Winter Flounder Abundance Near Brayton Point 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 Point 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

IntroductionPower generation at Brayton Point Station (BPS) on Mt. Hope Bay has been implicated

in the collapse of winter flounder in Mt. Hope 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 indigenous status by meeting state water quality

standards. The company that owns the facility, 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) variance request application, the company argues that over

fishing is the primary determinant of winter 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 model based on the Leslie matrix with density

dependent elements, to project the trajectory of the Mt. Hope Bay winter flounder stock.

An obvious problem is the lack of concordance between model estimates and observed

data in recent years. Specifically, the model projects an increase in winter flounder

abundance in response to reduced fishing mortality whereas the observed trawl and

impingement data indicate no increase. The evidence for a reduction in fishing mortality

rate in recent years is strong (Gibson 2000 and updates) suggesting 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 that the trends in Mt. Hope Bay near the

station were anomalous with respect to the 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 with an 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 negative intervention term was present in Mt.

Hope Bay data but not for other nearby areas. In this report, winter flounder abundance

trends are further analyzed to examine impacts from the facility and to gain incite on

permit conditions, which might lead to stock 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 fishery resources may have begun well before unit

4 modifications.

Methods and Data SourcesBACI Approach- The preferred method to assess environmental impacts from industrial

activity such as power generation is the before-after-control-impact (BACI) sample

design using analytical procedures that account for serial correlation in the observed data

(Madenjian et al. 1986, Manley 2001). The Gibson (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 four units is 1150 megawatts. Fish monitoring data for Mt.

Hope Bay are not available prior to 1972. Although unit 4 modifications likely played a

decisive role in the abrupt collapse of winter flounder, the population may have been

impacted earlier, particularly with the addition of the 650 MW unit 3 in July of 1969.

Lack of monitoring data prior 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 surveys 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 adjacent Block 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 Clark (1979). Briefly, MRI conducts monthly trawling at 6

fixed stations in Mt. Hope Bay. Five stations 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. Gibson (1987), computed abundance indices with

adjustments for area swept to be comparable with the modern survey. Since 1979, the

RIDFW has conducted spring and fall cruises 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 trawling in Mt. Hope Bay is limited to strata

south of Spar Island and does not overlap the MRI survey. The URIGSO trawl survey is

conducted at two fixed stations in the lower 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 RI area as a gross measure of winter flounder

abundance. Landings from NMFS statistical area 539, which includes Narragansett Bay

and RI coastal waters, were obtained. Winter flounder have been significantly exploited

in the New England area since the early 1900’s (Perlmutter 1947). Consequently, large

fluctuations in commercial landings are more likely related to changes in abundance than

to effort. For example, from 1975 to 1985 commercial landings in area 539 tripled while

Anthony (1990) reported less than a doubling of standardized fishing effort. Further,

commercial landings are significantly 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 demonstrated 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. Specifically, the MRI standardized impingement series

for Mt. Hope Bay was considered as an alternate impact series and the MRI impingement

data from the Providence River near Manchester 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 population in question and that 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 produce a ratio series without trend (Appendix).

A significant trend in the ratio series indicates a local impact or an inappropriate control

series. To reduce the likelihood of the latter, testing with multiple control series is

recommended (Underwood 1994). Jeffries et al. (1989) showed that winter flounder

abundance data for the period 1959-1985 had common signals regardless of where it was

collected in RI waters. The stock management areas specified in ASMFC and NMFS

stock assessments also imply commonality in gross abundance patterns. Rhode Island

winter flounder are part of the southern New 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 MRI standard trawl index and the control

abundance series as:

 Ztj= Mt/Itj (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 were log transformed and tested for time trends via linear

regression:

 ln(Ztj)= ln(Z0)+Gt+C (2)

where: Z0= ratio abundance at t=0

 G= instantaneous rate of population change.

 t= time

 C = combined process and measurement error term.

The logarithmic transformation in eq.2 stabilizes the variance in lognormally distributed

survey data and is consistent with an underlying 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. In the special case of G=0, the population is

stable without trend. The procedures above were applied to the MRI standard trawl index

(impact) and the five candidate control series (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 assumes that the error terms are independently

and identically distributed with constant variance and mean of zero. Serial correlation

occurs when the error terms are not independent but correlated with 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(Ct-Ct-1)

2

 / 6Ct

2

 . (3)

When serial correlation is present, 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 number 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 balanced and indigenous state. RIDEM has

maintained that pre-1985 operating conditions define possible limits for coolant flow and

waste heat rejection as this period preceded the 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 different from zero. If that hypothesis is rejected and there is

evidence that G<1.0, then the stock was declining in Mt. Hope Bay faster than in other

areas. One would conclude that the pre-1985 conditions were not conservative enough

and that the operation of units 1-3 had resulted 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 conditions when a stable stock of

winter flounder existed.

ResultsWinter flounder abundance data used in this analysis is summarized in Table 1. Trawl

survey results are plotted in Figure 1. Common features of the independently 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. The other surveys reached a historic low in

1993 but then showed some evidence of recovery. Of these, the NMFS survey shows the

greatest increase since 1993 while the URIGSO survey shows the least. Commercial

landings in statistical area 539 follow a similar pattern (Figure 2). Landings remain low

but have been increasing since 1993. Impingement 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 adjusted for 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 control 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 summarized in 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 RIDFW and MSS control series but the 95%

confidence bound included zero. This is not surprising since the RIDFW series changed

survey methodology in 1979 from fixed to random 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 (Table 3). Only the MSS

impingement (control) series did not reach conventional significance levels. Computed

Durban-Watson statistics are included in Table 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. Accordingly, the regression model used in eq. 2

should yield reliable slope estimates and identify significant trends without risk of

elevated type I error. Overall, the control-impact analysis provide strong evidence that

winter flounder in Mt. Hope Bay near BPS declined at faster rates than in other areas

during the period 1972-1985.

Trends in coolant water usage at BPS and waste heat rejected to Mt. Hope Bay are shown

in Figure 6. Coolant flow rose from 50 billion 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 power generation. Heat load to the Bay

fluctuated between 20 trillion and 30 trillion 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 waste 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 clear 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 with unit 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 all 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 that period 3 was a watershed event for the winter

flounder stock in Mt. Hope Bay. All of the adjusted abundance series were in a decline
8

that accelerated in 1985 or 1986. Viewed in the context of plant history, the operation of

units 1-3 was sufficient to cause a reduction 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 gallons and 28.9 (SE=0.9) trillion BTU

respectively. Since the average plant operational conditions during 1972-1985

contributed to a winter flounder decline, new 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.

DiscussionAn 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 should 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 control abundance series

measured in and outside of Narragansett Bay were used to characterize regional

abundance patterns and to reduce the likelihood of Type I error 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 conditions, 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 the 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 increase 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 with a significant decline in the abundance of

winter flounder in Mt. Hope Bay that was not observed in other areas. Accordingly, new

permit conditions should specify flow and heat rejection levels below the 1972-1985

mean levels. It is emphasized that the lack of monitoring data prior to 1972 precludes

direct estimation of overall flow and heat limits conducive to a healthy population of

winter flounder. Additional guidance from state water quality standards, life history

studies, and sustainable removal rates is needed. Also, there may be seasonal, life history

considerations that need be addressed within overall flow and heat limits.
9

Finally, there is no assurance that conditions that once supported a healthy population,

will allow for recovery of a collapsed population. Indeed, recovery may be difficult to

achieve without large reductions in plant induced and fishing mortality if long-term

environmental trends (increasing water temperature) and type III predation are present

(Collie and Spencer 1994). The former is known to be occurring 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). They note that under these circumstances, a

population may not recover after fishing pressure 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 the resource in question.

Literature Cited

Anthony, V.C. 1990. The New England ground fish fishery after 10 years under the

Magnuson Fishery Conservation and Management Act. 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 Division of Fish and Wildlife. Res. Ref.

Doc. 87/7.

Gibson, M.R. 1994. Population dynamics of winter 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 in the finfish assemblage of Mt. Hope

Bay and Narragansett Bay in relation to operations 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 RI Marine Fisheries Council.
10

Grosslein, M.D. 1969. Groundfish survey program 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-effort 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 and Wildlife. Annual Progress Report. Job

F-61-R-1.

Madenjian, C.P., J. Jude, and F. J. Tesar. 1986. Intervention analysis 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. Kutner. 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. Ecological 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. Barnthouse, and B.L. Kirk. 1981. An analysis of

the ability to detect reductions in year-class 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 independent series, both representative of the same

process, is a series without trend.

For this exercise, I assume that a simple exponential growth model governs the

population dynamics of a stock:

 Nt= N0 exp(Gt) exp(Cp) (1)

where: N= number in population

 N0= initial number

 t= time

 G= instantaneous rate of change.

 Cp= process error.

The rate parameter G has additive components due to births, natural deaths, and losses

from fishing and other sources. In fisheries terminology, these are equivalent to

recruitment (R), natural mortality rate (M1), fishing mortality rate (F), and anthropogenic

mortality rate (M2). For values of G greater than 1.0, the population 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 error term allows for deviations from the process due to

variability in growth, recruitment, natural mortality, etc. Error in eq. 1 is multiplicative in

nature, i.e. they are normally distributed in the log scale with zero mean and constant

variance.

Since actual population sizes are unknown, an observation model is needed which relates

true abundance to the relative abundance in a survey:

 Nt= It/q exp(Cm) (2)

Where: N= population size

 I= survey relative abundance

 Q= catchability coefficient

 Cm= measurement error

 t= time.

The multiplicative error term allows for deviations in the measured index from its true

value. As above, errors are normally distributed 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:

 It= I0 exp(Gt) exp(Cp+Cm) (3)

The usual form of analysis is to make a logarithmic transformation of eq.3 and use least

squares regression to estimate parameters:
12

 ln(It)= ln(I0)+Gt+(Cp+Cm) (4)

The rate of change in the population (G) is estimated as the slope of the regression. Note

that in eq.4, it is not possible to separate the process and measurement errors. When there

are two abundance indices, both representative of the stock, 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:

 Zt= I1t/I2t (5)

Substitution of eq.3 into eq.5 and carrying through the subscripts yields:

 Zt= [I10 exp(G1t) exp(C1p+C1m)]/ [I20 exp(G2t) exp(C2p+C2m)]. (6)

Because the surveys are representative of the stock in question, G1=G2 and C1p= C2p.

Expanding the errors, canceling common terms, and simplifying yields:

 Zt= I10/I20 exp(C1m-C2m). (7)

That is, the ratio series consists of a constant modified by a combination of the

measurement errors for the respective relative abundance series. The series has no trend

as exp(G1t-G2t)=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 G1γG2, then the

terms do not cancel and:

 Zt= I10/I20 exp((G1-G2)t)exp(C1m-C2m). (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

 CV 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

- 4.175660.00128

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

- 2.423340.03212

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

- 1.623470.13893

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 Impingement 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 2.168

URIGSO -0.086 0.035 6.105 0.029 1.534
20

Comm Landings -0.145 0.037 15.601 0.002 1.873

RIDFW Trawl -0.065 0.040 2.738 0.124 1.711

Manchester St -0.134 0.084 2.534 0.146 1.073

 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 1.456

Comm Landings -0.210 0.047 20.307 0.001 1.850

RIDFW Trawl -0.130 0.054 5.873 0.032 1.032

Manchester St -0.188 0.116 2.636 0.139 2.066

 /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

195919611963

1965

19671969

19711973

1975

1977

1979

1981

1983

1985

1987

19891991

1993

19951997

1999

2001

Year

No. per Tow

0

1

2

3

4

5

6

NMFS 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

19591961196319651967196919711973

1975197719791981198319851987

1989

19911993

1995

19971999

Year

Metric 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

Year

BP No. per Flow

0

5

10

15

20

25

30

MS 1000's Fish

Brayton Pt Manchester St
Fig.4- Winter Flounder Abundance in the MRI Trawl Survey Adjusted for Regional 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

Year

ln STD Inde

x

MRI/NMFS MRI/URIGSO MRI/Landings MRI/RIDFW MRI/Manchester St
Fig.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

Year

ln STD Impingement

IMP/NMFS IMP/URIGSO IMP/Lands IMP/RIDFW IMP/Manchester
Fig.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

Year

Billions Gallons

0

10

20

30

40

50

60

Trillions 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

30.74 30.88

38.3

76

88 89

90

87 91

9593 96 95

97

99 98 2000

86

85

80

84 83 84

73 78

7777

7981

7475

69

6767

68

64

63

0

10

20

30

40

50

0 50 100 150 200 250 300 350 400 450

Billions Gallons

Trillions BTU