Date June 13, 2012

To The Section 316(b) Existing Facilities Rule Record

From Erik Helm, U.S. Environmental Protection Agency

Subject Attribute Calculations within the 316(b) Stated Preference (SP) Survey

In reviewing the Abt Associates memorandum dated April 16, 2012, which documents the process used

to estimate attribute values for the 316(b) stated preference survey, EPA found one statement that

needs to be clarified and three instances where clarifications should be made to tables.  

First, the statement EPA would clarify occurs on page five, section 4.1, of the April 16,

2012

memorandum and states that: “For the Fish Saved attribute, baseline values represent maintaining the

status quo, a condition under which no fish are saved. Thus, the baseline value for all survey regions is

0.”  These sentences should be clarified to say: “The baseline value for the Fish Saved attribute is 0% for

all regions, which reflects the status quo level of I&E mortality before the implementation of regulatory

options.”  This change to the language reflects the fact that some level of I&E mortality reducing

technology does exist at many facilities.

Next, the three clarifying changes to make regarding tables are as follows:

1) Table 1, Baseline commercial Fish Population Values for Marine Fish Stocks, should have a

footnote indicating that the attribute value column values represents the percentage of

commercially‐targeted fish stocks with B > 0.8 x BMSY.

2) Table 2, Baseline Fish Population Values for Marine Fish Stocks, should have a footnote

indicating that the attribute value column represents the regional mean of the stock‐specific

ratio B / BK.

3) The final change occurs in two sentences on page six of the April 16, 2012 memorandum that

read: “To convert A1E reductions under the various regulatory options to the Fish Saved

attribute, EPA calculated the fraction of A1E loss reductions relative to baseline A1E losses.

Estimates of minimum and maximum loss reductions are presented in Table 3.” These should be

more clearly stated as: “To convert A1E reductions to the Fish Saved attribute, EPA calculated

the fraction of A1E loss reductions relative to baseline A1E losses for each proposed rule option.

Based upon these estimated changes, values representing minimum and maximum loss

reductions from the proposed options were identified, and are presented in Table 3.”
Abt Associates Inc. pg. 1

Environment and Resources Division

Date April 16, 2012

To Erik Helm, Tom Born, U.S. Environmental Protection Agency

From James Palardy

Subject Attribute Calculations within the 316(b) Stated Preference (SP) Survey

1 Introduction

The 316(b) survey is designed as a choice experiment, a stated preference technique in which 

people’s values are estimated based on their choices over a set of hypothetical but realistic policy 

options. Under the choice experiment (or choice modeling) format, respondents are presented 

with a set of multi-attribute alternatives and asked to select their preferred alternative, much as 

one might choose a preferred option in a public referendum. 

Within the context of the stated preference survey, the effects of hypothetical policy options are 

described in terms of a household cost and four environmental endpoints: (a) commercial fish 

populations, (b) fish populations (all fish), (c) fish saved, and (d) condition of aquatic ecosystems. 

Values are reflected in the survey by individuals’ willingness to vote for policies that would 

increase their cost of living, in exchange for specified changes in the four environmental 

attributes.

In this memorandum, EPA documents the methods it used to develop the four numerical attribute 

values used within the 316(b) stated preference survey.

2 Commercial Fish Populations

The Commercial Fish Populations score, measured between 0 and 100 percent, is a measure of 

the overall health of fish populations targeted by commercial and recreational fishers. The 

sustainability of commercial fisheries is not directly related to the relative number of fish in a 

population per se; instead, sustainability is related to the ability of fisheries managers to maintain 

populations at or above a threshold that maximizes long-term harvest. Thus, a score of 100 means 

that all stocks are at a size that maximizes long-term harvest while a score of 0 means that all 

stocks are heavily overfished. Notably, a score of 100 does not mean that populations of 

commercial fish species are equivalent to populations expected if no commercial fishing 

occurred. Similarly, a score of 0 does not indicate that there are no commercially targeted fish 

remaining. However, because fisheries managers are likely to close fisheries when stock 

populations are severely depressed, a score of 0 may indicate that no large-scale commercial or 

recreational harvests are permitted.

memorandum
Abt Associates Inc. pg. 2

2.1 Estimating Baseline Values

Due to data availability, values of the Commercial Fish Populations score were estimated using 

two separate methods. One method, used for the Northeast, Southeast, Pacific, and National 

survey regions, relied upon fish stock assessments conducted on marine fish. The second method, 

used for the Inland survey region, relied upon state- and lake-specific estimates of fish 

populations in fresh waters.

2.1.1 Marine Stocks

To estimate Commercial Fish Populations scores of marine fish within regional surveys, all 

commercially-targeted fish stocks with recent stock assessment reports produced by a reputable 

fisheries management organization [i.e., the National Oceanic and Atmospheric Administration’s 

(NOAA) National Marine Fisheries Service (NMFS), the Atlantic States Marine Fisheries 

Commission (ASMFC), the International Pacific Halibut Commission (IPHC)] were assessed

individually. To develop the commercial fishing populations score, we used stock status criterion

developed for the NMFS Fish Stock Sustainability Index (FSSI) and included in the annual Status 

of US Fisheries Report to the United States Congress (NMFS 2010). At the species or stock level, 

we used the ratio of stock biomass (B) to the biomass that produces maximum sustainable yield 

(BMSY), namely: B / BMSY. Consistent with how NOAA awards points within the FSSI, stocks with 

ratios exceeding 0.8 were given a score of 1 while stocks with ratios less than 0.8 were given a 

score of 0. All data used in calculating baseline values were taken from NMFS (2010)

1

.

Nationally, sufficient data were available to score 249 fish stocks. Although these data represent 

stocks of the most heavily-targeted and commercially important species, they likely represent less 

than 10% of all species with commercial fisheries: many stocks remain data-limited, particularly 

stocks managed as a species complex2. In the Northeast, Southeast, and Pacific survey regions, a 

total of 67, 56, and 54 fish stocks were scored, respectively. Within survey regions, the overall 

value of the Commercial Fish Population attribute represents the percentage of stocks receiving a 

score of 1 (i.e., the percentage of stocks with B > 0.8 x BMSY).

Table 1 – Baseline Commercial Fish Population 

Values for Marine Fish Stocks

Survey Region Stocks 

Assessed

B / BMSY

> 0.8

Attribute 

Value

Northeast 67 28 42%

Southeast 56 22 39%

Pacific 54 30 56%

National 249 126 51%

2.1.2 Inland Stocks

Population assessments of inland fish stocks are not as rigorous or frequent as those of marine 

stocks. Typically, fisheries are managed on a state-by state basis, with the exception of the Great 

Lakes, which are managed by the Great Lakes Fishery Commission. Additionally, commercial 

 1 Primary data sources are identified in Appendix 4 of NMFS (2010). 2 For example, the Hawaiian Archipelago Coral Reef Ecosystem Multi-species Complex, a data limited 

stock, contains up to 146 coral reef taxa currently harvested and “innumerable potentially harvested coral 

reef taxa." (NMFS 2010).
Abt Associates Inc. pg. 3

fishing outside the Great Lakes is limited, further reducing the impetus for detailed stock 

assessment surveys. As a result, stocks are poorly understood, and biological criteria necessary to 

estimate maximum sustainable yield is often missing. Consequently, the FSSI approach used in 

marine stocks could not be applied. Instead, recent stock assessments were used to estimate 

relative population levels of commercially and recreationally harvested fish species relative to 

historical estimates using similar field methods (e.g., electrofishing; trawl data; catch per unit 

effort, total landings). Data were obtained for inland rivers and lakes (Dieterman 2008, Meerbeek 

2008a, b), and for the Great Lakes (Bur et al. 2005, O'Gorman et al. 2005, Stockwell et al. 2005, 

Roseman et al. 2009, Schaeffer and O'Brien 2009, Madenjian et al. 2010, USGS 2010).

Within each publication, locations reporting single-species or commercially- and recreationallytargeted genera or families of species with time series data exceeding 20 years (to ensure recent 

changes and/or ephemeral conditions did not dominate results) were identified. Overall, data were 

obtained for 12 species within 21 geographical locations (including each of the Great Lakes). For 

each location, the most recent estimate of relative fish abundance was divided by the maximum 

relative abundance value found within the time series. Within each location, average commercial 

species abundances were estimated; these local averages were then averaged within two 

geographical regions – within (6 locations), and outside (15 locations), the Great Lakes, to more 

meaningfully weight the importance of the Great Lakes to freshwater commercial fisheries. 

Finally, the average of data inside and outside the Great Lakes was calculated to estimate 

Commercial Fish Populations in the Inland region. Overall, the attribute value was estimated to 

be 39%.

2.2 Estimating Maximum Changes to Values

To estimate the effects of regulatory options on the Commercial Fish Populations attribute, 

estimated changes in fish populations would be modeled using stochastic matrix population 

models, drawing upon data from NMFS, ASMFC, USGS, and EPRI. This modeling was not 

conducted; instead, maximum values of change were estimated from the literature (Lorda et al.

2000) and using preliminary modeling exercises conducted by EPA. Overall, changes in the 

Commercial Fish Populations attribute are likely to be very small, with feasible regional values 

likely to be less than 3%. To ensure the range of possible values were encompassed by the stated 

preference survey, the maximum estimate of policy effects used within the survey was 6%.

3 Fish Populations (All Fish)

The attribute of Fish Populations (All Fish) represents an estimate of current levels of fish 

populations relative to populations without large-scale fishing. The value of this attribute, unlike 

the Commercial Fish Populations attribute, is directly related to the relative number of fish in a 

population. Thus, a score of 100 means that all stocks are at their natural carrying capacity (i.e., 

the largest size sustainable without human influences), while a score of 0 means that no fish 

remain.

3.1 Estimating Baseline Values

As described in Section 2.1, fish population values were estimated using different methods for 

Marine and Freshwater survey regions. In both cases, due to data availability, the Fish Population 

metric relies heavily upon estimated relative populations of commercially and recreationally 
Abt Associates Inc. pg. 4

targeted fish with recent stock assessments. Consequently, available data represent only a small 

proportion of all fish species (< 1%). Additionally, and with notable exceptions (e.g., Atlantic 

Menhaden) populations of forage fish are under-represented in these data. Although forage fish 

populations may increase as their commercially-valuable predators are removed, a wide range of 

responses have been observed in nature including examples where invertebrates, rather than 

forage fish, respond to predator decline (Micheli 1999, Worm and Myers 2003, Frank et al. 2006, 

Baum and Worm 2009). Additionally, there is growing consensus that forage fish populations are 

often highly overfished (Pikitch et al. 2012). Thus, although available data may not be truly 

representative of all fish species, it is unlikely that relying upon stock assessments for 

commercially targeted fish species to estimate the population of all fish species will result in 

strongly biased attribute estimates. 

3.1.1 Marine Stocks

To estimate Fish Populations scores of marine fish within regional surveys, data sources from 

Section 2.1.1 were assessed individually. Using stock status data and estimates BMSY of included 

in the annual Status of US Fisheries Report to the United States Congress (NMFS 2010), in 

reputable online resources (NEFSC 2006), historical NMFS reports (e.g., SEFSC 1995) and the 

published literature, a ratio of current biomass (B) to biomass at carrying capacity (BK) was 

constructed, namely: B / BK. Carrying capacity was estimated as the greatest value obtained using 

the following three methods:

• the largest value of historical fisheries catch (by biomass),

• current biomass, or

• 3x BMSY (MSY normally ranges at between 30-40% of unexploited population size)

Nationally, 249 fish stocks, the same stocks used to estimate Commercial Fish Populations, were 

scored. Although sufficient data were found for several threatened and endangered (T&E) 

species, EPA chose not to include these species because it would incorporate a strong degree of 

bias into estimates: data are more likely to be available for T&E species because of their small 

populations, while similar estimates for species with elevated populations (for example, due to 

predator release) are unlikely to exist. 

Within survey regions, the overall value of the Fish Population attribute is the mean score for all 

stocks. Estimated values of Fish Populations are presented in Table 2

Table 2 – Baseline Fish Population Values 

for Marine Fish Stocks

Survey Region Stocks 

Assessed

Attribute 

Value

Northeast 67 26%

Southeast 56 24%

Pacific 54 32%

National 249 30%

3.1.2 Inland Stocks

Because population estimates of inland fish stocks are limited and often not standardized, the 

procedure used to estimate the Commercial Fish Populations attribute was used to estimate the 

Fish Population attribute, with minor changes. Instead of limiting its search to commercially 

relevant genera or families, EPA searched for all species of finfish. 
Abt Associates Inc. pg. 5

Overall, data were obtained for 26 species within 21 geographical locations (including each of the 

Great Lakes). For each location, the most recent estimate of relative fish abundance was divided 

by the maximum relative abundance value found within the time series. As described in Section 

2.1.2, this quotient was then used to derive average fish populations within the Inland survey 

region. Overall, the Fish Population attribute for the Inland region was estimated to be 33%.

3.2 Estimating Maximum Changes to Values

To estimate the effects of regulatory options on the Fish Populations attribute, estimated changes 

in fish populations would be modeled using stochastic matrix population models, drawing upon 

data from NMFS, ASMFC, USGS, and EPRI. As described in Section 2.2, this modeling was not 

conducted; instead, maximum values of change were estimated from the literature (Lorda et al.

2000) and using preliminary modeling exercises conducted by EPA. Overall, changes in the Fish 

Populations attribute are likely to be very small, with feasible regional values likely to be less 

than 2%. To ensure the range of possible values were encompassed by the stated preference 

survey, the maximum estimate of change used within the survey was 4%.

4 Fish Saved

The Fish Saved attribute represents an estimate of the proportional reduction in mortality 

(measured as age one equivalent (A1E) losses) relative to estimates of current annual mortality 

occurring at 316(b) facilities. Thus, a score of 0 represents the status quo (and baseline value for 

the attribute in all survey regions), while a score of 100 means that all mortality occurring at 

facilities regulated by the proposed 316(b) rule would cease. 

The A1E metric is an estimate of the number of entrained or impinged fish removed from a local 

population that otherwise would have survived to age 1. Thus, mortality occurring at early life 

history stages (e.g., egg, larval stages) are equivalent to less than an age 1 fish, while the loss of a 

single age 4 fish is equivalent to more than one age 1 fish. By converting all losses to a 

standardized value, losses can be expressed in a form useful for decision-making.

While the A1E terminology is not used specifically within the stated preference survey, pretesting during focus groups and cognitive interviews indicated that participants understood the 

“fish saved” attribute and the concept of “young fish” as reflecting an estimate that refers to fish 

that has not reached harvestable age and incorporates losses of eggs and other juvenile life stages 

into a single metric.

4.1 Estimating Baseline Values

For the Fish Saved attribute, baseline values represent maintaining the status quo, a condition 

under which no fish are saved. Thus, the baseline value for all survey regions is 0.

4.2 Estimating Maximum Changes to Values

To obtain regional estimates of impingement and entrainment (I&E) mortality reductions due to 

the regulatory options, EPA first extrapolated loss rates from facilities for which I&E mortality 

data are available (referred to as model facilities), to all in-scope facilities within the same region

using an equivalent adult model. To estimate changes due to the regulation, EPA altered the 
Abt Associates Inc. pg. 6

model to account for engineering changes required as a result of the proposed regulatory options, 

and re-estimated I&E losses. Model details, including detailed estimates for baseline A1E losses 

and reductions under regulatory options are found in Section 3 of the Environmental and 

Economic Benefits Analysis (EEBA) of the proposed rule (USEPA 2011).

To convert A1E reductions under the various regulatory options to the Fish Saved attribute, EPA 

calculated the fraction of A1E loss reductions relative to baseline A1E losses. Estimates of 

minimum and maximum loss reductions are presented in Table 3.

Table 3 – Estimated Minimum and 

Maximum Reductions in Fish Saved

Survey Region Minimum 

Saved (%)

Maximum 

Saved (%)

Northeast 4 94

Southeast 29 80

Pacific 1 94

Inland 55 94

National 24 92

5 Condition of Aquatic Ecosystems

Scores for the attribute Condition of Aquatic Ecosystems, ranging between 0 and 100, represent 

an estimate of the overall ecological condition of aquatic ecosystems in survey regions. The 

theoretical basis underpinning this attribute is that organisms at high trophic levels in aquatic 

systems require diverse ecosystem functions to survive, grow, and reproduce (Deegan et al.

1997). It is assumed that information on the structure, diversity and functional properties of 

resident ecological communities provides a better long-term, event-integrated evaluation of 

whether habitat quality is suitable for aquatic organisms than do water quality samples or other 

single-measurement datasets. 

Notably, biotic indices are not necessarily related directly to any single biological metric, and are 

often scored in a relative fashion (making direct comparisons between different metrics difficult). 

Thus, a score of 100 means that all measured locations contain high quality habitats that support a 

diverse range of life and a functioning food web. It does not necessarily mean that all locations 

are pristine (e.g., in a state that might be observed without human influence). Similarly, a score of 

0 does not mean that all measured locations cannot sustain life. Instead, such a score would 

suggest that these locations are severely degraded, may be dominated by one or few species, and 

are unlikely to have diverse biotic communities.

5.1 Estimating Baseline Conditions

To estimate the baseline values for the aquatic ecosystem score, EPA identified studies in a 

region that apply or define various indices of water quality based on biocriteria from the 

published literature or from state reports. To ensure meaningful comparisons could be made 

among indices within survey regions, publications were selected based upon indices used (i.e. 
Abt Associates Inc. pg. 7

individual criteria used to score habitat). In all regions, indices were chosen to ensure that data 

were available from tens of sites to ensure that localized effects did not dominate regional scores.

Data were obtained using the following groups of indices (although all references within a 

category use indices constructed using similar habitat metrics, not all indices within a category 

are identical; references may present data for more than one index):

• Estuarine Biotic Integrity Indices (Chun et al. 1996, Deegan et al. 1997)

• Benthic Indices of Biotic Integrity (Weisberg et al. 1997, Engle and Summers 1999, 

Dauer et al. 2000, Llansó et al. 2002a, Llansó et al. 2002b, Dauer and Llansó 2003, 

Hyland et al. 2003, Llansó et al. 2005, Astin 2007, Dauer et al. 2008)

• Indices of Fish Assemblage Integrity (Hughes et al. 1998)

• Urban Intensity Indices (Cuffney et al. 2005)

• Multivariate Benthic Indices (Hyland et al. 2003)

• Stream Indices of Biotic Integrity (Whittier et al. 2007)

• Great River Indices of Biotic Integrity (Gammon and Simon 2000)

Data from each publication were (when necessary) grouped by survey region and biotic index. 

Within regions and indices, average index scores were calculated for all sites (sample sizes 

ranged from 4 to > 200), the converted to a percentage value by dividing the average score by the 

index’s maximum score. Within survey region, these data were then averaged to estimate the 

baseline regional attribute score. The national baseline attribute score was calculated as the 

average of all regional scores. Estimated values of the Condition of Aquatic Ecosystem attribute 

within survey region are presented in Table 4.

Table 4 – Estimated Baseline Condition 

of Aquatic Ecosystem Values

Survey Region Ecosystem 

Condition (%)

Northeast 50

Southeast 68

Pacific 51

Inland 42

National 53

5.2 Estimating Maximum Changes to Values

To estimate the effects of regulatory options on the Condition of Aquatic Ecosystems attribute, 

estimated changes in fish populations would be modeled using stochastic matrix population 

models, estimated changes in river flow volume and thermal regime. As described in Section 2.2, 

this modeling was not conducted. Instead, maximum values of change were estimated using 

preliminary modeling exercises conducted by EPA. Overall, changes in the Condition of Aquatic 

Ecosystem attribute are likely to be very small, with feasible regional values likely to be less than 

2%. To ensure the range of possible values were encompassed by the stated preference survey, 

the maximum estimate of change used within the survey was 4%.
Abt Associates Inc. pg. 8

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