Environ. Sci. Technol. 2001, 35, 856-862
PCB Congeners in Lake Michigan Coho (Oncorhynchus kisutch) and Chinook (Oncorhynchus tshawytscha) Salmon L E L A N D J . J A C K S O N , * ,† STEPHEN R. CARPENTER,‡ JON MANCHESTER-NEESVIG,§ AND CRAIG A. STOW| Ecology Divison, Department of Biological Sciences, University of Calgary, 2500 University Avenue NW, Calgary, Alberta T2N 1N4, Canada, Center for Limnology, University of WisconsinsMadison, 680 North Park Street, Madison, Wisconsin 53706, Water Science and Engineering Labortatory, University of WisconsinsMadison, 660 North Park Street, Madison, Wisconsin 52706, and Nicholas School of the Environment, Duke University, Box 90328, Durham, North Carolina 27708
We determined PCB congener concentrations in coho and chinook salmon collected in two Lake Michigan tributaries during the fall of 1996. Chinook salmon were larger than coho salmon and contained higher concentrations of the 78 PCB congeners we detected. There were no differences between male and female chinook or coho salmon in size or their PCB concentrations. Among individual fish, we found little evidence for a relationship between congener concentrations and percent lipid; however, congener concentrations did show a generally positive relationship with salmon size. Fish and macroinvertebrate congener concentrations are clearly related, and PCB congeners biomagnify ∼20-30-fold as they flow from macroinvertebtates, two trophic levels below salmon, to the salmon. Slopes of regressions of salmonid congener concentrations on macroinvertebrate congener concentrations within homologs indicated that the degree of biomagnification generally increased with the degree of congener chlorination, although this pattern was much stronger for Mysis than for Diporeia. Log Kow and categorical variables for coplanar and “toxic” PCBs were not significant additional model terms, indicating that bioaccumulation of PCB congeners was not statistically related to these physicochemical attributes of the PCBs. The distribution of homologue PCBs shifts from a distinct predominance of hexachlorobiphenyls in macroinvertebrates to pentachlorobiphenyls and hexachlorobiphenyls in the salmon.
Introduction Polychlorinated biphenyls (PCBs) resist degradation in the environment and bioaccumulate in organisms to levels that * Corresponding author phone: (403)220-6790; fax: (403)289-9311; e-mail:
[email protected]. † University of Calgary. ‡ Center for Limnology, University of WisconsinsMadison. § Water Science and Engineering Labortatory, University of WisconsinsMadison. | Duke University. 856
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may impair normal organismal functioning (1-3). Thus, despite a cessation in their use nearly 30 years ago, PCBs continue to occupy the attention of scientists and the public. Since PCB use was banned, concentrations in Great Lake’s biota such as fishes (4, 5) and birds (6) have declined remarkably. Yet concern remains because of possible risks to humans (7, 8), wildlife (9, 10), and fish (11) that consume diets contaminated with PCBs. Numerous studies published over the past decade have reported declines in total PCBs in various biota, but far fewer studies (6, 12-14) have considered PCB congeners. Chemical behavior and biomagnification may vary among congeners with ecological consequences. For example, reproductive problems in Forster’s terns have been correlated to levels of specific PCB congeners (15). Congener profiles differ among sites within the Great Lakes, reflecting different Aroclor sources as well as differential volatilization and sedimentation since deposition (6). Different sources and aging of environmental PCB mixtures has led to different bioaccumulation patterns in herring gull eggs (6). Furthermore, groups of related congeners (homologues) are not transferred through Lake Ontario’s or Lake Michigan’s pelagic food web to the same degree. Hexachlorobiphenyls are transferred to a greater degree from plankton to macroinvertebrates than congeners from other homologue groups (12). Thus, changes in the mixture of PCB congeners in the environment may have effects that are not apparent from considering changes in total PCBs alone. The final concentration of PCBs in top predators such as salmonids is a function of the source concentration, the degree of transfer and biomagnification between successive trophic levels, and the number of trophic levels. Transfer of PCBs between selected trophic levels of Great Lakes pelagic food webs has been estimated from field studies (14, 16) and modeling exercises (4, 12), and the role of food chain length has been assessed (17, 18); however, relatively little is known of the relative contribution of benthic and pelagic sources of PCBs to salmonid body burdens. The Lake Michigan food web has become more complex as the lake has been invaded by exotic species [e.g., zebra mussels (Dreissena polymorpha), spiny water flea (Bythotrephes cederstroemi)] that alter pathways of energy and contaminant flow and the population dynamics of species that form key food web linkages in existing pathways [e.g., alewife (Alosa pseudoharengus)]. Here we report concentrations of individual congeners for coho (Oncorhynchus kisutch) and chinook (Oncorhynchus tshawytscha) salmon collected in Lake Michigan tributaries during the fall of 1996. These data are compared to those collected for benthic macroinvertebrates and water column plankton during the fall of 1995 (12) plus published concentrations for alewife collected during 1994-1995 (14). Our goal is to assess the degree to which congener profiles differ among trophic levels and the degree of trophic transfer of homolog groups from lower to higher trophic levels given uncertainty in the food web linkages and the relative importance of benthic and pelagic PCB sources.
Methods Sampling of Biota. Twenty-one salmon were collected from two Lake Michigan tributaries during the fall of 1996. Sixteen coho salmon were collected on October 22 from the Kewanee River, WI, near the Buzz Besadney fish hatchery. This river is a tributary to the main body of Lake Michigan. Five chinook salmon were collected on October 16 in Strawberry Creek, a Door County tributary to Sturgeon Bay of Lake Michigan. 10.1021/es001558+ CCC: $20.00
2001 American Chemical Society Published on Web 01/27/2001
These sites are ∼50 km apart. Nine coho salmon were female, 2 were male, and 5 were not sexed. Three chinook salmon were female, and two were male. The fish were frozen immediately following collection. The details of sampling the macroinvertebrates, water column plankton, and alewives to which we compare our salmon have been presented elsewhere (12, 19). Briefly, benthic macroinvertebrates and size-fractionated water column plankton were collected from multiple stations off Grand Haven, MI, during September 1995. Sample stations ranged in depth from 25 to 90 m, and we used a benthic sled (Diporeia spp.), vertical plankton net hauls (Mysis relicta), and horizontal plankton tows (63-243 and 243+ µm size fractions) to collect samples. These samples provided almost pure Diporeia, Mysis, and size-fractionated plankton with which to work. Alewife were collected from a variety of nearshore locations during the spring, summer, and fall of 1994 and 1995 (19). Congener Extraction and Analyses. We used the same procedure to extract and analyze PCB congeners in our samples. Plankton and benthic invertebrates were composited to produce sufficient material to solvent extract. For salmon, a ∼100-g steak was cut just in front of the dorsal fin to produce a “standard” steak that included skin, muscle, bone, and organ tissues. The standard steak was thawed and homogenized in a blender to produce a slurry. For plankton, macroinvertebrate, and salmon, PCB congener concentrations, water content, and lipid content were determined on approximately 8-10 g of the homogenized slurry. PCBs were separated from the bulk of the lipid by gel permeation chromatorgraphy and then run through a silica gel column to separate PCBs from interferents. IUPAC PCB congeners 14, 65, and 166 were added to all samples before extraction to monitor recoveries. For plankton and macroinvertebrates, those recoveries were (with one standard error in parentheses) 97% (5.1), 108% (3.6), and 106% (2.9) while for fish the recoveries were 86% (7.0), 71% (2.2), and 109% (3.0) for congeners 14, 65, and 166, respectively. Gas chromatography conditions were chosen to produce maximum separation of PCB congeners. We have reported those congeners that are quantifiable using standard capillary chromatography and are above the detection limits of quantification for North Atlantic cod, but because we employ relatively long run times, we can quantify many congeners that are otherwise difficult to resolve (20). Calibration was accomplished with a mixture of Aroclors 1232, 1248, and 1262 in a 25:18:18 ratio. The identity and concentration of each congener in this mixture has been previously determined (21). Total PCB concentrations were determined by summing the 78 individual congeners. Additional details on our extraction procedure has been outlined elsewhere (12). The extraction protocol for alewife samples presented in ref 19 have been outlined in that and other references contained therein and include similar quality control measure to that which we employed. We analyzed the data using the general linear models procedure in SAS software (22) at two levels of aggregation. Each congener was examined across the 21 individual fish to evaluate the relationship between congener concentration and fish lipid content, log Kow, and fish size. We then calculated the mean concentration of each congener in all fish and fit linear regressions of mean salmon congener concentration to mean macroinvertebrate congener concentration. Dummy variables for coplanar PCBs and “toxic” PCBs were added to test the effect of congener groupings not summarized by homologs on the observed patterns. We defined toxic PCBs as those congeners that (i) are non- or mono-ortho-substituted, (ii) have four or more Cl substitutions, (iii) have both para positions chlorinated, and (iv) have two or more meta positions chlorinated. To examine the
patterns within homolog groups, we first removed congeners from different homolog groups that coeluted and then calculated regressions for each homolog group. We obtained log Kow values for individual PCB congeners from ref 23. Standard assumptions regarding independence and homogeneity of variance were corroborated by viewing residual plots. Correlations between congener concentrations in our samples plus alewife were determined with the correlation procedure in SAS. For each combination examined, we assessed the correlation between the variables of interest with the effect of other variables removed.
Results and Discussion PCB Congener Concentrations in Lake Michigan Coho and Chinook Salmon. The chinook salmon in our samples were significantly (p < 0.001) larger than the coho salmon [mean mass (( 1 SE): coho salmon ) 3.02 kg (0.26), chinook salmon ) 7.45 kg (0.65); mean length: coho salmon ) 669 cm (14.62), chinook salmon ) 888 cm (15.52)]. There were no significant differences (p > 0.05) in the length or mass of males and females within species. Average total PCB concentrations in coho salmon (( 1 SE) were 1268 (77), 4519 (279), and 39870 (3841) ng/g wet wt, dry wt, and lipid-normalized wt, respectively. Chinook salmon contained an average total PCB concentration of 1941 (200), 7320 (988), and 53638 (21189) ng/g wet wt, dry wt, and lipid-normalized wt. The chinook salmon in our samples were more than twice the mass of the coho salmon, and their total PCBs were ∼37% higher when expressed per gram fresh weight. We calculated the expected salmonid total PCB concentrations for an alewife diet given published alewife PCB concentrations (24) and net PCB transfer efficiencies (16) calculated for Lake Michigan. We assumed the growth efficiencies averaged 0.2 for both salmon over their lifetimes. A 3.02-kg coho salmon would consume 15.10 kg of alewife to achieve this mass. At 0.43 mg/kg total PCBs, the alewife diet would contain 6.49 mg of total PCBs. The salmon would incorporate 3.25 mg of total PCBs. This yields a final salmon total PCB concentration of 3.25 mg/ 3.02 kg or 1.08 mg/kg (1080 ng/g) of total PCBs, which is a bit below our measured value of 1268 ng/g fresh wt. A similar calculation for chinook salmon yields a final total PCB concentration of 1220 ng/g fresh wt, which is also below our measured mean value. The chinook salmon expected concentration is 31% higher than the coho salmon expected concentration. This is remarkably similar the measured difference of 37%. The size of the fish in this analysis and their total PCB concentrations are greater than those (( 1 SE) of coho [mean length ) 55.2 (0.72) cm; mean total [PCB] ) 0.71 (0.029) mg/kg fresh wt.] and chinook [mean length ) 72.1 (0.85) cm; mean total [PCB] ) 1.42 (0.043) mg/kg fresh wt.] salmon reported in a previous Lake Michigan analysis (24). In the previous study, the tissues analyzed were skin-on fillets and did not include bones or organs; hence, the concentrations may not be directly comparable. Lake Michigan lake trout sampled during 1994-1995 at three locations had higher total PCBs (∼2970 ng/g wet wt.) even though the fish averaged ∼610 mm (24). These differences in PCB concentrations are consistent with lake trout having higher PCB and lower carbon assimilation efficiencies (26). The net effect for the same consumption of a given diet will be for lake trout to be smaller and more contaminated than either coho or chinook salmon. Diet differences very likely also contribute to the differences in total PCB concentrations between salmon and lake trout. Sculpins are an important diet item for young lake trout, and older lake trout rely less heavily on alewife than do coho or chinook salmon (27). The chinook salmon had higher concentrations of all measurable homolog PCBs than the coho salmon (Figure VOL. 35, NO. 5, 2001 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 1. Average concentrations of homologue PCBs (ng/g fresh weight) in Lake Michigan coho and chinook salmon collected from Wisconsin tributaries in the fall of 1996. Note the change of scale between panels. 1). This is expected for our samples given that the chinook salmon were roughly twice the size of the coho salmon. The physiological differences with respect to carbon assimilation and ecological difference with regard to diets are small between chinook and coho salmon (26-28). Field-based estimates of net PCB transfer for chinook salmon are approximately 30% higher than those for coho salmon (18). Our findings are consistent with these previous studies. The relative concentrations of homologue groups were similar between theses two species, further supporting the contention that differences in homolog concentrations are due to size differences between species. Pentachlorobiphenyls were the largest homolog group for chinook salmon (Figure 1). In contrast penta- and hexachlorobiphenyls were almost identical in coho salmon. Tetra- and heptachlorobiphenyls were the third and forth most abundant homolog groups in chinook salmon. This pattern was reversed in coho salmon. Unfortunately, we do not have the detailed information that would be necessary to reconstruct salmonid diets and PCB pathways through the Lake Michigan pelagic food web that would be necessary to test our hypothesis. As data on individual congeners in prey fish become available, it might be possible to infer PCB flow pathways by using individual congeners as food web markers. At the individual fish level, congener concentrations were generally positively correlated with mass (or length). Of the 78 congeners quantitated at detectable levels (including those that coeluted), two were negatively correlated with fish mass, and 76 were positively correlated. Correlation coefficients ranged from -0.34 to 0.63 with a mean of 0.38. Congener concentrations were generally slightly negatively correlated with percent lipid. Sixty-seven congeners were negatively correlated with lipid, while 11 had a positive correlation. Correlations ranged from -0.39 to 0.26 with a mean of -0.09. The absence of bivariate normality prevented the use of p values to assess the “significance” of these correlations; however, we viewed bivariate plots of each of these relation858
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ships to check for influential points and corroborate the generality of this inference. There is an interesting relationship between congener concentration and log Kow (Figure 2) with the highest concentrations occurring for the middle of the log Kow range. Congeners with log Kow values at the ends of the range had relatively low concentrations in the salmon. Lower biomagnification factors (BMFs) of low and high log Kow congeners have been previously reported (29, 30). Hypotheses to explain this finding range from nonequilibrium conditions, to molecular size limitation for transmembrane translocation (31), to decreased bioavailability of highly lipophilic compounds (32). Therefore, while our samples of Lake Michigan chinook and coho salmon show a consistent relationship between congener concentration and fish size, there is almost no relationship between congener concentration and percent lipid (25, 33-35). Fish and invertebrate congener concentrations are clearly related (Figure 3), and although Kow may play some role in determining the invertebrate:salmon BMF, that role is not at all influenced by salmon lipid levels. Relative Homologue Proportions as a Function of Trophic Level. Here we consider two possible pathways whereby PCBs move from either sediments or the water column as sources, through the pelagic food web, to top predatory salmonids. Other more convoluted pathways than the two we here consider certainly exist, and some biota may integrate alternative pathways based on their feeding ecology. For example, Mysis is an omniovre that feeds on benthic and pelagic materials (36), and alewives eat Mysis, benthic amphipods, and zooplankton (37). The Lake Michigan food web has undergone some important changes during the last few decades, including the addition of zebra mussles and spiny water fleas. Zebra mussels have been implicated in the decline of Diporeia in southern Lake Michigan (38), and Bythotrepes may have added an additional trophic level to the Mysis food chain (4, 38). These two alterations to Lake Michigan community composition have likely altered the flow of energy and contaminants from sources to sinks by altering the relative abundance of prey and increasing food chain length. We first discuss each scenario as if it is independent from the other and then consider the implications of these pathways being coupled. Scenario 1: PCB Flow from a Pelagic Source to Salmon. In Lake Michigan, dissolved phase homolog profiles were dominated by tetrachlorobiphenyls, followed by pentachlorobiphenyls and trichlorobiphenyls (13). We see a similar pattern with particulate phase homologue profiles dominated by pentachlorobiphenyls (63-243 µm size fraction) and tetrachlorobiphenyls (243+ µm size fraction). Our data show that the hexachlorinated biphenyls are by far the dominant congeners in Mysis relicta (12). This may come about by a greater transfer efficiency of hexachlorobiphenyls if Mysis is feeding exclusively on plankton or by consumption of benthic amphipods that are relatively enriched in hexachlorobiphenyls, as we found the case to be for Diporeia. With subsequent transfer from Mysis, via alewife, to coho and chinook salmon, there is a shift to dominance by pentachlorobiphenyls, although hexachlorobiphenyls are nearly as abundant. Thus, if the PCBs in salmon have as their source the water column, penta- and hexachlorobiphenyls are being transferred with the greatest efficiencies. Such changes in the relative composition of the homologue groups come about by higher transfers of pentachlorobiphenyls since the ratio of the absolute concentrations of pentachlorobiphenyls between coho salmon and Mysis (321.6) and chinook salmon and Mysis (446.1) is much larger than the ratios of hexachlorobiphenyls (33.6 and 46.6 for Mysis-coho and Mysischinook salmon, respectively). This suggests that pentachlorobiphenyls are transferred more efficiently than hexachlorobiphenyls between invertebrates and salmon in
FIGURE 2. Concentration of PCB congeners in Lake Michigan coho and chinook salmon collected from Lake Michingan tributaries in the fall of 1996 plotted against log Kow of the congeners. The center line in each box is the median, and the top and bottom lines represent the 75th and 25th quartiles, respectively. Whiskers represent 1.5 times the interquartile distance.
FIGURE 3. Relationships between the concentration of PCB congeners in Lake Michigan coho and chinook salmon and Mysis relicta and Diporeia spp., two trophic levels below the salmon. the Lake Michigan pelagic food web. Indeed, it has been shown that transfer across the gut wall (38) and net trophic transfer (14) is more efficient for penta- and hexachloro congeners than for tri- and tetrachloro congeners. Scenario 2: PCB Flow from a Benthic Source to Salmon. Diporeia is a benthic amphipod that we assume derives its PCBs from the sediments within which it lives and eats.
Diporeia has a homolog signature that, like Mysis, is dominated by hexachlorinated biphenyls. However, unlike Mysis, for which tetrachlorinated biphenyls form the second largest homolog group, pentachlorinated biphenyls are the second most abundant homolog group. This may reflect relative enrichment of the sediments with more chlorinated congeners relative to the water column. If all PCBs flow from VOL. 35, NO. 5, 2001 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 4. Relative homolog proportions in coho and chinook salmon (this study), alewife (from ref 14), size-fractionated plankton, and Diporeia (from ref 12), two trophic levels below the salmon. Homolog data are expressed as percentages of the total, which was calculated by summing the 21 individual congeners in this case. The same 21 congeners are represented in each species or trophic level. Diporeia to salmon, then there is a change in the congener distribution from dominance by hexachlorinated biphenyls to penta- and hexachlorinated biphenyls. Scenario 3: A Mixture of Benthic and Pelagic Sources. By whatever pathway PCBs flow from environmental source to salmon, there is a shift in relative homolog abundance dominated by hexachlorinated biphenyls to a roughly equal abundance of penta- and hexachlorinated biphenyls. A more likely scenario is that PCBs in salmon have benthic and pelagic sources. We cannot isolate these sources with our data, as would be the case if particular congeners were unique to the sediments, perhaps reflecting use 30-50 years ago, or the water column, perhaps reflecting recent inputs. As well, the feeding ecologies of Mysis and alewife, two key links between environmental sources and salmon, likely blends the contribution of sediments and the water column-derived PCBs. Our data are consistent with mixed sources. Missing from our data is congener information for alewife, the principle prey of the salmonids (14), and thus a nexus in the flow of PCBs to salmon. Data for 21 of the 78 congeners we have measured have recently been provided for Lake Michigan alewives (14). Combining these data with our data provides the first opportunity to compare congener and homolog profiles for a complete series of trophic levels in a Great Lakes pelagic food web (Figure 4). The following points are noteworthy. First, the relative homolog proportions are different for this 21 congener subset when compared to the 78 congeners we measured. All trophic levels, with exception of the 243+ µm plankton size fraction, are dominated by pentachlorobiphenyls, whereas in our larger data set the dominant congener in macroinvertebrates was hexachlorobiphenyls. This does not simply come about because homologue groupings have different numbers of congeners included. Penta- and heptachlorobiphenyls and hexa- and octachlorobiphenyls have 6 and 3 congeners included, respectively. Second, there is a decrease in the relative proportion of octachlorobiphenyls in successively higher trophic levels. A decrease of this homolog group also occurs 860
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when comparing salmon to Diporeia for our larger 78 congener data set, but the opposite pattern occurs for a Mysis-salmon comparison. Third, there is a decease in the proportion of tetrachlorobiphenyls in salmon relative to alewife, though this homolog group contains only congeners 66 and 74. The homolog distribution with 78 congeners does not show as clear a patttern since the tetrachlorobiphenyls decrease when comparing salmon to Mysis and Diporeia to coho salmon but increase when comparing chinook salmon to Diporeia. The 21 congener homolog profiles (Figure 4) are most consistent for the benthic source food chain. This suggests a benthic source if there is not substantial differential transfer of these 21 congeners from Diporeia, via alewife, to salmon. The alternative water column source food chain suggests that these 21 congeners exhibit differences in their trophic transfer. Trophic transfer of individual congeners has rarely been examined. One case where the prey fish-salmon trophic levels have been examined (14) in Lake Michigan suggests that tetrachlorobiphenyls are transferred less efficienctly than more chlorinated congeners, but the penta- through octachloro congers are transferred with approximately the same efficiency. We do not know if this pattern also holds for trophic transfer at lower trophic levels, and the mixed diets of Mysids and alewife make such an assessment problematic with our field data. This would be best addressed under laboratory control where diet and fish congener concentrations would be known. Biomagnification of Congeners from Macroinvertebrates to Salmon. We have the most complete congener information for plankton, macroinvertebrates, and salmon and have chosen to examine biomagnification between these trophic levels because they are well-established trophic linkages in Lake Michigan. The regressions of salmonid congener concentrations on macroinvertebrate congener concentrations are significant for all possible pairings (p < 0.001; n ) 78 for each), indicating strong correlations between
TABLE 1. Parameters for Regressions, across Homolog Groups, of PCB Concentration (mg/kg Fresh Weight) in Coho and Chinook Salmon, Mysis relicta, and Diporeia spp.a Mysis relicta-coho salmon
tri tetra penta hexa hepta octa
Diporeia spp.-coho salmon
m
b
r2
n
m
b
r2
n
9.13 (2.15) 13.99 (1.73) 14.09 (4.23) 19.63 (1.51) 29.92 (1.96) 36.28 (1.23)
0.15 (1.03) 0.96 (2.21) 6.97 (3.85) 3.81 (2.63) -0.48 (1.69) 2.85 (0.47)
0.75 0.86 0.55 0.95 0.97 0.99
8 13 11 11 10 3
11.66 (2.50) 20.77 (3.02) 16.26 (1.90) 17.06 (1.30) 17.85 (2.50) 16.66 (5.82)
-0.41 (1.08) -2.94 (2.99) 0.88 (2.23) 0.36 (2.76) 1.81 (3.28) 0.29 (5.68)
0.78 0.81 0.89 0.95 0.87 0.89
8 13 11 11 10 3
homolog
Mysis relicta-chinook salmon
Diporeia spp.-chinook salmon
homolog
m
b
r2
n
m
b
r2
n
tri tetra penta hexa hepta octa
22.32 (3.45) 29.17 (3.23) 21.75 (5.89) 26.50 (2.11) 40.81 (2.56) 48.27 (4.26)
-0.32 (1.65) 2.49 (4.13) 10.94 (5.36) 5.63 (3.66) -0.71 (2.20) 4.01 (1.64)
0.88 0.88 0.60 0.95 0.97 0.99
8 13 11 11 10 3
26.67 (5.45) 42.20 (6.50) 23.48 (3.29) 23.01 (1.79) 24.92 (2.84) 22.62 (6.56)
-1.49 (1.30) -3.72 (3.96) 3.45 (3.22) 0.73 (3.01) 1.00 (3.28) 0.18 (6.40)
0.80 0.79 0.85 0.95 0.86 0.92
8 13 11 11 10 3
a Individual congeners from different homologue groups that co-eluted were removed from the analysis. m is the slope of the regression line, b is the regression line intercept, and n is the sample size. One standard error of the parameter is given in parentheses.
PCBs in salmon and macroinvertebrates, two trophic levels below the salmon:
PCBchinook salmon ) 28.41 (1.32) PCBMysis relicta + 3.39 (1.21) r 2 ) 0.84 (1) PCBchinook salmon ) 24.95 (1.27) PCBDiporeia spp. + 1.77 (1.36) r 2 ) 0.81 (2) PCBcoho salmon ) 18.35 (1.01) PCBMysis relicta + 2.08 (0.94) r 2 ) 0.80 (3) PCBcoho salmon ) 17.21 (0.70) PCBDiporeia spp. - 0.39 (0.75) r 2 ) 0.88 (4) All PCB concentrations are ng/g fresh wt. The slopes of these relationships indicate that PCB congeners are biomagnified approximately ∼20-30-fold as they flow from macroinvertebrates, through prey fishes, to coho and chinook salmon in the Lake Michigan pelagic food web. Log Kow was not a significant term when added to these general models. The pattern in Figure 2 suggests that a quadratic function might fit the data. When we fit a quadratic model log Kow and log Kow2 were not significant terms in models 1, 2, and 4. The addition of log Kow2 made the Kow term barely significant (p ) 0.04) for model 3. Similarly, the dummy variables for coplanar and toxic PCBs were not significant additional terms. Thus, although there are strong relationships between PCB congeners in salmonids and macroinvertebrates, the bioaccumulation of PCB congeners at this scale of analysis does not appear to be significantly related to these physicochemical properties of the contaminants. This is perhaps surprising, for although there might not be a direct relationship of contaminant chemistry on contaminant uptake through diets, the partitioning of congeners into lipid-rich pools within individual fish might be expected to affect depuration across the range of all congeners considered and thus be related to the octanol-water partition coefficient. These physicochemical attributes may not affect contaminant metabolism within salmon, or the effect may be too small to be detected in our samples, perhaps because Kow values are laboratoryderived equilibrium partition coefficients and salmon are not in equilibrium with their environment. A similar conclusion has been reached for the Lake Superior pelagic food web (39).
Regressions of concentrations in salmon on concentrations in macroinvertebrates by homolog group allows a comparison of trophic transfers for individual congeners accounting for degree of chlorination. We found an increase in the slope of the relationship for concentrations in coho salmon and M. relicta (Table 1). The pattern was almost the same for concentrations in coho salmon and Diporeia spp., but tetra- and octachlorobiphenyls did not follow the trend of increasing slope of the relationship with increasing chlorination. A pattern of increasing slope with degree of chlorination was found for regressions between chinook salmon and Mysis. This pattern did not hold for regressions of concentrations between Diporeia spp. and chinook salmon as the hepta- and octachlorobiphenyls had similar slopes to the tri-, penta-, and hexachlorobiphenyls. However, the tetrachlorobiphenyls showed the same pattern of a higher slope when compared to tri-, penta-, hexa-, and heptachlorobiphenyls. We do not know why the rate of increase is higher for tetrachlorobiphenyls than for other homologs. To further explore the possibility that differences in bioaccumulation within homolog groups might be correlated to physicochemical properties of congeners we added, one at a time, log Kow and the categorical variables for coplanar and toxic PCBs. None of these variables were significant model terms.
Acknowledgments We thank Conrad Lamon for collecting and transporting salmon from the field to laboratory and three anonymous reviewers for improving this manuscript through their reviews. This work was funded by the University of Wisconsin Sea Grant Institute under grants from the National Sea Grant College Program, National Oceanic and Atmospheric Administration, U.S. Department of Commerce, and the State of Wisconsin. Federal Grant NA90AA-D-SG469, Project R/WM-41.
Supporting Information Available Concentrations of PCB congeners in individual coho and chinook salmon sampled from Lake Michigan (9 pages). This material is available free of charge via the Internet at http:// pubs.acs.org.
Literature Cited (1) Gilbertson, M.; Morris, R. D.; Hunter, R. A. Auk 1976, 93, 434442. VOL. 35, NO. 5, 2001 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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(2) Gilman, A. P.; Fox, G. A.; Peakall, D. B.; Teeple, S. M.; Carroll, T. R.; Haymes, G. T. J. Wildl. Manage. 1977, 41, 458-468. (3) Teeple, S. M. Can. Field Nat. 1977, 91, 148-157. (4) Jackson, L. J.; Carpenter, S. R. J. Great Lakes Res. 1995, 21, 112120. (5) Stow, C. A.; Carpenter, S. R.; Eby, L. A.; Amrhein, J. F.; Hesselberg, R. J. Ecol. Appl. 1995, 5, 248-260. (6) Hebert, C. E.; Norstrom, R. J.; Zhu, J.; Macdonald, C. R. J. Great Lakes Res. 1999, 25, 220-233. (7) Fein, G. G., Jacobson, J. L., Jacobson, S. W., Schwartz, P. M., Dowler, J. K. J. Pediatr. 1984, 105, 315-320. (8) Jacobson, J. L.; Jacobson, S. W. J. Great Lakes Res. 1983, 19, 776-783. (9) Heinz, G. H.; Erdman, T. C.; Haseltine, S. D.; Stafford, C. Environ. Monit. Assess. 1985, 5, 223-236. (10) Tillitt, D. E.; Ankley, G. T.; Giesy, J. P.; Ludwig, J. P.; KuritaMatsuba, H.; Wesloh, D. V.; Ross, P. S.; Bishop, C. A.; Sileo, L.; Stromborg, K. L.; Larson, J.; Kubiak, T. J. Environ. Toxicol. Chem. 1992, 11, 1281-1288. (11) Mac, M. J.; Edsall, C. C. J. Toxicol. Environ. Health 1991, 33, 375-394. (12) Jackson, L. J.; Carpenter, S. R.; Manchester-Neesvig, J.; Stow, C. A. J. Great Lakes Res. 1998, 24, 808-821. (13) Anderson, D. J.; Bloem, T. B.; Higgins, J. V. J. Great Lakes Res. 1998, 24, 838-844. (14) Madenjian, C. P.; Schmidt, L. J.; Chernyak, S. M.; Elliot, R. F.; Descorcie, T. J.; Quintal, R. T.; Begnoche, L. J.; Hesselberg, R. J. Environ. Sci. Technol. 1999, 33, 3768-3773. (15) Kubiak, T. J.; Harris, H. J.; Smith, L. M.; Schwartz, T. R.; Trick, J. A.; Docherty, D. E.; Erdman, T. C. Arch. Environ. Contam. Toxicol. 1989, 18, 706-727. (16) Jackson, L. J.; Schindler, D. E. Environ. Sci. Technol. 1996, 30, 1861-1865. (17) Rasmussen, J. B.; Rowan, D. J.; Lean, D. R. S.; Carey, J. H. Can. J. Fish. Aquat. Sci. 1990, 47, 2030-2038. (18) Bentzen, E.; Lean, D. R. S.; Taylor, W. D.; Mackay, D. Can. J. Fish. Aquat. Sci. 1996, 53, 2397-2407. (19) Madenjian, C. P.; Elliot, R. F.; Schmidt, L. J.; DeSourcie, T. J.; Hesselberg, R. J.; Quintal, R. T.; Begnoche, L. J.; Bouchard, P. M.; Holey, M. E. Environ. Sci. Technol. 1998, 32, 3063-3067. (20) Willman, E. J.; Manchester-Neesvig, J. B.; Anderson, D. E. Environ. Sci. Technol. 1997, 31, 3712-3718.
862
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 35, NO. 5, 2001
(21) Mullin, M. D. Presented at PCB Workshop. U.S. EPA Large Lake Research Station: Grosse Ile, MI, 1985. (22) SAS Institute Inc. SAS/STAT Guide for Personal Computers, Version 7.0; SAS Institute Inc.: Cary, NC, 1987. (23) Hawker, D. W.; Connell, D. W. Environ. Sci. Technol. 1988, 22, 382-387. (24) Madenjian, C. P.; DeSorcie, T. J.; Stedman, R. M.; Brown, E. H., Jr.; Eck, G. W.; Schmidt, L. J.; Hesselberg, R. J.; Chernyak, S. M.; Passino-Reader, D. R. J. Great Lakes Res. 1999, 25, 149-159. (25) Stow, C. A.; Jackson, L. J.; Amrhein, J. F. Can. J. Fish. Aquat. Sci. 1997, 54, 1031-1038. (26) Jackson, L. J. Ecol. Appl. 1996, 6, 1355-1364. (27) Stewart, D. J.; Ibarra, M. Can. J. Fish. Aquat. Sci. 1991, 48, 909922. (28) Jackson, L. J. Ecol. Appl. 1997, 7, 991-1001. (29) McCarty, L. S.; Mackay, D.; Smith, A. D.; Ozburn, G. W.; Dixon, D. G. Sci. Total Environ. 1993, 109/110, 515-525. (30) Willman, E. J.; Manchester-Neesvig, J. B.; Agrell, C.; Armstrong, D. E. Environ. Toxicol. Chem. 1999, 18, 1380-1389. (31) Opperhuizen, A.; Velde, E. W.; Gobas, F. A. P. C.; Lem, D. A.; Steen, J. M.; Hutzinger, O. Chemosphere 1985, 14, 1871-1896. (32) Gobas, F. A. P. C.; Clark, K. E.; Shiu, W. Y.; Mackay, D. Environ. Toxicol. Chem. 1990, 9, 715-724. (33) Stow, C. A. Environ. Sci. Technol. 1995, 29, 522-527. (34) Borgmann, U.; Whittle, D. M. J. Great Lakes Res. 1991, 17, 368381. (35) Miller, M. A.; Madenjian, C. P.; Masnado, R. G. J. Great Lakes Res. 1992, 18, 742-754. (36) Grossnickle, N. E. Hydrobiologia 1982, 93, 101-107. (37) Mills, E. L.; O’Gorman, R.; DeGisi, J.; Heberger, R. F.; House, R. A. Can. J. Fish. Aquat. Sci. 1992, 49, 2009-2019. (38) Nalepa, T. F.; Hartson, D. J.; Fanslow, D. L.; Lang, G. A.; Lozano, S. J. Can. J. Fish. Aquat. Sci. 1998, 55, 2402-2413. (39) Kucklick, J. R.; Baker, J. E. Environ. Sci. Technol. 1998, 32, 11921198.
Received for review August 7, 2000. Revised manuscript received December 4, 2000. Accepted December 5, 2000. ES001558+
