Ref: 8EPR-EP 12/12/11 

Amanda Smith 

Executive Director 

Utah Department of Environmental Quality 

195 North 1950 West, 4th Floor 

P.O. Box 144810 

Salt Lake City, Utah 84114-4810 

RE: EPA Action on the Gilbert Bay Selenium 

Criterion and Footnote (14) 

Dear Ms. Smith: 

The U.S. Environmental Protection Agency has completed its review of the Gilbert Bay selenium 

criterion and associated Footnote (14) that were adopted by the Utah Water Quality Board (Board) on 

November 10, 2008 and submitted to the EPA for review with a letter dated April 8, 2009. A letter from

the Office of the Attorney General, certifying that the revisions were adopted pursuant to State law, was 

enclosed with the submittal letter. Receipt of the revisions on April 29, 2009 initiated the EPA’s review 

pursuant to Section 303(c) of the Clean Water Act (CWA or the Act) and the water quality standards 

regulation. The EPA acted on many of the revisions in a letter dated September 30, 2009. The EPA has 

now completed its review of the remaining revisions, including the Gilbert Bay selenium criterion and 

associated Footnote (14). This letter is to notify you of our action. 

We commend the Department of Environmental Quality (DEQ) and the Division of Water Quality 

(Division) for their excellent collaboration with stakeholders when developing the Gilbert Bay selenium 

criterion. Several years of collaboration efforts including the development of a Steering Committee and 

expert Science Panel produced an egg tissue-based selenium criterion protective of aquatic dependent 

birds that nest in Gilbert Bay. This criterion is the first numeric criterion adopted for the Great Salt Lake. 

We thank the DEQ and Division for their commitment to protecting birds that utilize Gilbert Bay and for 

the considerable effort that was put into the development of the new and revised water quality standards. 

 

 UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

REGION 8 

1595 Wynkoop Street 

DENVER, CO 80202-1129 

Phone 800-227-8917 

http://www.epa.gov/region08 
CLEAN WATER ACT REVIEW REQUIREMENTS

CWA § 303(c)(2) requires States and authorized Indian Tribes1 to submit new or revised water quality 

standards to the EPA for review. The EPA is to review and approve or disapprove the submitted 

standards. Pursuant to CWA § 303(c)(3), if the EPA determines that any standard is not consistent with

the applicable requirements of the Act, the agency shall notify the State or authorized Tribe and specify 

the changes to meet the requirements. If such changes are not adopted by the State or authorized Tribe, 

the EPA shall promulgate the needed standard pursuant to CWA § 303(c)(4). The EPA’s goal has been, 

and will continue to be, to work closely with States and authorized Tribes throughout the standards 

revision process as a means to develop approvable water quality standards revisions. 

TODAY’S ACTION

Today, the EPA is approving the Gilbert Bay selenium criterion and taking no action on the remaining 

provisions in the associated Footnote (14).2 Enclosure 1 presents the rationale for the EPA’s action. 

ENDANGERED SPECIES ACT REQUIREMENTS

Section 7(a)(2) of the Endangered Species Act (ESA) requires federal agencies, in consultation with the 

U.S. Fish and Wildlife Service and/or the National Oceanic and Atmospheric Administration Fisheries 

Service (Services), to ensure that actions they authorize, fund, or carry out are not likely to jeopardize 

the continued existence of federally listed endangered or threatened species or result in the destruction 

or adverse modification of designated critical habitat of such species. 16 U.S.C. § 1536 (a)(2). However, 

consultation with the Services is not required where the action has no effect on listed species or 

designated critical habitat. 50 CFR § 402.14. Because there is no occurrence of listed species or critical 

habitat for listed species in Gilbert Bay, the EPA concludes that its approval of Utah’s Gilbert Bay 

selenium criterion has no effect on listed species or designated critical habitat. 

 

1 CWA Section 518(e) specifically authorizes the EPA to treat eligible Indian Tribes in the same manner as States for 

purposes of CWA Section 303. See also 40 CFR Section 131.8. 

2

 The EPA disapproved the 6.4 mg/kg trigger value in its September 30, 2009 action letter. The remaining provisions in 

Footnote (14) do not alter the magnitude, duration, or frequency of the criterion or any of the provisions that constitute the 

water quality standards for Gilbert Bay, or are otherwise not revisions requiring a CWA § 303(c) action. 
CONCLUSION

The EPA thanks DEQ, the Board, and the Division for their efforts to review and revise Utah’s water 

quality standards. The EPA looks forward to working with the State to make additional improvements to 

the water quality standards that apply to the Great Salt Lake ecosystem. If you have any questions 

concerning this letter, contact Lareina Guenzel on my staff at 303-312-6610. 

Sincerely, 

 

 Carol L. Campbell 

 Assistant Regional Administrator 

 Office of Ecosystems Protection 

 and Remediation 

Enclosure 

cc: Walt Baker, Director, Division of Water Quality 
1 

Enclosure 1 

RATIONALE FOR THE EPA’S ACTION ON THE REVISIONS TO UTAH WATER QUALITY 

STANDARDS 

Today’s EPA action letter addresses the new Gilbert Bay selenium criterion adopted into Utah’s water 

quality standards by the Water Quality Board (Board) on November 10, 2008. This enclosure identifies 

the new selenium criterion as well as the EPA’s rationale for approving the criterion for the purposes of 

Clean Water Act (CWA) §303(c). The enclosure also acknowledges certain portions of Footnote (14) 

upon which the EPA is taking no action. 

Gilbert Bay Selenium Criterion and Associated Footnote (14) 

The Board adopted a selenium criterion of 12.5 mg/kg dry weight in bird egg tissue that is a geometric

mean over the nesting season to be applied to Gilbert Bay of the Great Salt Lake (GSL). The Board also

adopted Footnote (14) which states: 

The selenium water quality standard of 12.5 (mg/kg dry weight) for Gilbert Bay is a tissue based 

standard using the complete egg/embryo of aquatic dependent birds using Gilbert Bay based upon a 

minimum of five samples over the nesting season. Assessment procedures are incorporated as a part 

of this standard as follows: 

Egg Concentration Triggers: DWQ Responses 

Below 5.0 mg/kg: Routine monitoring with sufficient intensity 

to determine if selenium concentrations within the Great Salt 

Lake ecosystem are increasing. 

5.0 mg/kg: Increased monitoring to address data gaps, 

loadings, and areas of uncertainty identified from initial Great 

Salt Lake selenium studies. 

6.4 mg/kg: Initiation of a Level II Antidegradation review by the 

State for all discharge permit renewals or new discharge permits 

to Great Salt Lake. The Level II Antidegradation review may include 

an analysis of loading reductions. 

9.8 mg/kg: Initiation of preliminary TMDL studies to evaluate 

selenium loading sources. 

12.5 mg/kg and above: Declare impairment. Formalize and implement TMDL. 

Antidegradation 

Level II Review procedures associated with this standard are referenced at R317-2-3.5.C. 

On September 30, 2009, the EPA Region 8 identified the portion of Footnote (14) dealing with the 6.4 

mg/kg trigger value (i.e., the text from “6.4 mg/kg” to “an analysis of loading reductions”) as a new water 

quality standard. The EPA disapproved that portion of Footnote (14) because it would not adequately 

maintain and protect assimilative capacity for selenium in the eggs of aquatic dependent birds using 
2 

Gilbert Bay. In this enclosure, the EPA acknowledges the remaining portions of Footnote (14), but takes no 

action upon them as they are not new or revised water quality standards. 

Background 

The Utah Division of Water Quality (UDWQ) established a multi-tiered review process to aid in the 

development of the Gilbert Bay selenium criterion. This process included the organization of a Steering 

Committee, Science Panel, and public involvement program that provided guidance, technical expertise 

and perspectives from a broad range of interests. The Steering Committee was composed of a wide 

range of stakeholders and included one or more representatives from the Utah Department of Natural 

Resources, EPA Region 8, U.S. Fish and Wildlife Service (FWS), U.S. Geological Survey (USGS), 

Utah Artemia Association, various industries that discharge to the GSL, GSL Alliance, University of 

Utah, the Nature Conservancy, West Side Associated Duck Clubs, and the Utah Department of 

Environmental Quality. The Science Panel was formed by the Steering Committee to provide technical 

guidance, oversight, and a review of the required research necessary to develop the criterion. The 

Science Panel was also tasked with providing recommendations to the Steering Committee on what 

would be considered a protective selenium criterion for Gilbert Bay of the GSL. Finally, the public 

involvement program included a number of stakeholder workgroup meetings, the development of a 

website, poster sessions, and public participation in the water quality standards hearing process. The

EPA commends UDWQ and the Board for their commitment to the protection of wildlife as well as their 

signification public participation efforts that went beyond the 131.20(b) public participation 

requirements. 

Multiple original research projects were organized by the Steering Committee and Science Panel to 

support the development of the Gilbert Bay selenium criterion. One example is a study that focused on 

analytical methods for measuring selenium in water samples collected from the GSL. Data collected 

from earlier studies showed high variability in water column selenium concentrations. Results from the

recent study indicate that much of the variability in the historic data was due to interferences caused by 

the high salinity, total dissolved solids (TDS), and alkalinity of GSL water when analyzed using 

inappropriate methods, rather than actual variability of selenium in the water column. Furthermore, a 

round-robin laboratory study concluded that only two methods (hydride generation atomic absorption 

and graphite furnace atomic absorption) produce relatively consistent and accurate results. Therefore,

only selenium data generated with these two methods were considered acceptable for use in the criteria

development. 

Additional studies focused on a conceptual model of selenium cycling in the GSL. Development of a 

conceptual model was necessary to answer questions concerning the sources, fate, transport, and trophic 

transfer of selenium in the GSL. The model developed in this effort focused on five components 

including: (1) selenium in the upper food chain; (2) selenium in the lower food chain; (3) selenium in the 

shallow layer of the lake; (4) selenium in the deep layer and sediment of the lake; and (5) selenium 

loading to the lake. Although the version of the model available when the standard was adopted was still 

considered preliminary, it provided information on how selenium cycles in the GSL and was taken into 

consideration by the Steering Committee and Science Panel when making criteria recommendations to 

the Board. 

Several scientific studies were conducted to answer questions on the current concentration of selenium

in shorebirds and potential effects of selenium on shorebirds of the GSL. These studies focused on bird 

species that actively feed and nest on the GSL including American avocet, black-necked stilt, California 

gull, eared grebe, and common goldeneye. Data collected included selenium concentrations in adult 
3 

blood and liver, food chain invertebrates, water column, and bird eggs, as well as multiple parameters

that evaluate the reproductive success of shorebirds (see Appendix C and D of the Final GSL Selenium 

report). 1 Study results showed egg selenium concentrations of birds utilizing the GSL were relatively 

low. Concentration of selenium in American avocets and black-necked stilt eggs ranged from 1.2 to 9.2 

mg/kg, with a mean of 2.7 mg/kg. Concentration of selenium in California gull eggs ranged from 2.0 to 

4.3 mg/kg, with a mean of 2.9 mg/kg. The average concentrations are within the range of concentrations

that are considered background (Ohlendorf 2003). 

Results from the reproductive success studies confirm that the existing low concentration of selenium in 

shorebird eggs is not affecting bird populations utilizing Gilbert Bay. Hatchability of American avocet 

and black–necked stilt eggs ranged from 94-100%.2

 These hatching success rates are within the range of 

conditions expected to be observed at non-contaminated sites. Additional results showed that in 

American avocet eggs, selenium concentrations of nests with 100% hatchability were not significantly 

different from nests with lower hatchability. Finally, of the 142 eggs that were dissected and observed 

for deformities, no malformations were observed and only one American Avocet egg had a possible 

Type I malposition.3 

Finally, the Science Panel was tasked with developing toxicity thresholds for birds that utilize the GSL. 

To accomplish this task, the panel considered the sensitivity of multiple aquatic-dependent birds and 

endpoints protective of reproductive success and body condition. Species that were evaluated included,

but were not limited to, the American avocet, black-necked stilt, California gull, and mallard duck. From 

these reviews, it was determined that selenium-related impairment of the open waters of the GSL should

be defined by the most sensitive endpoint of the most sensitive species. This resulted in the development 

of a criterion to protect the hatching success of the mallard duck. 

 

Details on the specific duties and objectives of the Steering Committee and Science Panel, as well as 

final reports and criteria recommendations are included in the Final GSL Selenium report available on 

UDWQ’s website. 

Criteria Development 

CWA § 101(a)(2) establishes as a national goal the achievement of water quality that provides for the 

protection and propagation of fish, shellfish, and wildlife and recreation in and on the water. CWA § 

304(a)(1) requires the Administrator to develop and publish criteria for water quality that accurately

reflect the latest scientific knowledge on the (1) kind and extent of all identifiable effects on health and 

welfare including, but not limited to wildlife, that may be expected from the presence of pollutants in 

any body of water; (2) concentration and dispersal of pollutants through biological, physical and 

chemical processes; and (3) effects of pollutants on biological diversity, productivity, and stability. 

The EPA does not have national guidance for deriving wildlife criteria. Therefore, the agency has not 

formally addressed endpoints necessary to protect wildlife on a national level. However, the Agency has 

taken a position on the level of protection for wildlife criteria as part of the Great Lakes Initiative (40 

 

1

 Great Salt Lake Water Quality Studies. Development of a Selenium Standard for the Open Waters of Great Salt Lake. Final 

Report May 2008. Prepared by CH2MHILL for the State of Utah Department of Environmental Quality, Division of Water 

Quality. http://www.deq.utah.gov/Issues/GSL_WQSC/docs/GLS_Selenium_Standards/index.htm 

2

 Hatchability was calculated as the #eggs hatched/# full term eggs in nest. 

3

 Malposition was classified according to Romanoff and Romanoff 1972. Type I malposition is when the head is between the 

thighs, when the head under the right wing is considered normal. 
4 

CFR § 132 Appendix D). In this effort, the agency used the no observed effect concentration (NOEC)4

in diet to calculate tissue-based wildlife criteria for DDT, mercury, PCBs and 2,3,7,8-TCDD. The final

avian wildlife value was calculated as the geometric mean of the kingfisher, gull, and eagle speciesspecific thresholds (EPA/820/B-95/008). Utah adopted a wildlife criterion for selenium using a 

methodology that fit toxicological data from six laboratory studies to a mathematical equation and then 

determined the concentration that would correspond to a hypothetical 10% adverse effect (EC10) for the 

most sensitive species. The resulting criterion is not significantly distinguishable from a criterion 

concentration derived from the same data using the NOEC concentration (see below). EC10s and NOECs 

are generally of similar magnitude, but EC10s have the advantage of being more reproducible and

reliable than NOECs, and are generally recommended as the replacement for NOECs (Crane and 

Newman 2000; Van der Hoeven 1997; Warne and van Dam 2008). Overall, use of an EC10 for wildlife 

criteria is generally less than or equivalent in magnitude to a NOEC, but more reliable than a NOEC and 

therefore is considered consistent with previous approaches approved by the EPA. 

Derivation of the Selenium Criterion 

The Gilbert Bay criterion was derived from a regression meta-analysis of six different laboratory studies 

that evaluated the effect of selenium on mallard egg hatchability (Heinz et al. 1987, 1989; Heinz and 

Hoffman 1996, 1998; Stanley et al. 1994, 1996). Having determined that mallards are the most sensitive

known species, a logistic curve was fitted to the combined, control normalized data from the six mallard 

studies. The logistic curve was used to estimate the egg selenium concentration that corresponds to an

EC10 for mallard egg hatchability (Figure 1). This value was thereby found to be 12.5 mg/kg. 

Six of the nine Science Panel Members recommended adoption of the 12.5 mg/kg dry weight in bird egg 

tissue criterion to the Board.5 The Science Panel Members that recommended adoption of the 12.5 

mg/kg criterion indicated that setting the Gilbert Bay criterion to the mallard EC10 is an appropriately 

conservative site-specific criterion since: the mallard duck is the most sensitive bird species tested to 

date; egg hatchability is the most sensitive endpoint; and the mallard duck is more sensitive to the 

effects of selenium than aquatic dependent birds that commonly feed and nest at Gilbert Bay (e.g., 

black-necked stilt).6

 Based on these recommendations, recommendations from the Steering Committee, 

and public comments, the Board adopted 12.5 mg/kg as the criterion for Gilbert Bay. 

 

4A NOEC is the highest tested concentration that did not yield an effect that is statistically significantly different from the 

control. The detectability of an effect depends on the quality and size of the experiment and the statistical procedure. 

5One Science Panel Member recommended adoption of a no effect concentration of 5.0 mg/kg, another Science Panel 

member recommended adoption of 10.4 mg/kg, and another Science Panel member abstained from making a 

recommendation. 

6When reviewing the Gilbert Bay criterion, the EPA also evaluated the sensitivity of the black-necked stilt to selenium. An 

extensive field dataset (n = 673 nests) has revealed that egg hatchability is also the most sensitive endpoint for black-necked 

stilt. Similar to the mallard duck, the EC10 values for stilt are sensitive to the method by which the data are organized and the 

model used to fit the data. When using a triangular tolerance distribution analysis, the median EC10 14.6 mg/kg when data are 

grouped into 10 bins. The stilt egg counting was conducted on a nest-wise or hen-wise basis (n = number of nests with 

typically 4 eggs per nest) rather than an egg-wise basis (n = number of eggs), which was used for the mallard duck studies. 

These counting methods are not directly equivalent, with the nest-wise approach typically producing more conservative 

results. 
5 

Figure 1: Mallard egg hatchability as a function of selenium concentration in eggs. Figure modified from Ohlendorf 

(2003). Dashed line identifies the 10 percent effect concentration (EC10). The data were normalized to their respective 

control hatchability values. LCL = lower confidence limit. UCL = upper confidence limit. 

The EPA closely examined the logistic curve model used to derive the 12.5 mg/kg EC10. Using the data 

from the six mallard studies, the EPA performed an independent evaluation, fitting the data using a 

variety of different approaches. In its analysis, the EPA eliminated certain treatments from the Stanley et 

al. (1994) study because they were potentially confounded by arsenic exposure, which was part of the 

study design. Removal of these data, however, did not have a significant influence on the final results. 

Some authors suggest that control normalization is inappropriate because control responses also contain 

variability. Normalizing for control response effectively removes this estimation error from the control 

values (OECD 2006). To address this concern, the EPA also analyzed the six-study data set without 

normalizing for control hatchability. Using a triangular tolerance distribution analysis, the EPA obtained 

an EC10 of 12.3 mg/kg, which is within 2% of the Gilbert Bay criterion of 12.5 mg/kg. The fit is shown 

in Figure 2. Using a Gaussian tolerance distribution analysis, which is more similar to the analysis 

performed by Ohlendorf (2003), the EPA obtained an EC10 of 12.0 mg/kg, which is within 4% of the 

Gilbert Bay criterion. 
6 

Figure 2. Mallard egg hatchability in the six studies, fitted by triangular tolerance distribution, without normalization 

to control values. 

Additional review of the model included an evaluation of the effect of mixing data from studies having

substantial differences in control hatchability. To do this, the EPA excluded the two studies with low

(<50%) control hatchability (Heinz and Hoffman 1996, 1998). This produced an EC10 of 12.7 mg/kg 

through the triangular tolerance distribution approach, which is within 2% for the GSL criterion. Thus

reducing the range of control hatchability included in the analysis did not have a significant effect on the 

resulting criterion value. 

The EPA also reviewed a logistic nonlinear regression fit to the data. The logistic nonlinear approach

yields an EC10 of 9.7 mg/kg. Although this estimate of an EC10 is different than the Gilbert Bay EC10-

based criterion, the EPA can find no scientific basis for concluding that a logistic nonlinear regression fit 

is more or less appropriate than tolerance distribution fit described in the previous paragraph. In the 

absence of any meaningful scientific justification to prefer the one approach over the other, the different 

values derived from the application of these two models to the same data are equally scientifically 

defensible. 

Protectiveness of the Adopted Criterion 

Gilbert Bay is a Class 5A waterbody which includes protection of the following designated uses: 

frequent primary and secondary contact recreation, waterfowl, shorebirds and other water-oriented 

wildlife including their necessary food chain. Based on the scientific literature and available information 

for selenium specific to the GSL, protection of aquatic-dependent birds is the most sensitive use 

assigned to Gilbert Bay, and reproductive success or egg hatchability is the critical endpoint to be used 

in the selenium criterion that would be protective of that designated use. 

The EPA considered multiple lines of evidence to determine if the adopted selenium criterion is 

protective of designated uses and approvable under the CWA and the EPA’s implementing regulation at 

40 CFR § 131.6. One line of evidence considered by the EPA was an evaluation of effects of selenium 

in egg tissue below the criterion of 12.5 mg/kg. Figure 3 shows the percent hatch in the six control 

L og of Se (mg/kg) in Eggs

Proportion of Eggs Hatching

-.5 0 .5 1.0 1.5 2.0

0

.1

.2

.3

.4

.5

.6

.7

.8

.9

1.0

1.1
treatments, one for each of six studies, and 

mg/kg criterion is represented by the vertical line.

12.5 mg/kg are within the range of the controls and 

which is shown by the lower horizontal dashed line.

than the criterion yielded hatchability below the lower confidence bound for the control mean and below 

the hatchability of any control. These data suggest that the 

EC10-based criterion of 12.5 mg/kg would be

and that selenium concentrations up to

hatchability beyond natural conditions.

Figure 3: Mallard percent hatch v. egg concentration for six studies

values. 1 = Heinz et al. (1989); 2 = Heinz et al 

Hoffman (1996); 6 = Heinz and Hoffman 

Another line of evidence the EPA considered is a

the NOECs and the lowest observed

derive the dose response curve presented in Figure 

treatment concentrations, not interpolations

selenium treatments. Use of the NOE

available data (Van der Hoeven 1997

selenium treatments for the majority of the tests

criterion is less than the NOECs for three of the four studies 

1). In the one study (Stanley et al. 1996

the confidence limits of the EC10 (5% LC

Overall, these data are not inconsistent

aquatic dependent birds utilizing Gilbert Bay

 

 

7 LOEC is the highest tested concentration that yielded an effect that is statistically significantly diffe

7 

ies, and the selenium exposed treatments from those studies.

mg/kg criterion is represented by the vertical line. Hatchability at all treatment concentrations less than 

f the controls and the lower 95% confidence range of the control mean,

n by the lower horizontal dashed line. By contrast, all treatment concentrations greater 

the criterion yielded hatchability below the lower confidence bound for the control mean and below 

These data suggest that the expected hatchability associated with the 

would be within the variability of background (control) responses, 

up to the proposed criterion would not lead to additional

beyond natural conditions.

: Mallard percent hatch v. egg concentration for six studies. Raw data without normalization to control 

; 2 = Heinz et al (1987); 3 = Stanley et al. (1996); 4 = Stanley et al. 

; 6 = Heinz and Hoffman (1998). 

considered is a comparison of the Gilbert Bay selenium 

effect concentrations (LOECs)7 of the six individual studies

e curve presented in Figure 1. NOECs and LOECs can only be 

, not interpolations, and therefore are strongly influenced by 

se of the NOECs and LOECs alone is not a preferred method to analyze the 

Van der Hoeven 1997; Warne and van Dam 2008), especially given the low number of 

selenium treatments for the majority of the tests; however, it is useful to recognize that 

three of the four studies for which a NOEC can be derived

tanley et al. 1996) where the NOEC is less than 12.5 mg/kg, the NOEC is

5% LCL = 6.4 mg/kg; 95% UCL = 16.5 mg/kg; see Figure

not inconsistent with the concept that an EC10-based criterion

aquatic dependent birds utilizing Gilbert Bay. 

LOEC is the highest tested concentration that yielded an effect that is statistically significantly diffe

s from those studies. The 12.5 

Hatchability at all treatment concentrations less than 

the lower 95% confidence range of the control mean,

l treatment concentrations greater 

the criterion yielded hatchability below the lower confidence bound for the control mean and below 

pected hatchability associated with the 

within the variability of background (control) responses, 

the proposed criterion would not lead to additional reductions in 

without normalization to control 

al. (1994); 5 = Heinz and 

the Gilbert Bay selenium criterion to 

individual studies used to 

NOECs and LOECs can only be a measured 

strongly influenced by the number of 

is not a preferred method to analyze the 

given the low number of 

it is useful to recognize that the adopted 

a NOEC can be derived (Table 

where the NOEC is less than 12.5 mg/kg, the NOEC is within 

; see Figure 1). 

criterion is protective of 

LOEC is the highest tested concentration that yielded an effect that is statistically significantly different from the control. 
8 

Table 1: NOEC and LOEC (mg/kg) for six studies evaluating egg hatchability in mallard duck. * Number of Se 

treatments excludes the control. The two Heinz and Hoffman (1996) treatments were fed the same selenium dose but 

differed in the form of selenium (seleno-DL-methionine vs. seleno-L-methionine). Differences between the two 

treatments were not statistically significant. 

Study #Se treatments* NOEC LOEC 

Heinz et al. 1989 5 36.3 59.4 

Stanley et al. 1996 2 11.6 23.4 

Heinz et al. 1987 1 >15.2 -- 

Heinz and Hoffman 1998 1 >25.1 -- 

Heinz and Hoffman 1996 2 -- <29.4/<30.4 

Stanley et al. 1994 1 -- <37 

 

The EPA also considered how the criterion is expressed (i.e., geometric mean over the nesting season) 

when evaluating the protectiveness of the Gilbert Bay selenium criterion by estimating the site-specific 

risk associated with the existing selenium in eggs collected from Gilbert Bay. The Society for Risk 

Analysis (2011) definition of risk is “the potential for realization of unwanted adverse consequences to 

human life, health, property, or the environment; estimation of risk is usually based on the expected 

value of the conditional probability of the event occurring times the consequence of the event given that 

it has occurred.” For the Gilbert Bay selenium criterion, the site-specific risk is the combination of the 

probability of bird eggs having particular concentrations multiplied by the effects predicted to be caused 

by those concentrations. It is the product of two mathematical curves, one describing the distribution of 

egg concentrations allowed by the criterion, and the other describing the concentration-response curve

from which Utah derived their EC10. 

The EPA utilized the egg selenium data presented in the Final GSL Report (Projects 1A and 1B) and a 

response curve similar to that of Ohlendorf (2003)(see Figure 1) in its analysis. Among the 130 

observations, the standard deviation of the natural logs of the egg concentrations is 0.33. (The base 10 

log standard deviation is 0.33/ln(10).) In contrast to the arithmetic standard deviation, the log standard 

deviation is a measure of relative variability, independent of the sample mean or the units of 

measurement. 

The general equation for relating concentrations in the normal (or probit) distribution curve is: 

ln x = μ + σz 

In this case x is the EC10; μ is ln(EC50); σ is the natural log standard deviation (and reciprocal of the log 

probit curve slope) and z is the normal deviate (from normal distribution tables or NORMSINV 

function) corresponding to 10% (for the EC10). Solving for σ yields a log standard deviation of 0.56, the 

inverse of which is the probit slope. 

The probability, pi

, of an individual having concentrations within some range increment ∆ln(Ci) is: 

 pi

 = φi ∆ln(Ci) 

where φi

 is the probability density function for the egg-ovary concentration Ci at the midpoint of each 

increment. (In Excel or Quattro Pro, φ is given by NORMDIST, selecting for the density function rather 

than the cumulative function. This is then multiplied by the increment breadth in natural log units.) 

Figure 4 illustrates how the allowed Gilbert Bay egg concentration distribution is combined with the 

concentration-response curve. The bell-shaped, lognormally skewed probability density function is the 
allowed Gilbert Bay egg concentrations, having median and geometric mean of 12.5 mg/kg.

shaped concentration-response curve defines potential reduction in hatchability.

predicted potential effect or site-specific risk

For Gilbert Bay, the summation of these products

Bay egg concentrations to have extremely low variability, the bell curve would be a narrow spike at 12.5 

mg/kg, and the site-specific risk would be 10%.

the sample concentrations would be below the 

prevents the reduced effects at concentrations <12.5 mg/k

at concentrations >12.5 mg/kg, thus raising the aggregate above 10%.

Figure 4. Allowed GSL egg concentrations (with current degree of variability) expressed as a probability density

function on an arbitrary y-scale, coupled with a concentration

Plotted on the arithmetic x-axis, both curves are skewed

To relate the site-specific risk to the

concentration that would be predicted to yield the potential 13.5% reduction 

response curve similar to that of Ohlendorf (2003)

each of the six studies used to develop 

the site-specific risk of 13.5% is 13.8

within the range of the control variability (

exceeds the control variability (15.86 mg/kg

mg/kg is also within the 95% confidence limit of the 

mg/kg; see Figure 1). Given the resul

criterion as a geometric mean is protective of the designated use given the 

selenium concentrations in bird eggs in 

indicating that such variability has significantly

protectiveness of the criterion in light of those new data, consistent with its obligations under 40 CFR 

131.20. The EPA would furthermore expect Utah to

the criterion is no longer protective of the existing and designated uses

 

9 

Gilbert Bay egg concentrations, having median and geometric mean of 12.5 mg/kg.

response curve defines potential reduction in hatchability. The calculated total 

specific risk is the summation ∑ pi Ei for all possible concentrations, C

summation of these products yields a site-specific risk of 13.5%.

Bay egg concentrations to have extremely low variability, the bell curve would be a narrow spike at 12.5 

specific risk would be 10%. Higher variability widens the bell curve.

the sample concentrations would be below the EC10 and half above, the nonlinearity of the S

prevents the reduced effects at concentrations <12.5 mg/kg from counterbalancing the increased effects 

at concentrations >12.5 mg/kg, thus raising the aggregate above 10%.

Allowed GSL egg concentrations (with current degree of variability) expressed as a probability density

scale, coupled with a concentration-response curve, expressed as fraction reduced hatch.

axis, both curves are skewed or asymmetric. 

protectiveness of the criterion, the EPA determined 

concentration that would be predicted to yield the potential 13.5% reduction in hatchability using 

Ohlendorf (2003) and compared this concentration to the raw data for

to develop the curve. The median egg tissue concentration associated 

specific risk of 13.5% is 13.8 mg/kg, which is greater than the highest concentration that is 

variability (12.07 mg/kg), but less than the lowest concentration that 

15.86 mg/kg)(see Figure 3). A bird egg-tissue concentration of 13.8 

is also within the 95% confidence limit of the EC10 (5% LCL = 6.4 mg/kg; 95% UCL = 16.5 

Given the results of these analyses, the EPA has determined that expressing the 

criterion as a geometric mean is protective of the designated use given the relatively low variability of

bird eggs in Gilbert Bay. In the event that new data becom

significantly increased, the EPA would expect Utah to reevaluate the 

in light of those new data, consistent with its obligations under 40 CFR 

would furthermore expect Utah to revise the criterion if those new data

of the existing and designated uses. 

Gilbert Bay egg concentrations, having median and geometric mean of 12.5 mg/kg. The SThe calculated total 

for all possible concentrations, Ci. 

specific risk of 13.5%. Were the Gilbert 

Bay egg concentrations to have extremely low variability, the bell curve would be a narrow spike at 12.5 

Higher variability widens the bell curve. Although half 

and half above, the nonlinearity of the S-curve 

g from counterbalancing the increased effects 

Allowed GSL egg concentrations (with current degree of variability) expressed as a probability density

response curve, expressed as fraction reduced hatch.

EPA determined the egg 

in hatchability using a 

and compared this concentration to the raw data for

The median egg tissue concentration associated with 

mg/kg, which is greater than the highest concentration that is 

west concentration that 

tissue concentration of 13.8 

(5% LCL = 6.4 mg/kg; 95% UCL = 16.5 

EPA has determined that expressing the 

relatively low variability of

become available, 

would expect Utah to reevaluate the 

in light of those new data, consistent with its obligations under 40 CFR § 

new data indicate that 
10 

Other Considerations 

EC10 Estimates Using a Biphasic Model 

One Science Panel member did not support the logistic analysis by Ohlendorf (2003) to derive an EC10. 

Rather, he advocated deriving the EC10 using the biphasic model as described by Beckon et al. (2008) 

(Skorupa 2008). Beckon et al. (2008) analyzed the data of Heinz et al. (1989) using a biphasic model 

(formerly called a hormetic model) and reported an EC10 of 7.7 mg/kg. With this approach, data from 

only one of the six studies included by Ohlendorf (2003) was used to calculate an EC10. 

The EPA’s review of the Gilbert Bay criterion included a careful evaluation of the Beckon et al. (2008) 

approach to estimating an EC10 using a biphasic model. The EPA has essentially reproduced the Beckon 

et al. (2008) result, obtaining an EC10 of 7.76 mg/kg, with the small difference due to the EPA using 

individual study values rather than cross-study averages in converting egg wet weight concentrations to 

dry weight concentrations. 

Using the biphasic model as described by Beckon et al. (2008) to derive the Gilbert Bay selenium 

criterion has several problematic implications. First, the EPA has determined that the recommendation 

from the majority of the Science Panel Members to utilize the data from all of the six studies and not

only rely on the Heinz et al. (1989) data is reasonable. Reducing the number of observations in the data 

set substantially increases the uncertainty in estimating an effect concentration. When analyzed by 

logistic regression apart from the rest of the data, the Heinz et al. (1989) data yields a high EC10

estimate, 29 mg/kg, which would not be considered protective. Also, further statistical analysis by the 

EPA indicates that the Heinz et al. (1989) dataset, by itself, is too small to support the biphasic model.8

Additionally, Beckon et al. (2008) assumes that the Heinz et al. (1989) control was deficient in selenium 

since the percent hatch in the control was less than the percent hatch of the first treatment (59.6% vs 

70.7%, respectively, see Figure 5).9

 This assumption was accepted by Beckon et al. (2008) even though 

the confidence bounds for the control (48.7 - 70.1) fall completely inside the confidence bounds for the 

first treatment (44.8 - 91.0). The Beckon et al. (2008) biphasic model relies on this difference in hatch 

not merely reflecting random experimental noise, but accurately reflecting a significant reduction in 

hatch due to a diet that was deficient in selenium. However, a post hoc statistical analysis of the raw data 

from the Heinz et al. (1989) study conducted by the EPA indicates that the difference between the 

control (59.6 percent hatch) and the first treatment (70.7 percent hatch) is not statistically significant at 

 

8The EPA does not believe a criterion set the EC10 of 29 mg/kg would be protective. Nevertheless, when the Heinz et al. 

(1989) data are analyzed by themselves, statistical measures indicate that the logistic fit to the Heinz et al. (1989) data 

yielding the EC10 of 29 mg/kg is generally better than the biphasic fit yielding the EC10 of 7.7 mg/kg. To some degree this is 

shown by the higher R2

 (better fit) for the logistic model (0.95) compared to the biphasic model (0.86). More important are 

the results for the Akaike Information Criterion, AIC (Burnham and Anderson 2002). The AIC is used to evaluate the 

tradeoff between model accuracy and complexity. The ACI metric penalizes overfitting by accounting for the number of 

parameters. The biphasic model uses four parameters and the logistic model uses three. When comparing two models 

describing the same data, a lower AIC value is better. Uncorrected for finite sample size, the smaller (more negative) AIC of 

-10.74 for the logistic model, compared to -3.90 for the biphasic model indicates that the logistic model may be preferred 

over the biphasic model. Corrected for finite sample size, the smaller AICc

 of 1.26 for the logistic compared to 36.10 for the

biphasic even more strongly indicates preference for the logistic model. These statistical measures indicate that the Heinz et 

al. (1989) dataset, by itself, is too small to support the biphasic model. 

9

In poultry, dietary Se concentration greater than 0.3 mg/kg dw are considered adequate for good adult health and Se egg 

concentrations less than 0.66 mg/kg dw may indicate inadequate Se in maternal diets (Puls 1988, Ohlendorf and Heinz 2011). 

Heinz et al. (1989) does not report the concentration of selenium in the control diet; however, Ohlendorf (2003) indicated that 

control diets typically contain 0.4 mg/kg selenium. The control egg concentration was 0.6 mg/kg dw. 
11 

either the α=0.05 or the α=0.5 confidence level. 10 That is, whether willing to accept a 5% or a 50% 

chance that the difference arose by chance, the difference is not statistically significant. Therefore, it 

cannot be concluded the difference in hatch is due to a selenium deficient diet.11

 

Figure 5. Biphasic analysis of Heinz et al. (1989) data assuming background egg concentration corresponding to the 

peak response. 

Furthermore, when comparing the EC10 values derived using a logistic curve model (as proposed by 

Ohlendorf (2003) and adopted by the Board) and biphasic model (as proposed by Beckon et al. (2008)), 

sidestepping the above statistical problems with the Beckon analysis, the EPA has found that the major

difference in the resulting EC10 is not a reflection of the model used to fit the data. Rather, the major 

difference is a reflection of the differing assumptions about the expected background response (percent 

hatchability) and background egg selenium concentration (collectively, we refer to this as background 

condition) in the absence of selenium contamination. When background conditions are similarly defined 

in both models, the difference between the EC10 values derived using the biphasic model and the logistic 

curve model is small. 

 

10Using the Games and Howell (1976) procedure and the Dunnett-Tukey-Kramer pairwise multiple comparison test (Dunnett 

1980). 

11The response patterns observed in the five studies not used by Beckon et al. (2008) and Skorupa (2008) also appear to 

disaffirm the biphasic model predictions. Although none of those studies contain exposures near enough to the biphasic 

model’s 2.7-2.8 mg/kg predicted optimum concentration range to allow visual determination of whether percent hatch attains 

a peak in that area, it is possible to examine the ratio of observed percent hatch at different concentrations within each study 

on either side of the biphasic model’s predicted optimum range, and compare them to the ratios predicted by the Beckon et al. 

(2008) biphasic model and the Ohlendorf (2003) logistic model. For example, in Stanley et al. (1996) the observed hatch 

response at 11.6 mg/kg was 98% of the hatch response at 0.89 mg/kg. This can be compared to the biphasic model’s 

predicted response ratio of 85% and the logistic model’s predicted response ratio of 92%. Comparing such response ratios 

within each of the five studies not used by Beckon et al. (2008) and Skorupa (2008) yields an observed-predicted r2

 of 0.46 

for the biphasic model and 0.72 for the logistic model. The five studies thus suggest that the monotonic response pattern of 

the Ohlendorf (2003) model (shown in Figure 1) is more appropriate than the biphasic response pattern of the Beckon et al. 

(2008) model (shown in Figure 5). 

Egg Se (mg/kg dw)

0.1 1 10 100

Egg Hatchability (proportion)

0.0

0.2

0.4

0.6

0.8

1.0

Se Control

Se Treatments

10% { } 10%

Max Hatchability

2.75 mg/kg

7.76 mg/kg 0.696 mg/kg
12 

In both cases, 10 percent reduction in hatchability is estimated relative to background percent 

hatchability. Ohlendorf (2003) calculated the background percent hatchability as the average 

hatchability of the background (control) exposures estimated from the fitted model. In contrast, Beckon 

et al. (2008) estimates background percent hatchability as the single peak value of hatchability at an 

optimum egg concentration of 2.75 mg/kg that is estimated by the biphasic model (Figure 5). This 

difference in how background is defined (i.e., an average or range vs. a single peak value) is the primary 

reason for the difference between the Ohlendorf (2003) EC10 of 12.5 mg/kg and the Beckon et al. (2008) 

EC10 of 7.7 mg/kg. Neither Beckon et al. (2008) nor Skorupa (2008) discuss the appropriateness of 

assigning this single optimum value as the applicable background condition. This assignment is 

particularly significant because of its corollary: that all increases or decreases in background selenium 

concentration represent a movement away from the optimal selenium concentration. 

It is difficult to justify and unrealistic to assume that a single optimum would apply to all real-world 

ecosystems. In fact, optimal egg hatchability and background concentration of Se in eggs can range 

widely; therefore, the EPA believes it is reasonable to define the background condition as a range or an 

average when modeling a response. For the same toxicity data, Figures 6 and 7 illustrate other possible 

applications of the biphasic approach when other, equally valid, methods are used to estimate 

background concentrations. In each figure, the horizontal line represents an alternative background 

hatchability level from which a 10% reduction in hatchability can be estimated. In Figure 6 the 

background response is calculated from the control egg concentrations in four studies included in 

Ohlendorf (2003). This yields an EC10 of 10.64 mg/kg. In Figure 7 the background response is derived 

from a log-triangular distribution of egg-concentrations in the range of 0.1–3.0 mg/kg. It yields an EC10

of 11.96 mg/kg, less than 5% different from the Ohlendorf (2003) value. Overall, when measured 

against an average hatchability across a range of possible background eggs concentration, the difference 

between the EC10 derived by the biphasic model and EC10s derived by other scientifically defensible 

methods decreases. 

The EPA review of the biphasic approach suggests that the value of the biphasic model is contextspecific. The EPA has determined it is not necessary to assume, as a starting point, that the background 

concentration of selenium is the optimum concentration to derive a protective criterion. Application of 

the biphasic approach may well best be confined to (a) studies specifically designed to elucidate the 

biphasic response – that is, studies designed to characterize both deficiency and toxicity; and (b) 

situations where the distribution of background concentrations is sufficiently well defined to reliably 

estimate the average background response against which a 10 percent reduction (EC10) is measured. 

Based on the absence of such studies and data respecting background concentrations in Gilbert Bay, the

EPA concludes that it was scientifically defensible, and consistent with CWA requirements, for Utah to

derive the Gilbert Bay selenium criterion without relying on the biphasic approach, and without 

discarding the data from five of the six studies noted above. As described above, the logistic curve 

model that Utah employed is scientifically defensible and consistent with CWA requirements. 
13 

Figure 6. Biphasic analysis of Heinz et al. (1989) data, assuming background egg concentration equal to the average of 

four study controls in Olhendorf (2003). 

Figure 7. Biphasic analysis of Heinz et al. (1989) data, assuming background egg concentration a log-triangular 

distribution between 0.1 and 3.0 mg/kg 

Egg Se (mg/kg dw)

0.1 1 10 100

Egg Hatchability (proportion)

0.0

0.2

0.4

0.6

0.8

1.0

Se Control

Se Treatments

10% { } 10%

Average Background

Hatch (0.17-1.6 mg/kg)

0.345 mg/kg 10.64 mg/kg

Egg Se (mg/kg dw)

0.1 1 10 100

Egg Hatchability (proportion)

0.0

0.2

0.4

0.6

0.8

1.0

Se Control

Average Background Se Treatments

Hatch (0.1-3.0 mg/kg)

0.262 mg/kg 11.96 mg/kg

10% { } 10%
14 

Use of Low-Level Effect Concentrations to Preclude the Possibility of Effects to Individual Migratory 

Birds. 

The Gilbert Bay selenium criterion was derived using a scientifically defensible method for calculating a 

tissue-based selenium criterion (i.e., logistic-curve fit to the control normalized six study data set). The 

U.S. Fish and Wildlife Service (FWS) has suggested that the criterion should be set to an EC0, rather 

than an EC10, to assure not only that Utah’s designated use for wildlife will protected, but also that no 

individual migratory bird would ever experience any effect from exposure to selenium so long as 

selenium does not exceed the criterion concentration. The EPA’s position is that it is not possible to

calculate a criterion that would achieve that objective (i.e., that would preclude the hypothetical 

possibility that an individual migratory bird could be affected by selenium, so long as selenium remains 

below that criterion concentration). 

 

With the Ohlendorf (2003) approach for deriving a tissue-based criterion, there is no threshold for 

determining no effect. The only EC0 is 0.0 mg/kg. Setting the criterion to zero would not be a valid 

criterion because selenium is an essential element that is found in all organic material. While FWS has 

put forward 5 mg/kg as an alternative selenium criterion for Gilbert Bay (and characterized it as an EC0), 

such a criterion would actually correspond to an EC3 by their own calculation, and thus (akin to Utah’s 

12.5 mg/kg EC10 criterion) would not preclude the hypothetical possibility that an individual migratory 

bird could be affected by selenium at the criterion concentration.12 While there is no criterion value that 

can preclude the possibility of calculating hypothetical, individual-level effects on migratory birds by 

one or the other model that has been proposed, an EC10 signifies a level of effect that cannot be 

distinguished from zero effect in real-world ecosystems. There is greater certainty regarding the midrange levels of effect, between 20% to 50% (that is, EC20s to EC50s), but a corresponding certainty of 

effect does not exist with respect to an EC10. 

The determination of an effect at a concentration at or less than an EC10 is more dependent on the 

selection of curve-fitting approach than the toxicity data set itself. Thus, any conclusion about whether 

an effect would or would not occur to an individual migratory bird, at a criterion set to a low level effect 

concentration, is heavily dependent on unverifiable assumptions built into the curve-fitting mathematics. 

Given that there is no objective scientific basis to preclude or establish that selenium produces 

individual-level effects on migratory birds at the EC10 level, the EPA disagrees that the hypothetical 

possibility of such effects weighs against approving Utah’s selenium criterion for Gilbert Bay. 

Revisions Upon Which EPA is Taking No Action 

The EPA does not consider the remaining portions of Footnote (14) (i.e., the provisions other than the

portion the EPA disapproved on September 30, 2009), to be new or revised water quality standards 

requiring a CWA § 303(c) action. This is because they do not describe a desired ambient condition of a

waterbody to support a particular designated use. They neither alter the magnitude, duration, or 

frequency of the adopted selenium criterion nor any of the other provisions that constitute the water 

quality standards for Gilbert Bay. Instead they outline research and monitoring programs that Utah will 

implement if triggered by particular concentrations of selenium in egg tissue. 

 

12 Skorupa (2008) used a biphasic equation with the Heinz et al. (1989) data to calculate an EC10 (see above discussion) and 

an estimated 3.0 mg/kg as a background concentration to justify a NOEC of 5 mg/kg. However, when the 5 mg/kg value is 

evaluated with the biphasic model, it is the EC3, not the EC0. Therefore, the 5 mg/kg proposed by Skorupa (2008) as a NOEC 

would be modeled as a concentration with effect to individual migratory birds. With use of the biphasic model, an EC0 is only 

equal to the optimal background concentration determined by the model. 
15 

Accordingly, the EPA is taking no action on these remaining portions of Footnote (14). Although the 

EPA is not acting on these remaining portions of Footnote (14), the EPA acknowledges them and 

commends the Board for adopting them. Footnote (14) requires routine monitoring of selenium in eggs 

collected from the Great Salt Lake and triggers increased monitoring and data collection if selenium 

concentrations exceed 5.0 mg/kg. These actions will provide early detection of potential selenium 

problems and allow the State to make proactive decisions to ensure the criterion will be effectively 

implemented. 

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