&EPA
            United States
            Environmental Protection
            Agency
            Policy, Planning,
            And Evaluation
            (PM-221)
EPA-230-05-89-055
June 1989
The Potential Effects
Of Global Climate Change
On The United States
Appendix E
Aquatic Resources

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THE POTENTIAL EFFECTS OF GLOBAL CLIMATE CHANGE
     VN         ON THE UNITED STATES:

          APPENDIX E • AQUATIC RESOURCES
              Editors: Joel B. Smith and Dennis A. Tirpak
         OFFICE OF POLICY, PLANNING AND EVALUATION
           US. ENVIRONMENTAL PROTECTION AGENCY
                  WASHINGTON, DC 20460

                       MAY 1989
                   Text Printed on Recycled Paper

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                             TABLE OF CONTENTS
APPENDIX E: AQUATIC RESOURCES


      PREFACE	  iii

      THE EFFECTS OF GLOBAL CLIMATE CHANGE ON THE WATER QUALITY
      OF MOUNTAIN LAKES AND STREAMS	   1-1
      Earl R. Byron, Alan Jassby, and Charles R. Goldman

      POTENTIAL RESPONSES OF GREAT LAKES FISHES AND THEIR HABITAT
      TO GLOBAL CLIMATE WARMING	   2-1
      John J. Magnuson, David K. Hill, Henry A. Regier, John A. Holmes,
      J. Donald Meisner, and Brian J. Shuter

      ECOLOGICAL EFFECTS OF GLOBAL CLIMATE CHANGE: WETLAND
      RESOURCES OF SAN FRANCISCO BAY	   3-1
      Michael Josselyn and John Callaway

      PROJECTED CHANGES IN ESTUARINE CONDITIONS BASED ON MODELS OF
      LONG-TERM ATMOSPHERIC ALTERATION	   4-1
      Robert J. Livingston

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                                              PREFACE


 The ecological and economic implications of the greenhouse effect have been the subject of discussion within
 the scientific community for the past three decades.  In recent years, members of Congress have held hearings
 on the  greenhouse effect and have begun to  examine its implications for public policy.  This interest was
 accentuated during a series of hearings held in June  1986 by the Subcommittee on Pollution  of the Senate
 Environment and Public Works Committee. Following the hearings, committee members sent a formal request
 to the EPA Administrator, asking the Agency to undertake two studies on climate change due to the greenhouse
 effect.

        One of the studies we are requesting should examine the potential health and environmental
        effects of climate change. This study should include, but not be limited to, the potential impacts
        on agriculture, forests, wetlands, human health, rivers, lakes, and estuaries, as well as  other
        ecosystems and societal impacts. This study should be designed to include original analyses, to
        identify and  fill in where important  research gaps  exist,  and to solicit the opinions of
        knowledgeable people throughout the country through a process of public hearings and
        meetings.

 To meet this request, EPA produced the report entitled The Potential Effects of Global Climate Change on the
 United States.  For that report, EPA commissioned fifty-five studies by academic and government scientists on
 the potential effects of global climate change.  Each study was reviewed by at least two peer reviewers. The
 Effects Report summarizes the results of all of those studies.  The complete results of each study are contained
 in Appendices A through J.


                               Appendix                       Subject

                                 A                            Water Resources
                                 B                            Sea Level Rise
                                 C                            Agriculture
                                 D                            Forests
                                 E                            Aquatic Resources
                                 F                            Air Quality
                                 G                            Health
                                 H                            Infrastructure
                                 I                            Variability
                                 J                            Policy
GOAL

The goal of the Effects Report was to try to give a sense of the possible direction of changes from a global
warming as well as a sense of the magnitude.  Specifically, we examined the following issues:


        o   sensitivities of systems to changes in climate (since we cannot predict regional climate change, we
            can only identify sensitivities to changes in climate factors)

        o   the range of effects under different warming scenarios

        o   regional differences among effects

        o   interactions among effects on a regional level


                                                 iii

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       o  national effects

       o  uncertainties

       o  policy implications

       o  research needs

The four regions chosen for the studies were California, the Great Lakes, the Southeast, and the Great Plains.
Many studies focused on impacts in a single region, while others examined potential impacts on a national scale.


SCENARIOS USED FOR THE EFFECTS REPORT STUDIES

The Effects Report studies used several scenarios to examine the sensitivities of various systems to changes in
climate. The scenarios used are plausible sets of circumstances although none of them should be considered to
be predictions of regional climate change.  The most common scenario used was the doubled CO2 scenario
(2XCO2), which examined the effects of climate under a doubling of atmospheric carbon dioxide concentrations.
This doubling is  estimated to raise average global temperatures by L5 to 4-5°C by the latter half of the 21st
century. Transient scenarios,  which estimate how climate may change over time in response to a steady increase
in greenhouse gases, were also used. In addition, analog scenarios of past warm periods, such as the 1930s, were
used.

The  scenarios combined average monthly  climate  change estimates for regional grid boxes from General
Circulation Models (GCMs) with 1951-80 climate observations from sites in the respective grid boxes.  GCMs
are dynamic models that simulate the physical processes of the atmosphere and oceans to estimate global climate
under different conditions, such as increasing concentrations of greenhouse gases (e.g., 2XCO2).

The scenarios and GCMs used in the studies have certain limitations. The  scenarios used for the studies assume
that temporal and spatial variability do not change from current conditions. The first  of two major limitations
related to the GCMs is their low spatial resolution. GCMs use rather large grid boxes where climate is averaged
for the whole grid box,  while in fact climate may be quite variable within a grid box.  The second limitation is
the simplified way that GCMs treat physical factors such as clouds, oceans, albedo, and land surface hydrology.
Because of these limitations,  GCMs often disagree with each other on estimates of regional climate change (as
well as the magnitude of global changes) and should not be considered to be predictions.

To obtain a  range of scenarios, EPA asked the researchers to use output from the following GCMs:

        o   Goddard Institute for Space Studies (GISS)

        o   Geophysical Fluid Dynamics Laboratory (GFDL)

        o   Oregon State University (OSU)

Figure 1 shows the temperature change from current climate to a climate with a doubling of CO2 levels, as
modeled by the  three GCMs.  The figure includes the GCM estimates for  the four regions.  Precipitation
changes are shown in Figure 2.  Note the disagreement in the GCM estimates concerning the direction of
change of regional and seasonal precipitation and the agreement concerning increasing temperatures.

Two transient scenarios from the GISS model were also used, and the average decadal temperature changes
are shown in Figure 3.
                                                iv

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                      FIGURE 1. TEMPERATURE SCENARIOS
                  GCM Estimated Change in Temperature from 1xCO2 to 2xCO2
8
                             8
   Great Southeast Great California United
   Lakes        Plains       States'
Great  Southeast Great California  United
Lakes        Plains        States*
                                                               4-
                                                               2
                                                                                  SUMMER
Great  Southeast Great California United
Lakes         Plains        States'
                                           GISS

                                           GFDL

                                           OSU
                                                                               * Lower 48 States

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                           FIGURE 2.  PRECIPITATION SCENARIOS
                      GCM Estimated Change in Precipitation from 1xCC>2 to 2xCO2
<
Q
0)
QC
HI
I.U
0.8
0.6
0.4
0.2
.0
-0.2-
•0.4-
-fl.fi.

ANNUAL


^nln l^ftfl
lP


       Great  Southeast Great California United
       Lakes         Plains        States*
 1.0
 0.8
 0.6
 0.4
 0.2
 0.0
-0.2
-0.4
-0.6
                                                           WINTER
                  I
Great  Southeast Great California United
Lakes         Plains        States*
 1.0
 0.8-
 0.6-
 0.4-
 0.2-
 0.0
-0.2-
-0.4-
-0.6
  No
Change
                                                  SUMMER
                                       Great  Southeast Great California Unite
                                       Lakes         Plains        State
                                                       GISS
                                                       GFDL
                                                       OSU
                                                                                    * Lower 48 States

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        4
       3.5
    O
    g  2.5
    UJ
UJ
       1.5
        1
       0.5
        0
    o
   3.5
    3
   2.5
flC
UJ   _
a.  1.5
UJ
I-   1
       0.5
        0
                                           3.72
                                               2.99
                                      2.47
                                    1.72
                           1.36
                0.70
                      0.88
           0.30
              1980s 1990s 2000s 2010s 2020s 2030s 2040s 2050s
                          TRANSIENT SCENARIO A
                                                    1.26
                                           1.(
                                  0.59
            0.18
                         0.35
          \/ / / A
Y77/
           1980s    1990s    2000s    2010s
                       TRANSIENT SCENARIO B
                                                   2020s
FIGURE 3
             GISS  TRANSIENTS  "A"  AND  "B" AVERAGE
             TEMPERATURE CHANGE FOR LOWER 48 STATES
             GRID  POINTS.
                                vu

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EPA specified that researchers were to use three doubled CO, scenarios, two transient scenarios, and an analog
scenario in their studies.  Many researchers, however, did not nave sufficient time or resources to use all of the
scenarios.  EPA asked the researchers to run the scenarios in the following order, going as far through the list
as time and resources allowed:

        1. GISS doubled CO2

        2. GFDL doubled CO2

        3. GISS transient A

        4. OSU doubled CO2

        5. Analog (1930 to 1939)

        6. GISS transient B


ABOUT THESE APPENDICES

The studies contained in these appendices appear in the form that the researchers submitted them to EPA.
These reports  do not necessarily reflect the official position of the ILS. Environmental Protection Agency.
Mention of trade names does not constitute an endorsement.
                                                vm

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THE EFFECTS OF GLOBAL CLIMATE CHANGE ON THE WATER QUALITY
                 OF MOUNTAIN LAKES AND STREAMS
                                 by
                             Earl R. Byron
                              Alan Jassby
                          Charles R. Goldman
                         Tahoe Research Group
                     Division of Environmental Studies
                      University of California, Davis
                            Davis, CA 95616
                      Contract No. CR-814581-01-0

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                                    CONTENTS

                                                                             Page

FINDINGS  	 1-1

CHAPTER 1:  INTRODUCTION 	 1-2
      PERSPECTIVE	 1-2
      SITE DESCRIPTIONS  	1-3

CHAPTER 2:  METHODS  	'	 1-4
      THE EMPIRICAL EFFECTS MODELS	 1-4
            Castle Lake	 1-4
            Snowfall Estimates  	1-5
            Model Using Estimated Snowfall	1-5
            Ward Creek, Lake Tahoe	 1-6
            Limitations of the Empirical Models 	1-6
      THE CLIMATE CHANGE SCENARIOS  	1-18
            The Scenarios Used	1-18
            Issues Resulting From the Scenarios	1-24

CHAPTER 3:  RESULTS  	1-25
      CASTLE LAKE  	1-25
      WARD CREEK, LAKE TAHOE  	1-25

CHAPTER 4:  INTERPRETATION 	1-33

CHAPTER 5:  IMPLICATIONS OF RESULTS	1-34
      ENVIRONMENTAL	1-34
      SOCIOECONOMIC  	1-34

CHAPTER 6:  POLICY IMPLICATIONS  	1-35

CHAPTER 7:  FUTURE RESEARCH 	1-36

REFERENCES	1-37

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                                             FINDINGS1
     We researched the effects of global climate change on the water quality of subalpine lakes and streams in
California Using our long-term data bases of Castle Lake and Ward Creek, Lake Tahoe, both in the mountains
of northern California, we were able to relate our water quality records to climatic variables. We then applied
several scenarios of global warming to the empirical models to predict the potential effects of climatic warming
on water quality in these systems.

    Our results indicated that the changing precipitation patterns and global warming predicted  by various
climate change scenarios would have a detrimental  effect on water quality for our systems. Algal growth rates
(primary productivity) in Castle Lake increased under all scenario projections. Average increases ranged from
16% to 87% over control values.  The enhanced algal productivity under scenario conditions resulted from the
combined effects of an increase in the length of the growing season and a change in  hydraulic loading. Our
preliminary analysis of Ward Creek did not reveal an increase in runoff and nutrient concentrations in the
scenario conditions. However, since we were not able to incorporate temperature change in our Ward  Creek
model, the results are much less certain than for Castle Lake.
        'Although the information in this report has been funded wholly or partly by the U.S. Environmental
Protection Agency under Contract No. CR-814581-01-0 with the University of California, Davis, it does not
necessarily reflect the Agency's views, and no official endorsement should be inferred from it.

                                                 1-1

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                                             CHAPTER 1

                                          INTRODUCTION


PERSPECTIVE

     The emphasis of our research on  the ecological effects of climate change has been directed toward
anticipated changes in the water quality of mountain lakes and streams.  The long-term limnological research
of the Lake Tahoe and Castle Lake research groups of University of California, Davis has provided a unique
opportunity to examine an extensive limnological data base that spans several decades. In our previous research,
we have established strong, quantifiable  relationships between climatic variables and water quality, and it is
logical that further research can be applied to the EPA effort in modeling the ecological effects of changing
climate.

    Our work interprets the  long-term limnological data bases of a northern California mountain lake and
stream.  Our first data set, from subalpine Castle Lake, spans the longest period of time and has yielded the
strongest relationships.  In this analysis we have taken advantage of 29 years of limnological and precipitation
data to establish relationships between annual  phytoplankton productivity and climatic variables.  Extensive
limnological research over the years has been focused on understanding the mechanisms  concerned with
nutrient dynamics and system productivity (e.g., Goldman, in press; Goldman  et al., submitted; Goldman and
de  Amezaga,  1984; Axler et al., 1981).  The  lake is clearly sensitive  to climatic  change, with  most  of the
interannual variability in heat budget (Strub et al., 1985) and  primary productivity accountable to variation in
climate (Goldman et al., submitted). One of the key variables responding to climate change is the extent and
timing  of ice cover.  Long-term records  have established clear responses between ice cover  and climate
(Semtner, 1984; Rannie,  1983; Kuusisto, 1987).  Long-term records have been  used empirically to project lake
ice changes, but not subsequent water quality effects in changing climate scenarios (Kuusisto, 1987).

    For our analysis of mountain stream water quality, we have integrated our 12 years of data on Ward Creek,
a tributary of Lake Tahoe, to yield precipitation-streamflow and streamflow-nutrient concentration relationships
useful in predicting the water quality changes associated with changing climate. Ward Creek has also shown
large water quality fluctuations in response to drought or high-precipitation years (Leonard et al., 1979; Byron
and Goldman, 1986).

    The study of the  causes of variability in the water quality of lakes and streams is of both theoretical and
practical interest.  Most previous research has focused  on the causes of long-term changes, but interannual
variability is receiving increasing attention as well.  For example, long-term records have revealed water quality
changes in lakes due  to eutrophication (Edmondson and Lehman,  1981; Goldman, 1986,  1981, 1974) and the
effects of climatic fluctuations (Wetzel,  1981; Strub et al.,  1985; Goldman et al.,  submitted).  In addition,
streams may experience  long-term changes in water quality due to anthropogenic disturbances of watersheds
(Leonard et al., 1979) and changing patterns of atmospheric deposition (Smith et al., 1987).

    The realization that unprecedented global climate changes brought about by projected CO2 and trace gas
increases will  occur,  has spawned a new body of research concerned with potential secondary effects (e.g.
Ramanathan,  1988). Hydrologic research has focused on projections of changes in water supply and subsequent
societal impacts (Cohen,  1986,1987; Kuusisto, 1987). Studies in California have projected significant hydrologic
changes under doubled  atmospheric CO2 scenarios (Gleick,  1987).  In this  study, we  propose to establish
empirical models of water quality and climatic variables from our long-term data sets. These models will allow
us to add water quality projections to the ongoing hydrologic and water supply modeling of other researchers.
                                                  1-2

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SITE DESCRIPTIONS

     Castle Lake is typical of subalpine lakes in the Western United States. It is located at an elevation of 1657
m in a protected cirque basin in the Klamath mountains of northern California  The lake has a surface area
of 0.20 km2, a maximum depth of 35 m, and a mean depth of 11.4 m.  It is meso-oligotrophic, dimictic, and
ice-covered in winter.  Ward Valley, Lake Tahoe, is also a rather typical subalpine watershed of the central
Sierra Nevada  Ward Creek runs shallow, cold,  and clear for the majority of the  year.  The  relief of the
watershed ranges from 2700 m on the surrounding peaks to 1898 m at the level of its drainage into Lake Tahoe.
The total area is 2510 ha, contributing approximately 6% of the annual surface runoff to the lake. The Ward
Valley watershed has been glaciated, and soil parent material is primarily andesitic and basaltic volcanic rock
(Leonard et al., 1979).

    The remaining portion of our report includes two major sections. First, we describe the development of the
empirical water quality models for Castle Lake and Ward Creek, Lake Tahoe, from our two long-term data sets.
Second, we apply our empirical models to the climatic variable outputs provided by EPA to examine the effects
of climate change scenarios on the water qualitv of the lake and stream. In each case, the results are discussed
with regard to model limitations  and to the environmental and socioeconomic implications of the predicted
water quality changes.
                                                 1-3

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                                            CHAPTER 2

                                            METHODS


THE EMPIRICAL EFFECTS MODELS

Castle  Lake

     The 29-year data record at Castle Lake has given us an excellent opportunity to understand the importance
of long-term variability in the water quality of a subalpine lake. Climatic events appear to dominate limnological
variation in Castle Lake, at least as indicated by the  primary productivity of the phytoplankton community.
Primary production makes an excellent water quality  indicator since it simultaneously reflects local weather
conditions, nutrient availability, and the size  of the producer populations.  Further, it indirectly reflects the
activities of other trophic levels, such as graying and nutrient regeneration. In addition, it is of direct importance
to our  investigation of oligotroptiic, subalpine lakes, since primary productivity is causally related to water clarity,
an important measure of water quality in these systems  (Goldman, in press). Although results based on primary
productivity are not immediately transferable to other  lakes,  in part because of the rarity of long-term
productivity records, the variable gives us an immediate measure of eutrophication, perhaps the most important
overall water quality indicator. Our empirical water quality model for Castle Lake gives us the ability to forecast
primary production from a knowledge of climatic variation and previous production.

    Our long-term record at Castle Lake results from samples collected over the entire water column at a single
deep station every 5 days during the summer months and irregularly during other months since 1959.  Our
model uses summer primary productivity estimates for the period of June 1 to September  30 of each year
(measured to be 75% of total annual production).  Temperature and precipitation estimates were provided by
the nearby Mt. Shasta City weather station (NWS #5983).

    Model development

    Annual primary production at Castle Lake is clearly related to climate.  In particular, Castle  Lake has
exhibited anomalous (unusually high or low) primary production during ENSO (El Nino/Southern Oscillation)
years.  In order to investigate the dependence of annual primary production on specific climate variables, the
1960-1984 series of primary productivity measurements was first prewhitened with a Markov process to remove
an autocorrelation at lag 1 year. The prewhitened series was created as follows:


    WPt = (Pt-a0)-a1*(Pt.1-a0)                     (1)


where WPt is the series of prewhitened production at  year t, Pt (mg C m"2d ) is the series of actual primary
production at year t, and the a,  are constants; ag = 355 ± 29 and a1  = 0.385 ± 0.189.

    A  significant cross-correlation at lag zero was detected between prewhitened primary production and both
winter snowfall and annual hydraulic load. Although  snowfall and hydraulic load are associated, the relation
between prewhitened primary production and each of these variables is still significant, even when adjusted for
the other variable. Snowfall is important because of its effect on ice breakup time; hydraulic load because of its
effect on outwash rates in the spring. The autocorrelation in primary production may arise from persistence of
certain phytoplankton populations from one year to the next (Vincent and Goldman, 1980).

    Based on these associations with the prewhitened  series, a time series regression model was built relating
measured annual primary production to snowfall and precipitation. Primary production was transformed with
logarithms to stabilize variance; the autocorrelation was addressed by including lag 1 primary production; climate


                                                 1-4

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                                                                                               Byron

effects were represented by the product of January-April snowfall and January-May total precipitation; and a
moving-average model for the residuals was included. All coefficients in the model were significantly different
from zero at the p <0.05 level.  The initial model accounted for 81% of the variability in the logarithm of
primary production:


    (1- S *B) In Pt =  WQ + Wl * St * Ht +  i\                (2)


where B is the backshift operator (i.e. B x. = x.,), Pt (mg C m d~1) is primary production during year t, S» is
January-April snowfall (actual snow depth in cm, not water equivalent) during year t, Hf (cm) is January-May
hydraulic loading during year t, ru is the residual series, and 5, WQ, and w1 are constants. P. is measured summer
productivity (June-September).  In 1968, when measurements were made year-round, these summer months
accounted for 75% of the annual production.

    Since some autocorrelation was found in the residual series at lag 2, we selected  a model for the residuals
as:


    r^ = aj + 0*^.2                                       (3)


where a^ is a series of independent white noise N(0, aa) and 6 is constant. The overall initial model using inputs
of measured productivity, snowfall,  and total precipitation became:


    (1- 5*B) In Pt = WQ  + w1 * St  * Ht + (1 + 0*B2)at        (4)


Snowfall Estimates

    Output  from the  global climate models does not include snowfall, which is required for the  final Castle
Lake production model (Eq. 4). We therefore attempted to develop a forecasting model for monthly snowfall,
based on monthly values for the climate model outputs of total precipitation and temperature. Using the actual
monthly data for the years 1960-1984, the same years for which Eq. 1 was developed, a variety  of approaches
was tried, including time-series regression models for individual months, and regression models developed using
data for all winter months.  A second-degree polynomial model applicable for all winter months was chosen;
it performed almost as well as the  more complicated time-series regression  models developed for individual
months:


    S = b,* H  + b2 * H2 + b3 * H * T                     (5)


where S is monthly snowfall (cm snow depth), H is monthly total precipitation (cm), and T is mean monthly
temperature (°K). The constants are b1 = 191 ± 27, b2 = -0.100 ± 0.015, and b^ = -0.672  ± 0.097. The variance
accounted for (r) is 56%, and the residuals exhibit no significant autocorrelation or  other internal structure.

Model Using Estimated  Snowfall

   An attempt was made to develop a new forecasting model for annual production that  excluded snowfall and
used temperature (monthly maximum, minimum, and average). However, no improvement could be made over
the form of  Eq. 4, even when St was estimated by summing values determined from Eq.  5 for January through
                                                 1-5

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April.  As a result, the parameters in Eq. 4 were estimated again using these predicted values of S.  The
resulting model explained much less of the variance (r2 = 58%), but the parameters remained significantly
nonzero (p <0.01) and a moving average term was not needed to remove residual  structure (i.e., 9=0).  The
new parameter values were S = 0.571a ± 0.142, WQ = 2.85 ± 0.81, and w1  = -(3.78 ± 0.79)*10'5  The structure
of the model is given  in Table 1.

Ward Creek. Lake Tahoe

    Our data records  for precipitation and streamflow for Ward Valley encompass 12 years of daily streamflow
and nutrient load estimates and storm-event based precipitation sampling.  In developing the water  quality
models, we limited the record to our  longest unbroken string of data: 1980-1986.  The period of modeling is
further limited by the  weather records  for this study, which end in 1984.  Approximately 100-150 stream samples
were taken each year to generate gauge height-concentration curves that allowed us to use continuously recorded
gauge height to estimate average daily concentration values.  Precipitation data were provided
by the VS. Weather Bureau, Tahoe City (NWS #8758).  While this is not a particularly long data set  compared
to our lake data, it has nevertheless yielded significant regressions relating  water quality to climatic variables.

    We had to examine several hydrologic parameters in order to establish the water quality regressions.  Our
basic  two-part approach was First to establish a relationship between precipitation and streamflow and then to
describe the relationship between flow and nutrient concentrations. Our data record is  long enough to include
a drought year (1981)  as well as very high water years  (1982, 1983).  Thus we feel that the  significant
relationships we have established encompass most of the present range of climatic variation.

    The precipitation-flow model for  Ward Creek used precipitation at lag 3  and 6 months  and a moving
average model for the residuals (1 month) to predict monthly total streamflow (Figure 1, Table 2). The model
was well behaved, i.e., all coefficients  were significant  and the residuals had all structure removed.  However,
only 55% of the variation was accounted for, and it was not possible to incorporate temperature into  the model
in a statistically acceptable manner.   Neither present  month  nor  lagged  temperature terms added  to the
explanation of variance.  The 3- and 6-month lag terms for precipitation were necessary to incorporate snowmelt
accumulation and melt effects into the model (and, thus, incorporate some potential temperature effects).

    The second step  of our model estimates various aspects of annual average  stream nutrient concentrations
as predicted from stream runoff.  In this case we were not limited by  the weather data, and our period of
analysis was from January 1980 through September 1986 (81 observations). Stream discharge was significantly
associated with both soluble phosphorus and nitrate concentrations for this period  (Figures 2, 3; Table  3). As
might be  expected, stream discharge was also significantly associated with loading  of both soluble phosphorus
and nitrate (Figures 4,5; Table 4).  The increase in predictive capability with loadings is due to the fact that load
is the product of  stream discharge and nutrient concentration.

Limitations of the Empirical Models

    In the case of Castle Lake, the model is limited to the prediction  of annual total'values of algal production.
Seasonal  effects are lost.  Still, the model was created empirically, using enough years of data to validate the
relationships for a wide range of climatic variation. It offers a good  unbiased estimate of direction and overall
magnitude of changes when using scenario data  For Ward Creek, the seasonal effects are preserved by the use
of  monthly data  However, the relative lack of change in cumulative precipitation in the climate  change
scenarios and inability to incorporate  temperature in the models precluded the use of the monthly data in any
attempts to model water quality changes.

    The models make use of effects of precipitation and temperature change  but do not incorporate the direct
effects of a doubling of CO2 in the  atmosphere into their predictions.  Atmospheric CO2 increase  is only
potentially important  in the case of the Castle Lake primary productivity model, but even in that case  the effects
of carbon limitation on algal growth are unlikely to be significant. Subalpine lakes like Castle are relatively rich
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                  Table 1.  Castle Lake Primary Production Model
     PPR  = "Annual"  primary  production  (June   September)
     LPPR = Ln(PPR)
TMPOit = mean temperature  (°K)  for year t and month i  (i = 1,...,4)
PRCOit = total precipitation  (cm)  for year t and month i (i = 1.....5)
SNWOit = MAX(191*PRCOit-0.0996*PRCOit2-0.672*PRCOit*TMPOit, 0)
PRCt = PRC01t+PRC02t+PRC03t+PRC04t+PRC05t
SNWt = SNW01t+SNW02t+SNW03t+SNW04t
LPPRt = MAX(2.85+0.571*LPPRt_r3.78*10-5*SNWt*PRCt, 0)
PPRt = EXP(LPPRt)
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 P.
             so
                                                 82
                                                                                     84
                                        Measured
                                                      Date
                                                                        Model
            Figure 1. Prediction of water discharge from precipitation, Ward Creek, Lake Tahoe.
                                                  1-8

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                      Table 2. Ward Creek Streamflow Prediction
Dependent Variable is Water Discharge
1980.07   1984.12
Number of observations:  54
Convergence  achieved after 5 iterations
VARIABLE
Precipitation (-3)
Precipitation (-6)
Residuals:
Moving Average (1)
R-squared
S.E. of regression
Durbin-Watson stat
F-statistic
COEFFICIENT STD. ERROR T-STAT. 2-TAIL SIG.
403 85.1 4.73 0.000
376 75.3 5.00 0.000

0.725 0.140 5.17 0.000
0.545
2600
1.98
30.6
                                       1-9

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            26
    a
    a
    o
    a.
                                                                            (Monthly Averages)
0
1 !
82
i
84
i i
86
                                                    Date
                                   a    Measured
+    Model
  Rgure 2. Prediction of soluble phosphorus concentrations from water discharge, Ward Creek, Lake Tahoe.
                                                  1-10

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               Table 3a. Ward Creek Soluble Phosphorus Concentration Prediction
 Dependent Variable  is  Soluble Phosphorus  Concentration
 1980.02  - 1986.09
 Number of observations:   80
 Convergence achieved after 3 iterations
VARIABLE
Constant
Water Discharge (-1)
Residuals:
Moving Average (1)
R- squared
S.E. of regression
Durbin-Vlatson stat
F-statistic
COEFFICIENT STD. ERROR T-STAT. 2-TAIL SIG.
10.1 0.350 28.8 0.000
-0.000436 7.81D-05 -5.59 0.000

0.503 0.114 4.41 0.000
0.422
2.39
1.95
28.1
                                        1-12

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3
                                                                84
86
                                       M*a»ur«d
                                                     Dot*
                                                                       Model
          Figure 3. Prediction of nitrate concentrations from water discharge, Ward Creek, Lake Tahoe.
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                  Table 3b. Ward Creek Nitrate Concentration Prediction
Dependent  Variable is  Nitrate Concentration
1980.04  -  1986.09
Number of  observations:   78
Convergence achieved after 5 iterations
VARIABLE
Constant
Water Discharge
Discharge (-1)
Discharge (-3)
Residuals:
Moving Average (1)
Moving Average (2)
R-squared
S.E. of regression
Durbin-Watson stat
F-stat1stic
COEFFICIENT STD. ERROR T-STAT. 2-TAIL SIG.
11.2 1.07 10.5 0.000
0.000782 0.000236 3.31 0.002
-0.000939 0.000235 -4.00 0.000
-0.000461 0.000193 -2.39 0.020

0.462 0.119 3.90 0.000
0.298 0.118 2.52 0.014
0.410
5.73
2.00
10.0
                                      1-13

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     u.
     i
                80
                                   D    Measured
     Figure 4.  Prediction of soluble phosphorus loading from water discharge, Ward Creek, Lake Tahoe.
                                                 1-14

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       280
 o
z

n
o
                              Q   M««ur»d
        Figure 5. Prediction of nitrate loading from water discharge, Ward Creek, Lake Tahoe.
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                  Table 4a.  Ward Creek Soluble Phosphorus Loading Prediction
Dependent Variable  is  Soluble Phosphorus  Loading
1980.02 - 1986.09
Number of observations:   80
Convergence achieved after 3 iterations
VARIABLE
Water Discharge
Discharge (-1)
Residuals:
Moving Average (1)
R-squared
S.E. of regression
Durbin-Uatson stat
F-statistic
COEFFICIENT STD. ERROR T-STAT. 2-TAIL SIG.
0.00733 0.000244 30.0 0.000
-0.000717 0.000239 -2.99 0.004
0.455 0.117 3.88 0.000
0.933
6.23
1.88
539
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                       Table 4b. Ward Creek Nitrate Loading Prediction
Dependent  Variable is Nitrate Loading,
1980.02  -  1986.09
Number of  observations:  80
Convergence achieved after 3  iterations
VARIABLE
Water Discharge
Discharge (-1)
Residuals:
Moving Average (1)
R-squared
S.E. of regression
Durbin-Watson stat
F-statistic
COEFFICIENT STD. ERROR T-STAT. 2-TAIL SIG.
0.014 0.000918 15.3 0.000
-0.00426 0.000898 -4.75 0.000

0.264 0.118 2.25 0.028
0.758
23.3
1.96
121
                                        1-17

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in free CO2 and low in bicarbonate and  carbonates, and experience  algal production limited by nutrient
availability, not carbon (Wetzel, 1983). Increased carbon in the system could be dealt with in a more complex
model.  The effects of increased carbonic acid in precipitation are not  incorporated into our models either.
However, precipitation acidity is presently dominated by the effects of strong acids. Presumably, future increases
in nitric and sulfuric acid (not estimated as part of the scenarios) would be more important in causing future
ecological changes.

    The Castle Lake algal productivity predictions that we have produced using the scenario results are only
realistic when productivity estimates fall within or close to the historic range.  As production estimates rise, the
limiting factors on total production at some point will undoubtedly shift from factors associated with hydraulic
washout and the length of the growing season (as the present model was developed) to nutrient input, speed
of recycling, or other limitations.  Nutrient inputs, for example, may change dramatically with forest vegetation
and runoff changes under a doubled CO2  regime.  The present model cannot account for the  possibility of
changing limitations to growth associated with changing climate. Microbial processes in the  soil and sediment
and watershed plant communities may be expected to respond to climatic change and deserve consideration in
terms of their  effect on nutrient flux.  A series of predictive ecological models should be incorporated into an
integrative ecosystem level model in order to most accurately predict water quality changes.


THE CLIMATE CHANGE SCENARIOS

The Scenarios Used

   We used the  GFDL, GISS, and OSU control and 2xCO~ and  the GISS transient A scenarios to apply to
our empirical water quality models. Surface temperature ana total precipitation were used from each. Although
this does  not represent  the  complete list of possible  scenarios, it gives a strong consensus in direction and
magnitude suggested by most of the climate change models. For Castle  Lake models we used weather station
data from Mt. Shasta City, California  For Ward Creek, we used Tahoe City, California, data

    The projected temperature and precipitation changes resulting from the  scenarios are shown in Figures 6
through 10.  In all cases, ratios of change from present conditions for each month were multiplied by historical
weather data to yield scenario climatic conditions. Note that temperature in the 2xCO, scenarios is higher than
historical values  (Figure 6), whereas precipitation changes are much less discernible (Figures 7, 8). The lack of
change hi precipitation can also be easily seen for the GISS transient A scenario (Figure 9).  For our empirical
model of Castle  Lake, it is of greater significance that snowfall estimates show a strong negative trend for the
transient scenario (Figure 10).

    It was necessary to change the application of the scenarios for  Castle Lake.  As described above, our
empirical model  requires that productivity estimates begin in a year for which the previous summer's productivity
was known. In our case, the model output was restricted to beginning in 1960, the second year of Castle Lake
data collection. Thus, the measured Castle Lake productivity for 1957 was used as input to both the control and
2xCO2 conditions. Since the years 1951-1959 were removed from the series, we included the four additional
years Tor which we had data (1981-1984) at the end of the data series.  Thus, the series for Castle Lake runs
from 1960 through 1984 for control and 2xCO2 data The GISS transient A starts by using the measured 1980
Castle Lake productivity to begin the model run in 1970 for the series through 2060.

    The Ward Creek models were run using the series 1980-1984 to establish  precipitation and temperature
conditions because our empirical models were developed for that period. The scenarios were not used to predict
water quality changes for the 1951-1980 period, since the only scenario to show  changing precipitation did not
cause a consequent change in stream water discharge (see results for GISS 2xCO2 below), and temperature was
not incorporated into the model. There is, therefore, no basis for examining predictions of Ward Creek water
quality based on the scenario outputs. The modeling of average concentrations of stream solutes as predicted
from precipitation would require more detailed information on rain, snow, and snowmelt than was available as
climate model output. Instead, we present  our empirical models of nutrient concentrations and load over time


                                                1-18

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        180
  I
170 -
160 -
150 -
140 -
130 -
120 -
110 -
100 -
 90 -
 80 -
 70 -
 60 -
 50 -
 40 -
 30 -
 20 -
 10 -
                               Mt.  Shasta  City,  CA.
                                   2XCO2 Scenario*. Coetle Lake
                   Total Praelp. (Jon-May)
            58
          i     i
              62

        Measured
66
  70
   Year
OFDL
 i
74
 i
78
82
86
                                                           GISS
                                            OSU
            Figure 7. Measured and 2xCO2 scenario, January-May precipitation, Castle Lake.
                                            1-20

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       12
I
I
11  -

10 -

 9 -

 8 -

 7 -

 6 -


 5 -


 4 -

 3 -

 2 -

 1 -
                                                                                 Byron
                            Mt.  Shasta  City,  CA.
                                2XC02 Scenarios. Cart* laks
                 Avg. T«mp. (Jan-Apr)
          98
             62
66
70
74
78
82
86
                                          Year
       Figure 6. Measured and 2xCO2 scenario, January-April average temperatures, Castle Lake.

                    GISS gridpoint:  120W, 39.13N;
                    GFDL gridpoint 120W, 40N;
                    OSU gridpoint:  120W, 40N.
                                          1-19

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    80
        Measured
   Date
GFDL
                                                    GISS
                                                                      OSU
Figure 8.  Measured and 2xCO2 scenario, monthly precipitation, Ward Creek, Lake Tahoe.

             GFDL gridpoint: 120W, 35.55N;
             GISS gridpoint:  120W, 39.13N;
             OSU gridpoint:  120W, 36N.
                                    1-21

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          180 - —
          150 -
          140 -
          130 -
          120 -
          110 -
          100 -
           90 -
           80 -
           70 -
           60 -
           SO -
           40-
           30 -
           20 -
           10 -
            0 -j-
              70
Prtdp. (Jon-May)
     90
10
30
90
70
                                                   Year
                       Figure 9. GISS transient A, monthly precipitation, Castle Lake.
                                                  1-22

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       350
       300 -
       230 -
       200 H
I      150-
       100 -
        50 -
                      Snow Depth (Jen-Apr)
           70
 r
90
10
30
50
70
                                                Year
 Figure 10.  Monthly snow estimates calculated using precipitation and temperature  results from GISS
            transient A, Castle Lake.
                                             1-23

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for the years 1980-1984 as predicted from the relationships between monthly stream water discharge and nutrient
concentrations.

Issues Resulting From the Scenarios

    Our empirical models require a knowledge of snow accumulation and melt to adequately predict water
quality parameters.  The present models would be greatly aided by refinements in the scenario outputs that
would allow more accurate predictions of precipitation, temperature, and snowfall on a fine-grained scale. The
coarse nature of the large global climate model grids yields ratios of change in parameters that are of dubious
value in predicting change in mountainous subregions of the larger grids.

    The years 1960-1984 are adequate for establishing a full range of baseline conditions.  In terms of hydrology
and water quality parameters,  they encompass an extensive range of drought, "normal,"  and high-water years.
Although these represent a deviation from the basic 1951-1980 data set, they are still representative of the same
hydrologic range.
                                                 1-24

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                                           CHAPTERS

                                             RESULTS
CASTLE LAKE
     Annual primary production was forecast for five cases using the equations shown in Table 1:  (1) actual
conditions (Table 5, Figure 11); (2) climate  output from the the three 2xCO2 models (Table 6, Figure 12); and
(3) climate output from the GISS transient scenario A (Figure 13).  Table 1 contains the actual equations used
in the simulation. Note that for all simulations, snowfall was set to 0 when Eq. 2 predicted negative snowfall.
The results for 2xCO2 are summarized in Figure 14.

     The simulation reproduced the actual primary production values quite accurately (Figure 11), although the
model had difficulty recreating the large swings experienced during the period 1969-1977, during which time
four major ENSO events occurred (1969,1972,1976,1977) (Strub et al., 1985). It should be noted that Figure
11 represents a dynamic simulation that propagates errors in  estimated primary production through the lag
1-year term.

     The three 2xCO2 simulations behaved in a similar fashion, although quite variable in magnitude (Figure
12). The increase in  temperature caused by the 2xCO, conditions  resulted in an increase in mean annual
primary production of 69.0% (GFDL), 87.0% (GISS), and 163% (OSU) over estimated "control" conditions for
the 2xCO2 scenarios. These changes are close to our measured historical range, but show a consistent direction
of change toward higher production under conditions of higher atmospheric CO, concentrations. It is possible
to substitute an average 2xCO2 value for our measured 19S9 value to start the 2xCO2 runs. The mean 2xCO2
primary production values increase slightly under those conditions, from 03% (OSU) to 3% (GISS) greater than
the averages shown in  Table 6.

     The GISS transient A scenario produced an increasing but variable primary productivity estimate for the
period 1970-2059 for Castle  Lake, showing the expected strong  30-year scale of variation inherent in the
1951-1980 data base (Figure 13).  In all scenarios, therefore, we project increasing productivity.  Whereas the
increases may be small at Castle Lake, the variability between the  lakes is unknown.  The direction of change
is clear. The implications of eutrophication are discussed below.


WARD CREEK, LAKE TAHOE

     Since our empirical models for stream water quality did not use temperature, we were limited in our ability
to apply our results to the 2xCO- and transient scenarios. Of the climate conditions we examined for the Tahoe
City weather station, the GISS £*CO>> precipitation estimates stood apart as representing  consistently wetter
conditions.  We tested whether the GISS-altered precipitation regime would affect  stream discharge by using
the empirical regression equation for precipitation-discharge described above.

     The results show isolated periods of deviation of GISS discharge from  measured streamflow but no
consistent trends in magnitude or direction (Figure 15).  Apparently, the slightly elevated precipitation regime
of the GISS scenario is not enough to significantly alter runoff conditions in our models, perhaps reflecting the
relatively small watersheds of mountain lakes. If we could incorporate temperature as weft as precipitation into
our precipitation-runoff model, there would be a greater likelihood of demonstrating some effect of the scenario
conditions.  With our present models we have no further basis for examining water quality parameters under
the scenario conditions.
                                               1-25

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                Table 5. Modeled vs. Measured Primary Production, Castle Lake
  1960  -  1984
  Number  of observations:   25
     SERIES
MEAN
S.D.       MAXIMUM       MINIMUM
  Measured Production
  Modeled Production
 349
 340
99.6
68.9
569
468
136
216
                             PPR,PPR1XO
                                                   Covariance      Correlation
                      427
                       0.648
                                        1-26

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I
r
       600
       500 H
400 H
       300 H
       200 H
       100
                                       htooaured
           58         62
                           i

                           66
                                                                    i     i
70         74         78          82         86

  Year
       Figure 11.  Measured and modeled annual primary production, Castle Lake, 1960-1984.
                                            1-27

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               Table 6. 2xCO2 Scenario Results, Castle Lake: Production Estimates
  1960  - 1984
  Number of observations:  25
 SERIES                        MEAN          S.D.        MAXIMUM        MINIMUM

 GFDL                            576          65.5          677            422

 GISS                            636          59.0          720            446

 OSU                             396          72.7          493            250
                                          1-28

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I
I
       800
       700 -
600 -
       SOO -
       400 -
       300 -
       200
                        Model
           58
                I

               62
 I

66
 i     i     i^     i     i

70         74         78


  Year
                                                                          I     I      I
82
86
     Figure 12.  Control and 2xCO, scenario, annual primary production estimates, Castle Lake.
                                            1-29

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   x
   04



   O


   I
          800
         500 -
400
          300 -
          200 -
          100 -
                                        Castle  Lake
                                      Annual Production EitimotM
                       1981-89 1990-99 2000-09 2010-19 2020-29  2030-39 2040-49 2050-99

                                       	QBS Traratwit A
                           Mean        UTft Standard Dwfatfon
 Figure 13.   GISS transient A, annual primary production estimates, Castle Lake, plus model "control" values.
            Transient values are decadal means. In all cases, one standard deviation is shown as added to the
            mean.
                                              1-30

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          TOO -i
   N



   O
          600 -
          900 -
          400 -
         300 -
         200 -
          100 -
                                        Castle  Lake
                                     Annual Production EitfmatM
              M«Mured           Model             GFDL


                    [771  Mean        U77\ Standard delation
OJSS
osu
Figure 14.   Summary of production estimates: all scenarios plus measured and model "control" values. One
           Standard deviation is shown as added to the mean.
                                            1-31

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          so
                                                                                84
                                       Control
                                                  Dot*
GISS
    Figure 15.  Modeled "control" and GISS 2xCO2 water discharge estimates, Ward Creek, Lake Tahoe.
                                               1-32

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                                            CHAPTER 4

                                        INTERPRETATION


     The changes in Castle Lake associated with the climatic change scenarios were driven primarily by
temperature. The increased temperature under the 2xCO, scenario conditions contributed to relatively less
snow accumulation and consequently an early melting of the lake ice. The trend in snowfall can clearly be seen
in the transient results (Figure 10). Early melting and a longer growing season resulted in higher annual primary
production estimates.  The relative lack of change of precipitation as compared to temperature in the scenarios
lessened the impact of total precipitation on the production estimates.

     The 2xCO2 conditions produced clearly altered productivity estimates (Figure 12) and  the transient
provided a definite trend  (Figure 13) for Castle Lake.  Short-term  changes are less clear in the transient,
however, owing to the masking effect of strong interannual variation. As can be seen from the summary figure,
the first two 30-year periods of the transient do not produce mean production estimates more than one standard
deviation from our measured historical values (Figure 14).  Short-term trends occurring at the rate of the GISS
transient A would be difficult to differentiate from interannual variability at a scale of 50 years or less.

     In the case of Ward Creek, Lake Tahoe, water quality changes associated with climate change projections
were undetectable in our analysis.
                                                1-33

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                                           CHAPTERS

                                   IMPLICATIONS OF RESULTS
ENVIRONMENTAL
     The implications of the projected direction of change in water quality center on the effects of enhanced
primary productivity on oligotrophic mountain lakes.  Our results suggest that algal production in Castle Lake
would be stimulated  through increasing hydraulic  inputs and a longer growing season.  The work on Ward
Creek, although inconclusive, indicates that enhanced precipitation (as demonstrated in the GISS scenario)
combined with a changing rain-snow balance (unknown in our model) could easily increase the in-stream
concentrations of particulates and soluble nutrients. There is thus the possibility of increased watershed nutrient
contributions as well as the direct  enhancement of production through an increase in length of the growing
season.   It seems certain that the projected global changes associated with the increasing atmospheric CO,
conditions would result in enhanced primary production for Castle  Lake and possibly for all similar high
mountain lakes.

     Enhanced primary production (eutrophication) would cascade to all elements of the food web and possibly
lake and downstream water quality through changing nutrient dynamics, storage, and release. As eutrophication
proceeds, the dynamics  of algal and bacterial blooms begin to dominate  the  changes in water quality.
Zooplankton, macroinvertebrates, and fish all increase in biomass but are subject to large seasonal changes.
Fish in the  Great Lakes have been projected to change dramatically in response to climate  change (Meisner
et al., 1987).  During stratification, decaying biota deplete oxygen in deep waters and  increase the solubility of
nutrients and potentially toxic compounds (e.g., metals, HjS) which could be added to the surface water and
downstream releases during mixing events. Although increased system productivity might affect such aspects
of community  dynamics  as species diversity  or the size,  age structure, and  dynamics of  populations, the
relationships are complex and require additional empirical and mechanistic models before reasonable predictions
can be made.


SOCIOECONOMIC

     The socioeconomic implications of mountain lake eutrophication and altered water quality are in terms
of recreational uses, fisheries, and the quality of water supplies. All are adversely affected by increasing algal
productivity with the exception of fisheries, which, at least in these unproductive lakes, might be enhanced  by
a greater system productivity.  The enhancement of system productivity could result in major shifts in fish
community structure as well. Lakes which are so warm and productive as to be marginal for trout production
might exceed the threshold of oxygen and temperature requirements and  revert to warm water fisheries
conditions, as has been projected for the Great Lakes area (Meisner et al., 1987). As  eutrophication proceeds,
deep lakes will show oxygen depletion in the hypolimnion and lose deep fish habitat during the summer
stagnation and winter ice cover. The eutrophication of mountain lakes and streams could  affect local and
downstream water supplies and recreation in a multiplicative fashion. Oxygen depletion, nutrient enrichment,
and algal blooms may contribute to locally decreased water quality for domestic or industrial use. Subsequently,
the eutrophication of upstream areas will have a negative impact on downstream receiving lakes and streams
independent  of climate-induced  changes.  The negative impact on water supplies will also be immediately
evident in recreational use. Although limited eutrophication could enhance fisheries, the deterioration of water
quality will negatively affect other recreational uses.
                                                1-34

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                                           CHAPTER 6

                                     POLICY IMPLICATIONS


     Lake water quality and eutrophication are important considerations in the regulation and planning of
recreation areas.  Cold water trout Usheries are important for recreation and for the economics of western
mountainous areas of the US. and Canada  A general warming of streams and lakes could eliminate or reduce
spawning success of resident and anadromous salmonids at lower elevations and allow for invasion of warm
water-tolerant species.  Water quality degradation could necessitate policy changes regarding in-streamflows
and the extent and type of water use.
                                               1-35

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                                            CHAPTER?

                                       FUTURE RESEARCH


     Several areas of applied research are suggested by the results of these initial studies.  We have touched
upon some of the potentially valuable resources of the long-term Tahoe and Castle Lake data bases, but much
more can still be learned concerning the effects of climate change on mountain lake and stream water quality.

     In the case of Castle Lake, we are now in the process of inputting and validating extensive data sets of zoo-
and phytoplankton abundance estimates and the results of stratification on water chemistry in both lakes.  Both
plankton and water chemistry are indicators of water quality that are expected to show important relationships
to climate.  Investigation of these with predictive models should further elucidate the effects of climate change.
An interpretation of this data should allow us to better understand the mechanisms by which climate influences
production in lakes and streams.

     In the case of Ward Creek, we have established in-stream models of water quality that only require better
climate model outputs to be useful in modeling potential effects of climate change. By working with hvdrologjsts
to refine the snow accumulation and melt predictions of the climate models we should be able to provide good
projections of nutrient and sediment loads under different climatic scenarios.
                                                 1-36

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                                           REFERENCES


Axler, R.P., G.W. Redfield, and C.R. Goldman.  The importance of regenerated nitrogen to phytoplankton
productivity in a subalpine lake." Ecology. 62:345- 354, 1981.

Brimblecombe, P. and D.H. Stedman. "Historical'evidence for a dramatic increase in the nitrate component of
acid rain."  Nature. 298:460-462, 1982.

Byron, E.R. and C.R. Goldman. "A Technical Summary of Changing Water Quality in Lake Tahoe: The First
Five Years of the Lake Tahoe Interagency Monitoring Program."  Tahoe Research  Group, Inst. of Ecology,
Univ. Calif., Davis, 1986.  62 pp.

Cohen, S J.  1986.  Impacts of CCL-induced climatic change on water resources  in the Great Lakes basin.
Climatic  Change. 8:135-153.

Cohen, S J.  1987. Projected increases in municipal water use in the Great Lakes due to COj-induced climatic
change.  Water Resources Bulletin. 23(1):
91-101.

Edmondson, W.T. and  J.T. Lehman.  "The  effect of changes in the nutrient income on the condition of Lake
Washington." Limnol. Oceanogr.. 27:272-293, 1981.

Gleick, P.H. 1987.  Regional hydrologic consequences of increases in  atmospheric CO- and other trace gases.
Climatic  Change. 10:137-161.

Goldman, C.R.  "Eutrophication of Lake Tahoe  emphasizing water quality."  EPA- 660/3-74-034.  U.S. Gov.
Printing Office, 1974. 408 pp.

Goldman, C.R.  "Lake  Tahoe: two decades of change in a nitrogen deficient oligotrophic lake."   Verb. Int.
Verein. Limnol.. 21:45-70, 1981.

Goldman, C.R.  "Lake Tahoe: an oligotrophic lake's response to nutrient loading."  Lake Pollution and Recovery.
Proceedings of an International Congress. European Water Poll.  Control. Assoc., 1986. pp. 329-334.

Goldman, C.R.  "Primary productivity and transparency: the early onset of eutrophication in ultra oligotrophic
Lake Tahoe, California-Nevada" Limnol. Oceanogr.. In Press.

Goldman, C.R.  and de  Amezaga, E. "Primary productivity and precipitation at Castle Lake and Lake Tahoe
during twenty-four years, 1959-1981" Verb. Int. Verein. Limnol.. 22:591-599, 1984.

Goldman, C.R., A. Jassby, and T. Powell. "Interannual fluctuations in primary production: impact of climate and
weather at two subalpine lakes."
Submitted.

Kuusisto, E. 1987. An analysis of the longest ice observation series made on Finnish lakes. Aqua Fennica.
17(2):123-132.

Leonard, R.L., LA. Kaplan, J.F. Elder, R.N. Coats, and C.R. Goldman.  "Nutrient transport in surface runoff
from a subalpine watershed, Lake Tahoe basin, California."  Ecol. Monogr.. 49(3):281-310,1979.

Meisner,  J.D., J.L. Goodier, HA. Regier, BJ. Shuter and WJ. Christie.   1987.  An assessment of the effects
of climate warming on Great Lakes basin  fishes. J. Great Lakes Res.. 13(3):340-352.


                                                1-37

-------
Byron


Ramanathan, V. 1988. The greenhouse theory of climate change: a test by an  inadvertent global experiment.
Science. 240:293-299.

Rannie, W.F.  1983.  Breakup and freezeup of the Red River at Winnipeg,   Manitoba, Canada in the  19th
century and some climatic implications,   riimatir ffiang»T 5:283-296.

Semtner, A J., Jr.  1984.   On modeling the seasonal thermodynamic cycle of   sea ice in studies of climatic
change. Climatic Change. 6:27-37.

Smith, RA.,  R.B. Alexander,  and M.G.  Wolman.  "Water quality trends in the nation's rivers."  Science.
235:1607-1615, 1987.

Soderlund, R. "NO  pollutants and ammonia emissions - a mass balance for the atmosphere over Northwest
Europe."  Ambio. 6(2-3):118-122,1977.
Strub, P.T., T. Powell, and C.R. Goldman. "Climatic forcing: effects of El Nino on a small, temperate lake."
Science. 227:55-57, 1985.

Vincent, W.F. and C.R.  Goldman.  "Evidence for algal heterotrophy in  Lake Tahoe, California-Nevada."
Limnol. Oceanogr.. 25(l):89-99,1980.
Wetzel, R.G. Limnology. 2nd Edition. W.B. Saunders Co., Philadelphia, Pennsylvania, 1983. 767 pp.

Wetzel, R.G. "Long-term dissolved and participate alkaline phosphatase activity in a hardwater lake in relation
to lake stability and phosphorus enrichments." Verb. Int. Verein. Limnol.. 21:369-381,1981.
                                                1-38

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POTENTIAL RESPONSES OF GREAT LAKES FISHES AND THEIR HABITAT TO
                      GLOBAL CLIMATE WARMING
                                  by
                            John J. Magnuson
                              David K. Hill
                           Center for Limnology
                      University of Wisconsin Madison
                           Madison, WI 53706

                                  and

                            Henry A. Regier
                             John A. Holmes
                            J. Donald Meisner
                          Department of Zoology
                           University of Toronto
                            Toronto, Ontario
                            Canada M5S 1A1

                                  and

                             Brian J. Shuter
                    Ontario Ministry of Natural Resources
                             Maple, Ontario
                            Canada LOJ 1EO
                       Contract No. CR-814644-01-0

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                                  CONTENTS
FINDINGS  	  2-1

CHAPTER 1: INTRODUCTION	  2-2
    DESCRIPTION OF ECOLOGICAL SYSTEM	  2-2
    ORGANIZATION OF THIS REPORT 	  2-4

CHAPTER 2: THERMAL NICHE	  2-5
    GREAT LAKES  	  2-5
        Methods	  2-5
        Scenarios	  2-6
        Results	  2-6
    HEADWATER STREAMS 	  2-8
        Methods	  2-8
        Scenarios	2-13
        Results	:	2-13

CHAPTER 3: GROWTH AND FOOD CONSUMPTION	2-15
    METHODS	2-15
    SCENARIOS	2-16
    RESULTS	2-19

CHAPTER 4: POPULATION RESPONSE	2-24
    METHODS	2-24
    SCENARIOS	2-26
    RESULTS	2-26

CHAPTER 5: ECOSYSTEM RESPONSE	2-28
    METHODS	2-28
    RESULTS	2-29

CHAPTER 6: INTERPRETATION OF RESULTS	2-32
    GENERAL  	2-32
    SPECIFIC INTERPRETATIONS	2-33
        Thermal Niche	2-33
        Growth and Food Consumption 	2-33
        Population Response	2-34
        Ecosystem Response	2-34

CHAPTER 7: IMPLICATION OF RESULTS	2-35

CHAPTER 8: POLICY IMPLICATIONS 	2-36

TERMS, ABBREVIATIONS, AND SYMBOLS  	2-37

REFERENCES	2-39
                                      u

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                                                                                           Magnuson
                                             FINDINGS1
      Global climate warming will directly influence fishes in the Great Lakes by significantly wanning their
 aquatic habitat.  These effects act on those fishes which do best in coldwater (e.g., salmon, trout and whitefish),
 in coolwater (e.g., yellow perch and walleye), and in warmwater (e.g., largemouth and smallmouth bass).

      Three types of direct effects of altered thermal structure were examined for fishes:  1) changes in the size
 of their optimum thermal habitat, 2) changes in growth and prey consumption, and 3) changes in reproductive
 success and population size. Generally, all three thermal groups of fishes experience an increase in the size of
 favorable thermal habitat integrated over the annual cycle and grow at faster rates and eat more, provided they
 can seek out favorable temperatures and prey is available. These  positive effects are apparent in all three
 (OSU, GISS, and GFDL) global climate model scenarios and  in cold regions (northern Lake Superior), cool
 regions (southern Lake Michigan), and warm regions (Lake Erie) of the Great Lakes.  Most of these positive
 effects result from an increased length of growing season.  However, in the warmest part of the summer the
 size of favorable thermal habitat decreases  for coldwater fishes like  trout both in the lakes and in headwater
 brook trout streams. During summer, species interactions like competition and predation should be intense and
 may have negative effects on fishes. The increased demand for forage may also intensify species interactions
 and alter food web  structure and efficiency.

     Smallmouth bass were used as an example of potential changes in reproductive success and population
 size.  This warmwater species, which lives in protected Great Lakes bays would experience marked increases
 in reproductive success and population size in cold and cool regions and would not be harmed in warm regions.
 Effects of climate change on the recruitment of other taxa were not  evaluated.

     The increase in thermal habitat for warmwater fishes may allow an increase in colonization and dispersal
 of exotic fishes with either negative or beneficial effects.

     To estimate whether there would be a general increase in primary and secondary production to meet the
 increased forage requirements of the fishes,  a simple ecosystem level model was applied to primary production
 (algae), secondary production (zooplankton), and fishery production (fish yields). In all regions of the Great
 Lakes primary production, zooplankton biomass, and fishery yields would be expected to increase significantly.

     Thus, for most regions and most fishes, habitat and productivity would increase following climate wanning.
 Even though productivity is expected to increase, surprises are expected involving  (1) the loss of important
 stocks and (2) intensified species interactions due to new colonizing species and habitat constriction among the
 existing fish communities. The higher productivity and the potential for more permanent thermal stratification
 increase the possibility of anoxia in some basins and bays, excluding such regions from fishes.

     Policy issues include the continued management of cold, cool, and warmwater fisheries for what appears
 to be higher recreational  and commercial fishery opportunities, continuing to protect the water quality of the
 lakes  especially in regard to nutrients contributing to  eutrophication and toxics that accumulate in fishes, the
 continuing value of lake trout for rehabilitation of populations, intensified predator pressure on the forage base
 and intensified species interactions, and the potential problem of negative species interactions between present
warm and coolwater fishes and exotic warmwater fishes that will find expanded habitats, especially in inshore
areas.
        'Although the information in this report has been funded wholly or in part by the U.S. Environmental
Protection Agency under Contract No. CR 814644-01-0 under the Office of Policy, Planning and Evaluation, it
does not necessarily reflect the Agency's views, and no official endorsement should be inferred from it.

                                                2-1

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Magnuson

                                            CHAPTER 1

                                          INTRODUCTION


DESCRIPTION OF ECOLOGICAL SYSTEM

     The Great Lakes lie within a relatively small watershed basin (Figure 1) that has long been the industrial
heartland of North America and is now home to some 37 million people. In a world in which fresh water is
becoming a scarce resource, our wealth is reflected in the fact that these lakes contain about one-sixth of all the
surface fresh water of the globe, not including the glaciers and groundwaters.  This chain of lakes drains through
the St. Lawrence  River into the Gulf of St. Lawrence, where  the freshwater outflow plays a role in the
reproduction and recruitment of the large herring fisheries of the gulf.

     By 1950, the southern third of the basin had become a vast ecological slum because of careless agricultural,
forestry, industrial, and urban development in preceding decades. A series of studies then led to binational
conventions and agreements to rehabilitate the fisheries  and remediate the bad practices that degraded water
quality. Many billions of dollars have been spent in these efforts by the various governments at different levels.
Some programs have been relatively successful: control of the predaceous sea lamprey, correction of overfishing,
reduction in loadings of old-fashioned industrial wastes and of plant nutrients from sewage, and discontinuation
of the  use of persistent pesticides. Prospects are improving that loadings of hazardous contaminants and acidic
substances will progressively be contained and reduced.

     By 1950 the ecological quality of the lakes, streams, and shores was generally offensive to humans, especially
in cities and towns.  People had turned their backs to the lakes. Some four decades later, many people are
rediscovering these ecosystems as they are now recovering from past abuses.  Shore properties are escalating in
value and are being redeveloped with sensitive uses in mind. Commercial fisheries have rebounded, but then-
value has been dwarfed by new recreational fisheries that are based, in part, on a new "manmade" association
of fish species thai is dominated by Pacific salmons. Parks, marinas, beaches, promenades, and condominiums
along the shorelines are increasing in number and quality as the demand for them grows.

     Commitment to a healthy Great Lakes ecosystem as reflected in an abundance of valued fish is part of a
regional policy to redevelop, on a sustainable basis, the cities, industries, farmlands, and forests of the basin, as
key to the resurgence of prosperity in the basin.  In the face of this optimistic prospect,  concern is growing
about  the likely effects of climate change.

     Will the warming trigger relapses in the ecological recovery of degraded parts of the lakes? Will increased
temperatures foster undesirable ecological productivity and thus impede recovery from cultural eutrophication?
Will the higher temperatures create difficulties, in southern waters of the basin, for the coldwater salmons and
lake trout? Will higher temperatures facilitate invasion of new species with some of them interacting to the
detriment  of some valued species of the present association?  WU1 valued new species appear in the warmer
habitats? Will sea lamprey benefit in Lake Superior to the detriment of lake trout and other valued fish species?
Such questions come to mind as we consider the possible ecosystem impacts of climate change.

     In its pristine state, the fish association of the Great Lakes could be  separated into five major types:

     i) The deeper, colder waters of the lakes and rivers were dominated by  the salmon family (lake trout, lake
whitefish, lake  herring, chubs), the cod family (burbot), and the cottid family (sculpins).

     ii) Waters of intermediate depth that were cool even in midsummer were dominated by the perch family
(yellow perch, walleye, sauger, etc.) and the pike family (northern pike, muskellunge, etc.). The sturgeon family
(lake sturgeon) and the ictalurids (bullheads, channel catfish) frequented the bottoms of cool waters.
                                                  2-2

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                                                                                             Magnuson
                                                                    Great Lakes Drainage Basin Boundary
                                 Southern Basin Silt


                   Chicago o\X  Eva5S'°n
Rgure 1.   Great Lakes drainage basin and sites referred to in text  Symbols: 0 « air temperature data site,
           X = water temperature data site.
                                                 2-3

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Magnuson

     iii)  In a few of the wannest waters of moderate depth, the "percichthyid" family (white bass) fluctuated in
abundance over the decades.

     iv)  Shallow, warm inshore waters, especially of bays and streams, were the home of the sunfish family
(sunfishes,  crappies, smallmouth bass, largemouth bass, etc.) and the cyprinid family (minnows).

     v)  In the forested coldwater tributary streams, again the salmon family (brook trout) and cottid family
(sculpins) predominated.

     At the outset, it seems likely that each of these five types of associations will react somewhat differently to
climate wanning in a particular locale.   Coldwater habitats may shrink toward  the north in the  lakes and
upstream into the headwaters; coolwater habitats may be shifted northward and upstream; wannwater habitats
may expand from the south northward and from the protected bays into the lake proper.


ORGANIZATION OF THIS REPORT

     Ecological  phenomena  in our  report are  addressed in a  hierarchic  context across several levels  of
organization, progressively from the organism level, to the population level, to the ecosystem level  First, the
report considers the potential influence of altered thermal structure on the size of the habitat within the thermal
niche of fishes which prefer and do best in cold, cool, or warm waters. Second, our report considers how the
body growth and the prey consumption of coldwater, coolwater, and wannwater fishes may be changed by living
near-shore in Great Lakes neritic habitats altered by climate change scenarios.  Third, for a wannwater fish
that lives in the shallowest inshore habitats and for which the best models were available, our report evaluates
the response of a population of fishes in respect to reproductive success and population size. Fourth, the report
steps back from the species level and estimates the response of phytoplankton production, zooplankton biomass,
and fishery yields as entire trophic levels to climate warming scenarios.  To seek generality among global climate
models, three climate change scenarios were applied: Oregon State University's (OSU) general circulation model,
the general circulation model from  the  Geophysical Fluid  Dynamics Laboratory (GFDL),  and the  general
circulation model from Goddard Institute of Space Studies (GISS). To seek generality across the Great Lakes,
simulations were made for a warm region in Lake Erie, a cool region in southern Lake Michigan or Lake Huron,
and a  cold region in northern Lake Superior. Finally, the report discusses what we do not know or have not
addressed especially in regard to the possibility of surprises that could occur in the dynamic food webs perturbed
by altered  fish species interactions.  A recent review paper by Meisner et al. (1987) is appended for  general
reference.
                                                 2-4

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                                                                                         Magnuson

                                            CHAPTER 2

                                        THERMAL NICHE
 GREAT LAKES
     Fishes of the Great Lakes have been grouped into three broad thermal guilds for which a central
temperature has been estimated by Hokanson (1977): coldwater ca 15.0°C; coolwater ca 24.0°C; and warmwater
ca 28.0°C.  Body temperatures of fishes are determined by the temperatures of the water in which they live, and
changes in their environmental temperatures directly influence their survival, physiology, and behavior (Fry,
1971; Hokanson, 1977; Magnuson et al., 1979). Fish species within a thermal guild have preferred temperature
ranges in which growth is maximal. The median temperature of the preferred range of a species is where the
species will be found at greatest densities if food resources are not limiting and interspecific competition is not
high. The thermal niche of a species was defined as ± 2.0°C of the median temperature by Magnuson et al.
(1979).

     The amount of thermal habitat within a fish's thermal niche has been shown to be a statistically significant
determinant of fishing yield (Christie and Regjer, 1988). Estimates of maximum sustained commercial fish yield
of lake trout, lake whitefish, and walleye were directly related to the amount of lake bottom area or pelagic
volume contained within the optimal temperature ranges of these species in a set of 21 large North American
lakes.

     Altered thermal structure of the Great Lakes owing to climate warming could increase or decrease the
size of optimum thermal habitats available for important fishes. This would ultimately change commercial and
sport fishing yields of valued species.  The above empirical model of thermal niche is used here to estimate the
amount of thermal habitat available for the thermal guilds of Great Lakes fishes before and after applying the
global climate warming scenarios.  Expected changes in potential yield of several commercial fishes are estimated
from the Christie  and Regier (1988) scaling of maximum sustained yields to summer thermal habitat.

Methods

     Potential effects of climatic warming on the size of thermal habitats of fishes representing several of the
thermal guilds were simulated using thermal structure data from southern Lake Michigan (McCormick 1988)
and the central basin of Lake Erie (Blumberg, 1988). These results were subsequently used to estimate changes
in fishery yields.

     Thermal habitat was estimated for individual species from three thermal guilds. The thermal niche was
defined in two alternative ways, one  narrow and the other broad: (1) the optimum ±2°C, which includes the
temperatures within which a fish in a preference tank in the laboratory will spend 2/3 of its time; and (2) the
optimum ±5°C, which includes the temperature a fish in the laboratory chooses to spend all of its time, if such
temperatures are  available  (Magnuson et al., 1979; Crowder and Magnuson, 1983).   Hokanson (personal
communication) has compiled data suggesting that stream fishes spend -90% of their time within the thermal
range of optimum ±5°C.  In some cases we estimated the thermal habitat  for the entire year, in others only
from June 5 to September 2 to be able to use the fishery yield prediction models of Christie and Regier (1988).
In some cases, the thermal niche was measured in terms of depth days or the number of meters of the water
column within the thermal niche summed across dates and expressed in terms of m-days of suitable space in the
water column. For southern Lake Michigan, the size of the thermal niche was estimated in terms of the volume
days of suitable thermal habitat and expressed as hm 3.7 days.  For walleye and yellow perch, we calculated the
weekly ha of bottom area exposed to suitable temperatures based on the approach of Christie and Regier (1988).

     The average  and minimum weekly thermal niche for the summer period (weeks 23-35) was also calculated
for some species by computing the weekly mean  and minimum weekly depth-days or volume-days of suitable
thermal habitat. This measure of habitat size provides insight to periods during which suitable thermal habitat


                                               2-5

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Magnuson

may be most limited (Coutant, 1987a; Rudstam and Magnuson, 1985). In the central basin of Lake Erie, the
mass of cold water is small and usually anoxic in summer, so we did not estimate any thermal habitat in Lake
Erie for coldwater fishes.

     Lake trout is an example of a coldwater fish with a lower optimum (10°C) (Stewart et al., 1983) than most
coldwater fishes. It is a species which, at first thought, should be most negatively influenced by warming of its
habitat.  Coho salmon is perhaps a more representative example of a coldwater fish with its optimum of 15° C
(Hewett and Johnson, 1987).  Yellow perch provide our example of coolwater species with an optimum of 23°C,
and largemouth bass serves as the wannwater fish with an optimum of 27.5°C (Hewett and Johnson,  1987).
Other individual species were used as well: lake whitefish at 12°C,  and walleye at 20°C (Christie and Regier,
1988).

     The statistical model equations relating summer thermal habitat to fishery yields from Christie and Regier
(1988) are:


     Lake trout                Iog10 SY = 0.807 loglO THV + 0.944,  r2 -  0.86;
     Lake whitefish            4og" SY = OJ76 loglO THV + 3.606,  r2 =  0.69;
     Walleye                  logjjj SY = 0.872 loglO THA + 0389,  r2 -  0.72


where SY is an estimate of sustained yield in kg/year, THV is summer thermal habitat (±2°C) volume in hm3 • 10
days from June 3 to September 2, and THA is summer thermal habitat (±2°C) bottom area in  ha. 10 days from
June 3 to September 2.

     The summer period as  defined by Christie and Regier (1988) omits habitat that might exist during the
spring and fall. Sufficient data for  spring and fall, necessary for their statistical analyses, were not available;
hence, their possible contribution to production could not be assessed. During the fall and late spring, there are
periods during which water temperatures in their study lakes were very likely within the optimal ranges for lake
trout and lake whitefish. The thermal habitat for  these species during these periods  may have contributed to the
sustained yields they used in their analyses.

Scenarios

     Thermal structure throughout the year was obtained from McCormick (1988) for Lake Michigan and from
Blumberg (1988) for Lake Erie.  OSU-simulated thermal structure was not available for Lake Erie. The three
simulations we used are BASE, the present conditions (IxCO,);  the OSU scenario from the OSU 2xCO2 general
circulation model; the GISS scenario derived from the GISS 2xCO, general circulation model; and the GFDL
scenario derived from  the GFDL 2xCO, general circulation  model.  For Lake Erie we used  the profiles
generated from 1975 (a cool year) and 19/0 (a warm year).

Results

     A profile of the thermal habitat of lake trout throughout the year (Figure 2) clarifies the concept of thermal
niche as the m-days of water within ±2°C (black) and ±5°C (grey) of optimum.  In these terms  the thermal
habitat under base climate is 1*500 m-days for i2°C  and 5322  m-days for ±5°C. Typically the temperature in
winter and spring mixis are too cold to be in the optimum range  for lake trout as are  the deeper waters in
summer. In summer the near-surface waters are  too warm. During fall mixis, much of the upper 100 m is within
their thermal niche.

     Under all three climate warming  scenarios  (OSU, GFDL, and GISS) the  quantity of suitable annual
thermal habitat increases  greatly for lake trout (Figure 2). The increases are most pronounced for the GFDL
and GISS scenarios where much of the  upper 100 m of the water column reaches temperatures above 5°C in
winter.  Temperatures between  5 and 15°C persist  long  into  the fall after climate wanning.  The size of


                                                2-6

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           50
         100
                                                                       Magnuson
                 BASE CLIMATE
    D
    E
    P
    T
    H
  (M)    50
          100
                            92            183           274

                                  DAY  NUMBER
                                                               365
Figure 2.
Extent of favorable thermal habitat before and after climate wanning for a coldwater fish (lake
trout) in southern Lake Michigan based on WC*TC (black) and KrC±5°C (grey). Vertical lines
span the period (June 5 to September 2) used in the fishery yield prediction models of Christie and
Regier (1988).  Thermal scenarios for Lake Michigan provided by McCormick (1988).
                                      2-7

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Magnuson

unsuitably warm waters near the surface in summer increases with the climate warming scenarios.  With the
GISS and GFDL scenarios, about 40% of the upper 100 m are within the thermal niche of lake trout compared
with 22%  with the OSU scenario and only 14% under the base climate.  Climate warming scenarios would
increase the amount of favorable thermal habitat over the year even for a coldwater fish like the lake trout.

     The size of the annual thermal habitat (Figure 3) not only increases for lake trout (factor of 1.5 to 2.7 x
BASE) in  Lake Michigan's southern basin under climate warming, but also for other coldwater fishes like coho
salmon (13 to 1.5 x BASE), and coolwater fishes like yellow perch and walleye (1.4 to 3.1 x BASE).  A small
amount of thermal habitat also is opened up or expanded for warmwater fishes.  In Lake Erie, climate warming
increases the thermal habitat both for coolwater (1.2 to 23 x BASE) and warmwater (23 or greater x BASE)
species (Figure 3). These results are robust and not sensitive to the particular method of estimation. The change
in the amount of thermal habitat for lake trout measured in various ways (Figure 4) indicates that when the
whole year is included, thermal habitat increases with climate warming regardless of whether ±2°C or ±5°C is
used as a criterion or whether depth days or volume days are used as a metric.

     The average summer habitat within the thermal niche of various fishes in Lake Michigan remains about
the same or decreases (Figure 5). For the lake trout (coldwater) and the yellow perch (coolwater), the summer
niche space decreases slightly as delineated by ±2°C of optimum and the vertical lines in Figure 2. Decreases
(Figure 5) with climate warming for walleye (coolwater) and lake whitefish (coldwater) were large.

     Sustained yields for Lake Michigan estimated by the Christie and Regjer (1988) model are predicted to
decrease only slightly for lake trout (2 to 6%) and lake whitefish (7 to 10%) with GISS and GFDL climate
scenarios but increase significantly for walleye (+29 to +33%) (Figure 6).


HEADWATER STREAMS

     Waters of many headwater streams in the Great Lakes Basin, especially those which originate in sandy and
gravelly moraines with copious groundwater flows  and springs, are inhabited by brook trout, a coldwater fish.
 Groundwater temperatures  track local average air temperatures and are expected to increase with climate
wanning (Meisner et al., 1988). The major impact of climate change on the streams maybe on the temperature
of the groundwater entering the streams and on the rate  of warming of water flowing downstream from
headwater springs. A calibrated hydrometeorological model of stream temperature (Delay and Seaders, 1966)
can be used to estimate summer stream temperatures before and after applying global climate change scenarios.
Longitudinal distributions of brook trout in nondegraded streams normally are bounded between 19 and 24°C
(Hawkes,  1975; Ricker, 1934),  but  Barton et  al. (1985) and Bowlby and Roff (1986) found that water
temperatures near 22°C marked the downstream habitat boundaries for  Ontario.  Combining the stream
temperature estimates along the course of a stream with the  thermal requirements of brook trout can provide
estimates  of changes in the size of thermal habitat for coldwater fishes in headwater streams.

Methods

     A hydrometeorological model of summer thermal regime was implemented for the brook trout zone of
two first-order headwater streams: the Rouge and Humber Rivers, which ultimately empty into Lake Ontario
east of Toronto (Figure 1). The streams originate as groundwater discharge at about 10°C. The Humber River
is about 5 km long with a mean discharge of .06 m3/s and has forested banks, while the Rouge River is about
3 km long, has a mean discharge of .075 m3/s, and flows through open fields. The streams are  similar to other
brook trout streams of southern Ontario.

     The  basic model (Delay and Seaders, 1966) simulates the change in temperature of the water as it flow
downstream:
                                                2-8

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                                                                             Magnuson
                                             LAKE  ERIE
      (0
      Z
      I-

      o

       I
      GC
      LU

      HI
500
400-
300
200
100
0-

•
COLDWATER
HABITAT
UNAVAILABLE
	 1 	

COLDWATER
HABITAT
UNAVAILABLE




• BASE
H QISS
m GFDL

__H.^B§1
                                        COLD  WARM  COLD  WARM
                                        YEAR  YEAR   YEAR   YEAR

     DC
     ULJ
     z
     h-
                                          LAKE MICHIGAN
             500
250-
                    COLDWATER    COLDWATER   COOLWATER
                       FISHES          FISHES         FISHES

                   (LAKE  TROUT) (COHO  SALMON)
                                                        WARMWATER
                                                            FISHES
Figure 3.  Amount of thermal habitat (optimum ±5°C) in the water columns of the central basin of Lake Erie
         and the southern basin of Lake Michigan for coldwater fishes (lake trout, coho salmon), coolwater
         fishes (yellow perch) and warmwater fishes (largemouth bass) for the BASE climate and for the
         OSU, GISS, and GFDL climate change scenarios using the thermal structure simulated by Blumberg
         (1988) for Lake-Erie and by McCormick (1988) for Lake Michigan. For Lake Erie, the cold year
         was 1975 and the warm year was 1970.
                                         2-9

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Magnuson
               DEPTH-DAYS  DEPTH-DAYS VOLUME-DAYS
               *c       ,
THERMAL  HABITAT
 FOR  ENTIRE YEAR
                                              C
VOLUME-DAYS   KG / YEAR
   *~2 °       YIELD
 THERMAL  CHRISTIE
 HABITAT   & REGIER
 SUMMER   MODEL
 Figure 4.   Change in the size of southern Lake Michigan thermal habitat expressed as a ratio of the GISS or
          GFDL estimates to the estimates from the BASE climate for a coldwater fish (lake trout) measured
          by various criteria; optimum ±2°C for the entire year or ±2°C for the summer months without and
          with the correction for lake hypsometry, and the estimated change in maximum sustained yields
          using the summer months and ±2°C criteria with the Christie and Regier  (1988) model  The
          temperature structure of Lake Michigan is from McCormick (1988).


                                        2-10

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                                                                            Magnuson
          AVERAGE
          THERMAL
          HABITAT
       (HM?7 DAYS OR
         HA-7 DAYS)
400000

300000
200000-
                       100000-
•  BASE

Bl  GISS
m  GFDL
#   HM3-7DAYS
*   HA -7 DAYS
                                  LAKE     LAKE   WALLEYE  YELLOW
                                 TROUT WHITEFISH    •       PERCH
                                   n        #
Figure 5.  Average size of thermal habitats averaged over the summer months (weeks 23-35) for four Lake
         Michigan fishes based on a thermal niche of optimum ±2°C for the BASE climate and GISS and
         GFDL climate warming scenarios. Temperature structure of southern Lake Michigan is from
         McCormick (1988), hypsometry is for all of Lake Michigan.
                                        2-11

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Magnuson
   SUSTAINED
   YIELD
   1,000,000
   KG/YR
                          LAKE TROUT
LAKE WHITEFISH
WALLEYE
 Figure 6.   Maximum sustained yields of lake trout, lake whitefish, and walleye in southern Lake Michigan
           based on summer optimum ±2°C thermal habitat volumes for the BASE climate and the GISS and
           GFDL climate change scenarios.  Yields estimated from the Regier and Christie (1988) model,
           thermal structure for southern Lake Michigan from McCormick (1988), hypsometry for the whole
           lake.
                                             2-12

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                                                                                          Magnuson
     AT,.. =     V             where,
        w
     ATW   = change in temperature of parcel of water;
      TW   = initial temperature of parcel of water;
      A    = surface area of parcel of water;
      V    = volume of parcel of water;
      v     = volume of inflow (e.g. groundwater, tributary);
      ty    = temperature of inflow;
     and S  = change in energy stored in parcel of water.

     S quantifies the energy flux across the air/water interface in a series of equations not shown here.  This
term treats the effects of air temperature, solar and longwave radiation, evaporation, conduction and wind on
stream temperature explicitly. The model adapts to varying hydrologjcal  conditions along the stream and
accounts for the thermal effects of shade.

     The model was used to estimate the farthest downstream extent of temperatures suitable for brook trout
in the wannest part of the diel cycle for a date in late July before and after applying the 2xCO2 climate wanning
scenarios.  Observations indicate that the thermal barrier defining the downstream limit of the brook trout zone
is 23°C in the Humber River and 24°C in the Rouge River.

Scenarios

    Simulations of maximum July water temperatures were made using July 24,1987, water temperatures from
the sites and 1987 meterological data from the sites for  BASE conditions and applying the 2xCO2/lxCO2
adjustment statistics for July and August for the OSU, GISS, and GFDL models.  Groundwater temperatures
in the 2xCO2 simulations were derived using the mean annual air temperature for each scenario.

Results

    The length of stream suitable for brook trout declines markedly in the 2xCO, simulations  of the Rouge
and Humber Rivers (Figure 7). The loss of thermal habitat is least pronounced in the OSU scenario and most
significant in the GFDL scenario, where suitable length in both streams is about  half that found in BASE
conditions.  Although changes in groundwater discharge were not included in these simulations,  any drying of
the climate would likely exacerbate the habitat  losses displayed here.
                                               2-13

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   Magnuson
O

LU
                                   ROUGE RIVER
                                 NUMBER RIVER
           BASE
OSU
GISS
GFDL
   Figure 7. Length of the brook trout zone in two river headwaters in southern Ontario during July for the
        BASE climate and the OSU, GISS, and GFDL climate warming scenarios.
                            2-14

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                                                                                          Magnuson


                                            CHAPTERS

                              GROWTH AND FOOD CONSUMPTION


     The physiological processes of fishes depend strongly on water temperature (Fry, 1971), and coupled with
 the rate of food consumption temperature determines the rate at which fish grow. The fish bioenergetics model
 of Kitchell et al. (1977) as given in Hewett and Johnson (1987) allows the computation of  growth given the
 food consumption and the water temperature environment of the fish. Alternatively prey consumption can be
 estimated from knowledge of water temperature and growth using the bioenergetic model.  This methodology
 has been applied to a variety of individual fish species (Kitchell et aL, 1977; Rice et aL, 1983; Stewart et al.,
 1983; Rice and Cochran, 1984; Stewart and Binkowski, 1986) as well as of interspecific interactions (Kitchell and
 Breck, 1980; Stewart et al., 1981, Lyons and Magnuson, 1987).

     The impact of increased water temperatures on fish growth and food consumption in the Great Lakes will
 be assessed using the bioenergetic models. The potential effects of a temperature change on growth and food
 consumption are estimated by-applying the model to temperature scenarios representing conditions before and
 after climate wanning. Simulations using different proportions of the maximum consumption rate were used in
 an attempt to bracket a range of potential energetics responses to climate warming.


 METHODS

     The bioenergetics model computes energy budget statistics for  individual fish using the equation G-C
 (R + F+U), where G is the growth rate, C the rate of food consumption, R the sum of metabolic costs, and F
 and U the waste products due to egestion and excretion (Kitchell et al. 1977).  All of the terms in this equation
 are strongly dependent on temperature.  The physiological processes (R, F, and U) are known functions of
 temperature and feeding rate. Therefore, fish growth may be computed if the temperature and feeding  rate are
 known.

     To evaluate the range of potential energetics responses of fish to climate warming, two sets of energetics
 simulations were run using different assumptions about prey  consumption rates.  The first  set of simulations
 were run under the assumption that as the climate changes, the  fish will continue to feed at the same  fraction
 of their maximum consumption rate as in the base period.  The  assumption that the proportion of maximum
 consumption (called the "P-value") is constant implies that prey availability will increase with climate warming
 at a rate such that actual consumption remains the same fraction of maximum potential consumption. P-values
 were chosen to produce growth in the range observed in the Great Lakes for baseline climatic conditions.
 Baseline P-values were 0.7 for largemouth bass, 0.45 for yellow perch, and 0.75 for lake trout. A P-value of 0.7
 means, for example, that a fish consumes prey at a rate of 0.7 times the maximum consumption rate, which is
 a function of temperature and body weight.   In the  second set  of simulations, annual prey consumption was
 assumed to remain constant at baseline levels in the 2xCO« scenarios.  This assumption means that the  P-value
 in the 2xCO, simulations is lower than in the  base scenarios.

     In both sets of simulations, growth was computed for representative "cold," "cool," and "warm" water fishes:
 lake  trout, yellow perch, and largemouth bass.  Each individual simulation consisted of a yearling fish exposed
 to one year of the water temperature scenarios, beginning in February, the coldest month in the annual cycle.
Starting weights were 8 g for yellow perch and 20 g for both lake trout and largemouth bass.  For all  species,
 a generic  prey was  used in the models with an energy density corresponding  to  a mixed diet of fish and
 invertebrates. The lake trout prey was given a higher energy density to reflect the importance of the  energy-
 rich alewife in their diet (Stewart et al., 1983).

    An important assumption used in our bioenergetic simulations is that fish behaviorally thermoregulate; they
 seek out a preferred temperature which is close to their optimal temperature for growth (Neill and Magnuson,


                                               2-15

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Magnuson

1974;  Magnuson et al., 1979).  Thus, the maximum water temperature in our scenarios was limited to the
preferred temperature of the species being modeled.  The assumption of behavioral thermoregulation includes
the implicit assumptions that the preferred temperature is available to the fish, that oxygen concentration is
adequate at that temperature, and that sufficient prey are available to support the assumed feeding rates. The
following preferred temperature values were used: 10°C for lake trout, 23°C for yellow perch, and 27.5°C for
largemouth bass (Hewett and Johnson, 1987).


SCENARIOS

    The energetics simulations with prey consumption held constant  at baseline levels in 2xCO2 climate
conditions were run using McCormick's (1988) water temperature scenarios for the southern basin of Lake
Michigan (see Figure 1). The simulations with prey consumption increasing with climate warming were applied
to three Great Lakes near-shore sites representative, respectively,  of "cold," "cool," and "warm" thermal
environments in baseline climate conditions: Thunder Bay, Ontario (Lake Superior), Evanston, Illinois (Lake
Michigan), and Put-In Bay (Lake Erie) (see Figure 1). EPA's standard procedure was followed to create air
temperature scenarios for the three sites.  Daily average air temperature observations from Cleveland, Ohio
(Put-In Bay site), Chicago, Hlinois'(Evanston site), and Thunder Bay, Ontario (Thunder Bay site) were used to
compute monthly average air temperatures.  These BASE scenarios covered the period 1951-1980 for Chicago
and Cleveland, and 1953-1980 for Thunder Bay. The OSU, GISS, and GFDL air temperature scenarios were
created by multiplying temperatures in the base period by the appropriate 2xCO2/lxCO2 ratio from the climate
model output, using the closest model gridpoint to each site of interest. The air temperature scenarios were
then used to create  water  temperature scenarios representing inshore epilimnetic conditions at each site.
Multiple regression models were developed relating the observed monthly averaged water temperature to the
current month's air  temperature and the previous month's air and water temperatures (see Table  1 for
equations). Three different seasonal models were developed for each site: a winter model and models for the
periods of rising and falling water temperatures.  Water temperature data for Put-In Bay were obtained from
the Put-In Bay fish hatchery.  The years 1954-1973 and 1976 were used to calibrate the model  The city of
Evanston water supply intake was the source of Lake Michigan temperature data, spanning the period 1974-1987.
Lake Superior data between 1958 and 1977 were obtained from the city of Thunder Bay water intake. For each
site, daily water temperatures were averaged to monthly values.

    The base period water temperature scenarios (BASE) were derived by applying the regression models to
the monthly average observed air temperatures for 1951-1980.  The model was run in an iterative fashion, using
the predicted  water temperature  for the current month in the  computation  of the next  month's  water
temperature.  This approach requires in addition to the air temperature  data only an initial guess of the water
temperature for January 1951 to generate the entire 30-year scenario. Similarly, the OSU, GISS, and GFDL
water temperature scenarios were derived by applying the regression equations to the respective air temperature
scenarios. Water temperature scenarios were then modified for thermoregulation by cutting off the temperature
at the fishes' preferred value.

    The water temperature scenarios are summarized in Figure 8. The left column displays the average and
extreme values for the water temperature scenarios over the entire 30-year period. For every site, the GFDL
climate change scenario is the warmest, the GISS scenario  intermediate,  and the OSU scenario the coolest In
each case, Lake Erie experiences the greatest increase in annual mean temperature, while Lake Superior changes
the least.  The GFDL mean annual water temperature is 33°C warmer  than the BASE scenario for the Lake
Superior site, 3.4°C warmer for Lake Michigan, and 5-8°C wanner for Lake Erie. The seasonal plot of average
monthly temperatures in the right column of Figure 8 shows that the GFDL scenario is uniformly warmer than
the OSU and GISS scenarios, with the largest difference occurring in midsummer. The horizontal lines bisecting
the annual temperature cycle are the optimal temperatures for  growth used in the bioenergetics simulations.
Under the behavioral thermoregulation assumption, the fish follow these lines as soon as the near surface waters
warm above their preferred temperature. The increased width of the thermoregulation lines for the OSU, GISS,
and GFDL scenarios over those for the BASE scenario means that the fish have a longer growing season wherein
they may, with behavioral thermoregulation, operate in optimal temperature conditions for growth.


                                                2-16

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Table 1. Regression Equations Relating Water Temperatures to Air Temperatures for Warm,
        Cool, and Cold Great Lakes Sites3
              Thunder Bay (Lake Superior)

              Winter: WT=0.418+0312WTL+0.036AT-0.023ATL  R2=038

              Warming:  WT=0.440+ 0.157WTL+0357AT+0.239ATL  R2-0.90

              Cooling: WT=2.534+0324WTL+0.164AT+0.174ATL  R2=0.91


              Evanston (Lake Michigan)

              Winter: WT=1.475+0.363WTL+0.106AT-0.027ATL  R2=0.57

              Warming:  WT=3347+0304WTL+0.217AT+0.208ATL  R2=0.90

              Cooling: WT=1.928+0377WTL+0382AT+0.067ATL  R2=0.%


              Put-In Bay (Lake Erie)

              Winter: WT=0.959+0.154WTL+0.140AT-0.053ATL  R2=0.49

              Warming:  WT=2.896+0.451WTL+0.524AT-0.009ATL  R2=0.%

              Cooling: WT=-7.642 + 1.022WTL+0386AT-0.173ATL  R2-0.94


aSymbols and units: WT =  current month's mean water temperature; WTL = last month's mean
 water temperature; AT = current month's mean air temperature; ATL = last month's mean air
 temperature (all temperatures °C); R2 = coefficient of determination.
                                          2-17

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Magnuson
                          SUMMARY
                                        MONTHLY  MEANS
     30-




     20-




     10-




      0-
                     COLD  AREA
                                                              COLD COLDWATER

                                                                   FISH
                                                                           GISS
           UJ

           GC
QC
UJ
Q.

UJ
H
QC
UJ


i
                30-
                20-
                10-
                     COOL AREA
                                I  T  I
                                                     COLO COLDWATER

                                                         FISH
                30-
                                                     COLD COLDWATER

                                                           FISH
                                                                           JAN
 Figure 8.  Water temperatures in cold, cool, and warm neritic areas of the Great Lakes (Thunder Bay, Lake

          Superior; southern Lake Michigan; Put-In Bay, Lake Erie) for the BASE climate and the OSU,

          GISS, and GFDL scenarios based on multiple regression relations between water intake and air

          temperatures. Optimum temperatures for three thermal guilds of fishes are shown.
                                         2-18

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                                                                                          Magnuson


     Water  temperatures in the base scenario for Evanston, Lake Michigan, are several degrees cooler in
 midsummer than those found for the central Lake Michigan site by McCormick (1988). This difference is likely
 owing to the fact that the water intake site at Evanston is in a nearshore site subject to rapid temperature drops
 caused by upweUing events, internal waves, etc. These factors may also cause our scenarios at Thunder Bay and
 Put-In Bay to underestimate actual epilimnetic temperatures.


 RESULTS

     The consequences of longer growing seasons under the 2xCO2 climate scenarios when prey consumption
 is allowed to increase with climate warming are readily apparent in Figure 9. Coldwater and coolwater fishes
 Qake trout and yellow perch) exhibit clear increases in annual growth over that in the base period The ratio of
 growth of cold coldwater and coolwater fishes in 2xCO2 conditions to that in the base scenario ranges from 1.2
 to 1.7 (mean 13).  Wannwater fishes (largemouth bass) cannot grow in any of the cold area temperature
 scenarios or in base conditions in the coolwater area  They experience minor growth in the 2xCO, scenarios
 in the cool area, and  large increases in growth in the warm area (2xCOu/lxCO2 ratios of 3.2, 43, and 5.7).
 Although in all cases the fishes under the GFDL scenarios grow faster than those under the GISS scenarios,
 differences are minor compared with differences between the 2xCO2 and baseline scenarios.

     For all  three thermal guilds of fishes,  the site with the largest simulated growth is  the warm area.  This
 may seem inconsistent with the intuitive notion that a coolwater fish should do best in a coolwater environment.
 However, Figure 8 shows that the water temperature scenarios for the warm area possess longer periods when
 preferred temperatures may be available for all three fish species. In the case of Lake Erie lake trout, suitable
 temperature and oxygen conditions are not found in the vicinity of the Put-in Bay site in summer.  Figure 8
 shows that Put-In Bay is potentially habitable for lake trout for 3-4 months during winter after climate warming.
 Historically,  lake trout in Lake Erie have used the eastern basin as a summer refuge and migrate into the central
 basin during the fall.  Also, other coldwater fishes (cisco and lake whitefish) use areas near Put-In Bay in fall
 for spawning, but are not present during summer.

     Results for prey consumption (Figure 10) closely parallel those for growth, because growth is an integrated
 form of consumption.  Changes in consumption between the BASE and 2xCO2 scenarios ranged from factors
 of 1.2 to 1.7 (mean 13) for cold coldwater and coolwater fish.  Wannwater fish consumption rates ranged from
 1.6 to 2.8 (mean 2.1) times higher than the base period  Similar to the  result seen for growth, Lake Erie fishes
 consumed more than their counterparts in the other lakes.

     The effect of the two assumptions about prey availability on fish growth following climate  warming is
 readily apparent in Figure 11.  When prey consumption is allowed to  increase with climate warming, growth
 increases. The  2xCO2/lxCO2  growth ratios are about  1.6 for coldwater fish and  1.4 for coolwater fish.
Wannwater  fish, which lost weight in the baseline scenario, are able to grow in the 2XCCL scenarios. When
consumption is held constant in the climate wanning scenarios,  growth for all  three  fish guilds declines.
Wannwater fish lose even more weight than in the base scenario. Coolwater and coldwater fishes also suffer
in the equal  consumption simulations (2xCO2/lxCO2 growth ratios of  0.7, 0.9). Differences in growth caused
by the two assumptions concerning prey consumption are much larger  than the differences in growth between
climate model scenarios. This implies that fish growth is more sensitive to changes in food web structure (Figure
12) than to differences in the temperature scenarios.
                                               2-19

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Magnuson
                   COLD
                   AREA
COOL
AREA
WARM
 AREA
                  400
           CC
           <
           LJJ
                        COLDWATER
                        FISH
           CO
           
           LL
           o
           I
           I-

           o
           DC
               COOLWATER
               FISH
         200
           z
           <
         150


         100


           50
               WARMWATER
               FISH
                        B   0  G  G
                        A   S   I   F
                        S   U  S  D
                        E       S  L
                                    B   O   G   G
                                    A   S   I    F
                                    S   U   S   D
                                    E       S   L
                 B  0  G  G
                 A  S   I   F
                 S  U  S  D
                 E      S  L
 Figure 9.
Annual growth (tlSD) of cold, cool, and warmwater fishes (lake trout, yellow perch, and largemouth
bass) in cold, cool, and warm neritic areas of the Great Lakes (Thunder Bay, Lake Superior; southern
Lake Michigan; Put-In Bay,  Lake Erie) for the BASE climate and the OSU, GISS, and GFDL
climate change scenarios. These simulations assume prey consumption rises with climate wanning.
                                       2-20

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                         COLD
                         AREA
COOL
AREA
WARM
AREA
                                                                            Magnuson
                     COLDWATER
                     FISH
                    WARMWATER
Figure 10.  Annual prey consumption (±1SD) by cold, cool, and warmwater fishes (lake trout, yellow perch, and
         largemouth bass).in cold, cool, and warm neritic areas of the Great Lakes (Thunder Bay, Lake
         Superior; southern Lake Michigan; Put-In Bay, Lake Erie) for the BASE climate and the OSU,
         GISS, and GFDL climate wanning scenarios.
                                        2-21

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        400
Magnuson
     300
LJLJ   200

S  1°°
GO      0
       50
       40
       30
       20
       10
£      °
O
CC
O
   cc
   <
   LU
   LL
   O
                                         COLDWATER  FISH
                                        COOLWATER  FISH
                                        WARMWATER FISH
               GISS     GFDL  ,  BASE  ,
                                         GISS     GFDL
               CONSUMPTION
                 INCREASES
                                         CONSUMPTION
                                           CONSTANT
Figure 11. Annual growth of cold, cool, and warmwater fishes (lake trout, yellow perch, and largemouth bass)
       in southern Lake Michigan with prey consumption allowed to increase or stay constant as climate
       warms. Lake Michigan thermal scenario provided by McCormick.
                               2-22

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                                                              Magnuson
               LAKE TROUT
      PACIFIC SALMON
         ISCULPINS
               J.
           [ALE WIFE]
          BENTHOS
           A
ZOOPLANKTON
   /\
              PRIMARY PRODUCERS AND
              DECOMPOSERS
Figure 12. Idealized Great Lakes food web diagram. Question marks and varied widths of energy transfer
       arrows highlight the uncertainties about whether production at lower trophic levels will be sufficient
       to meet the predatory demand of higher levels.
                                2-23

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Magnuson

                                           CHAPTER 4

                                    POPULATION RESPONSE


    Smallmouth bass, a wannwater fish, was used to demonstrate population responses to global climate
wanning.  These animals live in the shallowest, most protected areas of the Great Lakes and are, therefore,
likely to show the effects of climate wanning earlier than many other species. However, since the Great Lakes
watershed is near the northern limit of the natural range of the species, these effects will almost certainly be
positive.  This must be kept in mind while reviewing our results.  Our choice of smallmouth bass was dictated
solely by the fact that reliable recruitment and growth models for this  species were  already in existence and
could be used immediately to produce credible forecasts of the effects of climate change on year class strength
and population abundance.

    The reproductive rate of any fish population is strongly dependent on the average  survival of the young and
the age at which adults reach sexual maturity.  For smallmouth bass, both survival  and age of maturity  are
strongly dependent on temperature.  Overwinter survival of young bass depends on their size at the end of their
first summer, and this in turn depends on the length of the growing season (Shuter et aL, 1980).  Similarly,  age
of first maturity varies directly with growth rate and, therefore, with length of the growing season (e.g., Wrenn,
1980,1984). Thus, population responses of these fishes should be affected by a change in climate.


METHODS

    Three sites (Figure 1) were chosen for the estimated population response of smallmouth bass:  warm  - a
shallow bay near Long Point on Lake Erie; cool — a shallow bay, Baie du  Dore, Lake  Huron; and cold  ~ a
shallow bay near Thunder Bay on Lake Superior. Smallmouth bass populations presently exist at both the Lake
Erie and Lake Huron sites.  Data from these  populations were used extensively in developing forecasts of the
potential effects of climate change.  The Lake Superior site is an imaginary bay physically similar to Baie du
Dore.

    The smallmouth bass recruitment model of Shuter et al. (1980) was used to estimate winter mortality and
the length of the growing season for young-of-the-year fish. Growth for  all age groups was estimated using the
Kitchell bioenergetics model (Kitchell et al., 1977; Hewett and Johnson,  1987).  Age of first maturity (Tin) was
defined as the number of years of growth  required to exceed a total weight of 260 g.  This  weight is
representative of the size at first maturity among the males of both the Lake Erie and Lake Huron populations.
It  is also similar  to the values reported for other  smallmouth bass populations (Carlander, 1977).   The food
availability estimates used in the bioenergetics  model were derived from the actual growth rates observed in the
Lake Erie and Lake Huron populations in recent years. These estimates indicate that fish in these populations
are currently feeding at levels well below satiation. We assumed that these food availability parameters were
not affected by climate change.

    For each site, the combined growth recruitment model was run using current water temperatures and using
estimated water temperatures derived from GISS 2xCO, air temperatures. The population effects produced by
this change in climate were evaluated in terms of the following ratios:  (average YCS GISS 2xCO,/ average
YCS current) and (average adult abundance  GISS 2xCO2/ average  adult abundance current). These ratios
were calculated using equations derived from  the simple age-structured population model described  below.

    The model we use is constructed directly from the cohort and recruitment  models described  in Ricker
(1975).  Assume all population members aged 1 and over experience a constant instantaneous natural mortality
rate equal to M and that all fish aged Tin and over experience a constant instantaneous fishing mortality rate
equal to F. Assume a relation between YCS  at age 1 (Nj) and the number of breeding adults (S) which has
the following form:
                                               2-24

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                                                                                        Magnuson
                                                (1)
                             where  S=  £Nf   and  6<1.

                                       i=Tm

Preliminary analyses of the Lake Opebngo smalhnouth bass data base show that such an Nl-S relation does
hold for that population. At steady state, the following equations hold:
                                                (2)
                                                (3)
Now, by substituting equations 2 and 3 into 1, we obtain:
                    N1=a
                          r    -M(im-iy
                           N-)G   V     '
                          \
                                1-e
                                   -z
which simplifies to:
                             /  -M
                    N1=a
                          1-3
                                  1e
                                    -z
                                                (4)
Now, assume that a in equation 4 is proportional to winter survival (w). Further, assume that Z, M and ft are
constant. Under these conditions, equation 4 requires that:

                                          1
    Therefore, any change in first year winter survival (w) or age of first maturity (Tm) can be evaluated in
terms of the resulting changes in both steady-state YCS (using equation 5) and S (by substituting 5 into 3 and
then substituting the resultant equation for N-  into 2). Equation 5 shows that the only other parameters that
affect the results are M and ft.  The level of fishing mortality is not important.  All climate change scenarios
that we explore here are based on values for M and ft (03 and 0.65, respectively) derived from preliminary
analyses of the long term behavior of the Lake Opeongo smallmouth bass population.
                                               2-25

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Magnuson

SCENARIOS

    For both the Lake Erie (Ontario Ministry of Natural Resources, unpublished data) and Lake Huron (Shuter
et al., 1985) sites, littoral temperature data were available from several locations within the overall habitat of
resident populations of smallmouth bass.   These data were  used  to construct  annual temperature curves
representative of the average temperatures experienced by members of these populations over the last 10-20
years.  A current temperature curve for the Lake Superior site was derived using the Baie du Dore water
temperature curve, Baie du Dore and Thunder Bay air temperatures, and the equations of Shuter et al. (1983)
to translate observed air temperature  differences into water temperature differences.  The estimated GISS
2xCO2 effect on each water temperature annual cycle was determined by first determining the GISS-predicted
change in annual average air temperature and then using that change with the Shuter et al. (1983) equations to
estimate the resultant change in the annual water temperature  cycle.


RESULTS

    The smallmouth bass populations in shallow protected bay habitats experience an increase in reproductive
success and fishable population size in cold, cool, and warm areas with a GISS 2xCO~ climate wanning (Figure
13). As expected, the relative advantage is greater in the cold areas.  The imaginary Lake Superior population
probably is not viable prior to climate change but after climate change,  it appears quite viable.  The population
in the warm area near Long Point, Lake Erie is already doing well and would neither gain nor lose from climate
warming. The cool area near Baie du Dore, Lake Huron exhibits a positive response, with year class strength
increasing by a factor of 9 and the fishable population by a factor of 23, both of which are intermediate between
the response at the warm and cold sites.

    The increases in reproductive success and population size occur in the  model simulations because the
length of the growing season increases,  overwinter survival of young-of-the-year smallmouth bass increases, and
the fish spawn at earlier ages (Figure 13).  The growing season is simulated to increase with climate change by
a factor of approximately 13, for both young-of-the-year and adults.  Overwinter survival of young of the year
fish increases from 10% to 88% in cold areas and nears 100% in cool and warm areas.  Age of  maturity
decreases by 1 to 3 years, so generation time decreases.

    Our forecasts for smallmouth bass were uniformly positive. The reader must keep in mind that similar
detailed modeling for cool or coldwater species could well produce very negative forecasts. It was our intention
that the smallmouth bass work fulfill two primary functions:

    (1) provide a credible forecast, given our present state of knowledge, of the likely effects of climate change
on a typical warmwater species currently resident in the Great  Lakes drainage basin, and

    (2) provide an example of the kinds of detailed models necessary to forecast the effects of climate change
at the population level.   Our smallmouth bass forecasts are in no way representative of the response expected
from a "typical" species currently resident in the Great Lakes area.
                                                2-26

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                                                                  Magnuson
                   SMALLMOUTH BASS  POPULATIONS
             YEAR CLASS STRENGTH
                            FISHABLE POPULATION
            COLO
            AREA
    COOL
    AREA
       WARM
       AREA
COLD
AREA
COOL
AREA
WARM
AREA
    300
          YOUNG OF YEAR
          GROWING SEASON
          COLO
          AREA
COOL
AREA
WARM
AREA
                                        ADULT
                                    GROWING SEASON
                      WARM
                      AREA
     150
           YOUNG OF YEAR
           WINTER SURVIVAL
          COLO   COOL  WARM
          AREA   AREA  AREA
                                    AGE AT MATURITY
                                   COLD   COOL  WARM
                                   AREA   AREA  AREA
Figure 13. Changes in smallmouth bass populations with GISS climate wanning in shallow protected littoral
        areas of Lake Superior (cold), Lake Huron (cool), and Lake Erie (warm).
                                   2-27

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Magnuson

                                           CHAPTERS

                                     ECOSYSTEM RESPONSE


    Richard A. Vollenweider (personal communication) reminded us of works  by F. Ruttner (1931), A.
Thienemann (1930), and other early German limnologists comparing the tropical inland waters of Java, Sumatra,
and Bali and the temperate lake systems of Northern Europe. Lakes which appeared similar in all aspects except
temperature differed with respect to relative trophic status, with the tropical lakes being more productive and
appearing to be more eutrophic than the temperate lakes.

    The rate of photosynthetic production in well-lit surface waters is dependent on light intensity, temperature,
and abundance of nutrient substances and phytoplanktonic organisms. The rate of decomposition in poorly lit
deep waters depends on availability of oxygen, temperature, and abundance of decomposer organisms.  All of
these processes involve  chemical reactions.

    According to van't HofFs Law, the rate of chemical reactions is doubled or tripled by a 10°C increase in
temperature. Physiologists have  used  this law,  which they call the  Q1Q rule, to  compare  the  effects of
temperature on physiological rates (Hoar, 1975).  Ruttner (1931) invoked an ecological Q10 rule to explain the
differences in limnological features between tropical and temperate lakes as noted above. Regier and Henderson
(1973) also referred to  an ecological  Q1Q concept with respect to fisheries production in waters of different
latitudes.  Recent comparative fisheries-related studies of Pauly (1980) and Schlesinger and Regier (1982)
implicitly used the Q1Q  rule.  George Woodwell (1983) used an ecological Q1Q approach to derive  some first
approximations as to the likely impacts of climate warming on terrestrial ecosystems.

    The Arrhenius relationship has been used to describe the effect of temperature change on the "specific rate
constant," k, in chemical reactions.  For many physiological and ecological applications a first-order chemical
reaction has often been taken as an analogue of some living process. Here
                 k
           dt x " k-

where x is the mass or some surrogate measure of living substance.  If a particular ecological process can be
approximated by a first-order expression, then the relevant Arrhenius expression is

           k=Ae'E/RT,

where T is absolute temperature, and A^E, and R are constants. The Arrhenius expression is soundly based in
theory and in practice and, therefore, preferable to van't Hoffs empiric expression (Regier et aL, 1988). Regier
et al. (1988) provide a more detailed rationale for the use of the Arrhenius relationship.

     The Arrhenius relationship provides a simple model from which first approximations  of some potential
changes in lake productivity in response to climate warming can be derived.


METHODS

    Data from the literature at the ecosystem level of organization were fined to the Arrhenius relationship by
regression methods, and community Q10 values were then estimated for phytoplankton production, zooplankton
biomass, and fishery yields.  No data were located that treated an ecosystem as an integrated whole, but data
sets were found which  related three major ecosystem  components - algae, zooplankton, and  fish -  to
temperature.  The Arrhenius expression summarized the information available in the form:
                                                2-28

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                                                                                          Magnuson
                 =a(=?VbV+c
where: R is an ecosystem response variable, such as production;

     T is absolute temperature,°K;
     V is another environmental variaole, such as lake depth or fertility; and
      a, b, and c are estimated regression coefficients.

Here the Q10 measures the proportional change in R with a temperature increase of 10°K.

      The multiple regressions relating primary production, zooplankton biomass, and fish yields to temperature
(T) and Ryder's morphoedaphic index as V in the above equation are  either taken from the literature or
computed by us from literature values (Table 2).

      To estimate the relation for primary production and temperature in Table 2, primary production and MEI
data compiled by Oglesby (1977) and mean annual air temperature data from Wernstedt (1972) were used in
a stepwise multiple regression (SAS Version  6.02).  Temperature explained 51% of the variation in primary
production and MEI explained only 7%, and the model appears to have a good enough fit to estimate Q1Q.

      To estimate the relation for zooplankton biomass and temperature in Table 2,  zooplankton biomass,
mean epilimnetic temperature,  and MEI from Patalas (1975) were used in stepwise multiple regression.
Temperature explained 89% of the variation in zooplankton biomass while MEI only explained 3%. The overall
model accounted for 92% of the variation in zooplankton biomass among lakes.

      The relation for maximum sustained fish yields in Table 2 is a reparameterization  of the relationship
reported by Schlesinger and Regier (1982).

     In addition to the three relationships in Table 2, we also estimated two for marine taxa,  one on shrimp
with data from Turner (1977), and  another for fish stocks with data  from Pauly (1980).  The temperature
coefficients, not  shown here, were roughly comparable to those shown in Table 2 which gives us further
confidence in our estimates of community Q1Q values.


RESULTS

     Community Q1Q values ranged from 2.6 for  fishery yields to 3.7 for  primary production, and 4.0 for
zooplankton biomass (Figure 14). When these different processes were fitted by the Arrhenius expression, they
provided similar estimates of the temperature coefficients.  We do not understand enough about the relevant
theory to be able to explain this observation, but from a practical viewpoint we infer that the relevant community
rate processes respond, qualitatively and quantitatively, in ways similar to temperature. Temperature effects on
ecosystem processes or properties appear greatest on the primary producers and progressively less as one moves
up the food web.
                                               2-29

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Table 2.  Multiple Regressions Relating Community-Level Parameters to Temperature and R. A. Ryder's
         Morphoedaptic Index, Using the Arrhenius Relationship, and the Resulting Community Q1Q
         Values for Phytoplankton Production, Zooplankton Biomass and Freshwater Fish Maximum
         Sustained Yields*


                Phytoplankton Productionb

                (1) logel°=44.40-113(V?(l/T)+ 0.2908 lo^MEI^

                       N=32; R2=0.575;  r12=0.5lO; r2=0.065;
                        QlO=3'7

                Zooplankton Biomassb

                (2) logeZB=40.91-12074(l/EPTEMP)+0.4080

                       N=14;R2=0.918;  r2=0.887; r2=0.031;
                        Qio=4.o

                Freshwater Fish Maximum Sustained Yield0

                (3) logeMSY=29.74-8115.4(l/T)+0.4895 tog^MEI^

                       N=43; R2=0.826;  r2=0.737; r2=0.089
aSymbols and units: 1° * primary production (g C/mz.yr); ZB = Zooplankton biomass during
  the summer in wet weight (mg/L); MSY =  maximum aggregate sustainable yield (kg/ha.yr);
  T  = mean annual air  temperature (°K); EPTEMP = mean epilimnion temperature (°K);
  morphoedaphic index (Ryder, 1982) for maximum mean depth of 25m; Qin.3 proportional change
  in ecosystem variable  with a 10°K temperature change; N = sample size; K2  = coefficient
  of determination; r1 , r2  = fractions of variability explained by the first and second
  variables, respectively.
 Derived in this study, see text.
°From Schlesinger and Regier (1982).
                                                2-30

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                                                                  Magnuson
Q
10
             PHYTOPLANKTON  ZOOPLANKTON
               PRODUCTION        BIOMASS
                                                       FISH
                                                       YIELD
 Figure 14. Increases in primary production, zooplankton biomass, and maximum sustained fishery yields
         expected from estimated community Q10 values.
                                   2-31

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Magnuson

                                           CHAPTER 6

                                 INTERPRETATION OF RESULTS


GENERAL

    The initial interpretation of our results suggests that fishes in the Laurentian Great Lakes will benefit from
global climate warming as determined from the OSU, GISS, and GFDL 2xCO2 climate scenarios.  Based on
our model simulations, the size of thermal habitat within their optimum temperatures will generally increase in
basins with oxygenated hypolimnia (Figures 2, 3, and 4X their growth will increase provided prey consumption
increases with climate warming (Figures 9 and 10),  population size of an example wannwater fish will increase
(Figure 13), and the biological productivity will increase at all levels from from primary production, to secondary
production, to fish yields (Figure 14). These results were obtained not only for wannwater fishes like smallmouth
and largemouth bass but also for coolwater fishes like yellow perch and walleye, and even for coldwater fishes
like trout and salmon. Nor were the increases restricted to the northern cold waters of Lake Superior; similar
increases appeared in cool and warm  regions including neritic areas of Lakes Michigan, Huron, and Erie.

    Our simulations also revealed some potential  negative effects of climate warming.  During summer,  the
simulated thermal habitat size for a number of coldwater and coolwater fishes decreased (Figure 5) suggesting
that during summer,  species interactions such as competition and predation could intensify in the optimum
thermal habitats.  This restriction in  summer habitat could be a limiting factor  for some species as it is  for
certain species in southern U.S. reservoirs (Coutant, 1987b).  Simulations of the thermal habitat in brook trout
streams during summer showed that suitable temperatures decreased in a similar manner (Figure 7) and could
have similar consequences.  Increases in fish growth (Figure 9) and its corollary, increased prey consumption
(Figure 10), would intensify species interactions and put an increased demand on prey production. In some of
the lakes, forage for the salmonid fishery is believed to be at a premium (Stewart et al., 1981), and  the increase
in demand for forage could result in an overexploitation of prey resources by the salmonids.  If additional prey
are not available, then the increased temperatures will result in a decline in growth (Figure 11).  We have no
ecosystem level models for simulating the nature of such surprises.

    There are other unknowns for which models or model simulations are unavailable at present. (1) The
increase in wannwater habitats may encourage colonization by wannwater exotics (Mandrak, 1988), which could
destabilize fish communities in warm  areas (Magnuson, 1976). (2) The reduction of ice cover in bays could
reduce the reproductive success of lake whitefish (Taylor et  al., in press; Freeberg, 1985).  (3)  If the global
climate warming reduced vertical mixing of Lakes Michigan, Huron, and Ontario, and the deeper basin of Lake
Erie for a long period of years, the oxygen in these deep lakes could become limiting to the detriment of  the
coldwater fishes. Our simulations of increased growth assume that the fishes can behaviorally thennoregulate;
that is, when inshore or surface waters become too  warm, the fish can  find suitable thermal habitat offshore or
downslope or to the north.  This  means that the water in and below the thermocline  and below must contain
sufficient oxygen to support fish life.  Presently oxygen is depleted annually in the hypolimnia of the shallower
basins of Lake Erie,  and these waters cannot support coldwater fishes.  The increase in primary production
suggested in our simulations could exacerbate this process of developing  anoxia in  the  deep coldwaters by
increasing the annual hypolimnetic oxygen deficit.  (4) Our analyses have not included any effects on fishes
from loss of wetlands caused by water level changes. (5) Our analyses have not included any simulation of the
influence of climate wanning on the availability and accumulation of microcontaminants in Great Lakes fishes.
(6) Our analyses have not included effects of stream wanning on the reproductive success of sea lamprey.  (7)
Our analyses have not included predictions about  the smaller inland  lakes, which are sure to be affected by
climate warming.  Our general conclusion about the direct influence of temperature changes on  fishes in  the
waters of the Great Lakes, as induced by global climate change, is that it will be positive.  A number of the
indirect effects deserve research level  attention.
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                                                                                          Magnuson

 SPECIFIC INTERPRETATIONS

 Thermal Niche

     Thermal habitats for all thermal groups of fishes (coldwater, coolwater, and wannwater) increase from the
 BASE climate with the OSU, GISS, and GFDL 2xCO2 climate  wanning scenarios when the entire year is
 considered. In some cases, such as  for coldwater fishes, the increase is dramatic, averaging a factor greater
 than 2 or a doubling of thermal habitat. The increase results because warmer waters are found in spring, fall,
 or winter and at deeper depths than for BASE climate. However, during summer, the isotherms are compressed
 and the size of the thermal habitat (±2°C) in summer decreases under both scenarios by 2 to 47% depending
 on the species.  Inter- and intraspecific competition and predation may be intensified during these  months.
 Fishes may as a consequence occupy a broader range of temperatures than optimum (Rudstam and Magnuson,
 1985) and grow less rapidly. The interaction of warmer epilimnetic temperatures coupled with increased hypoxia
 in the hypolimnia of some Great Lakes basins and bays may lead to a "summer squeeze" on the  available habitat
 space for some species (Colby and Brooke, 1969; Coutant, 1985; Coutant, 1987a,b).

     The increased surface water temperature in open water opens up or expands thermal habitat for wannwater
 fishes in Lake Michigan and in Lake Erie. These conditions would provide more suitable habitat for white bass
 and white perch, two species which are currently expanding their ranges in Lakes Erie and Ontario.  Interest in
 stocking Lake Michigan with striped bass, which  has been introduced  to various fresh waters in the southern
 United States, may increase.

     The expansion of optimal habitat of all species in the spring and fall in both climate scenarios suggests that
 these "shoulder" seasons, if important to yield, may evolve as major contributors to fish yield and production if
 climate warms. This appears to be the case with another coldwater fish,  the burbot, in Lake Opeongo (Hackney,
 1973).  Of course, this depends on similar changes in other factors that control growth and yield, such as food
 supply and  interspecific competition.

 Growth and Food Consumption

     The bioenergetics simulations indicate that individual fish will grow faster under 2xCOU climate conditions,
 if sufficient and appropriate prey  are available. If prey consumption does not increase with climate warming,
 fish growth will decline.  If the forage base were  to decrease or remain the same following climate warming,
 predator growth  rates would be expected to decrease because of an inability to compensate for the increased
 metabolic costs of operating at higher temperatures. However, if prey availability increased so that fishes could
 function at  or near their optimal consumption rates, increases in growth rates would be even higher than those
 reported above.

     The Qj0 estimates of potential changes in zooplankton biomass and fish production indicate that at least
 part of the  forage base may increase with climate  warming. This result provides evidence that the assumption
 of a constant P-value may be reasonable.  However, because of the uncertainties in estimating prey availability,
 the effects of climate change on growth may be over- or underestimated.  The dynamics of the Great Lakes
 food webs (Figure 12) subjected to climate wanning must be considered  in detail in order to answer the question
 of whether primary and secondary production will  increase enough to meet the increased predatory demands of
fishes.

    Although  the results  for consumption are quite  similar to those for growth, total consumption is an
important parameter in its own right because it is a measure of the trophic interaction among fishes and between
fishes and their prey. Increased consumption implies an amplification of competition for food if the forage base
 does not grow as fast as the predatory demand on it.  Consumption may also be  thought of as  a measure of
 the potential predation pressure on the forage base.  Predation pressure increases significantly for the 2xCO2
water temperature scenarios (Figure  10).
                                               2-33

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Magnuson

Population Response

     Smallmouth bass populations in shallow protected bays in warm areas of the Great Lakes would not be
harmed and would continue to provide recreational fishing. The fishing would improve in cool areas, and new
fisheries would develop in cold areas.

     Other warmwater species and warmwater exotics might experience similar changes and opportunities from
range expansion.

Ecosystem  Response

     We infer from our statistical studies that the early observations by Ruttner (1931) and his colleagues were
generally reliable.  The rates of ecosystemic processes respond to temperature changes in an exponential way
broadly consistent with the Q10 rule. Most expressions in Table 2 from which Q10 values were estimated also
include data from the Great Lakes; hence, these broad generalization do apply specifically to the likely effects
of climate warming on ecosystemic processes in the Great Lakes.

     We expect that climate wanning would cause an overall increase in primary production (phytoplankton),
secondary production (zooplankton), and maximum sustained yields of fisheries across the Great Lakes (Figure
14).  Lower trophic levels would experience a greater increase  than  higher trophic levels.  The increased
ecosystemic productivity may be associated with shifts in community structure favoring organisms having higher
temperature preferences. The increased biological production will also be reflected in a long-term increase in
biogenic materials  in the lake basins, which will contribute to increased oxygen demand in the hypolimnia.  In
basins with small hypolimnia or with permanent thermal stratification, this increased oxygen demand would
contribute to hypoxia, with negative consequences  for fishes.
                                                2-34

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                                                                                          Magnuson


                                           CHAPTER?

                                   IMPLICATION OF RESULTS


    The five Great Lakes are inhabited by cold, cool, and warm water fishes and provide a range of thermal
habitats characteristic of a large, deep, cold lake (Lake Superior) to  a large, shallow, and warmer lake with
anoxic hypolimnia in some basins (Lake Erie). The lakes provide important recreational and food fisheries to
heavily populated regions of Canada and the United States.  For most regions, the climate warming would
increase the amount of optimum thermal habitat not only for warm water fishes, but also for cool and cold
water fishes.  Consequently, the productivity and potential  yields from these thermal guilds is predicted to
increase in general if climate warming occurs.  The productivity of zooplankton and phytoplankton is also
expected to increase. New warmwater fish species are expected to invade, especially into the littoral zones of
the more southern habitats.  Even though productivity is expected to increase in general, surprises involving
water quality interactions and loss of important stocks are expected from invasion of new species as well as
from intensified species interactions in the fish communities.
                                               2-35

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Magnuson


                                            CHAPTERS

                                      POLICY IMPLICATIONS


1.  Increased fish production should provide greater recreational, commercial, and artisanal fishery opportunities.

2.  Increased value of the Great Lakes in a warmer and dryer time should attract more people to the basin and
increase the consumptive use of the Great Lakes' water and living resources.

3.  Increased predation pressure on the forage base should require management agencies to pay even closer
attention to the management of food webs and prey-predator interactions.

4.  The potential for the further invasion of warmwater exotics and expansion of their thermal habitat should
require even greater attention to the effects of exotics on the stability and productivity of Great Lakes fisheries.

5.  Increasing value  of the fishery, should continue to make the reduction in toxins that bioaccumulate a high
priority for the Great Lakes.

6.  Heightened eutrophication effects due to greater primary production and greater isolation of the hypolimnia
should require continued and increased attention to nutrient reductions in some lakes.

7.  Water for the Great Lakes themselves should be more of an issue for fishery management agencies.

8.   Coldwater fishes:  thermal habitat for lake trout should  increase more  than for Pacific salmon;  thus
rehabilitation of lake trout populations should remain an important policy issue.

9.  Coolwater fishes: production of coolwater fisheries such as perch and walleye should increase and should be
expected to move northward and offshore.

10. Warmwater fisheries:  production of warmwater fishes should  increase in cool areas and develop in cold
areas. Managing the species structure of the inshore habitat should be an important policy issue.
                                                2-36

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                                                                                     Magnuson

                           TERMS, ABBREVIATIONS, AND SYMBOLS
Climate Scenarios
BASE = Scenario using actual air temperatures from 1951-1980.
OSU  =  Scenario produced by the Oregon State University general circulation model.
GISS  =  Scenario produced by the Goddard Institute for Space Studies general circulation
  model.
GFDL = Scenario produced by the Geophysical Fluid Dynamics Laboratory general
  circulation model.

Areas

Cold =  Northern Lake Superior
Cool -  Southern Lake Michigan, Baie du Dore (Lake Huron)
Warm = Lake Erie

Fish Thermal Guilds and Temperature Preferences

Coldwater = Lake whitefish, coho salmon (-15 C), lake trout (-10 C)
Coolwater - Yellow perch, walleye (-24 C)
Warmwater = Largemouth bass, smalhnouth bass, bluegill (28 C)

Thermal Niche Model

Depth day = The depth of the water column within the thermal niche summed over days
Volume  day * The volume of water within the thermal niche summed over days
THV  =  Summer thermal habitat volume in hm3.7 days or hm3.10 days (hm=hectometers)
THA  =  Summer thermal habitat lake bottom area in ha.7 days (ha=hectares)
SY =  Sustained yield of fishery in kg/yr

Brook Trout Stream  Model

FTw = Change in temperature of parcel of water
Tw a  initial temperature of parcel of water
A = Surface area of parcel of water
V = Volume of parcel of water
v = Volume of inflow (e.g. groundwater, tributary)
tv = Temperature of inflow
S = Change in energy stored in parcel of water

Bioenergetics Model (all units g/dav. e.g.)

G = Fish growth rate
C = Rate of food consumption
F - Egestion
U = Excretion
P-value  = Proportion of maximum consumption rate

Smallmputh Bass Model

M = Natural mortality rate
Tm = Age of first maturity


                                             2-37

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Magnuson

                     TERMS, ABBREVIATIONS, AND SYMBOLS (continued)

F = Fishing mortality rate
YCS = Year class strength
Nl =  Year class strength at year 1
NTm  = Year class strength at age of first maturity
S = Number of breeding adults
a,B = Empirically determined constants in the equation relating N, and S
w = First year winter survival

O10 Model

Q10 = Proportional change in R with a 10°K temperature increase
R = Ecosystem response variable
V = Environmental variable
a,b,c = Estimated regression coefficients
MSY  = Maximum Sustainable  Yield
T = Mean annual air temperature
EPTEMP  = Mean epilimnion temperature
MEI25 * Morphedaphic index for maximum mean depth of 25m
lo = Primary production in g C/m2/yr
                COMMON AND SCIENTIFIC NAMES OF FISHES CITED IN TEXT

Common Name                                  Scientific Name

Lake trout                                        Salvelinus namaycush
Lake whitefish                                    Coregonus clupeaformis
Coho salmon                                     Oncorhyncus kisutch
Yellow perch                                     Perca flavescens
Walleye                                          Stizostedion vitreum vitreum
Northern pike                                     Esox lucius
Largemouth bass                                  Micropterus salmoides
Bluegill                                          Lepomis macrocbirus
Smallmouth bass                                  Micropterus dolomieui
Sea lamprey                                      Petromyzon marinus
Cisco (lake herring)                                Coregonus artedii
Chubs                                            various Coregonus  sp.
Burbot                                           Lota lota
Sculpins                                          family Cottidae
Sauger                                           Stizostedion canadense
Muskellunge                                      Esox masquinongy
Lake sturgeon                                     Acipenser fulvescens
Bullheads                                        various Ictalurus sp.
Channel catfish                                   Ictalurus punctatus
White bass                                       Morone chrysops
Sunfish                                           family Centrarchidae
Crappies                                         family Centrarchidae
Minnows                                         family Cyprinidae
Brook trout                                      Salvelinus fontinalis
White perch                                      Morone americana
Striped bass                                      Morone saxatalis


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                                                                                           Magnuson


                                           REFERENCES


 Barton, D. R., W. D. Taylor, and R. M. Biette. 1985. Dimensions of riparian buffer strips required to maintain
 trout habitat in southern Ontario streams. N. Am. J. Fish. Mgmt. 5: 364-378.

 Blumberg, A.F.  1988.  The effects of climate warming on Lake Erie water quality.  Draft report to USEPA
 Office of Policy Analysis.

 Bowlby, J. N.  and J. D. Roff.  1986. Trout  biomass and habitat relationships in southern Ontario streams.
 Trans. Amer. Fish. Soc. 115: 503-514.

 Carlander, K. D.  1977.  Handbook of Freshwater Biology, Volume 2. Iowa State University Press, Ames, IA.

 Christie, G.C. and HA. Regier. 1988. Measures of optimal thermal habitat and their relationship to yields for
 four commercial fish species.  Can. J. Fish. Aquat. Sci. 45: 301-314.

 Colby, P. J. and L. T. Brooke. 1969.  Cisco (Coregonus artedii) mortalities in a southern Michigan lake, July
 1968. Limnol. Oceanogr.  14:958-960.

 Coutant, C. C.   1985.  Striped  bass, temperature, and  dissolved  oxygen:   a speculative  hypothesis for
 environmental risk.  Trans. Amer. Fish. Soc  114: 31-61.

 Coutant, C. C.  1987a.  Thermal preference:  when does an asset become a liability?  Environ. BioL fish.
 18:161-171

 Coutant, C. C. 1987b.  Poor reproductive success of striped bass from a reservoir with reduced summer habitat.
 Trans. Amer. Fish. Soc 116:154-160.

 Crowder,  L.B. and J. J. Magnuson.  1983.   Cost  benefit analysis  of temperature and food resource use: a
 synthesis with examples from fishes. Pp. 189-221 in W. P. Aspey and S. I. Lustick (eds.X Behavioral Energetics.
 Ohio State University Press, Columbus.

 Delay, W. H. and J. Seaders.  1966. Predicting temperatures in rivers and reservoirs. J. Sanit. Eng. Div., Proc
 Am. Soc Civil Eng. 92: 115-134.

 Freeberg,  M. H.  1985.  Early life history factors influencing lake  whitefish (Coregonus clupeaformis) year class
 strength in Grand Traverse Bay, Lake Michigan. MS Thesis, Michigan State University, East Tanking, MI. 89
 pp.

 Fry, F. E. J. 1971. The effect of environmental factors on the  physiology of fish.  Chapter 1 in W. S. Hoar and
 D. J. Randall (eds). Fish Physiology Vol VI Environmental Relations and Behavior. Academic Press, NY. 559
pp.

Hackney, PA. 1973.  Ecology of the burbot (Lota lota) with special reference to its role in the Lake Opeongo
fish community.  Pn.D.  Dissertation.  Department of Zoology, University of Toronto.  Toronto, Ontario. 209
pp.

Hawkes, HA.  1975.  River zonation and classification. In River Ecology, ed. B. A. Whitton, pp. 312-374.
University of California Press, Berkeley and Los Angeles.
                                                2-39

-------
Magnuson

Hewett, S. W. and B. L. Johnson. 1987. A generalized bioenergetics model of fish growth for microcomputers.
University of Wisconsin Sea Grant  Institute Technical Report No. WIS-SG-87-245.

Hoar, W. S. 1975. General and Comparative Physiology.  Prentice Hall, Inc. NJ. 848 pp.

Hokanson, K.  E. F.  1977.   Temperature  requirements of some percids  and adaptations to the seasonal
temperature cycle. J. Fish. Res. Board Can. 34:1524-1550.

Kitchell, J. F. and J. E. Breck.  1980. Bioenergetics model and foraging hypothesis for sea lamprey (Petromyzon
marinus).  Can. J. Fish. Aquat. Sci. 37: 2159-2168.

Kitchell, J.F., D. J. Stewart, and D. Weininger.  1977. Applications of a bioenergetics model to yellow perch
(Perca flavescens) and walleye (Stizostedion vitreum vitreum). J. Fish Res. Board Can. 34: 1922-1935.

Lyons, J. and J. J. Magnuson.   1987. Effects of walleye predation on the population dynamics of small littoral
zone fishes in a northern Wisconsin lake. Trans. Amer. Fish Soc. 116:  29-39.

Magnuson, J.  1976. Managing with exotics a game of chance.  Trans. Am.  Fish. Soc. 105: 1-9.

Magnuson,  J. J., L. B. Crowder, and  PA. Medvick.  1979.  Temperature as an ecological resource.  Amer.
Zool. 19: 331-343.

Mandrak, N. E.  1988.  Potential invasion of the Great Lakes by fish species associated with climate warming.
Env. Biol. Fish.  In Press.

McCormick, M. J. 1988. Potential climatic changes to the Lake Michigan thermal structure.  Draft report to
USEPA Office of Policy Analysis.

Meisner, J. D. J. S. Rosenfeld, and H. A. Regier.  1988.  The role of groundwater on the impact of climate
warming on stream salmonines.  Fisheries 13(3).  In press.

Meisner, J.D., J. L. Goodier, H. A. Regier, B. J. Shuter, and W. J. Christie. 1987. An assessment of the effects
of climate warming on Great Lakes basin fishes. J. Great Lakes Res. 13: 340-352.

Neill, W. H. and J. J. Magnuson.  1974. Distributional ecology and behavioral thennoregulation of fishes in
relation to heated effluents from a power  plant at Lake Monona, Wisconsin. Trans. Am. Fish. Soc 103:663-710.

Oglesby, R. T. 1977. Phytoplankton summer standing crop and annual productivity as functions of phosphorus
loading and various physical factors. J. Fish. Res. Bd. Can. 34: 2255-2270.

Patalas, K.  1975. The crustacean plankton communities of fourteen North American great lakes.  Verb.
Internac. Verein. Limnol. 19: 504-511.

Pauly, D. 1980. On the interrelationships between natural mortality, growth parameters, and mean environmental
temperature in 175 fish stocks. J. Cons.  Int. Explor. Mer. 39:175-192.

Regier, H. A. and H. F. Henderson.  1973. Towards a broad ecological model of fish communities and fisheries.
Trans. Am. Fish. Soc. 102: 56-71

Regier, H. A.,  J. A. Holmes and D. Pauly.  1988.  The influence of temperature on ectothermic aquatic living
systems: some elementary and general comments. Submitted to Trans. Am. Fish. Soc.

Rice, J. A., J. E. Breck, S. M. Bartell and J. F.  KitchelL  1983.  Evaluating the constraints of temperature,
activity and consumption on growth of largemouth bass. Env. Biol. Fish. 9: 263-275.


                                                2-40

-------
                                                                                           Magnuson


 Rice, J. A., and P. A. Cochran. 1984. Independent evaluation of a bioenergetics model for largemouth bass.
 Ecology.  65:732-739.

 Ricker, W. 1934.  An Ecological classification of certain Ontario streams. Univ. Toronto Studies Biol. Series,
 No. 37, Publ. Ont. Fish. Res. Lab. 49: 7-114.

 Ricker, W. E. 1975. Computation and interpretation of biological statistics of fish populations. Fish. Res. Bd.
 Can. 191: 382 pp.

 Rudstam, L. E. and J. J. Magnuson.  1985. Predicting the vertical distribution of fish populations:  analysis of
 cisco, Coregonus artedii, and yellow perch, Perca flavescens. Can. J. Fish. Aquat. Sci. 42:  1178-1188.

 Ruttner, F. 1931.  Hydrographische und hydrochemische Beobachtungen auf Java, Sumatra und Vali.  Arch.
 Hydrobiol., Suppl.  8: 197-454.

 Ryder, R. A. 1982. The morphoedaphic index use, abuse,  and fundamental concepts.  Trans. Am. Fish. Soc.
 111:154-164.

 Schlesinger, D. A.  and H. A. Regier. 1982.  Climatic and morphoedaphic indices of fish yields from natural
 lakes.  Trans. Am. Fish. Soc. Ill:  141-150.

 Shuter, B. J., J. A. Maclean, F. E. J. Frey, and H. A. Regier.  1980. Stochastic simulation of temperature effects
 on first year survival of smallmouth bass. Trans. Amer.  Fish. Soc 109: 1-34.

 Shuter, B. J., D. A. Schlesinger, and A. P. Zimmerman.  1983. Empirical predictors of annual surface water
 temperature cycles in North American lakes.  Can. J. Fish. Aquat. Sci. 40: 1838-1845.

 Shuter, B. J., Wismer, D. A., Regier, H. A., and Maruszek,  J. E. 1985. An application of ecological modelling:
 impact of thermal effluent on a smallmouth bass population. Trans. Amer. Fish. Soc. 114:  631-635.

 Stewart, D. J. and  F. P. Binkowski.  1986.  Dynamics of consumption and food conversion by Lake Michigan
 alewives: an energetics modeling synthesis.  Trans. Am. Fish. Soc. 115: 643-661.

 Stewart, D. J., J. F. Kitchell and L. B. Crowder.  1981.  Forage fishes and their salmonid predators in Lake
 Michigan.  Trans. Amer. Fish. Soc. 110:751-763.

 Stewart, DJ., D. Weininger, D. V. Rottiers  and T. A.  Edsall.   1983.  An energetics  model for lake  trout,
 Salvelinus namaycush: application to the Lake Michigan  population. Can. J. Fish. Aquat. Sci. 40(6): 681-698.

 Taylor, W.N., M.  A. Smale, and M. H. Freeberg,  1988.   Biotic and abiotic determinants of lake whitefish
 (Coregonus clupeaformis) recruitment in northeastern Lake Michigan. Can. J. Fish. Aquat. Sci. In Press.

Thienemann, A. 1930. Die deutsche limnologische Sunda-Expedition. "Deutsche    Forschung". Ausder Arbeit
 der Notgemeinschaft der Deutschen Wissenschaft.

Turner, R. E. 1977. Intertidal vegetation and commercial yields of penacid shrimp. Trans. Am. Fish. Soc. 106:
411-416.

Wernstedt, F. L. 1972.  World climatic data. Climatic Data Press, Lemont, PA. 523 pp.


Woodwell, G. 1983. Paper presented at Climate Change Conference, Washington, D.C., Proceedings edited by
John Topping. Climate Research Inst., Washington, D.C. In Press.


                                               2-41

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Magnuson


Wrenn, W.B. 1980.  Effects of elevated temperature on growth and survival of smallmouth bass.  Trans. Am.
Fish. Soc. 109: 617-625.

Wrenn, W. B.  1984.  Smallmouth bass reproduction in elevated temperature regimes at the species native
southern limit. Trans. Am. Fish. Soc 113: 295-303.
                                               2-42

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ECOLOGICAL EFFECTS OF GLOBAL CLIMATE CHANGE:
   WETLAND RESOURCES OF SAN FRANCISCO BAY
                        by
                   Michael Josselyn
                   John Callaway
               Romberg Tiburon Centers
            Center For Environmental Studies
             San Francisco State University
                    PO Box 855
                 Tiburon, CA 94920
            Contract Number CR-814594-01-0

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                CONTENTS

                                                                          Page

FINDINGS  	 3-1

CHAPTER 1: INTRODUCTION	 3-3
      OBJECTIVE OF REPORT	 3-3
      GENERAL DESCRIPTION OF THE ECOLOGICAL SYSTEM	 3-3
      SENSITIVITY TO PREHISTORIC CLIMATE CHANGE	 3-6
      SENSITIVITY TO PRESENT-DAY VARIABILITY	 3-8
      STUDIES APPLICABLE TO THIS PROBLEM  	 3-8

CHAPTER 2: METHODOLOGY	3-10
      MARSH AND MUDFLAT SEDIMENTATION  	3-10
      SALINITY INTRUSION	3-14

CHAPTER 3: RESULTS 	3-16
      SEA LEVEL RISE 	3-16
      FLOW MODIFICATION AND SALINITY INTRUSION  	3-21
            Marsh Plant Distribution	3-21
            Rare and Endangered Species Associated With Wetlands	3-21
            Phytoplankton Biomass 	3-22
            Benthic Invertebrates	3-22
            Fish Abundance	3-22
      SUMMARY OF RESULTS	3-23

CHAPTER 4: IMPLICATIONS OF RESULTS	3-27
      ENVIRONMENTAL IMPLICATIONS 	3-27
      SOCIOECONOMIC IMPLICATIONS	3-27

CHAPTER 5: POLICY IMPLICATIONS  	3-29
      ADDITIONAL RESEARCH	3-29

REFERENCES	3-30
                                      11

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                                                                                              Josselyn

                                             FINDINGS1


       This report examined the effects of global climate change on the deep-water and wetland habitats of the
San Francisco Bay estuary. Global wanning will result in increased sea level and modification of the freshwater
run-off into the estuary.

       Three scenarios were used for the sea-level rise rate: a 100-, 200-, and 300-cm rise by the year 2100.  The
relative rise at any one location in the estuary is a combination of the land  surface movement and sea level
change.  For the San Francisco Bay region, the lowest  rate of sea level rise  occurs around the Golden Gate,
with greater rates of relative sea level rise occurring in the northern and southern extremes of the estuary.
Thus, the effects of predicted sea level rise will be magnified by the regional  subsidence.

       Under the 100-cm rise scenario,  sediment entering the bay via the rivers is sufficient to meet  the
accretionary balance required by the tidal marshes and  mudflats, at least until 2040.  However, under the 200-
 and 300-cm rise, the sediment requirement by tidal marshes and intertidal mudflats win exceed the 50% of the
annual estuarine sediment input early in the 21st century. If the area of tidal inundation increases owing to levee
failure, it is expected that most of the current 100,000 acres of intertidal wetlands (marshes and mudflats) will
be converted to subtidal habitats owing to a negative  accretionary balance.

       The diked and managed wetlands behind levees face a double jeopardy. If the levees are maintained, the
diked wetlands will continue to decline in elevation relative to sea level and  will require substantial pumping
to remove standing water accumulated from land run-off and precipitation. Otherwise the standing water will
inundate the marsh vegetation.   The cost of levee  repair will also increase substantially  owing to greater
hydrostatic pressure exerted by sea level rise.  If the levees fail, over 130,000 acres of diked wetlands (seasonal
wetlands, fanned wetlands, and salt ponds) will be converted to  subtidal habitats with  resulting declines in
migratory waterbirds and extinction of many of the region's rare and endangered species. In some regions (such
as Suisun Marsh), additional wetlands may be created from higher  sea level, but in the south and central bay,
intertidal wetland habitat will be  completely eliminated owing to measures that will be necessary to protect
existing development in low-lying areas.

       The hydrologic changes in the estuary owing  to the combined effect  of rising sea level and changing
climate will be significant. Most of the inflow will occur in the winter, and spring flows will decrease owing to
smaller snowpacks in the Sierra  The greater volume of water in the estuary will cause salt intrusion into  the
Delta  To repel this salt from the intakes of the federal and state water projects, additional water (carriage
water) would have to be released from upstream reservoirs.  Nevertheless, despite increased freshwater inflows,
the average salinity within the estuary wul increase.

       All of the current regression models relating species abundance to flow are based on a positive correlation
between flow and abundance.  High flows result in regions of high plankton biomass in the San Pablo and Suisun
Bays. This region is called the null zone as it is found where the net landward bottom current is zero. The
null zone also  supports high numbers of benthic invertebrates and juvenile fish.  However, under the scenarios
projected for sea-level rise, increasing flows will not result in the positioning of the null zone within Suisun Bay,
but rather in the western Delta During two recent drought periods, plankton and fish biomass reached historic
lows when the null zone occurred in the western Delta

      The result of increasing salinization of the estuary will be a decline in brackish marsh plant productivity,
waterfowl habitat, and estuarine invertebrate populations.  In some cases, marine species will invade areas
formerly occupied by estuarine species. However, it is  anticipated that  overall productivity of the estuary will
        'Although the information in this report has been funded wholly or partly by the VS. Environmental
Protection Agency under Contract No. CR-814594-01-0, it does not necessarily reflect the Agency's views, and
no official endorsement should be inferred from it.

                                                 3-1

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Josselyn

decrease owing to decline in phytoplankton biomass.  Perhaps more critical to many anadromous fish  and
nesting waterfowl will be the increase in salinity in important spawning and breeding areas.  These  species
require nearly freshwater conditions, and the predicted salinity increase will significantly reduce their ability to
successfully reproduce.

       Society currently places a high value on both tidal and wetland habitats, investing money to  protect,
enhance, restore, and manage these areas.  Their loss would have significant economic effects  and would
also threaten the stability of developed areas behind levees. A number of recommendations  are made to
protect tidal and diked wetland habitats including dredge spoil disposal in  diked areas, high tide barriers, and
salt  barriers.  Each of these  engineering solutions have significant environmental  trade-offs and must be
evaluated now before their large-scale implementation.
                                                  3-2

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                                                                                            Josselyn

                                           CHAPTER 1

                                         INTRODUCTION
OBJECTIVE OF REPORT
       The objective of this study is to investigate the response of wetland habitats to two attributes of global
climate change: general sea level rise and modification of freshwater inflow to the San Francisco Bay estuary.

       Sea level change  affects the distribution of various wetland types by conversion to deep water habitats
due to inundation.  While some wetland types are tolerant of long periods of submergence, others such as tidal
marshes are severely impacted. The impacts of changing freshwater supply and sea level rise are related  ~ a
reduction in sediment supply from the rivers will increase the rate of tidal wetlands loss.

       Freshwater  supply to the estuary affects the longitudinal distribution of various wetland types, from
freshwater wetlands in the Delta region to saltwater marshes in San Francisco Bay.  In addition, freshwater
inflow drives the circulation pattern within the estuary and establishes the salinity gradient, which is important
in the distribution of the estuary's fish and wildlife populations.

       We have used available topographic, photographic, and sedimentation data to assess the ability of Bay
marshes to sustain themselves in light of increasing sea level.  Sea level rise scenarios were develop?
increases of 100, 200, and 300 cm by the year 2100. Sedimentation data and accumulation of sediment
marshes were  calculated using a modification of the model  given in Hatton et al. (1983).  The quantity ot
sediment required  as a  proportion of the riverine borne material is then estimated for various water flow
scenarios.

       To determine the effects of salinization, we compared the abundance or distribution of organisms as
related to various flow scenarios analyzed by Williams (1988).

       Based on these analyses, we have addressed the potential long-term impacts of global climate change
on the wetlands of the San Francisco Bay estuary and the policy implications which must  be considered to
protect these habitats.


GENERAL DESCRIPTION OF THE ECOLOGICAL SYSTEM

       The general features of the San Francisco Bay estuary have been described by Conomos (1979).  San
Francisco Bay  is the largest estuary on the west coast of the United States. The Bay is actually a complex of
interconnected  embayments, sloughs, marshes, channels, and rivers (Figure 1).  It  consists  of  two main
components, the northern reach containing San Pablo and Suisun Bays and the southern reach containing San
Francisco Bay proper.  Ninety percent of the mean annual river inflow to the estuary enters the northern reach
through the Sacramento-San Joaquin Delta. Most of the flow occurs in the winter and spring months at which
time the estuary functions as a partially mixed system, whereas during summer and fall low-flow periods, the
estuary is well  mixed.

       The estuary has been highly modified by man's activities (Nichols et al., 1986). Water development, in
particular, has  had tremendous impact on the distribution of plants and animals. Inflow to the estuary is highly
controlled by state and federal water projects. The Sacramento-San Joaquin Delta is a critical element in the
water management program: freshwater from the Sacramento River flowing through the Delta from the north
is diverted to federal and state pumping stations in the southern part of the Delta near Tracy. The integrity of
the Delta levees and channels is critical to the pumping efficiency and the maintenance of freshwater flows to
the estuary.  Nevertheless, at times during low-flow periods, the net  flow in the Delta is directed toward the
pumping stations instead of seaward through the estuary. Since the inception of the water projects in 1945,


                                                3-3

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Josselyn
                                                                            Suisun Marah
Figure 1.   Generalized map of San Francisco Bay estuary with tide station locations (from San Francisco Bay
           Conservation and Development Commission, 1987).
                                                3-4

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                                                                                              Josselyn

natural freshwater inflow has been reduced by over 50%, which has resulted in greater salinization of the estuary
with concomitant reductions in habitat for anadromous fish and landward shifts in salt marsh vegetation and
benthic invertebrates.

       In addition to changes in freshwater inflow, other man-induced changes have occurred in the estuarine
flora and fauna. Loss of wetlands owing to agriculture and urbanization has resulted in lowered populations of
many resident species and in reduced habitat for migratory species.  Introduced species of invertebrates and fish
have displaced many of the native species. Non-native marsh plants have invaded the remaining wetlands.

       Pollution has also affected organisms in the estuary. Domestic waste discharge and agricultural drainage
have increased the nitrogen and phosphorus content of the Bay's water.  Petroleum by-products have affected
the reproductive capacity of striped  bass.  Heavy metals such  as  silver  and mercury  are found in high
concentrations in bay invertebrates.   Despite  all these impacts, however,  the  San Francisco Bay estuary is
relatively low compared to other U.S. estuaries in its susceptibility to pollution and, therefore, still supports
important fish and wildlife resources despite significant urbanization (Klein et ah, 1988).

       The primary focus of this study is on the estuary's wetlands habitats.  Atwater et al. (1979) reviewed the
history of the tidal marshes around the Bay and their current condition. Josselyn (1983) provided a substantial
review of the ecology of tidal wetlands and the  San Francisco Bay and Development Commission et al. (1982)
of the diked wetlands.

       The urbanization of the San Francisco Bay estuary has greatly modified the wetlands around its margins.
Recent mapping by the U.S. Fish and Wildlife Service indicates that almost 200,000 acres of various wetland
habitats (exclusive of open water) are present,  compared to an estimated 540,000 (including the Delta) acres
prior to 1850.  However, the mixture and type of wetlands present today are primarily the result of human
activities such as diking, agriculture, and land development.  In most cases, tidal marshes have been replaced
by non-tidal wetlands such as salt ponds and seasonal freshwater and  brackish marshes. In addition, declines
in freshwater outflow (estimated at between 30 and 70% of the natural annual inflow) have resulted in a shift
of wetland vegetation from fresh to  saline tolerant species.  Despite this large-scale conversion  and the
substantial loss due  to urbanization, wetlands continue to provide substantial beneficial uses for flood control,
shoreline anchoring, sediment trapping, nutrient retention, and fish and wildlife  habitat.

       The Bay's wetlands are vital to migratory birds, commercial  and recreational fisheries, and a large
number of federal and state  rare  and endangered species.  Located along  the Pacific flyway, the estuary's
mudflats and deep water habitats provide important feeding and roosting areas for migratory waterfowl and
shorebirds.  Suisun  Marsh, the largest brackish water marsh  in the western United States,  is a major over-
wintering habitat for waterfowl and is specifically managed to produce food plants suitable for ducks and geese.
The estuary supports several anadromous fisheries including  striped  bass and  salmon. Key spawning areas
include the Delta and central valley rivers and streams. Important to the survival of the juvenile fish is the high
planktonic biomass  occurring in the north bay associated with the null zone or region where fresh and salt
water masses meet.  As a result of urbanization, a number of plants  and animals  are now listed as rare and
endangered by the federal government. These  species, such as the  salt marsh harvest mouse, clapper rail, salt
marsh bird's beak, and least tern, are largely restricted to marsh habitats. Any further loss or modification of
these wetlands will surely reduce these species.

      The primary types of wetlands present in the estuary are given in Table 1. The wetland acreages were
determined from digitized National Wetland Inventory Maps prepared by the U.S. Fish and Wildlife Service
from aerial photography taken in 1985 (Josselyn et al., in press). While over 200 different wetland  types were
identified, they were grouped into  eight major classifications for the  purposes  of this work:  (1) open water
(deep and shallow water submerged habitat); (2)  mudflat (unvegetated tidal areas); (3) tidal marsh (vegetated
tidal areas); (4) diked wetlands (non-tidal wetland areas [exclusive of fanned areas] that are generally within
the former boundary of historic tidal wetlands); (5) agricultural lands (former tidal wetlands now used for
fanning); (6) salt ponds (including crystallizers and bittern ponds); (7) lakes and ponds (shallow open water
areas); and (8) other wetland habitats (freshwater and  riparian wetlands).  Three geographic locations were


                                                 3-5

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Josselyn

considered: (1) Suisun Bay between the Delta (at Collinsville) and the Napa River; (2) North Bay between
Mare Island and the Oakland-San Francisco Bay Bridge; and (3) South Bay between Treasure Island and San
Jose.  The area! extent was determined for those areas generally within the 10-ft. elevational contour around
the Bay (the Bay Zone).

      For Suisun Bay, the largest component is the diked seasonal wetlands, which are managed for waterfowl
habitat.  The vegetation within these wetlands is typical of brackish water wetlands:  alkali bulrush, cattails,
brass-buttons, and pickleweed dominate. There is a substantial amount of tidal marsh as well, especially along
the southern shoreline of Suisun Bay.

      Open water  and mudflats are the most abundant wetland types in the north bay, especially along the
northern edge of San Pablo Bay.  It is also the location of the highest  acreage  of  tidal marsh, much of  it
created by hydraulic mining debris in the 1860*5. Much of the diked  historic marshlands are  now  farmed,
although some of the diked areas still function as wetlands. The tidal marshes  in  this sector are dominated
by saline tolerant vegetation: cordgrass and pickleweed.


Table   1.   Areal Extent  in Acres  for Eight Generalized Wetland
              Types Within  the San Francisco Bay Estuary.   Based
              on  U.S.  Fish  and Wildlife Service National Wetland
              Inventory and Josselyn et al.  (in press).

      HABITATSUISUN BAYNORTH BAYSOUTH  BAY
Open Water
Mudflats
Tidal Marsh
Seasonal Wetland
Farmed Wetlands
Salt Ponds
Lakes and Ponds
Other
Total
25,439
6,114
10,541
47,159
8,263
27
1,459
963
99,965
101,994
28,131
16,201
8,606
25,828
9,059
2,926
507
193,252
93,220
30,378
8,604
4,592
1,317
27.497
5,331
1,412
172,352
      In the south bay, non-vegetated wetland habitats make up the majority of the acreage. Salt ponds in
which water depths are controlled provide habitat for a number of fish-eating birds, long-legged shorebirds such
as avocets and stilts, and migratory waterfowL  Mudflats provide important habitat for short-legged shorebirds
such as sandpipers and willets.  Vegetated wetlands and diked wetlands comprise less than 13,000 acres, the
smallest acreage of these important habitats for any of the regions in the bay. Pickleweed, a plant tolerant of
high soil salinities, is the most prevalent plant.


SENSITIVITY TO PREHISTORIC CLIMATE CHANGE

      The evolution of tidal wetlands in San Francisco Bay has been the result  of a gradual inundation of
lowland areas balanced by sedimentation. The melting of glaciers has been the major cause for a rise in sea
level in the past 10,000 years. There have been two phases in the rate of rise during this period (Figure 2).
From approximately 10,000 years before present (YBP) to 7,000 YBP, sea level rose at an estimated rate of 20
mm/yr (Arwater et aL, 1977). At  this  rate, dryland was rapidly inundated and converted to tidal flats and


                                               3-6

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                                                                         Josselyn
    Mean
     tea
    level
     -10
 w
 IL
 IU
 I-
 W
 o
 UJ
 -i
 UJ
-20
     -30
     -40
     -50
                                  EXPLANATION
                     Probable range of error in age
                       and elevation of former sea  level.
                       Arrow indicates former
                       sea level below  some point
                       in rectangle
                _L
                         _L
I
I
I
I
I
                 123456789

               AGE. IN THOUSANDS OF  YEARS BEFORE PRESENT
                                                                         10
Figure 2.   Changes in sea level relative to land during the past 10,000 years at the site of southern San
         Francisco Bay (after Atwater et aL, 1977).
                                      3-7

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Josselyn

subtidal areas. Dated cores from San Francisco Bay indicate little marsh development during this period.  After
7,000 YBP, the rise in sea level slowed to the estimated current rate of 1-2 mm/yr. During this phase of sea
level rise, extensive marsh development occurred.

       Marsh development along the margins of San Francisco Bay has largely been a function of inundation
of dryland by rising sea level. As wetland plants colonized the newly inundated areas, they also retreated from
the bay edge as it  eroded. Sediments accreted on the marsh surface from this erosion as well as  from  the
suspended sediment derived from river drainage.  In the late nineteenth century, increased sediment supply
derived from mining and agricultural practices in the watershed resulted in the migration of marshes over former
mudflats. With the reduction of these sources and declining freshwater inflow, retreating shorelines along the
bay margin are again more prevalent than accreting ones (Atwater et al., 1979).

       Current scenarios for global climate change place the rate of sea level  rise at between 10 to 40 mm/yr,
which is an order of magnitude greater than at present.  The assumption is that tidal marshes would be rapidly
inundated.  However, the acreage of tidal wetland present today is only 10% of that which existed just 150 years
ago and,  therefore, one might anticipate that sufficient sediment supply is available to support these wetlands.


SENSITIVITY TO PRESENT-DAY VARIABILITY

       Two analogues are available to assess the response of present-day wetlands to variability in water flow
and sea level rise.  The first is the severe drought of 1976-77, which drastically reduced water supply to the
estuary, and the second is the relative change in sea level experienced by areas that have subsided due to ground
water withdrawal

       During the  1976-77 drought period, water supply to the estuary was reduced by 75% over normal
regulated inflow. A number of changes were observed in the vegetative composition of both tidal and non-tidal
(managed) wetlands.  A substantial decrease in the productivity of brackish water species such as alkali bulrush
and tules were observed  with a concomitant  increase in the  occurrence  of saline tolerant species  such as
cordgrass and pickleweed (Atwater et al., 1979; California Department  of  Fish  and Game,  1987).  Other
estuarine organisms were  also  affected.  Phytoplankton  productivity decreased within Suisun Bay (Ball and
Arthur, 1979), striped bass juveniles drastically decreased, and more saline-tolerant benthic organisms migrated
upstream.  These changes occurred within the 2-year period of the drought, and the marine species that invaded
the upper reaches of the estuary were eliminated once flows returned to previous levels. However, the recovery
of some estuarine species has been much slower. For example, the striped bass population remains critically low
to this date.

       The second analogue, groundwater withdrawal, has contributed to local subsidence in south San Francisco
Bay of up to 1.5 m within the recent past (Atwater et al., 1977).  Many diked wetlands  formerly at or above
MHHW are now at MLLW or below. Continued repair is required to maintain the levees around these areas
and pumps are necessary to remove accumulated precipitation. On the other hand, tidal marshes do not appear
to have been lost  owing  to inundation.   Patrick and DeLaune  (unpublished manuscript, Louisiana State
University) used isotopic data to  determine sedimentation rates in a tidal marsh near Alviso Sedimentation over
a 30-year period averaged 56 mm/yr.  At a tidal marsh near Foster City where subsidence is considerably less,
sedimentation in a  tidal marsh was less than 3 mm/yr during the same time  period. Thus, it appears that in
some locales, sedimentation can be sufficient to maintain tidal marshes despite large rises in relative sea level.


STUDIES APPLICABLE TO THIS PROBLEM

       Most studies provide only a glimpse of the impacts of long-term climatic change on estuarine wetlands
and their associated flora and fauna. In general, the focus has been placed on sedimentation rates in wetlands.
Stevenson et aL (1986) provide an excellent review of  sedimentation studies to date and found a strong positive
correlation between tidal range and sedimentation in wetlands experiencing annual sea level increases between


                                                 3-8

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                                                                                             Josselyn

2 to 10 mm/yr. Regions that have a very small tidal range, such as the Mississippi Delta, experience the greatest
accretionary deficit as sea level has risen whereas regions with higher tidal ranges have been able to sustain
elevations suitable for wetland vegetation.

       Regional studies on the  impacts of sea-level rise on the wetlands of Delaware and Chesapeake Bays
and the Mississippi Delta have also been completed (Hull and Titus,  1986; Stevenson et al., 1985; Salinas,
1986). These studies emphasize the complexity of the factors which contribute to wetland loss in these systems.
Salinity intrusion and sediment accretion deficits, however, are the most significant impacts resulting in either
modification or loss of wetlands.

       The literature for San Francisco Bay is much more limited than that in other areas.  Approximations
for sediment  input to the estuary have been derived (Krone, 1979). The amount of sediment entering the
estuary is directly related to total river discharge.  In the 1960's, the estimated annual sediment supplied  to the
Bay via the Delta was 3 million metric tons/yr.  By 1990, with projected water development, sediment supply
is estimated at only 1.6 million metric tons/yr, a 45 percent decrease.

       Less is known about the distribution of sediment once it enters the estuary.  Only a few studies have
been  completed on sedimentation on mudflats or within marshes (Nolan and Fuller, 1986; Pestrong, 1972;
Josselyn and  Buchholz,  1984).  Much of this data is site specific and difficult to generalize to other  areas.
Mudflats generally exhibit the highest rates of sedimentation, especially in regions where the tidal prism (volume
of water between high and low tide) has been reduced owing  to filling of wetlands. Accretion rates of 0.6 to
2.8 mm/yr were observed in a shallow embayment, whereas  rates of 70 mm/yr  occurred in subtidal waters
(Nolan and Fuller, 1986).

       Within marshes, seasonal variation in elevation may exceed long-term accretion (Josselyn and Buchholz,
1984). Generally, lower elevations within tidal marshes exhibit the greatest rates of sedimentation, which then
decrease rapidly with height above sea level (Krone, unpublished).  Josselyn and Buchholz (1984) measured
annual fluctuation in  sedimentation within tidal marsh channels ranging between 100 and 300 mm/yr, whereas
Martindale (1987) determined accretion rates of 1-5 mm/yr on the marsh surface.

       Recently, a number  of studies have  been completed by the San Francisco Bay Conservation and
Development Commission on the impact of sea level rise on the shoreline of the estuary and the reduction of
freshwater flow (San Francisco Bay Conservation and Development Commission, 1985,1987a,b,c). These reports
provide useful recommendations concerning the land-use policy implications of global climate change.
                                                3-9

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Josselyn
                                           CHAPTER 2

                                        METHODOLOGY


       To date, large-scale biological data bases for the estuary have not been integrated into a model  suitable
to study biological responses to changing water flow or sea level Therefore, the models used for this study are
based largely on empirical data, simple sedimentation budgets, and regression models.


MARSH AND MUDFLAT SEDIMENTATION

       Scenarios used for sea level rise were taken from Titus (personal communication). Three scenarios were
used: 100-, 200-, and 300-cm rise by the year 2100. The equations used for calculation were:

   100 cm scenario: RSL(t) • L(t) + 0.0012t +  0.000066422

   200 cm scenario: RSL(t) = L(t) + 0.0012t +  0.00014342

   300 cm scenario: RSL(t) - L(t) + 0.0012t +  0.00022032

   where: t   »   the number of years since 1986
         L(t)  *  the local component of relative sea level change
                  relative sea level at time t.
   L(t) is further refined as LYLE*t - 0.0012t where LYLE is the local relative sea level trend.  The value for
   LYLE for the San Francisco region was obtained from Lyle et al. (1987).

      The changes in sea level as predicted under these three scenarios and corrected for the San Francisco
Bay region are shown in Figure 3.  This model assumes an increasing rate of rise throughout the next century.
However, many other models may be equally valid such as a linear rise, a stepped rise, and a precipitous rise.

      Within San Francisco Bay, there is considerable variation in local sea level trend owing to differences in
crustal subsidence.  San Francisco Bay Conservation and Development Commission (1987b) provides local land
surface changes for 11 locations throughout the Bay (Figure 4). These data vary considerably, ranging from
0.06 mm/yr in Sausalito near the Golden Gate to 29.23 mm/yr in the south bay at Alviso.  To illustrate the
range of sea level change that might be expected to occur throughout the estuary, the 200-cm scenario for San
Francisco, Sausalito, Pittsburg, and Alviso is plotted in Figure 5.  Thus, the actual rise within the estuary for
any one scenario may vary significantly depending upon the location. Recently, actions taken to replenish the
groundwater aquifers in south San Francisco Bay have halted further subsidence (Santa Clara Water Agency,
pers. comm.) and, therefore, the range of potential sea level rise is more appropriately restricted to that shown
for Sausalito and Pittsburg on Figure 5.

      Sediment budgets for marsh and mudflat surfaces were calculated for each of the three scenarios using
the model equation from Stevenson et al. (1986):

Sediment necessary to maintain marsh surface =  BD * PI * AR * A

   where BD    = Bulk density of soil
       PI       = Percent of inorganic matter
       AR      * Accretion rate
       A       » Area of tidal marsh or mudflats
                                               3-10

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                                                                          Josselyn
      300
 u
I
o
V
in
0
y
o
0
                       PREDICTED  SEA  LEVEL  RISE
                              at San Francisco for 3 scenarios
         1990   2000  2010


      100 cm case
                                                                              210
     Year
200 cm cave
Figure 3.   Predicted sea level rise at San Francisco for three scenarios: 100-, 200-, and 300-cm rise by the
         year 2100.
                                      3-11

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Josselyn
        10
           CURRENT RELATIVE SEA  LEVEL  RISE  RATES
                                   Son Francisco Boy Eatuory
                                                                       29.2
E
"5

o
A
_
tr.
        9 -
6 -


5 -


4 -


3 -


2 -


1 -
            Pitts   Ben   Son   Pt O   Sous   Pres

            Suisun Bay  I San  Pablo & Central Bay
                                          T

                                          SF
 I
Ala
  H Pt

S.F.  Bay
 I
SM
 Dum B  Alv

(South S.F.
    Bay
Figure 4.   Current relative sea level rise rates for twelve sites in San Francisco Bay estuary. Abbreviations
          refer to tide station locations mapped on Figure 1 (after San Francisco Bay Conservation and
          Development Commission, 1987).
                                        3-12

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                                                                              Josselyn
                        PREDICTED SEA  LEVEL  RISE

                                      200 cm Scenario
u
i
o
W
y
Q

9
      600
      500 -
      400 -1
300 -
         1990  2000   2010  2020   2030   2040   2050   2060  2070   2080   2090   2100
200 -
      100 -
                                          Year
            Alviso
                           Pitt
SF
Sauiolito
  Figure 5.   The range of predicted sea level rise in San Francisco Bay estuary by the year 2100, including Alviso

           Slough, Pittsburg, San Francisco, andSausalito.
                                         3-13

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Josselyn

The data for bulk density and percent inorganic material were taken from Pestrong (1972), Mahall and Park
(1976), Perez (1981), Nolan and Fuller (1986), and Josselyn (unpublished) for a variety of geographic and
wetland types in the estuary. High, median, and low estimates were determined (Table 2).


Table  2. Ranges of Bulk Density and Inorganic  Content Used
           in Determining Sediment Budgets for Tidal  Marshes
           and Mudflats.
   WETLAND  TYPE       FACTOR           HIGH       MEDIAN      LOW
Tidal marshes

Mudflats

Bulk Density
Percent inorganic
Bulk Density
Percent inorganic
1.0
92
1.0
98
0.8
85
0.8
96.5
0.65 g/cm3
78
0.65
95
    The sediment quantity required to maintain marsh and mudflat surfaces was then calculated for various
accretion rate scenarios within each region of the estuary.  Finally, a comparison of the sediment required
under each of the scenarios against the sediment available was determined. The annual sediment supplied to
the Bay was determined from the relationship of flow to sediment supply as given in Krone (1979).  Sediment
supply was estimated under four inflow scenarios: BASE, GFDL, GISS, OSU outputs from Williams (1988). An
important assumption in Williams's estimate of inflow was that sufficient water would be released by state  and
federal water projects to retard salt intrusion to water project intakes within the Delta. This  additional water
is referred  to as  carriage water.  In addition,  we assumed that levees  around diked wetlands would   be
maintained. If not, these areas would also serve as sinks for sediment, increasing the percent necessary for
marsh accretion.

      In addition to sediment budget calculations, marsh erosion was also examined in locations with known
historic  changes in relative sea level.  We initially examined 1:24000 U.S.GS topographic maps from 1945 to
present  to determine if any areas showed accretion or erosion.  The scale of these maps prohibited accurate
measurements of shoreline change.  Therefore,  four locations in the estuary were selected for more detailed
analysis of shoreline change:  Palo Alto  and Alameda Creek Flood  Control  Channel in the south Bay and
Corte Madera and Southampton Bay in  the north Bay.  Aerial photographs from the 1950*8, mid-1960's,  and
present were enlarged to a scale of 1"=400' and the shorelines overlain. Levees and permanent structures were
used for horizontal control.  Erosion rates were  compared to relative sea level changes for nearby stations.


SALINITY INTRUSION

      Changes in water flow and average salinity for the estuary owing to climate change were taken from the
studies completed by Williams  (1988).  He examined the effect  of a 100-cm sea level change within several
climate model scenarios.  With the projected sea level rise, salt water penetrated further landward towards the
Delta.  In order to maintain water quality standards for water exported from the Delta, greater quantities of
freshwater  discharge through the Delta would  be needed to repel the  saltwater intrusion.  The  estimated
"carriage water" required to protect existing water intakes was determined to be twice current levels of discharge.
Despite  the increased inflow to the estuary, the larger volume of seawater in the Bay resulted in higher average
salinities in all of the basins.
                                               3-14

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                                                                                              Josselyn

       Williams also examined the impact of levee failure within the Delta  and along the margin of San
Francisco Bay.  This scenario resulted in a tripling of the existing area of the Bay and a doubling of its volume.
The greater volume resulted in stronger tidal currents at restrictions in the estuary (e.g., Golden Gate and
Carquinez Straits) and reduced tidal ranges further inland. However, salinity did not increase significantly
over the "intact levee" scenario.

       A number of linear relationships  have been observed between flow and the abundance of certain
organisms; however, these regressions are dependent upon the current geomorphology of the estuary.  If the
Delta islands were flooded, the inland sea created in this region would so alter the geomorphology of the
estuary that  current regressions would no longer be valid.  For example, studies on juvenile salmon migration
have shown  that higher flows are  necessary to assure high survival of smolt by moving them quickly through
the Delta region to avoid entrainment losses (Stevens and Miller, 1983). Under  the scenario where carriage
water is doubled, the regression model shows a higher survival by salmon smolt. However, the hydrology and
circulation in the Delta would be entirely different from the conditions on which the regression was based.

       Second,  most of the biological relationships observed in San Francisco  Bay are based on the indirect
relationship between flow and salinity. However, under conditions where sea level is rising, increased freshwater
inflow does not reduce salt intrusion into the estuary. Thus, existing models of organism response to changing
inflow are not applicable and any analysis of the changes which might occur within the estuary under various
climate scenarios must necessarily be qualitative.
                                                3-15

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Josselyn

                                            CHAPTERS

                                             RESULTS


SEA LEVEL RISE

       The rates of sea level rise for 5-year periods under each of the three scenarios is shown in Figure 6.
For the purposes of this report, the modei ^as ...ejected for a 50-year period only. The model predicts sea level
rat"   .creases throughout the next century.  For the 100-cm  rise, rates will range from less than 2 mm/yr in
1990 to over 8 mm/yr hi 2040. Given the range of accretion rates observed for San Francisco Bay and other
wetland systems with similar tidal ranges (Stevenson et aL, 1986), it is probable that sediment accretion on the
marsh surface could maintain the vegetated wetland for this scenario.

       On the other hand, for scenarios of 200- and 300-cm rise by the year 2100, the accretion rates necessary
to maintain the marsh surface relative to sea level rise are considerably higher than the average rates presently
observed. For the 200-cm scenario, sea level increase would exceed average rates for marsh surface accretion
by the year 2005 and, for the 300-cm scenario, by the year 2000.  Under the 300-cm scenario, the rate of rise
by 2030 will have exceeded that of recent prehistoric rates when it is presumed that tidal marshes were not very
prevalent owing to rapid inundation (Atwater,  1979).

       The amount of sediment necessary to sustain tidal marshes  under various accretion rates is shown in
Figure 7. The low, mid, and high estimates are based on the range of data for bulk density and inorganic
content given in Table 2.  An 11 mm/yr rise would require approximately 1.1 million metric tons (range of 0.8
to 1.5  me  : tons) of sediment to be deposited on all tidal marsh surfaces.  These estimates are based on
present-day tidal marsh acreage. As sea level rises, additional  acreage would be flooded. If the levees remained
intact,  the increase would be negligible. On the other hand, if the levees failed around diked baylands,  the
potential area over which sediments could be deposited increases by 3 (Williams, 1988).  In addition, many of
the diked areas are below sea level (compared to  tidal marsh surfaces which are generally near MHW) and
will be a substantial sink for suspended sediments.  Thus, under the  assumption of levee failure, the amount of
sediment deposited along the margins of the estuary could well exceed 5 million metric tons for an 11 mm/yr
rise in sea level

       These data can also  be expressed as a  percentage of  total sediment input from the Sacramento River
under various climate model scenarios (Figure 8). The amount of suspended sediments supplied to the estuary
were estimated from the regression of flow against suspended sediments given by Krone (1979), multiplied by
the flow rates projected by Williams for each of the three climate models. For example, for an 11 mm/yr sea
level rise, 20% of the total annual sediment input under the GFDL inflow scenario would be necessary to
maintain tidal marsh surfaces relative to sea level rise.  However, this is less than the 31% required under base
flow conditions because of the greater carriage water flows necessary to repel salt water intrusion.

       Under present-day conditions, only 5% of the total annual sediment input is necessary to maintain tidal
marshes at equilibrium with sea level rise. Under the lowest projected sea level rise scenario, only approximately
20% of the total sediment input would be needed to sustain marshes (i.e.,  for the 8 mm/yr rate of rise hi year
2040).  However, for the highest  projected sea level rise scenario, between 35 and 50% of the total sediment
input would be needed depending upon the climate scenario used.  Again, the amount of additional wetland
acreage formed when levees fail will add to the sediment budget requirements.

       The preceding discussion  did not consider accretion over mudflats. When mudflats do not accrete as
rapidly as increases in sea level, erosion of the marsh edge is often the result. Shoreline erosion is a presently
a common phenomenon throughout San Francisco Bay (Josselyn,  1983), and we examined  the relationship
between shoreline erosion  and relative sea level  change.  In all  sites examined using aerial photography,
significant shoreline erosion had occurred between  the early 1950's and the present (Figure 9).  Except at Palo
Alto, marsh edge erosion rates were generally between 0.6 to 0.9 m/yr since the mid-1950's. Palo Alto exhibited


                                                3-16

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       ?
       O
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       <8
                        RELATIVE  SEA LEVEL  RISE  RATES
                                        at San Francisco
2O.UU —
24.00 -
22.00 -

20.00 -

16.00 -


16.00 -


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12.00 -
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-------
Josselyn
              SEDIMENT  REQUIRED  BY  TIDAL MARSHES

                                       In metric tone
in

O


x

t-
•o
O
o
a
•o
0
VI
             0.2
                             Accretion Rates (Sea Level Rise) CM/YR
                   Low        PCSI  Mid       E5Z1  High Estimate
Figure 7.   Sediment required by tidal marshes in the San Francisco Bay estuary in metric tons under various
          hypothetical accretion rates as calculated from Table 2.
                                         3-18

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                                                                                    Josselyn
         100
                 SEDIMENT  REQUIRED   BY  TIDAL  MARSHES
                                Aa a p«rc«ntag« of total ••dlm«nt Input
  a'
  £

  |

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  &
  o
  +t
  c
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  a.
                0.2


                GFDL
0.5
       0.8
AccPBtlon Rates (Sea Level Rise) CM/TR
'  GISS         C/%>3  OREGON
                                                  BASE
Figure 8.   Sediment required by tidal marshes in the San Francisco Bay estuary as a percentage of total
          sediment input under various rates of predicted sediment input. The percentage of sediment input
          is indicated by the total bar height for each model scenario.  For example, at the 1.1 cm/year rise,
          the Oregon model predicts 31 percent (the sum of GFDL, GISS, and Oregon bars).
                                           3-19

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Josselyn
   Alomedo Creek
                 BAY
       LAND
                       Palo  Alto Baylonds
                                 BAY
                                              	1958
                                              	1966
                                              	1985
  South  Hampton Bay
               BAY
              LAND
—1957
—1966
	1987
                       Corte Modera Marsh
                                                            BAY
                                                           LAND
Figure 9.  Changes in land margins at selected locations in San Francisco Bay.
                                       3-20

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                                                                                              Josselyn

 a rapid rate between 1958-66, but it slowed considerably after this period, partially owing to accretion along
 the marsh channel. No relationship was observed between relative sea level change and rate of erosion.  Thus,
 given the scale of analysis, a general rate of erosion of 0.75 m is assumed under the most recent past when sea
 level rise in the Bay area has generally ranged between 2 and 5 mm/yr.

       Under the  100-cm scenario, shoreline erosion would probably continue at the present rate up to 2040.
 However, both the  200- and 300-cm  rise scenarios  double the probable erosion rate by 2030 and 2010,
 respectively.  This would have serious .consequences for the narrow fringing tidal marshes that surround much
 of San Francisco Bay, especially as many are outboard of levees that do not allow for marsh advance landward


 FLOW MODIFICATION AND SALINITY INTRUSION

       As explained above, it is much more difficult to predict the the changes in estuarine communities resulting
 from modifications of flow or salinity.  The detailed modeling does not exist and one must rely on regression
 analyses.  The results discussed below focus on marsh plant distribution, rare and endangered species associated
 with marshes, phytoplankton biomass, invertebrate  distribution, and fish abundance.

 Marsh Plant Distribution

    The longitudinal distribution of wetland plants within San Francisco Bay is directly related to the salinity
 gradient.  Suisun Bay is considered the  transitional  zone between  the freshwater wetlands of the Delta and the
 salt marshes of San  Pablo and San Francisco Bays. Spring salinities are most crucial to the development of
 wetland vegetation.   Using the 1976-77 drought analogue model for wetland plant distribution, the spring
 salinities predicted for Suisun Bay with the OSU hydrology and levee failure would reduce the productivity of
 brackish and freshwater species such as cattails, tules,  and alkali bulrush and favor more salt tolerant species
 such as pickleweed  and  saltgrass.  Salinities predicted  under the GFDL and GISS models are within the
 acceptable range for brackish water vegetation.

       The submergence tolerance of many brackish water species decreases as salinity increases (Atwater et
 al., 1979). Thus, they cannot extend as far down the intertidal. The net effect is a restriction in the intertidal
 range of species such as hardstem tules, Scirpus spp., the major shoreline plant in Suisun Bay.  This shift will
 reduce available habitat, primary production, and shoreline stabilization.

       The impact of the changes predicted under the OSU model would be  a reduction in waterfowl use of
 Suisun Marsh. Approximately 3 to 5% of the state's migratory waterfowl utilize Suisun Marsh during the winter
 months, feeding primarily on fresh and brackish water vegetation. If the levees around the managed wetlands
 of Suisun Marsh failed, the existing elevations would not support emergent vegetation and, thus, waterfowl would
 have to seek wintering grounds in the Central Valley. In addition, waterfowl nesting in Suisun Marsh would
 decline owing  to higher summer salinities projected under all scenarios.

Rare and Endangered Species Associated With Wetlands

       A number of federal and state rare and endangered species are associated exclusively with the salt and
brackish wetlands  of San Francisco Bay. These species include the salt marsh harvest mouse, the California
clapper rail, the least tern, Suisun shrew, and the black  rail. In addition, other species which may be considered
candidates for listing such as the salt marsh song sparrow and  the yellowthroat are found within vegetated
wetlands.

       These species are generally restricted to small home ranges within vegetated wetlands. Other migratory
species such as the least tern and snowy plover nest in low-lying areas adjacent to wetlands. Loss of vegetated
wetlands owing to  either gradual inundation by sea level rise or to catastrophic flooding following levee failure
                                                 3-21

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Josselyn


will cause local extinction of these species. As distances between the remaining populations increase, the chances
for long-term survival of the species are greatly reduced.

Phvtoplankton Biomass

       There are two peaks of phytoplankton biomass in the San Francisco Bay estuary, a early spring peak in
San Pablo Bay and a summer maximum in Suisun Bay. Generally, the peaks are associated with a circulation
phenomena called the null zone: the region where net bottom flow is zero. The null zone has longer residence
time than other  areas of the estuary and,  therefore, phytoplankton biomass  accumulates.  Flows necessary to
sustain these peaks are approximately 20,000 cfs during April and May for San Pablo Bay and at least 15,000
cfs during June and July for Suisun Bay (Williams and Hollibaugh, 1987a,b). These flows generally result in
average water column salinities between 2 and 6 ppt in these basins during the period of peak biomass.

       The projected salinities under  the OSU  hydrology indicate that  the  null zone and, hence,  the
phytoplankton biomass maxima would be reduced substantially in the San Pablo and Suisun Bays. Spring and
summer peaks would, instead, shift upstream into the Delta.  However, under GFDL and GISS conditions, it
is possible that biomass maxima could still occur depending upon whether or not the levees are maintained.
Levee failure would introduce vast expanses of shallow water and mudflats over which primary production may
be enhanced.

       During the 1976-77 drought and the 1987-88 dry period, phytoplankton biomass in Suisun Bay was near
zero during the summer months. Zooplankton, especially a mysid shrimp, Neomysis mercedis and a copepod,
Eurytemora sp.,  were also low.  These two zooplankters are the  major food source for juvenile striped bass,
Morone saxatilis. As a result, survival of juvenile striped bass during both dry periods was at  historic lows.
Based on these two analogues, we conclude that positioning of the null zone landward of Suisun Bay will result
in reduced primary and secondary productivity with a direct effect on an economically important fishery.

Benthic Invertebrates

       Many of the invertebrates now dominant in San Francisco Bay are introduced, non-native species.  They
are considered estuarine, opportunists in that they are tolerant of relatively low salinities and do not occur at
all in the marine environment outside the Bay.

       One explanation  given for a decline in phytoplankton biomass during drought years is the invasion of
benthic marine filter feeders into San Pablo and Suisun Bays (Thompson and Nichols, 1987).  These species
are very effective in removing phytoplankton,  and it was estimated that they are capable, in the densities
observed, of filtering the entire volume of the estuary in a single day.

       In addition to increasing abundance of marine infauna, epifauna such as dungeness crab also increased
during the periods of higher bay salinities.

Fish Abundance

       Two sets  of fish data exist:   commercial catches of species such as salmon, striped bass, and American
shad and shorter-term data on fish populations taken under the  State Board's Bay-Delta program.   Most of
the commercial data is  for the pre-water project development period.  Only salmon have  a long-term record
comparable to the base period selected for this study (1950-81). Rozengurt  et al., (1988) examined a number
of flow/catch relationships and determined that the best correlations occurred with lag periods of 4 to 5 years.
That is, the flows occurring in the previous years had the best relationship to the catch in any given year.  The
lag time is related to the age at which the fish reached spawning maturity. Two significant direct relationships
were observed: the preceding year(s) annual flow with total fish catch and preceding year(s) spring flow (Apr,
                                                3-22

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                                                                                              Josselyn

May, June) with total fish catch.  Though  the  specific regression equations  differed among species, the
relationships were similar for salmon, striped  bass, and shad

       When examining the flow scenarios produced by the hydrologic models, all scenarios provided in Williams
(1988) require higher total annual flow to reduce salt intrusion into the Delta due to sea level rise (Figure 10).
These annual flows  are significantly greater  than occurring presently and the regression using annual flow
predicts greater fish  abundance. However, the Williams report also predicts lower flows in the spring months
(April through June). High spring flows have been shown to be the most critical to young fish survival and to
greater abundance of spawning fish i-» subsequent years.  Rozengurt et aL (1987) and Kjeldson et aL (1981)
recommend spring flows greater than 40,000  cfs to support anadromous fish.  None of the climate scenarios
provide sufficient flows to meet these  criteria, and these fish populations would likely decline.

       A complicating factor to these scenarios is the probably of levee failure in the Delta creating a large
inland lake with fresh to brackish water quality.  Despite the larger winter flows predicted by Williams, average
salinities in the western Delta are predicted to increase over base conditions.  Striped bass and shad spawn in
essentially fresh water conditions, which under base conditions occur during winter and spring months in the
Delta. The predicted increase in salinity would  eliminate  this region as a spawning ground.

       A short-term  data base (1979-1987) for non-commercial fish species illustrates the difficulty of assessing
long-term changes in the estuarys fauna (California Department of Fish and Game, 1987). This report examined
the relationship between inflow and monthly fish abundances at over 70 deep and shallow-water sampling sites
in San Francisco Bay.  High correlations between flows and  abundance were observed for  a number of
crustaceans and fish. For these organisms, winter and spring flows (February-May) were commonly correlated
with abundance.  However,  for a great many species,  no  correlation or negative correlations were observed.
In addition, distribution in relation to  salinity  varied between years for many of the species and, therefore, no
reliable regression with salinity could be developed.

       Comparing the flow and salinity scenarios  developed by Williams with the Fish and Game data base
indicates that despite increasing flows, the higher salinities predicted for the Suisun and San Pablo Basin will
increase  the  abundance of  marine species occurring within  these  basins while decreasing freshwater  and
anadromous species. Specifically, species such as California halibut, jacksmelt, and  walleye surfperch will
increase their use of the estuary, while species  such as starry flounder, threespine stickleback, and longfin smelt
will decrease.  The data are insufficient to determine whether this species shift will result in lower or higher
fish biomass within the estuary.


SUMMARY OF RESULTS

       Specific responses of estuarine organisms to global climate change are difficult to assess from current
models. Presently, increased flows result in decreased salinities within the estuary and the available regressions
between  flow  and plant or animal distribution are influenced by this correlation.   Under the scenarios
investigated by Williams, the necessity to increase carriage water flow to retard salt intrusion to the state's water
supply does not result in decreased salinities in the estuary. Instead, average salinity in the basins also increases
owing to  the larger volume of water present.  The resulting conditions are not within the bounds of current
regression models. Therefore, our conclusions are primarily qualitative (Table 3).
                                                3-23

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Josselyn
                AVERAGE  MONTHLY DELTA  OUTFLOWS

                               Under various climate scenarios
       120
 38
 U- 3
                      DEC   JAN   FEB   MAR    APR    MAY   JUN   JUL   AUG    SEP
OCT
      BASEAVG
                  GFDLAVG
                                        MONTH
GISSAVG
OREGAVG
 Figure 10.  Projected monthly Delta outflows under various climate scenarios (from Williams).

                                                             ...•*


                                       3-24

-------
                                                                         Josselyn
Table 3. Summary of conclusions on the impact of global climate
         change on estuarine habitats and organisms
HABITAT OR
ORGANISM(S)
 SCENARIO
   LIKELY RESPONSE OR CHANGE
   DURING PERIOD 1990-2040
Tidal marsh
100 cm rise
Diked baylands
               200/300 cm rise
All sea level
rise scenarios
and with levee
maintenance
Rare and
endangered
species
Phytoplankton
biomass
                With levee
                failure
All models
GISS, GFDL
with levee fail-
ure

OSU with levee
failure
Benthic
invertebrates'
All models
Some decline in acreage, but
marshes able to maintain
accretionary balance; landward
shift in distribution of plant
species; decrease in production
of freshwater/brackish water
plants.

Likely inundation of many of
the fringing tidal marshes by
end of period.

Increasing degradation of
marsh vegetation and water
quality due to inability to
circulate water within diked
wetland channels, decline in
brackish water vegetation
used by wintering and nesting
migratory waterfowl.

Conversion to open water
habitat, loss of waterfowl
nesting habitat.

Decreased wetland habitat due
to tidal marsh loss from
increased shoreline erosion,
localized and regional
extinction.

Some reduction due to shift
upstream during some years
Shift of peak biomass
upstream  in  all years,  decline
in zooplankton biomass and
lower survivorship of juvenile
striped bass.

Invasion of marine species
and displacement of existing
estuarine species.
                                     3-25

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Josselyn
Table 3 (continued)
HABITAT OR
ORGANISM(S)
                 SCENARIO
                     LIKELY RESPONSE OR CHANGE
                     DURING PERIOD 1990-2040
                                  Shift upstream  in  distri-
                                  bution, greater abundance
                                  of marine species.

                                  Decline in  spawning  area
                                  within delta  due to  higher
                                  salinities; likely decline
                                  in abundance  due to  lower
                                  spring flows.
Fish distri-
bution
All models
Anadromous fish All models
                                      3-26

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                                                                                             Josselyn


                                            CHAPTER 4

                                   IMPLICATIONS OF RESULTS


ENVIRONMENTAL IMPLICATIONS

       Under the moderate and highest sea level rise scenarios, tidal marshes will be inundated as sediment
supply will be insufficient to maintain an accretionary balance. This will result in significant losses of animals
and plants associated with tidal marshes and adjacent mudflats. Migratory waterfowl and shorebird habitat will
be completely inundated. A number of rare and endangered species will become regionally extinct.

       On the other hand,  the implications to the estuary as a whole may not be significant.  This is largely
due to the substantial historical losses of tidal marshes which have already occurred.  At present, only about
10% of the historical tidal marsh acreage  surrounding San Francisco Bay exists.  In the past, the area of
marshland around the bay was twice as large as the open water area; ow it is less than 10%.  Unlike the vast
tidal marshes of the southeastern United States that export primary production to the estuary, phytoplankton
are responsible for the majority of primary production within the San Francisco Bay  estuary (Spiker and
Schemel, 1979).

       Levee failure will allow transgression of the Bay over former tidal marshes.  However, it is unlikely that
new emergent wetlands will be created for several reasons.  First, most of the diked baylands are currently at
or below sea level at present. In the Delta, some lands are 20 feet or more below sea level. Given the sediment
deficit predicted, these lands may not accrete rapidly enough to create emergent wetlands. Second, many of the
low-lying areas at elevations which might support emergent wetlands upon inundation are currently urbanized.

       If levees are maintained, the  diked wetlands behind these levees will degrade owing to the inability to
flush water through the channels without the use of large pumping systems. These areas will pond water during
the winter and then gradually dry out during the summer with a concomitant build-up  of salts in the soils.
Presently, salt build-up in low-lying diked wetlands of Suisun Marsh and in south San Francisco Bay is causing
a die-back in salt marsh vegetation and  anoxic conditions within marsh channels.

       Regardless of whether or not levees are  maintained, the primary fauna! impact will be  the loss of
vegetated  tidal marsh habitat that supports rare and endangered species.

       The changes in flow and salinity due to modification of freshwater inflow are more difficult to assess.
Marine and estuarine organisms will be distributed more landward due to the increases in average salinity
throughout the basin.   Circulation of the estuary will change dramatically as sea level rises and if levees fail.
Changes  in sedimentation,  retention times, and gravitational circulation are unknown, yet  are  crucial  to
understanding the biological responses. The predicted reductions in spring and early summer flow are the most
important to the functioning the estuary in its current configuration, especially to the transport of juvenile
anadromous fish such as salmon and striped bass.


SOCIOECONOMIC IMPLICATIONS

       Extensive public and private resources are invested in tidal and managed wetlands.  State and federal
agencies require  creation or restoration of wetlands as mitigation for development hi wetlands. To date, over
SO projects involving wetland creation have occurred in the estuary, ranging from 1 to 500 acres.  In addition,
the  44,000 acres of managed wetlands in Suisun Marsh represent a significant investment by public and private
interests.  These wetlands are used primarily for recreational hunting and fishing, itself an industry valued at
over $150 million annually (Meyer, 1987).  The state and federal governments are investing over $60 million
dollars to  construct new water supply facilities to the managed wetlands to reduce current salinity problems.


                                                3-27

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Josselyn

There are also indirect values attributable to public use of wetlands such as tourism, day recreation, and
non-consumptive uses. Finally, non-marketable values such as rare and endangered species and heritage values
must be considered.

       The losses  of wetlands, especially diked wetlands if levee  failure  occurs, would result in substantial
economic losses for the region.  If levees are to be maintained, the costs would also be significant.  The State
of California is currently embarking on a 10-year, $100 million levee stabilization project for the Delta. If the
higher ranges of sea level rise prove to be true, the cost of levee repair may be insurmountable.
                                                 3-28

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                                                                                             Josselyn

                                            CHAPTERS

                                      POLICY IMPLICATIONS


       San Francisco Bay conservation and Development Commission (1987b) has reviewed some of the local
policy decisions which must be made to deal with projected impacts to the estuarine shoreline. Generally, these
deal with implementing designs that take into account projected increases in sea level. The report also suggests
the establishment of monitoring stations around the bay to determine long-term changes in sea level and
provide data to estimate future changes.

       The most significant impact to the estuary will be the decision to maintain or abandon the current levee
system.  There is no choice for areas which are currently developed such as Foster City and Corte Madera.
Levees will  require replacement and pumping systems will have to be constructed The levees surrounding
less valuable land (agriculture and diked baylands) may not receive the same priority. Many of the levees are
poorly engineering (if engineered at all) and are subject to increasing rates of failure due  to high tides and
floods. Placing more rip-rap or higher levels of fill on these levees will not be an acceptable solution.  However,
maintenance of the levee system will be essential to reduce the amount of carriage water necessary to repel
salt water intrusion and to reduce flooding hazards to developed areas behind the diked baylands.

       Several engineering alternatives are possible. One is the construction of high tide/flood barriers which
will reduce the impact of high tides. Similar barriers are under construction on  the Thames River and the
Venice  Lagoon.  The cost would be substantial for San Francisco Bay and would have significant economic,
environmental,  and aesthetic impacts.   However, they would provide  protection to both  developed  and
undeveloped areas.

      A second alternative involves the construction of a salinity barrier to retard landward movement of salt
as sea level increases. The barrier could be a subsurface sill, a perforated wall, or a constricted channel located
along the Carquinez Straits. Whatever design is used, its function would be to regulate the location of the null
zone. Although there are a number of environmental problems associated with such a barrier, its primary
benefit would be a reduction in the amount of carriage water necessary to repel salt intrusion into the Delta.

      A third engineering alternative is to build up the elevations of diked baylands using dredge spoil or
other fill material.  This alternative would require making decisions which have severe short-term impacts such
as the temporary loss of seasonal wetlands (some of which harbor endangered species) in order to manage for
long-term impacts of inundation if the levees fail  Given the critical shortage of both tidal and seasonal wetlands
in the bay, few agencies will have the ability to suggest any alteration.  Nevertheless, it is important that some
initial projects be started to demonstrate the feasibility of altering elevations in diked wetlands with subsequent
restoration to functioning wetlands.  Otherwise, the ultimate fate of these areas will be complete loss.


ADDITIONAL RESEARCH

      All phases of estuarine research need to be continued, especially in the  area of circulation modeling
and factors affecting the distribution of organisms in the estuary. However, one  area which is  most lacking is
an understanding of sediment dynamics in the San Francisco Bay estuary.  Sedimentation and sediment
movement will play a crucial role in whether marshes, mudflats, and diked baylands will be sustained under
projected climate scenarios.   Most of the models  are based on relatively low  rates of marsh accretion
determined under current sea level rise.   However, several studies reviewed in this report showed that in areas
of subsidence, 'sedimentation can be extremely rapid.  Very few measurements of marsh accretion have been
conducted hi such conditions.  In addition, the rate of input of sediment relative to freshwater inflow needs to
be determined.
                                                3-29

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Josselyn

                                         REFERENCES


Atwater,  B.F., C.W. Hedel, and EJ.  Helley.  "Late Quaternary Depositional History, Holocene Sea Level
Changes, and Vertical Cmstal Movement, Southern San Francisco Bay." U.S. Geological  Survey Professional
Paper 1014. U. S. Geological Survey, Menlo Park, California, 1977. 15 pp.

Atwater, B.F., S.G. Conard, J.N. Dowden, C.W. Hedel, R.L. MacDonald, and W. Savage. "History, Landfonns,
and Vegetation of the Estuary's Tidal Marshes." In: San Francisco Bay: the Urbanized Estuary, T J. Conomos,
ed Pacific Div. Amer. Assoc. Advance. Sci., San Francisco, California, 1979. pp. 347-386.

Ball, M.D., and Arthur, J.F. Tlanktonic Chlorophyll Dynamics in the Northern San Francisco Bay and Delta"
In: San  Francisco Bay: the Urbanized Estuary, TJ. Conomos, ed Pacific Div. Amer. Assoc. Advance. Sci.,
San Francisco, California, 1979. pp. 265-285.

California Department of Fish and Game.  "Delta Outflow Effects on the Abundance and Distribution of San
Francisco Bay Fish and Invertebrates, 1980-1985."  Exhibit 60, entered by the California Department of Fish
and Game for the State Water Resources Control Board 1987 Water Quality/Water Rights Proceeding on the
San Francisco Bay/Sacramento-San Joaquin Delta.  California Department of Fish and Game, Sacramento,
California, 1987.  345pp.

California Department of Fish and Game. Testimony on the Suisun Marsh." Exhibit 5, entered by the California
Department of Fish and Game for the State Water Resources Control Board 1987 Water Quality/Water Rights
Proceeding on the San Francisco Bay/Sacramento-San Joaquin Delta.  California Department of Fish and
GAme, Sacramento, California, 1987b.

Conomos, T J. "Properties and Circulation of San Francisco Bay Waters."  IirSan Francisco Bay: the Urbanized
Estuary, TJ. Conomos, ed Pacific Div. Amer. Assoc. Advance. Sci., San Francisco, California, 1979.  pp47-84.

Davis, G.H. "Land Subsidence and Sea Level Rise on the Atlantic Coastal Plain of the United States."  Environ.
Geol. Water ScL, 10(2):67-80,1987.

DeLaune, R.D., R.H. Baumann, and J.G. Gosselink.  "Relationships  Among Vertical Accretion, Coastal
Submergence, and Erosion in a Louisiana Gulf Coast Marsh." J. of Sediment. Petrol., 53(1): 147-157,1983.

DeLaune, R.D., S.R. Pezeshki, and W.H. Patrick, Jr. "Response of Coastal Plants to Increase in Submergence
and Salinity." J. of Coastal Res., 3(4):535-546,1987.

Hackney, C.T., and W J. Cleary. "Saltmarsh Loss in  Southeastern North Carolina Lagoons: Importance of Sea
Level Rise and Inlet Dredging." J. of Coastal Res., 3(l):93-97,1987.

Harrison, E.Z., and AX. Bloom. "Sedimentation Rates on Tidal Salt Marshes in  Connecticut" J. of Sediment.
Petrol., 47(4):1484-1490,1977.

Hatton, RS~, R.D. De Laune, and W.H. Patrick, Jr. "Sedimentation, Accretion, and Subsidence in Marshes
of Barataria  Basin, Louisiana." Limnol. Oceanogn, 28(3):494-502,1983.

Hoese, H.D.  "Effect of Higher than Normal Salinities on Salt Marshes." Contrib. Mar. Set, 12:249-261,1967.

Hull, C.HJ., and* J.G. Titus, eds. "Greenhouse Effect, Sea Level Rise, and Salinity in the Deleware Estuary."
EPA-230-05-86-010.  U.S. Environmental Protection Agency, Washington, D.C., 1986.   88 pp.

Josselyn, M. "The Ecology of San Francisco Bay Tidal Marshes: A Community  Profile." FWS/OBS-83/23.
U.S. Fish and Wildlife Service, Division of Biological Services, Washington, D.G, 1983.102 pp.


                                               3-30

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                                                                                            Josselyn


Josselyn, M., and J.W. Buchholz. "Marsh Restoration in San Francisco Bay: A Guide to Design and Planning."
Technical Report No. 3. Tiburon Center for Environmental Studies, San Francisco State University, Tiburon,
California, 1984. 104 pp.

Josselyn, M., L. Handley,  M. Quammen, and D. Peters.  The Distribution of Wetland Habitats in the San
Francisco Bay Region." US. Fish and Wildlife Service, Office of Biological Services. Washington, DC. In press.

Kjelson, MA.,  P.F. Raquel, and F.W.  Fisher.  "Influences of Freshwater Flow  on Chinook Salmon in the
Sacramento-San Joaquin Estuary.  In:   Proceedings of the  National Symposium  on Freshwater Inflow  to
Estuaries, R.D. Cross and D.L. Williams, eds.  United States Department of the Interior, Washington, DC, 1981.
Vol. 2. pp. 88-108.

Klein ffl, CJ.,  S.P. Orlando, Jr., C.  Alexander, J.P. Tolson,  F.  Shirzad, R.B. Biggs, and E. Zolper. "How
Representative  Are the Estuaries Nominated for EPA's National  Estuary Program?  Strategic Assessment
Branch, National Oceanic and Atmospheric Administration. Washington, DC. 1988. 9pp.

Krone, R.B. "Sedimentation in the San Francisco Bay System."  In: San Francisco Bay: the Urbanized Estuary,
TJ. Conomos, ed. Pacific Div. Amer. Assoc.  Advance. Sci., San Francisco, California, 1979. pp. 85-96.

Krone, R.B. "A Method for Simulating Historic Marsh Elevations."  Unpublished 8 pp.

Letzsch, W.S., and R.W. Frey. "Deposition and Erosion in a Holocene Salt Marsh, Sapelo Island, Georgia." J.
of Sediment. Petrol., 50(2):529-542,1980.

Lyle, SJX, L.E. Hickman, Jr., and HA. Debaugh, Jr.  Sea Level Variations for the United States, 1855-1986.
National Oceanic and Atmospheric Administration, Rockville,  Maryland,  1987.

Mahall, B.E., and Park R.B. "The Ecotone between Spartina  foliosa Trin.  and Salicornia virginica L. in Salt
Marshes of Northern San Francisco  Bay: H. Soil Water and  Salinity." J. EcoL,    64:793-809, 1976.

Mall, R.E. "Soil-Water-Salt Relationships  of Waterfowl Food Plants in  the Suisun Marsh of California."
California Department  of  Fish and  Game,  Wildlife Bulletin No. 1.  California Department of Fish and
Game, Sacramento,  California, 1969. 59 pp.

Martindale, M. "Salicornia Europea L. and Salicornia virginica L. on a San Francisco Bay Salt  Marsh:  A
Study of  Factors Contributing to their Zonation Pattern."  MA. Thesis, San Francisco State University, San
Francisco, California, 1987. 52 pp.

Meyer, PA.  The value of wildlife in San Francisco  Bay." Exhibit 38,  entered by the  Bay Institute of San
Francisco for the State Water Resources Control Board 1987 Water Quality/Water Rights Proceeding on the
San Francisco Bay/Sacramento-San Joaquin Delta Bay Institute of San Francisco, Sausalito, California, 1987.
22pp.

Nichols, F.H., J.E. Cloern, S.N. Luoma, and D.H. Peterson.  The modification of an Estuary."  Science
231:525-648. 1986.

Nolan, K.M., and Fuller, C.C. "Sediment  accumulation in San Leandro Bay, Alameda County, California,
During the 20th Century:   A Preliminary Report." US. Geological Survey Water-Resources Investigations
Report 86-4057.  U.S. Geological Survey, Sacramento, California, 1986. 25 pp.

Orson, R., W. Panageotou, and S.P. Leatherman. "Response  of Tidal Salt Marshes of the U.S. Atlantic and
Gulf Coasts to Rising Sea Levels." J. of Coastal Res., l(l):29-37, 1985.
                                                3-31

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Josselyn

Patrick, W.H., Jr. and R.D. DeLaune. "Capacity of south San Francisco Bay Salt Marshes to Accrete Rapidly
Enough to Compensate for Subsidence and Sea Level Rise."  Manuscript.  Center for Wetland Soils, Louisiana
State University. Unpublished.

Pearcy, R.W., and  S.L. Ustin. "Effects of Salinity on Growth and Photosynthesis of Three California Tidal
Marsh Species." Oecologia, 62:68-73, 1984.

Perez,  RJ. "Salt Marsh Restoration from Former Salt Evaporators: Changes in Sediment Properties."  MA.
Thesis, San Francisco State University, San Francisco, California, 1981. 41 pp.

Pestrong, R.  "Tidal-Flat Sedimentation at Cooley Landing, Southwest San Francisco Bay." Sediment. GeoL,
8:251-288,1972.

Pethick, J.S. "Long-Term Accretion Rates on Tidal Salt Marshes."  J. of Sediment. PetroL, 51(2):571-577,1981.

Phillips, J.D. "Coastal Submergence and Marsh Fringe Erosion."  J. of Coastal Res., 2(4):427-436,1986.

Richard, GA. "Seasonal and' Environmental Variations in Sediment Accretion in a Long Island Salt Marsh."
Estuaries, l(l):29-35,1978.

Rosen, PJS. "A Regional Test of the Bruun Rule on Shoreline Erosion." Mar. GeoL, 26:M7-M16,1978.

Rozengurt, MA., MJ. Herz, and S.  Feld. "The Role of Water Diversions in the Decline of Fisheries of the
Delta-San Francisco  Bay and Other Estuaries."  Technical Report No. 87-8.  Romberg Tiburon Center for
Environmental Studies, San Francisco State University, Tiburon, California,  1987.158 pp.

Salinas, L.M., R.D. DeLaune, and W.H. Patrick, Jr.  "Changes Occurring along a Rapidly Submerging Coastal
Area: Louisiana, USA." J. of Coastal Res., 2(3):269-284, 1986.

San Francisco Bay Conservation and Development Commission. "Diked Historic Bayiands of San Francisco
Bay." Staf  Report. San Francisco, California, 1981 38 pp.

San Francisco Bay Conservation and Development  Commission.  "An Overview of the Impact of Accelerated
Sea Level Rise on San Francisco Bay."  Prepared by P.B.  Williams.  San Francisco, California, 1985. 25 pp.

San Francisco Bay Conservation and Development Commission. "An Overview of Flow and Salinity Standards
Required to Protect the Ecosystem of San Francisco Bay."  Prepared by P.B. Williams and M. Josselyn. San
Francisco, California, 1987a.  45 pp.

San Francisco Bay Conservation and Development  Commission.  "Future Sea Level Rise:  Predictions and
Implications for San  Francisco Bay." Prepared by  Moffatt and Nichol, Engineers, and Wetlands Research
Associates, Inc. San Francisco, California,  1987b. 98 pp.

San Francisco Bay Conservation and Development Commission.  "Recommendations for Salinity Standards to
Maintain the Wetlands of Suisun Marsh." Prepared by P.B.Williams and M. Josselyn. San Francisco, California,
1987c.  35 pp.

Spiker, E.C. and L.E. SchemeL  "Distribution and Stable-Isotope Compositon of Carbon in San Francisco  Bay."
In: San Francisco Bay: the Urbanized Estuary, T J. Conomos, ed. Pacific Div. Amer. Assoc. Advance. Sci., San
Francisco, California, 1979. pp. 195-212.

Stevens, D.E.  and L.W. Miller.  "Effects of river flow on abundance of young chinook salmon, American  shad,
longfin smelt, and delta smelt in the Sacramento-San Joaquin River system.  N. Amer. J. Fish. Manage., 3:425-
437,1983.


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                                                                                            Josselyn


Stevenson, J.C., L.G. Ward, and M-S. Kearney. "Vertical Accretion in Marshes with Varying Rates of Sea Level
Rise." In: Estuarine Variability, D. Wolf, ed. Academic Press, New York, 1986. pp. 241-260.

Stevenson, J.C., L.G. Ward, MS. Kearney, and T.E. Jordan. "Sedimentary Processes and Sea Level Rise in
Tidal Marsh Systems of Chesapeake Bay." In: Wetlands of the Chesapeake, HA. Groman, EJ. Meyers, D.M.
Burke, and JA. Kusler, eds. The Environmental Law Institute, Washington, D.C., 1985. pp. 37-62.

Stumpf, R.P. "The Process of Sedimentation on the Surface of a Salt Marsh."  Estuarine Coastal Shelf Sci.,
17:495-508, 1983.

Thompson, J JC and F.H. Nichols. "Food Availability Controls Seasonal Cycle of Growth in Macoma balthica
(L.) in San Francisco Bay, California."  J. Exp. Mar. Biol. Ecol., 116:43-61, 1988.

Titus, J.G. "Greenhouse Effect, Sea Level Rise, and Coastal Zone Management."  Coastal Zone Mngmnt. J.,
14(3):147-171, 1986.

Titus, J.G., C.Y. Kuo, MJ. Gibbs, T.B. LaRoche, M.K. Webb, and J.O. Waddell.  "Greenhouse Effect, Sea
Level Rise, and Coastal Drainage Systems."  J. Water Res. Planning Mngmnt.,  113(2):216-227, 1987.

United States Fish and Wildlife Service. "The Needs of Chinook Salmon, Oncorhvnchus tshawvtscha.  in the
Sacramento-San Joaquin Estuary." Exhibit 31, entered by the US. Fish and Wildlife Service for the State Water
Resources Control Board 1987  Water  Quality/Water Rights  Proceeding  on the  San Francisco Bay/
Sacramento-San Joaquin Delta.  U.S.  Fish and Wildlife Service, Sacramento, California, 1987. 179 pp.

Williams, P.B. "The Impact of Climate Change  on the Salinity of San Francisco  Bay."  Philip Williams and
Associates, San Francisco, California.  Environmental Protection Agency, Washington, DC.  1988.  38pp.

Williams, P.B. and J.T. Hollibaugh. "A Salinity Standard to Maximize Phytoplankton Abundance by Positioning
the Entrapment Zone  in Suisun Bay."  Exhibit 1, entered by the Contra Costa County Water Agency for the
State Water Resources Control Board 1987 Water Quality/Water Rights Proceeding on the San Francisco Bay/
Sacramento-San Joaquin Delta.  Philip  Williams and Associates, San Francisco, California, 1987a. 40 pp.

Williams, P.B. and J.T. Hollibaugh. "A Flow Standard to Maximize Phytoplankton Abundance by Positioning
an Entrapment Zone San Pablo Bay."  Exhibit 3, entered by the Contra Costa County Water Agency for the
State Water Resources Control Board 1987 Water Quality/Water Rights Proceeding on the San Francisco Bay/
Sacramento-San Joaquin Delta.  Philip  Williams and Associates, San Francisco, California, 1987b. 31 pp.
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PROJECTED CHANGES IN ESTUARINE CONDITIONS BASED ON MODELS OF
               LONG-TERM ATMOSPHERIC ALTERATION
                                 by
                          Robert J. Livingston
            Center for Aquatic Research and Resource Management
                        Florida State University
                         Tallahassee, FL 32306
                      Contract No. CR-814608-01-0

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                                  CONTENTS


                                                                         Page



ACKNOWLEDGMENTS	  iii

FINDINGS	  4-1

CHAPTER 1: INTRODUCTION	  4-4
      EXISTING DATA BASES: PROJECT SCOPE	  4-4
      THE APALACfflCOLA RIVER-BAY SYSTEM:  BACKGROUND 	  4-4

CHAPTER 2: METHODS  	4-13
      GENERAL APPROACHES  	4-13
      SCENARIOS FOR EFFECTS MODELING 	4-13

CHAPTER 3: RESULTS  	4-17
      RIVER FLOW SCENARIOS	4-17
            Water Quality Changes  	4-22
            Long-Term Biological Changes 	4-23
            Projections  	4-28
      TEMPERATURE	4-30
            Scenarios  	4-30
            Biological Response	4-30
      SEA LEVEL CHANGES	4-37
            Projected Effects	4-37

CHAPTER 4: DISCUSSION	4-44
      RIVER FLOW PROJECTIONS AND EFFECTS	4-44
      TEMPERATURE CHANGES AND EFFECTS	4-45
      PROJECTED CHANGES IN SEA LEVEL	4-46

REFERENCES	4-48
                                      11

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                                       ACKNOWLEDGMENTS


      This report is based on the work of literally hundreds of people over an 18-year period of study. The
funding of this work has been diverse, with various agencies (local, state, and federal) contributing over the years
to the final  product.  The Apalachicola data base is  the product of  the cumulative  efforts  of  students
(undergraduate and graduate), faculty, independent researchers, staff personnel, and a long line of volunteers
who have given their time in a most generous fashion. A series of project  managers and patrons have allowed
the continuous accumulation of field and experimental data during this period with the result that  we are now
in the final stages of analysis, modeling, and publication of the results.  Appendices cited in the text of this paper
were not included in this volume because of space considerations; however, they may be obtained from Jim Titus,
OPPE, EPA.  The appendices are:

I.     Analysis of existing (long-term, interdisciplinary) field data concerning a  series of river-estuaries in the
      southeastern United States (South Carolina, Georgia, Florida, Mississippi, Alabama).

II.    Curriculum vitae:  Robert J. Livingston.

HI.   Statistical analyses of epibenthic macroinvertebrates of the Apalachicola estuary.

IV.   Statistical analyses of fishes of the Apalachicola estuary.

V.    Data concerning sea level changes in the Apalachicola region.  (Dr.  Richard Park, Butler University)

VI.   Use of existing field data to evaluate the effects of projected long-term climatological changes in a south
      temperate gulf estuary.  (R J. Livingston, Florida State University)
                                                  ui

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                                                                                          Livingston


                                            FINDINGS1


      This paper addresses the problem of the potential effects of projected climatological changes on a
river-estuary  in the northern Gulf of Mexico. A series of scenarios was developed by other researchers
concerning long-term climatological changes due to atmospheric contaminants. Such scenarios involved changes
in  precipitation/river flow, temperature,  and sea  level  in the region  encompassing the Apalachicola-
Chattahoochee-Flint drainage system in Alabama, Georgia, and Florida Using long-term data sets (1972-1987)
from the Apalachicola estuary, it was possible to make estimates of potential changes in estuarine productivity
and dominant (commercially valuable) populations based on the long-term scenario predictions.


RIVER FLOW

      According to  models provided by Dr. C. F. Hains  (Northport, AL) concerning altered Apalachicola
River flow, the following projections (percent losses or gains) could be estimated concerning the response of
estuarine functions in the Apalachicola Bay system:

                                            SCENARIO

      FACTOR               GFDLx2       GISSx2        OSUx2
River flow                      -24.2          -6.2             -13.9
Mean salinity                   +113         +3.1             +6.5
Detritus/nutrients               -10-25         -3-7              -5-15
Detrital food web               -10-25         -3-7              -5-15
White shrimp                   -15-35         -5-10            -10-30
Pink shrimp                    +10-20       +3-5            +8-20
Blue crabs                     -15-35         -5-10            -10-30
Finfishes                       -15-35         -5-10            -10-30
Oysters                        -20-35         -7-10            -15-30


The above estimates are based on the combined losses of organic carbon and nutrients to the system and on
models of increased predation rate on nurserying juveniles of the top dominant estuarine populations due to
increased salinity. Oysters would be particularly vulnerable as the salinity increased owing in part to increased
predation and disease. These changes are predicated on the assumption that existing populations would not be
replaced either by adaptation of existing species or invasion by species new to the region.  Such an assumption
is somewhat unlikely but remains difficult to estimate in a quantifiable  fashion.  The increase in pink shrimp
would be due to the increased salinity and probable increases in the submerged aquatic vegetation that would
accompany the projected changes in river flow and estuarine habitat.


TEMPERATURE

      According to the GFDLx2 projections of changes in ambient temperature in the tri-river region, there
would be a mean increase of 3-5" C with even greater changes in the mean maximum temperatures of the estuary


        'Although the information in this report lias been funded wholly or partly by the US. Environmental
Protection Agency under Contract No. CR-814608-01-0, it does not necessarily reflect the Agency's views, and
no official endorsement should be inferred from it.

                                                4-1

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Livingston

with time. Such projections are based on the assumption that the estuarine water temperatures would closely
follow the air temperatures. Currently, the summer high temperatures of the Apalachicola estuary are close to
the upper thermal tolerance limits of most of the dominant (commercially important) species in the system.
With such assumptions in mind, the following projections were made based on the established thermal tolerance
limits and projected changes of water temperature in the Apalachicola estuary:

                                                         SCENARIO

      FACTOR                      GFDLx2         GKSx2      CURRENT (AMBIENT)*
Blue crab larvae                       -100             -100              -77
Blue crab juveniles                     -100             -100              -27
Spotted seatrout                        -91              -59              -5
Oyster larvae                           -91              -59              -5
Adult oysters                          ILC.              n.c.               ILC.
Pinfish                                 -91              -59              -5
Flounder                               -91              -59              -5
Redfish                                -55               -9               ILC.
Mullet                                  -5              ILC.               ILC,
White shrimp                            -5              n.c.               n.c.

•current loss of habitat use due to summer high temperatures


      The above estimates are indications of a loss of estuarine habitat to various commercially important
species due to increased water temperature. Currently, juveniles of such species use the estuarine habitat as a
highly productive nursery and as a haven from offshore (stenohaline) predators.  Such a loss of habitat could not
be replaced and the movement of the nurserying populations to offshore regions in the gulf would probably lead
to major losses of such populations owing to offshore predation.

      The possibility of adaptation and/or replacement by tropical species  is quite real but is  not easily
estimated in the above models. There are currently some very productive tropical estuaries which could serve
as models for projected changes in the Apalachicola region. Species such as mullet and white shrimp would not
necessarily be lost if temperature is the only variable under consideration. The above projections should also
be qualified by the various  assumptions implicit in the analysis. However, if the projected temperature changes
do occur, there will be some major changes in the structure and productivity of the Apalachicola Bay system with
considerable shifts in population dominance  and fisheries potential. By analogy, such changes could be projected
to other important river-estuaries along the northern gulf coast.


SEA LEVEL RISE

      Paniculate organic carbon (POC) can be used as an indicator of allochthonous and autochthonous sources
of production in the Apalachicola estuary. There are considerable data concerning the production, distribution,
and utilization of POC in the Apalachicola drainage system. According to a series of models developed by Dr.
Richard Park (Indianapolis, Indiana), various scenarios based on different levels of sea level rise were produced
concerning the changes in wetlands currently within the Apalachicola drainage system. Such changes were then
translated into overall gains or losses of POC over the period 1987-2100. The following is an estimate of the net
input of organic carbon (metric tons/year) in the Apalachicola system based on current conditions and projected
sea level rises in the region:
                                                4-2

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                                                                                             Livingston

                                           SCENARIO (by 2100)

 FACTOR                                 Current        1.2mm/yr     OJm/yr     l.Om/yr   2.0m/yr

 Freshwater wetlands                         30,000          26,100       24,000       21,300     4,980
 Seagrasses                                  27,200          28,700       28,800       30,100     31,035
 Saltmarsh                                  46,905          23,500       4,690        940       780
 Phytoplankton                              233,280         144,640      71,450       58,790     15,160
 Total                                       337,385         222,940      128,940      111,130    51,955


     Since there are various other limiting factors that contribute to the recreational and commercial fisheries
 of the region, it is difficult to estimate the potential losses of such populations.  However, the various wetlands
 serve both as habitats and sources of productivity to the bay system. Any loss of such wetlands would certainly
 lead to reductions of these estuarine populations.  One of the key issues in this estuary concerns the fate of
 phytoplankton production which is the single most important factor in the high level of productivity in  the bay.
 Such production is largely a measure of freshwater input (e.g., a major nutrient source) to the system and the
 shallowness of the estuary.   The relationship of the phytoplankton and the surrounding wetlands is poorly
 understood The above results are predicated in part on the correlation of phytoplankton productivity and the
 extent of wetland areas; this assumption is further complicated by the lack of an adequate forecast of microbial
 activity in the estuary (based on increasing losses of wetlands with time). Such twin uncertainties would thus
 serve to qualify the above projections and would limit any meaningful extrapolation to detailed projections of
 population response to such changes. Nevertheless, the progressive loss of wetlands to the region would lead
 to substantive changes in the habitat availability and nutrient/POC budgets in the Apalachicola drainage  system.

     It is quite likely that a substantial, perhaps even proportional, loss would occur of species that depend on
 the detrital food webs of the estuary.  Such species would include white shrimp, blue crabs, spot, Atlantic
 croakers, and white seatrout.  If, as postulated, the phytoplankton productivity were adversely affected by such
 losses,  species such as anchovies and oysters would also be adversely affected.  There would be a replacement
 of some of the primary productivity  of the estuary through increased sea grass  bed distribution. This would
 improve the production of species such as pink shrimp and spotted seatrout.

     There are certain shortcomings to the application of correlations, regressions, and multivariate statistical
 techniques to a complex data set that  includes many (diverse) biological variables. Accordingly, this analysis has
 included various assumptions that must be taken into  account when evaluating projected changes  in the system
 according to anthropogenous alterations in the various climatic features of the system.  In addition,  changes in
 both water quality and freshwater input to the system due to future human activities associated with municipal
 developments, industrial expansion, and agricultural activities could significantly alter these projections. An effort
 has been made to preclude such impacts, although various forms of deterioration due to human activities around
 the bay have already taken a serious toll on  the Apalachicola fisheries.  Such effects  can be  addressed.  By
 contrast, climatological changes would eventually be beyond any efforts of amelioration. It is almost a certainty
 that the Apalachicola estuary, as it  currently exists, would be  severely altered by a  global warming trend.
Although  it could be transformed into a productive tropical estuary dominated by seagrass productivity, it is
highly likely that there would be  an overall decrease in fisheries production and that qualitative changes in the
commercially valuable populations would lead to far-reaching changes in fisheries equipment and  practices and
the availability of seafood to people in the region.
                                                 4-3

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Livingston

                                            CHAPTER 1

                                         INTRODUCTION


EXISTING DATA BASES:  PROJECT SCOPE

     This project was designed to evaluate the possible effects of projected long-term changes in the climate
(e.g., temperature, precipitation) and associated variables (river flow, sea level changes) related  to increased
atmospheric CO2 and other  greenhouse gases. The analysis was based on a 15-year data base  taken in the
Apalachicola Bay system (East Bay, Apalachicola Bay:  Figure 1). Data were taken continuously on a weekly
to monthly  basis from  March 1972 through  October 1987.   Station groups were summed in  East Bay
(oligohaline) and Apalachicola Bay (mesohaline to polyhaline).  The Apalachicola River system (regional) and
Tale's Hell Swamp (local) constitute the two major sources of fresh water to the system.


THE APALACHICOLA RIVER-BAY SYSTEM: BACKGROUND

     The Apalachicola River is the terminus of a major drainage system that is located mainly in  Georgia and
Alabama Seasonal river flow patterns reflect Georgia precipitation and seasonal changes in evapotranspiration
rates of the tri-river (Apalachicola-Chattahoochee-Flint) system (Meeter et aL, 1987).  The winter-spring river
peak flows strongly influence various ecologically important estuarine  habitat variables (Livingston, 1984a).
Emergent and submergent vegetation (freshwater to marine), along with phytoplankton production, provide a
year-round source of energy  for the system (Livingston, 1983, 1984a) as mediated by river  flow and microbial
activities (Figure 2).  The relationship of river flow  and microdetritus is well established as a function of the
winter-spring flood events (Table 1). Long-term river flow changes follow interannual periods that approximate
5-7 year cycles (Meeter et al., 1979). Such trends determine salinity changes in the bay, which, in turn, have an
important impact  on the biological organization of the estuary (Livingston, 1984a).

     Organisms at different  levels of biological organization follow different  seasonal patterns of numerical
abundance and species richness (Figure 3).  Peak numbers of fishes, dominated by the sciaenids, occur in the
winter-early spring months whereas epibenthic invertebrates peak in the late summer and fall months and the
infaunal macroinvertebrates peak in the winter.  Specific dominant fish and invertebrate populations, some with
great commercial value, follow highly individualistic patterns that vary seasonally and yearly (Figures 3, 4, and
5).  Most such populations spawn offshore (Figure 6).  Others, such as oysters, spend their entire lives in the
estuary.   As a consequence of the river  input and basic climatological conditions, the Apalachicola system
produces a major  fishery with a dock value of oysters, penaeid shrimp, and finfishes of over $12 million in 1980.
According to Prochaska and Mulkey (1983), application of multipliers to such output would increase the regional
value of such fisheries to between $17 and $23 million for that year. Projected values of the  estuary by the year
2000 have varied  from $34 million (Colberg et al., 1968) to $67 million (Mulkey and Maturo,  1983).  The
Apalachicola Bay system also serves as a habitat for various rare, endangered or threatened species as well as
a major flyway for diverse birds (Livingston and Joyce,  1977).

     The basic model (Figure 7) for the  ongoing analysis of the Apalachicola system indicates  the extreme
complexity involved in any prediction of future changes based  on climatological alterations.  Because of this
complexity, there are various  assumptions that must be made when applying statistical models to the field data.
Such assumptions severely restrict a truly quantitative approach to the problem so that the following analysis
is basicly a first-cut attempt to project relatively broad responses to the projected changes in the system based
on the climatological scenarios provided by the Environmental Protection Agency.
                                                4-4

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                                                                                              Livingston
                                      jpalachicola
                                         t'nv
                                     lull ol
Figure 1.   The Apalachicola estuary showing loeal drainage areas and the river model showing relationships
           of detrital flow through the Apalachicola estuary (according to Livingston, 1984a). Also shown are
           sampling stations used in the long-term monitoring program.
                                                  4-5

-------
 Livingston
      RUVMOI-FLOW
         PRIMARY
        PRODUCTION
     SALINITY
DISSOLVED NUTRIENTS
DETRITUS   ^
                 substrate
                                  MICROBIAL
                               ?' SUCCESSION
                                .*
             GRAZING
            ORGANISMS
          TIDAL SUBSIDY
               WIND
           DISTURBANCE]
                              INCREASED   MICROBIAL
                                      BIQMASS
                PHB/LIPIDS
                14CO2 RELEASE
                O2UTILIZATION
             BASE,DETRITAL FOOD  WEBS
Figure 2. Model showing relationships of detrital flow through the Apalachicola estuary (according to
      Livingston, 1984a).
                            4-6

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                                                          Livingston

Table 1. Linear Regression (log/log) of Total Microdetritus (Ash-Free Dry Weight) and Riverflow (m3/sec)
      by Month/Year by Season (8/75-4/80)
  Station 7  (Surface.)

     December-February      0.70     0.49
     March-May               0.77     0.60
     June-August            0.08     0.01
     September-November     0.48     0.23
a (significance)

    0.00188
    0.00057
    0.39863
    0.03469
  Station 7  (Bottom)

     December-February      0.77     0.60
     March-May               0.55     0.30
     June-August            0.08     0.01
     September-November     0.21      0.04
    0.00037
    0.02253
    0.40243
    0.22867
  Station 8  (Mid-depth)

     December-February     0.64     0.40
     March-May              0.68     0.46
     June-August           0.35     0.12
     September-November    0.19     0.04
    0.00570
    0.00397
    0.11809
    0.25542
                               4-7

-------
Livingston
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Figure 3.  Mean monthly values of numerical abundance and species richness of infaunal macroinvertebrates,
          epifaunal macroinvertebrates, and fishes taken in the Apalachicola estuary from 1972-1982.
                                                 4-8

-------
 3000
 2000
 1000
                                                                  Livingston
                                                                     216
                                           Micropogon undulatus
Anchoa  mitchilli
      I
             Cynoscion
                arenarius
                                Leiostomus  xanthurus
           hloroscombrus
             chrysurus
                                Bairdiella  cnrysura
  240
  120
         MJSDMJSDMJSOM        MJSDMJSDMJSOM

                        TIME-MONTHS (3/72-3/75)

Figure 4. Monthly numerical abundance and mean lengths of dominant fishes in the Apalachicola estuary from
       3/72-3/75. Populations include anchovies, croaker, spotted seatrout, spot, Atlantic bumper, and silver
       perch.
                                   4-9

-------
Livingston
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                                     42-
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                                    s     s     s
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                MJ S OMJ S DMJ S DM
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Figure 5. Monthly numerical abundance and mean lengths of dominant invertebrates in the Apalachicola
       estuary from 3/72-3/75. Populations include white shrimp, paleomonetid shrimp, pink shrimp, blue
       crab, brown shrimp, and least squid.
                                   4-10

-------
                                                                                  Livingston
   OVIGEROUS
   FEMALES

   DEVELOPING
   STAGES

   OFFSHORE
   CURRENTS
megalops
                                                                             V  k.
                      'first  crab'
 Figure 6.  Schematic diagram of the life history movements of blue crabs along the Florida gulf coast. Ovigerous
         females move north to spawn in the waters offshore from the Apalachicola-Ochlockonee River
         systems. After spawning, the young move south, possibly using existing currents in offshore areas.
         The young crabs nursery in inshore waters such as the Apalachicola estuary.
                                           4-11

-------
                         REGIONAL
                          RAINFALL
                     Soil Compoiillon
                   Floodpioin Inundation
EVAPOTRANSPIRATION
             VEGETATION  DISTRIBUTION
                                                  MICROBIAL
                                                  SUCCESSION
t
Leaf Production
Decomposition
Fragmentation V
RIVER PEAKS
/ it\
NulrUnt/Ddrltut Flow
                                                   Tidal
                                                   Wind

                                                  Biological
                                               Oitiufbonc*
              Subwdy
                               I LOCAL  RAINFALL


                           Eimro«it  V»g«laHon  ^^V*
Nulriwili
                     SUBMERGED VEGETATION   PRODUCTIVITY
                              Salinity Gradltnli
                                                   PHYTOPLANKTON  PRODUCTIVITY
                      Conditioning: Porllculot* Organic
                                                                                  DETRITUS- FEEDING
                                                                                   INVERTEBRATES
                ASSOCIATED
           INVERTEBRATES
                               OLIGOCHAETE
                            kMPHIPODS and POL
                                                                  NKTON—t
                                                                  \. INVERT!
                                  EEDING
                                  BRATES
                                                                  RS (Inclu ling man )
                      DEC.
            APR.    MAY    JUN.

                TIME (months)
                                                                                                           NOV.
Figure 7. Model showing possible relationships of different variables in the Apalachicola estuary. The model incorporates the various
         sources of energy to the system in addition to the various forms of climatological factors that control the biological processes in
a         the estuary. The dominant populations are shown during months when organisms reach peak numbers in the estuary.

-------
                                                                                          Livingston

                                            CHAPTER 2

                                            METHODS
GENERAL APPROACHES
     Ideally, to develop the appropriate models  for an  assessment of the potential impacts of projected
climatological changes on the Apalacnicola estuary, we would need to build a comprehensive model of the
entire ecosystem and food web.  Such a model would have to involve both time series analysis and simulations
that would incorporate observed changes, temporal dynamics, and species interactions. Such an effort is outside
the scope of this effort.  Instead, we have used simple regression and multivariate analyses to evaluate  the
responses of various portions of the estuary over 15 years of continuous observation.  Thus, this analysis can be
considered preliminary to a more comprehensive modeling effort using more sophisticated modeling techniques.

     The Apalacnicola river-estuary has been studied for the past 18 years (15 years of field work, 3 years of
analysis) by scientists associated with the Center for Aquatic Research and Resource Management (Florida
State University).  A complete list of the data available  for this analysis along with a  summary of quality
assurance and analytical protocols is given in Appendix I (appendices are available from the U.S. Environmental
Protection Agency).  Background publications are  presented in Appendix n. The field data base  consists of
monthly collections of important variables taken over a 13-year period (Table 2).  The oyster populations were
studied  for an additional 2 years.  The exact methods of data collection have been given in Livingston (1983,
1984a) and associated papers (Appendix II). Apalacnicola River flow was a primary variable in the evaluation.
During  the study, there was a major river flooding  episode (1973-1975) and a prolonged drought (1980-1981).
The response of the estuary to these extreme conditions was evaluated relative to background levels of river flow.

     The Apalacnicola estuary  was the subject of the present analysis which  included  station-specific data
(Figure  1), grouped stations (East Bay, Apalacnicola Bay), and estuary-wide averages. A series of scattergrams
was developed using the monthly field data. Various transformations were  made to determine the best fit for
a normalized distribution when running the different statistical analyses.  Such transformed data were used for
all calculations.  Moving averages were  constructed to eliminate specific aspects of the short-term  variability.
Multiple correlation matrices were constructed using all variables. Such correlations were made with and without
lags of key factors. Regressions were used to determine specific relationships among the climatological, physical,
chemical, and biological variables. In this way, certain relationships between and among the different dependent
and independent variables were  ascertained.


SCENARIOS FOR EFFECTS MODELING

     This analysis required scenarios of future temperature, river flow, and wetland loss. Estimates of wetland
loss due to sea level rise were provided by Park (Volume B). Mains (Volume A) provided scenarios of future
river flow based on climate model projections of temperature, rainfall, and evapotranspiration. In addition, we
developed scenarios of river flow based solely on precipitation scenarios from the models.  The climate change
scenarios provided by the Environmental Protection Agency (EPA) were used as inputs to the effects models.
We used the GISS 2CO2 and the GFDL 2xCO2 models for our calculations. Data provided by the EPA were
used (e.g., means by month) for  surface air temperature (°C) and precipitation (mm day*1) levels in our region
(GFDL, 82^°W, 3333°N; GISS, 80°W, 35.22°N)  (see Table 3).  Rainfall data  (ambient) were used from
Columbus, Georgia (85°W, 32J°N). The NOAA data from Columbus, Georgia included minimum temperature
(°F), maximum temperature (°F), and precipitation  (0.01 inch) on a daily basis from 1/1/48 to 12/31/83. Data
bases were constructed for the entire data set and  for data from 1951 to 1980.  The period 1/84 to 7/84 was
added from our own data base so that the final numbers could then be applied Table 2.
                                                4-13

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Livingston

                  Table 2. Data Base Used in the EPA Climate Project
                 I.  Climatological  features
                       a.  temperature
                       b.  precipitation
                       c.  river flow

                 II.  Physical/chemical  factors
                       a.  temperature
                       b.  salinity
                       c. dissolved oxygen
                       d.pH
                       e.  color
                       f.  turbidity
                       g. oxygen anomaly

                 III.  Productivity features
                       a.  microparticulates
                       b.  macroparticulates
                             1.   leaf  matter-detritus
                             2. sea grasses

                 IV.   Biological  variables
                       a.  infaunal  macroinvertebrates
                             1. numbers
                             2. biomass
                       b.  epibenthic  macroinvertebrates
                             1. numbers
                             2. biomass
                       c.  fishes
                             1. numbers
                             2. biomass
                       d.  fisheries
                                    4-14

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         Table 3. Decadal Monthly Values for GISS and GFDL Models at Georgia Grid Points*
HONTH-
TYPE
GISST
GISSP
GFDLT
GFDLP
1

1.0128
1.0873
1.0122
.7689
2

1.0122
1.2439
1.0127
1.0537
3

1.0218
1.2369
1.0151
1.1465
4

1.0114
1.1783
1.0184
1.1493
5

1.0149
1.1392
1.0127
.8283
6

1.0128
1.3120
1.0209
.4408
7

1.0102
1.4133
1.0245
.6514
8

1.0073
1.2309
1.0148
.4257
9

1.0173
.7970
1.0206
.8082
. 10

1.0183
.9699
1.0161
.6737
11

1.0128
.7464
1.0190
1.0781
12

1.0097
.9169
1.0190
1.0716
T
P
    temperature
    precipitation
* Values for Columbus, GA (85°N, 32.5'W)

-------
Livingston

to our long-term (Apalachicola) data base, which ran from March 1972 through July 1984.  The calculated
adjustment factors for the GISS and the GFDL models were then applied to the data for the predicted effects
of the doubled CO2 scenarios. There was a different adjustment factor for each month in the respective models
for the surface air and precipitation values:

          Temperature: [(average TX 273.15)Xadj] - 273.15
          Precipitation: total precipitationXadj.

The  ambient data (precipitation, temperature maxima and minima) were then compared with the projected
(GISS.GFDL) results for the different time periods (1/1/48-12/31/83, 1/1/51-12/31/80, 3/1/72-7/31/84).

     The GISS precipitation model predicted generally higher levels of precipitation, whereas the GFDL
precipitation  model predicted somewhat less.  The study period (3/72-7/84) appears as part of an overall
(long-term) decline in precipitation with the drought of 80-81 shown clearly. When viewed as differences in the
respective moving averages, the GISS model predicts average increases of a little less than 2 cm/month whereas
the GFDL  model predicts average decreases of just under 2 cm/month.

     Using the previously developed regressions Unking precipitation to Apalachicola River flow at Blountstown,
Florida, we used the various data  projections to calculate new river  flows (based on the GISS and GFDL
scenarios).  The regressions were also used to calculate the salinity regimes in the Apalachicola estuary over the
period of observation (3/72-7/84). The various relationships (e.g., correlation, regression) were then calculated
for the different physical, chemical,  and biological variables  (Table 2).  Regressions were applied to the model
river data to get projected river flows according to the EPA models.  Such models were then applied to the
various biological variables taken hi the Apalachicola estuary (3/72-7/84).

     The various biological indices (e.g., population, community) were then analyzed in the existing data base
with  particular attention to responses to changes in river  flow, salinity, and temperature.  Such data were
supplemented with reviews of the literature concerning temperature ranges and optima for estuarine dominants
and commercially valuable species.  Using the above-outlined scenarios of climate change and the combination
of quantitative and qualitative analyses of biological response to such variables, a series of tentative conclusions
was outlined relative to the ranges of predicted events.
                                                4-16

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                                                                                          Livingston


                                            CHAPTERS

                                             RESULTS


 RIVER FLOW SCENARIOS

     An application of the models to the Columbus, Georgia precipitation data over the 13-year study period
 indicated differential seasonal differences. The GFDL model predicted considerable reductions in rainfall that
 are most evident during January and summer-fall periods.  The GISS model predicted general increases in
 precipitation that approach 8 cm in extreme cases. The overall trends showed differences approaching or
 ± 2 cm in the two models. There is a pronounced reduction in the differences in the GFDL model during the
 latter half of the record, which reflects general reductions of Georgia precipitation.

     The Apalachicola River flow data (Figure 8) show the extreme high and low level years quite clearly.
 Generalized peak flows were most consistent during 1973; the overall drought months (20) occurred during the
 period 1980-1981. These overall trends are evident in the 12-month summed data.  Peak flows occurred during
 1973; this was followed by an overall decline after a secondary peak in 1975. This decline ended in the drought
 year 1981, which was followed by a moderate increase in 1983-1984.  During the 24-month period of 1980-1981,
 15 months had monthly river flow averages that were below 500 m3; this was the most prolonged drought during
 the period of observations (3/72-7/84). The seasonal distribution of such flow conditions showed that, with the
 exception of the spring of 1979 and 1980, the overall decline of Apalachicola River flow from 1973 to 1981 was
 real. Summer-fall periods varied the least with  the general trend following the winter-spring flows.

     A statistical examination  of the relationship between Apalachicola River flow and rainfall (Columbus,
 Georgia; Apalachicola. Florida; East Bay, Florida) indicates that only Georgia rainfall is significantly associated
 with such river flow (r= 0.207, p < 0.0001). There is a close relationship of Georgia rainfall and river flow with
 some evidence of an averaged lag period of 3 months between the  Columbus, Georgia, precipitation and the
 Blountstown, Florida, Apalachicola River flow.  Using the established regression between these two variables,
 the GISS and GFDL models  were run to  determine the new river flow conditions under the projected
 precipitation change. The GISS model predicted an average increase in river flow conditions from the observed
 (from 722.277 to 827307 m  per second).  The minimal levels were  higher, whereas the maximum levels were
 lower.  The GFDL model, however, predicted an average decrease  (from 777369 to 722.277 m3 per second).
 Minimal values increased, while maximal values were less. In both cases, the extremes were moderated with less
 high flow in the winter-spring months and higher flow rates during the summer-fall months.

     The mean salinities for the Apalachicola estuary (Figure 10) show trends that are related to the above
 described long-term changes in meteorological conditions.  During the 1980-1981 period, 10 mean salinity points
 were above 15 ppt, the highest such number  in the entire survey.  The spring flooding  in 1979 and 1980 was
 particularly effective in driving down the bay salinity levels. A 12-month moving average of the data (Figure 11)
 shows the general decline in salinity prior to the major increases during the drought of 1980-1981. The spatial
 distribution of such salinity during the drought showed considerable vertical stratification and distinct differences
between the oligohaline East Bay and the meso- to polyhaline  Apalachicola Bay (Livingston, 1984a). Such
differences are crucial to an understanding of the biological organization of this estuary.

      The GISS and GFDL models were run with the Apalachicola River flow regressions (Table 4).  According
to the GISS model, mean salinity (overall) is decreased from 8.9 ppt to 8.4.  Model projections of minimum and
maximum salinities are considerably higher and lower, respectively, when compared to the actual data.  According
to the GFDL model, mean salinity increases from 8.9 ppt to 92 ppt. The relationship of the minimum and
                                               4-17

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Livingston
      5000
            1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982  1983 1984
                                           YEAR
WINTER
SPRING
SUMMER
FALL
 Figure 8.  Annual quarterly (seasonal) means of Apalachicola River flow from 1972-1984. Data show peak levels
         during 1973-1975 and the drought during 1981-1981
                                        4-18

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                                                                                          Livingston
                             y   «  -2.913X   *  10.996,   R-squared:   .461
     f
     T3
      M
     •3

     f
                                       2.6          2.8           3
                                           tog(1+x)  of river  flow
3.2
3.4
Figure 9.  Regression of the square root of salinity on the log (x+1) Apalachicola River flow (monthly data;
          1972-1984).  Salinity represents monthly means of all stations in the estuary, surface, and bottom.
          River'flow data include monthly means taken at  Blountstown, Florida.  There is a strong inverse
          relationship between these two variables.
                                                4-19

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                                     APALACHICOLA ESTUARY: TOTAL
Livingston
                       •       J«       <•      «•      ••      !••     12*     14*
                                               MONTH
Figure 10.  Average salinity data taken monthly in the Apalachicola Estuary from 3/72-7/84. Monthly means
          of all stations (surface and bottom) were used for this analysis.
                           APALACHICOLA SYSTEM: MA-12
I
e
z
                     14
                     12
                     10
                                                                1200
                                                                1000
                                                                400
                                                                      O
                                                                      HI
                                                                      9.
DC
UJ

E
    —  SALJIA-12
    -*•  R1VERI1A-12
                                                                200
                       ooooo*oooooooo«

                                  TIME IN MONTHS

Figure 11. Twelve month moving average of river flow  and average salinity data taken monthly in the
          Apalachicola estuary from 3/72-7/84.   River slow data are monthly mean values taken at
          Blountstown, Florida. Salinity data include monthly means of data taken from all estuarine stations
          (surface and bottom).
                                             4-20

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                                                                           Livingston

Table 4. A:   Regression Statistics Concerning the Relationship of Apalachicola River Flow and Mean Salinity
           Values in the Apalachicola Estuary
                      Mean	Std.  Dev.   Min.   Max. Ranoe   # cases

          River flow  8.9         5.7         0      25.0 25.0        149

          GISS       8.4         2.1         2.2   12.0 9.8         149

          GFDL       9.2         1.9         2.7   12.0 9.2         149
      B:  Regression Statistics Concerning the Relationship of Apalachicola River Flow and Salinity
          According to Hains' GISS, GFDL, and OSU Projections
          OSU
                      Data projections  are made monthly from  January,  1948
                            through July.1984.
          Nat, flow   9.9

          GISS
Mean Std. Dev Min.
9.9
10.
11.
10.

2
0
6
3.
3.
3.
2.
1
0
1
5
0
0
0
2.1
Max.
1
1
3.
3.
14.
1
3.
7
9
3
6
Ranae
1
1
3.
3.
14.
1
1.
7
9
3
5
# cases
372
372
372
372
                                        4-21

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Livingston

maximum salinities parallels that described for the GISS model.  In both models, the peaks and valleys of the
salinity levels are largely eliminated.  Such results are obvious artifacts of the methods (e.g., regression) used to
arrive at the final projections.

     River flow projections according to the Hains models indicated a 24.2% decrease in river flow projected
by the GFDLx2 model; lessor decreases were noted with the GISSx2 and OSUx2 models. Such changes in river
flow would result in a maximum (GFDLx2) increase of 113% of the mean salinity in the Apalachicola estuary
(Table 4).  Such a change would result in generally higher salinities over tune with such increases particularly
evident in the summer-fall highs. These differences approach a range of + /-10 ppt in a given month.  The
differences can go either way over the tune of projection although most such differences are positive.

Water  Quality Changes

     Various water quality factors in the Apalachicola estuary are strongly affected by river flow conditions
(Livingston, 1984a).   Mean turbidity values  for  the  estuary  reflect the major river flooding during the
winter-spring of 1973. The decreased turbidity in the following years indicates reduced flows; the same is true
for the latter portions of the 1980-1981 drought. Color levels follow a different pattern with the lowest levels
noted during the 1980-1981 drought. The long-term pattern of Secchi data reflects the above-noted episodes of
river flow; the highest Secchi readings (e.g., the clearest water) were noted during the drought period.  The
oxygen anomaly (AO2), denoted as excesses of dissolved oxygen above or below solubility, has been associated
by some  authors with productivity.


     AO2 = O2 - O2   (calculated from the equation  of Weiss 1970 as the difference between the measured
                       oxygen and the oxygen solubility at ambient temperature and salinity)

     Analyses of the oxygen anomaly in the Apalachicola estuary (Justic and Livingston, in review) indicate a
weak positive association with  temperature and a  weak negative association with river flow in East  Bay and
Apalachicola Bay. Relatively high correlations were noted in surface waters hi both systems between the oxygen
anomaly and salinity; lesser such correlations were noted at the bottom. This would indicate possible increases
in phytoplankton  activity during periods  of high temperature, low river flow, and high salinity.  Such trends
would be consistent with known patterns of phytoplankton productivity in the estuary (Livingston, 1983,1984a).
This would indicate that reduced river flow could be associated, at least during the short term, with increases in
phytoplankton productivity.

     River-driven estuarine systems such as the Apalachicola are often dependent, to a considerable degree, on
organic detritus that comes from upland wetlands and submerged aquatic vegetation. From January 1976 through
March 1982, a survey was conducted to determine the relative amounts of POM that were occurring in the bay.
Water from the lower end of the river was pumped through a series of sieves and analyzed for organic carbon
(total particulate organic carbon; sized fractions of POM from 2 mm to 0.1 /im). Such matter forms the substrate
for microbial activity that is directly linked to the detritus-based  food webs of the bay  (Livingston, 1984a,
unpublished data with D.C. White).  The regional  rainfall (Georgia and Florida) along with the Apalachicola
River flow was low from June 1980 through December 1981. Although concentrations of TPOC were high during
certain months of the drought, the overall mass transport of TPOC was relatively low throughout this period
Such mass flows are directly related to river flow levels. Most of the POC coming down the river is composed
of very fine particulate organic carbon (VFPOC).  The TPOC  (concentration and mass flow) was negatively
associated with temperature and positively associated with river  flow.  In East Bay during the drought, salinity
was uniformly high.  Color was low and Secchi readings were high, and the peak oxygen anomaly  readings
occurred at this time.  The bay salinity was very high during the drought; in most months, mean levels exceeded
20 ppt. Once again, Secchi readings were high and color was low. The POM was particularly low in all  its forms
during  the drought.  A summary of these data (Appendix VI) indicates that all forms of POM except  the  SAV
were lower during the drought  than at other times when rainfall/river flow rates were higher.
                                                4-22

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                                                                                             Livingston

      The data indicate that, during periods of low rainfall and river flow, the bay becomes clearer, more saline,
 and has less POM derived from the winter/spring winter flooding.  Such conditions could lead to short-term
 increases in  phytoplankton productivity and the proliferation of submerged aquatic vegetation.  When viewed
 on the basis of overall productivity in the bay, there would be a net increase in the spring to fall productivity at
 first. However, as nutrients became limiting, there would  probably be  a trend toward lower phytoplankton
 production and higher SAV production with conditions resembling the seagrass systems of Apalachee Bay (see
 Livingston, 1986, for a review of the long-term conditions in this system).  Those forms that utilize the estuary
 in the winter-early spring months (sciaenid fishes)  or are dependent  on phytoplankton productivity (oysters)
 would be  adversely affected  by such  conditions.  Other species,  such  as the spotted seatrout (Cvnoscion
 nebulosus) and other forms that depend on the seagrass beds would be favored. The data indicate that if river
 flows are reduced, the major commercial fisheries of the Apalachicola Bay system would be adversely affected
 whereas the sports fisheries associated with increased seagrass beds would be enhanced. Prolonged increases
 in regional rainfall  and river flow would have the opposite effect.

 Long-Term Biological Changes

      Salinity is one of the most important ecological variables in estuarine systems. Low and variable salinities
 tend to reduce the number of offshore species that can live in the estuary; this makes this kind of system a haven
 or sanctuary for those species that are euryhaline.  The complex community structure and food web patterns that
 are associated with the high productivity and salinity relationships tend to explain why estuaries are important
 centers  of sports  and  commercial  species.    However,  such complexity  precludes the  direct  use of
 correlation/regression analyses to predict specific relationships with estuarine habitat features and important
 forcing  functions.   Thus, the following is  a preliminary approximation of such relationships since  it is based
 primarily on such statistics.

      The overall trends of the infaunal  macroinvertebrates are shown in Figure 12. Such data indicate that both
 species richness and numerical abundance vary together (r2* 0.603; p < 0.0001). When viewed as a function of
 river flow (Figure 12), the increases in both indices are associated with  the drought period even though the
 general trends are not significantly correlated.  Salinity does not appear to be a major factor in these trends, at
 least directly; increased salinity could have an indirect effect by allowing predators into the area.  In a series of
 experiments (Mahoney and Livingston, 1982; Livingston, in press; Livingston, unpublished data), it was shown
 that direct predation has little impact on the infaunal associations in East Bay.  There is some  indication,
 according to these analyses, that the East Bay infaunal assemblages are physically controlled by temperature and
 river /rainfall conditions. The considerable drop in numbers after the drought could be an indication of a delayed
 response to the reduction of detrital matter associated with the drought.

      The epibenthic invertebrates represent some of the most important species in the estuary in terms of their
 commercial value.  The two top dominants are the white shrimp (Penaeus setiferus) and  the  blue crab
 (Callinectes sapidus) which together with the oyster (Crassostrea virginica). provide the basis for a multimillion
 dollar industry in the region.  Invertebrate numbers were highest during the years of highest rainfall, whereas
 invertebrate biomass was highest during the 1980-1981 drought (Figure 13). The penaeid shrimp tended to have
 peak numbers during periods of high river flow (Figure 14), whereas the blue crabs were in the highest numbers
during the drought (Figure 14). The regressions of invertebrate indices with physical/chemical factors (Appendix
VI) indicate reductions of numbers and biomass with increasing river flow, POC influx, and decreasing salinity.

     The fish associations of the Apalachicola estuary are well developed with numerical dominants including
the bay anchovy (Anchoa mitchilli). spot (Leiostomus xanthurus). croaker (Micropogonias undulatus). and white
seatrout (Cvnoscion arenarius).  The numbers of  such species usually peak during winter-early spring periods.
The 13-year trend (Figure 15) indicated the highest numbers of fishes during the 1980-1981 drought, whereas
fish biomass was highest during and immediately following  the  period of high river flooding.   Fish biomass
tended to follow the long-term trends of river flooding with particularly low biomass before,during, and after the
drought of 1980-1981.  Fish species richness followed a cyclical pattern with peaks in 1975 and
                                                 4-23

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                    30
Livingston
Cd

O
U

M
ta
O
&
Cd
                    20-
                                INFAUNAL NUMBER OF SPECIES
                                INFAUNAL NUMBERS
                                                                    n     i
                                                                     HA"^
                                                                                       200
                                                                                       100
C/5
«
U
a


z
                      20  30   40  50   60  70   80   90   100  110  120  130  140  ISO  160

                                                 MONTHS
                   100
           Cfl
           C£
           W
           ea
           2

           z
           fa.
           Z
                   80-
    60
                   40-
                   20-
                                INFNMAI2



                                RIVERKLOWMA12
                                                                                        1200
                                                                                        1000
                                                                        •800
                                                                                        600
                                                                                        400
                                                                                                U
                                                                                                Cd
                                                                                 I
                                                                                 fig
                                                                                 U
                                                                                        200
                     40    SO    60    70    80    90   100

                                                MONTHS
                                               110    120   130   140   150
Figure 12.  (a)  Species richness and numerical abundance of infaunal macroinvertebrates in East Bay (all
           stations) taken monthly from 3/75-7/84. Data represent summed data. Peak levels occurred during
           the drought months and low levels were reached during the heat following the drought, (b) Moving
           averages of infaunal macroinvertebrate numerical abundance and species richness from 3/75-7/84.
                                               4-24

-------
                                    APALACHICOLA ESTUARY: TOTAL
                                                                                        Livingston
                                   20      40     60     10      100    120     14»
                                                   MONTH
                         100
                   g
                   oc
                   CD
                    QC
                    UJ
                    z
                    o
                    o
                                  y = 10.0263 + 0.513x - 0.0096x*2 + 4.369e-5x*3 fl = 0.27
                                                       10     10*     120    140
                                                  MONTH
Figure 13.  (a)Trawl-susceptible invertebrates (numbers, dry-weight biomass) taken monthly in the Apalachicola
          estuary (3/72-7/84). Monthly samples were summed Log data were also analyzed using third order
          polynpial distribution showing a trend of abundance during the early years with low levels reached
          during the drought of the early 1980*8.  (b) Trawl-susceptible invertebrates Gog-transformed) taken
          monthly in the Apalachicola estuary 3/72-7/84.
                                              4-25

-------
                                       APALACHICOLA ESTUARY: TOTAL
Livingston
                         a.
                         E
                         a
                         UJ

                         ui
                         a.

                         I
                                      y . 1.5504 » 0 4759x • 0.008«*2 + 3.51 5o-5x*3 R • 0.21
                               15
                              •10
                          a
                          i
                          5
                          a
                          ui
                          w
                          a
                   ~     4
                   CO
                   E
                   UI
                   ffi
                   3     3
                   u
                   UI
                                                            II*    111    141
                                                            — TOTPEN-UA13
                                 •     II     4«    M     M    IM    111    141
                                                                    •*• CALSAPSH-9
                                                 MONTHS

 Figure 14   (a) Numbers of total penaeid shrimp taken monthly in the Apalachicola estuary (3/72-7/84). A third
            order polynomial distribution was used to show the trend of high numbers during the early years of
            the program. Also shown are moving averages of shrimp numbers (13-month) and moving averages
            of blue crab numbers (5-month) over the period of study, (b)  Moving average of numbers of total
            penaeid shrimp taken in the Apalachicola estuary, 3/72-7/84.  (c) Numbers of total blue crabs taken
            monthly in the Apalachicola estuary, 3/72-7/84.
                                                4-26

-------
                        APALACHICOLA ESTUARY: TOTAL
              APALACHICOLA ESTUARY: TOTAL
              1500
              1000
          in
          E
              500
                                        I*     II*     II*    14*
               300
          <-.   200
           O
               100
                         FN/TWL-SM13
                                 MONTH
O
ffi

(/)
u.
at
<

O
m
                                                                               y = 41.9058 + 1.6468x - 0.0334x"2 + 1.556e-4x*3 R . 0.26


                                                                                                         -*• MEAN B TH
                                                                        20
                       MONTH
Figure IS.  (a) Fish numbers taken monthly in the Apalachicola estuary from 3/72-7/84. Mean numbers summed over all stations were   <•
           used, (b) Moving average of fish numbers taken monthly in the Apalachicola estuary, 3/72-7/84. (c)  Total fish dry weight   5'
           biomass taken monthly in the Apalachicola estuary,  3/72-7/84. (d)  Moving average of total fish dry weight biomass taken   *S
           monthly in the Apalachicola estuary, 3/72-7/84.                                                                     §

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Livingston

1984. These cycles tended to follow a very complex series of climatologjcal changes and are currently under
study.   Low levels of species richness  occurred during the drought.   The statistical basis for the above
relationships is given in Appendix IV. These results, while extremely complex, are consistent with known patterns
of fish distribution in the Apalachicola system.  Fish numbers are high during winter-early spring periods of high
river flow, high POC, and organic carbon input from the river.  By the summer-early fall  months of high
temperature and low dissolved oxygen, fish numbers decrease but biomass per trawl tow increases with seasonal
growth and the arrival in the estuary of the larger predators. Fish species richness increases at this time.  A
multiple correlation matrix (with and without lags, Appendix IV) tends to corroborate this interpretation of the
data.  The various relationships shown with the lagged data indicate that a time series analysis may be more
effective in delineating the cyclic associations of these variables. This is  currently under study.

     A review of the long-term climatologjcal changes in the tri-river system and changes in the important
fisheries of the Apalachicola estuary is given by Meeter et al. (1979).  A spectral analysis of long-term trends
of Apalachicola river flow and Georgia rainfall indicated cycles on the order of 6-7 years with possible longer
term cycles that may reflect variations such as the low river flow during the mid-1950's. Cross-spectral analyses
of these two variables showed that river flow and precipitation in Georgia are  in phase.  When commercial
landings taken in the Apalachicola estuary are compared with the long-term trends of annual river flow, there
were diverse results. Penaeid shrimp catches were not correlated (p <0.05) with any of the other variables. Blue
crab catches were positively correlated with river flow and negatively correlated with oyster catches. The oyster
landings showed negative correlations with river flow and Apalachicola rainfall.

     Such data reflect complex biological processes and socio-economic  events which cannot be accounted for
by use of the relatively simple statistics of correlation and regression.  The considerable seasonal variations of
the various  commercial populations may  mask the possible associations of such catches with longer term
climatological changes and cycles.  Optimal oyster growth occurs at salinities ranging  from 15.0 to 225 ppt;
during periods of high salinity, oysters are preyed upon by various stenohaline organisms that are usually excluded
from the estuary by the low and varying salinities. However, extended periods of low salinity can have an adverse
impact on oyster  growth.  Young blue crabs have a preference for low salinity, whereas the euryhaline white
shrimp have no overt relationship to salinity. All such populations respond to complex combinations of variables
such as river flow, temperature, salinity, productivity, predation, and other  factors.  There is  little doubt that
without the input of the Apalachicola River, these fisheries would largely disappear.  However, the direct and
indirect effects of changes in regional precipitation and river flow on the local fisheries are still not entirely
clear owing in large part to the socio-economic variables that influence the commercial catch statistics.

Projections

     A decrease in river  flow of 24.2%  (Hains' estimated GFDLx2 scenario) would  cause  direct  losses of
nutrients and POC that eventually would lead  to proportionate losses in  the detrital- and phytoplankton-based
food webs of the estuary.  A possible impact  scenario for fishes is given in Figure 16, where the regressions
determined during the 13-year field studies are applied to the Hains GFDLx2 scenario. Fish numbers were
reduced in excess of 50% due to the loss of river flow according to this scenario.  Once again, such projections
are mere approximations owing to the problems associated with the statistical methods.  If invertebrates are
considered, after a brief increase of invertebrate numbers due to increased salinity and reduced predation, there
would be an eventual reduction of infaunal and epibenthic macroinvertebrates with shifts in species representation
as the estuary became less turbid and more saline. The reduction of food and increase of predators would lead
to reductions of the oyster productivity; more oysters would be eaten by offshore stenohaline predators and there
would be an increased toll due to oyster diseases that often accompany higher salinities. Such changes would
probably be proportional  to the change in river flow over the prolonged  period  of adjustment.  Thus, the
applications of the various scenarios of river flow changes could lead to proportional changes in the productivity
of the estuary.
                                                4-28

-------
                  300
                                                                                        Livingston
                                                                                 FN/TWL-NAT RIVER




                                                                                 FWTWL-GfDLX2
                                 100
                                             200
                                                                       400
                  100
                   SO
               UJ
               w
               <
               UJ
               cc
               o
               ui
               o
o-
                  -50-
                  -too
                                   •

                             if. *•*•/*.*,.*
                             *••  V  •  • •    • »
                              v-.j  /•.{.•/. v/'
                                 100
                                             200


                                          MONTHS
                                                         —i—
                                                          300
                                                   400
Figure 16.  (a)  Comparison of numbers of fishes taken per trawl under natural river flow conditions and

           numbers calculated from regressions of the GFDL river flow projections. Data are based on monthly

           averages of fishes taken from all stations in the Apalachicola estuary from 3/72-7/84. Also shown

           are the percent decrease of ratios of fish  numbers taken under actual conditions and numbers

           produced from regression of GFDL projections of river flow,  (b) Comparison of numbers of fishes

           taken per trawl under natural river flow conditions and numbers calculated from regressions on the

           GFDL river flow projections.  Figure is presented as the percent decrease of fish numbers.
                                              4-29

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Livingston


TEMPERATURE

Scenarios

     The EPA scenarios (GISS, GFDL) were run with ambient data taken within the tri-river drainage system.
Comparisons were made during the 13-year period of study (3/72-7/84).  Previous studies (Livingston, 1984b)
indicated that monthly average air temperatures in Apalachicola, Florida (NOAA station) are correlated (r =«
0.90, p < 0.00001) with water temperatures in the shallow estuaries along the north Florida gulf coast. The use
of air temperature as a predictor of estuarine water temperature in the Apalachicola Bay system is thus valid.
However, unlike river flow and salinity, no regressions were run with the biological data.  The temperature
changes would affect the bay thoughout any given year; however, the summer monthly temperatures would reach
lethal levels which differ from species to species. This is a threshhold effect that is difficult to quantify by the
use of correlations and regressions. Instead, the projected changes in temperature were calculated according to
the transformation of existing data by use of  the  various models.   A literature search  gave the various
temperature tolerances of the important estuarine  species  in the Apalachicola estuary.  From this analysis, a
frequency diagram was constructed  giving the projected changes in the estuary based on  the temperature
tolerances of indigenous species.

     The ambient (bay-wide mean) water temperature trends  over the period of study (Figure 17) indicated
that winter low temperatures showed  progressive declines from 1975  to  1977 at which  time  the lowest
temperature levels of  the period were observed. From 1977 to 1980, there was a progressive increase in low
winter temperature levels.  A time-series  analysis of air temperatures  in Apalachicola from 1930  to 1979
(Livingston, 1984b) showed that the lowest monthly minimum air temperatures occurred during winter months
in 1940, 1948, 1958, 1968, and 1977.  The  approximate period of such low winter temperatures appears to be
within a range of 8-10 years. Summer high mean temperatures varied less widely with a range approximating
28-31°C. The highest summer mean temperatures in the Apalachicola system occurred in 1972,1976,1977,1980,
and 1983. No long-term patterns of recurring high summer temperatures were evident.

     A comparison of the ambient temperature vs the GISS transformation showed relatively little difference
in the summer high temperatures (Figure 18); winter lows would increase somewhat more in terms of absolute
increases.  The GFDL model, however, showed substantial increases in the summer highs.   Such differences
range between 5 and 9°C (Figure 18). According to the GFDL model, summer high  average temperatures in
the Apalachicola estuary would range between 15 and 16°C.

Biological Response

      Thermal  stress  operates at both ends of  the temperature spectrum.   However, because the high
temperatures (summer) along the upper gulf coast approach the upper lethal levels of various estuarine species,
and because the GISS and GFDL projections indicate general increases in such temperature  levels, the upper
lethal levels will be emphasized in this review.

     There is a considerable literature concerning the effects of temperature on poikilothermk (aquatic)
organisms (Collins, 1985; Darcy, 1985; Lassuy, 1983; Muncy, 1984; Reagan ,1985;  Reagan and Wingo, 1985;
Stanley and Sellers, 1986; Sutler and McHwain, 1987;Van Den Avyie,  1984).  Brett  (1970) and Warren (1971)
have reviewed various  aspects of the effects of temperature on fishes and invertebrates. Temperature is a major
environmental determinant of the distribution of aquatic organisms in space  and  time.   Factors  such  as
acclimatization and adaptation complicate the interpretation of direct effects of temperature on the distribution
of aquatic organisms. Temperature tolerance is genetically controlled with modifying features including salinity,
photoperiod, season, size, diet, reproductive state, and the acclimatization history of the population in question.
The rate of temperature change along with the exact nature of the state variable in question (e.g., respiration,
behavior, reproduction, mortality) and the thermal history of the subject all interact to determine the mechanism
                                                4-30

-------
                                                               Livingston
                     APALACHICOLA ESTUARY: TOTAL
 o
 ,0,

 UJ
 DC

 UJ
 Q.


 UJ
                   20
60
80
100
120
140
                                     MONTH
Rgure 17.  Mopthly temperature changes in the Apalachicola estuary from 3/72-7/84. Numbers are based on

        mean values taken from all stations (surface and bottom) in the Apalachicola estuary.
                                 4-31

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Livingston
          U
           o

          Cd
          «
          3

          <
          at
          cd


          cd
          H
           U
           o

           Cd
           d


           I
           cs
           Cd
           eu

           Cd
               40
               30
               10-
                      10   20   30   40   50   60   70   80   90   100  110  120  130  140  150
                 0    10   20    30   40   50   60   70   80   90   100   110   120  130  140  150



                                                 MONTHS


Figure 18.  Comparisons  of ambient water temperature and  (a)  GISS transformations  and (b) GFDL
           transformations (projections) of water temperature in the Apalachicola estuary (3/72-7/84).  Data

           are based on monthly means of all stations.
                                               4-32

-------
                                                                                            Livingston

 of thermal tolerance in a given population. Indirect effects of associated variables such as dissolved oxygen (e.g.,
 temperature is a primary determinant of the level of dissolved oxygen in water) complicate the direct and indirect
 effects of temperature on aquatic organisms.

      A review was made concerning the upper thermal collection limits (e.g., the highest temperature at which
 an aquatic species was taken in  the field) and the upper  critical  or incipient lethal temperature (e.g., that
 temperature level which will eventually cause the death of a stated fraction of the test organisms, usually 50%)
 (Table 5). There are species-specific trends with organisms such as the lutjanids having relatively low upper
 thermal collection limits.  Important species in terms of abundance and biomass in the Apalachicola estuary
 include oysters (upper thermal collection limit or U.T.C. of 36° C), blue crabs (upper thermal tolerance or TJ.T.T.
 = 30-33°C), penaeid shrimp (U.T.C. = 35-35.5°C), stone crabs (U.T.C. = 32°C), seatrouts (U.T.C. = 34.9-35°C),
 catfishes (U.T.C. =  34.9-37°C), mullet (U.T.C.  = 345*0, and southern  flounder (U.T.C. = 35°C). Between
 the temperatures of 30-37S"C (usually not exceeding 35°C), there could be  a progressive reduction of the
 dominant estuarine species found during summer periods in the Apalachicola Bay system.

      An analysis was made of the major dominant species in the Apalachicola estuary with particular emphasis
 on commercially important species (Tables 6 and 7). According to the GFDL projections of summer high
 temperatures, blue crab larvae and juveniles, spotted seatrout, oyster larvae, pinfish, flounder, and croaker would
 be virtually eliminated. Severe impacts would occur with the  redfish population. In the GISS model projections,
 only blue crab larvae and juveniles would be eliminated, although spotted seatrout, oyster larvae, pinfish, and
 flounder would be severely affected.  Under  current  (ambient) conditions, only blue crab larvae  have been
 adversely affected by high summer temperatures; since larvae move into the  bay mostly during winter periods,
 such an impact would be  hardly felt.  In any case, the GFDL and GISS  projections of temperature increases
 would have a major  adverse impact on the estuary.

      The general increase of winter low temperatures would also  have  a profound  influence on the species
 composition of aquatic organisms in the Apalachicola estuary. The biota would probably become more tropical
 in terms of temperature-tolerant species with general increases in species richness and diversity and general
 reductions of overall fish and  invertebrate production in terms of dry-weight biomass.  Such projections would
 be strongly influenced by potential (co-existing) changes in other variables such as salinity and dissolved oxygen.
 According to Yanez-Arancibia et al. (1985), tropical estuaries in the Mexican portion of the Gulf of Mexico have
 the highest phytoplankton productivity in areas receiving high levels of river flow. Thus, the overall production
 of the Apalachicola estuary would depend to a considerable degree  on changes in the mass flow of freshwater
 into the estuary regardless of the ambient  temperature of the system.  Commercial fish catches in Mexican
 estuaries are directly dependent on the river discharge. Coastal wetlands are also an important component in
 such productivity (Yanez-Arancibia et al., 1985).

      Overall, an average increase of 4-5°C in the Apalachicola estuary would probably cause a major disruption
 of the aquatic fauna at both ends of the temperature spectrum.  It is doubtful that there would be enough time
 for species tolerant to the high temperature and accompanying low dissolved oxygen to adapt to such conditions,
 leaving only the organisms already tolerant to the projected temperature increases. When other more restrictive
 functions such as growth and reproduction are taken into consideration, the impact of the projected temperature
 increases in the Apalachicola region become even more meaningful since few organisms are adapted to maintain
 their  population numbers at temperatures higher than 35-36°C.  The major break point  in the Apalachicola
 estuary would be sustained summer maximum temperatures of 35°C and  higher.

     The question of acclimatization is difficult to address. Temperature  increases are projected to occur over
 a relatively short time period.  There  could be some adaptive response of existing  species,  although this is
 unlikely since there is some evidence (Livingston, unpublished data) that many of these species are already
 temperature limited. This is not a matter of succumbing to a single factor, since the effects  of such temperature
 changes would result in habitat alterations that would simply render  the estuary unfit for certain species.
Another complicating factor would be the synergjstic effects of temperature and other state variables such as
                                                4-33

-------
Livingston
                   Table 5.  Thermal Tolerance Levels of Selected Estuarine Species*
    Reference)
    Species
 Upper Thermal
Collection Limit
   Upper Thermal Tolerance
(Upper Critical or Incipient Lethal
       Temperature)
INVERTEBRATES

1) Caltinectes sapidus

2) Menippe mercenaria         32°C
3) Palaemonetes pugio         38° C

4) Penaeus douranun          35S"C
5) Penaeus setiferus
6) Penaeus axtecus             35° C
7) Crassostrea virginica         36°C

FISH

8) Anchoa milchelU            39.8°C
8) Anchoa hepsetus            34.9° C
9) Archosargus probatocephalus  3S.1°C
VS)Ariusfetis                  37DC
10) Bagre marinus              >34.9°C
11) Brevoortia patronus         34.9°C
12) Cynoscion arenarius         35°C
13) Cynoscion nebulosus        34.9°C
12) Cynoscion nothus           30°C
14) Fundulus heteroclUus        40-42°C
14) Fundulus majalis           40-42°C
15) Lagodon rhomboides        33-34°C
16) Lutjanus analis             27.8°C
16) Lutjanus griseus             32J°C
16) Lutjanus synagris           28.9°C
17) Micropogonias undulatus

18) A/ugi/ cepAoftu              34.5°C
16) Ocyurus chrysums
19) Orthopristes chrysoptera      36°C
20) Paralichtys lethostigmata     35°C
21) Pogonias cromis            33°C
22) Sciaenops oceUatus         375PC
                                   30°COarvae)
                                   33°C (juveniles)

                                   38°C
                                   3S°COarvae)

                                   42°C

                                   48°C
                                   >3S°C inhibits larval growth
                                   near39°C
                                   35-36°C avoided
                                                            36°C
                                   38°C
                                   413°C (juveniles)
* Data are given as the Upper Thermal Collection Limits and as the Upper Thermal Tolerances.
  Refemces and significance of the numbers are given in the text.
                                               4-34

-------
                                                                                            Livingston
Table 6.  Distribution of Upper Thermal Tolerances of Dominant Species in the Apalachicola Estuary from
          1972-1977.  Temperatures that Eliminate Species from the Bay are Listed Along With Projections
          that Would be Eliminated According to Different Scenarios During the Summer Months from 1972-
          1977 (Months Start in June of Each Year)
                                 AVERAGE MAXIMUM MONTHLY TEMPERATURE
                                  °C             °C            °C
DATE
summer
1972







1973.



GFDL

37.

39.

37.

38.

37.

40.


3

6

6

1

7

6

36.2



1974





1975





1976.


37.

36.

40.

36.

38.

37.

36.


6

5

1

4

0

9

7

36.6


40.0
. ELIMINATE GISS

bcl.bcj.
St.p.C.f
bcl.bcj,
st.p.c.r
bcl.bcj,
st.p.c.r
bcl.bcj,
st, p.c,
bcl.bcj.
st.p.c.r
bcl.bcj,
st.p.c.r
bcl.bcj,
st.p.c.f
bcl.bcj.
st.p.c.r
bcl.bcj.
st.p.c.f
bcl.bcj.
st.p.c.r
bcl.bcj.
st.p.c.f
bcl.bcj.
st.p.c.r
bcl.bcj.
st.p.c.r
bcl.bcj,
st.p.c.f
bcl.bcj,
st.p.c.f
bcl.bcj.

ol

ol
,f
ol
,f
ol
r.f
ol
,f
ol
,f
ol

ol
,f
ol

ol
,f
ol

ol
,f
01
,f
ol

ol

ol

34

35

35

37

35

36

33

36


.8

.2

.3

.1

.2

.2

.9

.6

33.4

34

35

34


.1

.7

.1

33.2

35

34

35

.5

.1

.6
st.p,c,r,f




1977

36.

35.

4

1

33.0


34.6



40.9



42.5






bcl.bcj.
st.p.c.f
bcl.bcj.
st.p.f
bcl.bcj

bcl.bcj

bcl.bcj.
st.p.c.r
bcl.bcj.
ws.st.p
m.r.f
ol

ol





ol
,f
ol
,c

34

34

35

38

38

.1

.1

.2

.4

.1

35.2




ELIMINATE ACTUAL

bcl.bcj

bcl.bcj,
st.p.f
bcl.bcj.
st.p.f
bcl.bc).
st.p.c.f
bcl.bcj,
st.p.f
bcl.bcj.
st.p.c.f
bcl.bcj,

bcl.bcj.
st.p.c.f
bcl.bcj

bcl.bcj

bcl.bcj.
st.p.f
bcl.bcj

bcl.bcj

bcl.bcj,
st.p.f
bcl.bcj

bcl.bcj,
st.p.f
bcl.bcj

bcl.bcj

bcl.bcj.
st.p.f
bcl.bcj,
st. p. r.f
bcl.bcj.
st.p.c.r
bcl.bcj.
st.p.f




ol

ol

ol

ol

ol



ol





ol





ol



ol





ol

ol

ol
.f
ol



30

32.

33

31


.9

.1

.1

.9

31.3


33.1

31.

31.

28.

33

32

30

32

28

30

32

31.

28

30

34

35

33



.7

3

9

.1

.6

.3

.5

.9

.3

.5

9

.9

.7

.5

.0

.0


ELIMINATE

bcl

bcl

bcl.bcj

bcl

bcl

bcl.bcj

bcl

bcl



bcl.bcj

bcl

bcl

bcl



bcl

bcl

bcl



bcl

bcl.bcj

bcl.bcj.ol
P.f
bcl.bcj


               SPECIES  UPPER THERMAL TOLERANCE (QC)

               blue crab larvae (bcl)	30 (TEMPERATURES THAT ELIMINATE
               blue  crab juveniles (bcj)	33
               white shrimp (ws)	42
               oyster larvae (ol)	35
               spotted seatrout(st)	34.9
               pinfish(p)	35
               croaker (c)	36
               mullet (m)	41.3
               redfish(r)	37.5
               flounder (f)	35
SPECIES FROM BAY)
    4-35

-------
Livingston

Table 7. Elimination of Species (%) Based on Projected Increases in Temperature According to a Comparison
       of the GFDL and GISS Scenarios With Ambient Conditions in the Apalachicola Estuary (Summer
       Months: 1972-1977)
                                     afdl        olss	ambient
            blue crab larvae	   22          22          17
            blue crab juveniles...   22          22          6
            spotted  seatrout	   20          13          1
            oyster larvae	   20          13          1
            pinfish	   20          13          1
            flounder	   20          13          1
            croaker	!	   19          4            0
            redfish	   12          2            0
            mullet	   100
            white shrimp	   100
                                      4-36

-------
                                                                                            Livingston

 salinity.  Since we know little about how the system would respond to each of the projected changes as sole
 entities, projections of interacting effects are even more problematical. The interactions of such changes on
 single populations should also be related to altered levels and timing of nutrient input and primary productivity
 in the Apalachicola estuary.

      According to Yanez-Arancibia et al. (1985), the temperature regime of the Terminos Lagoon, a tropical
 estuary on the Mexican Gulf of Mexico, approximates that of the Apalachicola estuary with high temperatures
 around 30-31° C.  The projected estuarine temperatures of the future Apalachicola system were predicated on
 an equivalence with increased air temperature. It is possible that conditions in the Gulf of Mexico could alter
 such a situation whereby the water temperature would be cooler than the air temperature.  Such equivalence
 should thus be acknowledged as an untested assumption subject to further review. In any case, such studies of
 tropical estuaries indicate that these systems are highly productive in terms of fisheries output and are dependent
 on factors (e.g., freshwater input, wetlands vegetation) similar to the temperate estuaries to the north.


 SEA LEVEL CHANGES

 Projected Effects

      One complicating feature of the projected impact of climatic changes on the Apalachicola system includes
 sea level alterations.  A series of projected changes have been developed by Dr. Richard Park (Table 8). Such
 changes should be placed in the perspective given by the various sources of POC for the Apalachicola estuary
 as summarized in Table 9 (Livingston, 1984a). The Apalachicola Bay system receives input in terms of organic
 carbon and nutrients from various portions of the drainage system. The river flooding during winter-early spring
 periods has been associated with input of POC and nutrients. Phytoplankton blooms during the spring and fall
 months combine with input from the seagrass beds and fringing salt water marshes during the summer and fall
 months to provide the bay with POC and dissolved and participate forms of nutrients. Such inputs are thought
 to be mediated by the activities of microorganisms, which transform the dissolved components into the enriched
 (e.g., N, P) participate organic matter that forms  the basis of the detrital food webs of the estuary. There is a
 certain equivalence of  input from  the freshwater wetlands, the coastal marshes,  and the seagrass beds.
 Phytoplankton productivity accounts for over 200% of the total of the above sources. The river has been
 associated with such production along with the movement of nutrients and POC within the river-bay system.

      The models provided by Dr. Richard Park (Butler University, Indianapolis, Indiana) will serve as the basis
 of this section of the report. No quantitative (statistical) projections can be made with such data, since there is
 little precedence for the major loss of wetlands in this region. However, there are enough data concerning the
 function of such areas that impacts can be evaluated concerning losses of major portions of the Apalachicola
 wetlands.

      Four scenarios of sea level change are presented (Tables 8 and 9): baseline projected changes (no change
 in current rate of sea level  increase); sea level increase of 0.5 m by 2100;  sea level increase of 1.0 m by 2100;
 and sea level increase of 2.0 m by 2100). The projected increases in sea level by the year 2100 (File FLAAPLAC;
Scenario 1) would be associated with some decrease in swamp habitat; high marshes would be hard hit and low
marshes would also suffer some losses. No significant changes would occur in the freshwater marshes or the
overall water surface.

      The  second scenario  (0.5-meter  increase by 2100) would be associated with more significant changes.
About 1/3 of the swamps and nearly 1/5 of the  freshwater marshes would be gone along with nearly all the
low and high marshes around the bay.  The increase of mangrove swamps is  difficult to factor into these
evaluations; if mangroves would eventually grow in the Apalachicola region, there would be some amelioration
of the effects owing to the projected losses of the existing wetlands. However, due  to the uncertainty involved
 in this projection, the mangroves will not be taken into account in this analysis.
                                                4-37

-------
Livingston

Table 8.  Projected Changes in the Apalachicola Region Due to Sea Level Rises (Based on Data Provided by
       Dr. Richard Park: Butler University, Indianapolis, Indiana)

     I. File  FLAAPLAC
          A. Scenario  1:

     Time  period:  1987-2100
     Sea level  rise: 1.2  mm/year (baseline).

     Projected  changes:

     1. Swamos: 9.4616 to 6.7053: decrease of  2.7563= 29.1% decrease.
     2. Freshwater  marshes: 1.4595 to 1.2708;  decrease of 0.1887=12.9%
     decrease.
     3. High  marsh: 1.1858 to 0.3656:  decrease  of 0.8202 = 69.1%
     decrease.
     4. Low marsh: 3.4213 to 2.3268: decrease of 1.0945 - 32.0%
     decrease.
     5. Mangrove:  no change.
     6. Water: 77.4019 to  81.5257: increase of 4.1238 - 5.3% increase.

           B. Scenario  2:

     Time  period:  1987-2100.
     Sea level  rise: 0.5 m increase.

     Projected  changes:

     1. Swamps: 9.456 to  6.2579; decrease of 3.1981; = 33.8% decrease.
     2. Freshwater marshes: 1.4595 to 1.1715 - decrease  of 0.2880=
     19.7% decrease.
     3. High  marsh: 1.1858 too.0353; decrease of 1.1505 = 97.0%
     decrease.
     4. Low  marsh: 3.4213 to 0.3879  - decrease of 3.0334 - 88.9%
     decrease.
     5. Mangrove: 0 to 3.0575  - increase of 3.0575.
     6. Water:  77.4019 to  81.857 - increase of  4.4551  - 5.8% increase.
                                 4-38

-------
                                                               Livingston
Table 8. (continued)


         C.  Scenario 3:

    Time period: 1987-2100.
    Sea level rise: 1 met-ar increase.

    Projected changes:

    1. Swamps:  9.456 to  5.4695 - decrease of 3.9865 - 42.2% decrease.
    2. Freshwater marshes:1.4595 to 0.999 = decrease of .4215 » 28.9%
    decrease.
    3. High marsh: 1.1858 to 0.0350 = decrease of 1.1508 = 97.0%
    decrease.
    4. Low  marsh: 3.4213 to 0.0616= decrease of  3.397 » 98.4% decrease.
    5. Mangrove: 0  to 2.1305= increase  of 2.1305.
    6. Salt  marsh: 4.6071 to 0.0966- decrease of 4.5105= 97.9%
    decrease.
    7. Water: 77.4019 to 85.2671; increase of 7.8652 = 10.2 % increase.

         D.  Scenario 4:

    Time period: 1987-2100.
    Sea level rise: 0.0756 to 2.004 =  2  meter increase.

    Projected changes:

    1. Swamps: 9.456 to  4.1158: decrease of  5.3402 = 56.4% decrease.
    2. Freshwater marshes: 1.4595 to 0.2461: decrease of 1.2134 =
    83.4% decrease.
    3. Hioh marsh: 1.1858 to 0.0199: decrease of  1.1659 = 98.3%
    decrease.
    4. Low  marsh: 3.4213 to 0.0322: decrease of3.3891  = 99.0% decrease.
    5. Mangrove » 0 tp 1.8006 « increase pf 1.8006.
    6. Salt  marsh: 4.6071 to .0521: decrease of 4.555 - 98.9% decrease.
    7. Water: 77.4019 to 88.3808: increase of 10.9789 -  14.1% increase.
                                 4-39

-------
Livingston
Table 9. Compendium of Data Concerning Different Terrestrial and Aquatic Habitat/Productivity V,
in the Apalachicola Drainage Basin
. Distribution and area of Mjor bodies of water along the coast of Franklin
County (north Florida) with areas of oysters, grassbeds, and contiguous Marshes.
Area
Hater, body tha)
St. Tincent Sound 5,539.
lay 20,959.
East Bay 3,980.
St. George Sound (West) 14,746.
St. George Sound (East) 16.015.
Alligator Harbor 1.637.0
Total 62.879.3
Percent of total water area 100
Terrestrial habitats and land-use
Apalachicola Bay system (Florida Bureau of
Category
Residential
Commercial, services
'Transportation, utilities
Mixed urban or built-up areas
Other urban or built-up areas
All urban or built-up areas
Cropland and pasture
Other agriculture
All agricultural land
Herbaceous range land
Range land
Evergreen forest land
Nixed forest land
All forest land
Streams and canals
Lakes
Reservoirs
Bays and estuaries
All water
Forested wetland
Hoof ores ted wetland
All wetlands
Beaches
Quarries and pits
Transitional areas
All barren land
Oysters Grassbeds Marshes
(ha) (ha) (ha)
1,096.5
1.658.5 1,124.7
66.6 1.433.5
1.488.8 624.3
2.6 2.767.3
36.7 261.3
4,349.7 6.211.1
7 10
patterns In the limed I ate
Land and Water Management
Total area (ha)
2.461
178
218
27
39
2.922
78
4
82
13
H.
68.598
36.396
104,994
1.469
452
10
62.879
64,810
25.562
8.465
34,027
1.441
25
110
1,575
i.noe
703
4.606
751
810
144
8.850
14
watershed of
1977).
X of total
113
0.1
O.I
0.0
0.0
1.5
0.0
0.0
0.0
0.0
0.0
35.7*
19.9
54.6
0.8
0.2
0.0
24.3
25.4
13.3
4.4
17.7
0.7
0.0
0.1
0.8
.9
.4
.1
.9
.8
.3
.4

the








Total area of Franklin County:              198.398



                                       4-40

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                                                                                              Livingston
Table 9. (continued)
Apalachlcola estuary
Net In situ
Vegetation productivity
mt C yr-1
Freshwater
wetlands
Coastal
marshes
Phyto-
p lank ton
Seagrass
beds



Mapping
category
Pine
Sweet gum-
Sugar berry-
Water oak-
Loblolly Pine
Water hlckory-
Sweetgum-
Overcup oak-
Green ash-
Sugarberry
Tupelo-cypress
with mixed
hardwoods
Tupelo-cypress
Pioneer
Marsh
Open water
Unidentified
Total
360,000
37.714
Apalachlcola Bav system
Net Input Net In situ Net Input Season of
mt C yr-1 productivity mt C yr'1 maximum input
mt C yr-1
30,000 360.000
37,714(7) 46,905
30,000 winter/spring
46.905(7) late summer.
fall(7)
103.080
8.953




Upper
river
136



642




12.500


1.170
2.420
0
0
2.730
1,020
20,600
103,080 233,284(7) 233,284(7) spring
fall
8,953




Middle
river
672



1.440




32.200


1,860
2,270
150
0
3,110
748
42.500
27,213

Acres
Lower
river from
Wewahltchka .
to Sumatra
0



154




15.800


8,310
6.240
19.2
0
1.540
81.3
32,100
27,213


Lower
river from
Sumatra
to mile 10
204



474




1,770


15,800
10,300
0
0
2.010
76.8
30.600
and
sunmer-fall


Lower
river from
mile 10
to mouth
0



0




48.0


6.920
456
0
9,030
1.260
19.2
17,700





Total
1,010



2,710




62.300


34,100
21.700
169
9,030
10,700
1,950-
144,000

Annual output minus Input
(metric tons)
•
Drainage basin
Apalachlcola-
Chattahoochee-
Fllnt
Chattahoochee-
Fllnt
Apalachlcola-
Chlpola
t Apalachlcola
Chlpola
Area
(km?)


50.800

44,600

6.200
3,100
3.100
• 9

Carbon Nitrogen


213

142

71
41
29


,800 21.480

.700 17,860

.100 3.620
.500 1.060
.600 2.560
Phos-
Areal yield
(q m-2 yr-M

Phos-
phorus Carbon Nitrogen chorus


1.652

1.340

312
237
75


4 0.4

3 0.4

12 0.6
13 0.3
10 0.8


0.03

0.03

0.05
0.08
0.02
                                                 4-41

-------
Livingston


     A one meter increase in the sea level would be accompanied by further reduction of the freshwater marshes
(28.9% decrease).  The rest of the marsh systems would be virtually gone with the exception of the mangrove
areas.  The 2-meter increase by the year 2100 would be associated with the loss of virtually all the wetlands
systems (freshwater, brackish, and marine) according to the model projections.

     An estimate of how such progressive losses of the wetlands in  the Apalachicola region would translate
into a loss of POC to the system is given in Table 10. It should be acknowledged that these calculations are
based on major assumptions that cannot be effectively evaluated in terms of their impact on the final results.
With the exception of the mangroves, the  model assumes no replacement of marshes as the sea level rises.
The exact relationship of the marshes to the seagrass beds remains undetermined; it is possible that an adverse
impact on the marshes and wetland systems associated with the river-estuary could adversely affect the SAV in
the estuary, since such plants may be dependent on nutrients from such areas. The SAV, on the other hand,
would have more area to grow as the water surface increased; hence the direct increase of SAV as the water
areas expanded with time (Table 10). The direct loss of wetlands may have an equivalent effect on phytoplankton
productivity which may, in turn, be inversely associated with the SAV  production (inversely due to competition
for nutrients and light). The projected changes in the carbon regime of the bay are also complicated by unknown
effects on the microbiota: microbial activity is critical to any evaluation  of POC input to the estuary.  The
extremely complex relationships of such elements have not been determined. However, for the sake of simplicity,
I related the direct loss of phytoplankton productivity with an average  of the loss of the freshwater marshes and
the high/low marshes of the system. There is little doubt that the nutrients from such systems, as mediated by
freshwater flow into the estuary, are an important component in the  high levels of phytoplankton productivity
in the bay system.

     Based on the above assumptions, the calculations (Table 10) would indicate a considerable loss of POC
to the estuary by the year 2100.  The salt water marshes would disappear quickly.  The phytoplankton losses
would  be considerable as the surrounding wetlands disappeared.   Even the baseline decreases would be
associated with about one-third of the POC of the system. The projected losses from the OS- to 2,0-m increases
in sea level would be considerable. The increase in SAV over this period would not appear to make up for such
losses. Overall, the input of POC due to the primary allochthonous and autochthonous sources would go from
337,385 metric tons annually in 1987 to 51,955 metric tons in 2100.  This loss could then be projected to those
elements of the estuary that are directly involved with the detrital foodwebs. Since most of the major fisheries
are tied in some way to the detrital food webs of the estuary, the projected loss could be applied to similar losses
of the shrimp, oyster,  and finfish production of the estuary.

     The projected changes in the upper regions of the tri-river basin are equivalent to those treated above
and would not appreciably change the estimates of carbon loss by the year 2100.  The speculation concerning
the appearance of mangroves in the Apalachicola region was  not addressed here and can be considered to be
a neutral issue by the  year 2100 owing to the eventual reduction and  loss of such vegetation.
                                                4-42

-------
                                                            Livingston

Table 10. Projected Changes of the Net Input of Organic Carbon (mtons/year) to the Apalachicola Bay system
      (Livingston, 1984a) According to Various Scenarios of Sea Level Rise (Table 8). (Seagrass Changes
      are Related to Increases in Water Areas; Phytoplankton Changes are Related to Changes in the
      Average Decreases of Freshwater Marshes, High Marsh Areas, and Low Marsh Areas)
  FACTOR    FRESH          SEA-  SALT       PHYTO-       TOTAL
             WETLANDS   GRASS  MARSHES   PLANKTON

  Scenario
  (1987-2100)


  CURRENT      30,000   27,200    46,905   233,280    337,385


  BASELINE     26,100   28,700    23,500   144,640    222,940
  INCREASES
  (1.2MM/yr)


  0.5 M          24,000   28,800     4,690    71,450    128,940
  INCREASE


  1.0 M          21,300   30,100       940    58,790    111,130
 INCREASE


 2.0 M           4,980   31,035        780    15,160     51,955
 INCREASE
                                4-43

-------
Livingston


                                            CHAPTER 4

                                            DISCUSSION


RIVER FLOW PROJECTIONS AND EFFECTS

     The GISS model of precipitation changes predicted an average increase of 2 cm of rainfall per month,
whereas the GFDL model predicted an average decrease of 2 cm per month when applied to the Columbus,
Georgia, precipitation records over the period of study (3/72-7/84).  However, the  Hains model (GFDLx2)
projected a reduction of over 25% of the river flow.  Such an  alteration could be associated with proportional
losses of nutrients and POC to the estuary in addition to changes in the salinity regime that could cause shifts
in the biotic relationships and losses of some part of the fisheries potential of the system.

     The Apalachicola River delivers considerable quantities of particulate organic matter and nutrients to the
estuary.  Such substances are associated with the various food webs in the estuary,  especially during the
winter-early spring peak flows. Phytoplankton production during the warmer periods is thought to be related
to nutrients  deposited in the bay during various times of the year.  The methods used to evaluate changes in
flow preclude an exact measure of the seasonal distribution of possible changes in the delivery of organic carbon
and nutrients to the system.  In addition,  the bay system, in terms of actual utilization of such substances, is
characterized by various complicating features such as species-specific lags in consumption, changes in microbial
utilization and  availability  of enriched detrital  material to the  estuarine food webs,  and differences in
phytoplankton production with time.  Consequently, the  overall  changes in the  amounts of such products
delivered .10  the bay can only be calculated hi a gross fashion. In general, the loss of the estuarine food webs
would be roughly proportional to the reduction of river flow delivered to the bay.

     Specific variables such as turbidity and color usually reflect the level and time of Apalachicola River flow
so that projected increases or decreases  would result in similar changes in such  variables in the estuary.
Short-term reductions in the river flow could be generally associated with increases in the oxygen anomaly,
although dissolved oxygen per se probably would not be affected.

     Reductions in river flow would result  in proportional decreases in nutrient delivery and phytoplankton
productivity.  At the same time, organisms that depend on phytoplankton, such as oysters, would be adversely
affected by such reductions.  The associated decrease of color and turbidity would, all other factors remaining
equal, probably allow an expansion of the submerged aquatic  vegetation (SAY) which, in turn, would provide
habitat and food for those organisms inhabiting the estuary during the warmer months of the year. With a shift
to higher salinity, white  shrimp, oysters, and various sciaenid fishes such as spot and croaker would be at a
disadvantage relative to  the  pink shrimp,  spotted seatrout, and other organisms that are  associated  with the
submerged aquatic vegetation. Because of the complexity of the interrelationships of habitat, physical constraints,
productivity, predator-prey relationships, and competition,  no exact sequence of response can be predicted
Overall, with reductions  of the river flow,  the major fisheries that currently are important  in the Apalachicola
estuary would give way to fisheries associated with the seagrass beds; the Apalachicola system would begin to
assume the characteristics of Apalachee Bay,  all other factors remaining the same. Increases in river flow would,
within limits, expand  the current  estuarine habitat and  increase  the level of  fisheries production in the
Apalachicola Bay system.

     A review of the long-term biological responses of the Apalachicola estuary to periods of drought and flood
indicate that the response to prolonged increases or decreases in precipitation and river flow would be extremely
complex and would, in large part, be based on the the responses  of individual populations to such  changes.
There would also be considerable differences between the short- and long-term responses of such organisms to
prolonged changes in state variables. The complex relationship of productivity and salinity in the estuary
complicates  this situation.  Reductions in river flow would result in increases in the numerical abundance and
species richness of infaunal macroinvertebrates. However, the relative dominance and overall production of this


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 group of organisms would be reduced by such changes.  Increases in river flow would expand the area of the
 highly opportunistic species that dominate the oligohaline and mesohaline infaunal associations.  As river flow
 decreased over time, there would be a reduction in the physically controlled infaunal groups with a corresponding
 expansion of the biologically controlled associations. In the long run, such changes would probably lead to an
 overall reduction in the standing crop biomass and numbers of such organisms.

      Increased salinity due to a reduction in river flow would allow oyster predators into the estuary; enhanced
 disease vectors and parasites could also take a toll of the oyster productivity. Coupled with a reduction in the
 phytoplankton productivity, the oyster1 industry would be adversely affected by the projected changes in river flow
 (hains GFDLx2). The same could be said for those organisms, such as white shrimp, blue crabs, and sciaenid
 fishes, which currently use the estuary as a nursery area and a haven from the stenohaline gulf predators that
 would be able to enter the bay as salinities increased. Although the habitat alterations would differ in various
 parts of the bay, the overall effects of reduced river flow would probably be consistent with general reductions
 in the various invertebrate and fish species which currently form the basis of the commercial fisheries of the
 region. The immediate estuarine response to changes in the river flow would probably be somewhat different
 than the long-term responses to such changes, and such  differences would be species-specific in terms of the
 exact timing of the response. There is some evidence that after an initial increase of certain populations relative
 to the increased salinity of the  system, there would be overall decreases in the numbers and biomass of the
 dominant epibenthic macroinvertebrates and fishes. Trends of numbers and biomass in the individual populations
 would not necessarily be parallel in such long-term responses.


 TEMPERATURE CHANGES AND EFFECTS

     Since the early 1940's, winter low temperatures in the estuarine systems along the north Florida gulf coast
 have varied considerably from year to year with long-term cycles (8-10 years) of the recurrence of very cold
 winters.  Summer high  mean temperatures are less variable from year to year  with a range approximating
 28-31°C. In terms of  changes in the average water temperature of the Apalachicola estuary, the GISS 2xCO2
 scenario predicted minimal increases  of summer high temperatures and increases of 2-3° C of the winter  lows.
 The GFDL 2xCO2 model predicted increases of 4-5° C in the average summer high temperatures with projected
 differences in the summer maximum temperatures of 5-9°C. Average winter low  temperatures would increase
 4-5°C according to the GFDL projections.  This would mean that there will be future summer temperatures
 (mean) along a range of 31-36°C with summer maximum temperatures occasionally exceeding 40°C.

     Temperature is  an important factor in  the  seasonal and interannual variation of organisms in the
 Apalachicola estuary.  Infaunal macroinvertebrates of East Bay have peak numbers/biomass during winter-early
 spring months of high  river flow and low winter temperatures.  Species richness/diversity tends to follow similar
 trends as the numerical abundance.  Such indices are associated  with  winter low temperatures and drought
 periods.  Epifaunal  macroinvertebrates, dominated by the commercially important penaeid shrimp  and blue
 crabs, are most abundant during summer-fall periods of high temperature hi the estuary. Temperature-related
 trends of such species  appear to be less important than precipitation/river flow variation over long (interannual)
 periods, although overall invertebrate biomass was associated with periods of very low winter temperatures. Fish
numbers tend to peak  during winter-early spring periods due to the migrations of juvenile spot and croakers into
 the estuary at that time. Fish species richness peaks during the fall when a number of species such as the
spotted seatrout are abundant: such species are important to the sports fisheries  of the region.  Absolute fish
numbers peaked during the drought  period, while fish dry weight biomass was highest during the period of
abundant river flow.   Thus, the  stereotypic  seasonal  temperature response of  the  dominant fishes  and
macroinvertebrates (infaunal and epifaunal) is complicated by long-term trends of regional precipitation,- there
are distinct interactions  of low winter temperatures and drought (e.g., rainfall/river flow) conditions  that tend
to drive the biological response.  There is considerable variation in such response from one level of biological
organization to the next.

     Based on.reviews  of upper  thermal  collection limits (e.g.,  the highest temperature at which different
estuarine species have been taken in the field) and the incipient lethal temperatures (e.g., that temperature


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Livingston

causing the death of a specific fraction of test organismsX there is good evidence that as the upper summer
mean and maximum temperatures increase, there will be a reduction and/or elimination of some of the most
important populations in the Apalachicola estuary. Blue crabs and stone crabs will no longer be in the estuary
at mean temperatures of 30-33°C. By the time the mean temperatures reach 35-36°C, oysters, penaeid shrimp,
catfishes, mullet, the seatrouts, and southern flounder will no longer be able  to use the estuary as a habitat if
such populations follow the experimentally determined sensitivities to high temperatures.

     Should the GFDL scenario of temperature increase be correct, there is a high probability or  likelihood
of a major change in the biotic composition and fisheries potential of estuaries  across the northern  Gulf of
Mexico.  The main uncertainty in this generalization rests on the possibility that  various species will adapt to
the temperature changes and/or be replaced by other species  that may or  may not take their place.  Such
alternatives are possible and cannot be determined here with any degree of certainty.  The relatively short
period over which  such temperature increases would occur would tend to preclude any significant adaptation
of these species to the projected summer high temperatures. Within the context of these uncertainties, it is
highly likely that temperature increases in the Apalachicola estuary of 4-5°C (mean high summer values) would
have a significant effect on the biota of this system. Since many of the organisms that inhabit the estuary during
summer periods are already close to limiting high temperatures, any sustained increase in such temperature levels
could have a negative impact on the fisheries potential of the estuary. The projected increases of the winter low
temperatures with time could have a profound influence on the species composition of the invertebrate and fish
faunas in the estuary with an overall trend toward an increase in tropical species, general increases  in species
richness and diversity, and associated reductions of fish and invertebrate production.  Such projected changes
would, of course, be dependent on associated changes in variables such as dissolved oxygen (which would be
reduced as the temperatures increased) and salinity, which is dependent on other state variables such as regional
precipitation and Apalachicola River flow.


PROJECTED CHANGES IN SEA LEVEL

     According to  projected changes of sea level from current (baseline) changes  to a 2-m increase by the year
2100, there would be progressive and almost total losses of freshwater marshes, the high and low marshes, and
the salt marshes of the Apalachicola system.  Over half the swamps would be lost with a 2-m increase hi sea level
by the year 2100.  Based on a series  of basic assumptions, such losses could be  associated with considerable
reductions of autochthonous and autochthonous sources of POC to the estuary. Seagrass beds would undergo
moderate increases, whereas phytoplankton productivity would be severely reduced The organic carbon input
from the freshwater and saltwater marshes would be virtually wiped out.  Overall POC levels, in metric tons/year,
would go from  337,385 hi 1987 to 51,955 by 2100.  Such losses could be directly related to losses of specific
populations within the important detrital food webs throughout the Apalachicola Bay system. Overall, the system
would be transformed into a seagrass-dominated  estuary with relatively minimal  input of POC  from  the
surrounding wetlands. The phytoplankton productivity, presently a primary source of organic carbon, would be
severely reduced to a relatively insignificant level The bay system would be much less  turbid  with most of the
nutrients held hi the seagrass vegetation.

     Ultimately, the various projected changes in the Apalachicola estuary over the next century would include
a simultaneous  yet gradual alteration of the system, whereby river flow would decrease as the wetlands were
inundated by sea level rise.  During this time, the water temperature would approach and possible exceed that
currently observed  in the tropics. Based on the combined effects, it would be virtually impossible to predict the
exact sequence  of the biological response of the estuary to such changes. However, the estuary would change
hi terms of species  composition, the source and level of productivity, and the fisheries potential Relative to the
current conditions, it would be a warm water seagrass system with less overall fisheries production.

     There are  certain shortcomings to the analysis presented in this report. A complete review of this problem
is given in Appendix VI.  The use of correlations, regressions, and other multivariate analyses with long-term,
multidisciplinary data is extremely limited in terms of scope, comprehensiveness, and the  development of
predictive models.  In essence, such analysis should be used as a preliminary review of the data so that the state


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                                                                                              Livingston

variables can be identified for more comprehensive modeling efforts. In this case, the weaknesses of the rather
simple statistical tests were obvious in that specific lags in the biological response to important climatological
features could not be included in the final interpretation of results.  It should be emphasized that, from the
beginning, this analysis was considered preliminary to a more rigorous application of more sophisticated statistical
models to the data base.

     Accordingly, it is appropriate that a more comprehensive analysis be made of the data with inclusion of
similar (currently available) information from other river-estuaries along the gulf coast. The data are sufficient
for the application of time-series analysis (ARIMA models, spectral analysis, intervention analysis); such models
do not have the rather restrictive assumptions that are associated with most multivariate statistical techniques.
In addition, there should be an application of simulation models. Again, the data base is sufficient for the
analysis of results with different portions of the available information, both from the standpoint of within-system
and between-system comparisons. A comparison of the various estuaries in which long-term field data have been
taken would thus be an important part of such an analysis. The use of the above techniques in the development
of predictive models could be considered the end result of a 20-year effort to understand river-estuaries along
the Florida gulf coast.
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Livingston


                                          REFERENCES


Brett, J. R. Temperature. la- Marine Ecology. Ed O.Kinne. Wiley-Interstience, New York, 1970. pp. 513-616.

Colberg, M.R., TJS.Dietrich, and D.M.Woodham. 1968. The social and economic values of Apalachicola Bay,
Florida. Final report. Contract NO. 14-12-117. U.S. Environmental Protection Agency, Washington, D.C.

Collins, M.R.1985. Species  profiles:  life  histories  and environmental requirements of coastal fishes  and
invertebrates (South Florida), striped mullet. U&Fish and Wildlife Service. FWS/OBS-82/11.21. U.S. Army
Corps of Engineers, TR EL-82-4.

Dubravko, J. and R. J. Livingston. Long-term oxygen cycling as an indicator of productivity in a river-dominated
estuary, (in manuscript).

Lassuy, D.R.1983. Species profiles: life  histories  and  environmental requirements of coastal fishes  and
invertebrates (Gulf of Mexico), spotted seatrout. U.S.Fish and Wildlife Service. FWS/OBS-82/11.21. U.S. Army
Corps of Engineers, TR EL-82-4.

Lassuy, D.R.1983. Species profiles: life  histories  and  environmental requirements of coastal fishes  and
invertebrates (Gulf of Mexico). Atlantic croaker. U^.Fish and Wildlife Service. FWS/OBS-82/11.21. U.S. Army
Corps of Engineers, TR EL-82-4.

Livingston, R. J. Resource atlas  of the Apalachicola estuary. Florida Sea Grant College Rep. No. 55. 1983. 64
pp.

Livingston, R.  J. The ecology of the Apalachicola Bay system: an estuarine profile. U. S. Fish Wildl. Serv.
FWS/OBS 82/05.1984a. 148pp.

Livingston, R. J. Trophic response of fishes to  habitat variability in coastal seagrass  systems. Ecology 65(4),
1984b.  pp. 1258-1275.

Livingston, R J. and EA Joyce, Jr. Proceedings of the conference on the Apalachicola drainage system. Florida
DNR Marine Research Publication No. 26. St. Petersberg, Florida.

Meeter, D. A., R. J. Livingston, and G. C. Woodsum. Long-term climatological cycles and population changes
in a river-dominated estuarine  system. la* Ecological  Processes in coastal and marine systems. Ed R. J.
Livingston. Plenum Press, New York.  1979. pp. 315-338.

Mulkey, D.  and F. Mature. 1983. A socioeconomic profile of the Apalachicola area of northwest Florida.
Apalachicola oyster industry: Proceedings of a conference. Ed Scott Andree. Sea Grant ReportNo.  SGR-57.
Gainesville, Florida.

Muncy, RJ. 1984. Species  profiles:  life  histories  and environmental requirements of coastal fishes  and
invertebrates (Gulf of Mexjco)--white shrimp. U^.Fish and Wildlife Service. FWS/OBS-82/11.21. US. Army
Corps of Engineers, TR EL-82-4.

Prochaska,  FJ. and  D. Mulkey.  1983. The Apalachicola oyster  industry: some economic considerations.
Apalachicola oyster industry: Proceedings  of a conference. Ed Scott Andree. Sea Grant  Report No. SGR-57.
Gainesville, Florida.
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                                                                                          Livingston

 Reagan, R.E.1985. Species  profiles: life histories and environmental requirements of coastal  fishes  and
 invertebrates (Gulf ofMexico). red drum. U.S.Fish and Wildlife Service. FWS/OBS-82/11.21. VS. Army Corps
 of Engineers, TR EL-82-4.

 Reagan, R.E. and W. M. Wingo.1985. Species profiles: life histories and environmental requirements of coastal
 fishes and invertebrates (Gulf of Mexico), southern flounder.U^.Fish and Wildlife Service. FWS/OBS-82/11.21.
 US. Army Corps of Engineers, TR EL-82-4.

 Stanley, J.G.  and MA. Sellers. 1984. Species profiles: life histories and environmental requirements of coastal
 fishes and invertebrates (Gulf of Mexico). American oyster. U.S.Fish and Wildlife Service. FWS/OBS-82/11.21.
 US. Army Corps of Engineers, TR EL-82-4.

 Sutler, F.C. and T.D.Mdlwain. 1984. Species profiles: life histories and environmental requirements of coastal
 fishes and invertebrates (Gulf of Mexico), sand seatrout and silver seatrout. U.S.Fish and Wildlife Service.
 FWS/OBS-82/11.21. US. Army Corps of Engineers, TR EL-82-4.

 Van Den Avyle, M J. 1984. Species profiles: life histories and environmental requirements of coastal fishes and
 invertebrates  (South Atlantic)~blue crab. U-S.Fish and Wildlife Service. FWS/OBS-82/11.21. US. Army Corps
 of Engineers, TR  EL-82-4.

 Warren, C. E. Biology and water pollution control W. B. Saunders Company, Philadelphia 1971. 434 pp.

 Yanez-Arancibia,  A., A.L.Laral-Dominguez, and  H. Alvarez-Guillen. 198S. Fish community ecology and
 dynamics in estuarine inlets. In; Fish commumity ecology in estuaries and coastal lagoons: toward an ecosystem
 integration. Ed.: A. Yanez-Arancibia. UNAM Press, Mexico.

Yanez-Arancibia, A., G. Soberon Chavez and P. Sanchez-Gil. 198S. Ecology of fish production in the coastal
zone. In; Fish commumity ecology in estuaries and coastal lagoons: toward an ecosystem integration. Ed: A.
Yanez-Arancibia, UNAM Press, Mexico.
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