f'.

■!^%„ ' ■::,

DIATOMS AS INDICATORS OF HISTORICAL
MACROPHYTE BIOMASS IN FLORIDA LAKES

By

THOMAS JAMES WHITMORE

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL

OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

1991

ACKNOWLEDGMENTS

I thank Daniel E. Canfield, Jr. for suggesting the concept of this
study in constructive criticisms he offered during my master's
defense. Dan's challenge provided me with motivation: I chose this
topic because I was determined to show him I could resolve the
problem. Mark Hoyer and Christine Horsburgh were responsible for
collection and management of macrophyte data, and Mark was
especially helpful in providing me with this information. The Florida
Museum of Natural History provided the vehicles, boats, and
equipment for field work. Brian Rood, J. Daniel Bryant and Dennis
Crumby assisted in the field. Silvia Ferguson gave much time and
hard work in field assistance, and has been a constant source of
support throughout this study.

I am grateful to Claire Schelske, Marty Fleisher, and Arthur
Peplow for radionuclide assay of samples used to calculate bulk
sediment accumulation rates. I thank Douglas Turley for the time
and computer expertise he offered for data management. Paul Zimba
gave generous assistance in computer programming during his
personal time, making it possible for me to use the CANOCO statistical
package. Dana Griffin provided funds for mainframe computing.
Martha Love and Sarah E. Whitmore kindly helped with manuscript
preparation.

11

■*%\l^:-

**l.

:■*:

||K

This dissertation work was begun under the guidance of Edward
S. Deevey, Jr., my major professor for more than 9 years. Ed's death
in November 1988 was a blow to many academic disciplines, and a
sad personal loss of which I am especially cognizant at this time. I
have benefitted from Frank Nordlie's particulalry competent
guidance through the completion of my studies. Claire Schelske
generously provided the space, resources and financial support that
allowed me to complete my graduate work. Joseph S. Davis and
Ronald G. Wolff have been supportive members of my graduate
committee for many years. It is largely due to Ron's encouragement
that I pursued graduate studies at the University of Florida. No list
of acknowledgements for my graduate work would be complete
without expressing sincere thanks for all manner of support and
encouragement to Mark Brenner, my colleague of 11-1/2 years who
introduced me to paleolimnology.

Ill

TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS ii

ABSTRACT vi

CHAPTER

1 INTRODUCTION 1

The Concept of Trophic State in Lakes 1

Macrophytes and the Lake Ecosystem 4

The Relationship of Macrophytes With Epiphyton 1 1

Methods for Reconstructing Historical Macrophyte

Communities 1 5

Diatom Methods in Paleolimnology 1 6

Effects of Spatial Variation on Diatom Assemblages 2 7

Purpose 2 9

2 METHODS 3 2

Collection of Sediment Samples 3 2

Laboratory Analyses 3 3

Quantifying Macrophyte Presence 3 6

Water Chemistry Data 3 8

Statistical Analyses 3 8

3 RESULTS 4 3

Results of Cluster Analyses 6 1

Results of Principal Components Analyses 6 8

Results of Stepwise Multiple Regression 7 6

Results of Canonical Correspondence Analyses 8 3

A New Predictive Model for Water-Column Total P 8 5

Assessing Confoundedness in the Water-Column Total

P Predictive Model 8 8

IV

- ^vV

*-;

Page

4 DISCUSSION 90

Dominant Environmental Variables and Scale

of Analysis 9 0

Response of Periphyton to Water-Column Nutrients 9 3

Recommended Predictive Models for Macrophyte

Variables 9 5

Applying Predictive Models to Obtain Historical TSI

Estimates 9 8

APPENDIX 1 1 0 1

APPENDIX 2 1 0 2

APPENDIX 3 1 0 9

APPENDIX 4 1 1 6

APPENDIX 5 1 1 8

APPENDIX 6 120

BIBLIOGRAPHY 123

BIOGRAPHICAL SKETCH 1 3 5

Abstract of Dissertation Presented to the Graduate School

of the University of Florida in Partial Fulfillment of the

Requirements for the Degree of Doctor of Philosophy

DIATOMS AS INDICATORS OF HISTORICAL
MACROPHYTE BIOMASS IN FLORIDA LAKES

By

Thomas James Whitmore
May 1991

Chairman: Frank G. Nordlie
Major Department : Zoology

Macrophytes represent an important component of primary
production in lakes that is usually ignored in trophic state
classification. Trophic classifications traditionally emphasize water-
column nutrient concentrations and phytoplankton biomass.
Predictive models have been developed from diatom assemblages to
assess historical changes in lake trophic state, but these models
usually infer water-column total P or chlorophyll a values and thus
also ignore macrophyte production. Lake-sediment core samples
often indicate former periods of low trophic state, although these
periods may instead represent low water-level events that
periodically occur in Florida lakes. Because macrophyte biomass is
negatively correlated with water-column nutrients, macrophyte
biomass may have been high at times when nutrient inferences
suggest that lakes were unproductive.

VI

The purpose of this study was to develop predictive models for
inferring historic macrophyte biomass using diatoms, and to
incorporate those models into a scheme that permits a more
complete assessment of former lake trophic state than do models
based solely on water-column nutrient concentrations.

Subfossil diatom assemblages were analyzed from the surface
sediments of 29 Florida lakes covering a range of macrophyte
abundance. Trophic-state, pH, and specific conductance formed an
environmental gradient that was the principal influence on diatom
communities in the limnologically diverse set of lakes. The
planktonic proportion of the diatom community was positively
correlated with trophic state, whereas the periphytic proportion was
negatively correlated with trophic state. Sedimentary diatom
concentrations, however, showed that both of these life-form
communities had a positive response to increase in water-column
nutrients.

Multivariate models are presented that permit estimates of
1 former macrophyte biomass from fossil diatom assemblages. The kg
of P contained in once-living macrophyte biomass can be estimated
using a mean percent P value for macrophyte taxa. This mass of P
divided by lake volume yields a concentration that can be added to
limnetic P inferences obtained from diatom predictive models to
estimate the potential total P content of the water column (WCP)
Trophic state index values calculated with historic WCP will reflect
both former macrophyte and phytoplankton aspects of trophic state.

Vll

CHAPTER 1
INTRODUCTION

The Concept of Trophic State in Lakes

Primary productivity in lakes can be defined as the rate at
which new organic matter is formed by photosynthesis in autotrophs
such as phytoplankton and macrophytes. Annual productivity is
typically expressed as the number of grams of carbon fixed per unit
of lake surface area per year. Lake productivity, however, has been
more traditionally thought of in conceptual terms referred to as
trophic state, and typologically described using categories ranging
from ultraoligotrophic at the low end of productivity to
hypereutrophic at the high end (cf. Shannon and Brezonik 1972).

Several trophic state indices (TSI) have been developed that
permit numerical expression of trophic state using biological,
physical and chemical characteristics. Shannon and Brezonik (1972),
for instance, used principal components analysis to reduce seven
variables including chlorophyll a (Chi a), primary productivity, and
total P to a single variable that described the trophic status of lakes.
Although it was inclusive, this TSI has been regarded as
cumbersome, especially because of the difficulty in obtaining
primary productivity values. Carlson (1977) sought a single, easily
obtained measure to describe lake trophic state, and he selected

1

Secchi depth, a convenient variable that reflected phytoplankton
standing crop in many lakes. Carlson scaled his TSI with the
intention that a ten-unit increase in TSI would be equivalent to a
doubling of algal biomass, but his index failed to relate in a uniform
way to Chi a. Carlson calibrated his TSI to limnetic Chi a and total P
for a set of north-temperate lakes and developed subindices to
permit expression of TSI from these latter two variables.

Kratzer and Brezonik (1981) later modified Carlson's approach
by constructing a subindex expressing TSI from water-column total N
concentrations after observing N limitation in several Florida lakes
located on phosphatic limestones. They proposed an averaged
subindex that used mean TSI values based on Chi a and Secchi depth,
and the lesser of the two TSI values based on total P and total N.

Baker et al. (1981) observed different relationships between
Secchi depth, total P and Chi a in Florida lakes than Carlson (1977)
observed in north-temperate lakes. Huber et al. (1982), therefore,
constructed a new TSI for Florida lakes using the Florida Lakes Data
Base, a large data set that is maintained at the Water Resources
Research Center at the University of Florida.

Huber et al. based their TSI on Chi a, a more direct measure of
phytoplankton biomass than Secchi depth, and they retained Kratzer
and Brezonik's total N and averaged subindex approach because of N
limitation in some Florida lakes. Huber et al. described lakes with
total N/total P values >30 as P-limited, and calculated averaged TSI
(TSI(AVG) for these lakes as the mean of TSIs based on Secchi depth
(TSI(SD)), Chi a (TSI(Chl a) and total P (TSI(TP)). Lakes with total
N/total P values <10 were described as N-limited, and TSI(AVG) was

calculated as the mean of TSI(SD), TSI(Chl a) and TSI(TN). Huber et
al. regarded lakes with total N/total P values between 10 and 30 as
nutrient-balanced, and noted that nutrient-Chl a relationships were
different in these lakes than in nutrient-limited lakes. They
constructed new TSI expressions for total P (TSI(TPB) and total N
(TSI(TNB) for nutrient-balanced lakes, and defined TSI(AVG) for
these lakes as the mean of TSI(SD), TSI(Chl a), TSI(TPB) and
TSI(TNB). The TSI of Huber et al. is the only TSI developed
specifically for use in Florida lakes.

All of the trophic state indices discussed above, however, share
in common their bias towards phytoplankton biomass and water-
column nutrient concentrations as the relevant indicators of primary
production in lakes, and they give no importance to the presence of
macrophytes. Porcella et al. (1980) constructed a multivariate index
based on Carlson's (1977) subindices and included a term derived
from percent-area coverage of macrophytes. Because Porcella et al.'s
TSI was developed for north-temperate lakes that are P-limited and
demonstrate hypolimnetic oxygen deficits during stratification, this
TSI might be inappropriate for use in Florida. The macrophyte term
also failed to quantify nutrients contained in macrophyte biomass as
other TSIs using water-column total P quantify the nutrients
contained in phytoplankton biomass.

Canfield et al. (1983a) proposed a new approach to trophic state
classification of lakes that gave consideration to nutrients contained
in macrophyte biomass as well as to water-column nutrient
concentrations. They quantified the amount of P contained per unit
of dry weight in many species of macrophytes from 6 Florida lakes.

Next they estimated the macrophyte biomass in each lake from the
area covered by macrophytes and the macrophyte density in kg of
wet biomass per square meter. This permitted calculation of the kg
of P contained in macrophyte biomass in each lake and the amount of
P that would be released to the water column assuming 100% death
and decomposition of the macrophytes. Dividing this mass of P by
the lake volume yielded a concentration that when added to water
column total P produced an estimate of the potential total P content
of the water column (WCP). This approach provided more realistic
estimates of trophic state for lakes such as Fairview in Orange Co.,
which appeared oligotrophic based on water-column nutrients and
Chi a, but contained a large standing crop of macrophytes. The
estimate of total water-column P content brought this lake to the
eutrophic range, which was edaphically consistent with other lakes in
the same physiographic region. Recent evidence shows that water-
column P concentration in Lake Fairview has increased to the
predicted WCP level because of macrophyte removal by grass carp
(Canfield pers. comm.). Canfield et al. (1983a) noted that the same
approach may be used with N for lakes that are N-limited.

Macrophytes and the Lake Ecosystem
Despite the emphasis on water-column nutrients and
phytoplankton biomass to characterize lake trophic state,
macrophytes are responsible for a substantial amount of the primary
production that occurs in many lakes. This is especially the case in
Florida because the shallow depths of Florida lakes, the high amounts
of insolation and the long growing season are conditions that support

. high macrophyte standing crops (Brenner et al. 1990). Many Florida
lakes have high nutrient concentrations because of edaphic reasons
or anthropogenic loading (Canfield and Hoyer 1988), and this also
stimulates macrophyte production.

Macrophyte Growth Forms

Macrophyte species are often grouped into growth-form
categories that describe whether or not the plants are rooted in
sediments and whether they grow laterally in the water or erect and
out of the water. Submerged macrophytes are those typically rooted
in sediments, growing completely under the water and usually
flexible due to a lack of rigid cellular tissue. Myriophyllum
heterophyllum Michx., Utricularia purpurea Walt, and Ceratophyllum
demersum L. are three examples of submerged taxa native to Florida,
while another common taxon, Hydrilla verticillata Royle is an
introduced exotic that has proliferated widely. Many submerged
' taxa, when growing in dense stands, are regarded as nuisance species
that have a negative effect on lake recreational uses (Brenner et al.
1990).

Floating-leaved plants can be divided into two categories
depending on whether they are rooted in sediments or not. Rooted
floating-leaved plants derive most of their nutrients from the
sediments (Carignan and Kalff 1980) and often have large peltate
leaves growing at the surface where they have access to sunlight and
atmospheric CO2 for photosynthesis. Common examples of these taxa
found in Florida are Nymphaea spp. (water-lily), Nelumbo lutea
(Willd.) Pers. (American lotus), Nuphar luteum (L.) Sibth. & Smith

(spadderdock), Nymphoides aquatica (Gmel.) O.Ktze and Brasenia
shreberi Gmelin. A second group of floating -leaved taxa are
unrooted in sediments and free-floating. These taxa, which include
Lemna minor L. (duckweed), Pistia stratiotes L. (water-lettuce) and
Salvinia rotundifolia Willd., obtain their nutrients from the water and
exhibit adaptations that keep the plant afloat. Eichhornia crassipes
(Mart.) Solms. is a floating-leaved species introduced to Florida,
which because of its rapid spread and prolific growth, has become a
severe economic and environmental problem (Tarver et al. 1979).

Emergent taxa, which grow erect in shallow aquatic areas and do
not depend on the water for support, demonstrate the third growth
form in macrophytes. Common examples of these taxa are Typha
spp. (cattails), which was the macrophyte with the most extensive
areal coverage in a large survey of Florida lakes (Schardt 1983), and
Sagittaria latifolia Willd.

Some taxa exhibit growth patterns that are typical of more than
one growth-form category. Hydrocotyl umbellata L., for instance,
grows mostly as a submerged plant though leaves are frequently
emergent in shallow water. Portions of Hydrocotyl mats occasionally
break away and are redistributed as floating vegetation.
Potamogeton spp. also exhibits extensive lateral submerged growth,
but bears some floating leaves at the surface.

Environmental Factors Influencing Macrophyte Distribution
Many studies have been conducted to determine which
environmental factors most affect the distribution and abundance of
macrophytes, and many of these studies have come to different

conclusions. Collins et al. (1987), for instance, compared macrophyte
biomass density with 13 different chemical, physical and biological
variables at various sites in Lake George, NY. They concluded that
water depth was the most important factor affecting macrophyte
biomass, and that substrate type and eutrophication status were of
secondary importance. Canfield and Hoyer (1988) studied the
influence of light and nutrient availability on macrophytes in Florida
streams, and they concluded that nutrients do not regulate the
abundance of macrophytes. Shading of macrophytes was the most
important factor regulating macrophytes in that study, while
substrate type, water depth and current velocity had a secondary
influence. Jackson and Charles (1988) studied macrophyte species
composition in 31 small, unproductive lakes in New York that were
low in specific conductance. They concluded that pH was the
regulating factor, that area, slope and substrate composition were of
secondary importance, and that macrophyte distribution bore no
relation to trophic state indicators. Crowder et al. (1977) concluded
that specific conductance was as important a macrophyte
determinant as pH in their study on circumneutral to hardwater
lakes. Duarte and Kalff (1990) determined that alkalinity and slope
were the most important factors in their study, but they explained
the discrepant conclusions between studies as the result of
differences in scale of analysis. When macrophytes are compared
between lakes in hardwater areas, water chemistry, including
specific conductance and trophic state are bound to be important
determinants of macrophyte disribution (Duarte and Kalff 1990,
Jackson and Charles 1988). Surveys of unproductive, dilute lakes

i^.

8

cover a small range of difference along the pH-alkalinity-
conductivity complex, and they are likely to conclude that pH is the
important variable affecting macrophyte distribution (Jackson and
Charles 1988). Surveys conducted within one or a few lakes will
cover only a small range of water chemisty differences, and site
characteristics, such as waves, slope and sediment type, will prove to
be the most important determinants (Duarte and Kalff 1990). Within
a single lake, wave exposure is likely to be a leading determinant of
macrophyte biomass at shallow littoral depths, whereas water
transparency will exert more influence at greater depths (Duarte and
Kalff 1990).

Effect of Macrophytes on Lake Ecosystem

Macrophytes seem to exert considerable effects on the nutrient
cycling, biology, sedimentation patterns and senescence of the lakes
in which they occur. Rooted macrophytes obtain most of their
nutrients from lake sediments and thus link the sediments with
overlying water (Carpenter 1981). This provides a mechanism for the
regeneration of sedimentary nutrients into the water-column.
Carignan and Kalff (1982) observed that living macrophytes were
responsible for a 2.2% daily increase in P that represented a net
seasonal input to the littoral zone because the P was derived from
sediments. While P is not released from living macrophytes at a
rapid rate, substantial amounts of nutrients in the macrophyte
biomass are released when macrophytes die back and shoots decay.
Approximately 75% of the P released is in a soluble reactive form,
and P becomes rapidly assimilated by phytoplankton, leading to an

increase in water-column Chi a (Carpenter and Lodge 1986). Landers
(1982) estimated that approximately 18% of the annual P loading in
Lake Monroe, Indiana originated from senescing macrophytes.
Carpenter (1981) also concluded that most of the dissolved organic
carbon and dissolved total P in Lake Wingra, Wisconsin was released
during decomposition of Myriophyllum spicatum in the littoral zone.
Filbin and Barko (1985) have concluded that the release of
sedimentary nutrients into the water column by macrophytes may
be more significant in lakes than in reservoirs because of the
riverine nature of reservoirs.

Macrophytes have several influences on the sedimentation
patterns in lakes where they are found. Macrophytes tend to
intercept or modify the flow of materials such as sediment from land
to the pelagic zone. By reducing water velocity and wave action,
macrophytes function as sediment traps in the littoral zone. This
effect was shown to be significant in historical changes in
sedimentation patterns of Lough Augher, Northern Ireland
(Anderson 1990b). When macrophytes die, their biomass increases
sedimentary organic matter content and leads to an accretion of
littoral sediment that promotes infilling of the lake basin and
expansion of emergent vegetation (Carpenter 1981, Carpenter and
Lodge 1986). Macrophyte presence in lakes, therefore, accelerates
infilling and senescence of lakes.

Macrophytes provide a complex habitat, and their presence leads
to an increase in those species commonly found in littoral areas.
When macrophytes are present, epiphytic algae proliferate and an
increase is observed in epiphytic grazers such as snails (Carpenter

10

and Lodge 1986). Zooplankton are abundant in weed beds and the
habitat complexity also provides cover and protection for spawning
and young fish (Carpenter and Lodge 1986). Dense infestations of
submerged macrophytes, nevertheless, have been shown to have a
negative effect on the presence of sport fish (Shireman and Maceina
1981).

Conflicting reports have been presented about the effects of
eliminating macrophytes in lakes through chemical or biological
control. Carpenter and Lodge (1986) stated that because of the
macrophyte role in enhancing sedimentary P recycling, an increase in
macrophyte standing crop will lead to an increase in phytoplankton
standing crop, whereas the long-term effect (>3 yrs.) of killing
macrophytes will lead to a decrease in water-column N and P and a
decrease in phytoplankton. This positive correlation between
macrophyte and phytoplankton standing crop is contrary to the
negative relationship reported by Canfield et al. (1984) between the
percent of lake volume infested with macrophytes and water-column
Chi a for 32 Florida lakes. An increase in water-column P
concentrations and phytoplankton standing crop has been shown
following herbicide application to macrophytes in Florida lakes
because of nutrient release by the decaying plant material (Richard
et al. 1984). An increase in water-column P was also reported
following biological control of macrophytes using the grass carp
Ctenopharyngodon idella because of nutrient release from feces,
although this increase seems less dramatic because of the retention
of P in the fish biomass (Richard et al. 1984, Canfield et al. l9S3h,
Canfield et al. 1984).

1 1

The Relationship of Macrophvtes With Epiphvton
Epiphytic algae growing in macrophyte beds often exhibit high
concentrations of biomass and are responsible for a significant
proportion of the primary production in a lake. Allen and Oceuski
(1981), for instance, determined that algal epiphytic production in
Lake Ohrid, Yugoslavia was higher than the production they
observed in littoral or pelagic algae. Cattaneo and Kalff (1980)
observed that the epiphytic algae in eutrophic portions of Lake
Memphremagog, Quebec fixed more carbon than macrophytes did
throughout the growing season. Fontaine and Ewel (1981) estimated
that macrophytes and their associated epiphytes were responsible
for 56% of the gross production in Little Lake Conway, Florida.
The question of macrophytes as a nutrient source for their
epiphytic algae has been a much-debated issue often referred to as
the "neutral substrate hypothesis" in the literature. Cattaneo and
Kalff (1979) observed no significant difference in epiphytic
production on Potamogeton richardsonii and artificial plants made of
plastic. They concluded that macrophytes functioned as neutral
support structures. Carignan and Kalff (1982) studied epiphytic
algae growing on fully 32p labelled Myriophyllum spicatum and
concluded that epiphytes derived only 3.4-9.0% of their P from the
labelled macrophytes, and that macrophytes were more important to
epiphytes for support than as a P source. Gough and Gough (1981)
took issue with Cattaneo and Kalff (1979), and cited Hutchinson's
(1975) statements that macrophytes release by-products of nutrient
assimilation, photosynthates and inorganic nutrients. They argued

12

that although some macrophytes may be neutral hosts, others affect
epiphytic production or community composition. Cattaneo and Kalff
(1981) replied that epiphytic biomass was mostly related to the
surface area of the substrate on which the epiphytes grow, and that
water chemistry exhibits a greater influence than macrophytes on
epiphytic production.

Recent studies by Burkholder and Wetzel (1990) seem to offer a
more definitive explanation of macrophyte influence on epiphytes.
They measured alkaline phosphatase (APA), an enzyme that
catalyzes hydrolysis of organic P compounds to release
orthophosphate, in epiphyton growing on natural and artificial
plants. They observed, as did Cattaneo and Kalff (1979), that APA
concentrations were higher in epiphyton growing on artificial
substrates than they were in epiphyton growing on macrophytes.
Burkholder and Wetzel concluded that epiphyton on artificial
substrates are P-limited and synthesize APA to provide a P source,
although the epiphyton growing on macrophytes were not P-limited
because of nutrient release by the macrophytes.

Epiphyton can in turn exert effects that influence the growth of
their macrophyte hosts. Nutrients are usually in abundant supply to
rooted macrophytes, and macrophytes generally do not seem to
compete with epiphyton for this resource. When epiphyton biomass
is high, however, epiphyton may shade their macrophyte hosts
(Eminson and Moss 1980). Filbin and Barko (1985) observed that
epiphytic biomass in Eau Galle Reservoir, Wisconsin comprised up to
33% of the macrophyte and epiphyte biomass, and they concluded
that epiphyton may have limited macrophyte growth by light

13

attenuation. Sand-Jenson and Sondergaard (1981) studied
phytoplankton and epiphyton shading effects on macrophytes in
Danish lakes. In oligotrophic, silicate-poor lakes, the water was
responsible for most of the light attenuation. Epiphyton were
responsible for 50% of the light attenuation to macrophytes in
oligotrophic, silicate-rich lakes receiving N supply. In a lake that had
a high nutrient supply, they determined that epiphytes were
responsible for 86% of the light attenuation to macrophytes.
Sand-Jensen and Sondergaard concluded that the shading effects that
epiphytes exert on macrophytes becomes a decisive factor limiting
depth distribution of macrophytes in lakes with high nutrient supply.

Substrate Specificity and Growth Forms of Periphyton

Some studies have indicated a high degree of substrate
specificity by epiphytic and periphytic diatoms. Round (1956)
characterized diatom taxa growing on plants (epiphytic) as
"attachment" types mostly of the genera Achnanthes, Cymbella and
Epithemia, whereas diatoms found on sediments (epipelic) were
actively motile and unattached, including the genera Navicula,
Amphora and Diploneis. Round noted, however, that diatom taxa
growing on stones (epilithic) were similar to epiphytic diatoms. Siver
(1978) observed that the diatom genera Achnanthes, Cocconeis and
Eunotia were the most abundant taxa growing on Potamogeton
robinsii. Blindow (1987) stated that the composition of epiphyton on
Potamogeton and Chara was different than the epiphytic composition
on Nitellopsis that was heavily marl-encrusted. Eminson and Moss
(1980) observed that host specificity of periphyton was greater in

14

oligotrophic lakes because of the importance of macrophyte nutrient
loss to epiphytes, whereas host specificity was less pronounced in
mesotrophic and eutrophic lakes because of the greater effect of
water-column nutrients on periphytic taxa.

Several studies have demonstrated the importance of diatom
growth form to patterns of colonization and physical structure of
epiphytic diatom communities. Achnanthes and Cocconeis are
solitary cells that lie adnate to the substrate and colonize
horizontally, and these genera are usually the initial colonizers on
new substrate (Robinson and Rushforth 1987). Later colonizers must
contend with space limitations, and genera such as Gomphonema and
Cymbella are at an advantage because they grow on long stalks and
colonize in a vertical orientation (Roemer et al. 1984, Robinson and
Rushforth 1987). This upward expansion of the epiphytic community
improves light and nutrient availability for taxa in the higher tiers
(Hudson and Legendre 1987), though some adnate forms below such
as Cocconeis placentula var. euglypta (Ehr.) CI. exhibit shade
tolerance (Robinson and Rushforth 1987). Swift-moving taxa capable
of complex movements including Nitzschia and Navicula can be
observed within the community matrix (Hudson and Legendre 1987).
As the thickness of periphyton on the substrate becomes too great,
cells on the outer tiers are subject to loss by grazing or sloughing off
by currents (Roemer et al. 1984, Hudson and Legendre 1987).
Sloughed off periphytic taxa can become part of the planktonic drift
and are then referred to as tychoplanktonic (Lowe 1974).

15

Methods for Reconstructing Historical Macrophyte Communities
Historical macrophyte presence has been traditionally
determined from lake sediments by methods that do not yield
quantitative estimates of standing crop. Macrophyte presence has
been assessed historically from macrophyte remains, pollen and
seeds that are found in lake sediment. Davis (1985) studied
historical macrophyte presence in upper Chesapeake Bay and
summarized many of the biological and diagenetic factors that
obscure accurate reconstruction of former macrophyte communities.
Seed preservation is poor in some taxa (e.g. Vallisneria and
Potamogeton) and seed dispersal is poor in others (e.g.
Myriophyllum) leading to under-representation of these taxa in
sediments. Pollen and seed production is variable among species of
macrophytes (Yeo 1966, Birks 1980), and plants producing larger
quantities of these may be over-represented in the sedimentary
record. Seeds and pollen also may be unreliable indicators of
macrophyte standing crop because a large number of species
reproduce vegetatively by budding, fragmentation and by plants
arising from stolons and rhizomes (Tarver et al. 1979).

Seed representation in the sedimentary record may be affected
by differential transport and palatability (Birks 1980). Birks (1973)
and Watts (1978) have shown that seed dispersal is often localized
for macrophyte taxa. Dispersal patterns, therefore, can cause a high
degree of spatial variability of macrophyte indicators in lake
sediment. Sampling from many littoral sediment cores is required to
obtain a reliable reconstruction of macrophyte history.

16

To summarize, traditional methods of macrophyte community
reconstruction tend to over-represent, under-represent or miss
entire portions of the macrophyte community. No single method has
been developed that will provide reasonably accurate quantitative
estimates of historical macrophyte standing crop.

Diatom Methods in Paleolimnology
Some Quantitative Diatom Methods Used in pH Reconstructions

Most quantitative work using diatoms to reconstruct past
limnological conditions has been concerned with lake acidification
due to anthropogenically induced acid precipitation. The large
number of lake acidification studies recently funded (Davis 1987)
indicates that lake acidification has occupied an important place on
the agenda of national and international environmental concerns.
The high costs of implementing more rigid air pollution standards
necessitated statistical rigor to determine if atmospheric loadings of
sulfur and N oxides were having significant fallout effects on aquatic
ecosystems. As a consequence, lake acidification studies received
priority funding and were numerous. Davis (1987) reviewed many
such studies that used diatoms to infer historical pH trends.

The earliest quantitative index relating diatom assemblages to
pH of lakewater was the a index described by Nygaard (1956), a
ratio of acidic to alkaline diatoms in a sample based on the pH
autecological classifications (Hustedt 1937-38) of the individual taxa.
Renberg and Hellberg (1982) developed the somewhat more
sophisticated index B that was also a ratio of the percentage of
diatoms in pH autecological categories. These authors regressed log-

17

transformed index B values with pH for a set of 30 Swedish lakes
and produced a model with which they assessed lake acidification
due to atmospheric deposition in Sweden. Index B is somewhat
statistically dubious, however, because coefficients for the
autecological terms could not have been calculated by a simple linear
regression between pH and log index B as indicated by Renberg and
Hellberg (Whitmore 1989).

Cluster analysis has been used to identify diatom assemblages
characteristic of various pH conditions (e.g. Davis and Anderson
1985). Charles (1985) used cluster analysis to group diatom species
with similar pH requirements and he performed a multiple
regression of these clusters with pH values of 38 Adirondack lakes.
His model explained approximately 90% of the variance in pH in his
calibration data set.

Davis and Berge (1980) performed a stepwise multiple
regression of 33 taxa in a set of Norwegian lakes and produced a
model consisting of 7 taxa that explained 93% of the variance in pH
(unadjusted R2). Dixit and Evans (1986) have shown, however, that
particularly in lakes with spatial variability in diatom assemblages,
the use of indicator assemblages rather than individual taxa in
predictive models will greatly reduce the error term. Hustedt's
(1937-38) pH autecological categories have also been used in
multiple regression equations to develop pH predictive models based
on diatom assemblages (Davis and Berge 1980, Charles 1984, 1985).

Ordination techniques, which reduce the number of diatom
variables in a model, have been used to construct pH predictive
equations. Principal components analysis is an indirect ordination

18

! .

•! technique that was used to develop models for assessing lake

X , acidification in Maine and Norway (Davis and Berge 1980, Davis and

' '■ *.

Anderson 1985). Davis and Anderson (1985) found that principal
;v% : V component models were less sensitive than multiple regression

models using individual taxa to variation in the frequencies of taxa
caused by environmental factors other than pH. Van dam et al.
(1980) also used a principal components procedure to calibrate
diatom models and assess the effects of acid precipitation on Dutch
moorland pools.

Reciprocal averaging (RA) is another indirect ordination
technique that has been used in diatom-based models. Charles
(1985) used reciprocal averaging to ordinate diatom data and he
correlated RA axes with environmental variables in a set of
Adirondack lakes. pH was a primary determinant of diatom
assemblage composition in that set of lakes and a regression equation
predicting pH from Charles' first RA axis explained 90% of the
variance in pH. In other environmental applications, Servant-
Vildary and Roux (1990) have used reciprocal averaging to
determine the effects of ionic elements on diatom species
composition in saline lakes of the Bolivian Altiplano.

Canonical correspondence analysis (CANOCO) is a direct
ordination technique that has been used in pH reconstructions to
define axes that are combinations of taxa responding directly to pH.
The axes have then been regressed with pH and the resulting models
used to document historical trends in lake acidification. Battarbee et
al. (1988), for example, used CANOCO to assess the acidification of

19

Scottish lochs and their recovery following abatement of atmospheric
sulfate emissions in the United Kingdom.

Diatom Methods for Reconstructing Trophic State

Historical trophic state studies generally have not received the
degree of quantitative treatment that studies of lake acidification
have. Diatom/trophic reconstructions have often relied heavily on
autecological information of specific taxa for qualitative
interpretation of diatom percentage diagrams from lake sediment
cores. Brugam (1978), for instance, documented the eutrophication
of Linsley Pond in Connecticut and Bradbury (1975) used diatoms to
interpret the history and eutrophication of Minnesota lakes.
Battarbee (1978) observed the influence of land use and sewage
effluent on the eutrophication of Lough Neagh, Northern Ireland.
Mkansson (1982) presented an excellent ecological analysis of the
diatom flora from HdvgSrdssjon in Sweden and documented
eutrophication after 1900 due to agricultural activity. Qualitative
>v, studies have provided understanding of gross trends in the trophic
trajectory of lakes because of climatic patterns and anthropogenic
influence, but they have lacked ability to discern subtle trophic
differences, assess rates of change or demonstrate statistical
significance.

Ratios have been proposed that quantitatively describe lake
productivity using the percentages of diatom species separated at
high taxonomic levels. Nygaard's (1949) C/P index was a ratio of the
number of valves in the diatom orders Centrales and Pennales. High
C/P values were thought to indicate eutrophic conditions because of

20

the supposed eutrophic preference of Centrales, a notion refuted by
the wide range of trophic preferences actually observed for centric
taxa (Battarbee 1979). Stockner and Benson (1967) studied historic
trends in Lake Washington and proposed the A:C index, a ratio of the
number of valves in the tribe Araphidiniae to the number of valves
in the order Centrales. Centrales were assumed to be oligotrophic
rather than eutrophic indicators in this scheme. Stockner (1971)
later qualified the conditions under which this index would
accurately indicate trophic state, but subsequent studies (e.g. Brugam
1979, Battarbee 1979, Carney 1982, Charles 1985, Whitmore 1985)
have shown that the A:C index is not a useful indicator of lake
trophic status. The essential problem with these indices is that they
assume ecological uniformity of diatom species over broad taxonomic
groupings, whereas the individual species actually have ecologically
diverse requirements (C. Reimer pers. comm.).

Schelske et al. (1983) examined concentrations of biogenic silica
in sediment cores from the Great Lakes. Increases in biogenic silica
were shown over time in the sediments of all of the Great Lakes
because eutrophication led to a more rapid production and
sequestering to sediments of diatom valves. The peak in
sedimentary storage of biogenic silica in Lakes Ontario and Erie
occurred in the 1800's and was followed by a decline that resulted
from silica limitation (Kilham 1971) as these lakes continued to
eutrophicate. Sedimentary biogenic silica increased after 1940 in
Lake Michigan and reached a maximum abundance in 1964, after
which it declined. Stoermer et al. (1990) used cluster analysis to
delineate diatom zonation in a sediment core from Lake Michigan

21

and determined that changes in diatom species composition support
the eutrophication inferences of sedimentary biogenic silica.
Schelske (1988) demonstrated that recent declines in sedimentary
biogenic silica are consistent with historic water concentration data
that showed a decline in dissolved silica in Lake Michigan.

Whitmore (in press) studied the relationship in Florida lakes
between sedimentary diatom concentrations and accumulation rates
and lake trophic state as indicated by a TSI based on water-column
Chi a. Both periphyton and planktonic diatom concentrations were
positively correlated with water-column Chi a. Because diatom
accumulation rates were determined by three order of magnitude
differences in sedimentary diatom concentrations rather than by the
small range in bulk sediment accumulation rates, sedimentary
diatom concentrations were shown to be more expedient predictors
of Chi a than diatom accumulation rates. Sedimentary concentrations
were found to be unreliable predictors of trophic state when factors
such as silica limitation or blue-green bacterial inhibition limit
phytoplankton production, or when post-depositional changes affect
preservation of diatom valves.

Bailey and Davis (1978) used a multiple regression of diatom
taxa to predict water-column total P in a set of 19 lakes in Maine.
The best model explained 96% of the variance of total P in these
lakes, but contained only a few species of Fragilaria as independent
variables. Such models based on a limited number of taxa may
prove unreliable when applied to lakes outside of their calibration
data sets because of the large number of environmental factors that
can influence the distribution and abundance of species (Patrick

22

1973). Predictive approaches that utilize groups of taxa are generally
more reliable than those based on a limited number of taxa
(Battarbee 1979).

Diatom indices have been proposed that used trophic
autecological classifications of taxa for lakes in Florida (Whitmore
1985, 1989) and in Canada (Agbeti and Dickman 1989). In both of
these studies, diatoms were classified into 5 autecological categories,
and their percentages were structured into an index similar to
indices used for pH reconstructions (Nygaard 1956, Renberg and
Hellberg 1982). Log-transformed values of the indices were
regressed with log-transformed total P and Chi a in the Canadian
lakes and with TSI(TP) and TSI(Chl a) in Florida lakes. Log-
transformed values of the diatom inferred trophic index (D. I.T.I.)
(Agbeti and Dickman 1985) explained 71% of the variance in log-
transformed total P in the Canadian lakes, and the TROPH 1 index
(Whitmore 1989) explained 83% of the variance in TSI(TP) in Florida
lakes. Agbeti and Dickman concluded that the D.I.T.I. diatom index
was influenced by unspecified environmental factors. Whitmore
showed that pH was an important covariable affecting diatom
assemblages in the Florida lakes, though partial correlations
demonstrated that the predictive model using the TROPH 1 diatom
index was not statistically confounded by pH. Despite the fact that
silica limitation or cyanobacterial inhibition may affect
paleoproductivity inferences based on diatom accumulation rates
(Anderson 1990c), the TROPH 1 index still seems useful because
diatom assemblages are qualitatively distinct at the high nutrient
conditions where their populations are limited (Whitmore in press).

i ■ ■:.. ■' f ■-: .■- ■- •■■►", '

5 • ; Canfield (pers. comm.) pointed out that while diatom indices such as
TROPH 1 may be accurate predictors of water-column total P, they
are not comprehensive indicators of lakewide trophic state because
they ignore the important component of primary production that is
in macrophytes,

Anderson et al. (1990) used the reciprocal averaging (RA)
indirect ordination method to study historical changes in lake trophic
state in Lough Augher, Northern Ireland resulting from loading and
later re-direction of nutrients from point sources. Anderson et al.
did not correlate RA axes with water quality indicators to
quantitatively assess trophic state changes, but instead plotted RA
axes against each other to graphically depict assemblage similarity
and ecological change.

Charles (1985) investigated the relationship between lake-water
characteristics and sedimentary diatom assemblages in 38
' Adirondack lakes. Charles used reciprocal averaging to determine
which environmental variables influenced the diatom assemblages,
and found that total P was a weak correlate with the first RA axis.
He concluded that pH proved a more important determinant of
diatom assemblage composition in the Adirondack lakes because
those lakes spanned a wider range of pH than of trophic state.

Huttunen and Merilainen (1986) used detrended correspondence
analysis, another indirect ordination method, to interpret historical
limnological trends in a Finnish lake and were able to demonstrate
eutrophication following deforestation and the inception of
agriculture, as well as recent lake acidification.

24

Fritz (1990) used the canonical correspondence option of CANOCO
(ter Braak 1987) in a constrained ordination of 127 diatom taxa in 64
lakes of the northern Great Plains. The ordination axis was
constrained by the variable salinity, which resulted in a predictive
model that Fritz used in reconstructing historical changes in the
salinity of Devils Lake.

In recent studies of Canadian lakes (Christie and Smol 1990, Hall
and Smol 1990), attempts to construct trophic predictive models
have involved canonical correspondence analysis as an explanatory
ordination method to identify limnological variables affecting diatom
assemblages in lakes having a wide range of trophic state but a
narrow range of pH. Weighted averaging calibration (Line and Birks
1990) was then used as the regression method to construct transfer
•^ functions and determine historical changes in trophic variables.

, '-: Anderson (1990c) examined the weighted averaging approach to

quantitative trophic-state reconstruction and warned that weighted
averaging studies largely utilize open water phytoplankton and v
chemical data, and that they ignore littoral community production
and chemistry. Anderson suggested that modeling methods should
be coupled with the use of multiple cores to calculate whole-basin
diatom accumulation rates that would give a reliable measure of both
plankton and periphytic paleoproduction.

Comments on Statistical Methods
Cluster analysis is a statistical method that groups observations
into clusters that reflect their similarity without a priori
consideration of which factors are influencing the similarities.

25

Cluster analysis of diatom assemblages from a set of lakes, for
instance, would result in clusters of diatom species that demonstrate
a similar response to the environmental variables responsible for
between-lake variance in the assemblages.

Indirect ordination techniques, which include reciprocal
averaging, principal components analysis and detrended
correspondence analysis, reduce the number of variables (e.g. diatom
taxa) by combining the variables into a series of linear combinations
of the original variables called ordination axes. With indirect
ordination methods, ordination axes are just particular combinations
of variables that are uncorrelated and appear in the order that best
explains the variance within the data set. The relationship between
indirect ordination axes and environmental variables that influence
the data set can then be determined by correlating axes with
environmental variables. Other ordination axes, however, may be
more suitable for establishing the relationship between the taxa and
specific environmental variables influencing diatom assemblages.

Canonical correspondence analysis (CCA), included in canonical
community ordination (CANOCO), is a direct ordination technique in
which variables are combined into ordination axes that are
constrained by specific environmental variables (ter Braak 1987).
Ordination axes are independent and uncorrelated, and are created in
order of their variance explained by the environmental variables.
CANOCO, therefore, effectively inserts a regression model into the
ordination model. When a single environmental variable is specified
in the CCA procedure, CANOCO can be used to obtain eigenvectors to

26

construct a calibration model for that environmental variable (ter
Braak 1987).

Principal components analysis is a linear ordination method in
which species demonstrate a linear response over the ordination axes
and species coefficients, called eigenvectors, are calculated as slopes
of those lines. In weighted averaging methods, which include
detrended correspondence analysis (DCA), species are assumed to
respond in a modal fashion over the ordination range, and
coefficients are equal to the center or optimum of their distribution
curve along the range of ordination values (ter Braak 1987). CANOCO
is an extension of DCA that also assumes a modal species distribution
over ordination axes (ter Braak 1987).

Reciprocal averaging (RA), or factor correspondence analysis, is
an extension of principal components. Hill and Gauch (1980)
compared RA and DCA and concluded that DCA was a better method.
A main fault they cite with RA is that the second ordination axis
demonstrates an 'arch effect' that is a mathematical artifact relating
to no real structure in the data. Charles (1985), for instance,
observed this arch effect in his study of diatom communities of
Adirondack lakes and found it made ecological interpretations
difficult. A second fault of RA is that it does not preserve ecological
distances between species along the ordination axes (Hill and Gauch
1980). Anderson et al. (1990) have presented, on the other hand, an
argument that RA is a preferred method over DCA because DCA
destroys spatial relationships between successive samples that is
necessary to demonstrate a time trajectory of community response in

27

sediment cores, and it interferes with assessment of the importance
of species on samples.

Effects of Spatial Variation on Diatom Assemblages
In paleolimnological work, there is frequently an implicit
assumption that diatom assemblages have been homogenized by
resuspension prior to deposition so that a single surface-sediment
sample or a sediment core reflects lakewide mean limnological
conditions. If spatial variability exists in species composition of
diatom assemblages in surficial sediments, variance is introduced
into the calibration data sets used for models describing
diatom/limnological relationships. Spatial variability in diatom
assemblages from sediment cores may also affect the precision of
historical inferences.

Anderson (1990a) studied variability in diatom concentrations
and accumulation rates in 10 sediment cores from Lough Augher and
found that diatom accumulation rates and concentrations were not
spatially uniform. Differences resulted partly from variance in bulk
sediment accumulation rates that was not related in a predictable
way to water depth (Anderson 1990b). Factors including localized
resuspension, stream inputs, slumping and the effects of
macrophytes on wind circulation patterns were responsible for the
spatial differences in sedimentation rates. Anderson concluded that
no single sediment core reflected the mean accumulation rate of the
whole basin.

Studies on the spatial heterogeneity of species composition in
surficial sediment samples have shown that no single sample

fc " ■* 1 ■

n

- 2 8

represents an "average" lakewide diatom assemblage (Earle et al.
1988). Variance due to site seems to increase when habitat
specificity of taxa is considered, i.e. when planktonic and periphytic
taxa are separated (Dixit and Evans 1986, Anderson 1990a).

^%? Planktonic taxa typically show greater representation in deeper

water, whereas periphytic taxa show relatively little redistribution
and are more abundant in the littoral zone. With respect to spatial

, ;. variation within a lake basin, periphyton contribute a substantial

■ . amount of the variance observed in diatom assemblages because

their substrate preferences lead to patchy distributions (Earle et al.
1988). In a sediment core from any particular site in a lake,
however, periphytic diatoms tend to demonstrate more even
accumulation rates than do planktonic taxa (Anderson 1990a,
1990b).

Spatial variation in subfossil diatom assemblages does not seem
to invalidate construction of calibration data sets using single

'*: samples from each lake when lakes are sampled over a limnological

range. Earle et al. (1988) have shown that single-sample, between-
lake differences are high enough to indicate that the comparison of
diatom assemblages between lakes is valid if samples are retrieved
from deeper areas with gentle slopes rather than from steep-sloped

; ; areas.

\ - Spatial variation does not preclude meaningful application of

predictive models to historical samples provided that the effects of

^0'- ■■■■ sample variance on inferences are understood. Anderson (1990a)
showed that sediment cores retrieved from deeper water sites
consistently demonstrated greater resolution of historical changes in

3..r

29

limnology than cores taken in littoral areas. Although taxon
resolution varied with sediment core site, each core from Lough
Augher gave fundamentally the same record of eutrophication. Dixit
and Evans (1986) concluded that when time or financial constraints
are important, a sediment core from the deepest site on a lake will
provide a reliable indication of historical trends in pH. Because of
differences in pH inferences from various sites, however. Dixit and
Evans indicate that it is important to analyze several sediment cores
and demonstrate replicability for absolute inferences.

Purpose

The purpose of this study is to develop methods that would
permit quantitative assessment of historical macrophyte biomass in
lakes using sedimentary indicators. Macrophyte standing crop has
been documented to be high (Canfield et al. 1983a) in lakes such as
Fairview in Orange Co., Florida that appear oligotrophic and have
been used for calibration of diatom/trophic state models (Whitmore
1989). Conventional paleolimnological reconstructions of trophic state
have focused on inferring water-column nutrient concentrations
principally from planktonic diatom assemblages, but they have
ignored the often substantial component of primary production
occurring in macrophytes.

Historical inferences of lower water-column nutrient
concentrations obtained with existing diatom predictive models don't
necessarily indicate that lakes were formerly less productive. A
negative correlation has been shown between water-column nutrient
concentration or phytoplankton biomass as measured by Chi a and

30

macrophyte biomass (Canfield et al. 1983a, Canfield et al. 1984).
Historic samples indicating low water-column nutrient concentrations
may represent times of high macrophyte production, especially if the
cyclic changes in water level of Florida's shallow lakes (Deevey 1988)
promote a periodic lakeward expansion of macrophyte beds. If the
trophic trajectory of lake ecosystems over time is to be fully
understood, a more holistic consideration of historical trophic state is
required, one that includes the macrophyte component of production.

Conventional sedimentary indicators of macrophytes are not
appropriate for quantitative reconstructions for a variety of reasons:

1 ) pollen and seed production is species specific quantitatively and
is often absent in plants such as Hydrilla that largely undergo
vegetative reproduction. Models predicting historical
macrophyte standing crop from sedimentary pollen or seeds
would have to be calibrated for each individual species;

2) there are no known photosynthetic pigments that would be
preserved in sediments and that are specific to macrophytes;

3) diagenesis often affects the preservation of macrophyte remains.

Diatoms are considered as potential macrophyte indicators in
this study because diatoms are usually well-preserved in lake
sediments and they are ecologically specific. Life-form classifications
are available (Lowe 1974) that permit separate consideration of
planktonic and periphytic taxa. Sedimentary concentrations and
accumulation rates of periphytic taxa might be expected to
demonstrate a positive correlation with the amount of submerged
macrophyte biomass for 2 reasons. First, a positive relationship
exists between periphytic biomass and the increased substrate area
afforded by macrophytes with many small or finely-dissected leaves
such as Hydrilla (Cattaneo and Kalff 1981). Secondly, evidence also

31

indicates that diatom biomass might be stimulated by nutrient
release from macrophyte substrates (Burkholder and Wetzel 1990).
The specific objectives of this study are to:

1 ) obtain information on diatom assemblages in a calibration set of
Florida lakes that represent a wide range of macrophyte
presence;

2) identify periphyton and planktonic components of the
assemblages;

3) perform explanatory analyses to determine which variables
influence diatom assemblages over the range of macrophyte
presence;

4) construct predictive models that could be used to quantitatively
assess historical macrophyte standing crop using percentages,
sedimentary concentrations or accumulation rates of diatoms;
and

5) derive a plan to assimilate historic macrophyte inferences into a
scheme that permits a more complete assessment of former lake
trophic state than models restricted to concentrations of water
column nutrients.

I propose to develop new multivariate models predicting

historical macrophyte presence in the following manner. For

explanatory analyses of diatom communities, a cluster analysis will

be used to identify diatom taxa with similar ecological responses. An

indirect (unconstrained) ordination method, such as PCA, will be used

to ordinate taxa in linear combinations that best explain the variance

between assemblages. The ordination axes will then be correlated

with environmental variables to determine which variables exert the

greatest influence on diatom assemblages. Multivariate predictive

models will be constructed by stepwise linear regression of taxa and

by the direct ordination method canonical correspondence analysis,

in which the linear combinations of diatom taxa are constrained in a

manner best explained by the limnological variables of interest.

CHAPTER 2
METHODS

, Collection of Sediment Samples
Sediment samples were collected from the sediment-water
interface of 30 Florida lakes. Lakes chosen for study were those
named in data sets (Canfield and Duarte 1988, Canfield unpub. data)
that contained data on macrophyte abundance. Lakes were selected
to cover the range of macrophyte presence as uniformly as possible.
Sediment samples were collected in two sets of field surveys. Survey
Set 1 consisted of samples from 10 lakes collected in the Spring of
1982. Samples were collected at a mid-lake station using an Ekman
dredge. Volumetric portions were removed with a pipette and
transferred to 125-ml Nalgene bottles. Sediment samples in Survey
Set 2 were collected between November 1987 and May 1988 from
20 additional Florida lakes. These samples were collected with a 77-
cm long, 8.8-cm diameter acrylic piston corer that was driven into
the sediment with 1 m long sections of magnesium-zirconium coring
rods. Water above the sediment-water interface was removed by
aspiration. The top 2 cm of sediment was collected with a syringe
and transferred to 125 ml Nalgene bottles.

32

33

Laboratory Analyses

Sediment samples were subdivided in the Paleoecology
Laboratory of the Florida Museum of Natural History for diatom
analyses, estimation of bulk sediment accumulation rates by 21 Op 5
assay, and determination of percent organic matter.

Sediment samples for diatom analyses were cleaned using
hydrogen peroxide and potassium dichromate (Van der Werff 1955).
Digested samples were diluted with deionized water in 400-ml
beakers and settled overnight. The supernatant solutions were
removed by vacuum aspiration and the process of dilution, settling
and aspiration was repeated until the dichromate color was no longer
visible. Cleaned samples were suspended in 60 ml of water and
settled onto coverslips in evaporation trays (Battarbee 1973). Dried
coverslips were mounted on glass slides using Hyrax mounting
medium.

A minimum of 500 diatom valves was counted and identified on
a single slide of each sample using an American Optical Microstar
microscope at 1500X with a dark-phase condenser. Each diatom
valve was identified to the lowest taxonomic level possible using
standard diatom floras including those of Patrick and Reimer (1966-
1975) and Hustedt (1930,1930-1966).

The concentration of diatoms (D CONC) in the sediment samples
was calculated with the following formula:

D-CONC = C(PVD)-1

where D-CONC = number of frustules g-^ dry weight of sediment
C = number of valves counted
P = proportion of settling tray area counted

34

V = cm3 of sediment in initial sample

D = g dry weight of sediment cm"3 of initial sample.

Life-form autecological information for the majority of diatom
taxa was obtained from the literature (Lowe 1974, Patrick and
Reimer 1966-1975, Hustedt 1930). The concentrations of periphytic
valves (PERICONC) and euplanktonic valves (PLNKCONC) were
calculated by multiplying the concentration of diatoms in the
samples by the proportion of periphyton and euplankton in each
sample. Tychoplanktonic diatoms normally show a periphytic life-
form, though are often suspended with the plankton (Lowe 1974).
Tychoplanktonic taxa were arbitrarily assumed to be 1/3
euplanktonic for these calculations.

Bulk-sediment accumulation rates were measured for each
sediment sample using the Binford and Brenner (1986) dilution
tracer method based on 2l0p5 assay. 210pb assay for 8 lakes from
Survey Set 1 was performed by a modification of the Eakins and
Morrison (1978) method that involved estimating 210pb recovery of
samples from the proportion of recovery onto copper planchettes of a
208po spike. Atmospherically derived (unsupported) ^lOp^ activity
was calculated as the difference between total residual 2l0pb activity
in the samples and an average supported 2l0pb activity (0.80 pCi g"^)
that was estimated from sediment cores of nine other Florida lakes
(Binford and Brenner 1986).

Sediment samples for 21 Opt, assay of Survey Set 2 lakes were
dried, ground with mortar and pestle and weighed. These samples
were sealed in polypropylene tubes for greater than 3 weeks to
permit ingrowth of 226Ra daughter products. Radionuclide activity

35

was then measured using an ORTEC Intrinsic Germanium Detector
connected to a 4096-channel multichannel analyzer. The
unsupported activity was obtained by difference between total
residual 2l0pb activity and supported activity of each sample as
assessed from ^Mgj

Bulk-sediment accumulation rates (SEDACCUM) were estimated
for samples from lakes in both survey sets using the following
formula:

SEDACCUM = F210pbA-l

where SEDACCUM = g dry sedimentcm-2 yr^

F210pb = flux of 210pb fallout (pCi cm-2 yr^)

A = unsupported 2l0pb activity in sediment (pCi g-^.

Annual diatom accumulation rates (D-ACCUM) were estimated as

follows:

D-ACCUM = (D-CONCXSEDACCUM)
where D-ACCUM = valves cm-2 yj-1

The annual accumulation rates of periphytic valves (PERIACCM) and
euplanktonic valves (EUPLACCM) were obtained by multiplying the
periphyton and euplankton proportions of each sample by the total
diatom accumulation rates.

Subsamples of 1.001 cm3 wet sediment were dried at 60 °C in a
drying oven. Dry weight per unit volume (rho) was then measured
for each sample by weighing the dry material on an Ainsworth 24N
analytical pan balance. Percent loss on ignition (% L.O.I.), a measure
of sedimentary organic matter content, was determined for each
sample by weighing the sediment samples before and after

36

combustion in a muffle furnace for 1 hr at 550 °C. Percent loss on
ignition was then calculated as:

% L.O.I. = 100 X (1- (sample weight before combustion/sample
weight after combustion)).

Organic matter accumulation rates (ORGACCUM) were calculated with

the following equation:

ORGACCUM = SEDACCUM x (% L.O.I./lOO)

where ORGACCUM = g dry organic matter cm'2 yr-l.

Quantifying Macrophyte Presence
Researchers from the Department of Fisheries and Aquaculture
at the University of Florida obtained quantitative macrophyte data
for the survey lakes in field work conducted between 1982 and
1988. The percentage of the lake area covered by macrophytes
(percent-area coverage) was obtained for 23 lakes by planimetry on
morphometric maps. The area covered by submergent vegetation as
estimated from recording fathometer transects (Canfield and Duarte
1988) was added to the area covered by emergent and floating-
leaved vegetation. The percentage of the lake volume filled with
macrophytes (percent-volume infestation) was also estimated
morphometrically for 30 lakes using the percent areal coverage of
submerged macrophytes and the height of submerged vegetation as
indicated by fathometry (Maceina and Shireman 1980).
Morphometric data including lake surface area, shoreline length,
shoreline development and mean depth were available for 24 of the
survey lakes.

37

The average wet-weight biomass per unit area of submerged,
emergent and floating-leaved macrophytes was determined from
0.25 m2 quadrats randomly sampled from 10 transects through the
littoral zones of 23 lakes. Above-ground biomass in each quadrat
was collected by divers, spun to remove excess water and weighed to
the nearest 0.1 kg (Canfield and Duarte 1988).

Water Chemistry Data

Median water chemistry values for the lakes in Survey Set 1
were obtained from the Florida Lakes Data Base of the Water
Resources Research Center at the University of Florida. Water
quality variables selected from this data set included median values
for total P, total N, Chi a, Secchi depth, specific conductance and pH.

Mean water chemistry values were obtained by the Department
of Fisheries and Aquaculture for each lake in Survey Set 2 by
averaging data from 3 mid-lake stations (M. Hoyer, pers. comm.).
Total P analyses were performed following persulfate digestion
(Murphy and Riley 1962, Menzel and Corwin 1965), and total N was
measured using a modified Kjeldahl technique (Nelson and Sommers
1975). Water samples were filtered through a Gelman type A-E glass
fiber filter and Chi a was measured using the Yentsch and Menzel
(1963) method and Parson and Strickland (1963) equations. Secchi
depth was measured with a 20-cm black and white Secchi disk.
Trophic state index (TSI) values (Huber et al. 1982) were calculated
using the mean or median values of total P, total N, Chi a and Secchi
depth for each lake in Survey Sets 1 and 2. Specific conductance was
measured with a Yellow Springs Instrument Company model 31

38

conductivity bridge. pH was measured with an Orion 601 A pH
meter.

Statistical Analyses

Data for diatom, macrophyte and water chemisty variables were
stored as Statistical Analysis System (SAS Inst., Inc. 1985) data sets
using the University of Florida's Northeast Regional Data Center. I
plotted the percent-area coverage, percent-volume infestation, and
submerged, emergent and floating-leaved biomass for macrophytes
against the percentage, concentration and accumulation rates of
diatom species to identify taxa responding to macrophyte presence.
Pearson product-moment correlation coefficients were obtained
between percent loss on ignition, organic-matter accumulation rates
and macrophyte variables using the SAS CORR procedure (SAS Inst.,
Inc. 1985). I also obtained correlation coefficients between
macrophyte variables and the percentages, concentrations and
accumulation rates of planktonic and periphytic diatoms. Correlation
coefficients were calculated between the macrophyte variables and
log-transformed concentrations and accumulation rates of diatoms.
In order to determine whether morphometric and chemical variables
have covariable effects on diatom-macrophyte relationships, I
obtained correlation coefficients between macrophyte, morphometric
and water chemistry variables.

For purposes of multivariate statistical analyses, it was essential
to reduce the number of diatom species from the 223 diatom taxa
observed. I examined plots of diatom percentages versus percent-
area coverage and percent-volume infestation, and selected forty-
seven taxonomic groups, several of which were the sum of taxa in

39

the same genus. Because of the large number of rare taxa present,
this preserved most of the diatom information in each sample (mean
= 96.6%) while substantially reducing the number of species.

Hierarchical cluster analysis was performed on the 47 taxonomic
groups using the SAS VARCLUS procedure (SAS Inst., Inc. 1985). I
applied this procedure to the percentages, concentrations and
accumulation rates of the taxonomic groups, and repeated the
procedures after partialling out the effects of TSI(AVG) and pH. Tree
diagrams of the hierarchical clusters from each analysis were
constructed using the SAS TREE procedure (SAS Inst., Inc. 1985).
Scores for diatom clusters were obtained for each lake in the survey
using standardized scoring coefficients from the cluster analyses and
the SAS SCORE procedure (SAS Inst., Inc. 1985). I then correlated the
scores for each cluster with macrophyte, chemical and morphometric
variables using the SAS CORR procedure to determine which
variables most influenced each cluster of taxa.

Principal components analyses of the percentages, concentrations
and accumulation rates of the 47 diatom taxonomic groups were
performed using the SAS PRINCOMP procedure (SAS Inst., Inc. 1985).
I repeated the PRINCOMP procedure for each of the three models
while partialling out the effects of TSI(AVG) and pH. The
standardized principal component scores of the first 8 principal
components in each test were calculated for the diatom assemblages
in the survey lakes using the SAS SCORE procedure. I then correlated
the principal component scores with macrophyte, water chemistry
and morphometric variables to identify the environmental variables
influencing each principal component.

40

Canonical correspondence analysis (CCA) was performed on the
percentage data for 47 diatom taxonomic groups using the canonical
community ordination (CANOCO) statistical package developed by ter
Braak (1987). In the first set of computations, the diatom groups
were ordinated in an axis constrained by the environmental variable
percent-volume infestation. In a second set of computations, 47
diatom taxa were ordinated into an axis constrained by percent-area
coverage.

Multivariate models that predict percent-volume infestation,
percent-area coverage and biomass for submerged, emergent and
floating-leaved plants were derived using the maximum R2
improvement method of the SAS STEPWISE procedure (SAS Inst., Inc.
1985). I reduced the number of independent diatom variables in
each stepwise regression to 20 or less, a recommended maximum for
this procedure (SAS Inst., Inc. 1985), after examining plots of diatom
taxonomic groups versus macrophyte variables. Regressions were
performed for each macrophyte variable using percentage data for
the diatom groups. The regressions for percent-area coverage and
percent-volume infestation were repeated using concentration and
accumulation rate data for the diatom groups.

I selected the best model in each STEPWISE regression
procedure by plotting Mallows' Cp statistic versus the number of
variables in each model (p) and selecting the model in which Cp was
approximately equal to p (Daniel and Wood 1971). Adjusted R^s,
which show the coefficient of determination after removing the
inflating effect of dependent variables, were calculated using the SAS
RSQUARE procedure (SAS Inst., Inc. 1985).

41

Partial correlations (Ott 1977) were used to determine if
predictive models were statistically free from confounding effects of
environmental variables correlated with their dependent variables.
I accomplished this by calculating multiple correlation coefficients as
the square root of the coefficients of determination for the
multivariate models. The multivariate models were then regressed
with the covariant dependent variables using the SAS GLM
procedure (SAS Inst., Inc. 1985), and I calculated multiple correlation
coefficients for these confounded forms of the model. Partial
correlation coefficients were calculated using the multiple correlation
coefficients to assess whether models with correct dependent
variables were significant when the effects of the covariables were
held constant.

Diatom-index values (TROPH 1) (Whitmore 1989) were
calculated for the subfossil diatom assemblages in the survey lakes.
A predictive model was developed to yield water-column total P
estimates from subfossil diatom assemblages for historic WCP
(Canfield et al. 1983a) inferences. The 51 lakes used to construct this
model were the lakes included in the present study, lakes in
Whitmore's (1989) study, and Lake Francis in Highlands County.
Water-column total P values for the lakes outside of the present
study were median values obtained from the Florida Lakes Data
Base. TROPH 1 values for subfossil diatom assemblages were
regressed with water-column total P values using the SAS GLM
procedure (SAS Inst., Inc. 1985).

Because of the significant negative correlation between
macrophyte presence and water-column nutrient concentrations

42

(Canfield et al. 1984), it was important to assess whether macrophyte
presence would have a significant confounding effect on the model
for predicting water-column total P. Log-transformed TROPH 1 index
values were correlated with log-transformed total P and percent-
area coverage values for the survey lakes.

CHAPTERS
RESULTS

Thirty lakes were sampled in the surface-sediment survey
(Table 1). Lake Miona was removed from the data set prior to
statistical analyses because the lake was observed to contain
virtually no macrophytes despite a percent-volume infestation value
of 86% measured by Canfield (unpub. data). Triploid grass carp had
been introduced to Lake Miona by the Florida Game and Freshwater
Fish Commission in the time between the macrophyte and surface-
sediment surveys in order to control aquatic vegetation (M. Hoyer,
pers. comm.).

Many correlation coefficients were used in this study to assess
relationships between morphometric, water-chemistry and
macrophyte variables and to determine which environmental
variables influenced specific groups of diatoms. The criterion for
significance of all correlation coefficients discussed below is the a =
0.05 level of significance.

Water-chemistry values (Table 2a) showed that survey lakes
ranged from ultraoligotrophic to hypereutrophic (TSI(AVG): 4.1 to
88.5) as determined by water-column nutrient concentrations, and
from acidic to alkaline conditions (pH: 4.61 to 9.03). Total N/total P
ratios suggested that Lakes Wauberg and Alligator were N-limited
(Ruber et al. 1982). Log-transformed water-column total P was

43

44

Table 1. Lake, county and sampling dates for macrophyte and
diatom surveys.

Lake

County

Macrophyte

Diatom

date

date

Alligator

Columbia

2 Jun ]

1987

19 Dec ]

1988

Apopka

Orange

Sep ]

[981

27 Jul ]

[982

Bonny

Polk

22 Sep ]

[987

17 Dec ]

1988

Carr

Leon

7 Jul ]

1987

15 Jan ]

[989

Catherine

Marion

8 Sep ]

1987

8 Dec

1988

Clay

Lake

15 Jul ]

1986

20 Jan ]

[989

Crooked

Lake

9 Jun ]

1987

17 May ]

[989

Deep

Putnam

16 Jun ]

[987

1 Feb ]

1989

Fairview

Orange

Oct ]

1982

28 Jul ]

[982

Harris

Lake

12 Oct ]

1987

25 Mar ]

[989

Hartridge

Polk

11 Aug ]

[987

17 Dec ]

1989

Keys Pond

Putnam

9 Jun ]

1986

2 Feb 1

1989

Lindsey

Hernando

10 May ]

[988

16 Nov ]

1988

Live Oak

Osceola

24 May ]

[988

12 Jan ]

1989

Lochloosa

Alachua

22 Aug ]

[988

9 Nov ]

1988

Loften Ponds

Leon

16 May 1

[988

14 Jan ]

[989

Miona

Sumter

19 Aug ]

[986

6 Jan ]

1989

Moore

Leon

18 May 1

[988

15 Jan ]

[989

Mystic

Madison

Jul ]

[982

4 May 1

[982

Ocean Pond

Baker

Aug ]

[982

3 May 1

[982

Okahumpka

Sumter

Aug ]

[981

26 May 1

[982

Orange

Alachua

Oct ]

1982

15 Aug ]

[982

Patrick

Polk

21 Jun ]

[988

18 Dec ]

1988

Rowell

Bradford

9 Aug ]

1988

5 Dec ]

1988

Stella

Putnam

Sep ]

981

10 Aug ]

[982

Tomohawk

Marion

18 Jul ]

1988

8 Dec ]

1988

Townsend

Lafayette

Jul ]

[981

4 May 1

[982

Watertown

Columbia

Aug ]

[982

3 May 1

[982

Wauberg

Alachua

22 Jul ]

1986

7 Nov ]

1988

Wildcat

Lake

Aug ]

982

3 Aug ]

1982

45

Table 2a. Summary water chemistry data for survey lakes.
Sources of data were Canfield (unpub. data) and
Florida Lakes Data Base.

Lake Water-column

TSI(AVG)

TN/TP

PH

Specific

total P

.■'-

conductance

(mgl-1)

(\iS/cm)

Alligator

0.320

74.9

7.4

7.9

144.0

Apopka

0.192

88.5

21.0

8.0

395.0

Bonny

0.050

71.2

37.2

7.7

255.8

Carr

0.015

42.4

58.0

6.3

25.3

Catherine

0.003

16.6

100.0

4.7

48.3

Clay

0.001

7.4

360.0

4.8

52.0

Crooked

0.007

24.3

47.1

4.6

44.3

Deep

0.002

4.1

80.0

4.6

37.0

Fairview

0.015

27.4

33.3

8.1

198.8

Harris

0.028

66.4

55.4

8.5

247.7

Hartridge

0.010

33.6

48.0

7.8

219.3

Keys Pond

0.002

6.7

85.0

5.4

42.7

Lindsey

0.017

41.2

38.2

6.8

34.0

Live Oak

0.014

35.3

25.0

7.0

131.0

Lochloosa

0.032

61.0

32.8

7.7

87.7

Loften Ponds

0.004

20.5

97.5

4.8

19.0

Moore

0.005

19.7

70.0

5.8

16.3

Mystic

0.015

25.7

34.7

6.7

28.0

Ocean Pond

0.040

46.0

10.5

5.0

45.5

Okahumpka

0.020

47.2

47.5

9.0

201.7

Orange

0.040

59.4

27.8

7.2

82.5

Patrick

0.010

35.5

147.0

8.1

320.3

Rowell

0.069

66.0

11.7

7.7

289.7

Stella

0.010

25.1

43.0

7.1

240.0

Tomohawk

0.004

15.7

52.5

4.9

34.7

Townsend

0.009

29.5

64.4

5.2

23.4

Watertown

0.062

51.7

16.9

7.4

153.3

Wauberg

0.158

77.1

9.9

7.4

80.0

Wildcat

0.008

20.9

23.9

4.8

33.0

46

Table 2b. Summary data for macrophyte variables of survey lakes.
Source of data was Canfield (unpub. data).

Lake

Percent

Percent

Floating

Submerged

1 Emergent

area

volume

-leaved

biomass

biomass

coverage

infestation

biomass

(kg m-2)

(kg m-2)

(kg m-2)

Alligator

10

10

1.2

0.0

1.7

Apopka

3

0

1.1

0.0

2.5

Bonny

10

7

0.0

3.0

8.1

Carr

100

100

7.0

9.9

12.7

Catherine

48

9

1.1

2.9

4.6

Clay

100

76

4.5

6.8

8.1

Crooked

27

2

3.8

2.4

26.8

Deep

97

21

2.5

11.7

10.6

Fairview

33

Harris

27

2

0.8

0.9

2.4

Hartridge

60

11

0.0

8.0

4.9

Keys Pond

40

8

0.0

1.0

2.8

Lindsey

100

80

1.3

1.8

3.0

Live Oak

100

55

0.3

1.6

2.0

Lochloosa

83

57

0.6

2.6

2.2

Loften Ponds

87

22

0.6

0.7

0.3

Moore

40

14

0.2

1.3

1.7

Mystic

78

Ocean Pond

0

Okahumpka

100

95

8.8

16.6

11.9

Orange

79

Patrick

93

42

0.4

1.3

1.1

Rowell

43

10

0.3

0.0

0.4

Stella

39

Tomohawk

43

12

0.5

1.0

1.4

Townsend

65

Watertown

7

1

0.0

0.0

1.0

Wauberg

0

1

11.2

4.4

12.1

Wildcat

2

47

Table 2c. Summary morphometric data for survey lakes. Sources
of data were Canfield (unpub. data) and Florida
Lakes Data Base.

Lake

Mean

Lake

Shoreline

Shoreline

depth

area

length

develop-
ment

Alligator

1.1

137

5.3

1.3

Apopka

1.6

12412

54.9

1.4

Bonny

2.0

143

6.4

1.5

Carr

1.9

254

5.1

Catherine

3.2

41

4.5

2.0

Clay

2.3

5

0.9

1.2

Crooked

2.3

8

2.0

2.0

Deep

3.0

4

1.6

2.3

Fairview

163

Harris

4.0

5580

61.3

2.3

Hartridge

3.4

176

5.5

1.2

Keys Pond

2.9

5

1.0

1.3

Lindsey

2.2

55

3.2

1.2

Live Oak

3.0

152

5.0

1.1

Lochloosa

1.8

2309

22.6

1.3

Loften Ponds

2.6

5

2.0

2.6

Moore

2.9

28

1.8

Mystic

19

Ocean Pond

722

Okahumpka

394

Orange

1.8

5142

Patrick

1.8

159

4.65

1.04

Rowell

1.3

147

5.18

1.21

Stella

Tomohawk

4.4

15

4.01

2.92

Townsend

1.5

44

3.58

1.52

Watertown

3.8

19

1.64

1.06

Wauberg

3.6

100

8.35

2.36

Wildcat

V

94

48

found to be highly correlated with specific conductance and pH
(Table 3, Fig. 1). Macrophyte variables were not significantly
correlated with water chemistry or morphometric variables, except
for percent-area coverage, that had a significant negative correlation
with TSI(AVG) (Fig. 2) and log-transformed total P (Table 3).

Two hundred twenty-three diatom taxa were found in the recent
sediments of the survey lakes (Appendix 1). Initially, 125 taxa had
life-form preferences described in the literature (Lowe 1974, Patrick
and Reimer 1966-1975), but the average percentage of valves with
unknown life-form preference was 15.04%. Life-form preferences
were assumed for 28 additional taxa (Appendix 1) based on valve
morphology. Taxa with a raphe that belonged to genera known to be
largely periphytic were assumed to have a periphytic life-form
preference. Two small species of Cyclotella and the long, lineate
Nitzschia romana and Synedra filiformis var. exilis were assumed to
be tychoplanktonic. These assumptions were discussed with Rex L.
Lowe (pers. comm.), who believed them to be correct. The new life-
form assignments reduced the mean percentage of valves with
unknown life-form preference in survey samples to 2.03%.

TSI(AVG) and pH were negatively correlated with the proportion
of periphytic diatoms (Fig. 3) and positively correlated with the
proportion of planktonic diatoms (Fig. 4) in surficial samples (Table
4). Diatom concentrations and accumulation rates were found to
vary as much as 4 orders of magnitude within the study lakes. Log-
transformed accumulation rates produced more significant
correlation coefficients with environmental variables than

f V

49

Table 3. Pearson product-moment correlation coefficients between
water quality and macrophyte variables for survey
lakes. * = p < 0.05.

correlation coefficient

prob. > 1 r 1 under Ho: rho

= 0

sampl

e size

log 10

TSI(AVG)

pH

Specific

total P

conductance

Percent

*-0.558

*-0.518

-0.185

-0.298

area

0.007

0.014

0.410

0.177

coverage

22

22

22

22

Percent

-0.169

-0.131

0.136

-0.212

volume

0.380

0.497

0.482

0.269

infestation

29

29

29

29

Floating-

0.124

0.134

0.072

-0.194

leaved

0.582

0.552

0.750

0.388

biomass

22

22

22

22

Submerged

-0.252

-0.219

0.033

-0.136

biomass

0.258

0.327

0.883

0.546

22

22

22

22

Emergent

-0.124

-0.121

-0.253

-0.251

biomass

0.582

0.592

0.256

0.260

22

22

22

*0.679

*0.713

*0.755

pH

<0.001

<0.001

<0.001

29

29

2 9

Specific

*0.493

*0.569

conductance

0.007
29

0.001
29

Table 3 cont'd.

50

<

correlation coefficient

prob.

> 1 r 1 under Ho:
sample size

rho = 0

Lake

Mean

Shoreline

Shoreline

surface

depth

length

develop.

Percent

-0.322

-0.137

-0.314

-0.173

area

0.145

0.553

0.165

0.452

coverage

22

21

21

21

Percent

-0.106

-0.396

-0.230

-0.401

volume

0.593

0.062

0.304

0.064

infestation

28

23

22

22

Floating-

-0.11

0.043

-0.084

0.184

leaved

0.626

0.852

0.717

0.426

biomass

22

21

21

21

Submerged

-0.2107

0.0739

-0.238

-0.007

biomass

0.347

0.750

0.299

0.976

22

21

21

21

Emergent

-0.165

-0.035

-0.175

0.168

biomass

0.462

0.880

0.449

0.467

22

21

21

21

0.343

-0.196

*0.508

-0.370

pH

0.074

0.371

0.016

0.090

28

23

22

22

Specific

*0.51

-0.216

*0.563

-0.241

conductance

0.005

0.322

0.006

0.281

28

23

22

22

51

y.3 -

•

8.5-

•

7.5-
« 6.5-

• •
•

• •
•

•

•

• •

• • •

•
•

5.5-
4.5-

•

•
•

•

•••

•

•

•

3.5-

1

1

r = 0.71
1 • 1 '

0

20 40

60 80 100

TSI(AVG)

Figure 1. Plot of pH versus TSI(AVG) for 29
lakes in synoptic survey.

52

a

u

>

o

a;

u

100-

•

^ M* r = -0.56

80-

•

•

•

60-

•

•

40-

. •. •

• •

20-

0-

« 1

• •
•

•
— 1 — 1 1 1 1 1 1 1—* 1 1

3.5 -3.0 -2.5
Log

10

2.0 -1.5 -1.0 -0.5 0.0
limnetic total P

Figure 2. Plot of percent area coverage
versus log-transformed limnetic
total P for 29 survey lakes.

53

a

1.00-

•

o

**

• •

03

0.90-

0.80-

>*

A

0.70-

«

&•

0.60-

o

fl

0.50-

0

»m>

0.40-

a
e
u
An

0.30

T ' r

40 60 80

TSI(AVG)

Figure 3. Proportion of diatom assemblage
that periphyton represent versus
TSI(AVG) for 29 lakes in survey.

100

54

u- ■

B

o

-5

o

a
o

■*^

u
o
o.

o

L.

u.o -

•

r

= 0.57

•

•
•

0.5-

•

•

^■■■■

0.4-

•

?

0.3-

•

•
•

•

0.2-

•

• •

• •

0.1-

•

•

• •

• •
% •

•

0.0-

■ ,

* r-

1

1 ■ 1

0

20

80

40 60

TSI(AVG)

Figure 4. Proportion of diatom assemblage
that plankton represent versus
TSI(AVG) for 29 lakes in survey.

100

55

Table 4. Correlation coefficients for diatom variables, which are
based on proportions, sedimentary concentrations and
annual accumulation rates of periphyton and plankton,
with water quality, macrophyte and morphometric
variables. Keys Pond was removed from periphyton
concentration data because of an anomalously high diatom
concentration value (see Fig. 5). * = p < 0.05.

correlation

coefficient

prob. > 1 r 1

under Ho: rho = 0

sample size

log

log

log

PERI-

PLNK-

PERl-

PLNK-

PERI-

PLNK-

PROP

PROP

CONC

CONC

ACCM

ACCM

logio

*-0.610

*0.613

*0.410

*0.644

0.377

*0.630

total P

<0.001

<0.001

0.030

<0.001

0.053

<0.001

29

29

28

29

27

27

*-0.566

*0.573

*0.507

*0.656

0.347

*0.578

TSI(AVG)

0.001

0.001

0.006

<0.001

0.076

0.002

29

29

28

29

27

27

*-0.404

*0.402

0.309

*0.492

0.300

*0.517

pH

0.030

0.031

0.109

0.007

0.130

0.006

29

29

28

29

27

27

Specific

-0.223

0.215

0.204

*0.387

*0.392

*0.480

conductance 0.244

0.262

0.298

0.038

0.041

0.011

29

29

28

29

27

27

Percent-

*0.445

*-0.438

-0.220

-0.353

-0.180

-0.280

area

0.038

0.042

0.339

0.107

0.422

0.206

coverage

22

22

21

22

22

22

Percent-

0.258

-0.243

-0.105

-0.186

-0.280

-0.203

volume

0.176

0.204

0.596

0.333

0.158

0.311

infestation

29

29

28

29

27

27

56

Table 4 cont'd.

correlation

1 coefficient

prob. > 1 r 1

under Ho: rho

. = 0

sample size

log

log

log

PERI-

PLNK-

PERI-

PLNK-

PERI-

PLNK-

PROP

PROP

CONC

CONC

ACCM

ACCM

Floating-

-0.097

0.126

0.221

0.088

*-0.205

0.019

leaved

0.668

0.576

0.336

0.695

0.360

0.932

biomass

22

22

21

22

22

22

Submerged

0.193

-0.173

-0.106

-0.140

-0.311

-0.137

biomass

0.391

0.440

0.647

0.534

0.159

0.543

22

22

21

22

22

22

Emergent

0.165

-0.143

0.028

-0.005

-0.278

-0.113

biomass

0.463

0.525

0.904

0.982

0.211

0.616

22

22

21

22

22

22

Mean

-0.195

0.169

-0.096

-0.059

-0.246

-0.108

depth

-0.372

0.440

0.669

0.789

0.270

0.633

22

22

22

22

22

22

Shoreline

-0.372

0.371

0.131

0.337

0.155

0.315

length

0.088

0.089

0.572

0.125

0.492

0.154

22

22

21

22

22

22

Shoreline

-0.002

-0.013

-0.273

-0.272

-0.413

-0.330

develop-

0.992

0.955

0.232

0.220

0.056

0.133

ment

22

22

21

22

22

22

57

untransformed values did. Table 4 shows that concentrations and
log-transformed accumulation rates of both periphyton (Fig. 5) and
plankton (Fig. 6) were positively correlated with TSI(AVG). pH, an
important correlate of TSI, was found to be negatively correlated
with the proportion of periphyton and positively correlated with the
proportion of plankton in recent sediments of survey lakes. pH was
also positively correlated with the log-transformed concentrations
and accumulation rates of plankton. Chi a, total N and Secchi depth
values used to calculate TSI(AVG) are shown in Appendix 4.
Appendix 6 lists the proportions, sedimentary concentrations and
accumulation rates of periphyton and plankton in the survey lakes.

The only macrophyte variable that demonstrated significant
correlation coefficients with diatom life-form variables was percent-
area coverage, which was positively correlated with the proportion of
periphyton, and negatively correlated with the proportion of
plankton (Table 4). Percent-area coverage was also negatively
correlated with the concentration of planktonic diatoms. Coefficients
of determination indicate that the 3 diatom life-form variables
correlated with percent-area coverage would each explain only 19-
24% of the variance in that variable. Percent loss on ignition and
organic matter accumulation rates (Table 5) were not significantly
correlated with any of the water chemistry or macrophyte variables.

Appendix 2 lists the 47 taxonomic groupings that were used in
multivariate analyses. Individual taxa were combined into groupings
based on taxonomic and ecological affinities, and selected for
multivariate analyses based on the results of plots of their
percentages versus macrophyte variables.

tl

,/■''

X r-

58

0)

E

o

^^

(0

Q.

<D

Q.

c CO
o

(0

c

Q>
O

c
o
o

8
_ 7
>, 6

"O

o> 5

(0

>
o 3-

^ 2-

1 -

0

(Keys Pond)

r = 0.51

• •

•••

2 0 4 0 6 0

TSI(AVG)

8 0

Figure 5. Concentration of periphytic
diatoms in surface sediments
versus TSI(AVG) for 29 lakes.

1 00

,*w^;*^'>i

59

f »

E

o

•mm

o
c

CO

B
O

u

c
o

U

6-

5-

4-

3-

-H 2-

1-

r = 0.52

20

Figure 6.

40 60

TSI(AVG)

80

100

Concentration of planktonic
diatoms in surface sediments
versus TSK AVG) for 29 lakes.

60

Table 5. Percent loss on ignition, bulk sediment accumulation
rates, and organic mater accumulation rates for lakes
in synoptic survey.

% Loss on

bulk sediment

Organic matter

Lake

ignition

accumulation

accumulation

@ 550 °C

(g cm-2 yr-1)

(g cm-2 yr-1)

Alligator

59.14

0.07

0.04

Apopka

64.10

0.09

0.06

Bonny

53.46

0.03

0.02

Carr

26.53

0.02

0.01

Catherine

55.21

0.03

0.02

Clay

43.98

0.03

0.01

Crooked

50.11

0.02

0.01

Deep

69.65

0.10

0.07

Fairview

8.20

0.22

0.02

Harris

80.93

0.04

0.03

Hartridge

53.56

0.05

0.03

Keys Pond

30.49

0.02

0.01

Lindsey

17.12

0.03

0.01

Live Oak

86.06

0.16

0.13

Lochloosa

36.10

0.04

0.01

Loften Ponds

33.60

0.02

0.01

Moore

17.21

0.04

0.01

Mystic

Ocean Pond

39.00

0.10

0.04

Okahumpka

67.10

0.06

0.04

Orange

Patrick

44.30

0.25

0.11

Rowell

69.00

0.31

0.21

Stella

14.00

0.17

0.02

Tomohawk

63.00

0.03

0.02

Townsend

79.60

0.04

0.03

Watertown

60.30

0.07

0.04

Wauberg

72.40

0.04

0.03

Wildcat

61

Results of Cluster Analyses
Cluster analysis based on the percentage data for 47 taxonomic
groups produced 16 clusters of taxa. Nine of these clusters
^K demonstrated significant correlation coefficients with TSI(AVG), and

i^ ' six clusters showed significant correlations with pH (Table 6). Four

clusters that were correlated with TSI(AVG) and pH also showed
significant correlations with specific conductance. Three clusters
were correlated with both lake surface area and shoreline length.
Mean depth and shoreline development were each correlated with
single separate clusters. Of the 80 correlation coefficients between
diatom clusters and macrophyte variables, only two were significant,
one with percent-volume infestation, and one with emergent
biomass. Navicula papula vars. (S-NAVPU) and Stauroneis spp. (S-
STAU) were the taxonomic groups in the cluster correlated with
percent-volume infestation, and these were included among the pool
of independent variables (Appendix 3.1) in a stepwise procedure to
construct a model that predicts percent-volume infestation. Caloneis
sp. A and Navicula seminulum vars. (S-NAVSEM) were the taxonomic
groups correlated with emergent biomass, but this correlation was
driven by a single datum because both species showed anamalously
high percentages in Crooked Lake.

Due to the overwhelming influence TSl(AVG) and pH had on
composition of taxonomic clusters, cluster analysis using taxonomic
percentages was repeated while partialling out the effects of pH and
TSI(AVG). Fourteen diatom clusters resulted from this procedure.
Seven of the clusters still demonstrated a significant correlation with
TSI(AVG), and six of these had significant correlations with pH. Two

62

Table 6. Pearson product-moment correlation coefficients for

clusters based on percentage data for diatom taxonomic
groups with macrophyte and water quality variables.
* = p < 0.05.

correlation coefficient

prob. > 1 r 1 under Ho: rho

= 0

sample

size

CLUSl

CLUS2

CLUS3

CLUS4

CLUS5

*-0.691

0.163

*0.547

-0.173

*0.528

TSI(AVG)

<0.001

0.397

0.002

0.368

0.003

29

29

29

29

29

*-0.692

*0.322

*0.409

-0.356

*0.389

pH

<0.001

0.009

0.0276

0.058

0.037

29

29

29

29

29

Specific

*-0.531

0.212

0.218

-0.253

0.154

conduc-

0.003

0.269

0.257

0.185

0.426

tance

29

29

29

29

29

Percent-

0.145

-0.084

-0.358

0.193

-0.271

area

0.519

0.710

0.102

0.388

0.215

coverage

22

22

22

22

22

Percent-

-0.124

-0.083

-0.194

-0.165

-0.120

volume

0.521

0.671

0.313

0.392

0.536

infestation

29

29

29

29

29

Floating-

-0.149

-0.116

-0.092

-0.019

0.408

leaved

0.508

0.607

0.685

0.934

0.060

biomass

22

22

22

22

22

Submerged

-0.024

0.121

-0.279

-0.113

-0.106

biomass

0.916

0.591

0.209

0.617

0.640

22

22

22

22

22

Emergent

-0.045

-0.096

-0.233

0.404

0.040

biomass

0.843

0.670

0.297

0.062

0.859

22

22

22

22

22

Table 6 cont'd.

63

correlation coefficient

prob. > 1 R 1 under Ho: rho

= 0

sample size

CLUS6

CLUS7

CLUS8

CLUS9

CLUSIO

-0.081

*0.485

*0.387

-0.101

-0.044

TSI(AVG)

0.677

0.008

0.038

0.603

0.819

29

29

29

29

29

-0.124

♦0.545

0.304

*0.381

0.250

pH

0.521

0.002

0.109

0.041

0.191

29

29

29

29

29

Specific

-0.150

*0.415

0.209

*0.392

0.335

conduc-

0.437

0.025

0.276

0.036

0.075

tance

29

29

29

29

29

Percent-

-0.058

-0.006

-0.296

0.226

-0.018

area

0.797

0.980

0.180

0.312

0.935

coverage

22

22

22

22

22

Percent-

-0.009

0.276

-0.151

0.107

0.018

volume

0.963

0.147

0.435

0.582

0.927

infestation

29

29

29

29

29

Floating-

0.1768

0.213

-0.225

-0.249

-0.103

leaved

0.431

0.341

0.312

0.262

0.645

biomass

22

22

22

22

22

Submerged

0.059

0.362

-0.190

-0.097

-0.225

biomass

0.793

0.097

0.396

0.666

0.313

22

22

22

22

22

Emergent

*0.724

0.0731

-0.251

-0.109

-0.259

biomass

<0.001

0.746

0.259

0.626

0.245

22

22

22

22

22

W*^.''

64

■':■■■'*"' '

Table 6 cont'd.

correlation coefficient

prob,

, > 1 R 1 under Ho: rho

= 0

■ ^'^ • , 'j^ ./'•

:

sample size

4' •/-■'..

CLUSll

CLUS12

CLUS13

CLUS14

CLUS15

CLUS16

■ .i

... .^

i 1 '

*-0.471

0.006

*0.413

0.205

*-0.4032

*-0.368

'} J^.

TSI(AVG)

0.009

0.971

0.025

0.284

0.0301

0.048

=• •*"

, ■ y .' '

29

29

29

29

29

29

*-0.515

0.112

0.318

0.142

-0.335

-0.330

PH

0.004

0.559

0.092

0.459

0.075

0.079

29

29

29

29

29

29

Specific

-0.310

-0.208

0.201

0.363

*-0.398

-0.331

conduc-

0.100

0.278

0.295

0.052

0.032

0.078

tance

29

29

29

29

29

29

Percent-

0.097

0.246

*-0.462

-0.190

0.303

0.334

area

0.667

0.268

0.030

0.396

0.170

0.128

coverage

22

22

22

22

22

22

Percent-

-0.185

*0.439

-0.227

-0.096

0.341

0.265

volume

0.335

0.017

0.234

0.618

0.063

0.163

infestation

29

29

29

29

29

29

Floating-

-0.099

-0.015

0.004

0.070

0.227

-0.175

-.■r '■■, .-

leaved

0.659

0.947

0.985

0.755

0.309

0.435

biomass

22

22

22

22

22

22

Submerged

0.140

-0.043

-0.001

0.080

0.262

0.008

biomass

0.532

0.847

0.995

0.722

0.237

0.968

22

22

22

22

22

22

Emergent

-0.011

0.021

0.044

-0.038

0.363

-0.003

biomass

0.958

0.925

0.843

0.864

0.096

0.989

22

22

22

22

22

22

65

of the three clusters that were significantly correlated with specific
conductance were also correlated with TSI(AVG) and pH. Among the
morphometeric variables, two clusters were signifcantly correlated
with mean depth, and one cluster was correlated with shoreline
development. Two clusters were significantly correlated with both
lake surface area and shoreline length. Of the 70 correlation
coefficients between macrophyte variables and diatom clusters, only
three correlation coefficients were significant. One cluster that was
composed of Caloneis sp. A and Navicula seminulum vars.
demonstrated a significant correlation with emergent biomass as in
the unpartialled analysis, and this again appeared spurious because
the correlation was driven by anomalously high percentages of both
taxa in Crooked Lake. The second cluster, which consisted of
Anomoeneis spp. (S-ANOM), Gomphonema spp. (S-GOMA), Navicula
pupula vars., and Stauroneis spp. (S-STAU), was correlated with both
percent-area coverage and percent-volume infestation. Taxonomic
groups in this cluster that showed a directional change in abundance
over the range of percent-area coverage and percent-volume
infestation were included among the independent variables in the
stepwise regression procedures to predict these macrophyte
variables (Appendix 3.1 and 3.4).

Cluster analysis based on sedimentary concentrations of diatoms
produced 12 clusters, 4 of which were significantly correlated with
TSI(AVG), and 5 of which were correlated with pH. The
morphometeric variables lake-surface area and shoreline length
were each significantly correlated with 2 separate clusters. Of 60
correlation coefficients between diatom clusters and macrophyte

l'..:X.

%'■ :-.

v.fv

i :■;■■«•

66

variables, 4 were significant. A cluster that was composed of the
taxon C alone is sp. A was significantly correlated with emergent
biomass, but this correlation was driven by a single datum point
because of the unusually high percentage of this taxon in Crooked

|(^J '. Lake. Another cluster consisted of Fragilaria brevistriata and

Gomphonema spp. (S-GOMA) and was signifcantly correlated with
percent-volume infestation. This correlation did not appear

^^fe : meaningful because F. brevistriata was represented by only two data
points, and G. spp. was correlated with percent-volume infestation
only because of anamalously high values in Lake Carr. In addition, F.
brevistriata and G spp. showed different slopes over the range of
percent-volume infestation. A third cluster, which seemed

#; spuriously correlated with emergent biomass, consisted of two

euplanktonic and two periphytic taxa. Asterionella spp. (S-AST) and

vxv Aulacoseira islandica were the two euplanktonic taxa in this cluster

,/; and they showed high values only in Crooked Lake. Tabellaria

flocculosa and T. fenestrata were the periphytic taxa in this cluster
and their slopes were different over the range of emergent biomass.

v|v A fourth cluster was positively correlated with floating-leaved

biomass (r = 0.554, p = 0.008, n = 22) and contained 9 taxonomic
groups (Appendix 3.7), seven of which were euplanktonic. These
taxa were used as independent variables in a stepwise regression
procedure to predict floating-leaved biomass.

When the clustering procedure was repeated partialling out the
effects of TSI(AVG) and pH, a cluster composed of the 7 euplanktonic
taxa last mentioned was again positively correlated with floating-
leaved biomass. Four clusters were still significantly correlated with

,-'-V-

67

TSI(AVG) and 5 were correlated with pH. Eunotia spp. (S-EUN) and
Gomphonema spp. (S-GOMA) composed a cluster that was
significantly correlated with percent-volume infestation, but plots
revealed that both of these taxonomic groups had low sedimentary
concentrations except for unusually high concentrations in Lake Carr.
Emergent biomass was significantly correlated with a cluster
composed of Asterionella spp., Aulacoseira islandica, Tabellaria
flocculosa and T. fenestrata as it was prior to partialling covariant
effects. Another cluster composed of Caloneis sp. A and Nitzschia
capitellata was correlated with emergent biomass, but these taxa
demonstrated opposite slopes over the range of emergent biomass.
None of the 66 remaining correlation coefficients between diatom
clusters and macrophyte variables was significant.

Cluster analysis of diatom taxonomic groups based on annual
diatom accumulation rates produced 11 clusters. Four of these
clusters were significantly correlated with TSI(AVG), and three of
these plus a fourth cluster were significanly correlated with pH. Four
clusters were correlated with shoreline length and two clusters were
correlated with specific conductance. Of the 55 correlation
coefficients between diatom clusters and macrophyte variables, only
one correlation coefficient was significant. Floating-leaved biomass
was found to be negatively correlated with a cluster composed of 4
periphytic and 3 planktonic taxonomic groups (Appendix 3.8). These
diatom groups were used in a stepwise regression procedure to
predict floating-leaved biomass.

The last cluster analysis was based on diatom accumulation rates
with the effects of TSI(AVG) and pH partialled out, and it produced

68

12 clusters of diatom taxa. TSI(AVG), pH and specific conductance
had 3 significant correlations each with diatom clusters. Shoreline
development and lake surface area were correlated with 2 clusters
each, and mean depth was correlated with one cluster. Of 60
correlation coefficients related to the macrophyte variables, only two
were statistically significant. Seven of the 9 taxa in one cluster that
was correlated with floating-leaved biomass were the same taxa
used in the stepwise multiple regression procedure described above
for unpartialled effects of TSI(AVG) and pH. Another cluster
consisted of Fragilaria crotonensis, a euplanktonic taxon occurring in
lakes high in water-column nutrients, that had an apparently
spurious correlation with floating-leaved biomass.

Results of Principal Components Analyses
Principal components analysis seems to be an appropriate
indirect ordination method to apply because species distributions
were observed to be linear or curvilinear, though not modal, over the
range of percent-area coverage and percent-volume infestation.
Correlation coefficients were examined between the first 8 principal
components based on percentage data for 47 diatom taxonomic
groups and environmental variables. Eigenvalues, which are equal to
the variances of the components, indicate that the first 3 principal
components account for 15.9%, 8.5% and 7.2% of the variance in
diatom assemblages, respectively. The first principal component was
found to be highly correlated with TSI(AVG), pH and specific
conductance (Table 7), indicating that these environmental variables
were responsible for most of the variance in the diatom assemblages.

69

Table 7. Correlation coefficients for principle components based on
diatom percentage data with water quality and
macrophyte variables. * = p < 0.05.

correlation coefficient

prob. > 1

r 1 under Ho: rho
sample size

= 0

1st.

2nd.

3rd.

1st.

principle

principle

principh

; principle

comp.

comp.

comp.

comp.
TSI(AVG) and
pH partialled out

Percent

-0.556

0.130

*0.395

-0.134

volume

0.774

0.500

0.034

0.653

infestation

29

29

29

29

Percent

-0.322

0.132

0.298

-0.171

area

0.144

0.557

0.176

0.444

coverage

22

22

22

22

Floating-

0.102

0.064

-0.146

0.086

leaved

0.649

0.776

0.515

0.703

biomass

22

22

22

22

Submerged

-0.108

-0.163

0.168

-0.185

biomass

0.632

0.467

0.453

0.409

22

22

22

22

Emergent

-0.144

0.114

0.082

-0.081

biomass

0.521

0.612

0.714

0.720

22

22

22

22

*0.753

-0.023

0.029

*0.414

TSI(AVG)

<0.001

0.901

0.880

0.026

29

29

29

29

f 3»»*x,

Table 7 cont'd.

70

correlation coefficient
prob. > 1 r 1 under Ho: rho = 0
sample size

1st.

2nd.

3rd.

1st.

principle

principle

principli

s principle

comp.

comp.

comp.

comp.
TSI(AVG) and
pH partialled out

♦0.775

-0.132

*0.374

*0.411

pH

<0.001

0.494

0.045

0.026

29

29

29

29

Specific

*0.539

-0.283

*0.373

0.187

conductance

0.003

0.135

0.045

0.330

29

29

29

29

Mean

-0.170

*-0.437

-0.154

-0.105

depth

0.437

0.037

0.481

0.633

23

23

23

23

Shoreline

0.200

*-0.457

0.446

-0.192

length

0.369

0.032

0.037

0.390

22

22

22

22

Lake

0.150

*-0.454

0.314

-0.260

surface

0.445

0.015

0.103

0.180

area

28

28

28

28

71

The second principal component was significantly correlated
with with the morphometric variables mean depth, shoreline length
and lake surface area. The third principal component was
significantly correlated with pH, specific conductance and percent-
volume infestation. A coefficient of determination indicates that the
third principal component would explain 16.0% of the variance in
percent-volume infestation, which is not sufficiently robust for
predictive purposes. None of the remaining 60 correlation
coefficients between principal components 4-8 and the
environmental variables was statistically significant.

Principal components analysis of percentage data was repeated
while TSI(AVG) and pH were partialled out. The first principal
component, which accounted for 11.8% of the variance in diatom taxa
still showed significant correlations with TSI(AVG) and pH (Table 7).
The second principal component explained 9.5% of the variance in
the diatom assemblages and was significantly correlated with mean
depth. The third principal component explained 8.6% of the variance
in the diatom assemblages and was not correlated with any of the
macrophyte or environmental variables considered. The fourth
principal component explained 7.5% of the variance, and was
negatively correlated with floating-leaved biomass (r = -0.442, p =
0.040, n = 22). A model based on this principal component would
explain approximately 19.5% of the variance in floating-leaved
biomass. None of the correlation coefficients between principal
components 5-8 and the environmental variables was significant.

Principal components analysis was performed on sedimentary
diatom concentrations for the 47 diatom taxonomic groups. The first

72

principal component explained 18.3% of the variance and was
significantly correlated with TSI(AVG) (r = 0.648, p < 0.001, n = 29)
and pH (r = 0.499, p = 0.006, n = 29). The second principal component
explained 10.3% of the variance in the diatom assemblages and was
not significantly correlated with any of the environmental or
macrophyte variables. The third principal component explained 9.9%
of the variance in the diatom assemblages. This component had a
significant negative correlation with floating-leaved biomass (r = -
0.536, p = 0.010, n = 22), and a positive correlation with pH (r =
0.400, p = 0.032, n = 29) and shoreline length. Scores obtained for
survey lakes using eigenvectors of the third principal component
(Table 8) were used to construct the following model:

floating-leaved biomass = 2.419 - 0.750(PRIN3) 3.1

R2 = 0.287, p = 0.010, n = 22
where PRIN3 is the sum of the products between eigenvectors and
sedimentary concentrations of the 47 diatom groups.
The majority of the taxa in Table 8 that show large, positive
eigenvectors (e.g. Fragilaria construens, F. pinnata, Navicula
lanceolata, N. pupula and vars., A^. radiosa and vars., A^. cuspidata,
Nitzschia amphibia, N. capitellata, Cocconeis placentula var. lineata),
have a periphytic life form. Many of the taxa with smaller, negative
eigenvectors {e.g. Asterionella spp., Aulacoseira islandica,
Cyclostephanos dubius, Fragilaria crotonensis) have a euplanktonic
life form. It appears that samples with large numbers of periphytic
diatoms and few planktonic diatoms would have large values of
PRIN3, and it would be reasonable to expect that large PRIN3 values
would be associated with greater floating-leaved biomass. Equation

73

Table 8, Eigenvectors of third principle component based on
sedimentary concentrations of 47 diatom taxonomic
groups. Taxonomic acronyms are defined in Appendix 2.

Taxonomic

Eigenvector

Taxonomic

Eigenvector

group

group

S-ACH

0.081

NAVGOT

0.061

S-ANOM

0.089

NAVLAN

0.261

S-AST

-0.177

S-NAVPU

0.209

AULAAM

0.186

S-NAVRA

0.320

AULADIS

-0.025

S-NAVSEM

0.192

S-AULAGR

-0.096

NAVSUBT

0.122

AULAISL

-0.141

NITZAM

0.235

AULAITAL

0.039
-0.013

NirZCAP

0.195

CALSPA

NirZFONT

-0.139

COCPLACL

0.138

NIlZhRUS

0.193

CYCMhN

0.124

NITZPAL

-0.128

CYCPSHUD

-0.023

SPIN

0.109

CYCXIH,

0.013

S-STAU

0.101

CYCSIKI O

0.005

S-SUR

-0.037

S-CYM

0.034

SYNDEI,

0.269

CYSIEPDU

-0.177

SYNEn EX

0.123

S-EP

0.035

S-SYNRUM

-0.014

S-EUN

0.007

TABhEN

-0.098

ACTPUNC

-0.075

NAVCUS

0.137

NAVCONF

0.115

TABFLOC

-0.168

S-NEI

0.103

ERAGBREV

0.069

S-ERAGCO

0.298

FRAGCROT

-0.166

ERAGPIN :

0.225

S-FRUSRH

0.096

S-GOMA

0.006

^^'

74

3.1 shows a negative coefficient for PRIN3, however, which is
contrary to the expectation that periphytic taxa would demonstrate
greater representation in lakes with greater floating-leaved biomass.

Partial correlation coefficients showed that the 3rd principal
component was significantly correlated with floating-leaved biomass
(r = -0.618, p = 0.003, n = 22) when the effect of pH was held
constant. Despite the non-significant correlation between pH and
floating-leaved biomass (Table 3), partial correlations showed that
pH was significantly correlated with the 3rd principal component (r =
0.521, p = 0.016, n = 29) when floating-leaved biomass was held
constant. This implies that the model predicting floating-leaved
biomass is confounded by the effect of pH, and should only be
applied historically when it can be demonstrated that significant
changes in pH have not occurred.

When the principal components analysis based on the
sedimentary concentration of diatom groups was repeated partialling
out the effects of TSl(AVG) and pH, the first principal component
explained 16.0% of the variance in the diatom assemblages and was
positively correlated with floating-leaved biomass (r = 0.460, p =
Uj\ 0.031, n = 22) and with TSI(AVG). This correlation with floating-
leaved biomass was less robust than in the previous correlation with
, unpartialled effects. None of the remaining principal components in

the partialled analysis had significant correlations with macrophyte
variables.

The principal components procedure was repeated using log-
transformed sedimentary diatom concentrations. The first principal
component accounted for 30.4% of the variance in the diatom

7 5

'. i' \'

assemblage and demonstrated stronger correlations with TSI(AVG) (r
:^ ■ = 0.783, p < 0.001, n = 29) and pH (r = 0.885, p < 0.001, n = 29) than
r • the procedure with untransformed concentration data. None of the
correlation coefficients between principal components and
macrophyte variables were significant in this procedure, even when
the effects of TSI(AVG) and pH were partialled out.

Principal components analysis using annual diatom accumulation
rates produced a first principal component that explained 21.4% of
the variance and was again significantly correlated with TSI(AVG) (r
= 0.460, p = 0.012, n = 29), pH (r = 0.464, p = 0.011, n = 29), and
specific conductance (r = 0.501, p = 0.006, n = 29). The only
significant correlation coefficient involving a macrophyte variable
was with the fourth principal component that was significantly
correlated with percent-area coverage (r = -0.448, p = 0.036. n = 22)
and more strongly correlated with TSI(AVG) (r = 0.501, p = 0.006, n =
29). A predictive model using eigenvectors of this principal
component would not be useful for predicting percent-area coverage
because it would be confounded by TSI(AVG).

Principal components based on diatom accumulation rates with
the effects of TSI(AVG) and pH partialled out produced a single
significant correlation with a macrophyte variable. The sixth
principal component, which accounted for 5.5% of the variance in the
diatom assemblages, had a significant negative correlation with
floating-leaved biomass (r = -0.574, p = 0.005, n = 22). A predictive
model using the eigenvectors for this principal component would
explain 33.0% of the variance in floating-leaved biomass but could

76

only be applied historically to lakes that had not undergone changes
in trophic state or pH.

Results of Stepwise Multiple Regression
Percent-Volume Infestation

The maximum R2 method of the SAS STEPWISE procedure (SAS
Inst., Inc. 1985) was applied to percentage data for 17 diatom
taxonomic groups selected from plots of their abundance versus
percent-volume infestation (Appendix 3.1). The plot of Mallows' Cp
statistic versus the number of variables in each model is shown in
Figure 7. Models with larger Cp values have larger total error than
models with smaller Cp values (Daniel and Wood 1971). Models in
which Cp is larger than the number of independent variables plus
the intercept (p) are subject to bias error. Models with Cp values less
than p are subject to random errors. Figure 7 shows that the model
for predicting percent-volume infestation that consisted of one
diatom taxon (p = 2) showed substantial bias. All other multivariate
models predicting percent-volume infestation from percentage data
were subject to random error.

Stepwise regression to predict percent-volume infestation was
attempted by using sedimentary concentration data for 17 diatom
taxonomic groups (Appendix 3.2). All models demonstrated random
error. Better results were obtained when log-transformed
sedimentary concentrations were used in the stepwise procedure.
Figure 8 is the plot of Cp versus p for models using log-transformed
diatom concentrations. The model using 2 diatom taxonomic groups
(p = 3) shows bias. The model using 3 taxonomic groups (p = 4)

77

0

10

15

20

Figure 7. Mallows' Cp statistic versus p for
model predicting percent volume
infestation from percentage data.
P is number of dependent variables + 1,

■■■ *' i

78

Figure 8. Mallows' Cp statistic versus p for
model predicting percent volume
infestation from log-transformed
diatom concentrations.

79

shows a slight random error, but a lower total error term. The best
model for predicting percent-volume infestation (PVI) from
sedimentary concentrations of diatom groups is therefore:

PVI = 36.960 - 4.2171og(ACHEX) + 7.8331og(STAUPH) -

3.641 log(SYNFILEX) 3.2

R2 = 0.519, p < 0.001, n = 29
where values for the 3 taxa, whose acronyms are defined in
Appendix 1, are expressed in valves g-^ dry weight of sediment.
The adjusted R2 value for this model is 0.461.

The stepwise procedure was applied to log-transformed annual
accumulation rate values for 17 diatom taxonomic groups (Appendix
3.3). The best model utilized 2 diatom taxa and showed an R2 of
0.312. The adjusted R2 for this model was 0.259. The model was
less robust than the model based on log-transformed sedimentary
diatom concentrations.

Percent-Area Coverage

Eleven diatom taxonomic groups (Appendix 3.4) selected from
plots were used in the stepwise procedure to construct models
predicting percent-area coverage from diatom percentage data. The
best model, as determined from Cp values was:

percent-area coverage = 27.294 + 3.971(S-CYM) -i- 0.871(S-

FRUSRH) + 50.967(STAUPH) 3.3

R2 = 0.502, p = 0.005, n = 22

where values for the 3 taxonomic groups, which are defined in

Appendices 1 and 2, are expressed as a percentage of each diatom

80

assemblage. The adjusted R2 for this model was 0.419. This
multivariate model was significantly confounded with TSI(AVG) (R2 =
0.665, p < 0.001) and would therefore be unsuitable for predictive
purposes.

The stepwise procedure was applied to sedimentary diatom
concentration data for 8 taxonomic groups (Appendix 3.5). Cp
statistics indicated that the best model was;

PAC = 44.408 + 2.14 X 10-6(ACHLIN) - 2.7 x 10-7(S-AULAGR) +
9.53 X 10-5(EUNINC) + 1.91 x 10-5 (STAUPH) 3.4

R2 = 0.607, p = 0.002, n = 22, adj. R2 = 0.514.
Acronyms for taxonomic groups are defined in Appendices 1 and 2.

The best model produced by the stepwise procedure using log-
transformed sedimentary diatom concentration data showed an R2 =
0.422, that was less robust than the model based on untransformed
data.

The stepwise procedure was applied to annual diatom
accumulation rates for 11 diatom taxonomic groups (Appendix 3.6).
The procedure was also used for log-transformed accumulation rate
data of these taxa. The best model included 3 diatom taxonomic
groups and showed an R2 = 0.415 (p = 0.006). This model bore a
stronger relationship with TSI(AVG) (R2 = 0.480, p < 0.001), and was
therefore significantly confounded by this trophic state variable.

Floating-Leaved Biomass

Stepwise multiple regression was performed on sedimentary
concentration and log-transformed sedimentary concentration of

81

valves of 9 diatom taxonomic groups (Appendix 3.7) that were
correlated with floating-leaved biomass in the cluster analysis
procedure. All models that resulted from these stepwise attempts
demonstrated random errors. Stepwise regression was also
performed on the 7 diatom taxa in the annual-accumulation rate
cluster analysis that were correlated with floating-leaved biomass.
The best model, as indicated by the Cp statistic, included a single
diatom taxon, and this model explained only 28.7% of the variance in
floating-leaved biomass.

Better results were obtained by stepwise regression of 12
diatom taxonomic groups (Appendix 3.9) that were selected from
plots of percentage data for diatom taxa versus floating-leaved
biomass. The best model obtained was the following:

FLOATING = 5.105 - 3.4551oglO(ACHS) - 2.4931oglO(CYMMUEL)
+ 0.264(CYSTEPDU) - 0.281(S-NAVS) - 3.2971oglO(S-
NITZS) 3.5

R2 = 0.866, p < 0.001, n = 22, adj. R2 = 0.825

where FLOATING = floating-leaved biomass in kg wet mass m-2
ACHS = ACHEX + ACHLIN + ACHLINCU + ACHMIN
S-NAVS = NAVGOT + NAVLAN + S-NAVPU + S-NAVRA
-hNAVSUBTS
NITZS = NITZAM + NITZCAP + NITZFRUS.

The values for all taxonomic groups, whose acronyms are defined in

Appendices 1 and 2, are expressed as a percentage of the diatom

assemblages. This model appeared to have a significant relationship

with submerged biomass (R2 = 0.502, p = 0.034). Partial correlation

coefficients showed that floating-leaved biomass was significantly

correlated with the model (r = 0.882, p < 0.001, n = 22) when the

[ '■ 7 ;: .i.,.,: ;-i .1 - X' ■ ' .jp-

i

82

effect of submerged biomass was held constant. The model was not
significantly correlated with submerged biomass (r = 0.431, p =
0.051, n = 22), however, when the effect of floating-leaved biomass
was held constant. This indicates that the above model predicting
floating-leaved biomass is not confounded by the variable
submerged biomass. The correlation coefficient between submerged
biomass and the model, however, failed to be significant at the a =
0.05 level of significance by a marginal amount, and the model may
prove to be confounded in certain applications.

Submerged Biomass

Stepwise multiple regression was performed on percentage data
for 20 diatom taxonomic groups (Appendix 3.10) to predict
submerged macrophyte biomass. Cp statistic values indicated that
the best model contained 11 diatom taxon variables and explained
91.6% of the variance in submerged biomass (p < 0.001, n = 22). The
adjusted R2 for the model was 0.824. This model, however, also
explained 86.9% of the variance in floating-leaved biomass (p =
0.004, n = 22) and seems to be confounded by that variable. The
partial correlation coefficient between the model and submerged
biomass was significant (r = 0.960, p < 0.001) when the effect of
floating-leaved biomass was held constant. The partial correlation
coefficient between the model and floating-leaved biomass (r =
0.938, p < 0.001) was also significant when the effect of submerged
biomass was held constant. The model to predict submerged
biomass, therefore, is not a useful predictive model because it is
significantly confounded by floating-leaved biomass.

83

Sum of Floating and Submerged Biomass

Because submerged macrophyte biomass could not be separated
from floating-leaved biomass in predictive models, an attempt was
made to combine these macrophyte variables in a single predictive
model. Sixteen diatom taxonomic groups (Appendix 3.11) were
selected that appeared to show response to both of these macrophyte
variables. The stepwise multiple regression procedure was applied
to these groups and it produced the following best model:

FLOAT-SUB = 13.292 - 0.384(S-ACH) - 1.159(S-AULA) -

23.126(EUNPEC) + 0.921(S-FRUSRH) - 0.912(S-
NAVS) + 7.115(S-STAU) +2.492(EUNVAN) 3.6

R2 = 0.592, p = 0.042, n = 22

in which FLOAT-SUB = sum of floating-leaved and submerged

macrophyte biomass in kg wet mass m"2. Acronyms for the

taxonomic groups are defined in Appendices 1 and 2. The adjusted

R2 for this model was 0.389.

Emergent Biomass

The stepwise multiple regression procedure was performed on
percentage data of 17 diatom taxonomic groups (Appendix 3.12) to
predict emergent biomass. Cp values of all models with significant R2
values were much less than the p values, which indicated that these
models were subject to substantial random error.

Results of Canonical Correspondence Analysis
The canonical correspondence analysis option of CANOCO (ter
Braak 1987) produced eigenvectors (Table 9) ordinating the 47

84

Table 9. Eigenvectors for percentage data of 47 diatom taxonomic
groups in CANOCO Axisl constrained by percent-volume
infestation. Eigenvectors are presented in descending
^' #-. order. Taxonomic acronyms are defined in Appendix 2.

Taxonomic
group

Eigenvector

Taxonomic
group

Eigenvector

S-GOMA

1.78

AULAAM

-0.40

S-STAU

1.34

ACTPUNC

-0.44

S-NAVPU

1.32

NAVCUS

-0.45

ERAGPIN

1.20

NIIZAM

-0.52

S-FRAGCO

1.14

CYCPSEUD

-0.60

S-EUN

1.00

NITZCAP

-0.61

S-ANOM

0.77

S-AULAGR

-0.70

S-NAVSEM

0.54

AULAISL

-0.76

S-CYM

0.50

NAVSUBT

-0.76

TABEEN

0.45
0.43

S-SUR
AULAITAL

-0.81

NllZhRUS

-0.85

SEP

0.31

SYNDFl,

-0.90

S-ACH

0.10

AULADIS

-0.96

CYCSm.

0.06

NAVCONF

-0.96

TABFLOC

-0.02

CYSTEPDU

-0.99

S-SYNRUM

-0.02

CALSPA

-1.04

S-NAVRA

-0.06

SYNFILEX

-1.10

NllZhONT

-0.08

S-AST

-1.55

CYCMHN

-0.10

FRAGBREV

-1.55

NAVGOT

-0.10

FRAGCROT

-0.11

COCPLACL

-0.21

S-FRUSRH

-0.21

NAVLAN

-0.23

S-NEI

-0.26

CYrS'lMO

-0.27

SPIN

-0.32

NirZPAL

-0.35

•*;■

85

diatom taxonomic groups into an axis constrained by percent-volume
infestation. When axis scores for the 29 survey lakes were regressed
with percent-volume infestation values using the SAS GLM
procedure (SAS Institute, Inc. 1985), the following predictive
equation was obtained:

percent-volume infestation = 33.8+ 0.7(Axis 1) 3.7

r2 = 0.600, p < 0.001, n = 29
where Axis 1 is the sum of products of eigenvectors and percentages
of the 47 diatom taxonomic groups.

When canonical correspondence was invoked to produce
eigenvectors ordinating the 47 diatom taxonomic groups into an axis
constrained by percent-area coverage, CANOCO returned eigenvectors
ordinating 45 of the taxonomic groups (Table 10). Tabellaria
fenestrata and T. floccolosa may have been eliminated by CANOCO in
this ordination because of under-representation. Axis scores for the
22 survey lakes with percent-area coverage values were regressed
with percent-area coverage using the SAS GLM procedure (SAS
Institute, Inc. 1985). The resulting equation was:

percent-area coverage = 53.6 - 0.5(Axis 1) 3.8

r2 = 0.447, p < 0.001, n = 22
where Axis 1 is the sum of products of the percentages and
eigenvectors of the 45 diatom taxonomic groups shown in Table 10.

A New Predictive Model for Water-Column Total P
Because inferences from Whitmore's (1989) TSI(TP) predictive
model cannot detransformed to yield total P values for WCP (Canfield
et al. 1983a) estimates, a new model is presented here using the

86

Table 10. Eigenvectors for percentage data of 45 diatom taxonomic
groups in CANOCO Axisl constrained by percent-area
coverage. Eigenvectors are presented in descending
order. Taxonomic acronyms are defined in Appendix 2.

;■-:"..*

Taxonomic

Eigenvector

Taxonomic

Eigenvector

group

group

FRAGBREV

2.11

S-NEI

-0.17

FRAGCROT

1.49

S-ACH

-0.18

NAVCONF

1.47

S-PIN

-0.19

S-NAVPU

1.26

S-STAU

-0.25

CYCPSEUD

1.17

S-FRAGCO

-0.28

CYSTEPDU

1.16

NAVSUBT

-0.30

NITZPAL

1.15

CYCS'lELO

-0.31

CALSPA

1.06

FRAGPIN

-0.35

SYNFn.FX

1.01

S-SUR

-0.48

AULAAM

1.00

S-FRUSRH

-0.71

NllZhONT

0.88

S-ANOM

-0.73

S-AULAGR

0.86

S-GOMA

-0.84

AULAITAL

0.86

S-EP

-0.86

S-AST

0.85

S-EUN

-1.08

COCPLACL

0.84

S-CYM

-1.18

NIIZCAP

0.59

ACIPUNC

-1.29

NAVCUS

0.55

CYCMFN

0.53

NITZAM

0.53

AULAISL

0.43

S-NAVRA

0.42

NAVGOT

0.25

NAVLAN

0.21

S-SYNRUM

0.19

S-NAVSEM

0.18

CYCSTHL

-0.07
-0.10

NirZhRUS

SYNDH,

-0.15

AULADIS

-0.16

87

Oh
O

H

OX)

o

1.00-

0.75-

0.50-

0.25-

0.00-

-0.25 -

-0.50 -

-0.75 -

-1.00

'.*•.*

+* ++

»V

■I--*-

••

•t

o N-limited
+ balanced
• P-limited

— « 1 ' 1 ' 1 ' 1 ' 1 ' 1 ^

3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0
Log limnetic total P

10

Figure 9. Log-transformed total P versus
log-transformed TROPHl diatom
index for 51 lakes.

88

TROPH 1 diatom index (Whitmore 1989) to predict log-transformed
water-column total P. Figure 9 shows a plot of log-transformed
water-column total P versus the TROPHl diatom index for 51 Florida
lakes. The four nitrogen-limited lakes shown demonstrated
anomalously low diatom index values for their total P values, and
were removed from the regression data set. The resulting predictive
model was:

logio(total P) = -1.795 + 0.973(logio(TROPH 1)) 3.9

r2 = 0.807, p < 0.001, n = 47.

Assessing Confoundedness in the Water-Column Total P Predictive

Model
The significant negative correlation coefficient that was observed
between log-transformed total P and percent-area coverage (Table
11) confirmed the inverse relationship previously observed (Canfield
et al. 1984) between macrophyte presence and water-column
nutrient concentrations. The correlation coefficient between the log-
transformed TROPH 1 diatom index and percent-area coverage,
however, was not significant (Table 11). The model for predicting
water-column total P from the TROPH 1 diatom index is not
confounded by the macrophyte variable percent-area coverage.

Mv.. 8 9

Table 11. Correlation coefficients between TSI(TP), percent area

coverage and the log-transformed diatom index TROPH 1.
i * = p < 0.05.

correlation coefficient
prob. > 1 r 1 under Ho: rho = 0

n.> ^

sample size

Water-column
total
P

Percent-
area
coverage

log(TROPH 1)

*0.838

<0.001

29

-0.359
0.101

22

Percent-
area
coverage

*-0.537

0.010

22

••^■IV^i »-,'-?-

CHAPTER 4
DISCUSSION

Dominant Environmental Variables and Scale of Analysis

The purpose of this study was to identify diatom taxa that are
indicators of macrophyte presence in lakes. Despite an appropriate
sampling design, macrophytes were not found to be a primary
determinant of diatom assemblages. A trophic-state/pH/specific-
conductance environmental gradient was the principal influence on
the diatom communities.

I sampled lakes along a wide gradient of macrophyte abundance
in order to develop predictive models with broad application for
inferring historical macrophyte standing crop, and by necessity I
transgressed the trophic-state gradient that is present among Florida
lakes. A negative correlation exists between macrophyte abundance
and water-column Chi a (Canfield et al. 1984) in Florida lakes. I also
observed a negative correlation between percent-area coverage of
macrophytes and water-column total P in the present study. Lakes
that were low in percent-area coverage (< 10%) were high in water-
column nutrients, whereas several lakes that were high in percent-
area coverage (>80%) were low in water-column nutrients.

pH, trophic state and specific conductance were mutually
correlated in this study as in other studies of Florida lakes (Canfield

90

91

1981, Brenner et al. 1990, Whitmore 1989). The majority of Florida
lakes are soft-water, acidic and low in alkalinity (Brenner et al.
1990). Nevertheless, many lakes on phosphatic sands or carbonate-
rich bedrock are naturally high in productivity and dissolved solutes
(Canfield 1981). Because the present survey spanned a wide range
of limnological conditions, trophic state, pH and specific conductance
formed a dominant environmental gradient that emerged as the
principal determinant of diatom community composition.

Jackson and Charles (1988) observed the effect of a similar
environmental gradient on macrophyte distribution in the
Adirondack lakes of New York. They found that alkalinity, pH and
ionic composition were interrelated factors that determined the
distribution and species composition of aquatic vegetation. Jackson
and Charles stated:

We conclude that the chemical gradient underlying
compositional variation among our Adirondack softwater
sites is the tail end of a broad pH complex-gradient that
extends to highly alkaline waters. At the scale of
environmental variation observed in Adirondack lakes,
the main factors associated with vegetation variation are
pH, alkalinity, Ca, Mg, and perhaps Al. In regions where
the gradient is broader, or at least where the hardwater
portion is represented, conductivity and trophic status
become more important (Jackson and Charles 1988, p.
1456-1457).

Because Florida lakes exhibit a wide scale of variation from

softwater to hardwater conditions, the resulting chemical gradient

determined diatom community composition in the same manner that

the chemical gradient in Adirondacks lakes determined macrophyte

composition. The fact that diatom community composition was

92

determined mostly by trophic-state, pH, and specific conductance
rather than by macrophyte presence was thus the result of the scale
of analysis as discussed by Duarte and Kalff (1990). The dominant
effects of trophic state and pH may have overridden differences in
community structure that resulted from the influence of
macrophytes. In addition, most lakes in the survey were shallow
(mean depth < 3.0 m), and periphytic diatom communities may be
less specific about substrate types than previous qualitative studies
(e.g. Round 1956) have suggested.

Future studies might minimize error variance in macrophyte
predictive models by focusing on a calibration set of lakes that
covers a narrower range of trophic state and pH. This approach
would sacrifice generality but improve the precision of prediction for
lakes with a limited range of macrophyte standing crop. Duarte and
Kalff warn, however, that "There is no reason to expect that patterns
found at any one scale are transferable to other scales" (Duarte and
Kalff 1990, p. 362). A problem of scale arises when any predictive
model derived from a set of limnologically diverse lakes is applied
historically to a single lake that has remained comparatively constant
in character over time. Confidence intervals are inappropriately
large for historical predictions because fewer factors affect the error
variance within a single basin than within a calibration data set.

Negative Relationship Between Chi, a and Macrophytes

Lower water-column total P values may occur in lakes with high
macrophyte standing crop for the following possible reasons invoked
by Canfield et al. (1984) to explain macrophytic influence on Chi a:

93

1 ) The phytoplankton community competes with macrophytes,
especially floating macrophytes (e.g. Eichhornia), and their
associated epiphyton for dissolved nutrients in the water
column. High macrophyte standing crop could therefore depress
water-column nutrient concentrations;

2) Macrophytes minimize wind mixing and resuspension of
nutrients from bottom sediments leading to a reduction in
nutrient cycling.

Other possible explanations for the negative correlation include:

1 ) Rooted macrophytes may proliferate in lakes that are naturally
low in water-column total P concentrations because they don't
depend on the water for their nutrient supply. Most of the P
they utilize is derived from the sediments (Carignan and Kalff
1980);

2) When water-column nutrient concentrations are high,
phytoplankton and epiphyton standing crop increases and may
limit submerged macrophytes by shading (Sand-Jensen and
Sondergaard 1981).

Response of Periphyton to Water-Column Nutrients
TSI variables demonstrated positive correlations with the
proportion of planktonic diatoms, and negative correlations with the
proportion of periphytic diatoms in the survey lakes. This suggests
that planktonic diatom populations assume greater importance
relative to periphyton populations in lakes that are higher in trophic
state. The positive correlations between water-column total P and
the concentrations and log-transformed accumulation rates of
periphytic diatoms, however, show that even if plankton assume
greater inportance at higher water-column nutrient concentrations,
periphyton production also increases with increasing trophic state.

94

A positive response of periphyton biomass to higher water-
column nutrients has been demonstrated in previous experimental
studies and lake surveys (Stockner and Armstrong 1971, Ennis 1975,
Shortreed et al. 1984). Sand-Jensen and Sondergaard (1981) came to
the unusual conclusion that epiphyton biomass increased more than
phytoplankton biomass in response to increased nutrient loading.
Other studies, which appear less conclusive, have shown that
periphytic biomass may be unaffected or even decline with
increasing water-column P concentrations. Cattaneo (1987)
concluded that periphytic biomass showed a low and variable
response to water-column total P in his survey of 10 Canadian lakes,
but admitted that wave intensity may have altered periphytic
biomass on the surface of the large stones in shallow water where he
obtained his samples. Hansson (1988) found a weak, negative
correlation between log-transformed periphytic algal biomass, as
assessed from colonization rates on nylon net, and log-transformed
total P in 20 Swedish lakes.

Hansson speculated that in lakes of higher trophic state, plankton
may limit periphyton populations to some extent by shading.
Hansson noted, however, that cold temperatures in the subalpine
lakes of his survey may have affected colonization rates and led to
some underestimates of periphyton standing crop.

One study of periphyton and plankton communities in a Florida
lake demonstrated a strong negative relationship between the
biomass of these groups over a 5-month period (Hodgson et al.
1986). This negative correlation, however, reflected seasonal
variation in community dominance within a single, closed system.

95

In the present study, subfossil assemblages integrated over
several recent years showed a clear positive correlation between
periphyton production and trophic state for a limnologically diverse
set of Florida lakes. Log-transformed values of planktonic and
periphytic diatom concentrations were positively correlated (r =
0.643, p < 0.001, n = 28), as were log -transformed planktonic and
periphytic diatom accumulation rates (r = 0.717, p < 0.001, n = 26). A
positive response of both periphyton and plankton communities to
water-column nutrient concentrations was observed in another study
in Florida that examined sedimentary diatom concentrations and
accumulation rates along a trophic gradient (Whitmore in press).
Although phytoplankton and periphyton may be negatively
correlated within a lake (Hodgson et al. 1986), this interpretation is
also influenced by the scale of analysis: they are both positively
affected by water-column nutrient concentrations when considered
over a broad scale of trophic state.

Recommended Predictive Models for Macrophyte Variables
During construction of the predictive models in this study,
particular attention was paid to the potential covariant effects non-
target environmental variables might have on the predictive models.
Equation 3.1, for example, which predicted floating-leaved biomass,
was shown to be confounded by pH. Equation 3.3 was a multivariate
regression equation predicting percent-area coverage that was
confounded by TSI(AVG). In general, I do not recommend historical
application of diatom predictive models that might relate to more

y f-

96

than one dependent variable because of the risk of error caused by
unassessed changes in the covariable.

None of the multiple regression methods produced a useful
model for predicting emergent biomass. All stepwise multiple
regression models for emergent biomass were subject to random
error. The reason that diatom taxa related so poorly to emergent
vegetation might be that the stems of emergent vegetation do not
provide the large surface areas for periphyton attachment that
submerged and floating-leaved macrophytes provide. Surface area
has been shown to be a primary factor influencing epiphytic biomass
(Cattaneo and Kalff 1980). Plants with many small or finely-
dissected leaves and more lateral growth patterns such as Hydrilla,
Valisneria and Myriophyllum would, therefore, demonstrate clearer
relationships with periphytic biomass than would emergent
macrophytes having vertical growth forms. Hoyer and Canfield
(1986), in addition, have shown that the mean surface area to
biomass ratio of submerged plants is higher than for emergent
plants, which permits a greater biomass of periphyton to be
supported on submerged plants than on an equal weight of emergent
plants.

The best model for predicting percent-volume infestation was
equation 3.7 that was derived by canonical correspondence analysis
and explained 60% of the variance in that macrophyte variable.
Equation 3.2, a mutlivariate equation that was based on three diatom
taxa, also explained 52% of the variance in percent volume
infestation. Percent-area coverage is best predicted with equation
3.4 that was based on 4 diatom taxa and explained approximately

97

61% of the variance in areal macrophyte coverage. Another good
model for predicting percent-area coverage was the canonical
correspondence equation 3.8 that explained 45% of the variance in
this variable. I prefer the use of the canonical correspondence
analysis models over the multiple regression models because many
environmental factors may affect species' distributions (Patrick
1973), and interpretations based on a few species are less likely to
be reliable. None of these 4 models was statistically confounded by
covariant environmental or macrophyte variables.

Macrophyte predictive models should prove useful for assessing
historical patterns in macrophyte standing crop that resulted from
changes in nutrient loading (Purohit and Singh 1985). Historical
water level changes might also be inferred from macrophyte
predictive models because macrophyte standing crop will increase
when lower water levels permit a lakeward expansion of rooted
macrophytes (Landers 1982), or when higher water levels permit
macrophyte colonization of a shallow littoral shelf (Anderson 1990).

Floating-leaved biomass is best predicted using equation 3.5 that
explains approximately 87% of the variance in this variable using 5
diatom taxonomic groups. Partial correlations showed that the
floating-leaved biomass model was marginally unconfounded by
submerged biomass. None of the models for predicting submerged
biomass was useful, however, because of statistical confoundedness
with floating-leaved biomass. Perhaps this confoundedness occurs
because periphytic taxa are not specific in their attachment sites
with respect to floating versus submerged vegetation, and planktonic
taxa are negatively affected to an equal degree by both types of

98

vegetation. Combined floating and submerged biomass can be
assessed historically using equation 3.6. Despite an R2 of
approximately 0.60, the adjusted R2 indicates that this model, which
is based on 7 diatom taxonomic groups, explains about 39% of the
variance in floating and submerged biomass.

Applying Predictive Models to Obtain Historical TSI Estimates
The predictive models shown above can be used to determine
the historical trophic state classification of lakes taking the nutrients
in macrophyte biomass into account (Canfield et al. 1983a). The
floating-leaved and submerged biomass model (equation 3.6) can be
used in conjunction with equations 3.4 or 3.8, predicting percent-
, ., ,.. area coverage, in order to estimate the total submerged and floating-
V'^I-'J leaved biomass for historical samples. Total floating-leaved and
submerged biomass (TFSB) is calculated as:
TFSB = SA X (PACi / 100) x Bi

I H

where SA = lake surface area (m^)

PACi = inferred percent-area coverage

Bi = inferred floating-leaved and submerged biomass (kg m-2).

Canfield et al. (1983a) report different percent P values for the
dry weight of individual macrophyte taxa, though variation is shown
between lakes in those values. It is difficult to calculate accurately
the amount of P contained in the inferred total biomass because the
biomass of individual macrophyte taxa cannot be determined from
the diatom predictive models, and inferences are for wet weight of
macrophyte biomass. If an overall mean percent P value (0.234%) is

J... '^'^ '-'- \ ■ ,: 9 9

used, however, and we assume that plant water content is 90% of
fresh weight (Canfield and Duarte 1988), it is possible to multiply
mean percent P by the total dry weight of floating-leaved and
submerged biomass to obtain an estimate of the kg of P that would
be released to the water column assuming 100% decomposition of
macrophyte biomass. Dividing this mass of P by the volume of the
lake produces a water-column P concentration that represents the
macrophyte component of trophic state. This P concentration can be
added to water-column total P inferences obtained using equation
3.9 to estimate the potential total P content of the water column
(WCP, Canfield et al. 1983a). Historical WCP estimates should take
macrophyte nutrients into account and thereby provide a more
complete assessment of historical trophic state than inferences based
solely on water-column total P.

Whitmore's (1989) model for predicting TSI(TP) from the TROPH
1 diatom index is useful for historically assessing changes in lake
trophic state, but inferred TSI(TP) values cannot be detransformed to
yield the water-column total P inferences necessary to calculate WCP.
Two separate equations are used to derive TSI(TP) from total P
values, depending on whether a given lake is P-limited or nutrient-
balanced (Huber et al. 1982). In historical applications, a lake may
have undergone changes in nutrient limitation over time, especially
if the lake received agricultural runoff, or if sewage effluent had
been directed into the lake. It would be difficult, therefore, to select
the appropriate equation to detransform an historic TSI(TP)
inference. Equation 3.9 in the present study will provide inferences
of log-transformed water-column total P, however, and these

100

inferences can be detransformed to provide the necessary water-
column total P values for WCP estimates.

Because of the long growing season and mild climate in Florida,
the annual dieback of macrophytes and nutrient release is not likely
to occur to the degree in Florida that it does in winter in cold-
temperate areas. Death and decomposition of macrophytes in warm-
temperate and subtropical Florida lakes are less synchronized and
dramatic than in colder regions. The nutrients present in
macrophytes are derived from the sediment, and their release to the
water column, however slow, does represent a source of nutrient
loading to lakes. Although nutrients in macrophytes of Florida lakes
may be less apt to appear in the water column in a seasonal pulse,
macrophytes represent a substantial component of primary
production and their nutrients are as germane to the concept of
trophic state as the water-column nutrients contained in
phytoplankton.

APPENDIX 1

SPECIES NAMES, ACRONYMS AND
LIFE-FORM CLASSinCATIONS

Key to Appendix

Life-form classifications

e
t

P
u

*

euplanktonic

tychoplanktonic

periphytic

unknown

assumed based on valve morphology

Species name

Life-
Acronym form
class.

Achnanthes biasolettiana (?Kiitz.) Grun. ACHBIAS

Achnanthes exigua Grun. var. exigua ACHEX

Achnanthes exigua var. constricta (Grun.) Hust. ACHEXOO

Achnanthes exigua var. heterovalva Krasske ACHEXHE

Achnanthes kryophila Pet. ACHKRY
Achnanthes lanceolata (Breb.) Grun. var. lanceolata ACHLAN
Achnanthes lanceolata var. dubia Grun.
Achnanthes linearis (W. Sm.) Grun. var. linearis
Achnanthes linearis f. curta H.L. Sm.
Achnanthes microcephala (Kiitz.) Grun. var.

microcephala ACHMIC

Achnanthes minutissima Kiitz. var. minutissima ACHMIN

Achnanthes pinnata Hust. var. pinnata ACHPIN

Achnanthes sp. ACHSP

Actinella punctata Lewis var. punctata ACHPUNC

u

P

P

P
u

P

ACHLANDU p
ACHLIN p'
ACHLINCU p =

P'

P
u

u

101

102

Amphora ovalis Kutz. var. ovalis
Amphora ovalis var. affinis (Kiitz.) V.H. ex Det.
Amphora ovalis var. pediculus (Kiitz.) V.H. ex Det.
Anomeoneis serians (Breb. ex Kiitz.) CI. var. serians
Anomeoneis serians var. acuta Hust.
Anomeoneis serians var. apiculata Boyer
Anomeoneis serians var. brachysira (Breb.

ex Kiitz.) Hust.
Anomeoneis vitrea (Grun.) Ross var. vitrea
Asterionella formosa Hass. var. formosa
Asterionella ralfsii W. Sm. var. ralfsii
Aulacoseira ambigua (Grun.) Sim. var. ambigua
Aulacoseira distans (Ehr.) Sim. var. distans
Aulacoseira granulata (Ehr.) Sim. var. granulata
Aulacoseira granulata (Ehr.) Sim. var. angustissima

O. Mull.
Aulacoseira islandica (O. Miill.) Sim. var. islandica
Aulacoseira italica (Ehr.) Sim. var. italica
Aulacoseira sp.

Caloneis bacillum (Grun.) CI. var bacillum
Caloneis latiuscula (Kiitz.) CI.
Caloneis sp. A

Caloneis ventricosa (Ehr.) Meist. var. ventricosa
Capartogramma crucicula (Grun. ex CI.) Ross var.

crucicula
Cocconeis placentula Ehr. var. placentula
Cocconeis placentula var. lineata (Ehr.) V.H.
Cocconeis sp.

Cyclostephanos dubius (Fricke) Round
Cyclotella meneghiniana Kiitz. var. meneghiniana
Cyclotella pseudostelligera Hust. var.

pseudostelligera
Cyclotella radiosa (Grun.) Lemmerman
Cyclotella stelligeroides Hust.
Cyclotella sp.
Cyclotella sp. A

Cyclotella stelligera CI. u. Grun. var. stelligera
Cymbella angustata (W.Sm.) CI. var. angustata
Cymbella lunata W. Sm. var. lunata
Cymbella microcephala Grun. var. microcephala
Cymbella minuta Hilse ex Rabh. var. minuta
Cymbella minuta var. silesiaca (Bleisch ex Rabh.)
Reim.

AMPOV

P

AMPOVAF

P

AMPOVPED

P

ANOMSE

u

ANOMSEAC

P

ANOMSEAP

P

ANOMSERB

P

ANOMVIT

P

ASTFOR

e

ASTRAL

e

AULAAM

e

AULADIS

P

AULAGR

e

AULAGRAN

e

AULAISL

e

AULAITAL

t-p

AULASP

u

CALSP

P

CALLAT

P

CALSPA

P*

CALVEN

P

CAPCRU

u

COCPLAC

p

COCPLACL

p

COCSP

p

CYSTEPDU

e

CYCMFN

e-p

CYCPSEUD

t*

CYCRAD

e

CYCSIH ,0

t-p

CYCSP

u

CYCSPA

u

CYCSTH,

t-p

CYMANG

P

CYMLUN

P*

CYMMIC

P

CYMMIN

P

CYMMINSI

P

103

Cymbella muelleri Hust. var. muelleri

Cymbella sp,

Diploneis elliptica (Kiitz.) CI. var. elliptica

Diploneis sp.

Epithemia adnata (Kiitz.) Breb. var. adnata

Epithemia argus var. alpestris Grun,

Epithemia sp.

Eunotia bidentula W. Sm. var. bidentula

Eunotia Carolina Patr. var. Carolina

Eunotia curvata (Kiitz.) Largerst. var. curvata

Eunotia diodon Ehr. var. diodon

Eunotia flexuosa Breb. ex Kiitz. var. flexuosa

Eunotia formica Ehr. var. formica

Eunotia incisa W. Sm. ex Greg. var. incisa

Eunotia indica Grun. var. indica

Eunotia luna Enr, var. luna

Eunotia maior (W. Sm.) Rabh. var. maior

Eunotia monodon Ehr. var. monodon

Eunotia naegeli Migula var. naegeli

Eunotia pectinalis (O.F. Miill.?) Rabh. var. pectinalis

Eunotia pectinalis var. minor (Kiitz.) Rabh,

Eunotia sp.

Eunotia vanheurckii Patr. var. vanheurckii

Eunotia vanheurckii var. intermedia (Krasske ex.

Hust.) Patr.
Fragilaria brevistriata Grun. var. brevistriata
Fragilaria brevistriata var. inflata (Pant.) Hust.
Fragilaria construens (Ehr.) Grun. var. construens
Fragilaria construens var. pumila Grun.
Fragilaria construens var. venter (Ehr.) Grun.
Fragilaria crotonensis Kitton. var. crotonensis
Fragilaria pinnata Ehr. var. pinnata
Fragilaria virescens Ralfs. var. virescens
Frustulia rhomboides (Ehr.) DeT. var. rhomboides
Frustulia rhomboides var. capitata (A. Mayer) Patr.
Frustulia rhomboides var. saxonica (Rabh.) DeT.
Gomphonema affine Kiitz. var. affine
Gomphonema gracile Ehr. var. gracile
Gomphonema grunowii Patr. var. grunowii
Gomphonema intricatum Kiitz. var. intricatum
Gomphonema parvulum (Kiitz.) var. parvulum
Gomphonema parvulum var. lanceolata Grun.
Gomphonema sp.

CYMMUKI ,

P

CYMSP

P

DIPEL

P

DIPSP

P

EPAD

P

EPARGAL

P*

EPSP

P

EUNBIDT

u

EUNCARO

u

EUNCUR

t-p

EUNDIOD

P

EUNH EX

p

EUNFOR

p

EUNINC

p

EUNIND

p

EUNLUNA

u

EUNMAI

p

EUNMON

p

EUNNAE

p

EUNPEC

p

EUNPECM

p

EUNSP

p

EUNVAN

p

EUNVANIN p*
FRAGBREV p
FRAGBRIN p
FRAGCON t-p
FRAGCONP t-p
FRANCONV t-p
FRAGCROT e
FRAGPIN p
FRAGVIR u
FRUSRH p
FRUSRHCA p
FRUSRHSA p*
GOMAAF p
GOMAGRAC t-p
GOMAGRUN p
GOMAIN p
GOMAPAR p
GOMAPARL p
GOMASP p

i 'V*

104

Gopmphona truncatum var. turgidum (Ehr.) Patr.
Gyrosigma obscurum (W. Sm.) Griff. & Henfr. var.

obscurum
Hantzschia amphioxys (Ehr.) Grun. var. amphioxys
Mastogloia smithii var. lacustris Grun.
Navicula accomoda Hust. var. accomoda
Navicula anglica var. subsalsa (Grun.) CI.
Navicula arvensis Hust. var. arvensis
Navicula confervacea (Kiitz.) Grun. var. confervacea
Navicula confervacea var. peregrina (W. Sm.) Grun.
Navicula cuspidata (Kiitz.) Kiitz. var. cuspidata
Navicula exigua Greg. ex. Grun. var. exigua
Navicula exigua var. capitata Patr.
Navicula gottlandica Grun. var. gottlandica
Navicula halophila (Grun.) CI. var. halophila
Navicula hustedtii Krasske var. hustedtii
Navicula kriegeri Krasske
Navicula lanceolata (Ag.) Hust. var. lanceolata
Navicula minima Grun. var. minima
Navicula mutica Kiitz. var. mutica
Navicula oblonga (Kiitz.) var. oblonga
Navicula pupula Kiitz. var. pupula
Navicula pupula var. elliptica Hust.
Navicula pupula var. rectangularis (Greg.) Grun.
Navicula radiosa Kiitz. var. radiosa
Navicula radiosa var. parva Wallace
Navicula rhyncocephala Kiitz. var. rhyncocephala
Navicula rhyncocephala var. germainii (Wallace)

Patr.
Navicula seminuloides Hust.
Navicula seminulum Grun. var. seminulum
Navicula seminulum var. hustedtii Patr.
Navicula seminulum var. intermedia Hust.
Navicula sp.
Navicula sp. F

Navicula subtilissima CI. var. subtilissima
Navicula tripunctata (O. Miill.) Bory var. tripunctata
Navicula viridula var. linearis Hust.
Neidium affine (Ehr.) Pfitz. var. affine
Neidium affine var. amphirhyncus (Ehr.) CI.
Neidium affine var. ceylonicum (Skv.) Reim.
Neidium apiculatum Reim. var. apiculatum
Neidium dubium (Ehr.) CI. var. dubium

GOMATRUT p

GYROOBS u
HANTAMP p
MASTSMLA p
NAV ACCOM u
NAVANGSU u
NAVARV p*
NAVCONF p
NAVCONFP p
NAVCUS p
NAVEX p

NAVEXCA p
NAVGOT p*
NAVHAL u
NAVHUST p*
NAVKRIEG u
NAVLAN p
NAVMIN p
NAVMUT t-p
NAVOBL p
NAVPU p

NAVPUEL p
NAVPURE p
NAVRA p

NAVRAPA p
NAVRHY p

NAVRHYGE u
NAVSE u

NAVSEM p*
NAVSEMHU u
NAVSEMIN p*
NAVSP p

NAVSPF u
NAVSUBT p*
NAVTRI p
NAVVIRL p
NEIAF p

NEIAFAMP p
NEIAFCEY p*
NEIAP p

NEIDUB u

105

Neidium floridanum Reim. var. floridanum

Neidium iridis (Ehr.) CI. var. iridis

Neidium iridis var. amphigomphus (Ehr.) A. Mayer

Neidium iridis var. ampliatum (Ehr.) CI.

Neidium ladogense var. densestriatum (0str.) Foged

Neidium sp.

Nitzschia amphibia Grun. var. amphibia

Nitzschia capitellata Hust. var. capitellata

Nitzschia fonticola Grun. var. fonticola

Nitzschia frustulum Kiitz. var. frustulum

Nitzschia gracilis Hantz.

Nitzschia Hantzschiana Rabh.

Nitzschia linearis (W. Sm.) var. linearis

Nitzschia obtusa W. Sm. var. obtusa

Nitzschia palea (Kiitz.) W. Sm. var. palea

Nitzschia romana Grun.

Nitzschia scalaris (Ehr.) W. Sm.

Nitzschia sigma (Kiitz.) W. Sm. var. sigma

Nitzschia sp.

Nitzschia tryblionella var. levidensis (W. Sm.) Grun.

Opephora americana M. Perag. var. americana

Opephora martyi Herib. var. martyi

Pinnularia abaujensis (Pant.) Ross var. abaujensis

Pinnularia abaujensis var. linearis (Hust.) Patr.

Pinnularia abaujensis var. rostrata (Patr.) Patr.

Pinnularia appendiculata (Ag.) CI. var. appendiculata

Pinnularia biceps Greg. var. biceps

Pinnularia biceps var. petersenii Ross

Pinnularia borealis var. rectangularis Carlson

Pinnularia braunii (Grun.) CI. var. braunii

Pinnularia braunii var. amphicephala (A. Mayer)

Hust.
Pinnularia caudata (Boyer) Patr. var. caudata
Pinnularia dactylus Ehr. var. dactylus
Pinnularia latevittata CI. var. latevittata
Pinnularia latevittata var. domingensis CI.
Pinnularia legumen (Ehr.) Ehr. var. legumen
Pinnularia sp.
Pinnularia sp. B

Pinnularia subcapitata Greg. var. subcapitata
Pinnularia subcapitata var. paucistriata (Grun.) CI.
Pinnularia viridis (Nitz.) Ehr. var. viridis
Pinnularia viridis var. minor CI.

NIEFL p

NEIIR p

NEIIRAMH p

NEIIRAML p

NEILADDE p

NEISP p

NITZAM p

NITZCAP t-p

NTTZFONT p

NITZFRUS e-p

NTTZGRAC u

NITZHANT u

NITZLIN p

NITZOBT u

NITZPAL t-p

NTTZROM t*

NITZSCAL u

NITZSIG p

NITZSP p

NTTZTRYL t-p

OPEAM p

OPEMAR p

PINABA p

PINABAL p

PINABAR p

PINAP p*

PINBI p

PINBIPET u

PINBORRE p

PINBR u

PINBRAMP p*

PINCAUD u

PINDAC p

PINLAT p

PINLATDO p

PINLEG p

PINSP p

PINSPB p*

PINSUB p

PINSUBPA p

PINVIR p

PINVIRMI p

106

Rhopalodia gibba (Ehr.) O. Miill. var. gibba
Stauroneis anceps Ehr. var. anceps
Stauroneis obtusa Lagerst. var. obtusa
Stauroneis pachycephala CI. var. pachycephala
Stauroneis palustris Hust.
Stauroneis phoenocenteron (Nitz.) Ehr. var.

phoenocenteron
Stauroneis phoenocenteron f. gracilis (Ehr.) Hust.
Stauroneis smithii Grun. var. smithii
Stauroneis sp.
Stenopterobia intermedia (Lewis) V.H. var.

intermedia
Stephanodiscus niagarae Ehr. var. niagarae
Stephanodiscus rotula var. minutula (Kiitz.) Ross

& Sims
Surirella biseriata Breb. var. biseriata
Surirella delicatissima Lewis
Surirella linearis W. Sm. var. linearis
Surirella linearis var. constricta (Ehr.) Grun.
Surirella robusta Ehr. var. robusta
Surirella robusta var. splendida (Ehr.) V.H.
Surrirella sp.

Surirella tenera Greg. var. tenera
Synedra acus Kiitz. var. acus
Synedra delicatissima W. Sm. var. delicatissima
Synedra delicatissima var. angustissima Grun
Synedra filiformis var. exilis Cl.-Eul.
Synedra incisa Boyer var. incisa
Synedra miniscula Grun. var. minuscula
Synedra parasitica (W. Sm.) Hust. var. parasitica
Synedra pulchella Ralfs ex. Kiitz. var. pulchella
Synedra pulchella var. lanceolata O'Meara
Synedra radians Kiitz. var. radians
Synedra rumpens Kiitz. var. rumpens
Synedra rumpens var. familiaris (Kiitz.) Grun.
Synedra rumpens var. fragiliariodes Grun.
Synedra sp.
Synedra sp. A

Synedra ulna (Nitz.) Ehr. var. ulna
Synedra ulna var. amphirhyncus (Ehr.) Grun.
Tabellaria fenestrata (Lyngb.) Kiitz. var. fenestrata
Tabellaria flocculosa (Roth) Kiitz. var. flocculosa
Triceratium sp.

RHOPGIB

P

STAUANC

P

STAUOBT

u

STAUPACH

u

STAUPAL

u

STAUPH

p

STAUPHGR

p

STAUSM

u

STAUSP

p

STENINT

u

Sl'EPNI

u

SIEPROMI

e

SURBIS

t-p

SURDET,

P*

SURLIN

P*

SURLINCO

P*

SURROB

u

SURROBSP

P

SURSP

u

SURIEN

u

SYNACUS

t-p

SYNDFI,

e

SYNDFT AN

e

SYNFILEX

t*

SYNINC

u

SYNMIN

u

SYNPAR

P

SYNPUL

P

SYNPULLA

P

SYNRAD

e

SYNRUM

P

SYNRUMFA

P

SYNRUMbR

u

SYNSP

u

SYNSPA

u

SYNUL

e

SYNULAMP

e

TABFEN

P

TABFLOC

t-p

TRICSP

APPENDIX 2

FORTY-SEVEN DIATOM TAXONOMIC GROUPS USED
IN MUTLIVARIATE ANALYSES

Acronym for
taxonomic
group

group composed of:
(see Appendix 1 for species acronyms)

S-ACH

ACTPUNC
S-ANOM

S-AST

AULAAM

AULADIS

S-AULAGR

AULAISL

AULAITAL

CALSPA

COCPLACL

CYCMEN

CYCPSEUD

CYCSTEL

CYCSTELO

S-CYM

CYSTEPDU

SEP

S-EUN

FRAGBREV
S-FRAGCO

ACHBIAS + ACHEX + ACHEXCON + ACHEXHE + ACHKRY +

ACHLAN+ ACHLANDU + ACHLIN + ACHLINCU + ACHMIC

ACHMIN +ACHPIN + ACHSP

ACTPUNC

ANOMSE + ANOMSEAC + ANOMSEAP + ANOMSERB +

ANOMVIT

ASTFOR + ASTRAL

AULAAM

AULADIS

AULAGR + AULAGRAN

AULAISL

AULAITAL

CALSPA

COCPLACL

CYCMEN

CYCPSEUD

CYCSTEL

CYCSTELO

CYMLUN + CYMMIC + CYMMIN + CYMMINSI + CYMMUEL

CTSTEPDU

EPARGAL + EPAD + EPSP

EUNBIDT + EUNCARO + EUNCUR + EUNDIOD + EUNELEG +

EUNEXIG + EUNFLEX + EUNFOR + EUNINC + EUNIND +

EUNLUNA + EUNMAI + EUNMON + EUNNAE + EUNPEC +

EUNPECM + EUNSP + EUNVAN + EUNVANIN

FRAGBREV

FRAGCON + FRAGCONP + FRAGCONV

107

108

FRAGCROT FRAGCROT

FRAGPIN FRAGPIN

S-FRUSRH FRUSPH + FRUSRHCA + FRUSRHSA

S-GOMA

NAVCONF

NAVGOT

NAVCUS

NAVLAN

S-NAVPU

S-NAVRA

S-NAVSEM

NAVSUBT

S-NEI

NITZAM

NITZCAP

NITZFONT

NTTZFRUS

NITZPAL

SPIN

S-STAU

S-SUR

SYNDEL

SYNFILEX

S-SYNRUM

TABFEN

TABFLOC

GOMAAF + GOMAGRAC + GOMAGRUN + GOMAIN +

GOMAPAR + GOMAPARL + GOMASP

NAVCONF

NAVGOT

NAVCUS

NAVLAN

NAVPU + NAVPUEL + NAVPURE

NAVRA + NAVRAPA

NAVSEM + NAVSEMIN

NAVSUBT

NEIIR + NEIIRAMH + NEIIRAML + NEIAP + NEIDUB +

NEIFL + NEISP + NEILADDE

NITZAM

NITZCAP

NITZFONT

NITZFRUS

NITZPAL

PINABA + PINABAL + PINABAR + PINBI + PINBIPET +

PINBORRE + PINBR + PINBRAMP + PINCAUD + PINDAC +

PINLAT + PINLATDO + PINLEG + PINSP + PINSPB +

PINSUB + PINSUBPA + PINVIR + PINVIRMI + PINAP

STAUANC + STAUPACH + STAUOBT + STAUPAL +

STAUPH + STAUPHGR + STAUSM + STAUSP

SURBIS + SURDEL + SURLIN + SURLINCO + SURROB +

SURROBSP + SURSP + SURTEN

SYNDEL

SYNFILEX

SYNRUM + SYNRUMFA

TABFEN

TABFLOC

APPENDIX 3

TAXONOMIC GROUPS USED IN STEPWISE REGRESSION PROCEDURES

(Taxonomic acronyms are defined in Appendices 1 and 2.)

Key to trend in diatom representation over range of macrophyte

variable
i = increasing over range
ic = increasing in curvilinear fashion
d = decreasing over range
dc= decreasing in curvilinear fashion
u = uniform over range

3.1. Predicting percent-volume infestation from diatom percentage
data beginning with 17 diatom taxonomic groups.

Acronym of taxonomic group Trend

ACHEX d

S-AST d

S-AULAGR d

AULAITAL d

CYMLUN i

EUNCUR i

FRAGBREV d

S-GOMA i

S-NAVPU i

NAVLAN d

NAVRA d

NAVSUBT d

NITZAM d

S-PIN d

S-STAU i

S-SUR d

SYNDEL d

109

no

3.2. Predicting percent-volume infestation from diatom

concentration data beginning with 17 diatom taxonomic groups.

Acronym of taxonomic group Trend

ACHEX dc

ACHMIN dc

ANOMSERB d

AULA AM dc

S-AULAGR dc

CALSPA dc

COCPLACL d

CYCSTEL d

CYCPSEUD d

NAVLAN d

S-NEI dc

NITZCAP dc

NTTZFONT dc

S-PIN dc

STAUPH i

SYNFILEX d

SYNRUM dc

3.3. Predicting percent-volume infestation from log-transformed

diatom accumulation rates beginning with 17 diatom taxonomic
groups.

Acronym of taxonomic group Trend

ACHEX d

ACHMIN d

ANOMSERB d

AULAAM d

S-AULAGR d

CALSPA d

COCPLACL d

CYCSTEL d

CYCPSEUD dc

Ill

NAVLAN

S-NEI

NITZCAP

NTTZFONT

SPIN

STAUPH

SYNFILEX

SYNRUM

d

d

d

d

d

u

dc

dc

3.4. Predicting percent-area coverage from diatom percentage data
beginning with 11 diatom taxonomic groups.

Acronym of taxonomic group

logio(ACHLIN)

S-ANOM

AULAAM

logio(AULAGRAN)

CYCPSEUD

S-CYM

S-EUN

S-FRUSRH

NAVRA

STAUPH

TABFEN

Trend

1
d
d
d

d
i

«

1

3.5. Predicting percent-area coverage from diatom concentration
data beginning with 8 diatom taxonomic groups.

Acronym of taxonomic group

Trend

ACHLIN

AULAAM

S-AULAGR

CYCPSEUD

CYCSTEL

EUNINC

STAUPH

SYNFILEX

ic

d

dc

d

d

ic

ic

d

Mr

I ■■

112

3.6. Predicting percent-area coverage from log -transformed diatom
accumulation rates beginning with 11 diatom taxonomic groups.

Acronym of taxonomic group

Trend

ANOMSERB

AULAGRAN

CYCPSEUD

CYMLUN

CYMMIN

S-EUN

FRUSRHCA

S-GOMA

NAVPU

STAUPH

SYNFILEX

1

d
d

d
i
d

3.7. Predicting floating-leaved biomass beginning with 9 diatom
taxonomic groups identified in cluster analysis of diatom
concentration data.

Acronym of taxonomic group

Trend

S-AULAGR

CYCMEN

CYCPSEUD

CYSTELO

CYSTEPDU

FRAGCROT

NITZFONT

NITZPAL

S-SYNRUM

dc

d

u

u

i?

i?

u

u?

u

113

3.8. Predicting floating-leaved biomass beginning with 7 diatom
taxonomic groups identified from cluster analysis of diatom
accumulation rates.

Acronym of taxonomic group Trend

S-AST

u?

AULAISL

d?

TABFLOC

d

S-NEI

d

S-NAVRA

dc

SYNFILEX

dc

TABFEN

dc

3.9. Predicting floating-leaved biomass from log-transformed

diatom percentage data beginning with 12 taxonomic groups.

Acronym of taxonomic group Trend

logio(ACHEX + ACHLIN + ACHLINCU

+ ACHMIN) d

logio(S-ANOM) d
logio(AULAGR + AULAITAL +

AULAISL + AULAAM) d

logio(CYMMUEL) d

logio(CYSTEPDU) i

logio(EUNPEC) d

logio(S-FRUSRH) d
logio(NAVGOT + NAVLAN -^
S-NAVPU + S-NAVRA +

NAVSUBT) d
logio(NITZAM -I- NITZCAP -h

NITZFRUS) d

logio(SYNDEL + SYNRUM) d

logio(S-STAU) d

logio(S-SUR) . d

114

3.10. Predicting submerged biomass from diatom percentage data
beginning with 20 taxonomic groups.

Acronym of taxonomic group Trend

S-ACH

dc

S-ANOM

d?

AULAAM

d?

S-AULAGR

d

(AULAISL ■

f AULAITAL)

d

(CYCMFN + i

CYCPSEUD + CYCS 1 hi , +

CYSlhLO)

d

S-CYM

d

CYSTEPDU

d?

EUNPEC

d

EUNVAN

d

FRAGCROT

d?

S-FKUSRH

dc

S-NAVRA

d

NAVSUBT

d

S-NEI

d

S-NITZ

d

S-PIN

d

S-STAU

d

S-SUR

d

(SYNDEL + ;

S-SYNRUM)

d

3.11, Predicting floating + submerged biomass from diatom
percentage data beginning with 16 taxonomic groups.

Trend

Acronym of taxonomic group Floating

Submerged

S-ACH d d

S-ANOM d d

(AULAISL + AULAITAL) d d

S-CYM dc dc

CYSTEPDU d? i?

115

EUNPEC d

S-FRUSRH dc

(NAVGOT + NAVLAN + S-NAVPU

+ S-NAVRA + NAVSUBT)

d

S-NITZ

u

S-STAU

d

S-SUR

d

(SYNDEL + SYNRUM)

d

FRAGCROT

d?

EUNVAN

d

S-AULAGR

u

AULAAM

d

d

dc

d

d

d

d

d

i?

d

d

d

3.12.

Predicting emergent biomass from diatom percentage data
beginning with 17 taxonomic groups.

Acronym of taxonomic group

Trend

(ACHEX + ACHLIN + ACHLINCU

+ ACHMIN) d
(AULAM + AULAGR + AULAITAL) dc

CYCMEN d

CYCPSEUD d

CYCSTEL d

S-CYM d

EUNPEC d

FRAGCONV d

S-FRUSRH d

FRAGPIN d ?

S-NAVRA d

NAVSUBT d

S-NITZ d ?

SPIN d

S-SUR d

TABFEN d

S-SYNRUM d

APPENDIX 4

CHL A, TOTAL N, AND SECCHI DEPTH VALUES USED TO CALCULATE

TSI(AVG)

Lake

Chi a

Total N

Secchi depth

(^ig 1-1)

(mg 1-1)

(m)

Alligator

25.4

2.37

0.46

Apopka

59.3

4.03

0.20

Bonny

36.5

1.86

0.58

Carr

7.0

0.87

1.81

Catherine

1.5

0.30

3.20

Clay

2.1

0.36

4.00

Crooked

1.8

0.33

3.13

Deep

0.9

0.16

Fairview

2.4

0.50

4.80

Harris

37.2

1.55

0.60

Hartridge

3.7

0.48

2.32

Keys Pond

1.0

0.17

5.25

Lindsey

5.2

0.65

1.95

Live Oak

5.3

0.35

2.55

Lochloosa

26.5

1.05

0.97

Loften Ponds

1.2

0.39

2.48

Moore

3.5

0.35

5.28

Mystic

3.6

0.52

7.00

Ocean Pond

3.9

0.42

1.10

Okahumpka

5.1

0.95

1.20

Orange

16.8

1.11

0.80

Patrick

4.0

1.47

2.00

Rowell

37.8

0.81

0.65

116

117

Stella

2.0

0.43

4.10

Tomohawk

1.2

0.21

4.00

Townsend

5.1

0.58

3.80

Watertown

15.9

1.05

1.90

Wauberg

114.7

1.57

0.57

Wildcat

1.2

0.19

3.61

APPENDIX 5

SUBJECTIVE TROPHIC STATE CLASSIHCATION OF
LAKES IN SURVEY

Key to Trophic Classification categories:

U

= ultraoligotrophic

0

= oligotrophic

M

= mesotrophic

E

= eutrophic

H

= hypereutrophic

Classification based on:

Lake water-column

macrophyte

overall

nutrients

presence

Alligator

H

0

H

Apopka

H

0

H

Bonny

E

0

H

Carr

M

H

H

Catherine

U

M

M

Clay

u

H

H

Crooked

0-M

0-M

0-M

Deep

U

E

E

Fairview

M

M

M

Harris

M

0-M

M

Hartridge

O

M

M

Keys Pond

U

M

M

Lindsey

M

H

H

Live Oak

M

H

H

Lochloosa

E

E

E

Loften Ponds

U

ME

ME

Moore

0

M

M

118

119

Mystic

M

Ocean Pond

E

Okahumpka

M

Orange

E

Patrick

M

Rowell

E

Stella

M

Tomohawk

U

Townsend

O

Watertown

E

Wauberg

H

Wildcat

0

H H

U E

H H

H H

H H

M E

E E

M M

H H

O E

U H

U O

APPENDIX 6

PROPORTIONS, SEDIMENTARY CONCENTRATIONS, AND ANNUAL
ACCUMULATION RATES OF PERIPHYTIC AND PLANKTONIC DIATOMS

121

^

^^

c

n

^ — V

o

e«

o

D

O-

•4-*

l-l

a

CN

u<

1

C

o

o

4-^

>>

C3

-C

>

^

a

c

CtS

£

o

>

3

D

o

a.

o

c«

P3

T3

V3

C

o

<->

o

00

• »-i o >

e«

.!r o

in— ivo-^ovoknooo-^^oovo-^cNO

^ r- vo r-

rt ON VO OO
OO (N 1— I

voooooovovoooootn

CO

^ ro o r^

>n in a\ O

0\ 04 On — 1 O

o vo o ^ >n

I— I t;1- On 0\ r-»

>n m »n oo cs

^ oo cs

o
oo

(S

ooor---Hininoovoor^'— I
or--oocnincn»oONoooo

t^ ON ^ (N ON

o o ^ >n »n

o r- vo ^

o
oo
o

O ^ VD O ON CS VD
Tt CO VO ^H oo ^H O

o CO r^ CO ^

>n(N>r)vooo^^-^coooo\c<it--

o

o

O

o

o

O

O

o

o

o

O

o

o

o

o

O

O

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

O

O

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

o

V~)

o

o

o

0\

o

o

o

o

NO

o

o

o

»n

On

m

(N

ON

vo

1— 1

<— 1

r-

CO

On

r-

NO

r--

CO

1— (

On

CO

1-^

On

CO

OO

1— t

oo

r-

vo

1— 1

oo

OO

r-

On

»n

CO

>n

CO

oo

ON

r-

CO

r~-

»n

oo

vo

vn

(N

CO

(N

NO

<N

in

oo

(N

»n

t^

i-H

04

CN

V~t

1— c

l-H

I— 1

(N

CN

r-

>— 1

^

.—1

?-H

o o

o o

o o

o o

o c--

^ »n oo

^ CS CO

CN CO in

o
o
o
o

o
o
o
o

00

r-H

WO

CO

O
O
r--

On

o
o
o
'^

NO

o
o
o

CN OO

ON

o o

o o

o o

o r~- CO

On -rt in

r-H r~-

o o

o o

o o

o o

O oo

CN ON oo

CO in r-

O oo

CO

o o o

o o o

o o o

oo O O

in CN -ri-

in r-- ON

CO 01 in

CN '-I

o
o
o
o
o

VO
oo

ON

o o o
t^ o o

CN NO O

CN NO '— I

oo CO NO

00 O

CN

NO— lOcor-OcoNoo

ONCNNOOCNinNO-^O

ONr-in-— ii— iNO^Hinin
inNooincNoooocN

incNO^CNCNOOO

oooooooooooooooooo

osrj-NOONcNNOt^Ttt^oocooinoooNONoin

OONOCNCNCO-^— iTtr-^ONONCOCOOOONO^

inooooc7NONONONONOo-rj-NooNOor~r~-ONONON
oooooooooooooooooo

u

if § >.'^ O^

C T3

^

lligato

popka

onny

arr

atheri

lay

rooke

eep

i-i

{«

rt c« (u .5 .>; o o

<<PQUUUUQ

PU

ffi K fcti J J J J

o
o

o

122

o

On

in

-^

'si-

,— 1

I— 1

m

in

,— 1

(N

in

r-

NO

I— 1

r-

m

"^

CO

1—1

Tt

r--

VD

00

^

o

NO

<N

1— 1

'^

O

>o

O

Tf

en

■*

OO

r-

1—1

NO

ON

o

r-

r-

Ti-

o

ON

1—1

o

(T)

r— (

1—1

oo

On

>n

1— (

in

m

t>

ON

ON

Ti-

<N

NO

1—1

o

r-H

ts

cn

<s

CO

»n

NO

^

NO

^

On

en

\D

1—1

On

On

>n

(N

^

in

ON

o

-^

OO

<S

On

m

m

T— 1

On

in

1—1

in

in

CO

f— ^

(S

r<^

^

Tf

On

On

OO

in

'=t

O

NO

1— 1

<s

00

(S

<N

00

o

NO

*-H

in

<N

ON

>n

r-

m

NO

^H

^H

O

in

On

NO

(N

1— 1

in

OO

OO

1-H

00

>— 1

fS

1-H

CO

o

o

O

o

o

o

o

o

O

o

o

o

o

O

o

o

o

o

o

o

O

o

O

o

O

o

o

o

O

o

o

O

o

o

ON

O

o

o

o

O

o

o

o

o

OO

OO

Tf

o

o

r-

OO

<N

o

o

(N

OO

1—1

O

NO

o

oo

O

^

^

^

00

r-

On

r-

<N

l-H

OO

r-

o

o

NO

CO

ON

r-

CO

On
CO

CO

oo

CO

On
CO

in

r~-

o

O

o

O

o

o

o

o

o

o

O

o

O

o

o

o

O

o

o

o

o

o

o

O

o

o

o

O

o

O

o

o

o

o

OO

o

o

o

O

o

r--

o

o

o

in

CO

CO

OO

CM

^H

(N

in

o

o

in

o

r~-

<N

OO

'^

O

CO

(N

o

(N

^

On

^H

NO

On

1—1

m

CO

<N

in

CO

On

OO

OO

r-

OO

^^

in
in

1—1
NO

in

coNOfSNONocsjinoooooin
t^>nc<ioo\or--coNOoocofO
Tti— iini— II— ifSOOininrj-

odoodooodoo

oot^r^t^-Ttcoi— loocoo
rocoNOONONO^H^HOin-^
inooTj-r-r^r^ONONTt-^in

-a

c
o

Oh

C
C3
(U
O

o

O

11

O

e

o

c

a
o

o

l-l
P3

00

X) O

^ a N^<i w N^-i.!^*-^

M V

BIBLIOGRAPHY

Agbeti, M. and M. Dickman. 1987. Use of lake fossil diatom

assemblages to determine historical changes in trophic status.
,.,. Can. J. Fish. Aquat. Sci., 46: 1013-1021.

Allen, H.L. and B.T. Ocevski. 1981. Comparative primary

productivity of algal epiphytes on 3 species of macrophyte in
the littoral zone of Lake Ohrid, Yugoslavia. Holarct. Ecol., 4(3):
155-160.

Anderson, N.J. 1990a. Variability of diatom concentrations and
accumultion rates in sediments of a small lake basin. Limnol.
Oceanogr., 35(2): 497-508.

. 1990b. Spatial pattern of recent sediment and

diatom accumulation in a small, monomictic, eutrophic lake.
Journal of Paleolimnology, 3: 143-160.

. 1990c. Diatom paleoproductivity versus diatom

inferred whole-lake paleoproductivity. 11th International
Symposium on Living and Fossil Diatoms, San Francisco, 13-17
August, 1990. Abstract.

Bailey, J.H. and R.B. Davis. 1978. Quantitative comparison of lake
water quality and surface sediment diatom assemblages in
Maine, U.S.A. lakes. Verh. Internat. Verein. Limnol., 20: 531.

Baker, L.A., P.L. Brezonik and C.R. Kratzer. 1981. Nutrient loading-
trophic state relationships in Florida lakes. Florida Water
Resources Research Center, Pub. no. 56. 126 p.

Battarbee, R.W. 1973, A new method for the estimation of absolute
microfossil numbers, with reference especially to diatoms.
Limnol. Oceanogr., 18(4): 647-653.

123

124

1978. Observations on the recent history of Lough

Neagh and its drainage basin. Phil. Trans. R. Soc. Lond. Ser. B,
281: 303-344.

. 1979. Diatoms in lake sediments, p. 177-225. In B.E.

Berglund (ed.), Paleohydrological Changes in the Temperate Zone
in the Last 15000 Years, Subproject B. Lake and Mire
Environments, V. II. Specific methods. International Geological
Correlation Programme. Lund, Sweden.

, R.J. Flower, A.C. Stevenson, V.J. Jones, R. Harriman and

P.G. Appleby. 1988. Diatom and chemical evidence for
reversibility of acidification of Scottish lochs. Nature
332(6164): 530-532.

Binford, M.W. and M. Brenner. 1986. Dilution of ^lOp^ 5y organic
sedimentation in lakes of different trophic states, and
application to studies of sediment-water interactions. Limnol.
Oceanogr. 31: 584-595.

Birks, H.H. 1973. Modern macrofossil assemblages in lake

sediments in Minnesota. Pages 173-188. In H.J.B. Birks and R.G.
West (eds.). Quaternary Plant Ecology. John Wiley and Sons, NY,
New York, U.S.A. 326 p.

. 1980. Plant macrofossils in Quaternary lake

sediments. In H.J. Elster and W. Ohle (eds.). Ergebnisse der
Limnologie. Heft 15. E. Schweizerbart'sche
Verlagsbuchhandlung (Nagele u. Obermiller), Stuttgart. 60 p.

Blindow, I. 1987. The composition and density of epiphyton on
several species of submerged macrophytes- the neutral
substrate hypothesis tested. Aquat. Bot., 29: 157-168.

Bradbury, J.P. 1975. Diatom stratigraphy and human settlement in
Minnesota. Geol. Soc. Am. Spec. Pap. 171. Boulder, Colorado.
74 p.

Brenner, M. , M.W. Binford and E.S. Deevey. 1990. Lakes, p. 364-
391. In R.L. Myers and J.J. Ewel (eds.). Ecosystems of Florida.
University of Central Florida Press, Orlando, FL.

125

Brugam, R.B. 1978. Human disturbance and the historical
development of Linsley Pond. Ecology, 59(1): 19-36.

. 1979. A re-evalution of the Araphidineae/Centrales

index as an indicator of lake trophic status. Freshwater Biol., 9:
451-460.

Burkholder, J.M. and R.G. Wetzel. 1990. Epiphytic alkaline

phosphatase on natural and artificial plants in an oligotrophic
lake: Re-evaluation or the role of macrophytes as a phosphorus
source for epiphytes. Limnol. Oceanogr., 35(3): 736-747.

Canfield, D.E., Jr. 1981. Final report: Chemical and trophic state
characteristics of Florida lakes in relation to regional geology.
Institute of Food and Agricultural Studies, Univ. Florida,
Gainesville, 434 p.

and CM. Duarte. 1988. Patterns in biomass and cover

of aquatic macrophytes in lakes: a test with Florida lakes. Can.
J. Fish. Aquat. Sci., 45:1976-1982.

and M.V. Hoyer. 1988. Influence of nutrient

enrichment and light availability on the abundance of aquatic
macrophytes in Florida streams. Can. J. Fish. Aquat. Sci., 45:
1467-1472.

, K.A. Langeland, M.J. Maceina, W.T. Haller and J.V.

Shireman. 1983a. Trophic state classification of lakes with
aquatic macrophytes. Can. J. Fish. Aquat. Sci., 40: 1713-1718.

, M.J. Maceina and J.V. Shireman. 1983b. Effects of

Hydrilla and grass carp on water quality in a Florida lake. Water
Resources Bulletin 19(5): 773-778.

, J.V. Shireman, D.E. Colle, W.T. Haller, C.E. Watkins, II,

and M.J. Maceina. 1984. Prediction of Chlorophyll a
concentrations in Florida lakes: importance of aquatic
macrophytes. Can. J. Fish. Aquat. Sci., 41: 497-501.

Carignan, R. and J. Kalff. 1980. Phosphorus sources for aquatic
weeds: water or sediments? Science, 207: 987-989.

126

and . 1982. Phosphorus release by

submerged macrophytes: significance to epiphyton and
phytoplankton. Limnol. Oceanogr., 27(3): 419-427.

Carlson, R.E. 1977. A trophic state index for lakes. Limnol.
Oceanogr., 22(2): 361-369.

Carney, H.J. 1982. Algal dynamics and trophic interactions in the
recent history of Frains Lake, Michigan. Ecology, 63(6): 1814-
1826.

Carpenter, S.R. 1981. Submersed vegetation: an internal factor in
lake ecosystem succession. Am. Nat., 118(3): 372-383.

and D.M. Lodge. 1986. Effects of submersed

macrophytes on ecosystem processes. Aquat. Bot., 26: 341-370.

Cattaneo, A. 1987. Periphyton in lakes of different trophy. Can. J.
Fish. Aquat. Sci., 44: 296-303.

and J. Kalff. 1979. Primary production of algae

growing on natural and artificial aquatic plants: A study of
interactions between epiphytes and their substrate. Limnol.
Oceanogr., 24: 1031-1037.

and . 1980. The relative contribution of

aquatic macrophytes and their epiphytes to the production of
macrophyte beds. Limnol. Oceanogr., 25: 280-289.

and . 1981. Reply to comment by Gough

and Gough. Limnol. Oceanogr., 26(5): 988-989

Charles, D. 1984. Recent pH history of Big Moose Lake (Adirondack
Mountains, New York, U.S.A.) inferred from sediment diatom
assemblages. Verh. Internat. Verein. Limnol., 22: 1-7.

. 1985. Relationships between surface sediment

diatom assemblages and lakewater characteristics in
Adirondack lakes. Ecol., 66(3): 994-1011.

127

Christie, C.E. and J.P. Smol. 1990. Paleolimnological reconstructions
of nutrient concentrations using diatom assemblages and
weighted averageing techniques. 11th International Symposium
on Living and Fossil Diatoms, San Francisco, 13-17August, 1990,
Abstract.

Collins, CD., Sheldon, R.B. and C.W. Boylen. 1987. Littoral zone

macrophyte community structure: distribution and association
ofspecies along physical gradients in Lake George, New York,
U.S.A. Aquat. Bot., 29: 177-194.

Crowder, A. A. , J.M. Bristow, M.R. King and S. Vanderkloet. 1977.

The aquatic macrophytes of some lakes in southeastern Ontario.
Nat. Can. (Que.), 104: 457-464.

Daniel, C. and F.S. Wood. 1971. Fitting equations to data. Wiley-
Interscience, N.Y. 342 p.

Davis, F.W. 1985. Historical changes in submerged macrophyte
communities of upper Chesapeake Bay. Ecol., 66(3): 981-993.

Davis, R.B. 1987. Paleolimnological diatom studies of acidification of
lakes by acid rain: an application of Quaternary Science. Quat.
Sci. Rev., 6: 147-163.

and D.S. Anderson. 1985. Methods of pH calibration of

sedimentary diatom remains for reconstructing history of pH in
lakes. Hydrobiologia, 120: 69-87.

and F. Berge. 1980. Atmospheric deposition in Norway

during the last 300 years as recorded in SNSF lake sediments II.

Diatom stratigraphy and inferred pH. Proc. Int. Ecol. Impact,

Precip., Norway 1980, SNSF project.

Deevey, E.S., Jr. 1988, Estimation of downward leakage from Florida
lakes. Limnol. Oceanogr., 33: 1308-1320.

Dixit, S.S, and R.D. Evans. 1986. Spatial variability in algal
microfossils and its bearing on diatom-inferred pH
reconstructions. Can. J. Fish. Aquat. Sci., 43: 1836-1845.

■5

.' *•■

128

Duarte, CM. and J. Kalff. 1990. Patterns in the submerged

macrophyte biomass of lakes and the importance of the scale of
analysis in the interpretation. Can. J. Fish. Aquat. Sci., 47: 357-
363.

Eakins, J.D. and R.T. Morrison. 1978. A new procedure for the

determination of lead-210 in lake and marine sediments. Int. J.
Appl. Radiat. Isotopes, 29: 531-536.

Earle, J.C, H.C. Duthie, W.A. Glooschenko and P.B. Hamilton. 1988.
Factors affecting the spatial distribution of diatoms on the
surface sediments of three Precambrian shield lakes. Can. J.
Fish. Aquat. Sci., 45: 469-478.

Eminson, D. and B. Moss. 1980. The composition and ecology of
periphyton communities in freshwaters. 1. The Influence of
host type and external environment on community composition.
Br. phycol. J., 15: 429-446.

Ennis, G.L. 1975. Distribution and abundance of benthic algae along
phosphate gradients in Kootenay Lake, British Columbia. Verh.
Internat. Ver. Limnol., 19: 562-570.

Filbo, G.J. and J.W. Barko. 1985. Growth and nutrition of submersed
macrophytes in a eutrophic Wisconsin (U.S.A.) impoundment.
Freshwater Ecol., 3(2): 275-285.

Fontaine, T.D., III and K. C. Ewel. 1981. Metabolism of a Florida lake
ecosystem. Limnol. Oceanogr., 26(4): 754-763.

Fritz, S.C. 1990. Twntieth-century salinity and water-level

fluctuations in Devils Lake, North Dakota: Test of a diatom-
based transfer function. Limnol. Oceanogr., 35: 1771-1781.

Gough, S.B. and L.P. Gough. 1981. Comment on "Primary production
of algae growing on natural and artificial aquatic plants: A study
of interactions between epiphytes and their substrate" (Cattaneo
and Kalff). Limnol. Oceanogr., 26(5): 987-988.

Hikansson, H. 1982. The recent diatom succession of Lake

HSvgardssjon, south Sweden. In D.G. Mann (ed.). Proceedings of
the Seventh International Diatom Symposium, Philadelphia, 22-
27 August,1982. p. 411-429.

129

Hall, R.I. and J.P. Smol. 1990. A calibration of surficial sediment

diatom assemblages from British Columbia lakes along a trophic
gradient. 11th International Symposium on Living and Fossil
Diatoms, San Francisco, 13-17 August, 1990. Abstract.

Hansson, L.A. 1988. Effects of competitive interactions on the
biomass development of planktonic and periphytic algae in
lakes. Limnol. Oceanogr., 33(1): 121-128.

Hill, M.O. and H.G. Gaugh, Jr. 1980. Detrended correspondence

analysis: an improved ordination technique. Vegetatio, 42: 47-
58.

Hodgson, L.M., S.B. Linda and D.E. Canfield, Jr. 1986. Periphytic algal
growth in a hypereutrophic Florida lake following a winter
decline in phytoplankton. Florida Scientist, 49: 234-241.

Hoyer, M.V. and D.E. Canfield, Jr. 1986. Surface area of aquatic
macrophytes. Aquatics. 8(2): 26-27.

Huber, W.C, P.L. Brezonik, J.P. Heany, R.E. Dickinson, S.D. Preston, D.S.
Dwornik and M.A. DeMaio. 1982. A classification of Florida
lakes. Final report to the Florida Department of Environmental
Regulation, Report ENV-05-82-1, Tallahassee, Florida, v. 1-2.
547 p.

Hudson, C. and P. Legendre. 1987. The ecological implications of
growth forms in Epibenthic diatoms. J. Phycol., 23:434-441.

Hustedt, F. 1930. Die siisswasser flora Mitteleuropas. Heft 10.
Bacillariophyta (Diatomeae). Jena. Gustav Fischer. 466 p.

. 1930-1966. Die Kieselalgen Deutschlands,

Osterreichs und der Schweiz. In Dr. L. Rabenhorst's
Kryptogamen Flora von Deutschlands, Osterreichs und der
Schweiz. Band 7. Teil 1-3.

. 1937-1938. Systematische und okologische

Untersuchungen iiber die diatomeen Flora von Java, Bali and
Sumatra. Arch. f. Hydrobiol., Supplement-Band, 15: 131-177, 16:
187-295, 16: 393-506.

130

Hutchinson, G.E. 1975. A treatise on limnology, v. 3. Wiley & Sons,
Inc. New York. 660 p.

Huttunen, P. and J. Merilainen. 1986. Applications of multivariate
techniques to infer limnological conditions from diatom
assemblages. In J.P. Smol, R.W. Battarbee, R.B. Davis and J.
Merilainen (eds.). Diatoms and Lake Acidity. Dr. W. Junk
Publishers, Dordrecht. 307 p.

Jackson, S.T. and D.F. Charles. 1988. Aquatic macrophytes in

Adirondack (New York) lakes: patterns of species composition in
relation to environment. Can. J. Hot., 66: 1449-1460.

Kilham, P. 1971. A hypothesis concerning silica and the freshwater
planktonic diatoms. Limnol. Oceanogr., 16(1): 10-18.

Kratzer, C.R. and P.L. Brezonik. 1981. a Carlson-type trophic state

index for nitrogen in Florida lakes. Water Resour. Bull., 17: 713-
715.

Landers, D.H. 1982. Effects of naturally senescing aquatic

macrophytes on nutrient chemistry and chlorophyll a of
surrounding waters. Limnol. Oceanogr., 27(3) 428-439.

Line, J.M. and H.J.B. Birks. 1990. WACALIB version 2.1- a computer
program to reconstruct environmental variables from fossil
diatom assemblages by weighted averaging. Journal of
Paleolimnology, 3(2): 170-173.

Lowe, R.L. 1974. Environmental requirements and pollution

tolerance of freshwater diatoms. U.S. Environmental Protection
Agency Report EPA-670/4-74-005, National Environmental
Research Center, Cincinnati, Ohio. 334 p.

Maceina, M.J. and J.V. Shireman. 1980. The use of a recording
fathometer for determination of distribution and biomass of
Hydrilla. J. Aquat. Plant Manage., 18: 34-39.

Menzel, D.W. and N. Corwin. 1965. The measurement of total

phosphorus in seawater based on the liberation of organically
bound fractions by persulfate oxidation. Limnol. Oceanogr., 10:
280-282.

131

Murphy, J. and J. P. Riley. 1962. A modified single solution method
for the determination of phosphate in natural waters. Anal.
Chim. Acta, 27: 31-36.

Nelson, D.W. and L.E. Sommers. 1975. Determination of total nitrogen
in natural waters. J. Environ, Qual., 4: 465-468.

Nygaard, G. 1949. Hydrobiological studies on some Danish ponds and
lakes, II. The quotient hypothesis and some new or little known
phytoplankton organisms. Det. Kingel. Dansk. Vid. Selsk. Biol.
Skr., 7: 1-293.

, 1956. Ancient and recent flora of diatoms and

chrysophyceae in Lake Gribs0, p. 32-94. In K. Berg and I. B.C.
Peterson. Studies on the Humic, Acid Lake Gribs0. Fol. Limnol.
Scand., 8.

Ott, L. 1977. An introduction to statistical methods and data
analysis. Duxbury Press. North Scituate, Mass., 730 p.

Parsons, T.R. and J.D. Strickland. 1963. Discussion of

spectrophotometric determination of marine-plant pigments,
with revised equations for ascertaining chlorophylls and
carotenoids. J. Mar. Res., 21: 155-163.

Patrick, R. 1973. Use of algae, especially diatoms, in the

assessment of water quality. Biological methods for the
assessment of water quality, ASTM STP 528, American Society
for Testing and Materials, 1973, p. 76-95.

and C.W. Reimer. 1966-1975. The Diatoms of the

United States. Monogr. Acad. Sci. Phila., No. 13, Part 1, v. 1-2.

Porcella, D.B., S.A. Peterson and D.P. Larsen. 1980. An index to

evaluate lake restoration. U.S.G.S., Water Supply Paper 1454,
U.S. Government Printing Office, Washington, D.C.

Purohit, R. and S.P. Singh. 1985. Submerged macrophytic vegetation
in relation to eutrophication level in Kumaun Himalaya (India).
Environ. PoIIut. Ser. A Ecol. Biol., 39(2): 161-174.

132

Renberg, I. and T. Hellberg. 1982. The pH history of lakes in

southwestern Sweden, as calculated from the subfossil diatom
flora of the sediments. Ambio, 11(1): 30-33.

Richard, D.I., J.W. Small, Jr. and J. A. Osborne. 1984. Phytoplankton
responses to reduction and elimination of submerged vegetation
by herbicides and grass carp in four Florida lakes. Aquat. Bot.,
20: 307-319.

Robinson, C.T. and S.R. Rushforth. 1987. Effects of physical

disturbance and canopy cover on attached diatom community
structure in an Idaho stream. Hydrobiologia, 154: 49-59.

Roemer, S.C, K.D. Hoagland and J.R. Roscowski. 1984. Development
of a freshwater periphyton community as influenced by diatom
mucilages. Can. J. Bot., 62: 1799-1813.

Round, F.E. 1956. A note on some communities of the littoral zone of
lakes. Arch. f. Hydrobiol., 52(3): 398-405.

Sand-Jensen, K. and M. Sondergaard. 1981. Phytoplankton and
epiphyte development and their shading effect on submerged
macrophytes in lakes of different nutrient status. Int. Rev.
Gesamten Hydrobiol., 66(4): 529-552.

SAS Institute, Inc. 1985. SAS User's Guide: Statistics. SAS Institute,
Inc., Gary, North Carolina. 956 p.

Schardt, J.D. 1983. 1983 Aquatic Flora of Florida Survey Report.
Bur. Aquat. Plant Res. Control, Florida Dep. Nat. Resour.,
Tallahassee. 116 p.

Schelske, C.L. 1988. Historic trends in Lake Michigan silica

concentrations. Int. Revue ges. Hydrobiol. 73(5): 559-591.

, E.F. Stoermer, D.J. Conley, J.A. Robbins and R.M. Glover.

1983. Early eutrophication in the lower Great Lakes: New
evidence from biogenic silica in sediments. Science 222: 320-
322.

Servant- Vildary, S. and M. Roux. 1990. Multivariate analysis of
diatoms and water chemistry in Bolivian saline lakes.
Hydrobiologia, 197: 267-290.

133

Shannon, E.E. and P.L. Brezonik. 1972. Relationships between lake
trophic state and nitrogen and phosphorus loading rates. Envir.
Sci. Tech., 6(8): 719-725.

Shireman, J.V. and M.J. Maceina. 1981. The utilization of grass carp,
Ctenopharyngodon idella Val., for hydrilla control in Lake
Baldwin, Florida. J. Fish. Biol. 19: 629-636.

Shortreed, K.S., A.C. Costella and J.G. Stockner. 1984. Periphyton
biomass and species composition in 21 British Columbia lakes:
seasonal abundance and response to whole-lake nutrient
additions. Can. J. Bot., 62: 1022-1031.

Siver, P. A. 1978. Development of diatom communities on
Potamogeton robbinsii Oakes. Rhodora, 80: 417-431.

Stockner, J.G. 1971. Paleolimnology as a means of assessing

eutrophication. Verh. Internat. Verein. Limnol., 18:1018-1030.

and F.A. Armstrong. 1971. Periphyton of the

Experimental Lakes Area (ELA), northwestern Ontario. J. Fish.
Res. Board Can., 28: 215-229.

and W.W. Benson. 1967. The succession of diatom

assemblages in the recent sediments of Lake Washington.
Limnol. Oceanogr., 12: 513-532.

Stoermer, E.F., J. A. Wolin, C.L. Schelske and D.J. Conley. 1990.
Siliceous microfossil succession in Lake Michigan. Limnol.
Oceanogr., 35(4): 959-967.

Tarver, D.P., J.A. Rodgers, M.J. Mahler, R.L. Lazor. 1979. Aquatic and
wetland plants of Florida. Bureau of Aquatic Plant Research and
Control, Flor. Dept. of Nat. Res., Tallahassee. 127 p.

ter Braak, C.J.F. 1987. CANOCO - a FORTRAN program for canonical
community ordination. TNO Institute of Applied Computer
Science, Wageningen, Netherlands.

134

Van Dam, H., G. Suurmond and C. ter Braak. 1980. Impact of acid

precipitation on diatoms and chemistry of Dutch moorland pools.
Proc. Int. Conf. Ecol. Impact Acid Precip., Norway 1980, SNSF
project.

Van der Werff, A. 1955. A new method of concentrating and

cleaning diatoms and other organisms. Verh. Internat. Verein.
Limnol., 12: 276-277.

Watts, W.A. 1978. Plant macrofossils and Quaternary paleoecology.
p. 89-99. In E.J. Gushing and H.E. Wright (eds.). Quaternary
Paleoecology. Yale University Press, New Haven.

Whitmore, T.J. 1985. Diatom transfer functions for assessing the
cultural eutrophication of Florida lakes. Master's Thesis, Univ.
of Florida, Gainesville. 143 p.

. 1989. Florida diatom assemblages as indicators of

trophic state and pH. Limnol. Oceanogr. 34(5): 882-895.
. in press. Sedimentary diatom concentrations and

accumulation rates as predictors of lake trophic state.
Hydrobiologia.

Yentsch, C.S. and D.W. Menzel. 1963. A method for the determination
of phytoplankton chlorophyll and phaeophytin by flourescence.
Deep Sea Res., 10: 221-231.

Yeo, R.R. 1966. Yields of propagules of certain aquatic plants.
Weeds, 14: 110-113.

BIOGRAPHICAL SKETCH

Thomas J. Whitmore was born in Bridgeport, Connecticut on July
27, 1955. Tom graduated from the University of Connecticut in 1977
with a Bachelor of Science degree from the Biology Department. His
studies emphasized geology and paleontology, and included a 12-
credit independent study on the paleoecology of the Miocene and
Pliocene sediments of the South Carolina coastal plain. Tom
completed a graduate course in micropaleontology at Western
Connecticut State College and entered the University of Florida for
graduate work in 1979.

Though intending research in invertebrate paleontology, Tom
met Edward S. Deevey, Jr. and began studies with Ed on the
paleolimnology of Florida lakes. Tom studied the "Ecology and
Systematics of Diatoms" with Charles Reimer at Iowa Lakeside
Laboratory in the summer of 1981, and submitted his master's thesis
entitled "Diatom Transfer Functions for Assessing the Cultural
Eutrophication of Florida Lakes" in 1985.

Tom continued graduate work in Ed Deevey's laboratory at the
Florida Museum of Natural History until Ed's death in 1988. In 1990,
Ed's lab relocated to join Claire Schelske in the Department of
Fisheries and Aquaculture at the University of Florida. Tom
continued graduate studies under the guidance of Frank Nordlie,
Chairman of the Department of Zoology. He also participated in field

135

136

work and research in 1989-90 on the paleolimnology of the Yunnan
Plateau of China under the direction of Mark Brenner, his long-time
friend and colleague.

Tom's professional interests include further research on
assessing the trophic trajectories of lakes, reconstructing climatic and
anthropogenic influences on lakes in southern China, and interpreting
long-term climatic patterns in Florida from lake sedimentary
indicators.

I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is
fully adequate, in scope and quality, as a dissertation for the degree
of Doctor of Philosophy.

Frank G. Nordlie, Chairman
Professor of Zoology

I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is
fully adequate, in scope and quality, as a dissertation for the degree
of Doctor of Philosophy.

Claire L. Schelske

Carl S. Swisher Professor of Water

Resources

I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is
fully adequate, in scope and quality, as a dissertation for the degree
of Doctor of Philosophy.

Daniel E. Canfield, J*

Professor of Forest Resources and

Conservation

I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is
fully adequate, in scope and quality, as a dissertation for the degree
of Doctor of Philosophy.

Ronald G. Wolff

Associate Professor of Zoology

I certify that I have read this study and that in my opinion it
conforms to acceptable standards of scholarly presentation and is
fully adequate, in scope and quality, as a dissertation for the degree
of Doctor of Philosophy.

^,7y*-xr4

)seph^. Davis
^rofessor of Botany

This dissertation was submitted to the Graduate Faculty of the
Department of Zoology in the College of Liberal Arts and Sciences and
to the Graduate School and was accepted as partial fulfillment of the
requirements for the degree of Doctor of Philosophy,

May 1991

Dean, Graduate School

'^l.

'■\:

.^,Miy,^''S'TY OF FLORIDA

3 1262 08556 9928

:--r'^^-

'*«

M.