### Winter Flounder Abundance Near Brayton Po int Station, Mt. Hope Bay Revisited: Separating Local from Regional Impacts using Long Term Abundance Data.

By

Mark Gibson RI Division of Fish and Wildlife Marine Fisheries 4808 Tower Hill Rd Wakefield, RI 02879 March 2002

RI Division of Fish and Wildlife Research Reference Document 02/1

### 2 Winter Flounder Abundance Near Brayton Po int Station, Mt. Hope Bay Revisited: Separating Local from Regional Impacts using Long Term Abundance Data.
By Mark Gibson RI Division of Fish and Wildlife Marine Fisheries 4808 Tower Hill Rd Wakefield, RI 02879 March 2002

Introduction

Power generation at Brayton Point Station (BPS) on Mt. Hope Bay has been implicated in the collapse of winter flounder in Mt. H ope Bay (Gibson 1994, 1996). As a result, the permitting process for the facility is focused on conditions that will allow for recovery of the fish population to a balanced and indi genous status by meeting state water quality standards. The company that owns the fa cility, USGEN of New England, does not accept that impacts from the plant have contributed to the decline of fish in the area. In their partial 316(a) and 316(b) vari ance request application, the company argues that over fishing is the primary determinant of wint er flounder abundance and that the failure to recover in Mt. Hope Bay is a result of continued over fishing (USGEN 2001). The company uses an age structured population mode l based on the Leslie matrix with density dependent elements, to project the trajectoryof the Mt. Hope Bay winter flounder stock. An obvious problem is the lack of concor dance between model estimates and observed data in recent years. Specifically, the m odel projects an increase in winter flounder abundance in response to reduced fishing mortality whereas the observed trawl and impingement data indicate no increase. The ev idence for a reduction in fishing mortality rate in recent years is str ong (Gibson 2000 and updates) sugges ting that other factors are responsible for the continued low abundance near Brayton Point Station. The Gibson (1996) report compared trawl survey abundance trends for a number of species including winter flounder and showed th at the trends in Mt. Hope Bay near the station were anomalous with respect to th e region. He concluded that adverse impacts from entrainment and waste heat rejection were the most likely cause. The methods used included an ARIMA time series model withan intervention term centered on 1985-1986. This period coincides with the conversion of unit 4 from closed to open cycle cooling. The results showed that a significant and ne gative intervention term was present in Mt. Hope Bay data but not for other nearby area s. In this report, winter flounder abundance trends are further analyzed to examine imp acts from the facility and to gain incite on permit conditions, which might lead to stoc k recovery. The Rhode Island Department of Environmental Management (RIDEM) has maintained from the onset of the process that plant operational conditions prior to 1985 are a logical starting point , but not necessarily

### 3 an end result, for future permit conditions. BPS has been in commercial operation since April of 1963 so that impacts to local fish ery resources may have begun well before unit 4 modifications. Methods and Data Sources

BACI Approach- The preferred method to as sess environmental impacts from industrial activity such as power gene ration is the before-after-control-impact (BACI) sample design using analytical procedur es that account for serial co rrelation in the observed data (Madenjian et al. 1986, Manley 2001). The Gi bson (1996) analysis was consistent with BACI design in that abundance series in the vicinity of the station and away from the station were examined before and after the 1985-1986 modification of unit 4. Time series methods that explicitly accounted for serial correlation were used. That analysis however dealt with the issue of additional impacts from unit 4. Brayton Point Station has four generating units, the first three of which went on line from 1963 to 1969. Total generating capacity of the f our units is 1150 megawatts. Fish monitoring data for Mt. Hope Bay are not available prior to 1972. A lthough unit 4 modifications likely played a decisive role in the abruptcollapse of winter flounder, the population may have been impacted earlier, particularly with the a ddition of the 650 MW unit 3 in July of 1969. Lack of monitoring data prio r to 1972 prevents full utilization of the BACI approach to estimate impacts prior to unit 4 modifications . However, the control-impact design (CI) post event may be used provided that care is exercised in selecting the control areas (Manley 2001). Relative Abundance Data- Four trawl survey s were used as the primary measure of winter flounder abundance in the Rhode Island area. Data from Marine Research Inc. (MRI) trawling in Mt. Hope Bay and from Rhode Island Division of Fish and Wildlife (RIDFW) and University of Rhode Island Graduate School of Oceanography (URIGSO) studies in Narragansett Bay and adjacentBlock Island and Rhode Island Sounds were examined from 1972 to 2000. I also used the National Marine Fisheries Service (NMFS) trawl survey conducted in the federal waters of the sounds. Details of the survey designs and methodology may be found in USGENMRI (1999), Lynch (1999), Jeffries et al. (1989), Grosslein (1969) and Cl ark (1979). Briefly, MRI conduc ts monthly trawling at 6 fixed stations in Mt. Hope Bay. Five stati ons are in the upper portion near BPS and one is located mid-bay near Spar Island. From 1969 to 1978, the RIDFW survey utilized a fixed station design with different trawl gear. Gi bson (1987), computed abundance indices with adjustments for area swept to be comparab le with the modern survey. Since 1979, the RIDFW has conducted spring and fall cruise s in Narragansett Bay and nearby coastal waters with a random stratified design and 42 tows per cruise. Stratification is by depth. Annually, between 3% and 10% of the RIDFW seasonal tows have fallen in the Rhode Island waters of Mt. Hope Bay. RIDFW traw ling in Mt. Hope Bay is limited to strata south of Spar Island and does not overlap th e MRI survey. The URIGSO trawl survey is conducted at two fixed stations in the lo wer west passage of Narragansett Bay on a weekly basis. J. Collie, URIGSO- pers. comm, provided recent data for the survey, which dates back to 1959. The NMFS survey is of random stratified design. Seasonal (spring and fall) cruises are made with stations allocated to inshore strata.

### 4 I also examined commercial landings in the RIarea as a gross meas ure of winter flounder abundance. Landings from NMFS statistical area 539, which includes Narragansett Bay and RI coastal waters, were obtained. Winter flounder have been si gnificantly exploited in the New England area since the early 1900 ’s (Perlmutter 1947). Consequently, large fluctuations in commercial landings are more likely related to ch anges in abundance than to effort. For example, from 1975 to 1985 co mmercial landings in area 539 tripled while Anthony (1990) reported less than a doubling of standardized fish ing effort. Further, commercial landings are significa ntly correlated (P<0.01) with the RIDFW, URIGSO and NMFS long-term trawl surveys. The Company identified impingement data at BPS as a more robust measure of abundance than trawl surveys in their 316(a) (b) partial demonstration (USGEN 2001). Although impingement indices have been de monstrated to be of limited value in detecting abundance changes (Van Winkle et al. 1981) and trawl survey data has been shown superior over fishery dependent data(Harley et al. 2001), I considered their impingement data in this study. Specificall y, the MRI standardized impingement series for Mt. Hope Bay was considered as an alte rnate impact series and the MRI impingement data from the Providence River near Manchest er Street Station (MSS) was considered as an alternate control series. Statistical Procedures- The CI design relies on an assumption that the control abundance series is broadly representative of the popul ation in question and th at the impact series contains additional information from a local influence. In this case, taking the ratio of corresponding series values produces a series indicative of the local impact as information common to both series is cancelled out. A test for trend in the adjusted series reveals the significance of the local impact. A control and impact abundance series that contain the same trend information will produ ce a ratio series without trend (Appendix). A significant trend in the ratio series indicate s a local impact or aninappropriate control series. To reduce the likelihood of the latter , testing with multiple control series is recommended (Underwood 1994). Jeffries et al . (1989) showed th at winter flounder abundance data for the period 1959-1985 had common signals regardless of where it was collected in RI waters. The stock manage ment areas specified in ASMFC and NMFS stock assessments also imply commonality in gross abundance patterns. Rhode Island winter flounder are part of the southern Ne w England and mid-Atlantic stock complex. These observations indicate that the use of multiple control series, sampled at different geographic scales, will be a powerful means to detect local impacts. Ratio series were formed between the MR I standard trawl i ndex and the control abundance series as:
Z tj 

### = M t 

### /I tj 

### (1) where: Z= ratio of impact to control abundance M= MRI standard trawl abundance I= control index abundance

### 5 t= year j= control series. The resulting ratio series we re log transformed and testedfor time trends via linear regression: ln(Z tj 

### )= ln(Z 0 

### )+G t 

### + C (2) where: Z 0 

### = ratio abundance at t=0 G= instantaneous ra te of population change. t= time C = combined process and measurement error term. The logarithmic transformation ineq.2 stabilizes the variance in lognormally distributed survey data and is consistent with anunderlying population dynamics model based on instantaneous rates of population change . For estimates of G<0, the population is declining while for G>0 it is increasing. Inthe special case of G=0, the population is stable without trend. The proced ures above were applied to the MRI standard trawl index (impact) and the five candidate control se ries (NMFS trawl, URIGSO trawl, RIDFW trawl, commercial landings, MSS impingement ). The procedures were then repeated substituting the BPS impingement index for the impact series. The analysis was restricted to the 1972-1985, pre-unit 4 period to evaluate conditions relative to permitting. The regression test for time trend in eq.2 a ssumes that the error terms are independently and identically distributed with constant variance and mean of zer o. Serial correlation occurs when the error terms are not indepe ndent but correlated w ith past error terms. Neter et al. (1983) summarize the problems encountered in regression analysis when serial correlation is present but not accounted for. The most important to this application is that standard errors of the slope parameter may be underestimated. This may result in type I error, that is concluding a significant trend when in fact none exists. Residuals from eq.2 were tested for serial correlation using the Durbin-Watson test (Neter et al. 1983). This test involves computing a ratio of the sum of squared residual differences to the sum of squared residuals (D):
D= 6 ( C t 

### - C t-1 

### ) 2 

### / 6C t 2 

### . (3) When serial correlation is pres ent, first differences in the residuals will be small and the numerator in eq.3 will be small relative to the denominator. Manley (2001) presents tabular values of D in relation to the num ber of independent variables and degrees of freedom in the regression model. Significant serial correlation is present when the computed D statistic is less than the lower 95% table value. No correlation is present when the computed D value exceeds the upper 95% limit. There is a range in the table for which the test is inconclusive. Plant Operational Data and Permit Conditions- Since permit conditions for the facility are the issue at hand, managers need advice on the conditions that will likely result in a

### 6 recovery of the fishery resource to a ba lanced and indigenous state. RIDEM has maintained that pre-1985 operating conditions de fine possible limits for coolant flow and waste heat rejection as this period precededthe abrupt collapse of the winter flounder stock following unit 4 conversion. If these conditions are appropriate, the adjusted abundance series should have no trend from 1972-1985. That is, estimates of G in eq.2 should not be significantly diffe rent from zero. If that hypothes is is rejected and there is evidence that G<1.0, then the st ock was declining in Mt. Hope Bay faster than in other areas. One would conclude th at the pre-1985 conditions we re not conservative enough and that the operation of units 1-3 had result ed in adverse impacts to winter flounder in Mt. Hope Bay. Data on average circulating coolant flow and waste heat rejection were taken from the company 316(a) and (b) demonstration. These data were examined for trends over time and compared with the trajectories of winter flounder abundance. Possible permit conditions were identified as historical operational conditi ons when a stable stock of winter flounder existed.
Results

Winter flounder abundance data used in this analysis is summarized in Table 1. Trawl survey results are plotted in Figure 1. Comm on features of the i ndependently conducted surveys are low abundance in the mid-1970’s followed by high abundance that persisted through about 1983. All surveys declined thereafter with the MRI survey showing a complete collapse to undetectable levels. Th e other surveys reached a historic low in 1993 but then showed some evidence of recove ry. Of these, the NMFS survey shows the greatest increase since 1993 while the URIG SO survey shows the least. Commercial landings in statistical area 539 follow a sim ilar pattern (Figure 2). Landings remain low but have been increasing since 1993. Impinge ment of winter flounder at BPS declined steadily from 1972 to 1988 with the exception of 1978, and has remained low since (Figure 3). Impingement at MSS featured a very large value in 1985 but otherwise was similar to trends in long-term trawl surveys. Recent levels are above that observed in the early 1990’s. This series needs to be adjustedfor coolant flow rates since it is not clear what impact station history and re -powering had on impingement rates. MRI standard trawl abundance of winter flounder, as adjusted by c ontrol data using the procedures described in equations 1 and 2, is plotted in Figure 4. The large drop in abundance in the mid-1980’s remains the dominant feature of the adjusted series. Clearly, a large reduction in winter flounder abundance occurred near BPS that did not occur in other areas. Of more interest to the permitting process are the slopes of the adjusted series during the period 1972 to 1985. All show evidence of decline. Detailed regression results are given in Table 2 and are summarizedin Table 3. For the period 1972-1985, all slope estimates are negative. Estimates of G in eq.2 were negative and significantly (P<0.05) less than zero for the NMFS, URIGSO, and commercial landings control series. Estimates of G were negative for the RI DFW and MSS control series but the 95% confidence bound included zero. This is not su rprising since the RIDFW series changed survey methodology in 1979 from fixed to ra ndom stations. Although efforts were made

### 7 to account for the change, the extended series may lack continuity. The MSS series is not free of power plant impacts and is short relative to the other series. BPS impingement, adjusted using the procedures described in eqs. 1 and 2, is plotted in Figure 5. Substitution of the impingement index at BPS for the MRI trawl survey confirms the trawl results for 1972-1985. Four of the five adjusted series have slope estimates (G) significantly less than zero at the P<0.01 level (Tab le 3). Only the MSS impingement (control) series did not reach conventional significan ce levels. Computed Durban-Watson statistics are included in Tabl e 3. Of the 10 series tested, none indicated significant serial correlation in the residuals. Three tests were inclusive while seven resulted in non-significant conclusions. Acco rdingly, the regression model used in eq. 2 should yield reliable slope estimates and id entify significant trends without risk of elevated type I error. Overal l, the control-impact analysis provide strong evidence that winter flounder in Mt. Hope Bay near BPS decl ined at faster rates than in other areas during the period 1972-1985. Trends in coolant water usage at BPS and wast e heat rejected to Mt. Hope Bay are shown in Figure 6. Coolant flow rose from 50 billi on gallons per year in 1963 to 300 billion in 1973 as generating units were brought on line . Cooling water usage fluctuated between 250 billion and 300 billion gallons per year from 1970 to 1984. A sharp increase began in 1985 and coolant flow exceeded 350 billion from 1986 to 1996. Recent levels have been between 300 billion and 350 billion. Waste heat to Mt. Hope Bay rose from 4 trillion in 1963 to 30 trillion in 1973 with increasing pow er generation. Heat load to the Bay fluctuated between 20 trillion and 30 tril lion BTUs from 1970 to 1981. In 1982, a steady increase began which accelerated in 1986 and peaked at around 50 trillion BTUs in 1988- 1990. Heat load declined somewhat in 1991 and has been steady at around 40 trillion BTU. Although plant coolant flow and wast e heat rejection are highly correlated (r=0.95), some deviations are noteworthy. The operational data are plotted as a BTUFlow phase diagram in Figure 7. There is a cl ear transition in plant operations in the mid1980’s when coolant flow increased before waste heat. The observational data may be broadly grouped into four periods: Period 1- 1963-1969, initial start up and addition of generating units Period 2- 1970-1981, operation of units 1-3 withunit 4 in closed cycle beginning in 1974. Period 3- 1982-1986, conversion of unit 4 from closed cycle cooling, to piggyback mode and then to open cycle cooling. Period 4- 1987-1996, high power operation of al l units with maximum coolant flow and waste heat rejection. Period 5- 1997-2000, MOA II operation with reduced output. Referring again to Figure 4, it is clear thatperiod 3 was a watershed event for the winter flounder stock in Mt. Hope Bay. All of the ad justed abundance series were in a decline

### 8 that accelerated in 1985 or 1986. Viewed in th e context of plant history, the operation of units 1-3 was sufficient to cause a reducti on in Mt. Hope Bay winter flounder abundance relative to other areas. The large increase in flow and waste heat associated with unit 4 conversion accelerated the decline. That winter flounder have not shown signs of recovery in either the trawl or impingement data indicates that flow and BTU limits under MOA II are above that needed to rebuild the population. Since the population was clearly in decline relative to other areas from 1972 to 1985, average operational conditions then were also excessive for the population. Mean flow and waste heat rejection at BPS from 1972 to 1985 were 266.0 (SE=5.7) billion ga llons and 28.9 (SE=0.9) trillion BTU respectively. Since the average plant operational conditions during 1972-1985 contributed to a winter flounder decline, ne w permit conditions should be significantly below this average. Resource monitoring did not commence until 1972, well after commercial operation began, so it is not clear from abundance and plant operational data how low flow and BTU need to be reduced to enable recovery. Discussion

An important permitting issue for the RIDEM, EPA, and MADEP is a set of conditions that will allow Mt. Hope Bay fish stocks to return to a balanced and indigenous state and to meet state water quality standards. It s hould be clear from the above analysis that winter flounder abundance in Mt. Hope Bay is anomalously low compared to other areas in and outside of Narragansett Bay. A control- impact analysis was used to confirm the large drop in abundance that occurred in 1985-1986. Multiple cont rol abundance series measured in and outside of Narragansett Bay were used to characterize regional abundance patterns and to reduc e the likelihood of Type I e rror in time trend analysis. Abundance of winter flounder in Mt. Hope Bay in 2000 is still well below that observed in other areas. With regard to permit conditi ons, there is statistical evidence of a decline in the Mt. Hope Bay winter flounder stock from 1972-1985 relative to other areas. These results are interpreted to mean that th e operation of units 1-3 during 1972-1985 was sufficient to induce a decline in the winter flounder stock. Conversion of unit 4 to open cycle cooling accelerated this decline and led to the complete collapse of the stock. The conversion was associated with a large in crease in coolant flow, followed by a large increase in waste heat rejection. Mean flow and waste heat rejection at BPS from 1972 to 1985 were 266.0 (SE=5.7) billion gallons and 28.9 (SE=0.9) trillion BTU respectively. As noted above, these operational conditions were associated witha significant declin e in the abundance of winter flounder in Mt. Hope Bay that was no t observed in other areas. Accordingly, new permit conditions should specify flow and heat rejection levels below the 1972-1985 mean levels. It is emphasizedthat the lack of monitoring data prior to 1972 precludes direct estimation of overall flow and h eat limits conducive to a healthy population of winter flounder. Additional guidance from st ate water quality standards, life history studies, and sustainable removal rates is needed. Also, there may be seasonal, life history considerations that need be addresse d within overall flow and heat limits.

### 9 Finally, there is no assuran ce that conditions that on ce supported a healthy population, will allow for recovery of a collapsed popu lation. Indeed, recovery may be difficult to achieve without large reductions in plant induced and fishing mortality if long-term environmental trends (increasing water temper ature) and type III predation are present (Collie and Spencer 1994). The former is known to be occurri ng and the latter is suspected in the form of invertebrate predation on larvae and YOY flounder. Long-term trends in environmental conditions and depensatory predation may interact with anthropogenic sources of mortality to drive a population into what is known as a “predator pit” (Hilborn and Walters 1992). Th ey note that under these circumstances, a population may not recover after fishing pr essure is reduced. This should not be interpreted to mean that nothing can be done but that, in the face of uncertainty over stock dynamics, permit conditions for power plants and fishery regulations should be overly protective of th e resource in question.

Literature Cited
Anthony, V.C. 1990. The New England ground fish fishery after 10 years under the Magnuson Fishery Conservation and ManagementAct. N. American J. Fish. Mgt. 10: 175-184. Clark, S.H. 1979. Application of bottom trawl survey data to fish stock assessment. Fisheries 4: 9-15. Collie, J.S. and P.D. Spencer. 1994. Modeling predator-prey dynamics in a fluctuating environment. Can. J. Fish. Aquat. Sci. 51: 2665-2672. Gibson, M.R. 1987. Preliminary assessment of winter flounder ( pseudopleuronectes americanus ) stocks in Rhode Island waters. RI Di vision of Fish and Wildlife. Res. Ref. Doc. 87/7. Gibson, M.R. 1994. Population dynamics of wint er flounder in Mt. Hope Bay in relations to operations at the Brayton Point Electric Plant. RI Division Fish and Wildlife. Report to the Brayton Point Technical Advisory Committee, May 1992. Revised December 1993. Gibson, M.R. 1996. Comparison of trends inthe finfish assemblage of Mt. Hope Bay and Narragansett Bay in relation to oper ations at the New England Power Brayton Point Station. RI Division Fish and Wildlife Research Reference Document 95/1. Revised August 1996. Gibson, M.R. 2000. Recent trends in abundance, recruitment, and fishing mortality for winter flounder in Narragansett Bay and Rhode Island coastal waters. RI Division of Fish and Wildlife. Report to the RIMarine Fisheries Council.

### 10 Grosslein, M.D. 1969. Groundfish survey progra m of the BCF Woods Hole Comm. Fish. Rev. 31: 22-35. Harley, S.J., R.A. Myers, and A. Dunn. 2001. Is catch-per-unit-e ffort proportional to abundance? Can. J. Fish. Aquat. Sci. 58: 1760-1772. Hilborn, R. and C.J. Walters. 1992. Quantitative Fish Stock Assessment: Choice, Dynamics, and Uncertainty. Chapman and Hall, New York. 570 p. Jeffries, H.P., A. Keller, and S. Hale. 1989. Predicting winter flounder (Pseudopleuronectes americanus) catches by time series analysis. Can. J. Fish. Aquat. Sci. 46: 650-659. Lynch, T.R. 1999. Assessment of recreationally important finfish stocks in Rhode Island coastal waters, 1979-1998. RI Division Fish a nd Wildlife. Annual Progress Report. Job F-61-R-1. Madenjian, C.P., J. Jude, and F. J. Tesar. 1986. Intervention analys is of power plant impact on fish populations. Can. J. Fish. Aquat. Sci. 43: 819-829. Manley, B.F.J. 2001. Statistics for Environmental Science and Management. Chapman and Hall/CRC. New York 326 p. Neter, J., W. Wasserman, and M.H. Kutn er. 1983. Applied linear regression models. Richard B. Irwin, Inc. Homewood, IL. 547 p. Perlmutter, A. 1947. The blackback flounder and its fishery in New England and New York. Bull. Bingham Oceanogr. Collect., Yale Univ. 11(2). 92 pp. Underwood, A.J. 1994. On beyond BACI: sampling designs that might reliably detect environmental disturbances. Ec ological Applications 4: 3-15. USGen New England, Inc. (USGEN). 2001. Variance request application and partial demonstration under the clean water act, section 316(a) and (b) in support of renewal of NPDES Permit No. MA 000 3654 for USGEN New England, Inc.’s Brayton Point Station. May 24, 2001. USGEN/MRI. 1999. Annual Hydrobiological Report. Van Winkle, W., D.S. Vaughan, L.W. Barnt house, and B.L. Kirk. 1981. An analysis of the ability to detect reductions in year-c lass strength of the Hudson River white perch (Morone americana ) population. Can. J. Fish. Aquat. Sci. 38: 627-632.

### 11 Appendix - Proof that the ratio of two independentseries, both representative of the same process, is a seri es without trend. For this exercise, I assume that a si mple exponential growth model governs the population dynamics of a stock:
N t 

### = N 0 

### exp(Gt) exp( C p 

### ) (1) where: N= number in population N 0 

### = initial number t= time G= instantaneous rate of change. C p 

### = process error. The rate parameter G has additive component s due to births, natu ral deaths, and losses from fishing and other sources. In fish eries terminology, thes e are equivalent to recruitment (R), natural mortal ity rate (M1), fishing mortalit y rate (F), and anthropogenic mortality rate (M2). For values of G greater than 1.0, the populati on increases. If G is exactly 1.0, the population is stable around a constant value. For G less than 1.0, the population is declining. The erro r term allows for deviations from the process due to variability in growth, recruitment, natural mo rtality, etc. Error in eq. 1 is multiplicative in nature, i.e. they are normally distributed in the log scal e with zero mean and constant variance. Since actual population sizes are unknown, an obs ervation model is needed which relates true abundance to the relative abundance in a survey:
N t 

### = I t 

### /q exp( C m 

### ) (2) Where: N= population size I= survey relative abundance Q= catchability coefficient C m 

### = measurement error t= time. The multiplicative error term allows for deviations in the measured index from its true value. As above, errors are normally distribu ted with zero mean and constant variance in the log scale. Substituting eq.2 into eq.1 and combining error terms yields an estimation model amenable to analysis when relative abundance data is available, for example in the form of a trawl survey:
I t 

### = I 0 

### exp(Gt) exp( C p 

### + C m 

### ) (3) The usual form of analysis is to make a l ogarithmic transformation of eq.3 and use least squares regression to estimate parameters:

### 12 ln(I t 

### )= ln(I 0 

### )+Gt+( C p 

### + C m 

### ) (4) The rate of change in the popul ation (G) is estimated as th e slope of the regression. Note that in eq.4, it is not possibl e to separate the process and measurement errors. When there are two abundance indices, bothrepresentative of the stoc k, a ratio series of the corresponding elements should be stationary with constant mean and variance. This can be shown as follows. For two series designated by subscript as 1 and 2, define a ratio series as:
Z t 

### = I 1t 

### /I 2t 

### (5) Substitution of eq.3 into eq.5 and carrying through the subscripts yields:
Z t 

### = [I 10 

### exp(G 1 

### t) exp( C 1p 

### + C 1m

### )]/ [I 20 

### exp(G 2 

### t) exp( C 2p 

### + C 2m

### )]. (6) Because the surveys are representative of the stock in question, G 1 

### =G 2 

### and C 1p 

### = C 2p. 

### Expanding the errors, canceling comm on terms, and simplifying yields:
Z t 

### = I 10

### /I 20

### exp( C 1m 

### - C 2m

### ). (7) That is, the ratio series consists of a constant modified by a combination of the measurement errors for the respective relativ e abundance series. The series has no trend as exp(G 1 

### t-G 2 

### t)=1. The linear combination of normally distributed measurement errors is itself normally distributed with constant and variance. The time pattern of the ratio series in eq.7 is therefore lognormal variation about a constant. In the case that G 1 

### γ G 2 

### , then the terms do not cancel and:
Z t 

### = I 10

### /I 20

### exp((G 1 

### -G 2 

### )t)exp( C 1m 

### - C 2m

### ). (8) The ratio series has a trend with the rate of change given by the difference in the slopes of the original two series. In the context of impact assessment, this would indicate a difference between the control and impact series.

### 13 Table 2- Regression Details for Tests for Time Trends in MRI Winter Flounder Abundance Data SUMMARY OUTPUT for NMFS adjusted log trend 72-85 Table 1- Winter Flounder Abundance Indices for the Rhode Island Area NMFS BIS URIGSO MRI RIDFW 539 Comm MRI Brayton Manchester St Year Kgs/tow No./tow No./tow No./tow Landings MT Impingement Impingement
1959 14.88 809.3 1960 13.94 864.9 1961 16.90 728.0 1962 18.30 728.4 1963 16.14 1045.5 1964 14.93 1591.5 1965 22.65 1117.8 1966 28.88 1617.0 1967 46.59 1320.3 1968 0.73 50.85 1274.7 1969 3.41 40.96 46.27 998.9 1970 1.33 27.77 49.01 860.0 1971 0.76 18.04 10.73 671.2 1972 0.66 15.36 43.65 18.31 438.5 17.11 1973 2.01 15.54 60.37 32.13 523.1 14.57 1974 1.04 13.67 36.24 33.57 390.7 20.16 1975 0.35 9.48 18.89 28.13 360.8 3.72 2381 1976 0.81 7.84 10.60 14.27 379.5 4.03 1148 1977 1.19 10.87 16.46 31.96 483.5 12.40 1503 1978 1.76 28.63 48.56 26.56 660.4 17.83 3017 1979 1.07 51.19 78.02 97.97 820.8 4.34 15101 1980 3.55 42.51 15.75 39.62 1276.3 6.67 8149.5 1981 4.76 31.74 19.35 56.59 1101.6 4.19 3273 1982 1.92 24.59 49.97 24.99 1324.0 1.71 11207 1983 2.47 41.40 43.01 38.13 1231.1 3.26 4212 1984 2.07 19.69 17.93 30.33 1292.5 5.89 1572 1985 1.98 8.76 14.97 28.20 1161.6 7.91 25340 1986 0.77 6.47 5.82 36.04 853.0 4.65 748 1987 0.57 16.44 2.09 38.56 621.6 9.46 7106 1988 0.73 13.80 0.73 22.54 505.2 1.86 5516 1989 0.58 9.11 0.37 14.02 444.1 4.19 2365 1990 0.47 5.10 0.97 14.58 402.0 1.55 10823 1991 0.69 4.25 0.57 21.86 660.8 2.48 2422 1992 0.44 3.05 0.49 7.50 358.7 0.93 1660 1993 0.22 4.96 0.99 4.79 246.8 4.34 1935 1994 0.33 3.11 0.44 6.23 380.0 2.48 912 1995 0.59 14.41 0.91 20.69 413.5 2.64 4295 1996 0.43 10.30 0.61 15.74 342.1 1.71 15360 1997 0.40 10.76 0.90 8.39 400.8 1.63 13398 1998 0.85 6.74 0.53 10.62 498.0 0.47 5902 1999 1.25 3.31 0.26 8.13 432.0 1.71 9716 2000 1.63 3.56 0.11 9.45 558.3 0.45 3948
Mean 1.27 18.27 16.88 26.43 766.40 5.67 6269.60 STD 1.05 13.54 21.83 18.83 379.39 5.53 5951.64 C V 0.83 0.74 1.29 0.71 0.50 0.98 0.95

### 14 Regression Statistics Multiple R 0.602155 R Square 0.362591 Adjusted R Square 0.309473 Standard Error 0.780515 Observations 14

ANOVA df SS MS F Significance F Regression 1 4.158547 4.15854 7 6.82620 7 0.022688 Residual 12 7.310438 0.60920 3 Total 13 11.46899
Coefficient s Standard Error t Stat P-value Lower 95% Upper 95% Intercept 268.1303 102.3829 2.61889 8 0.02243 45.05719 491.2034 X Variable 1 -0.1352 0.051748 -2.6127 0.02268 8 -0.24795 -0.02245

SUMMARY OUTPUT for URIGSO adjusted log trend 72-85 Regression Statistics Multiple R 0.580694 R Square 0.337206 Adjusted R Square 0.281973 Standard Error 0.525464 Observations 14

ANOVA df SS MS F Significance F Regression 1 1.685712 1.68571 2 6.10516 1 0.029447 Residual 12 3.313351 0.27611 3 Total 13 4.999062
Coefficient s Standard Error t Stat P-value Lower 95% Upper 95% Intercept 170.6969 68.92699 2.47648 8 0.02914 5 20.51787 320.8759 X Variable 1 -0.08608 0.034838 - 2.47086 0.02944 7 -0.16199 -0.01017

### 15 SUMMARY OUTPUT for Landings adjusted trend 72-85 Regression Statistics Multiple R 0.75182 R Square 0.565234 Adjusted R Square 0.529003 Standard Error 0.554505 Observations 14

ANOVA df SS MS F Significance F Regression 1 4.796951 4.79695 1 15.6010 5 0.001928 Residual 12 3.689714 0.30747 6 Total 13 8.486665
Coefficient s Standard Error t Stat P-value Lower 95% Upper 95% Intercept 287.9632 72.73643 3.95899 6 0.00189 7 129.4842 446.4423 X Variable 1 -0.14521 0.036763 - 3.94982 0.00192 8 -0.22531 -0.06511

SUMMARY OUTPUT for RIDFW adjusted 72-85 trend Regression Statistics Multiple R 0.431002 R Square 0.185762 Adjusted R Square 0.117909 Standard Error 0.595033 Observations 14

ANOVA df SS MS F Significance F Regression 1 0.969326 0.96932 6 2.73771 4 0.123904 Residual 12 4.248769 0.35406 4 Total 13 5.218095
Coefficient s Standard Error t Stat P-value Lower 95% Upper 95% Intercept 129.0209 78.05256 1.653 0.12423 4 -41.041 299.0828 X Variable 1 -0.06527 0.03945 -1.6546 0.12390 4 -0.15123 0.02068

### 16 SUMMARY OUTPUT for NMFS adjusted impingement Regression Statistics Multiple R 0.769635 R Square 0.592338 Adjusted R Square 0.558366 Standard Error 0.721328 Observations 14

ANOVA df SS MS F Significance F Regression 1 9.072269 9.07226 9 17.4361 4 0.001286 Residual 12 6.243768 0.52031 4 Total 13 15.31604
Coefficient s Standard Error t Stat P-value Lower 95% Upper 95% Intercept 394.3169 94.61914 4.16741 2 0.00130 5 188.1595 600.4743 X Variable 1 -0.1997 0.047824 - 4.17566 0.00128 6 -0.30389 -0.0955

SUMMARY OUTPUT for URIGSO adjusted impingement Regression Statistics Multiple R 0.574571 R Square 0.330132 Adjusted R Square 0.27431 Standard Error 0.933899 Observations 14

ANOVA df SS MS F Significance F Regression 1 5.15798 5.15798 5.91398 6 0.031624

### 17 Residual 12 10.466 0.87216 6 Total 13 15.62398
Coefficient s Standard Error t Stat P-value Lower 95% Upper 95% Intercept 296.8835 122.5028 2.42348 4 0.03211 2 29.97288 563.7941 X Variable 1 -0.15057 0.061917 - 2.43187 0.03162 4 -0.28548 -0.01567

SUMMARY OUTPUT for Landings adjusted impingement Regression Statistics Multiple R 0.792822 R Square 0.628566 Adjusted R Square 0.597613 Standard Error 0.701889 Observations 14

ANOVA df SS MS F Significance F Regression 1 10.00434 10.0043 4 20.3072 5 0.000719 Residual 12 5.911784 0.49264 9 Total 13 15.91612
Coefficient s Standard Error t Stat P-value Lower 95% Upper 95% Intercept 414.843 92.06931 4.50576 8 0.00071 9 214.2412 615.4448 X Variable 1 -0.2097 0.046535 - 4.50636 0.00071 9 -0.31109 -0.10831

SUMMARY OUTPUT for RIDFW adjusted impingement Regression Statistics Multiple R 0.57322 R Square 0.328581

### 18 Adjusted R Square 0.272629 Standard Error 0.807692 Observations 14

ANOVA df SS MS F Significance F Regression 1 3.831073 3.83107 3 5.87258 5 0.032121 Residual 12 7.828389 0.65236 6 Total 13 11.65946
Coefficient s Standard Error t Stat P-value Lower 95% Upper 95% Intercept 255.2075 105.9478 2.40880 5 0.03298 5 24.36717 486.0479 X Variable 1 -0.12977 0.053549 - 2.42334 0.03212 1 -0.24644 -0.01309

SUMMARY OUTPUT for Manchester St adjusted 72-85 trawl Regression Statistics Multiple R 0.468708 R Square 0.219687 Adjusted R Square 0.132985 Standard Error 0.885568 Observations 11

ANOVA df SS MS F Significance F Regression 1 1.987108 1.98710 8 2.53383 0.145893 Residual 9 7.058079 0.78423 1 Total 10 9.045187
Coefficient s Standard Error t Stat P-value Lower 95% Upper 95% Intercept 267.8731 167.18271.60227 0.14355 -110.321 646.067

### 19 7 8 X Variable 1 -0.1344 0.084436 -1.5918 0.14589 3 -0.32541 0.056602

SUMMARY OUTPUT for Manchester St. adjusted impingement Regression Statistics Multiple R 0.475936 R Square 0.226515 Adjusted R Square 0.140572 Standard Error 1.216323 Observations 11

ANOVA df SS MS F Significance F Regression 1 3.89929 3.89929 2.63564 9 0.138937 Residual 9 13.31498 1.47944 2 Total 10 17.21427
Coefficient s Standard Error t Stat P-value Lower 95% Upper 95% Intercept 372.3136 229.6246 1.62140 1 0.13938 2 -147.134 891.7609 X Variable 1 -0.18828 0.115972 - 1.62347 0.13893 7 -0.45062 0.07407

Table 3- Estimates of Instantaneous Rate of Decline in Winter Flounder Abundance for Period 1972-1985 Based on the MRI Standard Trawl and Brayton Impi ngement Series as Adjusted by Several Control Series.
Impact Series-MRI Standard Trawl Control Series Slope SE F Stat Prob D-W Stat
NMFS Trawl -0.135 0.052 6.826 0.023 2.16 8 URIGSO -0.086 0.035 6.105 0.029 1.53 4

### 20 Comm Landings -0.145 0.037 15.601 0.002 1.87 3
RIDFW Trawl -0.065 0.040 2.738 0.124 1.711 Manchester St -0.134 0.084 2.534 0.146 1.07 3
Impact Series- Brayton Impingement Control Series Slope SE F Stat Prob D-W Stat
NMFS Trawl -0.200 0.048 17.436 0.001 1.061
URIGSO -0.151 0.062 5.914 0.032 1.45 6 Comm Landings -0.210 0.047 20.307 0.001 1.85 0
RIDFW Trawl -0.130 0.054 5.873 0.032 1.03 2 Manchester St -0.188 0.116 2.636 0.139 2.06 6 /1 A ? indicates that the computed value of D falls within inconclusive range of the D-W test

### 21 Fig.1- Winter Flounder Abundance in the URIGSO, MRI, RIDFW and NMFS Trawl Surveys

0 20 40 60 80 100 120

1 9 5 9 1 9 6 1 1 9 6 3 1 9 6 5 1 9 6 7 1 9 6 9 1 9 7 1 1 9 7 3 1 9 7 5 1 9 7 7 1 9 7 9 1 9 8 1 1 9 8 3 1 9 8 5 1 9 8 7 1 9 8 9 1 9 9 1 1 9 9 3 1 9 9 5 1 9 9 7 1 9 9 9 2 0 0 1
YearNo. per Tow

0 1 2 3 4 5 6NMFS Kgs per Tow

URIGSO NEP/MRI RIDFW NMFS SNE

### 22 Fig.2- Winter FlounderCommercial Landings in Statistical Area 539

0 200 400 600 800 1000 1200 1400 1600 1800

19

59 19 61 19 63 19 65 19 67 19 69 19 71 19 73 19 75 19 77 19 79 19 81 19 83 19 85 19 87 19 89 19 91 19 93 19 95 19 97 19 99
YearMetric Tons

### Fig.3- Impingement of Winter Flounder at Power Stations in Narragansett Bay

0 5 10 15 20 25

1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 YearBP No. per Flow

0 5 10 15 20 25 30MS 1000's Fish



### Brayton Pt Manchester StFi g .4- Winter Flounder Abundance in the MRI Trawl Surve y Ad j usted for Re g ional Conditions Using Other Long Term Abundance Data

-6 -5 -4 -3 -2 -1 0 1 2 3

1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000

Yearln STD Index

MRI/NMFS MRI/URIGSO MRI/Landings MRI/RIDFW MRI/Manchester StFig.5- Winter Flounder Abundance from Brayton Point Impingement Adjusted for Regional Conditions Using other Long Term Abundance Data

-4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0

1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000

Yearln STD Impingement

IMP/NMFS IMP/URIGSO IMP/Lands IMP/RIDFW IMP/ManchesterFig.6- Mean Coolant Water Flow and Waste Heat Rejection to Mt. Hope Bay by Brayton Point Power Station, 1963-2000

0 50 100 150 200 250 300 350 400 450

1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 YearBillions Gallons

0 10 20 30 40 50 60Trillions of BTU

Coolant Flow Waste Heat

### 27 Fig.7- Waste Heat Rejected to Mt. Hope Bay vs. Amount of Coolant Water Used by Brayton Station, 1963-2000

16.55 30.88 30.74 38.3

76 88 89 90

87 91 93 95

95 96 97 99 98 2000 86 85
80 84 83 84 73 78 77 77 79 81 74 75 69 67 67 68 64

63 0 10 20 30 40 50

0 50 100 150 200 250 300 350 400 450 Billions GallonsTrillions BTU