Environ. Sci. Technol. 2007, 41, 1849-1855
Predicting Effects of Exploitation Rate on Weight-at-Age, Population Dynamics, and Bioaccumulation of PCDD/Fs and PCBs in Herring (Clupea harengus L.) in the Northern Baltic Sea H E I K K I P E L T O N E N , * ,† M I K K O K I L J U N E N , ‡ HANNU KIVIRANTA,§ PEKKA J. VUORINEN,⊥ MATTI VERTA,† AND JUHA KARJALAINEN‡ Finnish Environment Institute, P. O. Box 140, FI-00251 Helsinki, Finland; University of Jyva¨skyla¨, Department of Biological and Environmental Science, P. O. Box 35, FI-40014 University of Jyva¨skyla¨, Finland; Department of Environmental Health, National Public Health Institute, P.O. Box 95, FI-70701 Kuopio, Finland; and Finnish Game and Fisheries Research Institute, P.O. Box 2, FI-00791 Helsinki, Finland
The Baltic Sea ecosystem and fish stocks contain high concentrations of environmental chemicals such as polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs), and polychlorinated biphenyls (PCBs). This study forecasts how changes in fishing or natural mortality would probably influence concentrations of PCDD/F and PCB in the Bothnian Sea (Northern Baltic) herring (Clupea harengus L.). An age-structured simulation model was developed to forecast herring stock dynamics, catches, and weight-at-age under different assumptions about exploitation and natural mortality. The simulated herring weight-at-age estimates were employed in a bioenergetics model capable of simultaneous estimation of bioaccumulation of 17 PCDD/F and 37 PCB congeners. Although the natural variability in recruitment greatly influences the stock dynamics, considerable changes in weight-at-age would ensue changes in exploitation rate or in natural mortality rate. If exploitation rates increase, growth rates would be higher and herring in the weight categories of commercial fisheries would be younger and contain less PCDD/F and PCB. Hence, the average toxicant concentrations in catches would also decline. However, it is likely that only fairly small changes would occur in toxicant concentrations-atage. On the other hand, a drastic decrease in herring fishing would substantially increase PCDD/F and PCB concentrations in herring. The study indicated that, in spite of the clear influences of fishing on the toxicant concentrations, fishing alone cannot resolve the problems associated with a high concentration of toxicants in herring; further decreases in loading are still required.
* Corresponding author e-mail:
[email protected]. † Finnish Environment Institute. ‡ University of Jyva ¨ skyla¨. § National Public Health Institute. ⊥ Finnish Game and Fisheries Research Institute. 10.1021/es0618346 CCC: $37.00 Published on Web 02/10/2007
2007 American Chemical Society
Introduction Polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs, dioxins) as well as polychlorinated biphenyls (PCBs) are highly persistent substances that pose a threat to the environment and to human health. PCDD/F and PCB concentrations in Baltic Sea surface sediments have been declining since the 1970s (1), and a similar decline was observed in organochlorine (OC) concentrations of aquatic biota until the beginning of the 1990s. During the past 15 years, the concentrations of PCDD/Fs in particular, but also of PCBs, have been quite stable (2, 3, 4). Especially in the Bothnian Sea in the northern Baltic, the concentrations of PCDD/Fs and PCBs in large herring (Clupea harengus L.) exceed the maximum limits for foodstuff set by the European Commission (5). Fish and fish products play a significant role in the dietary intake of PCDD/Fs and PCBs in many countries around the Baltic Sea. In Finland they account for 82% of total PCDD/F intake from food, and Baltic herring alone constitute about 52% of total PCDD/F intake in humans (6). There is variability in the accumulation patterns of different dioxin congeners in fish tissue. Over 70% of dioxin congeners in Baltic herring and sprat (Sprattus sprattus (L.)) were comprised of two PCDF congeners, 2378-TeCDF and 23478-PeCDF, with the penta congener contributing approximately 50% of the total toxic equivalent (TEq) concentration (7). Of the PCBs, the PCB126 constituted about 56% of the TEq (8). In the middle of the 1990s, both dioxins and polychlorinated biphenyls each contributed about 50% of the total TEqs in herring. This has been changing so that, in 1999, dioxins contributed 63-70% of the total TEqs (8). Dioxin concentrations were observed to vary in different parts of the Baltic Sea. In general, the concentrations in herring increase from south to north (9). Because of bioaccumulation, the concentrations of organohalogens in herring increase with age and size (10). Besides size-dependent changes in diet, the shift from zooplankton to mysids was suggested to contribute to high OC concentrations, especially in old Bothnian Sea herring (8, 10). At present, substantial amounts of OCs are removed from the Baltic Sea with fish catches (11). More intensive fishing has been suggested as a means to reduce concentrations of OC in the Bothnian Sea herring (10). An intensively exploited stock consists of few young age groups which have low tissue OC concentrations. Besides, intensive fishing would also decrease OC concentrations if herring growth is densitydependent. Extremely large changes have occurred in the Baltic herring growth over the last few decades (12). There is evidence that Baltic main basin herring growth is regulated by density-dependence as high abundance of planktivorous fish, especially sprat and herring, deplete zooplankton resources and induce poor growth rates in these fish species (13-15). If in the Bothnian Sea, herring growth is dependent on herring abundance (12), decrease in stock density could lead to higher growth rates. We hypothesize that herring with a high growth rate allocate more energy for growth and less for maintenance than fish with a low growth rate. The less fish eat to attain a certain weight, the fewer persistent organic pollutants they get from food, and the lower concentrations of toxicant they have in their tissues. This study analyses density-dependence in Bothnian Sea herring growth and recruitment, and it develops a simulation model for forecasting the population dynamics of the herring. The model enables forecasting of growth rates, stock abundance-at-age, and potential catches under different VOL. 41, NO. 6, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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management and exploitation options. This study also presents a bioenergetic model to evaluate congener-specific bioaccumulation rates of PCDD/F and PCB in herring. The density-dependent changes in growth rates are applied in bioenergetics modeling to explore the dynamics of congenerspecific bioaccumulation of organochlorines under different fisheries management and exploitation options to assess whether concentrations of these pollutants could indeed be regulated with fisheries management.
Materials and Methods Weight-at-Age and Population Dynamics. A simulation model was applied to predict the stock dynamics and weightat-age of the Bothnian Sea herring, assuming alternative exploitation alternatives. The herring weights-at-age (Wt) were modeled with the von Bertalanffy growth equation (e.g. 16),
Wt ) W∞ (1 - exp(-k(t - t0)))b
(1)
where W∞ is the maximum asymptotic weight of an individual fish of age t years, k, and b parameters, and t0 is a hypothetical age of zero length. The model was modified to allow density dependence in weight-at-age by substituting W∞ with
W∞ exp(-((
∑N t
-1 -1 D
t
C ) ))
(2)
where ∑Nt t-1 ) average stock abundance during the life span of a cohort of age t, whereas C and D are parameters to fit the sigmoid decrease in weight-at-age relative to the increase in stock abundance. Thus, the weight-at-age was predicted with
Wt ) W∞ exp(-((
∑N t
-1 -1 D
t
C ) ))(1 - exp(-k(t-t0)))b (3)
The parameters were estimated with the method of leastsquares using the stock abundance and weight-at-age data from ICES (12). The spawning stock biomass and recruitment estimates in ICES (12) were applied to generate a Beverton-Holt type stock-recruitment model assuming log-normal distribution of residual errors (16). The stock-recruitment model was
Rt )
aSt (b + St)exp(-N(0,δ2))
(4)
where Rt is the recruitment in year t, St is the spawning stock biomass, a is the maximum recruitment level, b is the spawning stock that gives 50% of the maximum recruitment, and the residual errors are log-normally distributed with µ of 0 and variance δ2 (16). The parameters for the model were estimated with maximum likelihood methods (16). The recruitment was the abundance of the recruits at the end of the year (i.e., the abundance of 1-year-old herring at the beginning of the next year). Spawner abundance was estimated by assuming that 15% of fishing mortality and 30% of natural mortality occurs before spawning (12). The percentages of mature fish in each age group were from Bothnian Sea herring stock assessments (12). The abundance of herring in the beginning of each year after recruitment Na+1,t+1 was predicted with
Na+1,t+1 ) Na,t exp(-(F + M))
(5)
where F and M are the fishing and natural mortality rates, respectively. The catch at age a in year t was estimated as
Ca,t ) 1850
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F N (1 - exp(-(F + M))) F + M a,t
(6)
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The average F-at-age during 2002-2004 (12) was used as a reference when evaluating the influences of different exploitation options. Six different scenarios for fish stock dynamics and weights-at-age were produced: (a) mortality rates unchanged, (b) 50% increase in F, (c) 100% increase in F, (d) 50% increase in M (e) 50% decrease in F, and (f) fishing completely closed. The Monte Carlo simulations were conducted with the @RISK software (17), by incorporating the stochastic recruitment with the deterministic population model and the weight-at-age model. One thousand simulations were generated for each of the six different scenarios. Averages, as well as 0.05 and 0.95 fractiles of the weightat-age estimates for each scenario, were calculated. The averages were used in subsequent bioenergetics and contaminant accumulation estimation. Bioenergetics Accumulation Model. The accumulation rates of PCBs and PCDD/Fs in different herring age-groups were estimated with a bioenergetics accumulation model. This model relied on a mass balance energy budget in which energy consumed by a predator is equal to the sum of metabolism, waste loses, and growth (18). The model was used to predict the accumulation of 17 PCDD/F and 37 PCB congeners into herring tissue from their food consumed. Daily uptake of each congener was calculated as
Xc ) CcAEc
(7)
where Xc is assimilation of a congener into fish tissue, Cc is the total amount of a congener in food consumed (mass of prey consumed (grams) multiplied by the prey congener concentration (pg g-1)), and AEc is the assimilation efficiency of the congener (19). The food consumption estimates were calculated in grams of prey per 1 g of herring and were corrected for energy density (J g-1). All the calculations where done with time steps of 1 day. The bioenergetics model (19) parameters were set according to Rudstam (20). Rudstam (20) prevented excessive decrease in body weight at temperatures below 1 °C by adjusting seasonal consumption rates. We selected a different approach by adjusting the parameter K1, which is the proportion of the maximum daily intake (Cmax) at a certain temperature (θ1) (19). K1 was adjusted so that the annual variation in growth equalled the monthly weight variation of spring spawning Atlantic herring reported by Winters and Wheeler (21). Changing the K1 from 0.1 to 0.2 prevented too high seasonal variations in the body weight and excessive weight loss during winter. Diet. In the model, herring diet consisted of zooplankton, mysids, and amphipods, with seasonal- and length-dependent variations in proportions (22). The prey category “others” applied in ref 22 was combined with the group zooplankton. Energy densities of zooplankton, mysids, and amphipods were 2850 (23), 3472 (24-26), and 4429 J g-1 ww (27), respectively. Temperatures. The water temperatures in the model were average monthly temperatures measured in the Bothnian Sea in 1990-2000 (Finnish Environmental Institute Water Quality Register). For juveniles (0 and 1) we applied the average temperatures taken from the top 10 meters, and for adults, the average temperatures of the top 20 meters (22). Herring Energy Density. The energy density (J g-1 ww) was calculated with 3936 + 1268 log(ww) (28). This model fits well to energy content values from 15 pooled whole-fish herring samples from the Northern Baltic Sea. These 15 samples were freeze-dried, and energy densities were measured using bomb calorimetry (Automatic MK 200). Spawning. To estimate energy loss and elimination of toxicants during spawning, we analyzed the PCB and PCDD/F concentrations in the somatic parts and gonads of 20 individual spawning-ready herring with the methods de-
scribed in ref 8. These 20 specimens were divided equally between both sexes and two age-groups. Thus four pooled samples were analyzed. We estimated the eliminated proportion (EG) of each PCB and PCDD/F congener during spawning with
EG% )
JGCG JGCG + JBCB
(8)
where JG is the total energy content of the gonads (joules), CG is congener concentration of the gonads (pg g-1), JB is the total energy content of the fish without gonads (joules), and CB is the congener concentration of the fish without gonads (pg g-1). Gonad energy densities of 3551 and 5442 J g-1 were applied for milt and roe, respectively (29). The energy density of fish without gonads was 5533 J g-1 (20). It was assumed that herring mature at an age of 3 years and spawn annually thereafter. Our own analyses indicated that herring gonads constitute about 11% of fish weight (mean of both sexes). During spawning, this proportion of body weight was lost and the energy density of herring was readjusted accordingly. Spawning was assumed to take place in mid June and larvae to start exogenous feeding on 1 July. OC Calculations. All the calculations were done on a fresh weight basis. The herring TEq were determined from wholefish samples. For both mysids and amphipods, we used the same congener-specific OC concentrations (30). Congenerspecific zooplankton concentrations (Tables A and B, Supporting Information) were measured with methods as described in Kiviranta et al. (8). Zooplankton samples were collected from Bothnian Sea in late summer 2002 with a 200 µm mesh size vertical WP-2 net hauled from the near-bottom layer (10 m above bottom) to the surface. We assumed that herring assimilates each PCDD/F and PCB congener at a congener-specific constant rate, except 2,3,4,7,8-PeCDF (Table B, Supporting Information). Most of the congener-specific assimilation efficiencies were derived from Isosaari et al. (31). If the assimilation efficiency was not available for a certain congener, we used the average of the PCDD/F congeners with the same number of chlorine atoms, since accumulation efficiency of PCDD/Fs decreases with increasing substitutions (32). If assimilation efficiency was not available for a certain PCB congener it was replaced with the average of all the available PCB assimilation efficiencies. The total uptake of each individual congener was calculated as a cumulative sum of the daily uptakes and the final concentration estimates (pg g-1) were calculated by dividing the total uptake by fish mass. Toxic equivalents (WHOPCDD/FTEq and WHOPCB-TEq) were calculated using the toxic equivalent factors (TEFs for 12 PCBs and 17 PCDD/Fs) recommended by the WHO in 1998 (33). In the results the WHO-TEq values are presented as the sums of these two equivalents (WHOtotal-TEq). In EU, this unit, has a maximum level of 8 pg g-1 for fish and fisheries products (except 12 pg g-1 for eel) (5). Model Calibration. The predicted concentrations from the bioenergetics accumulation model were compared to the observed concentrations of PCDD/Fs and PCBs measured from pooled herring homogenates from five age-groups of Bothnian Sea herring collected in autumn 2003. The analytical methods were identical to those in ref 8. The model predicted the observed concentrations fairly well, except for the 2,3,4,7,8-PeCDF where the accumulation increased with herring size. Therefore, we calibrated the accumulation efficiency of the 2,3,4,7,8-PeCDF with the observed concentrations and fitted a weight-dependence model for assimilation of this congener (Table B, Supporting Information). After calibration, the accumulation model produced congener profiles that closely resembled those from the independent data from Bothnian Sea herring (Figures A and B, Supporting
FIGURE 1. Predicted and observed WHOtotal-TEq in Bothnian Sea herring. Information). PCB congeners substituted by three or four chlorines were overestimated by the accumulation model, and PCB congeners with five chlorines as substitutes depicted a transition phase after which the model slightly underestimates the concentration of PCB congeners. With PCDD/F congeners, the model predicted accurately the concentrations of tetra- and pentasubstituted congeners, but had difficulties predicting the concentrations of highly chlorinated congeners. In spite of the relatively poor fit of the latter congeners, the TEq estimates had a very good fit with the measured data (Figure 1), because the pentasubstituted congeners 2,3,4,7,8PeCDF and 1,2,3,7,8-PeCDD with a high TEF were wellpredicted and all the highly chlorinated congeners contributed little to the TEq values. The estimates suggested that there was a linear relationship between age and the TEq of PCDD/Fs and PCBs.
Results The weight-at-age model (eq 3) fitted to the 1981-2004 Bothnian Sea herring data (12) was
Wt ) 113 exp(-((
∑N t
-1
t
(5.18 × 106)-1)1.21))
(1 - exp(-0.0960(t - 0.375)))0.854 (9)
In agreement with the data, the model suggested a steep decline in weight-at-age in the most recent years (Figure C, Supporting Information). The fitted stock-recruitment model (eq 4) was
Rt )
9920000 St
(10)
(6400000 + St)exp(-N(0,0.6492))
where recruitment is in thousands of 1-year-old recruits and spawning stock biomass is in tonnes. The model suggests that the highest recruitment would occur with extremely high spawning stock biomass. However, incorporation of the density-dependence in the growth model produced plausible scenarios of stock dynamics. The weight-at-age, population dynamics, and catches were distinctly different under the scenarios with different exploitation and natural mortality rates (Figure 2), though variations occurred between individual randomizations (n ) 1000). The model predicted substantial changes in growth rates if exploitation or natural mortality rates change (Figure 2). A total closure of the herring fishery would, with a high probability, decrease growth rates, while acceleration of growth is expected if fishing mortality increases. Increase of fishing mortality by 50-100% would first increase catches over several years, but later, a substantial increase in fishing mortality would probably lead to decreasing spawning stock abundance and catches. Increase in M by 50% is predicted to produce almost as large increases in growth rates as a 100% increase in F (Figure 2). A 50% increase in M could significantly decrease the biomass and the potential catches. VOL. 41, NO. 6, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 2. The expected weight-at-age of 5-year-old herring, spawning stock biomass and catches under different fishing and natural mortality rates compared to the F and M estimates in the years 2002-2004. In each graph, the lowest and highest curves (dotted) are the 0.05 and 0.95 fractiles, respectively, and the solid curve is the average of the 1000 simulations. Our model simulations suggest substantial differences in bioaccumulation of PCDD/Fs and PCBs under different scenarios. Sustaining present fishing mortality rates would have no major effect on the sum of WHOtotal-TEq in herring (Figure 3a). In contrast, the changes in exploitation rate (Figure 3b, c, e) and subsequent changes in growth rates would influence the TEq-at-weight but only slightly on the TEq-at-age (Figure 3). Increasing F would produce only a slight decrease in the concentration in size-category 5 (Figure 1852
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3b, c), but in size-category 4c, a decline could be expected from about 25 to 15 pg g-1. On the other hand, closure of the fishery would clearly increase TEq (Figure 3f). Catch-composition-weighted average TEq in herring for human consumption (herring total mass g23 g, size categories 4c, 5, and 6) varied slightly between simulated scenarios (Figure D, Supporting Information). Again, higher mortality led to lower TEq. The differences between scenarios in TEq were, likewise, small when comparing the predicted
FIGURE 3. The shaded areas indicate the predicted temporal changes in individual mean WHOtotal-TEq of different herring size categories (classification used in fishing industry). The dotted lines indicate concentrations in age groups. Size categories 4c, 5, and 6 are those mainly used for human consumption. The scenarios include (a) mortality rates unchanged, (b) 50% increase in F, (c) 100% increase in F, (d) 50% increase in M, (e) 50% decrease in F, and (f) fishing completely closed. All the scenarios are the averages of the simulations. No fish were assumed to survive to ages older than 14. impacts of changes in mortality on the average length of herring with a WHOtotal-TEq of 8 pg g-1 (EU maximum limit) (Figure E, Supporting Information). The predicted increase in OC concentrations during the next few years (e.g., Figure 3) is especially due to the large contribution of the exceptionally strong slow-growing year-class 2002. Decrease in OC is again expected as the abundance of this year-class will gradually decrease.
Discussion Density-dependence in herring growth has been demonstrated in the Baltic Sea (12, 15) and in other sea areas (34, 35). The present study indicated that the density-dependence is a crucial factor behind the changes in herring growth in the northern Baltic Sea. The model which incorporated density-dependent growth with a stochastic stock-recruitment model produced plausible estimates of growth rates, stock dynamics, and potential catches. The results support Lorenzon and Enberg (36), who demonstrated that many fish populations are regulated by density-dependent growth in contrast to the frequently assumed regulation during juvenile stages. Obviously, some year-to-year changes in weight-at-age were faster than the model suggested though some of the variations in the weight-at-age data (12) evidently emerge, e.g., due to random variation in sampling the commercial catches. Especially before 1998, the sampling was not as intensive as during the past few years. In addition, some other environmental factors, such as temperature (37), may at least temporally influence growth rates of clupeids in the Baltic Sea. However, as population size is a factor that can be regulated, at least to some extent, by humans, a model especially considering the effects of population size may be
useful in exploring potential influences of various management options and exploitation rates on the long-term dynamics of the Bothnian Sea herring. As growth rates contribute to OC concentrations they may partly explain differences in OC concentrations around the Baltic Sea. Higher OC concentrations in the northern parts (9) could be a consequence of slower growth rates of these populations. Although we hypothesized that increase in growth rates would decrease OC concentrations, the sizedependent changes in food composition may, to some extent, counteract any decrease. Fish that grow faster are already, at younger ages, able to feed on larger food items such as mysids and amphipods, which have high OC concentrations. Nevertheless, if growth accelerates fish in a certain weight category would have lower OC concentrations. At present, the herring abundance in the Bothnian Sea is on a recordhigh level (ICES 2005); it is likely that somewhat moreintensive fishing could produce larger catches, higher growth rates, and lower concentrations of toxicants in herring. According to the simulations, it is likely that during the next two decades the spawning stock biomass would stay above the overfishing threshold for spawning stock biomass Blim reference point of 145 000 tons (38), except either with a 100% increase in F, or with a 50% increase in M. A substantial (100%) increase in F would, with a high probability, lead to a substantial decrease in the spawning stock biomass. A 100% increase in F to the value 0.29 (average in age-groups 3-7) would also bring the fishing mortality close to the overfishing limit Flim of 0.30 defined by ICES (38). The present study did not consider the decrease in dioxin concentrations in the environment which probably will take place compared to the levels in 1995-2000 (39). The results VOL. 41, NO. 6, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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suggest that, although the concentrations of OCs in prey species of herring would slightly decrease, it is possible that concentrations in herring decrease or increase depending on stock dynamics and exploitation of the stock. However, irrespective of the loading scenario, the present study strongly supports that OC concentrations in herring would be lower with fairly high fishing mortality than with a very low fishing (or natural) mortality. The assimilation efficiencies in the bioenergetics accumulation model originated from Atlantic salmon (31), because corresponding values were not available for herring. The observed differences between the modeled and measured concentrations were, at least in part, due to differences in assimilation efficiencies of PCDD/Fs and PCBs between these two fish species. On the other hand, especially with PCDD/ Fs, the differences in congener concentrations between the model estimates and actual herring samples can arise from the differences in PCDD/F congener profiles in zooplankton versus mysids/amphipods. In zooplankton, the contribution of 2,3,7,8-TCDF to the sum of dioxins was 32% while in mysids/amphipods it was only 4%. For example, the 25% larger modeled than measured 2,3,7,8-TCDF concentration (Figure B, Supporting Information) can arise from an overestimation of the proportion of zooplankton in the diets. The predicted and observed WHOtotal-TEq were similar, because the model accurately predicted the concentrations of all those congeners which, essentially, contribute to TEqs. However, the different accumulation pattern of the 2,3,4,7,8PeCDF to herring compared to the other congeners deserves more research. Increasing fishing mortality by 50% would decrease the mean WHOtotal-TEq of the catch used for human consumption by about 20%. However, the total exposure in humans would increase, if the additional catches would be used for human food. Moreover, the average concentrations in catches would still exceed the maximum limit value set by the European Commission (5) for the next two decades. Therefore, it is inevitable that fishing alone cannot solve this problem; restrictions of current sources of PCDD/Fs and PCBs will be needed as well. The current study also suggests that considerable increases in PCDD/Fs and PCBs are likely if fishing is severely limited. On the other hand, increase in natural mortality (e.g., predation by cod or seals) could substantially decrease biomass and potential catches, but also increase growth rates and decrease concentrations of PCDD/Fs and PCBs. The present study indicates that densitydependent growth may be a key mechanism in stock regulation in Baltic herring. Thus, density-dependence, or more broadly, ecosystem-based approaches, should be considered in fisheries assessment and in management of toxicants in Baltic herring.
Acknowledgments We are grateful to Timo Marjoma¨ki for his help with the model development, to Mika Vinni for sample processing, and to Juha Flinkman for providing the zooplankton samples. We also thank Roger Jones and two anonymous referees for helpful comments on the manuscript. This study was funded by the Academy of Finland (project DIOXMODE, no102557 in the Baltic Sea Research Programme BIREME).
Supporting Information Available Tables show OC congener concentrations in herring prey, congener-specific assimilation efficiency and proportion eliminated during spawning, and toxic equivalent factors of each congener. Graphs indicate the measured congener profiles of PCDD/Fs and PCBs compared to the estimates with the applied model. Observed annual changes in weightat-age of herring compared to those from the fitted growth1854
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model. Predicted temporal changes in mean WHOtotal-TEq of catch used for human consumption (g23 g) and in predicted impacts of different exploitation options on the average length of herring containing 8 pg WHOtotal-TEq g-1. This material is available free of charge via the Internet at http://pubs.acs.org.
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Received for review August 1, 2006. Revised manuscript received December 5, 2006. Accepted January 8, 2007. ES0618346
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