### ENTRAINMENT AND IMPINGEM ENT 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 2008TABLE 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 mort ality rate (CMR) in impact assessment..........................................................................................13 2.4 Role of long-term datasets in impact assessment ..............................14
2.5 Indicators of adverse impact s potentially re lated 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 ...............................................................................27ii 3.3.1 Response metrics for striped ba ss, 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 ...................................................56iii 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 ........................................................................................................................ . iiiiv Table 4 ........................................................................................................................ . iv

Table 5 ........................................................................................................................ ...v
Table 6 ........................................................................................................................ . vi

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

Table 8 ....................................................................................................................... v iii

Table 9 ........................................................................................................................ . ix
Figure 1 ....................................................................................................................... ...x Figure 2 ....................................................................................................................... . xi
Figure 3 ....................................................................................................................... xii
Figure 4 ...................................................................................................................... x iii
Figure 5 ...................................................................................................................... x iv
Figure 6 ....................................................................................................................... .xv Figure7a ..................................................................................................................... xv i
Figure 7b .................................................................................................................... xv i
Figure 8a ................................................................................................................... xvi i
Figure 8b ................................................................................................................... xvi i
Figure 9a .................................................................................................................. xvii i
Figure 9b .................................................................................................................. xvii i
Figure 10 .................................................................................................................... xi x Figure 11 ...................................................................................................................... xx Figure 12a .................................................................................................................. xxi
Figure 12b .................................................................................................................. xxi
Figure 13a ................................................................................................................. xxii
Figure 13b ................................................................................................................. xxii
Figure 14 .................................................................................................................. xxii iv 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 .............................................................................................................. xxxixvi Figure 30b .............................................................................................................. xxxix
Figure 31a .................................................................................................................... x l
Figure 31b .................................................................................................................... x l
Figure 32a ................................................................................................................... xl i
Figure 32b ................................................................................................................... xl i
Figure 33 .................................................................................................................... xl ii
Figure 34a ................................................................................................................. xlii i
Figure 34b ................................................................................................................. xlii i
Figure 34c ................................................................................................................. xlii i
Figure 35a ................................................................................................................. xliv
Figure 35b ................................................................................................................. xliv
Figure 35c ................................................................................................................. xliv
Figure 36 .................................................................................................................... xl v Figure 37a ................................................................................................................. xlvi
Figure 37b ................................................................................................................. xlvi
Figure 38 .................................................................................................................. xlvi i
Figure 39a ............................................................................................................... xlviii
Figure 39b ............................................................................................................... xlviii
Figure 40 ................................................................................................................... xli x Figure 41a ..................................................................................................................... . l
Figure 41b ..................................................................................................................... . l
Figure 42 ...................................................................................................................... . li
Figure 43 ...................................................................................................................... lii
Figure 44a ................................................................................................................... li ii
Figure 44b ................................................................................................................... li ii1 

### 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 es tablished definitions and standard s 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) whet her cooling-water withdrawals by IP2 and IP3 during this period could have been responsible for any such change s, or (3) whet her alternative stressors including stripe d 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, term ed the Spawning Stock Biomass per Recruit (“SSBPR”) model, to determine whether entrai nment and impingement at IP2 and IP3 could have adversely affected the sust ainability 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 deve loped 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 suppor t a conclusion that IP2 and IP3 have not
caused an AEI. Evaluation of alternative hypotheses concerni ng the causes of changes in abundance of Hudson River fish populations f ound no evidence supporting the hypothesis that IP2 and IP3 contributed to these changes. Inst ead, 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 sign ificant contributing factor. In creased 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 a nd bay anchovy. Striped bass predation probably2 contributed to the decline in abundance of white perch, although other unk nown causes were also
involved. Two additional lines of evidence support a c onclusion that entrainment and impingement
at IP2 and IP3 have not resulted in AEI. A pplication of the SSBPR m odel to stock assessment data for striped bass and American shad shows that mortality cau sed by entrainment at IP2 and IP3 is negligible, particularly compared to fish ing mortality, and does no t impair the ability of these populations to sustain themse lves. Analysis of community-l evel trends data show that species with relatively high suscep tibility to entrainment at IP 2 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 lim ited swimming abilities th at float in the water-column and are passive ly 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 throu gh a cooling water intake structure

Individual : A single organism

Population : A group of plants, animals, or other orga nisms, all of the same species, that live together and reproduce

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

Yolk-sac larvae (YSL) : Fish larvae that have recently ha tched 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 tr ansformation 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 survey4 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’ annua l 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 us ed 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 st ock 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 th e expected future egg production or biomass

Density-dependence : A relationship between the abunda nce of a population and the growth rates or mortality rates of indi viduals belonging to that population

Stressor : An anthropogenic or environmental factor th at increases mortality or decreases growth of organisms belonging to a populat ion exposed to that factor

Stressor metric : A measure of the intensity of a stressor

Response metric 

### : A measure of the response of an expos ed population to one or more stressors5 

### 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 a nd 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 definiti ons 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 fish eries 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 bega n commercial operation, (2) whether coolingwater withdrawals by IP2 and IP3 during this period could have been responsible for any such changes, or (3) whether altern ative 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, term ed the Spawning Stock Biomass per Recruit (“SSBPR”) model, to determine whether entrai nment and impingement at IP2 and IP3 could have adversely affected the sust ainability 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 deve loped 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 imp acts 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 impinge ment. Second, entrainment is the focus of the Department, as the existing retrofits (i.e., Ristro ph screens and fish returns) have resolved the Department’s concerns regarding impingement (Draft SPDES Permit, Special Condition 27).



### 2. Approach to Impact Assessment

Populations 2 and communities 3 are the proper focus for evaluating adverse impacts of cooling-water withdrawals on the Hudson River es tuary. The fundamental reason for focusing on populations and communities is that, whereas all individual organisms have finite life spans, populations and communities can persist. Beca use populations and communities can persist in spite of the inevitable mortality of the individua l organisms, populations and communities can be managed and restored. Most commonly, fisherie s management agencies establish harvesting policies to manage populations of fish while a llowing harvesting of individual fish to continue (Restrepo et al. 1998). The U.S. Environmen tal 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 principl es 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 reso urces of the Hudson River. Our evaluation is primarily based on an anal ysis of empirical data collected over the 30 years during which IP2 and IP3 have been operating, in a manner that appropriate ly accounts for other potential causes of change s in fish populations. This is because factors other than entrainment and impingement affect the abunda nce 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 th at occur together in space and time (Begon et al. 1996).7 natural environmental fluctuations, long-term environmental change, in troductions 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 poten tially 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 to mcod; (5) alewife; (6) bl ueback 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 th e 1970s (TI 1980). Six of these species, striped bass, white perc h, 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, bl ueback 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 addresse d 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); Techni cal 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 Atla ntic 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 (i ncluding 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 im pact in the context of fishery management

In the context of fisheries management, mortality per se could not be cons idered an AEI, because the act of fishing necessarily causes mort ality. To the contrary, fisheries management agencies, including NYSDEC, actively encourage the responsible harvesting of fish. For example, NYSDEC has issued a guide to saltw ater 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-S tevens Act”). The amended Act states: Fishery resources are finite but renewable. If placed under sound management before over-fishing ha s caused irreversible effects, the fisheries can be conserved an d 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 th at “[c]onservation and management measures shall prevent over-fish ing 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 harves ting is permissible, provided that this mortality does not threaten the long-term productivity of the popul ation. Over-fishing that threatens the long-term sustainability of harvests is consid ered to be adverse. The National Oceanic and Atmospheric Administration (“NOAA”) guidelin es and other related technical guidance documents (e.g., Restrepo et al. 1998) provide speci fic 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 exis t. 70 Fed. Reg. 36240, 36257 (June 22, 2005). The Magnuson-Stevens Act is often cited as th e “Sustainable Fisheries Act.” The term
“sustainable” is often used in a wider environmen tal policy context to refer to an approach to economic development and resour ce 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 Commi ssion 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 re fers to a resource-management approach that permits the popul ation to persist indefinitely into the future, while continuing to perform its normal ecologi cal function and support normal human use. Ecological function is included as part of the de finition 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 fis h, such as striped bass, control the abundance of other fish species upon which they prey, and forage fish, su ch 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 popul ation is a function of the a bundance and other characteristics of the population (e.g., age and si ze structure) and also of th e 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 ha rvest-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 cont ext of ecological risk assessment

USEPA’s Guidelines for Ecological Risk Assessment (USEPA 1998) provide a general discussion of adverse ecological effects of environmental stre ssors, including criteria for evaluating whether or not observe d 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 asse ssments” (USEPA 1998, p. 8). This guidance discusses adverse ecological effect s of environmental stressors, in cluding criteria for evaluating whether or not observed or pred icted changes should be consider ed adverse. According to USEPA and the scientific community, adverse ecol ogical effects are change s that “alter valued
structural or functional attributes of the ecol ogical 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 a ppear. “Potential for recovery” refers to the expected rate and
extent of return of an aff ected population or community follo wing elimination of the stressor responsible for an effect that has been de termined 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 po pulations 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 pote ntially 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 thes e populations and communities. An assessment whether adverse11 impacts have occurred (or will occur) should a ddress the three criteria provided in the
Guidelines.

2.1.3 Definition of adverse environmental im pact in the context of entrainment and impingement

The definition of sustainable use in the Ma gnuson-Stevens Act and the definition of ecological adversity in USEPA’s Guidelines for Ecological Risk Assessme nt provide a reasoned basis for a definition of AEI applicable to entr ainment and impingement at CWIS. A sustainable approach to managing a fishery would ensure th e long-term persistence and productivity of the population being managed. A non-sust ainable approach, in contrast , would cause harvest-related reductions in abundance, changes in age/size struct ure, increases in mortality, or reductions in reproduction that could threaten the capacity of a population to persist, perform its normal ecological function, an d support normal human uses. Si nce the ecological function of a population is understood by scient ists to include interacti ons with other populations, nonsustainable use of a population can affect an entire community. Abundance, age/size structure, mortalit y, and reproduction are examples of the
“structural and functional attributes” discus sed in the USEPA Guidelines. Hence, nonsustainable management of a fi shery would be an example of an AEI according to USEPA’s definition. Entrainment mortality differs from mo rtality caused by harvesting only in that the mortality is imposed on early life stages of fish or shellfish ra ther than on adults. Excessive levels of entrainment mortality could potentially a ffect most of the same structural and functional attributes affected by harvesting. In sum, the term AEI, as it relates to en trainment 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 sustainabili ty 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 re cognized 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 unders tanding what the term AEI reasonably ma y mean with respect to fisheries biology. As a matter of science and logic, losses, even la rge numbers of early life stage individuals do not necessarily equate to AEI. Th is is because fish species inhabiting the Hudson River exhibit either “periodic” or “o pportunistic” life history traits (Winemiller and Rose 1992). From an ecological perspective, pe riodic fish species ar e 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). O pportunistic 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 an chovy is an example of an opportunistic species. Periodic and opportunistic traits are advantag eous to fish species th at live in unstable or unpredictable environments, such as the Hudson River, which experiences significant withinyear and between-year variation in environmen tal conditions (e.g., te mperature, salinity, freshwater flow, etc.). In other words, the reproductive strategies of these fish in these unstable conditions, including the very la rge numbers of eggs produced, en sure 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 c ontain more than 3 million eggs (Hoff et al. 1988). A 16-year-old female striped bass exam ined 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 entr ained at IP2 and IP3 would have died in any case, counts of total numbers en trained 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 earl y period of high natural mo rtality. As discussed in the next sections, this fact was recognized more than 30 years ago by the scientists who performed the first entrainment impact assessme nts for IP2 and IP3, in conjunction with other Hudson River generating stations.

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

The first assessments of the effects of coo ling-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 math ematical 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 licensi ng proceedings for IP2 and IP3, and were incorporated in environmental impact statem ents prepared to support these proceedings (Barnthouse et al. 1984). At the time they we re 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 a dverse impacts on important fish populations. When data from riverwide ichthyoplankton sampling became availabl e in the late 1970s, scientists studying entrainment impacts developed an empirical m odel, 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 cal culated 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 neithe r the ETM, nor the EIM, accounts for the density-dependent processes that can partiall y offset mortality due to entrainment and14 impingement (Barnthouse et al. 1984). CMRs c ould, however, be used to compare the relative potential effectiveness of alternative tech nologies 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 su sceptible populations. Because it does not account for density-dependent effects, the CMR is not a valid measure of long-term entrainment impacts. De pending on the strength of density-dependence in a given population, a particular CMR value corres ponds 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 assessm ent, 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 fo r 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 y ears of intensive data co llection, 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 data sets in impact assessment

Today, nearly 30 years of data are availabl e from both generator and agency-sponsored monitoring programs. Together, these overlappi ng 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 ha rvested species, data on long-term

4 Although there can be substantial uncertainty concer ning 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 mo rtality of adult fish are available. These
datasets can be used both to asse ss trends in the status of importa nt fish populations and to test alternative hypotheses concerning potent ial causes of adverse changes. In this report, information concerning long- term trends on key popul ation characteristics and on the intensities of potential stressors is used to test specific hypotheses concerning the expected impacts of cooling-water withdrawal s, termed “risk hypotheses” in USEPA’s Guidelines for Ecological Risk Assessment (USE PA 1998). These hypothesis tests are used to distinguish changes that could ha ve been caused by cooling-water withdrawals from changes that are most likely related to other causes. The following generator-sponsored long-term data sets are the primary datasets used in assessing the effects of th e CWIS at IP2 and IP3:

• Longitudinal River Ichthy oplankton Survey (“LRS”) . This program samples eggs, larvae, and juvenile fish, weekly from April through July. The region between the George Wash ington Bridge and the Federal Dam at Troy ( Figure 1) has been sa mpled with only minor changes in methodology since 1974. In 1988, the LRS was extended to sample the region between the Battery and th e George Washington Bridge.

• Beach Seine Survey (“BSS”) . This program samples juvenile fish, also called “young-of-the-yea r” fish (“YOY”) (i.e.
, fish spawned earlier in the year) on alternate weeks from June through October. Sampling is conducted from the George Washington Br idge 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-m 2 Tucker trawl and a 3-m beam trawl. This16 program was also initiated in 1974, however, the b eam 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 Ma rk-Recapture Program . This program has been conducted in most years since 1974 to ge nerate 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 prim ary 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 st artup) and all of the period of commercial operation of IP3 (1976 startup). These four datase ts provide the most comprehensiv e 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, NYS DEC, 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 conditio n of the HRSA) to the Hudson River population. The program targets 1-yea r-old and 2-year-o ld striped bass,
and is conducted from November thr ough 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 b each seine survey in the lower Hudson River estuary. The prog ram 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 ot her 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 west ern 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 in formation on length, age and sex distribution, and mortality rates for a dult 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. Th e objective of th e 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 str ongly correlated with indices derived from the primary datasets. Th ese 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 av ailable from the National Marine Fisheries Service18 (“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 (ASM FC 2005) and American shad (ASMFC 2007a). These assessments provide additional informa tion for determining whether these populations have been harmed by CWIS.

2.5 Indicators of adverse impacts potentially related to CWIS

As discussed above, an adve rse impact of CWIS would consist of entrainment and impingement-related adverse ch anges in important population or community characteristics
sufficient to threaten the sust ainability of relevant populati ons, or to cause significant or potentially irreversible changes in community structure and function. Characteristics that influence the sustainability of a fish populatio n include the total size of the population, the
relative abundances of different life stages or age groups, the si zes and reproductive rates of the individual fish, and the rates of mo rtality of fish at different life stages or ages. Measures of any of these population characteristics c ould, at least in principle, be used as indicators of adverse impact. Some of these measures are not suitab le as indicators of adverse impacts potentially caused by CWIS, however, because they measure ch anges that cannot be reasonably attributed to cooling-water withdrawals. For example, a reduc tion 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 par ticularly 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 w ould have occurred naturally. Ther efore, characteristics that are either directly or indirectly affected by increased mortality of these life stages are potentially
useful as indicators of harm re lated to CWIS. Increased mortal ity imposed on a particular life stage would reduce the fraction of organisms in th at 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 evalua te whether the magnitude, spatia l extent, and duration of this mortality are large enough to constitute an advers e impact (USEPA 1998). Fisheries scientists have developed metrics, termed “biological reference points,” fo r determining whether harvested fish populations are being harmed by over-fishing (R estrepo 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 le ad to future declines in abundance and harvest.
The methods that fisheries scientists use to es timate 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 aff ected by cooling-water withdrawal s. Accordingly, for species for which published agency stock assessment re ports 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 sp awning potential have occurred and, if so, whether they may reasonably be attributed to the CWIS of IP2 and IP3.



### 3. Evaluation of changes in abundanc e of fish populations with life stages susceptible to entrainment

In complex ecological systems, such as th e Hudson River estuary, fish populations are influenced by many factors in addition to CWIS, including water quality impairment, introductions of non-native species, and overf ishing (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 r eason, investigations of the causes of changes in fish populations must consider mu ltiple hypotheses, weighing the evidence for and against each hypothesis (Hilborn and Mangel 1997, Su ter 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 stre ssor had been affecting a particular fish population. To test each hypothesis, this analysis utilizes rules for evaluating causal associ ations provided by Suter et al. (2007, p. 50). These author s identified five criteria that should guide analyses of potential causes
of adverse environmental effects:

1. Co-occurrence : An effect occurs where a nd 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 know ledge and theory.21 Evaluations of co-occurrence di scussed in this sections rely on a commonly-used and relatively straightforward statis tical 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 str ong that relationship is (Clarke and Kempson 1997). A positive
correlation between two variables i ndicates 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 tall er 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 (Clark e 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 criter ion. If, for example, entrainment mortality at IP2 and IP3 is reducing the survival of eggs a nd larvae of a particular fish species, then there should be a negative corr elation between entrainment mortalit y and a measure of the fraction of eggs and larvae that survive to reach older life st ages. 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. Da ta showing the presence of a negative correlation between early life stage survival and IP2 and IP 3-related mortality woul d constitute evidence supporting this impact hypothesis; data showing th e absence of a correlat ion would constitute evidence against this hypothesis. Evaluations of sufficiency in this assessmen t 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 st riped 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 declin e 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 in crease in the stressor, then the
stressor cannot have caused the decline.22 Evaluations of manipulation in this assessm ent 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 re ly 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 pr imarily 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 histor y characteristics (e.g., survival and growth) that would likely be affected, and (6) the type of measurable effects that would likely occu r (increase or decrease);
2. Identify appropriate sets of “stresso r metrics” and “response metrics” that can be used to test the potential influence of the various stressors;
3. Summarize the expected effect of th e stressor on each response metric;
4. Apply the five evaluation criteria disc ussed 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 im pingement impacts on striped bass ( Morone
saxatilis ), white perch ( Morone Americana ), Atlantic tomcod ( Microgadus tomcod ), bay anchovy ( Anchoa mitchilli ), American shad ( Alosa sapidissima ), alewife ( Alosa23 pseudoharengus ), blueback herring ( Alosa aestivalis ), and spottail shiner ( Notropis hudsonius ) (DEIS, Sections 5 and 6). This report asse sses entrainment and impingement impacts on these same species, focusing on the most economically important species (striped bass) and on the three species (white perch, Amer ican shad, and Atlantic tomcod) identified in the draft permit fact sheet as being of potentia l concern with respect to IP2 and IP3. Fact Sheet, Draft SPDES Permit, Attachment B, at 1 of 8. The datasets used in these an alyses are documented in the 2005 Year Class Report (ASA 2007). The stressor a nd response metrics are documented in Appendix
B.

3.2 Impact hypotheses and stressor metrics
This section documents expected effects of CW IS and four other stressors that are widely regarded as potentially having affected Hudson River fish popul ations: 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 planktonic 6 and are widely dispersed th roughout the estuary due to tidal and nontidal flows, cooling-water withdraw als would not be expected to alter the spatial distributions of the affected spec ies. In addition, the CWIS woul d not be expected to reduce the survival of fish that have grown through the most susceptible life st ages, 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 na tural 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 ear ly 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 Fi gure 2. As shown in Figure 2,
CWIS should affect the survival rates of the su sceptible life stages, bu t 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 susceptib le 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 negativ ely 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 pos sible that entrainment could di rectly 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. Ther efore, entrainment could affect the abundance
estimates derived from LRS data. If entrainm ent at IP2 and IP3 is reducing early life stage abundance, then the IP2 and IP3 CMR values s hould also be negatively correlated with PYSL
abundance estimates.

3.2.2 Fishing

Fishing imposes mortality primarily on harvestable-sized 7 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 pr edictable effects on the age distribution of adult fish and on the abundance (n umbers and biomass) of the spawning stock (Dayton et al. 2002). Measures of age distribution and sp awning stock abundance are used by fisheries managers as indicators of fishi ng (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 annu al 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 o ccur. This mortality is termed “bycatch” mortality.25Expected effects of fishing on age 0 life stages are summarized in Figure 3. Overharvesting reduces the size of the adult populati on 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. Fi shing should not reduce the survival or growth rate of any age 0 life stage, how ever, 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 co lonized water bodies. Because of their high filtering capacity, zebra mussels remove phytoplankt on 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 ava ilable 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 inhab it vegetated areas may increase because of increased habitat availability (Strayer et al. 2004) . Zebra mussels are limited to fr esh 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 al ter the spatial distributions of some species, reducing their abundance above RKM 100 as compared to below RKM 100. There is no readily availabl e quantitative metric for zebra mussel abundance. Due to the
discontinuous nature of the zebra mussel inva sion (absent prior to 1992; highly abundant after 1992), however, the qualitative evaluation can use pres ence/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” fo r 1993 and later). Expected effects of zebra mussels on age 0 life stages are summarized in Fi gure 4. Zebra mussels would be expected to reduce the survival and growth rates of post yolk-sac larv ae and YOY utilizing freshwater regions of the Hudson River. Thes e changes in survival and growth could result in a shift in the relative abundance of YOY present in predominantly freshwater regions (R egions 6-12; Figure 1) as compared to marine and brackish regions (Regions 0-5; Figure 1). Specifically, if zebra26 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 olde r striped bass, which are piscivorous 9 (Gardinder and Hoff 1982, Walter et al. 2003), could lead to increased predation mortality. Savoy and Crecco (2004) have attri buted a recent decline in Ameri can shad and blueback herring populations in the Connecticut Ri ver to predation by la rge adult striped bass on spawning adults of these species. Because the abundance of stripe d bass early life stages has been found to be strongly
correlated with the relative a bundance of adults (P ace et al. 1993; Barn thouse 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, redu ce the number of spawning adults and, as a consequence, the number of eggs spawned. The reduc tion in egg production would be expected to reduce the number of eggs surviv ing to become one-year-o ld 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 increa ses or decreases in the growth and survival of affected species, depending on species-specific temperature tole rances. Long-term trends in Riverwide temperatures could potentially lead to long-term changes in the abundance of sensitive species, such as Atla ntic tomcod (FEIS, pp. 65-66). E xpected 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 grow th of juvenile Atlantic tomcod in the Hudson River ceases during th e summer when river temperatures regularly exceed 25  ̊ C. The lethal temperature for juve nile Atlantic tomcod is 26.5  ̊ C (McLaren et al. 1988). Temperature records available from the Poughkeepsie Water Wo rks (PWW) were used to develop a degreeday index for evaluating the potential effects of elevated summer temp eratures 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 temper ature measured at the PWW on a given date was 27  ̊ C, then the degree-day value for that da te 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 eval uating all species, the response metrics used in this assessment are not the same for all species.

3.3.1 Response metrics for striped bass, wh ite 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 su rvival, growth, and spatial distri bution. Because th e durations of egg and YSL life stages are comparatively short, such that individual s 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 longe r, so that PYSL are susceptible to sampling for at least one and possibly tw o or more survey dates. For these reasons, estimates of total
larval abundance from the LRS are best interpreted as estimat es of the abundance of PYSL. Although the beach seine used in the BSS and th e 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 variab les that can be calculate d 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 aver age length of YOY fish from the BSS or FSS; and
5. Spatial distribution of PYSL and YOY re lative 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 metric s for spottail shiner

Because the LRS does not adequately sample ar eas 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 a nd 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 genera tors’ 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-r ecapture program provides annual estimates of age1 abundance, spawning stock size, and total egg production that can be us ed 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 char acterize both year-to-year variations in early life stage abundance and the distri bution of Atlantic tomcod larv ae and juveniles within the
Hudson River.29 For this species, the response variables include:

1. Abundance of PYSL and early juvenile s, 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 ear ly 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 th e response metrics are summarized below and in Tables 1 (striped bass, white perch, Amer ican shad, river herring, bay anchovy, and spottail shiner) and 2 (Atlantic tomcod):

• CWIS : Entrainment at IP2 and IP3 woul d be expected to reduce survival from the PYSL to the YOY stage, a nd 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 spaw ner 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 sp atial dist ribution of juveniles toward the lower regi ons 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, increase d summer temperatures would be expected to decrease the growth and su rvival 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 adul ts 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 th ese 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 th e 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). Th e 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 st ock (Young-Dubovsky et al. 1995). As first noted by Pace et al. (1993), and later confirmed by Barn thouse et al. (2003), there is no correlation between the abundance of striped bass PYSL a nd striped bass YOY (Figure 8a). There is a strong negative relationship betw een PYSL abundance and PYSL surv ival, however (Figure 8b). This negative correl ation has been interpreted by both Pace et al. (1993) and Barnthouse et al. (2003) as evidence for density-dependent mort ality 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 tim e as the size of the spawni ng 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 shoul d be a negative correlati on 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 e ither PYSL survival (Figure 9a) or PYSL abundance (Figure 9b) for striped bass. Henc e, the CWIS hypothesis fails the co-occurrence criterion for striped bass.

Sufficiency There are no independent measures of sufficie ncy that can be applied to this hypothesis. The objective of this report is to determine, usi ng all available and relevant evidence whether the magnitude of entrainment and impingement at I ndian Point have been sufficient to cause a
reduction in the abundance of im portant Hudson River fish speci es. 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 opera tions of IP2 (1974) and IP3 ( 1976). However, as shown in Figure 7, no such declines occurred. PYSL abu ndance 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 th e startup of IP2 and IP3. Hence, the CWIS hypothesis fails the temporality criterion for striped bass.

Manipulation No experimental manipulations of plant oper ations have been performed for the purpose of evaluating entrainment impacts on fish populat ions. However, outages, including refueling and maintenance outages mandated by the HRSA (E nglert et al. 1988), have frequently occurred32 during the months when entrainabl e striped bass are present in th e River. The peak abundance of striped bass eggs and larvae typically occurs du ring 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 Poin t 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 expecta tion. Figure 10a shows the time series of annual PYSL survival indices from 1975 through 2002. Th e horizontal line in Figure 10a shows the median survival index value for this time period. The median is defined as the mi dpoint of the entire distribution of survival index values, meani ng that one-half of the survival indices are above the median and
one-half are below the median. If striped bass PYSL survival we re 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 surviv al index was higher than the median for only 3 of the 11 years of one-unit operati on. 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 tota l water withdrawals by IP 2 and IP3 for the years
1975-2002. There is no correlatio n between withdrawals by IP2 and IP3 and striped bass PYSL survival. Hence, the CWIS hypothesis fails th e manipulation criterion for striped bass.

Coherence As noted above, the objective of this report is to determ ine, using all available and relevant evidence whether the magnitude of entr ainment and impingement at Indian Point have
been sufficient to cause a reduction in the abun dance of important Hudson River fish species. Including “coherence” as an explicit evaluation criterion for CWIS would be redundant. Hence, the coherence criterion is inappl icable to the CWIS hypothesis.33 3.4.1.2 Fishing

Co-occurrence Fishing indirectly affects th e abundance of early life stag es of fish by reducing the abundance of spawning adults (Goodyear 1993). If a populati on 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 increase d steadily throughout th e 1990s. This is the same period during which the adult stripe d bass population was e xpanding. Hence, the overfishing hypothesis satisfies the co-occurrence criterion.

Sufficiency Fishing mortality estimates for individual st riped 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 ma gnitude of fishing mortality imposed specifically on Hudson River striped bass has never been estimated, it is not possible to determine whether the fishing hypothesis sa tisfies 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 was34 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 depr essed 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, th e fishing hypothesis sati sfies 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 Ch esapeake Bay, the Delaware River, and the Hudson River are mixed and migrating along the Atla ntic coast (ASMFC 2003 , Waldman et al. 1990, Waldman and Fabrizio 1994). If reduced harvesti ng 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 af ter 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 quan tify the relationship between zeb ra mussel activity and fish growth or survival, and no mathematical models that could be used to quantify the indirect35 effects of zebra mussel activity have been deve loped. Hence, whether or not the zebra mussel hypothesis satisfies the suffici ency 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 PY SL survival began declining in the 1980s and had already fallen to a very low level by 199 0. Because the decline in striped bass PYSL survival preceded, rather than followed, the appear ance of zebra mussels in the River, the zebra mussel hypothesis fails the te mporality criterion.

Manipulation No deliberate manipulations of zebra mussel populations in the Hudson River have been performed, therefore, this criterion is in applicable to the zebra mussel hypothesis.

Coherence Because the proposed mechanism through wh ich zebra mussel activity could have affected striped bass in the Hudson River involve s reducing food availabili ty, 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 correl ation was found in the analyses performed to support this report (Appendix B, Table B-11). Zebra mussel activit y 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 sh ift 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 distri bution of PYSL was found, and an upstream shift (i.e., a shift in the opposite dire ction 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 j uvenile distribution. Hence, the zebra mussel hypothesis fails th e coherence criterion for striped bass.36 3.4.1.4 Summary evaluation of hypotheses

Table 3 summarizes the consistency of the st riped bass trends data with the CWIS, overfishing, and zebra mussel hypotheses. Two of th e five evaluation criteria – sufficiency and coherence – are inapplicable to the CWIS hypothesis. However, th is hypothesis fail s all three of the remaining criteria. Hence, the CWIS hypothesi s can be rejected as an explanation for longterm trends in the abundance of age 0 striped bass in the H udson River. The zebra mussel hypothesis passes the co-occurrence criterion, but fails the tempora lity and coherence criteria. Because striped bass PYSL survival declined se veral 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 PY SL and YOY were not observed, th e 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 co ntrast, passes four of the fi ve evaluation criteria. The remaining criterion (sufficiency) is inapplicable to this hypothesis. The abundance of striped bass PYSL in the Hudson began increasing s hortly following a reduction in striped bass harvesting. The reduction in harvest was sp ecifically intended to promote striped bass reproduction, and was followed by simultaneous in creases 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 abundan ce of juvenile white perch declined steadily throughout the 1980s, but has increased since 1990. De spite the recent increa se, over the entire time series, there is a statistically significan t 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 re lationship between PYSL abundance and YOY abundance in white perch (Figure 12a). The survival rate of white perch37 from the PYSL to the juvenile stage has declin ed (Appendix B, Table B-13). Moreover, there is a strong positive relationship between PY SL survival and YOY abundance (Figure 12b, Appendix B, Table B-14). B ecause YOY abundance in white perch is closely rela ted to PYSL survival but not to PYSL abundance, we can conclude that the decl ine in YOY abundance was
due to a decline in PYSL survival rather th an to a decline in white perch reproduction.

3.4.2.1 CWIS

Co-Occurrence Appendix B, Table B-13 and B-14 summarize th e results of the correlation analysis for white perch. If entrainment at Indian Poin t had caused the observed decline in white perch PYSL survival, there should be a negative rela tionship 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, a nd in years when the CMR was low, white perch PYSL survival should have been high. Ho wever, 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 ye ars when PYSL survival was high and the CMR was low in years when PYSL survival was low. There is a negative relationship between th e 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 juve nile 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, an d then increasing. It is impor tant to note that the recent
increase in survival occurred du ring a period in which the capaci ty factors for IP2 and IP3 have been higher than in earlier years ( Darla Gray, Entergy Corp., personal communication). Although there is a weak nega tive relationship between th e CMR for IP2 and IP3 and white perch PYSL abundance, the much stronge r positive relationship between the CMR and
PYSL to YOY survival must be accorded a hi gher weight. Because th is positive correlation clearly conflicts with the CWIS hypothesis, the CWIS hypothesis fails the co-occurrence criterion for white perch.

Sufficiency38 There are no independent measures of sufficie ncy that can be applied to this hypothesis. The objective of this report is to determine, usi ng all available and relevant evidence whether the magnitude of entrainment and impingement at I ndian Point have been sufficient to cause a
reduction in the abundance of im portant Hudson River fish speci es. Hence, the sufficiency criterion is inapplicable to the CWIS hypothesis.

Temporality As shown in Figure 14, white perch PYSL su rvival began to decline in 1977, one year following the startup of commer cial 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 wa s 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 PY SL 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 in consistent with this expectation. Figure 15a
shows the time series of annual PYSL surviv al indices from 1975 th rough 2002, which are the years for which cooling water flow data were ava ilable. The horizontal li ne in Figure 15 shows the median survival index value for this time pe riod. 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 medi an. 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 th e 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 the39 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, whic h shows the relationshi p 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 determ ine, using all available and relevant evidence whether the magnitude of entr ainment and impingement at Indian Point have
been sufficient to cause a reduction in the abun dance of important Hudson River fish species. Including “coherence” as an explicit evaluation criterion for CWIS would be redundant. Hence, the coherence criterion is inappl icable 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. He nce, the zebra mussel hypothesis satisfies the cooccurrence criterion.

Temporality As shown in Figure 14, however, the declin e in white perch PYSL to YOY survival occurred primarily between 1974 and 1986, prior to the zebra mu ssel invasion. PYSL to YOY survival has actually been incr easing since 1993, the fi rst 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 have40 been performed that could quan tify the relationship between ze bra 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 deve loped. Hence, whether or not the zebra mussel hypothesis satisfies the suffici ency criterion is unknown.

Manipulation No deliberate manipulations of zebra mussel populations in the Hudson River have been performed, therefore, this criterion is in applicable to the zebra mussel hypothesis.

Coherence Because the proposed mechanism through wh ich zebra mussel activity could have affected white perch in the Hudson River involve s reducing food availabili ty, the growth as well as the survival of white perch PYSL should have been reduced. Altho ugh Strayer et al. (2004) reported a negative relationship between zebra mussel activity and white perch growth, the analysis performed to support this assessmen t (Appendix B, Table B-13) found no significant relationship between zebra mussels and white perch growth. Mo reover, the perc ent of white perch juveniles downriver from RKM 100 is negativ ely, instead of positively, correlated with the zebra mussel index (Appendix B, Table B-13). Th is negative correlation implies that over this same period of years, the percentage of th e 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, th e zebra mussel hypothesis partially, but not fully, satis fies the coherence criterion.

3.4.2.3 Striped bass predation

Co-occurrence There is a weak negative co rrelation between the striped ba ss index and the white perch PYSL index (Appendix B, Table B-13). This relationship provides w eak evidence supporting the hypothesis that striped bass are preying on ad ult white perch. There is much stronger negative correlation between th e striped bass index and the YOY index (Figure 16a). This
correlation is consistent with the hypothesis that striped bass are pr eying on juvenile white perch. There is also a strong negative correlation between the striped bass index and white perch PYSL41 to YOY survival, however, this relationship is di fficult to interpret becau se striped bass would not be expected to prey on larval white perch. Overall, the striped ba ss hypothesis satisfies the
co-occurrence crite rion with respect to pr edation 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, Dunni ng 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) pe riods to changes in abundan ce of YOY fish in the Hudson River between these same two periods. The an alysis 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 wh ite perch, or if 0.3% of the age 1 through age 13 striped bass seasonal predatory demand wa s satisfied by YOY white perch. Hence, the striped bass predation hypothe sis satisfies the sufficiency criterion for white perch.

Temporality A sustained decline in white perch YOY a bundance began in 1989, at the same time the striped bass index began to increase (Figur e 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 perc h 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 ca nnot 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 in applicable 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 othe r known striped bass prey species, including river herring, American shad, bay anchovy, and A tlantic tomcod should also have declined. As shown in other Sections of this report, YOY abunda nce 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 pred ation 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 Ri ver to striped bass predation. Hartman (2003) estimated that the coastwide annual prey consum ption by striped bass between 1 and 10 years of age increased by more than a factor of 8 betw een 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 th e abundance of Atlantic menhaden in upper Chesapeake Bay from 1980 through 1999 to predation by striped bass. Because parallel declines in other suscepti ble species have occurred, and because the other published studies have docum ented the influence of striped bass predation on susceptible prey species, the striped bass predation hypot hesis satisfies the coherence criterion.

3.4.2.4 Summary evaluation of hypotheses

Table 4 summarizes the consistency of the wh ite 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. Th e CWIS hypothesis fails the cooccurrence and manipulation crite ria. Although the CWIS hypothesis satisfies the temporality criterion because the observed decline in white pe rch PYSL survival followed the startup of IP2 and IP3, the inconsistency of this hypothesi s with the co-occurrence and manipulation hypotheses means that the temporal corresponde nce 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-occu rrence criterion and at least partially satisfies the coherence criterion. However, it fa ils the temporality criterion because the declines in white perch PYSL survival and YOY abunda nce 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 explanati on 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 th e entire period from 1974 to 2004 (Figure 12b) cannot be explained by the predation hypothesis, b ecause striped bass abundance did not begin to increase until 1987. Hence, although striped bass pr edation 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 ha s declined significantly since the initiation of the generators’ monitoring program, with declines in the abundan ce 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 repr oduction 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 surviv al and the entrainment CMR at IP2 and IP3 (Figure 19a). The IP2 and IP3 CMR is also un correlated with American shad PYSL abundance (Figure 19b). Hence, the CWIS hypoth esis fails the co-o ccurrence criterion.

Sufficiency

There are no independent measures of sufficie ncy that can be applied to this hypothesis. The objective of this report is to determine, usi ng all available and relevant evidence whether the magnitude of entrainment and impingement at I ndian Point have been sufficient to cause a
reduction in the abundance of im portant Hudson River fish speci es. 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 year s after the startup of commerci al operations at IP3 and 12 years after the startup of IP2 (Figure 17). He nce, 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. Althoug h American shad eggs and larvae occur only at very low densities in the vicin ity 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 Am erican 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 are45 inconsistent with this expecta tion. Figure 20a shows the time series of annual PYSL survival indices from 1985 through 2002. Th e horizontal line in Figure 20a shows the median survival index value for this time period. The median is defined as the mi dpoint of the entire distribution of survival index values, meani ng 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 surviv al 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, wh ich shows the relationship between the
American shad PYSL survival index and the Ma y-June total water with drawals by IP2 and IP3 for the years 1975-2002. There is no correlati on between withdrawal s by IP2 and IP3 and American shad PYSL survival. Hence, the CW IS hypothesis fails the ma nipulation criterion for
American shad.

Coherence The objective of this report is to determine, using all ava ilable and relevant evidence whether the magnitude of entrainment and impingeme nt at Indian Point have been sufficient to cause a reduction in the abundance of import ant Hudson River fish species. Including “coherence” as an explicit evaluation criter ion for CWIS would be redundant. Hence, the coherence criterion is inapplicab le to the CWIS hypothesis.

3.4.3.2 Fishing

Co-Occurrence If a population is being overfished to the poi nt at which spawner abundance is reduced, then the number of eggs and la rvae 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 dramatically46 in recent years. In its most recent stock assessment for American shad (ASMFC 2007), the
ASMFC found that the abundance of adult Ameri can 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 sh ad PYSL and YOY in the Hudson River declined (Figure 17). Hence, the fishing hypothes is 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 fishi ng 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 pr obably being overfished, however, other sources of mortality cannot be excluded as contributing causes. Although there is still substantial uncertainty concerning causes of decline in Am erican shad population, this assessment accepts Hattala and Kahnle’s (2007) results and concludes that the overfi shing hypothesis satisfies the sufficiency criterion.

Temporality
The decline in American shad spawner abundance coinci ded with the decline in abundance of PYSL and YOY (Figure 17). Henc e, the overfishing hypothesis satisfies the temporality criterion.

Manipulation
Amendment 1 to the Interstate Fisheries Ma nagement Plan for Shad and River Herring (ASMFC 1999) directed all states to phase out the coastal fisher y for American shad over a five year period beginning in 2000. The phase-out should reduce fi shing mortality on American shad. If the coastal fishery had been contribu ting 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 abundan ce from the post-closure period are not yet47 available, so it is not yet possible to evaluate whether the overfishing hypothesis satisfies the manipulation criterion.

Coherence As noted above, there is stil l substantial uncertainty concer ning the impact of fishing on the Hudson River American shad population. Howe ver, available data are consistent with a conclusion that fishing is at least a significant co ntributor to the recent de cline in abundance of Hudson River American shad (Hattala and Ka hnle 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 Am erican 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 quan tify 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 deve loped. Hence, whether or not the zebra mussel hypothesis satisfies the suffici ency criterion is unknown.

Temporality
The decline in abundance of American shad PYSL and YOY began in the late 1980s (Figure 17), several years prio r 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 in applicable to the zebra mussel hypothesis.

Coherence Because the proposed mechanism through wh ich zebra mussel activity could have
affected American shad in the Hudson River i nvolves 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 ra te 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 al so have shifted the distribution of American shad PYSL and YOY downriver, away from the freshw ater zone in which zebra musse ls are abundant. Strayer et al. (2004) found a net downriver shift in the di stribution 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 significan t shifts in the distribution of either life stage was found. The observed changes in growth and distributio n 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 refl ects spawner abundan ce and reproduction, is negatively correlated with the striped bass inde x (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 Ameri can shad YOY index, (Figure 21b), however, this relationship is not statistically significant. He nce, 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 ad dress predation on YOY American shad. Hence, with respect to YOY American sha d, whether or not striped bass predation satisfies the sufficiency criterion is unknown. Kahnle a nd 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 insuffici ent to significantly affect th e abundance of adult Hudson River American shad. This assessment accepts the c onclusions of Kahnle and Hattala (2007) that striped bass predation on adult Hudson Ri ver 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 d ecline 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 in applicable to the striped bass hypothesis.

Coherence If predation by striped bass had caused the d ecline 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. Howe ver, PYSL abundance for river herring and Atlantic tomcod declined over the same peri od in which PYSL abundance for American shad declined. YOY abundance for all of the above sp ecies has declined sin ce the late 1980s, when50 striped bass abundance began to increase. Moreover , other published studies have concluded that striped bass predation is reduci ng 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 a nd Kahnle (2007) acknowledged that predation by striped bass on young American shad could be substa ntial and could be contributing to a decline in recruitment of young shad to the adult population. Hartman (2003) estimated that the coastwid e 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 docu mented the influence of striped bass predation on susceptible prey species, the striped bass predation hypothesis sa tisfies the cohe rence 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 th e American shad data with the CWIS, overfishing, zebra mussel, and striped bass preda tion hypotheses. Two of the five evaluation criteria – sufficiency and coherence – are inap plicable to the CWIS hypothesis. The CWIS hypothesis fails the co-occurrence, temporality, a nd manipulation criteria. Hence, the CWIS hypothesis can be rejected as an explanation fo r long-term trends in the abundance of age 0
American shad in the Hudson River. The overfishing hypothesis satisfies the co-o ccurrence, sufficiency, temporality, and
coherence criteria for American shad. The mani pulation criterion is ina pplicable at present, although applicable data may beco me 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-occu rrence, temporality, and coherence criteria for American shad. Whether the sufficiency criterion is satisfied is unknown, and the manipulation criterion is inapplicab le. 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 hypothesi s satisfies two and possibly th ree 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 inappl icable to this hypothesis. The simultaneous declines in abundance of susceptible life stages of other pr ey species in the Hudson River and the published studies documenting impacts of striped bass preda tion on prey species s upport for the predation hypothesis. However, substantial uncertainty re mains concerning the frac tion of the American shad YOY population that might be consumed. It appears reasonable to conc lude that the recent decline in abundance of Hudson River American shad is most likely a result of ove rfishing, but striped bass predation may be a contributing cause.

3.4.4. Atlantic tomcod

Figure 23 depicts long-term trends in the a bundance 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 inde x 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 generato rs’ monitoring programs, with the abundance of age 1 and older fish abundance showing an abrupt d ecline 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 surv ival rate is estimated by compari ng 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 relatio nship between egg deposition and resulting age 1 abundance (Figure 24a). There is a positive relati onship between egg to age 1 survival and age 152 abundance (Figure 24b). Hence, the decline in Atlantic tomcod abunda nce 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 cha nges in the abundance of this species. Thre e 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 correla tion between the IP2 and IP3 CMR and eggto-age 1 survival. There is a negative correla tion between the IP2 and IP3 CMR and the Atlantic tomcod LRS index (Figure 25b), but this correl ation is significant onl y at the 10% level (Appendix B, Table B-17). There is no correlat ion between the IP2 and IP3 CMR and the markrecapture index (Figure 25c). Because the IP2 a nd 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 sufficie ncy that can be applied to this hypothesis. The objective of this report is to determine, usi ng all available and relevant evidence whether the magnitude of entrainment and impingement at I ndian Point have been sufficient to cause a
reduction in the abundance of im portant Hudson River fish speci es. 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-19 80s and the decline in the LRS survey did not begin until 1990. Hence, the CWIS hypot hesis fails the temp orality criterion.53 Manipulation Although American tomcod spawn in Decembe r 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 Ag e 0 Atlantic tomc od, then egg to age 1 survival should have been higher in year s 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 se ries 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 ar e 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 si gnificantly more survival inde x values for years of one-year operation should be higher than the median than lower than th e median. However, Figure 26a
shows that the PYSL survival i ndex was higher than the median fo r 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, wh ich 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 co rrelation between withdrawals by IP2 and IP3 and Atlantic tomcod egg to age 1 survival. He nce, the CWIS hypothesis fails the manipulation
criterion for Atlantic tomcod.

Coherence The objective of this report is to determine, using all ava ilable and relevant evidence whether the magnitude of entrainment and impingeme nt at Indian Point have been sufficient to cause a reduction in the abundance of import ant Hudson River fish species. Including “coherence” as an explicit evaluation criter ion for CWIS would be redundant. Hence, the coherence criterion is inapplicab le to the CWIS hypothesis.54 3.4.4.2 Elevated summer temperatures

Co-occurrence As shown in Appendix B, Table B-17, egg to ag e 1 survival is negativ ely 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 load ing to the River from IP2 and IP3. Hence, the temperature hypothesis satisfies the co-occurre nce criterion, although
there is no evidence that IP2 and IP3 contribute to a temperature effect.

Sufficiency As discussed by McLaren et al. (1988), su mmer 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 (McLar en 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 hypot hesis satisfies the su fficiency 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 to mcod, for the period 1987-2001. For each year, the degree-day index is paired w ith the mark-recapture estimates generated during the following winter (e.g., the 1987 temperature va lue is paired with the mark-rec apture value for the winter of 1987-1988). As shown in Figure 2 7, a decline in Atlantic tomcod occurred from 1990-2001. However, elevated temperatures that could have explained this decline di d not occur. There is no long-term trend in the PWW degree-day index, a nd three of the four lowest values of the index have occurred since 1990. Hence, th e temperature hypothesis fails the temporality
criterion.

Manipulation No deliberate manipulations of Hudson River water temperatures ha ve been performed, therefore, this criteri on 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 consis tent with laboratory data on th ermal 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 reprod ucing 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, Dunni ng 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) pe riods to changes in abundan ce of YOY fish in the Hudson River between these same two periods. The an alysis 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 tw o periods if 1.4% of the age 1 and age 2 striped bass seasonal predatory demand was satisfi ed by YOY Atlantic tomcod, or if 0.4% of the age 1 through age 13 striped ba ss seasonal predatory demand was satisfied by YOY Atlantic tomcod. Hence, the striped bass predation hypoth esis satisfies the sufficiency criterion.56 Temporality The increase in striped bass abundance coin cides in time with the declines in both
Atlantic tomcod abundance metric s (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 in applicable to the striped bass hypothesis.

Coherence If predation by striped bass had caused the de cline in abundance of Atlantic tomcod in the Hudson River, then the YOY abundance of othe r 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 abunda nce 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 pred ation 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 Ri ver to striped bass predation. Hartman (2003) estimated that the coastwide annual prey consum ption 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 suscepti ble species have occurred, and because the other published studies have docum ented 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 th e Atlantic tomcod data with the CWIS, temperature, and striped bass pr edation hypotheses. Two of th e five evaluation criteria –57 sufficiency and coherence – are inapplicable to the CWIS hypothesis. The CWIS hypothesis weakly satisfies the co-occurren ce criterion, but fails the temporal ity, and manipulat ion criteria. The CWIS hypothesis can be rejected as an expl anation 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 criteri on is inapplicable to this hypothesis. Hence, the temperat ure 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 satisfie s all of the applicable criteria. The correlations between striped bass abundance and Atlantic tomcod abundance, the temporal correspondence between the timing of the striped ba ss increase and the Atlantic tomcod decline, the estimates of striped bass prey consumpti on, 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. Th ese two species must be considered together for purposes of evaluating impacts of CWIS, because their larv ae are indistinguishable. PYSL abundance for both species combined (Figure 30a) was stable un til 1985, and has since dec lined. With respect to YOY abundance, these two species have tende d to vary together (Figure 30b). YOY abundance in both species declined abruptly in the mid-1980s and ha s 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 a bundance (Figure 31b), alewife YOY abundance (Figure 32a), and58 blueback herring YOY abundance (Figure 32b). He nce, the CWIS hypothesis fails the cooccurrence criterion.

Sufficiency There are no independent measures of sufficie ncy that can be applied to this hypothesis. The objective of this report is to determine, usi ng all available and relevant evidence whether the magnitude of entrainment and impingement at I ndian Point have been sufficient to cause a
reduction in the abundance of im portant Hudson River fish speci es. Hence the sufficiency criterion is inapplicable to the CWIS hypothesis.

Temporality As shown in Figures 30a and 30b, alew ife and blueback he rring 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 te mporality criterion.

Manipulation The peak abundance of river herring eggs a nd larvae typically occurs during May and June (DEIS, Figures V-71 and V-74). IP2 was off line 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 op erating. As shown in Figure 33a, the measured
PYSL survival values are inconsistent with this expectation. Figure 33a s hows the time series of
annual PYSL survival indices from 1974 through 2002. The horizont al line in Figure 33a shows the median survival index value for this time pe riod. 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 medi an. If river herring PYSL survival were higher in years of one-unit operation than in ye ars 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 s hows that the PYSL surv ival index was higher59 than the median for 4 of the 11 years of one-un it operation. The PYSL wa s index lower than the median in 7 years of one-unit operation. This result is confirmed by Figure 33b, whic h shows the relationshi p between the river herring PYSL survival index a nd the May-June total water wit hdrawals by IP2 and IP3 for the years 1975-2002. There is no corre lation between withdrawals by IP2 and IP3 and river herring PYSL survival. Hence, the CWIS hypothesis fa ils the manipulation criterion for alewife and blueback herring.

Coherence The objective of this report is to determine, using all ava ilable and relevant evidence whether the magnitude of entrainment and impingeme nt at Indian Point have been sufficient to cause a reduction in the abundance of import ant Hudson River fish species. Including “coherence” as an explicit evaluation criter ion for CWIS would be redundant. Hence, the coherence criterion is inapplicab le 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-occu rrence 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 quan tify the relationship between zeb ra 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 deve loped. Hence, whether or not the zebra mussel hypothesis satisfies the suffici ency 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 in applicable to the zebra mussel hypothesis.

Coherence Because the proposed mechanism through wh ich zebra mussel activity could have affected river herring in the Hudson River involve s reducing food availabili ty, the growth as well as the survival of river herring PYSL and YOY s hould have been reduced. Strayer et al. (2004) found a decline in the growth ra te 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 si gnificant, 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 distribut ion of river herring PYSL and YOY downriver, away from the freshw ater zone in which zebra musse ls are abundant. Strayer et al. (2004) found net downriver shifts in the di stribution of alewife and blueback herring YOY, but a net upriver shift in the distribution of PY SL. None of these shifts was statistically significant, even at the 20% le vel. In the analysis perfor med 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 c oherence criterion for alewife and blueback herring.61 3.4.5.3 Striped bass predation

Co-occurrence The river herring PYSL abundance inde x, which reflects spawner abundance and reproduction, is negatively correlated with the st riped bass index (Figure 34a). The alewife YOY index, and the blueback herring YOY index are also negatively corr elated with the striped bass index, although only at the 10% significance le vel (Appendix B, Tables B-19 and B-21). (Figures 34b and 34c). Hence, the striped bass predation hypothe sis 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 a nd 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 blueb ack herring. Recently, Kahnle a nd Hattala (2007) reported that river herring were the most comm on prey item in the stomachs of adult striped bass captured in the Hudson River. Appendix C to this report do cuments an analysis of prey consumption by Hudson River striped bass. This analysis compar es 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 fi sh 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 th e decline in YOY river herring abundance between these two periods if 3% of the age 1 and ag e 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 he rring. Hence, the striped bass predation hypothesis satisfies the sufficiency criterion with respect to predation on YOY river herring. No quantitative estimates of consumption of adu lt river herring by stripe d bass are available.62 Temporality The decline in river herring abundance coincides in time with the incr ease in the striped bass index (Figure 35). Hence, the trends an alysis supports the hypothesis that predation by striped bass has contributed to the decline in al ewife 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 e xpected 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 in applicable to the striped bass hypothesis.

Coherence If predation by striped bass had caused the de cline in abundance of river herring in the Hudson River, then the YOY abundance of othe r 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 abunda nce 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 pred ation 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 stripe d bass predation. Th is conclusion is supported by a recent study of the diet compositi on 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 cons istent with the results publishe d by Kahnle and Hattala (2007), who found that river herring were the most abundant of the id entifiable prey items in the stomachs of adult striped bass cap tured in the Hudson River. Ha rtman (2003) estimated that the63 coastwide annual prey consumption by striped ba ss between 1 and 10 year s 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 susceptibl e species have occurred, because predation by striped bass on adult ri ver herring has been demonstrate d, and because th e other published
studies have documented the influe nce of striped bass predation on susceptible prey species, the striped bass predation hypothesis sa tisfies the coherence criterion.

3.4.5.4 Summary evaluation of hypotheses

Table 7 summarizes the consistency of the al ewife and blueback herring data with the CWIS, temperature, and striped ba ss 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 manipulat ion criteria. Hence, the CWIS hypothesis can be rejected as an explanation for long-term tren ds in the abundance of age 0 river herring in the
Hudson River. The zebra mussel hypothesis fails the co-occu rrence, temporality, and coherence criteria for river herring. Whether the sufficiency crit erion is satisfied is unknown, and the manipulation criterion is inapplicable. Hence, the zebra mu ssel hypothesis can be reject ed as an explanation for long-term trends in the abundance of age 0 river herring in the Hudson River. The striped bass predation hypothesis satisfie s all of the applicable criteria. The correlations between striped bass abundance and river herring abundance, the temporal correspondence between the timing of the striped ba ss 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 specie s 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 potentia l 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 th e abundance of bay anchovy YOY and PYSL in the Hudson. The abundance of juven ile bay anchovy, as measured by the FSS, has declined since
1985. There has been no tre nd 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 sufficie ncy that can be applied to this hypothesis. The objective of this report is to determine, usi ng all available and relevant evidence whether the magnitude of entrainment and impingement at I ndian Point have been sufficient to cause a
reduction in the abundance of im portant Hudson River fish speci es. 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 th e late 1980s, more than 10 year s following the startup of IP2 and IP3. Hence, the CWIS hypothesi s 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 duri ng 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 highe r 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 hor izontal line in Figure 38a shows the median survival index value for this time period. The me dian 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 ba y anchovy PYSL survival were higher in years of one-unit operation than in year s of 2-unit operation, then signi ficantly 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 PY SL 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, whic h 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 corr elation 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 impingeme nt at Indian Point have been sufficient to cause a reduction in the abundance of import ant Hudson River fish species. Including “coherence” as an explicit evaluation criter ion for CWIS would be redundant. Hence, the coherence criterion is inapplicab le to the CWIS hypothesis.66 3.4.6.2 Striped bass predation

Co-occurrence Bay anchovy juvenile abundance is negativel y correlated with the striped bass index (Figure 39a). Hence, the striped bass hypot hesis satisfies the co -occurrence criterion.

Sufficiency Striped bass larger than 200 mm in length ha ve 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 repo rt did not address pred ation on bay anchovy. Hence, whether the striped bass predation hypothes is satisfies the sufficiency criterion for bay anchovy is unknown.

Temporality The increase in striped bass abundance coinci des in time with the decline in bay anchovy juvenile abundance (Figure 39b). Hence, the st riped 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 in applicable to the striped bass hypothesis.

Coherence If predation by striped bass had caused th e decline in abundance of bay anchovy YOY in the Hudson River, then the YOY abundance of othe r known striped bass prey species, including white perch, American shad, river herring, and A tlantic 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 ba ss abundance began to in crease. Moreover, other published studies have concluded that striped bass predation is reducing the abundance of some prey species.67 Hartman (2003) estimated that the coastwid e 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 suscepti ble species have occurred, and because the other published studies have docum ented the influence of striped bass predation on susceptible prey species, the striped bass pr edation hypothesis satisfies the co herence 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 evalua tion criteria – sufficiency and coherence – are inapplicable to the CWIS hypothesis. The CWIS hypothesis fails the co-occurrence, temporality, and manipulation criteria. Hence, the CWIS hypot hesis can be rejected as an explanation for long-term trends in the abundance of ag e 0 bay anchovy in the Hudson River. The striped bass hypothesis satisfies three of th e five criteria. The manipulation criterion is inapplicable to this hypothesi s, and whether this hypot hesis satisfies the suffi ciency criterion is
unknown. The simultaneous declines in abundance of susceptible life stages of other prey species in the Hudson River and the published st udies documenting impacts of striped bass predation on prey species all provide relative ly strong support for the predation hypothesis. However, substantial uncertainty remains c oncerning 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 a bundance of spottail sh iners and YOY in the
Hudson River. The abundance of shiners has significantly declined, while the abundance of YOY has significantly increased. The increas e 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 unco mmon in the vicinity of Indian
Point. The causes of re cent changes in the abunda nce of this species ca nnot be identified using the data available for this report; howev er, the CWIS hypothesis can be rejected.

3.5 Summary evaluation of trends analysis

The results of the trends analysis are inconsis tent with the hypothesi s that entrainment at IP2 and IP3 is reducing the surviv al 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 ba ss predation being a potentially important contributing factor. For other sp ecies, the striped bass predation hypothesis is the most strongly supported hypothesis. This hypothesis satisfies th e 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 negativ ely correlated with abundance indices for white perch, American shad, Atlantic tomcod, river herri ng, and bay anchovy. With respec t to sufficiency, the analyses documented in Appendix C show that the increase in prey consumption by Hudson River striped bass in recent years is sufficien t to account for observed declin es in the YOY abundance of white perch, Atlantic tomcod, and river herring. With re spect 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, stri ped bass predation has been implicat ed in declines of susceptible species in other mid-Atlant ic northeastern estu aries (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 pred ation is a far more likel y cause of declines in69 the abundance of YOY white perch, American sh ad, Atlantic tomcod, river herring, and bay anchovy than are any of the ot her 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 dynam ics, entrainment and impingeme nt 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 re spective CWIS could have adversely affected Hudson River fish populations that support manage d fisheries. The method to be used, the SSBPR model, has a long history of applicati on 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 fisherie s management is how much spawning stock (essentially, the number of adult fi sh) must be protected from harv esting to allow a population to replace itself and pers ist through time (i.e., a sustainabl e 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 popul ations subjected to commercial and recreational fishing (Sissenw ine and Shepherd 1987, Gabriel et al. 1989, Goodyear 1993, Mace and Sissenwin e 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 abili ty of the population to sustain itself, its appli cation to entrainment and impingement is well-supported. The SSBPR model uses information on age-sp ecific 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 “r ecruit,” in fisheries te rminology). Expected lifetime reproduction is a function bo th of the average fecundity of female fish at each age and70 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 th e probability that spawned eggs will survive to become one-year-o ld recruits (in the case of CWIS), or reducing the fecundity of female fish (e.g., through adverse environmental conditions, such as toxi c chemicals). For the population to persist, each one-year-old female fi sh 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 potenti al to produce far more eggs th an are required to replace her (Mace and Sissenwine 1993). For example, a fema le 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 p opulation to maintain itself at a stable level, only one of the female eggs produced by each fish over her lifetim e 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 re cruit provides a useful measur e 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 cr itical 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 (t he 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 we re based on the SSBPR model,
and used the SSBPR model to evaluate over-fishi ng 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 me thods that can be used to establishing over-fishing reference points that comply with the Act.

SSBPR is estimated as:


## ∑ =
i i i i f m l SSBPR

where

l i = probability of survival from age 1 to age i

m i = fraction of the population of age i which are mature females; and f

i = 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

M a = age-specific instantaneous na tural mortality rate at age a;
F a = instantaneous fishing mortality rate at age a; and
P a = instantaneous power-plant mortality rate at age a.

The impact of fishing and power-plant mort ality on expected lifetime egg production is expressed as the ratio of SSBPR including both sources of mort ality to SSBPR without these sources of mortality. This ratio is often te rmed the “spawning potential ratio” (“SPR”):72 unfished fished SSBPR SSBPR SPR =

Rates of fishing mortality that would produ ce a given SPR value ar e used by fisheries management agencies to establish acceptable lim its 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 popu lation each year by fish ermen without affecting the sustainability of the populati on. 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 indicati ng over-fishing. If F consistently exceeds F .20 , then significant declines in the adult population ma y occur. Although some fish stoc ks may be able to maintain
recruitment at F .20 , other stocks are more sens itive to fishing and canno t 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 2 005) 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 bo th striped bass and American shad), the CMR calculated using the generators ’ empirical entrainment and im pingement 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. Fi rst, estimates of reduction in SSBPR due IP2 and IP3 are compared to reduc tions 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 ot her words, IP2 and IP3 reduce the spawning potential of the Hudson River striped bass popul ation to 92% of the value for an unfished population. Fishing for striped bass at the current target ra te established by the ASMFC (F=0.30) 10 corresponds to an SPR of 0.13. This mean s 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 curren tly 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 decl are the population to be over-fis hed and would take action to reduce harvesting. As shown in Figure 42, even when effects of fi shing 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 IP 3 have not jeopardized the sustainability of the Hudson River striped bass popula tion as defined by ASMFC regulati ons. Further, as is clear from Figure 42, the impacts of fishing on the su stainability of the Huds on 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 m easurable influence on the sustainability of the population.

4.3.2 American shad The ASMFC (ASMFC 2007a, 2007b) recently us ed the SSBPR model to assess impacts of increased mortality on the sustainability of Atlantic coastal American shad populations, including the Hudson River American shad populat ion. Because the relative contributions of fishing mortality and natural mortality to the increase are uncertain, the ASMFC expressed the
maximum sustainable rate of mortal ity 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 treat ed 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 va lue, as an excess mortal ity 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 20 07a). 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 pr imarily to overfishing. However, regardless of the actual cause, it is clear th at entrainment at Indian Point is a negligible contributor to American shad mortality. Figure 43 compares re ductions in spawning pot ential of American
shad due to IP2 and IP3 to redu ctions 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) a nd 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% compar ed to the baseline value. According to the ASMFC (2007a), the current rate of total mo rtality on age 1 and older American shad ( Z =0.87) corresponds to an SPR of 0.23, well below the thre shold level. Because it was derived from an analysis of long-term trends in abundance and age structur e of the Hudson River shad population, the total mortality rate estimate already includes the eff ects 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% increas e 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 in cremental 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 decline75 should have been greater for species with hi gh susceptibility than for species with low susceptibility. Among species that increased in abundance, the ma gnitude of the increase should have been lower for species with high susceptibil ity than for species with low susceptibility. This hypothesis can be tested using data ava ilable from the generato rs’ 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 calcula ted using distributional data obtained from the LRS, and indices of trends in age 0 abunda nce, obtained from the BSS and FSS. Evaluating the correlation between entrai nment susceptibility and change in YOY abundance requires selecting those species for which data are av ailable 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 progr am is designed to collect such data. The expected effect of con tinued annual entrainment lo sses of early life stages of a species, if losses are severe enough to reduce popula tion size, is a decrease in YOY abundance. YOY is the best stage to look for the effect of entrainment loss es because entrainment occurs prior to the YOY stage, and because most susceptible species are still in the river duri ng the YOY stage and thus their abundance is measurable. The generato rs’ BSS and FSS sampling programs are designed
to monitor YOY abundance.

5.1 Methods

The evaluation involves three steps: (1) calc ulate a species-specific numeric index of entrainment susceptibility based on data from th e 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 suscep tibility is related to ch ange in age 0 abundance. Susceptibility to entrainment at IP2 and IP3 was evaluated using an index of standing crop estimated from the generators’ LRS fo r 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 th e two adjoining regions as well. Therefore, relative abundance of a species in Regions 3-5 (Figure 1), as comparedto the riverwide abundance of that species, was76 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 regi ons. For those species occurring in more than
one of the three seas onal 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 EntSus SC EntSus

where EntSus i = fraction of species in the Hudson Rive r estuary in the IP2 and IP3 region in

year i ; SC ip = sum of abundance of the sp ecies within seasonal period p in year i ; and
EntSus ip = 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 ap peared in the Hudson River are pr ovided in Appendix D. The BSS and FSS programs were selected as th e best potential indica tors of long-term relative abundance of fish in the estuary. Thes e programs have sampled the estuary using similar gear and methodology since the early 1970s, although th ere have been varia tions in the regions sampled and in time of initiation and end of th e sampling across the years. To maintain consistent sampling effort and maximize comparabilit y of results, data are restricted to Regions 1-12, and weeks 31-42, approximately August through October. As documented in Appendix D, abundance da ta by species are categorized into two salinity zones, three habitats, and two time period s. 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 (botto m area in water 10 ft or less in depth), (b) benthic (volume of water between river bottom and 3 ft above th e bottom), and (c) water column (water volume not included in either the shorez one or benthic habitats ). Time series of abundance data are divided into two equal pe riods: Period 1, coveri ng 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 z one for freshwater guild; freshwater zone for marine guild) were excluded when calculating overa ll 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 divide d by abundance in Period 1). Details concerning these calculations are provided in Appendix D. The quantity and quality of abundance and dist ribution 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 el iminate 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 se ts of species, termed “Case A” and “Case B.” Species included in both cases were selected ba sed on the annual numbers of organisms collected in the LRS and BSS/FSS surveys. Species were in cluded in the Case A analysis if (1) an average of at least 100 larvae per y ear 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 pe riods. Species were included in the Case B analysis if (1) an average of at least 1000 larv ae per year of occurrence was collected in LRS samples 1974-2005 and (2) at leas t 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 incl uded 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 coeffi cient 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 stat istically significant. Figure 44 provide plots of mean entrainment sus ceptibility vs. the norma lized 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 re lative abundance change on E ntSus 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 hi gh susceptibility to entrainment (high E ntSus ) as for species with low susceptibility to entrainment. This result is inconsis tent with the hypothesis that the susceptibilities of species to entrainment at Indian Po int 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 numbe r 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 indi cated 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 rein forces the conclusion that the long-term trends in abundance of the fish species inhabiting the Hudson River estu ary are similar across all guilds
and are unrelated to entr ainment at IP2 and IP3.



### 6. Conclusions
The FEIS and the Draft Permit for IP2 and IP3 stated that three fi sh species (Atlantic tomcod, American shad, and white perch) have declined in abundance in recent years, and attributed these declines to cooling-water with drawals at IP2 and IP3. Analyses performed to test alternative hypothese s concerning the causes of these d eclines show that cooling water withdrawals by IP2 and IP3 did not cause these dec lines. Overharvesting is the most likely cause
of recent declines in the abundance of Ameri can shad, with striped bass predation being a79 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 stri ped 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 a nd Crecco 2004, Kahnle and Hattala 2007) and by an analysis of the increase in prey consump tion needed to support the recent growth of the Hudson River striped bass population (Appendix C). Two additional lines of evidence support a c onclusion that entrainment and impingement
at IP2 and IP3 have not resulted in AEI. A pplication of the SSBPR m odel to stock assessment data for striped bass and Ameri can shad (Section 4) shows that mortality caused by entrainment at IP2 and IP3 is negligible, particularly compar ed 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 w ith relatively high susceptibility to entrainment at IP2 and IP3 are no more likely to have declined in abundanc e 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 suscep tible populations cannot be detect ed, even after 30 years of intensive monitoring. Those changes that have occu rred are more likely attr ibutable to predation by the Hudson River’s rapidly grow ing striped bass population. For all of the above reasons, from the persp ective of a science-base d definition of AEI,
the available data demonstrate that entrainment and impingement associated with cooling-water withdrawals by IP2 and IP3 have not had an a dverse impact on Hudson River fish populations
and communities.80 

### 7. References

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Waldman, J. R., and M. C. Fabr izio. 1994. Problems of stock defi nition in estimating relative contributions of Atlantic stripe d bass to the coastal fishery. Transactions of the American Fisheries Society 123 :766-778.
Walter, J.F. III., A.S. Overton, K.H. Ferry, a nd M.E. Mather. 2003. Atlantic coast feeding habits of striped bass: a s ynthesis supporting a coast-wide u nderstanding of trophic biology. Fisheries Management and Ecology 10 :349-360.85 Winemiller, K. O., and K. A. Rose. 1992. Pattern s of life-history diversification in North American fishes: implications for population regulation. Canadian Journal of Fisheries and Aquatic Sciences 49 :2196-2218.
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Young-Dubovsky, C., G. R. Shepherd, D. R. Smith, and J. Field. 1996. Striped Bass Research Study: Final Report. Jointly published by the U. S. Fish and Wildlife Service and the National Oceanic and the National Marine Fisheries Service, Washington, D.C.i Table 1. Expected effects of stressors on H udson 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 Huds on 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 criteri a: striped bass.

CWIS Fishing Zebra Mussels Co-occurrence - + + Sufficiency N/A unknown unknown Temporality - + - Manipulation - + N/A Coherence N/A + - Summary evaluation CWIS and ze bra mussel hypotheses rejected Most likely cause: fishingiv 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 year s, but other unknown causes were responsible for declines o ccurring 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 factorvi 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 influe nce, but cannot explain post-1990 decline Most likely cause of dec line: striped bass predationvii Table 7. Consistency of hypotheses with evaluation criteri a: 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 ze bra mussel hypotheses rejected Most likely cause: striped bass predationviii 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 changeix Table 9. Pearson, Spearman, and Kenda ll correlation coefficients for the association between Log 10 (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 0.837x Figure 1. Hudson River map, with sample regionsxi Figure 2. Impacts of CWIS on Age 0 life stages, partitioned between abundance of each life stage and survival between life stages.



## Egg

YSL

PYSL YOY Age 1 Survival

Survival

Survival

Abundance Abundance Abundancexii Figure 3. Impacts of fishing on Age 0 life stages.



## Egg

YSL

PYSL YOY Age 1 Decline in spawning

Abundance

Abundance Abundance Abundancexiii Figure 4. Impacts of zebra musse l activity on Age 0 life stages.



## Egg

YSL

PYSL YOY Age 1

Survival Survival

Growth Growth

Abundance Abundancexiv Figure 5. Impact of striped bass predation on Age 0 life stages.



## Egg

YSL

PYSL YOY Age 1

Survival Decline in spawning

Abundance Abundance

Abundance

Abundancexv Figure 6. Impact of elevated summer temperatures on Age 0 Atlantic tomcod.



## Egg

YSL

PYSL YOY Age 1

Survival

Growth Abundance

Abundance Survivalxvi Figure 7a. Long-term trends in the a bundance of striped bass PYSL and YOY.

Figure 7b. Long-term trend in st riped bass PYSL to YOY survival. 0 1000 2000 3000 4000 5000 6000 7000

1974 1976 19

78 1980 1982 19

84 1986 19

88 1990 19

9

2

1994 19

96 19

98 2000 20

02 2004 0 500 1000 1500 2000 2500 3000 3500

PYSL YOYStriped Bass PYSL Index (millions)Striped Bass YOY Index (thousands)

0 0.005

0.01 0.015

0.02 0.025

0.03

1 9 7 4

197 6

197 8

198 0

198 2

1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2

004Striped bass PYSL survival indexxvii 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 IP 3 CMR for striped bass and striped bass PYSL survival index.

Figure 9b. Relationship between IP2 and IP3 CMR for striped bass a nd 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 we re operating during May and June, the peak months during which entrainable life stages of stri ped bass are present in the Hudson River. The horizontal line shows the median survival index value for the time seri es. (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 1 97

7 1979 1981 1 98

3 1985 1 9 87 1 9 89 1991 1 9

93 1 99

5 1997 1999 2 00

1 0 0.005

0.01 0.015

0.02 0.025

0.03

1975 1 97

7 1979 1981 1 98

3 1985 1 9 87 1 9 89 1991 1 9

93 1 99

5 1997 1999 2 00

1

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, 1975-2002Striped 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, 1975-2002Striped Bass PYSL Survival Indexxx Figure 11. Long-term trends in the abundance of white perch PYSL and YOY in the Hudson River.



### 0 500 1000 1500 2000 2500

1

974 1

976 197 8

198 0

198 2

1

9

8

4

1

986 1

988 1

990 199 2

1

9

9

4

1

996 1

9

9

8

2

0

0

0

2

002 200 4 

### 0 500 1000 1500 2000 2500 3000 3500

PYSL YOYWhite 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 pe rch 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 indexWhite Perch YOY abundance indexxxii 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 IP 3 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 Indexxxiii 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

197 4 1976 197 8 1 98

0 198 2 19 84 19 86 1 9

88 199 0 1 9

92 19 94 1996 1998 2 0

00 20 02 0 2 4 6 8 10 12 14 16 18 20

PYSL survival Indian Point CMRxxiv 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 du ring 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 seri es. (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 2002White perch PYSL to YOY survival index

0 0.001 0.002 0.003 0.004 0.005 0.006

0 50000 100000 150000White 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 150000White perch PYSL to YOY survival index

Combined May-June Flows for IP2 and IP3xxv Figure 16a. Relationship between white perch YOY abundance and the striped bass predation index.

Figure 16b. Long-term trends in white perc h 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 1

9

76 1

9

78 1

9

80 1

9

82 1

9

84 19

86 19

88 19

9

0

19

9

2

19

9

4

19

9

6

19

9

8

2000 2002 2004 0 500 1000 1500 2000 2500 3000 3500

SB Predation White perch YOYxxvi 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

19

74 197 6 1978 1 980 198 2 1984 1986 1 988 19 90 1992 1994 1 996 19 98 2000 2 002 200 4 0 500 1000 1500 2000 2500 3000 3500 PYSL Abundance YOY Abundancexxvii Figure 18a. Relationship between American sh ad 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 indexAmerican 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 IP 3 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 surviv al during years in which 1 unit (blue) and 2 units s(red) at Indian Point were operating du ring May and June, the peak months during which entrainable life stages of American shad are present in the Hudson Rive r. The horizontal line shows the median survival index value for the time series. (b) Relationship between total MayJune withdrawals by IP2 and IP3 an d 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 2001American 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 IP3American 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 IP3American shad PYSL to YOY survival indexxxx 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 sh ad PYSL abundance a nd in the striped bass predation index.

0 1000 2000 3000 4000 5000 6000 7000

19

7

4

197 6

19

78 198 0

1

982 19

8

4

198 6

19

88 199 0

1

992 19

94 1

996 19

98 20

00 2

002 20

04 0 20 40 60 80 100 120 140 160
SB Predation American shad PYSLAmerican 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

1 974 1 976 19 78 198 0 1 98

2 19 8

4 19 8

6 19 8

8 1990 1992 1994 1996 1 998 2 000 2 002 20 04 0 5 10 15 20 25 30

LRS Mark-Recapturexxxiii Figure 24a. Relationship betw een Atlantic tomcod egg deposition and resulting age 1 abundance.

Figure 24b. Relationship between At lantic tomcod egg to age 1 su rvival 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 indexAtlantic tomcod age 1 abundancexxxiv Figure 25a. Relationship between IP2 and IP3 CMR and Atlantic tomcod egg to age 1 survival.

Figure 25b. Relationship between IP2 and IP 3 CMR and Atlantic tomcod LRS index.

Figure 25c. Relationship between IP2 and IP3 CM R 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 indexxxxv Figure 26. (a) Atlantic tomcod age 0 survival dur ing years in which 1 uni t (blue) and 2 units (red) at Indian Point were operating during May and June, th e 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 A tlantic 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 19 7

6 1 9

78 1980 19 82 19 8

4 1 9

86 1988 19 9

0 1 9

92 1994 19 96 1 9

98 20 0

0Atlantic 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 Indexxxxvi 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

19

7 6 1 9 78 1980 19 8 2 1 9 84 1986 1988 19 9 0 1992 1994 1996 1998 20 0 0 2 0 0 2 0 5 10 15 20 25 30 PWW Degree-Days Age 1-2 tomcod (millions)xxxvii Figure 28a. Relationship between the striped bass predation inde x and the Atlantic tomcod LRS index.

Figure 28b. Relationship between the striped bass predation in dex and the Atlantic tomcod mark-recapture index.

0 5 10 15 20 25 30

0 1000 2000 3000 4000 5000 6000Atlantic 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

197 4

197 6

197 8

198 0

198 2

198 4

198 6

198 8

1990 199 2

199 4

199 6

1998 200 0

200 2

2

00

4 0 20 40 60 80 100 120 140 160 180 200Atlantic TomcodLRS Index (millions)Striped Bass Abundance Index (millions)

0 1000 2000 3000 4000 5000 6000 7000

197 4

1976 197 8

198 0

1982 198 4

1986 198 8

199 0

1992 199 4

19

9

6

199 8

200 0

2002 200 4 0 1 2 3 4 5 6Striped 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

1

974 1

9

76 197 8 1 9

80 19

82 1984 19 86 1

9

88 1

99

0 19 92 1994 1

996 19

9

8 200 0

2

00

2 20 04 

### River herring PYSL

0 200 400 600 800 1000 1200

1

974 1

97

6

1978 1

98

0

1982 1

984 1986 1

98

8

1

99

0

1992 1

9

94 1996 1998 2

000 2

00

2

2004 

### 0 5 10 15 20 25 30 35

alewife blueback herringxl Figure 31a. Relationship between the IP2 and IP3 CMR and river herring PYSL survival.

Figure 31b. Relationship between the IP2 and IP 3 CMR and river herri ng 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 blueba ck herring) PYSL to YOY survival during years in which 1 unit (blue) and 2 units (red) at Indi an Point were operating during May and June, the peak months during which entrainable life stag es of river herring are present in the Hudson River. The horizontal line s hows the median survival index value for the time series. (b) Relationship between IP2 and IP3 May-June wate r 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 2001River 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 150000River 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 150000River herring PYSL to YOY survival index

Combined May-June Flows for IP2 and IP3xliii Figure 34a. Relationship between the striped bass predation index and river herring PYSL abundance.

Figure 34b. Relationship between the striped bass predation in dex 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

1 97

4

19

7 6

1 97

8

1980 1 98

2

19

8 4

1 98

6

19

8 8

1 99

0

19

9 2

1 99

4

1 9 9 6

1998 20

0 0 2 00

2 2 0 0 4 0 0.5 1 1.5 2 2.5 3 3.5 4

SB Predation River herring PYSLStriped Bass Abundance Index (millions)River Herring PYSL Index (billions)

0 1000 2000 3000 4000 5000 6000 7000

1 97

4

1 9 76

1 9 78

1 98

0

1 9 82

1 9 84

1986 1 9 88

1 990 1 9 92

1 9 94

1996 1 9 98

2000 2 0 02

2 0 04 0 5 10 15 20 25 30 35

SB Predation Blueback herring YOY 0 1000 2000 3000 4000 5000 6000 7000

1 97

4

1 9 76

1978 1980 1 9 82

1 9 84

1986 19

8 8

1 9 90

1992 1 9 94

1996 1 9 98

2 000 2 0 02

2 0 04 0 200 400 600 800 1000 1200

SB Predation Alewife YOYxlv Figure 36. Long-term trends in abu ndance of bay anchovy PYSL and YOY.

0 1000 2000 3000 4000 5000 6000

197 4 197 6 1978 19 8

0 1 98

2 198 4 198 6 1988 19 9 0 1 99

2 199 4 199 6 199 8 20 0

0 2 00

2 200 4 0 50 100 150 200 250 300 350 400 450 500

PYSL YOYxlvi 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 abundancexlvii Figure 38. (a) Bay anchovy PYSL to YOY survival during years in whic h 1 unit (blue) and 2 units (red) at Indian Point we re operating during May and June, the peak months during which entrainable life stages of river herring are pr esent 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

1 98

5 1 987 1989 1 99

1 1 993 1995 1 99

7 1 99

9 2001Bay anchovy survival index

0.01 0.1

1 10 100 1000

0 50000 100000 150000 200000Bay anchovy survival index

Combined May-June Flows for IP2 and IP3 0.01

0.1

1 10 100 1000

0 50000 100000 150000 200000Bay anchovy survival index

Combined May-June Flows for IP2 and IP3xlviii 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

1974 197 6

197 8

1980 1982 1

984 1986 1988 1

990 1

992 1

994 1

996 1

998 200 0

2

002 200 4 

### 0 50 100 150 200 250 300 350 400 450 500

SB Predation Bay anchovy YOYxlix Figure 40. Long-term trends in the abunda nce of spottail shiner eggs and YOY.



### 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

1974 19

76 1978 1

980 1982 1984 198 6

1988 1990 1

992 1994 1

996 1998 200 0

200 2

2004 

### 0 0.5 1 1.5 2 2.5

Eggs (millions) YOY (millions)l Figure 41a. Relationship between the IP2 a nd 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 spawni ng 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 FSpawning Potential Ratiolii 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 ZSpawning Potential Ratioliii Figure 44a. Relationship between relative cha nge 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 cha nge 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 EntsusLog10(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 cha nge 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 EntsusLog10(R) Anadromous Estuarine Freshwater Marine