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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$33(1',; &21&(175$7,216 2) 1875,(176 = '5< '(7(&7$%/( /,0,76f $1' %,20$66 '5< 8762 0f %82< 180%(56 &255(6321' 72 7+26( ,1 $33(1',; 87L ,1',&$7(6 %(/2: 2) 68%0(56(' 0$&523+<7(6 '$7( 6(37(0%(5 2&72%(5 /$.( /$.( .(55 f %82< 63(&,(6 %,20$66 1 3 &$ 0* )( &8 0<5,23+/80 3,11$780 0$<$&$ $8%/(7,, '$7( 6(37(0%(5 2&72%(5 /$.( /$.( /2&+/226$ %82< 63(&,(6 %,20$66 1 3 &$ 0* )( &8 +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7 $ +<'5,//$ 9(57,&,//$7$ 183+$5 /87(80 +<'5,//$ 9(57,&,//$7 $ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ 183+$5 /87(80 +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7 $ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7 $ +<'5,//$ 9(57,&,//$7 $ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ n +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ t +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$
PAGE 115
$33(1',; &21&(175$7,216 2) 1875,(176 = '5< 87} ,1',&$7(6 %(/2: '(7(&7$%/( /,0,76f $1' %,20$66 '5< :764 0f 2) 68%0(56(' 0$&523+<7(6 %82< 180%(56 &255(6321' 72 7+26( ,1 $33(1',; '$7( 6(37(0%(5 2&72%(5 /$.( /$.( /2&+/226$ %82< 63(&,(6 %,20$66 1 3 &$ 0* )( &8 +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7 $ +<'5,//$ 9(57,&,//$7$ '$7( 6(37(0%(5 2&72%(5 /$.( /$.( 2.$+803.$ %82< 63(&,(6 %,20$66 1 3 &$ 0* )( &8 +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ 327$02*(721 ,//,12(16,6 +<'5,//$ 9(57,&,//$7$ 327$02*(721 ,//,12(16,6 +<'5,//$ 9(57,&,//$7$ 327$02*(721 ,//,12(16,6 +<'5,//$ 9(57,&,//$7$ 327$02*(721 ,//,12(16,6 +<'5,//$ 9(57,&,//$7$ 327$02*(721 ,//,12(16,6 9$/,61(5,$ $0(5,&$1$ +<'5,//$ 9(57,&,//$7$ 327$02*(721 ,//,12(16,6 9$/,61(5,$ $0(5,&$1$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ f +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ &(5$723+/80 '(0(5680 +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ ; +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ 9$/,61(5,$ $0(5,&$1$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ 9$/,61(5,$ $0(5,&$1$ &(5$723+/80 '(0(5680
PAGE 116
$33(1',; &21&(175$7,216 2) 1875,(176 = '5< 87} ,1',&$7(6 %(/2: '(7(&7$%/( /,0,76f $1' %,20$66 '5< 8764 0f 2) 68%0(56(' 0$&523+<7(6 %82< 180%(56 &255(6321' 72 7+26( ,1 $33(1',; '$7( 6(37(0%(5 2&72%(5 /$.( /$.( 2.$+803.$ %82< 63(&,(6 %,20$66 1 3 &$ 0* )( &8 +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7 $ +<'5,//$ 9(57,&,//$7 $ &(5$723+/80 '(0(5680 +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ &(5$723+/80 '(0(5680 +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ 9$/,61(5,$ $0(5,&$1$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ &(5$723+/80 '(0(5680 +<'5,//$ 9(57,&,//$7$ 9$/,61(5,$ $0(5,&$1$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ '$7( 6(37(0%(5 2&72%(5 /$.( /$.( 67(//$ %82< 63(&,(6 %,20$66 1 3 &$ 0* )( &8 +<'5,//$ 9(57,&,//$7$ 6U +<'5,//$ 9(57,&,//$7$ r &(5$723+/80 '(0(5680 +<'5,//$ 9(57,&,//$7$ 6 \ +<'5,//$ 9(57,&,//$7$ t‘ +<'5,//$ 9(57,&,//$7$ &(5$723+/80 '(0(5680 +<'5,//$ 9(57,&,//$7$ D +<'5,//$ 9(57,&,//$7$
PAGE 117
$33(1',; &21&(175$7,216 2) 1875,(176 '5< :7 W ,1',&$7(6 %(/2: '(7(&7$%/( /,0,76f $1' %,20$66 '5< 8764 0f 2) 68%0(56(' 0$&523+<7(6 %82< 180%(56 &255(6321' 72 7+26( ,1 $33(1',; '$7(f6(37(0%(5 2&72%(5 /$.( /$.( 67(//$ %82< 63(&,(6 %,20$66 1 3 &$ 0* )( &8 &(5$723+/80 '(0(5680 +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ &(5$723+/80 '(0(5680 +<'5,//$ 9(57,&,//$7$ &(5$723+/80 '(0(5680 +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ &(5$723+/80 '(0(5680 +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ • +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ ,6 +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ &(5$723+/80 '(0(5680 +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ &(5$723+/80 '(0(5680 +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ 1$-$6 48$'$/83(16,6 +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ 9$/,61(5,$ $0(5,&$1$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ 9$/,61(5,$ $0(5,&$1$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$
PAGE 118
$33(1',; &21&(175$7,216 2) 1875,(176 b '5< 87L ,1',&$7(6 %(/28 '(7(&7$%/( /,0,76f $1' %,20$66 '5< 8764 0f 2) 68%0(56(' 0$&523+<7(6 %82< 180%(56 &255(6321' 72 7+26( ,1 $33(1',; '$7( 6(37(0%(5 2&72%(5 /$.( /$.( 67(//$ %82< 63(&,(6 %,20$66 1 3 &$ 0* )( &8 +<'5,//$ 9(57,&,//$7 $ +<'5,//$ 9(57,&,//$7 $ +<'5,//$ 9(57,&,//$7$ '$7( -$18$5< )(%58$5< /$.e /$.( )$,59,(: %82< 63(&,(6 %,20$66 1 3 &$ 0* )( &8 +<'5,//$ 9(57,&,//$7$ 327$02*(721 ,//,12(16,6 +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ 1$-$6 48$'$/83(16,6 1,7(//$ 63 327$02*(721 ,//,12(16,6 +<'5,//$ 9(57,&,//$7$ 1,7(//$ 63 327$02*(721 ,//,12(16,6 +<'5,//$ 9(57,&,//$7$ 1,7(//$ 63 +<'5,//$ 9(57,&,//$7$ 1,7(//$ 63 +<'5,//$ 9(57,&,//$7$ 1,7(//$ 63 327$02*(721 ,//,12(16,6 +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ 1,7(//$ 63 327$02*(721 ,//,12(16,6 +<'5,//$ 9(57,&,//$7$ 1,7(//$ 63 327$02*(721 ,//,12(16,6 +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ 1,7(//$ 63 327$02*(721 ,//,12(16,6 +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ +<'5,//$ 9(57,&,//$7$ 1,7(//$ 63
PAGE 119
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PAGE 120
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PAGE 121
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PAGE 122
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