ECOLOGICAL CONDITION OF ALGAE AND 

NUTRIENTS 

IN FLORIDA SPRINGS 

DEP CONTRACT NUMBER WM858 

FINAL REPORT

Submitted to the Florida Department of Environmental Protection 

June 10, 2004 

Authors: 

R. Jan Stevenson 

Agnieszka Pinowska 

Yi-Kuang Wang
DEP Contract Number WM 858 

Ecological Condition of Algae and Nutrients in Florida Springs 

Submitted to: 

Russel Frydenborg 

Environmental Assessment Section 

Florida Department of Environmental Protection 

2600 Blair Stone Rd. MS 6511 

Tallahassee, FL 32399 

Phone 850-245-8082 

Fax 850-245-8063 

Submitted by: 

R. Jan Stevenson 

Department of Zoology 

203 Natural Science Building 

Michigan State University 

East Lansing, MI 48824 

Phone 517-432-8083 

Fax 517-432-2789 

June 8, 2004 
ACKNOWLEDGEMENTS 

The assistance of members of the Algal Ecology Laboratory in the Department of 

Zoology, Michigan State University: Lei Zheng, Julianne Heinlein, Vanessa 

Laugheed, Scott L. Rollins, Lara Panayotoff, Colleen McLean, and Daniel 

Wieserich is gratefully acknowledged. 

Silver River State Park in Florida graciously provided housing during the 

experimental streams study. We especially want to thank Robert LaMont, Steven 

Logan and a crew at Silver River State Park for their help in setting up experiments 

and changing flat tires in the middle of Ocala State Forest. 

Silver River State Park, Rainbow River State Park and Wakulla Springs State Park 

provided boat support. 

We also thank the DEP staff in Tallahassee and in the State Parks for their 

assistance in this study. 

ii
TABLE OF CONTENTS 

EXECUTIVE SUMMARY .............................................................................................................1

CHAPTER 1. Introduction..............................................................................................................2 

CHAPTER 2. Assessment of macroalgal biomass and taxonomic composition in Florida springs 

as a function of nutrient concentrations...................................................................4 

CHAPTER 3. Experimental confirmation of limiting nutrients using experimental streams ......46 

CHAPTER 4. Macroalgal growth in high and low conductivity spring water.............................62 

CHAPTER 5. Nutrient diffusing substrata assessment of nutrient limitation of microalgae .......71 

CHAPTER 6. Diatom indicators of nutrient conditions in Florida springs..................................76 

CHAPTER 7. Data........................................................................................................................92 

CHAPTER 8. References..............................................................................................................94 

 

CHAPTER 9. Appendices ..........................................................................................................101 

iii
EXECUTIVE SUMMARY 

Nuisance growths of macroalgae have been identified as a problem for the recreational use of 

and aquatic life support in Florida springs. Nuisance algal growths have been associated with 

human activity and increases in nutrients in the springs. Development of nutrient criteria and the 

tools to implement the criteria will help protect springs and other water bodies of the state. 

However, prior to this study, no systematic studies of macroalgal occurrence and their 

relationships to nutrients in Florida springs of this scope are available. 

A survey of filamentous macroalgae was conducted at 60 sites in 28 springs of north and central 

Florida, with the following results: 

• Macroalgae were found at all sites and covered over half of the bottoms of Florida 

springs. 

• Vaucheria and a noxious cyanobacterium, Lyngbya majuscula, were the two most 

common of a great diversity of algae at the sites. 

• The percent of the bottom of springs covered by Vaucheria was related to total nitrogen 

concentrations in springs, but Lyngbya occurrence was not constrained by the low 

nutrients in the ranges studied. 

Experiments, conducted using a variety of techniques to determine whether the nitrogen or 

phosphorus supply limited the growth of algae in springs, had the following results: 

• Laboratory bioassays using water from the springs indicated that algae were limited by P 

at 56% of spring sites, by N at 19%, and by both N and P at 22%. 

• In situ bioassays with nutrient-diffusing substrata embedded in sediments were not 

conclusive because of low colonization rates and the loss of many substrata. 

• Outdoor mesocosm studies in recirculating streams indicated Vaucheria growth rates 

were related to nitrate concentrations, but not phosphorus concentrations, and Lyngbya

growth rates were not limited by the range of nutrients tested. 

• Outdoor mesocosm studies in small tubs indicated that conductivity affected the response 

of Vaucheria to nutrients, but did not affect Lyngbya. 

Changes in microalgae on plants, macroalgae and sandy spring bottoms were surveyed to 

develop algal indicators of nutrient conditions in springs. These indicators will complement the 

measurement of nutrients in springs to characterize nutrient conditions.

• When variability in conductivity was reduced by eliminating high conductivity sites from

analyses, the importance of nutrient regulation of diatom species composition became 

evident. 

• Variation in nitrogen and phosphorus concentrations among spring sites was not 

correlated as in most studies, so different diatom indicators of nitrogen and phosphorus 

conditions were developed. 

• Existing diatom indicators of nutrient conditions were weakly correlated to P conditions 

in springs, but not to N; but newly developed indicators show value. 

Regulation of nitrogen may control macroalgal growths in Florida springs, but more evidence is 

required for development of specific nutrient criteria. 

1
CHAPTER 1. INTRODUCTION 

Nuisance growths of macroalgae have been identified as a problem in Florida springs, 

affecting both recreational use and support of aquatic life (Florida Springs Task Force, 2000). 

These springs have great economic importance for the state (Bonn and Bell, 2003) and represent 

unique resources for the support of recreation and biodiversity Florida Springs Task Force, 

2000). Nuisance algal growths have been observed in many springs and have been associated 

with increases in human activity and nutrients, particularly nitrate. Development of nutrient 

criteria and the tools to implement the criteria will help protect springs and other water bodies of 

the state (USEPA 1999). However, no previous systematic studies of macroalgal occurrence and 

their relationships to nutrients in Florida springs of this scope are available. 

 

Three separate lines of research were developed to determine the following: 

1. Nutrient and algal conditions; 

2. The relationships between nutrients and algae; and 

3. Monitoring and management tools to protect and restore the springs. 

First, a large number of sites in Florida springs were surveyed to determine the diversity 

of the algae; the factors affecting algal diversity; the range of nutrient conditions; and the 

relationships between the extent of algal growth and nutrients. Springs were sampled and 

surveyed during both the spring and the fall to determine whether conditions differed during the 

two seasons. The relationships among the macroalgal cover of spring bottoms, the thickness of 

macroalgae, and nutrient concentrations were the main focus. Results of these surveys are in 

Chapter 2. 

In many studies, nitrogen and phosphorus co-vary; so the nutrients that limit algal growth 

and would affect algae if reduced had to be identified. While the magnitude of correlations 

provides a hint about which factors most affect biology in ecosystems, experiments were 

necessary to show cause-effect relationships and determine whether management of nitrogen or 

phosphorus would be more effective (e.g., Pan et al., 2000). Therefore, experiments were 

conducted to determine the ranges of nitrogen and phosphorus that regulate algal growth in 

springs. Due to the variations in size and growth forms of algae in springs and the lack of 

research available on macroalgae in mesocosms, three different experimental approaches were 

used: 

1. Recirculating streams in an outdoor setting: Spring water was recirculated in the 

streams, with varying amounts of nutrients added. 

2. Algal growth rates were observed in small tubs in which conductivity and nutrients 

were manipulated. 

3. Small experimental devices were placed in streams. These devices slowly leaked 

nutrients through a fine-mesh screen upon which algae could grow in as natural 

setting a setting as possible. 

The results of these experiments can be found in Chapters 3-5. 

2
Assessing and monitoring nutrient conditions in streams and springs is challenging 

because nutrient concentrations vary with algal metabolism during the day, with weather-related 

runoff events that rinse nutrients into springs, and with natural weekly variations in algal accrual 

and die-off cycles (USEPA 1999). Diatoms are microscopic algae that are often the dominant 

photosynthetic organisms in aquatic habitats. They are an important base of the food web and 

are very sensitive to changes in nutrient concentrations. Because there are so many different 

species of diatoms, and the species have different sensitivities to nutrients, diatoms can be 

sensitive and precise indicators of nutrient conditions. In addition, because they live for 

extended periods of time in springs, diatoms provide a temporally integrated indication of 

nutrient conditions in springs that may more accurately and precisely characterize the nutrient 

regime of a water body than the sampling and measurement of nutrient chemistry (Stevenson and 

Smol, 2003). Thus, studies of diatoms and other microalgae in streams and their relationships to 

nutrients were conducted to better determine the effects of nutrients on algae and to develop 

indicators of nutrient conditions in springs. Results of this study can be found in Chapter 6. 

This research was intensive, but the study’s timeline was foreshortened by the state. 

Thus, the strategy for success was to collect as much information during surveys, experiments, 

and laboratory work as possible, use all viable experimental approaches, analyze the most 

promising data that resulted from this work, and present that data in this report. Sufficient time

was not available to analyze and present the results of all the data that were collected. However, 

those data have been organized in electronic format and re provided with this report to the 

Florida Department of Environmental Protection. The files in which data are stored are 

presented in Chapter 7. 

3
CHAPTER 2. ASSESSMENT OF MACROALGAL BIOMASS AND 

TAXONOMIC COMPOSITION IN FLORIDA SPRINGS AS A FUNCTION 

OF NUTRIENT CONCENTRATIONS 

Introduction

Florida Springs are an important and unique resource used by many for recreational 

activities, such as swimming, boating, and sight-seeing (Florida Springs Task Force, 2000). 

Concerns have developed that the occurrence and biomass of macroalgae in the springs have 

increased during the last decade (Southwest Florida Water Management District, 2003). In the 

last fifty years many of the springs experienced drastic changes in their watershed and in the area 

directly surrounding springs. An increase in nutrients, especially nitrate, was observed in some

springs (Rosenau et al., 1977). That the elevated nutrients associated with increased land use 

have stimulated the occurrence of nuisance algal blooms in Florida springs is cause for concern. 

Very few published studies document the occurrence of algae in Florida springs. Odum 

(1957) reported the presence of small Spirogyra sp., Oedogonium sp. and Rhizoclonium sp. mats 

in Silver Springs. Their biomass was very low compared to the rest of the primary producers in 

Silver Springs; consequently, macroalgal biomass was not included in the estimates of system

productivity in Odum’s study. A list of algae from Florida springs compiled by Whitford (1956) 

includes macroalgae taxa such as Vaucheria sp. and Cladophora sp., but mat thickness and 

spatial extend of cover were not recorded. Dichotomosiphon tuberosus was found in 

Ichetucknee, Turtle, Manatee, Poe and Silver Springs by Davis and Gworek (1972). 

Very few published studies document relationships between macroalgae and nutrients. 

Relationships between Cladophora and nutrients have been described, but in streams in 

Montana, Michigan, and Kentucky (Dodds et al., 1997, Stevenson et al., accepted). However, 

these relationships may not apply to the types of algae in Florida springs and to the physicalchemical conditions in Florida springs. 

The objectives of this study were to document the types of macroalgae occurring in 

Florida springs, the spatial extent and thickness of algal mats in springs, and the relationships 

among the amount of macroalgae, nutrients, and other environmental factors. A survey approach 

was used to observe and collect macroalgae at a large number of sites in a large number of 

springs. This approach was believed to be the best for characterizing the status of conditions in 

Florida springs and determining the relationships between the macroalgal biomass and nutrients. 

This study was the first systematic study of benthic algae in a large number of Florida springs 

that incorporated macroalgal percent cover and macroalgal mat thickness during more than one 

season. 

Methods 

Study sites 

Twenty eight springs, mostly of first and second magnitude and located in northern and 

central Florida, were selected for this study (Table 2.1, Figure 2.1). 

4
Table 2.1 

Study sites, spring and site codes, sampling dates, and latitude and longitude of transect 1 for 

each site. 

Spring Spring 

code Site name Site code Latitude Longitude

Date sampled

spring 

Date sampled

fall 

Head ALE-01 29.08128 81.57563 3/28/2003 10/29/2003 Alexander ALE 

Downstream ALE-02 29.08231 81.57754 3/28/2003 10/29/2003

Blue holes CHA-01 28.71617 82.57502 4/1/2003 11/2/2003

Chassahowitzka CHA Dock CHA-02 28.71558 82.57630 4/1/2003 11/2/2003

Brown spring CHA-03 28.71721 82.57586 4/1/2003 11/2/2003

Cypress CYP Head CYP-01 30.65855 85.68430 9/24/2003

Fanning FAN Head FAN-01 29.58757 82.93541 10/2/2003

Pipe GAI-01 30.42736 85.54827 4/26/2003 9/25/2003

Gainer

GAI 

Side boil GAI-02 30.42884 85.54854 4/26/2003 9/25/2003

Guranato GUR Head GUR-01 29.77973 82.94001 10/2/2003

Homosassa HOM After bridge HOM-01 28.79961 82.85905 4/2/2003 11/4/2003

Head ICH-01 29.98408 82.76184 3/16/2003 11/22/2003

Blue Hole ICH-02 29.98068 82.75866 4/20/2003 11/22/2003

Below Blue 

Hole ICH-03 29.98007 82.75895 3/17/2003 11/22/2003

Mission spring ICH-04 29.97628 82.75783 4/23/2003 11/9/2003

Devils Ear ICH-05 29.97388 82.75996 4/23/2003 11/9/2003

Mill Pond ICH-06 29.96658 82.76005 4/23/2003 11/9/2003

Before bridge ICH-07 29.95495 82.78507 4/24/2003 11/8/2003

Ichetucknee ICH 

Coffee spring ICH-08 29.95937 82.77526 4/21/2003 11/8/2003

Indian IND Head IND-01 30.25077 84.32203 9/26/2003

Head JAC-01 30.79037 85.13998 4/19/2003 9/24/2003

Boat ramp JAC-02 30.78249 85.16022 4/19/2003 9/23/2003 Jackson Blue JAC 

Arrowhead

camp ground JAC-03 30.75609 85.18680 4/18/2003 9/23/2003

Head JUN-01 29.18365 81.71201 3/27/2003 10/31/2003

Fern Hammock JUN-02 29.18364 81.70801 3/27/2003 10/31/2003

River fork JUN-03 29.18519 81.70726 3/27/2003 10/31/2003 Juniper JUN 

After bridge on 

route 19 JUN-04 29.21283 81.65431 3/26/2003 10/31/2003

Lafayette Blue LAF Head LAF-01 30.12592 83.22617 9/29/2003

Little River LTR Head LTR-01 29.99642 82.96675 9/28/2003

Madison Blue MAD Head MAD-01 30.48056 83.24439 9/29/2003

Manatee MNT Head MNT-01 29.48952 82.97692 10/3/2003

Ponce de Leon PON Head PON-01 30.72090 85.93071 4/26/2003 9/22/2003

Head RAI-01 29.10223 82.43741 4/4/2003 11/12/2003

KP Hole RAI-02 29.09294 82.42848 4/4/2003 11/12/2003

Before tubers 

sign RAI-03 29.06305 82.42788 4/4/2003 11/12/2003

Rainbow 

Spring 

RAI 

Before bridge RAI-04 29.05223 82.44700 4/4/2003 11/12/2003

Silver Glen SGL Head SGL-01 29.24603 81.64345 3/26/2003 10/30/2003

Head SLV-01 29.21619 82.05252 4/3/2003 11/13/2003

Second pool SLV-02 29.21584 82.04987 4/3/2003 11/13/2003

Birds of prey SLV-03 29.21561 82.04112 4/3/2003 11/13/2003

Old swimming 

area SLV-04 29.20500 82.02902 4/3/2003 11/13/2003

Silver River SLV 

Cabbage palm SLV-05 29.20211 82.01127 4/3/2003 11/12/2003

Troy TRY Head TRY-01 30.00598 82.99756 9/29/2003

Turtle TUT Head TUT-01 29.84742 82.89041 9/30/2003

5
Spring Spring 

code Site name Site code Latitude Longitude

Date sampled

spring 

Date sampled

fall 

Head VOL-01 28.94758 81.33969 3/29/2003 11/20/2003

Volusia Blue VOL Downstream

from stairs VOL-02 28.94679 81.33921 3/29/2003 11/20/2003

Head RR WAC-01 30.33979 83.99244 4/7/2003 9/27/2003

Wacissa WAC Minnow WAC-02 30.33020 83.98776 4/7/2003 9/27/2003

Big Blue WAC-03 30.32770 83.98484 4/7/2003 9/27/2003

Head WAK-01 30.23533 84.30287 4/8/2003 10/1/2003

Wakulla WAK Turnaround WAK-02 30.23318 84.28870 4/8/2003 10/1/2003

Bird colony WAK-03 30.22507 84.27470 4/8/2003 10/1/2003

Washington

Blue

WGT Head WGT-01 30.45279 85.53044 9/26/2003

Head WEK-01 28.51747 82.57349 3/24/2003 11/5/2003

Boat dock WEK-02 28.51901 82.57361 3/24/2003 11/5/2003

WMA WEK-03 28.52481 82.59583 3/24/2003 11/4/2003 Weeki Wachee WEK 

Roger's Park WEK-04 28.53057 82.62407 3/24/2003 11/5/2003

Head WKW-01 28.71193 81.46037 3/30/2004 11/21/2003 Wekiwa WKW 

Canoe launch WKW-02 28.71269 82.45948 3/30/2004 11/21/2003

Willford WIL Head WIL-01 30.43966 85.54763 9/25/2003

6
Figure 2.1 

Location of springs selected for the study. For spring codes, refer to Table 2.1. 

VOL

WKW

ALE

SGL JUN SLV

RAI

WEK

CHA

HOM

ICH

TUT

FAN

GUR

LTR

MNT

TRY

LAF

MAD

WAC

WAK

IND

JAC

WGT WIL GAI

CYP PON

This project began in March 2003. Samples were collected twice: 1) in the spring of 

2003 (March and April), and 2) in the fall of 2003 (September to November). The second 

sampling event was moved from summer to fall since many of the studied sites are highly 

affected by human recreational activity during the summer (Bonn and Bell, 2003) and that 

activity was believed to reduce macroalgal cover and biomass. In the spring, 48 sites within 16 

springs were sampled, and in the fall, 60 sites within 28 springs were sampled (Table 2.1). 

Originally, 53 sites were selected for both sampling events, but due to flooding along Suwannee 

River in the spring of 2003, 5 springs could not be sampled. The spring sampling showed few 

springs with low TP concentrations. Therefore, during the second sampling period (fall), the 

originally selected 53 sites were sampled, along with 7 additional sites that were selected 

specifically for their low TP concentrations. A description of the springs selected for this study 

can be found in Rosenau et al. (1977) and the Florida Springs Task Force Report (2000). Maps 

of sampled sites provided by DEP are in Appendix A.1. 

Rapid Habitat and Periphyton Assessment (RHPA) 

At each of the study sites a modified Rapid Habitat and Periphyton Assessment 

(Stevenson and Bahls, 1999) was conducted. Sites were sampled by wading, snorkeling, or 

making observations from a canoe or boat, depending upon the depth of the spring run and site 

accessibility. At each site, researchers designated 9 transects going across the spring run and 

positioned about 10 m apart. At most sites the first transect crossed the most upstream, boil area 

and the following transects were located downstream from the most upstream boil. Nine 

observation points were designated for each transect, resulting in a total of 81 observation points 

at each site. The length and buffer width were measured for each transect. Buffer width was 

7
defined within a 20 m range, unless more than 20 m was visible, in which case it was treated as 

50 m wide. Any bank conditions (binding roots, canalized or incised) were documented. For 

every second transect, the buffer composition (trees, shrubs, herbs or bare) was evaluated and the 

canopy cover was measured (with a spherical convex crown densiometer). At each point, the 

stream depth was measured, current velocity was estimated, and the substratum type was 

characterized. The presence and taxonomic identity of macrophytes (Dressler, 1991; Ramey, 

1995) and macroalgae and the thickness of the macroalgal mat were noted. The thickness of 

macroalgal mats was recorded on an ordinal scale: 0.5 for mat thickness <0.5 cm, 2 for mat 

thickness 0.5-2 cm, 5 for mat thickness 2-5 cm, 20 for mat thickness 5-20 cm, 50 for mat 

thickness 20-50 cm and 100 cm for mat thickness >50 cm. For other benthic algae, epiphyte 

thickness and epipelon thickness were estimated and also recorded on an ordinal scale: 0.5 for 

slimy biofilm, 1 for biofilm that tracks with a finger, 2 for biofilm 0.5-1 mm thick, 3 for biofilm

1-5 mm thick, 4 for biofilm 5-20 mm thick, and 5 for biofilm >20 mm thick. If the biofilm was 

formed by macroalgae and was more than 20 mm thick, it was recorded as macroalgae. At each 

site the RPHA was repeated at one randomly selected transect to characterize sampling error. 

However, it was impossible to go back exactly to the original points sampled, so the very patchy 

distribution of plants and macroalgae between the original and repeated transect assessment was 

characterized. Global positioning system (GPS) coordinates were recorded in the middle of

transect 1 and transect 9. 

The original RHPA was developed for sites that were wadeable and in which the stream

bottom could be viewed easily. However, most of the study sites in this project were very deep, 

with 65% of sampled points more than 1 m deep and an average depth in excess of 1 m at most 

sites. Thus, the original sampling methods had to be modified for deeper water. Measurement 

of flow with a flow meter was not possible, because the centers of the channel at almost all sites 

were too deep to conduct measurements with the instrument. Current velocity was estimated on 

a scale: 0 (below 5 cm/s), 1 (5-15 cm/s), 2 (16-50 cm/s) and 3 (>50 cm/s). Measurement of 

current velocity with a current meter at each observation point was impractical. 

The percent cover or percent presence of plants, macroalgae, different substrata types, 

etc., was based on results from the RHPA assessment and was calculated as the percent of 

sampled points where particular plant macroalgae or substrata were observed. Usually, at any 

one point more, than one plant, macroalga or substratum type was observed. 

The Landuse Development Intensity Index (LDI) was provided by FDEP. Spring 

discharge was based on USGS data (Rosenau et al., 1977). The distance from boil was measured 

by using the tracking tool along the river run in Microsoft Streets & Trips 2000. 

Algae samples 

Macroalgae 

From each transect, a macroalgae sample was collected to confirm the accuracy of field 

identification (198 samples in the spring and 463 samples in the fall). 

At each site, a composite macroalgal sample was also collected. This was a qualitative 

sample consisting of pinches taken from each transect. Forty-seven samples were collected in 

the spring and 60 samples (including 2 samples collected for QAQC) were collected during the 

8
fall. A subsample was taken for algal mat total nitrogen and total phosphorus analyses (analyzed 

by FDEP). For macroalgae chlorophyll a analyses, a small pinch was patted dry, weighed for 

fresh mass (FM) and frozen. For macroalgae volume and macroalgae ash free dry mass 

(AFDM), a pinch of algae was placed into a volumetric cylinder filled with water. Macroalgae 

were suspended to allow them to fill their natural volume, and the volume they filled (not the 

volume displaced by the macroalgae) was recorded. This measurement allowed the relationship 

between macroalgae mat thickness and macroalgae mass per surface area (1ml of volume used 

by macroalgae equals 1 cm thick macroalgal mat per cm2

) to be calculated. Then the macroalgae 

were patted dry, weighed for FM, and frozen for AFDM analyses. 

The dominant macroalgal taxa were identified under the dissecting scope (up to 100x 

magnification). A microscope (up to 400x) was used if identification was not possible under the 

dissecting scope. 

Epiphytes 

At each transect (if macrophytes were present), a mid-stem section of a plant was 

collected for a composite epiphyte sample. The original proposed collection of tips and midstem sections could not be executed at all sites due to the difficulty of sampling in deep water. 

Originally, SCUBA was planned to be used for sampling in deep water, however, this was not 

possible due to time limitations (in the trial it took a full working day to sample one site while 

using SCUBA) and personnel restrictions (volunteers were required to assist with SCUBA). In 

addition, sampled aquatic plants exhibited different growth patterns. Saggitaria sp. and 

Vallisneria sp. (the most common taxa in this study) have the oldest sections of the plant at the 

tip and youngest at the base, which is not the case for other plant taxa. To correct for this 

difference in growth patterns, only mid-stem sections were collected. Epiphytes were brushed 

off and rinsed from plant fragments. Forty-five samples were collected during the spring and 61 

(including 3 repeated samples for QAQC) samples were collected during the fall. A subsample 

was taken for chlorophyll a analysis and frozen immediately. Plants from which algae were 

removed were frozen for plant surface analysis. Macrophytes were spread flat on a white sheet 

of paper and scanned with a flatbed scanner. The plant surface area was calculated using the 

scanned image and Image-J software (Rasband, 2003). 

Epipelon 

At each transect, if sediments were exposed to microalgal colonization, a sediment core 

(5.2 cm in diameter and 0.3 cm deep) was collected. A composite quantitative epipelon sample 

consisted of all microalgae collected with cores at the site. Algae were removed from sediments 

by swirling and rinsing the sediments with distilled water (Stevenson and Stoermer, 1983). This 

swirl-pour procedure was repeated 10 times to remove most algae from sediments. Forty-six 

samples were collected during the spring and 21 samples were collected in the fall. The latter 

were collected from 6 sites sampled in the spring, 12 sites not sampled in the spring and 3 

repeated samples for QAQC. A subsample was taken for chlorophyll a analysis and frozen 

immediately. 

Chlorophyll a and AFDM 

Chlorophyll a analyses were conducted within 4 weeks of sample collection. 

Chlorophyll a was extracted with 90% ethanol and analyzed on a Turner Designs TD-700 

9
fluorometer (Clesceri et al., 1998). Samples for AFDM were preserved with M3 and were 

processed following Clesceri et al. (1998). 

Water analyses 

Water temperature, pH, conductivity and dissolved oxygen (DO) were measured at each site at 

transect 1 and 9 with YSI or Hydrolab. Water clarity (light extinction coefficient with PAR 

measured at the surface, at 0.5m and at 1m) was measured using a LICOR LI-250. From the 

most upstream transect, water samples were collected to analyze for alkalinity, ammonia 

nitrogen, calcium, chloride, iron, magnesium, Kjeldahl nitrogen, nitrates, orthophosphate, 

potassium, silica, sodium, strontium and sulfate. Samples were analyzed by FDEP. When the 

concentration of a compound was below the detection limit of the laboratory method, the method 

detection limit is reported (Table 2.2). Algal growth potential and algal limiting nutrient 

bioassays were conducted by FDEP on water samples collected from 15 sites in the spring and 

12 sites in the fall. 

Table 2.2 

Detection limits of water chemical analyses. When concentration of a compound was below 

detection limit of the laboratory method, the method detection limit is reported. 

Compound Detection limit (mg/L) 

Alkalinity 5

Ammonia-N 0.010

Calcium 0.050

Chloride 0.200

Iron 0.010

Magnesium 0.028

N_KJEL_TOT 0.060

NO2NO3-N 0.004

O-Phosphate-P 0.004

Potassium 0.025

Silica 0.300

Sodium 0.150

Strontium 7

Sulfate 0.200

Total-P 0.004

Soft epiphytic algae 

A subsample from a composite epiphyte sample was preserved with M3 for taxonomic 

algal analyses (Clesceri et al., 1998). Subsamples were collected by homogenizing samples on a 

magnetic stirrer, then collecting aliquots of each sample with a pipette for each subsample. 

Multiple aliquots were collected to reduce error due to patchiness. Algae were enumerated in a 

Palmer-Maloney counting chamber (Wildco®) under 400x. The count was continued until at 

least 300 live algal units were counted. Both live and dead diatoms were counted, but dead 

diatoms did not contribute to the 300 live units count. Each diatom cell was counted as one unit 

with the exception of diatoms that formed chains; then a chain was counted as a unit. Algae 

were identified to the lowest possible taxonomic level (Dillard, 1989; Komarek, 2003; Sheath, 

10
2003; Whitford and Schumacher, 1984; Komarek and Anagnostidis, 1999; Prescott, 1982; 

Starmach, 1963). 

Diatoms 

A subsample was taken for diatom analyses from each macroalgae, epiphyte and epipelon 

sample. Diatom subsamples were collected by homogenizing samples on a magnetic stirrer and 

collecting aliquots of each sample with a pipette for diatom subsample. Multiple aliquots were 

collected to reduce error due to patchiness. Diatoms were identified under 1000x after acidcleaning and mounting in Naphrax® on microscope slides. Data analyses of diatom samples are 

presented in Chapter 6. 

Statistical analyses 

Pearson correlations between measured environmental variables were calculated using 

SYSTAT 10 software. Repeated measures ANOVA on DM, AFDM, Chla and Phoephytin data 

was conducted using SAS/STAT. 

Multivariate ordination analysis was employed to determine the effect of environmental 

variables on the percent cover of macroalgae at the studied sites. Analysis was conducted with 

CANOCO v.4 for Windows. Species data were log-transformed and species scores were divided 

by the standard deviation. Detrended correspondence analysis (DCA) was used for indirect 

gradient analysis (only species data), and environmental variables were used as supplementary 

environmental data to calculate axes correlations with environmental variables (Leps and 

Smilauer, 2003). Since species distributions followed unimodal patterns (based on the maximum 

spread between sites and species distributions), canonical correspondence analysis (CCA) was 

used for direct gradient analysis (species and environmental variables) (Leps and Smilauer, 

2003). 

Results 

Rapid Habitat Assessment (RHA) 

Sites varied in their buffer width and the character of the bank. Data describing the 

buffers at the study sites are in Appendix A.2, and data describing the types of banks are in 

project Access database (from spring sampling in tblRHA_Canopy_Buffer_TLgth_spring2003 

and from fall sampling in tblRHA_Canopy_Buffer_TLgth_fall2003). Most of the study sites 

were deep and wading was not possible (Appendix A.3). Sites at the head of the spring had at 

least one boil, but in a few cases, it was not possible to sample directly over the boil. For a few 

springs, a different number of boils was observed in the spring and in the fall. Also, a few sites 

had high grazer density, which could have affected the epiphyte and epipelon biomass. Wide 

ranges of current velocity were observed; however, the average current velocity was low 

(Appendix A.3). Sand was the dominant substratum and was found at all sites (Appendix A.4). 

Chemical and physical water parameters 

A wide range of conductivity (113 - 3872 μS/cm) and dissolved oxygen (0.27-9.98 mg/L) 

was observed. However, water temperature (20.06-23.93°C) and pH (7.13-8.33) were relatively 

similar (Appendix A.5). Water chemistry data are in Appendix A.6. 

11
Macroalgae 

Taxonomic composition

Macroalgae were found at 59 out of 60 sampled sites and were present at 45% of all points 

sampled in Florida springs. The only spring in which no macroalgae were found was Cypress 

Spring. Troy Spring had macroalgae present but mat thickness was very low; thus, it was not 

possible to collect enough macroalgae for all of the analyses. In all, 24 taxa of macroalgae were 

found (Table 2.3). The most commonly observed taxa were Lyngbya majuscula and Vaucheria 

sp. (Figure 2.2). Thick algal mats were formed by Lyngbya majuscula, Vaucheria sp., 

Compsopogon sp., and Dichotomosiphon sp. 

Table 2.3 

Macroalgal taxa found in Florida Springs and codes for macroalgae used in this study. 

Taxa Algae code

Cyanophyta:

Lyngbya majuscula

Oscillatoria sp. 

Lyngbya aestuarii

Aphanothece sp. balls 

Phormidium sp. 

Rhodophyta:

Polysiphonia subtilissima

Caloglossa sp.

Compsopogon sp. 

Audouionella sp.

Batrachospermum sp. 

Bacillariophyta: 

Pleurosira leavis 

Terpsinoe musica 

Aulacosira sp. 

Xanthophyceae:

Vaucheria sp. 

Chlorophyta:

Spirogyra sp. 

Cladophora cf glomerata 

Rhizoclonium hieroglyphicum

Dichotomosiphon sp. 

Hydrodiction sp. 

Enteromorpha sp. 

Chaetomorpha sp. 

Stigeoclonium sp. 

Oedogonium sp.

Schizomeris sp. 

L 

O 

Ls 

Ap 

Ph 

Po 

Cg

Cm

U 

Br 

B 

V 

S 

C 

Rh

D 

Hd 

E 

Ch

Od 

12
Figure 2.2 

Proportion of macroalgal taxa found at 59 sampling sites during spring and fall sampling based 

on results of PRHA assessment (8676 points). 

Other

Diatoms forming filaments

Chaetomorpha sp.

Rhizoclonium hieroglyphicum

Hydrodictyon sp.

Cladophora cf. glomerata

Oscillatoria sp.

Spirogyra sp. Vaucheria sp.

Lyngbya majuscula

Figure 2.3 

Percent macroalgal cover at 59 sites in the fall. 

% cover 

<10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

13
Table 2.4 

Pearson correlation matrix for macroalgae and environmental variables in the fall (p<0.05 in 

bold). 

Average

cover 

thickness 

Percent 

cover 

DM AFDM Chl a Pheophytin

Lyngbya 

majuscula

percent

cover 

Vaucheria

sp. percent 

cover 

ALKALINITY 0.131 0.185 0.057 0.045 0.127 -0.050 0.012 0.465 

AMMONIAN -0.094 -0.080 -0.092 -0.074 -0.064 -0.006 -0.102 -0.104

CALCIUM 0.206 0.243 0.153 0.207 0.157 0.032 0.091 0.478 

CHLORIDE 0.030 0.029 0.028 0.086 0.092 0.088 -0.071 -0.014

IRON -0.122 -0.092 -0.133 -0.192 -0.183 0.005 -0.299 0.195 

N_KJEL_TOT -0.055 0.135 0.356 -0.029 0.196 0.111 -0.240 0.300 

NO2NO3N 0.023 0.057 0.031 -0.064 -0.082 -0.010 -0.114 0.375 

OPHOSPHATE -0.031 0.225 0.062 -0.021 0.082 0.191 0.094 0.130

SILICA 0.038 0.088 0.096 0.069 0.052 0.038 0.089 -0.165

SODIUM 0.028 0.029 0.028 0.085 0.085 0.090 -0.073 -0.023

SULFATE 0.119 0.135 0.178 0.263 0.073 0.148 0.143 0.045

TOTALP -0.033 0.221 -0.001 -0.019 0.056 0.153 0.093 0.152

TOTALN 0.019 0.065 0.053 -0.065 -0.069 -0.003 -0.128 0.391 

MAGNESIUM 0.024 0.040 0.051 0.084 0.056 0.087 -0.041 -0.009

STRONTIUM 0.158 0.209 0.202 0.328 0.203 0.165 0.229 0.071

LATITUDE -0.249 -0.346 -0.093 -0.286 -0.382 0.042 -0.361 -0.182

LONGITUDE -0.144 -0.375 -0.174 -0.240 -0.297 -0.104 -0.368 -0.212

PH 0.002 -0.076 -0.017 0.082 -0.019 -0.025 0.149 -0.533

CONDUCTIVITY 0.067 0.053 0.056 0.118 0.109 0.095 -0.048 0.051

DO -0.144 -0.293 -0.175 -0.085 -0.213 0.108 0.135 -0.328

TEMPERATURE 0.234 0.367 0.106 0.285 0.315 -0.001 0.341 0.140 

CANOPY -0.187 -0.081 -0.077 -0.211 -0.083 -0.112 -0.138 0.070

BUFFER -0.270 -0.271 -0.342 -0.258 -0.099 0.016 -0.176 -0.191

Transect Length 0.042 -0.033 0.018 0.096 -0.094 -0.040 -0.038 -0.135

TEES -0.137 -0.011 -0.249 -0.100 -0.093 0.159 0.063 -0.147

SHRUBS -0.062 0.016 -0.239 -0.100 0.031 0.034 -0.091 -0.202

HERBS 0.306 0.120 0.205 0.356 0.305 0.009 0.340 -0.008

BARE 0.279 0.288 0.356 0.207 0.119 -0.134 0.167 0.357 

LDI 0.149 0.104 0.097 0.123 0.215 0.043 0.091 0.475 

DISCHARGE 0.044 0.028 0.052 0.117 0.019 0.296 0.395 -0.028

MAT TN 0.097 0.092 0.008 0.07 0.08 0.024 0.157 -0.126

MAT TP -0.021 0.016 -0.068 -0.043 0.027 0.008 0.06 -0.124

MAT TN/TP 0.15 -0.092 0.008 0.102 -0.018 -0.12 -0.044 -0.116

 

14
Figure 2.4 

Average macroalgal mat thickness at 59 sites in the fall. 

Cover thickness (cm) 

Figure 

2.5 

Lyngbya majuscula percent cover in the fall. 

<10 

20 

30 

40 

50 

>50 

Lyngbya % cover 

<10 

20 

30 

40 

50 

60 

70 

80 

90 

15
Figure 2.6 

Vaucheria sp. percent cover in the fall. 

Vaucheria % cover 

<10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

16
Figure 2.7 

Macroalgae dry mass (DM) and ash free dry mass (AFDM) at 59 sites in the fall. 

DM mg/cm2

<50 

50 

100 

150 

>150 

AFDM mg/cm2

<50 

50 

100 

>100 

17
Figure 2.8 

Macroalgae chlorophyll a and pheophytin at 59 sites in the fall. 

Chla mg/cm2

<2 

2 

4 

6 

8 

10 

>10 

Pheophytin mg/cm2

<0.5 

0.5 

1 

1.5 

>2 

18
Percent cover and biomass 

The percentage of macroalgae cover varied from 0% in Cypress Spring to 94% in 

Manatee Spring and was higher for southern sites than for northern sites (Figure 2.3, Table 2.4, 

and Appendix A.7). 

The average macroalgae mat thickness varied from 0 cm for Cypress Spring to 55 cm at 

the head of Weeki Wachee Spring (Figure 2.4, Appendix A.7). The Lyngbya majuscula percent 

cover was the highest in the southern portion of our study area (Table 2.4), while the Vaucheria

sp. percent cover was the highest in Manatee Spring and Guranato Spring (Figure 2.5 and 2.6). 

Other macroalgal taxa were also common, but their percent cover was lower than that of 

Lyngbya majuscula and Vaucheria sp. (Appendix A.8). Macroalgae aerial AFDM and 

chlorophyll a were also higher in the south (Table 2.4, Figure 2.7 and Figure 2.8, Appendix A.9). 

Macroalgae, water TN and TP, and environmental variables 

The water in southern springs was characterized by higher TP (Figure 2.9, Table 2.5). 

There was no relation between macroalgae percent cover and macroalgal mat thickness, and TN 

and TP (Figure 2.10, Figure 2.11, and Table 2.4). There was no relation between Lyngbya 

majuscula percent cover and TN and TP (Figure 2.12, Table 2.4). However, Vaucheria sp. 

percent cover was positively correlated with TN and was not correlated with TP (Figure 2.13, 

Table 2.4). A threshold in percent cover of Vaucheria was indicated near 0.6 mg TN/L (Figure 

2.13). Below 0.6 mg TN/L, Vaucheria accrual was constrained to about 10% cover or less. The 

proportion of sites with high Vaucheria cover (> 10%) decreased with both TP and TN (Figure 

2.13), but these relationships were not evaluated with probability statistics due to time

constraints. Vaucheria sp. percent cover was positively correlated with alkalinity (Table 2.4). 

Mat TN and TP content was not correlated with the macroalgae percent cover and mat thickness 

for all taxa combined or for Lyngbya and Vaucheria percent cover (Table 2.4). Water alkalinity 

was not significantly correlated with latitude (Pearson correlation p= 0.686) or longitude 

(Pearson correlation p= 0.199) (Figure 2.14). 

Similarities in the macroalgal assemblages between sites were identified using DCA of 

macroalgal percent cover data. Axis 1 explained 14.4% and axis 2 explained 11% of the 

variance in the species data (Figure 2.15). The first axis grouped sites based on conductivity. 

High conductivity sites HOM-01 and CHA-03, characterized by Chaetomorpha sp. and 

Enteromorpha sp., grouped on the right side of the first axis. Sites with lower conductivity 

grouped on the left side of the first axis. The grouping along the second axis can be partially 

explained by nutrients and alkalinity. Higher nutrient sites grouped with Lyngbya majuscula, 

Vaucheria sp. and Dichotomosiphon sp., and grouped opposite to low nutrient sites characterized 

by Cladophora sp., Hydrodictyon sp. and Spirogyra sp. 

19
Figure 2.9 

Water total phosphorus and total nitrogen in spring water at 60 sites in the fall. 

TP mg/L 

<0.05 

0.05 

0.10 

0.15 

>0.15 

TN mg/L 

<1 

1 

2 

3 

4 

5 

6 

20
Table 2.5 

Pearson correlation matrix for Total P and Total N in the water and environmental variables in 

the fall (p<0.05 in bold). 

TOTALP TOTALN

ALKALINITY 0.197 0.414 

AMMONIAN -0.143 0.307 

CALCIUM 0.139 0.393 

CHLORIDE -0.034 -0.166

IRON -0.001 -0.014

N_KJEL_TOT 0.220 0.115

NO2NO3N 0.173 0.998 

PHOSPHATE 0.894 0.154 

SILICA 0.288 -0.310

SODIUM -0.033 -0.169

SULFATE 0.096 -0.137

TOTAL P 0.186 

TOTAL N 0.186 

MAGNESIUM 0.050 -0.155

STRONTIUM 0.139 -0.201

LATITUDE -0.286 0.113 

LONGITUDE 0.452 -0.059

PH -0.320 -0.466

CONDUCTIVITY -0.017 -0.096

DO -0.339 0.004 

TEMPERATURE 0.300 0.067 

CANOPY -0.109 -0.143

BUFFER 0.093 -0.307

Transect Length -0.062 0.325 

TREES 0.236 -0.178

SHRUBS -0.018 -0.193

HERBS -0.036 -0.119

BARE -0.020 0.268 

LDI 0.065 0.168

DISCHARGE 0.119 0.135

21
Figure 2.10 

Macroalgae percent cover (PCCOVER) as a function of water TN and TP for all sites (scatter 

plots) and grouped into 3 categories: 0-25%, 25-50% and over 50% (bar plots) in the fall. 

0.04 0.08 0.120.16

TOTALP

0

10

20

30

40

50

60

70

80

90

100

PC

C

O

VER

1 2 3 4 5 6

TOTALN

0

10

20

30

40

50

60

70

80

90

100

PC

C

O

VER

 

0.04 0.08 0.120.16

TOTALP

20

40

60

80

100

PC

C

O

VER

1 2 3 4 5 6

TOTALN

20

40

60

80

100

PC

C

O

VER

 

<0.025 .025-.050 >0.050

Total P

0

10

20

30

40

50

60

70

80

90

100

Proportion of Sites

<0.50 .50-1.0 >1.0

Total N

0

10

20

30

40

50

60

70

80

90

100

PCCOV50_100

PCCOV25_50

PCCOV0_25

22
Figure 2.11 

Macroalgae average cover thickness (AVGCOVTHK) as a function of water TN and TP for all 

sites (scatter plots) and grouped into 3 categories: 0-3 cm, 3-8 cm and over 8 cm (bar plots) in 

the fall. 

0.04 0.08 0.120.16

TOTALP

0

10

20

30

40

50

60

A

V

G

C

O

V

T

H

K

1 2 3 4 5 6

TOTALN

0

10

20

30

40

50

60

A

V

G

C

O

V

T

H

K

 

0.04 0.08 0.120.16

TOTALP

1.0

10.0

A

V

G

C

O

V

T

H

K

1 2 3 4 5 6

TOTALN

1.0

10.0

A

V

G

C

O

V

T

H

K

<0.025 .025-.050 >0.050

Total P

0

10

20

30

40

50

60

70

80

90

100

Proportion of Sites

<0.50 .50-1.0 >1.0

Total N

0

10

20

30

40

50

60

70

80

90

100

ACTGT8

ACT3_8

ACT0_3

23
Figure 2.12 

Lyngbya majuscula percent cover as a function of water TN and TP for all sites (scatter plots) 

and grouped into 3 categories: 0%, 0-10% and over 10% (bar plots). 

0.04 0.08 0.120.16

TOTALP

0

10

20

30

40

50

60

70

80

90

LPC

C

O

VER

1 2 3 4 5 6

TOTALN

0

10

20

30

40

50

60

70

80

90

LPC

C

O

VER

 

0.04 0.08 0.120.16

TOTALP

0.1

1.0

10.0

LPC

C

O

VER

1 2 3 4 5 6

TOTALN

0.1

1.0

10.0

LPC

C

O

VER

<0.025 .025-.050 >0.050

Total P

0

10

20

30

40

50

60

70

80

90

100

Proportion of Sites

<0.50 .50-1.0 >1.0

Total N

0

10

20

30

40

50

60

70

80

90

100

LPCCOVGT10

LPCCOV0_10

LPCCOV0

24
Figure 2.13 

Vaucheria sp. percent cover as a function of water TN and TP for all sites (scatter plots) and 

grouped into 3 categories: 0%, 0-10% and over 10% (bar plots). 

0.04 0.08 0.120.16

TOTALP

0

10

20

30

40

50

60

70

80

90

100

VPC

C

O

VER

1 2 3 4 5 6

TOTALN

0

10

20

30

40

50

60

70

80

90

100

VPC

C

O

VER

0.04 0.08 0.120.16

TOTALP

0.1

1.0

10.0

100.0

VPC

C

O

VER

1 2 3 4 5 6

TOTALN

0.1

1.0

10.0

100.0

VPC

C

O

VER

 

<0.025 .025-.050 >0.050

Total P

0

10

20

30

40

50

60

70

80

90

100

Proportion of Sites

<0.50 .50-1.0 >1.0

Total N

0

10

20

30

40

50

60

70

80

90

100

VPCCOVGT10

VPCCOV0_10

VPCCOV0

25
Figure 2.14 

Alkalinity of spring water at 60 sites in the fall. 

CaCO3 mg/L 

 Figure 2.15 

Ordination diagram based on DCA of macroalgal percent cover (60 sites and 20 species of 

macroalgae) displaying 25% of variance. Eigenvalues for the first three axes are 0.363, 0.277 and 

.0173. First and second axis were well correlated with environmental variables (r=0.872 for the 

first axis and r=0.911 for the second axis). 

-1.0 +7.0

-2.0 +2.5

CHA-03

HOM-01

CHA-02

JUN-04

SLV-05

SLV-02

SLV-04

SLV-01

SLV-03

TUT-01

MNT-01

WIL-01

ICH-02

ALE-01

RAI-03

PON-01

JUN-03

RAI-04

RAI-02

WGT-01

Ch

E

U

O

D

V

B

Hd

L

Ap

S

C

Rh

Ls

<50 

50 

100 

150 

200 

26
Figure 2.16 

Correlation triplot based on CCA of the macroalgal percent cover (20 macroalgae species, 60 

sites and 16 environmental variables). Number of sites and species presented were restricted by 

species weight and site fit. Eigenvalues of the first three axes are 0.319, 0.222 and 0.201. The 

sum of all canonical eigenvalues is 1.418. Significance of all canonical axes together was tested 

by Monte Carlo permutation test F-ratio=2.109, p=0.005. 

-2.5 +2.0

-2.0 +2.0

Alkalinity

pH

Sulfate

Canopy

Conductivity

Silica

TOTALN

Buffer

Magnesium

O-Phosph

LDI

TOTALP

DO Dischare

MNT-01

TUT-01

SLV-05

VOL-01

WAK-01

SLV-04

IND-01

VOL-02

PON-01

SLV-03

SLV-02

SLV-01

ICH-01

WGT-01

ALE-01

RAI-01

RAI-04

ICH-06

RAI-03

JAC-02

RAI-02

SGL-01

ICH-04

Hd

Ap

V

S

Rh O

Ls

C

B

L

27
Figure 2.17 

Correlation biplot based on CCA of the macroalgae percent cover (all data as on Figure 1.*2, 

only species and environmental variables are shown). 

-2.5 +2.5 -2.0 +2.5

Calcium

Alkalinity

Discharege

Strontium

pH

Sulfate

Canopy

TOTALN

Silica

Buffer

DO

ChSoldoiruidme

Ammonia

Magnesium

LDI

TOTALP

O-Phosph

Hd

Ap

V

Rh

S

O

C

L

28
Figure 2.18 

Relation between macroalgae mat thickness and distance from the boil. Macroalgae thickness 

was measured as up to 0.5cm, 0.5 – 2 cm indicated as 2 cm, 2 -5 cm indicated as 5 cm, 5-20 cm

indicated as 20 cm, 20-50 cm indicated as 50 cm and over 50 cm indicated as 100 cm. 

130m

1 10 100 1000 10000

Distance from the boil (m)

0

20

40

60

80

100

120

M

a

cro

a

gl a

e

m

at th

ci k

n

e

s

s (c

m)

RAI04

29
Figure 2.19 

Relation between macroalgae mat thickness and water depth. Macroalgae thickness was 

measured as up to 0.5 cm, 0.5 – 2 cm indicated as 2 cm, 2 -5 cm indicated as 5 cm, 5-20 cm

indicated as 20 cm, 20-50 cm indicated as 50 cm and over 50 cm indicated as 100 cm. 

0.10 1.00 10.00

Depth (m)

0

20

40

60

80

100

120

M

acroalgae m

at thickness (cm)

Figure 2.20 

Relation between macroalgae mat thickness and estimated current velocity (0 – current velocity 

less than 5 cm/s, 1 – current velocity 5-15 cm/s, 2- current velocity 16-50 cm/s, 3 – current 

velocity above 50 cm/s). Macroalgae thickness was measured as up to 0.5 cm, 0.5 – 2 cm

indicated as 2 cm, 2 -5 cm indicated as 5 cm, 5-20 cm indicated as 20 cm, 20-50 cm indicated as 

50 cm and over 50 cm indicated as 100 cm. 

-1 0 1 2 3 4

Current Velocity

0

20

40

60

80

100

120

Macroa gl ae mat thickness (cm)

30
Since high conductivity sites (HOM-01 and CHA-03) and taxa (Enteromorpha sp. and 

Chaetomorpha sp.) affected grouping in DCA, these sites and taxa were removed from CCA 

analysis. The first CCA axis grouped samples according to nutrient levels, alkalinity and pH 

(Figure 2.16), while the second axis grouped samples according to spring discharge, DO, silica 

and magnesium. Lyngbya majuscula was associated with high nutrient and high discharge sites, 

and Vaucheria sp. was associated with high alkalinity sites. Diatoms were associated with high 

silica sites, and Hydrodictyon sp. and Spirogyra sp. were associated with high pH sites (Figure 

2.17). 

Macroalgae mat thickness, water depth, distance from the boil and current velocity 

Analysis of macroalgae cover thickness based on RPHA data collected for 4851 points in 

the fall indicated that the thickest macroalgal mats (over 50 cm thick) were found only within 

130 m from the boil and mats 20 to 50 cm thick were found within 250 m from the boil (Figure 

2.18). The only site that had a mat 20 to 50 cm thick occurring far from the boil was RAI-04. 

This site is affected by an old phosphate mine pit called Blue Cove. Thicker macroalgal mats 

were found in water between 1 and 10 m deep (Figure 2.19). Current velocity did not affect 

macroalgal mat thickness (Figure 2.20). 

Macroalgal mat TN and TP 

Macroalgae mat TN and TP content was the highest for macroalgae from Ichetucknee 

Springs (Figure 2.21, Appendix A.10). Mat TN and TP content was positively correlated with 

water phosphate and was not affected by water TN and TP (Table 2.6). The molar TN:TP ratio 

in mats was greater than the 16:1 Redfield ratio for most of the study sites ((Borchardt, 1996), 

Figure 2.22). Only 6 sites (WEK-03, WKW-01, VOL-01, MNT-01, ICH-07 and ICH-06) had a 

TN/TP ratio below 16:1, indicating the phosphate limitation of macroalgal growth if nutrient 

concentrations were low enough to regulate algal growth. 

Algal Limiting Nutrients (ALN) and Algal Growth Potential (AGP) bioassays 

Phosphorus was the limiting nutrient at 15 and nitrogen at 6 out of 27 sites in the ALN 

bioassay (Appendix A.11). Five sites were co-limited by both phosphorus and nitrogen. Algal 

growth potential was highest for the water sample from WKW-01 and lowest for the water 

sample from MNT-01 (Appendix A.11). 

Epiphytes 

Epiphyte DM, AFDM, and chlorophyll a varied among study sites (Figure 2.23, 

Appendix A.9) and were as high as 7 mg/cm2, 6 mg/cm2

, and 40 μg/cm2, respectively. Epiphyte 

AFDM and chlorophyll a were not correlated with any of the environmental variables (Figure 

2.24, Table 2.7). DM was correlated with chloride, iron, sodium, magnesium, sulfate and 

conductivity (Table 2.7). Epiphyte thickness estimated in RHPA varied from slimy to over 5 

mm (Appendix A.12). Excluding diatoms, 62 taxa of soft epiphytic algae were identified 

(Appendix A.13; pictures of dominant taxa can be found in Appendix A.14). Epiphytic algal cell 

density per unit plant leaf surface area can be found in Appendix A.15. 

31
Table 2.6 

Pearson correlation matrix for macroalgal mat TN and TP content and environmental variables. 

Mat TN and TP measured in FM mg/Kg (p<0.05 in bold). 

Mat TN Mat TP Mat molar TN/TP 

ALKALINITY 0.187 0.174 -0.250

AMMONIAN -0.109 -0.112 0.097

CALCIUM 0.062 0.044 -0.242

CHLORIDE -0.142 -0.141 0.017

IRON -0.154 -0.127 0.030

N_KJEL_TOT -0.202 -0.161 -0.238

NO2NO3N -0.155 -0.145 0.096

PHOSPHATE 0.284 0.384 -0.420

SILICA 0.210 0.244 -0.150

SODIUM -0.141 -0.140 0.016

SULFATE -0.150 -0.130 -0.115

TOTAL P 0.108 0.251 -0.440

TOTAL N -0.166 -0.154 0.080

MAGNESIUM -0.138 -0.126 -0.036

STRONTIUM -0.135 -0.123 -0.195

LATITUDE 0.170 0.098 0.254

LONGITUDE -0.112 -0.151 0.385 

PH 0.059 0.053 0.301 

CONDUCTIVITY -0.133 -0.132 -0.007

DO -0.213 -0.209 0.282 

TEMPERATURE -0.180 -0.112 -0.150

CANOPY 0.320 0.334 0.110 

BUFFER 0.405 0.380 -0.130

Transect Length -0.222 -0.235 0.176 

TEES 0.078 0.070 0.071

SHRUBS 0.351 0.320 -0.078

HERBS 0.169 0.181 0.097

BARE -0.224 -0.188 -0.120

LDI -0.133 -0.099 -0.084

DISCHARGE -0.193 -0.180 -0.031

32
Table 2.7 

Pearson correlation matrix for epiphyte DM, AFDM (mg/cm2 ), epiphyte chlorophyll a and 

pheophytin (μg/cm2

) per leaf surface area and environmental variables in the fall (p<0.05 in 

bold). 

DM AFDM Chl a Pheophytin

ALKALINITY -0.077 -0.170 0.078 0.017

AMMONIAN 0.324 0.102 -0.056 -0.074

CALCIUM 0.055 -0.115 0.052 -0.026

CHLORIDE 0.435 0.194 -0.055 -0.065

IRON 0.369 0.185 -0.103 0.015

N_KJEL_TOT 0.286 0.139 -0.058 -0.054

NO2NO3N -0.017 -0.095 -0.072 -0.072

PHOSPHATE -0.062 0.040 0.201 -0.040

SILICA 0.167 0.162 0.181 0.176

SODIUM 0.434 0.196 -0.053 -0.063

SULFATE 0.301 0.118 -0.045 -0.052

TOTAL P -0.024 0.058 0.080 -0.050

TOTAL N 0.001 -0.085 -0.076 -0.075

MAGNESIUM 0.406 0.170 -0.060 -0.041

STRONTIUM 0.198 0.069 -0.018 -0.054

LATITUDE -0.006 -0.071 0.079 0.191

LONGITUDE 0.073 0.168 0.105 -0.067

PH 0.071 0.145 0.042 -0.013

CONDUCTIVITY 0.407 0.156 -0.065 -0.083

DO 0.169 0.076 -0.128 -0.071

TEMPERATURE 0.117 0.035 -0.077 -0.260

CANOPY -0.222 -0.039 0.073 -0.157

BUFFER 0.085 0.022 0.204 0.006

Transect Length 0.314 0.092 -0.056 0.092

TEES 0.164 0.100 0.072 0.037

SHRUBS 0.152 0.235 0.234 0.065

HERBS 0.109 0.151 0.157 0.196

BARE -0.212 -0.199 -0.146 -0.055

LDI 0.080 -0.072 -0.106 -0.187

DISCHARGE -0.046 -0.114 -0.076 0.003

33
Table 2.8 

Pearson correlation matrix for epipelon DM, AFDM (mg/cm2 ), epipelon chlorophyll a and 

pheophytin (μg/cm2

) and environmental variables for spring and fall data combined (p<0.05 in 

bold). 

DM AFDM Chl a Pheophytin

ALKALINITY -0.026 0.269 -0.06 0.045

AMMONIAN -0.026 0.026 0.064 0.01

CALCIUM -0.11 0.147 -0.091 -0.002

CHLORIDE 0.055 -0.047 0.047 0.019

IRON 0.085 0.076 -0.095 -0.067

N_KJEL_TOT -0.013 -0.121 0.146 0.097

NO2NO3N -0.127 -0.052 0.011 -0.031

PHOSPHATE -0.113 -0.103 0.065 -0.099

SILICA 0.325 0.339 -0.163 -0.019

SODIUM 0.053 -0.052 0.033 -0.001

SULFATE -0.026 -0.033 -0.063 -0.042

TOTAL P -0.068 -0.035 0.08 -0.091

TOTAL N -0.127 -0.059 0.02 -0.025

MAGNESIUM 0.098 0.039 0.006 0.012

STRONTIUM -0.053 -0.027 -0.036 -0.047

LATITUDE -0.019 0.158 -0.305 -0.149

LONGITUDE -0.02 -0.123 0.146 0.017

PH 0.078 -0.059 0.043 -0.035

CONDUCTIVITY 0.041 -0.024 0.045 0.026

DO 0.235 0.255 0.129 0.113

TEMPERATURE -0.045 -0.094 0.297 0.144 

CANOPY -0.252 -0.259 -0.231 -0.137

BUFFER 0.045 0.284 -0.112 0.019

Transect Length 0.172 0.234 0.089 0.094 

TREES 0.385 0.453 0.120 0.097

SHRUBS -0.027 -0.136 0.057 -0.042

HERBS 0.043 -0.150 0.277 0.260

BARE -0.185 -0.237 0.135 0.123

LDI -0.014 0.031 0.394 0.495

DISCHARGE 0.222 0.398 0.186 -0.002

34
Figure 2.21 

Macroalgal mat TN and TP content in FM mg/Kg. 

Mat TN FM mg/Kg 

<10000 

100000 

200000 

300000 

400000 

500000 

600000 

700000 

Mat TP FM mg/Kg 

<10000 

10000 

20000 

30000 

40000 

50000 

60000 

70000 

35
Figure 2.22 

Macroalgal mat molar TN/TP ratio. 

Mat TN/TP 

<10 

10 

20 

30 

40 

50 

60 

70 

80 

90 

36
Figure 2.23 

Epiphyte DM and AFDM expressed per surface of leaf surface are at 58 sites in the fall. 

0

1

2

3

4

5

6

7

Epiphyte DM mg/cm2

0

1

2

3

4

5

6

Epiphyte AFDM mg/cm2

37
Figure 2.24 

Epiphyte chlorophyll a and pheophytin expressed per surface of leaf surface are at 58 sites in the 

fall. 

0

10

20

30

40

Epiphyte Chl a μg/cm2

0

10

20

30

40

Epiphyte Pheophytin μg/cm2

38
Figure 2.25 

Epipelon DM and AFDM expressed per bottom surface area at 59 sites (based on spring and fall 

sampling). 

0

200

400

600

800

1000

1200

Epipelon DM mg/cm2

0

10

20

30

40

50

60

70

Epipelon AFDM mg/cm2

39
Figure 2.26 

Epipelon chlorophyll a and pheophytin expressed per bottom surface area at 59 sites (based on 

spring and fall sampling). 

0

20

40

60

80

100

120

Epipelon Chl a μg/cm2

0

100

200

300

400

500

600

Epipelon pheophytin μg/cm2

40
Table 2.9 

Aquatic plant taxa found in Florida Springs and codes for aquatic plants used in this study. 

Taxa Aquatic plant code

Atternantherea philoxeeroides A 

Ceratophyllum sp. Ce 

Chara sp. Ch 

Cladium jamaicense Cj 

Cobamba sp. Co

Crinum americanum Sl

Cycuta sp. Ct 

Egeria densa Ed 

Eichhornia crassiper W 

Eleocharis sp. Sg 

Hydrilla sp. H 

Hydrocotil sp. Hy 

Hygrophila polysperma Hp

Lemna sp. Le 

Ludwigiabsp. Lu

moss Mo

Myriophyllum sp. M

Najas guadalupensis N 

Najas sp. N2 

Nuphar sp. Nu

Nymphoides sp. Y 

Panicum repens Tr 

Pistia stratiotes Pi 

Pontedaria sp. Pn 

Potamogeton pectinatus Pp

Potamogeton pusillus Pu

Potamogeton spp. Ps 

Riccia sp. Rc 

Rorippa sp. R 

Sagittaria kurziana Sk

Sagittaria latifolia St 

Salvinia minima Sm

Scirpus sp. Br 

Typha sp. T 

Utricularia sp. U 

Vallisneria sp. Val 

Zizania sp. Z 

41
Epipelon 

Epipelon AFDM was correlated with large springs with high discharge (like SLV, WAK 

and WAC), indicating that a higher flow can support a larger biomass and greater deposition of 

organic material into the sediments. It was also positively correlated with tree cover (Figure 

2.25, Table 2.8, and Appendix A.9). Epipelon chlorophyll a and pheophytin were positively 

correlated with the Land Development Intensity index (LDI) (Figure 2.26). Epipelon thickness 

estimated in RHPA varied from slimy to over 5 mm (Appendix A.12). 

Aquatic plants 

In IND-01 and LAF-01, no aquatic plants were found. The remaining sites had aquatic 

plants present, with the highest aquatic plant cover at ICH-04 (Appendix A.16). A total of 37 

taxa of aquatic plants were found (Table 2.9). The most common were Vallisneria sp. and 

Sagittaria kurziana (Appendix A.17). At many sites both were present, and when sampling was 

conducted from a boat, there was a potential for confusion of the two. 

Seasonal changes 

AFDM and pheophytin measurements did not differ between spring and fall (repeated 

measures ANOVA for time effect AFDM p=0.8641, pheophytin p=0.4579). DM and 

chlorophyll a measurements differed between spring and fall (repeated measures ANOVA for 

time effect DM p=0.0033, Chl a p=0.0170) (results of analysis in 

Stats/AFDM_ChlaRMANOVA.doc). 

Discussion 

Macroalgae were found at almost all of the studied sites and on average, covered about 

half of the bottom area of the spring reaches studied. Lyngbya and Vaucheria were the most 

commonly observed macroalgae. Many of the macroalgal taxa observed in this study were also 

reported in previous studies (Davis and Gworek, 1972; Odum, 1957; Whitford, 1956). However, 

a large Lyngbya taxon, which we identified as L. majuscula, was not reported in any of these past 

studies. This indicates that it was not common or was not present at these sites in the past. 

Nutrients were not the most important factors affecting geographic distribution of 

different macroalgal taxa. Conductivity, alkalinity, pH, and factors related to the size of springs 

were indicated by ordination statistics as the most important determinants. This is one of the 

first surveys of macroalgal biodiversity and relationships to environmental variables. Although 

regulation of diatom biodiversity by conductivity, alkalinity, and pH is well known (e.g., Pan et 

al., 1996), the regulation of macroalgae had not been documented before this study. 

Overall, nutrient concentrations, whether soluble or total phosphorus or nitrogen, were 

not directly related to the total algal mat cover or cover by Lyngbya majuscula. However, 

Vaucheria cover was clearly related to NO2-NO3, total N concentrations and two indicators of 

human disturbance: % bare area of the riparian zone along the reach and LDI. The threshold in 

Vaucheria cover response at 0.6 mg TN/L was similar to observed responses in Cladophora in 

streams (Dodds et al., 1997; Stevenson et al., accepted). The response of Vaucheria to TP is not 

42
as certain, although a few sites had low TP concentrations. Of those sites with low TP (<0.025 

mg/L), less than 10% of the sites had a Vaucheria cover greater than 10% of the spring bottom.

Lyngbya majuscula is possibly an exotic species in a phase of expansion. It is a nitrogen 

fixer and is not affected by low nitrogen levels (Lundgren et al., 2003). It also grows well in low 

light levels (Rossi et al., 1997). It formed thick mats only in the proximity to the boil, which 

could indicate that very thick mats of Lyngbya majuscula are limited by carbon, and that CO2-

saturated water discharged from the boil facilitated the development of thick mats. 

Alternatively, unmeasured nutrients, such as micronutrients, may have been more available near 

the boil and depleted rapidly downstream. Southern sites had a higher macroalgal percent cover 

than northern sites. 

The taxonomic status of Lyngbya majuscula is not clear. It was suggested that it is 

originally a marine species that can adapt to fresh water (Shannon et al., 1992). It was also 

described by (Speziale and Dyck, 1992) as a new species, Lyngbya wollei. Recent molecular 

work by (Joyner, 2004) indicated that Lyngbya wollei is not related to Lyngbya majuscula. It is

possible that there are many species within what is termed Lyngbya majuscule. This would also 

explain why there were no strong relationships between the Lyngbya percent cover and the 

environmental variables in our study. The percent cover of Vaucheria was correlated with TN, 

and in Manatee Spring, Vaucheria covered the bottom completely. 

Cowell and Botts (1994), in the field survey of aquatic plants and macroalgae in Kings 

Bay/Crystal River estuarine system, also did not find a relation between water nutrients and 

Lyngbya biomass. Cowell and Botts suggested that a downstream drift of the Lyngbya mat might

contribute to error and might result in the underestimation of the true algal biomass. They also 

performed a series of laboratory experiments and did not find a direct relation between nutrients 

and growth of Lyngbya. In their study, the addition of calcium to the growth media stimulated 

the growth of Lyngbya, but only in low and medium phosphate and nitrate concentrations. 

The results of this survey of macroalgae in Florida springs do not provide the information 

needed to determine conclusively whether anthropogenic increases in nutrients in spring waters 

have caused higher algal biomass than naturally occurs. No quantitatively and systematically 

collected data is available that can be used to characterize the algal biomass and nutrient 

concentrations in Florida springs before humans altered these ecosystems. Modern human 

activity was likely preceded by disturbance by Native Americans. Native American activity 

dates back many years. Although there are records of increasing nitrogen levels (Rosenau et al., 

1977) and increasing phosphorus concentrations could be suspected, it is not known with 

absolute certainty that they caused an increase in algal biomass. 

The results of this study do indicate that regulating nutrients could constrain macroalgal 

cover, but more work is necessary before such an investment is made. Control of nitrogen could 

constrain nuisance growths of macroalgae, either directly or indirectly. The positive correlation 

between Vaucheria abundance in springs and TN concentrations indicate that TN regulates its 

abundance. Although this correlation between Vaucheria and TN does not prove causation, 

evidence from experimental streams in Chapter 3 does indicate that there is a cause-effect 

relationship between N and Vaucheria growth rates. However ,TN:TP molar ratios were usually 

43
greater than the 16:1 N:P in the macroalgae, indicating P would be limiting if nutrients were low 

enough. The N supply may limit the growth of algae in streams, although this has not been 

documented widely. N may be limiting even when water column N:P ratios are greater than 

16:1. P recycling in algal mats is considered to be high because of the luxury consumption of P 

by algae, and is probably more efficient than N recycling. Benthic algae may not be able to hold 

onto N as effectively as P. N:P ratios in benthic diatoms in experiments decrease as biomass 

increases during colonization, even though N and P concentrations remained the same in the 

water during that period (Humphrey and Stevenson 1992). N:P ratios in periphyton of Kentucky 

and Michigan streams were less than 16:1 despite higher ratios in overlying waters (Stevenson et 

al., unpublished data). 

Even though, in this study, Lyngbya biomass and growth (Chapter 3) did not show a 

relationship to nutrients, the control of nutrients could constrain proliferations of this important 

nuisance alga for several reasons. First, researchers may not have been able to measure Lyngbya

responses precisely enough. However, results indicate Lyngbya is adapted well to growth in low 

nutrient conditions. Second, Lyngbya proliferations could be controlled indirectly by reducing 

the proliferation of other benthic macroalgae that do respond to nutrient reductions. Lyngbya has 

no direct method to hold onto surfaces, except by becoming entangled with rough surfaces or 

objects attached to the bottom. Normally, Lyngbya was found attached to Vaucheria and aquatic 

plants. Proliferation of both Vaucheria and aquatic plants could be constrained by nutrient 

regulation. Cowell ant Botts (1994) suggested that Lyngbya colonizes new areas after a 

disturbance (prescribed removal of Hydrilla or after hurricane damage to beds of aquatic plants). 

Possibly the remains of aquatic plants form a good attachment point for Lyngbya, and since 

Lyngbya grows fast and shades the remains of aquatic plants, the macrophyte beds cannot 

recover. 

Many factors affecting algal cover were not considered for the analyses because there 

was insufficient information. Disturbance from flooding may affect macroalgae. Some sites 

never flood and some are flooded regularly. Sites along Suwannee River are flooded regularly 

with dark water that limits light availability for macroalgae and can result in macroalgal biomass 

depletion. Direct disturbance by human recreation in the springs may open space for macroalgal 

growth during periods when human activity levels are low. Human activity may have also 

reduced grazing by fish and manatees. All of these factors could have profound impacts on 

macroalgal accrual on substrata. They should be considered as factors that may affect nuisance 

growths of macroalgae and development of nutrient-response relationships. 

Macroalgal mats of the size observed in the Florida springs must have unique nutrient 

dynamics inside the mat. The response of these mats to water nutrients must also be different 

from the response of microalgal biofilms. Macroalgal mats were attached to the bottom and to 

aquatic plants. Usually at least part of the mat was attached directly to the bottom and was in 

contact with the sediment. Vaucheria mats develop rhizoids that grow directly into the sediment. 

This indicates that also nutrients from sediments could be very important to macroalgal growth 

(as has been observed for aquatic plants) and could contribute to the lack of relationships 

between macroalgal biomass and water nutrients. 

44
Overall, this study indicates that macroalgae are very common and diverse in Florida 

springs. Only a few taxa (L. majuscula, Vaucheria sp., Dichotomosiphon sp. and Compsopogon 

sp.) formed very thick mats. Lyngbya majuscula could be a new species that was recently 

introduced in the springs or a species that was historically present, but not sufficiently common 

to be listed. The apparent adaptation of this taxon to low nutrient concentrations indicates its 

control would be difficult. However, regulation of nuisance growths of Vaucheria, and 

indirectly Lyngbya, may be possible if low levels of nutrients constrain the growth of Vaucheria. 

45
CHAPTER 3. EXPERIMENTAL CONFIRMATION OF LIMITING 

NUTRIENTS USING EXPERIMENTAL STREAMS 

Introduction

Enrichment of surface waters with nutrients can result in increased growth of benthic 

algae (Stockner and Shortreed, 1978; Biggs, 2000). Nitrogen and phosphorus are the two main 

nutrients affecting benthic algal growth (Borchardt, 1996). Nitrate and ammonia are the 

bioavailable forms of nitrogen. Bioavailable phosphorus comes in the form of phosphates. 

Bioavailable forms of nutrients are rapidly taken up by autotrophic and heterotrophic microbes 

and plants in surface waters and transformed into organic form. Particulate and dissolved 

organic N and P can be recycled to bioavailable forms by microbial and photochemical 

processes. All of these forms of nutrients originate from both natural and anthropogenic sources. 

Important natural sources include nitrogen-fixing bacteria in soils and water and dissolution of 

minerals, such as the phosphate-rich rocks found in some regions of Central Florida. Important 

anthropogenic sources include septic tanks, waste water treatment plants, and fertilizers. 

Abandoned phosphate pits, which can be found along spring runs (e.g., Ichetucknee and 

Rainbow River), may also leak phosphate after rains and high water levels. 

Distinguishing effects of N versus P with correlations between the algal biomass and 

nutrient concentrations observed in field surveys of multiple sites is complicated. First, N and P 

concentrations covary among sites (Dodds and Welch, 2000; Stelzer and Lambertii, 2002; 

Stevenson, 1996), because many human activities produce both (Arbuckle and Downing, 2001). 

Second, bioavailable nutrients are consumed by benthic algae and can be depleted as algal 

biomass increases. TN and TP are more commonly related to the benthic algal biomass in 

streams than to dissolved inorganic nutrients, but this is likely the result of the sloughing of 

benthic algae and the accrual of suspended particulate N and P. Thus, TN and TP are imperfect 

indicators of bioavailable N and P in field surveys. The level of human activities in a watershed 

that generates nutrients, estimated loading rates of nutrients, and diatom indicators of nutrients 

can also be used to indicate the bioavailable nutrients in streams. 

Experiments provide a valuable complement to field surveys by documenting the 

independent and interactive effects of contaminants and natural environmental factors (Pan, et al. 

2000). Although the researchers would argue that the results of experiments should not take 

precedence over the results of field surveys to establish environmental criteria (Stevenson, et al. 

2004), experiments do indicate the range of nutrient concentrations. Without the clear effects of 

nutrients on Lyngbya and only the effects of N on Vaucheria in field surveys (Chapter 2), 

experiments could confirm the relative sensitivity of these two macroalgae to nutrients and the 

likely range of nutrient concentrations that would affect their growth. 

The objective of this study was to investigate the effect of an experimentally manipulated 

range of nitrate and phosphate concentrations on the growth of the macroalgae Lyngbya 

majuscula and Vaucheria. These two taxa were selected for study because they were the two 

most common algae in springs and the most likely to cause problems. The researchers 

46
independently manipulated nitrate and phosphate concentration in two experiments. They 

created a range of phosphate concentrations in artificial streams with elevated nitrate 

concentrations to determine the concentrations of phosphate that constrain growth of the two 

macroalgae. Then the researchers created a range of nitrate concentrations with elevated 

phosphate to determine the nitrate concentrations that constrain growth. 

Methods 

Experimental setup 

Experiments were conducted in Silver River State Park (N 29.20020 W -82.03518) in 

Ocala, Florida, in November and December 2003. Running experimental streams required 

access to electricity, well or stream water, access to spring water low in N and P, and a secure 

site with sufficient exposure to sunlight. Experimental streams (ES) were built from 2” PVC, 

were 1 m long and 30 cm wide, and were filled with from Juniper Spring (Figure 3.1). 

Figure 3.1 

Experimental setup showing single experimental stream. 

Juniper Spring water was judged to have the lowest nutrient concentrations in the region. 

An air pump produced bubbles at the bottom of the upstream vertical tube that lifted and 

circulated water in the ES. Current velocity was regulated at 25 cm/s (equivalent to mediumhigh current velocity observed in the field) with air flow. 

Fresh spring water used in the experiments was pumped into a large tank from Juniper 

Spring in Ocala Forest and transported three times a week to Silver River State Park. Juniper 

Spring has a low dissolved solids concentration compared with most springs in Florida, and its 

mean nutrient concentrations, based on measurements from 1984 to 2000, were 30 μg SRP/L, 30 

μg TP/L and 80 μg NOx/L (St. Johns River Water Management District, 2002). On March 27, 

2003, water from Juniper Spring had 29 μg SRP/L, 60 μg TP /L, 58 μg NOx/L and 120 μg TN/L. 

Well 

water 

Well water 

DI water 

Spring with nutrients Macroalgae water 

Air pump

Spring water 

Spring water 

with nutrients 

Well 

water 

overflow

overflow

47
A peristaltic pump continuously delivered spring water through large tubing and distilled water 

with dissolved nutrients through small tubing to the ES at the target rate of 12 mL/min for spring 

water and 0.68 mL/min for water with nutrients. Excess water in the streams flowed through an 

opening at the downstream end of each experimental stream. The lower 2/3 of the experimental 

streams were submerged in well water in a pool to reduce the high diurnal variability in water 

temperatures that could result in exposed streams and to better simulate the relatively stable 

temperatures of springs receiving groundwater. 

Before the experiments were started, the ES’s were power washed with a hose and well 

water. The large peristaltic pump tubing and experimental streams were then cleaned with a 5% 

bleach solution for several hours, rinsed with well water for at least 48 hours, and rinsed with 

spring water for at least 48 hours. The small peristaltic pump tubing was cleaned with 5% 

sulfuric acid and rinsed with distilled water for at least 96 hours. 

Experimental streams were covered with a screen made of 6 mil clear vinyl to protect 

them from rain and dust, and to shade them slightly (35% light reduction). The ES were located 

in an area exposed to direct sun from app. 8:00am to 11:30am and from 1:00pm to 6:00pm. The 

area was shaded by trees from 11:30am to 1:00pm. 

Nutrient dosing to the ES started 24 hours before algae were introduced. Algae were 

added to experimental streams on day 0. The phosphate experiment (21 streams) was conducted 

first and was finished on day 8. The nitrate experiment (20 streams) was conducted second and 

was finished on day 10. Based on the growth rates observed in the phosphate experiment, the 

researchers decided to extend the duration of this experiment. Every 2-3 days actual flow rates 

were tested from each piece of peristaltic pump tubing. Pump tubing that did not meet expected 

flows was replaced. 

Nutrient dosing 

Two experiments, nitrate additions and phosphate additions, were conducted. Sodium 

nitrate was employed as a source of nitrogen and potassium phosphate, as a source of phosphorus 

(Table 3.1). A gradient of either nitrate or phosphate conditions, ranging from 0 to saturating 

concentrations, was established among experiment channels. In the nitrate dosing experiment, 

nitrate was varied among channels (0, 0.005, 0.025, 0.1, 0.5, 1.0, and 5.0 mg N/L) and phosphate 

was added in all streams to attain a target concentration of 0.240 mg P /L, which was expected to 

saturate P demand. Mass-based Redfield ratios were less than 7 for all treatments except the 5 

mg N/L treatment. In the phosphate experiment phosphate concentrations were varied among 

channels (0, 0.003, 0.009, 0.027, 0.060, 0.120, and 0.240) and nitrate was added to the saturating 

level, which was targeted at 1 mg N/L. Here the mass-based Redfield ratios were more than 7 

for all treatments, except the 0.240 mg P/L. Also, due to an error, treatment 0 mg P/L had no 

nitrate added. 

48
Table 3.1 

Target concentration of P and N in the experimental streams and in the holding bottles from

which nutrients were being dosed. 

Nitrate dosing Phosphate dosing

Target addition 

of N mg/L in

ES 

Concentration of

N mg/L in dosing 

water 

n Target addition 

of P mg/L in ES 

Concentration of

P mg/L in

dosing water 

n 

0.000 

0.005 

0.025 

0.100 

0.500 

1.000 

5.000 

0.000 

0.094 

0.469 

1.876 

9.380 

18.760

93.800

3 

3 

3 

2 

3 

3 

3 

0.000 

0.003 

0.009 

0.027 

0.060 

0.120 

0.240 

0.000 

0.056 

0.169 

0.507 

1.126 

2.251 

4.502 

3 

3 

3 

3 

3 

3 

3 

Chemical and physical analyses of water and algae 

In each stream, the researchers measured the temperature, pH, dissolved oxygen and 

conductivity every 1-2 days. On day 1, day 5 and the last day of the experiment, samples were 

collected from each stream for water chemistry analyses (alkalinity, ammonia, calcium, chloride, 

iron, magnesium, Kjeldahl N, nitrates, phosphate, silica, sodium, sulfate and TP). Samples for 

nitrogen and phosphorus content in L. majuscula and Vaucheria sp. were collected in triplicate at 

the beginning of the experiment from the original algal mat and from one sample from each 

stream at the end of the experiment. Changes in algae TN and TP content are good indicators to 

confirm nutrient availability and measure physiological response to nutrient manipulations. 

Water nutrient analyses and algal mat nitrogen and phosphorus content were analyzed by FDEP. 

Algal mat Kjeldahl N and TP content are expressed per unit of macroalgae fresh mass. When the 

compound concentration was below the detection limit, the method detection limit was reported 

(for detection limits, refer to Table 2.2). 

Algae 

The experiment was conducted on two taxa of macroalgae: Lyngbya majuscula collected 

from Juniper Spring and Vaucheria sp. collected from Alexander Spring. Fragments of algal 

mats were gently patted dry between Nitex screens and weighed. The initial fresh mass of 

individual macroalgae fragments was 0.200±0.010g for both algal taxa. Algae were attached to 

clean (soaked in distilled water) 1”x1” unglazed tiles with clear thread and placed in the 

experimental streams. Four tiles with L. majuscula and four tiles with Vaucheria sp. were placed

in each stream. One of each was used for algae N and P content analyses, and the remaining 

three of each were used to determine growth rates with measures of fresh mass (FM) and ash free 

dry mass (AFDM)). The biomass of three nested replicates within each stream was combined 

and constituted one composite biomass per one stream (one experimental unit). In addition, at 

the beginning of experiment 10, algal samples from each taxon were weighed for FM and frozen 

to be processed for AFDM to measure initial FM and initial AFDM for algae placed in the 

streams. A lgae were weighed for FM and frozen at the end of the experiment. In the laboratory, 

49
all frozen samples were processed for AFDM (Clesceri et al., 1998). Algae relative growth rate 

was calculated as follows (Hunt, 1990): 

RGR=[ln(final mass)-ln(initial mass)]/# of days 

Statistical analyses 

The experiments had a factorial design with 7 nutrient levels (plus initial mat nutrient 

content) and 2 taxa (L. majuscula and Vaucheria sp.). (The number of replicates for each 

treatment is listed in Table 3.1). The biomass of 3 nested replicates within each stream was 

combined by summation and thereby constituted one composite initial and composite final 

biomass assessment per stream (1 experimental unit). ANOVA (glm model) was used to analyze 

data. Differences were considered significant if p<0.05 (SAS, 2000). Tukey’s or LS means test 

was used to test differences between means. All graphs show means and standard error (SE). 

Results 

Nitrate dosing experiment 

Nutrient dosing and nutrient concentration in the ES water 

The average actual flow rate of spring water from large tubing was 10.631 ml/min 

(SD=0.689, SE=0.089). For small tubing with distilled water with nutrients, the average flow 

rate was 0.621 ml/min (SD=0.032, SE=0.004). Nitrate and phosphate dosing resulted in the 

expected nitrate concentration gradient and high TP concentration in all treatments (Table 3.2). 

With control NOx averaging 0.075 mg N/L on day 1, nutrient additions resulted in NOx-N 

concentrations varying from 0.075 to 5.467 mg/L among treatments and TN varying from 0.135 

to 5.527 mg/L. Ammonia concentrations were mostly below the detection limit of 0.010 mg/L. 

NOx concentrations in the recirculating streams decreased about 0.05 mg/L in most treatments 

during the experiment, despite constant loading rates. This was likely due to increases in algal 

biomass and nutrient uptake by algae during the experiment. 

ES water temperature, pH, conductivity and DO 

Water temperature averaged 17.0°C (SD=2.1, SE=0.21, min=13.3, max=19.1). There 

were no differences between experimental streams (ANOVA, F=0.01, p=1). The pH averaged 

7.77 (SD=0.098, SE=0.010, min=7.58, max=7.94), and there were no differences between 

experimental streams (ANOVA, F=0.47, p=0.967). DO was saturating and averaged 9.61 mg/L 

(SD=0.7, SE=0.07, min=8.2, max=10.72), and there were no differences between experimental 

streams (ANOVA, F=0.12, p=1). Conductivity was higher in high nitrate dosing treatments (0.5, 

1 and 5 mg N/L; Table 3.3) than the control treatment. 

50
Table 3.2 

Average nutrient concentrations in the stream water in nitrate dosing experiment 

Target

N 

dosing 

mg/L

Day

TN 

mg/L

TP 

mg/L

Ammonia 

N 

mg/L

Kjeldahl

N 

mg/L

NOx 

mg/L

Calcium

mg/L

Chloride

mg/L

Iron 

mg/L

Magnesiu

m 

mg/L

0.000 1 0.135 0.257 0.010 0.060 0.075 11.600 4.333 0.010 4.200 0.000 5 0.107 0.267 0.010 0.060 0.047 12.133 4.267 0.010 4.167 0.000 10 0.081 0.260 0.012 0.060 0.021 12.667 4.167 0.010 4.233 0.005 1 0.156 0.263 0.010 0.080 0.076 11.733 4.233 0.010 4.167 0.005 5 0.117 0.267 0.010 0.060 0.057 12.567 4.733 0.010 4.300 0.005 10 0.087 0.267 0.010 0.060 0.027 12.733 4.200 0.010 4.233 0.025 1 0.149 0.257 0.010 0.060 0.089 11.867 4.233 0.010 4.167 0.025 5 0.128 0.267 0.010 0.060 0.068 12.167 4.333 0.010 4.167 0.025 10 0.102 0.273 0.010 0.060 0.042 12.700 4.200 0.010 4.233 0.100 1 0.270 0.275 0.010 0.095 0.175 11.950 4.200 0.010 4.250 0.100 5 0.205 0.275 0.010 0.060 0.145 12.400 5.250 0.010 4.300 0.100 10 0.175 0.290 0.011 0.060 0.115 12.500 4.200 0.010 4.200 0.500 1 0.717 0.277 0.010 0.060 0.657 11.533 4.133 0.010 4.100 0.500 5 0.643 0.273 0.010 0.060 0.583 12.533 4.200 0.010 4.267 0.500 10 0.617 0.277 0.011 0.060 0.557 12.733 4.167 0.011 4.233 1.000 1 1.260 0.267 0.010 0.060 1.200 11.500 4.200 0.010 4.133 1.000 5 1.233 0.273 0.010 0.100 1.133 12.500 4.267 0.010 4.267 1.000 10 1.193 0.270 0.011 0.060 1.133 12.833 4.200 0.010 4.333 5.000 1 5.527 0.260 0.010 0.060 5.467 11.800 4.200 0.010 4.200 5.000 5 5.860 0.273 0.010 0.060 5.800 12.533 5.700 0.010 4.300 5.000 10 5.893 0.293 0.013 0.060 5.833 12.600 4.167 0.010 4.233 

51
Table 3.3 

Conductivity (μS/cm) in nitrate dosing experiment (ANOVA, F=411.6, p<0.0001). Means 

sharing the same superscript letter did not differ (Tukey’s test). 

Nitrate dosing 

Mg/L 

Mean SD SE MIN MAX

0.000 

0.005 

0.025 

0.100 

0.500 

1.000 

5.000 

107.26a

107.60 a

107.33 a

107.50 a

111.60 b

116.00 c

150.60 d

0.79 

0.63 

1.05 

1.08 

0.91 

0.53 

7.48 

0.21 

0.16 

0.27 

0.34 

0.24 

0.14 

1.93 

105 

106 

104 

105 

110 

115 

136 

108 

108 

108 

108 

114 

117 

162 

Table 3.4 

Results of ANOVA for mat Kjeldahl Nitrogen and TP content at the end of nitrate dosing 

experiment for Lyngbya majuscula and Vaucheria sp. Data were log (x+1) transformed to meet 

the assumption of normality and variance homogeneity. 

Kjeldahl Nitrogen 

Source of Variation DF SS MS F p 

Nitrate 6 1.32 0.22 0.46 0.832

Algae 1 0.78 0.78 1.61 0.217

Nitrate*Algae 6 1.44 0.24 0.50 0.803

TP 

Source of Variation DF SS MS F p 

Nitrate 6 1.07 0.18 0.37 0.891

Algae 1 0.30 0.30 0.61 0.444

Nitrate*Algae 6 1.93 0.32 0.66 0.680

52
Figure 3.2 

Algal mat TP and Kjeldahl Nitrogen content in nitrate dosing experiment. Bars sharing the same

letter do not differ (LSmeans test). 

Vaucheria

0

50

100

150

200

250

300

Initial 0 5 25 100 500 1000 5000

Nitrate dosing μg/L

P g/Kg FM

ac ac

b

ac

c

a a a

Vaucheria

0

100

200

300

400

500

600

700

800

Initial 0 5 25 100 500 1000 5000

Nitrate dosing μg/L

N g/Kg FM

Lyngbya

0

50

100

150

200

250

300

Initial 0 5 25 100 500 1000 5000

Nitrate dosing μg/L

P g/Kg FM

Lyngbya

0

100

200

300

400

500

600

700

800

Initial 0 5 25 100 500 1000 5000

Nitrate dosing μg/L

g N/Kg FM

Nitrogen and phosphorus content in algal mats 

The water nitrate concentration gradient did not affect the final mat Kjeldahl Nitrogen 

and TP content, and there were no differences between two studied taxa (Figure 3.2, Table 3.4). 

The Kjeldahl Nitrogen content in the mat at the end of the experiment did not differ from the 

initial content for both taxa (ANOVA, F=0.68, p=0.780). TP in the mat of Lyngbya at the end of 

experiment also did not differ from the initial content (ANOVA, F=1.93, p=0.140). However, 

the final mat TP content in the Vaucheria sp. mat was higher than in the initial mat (ANOVA, 

F=5.54, p=0.005), indicating a possible P limitation in the mat collected from the field. The 

molar mat TN/TP ratio at the end of the experiment for both taxa did not differ from the initial 

ratio and was not affected by the nitrate concentration gradient (Table 3.5). All TN/TP ratios, 

except the initial ratio in the Lyngbya majuscula mat, were below 16, indicating a nitrogen 

limitation even in the highest nitrate dosing treatment. 

53
Table 3.5 

Mat molar TN/TP ratio in nitrate dosing experiment in the algal mat collected from the field 

(Initial) and algal mat at the end of experiment from different nitrate dosing concentrations. 

Mean ±SE. (ANOVA for Lyngbya sp. F=2.36, p=0.081; ANOVA for Vaucheria sp. F=1, 

p=0.477). 

Treatment (mg/L) Lyngbya sp. Vaucheria sp. 

Initial 22.66±0.5 14.3±1.6

0 10.7±8.9 5.4±3.7

0.005 8.2±4.5 4.5±2.8

0.025 8.3±5.1 5.0±3.2

0.100 9.6±6.6 6.5±4.1

0.500 4.3±0.6 1.9±0.5

1.000 8.2±3.2 5.5±3.7

5.000 4.3±0.4 5.2±2.5

Macroalgae growth rates 

Macroalgal growth rates were not affected by increased concentrations of nitrate (RGR 

based on FM for: L. majuscula ANOVA, F=1.21, p=0.359 and Vaucheria sp. ANOVA, F=2.43. 

p=0.085; and for AFDM for L. majuscula ANOVA, F=0.29, p=0.930 and Vaucheria sp. 

ANOVA, F=1.09. p=0.419) (Figure 3.3). Lyngbya majuscula had a growth rate averaging 0.059 

g FM/g FM/d and 0.028 g AFDM/g AFDM/d. Vaucheria sp. had a growth rate averaging 0.030 

g FM/g FM/d and 0.010 g AFDM/g AFDM/d. Lyngbya majuscula had a significantly higher 

growth rate than Vaucheria sp (based on FM, ANOVA for Algae effect, F=92.12, p<0.0001 and 

AFDM, ANOVA for Algae effect, F=35.19, p<0.0001). 

54
Figure 3.3 

Algal relative growth rate (RGR) in nitrate dosing experiment based on fresh mass and ash free 

dry mass. 

Lyngbya

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0 5 25 100 500 1000 5000

Nitrate dosing μg/L

RGR for FM (g/g/day)

Lyngbya

0

0.01

0.02

0.03

0.04

0.05

0 5 25 100 500 1000 5000

Nitrate dosing μg/L

RGR for AFDM (g/g/day)

Vaucheria

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0 5 25 100 500 1000 5000

Nitrate dosing μg/L

RGR for FM (g/g/day)

Vaucheria

0

0.01

0.02

0.03

0.04

0.05

0 5 25 100 500 1000 5000

Nitrate dosing μg/L

RGR for AFDM (g/g/day)

Phosphate dosing experiment 

Nutrient dosing and nutrient concentration in the ES water 

The average actual flow rate of spring water from the large tubing was 11.386 ml/min

(SD=0.653, SE=0.069), and the flow rate for small tubing with distilled water with nutrients was 

0.603 ml/min (SD=0.077, SE=0.009). Phosphate and nitrate dosing resulted in the expected TP 

concentration gradient and high nitrogen concentration in all treatments (Table 3.6). There was 

an error in phosphate dosing in 240μg/L treatment on days 1, 2 and 3 that resulted in a low TP 

concentration on day 1 (Table 3.6). With TP averaging 0.021 mg P/L in the control treatment 

during the whole experiment, phosphate additions resulted in TP concentrations varying from

0.021 to 0.268 mg/L among treatments. TP and NOx concentrations in the recirculating streams 

did not decrease during the experiment, as NOx did in the nitrate experiment. 

ES water temperature, pH, conductivity and DO 

Water temperature averaged 18.3°C (SD=2.1, SE=0.16, min=13.8, max=23.0), and there 

were no differences between experimental streams (ANOVA, F=0.02,p=1). The pH averaged 

7.72 (SD=0.166, SE=0.012, min=7.31, max=8.15), and there were no differences between 

experimental streams (ANOVA, F=0.15, p=1). Conductivity averaged 114.1 μS/cm (SD=2.93, 

SE=0.21, min=109, max=120), and there were no differences between experimental streams 

(ANOVA, F=0.78, p=0.730). DO was saturating and averaged 9.59 mg/L (SD=0.573, 

SE=0.044, min=8.60, max=10.96), and there were no differences between experimental streams 

(ANOVA, F=0.35, p=0.996). 

55
Table 3.6 

Average nutrient concentrations in the stream water in phosphate dosing experiment. 

Target

P 

dosing 

mg/L

Day

TN 

mg/L

TP 

mg/L

Ammonia 

N 

mg/L

Kjeldahl

N 

mg/L

NOx 

mg/L

Calcium

mg/L

Chloride

mg/L

Iron 

mg/L

Magnesiu

m 

mg/L

0.000 1 0.136 0.025 0.010 0.100 0.036 13.733 4.367 0.010 4.567 0.000 5 0.110 0.016 0.010 0.080 0.030 13.633 4.567 0.010 4.433 

0.000 8 0.093 0.023 0.010 0.060 0.033 13.267 4.500 0.010 4.500 0.003 1 1.020 0.028 0.010 0.080 0.940 12.567 4.100 0.010 4.167 

0.003 5 1.110 0.018 0.010 0.080 1.030 12.600 4.400 0.010 4.167 

0.003 8 1.073 0.021 0.010 0.060 1.013 12.300 4.267 0.010 4.200 0.009 1 1.050 0.033 0.010 0.100 0.950 12.700 4.100 0.010 4.233 

0.009 5 1.133 0.023 0.010 0.100 1.033 12.200 4.733 0.010 4.100 

0.009 8 1.043 0.031 0.010 0.060 0.983 12.400 4.300 0.010 4.233 0.027 1 1.240 0.058 0.010 0.080 1.160 12.733 4.033 0.010 4.200 

0.027 5 1.180 0.043 0.010 0.100 1.080 12.800 4.700 0.010 4.267 

0.027 8 1.110 0.038 0.010 0.060 1.050 12.467 4.300 0.010 4.233 0.060 1 1.030 0.078 0.010 0.100 0.930 12.867 4.100 0.010 4.333 

0.060 5 1.140 0.073 0.010 0.100 1.040 12.500 4.567 0.010 4.167 0.060 8 1.213 0.077 0.010 0.060 1.153 12.267 4.233 0.010 4.167 0.120 1 1.077 0.140 0.010 0.100 0.977 12.667 4.100 0.010 4.233 0.120 5 1.167 0.143 0.010 0.100 1.067 12.267 4.633 0.012 4.133 0.120 8 1.093 0.143 0.010 0.060 1.033 12.400 4.233 0.010 4.200 0.240 1 1.010 0.048 0.010 0.080 0.930 12.767 4.167 0.010 4.200 0.240 5 1.143 0.250 0.010 0.100 1.043 12.367 4.433 0.010 4.167 

0.240 8 1.335 0.287 0.010 0.065 1.270 12.000 4.267 0.010 4.100 

56
Table 3.7 

Results of ANOVA for mat Kjeldahl Nitrogen and TP content at the end of phosphate 

dosing experiment for Lyngbya majuscula and Vaucheria sp. Data were log(x+1) 

transformed to meet the assumption of normality and homogeneity of variance. 

Kjeldahl Nitrogen 

Source of 

Variation DF SS MS F p

Phosphate 6 1.14 0.19 0.41 0.867

Algae 1 8.56 8.56 18.38 0.000 

Phosphate *Algae 6 0.52 0.09 0.18 0.979 

TP 

Source of 

Variation DF SS MS F p

Phosphate 6 0.41 0.07 0.21 0.971

Algae 1 1.31 1.31 3.93 0.057

Phosphate *Algae 6 0.51 0.08 0.25 0.954 

Figure 3.4 

Algal mat TP and Kjeldahl Nitrogen content in phosphate dosing experiment. 

Vaucheria

0

10

20

30

40

50

60

70

80

90

100

Initial 0 3 9 27 60 120 240

Phosphate dosing μg/L

P g/Kg FM

Vaucheria

0

100

200

300

400

500

600

700

800

Initial 0 3 9 27 60 120 240

Phosphate dosing μg/L

N g/Kg FM

Lyngbya

0

10

20

30

40

50

60

70

80

90

100

Initial 0 3 9 27 60 120 240

Phosphate dosing μg/L

P g/Kg FM

ab

a

b ab ab ab ab

b

Lyngbya

0

100

200

300

400

500

600

700

800

Initial 0 3 9 27 60 120 240

Phosphate dosing μg/L

N g/Kg FM

57
Nitrogen and phosphorus content in algal mats 

The water phosphate concentration gradient did not affect final mat Kjeldahl 

Nitrogen and TP content (Figure 3.4, Table 3.7). At the end of the experiment, Lyngbya 

majuscula had a higher Kjeldahl Nitrogen content than Vaucheria sp. (ANOVA for 

Algae effect F=18.38, p=0.000). For TP, such a difference was not observed (ANOVA 

for Algae effect F=3.93, p=0.057). The Kjeldahl Nitrogen content in the mat at the end 

of the experiment did not differ from the initial content for both taxa (ANOVA, F=0.68, 

p=0.780). The TP content in the Lyngbya mat at the end of the experiment was higher 

than it was initially (ANOVA, F=3.09, p=0.029), indicating a possible P limitation in the 

mat collected from the field. The final TP content in the Vaucheria sp. mat did not differ 

from the initial content (ANOVA, F=0.96, p=0.489). 

The molar mat TN/TP ratio at the end of the experiment for both taxa did not 

differ from the initial ratio and was not affected by the phosphate concentration gradient 

(Table 3.8). All TN/TP ratios in the Lyngbya majuscula mat were higher than 16, 

indicating a phosphorus limitation even in the high phosphate treatment. All TN/TP 

ratios in Vaucheria sp. mat were below 16, indicating a nitrogen limitation. 

Table 3.8 

Mat molar TN/TP ratio in phosphate dosing experiment in the algal mat collected from 

the field (Initial) and algal mat at the end of experiment from different phosphate dosing 

concentrations (in mg/L). Mean ±SE. (ANOVA for Lyngbya sp. F=0.85, p=0.561; 

ANOVA for Vaucheria sp. F=1.22, p=0.349). 

Treatment Lyngbya sp. Vaucheria sp. 

Initial 22.3±1.1 12.8±0.1

0 20.2±1.5 13.5±1.6

0.003 23.4±1.7 12.5±0.9

0.009 19.9±1.1 12.1±1.1

0.027 20.7±1.2 10.9±2.7

0.060 21.8±0.9 13.1±1.2

0.120 19.1±2.8 9.2±0.9

0.240 21.5±0.9 14.9±2.2

Macroalgae growth rates 

Macroalgal growth rates were not affected by the increased concentrations of 

phosphate (RGR based on FM for: L. majuscula ANOVA, F=1.01, p=0.456 and 

Vaucheria sp. ANOVA, F=1.60. p=0.218; and for AFDM for L. majuscula ANOVA, 

F=1.45, p=0.264 and Vaucheria sp. ANOVA, F=1.89. p=0.154) (Figure 3.5). Lyngbya 

majuscula had a growth rate averaging 0.032 g FM/g FM/d and 0.037 g AFDM/g 

AFDM/d. Vaucheria sp. had a growth rate averaging 0.040g FM/g FM/d and about 0.025 

g AFDM/g AFDM/d. 

Lyngbya majuscula had a significantly higher growth rate than Vaucheria sp 

based on AFDM (ANOVA for Algae effect, F=11.68, p=0.002), but for FM the two taxa 

did not differ in their RGR (ANOVA for Algae effect, F=3.53.19, p=0.071). 

 

58
Figure 3.5 

Algal relative growth rate (RGR) in phosphate dosing experiment based on fresh mass

and ash free dry mass. 

Lyngbya

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0 3 9 27 60 120 240

Phosphate dosing μg/L

RGR for FM (g/g/day)

Lyngbya

0

0.01

0.02

0.03

0.04

0.05

0 3 9 27 60 120 240

Phosphate dosing μg/L

RGR for AFDM (g/g/day)

Vaucheria

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0 3 9 27 60 120 240

Phosphate dosing μg/L

RGR for FM (g/g/day)

Vaucheria

0

0.01

0.02

0.03

0.04

0.05

0 3 9 27 60 120 240

Phosphate dosing μg/L

RGR for AFDM (g/g/day)

Discussion 

No increase in macroalgal growth was observed with increased nutrient 

concentrations. The lack of macroalgal growth response to nutrients could be due to the 

short duration of experiments, artifacts of the experimental environment, growth 

limitation by microelements (like calcium or iron), unfavorable light conditions or the 

ability of these algae to efficiently utilize the relatively low nutrient concentrations in 

Juniper Spring water. 

The short duration of experiments can be a problem if algae have not had time to 

respond or have not become acclimated to environmental conditions. Both macroalgae 

did have measurable growth rates, so they likely did have time to respond. The growth 

rates were relatively slow compared to the growth rates of diatoms in other experiments, 

but within the range of rates observed in other studies (Stevenson, 1984; Rier et al., 

accepted). Algal responses to experimental treatments have been observed in other, 

short-term experiments within this short duration, which has the advantage of reducing 

artifacts due to removal from natural conditions and confinement in an experimental 

setting. Macroalgal responses in 1-week experiments were observed in the conductivity 

experiment (Chapter 3, this report). Algae can store luxury quantities of nutrients in their 

cells, and can use these to support growth during periods of low nutrient availability. The 

Vaucheria from Alexander Spring and Lyngbya from Juniper Spring may have had 

luxury quantities of nutrients when taken from their natural settings; however, these 

settings were chosen because of their relatively low nutrient supplies. In addition, 

59
exposure to elevated P concentrations in both experiments resulted in a significant 

increase in the P content of algal cells relative to their initial P content. The same was

true for N in the P experiment, but not in the N experiment. Therefore, acclimation to 

experimental treatments was probably not an issue. 

Artifacts of the experimental environment may have constrained algal responses 

to nutrients. Spring water close to the boil had high quantities of dissolved carbon 

dioxide that “bubbles” out to the atmosphere as soon as water reaches the surface due to a 

decrease in pressure as water emerges from the ground. Since the water used in the 

experiments was transported, the dissolved CO2 concentration had to have been much 

lower than that observed in the springs, where the thickest algal mats were observed only 

close to boils. Nutrient uptake by algae in experimental settings may constrain growth 

rates. Although N and P were added, the many micronutrients that algae require were not 

added. However, the macroalgae did grow, indicating that any limitation, if present, was 

not severe. The growth rates were low, but this was probably due to the binding of 

masses of macroalgae rather than limitations due to a low micronutrient supply. Masses 

of macroalgae were bound in tight balls, and growth patterns indicated that the surface 

algae were the only algae that grew. Algae deep in the bound masses probably had little 

exposure to light and nutrients. 

Lyngbya majuscula growth might have been restrained by very low iron 

concentration in the experimental stream water, which was below 0.01 mg/L. The 

optimum iron concentration for the growth of L. majuscula is 1.4-6.1 mg/L (Gross and 

Martin, 1996). Also, the calcium concentration in Juniper Spring water was low 

compared to other springs and in this study averaged 12.5 mg/L. Lyngbya majuscula (L. 

wollei) had higher growth rates when calcium was added to the growth media (Cowell 

and Botts, 1994). Lyngbya majuscula (L. wollei ) is a nitrogen fixer, and optimum

conditions for that process are low light and low oxygen concentration (Philips et al., 

1992), which is the opposite of the conditions in the experimental streams in this study. 

The accrual of thick mats of L. majuscula could facilitate nitrogen fixation by the 

filaments located deeper in the mat. The experimental algal mats were very small, and 

may have had good oxygen penetration in the mat. 

Algae in this experiment were exposed to relatively high light intensity. 

Vaucheria bursata growth in laboratory conditions was saturated at very low levels (20-

50 μmol/m2

/s) (Leukart and Hanelt, 1995). L. majuscula’s ability to fix nitrogen was 

reduced when exposed to light. 

The water used in the experiment, from Juniper Spring, the spring in the Silver 

River area with the lowest P, had higher TP than expected. So target concentrations from 

even control and low P treatments were higher than planned. Little is known about the 

nutrient concentrations that constrain the growth of Vaucheria and Lyngbya, however, 

there is considerable evidence that phosphate availability in streams with 0.030 mg TP/L 

is sufficient to support nuisance growths of Cladophora (Dodds et al., 1997; Stevenson et 

al., accepted). Cladophora growth seems to be constrained in streams with less 0.600 mg 

TN/L (Dodds et al. 1997). These nutrient concentrations constrain relatively dense 

60
growths of Cladophora, not the thin layer of macroalgae exposed on the surface of the 

bound masses in the experiment. Rapid nutrient uptake and slow mixing in dense mats of 

algae cause competition for nutrients in periphyton, which increases in intensity with the 

thickness of algal mats and algal density on substrata (Stevenson and Glover 1993; 

Dodds, 1991). The 0.020 mg TP/L and 0.100 mg TN/L found in Juniper Spring water 

should surely constrain the growth of dense mats of macroalgae, but perhaps not the 

relatively thin, active outer layer of bound masses used in the experiment. The accrual of 

thick benthic masses of macroalgae in Florida springs may be constrained by naturally 

low nutrient concentrations, but that hypothesis was not tested in the experiments that 

were conducted. 

The results of this study and previous studies on Lyngbya majuscula (L. wollei)

(Cowell and Botts, 1994) suggest that growth of this macroalga is probably controlled by 

a combination of factors: concentration of macro and micronutrients, flow, dissolved 

oxygen and CO2 concentrations, and light availability. Less is known about what affects 

the growth of Vaucheria sp. 

61
CHAPTER 4. MACROALGAL GROWTH IN HIGH AND LOW

CONDUCTIVITY SPRING WATER 

Introduction

Many factors may regulate the ability of algae to respond to nutrients. Temperature, 

light, pH, grazers, and conductivity are important factors. Conductivity is one of the most 

important variables regulating the changes of algal species composition in aquatic ecosystems 

(Pan et al., 1999; Chapter 6, this report). In the field survey, the researchers observed that sites 

with high conductivity often had thicker algal mats and a higher percentage of the spring bottom

was covered by macroalgae. Great inter-specific differences in species abilities to utilize 

nutrients and even differences among populations within species were expected (Carpenter and 

Guillard, 1971). 

To test the hypothesis that water conductivity and algal growth rates are positively 

correlated, the growth rates were compared of algae in mesocosms using water from 3 springs 

with similar nutrient concentrations but differing water conductivity. In addition, the hypotheses 

that growth rates of species and populations within species differ were tested by comparing 

growth rates of a Vaucheria population and two Lyngbya majuscula populations. 

Methods 

Experimental setup 

This experiment had 6 treatments with 3 replicates. A factorial design was used, with two 

factors: water conductivity (low, medium and high) and water nutrients (no nutrients added and 

nutrient enrichment with addition of 240 μg P/L as phosphate and 1000μg N/L as nitrate). Three 

kinds of algae were grown in each mesocosm: L. majuscula from 2 springs and Vaucheria sp. 

Eighteen Glad® containers were used as mesocosms. Each held 1.5 L of water. Water was 

mixed with aeratorsin each mesocosm to generate current. The mesocosms were placed in a 

water bath to moderate diurnal variability in water temperatures. The water bath was a small 

swimming pool filled with well water and heated with 3 aquarium heaters. This pool was 

covered with a screen (as described in Chapter 3). Water with low conductivity (110μS/cm) was 

collected from Juniper Spring, water with medium conductivity (1100 μS/cm) was collected 

from Alexander Spring, and water with high conductivity (2000 μS/cm) was collected from

Silver Glen Spring. It was decided not to amend low conductivity water with salts so as to 

reflect the conditions of natural springs as much as possible. Water in mesocosms was replaced 

with new water every two days. The water temperature, conductivity, pH and DO also were 

measured every two days. Fresh water was collected from the springs three times during the 

course of the experiment. This experiment started on day 0 and ended on day 7. 

Algae 

The experiment was conducted on two taxa of macroalgae. Lyngbya majuscula was collected 

from Juniper Spring and from Silver Glen Spring, and Vaucheria sp. was collected from 

Alexander Spring. Algae were prepared and attached to tiles as described in the previous 

experiments (Chapter 3). On day 0 algae were introduced to the mesocosms. Three tiles with L. 

majuscula from Juniper Spring, three tiles with L. majuscula from Silver Glen Spring and two 

tiles with Vaucheria sp. from Alexander Spring were placed in each mesocosm. One tile with L. 

62
majuscula from each of two springs was used for N and P content analyses and the remaining 

tiles were used for FM and AFDM analyses. Two tiles of each kind were treated as nested 

replicates, and the FM mass from each mesocosm was summed up. In addition, at the beginning 

of the experiment, 10 algal samples from each taxon were weighed for FM and frozen to be 

processed for AFDM to aid in calculating the initial AFDM from the initial FM for algae placed 

in the mesocosms. At the end of experiment, algae were weighed for FM and frozen. In the 

laboratory, all frozen samples were processed for AFDM (Clesceri et al., 1998). Biomassspecific growth rates of each algal taxon were calculated as in Chapter 3. 

Chemical and physical analyses of water and algae 

The temperature, pH, dissolved oxygen and conductivity were measured in each mesocosm

every two days. On day 1, day 5 and the last day of the experiment (day 7) samples were 

collected from each mesocosm for water analyses (alkalinity, ammonia, calcium, chloride, iron, 

magnesium, Kjeldahl N, nitrates, phosphate, silica, sodium, sulfate and TP). Samples for 

nitrogen and phosphorus content in L. majuscula from Juniper Spring and L. majuscula from

Silver Glen Spring were collected in triplicate at the beginning of the experiment from the 

original algal mat and one sample from each mesocosm was collected at the end of the 

experiment. Samples for nitrogen and phosphorus in Vaucheria sp. were not collected due to 

funding restrictions. Samples were analyzed as described in Chapter 3. 

Statistical analyses 

Biomasses for the two pseudoreplicates from each mesocosm were summed on respective dates 

and constituted one true replicate used for statistical analyses. ANOVA (glm model) was used to 

analyze data. Differences were considered significant for p<0.05 (SAS, 2000). The least squares 

(LS) means test was used to compare individual means. All graphs show means and standard 

error (SE). 

Results 

Nutrient concentration in mesocosm water 

Nutrient addition resulted in high TP, TN and nitrate concentrations (Table 4.1). Juniper Spring 

water (low conductivity) had the lowest concentration of calcium, chloride, magnesium, sodium

and sulfate. Alexander Spring water (medium conductivity) had higher concentrations of these 

salts, and Silver Glen Spring water (high conductivity) had the highest concentrations of these 

salts (Table 4.1). 

63
Table 4.1 

Average nutrient concentrations in the conductivity experiment. 

Spring water 

from

Nutri

ents Day

TN 

mg/L

TP 

mg/L

Ammoni

a N 

mg/L

Kjeldahl

N 

mg/L

NOx 

mg/L

Calcium

mg/L

Chloride

mg/L

Iron 

mg/L

Magn

um

mg/

Juniper 0 1 0.127 0.041 0.010 0.060 0.067 13.467 4.633 0.010 4.6Juniper 0 5 0.072 0.024 0.010 0.068 0.004 13.467 5.233 0.010 4.2

Juniper 0 7 0.082 0.028 0.011 0.077 0.005 13.933 5.833 0.010 4.3

Juniper 1 1 1.163 0.260 0.010 0.063 1.100 13.667 4.633 0.010 4.7Juniper 1 5 0.976 0.227 0.012 0.080 0.897 13.567 5.500 0.010 4.2

Juniper 1 7 0.917 0.240 0.011 0.077 0.840 13.433 6.233 0.010 4.2Alexander 0 1 0.175 0.056 0.010 0.137 0.038 49.300 270.000 0.010 22.3

Alexander 0 5 0.124 0.033 0.010 0.120 0.004 48.233 270.000 0.010 19.9

Alexander 0 7 0.191 0.041 0.010 0.187 0.004 48.800 276.667 0.010 20.0Alexander 1 1 1.183 0.270 0.010 0.183 1.000 49.367 273.333 0.010 22.4Alexander 1 5 0.943 0.237 0.022 0.183 0.760 48.233 263.333 0.010 19.9

Alexander 1 7 1.060 0.260 0.010 0.180 0.880 48.300 280.000 0.010 19.7Silver Glen 0 1 0.160 0.037 0.010 0.130 0.030 73.200 443.333 0.010 35.9

Silver Glen 0 5 0.137 0.024 0.015 0.133 0.004 73.533 466.667 0.010 33.3

Silver Glen 0 7 0.188 0.033 0.010 0.183 0.005 73.633 476.667 0.010 33.2Silver Glen 1 1 1.193 0.250 0.010 0.160 1.033 72.800 443.333 0.010 35.6Silver Glen 1 5 0.880 0.220 0.012 0.160 0.720 74.400 473.333 0.010 33.8

Silver Glen 1 7 0.983 0.243 0.010 0.183 0.800 74.867 483.333 0.010 33.5

64
Water conductivity, temperature, pH and DO 

The water temperature averaged 17.9°C (SD=2.4, SE=0.29, min=14.0, max=21.4), and 

there were no differences between mesocosms (ANOVA, F=0.02, p=0.999). The pH 

averaged 8.31 (SD=0.16, SE=0.019, min=7.99, max=8.60), and there were no differences 

between mesocosms (ANOVA, F=0.01, p=1). DO was close to saturating and averaged 

7.43 (SD=0.69, SE=0.082, min=6.06, max=8.44), and there were no differences between 

mesocosms (ANOVA, F=0.72, p=0.610). Water conductivity had a steep gradient 

between treatments (Table 4.2). Addition of nutrients slightly increased water 

conductivity in the low conductivity treatment (Juniper Spring water). 

Table 4.2 

Water conductivity (μS/cm) in mesocosms in the conductivity experiment (ANOVA, 

F=12293.3, p<0.0001) 

Spring water 

from

Nutrients Mean SD SE MIN MAX

0 118.91 4.87 1.41 114 130 Juniper 1 128.50 6.45 1.86 122 143

0 1156.83 19.17 5.53 1138 1197 Alexander 1 1168.83 12.14 3.51 1150 1191

0 1903.42 40.26 11.62 1852 1961 Silver Glen 1 1924.58 39.82 11.50 1871 1976

Nitrogen and phosphorus content in algal mats 

The final algal mat Kjeldahl Nitrogen and TP content, for Lyngbya majuscula from

Juniper Spring and L. majuscula from Silver Glen Spring, was not affected by water 

conductivity and by the addition of nutrients. It neither differed from the initial content 

nor between the two strains of L. majuscula (Figure 4.1, ANOVA for Kjeldahl N, 

F=0.28, p=0.989; ANOVA for TP, F=1.94, p=0.070). The molar mat TN/TP ratio was 

not affected by the experimental treatment but differed between the two strains of 

Lyngbya (Table 4.3). All TN/TP ratios were above 16 (Redfield ratio) indicating 

phosphorus limitation even in the treatments with nutrients (Table 4.4). 

65
Table 4.3 

Results of ANOVA for mat TN/TP ratio in conductivity experiment for Lyngbya 

majuscula from Silver Glen and Juniper Springs. Data were 1/(x+1) transformed to meet 

the assumption of normality and variance homogeneity. 

TN/TP 

Source of Variation DF SS MS F p 

Treatment 6 0.00034 0.00006 2.26 0.066

Algae 1 0.00293 0.00293 116.14 <0.0001

Treatment*Algae 6 0.00011 0.00002 0.75 0.618

Table 4.4 

Mat molar TN/TP ratio in conductivity experiment in the algal mat collected from the 

field (Initial) and algal mat at the end of experiment for two strains of Lyngbya. Mean 

±SE. (ANOVA for Silver Glen Lyngbya F=1.61, p=0.216; ANOVA for Juniper Lyngbya

F=1.45, p=0.263). 

Treatment 

Silver Glen 

Lyngbya

Juniper 

Lyngbya 

Initial 36.7±1.1 23.1±1.0

Alexander 32.3±5.1 19.1±2.1

Alexander with nutrients 29.2±1.9 22.3±0.9

Juniper 31.3±1.4 19.4±1.6

Juniper with nutrients 27.8±1.0 18.7±1.3

Silver Glen 31.5±1.7 20.5±0.6

Silver Glen with nutrients 28.3±0.3 19.9±0.1

Macroalgae growth rates 

Algae growth rates based on FM and AFDM differed among taxa (Tables 4.5 and 4.6). 

Lyngbya majuscula from Juniper Spring had a higher FM growth rate than L. majuscula 

from Silver Glen Spring, but AFDM growth rates did not differ (LS means p<0.05; 

Figure 4.2). Both Lyngbya majuscula populations had a higher growth rates than 

Vaucheria (LS means p<0.05). 

The source of spring water or conductivity affected the FM and AFDM growth of 

Vaucheria sp. (Table 4.7). Vaucheria sp. grew fastest in Alexander Spring water, which 

had intermediate conductivity levels (LS means p<0.05). The addition of nutrients did 

stimulate Vaucheria growth, especially in the moderate conductivity waters of Alexander 

Spring. Nutrients and spring water conductivity had no effect on the growth of either 

strain of Lyngbya. 

66
Figure 4.1 

Algal mat TP and Kjeldahl Nitrogen content in the conductivity experiment. Algae grown 

in water from different springs without (0) and with (1) addition of nutrients. 

Juniper Lyngbya

0

5

10

15

20

25

30

35

Initial 0 1 0 1 0 1

Juniper Alexander Silver Glen

P g/Kg FM

Juniper Lyngbya

0

50

100

150

200

250

300

350

Initial 0 1 0 1 0 1

Juniper Alexander Silver Glen

N g/Kg FM

Silver Glen Lyngbya

0

5

10

15

20

25

30

35

Initial 0 1 0 1 0 1

Juniper Alexander Silver Glen

P g/Kg FM

Silver Glen Lyngbya

0

50

100

150

200

250

300

350

Initial 01010 1

Juniper Alexander Silver Glen

N g/Kg FM

Table 4.5 

Results of ANOVA for algae RGR based on algal FM in the conductivity experiment. 

Source of Variation DF SS MS F p 

Conductivity (Spring) 2 0.00 0.00 6.13 0.005 

Nutrients 1 0.00 0.00 2.47 0.125

Algae 2 0.05 0.03 80.81 <.0001

Conductivity*Nutrients 2 0.00 0.00 1.57 0.221

Conductivity*Algae 4 0.00 0.00 1.79 0.151

Nutrients*Algae 2 0.00 0.00 4.05 0.026

Conductivity*Nutrients*Algae 4 0.00 0.00 1.04 0.403

Table 4.6 

Results of ANOVA for algae RGR based on algal AFDM in the conductivity experiment. 

Source of Variation DF SS MS F p 

Conductivity (Spring) 2 0.00 0.00 6.59 0.004 

Nutrients 1 0.00 0.00 2.06 0.160

Algae 2 0.01 0.01 25.87 <.0001

Conductivity*Nutrients 2 0.00 0.00 2.06 0.143

Conductivity*Algae 4 0.00 0.00 2.54 0.057

Nutrients*Algae 2 0.00 0.00 0.10 0.902

Conductivity*Nutrients*Algae 4 0.00 0.00 1.16 0.345

67
Table 4.7 

Results of ANOVA for RGR of Vaucheria sp. based on FM and AFDM. 

FM 

Source of Variation DF SS MS F p 

Spring 2 0.0055 0.0027 6.55 0.012 

Nutrients 1 0.0023 0.0023 5.38 0.039 

Spring*Nutrients 2 0.0018 0.0009 2.17 0.157

AFDM

Source of Variation DF SS MS F p 

Spring 2 0.0021 0.0011 5.02 0.026 

Nutrients 1 0.0001 0.0001 0.26 0.620

Spring*Nutrients 2 0.0011 0.0006 2.66 0.111

68
Figure 4.2 

Algal relative growth rate (RGR) in the conductivity experiment based on fresh mass and 

ash free dry mass. Algae grown in water from different springs without (0) and with (1) 

addition of nutrients. 

Juniper Lyngbya

0

0.02

0.04

0.06

0.08

0.1

0.12

010 101

Juniper Alexander Silver Glen

RGR for FM (g/g/day)

Juniper Lyngbya

0

0.01

0.02

0.03

0.04

0.05

0.06

0 1 0 1 0 1

Juniper Alexander Silver Glen

RGR for AFDM (g/g/day)

Silver Glen Lyngbya

0

0.02

0.04

0.06

0.08

0.1

0.12

0 1 0 1 0 1

Juniper Alexander Silver Glen

RGR for FM (g/g/day)

Silver Glen Lyngbya

0

0.01

0.02

0.03

0.04

0.05

0.06

0 1 0 1 0 1

Juniper Alexander Silver Glen

RGR for AFDM (g/g/day)

Vaucheria

0

0.02

0.04

0.06

0.08

0.1

0.12

0 1 0 1 0 1

Junip

Discussion 

This study was designed to address the role of conductivity on macroalgal growth. Since 

natural spring water was used, other factors in addition to conductivity were manipulated. 

Micronutrients as well as toxic salts may vary among springs. However, all salt 

concentrations measured increased in concentration with increases in conductivity among 

springs. If it is assumed that responses of macroalgae to spring waters were due to 

differences in conductivity and associated salts alone, then Vaucheria sp. grew best at 

intermediate conductivity levels. Salinity and conductivity can affect the distribution of 

Vaucheria sp. in streams (Moreno et al., 2001). In chapter 2 a positive correlation was 

reported between alkalinity and the distribution of Vaucheria sp. Freshwater species of 

Vaucheria sp. are known to adapt to different salinity levels (Schneider et al., 1996). 

er Alexander Silver Glen

RGR for FM (g/g/day)

Vaucheria

0

0.01

0.02

0.03

0.04

0.05

0.06

0 1 0 1 0 1

Juniper Alexander Silver Glen

RGR for AFDM (g/g/day)

b 

a 

a a a ad ac ad a a bcd bd 

a 

69
Nutrient enrichment had no effect on the Lyngbya growth rate, as in Chapter 2 (this 

report). Thus, these results confirm that Lyngbya is adapted to grow in very low nutrient 

concentrations. Vaucheria growth rates were stimulated by nutrient concentrations when 

higher conductivity conditions were created in the conductivity experiment than in 

recirculating streams. This result differs from the results obtained in the recirculating

stream experiment, which used low conductivity water from Juniper Spring. The 

response of Vaucheria to nutrients in the conductivity experiment indicates that 

Vaucheria proliferations in springs with conductivity similar to Alexander Spring may be 

managed by reductions in nutrient concentrations. 

The differences in the growth rates of two populations of Lyngbya majuscula indicated 

that genetically different Lyngbya taxa may be present. Thus, a distinction may not have 

been made between two different taxa, or phenotypes of populations may vary among 

springs. Both of these factors have implications for future work and management of 

Lyngbya. Lyngbya majuscula is known to adapt well to different environments (Burja et 

al., 2002; Rossi et al., 1997); it also requires iron for growth (Gross and Martin, 1996). 

In cultures, L. majuscula grows well in low light. In nutrient rich media its growth is

carbon-limited (Burja et al., 2002). Also, the taxonomic position of Lyngbya majuscula 

inhabiting fresh waters of Florida is not clear, possibly because it represents more than 

one species (Joyner, 2004; Shannon et al., 1992; Speziale and Dyck, 1992). 

70
CHAPTER 5. NUTRIENT DIFFUSING SUBSTRATA ASSESSMENT 

OF NUTRIENT LIMIATION OF MICROALGAE 

Introduction

Many approaches exist for the experimental assessment of nutrient limitation. 

Laboratory assays, field mesocosms, and nutrient diffusing substrata (NDS) are three 

such approaches. NDS is an established method of assessing potential nutrient 

limitations of microalgae in field settings (Fairchild and Lowe, 1984; Fairchild et al., 

1985; Fairchild et al., 1989; Francoeur et al., 1999; Marks and Lowe, 1993; Mosisch et 

al., 2001; Pillsbury et al., 2002; Pringle, 1990; Snyder et al., 2002). For this report, 

researchers investigated whether nitrogen and/or phosphorus limits periphyton growth in 

16 springs in northern and central Florida. Nutrient diffusing substrata were exposed to 

algal colonization for three weeks and periphyton chlorophyll a was measured. This 

study complemented the other methods, discussed in Chapters 1 and 2, that were used to 

assess the nutrients regulating algal growth in Florida springs. 

Methods 

NDS were prepared from clear polystyrene snap-cap vials (13 dr, 46 ml I.D. x H: 

30x53mm) with a 21 mm diameter hole cut out in the cap and a Nitex screen (30 μm

mesh) spread over the rim and held in place by the cut out cap. The Nitex screen served 

as a colonization surface for periphyton. The Nitex screen approach was modified from

Biggs et al. (1998) and also Francoeur et al. (1999). Each vial was filled with a 25 ml

agar (BactoAgarTM) and nutrient solution. There was a 3 cm clearance between solidified 

agar and the Nitex screen, which provided space for algal growth with exclusion of 

potential grazers. Potassium phosphate monobasic (KH2PO4) was used as a source of 

phosphorus and sodium nitrate (NaNO3) was used as a source of nitrogen. Four 

treatments were used: (C) control (2% agar), (N) nitrate enriched (2% agar, 0.5 M 

NaNO3), (P) phosphate enriched (2% agar and 0.5 M KH2PO4) and (NP) nitrate and 

phosphate combined (2% agar, 0.5 M NaNO3 and 0.5 M KH2PO4). Vials filled with agar 

were refrigerated prior to placement in the field. At the site, vials were filled with site 

water, and the opening was covered with the Nitex screen pulled tightly over the rim and 

secured with snap-cap. Four vials (C, N, P and NP) were glued with a craft hot glue gun 

inside a Glad® sandwich box (16.5x16.5x5cm deep, 739 ml volume). Each sandwich 

box was filled with sand collected at the site and the box was placed on the bottom of the 

spring. The upper 3 cm of each vial was positioned above the sand and was exposed to 

light. Six boxes containing 4 NDS (C, N, P and NP) were deployed at each site. When 

the initial NDS were deployed, they were placed in a sunny location close to the boil at 

the depth of approximately 1 m; however, most of the boxes placed in such a location

were missing or were damaged when the sites were revisited 7 days later. Consequently, 

boxes were placed in areas not easily accessible to people and hidden from view. NDS 

were left in the field for 3 weeks. In the initial trials NDS were left in the field for 2 

weeks. However, there was very little algal colonization after 2 weeks and an additional 

week of exposure was added. In all, 96 boxes (384 NDS) were placed at16 sites. 

Although 278 NDS were recovered, only 148 NDS were not damaged and remained 

71
exposed to the water column. Consequently, representative data from NDS recovered in 

good condition consisting of 3 replicates (at least 3 NDS recovered from each treatment) 

were collected from only 7 sites (GAI-02, ICH-01, JUN-02, RAI-01, SLV-01, VOL-01, 

WAC-02). See Table 5.1 for more complete statistics on recovery. 

Table 5.1. Number of NDS recovered from each site; number of NDS recovered in good 

condition and their exposure to light. 

Spring Site code

NDS 

recovered 

NDS 

recovered in

good condition 

Light exposure

Alexander ALE-01 13 8 sunny/shade

Chassahowitzka CHA-01 18 0 shade

Gainer GAI-02 24 20 sunny/shade

Homosassa HOM-01 20 8 sunny/shade

Ichetucknee ICH-01 24 16 sunny/shade

Jackson Blue JAC-01 12 0 sunny

Juniper/Fern Hammock JUN-02 20 16 shade 

Ponce de Leon PON-01 16 0 shade 

Rainbow Spring RAI-01 24 24 sunny 

Silver Glen SGL-01 8 0 sunny 

Silver River SLV-01 16 12 sunny/shade 

Volusia Blue VOL-01 20 20 shade 

Wacissa WAC-02 20 20 sunny/shade

Wakkulla WAK-01 16 0 sunny

Weeki Wachee WEK-01 16 0 sunny

Wekiwa WKW-01 11 4 shade

Total recovered 278 148 

Nitex screens were removed from the recovered substrata and discs colonized by 

algae were cut out and frozen immediately. In the laboratory, algae were rinsed off from

the Nitex discs with DI water, combined to form 3 replicates of each treatment per site (if

there were enough samples), and analyzed fluorometrically for chlorophyll a following 

extraction in 90% ethanol (Clesceri et al., 1998). 

Results 

The algal biomass on nutrient diffusing substrata varied among sites, but nutrient 

treatment effects were not significant (Table 5.2, Figure 5.1). The algal biomass was 

usually very low in all treatments, ≤ 1 μg chl a/cm2

. 

72
Table 5.2. Results of ANOVA for nutrient diffusing substrata experiment for 7 sites 

(GAI-02, ICH-01, JUN-02, RAI-01, SLV-01, VOL-01, WAC-02). 

 

Source SS df MS F P

Nutrient treatment 1.031 3 0.344 1.039 0.382

Site 21.953 6 3.659 11.062 0.000

Nutrient treatment * Site 2.758 18 0.153 0.463 0.963

Error 18.522 56 0.331

Figure 5.1. Algal biomass on nutrient diffusing substrata measured as chl a (Mean 

±SE).

GAI

C N P NP

TREATMENT

0

1

2

3

4

Chl a (ug/cm2)

ICH

C N P NP

TREATMENT

0

1

2

3

4

Chl a (ug/cm2)

JUN

C N P NP

TREATMENT

0

1

2

3

4

Chl a (ug/cm2)

RAI

C N P NP

TREATMENT

0

1

2

3

4

Chl a (ug/cm2)

SLV

C N P NP

TREATMENT

0

1

2

3

4

Chl a (ug/cm2)

VOL

C N P NP

TREATMENT

0

1

2

3

4

Chl a (ug/cm2)

WAC

C N P NP

TREATMENT

0

1

2

3

4

Chl a (ug/cm2)

73
Discussion 

Nutrient diffusing substrata are an excellent tool in assessing limiting nutrients 

directly at the study site, but they are easily damaged. The recovery rate of NDS was

very poor. Substrata were damaged by humans and animals and were covered with 

macroalgal mats, plants, and sediments. This indicates that springs are very dynamic 

environments with a lot of disturbance and with many factors affecting algal growth and 

distribution. Although a small grazer effect on the substrata was reduced by allowing

algae to colonize under Nitex, sites did have different exposures to light and current 

velocity. Although the effects of light and current velocity may have masked the effects 

of nutrient enrichment, the low algal biomasses obtained indicated that another factor 

may be important. With grazing minimized and saturating nutrient concentrations, high 

densities of algae should have accumulated during a 3-week period (e.g., Humphrey and 

Stevenson, 1992; Rier et al., accepted). The low colonization rates may have been due to 

the unique nature of springs, which likely have very low densities of suspended algae 

because they arise from groundwater. Suspended algae in most shallow streams with 

short residence time accumulate from the downstream transport of benthic algae 

(Stevenson and Peterson, 1993). Suspended algae provide the initial colonists for 

community development on artificial substrata (Stevenson, 1983). Low suspended algal 

densities would limit immigration, growth, and community development on any artificial 

substratum. Use of artificial substrata in springs may require either inoculation with 

algae by special means or much longer exposure times. 

74
NUTRIENT DIFFUSING SUBSTRATA (NDS) AND MACROALGAE 

Nutrient diffusing substrata were modified to expose macroalgae to increased 

nitrogen and phosphorus levels. The snap-cap vials nutrient diffusing substrata described 

above were modified by attaching Glad round 125ml cups with a hole cut in the bottom 

and a lid replaced by nylon mesh. A weighed sample of macroalgae was placed in the

cup. In the initial 5-day trials macroalgae were found to be doing well and some growth

was observed. In addition to nitrate and phosphate treatments, calcium and calcium

carbonate treatments were added. There were 6 treatments (C, N, P, NP, NPCa and 

NPCaCO3 ). Agar nutrient solutions were prepared as described above. The NPCa 

treatment was prepared by adding CaHPO4 (0.1M) and keeping N and P concentrations 

as in the NP treatment. The NPCaCO3 treatment was prepared by adding CaCO3 (0.1M) 

to solution, as in the NP treatment. There were 6 replicates of each treatment (36 

modified NDS per site). Approximately 1 g fresh mass of macroalgae was placed in each 

cup. Prepared substrata were filled with site water and pressed into the bottom at the 

study site. Modified NDS were placed at 3 sites with restricted access (no swimming 

area): JUN-02 (cups with Lyngbya majuscula collected from JUN-01), WAK-01 (cups 

with Vaucheria sp. collected at the site) and WEK-01 (cups filled with L. majuscula

collected at the site). A total of 108 substrata were left in the field for two weeks. After 

two weeks at JUN-01, the substrata were damaged (they appeared to have been chopped 

by an alligator), ripped out of the bottom, or covered by sediments. Less than half of the 

substrata placed were recovered. At WAK-01, the substrata were completely covered 

with Vaucheria sp., which had grown into the containers. At WEK-01, the substrata were 

covered with a L. majuscula mat or missing. 

75
CHAPTER 6. DIATOM INDICATORS OF NUTRIENT CONDITIONS IN 

FLORIDA SPRINGS 

Introduction

Using the species composition of diatoms can be sensitive, precise, and therefore, useful 

indicators of biological and nutrient conditions in streams (Pan et al., 1996; Chetelat et al., 1999; 

Wang et al., in press). Diatom indicators of nutrient conditions may be even more sensitive than 

chlorophyll and biomass measurements and more precise than a one-time measurement of 

nutrients (Stevenson and Bahls, 1999; Stevenson and Smol, 2003). Diatoms should be better 

indicators of biological responses to nutrient enrichment than macroinvertebrates and fish, because 

they respond directly to changes in nutrient concentration. Although the latter hypothesis has not 

been rigorously tested, that rationale and past results with diatom indicators have been used to 

support the potential value of this study. 

Florida springs water varies greatly in solute concentrations, and water age and origin 

among sites (Rosenau et al. 1977; Katz et al. 1999, 2001). Conductivity is recognized as one of the 

major factors affecting diatom assemblages (Pan et al. 1996). It is expected that differentiating the 

effects of nutrients from conductivity will be more challenging for nutrient indicator development

in Florida springs than in other regions. 

The overall objective of this study was to develop diatom indicators of nutrient conditions 

for Florida springs. To do this, first the relationships were determined between nutrients and other 

environmental variables in Florida springs. Second, the relationship were determined between 

nutrients and diatom assemblages in Florida springs. Finally, indicator species were determined 

for different nutrient conditions in the springs and multispecies indicators of these nutrient 

conditions were tested. 

Materials and Methods 

Study sites 

Twenty-eight springs located in northern and central Florida were selected for this study. 

Samples were collected twice: first in the spring of 2003 (March and April) and second in the fall 

of 2003 (September to November). In the spring 48 sites within 16 springs were sampled, and in 

the fall 60 sites within 28 springs were sampled. (For a description of study sites, refer to Chapter 

2.) 

Diatom sampling 

A detailed description of sampling protocol can be found in Chapter 2. A composite 

quantitative sample of epiphytes was made with algae brushed off and rinsed from the mid-section 

of plant fragments of known surface area collected at the site. Diatom subsamples were collected

by homogenizing samples on a magnetic stirrer, and collecting aliquots of each sample with a 

pipette for diatom subsamples. Multiple aliquots were collected to reduce error due to patchiness.

Diatoms were also sampled from the surface of macroalgae. Pinches of macroalgae were 

collected from 9 transects at each site to produce one composite macroalgal sample per site. Since 

76
it was impossible to homogenize macroalgae with a blender or Biospec biohomogenizer, a 

subsample for diatom analysis was taken by grouping pinches of different taxa of macroalgae into 

separate piles and cutting out 1/4 of each pile with a razor blade. Diatoms were separated from

macroalgae by oxidation and digestion of organic material with nitric acid, leaving just the glass 

cell walls of diatoms for identification. Only macroalgal diatom subsample DM was determined. 

The surface area of macroalgae was not determined. 

A composite quantitative epipelon sample consisted of multiple sediment cores 

(approximately 0.3 cm deep and 5.2 cm diameter) that were collected at each transect. Algae were 

collected through swirling and rinsing the sample 10 times with distilled water (Stevenson and 

Stoermer, 1983). Diatom subsamples were collected by homogenizing samples on a magnetic 

stirrer, and collecting aliquots of each sample with a pipette. Multiple aliquots were collected to 

reduce error due to patchiness. 

Diatoms were digested with boiling nitric acid and by adding a catalyst, potassium

dichromate. Diatoms were mounted in Naphrax® on microscope slides and identified to the 

lowest possible level, which was usually species. Identification of diatoms was primarily based on 

Krammer and Lange-Bertalot (1986, 1988, 1991a, 1991b); Patrick and Reimer (1966, 1975) were 

used as supplemental sources of information. 

Water analyses 

At each site, on transect 1 and 9 water temperature, pH, conductivity and dissolved oxygen 

(DO) were measured. From the most upstream transect water samples were collected for assays of 

alkalinity, ammonia nitrogen, calcium, chloride, iron, magnesium, Kjeldahl nitrogen (KN), 

nitrates, orthophosphate, silica, sodium, strontium and sulfate. Water chemistry samples were 

analyzed by FDEP. (For a description of water sample analyses, refer to Chapter 2.) 

Data Analysis 

Only data from research during the spring of the year were used in analyses for this chapter, 

because fall samples were still being counted at the time that data analysis began. The 

relationships among environmental variables were analyzed first with Pearson correlation analyses 

to determine the covariation among environmental attributes. After initial exploration, a new data

set was formed by removing high conductivity sites (> 2SD + mean), because conductivity is an 

important factor regulating diatom community structure. Sites with highly unusual species 

composition can greatly reduce precision when they are used in a dataset to develop indicators. 

Canonical correspondence analyses (CCA) were executed with the full dataset and then 

with a reduced dataset. For the reduced dataset, sites with unusual chemistry were removed to 

assess the relationship between diatom species composition and environmental variables and the 

scale at which diatom responses to nutrients could be observed. Epiphytes were analyzed first as 

an example for epipelon and epi-macroalgae. Nutrients and potassium were square-root 

transformed; the other environmental variables were natural-log transformed. 

Species composition of diatom assemblages on plants was used for testing nutrient indices. 

It was decided to limit analysis and presentation of data to one habitat and during one season 

because of time constraints. Analysis of plants was chosen, because past experience indicated that 

77
epiphytic diatoms are more sensitive to changes in nutrients than diatoms in other habitats (Wang, 

unpublished data). Nutrient indicator values were extracted from the literature, past analysis of 

Florida streams data, and recent results from the Mid-Atlantic Integrated Assessment (MAIA) 

conducted by the Environmental Monitoring and Assessment Program of the USEPA. Nutrient 

indices (NI) were calculated as a weighted average of species nutrient indicator values (θi) based

on species relative abundance in samples (pi). Thus, NIi = Σ (piθi)/Σpi for i=1,S, where S is the 

number of species for which a nutrient indicator value is known. The correlations between values 

of nutrient indices and nutrient concentrations were evaluated by Pearson correlation. 

In addition to use of old autecological characterizations of taxa, new characterizations were

developed with the epiphytic algal data sampled during the spring. Standard weighted average 

regression techniques (ter Braak and van Dam, 1989) were used to develop and test indicators of 

TN and TP using the CALIBRATE® program (Juggins and ter Braak, 1992). 

Species tolerances to low nutrient conditions and requirements for high nutrients were 

further analyzed with indicator species analysis (ISA) (Dufrene and Legendre 1997). TP was 

categorized according to its concentration range in the sampling sites. TP < 20 μg/L was assigned 

as 1, 20 ≤ TP < 30 μg/L was assigned as 2, 30 ≤ TP < 40 μg/L was assigned as 3, and TP ≥ 40 μg/L

was assigned as 4. A TP category of 5 was created for a few sites that had very high TP 

concentrations. TN was also categorized according to its concentration and range of sites sampled. 

One was assigned to TN < 0.5 mg/L, 2 was assigned to 0.5 ≤ TN < 1.0 mg/L, 3 was assigned to 1.0 

≤ TN < 1.5 mg/L, and 4 was assigned to TN ≥ 1.5 mg/L. Indicator species analysis was conducted 

with datasets that included all sites and with sites with unusual chemistry removed, as with most 

other analyses, to isolate the complicating factor, conductivity, from nutrients. Species were 

selected as indicator species if their p < 0.1 in the ISA. TN or TP categories were assigned 

according to results of ISA. 

Results 

When all sites were included in the analysis, N and P concentrations were not correlated. 

TN was positively correlated with NOx, alkalinity, and Ca and negatively correlated with Mg, Si,

and S (Table 6.1). KN was positively correlated with NH4-N, Cl, Na, K, and conductivity. NOx 

was positively highly correlated with TN and alkalinity and negatively correlated with Si, Na, Sr, S, 

and pH. NH4-N was positively correlated with KN and negatively correlated with PO4 and TP. TP 

was positively correlated with PO4, Cl, Mg, Si, Na, Sr, S, temperature, conductivity, and K but was

negatively correlated with DO and NH4. PO4 was also negatively correlated with DO, pH, and 

NH4 but was positively correlated with TP, Si, and S. 

Alkalinity was highly and positively correlated with Ca, but negatively correlated with pH 

(Table 6.1). Both Sr and conductivity were highly correlated with cations, Cl, and S. The pH data 

were negatively correlated with cations, S, and conductivity, but positively correlated with DO. 

78
Table 6.1. 

Correlations among nutrients and environmental variables at all sites during spring. Only correlations with P<0.0represents 0.01< p < 0.05, ** represents 0.01 < p < 0.001, *** represents p < 0.001. 

Alk NH4 Ca Cl Mg TN Kj-N NOx PO4 TP K Si Na Sr S 

NH4 R

P

Ca R 82

P **

Cl r 42

p *

g r 5 90

p ** **p *

O4 r 4

p

0.

*

0.*

M 0. 2 0.

* *

TN r 0.40

*

0.30

*

-0.33

*

Kjet-N r 0.51 0.49 0.32

p *** *** *

NOx r 0.44 -0.37 0.99

p ** ** ***

P -0.

**

0

TP r -0.34 0.35 0.45 0.86

p * * ** ***

K r 0.36 0.94 0.89 0.41 0.30

p * *** *** ** *

Si r 0.41 0.34 0.44 -0.31 -0.29 0.49 0.47

p ** * ** * * *** ***

Na r 0.44 1.00 0.92 0.47 -0.31 0.36 0.93

p ** *** *** *** * * ***

Sr r 0.61 0.75 0.80 -0.29 0.43 0.65 0.34 0.77

p *** *** *** * ** *** * *** 

S r 0.55 0.78 0.88 -0.31 -0.35 0.31 0.51 0.71 0.40 0.80 0.96

79
Alk NH4 Ca Cl Mg TN Kj-N NOx PO4 TP K Si Na Sr S 

p *** *** *** * * * *** *** ** *** ***

DO r -0.32 -0.59 -0.48 -0.47

p * *** *** ** 

pH r -0.76 -0.71 -0.41 -0.30 -0.32 -0.32 -0.30 -0.35 -0.39 

p *** *** ** * * * * * ** 

Temp r 0.47 0.31 0.31 0.31 0.36 0.44 0.51 0.47

p ** * * * * ** *** ** 

Cond r 0.63 0.94 0.91 0.48 0.33 0.93 0.94 0.83 0.84 

p *** *** *** *** * *** *** *** *** 

80
Four ordination axes explained 29.9% of the variance in species composition of epiphytic 

diatoms with CCA when all sites were included in the analysis (Table 6.2). The first and all 

canonical axes were statistically significant (F = 4.2, p = 0.005; F = 2.3, p = 0.005). The four axes 

explained 62.9% of species-environment variance. The first CCA axis was highly correlated with 

Cl and conductivity (r = 0.82, p < 0.001; r = 0.75, p < 0.001); the second CCA axis was positively 

correlated with Sr (r = 0.62, p < 0.001) and negatively correlated with alkalinity (r = -0.32, p = 0.03) 

(Table 6.3). A CCA biplot indicated that most sites clustered around the mid-point of the CCA 

with a few, high-conductivity sites strongly affecting the analysis (Figure 6.1). A table with 

species scores on the CCA axes can be found in Appendix B (Table A). 

Table 6. 2. 

The percentages of variance explained in the CCA. The CCA was analyzed with epiphytic diatoms 

and environmental variables and data from all sites during spring. 

Axes 1 2 3 4 Total 

inertia 

Eigenvalues (λ) 0.604 0.396 0.285 0.230 5.063

Species-environment correlation 0.953 0.865 0.93 0.849

Cumulative % variance of: 

Species data 11.9 19.8 25.4 29.9

Species-enviroment relationship 25.1 41.6 53.4 62.9

Test of significance of first canonical axis F=4.2, p=0.005

Test of significance of all canonical axes F=2.3, p=0.005

Sum of all unconstrained λ 5.063

Sum of all canonical λ 2.406

Table 6.3. 

The correlation between CCA and environmental variables on the first two axes. Epiphytic diatom 

data and environmental variables from all sites collected during the spring were used. * indicates p 

< 0.05, ** indicates p < 0.01, *** indicates p < 0.001. 

Environmental variables CCA1 CCA2 

Alkalinity 

NH4-N 

Cl 

TN 

Kjeldahl N 

PO4

TP 

Si 

Sr 

DO 

pH 

Temperature 

Conductivity 

0.82***

0.75***

-0.32* 

0.62***

81
Figure 6.1. 

A CCA biplot of the first 2 axes. The CCA was analyzed with epiphytic diatoms and 

environmental variables. 

-5.0 +15.0

-5.0 +10.0

Cl

Cond

Sr

NH4

pH

Temp

TN

Alk

TP

DO

KN

Si

CHA-03

HOM-01

WEK-04

SLV-05

SLV-04

JAC-03

ICH-07

SLV-03

ICH-08

WKW-01

CHA-02

GAi-01

ICH-06

ICH-05

ICH-04

RAI-01

JAC-02

RAI-04

GAi-02

RAI-03

JAC-01

RAI-02

SGL-01

VOL-01

CHA-01

WKW-02

WAC-01

ICH-03

ALE-01

WEK-03

Jun-03

Jun-01

ALE-02

SLV-01

ICH-01

PON-01

WAK-01

WAK-03

SLV-02

WAK-02

VOL-02

JUN-04

WAC-02

WAC-03

After removing high conductivity sites (ALE-01, ALE-02, CHA-03, HOM-01, JUN-04, 

SGL-01, VOL-01, and VOL-02), a second CCA was executed (Figure 6.2). Four ordination axes 

explained 29.3% of the variance in epiphytic diatom assemblage with CCA (Table 6.4). The first 

and all canonical axes were statistically significant (F = 2.62, p = 0.01; F = 1.83, p = 0.005). Four 

axes explained 59.9% of species-environment variance. The first CCA axis was positively 

correlated with Cl (p = 0.79, r < 0.001), but negatively correlated with NH4-N and TN (r = -0.38, p 

= 0.008; r = -0.39, p = 0.009, respectively). The second CCA axis was negatively correlated with 

TP and Si (r = -0.32, p = 0.06; r = -0.32, p = 0.06), but positively correlated with NH4-N, Cl, TN, 

KN, and DO (r = 0.72, p < 0.001; r = 0.38, p = 0.02; r = 0.48, p = 0.006; r = 0.80, p < 0.001; r = 0.55, 

p < 0.00; respectively) (Table 6.5). A table with species scores on the CCA axes can be found in 

the Appendix B (Table A). 

82
Figure 6.2. 

A CCA biplot of the first 2 axes. The CCA analysis was conducted using epiphytic diatoms and 

environmental variables. High conductivity sites were removed from this analysis. 

-4.0 +8.0

-2.5 +5.5

Cl

TN

NH4

Cond

Temp

Si

pH

Alk

DO

TP

Sr

KN

CHA-02

WEK-04

JAC-03

CHA-01

JAC-02

ICH-08

JAC-01

ICH-07

ICH-06

SLV-05

WKW-02

GAi-01

RAI-04

SLV-02

SLV-03

WAC-01

RAI-01

RAI-03

WAK-03

SLV-04

ICH-03

GAi-02

PON-01

WAC-03

Jun-03

WKW-01 ICH-01 Jun-01

WAK-01

ICH-05

WEK-03

ICH-04

WAC-02

WAK-02

SLV-01

RAI-02

Table 6.4. 

The variance explained by CCA axes. The CCA analysis was conducted using epiphytic diatoms 

and environmental variables. High conductivity sites were removed from this analysis. 

Axes 1 2 3 4 Total 

inertia 

Eigenvalues (λ) 0.351 0.293 0.205 0.155 3.428

Species-environment correlation 0.936 0.933 0.893 0.85

Cumulative % variance of: 

Species data 10.2 18.8 24.8 29.3

Species-enviroment relationship 21 38.4 50.7 59.9 

Test of significance of first canonical axis F=2.62, p=0.01

Test of significance of all canonical axes F=1.83, p=0.005 

Sum of all unconstrained λ 3.428

Sum of all canonical λ 1.675

83
Table 6.5. 

The correlation between CCA on the first two axes and environmental variables. Epiphytic diatom 

data and environmental variables collected during the spring were used. High conductivity sites 

were excluded from this analysis. * indicates p < 0.05, ** indicates p < 0.01, *** indicates p < 

0.001. 

Environmental variables CCA1 CCA2 

Alkalinity 

NH4-N 

Cl 

TN 

Kjeldahl N 

TP 

Si 

Sr 

DO 

pH 

Temperature 

Conductivity 

-0.38* 

0.79*** 

-0.39*

0.72*** 

0.38* 

0.48** 

0.80*** 

-0.32 

-0.32 

0.59* 

Conductivity was the dominant environmental constraint on species composition of diatoms 

on macroalgae and in epipelon, but nutrients were also important. Four ordination axes explained 

between 23.2 and 30.1 % of the variation in diatom species composition among sites, either with or 

without including high conductivity sites (Tables B, C, D and E in the Appendix B). Four axes 

explained between 52.1 and 60.4% of species-environment variance. Significant relationships 

between diatom species composition on macroalgae and nutrients were only observed after 

removing high conductivity sites from the dataset (Figures A and B in the Appendix B). 

Relationships between epipelic diatom species composition and nutrients were evident with both 

the full dataset and dataset without high conductivity sites, but relationships with nutrients in the 

later dataset were more important (Figures C and D in the Appendix B). In all cases, TN or NOx

were correlated to diatom species composition in different directions than TP or SRP. Table with 

species scores on the CCA axes can be found in the Appendix B (Tables G and J). 

Diatom indicators of nutrient conditions were complexly correlated to nutrient concentrations, 

and not correlated as predicted (Table 6.6). The MAIA trophic index was correlated to more 

measures of nutrient concentrations than other indicators. The FLavg and FLnew trophic status 

indicators were correlated to 3 of 8 nutrient measurements. Most of these indicators were 

negatively correlated with N measurements, except that the FL TP indicator was positively 

correlated with TN and NOx. All indicators were positively correlated with P measurements in 

springs. 

Diatom inference models of TP and TN developed with weighted averaging using diatoms

on plants were highly correlated to TP and TN concentrations (0.59 < r2

 < 0.71) before testing with 

jackknifing, but were not highly correlated afterwards (0.15 < r2

 < 0.26). Models for TP had 

higher r2 values than models for TN before jackknifing, but the opposite was true after jackknifing 

(Figure 6.3). Little difference in r2

 values was observed between models using datasets with all 

sites and without high conductivity sites. Indicator values for diatom taxa are provided in Tables 

K and L. Indicator values for epiphytes using the dataset with all sites are provided because these 

84
metrics with all sites performed as well as metrics based on the dataset without high conductivity 

sites. 

Table 6.6. 

Correlations between nutrient concentrations and previously developed diatom indicators of 

nutrient conditions using species composition of diatoms on plants in Florida Springs. Only 

correlations with P<0.05 are indicated. 

Ammonia TN Kje-N TNs NOx PO4 TP TPs

FL TP Pearson Correlation 

Sig. (2-tailed) 

0.355 

0.020 

0.345 

0.023 

FL TN Pearson Correlation 

Sig. (2-tailed) 

MAI Pearson Correlation 

Sig. (2-tailed) 

0.555 

0.000 

-0.481

0.001 

0.392

0.009

0.361

0.016

0.348

0.021

Slate SRP Pearson Correlation 

Sig. (2-tailed) 

Slate TP Pearson Correlation 

Sig. (2-tailed) 

0.306

0.044

WHITMORE Pearson Correlation 

Sig. (2-tailed) 

0.435 

0.003 

VD N Pearson Correlation 

Sig. (2-tailed) 

-0.450

0.002 

VD Salinity Pearson Correlation 

Sig. (2-tailed) 

VD Troph Pearson Correlation 

Sig. (2-tailed) 

FL avg 

Pearson Correlation 

Sig. (2-tailed) 

-0.362

0.016 

0.312

0.039

0.304

0.045

FL new Pearson Correlation 

Sig. (2-tailed) 

-0.399

0.007 

-0.349

0.020 

-0.377

0.012 

ISA showed lists of indicator species with respect to different groups of nutrients. The 

results from ISA generally supported the observations from CCA. Under low conductivity 

conditions, species such as Achnanthes exigua, Nitzschia inconspicua, Navicula arvensis, 

Nitzschia subacicularis, Navicula viridula var. rostrata, Fragilaria brevistriata, Sellophora

pupula preferred a high TP environment. Species such as Cymbella microcephala and Fragilaria

construens preferred low TP conditions (Table 6.7). Under low conductivity conditions, 

Achanthes minutissima, Cymbella affinis, Melosira varians, Cymbella microcephala, Fragilaria

construens, and Fragilaria capucina were grouped as category 4 in TN preference, while 

Nitzschia dissipata was grouped as category 1 in TN preference (Table 6.8). Under high 

conductivity conditions, species such as Navicula tenelloides, Bacillaria paradoxa, Fragilaria

brevistriata, Navicula gregaria, Fragilaria capucina, Gomphonema clevei, Navicula

sanctaecrucis, Synedra fasciculata, Nitzschia gracilis, and Gomphonema mexicana were 

categorized as group 3 or 4 of TP and group 1 or 2 of TN; thus they had a high TP and low TN 

85
preferences. In addition, species such as Navicula menusculus, Nitzschia recta, Fragilaria

pinnata, Eunotia subarcutoides, Gomphonema angustum, Gomphonema dichotumus, and 

Navicula contenta preferred both category 1 of both TP and TN (Table 6.9). 

Figure 6.3. 

Plots of observed nutrient concentrations (TN mg/L; TP μg/L) vs. weighted average inferred 

nutrient concentrations. A and C included all sites; B and D excluded high conductivity sites. 

 A C 

 C 

Weighted average inferred TP

0.00 .02 .04 .06 .08 .10 .12 .14 .16

Observed TP

.16

 B D 

.14

.12

.10

.08

.06

.04

.02

0.00

RMSE = 0.015

67

ackknifed RMSE = 0.024 

Jackknifed r2 = 0.21

r

2 = 0.

J

Weighted average inferred TP

0.00 .02 .04 .06 .08 .10 .12 .14 .16

Observed TP

.16

4

.12

.10

.08

.06

.04

.02

0.00

Weighted average inferred TN

-1.0 -.5 0.0 .5 1.0 1.5 2.0 2.5 3.0 3.5

Observed TN

3.5

3.0

2.5

2.0

1.5

1.0

.5

0.0

-.5

-1.0

RMSE = 0.47

r

2 = 0.51

Jackknifed RMSE = 0.59

Jackknifed r2 = 0.25

Weighted average inferred TN

-1.0 -.5 0.0 .5 1.0 1.5 2.0 2.5 3.0 3.5

Observed TN

3.5

3.0

2.5

2.0

1.5

1.0

.5

0.0

-.5

-1.0

RMSE = 0.47

r

2 = 0.51

Jackknifed RMSE = 0.59

Jackknifed r2 = 0.25

RMSE = 0.015

67

ackknifed RMSE = 0.024 

Jackknifed r2 = 0.21

r

2 = 0.

J

.1

Weighted average inferred TN

0.0 .5 1.0 1.5 2.0 2.5 3.0 3.5

Observed TN 3.5

3.0

2.5

2.0

1.5

1.0

.5

0.0

RMSE = 0.45

r

2 = 0.59

Jackknifed RMSE = 0.62

Jackknifed r2 = 0.26

Weighted average inferred TN

0.0 .5 1.0 1.5 2.0 2.5 3.0 3.5

Observed TN 3.5

3.0

2.5

2.0

1.5

1.0

.5

0.0

RMSE = 0.45

r

2 = 0.59

Jackknifed RMSE = 0.62

Jackknifed r2 = 0.26

Weighted average inferred TP

0.00 .02 .04 .06 .08 .10 .12 .14 .16

Observed TP

.16

.14

.12

.10

.08

.06

.04

.02

0.00

RMSE = 0.014

r

2 = 0.71

Jackknifed RMSE = 0.025 

Jackknifed r2 = 0.15

Weighted average inferred TP

0.00 .02 .04 .06 .08 .10 .12 .14 .16

Observed TP

.16

.14

.12

.10

.08

.06

.04

.02

0.00

RMSE = 0.014

r

2 = 0.71

Jackknifed RMSE = 0.025 

Jackknifed r2 = 0.15

86
Table 6.7. 

Species TP preference in different environmental conditions assessed by indicator species 

analysis. 

All sites Selected sites 

Species IV p Group Species IV p Group

NAtendes 74.1 <0.001 3 ACexigua 82 <0.001 5

ACdelica 44.5 0.040 3 NIincons 100 0.001 5

ROcurvat 38.3 0.068 3 NAarvens 87.2 0.002 5

PTlanceo 35.9 0.071 3 NIsubaci 91 0.004 5

NIspp 41 0.092 3 GOparvul 54.8 0.008 4

BAparado 92.4 <0.001 4 NAvirros 60.7 0.008 5

FRbrevis 83.9 <0.001 4 FRcapmes 56.7 0.011 4

NAgregar 98.6 <0.001 4 CAbacill 47.6 0.026 3

FRcapuci 78 0.005 4 NArhyger 54.5 0.026 4

GOclevei 48.7 0.010 4 AMveneta 54.4 0.038 3

NAsancru 50 0.012 4 FRconstu 55.9 0.044 1

SYfascic 50 0.012 4 CMaffins 51.3 0.046 1

NIgracil 62.1 0.018 4 FRbrevis 53.3 0.046 5

GOmexica 59.1 0.021 4 SFpupula 43.6 0.047 5

AMinarie 47.9 0.022 4 CCneodim 62.7 0.048 3

FRellipt 65.9 0.030 4 ACclevei 49.9 0.054 3

PTlanfre 46.4 0.041 4 PTlanros 53.7 0.055 5

EPadnata 37.4 0.049 4 GOclevei 50 0.059 5

GOgracil 43.4 0.087 4 NAgoeppe 50 0.059 5

NAcapita 31.6 0.092 4 NAingenu 50 0.059 5

NAmolest 50 0.059 5

NIgracil 44.2 0.064 4

SYulna 52.3 0.072 2

AUcrenul 44.1 0.078 4

ACminuti 53.2 0.082 1

 GOpumilu 46.9 0.092 2 

87
Table 6.8. 

Species TN preference in different environmental conditions assessed by indicator species 

analysis. 

TN (High cond) TN without outliers 

Species IV p Group Species IV p Group

AMlibyca 74.7 <0.001 2 ACminuti 66.6 0.005 4

NAcapita 72.5 <0.001 2 CMaffins 61.1 0.005 4

EPadnata 75 0.001 1 ACclevei 63 0.006 3

NIspp 74.7 0.002 1 FRbrevis 62 0.006 3

ACdelica 67.7 0.004 2 CCplacen 53.4 0.008 2

GOaffine 50 0.013 1 MEvarian 59.1 0.011 4

RPbrebis 50 0.013 1 CMmicroc 58.1 0.012 4

GOclevei 48.7 0.016 2 FRconstu 59.9 0.012 4

NAsancru 50 0.016 2 FRcapuci 61.5 0.015 4

SYfascic 50 0.016 2 NArecens 42.2 0.042 2

FRcapuci 73.7 0.021 1 NIsubaci 36.4 0.043 3

AMinarie 47.9 0.023 2 NIdissip 41.7 0.049 1

NAtendes 43.8 0.032 2 ACrupdes 52.7 0.065 3

PTlanfre 46.4 0.037 1 NAphylle 27.8 0.070 3

NAgregar 45.9 0.056 2 NAarvens 23 0.076 3

PTlanceo 35.9 0.072 1 SYulna 56.1 0.079 2

ROcurvat 38.3 0.082 1 CAbacill 33.4 0.083 3

 NIgracil 39.7 0.089 1 

88
Table 6.9. 

Species with preference for low NP conditions assessed by indicator species analysis. 

Species IV p Group

NAmenscl 95.9 0.001 1

NIrecta 64.4 0.007 1

FRpinnat 82.1 0.058 1

EUsubarc 33.3 0.065 1

GOangust 33.3 0.065 1

GOdichot 33.3 0.065 1

GOspp 33.3 0.065 1

NAconten 33.3 0.065 1

CMaffins 49 0.075 1

FRbidens 30.8 0.084 1

PTlanceo 31.5 0.084 1

CMsilesi 30.2 0.086 1

PTlanros 72.5 0.099 1

89
Discussion 

The lack of correlation among TN, TP, and conductivity in Florida springs is unusual. 

After removing high conductivity sites, we found that TN and TP were negatively correlated 

among most sites in FL springs. In most studies, TN and TP are positively correlated, and both are 

usually correlated with conductivity or Cl and human disturbance in watersheds (e.g., Johnson et 

al., 1997; Herlihy et al., 1998; Stevenson et al., accepted). In low conductivity waters, increases in 

conductivity can be related to landscape disturbance (Bolstad & Swank, 1997) and can be used as 

indicators of increased nutrient supply (Biggs, 1988). Variation in water chemistry among Florida 

springs represents a special case of nutrient relations in aquatic systems and likely reflects the 

diversity in sources of water and contamination among spring systems in Florida. 

The magnitude of correlations between diatom species composition and environmental 

variables in Florida springs was similar to values observed in other studies. CCA analyses 

commonly explain between 20 and 40% of variation in species composition among streams and 

lakes in a region (Hall and Smol, 1992; Pan et al., 1996). CCA analyses may explain less variation 

in diatom species composition when great physical-chemical diversity occurs among sites, such as 

wetlands (Stevenson et al., 1999). 

Conductivity explained more variation in diatom species composition among Florida 

springs than nutrients did. Conductivity, pH, or other factors indicating ionic/osmotic gradients 

are commonly a dominant attribute regulating variation in diatom species composition among 

streams, wetlands, and lakes (Hall and Smol, 1992; Pan et al. 1996; Stevenson et al., 1999). 

Florida springs were unusual, because the variation in conductivity was great and the effects of 

other environmental variables on species composition were masked unless the variation in 

conductivity was accounted for. A simple approach was used in this study to account for 

conductivity related variables. A limited number of sites with the highest conductivity were 

removed from the dataset. Results suggested that nutrients become important variables affecting 

species composition if the range in conductivity was constrained. 

CCA analyses indicated that diatoms responded differently to TN and TP variation among 

Florida spring sites. Indicator species analyses also indicated that different species were relatively 

more abundant in high N and high P environments, as well as low NP conditions. From ISA, in 

general, high TP species belonged to the low TN species group and low TP species belonged to the 

high TN group. In high conductivity sites, species listed in the table belonged to the low TN and 

high TP group. Another group of species preferred both low TN and low TP conditions. A similar 

pattern can be observed from the CCA. Although it is known that different species occur in low 

nutrient conditions than in high nutrient conditions, usually high nutrient conditions include both 

high N and P. Few cases exist in which we can distinguish diatoms that thrive in low N/high P 

conditions and high N/low P conditions. 

Existing diatom indicators of nutrient conditions need to be refined before application to 

Florida spring systems. Existing diatom metrics indicating nutrient conditions were unpredictably 

correlated to N and P concentrations in springs. Most existing trophic status metrics were 

positively correlated to TP, but unpredictably correlated to TN. Diatom indicators of nutrient 

conditions from other ecosystems were developed in conditions where N and P concentrations 

90
were positively correlated, and usually where P is hypothesized to be the most limiting factor 

(Stevenson, unpublished data and reports). If diatom taxa respond differently to independent N 

and P enrichment, as indicated by CCA, then, with the existing metrics, a more positive response

would be expected to the TP concentration than to TN. 

Refined diatom indicators of nutrient conditions based on autecological information 

developed with data from diatoms and environmental conditions in Florida springs show promise 

for application, but they should be tested more completely. Weighted average regression models 

developed separately to indicate TN or TP were relatively precise before testing by jackknifing. 

Correlations with r2

 greater than 0.5 usually indicate a good fit (Pan et al. 1996). However, poor 

correlations between diatom-inferred and measured nutrient concentrations (r2

 < 0.26) after 

jackknifing suggested caution should be exercised with use. This reduction in goodness of fit of 

nutrient models after jackknifing is common for nutrient models (see Pan et al., 1996, for 

discussion). In addition, the issue of whether N or P models should be used to imply trophic status 

should be explored. Thus, the N and P regulation of productivity in Florida springs and streams 

should be better understood. If springs or streams are P limited, then the TP model should be 

applicable. However, high TN may also be a problem. 

It is also recommended that weighted average models be used to compare the trophic status 

between reference and test sites and not be used to infer specific nutrient concentrations unless 

calibrated more thoroughly to conditions in those systems. Weighted average models are 

transferable among systems when the same nutrients regulate species composition, however, they

may be biased and over- or underestimate actual nutrient concentrations. In a study of 

transferability of nutrient indicators from streams to wetlands, Wang et al. (in preparation) found 

that the rank of trophic status was correctly identified, but the actual nutrient concentrations were 

underestimated. 

In conclusion, nutrient indicators developed in this study show significant promise. 

Further analysis of the data collected and comparison to existing data on Florida streams is 

warranted. Use of categorical autecological characterizations of taxa is recommended, like those 

used by van Dam et al. (1994), which will enable the ranking of sites. Indicator models using 

categorical autecological information can be calibrated to nutrient concentrations to infer specific 

nutrient concentrations if complements to nutrient measurements are needed. 

91
CHAPTER 7. DATA DESCRIPTION 

Data sets can be found in Appendix C. Data are organized in folder with corresponding 

chapter number. Data for each chapter can be found in the Excel files and in the Access 

databases. Excel files include the original data and the final data after calculations were 

performed. Access databases show calculations for the data sets. Summary of all data files can 

be found in Table 7.1. 

Chapter 2 

The original data from RPHA and field measurements can be found in 

RPHA_field_data_fall and in RPHA_field_data_spring Excel files. These two datasets were 

imported in parts to Access database - Florida Springs 20040531 for further analyses. 

Descriptions of codes and abbreviations used can be found in the Metadata for Chapter 2 Excel 

file. All tables in the Florida Sprigs 20040531 database can be linked by Site_code or 

Long_site_code. 

Epiphytes soft counts data can be found in Access database - Epiphytes_soft_counts. 

Final results for epiphytes soft counts can be found in Epiphytes_Cells_mm2 Excel file. 

Chapter 3 and 4 

The original data and results of calculations for nitrate and phosphate dosing experiments 

can be found in Nitrate_and_Phosphate_dosing_experiments Excel file. The original data and 

results of calculations for nitrate and phosphate dosing experiment can be found in 

Conductivity_experiments. Calculations for this data sets can be found in Access database 

Experimental_streams. Description of codes and abbreviations used can be found in the 

Metadata for Chapter 3 and 4 Excel file. 

Chapter 5 

The original data and results of calculations for NDS can be found in NDS_data Excel 

file. Calculations for NDS data can be found in NDS_chla Access database. 

Chapter 6 

Results of diatom counts for all samples from spring and fall are in the Diatom_counts 

Excel file. Description of codes for diatom taxa can be found in Diatom_taxa_codes Excel file. 

Images of common diatom taxa (contributing at least 5% to any of the diatom counts) can be 

found in a PowerPoint file Florida Springs Common Diatoms. 

92
Table 7.1 

Summary of data description. 

Chapter Data description Excel Access

2 - data sheet used in the 

field Datasheet fall 2003

2 

- RPHA 

- Chemical analyses of 

water and algal mats

RPHA_field_data_fall 

RPHA_field_data_spring

Florida Springs 

20040531

2 - Epiphytes soft counts Epiphytes_Cells_mm2 Epiphytes_soft_counts 

3 

- Nitrate dosing 

experiment

- Phosphate dosing

experiment

Nitrate_and_Phosphate_dosing_experiments Experimental_streams

4 - Conductivity 

experiment Conductivity_experiments Experimental_streams

5 - NDS NDS_data NDS_chla

6 - Diatom counts 

Diatom_counts

Diatom_taxa_codes 

93
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100
CHAPTER 9. APPENDICES 

APPENDIX A. Additional tables for Chapter 2. 

APPENDIX B. Additional tables and figures for Chapter 6 

APPENDIX C. Project original data and calculations 

101