a model framework for assessing the effects of selenium on aquatic

A MODEL FRAMEWORK FOR ASSESSING THE EFFECTS OF SELENIUM ON AQUATIC ECOSYSTEMS

GEORGE L. BOWIE and THOMAS M. GRIEB Tetra Tech, Inc. 3746 Mr. Diablo Blvd., Suite 300 Lafayette, CA 94549 USA

ABSTRACT. A model framework is being developed to assess the effects of Se toxicity on aquatic ecosystem s. The framework integrates several types of model components including a biogeochemical model, a pharmacokinetic model, a food web transfer model, a toxic effects model, and an ecosystem effects model that addresses both population dynamics and bioenergetics. The model is being used to identify data gaps and to test hypotheses concerning the biological cycling and toxic effects of different chemical forms of Se. An experimental research program is being conducted in conjunction with the model development to quantify Se cycling and toxicological processes, and to provide data to parameterize the model.

1. Introduction

A model framework is being developed to evaluate the effects of Se on aquatic ecosystems. The model includes five major components: a biogeochemical model, a phannacoldnetic model, a food web transfer model, a toxic effects model, and an ecosystem effects model. The components address all major aspects of aquatic toxicity problems including prediction of exposure levels, toxicant accumulation in tissues, sublethal and lethal toxic effects of long-term time-variable exposure, effects on population dynamics of various organisms, and ecological interactions that lead to changes in community structure. The model accounts for differences in the toxicological properties and biogeochemical processes associated with different chemical forms of Se. An experimental research program is being conducted in conjunction with the model development to determine uptake kinetics, elimination kinetics, biotransformation kinetics, and toxic effects on growth, reproduction, and mortality of different chemical forms of Se to various aquatic organisms ranging from microbial organisms to fish (Porcella et al., 1990). This paper gives an overview of the model framework and presents some initial analyses using the pharmacokinetic component.

2. Biogeochemicai Model

The biogeochemical model predicts Se exposure conditions in water and sediments. The bit- geochemical cycle of Se is complex since Se exists in four oxidation states (-II, 0, IV, VI) and several chemical forms within some oxidation states, each with different chemical and biological properties. Dissolved Se occurs in three major forms in oxygenated aquatic environments: dissolved organic selenides, selenite, and selenate (Cutter and Bruland, 1984; Cutter, 1989). Dissolved organic selenides (Se(-II)) consist of numerous organic compounds derived from the excretions of aquatic organisms and the microbial decomposition of Se-laden organic detritus (e.g., fecal material, dead

Water, Air, and Soil Pollution 57-58: 13-22, 1991. © 1991 Kluwer Academic Publishers. Printed in the Netherlands.

14 G.L. BOWIE AND T. M. GRIEB

organisms). Dissolved organic selenides are produced in the water column and sediments, and include compounds such as seleno amino acids (e.g., selenomethionine and selenocysteine) and methylated compounds (e.g., dimethyl selenide, dimethyl diselenide). Under oxidizing conditions, dissolved organic selenides are oxidized to selenite (Se(IV)), which is in tum slowly oxidized to selenate (Se(VI)) at high redox potentials. Although selenate is the stable form under normal pH and fully oxygenated conditions, the slow oxidation kinetics combined with the continual regeneration of reduced organic selenides by biological activity prevents thermodynamic equilibrium from being attained (Measures and Burton, 1978; Wrench and Measures, 1982; Cutter and Bmland, 1984). Under reducing conditions (e.g., 02 depletion in lake hypolimnions, pore waters of anaerobic sediments), selenate is reduced to selenite, which is further reduced to elemental Se (Cutter, 1982, 1990). Elemental Se is insoluble and precipitates to the sediments.

All dissolved forms of Se are removed from the water through uptake by aquatic organisms. Primary producers are able to concentrate Se in their cells on the order of 100 to 1000 times the concentration in water (Brooks, 1984; Foe and Knight, 1986). Primary producers metabolically reduce inorganic forms to organic forms (seleno amino acids, proteins) which are more efficiently assimilated by consumer organisms (Wrench, 1978; Bottino et al., 1984). Fish and aquatic invertebrates take up Se from both food and water. Se accumulated by aquatic organisms is recycled to the water with their soluble excretions and by the decomposition of fecal material and dead organisms. Much of this material settles to the bottom as detrital rain where it is later released by microbial decomposition processes in the sediments. The soluble decomposition products and most of the soluble excretions are probably initially dissolved organic selenides, which subsequently oxidize to selenite or selenate (Cutter and Bruland, 1984). Some of the particulate matter is also recycled directly through the food web by zooplankton, benthic invertebrates, and fish that feed on detritus in the water column and sediments.

One of the dissolved organic selenide compounds, dimethyl selenide, is of particular interest since it is highly volatile and is quickly removed from the water by outgassing. This may be an important removal process in some systems (Cooke and Bmland, 1987). Dimethyl selenide is produced by some algae and microorganisms (Chau et al., 1976; Doran and Alexander, 1977; Cooke and Bruland, 1987).

The biogeochemical model simulates the above processes and is based on a dynamic mass balance approach. As indicated above, a thermodynamic equilibrium approach is not appropriate for modeling Se since the system is never in equilibrium due to the slow kinetics of the oxidation reactions and the continual regeneration of soluble reduced organic forms by biological activity. The mass balance approach uses differential equations to describe the rates of change of each chemical form of Se due to the processes discussed above. These equations include terms describing the redox reactions between dissolved organic selenides, selenite, selenate, and elemental Se; decomposition of particulate organic Se in the water column and sediments; uptake and excretion of the three major dissolved forms of Se by organisms; production ofdimethyl selenide by algal excretion and microbial activity in the sediments and water column; volatilization of dimethyl selenide; settling of particulate organic and elemental Se; sediment exchange processes; and various external loadings and transport processes. The transport processes include advective transport (i.e., circulationprocesses), dispersive mixing, and lake outflows. The external loadings include ash pond or other pollutant loadings, river inflows, and atmospheric deposition.

An example mass balance equation, representing selenite dynamics in the water column, can be expressed as:

MODEL FRAMEWORK FOR ASSESSING THE EFFECTS OF SELENIUM 15

dSe(IV) V = -Ko2Se(IV)V + KolSe(-II)org V + KrlSe(VI)V- Kr2Se(IV) V -

dt

nt (Ku2iBi)Se(iV)V+ ~ (Ke2iBiSebody2i)V+ -Z i=1 i=l

+ E ak(1-Ek)( l - l : )k) CkjSebody2j V+sedex+trans k=l

(1)

where Kol and Ko2 are oxidation rates for organic selenides and selenite; Krt and Kr2 are reduction rates for selenate and selenite; V is the volume of the model spatial segment; subscripts i, k, andj refer to organism group i, predator group k, and food item j; n t, np, and nf are the numbers of organisms, predators, and food items; Ku2i and Ke2i are the selenite uptake and excretion rates; B i and B k are organism biomasses" Sebod , and Sebod ~ are tissue concentrations of selenite; E k is the selenite ' Y2i Y2j assimilation efficiency by consumers; Pk is the fraction of ingested but unassimilated selenite that is egested in particulate form; Ckj is the consumption rate; "sedex" is the net selenite exchange between the water column and sediments; and "trans" represents all transport processes including, advection, dispersion, inflows, outflows, and extemal loadings. The transport term "trans" is based on a 3- dimensional transport equation, hut the model is flexible and can be set up using either a 1-, 2-, or 3- dimensional grid, or by representing the whole water body as a single mixed element with no spatial resolution. The sediment exchange term "sedex" represents the net flux between the water column and sediments. The sediment portion of the biogeochemical model considers the settling of Se-laden organic detritus (i.e., algae, fecal pellets, dead organisms), settling of insoluble elemental Se, decomposition of sediment particles, other microNalprocesses such as production ofdimethylselenide, organic sediment consumption by benthic organisms, redox reactions in pore waters, and diffusion between the pore waters and overlying water column.

3. Pharmacokinetic Model

The pharmacokinetic model predicts the accumulation of Se in aquatic organisms based on exposure conditions predicted by the biogeochemical model. Organisms are simultaneously exposed to several different chemical forms of Se in aquatic systems, each with different pharmacokinetic properties. The uptake and elimination rates vary between selenite, selenate, and organic selenides (Sandholm et al., 1973; Kleinow and Brooks, 1986). Uptake rates are highest for organic selenides and lowest for selenate, with the uptake and accumulation of organic forms being significantly higher than inorganic forms (Sandholm et al., 1973; Kleinow and Brooks, 1986; Woock, 1986). Organic forms also have lower elimination rates than inorganic forms (Kleinow and Brooks, 1986; Bertram and Brooks, 1986) and accumulate more rapidly in tissues. Biotransformations convert some forms to other forms in the tissues or cells of some aquatic organisms (Bottino et al., 1984). Different forms of Se also produce different levels of toxic response (Niimi and LaHam, 1976; Woock et al., 1987).

Figure 1 summarizes the basic approach to the pharmacokinetic modeling utilizing three metabolic pools in each organism: selenite, selenate, and dissolved organic selenides. The example shows exposure to a single form of Se (organic selenides) taken up through food and water, metabolically transformed and redistributed between the various metabolic pools, and depurated from these pools via soluble excretions (urine and gills) and fecal elimination. Deputation kinetics are assumed to differ between metabolic pools and between different mutes of elimination (i.e., urine


Foo Uptake / ~ .. . . . . . . . . . ~ "~a~"~-~'/ Se

Water / ~ - ' ~ - - ~ . . ~ -]-- ~ N ~ N ~ ' ~ ' ~ " Uptake ~ ~ I " ~ " Fecal Se(-II) Elimination

Soluble Excretions

Figure 1. Pharmacokinetic modeling approach for organic selenide exposure.

and gills vs. bile). The soluble excretions of the urine and gills are distinguished from elimination via the fecal route for input to the biogeochemical model since Se in fecal pellets behaves differently than soluble excretions. Particulate forms settle, undergo decompositionprocesses, or are consumed by dental feeders, while soluble forms are subject to redox reactions and are immediately available for uptake from the water.

The pharmacokinetic model is based on mass balance equations that describe the tissue dynamics of Se for each metabolic pool. An example equation for the organic selenide pool in a particular fish is:

d SefiSh]dt - Kul BSe(-II)°rg + EIB ~ CjSeb°dylJ - Kel BSeb°dyl - Kfl BSeb°dyl - j=l

- K12BSebodyl - K13BSebody 1 + K21BSebody 2 + K31BSebody 3 (2)

where Sefishl is the organic selenide body burden; Ku 1 is the organic selenide uptake rate from water; B is the fish biomass; Se(-II)org is the dissolved organic selenide concentration; E 1 is the assimilation efficiency for organic selenides in food; Cj is the consumption rate of each food item; subscript j refers to each food item; nf in the number of food items; SebodYl, Setmdy 2, and Sebody 3 are the tissue concentrations of organic selenides, selenite, and selenate; Kel and Kfl are the organic selenide elimination rates in soluble excretions and fecal particulates; and K12, K13, K21, and K31 are biotransformation rates where the first subscript represents the donor metabolic pool and the second subscript represents the receptor pool. A similar equation is required for each metabolic pool and each type of organism.

Although the above equation represents uptake and elimination processes in terms of simple first- order coefficients, more mechanistic physiological formulations are being developed for these processes. Physiologically based pharmacokinetic models that simulate the dynamics of Se in various tissues are also being developed for fish.

MODEL FRAMEWORK FOR ASSESSING THE EFFECTS OF SELENIUM

4. Food Web Transfer Model

17

The food web transfer model is an extension of the pharmacokinetic model to predict Se accumulation in all major types of organisms in an aquatic food web. The model framework includes microbial organisms (bacteria, protozoans, microzooplankton), algae, aquatic plants, zooplankton, benthic animals, and fish. Each of these general types of organisms are partitioned into several functional groups (e.g., zooplankton are partitioned into rotifers, cladocerans, and copepods). Fish are further partitioned into several life stages and age classes. Each functional group and life stage represents a separate component of the food web through which toxicant dynamics are tracked over time.

The food web transfer model requires two sets of mass balance equations, one describing the dynamics of each Se metabolic pool in the organisms, and one describing the biomass dynamics of each organism. An example equation for the organic Se pool in a zooplankton group is:

dSez°°l =KulBSe(_Ii)org +EIB ~ (CjSebodylj)-KelBSebody I - dt j=l

- Kfl BSebodyl - K12BSebody 1 - K13BSebody I + K21BSebody 2 +

np

+ K31BSebody 3 - ~ (BkCk)Sebody 1 -mBSebody 1 +trans k=l

(3)

and the corresponding equation for the biomass dynamics of the zooplankton group is:

dB np - - = ( g - r - m ) B - ~ BkCk +trans (it k=l

(4)

where Sezool is the organic selenide pool in zooplankton (expressed as concentration in water), B is the zooplankton biomass (also in concentration units), g is the zooplankton growth rate, r is the respiration rate, m is the nonpredatory mortality rate, and all other variables are as defined previously in Equations (1) and (2).

These two sets of equations are solved simultaneously, and the results for the Se pools (expressed as mass Se/volume water) are divided by the biomass concentrations to get the tissue concentrations in the organisms (mass Se/mass organism). The equation for the organic Se pool in zooplankton includes terms for nonpredatory mortality, predatory mortality, and transport because the model must simultaneously account for the movement of Se and biomass through the ecosystem. Therefore, any processes that change or redistribute the biomass of a biotic group will affect the movement of Se associated with that group. Additional processes are required in the equations for the fish Se pools, including egg production, harvest, transfer between life stages, and migration out of the system.

5. Toxic Effects Model

The toxic effects model uses information on Se accumulation calculated from the pharmacold- netic or food web transfer models to predict the effects of Se exposure on the growth, reproduction,


and mortality of aquatic organisms. The toxic effects model will be based primarily on dose-response relationships derived from our experimental program and the literature, and will include sublethal effects on growth (e.g., growth rate, feeding rate, and conversion efficiency) and reproduction (e.g., fecundity, egg and larval development and survival), as well as direct lethal effects. Toxic effects will be related to internal tissue concentrations of Se, rather than exposure levels in water and food, because tissue accumulation integrates the effects of time variable exposure conditions, and because tissue accumulation most accurately reflects the internal toxicant exposure experienced by the organisms. The toxic effects model will also consider the temporal dynamics of tissue accumulation since physiological damage usually increases with duration of exposure, and repair and recovery processes occur when exposure levels drop. The model development efforts for this component are just beginning.

6. Ecosystem Effects Model

The ecosystem effects model simulates the population dynamics of each biological component of the ecosystem (different groups of fish, zooplankton, benthic animals, phytoplankton, microbial organisms, and aquatic plants)under the influence oftoxic stresses. Information from the toxic effects model conceming alterations in the normal rates of growth, reproduction, and mortality due to toxicant exposure are incorporated into equations describing the population dynamics of each biotic group. These equations are imbedded in an ecosystem framework that links all the equations through terms describing predator-prey interactions, competition for resources, and nutrient cycling processes. This framework predicts the direct effects of toxicants on individual populations, as well as more subtle indirect effects associated with community interactions between different populations such as shifts in dominant species.

Lower trophic level populations are modeled in terms of biomass dynamics using the same mass balance equations described previously in the food web transfer model [Equation (4)]. These equations calculate changes in the biomass of each biotic group due to differences between growth processes (both growth of individuals and reproduction) and loss processes such as predation, metabolic losses, and various sources of nonpredatory mortality. Growth and reproduction are functions of food supply (or nutrients and light for primary producers), which in turn depend on the densities of competing populations. Predatory losses depend on the densities of predator populations and their consumption rates. Most processes are functions of temperature.

Fish population dynamics are modeled using an age-structured approach since exposure conditions change significantly over their lifespan and since different age classes consume different food types, often have different sensitivities to toxicants, and many have different uptake and elimination rates due to metabolic differences. Fish dynamics are simulated using a combination of population dynamics models that track changes in the numbers of individuals in each age class over time and bioenergetic models that evaluate changes in the average weight of individuals in each age class:

dN - - = reproduction- predatory mortality - nonpredatory mortality + dt

+ recruitment- harvest + stocking_+ migration (5)

dW - - = consumption- metabolism - excretion - egest ion- egg production (6) dt


where dN/dt and dW/dt are the changes in numbers and weight, respectively. Bothmodels are linked using:

R d d(NW) dW dN --- = = N + W - (7) dt dt dt dt

where B is the biomass, N is the number of fish, and W is the average weight of fish in the age class. The combined use of age-structured population dynamics (numbers) models and bioenergetic models facilitates interfacing the ecosystem effects model with the food web transfer and biogeochemical models and provides a more accurate assessment of toxicant effects on fish population dynamics than a simple biomass dynamics approach.

7. Ini t ia l A p p l i c a t i o n

Some initial analyses have been conducted using a simplified version of the pharmacokinetic model to integrate information on Se dynamics in fish, identify data gaps, and perform some preliminary hypothesis testing. Model parameters were extracted from several laboratory studies in the literature and used to make predictions about the dynamics of Se accumulation in the field under time-variable exposure conditions. The field data were taken from a study by Woock (1984) that measured Se accumulation in caged golden shiners at four locations in Hyco Lake, North Carolina. Two cages were placed at each of two sites, a contaminated site and a less contaminated control site. At each site, one cage was suspended and one cage was placed on the bottom. Fish in the suspended cages were exposed to contaminated water and plankton, while fish in the bottom cages were exposed to contaminated benthos as well as plankton and water. Se was measured in the fish, water, plankton, and benthos approximately every four weeks during the 28-week study.

Several simplifying assumptions were made in applying the model: 1) first-order kinetics with constant rate coefficients were used for all modelparameters, 2) waterborne exposure was primarily through inorganic Se dominated by selenite, 3) foodborne exposure was dominated by organic forms, and 4) biotransformations between forms were ignored since no data are currently available to parameterize these processes. The daily consumption rate was estimated to be 1.25% of body weight per day, based on a typical gross growth rate of 1% d q and a food assimilation efficiency of 80% (Leidy and Jenkins, 1977).

The depuration rate forwaterborne inorganic Se exposure was set at0.026 d q (Bertram and Brooks, 1986; Gissel-Nielsen and Gissel-Nielsen, 1978; Sato et al., 1980) and the depuration rate for foodborne organic Se exposure was set at 0.014 d -1 (Bertram and Brooks, 1986). These rates represented total elimination through urine, bile, and gills.

After fixing the depuration rates for water and foodbome exposure, the three remaining model parameters, the uptake rate from water, the toxicant assimilation efficiency from food, and the partitioning of the diet between plankton and benthos for fish in the bottom cages, were adjusted until the model gave the best fit for all four cages. The calibrated uptake rate for waterborne exposure was 30 Lg-ld -1. This value is midway between the lower rates of 0.5 to 1.5 Lg-ld -1 calculated from Bertram and Brooks' (1986) data for fathead minnows exposed to selenate and the higher rates of 45 to 65 Lg-ld -1 calculated from Lemly's (1982) data for largemouth bass and bluegill exposed to selenite. The uptake rates for these studies were calculated from the reported steady-state tissue concentrations and waterborne exposure levels using the assumed depuration rate of 0.026 d -1. The calibrated assimilation efficiency for foodborne uptake was 20%. This is consistent with Bertram

20 G .L . BOWIE AND T. M. GRIEB

and Brooks' (1986) data for fathead minnows consuming Se-contaminated baphnia if a food consumption rate of 1.5% of body weight d -1 is assumed. They reported a foodborne uptake rate (Kfood) of 0.003 g g-ld-1, which is equivalent to the product of the food consumption rate (0.015 g g-ld-1) and the toxicant assimilation efficiency (0.2). The calibrated assimilation efficiency is also consistent with values of 8 to 30% reported by Turner and Swick (1983) for pike consuming Se-contaminated yellow perch. The calibrated assimilation efficiency is lower than the high assimilation efficiencies suggested by Hilton et al. (1982) and Kleinhow and Brooks (1986), but these studies did not use natural food sources and therefore may not have adequately represented realistic foodborne exposure conditions for Se. The calibrated partitioning of the diet for fish in the bottom cages was 2/3 plankton and 1/3 benthos, which also seems reasonable.

The results of the modelpredictions are shown in Figure 2. Although the model predictions gave reasonable results considering all of the simplifying assumptions, further modal refinements using more physiologicaUy-based formulations are planned to improve the predictions and to resolve some inconsistencies that were encountered in the experimental literature used to calibrate the model. Physiologically based pharmacokinetic modds that predict toxicant dynamics in various tissues are also being developed.

35: SURFACE CAGE - CONTAMINATED SITE

30'

~2o~

10"

5:

35-

30

25"

~ 1 5 ' v

10

5"

O-

50 100 150 Time (days)

200

SURFACE CAGE - C O N T R O L SITE

35]

301

25t

~ 2 o t

10]

BOTTOM CAGE CONTAMINATED SITE

i

o I 0

35

50 100 150 Time (days)

30t

25t

20t

15t

101

5t 04

200

BOTTOM CAGE - C O N T R O L SITE

50 100 150 200 0 50 100 150 Time (days) Time (days)

200

Figure 2. Model predictions of selenium accumulation in caged golden shiners (plotted symbols represent field data with standard deviations, from Woock, 1984).


Acknowledgments. Th~s work was funded by the Electric Power Research Institute under contract RP2020-11. Special thanks are due to Don Porcella, the EPRI project manager, for his help and support on the project.

References

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Bottino, N.R., Banks, C.H., Irgolic, K.J., Micks, P., Wheeler, A.E., and Zingaro, R.A.: 1984, PhytochCmistry. 23, 2445.

Brooks, A.S.: 1984, Selenium inthe environment: Anoldproblem withnew concerns. In: Workshop Proceedings: TheEffects ofTraceElements onAquaticEcosystems. EPRIEA-3329. ElectricPower Research Institute, Palo Alto, CA.

Chau, Y.K., Wong, P.T.S., Silverberg, B.A., Luxon, P.L., and Bengert, G.A.: 1976, Science. 192, 1130.

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Cutter, G.A.: 1989, Selenium in freshwater systems. In: Occurrence and Distribution of Selenium. Ihnat, M., editor. CRC Press.

Cutter, G.A.: 1990, An examination of selenium geochemistry in power plant receiving waters. Vol. 1. Old Dominion University Research Foundation. For Electric Power Research Institute, Palo Alto, CA.

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Porcella, D.B., Bowie, G.L., Sanders, J.G., and Cutter, G.A.: 1990, Assessing selenium cycling and toxicity in aquatic ecosystems. Water. Air. and Soil P0110t. (this volume).

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Turner, M.A. and Swick, A.L.: 1983, Can. J. Fish. Aquat. Sci. 40, 2241.

Woock, S.E.: 1984, Accumulation of selenium by golden shiners Notemigonus crysoleucas. Hyco Reservoir N.C. cage study 1981-1982. Carolina Power & Light Company. For the Electric Power Research Institute, Palo Alto, CA.

Woock, S.E.: 1986, The effects of chronic selenium exposure on bluegill, 1985 progress report. Carolina Power & Light Company, New Hill, NC.

Woock, S.E., Garrett, W.R., Partin, W.E. and Bryson, W.T.: 1987, Bull. Environ. Contami. Toxi¢ol. 39, 998.

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a model framework for assessing the effects of selenium on aquatic

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