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. Presser 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 Protection Agency interagency 

funding agreements. 

U.S. Department of the Interior 

U.S. Geological Survey 

ii
iii

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. 
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Contents 

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

Introduction .................................................................................................................................................................... 4

San Francisco Bay-Delta Estuary .................................................................................................................................. 6

Regulation .................................................................................................................................................................. 6

Setting ........................................................................................................................................................................ 6

Selenium Sources ...................................................................................................................................................... 6

Hydrodynamic Connections ....................................................................................................................................... 8

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

Fish and Wildlife ...........................................................................................................................................................10

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

Effects and Effect Levels ...........................................................................................................................................11

Estuary Food Web and Exposure Models .................................................................................................................12

Ecosystem-Scale Model Components ..........................................................................................................................13

Partitioning and Transformation ................................................................................................................................13

Biodynamics: Invertebrates, Fish, and Birds .............................................................................................................15

Available Data ..............................................................................................................................................................17

Application of Ecosystem-Scale Methodology ..............................................................................................................18

Estuarine Approaches ...............................................................................................................................................18

Dissolved and Suspended Particulate Material Selenium Profiles for Modeling .......................................................19

Dissolved and Suspended Particulate Material Selenium Speciation .......................................................................20

Bioaccumulated Selenium in Prey ............................................................................................................................20

Derivation of Site-Specific Model Components .............................................................................................................21

Environmental Partitioning Factors (Kds) ..................................................................................................................21

Trophic Transfer Factors (TTFs) ...............................................................................................................................21

Clam (C. amurensis) .............................................................................................................................................21

Aquatic Insect and Other Invertebrates .................................................................................................................23

Bird Egg ................................................................................................................................................................23

Fish Whole-Body or Muscle ...................................................................................................................................24

Validation ......................................................................................................................................................................24

Prediction of Selenium Concentrations in C. amurensis ...........................................................................................24

Prediction of Existing Conditions Across Media ........................................................................................................24

Modeling Scenarios and Predictions .............................................................................................................................25

Bay-Delta Continuum ................................................................................................................................................25

Suisun Bay-Carquinez Strait .....................................................................................................................................27

Landward Sites .........................................................................................................................................................28

Choices, Limitations, and Reduction of Uncertainty ......................................................................................................28

Data Collection, Model Updates, and Refinements ......................................................................................................29

Conclusions ..................................................................................................................................................................31

Acknowledgments ........................................................................................................................................................34

References Cited ..........................................................................................................................................................34
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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 selenium 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 approach 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 seaward clam-based food webs (A) and landward aquatic insect-based food 

webs (B) for modeling of the Bay-Delta. 

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 BayDelta. 

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

selenium. 

Figure 14. Profiles of hydrodynamically categorized suspended particulate material selenium concentrations 

from transects during 1997-1999. 

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). 

Figure 16. Profiles of observed dissolved selenium concentrations for transects across the estuary salinity 

gradient during 1997-1999. 

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

calculated Kds for transects across the estuary salinity gradient during 1997-1999. 
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Figure 18. Profiles of observed dissolved (A) and suspended particulate material (B) selenium 

concentrations and calculated Kds (C) for transects across the salinity gradient 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 gradient for September, 1986 and November, 1997. 

Figure 20. Profiles of observed suspended particulate material selenium concentrations and the observed 

percentages of suspended particulate organo-Se for transects across the 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 and 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-eating bird species showing predicted allowable 

dissolved, suspended particulate material, and dietary selenium concentrations for the Bay. 

Figure 25. Modeled scenarios for protection of juvenile Chinook salmon showing predicted allowable 

dissolved, suspended particulate material, and dietary selenium concentrations for the Delta.

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. 

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 discharged 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 protection of fish and aquatic-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. 
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Table 5. Selenium effect levels derived for protection of species at risk in the San Francisco Estuary. 

Table 6. Generic selenium effect levels for fish and birds. 

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

Table 8. Bay-Delta hydrologic conditions, Net Delta Outflow Index, salinity, observed dissolved Se 

concentrations, observed suspended particulate material Se concentrations, and calculated Kds. 

Table 9. Suisun Bay/Carquinez Strait hydrologic conditions, Net Delta Outflow Index, salinity, observed 

dissolved Se concentrations, observed suspended particulate material Se concentrations, and calculated 

Kds. 

Table 10. Landward hydrologic conditions, Net Delta Outflow Index, salinity, observed dissolved Se 

concentrations, observed suspended particulate material Se concentrations, and calculated Kds. 

Table 11. Bay-Delta mean, median, 75th percentile, and 25th percentile for observed dissolved Se 

concentrations, observed suspended particulate material Se concentrations, and calculated Kds. 

Table 12. Suisun Bay-Carquinez Strait mean, median, 75th percentile, and 25th percentile for observed 

dissolved Se concentrations, observed suspended particulate material Se concentrations, and calculated 

Kds. 

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

transects. 

Table 14. Validation for existing conditions at a seaward estuary location for November, 1999 or a 

generalized mean condition using observed Se concentrations in seaward and landward white sturgeon; 

derived Kds and TTFs; and a food web for suspended 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 observed Se concentrations in seaward white sturgeon; derived Kds and 

TTFs; and a food web for suspended particulate material>C. amurensis>white sturgeon. 

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

Se concentrations in landward largemouth bass; derived Kds and TTFs; and a food web for suspended 

particulate material>aquatic insect>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 protection (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-focused transects for suspended particulate 

material>aquatic insect>juvenile salmon or steelhead. 

Table 23. Prediction scenarios using landward-focused transects for suspended particulate 

material>aquatic insect>rail. 

Appendices (see separate files)

Appendix A 

Luoma, S.N., and Presser, T.S., 2009, Emerging 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 Methodology for Ecosystem-Scale Modeling of Selenium: 

Integrated Environmental Assessment and Management, 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 that the San Francisco Bay species Potamocorbula

amurensis is now the genus Corbula, but the species name is still unclear. 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. Presser and Samuel N. Luoma 

Executive Summary 

The San Francisco Bay-Delta Estuary (Bay-Delta) receives selenium (Se) internally from oil 

refinery effluents and externally through riverine agricultural 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 translating fish and wildlife tissue Se effect guidelines to dissolved Se 

concentrations. This change in regulatory approach requires consideration of intervening steps that 1) 

formally document system hydrology, biogeochemistry, 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 Bay-Delta would be the first regulatory action where a 

bioaccumulative element is managed to protect wildlife in a marine environment. Regulating seaward 

sites in the estuary also sets in motion consideration of upstream watershed sources. 

For regulators and scientists, our approach offers an understanding that 1) diet drives protection 

and 2) the choice of food web and predator species is critical because the kinetics of bioaccumulation 

differs widely among invertebrates. Further, adequately characterizing the transformation of dissolved 

Se to particulate Se and the type and phase of the resulting particulate material quantifies 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 kinetics). Our approach also includes opportunities to analyze alternative 

modeling choices explicitly 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 particulate material (Kds); 2) dietary 

biodynamic Trophic Transfer Factors (TTFs) for important 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 guidelines. Effect guidelines for species at risk in the Bay-Delta were 

provided by the U.S. Fish and Wildlife Service (USFWS). 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 considerations for the U.S. Endangered Species and Migratory Bird Treaty 

Acts. Knowing the details of an at-risk predator’s location during critical life stages for Se effects allows 

correlating trends in diet and exposure 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 effects to set choices in 

modeling scenarios. 

1
We employ both a salinity-specific transect approach, encompassing tidally-influenced sites 

across the Bay-Delta from near Chipps Island to the Golden Gate Bridge, and a geographically focused 

approach encompassing Suisun Bay and Carquinez Strait. The most recent transect data (i.e., matched 

datasets for dissolved and suspended particulate material) from 1997-1999 are used for modeling a 

seaward C. amurensis-based food web. Similarly, the most recent transect data from 2003-2004 are used 

for modeling a landward aquatic insect-based food web. Transect sampling 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 years in both low flow and high flow seasons. 

Profiles across the estuary 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 weight. In the more restricted approach used for 

Suisun Bay-Carquinez Strait that eliminates freshwater and ocean interfaces, the range of dissolved Se 

concentrations is 0.076-0.215 μg/L, with the range of suspended particulate material Se concentrations 

as 0.15-1.0 μg/g dry weight. 

Kds are the derived ratios of dissolved and suspended particulate material Se concentrations from 

transect sampling across the estuary. The operational Kds used here quantify the complex process of 

transformation to represent exposure and bioavailability at the base of the food web. The profiles of Kds 

across the estuary illustrate the range in biogeochemical transformations and their patterns as flow 

conditions change. Generally, Kds vary similarly as suspended particulate material Se concentrations 

across transects because of the narrowly defined range of dissolved Se concentration. Specifically, 

patterns during high flow conditions in April, 1999 and low flow conditions in November, 1999 are 

distinctly different. As residence time increases from 16 days in April to 70 days in November, the 

profile shape moderates and a hydrodynamic span of efficient transformation is identified. The range for 

the Bay-Delta continuum is 712-26,912, with mean Kds shown to increase with increasing residence 

time. Kds selected for use in modeling scenarios range from 3,198 to 7,614. The Kd range selected when 

the modeling location is limited to Suisun Bay-Carquinez Strait is 1,180-5,986. 

The range of derived TTFC. amurensis is 14-26 for local conditions, an increase when compared to a 

laboratory-derived mean value of 6.25. TTFinsect and TTFbird egg are not site-specific, but are selected 

from literature values (TTFinsect = 2.8; TTFbird egg = 2.6). For TTFfish, 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 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 aquatic insect to largemouth bass food web. Thus, the model is able to 1) quantify 

transformation and biodynamics processes for the estuary 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 based 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., TTFC. 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., TTFinsect = 2.8). The model also addresses an alternative dietary preference by predators: a mix of 

invertebrate species (i.e., a 50% C. amurensis and 50% amphipod diet generates a TTFmixed of 8.8). 

Allowable dissolved, particulate, and prey Se concentration calculated through modeling of a 

specified predator species are based not only on the dietary TTF for that species (i.e., exposure), but also 

2
on the toxicological sensitivity inherent to the predator (i.e., effects guideline provided by the USFWS 

for species at risk in the estuary). Hence, bioaccumulation in salmonids will be less than that in sturgeon 

because of dietary preference, but toxicity guidelines 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 guidelines and modeling choices from the range of 

temporal hydrodynamic conditions, geographic locations, foodwebs, Kd, and TTFs described above, 

bound allowable dissolved, particulate, and prey Se concentrations. Consideration of compliance with 

allowed Se concentrations across media (i.e., water, particulate, prey, and predator) harmonizes 

regulation and is a measure of ecological consistency and relevance of the links among exposure, 

transfer, and effects. The specificity of these scenarios demonstrates 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, suspended particulate material and C. amurensis Se concentrations and 

Kds for Suisun Bay-Carquinez Strait as a function of freshwater residence time (11, 16, 22 and 70 days) 

shows that critical ecological times are functionally connected to the underlying dynamics and processes 

of low flow periods. Transformation of dissolved Se to suspended particulate material Se (i.e., dissolved 

Se decreases as suspended particulate material Se concentrations increases) 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 particulate 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. Defining or conceptualizing a baseline dissolved Se 

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

Se concentrations. 

Predictions from modeling scenarios show that choices of geographic constraints, species, diet, 

and estuary conditions all are influential in risk management for Se. Thus, the more specificity added to 

the model, the less uncertainty in predictions. If, for 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 focus can be on times when the transformative nature 

of the estuary is elevated. Juxtaposition 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 bottlenecks). 

Further refinements to the approach would include consideration of: 1) contributions of Se 

source riverine end-members; 2) hydrodynamic relationships of riverine and internal Se sources to Se 

concentrations in the estuary (i.e., an Se budget through the estuary); 3) processes at the interfaces 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. Analysis of Se concentration and speciation for 

characterized particulate 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. Hence, future monitoring 

to increase the suspended particulate material database under a suite of flow conditions would enhance 

our understanding of estuarine transformation. Monitoring invertebrate Se concentrations in food webs 

also is a practical, informative step in monitoring because the first and second most variable aspect of Se 

dynamics (i.e., Kd and TTFinvertebrate) are integrated into invertebrate bioaccumulation. 

3
In particular for modeling of avian species, uncertainties exist around laboratory-derived 

biodynamic modeling parameters; movement and migration; and links of diet and tissue Se 

concentrations under site-specific conditions (i.e., field-derived TTFbird egg). Additionally, modeling 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 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 (decreased breeding propensity, reduced recruitment) and; 

• behavioral impairment (missed breeding window, delayed timing of departure). 

Predictions from a reference dose methodology for birds also would strengthen outcomes for protection 

of avian species. 

The methodology used here is able to document estuary and ecosystem fine-structure processes 

and provide the basis and context for future scenario development. The greatest strength of the 

analytical and modeling processes is that it is an orderly, ecologically harmonized derivation approach 

for assessing different choices of criteria for protection of fish and birds. Collection of modern data and 

additional modeling in collaboration with the final development of criteria would test if identified 

mechanisms and derived factors are applicable to the Bay-Delta of today. Further modeling also would 

provide decision-makers with additional 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 currently being revised [U.S. Environmental Protection Agency (USEPA), 

1992; 2004]. National freshwater water quality Se criteria (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 protection of marine aquatic 

life allow a maximum concentration of 290 μg/L and a continuous concentration of 71 μg/L, 

concentrations approximately an order of magnitude higher than freshwater criteria. What evidence is 

available from estuarine environments suggests that these guidelines are seriously under-protective for 

at least some predator species (Luoma et al., 1992; Presser and Luoma, 2006; Luoma and Presser, 

2009). 

Consideration of development of Se criteria specific to wildlife began in 1989 as an outcome of 

the ecological disaster at Kesterson National Wildlife Refuge, California, where aquatic birds 

experienced death and deformity (Presser and Ohlendorf, 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 developed methodologies for deriving water quality 

criteria that included protection of wildlife under 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 been 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 threshold estimate for protection of 

aquatic-dependent birds and mammals. Skorupa and Ohlendorf (1991) proposed a range of waterborne 

4
Se concentrations for the protection of nesting aquatic birds through use of field-derived regressions of 

food web and avian uptake. 

Adjustments to the development of Se criteria specifically for California were called for by 1) 

the USEPA through the National Toxics Rule (NTR) and the California Toxics Rule (CTR) (USEPA, 

1992; 2000); and 2) the USFWS and 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 bioaccumulative nature of Se in aquatic 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 threatened and endangered species. 

Specifically, pursuant to section 7(a) of the U.S. Endangered Species Act (ESA) (1973), the 

USEPA consulted with the USFWS and NMFS concerning USEPA’s rulemaking action for California. 

USEPA submitted a Biological Evaluation for their review as part of the consultation process in 1994. 

This evaluation found that the proposed CTR was not likely to jeopardize the continued existence of any 

federally listed species or result in the destruction or adverse modification of designated critical habitat. 

In April of 1998, the Services sent USEPA a draft Biological Opinion that found that USEPA’s 

proposed rule would jeopardize federally listed species. After discussions with the USFWS and NMFS, 

the USEPA agreed to several changes in the final rule and USFWS and NMFS, in turn, issued a final 

Biological Opinion finding that USEPA’s action would not likely jeopardize 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 (USGS) to address issues of 1) a 

methodology for translation of a tissue guidelines to protective site-specific dissolved Se concentrations 

(implementation of tissue criteria); 2) inclusion of protection of wildlife species (i.e., federally listed 

species) in regulatory methodologies; and 3) site-specific criteria development for the Bay-Delta 

(USEPA, 1999). 

A methodology for ecosystem-scale modeling 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 dissolved and particulate 

phases at the base of the food web (i.e., Kd); 

• collection of particulate material phases and analysis of their Se concentrations 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., 

TTFinvertebrate); 

• TTFfish 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 Bay-Delta would be inappropriate and promulgated the 

current national chronic freshwater selenium criteria for the Bay-Delta (USEPA, 1992; 2000). USEPA 

also reserved the acute freshwater aquatic life criterion for Se (USEPA, 2000). In doing so, USEPA 

disapproved the statewide Se objective for the Bay-Delta 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 issued 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 that contained effluent limits based on objectives 

greater than the freshwater criteria of 5 μg/L (four day average) 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 marshlands 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 here 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 different watershed than the Northern Reach (Figure 1). 

However, waters do exchange and similar estuarine processes, 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 seleniferous agricultural lands of the western San Joaquin Valley 

conveyed through the San Joaquin River; and 

• oil refinery wastewaters from processing of seleniferous crude oils at North Bay refineries. 

Regulation of Se for oil refiners is occurring through water quality Se criteria promulgated by USEPA 

for the Bay-Delta (USEPA, 1992; 2000) and limits on loads 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 North Bay and their discharge locations are: 

Chevron Refinery at Richmond, discharge to San Pablo Bay; Martinez (Shell) Refinery at Martinez, 

discharge to Carquinez Strait; Tosco (Conoco Phillips) Refinery at Rodeo, discharge to San Pablo Bay; 

Tesoro Golden Eagle Refinery at Martinez, discharge to Suisun Bay; Valero Refinery at Benicia, 

discharge to Suisun Bay. A compilation of refinery 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 Board 1992a,b; 1993). Proposed load reductions were 

achieved in 1998 and since then, the combined Se load from the refiners has remained at approximately 

1,200 pounds (lbs)/year. The target of 1,234 lbs/year was a balance between ecological, technological, 

and economic considerations. An iterative mass emissions strategy was used in lieu of site-specific 

water quality objectives because water-column Se concentrations 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 allowed daily maximum effluent limits now are within 

the range of 34-50 μg/L. Discharger’s outflows are designed to achieve a minimum 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, with an average daily flow range of 1.9-7.4 million 

gallons/day. The range of allowed average effluent Se limits is 0.85-2.0 lbs/day. 

Regulation of Se for the agricultural community of the Grassland 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 discharged 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 Grassland Bypass Project measured 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-negotiated 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-Mendota 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 generated annual drainage Se load to 

storage in groundwater aquifers and lands designated for disposal (San 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 such a shift in environmental compartments and 

increased storage of Se within the existing Se reservoir 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-necked stilt, American avocet, and killdeer that far 

7
exceed threshold Se concentrations for impairment of reproduction (San Francisco Estuary Institute, 

2004-2005; H.T. Harvey and Associates, 2004-2009). 

Restoration of the San Joaquin River is proceeding under a comprehensive program with many 

environmental goals such as increasing flows in the upper reaches of the river to re-establish salmon 

runs in the river (Natural Resources Defense Council and others, 1988, 1989, 1992, 1999; San Joaquin 

River Group, 2010). Also, regulation of salinity for the San Joaquin River is taking place at Vernalis 

and three locations interior to the southern Delta (California State Water Resources Control Board, 

1999). Few data are available to quantify a San Joaquin River end-member Se concentration at the head 

of the estuary. Dissolved Se concentrations for the San Joaquin River averaged 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 concentration 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, 2004). Other unregulated 

sources of Se include 1) effluents from wastewater treatment plants and industries other than refineries; 

and 2) discharges from watersheds that drain directly into the estuary. 

Restoration of the estuary also is underway. The Delta Regional Ecosystem Restoration 

Implementation Plan (DRERIP) is focusing on construction of conceptual models 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 concentrations in the Delta and estuary (Presser and 

Luoma, 2006). The Sacramento and San Joaquin Rivers are the main sources of inflow, with the 

Sacramento River being the dominant inflow under current management conditions. The Sacramento 

River dilutes the more concentrated Se inputs from other sources. 

Parameters critical in determining 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 Tracy and Clifton Court Forebay south to the DeltaMendota Canal and the California Aqueduct); 

• proportion of the San Joaquin River directly recycled south before entering the estuary; and 

• total outflow of the estuary to the Pacific 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 seasons of a water year and within types of water years (Presser and 

Luoma, 2006). Water years begin on October 1st 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-February and minimum discharge is during July through August (Conomos et 

al., 1979; 1985; Peterson et al., 1985; Presser and Luoma, 2006). 

Flow, and thus freshwater residence 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 and 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 (low flow and high flow), and water year type 

(critically dry, dry, below normal, normal, above normal 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 Presser, 2009; Presser and Luoma, 2010). With this 

approach, it is possible to differentiate consumer species and their food webs in terms of 

bioaccumulative potential and predict overall ecological risk. Specifically, modeling in support of 

development of wildlife Se criteria for the Bay-Delta is through adaptation of the San Francisco BayDelta Selenium Model (Luoma and Presser, 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 quantifies 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 concentration. 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. Modeling and prediction thus enables quantifying 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 tissues of a predator. Assuming the tissue guideline is 

generic for all fish or birds, the choice of the predator 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 invertebrate Se concentration to a suspended particulate material Se 

concentration that is the source of food for the invertebrate. An environmental partitioning factor (or a 

range of factors) for partitioning of Se between water and suspended particulate material (Kd) 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 predator (i.e., the applied criterion). Thus, the allowable water column 

concentration can differ among environments; an outcome that reflects the realities of nature. This 

biologically explicit approach also forces 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 for the Bay-Delta (e.g., sturgeon and 

salmonids) and their effect guidelines provided 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 oil refinery and watershed 

agricultural drainage) and supporting different food webs associated with a gradient of salinities. For 

example, agricultural Se loading is through the San Joaquin River into the Delta 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 Delta (e.g., the amount of Sacramento River and San 

Joaquin River allowed to enter the Bay) are considered in the future. 

Figure 9 shows site-specific processes and parameters 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, and flow seasons in modeling scenarios. Detailed model steps, 

parameters, and derivations are illustrated for a seaward C. amurensis food web and a landward aquatic 

insect food web (Figure 9). A spatial component for modeling is based 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 approach, as do the additions of details of species life 

cycles and habitat use. The more detailed the modeling 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, predator life cycles, and habitat use; and 

• residence time to further constrain transformation and biodynamic processes. 

Thus, a formalized approach captures both mathematical components and exposure gradients over time. 

A focused area approach would enable 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 directly in the benthic 

food web of the Bay-Delta were considered at greater risk to Se exposures than 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. The list included 27 bird species, 15 fish species, the 

salt marsh harvest mouse, the giant garter snake, and the Dungeness crab. 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 Bird Treaty Act (MBTA), Bald 

and Golden Eagle Protection Act (BGEPA)/protected, endangered; 

• California clapper rail (Rallus longirostris obsoletus): endangered/protected, 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 include bivalves, Delta smelt (Hypomesus transpacificus) is a 

threatened species that is endemic to the estuary and, hence, is considered by the USFWS (2008) as 

threatened overall. A reptile species (USFWS, 2006, 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 watershed 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 toxicological 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 developed as part of the DRERIP process are shown for diving ducks, 

sturgeon, and salmonids inhabiting the Bay-Delta (Figure 10, adapted from DRERIP Selenium Model, 

Presser, et al., in review). These effects can lead to changes within ecosystems including population 

reductions, loss of species or individuals, and community changes. 

The USFWS (2009b) provided Se effect guidelines and associated levels of protection (e.g., 

EC10 for birds is the Se concentration in eggs associated with a 10% reduction in hatchability) for 

predator species at risk in the estuary based on several different toxicity endpoints (Table 5). [Note: 

Technically, the term EC10 does not apply to quantitative reproductive performance endpoints. The 

proper term to apply to quantitative reproductive performance endpoints such as 10% reduction in egg 

hatchability is IC10 (or 10% Inhibition Concentration). However, the subtle conceptual distinction 

between these two technical terms has not been recognized in the avian toxicology literature for Se; 

therefore, we conform with the common use of the term 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 used 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 weight (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 (whole-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 concentrations also developed by the USFWS (USFWS, 

2005; 2009b; Skorupa, et al., 2004; Skorupa, 2008). A subset of the effects guidelines and associated 

levels of protection shown in Tables 5 and 6 are used in modeling to predict toxicity under different 

regulatory proposals. Emphasis here is on illustration of Se exposure for juvenile white sturgeon, diving 

ducks as represented by the mallard, and juvenile Chinook salmon. 

Estuary Food Web and Exposure Models 

Conceptual models for the estuary show clam-based 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 estuary 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 eagle 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 (Kds) 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 exposure media (water, suspended particulate material, 

and clams) and the potential for exposure based on the 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, habitat 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 male 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 lesser 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 reach 50-92% of migrating populations (Wainwright-De La Cruz et al., 

2008; Poulton et al., 2002) (Figure 11). Diving ducks arrive in the estuary when Se concentrations are 

elevated (Figure 11). The ducks eat voraciously as they stage 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 estimated 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 that is approximately 41% clams (USFWS, 2008). 

Green sturgeon is a federally listed endangered species 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 amount of their life in the estuary. White and green sturgeon are very 

long-lived (50-100 years) and have a two year internal egg maturation that is conducive to Se loading of 

eggs (Figure 12) (Linville, 2006). 

Sacramento splittail is a federally listed species 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 over for many months to a year and may not even emigrate to the 

ocean at all) (USFWS, 2008). Population numbers for the Delta smelt are alarmingly low, and thus the 

USFWS concluded that this species is particularly vulnerable to any adverse effect. 

The giant garter snake is a federally listed species that is known 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 web specific to the giant garter snake. 

Ecosystem-Scale Model Components 

Partitioning and Transformation 

Profiles of dissolved and suspended particulate material Se concentrations across the Bay-Delta 

(Cutter and Cutter, 2004, Doblin et al., 2006; Lucas and Stewart, 2007) initiate ecosystem-scale 

modeling by developing a detailed understanding of the relationship of dissolved and particulate Se 

concentrations at specific landward 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 Kd is used here to operationally 

characterize the bioconcentration of dissolved Se into the base of the food web (Figures 7 and 13). Kd

is environment specific and is the ratio of the particulate material Se concentration to the dissolved Se 

concentration. The specific equation is 

Kd = (Cparticulate material, μg/kg dw) ÷ (C water, μg/L) (1) 

13
Note that particulate Se concentrations 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 measure and model, although total water column Se 

(i.e., unfiltered Se) can be specified in the derivation of Kd for modeling to accommodate using existing 

datasets. Measurement of a total water column Se concentration would include a fraction attributable to 

digested suspended material Se. Specifically for Bay-Delta profiles or transects, dissolved Se samples 

were collected and dissolved Se concentrations are available (Cutter and Cutter, 2004). 

A particulate material Se concentration is the other component of Kd to measure and model 

(Figure 13). The base of the food web, as sampled in the environment, can include phytoplankton, 

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

(Presser and Luoma, 2010). For simplicity 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, Kd represents phase transformation in the system (i.e., the efficiency 

with which dissolved Se is converted to particulate material Se). Phase transformation reactions from 

dissolved to particulate material Se are of toxicological significance because particulate material Se is 

the primary form through which Se enters food webs (Luoma et al, 1992; Presser and Luoma, 2010; 

Stewart et al., 2010). The different biogeochemical 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 turn, 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 dynamics 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 assimilatory 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 biogeochemical reduction (e.g., Oremland et al., 1989); 3) 

adsorption as co-precipitated selenate or selenite through reactions with particle 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 Fisher, 1991; Zhang and Moore, 1996). Selenate is the least reactive of 

the three forms of Se and its uptake by plants is slow. If all other conditions are the same, Kd 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 Kds 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 (Besser 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 extent of biological transformations, and 

developing accuracy within the model. Data collection in site-specific field situations for particulate 

phases can include benthic or suspended phytoplankton, microbial biomass, detritus, biofilms, and 

nonliving organic materials associated with fine-grained (<100 μm) 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 Kd 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 that can be compared among locations, the greater the 

uncertainty in any predictions. Further information on choice of particulate material type, sample 

collection in aquatic systems, and modeling limitations are given in Presser and Luoma (2010). For 

example, Kd can be influenced by the type of particulate 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 dissolved Se concentration would 

yield a range of Kds 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 the least desirable choice for calculating Kd,. 

Biodynamics: Invertebrates, Fish, and Birds 

Kinetic bioaccumulation models (i.e., biodynamic models, Luoma and Fisher, 1997; Luoma and 

Rainbow, 2005) account for the now well-established principle that Se bioaccumulates in food webs 

principally through dietary exposure. Tissue Se attributable to dissolved exposure makes up less than 

5% of overall tissue Se in almost all circumstances (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 processes specific to each species (Reinfelder et 

al., 1998; Wang 2002; Baines et al., 2002; Stewart et al., 2004). Biodynamic models have the further 

advantage of providing a basis for deriving a simplified measure of the 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 the relationship between Se concentrations 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 biodynamic equation (Luoma and Rainbow, 2005): 

dCspecies/dt = [(AE) (IR) (Cfood)] - (ke +kg)(Cspecies) (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 particles (%); IR is the ingestion rate of particles (g/g/d); 

Cfood is the contaminant concentration in ingested particles (μg/g dw); ke is the efflux rate constant (/d) 

that describes Se excretion or loss from the animal; and kg is the growth rate constant (/d). The equation 

shows that key determinants of Se bioaccumulation are 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 protocols for measuring such 

parameters as AE, IR, ke are now well developed (Wang et al., 1996; Luoma and Rainbow, 2005). 

In the absence of rapid growth, a simplified, resolved biodynamic exposure equation for 

calculating a Se concentration in an invertebrate is 

Cinvertebrate = [(AE) (IR)(Cparticulate)] ÷ [ke] (3) 

where Cfood is defined as Cparticulate. 

For modeling, these physiological parameters can be combined to calculate a TTFinvertebrate, which 

characterizes the potential for each invertebrate species to bioaccumulate Se. TTFinvertebrate is defined as 

TTFinvertebrate = [(AE) (IR)] ÷ ke (4) 

Similarly, foodweb biodynamic equations for fish and birds are 

Cfish or bird = [(AE) (IR) (Cinvertebrate)] ÷ ke and TTFfish or bird = [(AE) (IR)] ÷ ke (5) and (6) 

15
When laboratory data are not available, a field TTFinvertebrate can be defined from matched 

datasets (in dw or converted to dw) of particulate and invertebrate Se concentrations as 

TTFinvertebrate = Cinvertebrate ÷ Cparticulate (7) 

A field derived species-specific TTFfish is defined as 

TTFfish = Cfish ÷ Cinvertebrate (8) 

where Cinvertebrate is for a known prey species, Cfish is reported as muscle or whole-body tissue, and both 

Se concentrations are reported in μg/g dw. If necessary, the modeling approach can represent a diet that 

includes a mixed proportion of prey in the diet through use of the equation 

Cfish = (TTFfish) [(Cinvertebrate a) (prey fraction) + (Cinvertebrate b) (prey fraction) + 

(Cinvertebrate c) (prey fraction)] (9) 

Once TTFs are know, invertebrate Se concentrations are calculated from particulate material Se 

concentrations through use of the equation 

Cinvertebrate = (TTFinvertebrate) (Cparticulate) (10) 

Equations are combined to represent step-wise bioaccumulation from particulate material through 

invertebrate to fish as 

Cfish = (TTFinvertebrate) (Cparticulate) (TTFfish) (11) 

Similarly for birds, the combined equation is 

Cbird = (TTFinvertebrate) (Cparticulate) (TTFbird) (12) 

Modeling can accommodate longer food webs that contain more than one higher trophic level consumer 

(e.g., forage fish being eaten by predatory fish) by incorporating additional TTFs. One equation for this 

type of example is 

Cpredator fish = (TTFinvertebrate) (Cparticulate) (TTFforage fish) (TTFpredator fish) (13) 

Modeling for bird tissue also can represent Se transfer through longer or more complex food webs (e.g., 

TTFs for invertebrate to fish and fish to birds) as 

Cbird = (TTFinvertebrate) (Cparticulate) (TTFfish) (TTFbird) (14) 

Variability or uncertainty in processes 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. Field 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 compared 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 concentration 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 species 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 relationships than single determinations. 

Additionally, in nature, if it is assumed that organisms regulate a constant minimum concentration of 

Se, then the observed TTF will increase when the concentration 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 sites exposed to Se contamination and identified as 

problematic because of Se bioaccumulation (Presser and Luoma, 2010). However, discretion was used 

when considering datasets from extremely contaminated sites (e.g., Kesterson). The relatively small 

16
variation of TTF within taxonomically similar animals is evidence that these potential sources of 

uncertainty may be minimal in terms of biodynamic kinetics variations (Presser and Luoma, 2010). 

Available Data 

Table 7 lists available data for the Bay-Delta. Comprehensive data collection to evaluate Se 

concentrations in the Bay-Delta began in 1986. Transects 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 material Se speciation; 

• amount of total suspended material; and 

• particulate carbon (C) concentration. 

Transects during July, 2000 to January, 2004 characterize the area mainly from Rio Vista and 

Stockton to Benicia near the Carquinez Strait (Lucas and Stewart, 2007) (Figure 1). These more 

landward transects were limited to: 

• dissolved Se; 

• dissolved Se speciation; and 

• suspended particulate material 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 particulate material Se in 

comparison to percentage of suspended particulate organo-Se) across the salinity gradient were not 

always available. 

The matched data pairs for dissolved Se concentrations and suspended particulate material Se 

concentrations used here are for tidally-influenced sites. Doblin et al. (2006) hydrodynamically 

categorized (i.e., binned), for the conditions of each transect, the most landward suspended particulate 

material Se samples as the Delta. These Delta sites are nominally upstream of Chipps Island (Doblin et 

al., 2006) and, thus, these sites 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 River at Vernalis). 

The methodology for collection and analysis of dissolved and suspended particulate material Se 

samples is described in Doblin et al. (2006). Methods for determining 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 available matched data on amount of total suspended 

material (in mg/L). Because of the limited data available 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 the Bay-Delta should consider collection of suspended 

particulate material Se concentration data as μg/g dw. All solids are 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 extracted pore waters from Bay-Delta 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, personal communication March, 2009) and in 2000 from 

three locations in the Delta (Lucas and Stewart, 2007) (Table 7). 

Datasets for Se concentrations in specific 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. Because there are minimal data available, data are 

generalized in model validations; however, data used in validation scenarios and illustrations are as 

closely matched as possible. 

Application of Ecosystem-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 near the Pacific Ocean at the Golden Gate Bridge 

(Cutter and Cutter, 2004; Doblin et al., 2006; Lucas and 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 Kds and TTFs and to predict allowable 

dissolved Se concentrations at each salinity measured across an estuary profile. This gradient modeling 

approach illustrates the variability across the estuary in terms of transformations, bioaccumulative 

potential, and protective dissolved allowable Se concentrations (Figures 2 and 9). A generalized 

approach (i.e., using a mean Kd from a transect) would add uncertainty to the derivations and 

predictions because of, for example, inclusion of samples from freshwater and ocean interfaces. Mean 

Se concentrations for transects can be used as a way to compare datasets through time, but that approach 

may be of limited applicability. Other statistical parameters or analysis techniques also could be used 

(i.e., median, 75th percentile value) for comparison of estuarine conditions. 

A second modeling approach, a focused location approach, uses compartmentalized data for 

Suisun Bay and Carquinez Strait (Doblin 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 four embayments: 1) Central Bay; 4) San Pablo Bay; 

3) Carquinez Strait-Suisun Bay; and 4) Delta. Figure 14 shows the range of suspended particulate 

material Se concentrations within the compartmentalized segments and the patterns within the range of 

illustrated flow conditions. Focusing on transect samples that specifically represent Carquinez StraitSuisun Bay allows modeling and prediction for the localized area most affected by internal oil refinery 

Se sources and for time periods of specified flow conditions. Again, a mean or other statistical measure 

for each transect, but within the Suisun Bay-Carquinez 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 uncertainties 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, freshwater residence times, water-year types, and flow 

seasons for transects of the Bay-Delta (Cutter and Cutter, 2004; Doblin 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 during these transects and the proportion of the recent historical record 

represented by these five transects 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 conditions in the estuary in 

the future. 

Dissolved and Suspended Particulate Material 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 included 13 sites. Conditions represented are all wet 

or above normal years, with sampling in June, 1998 and April, 1999 being during 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 extremely wet conditions related to El Niño (Figures 16). 

Approximately 70% of the data for this transect was obtained at sites with salinities < 5 psu. In contrast, 

profiles for residence times of 68 to 70 days in November, 1997 and 1999 show a span of salinities up to 

approximately 32 psu. 

Specifically, Figure 16 shows dissolved Se concentrations 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 estuary. 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 concentrations (0.15-2.2 μg/g dw) for all Bay-Delta 

transects is not as narrowly defined as that for dissolved Se (Figure 17; Table 8). The patterns of 

particulate enrichment vary with specified flow condition (e.g., April, 1999; November, 1999). The 

variation at freshwater and ocean interfaces would contribute differently (or may contribute 

substantially) to a calculated overall mean condition. Also depicted is the variation in calculated Kds 

across the estuary. These Kds will be used later as critical location-specific inputs for ecosystem 

modeling. 

A subset of dissolved and suspended particulate material Se concentrations 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 concentrations 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 Vista 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, and 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 extended to some seaward locations 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 below normal year-low flow condition. The range of 

dissolved Se concentrations is 0.068-1.01 μg/L and the range of suspended particulate material Se 

concentrations is 0.23-1.5 μg/g dw (Table 10). 

Dissolved and Suspended Particulate Material Selenium Speciation 

Selenium speciation in source discharges and within the gradient of the estuary itself are 

important in quantifying the efficiency of transformations from dissolved Se to particulate Se (Figure 

2). Profiles of dissolved Se speciation across the salinity 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). Other factors to consider in broad comparisons such as these, are 

that the salinity for Carquinez Strait near the refineries during November, 1997 ranged from 

approximately 12 to 19 psu (Doblin et al., 2006) and that the residence time was 24 days during the 

1986 transect and 70 days during the 1997 transect. 

Figure 20 shows profiles across the Bay-Delta of suspended particulate material organo-Se 

concentrations as the percentage of the total of the three suspended particulate material Se species 

analyzed [(i.e., organo-Se, elemental Se, and inorganic Se (adsorbed selenate and selenite), Doblin et al., 

2006]. The patterns of organo-Se particulate enrichment identified here serve as the basis for 

quantifying the effects of transformations to particulate material Se (i.e., Kd) and the assimilation 

efficiency of Se in the particulate material by prey (i.e., understanding the particulate 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 connection 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 within 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 landward 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 data, patterns and connections to hydrodynamic and ecological cycles are 

difficult to assess. 

Derivation of Site-Specific Model Components 

Environmental Partitioning Factors (Kds) 

Location-specific Kds 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, 75th percentile, median, and 25th percentile Kds (Tables 11 and 12). The location-specific Kds 

and set of statistical Kds are then used to represent conditions in the estuary for modeling a seaward 

clam-based food web and predicting an allowable dissolved Se concentration. The set of Kds used to 

represent conditions in the estuary for modeling a landward insect-based food web and predicting an 

allowable dissolved Se concentration is shown in Table 10. 

Location-specific Kds show the variation that can be expected across the estuary in the recent 

past (Figures 17 and 18). Kds vary similarly as suspended particulate material Se concentrations do 

across transects because of the narrowly defined range of dissolved Se concentration. For Bay-Delta 

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

transects, Kds range from 712 to 7,725 (Table 9). For Rio Vista and Stockton to Benicia transects, Kds 

range from 554 to 12,650 (Table 10). As noted previously, these latter transects also extend to seaward 

locations and, hence, calculated means include combinations of data from both landward and seaward 

locations. These means and ranges for Kds agree well with compiled field datasets for Kds for estuaries 

and choices used in previous Bay-Delta modeling 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). TTFC. 

amurensis derived from laboratory experiments averaged 6.25 over a range of assimilation efficiencies, 

ingestion rates, and efflux rate constants (Presser and Luoma, 2010). This average is within a range of 

0.6 to 23 for invertebrate species, with TTFs for species of bivalves being the highest (Presser and 

Luoma, 2010). 

Experimental physiological biodynamic parameters and rates are derived under idealized 

conditions in the laboratory. These biodynamic equations can be adjusted for a specific ecosystem by 

incorporating data from that system (Presser and Luoma, 2010). TTFC. 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-specific modification of equation (3) 

CC. amurensis = [(AE) (IR) (Csuspended particulate material] ÷ (ke) (14) 

where (AE) (IR)/ke is defined as TTFC. amurensis and Cfood is defined as the Se concentration in estuary 

suspended particulate material (Csuspended 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 elemental Se, adsorbed Se, and ogano-Se. 

Our site-specific approach here differs from broader approaches where 1) laboratory data for 

biodynamic parameters such as AE and IR of particulate material may be generalized; 2) particulate Se 

concentrations may be an average of several phases of material (i.e., particulate Setotal); or 3) field data 

may be sparse and thus applied across an entire watershed (Presser and Luoma, 2009). 

In general, for the purposes of a Bay-Delta location and estuarine processes, the suspended 

particulate material Se concentration carries with it assumptions about Se being associated primarily 

with organic material (detritus and living organisms). This allows us to determine IR on the same 

organic material basis (assuming clams seek organic material in the suspended particulate material) and 

to refine AE to account for suspended particulate material speciation (i.e., divide AE into three 

components of Se in suspended particulate material and their individual bioavailabilities). These 

assumptions are all rooted in well established biological understanding of bivalve feeding (Cammen, 

1980; Lopez and Levinton, 1987). We ignore the possibility of uptake directly from water by the clams 

because that has been shown in a large body of work to be trivial (Luoma and Rainbow, 2005).

Justifications for values used in each parameter of the equation for a site-specific approach are: 

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 suspended particulate material Se concentration (μg 

Se/g suspended particulate material) by mg C/mg suspended particulate material. For the 

present calculations we employ suspended particulate material Se concentrations as justified 

below. 

2. IR is determined by filtration rate (125 L/g clam/d, Cole et al., 1992) 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 across all data) they ingest 2.5 times their body weight per day in 

total suspended particulate material. If IR is calculated at each of three low river discharge 

months where data is available, the average is 1.7 g suspended particulate material/g clam/d. 

Experience has indicated that the ingestion model is more accurate when actual outcomes are 

used (or averaged) for the generic situation (i.e., 1.7 g suspended particulate material/g clam/d) 

as compared to taking the average of each component of the outcome and calculating a generic 

average. Therefore, we recommend using 1.7 g suspended particulate material/g clam/d for 

modeling. 

3. The derivation of a refined site-specific AE based on individualized bioavailabilities of Se in 

suspended particulate material uses observed fractions of particulate organo-Se, adsorbed Se, or 

elemental Se found in the estuary (Doblin et al., 2006) combined with individual AEs for those 

particulate Se species from the literature (living phytoplankton, AE = 60%; adsorbed on seston, 

AE = 40%; elemental, AE = 0%; Schlekat et al., 2004; Wang et al., 1996). The equation is: 

AE = (fraction organic particulate Se) (AE organic particulate Se) + (fraction adsorbed 

particulate Se) (AEadsorbed particulate Se) + (fraction elemental particulate Se) 

(AEelemental particulate Se) (15) 

For example, if a site-specific sample of suspended particulate material collected in the estuary 

contains 45% Se in phytoplankton at an assumed AE of 60%; 30% Se adsorbed on seston at an 

22
assumed AE of 40%; and 25% elemental Se in sediment at an assumed AE of 0%, then the 

composite AE = (0.45 x 0.6) + (0.30 x 0.40) + (0.25 x 0) = 0.39 or 39% AE. 

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

5. When we model for times when all data are available from the estuary, we use all data from that 

sampling date. When we model generically we employ mean parameters. 

Given the above protocol and assumptions, we can directly calculate C. amurensis Se 

concentrations for comparison to observed Se concentrations to validate predictions or calculate a TTFC. 

amurensis for use in modeling. If the data and assumptions given above are used in a site-specific 

modification of equation (4) 

(IR) (AE) ÷ ke = TTFclam (16) 

then 

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

Or, in terms of a C. amurensis Se concentration, if a 0.84 μg/g dw suspended particulate material Se 

concentration is assumed, then 

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

Salinity-specific or transect specific Se concentrations and TTFs for C. amurensis can be calculated 

using the same protocol as above, but with percentages of C and suspended particulate material Se 

species observed in that transect. Thus, an individual C. amurensis Se concentration and TTFC. amurensis

can be calculated from each matched set of data from the five suspended particulate material transects 

for the estuary (Doblin et al., 2006), making the predictions and derivations as detailed as the data 

permit. This data-intensive approach yields a mean TTFC. 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 TTFC.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 TTFinsect of 2.8 is used here for modeling a landward 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 datasets for particulate Se and insect Se concentrations in freshwater 

environments for several species of aquatic insects including mayfly, caddisfly, dragonfly, midge and 

waterboatman. TTFs for other potential invertebrates in landward food 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 derived 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 TTFbird egg is 2.7. If dietary Se concentrations 

that are unrealistic for estuary food webs are eliminated (< 1 μg/g dw and >18 μg/g dw), then a similar 

mean for TTFbird egg or 2.6 is calculated. A TTFbird 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 terms of dietary Se concentrations for birds and enough laboratory or 

field data are available. Modeling by Presser and Luoma (2010) showed a similar range for TTFbird 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 TTFfish of 1.1 is used here for modeling based 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 fish 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 derived from field studies range from 

0.6 to 1.7. TTFs derived specifically for white sturgeon range from 0.6 to 1.7, with a mean of 1.3. 

Selenium TTFs for fish also can be derived from data given in USFWS (2009b) (Table 5). If data 

provided for laboratory dietary Se concentrations are limited to a range of 1 to 20 μg/g dw and the 

corresponding fish tissue Se concentrations, then TTFs calculated from the USFWS data range from 

0.32 to 5.6, with a mean of 1.07. Again, as for modeling for birds, a regression equation for diet and fish 

whole-body or muscle Se concentrations could be used in future modeling if scenario choices are 

specific enough in terms of dietary Se concentrations 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 concentrations. Monthly mean observed clam Se 

concentrations from USGS monitoring station 8.1 near Carquinez Strait 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) and compiled observed Se concentrations for C. amurensis

from all monitoring stations during the transect collection period from November, 1997 to November, 

1999. Each transect time period was two days, but reported 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 predict the variability seen in clams during the low 

flow season with a residence time of 70 days (12.6 μg/g dw observed 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 material, and tissues of food-web species 

during times when observed datasets are available. The generalized equation for translation of a fish 

tissue Se concentration to dissolved or water-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 November, 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 possible given the scarcity of available data across all media. Several 

choices for TTFsturgeon, TTFC. amurensis, and Kd that are based on the ranges derived for the estuary are 

illustrated. 

24
Using existing Se concentrations in seaward white sturgeon, landward white sturgeon, and 

largemouth bass in the Delta (Stewart et al., 2004; Foe, 2010) as the starting points for modeling, 

predicted prey, suspended particulate material, and dissolved Se concentrations are comparable to the 

range of observed conditions and most are within the range of observed Se concentrations (Tables 14-

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

both a seaward and a landward observed sturgeon Se concentration to test the uncertainty within a 

continuum approach (Table 14). The more focused Suisun Bay-Carquinez Strait simulations better 

narrow the range of suspended particulate material Se concentrations (Table 15). Simulations for an 

insect-based food web are all within observed 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 phases and habitat are shown in Figure 

12. Tissue Se concentrations and specified EC levels 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 EC10, 05, and 0 (7.7, 5.9, 2.8 μg/g dw); 

• scoter or scaup (diet) at EC10, 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 encompassed within those for salmon 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 water-column Se concentration (Table 2 and Figure 7) is 

Cwater = (Cfish) ÷ (TTFfish) (TTFinvertebrate) Kd (17) 

where (Kd) (Cwater) is substituted for Cparticulate and the equation is solved for Cwater. An analogous 

equation for translation of a bird egg Se concentration is 

Cwater = (Cbird egg) ÷ (TTFbird) (TTFinvertebrate) Kd (18) 

Model scenarios and predicted allowable dissolved, 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 following section for mixed diet scenarios). Kds are transect 

specific and TTFs are those listed above (TTFclam for C. amurensis = 17.1; TTFinsect = 2.8; TTF bird egg = 

2.6; TTFfish = 1.1).

Hydrologic conditions (residence time, water-year type, flow season, and NDOI, Tables 17-19) 

are listed because of their importance in determining processes that affect Se transformations between 

dissolved and suspended particulate material Se concentrations and the 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 conditions. 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 exposure for low flow seasons is emphasized 

here in illustrated scenarios. Low flow seasons (and especially low flow seasons 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 Luoma, 2006). As discussed previously, Figure 12 illustrates the 

importance of the low flow season in terms of cycles of prey Se contamination and habitat-use by 

species important to the Bay-Delta. 

Modeling here predicts allowable Se concentrations that are linked to calculated Kds across the 

estuary for individual transects (Figures 23-25). Thus, a Bay-Delta continuum approach can be used to 

generate a set of salinity-specific predictions. The theoretical constructs 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 salinity-specific location, 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 particulate 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 overall regulatory and management considerations by 

addressing salinity-specific locations. 

Protection of fish for a seaward location is illustrated by specific exposure scenarios for an adult 

female white sturgeon (EC05 whole-body), a generic fish species (EC10 whole-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 for whole-body fish; observed Kds for November, 1999; and 

modeled dissolved, diet, and suspended particulate 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 generic 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 mallards. As above, shown are: guidelines for bird eggs; 

observed Kds for November, 1999; and modeled dissolved, diet, and suspended particulate material Se 

concentrations (Table 18). 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 (Table 18; Figure 24). 

Protection of fish for a landward location is illustrated by specific exposure 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 Kds for October 10, 2003 and April 22-23, 2003; 

and modeled dissolved, diet, and suspended particulate material Se concentrations (Table 19). Predicted 

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

to Benicia and observed dissolved Se concentrations are given for comparison. Interpretation across 

these transects is complex given the interface with freshwater and the variation in Kd. For landward sites 

(categorized as Delta, Figure 25; see discussion below) during conditions in the low flow season of 

October, 2003, observed dissolved Se concentrations exceed predicted allowable 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 Bay at Antioch, which is above Chipps 

Island. Data analysis and modeling for these transects assumes that an aquatic insect diet is consumed 

by fish even in habitats of higher salinity, a scenario 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 Kds. 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 concentrations 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 material in determining food-web bioaccumulation, 

Figure 26 shows observed and predicted suspended particulate material Se concentrations for the 

previously modeled exposure scenarios and set of guidelines (Figures 23-25). In addition, an exposure 

scenario for the estuary during June, 1998 (wet year, high flow season) is modeled (Tables 17 and 18). 

Patterns and ranges of particulate enrichment during 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-based food webs during the low 

flow season in November, 1999, observed suspended particulate material Se concentrations exceed 

predicted allowable suspended particulate 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 suspended particulate material Se 

concentrations exceed predicted allowable suspended particulate material Se concentrations at higher 

salinities (Figure 26B). For landward aquatic insect-based food webs (Delta) during October, 2003 (low 

flow season) and April, 2003 (high flow season), observed mean suspended particulate material Se 

concentrations exceed predicted allowable suspended particulate material Se concentrations for juvenile 

salmon, except at two low salinity locations (Figure 26C). 

Suisun Bay-Carquinez Strait

As previously described, a focused approach 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 processes (i.e., the variability at ocean-influenced and 

freshwater-influenced sites) that are part of the spectrum of the Bay-Delta. Landward sites can show the 

influence of elevated Se in allochthonous suspended particulate material and seaward 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-Carquinez 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 generated here illustrate the effect of a 

limited set of choices for Se effect guidelines, Kds, and TTFs (Tables 20 and 21). As discussed 

previously, model choices can be altered 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 identify data gaps and monitoring needs. 

27
Tables 20 and 21 show comparative prediction scenarios 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 Suisun Bay-Carquinez Strait, four choices for Kd are 

illustrated (mean Kds of 1,180; 2,666; 3,435; and 5,986 during increasing residence times in low and 

high flow transects in 1998 and 1999) (Tables 20 and 21). Choices for TTFfish are 0.8 and 1.1 and the 

choice for TTFbird egg is 2.6. Choices for TTFprey 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 into individual components 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 TTFC. 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 (TTFC. 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 significantly, with 0.428 μg/g allowed in the single species 

scenario and 0.826 μg/g in the mixed diet scenario. Overall though, the effect of this theoretical 

construct is to reduce the bioaccumulative potential of the modeled invertebrate species. 

Modeling for the area of Suisun Bay-Carquinez Strait within the specified set of parameters 

listed above, gives ranges of predicted dissolved, 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 combinations of model parameters will set the outcomes 

for criteria development and regulatory action in the future. 

Landward Sites 

Comparative prediction scenarios also are generated from transects that focus on landward sites 

(Lucas and Stewart, 2007). Comparative outcomes from scenarios for aquatic insect-based food webs 

are illustrated in Tables 22 and 23. For a landward aquatic insect-based food web four choices for Kd

are illustrated (means Kds of 2,268, 2,981, 2,684, and 5,855 during low and high flow transects in 2003 

and 2004) (Tables 22 and 23). Choices for predator TTFs are TTFfish = 1.1 and TTFbird eggs = 2.6. As 

above, ranges of predicted dissolved, suspended particulate 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 Bay-Delta. These figures represent the fine-scale 

information that defines and quantifies the ecological, 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 (Kd) 

(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) discuss the limitations of an ecosystem-scale modeling approach in 

general, but also note how models provide insights that advance understanding of value both to science 

and management. For the Bay-Delta, combining modeling with knowledge of fine 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 and a high flow season (April, 1999; 

November, 1999) across the Bay-Delta continuum. In further analysis of data for Suisun Bay-Carquinez 

Strait, Figure 27 shows mean observed dissolved and suspended particulate material Se concentrations 

and Kds as a function of residence time. Dissolved Se concentration decreases as residence time 

increases, but suspended particulate material Se concentrations increase sharply with increasing 

residence time. Including suspended particulate material 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 theoretical considerations of Se phase 

dynamics (see previous discussion and Presser and Luoma, 2010). Given the steepness of the curve, 

regulation of suspended particulate material Se concentration may be a more sensitive parameter on 

which to assess change and choice. Defining or conceptualizing a baseline dissolved 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 surrounding 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 reaches 50% in both plots at a residence 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 flow conditions (see also Figure 9 for linked mechanistic components of 

model approach). Further, Figure 27 helps establish the benefits of a Kd-approach in reducing 

uncertainties otherwise associated with modeling the complex processes of transformation and 

speciation, and of a biodynamic approach that incorporates the assimilation efficiency 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 concentrations (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 particulate material; seaward bivalves and amphipods (or other seaward 

invertebrate species); aquatic insects (or other landward invertebrate species); sturgeon, salmon, 

steelhead (or other fish species); and eggs and tissue from avian species (see complete list in Figure 11). 

A designated set of methods for collection and analysis of samples used in modeling of the Bay-Delta 

are needed to add consistency to model inputs. Further documentation of a predator’s dietary preference 

also would be desirable because food webs may change as criteria development goes forward. Follow29
up modeling can be done in response to collection of additional monitoring data and consideration of the 

pending USEPA national fish tissue guidance. 

Representation of Hydrologic Conditions: Analysis of flow conditions to give context to the 

environmental partitioning and foodweb biodynamic processes described here is fundamental to 

modeling for the Bay-Delta. For example, transect data for wet and above normal water years illustrate 

how Se concentration, Se speciation, and Kd profiles vary during conditions in April, 1999 (a high flow 

season) as compared to November, 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 data from a dry year to model an insect-based food 

web. Comparing model predictions for scenarios based 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 upstream river sources. For example, 

quantifying end-member Se concentrations for the Sacramento River and San Joaquin River would 

define the influence of riverine sources on Se concentrations in the estuary. Spatial and temporal 

definition in such a study should be such to resolve questions as future management strategies are 

implemented (Figure 2). 

Chronic Effects in Birds: Modeling of clam-eating migratory bird species, such as scoter and 

scaup, in reference to potential chronic 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 Dynamics and Species Diversity: Monitoring and comprehensive 

compilation of data for community change, introduction of species, loss of species, and loss of 

individuals that are threatened or endangered would document changes 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 further define bivalve kinetics. Modeling for C. amurensis also 

could be location-specific to add more specificity to modeling. Modeling could utilize TTFfish of up to 

1.9. Important site-specific Se TTFs to be updated include those for aquatic insects and other 

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

material and invertebrate Se concentrations 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 monitoring data available for modeling. This type of 

refinement to model parameters is discussed in Presser and Luoma (2010). For example, a concentration 

of Se in food can be calculated that takes into account site-specific bioavailability of particulate material 

to invertebrates. The generalized equation is 

Cparticulate= (AE) (Cparticulate a) (sediment fraction) + (AE) (Cparticulate b) (detritus fraction) + (AE) 

(Cparticulate 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, allowable dissolved Se 

concentrations could be calculated using the equation for a mixed invertebrate diet 

Cwater = (Cfish) ÷ (TTFfish) (Kd) [(TTFinvertebrate a) (prey fraction)] + [(TTFinvertebrate b) (prey 

fraction)] + [(TTFinvertebrate 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 invertebrates in the diet of each predator and a TTFinvertebrate 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, model scenarios could 

incorporate sequential bioaccumulation in longer food webs 

Cwater = (Cfish) ÷ (TTFfish) Kd (TTFinvertebrate) (TTFforage fish) (21) 

Cwater = (Cfish) ÷ (TTFfish) Kd (TTFTL2 invertebrate) (TTFTL3 invertebrate) (TTFTL3 fish) (22) 

For example, modeling a Dungeness crab food web would 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-salinity locations were not 

sampled on a consistent basis for the Bay-Delta during the analysis periods reported on here. 

Designation of specific sampling locations would greatly improve predictions for landward sites. Data 

analysis that compares dissolved and suspended particulate material Se concentrations and calculated 

Kds at specific locations across time also would be helpful to regulatory guidance. Datasets specific to 

Se concentrations in landward food webs (e.g., invertebrates 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 USFWS 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 for 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 analysis could be compared to those outcomes of modeling scenarios shown 

here to add weight to the conclusions drawn for the protection of predators 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 understanding (Reiley et al., 2003). 

Data Analysis: Ecosystem-scale modeling is more than mathematical correlations. Its success, in 

part, depends on formalization and conceptualization of existing data for food web ecology, system 

hydrology, and the biogeochemistry of partitioning. Thus, ultimately a comprehensive Bay-Delta model 

(i.e., addressing interconnection of estuarine processes, habitats, species, 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 dissolved and particulate 

phases at the base of the food web (i.e., Kd); 

• collection of particulate material phases and analysis of their Se concentrations 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 invertebrate physiology (i.e., 

TTFinvertebrate); 

• TTFfish 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) provides a record of supporting data on which to base 

decisions; 3) uses site-specific ecology, biogeochemistry, and hydrology; 4) includes choices explicitly 

throughout the decision-making process; 5) addresses uncertainties by showing outcomes of different 

choices in modeling scenarios; and 6) validates 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 for predators at risk that include dietary 

preferences (i.e., percentages 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 exposure route, effect endpoint, and 

magnitude of effect (EC0, EC05, and EC10); 

• analysis of spatially and temporally matched datasets for dissolved and suspended particulate 

material Se concentrations across the salinity gradient; 

• derivation of salinity-specific or location-specific Kds; 

• derivation of site-specific TTFC. amurensis; 

• selection or development of TTFfish, TTFbird, and TTFs for other invertebrates; 

• validation of modeling through comparison of predictions to observed Se concentrations; 

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

• prediction of allowable dissolved, suspended particulate material, and prey Se concentrations. 

Consideration of compliance with allowed Se concentrations across media (i.e., water, particulate, prey 

and predator) harmonizes regulation and is a measure of ecological consistency 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 insect-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 levels 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 aquatic insect to largemouth bass food 

web. 

Site-specific analysis and modeling show that: 

• estuarine approaches that focus on seaward, 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 conditions all are influential in risk 

management for Se; 

• the field-derived TTFC. amurensis that is derived here is the first instance of a field-derived TTF for 

a marine bivalve species; the value is appreciably higher than laboratory-derived values; 

• modeling of species at risk takes into account both 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 particulate 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 connected to the underlying dynamics and processes of 

low flow periods in Suisun Bay-Carquinez Strait thus allowing modeling and prediction as 

changes occur in management and regulations. 

The approach could be refined by: 

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

and birds as illustrated in Figure 11; 

• determining contributions of specific sources; 

• quantifying end-member Se concentrations and their hydrodynamic connection 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 chronic toxicity in avain species; and 

• further linking ecosystem-scale modeling to fine structure estuary processes. 

Analysis of Se concentration and speciation for characterized particulate 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 increase the suspended particulate material database 

under a suite of flow conditions would enhance our understanding of estuarine transformation. 

Monitoring invertebrate Se concentrations in food webs also is a practical, informative step in 

monitoring because the first and second most variable aspect of Se dynamics (i.e., Kd and TTFinvertebrate) 

are integrated into invertebrate bioaccumulation. 

Expressly for modeling of avian species, uncertainties exist around biodynamic modeling 

parameters (TTFbird egg); movement and migration; and links of bioaccumulation, exposure, and toxicity 

under site-specific conditions. Additionally, modeling 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 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 (decreased breeding propensity, reduced recruitment) and; 

• behavioral impairment (missed breeding window, delayed timing of departure). 

Predictions from a reference dose methodology for birds 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 specificity 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 species to set a range of limits and establish an understanding of the 

relevant conditions, biological responses, and ecological risks 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 influence exposure and uptake of Se by fish and birds. The 

33
uncertainty that stems from the variability in these processes reflects the complexity of the estuary. 

Nevertheless, the methodology used here is able to document fine-structure processes in different 

habitats and provide context for future scenario development. The greatest strength of the analytical and 

modeling processes is that it is an orderly, harmonized derivation approach across media for assessing 

different choices of Se criteria for 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 National Research and Toxics Substances Hydrology 

Programs. Thanks to Diane Fleck and Eugenia McNaughton of USEPA for support and insight 

throughout this process. Thanks also to the U.S. Fish and Wildlife Service, Division of Environmental 

Quality, Sacramento, California, for provision of effects levels for predators at risk in the San Francisco 

Bay-Delta Estuary. 

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42
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45
46

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Tables 1-23
1 

Table 1. Oil refinery Se loads discharged to the Bay-Delta during 1986-2009. [San Francisco Bay Board, 1992a,b; 1993; Lila Tang a

Bay Board, personal communication, 1999-2006; USEPA, 2010]. 

year 

Chevron Refinery 

(Richmond, CA; 

discharge to San Pablo 

Bay) lbs Se/year 

Martinez (Shell) Refinery 

(Martinez, CA; discharge 

to Carquinez Strait) lbs 

Se/year 

Tosco (Conoco Phillips) 

Refinery (Rodeo, CA; 

discharge to San Pablo Bay) 

lbs Se/year 

Tesoro Golden Eagle 

Refinery (Martinez, CA; 

discharge to Suisun Bay) 

lbs Se/year 

Valero Refinery (Ben

CA; discharge to Su

Bay) lbs Se/year 

1986 - - - - - 1987 - - - - - 1988 - - - - - 1989 - - - - - 1990 - - - - - 1991 - - - - - 1992 - - - - - 1993 - - - - - 1994 - - - - - 1995 - - - - - 1996 - - - - - 1997 - - - - - 1998 - - - - - 1999 314 441 107 129 133 2000 174 368 114 130 126 2001 282 451 123 100 144 2002 197 455 145 145 153 2003 239 464 90 144 175 2004 204 472 115 149 159 2005 276 490 154 154 177 2006 278 542 159 193 195 

2007a - - - - - 

2008 221 709 187 193b 160 2009 210 515 209 193b 160 

aData not available from USEPA (2010); bData not available from USEPA (2010), therefore estimated as 2006 Se load ; cIncludes estimated Se load for Tesoro 

defined as 1989-1991 average annual loading of 4,935 lbs Se/year.
2 

Table 2. Generalized steps in ecosystem-scale methodology for translation of a tissue Se concentration to a water-column Se conc

and aquatic-dependent wildlife. [Adapted from Table 5, Presser and Luoma, 2010.] 

Translation of Tissue Criterion to 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 TTFfish or use default TTFfish of 1.1; for birds, choose species-specific 

TTFibird or use default TTFbird of 2.0. 

• Identify appropriate food web(s) for selected fish or bird species based on species-specific diet. • Choose site-specific TTFinvertebrate for invertebrates in selected food web(s) or use default TTFinvertebrate

for species of invertebrate (see list in Presser and Luoma, 2010). 

• Choose site-specific Kd or use Kd indicative of a) generalized source of Se and receiving water 

conditions or b) site-specific hydrologic type and speciation; or a default Kd of 1000 (see list in Presser and Luoma, 2010). 

• Solve equation(s) for allowable water-column concentration for protection of fish or birds (i.e., 

predator) If assume single invertebrate diet, then 

o Cwater = (Cpredator) ÷ (TTFpredator) Kd (TTFinvertebrate) 

If assume a mixed diet of invertebrates, then 

o Cwater = (Cpredator) ÷ (TTFpredator) (Kd) [(TTFinvertebrate a) (prey fraction)] + [(TTFinvertebrate b) (prey 

fraction)] + [(TTFinvertebrate c) (prey fraction)] 

If assume sequential bioaccumulation in longer food webs, then 

o Cwater = (Cpredator) ÷ (TTFpredator) Kd (TTFinvertebrate a) (TTFforage fish) o Cwater = (Cpredator) ÷ (TTFpredator) Kd (TTFTL2 invertebrate) (TTFTL3 invertebrate) (TTFTF3 fish) 

where TL = trophic level 
3 

Table 3. List of species considered for evaluation of Se exposure risk in the San Francisco Bay/Delta. [Reproduced from USFWS, 2

communication, S. Detwiler, USFWS, Sacramento, California, 11/17/10). 

Common Name Scientific Name Federal Status California State 

Status 

Potential to be adversely affected by selenium in Bay/De

Mammals

salt marsh harvest mouse 

Reithrodontomys raviventris endangered protected As a terrestrial herbivorous mammal, unlikely to be among th

sensitive of wildlife species; therefore not likely to be a “spec

Birds

American white pelican 

Pelecanus erythrorhynchos MBTA concern SF Bay is North end of West Coast distribution of non-breed

bottom-feeding fish as well as schooling fish, but not likely to risk.”. 

California brown pelican 

Pelecanus occidentalis californicus 

endangered (delisted 11/2009, MBTA) 

protected, endangered (protected 2/09) 

SF Bay is North end of W Coast distribution. Feeds mainly otherefore, not part of benthic-based food chain and not likely risk.” 

white-faced ibis Plegadis chihi concern concern Breeds and winters in San Joaquin Valley. Inhabits mainly fr

also estuarine wetlands. Eats aquatic and moist soil inverteblikely to be a “species most at risk.” 

double-crested cormorant 

Phalacrocorax auritus MBTA concern Winters in Central Valley and SF Bay/Delta. Feeds on bottom

invertebrates as well as schooling fish. At some risk but not at risk.” 

American bittern Botaurus lentiginosus concern none Feeds mainly in freshwater marshes, eating mainly insects a

therefore not likely to be a “species most at risk.” 

western least bittern 

Ixobrychus exilis hesperis concern concern Breeds in SF Delta. Feeds in fresh and brackish water mars

fish and insects; therefore not likely to be a “species most at 

Aleutian Canada goose 

Branta canadensis leucopareia

delisted, MBTA none Winters in California, feeding primarily in upland crops and fa

selenium but unlikely to be exposed in estuary; therefore not 

at risk.” 

greater scaup Aythya marila MBTA none SF Bay is one of 2 major wintering areas on W coast of N A

mollusks that efficiently bioaccumulate selenium in the SF Bato be a “species most at risk.” 

lesser scaup Aythya affinis MBTA none SF Bay is an important wintering area; feeds on clams; there

most at risk.” 

black scoter Melanitta nigra MBTA none Winters along California coast, diving mainly for mollusks; the

“species most at risk.” 

white-winged scoter 

Melanitta fusca MBTA none Winters along California coast and estuaries, diving mainly fo

to be a “species most at risk.” 

surf scoter Melanitta perspicillata MBTA none Winters along California coast, diving mainly for mollusks; the

“species most at risk.” 

osprey Pandion haliaetus MBTA concern High trophic level piscivore; not at risk overall and exposure w

eagle. Therefore not treated here as a “species most at risk.

bald eagle Haliaeetus leucocephalus delisted, 

MBTA,BGEPA 

protected, endangered 

High trophic level piscivore; at risk overall and exposed to aqBay/Delta; therefore likely to be a “species most at risk.” 
4 

Common Name Scientific Name Federal Status California State 

Status 

Potential to be adversely affected by selenium in Bay/De

northern harrier Circus cyaneus MBTA concern High trophic level but less exposed to aquatic food chain tha

likely to be a “species most at risk.” 

white-tailed kite Elanus leucurus concern protected Feeds mainly on terrestrial mammals; minimal exposure to a

not likely to be a “species most at risk.” 

American peregrine falcon 

Falco peregrinus anatum delisted, MBTA protected, 

concern 

Delisted but monitored for population status and contaminanaquatic food chain as predator on piscivorous birds, but expoterrestrial component of diet; therefore not likely to be a “spe

prairie falcon Falco mexicanus MBTA concern Winters along California coast; high trophic level but in mainl

therefore not likely to be a “species most at risk.” 

California black rail 

Laterallus jamaicensis coturniculus 

MBTA protected, 

concern 

Inhabits tidal marsh in SF Bay estuary. Feeds on invertebratalso seeds; therefore not likely to be a “species most at risk.” 

California clapper rail 

Rallus longirostris obsoletus endangered protected, 

endangered 

Subspecies endangered and endemic to SF estuary; feeds oincluding filter-feeders that bioaccumulate selenium; thereformost at risk.” 

marbled murrelet Brachyramphus marmoratus threatened endangered Forages in bays along Pacific coast in summer, but not recor

for pelagic food: schooling fish and euphausiids (krill). Ther“species most at risk.” 

California least tern 

Sterna antillarum browni endangered protected, 

endangered 

Breeds primarily in Central San Francisco Bay but can nest tthroughout estuary, mainly on surface fish, not part of the bechain; therefore not likely to be a “species most at risk.” 

black tern Chlidonias niger concern concern Breeds in C Valley including SF Delta. Feeds on marine and 

and insects; therefore not likely to be a “species most at risk.

Caspian tern Sterna caspia MBTA none Preys heavily on juvenile salmonids, but not endangered ove

be a “species most at risk.” 

western snowy plover 

Charadrius alexandrines threatened concern Terrestrial component of diet likely provides dietary dilution o

exposures; have been shown to be very tolerant of selenium likely to be a “species most at risk.” 

mountain plover Charadrius montanus concern concern Winters in agricultural fields of Sacramento/San Joaquin Vall

therefore not likely to be a “species most at risk.” 

tricolored blackbird 

Agelaius tricolor concern concern Nests colonially, mainly in freshwater marshes. Feeds on te

freshwater insects; therefore not likely to be a “species most 

Reptiles

giant garter snake Thamnophis gigas threatened threatened Aquatic predator, but not known to inhabit the estuary; theref

“species most at risk” in the estuary. 

Fish

Chinook salmon Oncorhynchus tshawytscha endangered/ 

threatened 

endangered/ threatened 

Sensitive to selenium; most sensitive life stages occur in rivelikely to be a “species most at risk.” 

steelhead Oncorhynchus mykiss threatened none (in Central 

Valley) 

Sensitive to selenium; most sensitive life stages occur in rivelikely to be a “species most at risk.” 

delta smelt Hypomesus transpacificus threatened threatened Endemic to the Bay/Delta estuary. Feeds on zooplankton, no

exposure, but threatened overall, so included as a “species m
5 

Common Name Scientific Name Federal Status California State 

Status 

Potential to be adversely affected by selenium in Bay/De

longfin smelt Spirinchus thaleichthys concern endangered SF Bay/estuary is S end of distribution. Prefers more saline 

Overall less threatened and probably less exposed than delta represented by that species. Therefore not treated here as a 

green sturgeon Acipenser medirostris threatened concern; fishing 

prohibited 

Threatened overall, and vulnerable to selenium as a clam-eaSF estuary; therefore likely to be a “species most at risk.” Emissued by CDFG March 2006--Zero (0) bag limit for green stuareas. 

white sturgeon Acipenser transmontanus none limited fishing Population in the SF estuary not federally listed, but vulnerab

eating bottom feeder. Therefore, treated here as a “species and possession limit established by CDFG is one fish that mand 72 inches total length. The yearly limit is three. 

river lamprey Lampetra ayresi none watch list Anadromous; feeds on young salmon. Recorded from lower 

Joaquin Rivers. Not federally listed; therefore not considered risk.” 

Sacramento perch 

Archoplites interruptus concern concern Fry feed primarily on bottom-dwelling crustaceans, insect larv

captured in the Delta in 1992, not likely to represent an estabTherefore not considered to be a “species most at risk” in the plans for possible future reintroduction of this species in the Daccount possible risk to individuals of a recovering population Victoria Poage, Delta Native Fishes Recovery Coordinator, BOffice, USFWS.

Sacramento splittail 

Pogonichthys macrolepidotus concern threatened Vulnerable to selenium as clam-eating bottom feeder in the S

to be a “species most at risk.” 

striped bass Morone saxatilis none none Introduced sport fish in California. Population in Delta declin

but species overall not threatened. Therefore not considered risk.” 

threadfin shad Dorosoma pretenense none none Introduced in California as food for game fish. Population in 

early 2000s, but species overall not threatened. Therefore n“species most at risk.” 

tidewater goby Eucyclogobius newberryi endangered endangered Bottom-dwelling carnivore. Prefers semi-closed estuaries. P

found recently (since 1984) in the Bay area; therefore not comost at risk” in the SF Bay/Delta. 

California halibut Paralichthys californicus none none Bottom dweller inhabiting the SF Bay, but overall not threate

be a “species most at risk.” 

leopard shark Triakis semifasciata none none Bottom dweller inhabiting the SF Bay, but overall not threate

be a “species most at risk.” 

starry flounder Platichthys stellatus none none Bottom dweller inhabiting the SF Bay. Population in bay dec

but overall not threatened; therefore not likely to be a “specie

Invertebrates

Dungeness crab Cancer magister none none Estuary is nursery for this ocean-breeding bottom feeder, bu

therefore not likely to be a “species most at risk.” 
6 

Federal Status: Endangered: listed as endangered under the Federal Endangered Species Act; Threatened: listed as threatened under the Federal Endange

Proposed threatened: proposed as threatened under the Federal Endangered Species Act; Concern: designated a species of concern; Delisted: removed from and threatened species under the Federal ESA; MBTA: protected under Migratory Bird Treaty Act; BGEPA protected under the Bald and Golden Eagle Protecti

California State Status: Endangered: listed as endangered under the California Endangered Species Act; Threatened: listed as threatened under the Californ

Act; Concern: designated by the California Department of Fish and Game as a species of concern; Protected: Fully protected under the Fish and Game Code o

the 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 the San Francisco Estuary were considered to be at greater risk to selenium exposure than those species feeding in a pelagic/planktonic food web. This assumwork of Stewart et al. (2004). 
7 

Table 4. Species most at risk from Se exposure in the San Francisco Estuary: summary data. [Reproduced from USFWS, 2008 Tab

Common Name Scientific Name Probable critical life 

stage for Se effects1

Food ingestion rate at critical life stage (g ww/day) 2

Food ingestion rate at critical life stage (g dw/kg body weight/day) 3

Body weight at critical life stage (g) 4

Diet 

bald eagle Haliaeetus 

leucocephalus 

Adult female (egg laying) 

644 249 5275 

(female) 

fish, birds, mammals 

California clapper rail 

Rallus longirostris obsoletus 

Adult female (egg laying) 

172 46.8 346 mussels, spiders, clams, crabs, 

snails, marsh cordgrass seeds 

greater scaup Aythya marila Adult male and 

female (migration) 

313 85.8 1054 

(male) 

clams, snails, other mollusks, crustaceans, algae 

lesser scaup Aythya affinis Adult male and 

female (migration) 

246 67.5 734 

(male) 

clams, other mollusks, aquatic insects, crustaceans, plants 

white-winged scoter 

Melanitta fusca Adult male and 

female (migration) 

465 127.3 1917 

(male) 

clams, other mollusks, crustaceans, aquatic insects 

surf scoter Melanitta 

perspicillata 

Adult male and female (migration) 

314 86.0 1059 

(male) 

mussels, other mollusks, plants, crustaceans 

black scoter Melanitta nigra Adult male and 

female (migration) 

325 89.1 1117 

(male) 

mussels, clams, snails, barnacles 

Chinook salmon 

Oncorhynchus tshawytscha

Migrating/rearing juvenile 

23.3 0.5-18 insects, crustacea, juvenile fish 

steelhead Oncorhynchus 

mykiss

Migrating/rearing juvenile 

19.9 31-105 insects, annelids, Daphnia 

green sturgeon Acipenser 

medirostris 

Juvenile or adult female 

20 1300 (average 

caught) 

benthic crustacea, mollusks and fish 

white sturgeon Acipenser 

transmontanus 

Juvenile or adult female 

15-20 6280 

(mode) 

benthic mollusks and crustacea 

delta smelt Hypomesus 

transpacificus

Juvenile or adult female 

114 0.32 (average 

Jun-Aug) 

copepods, cladocerans, amphipods, insect larvae 

Sacramento splittail 

Pogonichthys macrolepidotus

Juvenile or adult female 

33.7 121 

(mode) 

benthic detritus, clams, other mollusks, mysids 

1. For most species it is premature and speculative to designate a critical life stage at this time. Such designation prejudges th

thorough search of the toxicology literature. 

2. Food ingestion rates based on wet weight can be calculated from available parameters (Nagy 2001) for birds, mammals, rep

but not, in general for fish. [Note: food ingestion rate for fish are available elsewhere (e.g., Baines et al., 2002); see text for 

3. For birds, the food ingestion rate as dry weight is calculated from the regression parameters for dry matter intake per day fr

(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 th

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 those waterfowl (scaups and scoters) that primarily feed on benth

etc.). 
8 

7. Percent of mollusks in gizzards of 819 adults and 4 juveniles collected in coastal Maine and Washington (Cottam C. U.S. D

643). 

8. Wet weight percents of summer and winter gizzard contents, British Columbia salt water (Vermeer K. 1981 Wildfowl 32:1

Bourne 1984 as summarized in Appendix 1 of Savard et 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 not known to be clam-based, these species may still be at risk fr

of greater sensitivity to selenium. The sensitivity of salmon and steelhead is documented below. The sensitivity of delta smunknown; population numbers are alarmingly low, so this species is particularly vulnerable to any adverse effect. 

11. Percentage clams by volume, fall, Suisun Bay and Carquinez Strait (Table 10 below).
9 

Table 5. Selenium effect levels derived for protection of species at risk in the San Francisco Estuary. [Reproduced from USFWS, 20

Concentration of selenium (μg/g dry wt.) corresponding to 

effect level: 

Se in diet Se in target species (whole body or egg) 

Species 

0% 5% 10% 0% 5% 10% 

Effect Exposure 

duration (days) 

Form of selenium 

Model 

Mallard 2.30 4.36 5.29 2.77 5.86 7.73 hatchability >40 (parental) seleno-DL- methionine Beckon 

al. 2008 

White sturgeon na 7.03 8.13 

larval edema and skeletal defects 

up to 6 months 

selenized yeast log-logi

 adulta

na 25.5 32.5 

assimilation 6 months selenized yeast power 

 juvenilea na 0.95 1.57 assimilation 56 seleno-L- methionine power Chinook salmon 1.54 2.25 2.67 1.01 1.53 1.84 

mortality 90 assimilated or 

seleno-DLmethionine 

Brain aCousen1989 

Rainbow trout 2.41 4.22 5.04 1.27 1.89 2.19 reduction in growth 140 sodium selenite Beckon 

al. 2008 

0.31 1.01 1.56 assimilation 90 seleno-L- methionine power 

a Adult and juvenile white sturgeon effect guidelines are being revised; b Revision, personal communication, USFWS, William Beckon, 10/27/10

Table 6. Generic selenium effect levels for fish and birds. 

Se (μg/g dw) Se (μg/g dw) 

bird (egg) 5.5 (NEC) (Skorupa, 2008) 7.7 (EC10) (USFWS, 2009b; Skorupa, 2008) 

fish (wb) - 5.0 (EC10) (USFWS, 2005; Skorupa et al., 2004) 

diet (fish and birds) 3.6 <4.9 (Skorupa et al., 2004) 
10 

Table 7. Available data for the Bay-Delta including transects and biota studies. [Water year classification based on precipitation in th

flow season is defined from December through May; a low flow season is defined as June through November.] 

study date water year/flow season 

residence 

time (days)/ 

salinity at 

Golden Gate 

Bridge (psu) 

reference Se da

Northern Reach from Sacramento/ San Joaquin Rivers to Golden Gate Bridge 

April 1986 wet/high 9.8/-- Cutter 1989; Meseck, 2002 dissolved; dissolved speciation; paSeptember 1986 wet/low 24.4/- Cutter 1989; Meseck, 2002 dissolved; dissolved speciation; paOctober 1987 critical/low 73.5/- Cutter and San Diego-McGlone, 1990 dissolved; dissolved speciation December 1987 critical/high 8.0/- Cutter and San Diego-McGlone, 1990 dissolved; dissolved speciation March 1988 critical/high 35.5/- Cutter and San Diego-McGlone, 1990 dissolved; dissolved speciation May 1988 critical/high 25/- Cutter and San Diego-McGlone, 1990 dissolved; dissolved speciation 1989-1990 critical - Urquhart and Regalado, 1991; Kroll and Doroshov, 1991 white sturgeon: flesh; ovary; egg yo1986-1990 wet 1986; dry 1987; 

1988 critical; 1989 dry; 1990 critical 

- White et al., 1987, 1988, 1989; Urquhart and Regalado, 

1991 

surf scoter, greater and lesser scaSan Pablo Bays 

1975, 1986, 1987 wet 1975; wet 1986; 

dry, 1987 

- Lonzarich et al., 1992 California clapper rail eggs from th

reaches of Bay 

1982; 1985 wet 1982; dry 1985 - Ohlendorf et al., 1986; 1991 surf scoter, greater scaup liver (souDecember 1986-1987 (early winter); March 1986-1987(late winter) 

wet 1986; dry 1987 - Takekawa et al., 2002 canvasbacks (n = 29), greater scau

and kidney from North, Central, an

1989 dry - Hoffman et al., 1998 surf scoter, greater scaup, ruddy d

Tomales Bay) 

1985-1986 dry 1985; wet 1986 - White et al., 1987, 1988, 1989; Urquhart and Regalado, 

1991; Johns et al., 1988 

sediment and clam 

1991, 1992, 1998, 1999 breeding seasons 

critical 1991, 1992; wet 1998, 1999 

- Schwarzbach et al., 2006 California clapper rail egg from six 

southern reaches of Bay 

1994, 1995, 1997, 1999, 2000, 2001 

critical 1994; wet 1995-1999; above normal 2000; dry 2001 

- CH2M HILL, 1994; 1995; 1998; 2000; 2001; 2002; 

Ohlendorf and Gala, 2000; Skorupa, 1998 

shorebird eggs from Chevron RichEnhancement Wetland 

November 1997 wet/low 68/32.5 Cutter and Cutter, 2004; Meseck, 2002; Doblin et al., 2006 Bay-Delta transects: dissolved; dis

particulate speciation 

June 1998 wet (El Niño) /high 11/24.8 Cutter and Cutter, 2004; Doblin et al., 2006 Bay-Delta transects: dissolved; dis

particulate speciation 

October 1998 wet/low 22/30.2 Cutter and Cutter, 2004; Doblin et al., 2006 Bay-Delta transects: dissolved; dis

particulate speciation 
11 

study date water year/flow season 

residence 

time (days)/ 

salinity at 

Golden Gate 

Bridge (psu) 

reference Se da

April 1999 wet/high 16/28.5 Cutter and Cutter, 2004; Doblin et al., 2006 Bay-Delta transects: dissolved; dis

particulate speciation 

November 1999 above normal/ low 70/32.2 Cutter and Cutter, 2004; Doblin et al., 2006 Bay-Delta transects: dissolved; dis

particulate speciation 

Nov 97, Jun 98, Oct 98, Nov 99 

see above for Cutter and Cutter, 2004 

- Meseck, 2002 sedimentary Se and speciation; po

Suisun Bay, Delta, mudflat marsh n

1995-1997 all wet years - Linville et al., 2002 (see Presser and Luoma, 2006, Fig 15) clams from 21 locations 1997-2000 1997-1999 wet; 

2000 above normal 

- Greenfield et al., 2005 sport fish at 6 locations including S

1999-2000 1999 wet; 2000 

above normal 

- Stewart et al., 2004 fall and early winter food webs 

1998-1999 wet 1998-1999 - Purkerson et al., 2003 zooplankton from stations in northe

reaches of Bay 

March to July, 2000; 2001 

above normal 2000; dry 2001 

- Schwarzbach and Adelsbach, 2003 aquatic bird eggs including Californ

Francisco Bay, Suisun Bay, and th

March, 2002 dry - Hunt et al., 2003 surf scoter and greater scaup musc

Bays: 

May, 1995- February, 2010 

- Kleckner et al., 2010 USGS clam database: monthly C. 

stations 

2004-2006 winter below normal 2004; 

above normal 2005; wet 2006 

- Wainwright-De La Cruz, et al., 2008 surf scoter liver :San Pablo, Suisun

Mar-Apr, 2005 above normal - Ackerman and Eagles-Smith, 2009 avocet, stilt, tern liver: north and so2003-2005 above normal 2003; 

2005; below normal 2004 

- Linares-Casenave et al., 2010 white sturgeon tissues (muscle, go

locations from Chipps Island to Sa

Rio Vista and Stockton to Benicia/Carquinez Strait 

October 7-8,1998 wet/low - Personal communication M. Doblin, March 2009 sediment cores from six Delta locaJuly 12-13, 2000 above normal/low - Lucas and Stewart, 2007 dissolved; dissolved speciation; paJanuary 22, 2003 above normal/high - Lucas and Stewart, 2007 dissolved; dissolved speciation; paApril 22-23, 2003 above normal/high - Lucas and Stewart, 2007 dissolved; dissolved speciation; paJune 17, 2003 above normal - Lucas and Stewart, 2007 dissolved; dissolved speciation; paOctober 10, 2003 below normal/low - Lucas and Stewart, 2007 dissolved; dissolved speciation; paJanuary 15, 2004 below normal/high - Lucas and Stewart, 2007 dissolved; dissolved speciation; pa2002 dry - Lucas and Stewart, 2007 sediment cores from three Delta lo
12 

Table 8. Bay-Delta hydrologic conditions, Net Delta Outflow Index, salinity, observed dissolved Se concentrations, observed suspen

concentrations, and calculated Kds. [Arranged by increasing residence time of transect, except for November, 1997. See text for 

hydrologic condition (transect, residence 

time, water year/flow season) 

Net Delta Outflow Index daily 

average per month (cfs) 

salinity 

mean and range (psu) 

observed dissolved Se mean 

and range (μg/L) 

observed parti

and range 

June 16-17, 1998 

11 day residence; wet/high 73,732 5.8 (0.01-24.5) 0.181 (0.101-0.303) 0.5

(0.150

April 13-14, 1999 

16 day residence; wet/high 35,034 11.4 (0-28.9) 0.116 (0.076-0.165) 0.6

(0.190

October 7-8, 1998 

22 day residence; wet/low 12,251 14.6 (0-30.1) 0.120 (0.077-0.164) 0.7

(0.289

November 4-5, 1999 

70 day residence; above normal/low 6,951 15.0 (0-32.2) 0.102 (0.070-0.137) 0.7

(0.428

November 5-6, 1997 

68 day residence; wet/low 9,632 17.2 (0.56-32.0) 0.192 (0.101-0.320) 0.8

(0.470

a Kd grand mean for 1998-1999 transects = 5,784 

Table 9. Suisun Bay-Carquinez Strait hydrologic conditions, Net Delta Outflow Index, salinity, observed dissolved Se concentrations

particulate material Se concentrations, and calculated Kds. [Arranged by increasing residence time of transect, except for Novem

discussion. See Doblin et al., 2006 and Figure 14 for division into subset.] 

hydrologic condition (transect, residence 

time, water year/flow season) 

Net Delta Outflow Index 

(daily average per month 

cfs) 

salinity 

mean and range (psu) 

observed dissolved Se mean 

and range (μg/L) 

observed p

(mean and ra

June 16-17, 1998 

11 day residence; wet/high 73,732 0.76 (0.44-1.08) 0.213 (0.211--0.215) 0.2

(0.150

April 13-14, 1999 

16 day residence; wet/high 35,034 5.82 (4.9-7.3) 0.118 (0.076-0.154) 0.3

(0.240

October 7-8, 1998 

22 day residence; wet/low 12,251 7.0 (2.5-11.6) 0.135 (0.128-0.151) 0.4

(0.289

November 4-5, 1999 

70 day residence; above normal/low 6,951 17.5 (11.4-23.1) 0.123 (0.104-0.132) 0.7

(0.428

November 5-6, 1997 

68 day residence 

wet/low 

9,632 16.1 (12.7-19.2) 0.210 (0.192-0.236) 0.7

(0.572

aKd grand mean for 1998-1999 transects = 3,317. 
13 

Table 10. Landward hydrologic conditions, Net Delta Outflow Index, salinity, observed dissolved Se concentrations, observed suspen

concentrations, and calculated Kds. 

hydrologic condition (transect, residence 

time, water year/flow season) 

Net Delta Outflow Index 

(daily average per month 

cfs) 

salinitya

 range (psu) 

observed dissolved Se mean 

and range (μg/L) 

observed p

(mean and ra

January 22, 2003 

 above normal/high 50,847 0.011-8.45 0.245 (0.111-0.599) 0.4

(0.27

January 15, 2004 

below normal/high 30,924 0.012-8.105 0.215 (0.114-0.523) 0.5

(0.23

April 22-23, 2003 

above normal/high 21,218 0.013-3.99 0.356 (0.115-1.008) 0.6

(0.28

October 10, 2003 below normal/low 4,350 0.019-12.68 0.174 (0.068-0.532) 0.7

(0.37

aCalculated from chlorinity. 
14 

Table 11. Bay-Delta mean, median, 75th percentile, and 25th percentile for observed dissolved Se concentrations, observed suspende

concentrations, and Kds. [Arranged by increasing residence time of transect, except for November, 1997. See text for additional d

Jun-1998 (11 day residence) Apr-1999 (16 day residence) Oct-1998 (22 day residence) Nov-1999 (70 day residence

dissolved Se μg/L 

mean 0.181 0.116 0.122 0.102 75th percentile 0.204 0.128 0.134 0.122 median 0.183 0.121 0.128 0.099 25th percentile 0.148 0.093 0.105 0.085 

particulate Se μg/g dw

mean 0.518 0.636 0.712 0.746 75th percentile 0.456 0.829 0.807 0.854 median 0.392 0.528 0.627 0.725 25th percentile 0.357 0.391 0.516 0.570 

Kd

mean 3198 5824 6501 7614 75th percentile 2491 7151 6525 8114 median 2330 5252 4963 6569 25th percentile 2059 3253 3782 5893 

Table 12. Suisun Bay-Carquinez Strait mean, median, 75th percentile, and 25th percentile for observed dissolved Se concentrations, 

material Se concentrations, and Kds. [Arranged by increasing residence time of transect, except for November, 1997. See text fo

Doblin et al., 2006 and Figure 14 for division into subset.] 

Jun-1998 (11 day residence ) Apr-1999 (16 day residence) Oct-1998 (22 day residence) Nov-1999 (70 day residence

dissolved Se μg/L 

mean 0.213 0.118 0.135 0.123 75th percentile 0.214 0.139 0.137 0.128 median 0.213 0.125 0.131 0.125 25th percentile 0.212 0.100 0.129 0.120 

particulate Se μg/g dw 

mean 0.252 0.303 0.462 0.740 75th percentile 0.303 0.335 0.606 0.892 median 0.252 0.319 0.447 0.738 25th percentile 0.201 0.280 0.308 0.597 

Kd

mean 1180 2666 3435 5986 75th percentile 1414 2861 4498 7089 median 1180 2555 3111 6142 25th percentile 946 2414 2286 5019 
15 

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

transect mean predicted clam Se 

μg/g dw 

mean observed clam 

Se (all stations) (μg/g 

dw) 

field location (station 

number) 

mean observ

m

June 16-17, 1998 4.4 all salinitiesa

1.6 Carquinez Strait/Suisun Bay salinitiesb

5.4 Suisun Bay (6.1) 

San Pablo Bay (12.5) 

Jun 5.1 Jun 5.8 

April 13-14, 1999 9.5 all salinitiesa

8.7 Carquinez Strait/Suisun Bay salinitiesb

7.3 Suisun Bay (6.1) 

Carquinez Strait (8.1) 

Mar 7.4; Apr 7.Jun 9.2 

October 7-8, 1998 13.1 all salinitiesa

11.2 Carquinez Strait/Suisun Bay salinitiesb

10.8 Chipps Island (4.1) 

Suisun Bay (6.1) Carquinez Strait (8.1) San Pablo Bay (12.5) 

Oct 5.6 Oct 12.3 Sep 15.5; Oct Sep 10.5; Oct 

November 4-5, 1999 12.6 all salinitiesa

12.0 Carquinez Strait/Suisun Bay salinitiesb

11.3 

(12.8 Carquinez 

Strait 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 1Sep 8.3; Oct 9Sep 8.4; Oct 1Sep 10.4; Oct Sep 8.3; Oct 1Sep 7.2; Oct 10

November 5-6, 1997 16.6 all salinitiesa

11.7 Carquinez Strait/Suisun Bay salinitiesb

14.3 Chipps Island (4.1) 

Suisun Bay (6.1) Carquinez Strait (8.1) San Pablo Bay (12.5) 

Nov 11.6 Nov 14.0 Oct 15.5; Nov Nov 14.9 

a Predicted 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 C
16 

Table 14. Validation for existing conditions at a seaward estuary location for November, 1999 or a generalized mean condition using 

seaward and landward white sturgeon; derived Kds and TTFs; and a food web for suspended particulate material>C. amurensis 

observed 

sturgeon muscle 

Sea μg/g 

site-specific 

TTFsturgeon

predicted C. 

amurensis Se 

μg/g 

mean observed 

C. amurensis

Seb μg/g 

biodynamic 

site-specific 

TTFC. amurensis

predicted 

particulate Se 

μg/g 

observed 

particulate 

Sec μg/g 

calculated 

Kd

p

dis

10.2 1.1 9.3 12.8 17 0.545 0.428-1.66 7614(Nov 99 mean) 10.2 1.1 9.3 12.8 17 0.545 0.150-2.21 5784 (grand mean) 

6.9 1.1 6.3 12.8 17 0.369 0.428-1.66 7614(Nov 99 mean) 6.9 1.1 6.3 12.8 17 0.369 0.150-2.21 5784 (grand mean) 

10.2 0.8 12.8 12.8 17 0.753 0.428-1.66 7614(Nov 99 mean) 10.2 0.8 12.8 12.8 17 0.753 0.150-2.21 5784 (grand mean) 

6.9 0.8 8.6 12.8 17 0.506 0.428-1.66 7614(Nov 99 mean) 6.9 0.8 8.6 12.8 17 0.506 0.150-2.21 5784 (grand mean) 

a1998-2001 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; nclams, 14.6 μg/g dw Se (Kleckner et al., 2010) (see also Table 13); c1998-1999 data (Doblin et al., 2006); d1998-1999 data (Cutter and Cutter, 2

Table 15. Validation for existing conditions in Suisun Bay-Carquinez Strait for November, 1999 or a generalized mean condition using 

seaward white sturgeon; derived Kds and TTFs; and a food web for suspended particulate material>C. amurensis >white sturgeo

observed 

sturgeon muscle 

Sea μg/g 

site-specific 

TTFsturgeon

predicted C. 

amurensis Se 

μg/g 

mean observed 

C. amurensis

Seb μg/g 

biodynamic 

site-specific 

TTFC. amurensis

predicted 

particulate Se 

μg/g 

observed 

particulate 

Sec μg/g 

calculated 

Kd

p

dis

10.2 1.1 9.3 12.8 17 0.545 0.428-1.03 5986 (Nov 99 mean) 10.2 1.1 9.3 12.8 17 0.545 0.150-1.03 3317 (grand mean) 10.2 0.8 12.8 12.8 17 0.753 0.428-1.03 5986 (Nov 99 mean) 10.2 0.8 12.8 12.8 17 0.753 0.150-1.03 3317 (grand mean) 

a1998-2001 data; seaward, 10.2 μg/g; landward, 6.9 μg/g (Stewart et al., 2004);bCarquinez Strait (USGS station 8): mean observed fall 1999; no14.6 μg/g dw Se (Kleckner et al., 2010) (see also Table 13). c1998-1999 data (Doblin et al., 2006); d1998-1999 data (Cutter and Cutter, 2004). 

Table 16. Validation for existing conditions at a landward estuary location for 2003-2004 using observed Se concentrations in landwa

and TTFs; and a food web for suspended particulate material>aquatic insect>largemouth bass food web. 

observed bass wb 

Sea μg/g 

generic 

TTFfish

predicted insect Se 

μg/g 

mean observed 

chironomid Seb

μg/g 

generic 

TTFinsect

predicted 

particulate Se 

μg/g 

observed 

particulate 

Seb μg/g 

calculated 

Kd

2.9 1.1 2.6 2.7 2.8 0.942 0.27-0.58 2268 (Jan 2003 mean) 2.9 1.1 2.6 2.7 2.8 0.942 0.23-1.0 2981 (Jan 2004 mean) 2.9 1.1 2.6 2.7 2.8 0.942 0.37-1.5 5855 (Oct 2003 mean) 

a 2007 data (Foe et al., 2010); b2002-2004 data (Lucas and Stewart, 2007) (see also Appendix D, Table D5). 
17

Table 17. Predicted allowed dissolved Se concentrations for Bay-Delta transects at different effect guidelines and associated levels o

a suspended particulate material>C. amurensis>sturgeon food web. Also shown are 1) observed dissolved Se concentrations, suconcentrations, and calculated Kds; and 2) hydrologic conditions including water-year type, flow season, residence time, and NDO

17.1; TTFfish = 1.1. Transect data and predictions for 1998 through 1999 are arranged by increasing residence time; transect dat

1997 are delineated separately (see text for explanation). Means and Kds are based on individual data points, not composites. Fu

consider sensitivity of green sturgeon]. 

calculated Kd mean 

and range 

food web: 

particulate material 

> C. amurensis

>fish 

tissue 

target Se 

(μg/g 

dw) 

EC 

predicted allowed 

dissolved Se 

mean and range 

(μg/L) 

predicted 

allowed 

particulate 

Se 

(μg/g dw) 

predicted 

allowed 

invertebrate 

Se 

(μg/g dw) 

hydrologic condition 

(transect, residence 

time, water year/flow 

season) 

obs

disso

me

rang

FISH (WHOLE-BODY) 

3,198 

(712-11,054) 

adult female white 

sturgeon 

wholebody 8.1 10 0.208 (0.039-0.605) 0.43 7.4 June 16-17, 1998 11 day residence wet/high 0

(0.10

5,824 

(1,151-13,317) 

0.110 

(0.032-0.374) 0.43 7.4 

April 13-14, 1999 16 day residence

wetl/high 

0

(0.07

6,501 

(2,202-26,912) 

0.096 

(0.016-0.196) 0.43 7.4 

October 7-8, 1998 

22 day residence

 wet/low 

0

(0.07

7,614 

(3,496-19,785) 

0.064 

(0.022-0.123) 0.43 7.4 

November 4-5, 1999 

70 day residence

above normal/low 

0

(0.07

4,652 

(2,333-8,349) 

0.108 

(0.052-0.185) 0.43 7.4 

November 5-6, 1997 

68 day residence 

wet/low 

0

(0.10

3,198 

(712-11,054) 

adult female white 

sturgeon 

wholebody 7.0 05 0.180 (0.034-0.523) 0.37 6.4 June 16-17, 1998 11 day residence wet/high 0

(0.10

5,824 

(1,151-13,317) 

0.095 

(0.028-0.323) 0.37 6.4 

April 13-14, 1999 16 day residence

wet/high 

0

(0.07

6,501 

(2,202-26,912) 

0.083 

(0.014-0.169) 0.37 6.4 

October 7-8, 1998 

22 day residence

wet/low 

0

(0.07

7,614 

(3,496-19,785) 

0.055 

(0.019-0.106) 0.37 6.4 

November 4-5, 1999 

70 day residence

above normal/low 

0

(0.07

4,652 0.093 0.37 6.4 November 5-6, 1997 0
18 

calculated Kd mean 

and range 

food web: 

particulate material 

> C. amurensis

>fish 

tissue 

target Se 

(μg/g 

dw) 

EC 

predicted allowed 

dissolved Se 

mean and range 

(μg/L) 

predicted 

allowed 

particulate 

Se 

(μg/g dw) 

predicted 

allowed 

invertebrate 

Se 

(μg/g dw) 

hydrologic condition 

(transect, residence 

time, water year/flow 

season) 

obs

disso

me

rang

(2,333-8,349) (0.045-0.160) 68 day residence 

wet/low 

(0.10

3,198 

(712-11,054) clam-eating fish whole- body 5.0 generic 0.128 (0.024-0.373) 0.27 4.5 June 16-17, 1998 11 day residence wet/high 0

(0.10

5,824 

(1,151-13,317) 

0.068 

(0.020-0.231) 0.27 4.5 

April 13-14, 1999 16 day residence 

wet/high 

0

(0.07

6,501 

(2,202-26,912) 

0.059 

(0.010-0.121) 0.27 4.5 

October 7-8, 1998 

22 day residence 

wet/low 

0

(0.07

7,614 

(3,496-19,785) 

0.040 

(0.013-0.076) 0.27 4.5 

November 4-5, 1999 

70 day residence 

above normal/low 

0

(0.07

4,652 

(2,333-8,349) 

0.066 

(0.032-0.114) 0.27 4.5 

November 5-6, 1997 

68 day residence 

wet/low 

0

(0.10

FISH (DIET)

3,198 

(712-11,054) 

juvenile white 

sturgeon diet 

1.6 

(=1.8 

wb) 

10 0.0452 (0.0085-0.1314) 0.094 1.6 June 16-17, 1998 11 day residence wet/high 0

(0.10

5,824 

(1,151-13,317) 

0.0247 

(0.0070-0.0813) 0.094 1.6 

April 13-14, 1999 16 day residence 

wet/high 

0

(0.07

6,501 

(2,202-26,912) 

0.0211 

(0.0035-0.0425) 0.094 1.6 

October 7-8, 1998 

22 day residence 

wet/low 

0

(0.07

7,614 

(3,496-19,785) 

0.0139 

(0.0047-0.0268) 0.094 1.6 

November 4-5, 1999 

70 day residence 

above normal/low 

0

(0.07

4,652 

(2,333-8,349) 

0.0234 

(0.0112-0.0401) 0.094 1.6 

November 5-6, 1997 

68 day residence 

wet/low 

0

(0.10

3,198 

(712-11,054) 

juvenile white 

sturgeon diet 

0.95 

(=1.0 

wb) 

05 0.0268 (0.0050-0.0780) 0.056 0.95 June 16-17, 1998 11 day residence wet/high 0

(0.10
19 

calculated Kd mean 

and range 

food web: 

particulate material 

> C. amurensis

>fish 

tissue 

target Se 

(μg/g 

dw) 

EC 

predicted allowed 

dissolved Se 

mean and range 

(μg/L) 

predicted 

allowed 

particulate 

Se 

(μg/g dw) 

predicted 

allowed 

invertebrate 

Se 

(μg/g dw) 

hydrologic condition 

(transect, residence 

time, water year/flow 

season) 

obs

disso

me

rang

5,824 

(1,151-13,317) 

0.0147 

(0.0042-0.0483) 0.056 0.95 

April 13-14, 1999 16 day residence 

wet/high 

0

(0.07

6,501 

(2,202-26,912) 

0.0126 

(0.0021-0.0252) 0.056 0.95 

October 7-8, 1998 

22 day residence 

wet/low 

0

(0.07

7,614 

(3,496-19,785) 

0.0082 

(0.0028-0.0159) 0.056 0.95 

November 4-5, 1999 

70 day residence 

above normal/low 

0

(0.07

4,652 

(2,333-8,349) 

0.0139 

(0.0066-0.0238) 0.056 0.95 

November 5-6, 1997 

68 day residence 

wet/low 

0

(0.10
20 

Table 18. Predicted allowed dissolved Se concentrations for Bay-Delta transects at different effect guidelines and associated levels o

for a suspended particulate material>C. amurensis>clam-eating bird species food web. Also shown are 1) observed dissolved Se particulate material Se concentrations, and calculated Kds; and 2) hydrologic conditions including water-year type, flow season, r

[Assumptions: TTFclam = 17.1; TTFbird = 2.6. Transect data and predictions for 1998 through 1999 are arranged by increasing respredictions for November, 1997 are delineated separately (see text for explanation). Means and Kds are based on individual data 

calculated Kd mean 

and range 

food web: 

particulate> 

C. amurensis 

>bird 

tissue 

target 

Se 

(μg/g 

dw) 

EC 

predicted allowed 

dissolved Se 

mean and range 

(μg/L) 

predicted 

allowed 

particulate 

Se 

(μg/g dw) 

predicted 

allowed 

invertebrate 

Se 

(μg/g dw) 

hydrologic condition 

(transect, residence 

time, water year/flow 

season) 

obse

dissol

mea

range 

BIRD (EGG) 

3,198 

(712-11,054) scoter and scaup egg 7.7 generic 10 0.0837 (0.0157-0.243) 0.17 3.0 June 16-17, 1998 11 day residence wet/high 0.1

(0.101

5,824 

(1,151-13,317) 

0.0440 

(0.0130-0.1505) 0.17 3.0 

April 13-14, 1999 16 day residence 

 wet/ high 

0.1

(0.076

6,501 

(2,202-26,912) 

0.0404 

(0.0064-0.0786) 0.17 3.0 

October 7-8, 1998 

22 day residence 

 wet/low 

0.1

(0.077

7,614 

(3,496-19,785) 

0.0258 

(0.0088-0.0495) 0.17 3.0 

November 4-5, 1999 

70 day residence 

above normal/low 

0.1

(0.070

4,652 

(2,333-8,349) 

0.0432 

(0.0207-0.0742) 0.17 3.0 

November 5-6, 1997 

68 day residence 

wet/low 

0.1

(0.101

3,198 

(712-11,054 scoter and scaup egg 5.9 05 0.0641 (0.0120-0.1864) 0.13 2.3 June 16-17, 1998 11 day residence wet/high 0.1

(0.101

5,824 

(1,151-13,317) 

0.0337 

(0.0100-0.1153) 0.13 2.3 

April 13-14, 1999 16 day residence 

wet/high 

0.1

(0.076

6,501 

(2,202-26,912) 

0.0310 

(0.0049-0.0603) 0.13 2.3 

October 7-8, 1998 

22 day residence 

 wet/low 

0.1

(0.077

7,614 

(3,496-19,785) 

0.0197 

(0.0067-0.0380) 0.13 2.3 

November 4-5, 1999 

70 day residence 

 above normal/low 

0.1

(0.070

4,652 

(2,333-8,349) 

0.0331 

(0.0159-0.0596) 0.13 2.3 

November 5-6, 1997 

68 day residence 

wet/low 

0.1

(0.101
21 

calculated Kd mean 

and range 

food web: 

particulate> 

C. amurensis 

>bird 

tissue 

target 

Se 

(μg/g 

dw) 

EC 

predicted allowed 

dissolved Se 

mean and range 

(μg/L) 

predicted 

allowed 

particulate 

Se 

(μg/g dw) 

predicted 

allowed 

invertebrate 

Se 

(μg/g dw) 

hydrologic condition 

(transect, residence 

time, water year/flow 

season) 

obse

dissol

mea

range 

3,198 

(712-11,054 scoter and scaup egg 2.8 0 0.0304 (0.0057-0.0884) 0.063 1.1 June 16-17, 1998 11 day residence wet/high 0.1

(0.101

5,824 

(1,151-13,317) 

0.0160 

(0.0047-0.0547) 0.063 1.1 

April 13-14, 1999 16 day residence 

wet/high 

0.1

(0.076

6,501 

(2,202-26,912) 

0.0140 

(0.0023-0.0286) 0.063 1.1 

October 7-8, 1998 

22 day residence 

wet/low 

0.1

(0.077

7,614 

(3,496-19,785) 

0.0094 

(0.0032-0.0180) 0.063 1.1 

November 4-5, 1999 

70 day residence 

above normal/low 

0.1

(0.070

4,652 

(2,333-8,349) 

0.0157 

(0.0075-0.0270) 0.063 1.1 

November 5-6, 1997 

68 day residence 

wet/low 

0.1

(0.101

BIRD (DIET) 

3,198 

(712-11,054 scoter and scaup diet 

5.3 

(=13.8 

egg) 

10 0.1498 (0.0280-0.4353) 0.31 5.3 June 16-17, 1998 11 day residence wet/high 0.1

(0.101

5,824 

(1,151-13,317) 

0.0818 

(0.0233-0.2693) 0.31 5.3 

April 13-14, 1999 16 day residence 

wet/high 

0.1

(0.076

6,501 

(2,202-26,912) 

0.0700 

(0.0115-0.1408) 0.31 5.3 

October 7-8, 1998 

22 day residence 

wet/low 

0.1

(0.077

7,614 

(3,496-19,785) 

0.0460 

(0.0157-0.0886) 0.31 5.3 

November 4-5, 1999 

70 day residence 

above normal/low 

0.1

(0.070

4,652 

(2,333-8,349) 

0.0774 

(0.0371-0.1328) 0.31 5.3 

November 5-6, 1997 

68 day residence 

wet/low 

0.1

(0.101

3,198 

(712-11,054 scoter and scaup diet 

4.4 

(=11.4 

egg) 

05 0.1244 (0.0233-0.3613) 0.26 4.4 June 16-17, 1998 11 day residence wet/high 0.1

(0.101

5,824 

(1,151-13,317) 

0.0679 

(0.0193-0.2235) 0.26 4.4 April 13-14, 1999 16 day residence 0.1

(0.076
22 

calculated Kd mean 

and range 

food web: 

particulate> 

C. amurensis 

>bird 

tissue 

target 

Se 

(μg/g 

dw) 

EC 

predicted allowed 

dissolved Se 

mean and range 

(μg/L) 

predicted 

allowed 

particulate 

Se 

(μg/g dw) 

predicted 

allowed 

invertebrate 

Se 

(μg/g dw) 

hydrologic condition 

(transect, residence 

time, water year/flow 

season) 

obse

dissol

mea

range 

wet/high 

6,501 

(2,202-26,912) 

0.0581 

(0.0096-0.1168) 0.26 4.4 

October 7-8, 1998 

22 day residence 

wet/low 

0.1

(0.077

7,614 

(3,496-19,785) 

0.0382 

(0.0130-0.0736) 0.26 4.4 

November 4-5, 1999 

70 day residence 

above normal/low 

0.1

(0.070

4,652 

(2,333-8,349) 

0.0642 

(0.0308-0.1103) 0.26 4.4 

November 5-6, 1997 

68 day residence 

wet/low 

0.1

(0.101

3,198 

(712-11,054 scoter and scaup diet 

2.3 

(=6.0 

egg) 

0 0.0650 (0.0122-0.1889) 0.13 2.3 June 16-17, 1998 11 day residence wet/high 0.1

(0.101

5,824 

(1,151-13,317) 

0.0355 

(0.0101-0.1169) 0.13 2.3 

April 13-14, 1999 16 day residence 

wet/high 

0.1

(0.076

6,501 

(2,202-26,912) 

0.0304 

(0.0050-0.0611) 0.13 2.3 

October 7-8, 1998 

22 day residence 

wet/low 

0.1

(0.077

7,614 

(3,496-19,785) 

0.0200 

(0.0068-0.0385) 0.13 2.3 

November 4-5, 1999 

70 day residence 

above normal/low 

0.1

(0.070

4,652 

(2,333-8,349) 

0.0336 

(0.0161-0.0576) 0.13 2.3 

November 5-6, 1997 

68 day residence 

wet/low 

0.1

(0.101
23 

Table 19. Predicted allowed dissolved Se concentrations for landward transects at different effect guidelines and associated levels of 

a suspended particulate material>aquatic insect>juvenile salmon food web. Also shown are 1) observed dissolved Se concentrat

material Se concentrations, and calculated Kds; and 2) hydrologic conditions including water-year type, flow season, and NDOI. 

TTFaquatic insect = 2.8. Means and Kds are based on individual data points, not composites.] 

calculated Kd mean 

and range 

food web: 

particulate >insect 

>fish 

tissue 

target Se 

(μg/g 

dw) 

EC 

predicted allowed 

dissolved Se 

mean and range 

(μg/L) 

predicted 

allowed 

particulate 

Se (μg/g 

dw) 

predicted 

allowed 

invertebrate 

Se (μg/g dw) 

hydrologic condition 

(transect, water 

year/flow season) 

obs

disso

mea

range 

FISH (WHOLE-BODY) 

2,268 

(554-3,503) insect-eating fish whole- body 5.0 generic 1.05 (0.463-2.93) 1.6 4.5 January 22, 2003 above normal/high 0.

(0.111

2,981 

(1,256-6,398)

0.701 

(0.254-1.29) 1.6 4.5 January 15, 2004 below normal/high 0.

(0.114

2,684 

(927-4,351) 

0.772 

(0.373-1.75) 1.6 4.5 April 22-23, 2003 above normal/high 0.

(0.115

5,855 

(1,628-12,650) 

0.382 

(0.128-0.997) 1.6 4.5 October 10, 2003 below normal/low 0.

(0.068

2,268 

(554-3,503) juvenile salmon whole- body 1.8 10 0.388 (0.170-1.078) 0.60 1.6 January 22, 2003 above normal/high 0.

(0.111

2,981 

(1,256-6,398)

0.258 

(0.0934-0.476) 0.60 1.6 January 15, 2004 below normal/high 0.

(0.114

2,684 

(927-4,351) 

0.284 

(0.137-0.644) 0.60 1.6 April 22-23, 2003 above normal/high 0.

(0.115

5,855 

(1,628-12,650) 

0.140 

(0.0472-0.367) 0.60 1.6 October 10, 2003 below normal/low 0.

(0.068

2,268 

(554-3,503) juvenile salmon whole- body 1.5 05 0.316 (0.139-0.897) 0.50 1.4 January 22, 2003 above normal/high 0.

(0.111

2,981 

(1,256-6,398)

0.210 

(0.0761-0.388) 0.50 1.4 January 15, 2004 below normal/high 0.

(0.114

2,684 

(927-4,351) 

0.232 

(0.112-0.525) 0.50 1.4 April 22-23, 2003 above normal/high 0.

(0.115

5,855 

(1,628-12,650) 

0.114 

(0.0385-0.299) 0.50 1.4 October 10, 2003 below normal/low 0.

(0.068

2,268 

(554-3,503) juvenile salmon whole- body 1.0 0 0.211 (0.0927-0.586) 0.33 0.91 January 22, 2003 above normal/high 0.

(0.111

2,981 

(1,256-6,398)

0.140 

(0.0507-0.258) 0.33 0.91 January 15, 2004 below normal/high 0.

(0.114
24 

calculated Kd mean 

and range 

food web: 

particulate >insect 

>fish 

tissue 

target Se 

(μg/g 

dw) 

EC 

predicted allowed 

dissolved Se 

mean and range 

(μg/L) 

predicted 

allowed 

particulate 

Se (μg/g 

dw) 

predicted 

allowed 

invertebrate 

Se (μg/g dw) 

hydrologic condition 

(transect, water 

year/flow season) 

obs

disso

mea

range 

2,684 

(927-4,351) 

0.154 

(0.0746-0.350) 0.33 0.91 April 22-23, 2003 above normal/high 0.

(0.115

5,855 

(1,628-12,650) 

0.076 

(0.0257-0.199) 0.33 0.91 October 10, 2003 below normal/low 0.

(0.068

FISH (DIET) 

2,268 

(554-3,503) juvenile salmon diet 

2.7 

(=3.0 

wb) 

10 0.632 (0.278-1.758) 0.97 2.7 January 22, 2003 above normal/high 0.

(0.111

2,981 

(1,256-6,398)

0.421 

(0.152-0.775) 0.97 2.7 January 15, 2004 below normal/high 0.

(0.114

2,684 

(927-4,351) 

0.463 

(0.224-1.051) 0.97 2.7 April 22-23, 2003 above normal/high 0.

(0.115

5,855 

(1,628-12,650) 

0.229 

(0.0770-0.598) 0.97 2.7 October 10, 2003 below normal/low 0.

(0.068

2,268 

(554-3,503) juvenile salmon diet 

2.2 

(=2.4 

wb) 

05 0.506 (0.222-1.406) 0.80 2.2 January 22, 2003 above normal/high 0.

(0.111

2,981 

(1,256-6,398)

0.337 

(0.122-0.620) 0.80 2.2 January 15, 2004 below normal/high 0.

(0.114

2,684 

(927-4,351) 

0.371 

(0.179-0.841) 0.80 2.2 April 22-23, 2003 above normal/high 0.

(0.115

5,855 

(1,628-12,650) 

0.183 

(0.0616-0.479) 0.80 2.2 October 10, 2003 below normal/low 0.

(0.068

2,268 

(554-3,503) juvenile salmon diet 

1.5 

(=1.65 

wb) 

0 0.348 0.153-0.967 0.54 1.5 January 22, 2003 above normal/high 0.

(0.111

2,981 

(1,256-6,398)

0.231 

0.0837-0.426 0.54 1.5 January 15, 2004 below normal/high 0.

(0.114

2,684 

(927-4,351) 

0.255 

0.123-0.578 0.54 1.5 April 22-23, 2003 above normal/high 0.

(0.115

5,855 

(1,628-12,650) 

0.126 

0.0423-0.329 0.54 1.5 October 10, 2003 below normal/low 0.

(0.068
25 

Table 20. Prediction scenarios using Suisun Bay-Carquinez Strait transects for a suspended particulate material>C. amurensis>white 

fish Se target (μg/g wb, dw) Kd predicted dissolved Se μg/L predicted particulate Se μg/g 

TTFfish = 1.1; TTFclam = 17 

8 1,180 (June 98, 11 days) 0.363 0.428 5 0.227 0.267 

1.8 0.082 0.096 

8 2,666 (Apr 99, 16 days) 0.160 0.428 5 0.100 0.267 

1.8 0.036 0.096 

8 3,435 (Oct 98, 22 days) 0.125 0.428 5 0.078 0.267 

1.8 0.028 0.096 

8 5,986 (Nov 99, 70 days) 0.071 0.428 5 0.045 0.267 

1.8 0.016 0.096 

TTFfish = 1.1; TTFclam + amphipod = 8.8a

8 1,180 (June 98, 11 days) 0.700 0.826 5 0.438 0.517 

1.8 0.158 0.186 

8 2,666 (Apr 99, 16 days) 0.310 0.826 5 0.194 0.517 

1.8 0.070 0.186 

8 3,435 (Oct 98, 22 days) 0.241 0.826 5 0.150 0.517 

1.8 0.054 0.186 

8 5,986 (Nov 99, 70 days) 0.138 0.826 5 0.086 0.517 

1.8 0.031 0.186 

TTFfish = 0.8; TTFclam = 17 

8 1,180 (June 98, 11 days) 0.499 0.588 5 0.312 0.368 

1.8 0.112 0.132 

8 2,666 (Apr 99, 16 days) 0.221 0.588 5 0.138 0.368 

1.8 0.050 0.132 

8 3,435 (Oct 98, 22 days) 0.171 0.588 5 0.107 0.368 

1.8 0.039 0.132 

8 5,986 (Nov 99, 70 days) 0.098 0.588 5 0.061 0.368 
26 

fish Se target (μg/g wb, dw) Kd predicted dissolved Se μg/L predicted particulate Se μg/g 

1.8 0.022 0.132 

TTFfish = 0.8; TTF clam + amphipod = 8.8 a

8 1,180 (June 98, 11 days) 0.963 1.14 5 0.602 0.710 

1.8 0.217 0.256 

8 2,666 (Apr 99, 16 days) 0.426 1.14 5 0.266 0.710 

1.8 0.096 0.256 

8 3,435 (Oct 98, 22 days) 0.331 1.14 5 0.207 0.710 

1.8 0.074 0.256 

8 5,986 (Nov 99, 70 days) 0.190 1.14 5 0.119 0.710 

1.8 0.043 0.256 

a TTF = 8.8 is a composite TTF of TTFclam + TTFamphipod where diet is assumed as 50% C. amurensis (TTF = 17) and 50

0.6). Predicted prey concentrations also are a composite that would need to be separated into components to assess the a

Se concentration and the allowable amphipod Se concentration. 
27 

Table 21. Prediction scenarios using Suisun Bay-Carquinez Strait transects for a suspended particulate material>C. amurensis>clam

bird egg Se target (μg/g wb, 

dw) Kd predicted dissolved Se μg/L predicted particulate Se μg/g 

TTFbird egg = 2.6; TTFclam = 17 

12 1,180 (June 98, 11 days) 0.230 0.271 

7.7 0.148 0.174 5.9 0.113 0.133 

12 2,666 (Apr 99, 16 days) 0.102 0.271 

7.7 0.065 0.174 5.9 0.050 0.133 

12 3,435 (Oct 98, 22 days) 0.079 0.271 

7.7 0.051 0.174 5.9 0.039 0.133 

12 5,986 (Nov 99, 70 days) 0.045 0.271 

7.7 0.029 0.174 5.9 0.022 0.133 

TTFbird egg = 2.6; TTF clam + amphipod = 8.8 a

12 1,180 (June 98, 11 days) 0.444 0.524 

7.7 0.285 0.337 5.9 0.219 0.258 

12 2,666 (Apr 99, 16 days) 0.197 0.524 

7.7 0.126 0.337 5.9 0.097 0.258 

12 3,435 (Oct 98, 22 days) 0.153 0.524 

7.7 0.098 0.337 5.9 0.075 0.258 

12 5,986 (Nov 99, 70 days) 0.088 0.524 

7.7 0.056 0.337 5.9 0.043 0.258 

a TTF = 8.8 is a composite TTF of TTFclam + TTFamphipod where diet is assumed as 50% C. amurensis (TTF = 17) and 50% 

0.6). Predicted prey concentrations also are a composite that would need to be separated into components to assess the a

Se concentration and the allowable amphipod Se concentration. 
28 

Table 22. Prediction scenarios using landward-focused transects for suspended particulate material>aquatic insect>juvenile salmon o

fish Se target (μg/g wb, dw) Kd predicted dissolved Se μg/L predicted particulate Se μg/g 

TTFfish = 1.1; TTFaquatic insect = 2.8 

8 2268 (50,847 cfs) 1.145 2.597 5 0.716 1.623 

1.8 0.258 0.584 

8 2981 (30,924 cfs) 0.871 2.597 5 0.545 1.623 

1.8 0.196 0.584 

8 2684 (21,218 cfs) 0.968 2.597 5 0.605 1.623 

1.8 0.218 0.584 

8 5855 (4,350 cfs) 0.444 2.597 5 0.277 1.623 

1.8 0.100 0.584 

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

fish Se target (μg/g wb, dw) Kd predicted dissolved Se μg/L predicted particulate Se μg/g 

TTFbird egg = 2.6; TTFaquatic insect = 2.8 

12 2268 (50,847 cfs) 0.727 1.648 

7.7 0.466 1.058 5.9 0.357 0.810 

12 2981 (30,924 cfs) 0.553 1.648 

7.7 0.355 1.058 5.9 0.272 0.810 

12 2684 (21,218 cfs) 0.614 1.648 

7.7 0.394 1.058 5.9 0.302 0.810 

12 5855 (4,350 cfs) 0.282 1.648 

7.7 0.181 1.058 5.9 0.138 0.810 
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

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

Carquinez Strait Antioch

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

Outflow to Bay • Net Delta Outflow Index = inflow-demand

San Joaquin River San Joaquin River

San Pablo

Bay

South

Bay

PACIFIC OCEAN

Suisun Bay Suisun Bay

NDOI = [Delta inflow] – 

[net Delta consumptive use]

– [Delta exports]

= NDOI

• Contra Costa 

County

recycle

Inflow

(import)

Demand

(export)

Suisun Bay and Carquinez Strait Sacramento/San Joaquin River Delta

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 2010

Se 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, 1999

wet/wet

wet/wet

wet/above normal

NDOI, 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 year

Load target, lbs Se

Measured 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 (Kd)

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

Kd

TTFinvertebrate

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

TTFfish TTFbird

Invertebrate

Se concentration

Fish

Se

concentration

Trophic

transfer

Bioaccumulation

ReceivingWater Hydrology

Environmental

Partitioning

Bioavailability

Transformation

R 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). [Kd = empirically 

determined environmental partitioning factor between water and particulate material; TTF = 

biodynamic food web transfer factor between an animal and its food.]
Foodweb

biodynamics

Cwater-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 (ke)

• site hydrology

• speciation

• particulate type

particulate

Cprey = (AE) (IR) (Cparticulate)/ke

Cpredator = (AE) (IR) (Cprey)/(ke)

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

Translation to Water-Column Concentration

Combined Equations

If longer food web

B. Modeling Equations

Cinvertebrate = (Cparticulate) (TTFinvertebrate)

Cfish = (Cinvertebrate) (TTFfish)

Cbird = (Cinvertebrate) (TTFbird)

Cfish = (Cparticulate) (TTFinvertebrate) (TTFfish)

Cbird = (Cparticulate) (TTFinvertebrate) (TTFbird)

Cbird = (Cparticulate) (TTFinvertebrate) (TTFfish) (TTFbird)

Cbird = (Cinvertebrate) (TTFfish) (TTFbird)

Cwater = (Cfish) ÷ [(TTFfish) (TTFinvertebrate) Kd]

Cwater = (Cbird) ÷ [(TTFbird) (TTFinvertebrate) Kd] 

[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 best

predictor 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 timing 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

TTFsturgeon TTFC.amurensis Kd

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)

emperical

salinityspecific

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

TTFsalmon TTFaquatic insect Kd

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)

emperical

salinityspecific

partitioning

factor for

dissolved and

suspended

particulate Se

Se in

dissolved

      

Site-Specific Model Approach

Within 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 from 

measured 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

Se in duck tissues decreased body condition disease

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 scoter1

greater scaup

black scoter2

white-winged scoter2

California clapper rail

% clam in diet*

96

86

81

80

75

64

bald eagle4 23% 

fish

white sturgeon

green sturgeon3

Sacramento splittail

% clam in diet*

41

41

34

Dungeness crab 

A. Seaward: 

clam-based 

food webs*

B. Landward:

insect-based 

food webs*

Kd

 TTFbird egg = 2.6

 TTFbird egg = 2.6

 TTFfish = 1.1

 TTFfish = 1.1

 TTFclam = 17

 TTFcrab = ?

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 

Kd 

 TTFbird egg = 2.6

 TTFfish = 1.1

 TTFinsect= 2.8

California black rail

Chinook salmon 

steelhead trout

delta smelt

 TTFbird 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= ? TTFbird egg = 2.6 TTFbird 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)

Central CA Coast steelhead 1, 2

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)

hydrodynamics

d

sources

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

]

benthic food web water-column food web

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

TTFinvertebrate

TTFinvertebrate

sediment

plants

particulate material Se*

dissolved Se

Kd =

bioavailability

partitioning Kd

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

0

Particulate 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

19981996 2000 2002 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, 1999

NDOI, 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, 1999

Dissolved Se, μg/L Dissolved 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

A. June, 1998* B. April, 1999

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

0

Particulate Se, μg/g dw

Dissolved 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

0

Kd Kd

B. April, 1999 - 16 day residence time

Dissolved Se

Particulate Se

arranged by 

 residence time

Kd

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 35

Particulate Se, μg/g dw

Dissolved Se, μg/L

Particulate Se, μg/g dw

Dissolved Se, μg/L

Kd

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 Kd

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

1.2

Dissolved Se, μg/L

0

2000

4000

6000

8000

10000

12000

Kd

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Particulate 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 Kd

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

C

Dissolved 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, psu

Particulate Se, μg/g dw

Particulate organo-Se, %

20 25 30 35

2.5

2.0

1.5

1.0

0.5

0 0 5 10 15

Particulate 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 15

Particulate 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, psu

Particulate organo-Se, %

20 25 30 35

100

80

60

40

20

0 0 5 10 15

Particulate 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 and 

the 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 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

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

4

Clam Se, μg/g dw

Sampling

Station

Chipps IslandSuisun BayCarquinez StraitSan Pablo Bay

4.1

(avg 14.3)

6.1 8.1 12.5

November 5-6,

1997 

Suisun Bay

Carquinez Strait

San 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 405 8.1 12.5 Carquinez Strait San Pablo Bay Suisun Bay Grizzly Bay

November 4-5,

1999

Suisun Bay

SuisunBay

San Pablo Bay

6.1 12.5

(avg 5.4)

Suisun Bay

6.1

(avg 7.3)

8.1Carquinez Strait

3 1 4 5

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, TTFclam = 18.9

Measured 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, TTFclam = 13.5

Measured particulate Se, μg/g dw

Clam Se, μg/g dw Clam 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

4

Clam Se, μg/g dw

18

16

14

12

10

8

6

4

Clam Se, μg/g dw

(avg 11.3)

6.1 411415 405 8.1 12.5 Carquinez Strait San Pablo Bay Suisun Bay Grizzly Bay Suisun Bay Suisun Bay San 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 EC05

Target Se (whole-body, diet and particulate), μg/g dw and Dissolved Se, μg/L

Kd

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 EC10

Kd

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 EC05

Kd

0 5 10 15 0

Salinity, psu

20 25 30 35

Dissolved Se, μg/L Dissolved Se, μg/L Dissolved Se, μg/L

5 10 15

Salinity, psu

20 25 30 35

fish diet (4.5)

Measured

dissolved Se (mean = 0.102)

mean Se

Kd

 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: TTFclam = 17.1; TTFfish= 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 Kd

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 EC05

Kd

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 EC10

Target Se (whole-body, diet and particulate), μg/g dw and Dissolved Se, μg/L

Kd

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 EC05

Kd

0 5 10 15 0

Salinity, psu

20 25 30 35

Dissolved Se, μg/L Dissolved Se, μg/L Dissolved 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: TTFclam = 17.1; TTFbird = 2.6

Measured

dissolved Se (mean = 0.102)

mean Se

Kd

 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 Salinity

Delta

Bay

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)

Kd Kd

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 EC05

fish 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: TTFinsect = 2.8; TTFfish = 1.1

Delta: April 2003, above normal year, high flow season

aquatic insect food web: TTFinsect = 2.8; TTFfish = 1.1

Measured

dissolved Se

mean Se 

Kd

 Predicted allowed

dissolved Se

target value Se

mean dissolved Se

mean (0.174)

Delta

Bay

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 dw and Dissolved 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 Kd

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.]
Delta

Bay

2.0

1.5

1.0

0.5

0

A. Bay: November 1999, 

 above normal year, low flow season, 

 70 day residence time

Particulate 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 time

Particulate 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 season

Particulate 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 80

Dissolved Se, μg/L or particulate Se, μg/g dw

C. amurensis Se, 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)

Kd

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

-2010 20 30 40 50 60 70 80

Particulate 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 C
Water-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 load

Water-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 load

Water-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 load

Water-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 load

Water-column selenium

(total recoverable)

Figure C5. Water-column selenium concentrations and selenium loads, Valero Renery, Benicia, California.
Appendix D
1 

APPENDIX D Compilation 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 d1999, 2000, 2005, and 2007. Table D4. White sturgeon and C. amurensis Se concentrations and calculated TTFs. Table D5. Invertebrate Se concentrations for Mildred Island and Franks Tract during 2003-2004. 
2 

Table D1. Bird liver, muscle, and egg Se concentrations for the Bay-Delta. species and location tissue Se (μg/g dw) Suisun and San Pablo Bays (2004-2006) (Wainwright-De La Cruz et al., 2008) liver (range) surf scoter overwintering 7.4-119 (n = 159) Suisun 

Pab

Suisun and San Pablo Bays (2004-2006) (Wainwright-De La Cruz et al., 2008) egg (mean) surf scoter late Jan to spring 1.7 ( n = 22) Central North Bay tidal marshes, 1998-1999(Schwarzbach et al., 2006) egg (mean and range) California clapper rail 1.93 (1.12-3.13) North and South Bays prebreeding season Mar-Apr, 2005 (Ackerman and Eagles-Smith, 2009) liver (mean and range) American avocet 7.9 (3.1-49) black-necked stilt 5.3 (2.3-41) Forster’s tern 7.1 (3.7-14.5) Caspian tern 6.7 (4.8-14.4) San Pablo Bay March 2002 (Hunt et al., 2003) muscle (mean) surf scoter 7.8 greater scaup 8.7 Suisun Bay, March 2002 (Hunt et al., 2003) muscle (mean) surf scoter 13.3 greater scaup 13.4 Table D2. Bird egg Se concentrations for the Bay-Delta (Schwarzbach and Adelsbach, 2003). Suisun Bay and San Francisco Bay, March-July, 2000 Suisun Bay, Delta, Stone Lake, Davis, Consumnes River, March-July, 2001 mean bird egg (μg/g dw) n minimum (μg/g dw) snowy plover 1.5 3 0.9 American avocet 1.8 6 1.3 California clapper rail 1.6 6 1.3 Brandt’s cormorant 2.1 2 1.9 Forester’s tern 2.4 6 2.0 black-necked stilt 2.4 2 2.2 double-crested cormorant 2.6 8 2.3 California least tern 2.8 6 2.4 black-crowned night heron 3.5 11 2.5 snowy egret 4.2 9 3.7 great egret 3.0 15 2.0 all groups 2.8 74 0.9 
3 

Table D3: Largemouth bass Se concentrations for the Sacramento River watershed, San Joaquin River watershed, and downstream 1999, 2000, 2005, and 2007 (Foe, 2010). [Data from 2007 are given in bold.] 

location largemouth bass Se μg/g wb dw

Sacramento R @ Veterans Bridge 2.27±1.57Sacramento R. @ RM 44 2.64±0.38; 1.47±0.65; 1.85±0.35Sacramento R. near Rio Vista 1.50±0.54; 1.74±0.43; 2.58±1.39San Joaquin R. @ Fremont Ford 1.94±1.12San Joaquin R. @ Crows Landing 2.54; 2.69±0.40; 2.86±1.37San Joaquin R. @ Vernalis 2.37;1.29±1.15;1.95±0.47; 2.44±0.13Old R. near Tracy 2.31±1.83; 2.41±1.11San Joaquin R. @ Potato Slough 1.59;1.36±0.38;1.32±0.53 2.57±2.12Middle R @ Bullfrog 1.76; 1.93±0.77; 2.14±0.37Franks Tract 1.66±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 Bay 1.63±0.35Whiskey Slough 1.74±2.39Table D4. White sturgeon and C. amurensis Se concentrations (Stewart et al., 2004; Kleckner et al., 2010) and calculated TTFs. 

seaward white sturgeon 

Se muscle (μg/g dw) 

landward white sturgeon 

Se muscle (μg/g dw) 

Carqueniz Strait (station 8.1) C. amurensis Se 

μg/g dw 

Chipps Island (station 4.1) C. 

amurensis Se μg/g dw 

10.2 mean 6.91 mean 14.0 (1998) mean 10.8 (range 5-16) 

12.6 (1999) 11.8 (2000) 14.1 (2001) 

12.9 grand mean (range 5.4-20) 

12.8 mean (2000-2001) 
4 

Table D5. Invertebrate Se concentrations for Mildred Island and Franks Tract during 2003-2004. (Lucas and Stewart, 2007). 

Mildred Island (August 2001) 

invertebrate Se μg/g dw 

stratiomyidae 0.60 corophium 1.1 damselfly 1.3 gammarus 0.88; 1.2 hyalella 1.4 aeshnidae 1.6 snail 1.8 bulk zooplankton 1.8 corbiculaa 2.3; 2.8; 2.9 isopod 3.7 oligochaete 4.1; 4.8 

fish (muscle) Se μg/g dw

threadfin shad 0.99 inland silverside 1.4 redear sunfisha 1.4; 1.5 largemouth bass 1.6 

Franks Tract (April 2002) 

invertebrate Se μg/g dw 

gammarus 0.91; 1.0 damselfly 1.3 hyalella 1.3 epiphytic material 1.5 bulk zooplankton 1.8 oligochaete 2.2 chironomid 2.7 planaria 3.6 corbicula 3.8 

fish (muscle) Se μg/g dw 

largemouth bass 1.3 redear sunfish 1.5 inland silverside 2.0 a1999-2000.