Environmental Pollution 186 (2014) 141e148
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Concentrations and trophic magnification of cyclic siloxanes in aquatic biota from the Western Basin of Lake Erie, Canada Daryl J. McGoldrick a, *, Cecilia Chan c, Ken G. Drouillard b, Michael J. Keir a, Mandi G. Clark a, Sean M. Backus a a b c
Water Science and Technology Directorate, Environment Canada, Burlington, ON, Canada L7R 4A6 Great Lakes Institute for Environmental Research, University of Windsor, Windsor, ON, Canada CASSEN Testing Labs, Toronto, ON, Canada M9W 6H3
a r t i c l e i n f o
a b s t r a c t
Article history: Received 29 August 2013 Received in revised form 2 December 2013 Accepted 5 December 2013
We examine the concentrations and food web biomagnification of three cyclic volatile methylsiloxanes (cVMS) octamethylcyclotetrasiloxane (D4), decamethylcyclopentasiloxane (D5), and dodecamethylcyclohexasiloxane (D6) using aquatic biota collected from Lake Erie. Concentrations of cVMS in biota were within the range reported for other studies of cVMS in aquatic biota. Trophic magnification factors (TMF) were assessed in various food web configurations to investigate the effects of food web structure. TMF estimates were highly dependent on the inclusion/exclusion of the organisms occupying the highest and lowest trophic levels and were >1 for D4 and D5, indicating biomagnification, in only 1 of the 5 food web configurations investigated and were 153 mm) Mayfly composite (Hexagenia sp.) Common Shiner (Luxilus cornutus) Yellow Perch (Perca flavescens) Emerald Shiner (Notropis atherinoides) Trout Perch (Percopsis omiscomaycus) White Perch (Morone americana) Freshwater Drum (Aplodinotus grunniens) Walleye (Sander vitreus) a
na
D4
D5
D6
PCB180
Total lipid
Trophic level
1
ND
5.2 (n/a)
ND
0.36 (n/a)
0.3 (n/a)
2.0 (0.32)
1
7.0 (n/a)
11 (n/a)
5.7 (n/a)
16 (n/a)
1.3 (n/a)
2.2 (0.08)
2
7.9 (0.49)
15 (5.6)
6.9 (9.7)
36 (0.70)
3.5 (2.0)
3.1 (0.08)
5
8.9 (1.6)
14 (3.6)
11 (7.7)
25 (12)
1.6 (1.1)
3.4 (0.10)
5
12 (2.3)
17 (5.7)
13 (2.2)
11 (2.1)
2.1 (0.47)
3.6 (0.07)
5
12 (2.9)
23 (5.3)
13 (4.4)
11 (1.5)
0.7 (0.51)
3.6 (0.08)
4
13 (4.5)
26 (5.4)
8.2 (8.2)
8.1 (0.41)
5.3 (2.4)
3.7 (0.05)
5
9.6 (2.1)
23 (12)
9.9 (5.6)
39 (33)
3.4 (2.1)
4.0 (0.12)
15
13 (4.1)
36 (15)
14 (7.2)
38 (13)
13 (2.9)
4.2 (0.12)
n refers to the numbers of samples analysed for each food web component.
The relative contribution of pelagic and benthic based carbon in the diets of each fish was estimated using a single isotope e two source mixing model (Phillips and Gregg, 2001). FP ¼ jdM edB j=jdP edB j
(3)
where FP is the fraction of planktonic carbon contribution to the mixture, dP is the isotope ratio of zooplankton, dB is the isotope ratio of Hexagenia, and dM is the isotope ratio of the fish tissue sample. Trophic levels were assigned relative to zooplankton which we assumed occupied trophic level 2. Trophic levels (TL) for each individual sample were determined using the following relationship: TLconsumer ¼ 2 þ
d15 Nconsumer d15 Nplankton
.
DN
(4)
where DN is the trophic enrichment factor of 3.4& (Fisk et al., 2001; Jardine et al., 2006) 2.5. Additional data treatments and analyses Where specified, contaminant concentrations in biota were lipid equivalent normalized following the methods of deBruyn and Gobas (2007). The formula accounts for the sorptive capacity of protein in samples with low lipid content such as those used in this study. Contaminant concentrations were normalized as follows: CLIPEQ ¼ Cww =ðfLIP þ 0:05*fLDP Þ
(5)
where, Cww is the wet weight concentration of the contaminant, fLIP is the fraction of lipid in the sample, and fLDP is the fraction of lean dry weight in the sample. Means and standard deviations for contaminant concentrations and estimated trophic levels were used to estimate population distributions for each component of the food web (Table 1; Table S4). Only 1 bulk composite sample was available for each of zooplankton and mayfly from the collection year used in this study. As these samples were homogenized composites, the resulting contaminant concentrations should approximate the arithmetic mean of the individuals in the samples but we could not calculate standard deviations. For these samples, standard deviations were estimated for each contaminant using the average coefficient of variation observed in the other components of the food web. Trophic magnification factors (TMF) were then estimated using a probability based approach (Powell, 2010a) and TMF
Table 2 Comparison of mean D4, D5, and D6 concentrations (ng/g ww) measured using the methods described in this study to the consensus value obtained on the same sample in a previous inter-laboratory comparison study. cVMS
D4 D5 D6 a
Previous study (n ¼ 28)a
This study (n ¼ 6) Mean (stdev)
%RSD
Mean (stdev)
%RSD
31.4 (2.5) 26.7 (4.8) 20.2 (7.2)
8 18 36
47.4 (6.5) 40.4 (6.5) 23.0 (4.4)
14 16 19
From (McGoldrick et al., 2011).
estimates were benchmarked against a ubiquitous and highly bioaccumulative contaminant (PCB180) (Adolfsson-Erici et al., 2012). This approach generates distributions of possible TMF for each contaminant using mean and standard deviation values for trophic level and contaminant concentrations for each food web component as inputs to Monte-Carlo simulations (n ¼ 10,000 model iterations). Resulting estimates of TMF incorporate error associated with both chemical concentration and trophic position of individual sample types. As the contaminant means and standard deviations were in log10 units, normal distributions were assumed for the input variables to the statistical simulations. Input variables used in the MonteeCarlo simulations are provided in Table S5.
3. Results & discussion 3.1. Data validation Prior to analysis for cVMS, background levels in the analytical instruments, particularly the Carbotrap tubes, were assessed and determined to be on average 0.2e0.3 ng for the compounds of interest and deemed insignificant as they were 5% or less than the lowest reported values in biota from this study (Table S6). The results of repeated measures (n ¼ 6) of a fortified fish homogenate material of known concentration were consistently lower for D4 and D5 (34%) and for D6 (13%) relative to the concentrations determined for the same material reported in McGoldrick et al. (2011); however, precision inferred by relative standard deviation of the repeated measurements were similar for D4 and D5 to those obtained other studies of cVMS in biota (Kierkegaard et al., 2011; McGoldrick et al., 2011; Kierkegaard et al., 2013) (Table 2). Relative standard deviations for repeated measures of D6 in this study (36%) were approximately double those reported in these same studies. Recoveries of 13C-D4 ranged from 40 to 117%, from 25 to 115% for 13C-D5, and from 19 to 104 for 13C-D6 and were always highest in low lipid plankton samples and generally decreased as the lipid content of the sample increased (Table S2). Due to the variable surrogate recoveries, each sample was analysed at least twice and the results averaged for reporting. Although the ranges of recovery were large when looking at all the samples combined, they were far more consistent for samples of the same species. Lower recoveries and higher variability in concentrations have been observed in other studies of cVMS in lipid rich biota (Kierkegaard et al., 2011). In this study, the low recoveries and increased variability could be related increased foaming of high lipid samples observed in the purging step of the extraction procedure interfering with the adsorption to the Carbotrap tubes.
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D.J. McGoldrick et al. / Environmental Pollution 186 (2014) 141e148
Repeated measures (n ¼ 15) of PCB180 in a fish tissue reference sample (internal reference sample - Common Carp GLCARP11) had a relative standard deviation of 8.3%. The mean concentration for PCB180 in the reference sample was found to be within 1 standard deviation of the control chart value generated by the laboratory for this homogenate over the past 2 years of reference tissue measurements. Recoveries of 13C labelled PCB34 surrogate in all biota samples ranged from 78 to 109 percent.
3.2. cVMS and PCB180 concentrations in biota Observed concentrations of cVMS materials and PCB180 in aquatic biota collected from the Western Basin of Lake Erie are presented in Table 1. The observed concentrations of D4 in the composite plankton samples were below the limit of detection ( 1 (%) 15
e e e
0.73 0.39e1.2 15
e e e
1.1 0.51e1.9 49
0.75 0.50e1.1 > 1 (%) 7.9
0.68 0.41e1.0 7.0
0.91 0.52e1.4 35
0.80 0.45e1.3 19
1.2 0.64e1.9 65
0.71 0.47e1.0 > 1 (%) 4.6
e e e
0.71 0.47e1.0 5.3
e e e
0.97 0.62e1.4 40
1.2 0.86e1.6 > 1 (%) 82
1.7 1.2e2.4 99
0.55 2.1 0.31e0.86 1.4e3.0 1.5 99
0.58 0.31e0.96 3.4
*D4 & D6 not detected in plankton samples.
3.4. Trophic magnification Trophic magnification of cVMS materials was assessed as the slope of lipid equivalent concentrations of D4, D5 and D6 regressed on trophic level for five separate food web configurations. TMFs were also estimated for PCB180 in each configuration as a benchmark to a compound that is known to magnify in aquatic food webs. The first configuration included all organisms described in this study and the other four configurations alternately excluded plankton and Hexagenia from the lower end of the food web and Walleye from the highest tropic level to investigate the sensitivity of TMF estimates to food web structure. The levels of D4 and D6 measured in the plankton samples were below the level of detection and thus trophic magnification factors (TMF) could not be estimated for these compounds in the food web configurations with plankton at their base. Bootstrapped estimates of TMF with the 95% confidence interval based on 10,000 MonteCarlo simulations for each of the food web configurations are presented in Table 4. The TMF distributions for D5 and PCB 180 obtained from these simulations are also provided in Fig. 1. Bootstrapped mean TMF values for D4, D5, and D6 were all 1 were below 15% as opposed to PCB180 which had a mean TMF of 1.2 and an 82% probability of observing a TMF > 1 (Table 4). Similar results were obtained for the food web configurations with Hexagenia and both Hexagenia and Walleye excluded. In these configurations, with plankton at the base of the food web, the estimated TMF values for D5 were also below 1 with 1 in the simulations (Table 4). In these same configurations mean TMF estimates for PCB180 were >1 with 99% of the simulations returning a TMF value >1 (Table 4). These results suggest that trophic magnification of D4, D5, and D6 is not likely with plankton occupying the lowest trophic level and are in line with the findings of studies in Lake Pepin and Oslo Fjord (Powell and Woodburn, 2009; Powell, 2010a). However, considering the d13C of the organisms collected suggested a strong reliance on benthic based carbon (Table 3), food web configurations with plankton excluded and both plankton and Walleye excluded are likely more appropriate as Hexagenia, a benthic burrowing mayfly, would be a more appropriate representative of the lowest trophic level. The mean estimated TMF for D5 (0.91) and the probability of observing a TMF value >1 (35%)
D.J. McGoldrick et al. / Environmental Pollution 186 (2014) 141e148
145
Fig. 1. Frequency distribution of trophic magnification factors determined for lipid normalized D5 and PCB180 in Lake Erie generated from bootstrapped Monte-Carlo simulation. TMF ¼ 1.0 (equilibrium) denoted by dashed black vertical line. The mean of each distribution are denoted with solid vertical lines.
with plankton excluded were greater than estimated for both the plankton based food web configurations (Table 4). The TMF values for D4 and D6 with plankton excluded were identical to the configurations containing all species as these analytes were not quantifiable above the detection limit in plankton. When both Walleye and plankton were excluded from the food web configuration the mean estimated TMF values were again higher for D4 (1.1), D5 (1.2), and D6 (0.97) with probabilities of TMF > 1 of 49%, 65%, and 40% respectively (Table 4). In the last two food web configurations, with Hexagenia occupying the lowest trophic level, the mean TMF estimates for PCB180 are well below 1 (0.55 & 0.58) with >4% probability of having a TMF > 1 and both measures were below those observed for D4, D5, and D6 (Table 4; Fig. 1). The high degree of variability of TMF dependent on food web structure was also noted in Borgå et al. (2012, 2013) who observed TMFs >1 in the pelagic based food webs of Lakes Mjøssa and Randsfjorden. In their 2013 study, they attributed variability and lower estimates of TMF to the influence of benthic food sources in the diets of certain species of fish. The food web in this study was largely supported by benthic carbon and lacked pelagic influence. Thus, the low TMF estimates we observed in Lake Erie seem consistent with the observations Borgå et al. (2013). The results we present differ from Borgå et al. (2012, 2013) in that the TMF estimates for PCB180 also varied considerably with food web composition and were 1 but would still vary considerably dependent upon the composition of the food web. The results presented both correspond to and disagree with published work on TMF investigations of cVMS materials in aquatic food webs which have provided evidence and explanations on how trophic dilution (Powell and Woodburn, 2009; Powell, 2010a), trophic magnification (Borgå et al., 2012; 2013), or bioaccumulation but not trophic magnification (Kierkegaard et al., 2011) can occur. The possible reasons for these mixed observations are varied, complex, and not fully understood; however, there are similarities in these studies and the observations in this study. On a wet weight basis, concentrations of cVMS and PCB180 in aquatic organisms increase with trophic level as does the lipid content of their tissues (Table 1; Fig. S2). When contaminant concentrations in these organisms are expressed as lipid equivalents the resulting relationship is reversed and often highest in plankton or benthic invertebrates (Powell, 2010a). This relationship likely explains a large part of the observed variability in TMFs which are dependent on the composition of the food web, specifically the occupants of the highest and/or lowest trophic levels (Table 4) (Powell, 2010a; Borgå et al., 2012). As the concept of TMF is dependent on lipid equivalent concentrations being a measure of
146
D.J. McGoldrick et al. / Environmental Pollution 186 (2014) 141e148
Fig. 2. Relationship between the concentrations of D5 and PCB180 and %lipid in Lake Erie biota expressed as wet weight (A), lipid-equivalent weight with NLOM partitioning equivalent value of 0.05 (B), and lipid-equivalent weight with NLOM of 0.07 (C).
the fugacity of lipid soluble chemicals within organisms and largely based on the behaviour of chemicals with the properties of PCBs or other legacy persistent organic pollutants (POPs), the assumption that cVMS partition within organisms in a similar manner to these chemicals is implicit (Gobas et al., 2009). In the case of cVMS materials this may not be the case as shown in the relationship between wet weight and lipid equivalent concentrations of D5 and PCB180 with lipid content in this study (Fig. 2). The slopes of linear
regressions of wet weight concentrations against lipid content for D5 and PCB180 were positive and similar to one another. When chemical concentrations were expressed on a lipid equivalent basis calculated using Eq. (5) and applying the recommended non-lipid organic matter (NLOM) partition equivalent value of 0.05 (deBruyn and Gobas, 2007), the regression shifted to a negative relationship for both PCB 180 and D5. However, it is worth noting that the slopes and intercepts of the above regression fits diverge between D5 and PCB 180, with D5 demonstrating elevated slope and intercept values compared to PCB 180. The lipid equivalent slopes and intercepts converge if the NLOM partitioning coefficient for D5 is changed from the recommended value of 0.05 to a value of 0.07 (Fig. 2). This provides supporting evidence that D5 has a higher association with non-lipid organic matter than PCB180 and may indicate that D5-tissue interactions are more complex than the simple partitioning behaviour demonstrated to occur for organochlorines (Drouillard et al., 2004). Potential inter-tissue partitioning differences between legacy POPs and emerging contaminants at the organismal scale and the associated effects on estimates of biomagnification at the food web scale require additional investigation. In the Lake Erie food web, the inclusion of Walleye at the highest trophic level resulted in lower TMF estimates for cVMS than when it was excluded (Fig. 1). This was due, in part, to Walleye having the lowest lipid equivalent concentrations of cVMS in the organisms we collected (Table S4). The observation of cVMS materials being higher in the 3rd trophic level than in the 4th was also noted in the Humber estuary and was attributed to sediment/biota partitioning differences between D5 and PCB180 from the organic carbon/water partitioning coefficient (Koc) (Kierkegaard et al., 2011). Their reasoning was that since Koc is 200 times greater for D5 than for PCB180 it would accumulate in the lower trophic level organisms with strong association with sediments at rates higher than metabolic elimination. Trophic dilution could still occur as metabolic elimination outpaces uptake as you move to higher trophic level organisms. The causes of declining D4, D5, and D6 concentrations in fish at higher trophic levels in Lake Erie, whether due to differing abilities to metabolize cVMS materials among species, or other mechanisms require additional study. Variability in TMF estimates for each food web configuration could also be due to sampling artifacts related to differing home or foraging ranges among the species collected in relation to the sources of contamination. In general, the home range of fish is positively correlated with body size, thus larger bodied fish occupying higher trophic levels have greater foraging ranges than their prey (Minns, 1995). It is possible that larger fish could consume forage of varying levels of contamination dependant on the proximity of the forage to sources of contamination. If sampling occurred in an area of high sediment contamination or in close proximity to waste water discharges, this phenomenon could result in higher levels of contamination in forage relative to consumer and explain the low TMF and strong sensitivity to food web composition we observed in Lake Erie. By using PCB 180 as a reference chemical, it is also assumed that water and/or sediment contamination patterns over spatial scales equivalent to the foraging ranges of animals sampled as part of the study are equivalent between PCBs and cVMS compounds. Unfortunately, there is insufficient information on sources and sediment concentrations of cVMS materials in the west basin of Lake Erie to confirm these hypotheses at present. However, the observation that PCB 180 and D5 showed differences in the sensitivity of TMFs when different food web combinations were used in the TMF calculation imply that differences in spatial contamination of the two chemicals were occurring. Thus future efforts on characterizing TMF should consider developing organismal sampling
D.J. McGoldrick et al. / Environmental Pollution 186 (2014) 141e148
strategies that consider both foraging range of the animals being sampled and choice of TMF reference chemicals such that the spatial contamination trends of the unknown and reference chemical show correlation to one another. In this study, we observed high variability in TMF estimates based on food web composition for cVMS materials and PCB180. Estimates were highly dependent on the inclusion/exclusion of the organisms occupying the highest and lowest trophic levels, which also contained the highest and lowest levels of tissue lipid, respectively. The occurrence of biomagnification of cVMS materials in Lake Erie is unclear. TMF >1 were observed for D4 and D5 in only 1 of 5 food web configurations and TMF for D6 were
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Environmental Pollution journal homepage: www.elsevier.com/locate/envpol
Concentrations and trophic magnification of cyclic siloxanes in aquatic biota from the Western Basin of Lake Erie, Canada Daryl J. McGoldrick a, *, Cecilia Chan c, Ken G. Drouillard b, Michael J. Keir a, Mandi G. Clark a, Sean M. Backus a a b c
Water Science and Technology Directorate, Environment Canada, Burlington, ON, Canada L7R 4A6 Great Lakes Institute for Environmental Research, University of Windsor, Windsor, ON, Canada CASSEN Testing Labs, Toronto, ON, Canada M9W 6H3
a r t i c l e i n f o
a b s t r a c t
Article history: Received 29 August 2013 Received in revised form 2 December 2013 Accepted 5 December 2013
We examine the concentrations and food web biomagnification of three cyclic volatile methylsiloxanes (cVMS) octamethylcyclotetrasiloxane (D4), decamethylcyclopentasiloxane (D5), and dodecamethylcyclohexasiloxane (D6) using aquatic biota collected from Lake Erie. Concentrations of cVMS in biota were within the range reported for other studies of cVMS in aquatic biota. Trophic magnification factors (TMF) were assessed in various food web configurations to investigate the effects of food web structure. TMF estimates were highly dependent on the inclusion/exclusion of the organisms occupying the highest and lowest trophic levels and were >1 for D4 and D5, indicating biomagnification, in only 1 of the 5 food web configurations investigated and were 153 mm) Mayfly composite (Hexagenia sp.) Common Shiner (Luxilus cornutus) Yellow Perch (Perca flavescens) Emerald Shiner (Notropis atherinoides) Trout Perch (Percopsis omiscomaycus) White Perch (Morone americana) Freshwater Drum (Aplodinotus grunniens) Walleye (Sander vitreus) a
na
D4
D5
D6
PCB180
Total lipid
Trophic level
1
ND
5.2 (n/a)
ND
0.36 (n/a)
0.3 (n/a)
2.0 (0.32)
1
7.0 (n/a)
11 (n/a)
5.7 (n/a)
16 (n/a)
1.3 (n/a)
2.2 (0.08)
2
7.9 (0.49)
15 (5.6)
6.9 (9.7)
36 (0.70)
3.5 (2.0)
3.1 (0.08)
5
8.9 (1.6)
14 (3.6)
11 (7.7)
25 (12)
1.6 (1.1)
3.4 (0.10)
5
12 (2.3)
17 (5.7)
13 (2.2)
11 (2.1)
2.1 (0.47)
3.6 (0.07)
5
12 (2.9)
23 (5.3)
13 (4.4)
11 (1.5)
0.7 (0.51)
3.6 (0.08)
4
13 (4.5)
26 (5.4)
8.2 (8.2)
8.1 (0.41)
5.3 (2.4)
3.7 (0.05)
5
9.6 (2.1)
23 (12)
9.9 (5.6)
39 (33)
3.4 (2.1)
4.0 (0.12)
15
13 (4.1)
36 (15)
14 (7.2)
38 (13)
13 (2.9)
4.2 (0.12)
n refers to the numbers of samples analysed for each food web component.
The relative contribution of pelagic and benthic based carbon in the diets of each fish was estimated using a single isotope e two source mixing model (Phillips and Gregg, 2001). FP ¼ jdM edB j=jdP edB j
(3)
where FP is the fraction of planktonic carbon contribution to the mixture, dP is the isotope ratio of zooplankton, dB is the isotope ratio of Hexagenia, and dM is the isotope ratio of the fish tissue sample. Trophic levels were assigned relative to zooplankton which we assumed occupied trophic level 2. Trophic levels (TL) for each individual sample were determined using the following relationship: TLconsumer ¼ 2 þ
d15 Nconsumer d15 Nplankton
.
DN
(4)
where DN is the trophic enrichment factor of 3.4& (Fisk et al., 2001; Jardine et al., 2006) 2.5. Additional data treatments and analyses Where specified, contaminant concentrations in biota were lipid equivalent normalized following the methods of deBruyn and Gobas (2007). The formula accounts for the sorptive capacity of protein in samples with low lipid content such as those used in this study. Contaminant concentrations were normalized as follows: CLIPEQ ¼ Cww =ðfLIP þ 0:05*fLDP Þ
(5)
where, Cww is the wet weight concentration of the contaminant, fLIP is the fraction of lipid in the sample, and fLDP is the fraction of lean dry weight in the sample. Means and standard deviations for contaminant concentrations and estimated trophic levels were used to estimate population distributions for each component of the food web (Table 1; Table S4). Only 1 bulk composite sample was available for each of zooplankton and mayfly from the collection year used in this study. As these samples were homogenized composites, the resulting contaminant concentrations should approximate the arithmetic mean of the individuals in the samples but we could not calculate standard deviations. For these samples, standard deviations were estimated for each contaminant using the average coefficient of variation observed in the other components of the food web. Trophic magnification factors (TMF) were then estimated using a probability based approach (Powell, 2010a) and TMF
Table 2 Comparison of mean D4, D5, and D6 concentrations (ng/g ww) measured using the methods described in this study to the consensus value obtained on the same sample in a previous inter-laboratory comparison study. cVMS
D4 D5 D6 a
Previous study (n ¼ 28)a
This study (n ¼ 6) Mean (stdev)
%RSD
Mean (stdev)
%RSD
31.4 (2.5) 26.7 (4.8) 20.2 (7.2)
8 18 36
47.4 (6.5) 40.4 (6.5) 23.0 (4.4)
14 16 19
From (McGoldrick et al., 2011).
estimates were benchmarked against a ubiquitous and highly bioaccumulative contaminant (PCB180) (Adolfsson-Erici et al., 2012). This approach generates distributions of possible TMF for each contaminant using mean and standard deviation values for trophic level and contaminant concentrations for each food web component as inputs to Monte-Carlo simulations (n ¼ 10,000 model iterations). Resulting estimates of TMF incorporate error associated with both chemical concentration and trophic position of individual sample types. As the contaminant means and standard deviations were in log10 units, normal distributions were assumed for the input variables to the statistical simulations. Input variables used in the MonteeCarlo simulations are provided in Table S5.
3. Results & discussion 3.1. Data validation Prior to analysis for cVMS, background levels in the analytical instruments, particularly the Carbotrap tubes, were assessed and determined to be on average 0.2e0.3 ng for the compounds of interest and deemed insignificant as they were 5% or less than the lowest reported values in biota from this study (Table S6). The results of repeated measures (n ¼ 6) of a fortified fish homogenate material of known concentration were consistently lower for D4 and D5 (34%) and for D6 (13%) relative to the concentrations determined for the same material reported in McGoldrick et al. (2011); however, precision inferred by relative standard deviation of the repeated measurements were similar for D4 and D5 to those obtained other studies of cVMS in biota (Kierkegaard et al., 2011; McGoldrick et al., 2011; Kierkegaard et al., 2013) (Table 2). Relative standard deviations for repeated measures of D6 in this study (36%) were approximately double those reported in these same studies. Recoveries of 13C-D4 ranged from 40 to 117%, from 25 to 115% for 13C-D5, and from 19 to 104 for 13C-D6 and were always highest in low lipid plankton samples and generally decreased as the lipid content of the sample increased (Table S2). Due to the variable surrogate recoveries, each sample was analysed at least twice and the results averaged for reporting. Although the ranges of recovery were large when looking at all the samples combined, they were far more consistent for samples of the same species. Lower recoveries and higher variability in concentrations have been observed in other studies of cVMS in lipid rich biota (Kierkegaard et al., 2011). In this study, the low recoveries and increased variability could be related increased foaming of high lipid samples observed in the purging step of the extraction procedure interfering with the adsorption to the Carbotrap tubes.
144
D.J. McGoldrick et al. / Environmental Pollution 186 (2014) 141e148
Repeated measures (n ¼ 15) of PCB180 in a fish tissue reference sample (internal reference sample - Common Carp GLCARP11) had a relative standard deviation of 8.3%. The mean concentration for PCB180 in the reference sample was found to be within 1 standard deviation of the control chart value generated by the laboratory for this homogenate over the past 2 years of reference tissue measurements. Recoveries of 13C labelled PCB34 surrogate in all biota samples ranged from 78 to 109 percent.
3.2. cVMS and PCB180 concentrations in biota Observed concentrations of cVMS materials and PCB180 in aquatic biota collected from the Western Basin of Lake Erie are presented in Table 1. The observed concentrations of D4 in the composite plankton samples were below the limit of detection ( 1 (%) 15
e e e
0.73 0.39e1.2 15
e e e
1.1 0.51e1.9 49
0.75 0.50e1.1 > 1 (%) 7.9
0.68 0.41e1.0 7.0
0.91 0.52e1.4 35
0.80 0.45e1.3 19
1.2 0.64e1.9 65
0.71 0.47e1.0 > 1 (%) 4.6
e e e
0.71 0.47e1.0 5.3
e e e
0.97 0.62e1.4 40
1.2 0.86e1.6 > 1 (%) 82
1.7 1.2e2.4 99
0.55 2.1 0.31e0.86 1.4e3.0 1.5 99
0.58 0.31e0.96 3.4
*D4 & D6 not detected in plankton samples.
3.4. Trophic magnification Trophic magnification of cVMS materials was assessed as the slope of lipid equivalent concentrations of D4, D5 and D6 regressed on trophic level for five separate food web configurations. TMFs were also estimated for PCB180 in each configuration as a benchmark to a compound that is known to magnify in aquatic food webs. The first configuration included all organisms described in this study and the other four configurations alternately excluded plankton and Hexagenia from the lower end of the food web and Walleye from the highest tropic level to investigate the sensitivity of TMF estimates to food web structure. The levels of D4 and D6 measured in the plankton samples were below the level of detection and thus trophic magnification factors (TMF) could not be estimated for these compounds in the food web configurations with plankton at their base. Bootstrapped estimates of TMF with the 95% confidence interval based on 10,000 MonteCarlo simulations for each of the food web configurations are presented in Table 4. The TMF distributions for D5 and PCB 180 obtained from these simulations are also provided in Fig. 1. Bootstrapped mean TMF values for D4, D5, and D6 were all 1 were below 15% as opposed to PCB180 which had a mean TMF of 1.2 and an 82% probability of observing a TMF > 1 (Table 4). Similar results were obtained for the food web configurations with Hexagenia and both Hexagenia and Walleye excluded. In these configurations, with plankton at the base of the food web, the estimated TMF values for D5 were also below 1 with 1 in the simulations (Table 4). In these same configurations mean TMF estimates for PCB180 were >1 with 99% of the simulations returning a TMF value >1 (Table 4). These results suggest that trophic magnification of D4, D5, and D6 is not likely with plankton occupying the lowest trophic level and are in line with the findings of studies in Lake Pepin and Oslo Fjord (Powell and Woodburn, 2009; Powell, 2010a). However, considering the d13C of the organisms collected suggested a strong reliance on benthic based carbon (Table 3), food web configurations with plankton excluded and both plankton and Walleye excluded are likely more appropriate as Hexagenia, a benthic burrowing mayfly, would be a more appropriate representative of the lowest trophic level. The mean estimated TMF for D5 (0.91) and the probability of observing a TMF value >1 (35%)
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Fig. 1. Frequency distribution of trophic magnification factors determined for lipid normalized D5 and PCB180 in Lake Erie generated from bootstrapped Monte-Carlo simulation. TMF ¼ 1.0 (equilibrium) denoted by dashed black vertical line. The mean of each distribution are denoted with solid vertical lines.
with plankton excluded were greater than estimated for both the plankton based food web configurations (Table 4). The TMF values for D4 and D6 with plankton excluded were identical to the configurations containing all species as these analytes were not quantifiable above the detection limit in plankton. When both Walleye and plankton were excluded from the food web configuration the mean estimated TMF values were again higher for D4 (1.1), D5 (1.2), and D6 (0.97) with probabilities of TMF > 1 of 49%, 65%, and 40% respectively (Table 4). In the last two food web configurations, with Hexagenia occupying the lowest trophic level, the mean TMF estimates for PCB180 are well below 1 (0.55 & 0.58) with >4% probability of having a TMF > 1 and both measures were below those observed for D4, D5, and D6 (Table 4; Fig. 1). The high degree of variability of TMF dependent on food web structure was also noted in Borgå et al. (2012, 2013) who observed TMFs >1 in the pelagic based food webs of Lakes Mjøssa and Randsfjorden. In their 2013 study, they attributed variability and lower estimates of TMF to the influence of benthic food sources in the diets of certain species of fish. The food web in this study was largely supported by benthic carbon and lacked pelagic influence. Thus, the low TMF estimates we observed in Lake Erie seem consistent with the observations Borgå et al. (2013). The results we present differ from Borgå et al. (2012, 2013) in that the TMF estimates for PCB180 also varied considerably with food web composition and were 1 but would still vary considerably dependent upon the composition of the food web. The results presented both correspond to and disagree with published work on TMF investigations of cVMS materials in aquatic food webs which have provided evidence and explanations on how trophic dilution (Powell and Woodburn, 2009; Powell, 2010a), trophic magnification (Borgå et al., 2012; 2013), or bioaccumulation but not trophic magnification (Kierkegaard et al., 2011) can occur. The possible reasons for these mixed observations are varied, complex, and not fully understood; however, there are similarities in these studies and the observations in this study. On a wet weight basis, concentrations of cVMS and PCB180 in aquatic organisms increase with trophic level as does the lipid content of their tissues (Table 1; Fig. S2). When contaminant concentrations in these organisms are expressed as lipid equivalents the resulting relationship is reversed and often highest in plankton or benthic invertebrates (Powell, 2010a). This relationship likely explains a large part of the observed variability in TMFs which are dependent on the composition of the food web, specifically the occupants of the highest and/or lowest trophic levels (Table 4) (Powell, 2010a; Borgå et al., 2012). As the concept of TMF is dependent on lipid equivalent concentrations being a measure of
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Fig. 2. Relationship between the concentrations of D5 and PCB180 and %lipid in Lake Erie biota expressed as wet weight (A), lipid-equivalent weight with NLOM partitioning equivalent value of 0.05 (B), and lipid-equivalent weight with NLOM of 0.07 (C).
the fugacity of lipid soluble chemicals within organisms and largely based on the behaviour of chemicals with the properties of PCBs or other legacy persistent organic pollutants (POPs), the assumption that cVMS partition within organisms in a similar manner to these chemicals is implicit (Gobas et al., 2009). In the case of cVMS materials this may not be the case as shown in the relationship between wet weight and lipid equivalent concentrations of D5 and PCB180 with lipid content in this study (Fig. 2). The slopes of linear
regressions of wet weight concentrations against lipid content for D5 and PCB180 were positive and similar to one another. When chemical concentrations were expressed on a lipid equivalent basis calculated using Eq. (5) and applying the recommended non-lipid organic matter (NLOM) partition equivalent value of 0.05 (deBruyn and Gobas, 2007), the regression shifted to a negative relationship for both PCB 180 and D5. However, it is worth noting that the slopes and intercepts of the above regression fits diverge between D5 and PCB 180, with D5 demonstrating elevated slope and intercept values compared to PCB 180. The lipid equivalent slopes and intercepts converge if the NLOM partitioning coefficient for D5 is changed from the recommended value of 0.05 to a value of 0.07 (Fig. 2). This provides supporting evidence that D5 has a higher association with non-lipid organic matter than PCB180 and may indicate that D5-tissue interactions are more complex than the simple partitioning behaviour demonstrated to occur for organochlorines (Drouillard et al., 2004). Potential inter-tissue partitioning differences between legacy POPs and emerging contaminants at the organismal scale and the associated effects on estimates of biomagnification at the food web scale require additional investigation. In the Lake Erie food web, the inclusion of Walleye at the highest trophic level resulted in lower TMF estimates for cVMS than when it was excluded (Fig. 1). This was due, in part, to Walleye having the lowest lipid equivalent concentrations of cVMS in the organisms we collected (Table S4). The observation of cVMS materials being higher in the 3rd trophic level than in the 4th was also noted in the Humber estuary and was attributed to sediment/biota partitioning differences between D5 and PCB180 from the organic carbon/water partitioning coefficient (Koc) (Kierkegaard et al., 2011). Their reasoning was that since Koc is 200 times greater for D5 than for PCB180 it would accumulate in the lower trophic level organisms with strong association with sediments at rates higher than metabolic elimination. Trophic dilution could still occur as metabolic elimination outpaces uptake as you move to higher trophic level organisms. The causes of declining D4, D5, and D6 concentrations in fish at higher trophic levels in Lake Erie, whether due to differing abilities to metabolize cVMS materials among species, or other mechanisms require additional study. Variability in TMF estimates for each food web configuration could also be due to sampling artifacts related to differing home or foraging ranges among the species collected in relation to the sources of contamination. In general, the home range of fish is positively correlated with body size, thus larger bodied fish occupying higher trophic levels have greater foraging ranges than their prey (Minns, 1995). It is possible that larger fish could consume forage of varying levels of contamination dependant on the proximity of the forage to sources of contamination. If sampling occurred in an area of high sediment contamination or in close proximity to waste water discharges, this phenomenon could result in higher levels of contamination in forage relative to consumer and explain the low TMF and strong sensitivity to food web composition we observed in Lake Erie. By using PCB 180 as a reference chemical, it is also assumed that water and/or sediment contamination patterns over spatial scales equivalent to the foraging ranges of animals sampled as part of the study are equivalent between PCBs and cVMS compounds. Unfortunately, there is insufficient information on sources and sediment concentrations of cVMS materials in the west basin of Lake Erie to confirm these hypotheses at present. However, the observation that PCB 180 and D5 showed differences in the sensitivity of TMFs when different food web combinations were used in the TMF calculation imply that differences in spatial contamination of the two chemicals were occurring. Thus future efforts on characterizing TMF should consider developing organismal sampling
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strategies that consider both foraging range of the animals being sampled and choice of TMF reference chemicals such that the spatial contamination trends of the unknown and reference chemical show correlation to one another. In this study, we observed high variability in TMF estimates based on food web composition for cVMS materials and PCB180. Estimates were highly dependent on the inclusion/exclusion of the organisms occupying the highest and lowest trophic levels, which also contained the highest and lowest levels of tissue lipid, respectively. The occurrence of biomagnification of cVMS materials in Lake Erie is unclear. TMF >1 were observed for D4 and D5 in only 1 of 5 food web configurations and TMF for D6 were
