Table of Contents
- Section 1
- Section 2
- Section 3
- Section 4
- Section 5
- Section 6
- Section 7
- List of Figures
- List of Tables
The Effect of Zebra Mussels on Cycling and Potential
Bioavailability of PCBs: Case Study of Saginaw Bay
5.1.1 System Diagnosis and Interpretation
a system-wide diagnostic, the analysis and interpretation of
dynamics and fate was conducted at a variety of spatial and temporal
scales. Figures 5.1 and 5.2 show the average annual
mass fluxes and compartment mass budget for the entire bay during the
steady state year (i.e. 71st year, after sediment
reached steady-state) in the absence and presence of mussels,
respectively. The external sources of PCBs to the Saginaw Bay include
tributary and atmospheric loads. The later load constitutes wet and dry
deposition, and absorption. The principal external source of
to Saginaw Bay is the Saginaw River and in 1991 contributed approximately
200 kg of PCBs. Since ∑PCB
load data from the other tributaries of the watersheds was not available,
so those load sources to the bay were neglected in the present study.
The major fluxes of ∑PCBs in Saginaw Bay included settling of various PCB sorbed particles, the resuspension of contaminated sediments, burial of PCBs associated with solids, volatilization and absorption of PCBs from air-water interface, and advective and diffusive transport across the sediment-water interface. All the fluxes shown in Figures 5.1 and 5.2 were expressed in kg/yr.
From Figure 5.1, the major annual mass fluxes were associated with volatilization, settling, and resuspension, with values of 212, 161, and 119 kg/yr, respectively. Other fluxes include absorption of PCBs from atmosphere, 54 kg/yr, burial loss from the surficial sediments to deep sediments, 42 kg/yr, and advective/dispersive loss of 25 kg/yr from bay to Lake Huron. The active sediments of the bay acts like a large reservoir of PCBs with a mass of 1503 kgs, whereas the total annual PCB mass in the water column was 13 kgs.
From Figure 5.2, the major mass fluxes were associated with volatilization, settling, resuspension, and shunting of PCBs mediated by zebra mussel activities, with values of 206, 128, 148, and 72 kg/yr, respectively. Other fluxes include absorption of PCBs from atmosphere, 54 kg/yr, burial loss to deep sediments, 51 kg/yr, and advective/dispersive loss of 25 kg/yr from bay to Lake Huron. Like the previous case, the active sediments of the bay acts like a large reservoir of PCBs with a annual mass of 1859 kgs whereas the total annual PCB mass in the water column was 14 kgs.
Comparison of results as demonstrated in Figures 5.1 and 5.2 illustrated the effect of zebra mussels on annual ∑PCB fluxes. The settling of ∑PCBs to the sediments associated with zebra mussel filtration has increased the ∑PCB mass in the sediments that has eventually increased the resuspension fluxes. With the introduction of zebra mussels the ∑PCB flux to the sediments is the sum of ∑PCBs associated with, 1) particulates subject to settling and 2) particles shunted by mussels. Though the settling flux of PCBs has reduced with the introduction of mussels, from 161 to 128 kg/yr, but the total transfer of ∑PCBs from water column to sediments has been increased from 161 to 200 kg/yr due to ∑PCBs transported to the sediments by zebra mussels. The mass of PCBs in the sediments has increased by 356 kgs in presence of mussels. This has caused an increment in resuspension flux of PCBs by 29 kg/yr. The volatilization loss of PCBs from the water column with zebra mussels has decreased. Various studies have shown that the invasion of zebra mussels has enchanced the blue-green blooms (Taylor 1995; Vanderploeg et al. 1996) thereby causing the introduction of another organic matter which allows ∑PCBs partitioning to them. Although the annual average fraction dissolved for PCBs has increased with the introduction of zebra mussels but its effect on volatilization is not the same throughout the year. For example, the volatilization is higher in late summer due to higher temperatures but the phytoplankton production is also higher in that period causing such an increase in particulate fraction of PCBs that it exceeds the dissolved fraction in that period. The possibility of larger fraction of ∑PCBs to partition to particulates has probably reduced the dissolved ∑PCB concentration in the water column, which has resulted in lower volatilization flux.
The shunting of particles and the change in contaminant transport pathways by zebra mussels has lead to increased contaminant accumulation in sediments allowing an increased exposure of PCBs to benthic organisms, which in turn may affect the biota in pelagic food chain through various food chain linkages.
PCB Mass Stored in Zebra Mussels
In present study, the metabolic degradation of PCBs was neglected since molluscs have extremely low levels of microsomal monooxygenases, which metabolize many organic compounds (Walker 1987). So, ∑PCB transfer to the zebra mussels through water and food is likely to be incorporated into soft-tissue of the mussels or tied up in the biodeposits of feces and pseudofeces. The cycling of ∑PCBs will depend on their resident time in various compartments of the ecosystem. For example, if there are large mass of ∑PCBs in the sediments it may be more readily available for resuspension but if it stays more in mussels’ tissue then the fate of ∑PCBs will depend on the bio-cycle of mussels. With their death it may become available in sediments and/or water depending on water column processes. If the mussels are consumed by their predators then PCBs will be transferred to them. So it is important to determine the ∑PCB mass stored in zebra mussels to understand the cycling behavior of ∑PCBs in an ecosystem.
The relative significance of ∑PCBs in zebra mussel biomass was examined by comparing the ∑PCB mass in zebra mussels (sum of ∑PCB mass in three age classes) (from Table 5.1) versus ∑PCB mass in the sediments (Figure 5.2). The results presented in Table 5.1 are using 1991-1995 average zebra mussel densities. Here, Class 3 refers to >2 year old, whereas Class 2 refers to 1-2 year old, and Class 1 refers to young-of-year or < 1 year old mussels. The ∑PCB mass in zebra mussel was determined by multiplying the annual average ∑PCB concentration in each cohort by annual average biomass of them in every segment. The total PCB mass in zebra mussels was calculated by summing ∑PCB mass of each cohort in all spatial segments.
|Table 5.1:||Zebra Mussel Wet Weight and Total PCB Concentration in Zebra Mussels|
Mussel Wet Weight
Concentration in Mussels
|Class 3||Class 2||Class 1||Class 3||Class 2||Class 1|
For example, at average
1991-1995 zebra mussel densities and with average annual
concentration in each cohort of mussels in each segment, zebra mussel in
Saginaw Bay contains about 8.07 kg of PCBs. This was calculated from Table
5.1 by multiplying wet weight of individual zebra mussel class with the
concentration in zebra mussels. The surficial sediments (0-5 cm) in
Saginaw Bay contain a mass of about 1859 kg, which in comparison of PCB
mass in zebra mussels is approximately 230 times higher. This implies that
the shunting of PCBs by zebra mussels is an important mechanism compared
to PCB mass storage in mussels.
5.1.3 Zebra Mussel Impact on Bioaccumulation in Pelagic Lower Food Web
The impact of zebra mussel on bioaccumulation in the pelagic lower food chain was evaluated during a steady-state year. The simulated results were for lower three-levels of the food chain consisting of five groups of phytoplankton, herbivorous zooplankton, and carnivorous zooplankton. These species linkage constitute the major energy transport in the pelagic food chain. Previous studies on ecosystem impacts of mussels have shown that the introduction of zebra mussels has affected phytoplankton and zooplankton densities (Leach 1993; Fahnenstiel et al. 1995a; MacIssac et al. 1992; Holland 1993; Nicholls and Hopkins 1993). Intuitively, this will impact bioaccumulation of PCBs in these ecosystem species. In order to quantify that impact, the model was run under four scenarios where the absence and presence of mussels was varied with different predator-prey relationships. Those scenarios are:
Scenario 1: Absence of zebra mussels, i.e. the pre-zebra mussel period (pre-1990)
Scenario 2: Presence of zebra mussels. Under this scenario zebra mussels were allowed to consume three groups of phytoplankton (diatoms, greens, and “others”) as well as on herbivorous zooplankton. Mussels selectively rejected blue-greens.
Scenario 3: Presence of zebra mussels. Under this scenario zebra mussels were allowed to consume three groups of phytoplankton (diatoms, greens, and “others”) but they did not filter herbivorous zooplankton. Mussels selectively rejected blue-greens.
Scenario 4: Presence of zebra mussels. Under this scenario zebra mussels were allowed to filter all five groups of phytoplankton (diatoms, greens, “others”, N2-fixing blue-greens and Non N2-fixing blue-greens) but they were not allowed to filter herbivorous zooplankton.
Scenario 1 was
designed to simulate bioaccumulation in pre-zebra mussel conditions,
whereas scenarios 2 to 4 simulated the results in presence of mussels. The
average 1991-1995 zebra mussel densities were used for different scenarios
with zebra mussels. Figure
5.3 shows the annual average biomass of herbivorous zooplankton under
above-mentioned four scenarios. The biomass of herbivorous zooplankton was
higher in scenario 1 i.e. in absence of mussels, compared to that in
scenarios 2, 3, and 4 in presence of mussels. In presence of mussels, for
example in segment 1, the herbivorous zooplankton biomass was 0.01, 0.03,
and 0.09 mg dry wt/L, in scenarios 2, 3, and 4, respectively. Other
segments showed the similar trends of herbivorous zooplankton biomass in
all four scenarios.
∑PCB concentrations in species on wet weight basis also termed as ∑PCB body burden of species under four scenarios was plotted in Figures 5.4 to 5.7. Here, ∑PCB body burden represented the assimilated ∑PCB concentration in organisms. Under all scenarios, ∑PCB concentration in species on wet weight basis was highest in segment 1 followed by segment 4. This was due to the flow patterns and the relative areas of contamination. Segment 1 is the area where Saginaw River enters the bay and impacts directly on ∑PCB concentration. The advective flow in the bay is from segment 1 to segment 4, which circulates the ∑PCBs from Saginaw River to segment 4 through segment 1 (see Figure 3.3).
Scenario 1: Figure 5.4 shows the annual average ∑PCB body burden (mg ∑PCB/g wet weight) of various species in absence of zebra mussels in various segments of the Bay. Higher up in the trophic ladder ∑PCB concentration in species increased due to predator-prey relationship, as being observed in bioaccumulation studies (Thomann and Connolly 1984; Thomann 1989; Endicott and Kandt 1994). Similar trends in ∑PCB concentration for various trophic levels were observed in other segments as well. As carnivorous zooplankton feed on herbivorous zooplankton and herbivorous zooplankton feed on three groups of phytoplankton (diatoms, greens, and “others”), obviously according to theory of bioaccumulation, ∑PCB concentration in carnivorous zooplankton should be highest, followed by herbivorous zooplankton, and phytoplankton.
Scenario 2: Figure 5.5 shows the results for ∑PCB body burden (mg ∑PCB/g wet weight) of various species in presence of mussels in various segments of the bay. For example, in segment 1, the total PCB concentration in phytoplankton, herbivorous zooplankton, and carnivorous zooplankton was 0.14, 0.23, and 0.15 mg PCB/g wet weight, respectively. Other segments showed similar trends in lower three trophic levels. The results showed that the ∑PCB body burden of herbivorous zooplankton was higher compared to that in phytoplankton. But ∑PCB body burden of carnivorous zooplankton was lower compared to that found in herbivorous zooplankton. This was in contrast with the general trends of bioaccumulation models, where higher concentration of ∑PCBs in successive trophic levels are observed than those present in the previous trophic level (Thomann and Connolly 1984; Thomann 1989; Endicott and Kandt 1994). The reasons for this deviation in results are two fold; (1) zebra mussels filter out three groups of phytoplankton that is the food sources for herbivorous zooplankton. The scarcity of food helped herbivorous zooplankton to assimilate lesser quantity of contaminated food than those would have been consumed with sufficient supply of food (even though may be less contaminated) (2) zebra mussels filter herbivorous zooplankton which serves as a lone food source for carnivorous zooplankton. The reduced biomass of herbivorous zooplankton due to the grazing by zebra mussels affected the ∑PCB concentration in carnivorous zooplankton.
In contrasting the biomass of herbivorous zooplankton in presence and absence of mussels, Figure 5.3 shows the reduction of biomass in the former case. The presence of mussels suppressed the herbivorous zooplankton biomass since mussels were allowed to filter those. This result is supported by a study conducted by MacIsaac et al. (1995), which showed a substantial decline in herbivorous zooplankton due to grazing by zebra mussels.
Scenario 3: According to biomagnification theory, the contaminant concentration is found to be increasing with the increasing trophic level (e.g. in Oliver and Niimi 1988). Since under scenario 2, ∑PCB concentration in the carnivorous zooplankton has not indicated the normal trends of bioaccumulation in the trophic ladder, it was hypothesized in this scenario that if zebra mussels are not allowed to graze on herbivorous zooplankton then bioaccumulation results may highlight the normal understanding of bioaccumulation. In other words, higher ∑PCB concentration in carnivorous zooplankton than that in herbivorous zooplankton may be observed. To test this hypothesis, scenario 3 was developed where zebra mussels were not allowed to filter herbivorous zooplankton. Figure 5.6 shows the results for ∑PCB body burden (mg ∑PCB/g wet weight) of various species in various segments under scenario 3. In general, the results showed that the ∑PCB body burden of carnivorous zooplankton was higher than what it was found in scenario 2. But the ∑PCB concentration in carnivorous zooplankton was still lower than that in herbivorous zooplankton.
As shown in Figure 5.3, under this scenario, the biomass of herbivorous zooplankton in the case of zebra mussels not filtering herbivorous zooplankton has allowed the herbivores to survive in the system. But their biomass was still lower than what was observed in absence of mussels. This may be due to the competition for food sources. Three groups of phytoplankton (diatoms, greens, and “others”) are the principal food sources for zebra mussels as well as for herbivorous zooplankton. The biomass of herbivorous zooplankton may be lower since there is another potential competitor for their food. With higher herbivorous zooplankton biomass, the quantity of food for carnivores increased, which resulted in higher concentration of ∑PCBs in carnivores as compared to those observed in scenario 2.
Scenario 4: Under scenario 3, although ∑PCB body burden of carnivorous zooplankton has increased compared with scenario 2, but ∑PCB body burden of carnivores was still lower than the ∑PCB body burden of herbivores. It was proved that the posed hypothesis in scenario 3 was not true that the ∑PCB body burden of carnivorous zooplankton will increase compared to ∑PCB concentration in herbivorous zooplankton if zebra mussels were not allowed to filter herbivorous zooplankton.
The next hypothesis formulated in this scenario was that if zebra mussels were allowed to filter all phytoplankton groups along with conditions posed in scenario 3, this may result in bioaccumulation in the higher trophic levels. To test this hypothesis, zebra mussels were allowed to filter all five groups of phytoplankton including blue-greens but not herbivorous zooplankton. This has further increased herbivorous zooplankton biomass than earlier scenario (Figure 5.3) and has potentially increased the food source for carnivorous zooplankton. In this scenario, the ∑PCB body burden (in mg/g wet weight) of species higher up in the food chain has increased in all the segments (Figure 5.7). A possible explanation for getting the usual trend of bioaccumulation according to predator-prey relationship is due to the fact that zebra mussels filter all five types of phytoplankton leaving a relatively higher food supply for herbivorous zooplankton than it was observed in scenario 3. Under this scenario, herbivorous zooplankton was not being grazed by mussels and they were also benefited for the sufficient resources available for their growth since the consumption by zebra mussels was distributed among all five phytoplankton groups. It was shown in the previous study by LTI (1997) that zebra mussel enhance the growth of blue-greens only under the case when they were not being grazed by mussels. So with mussels grazing on blue-greens, the growth of blue-greens was suppressed and that offered an advantage for the food source for herbivorous zooplankton than it was observed in previous scenarios. This scenario has tested the hypothesis true, implying an increase in ∑PCB bioaccumulation in the food chain if zebra mussels filtered all five groups of phytoplankton but not herbivorous zooplankton. This scenario has prompted the optimum biomass of herbivorous zooplankton, which has eventually transferred ∑PCB concentration to carnivorous zooplankton higher than the concentration in herbivorous zooplankton.
22.214.171.124 Effect on Mass-Specific Concentration in Various Species
Comparison of results in Figures 5.5 and 5.7 showed that the mass specific ∑PCB concentrations in species were higher in scenario 4 than in scenario 2. Although the differences were not very significant in the phytoplankton but a clear trend for an increase in ∑PCB concentration in zooplankton was observed. For example, in segment 1, ∑PCB concentration in phytoplankton, herbivorous zooplankton, and carnivorous zooplankton was 0.14, 0.23, and 0.24 mg/g wwt, respectively, in scenario 2, whereas those were 0.15, 0.25, and 0.27 mg/g wwt in scenario 4.
While comparing the dissolved fraction of PCBs in scenarios 2 and 4, an increase of 2 to 25% was evaluated in various segments of the bay. In scenario 4, the concentration of biotic solids in the water column had declined since zebra mussels were allowed to filter all five groups of phytoplankton. For chemicals like ∑PCBs, the dissolved ∑PCB concentration is a strong function of suspended solids. Moreover, the biotic solids have higher fraction of organic carbon compared to abiotic and detritus solids, so the partitioning of ∑PCBs was affected more with concentration of biotic solids.
In conjunction with the increase in dissolved PCBs, an increase in mass-specific concentration of PCBs in biotic solids was observed. Thomann (1989) and others have indicated that the food chain bioaccumulation of hydrophobic chemicals is quite sensitive to phytoplankton bioconcentration factor. Since the ∑PCB concentration in phytoplankton, the base of the food chain, was higher so the ∑PCB concentration in herbivorous zooplankton and carnivorous zooplankton was higher as compared to those found in scenario 2.
126.96.36.199 Summary of Bioaccumulation Results
Results in scenario 1 indicated that ∑PCB concentration in the higher trophic levels was higher than the lower trophic levels in absence of mussels. But biomagnification did not occur in the presence of mussels, under scenario 2, where zebra mussels were allowed to filter three groups of phytoplankton and herbivorous zooplankton. The higher concentration of contaminants in the higher trophic levels was not found in studies conducted by Hope et al. (1998) and Kucklick and Baker (1998). The conditions under scenario 2 are well documented in the literature. Zebra mussels selectively filter a wide range of particles of size varying from 0.7 mm (Sprung and Rose 1988) to 750 mm (Ten Winkel and Davids 1982). They remove nearly all particulate matter, which includes phytoplankton and many small invertebrates including protozoans and some small forms of zooplankton i.e. rotifer and naupli densities (MacIsaac et al. 1995), and their own veligers. A study conducted by Bridgeman et al. (1995) showed a decline in zooplankton abundance and biomass after zebra mussel establishment in Saginaw Bay. By removing the phytoplankton from water column, zebra mussels remove the food source of microscopic zooplankton, which in turn are the food for larval and juvenile fishes and other-plankton feeding forage fish. The depletion of food sources of forage fish impacts the sport and commercial fisheries. So under these two conditions, i.e., 1) zebra mussels filtration on herbivorous zooplankton and 2) selectively rejecting the blue-greens, the simulated results showed that the ∑PCB concentration in carnivorous zooplankton was lower than ∑PCB concentration in herbivorous zooplankton.
Further analysis under scenario 3 and 4 provided the quantitative understanding of the interactions occurring in the ecosystem. The competition for the food resources was one of the important factors affecting the ∑PCB concentration in the species. In these scenarios the competition for food has been altered. The simulated ∑PCB bioaccumulation comprises of both routes of ∑PCB transfer: uptake from water and through food. So if there is competition for phytoplankton, the base of the food chain, which is the major route of ∑PCB transfer to higher trophic levels, the ∑PCB concentration in the species feeding on it has been affected.
In summary, Figure 5.8 shows the interactions among five groups of phytoplankton, zooplankton, and zebra mussels. Three groups of phytoplankton and herbivorous zooplankton are the major food sources that zebra mussels remove from the water column along with other particulates, which directly impact on the ∑PCB concentration in carnivorous zooplankton. Zebra mussels and herbivorous zooplankton both compete for phytoplankton as their food source, particularly for diatoms, greens, and “others”.
Since zebra mussels also filter herbivores that limit the food for carnivorous zooplankton so a decline in the ∑PCB concentrations in carnivores was observed.
There has been controversial literature on the biomagnification of contaminants in the food chains. The concentrative process due to biomagnification was attributed to the fact that an efficient transfer (50-80%, Madenjian et al. 1988) of contaminants from one trophic level to the next occurs. This would result in higher concentrations of organochlorines in the successive trophic level than those present in the previous trophic level. The contaminant concentrations increased by increasing the trophic level, mainly in case of fish (Evans et al. 1991; Russell et al. 1995; Oliver and Niimi 1998) and aquatic mammals (Muir et al. 1988). Rasmussen et al. (1990) concluded that the PCB levels in higher trophic level depend on the length of food chain. The increase in contaminant concentrations with higher trophic levels have been attributed to lipid contents (Hope et al. 1998; Kucklick and Baker 1998), depuration rates (LeBlanc 1995; Sijm and van der Linde 1995), size (Bergner 1985), and exposure duration (Harding et al. 1997), rather to biomagnification.