### 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 B MSY


### . 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 / B K 

### . 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) fi sh 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 betw een 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 10 0 means

that all stocks are at a size that maximizes long

- term harves t 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 ther e are no commercially targeted fish remaining. However, b ecause fisheries managers are likely to close fisheries when stock populations are severely depressed, a score of 0 may indicat e that no large

- scale commercial or

recreational harvest s are permitted. 

# memorandumAbt 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 score s 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) Nationa l 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 F ish 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 ( B MSY ), namely: B / B MSY

. Consistent with how NOAA awards points within the FSSI, s tocks with ratios exceeding 0.8 were given a score of 1 while stocks with ratios less th an 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 1 0% of all species with commercial fisheries : many stocks remain data

- limited, particularly stocks managed as a species complex 2

. 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 re ceiving a score of 1 ( i.e., the percentage of stocks with B > 0.8 x B MSY ).

Table 1

– Baseline Commercial Fish Population

V alues for M arine F ish S tocks

Survey Region Stocks

Assessed B / B MSY > 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 i nland 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 u nderstood, 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 lev els 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 commerciall y

- and recreationally

targeted 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 geog raphical 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 specie s 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 a ttribute are likel y 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 (Al l 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 directl y 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 Estimat ing 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, d ue to data availability, the Fish Population metric relies heavily upon estimated relative populations of commercially and recreationallyAbt Associates Inc. pg. 4 targeted fish with recent stock assessments. Consequently , available data represent only a small proportion of all fis h 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

, W orm 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 B MSY 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 ( B K ) was

constructe d , namely: B / B K

. 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 B MSY (MSY normally ranges at between 30

- 40% of unexploited population size ) Nationally, 249 fi sh 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 elevat ed 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 V alues

for M arine F ish S tocks

Survey R egion 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 o f 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 Pop ulation 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 m atrix 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 esti mates 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, pre

testing 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 st atus 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 theAbt 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) o f 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. E stimates of

minimum and maximum loss reductions are presented in Table 3.

Table 3

– Estimated Minimum and

Maximum Reductions in Fish Sa ved

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 comm unities 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 wi thout 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 lit erature 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 e nsure 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 h abitat 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 )

• Ind ices 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. E stimated 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 modelin g 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 

### 6 References

Astin, L. E. 2007. Developing biological indicators from diverse data: The Potomac Basin

- wide Index of Benthic Integrity (B

- IBI). Ecological Indicators 7 :895

- 908. Baum, J. K. and B. Worm. 2009. Cascading top

- down effects of changing oceanic predator abundances. Journal of Animal Ecology 78 :699

- 714. Bur, M. T., M. A. Stapanian, P. M. Kocovsky, S. J. Galbreth, and A. M. Cznarnecki. 2005. Surveillance and Status of Fish Stocks in Western Lake Erie, 2004. Great Lakes Fisher Commission, Lake Erie Committee Meeting, Niagara Falls, ON. Chun, K., M. J. Weaver, and L. A. Deegan. 1996. Assessment of Fish Communities in New England Embayments: Application of the Estuarine Biotic Integrity Index. The Biological Bulletin 191 :320

- 321. Cuffney, T. F., H. Zappia, E. M. P. Giddings, and J. F. Cole s. 2005. Effects of Urbanization on Benthic Macroinvertebrate Assemblages in Contrasting Environmental Settings: Boston, Massachusetts; Birmingham, Alabama; and Salt Lake City, Utah. American Fisheries Society Symposium 47 :361

- 407. Dauer, D., J. Ranasinghe , and S. Weisberg. 2000. Relationships between benthic community condition, water quality, sediment quality, nutrient loads, and land use patterns in Chesapeake Bay. Estuaries and Coasts 23 :80

- 96. Dauer, D. M. and R. J. Llansó. 2003. Spatial Scales and Pro bability Based Sampling in Determining Levels of Benthic Community Degradation in the Chesapeake Bay. Environmental Monitoring and Assessment 81 :175

- 186. Dauer, D. M., R. J. Llansó, and M. F. Lane. 2008. Depth

- related patterns in benthic community

conditio n along an estuarine gradient in Chesapeake Bay, USA. Ecological Indicators

8 :417

- 424. Deegan, L. A., J. T. Finn, and J. Buonaccorsi. 1997. Development and validation of an estuarine biotic integrity index. Estuaries 20 :601

- 617. Dieterman, D. 2008. Stream Survey Report: Major River Survey, Upper MIssissippi River Pools 3, 4, 5, 5A, 6, 7, Upper 9 and Lower Vermillion River, 2007. Minnesota Department of Natural Resources, Division of Fish and Wildlife, Section of Fisheries. Engle, V. and J. Summers. 1999. Re finement, validation, and application of a benthic condition

index for Northern Gulf of Mexico estuaries. Estuaries and Coasts 22 :624

- 635. Frank, K. T., B. Petrie, N. L. Shackell, and J. S. Choi. 2006. Reconciling differences in trophic

control in mid

- lati tude marine ecosystems. Ecology Letters 9 :1096

- 1105. Gammon, J. R. and T. P. Simon. 2000. Variation in a Great River Index of Biotic Integrity over a

20

- year period. Hydrobiologia 422

- 423 :291

- 304. Hughes, R. M., P. R. Kaufmann, A. T. Herlihy, T. M. Kincaid , L. Reynolds, and D. P. Larsen. 1998. A process for developing and evaluating indices of fish assemblage integrity. Canadian Journal of Fisheries and Aquatic Sciences 55 :1618

- 1631. Hyland, J. L., W. L. Balthis, V. D. Engle, E. R. Long, J. F. Paul, J. K. S ummers, and R. F. Van Dolah. 2003. Incidence of Stress in Benthic Communities along the U.S. Atlantic and Gulf of Mexico Coasts within Different Ranges of Sediment Contamination from Chemical Mixtures. Environmental Monitoring and Assessment 81 :149

- 161.

Ll ansó, R., L. Scott, D. Dauer, J. Hyland, and D. Russell. 2002a. An estuarine benthic index of biotic integrity for the Mid

- Atlantic region of the United States. I. Classification of assemblages and habitat definition. Estuaries and Coasts 25 :1219

- 1230.

Lla nsó, R., L. Scott, J. Hyland, D. Dauer, D. Russell, and F. Kutz. 2002b. An estuarine benthic index of biotic integrity for the Mid

- Atlantic region of the United States. II. Index development. Estuaries and Coasts 25 :1231

- 1242.Abt Associates Inc. pg. 9 Llansó, R. J., F. S. Kelley, and L. C. Scott. 2005. National Coastal Assessment 2004: Benthic Community Condition in Maryland's Coastal Bays. Page 101. Prepared For Maryland Department of Natural Resources Tidewater Ecosystem Assessment Division by Versar, Inc., Columbia, MD. 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. Madenjian, C. P., D. B. Bunnell, J. D. Holuszko, T. J. Desorcie, and J. V. Adams. 2010. Status and Trends of Prey Fish Populations in Lake Michigan, 2009. Great Lakes Fishery Commission, Lake Michigan Committee Meeting, Winds or, ON. Meerbeek, J. R. 2008a. Completion Report: Angler Survey of Lake Pepin and Pool 4 of the Mississippi River, from 2005 to 2007. Minnesota Department of Natural Resources,

Section of Fisheries. Meerbeek, J. R. 2008b. Large Lake Monitoring Program Annu al Completion Report: Lake Pepin, 2007. Minnesota Department of Natural Resources. Micheli, F. 1999. Eutrophication, Fisheries, and Consumer

- Resource Dynamics in Marine Pelagic Ecosystems. Science 285 :1396

- 1398. NEFSC. 2006. Status of fishery resources off the Northeastern United States. Available at: http://www.nefsc.noaa.gov/sos/

. NMFS. 2010. Annual Report to Congress on the status of U.S. fisheries

- 2009. Silver S pring,

MD. O'Gorman, R., R. W. Owens, S. E. Prindle, J. V. Adams, and T. Schaner. 2005. Status of Major Prey Fish Stocks in the U.S. Waters of Lake Ontario, 2004. Great Lakes Fishery Commission, Lake Ontario Committee Meeting, Niagara Falls, ON. Pikitch, E ., P. D. Boersma, I. L. Boyd, D. O. Conover, P. Cury, T. E. Essington, S. S. Heppell, E. D. Houde, M. Mangel, D. Pauly, E. Plaganyi, K. Sainsbury, and R. S. Steneck. 2012. Little Fish, Big Impact: Managing a Crucial Link in Ocean Food Webs. Lenfest Ocean

P rogram, Washington, DC. Roseman, E. F., T. P. O'Brien, S. C. Riley, S. Farha, and J. R. P. French. 2009. Status and Trends of the Lake Huron Deepwater Demersal Fish Community, 2008. Great Lakes Fishery Commission, Lake Huron Committee Meeting, Ypsilanti, M I. Schaeffer, J. S. and T. P. O'Brien. 2009. Status and Trends of Pelagic Prey Fishes in Lake Huron, 2008. U.S. Geological Survey, Great Lakes Science Center, Ann Arbor, MI. SEFSC. 1995. Status of Fishery Resources off the Southeastern United States for 1993. NOAA

Technical Memorandum NMFS

- SSEFSC

- 368. Stockwell, J. D., L. M. Evrard, D. L. Yule, O. T. Gorman, and G. A. Cholwek. 2005. Status and Trends of Prey Fish Populations in Lake Superior, 2004. Great Lakes Fisher Commission, Lake Superior Committee Me eting, Ypsilanti, MI. 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. USGS. 2010. Commercial Fishing Reports: Total Pounds and Dollar Value of Commercial Catch in U.S. Waters of the Great Lakes by Year, State, Lake and Species, 1971

- 2009.,

Available at: http://www.glsc.usgs.gov/_f iles/cfreports/noaa1971

- 2010.zip

. Weisberg, S., J. Ranasinghe, D. Dauer, L. Schaffner, R. Diaz, and J. Frithsen. 1997. An estuarine benthic index of biotic integrity (B

- IBI) for Chesapeake Bay. Estuaries and Coasts

20 :149

- 158. Whittier, T. R., R. M. Hughes , J. L. Stoddard, G. A. Lomnicky, D. V. Peck, and A. T. Herlihy. 2007. A Structured Approach for Developing Indices of Biotic Integrity: Three ExamplesAbt Associates Inc. pg. 10 from Streams and Rivers in the Western USA. Transactions of the American Fisheries Society 136 :718

- 735. Worm, B. and R. A. Myers. 2003. Meta

- Analysis of Cod

- Shrimp Interactions Reveals Top

- Down Control in Oceanic Food Webs. Ecology 84 :162

- 173.