The use of the oyster Saccostrea glomerata as a biomonitor of

trace metal contamination: intra-sample, local scale and temporal

variability and its implications for biomonitoring

Wayne A. Robinson,ab William A. Maher,*a Frank Krikowa,a John A. Nellc and

Rosalind Handc

aEcochemistry Laboratory University of Canberra, University Drive 2601 ACT Australia.E-mail: Bill.Maher@canberra.edu.au; Fax: þ61262015305; Tel: þ61262012531

b Faculty of Science, University of Sunshine Coast, Maroochydore DC 4558 QLD AustraliacNSW Department of Primary Industries, Port Stephens Fisheries Centre, Taylors Beach 2316,NSW, Australia

Received 1st October 2004, Accepted 24th December 2004First published as an Advance Article on the web 9th February 2005

Cu, Cd, Zn, Pb and Se concentrations were measured in the bivalve mollusc Saccostrea glomerata (Iredaleand Roughly) from two uncontaminated locations, Clyde River Estuary, Batemans Bay and Moona MoonaCreek, Jervis Bay, to determine natural variability of metals associated with mass, gender, age, tissue typeand site within location. Trace metals were also measured in the Clyde River Estuary over an 11 year periodand in five other NSW estuaries (Hastings River, Hunter River, Georges River, Tillgerry Creek and LakePambula) over a 13-month period to determine temporal variability and if diploid and triploid oystersaccumulate trace metals differently. There were few significant relationships between trace metalconcentrations and mass and no significant differences in trace metal concentrations between female andmale oysters. Younger oysters (1.3 years) had significantly higher copper concentrations and higher tracemetal variability than mature oysters (3 years). Different tissues have different trace metal concentrationswith muscle tissues having lower concentrations. Considerable inherent variability occurs in oyster cohorts.Analysing specific tissues did not reduce variability of trace metal concentrations. Comparison of trace metalconcentrations at two sites within the Clyde Estuary showed a significant difference in zinc concentrations.Cu, Cd, Zn and Se concentrations were generally higher and less variable in triploids than diploids. Pb hada variable pattern of accumulation with no consistent elevation in diploids or triploids. Inter annualvariability of trace metal concentrations was considerable and trace metal concentrations also fluctuatedthroughout an annual cycle with no clear seasonal trends. Measurement of trace metals at knowncontaminated locations showed that Saccostrea glomerata accumulates metals in response to contamination.Saccostrea glomerata meet most of the requirements to be a biomonitor of trace metal contamination as theyare abundant, sessile/sedentary, easy to identify, provide sufficient tissue for analysis, and accumulate tracemetals in response to contamination. However, as trace metal concentrations can vary with mass, age,estuary position, ploidy type and temporally, care must be taken to collect individual organisms of similarmass, age and ploidy type to minimise variability, and from similar consistent positions and times to allowfor seasonal changes in environmental conditions. Trace metal concentration variability is higher in younganimals, thus to reduce variability, older mature animals could be selected. However, with immature oystersthere are no complications because of the effects of spawning i.e. sudden loss of trace metals or body mass.

Introduction

Australia is an arid country with limited freshwater resources.Over 90% of the population live within 50 km of the coast andmost industries are located on coastal plains or adjacent toestuaries.1 Similarly to most industrialised countries, urbanand industrial waste enters into coastal waterways and hasresulted in the degradation of aquatic ecosystems.1,2 Tracemetals and metalloids are of particular concern as dischargesfrom coal-fired power stations, smelters, stormwater drainsand sewage enter estuaries, which are important commercialand recreational fisheries.3

Before threats to aquatic ecosystems can be evaluated,assessment of the contamination occurring needs to be under-taken. Measurements of contaminants can be made in water,sediment and biota.4,5 Measurements of contaminants inwaters and sediments only give a measure of potential expo-sure. As pointed out by Phillips,4 measurements of contami-nants in biota need to be made if the bioavailable fraction is tobe determined.

Bivalves molluscs such as oysters and mussels are consideredsuitable for this purpose as they are hardy, sessile and do notregulate contaminants.6 There are a number of extrinsic andintrinsic factors that affect trace metal accumulation andretention by aquatic organisms. Extrinsic factors include foodavailability, salinity, and temperature.7 Mass, size, age, genderand reproductive status are examples of intrinsic factors.5,7–12

Each of these factors, as well as the combined effect of allfactors, governs the rate of metal uptake, assimilation andaccumulation in organisms.The oyster Saccostrea glomerata (diploid and triploid

forms), which is cultivated commercially along the NSW andQueensland coast, has been used extensively in Australia as abiomonitor of trace metal contamination.13–20 However, littleattention has been given to understanding the intrinsic factorsthat affect trace metal accumulation and retention by theseoysters and designing sampling programs to minimise varia-bility due to these factors. This paper uses trace metal con-centration measurements of oysters, undertaken over the last20 years to examine the influence of mass, gender, age and

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within site variation on trace metal concentrations. Intrinsicvariations in tissue metal concentrations in oyster tissues areexamined to determine if the analysis of a specific tissue wouldreduce trace metal variation. Trace metal concentrations inoysters collected over 11 years were measured to determineinter annual variation. Temporal trace metal concentrations inoysters collected from eight estuaries over 13 months wereexamined to determine the combined influences of seasonalvariations in reproductive state, food supply and water tem-perature. Estuaries were selected a priori based on knowncontaminant inputs to determine if the oysters reflect environ-mental trace metal contamination and to establish backgroundconcentrations. Diploid and triploid oysters were also exam-ined to determine any differences in metal concentrations thatoccurred due to chromosomal differences. Natural variabilityis assessed in terms of the numbers of organisms required toobtain a reliable estimate of the mean trace metal concentra-tion and to detect specified changes in trace metal concent-rations.

Methods

Study areas

Clyde River Estuary, Batemans Bay. The Clyde River Estu-ary (150.150 E, 35.450 S) is an open estuary with a waterwayarea of 25.4 km2 and a catchment area of 2900 km2. Thecatchment is mainly forested with the major coastal town ofBatemans Bay located immediately south of the Clyde Riverentrance.

Moon-Moona Creek Jervis Bay. Moona Moona Creek (1501400 E; 35 1030 S) is an open estuary with a waterway area of 1.2km2 and a catchment area of 165 km2. The catchment isconsidered pristine, with a mixture of forests and hobby farmswith no industrial inputs.

Hastings River. The Hastings River (152 1550 E; 31 1280 S) isan open estuary with a waterway area of 17.3 km2 and acatchment area of 3595 km2. The catchment is a mixture offorests and agricultural lands. The large town of Port Mac-quarie is located south of the entrance and there are some canalsubdivisions.

Tilligerry River. The Tilligerry River (152 1030 E; 32 1420 S) isan open estuary with a waterway area of 9 km2 and acatchment area of 82 km2. The catchment is a mixture offorests and agricultural lands.

Hunter River. The Hunter River (151 1480 E; 32 1550 S) is anopen estuary with a waterway area of 26 km2 and a catchmentarea of 22 000 km2. The catchment is mainly agricultural land.The Hunter River is approximately 300 km long and enters thesea through the Port of Newcastle, a major coal exporting portcapable of loading very large vessels—50% of ships presentlyloaded exceed 100 000 t.

Georges River. The Georges River (151 1100 E; 34 1000 S) isan open estuary with a waterway area of 12 km2 and acatchment area of 800 km2. It runs through the southernSydney metropolitan area and receives industrial and urbancontaminants (sediments, sewage and nutrients) from stormwater inputs and runoff from road surfaces.14

Lake Pambula. Lake Pambula (149 1550 E; 36 1550 S) is anopen estuary with a waterway area of 2.9 km2 and a catchmentarea of 275 km2 located approximately 5 km south of the townof Pambula. The catchment is mainly forested land.

Sample design and collection

Effect of mass and gender. Oysters were collected fromMoona Moona Creek Jervis Bay, Clyde River Estuary Bate-man Bay and Georges River Estuary for the mass and gendervariation studies.A single cohort of 100 diploid oysters of similar age,

collected from Moona Moona Creek in May 1994, was usedto establish whether mass and gender influence trace metalconcentrations. Oysters were collected prior to spawning froma single cohort. The gender of oysters was determined byexamination of the gonads using a microscope. Twenty diploidoysters of similar age collected in April–May from the ClydeRiver Estuary over 11 years from a single site and a singlecollection of thirty diploid oysters of similar age taken from thecontaminated Georges River Estuary during 1987 were alsoused to confirm the concentration–mass relationships found inthe Jervis Bay oysters.

Age. A single collection of forty-eight diploid oysters madefrom the Clyde River Estuary in May 1995 was used todetermine the influence of age on trace metal concentrations.Two age cohorts were collected, 1.3 years old and 3 years old,and oysters of similar mass in each cohort analysed. Age wasdetermined from oyster growth records from the lease that theoysters were obtained.

Intrinsic variation in tissue metal concentrations. A singlecohort of 30 diploid oysters of similar age, collected fromClyde River Estuary in May 1995, was used to measure thevariability of trace metals in four easily identifiable oystertissues (Gills, abductor muscle, visceral mass and mantle).A single cohort of 100 diploid oysters of similar age,

collected from Moona Moona Creek Jervis Bay, in May1994 were separated into gonad and non-gonad tissues todetermine if variation of trace metals in one tissue werereflected in the other and if there was an advantage in dissect-ing oysters into germinal and somatic tissues.

Local, regional and temporal scale variation of metal

concentrations

Local scale. Twenty diploid oysters collected from two siteslocated in the Clyde River estuary in May 1985 were used todetermine local scale variability in trace metal concentrations.Oysters were sampled randomly within the same age cohortwithin each site

Regional and temporal scale

Clyde River Estuary. Twenty diploid oysters of similar ageand mass were collected in April–May in each of eleven yearsfrom a single site to determine inter annual variation in tracemetal concentrations. Only animals that were less than oneyear old were analysed.

New South Wales Estuaries. Twenty-four diploid and tri-ploid oysters from cohorts identical in age and of similar mass/size, collected from commercial leases in five estuaries (Hast-ings River, Tillgerry River, Hunter River, Georges River andLake Pambula) each month between December 1995 andDecember 1996, were used to determine annual temporalvariability in trace metal concentrations. Estuaries were se-lected on a priori knowledge of contamination status. Datafrom this study were also used to establish backgroundconcentration ranges of oysters.

Measurement of trace metals

Oysters were placed in an Esky filled with clean seawater thatwas fitted with an aerator and left to depurate for 48 h. Oysters

J . E n v i r o n . M o n i t . , 2 0 0 5 , 7 , 2 0 8 – 2 2 3 2 0 9

were opened by slicing the adductor muscle, tissues removedand drained on adsorbent tissue. Further dissections, whencarried out, were with the aid of a dissecting microscope.Oysters were analysed individually except for the NSW Estu-ary temporal study in which four replicates of six oystersfrom each site at each time were homogenised for analysis.All tissues were stored at �20 1C prior to freeze drying at�80 1C for 48 h.

Samples were digested in nitric acid using either a hot waterbath21 or a low volume microwave digestion technique22,23 Forthe hot water bath digestion technique, approximately 0.5 g offreeze dried tissue was weighed into a 50 ml Pyrex test tube,3 ml of concentrated nitric acid (Aristar, BDH) added andheated at 100 1C until brown fumes had subsided. For themicrowave digestion technique, approximately 0.1 g of freezedried tissue was weighed into a 7 ml Teflon polytetrafluroace-tate (PFA) closed digestion vessel and 1 ml of concentratednitric acid (Aristar, BDH) added. Each 7 ml teflon vessel wasthen capped and tightened to 2.3 Nm, and placed into larger120 ml screw top pressure vessel tightened to 16.3 Nm. Amodel MDS-81 (CEM, Indian Trail, NC, USA) microwaveoven was used for all digestions, with the microwave timeprocedure consisting of three steps: 2 min at 600 W, 2 min at0 W; and 45 min at 450 W. After digestion, extracts wereallowed to cool to room temperature for 20 min, and thendiluted to 10 ml with de-ionised water. Digests were stored inlabelled polyethylene vials in a cool room (B0–5 1C) untilmetal analysis.

In the earlier part of the study, digests were analysed for Cuand Zn, using a Perkin Elmer flame atomic absorption spectro-meter.21 Se, Cd and Pb were analysed using a Perkin–Elmer5100 PC Electrothermal Atomic Absorption Spectrometerequipped with Zeeman background correction, HGA-600 gra-phite furnace and AS-60 auto-sampler. A palladium andmagnesium matrix modifier was used for Se.24 Cd and Pb wereanalysed using an ammonium dihydrogen phosphate andmagnesium nitrate matrix modifier (0.2 mg and 0.015 mg,respectively per 10 ml injection). The temporal trace metal datawas obtained using a Perkin Elmer Elan 6000 InductiveCoupled Plasma Mass Spectrometer fitted with a cross flownebuliser and a Ryton spray chamber.25

The accuracy of analyses was assessed using NIST 1566 and1566a Oyster tissue, which were routinely run in each samplebatch. Recoveries of all trace elements were in agreement withthe certified values (Table 1). Duplicates and blanks were alsoincluded in each sample batch. Evaluation of methods includedspiked recoveries of trace metals.25

Statistical analysis

Effect of mass and gender on trace metal concentrations.

Analysis of variance (ANOVA) was used to test for the effectof mass and differences in trace metal concentrations betweenmale and female oysters. After partitioning out the effects ofmass, significant differences in trace metal concentrationsbetween males and females were tested and significant effectsfollowed up using Scheffe’s correction for multiple compari-

sons. There were only occasional observations that were belowdetection limits and these values were set to half the detectionlimit. The ANOVA assumptions of normality and homosce-dasticity were verified using standard residual analysis. Insubsequent statistical analyses we reduced the variability frommass by; in the field collecting oysters that were generally ofsimilar size and from the same cohort, and; statistically byinserting dry mass of the animal in the ANOVA model (as acovariate) first.

Effect of age on trace metal concentrations. A two-sample Ftest was used to compare variability in each trace metal’sconcentration between the two Clyde River age group cohorts.Where the variances were not different (a ¼ 0.05), a student’s ttest was then used to compare the mean trace metal concentra-tion between the two groups. Alternatively, when the varianceswere found to differ between groups, Satterthwaites approx-imate t test26 was used to test for significant differences in meanconcentrations.

Tissue distribution of trace metals

Clyde River Estuary. A repeated measures ANOVA wasused and the effect of dry mass partitioned out as a covariatebefore differences between tissue type were analysed. In eachanalysis, significant effects were followed up using Scheffe’scorrection for multiple comparisons. All observations thatwere below detection limits or the occasional sample that waslost due to explosion during digestion were omitted from theanalyses to prevent skewing of the data. The ANOVA assump-tions of normality and homoscedasticity were verified usingresidual analysis.

Moona Moona Creek. A three factor partially hierarchaldesign with repeated measures was used for analysis. Gender isa fixed factor, oysters are a random factor nested withingender, and tissue type (Gonad or non-gonadal) is a repeatedmeasure fixed factor. Hence individual oysters were treated as arandom effect nested within gender and the variability betweenindividual oysters partitioned out before further analyses.After partitioning out the effects of dry mass and betweenoyster variation, significant differences in trace metal concen-trations between tissue type and sex and associated interactionswere tested. Significant effects were followed up using Scheffe’scorrection for type I error.Pearson’s Correlation analysis was used to test for relation-

ships between the concentrations of the trace metals betweeneach of the different tissues and for each tissue against thewhole animal concentration. Because of the large number oftests on the same data, a type I error was expected hence aBonferroni correction factor was applied and the family wiseerror rate held at 0.05.Distributions of trace metal concentrations in the Moona

Moona Creek oysters within each tissue type are displayedvisually with relative frequency polygons and analysed statis-tically using shape statistics for skewness, kurtosis and normal-ity. The Shapiro–Wilks test for normality is not powerful untilthe sample sizes are large, however, with samples of 100 as for

Table 1 Recovery of trace metals from certified reference materials (Mean � standard deviation)

Method

Cu/mg g�1 Cd/mg g�1 Zn/mg g�1 Pb/mg g�1 Se/mg g�1

Measured Certified Measured Certified Measured Certified Measured Certified Measured Certified

aTingii and Maher21 62 � 4 63.0 � 3.5 3.9 � 0.2 3.5 � 0.4 842 � 80 852 � 14 ndd nd nd ndbBaldwin et al.22 65 � 5 66.3 � 4.3 4.1 � 0.2 4.15 � 0.38 847 � 30 830 � 57 0.41 � 0.1 0.371 � 0.014 2.1 � 0.2 2.21 � 0.24cMaher et al.25 65 � 5 66.3 � 4.3 4.3 � 0.5 4.15 � 0.38 840 � 40 830 � 57 0.36 � 0.06 0.371 � 0.014 2.3 � 0.2 2.21 � 0.24

a NIST CRM 1566 Oyster tissue (n ¼ 50). b NIST CRM 1566a Oyster tissue (n ¼ 56). c NIST CRM 1566a Oyster tissue (n ¼ 70). d nd ¼ Not

determined.

2 1 0 J . E n v i r o n . M o n i t . , 2 0 0 5 , 7 , 2 0 8 – 2 2 3

the Moona Moona Creek data set the test was deemed able todetect non-normal curves.

Local scale spatial variability of trace metal concentrations.

The same analysis used for determining the effect of age wasused to compare trace metal concentrations between the twoClyde River Estuary sites. Furthermore, variance componentanalysis was used to allocate the proportion of variabilitybetween and within sites. The Restricted Maximum Likelihoodmethod (REML) was used to calculate variance components asit is recommended if there is the possibility of skewed data andit sets unrealistic (i.e. negative) variance estimates to zero.27

Temporal variation, large scale spatial variation, and

ploidy variation of trace metal concentrations

Clyde River Estuary Annual data. The variability in Cu, Cdand Zn concentrations were compared within and betweensampling months using REML variance components analysis.Spearman’s rank correlation analysis was used to determinewhether the mean annual concentrations of the three metalsvaried together or independently.

New South Wales Estuary data. A factorial ANOVA (ploidy,estuary and month) was used to test for differences in tracemetal concentrations between months, estuaries and ploidies.When there was a significant three way interaction involvingestuaries a separate two factor ANOVA was performed withineach estuary. Because of the resulting large number of tests,type I error was highly likely and the family wise error ratesubsequently set at 0.01 rather than the traditional 0.05 andwhen significant effects were found, multiple comparisons weremade using Scheffe’s correction for type 1 error. When a tracemetal concentration was below detection limits, the value wasset at half the detection limit. The ANOVA assumptions ofnormality and homoscedasticity were verified using residualanalysis. The site dates for the NSW estuary data were alsoplotted in trace metal ordination space using varimax rotationof axes after a principal components analysis. Only axes witheigenvalues 41 were included in the rotation.

Comparisons of sources of variability

Estimation of reliable mean trace metal concentrations. TheMoona Moona Creek oyster data set was used to assess thenumbers of organisms required to obtain a reliable estimate ofthe mean trace metal concentrations. For each trace metal set,we took 1000 random samples by bootstrapping (with replace-ment) from the original 100 oysters at each sample size from 1to 15 then 20, 25, 35 and 40. The 25th and 975th values of theordered means (2.5th and 97.5th percentiles) within eachsample size were then calculated giving the 95% confidenceinterval for that sample size.

Sample size to detect specified changes. The coefficient ofvariation was calculated and tabulated for each trace metal ineach of the studies for each level of each factor that had beentested. The minimum sample size required to detect changes inmean trace metal concentration of 10, 20, 30, 50 and 100% ofthe mean was then calculated using the formula,n ¼ s2

d2ðta;n þ tbð1Þ;nÞ2,26 where s is the sample standard deviation

from this study, d is the size of the difference to be detected, a isthe probability of type I error (held at 0.05 using a two-tailedtest), b is the probability of type II error (held at 0.20) and n isthe degrees of freedom. The degrees of freedom is dependenton n and cannot be determined prior to the calculation, hencethe calculations were performed iteratively as described inZar.26 For the first iteration the sample size was set at n ¼20, three iterations were performed and the smallest possiblesample size was manually restricted to n ¼ 2.

Results and discussion

Effect of mass

There were no significant relationships between the dry mass ofoysters and trace metal concentration in the single largecollection of oysters of similar age (B1 year) from MoonaMoona creek (Table 2, Fig. 1) and in older oysters (B3 years)collected from the Clyde river estuary (Fig. 2). Oyster cohortsfrom contaminated locations also did not show any significantrelationships between dry mass and trace metal concentrations(e.g. Georges River, r2 between 0.10–0.36). However, analysisof the oysters data set, collected over 11 years from the ClydeRiver Estuary, showed that significant relationships betweentrace metal concentrations and mass can sometimes occur(Table 3), even when oysters are from cohorts of a similar sizeand age. A trend of decreasing trace metal concentration withincreasing mass where growth dilutes the trace metal contenthas been widely reported in the literature8,12,28–36 but was notevident in our study. This trend is primarily attributed toincreased metabolic rates in smaller organisms.8,34 Thomson32

questioned if this trend was a function of measuring wet massrather than dry mass. He found that wet mass trace metalconcentrations decreased with an increase in wet mass whiledry mass trace metal concentrations did not decrease with anincrease in dry mass. Wet mass to dry mass ratios maysignificantly alter with age37 or shell size.38 Some studies havereported that trace metal concentrations did not vary, or arepositively correlated with organism mass.9,30,38–42 The relation-ships of trace metal concentration and mass reported in theliterature appear to be organism specific and vary with timeand geographic location.8 However, most of these reportedrelationships are confounded by age differences that have notbeen accounted for.When trace element content is directly related to body mass

(as indicated by no concentration dependency in this study)binding of trace metals into specific bioactive compounds isplaying a role. Oyster blood amebocytes are known to seques-ter Cu and Zn43 and oysters are expected to equilibrate withthese and other trace metals in their environment. This is incontrast with mussels such as Mytilus edulis, which sequestertrace metals into inert lipofuschin containing granules43 andthus increase their trace metal concentrations with mass.Although trace metal concentrations do not appear to vary

greatly with mass, minimisation of mass differences is advisa-

Table 2 Summary of ANOVA for differences in Moona Moona

Creek oyster trace metal concentrations between male and female

oysters after the effect of variability associated with the size (dry mass)

of the oyster is partitioned out

Metal Source df F P 4 F

Cu Dry mass 1 0.01 0.904

Gender 1 0.01 0.942

Dry mass * gender 1 0.36 0.549

Cd Dry mass 1 0.08 0.775

Gender 1 0.05 0.831

Dry mass * gender 1 1.47 0.228

Zn Dry mass 1 1.48 0.227

Gender 1 0 0.944

Dry mass * gender 1 0.14 0.706

Pb Dry mass 1 0.28 0.598

Gender 1 0.57 0.454

Dry mass * gender 1 0.08 0.783

Se Dry mass 1 0.66 0.418

Gender 1 0.01 0.940

Dry mass * gender 1 0.04 0.838

J . E n v i r o n . M o n i t . , 2 0 0 5 , 7 , 2 0 8 – 2 2 3 2 1 1

ble. Normally a minimum of three orders of magnitudedifference in mass is required before a significant relationshipbetween a trace metal concentration and mass is evident.44

Effect of gender

There was no difference in the trace metal concentrationsbetween the female and male oysters (Table 2, Table 4 and

Table 8), unlike for some bivalve species that have shown tracemetal concentrations to be influenced by gender.9,10,12,45–48

Effect of age

Although the mass range of oysters selected is small there maybe a wide range of ages. Age dependency of trace metalconcentrations in bivalves13,41,49 has been reported. Olderanimals have had longer exposure and may accumulate highertrace metal concentrations. Se, Zn and Cd mean concentra-tions measured in oysters showed no difference between thetwo age cohorts (Fig. 2). Copper concentrations were signifi-cantly higher (t ¼ 4.82, df ¼ 45, p o 0.0001) in the youngeranimals (Fig. 2) but differences in mean concentrations werenot great (112 and 67 mg g�1 for 1.3 and 3 year old oysters,

Fig. 1 Relationship of trace metal concentrations and mass in MoonaMoona Creek oysters.

Fig. 2 Trace metal concentrations in two age cohorts of Clyde RiverEstuary oysters.

Table 3 Relationship of trace metal concentrations and mass in

oysters collected from the Clyde River Estuary

Year 1985 1987 1989 1990 1991 1992 1993 1994 1995

Cu r2 0.53 0.05 0.44 0.12 0.63 0.14 0.08 0.33 0.34

p 0.1127 0.8583 0.0325* 0.6383 0.0689 0.4936 0.7614 0.2451 0.0795

Cd r2 0.37 0.34 0.20 0.69 0.11 0.21 0.03 0.30 0.22

p 0.2967 0.2217 0.342 0.001* 0.7805 0.296 0.9119 0.3055 0.2728

Zn r2 0.12 0.40 0.53 0.15 0.61 0.26 0.14 0.37 0.17

p 0.7351 0.1392 0.0075* 0.5416 0.0796 0.1961 0.5828 0.1963 0.3922

n ¼ 20. * ¼ significant p o 0.05

2 1 2 J . E n v i r o n . M o n i t . , 2 0 0 5 , 7 , 2 0 8 – 2 2 3

respectively). Mackay et al.13 reported that Cu, Zn and Cdconcentrations in S. glomerata decrease with age. However,their analyses have not taken out the effect of mass and onlyreport regression relationships between concentration and wetweight which describe some of the variation (Zn: r ¼ �0.26;Cu: r ¼ �0.58; Cd: r ¼ �0.58). Visual inspection of their datareveals little difference in trace metal concentrations betweenage cohorts.

There was no difference in the variability of Cd (F ¼ 1.68,df ¼ 26, 19, p ¼ 0.12) or Se (F ¼ 1.25, df ¼ 18, 19, p ¼ 0.31)concentrations of the 1.3 and 3 year old animals collected fromthe Clyde River (Fig. 2). The variability of concentrations ofCu (F ¼ 3.82, df ¼ 26, 19, p o 0.005) and Zn (F ¼ 3.1, df ¼ 26,19, p o 0.01) were significantly higher in the younger animals(Fig. 2). The findings for Cu and Zn are consistent with otherstudies that show that younger animals generally have morevariable trace metal concentrations. Again this is often attrib-uted to highly variable metabolic rates in younger, non-matureanimals.8,34 Oyster growth will depend on food availability andthe energy used by an oyster to survive. Differences in growthwill lead to differences in mass/size, and it is expected that evenanimals of similar age will probably have a spread of differentmasses and some variation in trace metal concentrations. Theseresults indicate that to reduce intrinsic variability in trace metal

concentrations, older mature animals could be selected. How-ever, with immature oysters there are no complications becauseof the effects of spawning i.e. sudden loss of trace metals orbody mass. The reproductive cycle does not contribute to thevariation in trace metal concentrations in non-mature oysters,as most of the energy will be allocated to growth rather thanreproduction.50

Intrinsic variation in tissue trace metal concentrations

Tissue distribution. There was a significant difference in theconcentrations of Cd, Cu, Se and Zn between the four differenttissue types of the Clyde River Estuary oysters (Table 4, Tables5 and 6). After correction for multiple comparisons (Scheffe’stest, family-wise error rate¼ 0.05) it was found that trace metalconcentrations in the adductor muscle were significantly lowerthan the other tissues for all trace metals (Fig. 3). Zn concen-trations were significantly higher in the mantle, and Se wassignificantly higher in the visceral mass (Table 5, Fig. 3). Theseresults are similar to other studies34,41,51,52 in that viscera, gillsand mantle tissues have higher Cu, Cd and Zn concentrationsthan muscle tissues. However, Martincic et al.53 andMcConch-ie et al.54 found significantly higher Cd concentrations inadductor muscle tissues, thus accumulation in tissues willultimately depend on the local source of trace metals. Thevisceral mass made up on average between 42% and 54% oftotal animal weight and subsequently whole animal trace metalconcentrations were strongly correlated with the concentrationin the visceral mass for all four metals analysed (Table 7).There was a significant difference in the concentration of Cd,

Se and Zn concentrations between the Moona Moona Creekgonad and non-gonad tissues (Table 5 and Table 8). Cd and Se

Table 4 Summary of ANOVA for differences in Moona Moona

Creek oyster trace metal concentrations between gender and tissue

type (gonad/visceral mass)

Metal Source df F Value Pr 4 F

Cu Dry mass 1 0.89 0.3489

Gender 1 0.58 0.4478

Tissue 1 1.03 0.3122

Gender * tissue 1 0.92 0.339

Cd Dry mass 1 0.28 0.5973

Gender 1 0.27 0.607

Tissue 1 433.97 0.0001

Gender * tissue 1 0.19 0.6667

Zn Dry mass 1 0.16 0.6871

Gender 1 0.28 0.5987

Tissue 1 47.14 0.0001

Gender * tissue 1 0.21 0.6484

Pb Dry mass 1 4.95 0.0287

Gender 1 0.17 0.6842

Tissue 1 1.98 0.1687

Gender * tissue 1 3.8 0.0598

Se Dry mass 1 0.5 0.4805

Gender 1 0.1 0.7584

Tissue 1 115.68 0.0001

Gender * tissue 1 0.23 0.6352

Table 5 Trace metal concentrations in oyster tissues

Location Tissue

Cu/mg g�1

dry mass

Cd/mg g�1

dry mass

Zn/mg g�1

dry mass

Pb/mg g�1

dry mass

Se/mg g�1

dry mass

Clyde River Estuary (n ¼ 26) Muscle 59 � 18 0.73 � 0.26 766 � 222 nmb 0.79 � 0.22

Mantle 237 � 61 1.7 � 0.5 3182 � 983 nmb 1.5 � 0.5

Visceral 193 � 56 1.6 � 0.6 2507 � 893 nmb 3.0 � 0.6

Gills 201 � 45 1.7 � 0.5 2351 � 640 nmb 2.0 � 0.5

Moona Moona Creek (n ¼ 100) Gonads 18 � 1 3.0 � 0.2 750 � 30 0.21 � 0.03 2.22 � 0.08

Non-gonads 18 � 1 nda 951 � 38 0.21 � 0.09 1.50 � 0.04

a nd ¼ not detectable. b nm ¼ not measured; � standard deviation.

Table 6 Summary of ANOVA for differences in Clyde River Estuary

oyster trace metal concentrations between four tissue types (muscle/

mantle/visceral mass/gills)

Metal Source DF F Value Pr 4 F

Cu Dry mass 1 0.02 0.8877

Dry mast * tissue 3 0.41 0.7487

Tissue 3 3.99 0.0101

Cd Dry mass 1 0.03 0.8651

Dry mass * tissue 3 0.83 0.4808

Tissue 3 4.03 0.0096

Zn Dry mass 1 0.3 0.5877

Dry mass * tissue 3 0.49 0.6874

Tissue 3 3.17 0.028

Se Dry mass 1 0.37 0.5461

Dry mast * tissue 3 1.26 0.2931

Tissue 3 7.3 0.0002

J . E n v i r o n . M o n i t . , 2 0 0 5 , 7 , 2 0 8 – 2 2 3 2 1 3

were significantly higher in the gonad tissues, but Zn was foundin significantly higher concentrations in the non-gonadal tis-sues (Table 5 and 8). Pb and Cu concentrations were notdifferent between tissue types (Table 5 and 8). Gender againhad no effect on the differences in trace metal concentrationsbetween tissue types for any of the trace metals analysed (Table8). Previous studies of Cu, Zn, Cd and Ni concentrations inbivalves such as Mytilus edulis, found higher concentrations inthe non-gonadal tissues than gonad tissues.47,55–57 WhereasMartinic et al.53 found high trace metal concentrations ingonad tissues. There was a positive correlation observedbetween Cu, Zn and Se whole animal body burdens (Pearsonsrcopper : zinc ¼ 0.85, rselenium : zinc ¼ 0.74, rcopper : selenium¼ 0.62,all p o 0.0001), indicating that the coaccumulation of some

trace metals may be a natural trace metal accumulationstrategy of oysters.There were positive correlations between trace metal con-

centrations in gonad, non-gonad and whole tissues (Cu: 0.79,0.85; Cd: 0.94, 0.46; Zn: 0.82, 0.90; Pb: 0.81, 0.75; Se: 0.88,0.59; p o 0.05, respectively), demonstrating that althoughmetal concentrations were different in the tissues, the propor-tion of trace metals in the tissues remained constant as thetissue mass increased. This indicates that there are no mechan-isms of trace metal exclusion from oyster tissues. Frew et al.41

also found that Cd concentrations in tissues remained rela-

Fig. 3 Frequency distribution of trace metal concentrations in four tissues of Clyde River Estuary oysters.

Table 7 Correlation coefficients for relationships of trace metal

concentrations in oysters from Clyde River Estuary between four

tissues and whole animal concentration. Significant relationships after

Bonferroni correction for multiple comparisons are notated by *p o0.05, **p o 0.005

Visceral Mantle Gill Whole

Cu Muscle �0.08 �0.34 0.31 �0.01Visceral 0.08 0.47 0.68*

Mantle �0.21 0.38

Gill 0.30

Cd Muscle 0.35 �0.01 �0.21 0.34

Visceral �0.05 �0.22 0.77**

Mantle �0.06 0.12

Gill �0.15

Zn Muscle 0.24 �0.19 0.40 0.27

Visceral 0.03 0.36 0.66*

Mantle �0.16 0.33

Gill 0.33

Se Muscle �0.21 0.18 0.01 0.11

Visceral �0.10 0.31 0.64*

Mantle 0.23 0.18

Gill 0.16

Table 8 Summary of ANOVA for differences in Moona Moona

Creek oyster trace metal concentrations between sex and tissue type

(gonad/non-gonad)

Metal Source Df F Value Pr 4 F

Cu Dry mass 1 0.89 0.3489

Gender 1 0.58 0.4478

Tissue 1 1.03 0.3122

Gender * tissue 1 0.92 0.339

Cd Dry mass 1 0.28 0.5973

Gender 1 0.27 0.607

Tissue 1 433.97 0.0001

Gender * tissue 1 0.19 0.6667

Zn Dry mass 1 0.16 0.6871

Gender 1 0.28 0.5987

Tissue 1 47.14 0.0001

Gender * tissue 1 0.23 0.6352

Pb Dry mass 1 4.95 0.0287

Gender 1 0.17 0.6842

Tissue 1 1.98 0.1687

Gender * tissue 1 3.8 0.0598

Se Dry mass 1 0.5 0.4805

Gender 1 0.1 0.7584

Tissue 1 115.68 0.0001

Gender * tissue 1 0.21 0.6484

2 1 4 J . E n v i r o n . M o n i t . , 2 0 0 5 , 7 , 2 0 8 – 2 2 3

tively constant with age, thus trace metal redistribution intissues must be fast. As oyster blood cells are known to berich in trace metals such as Cu and Zn,43,58,59 trace metalconcentrations measured in tissues at the time of samplingmaybe dependent on the presence of blood cells and tissuetrace metal concentrations will reflect this distribution. Thus as

individual tissue trace metal concentrations are correlated tototal tissue trace metal concentrations there is no advantage intaking an individual tissue for analysis. A major disadvantageof using only part of an oyster is that skilled staff are requiredto perform dissections and the amount of tissue available fordigestion is drastically reduced depending on the tissue.

Frequency distributions. Frequency distributions of tissuetrace metal concentrations, in oysters collected from the ClydeRiver Estuary (Fig. 3), showed slightly skewed distributions.Due to the low sample numbers, distribution statistics couldnot be calculated. The Moona Moona oysters (gonads, visceralmass and whole tissues) also displayed positive skewness(Table 9, Fig. 4). Some distributions appeared flatter than

Table 9 Shape statistics for distribution of Moona Moona Creek

oyster trace metal concentrations. Pr o W is the probability the

distribution is normal using the Shapiro–Wilks normality statistic W

Tissue Skewness Kurtosis Shapiro–Wilks W Pr o W

Gonads Cu 0.98 2.30 0.95 0.0007

Cd 0.72 1.14 0.97 0.0124

Zn 0.57 1.69 0.97 0.0123

Pb 0.98 0.18 0.83 o0.0001

Se 0.08 1.79 0.96 0.0033

Non-gonad Cu 0.89 1.52 0.94 0.0002

Cd 0.04 �0.87 0.98 0.0674

Zn 0.79 1.29 0.95 0.001

Pb 0.89 �0.34 0.83 o0.0001

Se 0.22 1.63 0.96 0.0063

Total Cu 0.44 0.25 0.97 0.0201

Cd 0.60 0.51 0.97 0.0228

Zn 0.55 1.86 0.97 0.0176

Pb 1.43 1.03 0.68 o0.0001

Se �0.70 3.22 0.93 o0.0001

Fig. 4 Frequency distribution of trace metal concentrations in gonadand non-gonad tissues of Moona Moona Creek oysters.

Fig. 5 Variation of trace metal concentration in Clyde River estuaryoysters over an eleven year period.

J . E n v i r o n . M o n i t . , 2 0 0 5 , 7 , 2 0 8 – 2 2 3 2 1 5

others but there was no apparent consistency in these trendsbetween tissues or metals. The Shapiro–Wilks test for normal-ity suggested all distributions were not from normal popula-tions although most probabilities for Cu, Cd, Zn and Seconcentration data were only marginally below 0.05. Pb con-centration data, however, was non-normal and had highlypositively skewed distributions.

Positive skewness of the distribution of tissue metal concen-trations of bivalve molluscs has been reported for populationsof molluscs from uncontaminated and contaminated loca-tions.37,57,60 Skewness seems to be a common factor in sampledistributions of natural populations and cannot be explainedon the basis of either mass, gender or habitat differences but isdue to natural variation between individuals. It has beensuggested that inherent variability may involve genetic differ-ences in uptake processes and or excretion rates and probablycan only be resolved at a molecular level.5,11 It is well docu-mented that variance increases as mean trace metal concentra-tions increase,61 thus variability should be greater atcontaminated sites.57

The shape analyses have important consequences for futurestudies and there are some principles that should be followed todeal with non-normalities. Statistical tests requiring the as-sumption of normality can be robust to slight deviations if thesample sizes are large and approximately equal.26 This is theprinciple that we have followed when applying these types oftests throughout this study. Furthermore we note that it isimperative that all analyses perform adequate post test exam-inations of residuals to determine when the normality andhomogeneity of variance assumptions are violated. We havefollowed this procedure as an essential component of ouranalyses and subsequently recommend that the Pb concentra-tion analyses using our data be treated with caution. Alter-

native steps that may be useful, depending on the study design,include data transformations and applying distribution freestatistical tests. With our large and approximately equal sam-ple sizes, the effects of skewed data on the power of the testswere minimal.

Local scale variation of trace metal concentrations

Zn concentrations in oysters were significantly different be-tween the two sites in the Clyde River Estuary (tzinc ¼ 4.07,df ¼ 38, po 0.0005) that were 0.5 km apart, but the differencesin mean concentrations were small (1161 � 239 mg g�1 cf. 1200� 300 mg g�1). There was no difference in the mean concentra-tions of Cu (47 � 14 mg g�1 and 41 � 8 mg g�1) or Cd (0.84 �0.91 mg g�1 and 0.9 � 0.6 mg g�1) between the sites (tcadmium ¼0.28, df ¼ 38, p ¼ 0.78, tcopper ¼ 1.56, df ¼ 38, p ¼ 0.13). Therewas a significant difference in the variability of Cu and Cdbetween the two Clyde River Estuary sites (Fcadmium ¼ 2.5,Fcopper ¼ 3.4, df ¼ 19, 19, p o 0.05). Both trace metalconcentrations were more variable at site 1. Zinc concentra-tions were equally variable at both sites (Fzinc ¼ 0.62, df ¼ 19,19, p ¼ 0.84). Ninety three percent of the variability in Cuconcentrations and 56% of variability in Zn concentrationswere because of within site variability. It should be noted thatthe within site variability also includes variability because ofinaccuracies in measurement. The remaining 7% and 44%,respectively could be attributed to between site variability. ForCd concentration, the effect of between site variability wasnegligible and 100% of variability was attributed to within sitevariability.The underlying cause(s) of the small but significant differ-

ence in Zn concentrations and trace metal variability areunknown, but probably reflect physiological differences. Therewere no obvious differences in food availability and food type,factors known to contribute to trace metal variability inmolluscs.62,63 When commercially grown oysters are used forbiomonitoring, users should be aware that oysters are oftenmoved within an estuary or between estuaries to maximisegrowth potential. The differences in trace metal concentrationsbetween the two Clyde river Estuary sites were small andprobably of no consequence. Thus differences in trace metalconcentrations between sites with similar environmental con-ditions due to natural causes are likely to be small. However,other studies13,14,17,39 have shown that differences of tracemetal concentrations within an estuary can be great. Differ-ences are probably due to point sources such as storm waterdrains and sewage inputs or natural factors such as salinitydifferences.4,32,64–67 Thus during the design of the samplingprogram particular attention will need to be given to theprevailing estuarine salinity regimes, potential trace metalinputs and to what information is to be obtained from the siteselected.

Temporal and regional scale variation of trace metal

concentrations

Clyde River Estuary annual data. Variation in Cu concentra-tion in oysters was equally partitioned between years (49%)and between oysters within years (51%). Zn and Cd concen-trations were relatively more stable between years (23 and14%, respectively, Fig. 5). Again the major source of tracemetal concentration variability is inherent variability withinoyster cohorts. The mean annual concentrations of Zn and Cdwere significantly correlated (r ¼ 0.65, n ¼ 10, po 0.05), whilstCu showed a large but non significant correlation with Cd (r ¼0.52, n ¼ 10, p ¼ 0.12) and no relationship with Zn (r ¼ 0.08,n ¼ 10, p ¼ 0.83). Others have shown trace metals concentra-tions in bivalve molluscs to be significantly corre-lated.9,14,40,45,68,69 Often this indicates a common source oftrace metals70 but as previously indicated coaccumulation of

Table 10 P values from ANOVAs to test between concentrations of

trace metals in diploid and triploid oysters sampled in thirteen

consecutive months in five New South Wales Estuaries

Element

River Effect

Georges

RiveresaHastings

River

Hunter

River

Lake

Pambula

Tilligery

Lake

Cu Date 0.0001 0.0001 0.0001 0.0001 0.0001

Ploidy 0.0001 0.0001 0.0001 0.0001 0.0001

Date *

ploidy

0.0369* 0.0001 0.0252* 0.0001 0.0289*

Cd Date 0.0001 0.0001 0.0001 0.0001 0.0863

Ploidy 0.0001 0.0001 0.0001 0.0001 0.4309

Date *

ploidy

0.0844 0.0001 0.0188* 0.0003 0.9992

Zn Date 0.0001 0.0001 0.0001 0.0001 0.0001

Ploidy 0.0001 0.0001 0.0001 0.0001 0.0001

Date *

ploidy

0.1034 0.0001 0.0003 0.0001 0.1515

Pbb Date 0.3305 0.0001 0.0035 0.0147* 0.1026

Ploidy 0.0348* 0.0001 0.0001 0.0005 0.8102

Date *

ploidy

0.3081 0.0001 0.0001 0.044* 0.9992

Seb Date 0.0007 0.0010 0.0001 0.0029 0.0200*

Ploidy 0.0001 0.0751 0.0001 0.8820 0.2028

Date *

ploidy

0.1452 0.5805 0.0423 0.2812 0.6591

* ¼ Not significant after correction for type I error.a Georges River

only sampled in 12 consecutive months. b Note selenium and lead

concentrations were often below the detectable limit resulting in

unbalanced sample sizes and violation of assumption of homogeneity

of variances. Hence, P values should be treated with caution.

2 1 6 J . E n v i r o n . M o n i t . , 2 0 0 5 , 7 , 2 0 8 – 2 2 3

some trace metals may be a natural trace metal accumulationstrategy of oysters.

New South Wales Estuary data. For all trace metals, therewere differences in concentrations between ploidy and datesbut it was always dependent on the estuary (Festuary*date*ploidy

always P o 0.0001) (Table 10, Fig. 6–10). Se and Pb concen-trations were below detectable limits on more than 50% ofoccasions and their distributions were severely skewed almostcertainly not meeting the requirements for ANOVA and resultsfor these trace metals are interpreted with caution. All othertrace metal concentrations reported here were measurable onat least 96% of sampling occasions. Cu, Cd, Zn and Se

concentrations were generally lower and less variable in thetriploid oysters than the diploid oysters regardless of theestuary or the date (Fig. 6–10), possibly because triploids donot spawn.71 In estuaries such as Hasting and the HunterRivers, in which the condition index (dry mass/cavity volume)of triploid oysters was significantly greater than diploidoysters,72 Cu, Cd and Zn concentrations were much lower intriploid oysters. This is probably due to growth dilution30 astriploids on average are 30.7% heavier than equivalent agediploid oysters.73

Fig. 6 Temporal variation in copper concentrations in oysters fromfive NSW estuaries over a thirteen-month period.

Fig. 7 Temporal variation in cadmium concentrations in oysters fromfive NSW estuaries over a thirteen-month period.

J . E n v i r o n . M o n i t . , 2 0 0 5 , 7 , 2 0 8 – 2 2 3 2 1 7

There was a marked temporal variation in trace metalconcentrations (Fig. 6–10). There appears to be a base lineset by local conditions on to which temporal (seasonal) varia-bility is superimposed. Saccostrea glomerata shows no evidenceof regulation of internal metals concentrations as contami-nated locations such as Georges, Hastings and Hunter Riverscontain higher metal concentrations (Fig. 6–10). Consistentwith other studies16,31,48,74–77 no systematic temporal covaria-tion of trace metal concentrations were observed. Temporalvariations in trace metals will result from the combinedinfluences of food supply, stored energy use, reproductivecycles, changes in water temperature and salinity and contami-nant supply.78,79 Sudden temporal mass changes can result inchanges in trace metal concentrations49,74,75,80 but the degreeto which fluctuations in trace metals can be explained by

variations in mass differs between locations and years.75 Gly-cogen is the major storage material in bivalve molluscs.81

Glycogen content varies markedly with season in oysters82 asglycogen is used up during gametogenesis. After spawning iscompleted, glycogen reserves are built up before winter.83

Talbot84 showed that mass loss occurs on spawning; spawningtimes are site specific and that Cu and Zn were lost duringspawning. Sydney rock oysters spawn between February andMay with individuals not spawning synchronously.85 Thustemporal (seasonal) variability due to sudden mass changes islikely to vary between estuaries.Analysis of oyster trace metal concentration data by a

Principle Component Analysis discriminated the estuaries withrespect to their trace metal concentrations (Table 11, Fig. 11).The first two principal components explained 80% of the total

Fig. 8 Temporal variation in zinc concentrations in oysters from fiveNSW estuaries over a thirteen-month period.

Fig. 9 Temporal variation in lead concentrations in oysters from fiveNSW estuaries over a thirteen-month period.

2 1 8 J . E n v i r o n . M o n i t . , 2 0 0 5 , 7 , 2 0 8 – 2 2 3

variation in trace metal concentrations between the NSWestuaries. After varimax rotation the first factor explained48% and was closely aligned with Cd, Pb and Se concentra-tions. The second factor explained 32% of variation and wasaligned with Cu and Zn concentrations. All five metals con-tributed strongly to the ordination space. Although Cu and Znconcentrations had the relatively highest communalities, Cdconcentrations with the lowest, was still a strong contributor at0.69. The ordination clearly separated the Georges, Hastingand Hunter Rivers, estuaries with catchments dominated byurban and industrial areas, from the other estuaries withcatchments dominated by agriculture and forests on factor 2,as they tended to have higher Cu and Zn concentrations (Fig.11). The Georges River oysters data plotted apart from allother estuaries on factor 1 as its oysters have higher in Cd, Pband Se concentrations. As expected, oysters collected from

Lake Pambula and Tilligerry River were generally lower inmost trace metal concentrations (Fig. 6–10) because of therelatively pristine estuary catchments. As found by others16,18

oyster tissue concentrations reflect local contamination.Using oyster data from these two relatively pristine estuaries

together with data from the Clyde River Estuary and MoonaMoona Creek, ranges can be defined that represent a firstapproximation to Eastern Australian background concentra-tions. These are Cu: 20–90 mg g�1; Cd: 0.76–1.5 mg g�1; Zn: 792–1465 mg g�1; Pb: 0.1–0.3 mg g�1 and Se: 1–2 mg g�1; and lowerthan the medians reported by Scanes and Roach18 i.e. Cu: 170mg g�1; Cd: 5 mg g�1; Zn; 2610 mg g�1; Pb: 0.6 mg g�1 and Se;3.7 mg g�1 dry mass. However, some variability in backgroundtrace metal concentrations will occur naturally depending onthe geology of the estuary catchment and rock and sedimentmineralogy will need to be carefully considered18,86 beforeassigning specific background concentrations for other partsof Australia. A problem that can be encountered is that datacollected from areas remote from urban/industrial inputs in arelatively undisturbed catchment for comparative purposes canhave high trace metal concentrations. For example, oystersfrom Shark Bay Western Australia, a pristine bay, have highCd concentrations (10 mg g�1) as Cd is being adsorbed on tothe surface of haematite and oysters ingest suspended haema-tite particles.87

Comparisons of sources of variability

The coefficients of variation ranged between 15.8% for Seconcentrations in 3 year old Clyde River oysters in 1995 to82.4% for Cu concentrations in 1.3 year old Clyde Riveroysters in 1989 (Table 12). Overall, Se concentrations werethe least variable regardless of the factors considered (averageCV ¼ 25%) and Cu and Cd concentrations the most variable(average CV’s 45% and 40%, respectively) (Table 12). Thecoefficients of variation for the trace metal concentrations inindividual tissues were similar to, or higher than the overallvariation for all trace metals analysed (Table 11). Therefore,

Fig. 10 Temporal variation in selenium concentrations in oystersfrom five NSW estuaries over a thirteen-month period.

Table 11 Principle components analyses of NSW estuary oyster trace

metal concentration data. Bold values indicate strong drivers of that

factor

Trace metal Factor 1 Factor 2 Communality

Cu 0.46 0.81 0.86

Cd 0.83 0.07 0.69

Zn �0.13 0.95 0.92

Pb 0.83 0.24 0.74

Se 0.89 �0.06 0.79

Variance explained 48% 32%

Fig. 11 Principal component analysis of trace metal concentrations inoysters from five NSW estuaries over a thirteen month period. Solidsymbols are for diploid oysters, hollow symbols for triploid oysters.

J . E n v i r o n . M o n i t . , 2 0 0 5 , 7 , 2 0 8 – 2 2 3 2 1 9

analysing individual tissues would not improve the precision ofthe analysis.

The Moona Moona Creek oyster data set was used to assessthe numbers of organisms required to obtain a reliable estimateof the mean trace metal concentrations. About 5–6 samples arerequired to confidently estimate the population mean (Fig. 12).Using sample numbers below 5 provides unstable estimates ofthe population mean.

The sample size required to detect changes in the meanconcentration of trace elements, which is dependent on varia-bility, varied considerably between trace metals and betweenthe studies (Table 13). Cd and Cu required the largest and Se

the smallest sample sizes. To detect a change in the meanconcentration of Cd, Cu, Se and Zn of 50% a sample size of 16animals is required at Moona Moona Creek, or 14 animals inthe Clyde River for non-annual data (Table 13). Validation ofthe results with the Clyde River Estuary annual data showedthat a sample size of 16 would detect a 50% change in the meanfor 9 of the 10 years for all trace metals (Table 13).The use of pooled samples in the temporal study reduced the

variance by a factor of 1/On such that less pooled samples needto be analysed. If a pooling design is initiated, the numbers ofoysters pooled cannot be changed, as parametric statisticalcomparisons between data sets will not be possible.88 Analysis

Table 12 Coefficients of variation for trace metal concentrations measured in Saccostrea glomerata studies on the South Coast of NSW, Australia

Table 13 Sample sizes required for measuring changes in the Saccostrea glomerata mean trace metal concentrations. Power is held at 0.8 for a two

tailed test with a ¼ 0.05. For example to detect a 50% change in the mean cadmium concentration in 1.3 year old animals in 1995 would require

12 oysters to be analysed

% difference in mean to detect

Cadmium Copper Selenium Zinc

10 20 30 50 100 10 20 30 50 100 10 20 30 50 100 10 20 30 50 100

Location Animals Year

Clyde River 3 Year old cohort 1995 251 65 30 12 4 123 33 16 7 2 39 12 6 2 2 139 37 18 8 2

1.3 Year old cohort 1995 242 62 29 12 4 92 25 12 5 2 29 9 5 2 2 59 17 9 3 2

Combined age cohorts 1995 261 67 31 13 5 185 48 23 10 3 33 10 5 2 2 113 30 15 6 2

Moona Moona Creek Tissue study 1995 294 75 35 14 5 337 86 40 16 6 97 26 13 6 2 160 42 20 9 2

Clyde River

Annual data

Tissue study 1995 55 16 8 3 2 60 17 9 3 2 39 12 6 2 2 77 21 11 5 2

1984 1984 315 81 37 15 5 135 36 17 7 2 163 43 20 9 2

1985 1985 345 88 41 16 5 116 31 15 7 2 42 12 6 2 2

1987 1987 501 127 58 22 7 310 79 37 15 5 86 23 12 5 2

1989 1989 309 79 37 15 5 717 181 82 31 10 373 95 44 17 6

1990 1990 209 54 25 11 3 52 15 8 3 2 75 21 10 5 2

1991 1991 192 50 24 10 3 133 35 17 7 2 112 30 15 6 2

1992 1992 144 38 18 8 2 112 30 15 6 2 149 39 19 8 2

1993 1993 279 72 33 13 4 75 21 10 5 2 50 14 7 3 2

1994 1994 147 39 19 8 2 67 19 10 4 2 63 18 9 4 2

1995 1995 140 37 18 8 2 123 33 16 7 2 139 37 18 8 2

Mean 246 64 30 12 4 176 46 22 9 4 48 14 7 3 2 120 32 16 7 3

2 2 0 J . E n v i r o n . M o n i t . , 2 0 0 5 , 7 , 2 0 8 – 2 2 3

of individual oysters is preferable as it reveals trace metalconcentration distributions and allows the estimation of po-pulation parameters. The detection of natural outliers i.e. notrepresentative of the population, which may be skewing resultsand having a disproportionate effect on the mean can also beremoved.

Measurement of wet or dry mass trace metal concentrations

In this study wet mass refers to blotted wet tissue and dry massto wet tissue that has been freeze dried and both exclude allshell material. Although we measured high correlations be-tween wet and dry mass (e.g. Clyde River 1989, r2 ¼ 0.9241,p o 0.0001), similar to others,84 we found that the regressionrelationships were variable. Wet mass to dry mass ratios alsomay significantly alter with age37 or shell size.38 Thus iflocations are to be compared, oysters should be dried before

analysis and dry mass results used. The dependence of tracemetal concentrations on mass is also eliminated or greatlyreduced. Our preference is to freeze dry samples, as it is easierto homogenise the sample for analysis. We have previouslyshown that freeze drying does not result in a loss of the tracemetals analysed in this study.89

Implications for use as biomonitors

Phillips4 has defined the attributes an animal needs to have tobe used as a biomonitor. The organism needs to be sessile,abundant, hardy to pollution, easy to identify, provide suffi-cient tissue for analysis and tissue concentrations should reflectenvironmental contamination loads. The oyster S. glomerata issessile and abundant, easy to identify, provides sufficient tissuefor analysis and, as illustrated in this study, is hardy topollution. Tissue trace metal concentrations also reflect local

Fig. 12 Mean and confidence interval for trace metal concentrations of Moona Moona Creek oysters determined by bootstrapping (n ¼ 1000) atvarious sample sizes.

J . E n v i r o n . M o n i t . , 2 0 0 5 , 7 , 2 0 8 – 2 2 3 2 2 1

contamination. Oyster tissues equilibrate with the trace metalsin their environment as illustrated by the little variation oftrace metal concentrations with mass and age. Considerabletemporal variation in trace metal occurs and the time ofsampling needs to be taken into account when undertakingspatial and temporal comparisons of trace metal concentra-tions. As trace metal concentrations can vary by at least afactor of 2–3 over a single year, a single collection may not beappropriate to estimate the magnitude of trace metal contam-ination. Diploid or triploid oysters can be used as biomonitorsas they both reflect trace metal contamination. However, thereare significant differences in the trace metal concentrations ofeach type and for consistency only one type should be used.The use of triploids may have the advantage of reducingvariability in trace metal concentrations. Considerable inherentvariability in trace metal concentrations in individual cohortsof oysters occurs which cannot be accounted for by variationsin mass, gender or age. Dissecting oysters and analysing non-gonadal (or visceral) tissues does not reduce inherent varia-bility. A sample size of at least 5–6 oysters is needed to estimatea population mean and 16 oysters is needed to detect a changeof �50% of the grand mean at any site.

We recommend that when oysters are used as biomonitorsthat oysters of similar mass, age and ploidy be selected. The useof mature oysters (3 years old) would reduce trace metalconcentration variability. Organisms must also be selectedfrom consistent positions and similar times avoiding the periodof reproduction if possible. If this protocol is adhered to, thevariability in trace metal concentrations measured at a site willbe minimised, and allow smaller environmental differencesbetween sites and background concentrations to be measuredwith confidence. However, a philosophical decision must betaken as to the magnitude of the difference that representscontamination that is of concern.90

Most studies of trace metals in oysters have used wildpopulations (passive biomonitors). Biological variability isinherent in natural populations because of differences ingenetics, ages and life histories. The use of hatchery rearedoysters transplanted to sites (active biomonitors) ensuresgenetic homogeneity and individual life histories. Total tissuetrace metal burdens could then be determined and net fluxesinto and out of oysters can be calculated to assess exposure.

References

1 SOE, Australia: State of the Environment, CSIRO Publishing,Collingwood, Australia, 1996.

2 SOE, Australia: State of the Environment, CSIRO Publishing,Collingwood, Australia, 2001.

3 R. J. Allan, P. G. C. Campbell, U. Forstner and K. Lum, Sci.Total Environ., 1990, 97/98, 1–872.

4 D. J. H. Phillips, Environ. Pollut., 1977, 13, 281–317.5 D. J. H. Phillips and P. S. Rainbow, in Biomonitoring of Trace

Aquatic Contaminants, Elsevier Science Publishers Ltd, Oxford,1992, ch. 5, pp. 79–132.

6 W. J. Langston, M. J. Bebianno and G. R. Burt, in Metalmetabolism in aquatic environments, Metal handling strategies inmolluscs, ed. W. J. Langston and M. J. Bebianno, Chapman andHall, London, 1998, ch. 8, pp. 219–285.

7 M. T. Brown and M. H. Depledge, in Metal metabolism in aquaticenvironments, Determinants of trace metal concentrations in marineorganisms, ed. W. J. Langston and M. J. Bebianno, Chapman andHall, London, 1998, ch. 7, pp. 185–217.

8 D. Cossa, E. Bourget, D. Pouliot, J. Puize and J. P. Chanut, Mar.Biol., 1980, 58, 7–14.

9 M. J. Orren, G. A. Eagle, H. F. K. O. Hennig and A. Green,Mar.Pollut. Bull., 1980, 11, 253–257.

10 M. Marino and O. Enzo, Mar. Pollut. Bull., 1983, 14, 342–346.11 P. B. Lobel, Comp. Biochem. Physiol., Part C: Comp. Pharmacol.,

1987, 87, 47–50.12 P. B. Lobel, C. D. Bajdik, S. P. Belkhode, S. E. Jackson and

H. P. Longerich, Arch. Environ. Contam. Toxicol., 1991, 21,409–414.

13 N. J. Mackay, R. J. Williams, J. L. Kacccprzac, M. N. Kazzacos,A. J. Collins and E. H. Auty, Aust. J. Mar. Freshwater Res., 1975,26, 31–46.

14 K. R. Brown and R. G. McPherson, Sci. Total Environ., 1992, 126,27–33.

15 S. Hardiman and B. Pearson, Mar. Pollut. Bull., 1995, 30,563–567.

16 P. Scanes, Mar. Pollut. Bull., 1996, 33, 226–238.17 W. J. Hayes, I. J. Anderson, M. Z. Gaffoor and J. Hurtado, Sci.

Total Environ., 1998, 212, 39–47.18 P. R. Scanes and A. C. Roach, Environ. Pollut., 1999, 105,

437–446.19 D. Spooner, W. Maher and N. Otway, Arch. Environ. Contam.

Toxicol., 2003, 45, 92–101.20 M. P. Lincoln-Smith and T. F. Cooper, Mar. Pollut. Bull., 2004,

48, 873–883.21 U. Tinggi and W. A. Maher, Microchem. J., 1986, 33, 304–308.22 S. Baldwin, M. Deaker and W. Maher, Analyst, 1994, 119,

1701–1704.23 M. Deaker and W. Maher, Anal. Chim. Acta., 1997, 350, 287–295.24 M. Deaker and W. Maher, J. Anal. At. Spectrosc., 1995, 10,

423–434.25 W. Maher, S. Forster, F. Krikowa, P. Snitch, G. Chapple and P.

Craig, At. Spectrosc., 2001, 22, 361–370.26 J. H. Zar, Biostatistical Analysis, Prentice Hall International,

Englewood Cliffs, NJ, 1984.27 R. R. Hocking, A Diagnostic Tool for Mixed Models with Applica-

tions to Negative Estimates of Variance Components, in SAS UsersGroup International Conference Proceedings, The SAS InstituteInc., Cary, NC, 1983, pp.711–715.

28 S. W. Fowler and G. Benayoun, Mar. Biol., 1976, 37, 59–68.29 D. Cossa, E. Bourget and J. Piuze, Mar. Pollut. Bull., 1979, 10,

174–176.30 C. R. Strong and S. N. Luoma, Can. J. Fish. Aquat. Sci., 1981, 38,

1059–1064.31 J. D. Thomson, Mar. Biol., 1982, 67, 135–142.32 J. D. Thomson, Aust. J. Mar. Freshwater Res., 1983, 34, 397–405.33 T. Lytle and J. Lytle, Bull. Environ. Contam. Toxicol., 1982, 45,

142–148.34 Y. K. Cheung and M. H. Wong, Environ. Manage., 1992, 16,

753–761.35 K. M. Y. Leung and R. W. Furness, Mar. Pollut. Bull., 1999, 39,

126–136.36 S. G. D. Kang, D. A. Wright and C. H. Koh, Sci. Total Environ.,

2000, 263, 143–153.37 D. A. Wright, J. A. Mihursky and H. L. Phelps, Mar. Environ.

Res., 1985, 16, 181–197.38 J. E. Harris, G. J. Fabris, P. J. Statham and F. Tawfik, Aust.

J. Mar. Freshwater Res., 1975, 30, 159–178.39 R. J. Huggett, M. E. Bender and H. D. Slone,Water Res., 1973, 7,

451–460.40 V. Talbot, Aust. J. Mar. Freshwater Res., 1985, 36, 169–175.41 R. Frew, K. A. Hunter and R. Beyer, Mar. Pollut. Bull., 1989, 20,

463–464.42 C. L. Brown and S. N. Luoma, Mar. Prog. Ser., 1995, 124,

129–142.43 J. D. Thomson, B. J. S. Pirie and S. G. George, J. Exp. Mar. Biol.

Ecol., 1985, 85, 37–45.44 C. R. Boyden, J. Mar. Biol. Assoc. UK, 1977, 57, 675–714.45 H. R. Watling and R. J. Watling, Mar. Pollut. Bull., 1976, 7,

91–94.46 D. W. Klumpp and C. Burdon-Jones, Aust. J. Mar. Freshwater

Res., 1982, 33, 285–300.47 Y. D. Latouche and M. C. Mix, Mar. Pollut. Bull., 1982, 13,

27–29.48 L. Krishna-Kumari, V. R. Nair and C. M. Moraes, J. Coastal

Res., 1992, 8, 347–354.49 D. J. Cain and S. L. Luoma, Mar. Prog. Ser., 1990, 60, 45–55.50 B. L. Bayne, J. Widdows and R. Thompson, in Physiological

integrations marine mussels: their ecology and physiology, ed.B. L. Bayne, Cambridge University Press, New York, 1976,pp. 262–291.

51 R. R. Brooks and M. G. Rumsby, Aust. J. Mar. Res., 1967, 15,53–61.

52 T. J. Ward, Aust. J. Mar. Freshwater Res., 1982, 33, 33–44.53 D. Martincic, H. W. Nurnberg, M. Stoeppler and M. Branica,

Mar. Biol., 1984, 81, 177–188.54 D. M. McConchie, A. W. Mann, M. J. Lintern, D. Longman,

V. Talbot, A. J. Gabelish and M. J. Gabelish, J. Coast. Res., 1988,4, 37–58.

55 G. W. Bryan, J. Mar. Biol. Assoc. UK, 1973, 53, 145–166.

2 2 2 J . E n v i r o n . M o n i t . , 2 0 0 5 , 7 , 2 0 8 – 2 2 3

56 M. Schulz-Blades, Mar. Biol., 1974, 25, 177–193.57 P. B. Lobel and D. A. Wright, Mar. Pollut. Bull., 1982, 13,

320–323.58 S. G. George and J. M. Frazier, Thalassia Jugosl., 1982, 18, 203–219.59 B. J. S. Pirie, S. G. George, D. G. Lytton and J. D. Thomson,

J. Mar. Biol. Assoc. UK, 1984, 64, 115–123.60 P. B. Lobel, P. Mogie, D. A. Wright and B. L. Wu, Mar. Pollut.

Bull., 1982, 13, 170–174.61 W. J. Langston and S. K. Spence, Biological factors involved in

metal concentrations observed in aquatic organisms, in Metalspeciation and bioavailability, ed. A. Tessier and D. R. Turner,John Wiley, New York, 1995, pp. 407–478.

62 W.-X. Wang and N. S. Fisher, Limnol. Oceanogr., 1996, 41,197–207.

63 W.-X. Wang and N. S. Fisher, Mar. Biol., 1996, 125, 715–724.64 D. W. Engle and B. A. Fowler, Health Perspect., 1979, 28, 81–88.65 J. M. Frazier, Environ. Health Perspect., 1979, 28, 75–79.66 C. A. Amiad-Triquet, B. Berthet and R. Martoja, Biol. Met., 1991,

4, 144–150.67 G. F. Riedel, G. R. Abbe and J. G. Sanders, Estuaries, 1995, 18,

445–455.68 V. Talbot and A. Chegwidden, Aust. J. Mar. Freshwater Res.,

1982, 36, 779–788.69 F. Paez-Osuna, M. G. Frias-Espericuela and J. I. Osuna-Lopez,

Mar. Environ. Res., 1995, 40, 19–31.70 J. M. Frazier, Chesapeake Sci., 1979, 16, 162–171.71 E. S. Cox, M. S. R. Smith, J. A. Nell and G. B. Maguire, J. Exp.

Mar. Biol. Ecol., 1996, 197, 101–120.72 R. E. Hand and J. A. Nell, Aquaculture, 1999, 171, 181–194.73 J. A. Nell and G. B. Maguire, Commercialisation of triploid Sydney

rock and Pacific oysters. Part 1, Sydney rock oysters, FRDCProject No 93/151, NSW Fisheries Final Report Series ReportNo. 10, 1998.

74 C. R. Boyden and D. J. H. Phillips, Mar. Ecol., 1981, 5, 29–40.75 D. J. Cain and S. L. Luoma, Mar. Ecol. Prog. Ser., 1986, 28,

209–217.76 J. Osuna-Lopez, H. M. Zazuela_Padilla, A. Rodiguez-Higuera

and F. Paez-Osuna, Mar. Pollut. Bull., 1990, 21, 486–488.77 N. Peerzada and E. Kozlik, Bull. Environ. Contam. Toxicol., 1992,

48, 31–36.78 D. J. H. Phillips, Use of macroalgae and invertebrates as monitors

of metal levels in estuaries and coastal waters, in Heavy metals inthe marine environment, ch. 6, ed. R. W. Furness and P. S.Rainbow, CRC Press, FL, 1990.

79 K. J. M. Kramer, Biomonitoring of coastal waters and estuaries,CRC Press, Boca Raton, FL, 1994, pp. 1–327.

80 R. D. Simpson, Mar. Pollut. Bull., 1979, 10, 74–78.81 P. A. Gabbott, in Proceedings of the 9th European Marine Biology

Symposium, Storage cycles in marine bivalve molluscs: a hypoth-esis concerning the relationship between glycogen metabolism andgametogenesis, ed. H. Barnes, Aberdeen University Press, 1975,pp. 191–211.

82 C. L. Mason and J. A. Nell, Mar. Freshwater Sci., 1995, 46,873–881.

83 G. F. Humphrey, Pamphlet Council Scientific and IndustrialResearch (Melbourne), 1941, 111, 21–40.

84 V. Talbot, Sci. Total Environ., 1986, 57, 217–230.85 J. A. Nell, Aust. Rev. Fish. Sci., 1993, 1, 97–120.86 H. Elderfield, L. Thorton and J. S. Webb,Mar. Pollut. Bull., 1971,

2, 44–47.87 D. M. McConchie and L. M. Lawrence, Arch. Environ. Contam.

Toxicol., 1991, 21, 303–310.88 M. Gorden, G. A. Knauer and J. H. Martin, Mar. Pollut. Bull.,

1980, 10, 195–198.89 W. A. Maher, Sci. Total Environ., 1983, 26, 173–181.90 P. M. Chapman, Mar. Pollut. Bull., 1997, 34, 282–283.

J . E n v i r o n . M o n i t . , 2 0 0 5 , 7 , 2 0 8 – 2 2 3 2 2 3