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| Citation |
- Permanent Link:
- https://ufdc.ufl.edu/UF00084175/00001
Material Information
- Title:
- Water, wetlands, and wood storks in southwest Florida
- Creator:
- Browder, Joan Arrington ( Dissertant )
Snedaker, Samuel C. ( Reviewer )
Johnston, David W. ( Reviewer )
Bayley, Suzanne E. ( Reviewer )
Huber, Wayne C. ( Reviewer )
Odum, Howard T. (Howard Thomas), 1924-2002 ( Thesis advisor )
- Publisher:
- University of Florida
- Publication Date:
- 1976
- Language:
- English
- Physical Description:
- ix, 406 leaves : ill. ; 28 cm.
Subjects
- Subjects / Keywords:
- Dissertations, Academic -- Environmental Engineering Sciences -- UF
Ecological surveys -- Wetland ecology -- Florida ( lcsh )
Ecology -- Florida ( lcsh )
Environmental Engineering Sciences thesis, Ph.D.
Wetlands -- Florida ( lcsh )
Wood stork ( lcsh )
Miami metropolitan area ( local )
Fish ( jstor )
Ponds ( jstor )
Rain ( jstor )
- Coordinates:
- 26.42 x -81.54
Notes
- Abstract:
- Energy circuit models were used to study an oscillating ecosystem, the seasonally expanding and contracting wetlands of southwest Florida. Analog and digital computers were used to simulate the effects of the natural rainfall pattern and of drainage on seasonal expansion and contraction of water area, production and concentration of fish, and feeding and reproduction of the Wood Stork (Mycteria americana). Information for quantification of the models was obtained from aerial surveys of storks nesting at Corkscrew Swamp Sanctuary, quantitative sampling of fish and invertebrates in a pond and marsh, measurement of wetlands area on infrared aerial photographs, topographic field surveys, and a literature search and field observation on demographic parameters of populations. There are approximately 30,000 ponds ranging in size from less than an acre to more than 100 acres in Lee, Hendry, and Collier counties. Freshwater wetlands once comprised 53% of the total area. Drainage, which has accelerated in the past 15 years, has reduced wetlands area by more than 60%, diminishing the capacity of the area to store water from wet years. Regional-scale depth-area, area-volume, and depth-volume curves were developed to relate the intensity and timing of rain to the seasonally changing area of surface water. Classical hydrologic equations were combined with the regional curves to produce a water model simulating seasonal and long-term water storage and runoff. Patterns of land area covered by water from the water model were used to drive three models relating water patterns to fish and storks. Seasonal oscillation of water area increases the efficiency of the flow of energy from prey to predator and allows a system to support more top consumer than would otherwise be possible. Concentration of the fish production of the wetlands of southwest Florida is increased approximately 25 times by the sun’s energy, acting seasonally through evaporation and the transpiration of plants to shrink water area. This extra concentration factor in the food chain give a very high “energy quality
value to the Wood Stork, which may serve as an indicator of the productivity of wetlands of the region. Drainage and a downward trend in rainfall for the past 15 years have stressed ecosystems causing a decline in wading birds. Simulations suggest that drainage more than decline in rainfall was responsible for the decrease in storks. Wood Storks have shown resiliency in adapting to the drained conditions, finding new feeding areas such as the marshes at Lake Okeechobee. Plans to raise lake regulation levels will eliminate half this area. Because of the topography of southwest Florida, present drainage has greatly decreased the area flooded but only slightly decreased the range of fluctuation. Therefore, a wetlands ecosystem dependent on oscillation continues to operate. If, however, the mean water level is further lowered, the fluctuation of water area may be drastically reduced and the ecosystem seriously disrupted.
- Thesis:
- Thesis--University of Florida.
- Bibliography:
- Bibliography: leaves 399-405.
- General Note:
- Typescript.
- General Note:
- Vita.
- Statement of Responsibility:
- by Joan Arrington Browder.
Record Information
- Source Institution:
- University of Florida
- Holding Location:
- University of Florida
- Rights Management:
- Copyright [name of dissertation author]. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
- Resource Identifier:
- 025386521 ( ALEPH )
03157178 ( OCLC )
AAU5731 ( NOTIS )
Aggregation Information
- UFIR:
- Institutional Repository at the University of Florida (IR@UF)
- ODUM:
- Howard T. Odum Center for Wetlands Publications
- UFETD:
- University of Florida Theses & Dissertations
- IUF:
- University of Florida
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WATER, WETLANDS, AND WOOD STORKS IN SOUTHWEST FLORIDA
By
JOAN ARRINGTON BROADER
A DISSERTATION PRESENTED TO THE GRADUATE COUNCIL OF
THE UNIVERSITY OF FLORIDA
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1976
Copyright
1976
Joan Arrington Browder
ACKNOWLEDGMENTS
I am indebted to my major professor, H. T. Odum, for
providing direction, advice, encouragement, support, and
introducing me to a new way of viewing the world. Other
members of my committee: S. Snedaker, W. Huber, S. Bayley,
and D. Johnson aided in many ways. S. Snedaker provided
laboratory space and equipment for the field study. W.
Huber 'helped develop the water model. M. Duever provided
facilities and the Corkscrew Sanctuary study area. Field
work was accomplished primarily with the assictrncc of E.
Sroka, E. Carlson, M. Chancy, and P. Schroeder. Others who
helped in the field work were M. Duever, L. Riopele, D.
Stellar, J. Hansen, and E. DeBellevue. R. Hartzog and B.
Oesterling measured, weighed, and identified the specimens
from the field collections.
M. Brown, R. Costanza, and E. Debellevue prepared the
maps and provided area measurements of ecosystems that
were the basis for the relationships of water depth, water
area, and water volume developed in the study. E.
DeBellevue participated in the development of the water
model.
Concepts of the study were discussed with F. C.
Craighead, Sr., J. Ogden, J. Kushlan, A. Sprunt, IV, M.
111
Duever, W. Dineen, P. Rhoads, and H. Klein. Unpublished
materials were provided by J. Kushlan, J. Ogden, J. Hansen,
S. Nesbitt, R. Chandler, and others. J. Fry conducted a
field trip over his property in southeastern Hendry County
and into the Kissimmee Billy cypress strand.
The National Audubon Society funded an aerial survey
of wading bird feeding sites in southwest Florida during
the 1973 1974 dry season. P. Schroeder, my husband, was
the pilot for the aerial survey and did many other noble
and ignoble tasks to help me complete my dissertation.
Work was supported by the following grants and
contracts administered by the Center for Wetlands of the
University of Florida, H. T. Odum, principal investigator:
Rockefeller Foundation, Grant RF-73029, National Science
Foundation, Grant AEN 73-07823 A01, and Contract
CX000130057 with the National Park Service, U. S.
Department of Interior, and State of Florida Division of
State Planning.
TABLE OF CONTENTS
ACKNOWLEDGMENTS .
ABSTRACT
. q .
Page
. IIi
. vii
INTRODUCTION .
Model of Water, Fish, and Wading Birds .
Climate .. .
Study Region .
Corkscrew Marsh and Mud Lake Pond .
Water Patterns and Biological Responses
Modeling Theory of Oscillating Systems
Population Biology of Wood Storks .
METHODS . .
Basis for Models .
Analysis of Rainfall .
Remote Sensing for Relating Water Area to.
Volume .
Field Study in Corkscrew Marsh and Mud Lake
Pond .
Aerial Survey of Feeding Wading Birds .
Characteristics of the Wood Stork Population
RESULTS . .
S 45
S 77
S 79
S 96
107
S 112
Rainfall Pattern .
Relationship of Water Volume to Surface Water.
Relationship between Water Levels and Fish
Populations .
Spatial-Temporal Aspects of Wading Bird
Feeding .. .
Wood Stork Population Dynamics .
QUANTIFICATION AND SIMULATION OF MODELS .
Water Model . .
Water Area, Fish, and Storks Models I and II
Water Area, Fish, and Storks Model III ..
112
119
15 7/
219/
235 i
25
251
283
303
3.
6
9
17
23
30
<32.
DISCUSSION .
Spatial and Temporal Patterns .
Comparison of Fish and Crayfish Production .
Energy Implications of Concentration .
Observations from Model III and the Field
Study .
The Biological Community in a Fluctuating
Habitat . .
Effects of Natural Storage and Impact of
Drainage .
Survival Prospects of Wood Storks in
Southwest Florida .
Recommendations for Wood Stork Protection .
General Recommendations for Water Management .
APPENDIX A SAMPLING SCHEDULE FOR FISH COLLECTIONS
AND TABLES OF DATA ON WETLANDS AREA, WATER
STORAGE, AND OCCURRENCE OF WOOD STORKS .
APPENDIX B TRIGONOMETRIC FUNCTIONS USED TO
GENERATE SEASONALLY-VARYING WATER AREA
AND SOLAR RADIATION IN MODELS I AND II OF
WATER AREA, FISH, AND STORKS .
APPENDIX C FORCING FUNCTIONS, INITIAL CONDITIONS,
THRESHOLDS, RATE COEFFICIENTS, AND OTHER
CONSTANTS AND TABLE FUNCTIONS OF THE MODELS
APPENDIX D DYNAMO PROGRAMS FOR THE DIGITAL
COMPUTER MODELS .
LIST OF REFERENCES .
BIOGRAPHICAL SKETCH .
PAGE,
320
320
323
325
332/
335
337
342
344 /
347
349
361
368
386
399
406
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
WATER, WETLANDS, AND WOOD STORKS IN SOUTHWEST FLORIDA
By
Joan Arrington Browder
August, 1976
Chairman: H. T. Odum
Major Department: Environmental Engineering Sciences
Energy circuit models were used to study an
oscillating ecosystem, the seasonally expanding and
contracting wetlands of southwest Florida. Analog and
digital computers were.used to simulate the effects of the
natural rainfall pattern and of drainage on seasonal
expansion and contraction of water area, production and
concentration of fish, and feeding and reproduction of the
Wood Stork (Mycteria americana). Information for
quantification of the models was obtained from'aerial
surveys of storks nesting at Corkscrew Swamp Sanctuary,
quantitative sampling of fish and invertebrates in a pond
and marsh, measurement of wetlands area on infrared aerial
photographs, topographic field surveys, and a literature
search and field observations on demographic parameters of
populations.
vii
There are approximately 30,000 ponds ranging in size
from less than an acre to more than 100 acres in Lee,
Hendry, and Collier counties. Freshwater wetlands once
comprised 53% of the total area. Drainage, which has
accelerated in the past 15 yrs, has reduced wetlands area
by more than 60?, diminishing the capacity of the area to
store water from wet years.
Regional-scale depth-area, area-volume, and depth-
volume curves were developed to relate the intensity and
timing of rain to the seasonally changing area of surface
water. Classical hydrologic equations were combined with
the regional curves to produce a water model simulating
seasonal and long-term water storage and runoff. Patterns
of land area covered by water from the water model were
used to drive three models relating water patterns to fish
and storks.
Seasonal oscillation of water area increases the
efficiency of the flow of energy from prey to predator and
allows a system to support more top consumers than would
otherwise be possible. Concentration of the fish
production of the wetlands of southwest Florida is
increased approximately 25 times by the sun's energy,
acting seasonally through evaporation and the transpiration
of plants to shrink water area. This extra concentration
factor in the food chain gives a very high "energy quality"
value to the Wood Stork, which may serve as an Indicator of
the productivity of wetlands of the region,
viii
Drainage and a downward trend in rainfall for the past
15 yrs have stressed ecosystems causing a decline in wading
birds. Simulations suggest that drainage more than decline
In rainfall was responsible for the decrease in storks.
Wood Storks have shown resiliency in adapting to the
drained conditions, finding new feeding areas such as the
marshes at Lake Okeechobee. Plans to raise lake regulation
levels will eliminate half this area.
Because of the topography of southwest Florida,
present drainage has greatly decreased the area flooded but
has only slightly decreased the range of fluctuation.
Therefore, a wetlands ecosystem dependent on oscillation
continues to operate. If, however, the mean water level is
further lowered, the fluctuation of water area may be
drastically reduced and the ecosystem seriously disrupted.
Chairman
INTRODUCTION
Understanding the structure, functions, spatial
patterns, and temporal variations of ecosystems subjected
to sharply pulsing climatic factors is an important general
problem in ecology. Subtropical freshwater wetlands
receiving seasonally varying inputs of rain are found on
most continents and provide interesting examples of
ecological systems under the influence of climatic pulses.
This dissertation is the study of the subtropical
freshwater wetlands located at the southwest end of the
Florida peninsula. The study simulates and evaluates the
water regimes and energy flows of the regional wetlands
ecosystem by means of analog and digital computer models
and addresses the following questions:
1) How does periodic expansion and contraction of water
area control fish and wading bird populations?
2) How are water volume and water surface area related?
3) What is the timing of the storage and release of
water in relation to the timing of the rain?
4) How does change in surface water area affect energy
flow?
5) What are the major pathways of energy flow and how
is the flow of energy organized in time and space?
6) How does productivity in a pulsing system compare
with productivity in systems with more constant
energy inputs?
7) What structural features and adaptations of the
system optimize energy flow?
The models describe the system in terms of primary
inputs, outputs, and internal components, and the major
flows of energy that connect them. The pathways of energy-
flow lead to large colonial-nesting wading birds at the top
of the food chain of the aquatic system. Field sampling
and measurements, aerial surveys, remote sensing, and
topographic surveys of the study were organized around the
design and quantification of the models.
Climatic factors such as rainfall and sunlight are the -
directing forces that orchestrate the seasonal pattern, but
the responses of the system to seasonal variations in
sunlight and rain are determined by structural
characteristics of the system at levels of organization
ranging from the geomorphology of the basins to the
population biology of the indicator species.
Structural characteristics of the southwest Florida
wetlands ecosystem were examined on three different scales
of organization: on the regional scale, considering all
the wetlands in southwest Florida as a functional unit,
with expansion and contraction of the water area and
sequential use of feeding areas by wading birds; on the
biological community scale, examining biological events
such as fish production and fish predation by wading birds
in relation to the change in water depths in a pond and its
surrounding marsh; and on the scale of the bioenergetics
and demography of an indicator species, the Wood Stork,
Mvcteria americana, with consideration of age structure,
age dependent survival rate, age of maturity, and other
factors.
Information for the models in this study on the
regional scale was obtained primarily by original work.
Information at the biological community scale was obtained
partially by original work and partially from previous
studies. Information concerning the indicator species was
obtained almost entirely from previous studies.
Demographic material was largely hypothetical, but based on
the best information available.
Model of Water, Fish, and Wading Birds
Figure 1 is a simple diagrammatic model of the
wetlands system of souLiiwest Florida that shows the
relationships between primary productivity, surface water
area, fish production, fish density, and breeding success
of wading birds. The model describes a connection between
the seasonal change in sunlight and rain and the timing of
biological events. In the model, fish production is a -
function of sunlight and total surface water area. Fish
density, as defined in this study, is total fish biomass
divided by surface water area. Both bird biomass and fish
kills are proportional to fish density rather than to
absolute fish biomass. Fish kills occur only when fish
density exceeds a certain threshold value. In the model
bird reproduction is dependent on the average weight of the
birds, which is total bird biomass divided by bird number.
Figure 1. Model of water area, fish, and wading birds.
NOTE: Key to symbols is in Methods section.
'RUNOFF AND
EVAPOTRANSPIRATION
Breeding takes place only when average bird weight rises
above a threshold value. In the model, breeding success of
one species, the Wood Stork is taken as an index not only
of the condition of the area's wading bird populations but
also of the general healthy functioning and overall
productivity of the wetlands system. The model tests the
effect of seasonal and long-term variations in rainfall and
the effect of drainage on expansion and contraction of
surface water area, fluctuation of the water table, total
annual fish production, fish density, and breeding success
of Wood Storks.
Climate
Figure 2 reveals seasonal and long-term patterns in
the rainfall of southwest Florida. The record of rainfall
is for the years 1951 through 1975 collected by ALICO
(Atlantic Land Improvement Company, La Belle) at Corkscrew
grove, a commercial orchard at latitude 260 25'N, located
30 km from the coast and immediately north of Corkscrew
Swamp (see Figure 4 for location). 5.
Average yearly rainfall was 1421 mm at Corkscrew
grove, which is slightly greater than the 1372 mm average
reported by the U. S. Weather Bureau near the Gulf Coast at
Page Field in Ft. Myers, Lee County. On the average,
Corkscrew grove received 68% of its annual rain during the
5 mos from June through October.
Figure 2. Monthly and
through 1975 in southwest
(ALICO, pers. comm.).
total annual rainfall from 1951
Florida at latitude 26 o 25 N
NOTE: Rain gauge is in Corkscrew grove, a commercial grove
of the Atlantic Land Improvement Company (ALICO),
immediately north of Corkscrew Swamp (see map in Figure 4).
2500.
1975
YEAR
Figure 3 shows the long term mean monthly moisture
relations in the vicinity of the study area (Royal Palm
Ranger Station, in the Everglades near the Tamiami Trail,
latitude 25000'N). The graph demonstrates an excess of
rainfall over evaporation during the summer and early fall,
but a moisture deficit, evaporation greater than rainfall,
during the spring and summer. In south Florida, pan
evaporation appears closely related to solar radiation,
also shown in Figure 3. Transpiration, the loss of water
through plants, changes seasonally. Transpiration rates
for some plants, such as cypress, willow, and many marsh
macrophytes are greatly reduced when the plants shed their
leaves or die back in late fall, suspending photosynthesis
during winter.
The Study Region
The study region can be roughly defined as the
noncoastal parts of Lee, Hendry, and Collier counties. It
also includes the seasonally drying marshes on the western
and northwestern shores of Lake Okeechobee and parts of
Charlotte and Glades counties (Figure 4). This area, which
lies between latitudes 250 47' and 270 07' N, comprises
numerous sloughs, marshes, cypress strands, wet prairies,
and ponds that are linked by flows of energy through the
movement of water and animals such as wading birds.
Although many of these wetlands are in their natural state,
Figure 3. Mean monthly rainfall (1940-1970) and mean
monthly pan evaporation (1940-1969) in south Florida at
latitude 250 00' N (redrawn from Kushlan, 1972), and semi-
monthly mean daily solar radiation (1971-1975) in south
Florida at latitude 26 42 N (R. J. Allen, Jr., pers.
comm.).
NOTE: Location of rainfall and pan evaporation data was
Royal Palm Ranger Station in the Everglades. Location of
solar radiation data was the University of Florida
Agricultural Experiment Station, Belle Glade. In the solar
radiation curve, the January values are based on 4 yrs'
date and February and March values are based on 3 yrs'
data. Ly is abbreviation for Langley, which is 1 cal/sq-cm.
250
200-
IS-
150-
E
E.
100-
50-
6C
5(
4C
. 3C
-J
a
JAIN FEB MAR APR MAY JUN -JUL AUG EP -OCT NOV DE
Jn.N FEB MAR APR MAYdUN JUL AUG SEP OCT NOV DEC
0J -- I I I I I I I I --
JAN FEB MAR APR MAY JUNE JULY AUG SEPT OCT NOV DEC
RAI
PAN
Figure 4.
study areas.
Map of south Florida showing boundaries of the
AREA OF MODEL (AREA QUANTIFIED FOR WATER AREA-
WATER VOLUME RELATIONS)
TOTAL REGIONAL STUDY AREA (AREA COVERED BY SURVEY
FLIGHTS)
COUNTY LINES
BORDER OF CORKSCREW SWAMP
others have been greatly changed by drainage accompanying
agricultural or urban development.
Two distinct physiographic regions within southwest
Florida, the Big Cypress and the sandy flatlands, were
recognized and described by John Henry Davis (1943). The
Big Cypress occurs throughout Collier County and in
southern Hendry County. The sandy flatlands cover Lee
County and all but the southern part of Hendry County and
include the area known as Devil's Garden (Craighead, 1971).
In the Big Cypress, the Tamiami formation, water-bearing -
limestone of pre-Pleistocene origin, is within a foot or
two of the surface. In the sandy flatlands, the Tamiami
formation is overlain by deposits of sand, shell, gravel,
marl, and clay 10 or more feet thick. The two regions are
connected by many shallow flow-ways that transect them.
Common to both regions are shallow depressions, or ponds,--
at all elevations, and cypress swamps, swamp hammocks,
freshwater marshes, and wet prairies.
Figure 5 from Parker and Cooke (1944) delineates the
major geological formations and physiographic regions of
the three-county study area. Maximum elevations in the
study area are on "Immokalee Rise" (Parker and Cooke,
1944), which is the area defined as the Talbot Formation.
The highest point on the rise is 14 m above mean sea level
(MSL). Below and south of the rise, elevations slope from
about 6 m MSL to sea level, with a gradient of
approximately 3,75 cm/km. Numerous broad, shallow drainage
Figure 5. Delineation of major geological formations and
physiographic regions of lower southwest Florida (from
Parker and Cooke, 1944).
LEE HENDRY
Palmico Formation
wi --=.= o Miami Oolite
<-> ----- -- lU
-) W
o z
1-
a- Talbot Formation
U.
I-
Q Fort Thompson Formation
o Tamiomi Formation
a.
Sandy Flatlands
Big Cypress
Everglades
Coastal Marshes and Mangrove Swamps
Approximate Drainage Divides
Direction of Surficial Flow
Sloughs
--- -
>--.*-,
ways connecting the sandy flatlands to the Big Cypress lead
eventually to the mangrove estuaries to the south and
southwest, or to the Everglades to the east. Some, such as
the Okaloacoochee Slough, also drain north into the
Caloosahatchee River.
Corkscrew Marsh and Mud Lake Pond
Figure 6 is a high altitude infrared aerial photograph
of Corkscrew Swamp, a major slough-strand system of
southwest Florida. Corkscrew Swamp is located in north
central Collier County, west-southwest of the town of
Immokalee, at the foot of the Immokalee Rise. In the
photograph, arrows point out (1) nearby Lake Trafford, (2)
the tall cypress trees at Corkscrew Swamp Sanctuary where a
Wood Stork population nests, and (3) a pond and adjacent
marsh that were the study site for quantitative sampling of
fish and aquatic invertebrate populations. The sampling
site is within 6.5 km of the Wood Stork nesting site, and
Wood Storks feed in the study pond late in the dry season.
Lake Trafford, 5.9 km east of the study site, has a surface-
water area of 599 ha and is the only large permanent body
of fresh water in southwest Florida.
The field study site is situated in a major flow-way of
the Corkscrew strand system (Duever, 1975). Figure 7 from
Duever (1976) shows the pattern of water movement through
Corkscrew Swamp. The study site is in the approximate
center of the figure, between the two small islands.
Figure 6. High altitude infrared photograph of Corkscrew
area, showing Lake Trafford (1), Wood Stork rookery (2),
and study site in Corkscrew marsh and Mud Lake Pond (3)
(Mark Hurd photography, 1972).
NOTE: Both the Wood Stork rookery and the study site are a
part of Corkscrew Swamp Sanctuary, owned by the National
Audubon Society.
19
--] 7.1 .7 ~
7.q.
I P : c~). -, I, Li .'C
~.,J 4?I ~ T 1 -
it
NI.- I A!,
j.r,' 'a' ~ ,
4. 1 -- U.
I- r
a ~ ~ ~ d -'-a---'
a~~j, .
I '4.' 7 %m&4
40 I 4
I'i"' **'
:NIV,
C~~~. a. IIii~i, :I
V*, -rt U
rt. z2 '2 ,
*:.J ~;~' -,IC~~~ :
*atia ****. p* '
Figure 7. Map of Corkscrew Swamp ,showing boundaries of
sanctuary (dashed line) and pattern of water flow (arrows)
(from Duever, 1976).
NOTE: Study site is located between the two small islands
in the upper right-hand extension of the sanctuary..
LEHIGH
ACRES
FT. MYERS
CORKSCREW SWAMP
SANCTUARY
7
~
N
AREA NORMALLY INUNDATED
> 6 MOS./YEAR
I I I I I I----
5 MILES
5 KM
CORKSCREW
WATERSHED
,FELDA
CO.
CO.
L.C. DUEVER
I i t f.
Approximately 0.8 ha of the study site are occupied by
an open pond and its border of vegetation. The pond is in
the deepest portion of a depression in the marsh
approximately 4 ha in extent; but, except for the pond and
its border, the depression cannot be distinguished from the
rest of the marsh by vegetational features visible on
aerial photographs. The pond is bordered on two sides by
islands of higher ground supporting tropical hardwood
trees. The rest of its margin is contiguous with the
marsh.
The pond is connected to the marsh during the wet
season, when the whole area is covered by a sheet of water.
During the dry season the pond shrinks to less than 0.2 ha.
Because this pond is in a depression in the center of a
slough, under most circumstances it would contain water
throughout the year and thereby act as a holding area for
aquatic life during the dry season and a center for its
redispersal at the beginning of the wet season. A shallow
well and deisel pump operated by owners of range cattle in
the area assure that the pond never dries. The pond has a
sand bottom covered with a layer of flocculent material,
which increases in thickness during the dry season, giving
the pond its name, "Mud Lake."
The pond's eulittoral zone is covered by a mixture of
marsh temperate species dominated by pickerel weed
(Pontederia lanceolata), maidencane (Panicum hemitomom),
and water hyssop (Baco a sp.). A solid stand of Sesbania
23
exaltata covers approximately one quarter of the pond
border. Except for clumps of button bush (Cephalanthus
occidentalis) and cordgrass (Spartina bakerii), the
vegetation of the marsh is the same as that covering the
eulittoral zone of the pond. An oblique view in Figure 8
shows the pond, neighboring islands, and nearby areas of
marsh, with the different communities defined.
Water Patterns and Biological Responses
Heavy seasonal rains and the flatness of the land,
cause water to stand on much of the land during several
months of each year. Water fills the shallow aquifer, then
the many ponds and sloughs, before running off to the
Everglades, the estuaries, or the river. A slow flow of
water into the estuaries also takes place through the
aquifer (Parker and Cooke, 1944).
Each summer all the depressions in southwest Florida
fill and overflow and the low, wet prairies become covered
with water for at least 6 mos. Sloughs, strands, and ponds
hold water longer (U. S. Department of Agriculture Soil
Conservation Service, 1968). In wetter than average years,
or if the rainfall is intense, surrounding higher areas of
slash pine or grasses also flood and may take several weeks
to several months to dry. As the dry season progresses,
the water surface area gradually shrinks with the ponds in
the uplands the first to dry. Sloughs are the last
widespread areas to dry, and, under normal conditions,
Figure 8. Features of the field study site and adjacent
areas of Mud Lake Pond and Corkscrew marsh from an oblique
low-level color aerial photograph.
... .
CORKSCREW' )'ISLAND
; HAMMOCK .
A S. I..k
MARSH:
: : SPARTIN Cttion 190 :
... ........ *,: : .
.......... ...
+r
26
ponds in the center of sloughs hold some water throughout
the year. Ponds at higher elevations may have localized
.holes, dug by alligators, which hold water through the dry
season (Craighead, 1968). During the dry season, sloughs
and ponds may receive groundwater flow from adjacent higher
land. Under other circumstances, surface water storage
may recharge soil water in surrounding areas. The flow
from ground to surface or surface to ground is governed by
relative water levels. An elevation profile through the
highest point in the study area shows that the water table
follows the general slope of the land (Figure 9). The
water table gradually flattens during the dry season.
Populations of wading birds that nest and roost in
large aggregations in scattered colonies throughout the
study region cover long distances to follow local patterns
of drying as they harvest the fish that concentrate in
numerous small, isolated pools. Wood Storks, which nest at
Corkscrew Swamp Sanctuary, coordinate their breeding season
with the dry season and are, for the most part, absent from
south Florida during the wet season. Because they locate
their food by touch as well as by sight, Wood Storks
require high concentrations of fish, particularly when
feeding young (Kahl, 1964). This species has been shown to
be very sensitive to changes in the seasonal pattern of
wetlands expansion and contraction that produces and
concentrates their food (Kushlan et al., 1975). Some
Figure 9. Elevation profile of highest point in study area
(immediately north of Immokalee), showing conformation of
water table with land slope characteristics (Florida
Department of Transportation, 1944).
NOTE: H. W. is high water fine in feet.
37.58
100 108 116 124 132 140 148
DISTANCE FEET x 102
29
years Wood Storks do not nest in the area; other years,
nesting is aborted with the young abandoned.
Populations of the food fish of Wood Storks change --
from one year to the next, and the pattern of the variation
is not completely understood. Fish production might be
expected to be greater in wet years; but, in a study of
fish populations in the Shark River Valley of Everglades
National Park, Kushlan et al. (1975) found that fish
populations decreased over a 3-yr period of above average
rainfall.
There has been a decline in Wood Stork populations in -
south Florida during the past 15 yrs, perhaps because they
have been nesting less frequently. The population decrease
is coincidental with a downward trend in rainfall. It also -
has occurred at the same time major changes in the water
patterns of the area have been made by drainage and diking.
During the past 15 yrs, networks of canals in .southwest
Florida have converted large areas of wetland to suburbs,
farms, and pastures. It is difficult to separate the
effects of drainage and the effects of weather on the Wood
Stork population and on other wading birds of south
Florida, which also are declining. To complicate the
problem of analysis and prediction, the Wood Stork is a
long-lived species in which individuals do not reach sexual
maturity until they are several years old (Kahl, 1963).
The young produced by the fish stock of one year mature and
reproduce several years later. This causes a lag in the
30
response of the Wood Stork population to changes in water
patterns and fish production,
Modeling Theory of Oscillating Systems
For many animal populations a change in population
number such as that exhibited by Wood Storks within the
past 15 yrs is part of a long-term cycle. There are many
known animal cycles in nature, and in many cases a cycle in
one species can be related to that in another. Predator
population cycles can be dependent on prey cycles. Prey
cycles can be dependent on predator cycles or on the cycles
of alternate prey species (Bulmer, 1975). Although it
seems reasonable to think that the ultimate cause of a
series of linked species cycles would be an oscillating
climatic factor, in a number of well-known cases,
particularly the 10 (9.6) yr animal cycles of the Canadian
northwest, climatic influences, though sought, have not
been demonstrated (Bulmer, 1974).
Systems showing population cycles generally have been
studied as systems with intrinsic oscillating capabilities.
Any simple two compartment population model may oscillate.
The classic two compartment prey-predator model of Lotka
(1925) and Volterra (1926) is a well known example of model
structure that will oscillate without oscillating input.
A model with a time delay such as that imposed by
delayed breeding is almost certain to oscillate (Smith,
1974), and a model subjected to an oscillating forcing
31
function also can be expected to oscillate (Rykiel and
Kuenzel, 1973). Some oscillating models demonstrate
persistence; others are unstable (Holling, 1973). Time
delays longer than the natural periodicity of the system
(in the equation dx/dt = rx, the natural period, or time
constant, is 1/r) almost inevitably cause instability
(Smith, 1974). On the other hand, simple changes in the
governing equations, representing adjustments or
modifications in the basic structure of the system, can
stabilize the system (Smith, 1974).
On the basis of mathematical analysis, Oster and
Takahashi (1974) predicted that, when intrinsic oscillating
factors (periodic forcing functions) are imposed on systems
with intrinsic oscillating factors (time delays), beat
frequencies may result. If, however, the frequency of the
intrinsic oscillation is close to the characteristic
frequency of the external oscillators than a phenomenon
known as entrainmentt" may occur, and the system will
respond to the external freauency only. This allows
biological oscillators to "latch on" to the environment
(Pavlidis, 1973). In the case of the Wood Stork, if
rainfall periodicity is operating on a 5 yr cycle and there
is a 4 to 5 yr delay between fledging and breeding, then a
synchronization of the intrinsic cycle and the extrinsic
cycle may occur.
The present existence of the birds is evidence that
their population was maintained under natural conditions.
32
To reflect the real system, the model also must maintain
the bird population under natural conditions. The first
question that arises is what structural conditions inherent
in the natural system must be included in the model to
insure a simulation of the long-term survival of the Wood
Stork population.
The second question of consequence is how have man's
changes in the structure of the system affected its ability
to maintain wading birds; or will the Wood Stork be
eliminated from south Florida under changed conditions? If
the Wood Stork is disappearing in south Florida, it may be
a number of years before the fact is obvious, and it may
then be too late to do anything about it. Model
simulations are able to provide clues to the future. They
allow the exploration of questions of both theoretical and
practical importance.
Population Biology of Wood Storks
Population size is ultimately controlled by energy,
usually food energy. Annual variation in population size
has been related to annual variation in the energy base of
a number of bird species, including the Great Tit (Perrins,
1965; Krebs, 1970) and the Tawny Owl (Southern, 1970).
Energetic influences limit bird populations by affecting
number of breeding pairs, clutch size, hatching success,
nestling survival, and mortality rates in the first (or
33
first few) years. Energy factors seldom limit bird
populations by shortening the life span of adults.
The research of Kahl (1963,1964) and J. Ogden (pers.
comm.) and records from Corkscrew Swamp Sarntuary (J.
Hansen, pers. comm.) suggest that food availability---.
regulates the size of Wood Stork populations by determining
whether nesting is attempted and carried to completion in a
given year; the percentage of the adult population
participating in the nesting effort; the number of
fledlings produced per nesting female (see Kahl's 1963
discussion of the effect of asynchronous hatching in this
species); and mortality during the year following fledging.
Kahl (1964) found that the availability of food to -
nesting Wood Storks depended more upon "ecological
density," or the concentration of food in numerous small
ponds, than on "absolute density," or the total amount of
food in the feeding range of the birds. In order for large
quantities of fish to be available to storks it is
necessary first that extensive areas of land be covered
with water so that fish may reproduce and grow, and then
that surface water area be reduced to bring about fish
concentration.
Kahl (1964) determined the energetic requirements for
reproduction in Wood Storks, including the daily energy
requirements of the adult birds and the total energy
requirements of growing young from hatching to fledging.
He calculated that, in the active state, mature Wood
34
Storks, which weigh 2 to 3 kg, require a daily food intake -
of approximately 570 kcal (95 g dry weight of fish), of
which 79 percent is assimilated. Nesting activities of the
individual pair of storks extend over approximately 130
days. Table 1 (from Kahl, 1963) shows the period of time
spent in each phase of reproduction.
During the 60-65 day nesting period (from hatching to
fledging) observed by Kahl (1962), each young stork
required a total of 2.81 kg dry weight (16,802 kcal) of
fish. As the nestlings grew, relative metabolic rates
decreased from 425 kcal/kg.day during the first and second
weeks to 80 kcal/kg.day during the ninth week (Kahl, 1962).
Kahl (1964) calculated that approximately 220,000 kcal
of food (34 kg dry weight of fish) are required by an
average Wood Stork family (2 adults and 2.25 fledglings)
during the breeding season. During the 1960-61 breeding
season, when the Corkscrew colony had 6,000 active nests,
the birds must have consumed approximately 200,000 kg dry
weight (1,200,000 kcal) of fish, which the adults collected
from ponds throughout the southwest Florida area.
Wood Storks range up to 130 km from their rookery on
daily food gathering flights. Whenever possible, the birds
conserve energy by taking advantage of local convection
currents from which they gain altitude for gliding, or
powerless flight. Thermal lift carries Wood Storks to 1500
m In south Florida during the latter months of the dry
season.
Table 1. Estimated duration of stages in the Wood Stork reproductive
cycle (from Kahl, 1963).
Stage
Pre-pairing presence in vicinity of colony
Pair-formation and nest-building
Incubation
Pre-flight nestling stage
Post-flight attachment to the nest
Total
Estimated Duration
days
Minimum Maximum
12 25
5 8
28 32
50 55
15 30
110 150
36
J. Ogden's (pers. comm.) observations of Wood Stork -
nesting colonies in Everglades National Park suggest that
the length of the breeding season, particularly the period
of raising nestlings, may vary according to the
availability of food. Nestlings may survive but grow at a
slower rate at suboptimal levels of food availability. J.
Ogden (pers. comm.) also noted that mortality was high
during the first year of life (from fledging to age 1) for
fledglings leaving the nest under conditions of poor food
availability near the rookery. First year mortality is
normally believed to be about 40% for this species (Allen
et al.,' 1958), but it may be 90% or higher during marginal
years (J. Ogden, pers. comm.).
Wood Storks lay as many as five eggs (Palmer, 1962),
but three eggs are usual. Howell (1941) inspected 200
nests in an Everglades mangrove rookery in December 1933
and found that "most" nests had four eggs and five nests
had five eggs each. Kahl (1963) found an average of 3..3
eggs per nest in a rookery in central Florida in 1960.
According to Kahl (1963) the average number of young
fledged per successful nest was much more variable than the
average number of eggs laid per nest. The mean number of
young fledged in the rookeries he studied during the years
1958-1961, varied from 1.34 per nest (based on 140 nests)
at Micanopy in northcentral Florida in 1959 to 2.92 per
nest (based on 100 nests) at Corkscrew in 1960. Kahl
(1962) said that the difference in fledgling production
37
between rookeries in northcentral Florida and rookeries in
south Florida during the same period indicated that
conditions for raising young were enhanced by greater
fJulctuation of the water table, causing greater
-concentration of food. The probability of nesting failure
with zero production in certain years was, however, also
increased in areas where the water level fluctuation was
the greatest.
Table 2 gives the number of fledglings produced at the
Wood Stork rookery at Corkscrew Swamp Sanctuary for the
past 19 yrs. As the Corkscrew population represents
approximately one half the Wood Stork population in the
United States (J. Ogden, pers. comm.), this table provides
a good index of the variation in the size of the nation's
Wood Stork population over this period. As the table
shows, a high percentage of nests were successful in only 7
out of 19 yrs. In 7 other years out of the 19, no young
were produced, either because nesting was not attempted or
because all the nests were abandoned before the young were
old enough to support themselves. In the other 5 yrs only
a small portion of the population successfully raised
young.
Kah1's (1964) observations, augmented by a comparison
of the fledgling production record at Corkscrew with the
weather record for the same period, suggest that successful
nesting for Wood Storks in this area depends on the
following factors: -..
Table 2. Wood Stork fledgling production at Corkscrew Swamp Sanctuary, 1957-1975.
Nesting No. of pairs of
No. of pairs of
nesting storks
0
500
425
500
4,000
5,000
6,000
0
3,000
500
Date started
Nesting
year
1956-57
1957-58
1958-59
1959-60
1960-61
1961-62
1962-63
Remarks
No nesting attempted
All nests deserted in early Jan.
due to cold temp. and over 8" rain
Successful nesting
All but 50 pairs deserted following
heavy rainfall in late December
Successful nesting
Successful nesting
Successful nesting
No nesting attempted
About 1,000 nests with young left after
rain and high winds on Feb. 19 & 26
Successful nesting
Estimated no.
of fledglings
0a
Oa
925a
9,000a
14,500a
17,000a
Oa
2,000b
1,000b
late November
April
late November
mid February
mid December
early December
early December
March
Table 2 continued.
Nesting
year
1963-64
1964-65
1965-66
Date started
No. of pairs of
nesting storks
0
1,300
3,000
3,000
3,500
180
5,000
2,200
3,500
1,900
Remarks
No nesting attempted
All nests deserted in early March
All young storks died from rain and
cold in late January
Rain and wind from hurricane on June
8 killed some young
Successful nesting
Successful nesting
All young were killed by rain, cold,
and high winds on Feb. 8 and 24
Most nests deserted after a rainy
period in early Jan.
Most nests deserted after a rainy
period March 6-9. About 150 nests
left on April 15
All nests deserted after 4.9" of
rain on March 7
Estimated no.
of fledglings
0
0a
a
0
3,500a
7,000a
a
350
0a
40a
300b
0b
0
mid January
late November
mid March
mid November
mid March
late November
early December
mid February
mid February
1966-67
1967-68
1968-69
1969-70
Table 2 continued.
Nestig No.of pars o
No. of pairs of
nesting storks
3,000
1,500
125
Nesting
year
1970-71
1971-72
1972-73
1973-74
1974-75
Date started
late November
mid January
mid March
early November
Remarks
Successful nesting
Most nests deserted in mid-Feb.
Most nests deserted after 5.45"
rainfall on March 31
No nesting attempted
Successful nesting
Successful nesting
Estimated no.
of fledglings
6,000b
Ob
200b
Ob
4,400b
6,600b
aCompiled by James L. Hansen, with information for 1956-62 from Kahl (1964) and information for 1963-75
from unpublished notes and reports of the National Audubon Society at Corkscrew Swamp Sanctuary.
bHansen's estimate based on number of nests reported.
2,000
3,000
---
1) early flooding, ample rain, and a large land area
covered by water during the summer and fall prior
to nesting so that reproduction and growth of fish
are maximized;
S2))cessation of rains no later than October so that
drying commences in early winter (if the dry-down
begins too late or progresses too slowly, the birds
start their reproductive activity so late in the
season that summer rains interrupt the nesting
effort by expanding the water and allowing fish to
disperse. The reduction in fish density causes
adults to abandon their young, resulting in
death of the nestlings).
3))few or no heavy rains during the winter and early
spring (untimely rains also cause fish populations
to spread out and not be accessible to Wood Storks-;
consequently rookeries and young are abandoned);
(4) a mild winter (storms and freezes cause heavy
\ /mortalities in the rookeries).
The level of fledgling production in successful
breeding seasons and the frequency with which successful
breeding seasons must occur to assure the perpetuation of
the species are dependent upon intrinsic characteristics of
the population such as survival rates, life expectancy, and
age of first breeding. Banding of Wood Stork nestlings in
rookeries at Everglades National Park has been carried out
since 1974 by John Ogden, research biologist of the
National Audubon Society, but it will be many years before
banding returns provide needed demographic information.
Meanwhile, the small amount of data available on Wood
Storks and information on related species must be used to
formulate initial estimates to use in predictive models.
A sole record from the wild indicates that a Wood
Stork lived at least 11.7 yrs (Kahl, 1959). Another Wood
42
Stork that was an adult when captured lived at least 30 yrs
in captivity (Kahl, 1963). Allen et al. (1958) estimate
that mortality in the Wood Stork is probably 20% after the
firs-t- year ofl i fe.
Life expectancy and survival rates of the Wood Stork
may be close to those of the White Stork of Europe (Ciconia
ciconia). Zink (1966) estimated that 1st and 2nd yr
mortality rates for this species are "about 30% and
probably higher" and that the average mortality rate for
older birds is 21%. Wood Stork mortality during the second
year probably is no higher than that of older birds. First
year mortality of Wood Storks, however, is highly variable
and may exceed 90% (J. Ogden, pers. comm.).
Zink (1.96U6) reported that the average life expectancy
of the White Stork is 4.5 yrs, but Kahl (1963) cited Flower
as giving an average age of 25.7 yrs for 20 captive storks
of genera Ciconia, Jabiru, and Leptophilos, suggesting that
average life expectancy in -the family as a whole may be
greater than 4.5 yrs. Miller et al. (1972) proposed that
mortality in long-lived species such as the Wood Stork may
have an age dependent as well as an age independent factor
and that, after the first year or two, mortality may
increase with age.
Kahl (1963) estimated that Wood Storks first breed
when they are 3 or 4 yrs old. Time delays as in delayed
breeding can have a strong impact on the way a system
functions.
43
Because the Wood Stork is a long-lived species with
relatively low mortality after the first year, Wood Storks
may not need to nest successfully every year to avoid a
gradual population decline. Important questions that
should be answered are what long term average level of
productivity must be maintained by the population to
prevent a trend toward extinction, and how does the long
term production record at Corkscrew compare with this
value.
METHODS
The model diagram in Figure 1 was used to organize
available information on the wetlands system. Data
collections were organized around the model to provide
quantitative information about interactions and energy
flows. Methods for data collection included analyzing the
rainfall record; following storks in a low-flying plane;
counting ponds; measuring the area extent of ponds,.
sloughs, and marshes; making hydrologic calculations;
sampling fish and aquatic invertebrates in a pond and
marsh; and making demographic calculations for the
population of Wood Storks.
Throughout this study all measurements, calculations,
and references related to hydrology or land surveying are
given in English units, which are standard units in these
fields in the United States. All other units are in the
metric system.
Following the measurements and calculations the
initial model evolved into three slightly differing models,
all of which were simulated and tested for their behavior
under different conditions of drainage. Models I and II
were simulated on an EAI Miniac analog computer using a
repeating seasonal pattern of expanding and contracting
water area. Model III was simulated on an IBM 370 digital
44
computer using the results from a water model based on
actual monthly rainfall at Ft. Myers (U. S. Department of
Commerce. National Oceanic and Atmospheric Administration,
1950 1975) for the 25-yr period from June 1950 through
May 1975. All models were written in the energy circuit
modeling language of H. T. Odum (1971).
Basis for Models
Models of Water, Fish, and Wading Birds
Model I is shown in Figure 10. The diagram indicates
most important compartments storagee) of the system, major
energy sources, energy sinks, pathways of energy
interaction, and thresholds (switching functions). Figure
11 is a key to the symbols used in Figure 10 and in the
model diagrams presented throughout this study. In Table 3
components of the model in Figure 10 are isolated and
explained in order to provide an understanding of the
model's structure and relationships. The symbols relate
flows to tanks in mathematical terms. Positive values are
flows entering the tank; negative values are those flows
leaving. The tanks change with time according to
differences between inputs and outputs. Flow rates change
with time according to the sizes of the tanks. Based on
the diagram differential equations are written for each
tank.
Figure 10. Model 1. Water area, fish, and Wood Storks
with initial fish stock and potentially breeding bird stock
unimportant.
(1) k (2)
F = klAS k2 F/A
B = k3 F/A(3) k4 B (4) (k7 B(7)x C1)
N = (k5 B xC) k6N(6)
(1) fish growth, g/sq-m.mo
(2) fish predation and death, g/sq-m-mo
(3) bird food intake, mg/sq-m-mo
(4) bird respiration, mg/sq-m-mo
(5) new bird production rate, birds/sq-m-mo
(6) mortality, birds/sq-m-mo
(7) energy expended in breeding effort, mg/sq-m.mo
C1 = 1 B/N > n
= 0 B/N < n
OTHER
PREDATION
AND FISH KILLS
Figure 11. Key to the symbols used In energy circuit
model diagram.
HEAT SINK
(Some energy is lost in
all interactions and from
all storage)
-
STATE VARIABLE
(storage) with inflows and
outflows
SIMPLE ONE-WAY FLOW
BETWEEN TWO STATE VARIABLES
J2 = ky
INTERACTION SYMBOL
(example' multiplier D or
divider )
POSITIVE FEEDBACK
or autocatalytic growth
J = kxQ
NEGATIVE FEEDBACK
or crowding effect, J =
kx(I-bQ)
CONSTANT GAIN AMPLIFIER
flow from E is proportional
to y.
SELF-MAINTAINING CONSUMER
MODULE (contains positive feed-
back for autocatalytic growth
PRODUCTION
MODULE
LOGIC FUNCTION:
COMPARATOR AND SWITCH
J > 0 if x> c
SOURCE
Table 3. Explanation of water area, fish, and Wood Stork Model I.
Forcing functions, or environmental /W
factors of importance to the system, AREA
are represented by circles. Both A
sun and water area vary seasonally. SUN
S
Tanks are the symbols for stocks,
or storage, within the system. FISH
Flows of energy, water, or other F
materials are shown by connecting
lines. Arrows indicate a one-way
force. There is an energy cost
associated with all transactions.
The energy loss is accounted for
with heat sink on flow lines
and other symbols.
Table 3 continued.
Fish production is a function of
radiation and the amount of land
surface covered by water. Depen-
dence on two or more such factors
is demonstrated by the work gate.
When the action appears multipli-
cative, the work gate is a simple
multiplier function.
Fish density is fish biomass di-
vided by water surface area, de-
noted by a divider work gate. As
surface water area shrinks, fish
density increases, its limit im-
posed by minimum water area and
maximum fish biomass.
Table 3 continued.
(
Wood Storks feed in proportion to
fish density rather than fish bio-
mass. Fish density controls the
flow of fish to the birds.
Bird breeding is stimulated by an
abundance of food, which is reflec-
ted in the individual weights of
the birds. Bird biomass divided by
bird number yields average weight,
which, by means of a comparator
operator, is compared with a breed-
ing weight threshold to determine
whether or not breeding will occur.
Average weight rising above the
threshold value switches on the
pathway from bird biomass to bird
number to initiate breeding activity.
53
The coefficients, or "k" values for the equations of
the model were computed by dividing the flow by the
upstream stock. Thresholds were estimated based on
literature values and measurements from the study. The
differential equations were the basis for an analog diagram
that served as a guide for patching the analog board. Pot
settings were calculated from "k" values. Differential
equations are given in the legend to Figure 10.
Outside inputs, or forcing functions of the model,
were solar radiation and surface water area, calculated as
percent of maxima and represented by sinusoidal curves with
surface water area lagging solar radiation by approximately
3 mos. The water area curve was approximated from the
simulation for the June 1974-May 1975 year from a water
model, which will be described later in this section. It
was possible by means of the water model to determine how
drainage changed the seasonal expansion and contraction of
surface water area. This change was approximated by
adjusting the mean and the amplitude of the sinusoid.
Several other levels of drainage were also tested for their
effect on fish growth and concentration and bird biomass
and breeding.
The model in Figure 10 (Model I) represents the
simplest possible interpretation of the system. It assumes
that seasonal expansion of fish biomass is a function only
of sunlight and water area and that initial fish stock, or
the parent population at the beginning of the wet season,
54
is not important. It also assumes that growth of bird
biomass is proportional only to fish density, and that the
number of potentially breeding birds at the beginning of
the nesting season is not important. Note that there is no
feedback from the fish compartment to the multiplier on
fish growth or from the bird compartment on food
consumption.
The structure of Model I suggests a system's
homeostasis in which fish recruitment and growth rates are
adjusted to counteract variation in initial fish stock.
When initial fish stock is low, recruitment and growth
rates are high; when initial fish stock is high,
recruitment and growth rates are lower. This model also
suggests that breeding activity is not limited by the
number of birds capable of breeding. It assumes that the
number of breeding birds is unlimited relative to the level
of breeding activity that can be supported by th.e system.
None of these assumptions is unrealistic if, in the prey
population, survival of young is inversely proportional to
the density of adults and if, in the predator population,
isolation from other breeding populations of the same
species is not complete.
In Model II (Figure 12) growth and survival rates of
fish stocks the previous year are factors in determining
the rate at which fish biomass increases. Equations for
Model II are shown in the legend to Figure 12.
Figure 12. Model II. Water area, fish, and Wood Storks,
with initial fish stock important.
F = k1 FAS(1) 2 F/A (2) + k7(7)
B = k3 F/A(3) k4 B(4)
N = (k5 B(5)C) kN(6)
(1) fish growth, g/sq-m-mo
(2) fish predation and death, g/sq-m.mo
(3) bird food intake, mg/sq-m-mo
(4) bird respiration, mg/sq-m.mo
(5) new bird production rate, birds/sq-m.mo
(6) mortality, birds/sq-m.mo
(7) immigration of fish, mg/sq-m-mo
MIGRATION
WATER
AREA
S
FISH a
57
In Model III (Figure 13) both fish history and bird
history are important. Climatic events of previous years
influence biological events of the current year. This
model includes the age structure of the birds, and only
those birds 5 yrs old or older are capable of breeding. It
also includes large populations of predatory fish (gar,
bowfin and bass) that expand in wet years, decrease in dry
years, and compete with wading birds for food fish.
Model III uses differential equations similar or
identical to those in Models I and II but makes increased
use of.discrete equations to handle logic functions such as
migration, fish kills, and the incrementation of age
cohorts. Modules on the right-hand side of the diagram
denote.age classes: 1, 2, 3, and 4 yr olds, which do not
breed; and the breeding stock, which consists of birds 5
yrs of age or older. Age cohorts are incremented once each
year, with mortalities subtracted. The number of breeding
birds increases or declines, depending on whether annual
recruitment (4-yr-olds becoming 5-yr-olds) exceeds average
mortality of the breeding stock.
The bird biomass tank empties abruptly each June and
remains empty all summer, indicating that migration out of
the region has occurred. When the birds return to the area
in the fall the bird biomass becomes the product of
"minimum weight" and "number of sexually mature birds."
Bird feeding and bird respiration switch on at that time,
EMIGF
Figure 13. Model III. Water area, fish, and Wood Storks with initial fish stock and breeding bird
stock important. Model includes age structure and breeding delay in the birds.
F klFAS blF2(1) k2FQ10(a2 b2F/A + c2(F/A)2)(2) -k6F/A (C2C3)(3) k5F/A(4)
k3k10B(a4 b4B/(Ny + N5)ln(c4F/A (5)
B = k3B(a4 b4B/(N + N5)n(c4F/A)(6) k4B(a4 b4(a4 4B/(N + N5)(7)
+ N5WL( C5)(8)
- BC4 (8)
G = k7G(a7 b7G)ln(l + F/A)(9) k11G(10) k12G(O.25C7 + 0.25C8 + 0.25C9 + 0.25C10)P(11)
N = N5EAC (12)
NO = ((B N5WA)/WA)C6P(13)
(1) consumption by prey fish
(2) prey fish respiration
(3) prey death in fish kill
and dry ponds
(4) general fish predation
(5) predation by storks and
other birds
N1 = NoS0Cp(14)
N2 = N1S12C6P(14)
(6)
(7)
(8)
(9)
(10)
(11 )
fish consumption by birds
bird respiration
migration
fish consumption by gar
gar respiration
gar death in dry ponds
and fish kills
N3 = N2S23C6P(14)
N4 = N3 S34C6(14)
(12)
(13)
(14)
(15)
N5 = (N4S45 N5M)C6(15)
nesting (laying eggs)
fledgling production
incrementation of age cohorts
accumulation of breeding stock
NOTE: Numbers in parenthesis correspond to those on the diagram.
S01
S12
S23
S34
S45
F biomass of fish
B biomass of breeding birds
and nestlings
WL minimum average weight
of birds
WA robust average weight of birds
WM robust weight plus weight of
egg
EA average number of eggs laid
Thresholds for Controls
s summer solar radiation
f fall solar radiation
1 low water area
d fish density
survival rate, fledgling to Age 1
survival rate, Age 1 to 2
survival rate, Age 2 to 3
survival rate, Age 3 to 4
survival rate, Age 4 to 5
Egg laying Control
1 B/N>WM
C :
1 0 B/N
Migration Controls
C 1 S>s
C4
C 6
6
0 S
1 S
C5 =
0 S>f
M average mortality
rate, birds 5 years
and older
Q10function for temper-
ature effect
P pulse (=1 for 1 dt)
Prey Fish Kill Switch
Control
1 F/A>d 1 A< 1
C = C =
0 F/A1
Predator Fish Kill
Control
I A
0 A>1 2
1 A<14
0 A>14
I A
C-=
0 A>13
1 A< 5
C0 = 6
and population biomass increases or declines according to
the balance between these two rates.
The feeding rate of the birds is a function of fish
density and bird biomass. It is an inverse function of
average bird weight, which decreases when nestlings join
the population but increases as they approach fledging. In
the model, breeding birds lay an average of 1.5 eggs each
in years when the laying weight threshold is exceeded by
average bird weight. The number of fledglings ultimately
produced in a given year is determined by the total weight
gain of the population.
In Model III, both prey fish and predator fish are
decimated when isolated ponds go dry. Prey fish, in which
recruitment and growth is a direct function of water area
and solar radiation, recover more quickly than predator
populations, in which growth is a function of prey density
and predator biomass.
Equations for Model III are shown in the legend to
Figure 13. The bases for those rate equations which differ
from those used in the simpler models are as follows.
Consumption by prey fish (rate equation 1 in
Figure 13). To the basic equation in which food
consumption is a function of solar radiation, water surface
area, and fish biomass was added a density dependent factor
to reduce fish growth when fish density becomes high. This
addition simulates the limiting of populations at high
densities by factors such as decreased fecundity or reduced
recruitment. The formula first used in Model III for the
density dependent function (normally expressed in energy
circiut language) was:
= k FAS kl F2AS (1)
This function produced instability in the model because of
the seasonally oscillating sunlight and water area. Two
variables, therefore, were dropped from the second term and
the formula became:
= kI FAS b F2 (2)
Fish respiration (rate equation 2 in Figure 13).
Respiration per unit of fish biomass in a seasonally
expanding fish population decreases with density at the
lower densities and increases with density -at higher
densities. The fish respiration rate equation and its
coefficients used in the model were similar to the
quadratic equation (equation 27 in Results section). The
respiration formula was:
= F(a2 b2F/A + c2(F/A)2) (3)
Temperature effect on fish respiration (equation 15 in
Figure 13). Respiration of all organisms is affected by
temperature. The smaller the organism, the greater the
63
effect of temperature on its respiration even in
subtropical climate where seasonal temperature differences
are relatively minor. "Q10," the factor by which the
respiration rate is changed by a change of 10 degrees from
a baseline environmental temperature, is the measure of the
effect of temperature on a given organism. In this model,
the Q10 of prey fish was 1.3, and the baseline temperature
(Q10 = 1) was 27 C. The Q10 equation used in the model
expressed this relationship:
Q10 = 1 + 0.03(T 27) (4)
Bird respiration (rate equation 7 in Figure 13). Bird
respiration in the model decreased as a function of average
weight of the breeding birds and their young in order to
reflect the increased energy demands of the population when
rapidly growing young are being raised. An approximate
average size and average energy demand per gram body weight
of one stork and its young were calculated for each week of
the 9-wk nesting period, based on information from Kahl
(1962, 1964). A simple linear regression of respiration
versus average weight over this period formed the basic
formula for bird respiration used in the model:
k4B(a4 b4B/(Ny + 1N5))
64
Fish consumption by birds (rate equation 6 in
Figure 13). Preliminary simulations by Model III
demonstrated that if stork food consumption were a simple
function of prey fish density, the tremendous difference in
fish density from year to year would cause instability.
Some measured prey-predator interactions are logarithmic
(Smith, 1974), and the natural log function of fish
density, substituted for the simple function commonly used
in energy circuit models, made the model more stable. The
basic food requirement equation of the stork population was
multiplied by the natural log of the product of prey fish
density and a constant to approximate the rate of feeding
by the birds. The food consumption formula became:
k3ln(c4F/A)B(a4 b4B/(N + N5)) (6)
Fish density was multiplied by the constant and a cut-off
was set at zero so that fractional densities would not
cause negative flows.
Fish consumption by predator fish (rate equation 9
in Figure 13). Consumption by gar was a function of the
natural log of one plus fish density in the model. The
value one was added to fish density so that negative flow
would not occur at low fish densities. The fish
consumption by gar formula was:
k7ln(1 + F/A)G(a7 b7G) (7)
Fledgling production (equation 13 in Figure 13). In
the model fledgling production was determined by the total
weight gain of the breeding population during the breeding
season. Average weight of healthy adults (2.5 kg)
multiplied by the number of breeding birds was subtracted
from total biomass of the birds and the remainder was
divided by average weight of fledglings (also 2.5 since
healthy fledglings can weigh as much as adults when they
first leave the nest according to Kahl, 1962) to determine
the number of fledglings raised. The formula used in the
model was:
= ((B N5 A )/A)C6P (8)
Death of gar in dry ponds and fish kills (equation 11
in Figure 13). A step-wise set of comparators was used to
simulate death of gar caused by drying of ponds and fish
kills because, in nature, conditions causing gar mortality
are encountered in ponds at different elevations throughout
the dry-down. The comparator value was incremented from
zero to one in 0.25 steps as surface water area dropped
below four threshold values (0.8, 0.6, 0.4, and 0.2 of
total wetland area).
The DYNAMO program for Model III is Appendix D.
Water Model
A diagrammatic representation of the model of water
volume and surface water area is shown in Figure 14.
Compartments of water storage are soil (includes ground)
and surface. Rainfall is channeled into the soil unless
this compartment is full (saturated), in which case it is
shunted directly into surface storage. Land surface area
covered by water is proportional to the volume of surface
water, according to the area-volume curve developed in this
study. Dry land area is total land area with wetlands area
subtracted from it. Water is removed from the soil and
aquifer by groundwater flow at a rate determined by the
gradient between the water table and sea level and by soil
and aquifer permeability characteristics. The water of
low-lying areas is augmented by runoff from surrounding
higher land. Input is by surface flow during the rainy
season and by horizontal flow through the ground during the
dry season. Under some conditions (i. e. when ground or
soil water level is lower than surface water level) surface
water sources restock soil water supplies. Overland flow
does not occur until surface storage areas are filled to
the lowest points on their rims. Surface water and soil
water are.subjected to evapotranspiration (ET), which is
greater during warm weather months than in winter because
of differences in wind and temperature, and also because of
seasonal physiological changes in predominant plants. In
areas where there is no standing water, ET is proportional
WATER VOLUI
Model relating rainfall timing and intensity to surface water area.
EQUATIONS
CHANGE IN SOIL WATER VOLUME
VD = QD QDG QDI RD DW
CHANGE IN SURFACE WATER VOLUME
VW = RDW + RW + QDI
- E QW
RAINFALL ON DRYLAND AREAS
RD = IAD
RAINFALL SWITCHED TO SURFACE WATER
FROM DRY LAND WHEN DRY LAND SATURATED
RDW = C4 {( I x AD) VDmax VD}
RAINFALL ON SURFACE WATER
RW = IAW
GROUND WATER FLOW TO SEA
hD
QDG = T B C1
dD
INTERFLOW BETWEEN SOIL AND
SURFACE STORAGE
hD hW
QDI = k p h hs C2
dW
EVAPOTRANSPIRATION FROM SURFACE
WATER
Ew = 0.75 P AW S C5
W e W 5
AREA OF LAND SURFACE
COVERED BY WATER
AW = fVW (Volume-Area curve)
DRY LAND AREA
AD = AT A
EVAPOTRANSPIRATION FROM SOIL
ED = 0.75
VD
Pe VDmax D S
RUNOFF FROM SURFACE WATER
AREAS
1.49
QW= n
AW
h 5/3 a(wW
w Wmax
(sin 9)1/2 C3
Figure 14.
Legend to Figure 14. continued.
SWITCHING FUNCTIONS
C = 0 Ll < VDme
1 L 1 >VDme
C 0 I l-Dme
C = 0 L < VDe and hD > h
1 0 L > Dme or h < W
C = 0
3 =
1
C 4 0
1
C = 0 L < 0
C5 L2 > 0
2
L2 < V
L2 > VWmax
2 -Wmax
L < V Dmax
L1 Dmax
Key to Symbols
AD = area of dryland, acres
.AW = area of wetland, acres
Awmax = maximum wetland area, acrea
AT = total land area, acres
An = P JbD hwl, area normal to flow, acres
a = width of overland flow: perimeter of all
primary and secondary canals; 2(N+Ca )
B = width of the edge of the aquifer, length
of coastline and river borders
b = exponent on proportionality factor
A /Awmax
Ca = length of all major and secondary canals
D = difference between maximum thickness of
monitored ground water and average
height of land above mean sea level
dW = average distance between highest
points in uplands and nearest edge of
pond of slough
dpD = average distance between inland areas
and coast
g = specific yield (porosity), a proportion-
ality factor
Legend to Figure 14. continued.
hD = thickness of monitored portion of ground-
water (maximum 8 ft), ft
hD = hG + D, elevation of water table, ft
h' = depth of surface water at local deepest
point, ft
hW = 0.25h'
I = rainfall, ft/mo
K = hydraulic conductivity (permeability
coefficient)
N = length of all natural flow-ways
Pe = potential evapotranspiration
P = width of overland flow, perimeter of
sloughs, ponds, and ditches
RD = rainfall on dry land
R DW = rainfall excess on dry land (shunted to
wetland)
Rw = rainfall on wetland
S = seasonality factor for plant transpir-
ation (with and without leaves)
T = transmissivity coefficient
VD = ground and soil water volume, acre ft
VW = surface water volume, acre ft
VDax = saturation volume of soil and surface
water, acre ft
VDme = moisture equivalent (field capacity)
volume of soil, acre ft
Vmax = surface water volume to lowest lip,
acres
9 = elevation gradient from border to in-
land areas
to the degree of soil saturation. The water supply is
replenished during the rainy season but gradually depleted
during the long dry season.
Inputs to the model are the monthly rainfall record
for a 25-yr period, June 1950 through May 1975 at the U. S.
Weather Bureau, Page Field, Ft. Myers, and average long-
term monthly pan evaporation at the Royal Palm Ranger
Station at the Tamiami Trail in the Everglades (U. S.
Department of Commerce. National Oceanic and Atmospheric
Administration, 1950 1974). Pan evaporationn represents
the integrated inputs of the energy sources, solar
radiation and wind.
Two runs of the model using the same rainfall and pan
evaporation inputs were made. In the first run, natural
conditions as they existed prior to drainage (circa 1900)
were simulated. In the later run, coefficients were
adjusted to create conditions representing the present
(1976) drainage density of the area. Only three
coefficients were changed to represent the difference
between predrained and postdrained conditions.
Equations for the model, are given in the legend to
Figure 14. The model is quantified and coefficients used
in the model are listed in the-section on quantification
and simulation of the models. Following are brief
explanations of some of the flow equations used in
developing the structure of the water model.
72
Interflow equation. Both ground water flow to sea and
interflow between soil and surface water are governed by
Darcy's law (Lindsley et al., 1958). The equation for
Darcy's law used as the basis of the interflow equation
was:
QDI = K i An (9)
where Q is flow rate, i is gradient, A is cross-sectional
area normal to water flow (seepage plane), and K is the
hydraulic conductivity (permeability) of the soil and
substrate. In the interflow equation of the model, both i
and A are functions of elevation of the water table and
change as the water table fluctuates. i is equal to h/dW ,
where d is the distance of water flow and h is the
absolute difference in elevation of groundwater and surface
water. An is equal to h x p, where p is the width of the
cross-sectional area normal to flow. For simulations under
primitive (predrained) conditions, p was the total gross
perimeter of the ponds, strands, and sloughs. For
simulations under present conditions, it was double the
length of drainage ditches plus the gross perimeter of the
natural drainage-ways.
Equation for groundwater flow to sea. Another form of
the equation for Darcy's law was used as the basis of the
equation for groundwater flow to sea. This form was:
QDG = T i B (10)
where QDG is flow rate, i is gradient, T is transmissivity
(hydraulic conductivity multiplied by the depth of the
aquifer), and B is the width of the cross-sectional area
perpendicular to water flow. In calculations for this
model, B was the length of the coastline plus the length of
the Caloosahatchee River. As transmiss'ivities vary
somewhat in different parts of the study region, the total
length was divided into three stretches and a different
transmissivity was estimated for each, based on Klein et
al. (1964). T x B was determined for each stretch, and the
three values were added together to give a weighted T B for
the total river and coastline of the study region. In this
equation i was calculated by dividing elevation difference,
h, by distance, d. h is the variable, (12 + D), and D is a
variable less than or equal to 8 that represents how many
of the top 8 ft of the aquifer and soil are saturated. The
maximum volume of water in the soil-substrate compartment
was the saturation volume of the top 8 ft of the soil and
substrate, because average water levels for the entire area
never fall below 8 ft. Twelve ft plus a maximum of 8 ft is
the approximate average elevation of the water table
relative to sea level when the soil is saturated. At less
than saturation, the water table is lower and the gradient
between water table and the sea is less.
74
Equation for surface water runoff. Runoff in the
water model is governed by the Manning equation (Lindsley
et al., 1958):
= 1.49 hw 5/3 L (sin 0)1/2 (11)
where QW is flow rate, hwis depth of the surface water, L
is the width of overland flow, and 8 is h/d, or the land
elevation gradient between upper and lower ends of the
flow-way. One quarter of the water depth at the deepest
local point (from the curve relating depth at deepest local
point to total surface water volume) was the estimate of h
used in the model. Runoff values simulated with the use of
this estimate of h were very similar to those simulated
with the use of "average depth," a value determined by
dividing total water volume by total area of surface water.
The first estimate, however, yielded a more realistic
simulation of seasonal change in surface water area. The
equation is:
hw= c h, (12)
where h' is depth at local deepest point and c is 0.25. In
the model L was a variable that changed according to a
power of the proportion of the total wetlands area
containing surface water. The equation is:
L = a (A /Awmax )b (13)
where a in the predrainage case is the perimeter of all
natural flow ways and in the present case is double the
length of all primary and secondary canals AW is surface
water area, AWmax is total wetlands area, and b is a number
between 1 and 2 that roughly relates width of the flow line
to water surface area. A /Awmax was less than 1 when
wetlands were not filled, equal to 1 when wetlands were
covered, and greater than 1 when uplands were flooded. The
maximum possible value of (A /Amax )b was 2. This allowed
for increased importance of connected shallow ditches for
removal of surface water when upland areas were flooded.
Evapotranspiration from surface water. The equation
for evapotranspiration from surface water in the model was:
Ew = 0.75 S Pe AW (14)
where Pe is long term average pan evaporation for each
month of the year, measured at Royal Palm Ranger Station in
the Everglades (U. S. Department of Commerce. National
Oceanic and Atmospheric Administration. 1974), AW is
surface water area, and S is a coefficient of plant
activity and shading that increases or decreases
evapotranspiration seasonally in correlation with leaf
senescence and regrowth of predominant vegetation cover.
This equation goes to zero as AW goes to zero.
Evapotranspiration from soil and substrate. The
equation for evapotranspiration from soil and substrate in
the model was:
ED = 0.75 S Pe (VD/VDmax ) AD (15)
where Pe is long term average pan evaporation, as above; S
is as above, VD is soil and substrate water volume to a
depth of 8 ft (generated by the model), V Dmax is saturation
volume of the soil and substrate to a depth of 8 ft, and AD
is the land area not covered by water. VD/VDmax is a
proportionality factor that reduces simulated
evapotranspiration as soil water becomes more difficult for
plants and wind to remove.
Depth and area of surface water. Both water depth at
the local deepest point and land covered by water are
variables related to surface water volume. The means by
which these relationships were determined are described
later in the Methods section. Plots of the relationships
are presented in the Results section. The plots were
translated into a table function in DYNAMO for
incorporation into the model.
Logic. In the model flows are limited by switching
functions and thresholds approximating real conditions.
For example, only gravity-flow water, or that volume of
water above the moisture equivalent volume of the soil, can
move as interflow from soil to surface water storage or as
groundwater flow to sea. Surface water runoff occurs only
when that volume is exceeded which is required to fill all
surface water areas to the estimated lowest points on their
rims. Only the top 5 ft (1.5 m) of soil-substrate water is
available to the roots of most plants and thereby subject
to removal by transpiration. Evapotranspiration from
surface water cannot remove a greater quantity of surface
water than is present. Rainfall is accepted by the soil-
substrate water tank only to the capacity of saturation
volume of the tank. Upland rainfall in excess of the
saturation volume of the soil-substrate tank becomes a part
of the surface water.
The water model was simulated on an IBM 370 digital
computer at the Northeastern Regional Data Center of the
University of Florida, using the DYNAMO modeling package.
Analyses of Rainfall
Trend Analysis
Moving 5-yr averages of annual, total wet season, and
total dry season rainfall for the 25-yr period of record at
Corkscrew grove (ALICO, pers. comm.) were graphed.
Graphing moving averages eliminates short term variation in
a periodic or noisy record so longer term periodicity or
trends, if present, can be identified. The 5-yr grouping
for the moving average was selected because Thomas (1970)
78
reported a 5-yr periodicity in the long term records of
some south Florida stations (although none of these
stations were in southwest Florida).
Frequency Analysis
Fourier analysis was used to look for cycles in the
rainfall record of Ft. Myers (U. S. Weather Bureau, Ft.
Myers). A fast Fourier transform program written by C.
Kylstra (pers. comm.) was used to convert both total
monthly and total annual data series to the frequency
domain so that the harmonic components of the signal could
be determined by a plot of amplitude versus frequency. The
equation used was as follows:
1 M y-j2rnm/M (16)
m=1
F = 1/2(an jb n); an = amplitude of real component; bn =
amplitude of imaginary component; M = total number of
Intervals; m = interval; n = harmonic.
Another Fourier equation, which converts a signal from
the frequency domain to the time domain, was used to
approximate the original signal as a sum of sines and
cosines. The equation is:
N j2(nm/M
ym = E Fe (17)
n=-1
where N = total number of harmonics.
The fast Fourier analysis program was executed several
times on the monthly data, using different numbers of data
points (multiples of 12) in order to assure that any low
frequency components that might be present would show as
clearly defined peaks and that non-even number harmonics
would not be spread out over several adjacent harmonics and
their importance possibly obscured. Due to limits of the
computer and the program, data sets greater than 36 yrs
(432 data points) in length could not be tested. Fourier
analysis of annual total rainfall at Ft. Myers tested 82,
81, 80, and 77-yr sequences. The programs were run on an
IBM 1180 digital computer at the Department of Nuclear
Engineering at the University of Florida.
Remote Sensing for Relating Water Area to Volume
Depth-area (hypsographic), depth-volume, and area-
volume curves of the three-county study region were
developed using remote sensing techniques, plan profiles
from state road and private canal surveys, and on-site
topographic surveys. Lands covered by water several months
annually were located and mea-sured on infrared aerial
photographs by means of soil and vegetation
characteristics. Relative elevations and elevation ranges
of the different vegetation communities were determined
with the plan profiles and the topographic surveys.
In this study eight wetland areas were defined. These
are marshes through which water flows (sloughs), the
dominant vegetation 'of which comprises pickerel weed
(Pontederia lanceolata), maidencane (Panicum hemitomom),
arrowhead (Sagittaria lancifolia), and cordgrass (Spartina
bakerii); cypress strands, dominated by baldcypress
(Taxodium distichum); swamp hammocks, in which grow pond
apple (Annona ,labra), water ash (Fraxinus caroliniana),
and red maple (Acer rubrum); wet pairies, dominated by
short sedges and grasses, different species predominating
on sand and on marl; shallow ponds, open or holding pond
cypress (Taxodium ascendens), water ash, pond apple, red
maple, pickerel weed, maidencane, and arrowhead; and deep
sink holes; large shallow ponds; and lakes, all of which
are open. A cross section showing relative elevations is
given in Figure 15. Throughout the study it was possible
to use the term "slough" and "marsh" interchangeably. In
common usage the term "swamp" applies to wetlands with
trees and "marsh" to wetlands without trees.
The high water line for the average wet season was
assumed to be the line where saw palmetto, slash pines, or
live oaks began (Davis, 1943). Rings of sparse vegetation,
showing white on the photographs, also helped delineate the
outer limits of many wetland areas, particularly ponds.
Following are details of area measurements made of the
different types of habitat.
Figure 15. Relative elevations of pineland, sloughs (also
marshes), wet prairie (also dwarf cypress), strands, and
ponds.
Iiy
a I
!Cypress Strand or i Dwarf Wet Prairie
Swamp Hammock! Cypress
a Water in ponds
b Water in strands
c Water standing above rim of
strand when marl prairie
(or dwarf cypress) is flooded
~i i
i i i
I Pine Slough iPine ;
I I or
j Forest ; I Forest!
d Water over marl prairie
(or dwarf cypress)
e Water in sloughs
Marshes, Cypress Strands, and SwamD Hammocks
Areas of major wetlands units were obtained from land
use maps for Hendry, Collier, and Lee Counties (DeBellevue,
1976; Brown,1976; Lehman, 1976). Maximum volumes of water
retained in sloughs were approximated by estimating average
depths of sloughs from elevation profiles (ALICO, pers.
comm. and Florida Department of Transportation, pers.
comm.). The profiles are listed in Table 4. Curves of
water depth versus water surface area were developed from
cross-sections from the profiles. Water volume was taken
as the integral of (or area under) each curve, and water
surface area was plotted against water volume.
Ponds of Less than 100 Acres
Area of ponds was obtained by multiplying number of
ponds by average size of ponds. The ponds in each county
were marked and counted on a 1:250,000 scale index sheet of
Hurd infrared photographs from 1950. Average size of ponds
in sample areas was determined from 1:20,000 scale
Agricultural Soil Conservation and Stabilization (ASCS)
infrared aerial photographs (9" x 9") in Hendry and Collier
Counties. Figures 16 and 17 show the locations of the
areas covered by the sample 9" x 9" photographs. Areas of
circular or near-circular ponds were calculated by
measuring average diameters and using the equation for the
area of a circle. Areas of irregular shaped ponds were
determined by a digital planimeter (Hewlett-Packard
Table 4. Plan profiles providing relative elevation information for
the study.
Transect
State Road 833
State Road 29
State Road 84
(Alligator Alley)
North Boundary
Canal
South Boundary
Canal
Location
Portion including 900 angle turn
in the Deer Fence area of southeastern
Hendry County
Portion in Cow Slough area, Immokalee
Rise, extreme northern Collier County
Portion in Big Cypress area, west of
Fahkahatchee in western Collier County
Devils Garden area between Ll Canal and
SR 833 in Hendry County
Devils Garden area between Ll Canal and
SR 833 in Hendry County
aFlorida Department of Transportation
bAtlantic Land Improvement Company
Source
DOTa
DOT'
DOT
ALICOb
ALICO
Figure 16. General Highway Map of Hendry County (Florida
Department of Transportation, 1969) showing location of
sample 1:20,000 scale photos used in determining area of
ponds.
T-
I UN
II
I
'rz
I
I
I
I
S
I
I
I
/
I
0 3 6 I;
SCALE IN MILES
HENRY COUNTY
SUN
20-
SUN' BUN BU
20- 4 50-
121 37
BUN
3D- 833
118 84
SUN
DI I183 -
g 54
I
I i
I i
! ,
' I
s *
L.- -- --- ....-
Figure 17. General Highway Map of Collier County (Florida
Department of Transportation, 1966) showing location of
sample 1:20,000 scale photos used in determining area of
ponds.
0 3 6 12
SCALE IN MILES
b I
COLLIER COUNTY
0 3 6 12
SCALE IN MILES
digitizer). A total of 751 ponds in Hendry County and 192
ponds in Collier County was used to estimate average pond
size. Lee County ponds were assumed to be similar. Ponds
were counted on the sample 9" x 9" aerial photographs, and
calculations of ponds per mile from the 9" x 9" aerial
photographs were compared with those from the same general
areas on the index sheet to test the accuracy of the
original count. A small correction factor thus developed
was applied to the total count for the counties. Figure 13
shows a sample aerial photograph with ponds outlined.
Pond area was subtracted from other wetlands types to
avoid double-counting. The area of ponds in each ecosystem
was approximated from observations made during the count.
The estimated percentage of pond area in each ecosystem is
given at the bottom of the tables describing wetlands area
for each county in Appendix A. Histograms showing the size
frequency distribution of ponds in Hendry and Collier
Counties were prepared.
Maximum capacities of the sample ponds were estimated
by the equation for the volume of a cone. Total capacity
of the sample ponds was multiplied by the total number of
ponds and divided by the number of sample ponds to obtain
the total volume of water the ponds of the two counties
could hold. The total capacity of ponds in Lee County was
estimated as the product of total number of ponds in Lee
County and the average size of ponds in Hendry and Lee
counties.
Figure 18. Copy of 1:20,000 scale infrared aerial photo
BUN-10-97 (U. S. Department of Agriculture. Agricultural
Soil Conservation and Stabilization) with ponds outlined.
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