Cover Art and Graphics: Jeanne DiLeo, USGS Office of Communications and Publishing, Menlo Park Publishing Service Center, Menlo Park, California.

# Ecosystem-Scale Selenium Modeling in Support of Fish and Wildlife Criteria Development for the San Francisco Bay-Delta Estuary, California

By Theresa S. Presse r and Samuel N. Luoma U.S. Geological Survey, Menlo Park, California



## Administrative Report

December, 2010

This work was performed with the support of U. S. Environmental Protec tion Agency interagency funding agreements.

U.S. Department of the Interior U.S. Geological Survey

iiiii 

## U.S. Department of the Interior KEN SALAZAR, Secretary

U.S. Geological Survey
Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia 2010

For more information on the USGS—the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment:

World Wide Web: http://www.usgs.gov Telephone: 1-888-ASK-USGS

Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.iv 

# Contents

Executive Summary ............................................................................................................. .......................................... 1

Introduc tion .................................................................................................................. .................................................. 4 San Francisco Bay- Delta Es tuary ............................................................................................... ................................... 6 Regulation .................................................................................................................... .............................................. 6 Setting ....................................................................................................................... ................................................. 6

Selenium Sources .............................................................................................................. ........................................ 6 Hydrodynamic Connecti ons ...................................................................................................... ................................. 8

Overview of Modeling .......................................................................................................... .......................................... 9

Fish and Wi ldlife ............................................................................................................. ..............................................10

Species at Risk ............................................................................................................... ..........................................10

Effects and Effe ct Lev els ..................................................................................................... ......................................11 Estuary Food Web and Exposure Models .......................................................................................... .......................12 Ecosystem-Scale Model Co mponents .............................................................................................. ............................13 Partitioning and Transformation ............................................................................................... .................................13 Biodynamics: Invertebrat es, Fish, and Bird s ................................................................................... ..........................15

Available Data ................................................................................................................ ..............................................17 Application of Ecosyst em-Scale Me thodology .................................................................................... ..........................18 Estuarine Ap proaches .......................................................................................................... .....................................18 Dissolved and Suspended Particulate Materi al Selenium Prof iles for M odeling ................................................... ....19 Dissolved and Suspended Particulate Ma terial Selenium Speciation .............................................................. .........20

Bioaccumulated Sele nium in Prey ............................................................................................... .............................20 Derivation of Site-Specif ic Model Co mponents .................................................................................. ...........................21 Environmental Partitioning Factors (K d 

### s) ..................................................................................................................21 Trophic Transfer Fa ctors (TTFs) ............................................................................................... ................................21 Clam ( C. amurensis ) ............................................................................................................................. ................21 Aquatic Insect and Ot her Invert ebrates ........................................................................................ .........................23 Bird Egg ...................................................................................................................... ..........................................23 Fish Whole-Body or Mu scle ..................................................................................................... ..............................24

Validat ion .................................................................................................................... ..................................................24

Prediction of Selenium Concentrations in C. amurensis ...........................................................................................24 Prediction of Existing Co nditions Ac ross Media ................................................................................ ........................24 Modeling Scenarios and Predict ions ............................................................................................ .................................25 Bay-Delta Co ntinuum ........................................................................................................... .....................................25 Suisun Bay-Carqui nez Strait ................................................................................................... ..................................27

Landward Si tes ................................................................................................................ .........................................28 Choices, Limitations, and R eduction of Un certainty ............................................................................ ..........................28 Data Collection, Model Updates, and Re finements ............................................................................... .......................29

Conclusi ons ................................................................................................................... ...............................................31

Acknowl edgment s ............................................................................................................... .........................................34

References Cited .............................................................................................................. ............................................34v 

# Figures (see separate file)

Figure 1 . Map of the San Francisco Bay-Delta Estuary.

Figure 2 . Conceptual details of sources of selenium, site-specific food webs, and hydrodynamic connections for the Bay-Delta.

Figure 3 . Refinery and agricultural selenium loads (1985-2009) ( A ) and flow conditions (1996-2009) ( B ).

Figure 4 . Grassland Bypass Project se lenium loads (1997-2009) ( A ) and proposed selenium load targets (2009-2019) ( B ).

Figure 5 . San Francisco Bay-Delta Selenium Model from Presser and Luoma (2006).

Figure 6 . General ecosystem-scale selenium model from Presser and Luoma (2010).

Figure 7 . Quantifying processes ( A ) and modeling equations ( B ) for ecosystem-scale selenium modeling.

Figure 8 . Ecotoxicology and effects of selenium for fish and birds.

Figure 9 . Site-specific modeling appro ach for Bay-Delta seaward ( A ) and landward ( B ) locations.

Figure 10 . Selenium effects models for diving ducks ( A ), white sturgeon ( B ), and juvenile salmonids ( C and

D ).

Figure 11. Conceptual details of seaw ard clam-based food webs ( A ) and landward aquati c insect-based food webs ( B ) for modeling of the Bay-Delta.

Figure 12 . Exposure and habitat use for pr edators at risk, patterns of C. amurensis selenium concentrations (1996), and hydrologic categories that may relate to environmental part itioning of selenium for the BayDelta.

Figure 13 . Conceptual details of environmental partitioni ng reactions between dissolved and particulate

selenium.

Figure 14 . Profiles of hydrodynamically categor ized suspended particulate mate rial selenium concentrations from transects during 1997-1999.

Figure 15 . Flow conditions in the Bay-Delta (1996-2009) with reference to samp ling transects by Cutter and Cutter (2004) and Doblin et al. (2006) (1997-1999).

Figure 16 . Profiles of observed dissolved se lenium concentrations for transects across the estuary salinity gradient during 1997-1999.

Figure 17 . Profiles of observed dissolved a nd suspended particulate material selenium concentrations and

calculated K d 

### s for transects across the estuary salinity gradient during 1997-1999.vi 

### Figure 18

. Profiles of observed dissolved ( A ) and suspended particulate material ( B ) selenium

concentrations and calculated K d 

### s ( C ) for transects across the salinity gr adient from Rio Vista and Stockton to Benicia, California (2003-2004).

Figure 19 . Profiles of observed dissolved selenium speciation [selenate ( A ), selenite ( B ), and organo-Se ( C )] for transects across the estuary salinity gr adient for September, 1986 and November, 1997.

Figure 20 . Profiles of observed suspended particulate materi al selenium concentrations and the observed percentages of suspended particulat e organo-Se for transects across th e estuary salinity gradient during

1997-1999.

Figure 21 . Observed selenium concentrations for C. amurensis collected from USGS monitoring station 8.1 at Carquinez Strait (1996-2009). Insets ( A and B ) show selenium concentrations in C. amurensis in the highlighted period from 1997 to 1999 when selenium transects were collected.

Figure 22 . Predicted C. amurensis selenium concentrations for November, 1999 and June, 1998 and

observed selenium concentrations in C. amurensis collected from several USGS monitoring stations for the same time periods as the transects in 1999 and 1998.

Figure 23 . Modeled scenarios for protection of sturgeon a nd a generic fish showing predicted allowable dissolved, suspended particulate material, and dietary selenium concentrations for the Bay

.

Figure 24 . Modeled scenarios for protection of clam-ea ting bird species showi ng predicted allowable dissolved, suspended particulate material, and dietary selenium concentrations for the Bay

.

Figure 25 . Modeled scenarios for protection of juvenile Chinook salmon showi ng predicted allowable dissolved, suspended particulate material, and dietary selenium concentrations for the Delta

.

Figure 26 . Observed and predicted allowabl e suspended particulate material selenium concentrations for modeled clam-based exposure scenarios in November , 1999 and June, 1998; and insect-based exposure scenarios in October, 2003 and April, 2003.

Figure 27 . Change in mean observed dissolved, suspended particulate material, and C. amurensis selenium concentrations during transects in 1998 and 1999 as a function of residence time.

Figure 28 . Change in mean observed suspended particulate material selenium species during transects in

1998 and 1999 as a function of residence time.



# Tables (see separate file)

Table 1 . Oil refinery selenium loads discha rged to the Bay-Delta during 1986-2009.

Table 2

. Generalized steps in ecosystem-scale methodology for translation of a tissue Se concentration to a water-column Se concentration for protec tion of fish and aqua tic-dependent wildlife.

Table 3

. List of species considered for evaluation of Se exposure risk in the San Francisco Bay/Delta.

Table 4 . Species most at risk from Se exposure in the San Francisco Estuary: summary data.vii 

### Table 5 . Selenium effect levels derive d for protection of species at ri sk in the San Francisco Estuary.

Table 6. Generic selenium effect le vels for fish and birds.

Table 7 . Available data for the Bay-Delta in cluding transects and biota studies.

Table 8 . Bay-Delta hydrologic conditions, Net Delta Ou tflow Index, salinity, observed dissolved Se concentrations, observed suspended particulate material Se concentrations, and calculated K d 

### s.

Table 9 . Suisun Bay/Carquinez Strait hydr ologic conditions, Net Delta Outf low Index, salinity, observed dissolved Se concentrations, observed suspended part iculate material Se concentrations, and calculated

K d 

### s.

Table 10

. Landward hydrologic conditions, Net Delta Outflo w Index, salinity, observed dissolved Se concentrations, observed suspended particulate material Se concentrations, and calculated K d 

### s.

Table 11 . Bay-Delta mean, median, 75 th 

### percentile, and 25 th 

### percentile for observed dissolved Se concentrations, observed suspended particulate material Se concentrations, and calculated K d 

### s.

Table 12 . Suisun Bay-Carquinez Strait mean, median, 75 th 

### percentile, and 25 th 

### percentile for observed dissolved Se concentrations, observed suspended part iculate material Se concentrations, and calculated

K d 

### s.

Table 13. Comparison of predicted and observed C. amurensis Se concentration during Bay-Delta

transects.

Table 14. Validation for existing conditions at a seaw ard estuary location for November, 1999 or a generalized mean condition using ob served Se concentrations in se award and landward white sturgeon;

derived K d 

### s and TTFs; and a food web for su spended particulate material> C. amurensis >white sturgeon.

Table 15. Validation for existing conditions in Suisun Bay-Carquinez Strait for November, 1999 or a generalized mean condition using ob served Se concentrations in s eaward white sturgeon; derived K d 

### s and TTFs; and a food web for susp ended particulate material> C. amurensis >white sturgeon.

Table 16. Validation for existing conditions at a landwar d estuary location for 2003-2004 using observed

Se concentrations in landwa rd largemouth bass; derived K d 

### s and TTFs; and a food web for suspended particulate material>aquatic in sect>largemouth bass food web.

Table 17. Predicted allowed dissolved Se concentrations for Bay-Delta transects at different effect guidelines and associated levels of protection (USFWS, 2009b) for a suspended particulate material> C.

amurensis >sturgeon food web.

Table 18. Predicted allowed dissolved Se concentrations for Bay-Delta transects at different effect guidelines and associated levels of protection (USFWS, 2009b) for a suspended particulate material> C.

amurensis >clam-eating bird species food web.viii 

### Table 19. Predicted allowed dissolved Se concentrations for landward transects at different effect guidelines and associated levels of protecti on (USFWS, 2009b) for a suspended particulate material>aquatic insect >juvenile salmon food web.

Table 20. Prediction scenarios using Suisun Bay-Carquinez Strait transects for a suspended particulate

material> C. amurensis >white sturgeon food web.

Table 21. Prediction scenarios using Suisun Bay-Carquinez Strait transects for a suspended particulate

material> C. amurensis >clam-eating bird species food web.

Table 22. Prediction scenarios using landward-focuse d transects for suspended particulate material>aquatic insect>juve nile salmon or steelhead.

Table 23. Prediction scenarios using landward-focuse d transects for suspended particulate material>aquatic insect>rail.



# Appendices (see separate files)

Appendix A Luoma, S.N., and Presser, T.S., 2009, Emergi ng opportunities in management of selenium contamination: Environmental Science and Technology, v. 43, no. 22, p. 8483-8487.

Appendix B Presser, T.S., and Luoma, S.N, 2010, A Methodol ogy for Ecosystem-Scale Modeling of Selenium: Integrated Environmental Assessment and Mana gement, v. 6, no. 4, p. 685-710 (plus Supporting Data, 32 p.).

Appendix C Selenium discharges from oil refineries ( Figures C1-C5 )

Appendix D Compilation of field data for the Bay-Delta ( Tables D1-D5 )

Note: A review by Coan (2002) concluded th at the San Francisco Bay species Potamocorbula

amurensis is now the genus Corbula , but the species name is still unc lear. Because of this uncertainty, reference to the bivalve is now suggested as Corbula ( Potamocorbula ) amurensis (Thompson, 2005). However, we have used Corbula amurensis throughout this report.ix

# Ecosystem-Scale Selenium Modeling in Support of Fish and Wildlife Criteria Development for the San Francisco Bay-Delta Estuary, California

By Theresa S. Presse r and Samuel N. Luoma



# Executive Summary

The San Francisco Bay-Delta Estuary (Bay-Delta) receives selenium (Se) internally from oil refinery effluents and externally th rough riverine agricultura l discharges. Predator species considered at risk from Se (e.g., green and white sturgeon, scoter, scaup) consume the estuary’s dominant bivalve, C.

amurensis , an efficient bioaccumulator of Se. Recently proposed water-quality regulations for protection of the estuary require tran slating fish and wildlife tissue Se effect guidelines to dissolved Se concentrations. This change in regu latory approach requires considera tion of intervening steps that 1) formally document system hydrology, biogeochemistr y, biology, ecology, and ecotoxicology; and 2) quantitatively link ecosystem media (water, particulate material , and tissues of different food web species) as Se is processed through site-specific food webs. Such a methodology to predict site-specific ecological risk and derive Se criteria for the Ba y-Delta would be the firs t regulatory action where a bioaccumulative element is managed to protect wild life in a marine environment. Regulating seaward sites in the estuary also sets in motion c onsideration of upstrea m watershed sources. For regulators and scientists, our approach offers an understanding that 1) diet drives protection and 2) the choice of food web and pr edator species is criti cal because the kinetics of bioaccumulation differs widely among invertebrates. Further, adequa tely characterizing the tr ansformation of dissolved Se to particulate Se and the type and phase of the resulti ng particulate material qua ntifies the effect of Se speciation on both Se partitioning and Se exposure to prey through the base of the food web (i.e., particulate material to prey kine tics). Our approach also includes opportunities to analyze alternative modeling choices explic itly throughout the decision-making process. Site-specific modeling for the Bay-Delta includes derivation of: 1) salinity -specific operationally defined factors for partitioning of Se between water and suspended pa rticulate material (K d 

### s); 2) dietary biodynamic Trophic Transfer Factors (TTFs) for importa nt food web inhabitants; 3) seasonal scenarios that illustrate hydrologic conditions, life-cycles of predator species, exposure cycles, and habitat use; and 4) species-specific effect guide lines. Effect guidelines for species at risk in the Bay-Delta were provided by the U.S. Fish and Wildlife Service (U SFWS). Effect guidelines are explicit to exposure route (e.g., maternal), endpoint (e.g. , hatchability) and magnitude of effect realized (EC0, EC05, and EC10) to address regulatory consider ations for the U.S. Endangered Sp ecies and Migratory Bird Treaty Acts. Knowing the details of an at-r isk predator’s location dur ing critical life stages for Se effects allows correlating trends in diet and expos ure that occur in the estuary. Thus , our approach uses a mechanistic biodynamic basis to quantify transformation and bioaccumulation as a foundation for criteria development and site-specific data for food webs, life cycles, habitat use, and e ffects to set choices in modeling scenarios.

1

### We employ both a salinity-specific transect a pproach, encompassing tidally-influenced sites across the Bay-Delta from near Chipps Island to th e Golden Gate Bridge, and a geographically focused approach encompassing Suisun Bay and Carquinez Stra it. The most recent transect data (i.e., matched datasets for dissolved and suspended particulat e material) from 1997-1999 are used for modeling a

seaward C. amurensis -based food web. Similarly, the most recen t transect data from 2003-2004 are used for modeling a landward aquatic in sect-based food web. Transect sa mpling from the 1990s represents wet and above normal years in both low flow and high flow seasons. Transect sampling from the 2000s represents above normal and below normal year s in both low flow and high flow seasons.

Profiles across the estuar y within a series of specified freshwater residence times (e.g., June, 1998, 11 days; November, 1999, 70 days) show the range of dissolved Se concentrations is narrowly defined as 0.070-0.320 μg/L. The profiles of suspended particulate material Se concentrations show a less narrow definition with a range of 0.15-2.2 μg/g dry we ight. In the more restri cted approach used for Suisun Bay-Carquinez Strait that eliminates freshwat er and ocean interfaces, the range of dissolved Se concentrations is 0.076-0.215 μg/L, with the range of suspended particul ate material Se concentrations as 0.15-1.0 μg/g dry weight.

K d 

### s are the derived ratios of disso lved and suspended particulate material Se concentrations from transect sampling across the estuary. The operational K d 

### s used here quantify the complex process of transformation to represent exposure and bioavailabi lity at the base of the food web. The profiles of K d 

### s across the estuary illustrate the range in biogeoche mical transformations and their patterns as flow conditions change. Generally, K d 

### s vary similarly as suspended partic ulate material Se concentrations

across transects because of the narrowly defined range of dissol ved Se concentration. Specifically, patterns during high flow conditions in April, 199 9 and low flow conditions in November, 1999 are distinctly different. As residence time increases fro m 16 days in April to 70 days in November, the profile shape moderates and a hydrodynamic span of ef ficient transformation is identified. The range for the Bay-Delta continuum is 712-26,912, with mean K d 

### s shown to increase with increasing residence

time. K d 

### s selected for use in modeling scenarios range from 3,198 to 7,614. The K d 

### range selected when the modeling location is limited to Su isun Bay-Carquinez Strait is 1,180-5,986. The range of derived TTF C. amurensis 

### is 14-26 for local conditions, an increase when compared to a laboratory-derived mean value of 6.25. TTF insect 

### and TTF bird egg 

### are not site-specific, but are selected from literature values (TTF insect 

### = 2.8; TTF bird egg 

### = 2.6). For TTF fish 

### , both a literature value of 1.1, and in the case of white sturgeon, a field-derived TTF of 0.8 are used.

Validation of the model shows the model is ab le to generate 1999-2000 seaward conditions for

Se concentrations in a C. amurensis to white sturgeon food web and 2003 landward conditions for Se concentrations in an aquatic insect to largemouth bass food web. Thus, the model is able to 1) quantify transformation and biodynamics processes for the es tuary and its food webs; and 2) predict that food webs dependent on C. amurensis are the most sensitive to Se inputs, provide the most Se exposure, and are highly vulnerable. Modeling to protect sturgeon and clam-eating bird species is ba sed on consumption of the clam

C. amurensis , an invertebrate that bioaccumulates Se approximately twenty-fold that of the concentration in suspended particulate material (i.e., TTF C. amurensis 

### = 17). Modeling to protect juvenile

Chinook salmon and steelhead trout is based on consumption of aquatic insects, an invertebrate that bioaccumulates Se approximately three-fold that of the concentration in suspended particulate material (i.e., TTF insect 

### = 2.8). The model also addresses an alternativ e dietary preference by predators: a mix of invertebrate species (i.e., a 50% C. amurensis and 50% amphipod diet generates a TTF mixed 

### of 8.8). Allowable dissolved, particulate, and prey Se concentration ca lculated through modeling of a specified predator species are base d not only on the dietary TTF for that species (i.e., exposure), but also

2

### on the toxicological sensitivity inherent to the pr edator (i.e., effects guideline provided by the USFWS for species at risk in the estuary). Hence, bioaccumul ation in salmonids will be less than that in sturgeon because of dietary preference, but toxicity guide lines for salmonids are lower due to increased toxicological sensitivity. In this case, the predicted allowable dissolved Se concentration is a value that

is a mathematical combination of the influences of the lower dietary TTF and the higher toxicological sensitivity. Illustrated scenarios using a set of specific gui delines and modeling choices from the range of temporal hydrodynamic conditions, ge ographic locations, foodwebs, K d 

### , and TTFs described above, bound allowable dissolved, particulate, and prey Se concentrations. C onsideration of compliance with allowed Se concentrations across media (i.e., wate r, particulate, prey, and predator) harmonizes regulation and is a measure of eco logical consistency and relevan ce of the links among exposure, transfer, and effects. The specific ity of these scenarios demonstrat es that enough is known about the

biotransfer of Se and the interconnectedness of habitats and species to set a range of limits and establish an understanding of the conditions, biological responses , and ecological risks critical to management of the Bay-Delta. Analysis of dissolved, suspe nded particulate material and C. amurensis Se concentrations and

K d 

### s for Suisun Bay-Carquinez Strait as a function of freshwater residence time (11, 16, 22 and 70 days) shows that critical ecological tim es are functionally connected to the underlying dynamics and processes of low flow periods. Transformation of dissolved Se to suspended partic ulate material Se (i.e., dissolved Se decreases as suspended particulate material Se concentrations increase s) occurs in the estuary as flow

slows down. C. amurensis Se concentrations also increase with increasing residence time, as does the presence of a majority of partic ulate organo-Se within a residence time of 22 days. Given the steepness of these curves, regulation of suspended particulate material Se concentration may be a more sensitive parameter on which to assess change and choice. De fining or conceptualizing a baseline dissolved Se

concentration or condition for the es tuary is less certain because of the small dynamic range of dissolved

Se concentrations. Predictions from modeling scenarios show that c hoices of geographic cons traints, species, diet, and estuary conditions all are influential in risk mana gement for Se. Thus, the more specificity added to the model, the less uncertainty in predictions. If, fo r example, the geographic range is narrowed by using data only from Suisun Bay, then freshwater and ocean interfaces are avoided. If the temporal range is narrowed to low flow seasons of dry years, then fo cus can be on times when the transformative nature of the estuary is elevated. Juxtapos ition of times when prey species achieve maximum Se concentrations and critical life stages of species at risk are present allows focus of regulatory considerations on times that govern Se’s ecological effects (i.e., ecological bottleneck s). Further refinements to the approach would incl ude consideration of: 1) contributions of Se source riverine end-members; 2) hydrodynamic relationshi ps of riverine and internal Se sources to Se concentrations in the estuary (i.e., an Se budget thr ough the estuary); 3) proce sses at the in terfaces of freshwater/bay/ocean; 4) collection of current temporally and spatially matched Se datasets for water, suspended particulate material, and food web species; and 5) further linkage of ecosystem-scale modeling to fine structure estuary processes. An alysis of Se concentr ation and speciation for characterized particulate phases ar e practical measures of the co mplex water/sediment/particulate milieu

that forms the base of the food web and is consumed as food by invertebrates. Hence, future monitoring to increase the suspended particulat e material database under a suite of flow conditions would enhance our understanding of estuarin e transformation. Monitoring invertebrate Se concentrations in food webs also is a practical, informative step in monitoring b ecause the first and second most variable aspect of Se dynamics (i.e., K d 

### and TTF invertebrate 

### ) are integrated into invertebrate bioaccumulation.

3

### In particular for modeling of avian species, uncertainties exist ar ound laboratory-derived biodynamic modeling parameters; movement and mi gration; and links of diet and tissue Se concentrations under site-specific conditions (i.e., field-derived TTF bird egg 

### ). Additionally, modeling of overwintering clam-eating migratory bird species, such as scoter a nd scaup, based on potential chronic Se effects that may impact staging would assess thes e species in scenarios relevant to their use of the estuary. Chronic toxicity effects include:

• compromised body condition (low body mass);

• oxidative stress (increased susceptibility to disease as immune system is suppressed);

• decreased winter survival;

• decreased reproductive fitness (d ecreased breeding propensity, reduced recruitment) and;

• behavioral impairment (missed breeding window, delayed timing of departure). Predictions from a reference dose methodology for bird s also would strengthen outcomes for protection of avian species. The methodology used here is ab le to document estuary and ecos ystem fine-structure processes and provide the basis and context for future scen ario development. The greatest strength of the analytical and modeling processes is that it is an orderly, ecological ly harmonized derivation approach for assessing different choices of criteria for protecti on of fish and birds. Collection of modern data and additional modeling in collaboration with the final development of cr iteria would test if identified mechanisms and derived factors are applicable to th e Bay-Delta of today. Fu rther modeling also would provide decision-makers with a dditional choices based on specific questions that arise during

collaborative discussions.



# Introduction

Aquatic-dependant wildlife are unprotected under national aquatic life water quality criteria for Se, but these criteria are curren tly being revised [U.S. Environmen tal Protection Agency (USEPA), 1992; 2004]. National freshwater water quality Se crit eria (5 μg/L chronic and 20 μg/L acute) for the protection of aquatic life are directed at protection of fish and are based on field data for effects in fish at Belews Lake (USEPA, 1987). National water quality Se criteria for the prot ection of marine aquatic life allow a maximum concentration of 290 μg/L and a continuous concentration of 71 μg/L, concentrations approximately an or der of magnitude higher than freshw ater criteria. What evidence is

available from estuarine environmen ts suggests that these guidelines are seriously unde r-protective for at least some predator species (Luoma et al., 1992; Presser and Luoma, 2006; Luoma and Presser, 2009). Consideration of developm ent of Se criteria specific to wild life began in 1989 as an outcome of the ecological disaster at Kest erson National Wildlife Refuge, Ca lifornia, where aquatic birds experienced death and deformity (Presser and Ohle ndorf, 1987; USEPA, 1989). The U.S. Clean Water Act (1972) provides the legal authority for deriving water quality criteria for the protection of aquatic life, wildlife, and human health. USEPA in 1985 de veloped methodologies for deriving water quality criteria that included protection of wildlife unde r determination of a Final Residue Value (FRV) (USEPA, 1985). A USEPA revision of criteria for the Great Lakes System [Great Lakes Initiative (GLI), USEPA, 1995] deleted the FRV method and applied a new methodology for contaminants and wildlife. Since that time, the GLI methodology has b een applied to DDT, PCBs, and mercury on a Great Lakes-specific basis for piscivorous birds and mammals. As an outgrowth of the GLI methodology, Petersen and Nebeker (1992) proposed a freshwater waterborne Se thre shold estimate for protection of aquatic-dependent birds and mamma ls. Skorupa and Ohlendorf (1991) pr oposed a range of waterborne

4

### Se concentrations for the protecti on of nesting aquatic birds through us e of field-derived regressions of food web and avian uptake. Adjustments to the development of Se criteria specifically for Californi a were called for by 1) the USEPA through the National Toxi cs Rule (NTR) and the Californi a Toxics Rule (CTR) (USEPA, 1992; 2000); and 2) the USFWS an d National Marine Fisheries Service (NMFS) through their Biological Opinion (USFWS and NMFS, 1998 and amended, 2000). In general, these adjustments were necessary to consider 1) the bioa ccumulative nature of Se in aqua tic systems; 2) Se’s long-term persistence in aquatic sediments and food webs; 3) the importance of dietary pathways in determining toxicity; and 4) protection of th reatened and endangered species. Specifically, pursuant to secti on 7(a) of the U.S. Endangere d Species Act (ESA) (1973), the

USEPA consulted with the USFWS and NMFS concer ning USEPA’s rulemaking action for California. USEPA submitted a Biological Evaluation for their revi ew as part of the cons ultation process in 1994. This evaluation found that the proposed CTR was not lik ely to jeopardize the continued existence of any federally listed species or result in the destruction or adverse modification of de signated critical habitat. In April of 1998, the Services sent USEPA a dr aft Biological Opinion that found that USEPA’s proposed rule would jeopardize fe derally listed species. After disc ussions with the USFWS and NMFS, the USEPA agreed to several change s in the final rule and USFWS and NMFS, in turn, issued a final Biological Opinion finding that USEP A’s action would not likely jeopard ize the continued existence of federally listed species. The agencies agreed that federally listed fish and wildlife species that are aquatic system foragers would be protected under future criteria and procedures for site-specific adjustments. To achieve these goals and as part of the remedy for these problems, the USEPA initiated an interagency project with the USFWS and the U.S. Geological Survey (USG S) to address issues of 1) a methodology for translation of a tissue guidelines to pr otective site-specific disso lved Se concentrations (implementation of tissue criteria); 2) inclusion of protection of w ildlife species (i.e., federally listed species) in regulatory methodologie s; and 3) site-specific criteri a development for the Bay-Delta (USEPA, 1999). A methodology for ecosystem-scale modeli ng of Se is now available (see Appendices A and B

, Luoma and Presser, 2009; Presser and Luoma, 2010). Analysis from this biodynamically-based methodology showed, in general, that:

• a crucial factor ultimately defining Se toxicity is the link between di ssolved and particulate phases at the base of the food web (i.e., K d 

### );

• collection of particulate material phases and anal ysis of their Se con centrations are key to representing the dynamics of the system;

• bioaccumulation in invertebrates is a major source of variability in Se exposure of predators within an ecosystem, although that variability can be explained by invertebrate physiology (i.e.,

TTF invertebrate 

### );

• TTF fish 

### is relatively constant over the range of species considered here; and

• Se concentrations are at least conserved and usually magnified at every step in a food web. Here, we specifically adapt this methodology to the conditions and food webs of the Bay-Delta and present ecosystem-scale Se modeling in support of fish and wildlife criteria development for the estuary.

5

# San Francisco Bay-Delta Estuary

Regulation

Habitats in California important to consider for site-specific Se criteria development include the Bay-Delta and its watersheds (Presser and Luoma, 2006) ( Figure 1 ). In 1992, USEPA found that the

utilization of the saltwater Se criteria for the Ba y-Delta would be inapprop riate and promulgated the current national chronic freshwater selenium criteria for the Bay-Delta (USEPA, 1992; 2000). USEPA also reserved the acute freshwater aquatic life cr iterion for Se (USEPA, 20 00). In doing so, USEPA disapproved the statewide Se objective for the Bay-Delt a on the basis that there was clear evidence that the objective would not protect the designated fish and wildlife uses (USEPA, 2000). For example, the California Department of Health Services had i ssued waterfowl Se consumption advisories and scientific studies had documented Se toxicity to fish and wildlife (USEPA, 2000; Presser and Luoma, 2006). The USEPA also re-stated its commitment to object to National Pollutant Discharge Elimination System (NPDES) permits issued for the estuary th at contained effluent limits based on objectives greater than the freshwat er criteria of 5 μg/L (four day aver age) and 20 μg/L (1 hour average).



## Setting

The Bay-Delta, the largest estuary on the west coast, has been described as the urbanized estuary
because of the extensive modification of its mars hlands and the hydrologic systems that feed it (Conomos et al., 1979; 1985; Nichols et al., 1986 ). Two major rivers, the southward flowing Sacramento and the northward flowing San Joaquin, join at the Delta, with seawater entering through the Golden Gate Bridge ( Figure 1 ). The generalized schematic of the estuary ( Figure 1 ) shows the

locations of:

• Sacramento River;

• San Joaquin River;

• Delta (nominally upstream of Chipps Island);

• North Bay (Suisun Bay, Carquinez Strait, San Pablo Bay);

• Central Bay;

• Pacific Ocean at the Golden Gate Bridge; and

• South Bay. The major portion of the estuary from the rivers to the Golden Gate Bridge is termed the Northern Reach. The North Bay and the Delta are emphasized he re as areas for criteria development. The South Bay is not a focus here. Although similar concepts apply, the South Bay can be modeled separately because it receives source inputs from a diffe rent watershed than the Northern Reach ( Figure 1 ). However, waters do exchange and similar estuarine pr ocesses, habitats, and inhabitants do occur within all segments of the estuary.



## Selenium Sources

Current major sources of Se to the Bay-Delta ( Figure 2) are:

• irrigation drainage from seleni ferous agricultural lands of the western San Joaquin Valley conveyed through the San Joaquin River; and

• oil refinery wastewaters from processing of sele niferous crude oils at North Bay refineries. Regulation of Se for oil refiners is occurring through wa ter quality Se criteria promulgated by USEPA for the Bay-Delta (USEPA, 1992; 2000) and limits on lo ads and concentrations enacted by the state in

6

### 1992 [San Francisco Bay Regional Water Quality Control (San Francisco Bay Board), 1992 a,b; 1993; 2010] ( Figure 3 ). The five refineries located in the No rth Bay and their discharge locations are: Chevron Refinery at Richmond, discharge to San Pa blo Bay; Martinez (Shell) Refinery at Martinez, discharge to Carquinez Strait; Tosco (Conoco Phillips) Re finery at Rodeo, discharge to San Pablo Bay; Tesoro Golden Eagle Refinery at Martinez, discha rge to Suisun Bay; Valero Refinery at Benicia, discharge to Suisun Bay. A compilation of re finery Se loads from 1986-2009 is shown in Table 1 (San Francisco Bay Board, 1992a,b; 1993; Lila Tang and Johnson Lam, San Francisco Bay Board, personal communication, 1999-2006; USEPA, 2010) and recent Se data are displayed in Appendix C, Figures

C1-C5 . Previous refinery mass emissions were reduced by 75% (cumulative reduction from baseline of 4,936 lbs during 1989-1991) (San Francisco Bay Boar d 1992a,b; 1993). Proposed load reductions were achieved in 1998 and since then, the co mbined Se load from the refiners has remained at approximately 1,200 pounds (lbs)/year. The target of 1,234 lbs/year wa s a balance between ecolo gical, technological,

and economic considerations. An iterative mass emissi ons strategy was used in lieu of site-specific water quality objectives because water-column Se concen trations were considered not predictive of Se bioaccumulation (San Francisco Bay Board, 1993). Daily water-column Se concentrations in effluents were as elevated as 300 μg/L before 1998, but allo wed daily maximum effluent limits now are within the range of 34-50 μg/L. Discharger’s outflows are de signed to achieve a mini mum initial dilution of 10:1, but the range of estimated initial dilutions is 15:1-200:1 (San Francisco Bay Board, 2009; 2010). Dilution credits of 8:1 and 10:1 are in-place, wi th an average daily flow range of 1.9-7.4 million gallons/day. The range of allowed averag e effluent Se limits is 0.85-2.0 lbs/day. Regulation of Se for the agricu ltural community of the Grassl and Drainage Area is occurring through the Grassland Bypass Project ( Figures 3 and 4 ). The project was initiated in 1996 and is for use

of the San Luis Drain and the tributaries of the San Joaquin River for discharge of agricultural drainage from approximately 100,000 acres of land [U.S. Bureau of Reclamation (USBR), 1995; 2001]. As noted below, the amount of agricultural Se load discharg ed to the Bay-Delta depends on the amount of San Joaquin River flow that is allowed to enter the Bay-Delta and how much is recycled back to the south (Presser and Luoma, 2006) ( Figure 2 ).

Historical and current Se loads from the Grassl and Bypass Project measur ed where the San Luis Drain discharges into a tributary of the San Joaquin River (i.e., Mud Slough) are shown in Figure 3

. The use agreement for the project was re-negot iated in 2001 and was to end in 2010 with zero discharge. However, the project did not meet its goals and is now being re-negotiated to continue through 2020 (USBR and San Luis and Delta-Mendot a Water Authority, 2009). Although dependent on water-year type, compliance with Se load targets gradually reduces the amount of Se allowed for discharge into the San Joaquin River ( Figure 4 ). For example, the Se load measured at the compliance point (i.e., the San Luis Drain at Mud Slough) was 7,096 lbs in 1998; 5,023 lbs in 2003; 4,286 lbs in 2005; 3,301 lbs in 2008; and 1,239 lbs in 2009 ( Figure 4 ). Imposition of more restrictive Se targets for the San Joaquin River is balanced by shifting a percentage of the gene rated annual drainage Se load to storage in groundwater aquifers and lands designated for disposal (S an Francisco Estuary Institute, 2004-2005). For example, drainage control activities resulted in storage of 4,200 lbs Se within the Grassland Drainage Area in 2005. For proposed targets from 2009-2019, wetter years allow greater discharge (e.g., 4,480 lbs Se/year during 2009-2014) than drier years ( Figure 4 ). Proposed targets continue to ramp down in the coming years with ultimate goals ranging from 150-600 lbs/year by 2019 ( Figure 4 ). The long-term ecological consequences of su ch a shift in environmental compartments and increased storage of Se within the existing Se rese rvoir in the San Joaquin Valley is currently under debate (Presser and Schwarzbach, 2008). However, data for the Grassland Bypass Project area show Se is accumulating to levels in bird eggs of black-nec ked stilt, American avocet, and killdeer that far

7

### exceed threshold Se concentrations for impairment of reproduction (San Fran cisco Estuary Institute, 2004-2005; H.T. Harvey and Associates, 2004-2009). Restoration of the San Joaquin River is pro ceeding under a comprehensive program with many environmental goals such as increasing flows in th e upper reaches of the river to re-establish salmon runs in the river (Natural Resources Defe nse Council and others, 1988, 1989, 1992, 1999; San Joaquin River Group, 2010). Also, regulation of salinity for th e San Joaquin River is taking place at Vernalis

and three locations interior to the southern Delt a (California State Water Resources Control Board, 1999). Few data are available to quantify a San Joaqui n River end-member Se concentration at the head of the estuary. Dissolved Se concentrations for the San Joaquin River aver aged 0.71 μg/L (range 0.40- 1.07 μg/L) at Vernalis during wet year and above normal conditions in 1998-1999 (Cutter and Cutter, 2004). Discharge of Se to the Sacramento River is unregulated. Again, few data are available to quantify a Sacramento end-member Se concentra tion at the head of the estuary. Dissolved Se concentrations in the Sacramento River averaged 0.07 μg/L (range 0.05-0.11 μg/L) at Freeport during wet year and above normal conditions in 1998-1999 (Cutter and Cutter, 2 004). Other unregulated sources of Se include 1) effluents from wastewater treatment plants a nd industries other than refineries; and 2) discharges from watersheds th at drain directly into the estuary. Restoration of the estuary also is underwa y. The Delta Regional Ecosystem Restoration Implementation Plan (DRERIP) is focusing on constr uction of conceptual mode ls that describe the processes, habitats, species, and stressors of aquatic environments of the estuary (http://www.dfg.ca.gov/delta/erpdeltaplan/ ). The models will be interconnected and used to help

evaluate future restoration actions.



## Hydrodynamic Connections

A current detailed Se budget or mass balance of Se as a function of source and conveyance is not available for the Bay-Delta. Riverine inputs as they mix with seawater and internal Se sources determine Se concentrations in the Bay. Seasonal and year-to-year variations in discharges from rivers, streams, and anthropogenic sources influence dissolved Se c oncentrations in the Delta and estuary (Presser and Luoma, 2006). The Sacramento and San Joaquin Rive rs are the main sources of inflow, with the Sacramento River being the dominant inflow under current management conditions. The Sacramento

River dilutes the more concentrat ed Se inputs from other sources.

Parameters critical in determin ing the balance of water and Se inputs for the Bay-Delta are:

• total river (Sacramento River and San Joaquin River) inflow;

• water diversions or exports (i.e., pumping at Trac y and Clifton Court Fore bay south to the DeltaMendota Canal and the Ca lifornia Aqueduct);

• proportion of the San Joaquin Rive r directly recycled south befo re entering the estuary; and

• total outflow of the estuary to the Pacifi c Ocean or Net Delta Outflow Index (NDOI). NDOI is essentially inflow minus demand (USBR, 2010) ( Figure 2 ). NDOI is related to residence time for freshwater in the Bay-Delta (Cutter and Cutter, 2004) and, hence, to processes that affect Se

transformations within flow seas ons of a water year and within types of water years (Presser and Luoma, 2006). Water years begin on October 1 st 

### and are classified here based on Sacramento Valley unimpaired runoff ( http://cdec.water.ca.gov/ cgi-progs/iodir/WSIHIST ). Maximum discharge from the rivers is during January-Februar y and minimum discharge is duri ng July through August (Conomos et al., 1979; 1985; Peterson et al ., 1985; Presser and Luoma, 2006). Flow, and thus freshwater re sidence time, vary dramatically during the year as water management and diversions take place (http://www.usbr.gov/mp/cvo/ ; Enright and Culberson, 2010)

8

### ( Figure 3 ). Processes such as phase transformation a nd uptake by prey depend on, to some extent, the hydrodynamics of the estuary (Meseck and Cutter, 2006; Presser and Luoma, 2006; Tetra Tech Incorporated, 2010). Residence time, seasonal period (l ow flow and high flow), and water year type (critically dry, dry, below normal, normal, above nor mal and wet) can be used to categorize modeling scenarios (see later discussion).



# Overview of Modeling

Used optimally, the modeling approach provided here is a tool to frame a site-specific ecological occurrence of Se exposure; quantify exposure within that ecosystem; and narrow uncertainties about how to protect it by understanding the specifics of the underlying system ecology, biogeochemistry, and hydrology (Luoma and Rainbow, 2005; Luoma and Presse r, 2009; Presser and Luoma, 2010). With this approach, it is possible to differentiate consum er species and their food webs in terms of bioaccumulative potential and predict overall ecol ogical risk. Specifically, modeling in support of development of wildlife Se criter ia for the Bay-Delta is through ad aptation of the San Francisco BayDelta Selenium Model (Luoma and Pre sser, 2000; Presser and Luoma, 2006) ( Figure 5 ) and the Ecosystem-Scale Selenium Model (Luoma and Presser, 2009; Presser and Luoma, 2010) ( Figure 6 ).

The linked factors that determine the effects of Se in ecosystems and the data needs for modeling and understanding these linkages are shown in Figure 6 . The organizing principle for the methodology is the progressive solution of a set of equations or models, each of which quan tifies a process important in Se exposure ( Figure 7 ). Table 2 compiles the generalized steps used to translate a predator tissue Se concentration guideline to a dissolved Se concentrati on. The ecotoxicology of Se and the specific effects

of Se on fish and birds are shown in Figure 8. Reproductive effects are key in Se’s actions, but chronic effects also are expressed. Modelin g and prediction thus enables quan tifying Se toxicity under different management or regulatory proposals. Modeling is used to quantify the environmental concentrations and conditions that would result from a pre-determined Se concentration in the tiss ues of a predator. Assuming the tissue guideline is generic for all fish or birds, the choice of the pred ator species in which to assess that concentration is still important because it determines the food web invertebrate species ( Figure 6 ). That specific predator’s feeding habits drive the choice of invertebrate, for which a species-specific transfer factor (i.e., TTF) connects an invertebra te Se concentration to a susp ended particulate material Se

concentration that is the source of food for the invertebrate. An envi ronmental partitioning factor (or a range of factors) for partitioning of Se betw een water and suspended particulate material (K d 

### ) feasible for that ecosystem is then used to determine the allowable water-column concentration, which is ultimately the concentration in that specific type of environment and food web that would result in the specified Se concentration in the predat or (i.e., the applied criterion). Thus, the allowable water column concentration can differ among environments; an outcome that reflects the real ities of nature. This biologically explicit approach also fo rces consideration of the desired uses and benefits in a watershed (i.e., which species of birds and fish are the most threatened by Se or are the most important to protect). To translate exposure into toxicity here, we employ species-at-risk fo r the Bay-Delta (e.g., sturgeon and salmonids) and their effect guidelines provi ded by the USFWS (see later discussion). Figure 2 illustrates some of the complexities that need to be addressed in developing a sitespecific approach for an estuary affected by several Se sources (i.e., internal oi l refinery and watershed agricultural drainage) and supporting di fferent food webs associated with a gradient of salinities. For example, agricultural Se loading is through the San Joaquin River into the De lta where food webs are modeled as aquatic insect-based. Yet, Se loading through the Delta affects the Bay and adds to oil refinery Se loads where food webs are modeled as C. amurensis -based. The North Bay, where C.

9

### amurensis is the dominant bivalve species and is a strong Se bioaccumulator, is the most affected by Se loading (Stewart et al., 2004; Presser and Luoma, 2006) ( Figure 2 ). Hence, overall, tracking and differentiation of Se sources is an important component of management for the estuary, especially as changes to the hydrologic configuration of the De lta (e.g., the amount of S acramento River and San Joaquin River allowed to enter the Ba y) are considered in the future. Figure 9 shows site-specific processes and paramete rs for the Bay-Delta and acts as a roadmap through the modeling process detailed in the sections below. The approach for the estuary is through specified food webs, locations, a nd flow seasons in modeling scenarios. Detailed model steps, parameters, and derivations ar e illustrated for a seaward C. amurensis food web and a landward aquatic insect food web ( Figure 9) . A spatial component for modeling is ba sed on a salinity gradient across the estuary or on a particular portion of the estuary (i.e., Suisun Bay). A temporal component for modeling addresses the effect of water-year type and within that type, a flow season (low flow, nominally June through November; high flow, December through May) . Addition of a temporal component based on residence time further delineates a fine-scale approac h, as do the additions of details of species life cycles and habitat use. The more detailed the modeli ng choices or approach, the less uncertainty there is in the forecasts. As illustrated ( Figure 9 ), the main considerations used here for a site-specific BayDelta approach are:

• species-specific effects guidelines to quantify regulatory concerns;

• food webs to define the choice of prey and predator pairs (i.e., TTFs);

• salinity to constrain locations and thus potential pathways for loading, transformation, and

exposure;

• flow seasons to connect to hydrology, pred ator life cycles, and habitat use; and

• residence time to further constrain tr ansformation and biodynamic processes. Thus, a formalized approach captures both mathema tical components and exposure gradients over time. A focused area approach would enab le regulatory consideration of sources or impacted downstream

areas.



# Fish and Wildlife

Species at Risk

The USFWS (2008) provided a comprehensive list of species for evaluation of Se exposure risk in the Bay-Delta ( Table 3 ). They stated that 1) aquatic dependent species feeding dire ctly in the benthic food web of the Bay-Delta were considered at greate r risk to Se exposures th an those feeding in the pelagic/planktonic food web; and 2) exposure assessment was based on a) dependence on a benthic food web, b) population status, and c) sensitivity to Se. Th e list included 27 bird species, 15 fish species, the salt marsh harvest mouse, the giant garter snak e, and the Dungeness cra b. The species listed in Table 3 then were narrowed to provide a list of species considered most at risk ( Table 4 ). Species most at risk from Se in the Bay-Delta and their status (federal/state) include:

• bald eagle ( Haliaeetus leucocephalus ): delisted, U.S. Migratory Bi rd Treaty Act (MBTA), Bald and Golden Eagle Protection Act (BGEPA)/protected, endangered;

• California clapper rail ( Rallus longirostris obsoletus ): endangered/prote cted, endangered;

• greater scaup ( Aythya marila ): MBTA/none;

• lesser scaup ( Aythya affinis ): MBTA/none;

• white-winged scoter ( Melanitta fusca ): MBTA/none;

• surf scoter ( Melanitta perspicillata ): MBTA/none;

10

### • black scoter ( Melanitta nigra ): MBTA/none;

• Chinook salmon ( Oncorhynchus tshawytscha ): endangered, threatened/endangered, threatened;

• steelhead ( Oncorhynchus mykiss ): threatened/none;

• green sturgeon ( Acipenser medirostris ): threatened/concern, fishing prohibited;

• white sturgeon ( Acipenser transmontanus ): none/limited fishing;

• Sacramento splittail ( Pogonichthys macrolepidotus ): concern/threatened; and

• giant garter snake ( Thamnophis gigas ): threatened/threatened. Although its diet does not incl ude bivalves, Delta smelt ( Hypomesus transpacificus ) is a threatened species that is endemic to the estuar y and, hence, is considered by the USFWS (2008) as threatened overall. A reptile species (USFWS, 20 06, 2009a) and an invertebrate species USFWS (2008) also are documented as important inhabitants of the estuary. The threatened giant garter snake ( Thamnophis gigas ) inhabits the Delta Basin and wate rshed valleys (USFWS and NMFS, 1998; amended 2000; USFWS, 2006). This species is an aquatic predator that feeds on small fish and larval/sub-adult frogs (USFWS, 2009a). The estuary is a nursery for the ocean-breeding, bottom-feeding Dungeness crab ( Cancer magister ). This species consumes C. amurensis , but invertebrates, in general, are known to have lower toxicologi cal sensitivity (Presser and Luoma, 2006). However, Dungeness crab may serve to further biomagnify Se by providing an additional trophic transfer step (i.e., C. amurensis to Dungeness crab to large predator fish or mammals).



## Effects and Effect Levels

Effects of concern for Se in fish and wildlife ( Figure 8 ) are:

• reproductive effects

o birds: hatchability, teratogenesis, chick survival and growth; and

o fish: deformity, larva and fry survival and growth

• chronic effects. Species-specific effect models deve loped as part of the DRERIP pr ocess are shown for diving ducks, sturgeon, and salmonids inhabiting the Bay-Delta ( Figure 10 , adapted from DRERIP Selenium Model, Presser, et al., in review). Th ese effects can lead to changes within ecosystems including population reductions, loss of species or individuals, and community changes. The USFWS (2009b) provided Se effect guideline s and associated leve ls of protection (e.g., EC10 for birds is the Se concentration in eggs asso ciated with a 10% reduc tion in hatchability) for predator species at risk in the estuary ba sed on several different toxicity endpoints ( Table 5 ). [Note: Technically, the term EC10 does not apply to quan titative reproductive performance endpoints. The proper term to apply to quantita tive reproductive performance endpoints such as 10% reduction in egg hatchability is IC10 (or 10% Inhibition Concentra tion). However, the subtle conceptual distinction

between these two technical terms has not been re cognized in the avian toxi cology literature for Se; therefore, we conform with the common use of the te rm EC10 with reference to avian egg hatchability and simply note here that we are aware of this issue (see Environment Canada, 2005)]. Data from the study of toxicity in mallards is us ed when modeling clam-eating bird species in the estuary because these are the most comprehensive studies available. The effect guideline ranges derived for tissue and diet in dry wei ght (dw) are:

• mallard (egg 2.8-7.7; diet 2.3-5.3 μg/g dw);

• adult female white sturgeon (whole-body 7.0-8.1 μg/g dw; diet 26-32 μg/g dw);

• juvenile white sturgeon (diet 0.95-1.6 μg/g dw);

• juvenile Chinook salmon (whole-body 1.0- 1.8 μg/g dw; diet 1.5-2.7 μg/g dw);

11

### • juvenile rainbow trout (w hole-body 1.3-2.2 μg/g dw; diet 2.4-5.0 μg/g dw); and

• larval rainbow trout (diet 0.31-1.6 μg/g dw).

Table 6 gives generic guidelines for Se effect con centrations also develo ped by the USFWS (USFWS, 2005; 2009b; Skorupa, et al., 2004; Skorupa, 2008). A subs et of the effects guidelines and associated levels of protection shown in Tables 5 and 6 are used in modeling to pred ict toxicity under different regulatory proposals. Emphasis here is on illustration of Se exposure for juveni le white sturgeon, diving ducks as represented by the malla rd, and juvenile Chinook salmon.



## Estuary Food Web and Exposure Models

Conceptual models for the estuary show clam-b ased food webs for seaward sites and aquatic

insect-based food webs for landward sites ( Figures 2 and 11 ). The C. amurensis -based food web has been of major importance to the es tuary since the clam’s invasion in 1986 (Nichols et al., 1990). Fish and bird species that consume C. amurensis are shown ( Figure 11 ). A Dungeness crab food web also is

shown because the diet of the crab includes C. amurensis . However, little Se-specific information is known for this crab. The bald eag le food web shows the complexity of a high order trophic level predator. USFWS suggested that the bald eagle would be representative of a resident high order predator for the purposes of modeling (USFWS, 2008). Chinook salmon and steelhead, along with the California black rail, are modeled for landward sites. Invertebrate prey items, in addition to aquatic insects, that may be of importance at landward sites also are listed. Environmental partitioning factors (K d 

### s) and Trophic Transfer Factors (TTFs) used to quantify the biotransfer of Se through food webs of the estuary

also are shown in Figure 11 . The development of these factors is shown in detail later (see Derivation of Site-Specific Model Components section). A diagram across flow seasons illustrates exposur e media (water, suspended particulate material, and clams) and the potential for exposure based on th e life cycles and habitat-use of predators in the estuary ( Figure 12) . Migratory and resident bird and fish species are illustrated. Knowing the details of a predator’s location during critical life stages for Se effects allows correlating trends in diet and exposure that occur in the estuary. This knowledge, in turn, sets choices in modeling scenarios. Combining food web, life cycle, ha bitat use, and effects data ( Figures 10, 11, and 12 ) results in BayDelta specific information for criteria development. The probable critical life stages of predators most at risk for Se effects as given in USFWS (2008) are:

• bald eagle and California clapper rail: adult female (egg laying);

• scoter and scaup: adult ma le and female (migration);

• Chinook salmon and steelhead: migrating/rearing juvenile; and

• green and white sturgeon and Sacramento splittail: juvenile or adult female. The estimated maximum percentage of diet that is clam-based for each predator most at risk (USFWS, 2008) ( Figure 11 ) is:

• lesser scaup 96%;

• surf scoter 86%;

• greater scaup 81%;

• black scoter 80%;

• white-winged scoter 75%;

• California clapper rail 64%;

• white sturgeon and assumed for green sturgeon 41%;

• Sacramento splittail 34%; and

12

### • bald eagle 23%. Specifically, migratory bird species such as surf scoter and greater and le sser scaup are at risk based on their consumption of a clam-based diet (75-96%) ( Figure 11 ). Overwintering populations of diving ducks in the estuary can r each 50-92% of migrating populations (Wainwright-De La Cruz et al., 2008; Poulton et al., 2002) ( Figure 11 ). Diving ducks arrive in the estu ary when Se concentrations are elevated ( Figure 11 ). The ducks eat voraciously as they stag e for migration in the spring, which puts

them at risk from chronic effects that influence many facets of their migratory and breeding behavior ( Figures 7 and 10 ). Surf scoters during overwintering move throughout the North Bay and thus can be exposed to different clam species (i.e., V. philippinarum in the Central Bay) (Wainwright-De La Cruz, 2008). Food webs for clapper rails with an estima ted 64% clam-based diet present opportunities for modeling of reproductive effects for resident species ( Figures 4 and 5 ). White and green sturgeon consume a diet th at is approximately 41% clams (USFWS, 2008). Green sturgeon is a federally listed endangered speci es that spends more time migrating than white sturgeon. Although white sturgeon migrate upstream to spawn, they are described as semi-anadromous because they spend a substantial am ount of their life in the estuary. White and green sturgeon are very long-lived (50-100 years) and have a tw o year internal egg maturation that is conducive to Se loading of eggs ( Figure 12 ) (Linville, 2006). Sacramento splittail is a federally listed speci es of concern that consumes a diet of approximately 34% clams (USFWS, 2008). This species spawns both in the upper Delta and the estuary and is known to inhabit Suisun Bay. The USFWS (2008) stated that although the diets of salmon and steelhead trout are not known to be clam-based, these species may still be at risk from Se because of their greater toxicological sensitivity to Se. Migratory salmon and trout are known to be in the Delta during migration upstream and emigration to the ocean ( Figure 12 ). Steelhead trout may be best described as nearly year-around spawners (i.e., juveniles may hold ov er for many months to a year and may not even emigrate to the ocean at all) (USFWS, 2008). Popul ation numbers for the Delta smelt are alarmingly low, and thus the USFWS concluded that this spec ies is particularly vulnerabl e to any adverse effect. The giant garter snake is a fede rally listed species that is know n to inhabit the Delta (USFWS and NMFS, 1998; amended 2000; USFWS, 2006; 2009a). The species is an aquatic predator that feeds on small fish and larval/sub-adult frogs. Modeling for this species of reptile is not included here, but future modeling could include a food we b specific to the giant garter snake.



# Ecosystem-Scale Model Components

Partitioning and Transformation

Profiles of dissolved and suspende d particulate material Se con centrations across the Bay-Delta (Cutter and Cutter, 2004, Doblin et al., 2006; Lu cas and Stewart, 2007) initiate ecosystem-scale modeling by developing a detailed un derstanding of the re lationship of dissolve d and particulate Se concentrations at specific la ndward and seaward locations ( Figure 2 ). Consideration of the transformations of dissolved Se phases to particulate Se phases is critical to quantifying the entrance of Se into food webs ( Figure 13 ). The environmental partitioning factor K d 

### is used here to operationally

characterize the bioconcentra tion of dissolved Se into the base of the food web ( Figures 7 and 13 ). K d 

### is environment specific and is the ratio of the partic ulate material Se concentration to the dissolved Se concentration. The specific equation is

K d 

### = (C particulate material 

### , μg/kg dw) ÷ (C water 

### , μg/L) (1)

13

### Note that particulate Se concentrat ions are usually expressed as μg/g dw. These units must be converted to μg/kg dw to make the particulate concentration comparable to the water concentration. Dissolved Se is the preferred parameter to m easure and model, although total water column Se (i.e., unfiltered Se) can be speci fied in the derivation of K d 

### for modeling to accommodate using existing

datasets. Measurement of a total water column Se c oncentration would include a fraction attributable to digested suspended material Se. Specifically for Ba y-Delta profiles or transects, dissolved Se samples

were collected and dissolved Se concentra tions are available (Cutter and Cutter, 2004). A particulate material Se concentration is the other component of K d 

### to measure and model ( Figure 13 ). The base of the food web, as sampled in the environment, can include phytoplankton, periphyton, detritus, inorganic suspe nded material, biofilm, sediment a nd/or attached vascular plants (Presser and Luoma, 2010). For simplic ity in our discussion here, we define this mixture of living and non-living entities as particulate material . Specifically for Bay-Delta profiles and transects, suspended particulate material samples were collected and suspended particulate material Se concentrations are available (Doblin et al., 2006).

As illustrated in Figure 13 , K d 

### represents phase transformation in the system (i.e., the efficiency

with which dissolved Se is convert ed to particulate material Se). Phase transformation reactions from dissolved to particulate material Se are of toxicological significance b ecause particulate material Se is the primary form through which Se enters food we bs (Luoma et al, 1992; Presser and Luoma, 2010; Stewart et al., 2010). The different bi ogeochemical transformation reactions result in different forms of Se in particulate material: organo-Se, elemental Se, or adsorbed Se ( Figure 13 ). The resulting particulate Se speciation, in tur n, affects the bioavailability of Se to invertebrates depending upon how an invertebrate “samples” the complex water/sediment/particulate milieu that composes its environment. Collection of a complete dataset of particulate phases and their Se concentrations and speciation can greatly aid in quantifying the biogeochemical dyna mics of an estuarine system and, hence, the prediction of prey and predator Se concentrations. Dissolved Se species that are present will influence the type of phase transformation reaction that creates particulate Se. Examples of types of reactions and the particulate species they produce ( Figure 13 ) include: 1) uptake by plants and phytoplankton of selenate, selenite or dissolved organo-Se and reduction to particulate organo-Se by assimilato ry reduction (e.g., Sandholm et al., 1973; Riedel et al., 1996; Wang and Dei, 1999; Fournier et al., 2006); 2) sequestration of selenate into sediments as particulate elemental Se by dissimilatory biogeo chemical reduction (e.g., Oremland et al., 1989); 3) adsorption as co-precipitated selenate or selenite through reactions with pa rticle surfaces; and 4) recycling of particulate phases back into water as detritus after organisms die and decay (e.g., Velinsky and Cutter, 1991; Reinfelder and Fish er, 1991; Zhang and Moore, 1996). Sele nate is the least reactive of the three forms of Se and its upt ake by plants is slow. If all ot her conditions are the same, K d 

### will

increase as selenite and dissolved organo-Se concentrations increase (even if that increase is small). Experimental data support this conclusion. Calculations using data from laboratory microcosms and experimental ponds show speciation-specific K d 

### s of 140-493 where selenate is the dominant form; 720- 2,800 when an elevated proportion of selenite exists; and 12,197-36,300 for 100% dissolved selenomethionine uptake into algae or periphyton (Besse r et al., 1989; Graham et al., 1992; Kiffney and Knight, 1990). Measurement of suspended particulate material Se concentrations in the Bay-Delta, therefore, is important for initiating modeling, understanding the ex tent of biological transformations, and developing accuracy within the model. Data collection in site-specific field s ituations for particulate phases can include benthic or su spended phytoplankton, microbial biom ass, detritus, biofilms, and nonliving organic materials associ ated with fine-grained (<100 μ m) surficial sediment (Luoma et al.,

14

### 1992). Analysis of particulate Se and particulate Se speciation of each phase collected would account for partitioning of Se in different media and elucidate how K d 

### may be best defined to represent the dynamic conditions present in the estuary. If few data are available to characterize particulate phases or data are inconsistent as to a particle type th at can be compared among locations, the greater the uncertainty in any predictions. Further informati on on choice of particulate material type, sample collection in aquatic systems, and modeling limitations are given in Presser and Luoma (2010). For example, K d 

### can be influenced by the type of particulat e material collected where a hierarchy of Se concentrations exist within an ecosystem (e.g., 2.4 μg /g in sediment; 3.2 μg/g biofilm, and 5.5 μg/g for filamentous algae). Using these concentrations with a field-measured dissolve d Se concentration would yield a range of K d 

### s that reflects the complexities of the system. In this regard, collection of one consistent type of material is an option, with bed sediments (especially if the sediments vary from sand to fine-grained) among the samples being th e least desirable choice for calculating K d 

### ,.


## Biodynamics: Invertebrates, Fish, and Birds

Kinetic bioaccumulation models (i.e., biodynami c models, Luoma and Fisher, 1997; Luoma and Rainbow, 2005) account for the now well-established pr inciple that Se bioaccumulates in food webs principally through dietary exposure. Tissue Se attributable to disso lved exposure makes up less than

5% of overall tissue Se in almost all circumst ances (Fowler and Benayoun, 1976; Luoma et al., 1992; Roditi and Fisher, 1999; Wang and Fisher, 1999; Wang 2002; Schlekat et al., 2004; Lee et al., 2006). Biodynamic modeling ( Figures 6 and 8 ) shows that the extent of Se bioaccumulation (the concentration achieved by the organism) is driven by physiological pr ocesses specific to each species (Reinfelder et al., 1998; Wang 2002; Baines et al., 2002; Stewart et al., 2004). Biodyna mic models have the further advantage of providing a basis for deriving a simplified measure of th e linkage between trophic levels: TTFs ( Figure 7) . For each species, a TTF can be derived from either experimental studies or field observations, where the TTF defines th e relationship between Se concentr ations in an animal and in its food ( Figure 7 ). Experimental derivation of TTFs is based upon the capability of a species to accumulate Se from dietary exposure as expressed in the bi odynamic equation (Luoma and Rainbow, 2005):

dC species 

### /dt = [(AE) (IR) (C food 

### )] - (k

e 

### +k g 

### )(C species 

### ) (2)

where C is the contaminant concentration in the animal (μg/g dw), t is the time of exposure in days (d); AE is the assimilation efficiency from ingested particle s (%); IR is the ingestion rate of particles (g/g/d);

C food 

### is the contaminant concentration in ingested particles (μg/g dw); k

e 

### is the efflux rate constant (/d)

that describes Se excretion or loss from the animal; and k g 

### is the growth rate c onstant (/d). The equation shows that key determinants of Se bioaccumulation ar e the ingestion rate of the animal, the efficiency with which Se is assimilated from food, and the rate constant describing Se turnover or loss from the tissues of the animal (Luoma and Rainbow, 2005) . Experimental protocol s for measuring such parameters as AE, IR, k

e 

### are now well developed (Wang et al., 1996; Luoma and Rainbow, 2005). In the absence of rapid growth, a simplif ied, resolved biodynamic exposure equation for calculating a Se concentrati on in an invertebrate is

C invertebrate 

### = [(AE) (IR)(C particulate 

### )] ÷ [k

e 

### ] (3)

where C food 

### is defined as C particulate



### . For modeling, these physiological parameters can be combined to calculate a TTF invertebrate 

### , which characterizes the potential for each invertebrate species to bioaccumulate Se. TTF invertebrate 

### is defined as

TTF invertebrate 

### = [(AE) (IR)] ÷ k

e 

### (4) Similarly, foodweb biodynamic equations for fish and birds are

C fish or bird 

### = [(AE) (IR) (C invertebrate 

### )] ÷ k

e 

### and TTF fish or bird 

### = [(AE) (IR)] ÷ k

e 

### (5) and (6)

15

### When laboratory data are not available, a field TTF invertebrate 

### can be defined from matched datasets (in dw or converted to dw) of particulate and inverteb rate Se concentrations as

TTF invertebrate 

### = C invertebrate 

### ÷ C particulate 

### (7) A field derived species-specific TTF fish 

### is defined as

TTF fish 

### = C fish 

### ÷ C invertebrate 

### (8)

where C invertebrate 

### is for a known prey species, C fish 

### is reported as muscle or whole-body tissue, and both Se concentrations are reported in μg/g dw. If necessary, the m odeling approach can represent a diet that includes a mixed proportion of prey in the diet through use of the equation

C fish 

### = (TTF fish 

### ) [(C invertebrate a 

### ) (prey fraction) + (C invertebrate b 

### ) (prey fraction) + (C invertebrate c 

### ) (prey fraction)] (9) Once TTFs are know, invertebrate Se concentrations are calculated from particulate material Se concentrations through use of the equation

C invertebrate 

### = (TTF invertebrate 

### ) (C particulate 

### ) (10) Equations are combined to repr esent step-wise bioaccumulation fr om particulate material through

invertebrate to fish as

C fish 

### = (TTF invertebrate 

### ) (C particulate 

### ) (TTF fish 

### ) (11) Similarly for birds, the combined equation is

C bird 

### = (TTF invertebrate 

### ) (C particulate 

### ) (TTF bird 

### ) (12) Modeling can accommodate longer food webs that cont ain more than one higher trophic level consumer (e.g., forage fish being eaten by pred atory fish) by incorporating addi tional TTFs. One equation for this type of example is

C predator fish 

### = (TTF invertebrate 

### ) (C particulate 

### ) (TTF forage fish 

### ) (TTF predator fish 

### ) (13) Modeling for bird tissue also can represent Se transfer through longe r or more complex food webs (e.g.,

TTFs for invertebrate to fish and fish to birds) as

C bird 

### = (TTF invertebrate 

### ) (C particulate 

### ) (TTF fish 

### ) (TTF bird 

### ) (14) Variability or uncertainty in pro cesses that determine AEs or IRs can be directly accounted for in sensitivity analysis (Wang et al, 1996). That is accomplished by considering the range in the experimental observations for the specific animal in the model. Fi eld derived factors require some knowledge of feeding habits and depend upon available data for that species. Laboratory and field factors for a species can be compar ed and refined to improve levels of certainty in modeling. Hence, physiological TTFs derived from kinetic experiments for a species and ecological TTFs derived either from data for a species across different field sites (global) or from one site (site-specific) are of value in modeling and understanding an ecosystem. TTFs are species-specific because of the influence of the physiology of the animal. They may vary to some extent as a function of the concentrati on in food or if AE or IR vary (Besser et al., 1993; Luoma and Rainbow, 2005). The approach here leads to consideration of a single TTF to quantify trophic transfer from diet to tissue for each specie s illustrated in modeling. If enough data are available to develop diet-tissue concentration regressions specific to inhabitants of an estuary or watershed, then use of those regressions would provide more detailed relationsh ips than single determinations. Additionally, in nature, if it is a ssumed that organisms regulate a c onstant minimum concentration of Se, then the observed TTF will increase when the con centration in food is insufficient to maintain the regulated concentration (Beckon et al., 2008). Datasets from which non-site-specific TTFs were derived for use in modeling here were collected from site s exposed to Se contamination and identified as problematic because of Se bioaccu mulation (Presser and Luoma, 2010). However, discretion was used when considering datasets from extremely contam inated sites (e.g., Kesterson). The relatively small

16

### variation of TTF within taxonomically similar anim als is evidence that th ese potential sources of uncertainty may be minimal in terms of biodynami c kinetics variations (Presser and Luoma, 2010).



# Available Data

Table 7 lists available data for the Bay-Delta. Comp rehensive data collection to evaluate Se concentrations in the Bay-Delta began in 1986. Tr ansects of the Bay-Delta from November 1997 to November 1999 provide spatially and temporally matched datasets for samples collected at one meter below the surface (Cutter and Cutter, 2004; Doblin et al., 2006). The parameters measured for these

datasets were:

• salinity;

• dissolved Se concentration;

• dissolved Se speciation;

• suspended particulate material Se concentration;

• suspended particulate ma terial Se speciation;

• amount of total suspe nded material; and

• particulate carbon (C) concentration. Transects during July, 2000 to January, 2004 char acterize the area mainly from Rio Vista and Stockton to Benicia near the Carquin ez Strait (Lucas and Stewart, 2007) ( Figure 1 ). These more

landward transects were limited to:

• dissolved Se;

• dissolved Se speciation; and

• suspended particulate mate rial Se concentration. Not all datasets are complete, so graphed profiles shown later may vary somewhat because matched pairs for each combination of data (e.g., dissolved Se and suspended partic ulate material Se in comparison to percentage of suspended particulat e organo-Se) across the salin ity gradient were not always available. The matched data pairs for dissolved Se concentr ations and suspended particulate material Se concentrations used here are for tidally-influenc ed sites. Doblin et al. (2006) hydrodynamically categorized (i.e., binned), for the conditions of each transect, the most landw ard suspended particulate material Se samples as the Delta . These Delta sites are nominally ups tream of Chipps Island (Doblin et al., 2006) and, thus, these site s are tidally influenced ( Figure 1 ). Therefore, our site-specific derivation

does not address Se concentrations in end-members such as the Sacramento and San Joaquin Rivers (i.e., Sacramento River at Freeport and the San Joaquin Rive r at Vernalis). The methodology for collection and analysis of di ssolved and suspended particulate material Se samples is described in Doblin et al. (2006). Methods for determin ing particulate Se can result in presentation of data either as μg/L or μg/g. For work here, direct determination of particulate Se concentrations as μg/g dw is preferable. However, a particulate Se concentration in μg/L can be converted to μg/g dw through division by the ava ilable matched data on amount of total suspended material (in mg/L). Because of the limited data av ailable for characterization of the Bay-Delta and the data needs of modeling for criteria development, all necessary conversions were made in order to make full use of available data. Future monitoring of th e Bay-Delta should consider collection of suspended particulate material Se concentrat ion data as μg/g dw. All solids ar e expressed in dry weight (dw). Other types of datasets are available for the Bay-Delta ( Table 7 ). Meseck (2002) collected sedimentary Se samples from box-cores and extracte d pore waters from Bay-De lta locations from 1997- 1999. Sedimentary Se samples (sediment cores at 2-4 meter-depth of water) also were collected in 1998

17

### from six locations in the Delta (M. Doblin, pe rsonal communication March, 2009) and in 2000 from

three locations in the Delta (Lucas and Stewart, 2007) ( Table 7 ). Datasets for Se concentrations in specif ic predators and food webs (e.g., the clam, C. amurensis

, white sturgeon, surf scoter) also are listed ( Table 7 ), but few current, matched datasets are available to provide comprehensive documentation of food webs. Fifteen years of monitoring data in the northern estuary for Se in C. amurensis was recently published (Kleckner et al ., 2010) and is illustrated later in the report. Appendix D ( Tables D1-D5) gives a compilation of some of the available food web Se data including for invertebrates, fish, and birds. Becau se there are minimal data available, data are generalized in model validations; ho wever, data used in validation s cenarios and illustrations are as closely matched as possible.



# Application of Eco system-Scale Methodology

Estuarine Approaches

A methodology based on a salinity gradient across the Bay-Delta, from the tidally-influenced landward sites above Chipps Island to seaward sites ne ar the Pacific Ocean at the Golden Gate Bridge (Cutter and Cutter, 2004; Doblin et al., 2006; Lucas a nd Stewart, 2007) is used here to provide locationspecific modeling for the estuary (Presser and Luoma, 2006). Given a specific food web and Se tissue guideline, the approach uses salinity-specific data to derive K d 

### s and TTFs and to predict allowable dissolved Se concentrations at each salinity measur ed across an estuary profile . This gradient modeling approach illustrates the variability across the es tuary in terms of transformations, bioaccumulative potential, and protective dissolved allowable Se concentrations ( Figures 2 and 9 ). A generalized approach (i.e., using a mean K d 

### from a transect) would add uncer tainty to the derivations and predictions because of, for example, inclusion of sa mples from freshwater a nd ocean interfaces. Mean

Se concentrations for transects can be used as a way to compare datase ts through time, but that approach may be of limited applicability. Other statistical para meters or analysis techni ques also could be used (i.e., median, 75 th 

### percentile value) for comparison of estuarine conditions. A second modeling approach, a focused location approach, uses compartmentalized data for Suisun Bay and Carquinez Strait (Dob lin et al., 2006) to illustrate how the Bay-Delta can be divided into segments for explicit regulatory consideration ( Figure 14 ). Doblin et al. (2006) grouped particulate material Se samples as a function of salinity into f our embayments: 1) Central Bay; 4) San Pablo Bay; 3) Carquinez Strait-Suis un Bay; and 4) Delta. Figure 14 shows the range of suspended particulate material Se concentrations within the compartmentali zed segments and the patter ns within the range of illustrated flow conditions. Focusing on transect sa mples that specifically re present Carquinez StraitSuisun Bay allows modeling and prediction for the locali zed area most affected by internal oil refinery Se sources and for time periods of specified flow c onditions. Again, a mean or other statistical measure for each transect, but within the Suisun Bay-Carqui nez Strait segment, can be used to characterize conditions through time, but thus at a more narrowly defined site. Modeling that specifies 1) water- year type and flow season; or 2) freshwater residence time

further narrows uncertaintie s within the estuarine approaches by addition of a temporal component. Modeling of the Bay-Delta based on hydrologic season or residence time also enables connection to hydrodynamic cycles, prey/predator exposure, and habitat-use ( Figure 12 ) in developing site-specific allowable Se concentrations. Specific dates, freshw ater residence times, wa ter-year types, and flow seasons for transects of the Bay-Delta (Cutte r and Cutter, 2004; Dob lin et al., 2006) are:

• November 5-6, 1997, 68 days, wet year, low flow season;

• June 16-17, 1998, 11 days, wet year (El Niño), high flow season;

18

### • October 7-8, 1998, 22 days, wet year, low flow season;

• April 13-14, 1999, 16 days, wet year, and high flow season; and

• November 4-5, 1999, 70 days, above normal year, low flow season. The conditions in the estuary duri ng these transects and the proportion of the recent hist orical record represented by these five transe cts are given context by showing the sampling dates within the variability afforded by NDOI for the period 1996-2009 ( Figure 15 ). During an 11-day residence time in June, 1998, NDOI is 73,732 cfs as a daily average/ month, but during a 70-day residence time in November, 1999, NDOI is 6,951 cfs as a daily average/month. Thus, consideration of a temporal component in modeling may be imperative for applying predictions here to condi tions in the estuary in

the future.



## Dissolved and Suspended Particulate Ma terial Selenium Profiles for Modeling

Modeling and predictions for criteria development for a C. amurensis food web uses Se data from the Bay-Delta transects listed above (November, 1997; June and October, 1998; April and November, 1999) ( Figures 16 and 17 ). Transect sampling for the Bay-Delta included 19 to 20 sites per transect, except for the June 1998 transect, which incl uded 13 sites. Conditions represented are all wet or above normal years, with sampling in June, 199 8 and April, 1999 being duri ng high flow seasons and October, 1998, November, 1997, and November , 1999 being during low flow seasons ( Figure 15 ). Salinity at the Golden Gate Bridge varies from 24.8 to 32.5 psu for the five transects. Distinctive profiles for dissolved Se concentrations from June 1998 shows conditions in the Bay-Delta when flows were exceptionally high because of extrem ely wet conditions related to El Niño ( Figures 16 ). Approximately 70% of the data for this transect was obtained at sites with salin ities < 5 psu. In contrast, profiles for residence times of 68 to 70 days in N ovember, 1997 and 1999 show a span of salinities up to approximately 32 psu. Specifically, Figure 16 shows dissolved Se concentrati ons across the estuary during a progression of residence times (11-70 days) from November, 1997 to November, 1999. The transect for November, 1997 is separated out from the main analysis here because of 1) decreasing refinery Se loads as proposed reductions took place ( Table 1; Figure 3 ); and 2) a noticeably higher dissolved Se concentration-profile across the estu ary. The range of dissolved Se concentrations is narrowly defined as 0.070-0.320 μg/L for all Bay-Delta transects ( Table 8 ). The range of suspended particulate Se concen trations (0.15-2.2 μg/g dw) for all Bay-Delta transects is not as narrowly defi ned as that for dissolved Se ( Figure 17; Table 8) . The patterns of particulate enrichment vary with specified flow condition (e.g., April, 199 9; November, 1999). The

variation at freshwater and ocean interfaces would contribute differently (or may contribute substantially) to a calculated overa ll mean condition. Also depicted is the variation in calculated K d 

### s across the estuary. These K d 

### s will be used later as critical location-specific inputs for ecosystem modeling. A subset of dissolved and suspe nded particulate material Se con centrations is developed using the samples defined as Suisun Bay-Carquinez Strait in Figure 14 (Cutter and Cutter, 2004; Doblin et al., 2006) ( Table 9 ). The range of dissolved Se concentrati ons is from 0.076-0.215 μg/L and the range of suspended particulate material Se concentrations is 0.15-1.0 μg/g dw. Profiles of dissolved and suspended particulate Se concentrations also are derived from more limited transects of the estuary from Rio Vi sta and Stockton to Benicia during 2003 and 2004 ( Figure

18 ). Four transects (January, April, and October, 2003; January, 2004) are used to model an aquatic insect food web. Specific dates, water-year types, a nd flow seasons for transects (Lucas and Stewart, 2007) are:

19

### • January 22, 2003, above normal year, high flow season;

• April 22-23, 2003, above normal year, high flow season;

• October 10, 2003, below normal year, low flow season; and

• January 15, 2004 below normal year, high flow season. As previously noted, samples for these transects were taken as part of work defining processes in the Delta (Lucas and Stewart, 2007), but sampling was exte nded to some seaward lo cations in the estuary (i.e., near Benicia). NDOI (daily average per month) varies from to 4,350 to 50,847 cfs over the range of transects, with October, 2003 representing a belo w normal year-low flow condition. The range of dissolved Se concentrations is 0.068-1.01 μg/L and the range of suspended pa rticulate material Se concentrations is 0.23-1.5 μg/g dw ( Table 10 ).



## Dissolved and Suspended Particulat e Material Selenium Speciation

Selenium speciation in source discharges and wi thin the gradient of the estuary itself are important in quantifying the effici ency of transformations from di ssolved Se to particulate Se ( Figure

2 ). Profiles of dissolved Se speci ation across the sa linity gradient for September, 1986 and November, 1997 show that the percentages of dissolved selenite generally have decreased over time (Cutter, 1989; Cutter and Cutter, 2004) ( Figure 19 ). During the period 1992-1998, new treatment technologies were put into place that were designed to reduce the amount of dissolved selenite in the effluent (San Francisco Bay Board, 1992a,b; 1993). Othe r factors to consider in broad comparisons such as these, are that the salinity for Carquinez Strait near the refineries du ring November, 1997 ranged from approximately 12 to 19 psu (Doblin et al., 2006) a nd that the residence time was 24 days during the 1986 transect and 70 days during the 1997 transect. Figure 20 shows profiles across the Ba y-Delta of suspended partic ulate material organo-Se concentrations as the percentage of the total of the three suspende d particulate material Se species analyzed [(i.e., organo-Se, elemental Se , and inorganic Se (adsorbed selena te and selenite), Doblin et al., 2006]. The patterns of organo-Se pa rticulate enrichment identified here serve as the basis for quantifying the effects of transformations to particulate mate rial Se (i.e., K d 

### ) and the assimilation efficiency of Se in the particulate material by pr ey (i.e., understanding the part iculate material to prey kinetics of bioaccumulation).



## Bioaccumulated Selenium in Prey

Central to the seaward ecosystem is the C. amurensis food web (Nichols et al., 1990; Linville et al., 2002; Presser and Luoma, 2006). Figure 21 shows monthly mean Se concentrations for C.

amurensis from several USGS monitoring stations for the time periods encompassed by the Bay-Delta transects (see inset). Mean observed C. amurensis Se (Kleckner et al., 2010) for each transect (Cutter and Cutter, 2004; Doblin et al., 2006) are shown in order of high flow seasons (June, 5.4 μg/g dw and April, 7.3 μg/g dw) to low flow seasons (October, 10.8 μg/g dw and November, 11.3; 14.3 μg/g dw) during wet or above normal years ( Figure 21 ) (see additional discussion in Choices, Limitations, and Reduction of Uncertainty section). Data here illustrate the connec tion of bivalve Se concentrations to the cumulative productivity of the estuary in terms of Se transformation, uptake, and exposure during low flow periods. The variability with in the available 15-year monthly C. amurensis Se concentration dataset is illustrated to give context to means for 1997-1999 (grand mean, 12.1 μg/g dw).

Less data are available for landw ard insect-based food webs ( Table 7; Appendix D, Table D5 ).

Data for invertebrate Se concentrations are from 2001 and 2002, with means ranging from 0.6-4.8 μg/g

20

### dw. With limited invertebrate da ta, patterns and connections to hydr odynamic and ecological cycles are

difficult to assess.



# Derivation of Site-Specific Model Components

Environmental Partitioning Factors (K d 

## s)

Location-specific K d 

### s based on salinity across the Bay-Delta are calculated from spatially and temporally matched datasets for dissolved and suspended particulate material Se ( Figures 17 and18; Tables 8, 9, and 10 ). Statistical evaluations of dissolved and suspended particulate material Se concentrations for complete transects or focused Suisun Bay-Carquinez Strait transect yield a set of
mean, 75 th 

### percentile, median, and 25 th 

### percentile K d 

### s ( Tables 11 and 12 ). The location-specific K d 

### s and set of statistical K d 

### s are then used to represent conditions in the estuary for modeling a seaward clam-based food web and predicting an allowabl e dissolved Se concentration. The set of K d 

### s used to represent conditions in the estuary for modeling a landward insect-based f ood web and predicting an

allowable dissolved Se concentration is shown in Table 10

. Location-specific K d 

### s show the variation that can be exp ected across the estuary in the recent past ( Figures 17 and 18 ). K d 

### s vary similarly as suspended particul ate material Se concentrations do

across transects because of the narrowly defined range of dissol ved Se concentration. For Bay-Delta
transects, K d 

### s range from 712 to 26,912 ( Figure 17; Table 8) . For Suisun Bay-Carquinez Strait
transects, K d 

### s range from 712 to 7,725 ( Table 9) . For Rio Vista and Stockton to Benicia transects, K d 

### s range from 554 to 12,650 (Table 10) . As noted previously, these latte r transects also extend to seaward locations and, hence, calculated means include comb inations of data from both landward and seaward locations. These means and ranges for K d 

### s agree well with compiled field datasets for K d 

### s for estuaries and choices used in previous Bay-Delta modelin g scenarios (i.e., 3,000 to 10,000) (Presser and Luoma, 2006; Presser and Luoma, 2009; Presser and Luoma, 2010).



## 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 efficiencies, ingestion rates, and efflux rate c onstants (Presser and Luoma, 2010). This average is within a range of 0.6 to 23 for invertebrate species, with TTFs for sp ecies of bivalves being the highest (Presser and Luoma, 2010). Experimental physiological biodynamic parame ters and rates are derived under idealized conditions in the laboratory. These biodynamic equati ons can be adjusted fo r a specific ecosystem by incorporating data from that sy stem (Presser and Luoma, 2010). TTF C. amurensis 

### is developed here for the estuary from a mechanistic equation for quantifying the biodynamics of C. amurensis and estuaryspecific data for suspended particulate material (i.e ., the food for clams). Selenium bioaccumulated at steady state by C. amurensis is calculated using a site-speci fic 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 sitespecific biodynamics of C. amurensis are spatially and temporally matched datasets from estuary transects (Doblin et al., 2006) for:

21

### • suspended particulate material Se concentration;

• suspended particulate material C concentration;

• percentage of C in suspended particulate material; and

• percentages of suspended particulate el emental Se, adsorbed Se, and ogano-Se. Our site-specific approach here differs from br oader approaches where 1) laboratory data for biodynamic parameters such as AE and IR of particul ate material may be generalized; 2) particulate Se concentrations may be an average of seve ral 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 lo cation and estuarine pro cesses, the suspended particulate material Se concentrat ion carries with it assumptions about Se being associated primarily with organic material (detritus a nd living organisms). This allows us to determine IR on the same organic material basis (assuming clams seek organic ma terial 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 particulate material and their individual bi oavailabilities). These assumptions are all rooted in well established bi ological understanding of bivalve feeding (Cammen, 1980; Lopez and Levinton, 1987). We i gnore the possibility of uptake di rectly 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:

1. We can either assume that Se is associated 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 suspe nded particulate material Se concentration (μg Se/g suspended particulate material) by mg C/ mg suspended particulate material. For the present calculations we employ su spended particulate material Se concentrations as justified

below.

2. IR is determined by filtration ra te (125 L/g clam/d, Cole et al., 1992) multiplied 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 available. At an average of 2% C in suspended particulate material (again, the average acr oss all data) they ingest 2.5 tim es their body weight per day in total suspended particulate material. If IR is calculated at each of th ree low river discharge months where data is available, the average is 1.7 g suspended particul ate material/g clam/d. Experience has indicated that the ingestion model is more ac curate when actual outcomes are used (or averaged) for the generi c situation (i.e., 1.7 g suspended particulate material/g clam/d) as compared to taking the average of each comp onent of the outcome and calculating a generic average. Therefore, we recommend using 1.7 g suspended particulate material/g clam/d for modeling.

3. The derivation of a refined site-specific AE based on individualized bioava ilabilities of Se in suspended particulate material us es observed fractions of particul ate organo-Se, adsorbed Se, or elemental Se found in the estuary (Doblin et al ., 2006) combined with individual AEs for those particulate Se species from the literature (liv ing 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 Se) (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 suspe nded particulate material collected in the estuary contains 45% Se in phytoplankton at an assumed AE of 60%; 30% Se ad sorbed on seston at an

22

### assumed AE of 40%; and 25% elemental Se in se diment 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 c onstant derived experimentally (Lee et al., 2006): k

e 

### = 0.03/d.

5. When we model for times when all data are availa ble from the estuary, we use all data from that sampling date. When we model generica lly we employ mean parameters. Given the above protocol and assump tions, we can directly calculate C. amurensis Se concentrations for comparison to obs erved Se concentrations to valida te predictions or calculate a TTF C.

amurensis 

### for use in modeling. If the data and assumpti ons 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 ma terial/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 particulate 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 specif ic Se concentrations and TTFs for C. amurensis can be calculated using the same protocol as above, but with percen tages 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 pred ictions and derivations as detailed as the data permit. This data-intensive approach yields a mean TTF C. amurensis 

### of 17.1 excluding April, 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. We assume a TTF C.amurensis 

### of 17 in modeling scenarios here. The range of

TTFs across all estuarine conditions 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.



## Aquatic Insect and Other Invertebrates

A Se TTF insect 

### of 2.8 is used here for modeling a la ndward aquatic insect food web based on a compilation of insect TTFs by Presser and Luoma (2010) ( Figure 11 ). This value represents a mean

TTF derived from matched field datase ts for particulate Se and insect Se concentrations in freshwater environments for several species of aquatic insect s including mayfly, caddisf ly, dragonfly, midge and waterboatman. TTFs for other potent ial invertebrates in landward f ood webs (range is 0.6 to 2.8) are

shown in Figure 11 (Presser and Luoma, 2010).



## Bird Egg

Selenium TTFs for aquatic bird eggs are deri ved from data listed in USFWS (2009b) that is compiled from Heinz et al. (1989). TTFs calculated from matched data pairs for diet and bird egg tissue show a range of TTF bird egg 

### from 0.87 to 4.7. The mean TTF bird egg 

### is 2.7. If dietary Se concentrations that are unrealistic for estuary food we bs are eliminated (< 1 μg/g dw and >18 μg/g dw), then a similar

mean for TTF bird egg 

### or 2.6 is calculated. A TTF bird egg 

### of 2.6 is used here for modeling ( Figure 11 ). A regression equation for diet and egg Se concentrations could be used in future modeling if scenario choices are specific enough in term s of dietary Se concentrations for birds and enough laboratory or

field data are available. Mode ling by Presser and Luoma (2010) showed a similar range for TTF bird egg 

### , but a somewhat lower TTF of 1.8 was chosen for modeling, which was near the lower limit for the captive mallard studies.

23

## Fish Whole-Body or Muscle

A Se TTF fish 

### of 1.1 is used here for modeling base d on a compilation of fish TTFs by Presser Luoma (2010) ( Figure 11 ). This value represents a mean TTF derived from laboratory experiments and

from matched field datasets for invertebrate and fi sh Se concentrations in saltwaters and freshwater environments (Presser and Luoma, 2010). TTFs derived from laboratory data from biodynamic experiments range from 0.51- 1.8. TTFs for different fish species derive d from field studies range from 0.6 to 1.7. TTFs derived specifically for white stur geon range from 0.6 to 1.7, with a mean of 1.3.

Selenium TTFs for fish also can be de rived from data given in USFWS (2009b) ( Table 5 ). If data provided for laboratory dietary Se co ncentrations are limited to a ra nge of 1 to 20 μg/g dw and the corresponding fish tissue Se concen trations, then TTFs calculated from the USFWS data range from 0.32 to 5.6, with a mean of 1.07. Again, as for modeling fo r birds, a regression equa tion for diet and fish whole-body or muscle Se concentrat ions could be used in future modeling if scenario choices are specific enough in terms of dietary Se concentrati ons for fish and enough laboratory or field data are

available.



# Validation

Prediction of Selenium Concentrations in C. amurensis

In general, biodynamic modeling is validated for a site location or food web by comparing predicted Se concentrations to observed Se c oncentrations. Monthly mean observed clam Se

concentrations from USGS monito ring station 8.1 near Carquinez St rait from 1996-2009 (Linville et al., 2002; Kleckner et al., 2010) show the range of Se concentrations in C. amurensis ( Figure 21 ). Figure

21 also shows the time period (see inset) a nd compiled observed Se concentrations for C. amurensis from all monitoring stations during the transect collection period from Nove mber, 1997 to November, 1999. Each transect time period was two days, but repor ted clam data are several monthly averages near

the transect collection.

Observed C. amurensis Se concentrations compare well with predicted Se concentrations using the biodynamic methodology described above ( Table 13 ). Specific illustrated examples from the November, 1999 and June, 1998 estuary transects predic t the variability seen in clams during the low

flow season with a residence time of 70 days (12.6 μg/g dw observe d versus 14.1 μg/g dw predicted) and a high flow season with a residence time of 11 days (4.4 μg/g dw observed versus 6.6 μg/g dw predicted), respectively ( Figure 22 ).



## Prediction of Existing Conditions Across Media

Comprehensive validation of Bay- Delta ecosystem-scale modeling ( Figure 9 ) is through prediction of Se concentrations in water, suspended particulate materi al, and tissues of food-web species during times when observed datasets are available. The generalized e quation for translation of a fish

tissue Se concentration to dissolved or wa ter-column Se concentration is shown in Table 2 and Figure

7 . Simulations here include conditions for 1) the estuary during November, 1999 for a clam-based food web ( Table 14) ; 2) Suisun Bay-Carquinez Strait during N ovember, 1999 for a clam-based food web ( Table 15) ; and 3) the estuary during 2003-2004 for a landward insect-based food web ( Tables 16)

. Datasets are matched as much as po ssible given the scarcity of available data across all media. Several

choices for TTF sturgeon 

### , TTF C. amurensis 

### , and K d 

### that are based on the ranges derived for the estuary are

illustrated.

24

### Using existing Se concentrations in seawar d white sturgeon, landwar d white sturgeon, and largemouth bass in the Delta (Stewart et al., 20 04; Foe, 2010) as the starting points for modeling, predicted prey, suspended particulate material, and dissolved Se concen trations are comparable to the range of observed conditions and most are with in the range of observed Se concentrations ( Tables 14-

16 ). Simulations across the gradient of the Bay-De lta for a clam-based food web are calculated using

both a seaward and a landward observe d sturgeon Se concentration to test the uncertainty within a continuum approach ( Table 14 ). The more focused Suisun Bay-Ca rquinez Strait simulations better narrow the range of suspended particul ate material Se concentrations ( Table 15 ). Simulations for an

insect-based food web are all within obs erved dissolved Se concentrations ( Table 16 ).



# Modeling Scenarios and Predictions

Bay-Delta Continuum

Site-specific model parameters and methodology steps are illustrated in Figure 9; exemplified

food webs are shown in Figure 11; and life cycles for critical pha ses and habitat are shown in Figure

12 . Tissue Se concentrations and specified EC le vels used as regulatory guidelines are from Tables 5

and 6. Species, modeled tissue guidelines, and associated ECs include:

• adult female white sturgeon (whole-body) at EC10 and 05 (8.1 and 7.0 μg/g dw);

• generic fish (whole-body) (5.0 μg/g dw);

• juvenile white sturgeon (diet) EC10 and 05 (1.6 and 0.95 μg/g dw);

• scoter or scaup (egg) at EC 10, 05, and 0 (7.7, 5.9, 2.8 μg/g dw);

• scoter or scaup (diet) at EC 10, 05, and 0 (5.3, 4.4, 2.3 μg/g dw);

• generic bird (egg) (same as above for EC10 egg of 7.7 μg/g dw);

• juvenile salmon (whole-body) at EC10, 05 and 0 (1.8, 1.5, 1.0 μg/g dw); and

• juvenile salmon (diet) at EC10, 05, and 0 (2.7, 2.2, 1.5 μg/g dw). Targets for trout inhabiting the Delta are encompa ssed within those for salm on with the exception of extremely low targets for diet of 0.31 μg /g dw (EC0) and 1.0 μg/g dw (EC05). Once choices for modeling scenarios are made, the generalized equation for translation of a fish

tissue Se concentration to wate r-column Se concentration ( Table 2 and Figure 7 ) is

C water 

### = (C fish 

### ) ÷ (TTF fish 

### ) (TTF invertebrate 

### ) K d 

### (17) where (K d 

### ) (C water 

### ) is substituted for C particulate 

### and the equation is solved for C water 

### . An analogous equation for translation of a bi rd egg Se concentration is

C water 

### = (C bird egg 

### ) ÷ (TTF bird 

### ) (TTF invertebrate 

### ) K d 

### (18) Model scenarios and predicted allowable dissolve d, suspended particulate material, and dietary

Se concentrations for C. amurensis -based food webs are compiled in Tables 17-18 and for aquatic insect-based food webs are compiled in Table 19 . Food webs assume exposure of predators through a 100% clam diet or a 100% insect diet (see fo llowing section for mixed diet scenarios). K d 

### s are transect specific and TTFs are those listed above (TTF clam 

### for C. amurensis = 17.1; TTF insect 

### = 2.8; TTF bird egg 

### = 2.6; TTF fish 

### = 1.1). Hydrologic conditions (residence time, wa ter-year type, flow season, and NDOI, Tables 17-19 ) are listed because of their importa nce in determining processes that affect Se transformations between dissolved and suspended particulate material Se concentrations and th e bioavailability of organic matter and Se to food webs (see additional discussion in Choices, Limitations, and Reduction of Uncertainty section). Modeling for a clam-based food web is limited to wet and above normal years because transects are not available for below normal, dry, or critically dry conditio ns. Landward modeling is

25

### limited to above normal (January, 2003 and April, 2003) and below normal (October, 2003 and January, 2004) water years because of data availability. Modeling e xposure for low flow seasons is emphasized here in illustrated scenarios. Low flow seasons (a nd especially low flow seas ons during dry years) are considered critical times (i.e., ecological bottlenecks ) that mainly will determine the ecological effects of Se on the estuary (Presser and Lu oma, 2006). As discussed previously, Figure 12 illustrates the importance of the low flow season in terms of cycl es of prey Se contamin ation and habitat-use by species important to the Bay-Delta. Modeling here predicts allowable Se concen trations that are li nked to calculated K d 

### s across the estuary for individual transects (Figures 23-25) . Thus, a Bay-Delta continuum approach can be used to generate a set of salinity-specifi c predictions. The theoretical cons tructs of predicted allowable

dissolved Se concentrations illustrated in Figures 23-25 are compared to observed dissolved Se concentrations in order to quantify the amount of reduction at a salin ity-specific locati on, if needed, to meet assumed tissue guidelines for fish and birds. In a broader application, the approach generates means and ranges for dissolved and suspended particul ate material Se concentrations across the estuary that can serve as an indicator to compare across time ( Tables 17-19; Figures 23-25 ). As noted previously, use of a continuum mean may increase modeling uncertainty, but use of a continuum approach for modeling can give context for overa ll regulatory and management considerations by addressing salinity-specific locations.

Protection of fish for a seaward location is illu strated by specific exposure scenarios for an adult female white sturgeon (EC05 whole-body), a generic fish species (EC10 whol e-body), and a juvenile white sturgeon (EC05 diet) under above normal water year and low flow season conditions ( Table 17; Figure 23 ). Shown are: guidelines fo r whole-body fish; observed K d 

### s for November, 1999; and modeled dissolved, diet, and suspended part iculate material Se concentrations ( Table 17 ). Predicted

allowed dissolved Se concentrations are shown across the salinity gradient and observed dissolved Se concentrations from the November 4-5, 1999 transect are given for comparison. All observed dissolved Se concentrations in November, 1999 exceed predicted allowable dissolved Se concentrations across the salinity gradient ( Table 17; Figure 23 ). Protection of aquatic birds at a seaward location is illustrated by specific exposure scenarios for a clam-eating bird species (EC05 diet and EC05 egg) and a generi c bird species (EC10 egg) under above normal water year and low flow season conditions ( Table 18; Figure 24 ). Both sets of scenarios are referenced to guidelines based on effects to malla rds. As above, shown are: guidelines for bird eggs;

observed K d 

### s for November, 1999; and modeled dissolved, di et, and suspended particulate material Se concentrations ( Table 18 ). Predicted allowed dissolved Se concen trations are shown across the salinity gradient and observed dissolved Se concentrations from the November 4-5, 1999 transect are given for comparison. All observed dissolved Se concentrations in November, 1999 exceed predicted allowable dissolved Se concentrations ( Table 18; Figure 24)

. Protection of fish for a landward location is i llustrated by specific e xposure scenarios for a juvenile Chinook salmon (EC05 diet and EC05 whole-body) under two different transect conditions (below normal, low flow season; above normal, high flow season) ( Table 19; Figure 25 ). As above, shown are: guidelines for whole-body fish; observed K d 

### s for October 10, 2003 and April 22-23, 2003; and modeled dissolved, diet, and suspended pa rticulate material Se concentrations ( Table 19 ). Predicted

allowed dissolved Se concentrations are shown across the salinity grad ient from Rio Vista and Stockton

to Benicia and observed dissolved Se concentrati ons are given for compar ison. Interpretation across these transects is complex given the interf ace with freshwater and the variation in K d 

### . For landward sites (categorized as Delta, Figure 25; see discussion below) during conditions in the low flow season of October, 2003, observed dissolved Se concentrati ons exceed predicted al lowable dissolved Se

26

### concentrations for fish whole-body targets of 1.5 and 2.4 μg/g dw ( Figure 25 ). For the furtherest landward sites during conditions in the high flow season of April, 2003, observed dissolved Se concentrations are less than predicted allowable dissolved Se concentrations for these targets ( Figure

25 ).

Noted on Figure 25 is a nominal division of Delta and Ba y at Antioch, which is above Chipps Island. Data analysis and modeling fo r these transects assume s that an aquatic inse ct diet is consumed by fish even in habitats of higher salinity, a scenar io that is unlikely. Additional data are needed to resolve food web questions such as this, along with monitoring at freshwater interfaces to better quantify and interpret the variation in location-specific K d 

### s. However, a broader point is proven by the results given in Figure 25 : if the Bay supported an aquatic insect -based food web rather than a clambased food web, then observed dissolved Se concentr ations in the Bay would not be above predicted allowable dissolved Se concentrations during times and locations modeled here for the Bay. Because of the importance of particulate mate rial in determining food-web bioaccumulation, Figure 26 shows observed and predicted suspended partic ulate material Se concentrations for the previously modeled exposure scen arios and set of guidelines ( Figures 23-25 ). In addition, an exposure scenario for the estuary during June, 1998 (w et year, high flow season) is modeled ( Tables 17 and 18 ). Patterns and ranges of particulate enrichment dur ing a low flow season and high flow season are distinctly different and underlie the outcomes of overall exposure in modeling (also see Choices, Limitations, and Reduction of Uncertainty section). For seaward clam-b ased food webs during the low flow season in November, 1999, observed suspended pa rticulate material Se concentrations exceed predicted allowable suspended particul ate material Se concentrations ( Figure 26A ). For a seaward clam-based food webs during the high flow season in June, 1998 (an El Niño event), outcomes are varied for low salinity sites ( Figure 26B ). However, observed suspende d particulate material Se concentrations exceed predicted allowable suspended pa rticulate material Se concentrations at higher salinities ( Figure 26B ). For landward aquatic ins ect-based food webs (Delta) during October, 2003 (low flow season) and April, 2003 (high flow season), observed mean suspended pa rticulate material Se concentrations exceed predicted allowable suspended pa rticulate material Se concentrations for juvenile salmon, except at two low salinity locations ( Figure 26C ).



## Suisun Bay-Carquinez Strait

As previously described, a focused appro ach for Suisun Bay-Carquinez Strait uses compartmentalized data to narrow modeling to a specific location ( Figure 14 ). Additionally, this site is especially impacted by oil refinery effluents. This narrowing of modeling eliminates some of the uncertainties associated with end-member proces ses (i.e., the variability at ocean-influenced and freshwater-influenced sites) that ar e part of the spectrum of the Bay- Delta. Landward sites can show the influence of elevated Se in allochthonous suspended particulate material and s eaward sites can show the influence of amplified Se processing, a pattern seen in other estuaries (LeBlanc and Schroeder, 2008; Presser and Luoma, 2009) ( Figures 16, 17, 20 ). For modeling, a focused approach for Suisun Bay-Car quinez Strait lends itself mathematically to representation by a bounded range of parameter choices for regulatory consideration. Hence, modeling scenarios and predictions for C. amurensis -based food webs gene rated here illustrate the effect of a limited set of choices for Se effect guidelines, K d 

### s, and TTFs ( Tables 20 and 21 ). As discussed previously, model choices can be al tered to illustrate sensitivity to model parameters and uncertainties in model predictions under a range of regulatory or management actions. Comparative scenarios thus develop a range of predictions and iden tify data gaps and monitoring needs.

27

### Tables 20 and 21 show comparative prediction scenario s using a general set of Se effect guidelines for whole-body fish (8, 5, and 1.5 ppm dw ) and for bird eggs (12, 7.7, 5.9 ppm dw) suggested through discussion with USEPA and USFWS. For Su isun Bay-Carquinez Strait , four choices for K d 

### are illustrated (mean K d 

### s of 1,180; 2,666; 3,435; and 5,986 during increa sing residence times in low and high flow transects in 1998 and 1999) ( Tables 20 and 21 ). Choices for TTF fish 

### are 0.8 and 1.1 and the

choice for TTF bird egg 

### is 2.6. Choices for TTF prey 

### are:

• C. amurensis , TTF = 17;

• mixed diet composite, TTF = 8.8 (50% C. amurensis , TTF = 17; 50% amphipod, TTF = 0.6);

• aquatic insect (TTF = 2.8). If a mixed diet composite TTF is used in modeling, then predicted prey Se concentrations also are composites that would need to be separated in to individual component s to assess allowable C.

amurensis and amphipod Se concentrations. For example, if the predicted particulate Se concentration of 0.826 μg/g is derived using a TTF C. amurensis + amphipod 

### of 8.8, then allowable individual prey Se

concentrations are (0.826 μg/g) (17) (0.5) = 7.02 μg/g for C. amurensis , and (0.826 μg/g) (0.6) (0.5) = 0.25 μg /g for a generic amphipod for a sum of 7.27 μg/g as a composite prey Se concentration. Therefore, C. amurensis could not exceed 7.02 μg/g in this mixed diet composite scenario (TTF C. amurensis + amphipod 

### ) as compared to 7.72 μg/g in a scenario using a 100% clam diet (TTF= 17). However, the predicted allowed particulate Se concentrations would be affected more significant ly, with 0.428 μg/g allowed in the single species scenario and 0.826 μg/g in the mixed diet scenari o. Overall though, the effect of this theoretical construct is to reduce the bioaccumulative pote ntial of the modeled invertebrate species. Modeling for the area of Suisun Bay-Carquinez St rait within the specified set of parameters listed above, gives ranges of predicted dissolve d, suspended particulate material, and prey Se

concentrations that can serve as the basis for regulatory consideration ( Tables 20 and 21 ). Choices by regulatory agencies of necessary and sufficient combin ations of model parameters will set the outcomes for criteria development and re gulatory action in the future.



## Landward Sites

Comparative prediction scenarios also are generate d from transects that focus on landward sites (Lucas and Stewart, 2007). Compar ative outcomes from scenarios fo r aquatic insect-based food webs

are illustrated in Tables 22 and 23 . For a landward aquatic insect-bas ed food web four choices for K d 

### are illustrated (means K d 

### s of 2,268, 2,981, 2,684, and 5,855 during low an d high flow transects in 2003 and 2004) ( Tables 22 and 23 ). Choices for predator TTFs are TTF fish 

### = 1.1 and TTF bird eggs 

### = 2.6. As above, ranges of predicted dissolved, suspended partic ulate material, and prey Se concentrations can serve as the basis for regulatory consideration.



# Choices, Limitations, and Reduction of Uncertainty

Several figures throughout the report illustrate processes and outcomes important to the sitespecific modeling approach used here for the Ba y-Delta. These figures represent the fine-scale information that defines and quantifies the ecolog ical, hydrodynamic, and biodynamic processes of the estuary that underlie and enable modeling. These figures include details of: sources and food webs ( Figure 2 ); site-specific modeling approach ( Figure 9 ); transformation and partitioning reactions ( K d 

### ) ( Figure 13 ); species and effects ( Figures 8, 10, 11, and 12 ); and hydrodynamics during sampling of the

estuary ( e.g., Figure 14 ).

28

### Presser and Luoma (2010) discus s the limitations of an ecosyst em-scale modeling approach in general, but also note how models provide insights that advance unders tanding of value both to science and management. For the Bay-Delta, combining mode ling with knowledge of fi ne structure estuary processes is important for reducing uncertainty and fortifying a mechanistic basis for modeling applications and predictions in the future. For example, Figure 17 shows the effect of estuary processes on suspended particulate material Se concentrations during a low a nd a high flow season (April, 1999; November, 1999) across the Bay-Delta continuum. In fu rther analysis of data for Suisun Bay-Carquinez Strait, Figure 27 shows mean observed dissolved and suspende d particulate material Se concentrations

and K d 

### s as a function of residence time. Dissolved Se concentration decreases as residence time increases, but suspended particul ate material Se concentrations increase sharply with increasing residence time. Including suspended particulate mate rial Se concentrations and residence time as

variables in Figure 27 illustrates that transformation of dissolved Se to particulate Se (i.e., dissolved Se decreases as suspended particulate Se concentrations increases) occurs in the estuary as flow slows down (i.e., during increased residence time) as expected from theoretic al considerations of Se phase dynamics (see previous discussion and Presser and Luoma, 2010). Give n the steepness of the curve, regulation of suspended particulat e material Se concentration may be a more sensitive parameter on which to assess change and choice. Defining or concep tualizing a baseline dissolv ed Se concentration or condition for the estuary is less certain because of the small dynamic range of dissolved Se

concentrations.

If mean observed C. amurensis Se concentrations measured in samples from Suisun BayCarquinez Strait during the months surroundi ng the transect sampling are added to Figure 27 to complete linkages of dissolved, particulate, and prey phases, then it is seen that C. amurensis Se concentrations also increase with increasing residence time ( Figure 27 ). To further elucidate the efficiency of Se assimilation in this food web, Figure 28 shows that the percentage of suspended particulate material organo-Se re aches 50% in both plots at a resi dence time of 22 days. Hence, the presence of a majority of organo-Se leads to efficient uptake into C. amurensis at increased residence

times. Thus, Figures 27 and 28 inform the model as to 1) the fundamental underlying mechanistic linkage between hydrodynamics and Se dynamics in the estuary and 2) why scenarios should be tied to specific transformation and fl ow conditions (see also Figure 9 for linked mechanistic components of model approach). Further, Figure 27 helps establish the benefits of a K d 

### -approach in reducing

uncertainties otherwise associated with modeli ng the complex processes of transformation and speciation, and of a biodynamic approach that incorporates the assimilation e fficiency of particulate

material.



# Data Collection, Model Updates, and Refinements

Current Data and Additional Modeling : Current data for dissolved, suspended particulate material, invertebrate, and predator Se concentrati ons (i.e., spatially and temporally matched datasets) are needed to update model predictions. Sampling and analysis would include Se concentrations for the dissolved phase; suspended partic ulate material; seawar d bivalves and amphipods (or other seaward invertebrate species); aquatic in sects (or other landward inverteb rate species); sturgeon, salmon, steelhead (or other fish species); and eggs and tissue from avia n species (see complete list in Figure 11 ). A designated set of methods for collection and analys is of samples used in modeling of the Bay-Delta are needed to add consistency to model inputs. Further documentation of a predator’s dietary preferencealso would be desirable because food webs may change as criteria development goes forward. Follow29

### up modeling can be done in response to collection of a dditional monitoring data and consideration of the pending USEPA national fish tissue guidance. Representation of Hydrologic Conditions : Analysis of flow conditi ons to give context to the environmental partitioning and foodweb biodynamic processes described here is fundamental to modeling for the Bay-Delta. For example, transect da ta for wet and above normal water years illustrate how Se concentration, Se speciation, and K d 

### profiles vary during conditions in April, 1999 (a high flow season) as compared to Novemb er, 1999 (a low flow season) ( Figures 17 and 20)

. Below Normal, Dry, and Critically Dry-Year Low-Flow Conditions : Available seaward datasets do not include data from a below normal, dry, or critically dry year to model a clam-based food web. Hence, modeling here could not assess effects in the North Bay during times of low flow in a dry year (i.e., the ecological bottleneck ) and locations where oil refinery Se effluents may exert their maximum

effect. Available landward datasets do not include da ta from a dry year to m odel an insect-based food web. Comparing model predictions for scenarios ba sed on a range of hydrologic conditions will help develop a more complete basis for regulatory guidance. The estuarine system is highly variable in terms of flow ( Figure 15 ) because of management demands and the natural variability induced by climate. Hydrodynamic Tracking of Se : A Se budget through the estuary is needed to differentiate sources and develop relationships to internal refinery sources and ups tream river sources. For example, quantifying end-member Se concentrations for th e Sacramento River and San Joaquin River would

define the influence of riverine sources on Se con centrations in the estuary. Spatial and temporal definition in such a study should be such to resolve questions as fu ture management strategies are implemented ( Figure 2 ).

Chronic Effects in Birds : Modeling of clam-eating migratory bi rd species, such as scoter and scaup, in reference to potential ch ronic Se effects that may impact staging of diving ducks overwintering in the estuary ( Figures 8, 10 and 12 ) would assess these species in scenarios relevant to the estuary use by these bird species. Changes in Population Dynami cs and Species Diversity : Monitoring and comprehensive compilation of data for community change, intro duction of species, loss of species, and loss of

individuals that are threatened or endangered would document ch anges to ecological pathways important to the sustainability and restoration of the estuary. Site-Specific TTFs : Updated Se TTFs for C. amurensis could be calculated from modern matched datasets for suspended particulate material and bivalve Se concentrations. Biodynamic parameters could be investigated to furt her define bivalve kinetics. Modeling for C. amurensis also could be location-specific to add more speci ficity to modeling. M odeling could utilize TTF fish 

### of up to 1.9. Important site-specific Se TTFs to be update d include those for aqua tic insects and other

invertebrates that serve as food for landward food chains. Matched datasets for suspended particulate

material and invertebrate Se c oncentrations would be needed. Field-derived TTFs for bird species : Field-derived TTFs for bird species (and other predators) would encompass habitat use and other factors that influence exposure.

Particulate Material Se Concentrations : In modeling, derivation of a particulate Se concentration can be very site-specific as defined by the mon itoring data available for modeling. This type of refinement to model parameters is discussed in Pr esser and Luoma (2010). For example, a concentration

of Se in food can be calculated th at takes into account site-specific bi oavailability of particulate material

to invertebrates. The generalized equation is

C particulate 

### = (AE) (C particulate a 

### ) (sediment fraction) + (AE) (C particulate b 

### ) (detritus fraction) + (AE) (C particulate c 

### ) (algae fraction)] (19)

30

### In terms of suspended particulate material as used for Bay-Delta modeling, a composite assimilation efficiency can be derived (see equation 15) to adequately represent food for clams.

Mixed Diet : Rather than assuming a 100% clam-diet for predators, allo wable dissolved Se concentrations could be calculated using th e equation for a mixed invertebrate diet

C water 

### = (C fish 

### ) ÷ (TTF fish 

### ) (K d 

### ) [(TTF invertebrate a 

### ) (prey fraction)] + [(TTF invertebrate b 

### ) (prey fraction)] + [(TTF invertebrate c 

### ) (prey fraction)] (20) The percentage of clam in the diet of species at risk ( Figure 11) could be used specifically. A choice as to the percentages of other types of inverteb rates in the diet of each predator and a TTF invertebrate 

### would need to be developed or assumed from literature sources for each additional invertebrate modeled. Longer Food Webs : For fish-eating birds or the bald eagle food webs, mode l scenarios could incorporate sequential bioaccu mulation in longer food webs

C water 

### = (C fish 

### ) ÷ (TTF fish 

### ) K d 

### (TTF invertebrate 

### ) (TTF forage fish 

### ) (21)

C water 

### = (C fish 

### ) ÷ (TTF fish 

### ) K d 

### (TTF TL2 invertebrate 

### ) (TTF TL3 invertebrate 

### ) (TTF TL3 fish 

### ) (22) For example, modeling a Dungeness crab food web w ould constitute an additional bioaccumulative step when juveniles are consumed by large predator fish or adults are consumed by mammals ( Figure 11 ). Specificity for Low-Salinity Locations : As noted previously, low-sa linity locations were not sampled on a consistent basis for the Bay-Delta during the analysis periods reported on here. Designation of specific sampling loca tions would greatly improve predic tions for landward sites. Data analysis that compares dissolved and suspended part iculate material Se concentrations and calculated

K d 

### s at specific locations across time also would be he lpful to regulatory guidance. Datasets specific to Se concentrations in landward food webs (e.g., i nvertebrates and salmonids) need to be collected because the current record is inadequate. Reference Dose Methodology Comparison : Ecosystem-scale modeling here is applicable to using a dietary Se concentration as a regulatory guideline. The USFW S provided, in some cases, both

tissue and diet Se concentrations as effects levels. An alternative approach would be to calculate a dietary Se concentration or dose fo r aquatic wildlife based on a protective reference dose and specific body weights of predators (USFWS , 2003; Presser and Luoma, 2010). Validation would be important; uncertainties in the relationship of body weight and ingestion rate, for example, would need to be considered. Results of this analys is could be compared to those out comes of modeling scenarios shown here to add weight to the conclu sions drawn for the protection of pr edators in the Bay-Delta estuary. Steps like this in the methodology could also serve to harmonize regulation, a goal long sought in obtaining consensus and understa nding (Reiley et al., 2003). Data Analysis : Ecosystem-scale modeling is more than ma thematical correlations. Its success, in part, depends on formalization a nd conceptualization of existing data for food web ecology, system hydrology, and the biogeochemistry of partitioning. T hus, ultimately a comprehensive Bay-Delta model (i.e., addressing interconnection of es tuarine processes, habitats, specie s, and stressors) as originally conceived by CALFED, would help with details of species, habitat use, competing contaminants, and estuary hydrodynamics.



# Conclusions

Analysis from the biodynamically-based methodology for ecosystem-scale modeling as presented in Presser and Luoma ( 2010) showed, in general, that:

• a crucial factor ultimately defining Se toxicity is the link between di ssolved and particulate phases at the base of the food web (i.e., K d 

### );

• collection of particulate material phases and anal ysis of their Se con centrations are key to representing the dynamics of the system;

31

### • bioaccumulation in invertebrates is a major source of variability in Se exposure of predators within an ecosystem, although that variability can be explained by invert ebrate physiology (i.e.,

TTF invertebrate 

### );

• TTF fish 

### is relatively constant over the range of species considered here; and

• Se concentrations are at least conserved and usually magnified at every step in a food web. In addition, an ecosystem-scale approach: 1) clearly documents pathways that connect dissolved Se to bioaccumulated Se in species of concern; 2) provi des a record of supporting data on which to base decisions; 3) uses site-specific ecology, biogeochemi stry, and hydrology; 4) includes choices explicitly throughout the decision-making proce ss; 5) addresses uncertainties by showing outcomes of different choices in modeling scenarios; and 6) validat es outcomes through comparison to field data. A site-specific methodology for development of Se criteria for the Bay-Delta includes the following steps:

• identification of predators at risk and their critical life stages;

• development of conceptual food-web models fo r predators at risk that include dietary preferences (i.e., percen tages of species of invertebrate consumed);

• development of seasonal-cycle and habitat-use diagrams for prey and predators at risk;

• derivation of tissue guidelines for species at risk specific to expos ure route, effect endpoint, and magnitude of effect (EC0, EC05, and EC10);

• analysis of spatially and tempor ally matched datasets for diss olved and suspended particulate material Se concentrations across the salinity gradient;

• derivation of salinity-specific or location-specific K d 

### s;

• derivation of site-specific TTF C. amurensis 

### ;

• selection or development of TTF fish 

### , TTF bird 

### , and TTFs for other invertebrates;

• validation of modeling through comparison of pr edictions to observed Se concentrations;

• development of exposure scenarios specific to location and season or residence time; and

• prediction of allowable dissolved, suspended part iculate material, and pr ey Se concentrations. Consideration of compliance with allowed Se concentrations across media (i.e., wa ter, particulate, prey and predator) harmonizes regulation and is a measur e of ecological consistenc y and relevance of the links among exposure, transfer, and effects. Modeling here for a seaward C. amurensis -based food web is referenced to data from transects from November, 1997 to November, 1999. Modeling for a landward aquatic inse ct-based food web is referenced to data from transects from January, 2003 to January, 2004 from Rio Vista and Stockton to Benicia. USFWS effect guidelines and associated leve ls of protection are used in modeling to predict toxicity under different regulatory proposals. Validation of the model shows the model is able to generate 1999-2000 seaward conditions for Se concentrations in a C. amurensis to white sturgeon food

web and 2003 landward conditions for Se concentrations in an aquati c insect to largemouth bass food

web. Site-specific analysis an d modeling show that:

• estuarine approaches that focus on seawar d, landward, and Suisun Bay-Carquinez Strait locations can illustrate influences of site, time, and flow-specific partitioning conditions;

• choices of geographic constraints, species, diet, and estuary conditi ons all are influential in risk management for Se;

• the field-derived TTF C. amurensis 

### that is derived here is the firs t instance of a fiel d-derived TTF for a marine bivalve species; the value is appr eciably higher than labor atory-derived values;

• modeling of species at risk takes into account bo th inherent sensitivity and potential exposure;

32

### • a C. amurensis -based food web in the estuary is highly vulnerable to Se inputs because of high potential exposure;

• regulation of suspended particulat e material Se concentration may be a more sensitive parameter on which to assess change and choice because of the small dynamic range of dissolved Se concentrations in the estuary; and

• critical ecological times are functionally connect ed to the underlying dynamics and processes of low flow periods in Suisun Bay-Carquinez St rait thus allowing modeling and prediction as changes occur in management and regulations.

The approach could be refined by:

• collecting modern matched datasets for water, susp ended particulate material , invertebrates, fish,

and birds as illustrated in Figure 11 ;

• determining contributions of specific sources;

• quantifying end-member Se concentrations a nd their hydrodynamic conn ection to estuary Se concentration;

• further limiting geographic (e.g., Suisun Bay) and temporal constraints (dry year, low flow season);

• analyzing processes at interfaces of freshwater/bay/ocean;

• addressing biodynamics of Se and chroni c toxicity in avain species; and

• further linking ecosystem-scale modeling to fine structure estuary processes. Analysis of Se concentration and speciation for characterized particul ate phases are practical measures of the complex water/sediment/particulate milieu that forms the base of the food web and is consumed as food by invertebrates. Future monitoring to incr ease the suspended particulate material database

under a suite of flow conditions would enhance our understanding of estuarine transformation. Monitoring invertebrate Se concen trations in food webs also is a practical, informative step in monitoring because the first and second most variable aspect of Se dynamics (i.e., K d 

### and TTF invertebrate 

### ) are integrated into invertebrate bioaccumulation. Expressly for modeling of avian species, uncertainties exist ar ound biodynamic modeling

parameters ( TTF bird egg 

### ); movement and migration; and links of bioaccumulation, exposure, and toxicity under site-specific conditions. Additionally, modeli ng of overwintering clam-eating migratory bird species, such as scoter and scaup, based on potential chronic Se effects that may impact staging would assess these species in scenarios re levant to their use of the estuary. Chronic toxicity effects include:

• compromised body condition (low body mass);

• oxidative stress (increased susceptibility to disease as immune system is suppressed);

• decreased winter survival;

• decreased reproductive fitness (d ecreased breeding propensity, reduced recruitment) and;

• behavioral impairment (missed breeding window, delayed timing of departure). Predictions from a reference dose methodology for bird s also would strengthen outcomes for protection of avian species. In sum, the amount of available data for the Bay-Delta may be limited, especially under below normal, dry, and criticallydry year conditions, but given the specifici ty of Se processes and food web species that is documented and modeled here, enough is known about the biotransfer of Se and the interconnectedness of habitats and speci es to set a range of limits and establish an understanding of the relevant conditions, biological respon ses, and ecological risk s critical to management of the Bay-Delta. Site-specific modeling here bounds predictions within spatial and temporal components and quantifies key characteristics of the system that can influenc e exposure and uptake of Se by fish and birds. The

33

### uncertainty that stems from the va riability in these processes reflect s the complexity of the estuary. Nevertheless, the methodology used he re is able to document fine-str ucture processes in different habitats and provide context for future scenario deve lopment. The greatest strengt h of the analytical and modeling processes is that it is an orderly, harmonized derivation approach across media for assessing

different choices of Se criteria fo r protection of fish and birds.



# Acknowledgments

This work was supported by U.S. Environmental Protection Agency (San Francisco, California, Region 9) and the U.S. Geological Survey Nationa l Research and Toxics Substances Hydrology Programs. Thanks to Diane Fleck and Eugeni a McNaughton of USEPA for support and insight throughout this process. Thanks also to the U.S. Fish and Wildlife Se rvice, Division of Environmental Quality, Sacramento, California, for pr ovision of effects levels for predat ors at risk in the San Francisco Bay-Delta Estuary.



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# Tables 1-231Table 1.Oil refinery Se loads discharged to the Bay-Delta during 1986-2009. [San Francisco Bay Board, 1992a,b; 1993; Lila Tang and Johnson Lam, San FranciscoBay Board, personal communication, 1999-2006; USEPA, 2010].yearChevron Refinery(Richmond, CA;discharge to San PabloBay) lbs Se/yearMartinez (Shell) Refinery(Martinez, CA; dischargeto Carquinez Strait) lbsSe/yearTosco (Conoco Phillips)Refinery (Rodeo, CA;discharge to San Pablo Bay)lbs Se/yearTesoro Golden EagleRefinery (Martinez, CA;discharge to Suisun Bay)lbs Se/yearValero Refinery (Benicia,CA; discharge to SuisunBay) lbs Se/yearrefinery totallbs Se/yearproposedpermittedloaddlbs Se/yr1986-----5783-1987-----4419-1988-----4417-1989-----3953-1990-----5222-1991-----5634-1992-----5592-1993------26661994------22221995------17271996------12341997------12341998------123419993144411071291331124123420001743681141301269121234200128245112310014411001234200219745514514515310951234200323946490144175111212342004204472115149159109912342005276490154154177125112342006278542159193195136712342007a------12342008221709187193b1601470c12342009210515209193b1601287c1234aData not available from USEPA (2010);bData not available from USEPA (2010), therefore estimated as 2006 Se load ;cIncludes estimated Se load for Tesoro Refinery;dbaseline for reductionsdefined as 1989-1991 average annual loading of 4,935 lbs Se/year.2Table 2.Generalized steps in ecosystem-scale methodology for translation of a tissue Se concentration to a water-column Se concentration for protection of fishand aquatic-dependent wildlife.[Adapted from Table 5,Presser and Luoma, 2010.]Translation of Tissue Criterionto Water-Column Concentration•Develop a conceptual model of food webs in watershed.•Choose toxicity guideline for fish or aquatic bird species in estuary.•Choose fish or bird species to be protected in watershed.•For fish, choose species-specific TTFfishor use default TTFfishof 1.1; for birds, choose species-specificTTFibirdor use default TTFbirdof 2.0.•Identify appropriate food web(s) forselected fish or bird species based on species-specific diet.•Choose site-specific TTFinvertebratefor invertebrates in selected food web(s) or use default TTFinvertebratefor species of invertebrate (see list in Presser and Luoma, 2010).•Choose site-specific Kdor use Kdindicative of a) generalized source of Se and receiving waterconditions or b) site-specific hydrologic type and speciation; or a default Kdof 1000 (see list in Presserand Luoma, 2010).•Solve equation(s) for allowable water-column concentration for protection of fish or birds (i.e.,predator)If assume single invertebrate diet, thenoCwater= (Cpredator) ÷ (TTFpredator) Kd(TTFinvertebrate)If assume a mixed diet of invertebrates, thenoCwater= (Cpredator) ÷ (TTFpredator) (Kd) [(TTFinvertebrate a) (prey fraction)] + [(TTFinvertebrate b) (preyfraction)] + [(TTFinvertebrate c) (prey fraction)]If assume sequential bioaccumulation in longer food webs, thenoCwater= (Cpredator) ÷ (TTFpredator) Kd(TTFinvertebrate a) (TTFforage fish)oCwater= (Cpredator) ÷ (TTFpredator) Kd(TTFTL2 invertebrate) (TTFTL3 invertebrate) (TTFTF3 fish)where TL = trophic level3Table 3.List of species considered for evaluation of Se exposure risk in the San FranciscoBay/Delta. [Reproduced from USFWS, 2008, Table 1.Updates, personalcommunication, S. Detwiler, USFWS, Sacramento, California, 11/17/10).Common NameScientific NameFederal StatusCalifornia StateStatusPotential to be adversely affected by selenium in Bay/Delta*Mammalssalt marshharvest mouseReithrodontomys raviventrisendangeredprotectedAs a terrestrial herbivorous mammal, unlikely to be among the most exposed andsensitive of wildlife species; therefore not likely to be a “species most at risk.”BirdsAmerican whitepelicanPelecanus erythrorhynchosMBTAconcernSF Bay is North end of WestCoast distribution of non-breeders. Preys on somebottom-feeding fish as well as schooling fish, but not likely to be a “species most atrisk.”.California brownpelicanPelecanus occidentaliscalifornicusendangered(delisted 11/2009,MBTA)protected,endangered(protected 2/09)SF Bay is North end of W Coast distribution. Feeds mainly on surface-schooling fish;therefore, not part of benthic-based food chain and not likely to be a “species most atrisk.”white-faced ibisPlegadis chihiconcernconcernBreeds and winters in San Joaquin Valley. Inhabits mainly freshwater wetlands, butalso estuarine wetlands. Eats aquatic and moist soil invertebrates. At some risk but notlikely to be a “species most at risk.”double-crestedcormorantPhalacrocorax auritusMBTAconcernWinters in Central Valley and SF Bay/Delta. Feeds on bottom-dwelling fish andinvertebrates as well as schooling fish. At some risk but not likely to be a “species mostat risk.”American bitternBotaurus lentiginosusconcernnoneFeeds mainly in freshwater marshes, eating mainly insects and small vertebrates;therefore not likely to be a “species most at risk.”western leastbitternIxobrychus exilis hesperisconcernconcernBreeds in SF Delta. Feeds in fresh and brackish water marshes, eating mainly smallfish and insects; therefore not likely to be a “species most at risk.”.Aleutian CanadagooseBranta canadensisleucopareiadelisted, MBTAnoneWinters in California, feeding primarily in upland crops and fallow fields. Sensitive toselenium but unlikely to be exposed in estuary;therefore not likely to be a “species mostat risk.”greater scaupAythya marilaMBTAnoneSF Bay is one of 2 major wintering areas on W coast of N America. Feeds on benthicmollusks that efficiently bioaccumulate selenium in the SF Bay/estuary, therefore likelyto be a “species most at risk.”lesser scaupAythya affinisMBTAnoneSF Bay is an important wintering area; feeds on clams; therefore likely to be a “speciesmost at risk.”black scoterMelanitta nigraMBTAnoneWinters along California coast, diving mainly for mollusks; therefore likely to be a“species most at risk.”white-wingedscoterMelanitta fuscaMBTAnoneWinters along California coast and estuaries, diving mainly for mollusks; therefore likelyto be a “species most at risk.”surf scoterMelanitta perspicillataMBTAnoneWinters along California coast, diving mainly for mollusks; therefore likely to be a“species most at risk.”ospreyPandion haliaetusMBTAconcernHigh trophic level piscivore; not at risk overall and exposure well represented by baldeagle. Therefore not treated here as a “species most at risk.”bald eagleHaliaeetus leucocephalusdelisted,MBTA,BGEPAprotected,endangeredHigh trophic level piscivore; at risk overall and exposed to aquatic food chain in the SFBay/Delta; therefore likely to be a “species most at risk.”4Common NameScientific NameFederal StatusCalifornia StateStatusPotential to be adversely affected by selenium in Bay/Delta*northern harrierCircus cyaneusMBTAconcernHigh trophic level but less exposed to aquatic food chain than bald eagle; therefore notlikely to be a “species most at risk.”white-tailed kiteElanus leucurusconcernprotectedFeeds mainly on terrestrial mammals; minimal exposure to aquatic selenium; thereforenot likely to be a “species most at risk.”Americanperegrine falconFalco peregrinus anatumdelisted, MBTAprotected,concernDelisted but monitored for population status and contaminants. Exposed to selenium inaquatic food chain as predator on piscivorousbirds, but exposure generally diluted byterrestrial component of diet; therefore not likely to be a “species most at risk.”prairie falconFalco mexicanusMBTAconcernWinters along California coast; high trophic level but in mainly terrestrial food chain;therefore not likely to be a “species most at risk.”California blackrailLaterallus jamaicensiscoturniculusMBTAprotected,concernInhabits tidal marsh in SF Bay estuary. Feeds on invertebrates, including snails, butalso seeds; therefore not likely to be a “species most at risk.”California clapperrailRallus longirostris obsoletusendangeredprotected,endangeredSubspecies endangered and endemic to SF estuary; feeds on benthic invertebrates,including filter-feeders that bioaccumulate selenium; therefore likely to be a “speciesmost at risk.”marbled murreletBrachyramphus marmoratusthreatenedendangeredForages in bays along Pacific coast in summer, but not recorded in SF Bay/Delta. Divesfor pelagic food: schooling fish and euphausiids(krill). Therefore not likely to be a“species most at risk.”California leastternSterna antillarum browniendangeredprotected,endangeredBreeds primarily in Central San FranciscoBay but can nest throughout estuary. Feedsthroughout estuary, mainly on surface fish, not part of the benthic mollusk-based foodchain; therefore not likely to be a “species most at risk.”black ternChlidonias nigerconcernconcernBreeds in C Valley including SFDelta. Feeds on marine and freshwater surface fishand insects; therefore not likely to be a “species most at risk.”Caspian ternSterna caspiaMBTAnonePreys heavily on juvenile salmonids, butnot endangered overall; therefore not likely tobe a “species most at risk.”western snowyploverCharadrius alexandrinesthreatenedconcernTerrestrial component of diet likely provides dietary dilution of aquatic system seleniumexposures; have been shown to be very tolerant of selenium exposure; therefore notlikely to be a “species most at risk.”mountain ploverCharadrius montanusconcernconcernWinters in agricultural fields of Sacramento/San Joaquin Valley. Diet mainly terrestrial;therefore not likely to be a “species most at risk.”tricoloredblackbirdAgelaius tricolorconcernconcernNests colonially, mainly in freshwater marshes. Feeds on terrestrial as well asfreshwater insects; therefore not likely to be a “species most at risk.”Reptilesgiant garter snakeThamnophis gigasthreatenedthreatenedAquatic predator, but not known toinhabit the estuary; therefore not likely to be a“species most at risk” in the estuary.FishChinook salmonOncorhynchus tshawytschaendangered/threatenedendangered/threatenedSensitive to selenium; most sensitive life stages occur in rivers and estuary; thereforelikely to be a “species most at risk.”steelheadOncorhynchus mykissthreatenednone (in CentralValley)Sensitive to selenium; most sensitive life stages occur in rivers and estuary; thereforelikely to be a “species most at risk.”delta smeltHypomesus transpacificusthreatenedthreatenedEndemic to the Bay/Delta estuary.Feeds on zooplankton, not a pathway of greatestexposure, but threatened overall, so included as a “species most at risk.”5Common NameScientific NameFederal StatusCalifornia StateStatusPotential to be adversely affected by selenium in Bay/Delta*longfin smeltSpirinchus thaleichthysconcernendangeredSF Bay/estuary is S end of distribution. Prefers more saline water than delta smelt.Overall less threatened and probably less exposed than delta smelt so adequatelyrepresented by that species. Therefore nottreated here as a “species most at risk.”green sturgeonAcipenser medirostristhreatenedconcern; fishingprohibitedThreatened overall, and vulnerable to selenium as a clam-eating bottom feeder in theSF estuary; therefore likely to be a “species most at risk.” Emergency regulationsissued by CDFG March 2006--Zero (0) bag limit for green sturgeon year-round in allareas.white sturgeonAcipenser transmontanusnonelimited fishingPopulation in the SF estuary not federally listed, but vulnerable to selenium as a clameating bottom feeder. Therefore, treated hereas a “species most at risk.” The daily bagand possession limit established by CDFG isone fish that must be between 46 inchesand 72 inches total length. The yearly limit is three.river lampreyLampetra ayresinonewatch listAnadromous; feeds on young salmon. Recorded from lower Sacramento and SanJoaquin Rivers. Not federally listed; therefore not considered to be a “species most atrisk.”SacramentoperchArchoplites interruptusconcernconcernFry feed primarily on bottom-dwelling crustaceans, insect larvae, snails, and fish. Onecaptured in the Delta in 1992, not likely torepresent an established population there.Therefore not considered to be a “species most at risk” in the Delta.Update: However,plans for possible future reintroduction of this species in the Delta should take intoaccount possible risk to individuals of a recovering population segment (pers. comm..,Victoria Poage, Delta Native Fishes Recovery Coordinator, Bay Delta Fish and WildlifeOffice, USFWS.SacramentosplittailPogonichthys macrolepidotusconcernthreatenedVulnerable to selenium as clam-eating bottom feeder in the SF estuary; therefore likelyto be a “species most at risk.”striped bassMorone saxatilisnonenoneIntroduced sport fish in California. Population in Delta declined sharply in early 2000s,but species overall not threatened. Therefore not considered to be a “species most atrisk.”threadfin shadDorosoma pretenensenonenoneIntroduced in California as food for game fish. Population in Delta declined sharply inearly 2000s, but species overall not threatened. Therefore not considered to be a“species most at risk.”tidewater gobyEucyclogobius newberryiendangeredendangeredBottom-dwelling carnivore.Prefers semi-closed estuaries. Potentially exposed, but notfound recently (since 1984) in the Bay area; therefore not considered to be a “speciesmost at risk” in the SF Bay/Delta.California halibutParalichthys californicusnonenoneBottom dweller inhabiting the SF Bay, but overall not threatened; therefore not likely tobe a “species most at risk.”leopard sharkTriakis semifasciatanonenoneBottom dweller inhabiting the SF Bay, but overall not threatened; therefore not likely tobe a “species most at risk.”starry flounderPlatichthys stellatusnonenoneBottom dweller inhabiting the SF Bay. Population in bay declined sharply since 1980,but overall not threatened; therefore not likely to be a “species most at risk.”InvertebratesDungeness crabCancer magisternonenoneEstuary is nursery for this ocean-breeding bottom feeder, but overall not threatened;therefore not likely to be a “species most at risk.”6Federal Status:Endangered: listed as endangered under the Federal Endangered Species Act; Threatened: listed as threatened under the FederalEndangered Species Act;Proposed threatened: proposed asthreatened under the FederalEndangered Species Act; Concern: designated a species of concern; Delisted: removed from the list of endangeredand threatened species under the Federal ESA;MBTA: protected under Migratory Bird Treaty Act; BGEPA protected under the Bald and Golden Eagle Protection Act.California State Status: Endangered: listed as endangered under the California EndangeredSpecies Act; Threatened: listed as threatened under the California Endangered SpeciesAct; Concern: designated by the California Department of Fish and Game as a species of concern;Protected: Fully protected under the Fish and Game Code of California predatingthe California Endangered Species Act* Assessment based upon population status, dependence upon benthic food web, and sensitivity to selenium. Aquatic dependent species feeding directly in the benthic food web ofthe San Francisco Estuary were considered to be at greater riskto selenium exposure than those species feeding in a pelagic/planktonic food web. This assumption is based upon thework of Stewart et al. (2004).7Table 4.Species most at risk from Se exposurein the San Francisco Estuary: summarydata. [Reproduced from USFWS, 2008 Table 2].Common NameScientific NameProbable critical lifestage for Se effects1Food ingestionrate at criticallife stage (gww/day)2Food ingestionrate at criticallife stage (gdw/kg bodyweight/day)3Body weight atcritical life stage(g)4DietMainly clambased foodchain?5Percent ofdiet that isclam-based(worst case)bald eagleHaliaeetusleucocephalusAdult female(egg laying)6442495275(female)fish, birds, mammalsno22.86Californiaclapper railRallus longirostrisobsoletusAdult female(egg laying)17246.8346mussels, spiders, clams, crabs,snails, marsh cordgrass seedsyes64.1greater scaupAythya marilaAdult male andfemale (migration)31385.81054(male)clams, snails, other mollusks,crustaceans, algaeyes80.7lesser scaupAythya affinisAdult male andfemale (migration)24667.5734(male)clams, other mollusks, aquaticinsects, crustaceans, plantsyes96white-wingedscoterMelanitta fuscaAdult male andfemale (migration)465127.31917(male)clams, other mollusks,crustaceans, aquatic insectsyes757surf scoterMelanittaperspicillataAdult male andfemale (migration)31486.01059(male)mussels, other mollusks, plants,crustaceansyes868black scoterMelanitta nigraAdult male andfemale (migration)32589.11117(male)mussels, clams, snails, barnaclesyes809ChinooksalmonOncorhynchustshawytschaMigrating/rearingjuvenile23.30.5-18insects, crustacea, juvenile fishno010steelheadOncorhynchusmykissMigrating/rearingjuvenile19.931-105insects, annelids,Daphniano010green sturgeonAcipensermedirostrisJuvenile or adultfemale201300(averagecaught)benthic crustacea, mollusks andfishprobablysubstantiallySee whitesturgeonwhite sturgeonAcipensertransmontanusJuvenile or adultfemale15-206280(mode)benthic mollusks and crustaceasubstantially41.111delta smeltHypomesustranspacificusJuvenile or adultfemale1140.32(averageJun-Aug)copepods, cladocerans,amphipods, insect larvaeno0SacramentosplittailPogonichthysmacrolepidotusJuvenile or adultfemale33.7121(mode)benthic detritus, clams, othermollusks, mysidssubstantially341.For most species it is premature and speculative to designate acritical life stage at this time. Such designation prejudges the outcome of athorough search of the toxicology literature.2.Food ingestion rates based on wet weight can be calculated from available parameters (Nagy 2001)for birds, mammals, reptiles and amphibians,but not, in general for fish. [Note: food ingestion rate for fish are available elsewhere (e.g., Baines et al., 2002); see text for further discussion].3.For birds, the food ingestion rate as dry weight is calculated from the regression parameters for dry matter intake per day from Table 3 in Nagy(2001), using categories of birds used to calculate food ingestion rate in terms of wet weight as described in the text below.4.See note 1 above. For anadromous species, a range of body weights is given corresponding to the period spent rearing in the estuary.5.We interpret “clam-based” broadly to mean filter-feeding benthic mollusk-based.6.For the worst case, we assume that all birds consumed are thosewaterfowl (scaups and scoters) that primarily feed on benthic mollusks (clams,etc.).87.Percent of mollusks in gizzards of 819 adults and 4 juveniles collected in coastal Maine and Washington (Cottam C.U.S. Dep. Agric. Tech. Bull.643).8.Wet weight percents of summer and winter gizzard contents, British Columbia salt water (Vermeer K. 1981Wildfowl32:107-116; Vermeer andBourne 1984 as summarized in Appendix 1 of Savardet al. 1998).9.Percent mussels, winter, coastal New England (reviewed in Bordage and Savard 1995).10.Although the diets of salmon, steelhead and delta smelt are notknown to be clam-based, these species may still be at risk fromselenium becauseof greater sensitivity to selenium. The sensitivity of salmon and steelhead is documented below. The sensitivity of delta smelt to selenium isunknown; population numbers are alarmingly low, so this species is particularly vulnerable to any adverse effect.11.Percentage clams by volume, fall, SuisunBay and Carquinez Strait (Table 10 below).9Table 5.Selenium effect levels derived for protection of species atrisk in the San Francisco Estuary. [Reproduced from USFWS, 2009b,Table 1).Concentration of selenium (μg/g dry wt.) corresponding toeffect level:Se in dietSe in target species (wholebody or egg)Species0%5%10%0%5%10%EffectExposureduration(days)Form ofseleniumModelData sourceMallard2.304.365.292.775.867.73hatchability>40(parental)seleno-DL-methionineBeckonetal. 2008Heinzet al. 1989Whitesturgeonna7.038.13larval edema andskeletal defectsup to 6monthsselenized yeastlog-logisticLinville 2006adultana25.532.5assimilation6 monthsselenized yeastpowerLinville 2006juvenileana0.951.57assimilation56seleno-L-methioninepowerTashjianet al2006Chinooksalmon1.542.252.671.011.531.84mortality90assimilated orseleno-DLmethionineBrain andCousens1989Hamilton et al.1990Rainbowtrout2.414.225.041.271.892.19reduction ingrowth140sodium seleniteBeckonetal. 2008Hiltonet al. 19800.311.011.56assimilation90seleno-L-methioninepowerVidalet al. 2005aAdult and juvenile white sturgeon effect guidelines are being revised;bRevision, personal communication, USFWS, WilliamBeckon, 10/27/10: EC05= 3.8; EC10 = 8.2.Table 6.Generic selenium effect levels for fish and birds.Se (μg/g dw)Se (μg/g dw)Se (μg/g dw)bird (egg)5.5 (NEC) (Skorupa, 2008)7.7 (EC10) (USFWS, 2009b; Skorupa, 2008)12 (>EC20)fish (wb)-5.0 (EC10) (USFWS, 2005; Skorupa et al., 2004)8.0 (EC40)diet (fish and birds)3.6<4.9 (Skorupa et al., 2004)5.710Table 7.Available data for the Bay-Deltaincluding transects and biota studies. [Wateryear classification based on precipitation in the Sacramento Valley. A highflow season is defined from Decemberthrough May; a low flow season isdefined as June through November.]study datewater year/flowseasonresidencetime (days)/salinity atGolden GateBridge (psu)referenceSe dataNorthern Reach from Sacramento/ San Joaquin Rivers to Golden Gate BridgeApril 1986wet/high9.8/--Cutter 1989; Meseck, 2002dissolved; dissolved speciation; particulateSeptember 1986wet/low24.4/-Cutter 1989; Meseck, 2002dissolved; dissolved speciation; particulateOctober 1987critical/low73.5/-Cutter and SanDiego-McGlone, 1990dissolved; dissolved speciationDecember 1987critical/high8.0/-Cutter and SanDiego-McGlone, 1990dissolved; dissolved speciationMarch 1988critical/high35.5/-Cutter and San Diego-McGlone, 1990dissolved; dissolved speciationMay 1988critical/high25/-Cutter and San Diego-McGlone, 1990dissolved; dissolved speciation1989-1990critical-Urquhart and Regalado, 1991;Kroll and Doroshov, 1991white sturgeon: flesh; ovary; egg yolk components; plasma1986-1990wet 1986; dry 1987;1988 critical; 1989dry; 1990 critical-White et al., 1987, 1988, 1989; Urquhart and Regalado,1991surf scoter, greater and lesser scaup liver and flesh: Suisun andSan Pablo Bays1975, 1986, 1987wet 1975; wet 1986;dry, 1987-Lonzarich et al., 1992California clapperrail eggs from the northern and southernreaches of Bay1982; 1985wet 1982; dry 1985-Ohlendorf et al., 1986; 1991surfscoter, greater scaup liver(southern and northern Bay)December 1986-1987 (earlywinter); March1986-1987(latewinter)wet 1986; dry 1987-Takekawa et al., 2002canvasbacks (n= 29), greater scaup, lesser scaup (n =30) liverand kidney from North, Central, and South Bays1989dry-Hoffman et al., 1998surf scoter, greater scaup, ruddy duck liver (Suisun Bay;Tomales Bay)1985-1986dry 1985; wet 1986-White et al., 1987, 1988, 1989; Urquhart and Regalado,1991; Johns et al., 1988sediment and clam1991, 1992, 1998,1999 breedingseasonscritical 1991, 1992;wet 1998, 1999-Schwarzbach et al., 2006California clapper rail egg from six tidal marshes in northern andsouthern reaches of Bay1994, 1995, 1997,1999, 2000, 2001critical 1994; wet1995-1999; abovenormal 2000; dry2001-CH2M HILL, 1994; 1995; 1998; 2000; 2001; 2002;Ohlendorf and Gala, 2000; Skorupa, 1998shorebird eggs from Chevron Richmond Refinery WaterEnhancement WetlandNovember 1997wet/low68/32.5Cutter and Cutter, 2004; Meseck, 2002; Doblin et al., 2006Bay-Delta transects: dissolved;dissolved speciation; particulate;particulate speciationJune 1998wet (El Niño) /high11/24.8Cutter and Cutter, 2004; Doblin et al., 2006Bay-Delta transects: dissolved;dissolved speciation, particulate;particulate speciationOctober 1998wet/low22/30.2Cutter and Cutter, 2004; Doblin et al., 2006Bay-Delta transects: dissolved;dissolved speciation, particulate;particulate speciation11study datewater year/flowseasonresidencetime (days)/salinity atGolden GateBridge (psu)referenceSe dataApril 1999wet/high16/28.5Cutter and Cutter, 2004; Doblin et al., 2006Bay-Delta transects: dissolved;dissolved speciation, particulate;particulate speciationNovember 1999above normal/ low70/32.2Cutter and Cutter, 2004; Doblin et al., 2006Bay-Delta transects: dissolved;dissolved speciation,particulate;particulate speciationNov 97, Jun 98,Oct 98, Nov 99see above for Cutterand Cutter, 2004-Meseck, 2002sedimentary Se and speciation; pore-water Se: San Pablo Bay:Suisun Bay, Delta, mudflat marsh near Martinez1995-1997all wet years-Linville et al., 2002 (see Presser and Luoma, 2006, Fig 15)clams from 21 locations1997-20001997-1999 wet;2000 above normal-Greenfield et al., 2005sport fish at 6 locations including San Pablo Bay1999-20001999 wet; 2000above normal-Stewart et al., 2004fall and early winter food webs1998-1999wet 1998-1999-Purkerson et al., 2003zooplankton from stations in northern, central and southernreaches of BayMarch to July,2000; 2001above normal 2000;dry 2001-Schwarzbach and Adelsbach, 2003aquatic bird eggs including California clapper rail eggs from SanFrancisco Bay, Suisun Bay, and the DeltaMarch, 2002dry-Hunt et al., 2003surf scoter and greater scaup muscle: Suisun and San PabloBays:May, 1995-February, 2010-Kleckner et al., 2010USGS clam database: monthlyC. amurensis: at seven USGSstations2004-2006 winterbelow normal 2004;above normal 2005;wet 2006-Wainwright-De La Cruz, et al., 2008surf scoter liver :San Pablo, Suisun, and Central BaysMar-Apr, 2005above normal-Ackerman and Eagles-Smith, 2009avocet, stilt, tern liver: north and south Bay, prebreeding season2003-2005above normal 2003;2005; below normal2004-Linares-Casenave et al., 2010white sturgeontissues (muscle, gonad, kidney, liver): sixlocations from Chipps Island to San Pablo BayRio Vista and Stockton to Benicia/Carquinez StraitOctober 7-8,1998wet/low-Personal communication M. Doblin, March 2009sediment cores from six Delta locationsJuly 12-13, 2000above normal/low-Lucas and Stewart, 2007dissolved; dissolved speciation; particulateJanuary 22, 2003above normal/high-Lucas and Stewart, 2007dissolved; dissolved speciation; particulateApril 22-23, 2003above normal/high-Lucas and Stewart, 2007dissolved; dissolved speciation; particulateJune 17, 2003above normal-Lucas and Stewart, 2007dissolved; dissolved speciation; particulateOctober 10, 2003below normal/low-Lucas and Stewart, 2007dissolved; dissolved speciation; particulateJanuary 15, 2004below normal/high-Lucas and Stewart, 2007dissolved; dissolved speciation; particulate2002dry-Lucas and Stewart, 2007sediment cores from three Delta locations12Table 8.Bay-Delta hydrologic conditions, Net Delta Outflow Index, salinity, observed dissolved Se concentrations, observed suspended particulate material Seconcentrations, and calculated Kds. [Arranged by increasing residence timeof transect, except for November, 1997.See text for additional discussion.]hydrologic condition (transect, residencetime, water year/flow season)Net Delta Outflow Index dailyaverage per month (cfs)salinitymean and range (psu)observed dissolved Se meanand range (μg/L)observed particulate Se meanand range (μg/g dw)calculated Kdmeanand rangeaJune 16-17, 199811 day residence; wet/high73,7325.8(0.01-24.5)0.181(0.101-0.303)0.518(0.150-1.59)3,198(712-11,054)April 13-14, 199916 day residence; wet/high35,03411.4(0-28.9)0.116(0.076-0.165)0.636(0.190-1.41)5,824(1,151-13,317)October 7-8, 199822 day residence; wet/low12,25114.6(0-30.1)0.120(0.077-0.164)0.713(0.289-2.21)6,501(2,202-26,912)November 4-5, 199970 day residence; above normal/low6,95115.0(0-32.2)0.102(0.070-0.137)0.746(0.428-1.66)7,614(3,496-19,785)November 5-6, 199768 day residence; wet/low9,63217.2(0.56-32.0)0.192(0.101-0.320)0.842(0.470-1.58)4,652(2,333-8,349)aKdgrand mean for 1998-1999 transects = 5,784Table 9.Suisun Bay-Carquinez Strait hydrologic conditions, Net DeltaOutflow Index, salinity, observeddissolved Se concentrations, observed suspendedparticulate material Se concentrations, and calculated Kds. [Arranged by increasing residencetime of transect, except for November, 1997. See text for additionaldiscussion. See Doblin et al., 2006 andFigure 14for division into subset.]hydrologic condition (transect, residencetime, water year/flow season)Net Delta Outflow Index(daily average per monthcfs)salinitymean and range (psu)observed dissolved Se meanand range (μg/L)observed particulate Se(mean and range) μg/g dwcalculated Kdmeanand rangeaJune 16-17, 199811 day residence; wet/high73,7320.76(0.44-1.08)0.213(0.211--0.215)0.252(0.150-0.354)1,180(712-1,647)April 13-14, 199916 day residence; wet/high35,0345.82(4.9-7.3)0.118(0.076-0.154)0.303(0.240-0.350)2,666(2,274-3,168)October 7-8, 199822 day residence; wet/low12,2517.0(2.5-11.6)0.135(0.128-0.151)0.462(0.289-0.667)3,435(2,202-5,212)November 4-5, 199970 day residence; above normal/low6,95117.5(11.4-23.1)0.123(0.104-0.132)0.740(0.428-1.03)5,986(3,496-7,725)November 5-6, 199768 day residencewet/low9,63216.1(12.7-19.2)0.210(0.192-0.236)0.710(0.572-0.809)3,381(2,722-4,078)aKdgrand mean for 1998-1999 transects = 3,317.13Table 10.Landward hydrologic conditions, Net Delta Outflow Index, salinity, observed dissolved Se concentrations, observed suspended particulate material Seconcentrations, and calculated Kds.hydrologic condition (transect, residencetime, water year/flow season)Net Delta Outflow Index(daily average per monthcfs)salinityarange (psu)observed dissolved Se meanand range (μg/L)observed particulate Se(mean and range) μg/g dwcalculated Kdmeanand rangeJanuary 22, 2003above normal/high50,8470.011-8.450.245(0.111-0.599)0.411(0.27-0.58)2,268(554-3,503)January 15, 2004below normal/high30,9240.012-8.1050.215(0.114-0.523)0.519(0.23-1.0)2,981(1,256-6,398)April 22-23, 2003above normal/high21,2180.013-3.990.356(0.115-1.008)0.614(0.28-1.31)2,684(927-4,351)October 10, 2003below normal/low4,3500.019-12.680.174(0.068-0.532)0.751(0.37-1.53)5,855(1,628-12,650)aCalculated from chlorinity.14Table 11.Bay-Delta mean, median, 75thpercentile, and 25thpercentile for observed dissolved Se concentrations, observed suspended particulate material Seconcentrations, and Kds. [Arranged by increasing residence time of transect, except for November, 1997. See text for additional discussion.]Jun-1998 (11 day residence)Apr-1999 (16 day residence)Oct-1998 (22 day residence)Nov-1999 (70 day residence)Nov-1997 (68 day residence)dissolved Se μg/Lmean0.1810.1160.1220.1020.19275thpercentile0.2040.1280.1340.1220.215median0.1830.1210.1280.0990.20025thpercentile0.1480.0930.1050.0850.163particulate Seμg/g dwmean0.5180.6360.7120.7460.84275thpercentile0.4560.8290.8070.8541.005median0.3920.5280.6270.7250.78325thpercentile0.3570.3910.5160.5700.609Kdmean3198582465017614465275thpercentile24917151652581146060median2330525249636569397025thpercentile20593253378258933173Table 12.Suisun Bay-Carquinez Strait mean, median, 75thpercentile, and 25thpercentile for observed dissolved Se concentrations, observed suspended particulatematerial Se concentrations, and Kds. [Arranged by increasing residence timeof transect, except for November, 1997.See text for additional discussion. SeeDoblin et al., 2006 andFigure 14for division into subset.]Jun-1998 (11 day residence )Apr-1999 (16 day residence)Oct-1998 (22 day residence)Nov-1999 (70 day residence)Nov-1997 (68 day residence)dissolved Se μg/Lmean0.2130.1180.1350.1230.21075thpercentile0.2140.1390.1370.1280.217median0.2130.1250.1310.1250.20825thpercentile0.2120.1000.1290.1200.200particulate Se μg/g dwmean0.2520.3030.4620.7400.71075thpercentile0.3030.3350.6060.8920.780median0.2520.3190.4470.7380.74025thpercentile0.2010.2800.3080.5970.637Kdmean1180266634355986338175thpercentile14142861449870893647median1180255531116142337825thpercentile946241422865019309115Table 13.Comparison of predicted and observedC. amurensisSe concentrations during Bay-Delta transects.transectmean predicted clam Seμg/g dwmean observed clamSe (all stations) (μg/gdw)field location (stationnumber)mean observed clam Se by station andmonth (μg/g dw)June 16-17, 19984.4 all salinitiesa1.6 Carquinez Strait/Suisun Bay salinitiesb5.4Suisun Bay (6.1)San Pablo Bay (12.5)Jun 5.1Jun 5.8April 13-14, 19999.5 all salinitiesa8.7 Carquinez Strait/Suisun Bay salinitiesb7.3Suisun Bay (6.1)Carquinez Strait (8.1)Mar 7.4; Apr 7.5; May 5.7; Jun 6.8Jun 9.2October 7-8, 199813.1 all salinitiesa11.2 Carquinez Strait/Suisun Bay salinitiesb10.8Chipps Island (4.1)Suisun Bay (6.1)Carquinez Strait (8.1)San Pablo Bay (12.5)Oct 5.6Oct 12.3Sep 15.5; Oct 13; Nov 14; Dec 14Sep 10.5; Oct 9.6November 4-5, 199912.6 all salinitiesa12.0 Carquinez Strait/Suisun Bay salinitiesb11.3(12.8 CarquinezStrait data only)Suisun Bay (6.1)Grizzly Bay (415)Grizzly Bay (411)Suisun Bay (405.1)Carquinez Strait (8.1)San Pablo Bay (12.5)Sep 9.4; Oct 12.7; Nov 12.5Sep 8.3; Oct 9.5; Nov 7.9Sep 8.4; Oct 11.3; Nov 11.7; Dec 13.3Sep 10.4; Oct 16.7; Nov 15.3Sep 8.3; Oct 15.3; Nov 14.7Sep 7.2; Oct 10.2; Nov 11November 5-6, 199716.6 all salinitiesa11.7 Carquinez Strait/Suisun Bay salinitiesb14.3Chipps Island (4.1)Suisun Bay (6.1)Carquinez Strait (8.1)San Pablo Bay (12.5)Nov 11.6Nov 14.0Oct 15.5; Nov 15.3Nov 14.9aPredicted clam Se concentrations calculated with outliers deleted (TTFs>35).bTable 1, Doblin et al. (2006) estuarine stations grouped into embayments: Delta; Carquinez Strait-Suisun Bay; San Pablo Bay; and Central Bay.16Table 14.Validation for existing conditions at a seaward estuary location for November,1999 or a generalized mean condition using observed Se concentrations inseaward and landward white sturgeon; derived Kds and TTFs; and a food web for suspended particulate material>C. amurensis>white sturgeon.observedsturgeon muscleSeaμg/gsite-specificTTFsturgeonpredictedC.amurensisSeμg/gmean observedC. amurensisSebμg/gbiodynamicsite-specificTTFC. amurensispredictedparticulate Seμg/gobservedparticulateSecμg/gcalculatedKdpredicteddissolved Seμg/LobserveddissolvedSedμg/L10.21.19.312.8170.5450.428-1.667614(Nov 99 mean)0.0720.070-0.13710.21.19.312.8170.5450.150-2.215784 (grand mean)0.0940.070-0.3206.91.16.312.8170.3690.428-1.667614(Nov 99 mean)0.0480.070-0.1376.91.16.312.8170.3690.150-2.215784 (grand mean)0.0640.070-0.32010.20.812.812.8170.7530.428-1.667614(Nov 99 mean)0.0990.070-0.13710.20.812.812.8170.7530.150-2.215784 (grand mean)0.1300.070-0.3206.90.88.612.8170.5060.428-1.667614(Nov 99 mean)0.0660.070-0.1376.90.88.612.8170.5060.150-2.215784 (grand mean)0.0880.070-0.320a1998-2001 data; seaward, 10.2 μg/g; landward, 6.9 μg/g (Stewart et al., 2004);bCarquinez Strait (USGS station 8.1): mean observed fall 1999; note also station 405clams, 14.6 μg/g dw Se (Kleckner et al., 2010) (see alsoTable 13);c1998-1999 data (Doblin et al., 2006);d1998-1999 data (Cutter and Cutter, 2004).Table 15.Validation for existing conditions in Suisun Bay-Carquinez Strait for November, 1999 or a generalized mean condition using observed Se concentrations inseaward white sturgeon; derived Kds and TTFs; and a food web for suspended particulate material>C. amurensis>white sturgeon.observedsturgeon muscleSeaμg/gsite-specificTTFsturgeonpredictedC.amurensisSeμg/gmean observedC. amurensisSebμg/gbiodynamicsite-specificTTFC. amurensispredictedparticulate Seμg/gobservedparticulateSecμg/gcalculatedKdpredicteddissolved Seμg/LobserveddissolvedSedμg/L10.21.19.312.8170.5450.428-1.035986 (Nov 99 mean)0.0910.104-0.13210.21.19.312.8170.5450.150-1.033317 (grand mean)0.1640.076-0.21510.20.812.812.8170.7530.428-1.035986 (Nov 99 mean)0.1260.104-0.13210.20.812.812.8170.7530.150-1.033317 (grand mean)0.2270.076-0.215a1998-2001 data; seaward, 10.2 μg/g; landward, 6.9 μg/g (Stewart et al., 2004);bCarquinez Strait (USGS station 8): mean observed fall 1999; note also station 405 clams,14.6 μg/g dw Se (Kleckner et al., 2010) (see alsoTable 13).c1998-1999 data (Doblin et al., 2006);d1998-1999 data (Cutter and Cutter, 2004).Table 16.Validation for existing conditions at alandward estuary location for 2003-2004 using observed Se concentrations in landward largemouth bass; derived Kdsand TTFs; and a food web for suspended particulate material>aquatic insect>largemouth bass food web.observed bass wbSeaμg/ggenericTTFfishpredictedinsect Seμg/gmean observedchironomid Sebμg/ggenericTTFinsectpredictedparticulate Seμg/gobservedparticulateSebμg/gcalculatedKdpredicteddissolvedSe μg/LobserveddissolvedSebμg/L2.91.12.62.72.80.9420.27-0.582268 (Jan 2003 mean)0.4150.111-0.5992.91.12.62.72.80.9420.23-1.02981 (Jan 2004 mean)0.3160.114-0.5232.91.12.62.72.80.9420.37-1.55855 (Oct 2003 mean)0.1610.068-0.532a2007 data (Foe et al., 2010);b2002-2004 data (Lucas andStewart, 2007) (see alsoAppendix D, Table D5).17Table 17.Predicted allowed dissolved Se concentrationsfor Bay-Delta transects at different effect guidelines and associated levels of protection (USFWS, 2009b) fora suspended particulate material>C. amurensis>sturgeon food web. Also shown are 1) observed dissolvedSe concentrations, suspended particulate material Seconcentrations, and calculated Kds; and 2) hydrologic conditions including water-year type,flow season, residence time, and NDOI. [Assumptions: TTFclam=17.1; TTFfish= 1.1. Transect data and predictions for 1998 through 1999 are arranged by increasing residence time; transect data and predictions for November,1997 are delineated separately (see textfor explanation). Means and Kds are based on individual data points, notcomposites. Further studies are needed toconsider sensitivity of green sturgeon].calculated Kdmeanand rangefood web:particulate material>C. amurensis>fishtissuetarget Se(μg/gdw)ECpredicted alloweddissolved Semean and range(μg/L)predictedallowedparticulateSe(μg/g dw)predictedallowedinvertebrateSe(μg/g dw)hydrologic condition(transect, residencetime, water year/flowseason)observeddissolved Semean andrange (μg/L)observedparticulate Se(mean andrange)μg/g dwNet DeltaOutflowIndex (dailyaverageper monthcfs)FISH (WHOLE-BODY)3,198(712-11,054)adult female whitesturgeonwholebody8.1100.208(0.039-0.605)0.437.4June 16-17, 199811 day residencewet/high0.181(0.101-0.303)0.518(0.150-1.59)73,7325,824(1,151-13,317)0.110(0.032-0.374)0.437.4April 13-14, 199916 day residencewetl/high0.116(0.076-0.165)0.636(0.190-1.41)35,0346,501(2,202-26,912)0.096(0.016-0.196)0.437.4October 7-8, 199822 day residencewet/low0.120(0.077-0.164)0.713(0.289-2.21)12,2517,614(3,496-19,785)0.064(0.022-0.123)0.437.4November 4-5, 199970 day residenceabove normal/low0.102(0.070-0.137)0.746(0.428-1.66)6,9514,652(2,333-8,349)0.108(0.052-0.185)0.437.4November 5-6, 199768 day residencewet/low0.192(0.101-0.320)0.842(0.470-1.58)9,6323,198(712-11,054)adult female whitesturgeonwholebody7.0050.180(0.034-0.523)0.376.4June 16-17, 199811 day residencewet/high0.181(0.101-0.303)0.518(0.150-1.59)73,7325,824(1,151-13,317)0.095(0.028-0.323)0.376.4April 13-14, 199916 day residencewet/high0.116(0.076-0.165)0.636(0.190-1.41)35,0346,501(2,202-26,912)0.083(0.014-0.169)0.376.4October 7-8, 199822 day residencewet/low0.120(0.077-0.164)0.713(0.289-2.21)12,2517,614(3,496-19,785)0.055(0.019-0.106)0.376.4November 4-5, 199970 day residenceabove normal/low0.102(0.070-0.137)0.746(0.428-1.66)6,9514,6520.0930.376.4November 5-6, 19970.1920.8429,63218calculated Kdmeanand rangefood web:particulate material>C. amurensis>fishtissuetarget Se(μg/gdw)ECpredicted alloweddissolved Semean and range(μg/L)predictedallowedparticulateSe(μg/g dw)predictedallowedinvertebrateSe(μg/g dw)hydrologic condition(transect, residencetime, water year/flowseason)observeddissolved Semean andrange (μg/L)observedparticulate Se(mean andrange)μg/g dwNet DeltaOutflowIndex (dailyaverageper monthcfs)(2,333-8,349)(0.045-0.160)68 day residencewet/low(0.101-0.320)(0.470-1.58)3,198(712-11,054)clam-eating fishwhole-body5.0generic0.128(0.024-0.373)0.274.5June 16-17, 199811 day residencewet/high0.181(0.101-0.303)0.518(0.150-1.59)73,7325,824(1,151-13,317)0.068(0.020-0.231)0.274.5April 13-14, 199916 day residencewet/high0.116(0.076-0.165)0.636(0.190-1.41)35,0346,501(2,202-26,912)0.059(0.010-0.121)0.274.5October 7-8, 199822 day residencewet/low0.120(0.077-0.164)0.713(0.289-2.21)12,2517,614(3,496-19,785)0.040(0.013-0.076)0.274.5November 4-5, 199970 day residenceabove normal/low0.102(0.070-0.137)0.746(0.428-1.66)6,9514,652(2,333-8,349)0.066(0.032-0.114)0.274.5November 5-6, 199768 day residencewet/low0.192(0.101-0.320)0.842(0.470-1.58)9,632FISH (DIET)3,198(712-11,054)juvenile whitesturgeondiet1.6(=1.8wb)100.0452(0.0085-0.1314)0.0941.6June 16-17, 199811 day residencewet/high0.181(0.101-0.303)0.518(0.150-1.59)73,7325,824(1,151-13,317)0.0247(0.0070-0.0813)0.0941.6April 13-14, 199916 day residencewet/high0.116(0.076-0.165)0.636(0.190-1.41)35,0346,501(2,202-26,912)0.0211(0.0035-0.0425)0.0941.6October 7-8, 199822 day residencewet/low0.120(0.077-0.164)0.713(0.289-2.21)12,2517,614(3,496-19,785)0.0139(0.0047-0.0268)0.0941.6November 4-5, 199970 day residenceabove normal/low0.102(0.070-0.137)0.746(0.428-1.66)6,9514,652(2,333-8,349)0.0234(0.0112-0.0401)0.0941.6November 5-6, 199768 day residencewet/low0.192(0.101-0.320)0.842(0.470-1.58)9,6323,198(712-11,054)juvenile whitesturgeondiet0.95(=1.0wb)050.0268(0.0050-0.0780)0.0560.95June 16-17, 199811 day residencewet/high0.181(0.101-0.303)0.518(0.150-1.59)73,73219calculated Kdmeanand rangefood web:particulate material>C. amurensis>fishtissuetarget Se(μg/gdw)ECpredicted alloweddissolved Semean and range(μg/L)predictedallowedparticulateSe(μg/g dw)predictedallowedinvertebrateSe(μg/g dw)hydrologic condition(transect, residencetime, water year/flowseason)observeddissolved Semean andrange (μg/L)observedparticulate Se(mean andrange)μg/g dwNet DeltaOutflowIndex (dailyaverageper monthcfs)5,824(1,151-13,317)0.0147(0.0042-0.0483)0.0560.95April 13-14, 199916 day residencewet/high0.116(0.076-0.165)0.636(0.190-1.41)35,0346,501(2,202-26,912)0.0126(0.0021-0.0252)0.0560.95October 7-8, 199822 day residencewet/low0.120(0.077-0.164)0.713(0.289-2.21)12,2517,614(3,496-19,785)0.0082(0.0028-0.0159)0.0560.95November 4-5, 199970 day residenceabove normal/low0.102(0.070-0.137)0.746(0.428-1.66)6,9514,652(2,333-8,349)0.0139(0.0066-0.0238)0.0560.95November 5-6, 199768 day residencewet/low0.192(0.101-0.320)0.842(0.470-1.58)9,63220Table 18.Predicted allowed dissolved Se concentrationsfor Bay-Delta transects at differenteffect guidelines and associated levels ofprotection (USFWS, 2009b)for a suspended particulate material>C. amurensis>clam-eating bird species food web. Also shown are 1) observed dissolved Se concentrations, suspendedparticulate material Se concentrations, and calculated Kds; and 2) hydrologic conditions including water-year type, flow season, residence time, and NDOI.[Assumptions: TTFclam= 17.1; TTFbird= 2.6. Transect data and predictions for 1998 through 1999are arranged by increasing residence time; transect data andpredictions for November, 1997 are delineated separately (see text for explanation). Means and Kds are based on individual datapoints, not composites.]calculated Kdmeanand rangefood web:particulate>C. amurensis>birdtissuetargetSe(μg/gdw)ECpredicted alloweddissolved Semean and range(μg/L)predictedallowedparticulateSe(μg/g dw)predictedallowedinvertebrateSe(μg/g dw)hydrologic condition(transect, residencetime, water year/flowseason)observeddissolved Semean andrange (μg/L)observedparticulate Se(mean andrange)μg/g dwNet DeltaOutflowIndex (dailyaverageper monthcfs)BIRD (EGG)3,198(712-11,054)scoter and scaupegg7.7generic100.0837(0.0157-0.243)0.173.0June 16-17, 199811 day residencewet/high0.181(0.101-0.303)0.518(0.150-1.59)73,7325,824(1,151-13,317)0.0440(0.0130-0.1505)0.173.0April 13-14, 199916 day residencewet/ high0.116(0.076-0.165)0.636(0.190-1.41)35,0346,501(2,202-26,912)0.0404(0.0064-0.0786)0.173.0October 7-8, 199822 day residencewet/low0.120(0.077-0.164)0.713(0.289-2.21)12,2517,614(3,496-19,785)0.0258(0.0088-0.0495)0.173.0November 4-5, 199970 day residenceabove normal/low0.102(0.070-0.137)0.746(0.428-1.66)6,9514,652(2,333-8,349)0.0432(0.0207-0.0742)0.173.0November 5-6, 199768 day residencewet/low0.192(0.101-0.320)0.842(0.470-1.58)9,6323,198(712-11,054scoter and scaupegg5.9050.0641(0.0120-0.1864)0.132.3June 16-17, 199811 day residencewet/high0.181(0.101-0.303)0.518(0.150-1.59)73,7325,824(1,151-13,317)0.0337(0.0100-0.1153)0.132.3April 13-14, 199916 day residencewet/high0.116(0.076-0.165)0.636(0.190-1.41)35,0346,501(2,202-26,912)0.0310(0.0049-0.0603)0.132.3October 7-8, 199822 day residencewet/low0.120(0.077-0.164)0.713(0.289-2.21)12,2517,614(3,496-19,785)0.0197(0.0067-0.0380)0.132.3November 4-5, 199970 day residenceabove normal/low0.102(0.070-0.137)0.746(0.428-1.66)6,9514,652(2,333-8,349)0.0331(0.0159-0.0596)0.132.3November 5-6, 199768 day residencewet/low0.192(0.101-0.320)0.842(0.470-1.58)9,63221calculated Kdmeanand rangefood web:particulate>C. amurensis>birdtissuetargetSe(μg/gdw)ECpredicted alloweddissolved Semean and range(μg/L)predictedallowedparticulateSe(μg/g dw)predictedallowedinvertebrateSe(μg/g dw)hydrologic condition(transect, residencetime, water year/flowseason)observeddissolved Semean andrange (μg/L)observedparticulate Se(mean andrange)μg/g dwNet DeltaOutflowIndex (dailyaverageper monthcfs)3,198(712-11,054scoter and scaupegg2.800.0304(0.0057-0.0884)0.0631.1June 16-17, 199811 day residencewet/high0.181(0.101-0.303)0.518(0.150-1.59)73,7325,824(1,151-13,317)0.0160(0.0047-0.0547)0.0631.1April 13-14, 199916 day residencewet/high0.116(0.076-0.165)0.636(0.190-1.41)35,0346,501(2,202-26,912)0.0140(0.0023-0.0286)0.0631.1October 7-8, 199822 day residencewet/low0.120(0.077-0.164)0.713(0.289-2.21)12,2517,614(3,496-19,785)0.0094(0.0032-0.0180)0.0631.1November 4-5, 199970 day residenceabove normal/low0.102(0.070-0.137)0.746(0.428-1.66)6,9514,652(2,333-8,349)0.0157(0.0075-0.0270)0.0631.1November 5-6, 199768 day residencewet/low0.192(0.101-0.320)0.842(0.470-1.58)9,632BIRD (DIET)3,198(712-11,054scoter and scaupdiet5.3(=13.8egg)100.1498(0.0280-0.4353)0.315.3June 16-17, 199811 day residencewet/high0.181(0.101-0.303)0.518(0.150-1.59)73,7325,824(1,151-13,317)0.0818(0.0233-0.2693)0.315.3April 13-14, 199916 day residencewet/high0.116(0.076-0.165)0.636(0.190-1.41)35,0346,501(2,202-26,912)0.0700(0.0115-0.1408)0.315.3October 7-8, 199822 day residencewet/low0.120(0.077-0.164)0.713(0.289-2.21)12,2517,614(3,496-19,785)0.0460(0.0157-0.0886)0.315.3November 4-5, 199970 day residenceabove normal/low0.102(0.070-0.137)0.746(0.428-1.66)6,9514,652(2,333-8,349)0.0774(0.0371-0.1328)0.315.3November 5-6, 199768 day residencewet/low0.192(0.101-0.320)0.842(0.470-1.58)9,6323,198(712-11,054scoter and scaupdiet4.4(=11.4egg)050.1244(0.0233-0.3613)0.264.4June 16-17, 199811 day residencewet/high0.181(0.101-0.303)0.518(0.150-1.59)73,7325,824(1,151-13,317)0.0679(0.0193-0.2235)0.264.4April 13-14, 199916 day residence0.116(0.076-0.165)0.636(0.190-1.41)35,03422calculated Kdmeanand rangefood web:particulate>C. amurensis>birdtissuetargetSe(μg/gdw)ECpredicted alloweddissolved Semean and range(μg/L)predictedallowedparticulateSe(μg/g dw)predictedallowedinvertebrateSe(μg/g dw)hydrologic condition(transect, residencetime, water year/flowseason)observeddissolved Semean andrange (μg/L)observedparticulate Se(mean andrange)μg/g dwNet DeltaOutflowIndex (dailyaverageper monthcfs)wet/high6,501(2,202-26,912)0.0581(0.0096-0.1168)0.264.4October 7-8, 199822 day residencewet/low0.120(0.077-0.164)0.713(0.289-2.21)12,2517,614(3,496-19,785)0.0382(0.0130-0.0736)0.264.4November 4-5, 199970 day residenceabove normal/low0.102(0.070-0.137)0.746(0.428-1.66)6,9514,652(2,333-8,349)0.0642(0.0308-0.1103)0.264.4November 5-6, 199768 day residencewet/low0.192(0.101-0.320)0.842(0.470-1.58)9,6323,198(712-11,054scoter and scaupdiet2.3(=6.0egg)00.0650(0.0122-0.1889)0.132.3June 16-17, 199811 day residencewet/high0.181(0.101-0.303)0.518(0.150-1.59)73,7325,824(1,151-13,317)0.0355(0.0101-0.1169)0.132.3April 13-14, 199916 day residencewet/high0.116(0.076-0.165)0.636(0.190-1.41)35,0346,501(2,202-26,912)0.0304(0.0050-0.0611)0.132.3October 7-8, 199822 day residencewet/low0.120(0.077-0.164)0.713(0.289-2.21)12,2517,614(3,496-19,785)0.0200(0.0068-0.0385)0.132.3November 4-5, 199970 day residenceabove normal/low0.102(0.070-0.137)0.746(0.428-1.66)6,9514,652(2,333-8,349)0.0336(0.0161-0.0576)0.132.3November 5-6, 199768 day residencewet/low0.192(0.101-0.320)0.842(0.470-1n58)9,63223Table 19.Predicted allowed dissolved Se concentrationsfor landward transects at different effect guidelines and associated levels of protection (USFWS, 2009b) fora suspended particulate material>aquatic insect>juvenile salmon foodweb. Also shown are 1) observed dissolved Se concentrations, suspended particulatematerial Se concentrations, and calculated Kds; and 2) hydrologic conditions including water-yeartype, flow season, and NDOI. [Assumptions: TTFfish= 1.1;TTFaquatic insect= 2.8. Means and Kds are based on individual data points, not composites.]calculated Kdmeanand rangefood web:particulate >insect>fishtissuetarget Se(μg/gdw)ECpredicted alloweddissolved Semean and range(μg/L)predictedallowedparticulateSe (μg/gdw)predictedallowedinvertebrateSe (μg/g dw)hydrologic condition(transect, wateryear/flow season)observeddissolved Semean andrange (μg/L)observedparticulate Semean andrange(μg/g dw)Net DeltaOutflowIndex (dailyaverage permonth cfs)FISH (WHOLE-BODY)2,268(554-3,503)insect-eating fishwhole-body5.0generic1.05(0.463-2.93)1.64.5January 22, 2003above normal/high0.245(0.111-0.599)0.411(0.27-0.58)50,8472,981(1,256-6,398)0.701(0.254-1.29)1.64.5January 15, 2004below normal/high0.215(0.114-0.523)0.519(0.23-1.0)30,9242,684(927-4,351)0.772(0.373-1.75)1.64.5April 22-23, 2003above normal/high0.356(0.115-1.008)0.614(0.28-1.31)21,2185,855(1,628-12,650)0.382(0.128-0.997)1.64.5October 10, 2003below normal/low0.174(0.068-0.532)0.751(0.37-1.53)4,3502,268(554-3,503)juvenile salmonwhole-body1.8100.388(0.170-1.078)0.601.6January 22, 2003above normal/high0.245(0.111-0.599)0.411(0.27-0.58)50,8472,981(1,256-6,398)0.258(0.0934-0.476)0.601.6January 15, 2004below normal/high0.215(0.114-0.523)0.519(0.23-1.0)30,9242,684(927-4,351)0.284(0.137-0.644)0.601.6April 22-23, 2003above normal/high0.356(0.115-1.008)0.614(0.28-1.31)21,2185,855(1,628-12,650)0.140(0.0472-0.367)0.601.6October 10, 2003below normal/low0.174(0.068-0.532)0.751(0.37-1.53)4,3502,268(554-3,503)juvenile salmonwhole-body1.5050.316(0.139-0.897)0.501.4January 22, 2003above normal/high0.245(0.111-0.599)0.411(0.27-0.58)50,8472,981(1,256-6,398)0.210(0.0761-0.388)0.501.4January 15, 2004below normal/high0.215(0.114-0.523)0.519(0.23-1.0)30,9242,684(927-4,351)0.232(0.112-0.525)0.501.4April 22-23, 2003above normal/high0.356(0.115-1.008)0.614(0.28-1.31)21,2185,855(1,628-12,650)0.114(0.0385-0.299)0.501.4October 10, 2003below normal/low0.174(0.068-0.532)0.751(0.37-1.53)4,3502,268(554-3,503)juvenile salmonwhole-body1.000.211(0.0927-0.586)0.330.91January 22, 2003above normal/high0.245(0.111-0.599)0.411(0.27-0.58)50,8472,981(1,256-6,398)0.140(0.0507-0.258)0.330.91January 15, 2004below normal/high0.215(0.114-0.523)0.519(0.23-1.0)30,92424calculated Kdmeanand rangefood web:particulate >insect>fishtissuetarget Se(μg/gdw)ECpredicted alloweddissolved Semean and range(μg/L)predictedallowedparticulateSe (μg/gdw)predictedallowedinvertebrateSe (μg/g dw)hydrologic condition(transect, wateryear/flow season)observeddissolved Semean andrange (μg/L)observedparticulate Semean andrange(μg/g dw)Net DeltaOutflowIndex (dailyaverage permonth cfs)2,684(927-4,351)0.154(0.0746-0.350)0.330.91April 22-23, 2003above normal/high0.356(0.115-1.008)0.614(0.28-1.31)21,2185,855(1,628-12,650)0.076(0.0257-0.199)0.330.91October 10, 2003below normal/low0.174(0.068-0.532)0.751(0.37-1.53)4,350FISH (DIET)2,268(554-3,503)juvenile salmondiet2.7(=3.0wb)100.632(0.278-1.758)0.972.7January 22, 2003above normal/high0.245(0.111-0.599)0.411(0.27-0.58)50,8472,981(1,256-6,398)0.421(0.152-0.775)0.972.7January 15, 2004below normal/high0.215(0.114-0.523)0.519(0.23-1.0)30,9242,684(927-4,351)0.463(0.224-1.051)0.972.7April 22-23, 2003above normal/high0.356(0.115-1.008)0.614(0.28-1.31)21,2185,855(1,628-12,650)0.229(0.0770-0.598)0.972.7October 10, 2003below normal/low0.174(0.068-0.532)0.751(0.37-1.53)4,3502,268(554-3,503)juvenile salmondiet2.2(=2.4wb)050.506(0.222-1.406)0.802.2January 22, 2003above normal/high0.245(0.111-0.599)0.411(0.27-0.58)50,8472,981(1,256-6,398)0.337(0.122-0.620)0.802.2January 15, 2004below normal/high0.215(0.114-0.523)0.519(0.23-1.0)30,9242,684(927-4,351)0.371(0.179-0.841)0.802.2April 22-23, 2003above normal/high0.356(0.115-1.008)0.614(0.28-1.31)21,2185,855(1,628-12,650)0.183(0.0616-0.479)0.802.2October 10, 2003below normal/low0.174(0.068-0.532)0.751(0.37-1.53)4,3502,268(554-3,503)juvenile salmondiet1.5(=1.65wb)00.3480.153-0.9670.541.5January 22, 2003above normal/high0.245(0.111-0.599)0.411(0.27-0.58)50,8472,981(1,256-6,398)0.2310.0837-0.4260.541.5January 15, 2004below normal/high0.215(0.114-0.523)0.519(0.23-1.0)30,9242,684(927-4,351)0.2550.123-0.5780.541.5April 22-23, 2003above normal/high0.356(0.115-1.008)0.614(0.28-1.31)21,2185,855(1,628-12,650)0.1260.0423-0.3290.541.5October 10, 2003below normal/low0.174(0.068-0.532)0.751(0.37-1.53)4,35025Table 20.Prediction scenarios using Suisun Bay-Carquinez Strait transects for a suspended particulate material>C. amurensis>white sturgeon food web.fish Se target (μg/g wb, dw)Kdpredicted dissolved Se μg/Lpredicted particulate Se μg/gpredicted prey Se μg/gTTFfish= 1.1; TTFclam= 1781,180 (June 98, 11 days)0.3630.4287.2750.2270.2674.551.80.0820.0961.6482,666 (Apr 99, 16 days)0.1600.4287.2750.1000.2674.551.80.0360.0961.6483,435 (Oct 98, 22 days)0.1250.4287.2750.0780.2674.551.80.0280.0961.6485,986 (Nov 99, 70 days)0.0710.4287.2750.0450.2674.551.80.0160.0961.64TTFfish= 1.1; TTFclam + amphipod= 8.8a81,180 (June 98, 11 days)0.7000.8267.2750.4380.5174.551.80.1580.1861.6482,666 (Apr 99, 16 days)0.3100.8267.2750.1940.5174.551.80.0700.1861.6483,435 (Oct 98, 22 days)0.2410.8267.2750.1500.5174.551.80.0540.1861.6485,986 (Nov 99, 70 days)0.1380.8267.2750.0860.5174.551.80.0310.1861.64TTFfish= 0.8; TTFclam= 1781,180 (June 98, 11 days)0.4990.5881050.3120.3686.251.80.1120.1322.2582,666 (Apr 99, 16 days)0.2210.5881050.1380.3686.251.80.0500.1322.2583,435 (Oct 98, 22 days)0.1710.5881050.1070.3686.251.80.0390.1322.2585,986 (Nov 99, 70 days)0.0980.5881050.0610.3686.2526fish Se target (μg/g wb, dw)Kdpredicted dissolved Se μg/Lpredicted particulate Se μg/gpredicted prey Se μg/g1.80.0220.1322.25TTFfish= 0.8; TTFclam + amphipod= 8.8a81,180 (June 98, 11 days)0.9631.141050.6020.7106.251.80.2170.2562.2582,666 (Apr 99, 16 days)0.4261.141050.2660.7106.251.80.0960.2562.2583,435 (Oct 98, 22 days)0.3311.141050.2070.7106.251.80.0740.2562.2585,986 (Nov 99, 70 days)0.1901.141050.1190.7106.251.80.0430.2562.25aTTF = 8.8 is a composite TTF of TTFclam+ TTFamphipodwhere diet is assumed as 50%C. amurensis(TTF = 17) and 50% amphipod (TTF =0.6). Predicted prey concentrations also are a composite that would need to be separated into components to assess the allowableC.amurensisSe concentration and the allowable amphipod Se concentration.27Table 21.Prediction scenarios using Suisun Bay-Carquinez Strait transects for a suspended particulate material>C. amurensis>clam-eating bird species food web.bird egg Se target (μg/g wb,dw)Kdpredicted dissolved Se μg/Lpredicted particulate Se μg/gpredicted prey Se μg/gTTFbird egg= 2.6; TTFclam= 17121,180 (June 98, 11 days)0.2300.2714.627.70.1480.1742.965.90.1130.1332.27122,666 (Apr 99, 16 days)0.1020.2714.627.70.0650.1742.965.90.0500.1332.27123,435 (Oct 98, 22 days)0.0790.2714.627.70.0510.1742.965.90.0390.1332.27125,986 (Nov 99, 70 days)0.0450.2714.627.70.0290.1742.965.90.0220.1332.27TTFbird egg= 2.6; TTFclam + amphipod= 8.8a121,180 (June 98, 11 days)0.4440.5244.627.70.2850.3372.965.90.2190.2582.27122,666 (Apr 99, 16 days)0.1970.5244.627.70.1260.3372.965.90.0970.2582.27123,435 (Oct 98, 22 days)0.1530.5244.627.70.0980.3372.965.90.0750.2582.27125,986 (Nov 99, 70 days)0.0880.5244.627.70.0560.3372.965.90.0430.2582.27aTTF = 8.8 is a composite TTF of TTFclam+ TTFamphipodwhere diet is assumed as 50%C. amurensis(TTF = 17) and 50% amphipod (TTF =0.6). Predicted prey concentrations also are a composite that would need to be separated into components to assess the allowableC.amurensisSe concentration and the allowable amphipod Se concentration.28Table 22.Prediction scenarios using landward-focused transects for suspended particulate material>aquatic insect>juvenile salmon or steelhead.fish Se target (μg/g wb, dw)Kdpredicted dissolved Se μg/Lpredicted particulate Se μg/gpredicted prey Se μg/gTTFfish= 1.1; TTFaquatic insect= 2.882268 (50,847 cfs)1.1452.5977.2750.7161.6234.551.80.2580.5841.6482981 (30,924 cfs)0.8712.5977.2750.5451.6234.551.80.1960.5841.6482684 (21,218 cfs)0.9682.5977.2750.6051.6234.551.80.2180.5841.6485855 (4,350 cfs)0.4442.5977.2750.2771.6234.551.80.1000.5841.64Table 23.Prediction scenarios using landward-focused transects for suspended particulate material>aquatic insect>rail.fish Se target (μg/g wb, dw)Kdpredicted dissolved Se μg/Lpredicted particulate Se μg/gpredicted prey Se μg/gTTFbird egg= 2.6; TTFaquatic insect= 2.8122268 (50,847 cfs)0.7271.6484.627.70.4661.0582.965.90.3570.8102.27122981 (30,924 cfs)0.5531.6484.627.70.3551.0582.965.90.2720.8102.27122684 (21,218 cfs)0.6141.6484.627.70.3941.0582.965.90.3020.8102.27125855 (4,350 cfs)0.2821.6484.627.70.1811.0582.965.90.1380.8102.27

# Figures 1- 28

### 122°30’

38°00’ 122°00’ 121°30’



# San
Pablo

Bay

South San
Francisco Bay Central

Bay



# PACIFIC

OCEAN Clifton Court Forebay Franks
Tract

Golden Gate Bridge Mildred
Island Mandeville Island Venice Island North Bay Aqueduct

Contra Costa Intake pump

SWPBanks

CVP Delta



### California Aqueduct South Bay Aqueduct Delta-Mendota
Canal 

## Sacramento
River



# Suisun Bay Grizzly Bay

Benicia Oil refineries Pumping plant

USGS Station 8.1

Rio Vista Freeport

Stockton ship channel

Smith’s

Canal



### Vernalis Calaveras River Honker Bay 3 Mile Slough

Middle
River



## San Joaquin
River San Joaquin

River CALIFORNIA

Drainage basin Map location 

### Yolo Bypass



### Big Break



### Stockton

San Francisco Suisun
Creek Napa
River Suisun Slough Montezuma Slough Nurse Slough

Cache Slough



### Chipps
Island 

### Martinez

Antioch 

# Carquinez Strait

0 0

10 10

mi km 

### Contra Costa

Canal



### Figure 1. Map of the San Francisco Bay-Delta Estuary.Clifton Court Forebay

California Aqueduct Delta-Mendota
Canal 

### Sacramento River Yolo Bypass*

State Water Project pumps California Aqueduct North Bay Aqueduct **Sacramento River***

**Eastside streams***

San Joaquin River Contra Costa Canal

**Central Valley Project pumps**

**Delta Mendota Canal • San Joaquin**  **Valley**

**• Los Angeles**
**• South Bay • North Bay** 

**O**

**u t**  **f l**  **o w** 

**t o** 

**B**

**a y**  **• N**  e t **D** e l t a **O** u t f l o w **I** n d e x = i n f l o w

- d e

m

a

n d



### San Joaquin River San Pablo
Bay

South
BayPACIFIC OCEAN 

### Suisun Bay

NDOI = [Delta inflow] – [net Delta consumptive use] – [Delta exports]

= NDOI **• Contra Costa County** 

recycle 

### Inflow **(import)**

**Demand (export)**  

### Sacramento/San Joaquin River Delta **Suisun Bay and Carquinez Strait**

Sacramento River watershed input

Se loads vary
with flow season and water-year type.

Riverine inputs as they

mix with

seawater determine Se
concentrations

for the Bay.

San Joaquin River watershed input Se loads depend on
amount of river flow entering the Bay-Delta. Impact of North Bay watershed
Se loads reduced

by seasonally high flows that

accompany their largest Se load inputs.**North Bay streams**  Food web: C. amurensis

to white sturgeon or diving ducks Food web: aquatic insect to juvenile Chinook salmon

or steelhead trout Regulated refinery Se loads*

*Privately owned treatment works and other industries also may contribute Se loads industry Se loads (?)* Regulated agricultural drainage Se load San Joaquin Valley Grassland Bypass Project annual restrictions apply Refinery

internal input Se

loads subjected

to annual

limit.



### Figure 2. Conceptual details of sources of selenium, site-specic food webs, and hydrodynamic connections for the Bay-Delta.

## 12,000

10,000

8,000

6,000

4,000

2,000

0 1985 1990 1995 2000 2005 2010

2000 2005 2010Se loads , lbs/year 

## Refinery and Agricultural

Se Loads agricultural

refinery

**Bay-Delta transects** November 5-6, 1997 June 16-17, 1998 October 7-8, 1998 April 13-14, 1999 November 4-5, 1999wet/wetwet/wetwet/above normalNDOI, cfs daily average per month Proposed, observed

data not available.



## 1999 1998 1997 275,000

250,000

225,000

200,000

175,000

150,000

125,000

100,000

75,000

50,000

25,000

0 

## A

B

Figure 3. Renery and agricultural selenium loads (1985-2009) ( A ) and ow conditions (1996-2009) ( B ).

### 0 2,000 4,000 6,000 8,000 10,000 12,000

Water yearLoad target, lbs SeMeasured load, lbs Se 

### start of Grassland Bypass Project (site B, San Luis Drain at Mud Slough) measured

Grassland Drainage Area target

2009

0 1,000 2,000 3,000 4,000 5,000 

## A. Grassland Drainage Area or Grassland Bypass Project Se loads (1986-2008)



## B. Proposed Grassland Bypass Project Se load targets (2009-2019)Target, lbs Se



### Year critically dry year dry/below normal year above normal year wet year proposed targets 1985 1990 1995 2000 2005 2010

2008 2010 2012 2014 2016 2018 2020 target 3,296 lbs Se dry/below normal

measured 1,239 lbs Se



### Figure 4. Grassland Bypass Project selenium loads (1997-2009) ( A ) and proposed selenium load targets (2009-2019) ( B ).

# ÷ =

Transformation partitioning (K d 

## )

clam

sturgeon diving ducks splittail striped bass splittail

flounder zooplankton amphipod dissolved selenium species (selenate, selenite, organo-Se) phytoplankton, algae, bacteria, suspended particulate material and bed sediment (elemental Se, particulate organo-Se, adsorbed selenite/selenate)

Bioaccumulation

Trophic Transfer composite source Se load

(oil refinery, agricultural drainage through San Joaquin

River or proposed San Luis

Drain, Sacramento River) composite volume (Sacramento River San Joaquin River) composite freshwater
end-member Se concentration (head of estuary)

Prey

Predator

impaired reproduction, teratogenesis, selenosis 

# San Francisco Bay-Delta Selenium Model

[adapted from Figure 2, Presser and Luoma (2006)] 

## Effects

Figure 5. San Francisco Bay-Delta Selenium Model from Presser and Luoma (2006).

### Wildlife Se concentration K d

TTF invertebrate ECOLOGY

EFFECTS

Reproduction in birds (hatchability;

teratogenesis; chick survival and growth) Reproduction in fish (deformity;

larva and fry survival and

growth)

Processes

Factors

Se measurements

Management guidelines

Effects and ecology

Predator/prey links Loss of individuals

Population decline

Loss of species

Community change Dissolved Se concentration

Particulate Se concentration

Particulate Se speciation (phytoplankton, suspended material, biofilm, sediment)

Dissolved Se speciation (selenate, selenite, organo-Se) 

## Ecosystem–Scale Selenium Model

TTF fish TTF bird Invertebrate Se concentration

Fish Se concentration Trophic transfer BioaccumulationReceivingWater Hydrology

Environmental

Partitioning

Bioavailability TransformationR I S K M A N A G E M E N T 

### Source

Se protective reference dose Se toxicity guidelines Volume (hydrodynamics) Load and speciation

[adapted from Figure 1, Presser and Luoma (2010)]



### Figure 6. General ecosystem-scale selenium model from Presser and Luoma (2010). [K
d 

### = empirically determined environmental partitioning factor between water and particulate material; TTF = biodynamic food web transfer factor between an animal and its food.]

### Foodweb **biodynamics**

C water-column C C invertebrate C or fish C bird

TTF invertebrate TTF fish TTF bird K d

C particulate

C water-column C invertebrate C fish C bird

C particulate = = = = C invertebrate C invertebrate 

## A. Quantifying Processes

**Environmental**

**partitioning**

• assimilation efficiency (AE) • ingestion rate (IR) • efflux (k e ) • site hydrology • speciation • particulate type

particulate C prey = (AE) (IR) (C particulate )/k e

C predator = (AE) (IR) (C prey )/(k e )

TTF = (AE) (IR)/(k)



### Translation to Water-Column Concentration Combined Equations

If longer food web 

## B. Modeling Equations

C invertebrate = ( C particulate ) ( TTF invertebrate )

C fish = ( C invertebrate ) ( TTF fish )

C bird = ( C invertebrate ) ( TTF bird )

C fish = ( C particulate ) ( TTF invertebrate ) ( TTF fish )

C bird = ( C particulate ) ( TTF invertebrate ) ( TTF bird )

C bird = ( C particulate ) ( TTF invertebrate ) ( TTF fish ) ( TTF bird )

C bird = ( C invertebrate ) ( TTF fish ) ( TTF bird )

C water = ( C fish ) 

### ÷ [ ( TTF fish ) ( TTF invertebrate ) 

### K d 

### ]

C water = (C bird ) 

### ÷ [ ( TTF bird ) ( TTF invertebrate ) 

### K d 

### ] [adapted from Figure 2, Presser and Luoma (2010)]



### Figure 7. Quantifying processes ( A ) and modeling equations ( B ) for ecosystem-scale selenium modeling.

### female adult bird **female adult fish Diet**  exogenous Se source

minor prehatch

exposure

failure of eggs to hatch (most sensitive endpoint) 

### egg prehatch exposure

embryo deformity

chicks

mortality (survival)
(little data available, may be most sensitive endpoint) 

### maternally
mediated Se reproductive
effects

juvenile

egg

exposure during yolk sac adsorption* **Liver**endogenous
Se source

fry

reduced growth

(growth rate in first 5 days is bestpredictor of posthatch survival)



### hatch into contaminated environment hatch into contaminated environment 

## Reproductive Effects

**Chronic Effects in Adults** reduced production of viable eggs (hatchability not affected)



### post-hatch reproductive
effects

direct Se
effects

direct Se
effects

Winter Stress Syndrome (metabolic stress imposed by low temperature during Se exposure increases toxicity of dietary Se) 

### over-winter mortality resulting in seasonal poisoning of young fish **Liver** endogenous
Se source **Diet** exogenous Se source

yolk-sac larvae **Se**

mortality deformity reduced growth

mortality liver
disease reduced growth

* yolk proteins precursor, vitellogenin; yolk proteins lipovitellin and phosvitin (liver Se stored in yolk proteins until metabolized during embryonic and larval stages vitellogenesis = yolk proteins are synthesized in liver and transported to egg) **Se**



### • compromised body condition (low body mass; edema) • oxidative stress (increased susceptibility to disease due to suppressed immune system)

• decreased winter survival • decreased reproductive fitness (decreased breeding propensity ; reduced recruitment) • behavioral impairment (missed breeding window; delayed timi ng of departure) • lowered saline tolerance and gill effects in fish



### Figure 8. Ecotoxicology and eects of selenium for sh and birds.**foodweb fish**  **guidline*wb**

**μg/g dw guideline**  **predicted**

**invertebrate**

**μg/g dw predicted**  **particulate**

**μg/g dw predicted** 

**dissolved**

**μg/L TTF**  **sturgeon K**  **d TTF**  **C.amurensis**

from

USFWS

or assumed

from

literature

Se in predator (sturgeon) speciesspecific

prey to predator trophic

transfer

factor sturgeon/

C. amurensis / suspended particulate

material Se in prey

C. amurensis speciesspecific particulate

to prey biodynamic

trophic

transfer

factor Se in food for prey (suspended particulate material) empericalsalinityspecific partitioning

factor for

dissolved and

suspended particulate Se Se in

dissolved       

**foodweb fish**  **guidline*wb**

**μg/g dw guideline**  **predicted**

**invertebrate**

**μg/g dw predicted**  **particulate**

**μg/g dw predicted** 

**dissolved**

**μg/L TTF**  **salmon K**  **d TTF**  **aquatic insect**

from

USFWS

or assumed

from

literature generic

field-derived

for fish generic

field-derived for aquatic

insect calculated

from transects

of measured

dissolved Se and suspended particulate Se

for transects Se in predator (salmon) speciesspecific

prey to predator trophic

transfer

factor Chinook salmon/
insect/ suspended particulate

material Se in prey aquatic

insect speciesspecific particulate

to prey biodynamic

trophic

transfer

factor Se in food for prey (suspended particulate material) empericalsalinityspecific partitioning

factor for

dissolved and

suspended particulate Se Se in

dissolved        

# Site-Specific Model ApproachWithin specified salinity and residence time

*USEPA, USFWS, or assumed from literature; wb = whole body, dw = dry weight. 

## A. seaward site-specific

B. landward site-specific **IR** calculated frommeasured suspendedparticulate material (or C) from transects and
literature filtration rate;

**AE** calculated from measured particulate species from transects

and literature bioavailability; **ke** assumed from

literature for C.

amurensis calculated from

transects of

measured

dissolved Se and

suspended particulate Se for

transects calculated from

measured sturgeon muscle Se; measured clam

Se (North Bay matched dataset)



### Figure 9. Site-specic modeling approach for Bay-Delta seaward ( A ) and landward ( B ) locations.

### A. **Diving duck**

**Se effects model** disease decreased body condition Se in duck tissues

delayed migration

decreased embryo survival and hatchability missed breeding

window decreased breeding propensity teratogenesis

reproductive impairment

reduced recruitment decreased winter survival



### D.

**Salmonids (upstream migration)** 

**Se effects model**

reduced reproduction

reduced recruitment 

### B. **White sturgeon**

**Se effects model** metabolized in 10 days

2 years of deposition maternal transport developing white sturgeon larvae spinal deformities

severe edema mortality

juvenile white

sturgeon Se in adult white sturgeon tissues

reproductive females

-all seasonsliver disease gill effects low growth

lowered saline

tolerance egg yolk



### C.

**Juvenile salmonids (downstream migration)** 

**Se effects model** Se in salmonid tissues

Se in salmonid tissues Se sequestration in eggs mortality reproductive impairment predation migration failure

mortality predation (fishing mating, spawning

behavioral impairment

reduced recruitment susceptibility

to disease growth/ development impairment olfactory impairment

behavioral impairment susceptibility

to disease developmental

malformations hatch, fry

survival olfactory impairment

migration

failure



### Figure 10. Selenium eects models for diving ducks ( A ), white sturgeon ( B ), and juvenile salmonids ( C and D ). [adapted from Figure 7, Presser et al. (in review)]

## clam ( Corbula amurensis ) dissolved Se

particulate Se

**bird** 

## lesser scaup

surf scoter 1


## greater scaup
black scoter 2 

## white-winged scoter 2 

## California clapper rail **% clam in diet***


## 96

86

81

80

75

64

bald eagle 4 

## 23% **fish** 

## white sturgeon
green sturgeon 3



## Sacramento splittail **% clam in diet***



## 41

41

34 Dungeness crab **A. Seaward:** 

**clam -based** 

**food webs***

**B. Landward:** 

**insect -based** 

**food webs*** 

## K d

TTF bird egg 

## = 2.6

TTF bird egg 

## = 2.6 TTF fish 

## = 1.1

TTF fish 

## = 1.1 **TTF clam**  

## = 17

TTF crab 

## = ?

1 see additional site-specific data in figure 12

2 infrequent visitor

3 assumed from white sturgeon

4 also represents exposure of osprey

TTFs shown are used in modeling. See text for additional information. 

### * USFWS (2008) 

## herring

sardine

Chinook

salmon sturgeon
halibut

sole

flounder

rockfish

skate

shark humans TTF = ? larvae juveniles adults

aquatic insects (midge, corixid, damselfly, mayfly, etc.) 

## dissolved Se

particulate Se K d

TTF bird egg 

## = 2.6 TTF fish 

## = 1.1 **TTF insect**  

## = 2.8

California black rail Chinook salmon

steelhead trout

delta smelt

TTF bird egg 

## = 2.6 seals

sea lions fish-eating birds

Other Delta food webs:

**invertebrate** 

## C. fluminea copepod amphipod mysid Daphnia juvenile fish Siberian shrimp **TTF**


## 2.8

1.35

0.6–0.9

1.3

1.9

1.1–1.6
? TTF = ? TTF = ? TTF bird egg 

## = 2.6 TTF bird egg 

## = 2.6

*****

Figure 11. Conceptual details of seaward clam-based food webs ( A ) and landward aquatic insect-based
food webs ( B ) for modeling of the Bay-Delta.

### Dec Primary
location 

### Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov

clapper rail*

bald eagle (assumed resident)



### Sacramento splittail***

(5 yrs)



### delta smelt** (1 yr) + 

### migratory birds

surf scoter

black scoter white-winged scoter

greater scaup lesser scaup

Chinook salmon* (3–7 yrs) + 

### spring-run Chinook salmon* (3–7 yrs) + 

### winter-run green sturgeon** (50 yrs) + 

### white sturgeon (100 yrs) + 

### dissolved and particulates partitioning/transformation

C. amurensis Se (μg/g dry wt.)

juveniles (4) 

### 1, 2, 3, 4

1, 2, 3 2, 3 1, 2, 3

2, 3, 4 1, 2, 3 1, 2, 3, 4

1, 2, 3 1, 2, 3

1, 2, 3, 4 spawning recovery



### migration north to breed a, b

c

c

b

b

d

e data: 12/1995–11/1996



### breeding surf scoter in Central Bay

over wintering (staging) surf scoter in Suisun

and San Pablo Bay

breeding

spawning in upper Delta

spawning in mixing zone

a. 50% of surf scoter population overwinters in Bay Suisun Bay diet is 89% C. amurensis San Pablo Bay diet is 71% C. amurensis Central Bay diet is V. philippinarum and soft-bodied prey b. arrive in Canada in late May and immediately breed c. infrequent visitor d. females require about two years for eggs to mature; small fraction move up rivers to spawn in late winter and early spring e. more marine than white sturgeon, juveniles spend 2—3 years in estuary before migrating to sea 2 year internal egg maturation



### migration upstream upstream spawning upstream spawning

upstream spawning

upstream spawning emigration to ocean emigration to ocean

emigration to ocean

migration migration

spawning in estuary

migration upstream



### 3, 4 high-flow season low-flow season

20

11 ppm



### Central Valley steelhead
trout (winter run)** (4–7 yrs) +

juveniles (2) 

### 1, 2 

### Central CA Coast steelhead
trout (winter run)** (4–7 yrs) + 

### emigration

emigration migration Chipps Island upstream spawning (ESU 12) ++

(ESU 9) ++ 

### 1, 2, 3, 4
juveniles (4)



### upstream spawning 

### 3 **2**

**1 4** 

*listed as endangered

**listed as threatened

***listed as of concern

+ Typical lifespan

++ Evolutionarily Significant Unit (ESU) 

## Species at Risk: Exposure and Habitat Use

Figure 12. Exposure and habitat use for predators at risk, patterns of C. amurensis selenium concentrations (1996), and hydrologic categories that may relate to environmental partitioning of selenium for the Bay-Delta.

## Se(VI) d



## Org-Se(II) p 

## Org-Se(II) d 

## Se(IV) dhydrodynamicssources 

## dissolved Se 

# Se(VI) **d Se(IV)**  **d org-Se(II)**  **d particulate Se** 
**(allochthonous)** 

### org-Se(II) **p Se(0)**  **p adsorbed Se(VI)**  p **and Se(IV)** p



# transformation **reactions**

**• uptake by plants** [assimilatory reduction of Se(VI)

d 

## , Se(IV)

d 

## and org-Se(II)

d 

## to org-Se(II)

p 

## ] **• sequestration by sediments** [dissimilatory reduction of Se(VI)

d 

## to Se (0)

p 

## ] **• adsorption of Se(VI)**

**d** 

## and Se(IV)

**d** 

## onto particle surfaces **• recycling as part of decay** [org-Se(II)

p 

## ] 

## water-column food web **benthic food web**

*phytoplankton, periphyton, detritus, inorganic suspended material, biofilm, sediment and/or attached vascular plants 

# TTF **invertebrate** 

# TTF **invertebrate**



## sediment

**plants**



# particulate material Se*

dissolved Se K
d 

# = **bioavailability** 

# p 

# a 

# r 

# t 

# i 

# t 

# i 

# o 

# n **i n**  **g K**  **d**

**bioavailability** 

## Se(VI) d



## Se(0) p 

## , Org-Se(II) p 

## adsorbed Se(VI) and Se(IV) Org-Se(II) d 

## Se(IV) d

sediment



### p p

Figure 13. Conceptual details of environmental partitioning reactions between dissolved and particulate selenium. [adapted from Figure 1, Luoma and Presser (2009)]

### Delta Suisun-Carquinez San Pablo Central Bay



### A



## . 11 day residence time

June 1998 wet, high



### C. 22 day residence time

October 1998
wet, low 

### B. 16 day residence time April 1999 wet, high



### D. 70 day residence time

November 1999
above normal, low 2.5

2.0

1.5

1.0

0.5

0

2.0

1.5

1.0

0.5

0Particulate Se, μg/g dw



### 2.0

1.5

1.0

0.5

0

2.0

1.5

1.0

0.5

0



### E. 68 day residence time

November 1997
wet, low 2.0

1.5

1.0

0.5

0 0 5 10 15



## Salinity, psu 20 25 30 35 [Data adapted from
Doblin et al. (2006)]



### Figure 14. Proles of hydrodynamically categorized suspended particulate material selenium concentrations from transects during 1997-1999. [Proles are arranged in order of increasing residence time, with the prole from November, 1997 separated out (see text for explanation).]Nov 5-6, 1997 68 days (wet/low)
9,632 cfs Apr 13-14, 1999 16 days (wetl/high)
35,034 cfs Oct 7-8, 1998 22 days (wet/low)
12,251 cfs Nov 4-5, 1999 70 days (above normal/low) **6,951 cfs (daily average/month)** Jun 16-17, 1998 11 days (wet/high)
**NDOI = 73,732 cfs** 

# 2000 2002 19981996 2004 2006 2008 2010 **Bay-Delta transects**

November 5-6, 1997 June 16-17, 1998 October 7-8, 1998 April 13-14, 1999 November 4-5, 1999NDOI, cfs daily average per month



# 1999 1998 1997 275,000

250,000

225,000

200,000

175,000

150,000

125,000

100,000

75,000

50,000

25,000

0

Figure 15. Flow conditions in the Bay-Delta (1996-2009) with reference to sampling transects by Cutter and Cutter (2004) and Doblin et al. (2006) (1997-1999).

### 0.35

0.30

0.25

0.20

0.15

0.10

0.05

0 0 5 10 15 

### D. 

### November, 1999Dissolved Se, μg/LDissolved Se, μg/L 

### 20 25 30 35



### Salinity,psu 0.35

0.30

0.25

0.20

0.15

0.10

0.05

0



### C. 

### October, 1998

0.35

0.30

0.25

0.20

0.15

0.10

0.05

0 

### E. 

### November, 1997 B. 

### April, 1999 A. 

### June, 1998*

0 5 10 15 20 25 30 35

0 5 10 15 20 25 30 35 

### Salinity, psu 11 day residence time 16 day residence time

22 day residence time 70 day residence time

68 day residence time 

### *arranged by

residence time



### Figure 16. Proles of observed dissolved selenium concentrations for transects across the estuary salinity gradient during 1997-1999. [Proles are arranged in order of increasing residence time, with the prole from November, 1997 separated out (see text for explanation).]

### 1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0Particulate Se, μg/g dwDissolved Se, μg/L



### 1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0 1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0 0 5 10 15

Salinity, psu

Salinity, psu 20 25 30 35

0 5 10 15 20 25 30 35 16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0

16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0KdKd 

### B. April, 1999 - 16 day residence time



### Dissolved Se

Particulate Se
arranged by

residence time K d 

### 19,785 1.66

C. October, 1998 - 22 day residence time 

### 26,912 2.2 

### 0 5 10 15 20 25 30 35

0 5 10 15 20 25 30 35

0 5 10 15 20 25 30 35Particulate Se, μg/g dwDissolved Se, μg/LParticulate Se, μg/g dwDissolved Se, μg/LKd 

### 16,000

14,000

12,000

10,000

8,000

6,000

4,000

2,000

0 * E. November, 1997 - 68 day residence time 

### A. June, 1998 - 11 day residence time*



### D. November, 1999 - 70 day residence time



### Figure 17. Proles of observed dissolved and suspended particulate material selenium concentrations and

calculated K
d 

### s for transects across the estuary salinity gradient during 1997-1999. [Proles are arranged in order of increasing residence time, with the prole from November, 1997 separated out (see text for explanation).]

### 0 0.2 0.4 0.6 0.8

1 1.2Dissolved Se, μg/L



### 0 2000 4000 6000 8000 10000 12000Kd 

### 0 0.2 0.4 0.6 0.8

1 1.2 1.4Particulate Se, μg/g dw



### 2003

Jan Apr

Jun

Oct

2004

Jan 

## A.

B.

C.

Increasing Salinity Rio Vista Benicia



### Figure 18. Proles of observed dissolved ( A ) and suspended particulate material ( B ) selenium

concentrations and calculated K
d 

### s ( C ) for transects across the salinity gradient from Rio Vista
and Stockton to Benicia, California (2003-2004).

### 90

80

70

60

50

40

30

20

50

40

30

20

10

0 50

40

30

20

10

0 0 5 10 15

Salinity 

## A

B

CDissolved organo-Se, %Dissolved selenate, %Dissolved selenite, %



### 20 25 30 35 Nov, 1997 - 70 days Sep, 1986 - 24 days



### Figure 19. Proles of observed dissolved selenium speciation [selenate ( A ), selenite ( B ), and organo-Se ( C )] for transects across the estuary salinity gradient for September, 1986 and November, 1997.

### E. November, 1997 - 68 day residence time 

### 2.5

2.0

1.5

1.0

0.5

0 100

80

60

40

20

0 0 5 10 15

Salinity, psuParticulate Se, μg/g dwParticulate organo-Se, %



### 20 25 30 35 2.5

2.0

1.5

1.0

0.5

0 0 5 10 15Particulate Se, μg/g dw



### 20 25 30 35 

### A. June, 1998 - 11 day residence time*



### C. October 1998 - 22 day residence time 

### 2.5

2.0

1.5

1.0

0.5

0 0 5 10 15Particulate Se, μg/g dw



### 20 25 30 35 

### B. April, 1999 - 16 day residence time 

### 100

80

60

40

20

0 0 5 10 15

Salinity, psuParticulate organo-Se, %



### 20 25 30 35

100

80

60

40

20

0 0 5 10 15Particulate organo-Se, %



### 20 25 30 35

Particulate Se Particulate organo-Se arranged by residence time 

### * D. November, 1999 - 70 day residence time



### Figure 20. Proles of observed suspended particulate material selenium concentrations andthe observed percentages of suspended particulate organo-Se for transects across the estuary salinity gradient during 1997-1999. [Proles are arranged in order of increasing residence time, with the prole from November, 1997 separated out (see text for explanation).]Clam Se, μg/g dw 

### 20

18

16

14

12

10

8

6

4



### 1996 1997 1998 1999 2003 2004 2005 2002 2007 2008 2009 2006 2001 2000 J M S J M S J M S J M S J M S J M S J M S J M S J M S J M J MS S J M S J M S J M JS 5. ( NDOI 6,951 cfs ) 70 day residence time 3. ( NDOI 9,632 cfs ) 68 day residence time
4. ( NDOI 12,251 cfs ) 22 day residence time 2. ( NDOI = 35,034 cfs ) 16 day residence time 1. ( NDOI = 73,732 cfs ) 11 day residence time 18

16

14

12

10

8

6

4Clam Se, μg/g dw

Sampling

StationChipps IslandSuisun BayCarquinez StraitSan Pablo Bay

4.1 (avg 14.3)

6.1 8.1 12.5 November 5-6,

1997Suisun BayCarquinez StraitSan Pablo Bay

4.1 6.1 8.1 12.5 (avg 10.8)Chipps Island October 7-8,

1998

(avg 11.3)

6.1 411415 8.1 405 12.5Carquinez StraitSan Pablo BaySuisun BayGrizzly Bay November 4-5,

1999SuisunBaySuisun BaySan Pablo Bay

6.1 12.5

(avg 5.4)Suisun Bay

6.1 (avg 7.3)

8.1Carquinez Strait 

## 3 4 5 1 B. Wet or Above Normal Water-Year

**Low Flow** Season (1997-1999) 

### A. Wet or Above Normal Water-Year **High Flow** Season (1998 and 1999)



## 2 June 16-17,

1998 April 13-14,

1999

3 1 4 2 5 Carquinez Strait (1996-2009)



### C. amurensis = station 8.1 monthly means (μg/g dw); Kleckner et al. (2010) (monthly means)



### Figure 21. Observed selenium concentrations for C. amurensis collected from USGS monitoring station 8.1 at Carquinez Strait (1996-2009). Insets ( A and B ) show selenium concentrations in C. amurensis in the highlighted period from 1997 to 1999 when selenium transects were collected.Mean particulate Se (0.49) Mean particulate Se (0.73) Mean clam (14.1) 6

5

4

3

2

1

0 30

25

20

15

10

5

0 **A.** Bay: November 1999, above normal year, low flow season, 70 day residence time, TTF clam = 18.9Measured particulate Se, μg/g dw

6

5

4

3

2

1

0 30

25

20

15

10

5

0 **B**

. June 1998, wet year, high flow season (El Niño), 11 day residence time, TTF clam = 13.5Measured particulate Se, μg/g dwClam Se, μg/g dwClam Se, μg/g dw

0 5 10 Predicted allowed clam Se

Measured clam Se Measured particulate Se

15

Salinity, psu 20 25 30 35 Mean clam (5.4) Mean clam (6.6) Mean clam (11.3)

Sampling Station 18

16

14

12

10

8

6

4Clam Se, μg/g dw

18

16

14

12

10

8

6

4Clam Se, μg/g dw (avg 11.3)

6.1 411415 8.1 405 12.5Carquinez StraitSan Pablo BaySuisun BayGrizzly BaySuisunBaySuisun BaySan Pablo Bay

6.1 12.5 (avg 5.4) 0 5 10 15 20 25 30 35

Salinity, psu

Sampling Station



### Figure 22. Predicted C. amurensis selenium concentrations for November, 1999 and June, 1998 and

observed selenium concentrations in C. amurensis collected from several USGS monitoring stations for the same time periods as the transects in 1999 and 1998. (monthly means)

(monthly means)fish whole body (7.0)

fish whole body (5.0) fish diet (6.4)

fish whole body (1.0)

fish diet (0.95) particulate (0.37)

particulate (0.27)

particulate (0.056) 

### 12

10

8

6

4

2

0 1.2

1.0

0.8

0.6

0.4

0.2

0 0.14

0.12

0.10

0.08

0.06

0.04

0.02

0

0.14

0.12

0.10

0.08

0.06

0.04

0.02

0

0.14

0.12

0.10

0.08

0.06

0.04

0.02

0 

### 20,000

17,500

15,000

12,500

10,000

7,500

5,000

2,500

0 

### A

. adult female white sturgeon EC05Target Se (whole-body, diet and particulate), μg/g dwandDissolvedSe, μg/LKd



### 8

6

4

2

0 0.8

0.6

0.4

0.2

0 

### 20,000

17,500

15,000

12,500

10,000

7,500

5,000

2,500

0 

### B

. generic fish EC10Kd



### 1.4

1.2

1.0

0.8

0.6

0.4

0.2

0 0.14

0.12

0.10

0.08

0.06

0.04

0.02

0 

### 20,000

17,500

15,000

12,500

10,000

7,500

5,000

2,500

0 

### C

. juvenile white sturgeon EC05Kd



### 0 0 5 10 15
Salinity, psu 20 25 30 35Dissolved Se, μg/LDissolved Se, μg/LDissolved Se, μg/L



### 5 10 15
Salinity, psu 20 25 30 35 fish diet (4.5) **Measured** dissolved Se (mean = 0.102)

mean Se

K d **Predicted allowed**

dissolved Se target value Se

mean dissolved Se

mean (0.055)

mean (0.055)

mean 0.040)

mean (0.040)

mean (0.0082) mean (0.0082) mean (0.102) **Bay: November 1999,** above normal year, low flow season, 70 day residence time

C. amurensis food web: TTF clam = 17.1; TTF fish = 1.1

mean (0.102)

mean (0.102)



### Figure 23. Modeled scenarios for protection of sturgeon and a generic sh showing predicted allowable dissolved, suspended particulate material, and dietary selenium concentrations for the Bay. [Predicted allowable selenium concentrations are keyed to calculated K
d 

### s across the estuary for November 1999 (above normal year, low ow season). Measured dissolved and suspended particulate material selenium concentrations are given for comparison.]bird egg (11.4)

bird egg (7.7)

bird diet (4.4)

bird diet (3.0)

bird egg (5.9)

bird diet (2.3) particulate (0.26)

particulate (0.17)

particulate (0.13) 

### 12

10

8

6

4

2

0 1.2

1.0

0.8

0.6

0.4

0.2

0 0.14

0.12

0.10

0.08

0.06

0.04

0.02

0

0.14

0.12

0.10

0.08

0.06

0.04

0.02

0

0.14

0.12

0.10

0.08

0.06

0.04

0.02

0 

### 20,000

17,500

15,000

12,500

10,000

7,500

5,000

2,500

0 

### A

. scoter and scaup EC05Kd



### 8

6

4

2

0 0.8

0.6

0.4

0.2

0 

### 20,000

17,500

15,000

12,500

10,000

7,500

5,000

2,500

0 

### B

. generic bird EC10Target Se (whole-body, diet and particulate), μg/g dwandDissolvedSe, μg/LKd



### 8

6

4

2

0 0.8

0.6

0.4

0.2

0 

### 20,000

17,500

15,000

12,500

10,000

7,500

5,000

2,500

0 

### C

. scoter and scaup EC05Kd



### 0 0 5 10 15

Salinity, psu 20 25 30 35Dissolved Se, μg/LDissolved Se, μg/LDissolved Se, μg/L



### 5 10 15

Salinity, psu 20 25 30 35 mean (0.102)

mean (0.102)

mean (0.102) mean (0.038)

mean (0.026)

mean (0.020) mean (0.038)

mean (0.026)

mean (0.020) **Bay: November 1999,** above normal year, low flow season, 70 day residence time

C. amurensis food web: TTF clam = 17.1; TTF bird = 2.6 **Measured** dissolved Se (mean = 0.102)

mean Se

K d **Predicted allowed**

dissolved Se target value Se

mean dissolved Se



### Figure 24. Modeled scenarios for protection of clam-eating bird species showing predicted allowable dissolved, suspended particulate material, and dietary selenium concentrations for the Bay. [Predicted allowable selenium concentrations are keyed to calculated Kds across the estuary for November 1999 (above normal year, low ow season). Measured dissolved and suspended particulate material selenium concentrations are given for comparison.]

### Increasing SalinityDeltaBay

Rio Vista

(0.013) Antioch (no data) Benicia (4.0) Rio Vista (0.013) Antioch (no data) Benicia (4.0) Rio Vista

(0.019) Antioch (1.4) Benicia (12.7) Rio Vista (0.019) Antioch (1.4)KdKd Benicia

(12.7)



### 12

10

8

6

4

2

0 1.2

1.0

0.8

0.6

0.4

0.2 

### C

. juvenile Chinook salmon diet EC05 

### D

. juvenile Chinook salmon whole-body EC05fish wholebody (2.4)

particulate (0.8) mean (0.114)



### Increasing Salinity 8

6

4

2

0 0.8

0.6

0.4

0.2 

### A

. juvenile Chinook salmon diet EC05

mean (0.183)

mean (0.232) **Delta: October 2003, below normal year, low flow season**
aquatic insect food web: TTF insect = 2.8; TTF fish = 1.1

**Delta: April 2003, above normal year, high flow season**
aquatic insect food web: TTF insect = 2.8; TTF fish = 1.1 **Measured**

dissolved Se

mean Se

K d

**Predicted allowed**

dissolved Se target value Se

mean dissolved Se mean (0.174)DeltaBay 

### 20,000

17,500

15,000

12,500

10,000

7,500

5,000

0 

### B

. juvenile Chinook salmon whole-body EC05

particulate (0.5) fish whole-body (1.5)

fish diet (1.4)

mean (0.371) 

### 20,000

17,500

15,000

12,500

10,000

7,500

5,000

0 mean (0.174)Target Se (whole-body, diet and particulate), μg/g dwandDissolved Se, μg/L

fish diet (1.4) mean (0.356)

fish wholebody (1.5)

particulate (0.50)fish wholebody (2.4) fish diet (2.2)
particulate (0.80) mean (0.356) fish diet (2.2)



### Figure 25. Modeled scenarios for protection of juvenile Chinook salmon showing predicted allowable dissolved, suspended particulate material, and dietary selenium concentrations for the Delta. [Predicted allowable selenium concentrations are keyed to calculated K
d 

### s across the estuary for October, 2003 (below normal year, low ow season) and April, 2003 (above normal year, high ow season). Measured dissolved and suspended particulate material selenium concentrations are given for comparison.]DeltaBay 2.0

1.5

1.0

0.5

0 

### A

. **Bay: November 1999,**

above normal year, low flow season, 70 day residence timeParticulate Se, μg/g dw

0 measured particulate Se fish bird (scoter and scaup)

EC05 adult sturgeon (0.37)

EC05 bird egg (0.13) EC10 bird egg (0.17)

EC05 juvenile sturgeon diet (0.56)

5 10 15

Salinity, psu 20 25 30 35 EC05 bird diet (0.26) EC05 generic fish (0.27)

EC05 adult sturgeon (0.37)

EC05 bird egg (0.13) EC10 bird egg (0.17)

EC05 juvenile sturgeon diet (0.56) EC05 bird diet (0.26) EC05 generic fish (0.27) 2.0

1.5

1.0

0.5

0 

### B

. **Bay: June 1998,**

wet year, high flow season (El Niño), 11 day residence timeParticulate Se, μg/g dw

0 measured particulate Se

5 10 15

Salinity, psu 20 25 30 35

2.0

1.5

1.0

0.5

0 

### C

. **Delta: October 2003,** below normal year, low flow season **Delta: April 2003,** above normal year, high flow seasonParticulate Se, μg/g dw

Rio Vista
(0.019) Antioch
(1.4) measured particulate Se October
April

EC05 juvenile salmon whole-body (0.50) EC05 juvenile salmon diet (0.80)

Benicia

(12.7)
Increasing Salinity



### Figure 26. Observed and predicted allowable suspended particulate material selenium concentrations for modeled clam-based exposure scenarios in November, 1999 and June, 1998; and insect-based exposure scenarios in October, 2003 and April, 2003.

### 14

12

10

8

6

4

2

0 1.4

1.2

1.0

0.8

0.6

0.4

0.2

0 0 10 20 30 40 50 60 70 80Dissolved Se, μg/L or particulate Se, μg/g dwC. amurensisSe, μg/g dw



## Residence time, days **suspended particulate material Se**



### dissolved Se **C. amurensis Se**  

## Suisun Bay/Carquinez Strait transect (means)

Oct 7-8, 1998 22 days
(wet/low) 

### Nov 4-5,1999 70 days
(above normal/low)



### Jun 16-17, 1998 11 days (wet/high) 

### Apr 13-14, 1999 16 days (wet/high)



### K **d**



### Figure 27. Change in mean observed dissolved, suspended particulate material,

and C. amurensis selenium concentrations during transects in 1998 and 1999 as

a function of residence time.

### 35% inorganic-Se

35% inorganic-Se 50% organo-Se 89% elemental-Se

0% organic-Se

60% elemental-Se

21% organic-Se 50% organo-Se 20% elemental-Se

19% elemental-Se 

### 100

80

60

40

20

0

-20 A. Suisun Bay - Carquinez Strait

100

80

60

40

20

0

-20 10 20 30 40 50 60 70 80Particulate species, %



### Residence time, days B. Bay - Delta

**NDOI** = daily average/month **Jun 16-17, 1998** 11 days (wet/high) NDOI = 73,732 cfs

**Apr 13-14, 1999** 16 days (wet/high) NDOI = 35,034 cfs

**Oct 7-8, 1998** 22 days (wet/low) NDOI = 12,251 cfs

**Nov 4-5, 1999** 70 days (above normal/low) NDOI = 6,951 cfs **Transects**



### Figure 28. Change in mean observed suspended particulate material selenium species during transects in 1998 and 1999 as a

function of residence time.

## For Appendices A and B see separate files:

Appendix A Luoma SN, Presser TS. 2009. Emerging opportunities in management of selenium contamination. Environmental Science and Technology 43(22):8483-8487 (and Supporting Information).

Appendix B Presser TS, Luoma SN. 2010. A methodology for ecosystem-scale modeling of selenium. Integrated Environmental Assessment and Management 6(4):685-710 (and Supporting Information).

# Appendix CWater-column selenium(total recoverable) 

### Chevron Renery, Richmond CA (Apr, 2007 through Mar, 2010)Selenium load 

### Appendix C Selenium discharges from oil reneries

Figure C1. Water-column selenium concentrations and selenium loads, Chevron Renery, Richmond, California.

### Martinez Renery (Shell), Martinez CA (Apr, 2007 through Mar, 2010)Selenium loadWater-column selenium(total recoverable)



### Figure C2. Water-column selenium concentrations and selenium loads, Martinez Renery (Shell), Martinez, California.

### Tesoro Golden Eagle Renery, Martinez CA (Apr, 2007 through Mar, 2010)Selenium loadWater-column selenium(total recoverable)



### Figure C3. Water-column selenium concentrations, Tesoro Golden Eagle Renery, Martinez, California.

### Tosco Renery, Rodeo CA (Apr, 2007 through Mar, 2010)Selenium loadWater-column selenium(total recoverable)



### Figure C4. Water-column selenium concentrations and selenium loads, Tosco Renery, Rodeo, California.

### Valero Renery, Benicia CA (Apr, 2007 through Mar, 2010)Selenium loadWater-column selenium(total recoverable)



### Figure C5. Water-column selenium concentrations and selenium loads, Valero Renery, Benicia, California.

# Appendix D1APPENDIX DCompilation of field data for the Bay-DeltaTable D1.Bird liver, muscle, and egg Se concentrations for the Bay-Delta.Table D2.Bird egg Se concentrations for the Bay-Delta.Table D3.Largemouth bass Se concentrations for the Sacramento River watershed, San Joaquin River watershed, and downstream Delta during1999, 2000, 2005, and 2007.Table D4.White sturgeon andC. amurensisSe concentrations and calculated TTFs.Table D5.Invertebrate Se concentrations for Mildred Island and FranksTract during 2003-2004.2Table D1. Bird liver, muscle, and egg Se concentrations for the Bay-Delta.species and locationtissue Se (μg/g dw)dietSuisun and San Pablo Bays (2004-2006) (Wainwright-De La Cruz et al., 2008)liver (range)surf scoter overwintering7.4-119 (n = 159)Suisun Bay: 100%C. amurensis; SanPablo Bay: 52%C. amurensisSuisun and San Pablo Bays (2004-2006) (Wainwright-De La Cruz et al., 2008)egg (mean)surf scoter late Jan to spring1.7 ( n = 22)Central Bay:Venerupis philippinarumNorth Bay tidal marshes, 1998-1999(Schwarzbach et al., 2006)egg (mean and range)California clapper rail1.93 (1.12-3.13)North and South Bays prebreeding season Mar-Apr, 2005(Ackerman and Eagles-Smith, 2009)liver (mean and range)American avocet7.9 (3.1-49)black-necked stilt5.3 (2.3-41)Forster’s tern7.1 (3.7-14.5)Caspian tern6.7 (4.8-14.4)San Pablo Bay March 2002 (Hunt et al., 2003)muscle (mean)surf scoter7.8greater scaup8.7Suisun Bay, March 2002 (Hunt et al., 2003)muscle (mean)surf scoter13.3greater scaup13.4Table D2. Bird egg Se concentrations for the Bay-Delta (Schwarzbach and Adelsbach, 2003).Suisun Bay and San Francisco Bay, March-July, 2000Suisun Bay, Delta, Stone Lake, Davis, Consumnes River, March-July, 2001mean bird egg (μg/g dw)nminimum (μg/g dw)maximum (μg/g dw)snowy plover1.530.92.0American avocet1.861.32.2California clapper rail1.661.31.7Brandt’s cormorant2.121.92.3Forester’s tern2.462.02.9black-necked stilt2.422.22.6double-crested cormorant2.682.33.0California least tern2.862.43.3black-crowned night heron3.5112.54.6snowy egret4.293.74.9great egret3.0152.07.8all groups2.8740.97.83Table D3: Largemouth bass Se concentrations for theSacramento River watershed, San JoaquinRiver watershed, and downstream Delta during1999, 2000, 2005, and 2007 (Foe, 2010). [Data from 2007 are given in bold.]locationlargemouth bass Seμg/gwb dwSacramento R @ Veterans Bridge2.27±1.57Sacramento R. @ RM 442.64±0.38; 1.47±0.65;1.85±0.35Sacramento R. near Rio Vista1.50±0.54; 1.74±0.43;2.58±1.39San Joaquin R. @ Fremont Ford1.94±1.12San Joaquin R. @ Crows Landing2.54; 2.69±0.40; 2.86±1.37San Joaquin R. @ Vernalis2.37;1.29±1.15;1.95±0.47;2.44±0.13Old R. near Tracy2.31±1.83; 2.41±1.11San Joaquin R. @ Potato Slough1.59;1.36±0.38;1.32±0.532.57±2.12Middle R @ Bullfrog1.76; 1.93±0.77;2.14±0.37Franks Tract1.66±0.70; 1.20±1.11;2.37±0.29Big Break (most seaward, near Antioch)1.57±0.67; 1.03±0.48;2.86±1.02Discovery Bay1.63±0.35Whiskey Slough1.74±2.39Table D4. White sturgeon andC. amurensisSe concentrations (Stewart et al., 2004;Kleckner et al., 2010) and calculated TTFs.seaward white sturgeonSe muscle (μg/g dw)landward white sturgeonSe muscle (μg/g dw)Carqueniz Strait (station 8.1)C. amurensisSeμg/g dwChipps Island (station 4.1)C.amurensisSe μg/g dwseawardTTFsturgeonlandwardTTFsturgeon10.2 mean6.91 mean14.0 (1998)mean 10.8 (range 5-16)0.790.6312.6 (1999)11.8 (2000)14.1 (2001)12.9 grand mean (range 5.4-20)12.8 mean (2000-2001)4Table D5. Invertebrate Se concentrations for Mildred Island and Franks Tract during 2003-2004. (Lucasand Stewart, 2007).Mildred Island (August 2001)invertebrateSe μg/g dwstratiomyidae0.60corophium1.1damselfly1.3gammarus0.88; 1.2hyalella1.4aeshnidae1.6snail1.8bulk zooplankton1.8corbiculaa2.3; 2.8; 2.9isopod3.7oligochaete4.1; 4.8fish (muscle)Se μg/g dwthreadfin shad0.99inland silverside1.4redear sunfisha1.4; 1.5largemouth bass1.6Franks Tract (April 2002)invertebrateSe μg/g dwgammarus0.91; 1.0damselfly1.3hyalella1.3epiphytic material1.5bulk zooplankton1.8oligochaete2.2chironomid2.7planaria3.6corbicula3.8fish (muscle)Se μg/g dwlargemouth bass1.3redear sunfish1.5inland silverside2.0a1999-2000.