July 2, 2012 316(b) SP Survey – Overview of Preliminary Modeling Exercises for Survey Attribute Values | pg 1

Date July 2, 2012 

To The Section 316(b) Existing Facilities Rule Record 

From Erik Helm, U.S. Environmental Protection Agency

Subject 316(b) Stated Preference (SP) Survey – Overview of Preliminary Modeling Exercises for 

Attribute Values

Methods used to calculate baseline and changes to attribute values in the 316(b) stated preference (SP) 

survey were described in a previous memorandum (“Attribute Calculations within the 316(b) Stated 

Preference (SP) Survey”, DCN 11-4533). That memo refers to “preliminary modeling exercises” (p. 3) 

used to support the maximum changes associated with attributes in the survey. This brief memo describes 

that effort in more detail. In particular, this memo describes maximum changes to the baseline values of 

the four environmental attributes included in the SP survey: (1) commercial fish populations, (2) fish 

populations (all fish), (3) fish saved, and (4) condition of aquatic ecosystems. 

1 Commercial Fish Populations 

To estimate the effect of regulatory options on the commercial fish populations attribute, EPA modeled 

populations of eight commercially harvested fish species using matrix models. To investigate the role that 

density dependence might play in limiting the effect of regulations, three density dependent functions 

were explored for each of these eight commercially targeted species (Abt Associates 2009a). 

Although the modeled effect of impingement and entrainment (I&E) mortality varied among species, it 

was minimal in all cases. Modeled population level changes to these stocks over ten years varied on a 

species-specific basis between < 0.1% and approximately 5%. These exploratory results were comparable 

to those in the literature (Lorda et al 2000). Because of the similar values these approaches produced, 

EPA assumes these estimates corroborate each other, such that the maximum feasible regional changes in 

commercial fish populations is about 5%. Using NOAA estimates of stock biomass compared to biomass 

at maximum sustainable yield (B / BMSY), EPA identified seven commercially-targeted stocks for which 

an increase of fish populations of 5% would result in B / BMSY > 0.8 (a threshold used by NOAA in their 

Fish Stock Sustainability Index (FSSI) to score commercial stock sustainability) (NMFS 2010). 

Regionally, these stocks account for between 1.5% and 5.4% all stocks for which biomass data are 

available. To ensure that the full range of possible levels was encompassed by the experimental design of 

the SP survey, and because integer percentages are easier to respond to, EPA increased this estimate to 

6%. Thus, the maximum policy effect presented to respondents was 6% for all SP survey regions. 

2 Fish Populations (All Fish) 

As described in Section 1, EPA modeled populations of eight commercially harvested species. Because 

the fish populations (all fish) attribute is measured against carrying capacity, rather than maximum 

sustainable yield, the relative magnitude of these population shifts are smaller for this attribute, typically 

by a factor greater than two (i.e., biomass at carrying capacity (BK) > 2x BMSY). Consequently, maximum 

change for the attribute will be smaller than those observed for changes in the commercial fish attribute. 

Given that integer percentages are easier for respondents to respond to, this suggests that changes to the 

fish populations (all fish) attribute should be no more than 2.5%, (i.e., half the maximum increase in fish 

populations observed). However, to ensure that the full range of possible levels was encompassed by the 

experimental design of the SP survey, and to present policy effects as an integer, EPA increased this 

estimate to a maximum of 4% for all SP survey regions. 
July 2, 2012 316(b) SP Survey – Overview of Preliminary Modeling Exercises for Survey Attribute Values | pg 2

3 Fish Saved 

Models used to estimate changes in the fish saved attribute are discussed in Chapter 3 of the 

Environmental and Economic Benefits Analysis (EEBA) for the proposed rule (USEPA 2011). Because 

of the long history of equivalent age modeling that has been conducted for 316(b) purposes, and the 

relative richness of data availability, meaningful estimates could be estimated for each region: they are 

presented in the memo “Attribute Calculations within the 316(b) Stated Preference (SP) Survey” (DCN 

11-4533), with raw data available in the document “316(b) Survey design values” (DCN 11-4523). 

Across regions, the maximum percentage of fish saved varied (achieved if all facilities were required to 

adopt closed cycle cooling) was between 80% and 94%. The maximum change assigned to this attribute 

was rounded to 95% for the Northeast, Pacific, Inland, and National SP surveys, and 90% for the 

Southeast SP survey, to ensure the full range of possible levels was encompassed by the experimental 

design of the SP survey. 

4 Condition of Aquatic Ecosystems 

EPA conducted an exploratory modeling exercise in the Chesapeake Bay using the Ecopath with Ecosim 

(EwE) software and the Chesapeake Bay Fisheries Ecosystem Model (CBFEM) to estimate the regulatory 

effects of the proposed rule on the condition of aquatic ecosystems (Abt Associates 2009b). This model 

produces a static mass-balanced snapshot of the bay, assuming a closed system. EPA was able to conduct 

this preliminary modeling only because of NOAA’s development of the CBFEM (Christensen et al. 

2009). Moreover, because of the detailed data requirements, time, and effort required to develop EwE 

models, it was not possible for EPA to conduct additional modeling exercises given time and resource 

constraints. 

Although results were preliminary and limited in geographical scope, the model indicated that minor 

changes to marine communities could occur as a result of the proposed rule. Additionally, because many 

of the indices used to estimate the condition of aquatic ecosystems are partially derived from community 

diversity and composition, it is possible that these changes would result in measurable changes to aquatic 

ecosystem scores. However, these changes are unlikely to be large. This is because indices of biotic 

integrity (IBI) used to estimate the condition of aquatic ecosystems rely upon several metrics (such as 

pollution, sedimentation, etc.) that are unlikely to be affected by the proposed rule. In many cases, fish 

communities either: (1) account for only a small portion of the overall score, or (2) require substantial 

changes in diversity, dominance, number of spawning species, number of benthic species, or abundance – 

not observed in CBFEM output – to have a measurable affect on IBIs. Thus, it is unlikely that reducing 

I&E mortality will alter overall ecosystem condition scores by more than 3% to 4%. After taking these 

considerations and the preliminary results of the CBFEM into account, EPA estimated that changes in 

aquatic condition as a result of the proposed regulations are unlikely to exceed 2%. Moreover, because the 

results observed from the CBFEM represent the potential for recovery within a heavily impacted estuary, 

this estimate is likely to be representative of an upper bound for all survey regions. However, because 

EPA’s estimate is made with incomplete data, and to ensure the range of all possible values were 

encompassed by the SP survey, the maximum estimate of changes presented in surveys was doubled to 

4% for all survey regions. 

5 References 

Abt Associates. 2009a. WA 2-09 Task 4: Matrix models of I & E effects on fish populations. 

Memorandum dated December 16, 2009. 

Abt Associates. 2009b. WA 2-09 Task 4: Ecopath simulations of Chesapeake bay. Memorandum dated 

November 18, 2009. 
July 2, 2012 316(b) SP Survey – Overview of Preliminary Modeling Exercises for Survey Attribute Values | pg 3

Christensen, V., A. Beattie, C. Buchanan, H. Ma, S.J.D. Martell, R.J. Latour, D. Preikshot, M.B. Sigrist, 

J.H. Uphoff, C.J. Walters, R.J. Wood, H.Townsend. 2009. Fisheries Ecosystem Model of the 

Chesapeake Bay: Methodology, Parameterization, and Model Explanation. U.S. Dep. Commerce, 

NOAA Tech. Memo. NMFS-F/SPO-106, 146 p. 

Lorda, E., D. J. Danila, and J.D. Miller. 2000. Application of a population dynamics model to the 

probabilistic assessment of cooling water intake effects of millstone Nuclear Power Station 

(Waterford, CT) on a nearby winter flounder spawning stock. Environmental Science & Policy

3:S471-S482. 

National Marine Fisheries Service (NMFS). 2010. Annual Report to Congress on the Status of U.S. 

Fisheries - 2009. Silver Spring, MD. 

U.S. Environmental Protection Agency (USEPA). 2011. Environmental and Economic Benefits Analysis 

for the Proposed Section 316(b) Existing Facilities Rule. Office of Science and Technology, 

Engineering and Analysis Division. EPA 821-R-11-002.