ENTRAINMENT AND IMPINGEMENT AT IP2 AND IP3: 

A BIOLOGICAL IMPACT ASSESSMENT 

Lawrence W. Barnthouse 

LWB Environmental Services, Inc. 

Douglas G. Heimbuch 

AKRF, Inc. 

Webster Van Winkle 

Van Winkle Environmental Consulting 

Boise, Idaho 

John Young 

ASA Analysis & Communications, Inc. 

January 2008
TABLE OF CONTENTS

Executive Summary.......................................................................................................1 

Glossary .........................................................................................................................3 

1. Introduction...........................................................................................................5 

2. Approach to Impact Assessment...........................................................................6 

 2.1 Definition of “adverse environmental impact”....................................8 

 2.1.1 Definition of adverse environmental impact in the context of 

 fishery management.............................................................................8 

 2.1.2 Definition of AEI in the context of ecological risk assessment.........10 

 2.1.3 Definition of adverse environmental impact in the context of 

 entrainment and impingement............................................................11 

 2.2 Why entrainment losses alone are insufficient to demonstrate 

AEI.....................................................................................................12 

 2.3 Role of the conditional mortality rate (CMR) in impact 

assessment..........................................................................................13 

 2.4 Role of long-term datasets in impact assessment ..............................14 

 2.5 Indicators of adverse impacts potentially related to CWIS ...............18 

3. Evaluation of changes in abundance of fish populations with life stages 

 susceptible to entrainment...................................................................................20 

 3.1 Species addressed...............................................................................22 

 3.2 Impact hypotheses and stressor metrics.............................................23 

 3.2.1 CWIS..................................................................................................23 

 3.2.2 Fishing................................................................................................24 

 3.2.3 Zebra mussels.....................................................................................25 

 3.2.4 Predation by striped bass ...................................................................26 

 3.2.5 Temperature .......................................................................................26 

 3.3 Response metrics ...............................................................................27 
 ii 

 3.3.1 Response metrics for striped bass, white perch, American shad, 

 alewife, blueback herring, and bay anchovy......................................27 

 3.3.2 Response metrics for spottail shiner ..................................................28 

 3.3.3 Response metrics for Atlantic tomcod...............................................28 

 3.4 Tests of impact hypotheses ................................................................29 

 3.4.1 Striped bass ........................................................................................30 

 3.4.1.1 CWIS..................................................................................................31 

 3.4.1.2 Fishing................................................................................................33 

 3.4.1.3 Zebra mussels.....................................................................................34 

 3.4.1.4 Summary evaluation of hypotheses ...................................................36 

 3.4.2 White perch........................................................................................36 

 3.4.2.1 CWIS..................................................................................................37 

 3.4.2.2 Zebra mussels.....................................................................................39 

 3.4.2.3 Striped bass predation........................................................................40 

 3.4.2.4 Summary evaluation of hypotheses ...................................................42 

 3.4.3 American shad ...................................................................................43 

 3.4.3.1 CWIS..................................................................................................44 

 3.4.3.2 Fishing................................................................................................45 

 3.4.3.3 Zebra mussels.....................................................................................47 

 3.4.3.4 Striped bass predation........................................................................48 

 3.4.3.5 Summary evaluation of hypotheses ...................................................50 

 3.4.4 Atlantic tomcod..................................................................................51 

 3.4.4.1 CWIS..................................................................................................52 

 3.4.4.2 Elevated summer temperatures..........................................................54 

 3.4.4.3 Striped bass predation........................................................................55 

 3.4.4.4 Summary evaluation of hypotheses ...................................................56 
 iii 

 3.4.5 Alewife and blueback herring............................................................57 

 3.4.5.1 CWIS..................................................................................................57 

 3.4.5.2 Zebra mussels.....................................................................................59 

 3.4.5.3 Striped bass predation........................................................................61 

 3.4.5.4 Summary evaluation of hypotheses ...................................................63 

 3.4.6 Bay anchovy.......................................................................................64 

 3.4.6.1 CWIS..................................................................................................64 

 3.4.6.2 Striped bass predation........................................................................66 

 3.4.6.3 Summary evaluation of hypotheses ...................................................67 

 3.4.7 Spottail Shiner....................................................................................67 

 3.5 Summary evaluation of trends analysis .............................................68 

4. Evaluation of impacts of cooling-water withdrawals on 

 spawning potential ..................................................................................69 

 4.1 History of the SSBPR model .............................................................69 

 4.2 Explanation of the SSBPR concept....................................................70 

 4.3 Application to Hudson River fish populations...................................72 

 4.3.1 Striped bass ........................................................................................73 

 4.3.2 American shad ...................................................................................73 

5. Community-Level Trends Analysis....................................................................74 

 5.1 Methods..............................................................................................75 

 5.2 Results and Discussion ......................................................................78 

6. Conclusions.........................................................................................................78 

7. References...........................................................................................................80 

Table 1 ........................................................................................................................... i 

Table 2 .......................................................................................................................... ii 

Table 3 ......................................................................................................................... iii 
 iv 

Table 4 ......................................................................................................................... iv 

Table 5 ...........................................................................................................................v 

Table 6 ......................................................................................................................... vi 

Table 7 ........................................................................................................................ vii 

Table 8 ....................................................................................................................... viii 

Table 9 ......................................................................................................................... ix 

Figure 1 ..........................................................................................................................x 

Figure 2 ........................................................................................................................ xi 

Figure 3 ....................................................................................................................... xii 

Figure 4 ...................................................................................................................... xiii 

Figure 5 ...................................................................................................................... xiv 

Figure 6 ........................................................................................................................xv 

Figure7a ..................................................................................................................... xvi 

Figure 7b .................................................................................................................... xvi 

Figure 8a ................................................................................................................... xvii 

Figure 8b ................................................................................................................... xvii 

Figure 9a .................................................................................................................. xviii 

Figure 9b .................................................................................................................. xviii 

Figure 10 .................................................................................................................... xix 

Figure 11 ......................................................................................................................xx 

Figure 12a .................................................................................................................. xxi

Figure 12b .................................................................................................................. xxi

Figure 13a ................................................................................................................. xxii

Figure 13b ................................................................................................................. xxii

Figure 14 .................................................................................................................. xxiii 
 v 

Figure 15 .................................................................................................................. xxiv

Figure 16a ..................................................................................................................xxv

Figure 16b ..................................................................................................................xxv

Figure 17 .................................................................................................................. xxvi

Figure 18a ............................................................................................................... xxvii 

Figure 18b ............................................................................................................... xxvii 

Figure 19a .............................................................................................................. xxviii 

Figure 19b .............................................................................................................. xxviii 

Figure 20 .................................................................................................................. xxix

Figure 21a ..................................................................................................................xxx

Figure 21b ..................................................................................................................xxx

Figure 22 .................................................................................................................. xxxi

Figure 23 ................................................................................................................. xxxii

Figure 24a .............................................................................................................. xxxiii 

Figure 24b .............................................................................................................. xxxiii 

Figure 25a .............................................................................................................. xxxiv 

Figure 25b .............................................................................................................. xxxiv 

Figure 25c .............................................................................................................. xxxiv 

Figure 26 ..................................................................................................................xxxv

Figure 27 ................................................................................................................ xxxvi 

Figure 28a ............................................................................................................. xxxvii 

Figure 28b ............................................................................................................. xxxvii 

Figure 29a ............................................................................................................ xxxviii 

Figure 29b ............................................................................................................ xxxviii 

Figure 30a .............................................................................................................. xxxix 
 vi 

Figure 30b .............................................................................................................. xxxix 

Figure 31a .................................................................................................................... xl 

Figure 31b .................................................................................................................... xl 

Figure 32a ................................................................................................................... xli 

Figure 32b ................................................................................................................... xli 

Figure 33 .................................................................................................................... xlii 

Figure 34a ................................................................................................................. xliii 

Figure 34b ................................................................................................................. xliii 

Figure 34c ................................................................................................................. xliii 

Figure 35a ................................................................................................................. xliv

Figure 35b ................................................................................................................. xliv

Figure 35c ................................................................................................................. xliv

Figure 36 .................................................................................................................... xlv 

Figure 37a ................................................................................................................. xlvi

Figure 37b ................................................................................................................. xlvi

Figure 38 .................................................................................................................. xlvii 

Figure 39a ............................................................................................................... xlviii

Figure 39b ............................................................................................................... xlviii

Figure 40 ................................................................................................................... xlix 

Figure 41a ...................................................................................................................... l 

Figure 41b ...................................................................................................................... l 

Figure 42 ....................................................................................................................... li 

Figure 43 ...................................................................................................................... lii 

Figure 44a ................................................................................................................... liii 

Figure 44b ................................................................................................................... liii
 1 

Executive Summary 

This report evaluates whether entrainment and impingement by the respective cooling 

water intake structures (“CWIS”) at Indian Point Unit 2 (“IP2”) and Indian Point Unit 3 (“IP3”) 

have caused an adverse environmental impact (“AEI”), using biologically-based definitions of 

AEI that are consistent with established definitions and standards of ecological risk assessment 

and fisheries management. 

 The approach involves three elements. First, we use the extensive Hudson River fisheries 

datasets to determine (1) whether changes in the status of species of interest identified by the 

New York State Department of Environmental Conservation (“NYSDEC”) have occurred since 

IP2 and IP3 began commercial operation, (2) whether cooling-water withdrawals by IP2 and IP3 

during this period could have been responsible for any such changes, or (3) whether alternative 

stressors including striped bass predation, zebra mussels, and harvesting are the more probable 

cause of perceived changes. 

Second, we use a widely-accepted method for quantifying the impacts of harvesting on 

the sustainability of fish populations, termed the Spawning Stock Biomass per Recruit 

(“SSBPR”) model, to determine whether entrainment and impingement at IP2 and IP3 could 

have adversely affected the sustainability of the Hudson River striped bass and American shad 

populations. 

Third, we examine long-term trends in the abundance of all Hudson River fish species for 

which adequate trends data sets can be developed to determine whether species with high 

susceptibility to entrainment at IP2 and IP3 are more likely to have declined in abundance over 

the past 30 years than are species with low susceptibility to entrainment. 

All three elements of the assessment support a conclusion that IP2 and IP3 have not 

caused an AEI. Evaluation of alternative hypotheses concerning the causes of changes in 

abundance of Hudson River fish populations found no evidence supporting the hypothesis that 

IP2 and IP3 contributed to these changes. Instead, the evaluation shows that overharvesting is 

the most likely cause of recent declines in abundance of American shad, with striped bass 

predation being a potentially significant contributing factor. Increased predation by the rapidly 

growing Hudson River striped bass population is the most likely cause of recent declines in the 

abundance of Atlantic tomcod, river herring and bay anchovy. Striped bass predation probably 
 2 

contributed to the decline in abundance of white perch, although other unknown causes were also 

involved. 

Two additional lines of evidence support a conclusion that entrainment and impingement 

at IP2 and IP3 have not resulted in AEI. Application of the SSBPR model to stock assessment 

data for striped bass and American shad shows that mortality caused by entrainment at IP2 and 

IP3 is negligible, particularly compared to fishing mortality, and does not impair the ability of 

these populations to sustain themselves. Analysis of community-level trends data show that 

species with relatively high susceptibility to entrainment at IP2 and IP3 are no more likely to 

have declined in abundance since 1974 than are species with relatively low susceptibility to 

entrainment. 

Considered together, the evidence evaluated in this report shows that the operation of IP2 

and IP3 has not caused effects on early life stages of fish that reasonably would be considered 

“adverse” by fisheries scientists and/or managers. The operation of IP2 and IP3 has not 

destabilized or noticeably altered any important attribute of the resource. 
 3 

Glossary

Ichthyoplankton: Eggs and larvae of fish with limited swimming abilities that float in the 

water-column and are passively transported by currents 

Entrainment: The drawing of ichthyoplankton and other small aquatic organisms through a 

cooling water intake structure into the cooling system of a power plant 

Impingement: The trapping of fish and other aquatic organisms against intake screens by the 

force of the water being drawn through a cooling water intake structure 

Individual: A single organism 

Population: A group of plants, animals, or other organisms, all of the same species, that live 

together and reproduce 

Community: An assemblage of species populations that occur together in space and time 

Yolk-sac larvae (YSL): Fish larvae that have recently hatched and are still receiving nutrition 

from yolk deposited in the eggs before they were spawned 

Post yolk-sac larvae (PYSL): Fish larvae that have absorbed the yolk and obtain nutrition by 

feeding 

Young-of-the-year (YOY): Fish that have completed the transformation from the larval to the 

juvenile stage and have grown large enough to be captured by the gear used in the generators’ 

Beach Seine Survey and Fall Shoals Survey 

Longitudinal River Survey (LRS): The Hudson River generators’ annual riverwide 

ichthyoplankton survey 
 4 

Beach Seine Survey (BSS): The Hudson River generators’ annual survey of YOY and older 

fish abundance in the shorezone 

Fall Shoals Survey (FSS): The Hudson River generators’ annual survey of YOY and older fish 

abundance in the shoal zone 

Early life stage: The collective term for the egg, YSL, PYSL, and early juvenile (juveniles too 

small to be captured by the gear used in the BSS and FSS) life stages 

Conditional mortality rate (CMR): A measure of the mortality imposed on a population by a 

stressor such as a cooling water intake structure 

Recruit: A fish that has grown large enough to be caught in gears used by agencies performing 

stock assessments for harvested fish species; as used in the spawning stock biomass per recruit 

model, a one-year-old fish 

Spawning stock biomass per recruit (SSBPR): The expected lifetime reproduction of a typical 

female recruit, measured in terms of the expected future egg production or biomass 

Density-dependence: A relationship between the abundance of a population and the growth 

rates or mortality rates of individuals belonging to that population 

Stressor: An anthropogenic or environmental factor that increases mortality or decreases growth 

of organisms belonging to a population exposed to that factor 

Stressor metric: A measure of the intensity of a stressor 

Response metric: A measure of the response of an exposed population to one or more stressors
 5 

1. Introduction 

This report evaluates whether entrainment and impingement by the respective cooling 

water intake structures (“CWIS”) at Indian Point Unit 2 (“IP2”) and Indian Point Unit 3 (“IP3”) 

has caused an adverse environmental impact (“AEI”), as that term is employed in §316(b) of the 

Clean Water Act (“CWA”) and 6 NYCRR §704.5 and reasonably may be interpreted by the 

scientific community.1 Our evaluation of whether entrainment and impingement by the 

respective CWIS at IP2 and IP3 has caused AEI is based on biologically-based definitions of 

“adverse environmental impact” consistent with established definitions and standards of 

ecological risk assessment (USEPA 1998) and fisheries management (Restrepo et al. 1998, 

Quinn and Deriso 1999). Our approach involves three elements. 

First, we use the extensive Hudson River fisheries datasets (prepared under the direction 

and oversight of the New York State Department of Environmental Conservation (“Department” 

or “NYSDEC”)) to determine (1) whether changes in the status of species of interest identified 

by NYSDEC have occurred since IP2 and IP3 began commercial operation, (2) whether coolingwater withdrawals by IP2 and IP3 during this period could have been responsible for any such 

changes, or (3) whether alternative stressors including striped bass predation, zebra mussels, and 

harvesting are the more probable cause of perceived changes. 

Second, we use a widely-accepted method for quantifying the impacts of harvesting on 

the sustainability of fish populations, termed the Spawning Stock Biomass per Recruit 

(“SSBPR”) model, to determine whether entrainment and impingement at IP2 and IP3 could 

have adversely affected the sustainability of the Hudson River striped bass and American shad 

populations. 

Third, we examine long-term trends in the abundance of all Hudson River fish species for 

which adequate trends data sets can be developed to determine whether species with high 

 

1

 As applicable here, the CWIS for IP2 and IP3 extend from the point at which water is withdrawn from the 

Hudson River (the “River”) up to, and including, the intake pumps. See, e.g., In Re Matter of Bowline, LLC, 

2001 WL 1587359 (N.Y. Dept. Env. Conserv.) (Nov. 30, 2001), at *6-7 (relying on USEPA definition, now 

codified at 40 C.F.R §125.93); 40 C.F.R. §125.93. The CWIS at IP2 and IP3 are shown schematically in 

Figures IV-12 through IV-15 of the Draft Environmental Impact Statement for State Pollutant Discharge 

Elimination System Permits for Bowline Point, Indian Point 2 & 3, and Roseton Steam Electric Generating 

Stations, dated December 1999 (the “DEIS”), subsequently incorporated into the Final Environmental Impact 

Statement by the New York State Department of Environmental Conservation, accepted June 25, 2003 (the 

“FEIS”). See FEIS, p. 12. These intake structures generally commence with bar racks and debris barriers at the 

point of entry, include modified Ristroph traveling screens and fish return systems upstream of the point of 

entry, and terminate with the circulating water pumps. 
 6 

susceptibility to entrainment at IP2 and IP3 are more likely to have declined in abundance over 

the past 30 years than are species with low susceptibility to entrainment.

Although the technical analyses documented in this report emphasize entrainment, the 

conclusions reached apply to the combined impacts of entrainment and impingement. There are 

two reasons for this. First, the trends data that are the primary focus of this assessment reflect 

the combined effects of entrainment and impingement. Second, entrainment is the focus of the 

Department, as the existing retrofits (i.e., Ristroph screens and fish returns) have resolved the 

Department’s concerns regarding impingement (Draft SPDES Permit, Special Condition 27). 

2. Approach to Impact Assessment 

Populations2 and communities3 are the proper focus for evaluating adverse impacts of 

cooling-water withdrawals on the Hudson River estuary. The fundamental reason for focusing 

on populations and communities is that, whereas all individual organisms have finite life spans, 

populations and communities can persist. Because populations and communities can persist in 

spite of the inevitable mortality of the individual organisms, populations and communities can be 

managed and restored. Most commonly, fisheries management agencies establish harvesting 

policies to manage populations of fish while allowing harvesting of individual fish to continue 

(Restrepo et al. 1998). The U.S. Environmental Protection Agency (“USEPA”) develops 

biological assessment methods, based on measures of aquatic community composition, to help 

states, tribes, territories, and interstate commissions identify communities that are impaired and 

in need of restoration (USEPA 2002). Established principles of population and community 

ecology underly both fisheries management and biological assessment. These scientific 

disciplines also provide a sound foundation for assessing impacts of entrainment and 

impingement on the biological resources of the Hudson River. 

Our evaluation is primarily based on an analysis of empirical data collected over the 30 

years during which IP2 and IP3 have been operating, in a manner that appropriately accounts for 

other potential causes of changes in fish populations. This is because factors other than 

entrainment and impingement affect the abundance of fish populations, including short-term 

 

2

 A population is a group of plants, animals, or other organisms, all of the same species, that live together and 

reproduce (Gotelli 1995). 3

 A community is an assemblage of species populations that occur together in space and time (Begon et al. 1996). 
 7 

natural environmental fluctuations, long-term environmental change, introductions of exotic 

species, pollution, and over-harvesting (Pew Oceans Commission 2003). The preamble to 

USEPA’s Phase II Rule, 69 Fed. Reg. 41588 (July 9, 2004), also acknowledges the potential 

influence of these factors on Hudson River fish populations. Where potentially adverse changes 

in Hudson River fish populations have occurred over the past 30 years, we attempt to determine 

whether those changes are reasonably attributable to entrainment and impingement, or whether 

they are more likely to have resulted from other factors. 

 This impact assessment focuses on eight of the ten species identified for quantitative 

assessment in NYSDEC’s October 1, 1992 Scope of Work for the DEIS: (1) striped bass; (2) 

white perch; (3) American shad; (4) Atlantic tomcod; (5) alewife; (6) blueback herring; (7) bay 

anchovy; and (8) spottail shiner. All of these species have been included in §316(b) studies for 

Indian Point and other Hudson River power plants since the 1970s (TI 1980). Six of these 

species, striped bass, white perch, Atlantic tomcod, alewife, and bay anchovy, were listed by 

USEPA as Representative Important Species (“RIS”) for the Hudson River (TI 1980). Although 

not officially listed as RIS, blueback herring was included in the list of species studied because 

of its abundance in impingement collections at Indian Point, and American shad was included 

because of its commercial importance (TI 1980). 

NYSDEC finalized the Scope of Work for the DEIS following a public scoping meeting 

and the integration of comments received from the generators, state and federal agencies, and 

environmental organizations. Two of the species identified in the Scope of Work, blue crab and 

shortnose sturgeon, are not addressed in this report. These two species are not addressed here 

because there is broad consensus that the CWIS at IP2 and IP3 have no impact on these species. 

See, e.g., DEIS, p. V-125, 126 (sturgeon); Technical Comments on the DEIS, Pisces 

Conservation, Ltd., June 2000 (“Pisces Comments”), p. 27 (“There seems no basis for suggesting 

that power plants are linked to [changes in Atlantic and shortnose sturgeon abundance].”); DEIS, 

p. V-157 (based on preferred habitat, blue crab eggs and larvae not entrained at IP2 and IP3; very 

high impingement survival); Pisces Comments, p. 28-29 (numbers of blue crab within the 

estuary have risen dramatically since 1980). 
 8 

2.1 Definition of “adverse environmental impact”

Neither §316(b) of the CWA (including USEPA’s Phase II Existing Facilities Rule), nor 

New York regulation provides a definition of the term “adverse environmental impact.” See, 

e.g., 6 NYCRR §704.5. However, both regulations governing fisheries management in the 

United States and other USEPA guidance provide a foundation for a scientifically appropriate 

definition of this term. 

2.1.1 Definition of adverse environmental impact in the context of fishery management

In the context of fisheries management, mortality per se could not be considered an AEI, 

because the act of fishing necessarily causes mortality. To the contrary, fisheries management 

agencies, including NYSDEC, actively encourage the responsible harvesting of fish. For 

example, NYSDEC has issued a guide to saltwater fishing in the New York City area 

(http://www.dec.ny.gov/outdoor/8377.html) that discusses equipment, fish identification, and 

specific fishing locations in all five New York City boroughs. 

Fishery policy in waters under the control of the U.S. federal government, including 

estuaries and rivers utilized by anadromous fish, is established in the Magnuson-Stevens Fishery 

Conservation and Management Act (“Magnuson-Stevens Act”). The amended Act states: 

Fishery resources are finite but renewable. If placed under sound 

management before over-fishing has caused irreversible effects, 

the fisheries can be conserved and maintained so as to provide 

optimal yields on a continuing basis. 

16 U.S.C. §1801(a)(5).

Federal guidelines implementing the Magnuson-Stevens Act state that “[c]onservation 

and management measures shall prevent over-fishing while achieving on a continuing basis, the 

optimum yield (“OY”) from each managed fishery for the U.S. fishing industry.” 70 Fed. Reg. 

36240, 36250 (June 22, 2005). Thus, a fish population is viewed by managers as a renewable 

resource for which mortality in the form of harvesting is permissible, provided that this mortality 

does not threaten the long-term productivity of the population. Over-fishing that threatens the 

long-term sustainability of harvests is considered to be adverse. The National Oceanic and 

Atmospheric Administration (“NOAA”) guidelines and other related technical guidance 

documents (e.g., Restrepo et al. 1998) provide specific procedures for determining whether over-
 9 

fishing is occurring. Fishery management councils are required to take action to reduce harvest 

levels if over-fishing is found to exist. 70 Fed. Reg. 36240, 36257 (June 22, 2005). 

The Magnuson-Stevens Act is often cited as the “Sustainable Fisheries Act.” The term 

“sustainable” is often used in a wider environmental policy context to refer to an approach to 

economic development and resource utilization that meets the needs of the present without 

compromising the ability of future generations to meet their own needs (World Commission on 

Environment and Development 1987). Sustainable uses of resources preserve those resources 

for future use; non-sustainable uses degrade or destroy the resources so that they may be 

unavailable in the future (World Commission on Environment and Development 1987). 

Applying the definition of sustainable use provided by the World Commission on 

Environment and Development, sustainable use in the context of a fish population refers to a 

resource-management approach that permits the population to persist indefinitely into the future, 

while continuing to perform its normal ecological function and support normal human use. 

Ecological function is included as part of the definition of sustainable use of fish populations 

because fish have a role in the maintenance of healthy aquatic systems that can be compromised 

by over-fishing (Dayton et al. 2002). Predatory fish, such as striped bass, control the abundance 

of other fish species upon which they prey, and forage fish, such as bay anchovy, serve as both 

food for other fish species and as controls on the abundance of smaller organisms at the base of 

the marine food chain (Dayton et al. 2002). Over-fishing has led to a wide variety of direct and 

indirect changes in the structure and function of fish communities throughout the world (Dayton 

et al. 2002). 

The sustainability of a population is a function of the abundance and other characteristics 

of the population (e.g., age and size structure) and also of the ability of members of the 

population to reproduce and replace themselves. Thus, with respect to the harvest-related 

mortality imposed on a fish population, an adverse impact consists of harvest-related reductions 

in abundance, changes in age/size structure, increases in mortality rates, or reduction in 

reproduction rates that threaten the capacity of the population to persist, perform its normal 

ecological function, and support normal human uses. 
 10 

2.1.2 Definition of AEI in the context of ecological risk assessment

USEPA’s Guidelines for Ecological Risk Assessment (USEPA 1998) provide a general 

discussion of adverse ecological effects of environmental stressors, including criteria for 

evaluating whether or not observed or predicted changes should be considered adverse. These 

guidelines were expressly issued to “set forth current scientific thinking and approaches for 

conducting and evaluating ecological risk assessments” (USEPA 1998, p. 8). This guidance 

discusses adverse ecological effects of environmental stressors, including criteria for evaluating 

whether or not observed or predicted changes should be considered adverse. According to 

USEPA and the scientific community, adverse ecological effects are changes that “alter valued 

structural or functional attributes of the ecological entities under consideration” (USEPA 1998, 

p. 106). USEPA (1998, p. 106) further states that the following criteria should be considered 

when determining whether an observed or predicted effect is adverse: 

• Nature and intensity of effects; 

• Spatial and temporal scale; and 

• Potential for recovery. 

“Nature and intensity of effects” refers to the types of effects that have occurred (or are predicted 

to occur), and the magnitude of the measured or predicted effects, the statistical significance of 

measured effects, and the ecological significance of the effects. “Spatial and temporal scale” 

refers to the size and location of the area within which an effect occurs, and the duration of the 

period required for the effect to appear. “Potential for recovery” refers to the expected rate and 

extent of return of an affected population or community following elimination of the stressor 

responsible for an effect that has been determined to be ecologically significant. 

USEPA’s definition and criteria for determining ecological adversity are consistent both 

with accepted principles of fishery management and with the current scientific understanding of 

the potential effects of harvesting on fish populations and communities. As noted in the 

introduction to this Section, in the context of §316(b) and §704.5, the ecological entities of 

interest are the populations and communities potentially affected by entrainment at CWIS. A 

definition of AEI of CWIS consistent with the Guidelines for Ecological Risk Assessment 

(USEPA 1998) should be expressed in terms of undesirable alterations in the structural or 

functional attributes of these populations and communities. An assessment whether adverse 
 11 

impacts have occurred (or will occur) should address the three criteria provided in the 

Guidelines. 

2.1.3 Definition of adverse environmental impact in the context of entrainment and 

impingement

The definition of sustainable use in the Magnuson-Stevens Act and the definition of 

ecological adversity in USEPA’s Guidelines for Ecological Risk Assessment provide a reasoned 

basis for a definition of AEI applicable to entrainment and impingement at CWIS. A sustainable 

approach to managing a fishery would ensure the long-term persistence and productivity of the 

population being managed. A non-sustainable approach, in contrast, would cause harvest-related 

reductions in abundance, changes in age/size structure, increases in mortality, or reductions in 

reproduction that could threaten the capacity of a population to persist, perform its normal 

ecological function, and support normal human uses. Since the ecological function of a 

population is understood by scientists to include interactions with other populations, nonsustainable use of a population can affect an entire community. 

Abundance, age/size structure, mortality, and reproduction are examples of the 

“structural and functional attributes” discussed in the USEPA Guidelines. Hence, nonsustainable management of a fishery would be an example of an AEI according to USEPA’s 

definition. Entrainment mortality differs from mortality caused by harvesting only in that the 

mortality is imposed on early life stages of fish or shellfish rather than on adults. Excessive 

levels of entrainment mortality could potentially affect most of the same structural and functional 

attributes affected by harvesting. 

In sum, the term AEI, as it relates to entrainment and impingement, is reasonably and 

appropriately defined as follows: 

An adverse environmental impact due to entrainment and impingement consists of 

adverse changes in important population or community characteristics sufficient 

to threaten the sustainability of susceptible populations or to cause significant or 

potentially irreversible changes in population or community structure and 

function. 

Such a definition would be consistent with recognized principles of both natural resource 

management and ecological risk assessment, as discussed above. 
 12 

2.2 Why entrainment losses alone are insufficient to demonstrate AEI 

Context is essential to understanding what the term AEI reasonably may mean with 

respect to fisheries biology. As a matter of science and logic, losses, even large numbers of early 

life stage individuals do not necessarily equate to AEI. This is because fish species inhabiting 

the Hudson River exhibit either “periodic” or “opportunistic” life history traits (Winemiller and 

Rose 1992). From an ecological perspective, periodic fish species are characterized by high 

fecundity (i.e., they spawn a large number of eggs), large size, and long life spans during which a 

female fish may spawn many times (Winemiller and Rose 1992). Striped bass is an example of 

a periodic species (Winemiller and Rose 1992). Opportunistic species are characterized by small 

body size, short life spans, and the ability to disperse offspring widely throughout the 

environment (Winemiller and Rose 1992). Bay anchovy is an example of an opportunistic 

species. Periodic and opportunistic traits are advantageous to fish species that live in unstable or 

unpredictable environments, such as the Hudson River, which experiences significant withinyear and between-year variation in environmental conditions (e.g., temperature, salinity, 

freshwater flow, etc.). In other words, the reproductive strategies of these fish in these unstable 

conditions, including the very large numbers of eggs produced, ensure that sufficient offspring 

will survive to sustain the populations, even in unstable environments characterized by the 

presence of multiple stressors. 

Entrainment losses consist mainly of eggs and larvae. Only a small fraction of the 

entrained fish would survive to adulthood, even if IP2 and IP3 did not exist. For example, an 18-

year-old Hudson River striped bass was found to contain more than 3 million eggs (Hoff et al. 

1988). A 16-year-old female striped bass examined by Olsen and Rulifson (1992) was found to 

contain nearly 5 million eggs. Since striped bass can live for up to 30 years (Secor and Piccoli 

1996), a single fish could potentially spawn tens of millions of eggs over her entire lifespan. 

According to early life stage survival estimates developed by Secor and Houde (1995), more 

than 99.99% of young striped bass eggs die from natural causes within 60 days following 

spawning. Less than one striped bass egg in 100,000 is likely to survive to become a one-yearold fish, and less than one in a million is likely to survive to reach six years of age, the median 

age at which female striped bass become sexually mature (EPRI 2005). 
 13 

Because nearly all of the eggs and larvae entrained at IP2 and IP3 would have died in any 

case, counts of total numbers entrained reveal nothing meaningful about the potential impact of 

IP2 and IP3 on fish populations. What matters is whether or not entrainment significantly 

reduces the number of fish that survive the early period of high natural mortality. As discussed 

in the next sections, this fact was recognized more than 30 years ago by the scientists who 

performed the first entrainment impact assessments for IP2 and IP3, in conjunction with other 

Hudson River generating stations. 

 

2.3 Role of the conditional mortality rate (CMR) in impact assessment 

The first assessments of the effects of cooling-water withdrawals on Hudson River fish 

populations, conducted on behalf of the Consolidated Edison Company of New York and various 

federal regulatory agencies were based on mathematical models that predicted the potential 

effects of entrainment losses on the abundance and other characteristics of fish populations, 

especially striped bass (Barnthouse et al. 1984). Many of these models were developed to 

support U.S. Atomic Energy Commission licensing proceedings for IP2 and IP3, and were 

incorporated in environmental impact statements prepared to support these proceedings 

(Barnthouse et al. 1984). At the time they were first developed, in the early and mid-1970s, 

modeling was undertaken because no actual fisheries data were available to test whether coolingwater withdrawals would have adverse impacts on important fish populations. When data from 

riverwide ichthyoplankton sampling became available in the late 1970s, scientists studying 

entrainment impacts developed an empirical model, termed the Empirical Transport Model 

(“ETM”, Boreman et al. 1981), and used it to estimate the impact of entrainment on the 

abundance of juvenile fish. The metric calculated using the ETM, which was termed the 

“conditional mortality rate” (“CMR”), provides an estimate of the fraction by which the 

abundance of young-of-the-year fish is reduced due to entrainment. A similar model, termed the 

Empirical Impingement Model (“EIM”, Barnthouse and Van Winkle 1988), was used to estimate 

a CMR for impingement. 

It was recognized at the time that the CMR could not be used to predict long-term 

impacts on populations, however, because neither the ETM, nor the EIM, accounts for the 

density-dependent processes that can partially offset mortality due to entrainment and 
 14 

impingement (Barnthouse et al. 1984). CMRs could, however, be used to compare the relative 

potential effectiveness of alternative technologies intended to reduce entrainment and 

impingement mortality. As discussed by Englert et al. (1988), CMRs calculated using the ETM 

also were used to develop the cross-plant outage credits that were included in the Hudson River 

Settlement Agreement (“HRSA”). CMRs were also used in the DEIS to compare alternative 

entrainment mitigation approaches. In all of these applications, CMRs were used usefully as 

measures of mortality caused by entrainment and impingement, not as measures of the impacts 

of that mortality on the long-term abundance or sustainability of susceptible populations. 

Because it does not account for density-dependent effects, the CMR is not a valid 

measure of long-term entrainment impacts. Depending on the strength of density-dependence in 

a given population, a particular CMR value corresponds to either a negligible or a substantial 

impact on the sustainability of a population.4

 CMRs can, however, be used as a measure of the 

annual rate of mortality imposed by entrainment and as inputs to assessment models that estimate 

the combined impacts of entrainment mortality and fishing mortality on the sustainability of 

populations (Goodyear 1977, 1993). For this assessment, CMRs are used for both of these 

purposes. They are not, however, used as measures of AEI, because CMRs are not appropriately 

used in that fashion and superior methods for assessing adverse impacts are available. As 

discussed in the following sections, analysis of long-term trends in the abundance of important 

Hudson River fish populations, available from 30 years of intensive data collection, is the best 

method available for assessing impacts of IP2 and IP3 on Hudson River fish populations. The 

trends analysis is supplemented by an analysis of the impacts of IP2 and IP3 on the sustainability 

of the Hudson River striped bass and American shad populations, using the SSBPR model. 

2.4 Role of long-term datasets in impact assessment 

Today, nearly 30 years of data are available from both generator and agency-sponsored 

monitoring programs. Together, these overlapping datasets provide information concerning 

long-term trends in the abundance and distribution of eggs, larvae, and juveniles of all of the 

species addressed in this report. For some commercially harvested species, data on long-term 

 

4

 Although there can be substantial uncertainty concerning the strength of density-dependence in specific 

populations, there is strong theoretical and empirical evidence that the great majority of biological populations, 

including fish populations, are regulated in part by density-dependent mechanisms (Murdoch 1994, Turchin 

1999, Rose et al. 2001, Brook and Bradshaw 2006). 
 15 

trends in the abundance, age distribution, and mortality of adult fish are available. These 

datasets can be used both to assess trends in the status of important fish populations and to test 

alternative hypotheses concerning potential causes of adverse changes. 

In this report, information concerning long-term trends on key population characteristics 

and on the intensities of potential stressors is used to test specific hypotheses concerning the 

expected impacts of cooling-water withdrawals, termed “risk hypotheses” in USEPA’s 

Guidelines for Ecological Risk Assessment (USEPA 1998). These hypothesis tests are used to 

distinguish changes that could have been caused by cooling-water withdrawals from changes that 

are most likely related to other causes. 

The following generator-sponsored long-term datasets are the primary datasets used in 

assessing the effects of the CWIS at IP2 and IP3: 

• Longitudinal River Ichthyoplankton Survey (“LRS”). This program 

samples eggs, larvae, and juvenile fish, weekly from April through July. 

The region between the George Washington Bridge and the Federal Dam 

at Troy ( Figure 1) has been sampled with only minor changes in 

methodology since 1974. In 1988, the LRS was extended to sample the 

region between the Battery and the George Washington Bridge. 

• Beach Seine Survey (“BSS”). This program samples juvenile fish, also 

called “young-of-the-year” fish (“YOY”) (i.e., fish spawned earlier in the 

year) on alternate weeks from June through October. Sampling is 

conducted from the George Washington Bridge to the Federal Dam. The 

BSS has been conducted annually with only minor changes in 

methodology since 1974. 

• Fall Shoals Survey (“FSS”). This program samples YOY and older fish 

in offshore habitats, on alternate weeks from the BSS. Approximately 200 

samples are collected per week, from Manhattan to the Federal Dam. The 

FSS uses two different gears in order to sample as much of the Hudson 

River as possible: a 1-m2

 Tucker trawl and a 3-m beam trawl. This 
 16 

program was also initiated in 1974, however, the beam trawl was not used 

until 1985. From 1974 through 1984 an epibenthic sled was used to 

sample near the river bottom. To ensure comparability between years, 

only the data collected from 1985 onward are used in this assessment. 

• Atlantic Tomcod Mark-Recapture Program. This program has been 

conducted in most years since 1974 to generate estimates of the number of 

tomcod in the winter spawning population.5

 Box traps and bottom trawls 

are used to collect fish for marking and recapture. 

The above datasets were selected as the primary datasets for this assessment because they 

have been conducted continuously since the mid-1970s. They cover nearly all of the period of 

commercial operation of IP2 (1973 startup) and all of the period of commercial operation of IP3 

(1976 startup). These four datasets provide the most comprehensive and consistent estimates of 

long-term trends in the abundance of multiple life stages of important Hudson River fish 

populations. More detailed descriptions of these datasets are provided in ASA (2007). 

A variety of other programs, conducted by the generators, NYSDEC, and federal resource 

management agencies provide information that can be used to test the validity of the primary 

trends data. These programs include: 

• Striped Bass Mark-Recapture Program. This program was initiated in 

1984, to estimate the contribution of the Hudson River striped bass 

hatchery (established as a condition of the HRSA) to the Hudson River 

population. The program targets 1-year-old and 2-year-old striped bass, 

and is conducted from November through March. Data from this program 

are used to estimate the numbers of striped bass greater than 150 mm in 

length overwintering in the lower estuary. Growth and survival rate 

estimates are also obtained from this program. 

 

5

 The program was not conducted in 1984 and 1986. 
 17 

• NYSDEC Beach Seine Survey. Since 1976, the NYSDEC Division of 

Marine Resources has conducted a beach seine survey in the lower 

Hudson River estuary. The program focuses on the Tappan Zee and 

Haverstraw Bay. It samples juvenile fish using a method similar, but not 

identical to, the generators’ BSS. 

• Juvenile Alosid Survey. NYSDEC conducts a beach seine survey in the 

middle and upper regions of the estuary (above River Mile 55) to estimate 

the relative abundance of YOY American shad and other juvenile fishes. 

This program was initiated in 1980 and continues to the present. 

• Western Long Island Survey. NYSDEC conducts a survey for subadult 

striped bass in the bays around western Long Island Sound. Sampling is 

conducted using a 200-ft. beach seine. The program was initiated in 1984 

and is continuing, although it has been modified over time. 

• Spawning Stock Assessment. NYSDEC conducts a haul seine survey in 

the Hudson River to provide information on length, age and sex 

distribution, and mortality rates for adult American shad and striped bass. 

The program was initiated in 1982 and continues to the present. 

• Commercial Fishery Monitoring. NYSDEC monitors the commercial gill 

net fishery for American shad. The objective of the program is to 

determine the relative abundance and age structure of the commercial 

catch of American shad. 

As shown in Appendix A, indices derived from these datasets are strongly correlated with 

indices derived from the primary datasets. These correlations support the use of the primary 

datasets in this assessment. 

In addition to the Hudson River monitoring programs, information on population status 

and trends for important fish species is also available from the National Marine Fisheries Service 
 18 

(“NMFS”) and the Atlantic States Marine Fisheries Commission (“ASMFC”). Quantitative 

stock assessments, which include estimates of age structure, natural mortality, and fishing 

mortality, are available for striped bass (ASMFC 2005) and American shad (ASMFC 2007a). 

These assessments provide additional information for determining whether these populations 

have been harmed by CWIS. 

2.5 Indicators of adverse impacts potentially related to CWIS 

As discussed above, an adverse impact of CWIS would consist of entrainment and 

impingement-related adverse changes in important population or community characteristics 

sufficient to threaten the sustainability of relevant populations, or to cause significant or 

potentially irreversible changes in community structure and function. Characteristics that 

influence the sustainability of a fish population include the total size of the population, the 

relative abundances of different life stages or age groups, the sizes and reproductive rates of the 

individual fish, and the rates of mortality of fish at different life stages or ages. Measures of any 

of these population characteristics could, at least in principle, be used as indicators of adverse 

impact. Some of these measures are not suitable as indicators of adverse impacts potentially 

caused by CWIS, however, because they measure changes that cannot be reasonably attributed to 

cooling-water withdrawals. For example, a reduction in fecundity could be an indicator of a 

potential impact caused by a toxic chemical but, because impingement and entrainment do not 

affect fecundity, this characteristic is not an appropriate indicator of impacts caused by CWIS. 

Similarly, some indicators of impact are not particularly useful in narrowing the potential causes 

of impacts. For example, a prolonged downward trend in the abundance of adult fish could be 

the result of any number of causes, including over-fishing or environmental factors. 

CWIS may impose mortality on early life stages of fish (i.e., eggs, larvae, and YOY) in 

addition to the mortality that would have occurred naturally. Therefore, characteristics that are 

either directly or indirectly affected by increased mortality of these life stages are potentially 

useful as indicators of harm related to CWIS. Increased mortality imposed on a particular life 

stage would reduce the fraction of organisms in that stage that survive to the next stage. 

Accordingly, this assessment focuses on whether CWIS have had a measurable influence on the 

survival of early life stages of fish in the Hudson River. 
 19 

As discussed in Section 2.1 of this report, however, mortality of early life stages as a 

result of CWIS is insufficient, of itself, to establish that an adverse impact has occurred. It is 

necessary, in addition, to evaluate whether the magnitude, spatial extent, and duration of this 

mortality are large enough to constitute an adverse impact (USEPA 1998). Fisheries scientists 

have developed metrics, termed “biological reference points,” for determining whether harvested 

fish populations are being harmed by over-fishing (Restrepo et al. 1998). These reference points, 

expressed in terms of either the total spawning stock biomass (“SSB”) or the SSBPR, are viewed 

as indicators of the risk that over-fishing will lead to future declines in abundance and harvest. 

The methods that fisheries scientists use to estimate effects of fishing mortality on SSB and 

SSBPR can also be used to estimate impacts of entrainment-related mortality on SSB and 

SSBPR (Goodyear 1993). Hence, the indicators used to determine whether fish populations are 

being adversely affected by fishing can also be used as indicators of whether these same 

populations are being adversely affected by cooling-water withdrawals. Accordingly, for species 

for which published agency stock assessment reports provide relevant information, this 

assessment addresses whether the magnitude of entrainment mortality (as measured using the 

CMR) is sufficient to produce an ecologically significant reduction in SSB or SSBPR. 

Information needed to estimate SSBPR is available for both striped bass and American 

shad. A coastwide SSB estimate is available for striped bass. 

The following indicators have been selected for this assessment: 

1. Long-term declines in the abundance of YOY fish belonging to species 

with life stages susceptible to impingement and entrainment, see, infra, 

Section 3; 

2. Reductions in the spawning potential of female fish below the sustainable 

level as estimated using the SSBPR approach, see, infra, Section 4; and 

3. Long-term trends in the abundance of species with high susceptibility to 

entrainment at IP2 and IP3 as compared to species with low susceptibility 

to entrainment at IP2 and IP3, see, infra, Section 5. 
 20 

The analyses documented in Sections 3, 4, and 5 of this report evaluate whether any such 

declines or reductions in spawning potential have occurred and, if so, whether they may 

reasonably be attributed to the CWIS of IP2 and IP3. 

3. Evaluation of changes in abundance of fish populations with life stages 

susceptible to entrainment 

In complex ecological systems, such as the Hudson River estuary, fish populations are 

influenced by many factors in addition to CWIS, including water quality impairment, 

introductions of non-native species, and overfishing (Pew Oceans Commission 2003). Many of 

these factors are discussed in the preamble to USEPA’s Final Phase II Existing Facilities Rule. 

69 Fed. Reg. 41575, 41588 (July 9, 2004). For this reason, investigations of the causes of 

changes in fish populations must consider multiple hypotheses, weighing the evidence for and 

against each hypothesis (Hilborn and Mangel 1997, Suter et al. 2007). This approach has been 

termed “ecological detection” by Hilborn and Mangel (1997) and “ecoepidemiology” by Suter et 

al. (2007). 

Most environmental factors affecting Hudson River fish populations vary in intensity 

over time. Knowledge of these variations can be used to predict the change in each metric that 

should have occurred, if that stressor had been affecting a particular fish population. To test each 

hypothesis, this analysis utilizes rules for evaluating causal associations provided by Suter et al. 

(2007, p. 50). These authors identified five criteria that should guide analyses of potential causes 

of adverse environmental effects: 

1. Co-occurrence: An effect occurs where and when its cause occurs and 

does not occur in the absence of its cause. 

2. Sufficiency: The intensity or frequency of a cause should be adequate to 

produce the observed magnitude of effect. 

3. Temporality: A cause must precede its effect. 

4. Manipulation: Changing the cause must change its effect. 

5. Coherence: The relationship between a cause and effect must be consistent 

with scientific knowledge and theory. 

 
 21 

Evaluations of co-occurrence discussed in this sections rely on a commonly-used and 

relatively straightforward statistical method known as correlation analysis (Clarke and Kempson 

1997). In simple terms, correlation is a measure of whether two different variables are related to 

one another and, if so, how strong that relationship is (Clarke and Kempson 1997). A positive 

correlation between two variables indicates that as the value of one variable increases, so does 

the other. For example, height and weight among people are positively correlated. Although 

some taller people weigh less than shorter people, on average the taller a person is, the more that 

person is likely to weigh. Conversely, a negative correlation indicates that, as the value of one 

variable increases, the other decreases (Clarke and Kempson 1997). For example, weight and 

fuel efficiency among automobiles are negatively correlated. Although some heavier cars get 

better gas mileage than some lighter cars, on average the heavier a car is, the lower its gas 

mileage will be. 

 The existence and strength of correlations between stressor metrics and response metrics 

provides evidence concerning the co-occurrence criterion. If, for example, entrainment mortality 

at IP2 and IP3 is reducing the survival of eggs and larvae of a particular fish species, then there 

should be a negative correlation between entrainment mortality and a measure of the fraction of 

eggs and larvae that survive to reach older life stages. This means that in years when mortality 

due to IP2 and IP3 is high, survival should be relatively low, and in years when mortality due to 

IP2 and IP3 is low, survival should be high. Data showing the presence of a negative correlation 

between early life stage survival and IP2 and IP3-related mortality would constitute evidence 

supporting this impact hypothesis; data showing the absence of a correlation would constitute 

evidence against this hypothesis. 

 Evaluations of sufficiency in this assessment rely on measures of the magnitude of the 

stressor, as compared to the magnitude required to cause the observed response. For example, 

the rate of fishing mortality imposed on the striped bass and American shad populations can be 

compared to overfishing thresholds established by the ASMFC. 

Evaluations of temporality in this assessment rely on time trends of the various stressor 

and response metrics. For any stressor to be a potential cause of a decline in the survival or 

abundance of a fish population, the decline should be preceded by an increase in the intensity of 

the stressor. If the decline in survival or abundance precedes the increase in the stressor, then the 

stressor cannot have caused the decline. 
 22 

Evaluations of manipulation in this assessment rely on observations of responses of 

populations to deliberate changes in the magnitudes of stressors, e.g., the harvesting restrictions 

imposed on the striped bass fishery in the 1980s. 

Evaluations of coherence in this assessment rely on the consistency of the responses with 

all relevant scientific information. 

 Because the focus of the permit proceedings is on entrainment and impingement of age 0 

fish, the analysis will focus primarily on age 0 response metrics. The steps in the analysis 

include: 

1. Develop a conceptual model of each stressor, including (1) a description 

of the stressor itself, (2) the reasonably expected causal mechanisms 

through which fish populations would be affected, (3) the species that 

would likely be affected, (4) the life stages (e.g., juveniles) that would 

likely be affected, (5) the life history characteristics (e.g., survival and 

growth) that would likely be affected, and (6) the type of measurable 

effects that would likely occur (increase or decrease); 

2. Identify appropriate sets of “stressor metrics” and “response metrics” that 

can be used to test the potential influence of the various stressors; 

3. Summarize the expected effect of the stressor on each response metric; 

4. Apply the five evaluation criteria discussed above to the available data for 

each fish species; and 

5. Summarize conclusions regarding (1) whether changes in the response 

metrics could have been caused by entrainment by CWIS at IP2 or IP3, or 

(2) whether other stressors are more likely to be responsible for these 

changes. 

3.1 Species addressed 

The DEIS assessed entrainment and impingement impacts on striped bass (Morone 

saxatilis), white perch (Morone Americana), Atlantic tomcod (Microgadus tomcod), bay 

anchovy (Anchoa mitchilli), American shad (Alosa sapidissima), alewife (Alosa 
 23 

pseudoharengus), blueback herring (Alosa aestivalis), and spottail shiner (Notropis hudsonius) 

(DEIS, Sections 5 and 6). This report assesses entrainment and impingement impacts on these 

same species, focusing on the most economically important species (striped bass) and on the 

three species (white perch, American shad, and Atlantic tomcod) identified in the draft permit 

fact sheet as being of potential concern with respect to IP2 and IP3. Fact Sheet, Draft SPDES 

Permit, Attachment B, at 1 of 8. The datasets used in these analyses are documented in the 2005 

Year Class Report (ASA 2007). The stressor and response metrics are documented in Appendix 

B. 

3.2 Impact hypotheses and stressor metrics 

This section documents expected effects of CWIS and four other stressors that are widely 

regarded as potentially having affected Hudson River fish populations: fishing, invasion of the 

Hudson River by zebra mussels (Dresseina polymorpha), temperature (Atlantic tomcod only) 

and predation by striped bass. 

3.2.1 CWIS

CWIS may cause mortality of fish due to entrainment and impingement. For most 

species, this mortality is largely limited to eggs, larvae, and YOY. Because most of the 

susceptible life stages are planktonic6 and are widely dispersed throughout the estuary due to 

tidal and nontidal flows, cooling-water withdrawals would not be expected to alter the spatial 

distributions of the affected species. In addition, the CWIS would not be expected to reduce the 

survival of fish that have grown through the most susceptible life stages, or to reduce fish growth 

rates at any life stage. 

As discussed in Section 2.3, the CMR is a direct estimate of the rate of mortality caused 

by entrainment and impingement, independent from natural mortality. Similar measures are used 

by fisheries scientists to estimate the rate of mortality imposed on adult fish by fishing. The 

CMR can have values ranging between 0.0 and 1.0. The higher the value of the CMR, the 

greater the mortality imposed on early life stages of fish. 

 

6

 Planktonic organisms are small organisms such as fish larvae that have limited swimming capabilities and are 

passively transported up and downriver with tidal currents. 
 24 

Expected effects of CWIS on the life stages potentially susceptible to entrainment and 

impingement (i.e., eggs, larvae, and YOY) are summarized in Figure 2. As shown in Figure 2, 

CWIS should affect the survival rates of the susceptible life stages, but should not affect the 

survival of stages that are not susceptible to entrainment or impingement. If entrainment or 

impingement were having a measurable impact on a fish population, then in years when the IP2 

and IP3 CMR is high, the survival rates of susceptible life stages of that species should be lower 

than in years when the IP2 and IP3 CMR is low. As a consequence, long-term trends in IP2 and 

IP3 CMR values for that species should be negatively correlated with long-term trends in the 

survival rates of susceptible life stages. 

Although entrainment would not affect the number of eggs spawned by females of 

susceptible species, it is still possible that entrainment could directly affect the abundance of 

early life stages. The reason for this is that the LRS is conducted during the period in which 

entrainment at IP2 and IP3 is occurring. Therefore, entrainment could affect the abundance 

estimates derived from LRS data. If entrainment at IP2 and IP3 is reducing early life stage 

abundance, then the IP2 and IP3 CMR values should also be negatively correlated with PYSL 

abundance estimates. 

3.2.2 Fishing

Fishing imposes mortality primarily on harvestable-sized7 fish.8 For managed Hudson 

River fish species (i.e., striped bass and American shad), harvesting is largely limited to age 1 

and older fish (ASMFC 1998, 2002). Fishing has predictable effects on the age distribution of 

adult fish and on the abundance (numbers and biomass) of the spawning stock (Dayton et al. 

2002). Measures of age distribution and spawning stock abundance are used by fisheries 

managers as indicators of fishing (Restrepo et al. 1998). Fishing reduces the total reproductive 

output of a fish population (Goodyear 1993). 

 The most appropriate estimate of stress due to fishing is the annual rate of fishing 

mortality (F) imposed on the population. Estimates of F for two of the species addressed in this 

analysis, striped bass and American shad, are available from the ASMFC. 

 

7

 Harvestable-size fish are fish that fall within the size range for which harvesting is permitted. 8

 Fish outside the permitted range are frequently caught by trawls and other fishing gear. Although they are 

returned to the ocean, substantial mortality may still occur. This mortality is termed “bycatch” mortality. 
 25 

 Expected effects of fishing on age 0 life stages are summarized in Figure 3. Overharvesting reduces the size of the adult population and necessarily the total number of eggs 

produced per year. The reduction in egg production would be expected to reduce the number of 

eggs surviving to become one-year-old fish. Fishing should not reduce the survival or growth 

rate of any age 0 life stage, however, because early life stages of fish are not susceptible to 

harvesting. 

3.2.3 Zebra mussels

Zebra mussels invaded the Hudson River in the early 1990s (Caraco et al. 1997). Zebra 

mussels form dense beds on the bottom of colonized water bodies. Because of their high 

filtering capacity, zebra mussels remove phytoplankton from the water column, thus reducing the 

food base that supports pelagic fish larvae, such as American shad, striped bass, and white perch 

(Strayer et al. 2004). Because less food is available to support fish species that feed in open 

water, the survival and growth of these species may decrease. The increased water clarity caused 

by zebra mussel filtration can result in improved growth of rooted vegetation. The survival and 

growth of species that inhabit vegetated areas may increase because of increased habitat 

availability (Strayer et al. 2004). Zebra mussels are limited to fresh water, and are not found in 

substantial numbers below approximately river kilometer (“RKM”) 100 in the Hudson River. 

For this reason, zebra mussels could potentially alter the spatial distributions of some species, 

reducing their abundance above RKM 100 as compared to below RKM 100. 

 There is no readily available quantitative metric for zebra mussel abundance. Due to the 

discontinuous nature of the zebra mussel invasion (absent prior to 1992; highly abundant after 

1992), however, the qualitative evaluation can use presence/absence to develop predicted effects, 

and the quantitative analysis can use a simple index to distinguish between these two periods 

(e.g., “0” for all years prior to 1993 and “1” for 1993 and later). Expected effects of zebra 

mussels on age 0 life stages are summarized in Figure 4. Zebra mussels would be expected to 

reduce the survival and growth rates of post yolk-sac larvae and YOY utilizing freshwater 

regions of the Hudson River. These changes in survival and growth could result in a shift in the 

relative abundance of YOY present in predominantly freshwater regions (Regions 6-12; Figure 

1) as compared to marine and brackish regions (Regions 0-5; Figure 1). Specifically, if zebra 
 26 

mussel activity reduces the growth and survival of pelagic fish species in freshwater regions as 

compared to marine and brackish regions, then during the post-invasion period a greater fraction 

of the populations of pelagic species, such as striped bass, white perch, alewife, and river 

herring, should be found in marine and brackish regions than during the pre-invasion period. 

3.2.4. Predation by striped bass

Increased abundance of yearling and older striped bass, which are piscivorous9

(Gardinder and Hoff 1982, Walter et al. 2003), could lead to increased predation mortality. 

Savoy and Crecco (2004) have attributed a recent decline in American shad and blueback herring 

populations in the Connecticut River to predation by large adult striped bass on spawning adults 

of these species. 

 Because the abundance of striped bass early life stages has been found to be strongly 

correlated with the relative abundance of adults (Pace et al. 1993; Barnthouse et al. 2003), 

estimates of striped bass larval abundance from the LRS can be used as a surrogate for adult 

striped bass abundance. 

Predation on adults would, like harvesting, reduce the number of spawning adults and, as 

a consequence, the number of eggs spawned. The reduction in egg production would be 

expected to reduce the number of eggs surviving to become one-year-old fish. Predation on 

YOY would directly reduce YOY abundance, over and above and reductions resulting from 

reduced egg production (Figure 5). 

3.2.5 Temperature

Changes in temperature can cause either increases or decreases in the growth and survival 

of affected species, depending on species-specific temperature tolerances. Long-term trends in 

Riverwide temperatures could potentially lead to long-term changes in the abundance of 

sensitive species, such as Atlantic tomcod (FEIS, pp. 65-66). Expected effects of elevated 

summer temperatures on age 0 temperature sensitive species are summarized in Figure 6. 

Elevated summer temperatures would be expected to cause decreases in survival and growth of 

temperature-sensitive species during this period. Growth and survival of early life stages would 

 

9

 Piscivorous fish are fish that eat other fish. 
 27 

not be depressed, however, because these life stages are present only during the winter and early 

spring, when temperatures would be well below adverse effects thresholds. 

According to McLaren et al. (1988), the growth of juvenile Atlantic tomcod in the 

Hudson River ceases during the summer when river temperatures regularly exceed 25 ̊C. The 

lethal temperature for juvenile Atlantic tomcod is 26.5 ̊C (McLaren et al. 1988). Temperature 

records available from the Poughkeepsie Water Works (PWW) were used to develop a degreeday index for evaluating the potential effects of elevated summer temperatures on Atlantic 

tomcod. A degree-day is defined as the number of degrees by which the temperature measured 

at the PWW on that day exceeds 24 ̊. If, for example, the temperature measured at the PWW on 

a given date was 27 ̊C, then the degree-day value for that date would be 3. If the temperature on 

a date is 24 ̊ or less, then the degree-day value for that date is recorded as 0. The degree-day 

index for a years is calculated by summing the degree-days for all days during that year. 

3.3 Response metrics 

Because not all data sets are suitable for evaluating all species, the response metrics used 

in this assessment are not the same for all species. 

3.3.1 Response metrics for striped bass, white perch, American shad, alewife, blueback 

herring, and bay anchovy

For species other than spottail shiner and Atlantic tomcod, the LRS and BSS provide the 

most reliable data concerning survival, growth, and spatial distribution. Because the durations of 

egg and YSL life stages are comparatively short, such that individuals can hatch and develop 

through one or both of these stages between survey dates, most of the fish captures in the LRS 

are PYSL. The PYSL stage is typically much longer, so that PYSL are susceptible to sampling 

for at least one and possibly two or more survey dates. For these reasons, estimates of total 

larval abundance from the LRS are best interpreted as estimates of the abundance of PYSL. 

Although the beach seine used in the BSS and the beam trawl used in the FSS do not capture 

larvae, they effectively sample YOY fish present in the sampled habitats (shore zone for the BSS 

and shoal zone for the FSS). The response variables that can be calculated from the generators’ 

survey data are: 
 28 

1. Abundance of PYSL, as measured in the LRS; 

2. Survival from the PYSL to the YOY stage, as measured by the ratio of 

densities of larvae in the LRS dataset to densities of juveniles in the BSS 

or FSS, 

3. Abundance of YOY, as measured in the BSS or FSS; 

4. YOY growth, as measured by the average length of YOY fish from the 

BSS or FSS; and 

5. Spatial distribution of PYSL and YOY relative to river regions with high 

zebra mussel densities, as measured by the per cent of the total population 

occurring downriver from RKM 100. 

3.3.2 Response metrics for spottail shiner

Because the LRS does not adequately sample areas of the Hudson River inhabited by spottail 

shiner, for this species, no estimates of egg and larval abundance are available. However, the 

BSS provides estimates of both YOY abundance and adult abundance (age 1 and 2 adults) for 

this species. For the purpose of trends analysis, adult abundance is used as a surrogate for egg 

production. 

3.3.3 Response metrics for Atlantic tomcod

Because a substantial fraction of Atlantic tomcod larvae and YOY occur downriver from 

the regions sampled by the generators’ surveys, for Atlantic tomcod, the data provided by the 

Atlantic tomcod mark-recapture program should be more reliable than the LRS, BSS, or FSS 

data for estimating survival rates. The mark-recapture program provides annual estimates of age1 abundance, spawning stock size, and total egg production that can be used to calculate the 

fraction of eggs produced during a given year that survive to become age-1 spawners the 

following year. The LRS data can be used to characterize both year-to-year variations in early 

life stage abundance and the distribution of Atlantic tomcod larvae and juveniles within the 

Hudson River. 
 29 

For this species, the response variables include: 

1. Abundance of PYSL and early juveniles, as estimated from the LRS; 

2. Abundance of Age-1 and Age-2 fish, as estimated from the markrecapture program; 

3. Total age 0 survival, as measured by the ratio of total egg production each 

year to age 1 abundance during the following year; 

4. Juvenile growth, as measured from growth rates of juveniles from the 

FSS; and 

5. Spatial distribution of PYSL and early juveniles, as measured by the 

fraction of the total PYSL/juvenile population found in river regions 1-5 

(LRS dataset). 

3.4 Tests of impact hypotheses 

 The predicted impacts of the stressors on the response metrics are summarized below and 

in Tables 1 (striped bass, white perch, American shad, river herring, bay anchovy, and spottail 

shiner) and 2 (Atlantic tomcod): 

 

• CWIS: Entrainment at IP2 and IP3 would be expected to reduce survival 

from the PYSL to the YOY stage, and could also reduce the abundance of 

PYSL. Entrainment should have no effect on growth or spatial 

distribution. 

• Fishing: Fishing would be expected to reduce the abundance of eggs and 

early larvae because of reduced spawner abundance, but should not reduce 

the survival of any age 0 life stage. 

• Zebra mussels: Zebra mussel activity would be expected to decrease both 

PYSL survival and YOY growth, and also to shift the spatial distribution 

of juveniles toward the lower regions and away from the freshwater 

regions where zebra mussels are abundant. 
 30 

• Temperature: Since Atlantic tomcod are known to be sensitive to high 

summer water temperatures, increased summer temperatures would be 

expected to decrease the growth and survival rates of life stages of this 

species that are present in the Hudson during this season. 

• Striped bass predation: Predation by older striped bass would be 

expected to decrease juvenile abundance, if the juveniles are susceptible to 

predation, and early life stage abundance, if adults are susceptible to 

predation. 

Appendix B documents the stressor and response metrics and statistical methods used in 

this analysis. The subsections below present the results of the analyses performed for each 

species, and evaluate the consistency of these results with the impact hypotheses. 

3.4.1 Striped bass

Figure 7a depicts long-term trends in the abundance of striped bass PYSL and YOY in 

the Hudson. Figure 7b depicts long-term trends in striped bass PYSL to YOY survival. The 

abundance of juvenile striped bass in the Hudson has shown no trend, even though the 

abundance of striped bass early life stages has greatly increased. The increase in abundance of 

striped bass larvae has occurred concurrently with an increase in the abundance of the Hudson 

River spawning stock of striped bass (Barnthouse et al. 2003). The increase in spawning size has 

been attributed to coastwide restrictions on harvesting that were imposed to promote the 

recovery of the Chesapeake Bay striped bass stock (Young-Dubovsky et al. 1995). As first noted 

by Pace et al. (1993), and later confirmed by Barnthouse et al. (2003), there is no correlation 

between the abundance of striped bass PYSL and striped bass YOY (Figure 8a). There is a 

strong negative relationship between PYSL abundance and PYSL survival, however (Figure 8b). 

This negative correlation has been interpreted by both Pace et al. (1993) and Barnthouse et al. 

(2003) as evidence for density-dependent mortality of striped bass larvae. This densitydependent mortality is reflected in the long-term trend in PYSL to YOY survival (Figure 7b), 

which has declined through time as the size of the spawning population has increased. 
 31 

3.4.1.1 CWIS 

Co-occurrence

Appendix B (Tables B-11 and B-12) summarizes the results of the correlation analysis 

for striped bass. If entrainment at IP2 and IP3 were reducing the survival or abundance of early 

life stages of striped bass, then there should be a negative correlation between the CMR and 

striped bass PYSL survival, PYSL abundance, or both. However, as shown in Figure 9, there is 

no correlation between the IP2 and IP3 CMR and either PYSL survival (Figure 9a) or PYSL 

abundance (Figure 9b) for striped bass. Hence, the CWIS hypothesis fails the co-occurrence 

criterion for striped bass. 

Sufficiency

There are no independent measures of sufficiency that can be applied to this hypothesis. 

The objective of this report is to determine, using all available and relevant evidence whether the 

magnitude of entrainment and impingement at Indian Point have been sufficient to cause a 

reduction in the abundance of important Hudson River fish species. Hence, the sufficiency 

criterion is inapplicable to the CWIS hypothesis. 

Temporality

If entrainment at IP2 and IP3 were reducing the survival or abundance of early life stages 

of striped bass, then a decline in PYSL survival, or PYSL abundance should have occurred after 

the startup of commercial operations of IP2 (1974) and IP3 (1976). However, as shown in 

Figure 7, no such declines occurred. PYSL abundance was relatively stable until 1985, and then 

rapidly increased. Striped bass PYSL survival has declined over time (Figure 7b), but the 

decline did not begin until several years after the startup of IP2 and IP3. Hence, the CWIS 

hypothesis fails the temporality criterion for striped bass. 

Manipulation

No experimental manipulations of plant operations have been performed for the purpose 

of evaluating entrainment impacts on fish populations. However, outages, including refueling 

and maintenance outages mandated by the HRSA (Englert et al. 1988), have frequently occurred 
 32 

during the months when entrainable striped bass are present in the River. The peak abundance of 

striped bass eggs and larvae typically occurs during May and June (Boreman and Klauda, 1988). 

IP2 was offline during the entire months of May and June in 1976, 1989, 1991, 1997, 1998, and 

2000. IP3 was offline during the entire months of May and June in 1975, 1982, 1993, and 1994. 

If entrainment at Indian Point were reducing the survival of striped bass PYSL, then PYSL 

survival should have been higher in years when one unit was offline than in years when both 

units were operating. As shown in Figure 10a, the measured PYSL survival values are 

inconsistent with this expectation. Figure 10a shows the time series of annual PYSL survival 

indices from 1975 through 2002. The horizontal line in Figure 10a shows the median survival 

index value for this time period. The median is defined as the midpoint of the entire distribution 

of survival index values, meaning that one-half of the survival indices are above the median and 

one-half are below the median. If striped bass PYSL survival were higher in years of one-unit 

operation than in years of 2-unit operation, then significantly more survival index values for 

years of one-year operation should be higher than the median than lower than the median. 

However, Figure 10a shows that the PYSL survival index was higher than the median for only 3 

of the 11 years of one-unit operation. The PYSL index was lower than the median in 8 years of 

one-unit operation. 

This result is confirmed by Figure 10b, which shows the relationship between the striped 

bass PYSL survival index and the May-June total water withdrawals by IP2 and IP3 for the years 

1975-2002. There is no correlation between withdrawals by IP2 and IP3 and striped bass PYSL 

survival. Hence, the CWIS hypothesis fails the manipulation criterion for striped bass. 

Coherence

As noted above, the objective of this report is to determine, using all available and 

relevant evidence whether the magnitude of entrainment and impingement at Indian Point have 

been sufficient to cause a reduction in the abundance of important Hudson River fish species. 

Including “coherence” as an explicit evaluation criterion for CWIS would be redundant. Hence, 

the coherence criterion is inapplicable to the CWIS hypothesis. 
 33 

3.4.1.2 Fishing 

Co-occurrence

Fishing indirectly affects the abundance of early life stages of fish by reducing the 

abundance of spawning adults (Goodyear 1993). If a population is being overfished, then 

reducing the rate of fishing should cause the spawning population, and therefore the number of 

eggs spawned, to increase. As discussed by Young-Dubovsky et al. (1994), a coastwide ban on 

harvesting of striped bass was imposed in 1986. Estimates of fishing mortality and adult 

population abundance developed by the ASMFC (2005) show that the coastwide adult 

population has increased greatly since 1986. As shown in Figure 7a, the abundance of striped 

bass PYSL began increasing in 1988 and increased steadily throughout the 1990s. This is the 

same period during which the adult striped bass population was expanding. Hence, the 

overfishing hypothesis satisfies the co-occurrence criterion. 

Sufficiency

Fishing mortality estimates for individual striped bass spawning stocks are not estimated 

by the ASMFC, because much of the fishing occurs along the Atlantic coast when fish from the 

individual spawning stocks are mixed (ASMFC 2003). Since the magnitude of fishing mortality 

imposed specifically on Hudson River striped bass has never been estimated, it is not possible to 

determine whether the fishing hypothesis satisfies the sufficiency criterion. 

Temporality

The ban on striped bass harvesting preceded the increase in abundance of striped bass 

PYSL in the Hudson River by approximately 2 years. Hence, the fishing hypothesis satisfies the 

temporality criterion. 

Manipulation

The 1986 ban on striped bass harvesting was described by Young-Dubovsky et al. (1996) 

as an “adaptive management experiment.” In other words, fishing was deliberately reduced in 

order to observe the response of the striped bass population to reduced harvesting. The fact that 

the adult population of striped bass began to increase immediately following the ban was 
 34 

interpreted by Young-Dubovsky et al. (1994) as strong evidence that overfishing was, if not the 

only cause, at least the primary cause of the depressed abundance of Atlantic striped bass prior to 

the ban. Because the response of the population to this management was consistent with the 

expectations from the fishing hypothesis, the fishing hypothesis satisfies the manipulation 

criterion. 

Coherence

Atlantic striped bass are managed as a single coastwide fishery because a large fraction of 

the harvest occurs when fish originating in Chesapeake Bay, the Delaware River, and the Hudson 

River are mixed and migrating along the Atlantic coast (ASMFC 2003, Waldman et al. 1990, 

Waldman and Fabrizio 1994). If reduced harvesting had been the cause of increases in the 

abundance of early life stages of striped bass in the Hudson River, then similar increases should 

have occurred in the Chesapeake Bay and the Delaware River as well. As shown in the 

ASMFC’s 2003 stock assessment, the abundance of juvenile striped bass in both Chesapeake 

Bay and the Delaware River grew rapidly after the harvest ban. Hence, the overfishing 

hypothesis is consistent with the coherence criterion. 

3.4.1.3 Zebra mussels 

Co-occurrence

As documented in Appendix B (Table B-11), the zebra mussel index is negatively 

correlated with the striped bass PYSL survival index. This correlation is consistent with the 

zebra mussel hypothesis. Hence, the zebra mussel hypothesis satisfies the co-occurrence 

criterion. 

Sufficiency

The potential effects of zebra mussel activity on early life stages of fish are indirect, and 

related to reductions in prey abundance and changes in habitat quality. No experiments have 

been performed that could quantify the relationship between zebra mussel activity and fish 

growth or survival, and no mathematical models that could be used to quantify the indirect 
 35 

effects of zebra mussel activity have been developed. Hence, whether or not the zebra mussel 

hypothesis satisfies the sufficiency criterion is unknown. 

Temporality 

Zebra mussels first became abundant in the Hudson River in 1992 (Caraco et al. 1997). 

However, as shown in Figure 7b, striped bass PYSL survival began declining in the 1980s and 

had already fallen to a very low level by 1990. Because the decline in striped bass PYSL 

survival preceded, rather than followed, the appearance of zebra mussels in the River, the zebra 

mussel hypothesis fails the temporality criterion. 

Manipulation

No deliberate manipulations of zebra mussel populations in the Hudson River have been 

performed, therefore, this criterion is inapplicable to the zebra mussel hypothesis. 

Coherence

Because the proposed mechanism through which zebra mussel activity could have 

affected striped bass in the Hudson River involves reducing food availability, the growth as well 

as the survival of striped bass PYSL and YOY should have been reduced. Although Strayer et 

al. (2004) found a negative relationship between the growth rate of YOY striped bass and the 

presence of zebra mussels, no significant correlation was found in the analyses performed to 

support this report (Appendix B, Table B-11). Zebra mussel activity should also have shifted the 

distribution of striped bass PYSL and YOY downriver, away from the freshwater zone in which 

zebra mussels are abundant. Strayer et al. (2004) found no downstream shift in the distribution 

of striped bass PYSL and YOY. In the analyses performed to support this report (Appendix B, 

Table B-11), no downstream shift in the distribution of PYSL was found, and an upstream shift 

(i.e., a shift in the opposite direction from the shift predicted by the zebra mussel hypothesis) in 

the distribution of YOY was found. The negative effect of zebra mussel activity on striped bass 

YOY growth that was reported by Strayer et al. (2004) conflicts with the findings in Appendix B, 

moreover, neither Strayer et al. (2004) nor the present analysis (Appendix B) found the predicted 

relationship between zebra mussel activity and striped bass PYSL and juvenile distribution. 

Hence, the zebra mussel hypothesis fails the coherence criterion for striped bass. 
 36 

3.4.1.4 Summary evaluation of hypotheses 

Table 3 summarizes the consistency of the striped bass trends data with the CWIS, 

overfishing, and zebra mussel hypotheses. Two of the five evaluation criteria – sufficiency and 

coherence – are inapplicable to the CWIS hypothesis. However, this hypothesis fails all three of 

the remaining criteria. Hence, the CWIS hypothesis can be rejected as an explanation for longterm trends in the abundance of age 0 striped bass in the Hudson River. The zebra mussel 

hypothesis passes the co-occurrence criterion, but fails the temporality and coherence criteria. 

Because striped bass PYSL survival declined several years prior to the invasion of the Hudson 

River by zebra mussels, and because predicted effects of zebra mussels on the growth and 

distribution of striped bass PYSL and YOY were not observed, the zebra mussel hypothesis also 

can be rejected as an explanation for long-term trends in the abundance of age 0 striped bass in 

the Hudson River. 

The overfishing hypothesis, in contrast, passes four of the five evaluation criteria. The 

remaining criterion (sufficiency) is inapplicable to this hypothesis. The abundance of striped 

bass PYSL in the Hudson began increasing shortly following a reduction in striped bass 

harvesting. The reduction in harvest was specifically intended to promote striped bass 

reproduction, and was followed by simultaneous increases in striped bass reproductive success in 

all three of the major east coast spawning populations. It is reasonable to conclude, therefore, 

that elimination of overfishing is the most likely cause of trends in the abundance of early life 

stages of striped bass in the Hudson River. 

3.4.2 White perch

Figure 11 depicts long-term trends in the abundance of white perch YOY and PYSL in 

the Hudson. As shown in Figure 11, the abundance of juvenile white perch declined steadily 

throughout the 1980s, but has increased since 1990. Despite the recent increase, over the entire 

time series, there is a statistically significant decline in YOY abundance (Appendix B, Table B13 and Figure B-4). There is no long-term trend in the annual abundance of PYSL (Figure 11), 

however, which suggests that larval production is stable. There is no relationship between PYSL 

abundance and YOY abundance in white perch (Figure 12a). The survival rate of white perch 
 37 

from the PYSL to the juvenile stage has declined (Appendix B, Table B-13). Moreover, there is 

a strong positive relationship between PYSL survival and YOY abundance (Figure 12b, 

Appendix B, Table B-14). Because YOY abundance in white perch is closely related to PYSL 

survival but not to PYSL abundance, we can conclude that the decline in YOY abundance was 

due to a decline in PYSL survival rather than to a decline in white perch reproduction. 

3.4.2.1 CWIS

Co-Occurrence

 Appendix B, Table B-13 and B-14 summarize the results of the correlation analysis for 

white perch. If entrainment at Indian Point had caused the observed decline in white perch 

PYSL survival, there should be a negative relationship between the entrainment CMR for white 

perch and white perch PYSL survival. This means that in years when the CMR was high, white 

perch PYSL survival should have been low, and in years when the CMR was low, white perch 

PYSL survival should have been high. However, as shown in Figure 13a, the opposite 

relationship exists. The IP2 and IP3 CMR is positively correlated with PYSL to juvenile 

survival, meaning that the CMR was high in years when PYSL survival was high and the CMR 

was low in years when PYSL survival was low. 

There is a negative relationship between the IP2 and IP3 CMR and white perch PYSL 

abundance (Figure 13b), but this correlation is significant only at the 10% level. Figure 14 plots 

time trends in both the CMR and in PYSL to juvenile survival for white perch. The two trend 

lines show similar patterns, with values decreasing from the mid-1970s to the mid-1980s, 

fluctuating until the mid-1990s, and then increasing. It is important to note that the recent 

increase in survival occurred during a period in which the capacity factors for IP2 and IP3 have 

been higher than in earlier years (Darla Gray, Entergy Corp., personal communication). 

Although there is a weak negative relationship between the CMR for IP2 and IP3 and 

white perch PYSL abundance, the much stronger positive relationship between the CMR and 

PYSL to YOY survival must be accorded a higher weight. Because this positive correlation 

clearly conflicts with the CWIS hypothesis, the CWIS hypothesis fails the co-occurrence 

criterion for white perch. 

Sufficiency
 38 

There are no independent measures of sufficiency that can be applied to this hypothesis. 

The objective of this report is to determine, using all available and relevant evidence whether the 

magnitude of entrainment and impingement at Indian Point have been sufficient to cause a 

reduction in the abundance of important Hudson River fish species. Hence, the sufficiency 

criterion is inapplicable to the CWIS hypothesis. 

Temporality

 As shown in Figure 14, white perch PYSL survival began to decline in 1977, one year 

following the startup of commercial operation at IP3. Since the startup of 2-unit operation 

preceded the decline in white perch PYSL survival, the CWIS hypothesis satisfies the 

temporality criterion. 

Manipulation

As discussed in Section 3.4.1.1, outages of IP2 or IP3 have frequently occurred during 

the entrainment season at Indian Point. The peak abundance of white perch eggs and larvae 

typically occurs during May and June (Klauda 1988). IP2 was offline during the entire months 

of May and June in 1976, 1989, 1991, 1997, 1998, and 2000. IP3 was offline during the entire 

months of May and June in 1975, 1982, 1993, and 1994. If entrainment at Indian Point were 

reducing the survival of white perch PYSL, then PYSL survival should have been higher in years 

when one unit was offline than in years when both units were operating. As shown in Figure 

15a, the measured PYSL survival values are inconsistent with this expectation. Figure 15a 

shows the time series of annual PYSL survival indices from 1975 through 2002, which are the 

years for which cooling water flow data were available. The horizontal line in Figure 15 shows 

the median survival index value for this time period. The median is defined as the midpoint of 

the entire distribution of survival index values, meaning that one-half of the survival indices are 

above the median and one-half are below the median. If white perch PYSL survival were higher 

in years of one-unit operation than in years of 2-unit operation, then significantly more survival 

index values for years of one-year operation should be higher than the median than lower than 

the median. However, Figure 15a shows that the PYSL survival index was higher than the 

median for only 4 of the 11 years of one-unit operation. The PYSL index was equal to the 
 39 

median in one year (1989) of one-unit operation, and lower than the median in 6 years of oneunit operation. 

This result is confirmed by Figure 15b, which shows the relationship between the white 

perch PYSL survival index and the May-June total water withdrawals by IP2 and IP3 for the 

years 1975-2002. There is no correlation between withdrawals by IP2 and IP3 and white perch 

PYSL survival. Hence, the CWIS hypothesis fails the manipulation criterion for white perch. 

Coherence

As noted above, the objective of this report is to determine, using all available and 

relevant evidence whether the magnitude of entrainment and impingement at Indian Point have 

been sufficient to cause a reduction in the abundance of important Hudson River fish species. 

Including “coherence” as an explicit evaluation criterion for CWIS would be redundant. Hence, 

the coherence criterion is inapplicable to the CWIS hypothesis. 

3.4.2.2 Zebra mussels 

Co-Occurrence

As shown in Appendix B, Table B-13, the zebra mussel index is negatively correlated 

with PYSL to YOY survival in white perch. Hence, the zebra mussel hypothesis satisfies the cooccurrence criterion. 

Temporality

As shown in Figure 14, however, the decline in white perch PYSL to YOY survival 

occurred primarily between 1974 and 1986, prior to the zebra mussel invasion. PYSL to YOY 

survival has actually been increasing since 1993, the first year in which zebra mussels were 

abundant enough to potentially affect fish populations (Strayer et al. 2004). Hence, the zebra 

mussel hypothesis fails the temporality criterion. 

Sufficiency

The potential effects of zebra mussel activity on early life stages of fish are indirect, and 

related to reductions in prey abundance and changes in habitat quality. No experiments have 
 40 

been performed that could quantify the relationship between zebra mussel activity and fish 

growth or survival, and no mathematical models that could be used to quantify the indirect 

effects of zebra mussel activity have been developed. Hence, whether or not the zebra mussel 

hypothesis satisfies the sufficiency criterion is unknown. 

Manipulation

No deliberate manipulations of zebra mussel populations in the Hudson River have been 

performed, therefore, this criterion is inapplicable to the zebra mussel hypothesis. 

Coherence

Because the proposed mechanism through which zebra mussel activity could have 

affected white perch in the Hudson River involves reducing food availability, the growth as well 

as the survival of white perch PYSL should have been reduced. Although Strayer et al. (2004) 

reported a negative relationship between zebra mussel activity and white perch growth, the 

analysis performed to support this assessment (Appendix B, Table B-13) found no significant 

relationship between zebra mussels and white perch growth. Moreover, the percent of white 

perch juveniles downriver from RKM 100 is negatively, instead of positively, correlated with the 

zebra mussel index (Appendix B, Table B-13). This negative correlation implies that over this 

same period of years, the percentage of the population present downriver from RKM 100 has 

declined, rather than increasing as predicted by the zebra mussel hypothesis. This result is also 

consistent with the findings of Strayer et al. (2004). Hence, the zebra mussel hypothesis 

partially, but not fully, satisfies the coherence criterion. 

3.4.2.3 Striped bass predation

Co-occurrence

There is a weak negative correlation between the striped bass index and the white perch 

PYSL index (Appendix B, Table B-13). This relationship provides weak evidence supporting 

the hypothesis that striped bass are preying on adult white perch. There is much stronger 

negative correlation between the striped bass index and the YOY index (Figure 16a). This 

correlation is consistent with the hypothesis that striped bass are preying on juvenile white perch. 

There is also a strong negative correlation between the striped bass index and white perch PYSL 
 41 

to YOY survival, however, this relationship is difficult to interpret because striped bass would 

not be expected to prey on larval white perch. Overall, the striped bass hypothesis satisfies the 

co-occurrence criterion with respect to predation on YOY white perch. 

Sufficiency

Striped bass larger than 200 mm in length have been shown to feed on white perch 

(Gardinier and Hoff 1982, Dunning et al. 1997). Appendix C to this report documents an 

analysis of prey consumption by Hudson River striped bass. This analysis compares the change 

in striped bass prey consumption requirements (August through October) between earlier (1983-

1990) and more recent (1991-2004) periods to changes in abundance of YOY fish in the Hudson 

River between these same two periods. The analysis shows that the increase in prey 

consumption from the earlier to the later period would be sufficient to explain the decline in 

YOY white perch abundance between these two periods if 1% of the age 1 and age 2 striped bass 

seasonal predatory demand was satisfied by YOY white perch, or if 0.3% of the age 1 through 

age 13 striped bass seasonal predatory demand was satisfied by YOY white perch. Hence, the 

striped bass predation hypothesis satisfies the sufficiency criterion for white perch. 

Temporality

 A sustained decline in white perch YOY abundance began in 1989, at the same time the 

striped bass index began to increase (Figure 16b). However, the historic peak in YOY 

abundance occurred in 1980 (Figure 16b), and PYSL to YOY survival declined substantially 

between 1975 and 1985 (Figure 14). White perch PYSL to YOY survival and YOY abundance 

are strongly correlated (Figure 12b), implying that declining YOY abundance must have been at 

least in part caused by a decline in PYSL to YOY survival. The decline in PYSL to YOY 

survival that declined between 1975 and 1985 cannot be explained by striped bass predation. 

Hence, the striped bass predation hypothesis only partially satisfies the temporality criterion. 

Manipulation

No deliberate manipulations of striped bass predation in the Hudson River have been 

performed, therefore, this criterion is inapplicable to the striped bass hypothesis. 
 42 

Coherence

 If predation by striped bass had caused the decline in abundance of YOY white perch in 

the Hudson River, then the YOY abundance of other known striped bass prey species, including 

river herring, American shad, bay anchovy, and Atlantic tomcod should also have declined. As 

shown in other Sections of this report, YOY abundance for all of these species has declined since 

the late 1980s, when striped bass abundance began to increase. Moreover, other published 

studies have concluded that striped bass predation is reducing the abundance of some prey 

species. Savoy and Crecco (2004) attributed recent declines in the abundance of both blueback 

herring and American shad in the Connecticut River to striped bass predation. Hartman (2003) 

estimated that the coastwide annual prey consumption by striped bass between 1 and 10 years of 

age increased by more than a factor of 8 between 1982 and 1995, from 17,900 metric tons (mt) to 

147,900 mt. Uphoff (2003) calculated even larger estimates of striped bass consumption, and 

attributed a 90% decline in the abundance of Atlantic menhaden in upper Chesapeake Bay from 

1980 through 1999 to predation by striped bass. 

Because parallel declines in other susceptible species have occurred, and because the 

other published studies have documented the influence of striped bass predation on susceptible 

prey species, the striped bass predation hypothesis satisfies the coherence criterion. 

3.4.2.4 Summary evaluation of hypotheses 

Table 4 summarizes the consistency of the white perch trends data with the CWIS, zebra 

mussel, and striped bass predation hypotheses. Two of the five evaluation criteria – sufficiency 

and coherence – are inapplicable to the CWIS hypothesis. The CWIS hypothesis fails the cooccurrence and manipulation criteria. Although the CWIS hypothesis satisfies the temporality 

criterion because the observed decline in white perch PYSL survival followed the startup of IP2 

and IP3, the inconsistency of this hypothesis with the co-occurrence and manipulation 

hypotheses means that the temporal correspondence between the beginning of the decline in 

survival and the startup of IP2 and IP3 is very likely a coincidence. Hence, the CWIS hypothesis 

can be rejected as an explanation for long-term trends in the abundance of age 0 white perch in 

the Hudson River. 
 43 

The zebra mussel hypothesis passes the co-occurrence criterion and at least partially 

satisfies the coherence criterion. However, it fails the temporality criterion because the declines 

in white perch PYSL survival and YOY abundance began prior to the appearance of zebra 

mussels in the Hudson River. Although zebra mussel activity might have contributed to a 

decline in white perch PYSL to YOY survival and YOY abundance from 1993 to 2004, zebra 

mussels could not have been the primary explanation for long-term trends in white perch survival 

and abundance. 

The striped bass predation hypothesis satisfies four of the five criteria. The fifth, 

manipulation, is inapplicable to this hypothesis. However, the strong relationship between white 

perch PYSL survival and YOY abundance over the entire period from 1974 to 2004 (Figure 12b) 

cannot be explained by the predation hypothesis, because striped bass abundance did not begin to 

increase until 1987. Hence, although striped bass predation likely contributed to the decline in 

white perch PYSL to YOY survival and YOY abundance, from 1987 onward, predation could 

not have been the primary cause of declines that took place between 1975 and 1985. 

3.4.3 American shad

Figure 17 depicts long-term trends in the abundance of American shad YOY and PYSL 

in the Hudson. The abundance of both life stages has declined significantly since the initiation 

of the generators’ monitoring program, with declines in the abundance of both life stages 

beginning in the late 1980s. As shown in Figure 18, there is a strong positive correlation 

between PYSL abundance and YOY abundance in American shad (Figure 18a), and no 

relationship between PYSL survival and YOY abundance (Figure 18b). Because YOY 

abundance is correlated with PYSL abundance but not with PYSL survival, we can conclude that 

the decline in YOY abundance is a consequence of reduced reproduction rather than reduced 

PYSL survival. 

Four hypothetical causes for these changes are evaluated below: the Indian Point CWIS, 

overfishing, zebra mussels, and striped bass predation. 
 44 

3.4.3.1 CWIS 

Co-Occurrence

 There is no correlation between PYSL survival and the entrainment CMR at IP2 and IP3 

(Figure 19a). The IP2 and IP3 CMR is also uncorrelated with American shad PYSL abundance 

(Figure 19b). Hence, the CWIS hypothesis fails the co-occurrence criterion. 

Sufficiency

There are no independent measures of sufficiency that can be applied to this hypothesis. 

The objective of this report is to determine, using all available and relevant evidence whether the 

magnitude of entrainment and impingement at Indian Point have been sufficient to cause a 

reduction in the abundance of important Hudson River fish species. Hence the sufficiency 

criterion is inapplicable to the CWIS hypothesis. 

Temporality

 American shad PYSL abundance grew from the mid-1970s, when IP2 and IP3 began 

commercial operations, until 1986 (Figure 17). The highest values for both PYSL and YOY 

abundance occurred in 1986, 10 years after the startup of commercial operations at IP3 and 12 

years after the startup of IP2 (Figure 17). Hence, the CWIS hypothesis fails the temporality 

criterion. 

Manipulation

As discussed in Section 3.4.1.1, outages of IP2 or IP3 have frequently occurred during 

the entrainment season at Indian Point. Although American shad eggs and larvae occur only at 

very low densities in the vicinity of Indian Point (DEIS, Figure V-68), the peak abundance of 

American shad eggs and larvae typically occurs during May and June (DEIS, Figure V-67). IP2 

was offline during the entire months of May and June in 1976, 1989, 1991, 1997, 1998, and 

2000. IP3 was offline during the entire months of May and June in 1975, 1982, 1993, and 1994. 

If entrainment at Indian Point were reducing the survival of American shad PYSL, then PYSL 

survival should have been higher in years when one unit was offline than in years when both 

units were operating. As shown in Figure 20a, the measured PYSL survival values are 
 45 

inconsistent with this expectation. Figure 20a shows the time series of annual PYSL survival 

indices from 1985 through 2002. The horizontal line in Figure 20a shows the median survival 

index value for this time period. The median is defined as the midpoint of the entire distribution 

of survival index values, meaning that one-half of the survival indices are above the median and 

one-half are below the median. If American shad PYSL survival were higher in years of oneunit operation than in years of 2-unit operation, then significantly more survival index values for 

years of one-year operation should be higher than the median than lower than the median. 

However, Figure 20a shows that the PYSL survival index was higher than the median for 5 of 

the 8 years of one-unit operation. The PYSL index was lower than the median in 3 years of oneunit operation. This difference could easily have arisen by chance. Moreover, 3 of the 5 years 

with the highest survival rates (1996, 1999, and 2002) were years of 2-unit operation. 

This result is confirmed by Figure 20b, which shows the relationship between the 

American shad PYSL survival index and the May-June total water withdrawals by IP2 and IP3 

for the years 1975-2002. There is no correlation between withdrawals by IP2 and IP3 and 

American shad PYSL survival. Hence, the CWIS hypothesis fails the manipulation criterion for 

American shad. 

Coherence

The objective of this report is to determine, using all available and relevant evidence 

whether the magnitude of entrainment and impingement at Indian Point have been sufficient to 

cause a reduction in the abundance of important Hudson River fish species. Including 

“coherence” as an explicit evaluation criterion for CWIS would be redundant. Hence, the 

coherence criterion is inapplicable to the CWIS hypothesis. 

3.4.3.2 Fishing 

Co-Occurrence

If a population is being overfished to the point at which spawner abundance is reduced, 

then the number of eggs and larvae produced by those spawners should decline. Historically, 

American shad supported very large unregulated commercial fisheries along the east coast of 

both the United States and Canada (ASMFC 1999). These harvests have declined dramatically 
 46 

in recent years. In its most recent stock assessment for American shad (ASMFC 2007), the 

ASMFC found that the abundance of adult American shad in the Hudson River peaked in 1985 

and 1986 and has since declined. This decline in adult abundance occurred during the same 

period in which the abundance of American shad PYSL and YOY in the Hudson River declined 

(Figure 17). Hence, the fishing hypothesis satisfies the co-occurrence criterion. 

Sufficiency

 There is conflicting information concerning whether the magnitude of fishing mortality 

imposed on Hudson River American shad has been sufficient to cause the declines in spawner 

abundance. According to the ASMFC (2007), many American shad stocks have declined in 

abundance in recent decades. Although the declines appear to be related to an increase in the 

mortality of adult shad, the contribution of fishing to the increase in mortality is unclear and 

probably differs between spawning populations. According to Hattala and Kahnle (2007), the 

Hudson River population of American shad is probably being overfished, however, other sources 

of mortality cannot be excluded as contributing causes. Although there is still substantial 

uncertainty concerning causes of decline in American shad population, this assessment accepts 

Hattala and Kahnle’s (2007) results and concludes that the overfishing hypothesis satisfies the 

sufficiency criterion. 

Temporality

 The decline in American shad spawner abundance coincided with the decline in 

abundance of PYSL and YOY (Figure 17). Hence, the overfishing hypothesis satisfies the 

temporality criterion. 

Manipulation

Amendment 1 to the Interstate Fisheries Management Plan for Shad and River Herring 

(ASMFC 1999) directed all states to phase out the coastal fishery for American shad over a five 

year period beginning in 2000. The phase-out should reduce fishing mortality on American 

shad. If the coastal fishery had been contributing to decreased abundance of Connecticut River 

American shad, then the abundance of this population should increase as a result of this action. 

Data on fishing mortality and population abundance from the post-closure period are not yet 
 47 

available, so it is not yet possible to evaluate whether the overfishing hypothesis satisfies the 

manipulation criterion. 

Coherence

 As noted above, there is still substantial uncertainty concerning the impact of fishing on 

the Hudson River American shad population. However, available data are consistent with a 

conclusion that fishing is at least a significant contributor to the recent decline in abundance of 

Hudson River American shad (Hattala and Kahnle 2007). Hence, the overfishing hypothesis 

satisfies the coherence criterion. 

3.4.3.3 Zebra mussels 

Co-occurrence

As shown in Appendix B, Table B-15, the American shad PYSL survival index is 

positively correlated with the zebra mussel index, rather than negatively correlated as predicted 

by the zebra mussel hypothesis. As can easily be seen from Figure 17, American shad PYSL to 

YOY survival has increased since the zebra mussel invasion. Hence, the zebra mussel 

hypothesis fails the co-occurrence criterion for American shad. 

Sufficiency

The potential effects of zebra mussel activity on early life stages of fish are indirect, and 

related to reductions in prey abundance and changes in habitat quality. No experiments have 

been performed that could quantify the relationship between zebra mussel activity and fish 

growth or survival, and no mathematical models that could be used to quantify the indirect 

effects of zebra mussel activity have been developed. Hence, whether or not the zebra mussel 

hypothesis satisfies the sufficiency criterion is unknown. 

Temporality

 The decline in abundance of American shad PYSL and YOY began in the late 1980s 

(Figure 17), several years prior to the zebra mussel invasion. Hence, the zebra mussel hypothesis 

fails the temporality criterion. 
 48 

Manipulation

No deliberate manipulations of zebra mussel populations in the Hudson River have been 

performed, therefore, this criterion is inapplicable to the zebra mussel hypothesis. 

Coherence

Because the proposed mechanism through which zebra mussel activity could have 

affected American shad in the Hudson River involves reducing food availability, the growth as 

well as the survival of American shad PYSL and YOY should have been reduced. Although 

Strayer et al. (2004) found a decline in growth rate of American shad PYSL and YOY following 

the zebra mussel invasion, this relationship was not significant even at the 20% level (Strayer et 

al. 2004, Fig. 7). No relationship between American shad YOY growth and zebra mussel 

activity was found in the analysis performed to support this assessment (Appendix B, Table B15). Zebra mussel activity should also have shifted the distribution of American shad PYSL and 

YOY downriver, away from the freshwater zone in which zebra mussels are abundant. Strayer et 

al. (2004) found a net downriver shift in the distribution of American shad YOY, but a net 

upriver shift in the distribution of PYSL. In the analysis performed to support this assessment 

(Appendix B, Table B-15), no significant shifts in the distribution of either life stage was found. 

The observed changes in growth and distribution predicted by the zebra mussel hypothesis were 

not observed. Hence, the zebra mussel hypothesis fails the coherence criterion for American 

shad. 

3.4.3.4 Striped bass predation

Co-occurrence

 American shad PYSL abundance, which reflects spawner abundance and reproduction, is 

negatively correlated with the striped bass index (Figure 21a), although this relationship is 

significant only at the 10% level. This correlation provides weak support for the hypothesis that 

striped bass are preying on adult American shad. There is a negative relationship between the 

striped bass index and the American shad YOY index, (Figure 21b), however, this relationship is 

not statistically significant. Hence, the striped bass predation hypothesis appears to marginally 

satisfy the co-occurrence criterion for predation . 
 49 

Sufficiency

Striped bass larger than 200 mm in length have been shown to feed on alosids such as 

American shad (Gardinier and Hoff 1982, Dunning et al. 1997). However, the prey consumption 

analysis documented in Appendix C to this report did not address predation on YOY American 

shad. Hence, with respect to YOY American shad, whether or not striped bass predation satisfies 

the sufficiency criterion is unknown. Kahnle and Hattala (2007) have argued that the great 

majority of adult striped bass in the Hudson are feeding on river herring rather than shad, and the 

striped bass predation is insufficient to significantly affect the abundance of adult Hudson River 

American shad. This assessment accepts the conclusions of Kahnle and Hattala (2007) that 

striped bass predation on adult Hudson River American shad is probably low. 

Temporality 

As can be seen from Figure 22, the increase in striped bass spawner abundance that began 

in the late 1980s closely coincides with the decline in American shad PYSL abundance. As 

shown in Figure 17, American shad YOY abundance has declined over this same period. Hence, 

the striped bass predation hypothesis satisfies the temporality criterion with respect to predation 

on both adults and YOY. 

Manipulation

No deliberate manipulations of striped bass predation in the Hudson River have been 

performed, therefore, this criterion is inapplicable to the striped bass hypothesis. 

Coherence

If predation by striped bass had caused the decline in abundance of American shad PYSL 

and YOY in the Hudson River, then the PYSL and YOY abundance of other known striped bass 

prey species, including white perch, river herring, bay anchovy, and Atlantic tomcod should also 

have declined. As discussed in other Sections of this report, no declines in white perch or bay 

anchovy PYSL abundance have occurred. However, PYSL abundance for river herring and 

Atlantic tomcod declined over the same period in which PYSL abundance for American shad 

declined. YOY abundance for all of the above species has declined since the late 1980s, when 
 50 

striped bass abundance began to increase. Moreover, other published studies have concluded that 

striped bass predation is reducing the abundance of some prey species. Savoy and Crecco (2004) 

attributed recent declines in the abundance of both blueback herring and American shad in the 

Connecticut River to striped bass predation on spawning adults, however, Kahnle and Hattala 

(2007) concluded that predation of striped bass on adult American shad in the Hudson River is 

relatively low. On the other hand, Hattala and Kahnle (2007) acknowledged that predation by 

striped bass on young American shad could be substantial and could be contributing to a decline 

in recruitment of young shad to the adult population. 

Hartman (2003) estimated that the coastwide annual prey consumption by striped bass 

between 1 and 10 years of age increased by more than a factor of 8 between 1982 and 1995, from 

17,900 mt to 147,900 mt. Uphoff (2003) calculated even larger estimates of striped bass 

consumption, and attributed a 90% decline in the abundance of Atlantic menhaden in upper 

Chesapeake Bay from 1980 through 1999 to predation by striped bass. 

Because parallel declines in YOY abundance of other susceptible species have occurred, 

and because the other published studies have documented the influence of striped bass predation 

on susceptible prey species, the striped bass predation hypothesis satisfies the coherence criterion 

with respect to predation on YOY American shad, but not with respect to predation on adults. 

3.4.3.5 Summary evaluation of hypotheses

Table 5 summarizes the consistency of the American shad data with the CWIS, 

overfishing, zebra mussel, and striped bass predation hypotheses. Two of the five evaluation 

criteria – sufficiency and coherence – are inapplicable to the CWIS hypothesis. The CWIS 

hypothesis fails the co-occurrence, temporality, and manipulation criteria. Hence, the CWIS 

hypothesis can be rejected as an explanation for long-term trends in the abundance of age 0 

American shad in the Hudson River. 

The overfishing hypothesis satisfies the co-occurrence, sufficiency, temporality, and 

coherence criteria for American shad. The manipulation criterion is inapplicable at present, 

although applicable data may become available once the response of the population to the phaseout of the ocean intercept fishery has been observed. 
 51 

The zebra mussel hypothesis fails the co-occurrence, temporality, and coherence criteria 

for American shad. Whether the sufficiency criterion is satisfied is unknown, and the 

manipulation criterion is inapplicable. Hence, the zebra mussel hypothesis can be rejected as an 

explanation for long-term trends in the abundance of age 0 American shad in the Hudson River. 

The striped bass predation hypothesis satisfies two and possibly three of the five criteria. 

Because no estimates of potential striped bass predation on YOY American shad have been 

developed, whether this hypothesis satisfies the sufficiency criterion is unknown. The 

manipulation criterion, is inapplicable to this hypothesis. The simultaneous declines in 

abundance of susceptible life stages of other prey species in the Hudson River and the published 

studies documenting impacts of striped bass predation on prey species support for the predation 

hypothesis. However, substantial uncertainty remains concerning the fraction of the American 

shad YOY population that might be consumed. 

It appears reasonable to conclude that the recent decline in abundance of Hudson River 

American shad is most likely a result of overfishing, but striped bass predation may be a 

contributing cause. 

3.4.4. Atlantic tomcod

Figure 23 depicts long-term trends in the abundance of Atlantic tomcod as measured by 

the LRS and the Atlantic Tomcod mark-recapture program. The LRS index reflects the 

abundance of late PYSL and early juvenile fish. The mark-recapture index reflects the combined 

abundance of age 1 and older (predominantly age 2) fish. The abundance of Atlantic tomcod has 

declined since the initiation of the generators’ monitoring programs, with the abundance of age 1 

and older fish abundance showing an abrupt decline beginning in 1990. The trend in abundance 

in the LRS time series is less clear, but the LRS index also has declined since 1990. Using 

Atlantic tomcod survival rates derived from annual mark-recapture surveys, for each year, the 

total egg to age 1 survival rate is estimated by comparing the total egg production during that 

year to the number of age 1 fish estimated to be present in the Hudson River during the following 

year. As shown in Figure 24, there is no relationship between egg deposition and resulting age 1 

abundance (Figure 24a). There is a positive relationship between egg to age 1 survival and age 1 
 52 

abundance (Figure 24b). Hence, the decline in Atlantic tomcod abundance is related to a 

decrease in survival rather than a decrease in egg production. 

Atlantic tomcod are uncommon in freshwater reaches of the Hudson River, therefore, 

they should not be susceptible to the effects of zebra mussel activity. This potential stressor is 

not evaluated as a cause of changes in the abundance of this species. Three hypothetical causes 

for these changes are evaluated below: the Indian Point CWIS, elevated summer temperatures, 

and striped bass predation. 

3.4.4.1 CWIS 

Co-occurrence

As shown in Figure 25a, there is no correlation between the IP2 and IP3 CMR and eggto-age 1 survival. There is a negative correlation between the IP2 and IP3 CMR and the Atlantic 

tomcod LRS index (Figure 25b), but this correlation is significant only at the 10% level 

(Appendix B, Table B-17). There is no correlation between the IP2 and IP3 CMR and the markrecapture index (Figure 25c). Because the IP2 and IP3 CMR are negatively correlated with only 

one of the three response metrics, and only at the 10% level, the CWIS hypothesis only weakly 

satisfies the co-occurrence criterion. 

Sufficiency

There are no independent measures of sufficiency that can be applied to this hypothesis. 

The objective of this report is to determine, using all available and relevant evidence whether the 

magnitude of entrainment and impingement at Indian Point have been sufficient to cause a 

reduction in the abundance of important Hudson River fish species. Hence, the sufficiency 

criterion is inapplicable to the CWIS hypothesis. 

Temporality

 As shown in Figure 23, the decline in abundance of Atlantic tomcod in the markrecapture survey did not begin until the mid-1980s and the decline in the LRS survey did not 

begin until 1990. Hence, the CWIS hypothesis fails the temporality criterion. 
 53 

Manipulation

Although American tomcod spawn in December and January, entrainable larvae and 

juveniles are still abundant in the lower estuary during May and June (DEIS, Figure 5-56). IP2 

was offline during the entire months of May and June in 1976, 1989, 1991, 1997, 1998, and 

2000. IP3 was offline during the entire months of May and June in 1975, 1982, 1993, and 1994. 

If entrainment at Indian Point were reducing the survival of Age 0 Atlantic tomcod, then egg to 

age 1 survival should have been higher in years when one unit was offline than in years when 

both units were operating. As shown in Figure 26a, the measured PYSL survival values are 

inconsistent with this expectation. Figure 26a shows the time series of egg to age 1 indices from 

1976 through 2001. The horizontal line in Figure 26a shows the median survival index value for 

this time period. The median is defined as the midpoint of the entire distribution of survival 

index values, meaning that one-half of the survival indices are above the median and one-half are 

below the median. If Atlantic tomcod survival were higher in years of one-unit operation than in 

years of 2-unit operation, then significantly more survival index values for years of one-year 

operation should be higher than the median than lower than the median. However, Figure 26a 

shows that the PYSL survival index was higher than the median for 3 of the 7 years of one-unit 

operation. The PYSL index was lower than the median in 4 years of one-unit operation. 

This result is confirmed by Figure 26b, which shows the relationship between the 

Atlantic tomcod egg to age 1 survival index and the May-June total water withdrawals by IP2 

and IP3 for the years 1975-2002. There is no correlation between withdrawals by IP2 and IP3 

and Atlantic tomcod egg to age 1 survival. Hence, the CWIS hypothesis fails the manipulation 

criterion for Atlantic tomcod. 

Coherence

The objective of this report is to determine, using all available and relevant evidence 

whether the magnitude of entrainment and impingement at Indian Point have been sufficient to 

cause a reduction in the abundance of important Hudson River fish species. Including 

“coherence” as an explicit evaluation criterion for CWIS would be redundant. Hence, the 

coherence criterion is inapplicable to the CWIS hypothesis. 
 54 

3.4.4.2 Elevated summer temperatures 

Co-occurrence

As shown in Appendix B, Table B-17, egg to age 1 survival is negatively correlated with 

the PWW degree-day index. Egg to age 1 survival is not, however, correlated with the August 

cooling water flows at IP2 and IP3, which is an index of the thermal loading to the River from 

IP2 and IP3. Hence, the temperature hypothesis satisfies the co-occurrence criterion, although 

there is no evidence that IP2 and IP3 contribute to a temperature effect. 

Sufficiency

 As discussed by McLaren et al. (1988), summer temperatures in the Hudson River 

frequently exceed optimal levels for juvenile Atlantic tomcod, and occasionally can exceed the 

lethal tolerance temperature (26.5 ̊C) for this species (McLaren et al. 1988). Although the 

temperature of the Hudson River is highly variable between locations, depth strata, and years, it 

can be concluded that the temperature hypothesis satisfies the sufficiency criterion. 

Temporality

Figure 27 compares long-term trends in PWW degree-day index to long-term trends in 

the abundance of age 1 and age 2 Atlantic tomcod, for the period 1987-2001. For each year, the 

degree-day index is paired with the mark-recapture estimates generated during the following 

winter (e.g., the 1987 temperature value is paired with the mark-recapture value for the winter of 

1987-1988). As shown in Figure 27, a decline in Atlantic tomcod occurred from 1990-2001. 

However, elevated temperatures that could have explained this decline did not occur. There is 

no long-term trend in the PWW degree-day index, and three of the four lowest values of the 

index have occurred since 1990. Hence, the temperature hypothesis fails the temporality 

criterion. 

Manipulation

No deliberate manipulations of Hudson River water temperatures have been performed, 

therefore, this criterion is inapplicable to temperature hypothesis. 
 55 

Coherence

If elevated temperatures were adversely affecting Atlantic tomcod in the Hudson River, 

then other temperature-sensitive species should also be declining. As noted in the FEIS (pp 66-

67), the abundance of rainbow smelt in the Hudson River has also been declining. In addition, 

the temperature hypothesis is consistent with laboratory data on thermal tolerances in Atlantic 

tomcod and with the geographic distribution of this species. As noted by McLaren et al. (1988), 

the Hudson River is the southern-most reproducing Atlantic tomcod population. Hence, the 

temperature hypothesis satisfies the coherence criterion. 

3.4.4.3 Striped bass predation 

Co-occurrence

Both the Atlantic tomcod mark-recapture index and the LRS index are negatively 

correlated with the striped bass index (Figure 28). Hence, the striped bass predation hypothesis 

satisfies the co-occurrence criterion. 

Sufficiency

Striped bass larger than 200 mm in length have been shown to feed on Atlantic tomcod 

(Gardinier and Hoff 1982, Dunning et al. 1997). Appendix C to this report documents an 

analysis of prey consumption by Hudson River striped bass. This analysis compares the change 

in striped bass prey consumption requirements (August through October) between earlier (1983-

1990) and more recent (1991-2004) periods to changes in abundance of YOY fish in the Hudson 

River between these same two periods. The analysis shows that the increase in prey 

consumption from the earlier to the later period would be sufficient to explain the decline in 

YOY Atlantic tomcod abundance between these two periods if 1.4% of the age 1 and age 2 

striped bass seasonal predatory demand was satisfied by YOY Atlantic tomcod, or if 0.4% of the 

age 1 through age 13 striped bass seasonal predatory demand was satisfied by YOY Atlantic 

tomcod. Hence, the striped bass predation hypothesis satisfies the sufficiency criterion. 
 56 

Temporality

The increase in striped bass abundance coincides in time with the declines in both 

Atlantic tomcod abundance metrics (Figure 29). Hence, the striped bass predation hypothesis 

satisfies the temporality criterion. 

Manipulation

No deliberate manipulations of striped bass predation in the Hudson River have been 

performed, therefore, this criterion is inapplicable to the striped bass hypothesis. 

Coherence

If predation by striped bass had caused the decline in abundance of Atlantic tomcod in 

the Hudson River, then the YOY abundance of other known striped bass prey species, including 

white perch, river herring, American shad, and bay anchovy, should also have declined. As 

shown in other Sections of this report, YOY abundance for all of these species has declined since 

the late 1980s, when striped bass abundance began to increase. Moreover, other published 

studies have concluded that striped bass predation is reducing the abundance of some prey 

species. Savoy and Crecco (2004) attributed recent declines in the abundance of both blueback 

herring and American shad in the Connecticut River to striped bass predation. Hartman (2003) 

estimated that the coastwide annual prey consumption by striped bass between 1 and 10 years of 

age increased by more than a factor of 8 between 1982 and 1995, from 17,900 mt to 147,900 mt. 

Uphoff (2003) calculated even larger estimates of striped bass consumption, and attributed a 

90% decline in the abundance of Atlantic menhaden in upper Chesapeake Bay from 1980 

through 1999 to predation by striped bass. 

Because parallel declines in other susceptible species have occurred, and because the 

other published studies have documented the influence of striped bass predation on susceptible 

prey species, the striped bass predation hypothesis satisfies the coherence criterion. 

3.4.4.4 Summary evaluation of hypotheses

Table 6 summarizes the consistency of the Atlantic tomcod data with the CWIS, 

temperature, and striped bass predation hypotheses. Two of the five evaluation criteria – 
 57 

sufficiency and coherence – are inapplicable to the CWIS hypothesis. The CWIS hypothesis 

weakly satisfies the co-occurrence criterion, but fails the temporality, and manipulation criteria. 

The CWIS hypothesis can be rejected as an explanation for long-term trends in the abundance of 

age 0 Atlantic tomcod in the Hudson River. 

The temperature hypothesis satisfies the co-occurrence, sufficiency, and coherence 

criteria, but fails the temporality criterion. The manipulation criterion is inapplicable to this 

hypothesis. Hence, the temperature hypothesis cannot be rejected. However, failure to satisfy 

the temporality criterion indicates that factors other than temperature were responsible for the 

decline in abundance of Atlantic tomcod that occurred after 1990. 

The striped bass predation hypothesis satisfies all of the applicable criteria. The 

correlations between striped bass abundance and Atlantic tomcod abundance, the temporal 

correspondence between the timing of the striped bass increase and the Atlantic tomcod decline, 

the estimates of striped bass prey consumption, the simultaneous declines in abundance of 

susceptible life stages of other prey species in the Hudson River, and the published studies 

documenting impacts of striped bass predation on prey species all provide relatively strong 

support for the predation hypothesis. 

3.4.5 Alewife and blueback herring

Figure 30 depicts long-term trends in the abundance of alewife and blueback herring 

PYSL and YOY in the Hudson. These two species must be considered together for purposes of 

evaluating impacts of CWIS, because their larvae are indistinguishable. PYSL abundance for 

both species combined (Figure 30a) was stable until 1985, and has since declined. With respect 

to YOY abundance, these two species have tended to vary together (Figure 30b). YOY 

abundance in both species declined abruptly in the mid-1980s and has fluctuated without 

apparent trend since that time, but without returning to previous abundance levels. 

3.4.5.1 CWIS 

Co-occurrence

 IP2 and IP3 entrainment CMR is uncorrelated with river herring PYSL survival (Figure 

31a), river herring PYSL abundance (Figure 31b), alewife YOY abundance (Figure 32a), and 
 58 

blueback herring YOY abundance (Figure 32b). Hence, the CWIS hypothesis fails the cooccurrence criterion. 

Sufficiency

There are no independent measures of sufficiency that can be applied to this hypothesis. 

The objective of this report is to determine, using all available and relevant evidence whether the 

magnitude of entrainment and impingement at Indian Point have been sufficient to cause a 

reduction in the abundance of important Hudson River fish species. Hence the sufficiency 

criterion is inapplicable to the CWIS hypothesis. 

Temporality

 As shown in Figures 30a and 30b, alewife and blueback herring PYSL and YOY 

abundance did not decline until the mid-1980s, nearly a decade after the startup of commercial 

operations at IP2 and IP3. Hence, the CWIS hypothesis fails the temporality criterion. 

Manipulation

The peak abundance of river herring eggs and larvae typically occurs during May and 

June (DEIS, Figures V-71 and V-74). IP2 was offline during the entire months of May and June 

in 1976, 1989, 1991, 1997, 1998, and 2000. IP3 was offline during the entire months of May and 

June in 1975, 1982, 1993, and 1994. If entrainment at Indian Point were reducing the survival of 

river herring PYSL, then PYSL survival should have been higher in years when one unit was 

offline than in years when both units were operating. As shown in Figure 33a, the measured 

PYSL survival values are inconsistent with this expectation. Figure 33a shows the time series of 

annual PYSL survival indices from 1974 through 2002. The horizontal line in Figure 33a shows 

the median survival index value for this time period. The median is defined as the midpoint of 

the entire distribution of survival index values, meaning that one-half of the survival indices are 

above the median and one-half are below the median. If river herring PYSL survival were 

higher in years of one-unit operation than in years of 2-unit operation, then significantly more 

survival index values for years of one-year operation should be higher than the median than 

lower than the median. However, Figure 33a shows that the PYSL survival index was higher 
 59 

than the median for 4 of the 11 years of one-unit operation. The PYSL was index lower than the 

median in 7 years of one-unit operation. 

This result is confirmed by Figure 33b, which shows the relationship between the river 

herring PYSL survival index and the May-June total water withdrawals by IP2 and IP3 for the 

years 1975-2002. There is no correlation between withdrawals by IP2 and IP3 and river herring 

PYSL survival. Hence, the CWIS hypothesis fails the manipulation criterion for alewife and 

blueback herring. 

Coherence

The objective of this report is to determine, using all available and relevant evidence 

whether the magnitude of entrainment and impingement at Indian Point have been sufficient to 

cause a reduction in the abundance of important Hudson River fish species. Including 

“coherence” as an explicit evaluation criterion for CWIS would be redundant. Hence, the 

coherence criterion is inapplicable to the CWIS hypothesis. 

3.4.5.2 Zebra mussels

Co-occurrence

As shown in Appendix B, Tables B-19 and B-21, there is no correlation between the 

zebra mussel index and any abundance index for either alewife or blueback herring. Hence, the 

zebra mussel hypothesis fails the co-occurrence criterion for both species. 

Sufficiency

The potential effects of zebra mussel activity on early life stages of fish are indirect, and 

related to reductions in prey abundance and changes in habitat quality. No experiments have 

been performed that could quantify the relationship between zebra mussel activity and fish 

growth or survival, and no mathematical models that could be used to quantify the indirect 

effects of zebra mussel activity have been developed. Hence, whether or not the zebra mussel 

hypothesis satisfies the sufficiency criterion is unknown. 
 60 

Temporality

The decline in abundance of alewife and blueback herring PYSL and YOY occurred 

during the mid-1980s, more than 5 years prior to the invasion of the river by zebra mussels 

(Figure 30). Hence, the zebra mussel hypothesis fails the temporality criterion. 

Manipulation

No deliberate manipulations of zebra mussel populations in the Hudson River have been 

performed, therefore, this criterion is inapplicable to the zebra mussel hypothesis. 

Coherence

Because the proposed mechanism through which zebra mussel activity could have 

affected river herring in the Hudson River involves reducing food availability, the growth as well 

as the survival of river herring PYSL and YOY should have been reduced. Strayer et al. (2004) 

found a decline in the growth rate of YOY alewife following the zebra mussel invasion using 

both the utility beach seine index and the NYSDEC beach seine index. Only the decline in the 

growth rate calculated from the NYSDEC index was statistically significant, and only at the 20% 

level. No relationship between alewife or blueback herring growth and zebra mussel activity 

was found in the analysis performed to support this assessment (Appendix B, Tables B-19 and B21). Zebra mussel activity should also have shifted the distribution of river herring PYSL and 

YOY downriver, away from the freshwater zone in which zebra mussels are abundant. Strayer et 

al. (2004) found net downriver shifts in the distribution of alewife and blueback herring YOY, 

but a net upriver shift in the distribution of PYSL. None of these shifts was statistically 

significant, even at the 20% level. In the analysis performed to support this assessment 

(Appendix B, Tables B-19 and B-21), no significant shift in the distribution of blueback herring 

was found, but an upstream shift in the distribution of alewife YOY was found. Only one of the 

predicted effects of the zebra mussel invasion on river herring was observed, in only one out of 

three analyses, and at a significance level (20%) not usually accepted in scientific studies. 

Hence, the zebra mussel hypothesis fails the coherence criterion for alewife and blueback 

herring. 
 61 

3.4.5.3 Striped bass predation

Co-occurrence

 The river herring PYSL abundance index, which reflects spawner abundance and 

reproduction, is negatively correlated with the striped bass index (Figure 34a). The alewife YOY 

index, and the blueback herring YOY index are also negatively correlated with the striped bass 

index, although only at the 10% significance level (Appendix B, Tables B-19 and B-21). 

(Figures 34b and 34c). Hence, the striped bass predation hypothesis satisfies the co-occurrence 

criterion for predation, on both adults and YOY. 

Sufficiency

Striped bass larger than 200 mm in length have been shown to feed on alosids, including 

alewife and blueback herring (Gardinier and Hoff 1982, Dunning et al. 1997). According to 

Savoy and Crecco (2004) and Davis et al. (2007), adult striped bass in the Connecticut River 

feed heavily on spawning blueback herring. Recently, Kahnle and Hattala (2007) reported that 

river herring were the most common prey item in the stomachs of adult striped bass captured in 

the Hudson River. Appendix C to this report documents an analysis of prey consumption by 

Hudson River striped bass. This analysis compares the change in striped bass prey consumption 

requirements (August through October) between earlier (1983-1990) and more recent (1991-

2004) periods to changes in abundance of YOY fish in the Hudson River between these same 

two periods. The analysis shows that the increase in prey consumption from the earlier to the 

later period would be sufficient to explain the decline in YOY river herring abundance between 

these two periods if 3% of the age 1 and age 2 striped bass seasonal predatory demand was 

satisfied by YOY river herring, or if 0.9% of the age 1 through age 13 striped bass seasonal 

predatory demand was satisfied by YOY river herring. Hence, the striped bass predation 

hypothesis satisfies the sufficiency criterion with respect to predation on YOY river herring. No 

quantitative estimates of consumption of adult river herring by striped bass are available. 
 62 

Temporality

The decline in river herring abundance coincides in time with the increase in the striped 

bass index (Figure 35). Hence, the trends analysis supports the hypothesis that predation by 

striped bass has contributed to the decline in alewife and blueback herring abundance. Alewife 

and blueback herring do not return to the Hudson as spawning adults until an age of at least four 

years (ASMFC 1998). Hence, if only juvenile river herring were susceptible to predation by 

striped bass, a four-year time lag would be expected between the increase in striped bass 

abundance and the decline in PYSL abundance. The fact that no such time lag is apparent over 

the substantial time series available (Figure 35a), is consistent with the hypothesis that spawning 

adults are also susceptible to predation. Hence, the predation hypothesis satisfies the temporality 

criterion for both predation on adults and predation on YOY. 

Manipulation

No deliberate manipulations of striped bass predation in the Hudson River have been 

performed, therefore, this criterion is inapplicable to the striped bass hypothesis. 

Coherence

If predation by striped bass had caused the decline in abundance of river herring in the 

Hudson River, then the YOY abundance of other known striped bass prey species, including 

white perch, American shad, Atlantic tomcod, and bay anchovy, should also have declined. As 

shown in other Sections of this report, YOY abundance for all of these species has declined since 

the late 1980s, when striped bass abundance began to increase. Moreover, other published 

studies have concluded that striped bass predation is reducing the abundance of some prey 

species. Savoy and Crecco (2004) attributed recent declines in the abundance of both blueback 

herring and American shad in the Connecticut River to striped bass predation. This conclusion is 

supported by a recent study of the diet composition of striped bass present in the Connecticut 

River during the spring shad and river herring spawning run (Davis et al. 2007). These authors 

found that striped bass between 600 and 800 mm in length feed predominantly on adult river 

herring. These results are consistent with the results published by Kahnle and Hattala (2007), 

who found that river herring were the most abundant of the identifiable prey items in the 

stomachs of adult striped bass captured in the Hudson River. Hartman (2003) estimated that the 
 63 

coastwide annual prey consumption by striped bass between 1 and 10 years of age increased by 

more than a factor of 8 between 1982 and 1995, from 17,900 metric tons (mt) to 147,900 mt. 

Uphoff (2003) calculated even larger estimates of striped bass consumption, and attributed a 

90% decline in the abundance of Atlantic menhaden in upper Chesapeake Bay from 1980 

through 1999 to predation by striped bass. 

Because parallel declines in other susceptible species have occurred, because predation 

by striped bass on adult river herring has been demonstrated, and because the other published 

studies have documented the influence of striped bass predation on susceptible prey species, the 

striped bass predation hypothesis satisfies the coherence criterion. 

3.4.5.4 Summary evaluation of hypotheses 

Table 7 summarizes the consistency of the alewife and blueback herring data with the 

CWIS, temperature, and striped bass predation hypotheses. Two of the five evaluation criteria – 

sufficiency and coherence – are inapplicable to the CWIS hypothesis. The CWIS hypothesis 

fails the co-occurrence, temporality, and manipulation criteria. Hence, the CWIS hypothesis can 

be rejected as an explanation for long-term trends in the abundance of age 0 river herring in the 

Hudson River. 

The zebra mussel hypothesis fails the co-occurrence, temporality, and coherence criteria 

for river herring. Whether the sufficiency criterion is satisfied is unknown, and the manipulation 

criterion is inapplicable. Hence, the zebra mussel hypothesis can be rejected as an explanation 

for long-term trends in the abundance of age 0 river herring in the Hudson River. 

The striped bass predation hypothesis satisfies all of the applicable criteria. The 

correlations between striped bass abundance and river herring abundance, the temporal 

correspondence between the timing of the striped bass increase and the river herring decline, the 

estimates of striped bass prey consumption, the simultaneous declines in abundance of 

susceptible life stages of other prey species in the Hudson River, and the published studies 

documenting predation by striped bass on spawning adult river herring, and studies documenting 

impacts of striped bass predation on prey species all provide relatively strong support for the 

predation hypothesis. 
 64 

3.4.6. Bay anchovy

Bay anchovy is a marine species and, because zebra mussels occur only in the freshwater 

zone of the Hudson River, bay anchovy should not be susceptible to the effects of zebra mussel 

activity. This potential stressor is not evaluated as a cause of changes in the abundance of this 

species. Two hypothetical causes for these changes are evaluated below: the Indian Point CWIS 

and striped bass predation. 

Figure 36 depicts long-term trends in the abundance of bay anchovy YOY and PYSL in 

the Hudson. The abundance of juvenile bay anchovy, as measured by the FSS, has declined since 

1985. There has been no trend in abundance of PYSL. 

3.4.6.1 CWIS 

Co-occurrence

 As shown in Figure 37, the PYSL to YOY survival rate (Figure 37a) and the PYSL index 

(Figure 37b) are both uncorrelated with the IP2 and IP3 CMR. Hence, the CWIS hypothesis fails 

the co-occurrence criterion. 

Sufficiency 

There are no independent measures of sufficiency that can be applied to this hypothesis. 

The objective of this report is to determine, using all available and relevant evidence whether the 

magnitude of entrainment and impingement at Indian Point have been sufficient to cause a 

reduction in the abundance of important Hudson River fish species. Hence, the sufficiency 

criterion is inapplicable to the CWIS hypothesis. 

Temporality

 There has been no decline in bay anchovy PYSL abundance, and bay anchovy YOY 

abundance did not decline until the late 1980s, more than 10 years following the startup of IP2 

and IP3. Hence, the CWIS hypothesis fails the temporality criterion. 
 65 

Manipulation

The peak abundance of bay anchovy eggs and larvae typically occurs during June and 

July (DEIS, Figures V-78). IP2 was offline during the entire months of June and July in 1976, 

1998, and 2000. IP3 was offline during the entire months of June and July in 1975, 1982, 1987, 

1993, 1994, and 1997. If entrainment at Indian Point were reducing the survival of bay anchovy 

PYSL, then PYSL survival should have been higher in years when one unit was offline than in 

years when both units were operating. As shown in Figure 38a, the measured PYSL survival 

values are inconsistent with this expectation. Figure 38a shows the time series of annual PYSL 

survival indices from 1985 through 2002. The horizontal line in Figure 38a shows the median 

survival index value for this time period. The median is defined as the midpoint of the entire 

distribution of survival index values, meaning that one-half of the survival indices are above the 

median and one-half are below the median. If bay anchovy PYSL survival were higher in years 

of one-unit operation than in years of 2-unit operation, then significantly more survival index 

values for years of one-year operation should be higher than the median than lower than the 

median. However, Figure 38a shows that the PYSL survival index was higher than the median 

for 4 of the 7 years of one-unit operation and lower than the median for the other 3 years. This 

difference could easily have arisen by chance. 

This result is confirmed by Figure 38b, which shows the relationship between the bay 

anchovy PYSL survival index and the June-July total water withdrawals by IP2 and IP3 for the 

years 1975-2002. There is no correlation between withdrawals by IP2 and IP3 and bay anchovy 

PYSL survival. Hence, the CWIS hypothesis fails the manipulation criterion for bay anchovy. 

Coherence

The objective of this report is to determine, using all available and relevant evidence, 

whether the magnitude of entrainment and impingement at Indian Point have been sufficient to 

cause a reduction in the abundance of important Hudson River fish species. Including 

“coherence” as an explicit evaluation criterion for CWIS would be redundant. Hence, the 

coherence criterion is inapplicable to the CWIS hypothesis. 
 66 

3.4.6.2 Striped bass predation

 

Co-occurrence

Bay anchovy juvenile abundance is negatively correlated with the striped bass index 

(Figure 39a). Hence, the striped bass hypothesis satisfies the co-occurrence criterion. 

Sufficiency

Striped bass larger than 200 mm in length have been shown to feed on clupeids such as 

bay anchovy (Gardinier and Hoff 1982, Dunning et al. 1997). However, the prey consumption 

analysis documented in Appendix C to this report did not address predation on bay anchovy. 

Hence, whether the striped bass predation hypothesis satisfies the sufficiency criterion for bay 

anchovy is unknown. 

Temporality

The increase in striped bass abundance coincides in time with the decline in bay anchovy 

juvenile abundance (Figure 39b). Hence, the striped bass hypothesis satisfies the temporality 

criterion for bay anchovy. 

Manipulation

No deliberate manipulations of striped bass predation in the Hudson River have been 

performed, therefore, this criterion is inapplicable to the striped bass hypothesis. 

Coherence

 If predation by striped bass had caused the decline in abundance of bay anchovy YOY in 

the Hudson River, then the YOY abundance of other known striped bass prey species, including 

white perch, American shad, river herring, and Atlantic tomcod should also have declined. As 

discussed in other Sections of this report, YOY abundance for all of the above species has 

declined since the late 1980s, when striped bass abundance began to increase. Moreover, other 

published studies have concluded that striped bass predation is reducing the abundance of some 

prey species. 
 67 

Hartman (2003) estimated that the coastwide annual prey consumption by striped bass 

between 1 and 10 years of age increased by more than a factor of 8 between 1982 and 1995, from 

17,900 mt to 147,900 mt. Uphoff (2003) calculated even larger estimates of striped bass 

consumption, and attributed a 90% decline in the abundance of Atlantic menhaden in upper 

Chesapeake Bay from 1980 through 1999 to predation by striped bass. 

Because parallel declines in other susceptible species have occurred, and because the 

other published studies have documented the influence of striped bass predation on susceptible 

prey species, the striped bass predation hypothesis satisfies the coherence criterion with respect 

to predation on YOY bay anchovy. 

3.4.6.3 Summary evaluation of hypotheses 

Table 8 summarizes the consistency of the bay anchovy data with the CWIS and striped 

bass predation hypotheses. Two of the five evaluation criteria – sufficiency and coherence – are 

inapplicable to the CWIS hypothesis. The CWIS hypothesis fails the co-occurrence, temporality, 

and manipulation criteria. Hence, the CWIS hypothesis can be rejected as an explanation for 

long-term trends in the abundance of age 0 bay anchovy in the Hudson River. 

The striped bass hypothesis satisfies three of the five criteria. The manipulation criterion 

is inapplicable to this hypothesis, and whether this hypothesis satisfies the sufficiency criterion is 

unknown. The simultaneous declines in abundance of susceptible life stages of other prey 

species in the Hudson River and the published studies documenting impacts of striped bass 

predation on prey species all provide relatively strong support for the predation hypothesis. 

However, substantial uncertainty remains concerning the fraction of the bay anchovy YOY 

population that might be consumed. 

3.4.7. Spottail shiner

Figure 40 depicts long-term trends in the abundance of spottail shiners and YOY in the 

Hudson River. The abundance of shiners has significantly declined, while the abundance of 

YOY has significantly increased. The increase in abundance of YOY spottail shiner is 

inconsistent with all of the hypotheses evaluated in this report. Hence, there is no need to 

perform a formal evaluation using the criteria from Suter et al. (2007). 
 68 

As shown in Figure 41, there is no correlation between the IP2 and IP3 CMR and either 

spottail shiner response metric. This result is not unexpected because, as discussed in the DEIS 

(Figure V-107), spottail shiner is a freshwater species that is uncommon in the vicinity of Indian 

Point. The causes of recent changes in the abundance of this species cannot be identified using 

the data available for this report; however, the CWIS hypothesis can be rejected. 

3.5 Summary evaluation of trends analysis 

The results of the trends analysis are inconsistent with the hypothesis that entrainment at 

IP2 and IP3 is reducing the survival or abundance of any of the eight Hudson River fish species 

considered in this assessment. Overfishing is the most likely cause of the recent decline in 

abundance of American shad, with striped bass predation being a potentially important 

contributing factor. For other species, the striped bass predation hypothesis is the most strongly 

supported hypothesis. This hypothesis satisfies the co-occurrence, sufficiency, temporality, and 

coherence criteria for many of the species evaluated. With respect to the co-occurrence criterion, 

the striped bass index is negatively correlated with abundance indices for white perch, American 

shad, Atlantic tomcod, river herring, and bay anchovy. With respect to sufficiency, the analyses 

documented in Appendix C show that the increase in prey consumption by Hudson River striped 

bass in recent years is sufficient to account for observed declines in the YOY abundance of white 

perch, Atlantic tomcod, and river herring. With respect to temporality, the increase in striped 

bass abundance that occurred following the imposition of harvest restrictions in the mid-1980s 

coincides in time with the declines in abundance of one or more life stages of all of these species. 

With respect to coherence, striped bass predation has been implicated in declines of susceptible 

species in other mid-Atlantic northeastern estuaries (Hartman 2003, Uphoff 2003, Savoy and 

Crecco 2004) and striped bass have been shown to prey on all of the species listed above 

(Gardinier and Hoff 1982, Dunning et al. 1997, Savoy and Crecco 2004, Kahnle and Hattala 

2007). 

The available evidence is sufficient to reject Indian Point CWIS as having a measurable 

effect on any of the species evaluated. Within the limits of the data available for this assessment, 

it can reasonably be concluded that striped bass predation is a far more likely cause of declines in 
 69 

the abundance of YOY white perch, American shad, Atlantic tomcod, river herring, and bay 

anchovy than are any of the other potential causes evaluated. 

4. Evaluation of impacts of cooling-water withdrawals on spawning potential 

Fisheries scientists have developed a variety of quantitative methods for determining 

whether the sustainability of a fish population is being harmed by excessive harvesting. From 

the perspective of population dynamics, entrainment and impingement have been characterized 

(somewhat over simplistically) as a type of “fishing,” imposed on early life stages rather than on 

adult fish (Goodyear 1977). For this reason, these methods may be used to determine whether 

entrainment or impingement by IP2 and IP3’s respective CWIS could have adversely affected 

Hudson River fish populations that support managed fisheries. The method to be used, the 

SSBPR model, has a long history of application both in power-plant impact assessment studies 

and in fisheries management (Goodyear 1993). 

4.1 History of the SSBPR model 

One of the critical questions in fisheries management is how much spawning stock 

(essentially, the number of adult fish) must be protected from harvesting to allow a population to 

replace itself and persist through time (i.e., a sustainable population) (Mace and Sissenwine 

1993). The so-called spawning stock biomass per recruit or SSBPR model is the most widely 

used approach for answering this important question for fish populations subjected to 

commercial and recreational fishing (Sissenwine and Shepherd 1987, Gabriel et al. 1989, 

Goodyear 1993, Mace and Sissenwine 1993, Rosenberg et al. 1994). Further, since it was 

originally developed by Goodyear (1977) as a method for assessing whether entrainment and 

impingement of striped bass at Hudson River power plants could, in combination with fishing 

mortality, threaten the ability of the population to sustain itself, its application to entrainment and 

impingement is well-supported. 

The SSBPR model uses information on age-specific mortality and fecundity (i.e., the 

number of eggs produced by a female fish of a given age) to calculate the expected lifetime 

reproduction of a one-year-old female fish (a “recruit,” in fisheries terminology). Expected 

lifetime reproduction is a function both of the average fecundity of female fish at each age and 
 70 

the probability that the female will survive to reproduce at that age (Goodyear 1977). Mortality 

due to fishing, CWIS, or other causes reduces expected lifetime reproduction either by reducing 

the probability of survival (in the case of fishing), reducing the probability that spawned eggs 

will survive to become one-year-old recruits (in the case of CWIS), or reducing the fecundity of 

female fish (e.g., through adverse environmental conditions, such as toxic chemicals). For the 

population to persist, each one-year-old female fish must produce at least one female egg that 

survives to become a one-year-old female recruit (Mace and Sissenwine 1993, Goodyear 1993). 

An average female has the potential to produce far more eggs than are required to replace her 

(Mace and Sissenwine 1993). For example, a female striped bass can spawn 3 million or more 

eggs in a single year (Hoff et al. 1988; Olsen and Rulifson 1992) and can live for up to 30 years 

(Secor and Piccoli 1996). For the population to maintain itself at a stable level, only one of the 

female eggs produced by each fish over her lifetime must survive to adulthood. This massive 

surplus of eggs ensures that the population will be able to persist in spite of natural and 

potentially extreme fluctuations in environmental conditions. This massive surplus of eggs also 

ensures that even substantial harvesting by commercial and recreational fishermen will not 

adversely affect the population. 

 

4.2 Explanation of the SSBPR concept 

The use of SSBPR in fisheries management derives from recognition that the lifetime 

reproductive capacity of a typical recruit provides a useful measure of the replacement capability 

of a population (Goodyear 1977, 1993, Sissenwine and Shepherd 1987, Mace and Sissenwine 

1993, Rosenberg et al. 1994). At low levels of fishing mortality, the lifetime reproductive 

capacity of a typical female recruit is far larger than is necessary to sustain the population. As 

fishing mortality increases, the expected life span of each fish decreases, resulting in a reduction 

in lifetime reproductive capacity. If fishing mortality exceeds a critical threshold, the number of 

eggs produced by a female over her lifetime will fall below the replacement level. Once egg 

production falls below this level, recruitment (the number of fish entering the population each 

year) will begin to decline, and will continue to decline unless fishing is reduced to a level that 

once again allows lifetime egg production to meet or exceed the replacement level (Sissenwine 

and Shepherd 1987, Mace and Sissenwine 1993). 
 71 

In a review of over-fishing definitions used in the management of marine fish stocks, 

Rosenberg et al. (1994) found that most of these definitions were based on the SSBPR model, 

and used the SSBPR model to evaluate over-fishing definitions used to manage the marine fish 

stocks. NOAA guidelines (Restrepo et al. 1998) for implementing National Standard 1 of the 

Magnuson-Stevens Act identify the SSBPR model as one of the methods that can be used to 

establishing over-fishing reference points that comply with the Act. 

SSBPR is estimated as: 

= ∑

i

i i i SSBPR l m f

where 

li = probability of survival from age 1 to age i 

mi = fraction of the population of age i which are mature females; and 

fi = average fecundity of a female fish at age i (average number of 

eggs/female of age i). 

The probability of survival to age i is estimated by combining age-specific rates of natural 

mortality, fishing mortality, and entrainment/impingement mortality: 

1

( )

1

aaa

a i

M F P

i

a

l e

= − − ++

=

= ∏

where 

Ma = age-specific instantaneous natural mortality rate at age a; 

Fa = instantaneous fishing mortality rate at age a; and 

Pa = instantaneous power-plant mortality rate at age a. 

The impact of fishing and power-plant mortality on expected lifetime egg production is 

expressed as the ratio of SSBPR including both sources of mortality to SSBPR without these 

sources of mortality. This ratio is often termed the “spawning potential ratio” (“SPR”): 
 72 

unfished

fished

SSBPR

SSBPR

SPR =

Rates of fishing mortality that would produce a given SPR value are used by fisheries 

management agencies to establish acceptable limits on fishing mortality. Historically, the two 

reference points most commonly used by fisheries managers are F.35 and F.20. F.35 is the fishing 

mortality rate that will lead to an SPR value of 0.35. F.35 has often been used as a default goal for 

achieving maximum sustained yield (“MSY”), i.e., the maximum amount of adult fish (in pounds 

or kilograms) that can be removed from the population each year by fishermen without affecting 

the sustainability of the population. Values of F greater than F.35 would lead to harvests greater 

than could be sustained over time. F.20 is the fishing mortality rate that will lead to SPR value of 

0.2, a default value indicating over-fishing. If F consistently exceeds F.20, then significant 

declines in the adult population may occur. Although some fish stocks may be able to maintain 

recruitment at F.20, other stocks are more sensitive to fishing and cannot sustain exploitation at 

this level (Mace and Sissenwine 1993, Rosenberg et al. 1994). 

4.3 Application to Hudson River fish populations 

Quantitative stock assessments and biological reference points are available for two of 

the species addressed in this report: striped bass (ASMFC 2005) and American shad (ASMFC 

2007). As long as mortality caused by entrainment and impingement is limited to fish that are 

younger than one year old (which is true for both striped bass and American shad), the CMR 

calculated using the generators’ empirical entrainment and impingement models provides a 

direct measure of the reduction in SSBPR caused by IP2 and IP3 (Goodyear 1977). The 

likelihood that entrainment and impingement at IP2 and IP3 have adversely affected the 

sustainability of these two species is evaluated in two ways. First, estimates of reduction in 

SSBPR due IP2 and IP3 are compared to reductions caused by fishing mortality. Second, 

estimates of combined reductions in SSBPR due to both IP2 and IP3 and fishing are compared to 

the biological reference points that are currently used to manage these species. 
 73 

4.3.1 Striped bass

As shown in Figure 42, the striped bass CMR for the 30 years for which data are 

available corresponds to an SPR of 0.92. In other words, IP2 and IP3 reduce the spawning 

potential of the Hudson River striped bass population to 92% of the value for an unfished 

population. Fishing for striped bass at the current target rate established by the ASMFC 

(F=0.30)10 corresponds to an SPR of 0.13. This means that fishing for striped bass, under the 

current management approach, has reduced the reproductive potential of a typical 1-year-old 

female striped bass to only 13% of the value that would be expected in an unfished striped bass 

population. The threshold fishing rate for striped bass is currently set at F=0.41 (ASMFC 2003). 

This value corresponds to an SPR of 0.096. If the rate of fishing were to rise above F=0.41, the 

ASMFC would be required to declare the population to be over-fished and would take action to 

reduce harvesting. 

As shown in Figure 42, even when effects of fishing are combined with effects of IP2 and 

IP3, the combined SPR is still above the threshold. Hence, either alone or in combination with 

fishing, entrainment and impingement at IP2 and IP3 have not jeopardized the sustainability of 

the Hudson River striped bass population as defined by ASMFC regulations. Further, as is clear 

from Figure 42, the impacts of fishing on the sustainability of the Hudson River striped bass 

population dwarf any impact of IP2 and IP3. Eliminating entrainment and impingement of 

striped bass at IP2 and IP3 would not have a measurable influence on the sustainability of the 

population. 

4.3.2 American shad

The ASMFC (ASMFC 2007a, 2007b) recently used the SSBPR model to assess impacts 

of increased mortality on the sustainability of Atlantic coastal American shad populations, 

including the Hudson River American shad population. Because the relative contributions of 

fishing mortality and natural mortality to the increase are uncertain, the ASMFC expressed the 

maximum sustainable rate of mortality in terms of total mortality (Z) rather than fishing 

mortality. The ASMFC selected Z.30, the total mortality rate at which SSBPR would fall to 30% 

 

10 For assessment purposes, Atlantic striped bass are treated as a single mixed population, and the same fishing 

mortality rate is assumed to be applicable to all of the individual spawning populations that contribute to the 

mixed coastal fishery. 
 74 

of an assumed baseline value, as an excess mortality threshold analogous to F.30. Using 

alternative assumptions concerning the operation of the American shad fishery, the ASMFC 

developed a range of estimates of Z.30 of Z=0.54 to Z=0.73 for the Hudson River American shad 

population. 

Empirical estimates of total annual mortality in Hudson River American shad are 

available for the years 1984-2004 (ASMFC 2007a). Total mortality has exceeded Z.30 in most 

years during this period. Hattala and Kahnle (2007) have contended that the excessive mortality 

imposed on Hudson River American shad is due primarily to overfishing. However, regardless 

of the actual cause, it is clear that entrainment at Indian Point is a negligible contributor to 

American shad mortality. Figure 43 compares reductions in spawning potential of American 

shad due to IP2 and IP3 to reductions due to other causes, including fishing. The calculations 

were performed using the Hudson-specific life history parameters from Tables 1.1.5.1-b (ageinvariant natural mortality) and 1.1.5.2-b of ASMFC (2007a) and the revised Type 1 fishery 

model from ASMFC (2007b). 

As shown in Figure 43, entrainment at IP2 and IP3 would reduce the spawning potential 

of Hudson River American shad by only 1% compared to the baseline value. According to the 

ASMFC (2007a), the current rate of total mortality on age 1 and older American shad (Z=0.87) 

corresponds to an SPR of 0.23, well below the threshold level. Because it was derived from an 

analysis of long-term trends in abundance and age structure of the Hudson River shad 

population, the total mortality rate estimate already includes the effects of entrainment at IP2 and 

IP3. If this contribution (as estimated using the CMR) is removed, the decrease in total mortality 

and increase in SPR level are negligibly small (Figure 43). Eliminating entrainment at IP2 and 

IP3 would result in less than a 1% increase in spawning potential, leaving the SPR still 

substantially below the threshold defined by the ASMFC. 

5. Community-Level Trends Analysis 

 Cooling-water withdrawals impose some incremental additional mortality on species 

susceptible to entrainment. If entrainment at IP2 and IP3 were having an adverse impact on the 

Hudson River fish community, then species with high susceptibility to entrainment would be 

more likely to have declined in abundance over the past 30 years than would species with low 

susceptibility. Among those species that declined in abundance, the magnitude of the decline 
 75 

should have been greater for species with high susceptibility than for species with low 

susceptibility. Among species that increased in abundance, the magnitude of the increase should 

have been lower for species with high susceptibility than for species with low susceptibility. 

This hypothesis can be tested using data available from the generators’ riverwide survey 

programs, using data for all Hudson River fish species for which an adequate trends dataset 

could be developed. The method used to perform the test is analysis of correlations between 

indices of entrainment susceptibility, as calculated using distributional data obtained from the 

LRS, and indices of trends in age 0 abundance, obtained from the BSS and FSS. 

 Evaluating the correlation between entrainment susceptibility and change in YOY 

abundance requires selecting those species for which data are available for both variables. 

Entrainment susceptibility at IP2 and IP3 can be estimated by evaluating the distribution of 

entrainable life stages in the region from which IP2 and IP3 withdraws water in comparison to all 

the regions sampled. The generators’ LRS program is designed to collect such data. The 

expected effect of continued annual entrainment losses of early life stages of a species, if losses 

are severe enough to reduce population size, is a decrease in YOY abundance. YOY is the best 

stage to look for the effect of entrainment losses because entrainment occurs prior to the YOY 

stage, and because most susceptible species are still in the river during the YOY stage and thus 

their abundance is measurable. The generators’ BSS and FSS sampling programs are designed 

to monitor YOY abundance. 

5.1 Methods 

The evaluation involves three steps: (1) calculate a species-specific numeric index of 

entrainment susceptibility based on data from the LRS; (2) calculate a species-specific numeric 

index of change in YOY abundance based on data from the BSS and FSS; and (3) determine 

whether entrainment susceptibility is related to change in age 0 abundance. 

Susceptibility to entrainment at IP2 and IP3 was evaluated using an index of standing 

crop estimated from the generators’ LRS for the 31-year period 1974-2004 (Appendix D). 

Indian Point is located in Region 4 (Figure 1), but because of tidal and nontidal flows, can 

withdraw water originating in the two adjoining regions as well. Therefore, relative abundance of 

a species in Regions 3-5 (Figure 1), as compared to the riverwide abundance of that species, was 
 76 

used to define a susceptibility index termed EntSus. For each sampled year (and each seasonal 

period when possible), EntSus is estimated for each species as the ratio of standing crop in 

Regions 3-5 to standing crop in all sampled regions. For those species occurring in more than 

one of the three seasonal periods, annual EntSus values are calculated as an average across 

periods, p, weighted by abundance for each period: 

∑

∑=

p ip

p ip ip

i SC

SC EntSus

EntSus

where EntSusi = fraction of species in the Hudson River estuary in the IP2 and IP3 region in 

year i; 

SCip = sum of abundance of the species within seasonal period p in year i; and 

EntSusip = value of EntSus for seasonal period p in year i. 

 Annual EntSus values for each species for each of 31 years (1974-2004) in which the 

yolk-sac or post yolk-sac stages appeared in the Hudson River are provided in Appendix D. 

The BSS and FSS programs were selected as the best potential indicators of long-term 

relative abundance of fish in the estuary. These programs have sampled the estuary using similar 

gear and methodology since the early 1970s, although there have been variations in the regions 

sampled and in time of initiation and end of the sampling across the years. To maintain 

consistent sampling effort and maximize comparability of results, data are restricted to Regions 

1-12, and weeks 31-42, approximately August through October. 

As documented in Appendix D, abundance data by species are categorized into two 

salinity zones, three habitats, and two time periods. The two salinity zones are brackish (Regions 

1-6; river miles 12-61) and freshwater (Regions 7-12; river miles 62-152). The three habitats 

sampled by these surveys are (a) shorezone (bottom area in water 10 ft or less in depth), (b) 

benthic (volume of water between river bottom and 3 ft above the bottom), and (c) water column 

(water volume not included in either the shorezone or benthic habitats). Time series of 

abundance data are divided into two equal periods: Period 1, covering the years 1974 through 

1989, and Period 2, covering the years 1990-2005. 
 77 

Because freshwater and marine species typically have strong salinity preferences, data 

from the non-preferred salinity zones (brackish zone for freshwater guild; freshwater zone for 

marine guild) were excluded when calculating overall relative change in abundance from Period 

1 to Period 2 for species in these two guilds. So that species with greatly differing abundances 

could be compared in the same scale, the between-period changes were expressed as a relative 

change index (i.e., abundance in Period 2 divided by abundance in Period 1). Details concerning 

these calculations are provided in Appendix D. 

The quantity and quality of abundance and distribution data vary greatly among species. 

The inclusion of species collected only rarely, or only in a small number of years, would weaken 

the analysis. Selection criteria are needed to eliminate species caught too infrequently to provide 

meaningful estimates of EntSus or meaningful abundance trends. However, any single choice of 

selection criteria can be questioned. For this reason, a sensitivity analysis was performed to 

evaluate influence of selection criteria on the outcome of the hypothesis test. The sensitivity 

analysis was performed by defining two cases, or sets of species, termed “Case A” and “Case B.” 

Species included in both cases were selected based on the annual numbers of organisms collected 

in the LRS and BSS/FSS surveys. Species were included in the Case A analysis if (1) an average 

of at least 100 larvae per year of occurrence was collected in LRS samples during 1974-2005 and 

(2) at least 100 YOY were collected in BSS or FSS samples in at least one salinity zone-habitat 

combination in at least one of the two time periods. Species were included in the Case B 

analysis if (1) an average of at least 1000 larvae per year of occurrence was collected in LRS 

samples 1974-2005 and (2) at least 1000 YOY were collected in BSS or FSS samples in at least 

one salinity zone-habitat combination in at least one of the two time periods. The species 

included in Case B are a subset of the species included in Case A. The selection criteria and the 

species included in each case are more fully documented in Appendix D. 

Three correlation metrics (Pearson, Spearman, and Kendall) were used to evaluate the 

association between entrainment susceptibility and YOY abundance change. There is no simple 

mathematical relation between any two of these three methods, and when the true correlation 

coefficient is not zero, it is likely that each coefficient is sensitive to different types of departures 

from independence (Sokal and Rohlf, 1995). 
 78 

5.2 Results and Discussion 

Table 9 shows the correlation coefficients and probability values, for both Case A and 

Case B, for all three correlation indices. None of the correlations are statistically significant. 

Figure 44 provide plots of mean entrainment susceptibility vs. the normalized index of relative 

change in YOY abundance from Period 1 to Period 2 for both Case A and Case B. 

 These figures illustrate the same two patterns. First, more species decreased in 

abundance than increased. For the 21 species in Case A, 71% decreased and 19% increased 

(Figure 44a). For the 11 species in Case B, 73% decreased and 17% increased (Figure 44b). 

Second, the regression of relative abundance change on EntSus is not statistically significant for 

any case, even at the 20% level. This means that relative change from the earlier to the later 

period was the same for species with high susceptibility to entrainment (high EntSus) as for 

species with low susceptibility to entrainment. This result is inconsistent with the hypothesis 

that the susceptibilities of species to entrainment at Indian Point influenced their rates of increase 

or decrease over the period 1974-2005. Although the number of taxa (19) included in this 

analysis is small compared to the total number of species present in the Hudson, these taxa 

represent approximately 94% (Case A) and 88% (Case B) of all age 0 fish captured in the 

BSS/FSS programs from 1974-2005. 

The guild to which each of the 21 species in Case A belongs is indicated in Figure 44a. 

Although each guild is represented by only four to six species, at least one species in each guild 

increased in abundance. This pattern further reinforces the conclusion that the long-term trends 

in abundance of the fish species inhabiting the Hudson River estuary are similar across all guilds 

and are unrelated to entrainment at IP2 and IP3. 

6. Conclusions 

The FEIS and the Draft Permit for IP2 and IP3 stated that three fish species (Atlantic 

tomcod, American shad, and white perch) have declined in abundance in recent years, and 

attributed these declines to cooling-water withdrawals at IP2 and IP3. Analyses performed to 

test alternative hypotheses concerning the causes of these declines show that cooling water 

withdrawals by IP2 and IP3 did not cause these declines. Overharvesting is the most likely cause 

of recent declines in the abundance of American shad, with striped bass predation being a 
 79 

potentially significant contributing factor. Striped bass predation is the most likely cause of the 

decline in abundance of Atlantic tomcod (as well as river herring and bay anchovy). Striped bass 

predation probably contributed to the decline in abundance of YOY white perch, although other 

unknown causes were also involved. The striped bass hypothesis is supported not only by 

analysis of species abundance trends, but also by four recently-published studies of striped bass 

predation (Hartman 2003, Uphoff 2003, Savoy and Crecco 2004, Kahnle and Hattala 2007) and 

by an analysis of the increase in prey consumption needed to support the recent growth of the 

Hudson River striped bass population (Appendix C). 

Two additional lines of evidence support a conclusion that entrainment and impingement 

at IP2 and IP3 have not resulted in AEI. Application of the SSBPR model to stock assessment 

data for striped bass and American shad (Section 4) shows that mortality caused by entrainment 

at IP2 and IP3 is negligible, particularly compared to fishing mortality, and does not impair the 

ability of these populations to sustain themselves. Analysis of community-level trends data 

(Section 5) shows that species with relatively high susceptibility to entrainment at IP2 and IP3 

are no more likely to have declined in abundance since 1974 than are species with relatively low 

susceptibility to entrainment. 

Considered together, the evidence evaluated in this report shows that the operation of IP2 

and IP3 has not caused effects on early life stages of fish that reasonably would be considered 

“adverse” by fisheries scientists and/or managers. The effects of mortality at IP2 and IP3 on the 

survival and abundance of susceptible populations cannot be detected, even after 30 years of 

intensive monitoring. Those changes that have occurred are more likely attributable to predation 

by the Hudson River’s rapidly growing striped bass population. 

For all of the above reasons, from the perspective of a science-based definition of AEI, 

the available data demonstrate that entrainment and impingement associated with cooling-water 

withdrawals by IP2 and IP3 have not had an adverse impact on Hudson River fish populations 

and communities. 
 80 

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 i 

Table 1. Expected effects of stressors on Hudson River fish populations (except Atlantic 

tomcod): age 0 growth, age 0 survival, and age 0 spatial distribution, and adult age structure. 

Response metric CWIS Fishing Zebra mussels Predation by 

striped bass 

PYSL Abundance Ð Ð __ Ð

PYSL→Juv survival Ð __ Ð __ 

Juvenile abundance __ __ __ Ð

Juvenile growth __ __ Ð __ 

Spatial distribution __ __ Ð __ 
 ii 

Table 2. Expected effects of stressors on Hudson River fish Atlantic tomcod population: Age 0 

survival, age 1 survival, juvenile growth, and spatial distribution. 

Response metric CWIS Temperature Striped bass 

predation 

PYSL/early juvenile 

abundance 

Ð __ Ð

Egg to age 1 survival Ð Ð Ð

Age 1 &2 abundance __ __ Ð

Age 1 to age 2 survival __ Ð Ð

Juvenile growth __ Ð __ 

Spatial distribution __ __ __ 
 iii 

Table 3. Consistency of hypotheses with evaluation criteria: striped bass. 

 CWIS Fishing Zebra Mussels 

Co-occurrence - + + 

Sufficiency N/A unknown unknown 

Temporality - + - 

Manipulation - + N/A 

Coherence N/A + - 

Summary evaluation CWIS and zebra mussel hypotheses rejected 

Most likely cause: fishing 
 iv 

Table 4. Consistency of hypotheses with evaluation criteria: white perch. 

 CWIS Zebra mussels Striped bass 

predation 

Co-occurrence - + + 

Sufficiency N/A unknown + 

Temporality + - + (?) 

Manipulation - N/A N/A 

Coherence N/A +(?) + 

Summary evaluation CWIS hypothesis rejected. 

Zebra mussels and striped bass predation may have contributed 

declines occurring in later years, but other unknown causes were 

responsible for declines occurring between 1975 and 1985. 
 v 

Table 5. Consistency of hypotheses with evaluation criteria: American shad. 

 CWIS Overfishing Zebra 

mussels 

Striped bass 

predation 

Co-occurrence - + - + (?) 

Sufficiency N/A + unknown unknown

Temporality - + - + 

Manipulation - N/A N/A N/A 

Coherence N/A + - + 

Summary 

evaluation 

CWIS and zebra mussel hypotheses rejected 

Most likely cause: fishing, with striped bass predation a potential 

contributing factor 
 vi 

Table 6. Consistency of hypotheses with evaluation criteria: Atlantic tomcod. 

 CWIS Temperature Striped bass predation 

Co-occurrence ± + + 

Sufficiency N/A + + 

Temporality - - + 

Manipulation - N/A N/A 

Coherence N/A + + 

Summary evaluation CWIS hypothesis rejected 

Temperature a significant influence, but cannot explain post-1990 

decline 

Most likely cause of decline: striped bass predation 
 vii 

Table 7. Consistency of hypotheses with evaluation criteria: River herring. 

 CWIS Zebra mussels Striped bass predation 

Co-occurrence - - + 

Sufficiency N/A N/A + 

Temporality - - + 

Manipulation - N/A N/A 

Coherence N/A - + 

Summary evaluation CWIS and zebra mussel hypotheses rejected 

Most likely cause: striped bass predation 
 viii 

Table 8. Consistency of hypotheses with evaluation criteria: bay anchovy. 

 CWIS Striped bass predation 

Co-occurrence - + 

Sufficiency N/A Unknown 

Temporality - + 

Manipulation - N/A 

Coherence N/A + 

Summary evaluation CWIS hypothesis rejected 

Striped bass predation most likely cause of change 
 ix 

Table 9. Pearson, Spearman, and Kendall correlation coefficients for the 

association between Log10(R) and mean EntSus. A value of p represents the 

probability of a sample correlation coefficient larger than the observed sample 

correlation coefficient, if the true correlation coefficient is zero. 

Case N Pearson Spearman Kendall 

r 0.225 0.182 0.129 A 19 

p 0.355 0.457 0.442 

r 0.157 -0.042 -0.046 B 12 

p 0.625 0.897 0.837 
 x 

Figure 1. Hudson River map, with sample regions 
 xi 

Figure 2. Impacts of CWIS on Age 0 life stages, partitioned between abundance of each life 

stage and survival between life stages. 

Egg

YSL

YOY PYSL

Age 1

Survival

Survival

Survival

Abundance Abundance 

Abundance 
 xii 

Figure 3. Impacts of fishing on Age 0 life stages.

Egg

YSL

YOY PYSL

Age 1

Decline in spawning

Abundance

Abundance Abundance

Abundance
 xiii 

Figure 4. Impacts of zebra mussel activity on Age 0 life stages. 

Egg

YSL

YOY PYSL

Age 1

Survival

Survival

Growth

Growth

Abundance

Abundance
 xiv 

Figure 5. Impact of striped bass predation on Age 0 life stages. 

Egg

YSL

YOY PYSL

Age 1

Survival

Decline in spawning

Abundance

Abundance

Abundance

Abundance
 xv 

Figure 6. Impact of elevated summer temperatures on Age 0 Atlantic tomcod. 

Egg

YSL

YOY PYSL

Age 1

Survival

Growth

Abundance

Abundance

Survival
 xvi 

Figure 7a. Long-term trends in the abundance of striped bass PYSL and YOY. 

Figure 7b. Long-term trend in striped bass PYSL to YOY survival. 

0

1000

2000

3000

4000

5000

6000

7000

197419761978

198019821984

19861988

1990

1992

1994

19961998

20002002

2004

0

500

1000

1500

2000

2500

3000

3500

PYSL

YOY

Striped Bass PYSL Index (millions)

Striped Bass YOY Index (thousands)

0

0.005

0.01

0.015

0.02

0.025

0.03

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Striped bass PYSL survival index
 xvii 

Figure 8a. Relationship between striped bass PYSL abundance and striped bass YOY 

abundance. 

Figure 8b. Relationship between striped bass PYSL abundance and PYSL survival. 

0

0.005

0.01

0.015

0.02

0.025

0.03

0 1000 2000 3000 4000 5000 6000 7000

Striped Bass PYSL Index (millions)

Striped Bass PYSL Survival Index

0

500

1000

1500

2000

2500

3000

3500

0 1000 2000 3000 4000 5000 6000 7000

Striped bass PYSL abundance index (millions)

Striped bass YOY abundance index (thousands)
 xviii 

Figure 9a. Relationships between IP2 and IP3 CMR for striped bass and striped bass PYSL 

survival index. 

Figure 9b. Relationship between IP2 and IP3 CMR for striped bass and striped bass PYSL 

abundance index. 

0

0.005

0.01

0.015

0.02

0.025

0.03

0 5 10 15 20

Striped Bass CMR (%)

Striped Bass PYSL Survival Index

0

1000

2000

3000

4000

5000

6000

7000

0 5 10 15 20

Striped Bass CMR (%)

Striped Bass PYSL index (millions)
 xix 

Figure 10. (a) Striped bass PYSL to YOY survival during years in which 1 unit (blue) and 2 

units (red) at Indian Point were operating during May and June, the peak months during which 

entrainable life stages of striped bass are present in the Hudson River. The horizontal line shows 

the median survival index value for the time series. (b) Relationship between total May-June 

withdrawals by IP2 and IP3 and striped bass PYSL survival. 

 (a) 

 (b) 

0

0.005

0.01

0.015

0.02

0.025

0.03

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

0

0.005

0.01

0.015

0.02

0.025

0.03

1975

1977

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

0

0.005

0.01

0.015

0.02

0.025

0.03

0 50000 100000 150000

Combined May-June Flows for IP2 and IP3, 1975-2002

Striped Bass PYSL Survival Index

0

0.005

0.01

0.015

0.02

0.025

0.03

0 50000 100000 150000

Combined May-June Flows for IP2 and IP3, 1975-2002

Striped Bass PYSL Survival Index
 xx 

Figure 11. Long-term trends in the abundance of white perch PYSL and YOY in the Hudson 

River. 

0

500

1000

1500

2000

2500

19741976

1978

1980198219841986

19881990

199219941996

1998

2000

2002

2004

0

500

1000

1500

2000

2500

3000

3500

PYSL

YOY

White Perch PYSL index (millions)

White Perch YOY index (thousands)
 xxi 

Figure 12a. Relationship between white perch PYSL abundance and YOY abundance. 

Figure 12b. Relationship between white perch PYSL survival and YOY abundance. 

0

500

1000

1500

2000

2500

3000

3500

0 500 1000 1500 2000 2500

White perch PYSL abundance index (millions)

White perch YOY abundance index (thousands)

0

500

1000

1500

2000

2500

3000

3500

0 0.001 0.002 0.003 0.004 0.005 0.006

White Perch PYSL survival index

White Perch YOY abundance index
 xxii 

Figure 13a. Relationship between the IP2 and IP3 CMR for white perch and the white perch 

PYSL survival index. 

Figure 13b. Relationship between the IP2 and IP3 CMR for white perch and the white perch 

PYSL abundance index. 

0

0.001

0.002

0.003

0.004

0.005

0.006

0 5 10 15 20

Indian Point CMR (%)

White perch PYSL survival index

0

500

1000

1500

2000

2500

0 5 10 15 20

Indian Point CMR (%)

White perch PYSL Abundance Index
 xxiii 

Figure 14. Long-term trends in IP2 and IP3 CMR for white perch and white perch PYSL 

survival. 

0

0.001

0.002

0.003

0.004

0.005

0.006

1974197619781980198219841986198819901992

1994199619982000

2002

0

2

4

6

8

10

12

14

16

18

20

PYSL survival

Indian Point CMR
 xxiv 

Figure 15. (a) White perch PYSL to YOY survival during years in which 1 unit (blue) and 2 

units s(red) at Indian Point were operating during May and June, the peak months during which 

entrainable life stages of white perch are present in the Hudson River. The horizontal line shows 

the median survival index value for the time series. (b) Relationship between total May-June 

withdrawals by IP2 and IP3 and white perch PYSL survival. 

 (a) 

 (b) 

0

0.001

0.002

0.003

0.004

0.005

0.006

1975 1978 1981 1984 1987 1990 1993 1996 1999 2002

White perch PYSL to YOY survival index

0

0.001

0.002

0.003

0.004

0.005

0.006

0 50000 100000 150000

White perch PYSL to YOY survival index

Combined May-June Flows for IP2 and IP3

0

0.001

0.002

0.003

0.004

0.005

0.006

0 50000 100000 150000

White perch PYSL to YOY survival index

Combined May-June Flows for IP2 and IP3
 xxv 

Figure 16a. Relationship between white perch YOY abundance and the striped bass predation 

index. 

Figure 16b. Long-term trends in white perch YOY abundance and the striped bass predation 

index. 

0.00E+00

5.00E+02

1.00E+03

1.50E+03

2.00E+03

0 1000 2000 3000 4000 5000 6000 7000

Striped Bass Abundance Index (millions)

White Perch YOY Index (thousands)

0

1000

2000

3000

4000

5000

6000

7000

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

0

500

1000

1500

2000

2500

3000

3500

SB Predation

White perch YOY
 xxvi 

Figure 17. Long-term trends in abundance of American shad PYSL and YOY abundance in the 

Hudson River. 

0

20

40

60

80

100

120

140

160

1974

1976

1978198019821984198619881990

199219941996

1998

20002002

2004

0

500

1000

1500

2000

2500

3000

3500

PYSL Abundance

YOY Abundance
 xxvii 

Figure 18a. Relationship between American shad PYSL abundance and YOY abundance in the 

Hudson River. 

Figure 18b. Relationship between American shad PYSL survival and YOY abundance in the 

Hudson River. 

0

500

1,000

1,500

2,000

2,500

3,000

3,500

0 0.02 0.04 0.06 0.08 0.1

American shad PYSL survival index

American shad YOY abundance index

0

500

1,000

1,500

2,000

2,500

3,000

3,500

0 50 100 150 200

American shad PYSL abundance (millions)

American shad YOY abundance (thousands)
 xxviii 

Figure 19a. Relationship between the IP2 and IP3 CMR for American shad and American shad 

PYSL survival. 

Figure 19b. Relationship between the IP2 and IP3 CMR for American shad and American shad 

PYSL abundance. 

. 

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

01234

Indian Point CMR (%)

American shadPYSL survival index

0

20

40

60

80

100

120

140

160

02468

Indian Point CMR (%)

American shad PYSL index (millions)
 xxix 

Figure 20. (a) American shad PYSL to YOY survival during years in which 1 unit (blue) and 2 

units s(red) at Indian Point were operating during May and June, the peak months during which 

entrainable life stages of American shad are present in the Hudson River. The horizontal line 

shows the median survival index value for the time series. (b) Relationship between total MayJune withdrawals by IP2 and IP3 and American shad PYSL survival. 

 (a) 

 (b) 

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

1985 1987 1989 1991 1993 1995 1997 1999 2001

American shad PYSL to YOY surivival index

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0 50000 100000 150000

Combined May-June Flows for IP2 and IP3

American shad PYSL to YOY survival index

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0 50000 100000 150000

Combined May-June Flows for IP2 and IP3

American shad PYSL to YOY survival index
 xxx 

Figure 21a. Relationship between American shad PYSL abundance and the striped bass 

predation index. 

Figure 21b. Relationship between American shad YOY abundance and the striped bass 

predation index. 

0

25

50

75

100

125

150

175

0 1000 2000 3000 4000 5000 6000

SB Predation Index (millions)

American shad PYSL Index (millions)

0

0 1000 2000 3000 4000 5000 6000

SB Predation Index (millions)

American shad YOY Index (millions)
 xxxi 

Figure 22. Long-term trends in American shad PYSL abundance and in the striped bass 

predation index. 

0

1000

2000

3000

4000

5000

6000

7000

1974197619781980198219841986198819901992199419961998200020022004

0

20

40

60

80

100

120

140

160

SB Predation

American shad PYSL

American shad PYSL Index (millions)

Striped Bass Abundance Index (millions)
 xxxii 

Figure 23. Long-term trends in the abundance of Atlantic tomcod in the Hudson River. 

0

20

40

60

80

100

120

140

160

180

200

1974

1976

197819801982

19841986198819901992199419961998

2000

2002

2004

0

5

10

15

20

25

30

LRS

Mark-Recapture
 xxxiii 

Figure 24a. Relationship between Atlantic tomcod egg deposition and resulting age 1 

abundance. 

Figure 24b. Relationship between Atlantic tomcod egg to age 1 survival and age 1 abundance. 

.

0

2

4

6

8

10

12

0 20 40 60 80 100 120

Egg deposition (Billions)

Resulting Age 1 (millions)

0

2

4

6

8

10

12

0 0.2 0.4 0.6 0.8 1

Atlantic tomcod egg to age 1 survival index

Atlantic tomcod age 1 abundance
 xxxiv 

Figure 25a. Relationship between IP2 and IP3 CMR and Atlantic tomcod egg to age 1 survival. 

Figure 25b. Relationship between IP2 and IP3 CMR and Atlantic tomcod LRS index. 

Figure 25c. Relationship between IP2 and IP3 CMR and Atlantic tomcod mark-recapture index 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 5 10 15 20 25 30

Indian Point Entrainment CMR (%)

Egg to Age 1 survival index

0

5

10

15

20

25

30

0 10 20 30 40

Indian Point Entrainment CMR (%)

Atlantic tomcod M-R index

0

20

40

60

80

100

120

140

160

180

200

0 10 20 30 40

Indian Point Entrainment CMR (%)

Atlantic tomcod LRS index
 xxxv 

Figure 26. (a) Atlantic tomcod age 0 survival during years in which 1 unit (blue) and 2 units 

(red) at Indian Point were operating during May and June, the peak months during which 

entrainable life stages of Atlantic tomcod are present in the Hudson River. The horizontal line 

shows the median survival index value for the time series. (b) Relationship between combined 

IP2 and IP3 May-June withdrawals and Atlantic tomcod egg to age 1 survival. 

 (a) 

 (b) 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1974

1976

1978

1980

1982

1984

19861988

1990

1992

19941996

19982000

Atlantic tomcod age 0 survival index

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 50000 100000 150000

Combined May-June Flows for IP2 and IP3 (MGal)

Atlantic Tomcod Egg – Age 1 Survival Index

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 50000 100000 150000

Combined May-June Flows for IP2 and IP3 (MGal)

Atlantic Tomcod Egg – Age 1 Survival Index
 xxxvi 

Figure 27. Comparison of long-term trends in the PWW degree-day index to long-term trends in 

the abundance of age 1 and age 2 Atlantic tomcod. 

0

20

40

60

80

100

120

140

160

19761978198019821984198619881990

1992

1994199619982000

2002

0

5

10

15

20

25

30

PWW Degree-Days

Age 1-2 tomcod (millions)
 xxxvii 

Figure 28a. Relationship between the striped bass predation index and the Atlantic tomcod LRS 

index. 

Figure 28b. Relationship between the striped bass predation index and the Atlantic tomcod 

mark-recapture index. 

0

5

10

15

20

25

30

0 1000 2000 3000 4000 5000 6000

Atlantic tomcod M-R Index (millions)

SB Predation Index (millions)

0

50

100

150

200

250

0 1000 2000 3000 4000 5000 6000

SB Predation Index (millions)

Atlantic tomcod LRS Index (millions)
 xxxviii 

Figure 29a. Long-term trends in the Atlantic tomcod LRS index and the striped bass predation 

index. 

Figure 29b. Long-term trends in the Atlantic tomcod mark-recapture index and the striped bass 

predation index.

0

1000

2000

3000

4000

5000

6000

7000

197419761978198019821984198619881990199219941996199820002002

2004

0

20

40

60

80

100

120

140

160

180

200

Atlantic TomcodLRS Index (millions)

Striped Bass Abundance Index (millions)

0

1000

2000

3000

4000

5000

6000

7000

197419761978

1980198219841986198819901992199419961998

2000

20022004

0

1

2

3

4

5

6

Striped Bass Abundance Index (millions)

Atlantic TomcodM-R Index (millions)
 xxxix 

Figure 30a. Long-term trend in abundance of river herring PYSL in the Hudson River. 

Figure 30b. Long-term trends in abundance of alewife and blueback herring YOY in the Hudson 

River. 

0

0.5

1

1.5

2

2.5

3

3.5

4

1974

1976

1978

1980

1982

198419861988

1990

1992

1994

1996

1998

2000

20022004

River herring PYSL

0

200

400

600

800

1000

1200

19741976

19781980

1982

198419861988

1990

1992

1994

19961998

2000

2002

2004

0

5

10

15

20

25

30

35

alewife

blueback herring
 xl 

Figure 31a. Relationship between the IP2 and IP3 CMR and river herring PYSL survival. 

Figure 31b. Relationship between the IP2 and IP3 CMR and river herring PYSL abundance. 

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0123456

Indian Point CMR (%)

River Herring PYSL Survival Index

0

0.5

1

1.5

2

2.5

3

3.5

4

0123456

Indian Point CMR (%)

River Herring PYSL Index (Billions)
 xli 

Figure 32a. Relationship between the IP2 and IP3 CMR and alewife YOY abundance. 

Figure32b. Relationship between the IP2 and IP3 CMR and blueback herring YOY abundance. 

0

200

400

600

800

1000

1200

0123456

Indian Point CMR (%)

Alewife YOY Index (thousands)

0

5

10

15

20

25

30

35

0123456

Indian Point CMR (%)

Blueback herring YOY Index (millions)
 xlii 

Figure 33. (a) River herring (alewife and blueback herring) PYSL to YOY survival during years 

in which 1 unit (blue) and 2 units (red) at Indian Point were operating during May and June, the 

peak months during which entrainable life stages of river herring are present in the Hudson 

River. The horizontal line shows the median survival index value for the time series. (b) 

Relationship between IP2 and IP3 May-June water withdrawals and river herring PYSL survival. 

 (a) 

 (b) 

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

1974 1977 1980 1983 1986 1989 1992 1995 1998 2001

River hering PYSL to YOY survival index

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0 50000 100000 150000

River herring PYSL to YOY survival index

Combined May-June Flows for IP2 and IP3

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0 50000 100000 150000

River herring PYSL to YOY survival index

Combined May-June Flows for IP2 and IP3
 xliii 

Figure 34a. Relationship between the striped bass predation index and river herring PYSL 

abundance. 

Figure 34b. Relationship between the striped bass predation index and alewife YOY abundance. 

Figure 34c. Relationship between the striped bass predation index and blueback herring YOY 

abundance. 

0

1

2

3

4

0 1000 2000 3000 4000 5000 6000

SB Predation Index (millions)

River Herring PYSL Index (billions)

0

200

400

600

800

1000

1200

0 1000 2000 3000 4000 5000 6000

SB Predation Index (millions)

Alewife YOY Index (thousands

0

5

10

15

20

25

30

35

0 1000 2000 3000 4000 5000 6000

SB Predation Index (millions)

Blueback Herring YOY Index (millions)
 xliv 

Figure 35a. Long-term trends in river herring PYSL abundance and in the striped bass predation 

index. 

Figure 35b. Long-term trends in alewife YOY abundance and in the striped bass predation 

index. 

 

Figure 35c. Long-term trends in blueback herring YOY abundance and in the striped bass 

predation index. 

0

1000

2000

3000

4000

5000

6000

7000

1974

1976

1978

19801982

1984

1986

1988

19901992

1994

1996

199820002002

2004

0

0.5

1

1.5

2

2.5

3

3.5

4

SB Predation

River herring PYSL

Striped Bass Abundance Index (millions)

River Herring PYSL Index (billions)

0

1000

2000

3000

4000

5000

6000

7000

1974

19761978

1980

1982198419861988199019921994

1996199820002002

2004

0

5

10

15

20

25

30

35

SB Predation

Blueback herring YOY

0

1000

2000

3000

4000

5000

6000

7000

1974

1976197819801982198419861988199019921994

1996

199820002002

2004

0

200

400

600

800

1000

1200

SB Predation

Alewife YOY
 xlv 

Figure 36. Long-term trends in abundance of bay anchovy PYSL and YOY. 

0

1000

2000

3000

4000

5000

6000

1974197619781980198219841986198819901992199419961998200020022004

0

50

100

150

200

250

300

350

400

450

500

PYSL

YOY
 xlvi 

Figure 37a. Relationship between the IP2 and IP3 CMR and bay anchovy PYSL to YOY 

survival. 

Figure 37b. Relationship between the IP2 and IP3 CMR and bay anchovy PYSL abundance. 

0

20

40

60

80

100

120

140

160

180

200

0 5 10 15 20 25

Indian Point CMR (%)

Bay Anchovy PYSL Survival Index

0

1000

2000

3000

4000

5000

6000

0 5 10 15 20 25

Indian Point CMR (%)

Bay anchovy PYSL abundance
 xlvii 

Figure 38. (a) Bay anchovy PYSL to YOY survival during years in which 1 unit (blue) and 2 

units (red) at Indian Point were operating during May and June, the peak months during which 

entrainable life stages of river herring are present in the Hudson River. The horizontal line 

shows the median survival index value for the time series. (b) Relationship between total IP2 

and IP3 June-July withdrawals and bay anchovy PYSL survival. 

 (a) 

 (b) 

0.01

0.1

1

10

100

1000

1985

1987

1989

1991

1993

1995

1997

1999

2001

Bay anchovy survival index

0.01

0.1

1

10

100

1000

0 50000 100000 150000 200000

Bay anchovy survival index

Combined May-June Flows for IP2 and IP3

0.01

0.1

1

10

100

1000

0 50000 100000 150000 200000

Bay anchovy survival index

Combined May-June Flows for IP2 and IP3
 xlviii 

Figure 39a. Relationship between bay anchovy YOY abundance and the striped bass predation 

index. 

Figure 39b. Long-term trends in bay anchovy YOY abundance and the striped bass predation 

index. 

0

100

200

300

400

500

600

0 1000 2000 3000 4000 5000 6000 7000

Striped Bass Predation Index (millions)

Bay Anchovy YOY Index (millions)

0

1000

2000

3000

4000

5000

6000

7000

197419761978198019821984

19861988

1990

19921994

1996

1998

2000

2002

2004

0

50

100

150

200

250

300

350

400

450

500

SB Predation

Bay anchovy YOY
 xlix 

Figure 40. Long-term trends in the abundance of spottail shiner eggs and YOY. 

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

19741976

197819801982

1984

1986

1988

199019921994199619982000

2002

2004

0

0.5

1

1.5

2

2.5

Eggs (millions)

YOY (millions)
 l 

Figure 41a. Relationship between the IP2 and IP3 CMR and spottail shiner egg to YOY 

survival. 

Figure 41b. Relationship between the IP2 and IP3 CMR and spottail shiner YOY abundance. 

0

20

40

60

80

100

120

140

0 2 4 6 8 10

Indian Point CMR (%)

Spottail Shiner egg to YOY survival index

0

0.5

1

1.5

2

2.5

0 2 4 6 8 10

Indian Point CMR (%)

Spottail Shiner YOY index (millions)
 li 

Figure 42. Relative influence of IP2 and IP3 vs. fishing on the spawning potential of Hudson 

River striped bass. 

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Unfished population Indian Point Target F Target F + IP

Spawning Potential Ratio (SPR)

SPR at Threshold F

Spawning Potential Ratio
 lii 

Figure 43. Comparative effects of Indian Point and fishing on Hudson River American shad 

SPR using data and modeling method from 2007 American shad stock assessment (ASMFC 

2007a). 

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Unfished population Indian Point Current Z Current Z - IP

Spawning Potential Ratio (SPR)

SPR at Threshold Z

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Unfished population Indian Point Current Z Current Z - IP

Spawning Potential Ratio (SPR)

SPR at Threshold Z

Spawning Potential Ratio
 liii 

Figure 44a. Relationship between relative change in YOY abundance from Period 1 to Period 2 

and entrainment susceptibility for the 21 fish species included in Case A. Zero on the 

logarithmic Y axis corresponds to no change in abundance from Period 1 to Period 2. 

CASE A

-2

0

2

0 0.1 0.2 0.3 0.4 0.5

Mean Entsus

Log10(R)

Anadromous

Estuarine

Freshwater

Marine

Figure 44b. Relationship between relative change in YOY abundance from Period 1 to Period 2 

and entrainment susceptibility for the 11 fish species included in Case B. Zero on the 

logarithmic Y axis corresponds to no change in abundance from Period 1 to Period 2. 

CASE B

-2

0

2

0 0.1 0.2 0.3 0.4 0.5

Mean Entsus

Log10(R)

Anadromous

Estuarine

Freshwater

Marine