A Model of PCB Bioaccumulation in the Sea Bass Food Web from the Seine Estuary (Eastern English Channel)

PII:

Marine Pollution Bulletin Vol. 43, Nos. 7±12, pp. 242±255, 2001 Ó 2001 Elsevier Science Ltd. All rights reserved Printed in Great Britain 0025-326X/01 $ - see front matter S0025-326X(01)00082-0

A Model of PCB Bioaccumulation in the Sea Bass Food Web from the Seine Estuary (Eastern English Channel) V. LOIZEAU*, A. ABARNOU, P. CUGIER, A. JAOUEN-MADOULET, A.-M. LE GUELLEC and A. MENESGUEN D epartement d'Ecologie C^ oti ere, IFREMER, Centre de Brest, BP70, 29280 Plouzan e, France

A bioaccumulation model was developed to simulate the PCB contamination in the sea bass food web from the Seine Estuary. The model relies upon a contaminant mass balance budget for each biological species. Biological processes determine the extent of bioaccumulation: respiration and feeding rates control the uptake of contaminants whereas excretion, spawning, and growth act on the chemicals removal. A step-by-step modelling approach was followed. A ®rst version was a steady-state model validated for the bioaccumulation processes. In the second version seasonal variation was taken into account, and ®nally in the third version, the model was coupled with a population dynamics model to describe PCB contamination in each age class. Ó 2001 Published by Elsevier Science Ltd. Keywords: bioaccumulation; PCBs; numerical model; Seine Estuary; sea bass; food web.

Introduction Polychlorinated biphenyls (PCBs) are a group of organic chemicals that possess the inherent properties of compounds that bioaccumulate, i.e. high octanol/water partition coecient (Kow ) and persistence. Through atmospheric deposition (Eisenreich et al., 1981) and river inputs (Larson et al., 1990), PCBs have spread to all aquatic environments and in turn to biota and sediment. In spite of their presence at a very low level in oceanic waters, where concentrations of individual compounds are typically at the pg l 1 level or even less (Schulz-Bull et al., 1991), PCBs have been found in very high concentrations in top predators. Concentrations between 1 and 100 mg kg 1 were determined in the fatty tissues of marine mammals (Law et al., 1995), which correspond to an overall bioaccumulation factor of about 108 . *Corresponding author. Tel.: +33-2-9822-4679; fax: +33-2-98224548. E-mail address: [email protected] (V. Loizeau).

242

The mechanisms leading to the PCB contamination of aquatic biota, especially top predators, have been greatly debated. Earlier studies have emphasized bioconcentration as being the primary mechanism governing contamination in biota (Mackay, 1982; Duursma et al., 1989). The term bioconcentration refers to the accumulation of a chemical through direct uptake from water. Fugacity-based models (Mackay and Paterson, 1982; Clark et al., 1990; Campfens and Mackay, 1997) simulate this process by assuming an equilibrium of chemical substances between the various environmental phases (water, sediment, biota, etc.). Such passive exchange processes should cause the distribution of PCBs and other hydrophobic chemicals to be proportional to the lipid content of organisms, irrespective of their position in foodwebs (Barber et al., 1988). However, several studies have shown that aquatic organisms can exhibit higher PCB concentrations that would be expected if only bioconcentration processes were involved (Evans et al., 1991; Oliver and Niimi, 1988; Van der Oost et al., 1988; Boon et al., 1994; Zaranko et al., 1997). This would suggest that some other mechanisms, such as biomagni®cation through trophic transfer, are governing contaminant accumulation in aquatic organisms and especially in top predators. This trophic transfer of contaminants has been taken into account by several authors in their approach to modelling the bioaccumulation in food webs (Thomann, 1980, 1989; Thomann and Connolly, 1984; Connolly, 1991; Thomann et al., 1992). A model of this type was developed successfully in a previous work and simulates PCB bioaccumulation in the dab from the `Baie de Seine' (Loizeau and Menesguen, 1993). The ability of models to reproduce measured concentrations in biota and, thus their value as predictive tools, depends on the care taken to describe the basic processes leading to bioaccumulation. The purpose of this paper is to present new models that simulate PCB bioaccumulation in the sea bass food web in the Seine Estuary (France). This estuary is heavily contaminated by various chemicals of terrestrial origin. The pollution monitoring programme RNO,

Volume 43/Numbers 7±12/July±December 2001

(`Reseau National d'Observation' or `the French mussel watch'), has pointed out the very high PCB contamination in coastal waters and more particularly in the Seine Estuary and in its surrounding bay (Abarnou and Simon, 1986; Claisse, 1989). For instance, in mussels sampled near the Seine Estuary, the concentrations of CB153, one of the most important PCB congeners in the environment, vary between 200 and 500 ng g 1 dry weight (d.w.) whereas the median concentration along the French coast is approximately 50 ng g 1 (d.w.). The awareness of such a high chemical contamination has led to the setting up of a multidisciplinary research project, the `Programme Scienti®que SEINE AVAL'. This applied research project aimed at a better understanding of the functioning of the estuary in order to improve its water quality and its management. Within such a framework, this model of PCB bioaccumulation in the sea bass food web was developed and validated using data from concomitant studies on the Seine Estuary biological resources.

Materials and Methods Study location The Seine is the fourth largest river in France, and the only large one discharging into the English Channel (Fig. 1). The catchment area (about 78 650 km2 ) is characterized by important industrial activities, is very urbanized with approximately 12 millions inhabitants, including Paris. The mean annual fresh water discharge of the Seine into the English Channel is about 410 m3 s 1 . The river carries a large quantity of suspended material, corresponding to mean annual in¯uxes between 0.2 and 1  106 t (Avoine, 1986; Guezennec et al., 1999). The estuary has a large tidal range ± up to 7.5 m during spring tides ± and can be classi®ed as well mixed, although it is partially mixed in its upper part (Salomon, 1988). Turbidity reaches a maximum within the region of mixing of fresh and marine waters. There, ®ne grain sediments are resuspended by strong tidal currents. From a biological point of view, the Seine Estuary was identi®ed as a nursery area for many ®sh and invertebrates species. The determination of the principal

Fig. 1 Study location.

benthic and pelagic species and their trophic inter-relationships were described elsewhere (Rochard et al., 1997; Bessineton and Simon, 1997; Mouny et al., 1997; Rybarczyck et al., 1997). Sampling and chemical analysis Sampling schemes and chemical analysis have been described previously (Loizeau et al., submitted) and are summarized below. The various biological species were sampled during the Seine Aval project cruises (Loizeau and Abarnou, 1996; Loizeau et al., 1997) and kept deep frozen until chemical analyses were carried out. After dissection and freeze-drying, biological tissues were solvent extracted using Soxtec apparatus. Appropriate clean-up of the extracts was then performed before the ®nal instrumental analysis by gas chromatography using two di€erent capillary columns and with electron capture detection. PCBs were quanti®ed individually using one standard solution containing 17 selected congeners. Model structure and equations Biological and experimental background. Several main biological functions such as respiration, nutrition and reproduction act on the uptake and elimination of contaminants. These biological functions interfere with each other adding an important part of the complexity and variability of bioaccumulation processes. The factors that increase bioaccumulation are: the uptake of contaminants from water and from food. The factors that decrease bioaccumulation are: excretion and defecation, growth acting as a dilution process, reproduction which is a loss of contamination as the contaminant is transfered from the parent to the eggs during spawning and ®nally biotransformation. The sea bass (Dicentrarchus labrax) was chosen as the top predator of a very simpli®ed food web. It is a commonly found species in European coastal waters, its biology is well known (Ramos et al., 1982; Bertignac, 1987; Pickett and Pawson, 1994) and it has an important commercial value. Sea bass is commonly found in the Seine Estuary which uses it as a spawning area (Masski, 1998). It is a euryhaline species which inhabits both estuarine and coastal waters. It is carnivorous and feeds on supra-benthic species such as small ®sh (e.g. gobies) and crustaceans (shrimps and mysidaceans). The steady-state model. In order to provide a tractable analysis across the food web, the ®rst bioaccumulation model was run to steady state. Only male sea bass in their third year in May were considered (no reproduction process). The compartmental structure of the food web is shown in Fig. 2. Six biotic compartments (state variables: xi which represent accumulation of chemical in each biotic compartment) are considered together with dissolved contaminant concentrations in the water column (Cw ), in suspended particular material (detritus) and in phytoplankton (Cw , detritus and phytoplankton are forcing variables). The basic model 243

Marine Pollution Bulletin

Fig. 2 Schematic structure of the sea bass food web.

equations are similar to those used in the equilibrium model of the pelagic food chain described by Thomann (1989) and in the steady-state model of PCBs in the dab food web (Loizeau and Menesguen, 1993). For a biotic compartment i, the rate of PCB uptake from the available dissolved pool is Ri (l g 1 day 1 ); the chemical assimilation eciency of dissolved chemical is ai;w ; the excretion rate is Ei and the growth rate is Gi , both in day 1 . The speci®c feeding rate of the organism i is Ni (g (prey w.w.) g 1 (predator w.w.) day 1 ). The chemical assimilation eciency of ingested chemical is bi (%) and the feeding preference of predator i for prey j (and for detritus and phytoplankton) is Qi;j . Thus the accumulation of chemical x by the biotic compartment (i) can be described by the following general equation: dx…i† ˆ0 dt ˆ Ri  Cw  ai;w ‡

n X

! bi  Qi;j  Ni  xj

jˆ1

… Ei ‡ G i †  x i :

…1†

The ®rst term of Eq. (1) represents the direct uptake of PCBs by animals from water. The second term refers to the inputs of contaminants into the animal through feeding. The third-term describes the decrease of contaminant due to excretion and growth, the latter acting as a dilution process. Bioaccumulation processes, environmental parameters and boundary conditions of this model have been described in an other publication (Loizeau et al., submitted). The dynamic bioaccumulation model. Biological processes like growth or reproduction vary with time and consequently cannot be described accurately by a simple steady-state model. The second version of this bioac244

cumulation model has been set up for both male and female sea bass. It takes into account the age and the gender of the ®sh. Most of the physiological processes depend on environmental conditions that vary during the year, such as temperature, dissolved oxygen, phytoplankton biomass, suspended particulate matter concentration as well as on the diet of the species. Cugier (1999) developed and validated an ecological model that can reproduce the main physical and biological characteristics of the Seine Estuary ecosystem. It has provided time series for temperature, suspended matters, dissolved oxygen, chlorophyll concentrations in the Seine Estuary that were used as forcing variables in the bioaccumulation model. In this dynamic version of the bioaccumulation model, the water contamination (Table 1), the lipid fraction in phytoplankton …Flip †, the organic carbon fraction …Foc † in detritus (Table 2) and the size of preys are parameters that are identical from one year to another. The dynamic model of bioaccumulation also takes into account the impact of seasonal variations of PCB contamination in supra benthic species but not its evolution over the years. This simpli®cation implies that, whatever their age, sea bass eat the same size prey. The model has been able to simulate the e€ects of reproduction on the elimination of contaminants. According to Jennings (1990) and to Pickett and Pawson (1994), the spawning of the English Channel sea bass TABLE 1 Concentrations of PCB congeners in Seine Estuary water (Munschy et al., 1996). CB CB52 CB153 CB180

Concentration (ng l 1 ) 0.315 0.075 0.040

Volume 43/Numbers 7±12/July±December 2001

occurs from February to May and ®sh reach sexual maturity in their third year. In the model, the contamination loss from female ®sh due to spawning is described by a gaussian function; weight losses are estimated using a function which depends on the weight of the ®sh and on the gonadosomatic index (GSI) and which is centred on the 90th day corresponding to the maximum of spawning : …Pi ˆ f …GSI; Wt ††. ! 2 1 …t 90† p  exp Pi ˆ GSI  Wt  ; 2  r2 r 2p

Environmental, chemical and biological parameters and biological processes are described, respectively, in Fig. 3 and Tables 2±4. Coupling the bioaccumulation model with a simple model of virtual population analysis. In its third version, the model was coupled with a dynamic population model of the sea bass. So, unlike previous versions which described the variation of contamination in a specimen, this model simulates PCB concentrations in the sea bass population. This means that this version calculates PCB concentration in individual ®sh of each age class at any time as well as the amount of PCB that could be found in each age class. In this model, the sea bass compartment has been divided into two groups: males and females. For each group, ®ve age classes have been considered to give ten di€erent state

…2† 1

where Pi is the weight loss by spawning (g g 1 day ), Wt is the weight of the female (g), and r is the standard deviation (10 days), and t is the time in Julian day.

Fig. 3 Environmental forcing parameters in the sea bass food chain model (results calculated from the ecological model Cugier (1999)).

TABLE 2 Environmental parameters used in the sea bass food chain model. Symbol Flip Foc

Meaning

Unit

Phytoplankton lipid fraction Organic carbon in detritus

mg g mg g

1 1

Value

SD (%)

References

8.4 35

8 10

Loizeau et al. (submitted) Munschy et al. (1996)

TABLE 3 Chemical parameters used in the sea bass food chain model Kow : octanol/water partition coecient (Hawker and Connell, 1988).a

CB52 CB153 CB180 a

Kow

b1

b2

b3

b4

b5

b6

5.84 6.92 7.36

0.5 0.45 0.609

0.5 0.18 0.241

0.5 0.03 0.038

0.5 0.122 0.165

0.5 0.63 0.85

0.5 0.02 0.03

bi : Assimilation eciency of the various compounds when taken up from the food by the ith predator.

245

Marine Pollution Bulletin TABLE 4 Biological processes and parameters used in the food chain model (the four feeding preference values correspond, respectively, to the four seasons: winter, spring, summer and autumn) (phyto ˆ phytoplankton; zoopk ˆ zooplankton). Symbol

Meaning

Zooplankton (1) Weight W1 (beginning of the year) Q1;phyto Feeding preference on phyto Feeding preference Q1;det on detritus Growth rate G1 Respiration rate (O2 ) R1 N1 E1

51.3

Food consumption rate

E…2†

Excretion rate

Loizeau et al. (submitted)

0.45

Dimensionless

Beghin et al. (1997)

0.5

0.25

0.45

0.55

Dimensionless

Beghin et al. (1997)

1

0.2496 R1 ˆ 0:059  ‰ChloroŠ ‡ 0:033  T

0:178

N1 ˆ 4:197  ‰ChloroŠ ‡ 1 E1 ˆ

2:4 : 0:75 ‡ ‰ChloroŠ

9.13

day lmol l 1 h lg l

1

h

1

lg l

1

h

1

Vidal (1980) Fourqurean et al. (1997) Hansen et al. (1995)

1

Durbin and Durbin (1978)

mg

Loizeau et al. (submitted)

0.55

0.65

0.75

0.7

Dimensionless

Aaser et al. (1995)

0.45

0.35

0.25

0.3

Dimensionless

Aaser et al. (1995)

mm

Mees et al. (1994)

t ˆ year lg

Irvine et al. (1995)

L2 ˆ 16 1

e…

3:35…t 0:25† 0:0276†




Ln…W2 † ˆ 1:432 ‡ 2:853  Ln…L2 †

N2 ˆ 0:265  W2  e…0:0875T E2 ˆ 1:225  W2 e…0:310T

1

ll ind

R2 ˆ W20:242 ‡ 0:505 0:0434†

0:0019†

h

1

1

Aaser et al. (1995)

day

1

Aaser et al. (1995)

mg

Loizeau et al. (submitted) Del Norte-Campos (1995) Del Norte-Campos (1995) Del Norte-Campos (1995) Meixner (1996)

0.6

0.5

Dimensionless

0.2

0.3

0.4

Dimensionless

0.1

0.1

0.1

Dimensionless

mm3 ind

R3 ˆ 0:170  T

Food consumption rate Excretion rate

1

day

G3 ˆ 0:367  W3

1

day

0:9292

N3 ˆ 0:193  W3 E3 ˆ 0:0194  W3

h

1

h

1

Taylor and Spicer (1987)

mg

Loizeau et al. (submitted) Del Norte-Campos (1995) Del Norte-Campos (1995) Del Norte-Campos (1995) Del Norte-Campos (1995)

0.6

0.4

Dimensionless

0.35

0.3

0.5

Dimensionless

0.15

0.1

0.1

Dimensionless 0:001786  L4

1

Del Norte-Campos (1995) Del Norte-Campos (1995)

0.5

G4 ˆ 0:13950 ‡ 0:008578T

Vasblom and Elgershuizen (1997)

day

0.7

Respiration rate

Palemon longirostris Weight 45.3 W4 (beginning of the year) Feeding preference 0.45 Q4;1 for zoopk Feeding preference 0.4 Q4;2 for Neomysis Feeding preference 0.15 Q4;det for detritus Growth rate G4

246

lg 0.55

‡ 0:6213 log…W3 †

E3

References

0.75

Crangon crangon (3) Weight 70.83 W3 (beginning of the year) Feeding preference 0.4 Q3;1 for zoopk Feeding preference 0.4 Q3;2 for Neomysis Feeding preference 0.2 Q3;det for detritus Growth rate G3

N3

Unit

0.5

Age±length relation Weight±length relation Respiration rate (O2 )

N…2†

R3

Equation

Food consumption rate Excretion rate

Neomysis integer (2) Weight W2 (beginning of the year) Q2;1 Feeding preference for zoopk Feeding preference Q2;det for detritus G2 Growth rate

R…2†

Value

mm d

1

Volume 43/Numbers 7±12/July±December 2001 TABLE 4 (CONTINUED) Symbol

Meaning

R4

Respiration rate

N4

Food consumption rate Excretion rate

E4

Value

Respiration rate

N5

Food consumption rate Excretion rate

E5

Sea bass male Weight W6 (beginning of the year) Feeding preference Q6;2 for neomysis Feeding preference Q6;3 for crangon Feeding preference Q6;4 for palaemon Feeding preference Q6;5 for gobies Growth rate G6

E4 ˆ 0:0027  W4

Food consumption rate

E6

Excretion rate

Sea bass female Weight W7 (beginning of the year) Q7;2 Feeding preference for neomysis Feeding preference Q7;3 for crangon Feeding preference Q7;4 for palaemon Feeding preference Q7;5 for gobies Growth rate G7 Age±length relation Weight±length relation

1

h

1

h

1

mg

Loizeau et al. (submitted) Del Norte-Campos (1995) Del Norte-Campos (1995) Del Norte-Campos (1995) Del Norte-Campos (1995) Founda and Miller (1981) Petersen and Pertersen (1990)

0.6

0.45

Dimensionless

0.3

0.25

0.45

Dimensionless

0.05

0.1

0.05

Dimensionless

0.15

0.05

0.05

Dimensionless mg day

G5 ˆ 0:01478  W5

1

ml g

R5 ˆ 0:151  W50:61

h

h

E5 ˆ 0:0012  W5 10.33

1

1

1

day

N5 ˆ 0:273  W5

Del Norte-Campos (1995) Del Norte-Campos (1995)

1

g

Bertignac (1987)

cm

Bessineton and Simon (1996, 1997) Bessineton and Simon (1996, 1997) Bessineton and Simon (1996, 1997) Bessineton and Simon (1996, 1997) Bertignac (1987)

g

Masski (1998)

0.15

0.13

0.20

0.18

Dimensionless

0.20

0.25

0.20

0.17

Dimensionless

0.40

0.45

0.50

0.40

Dimensionless

0.25

0.17

0.15

0.25

Dimensionless L6 ˆ 71:77 1 W6 ˆ 5  12

e 3

E6 ˆ

2




 L3:18 6

R6 ˆ 0:9883  W6 N6 ˆ 5:83:10

0:161…t‡0:079†

0:2209

 T 1:6867

 W6  e…0:04T

1:92 ‡ 0:221  LnW6

12.17

2:17†

mg kg

1

h

1

1

Ramos et al. (1982)

day

1

Ballestrazzi and Lanari (1996)

g

Bertignac (1987)

cm

Bessineton and Simon (1996, 1997) Bessineton and Simon (1996, 1997) Bessineton and Simon (1996, 1997) Bessineton and Simon (1996, 1997) Bertignac (1987)

g

Masski (1998)

0.13

0.20

0.18

Dimensionless

0.20

0.25

0.20

0.17

Dimensionless

0.40

0.45

0.50

0.40

Dimensionless

0.25

0.17

0.1

0.25

Dimensionless

W7 ˆ 5  12

e 3

0:127…t‡0:033†




Lemaire et al. (1992)

day

0.15

L7 ˆ 82:59 1

Szaniawska and Wolowicz (1984) Del Norte-Campos (1995) Del Norte-Campos (1995)

0.5

Weight±length relation Respiration rate

N6

h

References 1

mm ind

N4 ˆ 0:28  W4

Age±length relation

R6

Unit 3

R4 ˆ 0:1127  W40:7233

Pomatoschistus m. Weight 51.3 W5 (beginning of the year) Q5;1 Feeding preference 0.4 for zoopk Feeding preference 0.4 Q5;2 for neomysis Feeding preference 0.1 Q5;3 for crangon Feeding preference 0.1 Q5;det for detritus Growth rate G5 R5

Equation

 L3:18 7

247

Marine Pollution Bulletin TABLE 4 (CONTINUED) Symbol

Meaning

R7

Respiration rate

N7

Food consumption rate

E7

Excretion rate

P7

Spawning rate

Value

Equation R7 ˆ 0:9883  W7 N7 ˆ 5:83:10 E7 ˆ

2

Unit

0:2209

mg kg

 T 1:6867

 W7  e…0:04T

2:17†

1:92 ‡ 0:221  LnW7 …t

1 p  exp P7 ˆ GSI  W7  r  2p

variables. The structure of this third model is schematized in Fig. 4. This version can be divided into three sub-models which are coupled: · a model of PCB bioaccumulation in supra-benthic species, · a sea bass population dynamic model, · a PCB bioaccumulation model in di€erent age classes of the sea bass. The model of PCB bioaccumulation in supra-benthic species relies on the same processes as those described in the 1st version of model. Five state variables have been considered which represent PCB contamination in zooplankton, mysidaceans, gobies, and shrimps (Crangon crangon and Palaemon longirostris). Simulations have been performed with variable environmental conditions within a year but with the assumption of constant environmental conditions over the years. The population dynamics model provides an estimate of the ®sh abundance and age distribution and consequently the biomasses associated with each age class. The same demographical processes (recruitment, growth, mortality and ageing) have been taken into account for males and females. They control the age

90†2 2r2

1

References h

1

Lemaire et al. (1992)

day

1

Ramos et al. (1982)

day

1

Ballestrazzi and Lanari (1996) This study

! g day

1

class biomasses which can be described by the following equation:   dB…i† ˆ dN Recruitment  …i 1†  W…i 1† ‡N…i†  dW…i† Growth  …3†  N  W Z  dt Mortality …i†  …i†  …i† dN…i†  W…i† Ageing where N…i† is the number of ®sh in the ith class, W…i† the ®sh weight in the ith class, Z…i† the total ®sh mortality in the ith class, B…i† the ®sh biomass of the ith class. The rate of recruitment of new members to each age class is not constant during the year but the whole recruitment is assumed to occur around the spawning period. It is described by a gaussian function similar to Eq. (2): Rc…i† ˆ f  N…i

1†

with f

1 p  exp ˆ r 2p

…t 90† 2  r2

2

! :

…4†

For the 1 + age class recruitment is a forcing variable that corresponds to the number of ®sh coming from the 0 class (Table 5). Ageing of ith age class is the recruitment in the i ‡ 1 class and thus the same equation (Eq. (4)) can be used to describe the two processes which occur de facto at the same time. Therefore, in order to avoid numerical dispersion, each age class i was divided into two sub groups i1 and i2: recruitment occurs in the i1 sub-class and ageing in the i2 sub-class. The mortality process is constant within a year. For each age class it represents both natural mortality and mortality due to ®shing when ®sh are older than 3 years. TABLE 5 Recruitment and total mortality rate for each age classes of sea bass (Masski, 1998). Sea bass class

Fig. 4 Structure of the the third version of the bioaccumulation model of PCB in sea bass.

248

1+ 2+ 3+ 4+ 5+

Recruitment (number of ®sh)

Mortality rate (day 1 )

1028500

2.30E ) 3 2.19E ) 3 2.25E ) 3 2.30E ) 3 2.38E ) 3

Volume 43/Numbers 7±12/July±December 2001

Mortality is a forcing variable, its formulation is given (Table 5) according to the work of Masski (1998). This bioaccumulation model calculates the amount of PCB in each age class unlike the previous version which only calculates PCB concentration in a sea bass during its life. The `dynamic population' model and the `steadystate' model use the same approach to simulate age class contamination which depends in both models on four processes: respiration, nutrition, excretion/defecation and spawning rate. The coupling of the population dynamic model with a biomass model provides an estimate of the PCB concentration in each ®sh of each age class.

Results and Discussion Steady-state bioaccumulation model of PCBs in the sea bass food web The steady-state model has been validated in the case of the adult male sea bass. Calculated concentrations were consistent with those measured (Loizeau et al., submitted). This model has also shown that food is by far the major source responsible for bioaccumulation (Loizeau et al., submitted). For sea bass, the contribution of water, via respiration, is less than 10% for most of the PCB congeners and even less for the most chlorinated compounds (Loizeau et al., submitted). In this version the model was validated thrice with data describing year old sea bass in May 1996. Field measurement averages (analyses carried out on eight individual ®shes, SD <10%) were compared with simulated data. The model results are very promising: the agreement between modelling and ®eld measurement demonstrates that the key biological processes are properly described by the model and well parameterized. Dynamic model of PCB bioaccumulation: the in¯uence of seasonal variations. The second version of the model, the dynamic model, considers the time variation of the contamination in the food web taking into account the ¯uctuations of environmental conditions acting on the eciency of biological processes and, for females, the e€ect of reproduction. As for the steady-state model this version was validated with measurements on samples collected in 1996 and 1997 (Loizeau and Abarnou, 1996; Loizeau et al., 1997). For all biotic compartments, computed concentrations were compared to the mean of the PCB concentrations measured in each species of the same age class at the time of sampling. In order to simplify the discussion, only the results for CB153 are presented here. This is justi®ed ®rst because this compound is the congener found in the largest concentrations in biological species. Secondly, the relative distribution of PCB congeners is known to be roughly constant from one organism to another as shown for organisms included in the sea bass food chain in the Seine Estuary (Loizeau and Abarnou, 1996; Loizeau et al., 1997) and for other

organisms in di€erent environments (Oliver and Niimi, 1988; Lambeck et al., 1991; Falandysz et al., 1994; Norstrom and Muir, 1994; Teil et al., 1996). Finally, results obtained in the experimental part of this study suggest that in a speci®c species the PCB ®ngerprint does not vary with the age of the animal nor with the period of sampling (Loizeau et al., submitted). The seasonal variations of CB153 concentrations are well simulated by the model for supra-benthic species (Fig. 5) and for sea bass (Fig. 6). The simulated data show oscillations during the year which are not seen in measured data. This is a consequence of uncertainties in ®eld measurements (around 10±15%) carried out on limited sets of samples with their own biological variability as well as of the hypotheses taken to obtain modelled data (as mentioned in model structure and equation). Here, equations were derived from experimental or observed physiological laws obtained in speci®c conditions and calculations were simpli®ed by assuming that the size of preys, i.e. of the zooplankton and supra-benthic species, was constant from one year to another. This implies that, in the model, at the beginning of the year, oldest ®sh eat small prey which are less-contaminated than the prey they were eating at the end of the previous year. This allows for a small decontamination of the sea bass at the beginning of the year which appears as an oscillation in the model results. Another apparent shortcoming of the model is that it overestimates the PCB contamination in the oldest ®sh. This can be explained at least partly by the fact that real ®sh leave the Seine Estuary to reach the less contaminated `Baie de Seine' when they become 3-year old (Bessineton and Simon, 1996) whereas in the model they continue to consume food from the estuary. Hence the contaminant concentrations in the modelled oldest ®sh continue to increase while real ®sh undergo decontamination. This problem could only be overcome if ®sh migration was taken into account. This second version of the model distinguishes males and females and thus simulates the e€ects of spawning. Compared with that of males, the variation of PCB concentrations in females shows a pronounced decrease in spring when they are in the third-year class and thereafter (Fig. 7). According to the simulation these ®sh eliminate large amounts of PCBs during spawning : the concentration decreases markedly as shown by the example of CB153. The modelled loss of contaminants could be calculated from the concentration in ®sh and from their weight before and after the spawning period. It amounted approximately to 30 lg of CB153 for a 3year old female. This is slightly higher than actual measurements (18 lg per ®sh) but of the same order of magnitude. Similar observations have been made for ¯at®sh such as the ¯ounder of the Seine Estuary or dab from the `Baie de Seine' (Loizeau and Abarnou, 1994). After spawning, the feeding activity increases importantly because females have to restore their energy reserves (Jennings, 1990; Mayer et al., 1990). As a result, 249

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Fig. 5 Comparison of simulated (black line) with observed concentrations (grey squares) in zooplankton and supra-benthic species in the Seine Estuary (only CB153 concentrations are given and expressed on a dry weight basis).

Fig. 6 Comparison of simulated (black line) with observed PCB concentration for male sea bass in the Seine Estuary. (CB153 on a dry weight basis).

Fig. 7 Comparison of the sea bass contamination between males (black and dashed line) and females (grey line) showing the e€ect of the reproduction (circles).

PCB contamination in these animals rapidly reaches the levels found in males. The present approach to bioaccumulation modelling, using a bioenergetics-based pollutant accumulation model, can be compared with the study carried out by Luk and Brockway (1997), who have developed a similar model to simulate the total body burden of selected PCB congeners in the Lake Ontario lake trout. Age dependencies on the diet composition were incorporated in their model, but not the e€ects of reproduction. Their results demonstrated the

ability of their approach to show the in¯uence of the diet and of the size of prey on bioaccumulation process and con®rmed the signi®cance of the exposure through the food chain. They also support the explanation for the overestimation of PCB contamination in the oldest sea bass given above. Eby et al. (1997) have modelled the e€ect of changes in growth and diet on PCB bioaccumulation in herring (Coregonus hoyi). They concluded that ®sh that grew less consumed less food, and consequently accumulated less

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PCBs. On the contrary, ®sh that grew more eventually weighted more, and the amount of food required to merely maintain their mass increased, thus increasing their PCB body burden. In the second version of the model described here, females lose contaminants during spawning but the similar contamination levels are measured in females and males in late spring. According to Eby and co-workers, this can be explained by females higher growth rate in spring compared to that of males. Contamination in females is enhanced as they live longer and have a larger body weight than males (Pawson and Pickett, 1996). In conclusion, this dynamic model of PCB bioaccumulation in sea bass represents adequately the seasonal variation of PCB concentrations in species and simulates PCB concentration in sea bass along their life. But the model cannot achieve a simulation that describes the e€ects of an increase or a decrease of the PCB in the sea bass environment: for this purpose the distribution of the contamination in the whole sea bass population according to the various age classes is considered below. The dynamic bioaccumulation model coupled with a simple population dynamic model of sea bass The population dynamics model of sea bass in the Seine Estuary allows to evaluate the biomass associated to each age class. As in the case of the two previous versions, the simulated data were compared with observed data from Bessineton and Simon (1996, 1997) who had assessed the sea bass stock in the Seine Estuary by trawl sampling and ®sh density counting. There is a good agreement between simulated and observed data for the ®rst two age classes (Table 6). Small di€erences between measurement and simulation are observed : one explanation might be that classes are not exactly de®ned in the same way in the model as in the stock assessment. The results for the third-year class show that the model overestimates the biomass considerably, because, as mentioned before, the real oldest sea bass leave the Seine

Estuary for the `Baie de Seine' whereas the modelled sea bass are assumed to live their whole life in the estuary. The sea bass biomass (Fig. 8(a)) and PCB contamination (Fig. 8(b)) are presented for the di€erent age classes. These ®gures show that the 2- and 3-year classes represent the largest biomass whereas that the highest quantities of PCBs are found in the 3±4 year-classes This re¯ects the bioaccumulation processes : the oldest animal tissues contain the highest concentrations of contaminants. In this last version, the model can simulate the e€ects of an increase or a decrease of PCB concentration in water. First, the CB153 concentration in water set at 75 pg l 1 during the ®rst year was divided by two at the end of the ®rst year and the e€ects on the di€erent sea bass age classes was simulated. The decrease in contamination was immediate for one-year old ®sh who were born and grew in less-contaminated water than its elders. PCB concentration in 3-year old ®sh declined progressively (Fig. 9). This is because, each year, the new 3-year class had grown in highly contaminated water during a shorter period, one year less, than the 3-year class of the previous year. Each new 3-year old ®sh had therefore fewer opportunities to bioaccumulate PCB during their growth. A steady state was reached after 3 years as the sea bass spent the whole of their life in less-contaminated waters. Another approach consists in setting suddenly the water contamination to the ideal 0 level and to observe how the contamination would decrease in a 5-year old

TABLE 6 Comparison between simulated and observed biomass (Bessineton and Simon, 1997) associated with di€erent age classes of sea bass in the Seine Estuary. Measurement

Simulation

Size (cm) Weight (g) Age (year) Biomass (kg)

<16 <34.5 <1.5 8700

11±20 14±70 1+ 6750

Size (cm) Weight (g) Age (year) Biomass (kg)

16±21 23±83 1.5±2 11055

20±28 82±205 2+ 9430

Size (cm) Weight (g) Age (year) Biomass (kg)

21±32 120 2±3.5 3030

28±35 205±415 3+ 8300

Fig. 8 Biomass of sea bass (a) and their PCB contamination (b) associated with the di€erent age classes in the Seine Estuary.

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Fig. 9 Simulated PCB concentration for sea bass when the water contamination is divided by a factor two at the end of the ®rst year.

®sh. In this approach, food ( ˆ sea bass preys) contamination reaches 0 level in the ®rst modelled year. This is because preys take up the contaminants directly from water and from phytoplankton and detritus, which are forcing variables, in the model, whose contamination depends directly on PCB concentration in water (Munschy et al., 1996, Brown et al., 1982). Fig. 10 illustrates the decontamination of three congeners in sea bass. These compounds (CB28: 2,4,40 tri-, CB153: 2,20 ,4,40 ,5,50 hexa-; CB180: 2,20 ,3,4,40 ,5,50 hepta-chlorobiphenyl) have between three and seven chlorine atoms in various substitution positions on the biphenyl molecule. The results show that in the absence of contaminants in water, the 5-year class ®sh would see their concentration in PCB halved in about 6 years. It is only after ten years that PCB concentrations in ®sh would become insigni®cant. This approach shows the rate of decontamination through the ®sh physiological process, whereas the ®rst approach looked at the decontamination of the ecosystem. Fig. 10 suggests that modelled concentrations of various congeners decrease roughly at the same rate. To some extent, reality might be slightly di€erent because the various PCB congeners do not behave exactly in the same ways. The ®rst version of the model overestimated the concentration of low chlorinated congeners (Loizeau and Menesguen, 1993; Loizeau et al., submitted). This might be because some species such as shrimps and sea bass may be able to partially metabolize some com252

Fig. 10 Decontamination of a 5-year old male sea bass when PCB concentration is decreased to zero at the beginning of the year 1.

pounds, especially the light congeners up to 6 chlorine atoms in the molecule, depending on their substitution (Boon et al., 1989; Kannan et al., 1995). It would be necessary to include metabolization processes in this model in order to simulate more accurately the bioaccumulation of lighter congeners. The steady-state model also fails in simulating heptaand octa-chlorobiphenyl. This result had already been observed in the ®rst version model (Loizeau et al., submitted). Presumably, the assimilation eciency coecients of the more hydrophobic compounds (log Kow >6±7) are lower than assumed in the model. The third version of this PCB bioaccumulation model is similar to the one described by Thomann and Connolly (1984) on the PCB bioaccumulation in the food chain of the Lake Michigan lake trout. Their model can reproduce the age-dependent trends and magnitude of PCB contamination observed in alewife and lake trout. In the present study, as in theirs, the assimilation of PCBs from food is the major contributor to their accumulation by aquatic organisms. The feeding is responsible for more than 99% of the body burden in the adult lake trout from the Great Lakes. For the sea bass, the contribution of food is more than 90% for most of the congeners and even more for the most chlorinated

Volume 43/Numbers 7±12/July±December 2001

compounds (Loizeau et al., submitted). Last but not least, in the third version, the last scenario, where PCB concentration in water would be suddenly decreased to zero, has shown that in the ®ve-year class, the half-life of PCB is about six years. In theirs, the low contamination levels in old lake trout appears only after a period of ®ve years.

Conclusion This study of the PCB bioaccumulation in food web combines both analysis and modelling in a step-by-step approach. The ®rst version, the steady-state model, has been validated with ®eld measurements and the processes leading to bioaccumulation are correctly described. Feeding appears as the major source of contamination: the contribution of water via respiration was previously believed to be the main source but our results suggest that it accounts for less than 10% for most of the PCB congeners and for even less for more chlorinated compounds. Connolly (1991) has developed a similar model of bioaccumulation for lobster and the winter ¯ounder food chains in New Bedford harbour. In his study, as in the one presented here, the dietary uptake exceeds the uptake across the gill and becomes the dominant route for the higher chlorinated homologues. The present work can also be compared with the model developed by Thomann et al. (1992). Again these authors developed an equilibrium model for the accumulation of organic chemicals in aquatic food webs including sediment interaction. In their study, they normalized the PCB concentrations in the organisms to the organisms lipid concentrations. Application of their model to an amphipod-sculpin food web in the Lake Ontario indicates that water contamination is relatively important if log Kow < 5:5. For log Kow in the range 6.5±7.5, contamination is almost entirely due to food web transfer from sediment. The second version, the dynamics model, considers the ¯uctuation of the contamination in the food web with time. It takes into account the variation in environmental conditions that act on the eciency of biological processes as well as reproduction which is a major elimination process for 3-year old females. This loss of contaminants is however very rapidly compensated by enhanced feeding rates that follow the spawning period. Results obtained with this model were consistent with observed data except that the model overestimates PCB contamination in the oldest ®sh. This can be explained by the fact that real sea bass leave the Seine Estuary when they grow old and live in less-contaminated areas whereas their modelled counterparts continue their life in a contaminated environment. In its third version, the bioaccumulation model is coupled with a population dynamics model and can estimate the PCB concentration in ®sh in each age class. Therefore it may be used to predict the e€ects of changes

in PCB concentration in water on the level of contamination in the sea bass population. The results of this model are consistent with measurements except for the oldest sea bass, for the same reasons as mentioned for the ®rst version of the model. In its present form, our model appears very useful for the assessment of the fate of persistent contaminants in biota. Several improvements are nevertheless foreseen. Work is currently being undertaken to couple the sea bass bioaccumulation model to an ecological one so that major sources of contamination such as sediment, SPM and phytoplankton can be simulated. PCB concentrations in these compartments will become state variables. Spatial variations of these within the estuary will be simulated and then a more accurate estimation of PCB contamination in the oldest sea bass should be possible. Eventually, the model will be modi®ed so that bioaccumulation of other groups of contaminants can be simulated. Toxic compounds such as coplanar PCBs and poly-aromatic hydrocarbons (PAHs) are of great concern and particular interest. These chemicals behave di€erently in food webs, PAHs being rapidly eliminated at higher trophic levels. Preliminary results have been obtained for the levels of these compounds in the food chain of the Seine Estuary (Jaouen-Madoulet, 2000). Further improvements of the model are needed to simulate the biotransformation processes involved. This work was a contribution to the project `Programme Scienti®que Seine-Aval' led by L. A. Roma~ na (Ifremer, La Seyne / Mer) and was funded by the `Region Haute Normandie', the water authority `Agence de l'Eau Seine-Normandie', and several regional and national French agencies. The authors thank all their colleagues for their help during the sampling cruises, for fruitful discussions and for information given on the biological aspects of the work, particularly Pr J.C. Dauvin (Station marine de Wimereux); Dr P. Mouny (MNHN, Paris); Dr H. Rybarczyk (Univ. Paris 6); C. Bessineton and S. Simon (Cellule de Suivi du Littoral Haut Normand, Le Havre); Pr. P. Miramand (Univ. La Rochelle). They are also indebted to J.F Guillaud for his encouragement during the work and to Dr A.C. Le Gall for improving the English text. Aaser, H. F., Jeppesen, E. and Sondergaard, M. (1995) Seasonal dynamics of the mysid Neomysis integer and its predation on the copepod Eurytemora anis in a shallow hypertrophic brackish lake. Marine Ecology Progress Series 127, 47±56. Abarnou, A. and Simon, S. (1986) Contamination de l'estuaire et de la baie de Seine par les PCB. In La Baie de Seine, Groupe de recherches coordonnees Manche (Greco Manche), Actes de colloques No. 4, pp. 471±476. Ifremer, Universite de Caen. Avoine, J. (1986) Sediment exchanges between the Seine Estuary and its adjacent shelf. Journal of the Geological Society (London) 143, 1± 14. Ballestrazzi, R. and Lanari, D. (1996) Growth, body composition and nutrient retention eciency of growing sea bass (Dicentrarchus labrax L.) fed with ®sh oil or fatty acid Ca salts. Aquaculture 139, 99±107. Barber, C. M., Suarez, L. A. and Lassister, R. R. (1988) Modelling bioconcentration of nonpolar organic pollutants by ®sh. Environmental Toxicology and Chemistry 7, 545±558. Beghin, V., Thoumelin, G. and Wartel, M. (1997) Composition de la MOP et du contenu stomacal de Crangon crangon et de Palaemon longirostris; Utilisation des marqueurs biochimiques (acides gras et sterols). Rapport d'activite du Programme Scienti®que `Seine-Aval'. Rapport 1996/Fin-4, 99±120. Bertignac, M. (1987) `L'exploitation du bar'. Unpublished Ph.D. Thesis, ENSA de Rennes (France) 235 p.

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