1

Appendix E Additional Details for the Derivation of USGS Trophic Transfer Factor for C orbula amurensis 1

The original text concerning the derivation of a trophic transfer factor (TTF) for Corbula amurensis ( C. amurensis ) from field data for the San Francisco Bay and Delta estuary is given on pages 21

– 23 of USGS (2010). That original text is reproduced here and annotated with notes (Note 1 through Note 4) which refer to additional explanatory materials presented at the end of the discussion . The notes: a) clarify the text; and b) refer to a spreadsheet (also presented at the end of the discussion ) that shows the actual calculations, validation, and checks of the entries used in the derivation. See especially Note 4 that addresses specific questions concerning the cal culations of TTF C. amurensis one decision or assumption at a time. The derived
TTF C. amurensis is used in subsequent modeling of the selenium concentrations for C. amurensis as predicated from selenium concentrations for suspended particulate material for the estuary. Tables 13, 14 and 15 and Figure 2.2a in USGS (2010) provide additional illustrations of the agreement between model predictions and field observations.

Trophic Transfer Factors (TTFs) Clam ( C. amurensis )

The choice of food web is critical to modeling success because the particulate material to prey kinetics of bioaccumulation differs widely among invertebrates (Presser and Luoma, 2010). TTF C. amurensis derived from laboratory experiments averaged 6.25 over a range of assimilation efficienci es, ingestion rates, and efflux rate constants (Presser and Luoma, 2010). This average is within a range of 0.6 to 23 for invertebrate species, with TTFs for species of bivalves being the highest (Presser and Luoma, 2010).

Experimental physiological biod ynamic parameters and rates are derived under idealized conditions in the laboratory. These biodynamic equations can be adjusted for a specific ecosystem by incorporating data from that system (Presser and Luoma, 2010 ) ( see Note 1)

. TTF C. amurensis is developed here for the estuary from a mechanistic equation for quantifying the biodynamics of C. amurensis and estuary

- specific data for suspended particulate material (i.e., the food for clams). Selenium bioaccumulated at steady state by C. amurensis i s calculated using a site

- specific modification of equation (3)

C C. amurensis = [(AE) (IR) (C suspended particulate material ] ÷ (k e ) (14)

where (AE) (IR)/k e is defined as TTF C. amurensis and C food is defined as the Se concentration in estuary suspended particulate material (C suspended particulate material ). Among field data available to quantify site

- specific biodynamics of C. amurensis are spatially and temporally matched datasets ( see No te 2) from estuary transects (Doblin et al., 2006) for:

 suspended particulate material Se concentration;

 suspended particulate material C concentration;

 percentage of C in suspended particulate material; and

1 Also known as Potamocorb ula amurensis or P. amurensi s

.2

 percentages of suspended particulate elemental Se, adsorbed Se, and organo

- Se. (S ee Note 3)

Our site

- specific approach here differs from broader approaches where 1) laboratory data for biodynamic parameters such as AE and IR of particulate material may b e generalized; 2) particulate Se concentrations may be an average of several phases of material (i.e., particulate Se total ); or 3) field data may be sparse and thus applied across an entire watershed (Presser and Luoma, 2009).

In general, for the purposes of a Bay

- Delta location and estuarine processes, the suspended particulate material Se concentration carries with it assumptions about Se being associated primarily with organic material (detritus and living organisms). This allows us to determine IR on the same organic material basis (assuming clams seek organic material in the suspended particulate material) and to refine AE to account for suspended particulate material speciation (i.e., divide AE into three components of Se in suspended particul ate material and their individual bioavailabilities). These assumptions are all rooted in well

- established biological understanding of bivalve feeding (Cammen, 1980; Lopez and Levinton, 1987). We ignore the possibility of uptake directly from water by the clams because that has been shown in a large body of work to be trivial (Luoma and Rainbow, 2005).

Justifications for values used in each parameter of the equation for a site

- specific approach are ( see Note 4) :

1. We can either assume that Se is associate d with carbonaceous materials or Se is spread across all suspended particulate material. For the former, the concentration of Se is expressed as μg Se/g C. We obtain μg Se/g C by dividing the suspended particulate material Se concentration (μg Se/g suspend ed particulate material) by mg C/mg suspended particulate material. For the present calculations we employ suspended particulate material Se concentrations as justified below.

2. IR is determined by filtration rate (125 L/g clam/d, Cole et al., 1992) multip lied by C (median = 0.4 mg C/L) to achieve the units (g C/g clam/d) in the suspended particulate material at each sampling. In the average condition in the estuary, clams ingest 5% of their body weight per day in C across all days for which data is availab le. At an average of 2% C in suspended particulate material (again, the average across all data) they ingest 2.5 times their body weight per day in total suspended particulate material. If IR is calculated at each of three low river discharge months where data is available, the average is 1.7 g suspended particulate material/g clam/d. Experience has indicated that the ingestion model is more accurate when actual outcomes are used (or averaged) for the generic situation (i.e., 1.7 g suspended particulate mat erial/g clam/d) as compared to taking the average of each component of the outcome and calculating a generic average. Therefore, we recommend using 1.7 g suspended particulate material/g clam/d for modeling.3

3. The derivation of a refined site

- specific AE based on individualized bioavailabilities of Se in suspended particulate material uses observed fractions of particulate organo

- Se, adsorbed Se, or elemental Se found in the estuary (Doblin et al., 2006) combine d with individual AEs for those particulate Se species from the literature (living phytoplankton, AE = 60%; adsorbed on seston, AE = 40%; elemental, AE = 0%; Schlekat et al., 2004; Wang et al., 1996). The equation is:

AE = (fraction organic particulate S e) (AE organic particulate Se ) + (fraction adsorbed particulate Se) (AE adsorbed particulate Se ) + (fraction elemental particulate Se) (AE elemental particulate Se ) (15)

For example, if a site

- specific sample of suspended particulate material collected in the estuary contains 45% Se in phytoplankton at an assumed AE of 60%; 30% Se adsorbed on seston at an assumed AE of 40%; and 25% elemental Se in sediment at an assumed AE of 0%, then the composite AE = (0.45 x 0.6) + (0.30 x 0.40) + (0.25 x 0) = 0.39 or 39% AE.

4. We apply the efflux rate constant derived experimentally (Lee et al., 2006): k e = 0.03/d.

5. When we model for times when all data are available from the e stuary, we use all data from that sampling date. When we model generically we employ mean parameters.

Given the above protocol and assumptions, we can directly calculate C. amurensis Se concentrations for comparison to observed Se concentrations to valid ate predictions or calculate a TTF C. amurensis for use in modeling. If the data and assumptions given above are used in a site

- specific modification of equation (4)

(IR) (AE) ÷ k e = TTF clam ( 16)

then
TTF clam = (1.7 g suspended particulate material/g clam/d) (0.39) ÷ 0.03 = 22.1

Or, in terms of a C. amurensis Se concentration, if a 0.84 μg/g dw suspended part iculate material Se concentration is assumed, then

C C. amurensis = (0.84 μg Se/g) (1.7 g/g/d) (0.39) ÷ 0.03/d = 18.6 μg Se/g

Salinity

- specific or transect specific Se concentrations and TTFs for C. amurensis can be calculated using the same protocol as above, but with percentages of C and suspended particulate material Se species observed in that transect. Thus, an individual C. amurensis Se concentration and TTF C. amurensis can be calculated from each matched set of data from the five suspended particulate material transects for the estuary (Doblin et al., 2006), making the predictions and derivations as detailed as the data permit. This data

- intensive approach yields a mean TTF C. amurensis of 17.1 excluding A pril, 1999 transects data as out of the norm (i.e., El Niño condition in the estuary) or 18.1 using the focused approach for Suisun Bay

- Carquinez Strait.4

We assume a TTF C.amurensis of 17 in modeling scenarios here. The range of TTFs across all estuarine co nditions was 14

- 26. These values are higher than laboratory

- derived values primarily because ingestion rates are higher in these field systems than in experiments. This is the first calculation of a field

- derived TTF for a marine bivalve species.

Not es : Note 1: Why use Bay data to modify coefficients? The basic biodynamic model calculates Se bioaccumulation at steady state using the solution to the differential equation described
earlier:

C ss clam = (C SPM * AE * IR) / K e .

Coefficients for the model can be (and have been) directly derived in the laboratory (K e , AE and IR) under controlled conditions for specific species. In nature, however, the value for each coefficient varies to some degree with environmental conditions. In many cases there is not enough data from the ecosystem of interest to estimate the influences of environmental conditions. But that is not the case for San Francisco Bay. The model in this report incorporated specific influences on each coefficient u sing experimental studies of environmental influences; environmental data from two different transects in the Bay and basic principles of bivalve biology. Then TTFs were derived (or an average TTF was derived) based upon variations in those conditions in the Bay.

Uncertainty. The environment

- derived TTFs add multiple factors to the equation. In pure statistical terms, each factor comes with an uncertainty. The overall uncertainty is the sum of the uncertainties in each factor. On the other hand, the laboratory estimates of TTF carry an inherent uncertainty that cannot be quantified, stemming from the unknown (unincorporated) influence of environmental factors. Ultimately we can test the net influence of the uncertainty by evaluating how well the mode l predictions fit independently determined Se concentrations
in clams.

Note 2: Se and environmental data are used to derive one TTF for the Bay. Water

- column data and clam data were collected during the same cruise. Clams were not available from all

sta tions. For the Bay

- wide TTF
, we calculated a predicted composite TTF from all stations in the transect. To validate that prediction
, we compared the predicted TTF to an observed TTF based upon clams collected from the cruise. The field data on clam conc entrations were direct
observation s , completely independent of any data from the water

- column.

Note 3: Water

- column data sufficient to model concentrations in clams were collected by
Doblin et al . (2006) at 18 locations in the November 1999 transect (se e data below in the spreadsheet). At each location we calculated Se concentrations in clams at steady state, then determined the TTF at that location from the ratio of predicted clam/observed Se SPM at each5

site. The variation among the 18 locations is i ndicative of the degree of variability driven by both natural variability in the estuary and the summed uncertainty of the model. Using all
values from the transect to determine a mean gives the final value more statistical power than would any individual value. This assumes the TTF is a constant, an assumption in which there may be some small uncertainty. The very close fit between the observed Se concentrations in the clams and the predicted concentrations (or TTFs) suggests the uncertainties do not di stort the final an s wer.

Note 4: The following table goes through the calculations for the TTF one decision or assumption at a time. A spreadsheet follows showing data and calculations from the transect of Nov 1999. Th is is a low flow period. All the data necessary for the calculations and their alternatives are shown in the spreadsheet

.

Question Decision or Assumption How should Se concentration s in suspended particulate material (SPM) be expressed? One way to express Se concentrations in SPM is as a function of the organic material in particulate materials, because anthropogenic Se is primarily biogenically concentrated onto particles and mostly concentrated in the organic fraction of the SPM. The organic fraction can be represented by the carbon c ontent of the SPM. The proportion of the SPM that is inorganic varies widely in SF Bay, changing with changes in processes like river inflows and wind

- derived resuspenion. If the fraction of inorganic material in an SPM sample increases and the organic f raction stays the same, the total Se concentration in the sample could artificially decrease, due to the dilution effect of inorganics (adding more weight than Se). Vice versa is true if the inorganic fraction decreases. To eliminate this potential uncer tainty we did a set of calculations assuming that all Se is associated with the organic fraction of the sediment represented by carbon. Se was expressed as μ g Se per g carbon per liter. As stated in the text:

“ We obtain μg Se/g C by dividing the suspended particulate material Se concentration (μg Se/g suspended particulate material) by mg C/mg suspended particulate material.” Column K of the spreadsheet shows the outcome of this calculation for each site in the transect. The uncertainty associated with th is estimate (and the systematic estuarine variability) is expressed as the variability from one site to another. This is also represented in the first term in the equation in column J of the Nov 1999 spreadsheet calculation of TTF. See also point 1 in th e justification paragraph in the6 text.

How should the ingestion rate (IR) be determined? Cole et al

. (1992) conducted the most comprehensive study of ingestion rate in C. amurensis . This was a flume study in which IR was determined for a large number of animals under a variety of conditions (including different flow rates, feeding condition) typical of the estuary. The inclusion of different flow rates make s this study far superior to other studies of this issue. From those studies we assumed 125 L water w as ingested per g clam per day. To determine how much carbon would be ingested at each site in the transect we multiplied this by either the mg C/L that was determined at each site to obtain ingestion rate of C per day; or the mg SPM to express ing estion in terms of total mass of suspended material. Column L of the spreadsheet shows the outcome of this calculation; variability is indicative of uncertainty + estuarine variability, as described above. IR is represented in the second term in the eq uation in column J of the Nov 1999
transect.

How should Se concentrations be expressed in the general modeling, where such data is not available ? If we use the C

- based determination of SPM we must use the C

- based determination of IR. If we use the mg SPM basis for Se concentration, we must use the mg SPM basis for IR. To determine the influence of using either we predicted Se in clams using both approaches and compared them. The outcome, of course, was exactly the same because the C term, or the mg S PM term arithmetically cancel (see column s S and T of the spreadsheet). The calculation does also illustrate that the data and the assumptions were internally consistent and that either approach is equally valid to determining C and IR.

What is the be st bay

- wide ingestion rate for the general modeling? One way to address the uncertainty in any individual determination of IR from the field data is to do the calculation under a variety of conditions and gain statistical power by averaging the results. The resultant IR from a transect such as that in Nov 1999 would then be representative of the Bay as a whole. Point 2 explains that. To collect even more statistical power we also determined IR from additional transects and from the TSM data collected w ith the monthly suspended sediment determinations by Doblin et al. (2006)

. The average IR from Nov 1999 was 2.5 g/g clam/day. The average from Doblin et al. (2006) collections during low flows (Sep

– mid

Nov, 1996

- 2002) was 1.7 g/g clam/d. Other low flo w transects also showed IR in this range. We recommend that the 1.7 g/g7 clam/d value be employed in further modeling because it is at the low end of this range and came from only stations directly connected to the clam sampling.

How should assimilati on efficiency (AE) be determined? AE is determined in the laboratory with pure food sources. Data are available for pure algal cultures; field

- collected detritus and elemental Se, as cited in the text. Doblin et al

. (2006) determined the distribution o f Se between elemental Se, organic Se and what they called “adsorbed Se” at every site in the Nov 1999 transect. We assumed the detrital AE determined by Wang et al . (1996) represented “adsorbed” Se; consistent with the biogenic processes that concentrat e Se in the water

- column. Instead of using one AE determined from pure cultures, which is often the case when data is limited, we partitioned the AE into three parts based upon the AE of each and the relative abundance of each fraction of Se in the SPM

as determined at each site by Doblin et al. (2006). Outcomes are in column O in the spreadsheet. Variability across the transect shows effects of uncertainty + systematic estuarine variability.

Determination of concentrations in clams. The water

- column d ata used above is completely independen t of clam data collected in the transects. The assumptions and uncertainties of the model are tested by the difference between the predicted clam concentrations and observed clam concentrations. These are independen tly determined values and there is no a priori reason they should agree if the model does not work. The fact that the model predict i ons are in the same general range as the observations speaks to the veracity of the model assumptions. As noted above

ave raging predictions from the entire transect has more power than any one determination. The extremely close fit between the mean predicted clam concentration and the mean observed clam concentrations shows that the model, despite its assumptions and uncert ainties, gives a reasonable representation of what is actually happening in nature. The report shows details on the validation calculations.

How did we reach the conclusion about the TTF ? As the spreadsheet for Nov 1999 shows, the mean TTF across all stations in Nov 1999 was 18.9, the standard deviation was 47% of the mean and the 95% confidence limits were ± 3.9. So from this one transect a defensible TTF would range from 15.9

– 22.8. We did a similar calculation for a transect from Oct. 1998 for which all data were available (employing an IR of8 1.7 g/g clam/d). The mean TTF was 17.6, the standard deviation was 28% of the mean and the 95% CI was ± 2.2. A defensible TTF would range from 15.4

– 19.8. These values give a sense of the uncertainty in the calculations. Independent transects result in a coherent set of TTFs with a relatively small confidence interval, especially for field data. The mean across all transects, as stated in the text , was 17.1.9

References Cammen, 1980: L.M. Cammen. 1980. Ingestion rate: An empirical model for aquatic dep osit feeders and detritivores. Oecologia, v. 44, no. 3, p. 303

- 310.

Cole et al., 1992: B.E. Cole, Thompson , J.K., and Cloern, J.E. 1992. Measurement of filtration rates by infaunal bivalves in a recirculating flume. Marine Biology, v. 113, p. 219

– 225.

Doblin et al., 2006: M.A. Doblin, Baines, S.B., Cutter, L.S., and Cutter, G.A. 2006. Sources and biogeochemical cycling of particulate se lenium in the San Francisco Bay estuary. Estuarine, Coastal, and Shelf Science, v. 67, no. 4, p. 681

- 694.

Lopez and Levinton, 1987: G.R. Lopez and J.S. Levinton. 1987. Ecology of Deposit

- Feeding Animals in Marine Sediments. Quarterly Review of Biology , Vol. 62(3): 235

- 260.

Luoma and Rainbow, 2005: S.N. Luoma and P.S. Rainbow. 2005. Why is metal bioaccumulation so variable? Biodynamics as a unifying concept. Environmental Science and Technology, v. 39, no. 7, p. 1921

- 1931.

Presser and Luoma, 200 9: T.S. Presser and S.N. Luoma. 2009. Modeling of selenium for the San Diego Creek watershed and Newport Bay, Californi a. U.S. Geological Survey Open

- File Report 2009

- 1114, 48 p. At: http://pubs.usgs.gov/ of/2009/1114/

.
Presser and Luoma, 2010: T.S. Presser and S.N. Luoma. 2010. A Methodology for Ecosystem Scale Modeling of Selenium. Integrated Environmental Assessment and Management, v. 6, no 4, p. 685

- 710 (plus Supporting Information). At: http://wwwrcamnl.wr.usgs.gov/Selenium/Library_articles/Presser_Luoma_2010_IEAMv6no4_6 85_710p.pdf

.
Schlekat et al., 2004 : C.E. Schlekat
, D.G. Pu rkerson and S.N. Luoma. Modeling selenium bioaccumulation through arthropod food webs in San Francisco Bay, California, USA. Environmental Toxicology and Chemistry, v. 23, no. 12, p. 3003

- 3010.

Wang et al., 1996: W.

- X Wang, N.S. Fisher and S.N. Luoma. 1996. Kinetic determinations of trace element bioaccumulation on the mussel Mytilus edulis . Marine Ecology Progress Series, v. 140, p. 91

- 113.Cutter/Doblin transect Nov, 1999 w/equation

equations original check TTFclam = (AE) (IR)/ke calculated calculated calculated calculated calculated calculated assigned calculated calculated calculated calculated TTFclam Cclam Se = [(AE) (IR) (Cparticulate)]/ke salinity TSM carbon particulate Se particulate Se particulate inorganic Se particulate elemental Se particulate organic Se clam ppm dw ug Se/g C IR C in SPM IR AE ke TTF Carbon TTF SPM clam ppm dw clam ppm dw calc clam/particulate

Station # mg/L mg/L nmol/g ug/g fraction fraction fraction based on C fraction based on SPM based on C based on SPM 1 32.17 13.32 0.50 10.81 0.84 0.167 0.076 0.757 20.55 22.69 0.062 0.037 1.665 0.44 0.03 0.906 24.38 20.55 20.55 24.4 2 29.71 9.75 0.53 9.95 0.78 0.196 0.268 0.536 9.78 14.38 0.066 0.054 1.219 0.31 0.03 0.680 12.61 9.78 9.78 12.6 3 28.50 4.15 20.97 1.64 0.368 0.402 0.230 0.47 0.03 4 27.21 12.04 0.45 11.04 0.86 0.105 0.489 0.406 10.64 23.26 0.056 0.037 1.505 0.25 0.03 0.457 12.35 10.64 10.64 12.4 5 26.90 14.60 0.24 9.18 0.72 0.000 0.000 1.000 18.70 43.79 0.030 0.016 1.825 0.43 0.03 0.427 26.13 18.70 18.70 26.1 6 23.14 13.51 0.29 6.88 0.54 0.280 0.548 0.172 3.49 24.93 0.036 0.022 1.689 0.12 0.03 0.140 6.50 3.49 3.49 6.5 7 22.22 11.54 0.15 10.23 0.80 0.441 0.331 0.229 9.59 63.34 0.018 0.013 1.442 0.25 0.03 0.151 12.03 9.59 9.59 12.0 8 21.53 19.38 0.25 5.42 0.42 0.648 0.829 0.000 3.74 32.29 0.032 0.013 2.422 0.11 0.03 0.116 8.84 3.74 3.74 8.8 9 17.76 12.89 0.21 13.02 1.02 0.131 0.393 0.476 18.70 62.92 0.026 0.016 1.611 0.34 0.03 0.297 18.42 18.70 18.70 18.4 10 15.58 14.31 0.69 8.46 0.66 0.545 0.256 0.198 8.75 13.74 0.086 0.048 1.788 0.22 0.03 0.637 13.26 8.75 8.75 13.3 11 15.19 13.35 0.25 12.21 0.95 0.031 0.331 0.638 19.93 51.06 0.031 0.019 1.669 0.38 0.03 0.390 20.93 19.93 19.93 20.9 24 13.81 38.10 0.37 7.43 0.58 0.152 0.519 0.329 13.75 58.88 0.047 0.010 4.762 0.15 0.03 0.233 23.73 13.75 13.75 23.7 25 11.18 27.80 0.30 8.16 0.64 0.471 0.335 0.194 14.32 59.62 0.037 0.011 3.475 0.19 0.03 0.240 22.49 14.32 14.32 22.5 12 13.10 12.82 0.27 10.99 0.86 0.383 0.546 0.071 7.69 40.58 0.034 0.021 1.603 0.17 0.03 0.190 8.97 7.69 7.69 9.0 13 11.36 32.52 0.47 7.78 0.61 0.209 0.225 0.565 21.10 41.81 0.059 0.015 4.066 0.26 0.03 0.505 34.78 21.10 21.10 34.8 14 9.10 37.03 0.47 7.35 0.57 0.154 0.162 0.684 23.91 45.43 0.058 0.013 4.628 0.27 0.03 0.526 41.73 23.91 23.91 41.7 15 8.26 27.89 6.85 0.53 0.366 0.251 0.382 0.20 0.03 16 6.32 21.62 0.54 9.80 0.76 0.297 0.373 0.330 16.70 30.51 0.068 0.025 2.703 0.24 0.03 0.547 21.84 16.70 16.70 21.8 17 4.65 27.75 0.25 6.42 0.50 0.399 0.404 0.197 8.03 55.31 0.031 0.009 3.468 0.14 0.03 0.145 16.06 8.03 8.03 16.1 18 2.39 20.16 0.75 7.24 0.56 0.212 0.055 0.733 14.06 15.14 0.094 0.037 2.520 0.30 0.03 0.929 24.90 14.06 14.06 24.9 19 0.00 22.02 1.18 7.22 0.56 0.516 0.491 0.000 6.00 10.49 0.148 0.054 2.752 0.12 0.03 0.572 10.66 6.00 6.00 10.7 20 0.00 14.48 0.18 9.46 0.74 0.000 0.343 0.657 12.95 60.61 0.022 0.012 1.811 0.29 0.03 0.214 17.55 12.95 12.95 17.6 21 0.00 24.81 0.49 7.09 0.55 27.79 0.062 0.020 3.101 0.03 13.12 0.415 18.91 13.12 13.12 18.9 8.983943136 Stdev 3.937314331 Appendix E Additional Details for the Derivation of USGS Trophic Transfer Factor for Potamocorbula amurensis Page 10