• TABLE OF CONTENTS
HIDE
 Front Cover
 Acknowledgement
 Table of Contents
 List of Tables
 List of Figures
 Alphabetical list of botanical...
 Key to symbols
 Abstract
 General introduction
 Hydrilla growth response to concentrations...
 Hydrosoil related nutrition of...
 HRelationships amoung water chemistry....
 Relationships amoung water chemistry....
 Summary and conclusion
 Locations of biomass and hydrosoil...
 Concentrations of nutrients (%...
 Concentrations of nutrients in...
 Bathymetric maps of study...
 Hypsographic curves of study...
 Bibliography
 Bibliographical sketch
 Copyright






Title: Relationships among hydrosoil, water chemistry, transparency, chlorophyll a, and submersed macrophyte biomass
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Permanent Link: http://ufdc.ufl.edu/UF00084170/00001
 Material Information
Title: Relationships among hydrosoil, water chemistry, transparency, chlorophyll a, and submersed macrophyte biomass
Alternate Title: Macrophyte biomass
Physical Description: xiii, 143 leaves : ill. ; 28 cm.
Language: English
Creator: Langeland, Kenneth A
Publication Date: 1982
 Subjects
Subject: Lake ecology -- Florida   ( lcsh )
Water quality -- Florida   ( lcsh )
Phytoplankton -- Florida   ( lcsh )
Aquatic weeds -- Florida   ( lcsh )
Agronomy thesis Ph. D
Dissertations, Academic -- Agronomy -- UF
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
 Notes
Thesis: Thesis (Ph. D.)--University of Florida, 1982.
Bibliography: Bibliography: leaves 136-142.
Statement of Responsibility: by Kenneth A. Langeland.
General Note: Typescript.
General Note: Vita.
 Record Information
Bibliographic ID: UF00084170
Volume ID: VID00001
Source Institution: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: aleph - 000334713
oclc - 09490655
notis - ABW4356

Table of Contents
    Front Cover
        Page i
    Acknowledgement
        Page ii
    Table of Contents
        Page iii
        Page iv
    List of Tables
        Page v
        Page vi
        Page vii
    List of Figures
        Page viii
        Page ix
    Alphabetical list of botanical and common names
        Page x
    Key to symbols
        Page xi
    Abstract
        Page xii
        Page xiii
    General introduction
        Page 1
        Page 2
        Page 3
        Page 4
        Page 5
        Page 6
        Page 7
    Hydrilla growth response to concentrations of extractable nutrients in prepared substrates
        Page 8
        Page 9
        Page 10
        Page 11
        Page 12
        Page 13
        Page 14
        Page 15
        Page 16
        Page 17
        Page 18
        Page 19
        Page 20
        Page 21
        Page 22
        Page 23
        Page 24
        Page 25
        Page 26
        Page 27
        Page 28
        Page 29
        Page 30
        Page 31
        Page 32
        Page 33
    Hydrosoil related nutrition of submersed macrophytes in seven florida lakes
        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
        Page 53
        Page 54
        Page 55
        Page 56
        Page 57
        Page 58
        Page 59
        Page 60
        Page 61
        Page 62
        Page 63
        Page 64
        Page 65
    HRelationships amoung water chemistry. transparency, chlorophyll a, and submersed macrophyes in seven florida lakes
        Page 66
    Relationships amoung water chemistry. transparency, chlorophyll a, and submersed macrophyes in seven florida lakes
        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
    Summary and conclusion
        Page 81
        Page 82
        Page 83
    Locations of biomass and hydrosoil samples along transects in study lakes, during two sampling periods. Numbers correspond to buoy numbers in appendix 2 and appendix 3
        Page 84
        Page 85
        Page 86
        Page 87
        Page 88
        Page 89
        Page 90
        Page 91
        Page 92
        Page 93
        Page 94
        Page 95
    Concentrations of nutrients (% dry wt, 0 indicates below detectable limits) and biomass (G dry wt/sq m) of submersed macrophytes. Buoy numbers correspond to those in appendix 1
        Page 96
        Page 97
        Page 98
        Page 99
        Page 100
        Page 101
        Page 102
        Page 103
        Page 104
        Page 105
        Page 106
        Page 107
        Page 108
        Page 109
        Page 110
    Concentrations of nutrients in hydrosoil (mg/l, 0 indicates below detectable limits), organic matter (0m, % dry wt), and density (G dry wt/ml). Buoy numbers correspond to those in appendix 1
        Page 111
        Page 112
        Page 113
        Page 114
        Page 115
        Page 116
        Page 117
        Page 118
        Page 119
        Page 120
        Page 121
        Page 122
    Bathymetric maps of study lakes
        Page 123
        Page 124
        Page 125
        Page 126
        Page 127
        Page 128
        Page 129
    Hypsographic curves of study lakes
        Page 130
        Page 131
        Page 132
        Page 133
        Page 134
        Page 135
    Bibliography
        Page 136
        Page 137
        Page 138
        Page 139
        Page 140
        Page 141
        Page 142
    Bibliographical sketch
        Page 143
        Page 144
        Page 145
    Copyright
        Copyright
Full Text










RELATIONSHIPS AMONG HYDROSOIL, WATER CHEMISTRY,
TRANSPARENCY, CHLOROPHYLL a, AND SUBMERSED MACROPHYTE BIOMASS

By

Kenneth A. Langeland


A DISSERTATION PRESENTED TO THE GRADUATE COUNCIL
OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


1982














ACKNOWLEDGEMENTS

This work could not have been accomplished without the cooperation

of Center for Aquatic Weeds personnel. My appreciation for the efforts

of certain people deserves special mention: to Margaret Glenn, Mary

Rutter, and Julie Ziecina for their assistance; to Kat Perry for her

patience while typing the paper; to Dr. D.E. Canfield, Jr., Dr. W.T.

Haller, and Dr. D.L. Sutton for their guidance; and to Dr. J.V. Shireman

for setting an example of the "philosophical" and "scholarly" approach

to scientific investigation.

Funds were provided by the Agricultural Research Service,

U.S.D.A. under Cooperative Agreement No. 58-7B30-0-177. "Biomass

Galactica" was provided by the U.S. Army Corps of Engineers, Jacksonville

District through the efforts of Joe Joyce and Jim McGehee.
















TABLE OF CONTENTS


ACKNOWLEDGEMENTS . . . .

LIST OF TABLES . . . . .

LIST OF FIGURES . . . .

ALPHABETICAL LIST OF BOTANICAL AND COMMON NAMES

KEY TO SYMBOLS . . . . .

ABSTRACT . . . . .

CHAPTER


ONE GENERAL INTRODUCTION . . .

TWO HYDRILLA GROWTH RESPONSE TO CONCENTRATIONS OF
EXTRACTABLE NUTRIENTS IN PREPARED SUBSTRATES

Introduction . . . .

Materials and Methods . . .

Results and Discussion . . .

Conclusions . . . .

THREE HYDROSOIL RELATED NUTRITION OF SUBMERSED
MACROPHYTES IN SEVEN FLORIDA LAKES . .

Introduction . . . .

Materials and Methods . . .

Results and Discussion . . .

Conclusions . . . .


Page

. . ii



. . viii



. . xi

. . xii


S. .. 12

. 33


. 34

. 34

. 35

. 35

. 65










FOUR










FIVE

APPENDIX




APPENDIX




APPENDIX


APHY

ICAL


SKETCH . . .. . .


Page


RELATIONSHIPS AMONG WATER CHEMISTRY, TRANSPARENCY,
CHLOROPHYLL a, AND SUBMERSED MACROPHYTES IN SEVEN
FLORIDA LAKES . . .... ......

Introduction . . . . .

Materials and Methods . . . .

Results and Discussion . . . .

Conclusions . . . . .

SUMMARY AND CONCLUSION . . . .

1. LOCATIONS OF BIOMASS AND HYDROSOIL SAMPLES
ALONG TRANSECTS IN STUDY LAKES, DURING TWO
SAMPLING PERIODS. NUMBERS CORRESPOND TO BUOY
NUMBERS IN APPENDIX 2 and APPENDIX 3. . .

2. CONCENTRATIONS OF NUTRIENTS (% DRY WT, 0
INDICATES BELOW DETECTABLE LIMITS) AND BIOMASS
(G DRY WT/SQ M) OF SUMBERSED MACROPHYTES. BUOY
NUMBERS CORRESPOND TO THOSE IN APPENDIX 1 .

3. CONCENTRATIONS OF NUTRIENTS IN HYDROSOIL
(MG/L, 0 INDICATES BELOW DETECTABLE LIMITS),
ORGANIC MATTER (OM, % DRY WT), AND DENSITY
(G DRY WT/ML). BUOY NUMBERS CORRESPOND TO
THOSE IN APPENDIX 1. . . . .

4. BATHYMETRIC MAPS OF STUDY LAKES . .

5. HYPSOGRAPHIC CURVES OF STUDY LAKES . .


APPENDIX

APPENDIX

BIBLIOGRP

BIOGRAPH:















LIST OF TABLES


Page


Table 2-1.


Table 2-2.


Table 2-3.


Table 2-4.



Table 2-5.



Table 3-1.



Table 3-2.


Table 3-3.


Table 3-4.


Concentrations of nutrients (mg/m3) measured
in source water . . . 11

Means and confidence limits of extractable
nutrient concentrations, using different
extraction times with Mehlich's extractant. 13

Concentrations of nutrients and organic matter
content of prepared substrates. . 21

Correlation between concentrations of extract-
able nutrients in prepared substrates (Pearson
product moment correlation coefficients, P <.05) 23

Concentrations of nutrients in hydrilla
tissue and critical levels of these nutrients
in waterweed (Gerloff 1973). Numbers in
parentheses are standard errors of the mean 24

Locations of study lakes and dates of hydro-
soil and submersed macrophyte biomass
sampling. . . . . ... 36

Cumulative frequency of exchangeable nutrients
(mg/l hydrosoil) in the hydrosoil of six
Florida lakes in SeptemDer and October 1981. 37

Average submersed macrophyte biomass measured
in seven Florida lakes in 1981. . 39

Regression models relating submersed macrophyte
biomass to concentrations of nutrients in
hydrosoil (y=biomass, all regression coefficients
are significantly greater than 0 at a .1 level
of probability by students t, numbers in
parentheses indicate the proportion of the total
sums of squares explained by the variable above
it.) . . . . . .. 41








Page


Table 3-5.







Table 3-6.






Table 3-7.



Table 3-8.



Table 4-1.



Table 4-2.


Table 4-3.




Table 4-4.

Table 4-5.


Regression models relating total submersed
macrophyte biomass to concentrations of
nutrients in hydrosoil (y=biomass, all
regresssion coefficients are significantly
greater than 0 at a .1 level of probability
by student t, numbers in parentheses indicate
the proportion of the total sums of squares
explained by the variable above). . ... 42

Regression equations that relate levels of
nutrients in lake substrates to levels of
the corresponding nutrients in tissues of root
producing submersed macrophytes (all data is
from fall sampling, all regression coefficients
and intercept estimates are significant at the
.1 levels of probability). . . ... 45

Pearson product-moment correlation coefficients
between plant tissue nutrient concentrations and
plant biomass (p = .05) . . . 46

Water chemistry parameters measured during
September and October 1981 (average of 4 random
surface water samples, BDL = below detectable
limits). . . . . . 63

Measurements used to estimate total submersed
macrophyte biomass in study lakes and resulting
biomass estimates. . . . .... 0

Comparison of the availability of light in
Florida lakes to the occurrence of submersed
macrophytes . . . . 71

Chlorophyll a and Secchi transparency that was
measured, predicted from observed N and P, or
predicted from potentially available N and P
of submersed macrophytes (equations of Canfield
1981 and Canfield and Hodgson 1981). . 7

Water chemistry data of study lakes (Data
from Canfield 1981). . . . 7

Measured concentrations of plant nutrients in
lake water (average of 4 subsurface samples
BDL = below detectable limits). . ... .. 7









Page


Table 4-6.



Table 4-7.


Average potential concentrations of plant
nutrients in lake water (sum of observed
values and additions from submersed
macrophytes, assuming 100% release) . .. 78

Average nutrient concentrations of submersed
macrophytes (% dry wt, numbers in parenthese
are standard errors of the mean) . . 79














LIST OF FIGURES


Page


Figure 2-1.


Figure 2-2.



Figure 2-3.



Figure 2-4.





Figure 2-5.




Figure 2-6.




Figure 2-7.




Figure 3-1.


Effect of extraction time, with Mehlich's
extractant, on extractable P (dashed lines
enclose 95% confidence limits on predicted
means) . . . . .

Effect of extraction time, with Mehlich's
extractant, on extractable Fe (dashed lines
enclose 95% confidence limits) ....... .

Effect of extraction times, with Mehlich's
extractant, on extractable Cu (dashed lines
enclose 95% confidence limits) ...... .

Yield response of hydrilla to increasing
concentrations of P in the substrate.
(Vertical bars represent 95% confidence limits
of the predicted means and are offset from
the observed averages: '0', N=5; 'X',
N=15.) . . . . 2

Yield response of hydrilla to increasing
concentration of K in the substrate (Vertical
bars represent 95% confidence of the predicted
means and are offset from the observed
averages: '0', N=5; 'X', N=15.) . . 2

Concentrations of P in hydrilla tissue in response
to increasing concentrations of P in the substrate.
(Vertical bars represent confidence limits on the
predicted means, Gerloff's (1973) critical level
for waterweed = .14%). . . . 3

Concentration of K in hydrilla tissue in response
to increasing concentrations of K in the substrate.
(Vertical bars represent confidence limits on
predicted means, Gerloff's (1973) critical level
for waterweed = .80.) . . . 3

Cumulative frequency of N concentrations in
hydrilla tissues (Sept. through Oct. 1981). The
dashed line marks the critical level of N for 4
waterweed (Gerloff 1973) . . .


viii











Figure 3-2.



Figure 3-3.




Figure 3-4.



Figure 3-5.




Figure 3-6.



Figure 3-7.


Cumulative frequency of P concentrations in
hydrilla tissues (Sept. through Oct. 1981).
The dashed line marks the critical level
of P for waterweed (Gerloff 1973) . .

Cumulative frequency of K concentrations in
hydrilla tissue (Sept..through Oct. 1981).
The dashed line marks the critical level of K
for waterweed (Gerloff 1973) . . .

Cumulative frequency of Ca concentrations in
hydrilla tissues (Sept. through Oct. 1981).
The dashed line marks the critical level of
K for waterweed (Gerloff 1973) . . .

Cumulative frequency of Mg concentrations in
hydrilla tissues (Sept. through Oct. 1981).
The dashed line marks the critical level of
Mg for waterweed (Gerloff 1973) . .

Cumulative frequency of Fe concentrations in
hydrilla tissues (Sept. through Oct. 1981).
The dashed line marks the critical level of
Fe for waterweed (Gerloff 1973) . .

Cumulative frequency of Cu concentrations in
hydrilla tissues (Sept. through Oct. 1981,
BDL = below detectable limits) . ..


Page


51




53



55




57




59



61


I
















ALPHABETICAL LIST OF BOTANICAL AND COMMON NAMES


Botanical name

Bacopa caroliniana (Walt) Robins

Ceratophyllum demersum L.

Egeria densa (Planch.)

Elodea nuttallii (Planch.) St. John

Hydrilla verticillata (L.F.) Caspary

Mayaca aublettii Michx

Micranthemum umbrosum (Walt.) Blake

Myriophyllum alterniflorum D.C.

M. aquaticum (Vell.) Verdi.

M. spicatum L.

Potamogeton crispus L.

P. illinoensis Morong.

P. nodosus Poir

P. pectinatus L.

P. perforliatus L.

Proserpinaca palustris L.


Common name

bacopa

coontail

egeria

waterweed

hydrilla

bog-moss

baby-tears

watermilfoil

parrot's-feather

Eurasian watermilfoil

curled pondweed

Illinois pondweed

pondweed

sago pondweed

pondweed

mermaid weed

















KEY TO SYMBOLS

N nitrogen

P phosphorus

K potassium

Ca calcium

Mg magnesium

Fe iron

Cu copper

OM organic matter














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

RELATIONSHIPS AMONG HYDROSOIL, WATER CHEMISTRY
TRANSPARENCY, CHLOROPHYLL a AND SUBMERSED MACROPHYTE BIOMASS
By

Kenneth A. Langeland

December 1982

Chairman: William T. Haller

Major Department: Agronomy

The role of submersed macrophytes in nutrient cycling and the

subsequent influences on water quality parameters and phytoplankton

biomass are poorly understood. The relationships were, therefore,

studied among concentrations of N, P, K, Ca, Mg, Fe, and Cu in

water and hydrosoil, chlorophyll a concentration in water, trans-

parency, and submersed macrophyte biomass in seven Florida lakes,

which ranged from oligo-mesotrophic to eutrophic. The growth

response of hydrilla (Hydrilla verticillata) to extractable N, P,

K, Ca, Mg, Fe, and Cu concentrations in prepared rooting media was

also studied under laboratory conditions.

Hydrilla shoot weight increased linearly under laboratory

conditions as nutrient concentrations increased in the rooting media,

suggesting that growth was limited by nutrient availability to the

roots. High correlation among the nutrients prevented determination

of a single limiting nutrient. Concentrations of P and K in rooting

media were positively related to concentrations in the hydrilla tissue.









Above 153 pg P/g and 8 pg K/g in the rooting media, concentrations

of P and K in the hydrilla tissue remained constant. Concentrations

of N, Ca, Mg, Fe, and Cu remained constant throughout the range of

these nutrients in the rooting media.

Under natural conditions, there was no significant effect of

hydrosoil nutrient concentrations on submersed macrophyte biomass

among lakes in January through February 1981 when biomass ranged

from 12 to 51 g dry weight/m2. In September and October 1981 when

biomass ranged from 12 to 240 g dry weight/m2, K had a significant

positive effect on biomass; however, only 4% of the variability

could be explained. Hydrosoil concentrations of N, P, K, Ca, and

Mg explained small amounts of macrophyte biomass variability within

lakes.

The vertical distribution of submersed macrophytes was directly

influenced by water transparency and basin morphometry. Submersed

macrophytes were absent where water depths prevent sufficient light

for plant growth from reaching the lake bottom. This depth occurred

where 0.7% to 7% of full sunlight (PAR) was transmitted.

Submersed macrophyte biomass was directly related to lake trophic

state. However, where high macrophyte densities occurred, P and

chlorophyll a concentrations were lower than predicted for lakes in

the physiographic regions. The data suggest that high macrophyte biomass

suppresses phytoplankton production by acting as a P sink and results

in increased transparency. Using measurements of macrophyte biomass and

nutrient content, changes in water conditions after release of this

nutrient pool can be predicted.


xiii











CHAPTER 1.
GENERAL INTRODUCTION

Florida's 7,783 lakes (4 ha or more in size), totaling 9,266 km2

(Heath and Conover 1981) are an important recreational and economic

resource. The diversity of lake trophic types which include some

of the most oligotrophic lakes in the country and highly productive

eutrophic conditions (Canfield 1981) offers a variety of water uses.

The highly productive lakes generally provide excellent fishing for

both sport and commercial fishing. Sport fishing alone is a $400

million a year industry in Florida (Cato and Mathis 1979). On the

other end of the spectrum, the environment associated with oligotrophic

lakes is suitable for swimming, skin diving and water skiing. Many

Florida lakes, however, have increased dramatically in their nutrient

levels via cultural eutrophication (Shannon and Brezonik 1972). It

has been suggested that this enrichment of waters, particularly with

P, can encourage the growth of aquatic macrophytes to nuisance levels

which greatly reduce previous water uses or render them impossible.

It has consequently been suggested that aquatic weed growth may be

reduced in certain situations by abatement and or reduction of

nutrients in lakes (MacKenthun 1971; Sheffield 1970). The relation-

ship between algal biomass measured as chlorophyll a, and total P

concentrations has been well documented (Canfield and Bachmann 1981;

Dillon and Rigler 1974; Jones and Bachmann; 1976) and reductions in

chlorophyll a concentrations have been shown following reduction of

nutrient inputs to lakes (Edmondson 1979; Michalski and Conroy 1973;











Schindler 1975). However, similar relationships pertaining to

submersed macrophytes have not been reported.

It has been suggested that the importance of nutrients derived

from the hydrosoil by the roots of submersed macrophytes requires

that the hydrosoil be given important consideration in lake

restoration (Carignan and Kalff 1979). If absorption of nutrients

from the hydrosoil by the roots of submersed macrophytes is important

to their nutrition, then the hydrosoil must be considered since

nutrient concentration can be several orders of magnitude higher

than in the overlying water (Wetzel 1975). However, if nutrients

held in the hydrosoil are unavailable to submersed macrophytes,

then reduction of nutrient inputs and subsequent lowering of the

availability of nutrients for foliar absorption should be effective

in lowering macrophyte biomass providing limiting concentrations can

be achieved.

Nutritional requirements of submersed aquatic macrophytes have
only recently been studied in detail, and,the importance of root

nutrition is still a matter of debate (Cole and Toetz 1975; Waisel

and Shapira 1971). Gerloff (1973) has established critical levels

of most essential elements for the growth of waterweed under laboratory

conditions. Concentrations of P in waterweed tissues were found to

be close to the critical concentrations in a low fertility lake,

indicating that, in the lake studied, P was limiting to the growth of

waterweed. In addition, data were obtained that indicated growth-

limiting levels of Cu in some of the lakes studied. Basiouny and

Haller (1978) reported the nutritional requirements of hydrilla

and showed that the levels of nutrients in water required for optimum

hydrilla growth in laboratory experiments overlaps the range found in












natural waters. Their research has also indicated a Ca limitation

of hydrilla growth in Lake Jackson, Florida, which may be followed

by limitations of Mg and K.

Some early studies demonstrated that relationships exist between

the rooting medium and plant growth. Pond (1905) demonstrated that

several species of root-producing submersed macrophytes produced more

growth (length) when rooted in soil collected from a stream bed

than when rooted in sand or suspended over either the soil or sand.

Similar results were obtained by Snell (1907) which suggested the

importance of the rooting substrate to the growth and development of

root-producing submersed macrophytes. Works of Pearsall (1917, 1918,

1920, 1921) suggested that sedimentation rate, texture, and potash (K)

content of the substrates in English lakes were important factors

affecting plant species distribution. Likewise, Misra (1938)

demonstrated different growth responses of three aquatic species

when they were rooted in three different substrates. Misra concluded

that, for optimum growth, pondweed required intermediate organic

matter (12%), minimum C:N ratio, high levels of NH4 production, and

low redox potential. Denny (1972) demonstrated that nutrient-poor

sand or gravel substrates cannot support the plant production observed

on organic, nutrient-rich substrates, and the effect of substrates

can only be partially mediated by altering water chemistry.

Investigations using radioactive tracers, stable heavy isotopes,

and controlled environment which allow for the isolation of the root

and shoot portions of the plant have provided evidence for root

absorption of certain essential nutrients by several submersed

macrophytes. Bristow and Whitcombe (1971) have demonstrated that when












the shoots and roots are isolated and provided with phosphate, 90%,

59%, and 74% of the phosphate in the shoots of parrot's-feather,

Eurasian watermilfoil, and egeria respectively, are derived via the

roots as P32. Similar experiments have demonstrated thatN15H4 is

absorbed by the roots of egeria and translocated to the shoots

(Toetz 1974) and that Fe59 EDTA is absorbed by the roots of

hydrilla, 3% of which is translocated to the shoots (Basiouny et

al. 1977).

DeMarte and Hartman (1974) have shown equally efficient root

uptake and translocation of P32 from two distinct natural lake

sediments, muck and sand, by Eurasian watermilfoil. Carignan and

Kalff (1979) reported similar root uptake of P32 by nine submersed

hydrophytes when the roots were isolated and the shoots were exposed

to ambient lake water. All nine of these species appear to have

acquired all of the P in their shoots via the roots when grown in

a mesotrophic or mildly eutrophic environment, and an average of

72% when grown in hypereutrophic conditions. Bole and Allen (1978)

also demonstrated under laboratory conditions, root uptake of P

from a natural hydrosoil by Eurasian watermilfoil and hydrilla. In

their study, root uptake occurred even at the highest experimental

level of P in the shoot compartment. These were 0.5 ppm for hydrilla

and 2.0 ppm for Eurasian watermilfoil. DeMarte and Hartman (1974)

demonstrated root uptake of Fe 59 and Ca45 by Eurasian watermilfoil

under laboratory conditions. However, greater translocation of Fe59

occurred when the plants were rooted in muck, while Ca45 uptake was

greater when in sand. Likewise, root absorption and subsequent

translocation of P32 by sago pondweed and curled pondweed (Welsh and









5

Denny 1979), and by egeria, hydrilla and Eurasian watermilfoil have

been demonstrated (Barko and Smart 1980).

Barko and Smart (1981) have shown that the substrate can be a

source of N, P, and K for bacopa, parrot's-feather, Illinois

pondweed and mermaid weed under laboratory conditions; however,

K was absorbed and translocated only to a limited extent. Hydrilla

also absorbs K more readily from the water than from hydrosoil

(Barko 1982). Nitrogen was shown to be absorbed by the roots of

Eurasian watermilfoil by Best and Mantai (1978) and by Nichols and

Keeney (1976). In Best's studies, N limitation occurred for plants

grown in sand; however, P did not become limiting.

The preceding discussion suggests that when the roots of

certain submersed macrophytes are provided with available forms of

nutrients, the plants are able to absorb nutrients via their roots

and translocate them upward to the shoot. Other data, however,

indicate that root derived nutrition is not important to plant

growth (Cole and Toetz 1975; Waisel and Shapira 1971). Debusk

and Ryther (1981) reported that hydrilla grew equally well when

suspended on Vexar screen as rooted in substrates. This suggests

an independence to substrate derived nutrition, under the conditions

provided. Cole and Toetz (1975) demonstrated that N1 5 H4 was

absorbed and translocated by the roots of pondweed; however, they

strongly suggested that this source of N was not important to growth

of the plant. A question remains as to how well laboratory or

.enclosure derived data relate to natural conditions. Owing to the

difficulty of studying submersed macrophyte physiology in situ,

most submersed macrophyte nutrition studies have been conducted under

artificial conditions.











The growth of submersed aquatic macrophytes may be influenced

to a large extent by the levels of available essential elements in

both the water column and substrate, and by the interaction of

nutrient availability and physical and chemical parameters. Consider-

ing the complex chemical, physical, and biological interactions in

the aquatic environment, it is necessary to evaluate the relationships

between submersed macrophyte abundance and hydrosoil chemical

characteristics observed in the laboratory under natural conditions.

A diversity of physical and chemical properties exist in north and

central Florida lakes. These range from clear, softwater, low-

nutrient lakes to highly colored, hardwater nutrient-rich lakes

(Canfield, 1981). Hydrosoil in these lakes range from sandy, low

organic hydrosoils to highly organic mucks. It is, therefore,

possible that these lakes will have different abilities of supporting

both planktonic and macrophytic vegetation and hydrosoil characteristics

may explain low biomass of submersed macrophytes in Florida lakes

such as Lake Kerr (Marion Co.) and Lake Jackson (Leon Co.).

Nutrient uptake by submersed macrophytes may have an important

effect on nutrient cycling and water chemistry. Phosphorus, for

example, cycles between three major compartments in lakes, the

epilimnion, the biota of the littoral, and the hypolimnion and the

sediments (Rigler 1964). The role of submersed macrophytes in the

littoral has been variously described as transporting nutrients from

the hydrosoil to the epilimnion (Barko and Smart 1980; DeMarte and

Hartman 1974; McRoy et al. 1972), or as a P sink (Goulder 1969).

It has further been suggested that in acting as a N sink, submersed

macrophytes suppress phytoplankton abundance (Goulder 1969), which

in turn can increase water clarity (Dillon and Rigler 1974;








7

Jones and Bachmann 1976). The objectives of this study were as

follows: 1. Determine the relationships between nutrient concen-

trations in the water and hydrosoil and submersed macrophyte biomass.

2. Determine the relationships between water chemistry and clarity,

phytoplankton standing crops and submersed macrophyte biomass and

distribution. This knowledge will be useful for developing lake

management strategies.















CHAPTER 2.
HYDRILLA GROWTH RESPONSE TO CONCENTRATIONS
OF EXTRACTABLE NUTRIENTS IN PREPARED SUBSTRATES

Introduction

Investigations of nutrient absorption by submersed macrophytes

have shown that several species can absorb nutrients from hydrosoil

via their roots (Barko and Smart 1980, 1981; Carignan and Kalff

1979; Welsh and Denny 1979). These observations may help to explain

growth differences of submersed macrophytes on different substrate

types (Pond 1905; Pearsall 1920, 1921; Misra 1938; Denny 1972).

The objective of this investigation was to determine if hydrilla

growth and nutrient accumulation are influenced by different concen-

trations of extractable N, P, K, Ca, Fe, Mg, and Cu in prepared

substrates. Because there is no standardization for determining

nutrient availability of hydrosoils, a soil extraction procedure

which reportedly could be used for determining the availability of

a wide range of plant nutrients with a single extraction over a

wide range of soil properties was investigated (Mehlich 1978;

Mehlich 1980). This extraction procedure, if satisfactory, could

be useful for future nutrient analysis of submersed substrates.


Materials and Methods

Five substrates were prepared by enriching coarse builders sand

with varying volumes of an organic (muck-sand) soil. An aliquot of

each of these substrates was prepared for chemical analysis by oven

drying to constant weight in aforced-air drying oven and grinding in












a mortar and pestle to pass a 20 mesh screen. Chemical analysis was

performed on four subsamples from each of these aliquots. Organic

matter (OM) content was estimated as the percent dry weight loss on

ignition in a muffle furnace, at 850 C for 4 hr. Exchangeable N was

determined by extracting 5 g of soil with 50 ml 2N potassium chloride

for 60 min, distilling the extract in the presence of magnesium oxide

and Davarda's alloy into boric acid, and backtitrating with sulfuric

acid (Bremmer 1975). Exchangeable P, K, Ca, Fe, Mg and Cu were

extracted with a solution of the following composition (Mehlich 1978;
Mechlich 1980): 0.2 N acetic acid, 0.25 N ammonium nitrate, 0.015 N

ammonium fluoride, 0.012 N nitric acid, 0.002 N EDTA. Aliquots (1.0 g)

of a highly organic (44% OM) substrate, which was collected from

Lake Jackson (Leon Co., FL) were shaken with 25 ml of extract in

50 ml Ehrlenmeyer flasks for 5, 10, 15, 30 and 60 min to determine the

effect of extraction time on nutrient yield. Subsamples of the prepared

substrates were extracted as above for 30 minutes. After shaking,

the extract was filtered (Whatman #54) into glass vials. Determination

of P was by an ascorbic acid, molybdate reduction method which was

modified from Mehlich (1978) and standard methods (American Public

Health Service 1981) as follows: The mixed reagent was prepared by

combining 100 ml 5 N sulfuric acid, 10 ml 7.1 x 10-3 M antimony

potassium tartrate, 100 ml 8.1 x 10-3 ammonium molybdate, 0.88 g

ascorbic acid, and bringing to a final volume of 1.0 1. Color was

developed with 1 ml of extractant and 20 ml of mixed reagent.

Concentrations of K, Ca, Fe, Mg, and Cu in soil extracts were determined

by atomic absorption spectrophotometry by the University of Florida

Soil Testing and Analytical Research Laboratory (Mitchell and Rhue 1979).









Five replications each of the prepared substrates were placed in

plastic pots (10 cm x 10 cm x 10 cm) and arranged in two 5 x 5 latin

squares, so that substrate types were randomized over both lighting

effects and water flow, in an indoor fiberglass tank (207 cm long x 56

cm wide). Pondwater (Table 2-1) at a depth of 42 cm was continually

circulated through the tank in such a way that water entered at the

bottom of one end of the tank and exited at the top of the other

end. A single terminal shoot of hydrilla 6 cm in length was placed

into each pot. The hydrilla shoots were obtained from a culture which

was grown from tubers under similar conditions. Light was provided

for 14 hr by 2 sodium vapor lamps that produced 1755 pE/m -sec PAR

at the water surface in the center of the tank. After growing under

these conditions for 6 weeks, plants were removed for weight determination

and chemical analysis. The experiment was repeated twice, once

beginning in April 1980 and once beginning in June 1980.

The concentration of P in the incoming water was determined

colorametrically by ascorbic acid, molybdate reduction (Murphy and

Riley 1962), after persulfate digestions (Mentzel and Corwin 1965).

Total Kjeldahl nitrogen (TKN) was determined on incoming water by

steam distillation according to the methods of Nelson and Sommers (1975).

Concentrations of K, Ca, Fe, Mg, and Cu were determined as extractablee

metals" (American Public Health Service 1981).

Dry weight of roots and shoots was determined separately after

drying to constant weight at 70 C. Plant tissue (shoots only) obtained

from all replications of a prepared substrate, within a latin square,

were combined for nutrient analysis. The plant tissue digestion

procedure was adopted from Koch and McMeekin (1924). Sample aliquots







11

Table 2-1. Concentrations of nutrients (mg/m3) measured in source
water.



N (Total) 1000

P (Total) 2

K 400

Ca 22000

Fe 975

Mg 1900

Cu 53









of 100 mg were digested for 1 hr on a digestion block at 350 C with

1 ml of 20% H2SO4. After cooling, the samples were reheated for 15

min with 1 ml of 30% H202. The last process was repeated until the

sample solution became clear and then one additional time (a total

of 2 to 4 times). Finally, the digest was diluted to 50 ml. All

glassware used in the digestion procedure and the following nutrient

analyses was acid-washed with hydrofluoric acid.

Nitrogen was determined as for water on a 1 ml aliquot of the

digest. Analysis of other nutrients was performed on plant digests

as described for soil extracts.

All data were analyzed at the Northeast Regional Data Center

in Gainesville, FL, using the Statistical Analysis System (SAS),

statistical software.

Results and Discussion

Small 95 confidence limits on the means (CLM) of subsamples

within extraction times (Table 2-2) for each element analyzed in

the Lake Jackson substrate shows a high degree of precision that can

be obtained using the extraction procedure. Regression analysis

yielded an insignificant slope coefficient (a = .05) when K

concentration was regressed over extraction time, and only the

shortest extraction time yielded significantly lower yields of

Ca and Mg (Table 2-2). Extraction time has a greater effect on the

amount of P, Fe, and Cu extracted (Figure 1 through 3). Extractable

P and Fe increase as a function of time, whereas extractable Cu


decreases.










Table 2-2.


Means and confidence limits of extractable
concentrations, using different extraction
Mehlich's extractant.


nutrient
times with


Extraction time Average Concentrations 95% CLM
(min) (mg/kg) (mg/kg)


K

256
256
250
250
256


8.5
9.2
9.4
10.0
9.9


Fe

548
611
636
667
707


1169
1256
1256
1250
1262


Mg
5 175 3
10 184 3
15 186 2
30 186 2
60 186 4































Figure 2-1. Effect of extraction time, with Mehlich's extractant,
on extractable P (dashed lines enclose 95% confidence
limits on predicted means).


































- -


10.5








10.0



0)
E

0.
u 9.5
-j

I-



X
J 9.0
W 9.0


y=7.75 + 1.32Logx


r2=.82


/
/
/


10 20 30 40 50

EXTRACTION TIME (min)
































Figure 2-2. Effect of extraction time, with Mehlich's extractant,
on extractable Fe (dashed lines enclosed 95% confidence
limits).







































y=460 + 142Logx


750






700



E

u. 650

-J

I-
0
S600
I-




550


r2=.88


10 20 30 40 50
EXTRACTION TIME (min)


Sx "






























Figure 2-3. Effect of extraction time, with Mehlich's extractant,
on extractable Cu (dashed lines enclose 95% confidence
limits).


































y=23.07 12.17Logx

2
r .96


N


x


I I I I


20 30 40

EXTRACTION TIME (min)


* 10
0)
E


0
w
-J
.J
CD
<


o 5

I-
Lu


n |


_A








20

For most applications, an extraction time of 15 minutes will

sufficiently estimate all of the plant nutrients which were studied

with the exception of, perhaps, Cu. A 15-min extraction underestimates

the maximum P yield of 30-min by 7% and underestimates the maximum

Fe yield obtained with a 60-min extraction by 11%. The major problem

occurs with Cu where a 15-min extraction would underestimate, by 36%,

the maximum value obtained with the 5-min extraction recommended by

Mehlich (1978).

The growth response of hydrilla was studied over a wide range

of nutrient concentrations in the prepared substrates (Table 2-3).

Because the substrates were prepared by mixing different levels of

the same sand (0% OM) and organic soil (17% OM), all of the nutrient

concentrations and OM within a particular substrate should be highly

correlated. This relationship is consistent for all nutrients

except Cu (Table 2-4). The lower correlation of Cu with other nutrients

seems to result from a decrease in extractability of Cu at the higher

levels of OM and other nutrients (cf. Table 2-3 and Table 2-4).

This effect appears to occur with Fe but apparently to a lesser extent.

This may not necessarily indicate a shortcoming of the extraction

procedure but may be a real indication of nutrient availability.

Analysis of variance indicated that there was no significant row

or column effect on shoot weight, root weight or root to shoot ratio

within the separate latin squares on different experimental dates,

nor was there an effect of experimental dates. One of the latin

squares, however, in the April experiment, produced a yield response

significantly different (p = .05) from the other latin squares. Data










Table 2-3. Concentrations of nutrients
of prepared substrates.


and organic matter content


OM N P K Ca Fe Mg Cu


% g/g1

0.0 9 17 4 257 80 .02 .06

1.0 12 153 8 1438 198 41.0 2.50

2.3 31 334 12 3625 388 76.0 6.00

4.6 60 1526 24 5233 625 123.0 6.70/

17.0 120 2664 32 9468 669 253.0 5.30








from this latin square (N = 5) were therefore analyzed separately

from the combined data of the remaining three latin squares (N = 15).

Because plant tissue was pooled within latin squares for the nutrient

analysis, plant tissue nutrient data were analyzed together.

Regression analysis indicated nonsignificant slope coefficients
for both groups bf data when root weight or root to shoot ratio was

regressed over any of the soil chemical parameters studied. Significant

(p = .01), positive, linear, slope coefficients were obtained when shoot
weight was regressed over any of the soil parameters studied suggesting

substrate related nutrient limitation throughout the range of prepared

substrates. The responses of shoot weight to P and K concentrations

in the substrates are presented in Figures 2-4 and 2-5 respectively

as examples; however, due to the high correlation between concentrations

of nutrients in the substrates (Table 2-4) it is impossible to assign

cause and effect between shoot weight and individual nutrients.

Concentration of N, Ca, Mg, Fe, and Cu in the plant tissues were
not influenced by concentrations of nutrients in prepared substrates

(Table 2-5). The tissue concentrations of P and K were influenced

by the concentrations of the respective nutrients in the substrates.

However, only the lowest concentration of P in the substrate resulted

in a lower tissue concentration of P, after which the slope of the

regression line approached zero (Figure 2-6), and the effect of

concentrations of K in the substrate had only a limited effect on

the concentration of K in hydrilla tissue (Figure 2-7).

Concentrations of N, K, Ca, Mg, Fe, and Cu in hydrilla tissues

were above critical levels for waterweed (Gerloff 1973) (Table 2-5,

Figure 2-7). The concentration of P in hydrilla tissue was only









Table 2-4. Correlation between concentrations of extractable
nutrients in prepared substrates (Pearson product
moment correlation coefficients, P<.05).


N P K Ca Mg Fe Cu

OM 0.98 0.95 0.90 0.97 0.97 0.78 0.45

N 0.99 0.97 0.99 0.99 0.89 0.59

P 0.98 0.97 0.97 0.90 0.59

K 0.98 0.97 0.97 0.73

Ca 0.99 0.93 0.71

Mg 0.90 0.64

Fe 0.88









Table 2-5.


Concentrations of nutrients in hydrilla tissue and critical
levels of these nutrients in waterweed (Gerloff 1973).
Numbers in parentheses are standard errors of the mean.


Concentration in
Hydrilla Tissue
(% dry wt)


Element


Critical level
(% dry wt)


N 1.73 (.04) 1.6

Ca .75 (.03) .28

Fe .13 (.006) .006

Mg .23 (.003) .10

Cu .0006 (.00002)






























Figure 2-4. Yield response of hydrilla to increasing concentrations
of P in the substrate. (Vertical bars represent 95%
confidence limits of the predicted means and are offset
from the observed averages: '0', N=5; 'X', N=15.)


























y=.18 .58x, r2=
y=.18 + .58x, r =.78


y=.40 + .36x, r2=.58


2.0


1.5





1.0





0.5


1 2 3
P CONCENTRATION IN SUBSTRATE (mg/g)






























Figure 2-5. Yield response of hydrilla to increasing concentration
of K in the substrate. (Vertical bars represent 95%
confidence of the predicted means and are offset from
the observed averages: '0', N=5; 'X', N=15.)
























y=.06x.- .16, r2=.73


y=.18 + .03x, r2=.59


2.0


1.5





1.0





0.5


10 20 30
K CONCENTRATION IN SUBSTRATE (mg/kg)
































Figure 2-6.


Concentrations of P in hydrilla tissue in response to
increasing concentrations of P in the substrate.
(Vertical bars represent confidence limits on the
predicted means, Gerloff's (1973) critical level for
waterweed = .14%).





























x
Y=4.29x + .0008

r2.98


0.5 1.0 1.5 2.0 2.5 3.0
mg/g

P CONCENTRATION IN SUBSTRATE


.25




.20


.15




.10


.05































Figure 2-7.


Concentration of K in hydrilla tissue in response to
increasing concentrations of K in the substrate.
(Vertical bars represent confidence limits on
predicted means, Gerloff's (1973) critical level
for waterweed = .80.)





























x
=.37x .65

r2.97


2.5




2.0




1.5




1.0




0.5




0


5 10 15 20 25 30 35
(mg/kg)
K CONCENTRATION IN SUBSTRATE








slightly below Gerloff's critical level at the lowest concentration

of P in the substrate (Figure 2-6). Concentrations of these nutrients

above critical levels for optimum growth, and the constant concentration

over varying concentrations in the substrates, may suggest that none

of these nutrients were limiting to growth. However, since shoot

weight increased as concentrations of nutrients in the substrates

increased, the total amount of assimilated nutrients also increased.

It can, therefore, be reasoned that as nutrient availability in the

substrates increased the rate of nutrient assimilation increased

and allowed for a proportional increase in carbon dioxide assimilation

measured as dry weight. Those nutrients which were not at limiting

concentrations were apparently absorbed proportionally to the limiting

nutrientss.

Conclusions

The soil extractant used in this investigation (Mehlich 1978, 1980)

proved satisfactory for estimating concentrations of extractable P,

K, Ca, Mg, and Fe. However, with a highly organic hydrosoil the amount

of Cu extracted was very sensitive to extraction time, while amounts

of P and Fe extracted were sensitive to a lesser extent. Hydrilla

growth increased linearly in response to increasing concentrations of

nutrients as measured with the new extractant.














CHAPTER 3.
HYDROSOIL RELATED NUTRITION OF SUBMERSED
MACROPHYTES IN SEVEN FLORIDA LAKES

Introduction

Hydrosoil characteristics may influence the growth and distribution

of submersed macrophytes in lakes (Pond 1905; Pearsall 1920, 1921;

Misra 1938; Denny 1972). Evidence for root uptake of nutrients by

several submersed species (Bristowe and Whitcombe 1971; DeMarte and

Hartman 1974; Welsh and Denny 1979; Barko and Smart 1981) indicate

that this influence may be related to nutrient availability in the

hydrosoil; and, it has been suggested that this nutrient source is a

dominant factor influencing submersed macrophyte growth (Carignan and

Kalff 1979). However, since foliar uptake of nutrients can also occur,

the relative importance of the hydrosoil and water as a source of

nutrients is still in question (Cole and Toetz 1975).

A wide range of nutrient conditions and submersed macrophyte

abundance in Florida lakes suggests nutrient related growth limitation.

For example, hydrilla often causes severe weed problems by producing

dense infestations in many lakes but has not proliferated in other

lakes such as Lake Jackson (Leon Co., FL) or Lake Kerr (Marion Co., FL)

and seemingly disappeared from Lake Down (Orange Co., FL) after its

introduction. If hydrosoils are the dominant source of nutrients to

submersed macrophytes, then biomass should be related to concentrations

of nutrients in the hydrosoil when nutrient availability is limiting to

growth. This relationship has not been studied under natural conditions











and may explain the lack of hydrilla growth in Lakes Kerr, Jackson and

Down. The objective of this study was to determine the importance of

N, P, K, Ca, Mg, Fe, and Cu concentrations in hydrosoil on the biomass

and nutrient content of submersed macrophytes in Florida lakes with

differing water chemistry.

Materials and Methods

Six lakes were sampled during January and February 1981 (winter

sampling) and September and October 1981 (fall sampling) (Table 3-1).

Lake Jackson was not sampled during the fall due to low water. Lake

Down was included in the fall as a substitute. Fathometer (Raytheon

DE 719 Precision Survey Fathometer Depth Recorder) tracings were

made during each sampling along transects that were established

between fixed landmarks in each lake (Appendix 1). Buoys were

dropped along the transects to mark representative vegetation and

fixmarks were drawn simultaneously on the fathometer tracing. At each

buoy, a plant biomass sample and a hydrosoil sample were collected.

Plant biomass was sampled with a biomass sampler similar to the one

described by Nall and Schardt (1978). During the fall sampling, an

improved bucket designed by the USAE, WES (Vicksburg, MS) was used.

Hydrosoil samples were collected either directly from the biomass

sampling bucket or by dropping a Ponar dredge through the hole in the

vegetation created by the sampler. All samples were placed on ice

prior to preparation for analysis.

Results and Discussion

Concentration ragnes of N in the hydrosoil were similar among the

lakes (Table 3-2) suggesting that the large differences in biomass


1












Table 3-1. Locations of study lakes and dates of hydrosoil and submersed macrophyte biomass sampling.

Lake Location W Sampling Dates (1981)
Lake Location Winter Fall

Okahumpka S21 T19S R23E January September

Lochloosa S20 T11S R22E February September

Fairview S10 T22S R29E January September

Stella S30 T12S R28E January September

Jackson S33 T2N R1W February ---

Down S8 T23S R28E ---- October

Kerr S22 T13S R25E January September










Table 3-2.


Cumulative
hydrosoil)
September


frequency of exchangeable nutrients (mg/1
in the hydrosoil of six Florida lakes in


and October 1981.


Lake 0% 25% 50% 75% 100%


Okahumpka
Lochloosa
Fairview
Stella
Down
Kerr


Okahumpka
Lochloosa
Fairview
Stella
Down
Kerr


Okahumpka
Lochloosa
Fairview
Stella
Down
Kerr


Okahumpka
Lochloosa
Fairview
Stella
Down
Kerr


Okahumpa
Lochloosa
Fairview
Stella
Down
Kerr


N
6 12 21 28 50
10 16 19 22 35
8 14 18 23 34
6 12 15 18 29
7 11 14 17 33
6 14 20 24 32

P
1. 2 4 7 12
2 4 5 7 15
7 15 23 30 49
14 27 37 48 91
8 40 44 50 80
3 5 6 8 41

K
3 10 14 20 40
5 6 8 11 20
6 24 47 56 77
14 22 28 41 144
17 27 31 34 40
3 7 10 12 23

Ca
78 248 313 399 708
216 293 329 395 561
127 604 765 925 1678
332 542 661 783 1532
212 461 571 652 894
125 572 652 775 974

Mg
5 19 25 31 46
24 38 46 52 90
19 29 35 42 65
32 81 104 126 334
37 132 148 165 240
18 81 109 129 181









Table 3-2. Continued.

Lake 0% 25% 50% 75% 100%

Fe
Okahumpka 34 44 51 66 131
Lochloosa 15 27 34 43 68
Fairview 10 19 25 34 53
Stella 11 31 51 61 92
Down 7 24 29 39 89
Kerr 9 46 68 90 185
Cu
Okahumpka .01 .01 .01 .01 .03
Lochloosa .03 .05 0.07 .08 0.39
Fairview .05 .62 0.92 1.13 2.00
Stella .21 .72 1.14 1.76 3.06
Down .01 1.83 2.30 2.78 8.00
Kerr .01 .17 0.20 0.24 .57










Table 3-3. Average
Florida


Lake


Okahumpka

Lochloosa

Fairview

Stella

Jackson

Down

Kerr


submersed macrophyte biomass measured in seven
lakes in 1981.


Submersed Macrophyte biomass (g dry wt/m2)
January February September October


240

100

246

125

---

59

12










(Table 3-3) could not be attributed to N availability in the hydrosoil.

Large differences in P, K, Ca, Mg, Fe, and Cu concentrations were

encountered among lakes; however, these differences do not appear related

to the large differences in macrophyte biomass (cf. Table 3-2 and Table

3-3), or with other trophic state indicators such as N concentration of

water or chlorophyll a concentration (cf. Table 3-3 and Table 3-8).

Relationships between individual species biomass (where a species was

encountered in sufficient number, Appendix 2) and hydrosoil nutrient

concentrations (Appendix 3), within lakes, were analyzed by stepwise

regression with stepwise entry at a .15 entry level (SAS Institute Inc.

1979). Water depth and hydrosoil organic matter (OM) (Appendix 3) were

included in this analysis. The resulting regression equations are

presented in Table 3-4 along with the proportions of the total sums of

squares which are explained by the individual variables (calculated from

sequential sums of squares). Significant nutrient effects on biomass

were not observed in the winter in Lakes Kerr, Fairview, and Lochloosa,

which may be due to annual senescence of hydrilla (Berg 1977). The

significant effects in Table 3-2 are inconsistent among species and lakes,

and where a nutrient has a significant effect on biomass only a small

proportion of the total sums of squares is explained. The occasional

negative effects implied for P, Ca, and Fe are difficult to explain;

however, considering the very small contributions to the models in most

of these cases they are unimportant effects.

In order to remove the possible competitive effects of one species

over another, species weights were summed over individual buoys and the
resulting total biomass values were regressed over hydrosoil nutrient









Table.3-4.


Regressiom models relating submersed macrophyte biomass to
concentrations of nutrients in hydrosoil (y=biomass, all
regression coefficients are significantly greater than 0
at a .1 level of probability by students t, numbers in
parentheses indicate the proportion of the total sums of
squares explained by the variable above it.)


Lake Common name Regression


September


- October 1981


bogmoss


hydrilla


Illinois pondweed


y = 82LogloFe + 95LogloCu
(.09) (.09)
y = 4.3K
(.23)


y = -409-7P + 294LogloFe
(.02) (.13)


+ 97Depth
(.27)


y = -.28 + 21LogloP +
(.02)

y = 80 27LogloCa
(.24)

y = 20K + 5.4 Mg
(.35) (.11)

y = 21 + 2.7P
(.24)

y = 277 + 407LogloK -
(.05)


January February 1981


13LogloOM
(.42)


377 LogloMg
(.15)


y = 323 + 65LogloDepth
(.13)

No significance

No significance

No significance

No significance

y = 71 + .31K + .02Ca = 5LogloFe
9.22) (.14) (.15)


Down


Fairview


Fairview


Kerr


Kerr


Lochloosa


Stella


Okahumpka


hydrilla


bogmoss


hydrilla


hydrilla


hydrilla


Fairview


Kerr

Kerr


Lochloosa

Okahumpka

Stella


hydrilla


hydri lla

bogmoss

hydrilla

hydrilla

hydrilla









Table 3-5.


Regression models relating total submersed macrophyte biomass
to concentrations of nutrients in hydrosoil (y=biomass, all
regression coefficients are significantly greater than 0 at
a .1 level of probability by student t, numbers in parentheses
indicate the proportion of the total sums of squares explained
by the variable above).


September October 1981


Regression


y = 274 + 412Logo1K 375LogioMg
(.05) (.15)

y = 263 + 449LogloK .72Ca + 6Mg
(.37) (.01) (.13)


y = 3.7K
(.18)


y = 21 + 2.7P


Down

Kerr


Among lakes


y = 82LogloFe + 95Log10Cu
(.09) (.09)

y = 14 + 1.5K .02Ca
(.20) (.11)

y = 182 + 3.1K 1.4Mg
(.04) (.28)
January February 1981


No significance
y = 373-198P
(.16)


Y = -198 + 138LogloP
(.26)

y = .91N + .26K + .04Ca
(.03) (.19) (.23)


No significance


y = .54N
(.36)


No significance


Lake


Okahumpka

Lochloosa

Fairview


Stel la


Okahumpka

Lochloosa


Fairview


Stella

Jackson


Kerr


Among lakes









concentrations, OM and depth-(Table 3-5). Concentrations of K in the

hydrosoil had a significant effect on macrophyte biomass in Lakes

Okahumpka, Lochloosa, Fairview, and Kerr in the fall. In the winter,

K concentration in the hydrosoil explained 19% of the macrophyte biomass

variability in Lake Stella and Ca explained 23%. Concentrations of P

in the hydrosoil had a positive effect on macrophyte biomass in Lake

Stella in fall and Fairview in winter. Hydrosoil concentrations of N

explained 36% of the variability of biomass in Lake Kerr in winter.

In no case was a large proportion of the variability of biomass

explained by hydrosoil nutrient concentration. When total biomass

is regressed over hydrosoil nutrient concentrations, among lakes, only

32% of the variance can be explained by hydrosoil nutrient concentrations

by the fall data. The negative response to Mg concentration, which

accounts for 28% of the variance is difficult to explain. No significant

effects of hydrosoil nutrient concentration on plant biomass were

observed among lakes in winter. Hydrosoil nutrient concentrations did

not explain a large portion of submersed macrophyte biomass or nutrient

content variability. Part of the unexplained variability may result from

the method of sampling by Ponar dredge or biomass sampler; however,

it is believed that the samples represent the root zone of submersed

macrophytes (both methods yield samples from c.a. the top 12 cm of

hydrosoil under most conditions). Other factors such as nutrient

availability in the water apparently control submersed macrophyte

biomass in the lakes studied.

Plant tissue nutrient concentrations (Appendix 4) were regressed

over the concentrations of the corresponding nutrients in hydrosoils

(Appendix 2). Few significant effects were observed in this analysis












(Table 3-6). Although small proportions of the variability are explained,

a negative relationship occurs frequently for Mg.

If any of the nutrients which, as regression analysis suggests, have an

effect on plant biomass are causing growth limitation, an increase in

growth is expected as the concentration of that nutrient increases in the

plant tissue. None of the significant correlations between plant biomass

and plant tissue nutrient concentrations (Table 3-7) support the regres-

sion models in Table 3-4, again, suggesting that hydrosoil related nutrient

limitation is not important in these lakes. The few significant positive

correlations which appear in Table 3-7 may suggest nutrient limitation;

however, a high correlation was only observed between biomass and Fe.

The negative correlations may reflect the fact that greater biomass

results from larger, more robust plants with greater structural material

and carbon content which cause a smaller elemental content to dry

weight ratio.

If nutrient limitation of submersed macrophytes is occurring in the

study lakes, tissue nutrient concentrations which are below critical

levels for optimum growth should occur in fall when growth is approaching

maximal. Unfortunately, established critical levels are not available

for any of the plant species which were encountered in a sufficient number

of samples from which to make inferences. Ferloff (1973) has suggested

critical levels for waterweed, a closely related species (family

Hydrocharitaceae) to hydrilla. Comparing nutrient concentrations of

hydrilla tissues, collected during fall, to Gerloff's critical levels

(Figures 3-1 through 3-7) should give some indication of nutrient

limitation to hydrilla growth. Care must be taken, however, in these

interpretations because two different genera are being compared, and











Table 3-6. Regression equations that relate levels on nutrients in lake substrates to levels of the
corresponding nutrients in tissues of root producing submersed macrophytes (all data is
from fall sampling, all regression coefficients and intercept estimates are significant
at the .1 levels of probability).

Lake Species Regression r


Okahumpka hydrilla Plant-K=.57Loglo hydrosoil-K .06

Fairview Illinois pondweed Plant-P=.22 .06 hydrosoil-P .09

Fairview hydrilla Plant-Mg=.61 .005 hydrosoil-Mg .19

Fairview hydrilla Plant-N=-1.2Logo0 hydrosoil-N

Lochloosa hydrilla Plant-Mgl=.7 .68Logio hydrosoil-Mg .11

Stella hydrilla Plant-Mg=1.5 -.51Loglo hydrosoil-Mg .31

Stella hydrilla Plant-N=.46Loglo hydrosoil-N .10

Down bogmoss Plant-Mg=.57 -.0009 hydrosoil-Mg .18

Kerr bogmoss Plant-Mg=.35 -.0005 hydrosoil-Mg .25








Table 3-7.


Pearson product-moment correlation coefficients between
plant tissue nutrient concentrations and plant biomass
(p = .05).


Common name N P K Ca Mg Fe Cu

September October 1981

bacopa ns ns ns ns ns ns -.57

hydrilla ns ns -.22 ns ns ns ns

bogmoss -.36 -.43 ns ns .49 -.41 ns

Illinois pondweed ns ns ns .56 ns ns ns

January February 1981

bacopa ns ns ns ns ns ns ns

hydrilla .19 ns .19 ns ns ns ns

bogmoss ns ns ns ns ns .83 ns

Illinois pondweed ns ns ns ns ns ns ns











because Gerloff's values are based upon indicator segments and values

in this study are based upon composite tissue samples. In addition,

the values from Lake Okahumpka can be compared to the other lakes in

the study. Lake Okahumpka is a shallow, eutrophic lake in which

hydrilla surface-mats early in the year and remains in this condition

into winter months. It can be assumed, therefore, that competition

for available space occurs before nutrient limitation results in

this lake, and nutrient concentrations of hydrilla tissues from Lake

Okahumpka represent at least adequate concentrations.

Nitrogen concentrations of hydrilla tissue from Lake Okahumpka and

Lake Lochloosa are both well above the Gerloff critical levels (Figure

3-1); however, about 50% of the tissue samples from Lake Fairview and

Lake Stella are below the Gerloff critical levels. Because these

concentrations are also below the the minimum values in Lake Okahumpka and

Lake Lochloosa, it would appear that N may be limiting to hydrilla growth

in certain areas of these lakes. Both Lake Fairview and Lake Stella

produce high densities of hydrilla in nuisance proportions; therefore,

when this limitation to growth occurs, it does not become important until

high plant biomass is attained. Since no relationship between hydrosoil

nutrient concentrations and biomass, or plant tissue nutrient concentra-

tions was indicated, the apparent N limitation does not seem to be

hydrosoil related. Lake Okahumpka and Lake Lochloosa also have the

highest measured concentration of total N in the water column (Table

3-8). These observations suggest a dominant role of the water column

as a source of N to hydrilla. Since only about 10% of Lake Kerr hydrilla

tissues contained less than the Gerloff critical level of N, and the

remainder had concentrations equivalent to those of Lake Okahumpka and

































Figure 3-1. Cumulative frequency of N concentrations in hydrilla
tissues (Sept. through Oct. 198). The dashed line
marks the critical level of N for waterweed (Gerloff 1973).



























-90


-50 100-


0 -10


75-


S 75-


-L
UJ 25-



S 5-

U3
2 -



- 2


-90


75-


25-


100-


4_


-90


100-


25-


00 9-
--90


L -

<






























Figure 3-2. Cumulative frequency of P concentrations in hydrilla
tissues (Sept. through Oct. 1981). The dashed line
marks the critical level of P for waterweed (Gerloff 1973).











6 -
100-
100

-90


5-
Uw 75-

Sto100-


< 4
-J
-90 25-
z

oa
E >
S -90


O 5

< .-,
1 00- -10

- 10

z5
Z 75-

-- 25 0-o -50 --o
C -s
S-10 0 2 05 2- -I
S-50 5- 0
5-
0 -0





< <



2 O a- C


O u































Figure 3-3. Cumulative frequency of K concentrations in hydrilla
tissue (Sept. through Oct. 1981). The dashed line
marks the critical level of K for waterweed (Gerloff 1973).

































6






5





S4





3


25-


--




0
Z
w



Lu




I-
o
-J


-ti
0-






























Figure 3-4. Cumulative frequency of Ca concentrations in hydrilla
tissues (Sept. through Oct. 1981). The dashed line
marks the critical level of K for waterweed (Gerloff 1973).













20








100


100-
IS -



10000









jz 1






0 -9o
Z





-50
-50
-90 -90
75








-10 -SO 2- 75-
-- 5- -- :-- -- -50
-_ 0 -I0 ---


o
0o so

5 I-
I-e









5 -o



0 -5 -0 "





00J






























Figure 3-5. Cumulative frequency of Mg concentrations in hydrilla
tissues (Sept. through Oct. 1981). The dashed line
marks the critical level of Mg for waterweed (Gerloff 1973).













100-





U 100

C)




u.z
< 10 f
_{ >. 100 -90
-
O z

0



0 > I00-0
< -90 *-90 -90
I-I


75- -50
Z 0 -o 7 -
1050










0-OO
U 0 5 -50 S

S- "- -0
Z 210- oo
o 5 L I j L j l C
0 10 I0 2S 00- 10 50 -




0 D _- 0-- ->5a

-5




".j :R I- _J

LL V)
0 -1





























Figure 3-6. Cumulative frequency of Fe concentrations in hydrilla
tissues (Sept. through Oct. 1981). The dashed line
marks the critical level of Fe for waterweed (Gerloff 1973).












































100

90 75 -90
5- -2-
25 5 m


CO
0 <
WJo -


0 LL
o -


w-J

U)


w cr
Scr
wW






























Figure 3-7. Cumulative frequency of Cu concentrations in hydrilla
tissues (Sept. through Oct. 1981, BDL = below detectable
limits).








100



-50


100-


100-
-90


-90 -50

25-
BDL
IL


320 o


401


30-


201-


I '










Lake Lochloosa, low biomass and apparent inability of hydrilla to

proliferate in Lake Kerr cannot be explained in terms of concentrations

of N in hydrilla tissue.

Plant tissue data for P (Figure 3-2) present a situation very similar

to that for N, although somewhat less definitive. Greater than 50% of

Lake.Fairview and Lake Stella samples fall below the Gerloff critical

levels suggesting P limitation in these lakes. However, the overlap

of about 25% of the Lake Okahumpka samples complicate this relationship.

The significant explanation of 24% of hydrilla biomass in Lake Stella

on P concentrations in hydrosoil (Table 3-4) combined with the observa-

tion that tissue concentrations of P are close to probable critical levels,

present fairly convincing evidence for P limitation in Lake Stella and

evidence for some effect of the substrates in providing a source of

assimilable P. As with N, P is in sufficient supply to allow for high

plant densities. Since only 2% of the variability in hydrilla biomass in

Lake Kerr could be attributed to P concentrations in the hydrosoils

(Table 3-4), no relationships existed between P concentration in the

hydrosoil and plant tissues, and P concentrations in the plant tissues

are well above apparent critical levels, P concentrations in plant

tissue cannot be used to explain the low biomass of hydrilla in Lake

Kerr.

Concentrations of K in hydrilla tissues that would indicate limita-

tion to hydrilla growth were not observed (Figure 3-3). As with N, a

strong relationship between levels of K in the lake water and levels of K in

hydrilla tissue were evident. Lake Fairview and Lake Stella had the

highest concentrations in the water column (Table 3-8). This is in contrast

to the regression analysis which indicated some dependence of hydrilla











Table 3-8. Water chemistry parameters measured during September and October 1981 (average of 4 random
surface water samples, BDL = below detectable limits).

N P K Ca Fe Mg Cu Chla
Lake (mg/m3) (mg/m3) (mg/l) (mg/l) (mg/l) (mgAn3) (mg/m3) (mgmi3) pH


Lochloosa 1644 25 .20 2.58 .20 2.43 .0025 28.7 8.5

Okahumpka 1326 12 .15 4.65 .25 2.18 .0025 4.1 9.8

Fairview 595 10 2.63 5.83 .20 2.95 BDL 2.5 7.7

Stella 490 12 6.10 4.70 .10 6.40 BDL 1.6 8.0

Kerr 202 8 .65 2.03 .10 2.45 .0025 2.6 5.7









biomass on K concentrations in the substrates of Lake Okahumpka,

Lake Fairview, Lake Lochloosa, and Lake Stella (Table 3-1), and may

suggest loss of hydrosoil derived K.

Concentrations of Ca in hydrilla tissue (Figure 3-4), among the

study lakes, is difficult to interpret. The concentrations of Ca in

hydrilla tissue within all of the study lakes overlap the critical

level for waterweed. However, because there were no important effects

of hydrosoil concentrations of Ca on hydrilla biomass or concentrations

of Ca in hydrilla tissues, there is not a great difference in Ca

levels of lake waters, and the concentrations of Ca in the hydrilla

tissues within lakes between the 50th and 10th percentile are very

similar, Ca does not exert an important effect on plant biomass in these

lakes.

Essentially, all of the concentrations of Mg in hydrilla tissue are

well above the Gerloff critical levels (Figure 3-5). These concentrations

are similar among all of the lakes except Lake Kerr. All hydrilla samples

from Lake Kerr yielded Mg concentrations less than about 25% of the

samples in the other lakes. This observation may relate to the low biomass

of hydrilla in Lake Kerr.

Comparison of Fe concentrations (Figure 3-6) of hydrilla tissues

among the lakes follows a similar pattern to that of N. The levels of Fe

in tissues from Lake Okahumpka, Lake Lochloosa, and Lake Kerr are well

above Gerloff's critical levels, whereas approximately 50% of the samples

from Lake Fairview and Lake Stella are below this level and the other lakes.

However, since these tissue concentrations are not related to hydrosoil

or water levels, Fe levels in the hydrilla tissues probably do not effect

hydrilla biomass.









The data provide no indication of Cu limitation since all hydrilla

tissues had Cu concentrations as high or higher than the tissues from

Lake Okahumpka (Figure 3-7).

Conclusions

Concentrations of N, P, K, Ca, Mg, Fe, and Cu in the hydrosoil

explained only small amounts of the large variability in submersed

macrophyte biomass of the study lakes and; likewise, had little or

no positive relationship with nutrient concentrations of hydrilla

tissue. This suggests that other factors such as availability of

nutrients in the water are controlling macrophyte biomass.














CHAPTER 4.
RELATIONSHIPS AMONG WATER CHEMISTRY, TRANSPARENCY,
CHLOROPHYLLY a, AND SUBMERSED MACROPHYTES IN SEVEN FLORIDA LAKES
Introduction

Florida lakes exhibit a wide range in abundance of submersed

macrophytes, from the excessive growth of weed choked lakes to a

nearly complete absence of submersed vegetation. Although the

importance of the submersed macrophyte community is a consensus,

with respect to beneficial as well as detrimental habitat effects,

too little is known concerning relationships between submersed

macrophytes and water quality, as measured by chemical parameters,

chlorophyll a, and clarity.

Submersed macrophytes may affect the cycling of nutrients in

lakes by absorbing nutrients from the hydrosoil and releasing them

into the water column (Barko and Smart 1980, McRoy et al. 1972) or

by absorbing nutrients from the water, thereby representing a nutrient

sink (Goulder 1969). Either of these processes may have an important

influence on the P concentration of lake water as predicted by

empirical models (Jones and Bachmann 1976; Kirchner and Dillon 1975,

Vollenweider 1975). Influences of macrophytes on P concentration

should therefore influence chlorophyll a (Canfield and Bachmann

1981, Dillon and Rigler 1974, Jones and Bachmann 1976) and water

clarity (Bachmann and Jones 1974, Canfield and Hodgson 1981, Dillon

and Rigler 1975). It has been noted that where macrophytes are

abundant, water tends to be more transparent and have low chlorophyll a








concentrations (Fitzgerald 1969, Goulder 1969, Wiebe 1934). This

antagonism has been attributed to competition for light (shading)

and nutrients (Embody 1928, Hasler and Jones 1949, Mulligan and

Baranowski 1969, Nichols 1971, Philips et al. 1978) or allelopathy

(Hogetso et al. 1960). It has also been observed that P concen-

trations in lakes heavily infested with submersed macrophytes are

often well below that expected for the geologic region (Canfield

1981).

Maristo (1941) showed a direct relationship between water

clarity and the lower limit of macrophytes in Finish lakes. Decreased

water clarity caused by phytoplankton should, therefore, decrease

the extent of vertical distribution in lakes. Dense phytoplankton

standing crops can suppress or eliminate submersed macrophytes by

shading after fertilization of ponds (Embody 1928, Moss 1976, Smith

and Swingle 1941).

The purpose of this work was to study the relationship between

water chemistry, chlorophyll a, water clarity and submersed

macrophyte biomass in Florida lakes which cover a range of trophic

types. These relationships would be used to predict potential

changes in these parameters caused by lake management practices.

Materials and methods
Sampling of lakes and nutrient analyses were conducted as

described in Chapter 2.

Bathymetric maps of all study lakes (Appendix 5) except Lake

Okahumpka were constructed using aerial photographs (USGS, Salt

Lake City, UT or Florida Citrus Census, Orlando, FL) and fathometer








tracings. Lake area and volumes were determined by polar planimetry

using bathymetric maps and hypsographic curves (Lind 1974) (Appendix 6).
The morphometric measurements of Simmonds and German (1980) were used

for Lake Okahumpka.

Areal coverage of submersed macrophytes was determined for
individual lakes by:

%C = EVt (100)
T
where %C = percent cover, V = vegetated length of transect Vt(m), and

Tt = sum of all transect lengths (m). Total submersed macrophyte

biomass was determined by:

B=(A) (%C/100)(D)
where B = total submersed macrophyte biomass (kg dry wt), A = lake

surface area (m2), and D = average submersed macrophyte density

(kg dry wt/m2), as measured with biomass sampler. Potential lake-water

nutrient concentrations were calculated by:

Wc=[((B)(PC)/100)/V]+Mc
where We = potential concentration of nutrient c in lake water (mg/m3)

B = (mg); Pc = concentration of nutrient c in plant tissue averaged

over species, V = lake volume (m3), and Mc = measured concentrations

of nutrient c in lake water.

Color was determined by the platinum cobalt method and matched
Nessler tubes (American Public Health Service 1981) after filtering

through a Gelman type A-E glass fiber filter.

Transparency was measured with a Li-Cor Model 185A quantum

meter, fitted with a LI-1925B underwater quantum sensor, and with

a 20 cm black and white Secchi disc.










69
Results and Discussion

Submersed macrophyte coverage of the study lakes ranged from

14% in Lake Kerr to 100% in Lake Okahumpka (Table 4-1). With

respect to vertical distribution, the maximum depth to which vegeta-

tion occurs in lakes seems to be governed by transparency. In all

lakes except Lake Down, hydrilla is the dominant submersed macrophyte

and occurred in the deepest vegetated parts of the lake. The lower

limit of vegetation in the lakes is close to the depth where the light

compensation of hydrilla occurs, or c.a. 1% full sunlight (Van et al.

1976) (Table 4-2). This is, however, within morphometric constraints.

The maximum depth of Lake Okahumpka is less than the depth at which

1% light transmittance occurs; hence, light limitation does not occur

and Lake Okahumpka has a total areal coverage of submersed macrophytes.

The minimum depths at which 1% transmittance occurs in Lake Lochloosa

and Lake Fairview were observed during the winter, and these agree well

with the maximum vegetation depths observed during both fall and winter.

It would appear that the higher transparencies, as were measured in

the fall, do not occur frequent enough to allow for colonization or

that further colonization can be expected in these lakes. The

15 vE/m2xs compensation point was measured under laboratory conditions

(Van et al. 1976) and may differ under natural conditions. In Lake

Stella and Lake Jackson,1% transmittance occurs at a considerably

greater depth than the maximum vegetation depth; however, since

greater depths occur beneath less than 2% of the lake surfaces

(Appendix 6) light limitation does not occur in these lakes. The

water level of Lake Down was low- at the time of sampling. The maximum

vegetation depth, however, is close to the depth of 1% transmittance

during normal water level conditions and reflects the relationship












Table 4-1. Measurements used to estimate total sumbersed macrophyte biomass in study lakes and resulting
biomass estimates.

Surface Lake Volume Vegetation Macrophyte Submersed Macrophyte Biomass (mt dry wt)
Lake Area (ha) (m3 X 106) Cover Density 9 C U
g/m2 Average Lower 95% CLM Upper 95% CLM

January February 1981

Okahumpka 208 2.59 100 51 106 77 135

Lochloosa 2198 45.90 39 114 972 521 1423

Fairview 114 4.30 74 46 39 27 51

Stella 123 4.28 80 32 31 21 41

Jackson 1143 19.80 64 12 88 16 160

Kerr 1132 42.00 14 12 19 7 31
September October 1981

Ukahumpka 208 2.59 100 307 643 431 855

Lochloosa 2187 45.90 62 100 2165 1662 2090

Fairview 114 4.30 75 246 210 163 257

Stella 123 4.28 90 125 138 110 168

Down 360 12.00 39 59 81 62 100

Kerr 1123 42.00 13 12 18 12 24











Table 4-2.


Comparison of the
macrophytes.


availability of light in Florida lakes to the occurrence of submersed


Depth (m) at Area (% of total) Maximum vegetation % transmittance at
Lake 1% transmittance receiving 1% depth (m) maximum vegetation
transmittance depth

January February 1981
Okahumpka 3.1 100 -
Lochloosa 2.4 68 2.6 0.7
Fairview 6.7 88 6.9 0.9
Stella 10.2 100 5.8 7.0
Jackson 6.3 99 4.2 1.2
Kerr 4.6 65 3.8 3.1

September October 1981
Okahumpka 5.7 100 -
Lochloosa 3.0 89 2.6 1.8
Fairview 9.0 98 6.7 3.2
Stella 9.2 99 5.6 6.3
Down 6.6 90 5 2.6
Kerr 5.7 100 3.5 6.4










between the lower limit of vegetation in the lake, and light penetration.

When the observed vegetation cover in Table 4-1 is compared to

the area where sufficient light is available for plant growth (Table

4-2), agreement between minimum or maximum values measured in winter

or fall for Lakes Okahumpka, Lochloosa, Fairview and Stella is

observed. This suggests that light penetration is the major factor

influencing areal submersed macrophyte cover in these lakes. In

Lakes Jackson and Down the remaining unvegetated area can be accounted

for by shallow areas which are intermittently wet and dry and which

are characterized by coarse unstable substrates. The large area of

Lake Kerr bottom which is unvegetated cannot be explained by light

penetration or substrates.

Lake Okahumpka, Lake Lochloosa, and Lake Fairview are situated

in geologic regions which are characterized by lakes in the mesotrophic

to eutrophic classification (Canfield 1981). Lake Okahumpka and Lake

Fairview, however, had high transparency (Table 4-3), low chlorophyll a

concentrations (Table 4-3) and P concentrations at or near the

oligomesotrophic range in the fall when macrophyte biomass is maximal.

These lakes also were supporting a dense submersed macrophyte community

(Table 4-1). Lake Lochloosa, on the other hand, had a moderate

density of submersed macrophytes and concurrently high concentrations

of both chlorophyll a and P. Lake Stella is located in a geologic region

characterized by meso-oligotrophic conditions. It supports moderate

levels of submersed macrophytes and has P and chlorophyll a concentra-

tions indicative of oligotrophy. Lake Down and Lake Kerr lie in geologic

regions typified by oligotrophic lakes. These lakes do exhibit

oligotrophic conditions and support only low macrophyte biomass.


-----------------------------------------_____A












Table 4-3.


Chlorophyll a and Secchi transparency that was measured, predicted from observed N and P, or
predicted from potentially available N and P of submersed macrophytes (equations of Canfield
1981 and Canfield and Hodgson 1981).


Chl a (mg/1)


Predicted
from
Observed N,P


Predicted
from
Potential N,P


Secchi transparency (m)


Predicted
Observed from
Observed N,P


Predicted
from
Potential N,P


13.0
20.8
8.9
7.1
11.3
9.2


12.9
14.6
3.9
4.5
2.3
1.6


January February 1981

33.9
42.1
12.6
9.5
14.4
9.6


September October 1981
218
48.5
21.0
15.0
4.4
1.7


B = Bottom


Color
(mg/l Pt)


Lake


Observed


Okahumpka
Lochloosa
Fairview
Stella
Jackson
Kerr


7.7
7.3
2.5
1.8
3.8
1.4


1.0
.8
1.5
1.6
1.5
1.6 .


0.7
0.6
1.3
1.4
1.3
1.6


Okahumpka
Lochloosa
Fairview
Stella
Jackson
Kerr


1.2(B)


2.5
4.3
4.0
3.0


1.7(B)
0.8
4.8
5.1(B)
6.2
4.5


4.1
28.7
2.5
1.6
1.7
2.6


1.1
1.2
2.4
2.3
3.4
4.5









Hydrilla, which usually causes a severe weed problem when it is

introduced into a lake, has been present in Lake Kerr for several years

but has not proliferated. Hydrilla reportedly has been found in

Lake Down (Nick Sassic, personal communication) but did not become

established. Note, also that the winter sampling, when macrophytes

are at lower densities, that all lakes except Lochloosa yielded

higher N or P concentrations than in fall. Where high nutrient

conditions are expected, high macrophyte densities are observed in

fall, but P concentration in the water is lower than expected. The

data suggest that this depression in expected P concentration results

from assimilation into macrophyte biomass. In lakes like Lochloosa,

however, P concentrations exceed the macrophyte populations assimilatory

capacity which allows for concurrent high phytoplankton populations.

These observations support the views of Goulder (1969) who suggested

that the apparent antagonism between macrophytes and phytoplankton

was a result of competition for nutrients where the macrophtyes

act as nutrient sink. Goulder (1969) suggested that macrophytes

were an important N sink. The data from this study suggest that P

is sensitive to macrophyte biomass and that assimilation of this nutrient

by the submersed macrophyte community limits phytoplankton production.

It is difficult to assign a single factor as effecting macrophyte

biomass because several water chemistry parameters are related.

Study lakes with low biomass have low alkalinity, low pH, and the

dominant anion is sulfate (Table 4-4). Further investigation will be

needed to separate these factors. The preceding discussion suggests

that P is the limiting factor to submersed macrophyte biomass in these

lakes. And, combined with the data of Chapter 3, that the water column









Table 4-4. Water chemistry data of study lakes (Data from Canfield, 1981).

Total alkalinity Specific conductance HCO-3 C03 S04= C1-
Lake (mg/l as CaCO3) (pmhos/cm 25 C) ci) (o (%) (%)

Okahumpka 50 165 35 22 7 36 8.3

Lochloosa 23 105 58 0 11 31 7.7

Fairview 52 206 60 1 17 22 8.0

Stella 16 71 13 0 54 32 7.0

Jackson 5 26 38 0 29 33 6.5

Down 1 207 3 0 62 35 5.5

Kerr 2 44 6 0 41 54 6.1







is the major source of P nutrition.

Some of the ecological changes in the different lakes in response

to releasing the nutrient pool represented by the macrophyte biomass

back to the water can be predicted. The potential nutrient concen-

trations in Table 4-6 are calculated from the average biomass estimates

and lakes volumes in Table 4-1, the average plant nutrient concentration

in Table 4-7, and the observed nutrient concentrations in Table 4-5.

Using the potential concentrations ofN and P and the equations of

Canfield (1981) and Canfield and Hodgson (1981), the potential

chlorophyll a concentrations and Secchi transparencies can be predicted

and compared to the observed values when the macrophytes were present.

The equations tend to overestimate chlorophyll a; therefore, the

predicted Secchi transparency and chlorophyll a concentrations based

on predictions from observed N and P are included along with the

observed values.

Addition of the potential nutrient pool assimilated in the

macrophyte biomass in fall will have a dramatic impact upon the

chlorophyll a and transparency of some lakes but not others (Table 4-6).

While the potential phytoplankton bloom and resulting reduction in

transparency in Lake Okahumpka, Lake Lochloosa and Lake Fairview would

be sufficient to eliminate or severely suppress macrophyte recoloniza-

tion, Lake Down would be little effected and Lake Kerr virtually

uneffected. The effects on Lake Stella, with respect to basin

morphometry, would not be sufficient to suppress macrophyte regrowth to

a large extent. The predicted effects for the winter sampling are not

as severe because of lower biomass estimates. Although submersed

macrophyte biomass is probably lower in winter, values for the two









Measured concentrations of plant nutrients in lake water


of 4 subsurface samples BDL = below detectable


N P K Ca Mg Fe
Lake (mg/m3) (mg/m3) (mg/1) (mg/1) (mg/1) (mg/1)


January -


September


February 1981

2.8 17.6

0.5 8.9

3.6 23.9

6.5 16.6

0.3 0.9

0.3 3.1


- October 1981


16.6

3.0

3.4

7.4

1.2

1.6


2.4

2.2

3.0

6.4

8.8

2.5


.27

.10

BDL

BDL

BDL

BDL


.20

.25

.20

.10

BDL

.10


(average
limits).


Okahumpka

Lochloosa

Fairview

Stella

Jackson

Kerr


1003

2000

913

703

989

893


Okahumpka

Lochloosa

Fairview

Stella

Down

Kerr


1327

1233

447

490

284

202


---


Table 4-5.









Table 4-6. Average potential concentrations of plant nutrients in
lake water (sum of observed values and additions from
submersed macrophytes, assuming 100% release).

N P K Ca Mg Fe
Lake (mg/m3) (mg/m3) (mg/1) (mg/l) (mg/1) (mg/1)


January February 1981


1871

2555

1084

816

1106

904


111 3.9

73 1.1

24 3.9

26 6.8

37 0.5

0.3


19.2

8.9

24.2

16.7

1.0

3.1


18.3

3.4

3.6

7.9

1.2

1.6


6.30

0.59

0.03

0.04

0.21

0.01


September -

533

148

78

57

19

9


October 1981

5.4

5.8

4.2

7.6

8.6

0.7


Okahumpka

Lochloosa

Fairview

Stella

Jackson

Kerr


Okahumpka

Lochloosa

Fairview

Stella

Down

Kerr


7283

2440

1321

1009

429

212


13.8

6.1

10.0

5.4

3.4

2.1


13.8

4.9

4.6

8.2

9.1

2.5


22.91

1.25

0.40

0.33

0.09

0.13











Table 4-7.


Average nutrient concentrations of submersed macrophytes
are standard errors of the mean).


(% dry wt, numbers in parentheses


Lake N P K Ca Mg Fe


January February 1981


2.12(0.06)
2.62(0.13)
1.89(0.07)
1.56(0.08)
2.83(0.22)
2.59(0.20)



2.52(0.07)
2.56(0.08)
1.79(0.06)
1.61(0.08)
2.15(0.04)
2.31(0.08)


0.18(0.02)
0.28(0.02)
0.12(0.01)
0.18(0.02)
0.29(0.01)
0.34(0.04)



0.21(0.01)
0.26(0.01)
0.14(0.01)
0.14(0.01)
0.15(0.01)
0.35(0.02)


2.57(0.17)
2.88(0.14)
3.52(0.23)
4.63(0.18)
3.80(0.47)
3.23(0.47)


0.39(0.04)
0.20(0.01)
0.36(0.04)
0.18(0.01)
0.17(0.03)
0.30(0.07)


September October 1981


2.08(0.09)
2.40(0.13)
3.02(0.15)
4.66(0.16)
1.39(0.04)
2,.56(0.21)


0.45(0.04)
0.30(0.03)
0.86(0.06)
0.21(0.03)
0.13(0.01)
0.29(0.04)


0.41(0.07)
0.35(0.02)
0.23(0.01)
0.71(0.04)
0.36(0.06)
0.34(0.03)



0.46(0.02)
0.57(0.04)
0.32(0.01)
0.55(0.04)
0.43(0.01)
0.33(0.02)


1.49(0.48)
0.25(0.04)
0.03(0.01)
0.06(0.01)
0.47(0.12)
0.29(0.04)



0.91(0.14)
0.22(0.02)
0.04(0.01)
0.04(>.01)
0.13(0.01)
0.62(0.07)


Okahumpka
Lochloosa
Fairview
Stella
Jackson
Kerr


Okahumpka
Lochloosa
Fairview
Stella
Down
Kerr








sampling periods are not directly comparable because the improved

sampling head was used for the fall sampling which reportedly

yields biomass estimates 2 to 3 times higher (Bruce Sabol,

personal communication). This correction factor would make the

values between the sampling periods comparable. Predictions, such

as these, will be influenced by hudraulic flushing and loading

rates; however, the predictions for the lakes in this study are

in agreement with expected nutrient concentrations for the geologic

regions in which the lakes are situated (Canfield 1981).


Conclusions

The vertical distribution of macrophytes in all but one of the

lakes studied could be attributed to water transparency. Transparency

is directly effected by the interaction between the submersed macrophyte

community, phytoplankton, and P availability in the water. Macrophytes

suppress phytoplankton populations by acting as a P sink while the

reduction in water transparency caused by high phytoplankton density

suppress the submersed macrophyte community. When net P loading

exceeds the assimilatory rate of the macrophyte community, phytoplankton

dominance may occur. Using estimates of the total N and P represented

by the macrophyte community, we can predict what the effects of

releasing these nutrients will be on phytoplankton and water transparency.














CHAPTER 5.
SUMMARY AND CONCLUSION

There is strong evidence that the roots of submersed macrophytes

can absorb nutrients from the rooting medium, and that the plants

can subsequently translocate these nutrients acropetally. As

discussed in Chapter 1, this has been observed in laboratory

experiments and in lake-exclosures, when the root environment is

supplied with an available form of a nutrient. For the purpose

of isolating and studying a specific phenomenon, many naturally

occurring environmental pressures are removed from the experimental

environment and unnatural constraints are placed upon plant growth.

Specific questions relating to plant functions are answered in this

manner, but the conclusions are not necessarily directly applicable

to the interactions of plants with their natural surroundings.

Submersed macrophyte abundance and nutrient content was

intensively studied with respect to concentrations of N, P, K, Ca,

Mg, Fe and Cu concentrations in the hydrosoil of seven Florida lakes.

The data suggested in some instances that P and K in the hydrosoil

have a small influence on biomass but the response was inconsistent

among the lakes. Hydrosoil derived nutrition is not ecologically

important in a lake sample which spans commonly encountered

limnological conditions in Florida lakes.

The relationships among macrophyte abundance and distribution,

water chemistry, and phytoplankton, are discussed in Chapter 4. The

vertical distribution of submersed macrophytes is directly influenced









by water transparency and basin morphometry. Submersed macrophytes

are absent where water depths prevent sufficient light for plant

growth from reaching the lake bottom. This depth occurred where 0.7%

to 7% of full sunlight (PAR) was transmitted. However, water trans-

parency is ephemeral in nature and is greatly effected by water

chemistry, submersed macrophytes and phytoplankton.

Submersed macrophyte biomass is directly related to lake trophic

state. However, where high macrophyte density occurs, P and

chlorophyll a concentrations are lower than eutrophic conditions.

High macrophyte biomass suppresses phytoplankton production by acting

as a P sink and results in increased water transparency. When P

loading exceeds the assimilatory rate of the macrophyte community,

high phytoplankton density can occur and prevent further macrophyte

production by shading. Using measurements of macrophyte biomass

and nutrient content, changes in water conditions after release of

this nutrient pool can be predicted.


L


































APPENDIX 1.


LOCATIONS OF BIOMASS AND HYDROSOIL SAMPLES ALONG
TRANSECTS IN STUDY LAKES, DURING TWO SAMPLING
PERIODS. NUMBERS CORRESPOND TO BUOY NUMBERS IN
APPENDIX 2 and APPENDIX 3.


~









































500 m
LAKE OKAHUMPKA

JAN. 1981








































500 m

LAKE OKAHUMPKA

SEPT. 1981





86






















N


w E

S
1000 m
LAKE LOCHLOOSA


FEB. 1981














































1000 m

LAKE LOCHLOOSA

SEPT. 1981




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