NOAA Status and Trends Mussel Watch Project

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

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NOAA STATUS AND TRENDS
Mussel Watch Project
Technical Report
Year VIII

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Texas A&M Research Foundation

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OAA NATIONAL
STATUS AND TRENDS

Mussel Watch Project

Year 8 Technical Report

Prepared by

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

Submitted to Property of CSC Library

U.S. Department of Commerce
National Oceanic & Atmospheric Administration
1305 East-West Hwy.
38 Silver Spring, MD 209 10

U . S . DEPARTMENT OF COMMERCE NOA A
COASTAL SERVICES CENTER
2234 SOUTH HOBSON AVENUE
September 1994 CHARLESTON , SC 29405-24 11'@

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TABLE OF CONTENTS

Introduction ........................................................................................... 1

Reprint 1: Sources of Local Variation in Polynuclear Aromatic
Hydrocarbon and Pesticide Body Burden in Oysters
LCtassostrea. virgin from Galveston Bay, Texas ............................... 10

Reprint 2: Sediment Contaminants in Casco Bay, Maine:
Inventories, Sources, and Potentialfor Biological Impact ...................... 21

Reprint 3: Polynuclear Aromatic Hydrocarbon Contaminants
in Oysters from the Gulf of Mexico (1986-1990) .................................... 37

Reprint 4: Modeling Oyster Populations I[. Adult Size and
Reproductive Effort ............................................................................. 46

Reprint 5: Correlation Between Bioassay-Derived P4501AI
Induction Activity and Chemical Analysis of Clam
(Laternula. elliptica) Extracts from McMurdo Sound,
Antarctica .......................................................................................... 65

NOAX S NATIONAL STATUS AND TRENDS (NS&T) MUSSEL WATCH
PROGRAM - GULF OF M]EXIC0

The purpose of the NOAA National Status and Trends (NS&T)
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?
(2) Are these conditions getting better or worse?

This report represents the Year 8 Technical Report from this multi-
year project. These questions have been addressed in detail as evidenced
by the scientific papers and reports that have resulted from the
Geochemical and Environmental Research Group's (GERG)
interpretations of the Gulf Coast data (Table 1). Publications not
included in GERG's previous Technical Reports are contained in this
technical report.

This report is an update on the current condition of the Gulf of
Mexico coastal zone, based on results from Years 1 through 8 of the
NOAA NS&T Mussel Watch Project. Following is a brief sampling survey
of these years:

Year 1 - 49 sites (147 stations) of the original 51 sites were
successfully sampled. Sediments and oysters were
analyzed at triplicate stations from all sites.
Year 2 - 48 sites (144 stations) of the original 51 sites were
successfully sampled. Sediments and oysters were
analyzed at triplicate stations from all sites.
Year 3 - Twenty (20) sites were added to the original list of 51
sites for a total of 71 sites. Sixty-four (64) sites (192
stations) of the 71 sites were sampled (only 19 of the
new sites were sampled). Oysters were analyzed at
triplicate stations from all sites. Sediments were
analyzed at only the new sites (three stations analyzed
per site).
Year 4 - Seven (7) new sites were added (only six of the new sites
were successfully sampled). Sixty-seven (67) sites (201
stations) of the 78 total sites were sampled. Oysters
were analyzed at triplicate stations from all sites.
Sediments were analyzed at only the new sites (three
stations analyzed per site).
Year 5 - Three (3) new sites were added to the sampling project
(only two of these sites were successfully sampled;
79:MBDR and 80:PBSP). Sixty-eight (68) sites (204
stations) of the 80 total sites were sampled. Oysters
were analyzed at triplicate stations from all sites.

Sediments were analyzed at only the new sites (three
stations analyzed per site).
Year 6 - Two (2) new sites were added to the sampling project
(81:BHKF in Bahia Honda Key, FL and 63:LPGO in
Lake Pontchartrain, LA). Sixty-four (64) sites (192
stations) were sampled. Oysters were analyzed at
triplicate stations from all sites. Sediments were
analyzed at only the new sites (three stations analyzed
per site) -
Year 7 - Five new sites were established including three new
sites in Puerto Rico (Sites 86 to 88) and two new sites
in Choctawhatchee Bay (Sites 84 and 85). Sixty-seven
(67) sites were analyzed. Only one oyster analysis was
conducted at each of the old sites on a composite from
the three stations. Sediments were analyzed at the five
new sites and one site in Florida (PBPH) (three stations
analyzed per site).
Year 8 - Sixty-eight (68) existing sites were sampled. Only one
oyster analysis was conducted at each of the existing
sites on a composite from the three stations.
Sediments were not collected at any sites.

Details of the sample collection and location of field sampling sites are
contained in a separate report titled "Field Sampling and Logistics in
Year 8".

The oyster and sediment samples were analyzed for contaminant
concentrations [trace metals, polynuclear aromatic hydrocarbons (PAH),
pesticides and polychlorinated biphenyls (PCBs)], and other parameters
that aid in the interpretation of contaminant distributions (grain size,
oyster size, hpid content, etc.). The analytical procedures used and the
QA/QC Project Plan are detailed in a separate report titled "Analytical
Methods". The data that were produced from the sample analyses for
Year 8 are found in a separate report titled "Analytical Data".

A complete and comprehensive interpretation of the data from the
National Status and Trends Project for oyster data coupled with the
sediment data is an on-going 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). As part of the data interpretation and dissemination, over 40
presentations of the NOAA NS&T Gulf Coast Mussel Watch Project were
given at national and international meetings. With eight years of data,
the question of temporal trends of contaminant concentrations has been
addressed. A general conclusion found for most contaminants measured
is that the concentrations have remained relatively constant over the
eight-year sampling period. This general trend, however, is not observed
at all sites. Some sites show significant changes (both increases and
decreases) among the years. Continued sampling is addressing 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.

During Year 3 of this project, 20 new sites were added. These sites
were chosen to be closer to urban areas, and therefore, to the sources of
contaminant inputs. These 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. Over the subsequent years of the project (Years 4
through 7) additional sites have been added to increase the
representative coverage of the Gulf of Mexico and U.S. Caribbean
territories.

While sampling sites for this project were specifically chosen to
avoid known point sources of contamination, 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
further 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 through 7 are closer to urban areas and 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.

This document represents one of three report products as part of
Year 8 of the NS&T Gulf of Mexico projects. The other two reports are
entitled:

Analytical Data, Year 8
Field Sampling and Logistics, Year 8

Table 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 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

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

Wade, T.L., M.C. Kennicutt, 11 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
contaminant distributions in oysters from the Gulf of
Mexico. In: Proceedings, Oceans '89 Conference, Seattle,
WA, pp. 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. In: Proceedings, Oceans '89
Conference, Seattle, WA, pp. 550-553. IV

Wade, T.L. and C.S. Giam (1989) Organic contaminants in the
Gulf of Mexico. In: Proceedings, 22nd Waterfor Texas
Conference, Oct. 19-21, 1988, South Shore Harbour Resort
and Conference Center, League 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: 551-553. IV

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 PCB's in oysters
(Crassostrea virginica) and sediments from the Gulf of
Mexico, 1986-1987. Marine 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. coastal areas.
Chemosphere, 20: 647-662. IV

Brooks, J.M., M.C. Kennicutt H, T.L. Wade, A.D. Hart, G.J.
Denoux and T.J. McDonald (1990) Hydrocarbon
distributions around a shallow water multiwell
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. In: 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. In: Proceedings Galveston Bay
Characterization Workshop, February 21-23, pp. 65-67. V1

Wade, T.L. I.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 In: Proceedings Galveston Bay Characterization
Workshop, February 21-23, pp. 68-70. VI

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

Sericano, I.L., T.L. Wade and J.M. Brooks (1991) Transplanted
oysters as sentinel organisms in monitoring studies. In:
Proceedings Galveston Bay Characterization Workshop,
February 21-23, 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. In: Proceedings Galveston
Bay Characterization Workshop, February 21-23, 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. In: Proceedings 23rd Annual Offshore
Technology Conference, No. 6529, Houston, TX, May 6-
9,, pp. 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. SeaTechnology, 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 Prediction. 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., E.N. Powell, M.A. Craig, T.L. Wade and J.M.
Brooks (199 1) 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: 233-241. IV, V

Wilson, E.A., E.N. Powell, T.L. Wade, R.J. Taylor, B.I. Presley
and J.M. Brooks (1991) 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. Helgolander
Meeresunters, 46: 201-235. V, VI

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: Marine
Ecology, 13: 243-270. IV

Hofmann, E.E., E.N. Powell, J.M. Klinck E.A. Wilson (1992)
Modeling oyster populations 111. critical feeding
periods, growth and reproduction. J. Shellfish
Research, 2: 399-416. V

Sericano, J.L., T.L. Wade, A.M. El-Husseini and J.M. Brooks
(1992) Environmental significance of the uptake and
depuration of planar PCB congeners by the American
oyster (Crassostrea virginica). Marine Pollution Bulletin,
24: 537-543. 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. In:
Proceedings MTS '92, Marine Technology Society, Oct. 19 -
21, Washington, D.C. pp. 223-229. VI

Tripp, B.W., I.W. Farrington, E.D. Goldberg and J.L. Sericano
(1992) International mussel watch: the initial
implementation phase. Marine Pollution Bulletin, 24:
371-373. V1

Sericano, J.L., T.L. Wade and J.M.- Brooks (1993) The
usefulness of transplanted oysters in biomonitoring
studies. In: Proceedings of The Coastal Society Twetfth
International Conference, Oct. 21-24, 1990, San Antonio,
TX, pp. 417-429. V, VII

Wade, T.L., J.L. Sericano, LM. Brooks and B.J. Presley (1993)
Overview of the first four years of the NOAA National
Status and Trends Mussel Watch Program. In:
Proceedings of The Coastal Society Twelfth International
Conference, Oct. 21-24, 1990, San Antonio, TX, pp. 323-
334. V, V111

Sericano, J.L., T.L. Wade, E.N. Powell and J.M. Brooks (1993)
Concurrent chemical and histological analyses: Are
they compatible? Chemistry and Ecology, 8: 41-47. V, VI

Sericano, J.L., T.L. Wade, J.M. Brooks, E.L. Atlas, R.R. Fay and
D.L. Wilkinson (1993) National Status and Trends
Mussel Watch Program: chlordane-related compounds
in Gulf of Mexico oysters: 1986-1990. Environmental
Pollution, 82: 23-32. V, VI

Wade, T.L., T.J. Jackson, J.M. Brooks, J.L. Sericano, B. Garcia-
Romero and D.L. Wilkinson (1993) Trace organic
contamination in Galveston Bay oysters: results from
the NOAA National Status and Trends Mussel Watch
Program. In: Proceedings, The Second State of the Bay
Symposium, Galveston, TX, February 4-6, pp. 109-111. V11

Presley, B.J. and K.T. Jiann (1993) Indicators of trace metal
pollution in Galveston Bay. In: Proceedings, The Second
State of the Bay Symposium, Galveston, TX, February 4-6,
pp. 127-13 1. VII

Wade, T.L., T.J. Jackson, T.J. McDonald, D.L. Wilkinson, and
J.M. Brooks (1993) Oysters as biomonitors of the APEX
barge oil spill. In: Proceedings, 1993 International Oil
Spill Conference, Tampa, FL, March 29-April 1, pp. 127-
131. V11

Palmer, S.J., B.J. Presley, R.J. Taylor and E.N. Powell (1993)
Field studies using the oyster Crassostrea virginica to
determine mercury accumulation and depuration rates.
Bulletin Environmental Contamination Toxicology, 51:
464-470. VII

Morse, J.W., B.J. Presley and R.J. Taylor (1993) Trace metal
chen-dstry of Galveston Bay: water, sediment and biota.
Marine Environmental Research, 36: 1-37. VII

Sericano, J.L. (1993) The American oyster (Crassostrea
v&ginica) as a bioindicator of trace organic
contamination. Ph.D. Dissertation, Department of
Oceanography, Texas A&M University, 242 p. V111

Palmer, S.J. and B.J. Presley (1993) Mercury bioaccumulation
by shrimp (Penaeus aztecus) transplanted to Lavaca
Bay, Texas. Marine Pollution Bulletin, 26(10): 564-566. VII

Garcia-Romero, B., T.L. Wade, G.G. Salata, and J.M. Brooks
(1993) Butyltin concentrations in oysters from the Gulf
of Mexico during 1989-1991. Environmental Pollution,
81: 103-111. V1, V111

Ellis, M.S., K.-S. Choi, T.L. Wade, E.N. Powell, T.J. Jackson and
D.H. Lewis (1993) Sources of local variation in
polynuclear aromatic hydrocarbon and pesticide body
burden in oysters (Crassostrea virginica) from Galveston
Bay, Texas. Comparative Biochemistry and Physiology,
106C: 689-698. V1, VHI

Kennicutt, M.C. 11, T.L. Wade, B.J. Presley, A.G. Requejo, J.M.
Brooks and G.J. Denoux (1993) Sediment contaminants
in Casco Bay, Maine: inventories, sources and potential
for biological effects. Environmental Science and
Technology, 28(l): 1-15. VIII

Jackson, T.J., T.L. Wade, T.J. McDonald, D.L. Wilkinson and J.M.
Brooks (1994) Polynuclear aromatic hydrocarbon
contaminants in oysters from the Gulf of Mexico (1986 -
1990). Environmental Pollution, 83: 291-298. V1, VU, VM

Sericano, J.L., T.L. Wade, B. Garcia-Romero and J.M. Brooks
(1994) Environmental accumulation and depuration of
tributyltin by the American Oyster, Crassostrea
virginica. Marine Environmental Research (in press). IV

Hofmann, E.E., J.M. Klinck, E.N. Powell, S. Boyles, M. Ellis
(1994) Modeling oyster populations U. Adult size and
reproductive effort. Journal of Shel4flsh Research, 13(l):
165-182. V, Vin

McDonald, S.J., M.C. Kennicutt H, J.L. Sericano, T.L. Wade, H.
Liu, and S.H. Safe (1994) Correlation between bioassay-
derived P450 1A I -Induction activity and chemical
analysis of clam (Laternula efliptica) extracts from
McMurdo Sound, Antarctica. Chemosphere, 28(12):
2237-2248. Vin

Sericano, J.L., T.L. Wade and J.M. Brooks (1994) Accumulation
and depuration of organic compounds by the American
oyster (Cassostrea virginica). Science of the Total
Environment (in press). IX

Sericano, J.L., S.H. Safe, T.L. Wade, and J.M. Brooks (1994)
Toxicological significance of non-, mono-, and di-ortho
substituted polychlorinated biphenyls in oysters from
Galveston and Tampa Bays. Environmental Toxicology
and Chemistry, 13(11): x-xx (in press). Ix

Velinsky, D.J., T.L. Wade, C.E. Schlekat, B.L. McGee, and B.J.
Presley (1994) Tidal river sediments in the Washington,
D.C. area. 1. Distribution and sources of trace metals.
Estuaries, 17: 305-320. Ix

Wade, T.L., D.J. Velinsky, E. Reinharz, and C.E. Schlekat (1994)
Tidal river sediments in the Washington, D.C. area. U.
Distribution and sources of organic contaminants.
Estuaries, 17: 321-333. ix

Reprint 1

Sources of Local Variation in Polynuclear
Aromatic Hydrocarbon Pesticide Body
Burden in Oysters (Crassostrea virginica)
from Galveston Bay, Texas

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

Comp. Biochern. Physiol. Vol. I D6C, No. 3. pp. 689-698, 1993 Perpmon Press Ltd
Printed in Great BriWn

SOURCES OF LOCAL VARIATION IN POLYNUCLEAR
AROMATIC HYDROCARBON AND PESTICIDE BODY
BURDEN IN OYSTERS (CRASSOSTREA VIRGINICA)
FROM GALVESTON BAY, TEXAS

MATTIIEW S. ELLIS,* KWANG-SIK CHoi,* TERRY L. WADEJ ERic N. POWELL,*
THomAs J. JAcKsoNt and DONALD H. LEWIS:
*Department of Oceanography; tGeochemical and Environmental Research Group; and
tDepartment of Veterinary Pathobiology, Texas A&M University, College Station, TX 77843, U.S.A.

(Received 28 June 1993; acceptedfor publication 6 August 1"3)

Abstract-1. Eggs and sperm contain significantly more PAH (polynuclear aromatic hydrocarbon) than
somatic tissues in oysters (Crassostrea virginica) taken from Galveston Bay.
2. The quantity of gonadal material was the most important correlate of PAH body burden.
3. Eggs, but not sperm, were enriched in chlorinated compounds (e.g. DDD, chlordane), while both
eggs and sperm were enriched in total PCBs relative to somatic tissue.
4. Oysters may lose up to 50% of their total body burden of certain PAHs and pesticides in a single
spawn.

INTRODUMON correlated with latitude in the Gulf of Mexico. Con-
Bivalve molluscs have frequently been used as indi- taminant body burdens average higher at higher
cator organisms in studies monitoring levels of con- latitudes. Wilson el al. (1990) suggested that the
taminants in the environment. These organisms are latitudinal temperature gradient in the Gulf produced
utilized because of their ability to accumulate and variation in reproductive effort and that this variation
in reproductive effort affected PAH body burden
concentrate both metal and organic contaminants sufficiently to override the effect of local variation in
enabling them to serve as long-term integrators of contaminant loading. Wilson et al. (1992), in a more
their environment (Phillips, 1977). One such program thorough analysis, showed that PAH body burden
is the NOAA Status and Trends (NS&T) Program responds to climate change and that biological
("Mussel Watch") designed to monitor changes in factors are the likely intermediaries between the
environmental quality along the Atlantic, Pacific and climate's effect on temperature and freshwater inflow
Gulf'coasts of the United States by measuring levels and the final body burden of PAHs.
of chemical contaminants in fish, bivalves, and sedi- Two likely intermediaries are spawning and dis-
ments and identifying biological responses to those ease. Spawning has frequently been forwarded as an
contaminants (e.g. Wilson el al., 1992, 1990, Scricano important route of depuration (Marcus and Stokes,
el aL, 1990; Presley et aL, 1990). 1985; Jovanovich and Marion, 1987; Cossa. 1989)
Unfortunately, many biological and environmental because lipid loss peaks at this time (Chu ei al., 1990).
factors affect the rate and extent of bioaccumulation Parasites and pathogens are less frequently impli-
besides contaminant availability. Biological factors cated (Khan, 1987), but parasites and pathogens
include differential growth rate (Cunningham and should have an effect; if for no other reason, they
Tripp, 1975; Boyden, 1977), reproductive stage (Cun- frequently reduce spawning frequency or the number
ningham and Tripp, 1975; Frazier, 1975; Martinici& of gametes per spawn (Ak berali and Truema n, 1985;
et al., 1984), stress and disease (Shuster and Pringle, Ford and Figueras, 1988; Barber et al., 1988). In
1969; Sindermann, 1983; Moore et al., 1989). These oysters, both spawning frequency and disease are
biological factors make spatial and temporal com- significantly affected by temperature and salinity
parisons designed to evaluate the status and trends of (Hofmann et al., 1992, Soniat and Gauthier, 1989)
contaminant loading more difficult. The NOAA and thus could serve as important intermediaries by
Status and Trends Program has proven to be no which variation in climate might affect contaminant
exception. body burden.
In the Gulf of Mexico, the mollusc used for Climate exerts its influence over large geographic
monitoring by NOAA is the oyster Crassostrea vir- scales. Biological parameters capable of responding
ginica. Analysis of the first 4 yr of NS&T data has to climate change and, thus, affecting contaminant
shown that the body burden of polynuclear aromatic body burden on a large geographic scale, should
hydrocarbons (PAHs) and pesticides in oysters is 689 certainly do so as well on a local scale. Accordingly,

690 M. S. ELLis et al.

Table 1. The scale used for the analysis of gonadal stage (after GERG, 1990)
Assigned
numerical
Developmental stage value Description
Sexually undifferentiated I Uttle or no gonadal tissue visible
Early development 2 Follicles beginning to expand
Mid-development 3 FoIlicks expanded and beginning to coaksce; no
mature gametes present
late development 4 Folficles greatly expanded, coalesced, but
considerable connective tissue remaining; some
mature gametes present
Fully developed 5 Most 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

spawning frequency and disease should be important son to the Gulf-wide mean (Sericano et al., 1990;
sources of local (within population) variability in Wade et al., 1988). September is near the end of the
contaminant body burden. Monitoring programs spawning season; most individuals should have
typically sample infrequently (NS&T samples once spawned at least twice over the 4 previous months.
per year) so that the basis for within-sample variabil- The oysters were placed on ice and returned to the
ity is an important consideration. Accordingly, the laboratory. Maximum length and wet weight were
primary purpose of this study was to examine sources determined. The condition of each meat was rated on
of local variability in PAH body burden at any a serniquantitative scale from I (very good) to 9 (very
sampling period. Some analyses of chlorinated pesti- poor), according to Quick and Mackin (1971). A
cides and PCBs were also conducted. small section of gonadal tissue was taken and fixed in
Unfortunately, the variables likely of most import- Davidson's fixative (Fig. 28 in NOAA, 1983). A small
ance in determining local variability in body bur- section of mantle tissue was removed for determi-
den, spawning frequency and the time since the last nation of P. marinus infection following Ray (1966).
spawn, are variables that cannot be readily measured The remaining tissue was placed in a precombusted
even in a temporally-intensive sampling program mason jar with a teflon-lined screw cap and frozen for
because continuous (or dribble) spawning is a fre- PAH analyses.
quent condition at latitudes south of Chesapeake Perkinsus marinus infection intensity was rated
Bay, including the entire Gulf of Mexico (Hofmann on the 0 (uninfected) to 5 (highly infected) point
et al., 1992). Consequently, more readily measured scale of Mackin (1962) as modified by Craig et al.
variables must be. used as surrogates for the more (1989). Tissue samples were embedded in paraffin,
desirable variables. Thus, we examined a series of sectioned at 6,um and stained in Harris' hematoxylin
indices related to reproductive state, including stage and picro/Navy eosin (Preece, 1972). Reproductive
of reproduction and the quantity of gonadal material stage was rated on a scale of I (sexually undifferenti-
present, and a series of indices related to health, ated) to 8 (spawned out) slightly expanded from
namely digestive gland atrophy, condition and Ford and Figueras (1988) by GERG (1990)
Joerkbuw marinus infection intensity. PerkbLw mari- (Table 1). Digestive gland atrophy was rated semi-
nus, an endoparasitic protozoan, is responsible for quantitatively from 0 (no atrophy) to 4 (extreme
high mortality (typically > 50%) in market-sized atrophy) as described by Gauthier et al. (1990)
oysters in the Gulf each year (Hofstetter, 1977; (Table 2).
Osburn et al., 1985; Ray, 1987) and is known to delay The analytical procedures used for PAHs and
reproduction (White et al., 1988; Wilson et al., 1988). pesticides were based on NOAA's NS&T techniques
Digestive gland atrophy is a putatively pathogenic for organic compounds (MacLeod et al., 1985) with
condition (e.g. Marig6mez et al., 1990; Moore et al., some modification by Wade c- al. (1988). These
1989) common in Gulf coast oysters (Gauthier et al., methods have been detailed elsewhere (Wade el al.,
1990). 1988; Wade and Sericano, 1989; Sericano et al., 1990;

METHODS Table 2. The scale used for digestive gland atrophy
Assigned numerical
Within -population differences in body burden value Description
Oysters were collected in September, 1990, from 0 Normal
Less than one-half atrophied
Confederate Reef in the West Bay extension of 2 About one-half atrophied
Galveston Bay. Confederate Reef oysters normally 3 Greater than one-half atrophied
have a relatively high PAH body burden in compari- 4 Completely atrophied

PAH and pesticide body burden in oysters 691

GERG, 1990). Only a brief overview will be given &dy burden of eggs and sperm
here. In July 199 1, additional oysters were obtained from
Samples were extracted with methylene chloride Galveston Bay for examining the relative PAH, chlo-
after drying with Na2SO, The samples were then rinated pesticide, and PCB content of eggs, sperm
purified by silica/alurnina column chromatography' and the remaining body tissues. Most oysters were
In order to mmove lipids, a higli-performance liquid 7-12 cm, long and exhibited fully-developed gonads.
chromatography separation was performed. Purified Oysters were shucked and their sex determined by
extracts were then analyzed by gas chromatography microscope slide smear.
with a mass spectrometry detector, GC/MS/SIM for The contaminant content of the gametes, which is
PAHs and GC-ECD for chlorinated pesticides and the only tissue component lost during spawning, may
PCBs. All concentrations am reported as nanograms be dissimilar from the remaining gonadal tissue.
of analyte per gram dry weight of sample, or ppb. Therefore, the eggs and sperm were isolated from the
Concentrations in the procedural blanks were in all mmaining gonadal and somatic mass. The body of
cases, below reporting levels for each individual each oyster was separated from other somatic tissues.
analyte. The accuracy and precision of these methods The remainder including gill, mantle, adductor
have been established by several intercalibration exer- muscle and labial palps was stored at - 20'C for
cises overseen by the U.S. National Institute of PAH, 'pesticide, and PCB analysis. Gonads contain-
Standards and Technology.
ing eggs or sperm were excised from the visceral mass
Oyster gonadal tissue surrounds much of the body using scissors and forceps. Gonads were placed on a
mass and, thus, is difficult to excise cleanly and weigh petri dish and phosphate buffered saline (0. 15 M
(Kennedy and Battle, 1964; Morales-Alamo and NaCl, 0.003 M KC1, 0.01 M phosphate buffer,
Mann, 1989). Thus, a quantitative gonadal index pH 7.4) (PBS) was added. Eggs or sperm were ex-
based on gonad weight, as is frequently used in tracted by squeezing the gonads with a rubber-headed
invertebrates and fish, is not available. Accordingly, syringe piston. The egg extract was then filtered
a polyclonal rabbit anti-oyster egg antibody was used
to quantify the amount of egg protein present (Choi through a 100jurn nylon mesh screen; the sperm
et al., 1993). A single radial immunodiffusion assay extract was filtered through a 30,urn nylon mesh
(Mancini et al., 1965; Garvey et al., 1977) was screen.
performed to quantitate egg protein using 1.5% Oyster egg filtrates were washed 4 times by resus-
agarose in barbitone bufrer (0.01 M sodium barbital, pending the filtrates into 30 ml of PBS and centrifug-
0.0022 M barbital, 0.01 % sodium azide as preserva- ing at 700 g for 10 min. During each washing, tissue
tive, pH 8.6). Two millilitres of the rabbit serum debris and other impurities sedimented on the egg
containing anti-oyster antibody was mixed in 18 ml of pellets were removed by pasteur pipette. After the
the agarose gel and cast on a 10 x 10 cm glass plate. final washing, the egg pellets were resuspended into
Four millilitre diameter wells were made on the plate an equal volume of PHS. Five millilitres of the
using a gel puncher and 20 p I of oyster egg standard resuspension was transferred to a 15 ml centrifuge
(0.05 mg ml- I to 3.2 ing ml - 1) or the sample were tube, 7 ml PBS added to resuspend the eggs, and the
placed in the wells and incubated in a humid chamber suspension centrifuged at 5OOg for l5min. Any
for 48 hr at room temperature. After incubation, the remaining tissue debris layered on the egg pellet was
plate was pressed, dried, stained with 0.5% (w/,) removed using a pasteur pipette. Egg pellets from
Coomassie Brilliant Blue, and destained with 50% 10-20 oysters were pooled in a 50 ml centrifuge tube
EtOH and 10% acetic acid. Diameters of the precipi- and sedirriented by centrifugation (700 g for 15 min).
tation rings were measured to the nearest 0. 1 mm. A Oyster egg pellets were then resuspended into an
standard curve was constructed by plotting concen- equal volume of PBS. A 60% Percoll solution (4:6
tration of the egg standard against the diameter PBS/100% Percoll) (100% Percoll is 9:1 Percoll
squared of the precipitation rings, and the concen- stock: 10X PBS) was prepared. Five millilitres of egg
tration of each sample was read from the curve. suspension was mixed with 35 ml 60% Percoll and
Removal of the body section for histological analy- centrifuged at 900 g for 20 min. Oyster eggs formed
sis biases both the total PAH concentration and the an aggregate at the top of the centrifuge tube after
gonadal quantity as measured by us. Sericano et al. centrifugation. Purified eggs were harvested from the
(in press b) showed that the effect of this bias on PAH tube and washed twice by centrifuging at 700g for
content is an expected 10-20% reduction in measured 10 min.
body burden. For gonadal quantity, the percent Oyster sperm filtrates were washed 4 tinies with
reduction can be expected to be considerably higher. PBS by centrifuging at 700 g for 15 min. Tissue debris
Readers are cautioned not to accept the reported found at the top of the oyster sperm pellet was
measures of gonadal quantity as true measures of removed using a pasteur pipette during each washing
completely intact oysters. However, as most oysters step. After the final washing, the sperm extracts were
wem similar in size, the bias introduced in both msuspended into an equal volume of PBS. 70%
measures would be equivalent over all samples and Percoll was prepared and 35 ml 70% Percoll was
thus not compromise the data analysis. mixed with 5 ml sperm suspension and centrifuged at

692 M. S. F-- el al.

900 g for 20 min. Oyster sperm were found at the
bottom of the centrifuge tube and other impurities
found at the top of the Percoll as a float. Purified
oyster sperm were pooled from 20-30 oysters and
washed twice with PBS by centrifuging at 8OOg for
15 n-dn.
Because an involved procedure of this sort could
lead to significant contamination, each solution was
N subjected to chemical analysis. No solutions were
.'T .2 found to be significantly contaminated by PAHs,
;; r-: pesticides or PCBs.

RESULTS
Z S Within -population differences in PAH body burden
06
C4
Z 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 9.6 g, condition
r code 4.3 (fair plus), Perkinsus marinus infection inten-
sity 1.45 (light plus), and digestive gland atrophy 2.1
(about half atrophied). The sample contained individ-
uals covering nearly the entire range of condition
C6
00 00 C4
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
C El cc a variability in this data set is typical of single collec-
H-. Z Z Z
00 tions 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
8 heavier than males (9.9 g vs. 8.6 g). The weight differ-
ence is considerable since females are actually 0.2 cm
ValL
0 a shorter on average. Condition code for both sexes
L was also fairly close (4.6 for males vs. 4.2 for females)
as was digestive gland atrophy (1.8 for males and 2.2
for females). Perkinsus 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
V It 4.8 x 10' fully-developed eggs per female). As a sec-
tion 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 five most important
ro C0 C. PAHs: fluoranthene, phenanthrene, pyrene, naph-
Z thalene and chrysene. Males and females had similar
body burdens except for fluoranthene where females
had about one-third more. Means for both sexes
ranged from 12.0 ng g dry wt-' for phenanthrcne to
49.0 ng g dry wt -I 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
14 relationship with the PAHs, the relationships among

PAH and pesticide body burden in oysters 693

Table 4. Best 3-variable model for each biological variable for all Table 6. Best 3-variable model for each biological variable for male
oysters combined (i.e. both sexes combined) and the amount of oysters and the amount of variation explained (R'). Significant
variation explained (R). Significant partial correlations are shown partial correlations are shown by asterisks, as defined in Table 4
by asterisks: 00.05 < P < 0.01; **,0.025 < P < 0.05; 1*00.01 < Explanatory variable
P < 0.025; 00**0.001 < P < 0.01; ****00.0001 < P < 0,001 Variable R2 (N = 10)
Explanatory variable
Variable R2 (N = 39) Gonadal stage 0.70 Length
Perkbmw marbw 0.18 Condition code Wet weight
infiection intensity Wet weight Perkbuw marinus infwion
F,,*** intensity***
Digestive gland 0.14 Length Condition code 0.20 Length
atrophy Condition code Wet weight
Gonadal stage- Perkbuw marinw infection intensity
Sex 0.21 Length Perkinsus marinus 0.74 Length-
Condition code infection intensity Wet weight-
P. marinus infection intensity*** Digestive gland atrophy""
Gonadal stage 0.34 Condition code* Digestive gland 0.80 Perkinnis marinus
Wet weight**** atrophy infection intensity""
Digestive gland atrophy* Length..
Condition code 0.15 Gonadal stage* Wet weight"
Wet weight"
Digestive gland atrophy
Considering both sexes together, condition code
the biological variables themselves must be under- and sex were the most important variables correlating
stood. Because of the many significant correlations with the PAHs (Table 7). Among the females, go-
among them, we chose to identify the best 3-variable nadal quantity had a significant effect in three of five
model explaining variation for each of the important cases (Table 8): fluoranthene, pyrene and chrysene.
biological variables, as detailed in Tables 4 to 6. Each of the contaminant's concentrations was higher
Because gonadal quantity was measured in only 30 of in females having more eggs. Digestive gland atrophy
the 40 individuals and only in females, we examined was also a significant correlate of chrysene. Female
the data with and without this variable included. The oysters having a higher degree of atrophy had more
variables examined were length, wet weight, Perkin- chrysene. If gonadal quantity was removed, few
sus marinus infection intensity, digestive gland atro- significant correlations remained. Among the males,
phy, sex, condition code, gonadal stage and gonadal digestive gland atrophy was significantly correlated in
quantity. three of five cases (Table 9). PAH concentration was
lower in male oysters characterized by a greater
The important correlations were: (a) between sex degree of digestive gland atrophy. Condition c,)de
and P. marinus infection intensity, males had fighter was significant in two of five cases; higher condition
infections; and (b) between gonadal stage, condition code (less healthy) occurred with higher PAH concen-
code and digestive gland atrophy. Among the tration.
females, only the relationship between gonadal stage
and condition code remained significant. Among the
males, digestive gland atrophy was correlated with p. Body burden of eggs and sperm
marinus infection intensity. Inasmuch as the two sexes Samples of pure eggs and sperm, collected from
were distinctive in the relationships among biological oysters taken earlier in the spawning season than
attributes, we will consider the sexes separately in those supporting the previous data, had significantly
most of the remaining analyses. higher PAH levels than somatic tissue for all five

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. Significant partial correlations are shown by asterisks, as defined in Table 4
With gonadal quantity (N = 23) Without gonadal quantity (N = 29)
Variable R2 Explanatory variable R2 Explanatory variable
Gonadal stage 0.54 Length 0.47 Condition code
Condition code" Wet weight'-**
Wet weight- Digestive gland atrophy
Condition code 0.23 Length 0.11 Gonadal stage
Gonadal stage" Wet weight
Wet weight*** Digestive gland atrophy
Perkimus marinus infection 0.22 Length 0.06 Condition code
intensity Gonadal stage Gonadal stage
Digestive gland atrophy Digestive gland atrophy
Digestive gland atrophy 0.16 Perkkms marinus infection 0.11 Condition code
intensity Wet weight
Length Gonadal stage
Gonadal quantity Wet weight 0.07 Perkinsus marinus infection
intensity
Wet weight
Digestive gland atrophy

694 M. S. ELLis et al.

Table 7. Best 3-variable model for each PAH for ail oysters Table 9. Best 3-variable model for each PAH for male oysters and
combined and the amount of variation explained (fil). Significant the amount of variation explained (R'). Significant partial corm.
partial correlations am shown by asterisks, as defined in Table 4 lations am shown by asterisks. as defined in Table 4
Variable R2 Explanatory variable Variable R2 Explanatory variable
Flueranthene 0.49 Length
Fluoranthene 0.20 Length Gonadal stage
Perkkna maruna infection intensity Digestive gland atrophy*
Phmnthmne 0.11 F,,** Phenanthrene 0.67 Condition code**
Condition code Gonadal stage
Gonadal stage Digestive gland atrophy
Naphthalene 0.20 Sex Naphthalene 0.73 Condition code***
Condition oode*" Gonadal stage*
Gonadal stage Digestive gland atrophy
Pyrene 0.19 Sex PyMne 0.68 Length
Length Gonadal stage
Perkkww marbw infection intensity Digestive gland atrophy***
Chrysene 0.14 Sex** Chrysene 0.59 Condition code
Perkinm marinus infection intensity Gonadal stage
Wet weight Digestive gland atrophy*
Sex

PAHs (Table 10). A factor of 5 difference was typical. spawning. Eggs and sperm had PAH concentrations
Total PCBs were concentrated in eggs and sperm by 5 times higher than somatic tissue, 3-4 times higher
a factor of about 5 over the somatic tissue. The for pesticides, and the gonadal tissue can account for
chlorinated compounds like lindane, chlordane, 25% of animal dry weight prior to spawning (Choi
dieldrin and DDT (plus breakdown products) were et al., 1993; Klinck et al., 1992).
concentrated in eggs by about 4 times, but tended to (2) The quantity of gonadal material was the most
be equivalent to or lower than the somatic tissue in important correlate of PAH body burden and much
sperm. more important than, for example, gonadal stage.
Less gonadal material indicates recent spawning since
MSMSSION these oysters were collected well into the spawning
Spawning as a route of depuration season; all had certainly spawned at least once prior
to collection.
Our data suggest that reproduction is an important (3) Sex was an important determinant of body
depuration route for oysters; the frequency of repro- burden. PAH and PCB concentrations differed be-
duction is the most important determinant of body tween sexes in some cases, chlorinated pesticide con-
burden, under equivalent exposure levels. Sex and centrations were dramatically lower in male gametes,
health arc important secondary determinants of body and the factors correlating with body burden differed.
burden because both affect reproductive state and the Health-related factors were much more important in
frequency of reproduction. The three following ob- males. Factors decreasing health probably also
servations support these two conclusions: decrease spawning frequency. The most important
(1) Both eggs and sperm contain signilicantly more correlation occurred with digestive gland atrophy;
PAH and PCB than somatic tissue. Eggs also con- however in males, digestive gland atrophy was highly
tained more chlorinated pesticides. The concentration inversely correlated with Perkinsus marinus infection
factor is sufficient to conclude that over half of the intensity, so the two parameters behaved similarly in
PAH body burden, and somewhat Less of the pesti- explaining the variation in PAH body burden among
cide body burden, could be in gonadal tissue prior to oysters taken from the same site. PAHs were lower

Table 8. Best 3-variable model for each PAH for femake oysters and the amount of variation explained (R2). Analyses were conducted with
and without gonadal quantity included. Significant partial correlations am shown by asterisks, as defined in Table 4
With gonadal quantity Without gonadal quantity
Variable R2 Explanatory variable A 2 Explanatory variable
Fluoranthene 0.37 Condition code 0.18 Length
wet weight Condition code
Gonadal quantity*** Perkkw marinus infection intensity
Phenanthmne 0.18 Perkinsw marinw infection intensity 0.16 Condition code
Digestive gland atrophy PerkAw marinus infection intensity
Gonadal quantity Digestive gland atrophy
Naphthalene 0.21 Length 0.27 Length"O
Digestive gland atrophy Perkkw marinta infection intensity
Gonadal quantity Digestive gland atrophy*
Pymne 0.31 Gonadal quantity** 0.20 Length
Digestive gland atrophy Condition code
Gonadal stage Perkimw marinus infection intensity
Chryscne 0.51 Length**- 0.25 Perkkw marinus infection intensity*
Digestive gland atrophy" Wet weight*
Gonadal quantity""* Digestive gland atrophy

PAH and pesticide body burden in oysters 695

Table 10. PAH concentrations in pooled sarnples (groups) of purified oyster eggs, purified sperm and somatic tissue (in ppb)
Group A Group 0 Group C Group D Group E
Eggs Tissue Eggs I issue Eggs Tissue Sperm Tissue Sperm Tissue
Naphthalene 45.1 9.0 51.9 9.9 42.5 5.9 64.8 12.3 70.5 12.3
Phenanthmme 23.5 2.9 26.9 4.1 29.0 3.4 26. 1 5.6 29.9 5.6
Fluoranthem 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
Chrysem 11.5 2.4 12.5 2.0 10.9 2.2 7.2 2.4 16.6 2.4

with lower P. marinus infection intensity and P. that these are variables that can normally be easily
fftarinus is known to slow reproduction in oysters measured in oyster individuals, whereas spawning
(Wilson et al., 1988; White et al., 1988). time and frequency cannot. Nevertheless, under these
conditions, only the strongest relationships might be
Reproduction, health and body burden expected to generate a signal of sufficient intensity to
The importance of reproduction in molluscs in be observed as a significant correlation.
controlling or affecting body burden is open to Correlations were found, indicating the importance
disagreement. Mix et al. (1982) and DiSalvo et al. of reproductive state and health on body burden. The
(1975) found PAHs no more concentrated in Mytilus amount of variation explained among individuals in
eduhs gonadal material than somatic tissue (purified their PAH body burdens was generally low; however,
eggs were not measured), but noticed a significant this probably emphasizes the previous point, that
drop in body burden during the spawning season. each of the measured variables are themselves rela-
Sericano et al. (in press b) found that the central bo y tively poor indicators of how recently and how
region including the gonad contained proportionately frequently each animal had spawned. Stegeman and
more PAH in oysters. Lee el al. (1972), Fortner and Teal (1973) emphasized the importance of the total
Sick (1985) and Solbakken et al. (1982), as examples, ex .posure history of any individual organism in deter-
found the hepatopancreas to be an important depot mining body burden. One aspect of this exposure
for PAHs in bivalves; however, gonadal material, and history is the time since the last significant depuration
in particular, gametes, were not separately measured. event due to spawning.
In scallops where gonads can be separated from the Hydrocarbons can be taken up by feeding as well
somatic tissue by dissection, Friocourt et al. (1985) as in the dissolved phase (e.g. McElroy et al., 1989)
found gonadal material enriched in PAHs over and can affect filtration rate (Axiak et al., 1988;
muscle but not digestive gland tissue. Rossi and Barszcz et al., 1978). PAHs can also affect the
Anderson (1977) observed spawning to be an import- digestive gland (Nott and Moore, 1987). Theoreti-
ant depuration route in a polychaete Neanthes are- cally, digestive gland atrophy should be related to
naceodentata. nutritional state. Digestive gland atrophy was corre-
If spawning.is an important route of depuration, lated weakly with higher PAHs in females and more
then factors affecting spawning frequency and how strongly with lower PAHs in males. One possible
r=ntly the last spawn occurred prior to collection explanation for these divergent results is the strong
will alrect body burden. The biological variables correlation of digestive gland atrophy and Perkinsus
measured as surrogates of spawning frequency are marinus infection intensity in males. In any case, no
gonadal quantity and gonadal stage, Perkin3w mari- unambiguous effect of digestive gland atrophy could
nus infection intensity, and some general indicators of be discerned.
health. Few of these were correlated among them- Our data clearly support the importance of repro-
selves, so that most serve as separate, somewhat duction, at least in oysters, during the summer and
unique, indicators of the many factors that might fall. We suggest that the weak evidence for the
affect spawning frequency and how recently the last importance of reproduction in most time series of
spawn occurred prior to collection. Each has its own contaminant body burden generally stems from three
history, in some cases not necessarily related to factors: collection of animals out of spawning season
spawning frequency, so that each is only a poor when little gonadal material is present, failure to
surrogate for the desired variable, but we emphasize analyze purified gametes which are the primary ve-

Table 11. Pesticide concentrations in pooled samples (SToups) tof purified oyster eggs, purified sperm and somatic tissue (in ppb)
Group A Group B Group C Group D Group E
Eggs Tissue Eggs Tissue Eggs Tissue Sperm Tissue Sperm Tissue
LiDdane 9.4 2.1 5.5 2.2 8.2 1.8 < 1.0 2.2 < 1.0 2.2
Total BHCs 14.7 5.0 9.5 5.2 14.0 3.9 < 1.0 5.2 2.4 5.2
a-Chlordane 6.5 3.8 5.0 3.9 5.1 2.4 < 1.0 4.5 3.6 4.5
Dieldrin 6.3 2.2 6.1 1.9 5.8 1.7 < 1.0 1.8 1.7 1.8
4,4'DDE 32.1 9.1 26.0 9.2 26.7 7.5 4.1 11.9 6.6 11.9
4.4'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 )02.3 53.8

696 M. S. ELLIS et at.

hicle of depuration during spawning, and the poor substantial fraction of the body burden is lost in
understanding of the dynamics of uptake after spawning.
spawning. We suggest that the timing of the last Wilson et al. (1990) found the latitudinal gradient
spawning event prior to sampling-animals recover in PAH body burden to be stronger than the latitudi-
their body burden within a month or less after a nal gradient in pesticide body burden. We found
deputation event (Sericano et al., in press)- and the gonadal material concentrated much more highly in
degree of gonadal development (e.g. Hofmann et al., PAHs than pesticides and some pesticides are not
1992) are important variables affecting PAH body concentrated in male gonadal material at all. Our
burden in oysters. data would suggest that temperature, and therefore
Lowe and Pipe (1987) and Moore et al. (1989) latitude, should have a much greater impact on PAHs
observed gonadal resorption at high PAH concen- through reproduction than on pesticides, in agree-
trations. We observed no such effect in our analyses; ment with the findings of Wilson et al. (1990). Taken
however, body burdens were lower. together, our data and those of Wilson et al. (1990,
Variation between compounds 1992) suggest that interpretation of the results of
monitoring studies such as the Status and Trends
Fluoranthene, pyrene and chrysene were very simi- program using bivalves requires that close attention
lar in their response to the biological variables; be paid to the reproductive state and health of the
naphthalene and phenanthrene formed a second sampled populations.
group quite different from the other three. Certainly,
uptake, storage and deputation must be relatively Acknowkdgements-This research was supported by a grant
similar within these two groups but different between from the Center for Energy and Minerals Resources, Texas
them. Phenanthrene and naphthalene are lower mol- A&M University (TAMU), an institutional grant NA89-
AA-D-SG139 to TAMU by the National Sea Grant College
ecular weight, more water soluble compounds and Program, National Oceanic and Atmospheric Adminis-
equilibrate faster with the environment (Pruell et al., tration (NOAA), U.S. Department of Commerce, grant
1986; Sericano et al., in press). They might lose the 50-DGNC-5-00262 from the U.S. Department of Com-
signal imposed by spawning events faster than the merce, NOAA, Ocean Assessments Division, and computer
funds from the TAMU College of Geosciences and Mar-
larger three PAHs exaIrtined. Phenanthrene and itime Studies Research Development Fund. We appreciate
naphthalene supported fewer significant correlations, this support.
and none with reproduction, despite their enrichment
in eggs and sperm, but were correlated with general
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Toxic. 1, 879-896.
Aeproduction and the latitudinal gradient in bod Boyden C. R. (1977) Effect of size upon metal content of
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The data suggest one explanation for the latitudi- Quantitative measurement of reproductive output in the
nal gradient in FkH and pesticide body burden American oyster, Crassostrea virginica (Gmelin), using an
enzyme-finked immunosorbent assay (ELISA). Aquacult.
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and the relationship of PAH body burden and cli- Chu F,-L. E., Webb K. L. and Chen J. (1990) Seasonal
mate change (Wilson et al., 1992). Slight variations in changes of hpids and fatty acids in oyster tissues (Cras-
temperature, as affected by climate change, or vary. sosirea virginica) and estuarine particulate matter. Comp.
Biochern. Physiol. A 95, 385-391,
ing average temperature across latitudes will vary the Cossa D. (1989) A review of the use of Mytilus spp. as
reproductive season, the annual reproductive effort, quantitative indicators of cadmium and mercury contami-
and the frequency of spawning in oysters (Hofmann nation in coastal waters. Oceanol. Acta 12, 417-432.
et al., 1992). Small changes in temperature produce Craig A., Powell E. N., Fay R. R. and Brooks J. M. (1989)
large changes in reproductive effort. As a result, body Distribution of Perkinsus marinus in Gulf coast oyster
populations. Estuaries 12, 82-91.
burdens will vary even under similar exposure Cunningham P. A. and Tripp M. R. (1975) Factors affecting
Levels, and this variability may be considerable if a the accumulation and removal ormercury from tissues of

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(in press) Concurrent chemical and histological analyses: Biochem. Physiol. A 90, 279-290.
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Zool. Bot. 27, 21-27. ters. 46, 201-235.

Reprint 2

Sediment Contaminants in Casco Bay,
Maine: Inventories, Sources, and
Potential for Biological Impact

M.C. Kennicutt H. T.L. Wade, B.J. Presley,,
A.Q. Requejo, J.M. Brooks and G.J. Denoux

Sediment Contaminants In Casco Bay, Maine: Inventories, Sources, and
Potential for Biological Impact

U. C. Kordftuff 11, * T. L Wmle, 0. J. Pres1ey, A. G. Requelo, J. M. Brooks, mW Q. J. Denoux
Geocheffkal and Envhmmental Plesearch Group, Texas A&M UnNwsfty, 833 Graham Road, College Statlon, Texas 77845
An inventory-based approach to environmental assessment term accumulator of contaminants, which are probably
the main avenue of chronic exposure of the associated
that determines concentrations of sedimentary contam-
inants, defines their origins, and assesses the potential for ecosystem.
biological impact is illustrated in Casco Bay, ME. The Site Description
Most widespread contaminants in Casco Bay are petroleum
and petroleum byproducta. The highest concentrations Casco Bay is situated along the Atlantic Coast of Maine
of contaminants are associated with Population centers, and is bounded by Cape Small to the northeast and Cape
effluent outfalls, and spills. The majority of PAH in Elizabeth to the southwest (Figure 1). The bay has a
sediments are the product of high-temperature combustion wealth of natural resources and -srine habitats that
processes. PAH concentrations at sites in close proximity support a rich and diverse ecosystem. The bay proper is
to Portland exceed values believed to produce toxic a4OO-kM2 embayment of the Gulf of Maine which includes
responses in marine benthic organisms. Incontrast,PCB, Portland Harbor, a major docking facility and the principal
DDTs, and chlordane concentrations in the sediments are fishing port of Maine. More than 300 mi of coastline and
belo@w concentrations thought to produce toxic effects in nearly 400 islands are encompassed by the bay (1).
marine organisms. Metal concentrations in sediments are
also below those that elicit biological responses. The Methods
geographic distribution of contaminants is initially con-
trolled by the proximity to sources, and the regional Sediment samples were analyzed for trace metals,
differences in concentrations are the result of sediment aliphatic and polycyclic aromatic hydrocarbons, pesticides
accumulation patterns. Detrital (terrestrial), autochth- and PCBs (Table 1). Matrix spikes, laboratory sample
onous manne, pyrogenic, and petroleum sources for PAH, duplicates, and laboratory blanks were processed with each
alkanes, and trace metals are defined. batch of samples (10-20samples/batch). Duplicateswere
produced by subeampling in the laboratory. Standard
Introduction reference materials (National Institute of Standards and
Technology) were analyzed to audit the performance of
The systematic inventory of contaminants within mairtal the analytical methods. The quality assurance standards
environments is often a first step in developing a logical are those of the NOAA's National Status and Trend
and effective approach to preserving, protecting, and/or Program, of the RPks Environmental Monitoring and
reclaiming resources impacted by human activities. While Assessment Program-Near Coastal (EMAP-NC) and of
bulk inventories of chemicals alone cannot predict bio- the U.S. Fish and Wildlife Service (FWS) for trace
logical impacts or "ecosystem health", this first-order contaminant analyses (2). These methods have undergone
evaluation ofthe presence and magnitude ofcontamination extensive intercalibration with EPA, NOAA, NIST and
can indicate which processes are most influential in FWS. Detailed methods are provided elsewhere (3).
controlling ecosystem exposure. Cause and effect must Sample Collection. Sediment samples were collected
be linked by careful consideration of contaminant input, in August 1991 (Figure 1). Station locations were chosen
transport, ultimate fate, and biological impact. High- to provide good areal coverage, sediments of different ages
quality analyses, intensive sampling, and an evaluation of (including erosional features), and representative coverage
a broad spectrum of contaminants can contribute to of benthic communities. Bathymetry and sediment tex-
defining those processes or activities most closely linked bm also guided site selection. The sampling sites are
to detrimental or unwanted impacts. Innate in this type designated as CS, EB, IB, OB, SW, and WB (i.e., Cape
of approach is the generation of large, complex multi- Small, Fast Bay, Inner Bay, Outer Bay, Shallow Water,
component data sets that must be fully integrated and and West Bay, respectively). A number identifies the
rigorously evaluated, An approach utilizin comprehen- location within the bay. Samples were taken with either
grve chemical inventories and a detailed statistical analysis a Smith-McIntyre grab sampler, a ponar grab sampler, or
of the data is'reported for a study of Casm Bay, ME, by hand. All samples were carefully inspected to ensure
sediments. Surficial sediments were evaluated as a long- that undisturbed sediments were collected.
901343ex/%/09284001$04.60/0 0 IM Anwtan Chm" 900MY 22 EnvWn. Sd. Tedwid., Vol. 28, No. 1. 1994 1

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PWm 1. Location map for the Casco Bay dudy.

Hydrocarbons, Pesticides, and PCBs. The extrac- 100 mash) chromatography. The extracts were sequen-
tion method is that of Wade et aL (2). A total of 10 g of tially eluted from the column with 50 mL of pentane
freeze-dried sediment was Soxhlet-extracted with meth- (aliphatic fraction) and 200 mL of 1:1 pentane--dichlo-
y1ene chloride and concentrated in Kuderna-Danish U@bw romethane (aromatic/PCB/posticide fraction) and con-
The extracts were fractionated by aluminwailica gel (80-- 23 centrated for instrumental analysis.
2 Envkon. Sol. Technol., VOL 28, No. 1, 1904

______________________________________________________________________________________________
Table 1. Analytes Measured in Casco Bay Estuary Program

Total Metals

cadmium chromimum mercury
copper silver arsenic
lead xinc selenium
nickel iron

Hydrocarbons

naphthalene phenanthrene benzo[k]fluoranthene
2-methylnaphthalene anthracene benzo[a]pyrene
1-methylnaphthalene 2-methylphenanthrene benzo[e]pyrene
biphenyl fluoranthene indeno[1,2,3,-cd]pyrene
acenaphthene benz[a]anthracene dibenz[a,h]anthracene
acenaphthene chrysene benzo[g,h,i]perylene
fluorene benzo[b]fluoranthene

In Addtion

extended PAHs (alkylated homologues useful in differentiating oil from combustion sources)
aliphatic fraction quantitation including C12-C34 n-alkanes, pristane, phytane, and the
unresolved complex mixture

PCBs

congener-specific analysis of 20 individual PCBs including quantitative estimates of the
amount of arochlor mixtures

Pesticides

aldrin endosulfan I hexachlorobenzene
a-BHC endosulfan II 2,4'-DDE
p-BHC endosulfan sulfate 2,4'-DDD
-BHC endrin 2,4'-DDT
-BHC endrin aldehyde 4,4'-DDD
a-chlordane heptachlor 4,4'-DDE
-chlordane heptachlor epoxide 4,4'-DDT
dieldrin toxaphene

Aneillary Parameters
(1) TOC was determined by combustion in a Leco carbon analyzer to CO2 and
subsequent quantitation by IR
(2) grain size (sand, silt, and clay) was determined by the Folk settling method
(3) organic nitrogen was determined by a Kjeldahl digestion
(4) % solids (dry weight) are determined and reported for all samples

*Note: Organic analyte concentrations are reported on the basis of dry weight of sediment and are corrected for surrogate recoveries.
_______________________________________________________________________________________________________________________________________

Aliphatic hydrocarbons (n-C13-n-C34), pristane, and phytane were analyzed by gas chromatography (HP-5980) in the splitless mode with
flame ionization detection (FID). A 30 m x 0.32 mm i.d. fused-silica column with DB-5 bonded phase (J&W Scientific, Inc.)provided component
seperations. The FID was calibrated at five concentrations, and deuterated n-alkanes were used as surrogates and internal standards. Aromatic
hydrocarbons were quantified by gas chromatography with mass spectrometric detection (HP-5890-GC and HP-5970-MSD). The samples were injected
in the splitless mode onto a 30 m x 0.25 mm (0.32 mm film thickness) DB-5 fused silica capillary column (J&W Scientific Inc.) at an initial
temperature of 60 degree C and temperature programmed at 12 degree C/min to 300 degree C and held at the final temperature for 6 min.
The mass spectral data were acquired, and the molecular ions for each of the PAH analytes were used for quantification. The GC/MS was calibrated
by the injection of standards at five concentrations. Analyte identifications were based on the retaention time of the quantitation ion for each analyte
and a series of confirmation ions. Deurated aromatic compounds were used for surrogates and internal standards.
Pesticides and PCBs were separated by gas chromatography in the splitless mode using an electron capture detector (ECD).A 30 m x 0.32 mm
i.d. fused-silica column with DB-5 bonded phase (J&W Scientific, Inc.) Provided component separations. Four calibration solutions were used
to generate a nonlinear calibration curve. A sample containing only PCBs was used to confirm the identificaiton of each PCB congener. The surrogates
DBOFB (dibromooctafluorobiphenyl). PCB-103 and PCB 198 for pesticide and PCB analysis were added during the extraction. The
internal standard, TCMX (tetrachlorom-xylene), was added prior to GC/ECD analysis. The chromatographic conditions for the pesticide-PCB analysis were
100 degree C for 1 min, then 5 degree C/min until 140 degree C, hold for 1min, then 1.5 degree C/min to 250 degree C, hold for 1 min, and then 10 degree
C/min to a final temperature of 300 degree C, which was held for 5 min. Trace Metals. The major analytical technique used for trace metal determinaiton
was atomic absorption spectrophotometry (AAA) in the flame mode for those elements in high enough concentration. Graphite furnace (GC/AAS) or cold vapor
techniques were used when necessary. Samples were pressure-digested in 50-mL closed all-Teflon "bombs" (Savillex Co.; Brooks et al.,
1988). Sediment aliquota (ca. 200 mg) were digested at 130 degree C in a mixture of nitric, perchloric, and hydrofluoric acids. A saturated
boric acid solution was then added to

Environ. Sci. Technol,Vol.28,No.1 1994 S

complete the dissolution. Various dilutions were made and phytane, suggesting a phytoplankton input (8-10).
on the clear digest solutions to bring them within the Total alkanes and unresolved complex mixture (UCM)
calibration of the AAS. Standard reference materials and concentrations varied from 151 to 10 078 ppb dry wt and
blanks were digested and analyzed with every batch of from 2 to U5 ppm dry wt, respectively. PAHs were also
samples. detected at all locations sampled. The predominant PAHs
Concentrations of Fe, Mn, and Zn were determined by are highly condensed ring structures with few alkylations
flame AAS using a Perkin-Elmer Model 306 instrument, indicating a pyrogenic or combustion source (Figure 3;
following the manufacturer's recommendations with only refs 11-14). Four-ring and larger PAHs account for more
slight modifications. Calibration curves were constructed than 60 % ofsedimentary PAHs in Casco Bay. Total PAH
hm commercial standards. Concentrations of Ag, As, concentrations varied from 16 to 20 798 ppb dry wt.
Cd, Cr, Cu, Ni, Pb, and Se were determined with a Perkin- The western part of CascoBay (Inner Bay) is most highly
Zhner Zeeman 3030 instrument equipped with an HGA- contaminated with PAH. Sediments from the Fore River
SW furnace and AS-60 autosampler. Matrix modifiers area and locations close to Portland contain the highest
and analytical conditions for the furnace and spectro- concentrations of PAH. In general, contaminants decrease
photometer were based on the manufacturer's recom- in concentration with distance from populated areas.
n"n with modifications as appropriate to * i However, regionally elevated PAH concentrations are also
sensitivity and minimize interferences. Mercury was present at a few sites in Fast Bay and Cape Small. One
determined by cold vapor AAS following a slightly modified station in the Cape Small (CS-4) region was unusual
EPA Method 245.5 aqua-regia/permanganate digestion. compared to other sites in the region. Most Cape Small
A headapace sampling procedure was used to remove Hg stations contained <1.0% organic carbon and more than
from the digest in contrast to the more common stripping 65 % sand, whereas sediment from station CS-4 contained
procedure. A UV monitor (IAboratory Data Control Co.) 2.7 % organic carbon and only 29.9 % sand. Total alkanes,
with a 30-cm path length call was used for Hg detection UCK and total PAH concentrations were elevated at this
and quantification. location as well. Sediments at station EB-9 also had high
Organic Carbon and Grain Size. Organic carbon concentrations of total PAH. An organic carbon content
(OC) was determined by detection Of C02 by an infrared of 4.6% at EB-9 is the highest for all of the sediments
spectrometer after combustion in an 02 stream (LECO sampled.
WR-12 total carbon system). Samples were acidified using PCBs and Pesticides. Total PCB concentrations for
dilute HCl in methanol and then dried. Method blanks the study area range from 0.4 to 485 ppb dry wt with a
and duplicate samples were analyzed every 20 samples. median concentration of 15 ppb. Total PCBs are highest
Data are reported as micrograms of carbon per gram of in the Inner Bay in close proximity to Portland. Con-
dryweight. All glassware and utensils are preheated prior centrations are lowest in Cape Small and West Bay with
to use. a few anomalous values in East Bay. The site from Cape
Sediment grain size was determined by the procedure Small with a total PCB concentration of 40 ppb dry wt has
of Folk (4), utilizing sieving to separate gravel and send a higher TOC content (2.8%) than other samples from
fractions from the clay and silt fractions. The latter Cape Small.
fractions were subsequently separated by the pipet (set- Total DDT concentrations for the study area range from
ding rate) method. Detailed descriptions of the methods below the method detection limit (0.25 ppb) to 21 ppb dry
utilized in measuring OC and grain size are reported in weight. The DDTs were dominated by the pp'-isomers.
Brooks et at. (5). This is expected since technical-grade DDT is primarily
Principal Components Analysis (PCA). The organic the pp'- isomer (75-M%). In the environment, DDT is
and inorganic data were analyzed using PCA (6). The metabolized to DDD and DDE. In some samples, DDD
results of PCA are highly dependent on the pretreatment is the major metabolite while in other samples DDE
or scaling of the data matrix. The data for this study predominates. Samples from the Inner Bay and associated
consist of a wide variety of analytes that range several shallow water sites exhibit DDD > DDE while at most
orders of magnitude in their absolute values. Because other locations DDE > DDD. There is a relatively high
PCA is a least-squares method, variables with large percentage of undegraded DDT in Casco Bay sediments.
variance will have large loadings. To avoid this blas, the Th! geographic distribution of total DDT concentrations
entire datamatrix was firstscaled bydividing each variable IS similsk to that found for PCB9. The Inner Bay has the
by the standard deviation, This scaling assigns every highest concentration in Qum Bay. East Bay and Outer
variable a variance of 1.0 so that each variable has the Bay have intermediate concentrations, West Bay has lower
same influence in the PCA model. The technique of crow concentrations, and the Cape Small region has the lowest
validation was used to establish the significance of each concentrations.
principal component (7). PCA was performed on a The highest values of total chlordane are at Inner Bay
personal computer using the program SIRIUS (Pattern sites. East Bay and Outer Bay sites are intermediate,
Recognition Systems A/S, Bergen, Norway). while West Bay and Cape S-1111 sites exhibit the lowest
concentrations. Total chlordane concentrations range
Results from below the method detection limit (0.25 ppb) to 4.9
ppb dry wt. Other organochlorine pesticides including
Hydrocarbons. Aliphatic hydrocarbons were detected aldrin, BHC, dieldrin, endosulfan (1, 11, and sulfate),
at all stations sampled. The majority of resolved alkanes endrin, endrin aldehyde, heptachlor, heptachlor epoxide,
had odd-carbori chain lengths with 23-33 carbons indic- toxaphene, and hexad3lorobenzene were near or below
ative of plant biowaxes (Figure 2; refs 8-10). N-Cis,n-CM the method detection limit (<0.25 ppb).
n-Cm n-C21, and pristane were often more abundant than Trace Metals. Sediment trace metal data show con-
the co-occurring even carbon numbered normal alkanes aderable geographic variation with generally higher values
4 BrAoi SaL TechnaL, Val. 26, No. 1, 1994 25

7001 W 70000' w

i0c
;:1 . ........ .......

a d:
12 Ink
4c
4
2 9
7
I
Not 7
MF

.............. 5
12
Emst 09
a 9 4@3 Bay
3
10 160 ir
Soo
4
7
2
Inner
0
*3
so 4 BaY
20 Cqw
10
7
0 0 Small
4
0 2 Outer
0
........-........ 40 a Bay
0 5

3
0
2
...... ... ....
................. 121
X
1200-
104

1000-

43
moo

200
0 ShMlow WBW
IF sa"oft
0

4 k d: d:

RW* 2- Av"09 nwml AM@ and bOPVnM mms"Oftm (Ppb dry weW, su"gato conwW and dWbtalon In sodknwft from Casw Say.

4.59%

29.94%/ 24.90%
70010' 70*OV

00,
3.

p
430
37.21%

..........

Odwo 2

7
Wed
46 say
12
5.86% Fast 419
43* 8 9
'N3 8 Bay I
24.67%,,'
46:
3
10
t72 10
26.82%
06 4
7
AP 0
29
0.1 Inner
0% 4 BaY 3
5
2 0 Ce
2.94% 0 79 10
6
...... 4 SMA
2
30.71% 43 Wer
03
.............
40 a 09 BOY

4.38%

30.2296
22.47%
..............

430
3 0 2 RING
2.89%
0 3 RING
DST's
4 FMG
0 ShIdlow Wale
40.05% S+ puma sampl"

7001 V 70000'

Fkp" 3. Average PAN compositions in mdMents by region w" Casco Say.

= m = = m = m m m m = m m m =

0 to 0 m Numbw of Swnples

tz 0.02S
0.050

z 1-81,
ADD
rr tw
0.125
C"

0 C" (D
'a @- L" i. I J* 0 0.150
s C4 9: C- 8 1 1 0 P, w 1
014 It s
8--va. 0g,
16 0 0_ 0.200
r
.01 0 2,5
rr (D0 w
R
(D So 0 ma gT moo
it a
-V 0 cr 0.275
a- .01 aqm 0.300
m 5 IL
C
r!
f 0.325
4 A 0.350
r F - E@
0.4 'm & pl: 0.375
ftj CA Lr
2 R R. CMP
UT 0.400
5
It A . CD 0.425
1 (a @ cr
v rr p @ft m - 0.450
13 CD
rr 0.475

0.500

Nwnbw of Swnples
:4 1-3
0 oq a Z.
5

2
le 25
30
CA
rr 2 r, 35
rr 40
45
03
cr M 60
-4 45 V A
rr P. - E9 is 65
0
ts

ts
0

CD
rr
(D

TO, 75
go
NOR 95
100
log
C3
15
I'o

120
-4 pft P,

Figure 5. Relationship between chromium, lead, nickel, and zinc concentrations (ppm dry weight) and iron content (% dry weight) in sediments
from Casco Bay.

Outer Bay, three East Bay, and one Cape Small sites. Eight concentrations result in part from sediment accumulation
of the 10 most highly contaminated stations are located patterns. Thus, area of fine-grained sediment accumu-
in the Inner Bay region, including the six highest stations. lation such as the Inner Bay have high scores for PC I and
The lowest levels of organic contaminants are in the Cape exhibit high concentrations, while sediments in areas that
Small and West Bay regions. High levels of a variety of are characterized by a dynamic physical environment and
organic contaminants tend to occur at the same location. little sediment accumulation such as the Outer Bay have
For inorganic contamination, only those metals believed low scores for PC I and exhibit lower concentrations. It
to be influenced by anthropogenic inputs were used to is also notable that both the organic and inorganic
rank the sample locations, i.e., Ag, Cd, Pb, Zn, and Hg. contaminants exhibit the same general trend. Shallow
Based on the formation of inorganic contaminant rank- water samples SW-I and SW-2 were identified as outliers
ings, 25 % of the locations with the highest levels were as because their compositions were anomalous relative to the
follows: 12 Inner Bay, three East Bay, and one Outer Bay other sediments (extreme enrichment in PAH and PCB,
locations. Nine of the 10 highest locations are in the Inner respectively). These samples were excluded from the PCA
Bay region, including the eight highest stations. Lowest analysis.
metal concentrations occur in the Cape Small region. PC 2 (12.3 % of the total variance) and PC 3 (6.1 % of
Eleven stations are ranked in the highest 25 % on both the the total variance) are related to the composition of organic
inorganic and organic contaminat rankings (Figure 6). and inorganic contaminants in the sediments. Since
They are almost exclusively Inner Bay locations, i.e., 9 of principal components are orthogonal, the processes gov-
11. erning PC 2 and PC 3 are independent of PC 1. Hance,
Principal Components Analysis. A total of four the information contained in these principal components
significant principal components (PC) were extracted from is more representative of contaminant sources in the
the Casco Bay data. PC 1 accounts for 48.9 % of the total sediments and is not related to absolute concentration&
variance. The loadings for this PC show the sand content PC 2 is correlated positively with the Fe and saturated,
of the sediments inversely correlated with all other hydrocarbon content of the sediments (Figure 8). This
measured variables. PC 1 is inversely correlated with sand most likely reflects a detrital component enriched in plant
content and positively correlated with the TOC content wax n-alkanes and inorganic clastics derived from con-
of the sediments (Figure 7). This principal component tinental erosion (8-10). A loadings cross-plot for PC 2
reflects differences in the concentration of the targeted versus PC 3 (Figure 9) shows that, although all n-alkanes
analytes due to variations in sediment texture. This are positively loaded in PC 2, C23,C25,C27 adn C29 n-alkanes
finding is more significant than might appear at first have the highest loadings, consistent with this interpretation
consideration, as it implies that regional differences in Figure 9 also shows that nearly all the aromatice

8 Environ. Sci. Technol., Vol. 28, No. 1,1994

consideration, an it implies that regional differences in tation. Figure 9 also shows that nearly all the aromatic
29

Tale 2. Casoo Bay Ratuary Program Site Rankings Based on Organic Contaminant Data, I"I (Vpb dry wt surrogate

tow total total total total total total total total
PAHs PAH chlordane chlordane DDT9 DDT PCBs PCB organic
station no. (ppb) ranking (ppb) ranking (ppb) ranking (ppb) ranking ranking

CS-1 93 2 0.01 1 0.01 1 0.6 2 6
CS-7 16 1 0.02 3 0.02 2 0.4 1 7
CS-3 515 6 0.02 2 0.10 4 2.0 5 17
WB-3 421 4 0.07 4 0.18 6 2.6 6 19
SW-8 445 5 0.16 12 0.47 8 1.6 3 28
8W-10 595 8 0.11 6 0.30 6 4.5 9 29
CS-2 362 3 0.24 19 0.04 3 1.7 4 29
CS-6 672 9 0.15 10 0.50 9 3.8 7 35
SW-12 1094 16 0.23 16 0.72 10 5.5 11 53
WB-6 774 11 0.23 15 0.94 14 6.0 13 53
SW-9 734 10 0.23 17 0.73 11 &1 17 55
WB-2 146 22 0.16 11 1.01 15 7.2 14 62
SW-5 oil 13 0.15 9 1.63 26 7.3 16 64
WB-8 1112 is 0.11 5 1.52 25 8.4 is 66
SW-7 807 12 0.25 20 1.70 29 5.2 10 71
SW-13 961 14 0.19 14 1.23 21 9.8 23 72
CS-6 546 7 1.32 53 0.33 7 3.9 8 75
WB-7 1329 20 0.12 7 1.36 23 10.2 25 75
OB-1 1433 21 0.41 27 1.09 is 7.2 15 81
OB-11 1312 19 0.24 is 1.11 19 11.6 28 84
OB-7 1650 so 0.45 30 1.03 16 5.5 12 so
SW-14 1069 15 0.25 21 1.94 34 9.1 21 91
EB-1 2230 37 0.60 35 0.86 13 9.0 20 105
EB-2 2875 45 0.57 33 0.82 12 8.9 19 109
W'B-4 1496 24 0.56 31 1.83 31 11.5 27 113
SW-6 1526 26 0.30 23 2.29 41 10.0 24 114
WB-5 1102 17 0.57 32 1.91 33 14.1 34 116
EB-4 2791 0.16 13 1.37 24 14.3 35 116
OB-4 1964 36 0.64 37 1.26 22 9.6 22 117
OB-6 1631 28 0.13 8 2.33 43 18.8 42 121
OB-13 INS 29 0.85 42 1.69 28 11.5 26 125
OB-8 1865 33 0.39 25 1.72 30 17.4 39 127
EB-10 4545 65 0.43 28 1.12 20 13.5 31 134
WB-9 1901 34 0.33 24 2.28 40 16.3 as 136
WB-l 1490 23 0.91 43 2.42 45 11.8 29 140
OB-12 1696 31 0.74 39 2.00 35 14.4 36 141
SW-11 1501 25 0.98 46 3.10 49 13.9 32 152
OB-5 2964 48 0.60 34 1.65 27 18.9 43 152
IB-9 1946 35 0.78 41 3.56 50 13.4 30 156
EB-3 2939 46 1.06 47 2.26 39 14.0 33 165
IB-5 2545 40 0.96 45 2.40 16.8 37 166
OB-9 2706 41 0.77 40 2.08 36 22.2 49 166
EB-8 3459 52 0.26 22 2.81 48 19.6 46 168
EB-5 2944 47 0.40 26 2.55 47 23.7 50 170
SW-15 7180 59 1.60 56 1.07 17 17.9 40 171
SW4 1530 27 1.12 48 3.93 54 19.1 173
OB-2 1817 32 1.89 59 2.31 42 18.1 41 174
OB-10 2269 39 1.25 51 2.09 37 20.0 48 176
1" 3068 49 0.62 36 2.53 46 27.9 53 184
OB-15 4004 64 1.13 49 2.17 38 19.4 45 186
EB-8 2723 42 0.93 44 4." 57 19.9 47 190
CS-4 7454 61 0.71 38 1.89 32 40.0 59 190
OB-3 3727 63 0.43 29 4.12 55 30.7 64 191
M10 2737 43 1.13 60 3.69 51 27.9 52 196
EB-6 2233 38 1.72 57 3.86 52 35.7 57 204
E33-7 4872 W 1.30 52 3.86 53 23.9 51 212
M4 3273 51 1.39 54 7.63 59 31.8 65 219
M7 3109 50 1.84 58 C70 58 33.7 56 222
EB-9 7340 60 1.91 60 4.16 56 37.3 58 234
IB-2 6M 58 1.63 56 9.91 61 47.6 61 236
IB-3 5069 57 2.49 61 9.02 60 42.2 so 238
SW-1 20748 65 3.47 63 10.10 62 72.3 62 262
IB-1 9174 63 2.89 62 14.50 63 79.2 64 252
SW-3 7517 62 C91 65 20.42 65 77.1 63 255
9W-2 125M 64 3.98 64 16.81 64 48&0 66 257

hydrocarbons measured are loaded negatively in PC 2. in Casco Bay have different origins, which is generally
One exception is the alkylated chrysenes, which show a consistent with the known geochemistries of these cle
dight positive loading in PC 2. Thus, PC 2 can also be of compoun&
regarded a asaturate/aromatic hydrocarbon ratio. These PC 3 differentiates individual saturated and aromatic
reaft indicate that saturated and aromatic hydrocarbons hydrocarbons based on molecular weight (Figure 9). Most
30 Esw6oi.SaT*chrwL.VoL28,No.1.1W4 9

Table L Cameo Bay Estuary Program Site Rankin Based on Selected Metal Data. l"I Win dry wt)
Ag Cd Hg Pb Zn total
station no. Ag (ug/g) ranking Cd (Ag/g) ranking Hg (Ag/g) rankin Pb (Ag/g) ranking Zn GWg) ranking ranking
CS-7 0.05 1 0.069 5 <0.006 1 17.1 3 31 2 12
CS-3 0.06 1 0.053 3 0.008 1 17.6 4 35 4 13
CS-2 0.07 1 0.060 4 0.019 2 17.8 5 34 3 15
CS-1 0.05 1 0.071 6 <0.006 1 14.1 2 39 6 16
CS-5 0.09 3 0.036 1 0.031 3 20.0 6 38 5 is
CS-6 0.07 1 0.051 2 0.046 6 2D.8 9 46 9 27
Sw-8 0.09 3 0.150 14 0.019 2 20.5 7 34 3 29
SW-15 0.08 2 0.192 21 0.048 7 M6 1 28 1 32
SW-7 0.07 1 0.155 15 0.032 4 24.7 is 46 9 42
EB-4 0.10 4 0.076 7 0.058 10 23.3 11 59 11 43
EB-10 0.08 2 0.121 10 0.069 15 20.6 8 56 10 45
OB-11 0.10 4 0.168 17 0.049 8 25.5 14 43 a 51
EB-I 0.11 5 0.127 12 0.059 11 26.2 16 62 12 56
WB-3 0.11 5 0.258 28 0.031 3 20.5 7 69 14 57
EB-2 0.11 5 0.175 19 0.077 20 25.8 15 68 13 72
SW-5 0.12 6 0.245 27 0.062 13 27.5 20 40 7 73
OB-I 0.14 8 0.118 9 0.065 14 27.7 21 88 27 79
W]" 0.11 5 0.088 8 0.057 9 31.7 30 92 29 81
WB-8 0.13 7 0.293 30 0.077 20 26.8 17 68 13 87
SW-10 0.16 10 0.486 48 0.037 5 22.2 10 73 16 89
WB-7 0.11 5 0.312 32 0.071 17 27.1 18 80 20 92
SW-9 0.17 11 0.400 38 0.037 5 25.5 14 87 25 93
OB-15 0.16 10 0.155 15 0.102 28 29.3 24 75 17 94
SW-12 0.25 16 0.365 35 0.048 7 29.4 25 71 15 98
SW-4 0.19 12 0.213 24 0.097 27 32.0 32 35 4 99
SW-14 0.16 10 0.414 40 0.082 22 24.3 12 75 17 101
SW-13 0.15 9 0.125 11 0.073 18 31.5 28 101 36 102
OB-10 0.14 8 0.156 16 0.081 21 33.8 38 82 22 105
OB-2 0.12 6 0.133 13 0.058 10 37.7 49 92 29 107
OB-13 0.15 9 0.268 29 0.082 22 30.6 27 82 22 109
OB-8 0.14 8 0.176 20 0.087 24 35.7 43 76 18 113
SW-6 0.13 7 0.435 45 0.061 12 31.7 30 78 19 113
OB-5 0.15 9 0.200 .22 0.085 23 34.7 40 81 21 115
OB-4 0.17 11 0.226 25 0.104 29 33.1 36 75 17 118
WB-2 0.17 11 0.358 36 0.076 19 29.7 26 92 29 121
WB-1 0.15 9 0.430 42 0.087 24 28.4 22 93 30 127
OB-7 0.16 10 0.245 27 0.113 32 35.8 44 75 17 130
WB-4 0.17 11 0.444 46 0.082 22 28.6 23 94 31 133
WB-9 0.36 21 0.302 31 0.087 24 31.9 31 93 30 137
OB-9 0.17 11 0.174 is 0.113 32 38.3 51 91 28 140
CS-4 0.20 13 0.208 23 0.190 43 32.4 34 88 27 140
WB-5 0.15 9 0.529 52 0.069 16 27.4 19 140 45 141
sw-11 0.16 10 0.239 26 0.096 26 37.6 48 9.5 32 142
U315 0.20 13 0.325 33 0.094 25 38.1 50 84 23 144
EB-3 0.19 12 0.431 43 0.112 31 33.2 37 87 26 149
EB-9 0.19. 12 0.401 39 0.148 36 32.1 33 92 29 149
OB-12 0.19 12 0.434 44 0.118 33 35.1 41 92 29 159
OB-6 0.26 17 0.592 58 0.106 30 32.8 35 86 24 164
EB-7 0.20 13 0.608 59 0.153 37 31.6 29 100 36 173
OB-3 0.20 13 0.327 34 0.141 35 40.7 52 109 41 176
IB,10 0,23 14 0,501 50 0,170 39 36*0 41 98 34 1112
EB-8 0.23 14 0.720 so 0.181 42 U.1 39 97 33 188
UM 0.25 16 0.392 37 0.195 44 41.2 63 104 38 188
M8 0.24 15 0.573 56 0.168 38 35.3 42 104 38 189
EB-6 0.29 19 1.320 63 0.137 34 33.2 37 105 39 192
IB-9 0.23 14 0.557 63 0.173 40 36.2 46 106 40 193
EB-5 0.23 14 0.794 61 0.176 41 37.0 47 101 36 199
M-7 0.32 20 0.424 41 0.234 45 42.1 55 106 40 201
Sw-I 0.46 23 0.488 49 0.264 46 55.5 58 95 32 208
IB-4 0.27 111 0*171 55 0*274 49 41,5 54 102 37 213
M2 0.46 23 0.524 51 0.271 48 49.9 57 109 41 220
M-3 0.39 22 0.574 57 0.264 46 48.5 56 109 41
SW-2 0.67 24 0.478 47 0.392 50 70.3 60 117 43 224
IB-I 0.57 24 0.564 54 0.269 47 55.6 59 125 228
SW-3 0.78 25 0.908 62 0.424 51 7&6 61 112 42 241

n-alkanes in the range Clo-C22 are positively loaded in PC carbons loaded negatively in PC 3 include most parent
3, as are the more highly alkylated (C2 and higher) two- two- and three-ring compowds, their methyl-substituted
and three-ring aromatics: naphthalenes, fluorenes, phenan- homologs, and most four- and five-ring aromatic com-
threnes, and dibenzothiophenes. Pristane, phytane, and pounds.
UCM hydrocarbons are also loaded positively in PC 3. In Together, the loadings for PC 2 and PC 3 discz@ate
contrast, n-allranes, in the range C2s-C34 along with Cm sources of organic and inorganic materials in the Casco
and C17 are loaded negatively in PC 3. Aromatic hydro- Bay sediments. Hydrocarbons loaded positively in PC 2

10 ErMw. SoL TechW., VoL 28, No. 1, IM 31

70*10' 70000' 69050'

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

-M.
40
A# 0
430
-Nig
50
05

ff
.......... -x.
VA.
.....................
....... ...........-
2 9
04
7
7
West
Bay
East NP
43
Bay
59
45' 3
............
0 10 Ir
72

4
7 0
1 20 0
Inner 0
Bay *5 0
160 20 Cape
. . . . . . . . . .. . . . . . . . . . . 10
4M Small
M
f 0 V
6
2 Outer 10 430
430 0 %
00 5
4 8 09 Bay
5
6 0
3
2 7
01

;j PCB--485

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

430
43
35'

0 Shallow Water
Sarroes (SM

70010' 70000' 69050'

Pip" IL Localion of the 25 % high orgark (0) and kwgaric (12) mventrad" In w*nerft "M caw Bay.

5 22M
.-4

loco
% SM 00

0
13 2= go

cc
4- a
40 .0 0 10 20 30 @D -is _;0 ;0

fthwipid C-wo-" I MCI) P"nfAPM CD*4--" 2 (PC2)
rip" 7. RsbtbiM betwom PC 1. TOC (%), and wid corderd FW9 S. PAftdons* betwom PC 2. Fe coderi! (% L and ssbNated
for Qksoo Say so&. w. . all0hatle hydrocarbons (Ppb) for Casco Bay sefto.
and negatively in PC 3 (lower right quadrant, Figure 9) include Fe, NL Se, As, Cr, and percentage sflt and clay
include compounds of algal (Cis and C17) and higher plant (Figure 9). These distributions represent terrigenous
(CV-C@j) origin (8-10). Other similarly loaded variables detrital and autochthonous marine input& TOC is sim-

32 EmIrm ScL Tedvwl.. VOL 28, No. 1. 1104 11

025
Weathered Fresh Diesel (?)
C11
Petroleum
0.2"D

CIO
C]FLU R12
UCUr ja F'S
0.10 me
UNAP Mim CIO
0.05.
NO CIO CU
C* COW TCHLOR C22 ON
.0.00. ...-M ---------- caw ...... 04W .. ........ Ga .................. --------------------
IL PM CM
-0.06 US
BW CV
RAJO CM
;rl'Hci PERYL :
-0.10 C34
AGEY
-0.15.

-0.20- Pyrogenic FAMM Terrigenous Detrital &
P"
Hydrocarbons Autochthonous Marine
-0.25 i I I I I 1 1
-0.20 -0.15 -0.10 -0-05 0.00 0.05 0.10 0.15

PC2
F*" 9. Pabft*No betwom PC2 and PC3 for PCA of Casco Bay conftn*aM data.
ilarly loaded, suggesting that biogenic materials are an itive scores for PC 2, negative scores for PC 3) are found
important contributor to the organic richness of the in the upper Fast Bay (EB-3, -5, -6, -7, and -8), and also
sediments (17). Hydrocarbons loaded negatively in both at Outer Bay sites OB-1 and OB-12 and Inner Bay site
PC 2 and PC 3 (lower left quadrant, Figure 9) consist IB-9. In contrast, the lower East Bay (EB-1, -2,4,-9, and
primarily of four- and rive-ring aromatics that are gen- -10), as well as Outer Bay site OB-15 and shallow water
erated from both natural and anthropogenic combustion site SW-15, contains a greater component originating from
processes. A combustion origin for these hydrocarbons is pyrogertic sources (negative scores for PC 2 and PC 3).
also supported by the covariance of the parent two- and Site CS-4 in Cape Small exhibits a composition simila to
three-ring aromatics which are structurally stable at high the lower East Bay sites. These distributions are aignif-
temperatures (11-14). The departure of the alkylated icant in that the sites that are simila in composition are
chrysenes from this trend suggests either a biogenic source geographicaRy clustered. This suggests subtle differences
for these compounds or possibly some interference in their in the principal sources of hydrocarbons in the upper and
analysis from biogenic material. Hydrocarbons loaded lower East Bay.
negatively in PC 2 and positively in PC 3 (upper left Sites characterized by inputs of weathered petroleum
quadrant, Figure 9) include two- and three-ring aromatics (negative scores for PC 2, positive scores for PC 3) include
contsinin a C2 or greater alkylation. These compounds the Inner Bay and shallow water sites nearest the city of
are the most abundant aromatic hydrocarbons in petro- Portland (IB-1 and -2 and SWA 4, and -5). This is
leum and petroleum byproducts. Pristane and UCM are probably the result of chronic inputs from runoffand point
similarly loaded, suggesting a weathered petroleum origin sources associated with urban activities. Surprisingly,
(18,19). The source represented by the hydrocarbons that however, the sandy sediments from Cape Small (CS-1, -2,
are loaded positively in both PC 2 and PC 3 (upper right -3, -5, -7, and, to a lesser extent CS-6) have contaminant
quadrant, Figure 9) is equivocal. These consist primarily compositions that are nearly identical to site E13-1. This
of n-alkanes in the range CIO-C22, which might represent is illustrated in the scores cros&plot in Figure 10, where
a relatively unweathered petroleum product, i.e., diesel the majority of Cape Small sites plot intermediate between
fuel. Alternatively, the covariance of these hydrocarbons the lower East Bay and shallow water sites SW-3 and SW-4
with the metals Pb, Ag, and Hg and total DDTs and BHC from the Inner Bay. This likely reflects aromatic hydro-
concentrations (Figure 9) suggest possible inputs from carbon inputs from both pyrogenic and petroleum sources
runoff associated with either agricultural or industrial at these locations and suggests that, despite significantly
activities. Principal component 4 (5.4 % of the total lower concentrations, the assemblage of contaminants in
variance) is characterized by high positive loadings for Cape Small sediments is simila to those at some con-
most of the chlorinated hydrocarbons analyzed and is lea taminated Inner Bay sitea. Sites showing a relative
straightforward to interpret. It should be noted that the enrichment in CIO-C= n-alkanes (positive scores for PC
organochlorine compounds are generally low and near the 2 and PC 3) include nearly all the West Bay sites and
method detection limit, thus indicating a relatively"noiW shallow water sites SW-9, -10, -11, and -13 within the Went
data set Bay. Several nearby sites also exhibit a odmilsk compo-
Based on these interpretations, the distribution of sition. These include Outer Bay site OB-13 and Inner
samples in a scores cross-plot of PC 2 versus PC 3 (Figure Bay sites EB-6 and IB-10. Thus, although the origin of
can be used to assess the regional influence of a variety this compositional feature is uncertain, it appears to
ofsources. Sediments exhibiting a predominantly biogenic manifest itself over a limited portion of Casco Bay,
10

influence from detrital and autochthonous sources (pos- suggestinga localized source. Several Outer Bayadm (OB-
12 BrAw " T*OvwL. VOL 26, No. 1. IN4 33

Weathered Fresh Diesel (?)
Petroleum

0 - ------------------------------ -------------- ---------- ------ ... ..........................
-5

-10

Terrigenous Detrital &
Pyrogenic Autochthonous Marine
Hydrocarbons
-20 -15 -10 -5 0 5 10

Principal Component 2
Figure 10. Suggested model for determining the source of hydrocarbons and trace Metals In Casco Bay sediments.

Table 4. Comparison of ER-L, ER-M, Apparent Effects Thresholds, and Washington State Sediment Quality Criteria
Concentrations for Selected Chemicals in Sediments and Values Measured In Casco Bay (after Long and Morgan, 1990,
Washington State Dept. of Ecology Sediment Management Standards, Chapter 173-204 WAC)
Casco Bay regional
chemical deg of Inner Bay West Bay East Bay Cape Small Outer Bay
analyte ER-L* ER-M* AET* confidence* WSSQC# min max min max min max min max min max
Trace Elements (ppm dry wt)
arsenic 33 85 50 L/M 5.7 1.62 16.00 4.76 19.60 3.20 19.60 5.03 13.70 5.03 20.50
cadmium 5 9 5 H/H 5.1 0.213 0.908 0.088 0.529 0.076 1.320 0.036 0.208 0.036 0.592
chromium 80 145 NA4,M/M 26.0 31.00 91.00 35.00 100.00 29.00 105.00 37.00 93.00 43.00 93.00
copper 70 390 300 H/H 390 7.92 48.40 6.98 26.20 5.59 27.90 2.52 21.60 6.94 26.20
lead 35 110 300 M/H 450 27.50 75.60 20.50 37.60 13.60 37.00 14.10 32.40 25.50 40.70
mercury 0.15 1.3 1 M/H 0.41 0.061 0.424 0.019 0.096 0.048 0.181 <0.010 0.190 0.049 0.141
nickel 30 50 NSD M/M NA 7.81 37.80 9.67 38.60 8.36 38.40 12.90 30.60 14.50 39.80
silver 1 2.2 1.7 M/M 6.1 0.12 0.78 0.07 0.36 0.08 0.29 <0.07 0.20 0.10 0.26
zinc 120 270 160 H/H 410 36.00 125.00 34.00 140.00 28.00 105.00 31.00 KOO 43.00 109.00
a ER-L, effects range-low. b ER-M, effects range-median. c AET, apparent effects threshold. d L,low; M, medium; H, high. e WSSQC,
Washington State Sediment Quality Criteria, calculated ppb dry wt based on 2% TOC. / ppm dry weight. f NSD, not sufficient data h NA,
not available.

3, -5, -8, -9, and -11) exhibit a composition intermediate low to high. A 10th and 50th percentile were then
between the Inner Bay sites characterized by weathered determined. Those were designated "effects range low"
petroleum and the West Bay sites enriched in lower and "effects range median* (ER-L and ER-M). The
molecular weight n-alkanes. Washington State Sediment Quality Criteria, the summary
Potential for Biological Effects. Biological effects of data from Long and Morgan (16), and the Casco Bay
or sediment quality were not directly measured in this results are compared in Tables 4-6.
study. However, the concentrations of most organic The total PAH concentrations present in Inner Bay
contaminants detected are below the concentration levels sediments are above the PAH concentratiorm thought to
that are believed to evoke toxic responses in marine benthic produce toxic responses in marine benthic organisms, i.e.,
organisms (Tables 4-6). Long and Morgan (9) conducted total PAH >35 000 ppb (Table 4). Bioavailability and
an extensive review of articles that provide both concen- not necessarily absolute concentration are compared and
trations of contaminants in sediments and observed also a factor in determining whether a contaminant evokes
biological effects. Six different approaches used in these a biological response. For example, the mode of occurrence
studies were briefly described and reviewed. It was of PAH has been shown to vary widely depending on the
concluded that each approach had strengths and weak- original source (19). Coal or soot-associated combustion-
nesses, i.e., there is no perfect method for determining derived PAHs are often tightly bound or occur in the
specific threshold concentrations for contaminants in interiors of particles. This mode of occurrence renders
sediment. They therefore derive consensus values by these PAHs largely inert as far as biological effects. In
considering data from all of the studies reviewed. Sed- contrast, liquid hydrocarbons such as oil or creosote contain
iment concentrations shown by the studies to cause PAHs that are readily available to organisms and would
biological effects, and judged to be valid, were ranked from be expected to induce toxicological effects. A majority of
34 Environ. Sci. TechnoL., Vol. 28, No. 1, 1994 13

Table 5. Comparison of ER-L, ER-M, Apparent Effects Thresholds, and Washington State Sediment Quality Criteria
Concentrations for Selected Chemicals In Sediments and Values Measured in Casco Bay (after Long and Morgan 1990;
Washington State Dept of Ecology Sediment Management Standards, Chapter 173-204 WAC)
Casco Bay Regions
chemical deg of Inner Bay West Bay East Bay Cape Small Outer Bay
analyte ER-La ER-Mb AETc confidenced WSSQC min max min max min max min max min max
Polychlorinated Biphenyle (ppb)
total PCBs 50 400 370 M/M 240 7.31 484.97 1.58 16.32 8.89 37.30 0.44 40.02 5.50 30.67
DDT and Metabolites (ppb)
DDT 1 7 6 L/L 0.49 4.28 <0.20 0.96 0.40 2.01 <0.20 0.86 0.47 1.52
DDD 2 20 NSD M/L 0.67 15.09 0.08 1.49 0.31 1.98 <0.07 0.62 0.34 2.04
DDE 2 15 NSD L/L 0.18 3.84 <0.06 1.14 0.07 0.48 <0.06 0.40 0.06 0.63
total DDT 3 350 NA M/M 1.63 20.42 <0.20 3.10 0.82 4.16 <0.20 1.89 1.03 4.12
Other Pesticides (ppb)
lindane NA NA NSD NA <0.07 0.48 <0.07 0.22 <0.07 0.35 <0.07 0.11 <0.07 0.34
chlordane 0.5 6 2 L/L 0.15 4.91 0.07 0.98 0.16 1.91 <0.07 1.32 0.13 1.89
heptachlor NA NA NSD NA 0.08 0.13 <0.04 0.05 <0.04 0.13 <0.04 <0.04 <0.04 0.04
dieldrin 0.02 8 NA L/L <0.16 0.94 <0.16 <0.16 <0.16 0.43 <0.16 2.46 <0.16 1.40
aldrin NA NA NSD NA <0.28 <0.28 <0.28 <0.28 <0.28 <0.28 <0.28 <0.28 <0.28 <0.28
andrin 0.02 45 NSD L/L <0.06 0.84 <0.06 0.21 <0.06 0.17 <0.06 <0.06 <0.06 0.65
mirex NA NA NSD NA <0.04 0.29 <0.04 0.08 <0.04 0.49 <0.04 0.66 <0.04 0.16
ER-L, effects range-low. ER-M, effects range-median. c AET, apparent effects threshold. L, low, M, medium; H, high. WSSQC,
Washington State Sediment Quality Criteria calculated ppb dry wt based on 2% TOC. / ppm dry weight NSD, not sufficient data. NA,
not available.

Table 6. Comparison of ER-L, ER-M, Apparent Effects Thresholds, and Washington State Sediment Quality Criteria
Concentrations for Selected Chemicals in Sediments and Values Measured in Casco Bay (after Long and Morgan. 1990;
Washington State Dept. of Ecology Sediment Management Standards, Chapter 173-204 WAC)
Casco Bay regions
chemical deg of Inner Bay West Bay East Bay Cape Small Outer Bay
analyte ER-Le ER-Mb AETc confidenced WSSQC min max min max min max min max min max
Polynuclear Aromatic Hydrocarbons (ppb dry wt surrogated corrected)
acenaphthene 150 650 150 L/L 320 2 81 <1 3 2 19 <1 13 2 6
anthracene 85 960 300 L/M 4400 6 255 3 15 8 107 <1 99 14 50
benz(a)anthracene 230 1600 550 L/M 2200 30 655 12 56 34 481 1 360 48 173
benzo[a]pyrene 400 2500 700 M/M 1980 43 741 17 100 50 498 1 433 62 209
benzo[e]pyrene NA NA NSD NA 37 514 14 74 37 276 1 271 48 140
biphenyl NA NA NSD NA 3 29 <2 7 4 12 <2 10 4 12
chrysene 400 2800 900 M/M 2200 44 766 19 74 47 530 1 398 53 192
dibenz[a,h]anthracene 80 280 100 M/M 240 3 105 3 41 7 58 <0 64 11 73
2,6-dimethyinaphthylene NA NA NSD NA 4 130 1 9 3 28 <1 17 5 14
fluoranthene 600 3600 1000 H/H 3200 90 1444 34 144 82 639 2 522 118 304
fluorene 35 640 350 L/L 460 4 201 1 7 4 96 <1 27 6 16
1-methylnaphthalene NA NA NSD NA 3 81 1 7 3 31 <1 20 5 11
2-methylphenanthrene 65 670 300 L/M 760 5 95 2 11 5 37 <1 34 8 17
1-methylphenanthrene NA NA NSD NA 10 311 5 14 0 68 <1 49 8 33
naphthalene 340 2100 500 M/H 7400 8 135 2 14 7 46 <2 41 12 26
Perylene NA NA NSD NA 17 216 9 56 31 110 <4 94 21 77
Phenanthrene 225 1390 260 M/M 2000 42 1036 17 71 41 550 1 269 57 160
Pyrene 350 2200 1000 M/M 20000 82 1552 31 137 78 500 2 562 1127 302
2,3,5-trimethylnaphthalene NA NA NSD NA 3 187 1 4 2 34 <1 9 3 6
totel PAH 4000 35000 22000 L/L 911 20748 421 1901 1059 734O 16 7454 1312 4004
a ER-L, effects range-low. b ER-M, effects range-median. cAET, apparent effects threshold. d L, low, M, medium; H, high. e WSSQC,
Washington State Sediment Quality Criteria. calculated ppb dry wt based on 2% TOC. f ppm dry weight. g NSD, not sufficient data. h NA.
not available.

the PAHs in this study are combustion related and thus biological response have been used, resulting in a large
may be in a sequestered form that significantly reduces and confusing literature. Thomas (20) briefly describes
their toxicity. eight different approaches to setting toxicity criteria for
Long and Morgan (16) estimated that median concen- sediments, but no actual data are presented. Pavlov (21)
trations of total PCB above 400 ppb dry wt elicits a toxic compared results from one of these approaches, the
response in most benthic organisms. For this study, only equilibrium partitioning approach, to results from other
one site (SW-2) is above this threshold. The DDT commonly used methods. He shows that the concentration
concentrations are low compared to concentrations known of a given metal needed to elicit a biological response, as
to cause a toxic response in most benthic organisms (16). determined by equilibrium partitioning and other meth-
Chlordane concentrations are "low" based on the definition ods, does not vary widely (except for Hg). The threshold
Of O'Connor (15) and should pose little or no threat of concentrations for toxicity are much higher than those
toxic biological effects (16). found in Casco Bay sediment.
A number of different approaches to determining the None of the metal concentrations in the Casco Bay
trace metal concentrations in sediments which lead to a sediments are as high as Long and Morgan's (16) ER-M,

14 Environ. SoL TechnoL. Vol. 28, No. 1. 1994 35

and only a few are as high as the ER-Le. For example, (2) Wade, T. L.; Atlas, E. L.; Brooks, J. M.; Kennicutt, M. C.,
Casco Bay chromium concentrations are as high as 105 11; Fox, R. G; Sericano, J.; Garcia-Romero, B.; DeFreitas,
ppm, whereas the ER-L is 80 ppm. Many uncontaminated D. Estuaries 1998, 11, 171-179.
sediments from other parts of the world, however, contain (3) Kennicutt, M. C., 11; Wade, T. L.; Presley, B. J. Assessment
of Sediment Contamination in Casco Bay-, Interpretive
chrorniurn concentrations higher than 105 ppm, and it is Report prepared for Casco Bay Estuary Project; GERG
unlikelythat chromium in Casco Bay sediment would cause Technical Report92-157; US. EPA. Washington, DC, 1992;
a biological effect. The same can be said for nickel and 113 pp.
zinc, where Casco Bay concentrations are as high as 40 (4) Folk, R. L Petrology of sedimentary rocks; Hemphill
and 140 ppm compared to ER-Ls of 30 and 120 ppm, Publishing Co.: Austin, TX, 1974; 184 pp.
respectively. A few mercury concentrations in Casco Bay (5) Brooks, J. M.; Wade, T. L; Atlas, K L; Kennicutt, M. C.,
are also higher than the ER-L but are much lower than H, Presley, B. J.; Fay,& FL;Powell, K N.; Wolff, G. Analyses
those of highly contaminated sediments from Hudson- of bivalves and sediments for organic chemicals and trace
Raritan, Long Island Sound, Boston Harbor and elsewhere elements from Gulf of Mexico estuaries; Second annual
report for NOAA's National Status and Trends Program;
(15). It is unlikely that mercury in Casco Bay sediment Contract 50-DGNC-5-00262.
is causing an effect on marine organisms. As with PAH, (6) Wold, S. Technometric8 1978, 20, 397-406.
bioavailability is an issue in determining trace metal (7) Joliffe, J. Principal Components Analysis; Springer-Ver-
toxicity. 1W. Berlin, 1986.
(8) Brassell, S. C.; Eglinton, G.; Maxwell, J. &; Philp, R. P. In
Conclusions Aquatic Pollutants, Transformations and Biological Ef-
fects; Huntzinger, 0., van Lelyveld, L. H., Zoetman, B. C.
Detailed, high-quality assured analysis of a broad J., Eds.; Pergamon Press: Oxford, 1978; pp 69-N.
spectrum of contaminants can be utilized to understand (9) Clark, R., Jr.; Blumer, M. Limnol. Oceanogr. 1967,12,79--
the dynamics of pollutants in coastal environments. The 87.
potential processes implicated in releasing these contam- (10) Philp, F_ P. Fossil Fuel Biomarkers: Application and
inants to the marine environment can be identified and Spectra. Methods in Geochemi8try and Geophysics;
their relative importance can be estimated. Statistical Elsevier. New York, 1985; Vol. 23.
analysis of contaminant concentrations can be used to (11) ffites, K A.; La Flamme, & E.; Windsor, J. G., Jr.;
identify geographically consistent contaminant profiles Farrington, J. W.; Deuser, W. G. Geochim. Co8mochim. Acta
1980,44,873-878.
and suggest the source ofthese pollutants. Thisapproach (12) Wakeham, S. G.; Schaffner, C.; Giger, W. Geochim. C08-
was applied to Casco Bay, ME. mochim. Acta 1980, 44, 403-413.
Anthropogenic contaminants are widespread throughout (13) Wakeham, S. G.; Schaffner, C.; Giger, W. Geochim. Coo-
Casco Bay, but in most cases occur at exceedingly low mochim. Acta 1980b, 44, 415-429.
concentrations. The focus ofcontamination is in the Inner (14) La Flamme, & E.; Mtes, K A. Geochim. Cosmochim. Acta
Bay region directly associated with the densest population 1978, 42, 2&9-303.
centers and industrialization. Multiple processes add (15) O'Connor, T. P. Coastal Environmental Quality in the
contaminants to Casco Bay, and these chemicals have United States, 1990. Chemical Contamination in Sediment
and Tissues; A Special NOAA 20th Anniversary Report-
accumulated in bay sediments. Localized accumulations Coastal and Estuarine Assessment Branch, Ocean Assess-
of various chemicals do occur, but even these areas are ments Division, Office of Oceanography and Marine As-
mostly below levels suspected of evoking toxic biological sessment, National Ocean Service, National Oceanic and
responses. In order to more specifically assign the sources Atmospheric Administration: Rockville, MD, 1990; 34 pp.
of the observed contaminants, intense localized sampling (16) Long, E. &; Morgan, L. G. The potential for biological
and analysis of effluents and runoff patterns would be effects of sediment-sorbed contaminants tested in the
needed. To determine sediment quality, bioassays of National Status and Trends Program; NOAA Technical
Memorandum NOS OMA 52; NOAA Office of Oceanog-
sediments at suspect sites should be conducted to directly raphy and Marine Assessment, Ocean Assessments Divi-
assess the potential for biological impacts. sion: Seattle, WA, 1990; 173 pp and appendices.
(17) Boehm, P. D.; Requejo, AL G. Estuarine, Coastal ShelfSci.
Acknowledgments 1986, 23, 29-58.
(18) Jones, D. M.; Douglas, A. G.; Parkes, F_ J.; Taylor, J.; Giger,
This project has been funded wholly or in part by the W.; Schaffner, C. The recognition ofbiodegraded petroleum-
United States Environmental Protection Agency as part derived aromatic hydrocarbons in recent sediments. Mar.
of the Casco Bay Estuary Project underAassistance Pollut. Bull. 1983,14,103-108.
Agreement CE-001553-01 to the New England Interstate (19) McFlroy,A. E.; Farrington, J. W.;TeaLJ. M. InMetabolism
Water Pollution Control Commission. The contents of of Polycyclic Aromatic Hydrocarbons; VaranasL U., Ed.;
this document do not necessarily reflect the views and CRC Press. Boca Raton, FL, 1989; pp 1-39.
policiesofthe Environmental Protection Agency, nor does (20) Thomas, N. In Water Quality Standards for the 218t
Century, Proceedings of a National Conference, Dallas,
mention oftrade narnes or commercial products constitute TX, March 1-3,1989, US. EPA Office of Water. Wash-
endorsement or recommendation for use. We would also ington, DC, 1989.
like to thank the National Oceanic and Atmospheric (21) Pavlov, S. P. In Fate and Effects of Sediment-Bound
Administration (Contract 50-DGNC-5-00262), National Chemicals in Aquatic Systems, Dickson, K. L, Mak, A. W.,
Status and Trends Program, for providing baseline data Brungs, W. A., Eds.; Pergamon Press:- New York, 1987; pp
for comparison. 388-342.

Literature Cited Received for review November 16, 1992. Revised manuscript
received June 29, 1993. Accepted October 4, 1993.e
(1) Larsen, P. F.; Gadbois, D. F.; Johnson, A. C.; Doggett, L
F. Doggett. Bull. Environ. Contamin. Toxicol. 1993,30,530- 0 Abfftmct published in Advance ACS Abstracts, November 15,
5M. 199&
36 Emiw. ScL TedvioL, Vol. 28. No. 1. 1994 If

Reprint 3

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

Environmental Pollution 93 (1994) 291-298

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
& James M. Brooks
Geochemical and Environmental Research Group, College of Geosciences and Maritime Studies,
Texas A & M University, College Station, Texas 77845, USA

(Received I July 1992; accepted 25 September 1992)

Abstract equilibrium concentration for trace organic contami-
Polynuclear aromatic hydrocarbon (PAH) contaminant nants such as PAHs within approximately one month
concentrations in 870 composite oyster samples ftorn (Sericano & Wade, unpublished data).
coastal and estuarine areas of the Gulf of Mexico ana- To assess the spatial and temporal variation of con-
lyzed as part of National Oceanographic and Atmo- taminant levels of coastal and estuarine environments,
spheric Administration's (NOAA's) National Status and the National Oceanic and Atmospheric Administration
Trends (NS&T) Mussel Watch Program exhibit a log- (NOAA) instituted the National Status and Trends
normal distribution. There are two major populations in (NS&T) Mussel Watch Program under its Program for
the data. The cumulative ftequency function was used to Marine Environmental Quality (O'Connor, 1990). The
deconvolute the data distribution into two probability sample sites were selected to characterize the overall
density functions and calculate summary statistics for concentration of contaminants in coastal and estuarine
each population. The first population consists of sites ecosystems away from known point-sources of contam-
with lower PAH concentration probably due to back- ination.
ground contamination (ie. stormwater runoff, atmo- The focus of this paper is to examine the distribution
spheric deposition). The second population are sites with of the PAH contaminant concentrations in oysters
higher concentrations of PAHs associated with local collected from the Gulf of Mexico as part of NOAA's
point sources of PAH input (ie. small oil spills, etc.). NS&T Mussel Watch Program, and determine the
The temporal pattern for the mean concentration of the environmental factors controlling the concentration of
populations from the Gulf of Mexico is consistent with PAHs.
large-scale climatic factors such as the El Nifto cycles
which affect the precipitation regime. METHODS
INTRODUCTION Sample Collection
Oysters (Crassostrea virginica) were collected from
Oysters and other bivalve molluscs have been used for three stations at each site during the winter of each
monitoring contaminants in the environment (Farring- year (1986-1990). The number of sites per year varied
ton et al., 1983). Oysters are sentinel organisms which from 48 to 68. In some years not all sites had three
concentrate contaminants from the marine environ- stations due to the low abundance of oysters at a specific
ment, yet do not readily metabolize contaminants such site (Table 1). Sample sites give coverage of the Gulf of
as polynuclear aromatic hydrocarbons (PAHs) (Far- Mexico coastal and estuarine areas from southem-most
rington & Quinn, 1973). PAHs enter the near-coastal Texas to southern-most Florida (Fig. 1). Individual
environment through a number of mechanisms (e.g. stations at each site are generally from 100 to 1000 m
runoff, discharge of industrial waste or sewage, natural apart. An analysis at each station represents a com-
or industrial combustion processes, natural oil seep- posite of twenty individual oysters. Each year, the field
ages, and spills of petroleum or petroleum products). sampling returned to as many sites as possible. In some
The contaminants found in oysters reflect the current instances it was necessary to relocate or abandon an
contaminant burden of an ecosystem. The concentra- Table 1. National Status and Trends Oysters Gulf of Mexico
tion of a contaminant in an oyster is the difference Sampling Propmu-Summary of sampling
between uptake and excretion of that contaminant.
Galveston Bay oysters transplanted from a 'high' level 1986 1987 1988 1989 1990
site to a 'low' level site, and vice versa, come to a new
Year I IV V
Number of sites 49 48 65 62 68
F.nviron. Pollui. 0269-7491194/$06.00 C 1993 Elsevier Science Number of samples 142 144 195 186 203
Publishers Ltd, England. Printed in Great Britain 38

MISSISSIPPI ALABAMA GE

310
TEXAS LOUISIANA

Solon Rouge 0
Tellahm
0 62 10- 35 67 39 38 %Fwarnecity
Houstel 33 34 -
300 0 New ovise 0-30 40'4;@k
4: 2 32 We% 42
0,31
21 22
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23 4'1

0-64
Galveston
290 3C. 24 2m$ 7 29 65
57
14

280 C46
54
3

270

52 GJFoF AIDW
26*

NEX

250

240 0 Mile

0 KII,

970 960 950 940 930 920 910 900 890 880 870 860 850 840
4f

Fig. 1. Location of NS&T Mussel Watch Sites in the Gulf of Mexico (Sericano et al., 1990).

PAH contaminants in oysters ftom the Gutf of Mexico 293

established oyster site due to lack of suitable sized Gas chromatography-mass spectrometry (GC-MS)
bivalves (Wilkinson et al., 1991). The locations and PAHs were separated and quantified by GC-MS
designator for the oyster sites are found in Wilkinson et (HP5980-GC interfaced to a HP5970-MSD). The sam-
al. (199 1), Sericano et al. (1990) and Wade el al. (1990). ples were injected in the splitless mode on to a 30 in
XO-25 min (0-32 jAm film thickness) DB-5 fused silica
Tissue extraction capillary column (J&W Scientific Inc.) at an initial tem-
The tissue extraction process used was adapted from a perature of 60'C and temperature programmed at
method developed by MacLeod et al. (1985). Approxi- 120C/min to 300'C and held at the final temperature
mately 15 g of wet tissue were used for the PAH for 6 min. The mass spectral data were acquired using
analysis. After the addition of internal standards (surro- selected ions for each of the PAH analytes. The
gates) and 50 g of anhydrous Na2SO., the tissue was GC-MS was calibrated and linearity determined by
extracted three times with dichloromethane using a injection of a standard containing all analytes at five
tissuernizer. A 20 ml sample was removed from the total concentrations ranging from 0.01 nglAl to I ng/tLl.
solvent volume and concentrated to one ml for lipid Sample component concentrations were calculated
percentage determination. The 280 ml of remaining from the average response factor for each analyte.
solvent was concentrated to approximately 20 ml in a Analyte identifications were based on correct retention
flat-bottomed flask equipped with a three-ball Synder time of the quantitation ion (molecular ion) for the
column condenser. The tissue extract was then trans- specific analyte and confirmed by the ratio of quantita-
ferred to a Kuderna-Danish tube heated in a water bath tion ion to confirmation ion.
(60'C) to concentrate the extract to a final volume of Calibration check samples were run with each set of
2 ml. During concentration, the dichloromethane was samples (beginning, middle, and end), with no more
exchanged for hexane. than 6 h between calibration checks. The calibration
The tissue extracts were fractionated by alumina: silica check must maintain an average response factor within
(80-100 mesh) open column chromatography. The 101/6 for all analytes, with no one analyte greater than
silica gel was activated at 170'C for 12 h and partially �25% of the known concentration. A laboratory refer-
deactivated with 3% distilled water (v/w). Twenty ence sample (oil spiked solution) was also analyzed
grams of silica gel were slurry-packed in dichloro- with each set of samples to confirm GC-MS system
methane over 10 g of alumina. Alumina was activated performance and calibration.
at 400'C for 4 h and partially deactivated with 1%
distilled water (v/w). The dichloromethane was replaced RESULTS AND DISCUSSION
with pentane by elution. The extract was then applied
to the top of the column. The extract was sequentially Oyster site variations
eluted from the column with 50 n-d of pentane (aliphatic During the first five years of this study a total of 870
fraction) and 200 ml of I : I pentane: dichloromethane composited oyster samples have been analyzed for
(aromatic fraction). The aromatic fraction was further PAHs. The tPAH (total NS&T PAHs) is the sum of the
purified by HPLC to remove the lipids. The lipids were eighteen aromatic hydrocarbon analytes, as measured in
removed by size exclusion using dichloromethane as Year 1, with concentrations greater than 20 ng/g dry wt
an isocratic mobile phase (7 ml/min) and two 22-5 x (Table 2); this was the reporting limit for Year I data
250 min Phenogel 100 columns (Krahn et al., 1988). (Wade et al., 1988). The median PAH concentration at
The purified aromatic fraction was collected from a site is used as a measure of the best indicator of the
1.5 min prior to the elution of 4,4'-dibromofluoro- concentration. The median is a more stable (or resistant)
biphenyl to 2 min after the elution of perylene. The
retention times of the two marker peaks were checked Table 2. National Status and Trends oysters polynuclear
prior to the beginning and at the end of a set of 10 aromatic hydrocarbon analytes
samples. The purified aromatic fraction was concen-
trated to I ml using a Kuderna-Danish tube heated in Aromatic hydrocarbons
a water bath at 60*C. Low molecular weight High molecular weight
Quality assurance for each set of ten samples in-
cluded a procedural blank, matrix spike, duplicate, and Biphenyl Fiuoranthene
tissue standard reference material (NIST-SRM 1974) Naphthalene Pyrene
which were carried through the entire analytical scheme. 1-methylnaphthalenc Benz(a)anthracene
Internal standards (surrogates) were added to the sample 2-methylnaphthalene Chrysene
2,6-dimethylnaphthalene Indeo[1,2,3-cd]pyrene'
prior to extraction and were used for quantitation. The 1,6,7-trimethylnaphthalene' Benzo(a)pyrene
surrogates were dg-naphthalene, d,o-acenaphthene, Acenaphthene Benzo(e)pyrene
d,o-phenanthrene, d12-chrysene, and d,2-perylene. Surro- Acenaphthylene' Perylene
gates were added al a concentration similar to that Fluorene Dibenz[a,h]anthracene
expected for the analytes of interest. To monitor the Phenanthrene Benzo[g,hJ]perylene'
Anthraccne
recovery of the surrogates, chromatography internal 1-methylphenanthrene
standards d,o-fluorene and d,2-benzo(a)pyrene were
added just prior to GC-MS analysis. 40 'Analytes not used in tPAH summation.

294 T J Jackson et al.

Table 3. Total NS&T PAH concentration in oysters

No. Sitc Median concentration of tPAH Bay group No. Site Median concentration of tPAH Bay group
code median oDdc median
V IV III II I (ng/g) V IV 111 11 1 (ng/g)
1990 1989 1988 1987 1986 1990 1989 1988 1987 1986
(ng/g) (ng/g) (ng/g) (ng/g) (ng/g) (ng/g) (ng/g) (ng/g) (ng/g) (ng/g)

Texas Louisiana-cont.
I LMSB 22 20 30 20 25 65 MRTP 212 310 1410 - - 391 � 582
52 LMPI - - 3380 - - 30 � 58 64 MRPL 403 330 695 - -
78 LMAC 120 - - - - 31 BSSI 185 71 484 68 177 181 � 134
53 CCBH 1530 - 1 600 - - 30 BSBG 45 202 213 118 265
2 CCNB 161 264 598 434 45 565 � 725 32 LBMP 20 94 89 26 20 39 � 59
3 CCIC 137 430 848 - 1 140 62 LBNO - - 81 - -
54 ABHI - - 1870 - -
Mississippi
4 ABLR 20 20 20 21 20 33 MSPC 103 300 175 319 99
5 CBCR 88 - 20 20 22 20 � 1 34 MSBB 1 210' 893 1500 4310 1 600 322 � 654
6 MBAR 20 20 20 20 21 35 MSPB 59 306 776 300 246
7 SAPP 26 - - 51 45 Alabama
8 SAMP - - - 49 93 25 � 23 36 MBCP 20 90 288 137 31
9 ESSP 20 - - 21 20 66 MBHI 767 554 1110 - - 295 � 740
10 ESBD 21 70 21 - - 79 MBDR 1 520 - - - -
12 MBGP - 20 86 56 20 Florida
11 MBLR 96 348 - 59 90 45 � 48 67 PBPH 168 369 842 - -
56 MBCB 20 - 56 - - 37 PBIB - 21 204 250 406 197 � 198
13 MBTP 20 20 56 20 20 80 PBSP 130 - - - -
55 MBDI - - 53 - -
14 MBEM 201 200 23 22 78 138 � 119 73 CBJB 1680 8590 - - -
39 CBSP 225 355 703 543 428 429 � 1 140
72 BRCL 761 60 - - - 38 CBSR 69 21 2540 2470 209
57 BRFS 955 1 670 682 - - 792 � 792 74 PCLO 98 229 - - -
18 GBCR 370 1 170 525 478 1 070 68 PCMP 1 210 2690 4750 - - 1800 � 1 590
58 GBOB 315 593 543 - - 40 SAWB 1 150 2090 1990 1 970 11 800
16 GBTD 25 44 20 112 149 259 � 606 41 APDB 20 24 2800 20 20 57 � 530
15 GBYC 247. 132 207 568 1 030 42 APCP 269 1110 740 20 109
59 GBSC 1290 1 350 3 100 - -
17 GBHR 20 119 34 20 31 75 AESP 33 74 - - -
Louisiana 69 SRWP - - 119 - -
19 SLBB 108 154 169 26 247 154 � 72 43 CKBP 20 74 24 68 22 46 � 103
20 CLSJ 180 228 102 57 376 220 � 218 76 TBNP 269 394 - - -
60 CLLC 404 726 20 - - 47 TBMK 101 170 20 49 372
21 JHJH 88 72 20 84 43 44 � 50 44 TBPB 20 217 286 68 95
70 TBOT 112 357 212 - - 126 � 165
22 VBSP 189 31 20 118 79 79 � 108 77 TBKA 252 834 - - -
24 ABOB 20 28 192 115 32 22 � 42 45 TBHB - - 552 2150 460
25 CLCL 20 54 20 20 20 46 TBCB 20 65 94 22 20
26 TBLB 20 49 306 37 20 40 � 162 48 CBBI 20 83 31 43 20 51 � 180
27 TBLF 101 50 83 20 25 71 CBFM 69 546 272 - -
61 BBTB - - 20 - - 49 NBNB 87 203 253 108 228 72 � 129
28 BBSD 963 5480 44 25 57 963 � 1 020 50 RBHC 20 77 67 20 47
29 BBMB 1010 1 310 1 460 1 111 822 51 EVFU 47 68 257 20 112 68 � 125

estimator of the typical value than the mean for data MBLR, MBCB, MBTP & MBDI) and Aransas bays
which may contain outliers (Hensel, 1990). (ABLR, CBCR & MBAR) which exhibit low median
The data in Table 3 presents the spatial and temporal concentrations of tPAH and small variability in con-
variation for the median tPAH concentration in the centration. The highest median tPAH concentration for
coastal and estuarine areas of the Gulf of Mexico. The a bay group in Texas is the Brazos River (BRCL &
sites are separated into Bay groups (Wilson el al., 1992) BRFS), which carries the runoff from agriculture and
for data comparison. The variability for each Bay wastewater discharge from industrial point-sources
group is the standard deviation as computed from the (NOAA, 1985). For the entire coastal and estuarine
interquartile range (IQR) for the. five years of data area of the Gulf of Mexico (Table 3), the highest
(Hensel, 1990). In Texas, Corpus Christi (CCBH, median tPAH concentration for a bay group is near
CCNB, CCIC & ABHI) and Galveston bays (GBCR, Panama City, Florida (PCLO, PCMP & SAWB),
GBOB, GBTD, GBYC, GBSC & GBHR) are near which is close to a paper mill (NOAA, 1985; Wilkinson
industrial and population centers and exhibit high et al., 1991).
median concentrations of tPAH and large variability in There are ffteen sites (LMSB, ABLR, CBCR,
concentration compared to Matagorda (ESBD, MBGP, MBAR, SAPP, ESSP, ESBD, MBGP, MBCB, MBTP,
41

PAH contaminants in oystersftom the Gu@'of Mexico 295

NUT PAH Data - Years I to V NUT PAH Data - Years I to V
500- 70.

60,
400-

50,

3W
(a 40,

2DO- E 30-
n
Z
Z
20

'00.
10-

0 a @-- .= - I
CLU-1 CLCL-2 CLCL-3 <20 30 100 300 1000 3000 10000 '30000
Me and sww Medta-n of Site - NS&T PAH (Ppb)
Fig. 2. Total NS&T PAH concentration distribution during Fig. 4. Frequency distribution of the median total NS&T
the first five years for all three stations; Caillou Lake in PAH (tPAH) concentration in the Gulf of Mexico during the
Louisiana (Site 25-CLCL). first five years of the program.

CLCL, LBMP, TBCB, CBBI & RBHQ with low Cumulative frequency model.
concentration of tPAH (< 100 ng/g) and little variation Bar graphs (Wade et al., 1990) or crossplots (Wade &
in the observed values (Fig. 2). There are also six sites Sericano, 1989) of data comparing one year's data with
(GBSC, BBMB, MSBB, CBJB, PCMP & SAWB), of another have been used to display the general trend for
the seventy-eight different sites, where high concentra- tPAH data (Wade & Sericano, 1989; Wade et al., 1990;
tions of tPAH (>1000 ng/g) are observed. Four sites Wade et al., 1991). These data presentations easily
(CCIC, PBPH, PBIB & PCMP) exhibited a decrease in visualize the variation in concentration for a particular
the tPAH each year during the first five years of this site. In this report the cumulative frequency function is
study. Many sites exhibited a cyclic variation with time, used to examine the heterogeneous distribution of PAHs
At Choctawatchee Bay off Santa Rosa (CBSR, Fig. 3), in Gulf of Mexico oysters (Mackay & Paterson, 1984).
the order of magnitude increase in concentration of This approach has the advantage of examining the Gulf
tPAH in Years Il and III is probably due to relocation of Mexico as a single environmental system, determining
of the collection site to an area containing wood pilings, the percentage of sites exposed to a particular threshold
which if treated with creosote, are a source of PAHs. concentration, and providing information for environ-
The decrease in Years IV and V probably reflects relo- mental evaluation.
cation of the collection stations to an oyster reef away The distribution of the PAH data in Table 3 is best
from wood pilings. Due to prolonged freshwater condi- described by a lognormal distribution i.e. the distribu-
tions in San Antonio Bay during 1988 and 1989 (Years tion of data is skewed to low concentrations and has a
III IV), the oyster reefs experienced a die-off resulting in fraction which extends to high concentrations (Fig. 4).
no oysters being taken from SAPP, SAMP and ESSP. O'Connor (1990) used the lognormal distribution,
typical of environmental data, to define high concentra-
NS&T PAH Data - Years I to V tions as those whose logarithmic value is more than the
45W mean plus one standard deviation of the logarithms for
V all concentrations. The tPAH data in Fig. 4 is further
4M-
skewed in that analytes with concentrations less than
35W- 20 ng/g are not included in the sum of eighteen 2-5
3=- ring aromatic hydrocarbon analytes in Table 2, i.e. the
CL data has been censored. For Years 1-111, only censored
.9
2M- data was available, whereas for Years IV and V both
2=- censored and uncensored data was available. A regres-
sion analysis of the censored (tPAH) data versus
Z
15W- uncensored data for the sum of all analytes (T-PAH) in
1000 Table 2 from Years IV and V yields the best fit line as
WD y = 153.0 + 0.9834 x (r2 = 0-9989); where y = uncen-
I I I I I I

sored data, and x = censored data. Using the best fit
fine from the Year IV and V data, the censored data
cBsR_1 CBSR-2 CBSR-3
Sks and Stafim for the cumulative frequency data was corrected to be
Fig. 3. Total NS&T PAH concentration distribution during the same as the uncensored cumulative frequency data.
the first five years for all three stations; Choctawatchee Bay Distribution functions are useful measures of environ-
off Santa Rosa (Site 38-CBSR). 42 rkental quality data in that changes with time can be

296 T J. Jackson et al.

Year V lognormal MODEL Year V - lognormal MODEL-2 OpUlat*l ons
Mean 2 50 STD 2 18 Mean 1 214 mean 2 1 2P5

1 1 1- 0.9
0.9- -0.9 M0CW 0.9- 0.8
AIWW Aa-I
0.8- -0.8 41 C 0.8. 0.7
0.7- -0.7 0
-9 0.7-
ts 0.6
0.6- -0.6 0.6-
c
0.5 M
0,5 0.5 0.5 .0
05 0.4 M
0.4- -0.4 0.4-
E E -0.3 16
0.3- 0.3 02) :3 0.3- z
0.2. 0.2 cc 0 0.2- .2 cc

0.1- 0.1 0.1

0- a
10 100 i 0 10000 fo 100
Total NS&T PAHs (ppb) Total NS&T PAHs (ppb)
Fig. 5. Plot of the cumulative frequency distribution for Year Fig. 6. Plot of the cumulative frequency distribution for Year
V total NS&T PAH (tPAH) concentration. compared to the V NS&T PAH (tPAH) concentration, compared to the
Gaussian curve and its cumulative frequency distribution gen- Gaussian curves and their cumulative frequency distributions
erated from a lognormal model with a mean of 250 ppb and generated from a two population lognormal model with a
standard deviation of 218. mean of 214 ppb for Population I and a mean of 1205 ppb
for Population 2.
ascertained without being influenced by outliers. For
the cumulative distribution plot, the data is sorted from computed, but did not compare as well with the actual
the lowest value to the highest, similar to rank trans- data for Year V.
formation (Conover & Iman, 1981). Each observation The implication of the two populations in the data is
is I In fraction of the data set, where n is the number of that there are two primary mechanisms accounting for
samples in the data set. The sum of the fraction of the the distribution of T-PAH concentration in the Year V
samples less than the concentration is plotted against data. The sites with lower concentration PAHs are prob-
the concentration. From this plot the median can be ably due to low level background inputs from storm-
determined, since it is defined as the 50th percentile. water runoff, atmospheric deposition and sewage
The interquartile range (IQR) is used a measure of effluents, etc. (NOAA, 1985). The sites with higher con-
variability. The IQR is the 75th percentile minus the centration PAHs are probably due to local point-sources
25th percentile and equals 1-35 times the standard of PAH contamination (i.e. small spills). From the log-
deviation for a normal distribution (Hensel, 1990). normal cumulative frequency function two probability
To begin the examination of the distribution of the density functions were derived, the relative proportion of
PAH concentration data, the logarithm of the sum of the two populations were estimated to be 0.9 for popula-
all PAH analytes (T-PAH) for Year V data was plotted tion one and 0.25 for population two. Comparison of
as a cumulative frequency distribution. The 50th the cumulative frequency distribution derived from the
percentile was 250 ppb and the standard deviation as sum of the two probability density functions, in the
determined from the IRQ was 218. The log of the data above proportions, with the actual data for the cumula-
versus fraction of the samples was plotted and com- tive frequency disffibution (Fig. 7) indicates a good
pared with a lognormal distribution (Fig. 5). The shape correlation.
of the cumulative frequency curve (i.e. the positive
deviation from the lognormal model) for the T-PAH Year V-lognormal MODEL 2
data suggests two overlapping lognormal distributions. I Mean 2 14 Mean 2 i 2P50P'jIC4;"S
Making the assumption that there is a 2-5% overlap for 0.9-
MCM
the two distributions, the mean and standard deviation 0.8-
were computed for each data set, or population (Table r_
0 0.7-
4). The cumulative frequency distribution from the two r)
population model data compare well with the actual 0.6-
T-PAH data (Fig. 6). Other increments of overlap were 0.5-
0.4-
E
0.3- Popi 0 n. PW2
0.2-
Table 4. Two population lognormal distribution model. Year 0.1-
V-T-PAH data (2-5% overlap)
010 ... @60 1000 10000
Set Percentille STD= Log-mean STD of Total NS&T PAHs (ppb)
25% 500/6 75% IRQ/1,3 5 log-data
0
0
0
Z000@10000

- Ft. 7. Comparison of the cumulative frequency distributions
1 135 214 320 137 2.3308 0.278 3 for the actual Year V total NS&T PAH (WAH) concentra-
2 801 1 210 1 530 544 3-081 0 0.2093 tion data and the cumulative frequency distribution generated
43 from the two population model.

PAH contaminants in oystersftom the Gulf of Mexico 297

Table 5. Two population lognormal distribution model. Corrected tPAH data--Wg dry weight

Year Median Population I Population 2
total
data Mean (log) STD (log) Mean (log) STD (log)

1 229 197 (2-294 5) 108 (0129 8) 1075 (3-031 4) 714 (0-277 2)
11 208 186 (2-269 5) 87(0.196 7) 1 150 (3-059 9) 1 100 (0,381 1)
111 345 259 (2-413 3) 216 (0-343 5) 1910 (3-280 8) 1 190 (&261 8)
IV 352 269 (2-429 8) 174 (0-250 0) 1350 (3-131 6) 1 190 (0-303 9)
V 270 212 (2-326 3) 131 (0-263 9) 1170 (3-068 9) 637 (&243 5)

Since historical NS&T data (Table 3) is censored tration, while Year III had 80%, Year IV had 83%
data (Wade et al., 1988; Wade & Sericano, 1989; Wade and Year V had 87%. Alternatively, the cumulative
et al., 1990), the cumulative frequency distribution of frequency data can be used to calculate the percentage
this censored (tPAH) data was corrected using the best- of sites exposed to a concentration in excess of a partic-
fit-line from the data for Years IV and V. Data below ular threshold.
the reporting limit were extrapolated (Hensel, 1990, The cumulative frequency distribution was used in
Mackay & Paterson, 1984). The summary statistics for this study as an environmental evaluation too) to
the corrected data using the two population model for examine the heterogeneous distribution of total PAH
Years I-V data (Table 5) were calculated using the data contaminants in Gulf of Mexico oysters from coastal
from 0-80 for the original cumulative frequency distri- and estuarine areas collected during the winters of
bution for population I and from 77-5-100% for the 1986-1990. The PAH concentrations exhibits a log-
original cumulative frequency distribution for popula- normal distribution with two major populations in the
tion 2 (Table 6). data for each year. The two populations were decon-
The summary statistics for the first five years of voluted into probability density functions and sum-
measuring PAH contaminants in the Gulf of Mexico mary statistics for each population were calculated.
for NOAA's NS&T Mussel Watch Program (Table 5) The lower PAH concentrations are probably related to
show variation in the means for both populations, indi- chronic inputs. Many of these low PAH concentration
cating temporal change in the total Gulf of Mexico sites show little variability from year to year, support-
data and with the highest values found in Years III and ing the contention that the PAH contamination is on a
IV. The higher mean concentrations of PAHs in Years continual basis. The higher concentration PAHs are
III and IV and the lower abundance in Years 1, 1] and probably associated with local point-sources of PAH
V is a pattern which is probably related to large-scale contamination or spills. Most of the high concentration
climatic factors such as the El Niho cycles (Philander, sites (>1000 ng/g dry tissue) show large variability
1989) which affects the precipitation regime (Wilson et from year to year, supporting the contention tbat PAH
al., 1992). Examination of the PAH data for individual contamination for these sites is on an episodic basis. In
sites, as discussed above, does not show this pattern. addition, 20% of Gulf of Mexico sites in Year III were
The cumulative frequency data for Years I-V gives exposed to a PAH threshold concentration of greater
the percentage of sites whose PAH concentration is less than 1000 ng/g of dry oyster tissue. Whereas. in Years I
than a particular concentration (Table 6). As an exam- and 11 only 11% of the Gulf of Mexico sites had
ple, using 1000 ppb as an arbitrary concentration, 89% concentrations greater than 1000 ng/g of total NS&T
of the sites for Years I and Il are less than this concen- PAHs. The changes in the mean concentration of the
two populations between years display a cyclic patter
Table 6. NS&T concentration distribution data (cumulative which is probably due to large-scale climatic factors
frequency). Corrected tPAH data--Wg dry weight such as the El Niho cycles which affects the precipita-
tion regime (Wilson el al., 1992). The cyclic pattern
1990 1989 1988 1987 1986 was obtained by examining the Gulf of Mexico as a
Year V Year IV Year III Year II Year I single heterogenous system, since the PAH concentra-
100/0 110 171 110 110 110 tion data for individual sites does not clearly show this
20% 140 200 153 140 140 pattern.
3011/o 164 226 206 162 169
40% 212 269 259 186 197 ACKNOWLEDGEMENTS
W/o 270 352 345 208 229
60% 318 435 445 258 286 Funding for this research was supported by the
70% 191 119 112 110 371
80% 597 869 1030 480 557 National Oceanic and Atmospheric Administration,
9fto 1290 1440 2090 1300 1180 contract number 50-DGNC-5-00262 (National Status
95% 1670 2840 3020 2300 1750 and Trends Mussel Watch Program), through the Texas
91% 1920 1611 4510 3740 2450 44 A & M Research Foundation, Texas A & M University.

298 T J Jackson et al.

REFERENCES ment and Tissues. A Special NOAA 20th Anniversary
Report. 34 pp.
Conover, W. J. & Iman, R. L. (1981). Rank transformations Philander, G. (1989). El Niho and La Nifta. Amer. Scientist,
as a bridge between parametric and nonparametric statis- 77,451-9.
tics. The Amer. Statistician, 35, 124-9. Sericano, J. L., Wade, T. L., Atlas, E. L. & Brooks, J. M.
Farrington, J. W. & Quinn, J. G. (1973). Petroleum hydro- (1990). Historical perspective on the environmental
carbons in Narragansett Bay. 1. Survey of hydrocarbons in bioavailability of DDT and its derivatives to Gulf of
sediments and clams (Mercenaria mercenaria). Estuar. and Mexico oysters. Environ. Sci. Technol., 77, 1541-8.
Coast. Mar. Sci., 1, 71-9. Wade, T. L., Atlas, E. L., Brooks, J. M., Kennicutt 11, M. C.,
Farrington, J. W., Goldberg, E. D., Risebrough, R. W., Fox, R. G., Sericano, J. L., Garcia-Romero, B. & Defreitas,
Martin, J. H. & Bowen, V. T. (1983). US Mussel Watch D. A. (1988). NOAA Gulf of Mexico Status and Trends
1976-1978: An overview of the trace metal, DDE, PCB,. Program: Trace organic contaminant distribution in sedi-
hydrocarbon and artificial radionuclide data. Environ. Sci. ments and oysters. Estuaries, 11, 171-9.
Technol., 17, 490-6. Wade, T. L. & Sericano, J. L. (1989). Trends in organic
Hensel, D. R. (1990). Less than obvious. Statistical treatment contaminant distribution in oysters from the Gulf of
of the data below the detection limit. Environ. Sci. Tech- Mexico. Oceans '89 Proceedings, Marine Technology
nol., 24, 1766-74. Society, IEEE Publication Number 89CH2780-5, pp. 585--9.
Krahn, M, M., Moore, L. V, Bogar, R. G., Wigren, C. A., Wade, T. L., Sericano, J. L., Garcia-Romero, B., Brooks,
Chan, S-L. & Brown, D. W. (1988). High-performance J. M. & Presley, B. J. (1990). Gulf Coast NOAA National
liquid chromatography method for isolating organic con- Status & Trends Mussel Watch: The first four years. Proc.
taminants from tissue and sediment extracts. J. Chromaiogr., Mar. Tech. Soc., 1990,274-80.
437,161-75. Wade, T. L., Brooks, 1. M., Kennicutt 11, M. C., Denoux,
Mackay, D. & Paterson, S. (1984). Spatial concentration G. J. & Jackson, T. J. (1991). Oysters as biomonitors of
distributions. Environ. Sci. Technol., 18, 207A-14A. oil in the ocean. Proceedings of the 23rd Annual Offshore
MacLeod, W. D., Brown, D. W., Friedman, A. J., Burrows, Technology Conference, OTC 6529, pp. 275-90.
D, G,, Maynes, 0,, Pearce, R. W., Wigren, C, A, & Bo,ar, Wilkinson, D. L,, Brooks, 1. M* & Fay, R. R,,1991). NOAA
R. W. (1985). Standard analytical procedures of the NOAA Status and Trends: Mussel Watch Program-Field
National Analytical Facility 1985-1986. Extractable Toxic Sampling and Logistics Report-Year VI. GERG Technical
Organic Compounds, 2nd Ed. US Department of Commerce, Report 91-046, US Department of Commerce, National
NOAA/NMFS, NOAA Tech. Memo NMFS F/NWC,11, Oceanic & Atmospheric Administration, Ocean Assessment
NOAA (1985). Gulf of Mexico Coastal and Ocean Zones Division.
Strategic Assessment: Data Atlas, United States Depart- Wilson, E. A., Powell, E. N., Wade, T. L., Taylor, R. J.,
ment of Commerce, National Oceanic and Atmospheric Presley, B. J. & Brooks, J. M. (1992). Spatial and temporal
Administration. pp. 4.0-5.32. distributions of body burden and disease in the Gulf of
O'Connor, T. P. (1990). Coastal Environmental Quality in Mexico oyster populations: The role of local and large-
the United States, 1990. Chemical Contamination in Sedi- scale climatic controls. Helgol. Meeresunters. (in press).

Reprint 4

Modeling Oyster Populations. 11. Adult
Size and Reproductive Effort

Eileen E. Hofmann, John M. Klinck, Eric N.
Powell, Stephanie Boyles, and Matthew Ellis

J.1 of SM#ish R,,,.,,,h, Vol, 11, No* 1, 165-111,1114*

MODELING OYSTER POPULATIONS H. ADULT SIZE AND REPRODUCTIVE EFFORT

2
EILEEN E. HOFMANN,' JOHN M. KLINCK,' ERIC N. POWELL,
STEPHANIE BOYLES, 2 AND MATTHEW ELLIS2
'Centerfor Coastal Physical Oceanography
Crittenton Hall
Old Dominion University
Norfolk, Virginia 23529 USA
2Department of Oceanography
Texas A&M University
College Station, Texas 77843 USA

ABSTRACT A time-dependent model of energy flow in post-settlement oyster populations is used to examine the factors that
influence adult size and reproductive effort in a particular habitat, Galveston Bay, Texas, and in habitats that extend from Laguna
Madre, Texas to Chesapeake Bay. The simulated populations show that adult size and reproductive effort are determined by the
allocation of net production to somatic or reproductive tissue development and the rate of food acquisition, both of which are
temperature dependent. For similar food conditions, increased temperature reduces the aocation of net production to somatic tissue
and increases the rate of food acquisition. This temperature effect, however, is mediated by changes in food supply. Within the Gulf
of Mexico, oyster size declines from north to south because increased temperature decreases the allocation of net production to somatic
growth. An increase in food supply generally results in increased size as more energy is used in somatic growth; however, at low
Mvides, as food supply increases, adult size decreases because the allocation of more net production to reproduction outweighs the
effect of inicreased rates of food acquisition. Variations in temperature and food supply affect reproductive effort more than adult size
because the rate of energy flow through the oyster is higher in warmer months when most net production is aflocated to reproduction
and smaff changes in temperature substantially change the spawning season. The wide range of reproductive effort expected from small
changes in temperature and food supply suggest that comparisons of adult size and reproductive effort between oyster populations can
only be made within the context of a complete environmental analysis of food supply and associated physical parameters and an energy
flow model.

]INTRODUCTION size with latitude and the year-to-year variability in mean adult
size suggest that one or more climatic variables limit oyster size.
Populations of any species tend to have a characteristic mean The correlation with latitude suggests temperature as a likely vari-
adult size, which is defined as the size reached by the average able. From a physiological perspective, temperature may affect
surviving adult individual in the dominant cohort. When the char- adult size by regulating the division of net production into somatic
acteristic adult size is considerably below that characteristic of the and reproductive tissue growth and by regulating the relative rates
population, the population is described as stunted (Hallam 1965). of filtration and respiration. As temperature increases, more net
Stunting is generally considered to result from suboptimal condi- production is allocated to reproduction. Filtration and respiration
tions such as extreme environments or low food resources. rates also increase, but the rate of incmase in filtration rate is
In the Gulf of Mexico, populations of the American oyster greater (Powell et al. 1992b). Therefore, a complex interaction of
@Crassostrea virginica) exhibit a latitudinal gradient in character- temperature with oyster physiology may place an upper lin-dt on
istic adult size (Fig. 1, Table 1). Mean adult size decreases with adult size.
decreasing latitude on the eastern and western roasts of the Gulf. Related to adult size is the concept of reproductive senility
At the extremes of this distribution, most oysters fail to reach the (Peterson 1983) in which fecundity per unit biomass declines at
standard size limit of 7.6 cm that is required for commerical ex- large size or old age. The existence of reproductive senility in oys-
ploitation (e.g. Hofstetter 1977, Berrigan 1990). The nearly com- ters remains to be determined. However, respiration rate rises
plete restriction of the Gulf of Mexico oyster fishery to the north- faster than filtration rate with increasing body size (Klinck et al.
ern Gulf is the practical result of this trend. Additionally, year-to- 1992, Powell et al. 1992b). The different scaling of respiration and
year variations in mean adult oyster size show similar variations filtration with body size suggests that the scope for growth in
throughout the Gulf of Mexico (Wilson et al. 1992). That is, the oysters must eventually be curtailed at large size which will result
characteristic adult oyster size increases or decreases uniformly in declining fecundity per unit biomass (Powell et al. 1992b).
among the many populations in the Gulf, Variation in age cannot Consequently, populations of lower characteristic size may spawn
be completely excluded as a contributor to these trends; however, more per unit biomass.
the annual mortality in oyster populations from predators and dis- 'Me objectives of this study are to investigate processes that
caw exceeds 75% throughout the Gulf of Mexico (e.g. Butler contribute to variation in the characteristic adult size of oyster
1953a, Moore and Trent 197 1, Powell et al. 1992a) and fished and populations within a particular habitat and over a latitudinal gra-
unfished populations were included in the analysis. Accordingly, dient in temperature and to address the possible influence of re-
the oyster populations sampled in the Gulf of Mexico were com- productive senility in oyster populations. These objectives are ad-
posed primarily of individuals that were one to two years in age dressed using an energy flow model (Fig. 2) developed for post-
(Wilson et al. 1992). Hence, size rather than age accounts for the settlement oyster populations. A series of simulations are
trends seen in these populations. presented for Galveston Bay, Texas that consider the effect of
The similar bends on both sides of the Gulf of Mexico in oyster variations in temperature, food supply and salinity on adult oyster

166 HoFMANN ET AL.

30- q
q i
M i
25- f g M 0
C M n r k y 2 U
m k v
z 20- 9
f n k
b d 6 n0 U t
M is- 3 C d 0 0 U
2 - C f y t W v
be 10- a 2 C k
z a f W
' i 3 j b
5- 5 3 b u W v
4 2 P W
4 3 b P x
4 X v

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

RANK FOR LATITUDE
Figure 1. Mean adult oyster size (length) versus latitude plotted as the rank-order of latitude versus the rank-order of size [see Wilson et a].
(1"2) for details]. The four values for each size and latitude, referenced by letter (a-z) or number (1-5), are those given in Table I for 1986
to 1989. Bays with the characteristically smaller sizes am the more southerly bays on either side of the Gulf of Mexico (on the left), the bays
in the Florida Panhandle (right), and Tiger Pass and the Mississippi Delta.

TABLE 1.

Oyster population mean length (cm) and fraction of the population in advanced reproductive state (spawning or ready to spawn) for
thirly,one bay systems arou" the Gulf of Mexico that were sampled from 1916 to 1"9 as part of the NOAA Status and Trends program.
Details of the sampling sites an given in Wilson et al. (1"2). Bays are listed beginning with the southern most bay In Texas and proceding
clockwise around the Gulf of Mexico. The high fraction ready to spawn In the northern Gulf of Mexico in 1996 (bays I to s) resulted from
sampling late in the year. Year and Julian Day were used in the statistical analysis of these data to control for this effect.

Length Fraction in Advanced Reproductive State

Bay Systems 1986 1987 1988 1"9 1986 1987 1998 1989
aLaguna Madre 8.16 6.95 6.04 6.03 0.14 O@86 0.27 0.15
bCcnpus Christi Bay 7.41 5.67 5.52 7.04 0.13 0.00 0.14 0.23
cAransas Bay 8.47 8.20 8.19 6.38 0.05 0,02 0.04 0.05
dSan Antonio Bay 8.68 8.36 - - 0.09 O@70 - -
eMatagorda Bay 9.38 8.30 6.92 7.07 0.20 0.05 0.05 0.21
f East Matagorda Bay 10-13 8.37 6.72 6.29 0.10 0.00 0.14 0.23
gBrazos River - - 8.57 7.14 - - - 0.33
hGalveston Bay 9.03 8.56 8.55 8.33 0.14 0.09 0.04 0.10
iSabine Lake 10.44 9.65 9.66 8.40 0.00 0.15 0.00 0.00
jLake Calcasieu 11.48 8.27 7.99 9.32 0.00 0.00 - 0.00
kJoseph Harbor 8.36 8.79 8.19 7.06 0.67 0.00 - 0.14
1Vermillion Bay 8.72 9.66 9.91 9.06 0.93 0.00 0.25 0.00
m Caillou Lake 9.73 10.36 8.18 8.20 0.83 0.14 0.00 0.13
nLake Barre/Felicity 8.96 9.22 7.17 7.49 0.97 0.04 0.00 0.21
oBarataria Bay 10.08 9.57 7.04 6.86 0.89 0.00 0.15 0.35
pTiger Pass - - 5.80 5.72 - - - 0.27
qPass a Loutre - - 11.23 10.57 - - 0.00 0.00
rBreton Sound 9.66 8.50 7.71 8.47 0.93 0.07 0.04 0.04
sLake Borgne 8.94 7.27 7.52 5.68 1.00 0.00 0.07 0.00
tMississippi Sound 8.40 7.15 7.10 7.20 0.00 0.00 0.00 0.13
uMobile Bay 8.62 9.03 6.03 6.66 0.13 0.00 0.00 0.13
vPensacola Bay 9.09 4.55 6.02 6.46 0.08 0.00 0.05 0.09
wChoctawatchee Bay 7.74 4.95 6.67 5.97 0.09 0.00 0.00 0.03
x. St. Andrew Bay 6.01 4.81 6.53 6.35 0.64 0100 0.10 0.06
yApalachicola Bay 8.43 7.35 8.29 6.64 0.13 0.07 - 0.04
zApalachee Bay - - - 7.29 - - - 0.00
ICedar Key 7.44 5.16 6.71 5.39 0.07 0.00 0.08 0.00
2 Tampa Bay 6.58 5.90 6.37 6.44 0.25 0A1 0.23 0.57
30mulotte Harbor 6*12 1,30 6,47 6*64 0,00 0,01 0,41 0*27
4Rookery Bay 6.70 5.26 4.67 5.47 0.00 0.13 0.11 0.13
5Everglades 9.06 6.56 6.56 5.94 0.08 0.20 0.10 0.00

MODELING OYSTER POPULATIONS 167

Particulate Load TABLE 2.
Salinity mpersture Biomass and length dimensions of the oyster size classes used In the
model. Biomass is converted to size using the relationship given in
White et al. (1988), denoted by W1PR, and Paynter and DiMcbele
(1990), denoted by PD. The market-size/submarket-size boundary is
Filtration Rate about one size class smaller usling the conversion from Paynter and
DiMicbele (1990). The upper size class length conversions obtained
from the Paynter and DiMkhele (1990) relationship are
extrapolations and are, therefore, less accurate, as are the final two
Ingestion conversions obtained from the White et al. (1988) relationship. The
range of length to blomass relationships In Galveston Bay, Texas is
Assimilation shown in Figure 3.
Efficiency Model Size Biomass Length (WPR) Length (PD)
Class (g ash free dry wt) (mm) (mm)

1 1.3 x 10-7-0-028 0.3-25 0.15-21.4
Assimill tion 2 0.028-0.10 25-35 21.4-35.7
3 0.10-0.39 35-50 35.7-61.7
4 0.39-0.98 50-63 61.7-89.4
5 0.98-1.94 63-76 89.4-117.6
Respiratory ate Respiration 6 1.95-3.53 7&-88 117.6-149.5
7 3.53-5-52 88-100 149.5-178.9
8 5.52-7.95 100-110 178.9-207.1
9 7.95-12-93 110-125
Net Production 10 12.93-25.91 125-150

Division of 2.5 in, 3.0 in and 3.5 in, respectively. Adult oysters, those indi-
Net viduals capable of spawning, are defined as individuals weighing
Production more than 0.65 g ash-ftee dry weight, about 50 nun in length
(Hayes and Menzel 1981), although gonadal development has
S
Somatic Reproduction been observed at somewhat smaller sizes (Coe 1936, Burkenroad
o'
F Growth 1931). Hence, size classes I to 3 are juveniles.
Figure 2. Schematic of the oyster population model. The following conversions and scaling factors were used in the
oyster model. For simplicity, these are not explicitly shown in the
size. Aside from reductions in oyster growth rate from diseases governing equations that are described in the following section.
(Ray and Chandler 1955, Matthiessen et al. 1990) and perhaps First, all calculations were done in terms of energy (cal in-').
genetic differences (Grady et al. 1989, Reeb and Avise 1990) Oyster caloric content was obtained by applying a caloric conver-
these are likely to be the most important factors controlling size in sion of 6100 cal g dry wt-' (Cummins and Wuycheck 1971), and
oyster populations, The effect of latitudinal temperature effects is the food available to the oysters was converted to caloric equiva-
investigated with simulations that use environmental conditions lents by using 5168 cal g dry wt-'. The model calculations use
appropriate for the Laguna Madre, Apalachicola Bay and Chesa- biomass exclusively (and calories) and so are independent of oys-
peake Bay, as well as Galveston Bay. ter growth form and length-to-biomass relationships. To relate the
biomass size classes, defined in Table 2, to lengths for comparison
THE MODEL to the available measurements and the standard measures of fish-
ery ma agernent, the length-to-biomass conversion given in White
Basic Characteristics et al. (1988) was used. This conversion is only an approximation,
however, given the variation in growth forms found in oysters
The oyster population model (Fig. 2) is designed to simulate within bays and throughout their latitudinal range. The model
the dynamics of the post-settlement phase of the oyster's life from results are presented in terms of biomass, which can be converted
newly-settled juvenile through adult. Therefore, the oyster's size to any local specific lengths by using an alternative length-to-
spectrum was partitioned into 10 size classes ITable 21, that are not biomass relation and the size class boundaries given in Table 2 ,
equally apportioned across biomass. The lower size limit repre- One example, from Paynter and DiMichele (1990) is shown in
sents the size at settlement (Dupuy et al. 1977); the upper size limit Table 2 for comparison.
represents an oyster larger than those normally found in the Gulf Second, gains, losses or transfers of energy (or biomass) be-
of Mexico. In Galveston Bay, for example, the largest oysters tween oyster size classes were expressed as specific rates (day-')
routinely collected am 7 to 8 g dry wt (Fig. 3), which corresponds which were then applied to the caloric content in a size class. For
TO

R EE
@Z

to model size class 9. Thus, the largest size class, 10, is large example, ingestion (cal day - 1) divided by a caloric value in cal
enough to prevent boundary effects in the model solutions at the gives a specific rate (cal day - '/cal = day - '), which is then used
upper end of the size-frequency distribution. The boundaries be- to calculate incremental changes in a size class. Because the size
tween size classes 4 and 5, 5 and 6, and 6 and 7 represent size classes in the model are not of equal size, transfers between size
limits that have been used or considered for market-size oysters: classes were scaled by the ratio of the average weight of the

161 HOIFMANN ET AL.

120-
I Tom Roof
2 Dow Roof
04 3 Gaspips Roof
C% 4 Big Beazley Roof
100- It
Ln 5 Red Bluff Rest
tD 6 Stephenson Reef
7 Gale's Roof
E % 8 Yacht Club Roof
E 80- 9 Trinity Roof

1y=34.146 + 56.164*log(x) RA2=0.739
2y=29.408 + 49.922*log(x) RA2=0.828
60- A
3Y=37.977 + 43.078*iog(x) R 2=0.835
4y=39.262 + 40.094*log(x) RA2=0.847
U) - 5y=34.567 + 41.273*log(x) RA2=0.787
6y=31.624 + 41.740*log(x) RA2=0.729
7y=34.005 + 38.670*iog(x) R A2=0.733
40- 0y=36.511 + 36.01 S*Iog(x) RA2=0.871
9y=46.390 + 27.229*log(x) RA 2=0.770

20
0 10 20 30
Wet Weight (g)

120-
N 1 Four Bit Roof
2 South Redfish Rest (east)
V 3 Bull Shoals
100- 4 Buoy 73175
5 Bart's Pass Reef
6 Hanna Roof (north)
7 Scott Roof
CA 8 Hanna Reef (south)
E 9 Fisher Reef
go-
rM
Iy=31.8T7 + 60.170'log(x) RA2=0.808
2Y=37.976 + 52.423*log(x) RA 2=0.714
60 A
3Y=40.450 + 46.152*iog(x) R 2--0.807
4Y=39.977 + 44.369*log(x) R 2=0.792
5y--40.315 + 40.184*log(x) RA2=0.895
6Y=34.904 + 39.827*log(x) RA2=0.605
40- 7y--41.472 + 33.021*log(x) RA2--0.860
8y=31.653 + 39.104*log(x) RA2=0.841
9y=39.594 + 30.429*k>g(x) RA2=0.689

20
0 10 20 30
Wet Weight (g)
Figure 3. Shell length versus wet weight for oysters collected at eighteen locations in Galveston Bay, Texas. The curves indicate the empirical
relationships obtained using the data from the different locations. The numbers on the curves correspond to those for the empirical relationships
ftVm each site*

current size class (in g dry wt or cal) to that of the size class from Governing Equation
which energy was being gained or to which energy was being lost:
W The change in oyster standing stock with time in each size class
L or _W_j_ (0) is the result of changes in net production and the addition of
Wj- I Wj- I individuals from the previous size class or loss to the next largest
where W is the median value for biomass (in g dry wt) in size class size class by growth. Excretion was Dot included since it is a minor
j. This ensured that the total number of individuals in the simulated cmpo,,nt of the oyster's energy budget (Boucher and Boucher-
population was conserved in the absence of recruitment and mor- Rodoni 1988). Following VVhite et al. (1988), net production in
tality. Finally, each specific rate for each transfer between size any size class, NP,, is the sum of somatic (P,,) and reproductive
classes was scaled to the relative size of the respective classes: tissue (Pj) production which is assumed to be the difference be-
for transfers up: WiMi , , - Wi) tween assimilation (A) and respiration (R):
for transfers down: W/(Wi - wi-O. NPj = Pv + P, = A, - R, (1)
50

MODELING OYSTER POPULATIONS 169

Therefore, a govern ing equation for each oyster size class can be S -_ 7.5 ppt FRqj = FRj
written as 3.5 < S < 7.5 ppt FRj = FR@S- 3.5)14.0
!!@ij = Pjd + Pj + (gain from j - 1) - (loss to j + 1) (2) S :@z_ 3.5 ppt FRaj = 0
dr where S is the ambient salinity and FRj is the rate obtained from
equation (4). [Note that the second salinity relationship was mis-
for j = 1, 10, with Pri = 0 for j = 1, 3. printed in Powell et al. (1992b) and Hofmann et al. (1992).]
Resorption of either gonadal or somatic tissue results in loss Of The reduction in feeding efficiency at high particulate loads,
biomass. When NP, < 0, oysters lose biomass and transfer into the characterized by pseudofeces production, was included as a de-
next lower size class. This is an important difference between this pression in filtration rate rather than as a separate function as used
size class model and a size class model based on linear dimen- by Soniat (1982). From data presented in Loosanoff and Tommers
sions: shell size does not change, however biomass does during (1948), total particulate content can be related to a reduction in
periods of negative scope for growth. Ibis is the basis for the use filtration rate as
of condition index as a measure of health in oysters (e.g, Newell 10-4)100.41lx
1985, Wright and Hetzel 1985). To allow for a negative scope for T = (4.17 X (7)
growth, equation (1) is modified as where T is the total particulate content (inorganic + organic) in g
I` and x is the percent reduction in filtration rate. Solving equa-
Loj = Pgj + Pj + (gain from j - 1) tion (7) for the percent reduction in filtration rate gives an expres-
di - (loss to j + 1) + (gain from j + 1) sion for filtration rate modified by total particulate content, FR.,,
of the form:
- (loss to j - 1). (3)
The last two terms on the right side of equation (3) represent the FRrj = FRaj[ I _ .01 (loglo T + 3.3 (8)
individuals losing biomass and thus, translating down to the next 0.0-418
lower size class. Implementation of the model given by equation Equation (8), if applied to total particulate content (inorganic +
(3) requires that the processes that result in production and/or loss organic), approximates the results of Haven and Morales-Alamo
of somatic and reproductive tissue be described in mathematical (1966) and limits ingestion rate to approximately the maximum
terms. ne functional relationships used in the model and the value found by Epifanio and Ewart (1977). Therefore, an addi-
rationale for particular choices are given in the following sections. tional term to lower ingestion efficiency at high food concentra-
Filtration Rate, Ingestion and Assimilation tions was not used. We assume all particles are removed by fil-
tration, a slight overestimate (Palmer and Williams 1980), that
For this model, the filtration rate relationship given by Doering oysters feed more or less continuously (Higgins 1980a), and that
and Ovian (1986) was adapted to oysters using Hilbert's (1977) filtration rate does not vary with food availability (Higgins 1980b,
biomass-length relationship to obtain filtration rate for each size Valenti and Epifanio 1981).
class as a function of temperature (T) and biomass: Filtration rate times the ambient food concentration gives oys-
ter ingestion. To the extent that oysters can select nitrogen-rich
&,0-%T0.95
-j particles from the filtered material for ingestion, equation (8)
FRj @ 2.95 (4) yields an underestimate of ingestion (Newell and Jordan 1983).
Assimilation is obtained from ingestion using an assimilation ef-
and ficiency of 0.75, an average value obtained from Tenore and Dun-
WA.317100.w stan (1973), Langefoss and Maurer (1975), and Valenti and Epi-
Kj (5)
fanio (198 1).
where filtration rate, FR,, is given as ml filtered ind min and
W. is the ash-free dry weight in g for each size class. Powell et al. ResPb26on
(1992b) show that equations (4) and (5) yield results comparable to Oyster respiration, R,, as a function of temperature and oyster
a more general equation derived for all bivalves, including oysters, weight in each size class was obtained from Dame (1972) as
over the size range appropriate for this model. In addition, equa-
tion (4) has the advantage of containing the temperature- Rj = (69.7 + 12.67)Wjb-' (9)
dependency described in more detail by Loosanoff (1958), an
attribute not present in most other filtration rate equations (Doer- where b has the value 0.26. Equation (9) conforms to the more
ing and Oviatt 1986). Measurements (Loosanoff 1958) suggest general relationship for all bivalves obtained by Powell and Stan-
that the rate of increase of filtration rate moderates at temperatures ton (1985).
above 25*C, in accordance with a general trend for bivalves de- Salinity effects on oyster respimtion over a range of tempera-
scribed by Winter (1978), and declines above 32*C. However, tures were parameterized using data given in Shurnway and Koehn
equation (4) yields realistic values throughout the normal temper- (1982) as follows:
ature range, so it is used in the model without modification for T < 200C R, 0.007T + 2.099
lower filtration rates at even higher temperatures.
Equation (4) was modified to allow for salinity effects on fil- and
tmtion rate as described by LDosanoff (1953). Filtration rate de- T a@ 200C R, 0.0915T + 1.324;
creases as salinity drops below 7.5 ppt and ceases at 3.5 ppt. In
mathematical terms: where R, is the ratio of respiration at 10 ppt to respiration at 20 ppt:
51

170 HOFMANN ET AL.

R, = Rio pl,/R20 . Equations (9) and (10) were combined to disappear and for the oyster population to reach an equilibrium in
obtain respiration over a range of salinities as: response to a given set of environmental conditions.
S @-_ 15 ppt Rj = Rj, Numerous simulations (not shown) were performed initially
10 ppt < S < 15 ppt Rj = R@ I + [(R, - 1)/5((15 - S))]) using real and idealized time series for the environmental vari-
S IC 10 ppt Rj @ RjR, ables. These simulations, some of which are reported by Powell et
al. (1992b) and Hofmann et al. (1992), were used to calibrate and
Shurnway and Koehn (1982) identified effects of salinity on res- verify the transfers between size classes and the overall population
piration at 20 ppt; however, we used a 15 ppt cutoff to conform to characteristics and to provide guidance as to model sensitivity to
Chanley's (1958) observations on growth. various parameters. These simulations demonstrated that temper-
Reproduction ature and food concentration had more of an effect on the structure
and character of the simulated oyster populations than variations
For adult oysters 4, 10), net production was apportioned (i.e. � 10%) in individual model parameters. It should be noted
into growth and reproduction by using a temperature-dependent that all of the parameters in the model am specified from either
reproduction efficiency of the form field or laboratory measurements; no free parameters need to be
empirically determined. Therefore, the focus of this modeling
RIWJI = 0-054T - 0.729 (12) study is on the effect of variations in environmental conditions on
anuary to June and characteristic adult oyster size and fecundity.
The simulations described in the following sections used ob-
for RCO = 0.047T - 0.809 (13) served monthly-averaged time series of temperature of two years
for July to December. Equations (12) and (13) were derived em- length from Galveston Bay (Soniat and Ray 1985), the Laguna
pirically from the field observations of Soniat and Ray (14. 085). Madre (Powell et al. 1992b) and Chesapeake Bay (Galtsoff et al.
Disagreement exists in the literature concerning the extent to 1947). The temperature values were linearly interpolated to obtain
which oyster reproduction is temperature acclimatized (Loosanoff values at one day intervals to be consistent with the time step used
and Davis 1953, Stauber 1950, Loosanoff 1969). However, from in the model. For a six year simulation, the two-year temperature
the studies of Butler (1955), Kaufman (1979) and Quick and time series was repeated three times.
Mackin (197 1), acclimatization appears unimportant over the lat- For most of the simulations described in the following section,
itudinal range of Chesapeake Bay to the southern Gulf of Mexico. salinity values were held constant at 24 ppt to remove the effect of
Equations (12) and (13) may not hold north of Delaware Bay. low salinity on oyster respiration and filtration rates and to em-
The portion of new production that goes to reproduction is phasize temperature effects. For some Galveston Bay simulations,
given by a low salinity (7 ppt) event was imposed and one Chesapeake Bay
simulation used the salinity time series given in Galtsoff et al.
Pri = R.W_
,NPj, for j = 4, 10, (14) (1947). Food and turbidity values were specified as described for
Somatic growth is the remaining fraction. In cases where NPj < 0, each simulation. A summary of the environmental conditions used
we assume preferential resorption of gonadal tissue to cover the for the simulations is given in Table 3.
debt, although some data suggest the contrary (Pipe 1985). Go- RESULTS
nadal resorption is commonly observed in stressed oysters (e.g.
Gennette and Morey 197 1) and in the fall and winter when food is Basic Simulation
reduced (Kennedy and Battle 1964). For juveniles and adults with The time evolution of an oyster population that resulted from
no gonadal tissue, resorption of somatic tissue occurs. We assume the settlement of a cohort of ten individuals in mid-May (day 140)
reduced reproduction at low salinity (Engle 1947, Butler 1949) that were subsequently exposed to the monthly-averaged temper-
results from decreased filtration rate and increased respiratory rate atures from Galveston Bay, a constant salinity (24 ppt) and a
and so include no specific relationship for this effect. constant food supply of 0. 5 mg I - I was simulated. No recruitment
Although a considerable literature exists on factors controlling or mortality was allowed so that the same individuals were tracked
the initiation of spawning (e.g. Stauber 1950, Loosanoff 1965, from settlement onwards, about 5.5 years. This simulation pro-
Dupuy et al. 1977), including empirical temperature-dependent vided a basic case to which other simulations could be compared.
relationships (Loosanoff and Davis 1953, Kaufman 1979), little is Following settlement, the oyster population increases in biomass
understood about factors controlling the frequency of spawning during the first 1.5 years of the simulation (Fig. 4a) after which it
over the entire spawning season (e.g. Davis and Chanley 1956). In reaches a steady population distribution that is in equilibrium with
our model, spawning occurs when the cumulative reproductive the imposed environmental conditions. The majority of the popu-
biomass of a size class exceeds 20% of the standing stock; an lation at the end of the simulation is in size classes 5 and 6 (63 to
estimate based on data presented in Gallager and Mann (1986) and 98 mm) . In the first two years of the simulation, gonadal tissue is
Cboi et al. (1993).
present in size classes 4 to 6. However, as the population stabi-
Mo&I Implementation and Environmental Forcing lizes, gonadal tissue is confined to size classes five and larger.
Gonadal tissue development occurs in the adult size classes
The model described by equation (3) was solved numerically throughout the summer and into the fall, with the maximum de-
using an implicit (Crank-Nicolson) tridiagonal solution technique velopment as a fraction of body weight occurring in late July of
with a one day time step. The external forcing for the model is each year.
from time senes that specify ambient temperature, salinity, food A fall larval set, exposed to the same environmental condi-
concentration and turbidity conditions. Each simulation was run tions, results in a similar population distribution (Fig. 4b). The
for 6 years which is sufficient time for transient adjustments to oyster population stabilizes with the same size-frequency distribu-
52

MODELING OYSTER POPULATIONS 171

TABLE 3. Bay (Soniat et al. 1984) were tested. The pattern of development
Summary of the environmental conditions used for the oyster for an oyster population exposed to a food supply double that used
population simulations. Inclusion of a time varying in the basic simulation (Fig. 5a) is not substantially different. A
mouthly-averaged temperature, salinity, food concentration or stable size-frequency distribution develops in about 1.5 years.
torbidity time series is indicated by V. For simulations that used However, the details of the population do differ. The final size-
constant salinity or food conditions the values are given in ppt or frequency distribution shows that most of the individuals are in
mg 1-1, respectively. Some simulations used an idealized (I) food size classes 8 and 9, 100-125 mm. Gonadal tissue development
time wries that included increased concentrations in the spring and occurs throughout the year, but reaches maximum development in
fall to shoulate blooms. Exclusion of an environmental variable Is the larger animals in the fall. A further increase in food supply by
deDoted by N. 50% results in a simulated population that rapidly increases in size
- (Fig. 5b) and has the majority of the individuals in size class 8 and
Area Temperature Salinity Food Turbidity Figure larger. Development of gonadal tissue occurs in the larger indi-
Galveston Bay V 24 0.5 N 4a, b viduals throughout the year. Overall, these simulations demon-
Galveston Bay V 24 1.0 N 5a strate that oyster size increases with increasing food concentration.
Galveston Bay V 24 1.5 N 5b Food supply does not remain constant throughout the year in
Galveston Bay V V V N 6a Galveston Bay at the levels used in the previous simulations.
Galveston Bay V V V V 6b Rather, in many years, food supply shows maximum values in the
Galveston Bay V 7 0.5 N 7a spring and fall that are associated with the spring and fall plankton
Galveston Bay V 7 1.0 N 7b blooms and reduced food values in the winter. Hence, a monthly-
Galveston Bay V 7 1.5 N 7c averaged food time series from Galveston Bay (Soniat et al. 1984)
Chesapeake Bay V V V N 9a was used with the model. This simulation also used observed
Laguna Madre V 24 V N 9b salinity values for Galveston Bay. The time varying food supply
Laguna Madre V 24 0.5 N 10a
Apalachicola Bay V 24 0.5 N lob results in the simulated oyster population shown in Figure 6a. The
Chesapealce Bay V 24 0.5 N 10c final adult size for this population is intermediate between that
Laguna Madre V 24 1.0 N Ila obtained for the constant low and medium food simulations. The
Apalachicola Bay V 24 1.0 N Ilb majority of the adults are found in size classes 7 and 8 (88-110
Chesapeake Bay V 24 1.0 N llc mm). Maximum gonadal tissue production is also associated with
Laguna Madre V 24 1 N 13a these size classes and occurs in the late summer and fall. A con-
Galveston Bay V 24 1 N 13b stant salinity of 24 ppt results in a simulated population (not
Chesapeake Bay V 24 1 N 13c shown) that is almost identical to that shown in Figure 6a.
tion and gonadal tissue development is nearly identical. Conse- Turbidity
quently, a spring settlement is used to initialize the simulations In estuarine systems, like Galveston Bay, total seston includes
described in the following sections. inorganic particles that can interfer with filtration and reduce in-
Overall, the growth rates, gonadal tissue production and adult gestion rates at high enough concentrations. Hence, the overall
size of the simulated oyster populations shown in Figure 4 are in food supply is effectively reduced. When monthly-averaged tur-
agreement with measurements from Galveston Bay. Some oysters bidity values (Soniat et al. 1984) from Galveston Bay are included
reach size class 5 (63 mm) in about 45 days and size class 6 (76 as part of the food supply, the effect is to reduce the overall size
mm) in about 72 days after settlement. These growth rates are of the oyster population and gonadal tissue development (Fig, 6b).
similar to those found for oysters in Galveston Bay and around the 'Me final adult size is reduced to 63 to 88 mm (size classes 5 and
Gulf coast in general (Powell et al. 1992a, Ingle and Dawson 6) and is similar to that obtained at the low constant food supply
1952, Hayes and Menzel 1981). Gonadal tissue production and of 0.5 mg I`. Gonadal tissue development is confined to a
spawning in oyster populations in the northern Gulf of Mexico is smaller portion of the year.
normally restricted to the summer months (Wilson et al. 1990).
Consequently, reproductively-advanced oysters make up the ma- Salinity
jority of the population only from April to October. This same Estuarine systems are frequently characterized by extended pe-
pattern is seen in the simulated population. In Galveston Bay the riods of low salinity. As many laboratory and field studies have
upper limit on oyster size is 80 to 100 nun and the mean oyster shown, the filtration and respiration rates of oysters are adversely
length is about 85 nim (Table 1; Wilson et al. 1992). Adult oyster affected at salinities below 7.5 ppt and 15 ppt, respectively. Con-
size at the end of the simulation approaches this value. sequently, episodes of low salinity could result in reduced size and
I Con&ok on Aduh Size reduced gonadal tissue development. To test the effect of this
environmental variable, the development of oyster populations
Food Supply during extended periods of low salinity (7 ppt) over a range of food
concentrations was simulated (Fig. 7).
Food supply is an important factor governing the growth and The effect of low salinity is to reduce the overall size of the
development of post-settlement oyster populations, Within any adult population and to hinder the development of gonadal tissue
one bay, local conditions can result in large variations in the food at a given food concentration. The effect of low salinity is most
concentrations experienced by these populations. To investigate pronounced at low food concentration (Fig. 7a) where the scope
this effect on oyster adult size, constant food supplies that brack- for growth is most reduced. The final adult size is reduced relative
eted the range of typical food variations measured in Galveston to the equivalent high salinity case (cf. Fig. 4a) and gonadal tissue

172 HOFMANN ET AL.
0 . . . 'A 'B

9.0
'0

8.0

7.0

6.0

0 4.0

3.0
2.0 E3 3.0 3.5 ED 3.0 - 3.5
W 3-S - 4.0
IM 3.5 - 4.0
4.0 4.5 = 4.0-4.6
4.5 - 5.0 = 4.5 - 6.0
1.0 >5.0 111111 >5.0
j I I I , I . I @ I . I . I
400 600 800 1000 1200 1400 1600 1800 2000 200 400 600 800 1000 1200 1400 1600 1800 2000
TIME (Days) TIME (Days)
FUUM 4. Comparison of the time evolution of oyster populations and gone" tissue development produced by recruitment of a cohort of ten
individuals into size class I on A) Julian Wy 140 (mid-May) and B) Julian Day 240 (early August). Isolines represent the number of individuals
which an given in terms of the logarithm of the number of oysters (log,, N). Size class boundaries are defined in terms of biomass (ash free dry
weight) as shown in Table 2. Hence, the zero contour corresponds to one individual. Population values less than this are indicated by the dashed
lines; solid fines are population values greater than one individual. Shading for the amount or gonadal tissue development represents the
logarithm of calories (log,, cal) with the darkest shades corresponding to the highest values. Contour interval is 0.5 for the number of individuals
in-' and 1.0 for gonadal tissue production. Numbers of individuals or calories are plotted opposite the size elm designations, not halfway
between; hence, on day 140 all individuals are in size class I opposite the grid mark labeled 1. The caloric values can be expressed as Joules by
using a conversion of 4.18 Joules cal'.

production is less. Similar trends arc observed for low salin- 7) and high salinity conditions (Figs. 4 and 5) shows that the
ity conditions at the higher food concentrations (Fig. 7b, c). effect of reduced salinity is minor relative to that of reduced
However, higher food concentrations offset the deleterious food. Therefore, the detrimental effects of low salinity on oyster
effects of low salinity somewhat by providing more energy for populations can be reduced by high, but not unusually high food
growth. Comparison of the simulated populations at low (Fig. supplies.

a 9 1 r I -i -1 4 1 1 1 8 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
A B
10.0 -

9.0 -

8.0 0.

7.0
0 It's
&0

5.0

4.0

3
M 3.0 - 3.5 E3 3.0 - 3.6
2.0 W 3.5 - 4.0 103.5-4.0
4.0 - 4.5 = 4.0 - CS
4.5 - 5.0 4.6 - S.0
2.

1.0 W >5.0 >5.0

200 400 600 SW 1000 1200 140D 1600 18DO 2000 200 400 600 800 1000 1200 1400 1600 1800 2000
TIME (Days) TIME (Days)
Figure 5. Simulated oyster population distribution and gonadal dasue development that results from Galveston Bay environmental conditions
ther
Wid constant food concentrations or A) 1.0 mg I and B) 1.5 ing 1-'. 0 54 wise same as Figure 4.

MODELING OYSTER POPULATIONS 173

A B
10.0
'Zo

9.0

&0
0 -

7.0

&0 0.

5.0 0.5

0 4.0

3.0
3.0 - 3.6 M 3.0 - 3.S
2.0 3.6 '4.0 IM 3.5 - 4.0
4.0 -4.6 = 4.0 - 4.5
4.5 -S.0 IN 4.5 - 5.0
1.0 IN >6.0 101 >6.0
I . I I t I . I . I
200 400 600 800 1000 1200 1400 16W 1800 2000 200 400 600 800 1000 1200 1400 1600 1800 2000
TIME (Days) TIME (Days)
Figure 6. Simulated oyster population distribution and gonadal tissue development that results from Galveston Bay environmental conditions
and food conditions A) without, and B) with turbidity. Otherwise same as Figure 4.

Latitudinal Controls on Adult Size agree with those reported for Chesapeake Bay oyster populations
by Butler (1953b) and Beaven (1952). Yearly fluctuations in bio-
Temperature mass are higher in Chesapeake Bay because scope for growth is
The monthly temperature distributions that are characteristic of negative for longer periods during the winter.
Laguna Madre, Texas (26*N), Galveston Bay, Texas (290N), Adult size in Chesapeake Bay (size class 8) is larger relative to
Apalachicola Bay, Florida (30*N) and Chesapeake Bay, Virginia that in the Laguna Madre (size class 7). This difference arises
(38'N) show that all three bays reach about the same temperature despite the shorter growing season in Chesapeake Bay (Butler
(28'C) in the summer (Dekshenieks et al. 1993). The primary 1953b). The Chesapeake Bay simulation (Fig. 9a) allows more
difference over this latitudinal range is in the winter temperatures time at intermediate temperatures where somatic, but not repro-
and duration of cold conditions. To test the effect of temperature ductive, tissue is developed. The practical result is a larger adult
on oyster size and gonadal tissue development over such a latitu- population. Thus, the temperature range as well as the length of
dinal range, a series of simulations that used idealized temperature time exposed to a temperature are important determinants of adult
time series were done. All simulations used six months of warm size.
(280C) temperature. The remaining six months were set at 25*C,
20*C, 15*C and 10*C to rrpresent winter conditions in the four Food Supply
bays, respectively.
For all the temperature conditions, the mode of the oyster pop- A low (0. 5 ing I constant supply of food alters the size
ulation, after 5.5 years of simulation, was found in size class 7, distribution of adult oysters from Laguna Madre to Chesapeake
88-100 nun (Fig. 8). However, the population distribution about Bay (Fig. 10). The simulated adult size is essentially the same
this mode varied considerably from bay to bay. The small tem- throughout the Gulf of Mexico. Adult oysters in Laguna Madre
perature difference between winter and summer conditions in La- (Fig. 10al, Galveston Bay (Fig. 4a) and Apalachicola Bay (Fig.
guna Madre, resulted in the oyster population being dominated by 10b) are found in size class 6. Gonadal tissue production is about
essentially a single size class. Adult size increased between La- the same in the three bays, with that in Laguna Madre being
guna Madre and Galveston Bay, with about 40% of the population somewhat higher and extending over more of the year. Chesa-
found in size class 8. This model result agrees with observations of peake Bay oysters (Fig. 10c) are slightly smaller (size class 5)
hicreased adult oyster size in Galveston Bay relative to Laguna which results from decreased filtration rate and hence reduced net
Madre. However, the simulated size distributions suggest that production in response to the colder winter temperatures in this
adult size decreases between Galveston Bay and Chesapeake Bay, bay, Winter temperatures in Laguna Madre allow a higher rate of
which is opposite of the trend seen in the measurements. This filtration which results in this bay having the largest oysters at the
difference in simulated and observed adult size arises from the low food levels.
similar time periods used for the warm and cool temperatures. Doubling the available food supply to 1.0 mg I ', results in the
As a check on the above results, realistic temperature distribu- largest oysters being produced at the mid-latitude sites, Galveston
tions for Chesapeake Bay and Laguna Madre were used with the Bay (Fig. 5a) and Apalachicola Bay (Fig. I 1b). The smaller adult
model (Fig. 9). 'Me simulated population size-frequency distribu- size occurs in Laguna Madre (Fig. I I a) because mom of the aval)-
tion for Chesapeake Bay shows that oysters of size classes 6 and able food supply is used to produce reproductive rather than so-
7 (70-100 nun) are produced by the summer of the second year. matic tissue. Adult size in Chesapeake Bay (Fig. I 1c) is also
'Me juvenile growth rates and adult size obtained from the model smaller than that in the mid-latitude bays. However, this arises
55

174 HOFMANN ET AL.
10.0 - IA Size at 19W Days

9.0 .
IKO - 10-
7.0 $_ Winter: 25*C
s- Summer: 21r C
6.0 uib% 0
4-
0.5 N,__, CA
2
Z

05
0-

3.0

31 - 4.0
2.0 10-
4.0 - 4.5
4.6 - 5.0 Winter: 20'C
1.0
1 6- Summer: 2W C
400 600 Wo .1 -0
TWE (Days) E Z 4-
= -1
B z - 2-
0

10-

Winter: 15'C

6- Summer: 281 C

9
of #11 H 4
V
Z@ -5 2-

3.0-3A
3.6-4.0
4.0-4.9
4.6 - co 10-
.6.0 8- Winter: 1o'C
I . I . -62
200 400 GN $00 1000 1200 14M 1600 1600 2000 42 6- Summer: 281 C
TIME (Days) :2
Z 4-
C z -E 2-

0
of 1 2 3 4 5 6 7 8 9 10
Size Class
F%wv 8- Simulated size frequencY distribution from year i, for four
ideahz4ed temPerature time seiries. Other environmental conditions
Vri were constant salinitY (24 Plit), Galveston Bay food conditions and no
turbidity.

J. due to the colder temperatures which limit winter net production
1*01 rather than the production Of reproductive tissue.
3.0 - &5 Environmental Controls on Reproductive potential
3.6-4.0
4.0-4.6
4.5-6.0
@.Lo The simulations presented in Figures 4-11 show that gonadal
- tissue development changes for a given set of environmental con-
2 OL 'ICLO '12LO'14'm ISM 1800 20M ditions. This in turn determines the reproductive potential (spawn-
TWE (Days) ing) of an oyster population - "Me ability to check the accuracy of
F%ure 7. Shnubted oyster popubdon distribution and gonadal tissue the reproductive portion of the population model is limited due to
d
de, ,
el that results &= Galveston Bay temperatuires, low sa- the paucity of observations that provide measurements of oyster
Vmhy (7 plit) condidous and food concentrations of A) 0-5 mg 1-1, 8) reproductive state, oyster size, and environmental conditions con-
1.0 mg I-', and Q 1.5 mg 1-1. Otherwise same as Figure 4. currently. However, them arc some general trends that should
56 aripear in the simulated po pulations.

MODELING OYSTER POPULATIONS 175

B I
10.0

-2-0

q0
'Lo

6.0

4.0 %1 Of

0*
3.0

M 3.0 - 3.5 EM 3.0 - 3.5
2*0 W 3.5 - 4.0 10, IS-4.0
" 4.0.4'5 40-4.5
Z 4.5-50 C 4:5 - 5.0
1.0 = U.0
2
200 400 600 800 1000 1200 1400 1600 1800 2000 200 400 600 800 1000 1200 1400 1600 1800 2000
TIME (Days) TIME (Days)
Figure 9. Simulated oyster population distribution and gonadal tissue development that results from temperature, salinity and food conditions
characteristic of A) Chesapeake Bay and B) Lagun Madre. Observations on food distributions are lacking for Laguna Madre. Hence, the
Galveston Bay food time series was used in this simulation. Otherwise same as Figure 4.

The spawning frequency and pattern associated with the sim- period, recorded for Delaware Bay oysters held in the laboratory
ulated populations from Laguna Madre, Galveston Bay and Ches- was 3 X 10" to 4 X 107 eggs per female (Davis and Chanley
apeake Bay is shown in Figure 12. In general spawning is asso- 1955). This study did not report food levels. Egg number, esti-
ciated with the larger size classes and the spawning season tends to mated from the simulation results for Chesapeake Bay and
be longer at lower latitudes. Also, the most southerly bays tend to Galveston Bay, using the approach described in Klinck et al.
have continuous spawning; whereas, that in Chesapeake Bay tends (1992), is 1.7 X 10" and 3 X 10" eggs per female, respectively,
to be confined to discrete pulses. This same trend is observed in for a spawning period of about 100 days.
the observations from the NOAA Status and Trends program (Ta- The extent to which these differences and similarities in
ble 2). More oysters were found in late reproductive phase, ready spawning frequency and pattern result from variations in en-
to spawn or spawning at lower latitudes. vironmental conditions is discussed in Hofmann et al. (1992).
Spawning season is usually defined by the period of time dur- For this study, the interest is in the extent to which these differ-
ing which mature eggs are present or by the period of actual ences and similarities result from variations in adult size. Oyster
spawning. The simulated spawning season, as defined by signif- populations in Laguna Madre (Fig. 13a), Galveston Bay (Fig.
icant spawning events, is about 100 days in Laguna Madre (Fig. 13b) and Chesapeake Bay (Fig. 13c) show a restriction in the
12a), somewhat shorter in Galveston Bay (Fig. 12b) and even period of reproductive effort, as measured by spawn production,
shorter in Chesapeake Bay (Fig. 12c). A tendency towards a over the course of the six-year simulation. This is a conse-
spring and fall spawning peak occurs in Galveston Bay (last two quence of the increased size of the population rather than of in-
years of simulation) and an even stronger tendency towards this creased age. Smaller oysters are more likely to have a positive
occurs in Chesapeake Bay. Significant gonadal material is present energy balance and cat allocate a larger fraction of their total
for about 200 days (7 months) in Galveston Bay, 160 days (5 assimilated energy to reproduction. As a result, they can spawn
months) in Chesapeake Bay, and nearly all year in Laguna Madre. more frequently. This trend is independent of the pattern or fre-
These features of the stimulated spawning season are within the quency of spawning and is observed for all ranges of environmen-
range of values reported for oyster populations and fit the trend tal conditions.
toward shorter spawning seasons at higher latitudes (e.g. Hopkins A summary of reproductive effort, derived from the simula-
1935, Stauber 1950, Ingle 1951, Heffeman et al. 1989, and pre- tions, as it relates to average adult size, food supply and latitude is
vious references). The development of reproductive material in the given in Table 4. These results show the strong relationship that
simulated oyster populations, from initiation to first spawning, exists between reproductive effort, temperature and food supply.
takes about 40 days in Galveston Bay and 60 days in Chesapeake Overall reproductive effort is more variable than adult size. For
Bay. This is somewhat slower than the 20 to 40 days suggested by example, in Galveston Bay a reduction in food supply, produced
Kaufman (1979) and Loosanoff and Davis (1953). However, these by increased turbidity, gives a 67% reduction in average adult
time intervals were based on results from constant temperature size, but an 85% decrease in reproductive effort (Fig. 6a vs. Fig.
incubations, which will result in shorter times. Hayes and Menzel 6b). Similarly, the change in temperature that occurs between
(1981) recorded mature gametes in oysters that were 40 to 50 days Galveston Bay and Laguna Madre reduces adult size by 6%, but
old, which is similar to what is observed in the simulated popu- increases reproductive effort by 23%. Higher temperatures pro-
Mons from Galveston Bay. Egg production, over a two month duce higher filtration rates which give increased net production.

116 HOFMANN ET AL.

I I - I - a - 9 1 - I - I - I I i V
A. A
1.0
4.0 - 4.S
4.5 - &0
2.&0 . . . . . . . . .
9.0 8.0

7.0
7.0

9.0
0 U or

6.0 5.0
4.0 '*j
4.0

&0
3.0-3.6
3,5-4.0
10 zo 4.0-4.6
4.6 - LO
1 1.0 @5.0

200 400 600 WO 1000 1200 1400 1600 1800 2000 200 400 600 600 1000 1200 14W 16W IWO -2000
'nME (Days) TIME (Days)
i - r I
S.C.&S B
3.5-4.
M 4.0-4.05
4.6 - &0
;-S.O O.S

or. 0 a
Lo j.

MS V

x 3.0-3.5
3.S-4.0
4,0.4.5
4.6-6.0
>5.0
j I . 1
200 400 600 800 1000 12W 1400 1600 1800 2000 200 400 600 NO IWO 12W 1400 1600 1800 2000
TIME (Days) TIME (Days)

3.0-3.6 C. C
3.5-4.0
-4.5
4.SO-6.0

O.S

-2.

0A
V.0
of
00 &0-&S
3.6-4.0

LA -1 -LA _t__ I I t a I a I I I a I I a j
200 400 600 SOO 1000 1200 1400 1600 IWO 2000 200 400 600 NO IWO 1200 1400 1600 1800 2000
TIME (Days) TtME (Days)
F%m 10. Simulated oyster population distribution amd gonadal tis- Figure 11. Simulated oyster population distribution and gonadal tis-
sue development that remits from constant low food (0.5 mg V') sat development that results from medium food (1.0 mg 1-1) supply
supply and environmental conditions characteristic of A) Laguna and environmental conditions characteristic of A) I agma Madre, B)
Madre, B) Apalachicola Bay and Q Chesapeake Bay. Otherwise tune Apalachicola Bay and Q Chesapeake Bay. Otherwise same as
as Figure 4. Figure 4.

MODELING, OYSTER POPULATIONS 177

10.0 - A A

9.0 -
.0

8.0

7.0
7.0

6.0
Ilk

5.0
4.0
4t 4.0 4,
3.0 E3 1-2
W 2-3 3.0
2A - W 3-4
4-5 Lo 1W 2-3
1A - >6 - W 3.4
2 1.0 4-5
45 'I-OLO-0 t2ko '14'0-0 .1600 1800 2000' >S
TIME (Days) 200 400 600 $00 1000 1200 1400 1600 18W 2000
TIME (Days)
B

42 0.6
Ah

2
2-3
3-4 X%,
4'5
>6 NE. 2-3
I , I-1 W 3-4
200 600 600 $00 1000 1200 1400 1600 1800 2000 C 4-5
TIME (Days) W A
200 400 6W '14;'12'00'14;'d; 1k 2000
C TIME (Days)

1-2
2-3 00
3-4
4-5
>5 E-n
I . M 2-3
2W 400 600 800 1000 1200 14oo 16W ISW 2DOO M 3.4
TIME (Days) 4-5
>S
I%= 12. Comparison Of Spawning intensity Versus oyster population
du In A) Lwma Madre, B) Galveston Buy and Q Chesapeake Bay. 200 400 6W Soo 1000 1200 1400 1600 1600 2000
Spawning intensity 6 shown as loglo calories spawned with a contour TIME (Days)
hd"Val of " Spawning intensity for lasun, Madre and Chesapeake RgUre 13. Simulated oyster population distribution and spawn pro-
Bay was obtained from the simulated oyster populations shown in duction for A) I a0m, Madre, B) Galveston Bay and Q Chesapeake
Figures 9b and 9a, respectively. The Galveston Bay spawning Intensity Say obtained using an idealized food time series. Spawning Intensity is
was obtained trom the constant salinity simulation that was essentially dmnm as Iogjq calories spawned with a contour interval of 1. Other-
klentical to the simulation results shown In Figure wbe same as Figure 4.
59

178 HOFMANN ET AL.

TABLE 4. A C

Reproductive effort, average adult size and the ratio of the two
Calculated from year six of the simulated populations shown in the
bidicated figures. One simulation used is not shown (NS). This 10.0-
11111110111latim used 111001011bly-averaged temperature and food conditions
hm Galveston Bay, Texas, & constant salinity of 24 ppt and no
L6
ftrbidity. The results of this simulation were similar to those shown
9.0- u.
In Fligure 6a. Size and reproductive efforl are based on simulations U.
that used the environmental time series defined in Table 3. Lower
food supply, higher turbidity, or the inclusion of disease (e.g.
Perkinsus marinw) could be expeected to reduce thses values. 8.0-
0
C

Reproductive Average Ratio
7.0-
Effort Size (kcal:g dry FIgure U)
0 1 u. U.
Location (kcal) (g dry wt) wt-1) Number 0 j j
6.0- U.
Laguna Madre vs. 266.71 4.87 54.77 Ila U., I-11
j
Galveston Bay 260.92 5.12 50.% NS
Laguna Madre vs. 218.79 4.62 47.36 13a
Galveston Bay 179.03 4.89 36.61 13b 5.0-
Galveston Bay vs. 129.77 4.73 27.44 13a :0. to;
0 JJ
Chesapeake Bay 47.47 4.24 11.19 13c kn1 I
Galveston Bay vs. 156.49 5.18 30.21 6a 4.0- Ed
Galveston Bay 24.21 1.81 13.36 6b

3.0-
However, most of the net production is allocated to reproductive B Legend C Legend
rather that somatic tissue development. E Low Salinity Chesapeake Bay
DISCUSSION AND SUNUAARY 2.0 High Salinity Apalachicola Say
Laguna Madre
General Characteria*s 1.0 Galveston Say
Adult size and reproductive effort in oyster populations are Figure 14. Comparison of adult size from year six of the simulations
determined by the temperature- and season-dependent allocation from A) Galveston and Chesapeake Bays (Figs. 6a and 99), B)
of net production to somatic and reproductive tissue development Galveston Bay for high and low salinity at a range of food concentra-
which in turn depends upon the temperature regulation of filtration tions (Figs. 4a, 5 and 7) and C) four bays and a range of food con-
rate. Salinity and turbidity affect oyster physiology through a re- centrations. High and low salinity values are 24 ppt and 7 ppt and are
duction in the rate of food acquisition and cannot be distinguished designated by HS and LS, respectively. Designations for high (1.5 ing
from a simple reduction in food supply. Although respiration rate 1-1), medium (1.0 rag 1-1), and low (0.5 ing 1-1) food concentrations
varies non-linearly with body mass and is affected by salinity ,the are HF, MY and LF, respectively.
overall effect of environmental conditions on respiration rate is
small and can be ignored, in most situations. Variations in local environmental conditions also affect adult
A summary of simulated adult oyster size that results from oyster biomass. Low salinity conditions in an environment such as
variations in local and latitudinal controls on growth is given in Galveston Bay can result in reduced adult size (Fig. 14b). How-
Figure 14. These simulations considered only environmental con- ever, the effect of low salinity can be compensated for by increases
trol on oyster biomass. Oyster growth form is extremely plastic, in food supply. Low salinity conditions combined with high food
although Kent (1988) argues for some predictable influences of conditions can result in adult biomass that is similar to that ob-
local habitat. Nevertheless, the shell length achieved in the various tained during high salinity conditions. The largest reduction in
simulated populations may vary over a wide range (Table 2). adult oyster size occurs when low salinity is combined with a
Unfortunately, much of the available oyster measurements are in restricted food supply.
terms of shell length or condition index rather than biomass. In this The importance of food in determining adult biomass over a
discussion, except where noted, oyster size is considered strictly in latitudinal range is illustrated in Figure 14c. For all bays, low food
terms of biomass, and where needed, conversions to length are conditions produced adult oysters that were about the same size,
done as shown in Table 2. size classes 5 to 6. The only exception is Chesapeake Bay where
The simulations indicate that adult oysters in Chesapeake Bay somewhat smaller, size class 4, adult oysters are produced by low
tend to be about the same size in terms of biomass as those in food conditions. Medium food conditions result in larger adult
Galveston Bay (Fig. l4a), when presented with equivalent food oysters for all bays with minimal overlap with the size produced by
supplies, salinities and levels of turbidity, despite the difference in low food conditions. Galveston and Apalachicola Bays have sim-
AU

temperature regimes. Water temperatures in Chesapeake Bay tend ilar sized adult oyster populations. Individuals in Laguna Madre
to be colder for longer periods thart. in Galveston Bay. Thus, the tend to be a bit smaller. The warmer temperatures in Laguna
temperature-dependent control on the allocation of net production Madre result in more of net production going to form reproductive
results in more going to somatic rather than reproductive tissue tissue, thereby producing more spawn and smaller individuals.
development. Chesapeake Bay populations show a wider range of adult size, but
60

MODELING OYSTER POPULATIONS 179

many individuals reach adult size typical of the lower latitude sites increase in food apportioned to somatic growth and size remains
despite the cooler temperatures and more restricted growing sea- stable. Reproductive potential, however, declines in these popu-
son (e.g. Butler 1953b). lations.
Reduced size at lower latitudes is common in bivalves (e.g.
A&* Size (Biomws) Bauer 1992). Such a gradient in animal size can result from vari-
The shape of the growth curve for bivalves--whether size con- ations in temperature in one of two ways. First, an environment
tinuously increases at some declining rate or asymptotes to some characterized by low food supplies and warm temperatures can
maximum size (e.g. Levinton and Bambach 1970)--is probably produce large adult oysters despite increased reproduction because
more a function of environment than genetics. It is significant that the total gain in energy from higher winter filtration rates results in
the simulated oyster populations reached sizes characteristic of a net accumulation of somatic tissue. The decline in size at low
populations throughout the latitudinal range from Laguna Madre to latitudes in the Gulf of Mexico suggests that this is not the normal
Chesapeake Bay solely on the basis of physiology and environ- condition. Alternatively, an environment characterized by moder-
ment. No upper limit for oyster growth or adult size was included ate-to-high food supply and warm temperatures can produce
any of the formulations used to describe oyster physiology. smaller adult oysters because the greater allocation of net produc-
Limitations on size in the simulated populations come from the tion to reproduction balances the positive effect of temperature on
in

balance between winter and summer somatic production less the the rate of food acquisition. This is the more usual case.
energy expended in reproduction: Stunting, the presence of a relatively small adult size in a
P.i_ - Ps,_ @ Aj - Pj. (15) population, is generally considered to result from restricted food
supply. The results of this modeling study suggest that, at least for
In adult oysters, net production is normally negative in the winter oysters, temperature and reproductive effort are also important in
and for die most part is balanced by somatic growth in the spring restricting animal size. Hence, stunted populations can occur at the
and fall. Cessation or slowing of growth in the summer (e.g. edge of the species' range where physiology directly limits size as
Beaven 1950) in disease-free oyster populations is normally due to well as in populations that fail to reach the size expected for their
reproduction and spawning which accounts for most of the net position within the latitudinal range.
production in older animals. Hence, the relationship given above The observed oyster sizes from around the Gulf of Mexico
should result in a stable, but seasonally-oscillating, variation in (Fig. 1) show two exceptions to the general trend of decreasing
adult oyster size. In the simulated population distributions, the size at lower latitudes. It should be noted that the data presented in
balance between winter loss in net production and spring-summer- Figure I are in terms of length, rather than biomass, and so are
fall gain begins in the second or third year depending on the subject to the aforementioned caveats concerning the plasticity of
ambient temperature and food supply. Exceptions to this occur oyster growth form. First, the adult length observed at lower lat-
only when food supply is very high. itudes on both sides of the Gulf of Mexico is about I to 2 cm less
Growth rate in the hard clam, Mercenaria mercenaria, has a than the average length observed in the northern Gulf. Such a
concave parabolic relationship with temperature (Ansell 1968). length decrease is not easily produced in the simulated populations
Growth rates are lowest at low and high seasonal temperatures and with a simple reduction in temperature and one biomass-length
maximum at intermediate temperatures. Multiplying equations 4 relationship. A 0.5 to I cm reduction in length is typical of the
and 12, and assuming a food supply adequate to minimize the simulated populations. A temperature-dependent change in growth
effect of respiration on the energy budget and ignoring the depen- form modifying the size-to-biomass relationship may also be in-
dence of filtration rate on length, yields a parabolic dependence for volved. Second, oysters from Moblie Bay through the Florida
oyster growth rate on temperature of the same form Panhandle area and in Tiger Pass on the Mississippi Delta are
G a bT - a7"a (16) unusually small. This region characteristically has the coldest win-
ter temperatures in the Gulf of Mexico (Collier 1954). However,
where a and b are the constants in equation 12 and T is tempera- the possibility that the colder temperatures reduce the growing
ture. If equation (16) is applied over the latitudinal range from season and thus limit adult size is not supported by the simulated
Laguna Madre to Chesapeake Bay, then oyster growth rate and populations. Even colder temperatures in Chesapeake Bay fail to
hence size should decrease at the southern and northern ends of the reduce adult biomass. Either food supply is unusually meager in
distribution. Maximum growth rate and largest adult size would be these two areas or mortality rates are unusually high. Thus, stunt-
found near the center of this range, However, both the oyster and ing may be of local (Tiger Pass) or regional (Florida Panhandle)
the hard clarn (Ansell 1968) deviate from this expected distribution extent. The effect of a change in growth form can be discounted in
in that adult size remains constant over a wide latitudinal range this case because the length-biomass relationship given in White et
that includes habitats from the northern Gulf of Mexico to north of al. (1989) is adequate for at least some of these populations.
Delaware Bay. Butler (1953b) showed that oysters in Chesapeake Bay and the
The observed rather than expected [as suggested by equation northern Gulf of Mexico reached about the same size in terms of
(16)] latitudinal distribution in size is also reproduced in the sim- length. The simulations summarized in Figure 14 generally show
ulated oyster population distributions. This relationship between that Gulf of Mexico oysters slightly exceed Chesapeake Bay oys-
size and latitude arises through temperature effects on the alloca- ters in length when biomass is converted using a single length-
tion of net production to somatic and reproductive tissue growth biomass relationship. A latitudinal difference in growth form
and on filtration rate which determines the rate of food acquisition. would explain this differential. Kent (1988) describes a wide range
The longer periods of low temperature in the spring and fall found in growth forms from Chesapeake Bay, so that within-bay varia-
at higher latitudes result in more time in which food is plentiful tions cannot be discounted. However, the relationship given in
occurring at temperatures that favor somatic growth. As a result, Paynter and DiMichele (1990) for a Chesapeake Bay population
decreased filtration rates at lower temperatures are balanced by an from Tolley Point Bar predicts oysters much longer for a given

180 HOFMANN ET AL.

weight and this prediction agrees with a biomass-length relation- growth and reproduction. However, small changes in either result
ship obtained by Newell (University of Maryland, pers. comm.) in more pronounced changes in reproductive effort than in adult
from the Choptank River subestuary of the Chesapeake Bay. Lunz size. For example, the rate of food acquisition is higher in warmer
(1938) suggested that a primary influence of anthropogenic: activ- months when most net production is allocated to reproduction.
ities on oyster growth form was to decrease width and length, but Hence, small changes in available food are magnified during this
with more of an effect on width. If true, this would explain a period. The effect of small variations in environmental conditions
perceived variation between oyster size reported by Butler (1953b) on oyster reproduction and spawning is discussed in detail by
snd the more recent measurements reported by PaynteT and Hofmann et al. (1992).
DiMichele (1990) and Newell (University of Maryland, pers. The wide range of reproductive efforts produced from small
comm.). Unfortunately, the observations reported in Butler changes in temperature or food supply suggests that comparisons
(1953b) are not in terms of biomass. The same trend might explain of reproductive effort between oyster populations can only be
the tendency in the simulated oyster populations from Chesapeake made within the context of a complete environmental analysis of
Bay to be slightly lower in weight and, therefore, length, than the food supply, environmental conditions and a total energy budget
northern Gulf of Mexico oysters (e.g. Fig - H). The weight ob- for the animal. The wide range of reproductive efforts reported for
tained from the simulated populations would result in a longer bivalves in general (see Powell and Stanton 1985 for a review)
oyster in Chesapeake Bay using the conversions of Paynter and probably results from these interactions. Thus, correlations be-
DiMichele (1990) and Newell (University of Maryland, pers. tween size and reproductive effort will be location and time spe-
comm.). cific, and general conclusions based upon such correlations may
The simulated oyster populations suggest an explanation for the not be valid. For example, the relationship between temperature
concordance in year-to-year oscillations in oyster size throughout and reproduction given by Kaufman (1979) requires similar rates
the Gulf of Mexico (Wilson et al. 1992). Climatic cycles, such as of food acquisition among populations to provide valid compari-
El Nifto, change the Gulf-wide temperature and rainfall regime sons *
(Powell et al. 1992a). Size, through the direct effect of tempera- The assumption that populations of larger individuals should
ture on the allocation of net production to somatic and reproduc- reproduce more is not always correct. For many situations, pop-
tive tissue or indirectly through variations in food supply, could be ulations of smaller individuals may have a greater reproductive
affected by climatic variations in temperature and rainfall. Fur- effort per unit of biomass. The simulated population distributions
thermore, such climatic effects are likely introduced through vari- suggest that decreases in reproductive effort are related to in-
ations in temperature during the colder part of the year. For ex- creased size rather than to age. The apparent reproductive senility
ample, the difference between a warm and cold winter could be in these populations results from the differential scaling of filtra-
sufficient to significantly alter adult size. tion and respiration rate with body size, which reduces scope for
Reproduction growth at a given food supply in larger animals.
The reproductive processes included in the oyster population ACKNOWLEDGMIENTS
model are based upon simple empirical relationships-, however, the
simulated population distributions show trends typical of oyster We thank Elizabeth Wilson for help in data acquisition and
populations throughout the east coast of the U.S. and the Gulf of model formulation. The NS&T data were collected through the
Mexico. This suggests that reproductive effort in oysters is pri- efforts of too many to name; we thank the entire NS&T team at
marily a function of a genetical ly-determined temperature- Texas A&M University (TAMU). This research was supporled by
dependent allocation of net production into somatic and reproduc- institutional grant NA89-AA-D-SG]28 to Texas A&M University
dve tissue development and an environmentally determined scope (TAMU) by the National Sea Grant College Program, National
for growth. This temperature dependency may be described by Oceanic and Atmospheric Administration (NOAA), U.S. Depart-
simple linear relationships such as those given by equations (12) ment of Commerce, grants 50-DGNC-5-00262 and 46-DGNC-0-
and (13) which may reflect temperature-dependent reaction rates 00047 from the U.S. Department of Commerce, NOAA, Ocean
in protein synthesis or hormonal control. The mechanism under- Assessments Division, a grant from the Center for Energy and
lying the temperature-dependent allocation of net production Minerals Resources, Texas A&M University, a grant from the
would appear to be an important unknown in the reproductive U.S. Army Corps of Engineers, Galveston District Office
physiology of oysters. DACW64-91-C-0040 to TAMU and Old Dominion University
Reproductive potential is the result of the same physiological (ODU) and computer funds from the College of Geosciences Re-
and environmental conditions that govern adult size, i.e. the tem- search Development Fund. Additional computer resources and fa-
perature- and season-dependent rate of food acquisition and the cilities were provided by the Center for Coastal Physical Ocean-
temperature-dependent allocation of net production into somatic ography at ODU. We appreciate this support.

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Prof. Pap. Ser. 13:1-55.

Reprint 5

Correlation Between Bioassay-Derived
P4501AI Inductive Activity and Chemical
Analysis of Clam (Laternula elliptica)
Extracts from McMurdo Sound,
Antarctica

Susanne J. McDonald, Mahlon C. Kennicutt
11, Jose L. Sericano, Terry L. Wade, Long Liu,
and Stephen H. Safe

CORRELATION BETWEEN BIOASSAY-DREIVED P4501A1 INDUCTION
ACTIVITY AND CHEMICAL ANALYSIS OF CLAM (Laternula ellitica)
EXTRACTS FROM McMURDO SOUND, ANTARCTICA

Susanne J. McDonald, Mahlon C. Kennicutt, II, Jose Sericano,
Terry L. Wade, Hong Liu and Stephen H Safe

Geochemical and Environmental Research Group (S.J.M., M.C.K., J.S. and T.L.W.)
Texas A&M University

833 Graham Rd.

College Station, TX 77845

Department of Veterinary Physiology and Pharmacology (H.L. and S.H.S)
Texas A&M University
College Station, TX 77843-4466

Tel: 409-845-5988 FAX: 409-862-4929

(Received in Gummy 25 March 1994; accepted 18 April 1994)

ABSTRACT

Variable levesl of halogenated aromatic hydrocarbos were measured in clams (Laternula elliptica) collected
from McMurdo Sound, Antarctica. Clams collected in and near Winter Quarters Bay contained high levels of
organochlorine compounds, particularly pholychlorinated bigphenyls (PCBs). A strong gradient has been documented
in Winter Quarters Bay that been linked to human activities at McMurdo Station. The activity of clam extracts as
inducers of P4501A1 -dependent ethoxyresorufin 0-deethylase (EROD) activity was determined using in vitro
bioassays utilizing rat hepatoma H4IIE cells. The extracts which exhibited the highest induction activities were those
derived from clams collected in contaminated area . Additionally, there was an excellent linear correlation between
induced EROD activity versus total PCB levels (r2=0.96). The complimentary nature of both the analytical and
bioanalytical data confirms the utility of the latter assay and provides a method for estimating the 2,3,7,8-
tetrachlorodibenzo-p-dioxin (TCDD) toxic equivalents in extracts from marine biota.

2237

2239

DiTRODUCTION

Halogenated aromatic hydrocarbons (HAHs) are industrial compounds or combustion by-products which
have been widely identified as environmental contaminants in almost every component of the global ecosystem
(Tanabe, 1988; McFarland and Clarke, 1989; Safe, 1990, 1991, Rappe, 1993, Rappe el al., 1993). The HAHs
include the polychlorinated biphenyls (PCBs), dibenzoT-dioxins (PCDDs) and dibenzofurans (PCDFs). These
chmnicals exhibit a number of common properties including their structural similarities, chemical stability, lipophificity
and toxicological effects. The problems associated with the environmental persistence and transport of HAHs and
their preferential bioconcentration in the food chain is primarily due to their resistance to degradation and highly
hpophilic properties.
PCBs, PCDDs and PCDFs have been identified as complex mixtures in diverse environmental samples and
high resolution analytical procedures can give quantitative congener specific analysis of HAH mixtures (Muffin et
al., 1984; Tanabe, 1988; McFarland and Clarke, 1999; Schulz et al., 1989; Duarte-Davidson et al., 1991; Rappe,
1993; Rappe et aL. 1993). Risk assessment and risk management of these complex mixtures can be carried out using
a toxic equivalency factor (TEF) approach in which all the toxic HAHs have been assigned a fractional potency
rdative to 2,3,7,8-tetr&cWorodibenzop4iox:in (TCDD) (NATO/CCMS, 1998; Ahlborg, 1989; Safe, 1990; Ahlborg
et al.. 1992). The TCDD or toxic equivalents (TEQ) of a mixture can be readdy adculated from quantitative
congener-specific analytical data (Safe, 1990).
In vitro bioassays have also been developed to determine the TEQ values of extracts from various
environmental and indumial samples which exhibit *TCDD-like' activity (Bradlaw and Casterline, 1979,- Trotter et
al., 1982; Casterline el al., 1983; Zacharewski et al., 1989; Ankley et al., 1991, 1992, 1993 -1 Tillitt ei al., 199 1 a,
199 1 b, 1992, 1993; Jones et al, 1993). Since these compounds elicit similar toxic and biochemical responses via
the aryl hydrocarbon (Ah) receptor signal transduction pathway (Safe, 1990), various Ah receptor-mediated
msponses including P4501AI induction, antiestrogenicity and keratinization have been utilized to determine bioassay-
derived TEQ values for any mixture (Bradlaw and Casterline, 1979; Trotter el al., 1982,- Casterline et al., 1983;
Gerthy a al., 1994, 1993 - Zacharewski et aL, 1989,- TiHitt et al., 199 1 a, 1991b, 1992, 1993; Ankley el al., 1991,
1992, 1993; Krishnan el al.. 1992; Jones et al., 1993 - Krishnan and Safe, 1993). This approach is useful for
biomonitoring extracts since it obviates the need for relatively expensive chemical analysis, detects aU bioactive
components in a mixture and their possible interactions with coextracted non-TCDD-fike compounds. This approach
is particularly useful for invertebrates because, although the presence of P450 IA-monooxygenase enzymes has been
confirmed in a number of invertebrates (Lee, 1982; James, 1989.- Livingstone, 199 1), there is no conclusive evidence
showing they are inducible after exposure to aromatic hydrocarbons. Additionally, recent work by Hahn and
coworkers (1992) did not detect the presence of the Ah receptor in nine invertebrate species. This suggests that
invertebrates lack a functional Ah receptor, which is consistent with the failure to obsem induced P4501A-
dependent activity. McNIurdo Sound, Antarctica, was selected for study because high concentrations of PCBs and

2239

po4rnuclev aromatic hydrocarbons (PAILS) have been measured in sediments collected in Winters Quarter Bay and
marrounding area aAiihan el at., 1990; Risebrough el al., 1990; Lenihan, 1992). This paper reports the results of
a P45OIA14nduction bioassay using rat hepatorm H4HIE MRS exposed to extracts from clam (Laremula elliptica)
I * P I from both highly contaminated and control sites in McMurdo Sound. Additionally, the bioamy results are
compared to the results of chernical analyses of the same samples for organochlorine and aromatic hydrocarbon
pollutants.

MATERIALS AND METHODS

Sampling. Clam samples, in pools of 9 to 15 individuals, were collected by divers from impacted and
nonimpacted areas in McMurdo Sound, Antarctica. Contaminated clams were collected from two contaminated
locafions in the vicinity of the U.S. McMurdo Station, in Winter Quarters Bay and at the sewage outfall (WQB, Fig.
1). Clams collected from three sites located in remote areas of the McMurdo Sound were used as controls (Fig. 2).
Emwwfion and Cleanup. Approximately 5 to 15 g of wet tissue were used for the analysis of PAHs, PCBs
and chlorinated pesticides. Fifty grams of anhydrous Na2SO4 and the appropriate amount of surrogates were added
to each sample before extraction. The aromatic surrogate contained d4-1,4-dichlorobenzene, dg-naphthalene, d10-
acenaplithene, djo-phenanthrene, d,2-drysene, arW d12-perylene. The surrogate for PCBs and chlorinated pesticides
contained 4,4'-dibromooctafluorobiphenyl, PCB 103 and PCB 198. The tissue samples were then extracted with
methylene chloride (3 times x 100 frd) using a *Tissumizer' homogenizer. The combined extracts were concentrated
to 10-15 ml in a flat-bottom flask equipped with a three ball Snyder condenser and transferred to Kuderna-Danish
tubes. The tubes were heated in a water bath at 60'C to concentrate the extracts to a final volume of 1-2 ml in

hexane.
The tissue extracts were initially cleansed by alumina (20 & 5% deactivated with H20):silica (10 g, 1%
deactivated with H20) column chromatography. The columns were eluted with 200 ml of 1:1 methylene
chloride: pentane and the aluate was concentrated as described above. This fraction was further purified by high
performance liquid chromatography to remove excess of lipid materials. The extracts were concentrated to a final
volume of 0.5-a nil, hexane, for GC/MS and GC-ECD analyses. Extracts used in the bioassy were obtained as
described above except that the surrogates were not added and the extracts were concentrated and dissolved in
DMSO.
btsoumental Analysis. PAHs were analyzed by electron impact (70 eV) GC/MS in the selected ion mode
(ie. molecular ions) as previously described (Wade el al., 1988). The GC/MS was calibrated and linearity was
determined by injection of standards at five concentrations. Peak identity was confirmed by molecular ion, the ratio
of the primary (base) ion to the secondary ion, and retention time. Instrument calibration was checked daily by
reinjection of the original calibration mixture. The calibration check was maintained to within * 10% on average for

2240

Figure 1. 1=-ation of Winter
Quarters Bay and the svwa$e 3C
outfaU in McMurdo Sound,
Antarctica.
"Imp-

ntor Luk" Boy

0 50 100 200

Boy of "ts

McMurdo Sound

Figure 2. 'out ons of control sites in
McMurdo Sound, Antarctica.

TWO
McMurdo an&
Station cw.
emu

Ross ke Shelf
NOW I
VW

2241

all analytes of interest. Quality assurance for each set of sample included a system bland and a matrix spike which

were carried through the entire analytical scheme in a manner indentical to the claim samples.

PCBs and chlorinated pesticides were analyzed by fused-silica capillary column GC-ECD (Ni63) in spitless

mode. Capillary columns, 30 meters long x 0.25 mm i.d. with 0.25 pm DB-5 film thickness, were temperature-

programmed from 100 to 140 C at 5 C/min, from 140 to 250 C at 1.5 C/min, and from 250 to 300 C at 10 C/min

with 1 min hold time at the beginning of the program and before each program rate change. A hold time of 5 min

was used at the final temperature. Total run time was 94.33 min. Injector and detector temperatures were set at 275

and 325 C, respectively. Helium was used as the carrier gas. Nitrogen or argon/methane (95:5) were used as make-

up gases. The volume injected was 2 pl. The instruments were calibrated using authentic standards ar four different

conceptions to compensate for the non-linear response of the electron capture detector. Tetrachloro-m-xylene

(TCMX) was used as the GC internal standard to calculate the recoveries of the surrogates.

In Vitro Bioassay. H4IIE cells were grown as continous cell lines in -essential medium supplemented with

2.2 mg/ml tissure culture grade sodium bicarbonate, 5% fetal calf serum, and 10 ml/l antibiotic-antimycotic solution

(Sigma). Stock cultures were grown in 150-cm2 tissue culture flasks and incubated in a humidified mixture of 5%

CO2 and 95% air under atomspheric pressure. For enzyme assays, approximately 1 x 10^6 cells in 2 ml media/well

were passaged to 6-well plates. Solutions of the clam extracts dissolved in dimethyl sulfoxide (DMSO) were added

to the culture plates so that the final concentration of DMSO in the medium was < 0.25%. Cells were also treated

with DMSO (solvent control) and different concentrations of TCDD to determine maximal induction activity. Cells

were harvested by manual scraping from culture plates, centrifgued at 1000 g for 6 min at 4 C, and resuspended

in 100 pl of Tris-sucrose buffer (38 mM Tris-HCl, 0.2 M sucrose, pH 8.0) Alquots (50pl) of the cell suspension

were incubated with 1.15 ml cofactor solution (containing 1 mg bovine serum albumin, 0.1 mg NADH, 01. mg

NADPH, and 1.5 mg MgSO4 in 0.1 M HEPES buffer, pH 7.5) in a 37 C water bath for 2 min. The reaction was

started by adding 50 pl. ethoxyresofufin solution (1 mg/40 ml) for a 6-min incubation and stopped by adding 2.5 ml

methanol. Samples were centrifuged at 1000 g for 10 min. The supernatant was used for fluorescence measurement

at an excitation wavelength fo 550 nm, and an emission wavelength of 585 nm (Pohl and Fouts, 1980). Samples

were run in triplicate and the results are expressed a means SD.

RESULTS AND DISCUSSION

Studies have shown that a strong organic contaminant gradient exists within Winter Quarters Bay that has

been attributed to human activities associated with McMurdo Station (Risebrough et al. 1990; Lenihan et al., 1990;

Lenihan, 1992). Contamination has been linked to a dump site, active recent years; fuel storage tanks; shipping

and construction activites, and station runoff. Additionally, the only sewer outfall for McMurdo Station is located

at the mouth of Winter Quarters Bay where raw sewage is discharged. High concentrations of aromatic

hydrocarbons and organochlorines were measured in sediments near the back of Winter Quarters Bay and decreased

2242

with distance towards the mouth of the bay and with distance from the bay. Within Winter Quarters Bay, total
purgeable hydrocarbons ranged from non-detectable to 4500 pg/g and total estimated PCBs ranged from 110 to 1400
ng/g (Risebrough et al., 1990; Lenihan et al., 1990). In contrast, at control locations, no purgeable hydrocarbons
were detected and estimated total PCB levels varied from < 0.01 to 0.8 ng/g. The concentrations of aromatic
hydrocarbons and PCBs measured in Writer Quarters Bay sediments are considered high with respect to values
reported for contaminated temperate locations and significant charnges in the benthic community have been correlated
with the contaminant gradient (Lenihan el al., 1990; Lenihan, 1992).
Tqhe results in Table I summarize the quantitative analyses of organochlorine pesticides, total PCBs and PAHs
in clam extracts from McMurdo Sound, Antarctica. The range of total hexachlorocyclohexanes (HCHs), chlordanes
and DDT and related compounds varied from nondetectable to 2.83, non-detectable to 2.27, and 1.97 to 9.61 ng/g,
respectively. Tissue chlordane and DDT levels were significantly higher in clams collected at sites in and near Winter
Quarters Bay than at control locations. The highest levels of PCBs were measured (x=409 = 21 ng/g) in extracts
from clam samples 7, 8, 9 and 10 which were collected from Winter Quarters Bay and the sewage outfall (Fig. 1).
The PCB levels were significantly lower in samples collected al control locations in McMurdo Sound (Fig. 2). Total
PAH levels in the clam extracts were above detection limits only from locations 2 and 10 and were not significantly
different for control and contaminated sites.
The results in Table 2 summarize the induction of EROD activity in rat hepatoma H4IIE cells by aliquots of
the clam extracts. Initial induction studies utilized 2 ul aliquots (run #1q) for the induciion studies and the results
showed induced EROD activity in samples 6 through 9. In run #2, 5 pl aliquots, were used and higher induced
enzyme activities were observed in samples 6 through 9 whereas in samples I through 5 and 10, only low induction
was detected. Dose-response induction by the extracts was not possible due to limited availability of the extracts.
Sample 8A was a duplicate of 8 and there were no significant differences between the induced EROD activities in
these samples (for run #2), thus confirming the reproducibility of the induction bioassay (Tillitt et al., 1991b).
TCDD-induced EROD activity was used as a positive control and I nM TCDD (0.644 ng/plate) was utilized as a
100% maximal induced response. Since the dose-response curve for induction of EROD activity by TCDD was
nearly linear from 0 to 1 nM, the TCDD or toxic equivalents (TEQ) could be determine for the various extracts (see
Table 2).
Previous studies have demonstrated that both > 4-ring PAHs and several PCB congeners induce EROD
activity in rat hepatoma H4IIE cells (Bradlaw and Casterline, 1979, Trotter et al., 1982, Tillitt et al. 1991b; Sawyer
and Safe, 1982; Sawyer et al, 1984; Piskorska-Phswzynska et al., 1986; Kamps and Safe, 1987); however,
congener-specific chromatographic analysis of the "TCDD-like" coplanar and monoortho coplanar PCBs was not
obtained in this study and TEQs could not be calculated. However, there was a linear correlation between total PCB
levels and induction-derived TEQs (Fig. 3. r2 = 0.95). The other organochlorine compounds present in high
concentration in the extracts (Table 1) are not inducers of EROD activity. Thus, the high linear correlation between

Table 1. Organochlorine and PAH concentrations (ngig dry we' in Ldlernuld ellipfica extracts fforn McMurdo Sound, Antarc-tica.4

- Cinder Cones Bernaccm Bay Winter Quarters Ha_ Bay of Sails
Sarryle No. 1 -2 3 4 5 6 7 9 9 10
allCH ND ND ND ND 2.09 M ND 2.19 1.96 2.25 ND
HCB 0.40 0.27 0.41 0.34 0.31 0.43 0.34 4,75 9.43 0.32
aNCH ND ND ND ND ND ND ND ND ND ND
UHCH NO ND ND 0.38 0.37 0.61 0.64 0.43 0.41 0.42
O-HCH ND ND ND ND ND ND ND ND ND ND
Reptachlor ND ND ND ND 0.10 ND ND ND ND 0.03
Hepte-epoxide ND ND ND ND ND ND ND ND ND ND
Oxychlordane ND ND ND ND ND ND ND ND ND ND
11-Chlordane ND ND ND ND ND ND ND ND ND ND
DChlordane ND 0.36 0.25 ND ND ND ND 0.39 ND ND
tram-Nonectilor ND 0.24 0.20 0.19 0.31 ND ND ND 0.14 0.12
cis-Nonschlor ND ND ND 0.24 ND 1.53 1.61 1.88 1.75 ND
Aldrin 2.74 2.15 2.66 1.83 1.91 ND 1.66 1.64 1.88 ND
Dieldrin 0.77 0.71 0.79 0.66 ND ND ND ND ND 0.89 ND
Endrin ND 1.03 1.07 ND 0.41 ND ND 0.32 ND ND
Mitex ND ND ND ND ND ND ND ND HD ND
2.4'-DDE (O,P'-DDE) ND ND ND ND ND ND ND ND ND ND
4.4'-DDE (P,P'-DDE) O.S7 1.11 0.94 0.61 1.62 1.63 1.72 1.69 1.67 0.17
2.2'-DDD (O.P.DDD) ND ND 0.19 ND ND 0.40 0.34 0.36 0.24 ND
4.4'-DDD (P.P'-DDD) ND ND ND 0.80 0.36 .1.75 1.99 1.83 1.78 ND
2.4'-DDT (O.P'-DDT) 0.65 0.42 0.85 1.59 2.11 3.19 3.03 3.65 3DO 1.09
4,4'-DDT (P.P'-DDT) 0.67 0.86 0.92 1.11 1.11 1.91 1.90 2.07 1.94 0.60

Total HCI-Is ND ND ND 0.38 2.46 0.01 2.83 2.39 2.66 0.42
Total c1dordanes ND 0.59 0.45 0.43 0.41 1.53 1.61 2.27 1.98 0.15
Total DDrs 1.89 2.39 2.98 4.10 5.22 9.77 8.88 9.61 9.64 1.97
Total PCas 22.2 19.2 17.5 19.0 19.1 383 414 433 404 5.1

Total PAHs 74.61 155.9 69.2 J 32.6 J 56.2 J 145.3 J 148.3 J 159.51 177.0 67.3 J
Total PAI-Is (it 4-rinp) 16.7 J 8.9 1 12.0 J 5.0 j 6.6 J 1.63 21.2 J 31.7 J 77.3 J 7.1 J

Tin analysis ordarn extracts for organochlorinecompounds arA PAH9 was carried out as desc:ribed in the Materials and Mediods section; ND - non-detectable.
J Below method detection limit. Data reported as ng/g dry weight. Samples 1.2 and 3 were collected at Cinder Cone; samples 4 wA 5 were collected in Bernache Bay; samples 6 srW 7
were collected near sewage outfall-, samples 8 and 9 were collected in Winter Quarters Bay; and sample 10 was collected in Bay of Sails.
t'j

Table 2. Induction Of EROD actrvity in rat hepatorna H41M cells by Laternula elliplica 0dracts from McMurdo Sound, Antarctica.8

Run #1 Run #2

EROD Activity TEQ EROD Activity TEQ
Sample Number site (pmol/min/mgYe (ng)/g@ (Pm0Vmin1mgY0 (n9Y9

I Cinder Cones NDc ND ND ND

2 Cinder Cones ND ND 69.2 * 39.9 0.06 * 0.04

3 Cin(ler Cones ND ND 319.57*47.9 0.23:k 0.04

4 Bernache Bay ND ND 266.0*45.4 0.21.+0.04

5 Bernache Bay ND ND 191.9*44.6 0. 16A 0.04

6 Sewage outfall 735.7:k 107.1 0-98+0.15 1940.0 � 109.6 1.47 * 0.09
7 Sewage outfall 694.3*77.2 033:E0.10 1447.6137.7 1.16*0.03
8 Winter Quarters Bay 1329.1 + 60.6d 1.79 + 0.08d 2079.0 + 167.7d 1.66:k 0. 13d

9 Winter Quarters Bay 741.6:1213.9 0-99k 0.28 1558.5:k 32.9 1.241:0.03
10 Bay of Sails ND ND 201.4 � 45.8 0.16*0.04

I I Blank NDO ND ND ND
RA Winter Quarters Bay 960.6:k 186.2d 1.29 * 0.25d 1755.6 � 347.5d 1.40 + 0.29d

The dam C*wb were dissolved in 50ml DMSO and either 2 or 5 0 aliquots (nin I or 2) were used in the hxluction bioassay as described in the Materials wd Klethods.
The results expressed as means + SD for separate determinations for each .,le,
b The results are eXpreSSed as the rate of ethoXyreSorUf,
in Mdabol, (p I/ Mg)/g of
ND - non-&ftctable. zed mo mint dry tract.
d A replicate of sample 8; no signilicWt diffamee (p < 0.05) bdvvm the for
0 A sample blank. results the two separate determination in run #2,
r TEQ (ng)/g = 0.644 ng x (ER0D..0AR0DLrCDD) x dilution factor/dry tissue weight (g).

2245

Figure 3. Correlation between induced EROD activity versus PCB ( ) levels
in Laternula elliptica extracts. The induction bioassay and results are derived from
data in Table 2 and the analytical data are summarized in Table 1.

PCB levels versus induced EROD activity or TEQs (Fig. 3) coupled with the high concentrations of PCBs rel
to the 2 4-ring PAHs (Table 1) indicate that the PCBs are the major P4501A1 inducers in the clam extracts.
These data demonstrate the the EROD induction on clam extracts from the Antarctic can be utili
to detect uptake of PCBs from contaminated sediments in Winter Quarters Bay as evidenced by the correla
between total PCB levels versus TEQs (or induced activity). Ongoing studies in our laboratories are investiga
organochlorine pollutants and PAHs.

ACKNOWLEDGEMENTS

Samples were collected by John Oliver and coworkers. The financial assistance of the National Institut
Health (P42-ES04917), the Texas Agricultural Experiment Station, the National Science Foundation Polar Prog
(OPP-9022346) and the National Oceanographic and Atmospheric Status and Trends Contract (50-D6NC-5-00
is gratefully acknowledged. S. Safe is a Burroughs Wellcome Toxicology Scholar.

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