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
Since sediment PCBs take a considerable time to approach steady-state, therefore, the model was run for a seventy-year period without and with zebra mussels (densities used were mean levels observed during early-90's GLERL surveys, Figure 3.1). All other forcing functions (including PCB load) were kept the same and constant as in the base calibration of SAGZM to the 1991 data. Once the steady-state sediment concentrations for the two scenarios were obtained, the model was run with these initial conditions for a year to determine seasonal variations and annual average conditions during a steady-state year (with constant approximately 200 kg/yr PCB load). Since the measurements did not provide the exact spatial and temporal data required to calibrate the model, the validation of the Multi-stressor Aquatic Ecosystem Model was done by comparing the simulation results with the available field data. The predictions were field tested for ∑PCB concentrations in water column, sediments, and zebra mussels.
4.1.1 ∑PCB Concentration in Sediments
Results with seventy-year period are provided for the open water zone (Segment 3) and the near shore zone (Segment 4) of the bay. There are substantial differences in water quality responses between these two zones due to differences in zebra mussel densities, water column depth and the relative influence of Saginaw River inflow as described in report prepared by LTI (1997). Segment 3 contains 71 percent of the total inner bay water volume and is 8.0 meters deep. Segment 4 contains 11 percent of the total inner bay volume and is 3.8 meters deep. The burial rate for Segments 3 and 4 are 0.100x104 and 0.3x105 meters/day, respectively. As a broad generalization, water quality responses in Segment 3 are more strongly influenced by chemical-biological processes in the water column and responses in Segment 4 are more strongly influenced by Saginaw River inflow (see Figure 3.3 for advective flow field) and constituent fluxes across the sediment-water interface.
Figure 4.1 shows the surface sediment concentrations in open water and near shore zones of the bay as a function of time, both with and without zebra mussels in the system. For both zones at steady-state, ∑PCB concentration in surficial sediments with zebra mussels was higher compared with the no zebra mussel scenario. In the open water, the steady-state ∑PCB concentration in surficial sediments without zebra mussels was 52 ng/g and with zebra mussels was 61 ng/g. In near shore zones, the steady-state ∑PCB concentration in surficial sediments without and with zebra mussels was 57 and 74 ng/g, respectively.
The near shore area experiences nearly 30% increase in ∑PCB concentration in sediments with zebra mussels, which is nearly twice as high as an increase in sediment ∑PCBs of the open waters of the bay. The differential buildup in near shore area of the bay was due to two reasons: 1) shallow regions have high mussel densities; and 2) the near shore region falls in the path of Saginaw River inflow, which is the major source of ∑PCBs to the bay. The particles filtered from the water by zebra mussels are either assimilated and incorporated into biomass, or rejected as feces and pseudofeces (Schneider 1992). The larger filtered particles tend to bind with mucous and accumulate as pseudofecal pallets, which are ejected through the inhalant siphon (Morton 1993).
he ∑PCBs associated with the particulate material are also shunted to the sediments along with feces and pseudofeces. As a result, energy and contaminants are shifted from the pelagic food web to the benthic food web. The introduction of mussels has caused a shift in normal pathways by which nutrients and contaminants were utilized and cycled in the ecosystem. The ∑PCBs shunted to the sediments by mussels can be passed up the food chain through the benthic-pelagic coupling so that any fish or waterfowl consuming zebra mussels will also accumulate PCBs in them. Table 4.1 illustrates the comparison of model results for total PCB concentration in the surficial sediments with data of 1988 (Endicott and Kandt 1994). Data were estimated according to the segmentation scheme by area-weighted average approximation. Interpolation for the measured values was done by inverse distance weighting (IDW) method for a grid size of 10 km.
|Table 4.1:||Comparison of Modeled Steady-State ΣPCB Concentration in Surficial Sediments with Data of 1988 (Endicott and Kandt 1994)|
|Segment #||PCB Concentration (ng/g)|
modeled results are obtained in a steady-state year (after 70 year run)
while the measured data were for 1988 when the sediments have not reached
steady-state with the loads. So the comparison is not between the
steady-state concentrations but attempted to field test the model results
since this is the only measured data available. Apart from segments 1 and
3, the annual average model predicted PCB concentrations are near to the
measurements. The best comparison is obtained for segments 5 and 6 and the
worst for segment 3. The measured data for PCB concentration in segment 3
deviates maximum from model results. Since segment 3 is characterized as
depositional zone with lower resuspension and higher burial rates compared
to other segments, the measured concentration in sediments was the
highest. The response time of sediments in this segment is much longer
than other segments, which require long time to reach steady-state. This
is the possible explanation for a large discrepancy observed between the
model results at steady-state and measured data at non steady-state.
Segment 1 is highly affected by the inflow of Saginaw River, which has
affected the PCB concentration. The simulated results may have been
affected by the assumed routing scheme, solids dynamics or sediment water
exchange processes. However, the comparison of these results shows
reasonably well field testing of the simulated results.
4.1.2 ∑PCB Concentration in Water Column
The model results show that the water column ∑PCB concentration range from approximately 1.9 to 8.4 ng/L in segment 1 over a steady state year. Endicott et al. (1998) have plotted PCB concentration in water as a function of distance from the Saginaw River mouth. Their study reports the ∑PCB concentration measured in a location, which lies approximately in segment 1, varied from approximately 3 to 15 ng/L. Though the data may represent a particular time of the year when measurements were conducted but the present study simulates the ∑PCB concentration on daily basis in a year period. In spite of that, the model is able to predict the results, which are within the measurement data range. The comparison provides a level of confidence in the model predictions. It must be mentioned that the steady-state run was based on 1991 conditions and forcing functions but with average 1991-1995 zebra mussel densities. The fact that satisfactory results have been achieved gives the importance of a multi-stressor model where problems of eutrophication and contamination are dealt together.
4.1.3 ∑PCB Concentration in Mussels
Figure 4.2 shows the simulated ∑PCB concentration in zebra mussels for different log Kow and for two age classes of the mussels; >2 year and 1-2 year old. The measurements of ∑PCB concentration in zebra mussels were conducted from the samples collected from the buoys in the shipping channel, which approximate the PCBs concentration in mussels for two segments of the bay (segments 1 and 3) only. An additional measurement station lies in segment 6. The total PCB concentration in soft tissue of mussels range from about 0.1–1.2 ppm (wwt) and the highest PCB zebra mussels body burden was found near the mouth of the Saginaw River (Endicott et al. 1998). The model results follow the same trend as was observed in measurements on ∑PCB concentration in mussels. The ∑PCB concentration measured in mussels of segment 1 was the highest followed by those in segment 3 and 6. Though the water column ∑PCB concentration was higher in segment 4 compared to that in segment 3, but there was no data available for comparison of ∑PCB concentrations in mussels in segment 4. The higher Kow resulted in higher concentration in the mussels probably due to hydrophobicity of the compounds and lower excretion rates of the mussels. The comparison revealed that the model underestimates the concentrations. However, the available data was for sixteen samples collected in December 1991 and only for mussels colonizing each buoy. This may not be representing the exposure of PCBs to the mussels in the entire segment since the measurements were along the line (in navigation channel) that may be influenced heavily by the contaminated Saginaw River inflow. Nevertheless, the estimated ∑PCB concentrations in zebra mussels were within an order of magnitude of observations.
4.1.4 Zebra Mussels as Biomonitors
concentration in zebra mussels in various segments of the bay ranged from
0.03 to 0.85 mg/g wwt (Table 4.2). A study conducted by Endicott et al
(1998) showed that the ∑PCB
concentration in zebra mussels varied between 0.076 and 1.2 mg/g wwt in
Saginaw Bay and the highest ∑PCB
zebra mussels body burden was found near the mouth of the Saginaw River.
The simulation results showed that the annual average water column
concentration ranged from 0.30 to 4.81 ng/L.
A distinct gradient was observed as the waters of the Saginaw River travel farther from river mouth. The higher levels of PCBs were observed in Segments 1 and 4 as these segments fall in the path of the Saginaw River waters. Annual average ∑PCB concentration in segment 1 was 4.81 ng/L whereas in segment 4, it was 2.01 ng/L. The lowest concentrations were observed in segments 6 and 7, the areas close to Lake Huron. This variation of PCBs in segments reflects concentration gradients within the bay, which will impact the exposure of PCBs to species.
|Table 4.2:||Annual Average ΣPCB Concentration in Zebra Mussels and Water Column|
|Segment #||Zebra Mussels||
in Water Column
The PCB body burden of all three-age classes of mussels was the highest in Segment 1 followed by Segment 4. This is due to the fact the total PCB concentration in the water column was highest in Segment 1 followed by Segment 4. These results show that zebra mussels closely track fluctuations in environmental PCB concentrations. The results on difference in their body burden reflect the site-specific differences of contaminant exposure. These findings are very similar to the results reported in literature (Secor et al. 1993; Morrison et al. 1995; Chevreuil et al. 1996), where zebra mussels are suggested as biomonitors of contaminants.
4.2 Sensitivity Analysis
The calibration of model parameters is conducted to obtain an optimal agreement between the model calculations and the available data set. For the present study, all the available data bases did not serve the extensive data requirement for calibration of the developed Multi-Stressor Aquatic Ecosystem Model. The available data sets did not match with the model’s spatial and temporal resolutions and the number of data points were sparse. Moreover, the data sets did not include results for all study parameters. Also, in some cases the data was limited only to pre-zebra mussel invasion. The present data source did not contain the ∑PCB concentration in lower trophic levels of the food chain such as in phytoplankton and zooplankton although some researchers have measured ∑PCB concentration in higher trophic levels such as in fish (Giesy et al. 1997). Froesse et al. (1998) have analyzed the bioaccumulation of PCBs from sediments to aquatic insects and tree swallow eggs and nestlings in Saginaw Bay. The available data on ∑PCB body burden in zebra mussels did not differentiate PCB concentration among different age classes. Due to all these reasons, the calibration of the developed model was not possible but a thorough sensitivity analysis was conducted as an important part of this screening level model application. The validation of the model results was done by comparing the simulation results with the available field measurements.
Since model parameters have the greatest impact on model predictions, so relative importance of parameters was carried out by sensitivity analysis. The analysis consisted of varying parameters by a set percent or by changing their values within the observed ranges as reported in literature. This analysis helped in understanding the general behavior of the model with variation to different parameters. It also provided the system insights and filled the gaps in model understanding so as to direct the future research. The sensitivity analysis was conducted on the following parameters:
- PCB partition coefficient
- Zebra mussel density
- Phosphorus loadings
- PCB loadings
- Zebra mussel filtration on different types of particles
- Lipid content of zebra mussels
The simulations were conducted for total PCBs comprising of nine homologs. The chemical properties such as Henry’s law constant, molecular weight, and octanol-water partition coefficient for total PCBs were determined on the basis of individual PCB homolog’s contribution to the total.Based on the homolog distribution of PCBs in Lake Huron waters (Anderson et al. 1999), a partition coefficient (Log Kow) value of 6.1 was obtained and used for model simulations. In the sensitivity analysis, the different values 6.1, 6.4, and 6.7 for Log Kow were applied to examine the effect of hydrophobicity of chemicals on selected state variables.
For all the predictive simulations all other forcing functions were the same as in the base calibration to the 1991 field data. The PCB loading time-series was developed based on the data provided in literature for 1991 (Verbrugge et al. 1994).
The PCB and phosphorus loads were varied in the form of simple scale factors on the 1991 loading time series due to lack of recent data in literature. Specifically, three different phosphorus and PCB loads were used in predictive simulations: (1) actual 1991 loads, (2) actual 1991 loads plus 50%, and (3) actual 1991 loads minus 50%. Different scenarios were examined with different combinations of phosphorus and PCB loading. The range of variation of phosphorus and PCB loading reasonably represent the present and future conditions.
Lipids play an important role in the fate of PCBs in aquatic systems since PCBs are extremely lipophilic. The lipid content of organisms and trophic transfer of lipids seem to govern the distribution of organochlorines such as PCBs in the system. When the lipid solubility of the contaminant is high relative to its solubility in the source compartment, the contaminant will tend to pass into and accumulate in the organism (Landrum and Fisher 1998).
Lipids are the major energy source for the aquatic consumers. The lipid composition and quality differs among aquatic organisms that may affect the accumulation of contaminants (Swackhamer and Hites 1988; Bierman 1990; Ewald and Larsson 1994). Though the lipid content in organisms varies with season (Cavaletto and Gerdner 1998), size and age of the species, but for this study, lipid contents of zebra mussels were kept constant. Lipid fraction value of 0.05 was used in all simulations (Endicott et al. 1998). In this study, the effect of varying lipid content of mussels on PCB body burden of mussels was evaluated.
PCBs were not biomagnified in pelagic chain with two trophic levels; phytoplankton, herbivorous zooplankton and carnivorous zooplankton (see section 5.1.3). Wet weight normalized PCB concentration were lower in carnivorous zooplankton than in herbivorous zooplankton. The concept of biomagnification did not apply to lower trophic levels because of the re-direction of energy and nutrients (consequently PCB associated particles) from the pelagic food chain to the benthic food chain due to presence of zebra mussels. The presence of mussels along with the mussel’s interaction with different types of particles was responsible for the alteration in biomagnification. So, a sensitivity analysis of filtration of zebra mussels on different types of particles was conducted.
The results of sensitivity tests of different model parameters and their analysis are presented in the following sections. These results presented here are from a steady-state year model run.
4.2.1 Organic Carbon Normalized Octanol-Partition Coefficient
PCBs are hydrophobic and their hydrophobicity is described by octanol-water partition coefficient (Log Kow). The partitioning of PCBs between dissolved and particulate phases is dependent on the hydrophobicity of the chemical. The hydrophobicity of PCBs can also be expressed as organic carbon normalized partition coefficient (Log Koc). Endicott et al. (1990) have plotted partitioning data for Lake Ontario, Lake Michigan, and the Lake St. Clair, Detroit and Niagara Rivers, measured in various studies. The data of Log Koc and Log Kow have yielded the following regression (Endicott et al. 1990):
This relationship has been used to study the effect of partitioning of PCBs among different phases. Also, Log Kow affects the elimination rate of various aquatic organisms such as in zebra mussels (Morrison et al. 1995). Studies have shown that the depuration of organochlorines decrease with increasing fish or plankton size and increasing lipophilicity of the compound (LeBlanc 1995; Sijm and van der Linde 1995; Fisk et al. 1998). Sensitivity analysis not only predicted the response of the system to changes in Log Kow values, but also helped to mimic the fate and transport of various PCB congeners. Moreover, the values of Log Kow in literature differ, so model sensitivity to Log Kow was considered necessary and important. The sensitivity analysis was carried out by setting Log Kow to 6.1, 6.4, and 6.7. The effect of Log Kow, hydrophobicity of PCBs, on water column and sediment ∑PCB concentrations and ∑PCB body burden of zebra mussels were examined.
126.96.36.199 Effect of Log Kow on Water Column ∑PCB Concentration
The system response to different Log Kow values was evaluated by comparing the water column total and dissolved ∑PCB concentrations for Log Kow values of 6.1, 6.4, and 6.7. Figures 4.3 and 4.4 show response of varying Log Kow on annual average water column dissolved and total ∑PCB concentration, respectively. The results are shown for all the spatial segments of the bay in the presence of mussels. The highest ∑PCBs, both in total and dissolved, were found in segment 1 followed by those in segments 4, 5, 2, and 3. Segments 6 and 7 showed lower ∑PCB water concentrations as compared to the concentration in other segments. The ∑PCB concentration in various segments reflects the flow patterns of the bay and the influence of ∑PCB contamination through Saginaw River as explained in the previous section.
Figure 4.3 shows a decrease in annual average concentration of dissolved ∑PCBs in the water column with increasing Log Kow. The dissolved PCB concentration was highest for lowest Log Kow (6.1), and was lowest for the highest Log Kow (6.7) in all the segments. For example, segment 1 showed prominent differences in water column dissolved ∑PCB concentration for various Log Kow values. The dissolved ∑PCB concentrations were 2.0, 1.7, and 1.5 ng/L for Log Kow of 6.1, 6.4, and 6.7, respectively.
The total ∑PCB concentration in water column for different Log Kow shows the contribution from particulate fraction to the total concentration (Figure 4.4). The higher Log Kow (6.7) has resulted in the higher total ∑PCB concentration in the water column signifying the dominance of particulate phase PCBs over the dissolved phase. More hydrophobic chemicals with higher Log Kow tend to sorb onto the particle surfaces. Since the partitioning between dissolved and particulate phases was treated as a rapidly-equilibrating process, the higher concentration sorbed to particles has resulted in lower dissolved PCB concentration.
188.8.131.52 Effect of Log Kow on ∑PCB Body Burden of Mussels
4.5, 4.6, and 4.7 show the annual average
body burden of mussels in
seven segments of the bay for three different Kow values for
>2, 1-2, and <1 year old mussels, respectively. The common
observation in all these three figures is that the
body burden as
measured by mg per gram wet weight of the species is the highest for the
highest Log Kow.
The PCB assimilation efficiency,
uptakes and elimination rates of mussels are Kow dependent (Thomann
1989; Parkerton 1993; Bruner et al. 1994a, 1994b; Endicott et al. 1998).
PCB assimilation efficiency and uptake rate of mussels are directly
proportional to chemical Kow, but the elimination rate is
inversely proportional (Equation 7). However, in many organisms, chemical
assimilation efficiency has been observed to decline with increasing Kow
for PCB congeners (Parkerton 1993). In the present study, the combined
effect of chemical Kow on assimilation efficiency, uptake rate,
and elimination rate on the amount of PCBs retained by mussel soft-tissue
Higher PCB concentration in zebra mussels were noticed for more hydrophobic PCBs. However, this trend may be reversed for much higher hydrophobic PCBs with Log Kow>7 (Thomann et al. 1992). In segment 1, with Log Kow value of 6.7, ∑PCB concentration in >2, 1-2, and <1 year old mussels was approximately 0.9, 0.7, and 0.1 mg per gram wet weight, respectively.
Comparing Figures 4.5, 4.6, and 4.7 with Figure 4.4, the pattern of ∑PCB body burden of mussels closely followed the ∑PCB concentration in the water column. These results are in agreement with the reported results in literature on the use of mussels as biomonitors (Kraak et al. 1991; Secor et al. 1993; Morrison et al. 1995; Chevreuil et al.1996).
In general, ∑PCB body burden of >2 year old mussels was the highest followed by those in 1-2 and <1 year olds. This behavior was expected since >2 year old mussels remained longer times in contact with the polluted waters. Also, larger mussels have higher filtering rates than younger, smaller zebra mussels (Bierman et al. 1998). Moreover, the older mussels may have higher concentrations of ∑PCBs due to higher lipid contents than the younger mussels. Juvenile organisms have higher growth rates compared to adult, so the concentration of ∑PCBs may be lower due to growth dilution. The growth dilution hypothesis predicts a dilution of contaminants in fast growing aquatic organisms as described by Borgman and Whittle (1992), Sijm et al. (1992), and Stow and Carpenter (1994).
184.108.40.206 Effect of Log Kow on Surficial Sediment ∑PCB Concentration
In general, the ∑PCB in sediment increased in all the segments with increase in Log Kow values. The increase in concentration varied from 18 to 31% for increase in Log Kow from 6.1 to 6.4 and a smaller increase from 15 to 28% when Log Kow increased from 6.4 to 6.7.
4.2.2 Effect of Phosphorus and PCB Loads and Zebra Mussel Densities
External phosphorus, PCB loads and zebra mussel densities were varied over the ranges that represent reasonable perturbations about actual conditions for the period from 1991 to 1995. As mentioned earlier, the phosphorus and PCB loading was imposed on the model in the form of simple scale factors on the 1991 loading time series as was done in SAGZM model.
Zebra mussel densities used were the same as reported and used in SAGZM. Those values are estimated using the data reported by Nalepa et al. (1995) and additional data provided by the National Oceanic and Atmospheric Administration (NOAA), Great Lakes Environmental Research Laboratory (GLERL). The primary data consisted of size distributions, density estimates on hard and soft substrates, and biomass estimated. As reported in SAGZM there are large uncertainties in the estimated segment-specific densities due to a limited number of sampling stations (15), large variation in zebra mussel densities among individual sampling stations, and large uncertainties in relative proportions and areal extent of substrate type. So for the analysis, four different conditions were used in the predictive simulations:
- no zebra mussels,
- average of 1991-1995 mussel densities,
- average of 1991-1995 zebra mussel densities plus 100%, and
- average of 1991-1995 mussel densities minus 50%.
The effect of phosphorus and PCB loads with various zebra mussel densities were examined on annual average water and surficial sediment ∑PCB concentrations and on ∑PCB body burden of mussels.
Figure 4.8 shows the results of annual average water column ∑PCB concentration (sum of dissolved phase and particulate phase) in the open water and near shore zones. In general, ∑PCB concentration increased with increasing ∑PCB loads. For both zones, zebra mussel densities had no impact on ∑PCB concentration. In general, the water column ∑PCB concentration in the near shore zone was higher than that in open water zone, since the near shore falls in the path of Saginaw River flows.
Figure 4.9 shows the effect of zebra mussel densities and ∑PCB loads on annual average ∑PCB concentration in surficial sediments for both near shore and open waters. In general, ∑PCB concentration in sediments increased with increasing ∑PCB loads. For both zones, differences between absence and presence of zebra mussels were greater than differences in zebra mussel densities.
Figure 4.10 contains the results of ∑PCB body burden of >2 year old mussels in response to varying zebra mussel densities and ∑PCB loads for both open water and near shore zone. In general, ∑PCB body burden of mussels increased with increase in ∑PCB loads. ∑PCB concentration per gram wet weight of mussel decreased with increase in zebra mussel densities due to biomass dilution effect.
Figure 4.11 shows the results of ∑PCB concentration in water column with varying PCB and phosphorus loads in the open water and near shore zones. The increase in phosphorus loads and PCB loads showed the increase in water column ∑PCB concentrations. Higher ∑PCB concentration in near shore zone was due to the effect of routing path of Saginaw River waters.
Figure 4.12 shows the effect of phosphorus and ∑PCB loads on annual average dissolved ∑PCB concentration in the near shore and open waters. In general, dissolved ∑PCB concentration increased with increasing ∑PCB loads, whereas it decreased with increasing phosphorus loads. Changes with phosphorus loads for both zones were generally small, but were higher in near shore zones.
Figure 4.13 contains the results of ∑PCB concentration in the sediments for both open water and near shore zone. In general, ∑PCB concentration in the sediments increased with increase in ∑PCB and phosphorus loads. The higher concentrations in sediments in the near shore were attributed to the fact that there were higher zebra mussel densities when compared with the deep open waters.
Figure 4.14 contains the results of ∑PCB concentration in phytoplankton for both open water and near shore zone. In general, ∑PCB concentration in phytoplankton increased with increase in ∑PCB loads and decreased with increased in phosphorus loads. The higher concentrations in the near shore were due to higher water column concentration as compared those in open waters.
The excess of input of nutrients increases the overall biomass of plankton communities, especially that of phytoplankton community (Pace 1986; Mazumder et al. 1988). In eutrophic systems, an increase in phytoplankton biomass results in lower contaminant concentration in the water due to biomass dilution effect. This in turn results in lower ∑PCB concentration per gram wet weight of phytoplankton.
In present case, increase and decrease in the phosphorus load relative to the base case simulated the change in trophic state of the system. In case of phosphorus loads of 1991 plus 50% of 1991 loads (i.e. 1.5 times 1991 loads) resulted in higher concentration in the sediments. The increased phosphorus loads increased the production in the system (Bierman et al. 1998), which eventually served as increased food for the mussels. Figure 4.14 shows a small decline in ∑PCB concentration in phytoplankton per gram wet weight since the settling of more particles in a nutrient rich system together with shunting by mussels resulted in elevated ∑PCB levels in the sediments (Figure 4.13).
4.2.3 Zebra Mussel Filtration on Different Types of Particles
Although there is no available data on phytoplankton community composition after zebra mussels invaded Saginaw Bay, but there is enough evidence to suggest that zebra mussel activities have likely contributed to the relative abundance of different phytoplankton species. Lavrentyev et al. (1995) and Vanderploeg et al. (1996) have reported that the nuisance blooms of the blue-green alga Microcyctis have occurred in the bay in late summer and fall of 1994 and 1995. These may be related to zebra mussel dynamics and possible changes in the phosphorus loads. Allowing the zebra mussels to selectively reject blue-greens can evaluate this cause-effect mechanism. This mechanism was tested in the previous studies conducted by Bierman et al. (1998), where blue-green production was shown to have increased very strongly with increases in zebra mussel densities. As was done in Bierman et al. (1988) and in previous modeling report (LTI 1997), the diagnostic runs were carried out to determine the importance of selective rejection of blue green phytoplankton as a competitive mechanism.
According to MacIsaac (1992), decline in rotifer densities has been attributed to large populations of zebra mussels. In another study by Ten Winkel (1982) has shown that zebra mussels are capable of removing particles between 0.7 and 750 mm. So the effect of zebra mussels on filtration of herbivorous zooplankton (mean body length of the most common rotifer Polyathra spp. was 89 mm, n = 25, MacIsaac 1992) was tested.
sensitivity analysis, zebra mussels were hypothesized to filter all five
phytoplankton groups including blue-greens. Bierman et al. (1998) have
shown that with the removal of selective rejection by zebra mussels as a
competitive mechanism, blue green production decreased by almost 100% in
both the open water and near shore zones. Filtration of zebra mussels on
herbivorous zooplankton was restricted in order for herbivorous
zooplankton to grow and serve as a food for carnivorous zooplankton.
The sensitivity of zebra mussel filtration on different types of particles was tested and results were compared for presence and absence of zebra mussels. This resulted in following three cases:
Case 1: Without zebra mussels
Case 2: With zebra mussels, and mussels filter only three types of phytoplankton (diatoms, greens and “others” as those considered in the model) and herbivorous zooplankton
Case 3: With zebra mussels, and mussels filter all five groups of phytoplankton (diatoms, greens “others”, and two groups of blue-greens), but do not filter herbivorous zooplankton.
Table 4.3 shows the results for ∑PCB concentration in the water column and sediments in open water and near shore zones under three cases for zebra mussels' presence and their filtration on different particles. ∑PCB and phosphorus loads were taken as those of 1991 time series. Zebra mussel densities were kept constant as those of average 1991-1995.
|Table 4.3:||Comparison of ΣPCB Concentration in Water and Sediments in the Open Water and Near Shore Zones Under Different Conditions of Zebra Mussels Presence and Their Filtration on Different Types of Particles|
|Scenarios||Open Water||Near Shore|
∑PCB concentration in the water column was higher both in near shore and open water zones in absence of mussels (case 1) than under other two conditions where zebra mussels were present. On the other hand, ∑PCB concentration in the sediments was lower both in near shore and open water zones in the absence of mussels (case 1) than when mussels were present.
The presence of zebra mussels affects the particulate matter of the water column. The filtration by zebra mussels resulted in a decrease in fraction of organic carbon in the water column. This may explain the decrease in water column ∑PCB concentration, independent of the types of particles filtered since the organic carbon content of all phytoplankton groups were assumed to be the same. By shunting of organic matter from the water column to the sediments, zebra mussels have caused an increase in dissolved fraction of ∑PCBs. This has resulted in an increase in dissolved phase concentration making ∑PCBs more bioavailable. The increase in ∑PCB concentration in the sediments in the presence of mussels is due to the fact that ∑PCBs associated filtered material, which are not processed by mussels, get deposited to the sediments along with feces and pseudofeces.
In case 3, mussels are allowed to filter blue-green groups of algae along with other three forms of phytoplankton. This had further decreased the organic carbon content in the water column and consequently a decrease in water column ∑PCB concentration. Blue-green type of algae was not considered as the “food currency” of the mussels so the ∑PCB attached to the blue-greens eventually get deposited to the sediments.
Figure 4.15 shows the response of ∑PCB body burden of carnivorous zooplankton in three cases. Comparison of case 1 and case 2 shows that in the presence of mussels the ∑PCB concentration in carnivorous zooplankton declined both in open water and near shore zone. This result appears to be due to filtration of zebra mussels on herbivorous zooplankton. Since under case 3 zebra mussels were not allowed to filter herbivorous zooplankton the ∑PCB concentration in carnivorous zooplankton increased. ∑PCB concentration in cases 1 and 3 were very close.
4.2.4 Lipid Content of Zebra Mussels
Hydrophobic contaminants have very
different affinities to the tissue types of an organisms body. Lipids have
been identified as the potential storage of hydrophobic contaminants and
the partitioning of organic chemicals into aquatic organisms is governed
to first order by the lipid pool of the organism (Mackay 1982; Connolly
and Pederson 1988; Thomann 1989). Lipid content varies considerably over
the life cycle of many organisms. Various studies on zebra mussels have
shown that the change in the weight of Dreissena
occurs in the reproductive season and is dependent on food levels and
temperatures (Walz 1978; Tessier and Goulden 1982; bij de Vaate 1991;
Dorgelo and Kraak 1993; Garton and Haag 1993; Nalepa et al. 1993). The
increase in lipid levels in Dreissena is appears to be related to spring phytoplankton input,
and the lipids are then allocated for reproduction (Cavaletto and Gardner,
1998). It has been found that after zebra mussels spawning, lipid content
are reduced by 50% (Sprung 1993). For Saginaw Bay, weight loss occurs in
late summer/fall as the gametes are shed (Nalepa et al. 1995).
Study conducted by Lassiter and Hallam (1990) on toxic response by individuals concluded that the variation in lipid content can account for the variation in tolerance in some individuals. The study also suggested that for similarly exposed organisms, the fattest survives the longest. Organisms with higher lipid contents exhibit lower elimination rates than observed for leaner organisms (Landrum 1988a). In case of zebra mussels, the rate of accumulation can increase with in increasing lipid content (Bruner et al. 1994a). It is appropriate to conduct some numerical experiments to examine the effect of lipid content on the body burden of contaminant in mussels. The sensitivity analysis was conducted by varying lipid contents of zebra mussels, which affects elimination rates, to estimate the bioaccumulation in zebra mussels and the species of the lower food chain.
Nalepa et al. (1993) have examined the response of zebra mussels to changes in food levels, differences in weight and biochemical content of the soft tissue of mussels in Lake St. Clair. The mean lipid content reported in that study was 10.0% (by dry weight). This value was compared to the 11.44% lipid content reported for Dreissena from Lake Constance (Walz 1979), but was lower than the values, 15.7% and 17.8% for two depths found in mussels from the Fuhliger See (Sprung and Borcherding 1991). The lipid fraction reported by Endicott et al. (1998) for Saginaw Bay are in the range of 0.035 to 0.064 (by wet weight). In the sensitivity analysis lipid fraction (Fp) were varied from 0.05 to 0.11.
Total wet weight ∑PCB concentration in zebra mussels of three age classes with different lipid fractions for open water and near shore zones are shown in Figure 4.16. It is evident that the lipid fraction plays an important role in ∑PCB accumulation capacity of the species. As the lipid fraction was increased the ∑PCB retained in zebra mussel tissue were higher due to lower elimination rates. Again, due to higher water ∑PCB concentration in near shore zone, the ∑PCB concentration in zebra mussels was higher compared to those in open waters.