Group Title: Energy basis of a coastal region: Franklin County and Apalachicola Bay, Florida /
Title: Energy basis of a coastal region: Franklin County and Apalachicola Bay, Florida
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Title: Energy basis of a coastal region: Franklin County and Apalachicola Bay, Florida
Physical Description: xvii, 389 leaves : ill. ; 28cm.
Language: English
Creator: Boynton, Walter Raymond, 1947-
Publication Date: 1975
Copyright Date: 1975
Subject: Power resources -- Franklin County   ( lcsh )
Ecology -- Florida -- Franklin County   ( lcsh )
Marine ecology -- Florida -- Apalachicola Bay   ( lcsh )
Oyster culture -- Florida -- Apalachicola Bay   ( lcsh )
Franklin County (Fla.)   ( lcsh )
Apalachicola Bay (Fla.)   ( lcsh )
Environmental Engineering Sciences thesis Ph. D
Dissertations, Academic -- Environmental Engineering Sciences -- UF
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
Thesis: Thesis--University of Florida.
Bibliography: Bibliography: leaves 376-388.
General Note: Typescript.
General Note: Vita.
Statement of Responsibility: by Walter Raymond Boynton.
 Record Information
Bibliographic ID: UF00098138
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: alephbibnum - 000150187
oclc - 02352455
notis - AAR6425


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I wish to acknowledge the guidance and insights pro-

vided by H. T. Odum, the chairman of my committee. I was

fortunate to work with a man who so clearly sees the unity

of urban and natural systems. My supervisory committee in-

cluded E. E. Pyatt, P. L. Brezonik, C. Feiss, and R. L.


The research was supported by the Office of Sea

Grant, U. S. Department of Commerce, Grant 04-3-158-43 en-

titled "Simulation and Evaluation of Macromodels to Air

Coastal Planning," H. T. Odum, principal investigator. Ini-

tial work on the Franklin County project was done by C.

Gray and D. Hawkins.

Mr. C. Wojick assisted in producing the land-use

maps. Mr. James Estes, Franklin County Agricultural Ex-

tension Agent, provided much information on urban and

fishery matters in Franklin County. R. L. Iverson and

his students provided data critical to this study and ar-

ranged with R. C. Harriss for the use of the facilities

of the Florida State Marine Laboratory at Turkey Point on

Apalachicola Bay. Data were also provided by R. J. Living-

ston, and R. W. Menzel of Florida State University.

Field data from Apalachicola Bay were collected

with the assistance of M. Kemp, T. Ahlstrom, J. Schumacher,

H. McKellar, R. Livingston, G. Lewis, and H. Bittaker.

The Mobile District, U. S. Army Corps of Engineers pro-

vided information on Jim Woodruff Dam, and the Apalachicola

River. A. Clewell of the Botany Dept., Florida State Univer-

sity, was helpful in classifying local terrestrial ecosys-


I gratefully acknowledge my fellow graduate students

and especially my wife, Mary Ellen, for their support, as-

sistance and good humor which added much to both this work

and my graduate school experience.



ACKNOWLEDGEMENTS .................................... ii

LIST OF TABLES ...................................... vii

LIST OF FIGURES ..................................... ix

ABSTRACT ............................................ xv

INTRODUCTION ............ ............ .. ............... 1

Theoretical Coastal Zone Issues .................. 3
Description of Study Area ........................ 4
Previous Studies in Franklin County .............. 13
Issues for Decision in Franklin County ............ 17

METHODS ............................................. 19

Modeling Techniques .............................. 20
Simplification of Simulation Models ........... 22
Writing and Scaling Equations ................. 22
Simulation Techniques ......................... 24
Validation and Basis for Model Configurations 24

Field Study Techniques ........................... 27
Metabolic Measurements ........................ 27
Salinity Measurements ......................... 31
Dissolved and Particulate Carbon Analysis ..... 32


Energy Value Calculations .......................... 32
Energy Quality Concept .......................... 33
Energy Investment Ratio Theory .................. 39
Energy Evaluation of a Process or Alternative ... 40

Land-Use Maps from Aerial Photographs .............. 42

RESULTS ............................................... 43

Maps of Principal Ecosystems in Franklin County .... 43
Characteristics of Apalachicola Bay ................ 51
Salinity Measurements ........................... 51
Diurnal Metabolism Measurements ................. 56
Organic Carbon Measurements ..................... 68
Data Assembled from Other Sources ............... 68
Model of Diurnal Activities ..................... 89
Simulation Results and Comparisons with
Field Data ................................... 93
Characteristics of Franklin County Region .......... 126
Regional Model ..................................126
Assembled Data on Franklin County ............... 160
Simulation Results and Comparisons with
Assembled Data ...............................171

Energy Evaluations for Franklin County ............. 216
Calculation of Energy Basis .....................217
Primitive pattern ............................217
Present pattern ..............................218
Oyster Fishery ..................................234
Tourist Development .............................243

Energy Quality of Water at Jim Woodruff Dam ........255

DISCUSSION ............................................263
Main Energy Characteristics of the County .......... 263


Relationship of Economy to Natural Systems ....... 270
Feedback Required of Urban Systems ............ 270
Energy Value of Natural Pulses ................ 272

Alternative Factors Controlling Patterns in
the Region .................................... 274
Nutrient and Organic Matter Loading with
Oxygen Depletions .......................... 274
Salinity Control with Dams and Passes ......... 278
Urban Housing Developments .................... 289

Factors of Energy Investment Return .............. 294
Oyster Industry ............................... 294
Energy Costs of Basin Formation ............... 296

Energetic Basis of Regional Coastal Image ........ 297
World Conditions of Energy Prices and Future
Trends in Coastal Franklin County ............. 299
Fisheries and Urban Economy ...................... 302

APPENDICES ............................. ........... 304



ACTIVITY MODEL ................................ 319

APALACHICOLA BAY .............................. 335

LAND-USE PATTERNS ............................. 375

REFERENCES .......................................... 376

BIOGRAPHICAL SKETCH ................................. 389


Table Page

1 Energy quality factors relating different
work processes. 36

2 Area and percentage of total area of each
ecosystem type in primitive pattern in
Franklin County. 46

3 Area and percentage of total area of each
ecosystem type in present pattern in
Franklin County. 49

4 Summary of diurnal metabolism measurements
taken in Apalachicola Bay during the sum-
mer of 1973. 61

5 Summary of organic carbon measurements taken
in Apolachicola Bay in September, 1973. 69

6 Calculation of oyster biomass. 87

7 Summary of simulated values from diurnal
activity model. 124

8 Numbers used to evaluate regional model of
Franklin County and Apalachicola Bay given
in Fig. 28a. 131

9 Energy flow table for the primitive pattern
in the Franklin County region. 223

10 Energy flow table for the present pattern
in the Franklin County region. 225

11 New urban energy flows associated with
tourist development. 247

12 Energy flow table for localized impact of
island development. 254

13 Documentation of energy flows given in
Fig. 56. 259

C-1 Data used in the diurnal activity model. 320

C-2 Scaled differential equations and pot set-
tings used in simulating the diurnal activity
model. 330

C-3 Maximum values for state variables and forc-
ing functions used in diurnal activity model. 332

D-l Data used in the regional model of Franklin
County and Apalachicola Bay. 336

D-2 Scaled differential equations used in simu-
lation of the regional model of Franklin
County and Apalachicola Bay, Florida. 364

D-3 Scaled state variables and forcing functions
and scaled initial condition pot settings
used in simulating the regional model of
Franklin County and Apalachicola Bay. 369





Figure Page

1 Map showing major features of Franklin County
and Apalachicola Bay, Florida. 6

2 Map showing location of Franklin County and
Apalachicola Bay on the northwest Florida
coast and the extent of the Apalachicola
River drainage basin. 10

3 Simplified model of Franklin County and
Apalachicola Bay, Florida. 15

4 Map of Apalachicola Bay showing location of
stations used for diurnal metabolism and
organic carbon measurements. 30

5 Diagram showing example of the upgrade method
of calculating energy quality ratios. 38

6 Reduced map of primitive pattern of ecosystems
in Franklin County, Florida. 45

7 Reduced map of present pattern of ecosystems
(1973) in Franklin County, Florida. 48

8 Salinity pattern in Apalachicola Bay on
September 29, 1973, (a) surface; and (b)
bottom. 53

9 Salinity pattern in Apalachicola Bay on
April 20, 1974, (a) surface; and (b) bottom. 55

10 Examples of diurnal records used to calculate
community metabolism. 58

11 Graph of bay productivity for June-September,
1973. 65

12 Graph of daytime net photosynthesis as a func-
tion of respiration for systems in Apalachicola
Bay. 67

Figure Page

13 Monthly average flow (cfs) of the Apalachicola
River at Chattahoochee, Florida from October,
1928 to October, 1972. 72

14 Monthly river flow, (a) averaged for the
period 1929 through 1972; and (b) averaged
for the 15-year period before and after con-
struction of Jim Woodruff Dam at Chattahoochee,
Florida. 75

15 Monthly average insolation (a) and average
surface and bottom salinity for Apalachicola
Bay; and (b) averaged. 78

16 Average seasonal water temperature (a) and
average daily phytoplankton production in
Apalachicola Bay (b). 80

17 Seasonal record of nitrogen concentrations in
the Apalachicola River and in surface and
bottom waters of Apalachicola Bay. 83

18 Seasonal estimates of combined fish and ben-
thic invertebrate biomass in Apalachicola Bay. 85

19 Model of diurnal activity of Apalachicola Bay. 91

20 Justification of the configuration used in
the diurnal activity model shown in Fig. 19a. 95

21 Graphs of diurnal activity simulated with the
model in Fig. 19a using transfer coefficients
calculated from data in Fig. 19b. 106

22 Comparisons of field data with computer out-
put from the model given in Fig. 19a. 109

23 Simulation results from the model given in
Fig. 19a with input of sunlight as a square
wave. 112

24 Simulation results from the model given in
Fig. 19a with varying rates of nitrogen input. 115

25 Simulation results from the model given in
Fig. 19a with varying rates of detritus input. 117

Figures Page

26 Results of simulating the model given in
Fig. 19a with microbe respiration in-
creased by a factor of two above control
values. 121

27 Graphs of oxygen resulting from simulating
the model in Fig. 19a with the reaeration
rate reduced to 10 percent of control condi-
tions, varying inputs of detritus, nitrogen,
and sunlight, and with sunlight as a square
wave. 123

28 Regional model of Franklin County and
Apalachicola Bay, Florida, (a) energy cir-
cuit diagram; and (b) equations. 128

29 Justification of the configuration used in
the regional model of Franklin County and
Apalachicola Bay shown in Fig. 28a. 138

30 Yearly consumption of fuels in Franklin
County. 162

31 Annual number of visitors to (a) Florida,
(b) Franklin County, and (c) a summary of
average length of visit and expenditures. 165

32 Urban trends in Franklin County. 167

33 Annual oyster harvest data for Franklin
County including quantity, dockside value,
and price per pound in constant 1970
dollars. 170

34 Annual landings and dockside value of major
commercial species in Franklin County. 173

35 Simulation results from the model in Fig.
28a using transfer coefficients calculated
from 1970 data. 175

36 Simulation results from the model in Fig.
28a with six different rates of detritus
input associated with river flow. 179

37 Simulation results from the model in Fig.
28a for the response of estuarine vari-
ables and capital to three different rates
of nitrogen input associated with river flow. 182

Figure Page

38 Simulation results from the model in Fig.
28a for the response of all state vari-
ables to a two-fold increase in the input
rate of coliforms, nitrogen, and detritus. 185

39 Simulation results from the model in Fig.
28a for the response of adult oysters and
predators to various conditions of harvest
and predation. 187

40 Simulation results from the model in Fig.
28a with varying flow and amplitude of
river pulse. 190

41 Simulation results from the model in Fig.
28a for the response of (a) adult oysters,
(b) capital, and (c) structure to three
different rates of adult oyster recruitment. 194

42 Simulation results from the model in Fig.
28a for the response of (a) capital, (b)
structure, (c) population and (d) adult
oysters to four different rates of govern-
ment spending in the county. 196

43 Simulation results from the model in Fig.
28a for the response of (a) capital, (b)
population, and (c) structure to four
different rates of tourist and tourist
money inflow. 199

44 Simulation results from the model in Fig.
28a for the response of (a) capital, (b)
structure, and (c) image to pulses of
investment with three different rates of
money inflow. 201

45 Simulation results from the model in Fig.
28a for the response of capital, structure,
predators, and adult oysters to an infla-
tion rate of 10 percent for both purchases
and sales and changes in harvest effort. 204

46 Simulation results from the model in Fig.
28a for the response of structure, capital,
predators, and adult oysters to harvest
effort four times greater than in the con-
trol condition. 207

Figure Page

47 Simulation results from the model in Fig.
28a for the response of variables to in-
flation rates of 10 percent per year for
purchases and sales with increased investment. 210

48 Simulation results from the model in Fig.
28a for the response of variables to an
inflation rate of 20 percent per year for
purchases and sales with increased invest-
ment inflows. 213

49 Historical plot obtained from simulation of
model in Fig. 28a. 215

50 Primitive pattern of energy inputs available
to do work in the Franklin County region. 220

51 Present (1973) pattern of energy inputs avail-
able to do work in the Franklin County region. 222

52 Simplified energy circuit language diagrams
showing purchased and natural energy flows
involved in the Franklin County oyster
industry. 237

53 Simplified energy circuit language diagrams
showing aggregated inputs of natural and
purchased energies and effect of tourist
developments on St. George Island. 245

54 Map showing site of tourist development on
St. George Island. 251

55 Map view of proposed 800-acre tourist develop-
ment on St. George Island. 253

56 Simplified energy circuit language diagrams
showing the conversion of potential energy
of river head to electricity. 257

57 Plot of energy flow per area per year versus
percent of total study area in each ecosystem
type. 268

58 Plot of total salt versus river flow. 282




59 Plot of maximum salinity range per year
at 13 stations in Apalachicola Bay versus
yearly average species diversity recorded
at that station. 285

60 Salinity data at two oyster reefs in
Apalachicola Bay. 287

C-1 Analog patching diagram of the diurnal
activity model shown in Fig. 19. 334

D-1 Analog patching diagram for the regional
model of Franklin County and Apalachicola
Bay, Florida. 372

D-2 Analog patching diagram used to generate sun-
light, river flow and logic control of
oyster fishing season in regional simu-
lation model of Franklin County and
Apalachicola Bay, Florida. 374

Abstract of Dissertation Presented to the Graduate Council
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy



Walter Raymond Boynton

July, 1975

Chairman: H. T. Odum
Major Department: Environmental Engineering Sciences

The energy basis for a coastal region at Franklin

County and Apalachicola Bay, Florida was evaluated with

models, field measurements, energy calculations, land-use

maps from aerial photographs, and computer simulations.

Energetic relationships between urban and coastal ecosys-

tems were studied to help understand ecosystem relationships

on a regional scale and predict consequences of adding ur-

ban development and changing water quality to an economy

based on a marine fishery. Simulations of a regional model

suggested a near steady state pattern of urban activities

in the county which was consistent with previous studies.

A slow decline in urban activities was recorded when fur-

ther inflation was introduced. Trends were level tempor-

arily when investments and government spending were added.

Temporary growth was observed when the investment rate was

increased to twice the present rate. Growth could not be

stimulated by increasing oyster harvest effort without devel-

oping special feedback to stimulate the oyster reefs. Growth

trends initiated by investment in urban development were re-

versed and a decline observed if the oyster industry was

closed because of a decrease of water quality. Reducing the

seasonal pulse and increasing and decreasing river flow also

reduced oyster biomass and the urban economy. The model in-

dicated that natural predation rather than harvest was the

major cause of mortality of oysters.

An aggregated submodel of diurnal dissolved oxygen

dynamics in Apalachicola Bay was programmed to investigate

the possibility of low oxygen occurring under a variety of

conditions. Community metabolism and organic carbon stocks

in the bay and river were measured in the summer of 1973 to

evaluate models. Metabolism ranged from 3.1 to 21.6 g 02/

m2/day and was generally comparable to other estuarine areas.

Low predawn oxygen levels were not observed. Total organic

carbon averaged 6.4 and 5.3 g C/m3 in the bay and river,


Simulation of the model with reduced diffusion and in-

creased detritus input depressed oxygen levels to 3.2 ppm.

Nutrient loading produced larger diurnal changes but not

lower oxygen levels. Evaluation of this model suggested

that phytoplankton photosynthesis was the major source of

organic matter in the summer (75%). Eighty percent

of the nitrogen used in photosynthesis was from the recycle

of nitrogen from imported organic matter.

Energy calculations indicated that natural energy in-

puts accounted for 84% of the total energy input in the coun-

try. The ratio of purchased to natural, free energy inputs

(investment ratio) for the county was low compared to Flori-

da and the nation. On a county basis, full development of

the barrier island for tourism changed the ratio from 0.19

to 3.1, exceeding the 2.5 national average. On a local basis

calculations indicated that the planned development may be

too dense for best fit of natural and purchased resources.

The oyster industry accounted for 60% of the total

county income in 1970. Within the county, natural energies

predominated and accounted for 84% of the total energy used

in oystering as measured at dockside. When traced to final

consumption the natural energies involved in producing oy-

sters were matched elsewhere in the national economy with

the work of fossil fuels in ratio of 1:2.2. Preliminary

calculations suggested that the bay could support twice the

current oyster population without adding extensive fossil

fuel subsidies.



This is a study of principles organizing man and

nature in the coastal zone. The study considers purchased

and natural energy flows that together support a coastal

fishing economy. Questions are considered for fitting man's

increasing interaction with coastal waters into patterns

which maximize regional value. Included here are land-

use maps developed from aerial photographs, an aggregated

model of urban-coastal relationships, energy analyses of

development alternatives, simulation models of subsystems,

and examination of ways that changes in international energy

regimes control county activity. This study was made to

develop, apply, and extend ecological principles of organ-

ization and function to a regional scale.

Over the world, during the period of accelerating

urban growth based on fossil fuels, there was an increasing

harvest drawn from both recreational and commercial marine

fisheries at the same time that coastal developments were

removing habitat and interfering with the necessary food

chain base of coastal fisheries. In many areas there was

a burst of economic activity as boats utilized a virgin

fishery followed by periods of overfishing, coastal

disturbance, loss of the fishery, and replacement with ur-

ban development that may have unnecessarily decimated the

fishery resource. We need to test, clarify, and recommend

ways to retain the value of marine resources, while adding

fossil fuel-based developments.

However, in 1974 with changing patterns of fuel

availability and prices, the relative values of the natural

work and fossil fuel-based work began changing, and we need

an understanding of the trends ahead. As trends reverse,

strong marine resources may be needed as an economic cushion.

This study considered the general problem of co-

existence of economic development and marine resources

using Franklin County, Florida which includes the mouth of

the Apalachicola River and a very valuable resource of oy-

ster and shrimp landings. Major external developments

affecting Franklin County include changing prices of pur-

chased goods and fuels, river flow and coastal modifications,

development of coastal islands for tourism, and changing

populations pressures for tourist and retirement develop-

ments. To gain understanding of the energetic base of this

area, system models were constructed as follows: (1) an

oxygen balance model of production, respiration, exchange

and diffusion in Apalachicola Bay, with special emphasis

on the role of the river as a source of nutrients and or-

ganic matter; and (2) a regional model showing trends

caused by external driving functions on human activity on

land interacting with estuarine and fishery activities that

together constitute the economy of Franklin County, Florida.

Using numerically evaluated models, energy analyses were

made of the primitive and present patterns of land-use in

the county, the oyster industry, and a proposed tourist

development on the barrier island. Land-use maps were con-

structed from aerial photographs to show spatial relation-

ships of the primitive condition and current use by systems

of nature and man. The energy quality of the potential

energy of river head was also calculated and used in the

energy analyses. Lastly, calculations were made using an

energetic basis to estimate the appeal of the coastal zone

as a place for fossil fuel-based development.

Theoretical Coastal Zone Issues

In evaluating the energy basis of coastal Franklin

County, three theoretical issues were studied as follows:

(1) A seasonally pulsing energy flow may accelerate the

net yield of a system. The pulsing discharge of the

Apalachicola River may be an example affecting net

yields of the bay. What is the energy value of a

pulsing regime with net yield? How does it compare

to other river programs which have regular flow and

less net yield? Does the system receiving the pulse

benefit from it? What are the feedbacks or character-

istics exported to insure continued pulsing programs?

Is there a direct or indirect rate of the yield re-

lated back to the pulsing inflow?

(2) A second question is the relationship of the image of

the coastal zone to energy flow. Is it possible that

this attractiveness or image is based on the richness

both in types and magnitudes of natural coastal zone

energy flows which find viable couplings with urban


(3) The third question concerns the forward and feedback

exchange of value between estuarine productivity and

its coupled fishery harvest. In agriculture there

is a well-known give and take between farmer and field

with yields related to feedback studies of soil prepara-

tion, fertilization, breeding, weed control and the

like. What are the feedbacks from urban areas re-

quired to maintain yielding estuarine processes? Can

additional feedbacks be established to promote net

yields and prevent deterioration of fisheries?

Description of Study Area

Franklin County is located on the northwest Gulf

Coast of Florida (Fig. 1). It is bounded on the north by

Liberty and Wakulla Counties, on the west by Gulf County

and on the east and south by the Gulf of Mexico. The county

is rural with most of the 7,000 residents living along the














Cd C



coast. The gross area of the county consists of 361,000

acres, of which 18,400 acres are freshwater rivers and

lakes. The county consists mostly of flatlands, with

swampy forests covering over 90% of the land area. Apa-

lachicola Bay, a medium salinity estuary separated from

the Gulf of Mexico by several offshore islands, extends

nearly the entire length of the county.

The bay is approximately 36 miles long, 1 to 14

miles wide and covers an area of 104,320 acres (163 square

miles) including all of St. Vincent Sound on the west, East

Bay on the north, Apalachicola Bay proper, and St. George

Sound as far east as the western tip of Dog Island. Of

the 104,320 acres, 9,400 acres contain submerged vegetation

most of which occurs in St. George Sound. Species include

Thalassia testidinum, Syringodium filiforme, Diplanthera

wrightii, and Halophila engelmannii. Major emergent spe-

cies cover approximately 21,000 acres of marsh and include

Spartina alterniflora, S. patens, Juncus roemarinus,

Distichlis spicata, and Salicornia perennias (McNulty et

al., 1972).

Gorsline (1963) estimated that river water remains

in the bay system for a few days in the winter and up to

a month in the summer. However, the oyster producing area

of Apalachicola Bay is smaller than the bay area defined

by Gorsline and the retention time for this area may be

less. The mean depth of the bay system at low water is

2.3 meters (7.5 feet). During the winter season the bay

appears to be well mixed but has salinity induced strati-

fication around the inlets. During the warmer seasons,

stratification is generally observed with salt wedge pene-

tration extending into East Bay at times (Estabrook, 1973).

This condition may be a recent feature as both previous

hydrologic studies (Dawson, 1955a; Gorsline, 1963) reported

no stratification except in dredged channels. High turbidi-

ty was associated with river discharge, being highest in

the winter (Estabrook, 1973). Surface circulation in the

bay is partly wind-driven and partly driven by the river dis-

charge flowing to the westward most often. Water flows

east when west winds are strong. Bottom salinities had a

different distribution from the surface except when the

bay was well mixed (Estabrook, 1973).

The Apalachicola River has an average yearly dis-

charge of 26,713 cfs (cubic feet per second)(6.53 x 107

m3/day) draining a watershed of 18,000 square miles in-

cluding parts of Georgia and Alabama (Hawkins, 1973)(Fig.

2). River flow had a well-defined seasonal cycle with

highest and lowest flows occurring in March and September,

respectively. High and low flow monthly averages, over a

43-year recording period, were 52,000 cfs and 17,000 cfs,

respectively. The river is the major source of both coarse

quartz sands and clays. Most of the finer sediments are

located in the basin of Apalachicola Bay proper (Kofoed

and Gorsline, 1963).

Fig. 2. Map showing location of Franklin County and Apala-
chicola Bay on the northwest Florida coast and the
extent of the Apalachicola River drainage basin.

0 20 40





About 8% of the land area is owned by commercial

pulp and paper companies or is part of the Apalachicola

National Forest. Most of the land is unavailable for in-

dustrial or residential development in the foreseeable

future. Paper company activities account for a small part

of the economic base of the county (Colberg and Windham,

1965; Rockwood, 1973). The local economy is predominantly

dependent on water resource-based activities centered on

Apalachicola Bay. The Bay produces 90% of the commercial

oyster harvest in the state and supports shrimp, crab, and

finfish industries with an annual dockside value of several

million dollars (Rockwood, 1973). The largely undeveloped

forest lands, inland and coastal waters, and barrier islands

in Franklin County support a tourist business and provide

recreational opportunities for the residents (Florida Tour-

ist Study, 1970). Agriculture and manufacturing activities

are small at present. A large tourist-retirement community

is planned for one of the offshore islands. Most basic

goods, fuels, and services are purchased from outside the

county with money derived from oyster sales, tourism, and

land development income. Per capital income is low compared

to other areas of Florida and the nation. Population levels

have changed little due, in part, to emigration of residents

searching for more productive, stable jobs (Colberg and

Windham, 1965). In the 1930's Apalachicola was a boom town

of the Gulf Coast, second only to Mobile and New Orleans in

size. A major part of this growth was supported by cotton

trade with the interior which reached a peak during the

years 1835-1860. Some 75,000 bales of cotton were shipped

from Apalachicola in 1839, and 125,000 bales in 1843. The

population of Apalachicola was 2,060 in 1837. Timber, rice

and sugar cane production also played a role in the economy

during the territorial period. No mention was made of com-

mercial fisheries during this period (Martin, 1944). Fol-

lowing the Civil War, lumbering (primarily cypress) was a

major local activity with oystering, fin fishing, and boat

building also mentioned (Sawyer, 1974). Severe hurricanes

hit the county in 1873 and 1898, causing considerable damage.

A fire burned most of the business district in 1900. The

population of the county at that time was 3,077 (Sawyer,


Following the turn of the century, slow growth, un-

employment and underemployment were characteristic of the

county. Shipping declined on the Apalachicola River due

to a shallow channel to Columbus, Georgia and development

of more dependable rail transportation. Oysters and fish

were mentioned after 1900 as important products of the area

(Franklin County Overall Economic Development Program,

1965). The current economy retains its dependence on fish-

ery products (oysters, shrimp, and fin fish) with additional

income generated from tourism, government expenditures, and

some outside investment in retirement and tourist facilities.

A summary of county trends including fuel usage, fishery

yields and value, population, tourism, and income are given

in Figs. 30 through 34 in the Results section. A simpli-

fied model suggesting relationships within the county is

given in Fig. 3. This diagram shows major inputs, aggre-

gated storage of urban, plankton and oyster reef systems

and depreciation. Figure 28a shows the model with the com-

plexity used in simulations.

Previous Studies in Franklin County

Early studies conducted in Franklin County were

primarily concerned with the biology of the local oyster

fishery. Several economic and planning studies have been

recently completed. Early studies by Ingersoll (1881),

Swift (1897), and Danglade (1917) discussed oyster reef

position, density of oysters and associated oyster reef

fauna. Ingle and Dawson (1953) conducted a survey of oy-

ster reefs including information on spawning, setting,

growth, and condition of oysters (Ingle, 1951; Ingle and

Dawson, 1950, 1952). Menzel et al. (1957, 1966) published

data on oyster abundance in relation to oyster predation

as influenced by salinity. Menzel and Cake (1969) re-

ported data on temperature, salinity, diversity, and bio-

mass of invertebrates and fish, and commercial catch for

important species. Livingston and Thompson (1974) re-

ported pesticide concentrations and other characteristics

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of the bay (Livingston et al., 1974). Information on

nutrient stocks, phytoplankton biomass and productivity

was recently developed by Estabrook (1973). Cox (1970)

and Cox and Auth (1971) compiled information on water qual-

ity and fish populations in the Apalachicola River. Bark-

uloo (1961) has reported on the striped bass population in

the river.

Hydrographic studies were conducted by Dawson (1955a)

and Gorsline (1963). A single layer spatial simulation model

of salinity and other conservative materials was produced

by Swallows (1973). Estabrook (1973) also discussed hydro-

graphic features in the bay.

Jordan (1951) described the geology of the conti-

nental slope off the Apalachicola River and reef formation

in the adjacent Gulf of Mexico (Jordan, 1952). Kofoed and

Gorsline (1963) published maps of the sedimentary geology

of Apalachicola Bay. Summaries of water quantity informa-

tion, fishery yields, river flow, climatological data, and

coastal land uses were given in McNulty et al. (1972) and

Jones et al. (1973). A statistical summary and maps of

Florida coastal counties were produced by the Florida Coastal

Coordinating Council (1970).

Economic studies emphasized the importance of the

oyster industry to the local economy and included those by

Colberg and Windham (1965), Colberg, Dietrich, and Windham

(1968), and Rockwood (1973). The Rockwood report also .

included suggestions for oyster industry management. Whit-

field (1973) described the success of construction and re-

habilitation of oyster reefs in the county and elsewhere.

He also presented estimates of the value of submerged lands

based on present and projected dollar sales of marine products.

Regional studies include those which considered eco-

nomic and industrial development in Franklin County (Frank-

lin County Planning and Development Committee, 1965), com-

mercial tourism and land absorption (Northwest Florida De-

velopment Council, 1972), and an economic development pro-

file of Apalachicola, Florida (Florida Dept. of Commerce,

1973). Recently, the RMBR Planning/Design Group (1974),

under a federal grant, produced a county development plan

including information on social, economic, and environmental

conditions. Included in the report was a proposed set of

land development regulations. Hawkins (1973) and Boynton

et al. (1975) developed mathematical simulation models show-

ing relationships between the estuarine oyster industry and

coastal development. The Florida Statistical Abstract

(1973) contains detailed social, climatological and economic

data by both state and county areas. The Florida Division

of State Planning has developed a bibliography for Apala-

chicola Bay and the Apalachicola River basin.

Issues for Decision in Franklin County

From discussions with local leaders in Franklin

County, synthesis of data collected in previous and ongoing

studies in the county, and from general principles of sys-

tems ecology (Odum, 1971a, 1973) the following list of

questions needing understanding and decision was developed.

(1) What is the energy value of a generally higher more

stable salinity regime on an oyster producing estuary

adapted to seasonal pulses in river flow, sunlight,

and temperature?

(2) What is the value of proposed coastal island develop-

ment as a part of a coastal fishing economy?

(3) What are the prospects for future growth in Franklin

County and its impact on the oyster industry?

(4) What are estuarine responses to nutrient and detritus

loading from river and local sources?

(5) What are the major energy sources characterizing Apala-

chicola Bay estuarine ecosystems and oyster fishery?


The evaluation of the energy basis of Franklin

County included system models, direct field measurements,

energy calculations, and aerial maps. Energy flows and

storage were identified and their interactions charac-

terized with energy circuit diagrams. The model diagrams

indicated critical data to be collected in the field and

were the basis for simulations and energy value calcula-

tions. Field measurements were made of total metabolism,

seasonal salinity patterns, and organic matter inputs and

stocks. Nutrient flows and stocks, fishery stocks and

yields, and county-wide money flows were available from

other studies. Maps were used to quantify areas of urban

and natural land-uses and to show changing land-use patterns.

Computer simulations of simplified energy models were used

to test our concepts of interactions of processes as com-

pared to field data. After sensitivity checks and valida-

tion, simulation results suggested future county trends.

Energy calculations were made to determine which of several

development alternatives now facing the county has the

greatest survival contribution.

Modeling Technigues

From literature reviews, assembled field data, and

discussions with individuals familiar with local processes

in the study area, an initial list of model components was

prepared. The components included all storage of materials

in the system, external forcing functions and pathways of

interaction identified as important to the study area. In

translating the component list to diagram form, storage

or state variables were shown by the tank symbol, and forc-

ing functions or external energy sources which affect amounts

in storage by the circle symbol. Transfer relationships

between storage and between storage and forcing functions

were shown with solid lines. Money flows were shown as

dashed lines and flowed counter-current to material and

energy flows. Interactions were shown with the workgate

symbol and were some function of the interacting pathways.

Intersection functions may be additive, multiplicative,

logarithmic, exponential, or switching. An explanation of

energy circuit language symbols is given in Appendix A.

After further discussions and literature reviews an im-

proved diagram was made summarizing knowledge about form

and function within the study area.

In modeling efforts selection of system boundaries

determines which components are state variables, and which

are forcing functions and independent of model behavior.

If modeling boundaries are chosen one scale larger than

the size scale containing the specific component of inter-

est, there are fewer external driving functions. For ex-

ample, if understanding oyster production is a modeling

goal, modeling would be done at the scale of the estuary.

In this way, more components subject to change or inter-

action are contained within the model. The procedure avoids

the mistake of showing a storage, subject to change, as a

constant source independent of changes within the model.

When the full model was diagramed, numerical data

were obtained on observed chronological records. The model

was evaluated and simulated with initial values for an ini-

tial time period. In cases where local information was not

available for evaluating a portion of the model, data were

taken from the same process in a similar area. In some por-

tions of the model where no data were available, some rates

and storage values were calculated by difference as if the

system was in a temporary steady state.

When all components of the system have been evalu-

ated numerically, the relative sizes of storage and flows

become evident. Indications of the importance of flows

and the stability of state variables can be obtained by

calculating the residence time of materials in each storage

(calculated by dividing the amount in storage by the total

inflow). Flows that are of a similar nature are compared.

Those that are several orders of magnitude larger than others

are carefully considered because they may have controlling

actions in the model. Flows with effects several orders

of magnitude smaller than identical flows can be omitted.

Simplification of Simulation Models

Fully evaluated models were simplified prior to

simulation for several reasons. First, in the evaluated

model many pathways were small and thus not considered to

be essential. Rather than model these flows by themselves,

they were either lumped with similar, larger flows or ne-

glected. The same procedure was applied to state variables.

Thus some complexity was eliminated. Second, some factors

included in the evaluated model may not change or limit

any process. These factors can be eliminated from the

models while their effect remains implicit in the calcula-

tion of transfer coefficients. Comparison of simulation

results from condensed models with field and literature

data provide indications of model realism.

Writing and Scaling Equations

The set of differential equations associated with

each model is taken directly from the energy language dia-

grams. Scaled terms for each equation are given in Appen-

dix Tables C-3 and D-3. Odum (1972a) gives theoretical

derivations of mathematical equivalents of the energy symbols.

In analog simulations real world values contained

in equations must be magnitude-scaled so as not to exceed

the voltages available in the analog computer. To do this,

transfer coefficients must have scaled values between 0.001

and 0.999, and state variables and forcing functions must

have scaled values as a percent of full scale of the com-

puter. Each state variable and forcing function is assigned

a maximum value. This number is at least as large as the

maximum value expected for that variable but preferably

two to ten times larger so as to avoid overloads in the

early modeling stages. The calculated transfer coefficient

associated with each term in each equation is multiplied by

the maximum value (or product of the maximum values if the

term is multiplicative) associated with that term. This

product is then divided by the maximum value of the state

variable which the full equation describes. In this fashion,

proportionality of all terms is maintained while each term

is adjusted to stay within the capacity of the machine. If

after this procedure is completed, scaled transfer coeffi-

cients are less than 0.001 or exceed 0.999, time scaling

may be required. This procedure increases or decreases the

time period used to calculate flows. For instance, if a

scaled model had flows calculated in units of per year, di-

vision of transfer coefficients by a factor of 10 would

reduce the transfer coefficient value by a factor of 10

and flows to units of per 0.1 years. This discussion was

intended to provide an overview of the scaling process re-

quired in analog computation. Details of magnitude and

time scaling are given in Peterson (1967).

Simulation Techniques

The regional model was simulated on two slaved

Electronic Associates, Incorporated Model 580 computers.

The oxygen balance model was simulated on two slaved Elec-

tronic Associates, Incorporated MiniAc analog computers.

For each model an analog patching diagram was developed

directly from scaled differential equations and patched

onto appropriate analog boards. Analog patching diagrams

are given in Appendix Figs. C-l, D-1, and D-2.

Following checks to be sure all translational pro-

cedures to this point were correct, each model was tested

for responses to expected changes in the study areas. Each

computer run was similar to a controlled experiment in

which one or more factors were changed. Expected changes

were first calculated and then translated into new computer

settings and run. Model output was either photographed

directly from the oscilloscope attached to the computer or

obtained from a plotter.

Validation and Basis for Model Configurations

The following procedures were used in validating

models. For some variables and some rates, graphs of his-

torical data were assembled and compared to model output

with the model being started in the past. Good agreement

between model output and graphs indicates an absence of

contradictions. This procedure was especially useful when

systems had undergone appreciable changes in the past.

Simulations of natural system models were validated with

time graphs obtained from field studies in Apalachicola Bay,

microcosm studies and field studies in similar areas. In a

second validation technique, model sensitivities were com-

pared with single and multiple flow rate test changes.

This section also provides a rationale for the use

of the energy symbols in the models presented in this dis-

sertation. Given here are the criteria concerning the

selection of state variables, forcing functions, pathways,

and interactions. Storages (state variables) were used to

define functional groupings within the system which were

expected to vary with time and which had interaction with

other components or pathways in the model. Everything in-

cluded in a tank had characteristics which were similar for

the purposes of the simulation and could be considered as

a whole. For instance, the natural land tank in the re-

gional model (Fig. 28a) contained a variety of ecosystem

types (see Appendix B). When used for purposes other than

simulation this storage was broken down into 8 specific

ecosystems (see Figs. 6 and 7). Each of those could be

further subdivided if a finer level of detail was required.

However, each of the 8 ecosystems contained in the natural

land storage had the properties of occupying space in the

county and self-maintenance without urban inputs.

Obviously, not all storage of energy within the

boundaries of each model have been included. The same is

true for forcing functions and pathways. Those which were

expected to change were identified and retained in the model.

Those which were very large or not expected to change and

those which were small or had few or no major interactions

in the county were deleted. A similar process was followed

in arranging pathways and forcing functions.

Three types of interactions were used in the models

and included multiplicative and drag action workgates and

workgates operated by a force from a sensor. Multipliers

were used extensively because they had the property of re-

quiring all inputs to be present for a flow to occur. Of-

ten it was possible to identify factors required for a flow

to occur and thus multipliers were used to show this require-

ment. Secondly, the flow produced from this type of inter-

action is proportional to the levels in the input storage.

If one or both increased, then the flow increased until

one became limiting. This property was useful because it

was often possible to identify such relationships. In some

cases, as in primary production, the response of a multi-

plier is of the limiting factor type which is a well-estab-

lished photosynthetic response (Odum, 1971a; Odum, 1972a;

Rabinowitch and Govindjee, 1969).

Drag action workgates w2re used in cases where one

flow was inhibited by increases in one state variable, but

increased with increases in another. These were useful in

approximating situations having both stimulating and in-

hibiting factors interacting and producing a flow. For

instance, in the regional model shown in Fig. 28a, juvenile

oysters feed in proportion to the food available but are

inhibited when salinity is very low. If salinity reached

zero, there would be no food intake by the juvenile oysters.

While the exact nature of the feeding-salinity relationship

was not known, this interaction captured the basic features.

Lastly, workgates operated by a force from a sensor

were used in all cases where energy with or without mater-

ials flowed in proportion to some other flow. All river in-

puts were done this way as well as inflows of capital asso-

ciated with incoming tourists and new residents as shown in

the regional model (Fig. 28a).

Field Study Techniques

Field study techniques used here included measure-

ments of community metabolism in the bay, particulate carbon

concentrations in the bay and river, and two synoptic salin-

ity surveys in the bay.

Metabolic Measurements

Community metabolism was measured using the diurnal

oxygen sampling method of Odum and Hoskins (1958) and an

abbreviated dawn-dusk-dawn method used by McConnell (1962).

Oxygen was measured using the azide modification of the

Winkler technique (Strickland and Parsons, 1972) with a

further modification of thiosulfate normality which allowed

convenient use of small sampling bottles. Details of the

procedure used here are given by McKellar (1975).

Between June 22 and September 30, 1974, 36 diurnal

stations were occupied with stations AA, A, C, D, F, H, L

each occupied twice (Fig. 4). The sampling scheme was aimed

at generally characterizing the magnitude of metabolism and

the photosynthesis-respiration (P-R) balance in the dominant

bay environments. Stations were chosen to represent the

three dominant ecosystem types existing in the bay. Sta-

tions J, K, L, M, Q, Z, Y and the boat basin station at the

Florida State University Lab were established in areas

where respiration predominated. In the medium salinity

portion of the bay system stations AA, A, B, C, D, E, F, H

were established where phytoplankton activity predominated

and most oyster bars were located. Along the shorelines and

clearer areas where benthic grasses predominated stations

were established at the Florida State Lab and station N.

Two stations were also taken where wastes were suspected

of entering the bay (P and I).

Over the course of any one diurnal, stations were

sampled from 3 to 7 times depending on travel time between

stations and weather conditions. The minimum number of

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samples required for a complete diurnal was a dawn-dusk-

dawn sample sequence. Each sampling included measurements

of oxygen, salinity, temperature, and depth.

Diurnal oxygen data were plotted and calculated as

shown by Odum and Hoskins (1958), except for the diffusion

adjustment to the final rate of change curve. One measure-

ment of diffusion between the water and the air was measured

using a small nitrogen-filled plastic dome which floated on

the surface (Hall, 1972, based on original work by Copeland

and Duffer, 1964). The diffusion rate as g 02/m2/hr at 100%

deficit was calculated from the area and volume of the dome,

ambient oxygen concentration in the water, and the rate of

oxygen increase in the dome. Oxygen changes were measured

using a membrane oxygen probe. The calculation of a diffu-

sion coefficient followed the method used by McKellar (1975).

In calculating rate of change curves, diffusion coefficients

were estimated from a graph that included the one measure-

ment made in Apalachicola Bay and measurements reported by

McKellar (1975) for Crystal River, Florida.

Salinity Measurements

In September of 1973 and April of 1974 synoptic

salinity surveys were made in Apalachicola Bay. Stations

are shown as dots in Figs. 8 and 9. Each survey was started

about two hours before high tide and was completed about 2

hours after high tide. In each survey top and bottom


salinity and temperature measurements were taken at each

station using a Beckman electrodeless induction salinometer.

Dissolved and Particulate Carbon Analyses

In the summer of 1973 one effort was made to esti-

mate the standing.stock of particulate and dissolved organic

carbon in the water column and in the river for use in the

simulation models. Sixteen stations were sampled including

two stations in the Apalachicola River located approximately

1 and 2 miles upstream from the mouth. Water samples were

taken approximately 1 meter from the surface for all bay

stations and approximately 1 meter from the top and bottom

of the river. Samples were stored in acid-washed polyethe-

lene bottles and preserved immediately with 2-3 drops of

saturated mercuric chloride solution per 100 ml of sample.

Analyses for total organic carbon were made from unfiltered

water. Particulate carbon measurements were made with

material retained on precombusted 0.45 pm glass fiber fil-

ters. Dissolved organic carbon values were obtained by

difference. Subsequent techniques in the analysis followed

those specified for use with the Oceanography International

Total Carbon System (1972).

Energy Value Calculations

Energy value calculations were made to quantify

total work contributions from various components of man and

nature. Value was calculated as the work done for the

total economy of man and nature when all work processes

were expressed as work equivalents of one kind of energy.

The procedure takes as its theoretical base Lotka's maximum

power principle (Lotka, 1922) as developed by Odum (1971a).

This principle states that the system that tends to prevail

over alternative systems is the one that maximizes the use

of all energy flows available to it and is then one which

develops useful feedback roles for all participants for

the purpose of assuring continued energy flows and for cap-

turing any additional flows that come available. In a sys-

tem containing both man and nature (towns, estuaries, fish-

eries, river, industry, and forests), the principle requires

using the variety of components in partnerships rather than

in competition to obtain maximum work per time (power)

through the full system. Two additional concepts were used

in energy calculations and are presented next, followed by

the general procedure.

Energy Quality Concept

The value of a system process was defined as the

contribution of the process to the useful work of the sys-

tem. In evaluating energy models, all pathways were first

shown in units of kilocalories/area/time. Raw energy flows,

as measured in kilocalories of heat, do not represent the

ability to do work but rather show only the heat content of

that particular flow. Whereas any energy flow can be de-

graded to heat with 100% efficiency the ability of an energy

flow to do useful work depends on the packaging or concen-

tration of the energy flow. For instance, the kilocalories

associated with wood production in photosynthesis represent

the concentration to wood kilocalories of the dilute kilo-

calories of unprocessed sunlight and energy of collecting

wood to one location. Approximately 99% of the sunlight

kilocalories are degraded to heat to make one kilocalorie

of wood quality. While it is difficult for human systems

to use the dilute energy contained in sunlight directly,

the energy contained in wood has been concentrated or up-

graded at the expense of sunlight energy and thus represents

a higher quality energy. In the same way electrical energy

is at higher concentration than the energy contained in

coal; its generation requires approximately four kilocal-

ories of coal type energy to obtain one unit of electrical

type energy. Three kilocalories of coal energy are contrib-

uted from the coal to operate steam engines and one kilo-

calorie is expended to perform the work of constructing

arid maintaining the power plant structure. Several other

examples of conversion calculations are given by Odum et

al. (1974b) for relating kilocalories of wind, wood, and

electricity. Energy quality relationships relating pro-

ducers and many consumers in a shallow marine ecosystem

were given by McKellar (1975). One calculation of the

energy quality of the potential energy of river head being

converted to electricity was given by Young et al. (1974).

An additional calculation of river head-electrical relations

is contained in this dissertation for the dam on the Apalachicola

River. Thus, the above considerations suggest ways to com-

pare varying types of energy flows in macroscopic systems

of man and nature. Before comparisons are made, each flow

must be converted to a common baseline energy quality. In

this dissertation, all energy flows have been converted to

the fossil fuel quality level (expressed as KcalFFE). A

complete list of conversion factors used in this disserta-

tion is given in Table 1.

In this study, the following method is used to de-

termine the energy quality of a work flow relative to other

types of flows. The method involves calculating the energy

required to generate the high-quality work flow. As shown

in Fig. 5, the quality factor is obtained by comparing the

output of the conversion process with the input energy af-

ter all required feedbacks and subsidies have been converted

to equivalents of the same quality and subtracted. All

output feedback and subsidy energies must be of the same

quality before being manipulated. A complete calculation

of this type is given in Table 13, where potential energy

of river head is converted to fossil fuel and electrical

work equivalents. This method assumes that maximum energy

conversions done through one pathway equal those done

Table 1

Energy quality factors relating different work processes.

Energy Conversion Process

Sunlight to gross photosynthetic

Gross photosynthetic production
to wood

Wood to fossil fuel

Wood to electricity

Fossil fuels to electricity

Gross photosynthetic production
to fossil fuel

-Sunlight to fossil fuel

SWind to fossil fuel

Tidal energy to fossil fuel

Hydrostatic head to fossil fuel

Fresh/salt water concentration

Heat gradient to fossil fuels

Waves to fossil fuels

Fossil fuel flow to dollar flow
in U.S.

Energy Quality Factora

(divide by)














25,000 Kcal/dollar

aEnergy quality factor is the ratio of energy input
of one type required to generate another type of higher qual-
ity. (See Methods section) Energy quality factors used here
are preliminary and may be subject to revisions. For calcu-
lations leading to ratios given above see Odum et al. (1974b),
and Young et al. (1974).
Calculations for conversion of hydrostatic head to
electricity and fossil fuel are given in the Results section
of this dissertation.

Fig. 5. Diagram showing example of the upgrade method of
calculating energy quality ratios. Note that a
portion of the gross output is fed back to the
process, via the general economy, to cover full
cost of operating the process. This is required
to get a true net relationship between X and Y
forms. The energy quality ratio relating X to
Y is 20 in this example (100/5).

5 units/day
/ 5 units/day





through another if the systems are under competition for

survival and selected to operate at maximum power.

Thus, the application of energy quality ratios to

all energy flows allows for equal evaluation of all types

of work whether they be associated with man and money flows

or supplied without money exchanges.

Energy Investment Ratio Theory

In some energy evaluations, total energy flow fol-

lowing a proposed alteration is greater than before the

alteration. By criteria of maximum power, the new pattern

would seem better than the old and should be adopted. How-

ever, outside goods, services, and fuels have to be pur-

chased, and the income and investments for these purchases

can only be assured if the resident natural energies are

enough to attract the required capital. The energy invest-

ment ratio is the ratio of high quality external energies

that are attracted from outside the system and paid for by

some kind of exchange to the natural energies operating in

the study area. In 1973 this ratio was estimated to be 2.5

to 1 for the United States (Odum et al., 1974c). Systems

that have lower investment ratios can match high quality

external energies with more lower quality natural energies

and thus compete well with those things offered for ex-

change. The theory suggests that as the local investment

ratio exceeds the ratio of surrounding systems, the local

system generates less value per unit of high-quality energy

used. This decline in value per unit could be reflected

in higher prices required for exports and thus a disadvan-

tage in competing with other, less developed systems for

high-quality energies. The ultimate contribution of energy

flows depends both on high-quality purchased flows of fossil

fuels and resident natural energies with which the high-qual-

ity flows interact.

Energy Evaluation of a Process or Alternative

The first step in the energy evaluation of a proc-

ess or an alternative is to construct a diagram using the

energy symbol language including all major work processes

occurring in the study area. These diagrams include the

work done by nature in maintaining soils, water flows,

forests, estuarine and river systems, beaches, etc., as

well as the work associated with man's activities. By

putting the work processes of both man and nature on the

same diagram, we summarize all of the work processes that

contribute to the welfare of a region. The summary dia-

gram avoids the common mistake of counting as valuable

only those processes associated with money (Odum 1972b,


The next step is to evaluate all pathways in the

diagram in units of work (Kcal/area/yr). In this disser-

tation, natural system work was evaluated by using gross

community metabolism as an estimate of total work. Work

done by physical systems such as tides, waves, wind, etc.,

was calculated by standard formulas. Work done by human

activity was often available as dollar expenditures and

converted to work units in the following way. In the U. S.

economy as a whole, there is an average exchange ratio be-

tween work done and money flow (Kcal/dollar). After sev-

eral previous calculations (Odum et al., 1972), this ratio

in the United States was estimated to be approximately

25,000 KcalFFE/dollar in 1973 (Odum and Brown, 1975). This

figure was obtained by summing total fuel usage in the U.S.

per year and total work of the natural systems expressed

as KcalFFE required to do the same work. This figure was

then divided by the gross national product for that year

giving an average ratio of work that accompanies a dollar

expenditure expressed in kilocalories/dollar. Thus, when

data on human activity are given in dollars, the dollar

figure can be converted to kilocalories using the ratio

of 25,000 Kcal/dollars if the nature of that work has energy

uses similar to the national average.

When the energy diagram is fully evaluated a table

is constructed with each pathway in the diagram becoming

an entry in the table. Next, each work flow is expressed

in units of equivalent work by one type of energy. Thus,

all energy flows are converted to a common work quality

level. Once all entries are converted, they may be summed

to a total showing total value generated in the region per

year under the present pattern of man and nature. The in-

vestment ratio can also be calculated. Following this,

separate tables can be constructed for the totally natural

condition as it was thought to once exist and for antici-

pated outcomes or patterns of man and nature under each

alternative. Investment ratios for each alternative can

be calculated and compared to national and local averages.

Land-Use Maps from Aerial Photographs

Two sets of maps were constructed to show spatial

features, one of the primitive condition and one of the

present pattern of man and nature in the Franklin County


The primitive map was constructed using a soils map

of the region. The assumption was made that soil types re-

flect original vegetation types. The scale on the map was

1 inch equals 4 miles.

The map showing present land-use was developed from

aerial photographs taken in 1973 on a scale of 1 inch equals

2000 feet. The map was verified with checks on the ground

and with the Franklin County Agricultural Extension Agent.

Each system type shown on the maps is defined in Appendix B.


Maps of Principal Ecosystems in Franklin County

Shown in Figs. 6 and 7 are maps of the ecosystems

in Franklin County. A description of each type of system

is given in Appendix B. Figure 6 is the map showing the

primitive pattern prior to the first commercial cutting

of the forest (circa 1838). Areas of each ecosystem in

the primitive pattern are given in Table 2.

The present pattern is shown in Fig. 7. Areas of

present land-use are given in Table 3. In the present

pattern urban land accounts for only 0.5% of the total

regional area (about 1.2% of the land area). Approximately

15% of the original bay swamp and most of the original pine

palmetto flatwoods, high pinelands, and pine scrub have

been converted to pine forests. Cleared/planted lands

account for approximately 26% of the land area, most of

which was originally bay swamps and pine palmetto flat-

woods. The river swamp area is approximately 15% smaller

than in the primitive pattern. In the estuary, new systems

account for only 1.4% of the bay area and include boat

basins and harbors (200 acres), navigation channels (560








0 r



4J 0.
(a (A


0. Q

L) ~

0 0



1-tE 11 Il i tl It i I Xli i

1144 1 1II

-t 14 ZLh H
o u 14 1111 1
\\ .r n i w1:! ':~b Ii.

I **
A' Ni~, -
I C o


Table 2

Area and percentage of total area of each ecosystem
type in primitive pattern in Franklin County.

Ecosystem Type Area, Acres Percentage of Total

Oligohaline system 14,842 1.65
Grass flats 19,415 2.16
Salt marsh 7,701 0.86
Oyster reef 7,539 0.84
Sand bar 1,534 0.17
Medium salinity
plankton system 82,971 9.25
Coastal plankton system 406,400 45.30
High velocity channels 718 0.08
High energy beach (11,596) (1.30)
Coastal dunes 4,426 0.49

Shallow woodland ponds 1,388 0.15
Turbid piedmont river 3,644 0.41
Blackwater river 2,396 0.27

Pine scrub 43,118 4.81
High pineland 11,586 1.29
Pine-palmetto flatwoods 111,209 12.40
Bay swamp 118,044 13.16
River swamp 35,575 4.00
Freshwater marsh 13,052 1.45
TOTAL 897,154 100.00

aTotal county area includes all land and water eco-
systems in the county and adjacent Gulf of Mexico to the
10-fathom line.






o o.



0. ,


0 0r







Table 3

Area and percentage of total area of each ecosystem
type in present pattern in Franklin County.

Ecosystem Type Area, Acres Percentage of Total

Scraped land 1,134 0.13

Residential--light 1,789 0.20

dense 478 0.05

Commercial -indus-
trial 412 0.05

Airports 459 0.05


Oligohaline system 14,841 1.64

Grass flats 19,415 2.14

Salt marsh 7,072 0.78

Oyster reefs 8,870 1.00

Sand bars 1,534 0.17

Medium salinity
plankton system 80,217 8.85

Coastal plankton
system 406,400 44.84

High velocity
channels 767 0.08

High energy
beaches 11,595 1.28

aTotal county area includes all land and water eco-
systems in the county and adjacent Gulf of Mexico to the 10
fathom line.

Table 3 continued

Ecosystem Type Area, Acres Percentage of Total

Marine (cont.)

Coastal dunes 4,782 0.53

Boat basins and
harbors 200 0.02

Dredged navigation
channels 560 0.06


Shallow woodland
ponds 1,388 0.15

Turbid piedmont
River 3,644 0.40

Blackwater river 2,396 0.26


Pine forests 103,303 11.40

lands 93,015 10.26

River swamp 30,539 3.37

Bay swamp 99,494 10.98

marsh 12,100 1.33
TOTAL 906,401 100.00

acres), Bob Sikes Pass (49 acres) and planted oyster bars

(1331 acres).

Characteristics of Apalachicola Bay

This section brings together data on Apalachicola

Bay collected in this project and assembled from other in-

vestigators. Using these data, an aggregated model of the

diurnal properties of the bay ecosystem was evaluated,

simulated, and compared with field data.

Salinity Measurements

Figures 8 and 9 are maps showing the sur-
face and bottom salinity distribution on September 29,

1973 and April 20, 1974. Both sets of measurements were

taken on a rising tide. The September measurements were

taken when river flow was low (15,060 cfs) while the April

measurements were taken during high flow (49,390 cfs).

Salinity stratification was evident over most portions of

the bay in the fall (Fig. 8). This observation is supported

by data collected by Estabrook (1973). Area weighted aver-

age surface and bottom total salt content for the bay was

3.58 x 109 kg and 6.0 x 109 kg, respectively. These corre-

spond to average surface and bottom salinities of 15 and

26 ppt, respectively.

In the spring sample, both surface and bottom

salinities were lower, averaging 4.9 and 11.5, respectively

Fig. 8. Salinity pattern in Apalachicola Bay on September 29,
1973, (a) surface; and (b) bottom. River flow was
15,060 cfs and wind was from the southeast. Salin-
ity stations are indicated by dots.


Sept. 29, 1973


^ "O '15 20 25

15 a,



Sept. 29, 1973





Fig. 9. Salinity pattern in Apalachicola Bay on April 20,
1974, (a) surface; and (b) bottom. River flow was
49,390 cfs and the wind was from the east at 10-
15 knots. Salinity stations are indicated by dots.


April 20, 1974


0 0

ST. VINCENT -5 0 0 .'


April 20, 1974

A01 BAY5




(Fig. 9). However, the average relative difference between

surface and bottom salinity was greater in the spring sample

than in the fall sample. High salinity bottom water (20-

30 ppt) was evident in a large portion of the bay along the

barrier island on both sides of Bob Sikes Pass. Very little

stratification was evident nearer the river mouth and in

East Bay in the spring sample. There was no evidence of

thermal stratification. Surface and bottom temperatures

seldom differed by more than several degrees.

Diurnal Metabolism Measurements

Thirty-six diurnal metabolism measurements were

taken in the bay between June 22 and September 30, 1973.

Station locations were given in Fig. 4. Data from two

24-hour diurnal metabolism surveys (Station I, July 24-25,

1973, and Station C, September 29, 30, 1973) are shown in

Figs. lOa and lOb. In Fig. 1Oa respiration exceeded day-

time net production (see rate of change curve) by a factor

of four indicating that a large portion of the respiratory

activity was supported by organic matter supplied from

other areas. Photosynthesis was at a maximum at 1100 hours

(1.05 g 02/m2/hr) and declined rapidly in the early after-

noon. Respiration averaged about 0.5 g 02/m /hr during

the night. At this station the water was generally under-

saturated with oxygen. Hence, when the rate of change

curve was corrected for diffusion, hourly rates of

r "OE **-
I .0C ( V) u- U)
.0 01) 0 0- r- 0.-o 0)
41 f ( > 4 0 0- O4C S-
41 'Q)U 30 OS-r- )
S4-' > .0 0 4f 0 3
E 4/ 0 ) m r- '- E a-
C0 > .CT -4 0 E )
u 4oE r- u s- U .

4-> C- 5. 4 ) 0 ) 10 10
C t 3 *_0 CAS. O )
3-'a > 0 4- 3 **-X
E v 3 >>>, r- r-^-
E x c0 0 o 'o 'CDmS ..

u0.> C 4-'C OE + E -

4- 'C -- W-(-- Wc o
4-3 0 (t F- C C C E w
-0 U v 4-0 400 0U4'
OC Ot U) > .
r-COE C Or- nX *
3 0o00)-n2.Co00
0) s-4 o2 r- c 0 1
i- C 04- 0 0. 0 C *0-j 4
cn.0 L 0 = a.O- 04
-0 4)0 5.-0- 0 4 E 0

SI .(a .0 *a- O *0 -
-UCMWU -a>c4-l--u)
Iu t4 E o C2 *

3n S] 04- *44 C0-
aQ. Co> ,o *Or- 0)
co shu u c o ns u-CL Q O ,

&L. 00 0 0 0) fl tfl0 04
o-fl 4 J 3 'Vn L E r- a- "O
0 .0 4- 4* 20 ; d 0-
o) > S- a-r- u o)
5--.C'ri- 0rS- o sV W MU
O rn. n'o C 0) c5 -
C- cEnD oC ( ( l. c0-i
X 0-- 04- 5- L C = = S.- 0

S. 40 0u 4a r- 0 U -

SC/)m I -4 0)0 )-0 3 -'
C) -4 C 200 ) t O
4-.-- --.J CU- N O.0) &-
0m0 COn- 0)r- 3 -- 1 10
L-.ir4-'- ( C r-

r- E E 00) C- a r- *-
aEnnU N30) 0()

E4 *r- 2- a0 a0 0u 0u 0u ) O
Xr- 0) 0.C0.0C 0.C0 5- 0 5- 0
LU. O O ) 04 2 4 .0 4-' 0 0 04



0 x
uj t

mw 0 0


0 0

OD w o wN g~ a

% 'Nouwunivs IN3:)Vd Jtl/,W/ 0 5 39NVl(3 JO 31VU1

o W 0 (m 0 o l W
% W) C4 N N

LW/b 'N3DkXO wD Hld3a ) 3flnlVW3d3.L 0% S AIklI1rS

o "O N 0 0 co to0mto
D N %S -

WU/t ~N39AXO Wu 'Hld30 3o '3Yifl1VY3iri3 %S A~IfNI1VS


respiration became larger as shown on the corrected rate of

change curve. Most stations followed a pattern similar to

the one at Station I, except that the relative proportion

of photosynthetic and respiratory activity (P/R ratio)

varied from station to station. Figure 10b shows results

from a station (C) where photosynthesis was predominant and

the P/R ratio was greater than 1.0.

A summary of diurnal metabolism measurements taken

in the bay in the summer of 1973 is given in Table 4. Re-

sults are grouped in the table according to major features

characterizing the portion of the bay from which the samples

were taken (Odum et al., 1974a). During the sampling pe-

riod, metabolism (daytime net photosynthesis plus night

respiration) ranged from 3.1 g 02/m2 /24 hrs at Station L

to 21.6 g 02/m2/24 hrs at the FSUML boat basin. The latter

station was the deepest station where a measurement was

taken and this in part accounts for the large metabolism,

most of which was respiration. There was some variability

at stations where metabolism was measured twice.

Figure 11 shows total metabolism plotted against

time. No clear seasonal trend was evident over the four-

month sampling period. Average metabolism was 8.5 g 02/m2/

24 hrs. Figure 12 shows daytime net photosynthesis plotted

against nighttime respiration for each station. The diago-

nal line represents a P/R ratio of 1.0. When plotted in

this fashion those stations in East Bay (K, L, M, ), near



c .c
0 nD

*4 *J

Q. '1
V- 2



C 10


O o


0 0



In M 0M CM 0) Om 0 CO

o 0 0 0C 0 0 *- 0

o co O r- LO n r oC -

Ot CJ C" CM O i L

O m 0c to C o i- CM o o0 C' to C rM r. co




u) ai
01 4- C
S /- 0

10 '

0u .- $.

2 -0
E 0)

S u *
U ,

C 014
0 4 *r- *
= U-M -

ER. 'o u

= 06
< 2- r-
'0 >'
C >,.L-

4- 01

3 00 4-'
E'- 02E

1 2 CO
t_ e C e

0 =:

mO N CM - i-

-- O It N : :!

- Lm C"M CM -
re N 0 N N N

I- M LA Of W I LL. C <


0 00 O 0 0 0O
OO OO0 r--0 0 O a

Cn 9O O Q 0 ,

CM Ci C4 4 4 m O t 0o m


O 0)

0 o

L C0



I*- CM 'cl-

0 cd- C"

0 '0- (,I
S* C
t Ll) 0

Nr e -I

L Nl CJ I'- CM i- -

O r- 0 0 0 C --


( 0 to I CM JN r Co

0 C r- r-

*- l CO CO I-

C'J ID 01i t0 Cit 0

in cmi mj- 1 to Lo
mU N) we U m 0


C .

0 0

I0 *O
r E

Q. -N


c ,






* 0 (


un > *

= ( C


=3 Z


c O
r- r---

*r- Lt



C -


-J 1I

L() 4-'


4- 1
+3 I3
*r- O v

< +j roz

n vi

CD **- c S

Fig. 11. Graph of bay productivity for June-September, 1973.
Each point represents sum of daytime net photosyn-
thesis and nighttime respiration from a 24-hour rec-
ord of oxygen.





c *

a: *. .

5 -


Fig. 12. Graph of daytime net photosynthesis as a function
of nighttime respiration for systems in Apalachi-
cola Bay (grass flats x ; boat basins A ; medium
salinity plankton system and oligohaline system
o ). The diagonal line represents a P/R ratio
of one.

8 .



o 5 x /* (1
S* x 5/ 2, 16.4

SP/R 1.0


2 ** *A


1 o

I 2 3 4 5 6 7 9 10
R night, g 02 /m2/night

the river mouth (Z, J) and stations suspected of receiving

man-related wastes (I, P, FSUML boat basin) all had P/R

ratios less than 1.0. The medium salinity plankton system

stations (A, AA, B, C, D, E, F, H, R, S, T, and U) had P/R

ratios of about 1.0 or somewhat greater. Average metabolism

in this area was 6.8 g 02/m /24 hrs. The grass flat stations

(N, and FSUML stations) all had P/R ratios greater than 1.0.

Average metabolism at the grass flat station was 9.6 g 02/

m2/24 hrs.

Organic Carbon Measurements

Fourteen stations were sampled for particulate and

dissolved organic carbon between September 20 and September

30, 1973. Organic carbon data are summarized in Table 5.

The average concentration of total and particulate organic
carbon in the bay area was 5.9 and 0.8 g C/m3, respectively.

Surface and bottom concentrations of total and particulate

organic carbon in the river were 4.5 and 0.6 g C/m3 and

3.3 and 0.62 g C/m3, respectively. The dissolved to particu-

late organic carbon ratio for an average of all bay stations

was 6.4 This ratio was 5.3 for the river. In general,

organic matter concentrations in the bay were higher than

those in the river.

Data Assembled from Other Sources

Given in Fig. 13 is a plot of monthly average river

flow from October 1928 through October, 1972 (Mann, 1975).

Table 5

Summary of organic carbon measurements taken in Apala-
chicola Bay in September, 1973. Station locations are
given in Fig. 4. Numbers in parentheses indicate the
number of replicates averaged.

Date Total organic Particulate
Station 1973 carbon, organic
mg C/1 carbon, mg C/1

(boat basin)

(1/4 mi. offshore)

Station I

John Gorrie Bridgeb
(eastern end)

John Gorrie Bridge
(western end)

River Mouth

Station C

Station AA

Station D

Sept. 20

Sept. 20

Sept. 20

Sept. 20

8.5 (4)

8.6 (3)

7.8 (1)

4.8 (4)

Sept. 20 5.2 (4)

Sept. 20 6.1 (2)

Sept. 30 4.7 (3)

Sept. 30 5.4 (3)

Sept. 30 5.4 (2)

aFlorida State University
Franklin County, Florida.

Marine Lab at Turkey Point,

The John Gorrie Bridge crosses Apalachicola Bay con-
necting the towns of East Point and Apalachicola.

1.3 (2)

1.2 (2)

0.7 (3)

0.6 (3)

0.4 (2)

0.7 (2)

0.9 (1)

0.6 (1)

Table 5 continued

Date Total organic Particulate
Station 9 carbon, organic
193 mg C/i carbon, mg C/i

Station F Sept. 30 4.2 (1) 0.6 (1)

Station H Sept. 30 5.0 (1) 0.7 (1)

Station L Sept. 30 5.5 (3) 0.7 (1)

(bottom)c Sept. 30 4.5 (4) 0.6 (1)

(surface) Sept. 30 3.3 (3) 0.6 (1)

cThe river station was taken about a half mile
downstream of the railroad bridge crossing the Apalachi-
cola River.

Fig. 13. Monthly average flow (cfs) of the Apalachicola
River at Chattahoochee, Florida from October,
1928 to October, 1972.


Yearly average flow for this period is shown along the

dotted line. During this period, average flow was 21,690

cubic feet per second (cfs). The maximum and minimum flows

were of 293,000 and 4,950 cfs recorded in March, 1929 and

October, 1954, respectively. The flow rates presented in

Fig. 13 were recorded at Chattahoochee, Florida. An esti-

mate of river flow into Apalachicola Bay was made by adding

the flow of the Chipola River to that recorded at Chatta-

hoochee and increasing the total by 10 percent (Dawson,

1955a). With this adjustment, the 44-year average flow be-

comes 25,179 cfs.

Figure 14a shows average monthly river flow over

the period 1929 through 1972. Flow was adjusted to approxi-

mate the volume which enters the bay as described above.

Peak flow occurred in March (52,107 cfs) and minimum flow

in September (12,801 cfs). Peak flow occurred as a pulse

from January through April with the remaining monthly aver-

age flows near or below the yearly average. Figure 14b

shows monthly average river flow for 15-year periods before

and after construction of Jim Woodruff Dam at Chattahoochee,

Florida. The combined average flow for the full 43-year

recording period is also shown. All three curves are sim-

ilar. Apparently, the relatively small storage capacity of

Lake Seminole behind Jim Woodruff Dam did not affect the

timing of discharge.

Fig. 14. Monthly river flow, (a) averaged for the period
1929 through 1972; and (b) averaged for the 15-
year period before and after construction of Jim
Woodruff Dam at Chattahoochee, Florida.

J F' M A 'M J J A S 0 N D





- 30.




& 40_


0 -

1929 1972



Given in Figs. 15-17 are estuarine data developed

by Estabrook (1973) in a study of phytoplankton ecology

and hydrography of Apalachicola Bay. They were redrafted

and presented here for use in both the diurnal activity and

the regional models.

Figure 15a shows monthly average insolation for

1972-1973. The monthly averages show a seasonal cycle

typical of Florida with maximum insolation in May and

lower values later in the summer due to rainy season cloud

cover. Minimum insolation occurred in December. Figure 15b

shows average surface and bottom salinities for the bay.

The average salinity of the bay was lowest in February dur-

ing high river flow and highest in September during low

river flow. The average difference between surface and

bottom salinities was 8.5 ppt with a seasonal range of 1

to 18 ppt. Detailed salinity maps are given in Figs. 8

and 9 and in Estabrook (1973).

Average water temperature is shown in Fig. 16a.

The maximum average temperature was about 600C in September

with a minimum of about 100C in February. Figure 16b shows

average values for phytoplankton production as measured

by Estabrook (1973) using the C-14 technique. The dashed

lines are 95% confidence limits. Mean daily phytoplankton

production ranged between 1.69 g C/m2/day in April to 0.06

g C/m /day in February. However, there was a wide range in

productivity in the bay at any one sampling time as indi-

cated by the large confidence intervals.

Fig. 15. Monthly average insolation (a) and average surface
and bottom salinity for Apalachicola Bay (b). Data
are from Estabrook (1973).

1972 1973








U 10


Fig. 16. Average seasonal water temperature (a) and average
daily phytoplankton production in Apalachicola Bay
(b). Dashed lines in (b) are 95% confidence limits.
Data are from Estabrook (1973).


0 30 .

o .






0 .


1.5 I

. I '.0

I "
S0 N D J F 'M A M J ' A 'S

1972 1973


Shown in Figs. 17a and 17b are seasonal concentra-

tions of nitrate and ammonia in the surface and bottom

waters of the bay and from the surface of the river (Esta-

brook, 1973). Nitrate concentrations in the river were

rather constant ranging from 0.18-0.20 mg N/1. Average

nitrate concentrations in the bay showed a seasonal cycle

with high concentrations in February of about 0.18 mg N/1

when river flow was high. Low concentrations of about 0.05

mg N/1 occurred in the fall when river flow was low. Bottom

nitrate concentrations were generally less than surface

concentrations except when river flow was low, at which

time there was little difference between surface and bottom.

Ammonia concentrations are shown in Fig. 17b. Con-

centrations in the bay ranged from 7-40 pg N/1. No clear

seasonal trend was evident. Bottom water concentrations

of ammonia were generally higher than surface water levels.

Ammonia concentrations in the river varied considerably

ranging from about 7-35 pg N/1 over the year. At times

ammonia concentrations in the bay were higher than those

in the river.

Shown in Fig. 18 are monthly estimates of fish

and benthic invertebrate biomass from 15 stations in Apa-

lachicola Bay (Menzel and Cake, 1969). Oyster reef bio-

mass was not included in these estimates. Biomass was in

g/m2 wet weight. While there was considerable variation

in biomass between samples from any one sampling data,

Fig. 17. Seasonal record of nitrogen concentrations in the
Apalachicola River and in surface and bottom waters
of Apalachicola Bay. Data are from Estabrook (1973).
(a) Nitrate, (b) Ammonia.

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