• TABLE OF CONTENTS
HIDE
 Front Cover
 Title Page
 Acknowledgement
 Table of Contents
 Frontispiece
 Preface
 Appendix A: Methods and chemical...
 The process of eutrophication
 Rate of nutrient addition to Anderson-Cue...
 Physical characteristics of the...
 Biology
 Models of the eutrophication...
 Analysis of environmental factors...
 Routine chemical studies
 Trophic state of lakes in north...
 Appendix B: Methods for biological...
 Appendix C: Protozoa, microscopic...
 Appendix D: Multiple regression...
 References














Group Title: Water pollution control research series
Title: Eutrophication factors in north central Florida lakes
CITATION THUMBNAILS PAGE IMAGE ZOOMABLE
Full Citation
STANDARD VIEW MARC VIEW
Permanent Link: http://ufdc.ufl.edu/UF00081812/00001
 Material Information
Title: Eutrophication factors in north central Florida lakes
Series Title: Water pollution control research series
Physical Description: xi, 141 p. : illus. ; 28 cm.
Language: English
Creator: Putnam, H. D ( Hugh D )
Brezonik, Patrick L. ( joint author )
Shannon, Earl E. ( joint author )
United States -- Environmental Protection Agency. -- Office of Research and Monitoring
Publisher: U.S. Environmental Protection Agency; for sale by the Supt. of Docs., U.S. Govt. Print. Off.
U.S. Environmental Protection Agency for sale by the Supt. of Docs., U.S. Govt. Print. Off.
Place of Publication: Washington
Publication Date: 1972
Copyright Date: 1972
 Subjects
Subject: Eutrophication -- Florida   ( lcsh )
Lakes -- Florida   ( lcsh )
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
Spatial Coverage: United States of America -- Florida
 Notes
Bibliography: Bibliography: p. 133-141.
Statement of Responsibility: by H. D. Putnam, P. L. Brezonik, & E. E. Shannon. Prepared for the Office of Research and Monitoring, Environmental Protection Agency.
General Note: "16010 DON 02/72."
 Record Information
Bibliographic ID: UF00081812
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: oclc - 00515591
lccn - 72601390

Table of Contents
    Front Cover
        Front Cover
    Title Page
        Page i
    Acknowledgement
        Page ii
    Table of Contents
        Page iii
    Frontispiece
        Page iv
    Preface
        Page 1
        Page 2
    Appendix A: Methods and chemical analyses
        Page 85
        Page 86
    The process of eutrophication
        Page 3
        Page 4
        Page 5
        Page 6
        Page 7
        Page 8
        Page 9
        Page 10
        Page 11
    Rate of nutrient addition to Anderson-Cue Lake
        Page 12
        Page 13
        Page 14
        Page 15
    Physical characteristics of the research lakes and drainage basins
        Page 16
        Page 17
        Page 18
        Page 19
        Page 20
        Page 21
    Biology
        Page 34
        Page 35
        Page 36
        Page 37
        Page 38
        Page 39
        Page 40
        Page 41
        Page 42
        Page 43
        Page 44
        Page 45
        Page 46
        Page 47
        Page 48
        Page 49
        Page 50
        Page 51
        Page 52
    Models of the eutrophication process
        Page 53
        Page 54
        Page 55
    Analysis of environmental factors affecting primary production
        Page 56
        Page 57
        Page 58
        Page 59
        Page 60
        Page 61
        Page 62
        Page 63
        Page 64
        Page 65
        Page 66
    Routine chemical studies
        Page 22
        Page 23
        Page 24
        Page 25
        Page 26
        Page 27
        Page 28
        Page 29
        Page 30
        Page 31
        Page 32
        Page 33
    Trophic state of lakes in north central Florida
        Page 67
        Page 68
        Page 69
        Page 70
        Page 71
        Page 72
        Page 73
        Page 74
        Page 75
        Page 76
        Page 77
        Page 78
        Page 79
        Page 80
        Page 81
        Page 82
        Page 83
        Page 84
    Appendix B: Methods for biological procedures during the study
        Page 87
    Appendix C: Protozoa, microscopic algae and sulfur bacteria recorded from McCloud and Anderson-Cue Lakes on five recent dates
        Page 88
        Page 89
        Page 90
    Appendix D: Multiple regression analyses performed on ten day study data and on routine monthly data
        Page 91
        Page 92
        Page 93
        Page 94
        Page 95
    References
        Page 96
        Page 97
        Page 98
        Page 99
        Page 100
        Page 101
Full Text




ENGINEERING PROGRESS


at the


UNIVERSITY OF FLORIDA


Vol. XXIII. No. 8


August, 1969


Published monthly by the
FLORIDA ENGINEERING AND INDUSTRIAL EXPERIMENT STATION
COLLEGE OF ENGINEERING UNIVERSITY OF FLORIDA GAINESVILLE
Entered as second-class matter or the Post Office at Gainesville, Florida


A











EUTROPHICATION FACTORS IN

NORTH CENTRAL FLORIDA LAKES


by


P. L. Brezonik, W. H. Morgan, E. E. Shannon,
and
H. D. Putnam, Project Director


1969


This project was sponsored by Demonstration Grant DON 16,010 from the
Federal Water Pollution Control Administration, Department of the Interior,
Grant A-002-FLA from the Office of Water Resources Research, Department
of the Interior; the Trustees of the Internal Improvement Fund and the Game
and Fresh Water Fish Commission; State of Florida; and the Environmental
Engineering Department, University of Florida.


Bulletin Series No. 134
Water Resources Research
Center Publication No. 5


August 1969


Published monthly by the

FLORIDA ENGINEERING AND INDUSTRIAL EXPERIMENT STATION


College of Engineering University of Florida Gainesville






ACKNOWLEDGEMENTS


Special thanks go to Mr. Carl Swisher of Melrose on
whose property the research lakes are located. Without the
availability of these lakes the project field work, which will
continue for several years, would be impossible. The
cooperation of Mr. Swisher, will, we feel, shorten the time
for the accumulation of knowledge adequate to cope with
the serious problems of rehabilitating many eutrophic lakes
in Florida. Special thanks also go to Colonel Harold Ashley
of Melrose for his untiring efforts on behalf of the project.
Colonel Ashley, a former member of the State of Florida
Game and Fresh Water Fish Commission is a dedicated
conservationist who leaves no stones unturned in his efforts
to see that Florida's natural resources are not destroyed.

During the progress of this study many agencies and
individuals rendered essential and valuable assistance and
advice. Many technical reports, papers and communications
were contributed which provided important background
material for the staff. The following list illustrates some of
the sources from which cooperation was received.


PROJECT STAFF MEMBERS


Mr. Roger Yorton


Mrs. Zena Hodor


Mr. Howard Crown


Mr. Michael Long


Mr. Thomas Salmon


Mr. Samuel Richardson


Mr. Roger King


FEDERAL AGENCIES


DEPARTMENT OF THE INTERIOR.


Mrs. Jeanne Dorsey


Environmental
Department

Environmental
Department

Environmental
Department

Environmental
Department

Environmental
Department

Environmental
Department

Environmental
Department

Environmental
Department


Federal Water Pollution Control Administration

Office of Water Resources Research


Mr. Truman Perry

Dr. Jackson L. Fox


STATE AGENCIES


TRUSTEES OF THE INTERNAL IMPROVEMENT FUND

GAME AND FRESH WATER FISH COMMISSION


CONSULTANTS


Dr. James B. Lackey

Adm. Anthony L. Danis


Melrose, Florida

Melrose, Florida


Dr. Roy McCaldin


Prof. Thomas deS. Furman


Dr. H. K. Brooks


Mr. Paul Maslin

Mrs. Paul Maslin


Melrose, Florida


Environmental Engineering
Department

Environmental Engineering
Department


Environmental
Department


Engineering


Geology Department

Zoology Department

Zoology Department


Permission is given to reproduce or quote any portion
of this bulletin provided a credit line is given acknow-
ledging the source of information-the Engineering and
Industrial Experiment Station, College of Engineering,
University of Florida.


Engineering


Engineering


Engineering


Engineering


Engineering


Engineering


Engineering


Engineering


Geological Survey


OTHERS







CONTENTS

SECTION I. Preface .............................. ........................... 1


SECTION II.




SECTION III.



SECTION IV.




SECTION V.




SECTION VI.




SECTION VII.


SECTION VIII.




SECTION IX.






APPENDIX A.
APPENDIX B.
APPENDIX C.

APPENDIX D.


The Process of Eutrophication ............................................ 3
Figures II-1 11-4
Tables II-1 11-4

Rate of Nutrient Addition to Anderson-Cue Lake ............................ 12
Tables III-1 111-4


Physical Characteristics of the Research Lakes and Drainage Basins ............... 16
Figures IV-1 IV-5
Table IV-1


Routine Chemical Studies .............................................. 22
Figures V-1 V-16
Tables V-1 V-4


Biology ...................................................... ..... 34
Figures VI-1 VI-6
Tables VI-1 VI-14


Models of the Eutrophication Process ..................................... 53


Analysis of Environmental Factors Affecting Primary Production ................ 56
Figures VIII-1 VIII-8
Tables VIII-1 VIII-7


Trophic State of Lakes in North Central Florida ............................. 67
Figures IX-1 IX-11
Tables IX-1 IX-9




Methods and Chemical Analyses .................................. . . 85
Methods for Biological Procedures During the Study .......................... 87
Protozoa, Microscopic Algae and Sulfur Bacteria Recorded from
McCloud and Anderson-Cue Lakes on Five Recent Dates ...................... 88
Multiple Regression Analyses Performed on Ten Day Study Data
and on Routine Monthly Data ............ ......................... 91


REFERENCES .................................................. ............ 96

















































The Experimental Lake








SECTION I

PREFACE


Florida has a vast and valuable resource of fresh
water considering the springs and nearly 30,000 lakes
found within the state. Practically all of these surface
waters are useful in a recreational sense and for this
reason Florida appeals greatly to tourists everywhere
within this country and Canada. Fishing, boating,
and various contact water sports are enjoyed by both
residents and out-of-state visitors throughout the year.
Therefore, the conservation of this fresh water re-
source is most important to the state's economy.
However, since water is so intimately involved in
the total well being of the environment, impairment of
aquatic systems in varying degrees will affect all the
biota including man within a particular ecosystem. Es-
sentially, the water quality of lakes and other fresh
water resources mirrors the status of the total environ-
ment.
Over the years, Florida lakes have been enriched
gradually with nutrient salts from the land. Encroach-
ing urbanization and intensive agricultural practices
have, however, increased nutrient addition to lakes on
an unprecedented scale in recent years. This enrich-
ment has accelerated the eutrophication of surface
water thereby shortening the lives of lakes and gener-
ally impairing the quality of the water.
This problem, which can be reflected nationally, is
acute in Florida. The shallow lake basins, long hours
of sunlight and mild winter temperatures are some of
the factors which make surface water particularly sus-
ceptible to the effects of enrichment and lead to sus-
tained algal blooms throughout the year. The most
classic example in Florida is Lake Apopka near Or-
lando. This is a 30,000-acre lake (12,145 hectares)
which has been extensively enriched by fertilizers
from bordering citrus and winter vegetable farms, mu-
nicipalities, and citrus processing plants. A hyacinth
eradication program employing herbicides over the
last 20 years has left a flocculant bottom layer of un-
decomposed plant residues. These unconsolidated
sediments according to a recent Federal Water Pollu-
tion Control Administration report (1968) cover 90
percent of the lake bottom. Fish reproduction in Lake
Apopka is prevented by the lack of suitable areas for
spawning and by a persistent anaerobic environment.
A similar process is occurring in many other lakes
within the state. Although the visible effects of eu-
trophication are well documented, very little real
knowledge exists regarding the interplay of environ-
mental parameters during lake enrichment. Ultimately


management systems must be devised to include
whole drainage basins if the eutrophication problem
is to be dealt with effectively. First, however, it is
necessary to understand in quantitative terms what
eutrophication is; what are the most effective combi-
nations of enriching substances and how these relate,
for example, to the physical environment of lake mor-
phology, climate and various edaphic factors. To ac-
complish these objectives and ultimately to offer the
maximum use of a lake to those living within the basin
we must know what enrichment stress can be placed
on surface water without measurably impairing its
quality.
This can be brought about only by long-term re-
search. Projects such as that described herein, using
whole lakes as experimental units, are few in this
country. More are needed especially in varying geo-
graphic locations if we are to understand completely
the eutrophication process.
The site for this study was selected in the sandy,
scrub-oak terrain near Melrose, Florida, about 25 miles
east of Gainesville. There are numerous lakes in this
area. Two of these (Anderson-Cue Lake and Mc-
Cloud Lake) located on private property were se-
lected through the cooperation of the owners. The
isolated location of these lakes assures freedom from
outside interference and urban or agricultural influ-
ences. Considerable effort was exerted in 1966 to es-
tablish a field station at the lake site and to install ap-
propriate instrumentation. Background data on the
chemistry and biology of the lakes were obtained in
order to be certain of their similarity, and trophic
status. Nutrient enrichment of Anderson-Cue Lake
has been continuous since March, 1967, and during
this time both routine monitoring and special studies
have been carried out in both lakes comprising the ex-
perimental system.
In 1968 the eutrophication research program was
extended to include other lakes of various types and
exhibiting varying trophic stages in the north central
Florida area. For comparative purposes eutrophic
lakes in the Oklawaha chain were also included.
Useful information is gained from this kind of ac-
tivity since other lakes within the state can be catego-
rized and typed in relation to known eutrophic sys-
tems. In this way initial steps can be taken to deter-
mine the trophic status of a lake quantitatively. In
addition, valuable base line information is obtained







concerning water quality in north central Florida.
From studies of this kind will emerge patterns for lake
management. Since lakes are slow to change, a con-
siderable lag may ensue before lakes respond to re-
storative measures. Therefore it is essential to regu-


late the pollution stress on lakes so as to maintain a
desirable level of water quality at a stage where pre-
ventive measures will suffice. Hopefully ecosystem
management models of whole lake basins will provide
the means to accomplish these objectives.







APPENDIX A


METHODS AND CHEMICAL ANALYSES


Wherever possible Standard Methods (A.P.H.A.,
1965) has been used for routine chemical analyses.
However, this reference was not designed for such
dilute systems as the lakes in this study and modifica-
tions or alternative procedures have been necessary
for many parameters. Water samples are collected
with a Van Dorn sampler and stored in glass or poly-
propylene bottles (for silica and cations) in an ice
chest and refrigerator. Samples for nutrient analyses
are preserved with 1 ml saturated mercuric chloride
solution per liter of water sample. The following is
a summary of methods used for routine chemical
analyses of water samples:
1. Temperature (water )-measured by Whitney
underwater thermistor thermometer.
2. Dissolved oxygen: Winkler-azide modification
(A.P.H.A., 1965).
3. pH: determined with Beckman Zeromatic or
Corning pH meter on samples collected in
DO bottles to prevent CO2 transfer with the
atmosphere.
4. Acidity: potentiometric titration to pH 8.3
with 0.01 N NaOH.
5. Alkalinity: potentiometric titration to pH 4.5-
5.1 with 0.01 N HSO4.
6. Conductivity: Beckman conductivity bridge
(A.P.H.A., 1965).
7. Color: sample is centrifuged for 1 hour. The
supernatant is compared against standard
chloroplatinate at 425 mt with a Spectronic
20.
8. Turbidity: Hellige turbidimeter (A.P.H.A.,
1965).
9. BOD: undiluted, unseeded samples according
to A.P.H.A. (1965).
10. COD: A.P.H.A. (1965).
11. Total solids: 100 ml aliquot evaporated to
dryness at 105C in a tarred evaporating dish
(A.P.H.A., 1965).
12. Suspended solids: a suitable aliquot is filtered
through a glass fiber mat in a Gooch crucible
and dried at 1050C.
13. Chloride: mercuric nitrate procedure (A.P.
H.A., 1965).
14. Sulfate: turbidimetric method (A.P.H.A.,
1965).
15. Cations: Na, K+, Mg2-, Ca+", Fe, Mn: atomic
absorption spectrophotometry with a Beck-
man 979.


16. Fluoride: Orion ion specific electrode and
Corning Model 10 expanded scale pH meter.
17. Silica: heteropoly blue method for reactive
silica (A.P.H.A., 1965).
18. Total organic nitrogen: Kjeldahl method (A.
P.H.A., 1965).
19. Particulate organic nitrogen: 200-500 ml ali-
quot filtered through Millipore 0.8[ filter
covered with 5 ml of 2% MgCO suspension.
The cake is washed and scraped off and
nitrogen is determined by Kjeldahl procedure
as above.
20. Ammonia: determined by method of Grass-
hoff (1966) on Technicon AutoAnalyzer. The
method involves reaction of ammonia with
bromine under controlled pH to yield mono-
bromamine, which reacts with starch and
iodide to yield the blue iodine-starch com-
plex.
21. Nitrite: The method described by Jenkins
(1965) was adapted for the AutoAnalyzer.
Nitrite is reacted with sulfanilic acid to form
a diazonium ion which is coupled with N-
(1-naphthyl) -ethylenediamine dihydrochoride
to form a purple-colored azo dye.
22. Nitrate: The automated brucine method of
Kahn and Brezenski (1967) was modified to
produce optimum results on the AutoAnaly-
zer.
23. Ortho-phosphate: The single reagent molyb-
denum blue method of Murphy and Riley
(1962) was adapted to the AutoAnalyzer.
24. Total-phosphate: Samples are autoclaved at
15 psi for one hour in the presence of potas-
sium persulfate and sulfuric acid. The single
reagent method of Murphy and Riley (1962)
is used to measure the ortho phosphate
formed by digestion with a Klett colorimeter.
Sediment samples are collected with an Ekman
dredge and stored in wide mouth mason jars in a
refrigerator. The following procedures are used for
sediment analysis:
1. Samples are classified visually; i.e. sand, clay,
peat, coarse, fine, light, dark, odor, etc.
2. Samples are homogenized with a Waring
blender and diluted with NH3-free H1O to a
pouring consistency (if necessary).
3. A suitable aliquot of the homogenized sample
is placed in a tared evaporating dish (which







has been fired at 6000C). A dry weight and
an ash weight are obtained (110C and 6000C
respectively).
4. Free NH3-N is determined on a suitable ali-
quot of the homogenized sample by distil-
lation and titration with 0.02 N H2SO4. A
carbonate buffer is used. Paraffin is used to
prevent foaming.
5. Total organic nitrogen-Free NH3 is first
boiled off a suitable aliquot (enough to yield
0.5 g dry sediment) using a carbonate buffer
and paraffin. The residue is then digested


by the Kjeldahl method (A.P.H.A., 1965).
6. For iron, manganese and total phosphate, a
suitable aliquot is digested with nitric and
sulfuric acid. The digestate is neutralized
with 4 N KOH and 1 N H2SO4. The volume
is then adjusted to 250 ml.
7. Fe and Mn are determined on the atomic
absorption spectrophotometer on the neu-
tralized digestate.
8. Total PO4-determined on the neutralized di-
gestate using Murphy-Riley single reagent
method.







SECTION II


THE PROCESS OF EUTROPHICATION


A. Definitions

Progress in solving the lake eutrophication problem
has long been impeded because the process, its causes,
and its effects are vaguely defined and often confused.
Eutrophication is a complicated phenomenon with
ramifications extending to socioeconomic considera-
tions. The problem itself can be considered in two
parts. The process of eutrophication is simply the nu-
trient enrichment of natural waters. The process has
been defined only qualitatively, and quantitative load-
ing rates do not specify a particular rate of eutrophica-
tion. The second aspect of the problem is the effect of
eutrophication on the trophic state of lakes. Eutrophy
or the eutrophic condition (the result of eutrophica-
tion) is a lake state defined by a variety of biological
and chemical conditions; it is a hybrid concept. Un-
fortunately there are no well defined units or quanti-
tative measures of trophic state. Thus it has hereto-
fore been impossible to relate quantitatively the proc-
ess of nutrient enrichment eutrophicationn) to its ef-
fects on trophic state (viz. the degree of eutrophy).
The rate of eutrophication is of major concern to both
aquatic scientists and lake users. Both groups (for
perhaps different reasons) are interested primarily in
the rate of change in the trophic state (i.e. the rate of
change in the manifestations of nutrient enrichment).
Eutrophication is not a new phenomenon, nor is
identification of the problem particularly recent. For
example, Cowgill and Hutchinson (1964) concluded
from sediment cores that cultural eutrophication oc-
curred in a lake during the time of the Roman Empire.
Naumann (1919) originally defined the "eutrophic
formation" in terms of a phytoplanktonic assemblage
in nutrient rich waters. Later Naumann (1931) de-
fined eutrophication as "an increase of the nutritional
standards (of a body of water) especially with respect
to nitrogen and phosphorus." As originally defined,
the terms oligotrophic and eutrophic referred to water
types defined (ideally) by their chemical composition.
In later works the meanings of these terms became
more diffuse, and the terms were also taken to denote
lake types in which morphological and edaphic factors
affect the chemical and biological characteristics of
the water. Among most present-day workers, there is
little or no distinction between these meanings; if any-
thing the terms are used more frequently to denote
lake types than water types. Hutchinson (1967b) criti-
cized this extension of terms and proposed a return to


the original definitions. It would seem that the terms
are too widely used (and misused) for retrenchment
now, even if it were desirable. In that case there
would still remain the problem of defining lake types.
It should also be noted that the chemical (hence bio-
logical) composition of lake water is at least partly a
product of edaphic and morphological conditions. In
this report the terms will be used to denote both lake
and water types.
In a classic paper on eutrophication, Hasler (1947)
considered eutrophication as "enrichment of water, be
it intentional or unintentional." Enrichment was
broadly interpreted to include all nutritive substances.
Hasler (1947) and earlier, Strom (1928), and Linde-
man (1942) felt that all lakes are oligotrophic (low in
nutrients) in their original states. They regarded eu-
trophication as an inevitable successional process in
lake aging and extinction, and the eutrophic state as a
natural stage following initial oligotrophy and interme-
diate mesotrophy. Lindeman and Hutchinson and
Wollack (1940) felt that the initial period of oligotro-
phy was relatively short. Hasler's concept of eutro-
phication as a consequence of lake aging is shown in
Figure II-1. He viewed eutrophication as a compara-

Effect of Fertilizers
Artificial or Domestic


i- / Extinction

5

S





AGE OF THE LAKE

Figure 11-1. Hypothetical Curve of the Eutrophication Process in Lakes.
(After Hasler, 1947).

tively rapid process that might be autocatalytic (i.e.
self-accelerating). Hasler cited Mortimer (1941, 1942)
as proposing that acceleration results from release of
greater quantities of nutrients from the reducing muds
of eutrophic lakes than from the oxidized sediment





layer of oligotrophic lakes. According to Hasler, arti-
ficial or domestic fertilization results in almost instan-
taneous eutrophication. In both Lindeman's (1942)
model (Figure 11-2) and Hasler's (1947) model (Fig-
ure II-1), eutrophication eventually levels off, and a
long period of relatively constant production ensues in
the lake. This stable period is termed the stage-equi-
librium, during which the sediments act as a nutrient
reservoir or trophic buffer to maintain high produc-



l / Climax
.- / Eutrophy





Oligotrophy enescence





tion. During the stage-equilibrium, sediments con-

tinue to accumulate and the lake approaches extinc-
tion. Hasler's model suggests increased production as
the lake approaches extinction, but the. final succes-
sional stages are not described in detail. Lindeman
proposed a period of senescence when the lake be-
comes too shallow for maximum phytoplankton
growth. During senescence the lake is characterized
by a large littoral area predominated by rooted aquat-
ics and by increased marginal vegetation. Production
per unit volume of lake water may be at its highest
level during this time, but according to Lindeman's
model production per unit area is lower. In both
cases the lake eventually becomes a marsh or bog and

fills in completely to begin terrestrial stages of succes-
sion.
Definitions of eutrophication have been discussed
in detail by Wentz (1965) and Stewart and Rohlich
(1967). The latter reference, a comprehensive re-
view of the eutrophication problem, states that the
term eutrophication has been used rather freely by re-
searchers. There is general agreement among present
workers that eutrophication is the process of nutrient
enrichment, but many significant differences of opin-
ion appear concerning details of the process and its ef-
fects.
The classical scheme of eutrophy inevitably suc-
ceeding oligotrophy has been questioned by several
recent studies. Mackereth (1965) theorized that
lakes may be more productive in their earlier stages


(shortly after glaciation in this case) than later. On
the basis of sediment core analyses in the English lake
district, Mackereth proposed that lakes typically pro-
ceed from a eutrophic origin toward an oligotrophic
state unless another natural or human disturbance al-
ters their course. The concept of glacial scour caus-
ing initial eutrophy is not original with Mackereth.
For that matter, Lindeman (1942) and Hutchinson
and Wollack (1940) recognized that the influx of
phosphorus could be very high shortly after glaciation
in certain geological situations. Brundin (1958) found
glacial erosion to be responsible for eutrophy in Lake
Juvvatnet, Sweden. This is a true glacier lake (i.e.
the base of a glacier forms part of its shore), and
Brundin concluded that the glacier ice is a rich
source of phosphorus in the form of glacier-ground
mineral particles of colloidal size. Livingstone and
Boykin (1962) reported phosphorus distributions in a
sediment core from Linsley Pond (Connecticut)
which can be interpreted according to Mackereth's
hypothesis of eutrophy preceding oligotrophy. The
ontogeny of Linsley Pond's trophic state is still un-
settled (see Brooks and Deevey, 1963), but the above
studies indicate that the classical concept of succes-
sion from oligotrophy to eutrophy may not be univer-
sal. The supposed irreversibility of eutrophication was
questioned by Cowgill and Hutchinson (1964) and
Hutchinson (1967a), who reported a case of appar-
ently reversible eutrophication during the time of the
Roman Empire. Lago Monterosi rapidly became eu-
trophic when a Roman road was built around the lake
and later became oligotrophic just as quickly.
The Hasler-Lindeman concept of natural eutrophi-
cation clearly considered the process to be an irrever-
cible succession in a lake's evolution-or more precisely
in its devolution. Hasler (1947) seems to have con-
sidered cultural eutrophication in the same light and
made no distinction between the two processes with
regard to the lake's subsequent ontogeny. Lindeman
did not specifically consider the cultural eutrophica-
tion case. Most present workers have likewise re-
garded the two processes as identical (in their effects)
and have presumably considered both irreversible.
Association of cultural eutrophication with inevitable
lake aging has spawned some unfortunate and mis-
leading comparisons in the popular press, e.g., equat-
ing eutrophic lakes with "dead or dying lakes." This
comparison is somewhat ironic in that eutrophy im-
plies an abundance of life, which results from high nu-
trient conditions.
The role of dystrophy in lake succession and ex-
tinction has never been clear. The term "dystrophic"
(literally, ill-fed) generally refers to lakes high in
color and organic matter with low calcium and pH.
Such lakes may have high or low nutrient conditions







although the latter are perhaps more common. This
lake type has special relevance to limnological studies
in Florida because many of the state's lakes have high
organic color and exhibit other characteristics of dys-
trophy.
Naumann's original classification is somewhat am-
biguous with regard to the position of dystrophy.
Thienemann (1921, 1925) divided lakes into the fol-
lowing classes.

I
Lakes with colorless water
a. oligotrophic lakes
b. eutrophic lakes
II
Lakes with brown water


Because colored lakes were not well studied, Thiene-
mann did not attempt to subdivide them, but accord-
ing to Hansen (1962), it is clear that he intended a
bipartite or quartered system based primarily on color
and subdivided according to nutritional or trophic
bases. However, Hansen (1962) states that Thiene-
mann's system has always been interpreted as a tri-
partite system (i.e. three principal types: oligotrophic,
eutrophic, and dystrophic lakes), and probably most
American workers in lake eutrophication still interpret
dystrophy as a lake type parallel to oligotrophy and
eutrophy. This has caused much confusion in lim-
nology because dystrophy has no intrinsic relation to
the nutrient content of lakes, which in essence is the
basis of classifying lakes as oligotrophic, mesotrophic
or eutrophic (for low, intermediate or high nutritional
standards, respectively). Strom (1928) regarded
dystrophication as an alternative to eutrophication,
but Strom (1930) presented an extended classification
based on Thienemann's (above). Other workers have
considered dystrophy to be the successional phase to
eutrophy in lakes, and Lindeman's senescent stage has
some characteristics typical of dystrophy although he
did not use the term himself or emphasize the role of
color. Many Florida lakes have probably been colored
since shortly after their formation and will likely re-
main so throughout their existences unless human ac-
tivities change them.
The validity of the dystrophic lake type has been
discussed by Berg and Peterson (1956) and Jarnefelt
(1952, 1958). Hansen (1962) attempted to clarify the
position of dystrophy by refining Thienemann's origi-
nal bipartite system. Hansen established two princi-
pal classes of lakes: colorless and dystrophic (colored)
lakes. Oligotrophic and eutrophic subclasses could
occur in each category. Based on the sediment types


found in the main classes, Hansen recommended the
following nomenclature:


Col
a. oligotrophic
b. eutrophic

Dy
a. oligotrophic
b. eutrophic


orless or gyttja lakes


II
strophic or dy lakes


Hansen recommended the terms oligohumous and
polyhumous (euhumous) not be used to denote clas-
ses I and II, respectively, because of ambiguities in-
volved with the term humus in soil chemistry. How-
ever, Stewart and Rohlich (1967) used these terms,
which do offer some descriptive advantages. Hansen's
classification system has much to recommend it, but it
must be emphasized that clear and colored lakes may
be fundamentally different and may develop and age
differently. The nature of the eutrophication process
and the eutrophic state in oligo- and polyhumous lakes
may be rather dissimilar, and different criteria may be
required to define eutrophy in the two classes. As in
the case of oligotrophic and eutrophic designations,
there are probably all sorts of gradations between oli-
gohumous and polyhumous lakes, but as Hansen
(1962) points out, this is not a valid argument for
stating that colorless lakes are not fundamentally dif-
ferent from colored lakes.'
The above discussion indicates the need for more
precise definitions and distinctions regarding lacus-
trine trophic states. Whatever the position of natural
eutrophication in lake evolution-and it may have sev-
eral positions depending on the individual lake's cir-
cumstances-it seems proper to distinguish natural
and cultural eutrophication as clearly separate (non-
identical) processes. It may also be instructive to rec-
ognize Lindeman's senescent stage as another major
lake class. This would represent the final evolutionary
stage of a lake and would have the characteristics de-
scribed by Lindeman: shallowness, large littoral area,
high standing crop of macrophytes and high nutrient
conditions (which may be bound in the standing
crop). Some color would seem likely, but high color
would not be essential. Something should be done
with regard to the dystrophic category. Ideally the
word should be abandoned because of its equivocal
and ambiguous status, but it seems firmly entrenched

1 Hansen argues that a gradual transition between fresh and
saline waters occurs in estuaries, but no one would conclude
this implies that fresh and saline waters are not fundamen-
tally different.






in the literature and this may not be possible.2 The
oligo- and polyhumous nomenclature seems appropri-
ate, so long as it is realized that the nature of organic
color in water may not be humic in the original and
specific sense in which the word was derived for soil
fractions. If the dystrophic class is to be retained, it
should be more precisely defined. One alternative
would be to equate dystrophy with the main class of
polyhumous lakes and to provide oligo- and eutrophic
subclasses. A second alternative would be to define
dystrophy as one of the polyhumous subclasses; e.g.
oligotrophic polyhumous lakes could be defined as
dystrophic. This would leave the eutrophic polyhu-
mous lake class unnamed, but what limnology needs
least is another unsolicited addition to the prolifera-
tion of lake nomenclature, so that void will not be
filled here.
Some possible relationships among the lake types
discussed above are shown in Figure 1-3. The dia-
gram is not meant to be exhaustive, and further stud-


Senescence


Figure 11-3. Some Possible Relationships among Trophic States of Lakes in Relation to
the Aging Process. 0 oligliophic, M mesotrophic, E eutrophic, HE hypereutrophic,
D dystrophic.

ies in paleolimnology, palynology, and regional, de-
velopmental and experimental limnology may in-
crease or modify the interactions.
On the other hand, it is quite possible that lakes
are not meant to be classified so simply and unequivo-
2 Hutchison (1967b) proposed adoption of Jarnefelt's (1953)
term "chthoniotrophic" in place of dystrophic. This may be
more precise etymologically, but is less convenient and aes-
thetic. It still suffers from the connotation of nutritional re-
lationship because of its "-trophic" suffix. Jarnefelt (1958)
himself did not use the term in a later paper on lake typol-
ogy. The term "tyrfotrophic" has been used by a few work-
ers to denote dystrophy, but has largely been abandoned.


cally. The wide variety of lakes, the nearly continu-
ous series of gradations between major types, and the
number of anomalous lakes which don't seem to fit
into any category would seem to support this possibil-
ity. Larkin and Northcote (1958) found the above
phenomena discouraging in their attempts to classify
British Columbia lakes. However, they used only sim-
ple regression models (e.g. standing crop of plankton
versus total dissolved solids), whereas more sophisti-
cated multiple regressions and multi-variate tech-
niques may be more applicable to the classification
problem. Pennak (1958) likewise concluded that a
simple oligotrophic-mesotrophic-eutrophic-dystrophic
classification was not applicable to northern Colorado
lakes because of the large number of variable, inter-
grading and anomalous lake conditions. He proposed
an altitudinal division into plains, foothills, montane
and alpine zones, combined with certain chemical
characteristics, to realistically classify the lake types
in this region.
As Margalef (1958) and others have pointed out,
classical lake typology is based on a hybrid concept;
i.e., it considers the chemical nature of the water, mor-
phology of the lake basin, nature of the standing crop
and productivity. It is not always possible to define
eutrophy in terms of productivity in a manner conso-
nant with a definition in terms of the standing crop
(indicator organisms) or of the chemical nature of the
water. Zafar (1959) and others have proposed to use
the words "oligotrophic" and "eutrophic" to signify
only the chemical nature of the lake water. Zafar fur-
ther proposed that lake classification consists of three
components: the first indicating the chemical nature
of the water, the second indicating the climatic zone
and basic morphology, and the third indicating the
dominant class of organisms and productivity. This
interesting suggestion has merit in that the classical
trophic categories do suggest too many characteristics
which are not necessarily correlated. However, selec-
tion of appropriate quantifiable criteria to use within
a given component of the classification system is still a
problem. If improperly developed, Zafar's system
could lead to a proliferation of generated and imagi-
nary lake types. It would also be unwieldy to refer
to a lake, for example, as "y-eutrophic p-tropicoabso-
lutae Bacillariae," as Zafar suggested for Husain Sagar
Lake, India, but in spite of these deficiencies this sys-
tem seems to offer an approach to solution of the lake
typology problem.
European limnologists have devoted considerably
greater attention to lake typology than their American
counterparts. Macan (1958) summarized a sympo-
sium on lake typology held at the thirteenth Inter-
national Association for Pure and Applied Limnology







in Finland. General aspects of the topic have been
presented by Elster (1958, 1962), Faegri (1954),
Jarnefelt (1952), Okland (1964), and Thienemann
(1955), among others. A considerably larger body
of studies are referred to in these papers, indicating
much effort has been devoted to the topic. But the
problem still has not advanced beyond semantic
difficulties into discussion of quantifiable bases for
trophic state.
Hutchinson (1957) classified lakes into 76 cate-
gories according to their origin. This is not meant to
imply anything with regard to the actual number of
trophic types and successional patterns that may
eventually be delineated, but it does imply that lakes
are a diverse lot. As our knowledge about them be-
comes more complete, we may be able to delineate
many more distinct trophic types and successional
patterns, and eventually provide some quantifiable
bases for ranking within each trophic classification.

B. Factors Affecting Eutrophication

Most previous workers have cited nitrogen and
phosphorus as the main eutrophying elements. There
is no general agreement among authorities concern-
ing the relative importance of nitrogen and phos-
phorus as limiting elements. Cases can be made for
both elements, and bioassay methods have found both
to be limiting primary production in different lakes.
Many other elements and compounds (e.g., trace
metals and vitamins) are essential for algal growth.
However, little is known about their role in the eutro-
phication process. There are reasons-based on geo-
chemical and biological considerations-to believe
that lakes generally are limited by their nitrogen and
phosphorus inputs, but unambiguous proof would be
nearly impossible. It is also likely that input of minor


essential nutrients is highly correlated with one or
both of the major limiting elements.
One of the objects of the present research project
is to clarify the factors involved in eutrophication and
trophic state and to relate them quantitatively to
each other. The factors affecting nutrient enrich-
ment of lakes are largely geological and cultural, as
indicated in Table 11-1. The nutrient load imposed
on a lake is a function of the geochemistry of its drain-
age basin, the hydrology of the region, climate, and
other natural factors. Superimposed on these natural
factors are a variety of human factors, e.g., urban and
agricultural runoff and the amount of domestic sew-
age disposed into the lake. It should also be noted
that human factors can affect some of the natural
forces. For example, mining operations may change
the geochemistry and hydrology of the basin, and air
pollution may increase the nutrient content of rain-
fall. For a given total nutrient influx, net enrich-
ment may vary depending on the temporal varia-
tions in the input (whether it is a periodic slug or
steady addition) (Brezonik, 1965).
The effects of nutrient enrichment on the trophic
state of a lake are controlled by numerous physical
and chemical factors. These are summarized in Table
11-2. Rawson (1939) related the environmental fac-
tors affecting lake production to each other in the
classical diagram shown in Figure 11-4. Limnologists
have long realized that lake productivity is influenced
by factors other than the concentrations of nutrients.
This is apparent when it is realized that lakes of
similar chemical composition may vary significantly
in productivity. The other factors influence lake
productivity primarily by affecting the distribution,
availability, and utilization of nutrients. Thus they
influence productivity only indirectly, and conse-
quently it is much more difficult to assess their indi-


Table 11-1
FACTORS AFFECTING NUTRIENT ENRICHMENT
RATES EUTROPHICATIONN) OF LAKES


Natural Factors
Geochemistry of the basin
(Composition of underlying rock structures)
Soil types
Hydrology
Size of drainage basin
Short-circuiting
Detention time in lake
Groundwater composition
Climate
Precipitation
Thermal structure


Human Factors
Domestic sewage
Agricultural runoff
Type of farming
Fertilization practices and extent
Soil retentive capacity
Mining operations
Industrial wastes
Urban runoff
(Auto exhaust, lawn and garden fertilizing leaves, etc.)
Nutrient leaching from drained marshes and from
garbage dumps







Table 11-2
PHYSICAL AND CHEMICAL FACTORS CONTROLLING THE
EFFECTS OF NUTRIENT ENRICHMENT ON TROPHIC STATUS


Physical
Mean depth
Steepness of bottom contour
Shoreline irregularity
Percent littoral area
Mean depth/surface area ratio
Wind protection by surrounding terrain
Temperature
Insolation
Circulation which affects sedimentation rates

vidual importance. Similar statements can be made
with regard to these factors affecting overall trophic
state. Brezonik (1965) reviewed the effects of some
physical factors on lake productivity. Morphology
has a dominant influence on the availability and dis-
tribution of nutrients in a lake and thereby markedly
affects productivity and trophic status. Several mor-
phological parameters are important in this regard,
including mean depth, steepness of bottom contour,
percent littoral area, shoreline irregularity and mean
depth/surface area. ratio. Rawson (1955 and earlier
papers) considered mean depth of fundamental im-
portance with regard to lake productivity. He found


Chemical
pH
Balance of all nutrients needed for production
Suspended solids (as affecting transparency)
Nutrient concentrations
Dissolved oxygen





a hyperbolic relation between mean depth and long-
term fish production in the five Great Lakes and
seven large western Canadian lakes, indicating a
rapid increase in fish production as mean depth de-
creased below 25 meters and a slow decrease in pro-
duction as mean depth increased beyond 25 meters.
Similar results were obtained by relating phytoplank-
ton and bottom fauna standing crop to mean depth of
lakes, and Rawson concluded that a mean depth of
20-25 meters is the dividing point between oligo-
trophic and eutrophic lakes. Reasons for the domi-
nant effect of mean depth on lake productivity are
diverse but mostly relate to the possibility of nutrient


GEOGRAPHIC LOCATION

Human Geological
Influence Formation T


Sewage Composition
Agriculture of Substrate Shp
Mining


nation Heat Penetration Oxygen Penetration Development of Seasonal Cycle
and Stratification and utilization Littoral Region Circulation Stagnation
k___ ___ |Growing Season


TROPHIC NATURE OF THE LAKE
Amount, composition and distribution of plants
and animals. Also rates of circulation.
"PRODUCTIVITY"


Figure 11-4. Chart suggesting the interrelationships of factors affecting the
metabolism of a lake. (After Rawson, 1939).







replenishment from the underlying sediments in shal-
low unstratified lakes. This factor has special signi-
ficance for eutrophication in Florida since most of
its lakes are shallow and not stratified.
Climatic factors include temperature, insolation,
and wind as it affects circulation patterns in the lake.
Within a geographical region, climatic factors proba-
bly affect all lakes to similar degrees, and the differ-
ences that arise can be related to differences in lake
basin morphology and surrounding geography. This
is particularly evident for wind effects: a forest lake
and a lake surrounded by pasture are likely to be
affected by wind to different extents even though the
general wind pattern in the regions may be similar.
Between geographical regions with pronounced differ-
ences in climate, factors like temperature and season-
ality may influence the eutrophication process and its
manifestations to a marked degree.
Availability of nutrients is also affected by the
chemical composition of the water, as noted in Table
11-2. A proper balance of all nutrients is needed
for optimum production. Supra- and sub-optimal
concentrations of various ions occur and affect pro-
ductivity and the nature of the dominant organisms
in various ways. The presence of certain micro-
factors may partially obviate the requirements for
other nutrients. The unknown phosphate-sparing
factor long reported to be present in seawater is an
example. Algae cultured in defined inorganic media
require higher phosphate levels than they would re-
quire in seawater to achieve the same growth yields
and rates. Some organic factors may not be neces-
sary in an absolute sense, but their presence in the
water may eliminate the need for synthesis by grow-
ing algae. Vitamins are representative of such factors.
The ionic forms of many nutrients are functions of
pH, and their transport into cells may be pH-depend-
ent. Also, cell membrane permeability is pH-depend-
ent. Organic color may affect production in several
ways; by affecting pH, by chelating certain trace
metal nutrients and by absorbing light. Suspended


solids can affect production by reduction of light
transparency and in various other ways. Dissolved
oxygen affects availability of nutrients, especially in
relation to sediment regeneration and leaching. It
is well known that nutrient release from sediments is
enhanced by anoxic conditions in the overlying water.
Numerous other examples could be cited, but the
above discussion clearly indicates that a multiplicity
of physical and chemical factors control the availabi-
lity of nutrients in natural waters.


C. Measurement of Eutrophication

The trophic state or degree of eutrophy of a lake
may be considered to be a function of the amount
of eutrophication as modified by the edaphic factors
given in Table 11-2. Trophic state is defined by many
factors and cannot be adequately measured by any
single parameter. Commonly used physical, chemi-
cal, and biological indicators of trophic state are
shown in Table 11-3. Many parameters are only qual-
itative; some of the parameters are highly correlated
and interdependent, while others are at least quasi-
independent.
The indicators in Table 11-3 have long been em-
ployed by limnologists but are not completely satis-
factory. A discouraging fact is that sufficient back-
ground data are seldom available to determine
whether a lake is becoming more eutrophic and if
so at what rate. If long term data are available, their
reliability often is questionable. Even for a well-
studied case as Lake Mendota, Wisconsin, Fruh and
Lee (1966) were unable to conclude whether or not
the lake had become more eutrophic since man set-
tled around its shores. Considerable natural annual
fluctuations in such indicators as algal bloom inten-
sity, Secchi disc transparency and nutrient concentra-
tions intensify the difficulties.
Many of the physical and chemical indicators are
only indirectly related to nutrient conditions. Trans-


Table 11-3
INDICATORS OF TROPHIC STATE


Physical
Transparency

(Secchi disc reading)

Morphology

(mean depth, etc.)


Chemical
Sediment type
Oxygen supersaturation
in epilimnion
Hypolimnetic
Oxygen deficit
Conductivity
Dissolved solids
Nutrient concentrations
(at spring maximum)
Chlorophyll level


Biological
Algal bloom frequency
Algal species variety
Characteristic algal group
Littoral vegetation
Fish species and biomass
Characteristic zooplankton
Bottom fauna
Primary production







parency decreases as a result of algal blooms, but
color and suspended inorganic sediments have the
same effect. Conductivity and dissolved solids pre-
sumably increase as a lake becomes eutrophic since
other ions invariably accompany nutrient influxes.
This trend may be true in a general way for eutro-
phication in a given lake; but comparisons (based on
these parameters) of trophic states in a variety of
lakes are nearly meaningless. Hypolimnetic oxygen
deficit and the rate of oxygen disappearance from
the hypolimnion are presumed to be related to sink-
ing of oxygen-demanding organic material produced
in the surface water. In fact these phenomena are
also affected by sediment oxygen demand, by allo-
chthonous organic matter (such as leaves and organic
matter in sewage effluent) transported into the lake,
and by basin morphology, and these parameters are
not at all applicable to unstratified lakes. Since eu-
trophication is the process of nutrient enrichment,
nutrient concentrations in the lake logically would
seem to be the best indicators of trophic state. Un-
fortunately the situation is more complicated: con-
centrations of dissolved inorganic nutrients indicate
"the remains" or "leftovers" that organisms haven't
needed or been able to assimilate. Concentrations
of nutrients change considerably throughout the year.
Sawyer et al. (1945) proposed measurement of in-
organic nitrogen and phosphorus at the spring maxi-
mum (shortly after ice-out in their, study) and
suggested that 0.30 mg/1 of inorganic nitrogen and
0.015 mg/1 inorganic phosphorus are critical levels
above which algal blooms can be expected. Theoreti-
cal or laboratory justification for these empirical
values has not been accomplished.
Biologists have long favored the use of indicator
organisms to detect changes in trophic state. For
example, the cladoceran Bosmina longirostris has re-
placed B. coregoni in Lake Michigan, and Beeton
(1965) considered this indicative of eutrophication
in the lake. The appearance of Oscillatoria rubescens
in Untersee of Lake Zurich in 1898 (Minder, 1938)
and in Lake Washington in 1955 (Edmondson et al.,
1956) was cited as evidence of eutrophication in
these lakes. Bottom fauna, especially Chironomids,
have often been proposed as indicators of trophic
state (see Brundin, 1958, for a review). Numerous
studies have proposed various algae as trophic indi-
cators (e.g. Round, 1958; see Hutchinson, 1967b, for
further discussion). Temporally stable groups like
diatoms, which, like many green and blue green
algae, are likely to be present in lake water in vary-
ing concentrations throughout the year, are more
suitable indicator organisms than transients. One
problem with indicator organisms is that at best they


are only qualitative. Often several organisms give
contradictory results for a given lake.
Primary production is an obvious trophic criterion
and is generally accepted as appropriate in lake class-
ification systems; see Elster (1958) for a review.
Rohde (1958) argued in favor of primary produc-
tion as the main trophic criterion partly because it
is easily measured. One of the shortcomings of
this criterion is that other factors besides nutrient con-
centrations may control primary production (see
above). Thus some suggest that potential rather
than actual productivity be measured. Furthermore,
total annual production implies nothing regarding
maximum values encountered or the nature of the
organisms responsible. A moderately high, steady
production by an evenly distributed (with depth)
population of nannoplankton may be tolerable or even
desirable, but periodic blooms of floating, scum-form-
ing blue-green algae producing the same annual
production are a different matter. Findenegg (1964)
proposed that the shape of the vertical profile for
carbon assimilation (primary production) may be use-
ful in diagnosing the degree of eutrophication. He
found three main types of curves: one with maximum
assimilation at the surface, a second with no distinct
peaks and the third with a peak near the thermocline.
Lakes undergoing cultural eutrophication had the
third type curve. The general applicability of this
technique to lakes remains to be determined; for
shallow unstratified lakes its utility seems dubious.
Fruh et al. (1966) and Stewart and Rohlich (1967)
reviewed the parameters used to measure trophic
state in detail and discussed their assets and limita-
tions. Criteria used in the present study will be
further discussed in later sections.
The problem of defining trophic state is a serious
one. If progress is to be made in relating the pro-
cess of eutrophication to its effects, criteria for trophic
state will have to be well defined and quantified.
On a qualitative level the present criteria appear
adequate. Oligotrophic lakes are low in nutrients,
have low chlorophyll and primary production levels,
high transparency, are generally deep, and have small
numbers of organisms but large numbers of species.
Eutrophic lakes are generally the antithesis. They
have high nutrient and chlorophyll levels, high pri-
mary production, low transparencies, are usually shal-
low, and have high algal populations distributed
among few species. However, many criteria do not
lend themselves to quantification, and by using these
criteria it is impossible to state specifically how much
more eutrophic one lake is than another. Various
indicators sometimes present conflicting values for the
trophic state of a given lake. Beeton (1965) reported






such anomalies for some of the Great Lakes. While
all criteria indicate Lake Erie to be eutrophic, Lake
Ontario according to some biological and chemical
criteria is eutrophic and oligotrophic according to
other chemical and physical criteria.
Most previous studies on trophic state indicators
have been concerned with temperate lakes. Sub-
tropical lakes, such as those in Florida, have consider-
ably different characteristics, and the indicators of
trophic state in northern lakes may not be applicable
universally. Some differences between temperate
and subtropical lakes are summarized in Table 11-4.
In their natural states, some Florida lakes simultane-
ously have characteristics of oligotrophic, eutrophic,
and dystrophic temperate lakes, which implies that
these lake types may not be useful in subtropical
situations. Nearly all Florida lakes are shallow, and
considerable water fluctuations can occur between dry
and rainy periods. Water level fluctuations of plus or
minus five feet in lakes with normal maximum depths
of only 20 feet do occur, and many lakes dry up com-
pletely (or nearly so) in periods of prolonged drought.
In Florida lakes there is often no distinctive land-lake
interface, and the shore areas of lakes are often inter-
mittently submerged land. The littoral vegetation in


Florida lakes is composed of emergent and submer-
gent aquatic species as in northern lakes, but because
of the changing shore, terrestrial species are also
found in the littoral area. Extended growth periods
lead to large standing crops throughout the year
under subtropical conditions compared to highly sea-
sonal crops in north temperate lakes. In general,
seasonal changes in subtropical lakes are less evident
than in northern lakes. Temperature structuring is
less pronounced, and because of their shallowness
few Florida lakes exhibit stable thermal stratification.
The general chemical compositions of temperate and
subtropical lakes differ considerably. Typical tem-
perate lakes are calcareous, have pH values above 7
and moderate to high alkalinites. Florida lakes typi-
cally have soft acid waters and very low alkalinities.
Organic color is a more common constituent in Florida
lakes than in temperate lakes. Because of the differ-
ence in chemical composition, species of plankton
in Florida lakes tend to differ from those in temperate
lakes. Desmids are common in the soft, acid waters
of Florida, and diatoms are often sparse. However,
blooms of blue-green algae, such as Microcystis, Ana-
baena, and Aphanizomenon occur in fertilized Florida
lakes as in their northern counterparts.


Table 11-4
DIFFERENCES IN NORTH TEMPERATE AND SEMITROPICAL LAKES
WHICH MAY BEAR ON EUTROPHICATION PROCESS


Northern Lakes
1. Defined shoreline usually with beach
2. Thermally stratified, usually dimictic
3. Usually calcareous
4. Ice covered
5. Runoff from meltwater
6. Winter solar radiation and temperature limit
primary production


Semitropical Florida Lakes
1. Shore-water interface ill-defined
2. Little or no thermal stratification
3. Soft, acid water
4. Always ice free
5. No spring runoff
6. Low temperature and solar energy not evident; longer
periods for optimum plant growth. Sustained yields of
standing crop throughout year.







SECTION III


RATE OF NUTRIENT ADDITION TO ANDERSON-CUE LAKE


A. Previous Fertilization Studies

One approach to studying eutrophication is to
artificially enrich (eutrophy) a lake at a controlled
rate and measure all the parameters which define
trophic state. The problem then becomes a matter
of relating the response of a lake (in terms of trophic
structure) to the degree and rate of nutrient enrich-
ment. The lake thus serves as a general model for the
process; this approach has been used in the present
study. Intentional fertilization of lakes for scientific
purposes is not a new concept. Stewart and Rohlich
(1967) recently reviewed previous experiments on
lake fertilization. Einsele (1941) reported one of the
first experiments on lake fertilization. He applied
slug doses of superphosphate to a small German lake
in 1937 and 1938. Temporary increases in the phyto-
plankton of the lake were found but the lake soon
returned to normal. A number of investigators (e.g.
Ball, 1948a, b; Langford, 1950; Nelson and Edmond-
son, 1955; Hooper and Ball, 1964) have attempted to
increase the productivity of fish ponds by adding ferti-
lizer. These attempts have had only moderate suc-
cess. In most fertilization experiments, nutrients have
been added in high slug doses rather than continu-
ously. While temporary effects have been noted, the
ponds or lakes usually returned to their original condi-
tions in short periods of time.
Induced circulation by aeration or actual pump-
ing of hypolimnion water into the epilimnion has been
proposed by Hasler (1957) and Hooper et al (1952)
to render nutrients within a lake more available to
phytoplankton. However, unless the sediments were
actually disturbed by aeration, the long-term effect
of this process would seem to be the reverse of that
intended. Aeration would tend to keep the hypo-
limnion oxygenated and maintain an oxidized micro-
zone at the sediment water interface which decreases
the release of nutrients from the sediments.

B. Nutrient Loading Rates Into the Experimental
Lake

Because of the nature and purpose of previous
fertilization efforts, results from these studies are not
directly applicable to the problem of cultural eutro-
phication. Man-induced eutrophication is character-
ized by a more or less continuous addition of nutrients
to a lake from such sources as sewage effluent and


urban and agricultural runoff, while most previous
fertilization attempts have used sporadic or one-time
applications of fertilizer. Generally, sufficient back-
ground data were not obtained to describe the trophic
state of a lake and its natural temporal variations.
The study reported here is viewed as a long-term
effort to follow the effects of a controlled nutrient
input on a lake's trophic state. Two small lakes are
involved; one lake is serving as a control and the
other lake is being artificially eutrophied by contin-
uous and controlled addition of nutrients. A variety
of routine chemical and biological data on the lakes
is being collected along with routine physical and
climatic data in order to identify factors affecting the
rate and severity of eutrophication.
Chemical and biological measurements during 1966
and early 1967 established the oligotrophic nature of
Anderson-Cue Lake. In March, 1967, nutrient addi-
tions to the lake were begun. It was decided to add
sufficient nitrogen to raise the total N content of the
water 0.50 mg N/1 over a period of a year (assuming
all nitrogen would stay in solution). This is equiva-
lent to 500 mg/m3-yr or about 10 mg/m3-week. The
volume of Anderson-Cue Lake was estimated to be
248,000m3. Thus a weekly loading of 2.48 kg N was
desired. This was achieved by adding 21.2 lbs of am-
monium chloride to 300 gallons of sewage effluent,
which was trucked out to the lake site and fed into the
lake with a chemical feed pump at a rate of 1.8 gal/
hr. The nutrient outfall is located 2 ft below the sur-
face and 200 ft off the south shore of the lake in about
10 ft of water.
It was decided to increase the total phosphorus
content of the lake by 0.0427 mg P/1 in one year.
This is equivalent to 42.7 mg/m3-yr or 0.854 mg/m3-
week. For the whole lake a loading rate of 0.212 kg
P/week is indicated. This was achieved by adding
2.47 lb of Na3PO4 to the sewage effluent each week.
Originally it was planned to add only sewage
effluent to the experimental lake. This would have
been feasible if Berry Pond (1 acre surface, maxi-
mum depth, 13 ft) could have been used as originally
planned, but its trophic characteristics obviated this
plan. Nearly a million gallons of sewage effluent
would have to be transported to Anderson-Cue Lake
annually for the desired nutrient loading rate. Thus
logistics precluded the use of sewage effluent alone,
and it became necessary to enrich effluent with nitro-
gen and phosphorus compounds.







C. Nutrient Budgets for the Experimental and Other
Lakes

The above nutrient addition rates compare closely
with those estimated for the nutrient budget of Lake
Mendota, Wisconsin, by Lee et al (1966). This
eutrophic lake receives a heavy influx of nutrients
from agricultural drainage, but ground water and
atmospheric precipitation also make important contri-
butions. The nitrogen loading of Lake Mendota was
estimated to be 556,000 lb per year, or 534 mg/m3-yr.
Of this quantity, Brezonik and Lee (1968) have
estimated that two-thirds or 360,000 lb remains in
the lake and is deposited in the sediments, while the
remainder is lost through the outlet and by denitri-
fication. The phosphorus budget for Lake Mendota
was estimated to be 44,900 lb per year or 42.7 mg
P/m3-year. Table III-1 summarizes the computed
nutrient budget for Lake Mendota.
Relatively few other lake nutrient budgets have
been established. Mortimer (1939) constructed a
nitrogen balance for Lake Windermere (England).
He found a close balance between input and output
-326 and 318 metric tons, respectively. Hutchinson
(1957) felt that nutrient inflow and outflow normally
would balance closely in oligotrophic lakes, but not in
eutrophic lakes. Rohlich and Lea (1949) reported an
extensive nutrient balance on Lake Mendota, Wis-
consin. Of the estimated 156 metric tons of nitrogen
entering the lake annually, only 41 tons left through
the surface outlet. Corresponding values for phos-
phate were 16.4 and 11.6 metric tons. Partial nu-
trient budgets were determined for the lower Madi-
son lakes by Sawyer et al (1945). However, only


soluble phosphorus and inorganic nitrogen inputs
were measured rather than total values, and the use-
fulness of the results is thus impaired. Aside from
nutrient balances on Lake Tahoe (McGauhey et al,
1963) and Lake Washington (Edmondson, 1968) no
other definitive nutrient budgets are known. Less
detailed budgets have been drawn for a few other
lakes-for example, Lake Fure in Denmark (Berg,
1958), Castle Lake in California (Goldman, 1961)
and western Lake Erie (Curl, 1959). Various aspects
of nitrogen and phosphorus budgets and sources have
been treated by numerous workers. Brezonik (1968a),
Feth (1966), and Fruh (1967) have reviewed these
studies in considerable detail.
Table III-2 lists the most common nutrient sources
and sinks for lakes. Only some of these are appli-
cable to the study lakes. Artificial enrichment re-
presents the most significant nutrient source for
Anderson-Cue Lake. The possible natural sources of
nitrogen are biological fixation, atmospheric precipita-
tion, airborne particulates and surface and subsurface
runoff. Preliminary results indicate the rainfall
directly on the lake surface is the most important
natural source. Nitrogen fixation has not yet been
measured in the lake, but the near absence of blue-
green algae in the biota of the lake implies that it
does not occur. While bacterial fixation is possible,
available carbon substrates are low and indicate the
source is probably negligible. Contributions from
runoff also appear to be small. The amount of run-
off draining into the lake is apparently low, and the
soil is so nutrient depleted that rainfall runoff would
pick up little or no additional nutrients in passing
through and over the soil.


Table 111-1
ESTIMATED NUTRIENT BUDGET FOR LAKE MENDOTA, WISCONSIN',
Source Nitrogen Phosphorus Nitrogen
Contribution Contribution Contribution Contribution Sink' Lost Lost
kg. % kg. % kg. %
Municipal and Outlet loss 41,300 16.4
industrial wastewater 21,200 8 7,750 36 Denitrification 28,000 11.1
(total) (total)
Urban runoff 13,700 5 3,680 17 Fish catch 11,300 4.5
(soluble) (soluble)
Rural runoff 23,500 9 9,100 42 Weed removal 3,250 1.3
(soluble) (soluble)
Precipitation on 64 to Ground water
lake surface 43,900 17 3,460 2 recharge -
Ground water 113,000 45 274 2 Sediments and
other losses' 168,000 66.7
Nitrogen fixation 36,100 14 -
Marsh drainage -
Approx. Total 252,000 100 21,240- 100 Approx. Total 252,000 100
I After Lee et al. (1966) and Brezonik and Lee (1968)
2 The values presented are only rough approximations
' This total based on 455 kg. per year of phosphorus in precipitation on the lake surface
4 Phosphorus sinks have not been determined but outlet loss and sediment deposition probably account for most of the phosphorus
' By difference between total sinks (assumed to equal total sources) and sum of all other calculated sinks. Other sinks are probably small, and
sediment deposition accounts for most of the nitrogen in this category.






Table 111-2
SOURCES AND SINKS FOR THE NUTRIENT BUDGET OF A LAKE

Sinks


1. Airborne
Rainwater
Aerosols and dust
Leaves and miscellaneous debris
2. Surface
Agricultural runoff and drainage
Urban storm water runoff
Marsh drainage
Runoff and drainage from uncultivated land
Domestic waste effluents
Industrial waste effluents
Wastes from boating activities
3. Underground
Natural groundwater
Subsurface agricultural and urban drainage
Subsurface drainage from septic tanks near
lake shore
4. In situ
Nitrogen fixation
Sediment leaching


Effluent loss
Groundwater recharge
Fish caught or removed
Weed harvesting
Insect emergence
Evaporation (aerosol formation from surface foam)
Denitrification
Sediment deposition of detrital particles
Inorganic precipitation (for calcium phosphate, and some
trace metals) and deposition into sediments


Table 111-3
AMOUNT AND NUTRIENT CONTENT OF RAINFALL AT
ANDERSON-CUE LAKE, 1968


Date Amount' TON NHa-N NO3-N o-PO, t-PO,
Inches mg/1 mg/1 mg/1 tg/1 /g/1
2-19 1.00 0.46 0.40 230 -
2-26 0.25 0.23 0.94 20 -
3-4 0.35 0.80 0.26 25 -
3-11 1.20 0.57 0.86 0.24 18 -
4-15 0.65 0.67 0.33 0.27 2.2 -
5-6 0.45 0.64 0.10 0.30 28 -
5-13 0.75 0.33 0.0 0.15 20 -
5-27 3.35 0.11 0.02 0.05 -
6-24 6.65 0.24 0.02 0.14 9 30
7-5 4.00 0.39 0.05 0.09 10 30
7-25 4.65 0.01 0.01 0.08 8 -
8-2 0.85 0.50 0.29 20
8-19 2.10 0.34 0.14 0.22 33 70
9-3 10.85 0.07 0.09 20
9-16 4.30 0.07 0.11 0.16 5 -
10-11 2.85 0.12 0.05 0.11 12 -
10-19 3.70 0.11 0.21 0.04 4 -
11-11 3.60 0.23 0.07 0.05 25 30
12-9 2.25 0.54 0.18 0.11 18 -

1 Total amount of rain in period from date on previous line to the date listed on
the line. First line indicates rainfall from Jan. 1 to Feb. 19; last line indicates
amount of rain from Nov. 12 to the end of the year.


Sources







Measurements of the nutrient content of the rain-
fall were made periodically in 1967 and 1968. A
summary of the results are shown in Table III-3.
The nitrogen content of rainfall appears to be quite
variable. However, these results can be combined
with the rainfall amounts (see Section IV, Table IV-1)
to yield an estimate of the total nitrogen contribu-
tion of rainfall to Anderson-Cue Lake. For 1968, 44
kg nitrogen was added to the lake by rainfall directly
on the lake surface.' By comparison, about 124 kg N
1 This value corresponds with 49.4 Kg nitrogen added to the
lake by rainfall during 1967, as previously computed by Bre-
zonik and Putnam (1968).


was added by the nutrient mixture. Actually a
greater disparity between these two sources exists
than is indicated by the magnitude of the two num-
bers. The rainfall contribution is diluted in a large
volume of water, whereas the nutrient mixture is
highly concentrated and contributes an insignificant
amount of water to the lake. Phosphate analyses from
1968 indicate a wide range of phosphate in rain water.
The data indicate that about 2.7 kg P was contributed
by rainfall in 1968. This compares with 10.6 kg added
to the lake in the nutrient mixture. These results are
summarized as a nutrient budget for Anderson-Cue
Lake in Table III-4.


Table 111-4
PARTIAL NUTRIENT BUDGET FOR ANDERSON-CUE LAKE, 1968

Nitrogen


Sewage and nutrient mixture
Rainfall on lake surface


Total


mg/1 of lake water'
0.67
0.24

0.91


Other possible nitrogen sources not completely evaluated are nitrogen
fixation, groundwater seepage, subsurface runoff and airborne particulates (leaves,
etc.). Present information indicates all these except perhaps the last were insig-
nificant in 1968.

Phosphorus


Sewage and nutrient mixture
Rainfall on lake surface


Total


kg.
10.60
2.67

13.27


mg/1 of lake water
0.057
0.014

0.071


Other possible phosphorus sources are groundwater, subsurface runoff
and airborne particulates. Present information indicates the first two were in-
significant, but there is insufficient information to evaluate the last source.

1This is the concentration which would result if the amount of nutrient in column
1 were diluted to the volume of the lake (approximately 150 acre-feet or 185,000
m3).







SECTION IV


PHYSICAL CHARACTERISTICS OF THE RESEARCH LAKES AND DRAINAGE BASINS


A. General

Anderson-Cue and McCloud Lakes (see Figure
IV-1) are located in a region of high sand hills with
many circular to elliptical basins which have resulted
from solution of the underlying limestone. Both lakes
have small drainage basins with no influent or effluent
streams.
The tops of many of the surrounding hills reach
elevations of 190 to 220 ft., MSL. Westward at Mel-
rose the terrain changes from sand hills to the Oke-
feenokee Terrace, a poorly drained terrace 140 to
160 ft., MSL. Eastward, beyond Baywood, the sand
hills are bound by lower marine terraces. The imme-
diate area of the research lakes is the Trail Ridge por-
tion of the Central Highlands.
Three hydrographic surveys of Anderson-Cue Lake
have been made since the fall of 1965. Echo sound-
ings were made in November, 1965; a stadia survey
(including topography of the basin) was made in
July-August, 1967; and echo soundings were again
made in March, 1968. Using these data a topogra-
phic map of the lake and surrounding area was pre-
pared and is shown in Figure IV-2. The highest


100 0 100 200 300
Figure IV-2. Topography of Anderson-Cue Lake.


level shown on the map was the shoreline which
stood at 125.78 ft., MSL, in March, 1966, at which
time the lake had a surface area of 19.3 acres and a
volume of approximately 201 acre feet. When it was
decided to use McCloud Lake instead of Berry Pond
as the control body of water in late 1966 the volume
of water in McCloud Lake exceeded that in Ander-
son-Cue Lake by approximately 15 percent. This
information was obtained by stadia survey.
Yearly excess precipitation over evaporation (30
year record) is 12 to 18 in. in the xeric hills surround-
ing Anderson-Cue Lake1. The excess precipitation
and runoff percolate downward through breaks in
the sands and clays of an aquifuge that overlies the
Floridan aquifer. The influent drainage has resulted
in a subsidence karst landscape which forms a princi-
pal recharge area in North Florida for the Floridan
artesian system. Analyses of the water level data
for Anderson-Cue Lake, the surrounding water table
data, and the rainfall and evaporation records in-
dicate that there is very little contribution from
surface and subsurface runoff to the lake. Sands
covering the basin are porous. Downward seepage
rates are high and surface runoff is exceedingly low.

B. Geology

Materials exposed in the sand hills are largely of
two types: very fine surface sands and the underlying
kaolinitic gravels, sands and sandy clays. There sedi-
ments are known as the Citronelle Formation. Well
borings show an aquifuge of from 80 to 100 ft. of
phosphatic sands, sandy clays and clays lying below
the surface. These materials are known as the Haw-
thorn Formation of Lower and Middle Miocene Age.
Underlying the Hawthorn Formation is the Floridan
aquifer, the upper portion of which is the Ocala
Limestone of Eocene Age.
The piezometric surface of the water in the Flor-
idan aquifer is approximately 90 ft. above MSL in
the vicinity of Anderson-Cue Lake. The porous sand
and gravel of the Citronelle Formation contains a
perched water table above the aquifuge-the Haw-
thorn Formation. Anderson-Cue Lake is itself a
perched lake. The lake level is the result of a balance
between precipitation, evaporation and outflow into
the water table aquifer and Floridan aquifer.

1 These data are not applicable to the research lake itself.




















































Figure IV-1. Location Map of Anderson-Cue and McCloud Lakes.







The vegetation in both lake basins is sparse and
primarily scrub oak, indicative of poor nutrient condi-
tions. There is no human habitation in either basin.
The major source of nutrients for the lakes in their
natural states appears to be from the atmosphere via
precipitation and air-borne particulates.

C. Instrumentation

A Gurley water level recorder with staff gage
and a recording rain gage were installed at Anderson-
Cue Lake in February, 1966.
In September, 1967, an Aerovane wind recorder
and Foxboro hygrothermograph were installed. The
transmitter for the wind recorder was mounted on a
pole approximately 150 ft. from the south shore of
the lake and three feet above the water surface.
Examples of some of the instrumentation are shown
in Figure IV-3.

D. Meterological and Hydrological Phenomena

Anderson-Cue Lake lies in a shallow valley
oriented in a NNE to SSW direction and is sur-
rounded by scrub oak and pine trees. These charac-
teristics have a marked effect on the air-flow over the
water surface. The air speed in general is calm to
light (0-7 mph). The prevailing winds are from 30
to 60 degrees (NNE to NE) and from 210 to 240
degrees (SSW to SW). When a tropical storm or


SCALE: Imm I 3 HOURS
Caln4-47 MPH
1-3MPH--= | >7 MPH
Figure IV-4. Wind Rose, Anderson-Cue Lake, October 1967 September 1968.


frontal system passes over or close to NE Florida
the wind direction is influenced by such phenomena
and higher wind speeds are recorded. A wind rose
for the period October, 1967 to September, 1968,
is shown in Figure IV-4.
The only significant currents in shallow Anderson-
Cue Lake are wind currents. In bodies of water larger
than Anderson-Cue Lake such surface water cur-
rents flow to the right of the wind and set up a clock-
wise circulation. In Anderson-Cue Lake, however,
these currents cause a pileup of water on the leeward
shore which is returned by fanouts in both clockwise
and counterclockwise directions.
Many factors must be considered in attempting
to explain the fluctuations of the lake level and the
water table in the research area. These include
evaporation, precipitation, and flow to the water table
aquifer and Floridan aquifer. The most complex of
these factors is evaporation. The question arises as to
what percent of time in a certain period was the vari-
ation of dewpoint temperature with height such as to
lead to condensation on or evaporation from the lake
surface. An inversion of the dewpoint will develop if
the surface acts as a heat sink to remove water vapor.
This condition can be expected during clear nights
when there is strong radiation from the ground; dur-
ing times of high relative humidity; and during times
of the build-up of surface inversions which occur fre-
quently in the Anderson-Cue Lake area. In fact,
about 50 percent of the time the water vapor flux is
directed downward. This reversal of evaporation is
evident during non-daylight hours when winds are
persistently less than 7 mph and relative humidity
greater than 90 percent.
It is interesting to note from the rain gage records
of the past year that during the periods when the
vapor flux is directed upward (generally from 0800
hr to 1800 hr) approximately 0.12 to 0.15 of an inch
of water is evaporated daily. This indicates the
large amount of evaporation from the lake surface
that can be expected unless the amount of precipi-
tation plus condensation received when the lake
acts as a heat sink can overcome the evaporation
losses and losses to the water-table aquifer and Flori-
dan aquifer.
Water in the water-table aquifer is unconfined
so that its surface is free to rise and fall with the
variance in rainfalPl as shown in Figure IV-5 (refer
to Figure IV-2 for location of test wells). Because
2 Rainfall on the Anderson-Cue Lake basin for the period
March 28, 1966, through June 30, 1968, was deficient by
13.85 in. (the closest "departure from normal" data are ac-
cumulated at Gainesville, approximately 20 miles to the
west). For the period November 2, 1967 through June 30,
1968 the deficiency was 8.56 inches.





























View of Anderson-Cue Lake Checking Rain Gage at Lake Site
Looking Northwest


Unloading Sewage Effluent Checking Hygrothermograph
into Storage Tank at Lake Site


Figure IV-3.


?







the piezometric surface of the Floridan aquifer is
below the level of the lake, water cannot move from
the Floridan aquifer to the lake. The net ground-
water flow during the period of study has been com-
posed only of outflow to the water-table aquifer and
to the Floridan aquifer, the greater flow being to the
water-table aquifer east of the lake. Note the level
of Well No. 4 in Figure IV-5.
As shown in Table IV-1, for the period March 28,
1966 through November 30, 1968, evaporation losses
exceeded rainfall by 8.35 in. and approximately 65
acre feet of lake water was lost to the aquifers.
For this period, the residence time of water in Ander-
son-Cue Lake has been calculated to be 5.43 years.
The same calculation holds true for McCloud Lake
(control) which rises and falls at the same time and
in the same proportion as Anderson-Cue Lake.


.11 N., > --------------
2
3

S- Fig. IV-2 F6, 11


Fig- IV-5. G-d oM S,,fm, Wte, L-1, -- Ademo-C,* Wke.













Dates
From 3/28/66
To 4/26/66
5/25/66
6/22/66
7/21/66
8/18/66
9/13/66
10/11/66
11/08/66
12/06/66
1/03/67
3/28/67
5/02/67
5/31/67
6/27/67
8/01/67
9/06/67
10/03/67
11/02/67
12/03/67
1/04/68
1/31/68
2/29/68
3/31/68
4/30/68
5/31/68
6/30/68
7/30/68
8/31/68
9/30/68
10/31/68
11/80/68


Table IV-1
ANDERSON-CUE LAKE
Hydrological Data
(1) (2)
Lake Evaporation Rainfall
(in.) (in.)


4.73
4.89
6.04
6.48
6.25
4.84
3.87
3.17
2.53
2.12
9.53
7.18
7.58
5.35
6.24
5.77
5.24
4.19
3.11
2.37
2.10
2.59
4.14
6.63
6.95
6.09
5.89
4.82
5.07
3.62
2.41

151.79


1.42
3.99
5.96
2.45
8.70
5.00
5.45
1.56
0.05
2.67
9.15
1.65
7.92
7.66
8.12
10.91
1.38
1.32
0.00
5.60
0.24
1.35
1.42
0.45
6.09
5.38
9.55
12.90
5.15
6.50
3.45

143.44


(3)
Lake Level
(ft.-MSL)
125.78
125.33
125.04
124.69
124.25
124.43
124.41
124.51
124.21
123.75
123.61
123.61
122.99
122.83
123.07
123.33
123.81
123.66
123.46
122.99
123.07
122.63
122.17
121.59
120.83
120.49
120.02
119.54
120.45
120.75
121.09
121.19

-(55.08 in.)


Summary: 151.79 (1) 55.08 (3)
-143.44 (2) 8.35

8.35 in. 46.73 in. or approximately 65 acre ft.

NOTE: Lake evaporation is computed from data collected at the U.S. Weather
Bureau evaporation station at Gainesville, Florida. Pan coefficients are
those used for Lake Okeechobee, Florida: Kohler, M.A., 1954, Lake and
Pan Evaporation in Water Loss Investigations-Lake Hefner Studies,
Technical Report: U. S. Geological Survey Prof. Paper 269, p. 128.






SECTION VI


BIOLOGY


Biological studies of Anderson-Cue and McCloud
Lakes have included: 1) the species, succession and
productivity of algal forms, 2) the standing stock of
phytoplankton, 3) limiting nutrients for phytoplank-
ton growth, 4) standing crop estimates of littoral
vegetation and 5) production estimates of the zoo-
planktivorous fish Labidesthes sicculus. In addition
monthly diurnal variations in primary productivity
have been noted along with plant pigment levels as
a measure of algal biomass. One special 10 day
study dealt with an analysis of environmental factors
affecting primary production (See section VIII).
The methods for these procedures are outlined in
Appendix B.
The variety and succession of algal forms have
been followed both along the marginal shallow bottom
and among the littoral vegetation of both lakes. Com-
parisons have been made with the kinds and numbers
in the open surface water and with those close to the
water-sediment interface at maximum depth. This
early work which began in 1965 was reported pre-
viously (Lackey & Lackey, 1967) and serves as a
baseline for future changes in the microbiota as
nutrient enrichment continues.
Both lakes are similar in many respects. For
example Synura uvella occurs in both lakes and typi-
cally is more abundant in the deep open water areas.
Many species of colorless Euglenophyceae are found
in the lakes and each body of water supports a bloom


of Dinobryon at least once during the late winter.
Both support a varied dinoflagellate flora. Generally
the low population of photosynthetic forms indicates
limited algal growth substances and the low numbers
of zooflagellata and ciliates as well as the paucity
of open water green euglenids indicates the low
organic content of the lake waters.
As the experimental lake continues to eutrophy
the greatest fluctuation in species will be those which
occur in very small numbers. A question to be ans-
wered is whether the crop of Dinobryon, Synura,
Peridinium umbonatum and Stentor amethystinus will
increase considerably as enrichment proceeds or
whether species now encountered infrequently will
increase and supplant the present common organisms.
Rhode as discussed in Hutchinson (1967b) has re-
ported the disappearance of Dinobryon with increas-
ing phosphorus levels. It will be interesting to note
the effect of rising phosphorus levels in Anderson-
Cue Lake on the indigenous Dinobryon population.
Blooms of these chrysophytes occur annually between
December and February and the frequency of these
will be followed during the next several years.
Data for plankton analysis are presented in
Appendix C. A summary of this phase of the re-
search showing the number of species and frequency
of occurrence is outlined in Table VI-1. Generally
the total species of each lake is similar. This is so
because nutrients have been added so that biotic


Table VI-1
GROUPS OF MICROSCOPIC ALGAE AND PROTOZOA IN DETAILED ANALYSES
ON FIVE DATES IN 1967-68 IN McCLOUD AND ANDERSON-CUE LAKES,
AND THE NUMBER OF OCCURRENCES.


Organism Group
Sulfur Bacteria
Blue Green Algae
Green Algae
Volvocales
Euglenophyceae
Cryptophyceae
Chrysophyceae
Chloromonadida
Dinoflagellata
Bacillarieae (Diatoms)
Rhizopoda
Zooflagellata
Ciliata
Totals


Total
Species
2
26
58
7
39
5
23
4
17
7
31
22
59
300


McCloud
No. No.
Species Occur.
1 3
24 23
48 117
7 13
23 40
5 18
23 33
2 6
17 36
5 8
21 40
18 23
42 82
236 472


Anderson-Cue
No. No.
Species Occur.
2 4
21 33
39 101
2 5
29 50
5 14
18 24
4 8
14 31
4 6
24 33
15 19
41 82
218 410







changes can occur gradually and it is still early to
note marked differences. Some species variation ob-
viously does exist between the two lakes, but pre-
sently we can not determine whether this is a reflec-
tion of nutrient enrichment. The plankton data as
presented should be considered as representative for
the soft, acid, sand bottom lakes found in north-central
Florida.
Appendix C reflects the percent occurrence of spe-
cies during 1967 and 1968. The data are qualitative
since population and biomass estimates were infre-
quently carried out. The extensive list of species
includes littoral as well as open water forms and
therefore forms not usually associated with the open
water plankton are included. A great many inshore
forms are part of the periphyton. This community
although an important part of the lake biota, often
is not considered in limnological investigations. How-
ever, the periphyton can be the most productive
plant community in a lake system as was observed
by Wetzel in the study of Borax Lake in California.
While enrichment of Anderson-Cue Lake has not re-
sulted in a divergent plankton population (when com-
pared to McCloud) the phytoplankton community has
become more productive in the experimental lake
since nutrient additions began in March of 1967. The
data are presented in Figure VI-1 and Tables VI-2,
VI-3 and cover the years 1967 and 1968. Productivity
in both lakes the first 5 months in 1967 and 4 months


1 2 3 4 5 6 7 8 9 10
Month
Figure V-1 Annual Variation of Pritoary Productivity in
Anderson-Cue and McCloud Lakes


i i 12


the following year was the least productive with inte-
gral photosynthesis below 100 mg C/m2-day. As might
be expected the period May through June supported
greatest phytoplankton growth reaching a maximum
in both years of 300 mg C/m2-day.
Certain comparisons can be made with other
nearby lakes in Alachua County which demonstrate
eutrophic conditions. Productivity determinations
were made on these lakes in November, 1968 using
an enclosed box with a constant light source at an
ambient temperature of 200 C.


Hawthorn
Newnan's
Bivens Arm
Orange
Wauberg
McCloud
Anderson-Cue
*mg C fixed/m3-hour


55.45*
53.55
77.54
43.01
124.30
21.83
11.52


These data indicate that both lakes in the experi-
mental system are less productive and that An-
derson-Cue will require much more nutrient enrich-
ment to reach a level comparable to other eutrophic
lakes in this area. Interestingly enough comparative
productivity of Lake Apopka and Lake Dora in Or-
ange and Lake Counties which are recognized hyper-
eutrophic lakes in control Florida is 400 mg/Cs-hr.
and 1000 mg C/m3-hr. respectively. Both of these
lakes support continuous algal blooms throughout the
year. In addition the lakes are virtually useless for
recreational purposes.
The comparative production estimates of lakes
provide information regarding the trophic status
which in turn can be useful in establishing recrea-
tional and use potential of surface water. This tech-
nique is especially worthwhile in multi-lake studies
where surface waters are being examined on a re-
gional basis. In this kind of investigation compara-
tive productivity estimates can quickly determine
lakes where algal growth is high and where ecosystem
management procedures could be used effectively.
Also productivity techniques offer an advance
over simple microscopic counts of phytoplankton.
Our Anderson-Cue-McCloud lake studies show no
significant changes in the microbiota after nearly
18 months. However, other biological parameters
such as plant pigment levels and productivity have
undergone significant changes. For example, the
annual production of Anderson-Cue Lake in 1967
was 30.41 grams of carbon/m2 while McCloud was
35.26 grams. An estimate at the end of 1968 (exclu-
sive of an anomalous result in August) amounted to


1968 Anderson-Cue
10 .-- McCloud
X)0
O0-
)0-
O0
0 J I=






Table VI-2


Date
1/24/67
1/31/67
2/28/67
3/14/67
3/28/67
4/11/67
4/25/67
5/9/67
5/23/67
6/6/67
6/20/67
7/7/67
7/19/67
8/1/67
9/12/67
9/26/67
10/11/67
11/28/67
1/9/68
2/21/68
3/19/68
4/29/68
5/16/68
6/18/68
7/23/68
8/27/68
9/16/68
10/14/68
11/11/68
12/11/68


VALUES FOR ANDERSON-CUE LAKE


*Values for March, 1968 and months following were obtained from diurnal data,
calculated by averaging incubation periods at each depth and multiplying by
hours of daylight.


54.63 grams and 36.93 grams of carbon/m2 for Ander-
son-Cue and McCloud lakes respectively. While the
control lake did not change significantly the experi-
mental lake increased 48 percent in primary producti-
vity.
Diurnal variations in integral photosynthesis were
recorded monthly during 1968. These data are pre-
sented in Figure VI-2. Rather obviously marked vari-
ations in integral photosynthesis occurred during 1968
in both lakes. Maximum productivity in Anderson-
Cue lake was noted in September when fixation of
carbon amounted to 65 mg/m2-hr. Highest photosyn-
thesis between the two lakes was observed in Mc-
Cloud. Fixation rates here reached 80 mg C/m2-hr.
at the end of August. Considering daily variations
maximum production most frequently occurred after
midday. Always during periods of rainfall primary
production was reduced, but increased with a cessa-
tion of rain and a return of adequate sunlight.
Production maxima in 1968 were commonly ob-
served in the sub-surface layers of both lakes and most


often occurred at the 5 foot layer. Since neither chlo-
rophyll a nor an increase in phytoplankton could be
correlated with this rise in productivity it appeared
likely that this phenomenon was associated with light
effects. This conclusion is strengthened by the fact
that maxima occurred only during a few hours of a di-
urnal period. The remainder of the time productivity
in the sub-surface water was less than at the surface.
An examination of the chlorophyll data indicated no
increase of plant pigment at the 5-foot depth. The
most logical explanation for the increased productiv-
ity of this layer appears at this time to be associated
with light effects.
The frequency in which productivity maxima oc-
curred in the midlayer varied with each lake. Gener-
ally two patterns could be observed: 1) a maximum
which occurred only once during a diurnal period and
2) maxima occurring twice over the daily period.
Single maxima within the metalimnetic layer occurred
most frequently in McCloud Lake and were observed
from June through November. By comparison, no


PRIMARY PRODUCTIVITY
mg C fixed/day-m3
Surface 5'
7.18 9.21
2.30 9.39
19.70 9.23
20.80 6.24
7.47 14.60
34.80 25.60
47.30 32.40
9.53 14.00
28.90 27.50
31.60 27.20
68.40 47.70
96.00 57.50
127.50 98.10
33.80 66.70
40.10 14.40
31.90 15.04
22.70 51.30
17.00 21.70
17.40 18.40
33.10 31.80
26.70* 15.52*
35.91 34.45
105.63 59.60
153.34 72.01
113.16 107.36
128.52 41.76
160.90 140.90
151.08 83.88
16.29 13.61
15.64 18.19


10'
12.30
9.70
6.91
13.24
5.90
21.80
22.00
7.33
8.62
23.70
10.30
42.80
70.30
33.50
6.80
35.60
43.50
15.80
6.93
48.50
4.47*
1.40
9.32
6.78
5.66
9.70
8.62
11.76
4.18
16.25


mg C fixed/day
per m2 Langleys/day
29.84 402
26.44 382
32.56
27.28 375
38.40 203
81.40 540
100.80 652
37.60 703
75.60 400
84.00 461
138.80 483
185.20 456
303.20 607
183.60 504
53.60 225
62.60 560
141.60 573
61.20 461
50.20 294
101.00 426
48.20 499
90.00 574
181.60 600
230.80 560
294.00 560
152.00 580
397.00 380
252.50 476
39.00 110
53.00 315






Table VI-3
PRIMARY PRODUCTIVITY VALUES FOR McCLOUD LAKE


C fixed/day-m3
5'


2/7/67
2/14/67
2/28/67
3/14/67
3/28/67
4/11/67
4/25/67
5/9/67
5/23/67
6/6/67
6/20/67
7/7/67
8/1/67
8/29/67
9/12/67
9/26/67
1/9/68
2/22/68
3/19/68
4/29/68
6/18/68
7/23/68
8/27/68
9/16/68
10/14/68
11/11/68
12/11/68


mg C fixed/day
per m2 Langleys/day


8.28
3.27
13.80
5.21
24.92
24.90
2.00
10.50
30.86
59.50
59.56
32.69
68.03
6.49
53.78
109.59
7.70
2.10
3.14*
23.14
99.12
76.73
282.26
142.10
57.48
23.48
10.99


15.50
3.53
13.90
16.34
13.64
30.16
13.26
17.34
31.33
43.72
96.23
49.90
45.48
14.43
29.33
150.57
18.44
5.60
3.99*
22.21
61.56
54.92
138.48
113.12
47.16
24.98
11.04


7.14
3.77
7.07
13.17
36.62
23.33
6.37
23.58
22.44
67.20
169.06
37.00
19.07
7.55
13.21
37.41
15.10
4.81
3.35*
15.30
28.38
32.98
64.78
69.35
30.00
12.01
8.18


*Values for March, 1968 and months f
calculated by averaging incubation
hours of daylight.

uni-maxima occurred in Anderson-Cue Lake during
this same time. The experimental lake showed single
maxima only in February, March and November.
In July, September and November, sub-surface max-
ima occurred in McCloud during the afternoon. All
maxima in Anderson-Cue were observed before mid-
day. On 3 occasions (April, July and September)
productivity maxima occurred in subsurface water
twice during a diurnal period in Anderson-Cue Lake.
Maxima occurred at the 5 foot layer and were ob-
served once before mid-day and again during the late
afternoon period. The pattern was repeated in Mc-
Cloud Lake where maxima occurred morning and
afternoon.
As eutrophication of Anderson-Cue proceeds max-
ima within the metalimnetic layers may be expected
with increasing frequency. Findenegg (1964) made
observations on productivity patterns through the
water column in Alpine Lakes and observed the in-
creasing frequency of metalimnetic maxima as lakes
became more eutrophic. In general, he predicted a


following were obtained from diurnal data,
periods at each depth and multiplying by


shift in the shape of the curve from an orthograde
type to one where maxima in the subsurface water
are observed more frequently as the lake becomes
more enriched with nutrient substances.
Limiting nutrients for phytoplankton growth were
determined essentially using Goldman's technique
(1964a, 1965) for bioassay. Many results using this
method are varied and interpretation is difficult to
make. Generally three qualitative judgements can be
made. A particular substance may be limiting to plant
growth, result in no change or elicit an inhibitory re-
sponse. The results of these bioassays are shown in
Table VI-4. Considering Anderson-Cue lake phos-
phorus limited primary production 92 percent of the
time during 1967 trials. The phytoplankton did not
respond to nitrogen additions indicating that this
substance was not a limiting growth factor.
Verification of the nitrogen experiments has been
carried out using a laboratory technique proposed by
Fitzgerald (1968). The procedure essentially em-
ploys an exposure of ammonia nitrogen to a popula-


Date


41.10
10.72
39.36
39.20
55.00
87.30
33.28
52.80
92.00
151.80
312.00
139.00
137.00
37.64
94.60
392.00
50.40
15.64
11.54
64.20
192.00
168.80
460.00
337.00
139.00
70.30
32.00


mg
Surface


278
452

375
203
540
652
703
400
461
483
456
504
575
225
560
294
320
499
574
560
560
580
380
476
110
315







1/23 2/27





15- 15-


| 10- 10 -

5- 5

244 Langleys 0 449 Langleys (4


IIIII I I I I I
0800 1000 1200 1400 1600 0900 1100 1300 1500 1700








3/19 4/29





S 6 15-
-C

. 4 10 -



0-

499 Langleys O 574 Langleys


0900 1100 1300 1500 1700 1900 1000 1200 1400 1600 1800
Time EST Time EST

Anderson-Cue Q Clear
EST Eastern Standard Time A
EDT Eastern Daylight Time - McCloud O Overcast


Figure VI-2. Diurnal Variations in Primary Production During 1968.







5/16


I
30



co
S20

o>
E 10


0


0900 1100 1300


1500 1700


U 560 Langleys


1000 1200 1400 1600 1800


7/23


- .~.


560 Langleys

I I I


0900 1100 1300

Time


I I I


1500 1700

EDT


1900


8/27


/








580 Lang leys
/ \

/\






580 Langleys (I


1100


Time EDT


Figure VI-2. (Continued)


600 Langleys )

I I


40


30


S20

E 10


0


--- \


I ..


6/18









10/14


60


c, 50


40
-no
0)
x
S30
U
0)
E
20


10
















c- 8


46

5 4

E
2


I I


0900 1100 1300 1500 1700 1900

Time EDT


11/11


110 Langleys ()


I I I I I
0800 1000 1200 1400 1600

Time EST


476 Langleys

I I I I I
0900 1100 1300 1500 1700 1900

Time EDT






12/11


-- --0-


315 Langleys 0



0900 1100 1300 1500 1700

Time EST


Figure VI-2. (Continued)


380 Langleys
S I I


9/16







Table VI-4
ALGAL GROWTH RESPONSE TO NUTRIENTS


Anderson-Cue Lake 1967
1/4 1/24 3/14 3/28 4/11 5/23 6/6 6/20 8/1 8/29 9/12
N X X -
P + + + + + + + + + +
N,P + + + + +
Fe X + X X X -
Si X + X X X
S x x x x
Vitamins X -
Trace
Metals X X X -
EDTA X X -


McCloud Lake 1967
2/14 3/14 3/28 4/11 6/6 6/20 8/1 8/29 9/12
N X -
P + X X X + +
N,P + X + -
Fe + + X X
Si + + + X +
S + + + X + +
Vitamins X + +
Trace
Metals + + + X + +
EDTA + + + X X +

+ = Limiting
X = Inhibitory
- = No Change


Table VI-5
AMMONIA UPTAKE BY ALGAE IN ANDERSON-CUE WATER


Time (min.)


0
+15
+30
+45
+60
+90


Chlorella1


1.24*
1.30
1.35
1.36
1.88


Filamentous
Chlorophyceae2

0.83*
0.81
0.84
0.92
0.89
0.97


1 laboratory stock culture
2 filamentous green algae
probably Mougeotia collected in
Anderson-Cue Lake.
* NH,-N mg/1






tion of organisms and ammonia uptake is followed
over a set time period. The results from this proced-
ure (Table VI-5) showed that the natural plankton
from Anderson-Cue lake were not nitrogen deficient.
These data reflect the rising ammonia levels in the ex-
perimental lake as nutrient enrichment progresses
(see Section V).
The reasons why phosphorus concentrations in the
water do not reflect those added to the lake are ob-
scure. Certainly the supply is insufficient for the op-
timum growth of much of the phytoplankton. Phos-
phorus loss may be to the sediments although we have
been unable to determine this as yet.
Substances other than N and P appear to be limit-
ing during various times of the year. However, the
difficulties encountered with this particular method
make refined judgements questionable. A reliable
standardized algal growth potential procedure is
badly needed at this time for all those doing eutrophi-
cation research.
Figure VI-3 shows the results of phosphorus addi-
tion to natural phytoplankton in Anderson-Cue water
over a period of 120 hours. The experiment was car-
ried out to determine lag time in phosphorus uptake


- Anderson-Cue
SMCloud


S' Oct. '68


2 4 4 8 72 6 120
Hours
Figure VI-3. Carbon 14 Fixation by Phytoplankton Stimulated with
100,-g. Phosphorus.

by phytoplankton. It may be seen that maximum
stimulation of algae by phosphorus as detected by
labeled C-14 carbonate fixation occurs in 72 hours.
This particular time dependent bioassay method is
very useful as a tool which can be used to follow the
enrichment of single lakes by observing the change in
slope of the response line at successive time periods.
In addition comparative relationships among lakes


within a region can be made. As can be seen from
Figure VI-3 the greatest demand for phosphorus
occurs during the summer in Anderson-Cue lake and
decreases considerably during the fall when biological
activity is lower. Uptake of P in McCloud lake is
lower than Anderson-Cue and probably is due to a
lower biomass of plankton organisms. Maximum up-
take by organisms in the control lake occurs in 48
hours.
Goldman (1965) has noted photosynthetic stimu-
lation from the addition of various compounds and
elements in trace quantities. Lakes showing micronu-
trient deficiencies frequently can be found and such
substances as vitamins and various nations doubtless
play an important role in algal growth cycles. Sources
of these materials are provided to lakes by 1) tribu-
tary streams, 2) runoff from land, or 3) interchange
from sediments. Lakes near urban centers could very
well receive some micronutrients from rain falling
through polluted air.
Bioassay data on the two lakes show that nations
limited algal photosynthesis in trials carried out on
McCloud Lake. No similar pattern was observed in
Anderson-Cue Lake during the same period. Two
times during the study vitamin additions stimulated
algal growth in McCloud Lake with no correlative re-
sponse in Anderson-Cue water. It seems unlikely that
differences between the two lakes could be attribu-
table to variation in nutrient loading since enriched
sewage effluent was not added to Anderson-Cue Lake
until March, 1967. Therefore, at the present time dif-
ferences in algal growth response to micronutrients
appear to be inherent between the two lakes.
Variations in the vertical distribution of chlorophyll
a between McCloud and Anderson-Cue Lake are pre-
sented in Table VI-6, VI-7. The data are inclusive
beginning in January of 1967. Generally the mean
value for chlorophyll a at the surface, 5 and 10 foot
depths in Anderson-Cue Lake (1.99, 2.00, 2.12
mg/ml) were comparable to McCloud Lake (1.53,
1.89, 1.46 Mg/M3) during the first year. The standing
crop of phytoplankton increased significantly during
1968 in Anderson-Cue water, but remained virtually
the same as before in McCloud. Average values of
4.12, 4.12, and 4.41 mg cbl al M3 were noted in Ander-
son-Cue Lake at the 3 depths while McCloud Lake
supported an average of 2.14, 2.02, 2.24 mg chl a/M3.
Considering seasonal effects and based on integral
values chlorophyll a during 1967 gradually increased
from a range of 1-5 Mg/M2 during the first 4 months to
levels greater than 15 Mg/M2 in Anderson-Cue during
midsummer and fall, (Figure VI-4). Both lakes had
approximately the same phytoplankton biomass based
on pigment analyses except for peaks during July and







Table VI-6
CHLOROPHYL a LEVELS (mg/m3) in ANDERSON-CUE AND McCLOUD LAKES
DURING 1967


Date


1/17/67
1/24/67
1/31/67
2/7/67
2/14/67
2/28/67
3/14/67
3/28/67
4/11/67
4/25/67
5/9/67
5/23/67
6/6/67
6/20/67
7/7/67
7/19/67
8/1/67
8/29/67
9/12/67
9/26/67
10/11/67
Average


Surf


1.09
1.08
1.09
0.54

0.83
0.25
1.38
2.44
1.77
2.91
1.92
1.76
2.03
2.86
4.20
1.70
3.15
3.02
4.22
1.64
1.99


Anderson-Cue
5'


0.54
0.25
1.38
1.12

1.12
1.38
1.38
1.10
1.67
2.57
1.83
1.87
1.45
1.02
5.73
1.45
4.22
2.80
4.45
2.77
2.00


0.83
0.25
1.12
1.12

1.08
1.60
1.37
1.34
1.57
2.55
1.67
1.97
1.89
2.03
5.64
1.36
2.57
2.80
7.14
2.55
2.12


Surf


0.54
0.54
0.67
0.80
1.60

0.66
1.55
2.43
2.48
1.97
1.78
1.47
2.38
0.33
1.48
3.15
2.20

1.53


McCloud
5'


0.25
0.54
0.83
1.84
1.34

0.99
0.99
1.90
3.18
1.96
1.98
2.03
2.70
2.57
3.39
3.41
2.20

1.89


-
0.25
0.83
0.80
1.08
0.54

0.57
1.12
1.90
2.56
1.86
2.32
0.68
1.49
1.59
2.48
2.81
1.99

1.46


Table VI-7
CHLOROPHYLL a LEVELS (mg/m') IN ANDERSON-CUE AND McCLOUD LAKES
DURING 1968


Surf
2.57
7.74
3.00
4.62
5.07
3.57
5.50
4.60
4.18
4.56
2.07
1.92
4.12


Anderson-Cue
5'


0.77
8.77
3.75
3.27
5.06
3.24
7.13
4.66
4.05
4.95
1.90
1.92
4.12


*Diurnals Chlorophyll a values are an average of 5 samples per day


October in Anderson-Cue Lake. At these times chlo-
rophyll in the experimental lake reached a maximum
in excess of 15 mg chl a/m2.
The 1968 data show the divergent characteristics


of the two lakes with regard to pigment levels. Chlo-
rophyll a was 5 mg/m2 or higher at all times during
the year. Three peaks were evident. The highest
(>25 mg/m2) occurred in February during a winter


McCloud
5'


Date


1/9/68
2/21/68
3/19/68*
4/29/68*
5/16/68*
6/18/68*
7/23/68*
8/27/68*
9/16/68*
10/14/68*
11/11/68*
12/11/68*
Average


4.47
8.41
5.42
2.56
4.06
3.70
6.97
3.73
4.39
5.50
1.87
1.85
4.41


Surf
1.23
0.77
0.68
0.91

1.77
2.18
5.66
2.62
2.47
3.30
1.98
2.14


0.10
1.12
0.73
0.84

1.68
2.17
5.30
2.33
2.82
3.02
2.07
2.02


1.12
0.86
1.01

1.93
2.34
4.81
2.60
2.69
2.85
2.14
2.24






flowering of Dinobryon. The others as indicated in
Figure VI-4 were observed in May and late July. The
spring bloom coincided with growths of Synura.


I 2 3 4 5 6
Month


7 8 l10 i


Figure VI-4. Annual Variation of Chlorophyll a in Anderson-Cue
and McCloud Lakes.

It is worthwhile noting the significant algal growth
response to added nutrients as evidenced in the ele-
vated chlorophyll values. Most frequently chlorophyll
a was greater than 10 mg/m2. These data support
productivity data in that these two parameters show
the response to nutrient influx far more rapidly than
changes in species diversity.
McCloud Lake during the last half of 1968 had an
increased phytoplankton biomass reaching a peak in
late August. During the last two years the control
lake always supported a maximum population of phy-
toplankton during August and September.
Horizontal chlorophyll a distribution in Anderson-
Cue Lake was determined 4 times in 1968 from Feb-
ruary to the end of May. These measurements were
made only in surface water. No correlative production
estimates were completed. As indicated in Figure VI-
5 phytoplankton was unevenly distributed in surface
water on all sampling periods. Chlorophyll levels
were always highest in the near-shore environment
ranging from 6 to greater than 15 mg/m3. On three
occasions these occurred in the same extreme southern
part of the lake. Causes for the plankton patchiness
are likely a combination of wind effects and local
areas of enrichment especially in the shallow water


where animals visit for drinking. Nutrient regenera-
tion from sediments also play a contributary, but as
yet undetermined role.
Two experiments for coliform distribution were
completed in an attempt to trace animal movement in
the shallow water along the lake margin. Table VI-8
presents the results of this study. Samples collected
in January and July of 1968 showed that fecal coli-
forms were generally distributed in surface water
throughout the lake. No conclusive information could
be obtained relating to movement of livestock or other
animals although fecal coliforms were present in ex-
cess of 2.2/100 ml in 18 of 23 shore stations. Water
containing the greatest fecal coliform concentration
occurred at station 28 and 32 (Figure VI-6). Both of
these were shore stations. Fecal coliforms in the open
lake water in the southern portion of the lake likely
originated from the outfall.
Peripheral plant growth with respect to dry plant
weight has remained constant at both lakes. Since
this area experienced an extended drought during the
winter and early spring months of the past year both
lakes have dropped considerably, exposing former
submerged stations and those stations out of water
were not cut in December. A second problem en-
countered was that of migrant cows feeding on these
areas. Adequate fencing now prevents this from oc-
curring. Table VI-9 contains the results of this part
of the project. As might be expected plant growth
was greatest during the spring and summer months
and least in the fall and winter. Average dry weight
per square meter of littoral growth in Anderson-Cue
Lake amounted to 50 grams which represented an in-
crease from 1967 of 20 grams/m2. Nutrients added to
the lake expectedly would enhance the growth of mar-
ginal vegetation, but it is too soon to say that the 1968
data reflect the influence of induced eutrophication.
The research bearing on the determination of secon-
dary and tertiary trophic levels in both lakes has in-
volved work with zooplankton and the planktivorous
brook silverside (Labidesthes sicculus). This fish is a
significant element in the ecosystem of both Anderson-
Cue and McCloud Lakes feeding chiefly in pelagic
areas on zooplankton and small insects. It is very se-
lective in what it eats, the size of the prey being defi-
nitely correlated with fish size. Juvenile Labidesthes
feed chiefly on rotifers and copepod nauplii while
larger individuals eat cladocera, adult copepods and
occasional small insects. (Table VI-11 represents a
partial spectrum of zooplankton ingested by Labides-
thes based on examination of gut contents). Labides-
thes reproduce in spring and early summer and few
individuals live longer than a year, so the food re-
quirements of the population will change from sum-


1968 Anderson-Cue
?5 - - McCloud


5
0 -

5 -








2/15/68
S>1 Omg/M3
El 5-1 Omg/M3
g 0-5mg/M3


3/12/68
MH >10.6mg/M3
1I 9.5-10.5mg/M3
[_ 8.1-9.4mg/M3
IH 7.0-8.0mg/M3


4/9/68


E >15mg/Mj 5/29/68
10.1-15mg/M3
4.1-10mg/M3 4
0-4mg/M3 3














Figure VI-5. Horizontal Distribution of Chlorophyll a
Anderson-Cue Lake.


.5-6.2mg/M3
-4.5mg/M3


E
[

i













Table VI-8
BACTERIOLOGICAL DATA


1/23/68
MPN
Station Coliforms
1 8.0
2 23.0
3 2.0
4 5.0
5 5.0
6 49.0
7 5.0
8 5.0
9 5.0
10 2.0
11 <2.0
12 2.0
13 <2.0
14 5.0
15 <2.0
16 2.0
17 <2.0
18 8.0
19 <2.0
20 2.0
21 2.0
22 <2.0
23 49
24 2.0
25 2.0
26 2.0
27 <2.0
28 >1609
29 8.0


7/8/68
MPN
Enterococci


7/8/68
Total
SPC


1/23/68
MPN
Fecal
Coliforms
<2.0
<2.0
2.0
2.0
<2.0
4.0
<2.0
5.0
5.0
<2.0
<2.0
<2.0
<2.0
<2.0
<2.0
2.0
<2.0
5.0
<2.0
<2.0
2.0
<2.0
14
2.0
<2.0
<2.0
<2.0
>1609
<2.0


1/23/68
MPN
Enterococci


2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2


1/23/68
Total
SPC


1500
130
230
180
150
140
600
140
100
180
120
360
100
790
83
140
130
150
120
76
87
50
870
610
338
83
168
340
360


7/8/68
MPN
Coliforms


7.0
<2.0
9.2
5.1
<2.2
13.0
<2.2
9.2
5.1
5.1
5.1
2.0
9.2
9.2
2.2
17
2.2
2.2
<2.2
9.2
2.2
2.2
5.0


7/8/68
MPN
Fecal
Coliforms


7.0
<2.0
9.2
2.2
<2.2
13.0
2.2
9.2
2.2
2.2
5.1
2.0
2.2
9.2
2.2
7.0
<2.2
<2.2
<2.2
5.1
2.2
2.2
5.0


<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2










Table VI-8 (cont.)


1/23/68
Total
SPC


1500
24
170
170
68
65
69
52
63
110
84
27
65
300
600
610
830
21
140
17
110


Station


1/23/68
MPN
Coliforms


1/23/68
1 MPN
Fecal
Coliforms


2.0
<2.0
918
5.0
33.0
<2.0
2.0
<2.0
2.0
<2.0
<2.0
<2.0
11.0
23.0
<2.0
5.0
2.0
<2.0
<2.0
<2.0
<2.0


2.0
2.0
918
33.0
79.0
4.0
7.0
<2.0
2.0
2.0
2.0
2.0
17.0
70.0
13.0
8.0
8.0
<2.0
<2.0
<2.0
2.0


1/23/68
MPN
Enterococci


5.0
<2.2
23.0
2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
13.0
<2.2
13.0
<2.0
<2.0
<2.0
<2.2
<2.2


7/8/68
MPN
Coliforms


16.0
5.0
2.0
9.0
7.0
17.0
5.0
5.0
2.0
5.0
11.0
8.0
2.0
5.0

14.0

4.0
8.0
2.0
7.0


7/8/68
MPN
Fecal
Coliforms


2.2
<2.0
<2.0
9.0
7.0
13.0
5.0
5.0
2.0
2.0
11.0
5.0
<2.0
2.0

8.0

<2.0
2.0
<2.0
<2.0


7/8/68
MPN
Enterococci


<2.2
<2.2
<2.2
<2.2
2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2
<2.2

<2.2

<2.2
<2.2
<2.2
<2.2


7/8/68
Total
SPC


95
90
240
250
170
130
130
75
120
130
130
600
350
600

1000

160
320
190
110








Table VI-9
STANDING CROP ESTIMATES OF ANDERSON-CUE AND McCLOUD LAKES


Plant Plant %
Station Dry Wt. (g) Ash Wt. (g) Organic Matter Organic Matter
March May, 1967
Anderson-Cue
1 17.5749 4.1882 18.4367 76.4
3 12.2894 0.8118 11.4776 98.4
5 18.9235 4.4170 14.5065 76.7
6 44.8655 5.2483 39.1172 88.2
7 5.2778 1.3142 3.9631 75.1
McCloud
1 11.9673 0.7494 11.2179 93.7
2 55.4863 7.7995 47.6368 85.9
8 81.0448 1.6050 29.4393 94.8

June August, 1967
Anderson-Cue
1 36.6874 3.9242 32.7632 89.3
2 22.0299 2.0512 19.9787 90.7
8 19.3535 2.5064 16.8471 87.0
5 105.7579 6.6955 99.0624 93.7
7 89.8170 2.3864 37.4306 94.0
McCloud
1 21.8215 2.8341 18.4874 86.7
2 27.0365 1.2425 25.7940 95.4
3 50.8171 3.9842 46.8329 92.0

October December, 1967
Anderson-Cue
1 0.6651 0.0773 0.5878 88.4
2 5.0863 0.7867 4.2496 84.4
3 7.7053 2.0148 5.6905 73.8
6 10.1398 2.0935 8.0463 79.4
McCloud
1 3.6714 0.6179 3.0585 83.2

January March, 1968
Anderson-Cue
1 1.3599 0.2388 1.1211 82.4
2 2.8250 0.6043 2.2207 78.6
3 2.0906 0.2597 1.8309 87.6
5 5.9957 1.0297 4.9662 82.8
6 0.7454 0.1124 0.6330 84.9
7 2.2399 0.2808 1.9591 87.5

January March, 1968
McCloud
1 1.7174 0.8277 1.3897 80.9
2 1.9834 0.1669 1.8165 91.6
8 1.2515 0.1402 1.1113 88.8

April June, 1968
Anderson-Cue
1 44.5779 9.0419 85.5360 79.7
2 24.8772 6.4809 18.3963 74.0
3 21.4956 4.8200 16.6756 77.6
5 83.6573 9.7295 73.9278 88.4
6 36.9836 12.0098 24.9738 67.5
7 38.1837 13.8765 24.3072 63.7
McCloud
1 43.7322 5.9879 37.7443 86.8
2 23.5226 3.7630 19.7596 84.0
3 41.6614 8.6337 33.0277 79.3



Summary March 1967 March 1968
Anderson-Cue
1 56.2873 8.3785 47.9088 85.1
2 29.8912 8.4422 26.4490 88.5
3 41.4388 5.5927 35.8461 86.5
5 130.6771 12.1422 118.5351 90.7
6 55.2507 7.4542 47.7965 86.5
7 47.3342 3.9814 43.3528 91.6
McCloud
1 38.6776 4.5291 34.1485 88.83
2 84.4562 9.2089 75.2473 89.1
8 83.1129 5.7294 77.3835 93.1







mer to winter as the fish grow. Thus the food avail-
ability is related not so much to the total biomass of
zooplankton as to the biomass of acceptable food items
during a given season. The delicate balance involved
will be expected to change as the lake becomes more
eutrophic.
Zooplankton samples have been collected biweekly
with a tow net and microscopic counts have been
made according to Standard Methods for the Exami-
nation of Water & Wastewater (1965). Individuals
of each important species are picked out under a dis-
secting microscope, dried in a vacuum desiccator and
weighed on a calibrated quartz helix (Table VI-10).
In order to get an adequate deflection of the helix, 0
groups of 5-200 individuals are weighed at a time and
an average mass calculated. Average mass per indi- 0
vidual can be multiplied by number per square meter
of lake surface to obtain the biomass of the orga-
nism in gm/m2. Caloric values for species of zoo-
plankters is determined using a microbomb calori-
meter. Thus several years of biweekly data on zoo-
plankton populations can be readily converted into
biomass or energy content.


Table VI-10


DETERMINATION OF ZOOPLANKTON WEIGHTS
McCLOUD LAKES


0 0
3 11


0
0 9
0


Figure VI-6. Bacteriological Sam~pling Stations.


IN ANDERSON-CUE AND


Range
Organism No. of Dry Weight Avg. Dry Weight
Determinations Total Individuals (micrograms) (micrograms)
Keratella 4 320 0.036-0.053 0.0435
Larval Copepods 2 200 0.145-0.148 0.146
Tropocyclops 5 125 0.371-0.608 0.503
Bosmina 5 97 0.78-1.38 1.182
Diaphanosoma 6 110 0.872-1.82 1.423
Diaptomus 10 100 3.11-4.53 3.603
Daphnia 7 70 1.49-5.77 3.922
Holopedium 2 18 4.5-6.6 5.549
Mesocyclops 10 95 4.81-10.8 8.47
Chaoborus 5 35 14.7-84.6 45.6


Weights in this table were obtained by weighing groups of from 5-200 individuals of each species on a calibrated
quartz helix.


Not all data are as yet available for conversion of
zooplankton numbers to biomass. The accompanying
Tables VI-12 and VI-13 showing the results from Au-
gust 1967 and 1968 arc examples of the type of infor-


mation which will be available for each month of the
investigation.
Populations of many zooplankton species have
shown tremendous fluctuations over the time period







Table VI-11
FOOD OF LABIDESTHES SICCULUS


Standard length 19 mm


Gut Contents Number Weight (pg)
Bosmina 19 22.5
Tropocyclops 7 3.5
Larval Copepods 11 1.6
Chydorus 4 4.8


Labidesthes
Gut Contents


Standard length 29 mm
Number


Weight (/,g)


Bosmina 30 35.5
Small Cyclops 113 56.8
Larval Copepods 27 3.9
Chydorus 2 2.4
Diaptomus 1 3.6
Diptera 1 45.0


Standard length 37 mm


Gut Contents Number Weight (tkg)
Bosmina 14 16.5
Small Cyclops 30 15.1
Larval Copepods 3 0.4
Diaptomus 14 50.4
Daphnia 8 31.4
Diaphanosoma 1 1.4


of the study. Some of these fluctuations are seasonal
in nature or weather-induced but others may be cor-
related with changing physio-chemical parameters of
the lake. Definite trends caused by increased enrich-
ment have not yet been established.


Labidesthes


Approximate dry weight 49 mg.


Total weight (dry)
of food.
115 ug


The change in L. sicculus population in Anderson-
Cue Lake has been very spectacular. The popula-
tion has dropped from an estimated 130,000 in No-
vember, 1967, to virtually zero at present. The Mc-
Cloud population has shown no corresponding decline.


Table VI-12
ZOOPLANKTON DENSITIES AND CALCULATED BIOMASS FOR AUGUST 1967

Anderson-Cue Lake
3 Aug. 11 Aug. 26 Aug.
No/m2 mg/m2 No/m2 mg/m2 No/m2 mg/m2
Tropocyclops 4,715 2.372 2,160 1.086 19,742 9.930
Mesocyclops 3,169 26.841 1,452 12.298 13,271 112.405
Diaptomus 11,038 39.770 5,179 18.660 22,484 81.010
Larval Copepods 18,987 2.772 27,836 4.064 74,147 10.825
Bosmina 1,227 1.450 5,706 6.744 18,050 21.335
Daphnia 3,156 12.378 3,876 15.202 761 2.984
Diaphanosoma 879 1.251 0 0.0 0 0.0
Holopedium 0 0.0 0 0.0 0 0.0
Keratella 9,922 0.432 9,065 0.394 26,699 1.161
Total 87.266 58.448 239.650
Avg. Biomass (mg/m2) 128.45


Approximate dry weight 10 mg.


Total weight (dry)
of food.
32.4 jg


Approximate dry weight 23 mg.


Total weight (dry)
of food.
147 Ag


Labidesthes







Table VI-12 (Continued)
McCloud Lake
3 Aug. 11 Aug. 26 Aug.
No/mr2 mg/m2 No/m2 mg/m2 No/m2 mg/m2
Cyclops (sp. 1) 5,625 2.829 5,371 2.702
Cyclops (sp. 2) 3,781 32.025 3,611 30.585
Diaptomus 9,628 34.690 8,713 31.393
Larval Copepods 36,030 5.260 47,800 6.979
Bosmina 6,503 7.688 10,540 12.458
Daphnia 105 0.412 79 0.310
Diaphanosoma 367 0.522 26 0.037
Holopedium 0 0.0 0 0.0
Keratella 41,370 1.800 66,190 2.879
Total 85.226 87.343
Avg. Biomass (mg/m2) 86.28



Table VI-13
ZOOPLANKTON DENSITIES AND CALCULATED BIOMASS FOR AUGUST 1968

Anderson-Cue Lake
12 Aug. 19 Aug. 26 Aug.
No/m2 mg/m2 No/m2 mg/m2 No/m2 mg/m2
Cyclops (sp. 1) 24,200 12.173 34,496 17.351 43,847 22.055
Cyclops (sp. 2) 7,991 67.684 16,666 141.161 44.385 375.941
Diaptomus 144,514 520.684 92,690 333.962 291,058 1048.682
Larval Copepods 192,000 28.032 90,635 13.233 194,756 28.434
Bosmina 1,142 1.350 685 0.810 7,801 9.221
Daphnia 228 0.894 228 0.894 807 3.165
Diaphanosoma 0 0.0 228 0.324 4,035 5.742
Holopedium 0 0.0 0 0.0 0 0.0
Keratella 58,901 2.562 42,464 1.847 538 0.023
Total 633.379 509.583 1493,263
Avg. Biomass 878.74 mg/m2



12 Aug. 19 Aug. 26 Aug.
McCloud Lake No/m2 mg/m2 No/m2 mg/m2 No/m2 mg/m2
Cyclops (sp. 1) 36,528 18.374 23,743 11.943 27,976 14.072
Cyclops (sp. 2) 11,187 94.754 19,405 164.360 28,514 241.514
Diaptomus 25,570 92.129 34,702 125.031 91,998 331.469
Larval Copepods 86,982 12.699 101,137 14.766 80,162 11.704
Bosmina 27,168 32.113 24,656 29.143 112,980 135.542
Daphnia 1,142 4.479 0 0.0 0 0.0
Diaphanosoma 12,785 18.193 20,775 29.563 35,625 50.694
Holopedium 228 1.265 0 0.0 0 0.0
Keratella 2,511 0.109 1,826 0.079 0 0.0
Total 633.379 509.583 1493.263
Avg. Biomass 478.00 mg/m2







L. sicculus has previously been shown to be very
susceptible to high turbidities often accompanying
pollution, but Anderson-Cue turbidities have not risen
nearly as high as those in some local lakes where L.
sicculus is abundant. Possibly the decline was caused
by the extremely low water level and concomitant lack
of littoral vegetation suitable for spawning.
Table VI-14 contains a partial faunal list from the
study lakes. This information was provided by Paul


and Karolyn Maslin whose research dealing with sec-
ondary and tertiary production levels will form the
basis for a dissertation and a master's thesis.* Inves-
tigations of this kind are very important as virtually no
information is available for the southeast U.S. regard-
ing zooplankton species diversity, production rates
and changes in species composition as influenced by
the eutrophication process.
completion in August, 1969


Table VI-14
A PARTIAL FAUNAL LIST FROM LAKES ANDERSON-CUE AND McCLOUD


FISHES:
Chaenobryttus gulosus
Etheostoma fusiforme
Fundulus chrysotus*
Fundulus lineolatus*
Gambusia affinis
LIMNETIC CLADOCTERA:
Bosmina coregoni
Daphnia ambigua
LITTORAL CLADOCERA:
Acroperus harpae
Acantholeberis curvirostris
Alona affinis
A. costata
A. guttata
A. quadrangularis
Alonella globulosa
Anchistropus minor*
Camptocercus rectirostris
Ceriodaphnia pulchella
Chydorus bicornutus*
C. piger
C. sphaericus
*indicates species found only in Lake McCloud
LIMNETIC COPEPODS:
Diaptomus floridanus
Cyclops bicuspidatus
Tropolyclops prasinus
LIMNETIC ROTIFERS:
Conochiloides sp.
Conochilus sp.
Keratella americana (?)
K. taurocephala
Pedalia sp.
Polyarthra sp.


Heterandria formosa
Labidesthes sicculus
Lepomis macrochirus
Micropterus salmoides



Diaphanosoma brachyurum
Holopedium amazonicum


Eurycercus lamellatus*
Graptoleberis testudinata
Ilyocryptus spinifer
Macrothrix rosea
Monospilus dispar
Pleuroxus sp. (Hastatus?)
Simocephalus expinosus*


LITTORAL


COPEPODS:
Cyclops exilis
Eucyclops speratus


LITTORAL ROTIFERS:
Brachionus sp.
Keratella serrulata
Lecane spp.
Macrochaetus sp.
Monommata sp.
Monostyla sp.
Trichocerca spp.







SECTION VII


MODELS OF THE EUTROPHICATION PROCESS


It should be apparent from previous sections that
the problem of eutrophication is vastly complicated.
The natural and human factors in lacustrine nutrient
enrichment are multitudinous; environmental para-
meters affecting trophic state are beyond simple de-
scription. In addition, the eutrophication problem has
ramifications beyond the purely technical. In lake
management and eutrophication control and allevia-
tion, socioeconomic considerations become involved.
In effect, the eutrophication problem is not one but
many, and in order to solve it the efforts of many dis-
ciplines must be utilized. It is difficult to imagine so-
lution of the problem and its attendant ramifications
without some unifying and simplifying methodologies.
A group of such techniques has been developed in re-
cent years and is commonly referred to as systems ana-
lysis or operations research. This method, a new con-
cept for solving complex problems, relies heavily upon
advanced mathematical procedures and especially on
computer assisted solutions. One of the fundamental
principles of systems analysis is that complex prob-
lems or systems consisting of many variables can be
divided into a number of simpler components. each
containing few variables. The small components are
more amenable to detailed analysis. Solutions to the
overall problem can be attained in essence by inte-
grating the partial solutions. A second principle of
systems analysis is the concept of the model. A model
is simply an approximation of the real system. It con-
sists of the essential system elements and the variables
which affect their interactions. Quantitative system
models are constructed from various types of determi-
nate and stochastic mathematical expressions. With
the advent of high speed computer techniques, sys-
tems analysts have been able to construct sophistica-
ted models with the many components and variables
needed to describe involute real systems.
Systems analysis techniques have been widely ap-
plied to complicated industrial and governmental
management problems. Recent developments in the
area of water resources management have been
closely associated with theories of systems analysis
techniques (Hufschmidt and Fiering, 1966). Applica-
tions of these methods to water quality management is
somewhat less advanced and largely concerned with
effects of pollution on the dissolved oxygen regime of
rivers and streams (e.g., Thomann and Marks, 1966;
O'Connor, 1966). Ecosystems being characterized by
their labyrinthine natures, ecologists have increasingly


relied on the systems analysis approach in their
studies (Watt, 1968). Eutrophication is one of the
most involved and critical problems facing aquatic
scientists and water quality control agencies today.
The tools of systems analysis should be invaluable to
investigators of the problem. Heretofore there has
been no direct application of these methods to the
problem although there have been related applica-
tions in aquatic ecosystem studies. The remainder
of this section will briefly summarize the intended
application of systems analysis and mathematical
modeling in the present study on eutrophication.
The experimental portions of this study are still
under way, and much work remains. Since the pri-
mary functions of systems analysis techniques are in
data analysis and in synthesis of predictive models
and equations, the greatest application of the methods
to this project lies in the future. Five aspects of eu-
trophication can be mentioned as especially appropri-
ate for systems analysis and modeling, and work is
proceeding in these areas.

A. Derivation of a eutrophication rate function.
Eutrophication is the process of lake nutrient enrich-
ment and, as such, can be defined in terms of the net
nutrient flux to a lake. In order to quantify the process,
an equation or function should be derived such that a
given flux of the essential (limiting) nutrients defines
a unique rate of eutrophication. As an approximation,
eutrophication might be represented by some function
of nitrogen and phosphorus input, assuming trace ele-
ments do not limit lake productivity over long periods
of time:


E=F(N,P)


(7-1)


While many elements and compounds (including
trace metals and vitamins) are essential for algal
growth, there is reason (but no unambiguous proof) to
believe that lakes generally are limited by their N and
P inputs. It is also likely that input of other essential
nutrients is highly correlated with one or both of the
major limiting elements. Since organisms require both
nitrogen and phosphorus in order to grow, it would
seem that the function would not be simply additive,
i.e. F(kN+k_,P) for this would imply that the re-
quirements of organisms for either element could be
replaced by adding more of the other element. A
proper function in this case would seem to be multi-







plicative (E= F(NP)) or to at least contain a multi-
plicative or second-order term indicating the depend-
ence of the process on both (rather than either) of
the elements. Several possible eutrophication func-
tions might apply, for example:


E= kNP
E= kN"P"
E=kiN+kXP+k3NP


(7-2)
(7-3)
(7-4)


More complicated expressions could also be devel-
oped. Equation (7-4) is probably applicable to the
widest range of situations for it allows positive eutro-
phication when either N or P input is zero, but defines
E as zero when both N and P are zero. Such a situa-
tion would be possible, if, for example, a lake were
high in phosphorus but low in nitrogen; addition of ni-
trogen alone would then cause a certain amount of eu-
trophication. However, such limiting cases are prob-
ably rare, and lakes having a zero or near zero input
rate for one element and a relatively high input rate
for the other are unlikely. If N/P input ratios vary
only within a limited range, then equations (7-2) and
(7-3) would apply and, simplicity being a virtue,
equation (7-2) would be favored. This subject has
been discussed elsewhere (Brezonik, 1968b), but fur-
ther developmental work is needed. Such functions
are necessarily somewhat arbitrary, but this should not
limit their usefulness. As the contributions of nutri-
ents from various sources become better known and
understood, it should further become possible to
model the eutrophication rate function in terms of cer-
tain measured environmental parameters (amount of
rainfall, area and population of the drainage basin,
land usage patterns, etc.).

B. Dynamic models of aquatic ecosystems. The
effects of nutrient enrichment on the aquatic ecosys-
tem depend on a variety of edaphic factors. Ecosys-
tems can be described as multicompartment models
(each compartment representing a trophic level or
nutrient or energy reservoir), and the interactions
among the compartments can be defined by series of
differential equations. Environmental control of ex-
changes between ecosystem compartments can be in-
dicated in such models by appropriate forcing func-
tions or by transfer coefficients which could be func-
tions of certain environmental conditions. The effects
of nutrient enrichment on the dynamics and equili-
brium of aquatic ecosystems could be studied by ap-
propriate in situ experimentation and analysis of the
data with suitable dynamic ecosystem models. Much
theoretical work could also be done with such models
using artificial or computer generated data. The works


of Patten (1966) and Odum (1960, 1967) are noted as
pertinent to this approach. Some applications of this
method to the problem of eutrophication were also
discussed by Brezonik (1968b).

C. A trophic state equation. As previously noted,
trophic state is a loosely defined and only qualitative
term. No single parameter defines trophic state;
rather it is a summation of many chemical and biologi-
cal conditions. Little progress can be made until eu-
trophication and trophic state are more precisely de-
fined-even if the definitions are arbitrary. One possi-
ble avenue would be to develop a trophic state para-
meter as some simple function of the indicators shown
in Table II-1. The nature of the trophic state function
is still speculative; however, such an expression may
involve the following variables (among others):

Trophic State = F (annual primary production,
algal biomass, Secchi disc transparency, maxi-
mum spring nutrient concentrations) (7-5)

The relative importance of the different variables
would necessarily be arbitrary. Simple regression ana-
lysis would not be useful to develop this equation
since only independent variables are known while the
dependent variable trophicc state) is what we are
seeking to define. A possible approach would be to
arbitrarily define several trophic state equations and
determine which gives the highest correlation with the
nutrient input or eutrophication function. Canonical
correlation analysis may be useful in this regard. In
order to develop such trophic state parameters for
subtropical lakes, a sampling program was instituted
on various lakes in north central Florida during the
past year. Numerous lakes in the area have been ex-
tensively characterized with respect to their physical,
chemical and biological attributes. Work is presently
underway to classify the lakes into trophic types based
on selected physical, chemical and biological para-
meters. Indices of trophic state are being developed
from these data using the above-mentioned analytical
tools. The studies reported in Section IX of this report
are a summary of these efforts.

D. Factors affecting primary production. The im-
portance of primary production in any study of eutro-
phication or trophic state can hardly be overempha-
sized. Thus, studies which extend our knowledge of
the environmental factors controlling primary produc-
tion have important implications in the present prob-
lem. Controlling factors for primary production in-
clude physical parameters like temperature and light,
chemical factors such as nutrient concentrations, pH,







ionic balance, and biological parameters including
predation, competition and symbiosis. A variety of
bioassay techniques are available to determine the nu-
trient or nutrients limiting production. Some of these
have been used and described in an earlier section of
this report. Such techniques are useful in eutrophica-
tion studies in determining the elements least avail-
able to the algae (relative to their need), which
therefore are most critical in the eutrophication pro-
cess. An alternate procedure for delineation of the
various limiting factors is to make intensive field
studies of primary production and its controlling en-
vironmental factors.
Stochastic techniques such as multiple regression
and correlation analyses and multivariate procedures
like canonical correlation and factor analysis can then
be used to indicate likely controlling relationships. An
intensive 10-day field study on primary production
and its controlling factors in Anderson-Cue Lake was
conducted during May, 1968. Similar stochastic ana-
lyses are presently being made with the routine
(monthly) biological and chemical data from the con-
trol and experimental lakes. These results are de-


scribed in detail in Section VIII.

E. Economic analysis of the eutrophication prob-
lem. There are many degrees of eutrophication and
its manifestations, many alternatives for their control
and prevention, and only finite resources available to
do the job. Thus, it becomes a highly involved prob-
lem to determine how much eutrophication to allow
and how much money and other resources to spend
for its prevention, control, or alleviation. This is an
optimization problem in which we desire to maximize
the benefits at a given cost or minimize the cost at a
given level of benefits. Optimization is a familiar
problem in systems analysis. Involved are cost-benefit
analyses, i.e., for a given amount of resources spent to
remedy the problem, what and how many benefits
accrue. One of the major stumbling blocks is quanti-
fication of some of the aesthetic and recreational bene-
fits associated with oligotrophy and mesotrophy. How-
ever, further work on this aspect will have to await
clearer definitions of the eutrophication process and
its effects, and answers to some of the more basic
questions discussed above.







SECTION VIII


ANALYSIS OF ENVIRONMENTAL FACTORS AFFECTING PRIMARY PRODUCTION


A. Introduction

One of the major complicating factors in eutrophi-
cation research is the fact that no simple relationship
exists between the process of nutrient enrichment and
the trophic state of the lake. A multitude of environ-
mental factors as discussed in Section II control sever-
ity and degree of trophic change for any given rate of
nutrient enrichment. Similarly limnologists have long
realized that lacustrine primary productivity is affec-
ted by other factors besides the amounts of available
nutrients. These factors include physical parameters
such as light intensity, temperature, lake transparency
and turbulence, and chemical parameters such as mi-
cronutrient (trace metal and vitamin) concentrations,
pH, ionic balance and strength, alkalinity and others.
The relationship between primary production and
other influencing factors has been studied to some ex-
tent qualitatively, but only a few investigators have
taken a quantitative approach. Riley (1939) used
linear regression principles to derive an alignment
chart from which values of production in the ocean
could be determined for any given set of values of
chlorophyll, temperature and light. More recently
Goldman (1964b) discussed the feasibility of using the
statistical method of response surfaces for obtaining
optimum nutrient conditions in a lake, particularly
when primary production is expressed as a function of
several trace metal concentrations. Putnam (1966)
used multiple regression techniques to analyze limit-
ing factors for primary production in a Florida estu-
ary. Patten and Van Dyne (1968) considered photo-
synthesis and respiration of mixed plankton popula-
tions as a function of (1) species present, (2) their
physiological states, and (3) ambient environmental
factors, and described a nonlinear programming model
for estimating productivity parameters of individual
plankton populations from data on mixed species
water samples. Watt (1968) presented a review of
systems analysis techniques (including the method of
multiple regression) applicable to problems in popu-
lation productivity.
Hayes (1957, 1963), Hayes and Anthony (1959,
1964), and Anthony and Hayes (1964) used regres-
sion models to develop productivity indices for lakes.
Depth, area and several basic chemical parameters,
such as alkalinity, color, conductivity, were used as in-
dependent variables. Fish productivity (standing crop
or angling catch) and bacterial standing crop in sedi-


ments were used as indices of productivity (i.e. de-
pendent variables in regression equations). Indices
were developed using data from up to 150 lakes in the
northern United States and Canada. Northcote and
Larkin (1956) found a significant relation between
logarithmic standard haul of fish and logarithmic total
dissolved solids in 100 British Columbia lakes. Sev-
eral other workers in fisheries biology have attempted
to develop productivity indices based on simple physi-
cal and chemical parameters. See the above papers
for reviews.
Since primary production is a fundamentally im-
portant trophic state indicator, an intensive investiga-
tion of its variations and controlling factors in the ex-
perimental lake was considered essential. As pre-
sented in Section VI of this report, bioassay pro-
cedures offer one method of studying factors limiting
primary production. Another approach to the problem
is described in this section. A ten day field study was
conducted on Anderson-Cue Lake to determine the
short term variations in primary production and as-
sociated factors and to delineate the factors control-
ling production. The results of this ten day study are
discussed and statistically analyzed in this section. In
addition similar analyses have been made with the
routine monthly biological and chemical data reported
in earlier sections and these are discussed below.

B. Procedures

Six chemical, three biological and three physical
parameters were measured three times daily from
May 6, 1968, up to and including May 16, 1968. No
measurements were made on May 12. All of the bio-
logical and chemical parameters, except plankton
counts, were measured three times each day at 10:00
A.M., 12:00 Noon, and 4:00 P.M. plus or minus one
hour. Samples for plankton counts and identification
were taken only once each day.
Samples for measurement of the chemical para-
meters were taken at three depths: surface, 5 feet,
and 10 feet at station seven (See Figure IV-1). The
parameters measured were pH, ortho-phosphate, am-
monia, nitrate, dissolved oxygen and acidity. The bio-
logical samples were taken from the same sampling
locations as the chemical samples. Biological para-
meters measured were chlorophyll a, primary produc-
tion, and plankton identification and counts. The phy-
sical parameters measured were total radiation, cloud







cover, and air and water temperature. The proced-
ures used for physical, chemical and biological deter-
minations were the same as those described in earlier
sections of this report. The basic species diversity
concepts, including the computational equations, are
presented in Appendix B of this report. Species di-
versities were calculated from the 10 day plankton
counts by programming equations (B-1) to (B-4) on
an IBM 360 computer.

C. General Results

Detailed discussion of all the parameters and their
variations would be neither fruitful nor desirable.
However, a few general comments concerning the na-
ture of the results are in order. Daily averages of pri-
mary production at the three sampling depths are pre-
sented for the ten days in Figure VIII-1. The effect of
depth is clearly illustrated. In all cases surface pro-
181-


16

14

12

10
om


6

4

2


x 5 ft.
8 10 ft.


measured over the ten day period. Most of the chem-
ical and physical parameters varied within rather nar-
row ranges during the ten days and displayed consid-
erable randomness in their variations. Table VIII-1
indicates that ammonia, nitrate and pH changed over
quite a narrow range. Average ortho-phosphate levels
were higher in the surface waters than at the 5 foot
and 10 foot levels. This difference may be explained
in part by the periodic rainfalls which deposited water
high in phosphate on the lake surface. However, ni-
trate and ammonia levels did not display a similar pat-
tern.
Table VIII-1 indicates that some daily stratification
occurred in the lake; however, this was slight. Mean
dissolved oxygen and water temperatures decreases
from surface to bottom were 1 mg/1 and 2.50C, re-
spectively. Water temperatures rose as the day pro-
gressed; an average daily change in the order of 2C
occurred at the surface with smaller changes occurring
at the 5 foot and 10 foot depths. Air temperatures
reached a maximum in the early afternoon with lowest
values occurring in the morning. Acidity values also
showed a diurnal trend. Acidity was higher in the
morning and gradual decreases of 0.5 to 1.0 mg/1 oc-
curred during the day. However, pH values showed
very little fluctuation.
As shown in Table VIII-1, biological parameters
displayed the greatest variation. This wide fluctua-
tion implies that attempts to interpolate the value of a
biological parameter from encompassing sample points
could be erroneous. In Figure VIII-2, the 10 day


1 3 5 7 9 11
DAYS
Figure VIII-1. Variation of Average Daily Primary
Production Over the Study Period.

duction was the highest and the 10 foot production
was by far the lowest. Top and bottom primary pro-
duction rates generally differed by an order of magni-
tude or more. The peak in daily production at the
surface occurred on the sixth day and was correlated
with a moderate bloom of Synura. Temporal trends
in the 5 foot and 10 foot samples are not so pro-
nounced.
One of the questions this study sought to answer
concerns the degree to which a given sampling day is
representative of the conditions in the lake during the
period between sampling dates. This depends on the
frequency and amplitude of the temporal variations in
the parameters. A statistical analysis was made on the
variability of the data and is partially presented in
Table VIII-1. The table contains a summary of the
means and standard deviations of the parameters


O surface
X 5 ft.
f 10 t.


12:00 AM
Time of Day


4:00 PM


Figure VIII-2. Mean Primary Production vs Time
of Sampling.

mean primary production values are plotted versus
time of day. Mean primary production at the surface
gradually increased from the morning until late after-
noon, whereas primary production at the 5 foot level
appeared to peak in the early afternoon. Primary pro-







Table VIII-1
MEANS AND STANDARD DEVIATIONS OF PARAMETERS
MEASURED DURING TEN DAY STUDY OF
ANDERSON-CUE LAKE


Parameter Sampling Location
and Units Surface 5 Feet 10 Feet
Ortho-Phosphate .005 .004a .002 .002 .002 .002
(mg P/1) .005 .005b .001 .001 .001_+ .002
.005 .006c .001 .001 .001 .001

Ammonia Nitrogen .18 .04 .18 .04 .22 .08
(mg N/l) .18 .04 .18 .04 .22 .06
.18 .05 .17- .04 .18 .08

Nitrate Nitrogen .07 .02 .07 .02 .08 .03
(mg N/l) .06 .01 .06 .02 .07 .01
.06 .01 .07 .01 .06 .00

Acidity 2.95 .46 2.89 .66 4.061.26
(mg/1 as CaCOs) 2.75 .37 2.63 .32 3.27 .58
2.61 .40 2.55 .34 3.46 .96

pH 4.84 .05 4.85 .08 4.94 .12
4.98 .12 4.94 .05 5.00 .08
4.96 .07 4.94 .11 4.96 .14

Water Temperature 26.2 1.9 25.7 1.4 24.8 .4
(C) 27.2 2.2 25.9 1.4 24.9 .5
28.2 2.3 26.2 1.2 25.1 .5

Primary Productivity 9.863.54 4.021.50 .24 .13
(mg C/hr-m') 9.632.87 5.702.48 .33 .28
12.025.01 3.821.42 1.011.38

Chlorophyll a 12.439.12 11.583.82 6.343.06
(mg/m3) 11.935.89 10.054.31 5.14-2.33
13.989.39 10.998.23 4.342.10
Total Solar Radiation 542.191.2
(Langleys/Day)

Air Temperature 26.7 5.6
(oC) 30.1 2.8
29.8 2.6

Cloud Cover 3731
(%) 5927
5230

Plankton Counts 35271674 26751650 1237 759
(#/ml)

a Morning 10:00 AM
b Noon 12:00 N
c Afternoon 4:00 PM








duction levels at the 10 foot level were very small and
of little significance. Mean primary production for all
sampling times was higher at the surface than at the
5 foot level. This probably reflects a dominant role for
solar radiation in controlling primary production.


sults of species diversity calculations are presented in
Table VIII-2. The first and second figures in each
block respectively denote minimum and maximum di-
versities possible for the sample. The third figure rep-
resents the actual species diversity for the sample at
the indicated location and day. In this study the di-
versities tended to fall about half way between mini-


0 --


Ssur
x 5f
A 10

-- x- - X


20


face ;"
a 16

ft. at
12

8 a
-2
c 8


A ~ 5


1 2 3 4 5 6
Plankton Counts (Cells/ml x 103)


Figure VIll-3. Average Daily Primary Production
vs Plankton Counts.

Figure VIII-3 shows mean daily primary produc-
tion at each depth versus corresponding plankton
counts. The data are quite scattered, but trends are
apparent. Lines of linear regression (see Part D of
this section) are shown for each depth. The effect of
organism concentration on primary production at any
particular depth is small. Both primary production
and plankton counts were highest at the surface, but
population differences between the three depths (see
Table VIII-1) are insufficient to explain the differen-
ces in production. Apparently the surface plankton
were more efficient photosynthesizers than the plank-
ton at the 5-foot and 10-foot levels. In Figure VIII-4
average daily chlorophyll a is plotted versus plankton
counts. To an extent high chlorophyll a concen-
trations are associated with high plankton counts, but
there is considerable scatter of the data. The chloro-
phyll a concentrations were higher at the surface and
5 foot level than at the 10 foot level. With reference
to Figures VIII-3 and VIII-4, it would seem that high
primary production values were generally associated
with correspondingly high plankton counts and high
chlorophyll a concentrations.
The dominant planktonic genera during the study
were Synura, Dinobryon, Merotricha, Cryptomonas,
Chlamydomonas, Gymnodinium and Scenedesmus.
The sequence in which the organisms are listed repre-
sents their abundance in descending order. The re-


0
O


x /




/


*0


/


/


x


0

CHA= .0036 (PC)

0 surface
X 5 ft.
8 10 ft.


1 2 3 4 5 6 7


Plankton Counts (Cells/ml x 103)
Figure VIII-4. Average Daily Chlorophyll a vs
Average Plankton Counts.


mum and maximum diversity values. The fourth fig-
ure in each block of Table VIII-2 is the redundancy,
which is a measure of the dominance of a population
by one or more species. Redundancies in this study
ranged from .20 to .73. Samples with high redundan-
cies tended to be dominated by Synura.


0 surface
x 5 ft.
A 10 ft.


I I I I I I I I I I


1 2 3 4 5 6
DAY


7 8 9 10 11


Figure VIll-5. Species Diversities Over 10 Days.


I I


I


I


U


tl







Table VIII-2
SPECIES DIVERSITIES FOR TEN DAY STUDY


Location
Date Surface 5 Feet 10 Feet
May 6, 1968 32a* 25 46
5316b* 6633 4845
2669c* 4223 1962
.50d .36 .60

May 7, 1968 33 30 37
6076 3264 2684
3373 2229 1190
.45 .32 .56

May 9, 1968 35 42 35
10680 8546 1877
3630 3733 527
.66 .57 .73

May 13, 1968 52 44 21
3555 2992 2924
1095 1512 238
.71 .50 .20

May 16, 1968 72 36 48
6521 2625 2032
3701 1683 1036
.44 .36 .50

a-denotes minimum diversity
b-denotes maximum diversity
c-denotes actual diversity
d-redundancy
*-units are Species Diversity Units (SDU)


In general the surface and 5 foot samples dis-
played higher diversities than the 10 foot samples.
This is shown in Figure VIII-5, in which species di-
versities are plotted versus sampling day. It is inter-
esting to note that plankton counts were also higher
in the surface and 5 foot samples (See Table VIII-1).
This suggests species diversity is positively correlated
with total plankton counts, and Figure VIII-6 shows
this to be the case, at least for this study. The species
diversity values correlate with the fact that Anderson-
Cue Lake is in an oligotrophic state since high diver-
sities and relatively low total plankton counts are gen-
erally associated with such situations. As the lake
progresses toward a more eutrophic state diversities
should be expected to decrease as a few species in-
crease in numbers and become dominant. Patten
(1966) has also shown that species diversities and re-
dundancies for a particular aquatic environment show
definite seasonal fluctuations. In order to utilize this
technique to the fullest it would be necessary to deter-


mine species diversities throughout the year to obtain
an estimate of seasonal variability. Nevertheless, spe-
cies diversity concepts are valuable measures of trop-
phic state in a lacustrine system.

0
4-

0- 0
3 -



00
2I
y ~~ 00^


Total Plankton Count (Celli/ml x 103)


Figure VIll-6. Species Diversities vs Total Plankton Counts.







D. Multiple Regression Analysis

The results presented above indicate that primary
production is affected by a multiplicity of factors.
None of the parameters measured in the 10 day study
shows an obvious correlation with primary production
when the latter are plotted as a function of the former
using all the sampling points. Clear correlations are
apparently prevented by the dominance of depth and
time factors and the possibility that several different
factors may have controlled production at different
depths and times. This is indicated in Figure VIII-7,

0


tered points and indicates the positive correlation.
Certain trends become more apparent when the data
are grouped according to depth (Figure VIII-7).
While considerable scatter remains, there is a definite
trend toward lower production per unit chlorophyll a
concentration with increasing depth.
In order to further delineate the factors controlling
production, data from the 10 day study and some data
from the routine monthly sampling have been subjec-
ted to multiple regression analysis. The method of
linear regression as described by Ostle (1963) was uti-
lized. The model (Equation 8-1) assumes the de-
pendent variable Y is distributed normally with mean
, and variance ul.


Y = A+B1X1+B2X2+ .... +BpXp
where A=intercept value
B1, B2, ..... .B = regression coefficients
X1, X, ... .Xp = independent variables


(8-1)


a 0 0 PP- 87
0 0/

0 X
0 0o x 91
x x x
x x x x
/7 x
88 88 88 88
88 88x


0 4 8 12 16 20 24 28 32 36 40 44
Chlorophyll (gm3)
Figure VIII-7. Primary Production vs Chlorophyll .


.45 (CHA)

x 5t.
S10 ft.


48 52


in which primary production is plotted versus chloro-
phyll a for all values obtained in the study. In gen-
eral high chlorophyll a levels are associated with high
primary production values but the data are extremely
scattered. A straight line has been fitted to the scat-


A multiple regression and correlation program was
used with the IBM 360 computer to analyze the data.
In most of the regression analyses (See Appendix
D for a table of all regressions run), primary produc-
tion was selected as the dependent variable and re-
gressed against combinations of the physical, chemical
and biological parameters to determine the possible
relationships among the variables. Various combina-
tions of the data were analyzed. Most of the results
presented here are for the short term 10 day study
data, but some analyses were made on the routine
monthly data. In some runs all the sampling points
were considered as one set of about 90 results (3
depths x 3 times daily x 10 days); in other runs the
results were daily means at each sampling depth or
were integrated or mean values with respect to depth
at each sampling time. Averaging tended to lessen
the random component due to analytical errors and
unknown (and unmeasured) variables affecting pro-


Table VIII-3
ANALYSIS OF VARIANCE FOR PRIMARY
PRODUCTION VERSUS CHLOROPHYLL A


Degrees
Source of of Sum of Mean F
Variation Freedom Squares Squares Value

Regression 1 22.11023 22.11023 9.64281

Deviation From
Regression 8 18.34338 2.29292
Total 9 40.45361


"-denotes significance at 95% confidence level
Correlation Coefficient = .74







duction. Some of the more interesting results are pre-
sented below.
A simple regression of average daily primary pro-
duction (at given depths) versus average daily chlo-
rophyll a (at given depths) was made. The plot is
shown in Figure VIII-8 and the regression equation
given. There is a definite positive correlation between
primary production and chlorophyll a concentrations
at the surface and the 10 foot depth. Correlation co-
efficients of 0.74 and 0.76 were found for the two
depths, respectively. The regressions were significant
at the 95% confidence level. However, no significant
correlation was found for primary production versus
chlorophyll at the 5 foot level. The analysis of vari-
ance for average daily primary production at the sur-
face versus average daily chlorophyll a is presented in
Table VIII-3.
Average daily primary production at the surface
was regressed against average daily levels of dissolved
oxygen, ortho-phosphate, nitrate, and ammonia at the
surface. The analysis of variance, multiple correla-
tion coefficient and resulting regression equation are
given in Table VIII-4. The multiple regression coeffi-
cient is high and the-analysis of variance indicates sig-
nificant regression. It might be concluded that a defi-


PP 5.93 .36 (CHA)


0



0 surface
x 10 ft.


x
PP -.48 .20 (CHA)
0 2 4 6 8 10 12 14 16 18
Chlorophyll a (mg/m3)

Figure VIII-8. Average Daily Primary Production vs
Average Daily Chlorophyll a.

nite linear relationship exists between primary produc-
tion, dissolved oxygen, ortho-phosphate, nitrate and
ammonia. However, it should be noted that the range
of values for the independent variables is very narrow
(Table VIII-1). This restricts the confidence interval
of the regression equation, and attempts to use the


Table VIII-4
ANALYSIS OF VARIANCE FOR PRIMARY
PRODUCTION VERSUS DISSOLVED OXYGEN,
ORTHO-PHOSPHATE, NITRATE AND AMMONIA


Degrees
Souce of of Sum of Mean F
Variation Freedom Squares Squares Value
Due to
Regression 4 31.33093 7.83273 4.29800*
Deviation About
Regression 5 9.12268 1.82454
Total 9 40.45361


*--denotes significance at 90% confidence level
Cumulative Multiple Regression Coefficient = .88

Regression Equation:
PP = -79.2 + 10.9(DO) 269.3(OP) + 86.1(NO3) + 10.8(NH3)
PP = primary production (mgC/hr-m3)
DO = dissolved oxygen (mg/1)
OP = ortho-phosphate (,xg/1)
NOs = nitrate-nitrogen (mg/1)
NH, = amonia-nitrogen (mg/1)







equation for prediction outside this narrow range may
not be justified. Furthermore, regression of primary
production versus these same variables at the 5 and 10
foot levels yielded non-significant regressions with low
coefficients.
Primary production was also regressed against
acidity and pH levels at the surface with the resulting
regression equation:
PP=-32.4 + 3.3 (AC) + 6.8 (pH)
AC acidity (mg/1 as CaCOa)
pH=negative logarithm of hydrogen ion concen-
tration
Again, the range of acidity and pH values was quite
narrow and prediction outside this range would be
done with very little confidence. Regression of these
variables at the 5 foot level was not significant, but at
the 10 foot level regression was significant at the
90% confidence level.
Depth profiles of photometer readings were also
taken during the 10-day study. The relationship be-
tween average daily surface photometer readings and
average daily surface primary production is interest-
ing. The regression equation and correlation coeffi-
cient are:

PP=16.8-.94 (PHO)
correlation coefficient =-.60
PP =primary production (mg C/hr-m3)
PHO = photometer reading (light units)

The results indicate a negative correlation between
surface production and surface light intensity. This
suggests that increased light (above a certain level)
had an inhibitory effect on the carbon fixing organisms
present in the lake during this study. This result is
surprising in view of the fact that primary production
decreased so markedly with depth (hence production
increased with amount of light at the incubation
point).
Primary production values at each sampling time
were integrated with respect to depth giving one pri-
mary production value (mg C/hr-m') for each time.
The resulting values for the mid-day sampling period
were regressed against average chlorophyll a concen-
trations over depth for the same sample period giving
the following regression equation:

PP12= 6.1 -+1.2(CH12)
correlation coefficient =.62
PP12= primary production at 12:00 noon (mg
C/hr-m2)
CH12= average chlorophyll a concentration over
depth at 12:00 noon (mg/m3).


As shown previously a positive relationship exists be-
tween primary production and chlorophyll a concen-
trations. The results obtained in this study differ from
those obtained by Putnam (1966) and Saunders et al.
(1962) which indicated that there was little or no cor-
relation between primary production and chlorophyll
a levels. However, these workers attempted to corre-
late results over much more widely spaced time inter-
vals than the present study.
In one instance (5 foot depth), average chloro-
phyll a concentrations regressed against plankton
counts as the independent variable showed a signifi-
cant regression. The resulting equation is shown
below. There is a positive relationship between chlo-
rophyll a concentrations and plankton counts. This
might be expected since the organisms were phyto-
plankton, but the scatter indicates the chlorophyll con-
tent of individual cells varies considerably as other
workers have found.
CH=2.45+.002(OC)
correlation coefficient =.75
CH= chlorophyll a concentration (mg/m3)
OC=plankton counts at the 5 foot level (#/ml)

It should be noted that no definite correlations were
found between chlorophyll a and plankton counts at
the surface and 10 foot depths.
Many other regression combinations were tried
(see Appendix D), but most of the combinations pro-
duced either non-significant regressions or small re-
gression coefficients.
Some of the regressions run on the routine monthly
data on Anderson-Cue and McCloud Lakes are inter-
esting. The analysis of variance for primary produc-
tion versus ammonia, nitrate and ortho-phosphate
levels over a period of two years (1967-1968) is shown
in Table VIII-5. The regression equation and the
high multiple regression coefficient indicate primary
production and (N and P) nutrient levels have been
positively correlated in the experimental lake during
the two-year period.
As with the ten day study, many regression com-
binations produced non-significant results. For exam-
ple, a regression of surface primary production in An-
derson-Cue Lake versus total solar radiation for the
two year period produces the analyses of variance pre-
sented in Table VIII-6. The non-significant regression
and low regression coefficient indicate that a simple
linear relationship between surface primary produc-
tion and solar radiation does not exist. Variations in
solar radiation account for only a small part of the va-
riations in primary production; the relationship be-
tween primary production and environmental varia-
bles is thus a more complicated expression, encompas-







Table VIII-5
ANALYSIS OF VARIANCE FOR PRIMARY PRODUCTION
VERSUS AMMONIA, NITRATE, AND ORTHO-PHOSPHATE LEVELS
IN ANDERSON-CUE LAKE
(Monthly data from the period Jan. 1967-Sept. 1968)


Source of
Variation


Degrees
of
Freedom


Sum of
Squares


Mean
Squares


F
Value


Due to regression 8 29173.45312 9724.48438 11.943'**
Deviation from
regression 17 13841.88281 814.22827
Total 20 43015.33594

**-denotes significance at 99% confidence level
Cumulative Multiple Regression Coefficient = .82
Regression equation:
PP = 3.1 + 121.7 (NH3) + 2.90 (OP.) + 87.0 (NOs)
PP = primary production (mgC/hr-m3)
NH3 = ammonia-nitrogen (mg/1)
OP4 = ortho-phosphate (mg/1)
NOQ = nitrate-nitrogen (mg/I)


sing other variables. This, along with many other
non-significant regressions obtained from the data, im-
plies that there is much to be learned about the com-
plexity of variables influencing primary production.
Although the 10-day study and monthly data yielded
many significant regressions, it is not yet possible to
estimate primary production reliably by measuring a
few of the parameters from the multi-parameter en-
vironment. Therefore, the method of multiple regres-
sion analysis is still limited as a predictive tool. How-
ever, the general approach shows promise.

E. Canonical Correlation Analysis
One of the problems with the application of multi-
ple regression analysis to the primary production prob-
lem is that many of the environmental variables are
not really independent. The multiple regression
model (Equation 8-1) assumes the dependent va-
riable is a function of several independent variables
(which are orthogonal with respect to each other).
The various nutrient components are at least to some
extent related to each other and thus not completely
independent. This limits the statistical validity of
multiple regression analyses. However, there are mul-
tivariate methods which do not assume independence


among all the variables; one such technique is canoni-
cal correlation analysis.
Canonical correlation analysis is a method of de-
scribing the dependence between two sets of variates.
The technique was originated by Hotelling (1935).
Observations are considered in vector form, where the
vector consists of several individual variates measured
at the same time. For example, an observation vector
might consist of 5 components which are measure-
ments of primary production, chlorophyll a, nitrate
ammonia, and ortho-phosphate made on a particular
lake on a particular day. Vectors of observations from
the multidimensional population are assumed to be
distributed multivariate normal with mean vector p
and covariance matrix. This is analogous to the uni-
variate case where a variable is assumed to be nor-
mally distributed with mean / and variance a2. Sup-
pose certain variates within the vector X possess some
common feature. For example primary production
and chlorophyll a might be classified as biological ac-
tivity indicators and ammonia, nitrate and ortho-phos-
phate as available nutrient factors. The vector
can then be subdivided into two distinct smaller vec-
tors, X'=X', X'., with p and q components respec-
tively (' denotes the transpose). If N independent







Table VIII-6
ANALYSIS OF VARIANCE FOR SURFACE PRIMARY PRODUCTION
IN ANDERSON-CUE LAKE VERSUS TOTAL SOLAR RADIATION
(From routine monthly data for the period Jan. 1967-Sept. 1968)


Source of
Variation


Degrees
of
Freedom


Sum of
Squares


Mean
Squares


F
Value


Due to regression 1 4167.92969 4167.92969 2.04 N.S
Deviation about
regression 19 38847.40625 2044.60010
Total 20 43015.33594


N.S-denotes non-significance
Regression coefficient = .31


observation vectors are drawn from the multinormal
population and the covariance matrix S calculated, it
too can be partitioned as shown.

Sil S12
S = N>p+q+1
S12 S22

After testing demonstrates that there is in fact de-
pendence between the two sets of variates Xi and X2
(the procedure is not described here), the method of
canonical correlation may be applied. In canonical
correlation one is interested in the relationship be-
tween the two pairs of variates X, and X2. The follow-
ing question may be proposed:
What are the linear compounds


/s=a 'X,


v= blX2


with the property that the sample correlation of /A,
and vi is greatest, the sample correlation of /2 and v2
is greatest among all linear compounds uncorrelated
with /, and vi and so on for all s= min(p,q) possible
pairs? It can be shown that the coefficients of the i'h
pair are given by the homogeneous linear equations


(S12S22 '512' c151)ai = 0
(S12'S11 'Sl2 ciS,2)bi = 0


(8-2)
(8-3)


where ci is the i'" largest root of the detriminantal
equations


IS,2S-,'S12' AS,[I = 0 or
[S,,'S1 'S,, -AS. = 0


(8-4)


By computing the covariance matrix S and parti-
tioning as previously indicated it is possible to find
highly correlated pairs of new variates, that are com-
binations of the original variates. For example, it is
possible to define a biological activity variate (consist-
ing of a linear combination of primary production and
chlorophyll a) which may be highly correlated with
an available nutrient variate (consisting of a linear
combination of ammonia, nitrate, and ortho-phosphate
levels).
The example used for illustrative purposes in this
brief presentation on the method of canonical correla-
tion was run using routine monthly data from Ander-
son-Cue Lake, a canonical correlation program and
the IBM 360 computer. The results are presented in
Table VIII-7. The biological activity variable (pri-
mary production plus chlorophyll a) is highly corre-
lated with the available nutrient variable (ammonia,
nitrate, and phosphate); a canonical correlation co-
efficient of .84 was found. It should be noted that two
canonical correlations were obtained from the analysis
since s =min(p,q) where s is the number of correla-
ted pairs or the number of canonical correlations. The
first canonical correlation is the higher and is the one
presented here. The a's are the coefficients of pri-
mary production and chlorophyll a in the biological
activity vector and the b's are the coefficients of am-
monia, nitrate, and phosphate in the available nutrient
vector. The analysis weights the two parameters in
the biological activity variable equally but places
varying weights on the chemical factors in the avail-
able nutrient variable. The analysis indicates that
ammonia is the most important parameter in the nu-
trient variable; nitrate and then phosphate are next in
importance.







Table VIII-7
CANONICAL CORRELATION ANALYSIS RUN ON MONTHLY SURFACE
DATA FROM ANDERSON-CUE LAKE FOR THE PERIOD JAN. 1967-SEPT. 1968

Biological Activity Vector = PP, CHA
Available Nutrient Vector = N03,NH3,OP4
Number of variables in first set = p = 2
Number of variables in second set = q = 3
Number of observations = N = 21
PP = primary production (mgC/hr-m3)
CHA = chlorophyll a (mg/m3)
NO, = nitrate-nitrogen (mg/1)
NH3 = ammonia-nitrogen (mg/1)
OP, = ortho-phosphate (mg/1)
N = sample size = 21


First set of canonical correlations produces a biological activity variable=
PP + CHA, and an available nutrient variable = .44 (NO3) + .58 (NHa) + .32
(OP,), which have a canonical correlation coefficient of .84.


The example of canonical correlation analysis pre-
sented above is largely illustrative, but the potential
power of the method for analysis of lacustrine systems
should be obvious. The advantage of the method is
in lumping related parameters together into new vari-
ables. This should have valuable application to tro-
phic state studies, since trophic state is not a function
of one variable but of many interrelated variables.
Canonical correlation is but one method of multi-
variate analysis appropriate to study the interaction of
large numbers of variables. Other techniques like
factor analysis and discriminant analysis may also be
useful in analyzing the factors affecting primary pro-
duction and associated phenomena. It is planned to
continue these studies and further pursue the applica-
tion of statistical techniques to solution of this prob-
lem.

F. Conclusions

Several general conclusions can be drawn from the
above study. First, chemical parameters changed very
little during the 10-day field study, but biological
parameters showed relatively large changes. These
large day to day variations makes interpolation be-
tween successive sampling dates a risky venture. For
example, biweekly primary production measurements
give little indication of the values between two sam-
pling dates.
Multiple regression analysis demonstrated numer-


ous significant inter-parameter relationships for pri-
mary production and various environmental condi-
tions. No single factor is sufficiently correlated with
production to allow reliable estimates of the latter
from the former. However, estimates can be made
from regression equations involving combinations of
environmental factors. Caution must be observed in
applying the regression equations to predictive pur-
poses since a relatively narrow range of parameter
values were used in analysis.
This study also pointed out the existence of many
unexplained phenomena in a lacustrine system; for ex-
ample, a parameter may show high correlation with
another parameter at one depth but little or no corre-
lation at other depths. An adequate stochastic model
of a lacustrine system must be able to account for
these phenomena, at least in part. This implies the
desirability of using more sophisticated multivariate
techniques.
As indicated in the introduction of this section, a
few workers (Riley, 1939; Hayes, 1963; Goldman,
1964b; Patten and Van Dyne, 1968, have used stochas-
tic model techniques to analyze inter-variable rela-
tionships in aquatic environments. Few if any workers
have applied the more sophisticated multivariate tech-
niques to these purposes. The example of canonical
correlation analysis presented above indicates that
these techniques hold much promise for delineating
the complex relationships among biological, chemical
and physical phenomena in the lacustrine environment.







SECTION V


ROUTINE CHEMICAL STUDIES


A. Introduction

Trophic state is manifested by a variety of chemical
and biological parameters. This section will summar-
ize the routine chemical data obtained on the two
study lakes; biological results will be presented in the
following section. Results for Anderson-Cue Lake
extend for a period of nearly three years-from 1966
to the present. McCloud Lake has been sampled
routinely since the beginning of 1967. During 1966
and 1967 sampling was approximately biweekly, es-
pecially for the important nutrient parameters. Sam-
pling has been on a monthly basis since January, 1968,
because short term variations have been found to be
rather small. Monthly sampling has also allowed
more time for other special studies. The objective of
routine sampling is to define changes in the chemical
and biological composition of the lake as it undergoes
controlled eutrophication. To accomplish this requires
a representative sampling program with careful atten-
tion to possible temporal, lateral and depth variations
in composition.
Three permanent sampling stations were located
in Anderson-Cue Lake. Stations 4 and 7 are in the
centers of the lake's two basins, and Station 8 is on the
south shore in about 3 feet of water. The location
of these stations is shown in Figure IV-1. Two per-
manent stations are located in McCloud Lake, Station
11 near the center of the lake and Station 12 near
the north shore in 3 feet of water. Samples were
taken at three depths (top, middle and bottom)
at stations 4, 7 and 11, and at mid-depth at the shore
stations (8 and 12). For more detailed determina-
tions of lateral variability, a sampling grid of about
50 stations was established on Anderson-Cue Lake;
these are indicated in the isopleth maps shown in
later discussions of these studies. Parameters meas-
ured routinely (biweekly or monthly) include dis-
solved oxygen, pH, conductivity, acidity, dissolved
and suspended solids, ortho and total phosphate, total
and particulate organic nitrogen, ammonia, nitrite,
and nitrate. In addition, data have been routinely col-
lected on physical conditions such as water tempera-
ture and Secchi disc transparency. Other major and
minor chemical constituents have been determined
less frequently. These include chloride, sulfate, cal-
cium, magnesium, sodium, potassium, silica, iron,
manganese, chemical oxygen demand and biochemi-
cal oxygen demand. Chemical characterization of


lake sediments has included determination of per-
cent volatile solids, total organic nitrogen, ammonia,
total phosphate, iron, and manganese. Methods used
for the various chemical analyses are described in
Appendix A.

B. General Chemical Characteristics

The two lakes are typical of the small lakes in the
Trail Ridge portion of the Central Highlands of Flor-
ida. Table V-1 summarizes the chemical characteris-
tics of the lakes. Few significant changes in gross
chemical composition have been noted during the
period of record; hence the values in Table V-1 are
mean values for each lake during the period of record.
Both lakes are colorless, low in dissolved solids and ex-
tremely soft. The waters are acidic, with typical pH
values ranging between 4.6 and 5.5. The waters have
little buffer capacity and essentially no alkalinity. Con-
sequently, acidity titrations have been used to esti-
mate total CO,. Specific conductance has increased
in Anderson-Cue Lake from about 25 lmho cm-1 to
about 38 lmho cm-1 over the last 18 months. Corres-
ponding increases in McCloud Lake have been less-
from 30 to 35 e/mho cm-1. Some of the increase would
seem to be the result of excess evaporation over pre-
cipitation during the period; nutrient additions were
probably responsible in part for the increase in the ex-
perimental lake.
The low dissolved solids and ionic content of the
lakes are indicative of the waters' origin, i.e. atmos-
pheric precipitation. Table V-1 lists some compara-
tive values for the chemical composition of rain
water at the lake site. Concentrations of major ions
compare reasonably closely for the lakes and rain
water. The ionic content of rainwater varies con-
siderably, but the values in Table V-1 represent
approximate ranges for the various ions. The data are
too sparse for reliable estimates of mean rainfall com-
position, which would be useful in deriving a chemi-
cal model for the lake waters from rainfall composi-
tion and possible chemical interactions between rain-
water runoff and soil constituents. The primary rea-
son for studying the composition of rain is to deter-
mine its significance as a nutrient source. The rain-
fall values for nitrogen and phosphorus species in
Table V-1 are mean values of about twenty analyses
during 1968, and the results reveal the importance of
rain as a nutrient source, especially for lakes unaffec-







Table V-1
CHEMICAL COMPOSITION OF ANDERSON-CUE
AND McCLOUD LAKES AND RAINWATER


Anderson-Cue Lake


McCloud Lake2


Specific
conduct. 31.65 32.29 10-30
pH 4.98 4.85 5.3-6.8
Acidity as CaCO3 3.20 3.50
C1- 6.25 5.93 1.74
SO04 5.4 5.0 0.8
Na+ 2.49 2.81 0.29-1.85
K+ 0.51 0.25 0.13-0.21
Ca+2 0.74 0.61 1.01-2.06
Mg+2 0.58 0.57 0.06-0.35
Silica 0.14 0.10
Total org. N 0.47 0.42 0.32
Particulate org. N 0.26 0.21
NHS-N 0.234 0.105 0.208
NO2--N 0.0014 0.0012 0.005
NO3--N 0.067 0.041 0.209
Ortho phosphate 0.0084 0.006 0.027
Total phosphate 0.017 0.012 0.033
COD 10.7 10.7
BOD 1.02 0.86
Sus. solids 5.9 5.2
Turbidity 11.1 8.8
1 All values in mg/1 except specific conductance (t, mho con-1) and pH. Nitrogen
species are in mg N/1 and phosphate in mg P/1
2 Mean values for all samples from the mid-lake stations during 1967 and 1968.
3 Nutrient (and chloride) concentrations are mean values for all determinations
in 1968; other values represent range encountered in one to four determinations
during 1967-68.


ted by cultural sources. (See Section III for a more
detailed evaluation of rainfall as a nutrient source).
While concentrations of major ions are not likely
to limit primary production in either lake, the paucity
of several is likely to select against certain types of
organisms. Low silica probably is a contributor to
the small diatom populations; low calcium and mag-
nesium indicate the waters are unsuitable for macro-
phytes like Chara and some algae which prefer hard
water. The low pH of these lakes is undoubtedly
a contributing factor for the low populations of blue-
green algae, but encourages the maintenance of a
desmid population. The relatively low nutrient levels
favor organisms like Dinobryon and Synura, which
prefer such environments (Hutchinson, 1967b).

C. Temperature and Dissolved Oxygen

Neither lake shows much evidence for stable ther-
mal stratification at any time of the year. Table V-2
summarizes average temperature and dissolved oxy-
gen at the three depths sampled for stations 4, 7 and
11. The maximum difference in average annual tem-


perature from top to bottom was about 1.5C for sta-
tion 11 in 1967; differences at the other stations have
been 1C or less. Somewhat larger differences at
station 11 have been found from top to bottom on
particular days. During the period of high water in
summer of 1967, 30C differentials were sometimes
found, but the changes occurred in the bottom few
feet, and most of the lake was freely circulating.
Temperature profiles in Anderson-Cue Lake are norm-
ally within one degree Celsius from top to bottom.
During periods of intense warming and calm weather,
temporary stratification could occur in either lake,
but we have not yet found such conditions in over
two years of sampling. Water temperatures range
from about 12'C in winter to about 320C in mid-
summer.
Dissolved oxygen profiles also show little change
with depth. Average differences at stations 4 and 7
in 1967 were only 0.12 and 0.29 mg/1, respectively.
Slightly greater differences occurred in 1968: 0.46
and 0.74 mg/1 at stations 4 and 7, respectively; these
may reflect the somewhat greater production and
standing crop in Anderson-Cue in 1968. Vertical dif-


Constituent'


Rainwater3







Table V-2
TEMPERATURE AND DISSOLVED OXYGEN:
ANNUAL AVERAGES AT THREE DEPTHS IN STATIONS 4, 7, 11


Temperature1


1968
22.11
21.74
21.57
22.13
21.83
21.08
22.65
21.98
21.73


Dissolved Oxygen2
1967 1968


7.39
7.39
7.27
7.66
7.56
7.37
7.58
7.50
6.26


7.94
7.96
7.48
8.04
0.03
7.30
8.07
8.02
6.90


1 Temperature in *C
2 Dissolved oxygen in mg/1.


ferences at Station 11 were greater (1.2-1.3 mg/1)
than the Anderson-Cue results for both years, corrob-
orating the greater vertical stability of this lake. There
is no evidence of oxygen depletion in the bottom
water of either lake at any time, but considering the
lack of thermal stratification and oligotrophic condi-
tions, this is not surprising. Seasonal variations in dis-
solved oxygen largely reflect changes in solubility with
temperature. Figures V-1 and V-2 show the average
temperature and dissolved oxygen values for 1967 and
1968 at stations 7 and 11, respectively. Oxygen values
generally were near saturation, but a tendency toward
slight undersaturation is noted. Rates of photosyn-
thesis and respiration in either lake are too slow to
markedly influence dissolved oxygen, but this should
change in Anderson-Cue Lake as nutrient additions
are continued.

D. Variations in Biogenic Compounds

Greatest attention in routine chemical analyses
has been centered on nitrogen and phosphorus com-
pounds, which presumably are most critical for pri-
mary production, and on other substances whose con-
centrations are affected by activity of organisms.
Both lakes were extremely poor in nutrients before
enrichment began. Ammonia ranged between 0.02
and 0.06 mg N/1; nitrate was less than 0.04 mg N/l,
and total organic nitrogen averaged about 0.3 mg
N/i. Orthophosphate was often undetectable and
averaged less than 5 pg P/1. Total phosphate ex-
hibited similarly low concentrations. The above
concentration ranges are for 1966 and early 1967,
before nutrient enrichment of Anderson-Cue Lake.
Enrichment began in March of 1967 and is still


continuing. Figures V-3 to V-7 show the seasonal
variations in nitrogen and phosphorus forms in the
two lakes from January, 1967 to December, 1968.
The points on each plot represent mean values for
the mid-lake stations in each lake. Differences be-
tween the two lakes may have been somewhat greater
than the plots indicate. Occasional high nutrient con-
centrations were encountered in the bottom sample of
McCloud Lake; these may have resulted from stirring
the sediment during sample collection. These are in-
cluded in the averages, but are probably not repre-
sentative of the lake as a whole.
Seasonal patterns in both lakes are rather similar.
With the exception of total organic nitrogen (Figure
V-3), nutrient concentrations are consistently higher
in Anderson-Cue compared to McCloud Lake. Total
organic nitrogen does not appear to exhibit a marked
seasonality in either lake. The high and erratic
nature of the data in mid-1967 is partially the result
of analytical difficulties subsequently corrected. Con-
centrations were usually slightly higher in the experi-
mental lake during 1968.
The effect of enrichment on ammonia concen-
trations is clearly illustrated in Figure V-4. Ammonia
has been consistently higher in the experimental lake
throughout 1967 and 1968, but differences became
much more pronounced during 1968. There does
not seem to be a major seasonal influence on am-
monia; rather concentrations fluctuate considerably
from month to month. It is interesting to note that
the biological forces within the lake can exert an
over-riding influence on the general trend toward
increased concentrations resulting from nutrient input.
A decrease from about 0.34 mg NH3-N/1 to 0.06 mg
NH,-N/1 occurred in Anderson-Cue Lake during


Station &
Depth
4 Top
Mid
Bottom
7 Top
Mid
Bottom
11 Top
Mid
Bottom


1967
23.61
23.06
22.91
23.42
23.13
22.90
23.49
22.78
21.93









Anderson-Cue Lake


F M A M J i A S O N D J F M A M J J A S O N D
1967 1968

Figure V-1.



McCloud Lake


1967 1968
Figure V-2.


J F M A M


A S 0 N D J


Figure V-3.


25


F M A M


A S 0 N D


35


S30

25

20
? 20


E 15

0)
- 10

0


0 0


l 35
o


30


E 25


S20


15



10


0
0 0








1.1





0.5



O.E



o 0.6

z
.2 0.5


S0.4
o .










Anderson-Cue

0.7 - - McCloud


0.6 -


0.5


0.4-





0.2 s






J F M A M J J A S O N D J F M A M J J A S O N D
1967 1968
Figure V-4.


.18


.16


.14


. 12


.10


Z .08


.06


.04


1967 1968


Figure V-5.


-- Anderson-Cue
5-
- - McCloud

0


5


I'



5- I






0 iI I IlI I I I I
J F M A M J J A S 0 N D J F M A M J J A S 0 N D
1967 1968
Figure V-6.


26
















40

0
2E
S 30

'6 25

S15



0 1

0 I I I I I I l
J F M A M J J A S O N
1967


February, 1968. This corresponded to a winter
bloom of Dinobryon and Synura. After the bloom,
ammonia increased to 0.40 mg N/1 within the next
month. A similar but smaller decrease and sub-
sequent increase occurred contemporaneously in Mc-
Cloud Lake. Ammonia was being continuously fed
into the experimental lake at a rate equivalent to a 0.1
mg N/1 average increase in the water for the two
month period (February and March). With some
fluctuations ammonia has continued to increase in
Anderson-Cue Lake during 1968 while values in Mc-
Cloud Lake have shown a much smaller trend. Am-
monia in Anderson-Cue Lake has now increased to
levels commonly considered indicative of eutrophy.
Nitrate seasonal patterns (Figure V-5) are nearly
identical for the two lakes, but the experimental lake
has consistently higher values (by about 0.01 to 0.09
mg NO,-N/1). The peak concentrations for both
lakes in 1968 occurred in early February; minimum
values were found in late May. The seasonal pat-
tern probably can be explained by uptake of nitrate
during the late winter bloom and inhibition of nitri-
fication at warm summer temperatures. Nitrate con-
centrations have been below 0.10 mg N/I except
during the winter of 1968. No winter maximum was
found in 1967, but data from January, 1969 (not
shown in Figure V-5) implies that a late winter
maximum will probably occur at about the same time
as the 1968 peak.


1968
Figure V-7.


Ortho-phosphate concentrations in both lakes
(Figure V-6) are normally quite low and follow a
similar seasonal pattern. Peak concentrations occur
from late spring to mid-summer. Values were below
10 tg P/1 in both lakes during winter and early spring
and again in fall of both years. The low phosphate
values (except during summer) indicate phosphorus
is probably the limiting eutrophying factor in Ander-
son-Cue Lake. The seasonal pattern of ortho-phos-
phate is rather the opposite of that found in north
temperate lakes and is somewhat unexpected. This
pattern (summer maximum) has been found for am-
monia in some Polish lakes (Karcher, 1939). Data
on seasonal variations of phosphate in other unstrati-
fied lakes are rather sketchy. There seems to be a
consistent trend in the total phosphate of both lakes
to higher values in summer and minimum values
in early winter. With few exceptions concentrations
were higher in the experimental lake than in the
control, but the differences were usually not striking.
Concentrations increased greatly in both lakes in
early fall of 1968 and reached maximum values of
93 and 84 ug P/I (average concentrations) in Ander-
son-Cue and McCloud, respectively, in mid-October.
Data for this period in 1967 are not available for com-
parison. Such a rapid and large increase in total
phosphate and subsequent rapid decline would seem
to imply an important role for the sediments as a
phosphorus source and sink since they alone would







seem capable of providing such amounts to the water.
During the first 21 months of nutrient enrichment
(through December, 1968), approximately 217 kg
nitrogen and 18.5 kg phosphorus, mostly as ammonia
and ortho-phosphate, were added to Anderson-Cue
Lake through the nutrient outfall. This was sufficient
to increase the N and P levels in the lake by 0.87
and 0.082 mg/1, respectively, at the lake's volume in
1967, if all the nutrient material remained in the
lake. The actual nutrient increase should have been
larger because of the decrease in lake volume in
1968. Inspection of Figures V-3 to V-7 shows this


theoretical situation clearly does not apply. Increases
in total N and P concentrations do not approach
these levels and much of the added nutrient evi-
dently was deposited in the sediments or was lost
through ground water seepage. This has been found
to be the case in other lakes where nutrient budgets
have been constricted. This would seem to imply
an important role for sediment regeneration of nutri-
ents in the eutrophication process. Possibly the onset
of deleterious conditions in the eutrophication process
is contingent upon exhaustion of the sediment's capac-
ity to retain nutrients.


Table V-3
COMPARISON OF AVERAGE CONCENTRATIONS OF SOME
BIOGENIC PARAMETERS AT THREE DEPTHS
IN STATIONS 7 AND 11.


Top Mid
Parameter1 Station N2 Year Mean S.D.3 Mean


TON


7 18
10
11 18
10

7 11
9
11 11
9


PON


NH3




o-PO4


t-P04


21
7 10
21
11 10

7 18
9
11 18
9


1967
1968
1967
1968

1967
1968
1967
1968

1967
1968
1967
1968

1967
1968
1967
1968

1967
1968
1967
1968

1967
1968
1967
1968


4.67
5.20
4.90
4.97


0.41
0.33
0.40
0.30

0.15
0.17
0.14
0.17

0.104
0.363
0.052
0.136

4.0
11.5
3.9
7.5

9.3
24.6
7.7
10.6


0.50
0.49
1.12
0.31


0.15
0.06
0.19
0.06

0.08
0.05
0.23
0.06

0.057
0.199
0.033
0.083

4.6
8.8
5.8
5.4

9.9
29.0
8.8
6.1


4.75
5.08
4.75
4.93


0.49
0.37
0.43
0.37

0.23
0.22
0.19
0.19

0.126
0.368
0.060
0.133

5.5
12.0
3.3
8.4

12.8
20.1
9.7
12.4


Bottom
S.D.3 Mean S.D.3


0.64
0.24
1.29
0.34


0.20
0.09
0.16
0.07

0.20
0.07
0.16
0.07

0.112
0.179
0.036
0.068

7.0
8.9
4.9
5.9

7.4
9.3
8.2
5.4


4.74
5.27
4.70
4.92


0.65
0.38
0.50
0.38

0.35
0.21
0.26
0.21

0.102
0.393
0.093
0.179

6.1
13.4
3.1
11.9

11.1
24.0
13.4
18.1


0.72
0.35
1.25
0.48


0.48
0.09
0.22
0.11

0.45
0.08
0.27
0.10

0.069
0.191
0.077
0.097

8.2
11.3
. 5.0
12.7

8.2
7.4
9.5
15.7


1. Values for total organic nitrogen (TON), particulate
ortho and total phosphate in ug P/1.


organic nitrogen (PON) and ammonia in mg N/l;


2. N= number of determinations. Data from 1968 are for January to October, while data from 1967 are
for entire year.
3. S.D.= standard deviation = These are standard deviations of the results from the particular
station and depth for the given year and reflect the annual variability rather than the analytical preci-
sion of the test. They are presented primarily to indicate the former rather than for further statistical
testing.






Table V-4
COMPARISON OF AVERAGE CONCENTRATIONS OF SOME
BIOGENIC PARAMETERS AT THE
ROUTINE SAMPLING STATIONS


Anderson-Cue Lake
Station Station
7 8


1967
1968

1967
1968

1967
1968

1967
1968

1967
1968

1967
1968

1967
1968

1967
1968

1968

1967
1968


4.70
5.10

3.5
2.8

0.54
0.39

0.38
0.21

0.101
0.356

0.043
0.097

3.9
12.1

11.7
24.2

8.4

0.86
1.14


McCloud Lake
Station Station
11 12


4.72
5.18

3.6
3.0

0.52
0.36

0.24
0.20

0.111
0.374

0.040
0.090

5.2
12.3

11.1
22.8

9.7

0.89
0.80


4.76
5.15

3.3
2.9

0.62
0.59

0.37
0.24

0.112
0.337

0.041
0.088

4.2
13.3

11.6
22.8

20.7

1.83
1.33


Acidity in mg/1 as CaCO,; ortho and total
stands for particulate organic nitrogen.

Considerably greater detail is known about the
variations of chemical species in the lakes than was
presented above. The data in Figures V-1 to V-7
represent mean values for each lake on a particular
date. Data were collected from the 3 depths at each
mid-lake station on each date. Table V-3 summarizes
results for some biogenic elements at the 3 depths;
the data indicate the lakes are well mixed vertically
(as implied by the temperature and dissolved oxy-
gen data in an earlier section), and the vertical
differences in chemical species were usually very
small. Differences between the stations in each lake
are also small for most chemical species. Occasion-
ally, parameters such as total organic nitrogen have
exhibited significantly higher values at the shore
stations, apparently because of slough-off from littoral
vegetation. Table V-4 is a summary of some data
from the three stations in Anderson-Cue Lake and the


phosphate in pg P/1; all other values in mg/1. PN


two stations in McCloud Lake. Because of the large
number of samples even the small differences shown
for some parameters are statistically significant, but it
seems unlikely that the differences would be ecologi-
cally significant or that they would change one's opin-
ion about the lakes' homogeneity.
A detailed study of the lateral variations in am-
monia and orthophosphate was conducted in January,
1968, and gives further evidence of the experimental
lakes' comparative homogeneity. This is not to say
that there are no differences at all. Figure V-8 shows
a slight trend for higher ammonia near the southern
shore. In general ortho-phosphate was higher in shore
areas than in the lake center (Figure V-9), but values
for the southern shore were the lowest in the lake. The
high ortho-phosphate values in the northwest portion
of the lake probably represent a minor source of pollu-
tion from cattle grazing in this area during this period.


Parameter'


Year


Acidity

TON


PN


NHaN


NO3N

O-PO,


t-PO,


Station
4


4.78
4.94

4.1
3.3

0.44
0.35

0.20
0.19

0.068
0.149

0.024
0.058

3.7
9.2

10.3
13.7

11.4

0.96
0.58


4.64
5.03

3.1
2.6

0.48
0.49

0.26
0.29

0.064
0.121

0.019
0.064

2.9
7.6

9.2
15.3

8.5

0.73
1.54


COD

BOD







































Figure V-8. Lateral Variations of Ammonia in Anderson-Cue Figure V-9. Lateral Variations of Ortho-Phosphate in
Lake, January, 1968. Concentrations in mg N/I. Anderson-Cue Lake, January, 1968.
Locations of routine sampling stations shown in Concentrations in ug P/I. Location of
blocks, routine sampling stations shown in blocks.


The results indicate that the routine stations are rep-
resentative of the conditions throughout the lakes
within the limits of accuracy desired for this project.
A detailed survey of the area surrounding the nutrient
outfall was conducted in January, 1969. The results
shown in Figure V-10 imply rapid mixing in the lake
since no concentration gradients resulting from nutri-
ent additions were found.
Short-term temporal variations in chemical species
have also been investigated. An intensive ten day
study was performed in May, 1968. The results for
this are reported in a later section (Section VIII).
Several diurnal studies in which samples were taken
every hour or every two hours over a 24 hour period
have also been made, but in most cases the small
variations appear random rather than cyclic. A
few parameters do show a measurable diurnal vari-
tion; Figure V-11 illustrates this behavior for several
biogenic species during a diurnal study from January
31 to February 1, 1968. As Anderson-Cue Lake
becomes further enriched and more productive, bio-


genic species will undoubtedly show greater diurnal
periodicity, and studies of the lake's diurnal varia-
tions may be a useful indicator of the lake's advanc-
ing eutrophy.

E. Sediment Studies
Sediments may exert considerable influence on the
eutrophication process, both as nutrient sources and
as nutrient sinks. Furthermore they can exert large
oxygen demands on the overlying water and provide
a home and substrate for organisms which spend all
or part of their life cycles as benthos. A variety
of studies has been undertaken to provide a better
understanding of the role of lake sediments in eutro-
phication.
As a first step, chemical characteristics and varia-
tions in sediment types have been determined for
the study lakes. Representative results for Anderson-
Cue Lake are shown in Figures V-12 through V-14.
Several sediment types occur in the lake: near shore,
the bottom is sand covered with a thin layer of loose






pf. monia (Figure V-12), and most of the phosphorus
^G is also bound (presumably organic) rather than free
0 i?^t ortho-phosphate (Figure V-13). Leaching and incu-
^ >bation studies undertaken to reveal the importance
0020t 6 2.5
3.0 .25 60 -

3.0 OA27
32 .0 3.0 5
5I .. ...22
2.. ".. _- ..... .. ..




3 .0P 3.'oA 1
2 2J.1 3 Feb 0
.00 520










Around Nutrient Outfall --January 1969. .
I 4 ..z .22 o1.__00 .





.30251 27 D 03
220 1. 20 31 2-0 .







detritus and periphyton In parts of the deep regions, .
0.27

of McCloud Lake have not 19been as well character-
65 45 5











sized, but peat-like sediments are less in evidence
there. The results indicate that sediments from these
lakes are actually higher in nitrogen and phosphor C tusr




ample, sediments from Lake Mendota, Wisconsin,
have from 200 to 1200 ppm phosphorus and 2000 to ', 30
14,000 ppm total organic nitrogen ( Hasler, 1963). Theo
118














sediments in this alkaline lake are over 30 percent pre- 2 i
cipitated calcium carbonate, whereas those in the .1 2
lakes of this study are composed largely of organic I
matter. The absence of carbonate deposits in the .i
sediments of the study lakes permits volatile solids 2
determinations to approximate the organic content .08 36
of these sediments. The high values in Figure .23 08 16 9
V-14 indicate the largely organic nature of these25 .4.8


The sediments in the study lakes are obviously 6
enriched with nitrogen and phosphorus compared to 4
the overlying water and thus represent potential Figure V-12. Nitrogen in Anderson-Cue Sediments. Top
nutrient sources. However, most of the nitrogen is number is ammonia; bottom number is total
0 pm organic nitrogen. Values in mg N/g dry wt.
present in the organic form rather than as free am- o sediment.






































Figure V-13. Phosphate in Anderson-Cue Sediments. Top Figure V-14. Percent Volatile Solids (Top Number) and
number is ortho phosphate, bottom number is Total Iron (Bottom Number) in Anderson-Cue
total phosphate values in pg P/g dry wt. of Sediments. Iron values in mg Fe/g dry wt. of
sediments. sediment.


of sediments in nutrient storage and release must
consider the wide variations in available nutrient con-
tent of the various sediments in the lake as well as the
varying opportunities for transport from sediment to
water afforded at different locations in the lake. For
example, bottom currents are comparatively slow in
the center or deep portions of the lakes and nutrient
exchange may be a diffusion-controlled phenomenon.
On the other hand, the thin sediment layer in the
sandy littoral areas is frequently and easily mixed
with the overlying water by wind-generated currents
and waves and by movement of fish. Nutrient release
from these sediments is probably controlled by meta-
bolic rates rather than by physical factors. The over-
all question of nutrient exchange between sediments
and water (in either direction) is extremely complex
and a completely satisfactory answer is perhaps be-
yond the "present state of the art."
A variety of sediment exchange experiments, in-
cluding laboratory and in situ studies, have been un-
dertaken or are presently underway or planned. Some
initial results of a laboratory incubation study are


shown in Figures V-15 and V-16. Sediment from the
middle of Anderson-Cue Lake was incubated in 10
liter bottles under varying conditions, and ammonia
and ortho-phosphate in the over-lying water was fol-
lowed over a period of 20 days. About 0.5 liters of
sediment was placed in each bottle. Incubation was
conducted in the laboratory at 220C under conditions
of (artificial) laboratory lighting. One bottle (A) was
incubated under the above conditions; a second bottle
was treated similarly except that light was excluded
(C), a third bottle (B) was continuously purged with
nitrogen to maintain anoxic conditions and a fourth
bottle (D) was stirred to keep the sediment mixed
with the water. Changes in the ammonia content of
the overlying water are shown in Figure V-15. Anoxic
conditions allowed more ammonia leaching in bottle
(B) than in the oxygenated control (A). However,
stirring was more effective and maximum ammonia
was released immediately. A similar situation occur-
red with ortho-phosphate (Figure V-16). Anoxia
induced the release of considerably more phosphate
than oxygenated conditions in the control. The dark







bottle showed consistently lower ortho-phosphate
than in the control, but the trend was similar. Again
mixing was most effective in liberating phosphate.
The maximum phosphate was released almost immed-
iately and the concentration declined about 50 per-
cent over the 20 day incubation. It is obviously
premature to extrapolate these results to the in situ
role of sediments in nutrient recycling. But it would
seem that mixing is the most effective mechanism to


D (x0.5)


B


A A


S5 10 15 20
Days
Figure V-15. Temporal Changes of Aqueous Ammonia in Lake Water
Incubated With Sediment Under Varying Conditions:
A, control; B, anoxic; C, dark; D, stirred.


release sediment nutrients into the water if that were
desired. Alternately it is apparent that the single
most effective means of limiting release is prevention
of mixing. In deep lakes relatively little mixing occurs.
However, during periods of high winds, sufficient
currents may be generated in shallow lakes and
littoral zones of deeper lakes to stir sediments with
the water and release considerable amounts of nu-
trients.


Days
Figure V-16. Temporal Changes of Aqueous Ortho-Phosphate in
Lake Water Incubated With Sediment Under Varying
Conditions (see Figure V-15 for key).







SECTION IX


TROPHIC STATE OF LAKES IN NORTH CENTRAL FLORIDA


Limnological studies have been sparse in Florida;
consequently little background information is avail-
able concerning the quality and nature of most lakes
in the state. This is unfortunate since the state has
such a large number of lakes which figure heavily in
its recreational (hence economic) assets. It is not the
function of an academic research project to act in a
mere data gathering or surveillance capacity. This is
the responsibility of governmental survey and enforce-
ment agencies. On the other hand, some data collec-
tion is obviously necessary in a project like the present
one. The paucity of requisite background data has
necessitated a greater effort in this direction than per-
haps would have otherwise been appropriate.
A survey of the physical, chemical and biological
features of lakes in north central Florida was begun in
1968. This was undertaken for several specific objec-
tives: 1) to assess the present trophic quality of lakes
in this region; 2) to provide baseline data for future
studies on rates of changes in the quality of these
lakes; 3) to gather sufficient information to evaluate
the appropriateness of present trophic state criteria in
sub-tropical lakes; and 4) to provide necessary data to
construct an equation or index (or indices) for trophic
states in sub-tropical lakes.
Sampling programs have been initiated on lakes in
three regions of north central Florida: 1) Alachua
County (the location of Gainesville); 2) the Central
Highlands in Putnam and Clay Counties; and 3) the
lower Oklawaha River drainage basin in Lake, Semi-
nole and Marion Counties. The most extensive sam-
pling program is being conducted on the lakes in Ala-
chua County (for reasons of convenience and also be-
cause of the wide range of lake types available within
the county). Because of the large number of lakes in
the Central Highlands region, only selected lakes are
included in this survey. The two model lakes de-
scribed in earlier sections of this report are located
within this region. The lakes in this area are some-
what different from and generally higher in quality
than lakes in Alachua County. These lakes are be-
coming increasingly important recreationally as loca-
tions of homes for vacationers and retirees. Conse-
quently it was considered important to study the tro-
phic nature of these lakes. The lower Oklawaha River
basin contains some of the largest and most fertile
lakes in Florida. These lakes are important for recre-
ation, and in the past were considered among the
best bass fishing lakes in the country. Considerable


evidence of rapid eutrophication has been found in
some of the lakes within recent years, and agricultural
runoff has been implicated as a major contributor of
nutrients. In view of these facts it was felt that any
study of eutrophication and the trophic states of Flor-
ida lakes would be incomplete without some informa-
tion on these lakes. Numerous studies have already
been made on the Oklawaha lakes, and studies by
groups such as the Florida Game and Fresh Water
Fish Commission are continuing. It is not intended
that the present study duplicate these efforts but com-
plement them. Because biological information needed
in trophic state indices is not available for the lakes,
limited sampling on six of the largest lakes in this re-
gion has been undertaken.
Data gathering is still in progress. It is planned to
sample each lake several times to estimate seasonal
variability. The Alachua County lakes have been
sampled twice; the Oklawaha lakes once. Initial sam-
pling of the Central Highlands lakes is presently un-
derway, and a complete set of data is not yet avail-
able. The remainder of this section will describe the
results obtained thus far and discuss our initial at-
tempts to classify the lakes according to trophic type.

A. Alachua County Lakes

All the significant lakes in the county are included
in the survey; criteria for significance include size
(all lakes larger than about 3 hectares were sam-
pled) and economic or recreational value (any lake
with a public or private access road or a permanent
dwelling on its shore was considered significant in
this regard). Thirty-three lakes in or partly in
Alachua County were found to meet these criteria.
The locations were determined from U.S.G.S. topogra-
phic and county road maps. The lakes and their
locations are listed in Table IX-1 and shown in Fi-
gure IX-1. Not all the lakes have names, and some
of the names are known only to local residents. In
some cases conflicting names appear on different
maps; we have assumed the most recent map to be
correct in these instances. Several other lakes appar-
ently meeting the criteria are indicated on topo-
graphic maps but were either swamps or completely
dried land at the time of sampling.
Previous limnological efforts in Alachua County
have been rather sparse. Because of its unusual
characteristics, Lake Mize has been the subject of







Table IX-1
LAKES IN ALACHUA COUNTY


Number Name


Santa Fe Basin
1 Santa Fe
2 Little Santa Fe
3 Hickory Pond
4 Altho
5 Cooter Pond


Orange
6
7
8
9
10
11
12
13
14
15
16
17
18
19


Creek Basin
Elizabeth
Clearwater
Hawthorn
Little Orange
Unnamed
Moss Lee
Jeggord
Still Pond
Lochloosa
Orange
Palatka
Newnan's
Mize
Trout


No Surface Drainage Region
20 Meta
21 Unnamed
22 Bivens Arm
23 Alice
24 Clear
25 Unnamed
26 Unnamed
27 Unnamed
28 Kanapaha
29 Watermelon Pond
30 Long Pond
31 Burnt Pond
32 Wauberg
33 Tuscawilla


Location


North of Melrose
North of lake (1)
West of lake (2)
East of Waldo
North central part of county

Southwest of Melrose
Southeast of Melrose
Hawthorn
Southeast of Hawthorn
South of lake (9)
Southeast of lake (10)
South of Hawthorn
East of Lochloosa
Southeast part of county
Southeast part of county
South of lake (17)
East of Gainesville
Northeast of Gainesville
Southeast of Gainesville

Northwest Gainesville
South Gainesville
South Gainesville
U. of F. campus
Southwest Gainesville
West of Gainesville
South of lake (27)
Northwest of Gainesville
Southwest of Gainesville
Southwest part of county
West of Micanopy
West of lake (32)
Northwest of Micanopy
South of Micanopy


1 Depth in meters
2 Area in hectares
3 Symbols are as follows:
O oligotrophic
M = mesotrophic
E = eutrophic
HE = hypereutrophic
D = dystrophic
S = senescent
C = high organic color in lake water


Depth'


2.5
3
3.5
3
4

4

3
3
1
2
22
1.5

1.5
3
2
2
2
2

5
1
2
1.5
2.5
5
2


Area2


1760
464
32
228
124

57

35
241
50
57
85

2484
3105
25
2562
0.9
15

3.9
1.8
70
37
4.3
7.4
2.2
5.4
84
632

23
103
64


Type3


OC
OC
OC
OC
M

MC
0
EC
MC
MC

D-MC

E
E
D-S
HEC
D-OC
D-EC

M
E
HE
S
EC
M
D-MC
M
E-S
D-OC
D-OC
EC
E
MC






















































several studies, including Harkness and Pierce (1940)
and Nordlie (1967). Odum (1953) reported some
values for dissolved phosphorus in Lake Mize and
several other lakes in the county. Nordlie (1967)
studied primary production and its limiting factors
in Bivens Arm, Lake Mize and Newnan's Lake.
The nitrogen cycle, nitrogen fixation and organic
color in Lake Mize have been studied by Brezonik
(unpublished). Miscellaneous data on several of the
larger lakes can be found in Florida Geological Sur-
vey publications (e.g. Clark et al., 1962, 1964).


It is apparent from Figure IX-1 that the lakes are
not uniformly distributed throughout the county. Most
of the lakes are situated in the eastern half of the
county, and the four major lakes are in the eastern
third. The terrain surrounding the lakes is remark-
ably varied, considering the small geographical area.
Most of the lakes in the eastern part of the county
have heavily forested shorelines, but the type of forest
varies for different lakes. The large lakes have an
outlet and one or more inlet streams, but many of the
small lakes have neither.







On the basis of surface runoff, the county can be
divided into three zones as shown in Figure IX-2.
The southeastern third of the county lies within the
Orange Creek Basin; fourteen county lakes are in the
basin, but not all are connected by surface streams.
The Santa Fe River basin in northern Alachua County
has only five lakes but considerable swampland. The
southwestern part of the county, comprising some 300
square miles, is an area from which there is no sur-
face outflow. The few small streams terminate in
limestone sinkholes. Fourteen lakes are located in
this region, but most of them are small ponds in the
vicinity of Gainesville.
Figure IX-2 also indicates surface distribution of
geological formations in Alachua County. A brief
description of these surface formations (summarized
from Clark et al., 1962) is presented below. The
Hawthorn Formation is a marine deposit of Miocene


Age consisting of thick and sandy clays interbeddcd
with phosphatic limestone. This formation is exposed
in central, northern and eastern Alachua County.
Higher terrace deposits outcrop in a 70 square mile
area in northern Gainesville and north central Alachua
County. These deposits are of Pleistocene Age and
consist of fine to medium sands, clayey sands and
varicolored clays. The Citronelle and Alachua For-
mations are of Pliocene Age. A small area of Citronelle
outcrops in eastern Alachua County. This is a non-
fossiliferous deltaic deposit of sand, gravel and clayey
sand. The Alachua Formation is exposed in western
Alachua County and consists of terrestrial deposits
of white, gray and colored sands, clayey sands and
some varicolored clays. Vertebrate fossils, limestone
and phosphate pebbles and boulders are scattered
throughout the deposit. The Ocala Group of Eocene
Age limestones is at the surface in western and south-












Alachua
Formation


Ocala
Group


Hawthorne
Formation


Higher terrace
e deposits
Orange Creek
Citronelle
Formation


Figure IX-2. Drainage Basins and Geologic Map of Alachua County
(After Clark et al., 1962).







ern Alachua County. In much of the area shown in
Figure 1X-2 as having the Ocala Group at the surface,
a thin layer of residual sands and clays covers the
limestone. The Ocala Group consists of coquina,
hard and soft limestones, and dolomitic limestones.
The Ocala Group underlies all of Alachua County and
is the main part of the Floridan aquifer. Because
of the underlying Ocala limestones, Alachua County
can be described as generally having a karst topog-
raphy. Filled and open sinks, sinkhole lakes, solu-
tion pipes and lakes and prairies are typical of
such areas, and the county has these features in abun-
dance. Further details about the drainage basins
and general landforms in Alachua County can be
found in Clark et al. (1964).
Large areas of the county are covered with slash
pine for pulp purposes, and extensive swamplands
exist. Particularly in the northern and eastern areas,
the swamplands are forested with cypress and other
trees. Consequently much of the surface water in
the county is highly colored with organic exudates
and plant degradation products. While agriculture
in the broad sense occupies an important place in


the economy of the county, relatively little land is
devoted to annually harvested crops. Corn and
watermelons are perhaps the largest crops and are
grown in widely scattered areas. Some tobacco is
grown in the northwest part of the county, and a
few orange groves are located in the south-central
part near Orange Lake. Considerable land is devoted
to cattle grazing. In general, agriculture would seem
to have little effect on the quality of surface waters
in the county. The effects of geology and surface
land use on the water quality of individual Alachua
County lakes will be discussed in later paragraphs.
The physical, chemical and biological parameters
measured in each lake are enumerated in Table IX-2.
The lakes were sampled during late May-early June,
1968, and again in late November-early December,
1968. Processing and analysis of the data especially
for the third and fourth objectives listed above are
still underway and information on basin land use,
shoreline development and vegetation is incomplete.
The entire study will be published as a special bul-
letin when complete.
The lakes covered in this study range in size


Table IX-2
PARAMETERS MEASURED IN LAKE STUDY

Physical


Lake depth
Lake area
Temperature profile


Secchi disc transparency
Land use in lake basin
Shoreline development


Chemical


A. Water


Acidity
Alkalinity
Calcium
C.O.D.
Chloride
Color
Conductance
Fluoride
Iron


Magnesium
Manganese
Organic nitrogen
Ammonia
Nitrite
Nitrate
Oxygen, dissolved
pH
Orthophosphate


Total phosphate
Potassium
Silica
Sodium
Suspended solids
Total Solids
Sulfate
Turbidity


B. Sediments
Ammonia Percent volatile solids
Organic nitrogen Iron
Total phosphate Manganese
Sediment type (visual classification as peat, muck, etc.)

Biological


Algal identification and counts
Chlorophylls a, b, and c
Total carotenoids
Primary production


Species diversities indices of
algae
Visual classification of
vegetation surrounding lake







from 0.9 hectares (Lake Mize) to 3100 hectares
(Orange Lake). Only eleven of the lakes are larger
than 100 hctares, and only four of these are larger
than 1,000 hectares. Most of the lakes are quite
shallow, Lake Mize being the only significant excep-
tion. This small lake is a limestone solution sink and
has a depth of at least 25 meters. Santa Fe Lake
with a maximum depth around 8 meters is the second
deepest lake in the county. A majority of lakes
have maximum depths of 3-4 meters, but about a
third, including some of the larger lakes have maxi-
mum depths of 2 meters or less. Bathymetric maps
are available for only three (3) county lakes: New-
nan's, Mize and Orange. The depths recorded in
Table IX-1 are the maximum values found at several
widely scattered sampling stations on each lake.
More detailed soundings may locate deeper areas in
some lakes, but we believe the values in Table IX-1
to be closely representative of the maximum depths.


Because of their shallow depths, few of the lakes
in Alachua County show significant thermal stratifi-
cation. Lake Mize is the only known monomictic
lake, having a well developed thermocline from
March to November (Brezonik, unpublished data).
Santa Fe Lake may stratify at least for brief periods
in spring but data are not yet available to prove this.
Even shallow lakes will stratify briefly during calm
periods of intense warming. Such conditions occur
most commonly in spring, but can occur in fall and
winter also. Evidence for temporary stratification
has been found in a number of lakes, but tempera-
tures differ from top to bottom usually by only a
few degrees Celsius, which is insufficient to prevent
mixing by normal winds.
Conditions in three of the four large lakes (greater
than 1,000 hectares) in Alachua County leave much to
be desired. Relevant data concerning trophic condi-
tions in these lakes are summarized in Table IX-3.


Table IX-3
TROPHIC CHARACTERISTICS OF LARGE LAKES IN ALACHUA COUNTY1' 2


Parameter3


Seechi disc
Turbidity
Sp. conductance
Acidity
Alkalinity
pH
COD
Color
TON
Ammonia-N
Nitrate-N
Ortho P
Total P
Chloride
Sodium
Calcium
Iron
Manganese
Silica
Organism count
Chlorophyll a
Primary production


Santa Fe L.


2.4
5.4
48
0.6
2.6
6.5
16
130
0.44
0.22
0.0
0.001
0.11
10.4
7.5
1.3
0.02
0.008
0.11
28
5.56
13.5


Newnan's L.


0.6
5.8
60
0.9
9.0
7.6
79
490
1.13
0.02
0.0
0.006
0.05
10.8
9.8
4.0
0.06
0.003
0.68
2525
8.62
53.6


Orange L.


0.6
6.0
67
0.5
15
7.6
56
280
1.09
0.02
0.0
0.005
0.05
9.5
8.1
6.0
0.02
0.050
0.57
2234
9.68
43.0


Lochloosa L.


0.8
7.9
90
0.5
24
8.3
55
200
1.17
0.01
0.0
0.004
0.05
10.6
9.6
10.0
0.02
0.002
0.16
5670
12.5
35.6


SLakes larger than 1,000 hectares.
SData from December, 1968 sampling; data from June, 1968 show similar trends. Organisms counts
are from June samples since counts from December samples are not yet available. They are presented
for comparative purposes among themselves only and should not be compared with the other chem-
ical and biological results (from December). Each number represents a single determination on a
composite sample taken from three stations in the lake.
SUnits for the parameters are in mg/1 except as follows: Secchi disc transparency in meters; specific
conductance in umho cm-'; acidity and alkalinity in mg/1 as CaCO:,; pH in pH units; color in mg/1 as
P+; organism counts in numbers/ml; chlorophyll a in mg/m3; and primary production in mg C/m"-
hr. on composite samples run in a laboratory incubator.







Whether conditions have been affected by cultural
influences is not yet certain, but natural factors, es-
pecially geological and morphological, seem adequate
to explain most of the conditions. Santa Fe Lake is
the only oligotrophic lake in this size category. Its
maximum depth (8 meters) is not particularly im-
pressive, but it is by far the deepest of the large lakes.
An important factor relating to the lake's oligotrophy
is its small drainage basin, the majority of which is
forest. A moderate number of cottages and homes
dot the shoreline, but no other urban or agricultural
eutrophying influences are evident. Santa Fe Lake
lies wholly within the Hawthorn Formation, which
contains phosphatic clay deposits, but this circum-
stance is apparently mitigated by the small drainage
basin. Phosphate and inorganic nitrogen levels in
Santa Fe Lake are relatively high compared to the
critical levels suggested to stimulate blooms in north
temperate lakes, but concentrations are lower than in
the other large lakes. The biological data are all
indicative of oligotrophic conditions; color (which
limits light transmission) and depth are probably
contributing factors to this condition.
Newnan's, Orange and Lochloosa Lakes are in
the Orange Creek drainage basin, and all show con-
siderable evidence of eutrophy. Newnan's Lake lies
within the Hawthorn Formation outcrop, and its
major influent stream, Hatchet Creek, drains a large
area of the formation north of the lake. Water flows
from the southern end of Newnan's Lake through a
creek, to a man-made canal and a meandering river
into Orange Lake. The northern half of Lake Loch-
loosa and most of its drainage basin lie within the
Hawthorn Formation. Orange and Lochloosa Lakes
are connected by Cross Creek, but the major flow
from both lakes is through outlets to Orange Creek
to the southeast. The high trophic conditions of these
lakes are at least partially the result of edaphic con-
siderations. There are no major cultural sources of
nutrients for these lakes although fertilized pasture
land is drained by Hatchet Creek and the towns of
Orange Lake and McIntosh probably release some
sewage into Orange Lake.
Newnan's Lake is especially peccant; its extreme
shallowness must be a contributing factor. The maxi-
mum depth shown on the bathymetric map is 3.6
meters (12 feet), but the mean depth is under 2
meters. With the large surface area, winds must be
quite effective in mixing sediments with the overly-
ing water. Orange and Lochloosa Lakes are only
slightly deeper, and the geological and hydrological
situation implies they are following shortly behind
Newnan's Lake on the same avenue of degradation
and extinction. All three lakes had profuse algal


blooms in June of 1968. The November standing
crops were much lower though still blooms in Lackey's
(1945) definition (500 organisms/ml). Essentially all
the criteria (see Table IX-3) reaffirm the advanced
eutrophy of the three lakes. The large lakes in the
Orange Creek basin produce an abundance of fish and
are popular with sport fishermen. The advanced eu-
trophy suggests game fishing may be in a somewhat
precarious position, and takeover by rough and trash
fish could conceivably be imminent, particularly in
Newnan's Lake. Any plans to increase the man-made
sources of nutrients to these lakes should be viewed
askance.
Results from the medium sized lakes are sum-
marized in Table IX-4. These lakes are widely
scattered geographically and present an interesting
spectrum of trophic conditions. With the exception
of Lake Wauberg, the lakes are in good condition.
Wauberg is in an advanced state of eutrophy with
frequent and obnoxious algal blooms. The reasons
for this condition are not entirely clear. The lake is
not excessively shallow (maximum depth is at least
5 meters) and receives no urban runoff, sewage
effluent or large amount of agricultural runoff. The
drainage basin seems small and is in the Ocala
Limestone Formation. In past years the lake was
used extensively for picnicking and swimming; a semi-
private beach and restaurant were located on the lake.
But the potential nutrient additions from these sources
seems inadequate to explain the present conditions.
The University of Florida now owns the land around
the lake and operates it as a camp and recreational
facility for the university community.
Lake Altho and Little Santa Fe Lake are classi-
fied as colored oligotrophic. The two lakes are
connected by an improved canal, and together with
Santa Fe Lake, they form the headwater of the Santa
Fe River. Santa Fe and Little Santa Fe Lakes in a
sense are one lake with two basins separated by pen-
insulas which constrict the water to a short channel
several hundred yards wide. Both Altho and Little
Santa Fe are moderately deep (for this area) with
maximum depths of 5 and 6.5 meters, respectively.
Both have relatively small drainage basins and no
sources of cultural enrichment other than a relatively
small number of summer cottages and homes. Little
Orange Lake, in the Orange Creek basin, is somewhat
similar to the two above lakes, although it is more
highly colored (about 500 ppm in November) and
shallower (maximum depth 3.5 meters). The lake is
surrounded by forest and is dotted with cottages, but
there are apparently no major cultural influences.
The lake is somewhat difficult to classify. Its high
color implies dystrophy, but the lake has moderate













Table IX-4
TROPHIC CHARACTERISTICS OF MEDIUM-SIZE ALACHUA COUNTY LAKES1' 2

Parameter Altho Cooter Little Little Tuscawilla Watermelon Wauberg
Santa Fe Orange
Secchi disc 1.7 1.2 1.3 1.0 1.2 -3 0.9
Turbidity 3.3 4.5 6.3 4.4 3.3 4.0 10.1
Sp. conductance 49 63 50 54 55 32 59
Acidity 1.3 1.4 3.2 4.9 6.0 2.76 0.0
Alkalinity 3.2 4.8 1.6 1.1 12.8 0.0 15.7
pH 6.5 6.9 5.7 5.9 6.6 4.8 8.3
COD 22 87 31 68 52 23 25
Color 250 250 385 495 625 285 125
TON 0.62 1.12 0.57 0.89 0.85 0.86 1.71
NH3 0.23 0.45 0.26 0.16 0.03 0.20 0.09
NO- 0.03 0.01 0.02 0.00 0.03 0.03 0.00
- Ortho P 0.002 0.003 0.000 0.005 0.024 0.001 0.005
Total P 0.09 0.16 0.10 0.04 0.28 0.05 0.15
Chloride 10.1 7.6 9.7 8.8 7.7 6.2 8.7
Sodium 7.2 5.7 7.0 6.6 6.0 4.5 9.4
Calcium 2.0 2.2 1.3 2.2 4.0 0.5 6.0
Iron 0.01 0.01 0.03 0.04 0.03 0.01 0.00
Manganese 0.008 0.009 0.014 0.019 0.003 0.042 0.004
Silica 0.30 0.81 0.31 0.31 2.26 0.08 0.17
Organism count 633 431 150 2352 341 125 9288
Chlorophyll a 4.84 21.9 3.28 4.11 5.76 6.44 30.1
Primary production 10.3 87.0 6.6 12.7 12.2 5.33 124.3


Lakes between 100 and 1,000 hectares.
2 See Table IX-3 for sampling dates and units of parameters.
2 Disc visible to bottom (1.6m.).







nutrient levels and a varied population of diatoms,
blue-green algae and green algae. The lake has been
tentatively classified mesotrophic-colored.
Watermelon Pond is an irregularly shaped lake in
the southwest area of the county. The lake has a
moderate color, and except for its shallowness and
low pH, the data conform to the usual criteria for
oligotrophy. Cooter Pond is in the Santa Fe drainage
basin but is not connected to the river by a permanent
surface outlet. The lake is now surrounded by pas-
ture, but until recently groves of tung trees were
cultivated on the land. The general conditions now
suggest mesotrophic to eutrophic conditions. The
shallowness (2.5 meters maximum depth) implies
a small capacity to assimilate more nutrients, and the
lake appears to be susceptible to further eutrophica-
tion by agricultural runoff and cattle wastes. Tus-
cawilla Lake occupies a gently sloping depression in
a rather flat region south of Micanopy. The lake is
shallow and in periods of drought (such as spring of
1968) its area shrinks considerably. The lake is
highly colored and has a low ionic content. Nitrogen
levels are moderate but phosphorus is high, and
an abundance of macrophytes is found in the littoral
zone. The chemical conditions suggest mesotrophy,
but the shallowness and extensive macrophytes imply
the lake may be approaching senescence.
Thirteen lakes scattered throughout Alachua
County are in the size range 10-100 hectares. In-
cluded are lakes in all classification; conditions rele-
vant to trophic state are summarized in Table IX-
5. Space does not permit discussion of all these
lakes, but it is pertinent to note that three of the
four Gainesville area lakes in this size class show
evidence of cultural effects. Bivens Arm receives
urban runoff from a small stream in the southern
part of Gainesville and has probably been affected
by University of Florida experimental cattle farms on
its northwest shore. The lake has an interesting
history; the U.S.G.S. topographic map of Gainesville
dated 1895 shows Bivens Arm connected with Ala-
chua Lake (Payne's Prairie), both of which were then
dry. The latter never became a (water-filled) lake
again, but Bivens Arm is now a permanent lake
separated from Payne's Prairie by built-up roads.
Sediment cores would provide considerable informa-
tion on the nature of Bivens Arm when it was con-
nected with Alachua Lake and the ontogeny of the
lake since it was separated and refilled. The lake
may have been eutrophic all along; but it seems likely
that the above nutrient sources have intensified con-
ditions in the recent past.
Lake Alice has been classified senescent. This
lake was once eutrophic, but in recent years nearly
all of the lake's surface has been covered with water


hyacinths. The lake is shallow, and decaying vegeta-
tion produces obnoxious odors. Treated sewage from
the University of Florida waste treatment plant enters
the east side of the lake, but nutrient concentrations
in the water do not reflect this source of enrichment.
The extensive hyacinth growths are evidently effec-
tive nutrient removers. The lake is almost devoid of
phytoplankton as a result of the light cover provided
by the hyacinths, the relatively low nutrient concen-
trations and perhaps antibiotic effects of the macro-
phytes. A large volume of cooling water from the
University steam plant enters Lake Alice daily.
This undoubtedly has an important effect on the lake's
biota, and probably prevents large planktonic popula-
tions by a flushing effect.
Lakes Kanapaha and Hawthorn are also culturally
enriched. The former lake is connected with
Kanapaha Sink, the terminus of Hogtown Creek in
Gainesville; the latter lake receives some sewage
from the town of Hawthorn. Burnt Pond is appar-
ently naturally eutrophic. Its shallowness indicates
the lake may not be far from the senescent state.
Most of the other lakes in this size category are in
relatively good condition. High organic color and
relative isolation from urban and agricultural develop-
ment characterize these lakes.
Nine of the lakes in this survey are smaller than
10 hectares. There are also innumerable small ponds
(one hectare or less) scattered throughout the county
(particularly around Gainesville). These are usually
quite shallow and can hardly be considered as lakes in
the usual sense of the word. The nine lakes chosen in
this survey include all the large lakes in this size
category (i.e. lakes between 3 and 10 hectares) and a
few smaller lakes chosen because of their unusual
characteristics or potential significance in a broad re-
creational sense. The trophic conditions of these
lakes are summarized in Table IX-6. Lake Mize is
perhaps the most interesting of these small lakes, be-
cause of its great depth (about 25 meters). The lake
exhibits the usual characteristics of dystrophy-high
color and acidity, low pH, few organisms, etc. The
lake is remarkably similar morphologically and chemi-
cally to Lake Mary, Wisconsin, except for the latter's
mesomixis. Lake Mize is located in the University
of Florida Austin Cary Memorial Forest, which one
might presume would augur the preservation of this
unusual lake. Unfortunately an enclosure for main-
taining and raising waterfowl was recently allowed to
be built on the shore, and now over fifty ducks contri-
bute untreated excrement directly to the water.
Lake #27 (unnamed) is unusual in that it is com-
pletely covered with duckweed (Lemna) normally
indicative of high nutrient conditions. The lake is
isolated from urban and agricultural effects and














Table IX-5
TROPHIC CHARACTERISTICS OF SMALL LAKES IN ALACHUA COUNTY1 2


Parameter Alice Bivens Burnt Elizabeth Hawthorn Hickory Jeggord Kanapaha Long
Arm


Moss
Lee


Palatka Trout


Secchi disc -3 0.9
Turbidity 5.3 10.5
Sp. cond. 533 263
Acidity 13.4 0.0
Alkalinity 178 139
pH 7.4 8.5
COD 12 65
Color 130 75
TON 0.33 1.19
Ammonia-N 0.01 0.01
Nitrate-N 0.00 0.00
c Ortho-P 0.070 0.020
Total P 0.59 0.32
Chloride 16.7 15.3
Sodium 16.0 15.0
Calcium 71 50
Iron 0.01 0.01
Manganese 0.003 0.003
Silica 12.7 1.42
Organism
count 16 2600
Chlor a 1.60 11.0
Prim. prod. 0 77.5


0.9
4.9
67
3.7
15
6.8
66
620
1.87
0.47
0.00
0.039
0.36
9.6
6.5
10.0
0.04
0.007
0.64

1372
21.4
54.4


0.9
3.9
38
2.1
2.0
5.9
48
155
0.72
0.08
0.07
0.005
0.11
7.5
5.4
1.9
0.5
0.019
0.68


1.2
3.9
185
1.1
86
8.0
37
95
1.22
0.05
0.01
0.003
0.08
11.2
11.0
33.0
0.00
0.003
0.27


625 26,209
2.98 19.4
0.58 55.5


2.1
2.8
39
0.9
1.8
6.6
21
160
0.65
0.13
0.00
0.017
0.04
10.0
6.0
0.9
0.01
0.025
0.30


1.2
7.5
53
2.7
0.6
5.2
26
345
0.41
0.11
0.01
0.005
0.12
8.6
7.7
0.5
0.03
0.027
1.56


0.6
7.2
166
4.0
70.5
7.3
78
185
3.84
0.96
0.03
0.014
0.42
8.0
10.9
19.0
0.02
0.01
0.09


119 3824 11,736
6.36 4.28 9.48
7.52 4.26 26.9


3
3.3
16
6.4
0.0
5.1
38
240
0.89
0.05
0.02
0.001
0.04
4.4
2.5
0.6
0.01
0.033
0.18


2.0
5.4
43.0
2.8
1.2
5.9
46
278
.79
.04
.10
.010
.043
8.9
4.9
1.8
.00
.011
.017


- 1
2.56 3.14
1.42 12.9


-3
3.3
22
7.4
0.0
4.8
72
185
0.86
0.14
0.00
0.002
0.08
3.8
3.1
0.4
0.01
0.005
0.71


0.6
1.8
41
8.9
1.2
5.3
64
990
1.55
0.40
0.11
0.14
0.16
7.9
6.0
1.6
0.1
0.05
1.80


3.34 10.8
3.36 10.5


1 Lakes between 10 and 100 hectares.
2 See Table IX-3 for details on sampling dates and units of parameters.
3 Disc visible to bottom.


#10


1.6
4.4
44
5.0
2.5
6.0
48
305
0.81
0.06
0.01
0.027
0.10
8.8
6.6
1.7
0.02
0.012
0.35


3.29
17.4







Table IX-6
TROPHIC CHARACTERISTICS OF PONDS AND LAKELETS IN ALACHUA COUNTY1, 2

Still
Parameter Clear Clearwater Meta Mize Still #21 #25 #26 #27 Pond
Secchi disc 0.8 1.8 1.4 1.0 0.8 -' 1.8 .7
Turbidity 10.0 2.8 6.5 3.9 10.6 0.9 0.9 3.9 3.8
Sp. cond. 106 33 86 45 275 106 88 43.0 40
Acidity 0.0 1.5 1.4 5.8 0.3 0.0 9.8 9.0 1.3
Alkalinity 27.6 0.8 27.4 1.4 128 50.4 0.6 5.7 .6
pH 8.9 5.4 7.6 5.6 8.2 8.3 5.3 6.0 6.0
COD 44 14 40 34 50 17 29 28 20
Color 345 25 90 260 165 75 500 220 70
TON 1.38 0.59 0.83 0.51 1.33 0.73 0.74 0.56 .58
Ammonia-N 0.02 0.10 0.09 0.26 0.02 0.07 0.03 0.04 .07
Nitrate-N 0.00 0.00 0.00 0.01 0.00 0.03 0.01 0.00 .02
- Ortho-P 0.007 0.003 0.001 0.005 0.004 0.001 0.004 0.012 .006
Total P 0.17 0.05 0.06 0.09 0.25 0.06 0.12 0.20 .016
Chloride 8.2 7.4 8.2 9.2 17.7 3.4 9.1 7.7 7.8
Sodium 7.0 5.2 7.0 5.3 15.0 3.1 6.3 5.6 5.5
Calcium 10.0 0.7 10.0 0.8 48 20.0 0.9 2.1 1.0
Iron 0.04 0.01 0.00 0.05 0.00 0.00 0.05 0.02 .00
Manganese 0.005 0.023 0.003 0.010 0.003 0.016 0.015 0.014 .004
Silica 1.68 0.14 0.14 0.98 2.91 0.57 1.60 0.66 .002
Organism
count 5,896. 9 15,160. 88 10,840. 163 303 18 -
Chlor a 11.4 3.32 5.51 24.2 39.0 0.18 6.86 12.7 1.81
Prim. prod. 69.1 0.33 3.59 7.46 235 6.06 2.09 41.0 0.2

1 Lakes smaller than 10 hectares.
2 See Table IX-3 for details of sampling dates and units of parameters.
3 Disc visible to bottom.







otherwise exhibits oligotrophic characteristics. The
lake is moderately deep (for this area)-maximum
depth is at least 5 meters. There does not seem to
be a satisfactory explanation for the extensive duck-
weed growth in this lake and its virtual absence in
all the other lakes of this survey. Clear Lake was
evidently named before the onset of a eutrophic
condition which contradicts its name. The lake is
surrounded by homes with septic tanks and has
become eutrophic only in recent years (Furman,
personal communication). An extremely dense Ana-
baena bloom was present in the June, 1968, sample
and the lake oscillates between relatively clear and
bloom conditions. Lake Meta (in northwest Gaines-
ville) also shows some signs of cultural enrichment
from surrounding homes, but to a lesser extent.
Even cursory inspection of the chemical and
biological results obtained thus far reveals graphic
disparities in trophic conditions among the lakes.
In fact inspection of the lakes themselves illustrates
this in a dramatic albeit crude way. An attempt to
classify the lakes according to the usual trophic
types is included in Table IX-1. Some geographical
patterns in trophic type are evident but not altogether
conclusive. Lakes in the Santa Fe River basin are
oligotrophic and somewhat colored. Many of the
small lakes in the Orange Creek basin are also oligo-
trophic and most are highly colored, but the large
lakes are eutrophic. Lakes in the southwestern part
of the county are quite varied with respect to trophic
levels. Some of the small lakes in urban and suburban
Gainesville show evidence of eutrophy; cultural in-
fluences may have exerted some stress on these lakes.
The trophic criteria do not give clear indications
of trophic state in all cases. Some of the smaller
lakes have both oligotrophic and eutrophic character-
istics, and many lakes have dystrophic characteristics
along with oligotrophic or eutrophic conditions. The
lakes with oligotrophic and eutrophic criteria are
classified as mesotrophic; Lake Elizabeth and Cooter
Pond are examples. These lakes may be in a transi-
tional state between the two trophic levels. Al-
ternatively, these may be examples of the inadequacy
of trophic criteria developed for temperate lakes
when applied to subtropical lakes. The inadequacy
of the dystrophic classification (see Section II) is
amplified by the results from this study. Over half
of the lakes show some signs of dystrophy, i.e. high
color, low pH, low ionic solids. However, high color
is not always correlated with other criteria for dys-
trophy. Some colored lakes have neutral or alkaline
pH (e.g. Lake Kanapaha) or high calcium and alka-
linity (Lake #21). The range of nutrient concentra-
tions and plankton in these lakes is from near sterility


to hypereutrophy. It is obvious that one class (dys-
trophy) is insufficient to describe all these lakes, even
though each has several criteria indicating dystrophy.
Hansen's (1962) dichotomic classification (see Sec-
tion II), where lakes are classified as colored or clear
and each of these classes has the full range of trophic
states (oligotrophy to eutrophy) is an improvement
over classical typology. However, eutrophication in
colored and clear lakes may proceed along different
courses, and trophic states in the two classes may not
be strictly parallel (e.g. different biota and trophic
structures may result from eutrophication in the two
classes). Lakes showing partial dystrophy in this study
have been classified colored-oligotrophic, colored
mesotrophic, etc., as appropriate. A few lakes with
rather extreme dystrophic conditions (e.g. Lakes
Mize, Palatka, Jeggord, Long Pond) have been tenta-
tively classified as dystrophic along with an alternate
classification such as colored-oligotrophic. These are
subject to change as the classification scheme becomes
further developed and refined.
The shallow lakes present especially difficult prob-
lems in classification. Lakes with depths of two
meters or less are susceptible to take-over by macro-
phytes like hyacinths. Thus they could quickly
become swamps or bogs and must be near the sene-
scent stage. The present trophic state of these lakes
range from dystrophy (Palatka), mesotrophy (Tusca-
willa), hypereutrophy (Bivens Arm) to senescence
(Alice). It is not clear whether a lake like Tuscawilla
has already been through a eutrophic stage and is
now in a decreased state of production heading to-
ward senescence or it never reached eutrophic con-
ditions and will pass into senescence without doing
so. If lake classifications are to imply anything re-
garding a lake's ontogeny, and its probable future
development, it is important that such questions be
resolved. A corollary to the problem of shallow lakes
is the problem of lakes with extensive shallow areas
but some deeper holes. For example, Newnan's
Lake has large areas with a depth of 1 meter or less
but has several small regions nearly 4 meters deep
(mean depth is about 2 meters). The shallow areas
could give rise to extensive growths of rooted or at-
tached macrophytes and develop into swamp or bog,
which would indicate senescence, but the deeper
areas could remain lacustrine for much longer. In
fact, considerable difficulty has been encountered in
controlling water hyacinths (not an attached plant)
in recent years, which implies the lake, or a large
portion of it could become senescent in the near
future.
The data in Tables IX-3 to IX-6 show a number
of interesting correlations. Figure IX-3 indicates a









/
6 /
Theoretical /
forNaCI /
4/
/
2 /


0 *

8 /
6

4 -
2-
/ *
2
/
o p


Alice


Kanapaha


.~16.
.1 ;-. "
/ .". *
I I I I I I I I I I


16 18


800-

700 -

600-

500-

400 -

300 -


100


10 20 30 40 50 60
Calcium (ppm)
Figure IX-4.


70 80


positive correlation between sodium and chloride
concentrations in the 33 lakes, implying a common
origin for the two ions. However, the estimated line
of best fit deviates significantly from the theoretical
line for NaCI dissolution with an excess of sodium
over chloride. Most of the chloride presumably ori-
ginates as sodium chloride, probably from atmos-
pheric precipitation (from marine aerosols) and
cultural sources. But other salts and weathering of
clays must also act as sources of sodium. High sodium
and chloride concentrations correspond with lakes
having known pollution sources (e.g. Alice, Bivens
Arm, and Hawthorn).
Calcium and bicarbonate alkalinity are highly
correlated (Figure IX-4). The slope of the line of
best fit approaches the theoretical line for dissolution
of calcium carbonate:

CaCO: + CO.,+ H .O > Ca+2 + HCO,


0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
Total Nitrogen (ppm as N)
Figure IX-5.


2 4 6 8 10 12 14
Sodium (ppm)
Figure IX-3.




FiueA*,


S 1 I I I I I I I
10 20 30 40 50 60 70 80 90 100
COD (ppm)
Figure IX-6.

implying this to be the source of calcium and alkali-
nity in the lakes. Most of the lakes have low con-
centrations of these ions; only nine lakes have calcium
concentrations of 10 ppm (as Ca) or greater. The
major natural sources of water for the lakes in the
county are atmospheric precipitation, and surface
and sub-surface runoff through perched water tables
in the sandy soil; hence the low calcium concentra-
tions. High calcium suggests a lake receives ground
water from the Floridan aquifer (a limestone stra-
tum), which means the lake is either spring-fed or
receives waste water from supplies using well water
from the aquifer. Most of the lakes with high cal-
cium have known sources of waste water (e.g. Alice,
Bivens Arm, Clear, Kanapaha, and Hawthorn). Since
high nutrient concentrations are associated with such
waters, these lakes are all highly enriched. Thus for
most of the lakes in Alachua County, calcium ion ap-
pears to be a good indicator of cultural eutrophication.
The relatively high calcium content of Lochloosa Lake
probably results from Magnesia Spring, which drains
into Lochloosa Creek about 5 miles north of the
creek's confluence with the lake.


u- I I : a : I I I a


0.6 -







There was little apparent correlation between total
nitrogen and total phosphate in the December, 1968,
sampling of the 33 lakes, as indicated by the scatter
diagram in Figure IX-5. However, lakes with the
most extreme N/P ratios are seriously polluted (e.g.
Alice and Kanapaha) and there are some indica-
tions that lakes in a particular region have similar
N/P ratios. For example, lakes in the Santa Fe River
headwaters (Altho, Santa Fe, Little Santa Fe) have
similar ratios which are generally higher than the
larger lakes in the Orange Creek basin. The dashed
line in Figure IX-5 indicates the usually quoted
optimum N/P ratio of 15:1 by atoms for algal growth.
Points below the line suggest an excess of nitrogen
relative to phosphorus; most of the lakes belong in
this category. It should be emphasized that these
results are only for November, 1968, and more com-
plete sampling over several seasons may change these
ratios.

Chemical oxygen demand (COD) and color show
no correlation for the 33 lakes (Figure IX-6). Evi-
dently there is a considerable variability in non-
colored organic matter in the waters. The highest
color/COD ratio was 17 (Lake #26). Eight of the
lakes have ratios of 10-13 and the remainder of the
waters have lower ratios, suggesting large amounts of
non-colored organic matter. There is some evidence
that low color/COD ratios indicate organic pollution;
for example, Bivans Arm had the lowest color/COD
ratio (1.15), but other apparently unpolluted lakes
(e.g. Palatka) also had low ratios (2.6).
A plot of turbidity versus the inverse of Secchi
disc reading (Figure IX-7) also shows considerable
/
I 2.0 /
0 Trout L.
S 1.6 /

> 1.2 /
0.8 */ *

0.4 / *
/
0 2 4 6 8 10 12 14
Turbidity (ppm as SiO2)
Figure IX-7.
scatter, implying other factors control Secchi disc
visibility. Inspection of the data suggest color has
an important effect; Trout Lake, with a color of 990
ppm, had the highest ratio (0.93) of (Secchi disc)-'/
turbidity. A multiple regression of Secchi disc against
turbidity and color, after removal of Secchi disc
readings affected by shallow bottoms, would probably
account for most of the variance.


A wide range of primary production and chloro-
phyll a data are encountered in the 33 lakes. Rates
of the former ranged from negligible (Lake Alice) to
235 mg C/m3-hr in November, 1968 (Lake #21). In
order to facilitate comparison of rates in the lakes,
all incubations were carried out under constant light
and temperature conditions in a laboratory incubator.
In general highest rates of primary production are
associated with lakes showing eutrophic chemical
characteristics. However, there does not seem to be
any simple quantitative relationship between primary
production values and concentrations of nutrients.
For example, Figure IX-8 shows the scatter which
1601


(3.84)


A
*. :** J


0 0.5 1.0 1.5 2.0 2.5
Ton (mg/1)
Figure IX-8.
occurs when primary production is plotted vs. total
organic nitrogen. Plots of primary production vs.
inorganic nitrogen and phosphate show similar scatter.

There is also no simple relationship between pri-
mary production and depth (Figure IX-9), as Raw-
140
1 (235) I
120J !


100k


40-


I
* I
* I
* C I

.1


20- - - - -- - - - - -------
I (22)
1 2 3 4 5 6 7 8 9 1-
Depth (Meters)
Figure IX-9.
son (1955) found for the Great Lakes and some
Canadian lakes he studied. There is some uncer-
tainty concerning the meaning of depths used to
plot Figure IX-9 (see discussion above). Since








bathymetric maps are not available for these lakes,
mean depths are unknown; maximum depths for
most lakes are probably somewhat greater than the
values used here. Some of the variance may be re-
moved with accurate measures of mean depth, but
primary production values would still seem to be
grouped into classes rather along any linear or cur-
vilinear relationship. The horizontal dashed line in
Figure IX-9 represents an arbitrary division between
high and low productivity lakes. Points above this
line correspond with lakes classed as mesotrophic or
eutrophic. The dashed vertical line represents an
arbitrary division between deep and shallow lakes;
most of the lakes would be classified as shallow pro-
ductive or shallow unproductive on this basis.

Chlorophyll a concentrations also show a qualita-
tive correlation with other criteria for trophic state
(i.e., high chlorophyll a concentrations are associated
with lakes classified as eutrophic or mesotrophic).
However, quantitative correlations with nutrient con-
centrations are not evident. The relationship between
chlorophyll a and primary production also shows
considerable scatter (Figure IX-10), although there
would seem to be a significant correlation between
the two parameters.
B. Lakes in the Oklawaha River Basin
The region northwest of Orlando consists of roll
ing hills occupied by thousands of acres of citrus
trees. The area has an abundance of lakes situated
in its valleys, including a chain of large lakes in the
southern part of the Oklawaha River. These lakes
have been popular for recreation and in the past at


least one (Lake Apopka) was famous nationally for
bass fishing. The quality of several of the lakes
has declined radically in recent years and concern
has been expressed regarding the cultural eutrophi-
cation of the entire chain. Implicated as an impor-
tant nutrient source is agricultural runoff-primarily
from vegetable farms in reclaimed marsh and wet-
lands, but also from citrus groves; domestic waste
effluent from towns on the lakes' shorelines and waste
from citrus concentrate processing plants are also im-
portant nutrient sources.
The location of the lakes included in this survey
is shown in Figure IX-11 and physical data on the
250 -


225 -

200 -

175 -

150 -

125 -

100 -


A.
A.
A

~2.'.~.


0 4 8 12


16 20 24 28 32
Chlorophyll a (Mg/M3) 3
Figure IX-10.


36 40


Table IX-7
PHYSICAL CHARACTERISTICS OF LAKES IN OKLAWAHA BASIN


Lake


Area
(ha.)


Depth1 Surface
(m.) Temp.
C


Bottom
Temp.
oC


Lake
Type2


Apopka 12,200 2 19.8 19.6 HE
Dora 2,060 3.5 16.7 16.6 HE
Eustis 2,740 5 24.9 24.7 E
Griffin 3,455 3 19.8 18.9 E
Harris 7,090 5.5 22.9 20.0 E
Weir 2,420 7.5 25.7 24.0 M-E


1 Maximum depth found during sampling. In the case of Lake Apopka, this
value also represents the mean depth; maximum depth of this lake is 6 m. in a
small hole near the southern shore (see Schneider and Little (1968) for a bathy-
metric map). A map of Lake Weir is also available (Kenner, 1964) and indicates
a maximum depth of 10 m. in a small hole near the southern shore.
2 See Table IX-1 for list of abbreviations.


. . .. iiI


* *







lakes is summarized in Table IX-7. Lake Apopka is
the largest of these lakes, and also the first lake in
the Oklawaha chain. This lake is probably the most
famous of the chain, but is now considered as the
furtherest advanced in the eutrophication process.
The lake has been the subject of numerous investi-
gations and reports (see Burgess, 1964; Huffstutler
et al., 1965; Schneider and Little, 1968 for details on
the recent history of the lakes). Lake Apopka drains
into Lake Dora through the Beauclair Canal, and the
latter lake drains into Lake Eustis. Lake Harris also
drains into Lake Eustis, which drains into the last
important lake of the chain, Lake Griffin. While not
directly in the chain, Lake Weir, about 10 miles
northwest of Lake Griffin, is within the Oklawaha
drainage basin. None of these lakes has received
the attention accorded to Lake Apopka, but some
prior studies are available. The Florida Game and
Fresh Water Fish Commission is performing a de-
tailed study on the trophic states of Lakes Apopka,


1 0 1 2 4
M*ai


LEESBURG


WINTER GARDEN
Figure IX-1 l. Location of Lakes in Lower Oklawaho River Basin.

Eustis, and Weir, and their data will be useful in de-
veloping and analyzing trophic criteria.
The six lakes were sampled in October, 1968, for
the chemical and biological parameters shown in
Table IX-2. The results are summarized in Table IX-


8. The trophic characters of the lakes are revealed
quite unambiguously by these initial results. All
the lakes in the Oklawaha chain are eutrophic. Lake
Weir is in considerably better condition than the lakes
in the chain, but it is on the border between meso-
trophy and eutrophy. Lakes Apopka and Dora have
the most advanced cases of eutrophy; all relevant
chemical and biological data indicate a high degree
of enrichment and productivity. The shallowness of
Lake Apopka implies the lake could pass from hyper-
eutrophy into senescence if macrophytes (e.g. hya-
cinths) are allowed to take over. Lake Eustis and
Griffin present slightly improved characteristics com-
pared to the first two lakes. The chemical and bio-
logical conditions of Lake Harris are the best of the
five lakes in the chain. The trophic criteria definitely
indicate a eutrophic state, but perhaps not the hyper-
eutrophy of the other lakes. Perhaps some of the
water quality improvements in Lakes Eustis and Grif-
rin result from dilution of enriched Lake Dora effluent
by relatively nutrient poor Lake Harris water.
The water chemistry of Lake Weir differs con-
siderably from the other lakes. Alkalinity and hard-
ness are low in Lake Weir and pH is near neutrality.
Major cations and anions are moderate to low and a
specific conductance of 135 t/mho cm-1 reflects this
fact. Lakes in the chain have high alkalinities and
hardness and pH values near 9 or above. Major
ion concentrations are high and specific conductance
ranges from 230-335 e/mho cm-1. The correlation
between high trophic level and high concentrations
of major ions in these lakes and relatively low trophic
level and low ionic content in Lake Weir might imply
that eutrophication involves an increase in dissolved
solids and a change from soft to hard water. In fact
dissolved solids has been considered a trophic cri-
terion and was used as such by Beeton (1965) and
others. A certain degree of correlation would be
expected between increasing nutrients and increasing
dissolved solids where enrichment results from do-
mestic waste effluent and the town's water supply is
hard ground water. This is the case with Lake Apop-
ka, where the city of Winter Garden obtains its drink-
ing water from wells. The meager background chem-
ical data on the lake indicate some increases in dis-
solved solids and hardness in the period of record,
but the lake water was relatively hard and high in
dissolved solids over 40 years ago. Black and Brown
(1951) reported a chemical analysis of Lake Apopka
from August, 1924, made by the U.S. Geological
Survey. Table IX-9 compares their results for major
ions with the results from this study. While the lake
was probably quite productive in 1924, serious de-
gradation in water quality has occurred only in the







Table IX-8
CHEMICAL AND BIOLOGICAL CONDITIONS
IN OKLAWAHA LAKES, OCTOBER, 19681

Constituent2 Apopka Dora Harris Eustis Griffin Weir
Diss. 02 11.2 11.4 9.7 8.78 8.4 7.60
Cond. 330 335 230 275 290 135
pH 9.5 9.5 8.8 8.9 8.9 7.2
Alkalinity 126 130 91 104 112 15.1
COD 113 157 48 112 78 25.
Color 30 43 13 28 25 5
Secchi Disc 0.3 0.3 0.8 0.6 0.5 1.2
Turbidity 27 28 20 25 28 3
Sus. solids 43 77 13 46 27 22
TON 8.8 5.3 1.7 4.1 3.0 0.97
NH3-N 0.18 0.17 0.19 0.20 0.18 0.25
Ortho P04 0.016 0.108 0.010 0.014 0.014 0.006
NOI-N 0.004 0.005 0.004 0.006 0.007 0.004
NOs-N 0.09 0.11 0.05 0.24 0.09 0.05
Total P 0.24 0.39 0.035 0.22 0.20 0.021
SiO2 6.4 0.05 3.4 1.4 0.40 0.34
Fe (total T 0.01 0.01 0.01 0.01 T
Mn (total) T 0.003 0.006 0.005 0.003 0.005
Ca+2 25.1 30.5 23.1 26.3 28.9 1.4
Mg+2 14.7 14.2 6.1 10.7 9.6 3.1
Na+ 11.7 18.1 9.7 14.5 14.0 15.5
K.+ 2.9 3.4 0.9 2.4 2.1 1.0
Cl- 21 22.3 12.8 18.7 17.5 26.8
SO = 10.2 12.3 4.9 7.1 6.7 4.8
Chor a 34.1 72.1 17.7 33.1 45.4 8.4
Prim. prod. 0.386 1.02 0.037 0.274 0.183 0.011

1 Results are average values for composite samples from three stations in each
lake.
2 Chemical species in mg/1 except as follows: alkalinity in mg/1 as CaCO3, nitro-
gen species in mg N/l, phosphorus species in mg P/1, color in mg/1 as Pt.
specific conductance in f/mho cm-1, Secchi disc visibility in meters, chlorophyll
a in mg/m3.
3 Primary production in mg C fixed/1-hr. Composite lake water samples were
run in a laboratory incubator.







Table IX-9
COMPARISON OF MAJOR IONS IN LAKE APOPKA IN 1924 AND 19681

Constituent 19242 19683


Total dissolved solids
Calcium
Magnesium
Sodium and Potassium
Iron
Bicarbonate
Sulfate
Chloride
Silica


8.3
11
0.17
89


184
25.1
14.7
13.4
T (<0.01)
152
10.2
21
6.4


1 Results in mg/1; potassium expressed in terms of sodium (Na).
2 From Black and Brown (1951); original data by the U. S. Geol. Surv. (Water
Supply Paper 596-G), Washington, D.C. (Collins and Howard, 1927).
3 Data from this study.


last 15-20 years. Thus increasing hardness and dis-
solved solids would not seem to be a necessary fea-
ture of cultural eutrophication. Quite possibly eutro-
phication in Lake Weir may proceed along a different
course not involving increases in solids and hardness
-as has apparently been the case with lakes in the
Orange Creek basin (e.g. Newnan's Lake).
It is difficult to draw firm conclusions regarding
trophic state from one set of samples. Various
parameters may respond to seasonal changes differ-
ently in each lake. With this limitation in mind,
we would tentatively systematize the trophic condi-
tions of these lakes in the following sequence:
Lake Apopka= hypereutrophic-near senescence
Lake Dora = hypereutrophic
Lakes Eustis and Griffin = eutrophic
Lake Harris = moderately eutrophic
Lake Weir= mesotrophic to moderately eutrophic
The above sequence is somewhat relative since it
is difficult to compare lakes in different regions in a
quantitative manner. Eutrophy in Lake Apopka is
considered more advanced than in Lake Dora because
of the extreme shallowness of the former lake. The
chemical and biological results are similar for the
two lakes; in fact Lake Dora had more extreme values
for some trophic criteria. But it is somewhat deeper
and because of this perhaps more amenable to restor-
ation.


C. Conclusions
The results obtained thus far in this phase of the
eutrophication study substantiate the complexities
involved in defining and quantifying lacustrine tro-
phic states. The chemical and biological information
obtained on each lake has enabled us to classify the
lakes qualitatively as oligotrophic or eutrophic, etc.
But quantitative classification is still impossible since
more than one criterion is required to define trophic
state and the relationships among the various criteria
remain undefined. The correlations among different
trophic indicators are generally good for these lakes-
on a qualitative basis. But simple regressions of one
criterion versus another in all cases showed large
amounts of scatter or unexplained variance. Trophic
state and the interrelations among trophic criteria
are multivariate functions. To quantify these pheno-
mena will require more sophisticated mathematical
analysis than those employed above. Stochastic
modeling procedures would seem to be particularly
appropriate to the problem of quantifying trophic
indices with masses of data collected from different
lakes. After data collection is completed on lakes
in the three regions described in the introduction to
this section, such multivariate techniques as canoni-
cal correlation, multiple discriminant analysis and fac-
tor analysis (principal component analysis) will be
used to synthesize quantitative measures of trophic
state for sub-tropical lakes.







APPENDIX B


METHODS FOR BIOLOGICAL PROCEDURES DURING THE STUDY


1. Systematics of plankton organisms-This pro-
cedure was the Drop-Sedimentation method out-
lined in the 12th Edition of Standard Methods for
Water and Wastewater (1965). Samples of lake
water were examined fresh within a few hours of
collection.
2. Productivity estimates of phytoplankton-The
light and dark bottle method was used employing
NaHC14QO. Sterile one ml volumes containing 5 A
curies of Carbon-14 were prepared by Tracer Lab
of Waltham, Mass. Paired clear and dark bottles
containing lake water and isotope were incubated
in situ at 3 depths during four hours of daylight usual-
ly between 9 a.m. and 3 p.m. The contents were
formalized following incubation and membrane fil-
tered after returning to the laboratory at the end of
the field day. Counting procedures and daily photo-
synthesis estimates, were similar to those reported
previously by Putnam (1966).
3. Standing crop of phytoplankon-Chlorophyll a
levels were used to determine algal biomass. The
standard procedure originally outlined by Richards
and Thompson (1952) and Crietz and Richards
(1955) was followed. Parsons and Strickland equa-
tions (1963) were employed to calculate chlorophyll
concentration.
4. Bioassay of limiting nutrients-Essentially the
procedure proposed by Goldman (1965) was fol-
lowed to determine limiting substances for phyto-
plankton growth. This method must be carefully
employed as the natural phytoplankton was very
sensitive to excess addition of growth promoting
substance. Too frequently inhibitory responses were
observed which were caused from an over addition
of nutrient. Clearly a standardized algal growth
potential test is a necessary method for future lake
studies.
5. Standing crop of littoral vegetation-Selected
stations surrounding the experimental and control
lakes were marked off in one meter square plots.
Vegetation within the meter squared area was har-
vested on a quarterly basis. Dry and ash weights
were obtained on samples by standardized proce-
dures.
6. Population estimates of Labidesthes sicculus
were made using the Peterson Mark recapture
method outlined by Ricker (1968).
7. Species diversity indices, which are measures
of the disorder present in a planktonic population,


were calculated from plankton counts using the infor-
mation-theoretic approach. This theory was devel-
oped originally by Shannon (1948) and Weiner
(1948) and has been applied to plankton diversity
by Margalef (1956), Patten (1962, 1966) and others.
For a mixed species population of n1, n, . nm
individuals in m different species, the total number
of individuals is given by

m
N= i n,. (B-l)
i=l 1
The community diversity is determined according to
the distribution of individuals among the various
species and is calculated from


m
D = In NI - In nn


(B-2)


i=1
For large populations eq. (B-2) is equivalent to


m
D =- I niln(n,/N)


(B-3)


i=
The mean diversity per individual is obtained by di-
viding eq. (B-3) by N:


m
D = (n,/N)1n(n1/N)

m
D = X pl1npi


(B-4)


(B-5)


i = I
where pi=ni/N is the probability of occurrence of
the i"h species.
When m = 1, D= 0. When m > 1, minimum diversity
occurs when all but (m-1) individuals belong to a
single species and the remainder are distributed one
each to the other species. Then
D.in = In N! ln[N-(m-1)]! (B-6)
Maximum diversity occurs when individuals are equal-
ly apportioned among the species:
Dm.x = In NI m In(N/m)l (B-7)
The position of D between Dmin and Dm.ax is given
by the redundancy,
R = Dmax-D (B-8)
Dmax-Dmin
Equations B-3, 6, 7 and 8 were programmed on an
IBM 360 computer in order to calculate minimum,
maximum and actual diversities and redundancy for
plankton samples.









APPENDIX C

PROTOZOA, MICROSCOPIC ALGAE AND SULFUR BAC-
TERIA RECORDED FROM McCLOUD AND ANDERSON-
CUE LAKES ON FIVE RECENT DATES


Organism Group and Species

Sulfur Bacteria
Achromatium oxaliferum
Beggiatoa alba

Blue-Green Algae
Anacystis sp.
Anabaena spp.
Aphanocapsa pulchra
Aphanothece sp.
Aulosira implexa
Calothrix sp.
Chroococcus planktonica
Cylindrospermum sp.
Eucapsis alpina
Eucapsis major, p.n.
Gloeocapsa magma
Gloeothece sp.
Hapalosiphon pumilus
Lyngbya spp.
Merismopedia glauca
Merismopedia punctata
Microcystis incerta
Nodularia spumigena
Oscillatoria spp.
Phormidium sp.
Pleurocapsa fluviatilus
Rhabdoderma lineare
Schizothrix calcicola
Scytonema sp.
Stigonema turfaceum
Synechococcus aeruginosus

Chlorophyceae, Green Algae
Ankistrodesmus falcatus
Arthrodesmus sp.
Asterococcus limneticus
Chaetonema irregulare
Characium sp.
Chlorella sp.
Closterium acerosum
Closterium sp.
Coelastrum chodati
Cosmarium spp.
Crucigenia apiculata
Cylindrocystis sp.
Desmidium Baileyi
Dictyosphaerium Nagelianum
Dictyosphaerium puchellum
Docidium sp.
Elakatothrix gelatinosa
Eremosphaera viridis
Euastrum spp.
Gymnozyga moniliformis
Hormidium sp.
Hyalotheca mucosa
Kirchneriella obesa
Kirchneriella solitaria, p.n.
Micractinium pusillum
Microthamnion Kuetzingianum
Mougeotia spp.


Percent Occurrence
McCloud Anderson-Cue

60 40
40


Organism Group and Species

Chlorophyceae, Green Algae
Mougeotiopsis calospora
Netrium sp.
Oedogonium sp.
Oocystis spp.
Ourococcus bicaudatus
Pediastrum tetras
Penium sp.
Planktosphaeria gelatinosa
Pleurotaenium sp.
Quadrigula closterioides
Scenedesmus spp.
Sirogonium sp.
Sphaerocystis Schroeteri
Sphaerozosma excavata
Spirogyra sp.
Spirotaenia condensata
Spondylosium planum
Staurastrum spp.
Tetraedron constrictum
Tetraedron muticum
Tetraedron trigonum
Tetmemorus granulatus
Triploceras verticillatum
Triploceras gracile
Triploceras sp.
Westella botryoides
Xanthidium spp.
Zygnema sp.
Green cells

Volvocales
Carteria sp.
Chlamydomonas spp.
Chlorogonium sp.
Eudorina elegans
Gonium pectorale
Gonium social
Green flagellates

Euglenophyceae
Anisonema emarginata
Astasia Klebsii
Astasia longa
Copromonas subtilis
Cyclidiopsis pseudomermis
Dinema griseolum
Distigma proteus
Entosiphon sulcatum
Euglena deses
Euglena minima
Euglena mutabilis
Euglena pisciformis
Euglena polymorpha
Euglena spirogyra
Euglena viridis
Euglena sp.
Gyropaigne kosmos
Heteronema acus
Heteronema trispira


Percent Occurrence
McCloud Anderson-Cue


60 80
20 40
20
60 80
20
20 20


40 20
20
20 20


80 80
20
20
80 60
60
20
40 40
80 40
40 20
20 20








Organism Group and Species

Euglenophyceae
Menoidium sp.
Notosolenus apocamptus
Notosolenus trichophorum
Peranema trichophorum
Petalomonas carinata
Petalomonas praegnans
Petalomonas pusillus
Petalomonas quadrilineata
Petalomonas sp.
Phacus caudatus
Phacus longicauda
Phacus pleuronectes
Phacus triqueter
Rhabdomonas incurva
Sphenomonas teres
Trachelomonas cylindrica
Trachelomonas euchlora
Trachelomonas hispida
Trachelomonas volvocina
Tropidoscyphus octocostatus
Dinoflagellata
Bernardinium bernardinense*
Ceratium curvirostre**
Glenodinium sp.
Gonyaulax triacantha
Gymnodinium oculatatumn
Gymnodinium microns
Gymnodinium sp.
Hemidinium nasutum
Massartia Musei
Peridinium cintum
Peridinium umbonatum
Peridinium vanccuverensis
Peridinium volzii
Peridinium willei
Peridinium wisconsineusis
Peridinium sp.
Rhizopoda
Acanthocystis aculeata
Actinosphaerium eichhornii
Amoeba radiosa
Amoeba vespertilis
Amoeba villosa
Arcella dentata
Arcella discoides
Arcella mitrata
Arcella vulgaris
Astrodisculus radians
Clathrulina elegans
Cochliopodium bilimbosum
Difflugia acuminata
DiflBugia globosa
Difflugia lebes
Difflugia pyriformis
Euglypha alveolata
Heterophrys myriopoda
Lesquereusia spiralis
Nuclearia simplex
Pelomyxa sp.
Phryganella sp.
Pseudodiffluglia gracilis
Raphiophrys elegans
Raphiophrys pallida

*from Ward & Whipple, p. 161
**now called C. caroliniava (Smith)


Percent Occurrence
McCloud Anderson-Cue


40
20
40
20
40 40
20


Organism Group and Species

Cryptophyceae
Chroomonas sp.
Cryptomonas erosa
Cryptomonas ovata
Rhodomonas lacustris


Chrysophyceae
Botryococcus Braunii
Chlorobotrys limnetica
Chlorodesmus hispidus
Chromulina globosa
Chromulina ovalis
Chromulina sp.
Chrysidiastrum ocellatum
Chrysochromulina parva
Chrysocapsa planktonica
Chrysococcus cordiformis
Chrysococcus rufescens
Chrysopyxis bipes
Chrysostephanosphaera globulifera
Conradocystis dinobryonis
Dinobryon divergens
Dinobryon sertularia
Lagynion ampulla
Lutherella adhaerens
Mallomonas tonsurata
Mallomonas sp.
Ochromonas sp.
Peroniella planktonica
Synura uvella


Chloromonadida
Gonyostomum lata
Gonyostomum semen
Merofrichia capitata
Vacuolaria virescens


Diatoms
Asterionella formosa
Cocconeis sp.
Frustulia rhomboides
Melosira granulata
Navicula spp.
Synedra ulna
Diatoms, unid.
Sphenoderia lenta
Trinema lineare
Vahlkampfia limax
Vampyrella lateritia
Zonomyxa violacea


Zooflagellata
Biocoeca lacustris
Bodo agilis, p.n.
Bodo celer, p.n.
Bodo elongata
Bodo sp.
Calycomonas ovale (Kephyrion ovale)
Kephyrion ovum
Mastigamoeba reptans
Mastigamoeba sp.
Monas spp.
Monosiga ovata
Oicomonas ocellata
Oicomonas termo


Percent Occurrence
McCloud Anderson-Cue


20
60 80
60 40
20


20
20
20 20
20 20


60 20
20
40 20








Organism Group and Species

Zooflagellata
Phalansterium digitatum
Phanerobia pelophila
Pleuromonas jaculans
Rhipidodendron splendidum
Rynchobodo nasuta
Sphaeroeca volvox
Spiromonas angusta
Spongomonas uvella
Zooflagellata unid.

Ciliata
Acineta sp.
Aspidisca costata
Aspidisca linneaus
Aspidisca turrita
Aspidisca sp.
Bursaridium difficile
Chaena elongata
Chilodonella cucullus
Cinetochilum margaritaceum
Cinetochilum marina
Coleps hirtus
Cothurnia butachlii
Cristigera phoenix
Cyclidium glaucoma
Cyrtolophosis mucicola
Dileptus anser
Dileptus gigas
Drepanomonas dentata
Drepanomonas sp.
Enchelydon sp.
Epiclintes ambiguous
Espejoia sp.
Frontonia acuminata


Percent Occurrence
McCloud Anderson-Cue

20
20
20 40
20 20
20
20
20 20
40 20
20 60


20
40 20
20
60 40
20
40 40


Ciliata
Frontonia leucas
Halteria grandinella
Hemiophrys sp.
Holophrya nigricans
Hypotrichida unid.
Lembus fusiformis
Lembus infusionum
Lembus pusillus
Loxocephalus granulosus
Mesodinium acarus
Mesodinium cinctum
Metopus es
Ophrydium versatile
Oxytricha discocephalus
Oxytricha pelionella
Paramecium bursaria
Podophrya fixa
Prorodon sp.
Sacculus sp., n.g.
Saprodinium sp.
Spathidium spathula
Spirostomum teres
Stentor amethystinus
Stentor coereulus
Stentor sp.
Stichotricha segunda
Strobilidium humile
Strombidium sp.
Tetrahymena sp.
Urocentrum turbo
Uroleptus rattulus
Urotricha farcta
Vasicola parvula
Vorticella sp.
Ciliata unid


20 80
20
20
40
20
40 40
60
40 20
60 40
20
60 60


20
40 60
20 20
20
60 20
60 40
20 20
20 40
40 20







APPENDIX D


MULTIPLE REGRESSION ANALYSES PERFORMED ON TEN DAY STUDY DATA
AND ON ROUTINE MONTHLY DATA

10 Day Study-All Sample Points (N= 90)

Dependent Variable Independent Variables Significance of Multiple
Regression Regression
Coefficient

Primary Production PO, CHA, DO, AC, NO3, ** .87
WT, pH, PO4, NH,

Primary Production DO ** .42

Primary Production NH, NS .17

Primary Production NO, NS .03

Primary Production PO4 ** .41

Primary Production DO, PO4, NH3, NO3 ,* .58

Primary Production WT "* .38

Primary Production CHA "* .61

Primary Production pH, AC .33

pH AC ** .38

DO WT NS .11

Primary Production PO ** .76






10 Day Study-Regressions on Sample Points
At Same Depth & Time (N = 10)


Dependent Independent Significance of Multiple
Variable Variable Regression Regression
Coefficient
Primary CHA 10:00AM 12:OON 4:00PM 10:00AM 12:OON 4:00PM
Production a NS *** *** .54 .73 .70
b NS NS NS .01 .27 .37
c NS NS NS .43 .37 .35

Primary DO, WT, NH, NS NS *** .94 .85 .99
Production NOT PO4 *** NS NS .99 .98 .87
SOLR, CL NS ** NS .97 .99 .87

Primary AT, CL, SOLR, NS NS NS .93 .89 .87
Production WT, pH, AC NS NS NS .74 .80 .87
NS NS .80 .87 .94

Primary WT, CL NS NS .43 .76 .62
Production NS NS .74 .48 .49
NS NS NS .18 .35 .40

Primary CL NS NS .13 .04 .56
Production NS NS NS .50 .03 .13
NS NS NS .16 .15 .40

Primary DO, AC NS NS .55 .33 .80
Production NS NS NS .57 .22 .29
NS NS NS .15 .50 .39

Primary NOT, PO4 NS NS NS .59 .62 .42
Production NH3 NS NS NS .63 .35 .25
NS NS NS .65 .72 .22







10 Day Study-Regressions on Averages over Depth
At Three Times (N= 10)


10:00AM 12:00N 4:00PM 10:00AM 12:OON 4:00PM
Primary CL, WT, PO NS NS .81 .66 .76
Production

Primary DO, P04, NOT NS NS NS .47 .64 .70
Production NH,

Primary AC, pH NS NS NS .59 .50 .26
Production

Primary CHA NS .41 .62 .53
Production


10 Day Study-Regressions on Averages
Over Day at Three Depths (N = 10)

Surface 5 Ft. 10 Ft. Surface 5 Ft. 10 Ft.
Primary SOLR NS NS NS .43 .32 .22
Production

Primary PO NS NS -.60 .04 .04
Production

Primary DO, P04, NOT NS NS .88 .65 .32
Production NH,

Primary AC, pH NS .75 .33 .74
Production

Primary CHA ** NS ** .74 .21 .76
Production

Primary WT NS NS NS .53 .06 .15
Production







Monthly Study (N = 21)
L Significance of Multiple
a Regression Regression Coefficient
Dependent Independent k
Variable Variables e Top Middle Bottom Top Middle Bottom
Primary TP4, NHa, PO4, A NS NS .93 .80 .79
Production TON, pH, WT, ** .89 .88 .89
DO, AC B

Primary SOLR A NS ** NS .31 .44 .27
Production NS NS NS .31 .12 .12
B

Primary CHA A ** NS .40 .48 .32
Production *** *** NS .62 .66 .39
B

Primary NH3, PO0, A NS .82 .70 .32
Production NOs *** *** NS .82 .84 .37
B

Primary DO, AC A ** NS .52 .56 .16
Production NS ** NS .46 .59 .43
B

Primary TP4, TON A *** *** .73 .64 .67
Production *** .48 .71 .56
B

Primary WT A NS .61 .62 .12
Production ** *** ** .48 .65 .53
B

pH AC A NS NS NS .15 .07 .24
NS NS NS .23 .28 .11
B

PO, TP, A *** *** *** .60 .50 .62
** *** *** .70 .57 .73
B

NH, TON A NS NS NS .02 .07 .09
NS NS NS .01 .11 .18
B






Biweekly Study (N = 14)

Primary PN A ** 0 ** .65 .58 .64
Production NS *** *** .20 .63 .76
B

PN TON A NS ** *0* .37 .68 .83
*0* 00* o* .84 .81 .79
B

NOs- NO2- A NS NS NS .20 .26 .06
NS NS NS .44 .06 .27
B

Primary PO4, PN, TP4, A ** NS ** .97 .79 .97
Production SC, TON, NO, * NS .94 .97 .84
NO-3 NH3 B

Primary PO., TP, A *** NS .79 .68 .63
Production NS ** NS .36 .74 .34
B

Primary PN, NHa, TON, A NS NS .80 .69 .86
Production NO, NOs- ** * NS .92 .89 .80
B


Key to Abbreviations


AC-Total Acidity (mg/L as CaCO3)
AT-Air Temp. (C)
CHA-Chlorophyll a (mg/ms)
CL-Cloud Cover (%)
DO-Dissolved Oxygen (mg/L)
NH3-Ammonia-Nitrogen (mg/L)
NO--Nitrite-Nitrogen (mg/L)
NO -Nitrate-Nitrogen (mg/L)
PO,-Ortho-Phosphate (mg/L)
pH-Hydrogen Ion Concentration
PN-Particulate Nitrogen (mg/L)
PO-Photometer Readings (light units)
PP-Primary Production (mgC/hr-m3)
SC-Specific Conductance (Mmho/cm@250C)


SOLR-Total Solar Radiation (Langleys/Day)
TP,-Total Phosphate (mg/L)
TON-Total Organic Nitrogen (mg/L)
WT-Water Temperature (C)
*-Denotes Significance at 90% confidence level
**-Denotes Significance at 95% confidence level
**-Denotes Significance at 99% confidence level
NS-Denotes nonsignificance
a-denotes surface samples
b-denotes 5 ft. samples
c-denotes 10 ft. samples
A-denotes samples taken from Anderson-Cue Lake
B-denotes samples taken from McCloud Lake
N-denotes sample size




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