# 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. Sickman2 

### 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 517-432-27893 ACKNOWLEDGEMENTS

This research was supported by the Florida Depa rtment of Environmen tal Protection. Russel Frydenborg oversaw the project for FDEP and provi ded invaluable information and logistical support for the project.

Denise Miller supervised the budget process for FD EP and facilitated completion of the project. Randy Sheets and Sharon Nichols managed the b udget process at Michig an State University (MSU). Kelly Lewis and Cheryl Stephens mana ged 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, Ju lianne Heinlein, Vanessa Lougheed, Scott L. Rollins, Lara Panayotoff, Kalina Manoylov, Colleen McLean, Daniel Wieserich, Elizabeth Mullen, Kevin Geyer, Amy Messick, Nicole Hedquist, Ja n Seidler, and Scott Fullbright.

Many students and staff at the Univ ersity of Florida also assisted with the project: Mi Youn Ahn, Delores Lucero, Francisco (Chico) Alvarado, Er ic Ostmark, Martin Sandquist, Alyson Dagang, Solomon Haile, Yu Wang, Gavin Wilson, Yubao Cao and Xiaowei Gu. Marty Anderson, a coauthor on other reports, pr ovided field and GIS support.

Silver River State Park in Flor ida graciously provided housing during the experimental streams study. We especially want to thank Robert LaM ont, Steven Logan and a crew at Silver River State Park for their help in setting up experime nts 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 thei r assistance in this study.

This report, as the other reports submitted for th is 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 NUT RIENTS 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 AB OVE, 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 M ACROALGAE, 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 515 EXECUTIVE SUMMARY

1. Surveys of Florida springs indicated that almo st all springs had macroscopic algae growing in them, an average of 50% of the spring bot toms 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 comm on 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 thres hold increase at 0.59 mg total nitrogen/L. 4. Abundance of Lyngbya wollei in surveys of springs was not re lated well to either nitrogen or phosphorus concentrations in spring water, howev er it was related to sediment phosphorus concentrations and indices of human ac tivities within 1000 m of sampling sites. 5. Interstitial waters of macroalgal mats had higher nutrient concentrations than overlying waters, indicating entrained particulate matte r, long term growth and decomposition, or groundwater may supply nutrients to supplement water column supplies. 6. Seasonal variation in macroalgae was largely re lated 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 concentr ations in Florida springs could regulate growth rates of both Vaucheria spp. and Lyngbya wollei

. 9.2. algae grew in almost all nu trient treatments, even at ve ry low nutrient concentrations, and showed a logistic respons e 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 th an 10% below 0.011 mg PO 4 -P/L, increased substantially from 0.011-0.028 mg PO 4 -P/L, and did not respond more than 10% to further phosphate concentr ation increases above 0.028 mg PO 4 -P/L (in microcentrifuge microcosms that provided the most accurate control of nutrient concentrations); 9.4 when nitrate concentrations were manipul ated and phosphate was maintained in luxury supply, growth rates of Lyngbya wollei did not respond more than 10% below 0.034 mg NO 3 -N/L, increased substant ially from 0.034-0.230 mg NO 3 -N/L, and did not respond more than 10% to further nitrate co ncentration increas es above 0.230 mg NO 3 -N/L (in microcentrifuge microcosms that provided the most accurate control of nutrient concentrations); 9.5 growth of Lyngbya wollei increased with nitrogen con centrations, even if phosphorus concentrations were very low; 9.6 when nitrate concentrations were manipulat ed and phosphate was maintained in very low supply, growth rates of Lyngbya wollei did not respond more than 10% below 0.015 mg NO 3 -N/L, increased substant ially from 0.015-0.110 mg NO 3 -N/L, and did not respond more than 10% to further nitrate co ncentration increas es above 0.100 mg NO 3 -N/L (in6 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 PO 4 -P/L, increased substantially from 0.006-0.022 mg PO 4 -P/L, and did not respond more than 10% to further phosphate concentrati on increases above 0.022 mg PO 4 -P/L (in microcentrifuge microcosms that provided the most accurate control of nutrient concentrations); 9.8 when nitrate concentrations were manipul ated and phosphate was maintained in luxury supply, growth rates of Vaucheria did not respond more than 10% below 0.069 mg NO 3 - N/L, increased substantially from 0.069-0.644 mg NO 3 -N/L, and did not respond more than 10% to further nitrate concen tration increases above 0.644 mg NO 3 -N/L, but this is probably an overestimate of regulating nitrat e concentrations becau se it was determined in raceway microcosms in which substantial nu trient depletion occurr ed and could not be accounted for; and 9.9 fastest algal growth rates in high nutrient co ncentrations 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 reduc tions 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 th e only practical restor ation strategy because natural phosphorus concentrations may be higher than the concentr ations 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 quali ty of lakes, rivers, and coastal waters. Great accumulations of macroscopic algae on th e 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. Bi ofilms of algae fowl the bottoms of ships and many underwater surfaces. Excessive accumulations of phytoplan kton and benthic macroalgae can reduce oxygen concentrations and produce toxins that reduce both human and eco logical health. These nutrient problems are so extensive that the USEPA has es tablished a program with guidance manuals for nutrient regulations for all waterbodies th roughout the United States (USEPA 2000).

In many cases these proliferations of phytoplankt on and benthic macroalgae have been related to nutrients added to waters by hum an 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 indi rectly via groundwater (Carpenter et al. 1998). Nutrie nts (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 an d 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 growth s 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 macr oalgae in the springs of Florida and their likely relations to human activities.

Florida springs are highly valued resources for their aesthetic a nd recreational value as well as their support of biodiversity (B onn and Bell 2003). Geologists estim ate 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 vi sit these springs each year for swimming and boating because of their clear waters. Marjory Stoneman Douglas, a re vered author and enviro nmentalist in Florida, observed that “Springs are bowls of liquid light.” Thousands use water from the springs for drinking. The network of waterways through ca ves and surface channels also support a unique biota, including the manatee.

Environmental problems in the Florida springs rais ed sufficient concern that David Struhs, then Secretary of the Florida Departme nt of Environmental Protection, called for the organization of a multi-agency group, the Florida Springs Task For ce. The Florida Springs Task Force (2000) identified known threats to the sp rings 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 accumu lations, and microbial contamination from human and animal wastes. Strategies for prot ecting and restoring Flor ida springs were grouped into five categories: outreach, information, ma nagement, regulation, and funding. The increased funding resulting from Florida Springs Task Forc e (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 reduction8 strategies and regulations that will reduce risk of problems fr om excessive accumulations of benthic macroalgae in springs.

Benthic macroalgae cause many problems relate d 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 occupi ed by other organisms and can re duce 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 prov ide habitat and nutrition to bacteria that may include pathogens (Ksoll et al . 2007). Either thro ugh contact while swimming or on shorelines, human health risks are an issue. Macroalgae pr oliferations 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 e nough 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 at tach directly to a bottom substratum or become entangled with it. As they grow, they cover gr eater portions of the botto m 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 disturban ce of algae, herbivory, and death due to aging or even disease cause loss es 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, ma rine habitats are most commonly limited by nitrogen and freshwater ecosystems are usually limited by phosphorus. However, reviews of nutrient-enrichment experiments in st reams showed that alg ae in about 30-35% of streams studied were nutrient limited and of thos e, 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 gr owth of algae in a stream can be co-limited by N and P, and still be consistent with the single nutrient lim itation hypothesis (Leibig’s Law of the Minimum), is that many species of algae oc cur in a stream and some are limited by N and others by P.

The overarching goal of our rese arch was to determine the relationship between macroalgal proliferations and nutrients in Fl orida springs in a way that manage rs could assess the benefits of different nutrient regulations. Hi storically, nitrate concentrati ons have increased greatly in springs at the same time that population density in sourcesheds has increased. During the same9 period, macroalgae have increased in the springs (Figure 1.1). We de veloped a research plan that utilized both survey and experimental methods fo llowing the model of resear ch in the Everglades (McCormick and Odell 1996, Stevenson et al. 2002) and many recommendations from USEPA nutrient criteria guidel ines (USEPA 2000). The surveys of macroalgae and nutrients in springs were designed to show the threshold in nitroge n or phosphorus concentra tions associated with proliferations of macroalgae. The experiments were intended to confirm the cause-effect relationships between nitrate and pho sphate 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 prolifera tions 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 co nducted during the la st four years.

Our results show great abundances of macroalg ae in some spring runs. They also show sufficient reduction of either phosphorus or nitr ogen could reduce risk of nuisance levels of macroalgae in Florida springs. Na tural 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 macroalg ae 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

195410 growth, but current levels of phosphorus in many springs where we have macroalgal proliferations are not much higher than natura l concentrations. Seve ral reasons explain how thick mats of macroalgae can accumulate in spri ng runs with low nutrient concentrations in the water column. These macroalgae may entrain nut rients from the water column and sediment sources that sustain slow grow th. Without disturbance by physi cal 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. Tw o of these findings contradict prevailing wisdom about relationships between algae and nutrients.

This report is organized by the different studie s that we used to understand the macroalgal accumulations in Florida springs which are rela ted to specific hypothe ses 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 macr oalgae in two springs and nutrient fluxes in macroalgal mats; finally we describe the experi ments that we conducted on macroalgal recovery after disturbance and the many nutrient experiments manipulati ng 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 as pects 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 Flor ida 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 us e 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 bette r understand algal metabolic pro cesses. Sickman et al. (2007) described the seasonal variation in macroalgae and nutrient conditi ons within macroalgal mats. Pinowska et al. (2007b) and Albe rtin 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 betw een the nutrient concentrations in a habitat, the response of the ecosystem to observed c oncentrations, and linking these observations to human activities in sourcesheds. In the survey s of macroalgae and nutrients that we conducted, we hypothesized thresholds in macroalgal accumula tion at specific nutrient concentrations that released macroalgal accumulation from constrai nts by nutrient concentrations. These responses often appear as thresholds, because once accu mulation is unconstrained by resources or herbivory, macroalgae can accumulate either slow ly or rapidly to a peak biomass that is constrained by other pro cesses (Stevenson 1997).11 Peak biomass, i.e. the maximum biomass that ca n accumulate in a habitat, can be regulated by current and nutrient availability within the mat (Stevenson 1996). Previous work indicates nutrient supply within benthic al gal mats is positively related to to current velocity, because nutrient uptake rates by algae can ex ceed the rate at which nutrient-depleted waters within mats are replaced (Stevenson and Gl over 1993). As benthic algae a ccumulate more thickly on a substratum, current velocity, nutr ient supply, waste removal, and light for algae at the bottom of the mat decrease and cause these algae to senesc e (Peterson 1996). As algae at the mat bottom senesce and decompose, the bond between algae a nd substratum weakens and increases the risk of current washing loosened algal mats downs tream. Current positively affects nutrient supply, but it also creates drag on mats that can remove them from substr ata and wash them downstream. Thus, peak biomass of algae is typically obs erved 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 con centrations observed in water bodies, such as springs, that support inte nded 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 r ecommended by USEPA (2000) for developing and assessing complian ce with nutrient criteria. The USEPA also suggests a weight of evidence approach in which response variable s are also used to dete rmine whether goals of nutrient criteria have been met. The results of experiments provide valuable information about the relationships between algal growth and other measurable pro cesses and the nutrient concentrations that are manipul ated, but experimental systems usually do not include all the complexities of ecological processes affecting nut rient concentrations in natural water bodies. These considerations have been taken into accoun t in the conclusions where we have integrated the results of our work in experiment al settings and the spring surveys.

In this section of the re port we describe the results of survey s of macroalgal biomass plus several indicators of nutrient concentrations at as ma ny as 60 spring sites in 29 first magnitude springs (i.e. springs with greater than 100 cfs, Scott et al. 2003). Survey s were conducted to identify the kinds of macroalgae in Florid a springs, the percentage of the spring bottoms covered by macroalgae, the thickness of the macroalgal ma ts, and nutrient concentrations and sources of nutrients in the mats. Land use in sourcesheds and riparian zones was related to nutrient concentrations to distinguish an thropogenic from natu ral sources of nutrien ts in springs. A variety of statistical methods were used to relate percent cover and thickn ess 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 ma gnitude 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 sample d during spring 2003 because of flooding in the Suwannee basin. During fall 2003 60 sites within 28 springs were sampled. Sixty-three sites12 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 modifi ed during winter 2006 beca use of financial and resource constraints such that macroalgal a ssessments 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, a nd 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 samp ling or if sites had not been assessed before (see Pinowska et al. 2007a).

Landuse was provided by FDEP for 100 m a nd 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 tracki ng tool along the river run in Mi crosoft 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 analys es (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 charac terize biological cond ition and infer nutrient conditions with diatom indicato rs. Microalgal samples were collected from plants (epiphytes) and sediments using quantitative methods to enab le 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 (A PHA 1998). AFDM was determined after drying and ashing using standard methods (APHA 1998). Non-diat om 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 Stevens on et al. (2004) and Pino wska 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. Wate r clarity was measured using a LICOR LI-250. Water samples were collected for analysis of alkalinity, ammonia nitrogen, calcium, chloride, iron, magnesium, Kjeldahl nitroge n, 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 dete ction limit of the laboratory method, the method detection limit was reported. Algal growth pot ential and limiting nutr ient bioassays were conducted by FDEP for 15 sites in th e spring and 12 sites in the fall.

Isotopic analyses of water and algal tissues fr om springs were conducted to (1) assess nitrate sources to spring water using the du al-isotopic analysis of nitrate ( δ 15 N-NO 3 and δ 18 O-NO 3 ), (2) determine the δ 15 N and δ 13 C composition of macroalgae and sp ring sediments through one-time surveys at a regional scale and along four spri ng river runs, and (3) as sess the relationships between the δ 15 N and δ 13 C of macroalgae and indicators of nut rient availability and sources at the regional scale. The dual isotope analysis of nitrate in water ( δ 15 N-NO 3 and δ 18 O-NO 3 ) was14 used to further differentiate sources of nitrate when δ 15 N ranges overlap (Kendall, 1998), such as NO 3 fertilizer from soil nitrogen and NH 4 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 rela ted to nutrient concentrations to determine likely contributions by natural and human sources. Latitude and longitude were used as simple variables to characterize region. Stepwise forw ard multiple regression analysis was used to evaluate covariation among vari ables and thereby determine wh ether relationships could be explained or masked by other factors.

Table 2.1. Dependent and indepe ndent variables in correla tions relating macroalgae to environmental characteristics in Florida springs . Sediment chemistry was determined for sediments under mats and in open areas of springs. Independent Variables Dependent 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 ( μ g/g) Latitude

Avg Vaucheria % cover (dominant) Total Nitrogen Available N ( μ g/g) Riparian Trees

Avg Vaucheria % cover (presence) Temperature Available NH4 ( μ g/g) Riparian Shrubs

Avg Vaucheria mat thickness DO Available NO3 ( μ g/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 macroa lgae 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 has15 significant effects on algal specie s composition (Table 2.1). Alga l biomass was characterized by the % cover of the bottom and by thickness. Av erage 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 pr esent. Therefore one set of statistics were run relating environmental para meters 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 domin ant alga at the site.

In addition to measured water chemistry paramete rs, nutrient concentratio ns were inferred using diatom indicators of nutrient conditions. Diat om indicators of nutri ent concentrations are important, because they can more precisely and accu rately measure nutrient availability than one time sampling of nutrient concentrations (Steve nson 2006). In this stud y, 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 nutrien t concentrations in the daytime water column. Different species of diatom grow faster in low and hi gh nutrients (Manoylov and Stevenson 2006). The nutrient c oncentrations in which diatom species have highest relative abundances can be characterized and used as tra its for each species that indicate the so-called “optimum” nutrient concentration fo r 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 Fl orida 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 thes e results can be found in a report to FDEP by Stevenson and Pinowska (2007).

Correlations were calculated betwee n all measures of algal biomass and all measures of nutrient concentrations resulting from la boratory analysis of water sample s and diatom inference models. In addition, correlations were cal culated 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 signi ficant were graphed and analyzed with quantile regression and regression tree an alysis to further explore fact ors 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 conf ounding factors constrain abundance of algae in subsets of springs with specific environmental conditions. Regression tr ee analysis evaluated non-linear relationships and helped identify algal response threshol ds 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 nut rients. Nitrogen co ncentrations (NO x 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 acco unted for with multiple regression, both TN and NO x 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 regre ssion indicated that the correlation was confounded with regional variati on. 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 corre lated 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 af ter variation in SRP related to latitude and longitude was accounted for using multiple regr ession analysis; then SRP was positively, but poorly correlated to land use (p=0.168).

Table 2.2. Correlations between nutrient concentrati ons, land use metrics, la titude, and longitude. Nutrients include (ammonia, nitrate-nitrate, to tal dissolved nitrogen, to tal nitrogen, soluble reactive phosphorus, tota l dissolved phosphorus, and total pho sphorus). 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 percen t watershed disturbed within 100 m and 1000 m of sampling sites. LDI_100m and LDI_1000 m 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 ra tios and land use to he lp distinguish sources of nutrients (Albertin et al . 2007a). However, the cons istent relationship between δ 18 O-NO 3 and δ 15 N-NO 3 indicated that fertilizer was the prim arly source of nitrogen in springs and denitrification of the fertilizer explained variation among springs. Correlations between δ 15 N or δ 13 C and either diatom indicators of nutrients, pH, or nitrogen c oncentrations in springs were hypothesized to be caused by complex relations hips between N and P supply rates, water hardness, and P transport via CaPO 4 . 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 al gae: 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 structur es were not common. Lyngbya wollei and Vaucheria spp. were found in many more springs than the other mat-forming macroa lgae 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 concen trations (Table 2.3). Average mat thickness on spring bottoms by all m acroalgae ranged from near zero to 0.25 m and also was not related to TN, TP, or soluble nu trient 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 macroa lgae abundance and water chemistry (p<0.05 in bold, Pinowska et al. 2007a).

Macroalgae abundance Ammonia Kjeldahl 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 spring18 water (Figures 2.3). Non-linear models of Vaucheria % cover and thickness along the TN and nitrate gradients explained substantially more vari ation than a linear model, with thresholds in Vaucheria response at 0.454 and 0.591 mg N/L as nitr ate 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 macr oalgae and N concentrations in spring water, laboratory bioassays using water from the springs indicated that algae we re limited by P at 56% of spring sites, by N at 19%, and by both N and P at 22%.

The percent cover a nd mat thickness of Lyngbya wollei and Vaucheria were positively correlated to cellular N and P concentra tions (Pinowska et al. 2007a). Lyngbya wollei % cover and mat thickness were positively correla ted to sediment phosphorus c oncentrations, 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 con centrations explain more variat ion in macroalgal biomass and thickness than measured nutrien t concentrations in Florida springs (Table 2.4). Whereas Lyngbya wollei % cover was not related to nutrient c oncentrations, it was related to diatom indicators of nutrient concentratio ns 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 rela ted to diatom indicators of nutrient concentrations with coefficients usually ranging fr om 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, wh ich then leak through the epidermis of leaves (Burkholder et al. 1990) which would cause chan ges 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 ther efore could reflect the sediment nutrient supplies that affect Lyngbya wollei and Vaucheria.

Percent cover measures of Lyngbya wollei were more highly corre lated 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 macroalg al 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 af fect 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 relativ ely narrow ranges, which were assumed to be related to natural factor s, 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 relate d to conductivity. The relationship between Lyngbya wollei and temperature was non-linear, w ith a threshold observed at 21.5 ◦ C. In contrast, Vaucheria % cover and thickness were negatively corr elated 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 factors21 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 macr oalgae abundance, percent disturbed land and LDI scores in 100m radius around sampling lo cation, 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 attr ibutes 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 th reshold around -83 ◦ W. This may be related to phosphorusrich geology in specific regions of the state.22 Details of these analyses and more thorough repo rt of relationships between all dependent and independent variables can be found in Stevenson et al. (2004) and Pinowsk a et al. (2007a).

2.4 – Discussion

Lyngbya wollei and Vaucheria , the two most abundant macroalg ae 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 hol ding onto a substratum. Lyngbya wollei can produce a variety of toxins (Carmichael et al. 1997, Onode ra et al. 1997, Yin et al. 1997). Vaucheria is approximately the same diameter, but is a coenoc ytic 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 f ilaments 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 diacritic al 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 vari ability. Historically this taxon was probably identified as Plectonema wollei Farlow ex Gomont in Whitfor d’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 consid erations and rejected Lyngbya majuscula Harvey ex Gomont as a correct name, because the latter is from brack ish and marine habitats. Ecology and morphology are important characteristics in modern taxonom y 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 al ga 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 mu ch 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 materi al 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 dominate d 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 (F igure 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). Tota l nitrogen, nitrate, and ammonia, increased with indicators of human activities, where as increas es in phosphorus were less signi ficantly 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 ha ve 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 wate r. 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 c over and thickness of Lyngbya wollei mats, as well as Vaucheria mats, were positively related to phosphorus in spring sediments, nutrient concentra tions in macroalgal cells, di atom 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 presente d in the following sections addr esses these questions. What are nutrient concentrations and fluxes in mats of Vaucheria and Lyngbya wollei compared to sediments and the water column? What nutrien t concentrations affect the growth of Vaucheria as well as Lyngbya wollei ? Do N:P ratios di ctate 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 bent hic algae may vary greatly with density of those algae in a habitat. The phosphorus and nitr ogen concentrations required to produce maximum growth rates of diatom-dominated benthic algae increases with increasing biomass of algae on substrata (Bothwell 1989, Rier and Steven son 2006). This density-depende nt demand for nutrients has been explained by reduced mixing of nutrients fr om overlying surface waters into mats coupled with high cellular uptake rates as biomass of diat om-dominated mats increases. Therefore, we would expect higher nutrient concentrations to be required to generate thic k 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 th e 2-3 cm of a diatomdominated mat. Typically, cells of filament ous macroalgae are not packed as densely as microalgae. Other processes, such as entrai nment 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 ma y enable support of thic k algal mats without high nutrient supply rates, if algae can grow slow ly without great disturba nce for long periods of time. As mats thicken and age, lower and inte rior portions of the mats die, which has been attributed to reductions in nutri ent supply and light in diatom- dominated benthic algae. The same resource limitation weakens health of macroa lgae in the interior and lower portions of mats and makes them more susceptible to bacterial a ttack and decomposition. In addition, particulate organic nitrogen and phosphorus from upstream may become entrained within mats and decompose. If mixing ra tes between interstitial waters of the mat and overlying waters are less than decomposition rates, decomposition could b ecome an important source of nutrients to support deep accumulations of benthic macroalg ae. Several studies show that nitrogen regeneration may help sustain benthic macroalgal productivity and some systems may function as self-regenerating th rough this recycling of nitrogen (M cGlathery 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 hyporhe ic flows and could increase nutrient availability. Macroalgal accumulation in Florida springs could also be supported by groundwater sources of nitrogen or phosphorus that become entr ained in mats or groundwater. Such sources could provide a partial explana tion 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 wa ter because of dilution.

In this study benthic macr oalgal 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 measured25 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 nut rient turnover in mats, and to estimate advective movement of dissolved nutrients out of mats. Results of th is study will help us de termine whether nutrient depletion within macroalgal mats could constr ain 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 multisampler s, 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 du ring 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 surf ace 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, disso lved 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 multisam p ler

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 multisam p ler

The end of a plastic tube covered by 500 micron screen26 Subsamples for δ 13 C and δ 15 N isotope analysis were also collected and submitted for analysis by the Stable Isotope Laboratory at UC Davis.

To estimate advective movement of water and nutrie nts into and out of thic k algal mats, a tracer study was conducted in August 2006 at Weeki Wachee, Manatee and Silver Glen springs using multisamplers. NaCl tracer solution was inje cted 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 us ing a conductivity meter to quantify NaCl trac er. The rate of decrease of NaCl tracer, expressed as the per cent 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/LDepth 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/LDepth 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/LDepth 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/LDepth 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/LDepth 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/LDepth 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/LDepth in multisampler (cm) SRP
TP

Sediment Algae Water Column 

### Replicate 1 Replicate 2

0 20 40 60 80 100 120 140 160

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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/LDepth in Multisampler (cm) Fe
TKN
NH4
NO3

Sediment Algae Water Column

0 20 40 60 80 100 120 140 160

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Sediment Algae Water Column

0 20 40 60 80 100 120 140 160

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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/LDepth 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/LDepth in Multisampler (cm) Fe
TKN
NH4
NO3

Sediment Algae Water Column

0 20 40 60 80 100 120 140 160

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0 20 40 60 80 100 120 140 160

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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/LDepth 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 ver tical profiles through the algal mats, and patterns varied to some degree among springs and macroalg al 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 amm onia 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 wa ter 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/LDepth in Multisampler (cm) Fe
TKN
NH4
NO3

Sediment Algae Water Column

0 20 40 60 80 100 120 140 160

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0 20 40 60 80 100 120 140 160

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0 20 40 60 80 100 120 140 160

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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/LDepth 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/LDepth in Multisampler (cm) Fe
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Sediment Algae Water Colum n

0 20 40 60 80 100 120 140 160

0.0 1.0 2.0 3.0 4.0 5.0 mg/LDepth 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/LDepth 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/LDepth 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/LDepth in Multisampler (cm) Fe
TKN
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NO3

Sediment Algae Water Colum n

0 20 40 60 80 100 120 140 160

0.0 1.0 2.0 3.0 4.0 5.0 mg/LDepth 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/LDepth 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/LDepth 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/LDepth in Multisampler (cm) Fe
TKN
NH4
NO3

Sediment Algae Water Colum n

0 20 40 60 80 100 120 140 160

0.0 1.0 2.0 3.0 4.0 5.0 mg/LDepth 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/LDepth 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. Al though 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 w ith depth in the mat, however these patterns were not highly consistent across springs. The δ 13 C values increased with depth in the Lyngbya mats of Weeki Wachee, but δ 15 N values varied unpredictably with ma t depth. In Silver Glen Lyngbya mats, δ 13 C values varied little with depth, but δ 15 N 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 consiste nt 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 ratio s was observed in Silver Glen. Thus we could not conclude that nutrient depl etion usually increased with dept h 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 MinuteDistance 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 MinuteDistance From Bottom (cm)

Silver Glen Manatee Weeki Wachee * *

* above algal mat29 actual water velocities, we made a first approx imation of rates of wa ter 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, disc harge 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 overlyi ng waters above mats commonly ranging from 0.01-0.5 m/s.

Thus, considerable evidence indicated that macroa lgal mats function very differently than mats of diatom-dominated microalgae. Nutrient conc entrations were not lower in interstitial mat waters than in overlying waters as predicted based on benthic microalg al 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 ma tter 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 partic ulate 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 fl ows in mats indicate th at mineralized nitrogen and phosphorus would have long periods of time to be taken up by cells with the algal mats. Thus, thicker mats of macroalg ae may not require higher nutrient concentrations than thinner mats if loss and disturbance rates are low. Mo re information is certainly needed to better understand these processes before broad conclusions can be drawn.

4 – SEASONAL VARIABILITY IN MACRO ALGAE, 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 whet her seasonal factors could help us understand macroalgal ecology. Thus we monitored the mont hly variability in discharge, nutrients, and macroalgal cover in Manatee and Ichetuckn ee Springs for a year to document seasonal variability. In addition, we comp ared the amount of nutrients in macroalgal mats to downstream nutrient flux to determine the pot ential of macroalgae for depleti ng nutrient supplies in overlying waters of springs. The same methods were used in this study as th e 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 botto m 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 Ichetuckne e formed in the lee of the swim fence where water velocity was relatively low. In contra st, at Manatee Springs a semi-continuous mat30 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 th e late spring and early summer of 2005 to a peak of 3500 m 2 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 m 3 . 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 alga l cover varied between 465 to 754 m 2 . Patterns of mean algal thickness often tr ended inversely with algal area a nd ranged from 0.08 to 0.26 m. Gross algal mat volume at Ichetucknee ranged from 48 to 166 m 3

. At Ichetucknee Blue Hole, discharg e from the boil ranged from 6.0 – 6.9 m 3 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

May05 Jun05 Jul05 Aug05 Sep05 Oct05 Nov05 Dec05 Jan06 Feb06 Mar06 Apr06 May06Algal Mat Area m2or Algal Mat Volume m3

0 0.1 0.2 0.3 0.4 0.5Algal Thickness m

Mat Area Mat Volume
Mean Thickness Ichetucknee Spring

0 500 1000 1500 2000 2500 3000 3500 4000

May05 Jun05 Jul05 Aug05 Sep05 Oct05 Nov05 Dec05 Jan06 Feb06 Mar06 Apr06 May06Algal Mat Area m2or Algal Mat Volume m3

0 0.1 0.2 0.3 0.4 0.5Algal 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

May05 Jun05 Jul05 Aug05 Sep05 Oct05 Nov05 Dec05 Jan06 Feb06 Mar06 Apr06 May06Algal Mat Area m2or Algal Mat Volume m3

0 0.1 0.2 0.3 0.4 0.5Algal Thickness m

Mat Area Mat Volume
Mean Thickness Ichetucknee Spring

0 500 1000 1500 2000 2500 3000 3500 4000

May05 Jun05 Jul05 Aug05 Sep05 Oct05 Nov05 Dec05 Jan06 Feb06 Mar06 Apr06 May06Algal Mat Area m2or Algal Mat Volume m3

0 0.1 0.2 0.3 0.4 0.5Algal Thickness m

Mat Area Mat Volume Mean Thickness Manatee Spring 0 100 200 300 400 500 600 700 800

May05 Jun05 Jul05 Aug05 Sep05 Oct05 Nov05 Dec05 Jan06 Feb06 Mar06 Apr06 May06Algal Mat Area m2or Algal Mat Volume m3

0 0.1 0.2 0.3 0.4 0.5Algal Thickness m

Mat Area Mat Volume
Mean Thickness Ichetucknee Spring

0 500 1000 1500 2000 2500 3000 3500 4000

May05 Jun05 Jul05 Aug05 Sep05 Oct05 Nov05 Dec05 Jan06 Feb06 Mar06 Apr06 May06Algal Mat Area m2or Algal Mat Volume m3

0 0.1 0.2 0.3 0.4 0.5Algal 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 m 3 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 contai ned 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 c ontained 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 fluxe s 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 la rgest algal mats observed in Manatee and Ichetucknee contained no more than 24 hour s 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, slough ing during senescence stages of community development, and biotur bation by animals. Disturbance of benthic macroalgae in Florida springs resu lts as larger rivers backup into springs, carrying turbid water and silts that shade and bury algae (Sickman et al. 2007).

Recolonization of macroa lgae in Florida springs after distur bance 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, reproducti on, death, grazing, disease, competition, and sloughing have been characterized. Colonizat ion by macroalgae is more complex than unicellular microalgae such as diatoms, because immigration occurs by filament fragments or by spores, which are special produc ts of pre-existing cells. Vaucheria produces spores both asexually and sexually. Lyngbya can recolonize by fragments of filaments. Thus, their recolonization is probably regulate d by the rates of settl ing of new spores or filament fragments into the space opened by disturbance and subseq uent reproduction of thes e new cells and cells that persisted through the disturbance.

The objective of this study was to de termine the rates of recovery of Vaucheria and Lyngbya wollei in Florida springs. In this study we remove d 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, distur bance effect p< 0.0021) . Mat thickness of Vaucheria spp. in the control plots was greatest on Febr uary 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 quali ty conditions, however Manatee springs was characterized by a higher variability in conductivity and dissolved oxygen

The difference in reco lonization 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 concentrati ons 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 as exual production of zoospores, akinetes and aplanospores, and by spores from sexual reproduction (Starmach 1972, Johanson 2005). Lyngbya reproduces only by fragmentation of tric homes (Komárek and Anagnostidis 2005). Thus Vaucheria has the capability of disper sing much more rapidly than Lyngbya , which could explain more rapid recolonizat ion after disturbance. This probably explains why Vaucheria is the dominant macroalga in springs along the Su wannee River where disturbance is high. Manatee, Fanning and Guaranto sp rings are dominated by mats of Vaucheria sp. These springs experience frequent intrusions of turbid wa ters from the Suwannee River during floods.

6 – EXPERIMENTAL ASSESSMENT OF LYNGBYA AND VAUCHERIA GROWTH RATES ALONG NITRATE AND PHOSPH ATE CONCENTRATION GRADIENTS

6.1 – Introduction

Slow accumulation rates with low loss rate s 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 al gal biomass, but these processes are complexly related to environmental factors (Steve nson et al. 1996, Stevenson 1997, Biggs 2000). Immigration positively affects al gal 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 pro cess, 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 ti me, even if reproduction rates are low.

Little is actually known about growth of macroalgae in diffe rent nutrient conditions and interactions with other environmental factors, such as light , micronutrient c oncentrations, and conductivity. Many of these factor s are known to affect algal gr owth, but precis e quantitative relationships are not well defined. To assess effects of nutrients on algal growth, we should determine those effects in conditions that are othe rwise the best for algal growth. Therefore, we34 conducted a set of preliminary experiments to determine effects of li ght, conductivity, and Fe, which was suspected to be an important micronutrient for Lyngbya wollei growth.

Growth rates of algae increase rapidly with increa sing 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 effect s at low and high nutrient concentrations, which is an important consideration for nutrient management strategies. A Monod model is usually used to predic t 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 PO 4 -P/L and 0.250 mg NO 3 -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 nut rients are supplied. Nutrients have been assumed to be supplied primarily by mixing from overlying surface waters w ith 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 ex ceeds 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 nutri ent concentrations needed to pr oduce peak algal biomass. This historic model of competition by algae for nutrien ts 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 “D oes nitrogen or phosphorus limit growth of macroalgae in Florida springs?” Historic trends in nitrogen and phosphorus concentrati ons show that nitrogen has increased much more than phosphorus in Florida springs. Relationships between human activities around sites and nutrient concentrations indicate mu ch 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 th at 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 ( μ max ) occurs at the half-saturation constant (K
s ). Here, μ = μ max (S/(K
s +S)). μ maxGrowth Rate (μ)

0.5 μ max

0
0

S (e.g. PO 4 or NO 3 , mg/L ) K s

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 ( μ max ) occurs at the half-saturation constant (K
s ). Here, μ = μ max (S/(K
s +S)). μ maxGrowth Rate (μ)

0.5 μ max

0
0

S (e.g. PO 4 or NO 3 , mg/L ) K s μ maxGrowth Rate (μ)

0.5 μ max

0
0

S (e.g. PO 4 or NO 3 , mg/L ) K s35 Is it possible that changes in nitrogen alone coul d 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, whic h 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 on e resource at time limits growth of a species. According to current N:P ratios in Florida spring s and the Redfield ratio benchmark of 16N:1P, macroalgal growth should be limited by phosphor us rather than nitrogen (Stevenson et al. 2004, Pinowska et al. 2007a). Howeve r 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 ot her 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 se ction were to determine effects of nutrients on Vaucheria and Lyngbya wollei growth rates in highly controlled laboratory experiments. Our goal was to determine the nutri ent concentrations, both nitrat e and phosphate, that severely limited growth and that saturated growth rates (i .e. that produced no furt her increase in growth rates) of both macroalgal taxa. We conducted a series of experiments to determine the best conditions for assessing nitrate and phosphate re gulation of growth of these macroalgae. We then conducted experiments to determine th e limiting and saturating concentrations along nutrient gradients in which all ot her nutrients were in luxury suppl y. In these experiments, we varied light, micronutrient concen trations, iron, and conductivity, wh ich have been shown to be important in experiments with ot her alga and other species of Lyngbya (e.g. Ahern et al. 2006, Watkinson et al. 2005). Finally, we conducte d 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 th ree types of experimental system s to address slightly different hypotheses and to confirm results of the same expe rimental treatments in two or more settings (Figure 6.2-6.3). The small microcosms were 1.5 mL microcentrifuge tube s filled with growth media and placed on a shaker unde r lights in a cold room (22.5 ◦ C) at Michigan State University. The large microcosms were a set of 49 circul ar raceways (donut-sha ped) made of clear plexiglass, filled with 10 0 mL of media, and plac ed on a shaker table under lights in a cold room (22.5 ◦ C) at Michigan State University. The meso cosms were rectangular loops of 5 cm PVC pipe, 122 cm long and 91 cm tall, oriented vertica lly, 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. Detail s 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 microc osms, respectively, to evaluate good non-nutrient growth conditions and effects of nitrate, phosphate, and ammonia on Lyngbya wollei and Vaucheria growth. The nutrient levels us ed in the experiments reflected phosphate and nitrate concentr ations observed in Florida springs water. Ammonia concentrations used in the experiments were high er than those observed in Florida springs water but in the range of t hose observed inside the L. wollei mats (Sickman et al. 2007). The small microcosms were valuable for high replication and use of individual f ilaments. The large microcosms enabled manipulation of biomass in highly controlled experimental settings. The mesocosms were used to reflect the most natura l conditions with natural groundwater. To enable enrichment with nutrients to create treatment le vels, we used groundwater with very low nutrient Aquifer water with nutrients

Water bath to regulate temperature NO 3 and Micronutrients in DDI Water

Floridian Aquifer Water

Air Pump Pump

Macroalgae Pump

Excess water outflow

Figure 6.3. The recirculating stream mesoscosm. 

## Flow

Donut - Microcosms Microcentrifuge - Microcosms

Figure 6.2. The microcentrifuge and donut mesoscosms.

Aquifer water with nutrients

Water bath to regulate temperature NO 3 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 NO 3 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 NO 3 and Micronutrients in DDI Water

Floridian Aquifer Water

Air Pump Pump

Macroalgae Pump

Excess water outflow

Figure 6.3. The recirculating stream mesoscosm. 

## Flow

Donut - Microcosms Microcentrifuge - Microcosms

Figure 6.2. The microcentrifuge and donut mesoscosms. Donut - Microcosms Microcentrifuge - Microcosms

Figure 6.2. The microcentrifuge and donut mesoscosms.37 concentrations. As a result, nutri ent concentrations in the groundwater used could have been less than natural background nutri ent 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 gr own in microcentrifuge tubes for 11-12 days. Each filament was photographed before and afte r incubation in the microcentrifuge tubes to determine changes in filament lengths, which were used to calculate th eir growth rates. The growth medium was changed every 48 hours to replenish nutrients supplies depleted by the algae. Temperature was maintain ed 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 Florid a spring water (referred to as medium Z8Fl). The formula for the growth medium was actuall y the study of preliminary experiments, which will be described subsequently. Ammonia nitroge n, Kjeldahl nitrogen, nitrite-nitrate nitrogen, soluble reactive phosphorus, a nd total phosphorus concentrations were analyzed routinely by DEP. Water conductivity was measured using YS I 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 condi tions 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 fr om Alexander Spring, water from Silver Glen spring, and Z8Fl medium with either 1 or 5 f ilaments of macroalgae pe r centrifuge tube. The second experiment evaluated effects of conductivity where major cation and anion concentrations were varied in the medi um to produce conductivities of 100, 300, 500, 1000 and 3000 μS/cm. Both conductivity an d light were varied in expe riment 3, with 3 conductivity levels (100, 300, 1000 μS/cm) and 5 light levels (1, 2.5, 5, 25, 250 μmol/m 2 /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 μ S/cm, had a negative effect on growth of both macroa lgae compared to lower conductivity treatments, which indicated that both taxa we re 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 leve ls. Both macroalgae grew better in 25 μ mol quanta m -2

s -1 than in higher and lower light intensities.

6.2.1.2 Microcosm experiments to determ ine 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/m 2 /s of light, at 22.5 ◦ C, and intermediate iron and conductivity levels. Typically each treatment was replicated 10 times, or 5 times if two38 populations (from 2 different spri ngs) were used. Each experi ment was designed to evaluate effects of 1 nutrient when other nutrients (macr o- and micronutrients) were in luxury supply. The experiments conducted for Lyngbya wollei and Vaucheria were very much the same, but slight differences should be not ed. Experiment numbers in th e following list reflect numbers associated with tasks in the project proposal. Th ey are meant to identify the experiments, but do not reflect their order of presen tation. Nutrient concentrations are stated in the following methods in μ g/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 PO 4 treatments (1, 5, 10, 20, 30, 40, 60, 80, 100 and 250 μg P/L) and 2 populat ion location treatments. The NO 3 -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 NO 3 -N concentrations: 10, 30, 60, 125, 250, 500, 750, 1000, 1500, 2500 and 5000 μg N/L. The PO 4 -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 NO 3 -N concentrations (10, 30, 60, 125, 250, 500, 750, 1000, 2000 and 5000 μg N/L) and 2 population location treatments. The PO 4 -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 NH 4 (10, 30, 60, 125, 250, 500, 750, 1000, 1500, 2500 and 5000 μg N/L). The PO 4 -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 PO 4 treatments (1, 5, 10, 20, 30, 40, 60, 80, 100 and 250 μg P/L) and 2 population location treatments. The NO 3 -N concentration in experiment 1 was 1000 μg/L.

• Experiment 3 had a two-factor design with 10 NO 3 treatments (10, 30, 60, 125, 250, 500, 750, 1000, 2000 and 5000 μg N/L) and 2 population location treatments. The PO 4 -P concentration in experiment 3 was 100 μg/L.

• Experiment 5 had a one-factor design with 11 nutrient treatments (NH 4 -N concentrations: 10, 30, 60, 125, 250, 500, 750, 1000, 1500, 2500 and 5000 μg N/L). The PO 4 -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 PO 4 -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/m 2 /s of light. Each microcosm was filled with 100 ml of growth medium, which was changed daily. Small39 fragments of Lyngbya wollei mats were grown in the large microcosms. Changes in their fresh mass and dry mass were determined to calculate gr owth rates. All experiments had a complete factorial ANOVA design with two experimental f actors: nutrient concen tration and initial biomass.

• Experiment 4 had 15 treatments which were a combination of five phosphate concentrations (PO 4 -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 NO 3 -N concentration in experiment 4 was 1000 μg/L.

• Experiment 9 had a total of 15 treatments wh ich were a combination of five nitrate concentrations (NO 3 -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 PO 4 -P concentration in experiment 9 was 100 μg/L.

• Experiment 11 had 15 treatments which we re a combination of five ammonia concentrations (NH 4 -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 PO 4 -P concentration in experiment 11 was 100 μg/L.

6.2.3 Mesocosm Experiments

Two mesocosm experiments were conducted cons ecutively to evaluate effects of nitrate enrichment when phosphorus concentration was lo w. Mesocosms were located in a climate controlled greenhouse on the University of Florid a, Gainesville. Groundwater from the Floridan Aquifer, nitrate, and micronutrien ts were added continuously to the channels with peristaltic pumps. The groundwater had low nutrient concen trations (NO 3 < 0.001 mg N/L, PO 4 -P = 0.009 mg P/L) and was intended to simulate spring wa ter. Water temperature in the recirculating stream channels averaged 21°C during Expe riment 1. The pH ranged from 6.27 to 8.76, the dissolved oxygen ranged 8.28 to 12.00 mg/L, a nd the conductivity range was 292 to 357 μ S/cm. During Experiment 2, water temperature in the re circulating stream channels averaged 20.5°C. The pH ranged from 7.03-8.75 and the dissolv ed oxygen averaged 7.01 mg/L. Conductivity ranged from 228 to 323 μ S/cm. Light averaged 650 μ mol m -2 s -1 and varied with cloudiness during both experiments. Details about the expe rimental 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 trea tments of nitrate additions (ranging from 0.001to 5.000 mg N/L) in the form of NaNO 3 . All treatments except Control A received a Z8Fl micronutrient supplement. Phosphorus was not a dded to any treatment. Experiment 1 was designed to identify the range of nitrate con centrations in which th reshold responses in macroalgal growth rates would be observed, which would be inve stigated more thoroughly in Experiment 2. Experiment 2 ran for 21 days an d had seven treatments: one control treatment with no nitrate addition and six nitrate treatmen ts ranging from 0.025 to 0.750 mg NO 3 -N/L. All seven treatments in Experiment 2 received th e Z8Fl micronutrient supplement. No phosphorus was added to any treatment. In both experi ments, phosphate was depl eted rapidly in the mesocosm from groundwater concentrati ons of 0.009 to less than 0.005 mg/L PO 4 -P. Phosphate concentrations were us ually 0.002-0.003 mg/L PO 4 -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 meso cosms. 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 stre am channel for Experiment 2.

During Experiments 1 and 2, temperature, conductivity, pH, and dissolved oxygen were measured every three days in every stream channe l. Water samples for chemical analyses were taken five times during Experiment 1 (Day s 0, 7, 14, 21 and 28) and four times during Experiment 2 (Days 0, 7, 14 and 21). The sample s were analyzed for total Kjeldahl nitrogen, total phosphorus, soluble reactiv e phosphorus, nitrate, ammonium

, dissolved organic carbon, cations, and trace metals. Nitrogen and phosphor us in algal mats was also determined.

6.2.4 Data Transformations and Analyses Algal relative growth rate (RGR) in the sma ll 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 tran sformed to meet the assumptions of normality and variance homogeneity.

To determine threshold values of phosphate a nd nitrate concentrati ons 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 PO 4 was manipulated and nitrate was in luxury concentrat ion. 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) a nd 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. Ther efore, the data were analyzed using a four-parameter logistics model in the drc (dose response curv e) package of R statistical soft ware (Ritz and Streibig 2005).

Only statistical results that were statistica lly 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 amm onia stimulated growth of Lyngbya wollei and Vaucheria spp. (Figures 6.4-6.7). In nitrate a nd phosphate experiments, both macr oalgae 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 increa sed past a threshold, growth of the macroalgae days of # Length) ln(Initial Length) ln(Final

= RGR −41 were stimulated until peak growth rates were reached and additional nutrient supply had no effect on growth. Ammonia, how ever, did have a negative eff ect 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 phosphat e enrichment followed a classic logistic doseresponse curve observed in many t oxicological studies, but instead of a negative effect of the contaminant, nutrients had a positive effect on gr owth rate. Using parameters from the dose response curve statistics, several parameters could be estimate d that have value for macroalgal management (Table 6.1):

• minimum growth rates (r min );

• maximum growth rates (r max );

• the increase in growth rate with nutrient enrichment (r inc );

• constraining concentrations: the nutrient con centration at which growth was predicted to be elevated by 10%, below which growth of th e macroalgae is constrained about as much as possible (ed10 – effective dose with 10 % effect); and

• saturating concentrations: the nutrient concentr ation at which growth was predicted to be elevated by 90%, above which no effects of nut rient reduction would be expected (ed90 – effective dose with 90 % effect).

• the nutrient concentration at wh ich growth was predicted to be elevated by 50% of the predicted change between r min and r max (ed50).

In all cases reported in the text of these results, there were significant increases in algal growth with nitrate and phosphate concen trations. Ammonia concentrat ions also stimulated algal growth at low concentration range s, 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 con centrations in which effects are observed.

Before evaluating the specific nutrient concentrati ons affecting algal growth, it is important to examine the relative consistency in parameters differences among experiments. The nutrient concentrations at which effects were obs erved (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 concentratio ns during the incubation period were greater for donuts th an the microcentrifuge microcosms (Pinowska et al. 2007b). Increases in biomass in donuts increased th e ed50 and ed90, consistently decreased r max , and usually decreased r min 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 nut rient 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 fr om which the algae came, i.e. to either preconditioning or genetic di fferences among populations.

Lyngbya wollei in mescosms responded to NO 3 at lower concentrations, with lower growth rates, and with smaller increases in gr owth rates than in microcosms or donuts. This was probably related to the nature in which filaments were bu ndled and P concentrations were much lower in mesocosms than both microcosm types. Filament s were bundled together in relatively tight masses, which would have reduced light penetrat ion and mixing of water and nutrients into the masses. This reduction in mixing is known to redu ce nutrient concentrations in some microalgal mats, such as those dominated by diatoms (Ste venson and Glover 1993). However, our work did43 show that this is not always the case when mats become thicker and other processes become important in producing nutrient s in interstitial wa ters of mats (Sickman et al. 2007).

The range of nutrient concentrations that constrained and sa turated growth of Lyngbya wollei and Vaucheria spp. were very similar in experiments. R max 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 ra tes 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 potentia l 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 ( μ g/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 ( μ g/L) when phosphate concentrations were high. Figure 6.7. Growth rates (RGR) of Lyngbya wollei in mesocosms at different nitrate concentrations ( μ g/L) when phosphate concentrations were high.44Table 6.1. Summary of macroalgal responses to nutrient (PO4or NO3) enrichment in different experiments, in different systems(small microcosms – microcosm, large microcosms – donuts, andmesocosms). Biomass indicatesthe amount of algae in themicrocentrifuge tube, donut, and mesocosm. Model parameters for the logistic dose response curve and their column heading codesare described in the text above. Model parameters are omitted forsets of experiments in which a logistic response could not be fit.Model parameters are in bold type if theywere determined to be statistically significant. ed10, ed50, and ed90 are expressed in mg/L.In the case of the ed10 and ed90, which were harder to estimate,if the standard errorof estimates was less than half of the parameter,then they are in bold type.Taxon Exp System Biomass Nutrient ed10 ed50 ed90 r(min) r(max) r increaseLyngbyaMicrocosm 1 filament PO40.011 0.017 0.028 0.14 0.280.14LyngbyaDonuts 0.01g PO40.024 0.049 0.101 0.15 0.230.08LyngbyaDonuts 0.05g PO4 xLyngbyaDonuts 0.2 g PO4 xVaucheriaMicrocosm 1 filament PO40.006 0.012 0.022 0.16 0.270.11VaucheriaDonuts 0.01g PO40.017 0.0370.078 0.020.150.13VaucheriaDonuts 0.05g PO4 0.015 0.0760.06 0.110.05VaucheriaDonuts 0.2 g PO4 xLyngbyaMicrocosm 1 filament NO30.034 0.088 0.230 0.10 0.260.16LyngbyaDonuts 0.01g NO30.327 0.519 0.821 0.15 0.270.12LyngbyaDonuts 0.05g NO3 0.0870.5463.4400.11 0.240.13LyngbyaDonuts 0.2 g NO3 0.058 0.808 11.2830.11 0.150.04LyngbyaMesocosm masses NO30.015 0.042 0.110 0.09 0.130.04VaucheriaMicrocosm 1 filament NO3 xVaucheriaDonuts 0.01g NO30.069 0.210 0.644 0.10 0.250.15VaucheriaDonuts 0.05g NO3 0.0360.2081.2000.07 0.190.12VaucheriaDonuts 0.2 g NO3 0.2930.4990.8500.05 0.110.06VaucheriaMesocosm masses NO3 x45 However, recolonization of disturbed areas of springs was relatively slow (Pinowska et al. 2007b), even after initial colonizat ion of filaments, which indi cated that growth of these macroalgae was relatively slow in springs.

The constraining nutrient concentration (ed10) for PO 4 for both Lyngbya wollei and Vaucheria ranged from 0.006-0.015 mg PO 4 -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 actu al 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 esti mates of ed10 and ed90. Since nutrients are not depleted within the macroalgal mats (Sickman et al. 2007) and nutrien t depletion in donut experiments was great, results from the micro centrifuge tube experime nts should be assigned more significance when predicting the nutrient concen trations in springs that regulate macroalgal growth.

The growth-saturating nutrient concentrations (ed90) for PO 4 -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 microcen trifuge tube experiments were higher than observed for lowbiomass diatom-dominated periphyton, which have been reported to reach peak growth rates around 0.001 mg PO 4 -P/L (Bothwell 1989) and 0.016 mg PO 4 -P/L by Rier and Stevenson (2006).

The NO 3 -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 NO 3 -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 nutr ients would not have been depleted. Few other studies have estimated limiting and saturating nutrie nt concentrations of nitrate for algae. Rier and Stevenson (2006) found growth of low-biom ass 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 NO 3 -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 Da wes (2004) is that they did not have lower NO 3 treatments than 0.070 mg/L and they incubate d masses of algae, rather than individual filaments, so nutrient depletion may have occu rred even though they continuously supplied new medium to cultures.

Given historic records of NO 3 being around 0.050 mg/L, our experi mental results indicate that nitrate loading into springs from human sources has elevated growth rates of macroalgae from a range of concentrations that se verely constrained growth of Lyngbya wollei and Vaucheria spp. to concentrations that commonly saturate their ni trogen requirements.

The response of Lyngbya wollei to nitrate enrichment in the me socosm experiments, in which no phosphate was added, provides insight into how n itrate additions in all springs can stimulate algal growth, whether spring phosphorus is high or low. We usually assume that either nitrate or46 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 in dications were that phosphorus should have been the primary limiting nutrient, nitrate enrichment stimulated growth of Lyngbya wollei . The following conditions indicate that phosphorus shou ld have been the primary limiting nutrient: phosphate concentrations were low in the meso cosm water, and both N:P and C:P ratios of Lyngbya wollei were well above the 16:1 a nd 106:1 ratios that indicate relative cell requirements for these elements. Therefore, stored phosphorus in cells did not provi de 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 concentra tions were not available in that st udy 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 tr eatment concentrations (NO 3 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
C:N 0 7 7 7 7 7 7 7 Lyngbya 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

C:N 0 9 9 9 9 9 9 9 Vaucheria
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 Florid a springs during the last 50 years, which has corresponded with increases in nitrate concentr ations 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 nutrie nt data collected during the past 50-70 years, Hand (unpublished data, FDEP) observe d that significant increases in nitrate47 concentration were observed in 48% of Florida’s springs and they correspond with increases in chloride concentration, an indica tor of human sources of pollutant s. 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 s ites 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 wa ter nutrient concentra tions, however it was positively related to the human activities around sa mpling sites and other indicators of nutrient supply.

Water column nutrient concentrat ions may only tell part of th e 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 sedime nts. 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 di atom indicators of phosphorus concentration indicates that nutrient supply ra te was related to abundance of Lyngbya wollei . The source of the phosphorus in these sediments should be investig ated. Is it transpor ted through the shallow groundwater pathways from watershed sources in dissolved organic or inorganic form or as calcium phosphate? Does P in sediments aris e 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 nutrien t 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 nutrien t concentrations high accumulations could accumulate over time given gr owth rates measured in our experiments and low loss rates. This would explain how sediment s 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 beco me self-sustaining as they accumulate. Nutrient concentrations were high within the macroalgal mats (Sickman et al. 2007). This indi cates that sequestration and production of nutrients is greater than efflux a nd uptake of nutrients. In addition to uptake of dissolved inorganic nutrients by the algae, dissolved and pa rticulate organic matter and particulate inorganic matter could get entrained in mats. After decomposition, this material could be an important nutrient source for surrou nding algae. Research to further explore the48 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 sour ce of nitrogen to Lyngbya wollei is nitrogen fixa tion (conversion of atmospheric N 2 to the bioavailable NH 3 ). Lyngbya wollei has the gene and the capability to convert the unlimited supply of N 2 into a bioavailable form (Phlip s et al. 1992). But response of Lyngbya wollei growth to nitrate in experiments indicat es that its growth is not saturated by atmospheric sources of N 2 . 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 gr owth of macroalgae. Such reductions should be effective as long as target concentrations (e.g. nutrient crite ria or pollution load reduction goals) are below saturating nutrient concentrations . What are saturating concentra tions 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 microcen trifuge tube experiments, suggest that nutrient concentrations less than 0.028 mg PO 4 -P/L and 0.230 mg NO 3 -N/L are needed to slow growth (i.e. ed90) of Lyngbya wollei, and concentrations less than 0.022 mg PO 4 -P/L are needed to slow growth of Vaucheria spp

. The same experiments show that further reductions to as low as 0.011 mg PO 4 -P/L and 0.034 mg NO 3 -N/L will continue to slow growth (i.e. ed10) of Lyngbya wollei and concentrations to as low as 0.006 mg PO 4 -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 th ese ratios to convert responses of Vaucheria growth in low biomass donut experiments to comparable benc hmarks for nitrate, the ed10 would be 0.017 and the ed90 would be 0.225-0.296 (mean 0.261) mg NO 3 -N/L.

To convert these benchmarks from microcentrifuge tube experiments to to tal nitrogen and total phosphorus concentrations for nutri ent criteria in Florid a springs, we need to account for the differences in nutrient forms. The benchmarks fr om 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 be nchmarks in the form of total nutrient concentrations. Average TP and TN concentr ations 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 sta ndard deviation) in our 2003 survey results. The average difference between nitrate con centration and TN wa s 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 di ssolved inorganic fractions of total nutrient concentrations are high in Florida springs, proba bly due to the relatively short distances and large discharges that limit accumulation of pa rticulate 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. Sin ce ratios of dissolved inorganic to total nutrients49 should be relatively consistent w ithin streams, such variations in fractions could be taken into account to develop spring-specific nutr ient criteria. Given average TN:NO x 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:NO x and TP:SRP ratios for Florida sp rings, 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 (PO 4 -P) and nitrate (NO 3 -N) from microcentrifuge tube experiments (MTM Expt) a nd 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 whet her nutrients are supplie d from groundwater or surface water will affect the eff ectiveness of a target concentration. Water column nutrient concentrations, especially expressed as total ni trogen and total phosphorus , are only indicators of actual nutrient supplies supporting al gal growth. However, the ra nge of saturating nitrate and phosphate concentrations from experiments, esp ecially microcentrifuge experiments, provide valuable benchmarks for consideration in nutri ent load reduction goals. The certainty of macroalgal reductions should be greater if nutrient loads are re duced sufficiently that nutrient concentrations in spring waters are less than the saturating concentrations indicated in the experiments and Table 7.1. Of course the cert ainty of restoration w ill increase with greater reductions until we reach concentrations that lim it algal growth as much as possible. This concentration is likely less than even the ed10, given the ma ny sources of nutrients and factors affecting nutrient-gr owth relationships.

The relationship between the extensive results from experiments and limite d 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 NO 3 -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 experi ments (Table 7.1). Using the same rationale, the ed90s could be assumed to be reasonable benchmarks for first-st age 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 Manageme nt District (SJRWMD) for the Wekiva River (Mattson et al. 2006), which was 0.156 mg NO 3 -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 natura l concentrations of phos phate 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 ni trogen benchmarks provided in this study and by SJRWMD.

Reducing nutrient loads sufficiently to stop vers us 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 hi gh to stop their growth and controlling nitrate sufficiently may be impractical. Evidence indica tes these macroalgae likely occurred naturally in Florida springs, but not in th e excessive abundance as today. Vaucheria is reported in spring seeps in areas around the world with very li mited human activity. Historic records of Lyngbya wollei exist from Silver Spring (Pin owska et al. 2007a). But pict ures of these Florida springs show no nuisance algal growths prio r 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 di fficult to explain. We observed a relationship between growth and nutrient concentr ations that fit in most cases, fit a logistic model rather than a Monod model. Our results we re similar to the Monod model in that nutrients stimulated algal growth within a specific range of nutri ent concentrations, a nd above that range, additional increases in nutrients had little effect on growth rate s. Unlike predictions of the Monod model, both macroalgae were able to sustain low growth rates when no and low concentrations of nutrients we re added to the culture media. Although it does not seem likely that intracellular nutrient stores were sufficient to sustain growth rates, th at does seem to be the only reasonable explanation. Cells must have real located nutrients to supp ort the most essential processes to maintain sustained growth with no new nutrient allocations. Perhaps cells allocated elemental resources to different processes at different times duri ng the growth cycle to continue to produce new cells with lower and lower nitrogen and/or phosphorus conc entrations relative to carbon. More long-term experiment s 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 consideratio ns, 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 an other reason that nutrient load reductions can not be expected to stop accumulation of these macroalgae.

Reducing macroalgal growth rates by reducing n itrate and phosphate load ing in springs should have substantial effects on the frequency, in tensity, and duration of nuisance macroalgal growths. Slight differences in growth rates pr oduce 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 give n recovery and seasonal persistence patterns observed in our disturbance expe riment and the seasonal monitori ng after a flood disturbance. Assuming no losses and no other sources of macroa lgae (such as algae settling on the bottom), 1, 20, and 8104 grams of biomass would be presen t if growth rates were 0.01, 0.10, and 0.30 g g -1 d -1 . Reducing macroalgal grow th rates from the common r max near 0.30 to just 0.20 would51 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 incr ease with the length of time that algal mats accumulate. Thus decreases in nutrient concentr ations in Florida spri ngs could greatly reduce nuisance macroalgae problems.

8 – REFERENCES

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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 9:55-63.56Appendix 1. Study sites, spring names and sitecodes, sampling dates, and latitude and longitude of transect 1 for each site.SpringSpringcodeSite name Site codeBoilLatitude LongitudeDate sampledspring 2003Date sampledfall 2006Datesampled2006Head ALE-01 Yes 29.08128 81.57563 3/28/2003 10/29/2003 2/2/2006Alexander ALEDownstream ALE-02 No 29.08231 81.57754 3/28/2003 10/29/2003 2/2/2006Blue holes CHA-01 Yes28.71617 82.57502 4/1/2003 11/2/2003 1/21/2006Dock CHA-02 Yes 28.71558 82.57630 4/1/2003 11/2/2003 1/21/2006Chassahowitzka CHABrown spring CHA-03 Yes 28.71721 82.57586 4/1/2003 11/2/2003 1/21/2006Cypress CYP Head CYP-01 Yes 30.65855 85.68430 9/24/2003 1/13/2006Fanning FAN Head FAN-01 Yes 29.58757 82.93541 10/2/2003 7/26/2006Pipe GAI-01 Yes 30.42736 85.54827 4/26/2003 9/25/2003 1/12/2006Side boil GAI-02 Yes 30.42884 85.54854 4/26/2003 9/25/2003 1/12/2006Gainer GAIMorten Spring GAI-03 Yes 30.42875 85.54649 1/12/2006Guranato GUR Head GUR-01 Yes 29.77973 82.94001 10/2/2003 7/26/2006Homosassa HOM After bridgeHOM-01 Yes 28.79961 82.85905 4/2/2003 11/4/2003Head ICH-01 Yes 29.98408 82.76184 3/16/2003 11/22/2003 1/29/2006Blue Hole ICH-02 Yes 29.98068 82.75866 4/20/2003 11/22/2003 1/29/2006Below Blue Hole ICH-03 No 29.98007 82.75895 3/17/2003 11/22/2003 1/29/2006Mission spring ICH-04 Yes 29.97628 82.75783 4/23/2003 11/9/2003 1/29/2006Devils Ear ICH-05 Yes 29.97388 82.75996 4/23/2003 11/9/2003 1/29/2006Mill Pond ICH-06 Yes 29.96658 82.76005 4/23/2003 11/9/2003 1/29/2006Before bridge ICH-07 No 29.95495 82.78507 4/24/2003 11/8/2003 1/29/2006Ichetucknee ICHCoffee spring ICH-08 Yes 29.95937 82.77526 4/21/2003 11/8/2003 1/29/2006Indian IND Head IND-01 Yes 30.25077 84.32203 9/26/2003 1/16/2006Head JAC-01 Yes 30.79037 85.13998 4/19/2003 9/24/2003 1/14/2006Boat ramp JAC-02 No 30.78249 85.16022 4/19/2003 9/23/2003 1/14/2006Arrowhead campgroundJAC-03No30.75609 85.18680 4/18/2003 9/23/2003Jackson Blue JACRock cliff JAC-04 Yes 30.79023 85.14290 1/14/2006Head JUN-01 Yes 29.18365 81.71201 3/27/2003 10/31/2003 2/2/2006Juniper JUNFern Hammock JUN-02 Yes29.18364 81.70801 3/27/2003 10/31/2003 2/2/200657SpringSpringcodeSite name Site codeBoilLatitude LongitudeDate sampledspring 2003Date sampledfall 2006Datesampled2006River fork JUN-03 No 29.18519 81.70726 3/27/2003 10/31/2003After bridge onroute 19JUN-04No29.21283 81.65431 3/26/2003 10/31/2003Lafayette Blue LAF Head LAF-01 Yes 30.12592 83.22617 9/29/2003 7/26/2006Little River LTR Head LTR-01 Yes 29.99642 82.96675 9/28/2003 7/26/2006Madison Blue MAD Head MAD-01 Yes 30.48056 83.24439 9/29/2003 7/26/2006Manatee MNT Head MNT-01 Yes 29.48952 82.97692 10/3/2003 1/31/2006Pitt PIT Head PIT-01 Yes 30.43288 85.54616 1/12/2006Ponce de Leon PON Head PON-01 Yes 30.72090 85.93071 4/26/2003 9/22/2003 1/10/2006Head RAI-01 Yes 29.10223 82.43741 4/4/2003 11/12/2003 1/19/2006KP Hole RAI-02 Yes 29.09294 82.42848 4/4/2003 11/12/2003 1/19/2006Before tuberssignRAI-03No29.06305 82.42788 4/4/2003 11/12/20031/19/2006Before bridge RAI-04 No 29.05223 82.44700 4/4/2003 11/12/2003 1/19/2006RAI-05 No 29.09275 82.43133 1/19/2006RAI-06 No 29.07650 82.4276 1/19/2006Rainbow Spring RAIAfter bridge RAI-07 No 29.05407 82.44717 1/19/2006Head SGL-01 Yes 29.24603 81.64345 3/26/2003 10/30/2003 2/01/2006Natural Well SGL-02 Yes 29.24583 81.64385 2/01/2006Silver Glen SGLTrial in thewoodsSGL-03Yes29.24400 81.64632/01/2006Head SLV-01 Yes 29.21619 82.05252 4/3/2003 11/13/2003 1/23/2006Second pool SLV-02 Yes 29.21584 82.04987 4/3/2003 11/13/2003 1/23/2006Birds of prey SLV-03 No 29.21561 82.04112 4/3/2003 11/13/2003 1/23/2006Old swimmingareaSLV-04No29.20500 82.02902 4/3/2003 11/13/20031/23/2006Silver River SLVCabbage palm SLV-05 No 29.20211 82.01127 4/3/2003 11/12/2003 1/23/2006Troy TRY Head TRY-01 Yes 30.00598 82.99756 9/29/2003 7/26/2006Turtle TUT Head TUT-01 Yes 29.84742 82.89041 9/30/200358SpringSpringcodeSite name Site codeBoilLatitude LongitudeDate sampledspring 2003Date sampledfall 2006Datesampled2006Head VOL-01 Yes 28.94758 81.33969 3/29/2003 11/20/2003 1/30/2006Volusia Blue VOLDownstreamfrom stairsVOL-02No28.94679 81.33921 3/29/2003 11/20/20031/30/2006Head RR WAC-01 Yes 30.33979 83.99244 4/7/2003 9/27/2003Minnow WAC-02 No 30.33020 83.98776 4/7/2003 9/27/2003Wacissa WACBig Blue WAC-03 Yes 30.32770 83.98484 4/7/2003 9/27/2003Head WAK-01 Yes 30.23533 84.30287 4/8/2003 10/1/2003 1/17/2006Turnaround WAK-02 No 30.23318 84.28870 4/8/2003 10/1/2003 1/17/2006Bird colony WAK-03 No30.22507 84.27470 4/8/2003 10/1/2003 1/17/2006WAK-04 No 30.23650 84.29831 1/17/2006WAK-05 No 30.23439 84.29505 1/17/2006WAK-06 No 30.22836 84.28001 1/17/2006Wakulla WAKUpstream frombridge on 98WAK-08No30.18037 84.248171/16/2006WGT-01 Yes 30.45279 85.53044 9/26/2003 1/13/2006WashingtonBlueWGT HeadWGT-02 Yes 1/13/2006Head WEK-01 Yes 28.51747 82.57349 3/24/2003 11/5/2003 1/25/2006Boat dock WEK-02 No 28.51901 82.57361 3/24/2003 11/5/2003 1/25/2006WMA WEK-03 No 28.52481 82.59583 3/24/2003 11/4/2003 1/26/2006Weeki Wachee WEKRoger's Park WEK-04 No 28.53057 82.62407 3/24/2003 11/5/2003 1/26/2006HeadWKW-01No28.71193 81.46037 3/30/2004 11/21/20031/26/2006Wekiwa WKWCanoe launchWKW-02No28.71269 82.45948 3/30/2004 11/21/20031/26/2006Willford WIL Head WIL-01 Yes 30.43966 85.54763 9/25/2003 1/15/2006