ECOLOGICAL CONDITION OF ALGAE 

AND NUTRIENTS 

IN FLORIDA SPRINGS: 

THE SYNTHESIS REPORT 

Submitted to the Florida Department of Environmental Protection 

 

October 31, 2007 

 

 

 

Authors: 

 

R. Jan Stevenson 

Agnieszka Pinowska 

Andrea Albertin 

James O. Sickman
2

 

DEP Grant Number S0291 

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 850-245-8063 

Fax 850-245-8082 

 

 

 

 

 

 

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 

 

 

 

 
3

ACKNOWLEDGEMENTS 

 

This research was supported by the Florida Department of Environmental Protection. Russel 

Frydenborg oversaw the project for FDEP and provided invaluable information and logistical 

support for the project. 

Denise Miller supervised the budget process for FDEP and facilitated completion of the project. 

Randy Sheets and Sharon Nichols managed the budget process at Michigan State University 

(MSU). Kelly Lewis and Cheryl Stephens managed the budget process at the University of 

Florida. 

Many members of the Algal Ecology Laboratory in the Department of Zoology at MSU assisted 

with the project: Lei Zheng, Julianne Heinlein, Vanessa Lougheed, Scott L. Rollins, Lara 

Panayotoff, Kalina Manoylov, Colleen McLean, Daniel Wieserich, Elizabeth Mullen, Kevin 

Geyer, Amy Messick, Nicole Hedquist, Jan Seidler, and Scott Fullbright. 

Many students and staff at the University of Florida also assisted with the project: Mi Youn Ahn, 

Delores Lucero, Francisco (Chico) Alvarado, Eric Ostmark, Martin Sandquist, Alyson Dagang, 

Solomon Haile, Yu Wang, Gavin Wilson, Yubao Cao and Xiaowei Gu. Marty Anderson, a coauthor on other reports, provided field and GIS support. 

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 Tom Frick and other staff with FDEP in Tallahassee and 

with the State Parks for their assistance in this study. 

This report, as the other reports submitted for this project, was extensively peer-reviewed. We 

thank those reviewers for their valuable comments. 
4

TABLE OF CONTENTS 

 

EXECUTIVE SUMMARY 5 

1 – INTRODUCTION 7 

2 - SURVEYS OF MACROALGAE AND NUTRIENTS IN FLORIDA SPRINGS 10 

 2.1 – Introduction 10 

 2.2 – Overview of Methods 11 

 2.2.1 Field Sampling and Laboratory Analyses 11 

 2.2.2 Data Transformations and Analyses 14 

 2.3 – Overview of Results 16 

 2.3.1 Region, land use, and nutrients 16 

 2.3.2 Algae and relationships to nutrient concentrations 17 

 2.3.3 Algae and relationships to local and regional environmental factors 20 

 2.4 – Discussion 22 

3 – NUTRIENT CONCENTRATIONS ABOVE, WITHIN, AND BELOW 

LYNGBYA WOLLEI AND VAUCHERIA MATS 24 

 3.1 – Introduction 24 

 3.2 – Overview of Methods 25 

 3.3 – Overview of Results and Discussion 27 

4 – SEASONAL VARIABILITY IN MACROALGAE, DISCHARGE, AND 

 NUTRIENTS IN FLORIDA SPRINGS 29 

 4.1 – Introduction and Overview of Methods 29 

 4.2 – Overview of Results and Discussion 29 

5 – EXPERIMENTAL ASSESSMENT OF LYNGBYA WOLLEI AND VAUCHERIA

RECOLONIZATION AFTER PHYSICAL DISTURBANCE 31 

 5.1 – Introduction 31 

 5.2 – Overview of Methods 32 

 5.3 – Overview of Results and Discussion 32 

6 – EXPERIMENTAL ASSESSMENT OF LYNGBYA AND VAUCHERIA GROWTH 

RATES ALONG NITRATE AND PHOSPHATE CONCENTRATION 

 GRADIENTS 33 

 6.1 – Introduction 33 

 6.2 – Overview of Methods 35 

 6.2.1 Microcosm Experiments 37 

 6.2.2 Large Microcosm (Donut) Experiments 38 

 6.2.3 Mesocosm Experiments 39 

 6.3 – Overview of Results and Discussion 40 

7 - OVERALL DISCUSSION AND CONCLUSIONS 46 

8 – REFERENCES 51 
5

EXECUTIVE SUMMARY 

1. Surveys of Florida springs indicated that almost all springs had macroscopic algae growing 

in them, an average of 50% of the spring bottoms were covered by macroalgae, and thickness 

of macroalgal mats was commonly 0.5 m and as thick as 2 m in one spring boil. 

2. Lyngbya wollei and Vaucheria spp. were the two most common taxa of macroalgae that 

occurred in extensive growths in the studied springs, however 23 different macroalgal taxa 

were observed in the spring survey. 

3. Abundance of Vaucheria spp. in surveys of springs was positively related to nitrogen and 

phosphorus concentrations with a clear threshold increase at 0.59 mg total nitrogen/L. 

4. Abundance of Lyngbya wollei in surveys of springs was not related well to either nitrogen or 

phosphorus concentrations in spring water, however it was related to sediment phosphorus 

concentrations and indices of human activities within 1000 m of sampling sites. 

5. Interstitial waters of macroalgal mats had higher nutrient concentrations than overlying 

waters, indicating entrained particulate matter, long term growth and decomposition, or 

groundwater may supply nutrients to supplement water column supplies. 

6. Seasonal variation in macroalgae was largely related to flooding and resulting turbidity and 

sedimentation that reduce macroalgal biomass. 

7. Macroalgal removal experiments showed that Vaucheria spp. recovered rapidly after 

removal, whereas Lyngbya wollei recovered slowly and incompletely after removal. 

8. Laboratory experiments showed that conductivity, iron, and light as well as nutrients affected 

growth of both Vaucheria spp. and Lyngbya wollei. 

9. Nutrient enrichment experiments showed that: 

9.1. the range in nitrate and phosphate concentrations in Florida springs could regulate 

growth rates of both Vaucheria spp. and Lyngbya wollei. 

9.2. algae grew in almost all nutrient treatments, even at very low nutrient concentrations, 

and showed a logistic response to nutrients, versus a simple asymptotic response; 

9.3. when phosphate was manipulated and nitrate was maintained in luxury supply, growth 

rates of Lyngbya wollei did not respond more than 10% below 0.011 mg PO4-P/L, 

increased substantially from 0.011-0.028 mg PO4-P/L, and did not respond more than 

10% to further phosphate concentration increases above 0.028 mg PO4-P/L (in 

microcentrifuge microcosms that provided the most accurate control of nutrient 

concentrations); 

9.4 when nitrate concentrations were manipulated and phosphate was maintained in luxury 

supply, growth rates of Lyngbya wollei did not respond more than 10% below 0.034 mg 

NO3-N/L, increased substantially from 0.034-0.230 mg NO3-N/L, and did not respond 

more than 10% to further nitrate concentration increases above 0.230 mg NO3-N/L (in 

microcentrifuge microcosms that provided the most accurate control of nutrient 

concentrations); 

9.5 growth of Lyngbya wollei increased with nitrogen concentrations, even if phosphorus 

concentrations were very low; 

9.6 when nitrate concentrations were manipulated and phosphate was maintained in very low 

supply, growth rates of Lyngbya wollei did not respond more than 10% below 0.015 mg 

NO3-N/L, increased substantially from 0.015-0.110 mg NO3-N/L, and did not respond 

more than 10% to further nitrate concentration increases above 0.100 mg NO3-N/L (in 
6

microcentrifuge microcosms that provided the most accurate control of nutrient 

concentrations); 

9.7 when phosphate was manipulated and nitrate was maintained in luxury supply, growth 

rates of Vaucheria did not respond more than 10% below 0.006 mg PO4-P/L, increased 

substantially from 0.006-0.022 mg PO4-P/L, and did not respond more than 10% to 

further phosphate concentration increases above 0.022 mg PO4-P/L (in microcentrifuge 

microcosms that provided the most accurate control of nutrient concentrations); 

9.8 when nitrate concentrations were manipulated and phosphate was maintained in luxury 

supply, growth rates of Vaucheria did not respond more than 10% below 0.069 mg NO3-

N/L, increased substantially from 0.069-0.644 mg NO3-N/L, and did not respond more 

than 10% to further nitrate concentration increases above 0.644 mg NO3-N/L, but this is 

probably an overestimate of regulating nitrate concentrations because it was determined 

in raceway microcosms in which substantial nutrient depletion occurred and could not be 

accounted for; and 

9.9 fastest algal growth rates in high nutrient concentrations were usually 2 or more times 

greater than in low nutrient concentrations, which caused great differences in algal 

biomass accumulation of a 30 day colonization period. 

10. Simple mathematical models show that reductions in algal growth rates from maximum rates 

to 33 or 66 % lower rates with nutrient load reductions should have great benefits for 

controlling macroalgal accumulation. 

11. Reductions in TN and TP concentrations to less than 0.591 or 0.026 mg/L, respectively, 

should reduce the extent of cover of spring bottoms by Vaucheria spp. in Florida springs; 

however greater reductions in TN and TP will likely be necessary to substantially reduce 

Vaucheria cover. 

12. Reductions in TN and TP concentrations to less than 0.250 or 0.033 mg/L respectively, 

should reduce the extent of cover of spring bottoms by Lyngbya wollei in Florida springs; 

however greater reductions in TN and TP will likely be necessary to substantially reduce 

Lyngbya wollei cover. 

13. In many springs, nitrogen reductions may be the only practical restoration strategy because 

natural phosphorus concentrations may be higher than the concentrations that constrain algal 

growth. 
7

1 - INTRODUCTION 

Proliferations of nuisance and hazardous algae have become a common problem in freshwater 

and marine habitats throughout the world (Carpenter et al. 1998, Lembi 2003). Blooms of 

phytoplankton reduce water clarity and aesthetic quality of lakes, rivers, and coastal waters. 

Great accumulations of macroscopic algae on the bottom of these waters by both native and nonnative species smother habitats once occupied by other organisms in streams, lakes, coastal 

embayments, and coral reefs. Biofilms of algae fowl the bottoms of ships and many underwater 

surfaces. Excessive accumulations of phytoplankton and benthic macroalgae can reduce oxygen 

concentrations and produce toxins that reduce both human and ecological health. These nutrient 

problems are so extensive that the USEPA has established a program with guidance manuals for 

nutrient regulations for all waterbodies throughout the United States (USEPA 2000). 

In many cases these proliferations of phytoplankton and benthic macroalgae have been related to 

nutrients added to waters by human activities. Fertilizers, human and domesticated animal 

waste, and simply clearing trees and grasses from nearby lands can increase nutrient loading into 

surface waters, either directly by runoff into surface water or indirectly via groundwater 

(Carpenter et al. 1998). Nutrients (both N and P) flowing into the groundwater through soils can 

be transported to surface waters. Natural sources of nutrients, such as rain, soils, and bedrock 

also contribute to the nutrient supply of streams and lakes and these contributions vary among 

regions depending on climate and geology. However, natural sources of nutrients are usually 

much less than human sources, to the extent that excessive algal growths are rare when human 

sources of nutrients do not exist (Stevenson et al. 2006). This report describes a study of the 

causes of excessive accumulations of benthic macroalgae in the springs of Florida and their 

likely relations to human activities. 

Florida springs are highly valued resources for their aesthetic and recreational value as well as 

their support of biodiversity (Bonn and Bell 2003). Geologists estimate as many as 600 springs 

occur in the state of Florida, which may be the highest density of springs in the world (Scott et 

al. 2004). Millions of people visit these springs each year for swimming and boating because of 

their clear waters. Marjory Stoneman Douglas, a revered author and environmentalist in Florida, 

observed that “Springs are bowls of liquid light.” Thousands use water from the springs for 

drinking. The network of waterways through caves and surface channels also support a unique 

biota, including the manatee. 

Environmental problems in the Florida springs raised sufficient concern that David Struhs, then 

Secretary of the Florida Department of Environmental Protection, called for the organization of a 

multi-agency group, the Florida Springs Task Force. The Florida Springs Task Force (2000) 

identified known threats to the springs and a series of strategies for protecting and restoring the 

springs. The most common and important threats were increased nitrate concentrations, flow 

reductions, nuisance plant and macroalgal accumulations, and microbial contamination from 

human and animal wastes. Strategies for protecting and restoring Florida springs were grouped 

into five categories: outreach, information, management, regulation, and funding. The increased 

funding resulting from Florida Springs Task Force (2000) report, through the Florida Department 

of Environmental Protection, supported the nuisance algae-nutrient research in our report. Our 

report will provide information that will help resource managers develop nutrient reduction 
8

strategies and regulations that will reduce risk of problems from excessive accumulations of 

benthic macroalgae in springs. 

Benthic macroalgae cause many problems related to ecological and human health, as well as 

quality of life. Caulerpa in coastal embayments and on coral reefs and Cladophora in streams 

and lakes smother habitat occupied by other organisms and can reduce oxygen levels in the water 

(Dudley et al. 1986, Meinesz 1999). When these algae detach from substrates, they can wash 

ashore, foul beaches and shorelines, and thereby reduce recreational quality of these resources. 

In addition, recent reports suggest macroalgae provide habitat and nutrition to bacteria that may 

include pathogens (Ksoll et al. 2007). Either through contact while swimming or on shorelines, 

human health risks are an issue. Macroalgae proliferations may increase the frequency, intensity, 

and duration of beach closing due to microbial contamination. Macroalgae fowl boat props. The 

macroalga Lyngbya, a cyanbacterium, produces toxins that may affect biota in springs and cause 

skin irritations to humans (Carmichael et al. 1997, Onodera et al. 1997, Yin et al. 1997, Camacho 

and Thacker 2006). 

Benthic macroalgae are a diverse group of algae that are large enough that individual filaments, 

colonies, or thalluses of algae are visible without a microscope. Problems in freshwaters are 

usually caused by cyanobacteria and green algae. Most nuisance macroalgae in flowing waters 

are filamentous, that is cells attach end to end in long strings that branch in some species and not 

in others. At one end of the filament they attach directly to a bottom substratum or become 

entangled with it. As they grow, they cover greater portions of the bottom by attachment of new 

cells or filaments to new substrate locations and by growth and extension of pre-existing 

filaments. These two processes that enable increased accumulation of algae are called 

colonization (immigration) and growth. Physical and biological disturbance of algae, herbivory, 

and death due to aging or even disease cause losses of benthic algae. The specific interactions 

between the algae and their environment, regulated through these accumulation and loss 

processes, vary greatly among species of algae. 

Nitrogen (N) and phosphorus (P) are the two most common nutrients associated with pollution 

that regulate the growth of algae in freshwater and marine habitats. In theory, only one of these 

nutrients regulates growth of an alga at a time. Overall, marine habitats are most commonly 

limited by nitrogen and freshwater ecosystems are usually limited by phosphorus. However, 

reviews of nutrient-enrichment experiments in streams showed that algae in about 30-35% of 

streams studied were nutrient limited and of those, almost half were N-limited and about half 

were P limited, whereas a few were limited by both N and P (Dodds and Welch 2000, Francoeur 

2001). Different supply rates of N and P by both natural and human sources were the cause of 

these differences among streams. A reason that growth of algae in a stream can be co-limited by 

N and P, and still be consistent with the single nutrient limitation hypothesis (Leibig’s Law of 

the Minimum), is that many species of algae occur in a stream and some are limited by N and 

others by P. 

The overarching goal of our research was to determine the relationship between macroalgal 

proliferations and nutrients in Florida springs in a way that managers could assess the benefits of 

different nutrient regulations. Historically, nitrate concentrations have increased greatly in 

springs at the same time that population density in sourcesheds has increased. During the same 
9

period, macroalgae have increased in the springs (Figure 1.1). We developed a research plan that 

utilized both survey and experimental methods following the model of research in the Everglades 

(McCormick and Odell 1996, Stevenson et al. 2002) and many recommendations from USEPA 

nutrient criteria guidelines (USEPA 2000). The surveys of macroalgae and nutrients in springs 

were designed to show the threshold in nitrogen or phosphorus concentrations associated with 

proliferations of macroalgae. The experiments were intended to confirm the cause-effect 

relationships between nitrate and phosphate and macroalgal growth. 

Preliminary results from 

the surveys of macroalgal 

cover and water-column 

nutrient concentrations 

did not show clear 

patterns between nitrogen 

or phosphorus 

concentrations and 

macroalgal growth, even 

though they were 

thorough and the most 

extensive spring 

assessments of their kind 

(Stevenson et al. 2004). 

Further research was 

planned to better 

understand relationships 

between both nitrogen 

and phosphorus and 

macroalgal growth in 

Florida springs. We 

evaluated seasonal 

variation, internal fluxes 

of nutrients in macroalgal 

mats, and sediment 

sources of nutrients to 

explain why macroalgal proliferations were not related well to nutrient concentrations in the 

overlying water of the spring runs. In addition, we conducted extensive experiments in multiple 

experimental settings to determine relationships between macroalgal growth and nitrogen and 

phosphorus concentrations. This report summarizes the findings from the many aspects of this 

research program that have been conducted during the last four years. 

Our results show great abundances of macroalgae in some spring runs. They also show 

sufficient reduction of either phosphorus or nitrogen could reduce risk of nuisance levels of 

macroalgae in Florida springs. Natural concentrations of nitrate in springs, recorded to have 

been as low as 0.05 mg/L (Scott et al. 2003), were below thresholds that stimulate growth of the 

two most common macroalgae in Florida springs, Vaucheria spp. and Lyngbya wollei. Nitrate 

has now accumulated to the point that phosphorus is the most limiting nutrient for macroalgal 

Figure 1.1. Pictures of mermaids and underwater hunters in the 

Weeki Wachee show in the past when no benthic macroalgae are 

visible (1951 & 1954) and during the last 6 years when 

macroalgae (Lyngbya wollei) are abundant (2001 & 2003). 

Photos from 1951, 1954, and 2001 were provided by John 

Athanason, Weeki Wachee. Photo from 2003 by Pinowska.

2003

2001 1951

1954

Figure 1.1. Pictures of mermaids and underwater hunters in the 

Weeki Wachee show in the past when no benthic macroalgae are 

visible (1951 & 1954) and during the last 6 years when 

macroalgae (Lyngbya wollei) are abundant (2001 & 2003). 

Photos from 1951, 1954, and 2001 were provided by John 

Athanason, Weeki Wachee. Photo from 2003 by Pinowska.

2003

2001 1951

1954
10

growth, but current levels of phosphorus in many springs where we have macroalgal 

proliferations are not much higher than natural concentrations. Several reasons explain how 

thick mats of macroalgae can accumulate in spring runs with low nutrient concentrations in the 

water column. These macroalgae may entrain nutrients from the water column and sediment 

sources that sustain slow growth. Without disturbance by physical or biological processes, 

including humans, macroalgae can slowly accumulate over extensive proportions of spring runs 

and to great thickness. We also found one macroalgae, Lyngbya wollei, can grow faster if given 

more nitrate even if phosphorus is in relatively short supply. Two of these findings contradict 

prevailing wisdom about relationships between algae and nutrients. 

This report is organized by the different studies that we used to understand the macroalgal 

accumulations in Florida springs which are related to specific hypotheses tested, but also 

somewhat chronological in association with the successive sets of new observations and 

alternative hypotheses that needed to be tested. First, we describe the results of the spring 

surveys and then seasonal patterns in macroalgae in two springs and nutrient fluxes in 

macroalgal mats; finally we describe the experiments that we conducted on macroalgal recovery 

after disturbance and the many nutrient experiments manipulating both nitrate and phosphate 

concentrations. At the end, we summarize the multiple lines of evidence for specific effects of 

specific nutrient concentrations on macroalgae in Florida springs to provide information for the 

development of nutrient criteria. 

Detailed descriptions of most aspects of this synthesis report can be found in one of seven reports 

submitted to FDEP that provide more information about our subprojects. Stevenson et al. (2004) 

described the preliminary results of the Florida springs survey, which has been updated and 

expanded in Pinowska et al. (2007a). Stevenson and Pinowska (2007) described the relationship 

of diatoms to nutrients in Florida springs and use of them as indicators of nutrient conditions. 

Albertin et al. (2007a) described the stable isotope patterns in water and algae among springs to 

track nutrient sources and better understand algal metabolic processes. Sickman et al. (2007) 

described the seasonal variation in macroalgae and nutrient conditions within macroalgal mats. 

Pinowska et al. (2007b) and Albertin et al. (2007b) describe the extensive experiments 

evaluating effects of nutrients on Lyngbya wollei and Vaucheria. 

2 - SURVEYS OF MACROALGAE AND NUTRIENTS IN FLORIDA SPRINGS 

2.1 – Introduction 

Survey methods enable observing relationships between the nutrient concentrations in a habitat, 

the response of the ecosystem to observed concentrations, and linking these observations to 

human activities in sourcesheds. In the surveys of macroalgae and nutrients that we conducted, 

we hypothesized thresholds in macroalgal accumulation at specific nutrient concentrations that 

released macroalgal accumulation from constraints by nutrient concentrations. These responses 

often appear as thresholds, because once accumulation is unconstrained by resources or 

herbivory, macroalgae can accumulate either slowly or rapidly to a peak biomass that is 

constrained by other processes (Stevenson 1997). 
11

Peak biomass, i.e. the maximum biomass that can accumulate in a habitat, can be regulated by 

current and nutrient availability within the mat (Stevenson 1996). Previous work indicates 

nutrient supply within benthic algal mats is positively related to to current velocity, because 

nutrient uptake rates by algae can exceed the rate at which nutrient-depleted waters within mats 

are replaced (Stevenson and Glover 1993). As benthic algae accumulate more thickly on a 

substratum, current velocity, nutrient supply, waste removal, and light for algae at the bottom of 

the mat decrease and cause these algae to senesce (Peterson 1996). As algae at the mat bottom 

senesce and decompose, the bond between algae and substratum weakens and increases the risk 

of current washing loosened algal mats downstream. Current positively affects nutrient supply, 

but it also creates drag on mats that can remove them from substrata and wash them downstream. 

Thus, peak biomass of algae is typically observed at high nutrient concentrations and 

intermediate current velocities (Stevenson 1996). 

The benefit of using results of surveys for nutrient criteria development is the direct 

transferability to implementation of criteria based on the nutrient concentrations observed in 

water bodies, such as springs, that support intended uses. Whether a water body meets or does 

not meet nutrient criteria is evaluated, at least in part, on nutrient concentrations measured in the 

water body. Total phosphorus and total nitrogen are recommended by USEPA (2000) for 

developing and assessing compliance with nutrient criteria. The USEPA also suggests a weight 

of evidence approach in which response variables are also used to determine whether goals of 

nutrient criteria have been met. The results of experiments provide valuable information about 

the relationships between algal growth and other measurable processes and the nutrient 

concentrations that are manipulated, but experimental systems usually do not include all the 

complexities of ecological processes affecting nutrient concentrations in natural water bodies. 

These considerations have been taken into account in the conclusions where we have integrated 

the results of our work in experimental settings and the spring surveys. 

In this section of the report we describe the results of surveys of macroalgal biomass plus several 

indicators of nutrient concentrations at as many as 60 spring sites in 29 first magnitude springs 

(i.e. springs with greater than 100 cfs, Scott et al. 2003). Surveys were conducted to identify the 

kinds of macroalgae in Florida springs, the percentage of the spring bottoms covered by 

macroalgae, the thickness of the macroalgal mats, and nutrient concentrations and sources of 

nutrients in the mats. Land use in sourcesheds and riparian zones was related to nutrient 

concentrations to distinguish anthropogenic from natural sources of nutrients in springs. A 

variety of statistical methods were used to relate percent cover and thickness of benthic 

macroalgae to nutrient concentrations in springs. Details of the methods and results can be found 

in reports by Pinowska et al. (2007a), Albertin et al. (2007a) and Sickman et al. (2007). 

2.2 – Overview of Methods 

2.2.1 Field Sampling and Laboratory Analyses 

Seventy-one sites in 29 first and second magnitude springs (Appendix 1) were sampled 

throughout central and northern Florida during 3 seasons, spring and fall of 2003 and winter of 

2006 (Figure 2.1). Only 48 sites were sampled during spring 2003 because of flooding in the 

Suwannee basin. During fall 2003 60 sites within 28 springs were sampled. Sixty-three sites 
12

were sampled during winter 2006 with addition of new sites in springs for within spring run 

gradient studies and exclusion of eight sites that were previously sampled. 

At each site, researchers 

designated nine transects 

perpendicular to flow across 

the spring run and spaced 10 m 

apart to define the sampling 

reach (stream section) for the 

site (Figure 2.2). 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 spaced 

along each transect, resulting 

in a total of 81 potential 

observation points at each site. 

Channel dimensions, bank 

condition, canopy cover, and 

riparian buffer vegetation were 

characterized at transects. 

Algal attributes, current velocity, 

substrate type, were 

characterized at each of the 81 

observation points. 

At each of the study sites a 

modified Rapid Periphyton 

Assessment (Stevenson and 

Bahls 1999) was conducted 

during spring and fall 2003. The 

presence and taxonomic identity 

of macrophytes and macroalgae 

and the thickness of the 

macroalgal mats were recorded 

at each of the 81 observation 

points. The percent cover of 

plants, macroalgae, and different 

substratum types was calculated 

as the percent of sampled points 

where particular plant, 

macroalgae, or substratum was observed. See Stevenson et al. (2004) for details of these 

methods. The protocol for sampling was modified during winter 2006 because of financial and 

resource constraints such that macroalgal assessments were only conducted at sites were 

Figure 2.1. Location of 23 springs in Northern Florida 

that were sampled during the survey.

Figure 2.2. Rapid periphyton survey pattern. 

Macroalgae, substrate, and water conditions were 

characterized at nine points along each of nine transects 

in a section of spring run. 
13

substantial differences were evident since 2003 sampling or if sites had not been assessed before 

(see Pinowska et al. 2007a). 

 

Landuse was provided by FDEP for 100 m and 1000 m regions circumscribed around the 

sampling sites. Percent of watersheds disturbed by human activities and Landscape 

Development Intensity Index (LDI) scores were calculated for each site for the 100 m and 1000 

m zones. Percent watershed disturbed included all urban, agricultural, and industrial land uses. 

Landuse Development Intensity Index (LDI) for sourcesheds of each spring was calculated using 

weights for different land uses reflecting the potential of each for effects (Brown and Vivas 

2005). Spring discharge was based on USGS data (Scott et al. 2003). The distance from boil 

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

 

A macroalgal sample was collected from each transect to confirm the accuracy of field 

identification (198 samples in the spring and 463 samples in the fall of 2003). At each site, a 

composite macroalgal sample was also collected from which a subsample was taken for algal 

mat total nitrogen and total phosphorus analyses (analyzed by FDEP). Subsamples of 

macroalgal mats were also taken to measure chlorophyll a, ash-free dry mass, fresh mass, and 

macroalgal volume and estimate areal algal biomass with these parameters based on 

measurements of % cover and thickness of macroalgae. 

Microalgal samples were collected to characterize biological condition and infer nutrient 

conditions with diatom indicators. Microalgal samples were collected from plants (epiphytes) 

and sediments using quantitative methods to enable determination of algal biomass as well as 

species composition. Chlorophyll a was extracted with 90% ethanol and analyzed on a Turner 

Designs TD-700 fluorometer (APHA 1998). AFDM was determined after drying and ashing 

using standard methods (APHA 1998). Non-diatom species composition and diatom species 

composition were determined using a Palmer-Maloney counting chamber and cleaned-diatoms 

mounted in NAPHRAX, respectively. Subsamples of macroalgae were digested to determine N 

and P content of algal cells. Again, see Stevenson et al. (2004) and Pinowska et al. (2007a) for 

details. 

 

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

upstream and downstream ends of the reach. Water clarity was measured using a LICOR LI-250. 

Water samples were collected for analysis of alkalinity, ammonia nitrogen, calcium, chloride, 

iron, magnesium, Kjeldahl nitrogen, nitrates, soluble reactive phosphorus (indicating phosphate), 

total phosphorus, potassium, silica, sodium, strontium and sulfate by FDEP. When the 

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

detection limit was reported. Algal growth potential and limiting nutrient bioassays were 

conducted by FDEP for 15 sites in the spring and 12 sites in the fall. 

Isotopic analyses of water and algal tissues from springs were conducted to (1) assess nitrate 

sources to spring water using the dual-isotopic analysis of nitrate (δ15N-NO3 and δ18O-NO3), (2) 

determine the δ15N and δ13C composition of macroalgae and spring sediments through one-time 

surveys at a regional scale and along four spring river runs, and (3) assess the relationships 

between the δ15N and δ13C of macroalgae and indicators of nutrient availability and sources at 

the regional scale. The dual isotope analysis of nitrate in water (δ15N-NO3 and δ18O-NO3) was 
14

used to further differentiate sources of nitrate when δ15N ranges overlap (Kendall, 1998), such as 

NO3 fertilizer from soil nitrogen and NH4 in fertilizer and rain. Details of this analysis can be 

found in Albertin et al. (2007a). 

 

2.2.2 Data Transformations and Analyses 

Land use and some landscape attributes were related to nutrient concentrations to determine 

likely contributions by natural and human sources. Latitude and longitude were used as simple 

variables to characterize region. Stepwise forward multiple regression analysis was used to 

evaluate covariation among variables and thereby determine whether relationships could be 

explained or masked by other factors. 

Table 2.1. Dependent and independent variables in correlations relating macroalgae to 

environmental characteristics in Florida springs. Sediment chemistry was determined for 

sediments under mats and in open areas of springs. 

Dependent Variables Independent Variables 

Water Chemistry Sediment 

Chemistry 

Habitat and 

Land Use 

Avg % cover Ammonia %N Depth 

Avg cover thickness Kjeldahl nitrogen %C Current 

Avg Lyngbya % cover (dominant) Nitrates TP (mg/g) Transect Length 

Avg Lyngbya % cover (presence) Phosphate TP LOI % Longitude 

Avg Lyngbya mat thickness Total Phosphorus Available P 

(μg/g) 

Latitude 

Avg Vaucheria % cover (dominant) Total Nitrogen Available N 

(μg/g) 

Riparian Trees 

Avg Vaucheria % cover (presence) Temperature Available NH4 

(μg/g) 

Riparian Shrubs 

Avg Vaucheria mat thickness DO Available NO3 

(μg/g) 

Riparian Herbs 

Max % cover Conductivity % water Riparian Bare 

Max cover thickness pH % ash Riparian Canopy 

Max Lyngbya % cover (dominant) Riparian Buffer 

Max Lyngbya % cover (presence) 

Max Lyngbya mat thickness 

Diatom 

Indicators of 

Nutrients 

Max Vaucheria % cover (dominant) 

Percent 

Watershed 

Disturbed 

Max Vaucheria % cover (presence) 

Max Vaucheria mat thickness 

Landscape 

Disturbance 

Index 

 

Algal biomass was characterized for all macroalgae and for two dominant macroalgal species 

independently, Vaucheria spp. and Lyngbya wollei. “Species” level analysis was important 

because different species could respond differently to nutrients and interact with other water 

chemistry parameters, such as conductivity, which varies greatly among springs and often has 
15

significant effects on algal species composition (Table 2.1). Algal biomass was characterized by 

the % cover of the bottom and by thickness. Average and maximum values for both % cover and 

thickness were determined, where average and maximums were determined across seasons. 

Percent cover for Lyngbya wollei and Vaucheria was determined two ways: as percent of 

observation points at which either alga was dominant or just present. Therefore one set of 

statistics were run relating environmental parameters to macroalgal abundances where it was 

dominant, and the same statistics were run when abundance was based on it just being present. 

Both percent cover variables were evaluated to ensure thorough analysis of the data. In many 

cases, macroalgae were quite abundant, even if they were not the dominant alga at the site. 

In addition to measured water chemistry parameters, nutrient concentrations were inferred using 

diatom indicators of nutrient conditions. Diatom indicators of nutrient concentrations are 

important, because they can more precisely and accurately measure nutrient availability than one 

time sampling of nutrient concentrations (Stevenson 2006). In this study, where sediments may 

be an important source of nutrients, epiphytic diatoms may provide an indication of nutrient 

availability to algae that are not reflected in measured nutrient concentrations in the daytime 

water column. Different species of diatom grow faster in low and high nutrients (Manoylov and 

Stevenson 2006). The nutrient concentrations in which diatom species have highest relative 

abundances can be characterized and used as traits for each species that indicate the so-called 

“optimum” nutrient concentration for species (Potapova et al. 2004). Nutrient concentrations in 

water at a site can be inferred subsequently by summing the products of relative abundances and 

nutrient optima of individual diatom species and correcting for lack of information about some 

species. Multiple indicators of nutrient conditions were calculated using diatom species nutrient 

optima that were derived with data from the Florida springs and from other studies. Diatom 

species composition responded much more to changes in total phosphorus than total nitrogen 

among springs. Therefore, diatom indicators of nutrients more precisely inferred nutrient 

concentrations for TP than TN. Details of this analysis and these results can be found in a report 

to FDEP by Stevenson and Pinowska (2007). 

Correlations were calculated between all measures of algal biomass and all measures of nutrient 

concentrations resulting from laboratory analysis of water samples and diatom inference models. 

In addition, correlations were calculated between all measures of algal biomass and non-nutrient 

water parameters, stable isotopes, sediment parameters, riparian parameters, and land use 

parameters (Table 2.1). 

All correlations that were statistically significant were graphed and analyzed with quantile 

regression and regression tree analysis to further explore factors that regulate macroalgal 

biomass in Florida springs (see Pinowska et al. 2007a for these figures and results). Lowess 

smoothing was used to evaluate relationships between highly correlated variables without a 

priori assumptions of the shape of relationships. Quantile regression enabled evaluation of the 

potential for algal proliferation when confounding factors constrain abundance of algae in 

subsets of springs with specific environmental conditions. Regression tree analysis evaluated 

non-linear relationships and helped identify algal response thresholds along nutrient gradients. 
16

2.3 – Overview of Results 

2.3.1 Region, land use, and nutrients 

Nutrient concentrations were related to both region of the state and land use indicating both 

natural and anthropogenic sources of nutrients. Nitrogen concentrations (NOx and TN) were 

significantly correlated to land use within 1000 m, but not 100 m of the site or directly to region 

(Table 2.2). After effects of land use were accounted for with multiple regression, both TN and 

NOx were positively correlated to latitude (p<0.05). Total dissolved phosphorus (TDN) was 

correlated to land use within 100 m of sites, but stepwise multiple regression indicated that the 

correlation was confounded with regional variation. Phosphorus measures (SRP, TDN, and TP) 

were significantly related to both latitude and longitude. After accounting for covariation with 

multiple linear regression, SRP was positively correlated with latitude and longitude, TP was 

positively correlated with longitude, and TDN was negatively correlated with latitude. The only 

evidence of a P relationship with land use was after variation in SRP related to latitude and 

longitude was accounted for using multiple regression analysis; then SRP was positively, but 

poorly correlated to land use (p=0.168). 

Table 2.2. Correlations between nutrient concentrations, land use metrics, latitude, and longitude. 

Nutrients include (ammonia, nitrate-nitrate, total dissolved nitrogen, total nitrogen, soluble 

reactive phosphorus, total dissolved phosphorus, and total phosphorus). Nutrient and landuse 

metrics were log transformed before analysis. Correlation coefficients in bold type have a 

p<0.05. PWD_100m and PWD_1000m are the percent watershed disturbed within 100 m and 

1000 m of sampling sites. LDI_100m and LDI_1000m are the landscape disturbance intensity 

index within 100 m and 1000 m of sampling sites. 

Nutrient PWD_100m PWD_1000m LDI_100M LDI_1000M Latitude Longitude

ln(NH3) 0.107 0.106 0.200 0.291 -0.054 -0.063

ln(NOx) 0.238 0.454 0.276 0.469 0.044 -0.148

TDN -0.114 -0.044 -0.114 0.010 -0.173 0.266

ln(TN) 0.257 0.434 0.274 0.458 0.000 -0.058

ln(SRP) 0.019 0.038 -0.207 -0.073 -0.130 0.416

ln(TDP) 0.298 0.109 0.284 0.193 -0.561 0.379

ln(TP) 0.183 0.158 -0.038 0.091 *-0.414 0.600

No relationship was found between stable isotope ratios and land use to help distinguish sources 

of nutrients (Albertin et al. 2007a). However, the consistent relationship between δ18O-NO3 and 

δ15N-NO3 indicated that fertilizer was the primarly source of nitrogen in springs and 

denitrification of the fertilizer explained variation among springs. Correlations between δ15N or 

δ13C and either diatom indicators of nutrients, pH, or nitrogen concentrations in springs were 

hypothesized to be caused by complex relationships between N and P supply rates, water 

hardness, and P transport via CaPO4. However, no conclusions about nutrient sources and 

effects on algae could be drawn from these latter correlations. 
17

2.3.2 Algae and relationships to nutrient concentrations 

Macroalgae were found at 59 of the 60 sampled sites, and on average, covered almost half of the 

bottoms of springs at those sites (Stevenson et al. 2004). Twenty-four taxa and 23 genera of 

macroalgae were found from five major groups of algae: the cyanobacteria (five taxa), red algae 

(five taxa), diatoms (three taxa), Xanthophyceae (one taxon), and green algae (10 taxa). Thick 

algal mats were formed by four taxa: Lyngbya wollei, Vaucheria spp., Compsopogon spp., and 

Dichotomosiphon spp. Species of most genera were not identified because diagnostic features of 

sexual reproductive structures were not common. Lyngbya wollei and Vaucheria spp. were 

found in many more springs than the other mat-forming macroalgae and therefore, became the 

focus of most of our research. 

Both nitrogen and phosphorus concentrations in spring water ranged greatly among springs, but 

positive relationships were only observed between Vaucheria and nitrogen and phosphorus 

concentrations. Percent cover of spring bottoms by all macroalgae ranged from close to zero to 

almost 100% and was not related to TN, TP, or soluble nutrient concentrations (Table 2.3). 

Average mat thickness on spring bottoms by all macroalgae ranged from near zero to 0.25 m and 

also was not related to TN, TP, or soluble nutrient concentrations. Percent cover of spring 

bottoms by Lyngbya wollei ranged from zero to almost 80% and its mat thickness ranged from 

zero to 0.25 m, but neither were related to TN, TP, or soluble nutrient concentrations (Table 2.3). 

Table 2.3. Pearson correlation matrix for macroalgae abundance and water chemistry (p<0.05 in 

bold, Pinowska et al. 2007a). 

Macroalgae abundance AmmoniaKjeldahl 

nitrogen Nitrates Phosphate Total Phosphorus

Total 

Nitrogen

Avg % cover of all macroalgae 0.161 0.061 0.022 0.080 0.098 0.042

Avg cover thickness all macroalgae -0.021 -0.095 -0.005 0.065 0.105 -0.023

Avg Lyngbya % cover (dominant) 0.186 -0.281 -0.073 0.116 0.089 -0.058

Avg Lyngbya % cover (presence) 0.204 -0.266 -0.052 0.138 0.119 -0.041

Avg Lyngbya mat thickness 0.166 -0.337 -0.099 0.112 0.106 -0.101

Avg Vaucheria % cover (dominant) 0.182 0.215 0.288 0.193 0.246 0.310

Avg Vaucheria % cover (presence) 0.194 0.198 0.304 0.220 0.275 0.325

Avg Vaucheria mat thickness 0.151 0.093 0.257 0.253 0.302 0.261

Max % cover 0.126 -0.025 -0.004 0.161 0.173 0.007

Max cover thickness -0.120 -0.190 -0.006 0.078 0.123 -0.037

Max Lyngbya % cover (dominant) 0.175 -0.338 -0.056 0.146 0.122 -0.052

Max Lyngbya % cover (presence) 0.197 -0.317 -0.033 0.179 0.162 -0.032

Max Lyngbya mat thickness 0.152 -0.371 -0.101 0.129 0.124 -0.111

Max Vaucheria % cover (dominant) 0.121 0.184 0.258 0.203 0.269 0.273

Max Vaucheria % cover (presence) 0.145 0.169 0.277 0.229 0.298 0.289

Max Vaucheria mat thickness 0.115 0.057 0.238 0.259 0.309 0.238

Percent cover of spring bottoms by Vaucheria ranged from zero to 80 % and its mat thickness 

ranged from zero to 0.20 m and both were related positively to N and P concentrations in spring 
18

water (Figures 2.3). Non-linear models of Vaucheria % cover and thickness along the TN and 

nitrate gradients explained substantially more variation than a linear model, with thresholds in 

Vaucheria response at 0.454 and 0.591 mg N/L as nitrate and as TN, respectively. Average 

Vaucheria cover was only 2.3 % below the 0.591 mg TN/L threshold. 

Figure 2.3. Relationship between concentrations of total nitrogen (TN) and total 

phosphorus (TP) and Vaucheria percent cover at sites in 23 springs across 

northern Florida. See Pinowska et al. (2007a) for details.
19

Despite the stronger correlations between macroalgae and N concentrations in spring water, 

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%. 

The percent cover and mat thickness of Lyngbya wollei and Vaucheria were positively correlated 

to cellular N and P concentrations (Pinowska et al. 2007a). Lyngbya wollei % cover and mat 

thickness were positively correlated to sediment phosphorus concentrations, both when the 

sediment P was measured under mats and in open areas (Figure 2.4). Lyngbya wollei % cover 

was also related to nitrate concentrations in sediments below mats, but not in open areas. 

Vaucheria % cover was correlated with % carbon in sediments under mats, but not to nutrient 

conditions in sediments (Pinowska et al. 2007a). 

Diatom indicators of nutrient concentrations explain more variation in macroalgal biomass and 

thickness than measured nutrient concentrations in Florida springs (Table 2.4). Whereas 

Lyngbya wollei % cover was not related to nutrient concentrations, it was related to diatom 

indicators of nutrient concentrations and particularly to indicators of P concentrations. Percent 

cover of Vaucheria was usually correlated to N and P concentrations with correlation 

coefficients 0.2-0.3, but it was related to diatom indicators of nutrient concentrations with 

coefficients usually ranging from 0.3-0.4. This could be due to epiphytic diatoms better 

reflecting nutrient supply from sediments and interstitial waters than water column nutrients. 

Plants take up nutrients from sediments, which then leak through the epidermis of leaves 

(Burkholder et al. 1990) which would cause changes in diatom species composition. These 

Figure 2.4. Average Lyngbya % cover (presence) as a function of sediment TP 

including lowess smooth, least square regression, quantile regression and CART 

(Pinowska et al. 2007a).
20

changes in species composition therefore could reflect the sediment nutrient supplies that affect 

Lyngbya wollei and Vaucheria. 

Percent cover measures of Lyngbya wollei were more highly correlated to land use around 

sampling sites than Vaucheria (Table 2.5). Percent cover of Lyngbya wollei was correlated to 

LDI in the 100 m and 1000 m zone around sites. Percent cover of Vaucheria was not 

significantly correlated with percent of the watershed disturbed or with LDI in either the 100 m 

or the 1000 m zone around sites. These correlations indicated Lyngbya wollei abundances in 

springs were associated with human activities. 

Table 2.4. Pearson correlation matrix for macroalgal abundance and diatom indicators of nutrient 

concentrations (p<0.05 in bold, Pinowska et al. 2007a). 

Macroalgal 

Taxon 

Diatom Indicator of 

Nutrient Conditions 

Trait Dat 

Source 

Avg % 

cover 

(present) 

Avg mat 

thickness

Max % 

cover 

(present) 

Max mat 

thickness

Lyngbya FLS Nutrient Index Spring Epi 0.246 0.259 0.269 0.267

Lyngbya FLS Nutrient Index F&S Epi 0.241 0.216 0.268 0.220

Lyngbya FLS TP Index F&S Epi 0.275 0.252 0.285 0.255

Lyngbya % High P Individuals all datasets 0.318 0.264 0.314 0.256

Lyngbya FLS Nutrient Index Spring Epi 0.253 0.255 0.261 0.262

Lyngbya FLS Nutrient Index F&S Epi 0.160 0.099 0.170 0.100

Lyngbya FLS TP Index F&S Epi 0.199 0.169 0.186 0.160

Vaucheria FLS Nutrient Index Spring Epi 0.273 0.353 0.302 0.364

Vaucheria FLS Nutrient Index F&S Epi 0.370 0.386 0.353 0.359

Vaucheria FLS TP Index F&S Epi 0.320 0.398 0.338 0.380

Vaucheria % High P Individuals all datasets 0.117 0.138 0.096 0.101

Vaucheria FLS Nutrient Index Spring Epi 0.354 0.434 0.378 0.445

Vaucheria FLS Nutrient Index F&S Epi 0.357 0.355 0.340 0.335

Vaucheria FLS TP Index F&S Epi 0.360 0.396 0.358 0.372

 

2.3.3 Algae and relationships to local and regional environmental factors 

Some natural environmental factors that could affect macroalgae varied greatly among springs. 

Conductivity ranged from 113 to 3872 μS/cm and dissolved oxygen ranged from 0.27 to 9.98 

mg/L). Water temperature and pH had relatively narrow ranges, which were assumed to be 

related to natural factors, from 20.06 to 23.93°C and from 7.13 to 8.33, respectively. 

Lyngbya wollei % cover and mat thickness were positively correlated with water temperature, 

DO concentration, and pH, but were not related to conductivity. The relationship between 

Lyngbya wollei and temperature was non-linear, with a threshold observed at 21.5◦

C. In contrast, 

Vaucheria % cover and thickness were negatively correlated to DO, conductivity, and pH, but 

were not related to temperature. Note: Hand (unpublished results, FDEP) shows that 

temperatures of many springs have increased during the last 40-50 years. Many of these factors 
21

may covary with other factors, but additional analysis of covariation was beyond the scope of 

this study. 

Table 2.5. Pearson’s correlation matrix for macroalgae abundance, percent disturbed land and 

LDI scores in 100m radius around sampling location, 1000 m radius around sampling location, 

and in the whole springshed for a particular spring system (p<0.05 in bold, Pinowska et al. 

2007a). 

Macroalgae abundance metric 

PW_ 

100 m 

PW_ 

1000 m

PW 

springshed

LDI 

100 m

LDI 

1000 m 

LDI 

springshed

Avg % cover 0.155 0.106 0.177 0.170 0.174 0.132

Avg cover thickness 0.171 -0.028 0.135 0.136 0.050 0.134

Avg Lyngbya % cover 

(dominant) 

0.315 0.245 0.255 0.294 0.231 0.219

Avg Lyngbya % cover 

(presence) 

0.323 0.279 0.293 0.294 0.268 0.293

Avg Lyngbya mat thickness 0.299 0.200 0.225 0.275 0.195 0.259

Avg Vaucheria % cover 

(dominant) 

-0.101 -0.074 0.106 -0.193 -0.054 0.279

Avg Vaucheria % cover 

(presence) 

-0.098 -0.083 0.133 -0.195 -0.066 0.316

Avg Vaucheria mat thickness -0.119 -0.016 0.115 -0.206 -0.002 0.306

Max % cover 0.202 0.127 0.207 0.189 0.173 0.138

Max cover thickness 0.215 -0.029 0.212 0.221 0.034 0.167

Max Lyngbya % cover 

(dominant) 

0.332 0.262 0.277 0.308 0.239 0.229

Max Lyngbya % cover 

(presence) 

0.329 0.288 0.307 0.290 0.266 0.305

Max Lyngbya mat thickness 0.303 0.185 0.216 0.276 0.178 0.260

Max Vaucheria % cover 

(dominant) 

-0.119 -0.091 0.083 -0.203 -0.075 0.263

Max Vaucheria % cover 

(presence) 

-0.103 -0.087 0.122 -0.194 -0.072 0.311

Max Vaucheria mat thickness -0.124 -0.032 0.096 -0.207 -0.021 0.282

Percent cover and thickness of Lyngbya wollei and Vaucheria mats were not related well to local 

factors such as depth, canopy cover, and channel width. Vaucheria was positively related to 

higher current velocity. Few attributes measured as part of the habitat characterization for the 

riparian buffer were correlated to macroalgal attributes. 

Percent cover and thickness of Lyngbya wollei mats were negatively correlated to both latitude 

and positively related to longitude. Only Vaucheria mat thickness was positively correlated to a 

regional factor, and that was to longitude. Lyngbya wollei % cover and thickness were nonlinearly related to longitude, with a threshold around -83◦

 W. This may be related to phosphorusrich geology in specific regions of the state. 
22

Details of these analyses and more thorough report of relationships between all dependent and 

independent variables can be found in Stevenson et al. (2004) and Pinowska et al. (2007a). 

2.4 – Discussion 

Lyngbya wollei and Vaucheria, the two most abundant macroalgae in Florida springs are very 

different algae. Lyngbya wollei is a filamentous cyanobacteria with cells approaching 0.5 mm 

wide, which is very wide compared to most other filamentous cyanobacteria (Fig. 2.5A). It has a 

thick firm mucilaginous sheet around the trichome of cells. It grows in entangled masses without 

a specialized method of holding onto a substratum. Lyngbya wollei can produce a variety of 

toxins (Carmichael et al. 1997, Onodera et al. 1997, Yin et al. 1997). Vaucheria is 

approximately the same diameter, but is a coenocytic filament in which the cytoplasm is not 

separated into small cells by cross-walls. Its multinucleate cytoplasm is arranged in a tube 

around a large central vacuole (Fig. 2.5B). These filaments grow in a thick turf that is connected 

and expanding by rhizoids. Different species of Vaucheria could not be identified because 

because sexual reproductive structures, the diacritical characteristics, were not observed in most 

cases.

The taxonomy of the Lyngbya wollei (Farlow ex Gomont) Speciale and Dyck is complicated 

because of its morphological and ecological variability. Historically this taxon was probably 

identified as Plectonema wollei Farlow ex Gomont in Whitford’s (1957) survey of Florida 

springs. In Stevenson et al. (2004), we referred to it as Lyngbya majuscula Harvey ex Gomont. 

Speciale and Dyck (1992) renamed Plectonema wollei Farlow ex Gomont as Lyngbya wollei 

based on morphological considerations and rejected Lyngbya majuscula Harvey ex Gomont as a 

correct name, because the latter is from brackish and marine habitats. Ecology and morphology 

are important characteristics in modern taxonomy of the cyanobacteria. Joyner (2004) shows 

this alga in Florida springs is genetically different than Lyngbya majuscula. Lyngbya wollei is 

currently the most commonly used name for this alga in freshwater ecosystems. Therefore, we 

use this name for this extremely large-celled filamentous cyanobacterium. 

A B

Figure 2.5. Lyngbya wollei (A) and Vaucheria (B). Insets show photomicrographs 

of the algae. Larger pictures show masses growing on spring bottom.
23

The extent of Lyngbya wollei and Vaucheria occurrence seems to be much greater than in the 

past. Both taxa were observed by Whitford (1957) in his study of algae in Florida springs. He 

noted Plectonema wollei Farlow “forms abundant mats in the fresh-water springs.” Pinowska 

found a collection of Lyngbya at Harvard’s Farlow Herbarium, which was collected from Silver 

Springs in 1939. Her examination of that material showed that it looked like the alga that we 

now identify as Lyngbya wollei. However, Odum (1956a, 1956b) does not identify Lyngbya

wollei or Vaucheria as important primary producers in this work in Silver Springs or eleven 

other Florida springs. Odum characterized most springs being dominated by aquatic vascular 

plants. Photographic documentation of Weeki Wachee also indicates great loses of aquatic 

vascular plants and increases in macroalgae from the 1950s to present (Figure 1.1). Quinlan et 

al. (submitted) concluded that the epiphytic and algal mat biomass in Silver River Springs is 

higher today than reported by Odum (1957a). 

Increases in nitrogen concentrations in Florida springs were clearly related to the increases in 

population density over time (Scott et al. 2003) and increases in human activities among springs 

at present (our analyses). Total nitrogen, nitrate, and ammonia, increased with indicators of 

human activities, where as increases in phosphorus were less significantly related to humans. 

However, the high P concentrations, sometimes similar to natural background concentrations, 

seem sufficient to stimulate growth of macroalgae, such as Cladophora in streams of the 

Montana and the Midwest where 0.015-0.030 mg TP/L regulate growth (Dodds et al. 1997, 

Stevenson et al. 2006). Historic levels of nitrogen in Florida springs, which were predicted to 

have been around 0.050 mg/L, would have constrained growth of both Lyngbya wollei and 

Vaucheria (Albertin et al. 2007b). 

Only % cover and thickness of Vaucheria mats were related to nitrogen and phosphorus 

concentrations in spring water. A clear threshold in Vaucheria response to TN at 0.591 mg/L 

provides a benchmark for nitrogen criteria that should prevent nuisance levels of Vaucheria. 

The other common macroalga, Lyngbya wollei, was not correlated to either nitrogen or 

phosphorus concentration in the water column. However, percent cover and thickness of 

Lyngbya wollei mats, as well as Vaucheria mats, were positively related to phosphorus in spring 

sediments, nutrient concentrations in macroalgal cells, diatom indicators of nutrient 

concentrations, and land use around sampling sites. N:P ratios in the water column and algal 

growth assays indicated phosphorus is the most limiting nutrient in many Florida springs. 

Could Lyngbya wollei be getting its nutrient supply from sediment sources? Would nitrogen 

reductions have any effect on Lyngbya wollei in Florida springs if phosphorus is the limiting 

resource? The research presented in the following sections addresses these questions. What are 

nutrient concentrations and fluxes in mats of Vaucheria and Lyngbya wollei compared to 

sediments and the water column? What nutrient concentrations affect the growth of Vaucheria as 

well as Lyngbya wollei? Do N:P ratios dictate responses of Vaucheria and Lyngbya wollei to 

nutrient reductions? 
24

3 – NUTRIENT CONCENTRATIONS ABOVE, WITHIN, AND BELOW LYNGBYA 

WOLLEI AND VAUCHERIA MATS 

3.1 – Introduction 

The effects of nutrients on benthic algae may vary greatly with density of those algae in a 

habitat. The phosphorus and nitrogen concentrations required to produce maximum growth rates 

of diatom-dominated benthic algae increases with increasing biomass of algae on substrata 

(Bothwell 1989, Rier and Stevenson 2006). This density-dependent demand for nutrients has 

been explained by reduced mixing of nutrients from overlying surface waters into mats coupled 

with high cellular uptake rates as biomass of diatom-dominated mats increases. Therefore, we 

would expect higher nutrient concentrations to be required to generate thick than thin macroalgal 

mats if the same relationship between mixing and cellular uptake rates exists for macroalgae as 

microalgae. 

However, macroalgae accumulate to produce much greater depths than the 2-3 cm of a diatomdominated mat. Typically, cells of filamentous macroalgae are not packed as densely as 

microalgae. Other processes, such as entrainment of particulate nutrients and decomposition, 

may be more important as thickness of mats increases so greatly. Thus, entrainment and 

decomposition with slow interstitial flux rates may enable support of thick algal mats without 

high nutrient supply rates, if algae can grow slowly without great disturbance for long periods of 

time. As mats thicken and age, lower and interior portions of the mats die, which has been 

attributed to reductions in nutrient supply and light in diatom-dominated benthic algae. The 

same resource limitation weakens health of macroalgae in the interior and lower portions of mats 

and makes them more susceptible to bacterial attack and decomposition. In addition, particulate 

organic nitrogen and phosphorus from upstream may become entrained within mats and 

decompose. If mixing rates between interstitial waters of the mat and overlying waters are less 

than decomposition rates, decomposition could become an important source of nutrients to 

support deep accumulations of benthic macroalgae. Several studies show that nitrogen 

regeneration may help sustain benthic macroalgal productivity and some systems may function 

as self-regenerating through this recycling of nitrogen (McGlathery et al. 1997; Stimson and 

Larned 2000; Trimmer et al. 2000; Sundbäck et al. 2003). 

Groundwater may be another source of nutrients that is not accounted for when only measuring 

surface water nutrient concentrations. Hendricks and White (2000) describe how mounds of the 

macroalga Chara in sandy streams alter surface and hyporheic flows and could increase nutrient 

availability. Macroalgal accumulation in Florida springs could also be supported by 

groundwater sources of nitrogen or phosphorus that become entrained in mats or groundwater. 

Such sources could provide a partial explanation for how we observe extensive growths of 

Lyngbya wollei mats in low nutrient springs. Groundwater nutrient sources could be just enough 

to support macroalgal growth in streams, yet not enough to increase measured nutrient 

concentrations in overlying spring water because of dilution. 

In this study benthic macroalgal mats dominated by Lyngbya wollei and Vaucheria spp. in 

Florida springs were evaluated to determine whether nutrients accumulated within mats. 

Nutrient profiles, isotopic composition of algae, and advective water movement were measured 
25

within, below, and above algal mats at Weeki Wachee, Manatee and Silver Glen Springs to 

characterize nutrient profiles of Lyngbya wollei and Vaucheria spp. mats, to determine if higher 

isotopic signatures indicated nutrient turnover in mats, and to estimate advective movement of 

dissolved nutrients out of mats. Results of this study will help us determine whether nutrient 

depletion within macroalgal mats could constrain growth or whether groundwater, sediments, 

entrainment, and decomposition may be sources of nutrients that sustain accumulation of thick 

mats of macroalgae. Details about the methods and results of this research can be found in 

Sickman et al. (2007). 

 

3.2 – Overview of Methods 

Interstitial water samplers, known as multisamplers, were used to collect water above, within, 

and below macroalgal mats on two dates and in three springs: in Weeki Wachee and Manatee 

Springs on April and August 2006 and in Silver Glen Springs (August 2006). The multisamplers 

were sampled at different times of the year to observe nutrient cycling during different stages of 

algal growth cycles and environmental conditions. Weeki Wachee had the thickest Lyngbya 

wollei mats and Manatee Springs the thickest Vaucheria spp. mats observed during surveys 

conducted in January 2006. Silver Glen was added as a comparison site to Weeki Wachee 

because it also has thick Lyngbya wollei mats, but the water chemistry varied between the two 

sites. Duplicate multisamplers were placed where the algal mats were thickest. 

The multisamplers were 1.5 m tall PVC pipes 

with small holes drilled every 10 cm 

(modified from Martin et al. 2003)(Figure 

3.1). Two layers of 500 μm nylon screen 

were glued to the outside of each hole to filter 

particulates. Tygon tubing (0.25 inch ID) was 

glued to the inner surface of each hole and ran 

the length of the water sampler. The 

multisamplers were inserted vertically through 

the thickest part of the algal mat to a depth of 

approximately 20-30 cm into the sediment. 

After a week of equilibration, water samples 

were drawn with a syringe from specific 

depths in the mats by using tubes connected to 

different holes in the multisampler. Both total 

and soluble nitrogen and phosphorus 

concentrations, dissolved organic carbon 

(DOC), and trace metals were measured in the 

water samples. 

Additionally, algal and sediment samples 

were taken from the algae/sediment interface, 

in the outer (surface) portion of the mat, and 

in the deeper portions. They were analyzed 

for % C, N and P as described in section 2.2. 

Figure 3.1. Multisampler device used to 

collect water column, algal mat and sediment 

interstitial waters.

Sediment

Algal Mat 

Water 

Column Each plastic 

tube 

corresponds to a 

distinct depth 

within the 

multisampler

The end of a plastic 

tube covered by 500 

micron screen 

Figure 3.1. Multisampler device used to 

collect water column, algal mat and sediment 

interstitial waters.

Sediment

Algal Mat 

Water 

Column Each plastic 

tube 

corresponds to a 

distinct depth 

within the 

multisampler

The end of a plastic 

tube covered by 500 

micron screen 
26

Subsamples for δ13C and δ15N isotope analysis were also collected and submitted for analysis by 

the Stable Isotope Laboratory at UC Davis. 

To estimate advective movement of water and nutrients into and out of thick algal mats, a tracer 

study was conducted in August 2006 at Weeki Wachee, Manatee and Silver Glen springs using 

multisamplers. NaCl tracer solution was injected along the edge of replicate multisamplers 

facing the boil (i.e., what we interpreted to be the “upstream” side of the sampler). Water 

samples were collected 5 minutes, 15 minutes, 30 minutes and 60 minutes after injection. The 

samples were then analyzed using a conductivity meter to quantify NaCl tracer. The rate of 

decrease of NaCl tracer, expressed as the percent change, was computed for each multisampler 

port to indicate the rate of advective water movement through the mat. 

0

20

40

60

80

100

120

140

160

0.00 0.01 0.10 1.00 10.00

mg/L

Depth in Multisampler (cm)

Fe

TKN

NH4

NO3

Sediment

Algae

Water 

Column

0

20

40

60

80

100

120

140

160

0.0 0.0 0.1 1.0 10.0

mg/L

Depth in Multisampler (cm)

Fe

TKN

NH4

NO3

Sediment

Algae

Water 

Column

0

20

40

60

80

100

120

140

160

0.00 0.10 0.20 0.30 0.40 0.50

mg/L

Depth in multisampler (cm)

SRP

TP

Sediment

Algae

Water 

Column

Replicate 1 Replicate 2

0

20

40

60

80

100

120

140

160

0.00 0.10 0.20 0.30 0.40 0.50

mg/L

Depth in multisampler (cm)

SRP

TP

Algae

Sediment

Water Column

Weeki Wachee

April 19, 2006

0

20

40

60

80

100

120

140

160

0.00 0.01 0.10 1.00 10.00

mg/L

Depth in Multisampler (cm)

Fe

TKN

NH4

NO3

Sediment

Algae

Water 

Column

0

20

40

60

80

100

120

140

160

0.0 0.0 0.1 1.0 10.0

mg/L

Depth in Multisampler (cm)

Fe

TKN

NH4

NO3

Sediment

Algae

Water 

Column

0

20

40

60

80

100

120

140

160

0.00 0.10 0.20 0.30 0.40 0.50

mg/L

Depth in multisampler (cm)

SRP

TP

Sediment

Algae

Water 

Column

Replicate 1 Replicate 2

0

20

40

60

80

100

120

140

160

0.00 0.10 0.20 0.30 0.40 0.50

mg/L

Depth in multisampler (cm)

SRP

TP

Algae

Sediment

Water Column

Weeki Wachee

April 19, 2006

Figure 3.2. Chemical profiles measured by multisamplers through Lyngbya wollei on dates 

indicated below for two replicate samplers. Approximate depth zones of sediment, algae and 

water column are indicated along the right side of each figure. For panels showing N species 

and Fe the x-axes are logarithmic (Sickman et al. 2007).

0

20

40

60

80

100

120

140

160

0.00 0.01 0.10 1.00 10.00

mg/L

Depth in Multisampler (cm)

Fe

TKN

NH4

NO3

Sediment

Algae

Water 

Column

0

20

40

60

80

100

120

140

160

0.0 0.0 0.1 1.0 10.0

mg/L

Depth in Multisampler (cm)

Fe

TKN

NH4

NO3

Sediment

Algae

Water 

Column

0

20

40

60

80

100

120

140

160

0.00 0.10 0.20 0.30 0.40 0.50

mg/L

Depth in multisampler (cm)

SRP

TP

Sediment

Algae

Water 

Column

Replicate 1 Replicate 2

0

20

40

60

80

100

120

140

160

0.00 0.10 0.20 0.30 0.40 0.50

mg/L

Depth in multisampler (cm)

SRP

TP

Algae

Sediment

Water Column

Weeki Wachee

April 19, 2006

0

20

40

60

80

100

120

140

160

0.00 0.01 0.10 1.00 10.00

mg/L

Depth in Multisampler (cm)

Fe

TKN

NH4

NO3

Sediment

Algae

Water 

Column

0

20

40

60

80

100

120

140

160

0.0 0.0 0.1 1.0 10.0

mg/L

Depth in Multisampler (cm)

Fe

TKN

NH4

NO3

Sediment

Algae

Water 

Column

0

20

40

60

80

100

120

140

160

0.00 0.10 0.20 0.30 0.40 0.50

mg/L

Depth in multisampler (cm)

SRP

TP

Sediment

Algae

Water 

Column

Replicate 1 Replicate 2

0

20

40

60

80

100

120

140

160

0.00 0.10 0.20 0.30 0.40 0.50

mg/L

Depth in multisampler (cm)

SRP

TP

Algae

Sediment

Water Column

Weeki Wachee

April 19, 2006

Figure 3.2. Chemical profiles measured by multisamplers through Lyngbya wollei on dates 

indicated below for two replicate samplers. Approximate depth zones of sediment, algae and 

water column are indicated along the right side of each figure. For panels showing N species 

and Fe the x-axes are logarithmic (Sickman et al. 2007).
27

3.3 – Overview of Results and Discussion 

Nutrient concentrations varied greatly in vertical profiles through the algal mats, and patterns 

varied to some degree among springs and macroalgal taxon. But overall, nutrient concentrations 

were higher in interstitial waters within algal mats and sediments below algal mats than in the 

spring water above algal mats (Figure 3.2-3.3). Total phosphorus, soluble reactive phosphorus, 

total Kjeldahl nitrogen, and ammonia were consistently higher in water samples drawn from 

multisamplers at depths within the algal mats and sediments than in overlying waters. Only 

concentrations of nitrate decreased within algal mats, which may be attributed to the 

development of anoxic conditions within the mats, which were indicated by high iron 

concentrations. 

High nutrient concentrations relative to overlying spring water occurred higher in the Lyngbya 

wollei mats of Weeki Wachee than Lyngbya wollei mats of Silver Glen or Vaucheria mats of 

Manatee Spring. Elevated nutrient concentrations in mats tended to be toward the bottom and 

Replicate 1 Replicate 2

Manatee

April 19, 2006

0

20

40

60

80

100

120

140

160

0.00 0.01 0.10 1.00 10.00 100.00

mg/L

Depth in Multisampler (cm)

Fe

TKN

NH4

NO3

Sediment

Algae

Water 

Column

0

20

40

60

80

100

120

140

160

0.00 0.01 0.10 1.00 10.00 100.00

mg/L

Depth in Multisampler (cm)

Fe

TKN

NH4

NO3

Sediment

Algae

Water 

Column

0

20

40

60

80

100

120

140

160

0.0 1.0 2.0 3.0 4.0 5.0

mg/L

Depth in multisampler (cm)

SRP

TP

Sediment

Algae

Water 

Column

0

20

40

60

80

100

120

140

160

0.0 1.0 2.0 3.0 4.0 5.0

mg/L

Depth in multisampler (cm)

SRP

TP

Sediment

Alga

e

Water 

Column

Replicate 1 Replicate 2

Manatee

April 19, 2006

0

20

40

60

80

100

120

140

160

0.00 0.01 0.10 1.00 10.00 100.00

mg/L

Depth in Multisampler (cm)

Fe

TKN

NH4

NO3

Sediment

Algae

Water 

Column

0

20

40

60

80

100

120

140

160

0.00 0.01 0.10 1.00 10.00 100.00

mg/L

Depth in Multisampler (cm)

Fe

TKN

NH4

NO3

Sediment

Algae

Water 

Column

0

20

40

60

80

100

120

140

160

0.0 1.0 2.0 3.0 4.0 5.0

mg/L

Depth in multisampler (cm)

SRP

TP

Sediment

Algae

Water 

Column

0

20

40

60

80

100

120

140

160

0.0 1.0 2.0 3.0 4.0 5.0

mg/L

Depth in multisampler (cm)

SRP

TP

Sediment

Alga

e

Water 

Column

Figure 3.3. Chemical profiles measured by multisamplers through Vaucheria mats on dates 

indicated below and for two replicate samplers. Approximate depth zones of sediment, algae 

and water column are indicated along the right side of each figure. For panels showing N 

species and Fe the x-axes are logarithmic (Sickman et al. 2007). 

Replicate 1 Replicate 2

Manatee

April 19, 2006

0

20

40

60

80

100

120

140

160

0.00 0.01 0.10 1.00 10.00 100.00

mg/L

Depth in Multisampler (cm)

Fe

TKN

NH4

NO3

Sediment

Algae

Water 

Column

0

20

40

60

80

100

120

140

160

0.00 0.01 0.10 1.00 10.00 100.00

mg/L

Depth in Multisampler (cm)

Fe

TKN

NH4

NO3

Sediment

Algae

Water 

Column

0

20

40

60

80

100

120

140

160

0.0 1.0 2.0 3.0 4.0 5.0

mg/L

Depth in multisampler (cm)

SRP

TP

Sediment

Algae

Water 

Column

0

20

40

60

80

100

120

140

160

0.0 1.0 2.0 3.0 4.0 5.0

mg/L

Depth in multisampler (cm)

SRP

TP

Sediment

Alga

e

Water 

Column

Replicate 1 Replicate 2

Manatee

April 19, 2006

0

20

40

60

80

100

120

140

160

0.00 0.01 0.10 1.00 10.00 100.00

mg/L

Depth in Multisampler (cm)

Fe

TKN

NH4

NO3

Sediment

Algae

Water 

Column

0

20

40

60

80

100

120

140

160

0.00 0.01 0.10 1.00 10.00 100.00

mg/L

Depth in Multisampler (cm)

Fe

TKN

NH4

NO3

Sediment

Algae

Water 

Column

0

20

40

60

80

100

120

140

160

0.0 1.0 2.0 3.0 4.0 5.0

mg/L

Depth in multisampler (cm)

SRP

TP

Sediment

Algae

Water 

Column

0

20

40

60

80

100

120

140

160

0.0 1.0 2.0 3.0 4.0 5.0

mg/L

Depth in multisampler (cm)

SRP

TP

Sediment

Alga

e

Water 

Column

Figure 3.3. Chemical profiles measured by multisamplers through Vaucheria mats on dates 

indicated below and for two replicate samplers. Approximate depth zones of sediment, algae 

and water column are indicated along the right side of each figure. For panels showing N 

species and Fe the x-axes are logarithmic (Sickman et al. 2007). 
28

sediments in Silver Glen and Manatee Spring. Although it is tempting to relate these differences 

to nutrient concentrations in springs and type of macroalgae, our sampling design does not allow 

testing those hypotheses without confounding variables associated with other factors in the 

different springs. 

Trends in stable isotope ratios of macroalgae with depth through mats indicated that heavier 

isotopes proportions increased with depth in the mat, however these patterns were not highly 

consistent across springs. The δ13C values increased with depth in the Lyngbya mats of Weeki 

Wachee, but δ15N values varied unpredictably with mat depth. In Silver Glen Lyngbya mats, 

δ13C values varied little with depth, but δ15N values increased with depth. Stable isotope ratios 

should increase with greater age and turnover of N and C in cells, but this pattern was not 

observed. 

Mat C:N:P ratios indicated that macroalgae in Weeki Wachee and Silver Glen were highly 

phosphorus limited, but little consistent difference occurred with depth in mat chemistries. 

Element ratios for C:P and N:P ranged from 126-971:1 and 12-73:1, respectively. When 

compared to the Redfield benchmarks for C:P and N:P ratios of 106:1 and 16:1, phosphorus is in 

relatively short supply compared to both carbon and nitrogen. Although C:N, N:P, and C:P 

increased with depth into mats in Weeki Wachee, with the highest values occurring toward the 

bottoms of the mats, no pattern in elemental ratios was observed in Silver Glen. Thus we could 

not conclude that nutrient depletion usually increased with depth in macroalgal mats. More 

extensive study of more mats over longer time periods would be valuable in future 

investigations. 

Percent change in tracer 

concentrations suggested 

that advective flow varied 

as a function of the 

distance above the spring 

bottom (Figure 3.4), with 

lowest flow at mid-depths 

of the mat. Higher 

advective flow at the 

bottom than the middle of 

the mats could indicate 

water movement out of 

sandy bottom sediments. 

Higher advective flow at 

the top than the middle of 

the mats indicated mixing 

of overlying surface waters 

with interstitial waters at 

the top of the mat. 

While it is difficult to 

translate the tracer data into 

Figure 3.4. Profiles of tracer dilution at three springs. The rate 

of decrease of NaCl tracer (measured in units of conductivity) 

expressed as percent change from peak concentration are 

plotted on the x-axis (Sickman et al. 2007).

0

10

20

30

40

50

60

70

80

90

100

0.0% 0.1% 1.0% 10.0%

Decrease in Tracer Concentration per Minute

Distance From Bottom (cm)

Silver Glen

Manatee

Weeki Wachee

*

*

* above algal mat

Figure 3.4. Profiles of tracer dilution at three springs. The rate 

of decrease of NaCl tracer (measured in units of conductivity) 

expressed as percent change from peak concentration are 

plotted on the x-axis (Sickman et al. 2007).

0

10

20

30

40

50

60

70

80

90

100

0.0% 0.1% 1.0% 10.0%

Decrease in Tracer Concentration per Minute

Distance From Bottom (cm)

Silver Glen

Manatee

Weeki Wachee

*

*

* above algal mat
29

actual water velocities, we made a first approximation of rates of water movement by making 

assumptions about the distribution of tracer around the multisampler and by treating the algal 

mat as a porous medium like soil. Based on these assumptions, discharge through the mats 

ranged from 4.0 x 10-8 m s-1 at Silver Glen to 9.0 x 10-7 m s-1 at Weeki Wachee. For comparison, 

hydraulic conductivity through silty sand ranges from approximately 10-3 to 10-7 m s

-1. This 

compares to current velocities in overlying waters above mats commonly ranging from 0.01-0.5 

m/s. 

Thus, considerable evidence indicated that macroalgal mats function very differently than mats 

of diatom-dominated microalgae. Nutrient concentrations were not lower in interstitial mat 

waters than in overlying waters as predicted based on benthic microalgal ecology; they were 

usually higher in mats. This may be due to many factors. The macroalgae in Florida springs 

grow on soft sediments with varying organic matter content, which may supply nutrients to the 

macroalgae. Groundwater may supply nutrients to macroalgae in Florida springs through the 

sandy substratum. Nutrient rich silt and particulate organic matter may become entrained in 

mats as it is transported downstream. Low redox conditions in mats may release phosphorus 

bound to inorganic particles. Inorganic nutrients taken up from the water column by the mats 

may become entrained mats. Decomposition of accumulating organic matter may provide a 

source of nutrients in mats. The low advective flows in mats indicate that mineralized nitrogen 

and phosphorus would have long periods of time to be taken up by cells with the algal mats. 

Thus, thicker mats of macroalgae may not require higher nutrient concentrations than thinner 

mats if loss and disturbance rates are low. More information is certainly needed to better 

understand these processes before broad conclusions can be drawn. 

4 – SEASONAL VARIABILITY IN MACROALGAE, DISCHARGE, AND NUTRIENTS 

IN FLORIDA SPRINGS 

4.1 – Introduction and Overview of Methods 

Seasonal variability in macroalgal cover has not been documented to determine the times of year 

in which problems may be greatest and whether seasonal factors could help us understand 

macroalgal ecology. Thus we monitored the monthly variability in discharge, nutrients, and 

macroalgal cover in Manatee and Ichetucknee Springs for a year to document seasonal 

variability. In addition, we compared the amount of nutrients in macroalgal mats to downstream 

nutrient flux to determine the potential of macroalgae for depleting nutrient supplies in overlying 

waters of springs. The same methods were used in this study as the large springs survey 

(Pinowska et al. 2007a), except that water chemistry was analyzed at the University of Florida 

versus the FDEP. Details of this study are described in Sickman et al. (2007). 

4.2 – Overview of Results and Discussion 

Vaucheria mats covered a majority of the bottom of Manatee Springs in August 2005, while 

Lyngbya wollei mats at Ichetucknee covered less than 50% of the bottom. The thickest (40-50 

cm) and most extensive algal mats at Ichetucknee formed in the lee of the swim fence where 

water velocity was relatively low. In contrast, at Manatee Springs a semi-continuous mat 
30

stretching along nearly the entire length of the spring run was observed with peak thickness of 

50-60 cm. 

The area of spring bottom 

covered by macroalgae and the 

mean thickness of algal mats in 

the Manatee and Ichetucknee 

Springs varied considerably 

during the study period (Figure 

4.1). At Manatee spring, 

macroalgal area varied from 

near zero in the late spring and 

early summer of 2005 to a peak 

of 3500 m2

 in late winter 2006. 

Over the course of the study, 

mean thickness varied from 

0.03 to 0.18 m and gross 

volume ranged from near zero 

to 518 m3

. Both algal area and 

thickness appeared to respond 

strongly to water levels in the 

Suwannee River. Low algal 

biomass in June and July of 

2005 was likely a result of high 

water levels in the Suwannee 

River, which caused flooding 

of Manatee spring by dark 

tannic waters and thereby 

reduced sunlight available for 

algal growth. After the flood 

water receded, water clarity 

improved, algal growth 

increased rapidly and stayed 

relatively high until spring 

2006 (Figure 4.1). 

Algal mat dynamics at 

Ichetucknee spring exhibited 

less variability than at Manatee 

spring (Figure 4.1). Over the year-long study there was a gradual decline in algal area, thickness 

and volume. The areal extent of algal cover varied between 465 to 754 m2

. Patterns of mean 

algal thickness often trended inversely with algal area and ranged from 0.08 to 0.26 m. Gross 

algal mat volume at Ichetucknee ranged from 48 to 166 m3

. 

At Ichetucknee Blue Hole, discharge from the boil ranged from 6.0 – 6.9 m3 s

-1 between May 

2005 and May 2006, and total Kjeldahl nitrogen (TKN) and total phosphorus (TP) concentrations 

0

100

200

300

400

500

600

700

800

May05Jun05Jul05Aug05Sep05Oct05Nov05Dec05Jan06Feb06Mar06Apr06May06

Algal Mat Area m2 or Algal Mat Volume m3

0

0.1

0.2

0.3

0.4

0.5

Algal Thickness m

Mat Area

Mat Volume

Mean Thickness

Ichetucknee Spring

0

500

1000

1500

2000

2500

3000

3500

4000

May05Jun05Jul05Aug05Sep05Oct05Nov05Dec05Jan06Feb06Mar06Apr06May06

Algal Mat Area m2 or Algal Mat Volume m3

0

0.1

0.2

0.3

0.4

0.5

Algal Thickness m

Mat Area

Mat Volume

Mean Thickness

Manatee Spring

Figure 4.1. Variation in area of spring bottom covered by a 

Lyngbya-dominated mat at Ichetucknee Spring and a 

Vaucheria-dominated mat at Manatee Spring (Sickman et 

al. 2007).

0

100

200

300

400

500

600

700

800

May05Jun05Jul05Aug05Sep05Oct05Nov05Dec05Jan06Feb06Mar06Apr06May06

Algal Mat Area m2 or Algal Mat Volume m3

0

0.1

0.2

0.3

0.4

0.5

Algal Thickness m

Mat Area

Mat Volume

Mean Thickness

Ichetucknee Spring

0

500

1000

1500

2000

2500

3000

3500

4000

May05Jun05Jul05Aug05Sep05Oct05Nov05Dec05Jan06Feb06Mar06Apr06May06

Algal Mat Area m2 or Algal Mat Volume m3

0

0.1

0.2

0.3

0.4

0.5

Algal Thickness m

Mat Area

Mat Volume

Mean Thickness

Manatee Spring

0

100

200

300

400

500

600

700

800

May05Jun05Jul05Aug05Sep05Oct05Nov05Dec05Jan06Feb06Mar06Apr06May06

Algal Mat Area m2 or Algal Mat Volume m3

0

0.1

0.2

0.3

0.4

0.5

Algal Thickness m

Mat Area

Mat Volume

Mean Thickness

Ichetucknee Spring

0

500

1000

1500

2000

2500

3000

3500

4000

May05Jun05Jul05Aug05Sep05Oct05Nov05Dec05Jan06Feb06Mar06Apr06May06

Algal Mat Area m2 or Algal Mat Volume m3

0

0.1

0.2

0.3

0.4

0.5

Algal Thickness m

Mat Area

Mat Volume

Mean Thickness

Manatee Spring

Figure 4.1. Variation in area of spring bottom covered by a 

Lyngbya-dominated mat at Ichetucknee Spring and a 

Vaucheria-dominated mat at Manatee Spring (Sickman et 

al. 2007).
31

ranged from 0.09-0.23 mg L-1 and 0.03-0.05 mg L-1, respectively. Discharge from the boil of 

Manatee Spring ranged from 5.7 – 7.8 m3

 s

-1 between May 2005 and May 2006 and TKN and TP 

concentrations ranged from 0.09-0.29 mg L-1 and 0.02-0.04 mg L-1, respectively. 

For both Springs the amount of C, N, and P contained in algal biomass was typically in the range 

of 100-300 kg C, 10-150 kg N and <1-5 kg P. Daily C, N and P fluxes from the boils at Manatee 

and Ichetucknee Springs exceeded the amount contained in algal biomass on all dates. At 

Ichetucknee Springs mean C, N and P flux in boil water were 436 kg-C d-1, 80 kg-N d-1, 23 kg-P 

d-1. At Manatee Springs, mean fluxes of C, N and P were 864 kg-C d-1, 107 kg-N d-1, 16 kg-P d-1

Comparing algal mat and boil flux, we observed that the amount of C, N and P contained in algal 

biomass represented less than one-day’s flux from the boils. These data suggest that the entire 

carbon and macronutrient content of even the largest algal mats observed in Manatee and 

Ichetucknee contained no more than 24 hours worth of nutrient flux from boil water. 

5 – EXPERIMENTAL ASSESSMENT OF LYNGBYA WOLLEI AND VAUCHERIA

RECOLONIZATION AFTER PHYSICAL DISTURBANCE 

5.1 – Introduction 

Disturbance and recolonization is a natural cycle in benthic algal ecology. Benthic algae can be 

removed from substrata by floods and waves that disturb substrata, sloughing during senescence 

stages of community development, and bioturbation by animals. Disturbance of benthic 

macroalgae in Florida springs results as larger rivers backup into springs, carrying turbid water 

and silts that shade and bury algae (Sickman et al. 2007). 

Recolonization of macroalgae in Florida springs after disturbance is poorly understood. Such 

events have not been studied for macroalgae as much as microalgae, and especially Lyngbya 

wollei or Vaucheria. Recolonization of diatom-dominated periphyton has been studied rather 

thoroughly in streams. In these assemblages, the roles of persistence of cells through disturbance 

events, immigration of new cells, reproduction, death, grazing, disease, competition, and 

sloughing have been characterized. Colonization by macroalgae is more complex than 

unicellular microalgae such as diatoms, because immigration occurs by filament fragments or by 

spores, which are special products of pre-existing cells. Vaucheria produces spores both 

asexually and sexually. Lyngbya can recolonize by fragments of filaments. Thus, their 

recolonization is probably regulated by the rates of settling of new spores or filament fragments 

into the space opened by disturbance and subsequent reproduction of these new cells and cells 

that persisted through the disturbance. 

The objective of this study was to determine the rates of recovery of Vaucheria and Lyngbya 

wollei in Florida springs. In this study we removed algae from areas of spring bottoms in which 

these algae occurred naturally and monitored recolonization. 
32

5.2 – Overview of Methods 

The macroalgal recolonization 

experiments were conducted in 

Manatee Spring and the Blue 

Hole of the Ichetucknee Spring 

system in which, respectively, 

Vaucheria and Lyngbya wollei

dominated. Theses sites were 

chosen because of differences 

in dominant algae and 

sufficiently high nutrient 

concentrations that regrowth of 

algae would not be limited. 

All algae were removed for 2 

plots (each 50 cm x 50 cm) 

with a rake. The percent cover 

and thickness of macroalgae in 

removal plots and control plots 

were monitored every 2-4 

weeks, depending on time after 

the disturbance and 

recolonization rates. The water 

depth, dissolved oxygen (DO), 

pH, conductivity and 

temperature were also 

measured. Data were square 

root transformed to reduce 

deviations from normality and 

homoscedasticity. Data were 

analyzed using repeated 

measures ANOVA with LS 

means test with SAS statistical 

software (SAS 2000) and R 

statistical software (R 

Development Core Team 

2006). 

5.3 – Overview of Results 

and Discussion 

Mat thickness of Lyngbya 

wollei in control plots did not 

change during the course of 

this study (Figure 5.1). 

Disturbed plots of Lyngbya 

Figure 5.1. Left - Average thickness of the mats of Lyngbya 

wollei at Blue Hole in Ichetucknee springs. Disturbed sites 

were completely scraped on the 31 of January and control sites 

were undisturbed. Right - Average thickness of the mats of 

Vaucheria sp. at Manatee spring. Disturbed sites were 

completely scraped on January 31 and control sites were 

undisturbed.

Figure 5.1. Left - Average thickness of the mats of Lyngbya 

wollei at Blue Hole in Ichetucknee springs. Disturbed sites 

were completely scraped on the 31 of January and control sites 

were undisturbed. Right - Average thickness of the mats of 

Vaucheria sp. at Manatee spring. Disturbed sites were 

completely scraped on January 31 and control sites were 

undisturbed.
33

wollei slowly grew back but did not recover from the disturbance even after 108 days (repeated 

measures ANOVA: time effect p<0.0013, disturbance effect p< 0.0021). Mat thickness of 

Vaucheria spp. in the control plots was greatest on February 17 but decreased in April as a result 

of senescing and decomposing. Vaucheria spp. recovered very quickly in disturbed plots; after 

24 days there was no difference in the thickness of the mat at the disturbed and control sites (LS 

means for 24 Feb p=0.2067, for 3 Mar p=0.3108, for 10 Mar p=0.2600, 7 Apr p=0.8269). 

The two sites studied had similar water quality conditions, however Manatee springs was 

characterized by a higher variability in conductivity and dissolved oxygen 

The difference in recolonization rates of Vaucheria sp. and Lyngbya wollei in the two springs 

was probably related to the dispersal mechanisms of the two taxa. Both taxa had similar growth 

rates in experiments (Pinowska et al. 2007b). Nutrient concentrations in these two springs should 

have been high enough to support rapid growth. Mechanisms of dispersal vary greatly. 

Vaucheria disperses by fragmentation of mats, by asexual production of zoospores, akinetes and 

aplanospores, and by spores from sexual reproduction (Starmach 1972, Johanson 2005). 

Lyngbya reproduces only by fragmentation of trichomes (Komárek and Anagnostidis 2005). 

Thus Vaucheria has the capability of dispersing much more rapidly than Lyngbya, which could 

explain more rapid recolonization after disturbance. This probably explains why Vaucheria is 

the dominant macroalga in springs along the Suwannee River where disturbance is high. 

Manatee, Fanning and Guaranto springs are dominated by mats of Vaucheria sp. These springs 

experience frequent intrusions of turbid waters from the Suwannee River during floods. 

6 – EXPERIMENTAL ASSESSMENT OF LYNGBYA AND VAUCHERIA GROWTH 

RATES ALONG NITRATE AND PHOSPHATE CONCENTRATION GRADIENTS 

6.1 – Introduction 

 

 Slow accumulation rates with low loss rates could explain the great abundances of Lyngbya 

wollei and Vaucheria biomass in low nutrient as well as high nutrient springs (Pinowska et al. 

2007a, Section 2 of this report). Accumulation processes are important for understanding the 

spatial and temporal variability in benthic algal biomass, but these processes are complexly 

related to environmental factors (Stevenson et al. 1996, Stevenson 1997, Biggs 2000). 

Immigration positively affects algal accrual. Grazing, disturbance by physical or biological 

processes, and death by senescence, disease, and decomposition also are regulated by a variety of 

factors. Reproduction, another accumulation process, is regulated by light and nutrients, the 

latter with is the focus of this study. Without significant loss by grazing or physical disturbance, 

large algal biomass could accumulate over time, even if reproduction rates are low. 

Little is actually known about growth of macroalgae in different nutrient conditions and 

interactions with other environmental factors, such as light, micronutrient concentrations, and 

conductivity. Many of these factors are known to affect algal growth, but precise quantitative 

relationships are not well defined. To assess effects of nutrients on algal growth, we should 

determine those effects in conditions that are otherwise the best for algal growth. Therefore, we 
34

conducted a set of preliminary experiments to determine effects of light, conductivity, and Fe, 

which was suspected to be an important micronutrient for Lyngbya wollei growth. 

Growth rates of algae increase rapidly with increasing levels of nutrients when nutrients are in 

relatively low supply, but then saturate 

(reach an asymptotic maximum) at high 

nutrient concentrations. Thus, incremental 

effects of nutrient increases and decreases 

have different effects at low and high 

nutrient concentrations, which is an 

important consideration for nutrient 

management strategies. A Monod model is 

usually used to predict response of growth 

rates to increasing phosphate or nitrate 

concentrations when the other resources do 

not constrain growth (Figure 6.1). 

Saturating nutrient concentrations for many 

algae are less than 0.030 mg PO4-P/L and 

0.250 mg NO3-N/L (Bothwell 1990, Rier 

and Stevenson 2006, Stevenson et al. 2006). 

As you will see in this research, the Monod 

model was only partially sufficient to 

explain response of algae to increasing 

nutrient concentrations. 

Competition is one of the most important factors affecting growth of algae and affecting 

maximum biomass accrual. As algae accumulate in the water column or on substrata, they can 

consume nutrients faster than nutrients are supplied. Nutrients have been assumed to be supplied 

primarily by mixing from overlying surface waters with interstitial waters in benthic mats. In 

benthic algae, mixing of overlying surface water around cells decreases as benthic algae density 

on substrata increase (Carlton and Wetzel 1988). Therefore, nutrient transport into mats 

decreases because uptake by overlying cells exceeds the mixing rate (Stevenson and Glover 

1993). Thus, we would expect that the nutrient concentrations producing maximum algal growth 

rate would be lower than the nutrient concentrations needed to produce peak algal biomass. This 

historic model of competition by algae for nutrients may be too simple for macroalgae, as we 

showed in Section 3 (Sickman et al. 2007). Decomposition or groundwater upwelling in and 

under mats may also be important sources of nutrients. 

Another critical question is “Does nitrogen or phosphorus limit growth of macroalgae in Florida 

springs?” Historic trends in nitrogen and phosphorus concentrations show that nitrogen has 

increased much more than phosphorus in Florida springs. Relationships between human 

activities around sites and nutrient concentrations indicate much greater effect on nitrogen than 

phosphorus. Thus, reductions in nitrogen loading with best management practices may be much 

more practical than reductions in phosphorus concentration. However, N:P ratios and algal 

growth potential assays indicate that P alone or both N and P limit algal growth in many Florida 

springs. 

Figure 6.1 The Monod Model. Here growth 

rate (μ) increases asymptotically with 

increasing nutrient concentration (S) such that 

half of the maximum growth rate (μmax) occurs 

at the half-saturation constant (Ks

). Here, 

μ=μmax(S/(Ks+S)).

μmax

Growth Rate (

μ) 0.5 μmax

0

0

S (e.g. PO4 or NO3, mg/L)

Ks

Figure 6.1 The Monod Model. Here growth 

rate (μ) increases asymptotically with 

increasing nutrient concentration (S) such that 

half of the maximum growth rate (μmax) occurs 

at the half-saturation constant (Ks

). Here, 

μ=μmax(S/(Ks+S)).

μmax

Growth Rate (

μ) 0.5 μmax

0

0

S (e.g. PO4 or NO3, mg/L)

Ks

μmax

Growth Rate (

μ) 0.5 μmax

0

0

S (e.g. PO4 or NO3, mg/L)

Ks
35

Is it possible that changes in nitrogen alone could regulate the growth of benthic macroalgae in 

Florida springs, even if P supply is indicated to be relatively low supply? “Not in many springs,” 

according to Leibig’s Law of the minimum, which holds that only one resource at a time limits 

growth of an alga. If nutrient concentrations are low enough to limit growth of algae, then ratios 

of nutrients should determine whether nitrogen or phosphorus reductions will lower growth rates. 

Leibig’s Law of the Minimum holds that only one resource at time limits growth of a species. 

According to current N:P ratios in Florida springs and the Redfield ratio benchmark of 16N:1P, 

macroalgal growth should be limited by phosphorus rather than nitrogen (Stevenson et al. 2004, 

Pinowska et al. 2007a). However Stevenson and Pan (1995) show that Liebig’s Law of the 

Minimum may not hold. We observed growth of a diatom species being stimulated by both 

nitrate and phosphate when the other nutrient was presumed to be in limiting supply. Could the 

same be true for macroalgae in Florida springs? 

The objectives of research presented in this section were to determine effects of nutrients on 

Vaucheria and Lyngbya wollei growth rates in highly controlled laboratory experiments. Our 

goal was to determine the nutrient concentrations, both nitrate and phosphate, that severely 

limited growth and that saturated growth rates (i.e. that produced no further increase in growth 

rates) of both macroalgal taxa. We conducted a series of experiments to determine the best 

conditions for assessing nitrate and phosphate regulation of growth of these macroalgae. We 

then conducted experiments to determine the limiting and saturating concentrations along 

nutrient gradients in which all other nutrients were in luxury supply. In these experiments, we 

varied light, micronutrient concentrations, iron, and conductivity, which have been shown to be 

important in experiments with other alga and other species of Lyngbya (e.g. Ahern et al. 2006, 

Watkinson et al. 2005). Finally, we conducted experiments to determine the limiting and 

saturating nitrate concentration for Lyngbya wollei and Vaucheria when phosphorus was in very 

low concentrations, as in many Florida springs. We conducted these experiments in multiple 

experimental systems to confirm that we would find similar relationships in all experimental 

systems. 

 

6.2 – Overview of Methods 

Experiments were conducted in three types of experimental systems to address slightly different 

hypotheses and to confirm results of the same experimental treatments in two or more settings 

(Figure 6.2-6.3). The small microcosms were 1.5 mL microcentrifuge tubes filled with growth 

media and placed on a shaker under lights in a cold room (22.5◦

C) at Michigan State University. 

The large microcosms were a set of 49 circular raceways (donut-shaped) made of clear 

plexiglass, filled with 100 mL of media, and placed on a shaker table under lights in a cold room 

(22.5◦C) at Michigan State University. The mesocosms were rectangular loops of 5 cm PVC 

pipe, 122 cm long and 91 cm tall, oriented vertically, in which water was circulated; the top of 

the upper section of the rectangular loop was cut off to allow light to reach the water and algae; 

and 20 PVC loops were oriented vertically in water baths for cooling in a greenhouse at the 

University of Florida. Details about these experimental systems can be found in Pinowska et al. 

(2007b) and Albertin et al. (2007b). 
36

Nine and three experiments were conducted in small and large microcosms, respectively, to 

evaluate good non-nutrient growth conditions and effects of nitrate, phosphate, and ammonia on 

Lyngbya wollei and Vaucheria growth. The nutrient levels used in the experiments reflected 

phosphate and nitrate concentrations observed in Florida springs water. Ammonia 

concentrations used in the experiments were higher than those observed in Florida springs water 

but in the range of those observed inside the L. wollei mats (Sickman et al. 2007). The small 

microcosms were valuable for high replication and use of individual filaments. The large 

microcosms enabled manipulation of biomass in highly controlled experimental settings. The 

mesocosms were used to reflect the most natural conditions with natural groundwater. To enable 

enrichment with nutrients to create treatment levels, we used groundwater with very low nutrient 

Aquifer water with nutrients

Water bath to regulate temperature

NO3 and Micronutrients 

in DDI Water

Floridian 

Aquifer Water

Air Pump

Pump

Macroalgae Pump

Excess water outflow

Figure 6.3. The recirculating stream mesoscosm.

Flow

Microcentrifuge - Microcosms Donut - Microcosms

Figure 6.2. The microcentrifuge and donut mesoscosms.

Aquifer water with nutrients

Water bath to regulate temperature

NO3 and Micronutrients 

in DDI Water

Floridian 

Aquifer Water

Air Pump

Pump

Macroalgae Pump

Excess water outflow

Figure 6.3. The recirculating stream mesoscosm.

Aquifer water with nutrients

Water bath to regulate temperature

NO3 and Micronutrients 

in DDI Water

Floridian 

Aquifer Water

Air Pump

Pump

Macroalgae Pump

Excess water outflow

Aquifer water with nutrients

Water bath to regulate temperature

NO3 and Micronutrients 

in DDI Water

Floridian 

Aquifer Water

Air Pump

Pump

Macroalgae Pump

Excess water outflow

Figure 6.3. The recirculating stream mesoscosm.

Flow

Microcentrifuge - Microcosms Donut - Microcosms

Figure 6.2. The microcentrifuge and donut mesoscosms.

Microcentrifuge - Microcosms Donut - Microcosms

Figure 6.2. The microcentrifuge and donut mesoscosms.
37

concentrations. As a result, nutrient concentrations in the groundwater used could have been less 

than natural background nutrient concentrations in some regions of Florida.

6.2.1 Microcosm Experiments 

Individual filaments of Lyngbya wollei and Vaucheria spp. were placed in dishes under the 

dissecting scope, cut with a scalpel, and then grown in microcentrifuge tubes for 11-12 days. 

Each filament was photographed before and after incubation in the microcentrifuge tubes to 

determine changes in filament lengths, which were used to calculate their growth rates. The 

growth medium was changed every 48 hours to replenish nutrients supplies depleted by the 

algae. Temperature was maintained in the incubation room at 22.5◦

C to simulate the average 

temperature of water in Florida springs. The Z8 medium (Kotai 1972) was modified to simulate 

the median chemistry of macronutrients in Florida spring water (referred to as medium Z8Fl). 

The formula for the growth medium was actually the study of preliminary experiments, which 

will be described subsequently. Ammonia nitrogen, Kjeldahl nitrogen, nitrite-nitrate nitrogen, 

soluble reactive phosphorus, and total phosphorus concentrations were analyzed routinely by 

DEP. Water conductivity was measured using YSI 556 MSP. Details about the experimental 

set-up, media, and treatments for the microcosm experiments can be found in Pinowska et al. 

(2007b). 

6.2.1.1 Microcosm experiments to determine good non-nutrient conditions for growth. Five 

experiments were conducted using small algal microcosms to determine good non-nutrient 

conditions for growth. The first experiment compared growth of Lyngbya wollei and Vaucheria

in 3 media and with 2 biomass levels: water from Alexander Spring, water from Silver Glen 

spring, and Z8Fl medium with either 1 or 5 filaments of macroalgae per centrifuge tube. The 

second experiment evaluated effects of conductivity where major cation and anion 

concentrations were varied in the medium to produce conductivities of 100, 300, 500, 1000 and 

3000 μS/cm. Both conductivity and light were varied in experiment 3, with 3 conductivity 

levels (100, 300, 1000 μS/cm) and 5 light levels (1, 2.5, 5, 25, 250 μmol/m2

/s). Experiment 4 

was relatively simple, with just 2 treatments: with and without micronutrients. Experiment 5 

determined the effect of iron on macroalgal growth with 4 iron treatments (0, 20, 200, 600 μg 

Fe/L). 

The results of these experiments showed that Lyngbya wollei and Vaucheria grew best in Z8Fl 

medium with single filaments in microcentrifuge tubes. High conductivity, about 3000 μS/cm, 

had a negative effect on growth of both macroalgae compared to lower conductivity treatments, 

which indicated that both taxa were freshwater algae, and not of marine origin. Micronutrients 

had a negative effect on Lyngbya wollei and no effect on Vaucheria. Iron concentration in the 

range of 0.020-0.200 mg/L had optimal effect on growth of both macroalgae, with negative 

effects at higher and lower treatment levels. Both macroalgae grew better in 25 μmol quanta m-2

s

-1 than in higher and lower light intensities. 

6.2.1.2 Microcosm experiments to determine nutrient effects on growth. Single filaments of 

Lyngbya wollei and Vaucheria from Florida springs were grown in Z8FL medium-filled 

microcentrifuge tubes under 25 μmol/m2

/s of light, at 22.5◦C, and intermediate iron and 

conductivity levels. Typically each treatment was replicated 10 times, or 5 times if two 
38

populations (from 2 different springs) were used. Each experiment was designed to evaluate 

effects of 1 nutrient when other nutrients (macro- and micronutrients) were in luxury supply. 

The experiments conducted for Lyngbya wollei and Vaucheria were very much the same, but 

slight differences should be noted. Experiment numbers in the following list reflect numbers 

associated with tasks in the project proposal. They are meant to identify the experiments, but do 

not reflect their order of presentation. Nutrient concentrations are stated in the following 

methods in μg/L to make numbers easier to read, whereas they are in mg/L in the results and 

discussion to match the rest of the reports. 

The following experiments were conducted with Lyngbya wollei: 

• Experiments 1, 2 and 3 had a 2 factor design with 10 PO4 treatments (1, 5, 10, 20, 30, 40, 

60, 80, 100 and 250 μg P/L) and 2 population location treatments. The NO3-N 

concentrations in experiments 1, 2 and 3 were 1000 μg/L, which was assumed to be a 

luxury supply. 

• Experiment 5 had a one factor design with 11 NO3-N concentrations: 10, 30, 60, 125, 

250, 500, 750, 1000, 1500, 2500 and 5000 μg N/L. The PO4-P concentrations in 

experiment 5 was 100 μg/L, which was assumed to be a luxury supply. 

• Experiments 6, 7 and 8 had a 2 factor design with 10 NO3-N concentrations (10, 30, 60, 

125, 250, 500, 750, 1000, 2000 and 5000 μg N/L) and 2 population location treatments. 

The PO4-P concentrations in experiments 6, 7, and 8 were 100 μg/L, which was assumed 

to be a luxury supply. 

• Experiment 10 had a 1 factor design with 11 NH4 (10, 30, 60, 125, 250, 500, 750, 1000, 

1500, 2500 and 5000 μg N/L). The PO4-P concentration in experiment 10 was 100 μg/L, 

which was assumed to be a luxury supply. 

• Experiment 12 had a 2 factor design with 16 nutrient treatments: a combination of 4 

ammonia concentrations (20, 100, 500 and 1000 μg N/L) and 4 nitrate concentrations 

(20, 100, 500 and 1000 μg N/L). 

The following experiments were conducted with Vaucheria: 

• Experiment 1 had a two-factor design with 10 PO4 treatments (1, 5, 10, 20, 30, 40, 60, 80, 

100 and 250 μg P/L) and 2 population location treatments. The NO3-N concentration in 

experiment 1 was 1000 μg/L. 

• Experiment 3 had a two-factor design with 10 NO3 treatments (10, 30, 60, 125, 250, 500, 

750, 1000, 2000 and 5000 μg N/L) and 2 population location treatments. The PO4-P 

concentration in experiment 3 was 100 μg/L. 

• Experiment 5 had a one-factor design with 11 nutrient treatments (NH4-N concentrations: 

10, 30, 60, 125, 250, 500, 750, 1000, 1500, 2500 and 5000 μg N/L). The PO4-P 

concentration in experiment 5 was 100 μg/L. 

• Experiment 7 had a two-factor design with 16 nutrient treatments: a combination of 4 

ammonia concentrations (20, 100, 500 and 1000 μg N/L) and 4 nitrate concentrations 

(20, 100, 500 and 1000 μg N/L). The PO4-P concentration in experiment 7 was 100 μg/L. 

6.2.2 Large Microcosm (Donut) Experiments 

The large microcosm experiments were conducted under 150 μmol/m2/s of light. Each 

microcosm was filled with 100 ml of growth medium, which was changed daily. Small 
39

fragments of Lyngbya wollei mats were grown in the large microcosms. Changes in their fresh 

mass and dry mass were determined to calculate growth rates. All experiments had a complete 

factorial ANOVA design with two experimental factors: nutrient concentration and initial 

biomass. 

• Experiment 4 had 15 treatments which were a combination of five phosphate 

concentrations (PO4-P concentrations: 2, 10, 30, 60, 150 μg P/L) and three initial fresh 

mass levels (0.01 g, 0.05 g, 0.2 g). The NO3-N concentration in experiment 4 was 1000 

μg/L. 

• Experiment 9 had a total of 15 treatments which were a combination of five nitrate 

concentrations (NO3-N concentrations: 20, 125, 500, 1000, 2500 μg N/L) and three initial 

fresh mass levels (0.01 g, 0.05 g, 0.2 g). The PO4-P concentration in experiment 9 was 

100 μg/L. 

• Experiment 11 had 15 treatments which were a combination of five ammonia 

concentrations (NH4-N concentrations: 20, 125, 500, 1000, 2500 μg N/L) and three initial 

fresh mass levels (0.01 g, 0.05 g, 0.2 g). The PO4-P concentration in experiment 11 was 

100 μg/L. 

6.2.3 Mesocosm Experiments 

Two mesocosm experiments were conducted consecutively to evaluate effects of nitrate 

enrichment when phosphorus concentration was low. Mesocosms were located in a climate 

controlled greenhouse on the University of Florida, Gainesville. Groundwater from the Floridan 

Aquifer, nitrate, and micronutrients were added continuously to the channels with peristaltic 

pumps. The groundwater had low nutrient concentrations (NO3 < 0.001 mg N/L, PO4-P = 0.009 

mg P/L) and was intended to simulate spring water. Water temperature in the recirculating 

stream channels averaged 21°C during Experiment 1. The pH ranged from 6.27 to 8.76, the 

dissolved oxygen ranged 8.28 to 12.00 mg/L, and the conductivity range was 292 to 357 μS/cm. 

During Experiment 2, water temperature in the recirculating stream channels averaged 20.5°C. 

The pH ranged from 7.03-8.75 and the dissolved oxygen averaged 7.01 mg/L. Conductivity 

ranged from 228 to 323 μS/cm. Light averaged 650 μmol m-2 s

-1 and varied with cloudiness 

during both experiments. Details about the experimental set-up are in Albertin et al. (2007b). 

The first experiment ran for 28 days and had seven treatments: 2 treatments had no nitrate 

addition (Control A and Control B) and 5 treatments of nitrate additions (ranging from 0.001to 

5.000 mg N/L) in the form of NaNO3. All treatments except Control A received a Z8Fl 

micronutrient supplement. Phosphorus was not added to any treatment. Experiment 1 was 

designed to identify the range of nitrate concentrations in which threshold responses in 

macroalgal growth rates would be observed, which would be investigated more thoroughly in 

Experiment 2. Experiment 2 ran for 21 days and had seven treatments: one control treatment 

with no nitrate addition and six nitrate treatments ranging from 0.025 to 0.750 mg NO3-N/L. All 

seven treatments in Experiment 2 received the Z8Fl micronutrient supplement. No phosphorus 

was added to any treatment. In both experiments, phosphate was depleted rapidly in the 

mesocosm from groundwater concentrations of 0.009 to less than 0.005 mg/L PO4-P. Phosphate 

concentrations were usually 0.002-0.003 mg/L PO4-P. 
40

Vaucheria spp. and Lyngbya wollei from Florida springs were collected for mesocosm 

experiments. Fresh mass of small fragments of algal mats of each species were determined 

before and after incubation in the mesocosms. Six mat fragments each of Vaucheria spp. and 

Lyngbya wollei were randomly placed in each channel for Experiment 1. Nine mat fragments per 

genus were placed in each stream channel for Experiment 2. 

During Experiments 1 and 2, temperature, conductivity, pH, and dissolved oxygen were 

measured every three days in every stream channel. Water samples for chemical analyses were 

taken five times during Experiment 1 (Days 0, 7, 14, 21 and 28) and four times during 

Experiment 2 (Days 0, 7, 14 and 21). The samples were analyzed for total Kjeldahl nitrogen, 

total phosphorus, soluble reactive phosphorus, nitrate, ammonium, dissolved organic carbon, 

cations, and trace metals. Nitrogen and phosphorus in algal mats was also determined.

6.2.4 Data Transformations and Analyses 

Algal relative growth rate (RGR) in the small microcosm experiments was calculated using the 

following formula (Hunt 1990): 

Data were analyzed using R statistical software (R Development Core Team 2006). If necessary, 

data were square root or 1/square root transformed to meet the assumptions of normality and 

variance homogeneity. 

To determine threshold values of phosphate and nitrate concentrations causing increased 

macroalgal growth, the data from several experiments with the same treatments were combined. 

For example, data were combined for all experiments in microcosms in which PO4 was 

manipulated and nitrate was in luxury concentration. A comparison of the experiments listed 

above and results in Table 6.1 below show how data were combined; details can be found in 

Pinowska et al. (2007b) and Albertin et al. (2007b). Response of both Lyngbya wollei and 

Vaucheria growth showed a response in which growth rates were only affected at intermediate 

nutrient concentrations. Neither a linear or Monod model fit this response shape, which seemed 

more like a logistic curve. Therefore, the data were analyzed using a four-parameter logistics 

model in the drc (dose response curve) package of R statistical software (Ritz and Streibig 2005). 

Only statistical results that were statistically significant at the P<0.05 level of attained 

significance are described in this report, unless otherwise noted. This means that differences 

related to treatments could be observed by chance less than 1 in 20 times if the experiment were 

repeated. 

6.3 – Overview of Results and Discussion 

Nitrate, phosphate, and ammonia stimulated growth of Lyngbya wollei and Vaucheria spp. 

(Figures 6.4-6.7). In nitrate and phosphate experiments, both macroalgae were able to maintain 

low growth rates, around 0.10-0.15 (ln(mm mm-1) d-1 or ln(g g-1) d-1), at very low nutrient 

concentrations. As nitrate and phosphate increased past a threshold, growth of the macroalgae 

# of days

ln(Final Length) ln(Initial Length) RGR = −
41

were stimulated until peak growth rates were reached and additional nutrient supply had no 

effect on growth. Ammonia, however, did have a negative effect on growth rates of both 

macroalgae in the high concentration treatments, which were the same as observed in some thick 

macroalgal mats (Sickman et al. 2007). 

Figure 6.4. Growth rates (RGR) of Lyngbya wollei (left) and Vaucheria spp. (right) at 

different phosphorus concentrations in microcentrifuge tubes when nitrate concentrations 

were high.

Figure 6.5. Growth rates (RGR) of Lyngbya wollei (left) and Vaucheria spp. (right) at 

different nitrate concentrations in microcentrifuge tubes when phosphate concentrations 

were high.

Figure 6.4. Growth rates (RGR) of Lyngbya wollei (left) and Vaucheria spp. (right) at 

different phosphorus concentrations in microcentrifuge tubes when nitrate concentrations 

were high.

Figure 6.4. Growth rates (RGR) of Lyngbya wollei (left) and Vaucheria spp. (right) at 

different phosphorus concentrations in microcentrifuge tubes when nitrate concentrations 

were high.

Figure 6.5. Growth rates (RGR) of Lyngbya wollei (left) and Vaucheria spp. (right) at 

different nitrate concentrations in microcentrifuge tubes when phosphate concentrations 

were high.

Figure 6.5. Growth rates (RGR) of Lyngbya wollei (left) and Vaucheria spp. (right) at 

different nitrate concentrations in microcentrifuge tubes when phosphate concentrations 

were high.
42

Response of the macroalgae to nitrate and phosphate enrichment followed a classic logistic doseresponse curve observed in many toxicological studies, but instead of a negative effect of the 

contaminant, nutrients had a positive effect on growth rate. Using parameters from the dose 

response curve statistics, several parameters could be estimated that have value for macroalgal 

management (Table 6.1): 

• minimum growth rates (rmin); 

• maximum growth rates (rmax); 

• the increase in growth rate with nutrient enrichment (rinc); 

• constraining concentrations: the nutrient concentration at which growth was predicted to 

be elevated by 10%, below which growth of the macroalgae is constrained about as much 

as possible (ed10 – effective dose with 10 % effect); and 

• saturating concentrations: the nutrient concentration at which growth was predicted to be 

elevated by 90%, above which no effects of nutrient reduction would be expected (ed90 – 

effective dose with 90 % effect). 

• the nutrient concentration at which growth was predicted to be elevated by 50% of the 

predicted change between rmin and rmax (ed50). 

In all cases reported in the text of these results, there were significant increases in algal growth 

with nitrate and phosphate concentrations. Ammonia concentrations also stimulated algal 

growth at low concentration ranges, but high ammonia concentration had a negative effect. This 

presentation of results will focus on nitrate and phosphate as management targets and the nutrient 

concentrations that delineate the range of concentrations in which effects are observed. 

Before evaluating the specific nutrient concentrations affecting algal growth, it is important to 

examine the relative consistency in parameters differences among experiments. The nutrient 

concentrations at which effects were observed (ed10, ed50, and ed90) were lower for 

microcentrifuge tubes than donuts, which may have been due to less depletion of nutrients in 

microcentrifuge tubes than in donuts. Decreases in nutrient concentrations during the incubation 

period were greater for donuts than the microcentrifuge microcosms (Pinowska et al. 2007b). 

Increases in biomass in donuts increased the ed50 and ed90, consistently decreased rmax, and 

usually decreased rmin for Vaucheria. Increased biomass had a negative effect on Lyngbya wollei

growth rates, but the response of Lyngbya wollei to nutrients could not be expressed with 

parameters of the logistic model because nutrient responses were reduced greatly in high 

Lyngbya wollei biomass treatments. This too may have been related to the greater depletion of 

nutrients when more algae were present to levels that were below saturation levels. No effects 

on growth rate were related to the spring from which the algae came, i.e. to either 

preconditioning or genetic differences among populations. 

Lyngbya wollei in mescosms responded to NO3 at lower concentrations, with lower growth rates, 

and with smaller increases in growth rates than in microcosms or donuts. This was probably 

related to the nature in which filaments were bundled and P concentrations were much lower in 

mesocosms than both microcosm types. Filaments were bundled together in relatively tight 

masses, which would have reduced light penetration and mixing of water and nutrients into the 

masses. This reduction in mixing is known to reduce nutrient concentrations in some microalgal 

mats, such as those dominated by diatoms (Stevenson and Glover 1993). However, our work did 
43

show that this is not always the case when mats become thicker and other processes become 

important in producing nutrients in interstitial waters of mats (Sickman et al. 2007). 

The range of nutrient concentrations that constrained and saturated growth of Lyngbya wollei and 

Vaucheria spp. were very similar in experiments. Rmax often ranged from 0.1-0.3 d-1, depending 

largely on biomass and nutrient depletion in the experiment. Peak growth rates of many algae 

are 0.3 (Stevenson 1984), and higher growth rates have been observed (Manoylov and 

Stevenson. 2006, Rier and Stevenson 2006). Even though we optimized as many conditions as 

possible in experiments, experimental handling may have reduced potential growth rates. 

Figure 6.6. Growth rates (RGR) of Lyngbya wollei (left) and Vaucheria spp. (right) at 

different ammonia concentrations in microcentrifuge tubes when phosphate concentrations 

were high.

Figure 6.7. Growth rates (RGR) of Lyngbya wollei in mesocosms at different nitrate 

concentrations (μg/L) when phosphate concentrations were high.

Figure 6.6. Growth rates (RGR) of Lyngbya wollei (left) and Vaucheria spp. (right) at 

different ammonia concentrations in microcentrifuge tubes when phosphate concentrations 

were high.

Figure 6.6. Growth rates (RGR) of Lyngbya wollei (left) and Vaucheria spp. (right) at 

different ammonia concentrations in microcentrifuge tubes when phosphate concentrations 

were high.

Figure 6.7. Growth rates (RGR) of Lyngbya wollei in mesocosms at different nitrate 

concentrations (μg/L) when phosphate concentrations were high.

Figure 6.7. Growth rates (RGR) of Lyngbya wollei in mesocosms at different nitrate 

concentrations (μg/L) when phosphate concentrations were high.
Table 6.1. Summary of macroalgal responses to nutrient (PO4 or NO3) enrichment in different experiments, in d(small microcosms – microcosm, large microcosms – donuts, and mesocosms). Biomass indicates the amount of 

microcentrifuge tube, donut, and mesocosm. Model parameters for the logistic dose response curve and their colare described in the text above. Model parameters are omitted for sets of experiments in which a logistic respons

Model parameters are in bold type if they were determined to be statistically significant. ed10, ed50, and ed90 ar

In the case of the ed10 and ed90, which were harder to estimate, if the standard error of estimates was less than hthen they are in bold type. 

Taxon Exp System Biomass Nutrient ed10 ed50 ed90 r(min) r(ma

Lyngbya Microcosm 1 filament PO4 0.011 0.017 0.028 0.14 0.2Lyngbya Donuts 0.01g PO4 0.024 0.049 0.101 0.15 0.2

Lyngbya Donuts 0.05g PO4 x Lyngbya Donuts 0.2 g PO4 x Vaucheria Microcosm 1 filament PO4 0.006 0.012 0.022 0.16 0.2Vaucheria Donuts 0.01g PO4 0.017 0.037 0.078 0.02 0.1Vaucheria Donuts 0.05g PO4 0.015 0.076 0.06 0.1Vaucheria Donuts 0.2 g PO4 x Lyngbya Microcosm 1 filament NO3 0.034 0.088 0.230 0.10 0.2Lyngbya Donuts 0.01g NO3 0.327 0.519 0.821 0.15 0.2Lyngbya Donuts 0.05g NO3 0.087 0.546 3.440 0.11 0.2

Lyngbya Donuts 0.2 g NO3 0.058 0.808 11.283 0.11 0.1Lyngbya Mesocosm masses NO3 0.015 0.042 0.110 0.09 0.1

Vaucheria Microcosm 1 filament NO3 x Vaucheria Donuts 0.01g NO3 0.069 0.210 0.644 0.10 0.2Vaucheria Donuts 0.05g NO3 0.036 0.208 1.200 0.07 0.1

Vaucheria Donuts 0.2 g NO3 0.293 0.499 0.850 0.05 0.1

Vaucheria Mesocosm masses NO3 x 
45

However, recolonization of disturbed areas of springs was relatively slow (Pinowska et al. 

2007b), even after initial colonization of filaments, which indicated that growth of these 

macroalgae was relatively slow in springs. 

The constraining nutrient concentration (ed10) for PO4 for both Lyngbya wollei and Vaucheria

ranged from 0.006-0.015 mg PO4-P/L and was generally lowest for the microcentrifuge 

treatments in which nutrient depletion was least of all experimental settings. In donuts, when 

nutrients were depleted, the actual nutrient concentrations were much less than the treatment 

level that was used to characterize exposure level of algae to the nutrients, plus the low number 

of treatment levels constrained precision in estimates of ed10 and ed90. Since nutrients are not 

depleted within the macroalgal mats (Sickman et al. 2007) and nutrient depletion in donut 

experiments was great, results from the microcentrifuge tube experiments should be assigned 

more significance when predicting the nutrient concentrations in springs that regulate macroalgal 

growth. 

The growth-saturating nutrient concentrations (ed90) for PO4-P were 0.028 and 0.022 mg/L in 

microcentrifuge tubes for Lyngbya wollei and Vaucheria, respectively. Growth-saturating 

nutrient concentrations were many times higher in low biomass treatments in donuts. The ed90 

concentrations from microcentrifuge tube experiments were higher than observed for lowbiomass diatom-dominated periphyton, which have been reported to reach peak growth rates 

around 0.001 mg PO4-P/L (Bothwell 1989) and 0.016 mg PO4-P/L by Rier and Stevenson 

(2006). 

The NO3-N ed10 and ed90 for Lyngbya wollei were 0.034 and 0.230 mg/L, respectively, in 

microcentrifuge tubes. Unfortunately, Vaucheria did not respond to nitrate manipulations in the 

microcentrifuge tube experiments. However, the NO3-N ed10 and ed90 for Vaucheria were 

0.069 and 0.644 mg/L in the low biomass treatments in donuts, which would be predicted to be 

much lower in microcentrifuge tubes because nutrients would not have been depleted. Few other 

studies have estimated limiting and saturating nutrient concentrations of nitrate for algae. Rier 

and Stevenson (2006) found growth of low-biomass diatom-dominated periphyton was saturated 

by dissolved inorganic nitrogen (mostly nitrate) at 0.086 mg/L. Cowles and Dawes (2004) found 

Lyngbya wollei growth rates were similar between 0.070 and 0.300 mg NO3-N/L, but 

substantially greater and likely saturated at 0.600 mg/L. Likely reasons for higher estimates of 

limiting nutrient concentrations by Cowles and Dawes (2004) is that they did not have lower 

NO3 treatments than 0.070 mg/L and they incubated masses of algae, rather than individual 

filaments, so nutrient depletion may have occurred even though they continuously supplied new 

medium to cultures. 

Given historic records of NO3 being around 0.050 mg/L, our experimental results indicate that 

nitrate loading into springs from human sources has elevated growth rates of macroalgae from a 

range of concentrations that severely constrained growth of Lyngbya wollei and Vaucheria spp. 

to concentrations that commonly saturate their nitrogen requirements. 

The response of Lyngbya wollei to nitrate enrichment in the mesocosm experiments, in which no 

phosphate was added, provides insight into how nitrate additions in all springs can stimulate 

algal growth, whether spring phosphorus is high or low. We usually assume that either nitrate or 
46

phosphate, or some other nutrient limits growth of a species of algae (i.e. Leibig’s Law of the 

Minimum). But in this experiment, when all indications were that phosphorus should have been 

the primary limiting nutrient, nitrate enrichment stimulated growth of Lyngbya wollei. The 

following conditions indicate that phosphorus should have been the primary limiting nutrient: 

phosphate concentrations were low in the mesocosm water, and both N:P and C:P ratios of 

Lyngbya wollei were well above the 16:1 and 106:1 ratios that indicate relative cell requirements 

for these elements. Therefore, stored phosphorus in cells did not provide an unmeasured supply 

of phosphorus for cell growth. This appears to be a clear violation of Leibig’s Law of the 

Minimum. Such a violation has also been observed for a diatom species, Synedra ulna, but 

tissue nutrient concentrations were not available in that study to eliminate the hypothesis that 

intracellular phosphorus stores supported growth (Stevenson and Pan 1995). 

Table 6.2. Initial and final C:N, C:P and N:P molar ratios of Lyngbya wollei and Vaucheria spp. 

in Experiment 1. Target treatment concentrations (NO3 mg/L) are shown below treatment 

numbers. 

Treatment 

1 2 3 4 5 6 7 

Species 

Molar 

Ratio Day Cont A Cont B 0.0005 0.005 0.05 0.5 5 

Lyngbya C:N 0 7 7 7 7 7 7 7

wollei 28 12 15 15 16 15 10 11

 C:P 0 198 198 198 198 198 198 198

 28 286 319 299 345 436 543 630

 N:P 0 29 29 29 29 29 29 29

 28 23 22 20 22 30 52 57

 

Vaucheria C:N 0 9 9 9 9 9 9 9

spp. 28 17 17 17 19 20 13 14

 C:P 0 163 163 163 163 163 163 163

 28 293 341 330 351 451 606 672

 N:P 0 18 18 18 18 18 18 18

 28 17 20 19 19 23 45 51

7 - OVERALL DISCUSSION AND CONCLUSIONS 

Macroalgae have increased in abundance in Florida springs during the last 50 years, which has 

corresponded with increases in nitrate concentrations associated with human activities. 

Macroalgal abundance in Florida springs is now extensive, with almost all springs having 

macroalgae in them and an average of 50% of spring bottoms being covered by macroalgae. 

Photographic documentation and historical accounts from many long-time observers record the 

increase in algae during the last 30-50 years, which corresponds with measured increases in 

nitrate in springs (Scott et al. 2003). In a review of springs nutrient data collected during the past 

50-70 years, Hand (unpublished data, FDEP) observed that significant increases in nitrate 
47

concentration were observed in 48% of Florida’s springs and they correspond with increases in 

chloride concentration, an indicator of human sources of pollutants. Phosphate concentrations 

decreased in 60% of Florida springs according to Hand’s review; however, they did increase in 

25% of the springs. 

We found that nitrogen concentrations of spring waters were related to the extent and intensity of 

human activities in the region around sampling sites and abundance of one common macroalga, 

Vaucheria. Abundance of Lyngbya wollei, the second of the two most common macroalgae in 

Florida springs, was not related to spring water nutrient concentrations, however it was 

positively related to the human activities around sampling sites and other indicators of nutrient 

supply. 

Water column nutrient concentrations may only tell part of the story about nutrient supply to 

these large macroalgae. The correlation between abundance of Lyngbya wollei and phosphorus 

in sediments both under and throughout the spring reach indicate that either groundwater or 

accumulations of organic matter in sediments are contributing phosphorus that may also support 

growth of Lyngbya wollei (Pinowska et al. 2007a). These focused sources of nutrients may 

become sequestered in algae that cover sediments. Low to moderate levels of groundwater 

nutrient loading could be difficult to detect in simple measurements of surface water chemistry 

because they would be greatly diluted by the relatively large quantities of overlying spring 

waters. The positive relationship between diatom indicators of phosphorus concentration 

indicates that nutrient supply rate was related to abundance of Lyngbya wollei. The source of the 

phosphorus in these sediments should be investigated. Is it transported through the shallow 

groundwater pathways from watershed sources in dissolved organic or inorganic form or as 

calcium phosphate? Does P in sediments arise from mat accumulation and burial? Although the 

correlation between Lyngbya wollei % cover and sediment P in exposed sediments as well as 

under mats suggested that P was coming from the groundwater, exposed sediments may have 

become contaminated by previous cover by mats or shallow flows in the hyporheic zone. 

High biomasses of benthic algae in low nutrient habitats could also be explained by slow 

accumulation of disturbance resistant algae over prolonged periods. This is the likely 

explanation for extensive calcareous algal mats in the oligotrophic waters of the Everglades. The 

algae in the calcareous mats are relatively resistant to disturbance by wind, rain, or grazers. 

Vaucheria and Lynbgya wollei mats accumulate during long periods, usually much longer than a 

month (Sickman et al. 2007). With even slow growth in low nutrient concentrations high 

accumulations could accumulate over time given growth rates measured in our experiments and 

low loss rates. This would explain how sediments could become contaminated even if nutrient 

supply in both ground and surface waters were low. 

Another mechanism for producing large mats of macroalgae in relatively low nutrient 

concentrations is they could become self-sustaining as they accumulate. Nutrient concentrations 

were high within the macroalgal mats (Sickman et al. 2007). This indicates that sequestration 

and production of nutrients is greater than efflux and uptake of nutrients. In addition to uptake of 

dissolved inorganic nutrients by the algae, dissolved and particulate organic matter and 

particulate inorganic matter could get entrained in mats. After decomposition, this material 

could be an important nutrient source for surrounding algae. Research to further explore the 
48

metabolic processes within these macroalgal mats will provide a more complete understanding of 

how such large accumulations of algae can occur even in low nutrient habitats. 

Another potential source of nitrogen to Lyngbya wollei is nitrogen fixation (conversion of 

atmospheric N2 to the bioavailable NH3). Lyngbya wollei has the gene and the capability to 

convert the unlimited supply of N2 into a bioavailable form (Phlips et al. 1992). But response of 

Lyngbya wollei growth to nitrate in experiments indicates that its growth is not saturated by 

atmospheric sources of N2. Thus nitrate reductions in springs could be effective in reducing both 

Lyngbya wollei and Vaucheria growth if reductions are sufficient. 

Both nitrogen and phosphorus reductions in Florida springs should reduce macroalgal 

accumulation because it will slow growth of macroalgae. Such reductions should be effective as 

long as target concentrations (e.g. nutrient criteria or pollution load reduction goals) are below 

saturating nutrient concentrations. What are saturating concentrations in the springs? That may 

depend on a lot of factors and may be difficult to pinpoint exactly. Our most accurate and 

conservative experimental results, those from microcentrifuge tube experiments, suggest that 

nutrient concentrations less than 0.028 mg PO4-P/L and 0.230 mg NO3-N/L are needed to slow 

growth (i.e. ed90) of Lyngbya wollei, and concentrations less than 0.022 mg PO4-P/L are needed 

to slow growth of Vaucheria spp. The same experiments show that further reductions to as low 

as 0.011 mg PO4-P/L and 0.034 mg NO3-N/L will continue to slow growth (i.e. ed10) of 

Lyngbya wollei and concentrations to as low as 0.006 mg PO4-P/L will continue to slow growth 

of Vaucheria. Unfortunately, Vaucheria spp. did not respond to nitrate enrichments in the 

microcentrifuge tube experiments, but it did in low biomass donut experiments. If we use other 

algae-nutrient combinations to develop ratios for ed90 and ed10 between microcentrifuge and 

low biomass donut experiments, the ratio for ed90s is about 0.25 and the ratio for ed10s is 0.35-

0.46 (Table 6.1, excluding the unusually high ed10 for Lyngbya wollei in the low biomass donut 

nitrate experiments). If we use these ratios to convert responses of Vaucheria growth in low 

biomass donut experiments to comparable benchmarks for nitrate, the ed10 would be 0.017 and 

the ed90 would be 0.225-0.296 (mean 0.261) mg NO3-N/L. 

To convert these benchmarks from microcentrifuge tube experiments to total nitrogen and total 

phosphorus concentrations for nutrient criteria in Florida springs, we need to account for the 

differences in nutrient forms. The benchmarks from the microcentrifuge tubes were in dissolved 

inorganic form. Therefore, if we determine the difference between nitrate and TN concentrations 

or phosphate and TP concentrations in Florida springs, we can calculate factors that could 

convert dissolved nutrient benchmarks to benchmarks in the form of total nutrient 

concentrations. Average TP and TN concentrations in Florida springs were 0.043 and 0.983 

mg/L, respectively, during the 2003 surveys. The average difference between phosphate 

concentration and TP was 0.007 mg P/L (± 0.009 standard deviation) in our 2003 survey results. 

The average difference between nitrate concentration and TN was 0.080 mg N/L (± 0.009 

standard deviation) in our 2003 survey results. These differences represented 16 and 8 % of the 

total TP and TN fractions, respectively. Thus dissolved inorganic fractions of total nutrient 

concentrations are high in Florida springs, probably due to the relatively short distances and 

large discharges that limit accumulation of particulate matter. Note, dissolved inorganic 

fractions were variable among springs, with as little as 6 % and 64 % of phosphorus and nitrogen 

being particulate fractions in some streams. Since ratios of dissolved inorganic to total nutrients 
49

should be relatively consistent within streams, such variations in fractions could be taken into 

account to develop spring-specific nutrient criteria. Given average TN:NOx and TP:SRP ratios 

for Florida springs, the benchmarks for ed10 for Lyngbya wollei are 0.013 mg TP/L and 0.037 

mg TN/L and for ed90 are 0.033 mg TP/L and 0.250 mg TN/L (Table 7.1). Given average 

TN:NOx and TP:SRP ratios for Florida springs, the benchmarks for ed10 for Vaucheria are 0.007 

mg TP/L and 0.018 mg TN/L and for ed90 are 0.026 mg TP/L and 0.284 mg TN/L, respectively 

(Table 7.1). 

Table 7.1. Nutrient parameter benchmarks for phosphate (PO4-P) and nitrate (NO3-N) from 

microcentrifuge tube experiments (MTM Expt) and their conversion to total phosphorus and total 

nitrogen benchmarks for nutrient criteria based on average particulate phosphorus and nitrogen 

fractions in Florida springs. 

Benchmark 

Macroalgae 

Logistic 

Parameter

MTM Expt 

PO4 (mg/L) 

MTM Expt 

NO3 (mg/L)

TP 

(mg/L) 

TN 

(mg/L) 

Lyngbya ed10 0.011 0.034 0.013 0.037 

Vaucheria ed10 0.006 0.017 0.007 0.018 

Lyngbya ed90 0.028 0.230 0.033 0.250 

Vaucheria ed90 0.022 0.261 0.026 0.284 

Current, light, and mat thickness as well as whether nutrients are supplied from groundwater or 

surface water will affect the effectiveness of a target concentration. Water column nutrient 

concentrations, especially expressed as total nitrogen and total phosphorus, are only indicators of 

actual nutrient supplies supporting algal growth. However, the range of saturating nitrate and 

phosphate concentrations from experiments, especially microcentrifuge experiments, provide 

valuable benchmarks for consideration in nutrient load reduction goals. The certainty of 

macroalgal reductions should be greater if nutrient loads are reduced sufficiently that nutrient 

concentrations in spring waters are less than the saturating concentrations indicated in the 

experiments and Table 7.1. Of course the certainty of restoration will increase with greater 

reductions until we reach concentrations that limit algal growth as much as possible. This 

concentration is likely less than even the ed10, given the many sources of nutrients and factors 

affecting nutrient-growth relationships. 

The relationship between the extensive results from experiments and limited results from field 

surveys also provide insight into how results from experiments can be applied in nutrient criteria 

development. The threshold response in Vaucheria biomass at 0.454 mg NO3-N/L and 0.591 mg 

TN/L in the spring survey (Pinowska et al. 2007a) are greater than the benchmarks established 

using the microcentrifuge tube microcosm experiments (Table 7.1). Using the same rationale, 

the ed90s could be assumed to be reasonable benchmarks for first-stage nutrient criteria 

development, i.e. interim goals for long-term nutrient load reductions. However, the ed90 

benchmarks for nitrate are substantially higher than the nitrogen pollution load reduction goals 

proposed by the St. Johns River Water Management District (SJRWMD) for the Wekiva River 

(Mattson et al. 2006), which was 0.156 mg NO3-N/L; but our ed90 benchmarks for phosphate are 

about half of the Wekiva River phosphate goal, 0.047 mg PO4-P/L. The higher phosphate goal 

for the Wekiva River may be due to high natural concentrations of phosphate in the groundwater. 
50

Our phosphorus benchmarks are in the range of the 0.030 mg TP/L benchmark for control of 

Cladophora (Dodds et al. 1997, Stevenson et al. 2006). No detailed studies of nitrogen effects 

on macroalgae have been done to evaluate the nitrogen benchmarks provided in this study and by 

SJRWMD. 

Reducing nutrient loads sufficiently to stop versus slow growth of the macroalgae may be 

unrealistic for many reasons. First, results from our experiments show these algae seem to be 

able to continue to grow, even in very low nutrient concentrations. Second, natural phosphate 

levels in many Florida springs may be too high to stop their growth and controlling nitrate 

sufficiently may be impractical. Evidence indicates these macroalgae likely occurred naturally 

in Florida springs, but not in the excessive abundance as today. Vaucheria is reported in spring 

seeps in areas around the world with very limited human activity. Historic records of Lyngbya 

wollei exist from Silver Spring (Pinowska et al. 2007a). But pictures of these Florida springs 

show no nuisance algal growths prior to the last 20-30 years. 

The continued growth of both Lyngbya wollei and Vaucheria, even when we added no nitrate or 

no phosphate to cultures, is unusual, contradicts theory, and is difficult to explain. We observed 

a relationship between growth and nutrient concentrations that fit in most cases, fit a logistic 

model rather than a Monod model. Our results were similar to the Monod model in that nutrients 

stimulated algal growth within a specific range of nutrient concentrations, and above that range, 

additional increases in nutrients had little effect on growth rates. Unlike predictions of the 

Monod model, both macroalgae were able to sustain low growth rates when no and low 

concentrations of nutrients were added to the culture media. Although it does not seem likely 

that intracellular nutrient stores were sufficient to sustain growth rates, that does seem to be the 

only reasonable explanation. Cells must have reallocated nutrients to support the most essential 

processes to maintain sustained growth with no new nutrient allocations. Perhaps cells allocated 

elemental resources to different processes at different times during the growth cycle to continue 

to produce new cells with lower and lower nitrogen and/or phosphorus concentrations relative to 

carbon. More long-term experiments are needed to explore these processes and the seemingly 

indisputable assumption that algae will not grow if nutrient concentrations are sufficiently low 

for a sufficiently long time. Regardless of these theoretical considerations, the observation that 

Lyngbya wollei and Vaucheria were able to continue to grow in experiments with very low 

nutrient supplies is also indisputable and is another reason that nutrient load reductions can not 

be expected to stop accumulation of these macroalgae. 

Reducing macroalgal growth rates by reducing nitrate and phosphate loading in springs should 

have substantial effects on the frequency, intensity, and duration of nuisance macroalgal 

growths. Slight differences in growth rates produce great differences in how rapidly macroalgae 

accumulate. To illustrate this, a very simple model of algal accumulation was used simulate 

algal accumulation with growth rates ranging from 0.01 to 0.30 g g-1 d-1. If we started with 1.0 

gram of biomass on day 1, we would have very different biomasses after just a month of 

accumulation, which is a short recovery period given recovery and seasonal persistence patterns 

observed in our disturbance experiment and the seasonal monitoring after a flood disturbance. 

Assuming no losses and no other sources of macroalgae (such as algae settling on the bottom), 1, 

20, and 8104 grams of biomass would be present if growth rates were 0.01, 0.10, and 0.30 g g-1

d-1. Reducing macroalgal growth rates from the common rmax near 0.30 to just 0.20 would 
51

reduce biomass accrual over 30 days from 8104 g to 403 g – almost a 20 fold reduction. The 

benefits of nutrient reduction on algal accrual increase with the length of time that algal mats 

accumulate. Thus decreases in nutrient concentrations in Florida springs could greatly reduce 

nuisance macroalgae problems. 

8 – REFERENCES 

Ahern, K.S., J.M. O’Neil, J.W. Udy, and S. Albert. 2006. Effects of iron additions on filament 

growth and productivity of the cyanobacterium Lyngbya majuscula. Marine and 

Freshwater Research 57:167-176. 

Albertin, A., J. O. Sickman, M. Anderson, A. Pinowska, and R. J. Stevenson. 2007a. Stable 

isotope compositions of macroalgae, sediment and nitrate in Florida springs. Florida 

Department of Environmental Protection, Tallahassee, Florida. 

Albertin, A., J. O. Sickman, M. Anderson, A. Pinowska, and R. J. Stevenson. 2007. Macroalgal 

responses to nitrate additions under conditions of low phosphorus in recirculating stream 

channels. Florida Department of Environmental Protection, Tallahassee, Florida. 

APHA. 1998. Standard methods for the evaluation of water and wastewater, 20th edition. 

American Public Health Association, Washington, DC. 

Biggs, B. J. F. 2000. Eutrophication of streams and rivers: dissolved nutrient- chlorophyll 

relationships for benthic algae. Journal of the North American Benthological Society 

19:17-31. 

Bonn, M. A., and F. W. Bell. 2003. Economic impact of selected Florida springs on surrounding 

local areas. http://www.dep.state.fl.us/springs/reports/EconomicImpactStudy.doc, Florida 

Department of Environmental Protection, Tallahassee, Florida. 

Bothwell, M. L. 1989. Phosphorus-limited growth dynamics of lotic periphytic diatom 

communities: areal biomass and cellular growth rate responses. Canadian Journal of 

Fisheries and Aquatic Sciences 46:1293-1301. 

Brown, M. T., and M. B. Vivas. 2005. Landscape development intensity index. Environmental 

Monitoring and Assessment 101:289-309. 

Burkholder, J. M., R. G. Wetzel, and K. L. Klomparens. 1990. Direct comparison of phosphate 

uptake by adnate and loosely attached microalgae within an intact biofilm matrix. 

Applied and Environmental Microbiology 56:2882-2890. 

Camacho, F.A. and R.W. Thacker. 2006. Amphipod herbivory on the freshwater cyanobacterium 

Lyngbya wollei: chemical stimulants and morphological defenses. Limnology and 

Oceanography 51: 1870–1875. 

Carlton, R., and R. G. Wetzel. 1988. Phosphorus flux in lake sediments: effect of epipelic algal 

oxygen production. Limnology and Oceanography 33:562-570. 

Carpenter, S. R., N. F. Caraco, D. L. Correll, R. W. Howarth, A. N. Sharpley, and V. H. Smith. 

1998. Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecological 

Applications 8:559-568. 

Carmichael, W.W., W.R. Evans, Q.Q. Yin, P. Bell, and E. Moczyydlowski. 1997. Evidence for 

paralytic shellfish poisons in the freshwater cyanobacterium Lyngbya wollei (Farlow ex 

Gomont) comb. nov. Applied Environmental Microbiology 63:3104–3110. 

Dodds, W. K., V. H. Smith, and B. Zander. 1997. Developing nutrient targets to control benthic 

chlorophyll levels in streams: A case study of the Clark Fork River. Water Research 

31:1738-1750. 
52

Dodds, W. K., and E. B. Welch. 2000. Establishing Nutrient Criteria in Streams. Journal North 

American Benthological Society 19:186-196. 

Dudley, T. L., S. D. Cooper, and N. Hemphill. 1986. Effects of macroalgae on a stream 

invertebrate community. Journal of the North American Benthological Society 5:93-106. 

Florida Springs Task Force. 2000. Florida's springs: strategies for protection & restoration. 

http://www.dep.state.fl.us/springs/reports/index.htm, Florida Springs Report., Florida 

Department of Environmental Protection, Tallahassee, Florida. 

Francoeur, S. N. 2001. Meta-analysis of lotic nutrient amendment experiments: detecting and 

quantifying subtle responses. Journal of North American Benthological Society 20:358-

368. 

Gaiser, E. E., D. L. Childers, R. D. Jones, J. H. Richards, and L. J. Scinto. 2006. Periphyton 

responses to eutrophication in the Florida Everglades: cross-system patterns of strucural 

and composition changes. Limnology & Oceanography 51:617-630. 

Hunt, R. 1990. Basic growth analysis. Unvwin Hyman Ltd., London. 

Johanson, L. R. 2005. Phylum Xanthophyta (yellow-green algae). Pages 245-270 in D. M. John, 

B. A. Whitton, and A. J. Brook, editors. The freshwater algal flora of the British Isles. 

Cambridge University Press, Cambridge. 

Joyner, A. J. J. 2004. Molecular techniques applied to environmental samples of the freshwater 

nitrogen-fixing cyanobacterium, Lyngbya wollei. In: ASLO Summer Meeting Abstracts 

Kendall, C. 1998. Tracing Nitrogen Sources and Cycling in Catchments. Pages 519-576 in C. 

Kendall, and J. J. McDonnell editors. Isotope Tracers in Catchment Hydrology. Elsevier, 

Lausanne. 

Komárek, J., and K. Anagnostidis. 2005. Cyanoprokaryota. 2. Teil: Oscillatoriales. Gustav 

Fischer, Jena. 

Kotai, J. 1972. Instructions for preparation of modified nutrient solution Z8 for algae. Norwegian 

Institute for Water Research, publication B-11/69., Blindern, Oslo. 

Ksoll, W. B., S. Ishii, M. J. Sadowsky, and R. E. Hicks. 2007. Presence and sources of fecal 

coliform bacteria in epilithic periphyton communities of Lake Superior. Applied and 

Environmental Microbiology 73:3771-3778. 

Lembi, C. A. 2003. Control of nuisance algae. Pages 805-834 in J. D. Wehr and R. G. Sheath, 

editors. Freshwater Algae of North America. Academic Press, New York, NY. 

Manoylov, K. M., and R. J. Stevenson. 2006. Density-dependent algal growth along N and P 

nutrient gradients in artificial streams. In:. Pages 333-352 in N. Ognjanova-Rumenova 

and K. Manoylov, editors. Advances in Phycological Studies. Pensoft Publishers., 

Moscow, Russia. 

Martin, J., K. Hartl, D. Reide Corbett, and P. Swarzenski. 2003. A multi-level pore-water 

sampler for permeable sediments. Journal of Sedimentary Research 73:128-132. 

Mattson, R. A., E. F. Lowe, C. L. Lippincott, D. Jian, and L. Battoe. 2006. Wekiva River and 

Rock Springs Run Pollution Load Reduction Goals. St. Johns River Water Management 

District, Palatka, Florida. 

McCormick, P. V. and M. B. Odell. 1996. Quantifying periphyton responses to phosphorus in 

the Florida Everglades: A synoptic-experimental approach. Journal of the North 

American Benthological Society 15:450-468. 

McGlathery, K.J., D. Krause-Jensen, D. Rysgaard, P.B. Christensen. 1997. Patterns of NH+ 

uptake within dense mats of the filamentous macroalga Chaetomorpha linum. Aquatic 

Botany 59:99-115. 

Meinesz, A. 1999. Killer Algae. The University of Chicago Press, Chicago, Illinois. 
53

Onodera, H., M. Satake, Y. Oshima, T. Yasumoto, W.W. Carmichael. 1997. New saxitoxin 

analogues from the filamentous cyanobacterium Lyngbya wollei. Natural Toxins 5: 146–

151. 

Peterson, C. G. 1996. Response of benthic algal communities to natural physical disturbance. 

Pages 375-402 in R. J. Stevenson, M. L. Bothwell, and R. L. Lowe, editors. Algal 

ecology: freshwater benthic ecosystems. Academic Press, San Diego, California. 

Phlips, E. J., J. Ihnat, and M. Conroy. 1992. Nitrogen fixation by the benthic freshwater 

cyanobacterium Lyngbya wollei. Hydrobiologia 234:59-64. 

Pinowska, A., R. J. Stevenson, J. O. Sickman, A. Albertin, and M. Anderson. 2007a. Integrated 

interpretation of survey for determining nutrient thresholds for macroalgae in Florida 

Springs: Macroalgal relationships to water, sediment and macroalgae nutrients, diatom 

indicators and land use. Florida Department of Environmental Protection, Tallahassee, 

Florida. 

Pinowska, A., R. J. Stevenson, J. O. Sickman, A. Albertin, and M. Anderson. 2007b. Integrated 

interpretation of survey and experimental approaches for determining nutrient thresholds 

for macroalgae in Florida Springs: Laboratory experiments and disturbance study. Florida 

Department of Environmental Protection, Tallahassee, Florida. 

Potapova, M. G., D. F. Charles, K. C. Ponader, and D. M. Winter. 2004. Quatifying species 

indicator values for trophic diatom indices: a comparison of approaches. Hydrobiologia 

517:25-41. 

Quinlan, E. L., E. J. Phlips, K. A. Donnelly, C. H. Jett, P. Sleszynski, and S. Keller. 2007. 

Estimating Primary Producer Biomass in Silver Springs, Florida, USA. Journal of 

Applied Ecology. 

R Development Core Team. 2006. R: a language and environment for statistical computing. in, 

Vienna, Austria. 

Rier, S. T., and R. J. Stevenson. 2006. Response of periphytic algae to gradients in nitrogen and 

phosphorus in streamside mesocosms. Hydrobiologia 561:131-147. 

Ritz, C., and J.C. Streibig. 2005. Bioassay analysis using R. Journal of Statistical Software 12:1-

22. 

SAS. 2000. SAS/STAT. in. SAS Institute, Cary, N.C. 

Scott, T. M., G. H. Means, R. P. Meegan, R. C. Means, S. B. Upchurch, R. E. Copeland, J. Jones, 

T. Roberts, and A. Willet. 2004. Springs of Florida. Florida Geological Survey, 

Tallahassee, Florida. 

Sickman, J. O., M. Anderson, A. Albertin, A. Pinowska, and R. J. Stevenson. 2007. Estimation 

of Autochthonous Sources of C, N, and P in Florida Springs. Florida Department of 

Environmental Protection, Tallahassee, Florida. 

Starmach, K. 1972. Chlorophyta III. Zielenice nitkowate: Ulotrhichales, Ulvales, Prasiolales, 

Shaeropleales, Cladophorales, Chaetophorales, Trentepohliales, Siphonales, 

Dichotomosiphonales. Panstwowe Wydawnictwo Naukowe, Kraków. 

Stevenson, R. J. 1984. How currents on different sides of substrates in streams affect 

mechanisms of benthic algal accumulation. Internationale Revue gesamten 

Hydrobiologie 69:241-262. 

Stevenson, R. J. 1996. The stimulation and drag of current. Pages pp. 321-340 in R. J. Stevenson, 

M. Bothwell, and R. L. Lowe, editors. Algal Ecology: Freshwater Benthic Ecosystems. 

Academic Press, San Diego, CA. 
54

Stevenson, R. J. 1997. Resource thresholds and stream ecosystem sustainability. Journal of the 

North American Benthological Society 16:410-424. 

Stevenson, R. J., and L. L. Bahls. 1999. Periphyton protocols. Pages 6-1 through 6-22 in M. T. 

Barbour, J. Gerritsen, and B. D. Snyder, editors. Bioassessment Protocols for Use in 

Wadeable Streams and Rivers: Periphyton, Benthic Macroinvertebrates, and Fish, Second 

Edition. U. S. Environmental Protection Agency, Washington, D.C. 

Stevenson, R. J., M. L. Bothwell, and R. L. Lowe, editors. 1996. Algal Ecology: Freshwater 

Benthic Ecosystems. Academic Press, San Diego. 

Stevenson, R. J., and R. Glover. 1993. Effects of algal density and current on ion transport 

through periphyton communities. Limnology & Oceanography 38:1276-1281. 

Stevenson, R. J. 1996. The stimulation and drag of current. Pages pp. 321-340 in R. J. Stevenson, 

M. Bothwell, and R. L. Lowe, editors. Algal Ecology: Freshwater Benthic Ecosystems. 

Academic Press, San Diego, CA. 

Stevenson, R. J., S. T. Rier, C. M. Riseng, R. E. Schultz, and M. J. Wiley. 2006. Comparing 

effects of nutrients on algal biomass in streams in two regions with different disturbance 

regimes and with applications for developing nutrient criteria. Hydrobiologia 561:140-

165. 

Stevenson, R. J., and Y. Pan. 1995. Are evolutionary tradeoffs evident in responses of benthic 

diatoms to nutrients? Pages 71-81 in D. Marino, editor. Proceedings of the 13th 

International Diatom Symposium 1994. 

Stevenson, R. J., Y. Pan, and P. Vaithiyanathan. 2002. Ecological assessment and indicator 

development in wetlands: the case of algae in the Everglades, USA. Verhandlungen 

Internationale Vereinigung für Theoretische und Andgewandte Limnologie 28:1248-

1252. 

Stevenson, R. J., and A. Pinowska. 2007. Diatom indicators of ecological conditions in Florida 

springs. Florida Department of Environmental Protection, Tallahassee, Florida. 

Stevenson, R. J., A. Pinowska, and Y.-K. Wang. 2004. Ecological condiiton of algae and 

nutrients in Florida springs. Florida Department of Environmental Protection, 

Tallahassee, FL. 

Stevenson, R. J., S. T. Rier, C. M. Riseng, R. E. Schultz, and M. J. Wiley. 2006. Comparing 

effects of nutrients on algal biomass in streams in two regions with different disturbance 

regimes and with applications for developing nutrient criteria. Hydrobiologia 561:140-

165. 

Stimson, J. and S.T. Larned. 2000. Nitrogen efflux from the sediments of a subtropical bay and 

the potential contribution to macroalgal nutrient requirements. Journal of Experimental 

Marine Biology and Ecology 252:159-180. 

Sundbäck, K., A. Miles, S. Hulth, L. Pihl, P. Engström, E. Selander, A. Svenson. 2003. 

Importance of benthic nutrient regeneration during initiation of macroalgal blooms in 

shallow bays. Marine Ecology Progress Series 246:115-126. 

Trimmer, M., D.B. Nedwell, D.B. Sivyer, and S.J. Malcolm. 2000. Seasonal organic 

mineralisation and denitrification in intertidal sediments and their relationship to the 

abundance of Enteromorpha sp. and Ulva sp. Marine Ecology Progress Series 203:67-80. 

USEPA. 2000. Nutrient criteria technical guidance manual: rivers and streams. EPA-822-B-00-

002, United States Environmental Protection Agency, Washington, D.C. 

Watkinson. A., O’Neil, J.M., Dennison, W.C. 2005. Ecophysiology of the marine 

cyanobacterium Lyngbya majuscula (Osciallatoriacea). Harmful Algae 4:697-715. 
55

White, D. S., and S. P. Hendricks. 2000. Lotic macrophytes and surface-subsurface exchange 

processes. Pages 363-379 in J. J. Jones and P. J. Mulholland, editors. Streams and 

Ground Waters. Academic Press. 

Yin, Q. Q., W. W. Carmichael, and W. R. Evans. 1997. Factors influencing growth and toxin 

production by cultures of the freshwater cyanobacterium Lyngbya wollei Farlow ex 

Gomont. Journal of Applied Phycology 9:55-63. 
Appendix 1. Study sites, spring names and site codes, sampling dates, and latitude and longitude of transect 1 for 

Spring Spring code Site name Site code Boil Latitude Longitude Date sampled spring 2003 Date 

fall 2

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

Alexander ALE Downstream ALE-02 No 29.08231 81.57754 3/28/2003 10/29

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

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

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

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

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

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

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

Morten Spring GAI-03 Yes 30.42875 85.54649 

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

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

Head ICH-01 Yes 29.98408 82.76184 3/16/2003 11/22Blue Hole ICH-02 Yes 29.98068 82.75866 4/20/2003 11/22Below Blue Hole ICH-03 No 29.98007 82.75895 3/17/2003 11/22Mission spring ICH-04 Yes 29.97628 82.75783 4/23/2003 11/9/Devils Ear ICH-05 Yes 29.97388 82.75996 4/23/2003 11/9/Mill Pond ICH-06 Yes 29.96658 82.76005 4/23/2003 11/9/Before bridge ICH-07 No 29.95495 82.78507 4/24/2003 11/8/

Ichetucknee ICH 

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

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

Head JAC-01 Yes 30.79037 85.13998 4/19/2003 9/24/Boat ramp JAC-02 No 30.78249 85.16022 4/19/2003 9/23/Arrowhead camp ground JAC-03 No 30.75609 85.18680 4/18/2003 9/23/

Jackson Blue JAC 

Rock cliff JAC-04 Yes 30.79023 85.14290 

Juniper JUN Head JUN-01 Yes 29.18365 81.71201 3/27/2003 10/3

Fern Hammock JUN-02 Yes 29.18364 81.70801 3/27/2003 10/3
Spring Spring code Site name Site code Boil Latitude Longitude Date sampled spring 2003 Date 

fall 2

River fork JUN-03 No 29.18519 81.70726 3/27/2003 10/3

After bridge on route 19 JUN-04 No 29.21283 81.65431 3/26/2003 10/3

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

Little River LTR Head LTR-01 Yes 29.99642 82.96675 9/28/Madison Blue MAD Head MAD-01 Yes 30.48056 83.24439 9/29/Manatee MNT Head MNT-01 Yes 29.48952 82.97692 10/3/Pitt PIT Head PIT-01 Yes 30.43288 85.54616 Ponce de Leon PON Head PON-01 Yes 30.72090 85.93071 4/26/2003 9/22/

Head RAI-01 Yes 29.10223 82.43741 4/4/2003 11/12KP Hole RAI-02 Yes 29.09294 82.42848 4/4/2003 11/12Before tubers sign RAI-03 No 29.06305 82.42788 4/4/2003 11/12

Before bridge RAI-04 No 29.05223 82.44700 4/4/2003 11/12 RAI-05 No 29.09275 82.43133  RAI-06 No 29.07650 82.4276 

Rainbow Spring RAI 

After bridge RAI-07 No 29.05407 82.44717 Head SGL-01 Yes 29.24603 81.64345 3/26/2003 10/30

Natural Well SGL-02 Yes 29.24583 81.64385 

Silver Glen SGL Trial in the 

woods SGL-03 Yes 29.24400 81.6463 

Head SLV-01 Yes 29.21619 82.05252 4/3/2003 11/13Second pool SLV-02 Yes 29.21584 82.04987 4/3/2003 11/13Birds of prey SLV-03 No 29.21561 82.04112 4/3/2003 11/13Old swimming area 

SLV-04 No 29.20500 82.02902 4/3/2003 11/13

Silver River SLV 

Cabbage palm SLV-05 No 29.20211 82.01127 4/3/2003 11/12

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

Turtle TUT Head TUT-01 Yes 29.84742 82.89041 9/30/
Spring Spring code Site name Site code Boil Latitude Longitude Date sampled spring 2003 Date 

fall 2

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

Volusia Blue VOL Downstream 

from stairs VOL-02 No 28.94679 81.33921 3/29/2003 11/20

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

Wacissa WAC Minnow WAC-02 No 30.33020 83.98776 4/7/2003 9/27/

Big Blue WAC-03 Yes 30.32770 83.98484 4/7/2003 9/27/Head WAK-01 Yes 30.23533 84.30287 4/8/2003 10/1/Turnaround WAK-02 No 30.23318 84.28870 4/8/2003 10/1/Bird colony WAK-03 No 30.22507 84.27470 4/8/2003 10/1/ WAK-04 No 30.23650 84.29831  WAK-05 No 30.23439 84.29505  WAK-06 No 30.22836 84.28001 

Wakulla WAK 

Upstream from bridge on 98 WAK-08 No 30.18037 84.24817 

Washington WGT-01 Yes 30.45279 85.53044 9/26/Blue WGT Head WGT-02 Yes 

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

Boat dock WEK-02 No 28.51901 82.57361 3/24/2003 11/5/WMA WEK-03 No 28.52481 82.59583 3/24/2003 11/4/

Weeki Wachee WEK 

Roger's Park WEK-04 No 28.53057 82.62407 3/24/2003 11/5/Head WKW- 01 No 28.71193 81.46037 3/30/2004 11/2

Wekiwa WKW 

Canoe launch WKW- 02 No 28.71269 82.45948 3/30/2004 11/2

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