With the exception of sites in Galveston Bay, the lowest

concentrations of total chlordane were encountered in samples collected to the west of

the Mississippi River. The addition of the new sites after 1987 (Figure 1). adds to the

definition of the distribution of pesticide concentrations for the first two years of this

program (6).

Residue composition. Alpha-chlordane and trans-nonachlor comprised from 38

to 48% and 36 to 49% of the total chlordane load. respectively. in oyster samples

4-44

collected between 1986 and 1990. The contribution of heptachlor and Its epoxide to the

total oyster burden were between 2-9% and 6-17%, respectively. Relative ratios of

alpha-chlordane, trans-nonachlor and heptachlor in Gulf of Mexico oysters.were

1.00-0.92:0.05, in 1986. 1.00.0.82:0.04, in 1987; 1.00:0.92:0.05. In 1988; 1.00.1.11:0.09.

in 1989; and 1.00:0.98:0.21. In 1990. Ratios for the last three years correspond to the

average values between both oyster data sets. Puri et aL (13) reported the percent

composition of chIordane constituents in four different technical chlordane

formulations. Thd average percent contribution of alpha-chlordane. &ans-nonachIor

and heptachlor to the total technical mixtures are 16.5�2.79. 13.4�4.34 and

12.5�2.190/o, respectively. Heptachlor epoxide is reported to be present as tracc levels.

Relative ratios among alpha-chlordane. trans-nonachlor and heptachlor in the

average technical chlordane mixture are 1.00:0.81:0.75. The NS&T data clearly show

that alpha-chIordane:trans-nonachlor ratios for the average technical chlordane

mixture and Gulf 6f Mexico oysters are similar. There is. however, a marked depletion

in the relative concentration of heptachlor in oyster, samples. -This decrease in the

relative concentration of heptachlor is commonly reported for biota tissues (13-15).. In

1990, however. the higher relative alpha-chlordane:heptachlor ratio in oyster tissues

Indicate a somewhat reduced chemical/blochemical epoxidation of heptachlor, which

is confirmed by an increase in the heptachlor- heptachlor epo-xide ratio from 1:6. in
1986@@;9.; *6 1:2. in 1990. This suggests fresh inputs of technical chlordane mixtures or

the related pesticide heptachlor into the coastal marine environment- The Increase in

the concentrations of heptachlor and its metabolite heptachlor epoxide in oyster

tissues during 1990, which are not accompanied by a proportional increase in the

concentration of alpha-chlordane and trans- nonachlor. might point to the second

source as the most proVable. However, this possibility must be taken with caution until

more data. from this or other studies, becomes available.

4-45

It
Z 8

Temporal trends. A five years database is available to look for trends in

chlordanc concentrations with time. Although with overlapping standard deviations,

the annual average concentrations of total chlordane have been decreasing in oyster

samples from the Gulf of Me)dco. This tendency is supported by a shift in the percent

distribution of concentrations to lower values (Table I and 11). For example. in 1986, the

total chlordane concentration in oysters from the original sites were largely dominated
by concentrations between 10.0 and <100 ng g-1 with some values over 100 ng g-1 trable
1). . In 1990. the t6tal residue concentrations were ahnost equally distributed between
the 1.00-<10.0 and 10.0-<100 ng g-I ranges with 4% of the samples below the

quantitation limit. The same analysis can be made for the individual analytes except

heptachlor. Heptachlor concentrations have been shifting in the opposite direction

since 1987. Agairi. this suggests that fresh heptachlor has been or is entering the

coastal marine environment. The analysis of the samples already collected for the

sixth sampling period (199 1). and those of the following years. will probably assist in

this matter.

Table III indicates the average total chlordane concentrations at each site between

1986 and 1990. Sites listed in this table are shown in geographical order from the U.S.-

Me:xico border to the southernmost Florida site. In general, the total chlordane

concentrations seem to be oscillating around a certain value without showing any trend

with time. Statistical analysis of the slopes of the regression lines of concentrations

versus time at those sites with at least four years of collected data identified only a

thirteen sites with significant decreases. These sites are marked with an asterisk in

Table 111. . Most of these sites are located along the southern Texas coast from Corpus

Christi to Matagorda Bays. Paradoxically, the only site that showed a statistically

significant increase in concentration with time. Copano Bay, is located within this

area.

4-46

Aknowledgement

This research was supported by the National Oceanic and Atmospheric

Administration. Contract 50-DGNC-5-00262, through the Texas A&M Research

Foundation, Texas A&M University.

Literature Cited

(1) Robertson, A. Monitoring coastal water environmental quality: The U.S.

National Status and Trends Program. In G.E. Schweitzer and A.S. Phillips

(eds.), Monitoring and Managing Environmental Impact: American and Soviet

Perspecttves. National Academy Press. 281 pp. qr

(2) Wade, T.L., Garcia-Romero, B. and Brooks. J.M. Erwtron. Sci. Technol. 1988,

22,1488-1493.

(3) MacDonald, D.A. Oceans '89 Conf. Proc., 1989, 2,647-651.

(4) O'Connor. T.P., Price. J.E. and Parker. C.A. Oceans '89 Conf. Proc., 1989. 2 569-

572.

(5) Presley. B.J., Taylor, R.J. and Boothe, P.N. Sci. Total Environ. 1990.97/98,

551-593..

(6) Sericano, J.L., Atlas, E.L., Wade, T.L. and Brooks. T.L. Mar. Environ. Res., 1

990a. 29.161-203.

(7) Sericano. J.L., Wade. T.L., Atlas, E.L.: Brooks, J.M. Environ. Sci. Technol.,

1990b, 24,1541-1548.

(8) Dearth, M. A. and Hites, R. A. Environ. Sci Technol. 1991, 25. 245-254.

(9) Shigenaka, G. Chlordane in the Marine Environment of the United States:

Review and Results from the National Status and Trends Program. NOAA

Tech. Memo. NOS OMA 55,Seattle, Washington. 1990. 230 pp..

(10) MacLeod, W. D.; Brown. D. W.; Friedman, A. J.: Burrows. D.G.: Maynes, O.;

Pearce, R. W.; Wigren. C. A. and Bogar, R.G. Standard analytical procedures of

4-47

the NOAA National Analytical Facility. 1985-1986. Extractable toxic organic

compounds. 2nd. Edition. U. S. Department of Commerce, NOAA,/NMFS. NOAA

Tech. Memo. NMFS F/NWC-92. 1985. 121 pp.

(11) Ramos, L.: Prohaska. P. G. J. Chromatogr., 1981, 211, 284-289.

(12) Krahn, M.M.; Wigren, C.A.: Pearce. R.W.; Moore. L.K.; Bogar, R.G.: MacLeod.

W.D.; Chan. S-L. and Brown. D.W. Standard analytical procedures of the NOAA

National Analytical Facility. 1988. New HPLC cleanup and revised extraction
procedures for organic contaminants. NOAA Tecn. Memo. 1988. 52,pp.

(13) Puri, R.K., Orazio. C.E., Kapila, S., Clevenger, T.E., Yanders, A.F., McGrath, K.E.,

Buchanan. A.C., Czarnezki, J. and Bush, J. Studies on the Transport and Fate of

Chlordane in the Environment. In: D.A. Kurtz (ed.), Long Range Transport of

Pesticides. Lewis Publishers. 1990. pp 271-289.

(14) Muir. D.C.G.; Norstrom, R.J.; Simon. M. Environ. Sci. Technol., 1988,22.1071-

1079.

(15) Kawano. M.; Inoue, T.; Wada, T.,; Hidaka, H.: Tatsukawa, R Environ. Sci.

Technol., 1988, 22.792-797.

4-48

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Table 1: Chlordane-related Compound Concentrationsa. and Distribution Frequencies in Gulf of Mexico Oysters

(original Sites), 1986-1.990

concn, ng/g distribution

median mean�1 STD range P.00-0.25 0.25-<1.00 1.00-<10.0 10.0-<100 100+, (ng/g)

1986 (n-147)

Heptachlor 25 0.51�0.69 <0.25-4.62 63 29 8

Heptachlor Epoxide 1.87 2.71�3.31 <0.25-24.5 14 12 70 4

Alpha-chlordane 5.23 10.9�14.4 0.91-96.3 1 72 27

Trans-nonachlor 4.58 10.0�13.8 0.60-71.9 1 76 23

ZChlordane 13.1 24.1�30.3 2.00-175 36 61 3

1987 (n=143)

Heptachlor <0.25 0.54�0.99 <0.25-7.04 77 14 9

Heptachlor Epoxide 2.45 3.30�3.93 <0.25-27.3 2 11 82 5

Alpha-chlordane 6.42 14.1�29.0 0.65-292 1 71 27 1

Trans-nonachlor 4.78 11.6�27.7 <0.25-289 1 5 73 20 1

7,Chlordane 14.4 29.5�58.5 2.12-590 28 66 6

M.IRA

Table I (continuation)

concn, ng/g distribution-

median mean�1 STD range 0.00-<0.25 0.25-<1.00 1.00-<10.0 10.0-<100 100+, (ng/g)

1988 (n-132)

Heptachlor <0.25 0.49�0.66 <0.25-5.81 73 16 11

Heptachlor Epoxide 1.69 2.44�2.33 <0.25"14.3 3 15 80 2

Alpha-chlordane 5.80 9.76�10.7 0.40-60.2 2 72 26
@n
Tran's-nonachlor 5.02 8.98�11.5 <0.25-81.6 1 4 70 25

7-Chlordane 13.9 21.7�22.8 1.29-132 35 64 1

1989 (n=135)

Reptachlor 0.33 0.78�1.03 <0.25-8.23 40 40 20

Heptachlor Epoxide 0.60 1.25�1.41 <0.25-9.33 20 41 39

Alpha-chlordane 3.14 7.00�9.84 <0.25-48.3 1 7 76 16

Trans-nonachlor 2.33 7.29�14.5 <0.25-99.1 1 16 69 14

7,Chlordane 7.68 16.3�25.0 1.37-159 61 37 2

Table I (continuation)

conen, ng/g distribution-

median mean:tl STD range 10. 00-<O. 25 0.25-<I. 00 1. 00-<10. 0 10, 0-<100 100+, (ng/g)

1990 (n=138)

Heptachlor .0.72 1.34�1.81 <0.25-15.2 30 28 41 1

Reptachlor Epoxide 1.20 .2.63�4.53 <0.25-29.9 27 19 50 4
Alpha-chlordane 4.16 5.81�5.66 <0.25-36.3 5 6 72 17

Trans-nonachlor 3.13 5.55�6.49 <0.25-29.8 4 17 62 17

IChlordane 10.7 15.3�14.0 <1.00-69.4 4 42 54

a Concentrations on a dry weight basis

Table II: Chlordane-related Compound Concentrationsa and Distribution Frequencies in Gulf of Mexico oysters

(Complete Data Set), 19'88-1990

conch, ng/g distribution'

median mean�l STD range .0.00-<0.25 0.25-<1.00 1.00-<,10.0 10.0-<100 100+, (ng/g)

1988 (n-189)

Heptachlor <0.25 0.58�0.81 <0.25-5.81 68 18 14

Heptachlor Epoxide 1.73 2.95�3.63 <0.25-21.5 5 is 75 5

Alpha-chlordane 6.31 11.4�13.3 0.40-88.4 2 66 32

Trans-nonachlor 5.46 10.5�12.8 <0.25-81.6 1 3 65 31

7-Chlordane 14.3 25.4�27.7 1.29-182 34 64 2

1989 (n=186)

Heptachlor 0.32 0.74�0.98 <0.25-8.23 43 38 18 1

Heptachlor Epoxide 0.88 1.50�1.65 <0.25-10.7 16 37 46 1

,Alpha-chlordane 4.01 10.2�15.3 <0.25-116 1 5 69 24 1

Trans-nonachlor 3.02 11.8�22.7 <0.25-183 1 13 64 21 1

7,Chlordane 9.63 24.2�38.4 1.37-302 52 43 5

Table II (continuation)

concn, ng/q distribution'

median meanil STD range 10.00-0.25 0.25-<1,00 1.00-<10.0 10.0-<100 100+, @ng/q)

1990 (n-203)
Heptachlor -0.84 1.36�1.63 <0.25-15.2 24 30 45 1
Heptachlor Epoxide 1.24 2.51�4.10 <0.25-29.9 26 19 51 4
Alpha-chlordane 4.97 7,74�8.37 <0.25-59.0 4 6 65 25
Trans-nonachlor 4.37 7.72�9.85 <0.25-73.6 3 12 59 26

6 19.4�19.3 <1.00-139 1 36 62 1
7,Chlordane 12.

a Concentrations on a dry weight basis

dooms @k I k

M Mae M M1 M M, M M M M M M M IM.M

Table III. Total Chlordane Concentrations in oyster Samples from NS&T Program Sites, 1986-1991a.
Site Location State mean:@l SD
1986 1987 1988 1989 1990
I LMSB Laguna Madre TX 3.77�0.60 2.83�0.71 3.68�3.92 3.63�1.06 1,771:0.87
2 CCNB Corpus Christi TX 19.1�12.3 13.9�4.37 13.8�10.5 8.1@�2.52 13.2�2.48
52 LMPI Laguna Madre TX 8.78�2.81
78 LKAa Laguna Madre TX 8.8W.43
14.9�7.50 7.76�1@35
53 CCBH Corpus Christi TX
3 CCIC* Corpus Christi TX 23.8�1.61 4.62�1.70 3.46�1.07 3. 7 O:k2. 9 0

54 ABHI Aransas Bay TX
4 ABLR* Aransas Bay TX 8.82�3.12 9.76�2.22 4.86�1.15 3.57�1.05 4.49�2.48
5 CBCR' Copano Bay TX 8.84�2.41 12A�0.21 9.70�5.48 19.4�11.8
6 14BAR* Mesquite Bay TX 8.56�2.95 11.0�3.13 8.22�2.33 5.09�2-01 6.17�5.40
sAP? San Antonio Bay TX 9.61�3.61 9.20�2.38 4.96�2.82
6 SANP San Antonio Bay TX 11.9�2.42 i"4. 8�3. 52
9 E s s P, Espiritu Santo TX 5.08�1.81 8.97�0.59 3.81�1.59
10 SSBD Espiritu Santo TX 1.97�1.65 3.19�0.82 2.76�3.12
11 MBLR* Matagorda Say TX 16.5�6.08 16.4�2.31 4.71�1.40 i.76�i.26
8.21�0.22 1818�17.2 9.36�3.33 3.69�1.04
12 MBG? Matagorda Bay TX
9.04�4.58 3.65�1.98
56 MBCB Matagorda Bay TX

Table III (continuation)

site Location state mean:tl SD

1986 1987 1988 1989 1990

26
13 MBTP* Matagorda Bay tx 14.8�10.3 19.7�4.40 8. 79�2.. 95 4.50�1.23 6.66�5.

8.41�0.21
55 MBDI Matagorda Bay TX
14 MBEM* Matagorda Bay TX 16.3�9.52 19.5�iO.9 8.49�3.19 6.96�1.17 7.51�4.29
10.1�1.76 7:25�3.04 5.85�4.86
57 BRFS Brazos River TX
72 BRCL Brazos River TX 7.31�6.90 19.3�5.69
15 GBYC* Galveston Bay TX 124�24.9 136�30.3 60.8�8.95 20.4�3.57 42.li9.63
59 GBSC Galveston Bay TX - - 139�38.5 35.2�M6 51.7�13.6
58 GBOB Galveston Bay TX 88.2�15.2 98.2�11.7 111�35.9
16 GBTD Galveston Bay TX 25.5�4.68 55-218-13 14.2�2.93 11.4�1.25 27.7�6.03
13 GBHR Galveston Bay TX 10.7�2.14 15.6�7.89 8.06�2.42 7.36�0.48 21.0�13.4
18 GBCR Galveston Bay TX 9.92tl.78 14.8�2.78 14.2�7.33 7.93�2.37 11.0�1.20
19 SLBB Sabine Lake TX 11@9�4:31 9.23�1.36 20.4�11.4 6.99�1.18 12.4�6.63
20 CLSJ* Calcasieu Lake LA 14.0�1.35 1*6.2�3.71 14.5�3.19 7.94�7.19 6.65�4.28
60 CLLQ- Calcasieu Lake LA 45.1�3.88 6.63�1.56 20.2�4.51
21 JHJH Joseph Harbor LA 11.2�3.03 11.1�1.22 5.26�0.78 7.24�3.16 14.0�9.92
22 VBSP Vermilion Bay LA 15.2�2.36 21.4�6.25 15.2�1.19 12,0�1.60 32.7�14.9

23 ZCSP East Cote Blanche LA
24 ABOB* Atchafalaya Bay LA 13.8�1.34 20.3�6.84 15.1�3.00 9.11�1 '..48 8.32�2.78

Ilk

Table III (continuation)

site Location state meardl SD

1986 1987 1988 1989 1990

25 CLCL Caillou Lake LA 8.30�0.91 10.2�4.35 5. 2 8�1,. 11 4.77�2.56, 9.02�3.44

26 TBLB Terrebone Bay LA 6.77�3.25 8.88�2.34 11.8�9.95 5.38�1.05 9.97�4.51
27 TBLF Terrebone Bay LA 6.69�2.01 .5.67�2.23 5.55�1.78 3.9@�1.31 10.1�4.40

61 BBTB Barataria Bay LA 7.86�4.62

28 BBSD Barataria Bay LA 10.7�1.86 12.0�7.60 20.0�7.25 7.21�2.51 8.20�1.92

29 BBMB* Barataria Bay LA 35.3�21.6 16.2�3.98 14.3�1.53 7.86�3.69 7.60�0.86

65 MRTP Mississippi River LA 27.1�11.0 17.3�2.39 13.3�2.04

64 MRPL Mississippi River LA 61.3�17.1 66.9�1.37 39.0�15.4
00
30 BSBG Breton Sound LA 10.5�7.14 10.8�3.51 12.5�4.60 5.71�1.75 8.03�3.33

31 BSSI* Breton Sound LA 45.4�18.0 12.8�1,39 28.8�10.2 13.1�6.14 12.6�3.35

32 LBMP Lake Borgne LA 12.6�5.75 10.4�4.57 11.0�9.94 7.11�2.22 17.4�2.16

62 LBNO Lake Borgne LA 8.82�3.25
33 MSPC Mississippi Sound MS 21.5�3.13 6:9.0�41.2 15.9�4.63 22.7�7.70 14.8�2.03

34 MSBB Mississippi Sound MS 98.0�67.8 86.0�55.6 71.4�22.3 71.2�18.7 49.8�16.2

35 MSPB Mississippi Sound MS 12.0�4.25 18.9�1.34 28.0�7.81 18.5�7.60 28.6�32.7

36 MBCP Mobile Bay AL 14.8�7.07 24,1�16.3 14.7�3.36 25.3�10.9 17.1�3.54

66 MBH1 Mobile Bay AL 34.3�7.12 40.3�3.22 34.3�2.98

79 MBDR Mobile Bay AL 31.6�2.09

Table III (continuation)

site Location State mean�1 SD

1986 1987 1988 1989 1990
A
67 PBPH Pensacola Bay FL 35.6�@.67 17.0�0.80 28.6�8.36

37 PBIB Pensacola Bay FL 15.0�0.88 24.'5�4.55 17.4�2.31 13.7�1.13
80 PBSP Pensacola Bay FL 14.9�1.84

38 CBSR Choctawhatchee Bay FL 10.5�3.63 8.37�1.43 14.5�5.66 5.92�1.25 8.92�3.39
39 CBSP Choctawhatchee Bay FL 57.5�20.5 288�256 45.4�20.8 65.8�36.8 39.3�26.6
73 CBJB Choctawhatchee Bay FL 17.4�13.9 20.2�8.25
68 PCMP Panama City FL 25.8�9.48 17.0�1.29 16.9�7.55
74 PCLO Panama City FL 8.42�1.00 12.1�1.46
40 SAWB* St. Andrew Bay FL 79.8�26.7 57.8�7.39 37.5�22.4 38.5�8.33 26.1�10.3
41 APDB Apalachicola Bay FL 8.28�1.49 13.2�0.44 53.9�34.1 2.95�0.86 22.7�7.62
42 APCP Apalachicola Bay FL 12.1�1.*92 11.6�2.14 14.9�4.72 13.5�2.61 11.4�3.51
75 AESP Apalachee Bay FL 5.11�2.31 13.7�12.7

69 SRWP Suwannee River FL 11.2�1.91
43 CKBP Cedar Key FL 8.90�3.15 14.4�3.16 25.6�31.2 8.18�5.33 6.71�2.33
44 TBPB Tampa Bay FL 58.0�13.2 98.9�66.7 60.1�20.1 81.4�3.90 24.0�10.9
70 TBOT Tampa Bay FL 34.8�5.85 28.1�11.8 12.2�6.96'

45 TBHB Tampa Bay FL 21.8�4.46 17.6�1.04
46 T5CB Tampa Bay FL 73.8�16.6 45.1�8.58 107�47.2 127�27.3 41.3�3.42

Table III (continuation)

site Location State mean�1 SD

1986 1987 1988 1989 1990

76 TBNP Tampa Bay FL 117�66.3. 47.1�5.99

77 TBKA Tampa Bay FL 113�50.8 62.0�1.07

47 TBMK Tampa Bay FL 23.7�4.20 '21.0�3.63 21.4�13.4 18.2�8.05 23.0�17.9
48 CBBI 'Charlotte Harbor FL 9.56�2.98 23.8�15.9 8'. 69�1.17 8.3@�3.31

71 CBFM Charlotte Harbor FL - 73.3�4.81 172�114 36.8�11.0

49 NBNB* Naples Bay FL 114�27.4 40.0�17.2 31.3�9.16 17.0�2.58 11.5�3.08

50 RBHC Rookery Bay FL 4.26�2.;52 3.87�2.13 19.4�15.8 3.51�0.23 3.00�1.07

51 EVFU Everglades FL 2.51�0.46 5.70�2.31 22.7�12.0 1.91�0.72 25.3�20.8

a Concentration (ng g-1 on a dry weight basis); no sample or indicate sites that have shown

statistically concentration increases or decreases, iespectively, with time (see text)

Preprint 4

Concurrent Chemical and Histological Analyses:
Are They Compatible?

J.L. Sericano, T.L. Wade, E.N. Powell, and J.M. Brooks

CONCURRENT Cj1EAHCAL AND HISrOLOGICAL ANALYSES:

ARE THEY COMPATUBLE?

J. L. Sericano(l); T. L. Wade(l); E. N. powell(2) and J. M. Brooks(l)

Geo chemical and Environmental Research Group, College of

Geosciences, Texas A&M University, 833 Graham Rd,

College Station, Texas 77845.

Department of Oceanography, Texas A&M University,

College Station, Texas 77840.

Bivalves are often*' used as sentinel organisms in monitoring programs for trace
organic contaminants. The animal's physiological state may be important in
interpreting trends in contaminant body burden- Simultaneous evaluation of
physiological state and organic contaminant concentration in bivalves typically
involves removal of a lipid-rich cross-section of the body mass for
histopathological and/or gonadal analysis.
In this study, the bias introduced by this technique in the final trace organic,
e.g. polynucl.ear aromatic hydrocarbons, chlorinated pesticides and
polychlorinated biphenyls, toncentrations are evaluated on five different size
groups of oysters. As a test case, we evaluated the use of this method in the NOAA:s
Status & Trends Mussel Watch (NS&T) program. The average biases introduced
by this technique in the final trace organic concentrations in Gulf of Mexico
oysters have been increasing since 1986 as a consequence of a continuous decrease
in the sizes of the individuals sampled.

4-62

INTRODUCTION

Seasonal variations in organic contaminant concentrations in

bivalves have been attributed to a number of Aifferent factors

including the stage of the reproductive cycle, nutritional Status and

ambient temperature (Wormell, 1979; Neff & Anderson, 1981;

Jovanovich & Marion, 1987). Several studies have indicated the

importance of considering the bivalve's physiological state when
measuring contaminant loads (Fossato & Canzonier, 1976; Boe_hi@-&

Quinn, 1977; Mix & Schaffer, 1979; Lunsford & Blem, 1982; Widdows

et at., 1982; Jovanovich & Marion, 1987). Monitoring programs that

use bivalves as sentinel organisms typically try to assess some of

these problems. In NOAA's National Status and Trends Mussel

Watch (NS&T) Program, for example, reproductive state, condition
index and disease incidence in oyster samples from .the Gulf of

Mexico have been monitored since 1986 (e.g. Craig et al., 1989; Wilson

et al., in preparation).

One aspect of the problem involves the determination of the

reproductive state, which typically requires a histological analysis in

most bivalves (e.g. Morales-Alamo & Mann, 1989). In some cases,

where the seasonal variability in contaminant concentrations in

bivalves was followed in relation to their reproductive cycle, the

chemical and biological analyses were performed on t wo different

groups of individuals collected at the same site (e.g. Jovanovich &

Marion, 1987). Since the reproductive state of bivalves may vary
considerably among individuals at certain times of the year -,(e.g.

4-63

Wilson et al., in preparation), adequate comparison requires a large

sample size and the approach necessarily restricts statistical

analysis.

An alternative approach is to take a cross-section of tissue from

the same individual that is used for trace organic analyses. Bivalves

where the gonadal material is in the mantle, such as mussels

(Bullogh, 1970), present only a minor problem; but oysters, where the

gonadal tissue surrounds the visceral mass (Morales-Alamo &

Mann, 1989), require removal of a tissue cross-section that maybe

rich in trace organic contaminants. Any additional

histopathological analysis would, of course, require removal of a

larger tissue cross-section in either species.

'The objecti ve of this study was to evaluate the bias in the final
organic conf,4@nant concentrations introduced by the selective

removal of a tissue cross section for histopathological or gonadal

analysis. Five groups of oysters, Crassostrea virginica, of different

average sizes were dissected and the portions normally used for

his to pathological and trace organic analysis were separately

analyzed for selected polynuclear aromatic and chlorinated

hydrocarbons to evaluate this bias.

MATERIALS AND METHODS

Oysters were collected from Galveston Bay, Texas, near the Houston

Ship Channel in December 1988. This area is one of the 71 sites that

4-64

was sampled during the NS&T program in the Gulf of Mexico
(Sericano et al., 1990). The site, Galveston Bay Ship Channel (GBSC),

is located at the mouth of Goose Creek in Tabbs Bay. Immediately

after collection, the oysters were transported to the laboratory and

sorted into five different size groups. A cross-section of the body of the
oysters was separated by first making a transverse cut where the
palps..and gills meet. A second parallel cut was made about 5 mm

from the first cut toward the center of the organism. This cross-

section contained portions of gonad-, stomach, intestine, dig-esti@ve

diverticula. and connective tissue as well as mantle.and gill. In

standard practice, 3 to 5 mm. sections are cut for histological

analysis; consequently, the 5 mm cross-section would represent a
maximum estimate of any bias incurred. The cross-section and
remaining b6d@ tissues from oysters within each size group were
pooled into two separated samples and analyzed' for PAHs,

chlorinated pesticides and PCBs. The methods used to measure the

analyte concentrations were fully described elsewhere (e.g. Sericano

et al., 1990).

RESULTS AND DISCUSSION

Average lengths of the five different groups of oysters used in this

study ranged from 6.1 to 9.5 cm (Table 1). Also shown are the mean

percent contribution on a dry weight basis of the cross-section and

4-65

remaining body tissues to the total body mass, and the percentage of

extractable lipids corresponding to each of these fractions.
In.,general, the concentrations of PAHs, pesticides and PCBS
measured in oysters are similar in each of the five'size groups when

the same subsamples, cross-section (A) or remaining body tissues
(B), are compared (Table 2j. In contrast, the trace organic

concentrations of the two subsamples differ substantially in all five
size classes. The cross-section (A) is the portion _ffiat normally would
have been used for histological analysis.. Since aromatib: and
chlorinated hydrocarbons* are hydrophobic, they tend to be associated

with lipid-rich tissues. This could in part explain the higher

concentrations measured in the cross-section tissues which contain

between 35 t o 50% more extractable lipid than the. remaining body

tissues (Table 1).

The removal of the tissue cross-sections from* the sample

analyzed for trace organic compounds will introduce a bias towards

lower total concentrations in the sample. The magnitude of this bias

will largely depend on the sizes of the oysters sampled. In this study,

the tissue cross-sections accounted for about 15% of the tissue dry

weight in a 9 cm oyster, but for nearly 23% in a 6 cm oyster (Table 1).

Accordingly, in large oysters, a proportionally smaller fraction is

used for biological assays whereas, in smaller oysters, a 5 mm cross-

section represents the removal of a comparatively large fraction of
the total body mass. In the extreme, the cross-section of a very small

specimen may include all of the tissues from where the palps and

4-66

gills meet to the adductor muscle which removes most of the lipid-

rich internal organs.

The concentrations of PAHs, chlorinated pesticides and PCBs

can be corrected for the contribution to the total b6dy burden of the

cross-section removed for histology. The differences between the

uncorrected, which represent the values that would normally be

reported, and corrected concentrations for each of the oyster groups

are shown in Figure 1. As expected, the biases in the individual
concentration of trace organic compounds increase as the dysiier

sizes decrease. Average biases are 6.1�2.0, 8.8�2.4, 10.5�2.7, 14.0�2.6,

and 14.3�2.4%, for PAHs, 10.5�2.1, 11.7-+0.7, 13.3�1.0, 13.9�0.8 and

16.8�1.5, for pesticides, and 6.3�0.9, 8.9�1.7, 10.9-+1.2, 13.3�1.4 and

13.9-+0.9, for PCBs, in oysters groups I to V, respectively.

As an example for this study, we consider the NS&T program in

the Gulf of Mexico. In this program, oysters are used as sentinel

organisms to monitor the current status and long-term trends of

selected organic and inorganic environmental contaminants along

the Atlantic, Pacific and Gulf coasts of the United States. In 1986, the

overall average oyster size collected for the Gulf of Mexico portion of

the NOAA's S&T Program was 8.5�1.4 cm, (Brooks et al., 1987)

(Figure 2). During the following sampling years there was a
continuous decrease in the sizes of the oysters that were sampled. In

1987, the average oyster size for the Gulf of Mexico was 7.6�1.8 cm

(Brooks et al, 1988); in 1988, the average oyster size was 7.2�1.4 cm

(Brooks et al., 1989); and in 1989, the average oyster size was 7.0�1.3

cm (Brooks et al., 1990).

4-67

Wilson et al. (in preparation) discuss the possible reasons for

this decline in the sizes of the sampled oysters and concluded that the

trend toward smaller sizes was probably a manifestation of decreased

population health. For our purposes, this downward trend could

introduce a bias in the trace organic values.

The bias imposed by the continuous decrease in oyster sizes with

the successive sampling years in the observed PAH concentrations in

the Gulf of Mexico can be estimated from the regression lines in

Figure 1. Assuming that the cross-section was 5 mm in each case,

the average percent biases in PAH concentrations that were reported

for 1986, 1987, 1988 and 1989 can be estimated as 9.7, 11.7, 12.5 and

13.0%, respectively. Similarly, average percent biases for chlorinated

pesticides and PCBs can be estimated as 12.6, 13.8, 14.4 and 14.7%
and 9.7, 11.4,'12.2 and 12.6%, respectively. However, under the
protocol used for the NS&T program, a cross-section' of tissue is

removed from only 10 of the 20 oysters collected per sampling station.

Thus, the estimated bias for each group of analytes would be about

half of these values.

In order to avoid misleading interpretations of comparative

spatial and temporal data, it is imperative to understand how the

methodology affects the trace organic concentration measurements

in bivalves. This understanding is of particular importance if tissue

cross sections are removed for histological analysis and it is

especially important in sites where considerable variability exists in

the sizes of the individuals sampled over the years and in cases

where smaller organisms must be used. The development of -non-

4-68

histologically based gonadal indices (e.g. Choi et al-, 1989; 1990) offers

one way to avoid this problem.

ACKNOV,TLEDGEMENTS

This research was supported by the National Oceanic and

Atmospheric Administration, - contract No. 50-DGNC-5-00262,

through the Texas A&M Research Foundation, Texas - A&M

University.

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accumulated hydrocarbons in the hard shell clam, Rangia

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Brooks, J.M., Wade, T.L., Atlas, E.L., Kennicutt II, M.C., Presley,

B.J., Fay, R.R., Powell, E.N. and Wolff, G. (1987). Analyses of

bivalves and sediments for organic chemical and trace elements

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4-69

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Craig, A., Powell, E.N., Fay, R.R. and Brooks, J.M. (1989)_
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populations. Estuaries, 12, 82-91.

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loss by the mussel Mytilus edulis. Marine Biology, 36, 243-250.

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uptake and depuration of anthracene by the brackish water clam

Rangia cuneata. Marine Biology, 95, 395-403.

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residue and lipid content of the estuarine clam, Rangia cuneata.

Estuaries, 5, 121-130.

Mix, M.C. and Schaffer, R.L. (1979). Benzo(a)pyrene concentrations

in mussels (Mytilus edulis) from Yaquina Bay, Oregon, during

June 1976-May 1978. Bulletin of Environmental Contamination
and Toxicology, 23, 667-684.

Morales-Alamo, R. and Mann, R. (1989). Anatomical features in

histological sections of Crassostrea virginica (Gmelin, 1971) as

an aid in measurement of gonad area for reprodu.ctive

assessment. Journal of Shellfisheries Research, 8, 71-82.

Neff, J.M. and Anderson, J.W. (1981). Response of marine animals

to petroleum and specific petroleum hydrocarbons, Applied

Science Publishers Ltd, London, 177 pp.

Sericano, J.L., Atlas, E.L., Wade, T.L. and Brooks, J.M. (1990).

NOAA's Status and Trends Mussel Watch Program:

Chlorinated pesticides and PCBs in oysters (Crassostrea

4-71

virginica) and sediments from the Gulf of Mexico, 1986-1987.

Marine Environmental Research, 29, 161-203.
Widdows, J.' Bakke, T., Bayne, B.L., Donkin, P., Livingstone, D.R,

Lowe, D.M., Moore, M.N., Evans, S.V. and Moore, S.L. (1982).

Responses of Mytilus edulis on the exposure to the water-

accomodate fraction of north sea oil. Marine Biology, 67, 15-31.

Wilson, E.A., Powell, E.N., Wade, T.L., Taylor, R.J., Presley, B.J.

and Brooks, J.M. Spatial and temporal distributions of

contaminant body burden and disease in Gulf of Mexico -oyster

populations: The role of local and large-scale climatic controls

(in preparation).

Wormell, R.L. (1979). Petroleum hydrocarbon accumulation patterns

in Crassostrea virgainica: analyses and interpretations. Ph.D.

Dissertation, Rutgers University, The State University of New

Jersey (new Brunswick), 189 pp.

4-72

TABLE 1. Average shell length and percent contribution of the cross-section

and remaining-body tissues to the total body weight corresponding to

the five group of oysters analyzed. Lipid percentages for each

fraction are also indicated.

Oyster n Shell Cross-Section Tissues Remaining Body Tissues

Size Length Dry Weight. Lipids Dry Weight Lipids

(CM) M (%) M M

8 9.5�0.8 15.6�2.7 14.2 84.4�2.7 9.6

8 9 . 3�0 . 9 17.0�1.9 13.3 83.0�1.9 9.0

8 8.5�1.4 18.5�3.0 12.7 81.5�3.0 8.9

IV 14 6.7�0.9 22.2�3.4 14.9 77.8�3.4 10.2

V 14 6.1�0.6 22.5�2.7 14.5 77.5�2.7 10.9

11 A I

TABLE 2. (cont.)

Analyte Oyster size Average

IV V

A A B A B A B A A B A%

Clorinated Pesticides

Gamma-chlordane 20.0 11.2 21.1 12.1 21.3 12.2 23.1 13.8 23.7 13.2 21.8�1.52 12.5�1.01 74

Alpha-chlordane 18.9 11.8 21.4 13.0 21.9 12.9 23.0 14.7 23.4 13.9 21.9�1.94 13.3�1.10 65

Trans-nonachlor 17.2 10.0 18.7 10.9 19.2 10.8 20.0 12.1 20.8 11.6 19.2�1.36 11,1�0.80 73

p-p'DDE 42.2 28.5 48.1 28.6 43.5 26.5 50.2 31.7 47.8 28.6 4 6 . 4�3 . 3 7 28.8�1.86 61
p-p'DDD 45.2 25.2 48.0 28.8 49.1 28.3 51.6 31.9 53.8 30.2 4 9 . 5�3. 31 28.9�2.49 71

52 71.3 48.1 82.5 49.7 75.8 46.6 81.9 52.3 79.0 47.6 78.1�4.64 4 8. 9�2. 2 3 60

101 102 75.3 109 77.1 127 76.3 132 78.1 122 78.1 118�12.5 77.0�1.20 53

105 26.6 18.4 32.6 20.7 32.2 20.4 34.1 22.2 33.6 20.9 31 . 8�3 . 02 20 . 5�1. 37 55

118 74.0 54.2 82.6 56.3 82.9 55.6 93.3 57.7 92.2 55.6 85 . 0�7 . 9 4 55.9�1.23 52

138 52.5 38.5 64.6 42.8 67.5 42.8 66.0 42.1 68.0 42.1 63.7�6.41 41.7�1.80 53

A- Cross-section Tissues

B- Remaining-body Tissues

TABLE 2. Cross-section and remaining-body PAH, pesticide and PCB concentrations, ng g-1, measured

in the five different groups of oysters. Average concentrations for each analyte in the

subsamples and percent differences are also listed.

Analyte Oyster size Average

I II III IV V

A B A B A B A B A B A B A%

Uhl

2,3,4 Trimethyl Naphthalene 95.2 64.6 106 64.5 98.4 57.2 101 59.6 124 68.2 105�11.3 62.8�4.38 67

A, I Methyl Phenanthrene 111 86.3 112 91.8 123 80.7 104 63.3 158 93.0 121�21.5 83.0�12.1 46

Fluoranthene 615 462 676 446 626 392 686 402 766 474 674�60.0 435�36.0 55

Pyrene 1300 1030 1430 1070 1470 970 i440 976 1750 1130 1480�165 1040�67.1 42

Benz(a)anthracene 210 132 229 147 204 132 214 131 219 142 215�9. 4 7 137�7.26 57

Chrysene 392 281 439 277 426 264 443 273 487 321 437�34.2 283�22.1 54

Benzo(b+k)fluoranthene 220 170 221 147 232 169 254 172 299 186 245�33.0 169�14.0 45

Benzo (e) pyrene 253 201 282 172 267 290 298 204 352 226 290�38.3 201�19.2 44

Benzo(a)pyrene 86.4 58.6 84.6 55.8 100 58.3 98.3 59.8 107 62.1 95.3�9.51 58.9�2.30 62

Perylene 140 85.5 155 94.8 160 89.6 173 96.0 182 101 162�16.3 93.4�5.99 73

FIGU RE CAPTIONS

Figure 1.

Percent differences between corrected and remaining-body PAH,

chlorinated pesticide and PCB concentrations versus oyster size.

Figure 2.
Size distributions of oysters sampled in the Gulf of Mexico during the

N0A.Xs S&T Program between 1986 and 1989.

4-76

25-
.A 0 2,3.4 Trimethyl Naphth. A Pyrene
0 1-Methyl Phenanthrene 0 Chrysene
E3 Fluoranthene # Benzo(e)vYrene
20 0 Benzo(a)anthracene + Benzo(a)pyrene
XO x A Benzo(b+k)fluoranthene x Perylene
15 0 x
13,& + a
10 Ao@ x
A K+
A
A
5

y 28.156 - 2.1716x R,"-2 0.920
0 ......... ......... ...... ...... ......

25-
B 0 52
0+ 103L
13 105
20- N 138
Q A 118

15-

0
ILO A 13
Q

y 26.031 - 1.9187x RA2 0.888
ol ......... ......... j ....
m

25
c 0 Gamma-chlordane
* Alpha-chlordane
20- 13 Trans-nonachlor
N p-p'DDD
15 A p-p'DDE

j y 24.776 7 1.4380x RA2 0.862

5.5 6.5 7.5 8.5 9.5

Oyster Size (cm)
0
13

4-77

40-
1986

1987

cn 19as
30'
Ea 19S9

Cd
U)

10
PL4

0
3-<4 4-<5 5-<6 6-<7 7-<8 8-<9 9-<10 10-<Il ll-<12 12-<13

Oyster Size (cm)

4-78

In-.-Lveprint 5

Environmental Significance of the Uptake and Depuration of
Planar PCB Congeners by the American Oyster (Crassostrea
virginica)

Jos6 L. Sericano, Terry L. Wade, Amani M. El-Husseini, and James M. Brooks

Environmental Significance of the Uptake and Depuration
of Planar PCB congeners by the American Oyster
(Crassostrea v&giniea)

Jos6 L. Sericano, Terry L Wade, Amani M. El-Husseini

and James M. Brooks

Geochemical and Environmental Research Group

College of Geosciences, Texas A&M University

College Station, Texas 77845, U.S.A.

Uptake and depuration of three highly to3ic PCB congeners, i.e.

PCBs 77, 126 and 169, by the American oysters (Crassostrea
virginica) were study under environmental conditions. Compared

with other PCB congeners, these compounds can be considerably

bioconcentrated, and retained, by bivalves and constitute a potential

health hazard for higher consumers. To evaluate the health risks

that these PCE congeners pose for human beings, concentrations in

oyster samples from two of the largest bays on the northern Gulf of

Mexico coast, Galveston and Tampa Bays, sampled as part of the

NOAA's National Status and Mrends'TAussel Watch!'Program, are

discussed.

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Of -the 209 possible PCB congeners that can be produced by the

extensive chlorination of biphenyl, only 20 have non-ortho chlorine

substitutions in the biphenyl rings. These congeners can attain

planarity which makes their structure similar to the highly toxic

dibenzo-p-dioxins and dibenzofurans (McKinney et al., 1976, 1985;

Hansen, 1987). Particularly important within this group are the

PCBs having four, five or six chlorines in non-ortho positions, for

example, congeners 3,3,4,4' tetrachlorobiphenyl (IUPAC No 77),
3,3',4,4',5 pentachlorobiphenyl (IUPAC No 126), and 3,3',4,4%5,5'

hexachlorobiphenyl (IUPAC No 169) which are very potent mimics of

the 2,3,7,8 tetrachlorodibenzo-p-dioxin (TCDD) and 2,3,7,8

tetrachlorodibenzofuran (TCDF) both in P-450 induction and toxic

effects, e.g. body weight loss, dermal disorders, liver damage,

thymic atrophy, reproductive toxicity and immunotoxicity (Poland &

Knutson, 1982; Safe, 1984,1986,1990; Goldstein & Safe, 1989).

Although these planar PCB congeners represent a small portion

of the total technical PCB mixtures (Duinker & Hillebrand, 1983;

Kannan et al., 1987; Schulz et al., 1989), a worldwide environmental

occurrence should be expected and monitoring of these compounds is

needed. Until recently, however, quantitation of individual non-ortho

substituted PCB congeners was very difficult because of their

extr emely low concentrations and routine high-resolution capillary

gas chromatography analyses failed to separate some of these planar

PCBs from other ortho-PCB congeners. Although, this separation

can now be achieved with, more expensive, techniques such as
multidimensional gas chromatography (Duinker et al., 1988),

simpler and less expensive methods, carbon chromatography for

4-81

example, are available for routine analysis of planar PCBs (Hong &
Bush, 1990; Kuehl et al-, 1991; Sericano et al., 1991)

This paper, which is part of a more comprehensive study, reports
the uptake'and depuration of three highly toxic PCB congeners, i.e.

PCBs 77, 126 and 169, by the American oysters (Crassostrea

virginica) under environmental conditions using a newly developed

carbon chromatographic method (Sericano et al., 1991) and evaluate

the health risks that these congeners pose in two of the largest bays

on the northern Gulf of Meidco coast, Galveston and Tampa Bays.

As part of the NOAA!s National Status and Trends "Mussel Watch"

Program, oyster samples from these, and other areas, have been

analyzed for selected organic pollutants since 1986. Although PCBs
are-6ne of the most commonly found contaminants in Gulf of Mexico

oysters (e.g., Sericano et al., 1990a), the occurrence of planar PCB,

congeners have not been previously reported.

Materials and Methods

Uptake and depuration experiments

Approximately 250 oysters were collected by dredge at Hanna

Reef, a relatively pristine area in Galveston Bay (Fig. 1). Collected

oysters were immediately transplanted live in nets to a site near the

Houston Ship Channel, an area where oysters have shown high PCB

concentrations. , Thereafter, oysters were sampled in groups of 20

individuals during the 3rd, 7th, 17th, 30th and 48th days after

transplantation, respectively. During the uptake period, native

oysters were collected from the Ship Channel area to compare their

4-82

concentrations of these trace organic contaminants with those

encountered in transplanted Hanna Reef oysters. The remaining
transplanted oysters, i.e. approximately 150 individuals, were re- F

located to the Hanna Reef area and sampled in groups of 20

individuals during the 3rd, 6th, 18th, 30th, and 50th days after

transplantation.

Extraction and initial sample fractionation

The analy@ical procedure used for the extraction, initial

fractionation and cleanup of oyster tissue samples for aliphatic and

aromatic (PAHs) hydrocarbons, polychlorinated biphenyls (PCBs),

including planar congeners, and chlorinated pesticides analyses is

based on a method developed by MacLeod et al. (1985) with a few

modifications that. proved to be equivalent or superior to the original

technique. This method and its modifications. have been fully'

described elsewhere (Sericano et al., 1990a) and is not repeated here.

Isolation of planar PCB congeners

For the isolation and analysis of planar PCB congeners, 250 ul

fractions were withdrawn from the final 1 ml extract reserved for

PCB analyses (Sericano et al, 1990a). Before proceeding with the

isolation of planar congeners, PCB #81 was added to the extracts as

internal standard.

The methodology to analyze planar PCBs in tissue samples has

being published elsewhere (Sericano et al., 1991). Briefly, glass

chromatographic columns (10 mm i.d.) were packed in methyleine

chloride. Two grams of the adsorbent, a 1:20 mixture of activated

4-83

AX-21 charcoal (Super-A activated carbon) and LPS-2 silica gel (Low-
pressure silica gel, particle size 37-53 @tm, 450 m2 9-1), were packed

between two layers of anhydrous sodium sulfate. The adsorbent

mixture was carefully checked for interfering compounds by

running blanks with the solvent mixtures used to elute the columns

and concentrating them to a final volume of approximately 0.1 times

the working volume. Oyster tissue extracts were sequentially eluted

from the column with 50 ml of 1:4 methylene chloride and

cyclohexane, 30 ml of 9:1 methylene chloride and toluene, and 40 ml
of toluene. The flow rate through the column was 1.5 to 2.0 ml min-1.

The first two solvent mixtures were collected as one fraction M) and

contained the bulk of PCB congeners. The second -fraction (M),

containing the planar PCB congeners with four, five and six
chlorines in meta and para positions, was concentr aited to a final

volume of 0.1 ml, in hexane, for GC-ECD analysis.

Instrumental analysis

Planar PCB congeners were analyzed by fused-silica capillary
column GC-ECD (Ni63) using a Hewlett Packard 5880A GC in

splitless mode. Capillary columns, 30 meters long x 0.25 mm, i.d.

with 0.25 mm DB-5 film thickness, were temperature-programmed
from 100 to 1500C at 100C min-1 and from 150 to 2700C at 60C min-1

with I min hold time at the beginning of the program and the

program rate change. A hold time of 3 min was used at, the final

temperature. Total run time was 30 min. Injector and detector

temperatures were set at 275 and 3250C, respectively. Helium Was
used as carrier gas at a flow velocity of 30.0 cm sec-1 at 1000C.

4-84

Argon:methane (95:5) was used as make-up gas at a flow rate of 20
ml min-1. The volume injected was 2 ul. Planar PCBs were

quantitated against a set of authentic standards which were injected r
at four different known concentrations, i.e., 1, 5, 20 and 50 pg ul-1, to
calibrate the instrument and to compensate for a non-linear response r

of the electron capture detector. PCB congeners 103 and 198 were

used as the GC internal standard to estimate the recovery of the

internal standard. The detection limits for individual planar PCB

congeners, calculated on the basis of 2 grams (dry weight) sample

size with 2% by volume of the extract injected into the G-C-ECD, was
50 pg g-1 dry weight.

Results and Discussion

Uptake and depuration of planar PCB congeners by transplanted
oysters

The concentrations of the three highly toxic planar congeners,

i.e., 3,3',4,4' (77), 3,3',4,4',5 (126) and 3,3',4,4',5,5' (169), in

tr-ansplanted and indigenous oysters are summarized in Table 1.

P lanar PCBs were found at low concentrations, e.g. part per trillion
(pg g-1) to part per billion (ng g-1). Congener 169 was present at

concentrations near or below the detection limits.

Congeners 77 and 126 have well defined uptake and depuration

curves as seen when the concentrations of these congeners versus
time are plotted during both stages of this study (Fig.-2). The

concentrations of these two planar PCB congeners in transplanted

Hanna Reef oysters increased over the seven week exposure peri od.

4-85

PCB congener 77 reached a concentration similar to that encountered

in indigenous Ship Channel oysters within 30 days. The uptake of

congener 126 was slower and only approximated the concentration of

Ship Channel oysters by the end of the exposure period. Contrasting

with congeners 77 and 126, and because the extremely low

concentration, it was not possible to observe a clear trend for

congener 169.
The decreasing concentrations of accumulated planar PCBs with

the increasing number of chlorines substituted in the biphenyl rings

observed during this study in transplanted oysters were also reported

to occur in transplanted green-lipped mussels (Perna viridis) during

a 32 day exposure experiment-in Hong Kong waters (Kannan et al.,

1989). Kannan et al. (1987) reported the concentrations of these

planar congener in different commercial PCB mixtures. In general,

congener 77 is 1 to 2 and 3 to 5 orders of magnitude higher than

congeners 126 and 169, respectively. Comparing this relative

concentrations with those observed in transplanted oyster samples, it

appears that the high molecular weight congeners in oyster tissues

are enriched with respect to congener 77. The same observatio n was
made by Kannan et al. (1989). This is not surprising since the Kow

(octanol-to-water partition coefficient) increases with the IUPAC
number of the PCB congener, e-g. 6.36, 6.89 and 7.42 for congeners 77,

126 and 169, respectively (Hawker & Connell, 1988).

When transplanted to the Hanna Reef area, exposed oysters

slowly depurated the concentrated planar congeners. These PCBs
were still present at relatively high concentrations by the end of the

50-days depuration period. Kannan et al. (1989) also observed that the

4-86

concentrations of these planar PCB congeners in transplanted green-
lipped mussels (Perna viridis), at the end of the exposure period (32
days), were substantially higher than those found in native

individuals.

Kinetics parameters describing the uptake and depuration of

planar PCB congeners by the oyster Crassostrea virginica can be

calculated according to the first-order equation:

dCt/dt = ku CW - kd Ct

where Ct is the concentration of the analyte in the tissue at time = t

and Cw is the concentration in water. If the concentration in the

depuration site is regarded as zero, i.e., Cw = 0, equation (1) can be

reduced to:

dCt/dt kd Ct (2)

From this equation, the relationships to calculate Kd., biological

half-life and time to reach a concentration equal to 90% the

equilibrium concentration, i.e. concentration at time infinity, can

be deduced. Respectively, these equations are:

Log Ct = log GD - kd t / 2.303 (3)

tj/2 = 0.693 / kd (4)

t90% = 2.303 / kd (5)

where Co is the initial concentration, i.e. time zero, during

depuration.

4-87

Pepuration rate of congener 77 was higher than the rate observed

for congener 126. The estimated depuration constants for congeners
77 and 126 were 0.0079 and 0.0064 days-1, respectively. These values

were lower than the range of values observed for other PCB

congeners within the same homolog group (Sericano, unpublished

data). This would indicate longer biological half-lives for congeners

77 and 126 (88 and 107 days, respectively) and longer time to reach a

concentration within 10% the concentration at equilibrium (291 and

360 days, respectively-, Fig. 3).

The estimated biological half-lives for these toxic planar PCB

congeners during this study were significantly higher than those

reported by Kannan et al. (1989) for mussels (9 and 13 days,

respectively). However, it must be noted that all the reported

biological half-lives for different PCB, congeners corresponding to that

transplantation study (i.e. Tanabe et al., 1987; Kannan et al., 1989)

were significantly lower than the estimated half-lives during this

study and previous reports involving different organisms (Table 2).

Despite this disagreement, both studies indicate that, compared to

other ortho-substituted congeners within the corresponding homolog

groups, planar PCBs take longer to equilibrate into and out of the

lipid pools of these organisms.

NOAA's National Status and Trends "Mussel Watch" Program

Details regarding site locations and oyster collection during this

program are given elsewhere (Sericano et al., 1990a, 1990b). The

concentrations of PCB congeners 77, 126 and 169, as well as the

concentrations of selected predominant mono- and di-ortho

4-88

substituted congeners and total PCBs in oyster samples from sites in

Galveston and Tampa Bays (Fig. 4) are summarized in Table 3.

In Galveston Bay, the highest concentration of these planar PCBs

was found in samples collected near the area where the Houston

Ship Channel enters the upper Galveston Bay (GBSC) and decreases

seaward. The second highest total concentration was encountered in

samples from a site near the city of Galveston (GBOB). The general

distribution of planar congener concentrations in Galveston Bay.

clearly indicates high values near population centers. The same

correlation between urban, centers and concentrations of planar

PCBs can be observed in Tampa Bay. The highest concentrations

were measured in samples collected near Tampa (TBKA).

As expected from the small contributions of these planar-

congeners to the total commercial PCB mixtures (Kannan et al.,

1987), these congeners were detected at much lower concentrations

than other mono- and di-ortho substituted PCB congeners. However,

as discussed previously, it appears that congeners 126 and 169 are

enriched with respect to congener 77. On average, the sum of these

three highly toxic congeners ranged from. 0.26 to 0.62% and from 0.31

to 1.40% of the total PCB load in Galveston and Tampa Bays,

respectively.

In a recent review, Safe (1990) discussed the environmental and

mechanistic considerations behind the development of the Toxic

Equivalent Factor (TEF) concept. He proposed provisional TEF values
of 0.01, 0.1 and 0.05 for planar congeners 77J26 and 169, respectively.

Calculated TEF in oysters tissues collected from Galveston and
Tampa Bay, as well as their averages, are listed in Table, 4. In

4-89

Tampa Bay, the total TEF values ranged from 14 to 52 whereas in

Ga Iveston Bay the TEP values were between 13 and 280. The data

show that, except for the sample collected near the Houston Ship

Channel, oysters from Tampa and Galveston Bays are similar in

terms of total toxicity. Oysters collected near the Houston Ship

Channel (GBSC), in Galveston Bay, were clearly the most toxic. This

area is closed to commercial or sport oystering.

In conclusion, two of the most toxic planar PCB congeners, i.e.

congeners 77 and 126, were bioconcentrated by transplanted oysters

during a seven-week exposure period. Congener 77 attained an

equilibrium concentration in a shorter period of time than congener

126. When contaminated oysters were back transplanted to the

Hanna Reef area, they significantly depurated both planar PCB

congeners; however, the estimated depuration half-lives were

significantly longer than those corresponding to different PCBs

within the same homolog groups. Because of their potential toxicity,

this persistency of highly toxic planar congeners is of significant

importance in environmental studies. These congeners can be

considerably bioconcentrated, and retained, by bivalves and constitute

a potential health hazard for higher consumers, including human

beings.

This research was supported by the National Oceanic and

Atmospheric Administration (NOAA), contract No 50-DGNC-5-00262,

through the Texas A&M Research Foundation, Texas A&M

University.

4-90

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Safe, S. (1986). Comparative toxicology and mechanisms of action of

polychlorinated dibenzo-p-dioxins and dibenzofurans. Ann. Rev.

PharmacoL ToxicoL 26,371-399-

Safe, S. (1990). Polychlorinated biphenyls (PCBs), dibenzo-p-dioxins

(PCDDs), dibenzofurans (PCDFs). -and related compounds,

envirorunental and mechanistic -considerations which support the

development of toxic equivalency factors (TEFs Crit. Rev.

Toxicol. 21, 51-88.

Schulz, D.E., Petrick, G & Duinker, J.C. (1989). Complete
characterization of polychlorinated biphenyl congeners in

commercial Aroclor and Clorphen mixtures by multidimentional

gas chromatography-electron capture detection. Environ. Sci.

Technol- 23, 852-859.

Sericano, J.L., Atlas, E.L., Wade, T.L & Brooks, J.M. (1990a).

NOAA's Status and Trends Mussel Watch Program: Chlorinated

pesticides and PCBs in oysters (Crassostrea virginica) and

sediments from the Gulf of Mexico, 1986-1987. Mar. Environ. Res.

29,161-203.

Sericano, J.L., Wade, T.L. Atlas, E.L. & Brooks, J.M. (1990b).

Historical perspective on the environmental bioavailability of DDT

4-93

and its derivatives to Gulf of Mexico oysters. Environ. ScL

Technol. 24,1541-1548.

Sericano, J.L., El-Husseini, A.M. & Wade, T.L. (1991). Isolation of

planar polychlorinated biphenyls by carbon column

chromatography. Chemosphere 23, 915-924.

Tanabe, S., Tatsukawa, R. & Phillips, D.J.H. (1987). Mussels as

bioindicators of PCB pollution: A case study on uptake and

release of PCB isomers and congeners in green-lipped mussels

(Perna viridis) in Hong Kong waters. Environ. Pollut. 47, 147-163.

4-94

TABLE I
Planar PCB concentrations in oysters (Crassostrea virginica) during the
uptake and depuration. phases in Galveston Bay

Sample Sampling Concentration of Planar PCBs Total PCBs
77 126 169
pg 9-1 Pg g- I pg g- I ng g-I

HRSCM 3 330 110 ND 220
HRSC 7 560 140 ND 380
HRSC 17 630 140 ND 500
HRSC 30 920 160 ND 650
HRSC 48 1000 220 77 830

HRHR(2) 3 900 170 106 850
HRHR 6 800 250 ND 670
HRHR 19 750 210 76 470
HRHR 30 740 190 N D 400
HRHR 50 630 150 N D 380

SC(3) 3 1070 370 340 1500
SC 17 1040 250 120 1200
SC 30 1000 230 320 960
SC 48 980 220 96 1100

(1) Hanna Reef-to-Ship Channel oysters
(2) Hanna Reef-back-to-Hanna Reef transplanted oysters
(3) Ship Channel oysters
ND = not detected

4-95

TABLE 2
Biological half-lives of selected PCBs in different organisms

Congener Oystersa Musselsb Musselsc Wormsd

Planar PCBs

3,3', 4,4' (77) 88 9
3,3',4,4',5 (126) 107 13
3,3',4,4',5,51 (169) - 26 -

Selected non-planar PCBs

2,4,4' (2@8) 17 7 16
2,2', 5,5' (52) 55 6 28 -
2,2',4,5,5' (101) - 76 7 37 50
2.2'.3,3'.4,4' (128) 51 9 46 92
2,2',4,4',5,51 R53) 27 6 36

a This study; b Tanabe et aL (1987) and Kannan et al. (1989); c Pruell
et aL (1986); d Oliver (1987)

4-96

TABLE 3
Planar- PCB concentrations in oysters (Crassostrea virginica) from
Galveston and Tampa Bays

Sample Concentration of Planar PCBs Total PCBs
77 126 169
Pg g- p9 9-1 Pg g-I ng g-1

Galveston Bay

GBSC 2000 2200 790 1100
GBYC 330 210 190 210
GBID 140 120 54 110
GBHR 89 110 89 50
GBCR 100 94 51 77
GBOB 500 400 93 160

Tampa Bay

TBOT 170 320 280 55
TBKA 1500 330 84 580
TBPB 85 100 51 75

TBNP 260 140 150 120

TBCB 200 290 100 49
TBMK ND ND ND 38

ND not detected

4-97

TABLE 4
Toxic Equivalent Factors (TEF) in Crassostrea virginica Oysters from
Galveston and Tampa Bays.

Sample Twic Equivalent Factors Total TEF
77 126 169

Galveston Bay

GBSC 20 220 40 280
GBYC 3.3 21 9.5 34
GBTD 1.4 12 2.7 16
GBHR 0.9 11 4.5 16
GBCR 1.0 9.4 2.6 13
GBOB 5.0 40 4.7 50

Tampa Bay

TBOT 1.7 32 14 48
TBKA 15 33 4.2 52

TBPB 0.9 10 2.6 14
TBNP 2.6 14 7.5 24
TBCB 2 29 5.o 36

TBMK - -

4-98

F4--ure caT)tions

Fig. I Galveston Bay transplantation sites

Fig. 2 Planar PCB concentrations in Hanna Reef oysters

during the uptake and depuration phases of the

transplantation experiments at Galveston Bay. Planar

concentrations in Ship Channel oysters during the

uptake phase are also indicated

Fig. 3 Depnmtion constants (kd) and biological half-lives (BHL)

of planar PCB congeners compared to the ranges of

values calculated for non-planar PCBs (Sericano,

unpublished data).

Fig. 4 Location of Galveston and Tampa Bays sampling sites

(NOANs National Status and Trends Mussel Watch

Program)

4-99

T E X A S

Y.
A

LA PORTE

Z-

0
EAST

SAN LEO

TEXA.!@CITY

GALVESTON

Si-te 1: Hanna Reef

S-ite 2: Ship Channel

4-100

3.3'4.4'TETRACHLOROBEPHENYL (rLYPAC No 77)
10000-
Hanna Reef Oysters
Ship Channel Oysters

1000-.0
0

100 ......... ..... ...
0 10 .20 30 40 50 60 70 so 90 100
Time (days)

3.T.4..V.5 PENTACHLOROBIPHENYL MWAC No 126)
10007
0 Hanna Reef Oysters
Ship Channel Oysters

100
0
4@

0
U
10 1
0 10 20 30 40 50 60 70 so 90 100
Time (days)

4-101

4 0 PCB 77

0
0 5 0
PCB 126
0

.8 .........
0.0000 0.0100 0.02W 0.0300 0.0400 0.0500

kd (day-1)

-2

4 0 PCB 77

0
4
0 5 oPCB126
5
0

-6

0 50 100 150

BIHL (day)

4-102

X A. TAMPA BAY OLD
14OUST014 A B - TAMPA BAY MG
C - TANIPA BAY PAP)
D - TAMPA DAY NAX
I00 E - TAMPA BAY COC
F - TAMPA BAY MULI

B
@A S A
low,

I- LEO
c Clearwater*
Ta

imsory

..A
GALVESTON c
0A.
F
t. Petersburg
GULF
D
W'S' OF
PL ptmfts
C) .......
mEX/cO a

A
-G BSC)
ALVESTON BAY SHIP CHANNEL (G
B - GALVESTON BAY YACHT CLUB (GBYC)
- GALVESTON DAY
C TODD's DLW (GBTD)
D. GALVESTON BAY HANNA REEF (GBHR)
E - GALVESTON BAY CONFEDERATE REEF (GBCR) Anna
F - GALVESTON BAY OFFATS BAYOU (GBOBI Maria Isla

5.0 Trace Metals Results

5.1 Laborato1y Intercalibration Results

As was discussed in the Laboratory Procedures section, standard
reference materials. U.S.G.S. standard rocks and other materials of
known trace metal concentrations were analyzed with almost every
batch of samples. In the case of INA& these materials were used to
quantify the amount of trace element in the sample, whereas in the
AAS analysis. working curves made from commercial standards were
used for quantification and the reference materials were used to verify
results and identify recovery problems.

In addition to the reference materials we had obtained from
NIST, USGS and other sources, we received interealibration materials
from the National Research Council of Canada (Dr. Shier Berman) again
this year as we have each year since the NS&T program began.

5.2. Trace Metal Concentrations in Year 6 Oysters

In an attempt to bring out geographic and temporal trends,
trace metal concentrations in oysters (the average of the three stations
for each site), were plotted as a function of site location from Lower
Laguna Madre, Texas, through the Everglades, Florida, in the annual
reports for the first five years of this project. Each plot showed the
geographic distribution of one of the trace metals determined. The
plots of averaged data for -the first five years are shown updated here
by addition of the Year 6 data (Figures 5.1 to 5.13). Some observations
based on these plots are given below, with emphasis on higher than
average values for a given element that persist for more than one year
and values that changed significantly in year 6 when compared to
average values for the first 5 years. The plots will continue to be
updated annually as the project proceeds, and both geographic and
temporal trends will be sought,

Silver concentration in oysters was very high at Copano Bay,
Texas, and at the nearby San Antonio and Matagorda bays during Year
I of the project. During Year 2, the Copano Bay site, which at 7ppm
was highest in Ag not only for the Gulf but for the entire U.S. in Year 1,
was about 50% lower in Ag. Four other Gulf sites had higher Ag
concentrations than it did. It was, however, still enriched relative to
most other sites along the Gulf Coast and remained enriched in Year 3
with a concentration very similar to the Year 2 value. Year 4 saw a
further decrease at this site to a near average Ag value. However, in
Year 5, two of the three stations at this site gave oysters which were
very enriched in Ag, even more so than in Year I of the project.. In
year 6 Ag was again down at this site.

5-1

The East Matagorda site was similar in Ag concentration in Years
I and 2 and was greatly enriched compared to most other sites. It
decreased to average values in Years 3 and 4 but was again enriched in
Year 5. The other three sites from Matagorda Bay were not enriched
in Ag in Years I and 2, but one site showed enrichment in Year 3. In
general during Years 4 and 5, slight enrichments were seen
throughout this part of Texas, but in year 6 Ag decreased to average
values at all but one of these sites. East Matagorda oysters increased in
year 6.

The south central Texas (Matagorda) areas where Ag is so
variable are generally areas of low population density and relatively
little commercial activity. There are, however, several large isolated
petrochemical plants in the area as well as a large aluminum refining
plant (ALCOA). It seems unlikely that human activity is involved in Ag
variability in this area, but it's not impossible.

The Galveston Bay area is much more industrialized than is the
Matagorda Bay area, but it produced oysters lower in Ag for the first 4
years. In Year 5, for some unknown reason, all stations at one site
(GBCR) in Galveston were highly enriched in Ag and this same site was
again greatly enriched in year 6. It seems that Ag input to this area of
Texas varies from year to year, but we can not explain why or why the
enrichments are so geographically localized.

Sabine Lake, Texas, just east of Galveston, was average in Ag in
Year 1, very high in Year 2, and moderately high in Years 5 and 6.
This area is heavily industrialized and was enriched in several metals
(Ag, Cd, Cu) in Year 2 compared to Year 1, but slightly depleted in
others (Cr, Fe, Zn). These large changes in Sabine Lake oysters might
be due to inputs of specific pollutants at specific times. (Cu, for
example, changed from a less than average value in. Year 1 to a value
more than three times greater than average in Year 2 to an average
value in Year 3 and 4, whereas Zn was very much above average in Year
1, decreased in Year 2, and decreased further in Years 3, 4, 5 and 6.)
However, we have no data on pollutants inputs that would confirm this
hypothesis.

In Louisiana, a high Ag value of about twice the Gulf average was
found at Vermilion Bay in central Louisiana in both Years I and 2.
There was a slight decrease at this site in Year 3 but in Year 4 it had
the highest Ag in the Gulf and it remained high in Years 5 and 6. As
with the Texas situation, a variable input of Ag from some unknown
source is suggested. Further east, the Ag concentration dropped
drastically (Figure 5. 1) through the next five sites and reached a
distinct minimum at Barataria Bay, just west of the Mississippi River
delta in Years 1 and 2. The pattern was similar in Years 3, 4, 5 and 6,
but the minimum at Barataria Bay was not as distinct because the

5-2

nearby Terrebonne Bay samples were also very low in Ag. Moving
eastward from Barataria Bay, scattered high values were found east of
the Southwest Pass of the Mississippi River delta, especially at the
Pass A Loutre site on the Mississippi River Delta, during Year 5, but
this site was not sampled for Year 6. Far-ther cast, site MBHI first
sampled in Year 3 almost doubled in Ag in Year 6 to become one of the
highest sites in the Gulf. Nearby site MBDR was high last year but was
not sampled this year.

Both high and low Ag values are found in Florida. Like the
Louisiana sites, the Florida sites were generally, but not always, very
similar for all 6 years, whether they were high, low, or average in
silver concentration. For example, Choctawhatchee Bay was much
above average all 6 years and Tampa Bay Mullet Key was much below
average. The CBSP site in Choctawhatchee Bay gave oysters averaging
6.4ppm in year 6, the highest in the Gulf. Oysters from this site were
also enriched in Pb and Se.

Barataria Bay, and the surrounding bays with low concentrations
of Ag in oysters, have probably been as physically disturbed by man as
any bays on the Gulf Coast based on the information we have at this
time. These are areas of extensive petroleum development and
widespread dredging and channel cutting. Almost every square foot
has been disturbed by man. Furthermore, these bays are directly
downstream of the Mississippi River outflow, which is usually
considered to be a major source of pollutants to the Gulf of Mexico.
Why, then, is Ag so low and does only Ag show this apparently
anomalous behavior? The second part of the question is easy. Several
other metals show distribution patterns almost identical to that of Ag
(e.g. Cd and Cu-, Figures 5.3 and 5.5); other metals (e.g. Fe, Cr, Se, and
Hg) show similar but not identical patterns. Something.about the
muddy, frequently stirred Louisiana bays may be keeping the
concentration of some trace metals in oysters low. Perhaps the large
amount of fine-grained clay from the Mississippi River effectively
competes with the oysters by adsorbing dissolved metals. The clay
itself would not become greatly enriched in trace metals due to
dilution by its large mass (- 3 x 1014g of sediment are transported by
the Mississippi River each year) and would not be a clear indicator of
pollution.

The idea that the amount and/or kind of suspended material in
the water might control the amounts of trace metals in oysters by
controlling the concentration of dissolved trace metal is one of several
possible explanations for the patterns seen. It is also possible that
local anthropogenic inputs influence trace metal concentration
patterns, even though such inputs have not yet been identified. As
noted above, it is interesting that oysters from Sabine Lake, Texas,
were greatly enriched in Cu in Year 2 compared to Year 1, and also in
Ag and Cd, but not in Fe, Hg, Pb or other metals. The extreme

5-3

enrichment in Ag of oysters taken at Confederate Reef in Galveston Bay
during Years 6 should also be noted, as well as the big increase in Cu at
a Lake Borgne site (LBMP) in Year 5 and the big increases in Cu. Pb
and Zn at Knight's Airport in Tampa Bay in Year 6. This shows that
oysters can change drastically in trace metal content in a one year
period under certain circumstances, even though the general pattern
in the Gulf of Mexico is to have similar concentrations of a given metal
at a site year after year. The implication is that the oysters do respond
to added pollutants.

Other anthropogenic-looking trace metal values include high Hg
in Lavaca Bay, Texas, and at some Florida sites, especially Old Tampa
Bay, very high Pb, increasing in concentration each year at one of the
two Choctawhatchee Bay sites: a two fold increase in Pb at the Houston
ship channel site in Year 5, very high Zn at the old Tampa Bay site;
and higher than average Ag, Cd and Cu at the Vermilion Bay site.
These abnormally high values may well be a result of anthropogenic
inputs of metals. It is also possible that some of the tissue metal
concentration variability is determined by the oysters themselves
through "natural" processes. As mentioned earlier, such parameters as
size and sexual stage of the oysters are being examined in this study
because trace metal levels in oysters are reported to vary with these 01
and other physiological parameters. In other work we have sampled
oysters in Mobile Bay four times over a year period and Galveston Bay
oysters in June and September (vs NS&T sampling in December). In
repeated sampling from the same reefs, we have found concentrations
of some metals as much as a factor of two lower in September than in
March-June. Thus, physiology may play a role in some metal variation.

We have not yet completed attempts to correlate oyster metal
data with the other information we have about the oysters, but this is
being done and preliminary work shows no simple relationships
applicable to the whole data set. One observation made in our earlier
reports is that the oysters around Barataria Bay, which were much
lower than average in trace metal content, were among the last oysters
collected in Year I of the project, and apparently were among the few
that had either just spawned, or were about to spawn, although
spawning state is not easy to determine for Gulf oysters. This
relationship obviously cannot explain all variability in the data,
however. Otherwise, all metals would correlate perfectly with each
other, which they do not. In fact, in some cases some metals are in
high concentration precisely where others are low. Likewise, the
sampling that occurred after Year I in Louisiana apparently did not
collect oysters at the same spawning state as the first year sampling,
yet trace metal levels were similar to those found in Year 1.

The Louisiana oysters for most years have been larger than
average, and trace metal content of all Gulf of Mexico oysters showed a
weak negative correlation with size, although there were exceptions to

5-4

this tendency- We have not seen any correlation between metals in
oysters and salinity, water depth, or such variables. In short, there is
no strong indication that physiological parameters have obscured
metal concentration variations which are due to environmental factors
such as variable input of pollutant metals. The strategy of sampling
during, the winter each year does seem to reduce "natural" variability
in oyster metals.

Some of the high metal levels in oysters from the clear waters of
western Florida are surprising. Arsenic especially is much higher in
some of the Florida oysters than it is elsewhere on the Gulf Coast, yet
some Florida oysters, for example those from most sites in Tampa Bay.
were very low in As all 6 years. Only the Tampa Bay site at Navarez
Park near the city of St. Petersburg was significantly enriched in As. It
was first sampled in Year 4, at which time it had the highest As
concentration in the Gulf. In Year 5 the As level was even higher and
in Year 6 the concentration more than doubled from the high Year 5
value. Arsenic at this site is now four times higher than that at any
other site. The new site at Knight Airport on the edge of the city of
Tampa was low in As in Years 4. 5. and 6. It is possible that the
extensive phosphate rock deposits in Florida are a source of arsenic,
but based on the limited data we have, there is no correlation between
phosphate rock occurrence, shipping, or mining, and As
concentration in oysters. For example, a phosphate plant is reportedly
adjacent to the TBHB site, yet oysters from it were low in As.

The As distribution in Florida does seem to call for some kind of
local environmental control, as do certain other metal distributions.
There seems to be no other explanation for high and low values of
trace metals to occur at adjacent sites, often in a given bay, and to have
these values repeat year after year. This suggestion is further
strengthened by the Year 5 and 6 results which show the As level at
CBBI, NBNB and RBHC dramatically lowered than was found through
the first four years. Some unknown environmental change, perhaps
rainfall and runoff or a change in pest control practices, must be
responsible for the As decrease.

The local patterns discussed above are imposed on regional
trends, for example, Hg is enriched in Florida sites where twelve of
the 25 sites are well above average. The oysters from Old Tampa Bay
are especially high in Hg, rivaling even those from Lavaca Bay, Texas,
which are known to be contaminated with Hg and to be a human
health threat. Se, Cd, and Ag, on the other hand, are generally lower
in Florida oysters than those collected elsewhere. Other metals show
no obvious regional trends, but subtle trends may exist.

Zn shows especially great variability from place to place, not only
in Florida but throughout the Gulf Coast, and can even vary widely
within a given bay. For example, Old Tampa Bay oysters averaged -8300

5-5

ppm Zn in Year 3 and 6700 ppm Zn in Years 4, 5, and 6, whereas
oysters from Mullet Key in Tampa Bay averaged only 240 ppm in Year
3 and only about 325 ppm in Years 4, 5, and 6. Apalachicola Bay
oysters were even lower in Zn than those from Mullet Key during the
six years of the project. Should the APD13 low Zn values be considered
background for all Florida oysters, or does the natural background
concentration vary by more than the observed factor of 20 from site to
site? It seems very unlikely that the background would vary so
drastically within a given bay. Human activity must somehow be
involved in these drastic differences in Zn content. This suggestion is
supported by the observation that Zn concentrations in oysters does
seem in a qualitative way to correlate with proximity to population and
industry.

St. Andrews Bay in north Florida provided oysters greatly
enriched in Zn and Cu in Yeax I but somewhat less enriched in later
years. These same oysters were depleted in Cd by almost a factor of
four during all six years and had less than average concentrations of
several other metals. This may be a case in which large amounts of
one or two metals inhibit the uptake of other metals, but again the
situation is ambiguous because high Cu- and Zn in Sabine Lake and
Vermillion Bay oysters are accompanied by high Ag, Cd, etc. It is
likely that the form (species) of metal in the environment is as
important as the amount where uptake by oysters is concerned.

5.3 Summ= and Conclusions from Six Years of Trace Metals in
Qysters Data

The trace metal concentrations found in the Gulf of Mexico,
oysters were generally less than or equal to literature values from
other parts of the world that are thought to be uncontaminated by
anthropogenic activity. A few sites, however, did show apparent trace
metal pollution, and other sites gave anomalous values that cannot be
readily explained by either known anthropogenic or niatural causes.

The range of values for the overall data set (maximum/minimum)
varied from 15-fold for Mn to more than 600-fold for Pb, whereas the
coefficient of variation (standard deviation/mean) was generally in the
50-60% range for most metals. Variations were much greater
between stations than between years at a given station. Enrichments
usually occurred in suites of 3-4 elements with Ag. Cd, Cu and Zn
being the most common suite: thus, several strong inter-element
correlations were found. There was, however, little correlation
between metal levels in oysters and in sediments from the collection
sites even when sediment data were ratioed to Al. There was likewise
little correlation between oyster metal levels and size, sex, or
reproductive stage of the oysters.

5-6

Geographically, appreciably elevated (>3 x average) metal levels
were generally restricted to single sites within bays or estuaries which
implies local control (Reprint 8). On the other hand, Ag, Cd and Se
levels were somewhat higher in Texas oysters than in those from
Florida, whereas the reverse was true for As and Hg. Concentrations
were lower than average for several metals in oysters from central
Louisiana, especially Ag, Cd, and Cu. Thus, the Mississippi River
outflow and extensive offshore oil development do not seem to enrich
oysters in trace metals.

5-7

w p. tA

LMSB CLSJ PBPH
PBM
< LMPI CLLC
LMAC PBSP
p JWH
CCBH CBJB
CCNB VBSP CIISR
co ABOB Cl
ccic BISP
A13LR CLCL PCLO
AMU IBL13 PCMP
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n SAPP
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Cl. FSBD SRWP
MRI?
MRPL CKBP
o in
M.. T13NP
cn
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0 MBTP HSBG TBPB
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L13NO
cn BRCL LPGO LA 7BHB
r-t' BRFS ....... ............................... T13CB
CD MSPC
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p GBTD MSPB
0 . . ............................................. NBNB
BYC
:or G C MBCP RBHC
Z GBSC 1VOU -\ \ \ \ " 7 AL EVFU
Cn GBHR YlBDR B HWU
Sur,
WF
SEEM

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0 APCP
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MRPL
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cn 1-114 LPGO
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CBFM
C;B7D MSPB
.. . ......... .................................. . . ........... NBNB
0 GBYC MBCP RBHC
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(D M GBIHMR
p MRDR BHKF
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0.1 00 00
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> Lmpl CLLC PBIB
L
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(D 17 1
@a3 D LBNT S!K2= TBOT
MBEM TBKA
0
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...............
A) BRFS 7BCH
MSPC
GBCR CBBI
GBOB MSBB NIS CBFM
GBM MSPB NBNB
. . ........... ................ ............... .....
0 0 MBCp RBI3HC
0 : SYCC
IF AL EVFU
(,D GBHR
PO MBDR BHKF
0 SLBB..

41 0
tA LA LA
LAISB CLSJ PBPH
z > L.MPI CLLC PBE3
LMAC PBSP
JHJH
CCBH
CCNB VBSP CMBR
ccic
r: 0 ABOB CBSP
W Cn @3' ABLRR CLCL PCLO
Z C/) "
CL (D 0 ABW TBLI3 PCMP
C13CR TBLF SAWB
M13AR
APDB
BM
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0 SAMP BBSD
0 AESP
LTI ESSP BBNM SRIWP
ESBD MRTP CICBP
MMBGP
MRPL TBNP
o MBLR
ssi TBMX
I@MCB
MOP RSBG S-= T13PB
X NMDI LBMP S= 713ar,
F M13EM TBKA
0 BRCL LBNO LA IMHB
cn 0 Lprjo
'< BRFS S:m ....................................................... 713CB
cn mspc
r+ GBCR CBBI
F@
110 I-S GBOB MSBB ms
cn
GBTD MSPB
::@ . . . .. . ..........................................
0 GBYC MBCP B C
GBSC FIV
I@mm AL
GBHR MBDR BHKF
SL13B

L.MSB CLSJ PBPH
> LMPI PBE3
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I
I
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I
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I
I Reprint 8
I Trace Metals in Galveston Bay Oysters
I I B.J. Presley, R.J. Taylor, and P.N. Boothe
I
I
I
I
I
I
I
I

Trac e Metals in Galveston Bay Oysters

B. J. Presley, R. J. Taylor and P. N. Boothe
Oceanography Department, Texas A&M University

Oysters and other bivalves have been used as "sentinel" organisms for assessing
the pollution status of marinewater bodies for almost twenty years. For example,
Goldberg, et al., (1983) report data for a U.S. EPA funded "Mussel Watch"
program conducted in 1976-78 and the current NOAA funded "National Status
and Trends Program" (NS&T) is an outgrowth and extension of the "Mussel
Watch" concept. Bivalves are widely recognized as being responsive to changes in
pollutant levels in the environment, good accumulators of pollutants, widely
distributed along coasts, and easy to collect and analyze. They integrate pollutant
levels in the environment over weeks to months and therefore allow areas to be
compared even when sampling is done only once or twice per year.

Oysters (Crassostrea virginica) were collected at six different sites in Galveston
Bay during 1986-1989 as part of NS&T (see Fig. 1, page 69). Each site was on an
identifiable oyster reef and at each, twenty oysters were taken from each of three
stations, the stations being 100 to 500 m apart. Each site was sampled once each
year, except two of the sites (stations 58, 59) were not sampled the first two years.
The twenty oysters from each station were combined and analyzed as a single
sample each year. In most cases stations were located hundreds of meters to
many kilometers away from any obvious point sources of pollutant inputs in an
attempt to characterize large areas of Galveston Bay, rather than to identify
specific point sources of pollutant input.

Frozen oysters were returned to the lab where they were opened under clean room
conditions. The oyster tissue was put into teflon jars which were loaded into an
industrial paint shaker and shaken vigorously for 15-20 minutes to completely
homogenize the samples. An aliquot of the combined and homogenized sample
was freeze-dried, re-homogenized by ball milling in plastic, and weighed into a
digestion vessel. Digestion of the approximately 200 mg dry weight samples of
oyster tissue used three ml of a four to one mixture of ultra-pure nitric and
perchloric acids.

Two blanks and two reference materials were digeste d with every set of 20-40
samples. Repeated analysis of these reference materials a Cd participation in
several intercalibration exercises give an estimate of ten perce@t or better for both
the precision and accuracy of the data reported here.

All data reported here were obtained by atomic absorption spectrophotometry
(AAS). The samples were analyzed for Ag, As, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb,
Se, Si, Sn, and Zn. Flame AAS was used for Cu, Fe, and Zn which exhibit high
concentrations in oysters, cold vapor AAS for mercury, andgraphite furnace
AAS for the remaining elements.

Trace metal concentrations found in oysters collected along the entire Gulf of

5-22

Mexico coastline during the first four years of NS&T were generally similar to
those reported in oysters, taken from non-contaminated water in other parts of the
world (Texas A&M Geochemical and Environmental Research Group, 1990).
Only a few sites showed obvious trace metal pollution and these were restricted
geographically such that nearby sites were usually unaffected. Abnormally high
or low values at a site did, however, usually repeat year after year suggesting
local control. Abnormal sites for most metals were just as likely to be visibly
pristine as to be highly industrialized. -

The oysters collected in Galv 'eston Bay for NS&T were similar in trace metal
content to those collected elsewhere along the Gulf coastline, i.e., there is no
indication of generalized trace metal pollution in Galveston Bay (Table 1). The
average Ag, Cd, Cr, Fe, Mn and Pb in Galveston Bay oysters differs by 10% or less'
from the Gulf-wide average. Copper is 13% higher in Galveston Bay, while Ni is
15% higher and Se is 16% higher. A "t-test" of the significance of those
differences shows that only the Se averages are significantly different at the 95%
confidence leveL Arsenic in Galveston Bay oysters is less than one-half the Gulf-
wide average, but the Gulf average is greatly influenced by several sites in
southern Florida that produce oysters greatly enriched in As. Oysters from other
Texas and Louisiana bays are similar in As content to those in Galveston Bay.
Tin seems to be about 20% lower than Gulf averages in Galveston Bay, but all Sn
values are near the detection limit of the method used and a 20% difference is not
significant. Finally, Zn is 43% higher in Galveston Bay oysters than in Gulf-wide
average oysters.

Table 1. Average trace metal concentrations in 1200 oysters from Galveston Bay and 14,000
oysters from the entire U.S. Gulfcoastline. All elements in ppm, pglg dry wt.

AP, As Cd -Cr Cu Fg@ HP, Mn Ni Pb Sn Zn

GB avg. 2.35 4.74 4.29 0.53 166 Z79 .0815 16.2 2.01 0.66 3A6 0.26 3220
GOM avg. 2.13 9.94 4.06 0.57 148 309 0.135 15.1 1.75 0.68 2.96 0.33 2250
GB/GOM 1.10 0.48 1.06 0.93 1.12 0.90 0.60 1.07 1.15 0.97 1.17 0.79 1.43

Discussion of metals in Galveston Bay oysters averaged over all sites and all years
obviously cannot show possible geographic and temporal trends within the bay.
In the case of Zn, for example, three of the six Galveston Bay sites had oysters
with near Gulf average Zn, with relatively little year to year variation. The other
three sites had much higher Zn. Two of the high Zn sites, Ship Channel and
Yacht Club, are in extreme northwestern Galveston Bay near industrial waste
water inputs and boat basins where Zn contamination might be expected. The
other high Zn site was in Offatfs Bayou on Galveston Island and is surrounded by
residential development and private boat moorings. This apparent local control
on Zn, and in some cases on other metals, is seen not only in Galveston Bay but
also throughout the Gulf of Mexico. Large site to site and time to time changes in
trace metal concentration might be due to man, but the exact activity responsible
has not been identified.

5-23

Cadmium, Pb, Ag and Hg are often added to the environment by man in amounts
rivaling those added by nature but there is no evidence of anthropogenic inputs of
these metals in the Galveston Bay oyster data. Rather, except for Zn, trace metal
concentrations in oysters from Galveston Bay are similar to those in oysters from
pristine areas elsewhere and do not reflect the big differences in proximity to
population and industrialization of the different sites in the bay.

Uterature Cited

Texas A&M Geochemical and Environmental Research Group. 1990. NOAA
status and trends mussel watch program for the Gulf of Mexico. Technical
Report submitted to National Oceanic and Atmospheric Administration,
Rockville, MD.

Goldberg, E. D., M. Koide, V. Hodge, A. R. Flegal and J. Martin. 1983. U.S.
mussel watch: 1977-1978 results on trace metals and radionuclides.
Estuarine, Coastal and Shelf Science, 16: 69-93.

5-24

6.0 Butyltin Results

The analyses of butyltins as part of the National Status and
Trends (NS&T) Project for Marine Environmental Quality, Mussel
Watch Project was initiated in 1987 by GERG. Butyltin analyses was
added to the NS&T project because of the growing concern about the
effects of tributyltin from antifouling paints on non-target organisms.
This section serves as an overview of several papers that have resulted
from the butyltin studies performed by GERG (Table 1. 1).

Organotin compounds have a range of toxicity and as such have
found a broad spectrum of applications including use as fungicides,
bactericides, pesticides, anti-cancer agents, and biochemicals.
Tributyltins (TBTs) were a major component of many antifouling
paints because they are 10-100 times more effective than copper-
containing paints. . It is estimated that 140,000 kg of TBT-containing
antifouling paint was used each year prior to 1988 in the United States
on commercial and recreational boats and ships to retard fouling. The
U.S. Navy estimates that it could save $155 million annually by
repainting its fleet,of 550 ships with TBT-containing antifouling paint,
but has not begun this process because of the mounting evidence that
TBT compounds may have acute and chronic effects on non-target
organisms. However, the recreational and smaller commercial fleets
are potentially the most deleterious to estuarine resources because
these vessels spend most of their time in port and their antifouling
paints are formulated to give high static release rates.

Studies conducted in the United Kingdom and France have
determined that the short-term acute toxicity of TBT in water is at the
nanogram per liter level for oysters and other non-target molluscan
species. The re8ults of these studies and the finding that TBT water
column concentrations appear to be increasing in selected harbors
and marinas in the United States have prompted the U.S.
Environmental Protection Agency (EPA) to initiate a special review
study.

After review of the available data, the U.S. EPA concluded that
low concentrations (20 ppt) of TBT in the water can cause irreversible
chronic effects to a broad spectrum of aquatic organisms. This led to
the President signing the Organotin Paint Control Act of 1988
(OPACA). The act contained interim and permanent TBT use
restrictions as well as research and monitoring requirements. The
application of TBT antifoulant was prohibited from vessels under 25
meters (82 ft.) and the maximum average daily release rate was set at
4 Mg/CM2/day. These requirements of OPACA should reduce the total
amount of TBT entering the marine environment to about 10% of its
pre-OPACA levels. Furthermore, most of the reduction should come in

6-1

estuarine and fresh water areas where small vessels are used and
moored and where the risk from TBT input is greatest.

Bivalves (oysters and mussels) have been widely used as sentinel
organisms for monitoring the contamination burden of estuarine
ecosystem because they filter feed and bioaccumulate contaminants.
Oysters have been found to have bioconcentration factors for TBT that
range from 2300 to 11,400 times the water concentration, rapidly
reaching an equilibrium plateau and slowly depurating. Thus, analysis
of TBT concentrations in bivalves from coastal waters should provide
information by which to assess the extent of butyltin contamination.

In 1987, GERG analyzed bivalves (mussels and oysters) and
sediments from 36 coastal sites distributed on the Atlantic, Gulf, and.
Pacific Coasts, including one site in Hawaii. These selected S&T sites
were chosen as the NS&T sites closest to suspected sources of input
(for example-, near marinas, dry docks, etc.). It was anticipated that
no butyltins would be detected because the half-life of tributyltin in the
water column measured by C14 label techniques was estimated to be 2-
14 days. However, all but one of the 36 bivalve samples analyzed
contained TBT and its less toxic breakdown products (dibutyltin and
monobutyltin). The concentrations of TBTs ranged from <5 top 1560
(366 av) ng of Sn/g dry weight as tin and accounted on average for
74% of the tin present as butyltins. Replicate oyster samples from a
specific site concentrate TBT to the same level. Concentrations of
TBT found in oysters varied both spatially and temporally. Both
oysters and mussels concentrate TBT from their environment and are
therefore excellent sentinel organisms to monitor the environmental
levels of TBT available to marine organisms.

Butyltin concentrations in sediment samples. from U.S. coastal
areas ranged from <5 to 282 ng Sn/g. - Butyltins were detected in 75%
of the sediment samples analyzed. The predominant butyltin was TBT,
which is also the most toxic. DBT and MBT were detected in 30% of
the sediment samples analyzed: the TBT degradation products were
only found when TBT was present, usually at high concentrations.
Mean bivalve butyltin concentrations were 18 times higher than mean
sediment concentrations. Based on bivalve analyses, bioavailable
butyltins were present at all the sites where butyltins were detected in
the sediment. The sediments are one possible source of these
bioavailable butyltins. However, the lack of correlation between
sediments and bivalve butyltin concentration indicates that other
sources may be predominant.

The purposes of the NS&T project is to determine the current
status and the long term trends of contamination in U.S. Coastal areas.
With the limitations imposed on the usage of TBT antifouling paint by
OPACA, a decrease in the concentration of TBT in the environment
would be expected. Studies done at GERG were designed to, examine

6-2

the uptake and depuration rates of TBT compounds in oysters
(Crassostrea virginica) through transplantation experiments at two
locations in Galveston Bay, Texas. Oysters from a relatively
uncontaminated area (Hanna Reef) were transplanted to a new site
known to have indigenous oysters with higher TBT concentrations
(Houston Ship Channel). Total butyltin concentrations increased
rapidly from 62 to 380 ng Sn/g during the exposure period (48 days)
with TBT accounting for most of the increase. After the uptake
period, transplanted and indigenous oysters were relocated to the
relatively pristine location. During the depuration period (50 days),
oysters originally from the clean location depurated at a faster rate
than oysters from the chronically exposed population. This is
reflected in half-fives for TBT of 15 and 26 days for these oysters,
respectively.

These experiments indicate that we should see a decrease in
environmental levels of TBT in NS&T oyster samples from the Gulf of
Mexico. This was indeed found to be the case. The concentration of
total butyltins and tributyltin in oysters are decreasing at many Gulf of
Mexico sites (Reprint 6 and Preprint 6). The decrease is more than a
factor of two for 75% of these sites. The environmental half-life for
butyltins for some sites is less than 3 years. At several sites there was
no measurable decrease in butyltin concentration, but no site had an
increase in concentration between 1987 and 1990. The indications of
a decrease in TBT environmental levels appear to be reflected in the
1990 data. However, it is only one data point for these sites. The data
from the 1991 collection will determine if the trend of decreasing
concentrations continues.

6-3

Preprint 6

Butyltin Concentrations on Oysters from the Gulf of Mexico
during 1989-1991

Bemardo Garcia-Romero, Terry L. Wade,
Gregory G. Salata, and James M. Brooks

Butyltin Concentrations in Oysters
from the Gulf of Me@deo during 1989-1991

Bernardo Garcia-Romero, Terry L. Wade. Gregory G. Salata
& James M. Brooks

Department of Oceanography, Texas A&M University,
Geochemical and Environmental Research Group,
833 Graham Road @ - -
College Station, Texas 77845, USA

ABSTRACT

Oyster samples from 53 Gu!f of Mexico coastal sites were collected
and analyzed for butyltins during 1989, 1990, and 1991. The

geometric mean tributy[tLn concentrations were 85, 30, and 43 ng

Snlg.for 1989,1990, and 1991, respectively. The tributyltin
concentrations are best represented by a log normal disiribution. A

decline of the butyltin concentrations at sites with relatively low

butyltin concentrations for 1989 compared to 1990 and 1991 was

observed, while at relatively high butyltin concentrations (>400 ng

Sn/g), there was almost no difference between 1989 and 1991 but

lower concentrations were present in 1990. Continued monitoring

is needed in order to determine if bubjltin contamination of the

coastal marine environment is decreasing in response to use

limitations.

INTRODUCTION

The presence of tributyltin and its degradation products in the
environment continues to be of environmental concern. Tributyltin (TBT)

anti-fouling paints are a solution to the costly problem of fouling organisms

6-5

which attach to the bottom of the hulls of boats and ships (Huggett et at.,
1992). Although an effective antifouling agent@ tributyltin was found to
adversely affect non-target organisms (Bushong et aL. 1987; Hall & Pinkney,

1985: Minchin et al., 1987; Short & Thrower, 1986; Thain, 1986;

Thompson et at., 1985: Alzieu, 1991). For example, commercially valuable

species were adversely affected in France (Alzieu, 1991). The presence of

TBT and its degradation products, dibutyltin (DBT) and monobutyltin (MBT),

in samples removed from input sources (Wade et al., 1988; 1991b) suggest

that environmental half-lives in the marine environment may be longer than

reported values (Lee et al., 1987; Olson & Brinckman, 1986; Seligman et

al.,1986a,b & 1988a). After the use of TBT-based paints was limited in

countries such as France, England, and the United States, the concentration
of organotins in water and oysters was shown to decline (Shor-t & Sharp,
1989: Wade et al., 1991b; Alzieu. 1991: Page & Widdows, 1991; VaMirs et

al., 1991: Waite et al., 1991). In the United States, however continuous M

monitoring is needed in order to provide information on the long term

response of butyltin concentrations in the marine environment to these

regulations.

OYS ters are excellent sentinels of TBT contamination. Bivalves have

been used in uptake and depuration studies (Laughlin, et al., 1986: Langston

& Burt, 1991; Sericano et al., in press; Alzieu et al., 1991; Ritsema et al.,

1991: Salazar & Salazar, 1991) and to determine temporal and spatial
variations of butyltin concentrations (Short & Sharp, 1989; Wade et al.,

1988; Page & Widdows, 1991). These studies indicated that oysters
integrate bioavailable TBT with equilibration rates on the order of weeks.
This indicates that continuous and carefully planned sampling should be

6-6

carried out. in order to determine trends in the variation of TBT

concentrations in the environment.

Tributyltin and its degradation products have been determined in

oysters from 53 sites in the Gulf of Mexico from 1989 to 1991. The overall

butyltin concentrations showed a decline from 1989 to 1990 (Wade et aL,

199 la,b). If this decline resulted from the implementation of the

Iimitations on the use of TBT in the United States by the Organotin Anti-

Fouling Paint Control Act of 1988 (OAPCA), a continuous decline would be
expected. The results are now available for 1991. This report compares
three years of data for the Gulf of Mexico to determine if there is a trend in

butyltin concentrations.

METHODS

Oyster (Crassostrea virginica) samples were collected at 73 different

sites along the Gulf of Mexico coast in the winter of 1989, 1990, and 1991.

Table I shows the geographic location of the sites sampled and the symbols

used to identify each site. Although known point sources of TBT such as
marines of'dr-y docks were avoided, some locations are closer to such TBT

sources. A complete description of field sampling and logistics has been

reported (GERG, 1991).

The same sampling and analytical procedures were used for all oyster

samples reported. A detailed description of these procedures has been
previously reported (Arade et at., 1988; Wade & Garcia-Romero, 1989).

Briefly, oyster tissues were homogenized, weighed, spiked with a surrogate

standard, extracted with 0.2% tropolone in methylene chloride, hexylated,

purified using Si/Al columns, and analyzed by gas chromatography w. ith a tin

6-7

selective flame photometric detector. Quality control consisted of duplicate
samples, procedural blanks, and spike blanks. Quadruplicate analysis of one
sample yielded the following means and standard deviations 395 � 14.5 ng
Sn/g for TBT: 74.5 � 5.80 for DBT: and 32.5 � 6.5 for MBT. Method
detection limit (MDL) on average for TBT and DBT was 5 ng Sn/g and for

MBT was 10 ng Sn/g.

RESULTS and DISCUSSION

Annual Variation of Butyltins at Individual sites

Oyster butyltin concentrations determined in 1989, 1990, and 1991
were compared. In order to simplify the presentation of 'data, the sites
sampled have been divided into three geographical zones: Florida, Louisiana-
Mississippi-Alabama (LA MS AL), and Texas. Only 730/6 of the sites reported

were sampled during all three years. In some instances, some sites were

not sampled because no oysters were available. Butyltin concentrations in
oysters are reported in ng Sn/g dry weight (Maguire, 1991). Sites with an

incomplete set of data are indicated with a star in Table I and Figures 1, 2,

and 3.

The concentration of total butyltins. in 1989, 1990, and .1991 ranged
from below the limit of detection (<5 ng Sn/g) to 1880 (TBKA), 850 (TBKA),

and 1300 ng Sn/g (BBMB), for 1989, 1990, and 1991, respectively. In

general the butyltin concentrations decreased from 1989 to 1990 and then

increased slightly between 1990 and 1991.

Tributyltin, the most toxic butyltin, was the predominant butyltin

found in oysters during the three-year sampling. Percentages of TBT

6-8

determined .. were 85�15% for all years. Near the limit of detection the
percentage of TBT is more variable- The high percentage of, TBT for C.
virginica agrees with other reports (Wade et aL, 1988; Uhler et aL, 1989).

Uhler et aL (1989) reported that bivalves have approximately constant ratios
of TBT/DBT. The TBT percentages observed are the result of the uptake of

TBT and DBT from the water column (Lee et aL, 1987; Olson & Brinckman.

1986; Seligman et aL, 1986a: 1988)-, TBT degradation to DBT by oysters
(Lee, 1985); and different rates of depuration for TBT, DBT, and MBT (Lee.

1991). There is no evident relationship between the TBT concentration and
the percentage of TBT present in the oysters for this study. Therefore. the
fluctuation of percentage TBT around 85% is probably the result of a
dynamic equilibrium between uptake, metabolism, and depuration.

The TBT concentrations determined for each site during 1989. 1990,

and 1991 are shown in Figure 1. Sites are shown in e
. g ographical order
from Texas to Florida. Tiibutyltin concentrations ranged from <5 ng Sn/g to
1450 (TBKA), 770 (BBMB), and 1160 ng Sn/g (BBMB) in 1989, 1990, and

199 1. respectively. TBT concentrations increased monotonically at some

sites from 1989 to 1991, while at others sites concentrations decreased

monotonically. For example. oyster TBT concentrations increased from
1989 to 1991 at CLLC, BBMB and GBTD (Figure 1). Decreasing TBT

concentrations from 1989 to 1991 were observed for oysters from PBPH,

SAWB, TBCB, MBLR, and MBEM (Figure 1). Concentrations of TBT were the

same at TBOT and GBCR during all three years. In general, higher

concentrations of TBT were determined in Florida sites than in Texas,

Louisiana, Mississippi, or Alabama sites. TBT was below the detection limit
at I of 53 sites in 1989 and at 10 and I I sites during 1990 and 1991,

6-9

respectively, Although the concentrations were low, butyltins were detected
in oysters from every site sampled. in at least one sampling year.
Dibutyltin concentrations determined in oysters during 1989, 1990,
and 1991 are shown in Figure 2. Dibutyltin concentrations ranged from <5
ng Sn/g to 380 (TBKA), 160 (TBKA), and 200 ng Sn/g (TBKA), in 1989,

.1990, and 1991, respectively. Sites sampled in Florida had the highest DBT

concentrations. With the exception of five sites (CBJB, TBK& CBFM, BBMB,

and BRFS), annual variation of DBT concentrations did not mimic the annual

variation of TBT concentrations. Ship and boating activity have been cited as
potential factors that may affect DBT fluctuations (Short & Sharp, 1989;

Uhler et al., 1989). Also, the commercial usage of DBT as a stabilizer for

plastics including PVC pipes may be another important source of input to
the marine environment and may result in DBT fluctuations that do not
mimic TBT fluctuations Went et aL, 1991; Maguire, 1991). At this point it

is not possible to estimate the influence of the factors discussed above on

the DBT concentrations present in the oysters. Monotonic increases or
decreases of DBT were observed at specific sites during the three year
period. For example, Middle Bank (BBMB, @ Figure 1 and 2) showed not only
increasing concentrations of TBT during the three year sampling period but

also showed a steady increase of DBT in the same period. DBTwas detected
in 39, 38, and 33 out of the 53 sites sampled each of the three years. In

many instances DBT was not detected in any of the sampling years.

Regional MBT concentrations are shown in Figure 3. Since the MBT

concentrations are low, annual variations in MBT concentrations for each

site are large. The precision of MBT determination is also not as good as

that of TBT and DBT (Wade et al.,1988). Monobutyltin concentrations

ranged from <5 ng Sn/g to 145 (NBNB), 25 (CCIC), and 42 ng Sn/g (TBKA),

6-10

in 1989, 1990, and 1991, respectively. Generally, sites with high TBT

concentrations had high DABT concentrations- MBT was detected in 21, 4.
and 19 of the 53. sites during 1989, 1990, and 1991, respectively. During

all three years, A4BT was only detected at three sites in Florida (CBJB. TBEA,

and CBFM) and at one site in Texas (CCIC). The fact that MBT was found in

lower concentrations than DBT, and DBT was found in lower concentrations

than TBT is consistent with the fact that TBT is the ma or constituent of

antifouling paints while DBT and MBT are environmental breakdown
products of TBT. This may indicate that only a limited degradation of TBT

has occurred or that the more water soluble DBT and MBT are assimilated

by the oysters at a slower rate than TBT.

Annual Variation of Butyltins in the Gulf of Mexico
A graphic representation of the TBT data for the 53 "sites sampled in
1989, 1990, and 1991 is shown in Figure 4. The graph is a plot of 1989
concentrations vs 1990 and 1991. The 'Y' and "Y' scales are identical. If no

change occurs in the TBT concentration at a site, that data will be plotted

on the center line. Sites that fall below the line show a decrease while

points that rise above the line show an increase compared to 1989. Two

other lines also appear in Figure 4. These are the lines that form the
boundary of sites with a factor of 2 increase (top line) or decrease (bottom
line). Only 6 sites for 1990 and 8 for 1991 of the 53 sites plotted for each

year are above the center line. Therefore, "over 85% of the TBT

concentrations in 1990 and 1991 were less than the concentration

measured in oysters at that site in 1989. There were 30 sites (570/6) in

1990 and 20 sites (38%) in 1991 that had decreases of more than a factor of

6-11

two. There was only one site that had an increase of TBT concentration of

more than a factor of 2.

In order to detect temporal trends, the butyltin oyster concentrations
for the entire Gulf of Mex:ico from 1989 to 1991 are compared. Annual
variation of butyltins for the entire Gulf of Mex:ico are not readily apparent in

Figures 1. 2. or 3 where only annual concentrations at individual sites are

compared. Comparisons of arithmetic mean, geometric mean, and medians
(Table 11) for butyltin concentrations determined during 1989, 1990, and
1991 are based only on the 53 sites that were sampled all three years. All of
these parameters were calculated by assigning 5 ng Sn/g to all of those
samples with concentrations below the limit of detection. The percentage

of samples below the detection limit is listed in Table 11. The median and

geometric means are similar in, all cases, while the arithmetic mean is

always higher. The median or the geometric means appear to be the better
estimators of the central tendency of the data. Based on the median or the

geometric means, there was a decrease in TBT oyster concentrations when

1989 is compared to 1990 or 1991.
A complete view of butyltin concentrations for the whole Gulf of

Mexico for a given year can be achieved using either cumulative percentage

distribution or probability distribution curves (Mackay & Paterson, 1984;

O'Connor & Ehler, 1990: Jackson.et al., in press). Although both types of

curves may describe a distribution of butyltin concentration for each year.

probability distribution curves were chosen because they are more easily

compared. Use of this type of curve assumes that the-log of the

concentration produces a normal distribution. Log normal distributions have

already been reported for environmental data obtained in the NOAA National

6-12

Status and Trends Mussel Watch Program (O'Connor & Ehler, 1990;

Jackson et aL, in press).

TBT log distribution curves are shown in Figure 5 for 1989, 1990, and
1991. These curves were obtained by using the following equation (Milton

& Arnold, 1986):
f(X) = IS [SQR(2n)])-1 EXp - [1/2 [Ix - M/S 12 1 (1)

where f(x) is the distribution probability of the log butyltin concentration, s
is the standard deviation, SQR is the square root, x is the log of the butyltin
concentration, and X is the geometrical mean. Then each Ax) was divided
by the sum of the Ax) as shown by equation (2)
NX)i = f(X)i / E f(X)i (2)

in such a way that
E f wi (3)

TBT concentrations curves from 1989, 1990, and (Figure 5) are
Gaussian with some degree of skewness. DBT had a log normal distribution

only in 1989 and 1990, while MBT does not follow a log normal distribution
for any of the years. the geometric mean concentrations are indicated by
solid lines for 1989, dotted for 1990, and dashed for 1991 (Figure 5) and

are also reported in Table Ila. The TBT. DBT and MBT concentrations for

plus and minus one standard deviation from the geometric mean are listed
in Table IIb. Probability distribution curves of TBT in oysters from the Gulf

of Mexico provide information about annual variations at low, medium, and

high ranges of concentration. Although the standard deviations quantify the

spread of a data set, they provide no information about how-low or high
concentrations changed with time. TBT concentrations decreased from

1989 to 1990 at all concentrations; while TBT concentration decreased

from 1989 to 1991 at. low and medium concentrations, but were similar at

6-13

high concentrations (Figure 5). This decrease may be the result of the TBT
regulation of 1988 and/or development and use of lower release rate TBT
paint formulations. Initial TBT regulations probably resulted in a marked
reduction in private boat owners painting their own vessels. The fact that
newer TBT containing paints are rated to be good for up to 5 years and '113T

was not banned but its use limited probably leads to decreased TBT inputs.

This may have resulted in the observed decreases in TBT concentrations in
1990 and 1991. The decrease observed at high concentrations from 1989
to 1990 but not in 1991 may be due to the naturally higher variation of TBT
concentrations near input areas (Seligman et aL, 1988). Therefore, TBT
lower concentrations ranges may have decreased as a consequence of TBT
regulations or changes in TBT-based paint formulations, but the effect are

not as apparent at sites with high TBT concentrations.. Distribution curves

for DBT and MBT concentrations did not follow a log normal distribution but

also show annual variations. This may be due to the high percentage of

values below the MDL (Table 11).

CONCLUSION

Oysters are valuable biomonitors for butyltins. The percentage of '!BT

present with respect to the total butyltins oscillated around 85% during the
three years sampled. There was a decrease of the butyltin concentration

from 1989 to 1990 or 1991. However at high concentrations there was

little difference between 1989 and 1991. Environmental response to the

TBT regulation in 1988 is not yet apparent. The decline between 1989 and

1990. 1991 may have resulted from previous changes in antifouling paint
formulation with lower TBT release rates or suspension of painting activities

6-14

by individual boat owners after 1988. Because the newer TBT paints were
formulated to last 5 years or more, there are many boats still in use that
were painted with TBT containing paints before the ban. Consequently,

continuous monitoring is necessary to determine trends in butyltin

contamination of the marine environment.

ACKNOWLEDGMENTS

This Research was supported by the National Oceanic and

Atmospheric Administration Grant Number 50-DGNC-5-00262 (National

Status and Trends Mussel Watch Program).

6-15

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01
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6-18

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6-19

Table Ila.. Arithmetic, geometric means and medians (ng SnLgL
Numbers in parenthesis indicate percentage of samples below MDL.

TBT DBT MBT
1989 OF
Mean
Arith. 176 32 13
Geom. 85 14 8
Median 77(2%) 12(26%) 5(600/6)

1990
Mean
Arith. 96 17 6
Geom. 30 8 6
Median 24(17%) 5(72%) 5(900/0)

1991
Mean
Arith. 150 25 8
Geom. 43 13 6
Median 42(17%) 8(40%) 5(660/6)

TableIlb. Geometricmean. plus or minus one standard deviation of
the log butyltin. concentrations (ng Sn/g).

TBT DBT MBT
1989
Plus 293 44 18
Minus 25 5 3

199-0;: .
Plus 141 21 8
Minus 6 3 4

1991
Plus 233 37 10
Minus 8 4 4

6-20

Table 1. Sampling locations and site designators.
Desig. Site Location TEXAS Latitude Longitude
LMSB South Bay Lower Laguna Madre .260 02.58' 970 10.49'
LMAC* Arroyo Colorado Laguna Madre 26 16.80 97 17.30
CCBH* Boat Harbor Corpus Christi 27 50.00 97 23.00
CCNB* Nueces Bay Corpus Christi 27 51.70 97 21.00
CCIC Ingleside Cove Corpus Christi 27 50.30 97 14.25
ABLR Long Reef Aransas Bay 28 03.30 96 57.50
CBCR* Copano Reef Copano Bay 28 08.20 97 07.58
MBAR Ayres Reef Mesquite Bay 28 10.30 96 49.70
SAPP* Panther Pt. Reef San Antonio Bay 28 13.20 96 43.00
SAMP* Mosquito Point San Antonio Bay 28 19.00 96 42,20
ESSP* South Pass Reef Espiritu Santo Bay 28 17.83 96 37.50
ESBD* Bill Days Reef Espiritu Santo Bay 28 25.00 96 27.00
MBGP* Gallinipper Pt. Matagorda Bay 28 35.00 96 34.00
MBLR Lavac River Mouth Matagorda Bay 28 39.30 96 35.00
MBCB* Carancahua Bay Matagorda Bay 28 40-00 96 23.20
MBTP Tres Palacios Bay Matagorda Bay 28 39-00 96 15.50
MBEM East Matagorda Matagorda Bay 28 42.30 95 53.00
BRCL* Cedar Lakes Brazos River 28 51.50 95 27.90
BRFS Freeport River Brazos River 28 55.00 95 20.50
GBCR Confederate Reef Galveston Bay 29 15.75 94 50.50
GBOB Offatts Bayou Galveston Bay 29 16.70 94 50.70
GBTD Todd's Dump Galveston Bay 29 30.10 94 54.00
GBYC Yacht Club Galveston Bay 29 37.00 94 59.50
GBSC* Ship Channel Galveston Bay 29 42.50 94 59.50
GBHR Hanna Reef Galveston Bay 29 29.50 94 42.50
SLBB "Blue Buck Point Sabine Lake 29 48.00 93 54.42

*Sites that were not sampled consecutively from 1989 to 1991.

Table 1. Continuation.
Desig. Site Location LOUISIANA Latitude Longitude
CI,Sj St. Johns Island Qalcasieu Lake 290 50.00' 930 32.00'
CLLC Lake Charles Calcasieu Lake 30 03.50 93 17.50
JHJH Joseph Harbor Bayou Joseph Harbor Bayou 29 37.75 92 45.75
VBSP Southwest Pass Vermillion Bay 29 34.70 92 04.00
ABOB Oyster Bayou Atchafalaya Bay 29 13.00 91 08.00
CLCL Caillou Lake Caillou Lake 29 15.25 90 55.50
TBLB Lake Barre Terrebonne Bay 29 15.00 90 36.00
TBLF Lake Felicity Terrebonne Bay 29 16.00 90 24.50
BBSD Bayou St. Denis Barataria Bay 29 24.10 89 59.80
BBMB Middle Bank Barataria Bay 29 17.20 89 56.60
MRTP Tiger Pass Mississippi River 29 08.69 89 25.67
MRPL* Pass a Loutre Mississippi River 29 04.30 89 04.60
BSSI Sable Island Breton Sound 29 24.70 89 28.70
BSBG Bay Garderne Breton Sound 29 35.87 89 38.50
LBMP Malheureux Point Lake Borgne 29 52.30 89 40.70
LPGO* Gulf Outlet Lake Ponchartrain 30 02.20 89 03.00
MISSISSIPPI
MSPC Pass Christian Mississippi Sound 30 19.75 89 19.58
MSBB Bilo)d Bay Mississippi Sound 30 23.38 88 15.42
MSPB Pascagoula Bay Mississippi Sound 30 21.05 88 37.00
ALABAMA
MBCP Cedar Point Reef Mobile Bay 30 19.40 88 07.30
MBHI Harbor Island Mobile Bay 30 33.59 88 02.80
MBDR* Dog River Mobile Bay 30 35.50 88 02.72

*Sites that were not sampled consecutively from 1989 to 199 1.

Table 1. -Continuation.
Desig. Site Location FLORIDA Latitude Longitude
.50'
PBPH Public Harbor. Pensacola Bay 300 34.80' 870 11
PBIB* Indian Bayou Pensacola Bay .30 30.83 87 04.00
PBSP* Sabine Point Pensacola Bay 30 20.80 87 08.10
CBJB Joes Bayou Choctawhatchee Bay 30 24.70 86 29.55
CBSP Shirk Point Choctawhatchee Bay 30 28.95 86 28, 60
CBSR Off Santa Rosa Choctawhatchee Bay 30 23.50 86 10.60
PCLO Little Oyster Bay Panama City 30 15.00 85 40.87
PCMP* Municipal Pier Panama City 30 08.20 85 37.50
SAWB Watson Bayou St. Andrew Bay 30 08.50 85 37.58
APDB Dry Bar Apalachicola Bay 29 41.50 85 05.00
APCP Cat Point Bar Apalachicola Bay 29 43-00 84 52,50
AESP Spring Creek Apalachee Bay 30 30.50 84 19.38
CKBP Black Point Cedar Key 29 10.25 83 03.00
TBNP Navarez Park Tampa Bay 27 48.30 82 45.28
TBMK Mullet Key Bayou Tampa Bay 27 37.17 82 43.62
TBPB Papys Bayou Tampa Bay 27 50.72 82 36.75
TBOT Old Tampa Bay Tampa Bay 28 01.48 82 37.95
TBKA K. Airport Tampa Bay 27 54.46 82 27.29
TBCB Cockroach Bay Tampa Bay 28 40.55 82 30.56
CBBI Bird Island Charlotte Harbor 26 31.00 82 02.60
CBFM Fort Meyers Charlotte Harbor 26 38.64 81 52.48
NBNB Naples Bay Naples Bay 26 00-00 81 32.00
Henderson Creek Rookery Bay 26 01.83 81 43.75
RBHC* 25 54.27 81 30.60
EVFU Faka Union Bay Everglades
BHKF*

*Sites that @vere not sampl ed consecutively from 1989 t o 1991.

Table Ila. Arithmetic, geometric means and medians (ng Sn/g).

TBT DBT MBT
IL989
Mean
Arith. 176 32 13
Geom. 85 14 8
Median 77 12 5

1990
Mean
Arith. 96 17 6
Geom. 30 8 6
Median 24 5 5

1991
Mean
Arith. 150 25 8
Geom. 43 13 6
Median 42 8 5

Table Ilb. Geometric mean plus or minus one standard deviation of
the log butyltin concentrations (ng Sn/g).

TBT DBT Mar
1989
Plus 293 44 18
Minus 25 5 3

1990
Plus 141 21 8
Minus 6 3 4

1991
Plus 233 37 10
Minus 8 4 4

6-24

0) co
0 0 AD

P13PH
LMSB CLSJ 'PI31D
'LMAC CLLC PBSP
*CCBH JHJH
CBJB
-CCNB
VBSP
x 0 cclo CBSP
C)
ABOB CBSR
rD 0 (n W ABLE
rt 10 PCLO
m ::7- ICBC8 CLCL
0 ., H. m IPCMP
w (, 0 MBAS TGL8
V) D)

SAWB
0 'SAPP T13LF
0 ta. APDO
u) Pi 11, 'SAMP
m (n 0 (A o8so
n U) C? IESSP APCP
0 BBMB
0 AESP
0 'Esso
" r- IMBGP MRTP CKBP
M rt
z M H. TUNP
in.0 0 MBLR 'MRPL
H, :j TBMK
n 4 IMBCB 13SSI
w H. 0 TBPB
rt P-1 MSTP BSl3G
(D 00 TBOT
H. r? MBEM
rt :j LOW
.13 TSKA
0 0 V SOL
W 0 C: 'LPGO, LA TBCB
rD rt BRFS
,--,I MSPO caal
I., GBCR
rt 0 H. GBOB Me CBFM
(D El @3
U) rt 0 GBTO MSPB ms NBNB
rt 0 ......................... ..................................... WHO
:1, :j Gayc
w n MBCP EVFU
0 (D GBSC
C: :3 MBHI
rt BHKF
(D rl GBHR
ri co IMBDR AL,
(D 0 rt, SL89
:1 0 -MS-AL
0 @3 TEXAS LA
...........

01
LMS13 CLSJ P13PH
'LMAC CLLC IPBIB
IcceH IPBSP
'CCNB JHJ H CBJG
P. 0 VBSP
0 OQ ceic CBSP
ABLR ABOB CBSR
V -ceon CLCL PICLO
al 0 MBAR TBLB IPCMP
H co
0 :3 H
V) H. 'SAPP SAWB
(D 0 a. TBLF
-SAMP APDB
BBSD
'ESSP APOP
0 BBMB
(D 0 H.
E3 a. -4 'ESBD AESP
c m
rt rt r"t IMBGP MFITP CKBP
::71 :r H.
p m 0 MBLR *MRPL TBNP
f-t :5
'MBCB asst TMI3MK
MBTP T13PS
rl m BSBG
(D Q@
0 FJ- MSEM TBOT
t-h cr LBMP
0 r_ TRCL TIBKA
r? rr 'LPOO
BRFS LA TBCB
......................................................... ....................
0 H. rt GDCR MSPO ceal
E3 0 W.
0 ::s
GBOB MSBB CBFM

NBNB
0 0 GBTD MSPB ms
03 0 ............ .............. ................................. . ........
(A 0 GBYC WHO
rT (D macp
. :3
rt 'GBSC EVFU
0 pi MBHI
:3 M 0) GBHR *BHKF
Ch rT rT MBDR AL
(D w H. SLBB
n 0
.rt TEXAS LA-MS-AL

rTj I
8 0 8 tn
0
t7l LMSB
PBPH
CLSJ
*LMAC CLLC
'CCBH 'PBSP
0 'CCNB JHJH CI3JB
M 0 M VBSP
@j CLI 0 CCIC cesp
ABLR ABOO CBSR
e. 10 IcBcR CLCL PCLO
ri H.
0 MBAR TBL8 IPcmP
Oj
H 'SAPP SAWB
W W, TELF
0 CL 'SAMP APOB
ru P.
W IISSP BOSO APCP
Boma
0 H. --4 'ESOD AESP
60 M
'MBGP MRTP CKSP
rIr CI r?
v ::r H. MeLR *MIIPL TBNP
w (D 0
rt 0 0 wace BSSI' TBMK
C f: 0
(D MBTP T13Pb:
ri " MCI
(D 5 MEIEM TBOT
0 0 L8MP
" Z 'SRCL MKA
0 0 . . ..........
rr :3@ cr 'LPGO
(D r. BRFS LA TBCB
V) x r? GBCR MSPC CBBI
E3 0
,Z) 0 rt GBOB Me CBFM
GGTD MSPO Ms NBNB
a. 0
W n GBYC 115 .............................. ............................................... 'RBHC
Ch 0
rt z MBCP
r) 'GBk EVFU
(D MSHI
0 GBHR 'BHKF
rr 'MBDR AL A
ri SLBB
(D W 00
TEXAS LA-MS-AL
0
L

..............

.................

10000--

GULF COAST OYSTERS

19W
1000 1991

10011:

co 10
0 91i ENS

1 10 100 1000 10000

TBT (ng Sn/g) 1989

FIGURE 4. Tributyltin concentrations determined in 1989 versus the tributyltin concentrations
determined in 1990 and 1991. Points falling along the center line have equal
concentrations, colateral lines indicate a factor of two greater or lower than
the concentrations determined in 1989.

0.03
--Cl-- 1989
11111111101111111 1990
e@A
am*-- 1991
#A A
A
ASA
0.02-

001

0.00
1 10 100 10 00 10000
TBT (ng Sn/g)

FIGURE 5. Log normal distribution of tributyltin concentrations determined in oysters
in 1989, 1990, and 1991.
A

7.0 Biological Results

7.1 Condition Index/Shell Length /Condition Code

Condition code was rated as described in the Analytical Methods
volume. Higher numbers indicate oysters in better condition. Most
sites averaged 3 or 4. The highest median condition was 2 at five
sites, the lowest was 7 at three sites.

Condition index is an estimate of the relative health of oysters.
Healthy oysters, generally have more tissue dry weight compared to
the cavity volume of their shells. Condition index is calculated by
dividing the tissue dry weight of oysters by their shell. volumes. The
higher the number, the healthier the oyster.

Condition index varied from 0.062 gm dry wt - mI-1 at Brazos
River, Freeport Surfside (BRFS) to 0. 197 gm dry wt * ml- 1 at. Aransas
Bay,. Long Reef (ABLIZ). Condition index usually varies with the
reproductive cycle being higher during the period of gonadal
development and decreasing after spawning. Lower condition indices
have also been associated with pollutant stress. Condition index varied
concordantly from year to year among the Gulf sites. Condition index
was relatively high in years 1987 and 1988 and was low in 1986 and
1989. P. marinus infection intensity followed the exact opposite
trend. In 1990, condition index fell between these two extremes, as
did P. marinus infection intensity. Length decreased steadily from
1986 to 1989 and declined at lower latitude sites. Length was
unusually low in the panhandle of Florida relative to that expected
from other sites in the equivalent latitude range. Both trends
continued in 1990.

7.2 Gonadal/Somatic Index

Assessment of the physiological state of an oyster population
requires an analysis of the state of gonadal development. Typically,
oysters are undifferentiated in the winter, the gonads begin to develop
in early spring, and spawning occurs during late spring through early
fall. Most Gulf coast oysters spawn at least twice during that time
period. The state of gonadal development is determined by
observation of histological sections after staining. Oysters are sexed
and assigned to a semiquantitative state of reproductive development
as detailed in the Analytical Methods volume. Four stages of gonadal
state were used as detailed below. This scale has been further refined
as described in the Analytical Methods volume last year.

7-1

Stage 1. Undifferentiated/Mid Development Gonad:

Little or no gonadal tissue visible. Sex cannot be
determined.

Early Development: Follicles beginning to expand, no ripe
gametes visible. Primary and secondary gametocytes
present. Sex can be determined.

Mid-Development: Follicles expanded and beginning to
coalesce; no mature gametes present.

Stage 2. Undifferentiated /Mid -Development Gonad:

Late Development: Follicles greatly expanded, coalesced, r
but considerable connective tissue remaining; gametocytes
and some mature gametes present.

Stage 3. Fully Developed Gonad:

Follicles packed with mature gametes. Most gametes
mature; little connective tissue remaining within the
gonadal tissue.

Stage 4. Spawning/Spent gonad:

Spawning: Gametes visible in gonoducts.

Spawned: Reduced number of gametes; some mature
gametes still remaining; evidence of renewed reproductive
activity.

Spent: Few or no gametes visible, gonadal tissue atrophying.

Gonadal index is calculated as the mean stage obtained from a
minimum of 15 individuals per site. This gonadal index in oysters is a
qualitative estimation of the state of reproductive development. It
does not allow direct comparison (or normalization) of other data (e.g.
hydrocarbon content) with reproductive development because a
histological determination of reproductive state does not measure the
total volume of gonadal material consistently present at all stages and
all individual sizes.

Most collected oysters that could be assigned to a sex were
female, as expected. Large oysters are usually female. and large oysters
were preferentially collected. There has been no consistent
relationship among the years in sex ratio. Sites with more males in
any one year were not necessarily sites with more males in another

7-2

year. For example, the site (BBSD) which had the most males (6) in
1989 had few males in 1990.

In Year 1, except extreme south Texas, nearly all oysters
collected west and south of Joseph Harbor, Louisiana and east and
south of Lake Borgne were in the earliest stages of gonadal
development. Little gonadal tissue was present. Oysters from sites
between Joseph Harbor and Lake Borgne were collected later in the
season. Nearly all individuals were in late stages of development or
ready to spawn. A few individuals had already spawned and appeared
to be developing new gonadal material again. In Years 2 and 3,
collections were made earlier. Louisiana oysters were for the most
part in early development. Sites in Texas and South Florida, however,
provided some oysters in late development, full development or
spawning. Sites having the most oysters in late development or ready
to spawn were in the Laguna Madre and Corpus Christi Bay areas of
Texas (>20%), and in Tampa Bay and Charlotte Harbor, where 50% or
more of the oysters were ready to spawn at some sites. Elsewhere in
Texas and south Florida values averaged below 15%. In Year 4, the
majority of oysters in early development were in Florida, from the
Brazos River South in Texas, and in the Mississippi River Delta area.
Sites having >50% of the oysters in late development or ready to.
spawn occurred mostly in South Texas (Corpus Christi Bay, Matagorda
Bay, and Laguna Madre Bay). along with some isolated sites in the
Mississippi River Delta (Barataria Bay) and the Tampa Bay area. In Year
5, South Texas and South Florida again accounted for most of the sites.
In Year 6, sites were generally higher around the Gulf, particularly in
the northern Gulf- many more reproductive/advanced individuals
were collected.

7.3 Lona-Term Changes

We reported the Gulf-wide distributions of certain parameters in
Preprint 7. In the following figures (7.1-7.14), we have extended this
analysis to six years. The statistical approach is detailed in Preprint 7.
In the first set of figures (7.1-7.7), we asked the question "How far
apart must bays be before the similarity in yearly changes in certain
parameters no longer exists?" We expect nearby bays to behave
similarly. We expect bays far apart to behave differently. The evidence
present so far suggests that large-scale climatic shifts dictate year-to-
year changes so that bays within 500 to 10OOkm of each other may
behave similarly.

Figure 7.1 shows the situation for gonadal stage. The y-axis is a
plot of the log of the p value. Any value below -I indicates a significant
similarity. In this graph, nearby bays are very similar (PL .000001 at
200km). Similarity disappears at about 10OOkm (IogP >-1 at
10OOkm). Accordingly, year-to-year changes in the frequency of
reproductive /active individuals were similar in bays up to 10OOkm

7-3

apart. On the average, bays within 1000kni rose or fell in unison in
the biological attribute from one year to the next. This would be
expected if climatic conditions, such as the average winter
temperature, controlled by climate cycles, like El Nifto, were
responsible for year to year shifts.

The following is clear from these graphs: P. marinus prevalence
and sex ratio were affected minimally by local conditions. Nearby bays
were as dissimilar as bays far apart. Like gonadal stage, condition
index, length, and P. marinus infection intensity all showed scales of
similarity in the range of 10OOkm. So, the primary indicators of
population health were impacted significantly by climate change on a
scale of 1000km over the six years of study.

In the second set of graphs (Fig. 7.8-7.14), we look at groups of
ten adjacent bays. This analysis searches. for the pattern of regional
similarity around the Gulf of Mexico. Preprint 7 has a detailed
description of the method. In this analysis, values above the. dark line
indicate significant similarity. The biological attributes show the
following pattern: Length, P. marinus prevalence, gonadal stage, and
sex show similarities in the southern Gulf, east or west or both. This
similarity suggests a subtropical climate control like El Nifto.
Condition index shows the opposite trend: similarity in the northern
Gulf of Mexico from approximately Galveston Bay to Tampa Bay. P.
marinus infection intensity reacts similar throughout the, Gulf. These
latter two indicate that temperate weather patterns are also
important. Overall, the data clearly show that weather patterns exert
control on oyster population health throughout the Gulf of Mexico and
can account for the assessed pattern of year-to-year changes in
population health over the six years of the study.

7-4

Bays
Log p Values for Stage 31

I A, I f 1 -
0

A ..... . ....... ........
it .................. ......
.... ........ ....... ....... ........ ........ t........ ................. ........

................. ........ ........
................. ........ ........ ....... ....... ........ ..... . ......
- - --------
2

..............................
................. I .... ....
........ ... . ........
. ........ ........ ........ ...... . .....
-3

........ .... ........ ........ ...................... . . ........
....... ........ ................. ................. .... . ........ ... . .......
................. . .......

....... . ........ ........ ........ ........ ....... ....... ........ . . .
-5

0 500 1000 1,500 2000 2500
Distance, KM

j@og p Values for Mean Infection Intensity
31 Bays

>1
0
U)

........ ......
.......... ....... ....... . ....... ..... ........ ........ .................. ........ ........ . . ...... .................. ....... ........ ........ -

4-j

........ ............ ... ....... ....... 4
................ ........ ........ ........ ........ ........

4 4....... .......
........ ....... ....... ........ ........ ........ ....... ........ ........

..... . ..... ........ ....... ....... ........ ........ ........ ........ ........ . ....... . ....... . ........
. ......... ........ ................. ........ ....... I ..
-4

.. ........ ........ ........ ........
................. ........ ........ ........ ........ . .......
!........ . ....... 1. .....
CZ -5

............. ...... . ..... I. ............... . ................. ........ j................. ....... . ........ ........ ........ ........ ........ ........ . ....... ........
6

. ....... ....... ........ ....... I ....... ........
........ ........ ........ ..... ........ ....... ....... ........ ........ ........
7

8
0 500 10 00- 1500 2000 2500
Distance, KM

6,iog p Values for Median Infection Intensity
(D 31 Bays
>1

............... . ...... . ................ ....... . ............... .......
0 ......

........ . ........ ........ ........ ...... ........ ....
. ......... ..... ........ ........ ... . ....... . ........ ....... ...

....... ................ ......... ....... ....... ....... ....... .
T T ........ ...... ....... .......
........ ...... . .......
-2
+
Cz

.................. ........ . .. . ....... ................. ........ ........ ...................
-3

................. ........ ....... .......
...............
0-4

----------

0 500 1000 1500 2000 2500
Distance, KM

Olt%
z Log p Values for Length 31 Bays
CD
-4
;PI

................ :. ....... ....... ........ .................. . ....... ....... ..... + ........... .................
0

...... . ..... I *....... ........I........ ........ 4 ...... ........ ........ ....... ........
............................ ...... ...... ................. ..............
+

................. . ....... . ....... ....... :. ........ ..................... ....... . . .
-2

................. . ....... . ....... . ....... ........ ........ ::.6
.. . ....... . ....... . ........
-3 ........ .......

7-7

0 500 1000. 15 0 0 2000 2500
Distance, KM

mm m

Log p Values for' Co,ndition Index 31 Bays

+
............... . ....... .........
. ................................... ................. ....... ....... I ..... ........ ....... ........ .......

. . . . . . . . . . .
. ........ ........ ........ ........ ........ ....... ......
........ ........ ........ ........ ........ . ........ ........ ........
-2

............ . ....... . ....... . ........
....... .. ....... ...... ..... .... ........ ........ ........ ........
I. . .............................
..... .....
... ........ ... .. ..

.... ................. ........ ........ ........ . ....... . ....... . ........
........ . .... .. ....... ........ .......................... ........ ........ ........ .
-4

........ .... ....... ....... .... . ........ ..... ........ ........ ........ ........ ....... ........ ........-
...... .......

-6
0 500 1000 1500 2000 2500
Distance, KM

Log p Values for Sex 31 Bays

0.5

.......... ....... ........
........ ..... ........ ........ ... ............ ...... .... . ........ ........ ......... i.......... ........
.....................
0
+
........ . .....
........ ....... ..... . ..... ...... ..... . ......... ........ . ........ ..... ........ ....... ........ ........ -
-0.5

...... ...... ..... It ....... 4........ ..... . ..... ... ....... ...... . ..... ........ ....... . ....... 4.......

. ......... ....... .... ... ................. ........ ..... ......... . ... ....... . .... ...
... .......
........ ........ ...... ........ I. . .. ....... ........ ........ ..... . ....... ..... . ........ ....... .
.5 ........ .....

.............................. ................. ....... I I....... . ........
.......... ............. ........ ........................... ........
...... ....... ...
-2

................. ...... .......................... ........ ........ ........ ....... ........ ........ ........
........ ........ .......
-2.5

...... ........ ....... ........ i........ I........i........ ..... ......... ....... ....... ........ -
...... ..... ........ ....... ........ ........ ........

-3.5
0 500 1500 2000 2500
Distance, KM

M Mao M MMM M M m M

Log p Values for Prevalence 31 Bays

0.5

........ ....... ........ ......... ...... L....... I....... .................

................... . ...... ........ ........ ....... ........ ........ ........ ........ ........: ....... ........
0.5
....... ....... ........

....... .......................... ........ . ....... ........ . ....... ........ ........
....... ..... ..... ...... ........ ........ .....
. . ....... .

Cm ...... ........ ..... ................. ........
......... ........ .... ........ .................. ........ ........ ....... ........ ......... .......
2 . ........ ...... . .....

t ........ 1@ .......4
-2.5

-3
500 1000 1500 2000 2500
Distance,. KM

Condition Index 31 Bays

0.6

................... ..... ........................... ...... ....... . . . ... + ... ; ..... ........... ..... . .....
0.5 . ..... I I I ..........

...... . ..... ..... ..... . ..... ...... ....
0.4
C)

..... . .....
..... ..... ............. ..... ........... .... . ..... ..... ............ ...... ..... . ..... .....
............. ..... . ..... ..... . .....
4-a 0.3
+

. ...... ..... ..... ... ..... . .....
...... ........................ . ............. .... A ..... ........... . ...... ..... I .. ..... I
0.2

..... ..... ...... ............ ...... ...... ..... ..... .. ...... ..... . .....
0.1

I I A I I I I I

0 5 10 15 2 0 2 5 30 35
Steps Around Gulf -

P. marinus Median Infection Intensity

0.5

.......... ......... .......
.................. ...... ...... ..... ..... . ........ . ..... ..... . ..... ........... ..... . ..... ...........
0.4

Q)
. .... ..... .... ..... ...... ..... ..... ..... ........... ....... ......................... ..... ..... .. ...... . .....
..... . .. . .......................
0.3

........ .....
..... I ...... ............ ..................
........... . ...... ......I...... ..... ...... ........... ............ . ............ ..... .......
lli!:j
0.2
E

0.1 ..... .... ..... ..... ..... ..... ... ..... ..... .... ..... ...... ..... ..... ..... ... . .....

I F-71
0
0 5 1 0 1 5 20 25 30 35
Steps Around Gulf

P. marinus Mean Infect-ion Intensity

0.55

..... . .... ........................... . .... . ..... ..... . ....
. ........... 1. ..... ..... .. ..... ........... ...... ............
0.5
Hill

........... ..... ..... ..... ...... ...... ..... I ..... ..... 1. ..... .....
.... .... .. ..... ... ... ..... ....... ..... ..... ..... .....
o.45
+ +

..... ..... ......
... ..... .....
0.4 ...... ......

........... ..... . .....
...... ........... . .... ..... . ..... ..... ..... ..... . .....
0.35 ..... ............

C'D
..... ..... ..... ..... ..... .. . ... .....
CO 4-
0.3 1 :1: : : t

..... ..... ... . .. ........... ......
...... ..... .. .... ... ..... ..... ........... .
0.25 .....

. ..... . .... . ... ...... ..... I .... ..... ..... .....
........... .
0.2 . . .

0.15
0 5 10 15 20 25 30 35
Steps Around Gulf

Length 31 13 ays

0.45

..... ..... .....
..... ..... ..... ...... .....
T T .....
............. ..... ... ......
0.4

....... .... ..... ..... ..... ...........
... ..... ... ..... ...... ..... . ..... ..... . ....I
0.35

..... ......
..... ..... .....
0.3

U1
............. .......... ........... .. ..... ..... .....
.....+ ..... ..... .....
. ..... ...... ...... ..... I ..... .......... I ..... ..... I .....
0.25

...... ........ .......... . ..... . ..... . ............ ...... ..... ...... ........... ..... . ..... ...... ............ ...... ..... . .....
0.2

... .... ............ ..... ..... ..... ..... ...I.... .... .... ....
0.15 1w .......... 1; 1

.............. ...... ..... ..... ..... ..... I ..... . ..... ..... ..... ..... . ...........
0.1
+
0.05
0 5 10 15 20 25 30 35
Steps Around Gulf

P. marinus Prevalence 31 Bays

0.4

............ ...... ...... ............ ...... ..... ...... ................. ..... ......
0.35

............ ............ .......... ...... ............ ..... . .....
..... . ..... ..... . .....
0.3

i.....4 1 -S .....I......I.....3..... 4. .1 ..... I i.....i.....i
..... ..... ... ..... .....
P's 0.25
+

... . ... ... .....
..... . ..... .... ..... .... .. . ..... ...................... ..... .... .... .... .. I .. I . .... ..... .
0.2 7 V

i t i i i

..... ..... ..... . ............ ............. ............ ................ ..... .....I............ ............ . ..... ..... . ..... ..... . ..... ........... ..... ..... ......-

jilf:
:i.%
U) *+
.................. ...... ..... I ... +..: . .....i. ..... ...........
0.1

......... .....
..... . ...... ..... ..... . . ......... ..... ..... ..... ..... ..... .... .....
0.05
+

I I i I-T-T-r- I I I I I F-r I I I I I I I I I
0
0 5 .10 15 20 25 30 35
Steps Around Gulf

stage 31 Bays

I I f i
0.6 -

............ ..... ..... ..... .
............. ............
0.5 . ..... ...... L* ... I ...... . ... . ........ ............. I .....

0 ...... ...... ...... ..... ..... ...... ........... ...... ......
64
cz

..... ..... .... . ..... ...... .....
..... .....
..... I .....
... ......... .....
..... . ..... ..... ..... ................. ..... . ..
0.3

.............. ..... ........................... ..... I ... . ..... ......... . .
0.2

............
0.1 . ..... ...... ...... ...... ..... ...... ..... ..... . ... ...

0
0 5 10 15 20 25 30 35
steps Around Gulf

Sex 31 Bays
CD

I tt
0.4
t ii it

t............ ............. ........... .................. ........... ..... . ..... ............ ..... ..... ..... ......
P@ 0.35 1 1 t i

..... . ........... . .....
..... ............ ...... ...... .....
.0.3 T i t

..... ..... .....
.... ..... ..... ...
..... .... ... ........ ..... .....
x 0.25 ...... ....

co
I...........:. ..... .... .....
A..... . ..... .................. ..... . ..... ....... I ....... . ..... .......................i..... ......
0.2
+ i
.0077=77 ..... ... .. .... ..... ....... ..... .... .....
0.15

..... .... ... ...... ........... ..... . .. .. ..... ..... ..... ..... . ..... ..... . .....
..... ..... ..... ..... .....
:11 it 1:::!!;I:

..... ..... . 1.
...... .....
............ ....... i ... ....... ..........I..... .A
0.05

0
0 5 10 15 20 25 30 35
Steps Around Gulf

Preprint 7

Spatial and Temporal Distributions of Contaminant Body
Burden and disease in Gulf of Mexico Oyster Populations:
The Role of Local and Large-Scale Climatic Controls

E.A. Wilson, E.N. Powell, T.L. Wade,
R.J. Taylor, B.J. Presley, and J.M. Brooks

HELCOLANDER MEERESUNTERSUCHUNGEN
Helgolander Meeresunters 46 (1992)

Spatial and temporal distributions of contaminant body
burden and disease in Gulf of Mexico oyster
populations: The role of local and large-scale climatic
controls
E.A. Wilson, E.N. Powell, T.L. Wade, R.J. Taylor, B.J. Presley &
J.M. Brooks
Department of Oceanography. Texas A&M University: College Station, TX 77843, USA

ABSTRACT: As part of NOAA's Status and Trends Program. oysters were sampled from 43 sites
throughtout the Gulf of Mexico from Brownsville, Texas, to the Florida Everglades from 1986 to 1989.
Oysters were analysed for body burden of a suite of metals and petroleum aromatic hydrocarbons
[PAIis]. the prevalence and intensity of the oyster pathogen, Perkinsus marinus, and condition
index. The contaminants tell into two groups based on the spatial distrubution of body burden
throughout the Gulf Arsenic, selenium, mercury and cadmium were characterized by clinal
reduction in similarity with distance reminiscent of that followed by mean monthly temperature and
precipitation. Zinc, copper, PAHs and silver showed no consistent georgraphic trend. Within local
regions, industrial and agricultural land use and P. marinus prevalence and infection intensity
frequently correlated with body burden.
Contaminants and biological attributes followed one of three temporal trends. Zinc, copper and
PAHs showed concordant shifts over 4 years throughout the eastern and southern Gulf, Mercury and
cadmium showed concordant shifts in the northwestern Gulf, Selenium, arsenic, length, condition
index and P. marinus prevalence and infection intensity showed concordant shifts throughout most
of or the entire Gulf. Concordant shifts suggest that climatic factors, the El Nino/Southern Oscillation
being one example, exert a strong influence on biological attributes and contaminant body burdens
in the Gulf. Correlative factors are those that probably affect or indicate the rate of tissue turnover
and the frequency of reproduction; namely, temperature, disease intensity, condition index and
length.

INTRODUCTION

Bivalve molluscs have frequently been used as indicator organisms in studies
monitoring levels of contaminants in the environment. These organisms are preferred
because of their ability to accumulate and concentrate both metal and organic contamin-
ants enabling them to serve as long-term integrators of their environment (Phillips,
1977a). However, many biological and environmental factors affect the rate and extent of
bioaccumulation. Biological factors including differential growth rate (Cunningham &
Tripp. 1975a; Boyden. 1977). reproductive stage (Cunningham & Tripp. 1975a; Frazier,
1975; et al., 1984). and general physiological condition, stress and disease
(Shuster & Pringle, 1969; Sindermann, 1983) affect incorporation and depuration rates.
Similarly, changes in environmental parameters such as salinity (Denton & Burdon-

7-21

James 1981; Wright & Zamuda, 1987), freshwater runoff (Windom & Smith, 1972; Phillips, 1976a; Zariugian & Cheer, 1976),
duration of exposure to contaminants (Shuster & Pringle, 1969, Scott & Lawerence. 1982). Temperature (Shuster & Pringle.1969;
Zaroogian & Cheer, 1976; Denton & Burden-Jones, 1981). resuspension of sediments (Uncles et al.
1988) and proximity to point sources (Farrington & Quinn, 1973: Ratkowsky et al., 1974;
Phillips. 1976b) can affect the bioavailability of environmental contaminants.
Although these local environmental and biological controls on variability in pollutant
body burden are important; they, themselves, may be affected by long-term, large-scale
phenomena such as climatic cycles. Such phenomena may override local controls and
impose large-scale, concordant oscillations in environmental and biological parameters.
Seasonal, climatic and other longterm cycles have been used in predicting harvests of
commercially important fish and shellfish, including oysters (Dow, 1977; Ulanowicz et al.,
1980l Allen & Turner, 1989), and have been implicated in the distribution and intensity of
oyster disease (Powell et al., in press). By imprinting themselves on the local environ-
ment, these long-term cycles may also alter the bioavailability of contaminants and
therefore, contaminant body burden. Accordingly, explaining spatial and temporal varia-
bility in contaminant body burden may require understanding both local and large-scale
environmental phenomena.
The NOAA Status and Trends Program ("Mussel Watch") is an environmental moitoring program
designed to monitor changes in environmental quality along the Atlantic,Pacific and Gulf coasts
of the United States by measuring levels of chemical contaminants in fish, bivalves, and sediments
and identifying biological responses to those contaminants. As part of the program, pollutant body
burdent of trace metals and polynuclear aromatic hydrocarbons (PAHs) were measured in oysters
(Crassostrea virginica) collected from sites along the Gulf of Mexico coast from Brownsville, Texas
to the Florida Everglades. The biological component of this study included determining the prevalence
and intensity of infection by teh endoparasitic protozoan Perkinsus marinus in these oyster populations.
Over four years (1986-1989), this program has produced the most extensive spatial and temporal data set
on contaminant body buden and disease prevalence and intensity available for natural oyster populations in
the Gulf of Mexico and has implicated the El Nino/Southern Oscillation cycle as an important factor
controlling large-scale tempral variability in oyster disease. The goal of this paper is to integrate the
biological and chemical data to determine: (1) the spatial and temporal distributions of contaminent body
burden as they compare to P. marinus in oyster populations: (2) the biological and environmental factors
important in determining these distributions and (3) the role of local and long-term controls on contaminant
body burdens.

MATERIALS AND METHODS

Sample collection

Oysters were collected from natural populations along the coast of the Gulf of Mexico during December
to February of each year from 1986 to 1989. In all, 75 sites were sampled; 43 sites were sampled in all
4 years. Forty oysters were collected at each three stations at each site: twenty for trace metal analysis and
twenty for biological and trace organic analysis. Temperature and salinity were recorded at the time of collection.

(JN)1852 (KL)150 (SW)Wilson

The maximum anterior-posterior length was measured for each oyster (Morales-Alama & Alama, 1989).
Displacement volume of the 20 oysters collected for the biological and trace organic analyses was determined
before and ager shucking. Each oyster was sampled for the presence and intensity of infection by Perkinsus
marinus (Ray, 1966). Prevalence of infection was calculated as: (the number of infected oysters/number of
oysters sampled). Infection intensity was ranked on the 0-to-5 point scale of Mackin (1962) as modified by
Craig et al. (1989). After the biological sample was removed, the remainder of the oyster tissue was placed
in precombusted jars, sealed with Teflon lids, weighed and frozen for trace organic analysis. Tissue dry
weight and displacement volume were used to calculate condition index=dry weight of tissue/internal volume of
shell cavity (Lawrence & Scott, 1982). The twenty oysters collected for trace metal analysis were scrubbed,
frozen in the shell and returned to the laboratory. Further sample preparation and the analytical techniques
employed for both trace organic and trace metal analyses were described in Brooks et al. (1989).

Statistical analysis

Data reduction

Within-site variability was typically low (Craig et al.,1989; Wilson et al.,1990), so the three stations
were combined for the following statistical analyses. Statistical analysis of the data was limited to the 43
sites sampled in each of the 4 years. Each site was assigned to one of the 26 bay systems as slightly modified
from Broutman & Leonard (1988) (Table 1)[see Craig et al. (1989) or Presley et al. (1990) for site maps, and
Wilson et al. (1990) and Powell et al. (in press) for further information on the sites]. Salinity and temperature
data for the sites are given in Brooks et al. (1989). Powell et al. ( in press) list mean P. marinus prevalences
and infectioin intensities for the bay groups. Mean values of condition index and length for the bay groups are
presented in Table 2.
Seven of the 13 metals analysed as part of the Status and Trends protocol were chosen for further
consideration: arsenic, cadmium, copper, silver, mercury, zinc and selenium. These metals were selected because
they generally were present in highest concentration among the metals measured and because they exhibited some of
the most dramatic differences in body burdens in populations around the Gulf of Mexico and among the 4 years of
the study. Wade et al. (1988) and Brooks et al. (1989) list the individual PAHs analyzed, but, for this study,
body burdens of the individual PAHs were summed and a total value was used for statistical analysis. Contaminant
data are presented as the geometrc mean of all sites included in each bay group (Tables 3,4,and 5).
Values for mean monthly precipitation and mean monthly temperature were obtained from NOAA (1985-1989). The
values used were averages of several stations around each bay system. Average monthly stream flow from gauged
streams -Rio Grande (IBWC, 1985-1989), the Mississippi and atchafalaya Rivers (Army Corps of Engineers, personal
communication) and the remaining gauged rivers and streams (USGS, 1985-1989)-and estimated freshwater runoff
(from precipitation data) for areas downstream of gauges, estimated from the total watershed area (NOAA, 1987),
were summed to estimate total freshwater input to each bay system. Land use around the bay systems, classified
as either industrial, agricultural or residential, was compoled from NOAA (1987).

7-23

Nil-
43 @md Ttvlid.-@ Nilv, ill 4 y,-tiN, 11 11w SIM1%,

Silt- :1,111w Localilm
I-alifilde Lungittide

T t, N
I Lagmic, Nladic. Sowh IMN- UNIS11 26 2.58 97 10.49
'i Corpus Chirisli Bay. Nueces Isay CCNB 27 51.70 97 21-00
Aransas Day. Long R(:(-( A13LR 28 3.30 96 57.50
wi Day. Collano lzi,@O CIICR 28 8.20 97 7.58
NIt.squite Bay. Avres 14-0 NIBAR 28 10.30 96 49.70
Nlaiagorda [lay. Gallinipper 13(fint J\.IBC;p 28 35.00 96 34.00
Matagorda Bav.Trei Palacios Bay NIRTP 28 39.00 96 15.50
East Matagorda Bay 113 E- M 28 42.30 95 :53.00
Galvesimi Bay. Yarlit Club R(tf.-[ GBYC 29 37.00 94 59.50
Galveslon flay. Twild's Dump R(:ef GBTD Z9 30.10 94 54.00
Galvesum Bay. Hanna CAMIZ 29 27.50 94 42.50
Galveston Bay. Conlederat(! 11'(@ef
C.RCR '29 15.75 94 50.50

9 Sabitie Lake. 111tic Buck Point SLI311 29 48.00 93 511.42

Lo u i s i a n a
10 Lake calrdsk-w. St. Johns Island CLSJ 29 50.00 93 23.00

11 Josepli I farbur Bayou 2ii 37.75 92 45.75

1*2 VC-1-million Bay.Soulhwesl Pass V13SIl 29 34.70 92 14.00
I Atchafalaya Ray. Ovstt-r Baymi AR013 29 13.00 91 8.00
Caillou Lak(- CLCL 29 15.25 90 55.50
14 Lal,(! Barre THLB 29 15.00 90 36.00
La k(- N-1 ic-i I y TIILF 129 16.00 90 24,5o
1.5 laralaria Bav. flavtsit St. I`X-tiis 1313sr.) 29 24.10 89 59.80
nara(aria flay. Middle saill, IIHNIB 29 17.20 89 56.50
18 BrOon Sound. Bay Gardvrne IISBG 29 35.87 89 38.50
Br.@:umn S(sund. Salille Island Bssl 19 24.70 89 28.70

19 Lake flobit UNIP 19 52.30 89 40.70

IN'll i s s i s s i p I I i
PJSSChJ-iSliIII N IS PC :to 19.75 199 19.58
,2 0 Bilo.Ni Bay NISIM 30 23.38 88 55. 4 2
I'd-waqutil'i flay NISI'll AO 21.05 88 17.0u

A I d It it tit it
I N.I.-hile BJj-, Ct-dal Voilit lwel N 11WI, 30 19.40 118 "I.3o

F I (I r i d a
2*2 114-11sactila flay. Indian BaVIIII Illsill 3o 30.83 87 4.00

CIIISR :10 213.50 W; I (I.(;()
CII.-clawlialchl-k- Bav. shirt, Poilif 341 '111-95 1*,(;

2 4 St. Aitifirt-w Bay. %Vatm,is llavmt SAI'M :10 11.50 85 3 7.5 11

7-24

IN') I AG-201 0,L),50

Tot 11@ I I

F I i d
1 Alial'Ichicola flay. Dly Bal APDB 21! 1 4 LiU M 5.00
Alwhichicola Bay. Cto hiiiii Bar A PC P 29 4:1.00 84 52.50
'!7 Kev. Mark Point 9 10.25 83 3.00
T,jmpa flay, l1apys Baymi T131313 27 50.72 82 36.75
@i [email protected] Bav. Cockroach Boy TBCH 2 7 40.55 8*2 30.56
Tompt Bay. klullel Key Bay(m TBMK '27 37.28 82 43.61
Chadolle Hatb4ir. Bird I.-J'ald ("BlU 26 31.00 82 IGO
30 NUPIcs Bay NBNR 16 7.00 ..81 47.10
Rtwk4-ry flay. I Iendt!rscilt CJ'(!(-k- Rill Ic '2 6 1.83 81 43.75
0
to J I Everglades. Faka Union flay EVFU 25 54.27 81 30.60

Tifljl(@ 2. Arillinwlic ineans lor curidifion index (g cin ') and length (cm) for the 26 bay groups for
each cii Ow 4 years of tlie sludy. Nittans are determined from all sit" within eacli designated bay
group

Cl)
0 13dy conditicift iodex Length
cri
z Nar, IS187 1988 1! 189 1986 1987 1988 1989
-2
1 0.086 0.076 1 i2 0.115 S. 163 6.95-3 6.035 6.028
2 0.087 0.131 0.110 0.065 7.407 5.673 5.521 7.042
3 0.092 0.141 0.104 0.0118 8.470 8.197 8.187 6.383
3 0.0519 0.137 0.11.4 0.075 9.378 8.299 6.916 7.071
(1 0.087 0.119 o.100 0.099 10.132 8.370 6.7 17 6.292
8 0. 1 Mi 0. 119 o. I Off omk 9.032 a35(i 8.546 11.332
@1 OA75 0. 130 o.088 0.043 10.440 9.648 9.655 8.395
111 0. 100 0.108 o. 1:35 0.054 11.477 8.265 7.9118 9.323
1 1 0. 120 0. 126 0.081 0.112 8.358 8@787 8.187 7.062
12 0.108 0.097 0.088 0.081 8.715 9.658 9.908 9.057
13 0.10.5 0.112 0. 1,22 0.0.51 9.731 lo.360 8.182 8.197
1-1 0.096 0-106 (1. 107 0.071 8.116 1 $ 1.223 7.178 7.488
13 (1.088 0.114 0. 125 0.068 it).w@i 9_566 7.040 6.861
18 0. 146 0. 103 0. 1 13 0-058 9.657 8.504 7.708 13.465
1.1 ItMW (1. 128 4 1. 125 0.0.53 8.942 7.270 7-524 5-682
1 0. 1 t 9 0. 11 it. 144 0.093 H.399 7. 153 7-104 7.204
1 11.14.1 0.105 41.11:1 0.096 8.622 @1.003 (3.033 (1.663
109 0. IA5 o. 14 4 O.Mi 1 9.090 4.558 G.0 17 6.456
i OA 19 0. 122 ().fig[ 0.063 7.747 4.949 6.673 5.974
'i-I 0.141 0. 130 0.140 0.08il 6.008 4.1110 (71.5iii 6.347
0. 149 0. 109 OA 19 0. 102 8.4.33 7.347 8.1286 6.637
o. I i@j 0.104 o.100 Omg@l 7.438 .5.515 6.714 5.390
_211 0.087 0. 120 o.117 0_086 6.578 6.373 6.443
(1.(181 0. 1 O@j o,171,1 0.06 1 fi.517 5.2 9 5 6.466 15.640
'to OA H; (I. I (if, 0 @ 11,12 0.078 6.702 G2 -1-673 5_46o
JI o.1213 0. 125 8.060 6.56io 6.558 5.11:15

7-25

(J(411.864 (KL,1150 ISVNW.Mio.'!

Taljl(- :1. G(-orm-fric Incw-.- (d III-fly li,.:rcl(-Il lor Ow melals arsenic. cadmillill 1111clselellium I)zi\. (@Voup @1110 (-Zich vvc! e.,; ;I-,(-
study. Values given are paris per million (Ppoll

Pollulaill body burclen
Si:"...-r Arsenic cb(lillikull Selenium
group ING 1987 S188 198s, 1 Si8r) IS)87 1988 1989 1986 1987 1988 1 @189 1 98G 1 @167 1988 1 slb@l

1 1.73 ).14 088 i 68 21.67 15.10 18.67 14.84 3.83 2.72 2.13 2. 64 2.87 21. 13 -66 2 4
2 1.33 1 .80 1.9) 1 .4 P, 10.95 1 O.U 10.66 8. 210 6,30 11.88 4.74 4 8 'j - 3.5 4
3 4.05 2.08 9 1-0-63 7.47 6.36 G. 3 7 7.14 7.63 5.14 6.34 5.73 4.43 2.7@1 3.6 1
1. B-1 I . (Ili 3. i 0 5 8.24 5.88 7.914 9.37 4.37 3.90 4.84 3. 16 2.63 2. ' .5 82
6 4 . 6 7 3.14 1-1.50 8.33 5.77 5.93 6.84 7.33 6.61 4.70 5.38 4.07 3.50 4,57 1"', -
8 1. 7 6 2,30 2. '-' 4 21 - 35 4.81 4A8 3.48 4.44 4.33 4.11 3.16 5.00 3.04 3.07 4.04 3. 3 4
-.6t, 7.13 4.60 3.-@) 5.50 4.10 5.50 5.80 4.17 7.16 4.3 .5.13 2.53 2.68 1) 7
10 2.00 2. 11) '2.4 3 1.74 10.33 6.60 4.37 5.91 4.33 5.08 3.49 3.88 2.30 2.25 3.00 31 1
11 '2.3? 3.16 '2.S,O 2,. 21 @ 1 8.00 7.97 4.80 5.63 4.83 3.61 4.57 5.09 2.13 3.118 3.32 4 -^' 7
1 1, 5.00 4.55 3.47 5.08 @1.67 8. iQ 4.67 6.13 9.67 9.25 6.43 10.39 2.23 4.37 5.43 4 A I
13 1.04 1.61 2.01 1.67 10.09 7 * 14 7.20 5.69 3.34 5.28 4.84 3.53 1.78 2.61 3.27 'J. 17
14 0.43 0.51 0.93 0.68 10.25 8.78 7.39 4.98 1.98 2.29 3.69 3.13 1.54 2.22 5. 1 r) '21. 21 1
15 0,3G 0.73 1.23 O@77 9,75 10.77 7.89 7.55 1.44 1.46 2.17 2.17 1.03 1.73 3.38 2. 24
18 1.07 (P. 93 1. 4 @l 0. @' 1 6.83 9.28 10.22 6.34 2.99 6.76 7.04 5.10 1.85 2.15 3.8!? :16
19 1.83 0.78 1 - 4 9 1.47 6.33 4.27 4.90 4.46 3.43 3.26 5.68 6.13 2,57 2 @ 321 4 - 21 9
20 3.29 2.03 2.12 2.79 14.96 9.66. 14.30 8.14 4.14 3.79 3.87 4.07 2.09 2. 6 1 3.41 21, , 3
,)I
2.20 1.84 1 -98 2.01 15.66 6.33 7.04 6.45 2.50 2.38 3.56 3.73 1.63 1.78 2.3.1 7.
22 1,213 2.80 1.66 1.50 11.33 17.2 0 12.14 8.84 3.87 2.83 2.86 2.74 2.40 2.13 3.42 '-, 9
23 3.99 2.49 2.79 1,69 6.71 8.01 6.21 9.35 3.57 2.51 4.29 3.09 3.27 3.62 5.87 4. 21 5
24 1.70 1.67 1.64 1.85 13.67 12.97 17.48 12.412 1.13 1.16 1.06 1.35 1 .210 1.68 21. 7 t^) 2. ^14
23 1.72 2.62 1.76 1.26 10.05 11.93 11.54 9.89 2V 2.59 2.55 2.12 1.61 1.99 3.2 7 2.03
27 0.33 0,45 0.42 1. 12 39.00 23.70 18.86 18.90 2.10 1.72 2.44 3..1 *1 1.37 2.43 2@03 3.69
28 0.90 1.15 0.85 1.08 7.32 6.66 8.66 7.26 2.29 M4 2.97 2.34 1.36 2.09 2.39 1 1
29 1.53 3.38 1.27 2.13 38.67 31.13 11.67 106 3.90 4.06 3.01 2.86 1.70 2.63 1.77 9 1
30 2.51 2.98 3.26 2.18 24.52 27,97 28-16 19.28 2.02 1.38 1.81 1.43 1.49 2.20 2.09 1.61
31 0.70 1.39 0.7 7 1.29 8.83 7.63 7.47 7.97 3.20 1.83 2.21 0 2.27 1.87 1.97 2.37 2

ON)1865 iKL)150 (SV%I)\A'iIS@)n

1@' Iol' Ill(- ll)LIals C(lpper. zinc all(i 11"("'""Y 't)l' (,'Lldl IJOV (1111111) bl)d IULII VQcIl 01 llll,@ lu(!\
Ima in parlst 1)('I- Illillion

P0111118111 body )AIrden
Say Copp(f r Zinc Mercurv
g 1-0 U 1) 1986 1087 j5i88 198.9 1986 1 q87 1988 1989 1986 1987 988

1 120.00 120.33 169,84 1,10.08 1633.33 1257.67 4 94 5.86 162 1.712 130-00 196.07 00.14 124.
2 110-55 1 C, 2. 3 3 19 1. 6 1 111.18 3343.98 3260.00 4 3 4 G. 3 1 4137.87 79.86 22.00 1 31 0. 25 1 -3 1;
3 172.61 98.4 5 1 @, 7 - G 8 113.84, 1211.01 724.60 1304.14 1286.4 3 103.69 54.11 3 42. 0 0 1) 4 4
5 11 G,O I 10 6. 2'21 182.32 233.00 1137.03 94 I.G9 1382.09 1700.61 173.69 10 6.2 7 14 8.38 7 '2
6 1 0.00 12 6-1 14. @s b 3 B. 3@ 983.33 312-1.00 10', 2. 67 1292.25 V, . r) -111.33 69. 00
8 121.57 161.71 156.76 193.06 1186.77 2544.19 3 186.08 34 38.99 56.93 92.16 47.31 1
9 K.00 4 93.67 '279.33 220.52 8000.00 3989.00 4146.67 3370.@)g 143.3.1 14 6.67 108.33 I's 29
10 1,83.33 180.00 6. 5 8 185.80 2600.00 1899.33 3093.52 234 3.64 111.67 89.33 9 R. 12 0 6a 1 @I
11 173.33 182.67 170.00 192.22 1200.00 1324.33 1625.67 2129,86 4 7.67 50.33 70.00 5 6
12 353.33 509.33 248.67 611.67 2300.00 2896.67 1435.33 4089.20 39.00 - 77.67 38.33 C) 6 i
13 105.28 18().8.1 1'63.18 148.00 1264.9 1 2353.74 2143,93 17 83.13 32.44 46.47 45-34 33. '3
14 63.89 75. i2 114.35 86.43 1568.44 1578.96 2104.39 2388.97 43.72 77.81 87.49 7 7. C) 6
15 39.28 86.93 134.88 71.44 9 16.67 2097.02 2593.78 2625.89 413.72 77.81 87.49 7 -) 6
18 921 . 4 9 1 u 5.32 95. 1 CI 67.39 1052.78 1491.33 1205M 03.89 32.79 108.78 5 2.2 8 C. '3 '6
19 2) 9 3.3 3 116.00 280 1.99 228.19 3400.00 1285.00 3433.13 3369.99 33.33 153.33 124.37
128.52 146.79 i 72.63 208.14 3215,66 3229,64 2762.19 4555.68 130.52 136.36 1 13.7 G 111
21 100.00 59.33 133.86 132.51 916.67 956.00 1891.42 1957.27 70.00 67.67 73.71 6!.09
22 75.00 43.33 65.60 53.19 2133.33 470.00 1572.89 1356.49 243.33 84.33 119.58 1, 2.2 3
'13 -7 -1. 12 8 54.89 99.86) 114.03 2178.69 1983.16 3331.22 2170.02 256.48 2 P G. 3 7 '233.33 '21 6.7 q
24 4 )(3.67 '2 7 Sj. G 7 210.71 139.8G i4 00.00 3316.00 3150.72 3657.02 71.67 43.00 71.40 132.34
25 34.21 41.16 49.67 71.32 530.72 333.13 336.36 529.04 117.36 100.43 74.09 113.64,
27 14.67 20.33 18-07 38.64 300.00 483.00 916.73 488.36 :106.67 138.00 139.02 91.03
28 68.06 52.83 67.33 74,37 1666.15 1211.38 1859.14 2026.37 230,97 1 C)GISP 23V.87 2 ! .1 ?1 -1
29 85.00 153.00 103.30 163.71 1300,00 1679.67 1706.11 2695.68 313.33 296.67 188-30 1., @4.1 6 7
30 149.40 237.02 202.52 189.0 1302.35 1827.04 2089.41 1963.47 187.08 164.92 151.44
31 44,67 40.33 48.33 69.88 933.33 664.67 1167.67 11133.40 246.67 190.00 153.00 181.52

i,;Nj1C,,6G (KL)150 (StY)VJ!ir1o,,

lwy@ll'.Jlll @llldvac!l v-;'l (d lilt- ill peols I)k!j 11'.Mi"ll (ppil"

PA I I I -t 1 1. (11 dvi S

iM7

4 8.00
5 7. .117.00 9611.89 408.85
32. i 4 -.!4. 1 () 1 (1(;. 1 t; 29.67
5 4 1 _'25 711.4 R 93.97 7.5.12
10_67 64,67 '2! j. (; 7
2:13.97 27.5.611 3 1 j. 5,.4
I V.3 3 511.33 17 1.(17 171(17
6A.33 252. 44 G 364.4 1
1 -11.33 28.00 82.67
12 68-67 ua.67 i 1.67 -30.00
13 15:1.33 61.65 57.04 81.99
14 '213-ii2 !Of J. 7 7 354.18 204.29
15 257.28 24 11.2 1 14 7. 85 16710.34
111 '203.63 65.4 1 263.38 131.48
19 7_00 34.67 89.63 101.50
47 I.-W 687.311 '1611.32 453.26
7:!.-;7 :133.33 .535.73 214.97
4:15.67 187.67 3 13.99 139.03
23 298.80 940.56 1121.29 398.74
.!4 13277.67 2709.0 '2533.99 961.36
25 i1o'2 22.33 IT3 4. 12 1025.00
27 38.33 44.33 :480.11 72.50
'18 144.93 71.73 111.361 176.94
20.1;7 20 1*.(;7 84.85 509.35
:M 100.18 51.77 68.90 134.98
Al 98-CIO 20.00 19G.G7 108.00

Spatial Distribution
The spalial distributk)ji .if (ta(-11 containinant WiIS CXalllillCd Using a spatial autocorre-
Idlioll 11101110d (lCSCj-ibCd bV Cliff & Ord (1973). We used Moran's I as the test statistic,

W" Z' Z'

it
I (11/w)

dild

W wjj; 7.

fl __ IMIllber of saniples. X, Value (if each Samole and %\,,i it welghling ni(_-asure tis
dt*-Scrib'-d below.

7-28

Moran's I is sensitive to the location of extreme departures from the mean [x1-x]. For example, in a
patchy distribution, adjacent samples would both tend to be much above or below the mean more frequently than
would be expected by chance. Significance levels were calculated after Jumars et al. (1977) under the assumption
of randomization. Cliff & Ord (1973) showed, for samples that are spatially randomly distributed, that the
expected value of I is -(a-1)'. Hence, values of I below -(n-1)-1 indicate negative spatial autocorrelation
(an even distribution) and values above -(n-1)-1 positieve spatial autocorrelation (a patchy distribution).
The use of this technique depends upon the choice of a weighting system (wq) which is a mathematical
expression of teh spatial relationship between the sampled sites. Factors involved in the choice of a weighting
system were discussed by Jumars et al. (1977), Sokal & Oden (1978a) and Cliff & Ord (1973). We constructed a
Gabriel-connected graph (Gabriel & Sokal, 1969) for the bays sampled in all 4 years. In this case, two sites (AB)
were connected between the other two (<ACB). Gabriel-connected pairs were given a weight (wq) of 1.0 and all other
pairs wq=0 (Fig. 1)
The change in spatial relationship among samples at varying distances can be used to identify the scale of
spatial variation. For example in a patchy population samples closer than patch size will be more similar than
expected by chance (e.g. Moran's I>-(n-1), whereas samples further apart than patch size will be less similar e.g.
Moran's I <-(n-1)1). We examined the change in spatial relationship with distance using correlograms (plots of
sample similarity versus distance between samples) calculated as discussed by Sokal & Oden (1978a,b). Distances
were calculated along the Gabriel network by Marble's method (1967). Bays within a given distance from one another
when joined along the Gabriel network were given wq =1.0; for all others wq=0. Therefore, our correlograms were
distance-corrected using the terminology of Sokal & Oden (1978a).

Temporal changes in spatial distribution

To examine the spatial scale of yearly changes in the biological variables and contaminant body burdens
and to determine whether concerdant changes occurred among several variables, we used the analytical approach of
Powell et al. (1984) as adapted by Powell et al. (in press). First, we ranked each of the 4 years for each bay
group from 1 (highest) to 4 (lowest) for prevalence and mean infection intensity of P. marinus, lenght, condition
index and each of the eigbht contaminants. Two bays or two parameters were compared by subtracting each year's
rank for one from the corresponding rank for the other and summing the absolute value of the 4 differences. As
an example, if the data for hay group 1 were ranked 1,2,3 and 4 years and the data for bay group 2 were ranked
1,3,2 and 4, then the differences would be 0,-1,1,0 and the absolute value of teh sum would be 2. The values of
the sums obtained in this way can only take the values 0,2,4,6 and 8. The frequency spectrum of occurences of
possible sums between site pairs having randomly distributed ranks is 0 (.042).2 (.125),4(.292),6 (.375) and 8(.167).
A frequency spectrum of sums calculated from the data in this way was compared to the frequency expected by chance
combinations of the rankings using Kolmogerov-Smirnow (K-S) one-sample two-sided tests. Significance of the K-S
statistic was judged using Conover's (1972) method for calculating exact P-values for discrete

7-29

MSPB M13CP

-LBMP
si
c CL
' *.. . :..:. .:.- 13S8G
H H V(3Sp
BOB T8 --BSSI

86M
TSL

msas msps
MSPC

MBCP
CLSJ ABOB L84MP
CLCL 138so BS8G
TSLF Bssl
CL
JHJH
VBSIP

TBLB

Ps cesp C 8 SR-t
-SAWS
APCP

APOB
CKSP

rOsti
P,1.8 CBSP
SAINS APCP -XSPS

TBMK
ADPS

CKBP TlIce

TOPS ceal
NSN13'k"-
TStJK 118HC
T3CO FU

COW
Nolu
AOHC
EVFu
MBCP
L @8
4M P
_CL 13 0 BS8G
1, TE3LF SSSI
R
R,
"R
,@_: Ips.-s-

1@@SA ZIS

7-30

*GBYC
*SLBB
*GBTD *GBHR
*GBCR

*MBTP
*MBGP *MBEM
*GBYC *SLBB
*CBCR *GBTD *GBHR
*MBAR *GBCR
*CCNB *ABLR

*MBTP
*MBGP *MBEM
*CBCR
*MBAR
*CCNB *ABLR

*LMSB

Fig. 1. Distance-corrected Gabriel graphs for the sites sampled around the Gulf of Mexico in each of the 4 years
of the Status and Trends Program. Four-letter site designations correspond with those in Table 1. Dots indicate
site locations. Graph is drawn opposite for clarity. See Powell et al. ( in press) for more details

data. In this analysis, if the year-to-year change in any variable between bay systems tended to co-vary, most
going down, then values of 0 or 2 in the previous example would occur more frequently than expected by chance. If
the yearly changes tended to oppose one another (for example some bays going up, the others down) then values
of 8 would be frequently obtained.
We utilized the preceeding approach on two geographical scales, the entire Gulf of Mexico (all bay systems)
and sets of ten contiguous bay systems, contiguous being defined along the Gabriel graph. In the latter case, 10
bay systems were chosen because the number of bay systems in Texas and extreme wester Louisiana, as defined here
was

7-31

10. To determine hte extenl of regional sieilarity sels sets of nine hay system were examined step by step orounal the Gulf of Mexico in the following manner. Step one always comparel lays from the Liguna Aledre in South Texas through Vermillion Bay
in western Louidiana. Step 2 was generated by deleting the most Southern Bay system (Laguma Medre) and adding the next bay system to the cast (Alvhafalaya Bay and Cailou Lake)
Consecutive steps followed the same protocol with one exception. Step originaling on the Eastern Gulf coast were alloud t wrap around the Gulf. For Example, Step 22
compard the five Southern most Florida Bay system (Cedar Key to the Everglades0 and the 5 southern most Bay system in Texas ( Laguna Madre through East Matagerda Bay). We exanmined all possible
pair -wise combinations within the set of 10 Bay systemd. This gererated 45 sunis. The frequency of thesre sums was compared against the expected
frequency of sums using K-S tests as previously described. A non-signficant value for the K-S statistic among the 10 Bay systems making up one step indicates local control of the tempotal variation in the variable
(E-G. pollutant simultaneously going up or down in value) from one year to the next did not occur among the 10 Bay systems more frequently than expected by chance over the spaliat scale encompassing the 10 Bay system.
This result would suggest no regional imprint on local control of variability. Similarly, a significant vale of the K-S statistic would suggest that regional influences overrode local controls so that
temporal variation, in contaminant body burden or disease for example was substantially affected by climatic, as well as local, factor. Plots of the K-S statistic as a function of steps around the Gulf give a graphical representation of the spatial extent of the similarity.

Within those geographic regions where yearly changes in pollutant body burden were concordant using the K-S test. R improvment test were conducted. Factors tested in most
models included that might have produced the odserved concordancy. Factors tested in most models included length conition index, mean temperature, mean precipitation, P.
marinus prevalence and infection intensity. And agricultural and industrail land use. The parameters that produce the best R model were than used in regression analysis, again
only in regions of yearly concordancy as defined by consecutive significant results of the K-S test. Not knowing the response time for pollutant body burdens to shifts in environmental regimes in most casses analyses were
conducted using the average precipitation and tempuature values for the 5 months prior to sampling and the 2 months prior to sampling.
Prevalence and infection intensity of P. mainus were used in R improvement and regression analyses only with the average precipitation and temperature data for the 2 months prior to sampling because P. marinus responds do rapidly to changes in the Environmental Regime.

RESULTS

Spatial Distribution, Gulf-Wide

Correlograms calculated along the Gabriel network for the Distribution of contaminant body burden with distance around the Gulf Of Mexico are given in Figures 2 to 3. The
contaminants can be divided into two distinct groups. For mercury, selenium, arsenic and casmium site-to-site similarty gradually declines with distance over the first approxi

7-32

1,0-

0.5-
L

%
A.
-0.5 - ----- 1986
1987
199S
19S9
-1.0 i - I .
0 200 400 600 Boo 1000 1200 1400 1600 1800 2000
Distince (km)

Selenium

0.5-

'A
0.0 )K
%

-0-5 -

1987
-1.5 - ---- 1988 41
1989

-2.0f
0 '200 4@O 6@O 8@O 10'00 1200 1400 16,00 ieDO 2000
Dist:incc (km)

2. Corlolotirallis ol-lalinq dislance (kiii) Us Nhirail*s I (ditaitieJ ti-sing licidy litirden of inercurN and
st-k-Ilitful jor all sil(@.-; sallipled in ea(-h year. Di.-.1imccts were calculakid along the Gabriel
wiwiv slations separdled Ily. lor 10 1 alld 200 1,111 werc. It-,4:ILI to slew.-rate. the 200-1,111 J)'Jilll.
I'lle itik-al rolid(lill v,tltt(! Im- P Itirais's I i-@ approximately -0.04

incitc-ly IGOO kill in (:cich of flie 4 The C.-0rl-V.I(I(jralllS I)iISS tlll-OLI(jll I = -(]I - ])-@ Zit
(11101.11 -100 kill. I'll'a is. Ijay groups k-ss than 400 kin iparl are more similar in b(gly Ijuv(ien
(01 llltl*@C 11101cif" 111(ill Vxjwclod hy Cluilict. alld Sit4L-.-, become. less and less similar at larger
xMd larq(tr spitial sc,fles. Anollwr characteri,,tic of [lie (listribitticin of groUl) I C011taillin-
aills 'is Ihe closic. association between the fir-st '! years ( 19116 and MR71 and the last 2 y@!ars
(PIN'l 'Illd P1,191 <11 dw It'll(lost spali'll scalos.. Body 13111-dell 411 --y-ilers Iroill IIIC east 'Ind
co'ININ of Ow. Gtflt variod similarly alliollig tht. bays within both pairs of years and

7-33

Arsenic

0.5-

0.0-

-0.5 - 'A

-1-0-

x987
-1.5- 1988
1989

.2.0 4 4 4 . . I I. - . I
0 200 400 coo 800 1000 1200 1400 1600 1.8.00 2000
DLst2nce (km)

Cadmium

1.0

05-

-A@
A,.

-0.5-
*A'
2 -1.0-
1986
1987
1988
1989

-2.0- 4
0 200 400 6@O 6@O 1000 1200 1400 1600 1800 2c;00

Dislaticc (kill)

III, w1atill(I dist.111ce (kill] Ill 141litailled 41iiII(I 111)(IN. Istil-doll (Ifarsellicand

lw!.- -lall-11, -'1411011"d 131'. 11W.'XIIIIIIIIv, 101 and 200 kill wt-we tis4-.(I to gencrik! III(-, '200-kin 1)(4111.
The idv'd I'alldt'Ill valm- I(Ov MocaWs I i% -0.04

I. cl I Ict 14 .0 sI Ill I I it Ov h Y s(l I I It.! I -(I(. I(! ev(!111 %v I, icli occorred Ix.-tween 1987 a I I (I

Ill c(IIIII-cls-I 14) (11-oup I c(JI11,1111itliIIIIS, Ille diitribtilicin of gamp 1 C011till1lillaillS. Copper.
Z1111'. N'Ilvor '111d I(,'U(1 PAI Is. ShOwS, Ito coverall (11(@ piit(erli tell(Is to ().sCiliate
ill(.. ldcal rawl(IIII valw. ill ntos( spallill scilics for all 4 years. This pjdj(-rn itl%o (10(!S
11-1 @.Iv'lrly sIval, Ow dis(inct bi-cal, 19416-1987 and 19811-19119.

7-34

Copper

0.5-

0.0- _Ak

)IL
'A
%
1987
1983
1989
-1.0 i
0 200 4@O 6@O 600 1000 1200 1400 1600 1800 2000
DiSt2rKV (kM)

Zinc

0.0-

,It

-0-5 19M
1967
Im
1989

-1.0
0 200 4@0 6@O 8@0 1000 1200 1400 1600 1800 2000
Di:auncc (km)

Fig. 4. Correlograms rt--lating diNutru-41 (luill to Morall's I Obldillt.-d using body burdeve of copper all([
zillc i4jr all Sampled ill (-ach yt:ar. Distanc-c!s xvcre rzdculai(--(t alunq dic Gabriel network. where
11jr. 101' ('XdIlljJI(-. 101 md '100 kill mcd to cl(itwr@alt-. Itie 100-kin point. The
idt-,it random tor Mor,m*s I is -0.04

perk-ills(IS marillus is all impOl'UtIlt J)dtlLO(J(211 ill I)OI)LIlatiOlIS ill the GUlf Of
NlexiCO. and is responsible lor high mortality ill most years (I lofstetter. 1977). Correlo-
grCillis lot, ttleall prevaterice wid meim itifection ititeresity (if 1@ mariijus for each of Ific 4
yoars are clivell ill Powell (.-( at. (in press). Overall, the spattal distributioti of A marinus
prevalviiCk! all([ infection inteitsi(y. while not identical, retaires many of the spatial
(I k(I 1-@rl Cl erest Ics of grollp I cuntaminatits- Correlograms for P. mariijus prevaterece aged

7-35

Silver

1 1-8
0.5- 1939]
A@
0.0-

ILI
'XI

0 200 400 600 Boo 1000 1200 1400 1600 1800 2000

Distanct (km)

Total PAH

1.0

0.5-

A
0-0-
--A

Ik
-0.5- 1986
1987
1988
--- 1989

-1.0
0 200 400 600 800 1000 1200 1-100 1600 1800 2000
Distance (km)

Fiq. 5. Corm-locirams r0ating clislan(:c (kill) to N-loran's I ubtained using budy burden (of silver and
I-I.d PAI I for all siles sampit-d in vach yvar. Dislallc(-., vvd-rc cal4rulaied alung (lie Gabriel network.
X-divit- .'Idliolls separdled by. for R, I and '100 kill werit Used to (Itnierate Ow 2004,111 julint.
'mi.. itit!.l racidmn valuca I(jr I is isppruximatOy -0.04
- '. .- . I
Ilifecliull ilitensily (lei'. lolls( 1-41 le it VC1,11 strong relationship between year pairs 1.986/87
and 1988/89. Again. the I)reak- between. 1987 and 1988 I-CSIARS in two very different
di@,Irihtltiolwl patterns Cit C@-rl(titi sp,diiii scales. Similarity dedines over (lie first approxi-
m,itt-lv 1-100 kill. allhoti(th mork. rallidly Illall It does for Ille conlaininants. and relurns
citizilit <if Imi(Icr spatial scak-s.

7-36

Temporal changes in spatial distribution

Utilizing test the spatial scale of the Gulf of Mexico (2000 km along the Gabriel
network), few contaminents had significantly concordant shifts (Table 6). For selenium.
and to a lesser extent zinc,copper and arsenic,however,many more bay systems in the
Gulf tended to vary simularly year-to-year than would be expected by chance. That is the
tissue concentration of these contaminants tended to increase or decrease uniformly from

Table G. Results of analyses to detect concordent temporal shifts among all 26 bay systems in the
Gulf of Mexico. A significant result indicates that temporal shifts of the measured variable were of
the same sign (values increasing or decreasing) in most of the bay systems around the Gulf

Patameter KS Statistic P Value
Silver 0.11218 0.3833
Arsenic 0.17949 0.0640
Cadmium 0.11859 0.3744
Copper 0.19551 0.0601
Mercury 0.11859 0.3744
Selenium 0.50321 6.0x 10-m
Zinc 0.21795 0.0313
Total PAH 0.05128 1.00
Condition index 0.38782 5.9x10-5
Length 0.34936 3.4x10-4
P.marinus mean infection 0.48718 6.0x10-8
P.marinus mean prevalence 0.21795 0.0313

one year to the next in all or a significant portion of the bay systems. Such coincident
shifts in body burden would indicate some regional or Gulf-wide control on body burden.
Among the biological indices,condition index,length. Perkinsus marinus prevalence and
P.marinus infection intensity all were characterized by nearly Gulf-wide coincident
oscillations in yearly values.
Meteorological data (Trenberth et al. 1988; Ropelewski & Halpert, 1986; Douglas &
Englehart. 1981) suggest that the eastern and southern Gulf are dissimilar from the
western Gulf. Powell et al. (in press) found that P.marinus prevalence followed this trend.
Consequently, substantial geographic areas of similarity might go unrecognized at a
spatial scal encompassing the entire Gulf of Mexico. Accordingly, we also looked at
groups of 10 bay systems covering approximately 600 km of coastline (range
500-900 km,excepting those that "wrap around" the Gulf,thereby including the eastern
and western portions of the southern Gulfs.
Average length of the oysters sampled tended to decrease in each year throughout
the study. The largest oysters were always preferentially sampled at each site. The
decrease in length could represent teh depletion of the largest individuals over time due
to this sampling strategy: however, the decrease occurred in fished and unfished
populations and in many areas most collected oysters were no more than 2 years old.
Accordingly collections the previos year would not have sampled the same cohort.
Trends in size then are probably a natural phenomenum. Yearly trends in length are
coincident over most of the Gulf,except the Galveston Bay/Sabine Lake area of Texas.

7-37

Cadmium Arsenic
0.20 0.40-

0.30-

.2 0.20
LOI) ------- 3, A,
0.10- 4@
------------ - -----

0.10-

0.05- 0.00-1
0 5 10 15 20 25 1 6 1 1 16 21 26
---I Steps Around Gulf Steps Around Gulf
C@
00 Mercury Silyer
0,2 0.20-

0,151- - - - - - - - - - - - - - - - - - - - - 0.15 -- - - - - - - - - - - - - - - - - - - - - - - - - - - -
U

O.io- .0.10-

x 0.05 0,05-

0.00 0.00
1 6 1 1 1 G 21 2 G I G I i I G 2 1 2 G
Stens Around Gulf Steps Around Gulf
Fig. 6. Graphical representation of results of the Kolmogorov-Smirnov test for arsenic, silver, cadmium, and mercury using all bay pairs in ea
group of 10 bay systems (one step), The two lines indicate the a 0.05 (solid) and 0.10 (dashed) significance levels for an n of 45 (number of SI(C.-
pairs used)

MEMO& Wt

=ago MIMM

Zinc Total PAH
0.507 0.4

0.40-
0.30-

0.30

0.20-

0.20
-- - - - - - - - - - - - - - - - - - - - - - - - -
- - - - - - - - - - - - - - - - - - - too)
0.10-
0.10 Id

0. DO . . . . . . . . . 0.00
1 6 1 1 1 G 21 26 1 1 16 2 1 2 G
Steps Around GuirI Steps Around Gu)t

Copper Selenium
0.25 0.70
0.20 0.60
0.50

0.15 - - - - - - - - - - - - - - - - - - - - - - 0.40-

V)
0,10 0.30-,
V)
X 0.20
0.05 -------- --------
0.10-,

0. 0.00
1 6 It iG 21 26 1 8 11 16 21 26
Steps Around Guir Steps Around Guir
Fig. 7. GraphiW representation of results of the Kolmogorov-SmImov test for seleni=, copper, zinc, and PAH using all bay pairs in each goup
of 10 bay systems (one step), The two lines indicate the cc - 0,05 (solid) and 0.10 (dashed) significance levels for an n of 45 (number of sitepairs
used)

length increasing or decreasing from one year to the next coincidentally in most bays
within a contiguous group of 10(Figure B). Of particular note are the highly significant
concordancies in the eastern and southern Gulf of Mexico. Yearly trends in length in
southern Texas and southern Florida were nearly identical,small oysters being collected
in certain years and large oysters in other years.
Low values of condition index typically indicate a stressed or unhealthy population.
Condition index also varies with the reproductive cycle in 1989, 3 of the 26 bay systems
had condition indices greater than 0.1. while 1986 had 1.5, 1987 24 and 1988 21. The two
years with lower mean prevalences of P.marinus had higher average condition indices,
as might be expected. Similar year-to-year variations in condition index occurred
throughout the Gulf of Mexico(Figure 8). As the time of sampling of the populations was
similar in all cases, except the Louisiana bays in 7986(Craig et at., 7989; Wilson et al.,
1990), this trend indicates that a Gulf-wide variation in climatic conditions probably
controls condition index in Gulf oysters.
Similar year-to-year variations in prevalence of P. marinus occurred throughout the
Gulf with the exception of the central-northern region represented by bays on both sides
of the Mississippi River(Figure 8). Powell et al.(in press) noted that the Mississippi River
represents an important boundary in P. marinus infection. The only uninfected popula-
tions in the Gulf of Mexico are regularly found on the Mississippi delta. Concordant year-
to-year variations in mean infection intensity of P. marinus occurred throughout the Gulf
of Mexico, as was the case for condition index and nearly so for length, suggesting a
similar relationship with climatic variables, if not a causal process. P. marinus infection
intensity could be a controlling factor in both length and condition index. Again, in both
prevalence and infection intensity, the similarity in yearly trends on both sides of the
southern Gulf is noteworthy.
The pollutants divide into 3 groups based on their temporal variations (1) Like
condition index, length and P. marinus infection intensity, year-to-year variations in
selenium coincided throughout the Gulf. The similarity between selenium and condition
index is particulary noteworthy. Year-to-year variations in arsenic were only slightly
lower than selenium in their regional scale of concordancy; concordancy occurred over
much of the eastern and western Gulf,only failing to encompass the Louisiana region.
(2)Mercury and cadmium varied similarly in the western Gulf,but not in the eastern
Gulf. The degree of concordancy was low, large-scale control of body burden occurred
only in the Texas region. This pattern,then,is different from all biological parameters.
(3)Year-to-year variations in copper,zinc and PAH body burden were concordant in
the eastern and southern Gulf,particularly Florida and southern Texas, but not in the
northern and western Gulf, a trend exactly opposite of that noted for cadmium and
mercury. The concordance of yearly variations in body burden on both sides of the
southern Gulf similar to that noted previously for P. marinum prevalence and infection
intensity and condition index. Again the region of concordancy begins in the Mississippi/
Alabama area of the Gulf at a similar location as it does for P. marinus, suggesting a
similar climatic control.
(4)Silver failed to show regional concordancy anywhere in the Gulf.

7-40

Condition Index Mean Infection Intensity
0.60 0.00

0.50- . o.7o-

0.60-
O.AO
0.50-
V) . . . . . .
0.30 01.40-

X 0.30.
0.20'
-- - - - - - - - - - - - - - - - - - - - - - - - - - - - 0.20.
----------------------
OJ 0 O.j 0
1 6 21 26 a 21 26
Stcps Around Gulf Steps Around Gulf

Length Prevalence
0.60 0.60-

0.50
0.50-

0,40-,
0.40
0.30
i7 GO
0.30 0.20
- - - - - - - - - - - - - --
o.20- 0.10.

0.00
0.10 . . . . . . 21 26
1 6 1 1 1 G 21 26 1 6 16
Steps Around Gulf Steps Arour\d Gulf
Fig, 8, Graphical representation of resWts of'the Kolmogorov-Synimov test for condition index, length and t@e inean infection inkcns:ty and.
prevalence of infection of Perkinsus rnarinus for all pairs in each group of 10. The two lines indicate Lhe oc - 0.05 (solid) and 0.10 (': ' '
'as.'Iocl
significance levels for an n of 45 (number of site pairs used)

DISCUSSION

Explaining the temporal and spatial variation in contaminant body burdens is complicated because body burden may be
affected by so many environmental and biological factors (Farrington et al. 1983). Oysters can incorporate metal and
organic contaminants either through direct absorption from water or ingestion with food particles (Ehrhardt, 1972;
Stegeman & Teal, 1973). Therefore, any factor which affects the bioavailability of pollutants such as changes in
environmental condition, physiological condition or food supply may ultimately affect contaminant body burden
(Farrington et al.,1983). Factors controlling these conditions are, in teh extreme, of two kinds: local an large-scale.
These, in the extreme, offer two opposing expectations. In the local case, temporal varioations in body burden should
never occur simultaneaously in adjacent bays more frequently than expected by chance. In the large-scale case, we
should expect coincidental variations within some large geographic region. It may be true that local variations outweigh
large-scale factors in some regions and not in others, depending upon the strength of the two signals. El Nino, for
example, affects the southern and eastern Gulf. A contaminant significantly affected by environmental conditions associ-
ated with El Nino might show coincidental changes in the eastern and southern Gulf while local variables controlled
its temporal variability in the northwestern Gulf. Understanding the variation in contaminant body burden, then, requires
investigating the effects of biological and environmental factors on both the local and larger geographic scales.

Temporal distribution and climatic control on variability

The biological attributes and contaminants can be placed into three groups based on the regional scale of their
concordant temporal changes in the Gulf. For selenium and arsenic body burden, condition index, length, and prevalence
and mean infection intesity of Perkinsus marinus, year-to-year variations are similar in nearly every bay around the
Gulf. This implies controlling factors Gulf-wide of nearly Gulf-wide in scope. The geographic scale is largest for
selenium. P. marinus infection intensity, condition index and length, but encompasses all save the Louisiana bays for
arsenic and P. marinus prevalence. The second group, including mercury and cadmium, varies concordantly from southern
Texas to approximately the Mississippi delta, suggestive of some large-scale phenomenon in the northwestern Gulf
producting similar changes in body burden for these contaminants. The last group, including copper, zinc, and total
PAHs, varies concordantly in southern Texas and southern Florida, suggesting a subtropical control on body burden for
these contaminants.
Of particular note are the geographic boundaries of these three groups. The boundaries between the northwestern
and the southern/eastern Gulf are clear; the vicinity of the Mississippi River delta and the Matagorda/Aransas Bay
area of Texas. The break in similarity for arsenic and P. marinus prevalence in the Mississippi River region also marks
the eastern extent of similarity for mercury and cadmium and the western extent for copper, zinc, and PAHs. The western
extent of similarity for mercury and cadmium, Matagorda Bay, approximates the northern extent of similarity for PAHs,
zinc and copper.
These groupings of contaminants and biological attributes require three levels of

explanation. First, if only climatic factors are of sufficient scale to explain the concord-
ances observed. what climatic factors are ultimately responsible? Second, why certain
grops of pollutants climatically controlled only in one part of the Gulf; do geochemical
similarities for example, explain these groupings? Third, what factors mediate the
climatic control on body burden?
Climatic controls. Large-scale concordances in temporal change can only be
explained by climatic factors; only these operate on an appropriate geographic scali.
Choices for the climatic factors ultimately responsible are relatively limited. (Of course,
our data do not permit us to cerify what the ultimate caosative factors are. A 4-year time
series is inadequate for statistical treatment.) The concordant shifts in the easetern and
southern Gulf suggest a tropical or subtropical control. The El Nino/Southern Oscillation
phenomena is of appropriate scale and location. El Nino occurs in the Pacific, but affects
temperature and rainfall in the Gulf of Mexico region by altering dominant weather
patterns (Trenberth et al., 1988: Philander, 1989) and has been implicated in temporal
variations in P. matinus prevalence and infection intensity. El Nino/La Nina events
typically affect the Gulf from the panhandle of Florida through southern Florida and
southern Texas, where concordancy for selenium, arsenic, copper, zin, PAHs and most
biological cariables occur. A strong El Nino/La Nina shift occured between 1987 and
1988 and contributed to the Noth American drought that summer (Philander, 1989). We
noted that the year that the groups 1986/87 tended to be statistically similar in many
analyses, as did Powell et al. (in press). A second large-scale meteorological phenome-
non, the Pacific North American Teleconnection (PAMT), control the number and
severity of winter storms in the northwest Gulf region (Wallace & Gutzer, 1981) where
concordancy for mercury and cadmium, as well as selenium, aresenic, and most biological
cariables occurs. Combined, these two weather patterns could explain the geographic
scale of concordancy observed in each of the contaminants and biological variables.
Why groupings exist. Any of the contaminants of biological attributes might
respond to two scales of environmental change. Local changes, originating for example
from the nearness of urbanized areas, the presence of certain contaminants in particular
drainage basins and the extent of agricultural development, should produce discordance
between adjacent bay systems. Galveston bay, for example, might drain a large geo-
graphic area wheras an adjacent bay, East Matagorda Bay, may receive only local
precipitation. Contrasting wtih this ate large-scale climatic trends which affect weather
patterns at least regionally. Changes in the precipitation regime during El Nino cycles are
a good example. All watershesds in regional areas may be affected in the same way.
Depending upon the conpeting strengths of local and climatic variability, biological
attributes or contaminants might respond most strongly to one or the other. In our case,
the body burdens of copper, zinc, and PAHs would apear to be predominantly under
local control in the northwestern Gulf and under climatic control in the eastern/
southern Gulf. Mercury and cadmium have the opposite distinction. Selenium, arsenic
and many of the biological attributes respond regionally in both areas. Silver seems
generally to be under local control.
For the first two groups, the reason why local controls are relatively more important
in one region than another is unclear, nor is it clear why cadmium and mercury behave
similarly, as do copper, zinc, and PAHs. To the extent that copper and zinc often behave
similarly in bivalves (Phillips, 197Ga, b; Phelps et al., 1985; Roesijadi & Klerks, 1989) and

7-43

quite diffierently from Cadanae: tBrooks & Rumsby. 1965; Boyden, 1974; Cheng, 1988a,b:
bet see Roesquda et al.,1989 these data fit an expected scenario.
Mediating factors We used R2 improvement and regression anayses on the
yearly rankings for those locations in the Gulf demonstrating concordant yearly shifts to
examine what biological and climatic parameters might contribute most to the observed
concoidancy. Inasmuch as the biological parameters certainly were also influenced by
climatic parameters, the set of independent variables were not in themselves completely
independent: thus the analyses serve as a guide to the mediating factors responsible only
in this context. Moreover, we expected factors to differ between the northwestern and the
eastern and southern Gulf regions.
In detemining which variables might affect P. marinus, we considered condition
index, monthly mean temperature, monthyly mean precipitation, length, cadmium,
selenium, zinc, copper, PAHs, silver and mercury (Table 7). P. marinus prevalence was
positively correlated with mercury body burden and negatively correlated with tempera-
ture and condition index in the western Gulf. Negative correlations existed for zinc,
selenium, copper and cadmium and positive correlations for PAHs and arsenic in the
eastern and southern Gulf (Table7). P. marinus infection intensity responded negatively
to selenium, condition index, and length and positively to copper in the western Gulf;
condition index and selenium demonstrated negative correclations in the eastern and
souther Gulf. These correlations demonstrate serveral important trends. (1) Biological
variables were the most important correlates of the distribution of P. marinus in the
western Gulf where concordance in contaminant body burdne was least well developed;

Table 7. Results of regression analyses within regions of concordancy of yearly changes for
Perkinsus marinus prevalence and infection intensity. Possible significant results represent the
number of steps or groups of 10 bay systems tested individually.Number given indicates the number
out of that possible number significant at a =0.10.N, a negative correclation; P, a positive correlation

P. marinus prevalence
Western Gulf Eastern/southern Gulf

(10 possible) (11 possible)
Temperature 9 N Arsenic 6 N
Condition index 9 N Copper 4 P
Mercury 7 P Cadmium 5 N
PAH 4 P
Zinc 6 N
Selenium 8 N

P. marinus infection intensity

Western Gulf Eastern/southern Gulf

(15 possible) (10 possible)

Copper 7 P Condition index 10 N
Length 12`N Selenium 5 N
Condition index 15 N
Selenium 8 N

7-44

contaminants were most important in the eastern and southern Gulf where the El Nino
signal was strongest (2) Most correlations were negative for biological and environ-
mental variables and contaminants. Mercury, copper and PAHs were important excep-
tions. (3) The negative relationships with condition index and length are, perhaps,
expected; that with temperature is a surprise as is the absence of an effect of precipita-
tion. (4) The most consistent Gulf-wide signals were negative correlations with selenium
body burden and condition index. Both of these responded concordantly throughout the
Gulf as did P. marinus prevalence and infection intensity.
The parameters used for the analyses of the contaminants were length, condition
index, P. marinus prevalence and mean infection intensity, and mean monthly tempera-
ture and mean monthly precipitation for the two months prior to sampling. Cadmium,
mercury and arsenic varied concordantly in the northwestern Gulf (Table 8). Tempera-
ture (negative), length and condition index (positive) generally explained about 35% of
the variation for arsenic; temperature, length (negative) and mean infection intensity
(positive) explained 25-35% of the variation for cadmium. Mercury responded positively
with temperature and P. marinus prevalence.
Copper, zinc, arsenic and PAHs generally varied concordantly in the eastern and
southern Gulf. Temperature (negative), mean infection intensity (positive) and preva-

Table 8. Results of regression analyses with regions of concordancy of yearly changes in contami-
nant body burden. Possible significant results represent the number of steps or groups o 10 bay
systems tested individually. Number given indicates the number out of that possible number
significant at a -0.10.N, a negative correclation; P, a positive correlation.

Arsenic (Western Gulf) Arsenic (Eastern/southern Gulf)
(8 possible) (7 possible)
Temperature 6 N Precipitation 4 N
Condition index 5 P Temperature 3 N

Mercury (Western Gulf) PAH (Eastern/southern Gulf)
(3 possible) (6 possible)
Temperature 3 P Temperature 3 N
Precipitation 3 P Length 3 N

Cadmium (Western Gulf) Copper (Eastern/southern Gulf)
(9 possible) (6 possible)
Perkinsus Marinus intensity 4 N Perkinsus marinus prevalence 3 N
Length 3 N Length 6 N
Condition index 2 N

Selenium (Entire Gulf) Zinc (Eastern/southern Gulf)
(26 possible) (10 possible)
Length 24 N Perkinsus marinus prevalance 9 N
Condition index 15 N Temperature 6 N
Temperature 7 N Perkinsus marinus intensity 6 P
(only southern sites)
Precipitation 11 N
(only northern sites)

7-45

(negative) explained 30 to 50 of the yearly variation in zinc. Prevalence {nega-
tive). condition index (negative ) and length (negative)explained 35 to 55% of the
variation in copper. For PAHs. temperature (negative) and length (negative) were most
important. Temperature and precipitation were most important for arsenic.
Selenium body burden varied concordantly over most of the Gulf. In the northwest-
ern Gulf, precipitation (negative). lenght (negative) and condition index (negative)
explained 35 to 75% of the variation. In the eastern and southern gulf, condition index
(negative), length (negative) and temperature (negative) explained 25 to 45% of the
variation.
Overall, then, a few trends were evident. (1) Regressions with condition index and
length were generally negative, higher body burdens occurred in smaller oysters, which
is a general phenomenon (Boyden, 1977; and others referenced previously). Lower
condition index suggests that small size was not just indicative of young oysters, but in
fact indicates oysters in poorer health (less biomass per length or mantle cavity volume).
The relationship between P. marinus infectin intensity and condition index corroborates
this view. (2) Temperature was usually negatively correlated. the exception was mer-
cury, where temperature was a positive factor. Although temperature might directly
affect body burden, we would suggest that temperature probably controls the frequency
of fall spawning and spawning generally results in lower pollutant body burdens ie.g.
Frazier, 1975, 1976; Boyden & Phillips, 1981; Wilson el al. 1990), hence the higherbody
burdens at lower temperatures. (3) Precipitation was generally negatively correlated,
suggesting higher salinities corresponded to higher body burdens, but precipitation was
only important in selenium and arsenic. For the most part, temperature and precipitation
were not themselves correlated, the exception being the north-central Gulf. (4) Arsenic is
taken up primarily from food (Sanders, 1980. Sanders et al. 1989): accordingly, it is likely
that the response of body burden to climatic factors was biologically mediated in at least
thist case. (5) P. marinus prevalence and mean infectin intensity were important in
copper, zinc, mercury and cadmium. Correlations were generally negative with preva-
lence but positive with infectin intensity. Again, mercury was the exception. Lower
prevalence would correspond with lower temperatures (Sonial & Gauthier, 1989). Preva-
lence includes many light infectins which probably are meaningless with respect to
body burden. High infection intensities, on the other hand, probably slow reproduction
(White el al. 1988; Wilson el al. 1988; Wilson el al. 1990) and are likely responsible for
the observed reductions in condition index and length.
Again, we emphasize the intimate relationship between P. marinus and the other
biological and environmental variables; consequently, the analyses can only provide a
rough estimate of the relative importance of these variables without the actual processes
being more completely understood. Overall, the factors affecting the rate of tissue
turnover, particularly the ganietogenic cycle and general health, determined in part by
the temperature reginie and disease intensity, would seem to be of the primary importance in
determining yearly trends in contaminant body burden (see also Wilson el al., 1990).
Generally, higher contaminant body burdens were found in populations characterized by
lower health.

7-46

Spatial distribution and climatic control on variability
Gulf-wide trends. As with P matinas, most characteristics of the spatial
distribution of the pollutants were conservative features; they were repeated in each of
the 4 years. A general clinal relationship might be expected to dominate the spatial
distribution of contaminants; bays farther and farther apart being less and less similar in
body burden. Temperature and precipitation show this clinal relationship (Fig. 9). The
farther sites are apart, the less similar the local weather regines are likely to be. Many
geographical variables related to contaminant source availability probably do so as well.
River inflow does not (Fig. 9).

Total inflow and prevalence
05

05
Total inflow:April-September 1985
Total inflow:April-September 1988
07 Prevalence:Bay average 1986
Prevalence:Bay average 1989
09

0 200 400 600 800 1000 1200 1400 1600 1800 2000

Distance
Fig. 9. Correlograms relating distance (km) to Moran's I obtained using temperature precipitation
and total freshwater inflow for all sites sampled in each year. Distances were calculated along the
Gabriel network, where stations separated by, for example, 101 and 200 km were used to generate
the 200-km point. See Powell et al (in press) for more details

Arsenic,selenium,mercury and cadmium show gradually declining similarities with
distance; the clinal variation predicted from the precipitation and temperature regime.
The correlograms of copper,zinc,silver and PAHs do not; the spatial extent of regional
similarity is of varying size throughout the Gulf so that no consistently significant spartial
scale exists. Why these two groups differ can be related to the temporal trends previously
discribed. From one year to the next, the body burden of selenium and arsenic varied
concordantly throughout the Gulf; the body burden of cadmium and mercury was
predominately affected by local factors throughout the Gulf. In both cases, the Gulf-wide

7-47

trends were suficiently uniform that local factors of a clinal nature might successfully
generate a strong spatial signal throughout the Gulf in any given year. In constrast, for
those contaminants having a strong regional response in the year-to-year variability in
the eastern Gulf, but which were locally controlled in the western Gulf (copper,zinc and
PAHs), fundamental differences in the controlling factors between the two regions
probably prevented a general clinal relationship from being observed throughout the
entire Gulf.
None of the correlograms mimic those of local argricultural or urban land use(Craig et
al. 1989) or P. marinus prevalence and infection intensity (Powell et at.,in press). It is
tempting, therefore, to suggest that the precipitation and temperature regimes are
important in controlling site-to-site trends in contaminant body burden over large
geographic areas, whereas organism health modifies these bay-to-bay relationships on
smaller regional scales. A correlation with latitude and body burden of some
contaminants does exist in Gulf coast oysters (Wilson et al., 1990). Temperature and
feshwater inflow can change the supply of contaninants and therefore their biovail-
ability (Shuster & Pringle, 1969;Zaroogian & Cheer, 1976;Denton & Burdon-Jones 1981).
Cunningham & Tripp (1973, 1975b), Zaroogian & Cheer (1976). Zaroogian (1980), and
Zaroogian & Hofmann (1982), for example, comment on the temperature dependence of
body burdens in cadmium,mercury and arsenic either linked directly to temperature or a
seasonal biological cycle, such as reproduction that correlates directly with temperature
(Wilson et al., 1990),and Parizek et al. (1974) describe a relationship between selenium,
cadmium and mercury. Several sources cite the co-occurrence of zinc,copper and silver,
and that a common source for these metals in freshwater runoff (Windom & Smith, 1972:
Frazier, 1975: Phillips, 1977b. c), as is also likely for PAHs (Wade et al., 1988). Body
burdens of copper and zinc may also be related to salinity (Wright & Zamuda, 1987).
Nevertheless, sufficient data is not now available to identify the primary controlling
factors behind the large-scale distribution of contaminants in the Gulf.
Regional correlations. We examined the correlations between various
environmental and biological variables and contaminant body burdens within the re-
gions observed to have concordant yearly shifts in body burden; the reason being the
expectation that the significant variables controlling body burden may be different in
different areas of the Gulf and that the areas providing concordant temporal trends might
offer some guidance in dividing the Gulf into regional areas.
Using the 5-month average for precipitation and temperature (no measures of P.
marinus infection included)(Table 9) shows that precipitation and temperature are only
significantly correlated with some contaminants and are only significant in 1986 and
1988. Whereas precipitation is always positively correlated, temperature is negatively
correlated,indicating that high precipitation and low temperatures over long periods of
time before sampling may influence body burden. Agricultural and industrial land use
are also important for some contaminants. Including P. marinus and necessarily using a
shorter time scale (2 month) (Table 10). shows the P. marinus prevalence and mean
infection intensity are often significantly corelated with contaminant body burdens
within regions showing similar temporal responses to climatic variation.
Despite the seeming likelihood that large-scale trends in the Gulf must ultimately
originate in Gulf-wide trends in temperature and freshwater inflow, few of the contamin-
ants show consistent correlations with either temperature or precipitation on the regional

7-48

(JN)1870 (KL)150 (SW)WilsOn

Table 9. q(:'sults Of @(-grc!ss'on 6nal" s \N,it,lin regions of SillI)arily in pollutant body bUrden as cic@ttrminvd using the K-S tc-si. Tht-@(,
tile average of the 5 months prior to s6nipling for precipitation and temperature. ' @ignifies a significant negative ceinc-latior, C! = Co:-.('@iiun
ind(m Industn! refers to indusuial land use; Agriculture refers to agricOurb) land use

Pollutant 1@8G 15,67 1 @188 1

A t s e 1) i c .... .... Length P 0.0099'
C III i LI III prvcipilalion 11 0. o -I i 7 Indti-on- 11 n 0.0473 Length 1) 0,0057
'rempt..'raturc, 1) = 0.0)67,
C p . . . . . . . . . . . . . . . .
N1 e r c u r 1, Industry P - 0.0441 P @ 0.0)30' Indkistry P = 0 0,27
AgriCUNUIle P = 0,0446
S I c- n im Agriculluit? P 0.0054 . . . . Agriculture P 0.0021 A cri c t,,:,"e P = 0 0 46

5 1 v e r
n c Precipitation P 0.0074 Pr(,cipifntion P 0.01-15
C) 11 0,0035

P H . . . . . . . . . . . . . . .

ij,40871 iKI-050

T@i!@!-' , 10. Rf-su)!@ ol ...!?hm (11 similarily ill pollulaw body hurden as ds?l(-rmini@d using 0-,(, I\*-S ie@@. T"w'-i.
u I i I i z v i I-) (. u vr i i q f., cIt I i v 21 111 r, i hr ir ii s b i i iI I n gIm, pRCil)ilbli0ll and Meall and Medilin bn (I
Perkinw.s metrinus. 011it-r obbrevialions and svjiflluls iis dem,ib(-d in Tabic. @i

Pullutall;
19 8 r, 1987 1988

A r s (- 1) i C Indusirv P = 0.0002' Length P = 0.0061
Pl-(.6pilalion P = 0.0111 prec;p@!1i lion p
Prevalence 1) = 0.0472 C) P = 0,0098
Median Inlection 1) 0.(11,35'
C a d ill I u ill C: 1 1) = 0. L, 1152, Industry P = 0,0483 Precipitation P 0.0087' . . . .
Pr(-vaionce P = 0.0.'J)O Length P = 0,0037
(t 6 n I n f c, c, I on P = 0.004 4 Mean Infection P = 0.0132'
C 0 p 1) C. r Temperature P = 0.03,311) Mean Infection P = 0.0062 Temperature P = 0.0230 Industry 11 = 0.040'2'
Median Infection P = 0.0033' Median Infection P = 0 029C,
Prevalence P = 0 0308.
NI e r c u r y I n d L; s try 1) - 0. 04 10' InduFtry P = 0.0199, . . . . Temperature P = 003c.@."'
Prevalence 11 0.0116 Njebll Infection P = 0.0187 Agriculture P - 0.01C.
Industry P = 0.0204'
11 1 u ill .-\(Iriculture P 0.0023 Agricullure P @ 0.0283 Agriculture P = 0.0037 Agriculture P = 0.0'-,-*)4
Mc-an Infection P = 0.0159' Nlean Infection P = 0.0159
Median Infection P 0.0173'
S i I v e r Precipitation P = 0.0082' . . . . . . . . Length P = 0.0168
P.evalence P = 0.0429 Cl P = 0.0073
Mean Infection P = 0.0042' Prevalence p
Median Infection P = 0.0177 Mr-dian Infection P 0.',)010
Z i 11 Ill 1.-Opll al ion 1, Om(OP2, . . . . Median h-Ilt-ction P 0.02167' L(-nclih 11 0.02,@)7

Cl P 0.0039,
P A H . . . . Prevalence P = 0.0247 Length P 0.0101
Mean Inlection P 0.024 1

level, and these regions typically cover a substantial range in latitudes (Tables 9, 10). In
fact from these analyses local imput from industrial and agricultural land use and levels
of P. marinus infection appear to be more important. Most pollutants show a significant
correlation with some measure of P. marinus infection in at least one instance. The
relationship between P. marinus and temperature and salinity (again, related to precipi-
tation) is well documented in the literature (Mackin, 1962; Soniat, 1985; Soniat &
Gauthier, 1989). correlations are more frequent using the average of the climatic date for
the 2 months before sampling rather than for the 5 months before sampling, suggesting
that response times to variations in environmental varibles might be more nearly 2 than
5 months, and this is the response time expected if P. marinus was an important factor in
body burden (Choi et al., 1989). The infrequent correlations with length, condition index
or P. marinus prevalence (as opposed to infection intensity) are also noteworthy, particu-
larly considering the frequent importance of these biological indices generally (Cossa et
al., 1980;Scott & Larence, 1982;Lytle & Lytle, 1990; Paez-Osuna & Marmolejo-Rivas,
1990; and others referenced previously) and in the temporal trends we observed. Recall,
however, that P. marinus infection intensity condition index and length are correlated on
most spatial scales.
Whether a cause and effect relationship exists between disease and contaminant
body burden has not been demonstrated. P. marinus can produce physiologic abnor-
malities in its oyster host that in turn can affect the oyster's ability of feed (Mackin & Ray,
1955). Since feeding is one method of uptake for certain contaminants, such as arsenic
(Sanders et al., 1989). boyd burden could be reduced with increased infection of P.
marinus. Contaminant exposure and disease may also affect the health of the digestive
gland (Bayne et al., 1979; Axiak et al., 1988). Cadmium has been shown to stimulate
phagocytosis (Cheng, 1988a) which is one means of defense against disease (Fisher &
Nowell, 1986; Fisher & Tamplin, 1988). The negative correlation between cadmium body
burden and P. marinus may be a reflection of this stimulatory action.

CONCLUSIONS

The results of environmental monitoring studies have long been looked upon with
suspicion when the results have been compared over varied environmental conditions
(Phillips, 1977a). Our results stress the variability of pollutant body burdens as they relate
to variations in environmental and physiological conditions. Consideration of local
controls are important, but so are large-scale geographic and climatic controls which can
override the local controls. All biological variabls responded regionally on a Gulf-wide
scale. Local controls were relatively unimportant throughout the Gulf in explaining
temporal trends,albeit of more importance in explaining the spatial relationships within
any one year. Among the contaminants, local and regional controls were important in
discrete geographic areas in most cases. Some contaminants responded primarily region-
ally (e.g. selenium), some primarily locally (e.g. silver). These regional differences
affected not only the temporal trends, but also the spatial distribution of body burden
within any one year. Accordingly, consideration of the spatial distribution of body
burden, and particularly, consideration of the temportal trends in body burden must take
into account that climatic factors may be more important in some regions than others and
that the health of the population may contribute markedly to body burden;consequently,

7-51

about controlling the health of populations may moderatly affect temporal trends by
medtating the climatic response.
Variations in source content have not been included in the analysis. Inasmuch as
arguably the most important parameter controlling body burden has not explicitly been
included in the analysis, the fact that several environmental and biological parameters
nevertheless demonstrated significant correlations with body burden is noteworthy.
Among the biological parameters, factors related to disease, and among the environ-
mental parameters,factors related to land use and meterological conditions are likely to
play an important role in determining pollutant body burden at least regionally. It is
particularly important to recognize that regional factors of importance may go unrecog-
nized at larger geographic scales because statistical analyses may be compromised by
varying responses to selected variables in different regions. We should not expect
selected variables to be consistently of paramount importance everywhere.
Both P. marinum prevelence and intensity, as well as the other biological variables,
and contaminant body burdens must ultimately respond to temperature and rainfall.
These latter two parameters should be the initial factors mediating the effect of climate on
pollutant body burden. Whether they are the proximate causes of whether biological
parameters intercede, remains unclear. Certainly, however, factors like disease intensity
and the gametogenic cycle play an important role in determining the health and
condition of Gulf oyster populations. That the contaminant body burdens in the Gulf are
almost uniformly relatively low suggests that the correlations observed between body
burden and biology originate either in biological control of contaminant body burden or
coincident control of both directly by climatic cycles rather than the impact of pollution on
organism health (e.g. Khan, 1990) which might result at higher exposure levels.

Acknowledgements. The authors wish to thank the Status and Trends field and laboratory crews at
Texas A & M University who collect and analyse the trace metal and PAH samples. This research was
conducted under grants from the U.S.Department of Commerce,National Oceanic and Atmospheric
Administration Ocean Assessments Division #50-DGNC-5-00262 and #46-DGNC-0-00047, the Sea
Grant College Program #NA89AA-D-SG139 and the Center for Energy and Minerals Resources and
a Sea Grant Marine Fellowship to E.W.Funds for computer analysis were provided by the TAMU
College of Geosciences. We appreciate this support.

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