Application of a polychlorinated biphenyls bioaccumulation model to Lake Ontario lake trout

ELSEVIER

Ecological Modelling 101 (1997) 97-111

Application of a polychlorinated biphenyls bioaccumulation model to Lake Ontario lake trout G.K. Luk

a,*, F. Brockway b

a Department of Civil Engineering, Ryerson Polytechnic University, 350 Victoria Street, Toronto, Ontario M5B 2K3, Canada b Municipal Department, Marshall, Macklin and Monaghan Consulting Co., 80 Commerce Valley Drive, Thornhill, Ontario L3T 7N4, Canada

Accepted 19 December 1996

Abstract

The total body burden of specific Polychlorinated biphenyl (PCB) congeners in Lake Ontario lake trout (Salvelinus namaycush) was studied with a bioenergetics-based pollutant accumulation model. Concentrations for PCB-congeners 101 and 153, which when combined account for approximately 16% of total PCB burden in Lake Ontario fish species, were analyzed with respect to trout age and weight. Age-class dependencies for diet composition and energy densities of prey and consumer were incorporated in the analysis. Parameters obtained from independent metabolic and fish growth studies were further refined, using recent data in the fields of pollutant kinetics, bioenergetics and freshwater fish production. Sensitivity analyses indicated a general robustness of the model, with metabolic and growth related parameters found to be most critical• The model was fitted to Lake Ontario water and fish pollutant concentrations data. Results demonstrated the effectiveness of the approach, and confirmed the significance of the acclaimed food-chain route of exposure. © 1997 Elsevier Science B.V. Keywords: Bioaccumulation models; PCBs; Fish; Body burden

1. Introduction

Polychlorinated biphenyls (PCBs) are a family of chlorinated hydrocarbons, in which one or more hydrogen atoms in the biphenyl molecule (C12H,0) are replaced by chlorine. The properties * Corresponding author. Tel.: + 1 416 9795345; fax.: + 1 416 9795122; e-mail: [email protected]

of these compounds, such as stability, low volatility, and fire resistance, are very suitable for industrial applications. Since 1930 over 1.2 million tonnes of PCBs have been produced worldwide, of which an estimated 31% has been released to the environment (WHO, 1976). Recent studies have linked PCBs with various forms of cancer affecting the liver, gall bladder and biliary tract. It is suspected that long term exposure may result in

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G.K. Luk, F. Brockway / Ecological Modelling 101 0997) 97-111

cell mutagenicity in human tissues. In light of this data, the Ontario government has added PCBs to a list of 29 chemicals x~hich are candidates for a total ban. It has been observed that even extremely low background PCB concentrations in the aquatic environment may result in elevated levels in aquatic plant life and at all trophic stages of the food-chain. The demonstrated potential for living organisms to concentrate PCB residues to greater than a million times the background water levels, commonly termed the bioaccumulation effect, is an established environmental concern. Despite mitigative measures to reduce discharges, top predator level game fish in the Great Lakes continue to demonstrate whole-body PCB residues in the order of 4-10 ppm. This is outside the Canadian government's guideline for safe consumption, which has been set at 2 ppm. Sound management, mitigation and control of pollutant discharges to the aquatic ecosystem are reliant on our understanding of the factors which influence deposition and clearance of PCBs in fish. To this end, bioaccumulation models of different vigour have been proposed to explain accumulation behaviour of persistent chemicals. The traditional approach has been to use a linear sorptive-desorptive kinetic relation to represent the transfer of pollutants from food and environment into fish tissues. Another method, termed the 'fugacity' approach, is based on the thermodynamically-driven partitioning tendency of the chemical. For both of these methods, the rate or tendency of chemical transfer between the different phases, from water to body liquids to lipids, can be determined from laboratory study where the steady-state effects of constant exposure are measured. No consideration for entry through the food vector or growth dilution is generally given. While this works well with simple chemicals that are rapidly equilibrated within the fish body, it has poor performance with compounds that have complex atomic structure or are slowly metabolized, such as chlorinated hydrocarbons. For these substances, the most commonly accepted approach is the bioenergetics-based model. Essentially, this concept links pollutant accumulation to metabolic demands for food and oxygen. Some

proportion of the chemical to which the fish is exposed through these routes is absorbed into the body. Various physical, environmental and biological factors can be incorporated into the model. Examples of application include works of Jensen et al. (1982), Thomann and Connolly (1984), and recently Harris and Snodgrass (1993). The accuracy of these attempts hinges on the metabolic function of the fish under field conditions. Unfortunately, there has been a lack of measured data and general consensus on this issue. As a result, most investigators had to adjust the metabolic function in order to get matching pollutant concentration data. The objective of this paper is to extend an existing bioenergetics-based model (Fagerstr6m and ~,sell, 1973; Norstrom et al., 1976; Jensen et al., 1982) to describe the bioaccumulation behaviour of specific PCB-congeners in Lake Ontario lake trout (Salvelinus namayeush). In particular, the metabolic function will be determined independently with recent data and models on fish metabolism. Consequently, advance in the fields of bioenergetics, pollutant kinetics, and fish farming could be incorporated to refine the performance of the model. The goal is to provide an improved protocol, with an application example, to represent more accurately the various pathways and mechanisms of entrance and clearance of PCBs in top predator species of the food-chain.

2. Theoretical development of bioenergetics model The generalized bioaccumulation model consists of terms to account for food and water uptake vectors, as well as clearance or depuration. When the bioenergetics-based approach, which provides estimates of food and oxygen needed to satisfy metabolic demands, is incorporated in the model, the following equation for the total body burden, P (pg), is obtained: dP -d? = epfRCpf +

EpwpVCpw - ICP

(1)

where R (g/wk) is the food ration, p (g/ml) is the density of water, V (ml/wk) is the volume of water processed, and K' (g/(g. wk)) is the clearance rate.

99

G.K. Luk, F. Broekway/ Ecological Modelling 101 (1997) 97-111 In the equation, Cp (/tg/g) and Ep (dimensionless) represent the pollutant concentration and absorption efficiency respectively. The second subscript defines the vector from which the pollutant derives, which is either food (f) or water (w). It has been established that growth dilution is the chief mechanism in the reduction of whole body residues of the higher PCB-congeners. For these slowly-cleared pollutants with long biological half-lives, a simpler clearance expression, K', summarizing the effects of body egestion and growth dilution, is generally sufficient. Ration is estimated from the energy requirements for respiration and growth of the fish. Energy must be available to sustain basal metabolic activity as well as for normal swimming and foraging activities. In addition, energy is deposited in the form of body tissues, resulting in fish growth. Taking these into consideration, the expression for ration in terms of total food energy required per week is obtained as: R=

1 [

~rd _el"

W~

1 dW-I

+ (fl + )--~-- J

(2)

where 0~lr(kcal/(wk.g~)) represents the low-routine metabolism per unit weight of fish, W(g) is the fish weight, ~ (dimensionless) is the metabolic exponent, fl (dimensionless) is the proportion of the growth rate that represents the energy for food conversion, and Efd (dimensionless) is the efficiency of conversion of food to energy. In order for the equation to be dimensionally homogeneous, the fish weight has to be converted to energy equivalents. Norstrom assumed a constant value of 1 g/kcal for their model, which implies a gram of fish tissue growth to be equivalent to 1 kcal of energy contents. If this equivalence is used, ration (kcal/wk) is related directly to grams of food consumed in a week. An estimate of the volume of water passing the fish gills is obtained from the oxygen requirement to satisfy total metabolic demand except growth. Combining this with the expression for total metabolic rate, the following equation is obtained for the volume of water, V (ml/wk), passing over the gill lining per unit time:

~r W~ + fl(dW/dt)

v-

(3)

EoxCoxqox where Eox (dimensionless) is the efficiency of assimilation of oxygen from the water, Cox (g/g) is the concentration of oxygen in the water, and qox (kcal/ml O2) is the equivalence coefficient for converting oxygen respired to energy utilized. When the expressions for R and V are substituted into the basic pollutant accumulation expression in Eq. (1), the following model of Norstrom et al. (1976) is obtained:

EpfCp~o~lrWrqL(~Aff 1 ) a w l

de -=

E

,,L

EpwCpwp I/

r

+ EoxCoxqox~CtlrW_

--~dW) + fl

/ - K'P

(4)

When the simplifying assumption of constant energy density (1 g/kcal) of Norstrom is adopted, direct conversion of energy consumed to gram weight equivalents is possible. This approach, however, can be refined using data from Stewart et al. (1983), whose experiments confirmed that the energy density of fish flesh actually varies with species, season and age. Energy density of lake trout, for example, can double over the life span. Larger, older trouts must therefore accumulate twice as much energy by doubling their food intake in order to gain the equivalent weight as young trouts. This new insight may be incorporated into the existing bioaccumulation model by applying the appropriate energy equivalent of each species in the conversion, giving the following form of the bioaccumulation equation: dPd__t_ Epf Cp,-[-

~ -~d W1 = qfd~lr.-L Et~'[ W + qv(fl + 1) _1 gpwCpwp {

,,,

dm~

+ --ICtlr w r + qvfl J -- K'P (5) EoxCoxqoxk, --~ where qv and qfd (both in kcal/g) represent the energy density of the fish and the food respectively.

3. M o d e l parameters

The present work involves the modeling of bioaccumulation behaviour for specific PCB-con-

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G.K. Luk, F. Brockway / Ecological Modelling 101 (1997)97-111

geners. Oliver and Niimi (1988) have measured concentrations of PCB-congeners in Lake Ontario water and a few species of fish. Their measurements indicate that the penta- and hexa-CB isomeric groups consistently account for about 60% of total PCB residue in fish at the top predator levels. PCB-congeners 101 and 153 were chosen as representatives of the penta- and hexa-congeners respectively. These two congeners alone account for 15-18% of the whole body PCB residues in larger fish in Lake Ontario (Oliver and Niimi, 1988). Studies related to fish ecology and production have also been used to gather data related to the parameters of the model. 3. I. Growth parameters (dW/ dt, qF and qfd) Table 1 summarizes the values of the growthrelated parameters of the model. Chen et al. (1992) have suggested that the von Bertalanffy Growth Functions (VBGF) are statistically superior to other expressions in describing fish growth. A function of this form is used to describe the 10-year growth of lake trout in Lake Ontario: W( t ) = Wy(1 - e - B') N

(6)

where Wf (g) is the maximum fish weight, and B ( w k - l ) and N (dimensionless) are empirical constants. Fig. 1 shows the fitted VBGF to the lake trout data collected by Borgmann and Whittle (1992a). Table 1 Growth-related model parameters Parameter

Symbol (units)

Adopted function or value

Weight Energy density

W (g) qv

For fish

(kcal/g)

Energy density

qrd

For preys

(kcal/g)

8400 (1-e-°°35t)2"~ (t in weeks) 1.362+7.36X 10 -4 W (W --___1470 g) 2.172+ 1.86 x 10 -4 W (W ~ 1470 g) 1.00 kcal/g (invertebrates) 1.36 kcal/g (sculpins and smelts) 1.60 kcal/g (alewife)

Constant values for qv and qfd have been assumed in previous bioaccumulation models (Norstrom et al., 1976; Jensen et al., 1982; Harris and Snodgrass, 1993). It has been demonstrated, however, that older and larger fish must consume more calories than young fish to gain an equivalent weight. Accordingly, a functional relationship incorporating the ontogenetic energy density increase (Stewart et al., 1983) has been adopted, as shown in Table 1. The relative energy density of prey species upon which lake trout feed should also be considered in the model. As Great Lakes lake trout grow, they appear to preferentially shift their diet, from slimy sculpin (Cottus cognatus) and invertebrates, to smelt (Osmerus mordax) and alewife (Alosa pseudoharengus) (Elrod, 1983; Stewart et al., 1983; Christie et al., 1987). Relative energy densities, qfd, of the main prey species in lake trout diets are summarized in Stewart et al. (1983). 3.2. Metabolic parameters (air, ~ and fl) Estimates of field metabolism of lake trout present the major challenge to model application. The effects of changing metabolic parameters on the model performance are demonstrated in Fig. 2. As evident from the figure, the model output is particularly sensitive to changes in T, the exponent for body weight dependence of metabolism. Previous authors have fitted pollutant uptake models by adjusting these parameters within ranges provided through the literature (Jensen et al., 1982; Harris and Snodgrass, 1993). The validity of this approach is questionable because metabolism is a distinct characteristics and should be studied independently. Therefore, a new approach is undertaken in the present work to provide an adequate representation of field metabolism of the species under study prior to any association with pollutant accumulation model. The parameters 0~lrand z are used in fish bioenergetics modeling to relate the level of metabolism to fish body mass, at any level between standard and maximum activity levels. At low activity levels, a well-defined relationship is usually evident between metabolic rates and body mass over wide temperature regimes. At metabolic rates above

G.K. Luk, F. Brockway / Ecological Modelling 101 (1997) 97-111

101

6000

5000

4000

,

°

,

°=?I I

j-.

~~ - - - -

2000 n

1000

-

0.0

-

=

2.0

=61

:

~

4.0

-

6.0

8.0

-

10.1

LakeTroutAgeClass(years) Fig. 1. Wet mass of Lake Ontario lake trout for different age-classes. The symbols represent the mean and standard deviation of n-samples of measured data (Borgmann and Whittle, 1992a). The line represents the von Bertalanffy growth function fitted to the data.

standard, however, this relationship can change, and, at maximum activity levels, body weight dependence may no longer be evident. For salmonids, the scope for metabolic activity appears to increase with temperature to a maximum, with a decline above an optimum temperature (Webb, 1978). Values for c¢ and z are usually adjusted to fit measured laboratory data for oxygen consumption at various temperature, activity and feeding levels. Neely (1979) has tabulated data showing ~ values at active levels exceed those at standard levels by a factor of three to ten for salmonids. Weiser (1985) provided an illustration of increased respiratory requirements for rainbow

trout (Salmo gairdneri) between standard and active levels. With regards to pollutant modeling, Norstrom et al. (1976) used constant values of 0~lrfor various age-class fish. These values were normalized with respect to seasonal temperature and growth fluctuations for Ottawa River yellow perch (Perca flavescens). A range of 0.136-0.112 kcal/(wk.g ~) for ~lr was suggested, reflecting the decline of weight-specific metabolism with age. The body weight exponent for metabolism (r) was set at a constant value of 0.81 based on literature review. Jensen et al. (1982) applied a PCB bioaccumulation model to five species of salmonids. Using a

G.K. Luk, F. Brockway / Ecological Modelling 101 (1997)97-111

102

constant value of 0.121 kcal/(wk'g ~) for ~lr, z values were adjusted over a range of 0.80-1.25 to f i t the observed fish pollutant level data. In view of the inconclusive literature review, a modified approach is used herein to provide estimates of ~lr and r for the model. Consumption estimates for Lake Ontario lake trout are compared with two energetics models to provide bestfit energetics parameters. These parameters are used to estimate average weekly metabolic expenditures over the expected range of feeding and activity levels. The first model was developed by Stewart et al. (1983) on the Lake Michigan lake trout population. The model was proposed to re-construct food consumption based on observed growth patterns and predict future forage requirements. SeaI Lalm Trout, c~v-, 0.149

I

sonal fluctuated, weight specific metabolism (Qs) was modelled by combining functions for consumption and typical in-situ swimming speeds, both temperature and weight dependent, using the following expression: Qs= 0 . 0 0 4 6 3 ( ~ ) W - °'295e°°59 Te0"°232( 11.7 W0-05e0-04OST)

(7)

where Qs (g/(g-day)) is the rate of consumption of prey and T (°C) is the temperature. Oxygen requirement to satisfy metabolic demand is converted to equivalent food energy units required (kilocalories) using the (qox/qv) conversion. The second model is taken from Thomann and Connolly (1984), in which a generalized relationship for metabolic rate of lake trout may be found: Qs = 0.020 w - 0.2

3

I] o 0

(a)

1C(]0

2000

3CC0

40[:0

SCfl0

W~lht ~ [ Lake Trout, z - 0.91

i

J 0

.

0

(b)

.

.

1(]00

.

.

.

2000

.

30[:0

.

40QO

.

.

.

5000

~00

w.,~t (:..,,.:

When the negative body-weight exponents in these expressions are multiplied by the fish wet weight, equivalent values of 0.705 and 0.80 for r are obtained. Using the body weight information for lake trout, the above metabolic expressions are converted to metabolic costs and plotted over a tenyear growth period in Fig. 3. Seasonal temperature variability is taken from the data of Stewart et al. (1983). Lake water at 10°C is assumed to be occupied by lake trout during much of the year, but during the winter months the fish must occupy colder water (Stewart et al., 1983; Olson et al., 1988). It may be observed from the figure that the 'scope of activity' implicated in many metabolic studies is quite evident. Thomann's model (upper line) gives a higher estimate of metabolic costs representing an active level metabolism, where Stewart's model gives the standard level of activities of fish. When the metabolic function is fitted to the two models, the following parameter values are obtained: 0~lr 0.149 kcal/(wk.g~), and r ranges from 0.88 for the lower curve to 0.92 for the upper one. These fitted metabolic functions are plotted as the broken lines in Fig. 3. An average =

Fig. 2. Sensitivity of model output to metabolic parameters. (a) with ~qr = 0.149 kcal/(wk.g'), and r = 0.8, 0.9 and 1.0. (b) with z = 0.9, and Cflr= 0.14, 0.15 and 0.16 kcal/(wk-g~).

G.K. Luk, F. Brockway /Ecological Modelling 101 (1997)97-111

103

450 ::::

4OO

350

3OO 2

J 250

R

200

tu 150

100

50

0 0

2

4

8

8

10

Lake Trout Age Glass 0¢~mm) Fig. 3. Field metabolism of Lake Ontario lake trout. The solid lines represent metabolic models of Thomann and Connolly, 1984 (upper) and Stewart et al., 1983 (lower). The broken lines are the fitted metabolic functions to the two models.

value of ~ = 0.90 is used in the bioaccumulation model to reflect the general metabolism of the fish somewhere between the two extremes. Borgmann and Whittle (1992a) have produced estimates of field metabolism for Lake Ontario lake trout, based on observations of pollutant kinetics. Their values for metabolism over a range of fish weights are similar to values obtained from the above simulations. For purposes of the present work, more confidence can be placed in studies which derive estimates of field metabolism using bioenergetics-based approaches, rather than the ancillary approach used by previous authors.

Norstrom et al. (1976) used the metabolic parameter, fl, to estimate the amount of energy required to metabolize and store food, Q¢: dW

Q~=fl dt

(9)

Alternatively, Qc may be expressed as a fraction of metabolizable ration according to Webb (1978). Stewart et al. (1983) have suggested a value of 17% for this fraction for lake trout, independent of temperature and ration size. Therefore, Qc may also be given as:

G.K. Luk, F. Brockway ~Ecological Modelling 101 (1997)97-111

104

+ 1 dWq Qc=0"17[ ~qrW~ ( f l + ) - ~ d t J

(10)

Equating the above equations and substituting the derived values of ~lr and z, the following expression for fl is obtained: /? =

0.0305 W ~

d W/dt

+ 0.205

(11)

With the fitted weight for lake trout, a range of from 0.25-8.5 times the growth rate has been obtained over the 10-year growth simulation. This increase is consistent with the fact that decreased proportions of assimilated food are used for growth as fish age (Stewart et al., 1983; Borgmann and Whittle, 1992a).

3.3. Efficiency factors (Efd, Epf, Eox, E~,w) The efficiency of food assimilation by fish, Erd, is an important determinant in the pollutant uptake model; food energy excreted or egested is not available for growth and respiration. A description of food energy loss mechanisms is given by Webb (1978). The most comprehensive laboratory study was provided by Stewart et al. (1983), who modelled excretion and egestion of lake trout on mixed diets as functions of temperature and ration size (percentage of maximum ration consumed). Total proportion of consumption going to waste losses is estimated at between 16 and 36%, varying with season, ration size and type of prey ingested. For typical conditions when temperature ranges from 4 to 10°C, and ration of 25-85%, the suggested value is 22% (or Efd = 0.78). This value is adopted in the present study. The efficiency of assimilation of total PCBs from food, Ep¢, was assumed as 0.8 by Norstrom et al. (1976) and Thomann and Connolly (1984). This value should be further refined, because individual PCB-congeners have different chemical structures which cause them to be absorbed differently. Niimi and Oliver (1983) have calculated uptake efficiencies for PCB-congeners 101 and 153, for rainbow trout exposed to a single oral dose, at 0.78 and 0.75 respectively. Pollutant absorption efficiencies vary according to specific environmental and biological conditions, and

generally decrease with continuous exposure (Neely, 1979; SpigareUi et al., 1983; Barber et al., 1991); thus the values of Niimi et al. may be high. Theoretically, it is more appropriate to consider the net pollutant assimilation efficiency, which is defined as the product of Epf and Era. Sijm et al. (1992) calculated the net assimilation efficiency to be 0.37 for PCB-153 in guppies (Poecilia reticulata) after 30 weeks of exposure. This value may have been low as a result of poor food absorption by guppies. Barber et al. (1991) listed a net assimilation efficiency of 0.371 for PCB-153 in coho salmon (Oncorhyncus kisutch) after 108 days of exposure, following a general pattern of decrease with exposure. With the adopted Era (=0.78) from above, the equivalent value of Epf from both studies is 0.48. The recommended value for longterm exposure for lake trout is much higher than this. Consequently, a value of Epf = 0.55 has been selected initially for this study, giving a net assimilation efficiency of 0.43. The efficiency of oxygen transfer, Eox, across the gills is taken as 0.75, following recommendations of Norstrom et al. (1976) and Jensen et al. (1982). It is likely that oxygen transfer efficiency varies with temperature and activity level. This factor is not considered in the present work, in lieu of the minimal contribution of the water uptake vector. Efficiency of pollutant transfer across the gills, Epw, is controlled to a large degree by molecular size and weight, stereochemistry and water solubility (Connell, 1988). Attempts to establish relationships between bioaccumulation potential and one or more of these physicochemical properties have often been made. Barber et al. (1988) have plotted predicted gill uptake efficiencies of organochlorines for rainbow trout. From their plot, an estimate of Epw for PCBs-101 and 153 are 0.42 and 0.40 respectively. Thomann (1989) has discussed the factors affecting the uptake efficiency, and proposed a relationship between Epw and the octanol-water partition coefficient. This gives values of Epw = 0.40 and 0.30 for PCBs-101 and 153 respectively. Since the proposed values from both studies are quite close, the average of the suggested values is adopted. Absorption efficiencies at the gill membrane are lower than at the

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G.K. Luk, F. Brockway/ Ecological Modelling 101 (1997)97-Ill

intestinal linings, which is consistent with the longer contact times expected in the gut.

3.4. Miscellaneous factors (K, qox and Cox)

A simplified kinetic approach for clearance, incorporating the effects of elimination and growth dilution, is used in the present model. This approach is considered satisfactory, given the low clearance rates associated with PCB compounds. Elimination rates for PCBs vary with specific congeners, but are known to be very low for the higher chlorinated congeners. Niimi and Oliver (1983) established the biological half-lives of PCBs-101 and 153 as well over 1000 days. Assuming first order elimination kinetics, this halflive gives an equivalent elimination rate of less than 0.005 w k - 1 . Sijm et al. (1992) found elimination rates of 0.0019 wk -1 for PCB-153 in guppies. A value of 0.002 w k - 1 for the elimination rate of PCBs-101 and 153 is assumed for the initial model runs. This low value reflects the extremely low clearance rates observed in the above studies. Weight specific growth dilution is the second component of the clearance coefficient, and must be included since it is the most important factor in the reduction of whole body PCB residues (Thomann and Connolly, 1984). This value may be calculated for each time step by dividing growth by the body weight. The energy equivalent of respired oxygen, qox, was measured as 3.24 kcal/g 02, which is consistent with values used by N o r s t r o m et al. (1976) and Stewart et al. (1983). The saturation concentration of oxygen in water, Cox, is given as a function of temperature: Cox = 14.45 - 0 . 4 1 3 T + 0.00556T 2

(12)

When the actual dissolved oxygen is above 70% saturation level, the rate of oxygen consumption is independent of Cox (Stewart et al., 1983). Therefore, normal fluctuations in oxygen concentration do not affect the model.

4. Model sensitivity analysis A sensitivity analysis was conducted to determine how the model might be affected by input error. The approach is similar to the method used by Stewart et al. (1983). The sensitivity of model output, x, to each input parameter, P, is measured by the dimensionless parameter Sx: S~ =

Ax/x

(13)

AP/P

where AP and Ax are the corresponding changes in P and x respectively. Hence, a sensitivity of + 1 indicates a change in the input parameter will result in an identical change in the same direction for the output value. Results of the analysis, as generated with a + 10% change in model parameters, are tabulated in Table 2, along with the model values. It

Table 2 Sensitivity analysis on model parameters Parameter

cqr z ~' B Cot Cpw

Cox Efd Eox Epr Epw K N qrd qv qox Wf

Modelvalue

0.149 kcal/ (wk .gr) 0.90 var 0.0035 wk i var pg/g 130 pg/g (PCB-- 101) 50 pg/g (PCB-153) 10.9 pg/g 0.78 0.75 0.55 0.41 (PCB-101) 0.35 (PCB-153) 0.002 wk-i 2.5 var kcal/g var kcal/g 3.24 kcal/g 8400 g

SensitivitySx Ax/x = + 10%

Ax/x = - 10%

0.57

-0.57

10.47 0.36 0.44 0.96 0.04

-4.90 -0.36 -0.43 -0.96 -0.04

-0.04 -0.87 -0.04 0.96 0.04

0.05 1.07 0.05 -0.96 -0.04

-0.20 -0.45 - 0.87 0.43 -0.04 0.04

0.21 0.51 1.07 --0.43 0.05 --0.03

106

G.K. Luk, F. Brockway / Ecological Modelling 101 (1997) 97-111

may be observed that the model is extremely sensitive to variation of the metabolic parameter ~. In the present work, the metabolism function was specified a prior from independent information, thus eliminating the need for adjustment and hence the possible error introduced by this parameter. The model shows an average sensitivity to changes in Epf, Efd and Cpr, and much less sensitivity for K' and other water uptake parameters. It is apparent that the model is most sensitive to changes in food and growth related parameters, whereas generally low sensitivity was indicated for similar changes in water uptake related parameters.

5. Model application The model is applied to predict accumulation trends of PCB-congeners 101 and 153 by Lake Ontario lake trout. This species is representative of the top predator level of the food-chain in Lake Ontario. Calibration of the model requires estimates of pollutant levels in water and food, as well as diet composition of lake trout over the chosen modeling period. Recent pollutant level data and a probable diet composition scenario were constructed based on available data for Lake Ontario. The Canadian Department of Fisheries and Oceans has monitored levels of PCBs in Lake Ontario since 1977 as part of an ongoing surveillance program (Borgmann and Whittle, 1991). The most recent figures available for pollutant levels were from 1988; therefore the model is applied for the period 1977-1986, representing a 10-year lifespan for the trout.

5.1. Prey composition and PCB levels An estimate of contaminant concentration in lake trout prey items must reflect the levels of contamination in the individual prey species, as well as the relative proportions of these species in the diets of different-aged trout. A dietary shift appears evident over lake trout life spans. Younger trout feed mainly on sculpin and inverte-

brates and older trout on smelt and alewife (Stewart et al., 1983; Christie et al., 1987). The proportions of individual prey species in the diet is dependent on habitat selection of the trout and relative availability of the various prey species within this habitat (Olson et al., 1988). Stewart et al. (1983) have provided a re-construction of food habits of Lake Michigan lake trout. Relative size and percentage composition of prey items were given for all age classes. Age 1 + trout consumed 40% invertebrates and 60% mixed fish species. After the second year, smelt, sculpin and alewife are considered to make up the complete diet, with alewife becoming increasingly more important from age 4 + (75%) to age 8 + (95%). Based on the data of Christie et al. (1987), Borgmann and Whittle (1992a) have estimated the average proportions of the three main prey species (sculpin, smelt and alewife) in the diet of Lake Ontario lake trout. Sculpin and smelt are the dominant forage until age 3 + , with alewife becoming more important at age 5 + (70% of diet for all subsequent age classes). Invertebrate prey is not considered in their model. Relative proportions of the different prey species are comparable to Stewart et al. (1983) after age 1 + , with alewife considered less significant in diets of larger fish. Brandt (1986) has collected data on stomach contents of larger lake trout from Southern Lake Ontario during the April to September periods of 1983 and 1984. Their data shows that alewife made up the largest proportion of stomach contents for all months in both years. Averaging the data for percentage occurrence of smelt and alewife, alewife was found to make up roughly 75% of identifiable stomach contents. In comparison, smelt accounted for a maximum of 25% in 1984. Diet composition of Lake Ontario lake trout has been monitored by the Canadian Department of Fisheries and Oceans. Alewife composed 7581% of lake trout diet in the period from 1983 to 1986, with smelt accounting for 15-21% of the remainder. Based on this and the above information, a reconstruction of diet composition for lake trout over the modeling period is attempted in Table 3. The proportions indicated are based on the data of Brandt (1986) and Department of

G.K. Luk, F. Brockway / Eeological Modelling 101 (1997)97-111

107

Table 3 Diet composition and contamination of lake trout Lake trout age-class

1+ 2+ 3+ 4+ -10+

Sculpin (%)

50 25 10 0

Smelt (%)

25 25 25 25

Fisheries and Oceans. Within the scope of the present work, these values are taken as an adequate representation of food-chain delineation for lake trout. PCB concentration levels in lake trout prey are estimated based on the data of Borgrnann and Whittle (1992b). A total of 772 Lake Ontario smelt and sculpin were analyzed for PCB content for the period from 1977 to 1988. The authors reported no significant change in the trend of PCB levels over the monitored period. There were significant site-to-site variations in concentrations for both species, with decrease in PCB levels from West to East of the lake. Lake wide, average total PCB levels have remained markedly consistent at around 1 pg/g for both species. From the data of Borgrnann et al., average total PCB concentrations over the modeling period are 0.86 pg/g for smelt and 1.23 pg/g for sculpin. PCB levels in alewife were not monitored as part of the surveillance program. The authors suggest that alewife are less contaminated than smelt or sculpin based on data for 1986. Oliver and Niimi (1988) have reported PCB levels for a number of freshwater species in Lake Ontario. The values for total PCB levels reported are higher than those reported by Borgmann above. This can be explained by the fact that most of their samples were taken from the nearshore areas in the vicinity of the Niagara River, at which higher concentrations are expected due to dynamic sediment re-suspension and proximity to contamination source. The congener specific data is used to provide estimates of relative content of PCBs-101 and 153 with respect to whole body PCB levels. Applying the typical fraction of 7% for PCB-101 and 10%

Alewife (%)

25 50 65 75

Food contamination (/tg/g) PCB-101

PCB-153

0.073 0.067 0.063 0.060

0.105 0.095 0.090 0.086

for PCB-153 to the concentration levels reported by Borgrnann and Whittle (1992b), the following average congener concentrations are obtained: PCB-101:0.060 ltg/g in smelt; 0.086 pg/g in sculpin PCB-153:0.086 /tg/g in smelt; 0.123 pg/g in sculpin These values are representative of lake-wide averages over the 10 years of modeling period. The data from Oliver and Niimi (1988) indicates that alewife are only slightly less contaminated than smelt and sculpin, with respect to whole-body PCB levels. Therefore, the congener concentrations in alewife are accepted as being identical to smelt as a conservative estimate. Adopting the diet composition derived previously and these concentration values, weighted averages for the concentrations of congeners 101 and 153 for the different ages of trout may be obtained. These are also tabulated in Table 3. The above values reflect the expected decline in food exposure concentrations as growing lake trout shift their diets from more highly contaminated sculpin to alewife and smelt. Invertebrate prey is not directly considered in the model. This should not seriously affect the output, as invertebrates do not constitute a large portion of the lake trout diet for older fish in Lake Ontario (Elrod, 1983; Brandt, 1986; Christie et al., 1987). No predator to prey size relationships were considered in the modeling attempt. Brandt (1986) did not find evidence to strongly support such relationships.

5.2. Water concentrations Background PCB water concentrations in Lake Ontario have shown a slow decline with a half-life

108

G.K. Luk, F. Brockway / Eeological Modelling 101 (1997)97-111

in the order of 10 years (Environment Ontario, 1989; Borgrnann and Whittle, 1992b). PCB water concentrations for 1984 were 130 + 36 ppm for PCB-101 and 50 __. 13 ppm for PCB-153, representing 12% and 4.6% of total PCBs measured respectively (Oliver and Niimi, 1988). These values represent a lake-wide mean of seven offshore sites taken in 1984, and are accepted as representative values for Cpw over the modeling period. Although concentrations would have been higher for previous years (1977-83), the authors have also noted that a large percentage of measured concentrations may not be available for bioaccumulation because PCBs preferentially bind to organic matter in the water column. Barber et al. (1991) estimated this fraction as 10-20% for penta- and hexa-CBs in Lake Ontario. Larsson et al. (1992) have discussed the influence humic substances in the water column may have in reducing bioavailability of pollutants. The water concentrations chosen for the model may be in error in either direction, but the expected effect on model results is minimal.

6. Model results

Several simulations for PCB bioaccumulation by lake trout were run using a computer program written in the C-language. The parameter values listed in Table 2 have been used initially. With a weekly update on parameter values, the change in body burden for each week was calculated and added to the previous cumulative value. The total was then divided by new body weight to give an equivalent whole body concentration. Observed values for PCB levels in lake trout were taken from Borgmann and Whittle (1991) for age classes up to 8 + and from Niimi (personal communication, 1994) for age classes 9 + and 10 + . Estimates of percentages of congeners 101 and 153 present in the fish body at any given time were taken as 6% and 10% of total congener levels respectively, based on the analysis of Oliver and Niimi (1988). Initial model runs gave a slightly lower (by 5-10%) estimate of final congener levels. From an examination of the results of the sensitivity analy-

sis, it was apparent that a low estimate of metabolism and uptake efficiencies, or an erroneously-high estimate of elimination rate, are the most probable causes. As a first attempt, the uptake efficiency factor was increased from Epf = 0.55 to 0.60, a value closer to that used by Niimi and Oliver (1983) for rainbow trout. This change resulted in a much closer estimate of ultimate PCB concentrations. A final minor adjustment on the clearance coefficient for PCB-153 (K lowered from 0.002 to 0.001 wk -~) was all that was required to produce an excellent match to the measured concentrations. Fig. 4 is a plot of the final modelled PCB-congener concentrations for lake trout at different stages. It could be observed from the graph that both of the two PCB-congeners were modelled very well simultaneously with the same set of fish metabolic information. This is significant because, as opposed to previous attempts, the metabolic function was fitted to the fish prior to the pollutant modeling. The excellent performance of the model affirms that this approach is not only indisputably superior in theory to the existing techniques, but also offers accurate results. A bi-phasic uptake trend is also evident from the graph. The trend appears to coincide with variations in the growth and metabolic rates. Growth dilutive effect on clearance for juvenile fish may be moderated by the fact that the fish are consuming more highly contaminated food (sculpin). Slow growth rates (translating into low overall elimination) combined with larger rations needed for equivalent weight gain combine to give a second rapid uptake phase for age 4 + lake trout. Jensen et al. (1982), Thomann and Connolly (1984) and the data of Borgmann and Whittle (1992a) indicate that PCB concentrations show a bi-phasic increase pattern when plotted against body-weight. PCB concentrations increase rapidly in young trout before a leveling off at ages 3-4 and a second phase of rapid increase after this age. Jensen et al. (1982) were able to duplicate this pattern of bi-phasic increase only by modifying arbitrarily the body weight exponent for metabolism. In our approach, coefficients used to estimate metabolism were specified prior to other model inputs.

G.K. Luk, F. Brockway / Ecological Modelling 101 (1997) 97-111

109

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7. Discussions and conclusions

The bioenergetics-based model described herein was successfully applied to predict uptake trends and final residue levels for individual PCB-congeners in lake trout in Lake Ontario. Final congener levels were duplicated after minor adjustments to food uptake and clearance related parameters, within the range of values cited in the literature. The importance of reliable estimates for the metabolic and food related parameters must therefore be stressed. It is possible that our estimate of metabolism is low, but verification is impossible without more comprehensive field data.

The model verifies that a bi-phasic uptake pattern for PCB accumulation by larger predators can be explained by ontogenetic changes in growth and metabolic rates, in conjunction with accurate delineation of food web relationships. Spigarelli et al. (1983) have discussed how uptake is closely related to growth and metabolic rates and factors controlling these rates (such as temperature and temperature cycles). The food-chain route accounted for at least 94% of congener residues over the fish life span within this model framework. Levels of PCB-153 are considerably higher in lake trout and their prey than in the water column, although water column concentrations of PCB-101 are 2.5 times PCB-153 levels.

110

G.K. Luk, F. Brockway /Ecological Modelling 101 (1997)97-111

This preconditions the conclusions of T h o m a n n and Connolly (1984) and Oliver and Niimi (1985), and confirms the theory that food-chain exposure is the dominant source of PCBs for top predators. Rasmussen et al. (1990) and R o w a n and Rasmussen (1992) have discussed the importance of food-chain length in determining uptake magnitudes. A bio-magnification effect at each level of the food-chain results in higher residues at each successive level. Food-chain relationships are complex and difficult to substantiate given a lack of field data. An advantage of the bioenergetics approach is that only data on prey items in a top predator diet need be established. This can be accomplished using stomach content analyses, preferrably based on a year-round consumption study. In contrast to the complete food-chain model, which is much more difficult to validate with the possibility of extra error being introduced at each trophic level, this provides a welcomed alternative. F r o m the model results, it is apparent that food-chain related parameters including relative energy densities o f predator and prey, as well as consumption patterns, are all significant for model performance. On the other hand, it appears that estimates obtained for average pollutant transfer efficiencies, under expected environmental conditions, can be used with reliability to predict uptake trends. These estimates are readily available using existing laboratory techniques. In conclusion, it has been confirmed that bioenergetics-based models, with accurate estimates of field metabolism and growth, can be a useful tool to predict bioaccumulation of slowly eliminated organic chemicals such as PCBs. These models, although require prior specification of prey concentrations, are still more tractable than complicated partitioning approaches. In addition, the models may be applied to most species of salmonids with little modification. Using the model to study a range of species and other PCB-congeners should make further refinement possible. Uptake and elimination rates can be verified, and the model m a y eventually be used to estimate fish metabolism under field conditions. This is seen to be very important from an ecological standpoint.

Acknowledgements This work is supported in part by the Natural Science and Engineering Research Council (NSERC) of Canada. The authors are grateful for the technical assistance of Paul Collins, Elena Cigala-Fulgosi, and Sean Langan, and the useful insights and data supplied by Drs A.J. Niimi and M. Whittle of the National Water Research Institute.

References Barber, M.C., Suarez, L.A. and Lassiter, R.R., 1988. Modelling bioconcentration of nonpolar organic pollutants by fish. Environ. Toxicol. Chem., 7: 545-558. Barber, M.C., Suarez, L.A. and Lassiter, R.R., 1991. Modelling bioaccumulation of organic pollutants in fish with an application to PCBs in Lake Ontario salmonoids. Can. J. Fish. Aquat. Sci., 48: 318-337. Borgmann, U. and Whittle, D.M., 1991. Contaminant concentration trends in Lake Ontario lake trout (Salvelinus namaycush): 1977 to 1988. J. Great Lakes Res., 17: 368-381. Borgmann, U. and Whittle, D.M., 1992a. Bioenergetics and PCB, DDE, and mercury dynamics in Lake Ontario lake trout (Salvelinus namaycush): A model based on surveillance data. Can. J. Fish. Aquat. Sci., 49(10): 1086-1096. Borgmann, U. and Whittle, D.M., 1992b. DDE, PCB and mercury concentration trends in Lake Ontario rainbow smelt (Osmerus mordax) and slimy sculpin (Cottus cognatus): 1977 to 1988. J. Great Lakes Res., 18: 298-308. Brandt, S.B., 1986. Food of trout and salmon in Lake Ontario. J. Great Lakes Res., 12(3): 200-205. Chen, Y., Jackson, D.A. and Harvey, H.H., 1992. A comparison of von Bertalanffy and polynomial expressions in modelling fish growth data. Can. J. Fish. Aqua. Sci., 49: 1228-1235. Christie, W.J., Scott, K.A., Sly, P.G. and Strus, R.H., 1987. Recent changes in the aquatic food web of eastern Lake Ontario. Can. J. Fish. Aqua. Sci., 44(2): 37-52. Connell, D.W., 1988. Bioaccumulation behaviour of persistent organic chemicals with aquatic organisms. Rev. Environ. Contam. Technol., 102:117-154. Elrod, J.H., 1983. Seasonal food of juvenile lake trout in U.S. waters of Lake Ontario. J. Great Lakes Res., 9(3): 396402. Environment Ontario, 1989. Proceedings of the Environmental research technology transfer conference. Environ. Ontario, 1. Fagerstrrm, T. and ,A,sell, B., 1973. Methyl mercury accumulation in an aquatic food chain: A model and some implications for research planning. Ambio, 2: 164-171.

G.K. Luk, F. Brockway / Ecological Modellhzg 101 (1997) 97 111 Harris, R.C. and Snodgrass, W.J., 1993. Bioenergetic simulations of mercury uptake and retention in walleye (Stizostedion vitreum) and yellow perch (Perca flavescens). Water Pollut. Res. J. Can., 28(1): 217-236. Jensen, A.L.S., Spigarelli, S.A. and Thommes, M.M., 1982. PCB uptake by five species of fish in Lake Michigan, Green Bay of Lake Michigan, and Cayuga Lake, New York. Can. J. Fish. Aquat. Sci., 39(6): 700-709. Larsson, P., Collvin, L., Okla, L. and Meyer, G., 1992. Lake productivity and water chemistry as governors of the uptake of persistent pollutants in fish. Environ. Sci. Technol., 26(2): 346 352. Neely, W.B., 1979. Estimating rate constants for the uptake and clearance of chemicals by fish. Environ. Sci. Technol., 13(12): 1506 1509. Niimi, A.J. and Oliver, B.G., 1983. Biological half-lives of polychlorinated biphenyl (PCB) congeners in whole fish and muscle of rainbow trout (Salmo gairdneri). Can. J. Fish. Aquat. Sci., 40: 1388-1394. Norstrom, R.J., McKinnon, A.E. and DeFreitas, A.S.W., 1976. A bioenergetics-based model for pollutant accumulation by fish: Simulation of PCB and methylmercury residue levels in Ottawa River yellow perch (Perca flavescens). J. Fish. Res. Board Can., 33(2): 248 267. Oliver, B.G. and Niimi, A.J., 1985. Bioconcentration factors of some halogenated organics for Rainbow Trout: Limitations in their use for prediction of environmental residues. Environ. Sci. Technol., 19(3): 842-849. Oliver, B.G. and Niimi, A.J., 1988. Trophodynamic analysis of polychlorinated biphenyl congeners and other chlorinated hydrocarbons in the Lake Ontario ecosystem. Environ. Sci. Technol., 22(4): 388 397. Olson, R.A., Winter, J.D., Nettles, D.C. and Haynes, J.M., 1988. Resource partitioning by salmonids in South-Central Lake Ontario. Trans. Am. Fish. Soc., 117:552 559.

111

Rasmussen, J.B., Rowan, D.J., Lean, D.R.S. and Carey, J.H., 1990. Food chain structure in Ontario Lakes determines PCB levels in lake trout (Salvelinus namaycush) and other pelagic fish. Can. J. Fish. Aquat. Sci., 47: 2030-2038. Rowan, D.J. and Rasmussen, J.B., 1992. Why don't Great Lake fish reflect environmental concentrations of organic contaminants? An analysis of between-lake variability in the ecological partitioning of PCBs and DDT. J. Great Lakes Res., 18(4): 724-741. Sijm, D.T.H.M., Seinen, W. and Opperhuizen, A., 1992. Lifecycle biomagnification study in fish. Environ. Sci. Technol., 26(11): 2162-2174. Spigarelli, S.A., Thommes, M.M. and Prepejchal, W., 1983. Thermal and metabolic factors affecting PCB uptake by adult brown trout. Environ. Sci. Technol., 17(2): 88--94. Stewart, D.J., Weininger, D., Rottiers, D.V. and Edsall, T.A., 1983. An energetics model for lake trout (Salvelinus namaycush): Application to the Lake Michigan population. Can. J. Fish. Aquat. Sci., 40:681 698. Thomann, R.V. and Connolly, J.P., 1984. Model of PCB in Lake Michigan lake trout food chain. Environ. Sci. Technol., 18(2): 65 71. Thomann, R.V., 1989. Bioaccumulation model of organic chemical distribution in aquatic food chains. Environ. Sci. Technol., 23 (6): 699-707. Webb, P.M., 1978. Partitioning of energy into metabolism and growth. In: S.D. Gerking (Editor), Ecology of Freshwater Fish Production, Blackwell Scientific Publications, Great Britain, pp. 184 204. Weiser, W., 1985. Developmental and metabolic constraints of the scope for activity in young rainbow trout (Salmo Gairdneri). J. Exp. Biol., 118:133 142. WHO (World Health Organization), 1976. Environmental health criteria 2: Polychlorinated biphenyls and terphenyls. WHO.