U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2009-1114

U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2009-1114

Modeling of Selenium for the San Diego Creek 

Watershed and Newport Bay, California
Cover Photos: In order from top, space radar image of Orange County, California, 

NASA, 2006 [http://visibleearth.nasa.gov]; birds: curlew, skimmer, pelican, and mallard; 

Upper Newport Bay Ecological Reserve; middle portion of Newport Bay, Lower 

Newport Bay, California State University, Long Beach, College of Natural Science and 

Mathematics [http://www.cnsm.csulb.edu].

Cover art by Jeanne DiLeo, USGS, Menlo Park, Calif.
Modeling of Selenium for the San Diego 

Creek Watershed and Newport Bay, 

California

By Theresa S. Presser and Samuel N. Luoma

Open-File Report 2009-1114

U.S. Department of the Interior

U.S. Geological Survey
ii

U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia 2009

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Suggested citation:

Presser, T.S., and Luoma, S.N., 2009, Modeling of selenium for the San Diego Creek watershed and 

Newport Bay, California: U.S. Geological Survey Open-File Report 2009-1114, 48 p. 

[http://pubs.usgs.gov/of/2009/1114/].

Any use of trade, product, or firm names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

Although this report is in the public domain, permission must be secured from the individual copyright 

owners to reproduce any copyright the material contained within this report.
iii

Contents

Abstract........................................................................................................................................... 1

Introduction ..................................................................................................................................... 1

Conceptual Models.......................................................................................................................... 3

Partitioning Coefficient (Kd) .............................................................................................................. 5

San Diego Creek Watershed ....................................................................................................... 5

Newport Bay ................................................................................................................................ 7

Trophic Transfer Factors (TTFs)...................................................................................................... 8

Invertebrates................................................................................................................................ 9

Fish............................................................................................................................................10

Birds ..........................................................................................................................................10

Modeling........................................................................................................................................11

Scenarios...................................................................................................................................11

Validation...................................................................................................................................12

Summary.......................................................................................................................................12

Acknowledgments .........................................................................................................................12

References Cited...........................................................................................................................13

Tables ...........................................................................................................................................19

Figures ..........................................................................................................................................44
iv

Figures

Figure 1. Schematic of San Diego Creek watershed and Newport Bay. The Upper Newport 

Bay Ecological Reserve is shown. .................................................................................................. 44

Figure 2. Conceptual model for the San Diego Creek watershed illustrating linked factors that 

determine the effects of Se on ecosystems. ................................................................................... 45

Figure 3. Conceptual model for the upper Newport Bay illustrating linked factors that 

determine the effects of Se on ecosystems. ................................................................................... 46

Figure 4. Conceptual model for the lower Newport Bay illustrating linked factors that determine 

the effects of Se on ecosystems..................................................................................................... 47 

Figure 5. Extrapolation of dissolved Se at 15.1 ppt to 0 ppt. Data from transect of the upper 

Newport Bay from the mouth of San Diego Creek into the upper bay (CH2MHILL, 2008)............... 48

 

Tables

Table 1. Total water-column Se concentrations and Se speciation for the San Diego Creek 

watershed....................................................................................................................................... 19

Table 2a. Dissolved or total water-column Se concentrations; Se concentrations in bed 

sediment and algae; and calculated Kds for the San Diego Creek watershed (freshwater sites)..... 20

Table 2b. Statistics for calculated Kds based on table 2a data........................................................ 21

Table 3. Dissolved water-column Se concentrations and speciation for the Newport Bay 

estuary. .......................................................................................................................................... 22

Table 4. Water-column and suspended particulate Se concentrations in Upper Newport Bay in 

a transect from the near the mouth of San Diego Creek into the upper bay to salinities of 33.1 

ppt.................................................................................................................................................. 22

Table 5. Water-column and sediment Se concentrations and calculated Kds in a gradient from 

landward to seaward in Newport Bay.............................................................................................. 22

Table 6. Experimental data for invertebrate physiological factors and calculated kinetic TTFs 

for invertebrates. ............................................................................................................................ 23

Table 7. Field-derived TTFs for invertebrates from field datasets (temporally and spatially 

matched data-pairs), with experimentally derived TTFs given for comparison ................................ 24

Table 8. Invertebrate Se concentrations for the San Diego Creek watershed. ............................... 25

Table 9. Invertebrate Se concentrations for Newport Bay............................................................... 25

Table 10. Selected particulate Se concentration and a comparison of calculated invertebrate 

TTFs for the San Diego Creek watershed and invertebrate TTF derived from the literature. ........... 26

Table 11. Experimentally or field derived TTFs for fish................................................................... 27

Table 12. Fish Se concentrations for the San Diego Creek watershed. .......................................... 28

Table 13. Invertebrate and fish Se concentrations for San Diego Creek watershed and 

derivation of watershed-specific TTFs for fish................................................................................. 29

Table 14. Invertebrate and fish Se concentrations for Newport Bay and derivation of bayspecific TTFs for fish. ..................................................................................................................... 30

Table 15. Mean and ranges for bird egg Se concentrations for the San Diego Creek 

watershed....................................................................................................................................... 31

Table 16. Mean and ranges for bird egg Se concentrations for Upper Newport Bay....................... 31
v 

Table 17. Calculated TTFs from laboratory mallard studies; the endpoint is reduced 

hatchability in eggs (see Ohlendorf, 2003 for complete dataset)..................................................... 31

Table 18. Modeled Se exposure for the San Diego Creek watershed and Newport Bay................. 32

Table 19. Modeled Se exposure for the San Diego Creek watershed and Newport Bay. ............... 33

Table 20. Modeled Se exposure for the San Diego Creek watershed and Newport Bay................. 34

Table 21. Generalized modeled Se exposure scenarios for the watershed based on protection 

for fish. ........................................................................................................................................... 36

Table 22. Generalized modeled exposure scenarios for the watershed based on protection of 

birds. .............................................................................................................................................. 37

Table 23. Model validation for Newport Bay showing predicted and observed fish Se 

concentrations. ............................................................................................................................... 38

Table 24. Model validation for bay showing (1) predicted and observed fish Se concentrations 

and (2) predicted and observed bird egg Se concentrations. ........................................................ 39

Table 25. Model validation for bay showing predicted and observed bird egg Se 

concentrations. ............................................................................................................................... 40

Table 26. Model validation for watershed showing (1) predicted and observed invertebrate Se 

concentrations and (2) predicted and observed bird egg Se concentrations. ................................. 41

Table 27. Model validation for watershed showing (1) predicted and observed fish Se 

concentrations and (2) predicted and observed bird egg Se concentrations. .................................. 42

Table 28. Model validation for watershed showing predicted and observed bird egg Se 

concentrations. ............................................................................................................................... 43
1 

Modeling of Selenium for the San 

Diego Creek Watershed and Newport 

Bay, California

By Theresa S. Presser and Samuel N. Luoma

Abstract

The San Diego Creek watershed and Newport Bay in southern California are 

contaminated with selenium (Se) as a result of groundwater associated with urban 

development overlying a historical wetland, the Swamp of the Frogs. The primary Se 

source is drainage from surrounding seleniferous marine sedimentary formations. An 

ecosystem-scale model was employed as a tool to assist development of a site-specific Se 

objective for the region. The model visualizes outcomes of different exposure scenarios 

in terms of bioaccumulation in predators using partitioning coefficients, trophic transfer 

factors, and site-specific data for food-web inhabitants and particulate phases. Predicted 

Se concentrations agreed well with field observations, validating the use of the model as 

realistic tool for testing exposure scenarios. Using the fish tissue and bird egg guidelines

suggested by regulatory agencies, allowable water concentrations were determined for 

different conditions and locations in the watershed and the bay. The model thus 

facilitated development of a site-specific Se objective that was locally relevant and 

provided a basis for step-by-step implementation of source control. 

Introduction

Understanding the biotransfer of selenium (Se) is essential for evaluating the effects of Se 

on ecosystem resources. The linked approach presented in the San Francisco Bay-Delta 

Selenium Model (Presser and Luoma, 2006) considers progressive modeling of Se 

through water-column loads, concentrations, and speciation; particulate matter 

transformation and bioavailability; the biodynamics of bioaccumulation in prey; and 

trophic transfer to predators. This approach can conceptualize and model Se exposure 

through site-specific food webs and determine the vulnerability of predator species in an 

ecosystem. This approach also can be used to generate forecasts of how Se moves 

through a watershed and estuary based on selected management and regulatory options. 
2 

Groundwater- and wastewater-related discharges of Se within the San Diego Creek 

watershed have the potential to adversely affect surface waters and, hence, habitats within 

the watershed and Newport Bay (fig. 1) (U.S. Environmental Protection Agency, 2002). 

In 2004, the California Santa Ana Regional Water Quality Control Board issued a revised 

National Pollutant Discharge Elimination System permit regulating groundwater-related 

discharges in the watershed (California Santa Ana Regional Water Quality Control 

Board, 2004). This specific revised regulatory approach addressed (1) the presence of Se 

and nitrogen and (2) the ability to comply with established Total Maximum Daily Load 

requirements for impaired water bodies (that is, San Diego Creek and Newport Bay) 

(U.S. Environmental Protection Agency, 2002). This action initiated an alternative 

compliance approach allowing stakeholders to participate in Work Groups to develop and 

implement a comprehensive Work Plan to address Se and nitrogen. This process included 

Federal agencies (U.S. Environmental Protection Agency, U.S. Fish and Wildlife, U.S. 

Geological Survey), State and County agencies (California State and Regional Boards; 

California Department of Fish and Game; County of Orange), and three consulting 

groups (CH2M Hill, RBF Consulting, and Larry Walker Associates) working 

cooperatively as the Resource and Regulatory Agency Group with the regulated 

community to develop a site-specific Se objective to integrate into a watershed scale 

management strategy for Se (California County of Orange, 2006a; Larry Walker 

Associates and CH2M Hill, 2007; California Nitrogen and Selenium Management 

Program, 2006a). A primary task included in the development of the site-specific Se 

objective was the adaptation of the San Francisco Bay-Delta Selenium Model to the San 

Diego Creek watershed and Newport Bay. The modeling would translate selected tissue 

guidelines for predators (that is, whole-body fish tissue and bird egg tissue) to dissolved 

Se concentrations at chosen sites in the watershed. The dissolved Se concentrations 

would then facilitate calculation of Total Maximum Daily Load Se requirements for the 

watershed (U.S. Environmental Protection Agency, 2002). 

This report is supplemental to the larger effort of justifying, deriving, and implementing a 

site-specific Se objective for San Diego Creek watershed and Newport Bay (California 

Santa Ana Regional Water Quality Control Board, 2004). In that regard, this report is 

written mainly for a specific audience that has considerable knowledge of the watershed 

and has participated in Resource and Regulatory Agency Group and Work Group 

meetings from 2007 to 2009. In addition to this project report, a report is planned that 

further details and justifies the approach for ecosystem-scale modeling of Se.

This report (1) designs conceptual models for the San Diego Creek watershed and 

Newport Bay; (2) derives and justifies partitioning coefficients (Kds) and trophic transfer 

factors (TTFs) used in modeling of the watershed and bay; (3) models different food-web 

scenarios to elucidate Se exposure within the watershed and bay; and (4) compares 

predicted Se concentrations to modeled Se concentrations (that is, validation). The work 

presented here shows the nature of a cooperative iterative process resulting in sitespecific regulation and the evolution of our modeling effort. During this time, additional 

field data became available, data gaps were addressed, feedback was given, guidelines 

were developed, and revised exposure scenarios were completed. Thus, this report, in 

addition to illustrating modeling of Se for the watershed and bay, serves as a record to 

support decision-making within the larger group of resource and regulating agencies. 
3 

From a scientific perspective, this watershed-scale project illustrates the importance of 

ecosystem knowledge, especially ecology, for successful Se modeling.

Conceptual Models

A generalized schematic of the San Diego Creek watershed and Newport Bay is 

presented in Figure 1. The watershed is in an urban setting that is intensively developed 

(93 percent urban uses; 7 percent agricultural uses, including plant nurseries). Newport 

Bay is an important southern California estuary that is an ecological reserve in the upper 

portion and a developed marina for recreational boating and fishing in the lower portion. 

Figure 1 shows the boundaries of historical marshes (Swamp of the Frogs and an 

ephemeral lake), and these areas overlay aquifers containing the most elevated Se 

concentrations (Hibbs and Lee, 2000; Meixner and others, 2004). Geologic Se sources 

are implicated for the watershed including the surrounding Monterey Formation (Piper 

and Isaacs, 1994). Ninety-six percent of Se loading is estimated to be from groundwater 

sources in the watershed (California Nitrogen and Selenium Management Program, 

2006b; 2009). Drains and drainage channels are important parts of the hydrology of the 

watershed because of the presence of a high ground water table (Hibbs and Lee, 2000; 

Meixner and others, 2004). 

Sites for modeling (Larry Walker Associates Inc. and CH2M Hill, 2007) within the San 

Diego Creek watershed and Newport Bay include: 

• marsh drains (drains within the area historically designated as the Swamp of the 

Frogs); 

• non-marsh drains (drains outside the area of the historical swamp);

• upper and lower San Diego Creek (SDC); 

• upper and lower Peters Canyon Wash (PCW); 

• Irvine Ranch Water District (IRWD) off-channel wetlands; 

• University of California at Irvine (UCI) off-channel wetlands; and 

• Upper and Lower Newport Bay. 

Some data exist for other freshwater drainages to the bay [for example, Santa Ana Delhi 

Channel (SADC), Big Canyon Wash], but these sites are not included in the final 

exposure analysis. 

Aquatic habitat categories within the area are: riparian channels and pools; control basins; 

freshwater wetlands; salt marsh; tidal mudflat; estuarine open-water; and marine openwater. The quality of the habitats varies, as does the potential for Se interaction (for 

example, low habitat value and high Se exposure in some marsh drains). Many of the 

habitat components are highly engineered and managed to serve several purposes, 

including treatment for nitrogen and phosphorus and sediment control (for example, inchannel or off-channel wetlands; detention basins in lower San Diego Creek). 

The Upper Newport Bay Ecological Reserve and the bay itself (fig. 1) are important 

stopovers for birds on the Pacific Flyway. Threatened or endangered bird species include 

the light-footed clapper rail (Rallus longirostris levipes), California brown pelican 
4 

(Pelecanus occidentalis californicus), and California least tern (Sterna antillarum 

browni) (Sutula and others, 2005; Allen and others, 2008). Bird species of concern in 

California are osprey (Pandion haliaetus), double-crested cormorant (Phalacrocorax 

auritus), elegant tern (Thalasseus elegans), black skimmer (Rhynchops niger), and 

California gull (Larus californicus). Forage fish species that serve as food for birds 

include killifish, goby, juvenile halibut, sculpin, and topsmelt (Allen and others, 2004). 

Larger predatory fish species include sea basses, halibut, sand bass, croaker, and 

jacksmelt. Shellfish sampling of bivalves showed 24 species of bivalves, with the lower 

bay supporting more species (California County of Orange, 2004). However, lack of 

previously available commercial species has deterred studies to evaluate the suitability of 

existing bay habitats and water quality for renewal of a shellfish industry. Several organic 

chemicals, including modern pesticides (diazinon, chlorpyrifos) and legacy pesticides 

(DDT, Chlordane), as well as heavy metals (cadmium, copper, lead, zinc, and mercury) 

also are of concern in the watershed and bay and may have affected the status of specific 

food webs and their inhabitants (U.S. Environmental Protection Agency, 2002). 

Conceptual Se models for the San Diego Creek watershed and Newport Bay are based on 

the approach of Presser and Luoma (2006) (figs. 2-4). Important components of food 

webs identified from site-specific monitoring data (CH2M Hill, 2008a) for the freshwater 

portions of the watershed are: 

• invertebrates: aquatic insect, crayfish, clam, snail, worm, leech. 

• fish: minnow, sunfish, carp shiner, crappie, catfish, bass, mosquitofish. 

• birds: avocet, stilt, rail, killdeer, coot, mallard, grebe. 

Important components of food webs identified from site-specific monitoring data (CH2M

Hill, 2008a) for the estuarine and marine portions of bay are: 

• invertebrates: amphipod, isopod, clam, mussel, crab, snail. 

• fish: mullet, killifish, goby, anchovy, sandbass, turbot, halibut, sculpin, perch, 

topsmelt. 

• birds: avocet, stilt, rail, killdeer, tern, skimmer.

Some species not represented in the collected species (for example, polychaetes) were 

modeled because of implications for past and future food webs. 

Datasets compiled for modeling are food-web-specific and include Se data for the water 

column (dissolved or total Se); particulate phases; invertebrates; fish (whole-body); birds 

(egg); and, when available, dissolved Se speciation and salinity (CH2M Hill, 2008a). 

These datasets are limited to data that are temporally and spatially matched [that is, 

datasets gathered across media (water, particulate material, invertebrates, fish and/or bird 

tissue) and during a constrained time period]. Summaries of the data are presented in this 

report, with extensive documentation available in the spreadsheet datasets that are part of 

the site-specific Se objective process (CH2M Hill, 2008a). Data are sorted according to 

seasonal conditions (wet season, November through April, and dry season, May through 

October). However, the small number of data for prey and predators precluded 

conclusions based on season. All solids data are given in dry weight (dw). 
5 

Partitioning Coefficient (Kd) 

San Diego Creek Watershed

Partitioning of Se between water and particulate material is a dynamic biogeochemical 

process. Because of the biology involved, it is complex to model (that is, geochemical 

modeling misses major biological processes). At any point in time and space, Se 

partitioning can be described by a distribution coefficient, which is the ratio 

 Kd = Se concentrations in particulate (μg/kg dw) / Se concentrations in water (μg/L).

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 concentrations comparable to 

water concentrations. Presser and Luoma (2006) explained this coefficient in detail and 

presented data from the literature showing the wide range of distribution coefficients 

(Kd’s) that occurred in different environments. They recognized that the use of Kd is an 

operational, descriptive choice for characterizing Se dynamics in an environment. As a 

practical measure Kd is very useful, but there are limits to its interpretation in terms of 

describing and quantifying phase transformation processes (Presser and Luoma, 2006). 

A major factor determining the magnitude of the partitioning coefficient is Se speciation 

in the water column (Presser and Luoma, 2006). Dissolved Se can exist as selenate, 

selenite, and organo-Se. Selenium speciation also differs in nature with the character of 

the input to a water body and the residence time in the water body. For example, Se often 

enters a stream (for example, San Diego Creek) as selenate. If that stream flows into a 

wetland and is retained in a wetland (that is, in-stream water becomes off-stream water) 

with sufficient residence time, then recycling of Se may occur. During recycling, some 

selenate is taken up by bacteria and/or plants and converted to selenite and organo-Se. 

These reduced species then are released back to the water as these organisms die and 

decay. The more recycling, the more selenite and organo-Se are produced. Neither of the 

latter forms can be re-oxidized to selenate; that reaction takes hundreds of years (Cutter 

and Bruland, 1984). Therefore, the net outcome of recycling in a watershed is a gradual 

buildup of selenite and organo-Se in the water. This process is evidenced in the San 

Diego Creek watershed by a small increase in selenite (21 to 25 percent) toward the 

downstream end of the system, 39-100 percent selenite in watershed wetlands, and as 

much as 78 percent selenite in the estuary (table 1). 

Reactions between particles and dissolved Se are dominated by biological uptake into 

bacteria, plants, and their decaying debris termed detritus. Selenate is the least reactive of 

the three forms of dissolved Se in generating particulate material. Thus, if selenate is the 

only form of Se, then the Kd tends to be low. This condition reflects low Se 

concentrations on particulate material compared to Se concentrations in the water

column. As selenite concentrations increase (even if that increase is small) the ratio 

begins to increase. In the case here, as the San Diego Creek watershed changes from 

freshwater to the saline water of the estuary and bay, residence time increases, recycling 

increases, and eventually the ratio of particulate to dissolved Se can be extremely high as 

Se is stripped from the water column with increasing effectiveness.
6 

Presser and Luoma (2006) discussed several studies that supported the link between 

speciation and partitioning coefficient; however, several additional studies are included in 

this report. Calculations using data from laboratory microcosms and experimental ponds 

show speciation-specific Kds of 140-493 for selenate; 720-2,800 when an elevated 

proportion of selenite exists; and 12,197-36,300 for 100-percent dissolved selenomethionine uptake into algae or periphyon (Besser and others, 1989; Graham and others, 

1992; Kiffney and Knight, 1990). These ranges are consistent with the general ranges for 

Kd used by Presser and Luoma (2006) for modeling of the San Francisco Bay-Delta 

Estuary when they developed scenarios based on several possible fates for Se. These 

ranges also are consistent with the full range for Se in the San Diego Creek watershed 

and Newport Bay. 

The Kd also can be influenced by the type of material in the sediment, although to a lesser 

degree than by speciation. For example, field data for Luscar Creek in Alberta, Canada, 

show a hierarchy of Se concentrations: 2.4 μg/g dw in sediment, 3.2 μg/g dw in biofilm, 

and 5.5 μg/g dw for filamentous algae (Casey, 2005). Using these concentrations with a 

field-measured dissolved Se concentration of 10.7 μg/L yields Kds of 224, 299, and 514 

respectively, with an average Kd of 346. Similarly, field data for a slough tributary to the 

San Joaquin River, California, show a hierarchy of Se concentrations: 0.47 μg/g dw in 

sediment, 2.4 μg/g dw in algae, and 7.9 μg/g dw in detritus (Saiki and others, 1993). 

Using these concentrations with a field-measured dissolved Se concentration of 13 μg/L

yields Kds of 36, 185, and 608 respectively, with an average Kd of 276. 

Interpretation of particulate Se concentrations should take into consideration, where 

possible, the nature of the particulate material. Bed sediments are the least desirable 

choice for calculating Kd, especially if the sediments vary from sand to fine-grained 

material among the samples. Sandy sediments dilute concentrations with a high mass of 

inorganic material and may yield Kds that are anomalously low. On the other hand, bed 

sediments samples are the most traditional type of sample to collect; and Se 

concentrations in bed sediment are commonly all that are available. In that case, these 

data are used for determination of Kd. Further operational choices include comparison of 

one consistent type of material among locations or comparison of an average of the 

different types of materials (that is, all relevant particulate phases). In practical terms 

however, modelers usually have to work with the particulate Se data that are available 

and try to find ways to both minimize the influences caused by particle type and capture 

the influences of changes in speciation. This is the approach taken below. 

Several approaches are possible in choosing a Kd(s) for the San Diego Creek watershed. 

The dataset for Se concentrations in the water column is extensive, but a smaller dataset 

exists for particulate Se concentrations (CH2M Hill, 2008a). Table 1 is a summary of 

total water-column Se concentrations and the percentages of dissolved selenite for the 

watershed during the dry season. The datasets for water and sediment data pairs were 

more numerous for the dry season than for the wet season. Table 2a shows a summary of 

available Se concentrations for bed sediment and algae samples collected in the same 

place and at the same time as the water-column samples (that is, matched samples). The 

75th percentile values for all data collected at each of the sites also are shown. Use of a 

75th percentile value was chosen by the Resource and Regulatory Agency Group as a 
7 

useful statistical representation for the data. The location-specific Kds are calculated from 

these data and are shown in table 2a. Table 2a was assembled at the request of the 

Resource and Regulatory Agency Group for the purpose of illustrating the basis for 

decisions (and the uncertainty around those decisions) concerning Kds for the watershed. 

Table 2a is complex, but the compilation is intended to show all the available data and 

the limitations of the datasets for particular sites. 

The Kds for different environments in the San Diego Creek watershed vary from <100 to 

a maximum of 938, averaging 241 (table 2b). The UCI marsh is a consistent exception 

from the rest of the watershed, with Kds from 656 – 939, averaging 791. Excluding UCI 

marsh, the range is 19 to 444, averaging 168 across the entire creek. Thus, the variability 

(based on a coefficient of variation) in the Kd in this area is about twofold. The 75th 

percentile for all values (excluding UCI marsh) is 238. The type of particulate materials 

used (for example, sediment or algae) has a small influence on Kd in this dataset. 

Differences are not large between Kds determined from matched data-pairs (particulate 

and water samples collected in the same place and at the same time) and Kds calculated 

from the 75th percentile of all available water and sediment data (where enough sediment 

data are available to do that calculation). The greatest differences in Kds, however, are 

due to the inherent spatial variability in the data (table 2). 

Choices for Kd used in freshwater modeling (shown later in tables 18-22) are: 

1. A choice for Kd of approximately 200 would illustrate a representative freshwater 

average scenario. This choice of Kd reflects the mean (168) and 75th percentile 

(238) Kds for the San Diego Creek watershed. Predictions using this Kd would 

represent water-column Se concentrations if conditions in the watershed stayed 

much like they are now.

2. A more environmentally conservative approach is illustrated using the 75th 

percentile of all Kds (approximately 400). Predictions using this Kd would 

represent water-column Se concentration if conditions in the watershed change, 

for example, to allow more recycling. Use of this environmentally conservative 

Kd would be desirable to assure the greatest likelihood of remaining below fishtissue guidelines within the entire watershed. 

3. An alternative approach would be to assume that any areas that behaved like the 

UCI wetlands must be protected. In that case, a Kd of 800 could be used in 

modeling. 

Newport Bay

Table 3 shows a summary of dissolved Se concentrations for Newport Bay as a function 

of salinity (parts per thousand, ppt) and the percentage of selenite as a fraction of the 

dissolved Se. Concentrations decline dramatically between the mouth of San Diego Creek 

and the lower bay. Dissolved Se concentration data in a transect from near the mouth of 

the estuary at San Diego Creek into Upper Newport Bay (table 4) were used to predict the 

Se concentration at the mouth of the estuary (at 0 ppt salinity) (fig. 5; table 3). The 

extrapolation of the dissolved Se concentration from a salinity of 15.1 ppt to a salinity of 
8 

0 ppt is based on Se being conservative (that is, no loss into other phases of the 

environment). Table 4 also shows the results of Se analysis of suspended particulate 

samples for the upper bay taken at the same time as the dissolved samples. The Kds 

calculated from the matched dissolved and particulate Se concentrations increase from 

107 in the landward-most sample to as high as 578 and 776 seaward. One choice for a Kd 

for modeling the upper bay is the mean of these values: 264. Another alternative is the 

75th percentile value: 353. 

Table 5 shows the mean of 11 samples of dissolved and bed-sediment Se concentrations 

collected at different harbors of Newport Bay, most of which are in the lower bay. The 

locations closest to where San Diego Creek enters the upper bay (Newport Dunes and 

Bayside DeAnza) had the lowest Se concentrations in sediment and the highest dissolved 

Se concentrations for this dataset. The most enclosed location (Harbor Towers), which is 

likely to have the poorest circulation and longest residence, had the highest Se 

concentrations in sediments and low dissolved Se. These patterns suggest that 

biogeochemical processes progressively transform Se to particulate forms as the 

contaminants are transported seaward and residence time allows longer reaction times. 

The distribution also could reflect some transport seaward of fine-grained sediments from 

the watershed. Whatever the processes involved, most of the Se coming from the 

watershed is trapped in these harbors. Kds also increase dramatically in these 

environments (from approximately 7,000 to 43,000). Unfortunately no data are available 

to track Se concentrations on suspended particulate concentrations in the water column, 

making it difficult to draw conclusions about how tightly the water-column community of 

invertebrates, fish, and birds is linked to these contaminated sediments. Modeling for the 

bay depends on particulate Se data and an assumption that bed-sediment Se 

concentrations are linked to the Se concentrations in invertebrates, fish, and birds in the 

system. Choices for estuary and bay modeling shown later employ Kds of 200, 1,000, 

10,000 and 20,000 to represent the various conditions from the upper bay to the most 

isolated harbors. 

Trophic Transfer Factors (TTFs)

Traditional models for aquatic systems attempt to quantify the linkage between Se 

concentrations in invertebrates, fish, and aquatic birds and those in the environment. The 

traditional approach was to employ a bioconcentration factor (BCF), which is the ratio 

between Se accumulated by an organism from water and Se concentrations in the water; 

or a bioaccumulation factor (BAF), which is the ratio between the concentration 

accumulated by the animal from all sources (for example, in the field) and the 

concentration in the water column. It is well recognized that BCFs and BAFs are 

extremely variable among circumstances and are not constants (McGeer and others, 

2003; DeForest and others, 2007). The latter means that both factors can change as Se 

concentrations change, even if all other conditions are held constant. It is also widely 

recognized that this approach is not very useful for understanding Se exposures because it 

skips links among components of food webs (figs. 2-4). 

Selenium trophic transfer factors (TTFs), on the other hand, take account of the wellestablished principle that Se is bioaccumulated almost entirely from the food an animal 
9 

eats (Luoma and others, 1992; Presser and Luoma, 2006). In addition, the extent of 

bioaccumulation (that is, the concentration achieved by the organism) is driven by 

physiological constants that are specific to each species (Luoma and Rainbow, 2005). 

Experimental protocols are now well developed for defining those constants. The 

constants are assimilation efficiency (AE), ingestion rate (IR) and the rate constant that 

describes Se excretion or loss from the animal (ke). In the absence of rapid growth, the 

species-specific potential for an organism to bioaccumulate Se is

TTF = [(AE) (IR)]/ke.

as defined by Reinfelder and others (1998) and reviewed by Wang (2002). If field data 

are available, derivation is from matched data-pairs using the equation 

TTF = Canimal ÷ Cdiet

Invertebrates

TTFs from kinetic experiments

Table 6 shows a summary of Se TTFs determined in robust kinetic experiments over the 

past decade for a variety of marine invertebrate taxa and the freshwater zebra mussel. The 

clam Corbicula fluminea can exist in both freshwater and marine environments. The table 

also illustrates the degree of variability among different determinations of TTF. The 

variability typically reflects the influences of different factors, such as food type and food 

availability, but also includes some influence from experiment-to-experiment variability. 

The variability among studies for each species is typically much smaller than the 

variability among species. 

TTFs from field data 

Selenium TTFs also can be derived from field data, using the ratio of bioaccumulated Se 

in the animal versus the concentration of Se in its food. Table 7 shows a summary of 

TTFs derived for different animal taxa from field studies for which robust data are 

available. Kinetic TTFs are given where appropriate for comparison. The use of these 

field data allows us to expand the number of taxa for which TTFs are available for 

modeling. 

TTFs from San Diego Creek

Some data for Se concentrations in invertebrates are available from both the San Diego 

Creek habitat and from Newport Bay. Tables 8 and 9 show summaries of available Se 

concentrations for invertebrates collected in the San Diego Creek watershed and Upper 

and Lower Newport Bay. One way to verify if data from other locations are appropriate 

for use in the San Diego Creek situation is to compare TTFs from the small data set 

available for the local environment with those from elsewhere. Table 10 shows a 

comparison of the TTFs derived locally with a summary of those derived in tables 6 and 

7. The local values, even though based upon a limited number of data-pairs in many 

cases, are comparable to the literature-derived values, verifying that studies in other 
10 

situations are applicable to the local conditions in the San Diego Creek watershed (table 

10). From these summaries, choices are possible with regard to which TTFs to use in 

modeling for major groups of taxa:

• Freshwater: 

o insects in general: 2.8 

o zooplankton: 1.5 (amphipod: 0.6; copepod: 2.05) 

o freshwater clams: 2.7 

o freshwater mussels: 6

• Bay 

o deposit-feeding clams: 7.4

o marine mussels: 6

o amphipods: 0.6 

o zooplankton: 1.5 

o polychaete worm: 4.5 

Although some clams show much higher potential for bioaccumulation, this has not been 

verified in the field.

Fish

Table 11 shows a compilation of Se TTFs for fish derived from experimental studies and 

from sets of matching field data for invertebrates and fish. The average TTF for this set 

of data is 1.18 and the 75th percentile is 1.34. Table 12 shows a summary of available Se 

concentrations for fish tissue collected in the watershed. Table 13 shows the derivation of 

site-specific Se TTFs for fish in the watershed based on matched fish and invertebrate 

data. Fewer data are available for fish than for invertebrates in the watershed. The range 

of site-specific TTFs is 0.8 to 4.6, so choosing a Se TTF for fish of 1.1 to 1.2 would be 

conservative on the basis of local data. Table 14 shows a summary of available Se 

concentrations for fish tissue collected in the bay and calculated site-specific TTFs based 

on matched fish and invertebrate data. The range of site-specific TTFs is 0.62 to 1.3. 

Most modeling scenarios use a default TTF for fish of 1.1. 

Birds

Tables 15 and 16 show summaries of available Se concentrations for bird eggs collected 

in the watershed and bay. Table 17 shows the laboratory data used to derive a TTF for 

birds for modeling. A range of 1.8 to 2.9 is shown. The toxicity endpoint for the 

experimental studies was reduced hatchability in mallard eggs (Ohlendorf, 2003). A TTF 

of 1.8, the lowest value derived from this dataset, is chosen for modeling. This choice 

would represent the least effective bioaccumulative potential derived from the dataset. 

The choice of 1.8 can be modified as part of exposure scenarios if regulating agencies 

such as the U.S. Fish and Wildlife Service decide to do so. The TTFs derived from 

experimental data may be most relevant to resident birds rather than to birds feeding in a 

larger range of diverse habitats. Another compilation of studies relevant to calculating 

TTFs for birds concluded that a TTF of 1.4 was appropriate for the watershed (CH2M 

HILL, 2008b). 
11 

Modeling

Scenarios

Modeling scenarios were constructed throughout the course of the development of a sitespecific Se objective for the San Diego Creek and Newport Bay watershed. The 

generalized equations (Presser and Luoma, 2006) used for translation of tissue Se 

guidelines to dissolved Se concentrations are 

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

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

Equations may be modified for more complex food webs by inserting additional TTFs. In 

addition to modeling scenarios chosen for illustration of specific food-web exposure in 

the watershed and bay, some modeling conditions were stipulated by those collaborating 

in the site-specific Se objective process.

A series of exposure scenarios are shown in tables 18-20 (for example, particulate to 

invertebrate to fish to bird; particulate to invertebrate to bird; particulate to invertebrate to 

forage fish to predator fish). The scenarios employ a fish-tissue target of 5.0 μg/g wholebody dw or a bird-egg target of 8.0 μg/g whole-egg dw. These targets were chosen as part 

of the regulatory process of the Resource and Regulatory Agency Group for the San 

Diego Creek watershed and Newport Bay. The Se TTF used for modeling exposure of 

fish is 1.1, except for mosquitofish, where a TTF of 2.2 was used. The TTF used for 

modeling exposure of birds is 1.8. For the watershed, aquatic insects (TTF = 2.8) were 

used as the selected invertebrate on which to base translation for invertebrate-eating fish 

or birds. Selenium TTFs for invertebrates used in modeling for the mouth of the estuary 

and the upper and lower bay varied from 2.05 for copepod, 4.5 for polychaete, and 7.4 for 

clam. Modeled Kds were those derived previously to represent the habitats of the 

watershed (200-1,000) and bay (200 to 20,000). Tables 21 and 22 show requested 

generalized exposure scenarios for the watershed based on protecting fish or birds. 

Results of modeling are shown for varying Kds, guidelines for the protection of fish and 

birds, and the TTFs for birds. 

Modeling of the conditions and habitats of the San Diego Creek watershed and Newport 

Bay shows the importance of (1) wetlands within the watershed and (2) estuary 

interaction within the lower and upper bays in determining the fate of Se discharged 

within the watershed. Food-web modeling of the watershed predicts increased Se transfer 

from the upper watershed through lower San Diego Creek to the lower wetlands. Foodweb modeling of Newport Bay predicts that if predators are limited to feeding on prey 

from the water column (the current main food webs), then trophic transfer will be less 

than if transfer is through a food web consisting of benthic predators (restored food 

webs). 
12 

Validation

Comparisons were made between predicted concentrations and available observed Se 

concentrations for invertebrate, fish, and bird species to test the validity of the selected 

approach and site-specific assumptions for the San Diego Creek watershed and Newport 

Bay (tables 23-28). In some cases, few data were available for comparison. Predicted Se 

concentrations were in the range of observed Se concentrations for most modeled cases. 

Where there were discrepancies, modeling factors were examined and redefined or it was 

recommended that additional data be collected to confirm modeling expectations. 

Summary 

Critical variables to be considered in modeling and protection of a watershed or estuary 

are (1) Se speciation in the water column, (2) bioavailablity of particulate material as 

food for invertebrates, and (3) the biodynamics of food ingestion in invertebrates, fish, 

and birds. Site-specific modeling of Se exposure in prey-predator pairs in the San Diego 

Creek watershed and Newport Bay illustrates the differences in invertebrate physiology 

that are propagated to higher trophic levels as a main determinant of exposure and, hence, 

risk. Modeling of the estuary and bay illustrates that biogeochemical processes 

progressively transform Se to particulate forms as the contaminants are transported 

seaward and residence time allows longer reaction times.

A final recommendation of a site-specific Se objective for San Diego Creek watershed 

and Newport Bay involves more choices than analyzed here. These choices include 

collaborative resolution of (1) what habitat to protect (the most sensitive part of the 

watershed or bay; a generalized watershed; individual components of the watershed or 

bay); (2) what predators and food webs to protect (endangered and threatened species; 

commercially valuable species; existing abundant species); (3) what parameters to use in 

modeling (maximums; averages; or 75th percentile); and (4) what type of guideline to 

adopt (fish or bird tissue; dietary; or water column). An overarching question for those 

confronted with protection of a watershed/estuary that is affected by a mixture of legacy 

pollutants, such as the San Diego Creek watershed and Newport Bay, is whether to 

restore the ecosystem and consider exposure scenarios based on potential future 

inhabitants of the ecosystems. 

Acknowledgments

We wish to thank Orange County for providing cooperative funding with the U.S. 

Geological Survey for this project. We also thank participating members of the U.S. 

Environmental Protection Agency, U.S. Fish and Wildlife Service, the California State 

Water Resources Control Board, the Santa Ana Regional Water Resources Control 

Board, the California Department of Fish and Game, CH2M Hill, Larry Walker 

Associates, and RBF Consultants for their insights and support during this process. We 

wish to especially thank Christine Arenal of CH2M Hill for the compilation of a 

comprehensive Se dataset and Jeanne DiLeo of USGS for the illustration of the Se 

pathway in the watershed and bay. 
13 

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19 

Tables

Table 1. Total water-column Se concentrations and Se speciation for the San Diego Creek watershed. 

location season 75th percentile 

(μg/L)

mean 

(μg/L)

minimum 

(μg/L)

maximum 

(μg/L)

selenite (%)

(selenite/dissolved Se)

Upper PCW dry 2.5 5.0 0.1 24.7 up to 21%

Upper SDC dry 4.6 2.9 0.7 6.1 up to 21%

Lower PCW dry 31.0 21.8 1.6 44.0 up to 22%

Lower SDC dry 16.0 12.3 1.2 37.5 up to 25%

IRWD wetlands dry 15.6 11.7 4.0 16.9 up to 39%

UCI wetlands dry 2.9 2.3 1.3 3.0 up to 100%

OTHER

Marsh Drains dry 24.2 14.9 0.1 162.0

Non-Marsh Drains dry 4.7 5.4 0.5 79.8

SADC dry 14.0 9.9 2.2 19.0 up to 11%

IRWD (IN)a dry 19.1 12.9 4.0 20.7

IRWD (OUT)a dry 13.3 10.5 4.0 14.1 a denotes location near inflow or outflow.
20 

Table 2a. Dissolved or total water-column Se concentrations; Se concentrations in bed sediment and algae; and calculated Kds 

for the San Diego Creek watershed (freshwater sites). [Kds are calculated from matched (that is, temporally and spatially paired) 

and 75th percentile datasets when data are available. The average of the sediment and algae Se concentrations are 

preferentially used, if available, to calculate Kd. Additional available data are listed in brackets ([ ]) to substantiate data ranges 

for sites. See additional notes in text on assemblage of table. dw = dry weight; n = number of samples.]

location, season, and speciation water (μg/L) bed sediment

(μg/g dw)

algae

(μg/g dw)

average 

(sediment and 

algae) (μg/g dw)

Kd

sediment/water

(μg/kg/μg/L)

Upper SDC (matched) DRY 0.79 0.2 253

Upper SDC (75th percentile) DRY 4.6 0.2 (n=1) 43

Upper PCW (matched) DRY

(up to 21% selenite) 

3.4 total

3.2 dissolved

0.6

0.6

1.2

1.2

0.9

0.9

264

281

Upper PCW (75th percentile) DRY 2.5 0.6 (n=1) 240

Non-marsh drains (matched) none none none

Non-marsh drains (75th percentile) 4.7 DRY

3.65 WET

none none

Marsh drain (matched) DRY 0.25-28.1

11.1

none 0.6

2.4

3.1

3.8

3.1 279

Marsh Drains (75th percentile) 24.2 DRY

17 WET

none none

Lower PCW (matched) DRY

(up to 22% selenite)

8.1 ---

[0.86]

3.6

[2.5, 1.6]

444

based on algae

Lower PCW (matched) WET 30 6.7 223

Lower PCW (75th percentile) 31 ALL 4.1 n=6 ALL

(0.7-13.8)

132

Lower SDC (matched)

(up to 25% selenite) 

3.3 DRY (n=1)

29 DRY (n=1)

4.1 DRY (May)

19.2

25.3 WET (n=1)

18.4 WET (n=1)

0.2 (n=1) DRY

0.54 (n=2) DRY

0.66 (range 0.5-1.6)a 

2.6 (range 0.17-2.6) WET

2.6 (range 0.17-2.6) WET

[3.1]

0.64 (n=1) 

DRY

61

19

156

34

103 

141

Lower SDC 75th percentile 

mean

17 DRY

12.7

1.16

0.95

68

75
21 

minimum

maximum

1.2

37.5

0.09

6.2

75

166

Lower SDC, 75th percentile 

mean

minimum

maximum

19.7 WET

13.3

0.75

39

1.16

0.95

0.09

6.2

59

71

120

159

IRWD wetlands (matched) WET

pond 1

pond 1 anti-predator trench

pond 3

pond 3 anti-predator trench

stream and riparian zone

(up to 39% selenite)

21.1

21.1

20.4

20.4

(assume 21)

2.3

2.7

4.5

5.8

6.9 5.7; 9.3 7.3

109

128

221

284

348

IRWD wetlands (75thpercentle) ALL 16.0 3.72 232

IRWD wetlands IN 

(75th percentile) ALL

(up to 9% selenite)

21.5 in

IRWD wetlands OUT

(75th percentile) ALL

(up to 39% selenite)

12.3 out

UCI wetlands (matched)

(up to 100% selenite)

none none

UCI wetlands (DRY) 75th percentile

mean

minimum

maximum

2.77 (n=2)

2.04

1.31

2.77

2.18 (n=5)

1.6

0.86

2.6

787

784

656

939

Delhi Channel (SADC) DRY

(up to 11% selenite)

0.49 2.0 none

Delhi Channel (75th percentile) 15 1.23 (range 0.5-4.0) 82 a sand to silty-sand sample.

Table 2b. Statistics for calculated Kds based on table 2a data.

Kd all sites all sites except UCI wetlands UCI wetlands IRWD wetlands UCI and IRWD wetlands

mean 241 168 792 220 449

median 159 136 786 226 316

75th percentile 279 238 825 271 752

Maximum 939 444 939 348 939

Minimum 19 19 656 109 109

n (number of data points) 33 29 4 6 10
22 

Table 3. Dissolved water-column Se concentrations and speciation for the Newport Bay estuary. 

location dissolved or range of dissolved (μg/L) selenite or range of selenite (%)

mouth of estuary at 0 ppt 24 extrapolated (see figure 5)

Upper Bay at 15 ppt 14.4 11

Upper Bay at 27 ppt 3.5 19

Upper Bay at 28 ppt 2.4-1.8 18-32

Upper Bay at 30 ppt 1.3-1.0 14-25

Upper/Lower Bay at 33 ppt 0.97-0.29 21-48

Lower Bay 0.15-0.11

Lower Bay 0.08-0.06

Table 4. Water-column and suspended particulate Se concentrations in Upper Newport Bay in a 

transect from the near the mouth of San Diego Creek into the upper bay to salinities of 33.1 ppt. 

[Calculated Kds and percentages of dissolved selenite also are shown. ppt = parts per thousand; 

dw = dry weight]

salinty

(ppt)

dissolved

(μg/L)

total 

(μg/L)

particulate

(μg/g dw) Kd

% selenite

(selenite/dissolved)

15.1 14.4 18.4 1.54 107 10.7

26.6 3.50 4.02 0.39 111 18.6

30.2 1.79 2.26 0.18 101 20.1

28.0 2.32 2.47 0.27 116 22.0

31.4 1.13 1.60 0.24 212 31.9

28.7 2.42 2.97 0.21 87 17.8

31.6 1.27 1.43 0.17 134 22.0

30.5 1.31 1.67 0.24 183 13.7

31.0 1.32 1.72 0.25 189 18.9

30.8 1.18 1.53 0.17 144 25.4

31.4 0.991 1.37 0.21 212 27.2

31.6 0.837 1.02 0.24 287 25.1

32.0 0.967 0.985 0.17 176 14.5

32.8 0.445 0.548 0.17 382 31.5

33.0 0.482 0.624 0.17 353 29.0

32.8 0.438 0.744 0.34 776 32.0

32.2 0.674 0.839 0.20 297 20.8

32.8 0.508 0.698 0.17 335 27.6

32.9 0.465 0.506 0.17 366 30.1

33.0 0.294 0.351 0.17 578 47.6

33.1 0.417 0.466 0.17 408 33.6

264 mean

353 75th percentile

Table 5. Water-column and sediment Se concentrations and calculated Kds in a gradient from 

landward to seaward in Newport Bay. [dw = dry weight]

location (with increasing residence time) sediment (μg/g dw) dissolved (μg/L) Kd

Newport Dunes 1.07 0.15 6,933

Bayside DeAnza 1.05 0.11 9,679

Bahia Corinthian 1.17 0.07 16,675

Balboa Yacht Basin 1.06 0.06 17,026

H&J Moorings 1.10 0.08 13,508

Lido Village 1.32 0.06 20,000

Lido Anchorage 1.57 0.07 25,000

Harbor Towers 2.98 0.07 42,715
23 

Table 6. Experimental data for invertebrate physiological factors and calculated kinetic TTFs for invertebrates.

Marine Environments

species AE IR ke TTF (AE X IR/ke) Reference

copepod (Acartis tonsa) 0.97 0.42a 0.13b 3.1 Fisher and Reinfelder, 1991

copepod (Temora longicornis) 0.55 0.42 0.155 1.5 Wang and Fisher, 1998

copepod (A. tonsa; T. longicornis) 0.8 0.42a 0.19 1.8 Wang and othersl., 1996a

copepod (Oithona; Limnoithona; Tortanus; Acartia; T. 

longicornis)

0.50 0.42 0.155 1.35 Schlekat and others, 2004

mysid (Neomysis mercedis) 0.61-0.73 0.45 0.25 1.1-1.3 Schlekat and others, 2004

amphipod (Leptocheirus plumulosus) 0.45 0.20 0.15 0.6 Schlekat and others, 2004

clam (Corbula amurensis) 0.45-0.80 0.25 0.025 4.5-8.0 Schlekat and others, 2002

clam (C. amurensis) 0.36-0.54 0.25 0.025 3.6-5.4 Lee and others, 2006

clam (Corbicula fluminea) 0.29-0.81 0.05 0.010 1.45-4.05 Lee and others, 2006

clam (Macoma balthica) 0.22-0.86 0.25 0.012c 11.2 (using mean AE) Luoma and others, 1992

clam (M. balthica) 0.54 (mean) 0.25 0.030d 4.5 Luoma and others, 1992

clam (Ruditapes philippnarum) 0.52-0.70 0.25a 0.013e 10-13.5 Zhang and others, 1990

clam (Mercenaria mercenaria) 0.92 0.25a 0.010f 23 Reinfelder and others, 1997

oyster (Crassostrea virginica) 0.45-0.70 0.25a 0.069g 1.6-2.5 Reinfelder and others, 1997

oyster (Crassostrea rivularis) 0.55 0.45 Ke and Wang, 2001

oyster (Saccostrea glomerata) 0.45 0.32 Ke and Wang, 2001

mussel (Mytilus edulis) 0.31 0.27 0.022 3.8 Wang and others, 1996b

mussel (M. edulis) 0.72 0.27 0.022 8.8 Reinfelder and others, 1997

barnacle (Balanus amphitrite) 0.63-0.79 0.40 0.014 18-22.6 Wang and others, 1999

barnacle (Elminus modestus) 0.34-0.74 0.40 0.0137 9.9-21.6 Rainbow and Wang, 2001

Freshwater Environments

Species AE IR ke TTF (AE X IR/ke) Reference

zebra mussel (Dreissena polymorpha) 0.28-0.46 0.42 0.026 4.5-7.4 Roditi and Fisher, 1999

clam (Corbicula fluminea) 0.29-0.81 0.05 0.010 1.45-4.05 Lee and others, 2006 a assumed IR

b calculated from 5.25 day half-life c chosen from Fowler and Benayoun, 1976a; Zhang and others, 1990; and Okazaki and Panietz, 1981

dcalculated from 23 day half-life [efflux (/d) = 0.69/half-life (d)], Reinfelder and others, 1997 e calculated from 52 day half-life

f calculated from 70 day half-life

g calculated from 10 day half-life
24 

Table 7. Field-derived TTFs for invertebrates from field datasets (temporally and 

spatially matched data-pairs), with experimentally derived TTFs given for comparison

invertebrate kinetic TTF field TTF reference

Freshwater

amphipod 0.94 Birkner, 1978; Saiki and others, 1993

zooplankton 1.5 Saiki and others, 1993

mayfly 2.7 Casey, 2005

caddisfly 3.2 Casey, 2005

cranefly 2.3 Casey, 2005

stonefly 2.6 Casey, 2005

all flies composite 3.2 Harding and others, 2005

damselfly 2.6 Birkner, 1978

midge (chironomid) 2.7 Birkner, 1978; Saiki and others, 1993

corixid (waterboatman) 2.14 Birkner, 1978

crayfish 1.5 Birkner, 1978; Saiki and others, 1993

daphnia 1.9 Besser and others, 1989

zebra mussel (D. polymorpha) 4.5-7.4 Roditi and Fisher 1999

clam (C. fluminea) 1.5-4.1 Lee et al 2006

Marine/Estuarine

amphipod 0.6 Schlekat and others, 2004; 

copepod 1.35 Schlekat and others, 2004

euphausiid 1.3 Fowler and Benayoun 1976b

mysid 1.3 Schlekat and others, 2004;

brine shrimp adult 4.2 Marden, 2008

brine shrimp nauplius 2.6 Marden, 2008

brine shrimp cysts 2.2 Marden, 2008

brine fly adult 1.8 Cavitt, 2007; Wurtzbach, 2007

brine fly larvae 1.5 Cavitt, 2007; Wurtzbach, 2007
25 

Table 8. Invertebrate Se concentrations for the San Diego Creek watershed. [Means and ranges or values (if only two values are available) are shown. dw = 

dry weight; n = number of samples.]

species Upper SDC and PCW Marsh Drains Lower SDC (DRY) Lower PCW IRWD wetlands UCI wetlands

invertebrate (μg/g dw)

zooplankton 6.8 (n=1)

amhipod 8.7 (n=1)

midge larvae (chironomid) 26.7 (8.1, 45.3) (n=2) 11 (8.4-15.7) (n=3)

waterboatman (corixid) 7.5 (2.3-13.6) (n=4) 11.6 (1.8-23.6) (n=5)

scud 2.7 (n=1)

snail 1.4 (1.2, 1.7) (n=2) 4.9 (n=1) 7.3 (n=1) 5.3 (n=1)

crayfish 4 (2.6-4.9) (n=3) 5.8 (n=1) 10.7 (n=1) 12.1 (8.5-15) (n=4)

dragonfly (nymphs or larvae) 12.6 (n=1) 10.2 (12.5, 7.8) (n=2) 5.6 (3.2, 7.9) (n=2)

clam (Corbicula if freshwater) 7.5 (6.1-8.5) (n=9) 7.6 (n=1)

clam (transplanted) 5.4 (n=1) 5.2 (n=1)

backswimmer 4.2 (n=1)

worm 14.9 (n=1)

leech 20.9 (n=1)

Table 9. Invertebrate Se concentrations for Newport Bay. [Means and ranges are 

shown. dw = dry weight; n = number of samples.]

species Upper Bay

(μg/g dw)

Lower Bay 

(μg/g dw)

amphipod 4.8 none

isopod 8.4 (6.5 - 10.4)

bivalve 6.1 (4.8 - 7.8) 

clam 2.9 (2.0 - 3.8) 

crab 6.1(2.5 - 8.0) 

snail 6.2 (2.3 - 12.0) 

mussels 4.4 (4.1, 4.7) 

transplanted mussels 3.9 (2.7 - 5.3)
26 

Table 10. Selected particulate Se concentration (see particulate Se concentration data in CH2M Hill, 2008a, matched and 75th percentile concentrations) and 

a comparison of calculated invertebrate TTFs for the San Diego Creek watershed and invertebrate TTF derived from the literature. [Means and ranges are 

shown. Two TTFs are calculated if two particulate Se concentrations were available or chosen as representative of a site. See table 8 for invertebrate Se 

concentrations use in calculations of TTF. dw = dry weight; n = number of samples; NA = not available.]

Upper SDC 

and PCW

Marsh 

Drains Lower SDC Lower PCW IRWD wetlands UCI wetlands

San Diego Creek 

watershed

(field average

by invertebrate species)

other studies

(by invertebrate 

species)

(See tables 6 and 7 

for data and 

references)

particulate Se (μg/g dw) 0.9 or 0.4 2.6 2.8 or 1.2 4.1 or 3.2 5.3 or 3.7 2.2 NA NA

TTF-zooplankton 1.3 or 1.8

n=1

1.6 1.5 freshwater

zooplankton 

composite;

1.4 copepod;

1.3 euphausiid;

1.2 mysid

1.9 daphnia

TTF-amphipod 2.1 or 2.7 n=1 2.4 0.6 marine

0.94 freshwater

TTF- chironomid 2.0 or 2.5 n=1 2.1 or 3.0 n=3 2.4 2.7

TTF-corixid 2.7 or 6.2 n=4 2.2 or 3.1 n=5 3.6 2.1

TTF-scud 1.0 n=1 1.0 n=1 1.4 copepod;

1.3 euphausiid;

1.2 mysid;

1.9 daphnia

TTF-snail 5.6 or 3.5 n=2 1.8 or 4.1 n=1 1.8 or 2.3 n=1 1.0 or 1.4 n=1 2.7

TTF-crayfish 1.5 n=3 2.1 or 4.8 n=1 2.6 or 3.3 n=1 2.3 or 3.3 n=4 2.8 1.5 freshwater

TTF-dragonfly (nymphs/ larvae) 3.1 or 3.9 n=1 1.9 or 2.8 n=2 2.5 n=2 2.8 2.8 all insects

TTF-clam (Corbicula) 2.7 or 6.2 n=9 1.4 or 2.1 n=1 3.1 2.7Corbicula

TTF-clam (transplanted) 1.9 or 4.5 n=1 1.3 or 1.6 n=1 2.3 n=1

TTF-backswimmer 1.9 n=1 1.9 n=1 2.8 all insects

TTF-worm 3.6 or 4.6 n=1 4.1 n=1

TTF-leech 5.1 or 6.5 n=1 5.8 n=1
27 

Table 11. Experimentally or field derived TTFs for fish. [Matching field datasets (temporally/spatially matched data-pairs) are used in the derivation. Unless 

noted, transfer is from invertebrate to fish.]

species kinetic TTF field or lab TTF experimental physiological factors reference 

mangrove snapper 1.1 ke 0.031; IR 5%; 69% AE) Xu and Wang, 2002 

juvenile striped bass 0.89 ke 0.08; IR 17%; 42% AE) Baines and others, 2002 

mudskipper 0.84 ke 0.019; IR 5%; 32% AE) Ni and others, 2005 

smooth toadfish 0.80 ke 0.015-0.032; IR, 7-10%; AE 24%) Alquezar and others, 2008

fathead minnowa 1.0b ke 0.025; assumed IR 5%; AE 50%) Bennett and others,1986 

fathead minnow adult 0.71-1.8b ke 0.014;assumed IR 5%; AE 20-50% Bertram and Brooks,1986

bluegill (fry/juvenile)a 0.51b ke 0.035; assumed IR 5%; AE 36%) Besser and others, 1993 

bluegill (fry/juvenile) 0.61b ke 0.041; assumed IR 5% AE 50%) Cleveland and others, 1993 

bluegill (juvenile) 1.15 (lab) Lemly, 1993 

bluegill 1.06 Saiki and others, 1993

white sturgeon range 1.0-1.7 Presser and Luoma, 2006 

mosquitofish 0.71-1.8 (average 1.2) Saiki and others, 1993

mosquitofish 1.3 Saiki and others, 2008

largemouth bass 1.1; 0.95 (average 1.0) Saiki and others, 1993

sailfin mollie 1.4 Saiki and others, 2008

rainbow trout 0.91; 0.71; 1.1; 0.96 (average 0.91) Casey, 2005

rainbow trout 1.04 Greater Yellowstone Coalition, 2005; 2006; Hamilton and Buhl

2004, 2005; Hamilton and others, 2004

cutthroat trout 1.0 McDonald and Strosher, 1998

cutthroat trout from dace 0.87; 0.99 (average 0.93) Greater Yellowstone Coalition, 2005; 2006; Hamilton and Buhl

2004, 2005; Hamilton and others, 2004

cutthroat trout from sculpin 0.95; 1.0 (average 0.98) Greater Yellowstone Coalition, 2005; 2006; Hamilton and Buhl

2004, 2005; Hamilton and others, 2004

cutthroat trout 0.97; 1.53 (average 1.25) Greater Yellowstone Coalition, 2005; 2006; Hamilton and Buhl

2004, 2005; Hamilton and others, 2004

brook trout 0.77 Hamilton 2004a,b;2005

brown trout 1.45; 1.18 (average 1.32) Greater Yellowstone Coalition, 2005; 2006; Hamilton and Buhl

2004, 2005; Hamilton and others, 2004

sculpin 1.23; 1.66 (average 1.45) Greater Yellowstone Coalition, 2005; 2006; Hamilton and Buhl

2004, 2005; Hamilton and others, 2004

dace 1.41; 1.65 (average 1.53) Greater Yellowstone Coalition, 2005; 2006; Hamilton and Buhl

2004, 2005; Hamilton and others, 2004

chub 1.03; 1.38 (average 1.21) Greater Yellowstone Coalition, 2005; 2006; Hamilton and Buhl

2004, 2005; Hamilton and others, 2004

shiner 1.5 Greater Yellowstone Coalition 2005; 2006

whitefish 1.3 Greater Yellowstone Coalition 2005; 2006

sucker 0.97 Greater Yellowstone Coalition 2005; 2006 a rotifer to larval fish microcosm or experimental microcosm

b with assumptions 
28 

Table 12. Fish Se concentrations for the San Diego Creek watershed. [Means and ranges by species are shown. dw = dry weight; n = number of 

samples.]

species Upper SDC

(μg/g, whole-body, 

dw)

Marsh Drains

(μg/g, whole-body, 

dw)

Lower SDC

(μg/g, whole-body, 

dw)

Lower PCW

(μg/g, whole-body, dw)

IRWD wetlands

(μg/g, whole-body, dw)

UCI wetlands

(μg/g, whole-body, 

dw)

mixed fish 5.8

n=1

5.5 (2.4-9.0)

n=3

13

n=1

fathead minnow 9.0 (8.9-9.1)

n=3

6.5

n=1

bluegill 9.1 (5.6-12.7) n=2

carp 9.3 (5.7-14.5) n=4 19 (dead fish filet)

n=1

red shiner 7.9 (7.4-8.5)

n=2

6.3

n=1

black crappie 6.7 

n=1

catfish 2.4 (2.2-2.7)

n=2

mosquitofish 12.5

n=1

17

n=1

21 n=2

(12.1, 29.9)

10.1 (1.3-18.9)

n=10

5.8 (5.6-6.0)

n=2
29 

Table 13. Invertebrate and fish Se concentrations for San Diego Creek watershed and derivation of watershed-specific TTFs for fish. [For Se concentrations, 

means and ranges are shown. dw = dry weight; wb = whole-body; n = number of samples; avg = average.]

location and 

speciation food web observed Se in invertebrate(μg/g dw)

observed Se in fish

(μg/g wb, dw) watershed-specific TTF invertebrate TTF source

Upper SDC and PCW

(up to 21% selenite)

sediment> 

invertebrate>

fish

1.4 snail n=2

5.8 tadpole composite no fish snail=mollusk= corbicula =2.8

Non-marsh drains If clam, 2.8

Marsh drains sediment>

invertebrate>

fish

2.7 scud n=1

4.0 crayfish n=3 12.5 mosquitofish n=1

12.5 mosquitofish

/2.7 amphipod=4.6

12.5 mosquitofish

/4 crayfish=3.1

crayfish=1.6; 

scud=amphipod= 0.9

average=1.3

Lower PCW

(up to 22% selenite)

sediment>

invertebrate>

fish

8.7 amphipod, n=1

27 midge (8.1; 45) n=2

7.3 snail n=1

11 crayfish n=1

12.6 dragonfly n=1

21 leach n=1

15 worm n=1

5.4 trans clam n=1

(all average 11)

6.5 fathead n=1

6.3 shiner n=1

21 mosquitofish (n=2)

6.4 fathead;shiner/8.1 midge=0.8

21 mosquitofish/11 avg invert=1.9

amphipod=0.6

midge,

dragonfly=2.9

crayfish=1.6

clam=2.8

leech, worm=2.8?

average = 2.1

Lower SDC

(up to 25% selenite)

sediment>

invertebrate>

fish

7.5 corixid (2.3-14) n=4

5.8 crayfish n=1

4.9 snail n=1

7.5 clam (6.1-8.5) n=9

(all average 6.4)

7.8 avg (2.2-14.5) bluegill, carp, fathead minnow, red shiner, 

largemouth bass, blk crappie, wht 

catfish, mixed fish, n=17

17 mosquitofish n=1

7.8 avg fish/7.5 corixid =1.04

17 mosquitofish/6.4 avg=2.6

crayfish=1.6

corixid=2.9 (insect)

clam (freshwater) =2.7

average =2.4

IRWD wetlands

(up to 39% selenite)

sediment>

invertebrate>

fish

6.8 zooplankton n=1

11 midge (8-16) n=3

12 corixid (2-24) n=5

5.3 snail n=1

12 crayfish (8-15) n=4

10 dragonfly (12; 7.8)

7.6 clam n=1

(all average 9.2)

13 mixed n=1

19 dead carp n=1

10 mosquitofish n=10

13 mixed/11.5 midge, corixid=1.1

13 mixed/9.2 avg=1.4

10 mosquitofish/9.2 avg =1.1

midge, corixid, dragon= 2.9

crayfish=1.6

copepod=1.6

clam=2.8

snail=2.8

average =2.3

UCI wetlands

(up to 100% selenite)

sediment>

invertebrate>

fish

5.6 dragonfly (3.2; 7.9)

4.2 back-swimmer n=1

(4.9 average)

5.8 mosquitofish n=2 5.8 mosquitofish/4.9=1.2 aquatic insects=2.9

Santa Ana Delhi 

Channel

(up to 11% selenite)

sediment>

invertebrate 4.3 clam n=1 clam=2.8
30 

Table 14. Invertebrate and fish Se concentrations for Newport Bay and derivation of bay-specific TTFs for fish. [Means and ranges are shown. The 

percentage of food items in diets of specific fish species also are given. dw = dry weight; wb = whole-body; n = number of samples.]

species Upper Bay

(μg/g, wb, dw)

Lower Bay

(μg/g, wb, dw)

% in diet (Allen and others, 

2008)

Upper Bay invertebrate

μg/g dw (clapper rail study)a TTF

striped mullet 5.5 (2.4-9.5) n=7 copepod 78%

(bivalve 13%)

California killifish

5.8 (2.6-9.5) n=5 3.9 (1.6-4.6)

n=9

bivalve 62%

**isopod 3%

6.1 bivalve n=5

2.9 clam n=4

4.4 mussel n=2

4.5 average

8.4 isopodb

5.8/4.5= 1.3

shadow goby 5.1 n=1 amphipod 70% amphipod 4.8 n=1 5.1/4.8 =1.1

arrow goby 4.0 (1.8-8.0) n=4 1.6 n=1 copepod 94%

cheekspot goby 2.3 (1.4-3.3) n=2 copepod 70%

anchovy 3.0 (3.0-3.1) n=2 amphipod 99% amphipod 4.8 n=1 3.0/4.8=0.62

barred sandbass 2.2 (2.2, 2.2) n=2

spotted sandbass

3.2 (3.1-3.4) n=2

fish 32%

bivalve 26%

crab 15%

assume fish 3.0

bivalve 6.1 n=5

crab 6.1 n=5

3.2/4.6=0.70

diamond turbot 3.3 (2.4-4.0) n=3 2.7 (1.5-4.0) n=2

California halibut 4.0 (3.4-4.5) n=4 1.5 (0.9-3.1) n=4 mysid 90% (eating from 

water column)

staghorn sculpin 1.6 n=1 2.0 (1.6-2.4) n=3

black surfperch 1.1 (1.0-1.2) n=3

shiner perch 1.2 (1.2, 1.2) n=2

pile surfperch 1.8 (1.7-1.8) n=2 1.7 n=1

topsmelt 3.4 (1.7-8.0) n=24 2.6 (1.3-4.1) n=26 copepod 57%

bivalve 31%

4.5 average of bivalve, 

clam, mussel a other data: 6.2 μg/g snail (n=15) 

b killifish is the only fish that eats isopods (with highest Se in invertebrates, 8.4 μg/g dw)
31 

Table 15. Mean and ranges for bird egg Se concentrations for 

the San Diego Creek watershed. [dw = dry weight; n = number of 

samples.]

species Lower SDC

(μg/g, bird egg dw)

IRWD wetlands

(μg/g, bird egg, dw)

UCI wetlands

(μg/g, bird egg, dw)

American avocet 2.8 (1.9-3.9) n=3* 6.4 (2.0-17.2)

n=8

5.1 (3.3-11)

n=5

black-necked stilt 6.9 (3.6-8.7) n=11 5.0 (3.0-9.8)

n=4

killdeer 9.5 (9.1-9.9) n=2 4.8 (2.6-7.1)

n=2

American coot 5.2

n=1

2.7 (1.9-4.5)

n=9

mallard 6.2 (4.0-7.0) 

n=4

pied-billed grebe 5.8 

n=1

6.2 (4.0-11)

n=4

Table 16. Mean and ranges for bird egg Se concentrations for 

Upper Newport Bay [dw = dry weight; n = number of samples.]

species Upper Bay

(μg/g, bird egg, dw)

American avocet 5.3 (1.6-10.7)

n=12

clapper rail 3.7 (3.1-4.5)

n=6

black-necked stilt 5.1 (3.4-8.5)

n=3

killdeer 2.6 (2.3-2.9)

n=2

tern 4.4 (2.5-8.4)

n=18

black skimmer 3.1 (2.1-5.6)

n=21

Table 17. Calculated TTFs from laboratory mallard studies; the 

endpoint is reduced hatchability in eggs (see Ohlendorf, 2003 for 

complete dataset). [Selenium concentrations in diet and bird 

eggs used to calculate TTFs also are shown. dw = dry weight; n 

= number of samples; EC = effect concentration.]

TTF diet, μg/g dw bird egg, μg/g dw

1.8 3.6 (EC3) 6.4

2.55 4.9 (EC10) 12.5

2.9 5.7 (EC18) 16.5
32 

Table 18. Modeled Se exposure for the San Diego Creek watershed and Newport Bay. [Two scenarios are shown: scenario A: particulate to invertebrate to

average fish to bird; and scenario B: particulate to invertebrate to mosquitofish to bird. The modeled Se criterion is 8.0 μg/g in bird eggs (dw). dw = dry 

weight; wb = whole-body; n = number of samples; NA = not applicable.]

location food web Se in water 

column (ug/L) Kd

Se in 

particulate 

(μg/g, dw)

TTF for 

invertebrate

Se in 

invertebrate 

(ug/g, dw)

TTF for 

fish

Se in fish 

(ug/g wb, dw)

TTF for 

bird

target Se in 

bird egg (μg/g 

dw)

Upper watershed sediment>insect>

average fish>bird

7.0 200 1.4 2.8 4.0 1.1 4.4 1.8 8.0

Upper watershed sediment>insect>

mosquitofish>bird

3.6 200 0.71 2.8 2.0 2.2 4.4 1.8 8.0

Lower SDC sediment>insect>

average fish>bird

4.4 320 1.4 2.8 4.0 1.1 4.4 1.8 8.0

Lower SDC sediment>insect>

mosquitofish>bird

2.2 320 0.71 2.8 2.0 2.2 4.4 1.8 8.0

IRWD wetlands sediment>insect>

average fish>bird

3.5 400 1.4 2.8 4.0 1.1 4.4 1.8 8.0

IRWD wetlands sediment>insect> 

mosquitofish>bird

1.8 400 0.71 2.8 2.0 2.2 4.4 1.8 8.0

UCI wetlands sediment>insect>

average fish>bird

1.4 1000 1.4 2.8 4.0 1.1 4.4 1.8 8.0

UCI wetlands sediment>insect>

mosquitofish>bird

0.71 1000 0.71 2.8 2.0 2.2 4.4 1.8 8.0

Near mouth of 

estuary

sediment>isopod>rail 10.5 200 2.1 2.05 4.4 NA NA 1.8 8.0

Near mouth of 

estuary

sediment>clam>rail 3.0 200 0.60 7.4 4.4 NA NA 1.8 8.0

Near mouth of 

estuary

sediment>copepod>

topsmelt>tern

10.0 200 2.0 2.05 4.0 1.1 4.4 1.8 8.0

Near mouth of 

estuary

sediment>polychaete>

turbot> tern

4.4 200 0.89 4.5 4.0 1.1 4.4 1.8 8.0

Upper Bay sediment>isopod>

scaup or rail

2.1 1000 2.1 2.05 4.4 NA NA 1.8 8.0

Upper Bay sediment>clam

>scaup or rail

0.60 1000 0.60 7.4 4.4 NA NA 1.8 8.0

Upper/Lower Bay sediment>copepod>

topsmelt>tern

0.20 10,000 2.0 2.05 4.0 1.1 4.4 1.8 8.0

Upper/Lower Bay sediment>polychaete>

turbot> tern

0.09 10,000 0.89 4.5 4.0 1.1 4.4 1.8 8.0

Lower Bay sediment>copepod>

topsmelt>tern

0.10 20,000 2.0 2.05 4.0 1.1 4.4 1.8 8.0

Lower Bay sediment>polychaete>

turbot> tern

0.04 20,000 0.89 4.5 4.0 1.1 4.4 1.8 8.0
33 

Table 19. Modeled Se exposure for the San Diego Creek watershed and Newport Bay. [The modeled scenario is particulate to invertebrate to bird. The 

modeled Se criterion is 8.0 μg/g in bird eggs (dw). dw = dry weight; n = number of samples.]

location food web Se in water

column (ug/L) Kd

Se in particulate

(μg/g, dw)

TTF for 

invertebrate

Se in invertebrate 

(ug/g, dw) TTF for bird target Se in bird egg (μg/g dw)

Upper watershed sediment>insect>>bird 8.0 200 1.6 2.8 4.4 1.8 8.0

Lower SDC sediment>insect>>bird 5.0 320 1.6 2.8 4.4 1.8 8.0

IRWD wetlands sediment>insect>>bird 4.0 400 1.6 2.8 4.4 1.8 8.0

UCI wetlands sediment>insect>>bird 1.6 1000 1.6 2.8 4.4 1.8 8.0

Near mouth of 

estuary sediment>isopod>rail 10.5 200 2.1 2.05 4.4 1.8 8.0

Near mouth of 

estuary sediment>clam>rail 3.0 200 0.60 7.4 4.4 1.8 8.0

Near mouth of 

estuary sediment>copepod>bird 10.05 200 2.1 2.05 4.4 1.8 8.0

Near mouth of 

estuary sediment>polychaete>bird 4.9 200 0.98 4.5 4.4 1.8 8.0

Upper Bay sediment>isopod>scaup or rail 2.1 1000 2.1 2.05 4.4 1.8 8.0

Upper Bay sediment>clam>scaup or rail 0.60 1000 0.60 7.4 4.4 1.8 8.0

Upper/Lower Bay sediment>copepod>bird 0.21 10,000 2.1 2.05 4.4 1.8 8.0

Upper/Lower Bay sediment>polychaete>bird 0.098 10,000 0.98 4.5 4.4 1.8 8.0

Lower Bay sediment>copepod>bird 0.105 20,000 2.1 2.05 4.4 1.8 8.0

Lower Bay sediment>polychaete>bird 0.049 20,000 0.98 4.5 4.4 1.8 8.0
34 

Table 20. Modeled Se exposure for the San Diego Creek watershed and Newport Bay. [Two scenarios are modeled: scenario A: particulate to invertebrate to 

average fish; and scenario B: particulate to invertebrate to forage fish to predator fish. The modeled Se guideline is 5.0 μg/g in fish tissue (wb, dw). dw = dry 

weight; wb = whole-body; n = number of samples; NA = not applicable.]

location food web Se in water

column (ug/L) Kd

Se in 

particulate

(μg/g, dw)

TTF for

invertebrate

Se in 

invertebrate 

(ug/g, dw)

TTF for 

fish

Se in fish 

(ug/g wb, 

dw)

TTF for fish target Se in fish 

(μg/g wb, dw)

Upper watershed sediment>insect>

average fish

8.0 200 1.6 2.8 4.5 NA NA 1.1 5.0

Upper watershed sediment>iinsect>

average fish>LM bass

7.5 200 1.5 2.8 4.1 1.1 4.5 1.1 5.0

Lower SDC sediment>insect>

average fish

5.0 320 1.6 2.8 4.5 NA NA 1.1 5.0

Lower SDC sediment>insect>

average fish>LM bass

4.7 320 1.5 2.8 4.1 1.1 4.5 1.1 5.0

IRWD wetlands sediment>insect>

average fish

4.0 400 1.6 2.8 4.5 NA NA 1.1 5.0

IRWD wetlands sediment>insect> average 

fish>LM bass

3.8 400 1.5 2.8 4.1 1.1 4.5 1.1 5.0

UCI wetlands sediment>insect>

average fish

1.6 1000 1.6 2.8 4.5 NA NA 1.1 5.0

UCI wetlands sediment>insect>

average fish>LM bass

1.5 1000 1.5 2.8 4.1 1.1 4.5 1.1 5.0

Near mouth of 

estuary

sediment>copepod>

topsmelt

11 200 2.2 2.05 4.5 NA NA 1.1 5.0

Near mouth of 

estuary

sediment>polychaete>

turbot

5.0 200 1.0 4.5 4.5 NA NA 1.1 5.0

Upper Bay sediment>copepod>

topsmelt

2.2 1000 2.2 2.05 4.5 NA NA 1.1 5.0

Upper Bay sediment>copepod>

topsmelt>halibut

2.0 1000 2.0 2.05 4.1 1.1 4.5 1.1 5.0

Upper Bay sediment>polychaete>

turbot

1.0 1000 1.0 4.5 4.5 NA NA 1.1 5.0

Upper Bay sediment>polychaete>

turbot>halibut

0.91 1000 0.91 4.5 4.1 1.1 4.5 1.1 5.0

Upper/Lower Bay sediment>copepod>

topsmelt

0.22 10,000 2.2 2.05 4.5 NA NA 1.1 5.0

Upper/Lower Bay sediment>copepod>

topsmelt>halibut

0.20 10,000 2.0 2.05 4.1 1.1 4.5 1.1 5.0

Upper/Lower Bay sediment>polychaete>

turbot

0.10 10,000 1.0 4.5 4.5 NA NA 1.1 5.0

Upper/Lower Bay sediment>polychaete>

turbot>halibut

0.09 10,000 0.91 4.5 4.1 1.1 4.5 1.1 5.0

Lower Bay sediment>copepod> 0.11 20,000 2.2 2.05 4.5 NA NA 1.1 5.0
35 

location food web Se in water

column (ug/L) Kd

Se in 

particulate

(μg/g, dw)

TTF for

invertebrate

Se in 

invertebrate 

(ug/g, dw)

TTF for 

fish

Se in fish 

(ug/g wb, 

dw)

TTF for fish target Se in fish 

(μg/g wb, dw)

topsmelt

Lower Bay sediment>copepod>

topsmelt>halibut

0.10 20,000 2.0 2.05 4.1 1.1 4.5 1.1 5.0

Lower Bay sediment>polychaete>

turbot

0.05 20,000 1.0 4.5 4.5 NA NA 1.1 5.0

Lower Bay sediment>polychaete>

turbot>halibut

0.046 20,000 0.91 4.5 4.1 1.1 4.5 1.1 5.0
36 

Table 21. Generalized modeled Se exposure scenarios for the watershed based on protection for fish. [dw = dry weight.]

Se guideline for 

fish (μg/g dw) TTF for fish Se in invertebrate(μg/g dw) TTF for aquatic insect Se in particulate (μg/g dw) Kd

Se in water column 

(ug/L)

Effect of varying Kd

5 1.1 4.55 2.8 1.62 136 (median) 11.94

5 1.1 4.55 2.8 1.62 168 (mean) 9.66

5 1.1 4.55 2.8 1.62 238 (75th percentile) 6.82

5 1.1 4.55 2.8 1.62 400 (wetland A) 4.06

5 1.1 4.55 2.8 1.62 800 (wetland B) 2.03

5 1.1 4.55 2.8 1.62 791 (UCI wetland mean) 2.05

Effect of varying fish guideline

8 1.1 7.27 2.8 2.60 136 (median) 19.10

7 1.1 6.36 2.8 2.27 136 (median) 16.71

6 1.1 5.45 2.8 1.95 136 (median) 14.32

5 1.1 4.55 2.8 1.62 136 (median) 11.94

Effect of varying fish guideline at a 75th percentile Kd

8 1.1 7.27 2.8 2.60 238 (75th percentile) 10.91

5 1.1 4.55 2.8 1.62 238 (75th percentile) 6.82
37 

Table 22. Generalized modeled exposure scenarios for the watershed based on protection of birds. [dw = dry weight]

Se guideline for bird egg 

(μg/g dw)

TTF for 

bird

Se in fish 

(μg/g dw)

TTF for 

fish

Se in invertebrate 

(μg/g dw)

TTF for aquatic 

insect

Se in particulate 

(μg/g dw)

Kd Se in water column 

(ug/L)

Effect of varying Kd

8 1.8 4.44 1.1 4.04 2.8 1.44 136 (median) 10.61

8 1.8 4.44 1.1 4.04 2.8 1.44 168 (mean) 8.59

8 1.8 4.44 1.1 4.04 2.8 1.44 238 (75th percentile) 6.06

8 1.8 4.44 1.1 4.04 2.8 1.44 400 (wetland A) 3.61

8 1.8 4.44 1.1 4.04 2.8 1.44 800 (wetland B) 1.80

8 1.8 4.44 1.1 4.04 2.8 1.44 791 (UCI wetland mean) 1.82

Effect of varying bird egg guideline

12.5 1.8 6.94 1.1 6.31 2.8 2.25 136 (median) 16.58

12.5 1.8 6.94 1.1 6.31 2.8 2.25 238 (75th percentile) 9.47

Effect of varying TTF for bird

8 1.6 5.00 1.1 4.55 2.8 1.62 238 (75th percentile) 6.82

8 1.6 5.00 1.1 4.55 2.8 1.62 136 (median) 11.94
38 

Table 23. Model validation for Newport Bay showing predicted and observed fish Se concentrations. [SPM = suspended particulate 

material; max = maximum; dw = dry weight; n = number of samples.]

food web

Se in 

particulate 

(μg/g dw)

TTF for 

invertebrate

predicted Se in 

invertebrate

(μg/g dw)

TTF for 

fish

predicted Se in 

fish

(μg/g, wb, dw)

observed Se in 

fish

(mean μg/g, wb, 

dw)

observed Se 

range in fish 

(μg/g, wb, dw)

Upper Bay

mean SPM>copepod>topsmelt 0.21 2.05 0.43 1.1 0.47 3.4 2.4-9.5 n=7

max SPM>copepod>topsmelt 1.00 2.05 2.05 1.1 2.26

mean SPM>polychaete>turbot 0.21 4.5 0.94 1.1 1.03 3.3 2.4-4.0 n=3

max SPM> polychaete>turbot 1.00 4.5 4.5 1.1 5.0

mean SPM>polychaete>killifish 0.21 4.5 0.94 1.1 1.03 5.8 2.6-9.5 n=5

max SPM> polychaete>killifish 1.00 4.5 4.5 1.1 5.0

mean SPM>clams>turbot 0.21 7.40 1.55 1.1 1.71 3.3 2.4-4.0 n=3

creekSPM>clams>turbot 1.00 7.40 7.40 1.1 8.14

meanSPM>anchovy>halibut 0.21 1.70 0.36 1.1 0.39 4.0 3.4-4.5 n=4

meanSPM>anchovy>halibut 1.00 1.70 1.70 1.1 1.87

sediments>mullet 2.00 2.0 4.00 5.5 2.4-9.5 n=7

Lower Bay

sediments>amphipods>killifish 1.10 0.60 0.66 1.1 0.73 3.9 1.6-4.6 n=9

sediments>amphipods>killifish 2.98 0.60 1.79 1.1 1.97

sediment>copepod>topsmelt>halibut 1.10 2.05 and 1.1 2.50 1.1 2.75 1.5 0.9-3.1 n=4

sediment>copepod>topsmelt>halibut 2.98 2.05 and 1.1 6.70 1.1 7.40 1.5

sediment>polychaete>turbot 1.1 4.5 5.0 1.1 5.5 2.7 1.5-4.0 n=2

sediment>polychaete>turbot 2.98 4.5 13.4 1.1 14.7
39 

Table 24. Model validation for bay showing (1) predicted and observed fish Se concentrations and (2) predicted and observed bird egg Se concentrations. [SPM = 

suspended particulate material; max = maximum; dw = dry weight; n = number of samples; NA = not available.]

food web

Se in 

particulate 

(μg/g dw)

TTF for 

invertebrate

predicted Se 

in 

invertebrate

(μg/g dw)

TTF for 

fish

predicted Se in 

fish

(μg/g, wb, dw)

observed Se 

in fish

(mean μg/g, 

wb, dw)

TTF for 

bird

predicted Se 

in bird egg, 

(μg/g dw)

observed Se in 

bird egg (μg/g 

dw)a

(skimmer/tern)

Se in bird 

egg from 

observed fish 

( μg/g dw)

Upper Bay

mean SPM>copepod>topsmelt> 

skimmer, tern, grebe 0.21 2.05 0.43 1.1 0.47 3.4 1.8 0.85 3.1/4.4 6.1

max SPM 1.00 2.05 2.05 1.1 2.26 1.8 4.07 3.1/4.4

mean SPM>polychaete>turbot> 

skimmer, tern, grebe 0.21 4.5 0.94 1.1 1.03 3.3 1.8 1.85 3.1/4.4 5.9

max SPM 1.00 4.5 4.5 1.1 5.0 1.8 9.0 3.1/4.4

mean SPM>polychaete>killifish>

skimmer, tern, grebe 0.21 4.5 0.94 1.1 1.03 5.8 1.8 1.85 3.1/4.4 10.4

max SPM 1.00 4.5 4.5 1.1 5.0 1.8 9.0 3.1/4.4

mean SPM>clams>turbot>

skimmer, tern, grebe 0.21 7.40 1.55 1.1 1.71 3.3 1.8 3.1 3.1/4.4 5.9

max SPM>clams>turbot 1.00 7.40 7.40 1.1 8.14 1.8 14.6 3.1/4.4

mean SPM>anchovy>halibut>

skimmer, ern, grebe 0.21 1.70 0.36 1.1 0.39 4.0 1.8 0.70 3.1/4.4 7.2

max SPM 1.00 1.70 1.70 1.1 1.87 1.8 3.4 3.1/4.4

mean sediment>mullet> skimmer, 

tern, grebe 2.00 2.0 4.00 5.5 1.8 7.2 3.1/4.4 9.9

Lower Bay

sediment>amphipods>killifish>

skimmer, tern, grebe 1.10 0.60 0.66 1.1 0.73 3.9 1.8 1.31 3.1/4.4 7.0

max sediment 2.98 0.60 1.79 1.1 1.97 1.8 3.54 3.1/4.4

sediment>copepod>topsmelt>

halibut>pelican, osprey 1.10 2.05 and 1.1 2.50 1.1 2.75 1.5 1.8 4.95 3.1/4.4 2.7

max sediment 2.98 2.05 and 1.1 6.70 1.1 7.40 1.5 1.8 13.32 3.1/4.4

sediment>polychaete>turbot> 

skimmer, tern, grebe 1.1 4.5 5.0 1.1 5.5 2.7 1.8 9.9 3.1/4.4 4.9

max sediment 2.98 4.5 13.4 1.1 14.7 1.8 26.5 3.1/4.4

mean sediment>mullet>skimmer, 

tern, grebe 2.00 2.0 4.00 5.5 1.8 7.2 3.1/4.4 9.9

sediment 2.00 2.0 4.00 5.5 1.8 7.2 3.1/4.4 a ranges in bird egg Se concentrations (mean, range, and number of samples): skimmer, 3.1 μg/g dw (2.1-5.6) n=21; tern, 4.4 μg/g dw (2.5-8.4) n=18; and grebe, (from watershed) 

6.1 μg/g dw (4-11) n=5
40 

Table 25. Model validation for bay showing predicted and observed bird egg Se concentrations. [dw = dry 

weight; n = number of samples.]

food web particulate 

Se (μg/g dw)

TTF for 

invertebrate

predicted Se 

in 

invertebrate

(μg/g dw)

TTF for

bird

predicted 

Se in bird

egg

(μg/g, dw)

observed 

Se in bird

egga 

(μg/g, dw)

Se in bird egg 

(range, μg/g, 

egg dw)

number of 

samples

Upper Bay

mean SPM>isopod>avocet 0.21 2.05 0.43 1.80 0.77 5.35 (1.6-10.7) n=12

creekSPM>isopod>avocet 1.00 2.05 2.05 1.80 3.69 5.35

mean SPM>isopod>rail 0.21 2.05 0.43 1.80 0.77 3.7 (3.1-4.5) n=6

creekSPM>isopod>rail 1.00 2.05 2.05 1.80 3.69 3.7

mean SPM>clams>scaup 0.21 7.40 1.55 1.80 2.80 NA

creekSPM>clams>scaup 1.00 7.40 7.40 1.80 13.32 NA

mean SPM>clams>rail 0.21 7.40 1.55 1.80 2.80 3.7 (3.1-4.5) n=6

creekSPM>clams>rail 1.00 7.40 7.40 1.80 13.32 3.7

Lower Bay

sediment>clam>scaup 1.1 7.40 8.14 1.80 14.65 NA

sediment>clam>scaup 2.98 7.40 22.05 1.80 39.69 NA

sediment>clam>scaup 2 7.40 14.80 1.80 26.64 NA

sediment>clam>scaup 2 7.40 14.80 1.80 26.64 NA a

 additional bird egg Se concentrations (mean, range, and number of samples): stilt, 5.1 μg/g dw (3.4-8.5 ) (n=3); and killdeer, 

2.6 μg/g dw (2.3-2.9) (n=2)
41 

Table 26. Model validation for watershed showing (1) predicted and observed invertebrate Se concentrations and (2) predicted and observed bird egg Se 

concentrations. [dw = dry weight; n = number of samples]

location food web invertebrate

Se in 

particulate 

(μg/g dw)

TTF for 

invertebrate

predicted Se in 

invertebrate

(μg/g dw)

observed in 

invertebrate

(average)

TTF for 

bird

predicted 

Se in bird 

egg

(μg/g, dw)

observed Se 

in bird egg

(μg/g, dw)

species and 

number of 

samples

Lower SDC sediment>invertebrate> 

avocet

corixid, crayfish, snail, 

clam

1.2

2.8

2.8

2.8

3.4

7.8

6.2 1.8

1.8

6.2

14.0

2.8

(1.9-3.9)

avocet (3)

Lower SDC sediment>invertebrate>

stilt

corixid, crayfish, snail, 

clam

2.8 1.8 6.9

(3.6-8.7)

stilt (11)

Lower SDC sediment>invertebrate> 

killdeer

corixid, crayfish, snail, 

clam

2.8 1.8 9.5

(9.1-9.9)

killdeer (2)

Lower SDC sediment>invertebrate> 

grebe

corixid, crayfish, snail, 

clam

2.8 1.8 5.8 pied-billed 

grebe (1)

Lower SDC sediment>invertebrate>

coot

corixid, crayfish, snail, 

clam

2.8 1.8 5.2 coot (1)

IRWD wetlands sediment>invertebrate>

avocet

midge, corixid, 

dragonfly

3.7

5.3

2.8 10.4

14.8

10.9 1.8 18.7

26.6

6.4

(2-17.2)

avocet (8)

IRWD wetlands sediment> invertebrate>

stilt

zooplankton, midge, 

corixid, snail, crayfish, 

dragonfly, clam

2.8 1.8 5

(3-9.8)

stilt (4)

IRWD wetlands sediment> invertebrate>

mallard

zooplankton, midge, 

corixid, snail, crayfish, 

dragonfly, clam

2.8 1.8 6.2

(4-7)

mallard (4)

IRWD wetlands sediment> invertebrate> 

killdeer

zooplankton, midge, 

corixid, snail, crayfish, 

dragonfly, clam

2.8 1.8 4.8

(2.6-7.1)

killdeer (2)

UCI wetlands sediment> invertebrate> 

coot

dragonfly 2.2 2.8 6.2 5.6 1.8 11.2 2.7

(1.9-4.5)

coot (9)

UCI wetlands sediment> invertebrate> 

avocet

dragonfly 2.8 1.8 5.1

(3.3-11)

avocet (5)

UCI wetlands sediment> invertebrate>

grebe

dragonfly 2.8 1.8 6.2

(4-11)

pied-billed 

grebe (4)
42 

Table 27. Model validation for watershed showing (1) predicted and observed fish Se concentrations and (2) predicted and observed bird egg Se 

concentrations. [dw = dry weight; n = number of samples.]

location food web fish 

species

particulate Se 

(μg/g dw)

TTF for 

invertebrate

predicted Se in 

inverte-brate

(μg/g dw)

TTF for

fish

predicted 

Se in fish

(μg/g dw)

average 

observed 

in fisha

TTF for

bird

predicted

Se in bird 

egg

(μg/g, dw)

observed 

Se in bird 

egg

(μg/g, dw)

species 

and 

number of 

samples

Lower San 

Diego Creek

sediment> 

invert>fish>bird

average 

fish

1.2

2.8

2.8 3.4

7.8

1.1

1.1

3.7

8.6

8.4 1.8 15.1 5.8 pied-billed 

grebe (1)

IRWD wetlands sediment> 

invert>fish>bird

average 

fish

3.7

5.3

2.8 10.4

14.8

1.1

1.1

11.4

16.3

14 1.8 25.2

UCI wetlands sediment> invert> 

grebe

average 

fish

2.2 2.8 6.2 1.1 6.8 5.8 1.8 10.4 6.2

(4-11)

pied-billed 

grebe (4) a mosquitofish for marsh drains, lower San Diego Creek, and Lower Peters Canyon Wash were 12.5, 17 and 21 μg/g dw, respectively; thus, transfer for mosquitofish at these 

sites are underestimated by a TTF of 1.1 (see table 11 for range of TTFs for mosquitofish) 
43 

Table 28. Model validation for watershed showing predicted and observed bird egg Se concentrations. [dw = dry weight; n = number of samples.] 

location food web Se in fish

(μg/g, wb, dw) fish species TTF forbird

predicted Se in bird 

egg (μg/g, dw)

observed Se in 

bird egg

(μg/g, dw)

species and 

number of bird 

egg samples

Upper SDC and PCW sediment>invert>fish>bird 5.8 mixed fish 1.8 10.4

Marsh drains sediment>invert>fish>bird 12.5 mosquitofish 1.8 22.5

Lower SDC sediment>invert>fish>bird 7.8 bluegill, common carp, fathead 

minnow, red shiner, largemouth bass, 

black crappie, white catfish, mixed fish

1.8 14.0 5.8 pied-billed 

grebe (1)

Lower SDC sediment>invert>fish>bird 17 mosquitofish 1.8 30.6

Lower PCW sediment>invert>fish>bird 6.4 fathead minnow, red shiner 1.8 11.5

Lower PCW sediment>invert>fish>bird 21 mosquitofish (12.1; 29.9) 1.8 37.8

IRWD wetlands sediment>invert>fish>bird 19.3 dead carp (filet) 1.8 34.7

IRWD wetlands sediment>invert>fish>bird 13 mixed fish 1.8 23.4

IRWD wetlands sediment>invert>fish>bird 10.1 mosquitofish (2.3; 15.3) 1.8 18.2

UCI wetlands sediment>invert>fish>bird 5.8 mosquitofish 1.8 10.4 6.2 (4.0-11) pied-billed 

grebe (4)
44 

Figures

Figure 1. Schematic block diagram of the San Diego Creek watershed and Newport Bay highlighting assessment 

areas and Se pathway. Map enlargement shows Upper and Lower Newport Bay. 
45

Figure 2. Conceptual model for the San Diego Creek watershed illustrating linked factors that determine the effects of Se on ecosystems. 
46

Figure 3. Conceptual model for the Upper Newport Bay illustrating linked factors that determine the effects of Se on ecosystems. 
47

Figure 4. Conceptual model for the Lower Newport Bay illustrating linked factors that determine the effects of Se on ecosystems. 
48 

Figure 5. Extrapolation of dissolved Se to 0 ppt. Data from transect of the Upper Newport Bay from 

near the mouth of San Diego Creek into the upper bay (CH2M Hill, 2008a).

0

5

10

15

20

25

0 5 10 15 20 25 30 35

Dissolved Se, micrograms per liter (

μg/L)

Salinity, parts per thousand (ppt)

y = 24.53 - 0.74507x

R2

 = 0.96067