# ECOLOGICAL CONDITION OF ALGAE AND

NUTRIENTS

IN FLORIDA SPRINGS

DEP C ONT RACT 

### N U M BE R 

### W M 858

F INAL 

### R EPOR T



## Subm itted to the Florida Dep a rtme nt of Environm en tal Protection

June 10, 2004

Authors:

R. Jan Stevenson

Agnieszka Pinowska

Yi-Kuang Wang

## DEP Contract Number WM 858

Ecological Condition of Algae and Nutrients in Florida Springs

Submitted to:

Russel Frydenborg

Environmental Assessment Section

Florida Department of Environmental Protection

2600 Blair Stone Rd. MS 6511

Tallahassee, FL 32399

Phone 850-245-8082

Fax 850-245-8063

Submitted by:

R. Jan Stevenson

Department of Zoology

203 Natural Science Building

Michigan State University

East Lansing, MI 48824

Phone 517-432-8083

Fax 517-432-2789

June 8, 2004

## ACKNOWLEDGEMENTS

The assistance of m e m b ers of the Algal Ecology Laboratory in the Department of

Zoology, Michigan State University: Le i Zheng, Julianne Heinlein, Vanessa

Laugheed, Scott L. Rollins, Lara Pana yotoff, Colleen McLean, and Daniel

Wieserich is gratefull y acknowledged.

Silver River State Park in Flori d a graciously provided housing during t h e

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

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

and changing flat tires in the mi ddle of Ocala Stat e F o rest.

Silver River State Park, Rainbow River State Park and Wakull a Springs State Park

provi ded boat support.

We also thank the DEP staff in Tallah assee and in the State P a rks for their

assistance in this study.

ii

## TABLE OF CO NTENTS

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

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

CHAPTER 2. Assessment of m acroalgal b i om a ss and taxono m i c com position in Florida springs

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

CHAPTER 3. Experimental confirm a tion of lim iting nutrients using experim e ntal stream s ......46

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

CHAPTER 5. Nutrient diffusing substrata assessm e nt of nut rient lim itation of m i croalgae .......71

CHAPTER 6. Diatom indicat ors of nutrient conditions in Florida springs ..................................76

CHAPTER 7. Data

............................................................................................................... .........92

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

CHAPTER 9. Appendices

......................................................................................................... .101

iii

## EXECUTIVE S U M M ARY

Nuisance growths of m a croalg ae hav e been iden ti fied as a pro b lem for the recreational use of

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

hum a n activ ity and incre a ses in nu tr ients in the s p rings. Developm ent of nutrient criteria and the

tools to im plem ent the cr ite ria will he lp protect springs and other water bo dies of the s t ate.

However, prior to this study, no system atic st udies of m acroalgal occurrence and their

relationships to nutrients in Florida sp rings of this scope are available.

A survey of filam e ntous m acroalgae was conducted at 60 sites in 28 springs of north and central

Florida, with the following results:

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

springs.

• Vaucheria and a noxious cyanobacterium

, Lyngby a majuscula

, were the two m o st

common of a great diversity of algae at the sites.

• The percen t of the botto m of springs covered by Vaucheria was rela ted to tota l nitro g en

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

nutrients in the ranges studied.

Experim e nts, conducted using a variety of techni ques to determ ine whether the nitrogen or

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

• Laboratory bioassays using wate r f r o m the sprin g s indic a ted th at algae w e re lim ited by P

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

• In situ bioassays with nutrient-diffusing substrata em bedded in sedim e nts were not

conclus i ve b ecause of lo w colonization rates and the loss of m a ny substrata.

• Outdoor m e socosm studies in re circulating stream s indicated Vaucheria growth rates

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

growth rates were not lim ited by the range of nutrients tested.

• Outdoor m e socosm studies in sm all tubs indica ted that conductivity affected the response

of Vaucheria to nutrients, but did not affect Lyngbya

.

Changes in m i croalgae on plants, m acroalgae and sandy spring bottom s were surveyed to

develop a l ga l indicato r s of nutrient c onditions in springs. Th ese ind i cato r s will com p lem e nt the

m easure m ent of nutrients in spring s to characterize nutrient conditions.

• When variability in conductivity was reduced by elim inating high conductivity sites from

analyses, the im portance of nutrient regulat ion of diatom species com position becam e

evident.

• Variation in nitrogen and phosphorus c oncentrations am ong spring sites was not

correlated as in m o st studies, so different diatom indicators of nitrogen and phosphorus

conditions were developed.

• Existing d i atom indicato r s of nutrien t conditi ons were weakly correlated to P conditio ns

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

Regulation of nitrogen m a y control m acroalgal growths in F l orida sp rings, but m o re evidence is

required for developm ent of specific nutrient criteria.

1

## CHAPTER 1. INTRODUCTION

Nuisance growths of m a croalg ae hav e been id entified as a problem in Florida springs,

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

These springs have great econom ic importance fo r the state (Bonn and Be ll, 2003) and represent

unique resources for the support of recreation and biodiversity Florida Springs Task F o rce,

2000). Nuisance algal growths have been observe d in m a ny springs and have been associated

with inc r eas es in hum an activ ity and nutrients, particularly nitr ate. Developm ent of nutrient

criteria and the tools to implem e nt the criteria wi ll help protect springs and other water bodies of

the state (USEPA 1999) . However, no previous sy stem atic studies of m a croalgal occurrence and

their re latio n ships to nu trien t s in Flo r ida sp rings of this scope are ava ilab l e.

Three separate lines of research were developed to dete rm ine the f o llowin g :

1. Nutrient and algal conditions;

2. The relationships between nutrients and algae; and

3. Monitoring and m a nage m e nt tools to protect and restore the springs.

First, a la rge num b er of sites in Flor id a spri ngs were surveyed to determ ine the diversity

of the algae; the factors aff ecting algal diversity; the rang e of nutrient conditions; and the

relationships between the extent of algal grow th and nutrients. Springs were sam p led and

surveyed during both the spring and the fall to determ ine whether conditions differed during the

two seasons. The relationships am ong the m acroa lgal cover of spring bottom s , the thickness of

m acroalgae, and nutrient concentratio ns were the m a in focus. Results of these survey s are in

Chapter 2.

In m a ny studies, nitrogen and phosphorus co-vary; so the nutrients that lim it algal growth

and would affect algae if reduced had to be identif ied. W h ile the m a gnitude of correlations

provides a hint about which f actors most affect biology in ecosystem s , experim e nts were

necessary to show cause-effect relationships and determ ine whether m a nagem e nt of ni trogen or

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

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

springs. Due to the variations in size and grow th for m s of a l gae in springs and the lack of

research availab l e on m a croalg ae in m e socosm s, three d i fferent experim e ntal app r oaches were

used:

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

stream s, with varying amounts of nutrients added.

2. Algal growth rates were observed in sm a ll tubs in which conductivity and nutrients

were m a nipulated.

3. Sm all experim e ntal devices were placed in s t ream s. These devices s l owly leaked

nutrients through a fine-m esh screen upon wh ich algae could grow in as natural

setting a setting as possible.

The results of these experim e nts can be found in Chapters 3-5.

2

### Assessing and m onitoring nutrien t c onditions in stream s a nd springs is challenging

because nu trient con centrations vary with alga l metabolism during th e da y, with weather-related

runoff events that rins e n u trients into springs, and with natural week ly var i ations in alg a l acc rual

and die-off cycles (USEPA 1999). Diatom s are mi croscopic algae that are often the dom i nant

photosynthetic organism s in aquatic habitats. Th ey are an important ba se of the food web and

are ve ry sen s itiv e to ch a nges in nutrient concen tr ations. Bec a use the r e a r e so m a ny dif f

e rent

species of diatom s, and the species have differe nt sensitivities to nutrients, diatom s can be

sensitive an d precise ind i cato r s of nutrient cond itions. In add ition, b ecause they live for

extended periods of tim e in springs, diatom s pr ovide a tem p orally integrated indication of

nutrient con d itions in sp rings th at m a y m o re accu rately and precis e ly ch aracterize th e nutrient

regim e of a water body than the sam p ling and m easurem ent of nutrient chem istry (Stevenson and

Sm ol, 2003). Thus, studies of diatom s and other m i croalgae in stream s and their relationships to

nutrients were conducted to better determ ine th e effects of nutrients on algae and to develop

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

This research was intensive, but the study’ s tim e line was foreshortened by the state.

Thus, the strategy for su ccess was to collect as m u ch inform a tion during surveys, experim e nts,

and laboratory work as possible, use all viable experim e ntal approaches, analyze the most

prom ising data that resulted from this work, and pr esent tha t d a ta in this re port. Suf f i cient tim e

was not available to analyze and pres ent the resu lts of all the data th at we re collected. However,

those data have been organized in electronic format and re prov ided with this repo rt to the

Florida Departm e nt of Environm ental Protection. The f iles in which data are sto r ed a r e

presented in Chapter 7.

3

## CHAPTER 2. ASSESSMENT OF MACROALGAL BIOMASS AND

TAXONOMIC COMPOSITION IN FLOR IDA SPRINGS AS A FUNCTION

OF NUTRIENT CO NCENTRATIONS

Introductio n

Florida Springs are an important and unique resource used by m a ny for recreational

activities, such as swimm i ng, boating, and sight-seeing (F lorida Springs T a sk Force, 2000).

Concerns have developed that the occurren ce an d biom ass of m a croalgae in the springs have

increased du ring th e las t decade (Sou thwest Flor ida W a ter M a nagem e nt District, 200 3). In the

last fifty years m a ny of t h e springs experien ced drastic chan ges in their watershed and in the area

directly surrounding springs. An in crease in nutrients, especially nitrate, was observed in som e

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

have stim ulated the occurrence of nuisance algal bloom s in Florid a springs is cause for concern.

Very few published studies docum ent the occu rrence of algae in Fl orida springs. Odum

(1957) reported the presence of s m all Spirogyra sp., Oedogonium sp. and Rhizoclon i u m sp. m a ts

in Silver Springs. Their biom ass was very low co mpared to th e rest of the prim ary producers in

Silver Springs; consequently, m acroalgal biom a ss was not included in the estim a tes of system

productivity in Odum ’s study. A list of algae fr om Florida springs com p iled by W h itford (1956)

includes m a croalg ae tax a such as Vaucheria sp. and Cladophora sp., but m a t thickness and

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

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

Very few published studies docum ent relations hips between macroalgae and nutrients.

Relation s hip s between Cladophora and nutrients have been described, but in stream s in

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

these relationships m a y not apply to the types of algae in Florid a springs and to the physicalchem ical conditions in F l orida springs.

The objectives of this study were to do cum e nt the types of m a croalgae occurring in

Florida sp rings, the spa t ial ex tent and thickness of algal m a ts in springs, and the relationships

am ong the amount of m a croalg ae, nutrients, and other environm en tal factors. A survey approach

was used to observe and collect m acroalgae at a large num ber of sites in a large num ber of

springs. This approach was believed to be the be st forchar act e ri zi ng t h e status of conditions in

Florida springs and determ ining th e relationships between the m acr oalgal biom ass and nutrients.

This study was the first system atic study of bent hic algae in a large num ber of Florida springs

that incorpo r ated m acro a lgal p e rcen t cover and m acroalgal m a t thickness during m o re than one

season.

Methods

Study sites

Twenty eight springs, mostly of first and second m a gnitude and located in northern and

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

4

### Table 2.1

Study sites, spring and s i te codes, s a mpling dates, and latitu de and longitude of trans e ct 1 for

each site.

Spri ng Spri ng

code Site n a m e Site co d e Latitu d e Lon g itud e Date sam p led

spri ng Date sam p led

fall

Head ALE

- 0 1 2 9 .0 8 1 2 8 81 .5 7 5 6 3 3/ 2 8 / 2 00 3 10/ 29/ 20 0 3 Al exa nde r ALE

Downstrea m ALE

- 0 2 2 9 .0 8 2 3 1 81 .5 7 7 5 4 3/ 2 8 / 2 00 3 10/ 29/ 20 0 3

Blue holes CH A- 01 2 8 .716 17 8 2 .575 02 4 / 1 / 200 3 1 1 / 2 / 20 03

Dock CH A- 02 2 8 .715 58 8 2 .576 30 4 / 1 / 200 3 1 1 / 2 / 20 03 Chassa howitzka CHA

Br own spr i ng CH A- 03 2 8 .717 21 8 2 .575 86 4 / 1 / 200 3 1 1 / 2 / 20 03

Cypress C Y P H e a d CY P-01 3 0 .658 55 8 5 .684 30 9 / 24 /20 0 3

Fanning F A N H e a d FAN

- 0 1 2 9 .587 57 8 2 .935 41 1 0 / 2 / 20 03

Pipe G A I

- 01 3 0 .427 36 8 5 .548 27 4 / 26 /20 0 3 9 / 25 /20 0 3

Gaine r GAI

Side boil G A I

- 02 3 0 .428 84 8 5 .548 54 4 / 26 /20 0 3 9 / 25 /20 0 3

Gu ra nato G U R H e a d G U R

- 0 1 2 9 .779 73 8 2 .940 01 1 0 / 2 / 20 03

Hom o sassa H O M A f ter b r i d g e HOM -0 1 2 8 .7 9 9 6 1 82 .8 5 9 0 5 4/ 2/ 2 0 0 3 11/ 4/ 2 0 0 3

Head IC H- 0 1 2 9 .9 8 4 0 8 82 .7 6 1 8 4 3/ 1 6 / 2 00 3 11/ 22/ 20 0 3

Blue Hole IC H- 0 2 2 9 .9 8 0 6 8 82 .7 5 8 6 6 4/ 2 0 / 2 00 3 11/ 22/ 20 0 3

Below Blue

Hole IC H- 0 3 2 9 .9 8 0 0 7 82 .7 5 8 9 5 3/ 1 7 / 2 00 3 11/ 22/ 20 0 3

M i ssion spr i ng I C H

- 04 2 9 .976 28 8 2 .757 83 4 / 23 /20 0 3 1 1 / 9 / 20 03

Devils Ear I C H

- 05 2 9 .973 88 8 2 .759 96 4 / 23 /20 0 3 1 1 / 9 / 20 03

Mill Pond I C H

- 06 2 9 .966 58 8 2 .760 05 4 / 23 /20 0 3 1 1 / 9 / 20 03

Befor e br idge I C H

- 07 2 9 .954 95 8 2 .785 07 4 / 24 /20 0 3 1 1 / 8 / 20 03 Ichet u cknee ICH

Co ff ee sp rin g I C H

- 08 2 9 .959 37 8 2 .775 26 4 / 21 /20 0 3 1 1 / 8 / 20 03

In dian I N D H e a d I N D

- 01 3 0 .250 77 8 4 .322 03 9 / 26 /20 0 3

Head JAC

- 0 1 3 0 .7 9 0 3 7 85 .1 3 9 9 8 4/ 1 9 / 2 00 3 9/ 2 4 / 2 00 3

Boat ra m p JAC

- 0 2 3 0 .7 8 2 4 9 85 .1 6 0 2 2 4/ 1 9 / 2 00 3 9/ 2 3 / 2 00 3 Jacks o n Blue JAC

Arro wh ead

cam p gr ound JAC

- 0 3 3 0 .7 5 6 0 9 85 .1 8 6 8 0 4/ 1 8 / 2 00 3 9/ 2 3 / 2 00 3

Head JUN

- 0 1 2 9 .1 8 3 6 5 81 .7 1 2 0 1 3/ 2 7 / 2 00 3 10/ 31/ 20 0 3

Fer n Ha m m ock JUN

- 0 2 2 9 .1 8 3 6 4 81 .7 0 8 0 1 3/ 2 7 / 2 00 3 10/ 31/ 20 0 3

Riv e r f o rk JUN

- 0 3 2 9 .1 8 5 1 9 81 .7 0 7 2 6 3/ 2 7 / 2 00 3 10/ 31/ 20 0 3 Juni per JUN

After br idge on

r oute 19 JUN

- 0 4 2 9 .2 1 2 8 3 81 .6 5 4 3 1 3/ 2 6 / 2 00 3 10/ 31/ 20 0 3

Lafayette Blu e L A F H e a d LA F- 01 3 0 .125 92 8 3 .226 17 9 / 29 /20 0 3

Little Riv e r L T R H e a d LTR- 0 1 2 9 .996 42 8 2 .966 75 9 / 28 /20 0 3

M a di so n B l ue M A D H e a d MA D- 01 3 0 .480 56 8 3 .244 39 9 / 29 /20 0 3

Manatee M N T H e a d MN T-0 1 2 9 .489 52 8 2 .976 92 1 0 / 3 / 20 03

Ponce de Le on P O N H e a d PO N- 0 1 3 0 .7 2 0 9 0 85 .9 3 0 7 1 4/ 2 6 / 2 00 3 9/ 2 2 / 2 00 3

Head R A I- 0 1 2 9 .1 0 2 2 3 82 .4 3 7 4 1 4/ 4/ 2 0 0 3 11/ 12/ 20 0 3

KP Hole R A I- 0 2 2 9 .0 9 2 9 4 82 .4 2 8 4 8 4/ 4/ 2 0 0 3 11/ 12/ 20 0 3

Bef o re tu b e rs

sign R A I- 0 3 2 9 .0 6 3 0 5 82 .4 2 7 8 8 4/ 4/ 2 0 0 3 11/ 12/ 20 0 3 Rain bo w

Spri ng RAI

Befor e br idge R A I- 0 4 2 9 .0 5 2 2 3 82 .4 4 7 0 0 4/ 4/ 2 0 0 3 11/ 12/ 20 0 3

Silver Glen S G L H e a d SGL

- 0 1 2 9 .2 4 6 0 3 81 .6 4 3 4 5 3/ 2 6 / 2 00 3 10/ 30/ 20 0 3

Head SLV

- 0 1 2 9 .2 1 6 1 9 82 .0 5 2 5 2 4/ 3/ 2 0 0 3 11/ 13/ 20 0 3

Second pool SLV

- 0 2 2 9 .2 1 5 8 4 82 .0 4 9 8 7 4/ 3/ 2 0 0 3 11/ 13/ 20 0 3

Bir d s of pr ey SLV

- 0 3 2 9 .2 1 5 6 1 82 .0 4 1 1 2 4/ 3/ 2 0 0 3 11/ 13/ 20 0 3

Old swim m i ng

area SLV

- 0 4 2 9 .2 0 5 0 0 82 .0 2 9 0 2 4/ 3/ 2 0 0 3 11/ 13/ 20 0 3 Silv er Riv e r SLV

Cabbage palm SLV

- 0 5 2 9 .2 0 2 1 1 82 .0 1 1 2 7 4/ 3/ 2 0 0 3 11/ 12/ 20 0 3

Troy T R Y H e a d TRY

- 01 3 0 .005 98 8 2 .997 56 9 / 29 /20 0 3

Tu rtle T U T H e a d TU T- 01 2 9 .847 42 8 2 .890 41 9 / 30 /20 0 3



### 5Spri ng Spri ng

code Site n a m e Site co d e Latitu d e Lon g itud e Date sam p led

spri ng Date sam p led

fall

Head VOL

- 0 1 2 8 .9 4 7 5 8 81 .3 3 9 6 9 3/ 2 9 / 2 00 3 11/ 20/ 20 0 3

Vol u sia Blue VOL Downstrea m

fr o m sta i r s VOL

- 0 2 2 8 .9 4 6 7 9 81 .3 3 9 2 1 3/ 2 9 / 2 00 3 11/ 20/ 20 0 3

Head RR WAC

- 0 1 3 0 .3 3 9 7 9 83 .9 9 2 4 4 4/ 7/ 2 0 0 3 9/ 2 7 / 2 00 3

M i nnow WAC

- 0 2 3 0 .3 3 0 2 0 83 .9 8 7 7 6 4/ 7/ 2 0 0 3 9/ 2 7 / 2 00 3 Wacissa WAC

Big Blue WAC

- 0 3 3 0 .3 2 7 7 0 83 .9 8 4 8 4 4/ 7/ 2 0 0 3 9/ 2 7 / 2 00 3

Head W A K

- 01 3 0 .235 33 8 4 .302 87 4 / 8 / 200 3 1 0 / 1 / 20 03

T u r n ar ound W A K

- 02 3 0 .233 18 8 4 .288 70 4 / 8 / 200 3 1 0 / 1 / 20 03 Waku lla WAK

Bir d colony W A K

- 03 3 0 .225 07 8 4 .274 70 4 / 8 / 200 3 1 0 / 1 / 20 03

Was h in gto n

Blu e WG T H e a d WG T- 0 1 3 0 .4 5 2 7 9 85 .5 3 0 4 4 9/ 2 6 / 2 00 3

Head WE K- 0 1 2 8 .5 1 7 4 7 82 .5 7 3 4 9 3/ 2 4 / 2 00 3 11/ 5/ 2 0 0 3

Boat dock WE K- 0 2 2 8 .5 1 9 0 1 82 .5 7 3 6 1 3/ 2 4 / 2 00 3 11/ 5/ 2 0 0 3

WM A WE K- 0 3 2 8 .5 2 4 8 1 82 .5 9 5 8 3 3/ 2 4 / 2 00 3 11/ 4/ 2 0 0 3 Weeki Wac h ee WE K

Ro g e r's Park WE K- 0 4 2 8 .5 3 0 5 7 82 .6 2 4 0 7 3/ 2 4 / 2 00 3 11/ 5/ 2 0 0 3

Head WK W- 0 1 2 8 .7 1 1 9 3 81 .4 6 0 3 7 3/ 3 0 / 2 00 4 11/ 21/ 20 0 3 Weki wa WK W

Canoe launch WK W- 0 2 2 8 .7 1 2 6 9 82 .4 5 9 4 8 3/ 3 0 / 2 00 4 11/ 21/ 20 0 3

W illfo r d WI L H e a d WIL

- 0 1 3 0 .4 3 9 6 6 85 .5 4 7 6 3 9/ 2 5 / 2 00 3



### 6

### Figure 2.1

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

VO L

WK

W AL E SG L

JU N SL V

RA

I

WE K CHA HO M IC H

TU T

FA

N GU R LT R

MN T TR Y LA

F MA

D

WA

C

WA

K IN D JA

C

WG T

WI L

GA

I CY

P PO N



### This project began in March 2003. Sam p les we re collected twice: 1) in the spring of

2003 (March and April), and 2) in the fall of 2003 (Septem b er to Novem b er). The second

sam p ling event was m o ved f r om summ er to f a ll since m a ny of the studied sites are highly

affected by hum a n recreationa l activity during the summ er ( B onn and Bell, 2003) and that

activ ity was believ e d to r e duce m acroalgal cover a nd biom ass. In the spring, 48 sites w ithin 16

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

Originally, 53 sites were selected for both sa m p ling events, but due to flooding along Suwannee

River in the spring of 2003, 5 springs could not be sam p led. The spring sam p ling showed few

springs with low TP concentrations. Therefor e, during the second sam p ling period (fall), the

originally selected 53 sites were sam p led, along with 7 additional sites that were selected

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

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

of sa m p led sites provided by DEP are in Appendix A.1.

Rapid Habitat and Periphyton Assessment (R HPA)

At each of the study sites a m odified Ra pid Habitat and Perip hyton Assessm e nt

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

m a king observations from a canoe or boat, depe nding upon the depth of the spring run and site

access i bility . At each site, rese arche r s designa ted 9 transe cts going acros s the spring run and

position e d about 10 m a p art. At m o st sites th e first transect crossed th e most upstream , boil area

and the following transects were located downs tream from the m o st upstream boil. Nine

observation points were designated for each trans ect , resultin g in a to tal o f 81 observation poin t s

at each site. The length and bu ffer width were m easured for each transect. Buffer wi dth was

7

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

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

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

canopy cover was m easured (with a spherical con v ex crown densiom e ter). At each point, the

stream depth was m easured, current velocity was estim ated, and the substratum type was

characterized. The pres ence and tax onom i c iden tity of m acrophytes (Dressler, 1991; Ram e y,

1995) and macroalgae and the thickn ess of the macroalgal m a t we re noted. The thickness of

m acroalgal m a ts was recorded on an ordinal scale: 0.5 for mat thickness <0.5 cm , 2 for m a t

thickness 0.5-2 cm , 5 for m a t thickness 2-5 cm , 20 for m a t thickness 5-20 cm , 50 for m a t

thickness 20-50 cm and 100 cm fo r m a t thickness >50 cm . For other benthic algae, epiphyte

thickness and epipelon thickness were estim a ted a nd also recorded on an ordinal scale: 0.5 for

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

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

for m ed by m acroalgae and was m o r e than 20 mm thick, it w a s reco rded as m acroalgae. At each

site the RPHA was repeated at one random l y sele cted transect to characterize sam p ling error.

However, it was im possible to go back exactly to the original points sam p led, so the very patchy

distribution of plants and m acroa lga e between th e orig inal an d re peated transect assessm e nt was

charac ter i ze d. Global p o sition i ng s y stem (GPS) coordin a te s were reco r d ed in the m i ddle of

transect 1 an d transect 9.

The original RHPA was developed f o r sites th at were wadeable and in w h ich the stream

bottom could be viewed easily. However, m o st of the study sites in this pr oject were very deep,

with 65% of sam p led points m o re than 1 m deep and an average depth in excess of 1 m at m o st

sites. Thus, the original sam p ling methods had to be m odifie d for deeper water. Measurem ent

of flow with a flow m e ter was not po ssible, b ecau se the centers of the chan nel at alm o st all s ites

were too deep to conduct m easurem e n ts with the instrum e nt. Current ve locity was estim a ted on

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

curren t velo city with a current m e ter at each ob s e rvation point was im practical.

The percent cover or percent presence of pl ants, m acroalgae, different substrata types,

etc., was based on results from the RHPA asse ssm ent and was calcu lated as the percent of

sam p led points where particular plant m acroalgae or substrata were observed. Usually, at any

one point m o re, than one plant, m acroalga or substratum type was observed.

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

discharge was based on USGS data (Rosenau et al., 1977). T h e distance from boil was m e asured

by using the tracking tool along the river run in Microsoft Streets & T r ips 2000.

Algae samples

Macroalgae

From each transect, a m a croalg ae sample was co llected to co nfirm the accuracy of field

identification (198 sam p les in the spring and 463 sam p les in the fall).

At each site, a com posite m acroalgal sam p le was also co llected. This was a qualitative

sam p le consisting of pin c hes tak e n from each tran sect. Forty

- seven sam p les were collected in

the spring and 60 sam p l e s (including 2 sam p les collected for QAQC) were collected during th e

8

### fall. A subsam ple was taken for algal m a t total nitrogen and total phosphor us analyses (analyzed

by FDEP). For m acroalgae chlo rop hyll a analyses, a sm all pinch was patted dry, weighed for

fresh m a ss (FM) and frozen. For m a croa lg ae volum e and m acroalg ae ash free dry m a ss

(AFDM), a pinch of algae was pla c e d into a vo lu m e tric cylin der f illed with water. M acroa lgae

were suspen ded to allow them to f ill their natu ral volum e, and the volum e they f illed ( not the

volum e displaced by the m acroalgae) was recorde d. This m e asurem ent allowed the re lationsh i p

between m a croalg ae m a t thickness and m acroalg ae m a ss per surface area (1m l of volum e used

by m acroalgae equals 1 cm th ick m a croalgal m a t per cm 2 

### ) to be calculated. Then the m a croalgae

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

The dom ina n t m acroalgal taxa were identi fied under the dissecting scope (up to 100x

m a gnification). A m i croscope ( up to 400x) was used if identifi cation was not possible under the

dissecting scope.

Epiphytes

At each tran sect (if m acrophytes were pres en t), a m i d-stem se ction of a plant was

collected for a com posite epiphyte sample. The original proposed coll ection of tips and m i dstem sections could not be execute d at all sites d u e to the d i f f i culty of sa mpling in de ep water.

Originally, SCUBA wa s planned to be used for sam p ling in deep water, however, this was not

possible du e to tim e lim itations ( i n th e tr ial it too k a f u ll working day to sam p le one site while

using SCUBA) and personnel restrictions (volunteer s were required to assist with SC UBA). In

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

Vallisn e ria sp. (the m o st common taxa in this study) ha ve the oldest s ectio ns of the pla n t at the

tip and youngest at the base, which is not the cas e for other plant taxa. T o correct for this

difference in growth patterns, only m i d-stem sect ions were collected. Epiphytes were brushed

off and rinsed from plant fragm e nts. Forty-fi ve sam p les were collected during the spring and 61

(including 3 repeated samples for QAQC) sa m p l e s we re collected during th e fall. A subsam ple

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

rem oved we re frozen for plant surface analysis. Macrophytes were spread flat on a white sheet

of paper and scanned with a flatbed scanner. The plant su rface area was calculated us ing the

scanned im age and Im age-J software (Rasband, 2003).

Epipelon

At each tran sect, if sedim e nts were exposed to m i croalgal colonization, a sedim e nt core

(5.2 cm in diam eter and 0.3 cm deep) was collect ed. A com p osite quantitative epipelon sam p le

consisted of all m i croalg ae collect ed with cores at the site. Al gae were removed from sedim e nts

by swirling and rinsing the sedim e nts with distilled water (Steve nson and Stoerm er, 1983). This

swirl-pour procedure was repeated 10 tim es to re move m o st algae from sedim e nts. Forty-six

sam p les were collected d u ring th e spring and 21 s a m p les were collected in the fall. Th e latter

were collected from 6 sites sam p led in the sp ring, 12 sites not sam p le d in the spring and 3

repeated samples for QAQC. A subsam ple wa s taken for chlorophyll a analysis and frozen

imm e diately.

Chlorophyll a and AFDM

Chlorophyll a analyses w e re conducted within 4 weeks of sample collection.

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

9

### fluorom eter (Clesceri et al., 1998). S a m p les for AFDM were preserved with M3 and were

processed following Clesceri et al. (1998).

Water analyses

Water tem p erature, pH, conductivity and dissolved oxygen (DO) were measured at each site at

transect 1 and 9 with YSI or H ydrolab. W a ter clarity (light ex tinction coef ficient with PAR

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

most upstre a m transec t , water sam p les were co llected to analyze for alkalinity, ammonia

nitrogen, calcium , chloride, iron, m a gnesium , Kj eldahl nitrogen, nitr ates, orthophosphate,

potassium , silica, sodium, strontium and sulfate. Sa m p les were analyzed by FDEP. When the

concentration of a com p ound was below the dete ction lim it of the laboratory m e thod, the m e thod

detection lim it is reported (Table 2.2). Al gal growth potential and algal lim iting nutrient

bioassays w e re conducted by FDEP on water samples collected fr om 15 sites in the spring and

12 sites in th e f a ll.

Table 2.2

Detection lim its of water chem ical analyses . W h en concentration of a compound was below

detection lim it of the laboratory m e thod, the m e thod detection lim it is reported.

C o m pou nd Det ect i on l i m i t (m g/ L)

Alk a lin i t y 5

Am m oni a

- N 0

. 01 0

C a l c i u m 0

. 05 0

C h l o ri d e 0

. 20 0

Ir o n 0

. 01 0

M a gnesi um 0

. 02 8

N_ KJEL _T OT 0

. 06 0

NO 2 N O 3

- N 0

. 00 4

O-P h os phat e -P 0

. 00 4

Pot a ssi um 0

. 02 5

Si l i c a 0

. 30 0

So di um 0

. 15 0

Stron tiu m 7

Sul f at e 0

. 20 0

Tot a l

- P 0

. 00 4



### Soft epiphytic algae

A subsam pl e from a composite epiphyte sam p le was preserved with M3 for taxonom ic

algal analyses (Clescer i et al., 1998). Subsam ple s were collected by homogenizing samples on a

m a gnetic stirrer, then co llecting aliq uots of each sam p le with a pipette for each subsam ple.

Multip le aliquots were collected to reduce error due to patch i ness. Algae were enumerated in a

Palm er-Maloney counting cham ber (W ildco®) unde r 400x. The count was continued until at

least 300 live algal units were c ounted. Both live and dead diatom s were counted, but dead

diatom s did not contribute to th e 300 live units count. Each diat om cell was counted as one unit

with the exception of diatom s that form ed chai ns; then a chain was counted as a unit. Algae

were identified to the lowest possible taxonom ic level (Dillard, 1989; Kom a rek, 2003; Sheath,

10

### 2003; W h itford and Schum acher, 1984; Kom a re k and Anagnostidis, 1999; Prescott, 1982;

Starm ach, 1963).

Diatoms

A subsam pl e was taken for diatom analyses fro m each m acr oalgae, ep ip hyte and ep ipelon

sam p le. Diatom subsa m ples were collected by hom ogenizing sam p les on a m a gnetic stirrer and

collecting aliquots of each sam p le with a pipette for diatom subsam ple. Multip le aliqu o ts were

collected to reduce error due to patchiness. Diatom s were identified under 1000x after acidcleaning and m ounting in Naphrax® on m i croscope slides. Data an alyses of diatom sa mples are

presented in Chapter 6.

Statis tica l a n alyses

Pearson correlations between m easured enviro n m ental varia b les were ca lcula t ed us in g

SYSTAT 10 software. Repeated m easures ANOVA on DM, AFDM, Chl a and Phoephytin data

was conducted using SAS/STAT.

Multiv ar iate ordina tion a n alysis was em ployed to determ ine the ef f

e ct of environm ental

variables on the percent c over of m acroalgae at the studied si tes. Analysis was conducted with

CANOCO v.4 for W i ndows. Species data were log-transform e d a nd spe c ies scores were divided

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

gradient analysis (only species data), and environm ental variables were used as supplem entary

environm ental data to calculate axes correla tions with environm ental variables (Leps and

Sm ilauer, 2003). Since species distributions fo llowed unim o dal patterns (based on the m a xim u m

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

used for direct gradient analys is (species and environm ental variables) (Leps and Sm ilauer,

2003).

Results

Rapid Habitat Assessment (RHA)

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

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

project Access database (from spring sa m p ling in tblRHA_Ca nopy_Buffer_TLgth_spring2003

and from fall sam p ling in tblRHA_Canopy_Buffer_ TLgth_fall2003). Most of the study sites

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

least one boil, but in a few cases, it w a s not possibl e to sam p l e directly over the boil. For a few

springs, a different number of boils was observed in the sp ring and in th e f a ll. Also, a f e w sites

had high grazer density, which c ould have affected the epiphyte and epipelon biom ass. W i de

ranges of current velocity were observed; how ever, the av erag e current velocity was lo w

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

Chemical and physical water parameters

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

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

sim ilar (Appendix A.5). Water ch emistry data are in Appendix A.6.

11

### Macroalgae

Taxonomic composition

Macroalgae were found at 59 out of 60 sam p led s ites and were present at 45% of all points

sam p led in Florida springs. The only spring in which no macroalgae w e re found was Cypress

Spring. Troy Spring had m acroalgae present but m a t thickness was very low; thus, it was not

possible to collect enough m acroalgae for all of th e analyses. In all, 24 taxa of m acroalgae were

found (Table 2.3). The most commonly observed taxa were Lyngbya majuscula a n d Vaucheria

sp. (Figure 2.2). Thick algal m a ts were form ed by Lyngbya majuscula

, Vaucheria sp.,

Compsopogon sp., and Dichotomosiphon sp.

Table 2.3

Macroalgal taxa found in Florida Springs a nd codes for m acroalgae used in this study.

T a x a A l g a e c o d e

C y ano phy t a :

Lyn gby a m a j u s c ul a

Oscilla to ria s p

. Lyn gby a aest u a ri i

Aph ano th ece s p

. balls

Ph ormi di u m s p

.

Rh odo ph yta:

Po lysiph on ia su b tilissima

C

a l o gl oss a sp

. Co mp so pog on sp.

Au do ui o n el l a sp

. Batrac hos p er mum s p

.

Bacillariophyt a :

Pleurosira le avis

Terpsi noe musi ca

Aulac o sir a sp

.

Xant hophyceae:

Va ucheri a sp

.

Ch lo rop h y ta:

Spi r o gyra s p

. C l ad op h o ra cf gl o m er at

a

Rhi z ocl oni um hi ero g l y p h i c u m

D i cho t om osipho n sp.

Hyd r od ictio n s p

. Enteromor p ha sp.

C

haet o m o r p h a sp.

Stigeocl oni um sp.

O e dog on ium sp

. Schi z o meri s s p

. L

O

Ls

Ap

Ph

Po

Cg

Cm

U

Br

B

V

S

C

Rh

D

Hd

E

Ch

Od



### 12

### Figure 2.2

Proportion of m acroalgal taxa found at 59 sam p ling sites during spring and fall sam p ling based

on results of PRHA asse ssm ent (8676 points).

Ot h e r D i at om s f o r m i n g f i l a m e n t s

C h aet om or ph a s p

.

R h i z oc l o n i u m h i er ogl y p h i c u m

H y

dr odi c t y o n s p

.

C l adoph or a c f

. gl om er at a

O sci l l a t o r i a sp

.

S p i r ogy r a s p

. Va u c h e r i a s p

. Ly n gby a m a j u s c u l a



### Figure 2.3

Percent m acroalgal cover at 59 sites in the fall.



## % cover

<10 20 30 40 50 60 70 80 90 100

13

### Table 2.4

Pearson correlation m a trix for m acroalgae and env i ronm ental v a riab les in the fall (p<0. 05 in

bold).

A v er ag e

cove r

th ick n e ss Perce n t

cove r DM A F DM C h l a Phe o p h y t i n Lyn gby a

maj u sc ula

perce n t

cove r Va ucheri a

sp. pe rcent

cove r

ALK A L I N I T Y 0. 13 1 0

. 18 5 0

. 05 7 0

. 04 5 0

. 12 7 -0 .0 5 0 0. 01 2 0. 46 5

A MMON I A N

- 0 .094

- 0 .080

- 0 .092

- 0

. 074

- 0 .064

- 0 .006

- 0 .102

- 0 .104

C A LC I U M 0. 20 6 0

. 24 3 0

. 15 3 0

. 20 7 0

. 15 7 0

. 03 2 0

. 09 1 0. 47 8

C H LOR I D E 0. 03 0 0

. 02 9 0

. 02 8 0

. 08 6 0

. 09 2 0

. 08 8 -0 .0 7 1 -0 .0 1 4

I R ON

- 0 .122

- 0 .092

- 0 .133

- 0 .192

- 0 .183 0

. 0 0 5

- 0 .299 0. 19 5

N_ KJEL _T OT

- 0 .0 5 5 0. 13 5 0. 35 6 -0 .0 2 9 0

. 19 6 0. 11 1 -0 .2 4 0 0. 30 0

NO 2 N O 3 N 0. 02 3 0. 05 7 0. 03 1 -0 .0 6 4

- 0 .0 8 2

- 0 .0 1 0

- 0 .1 1 4 0. 37 5

OP HO SP HAT E -0 .0 3 1 0. 22 5 0

. 06 2 -0 .0 2 1 0. 08 2 0

. 19 1 0

. 09 4 0

. 13 0

SIL I C A 0. 03 8 0

. 08 8 0

. 09 6 0

. 06 9 0

. 05 2 0

. 03 8 0

. 08 9 -0 .1 6 5

SO DI UM 0. 02 8 0

. 02 9 0

. 02 8 0

. 08 5 0

. 08 5 0

. 09 0 -0 .0 7 3 -0 .0 2 3

SULF AT E 0. 11 9 0

. 13 5 0

. 17 8 0. 26 3 0. 07 3 0

. 14 8 0

. 14 3 0

. 04 5

TOT A L P -0 .0 3 3 0. 22 1 -0 .0 0 1 -0 .0 1 9 0. 05 6 0

. 15 3 0

. 09 3 0

. 15 2

TOT A L N 0. 01 9 0. 06 5 0. 05 3 -0 .0 6 5

- 0 .0 6 9

- 0 .0 0 3

- 0 .1 2 8 0. 39 1

M A G N ES IU M 0. 02 4 0

. 04 0 0

. 05 1 0

. 08 4 0

. 05 6 0

. 08 7 -0 .0 4 1 -0 .0 0 9

STR O NTI U M 0. 15 8 0

. 20 9 0

. 20 2 0. 32 8 0. 20 3 0

. 16 5 0

. 22 9 0

. 07 1

LAT I T U D E

- 0

. 2 4 9

- 0 .346

- 0 .093

- 0 .286

- 0 .382 0. 04 2

- 0 .361

- 0 .182

LONGITUDE

- 0

. 1 4 4

- 0 .375

- 0 .174

- 0 .240

- 0 .297

- 0 .104

- 0 .368

- 0 .212

P H 0. 00 2

- 0 .0 7 6 -0 .0 1 7 0. 08 2

- 0 .0 1 9 -0 .0 2 5 0. 14 9

- 0 .533

C O N DUC T I V I T Y 0. 06 7 0

. 05 3 0

. 05 6 0

. 11 8 0

. 10 9 0

. 09 5 -0 .0 4 8 0. 05 1

D O

- 0 .1 4 4

- 0 .293 -0 .1 7 5

- 0 .0 8 5

- 0 .2 1 3 0. 10 8 0. 13 5

- 0 .328

TEM PER A T U R E 0

. 23 4 0. 36 7 0. 10 6 0. 28 5 0

. 31 5

- 0 .001 0. 34 1 0. 14 0

CA NO PY

- 0 .187

- 0 .081

- 0 .077

- 0

. 211

- 0 .083

- 0 .112

- 0 .138 0

. 0 7 0

BUFFER

- 0 .270

- 0 .271

- 0 .342

- 0 .258

- 0 .099 0

. 0 1 6

- 0 .176

- 0 .191

Tran sect Le ngt h 0. 04 2 -0 .0 3 3 0. 01 8 0. 09 6 -0 .0 9 4

- 0 .0 4 0

- 0 .0 3 8

- 0 .1 3 5

T E E S

- 0

. 1 3 7

- 0

. 0 1 1

- 0

. 2 4 9

- 0

. 1 0 0

- 0

. 0 9 3 0.15 9 0

. 0 6 3

- 0

. 1 4 7

SHR U B S -0 .0 6 2 0

. 01 6 -0 .2 3 9 -0 .1 0 0 0

. 03 1 0. 03 4 -0 .0 9 1 -0 .2 0 2

HERBS 0. 30 6 0. 12 0 0

. 20 5 0. 35 6 0

. 30 5 0. 00 9 0. 34 0

- 0 .008

BARE 0. 27 9 0

. 28 8 0

. 35 6 0. 20 7 0

. 11 9 -0 .1 3 4 0. 16 7 0. 35 7

L D I 0. 14 9 0

. 10 4 0

. 09 7 0

. 12 3 0

. 21 5 0

. 04 3 0

. 09 1 0. 47 5

DISC H A R G E 0. 04 4 0

. 02 8 0

. 05 2 0

. 11 7 0

. 01 9 0. 29 6 0

. 39 5

- 0 .028

MA T TN 0. 09 7 0

. 09 2 0

. 00 8 0. 0 7 0. 0 8 0. 02 4 0

. 15 7 -0 .1 2 6

MAT TP -0 .0 2 1 0

. 01 6 -0 .0 6 8 -0 .0 4 3 0

. 02 7 0. 00 8 0. 0 6 -0 .1 2 4

MA T TN /TP 0. 1 5

- 0 .0 9 2 0. 00 8 0. 10 2 -0 .0 1 8 -0 .1 2 -0 .0 4 4 -0 .1 1 6



### 14

### Figure 2.4

Average m a croalg al m a t thickne ss at 59 sites in the f a ll.



## Cover thi ckness (cm)

Figure

2.5

Lyngbya majuscula p e rc ent cove r in the f a ll. <10 20 30 40 50 >50



## Lyngbya % cover

<10 20 30 40 50 60 70 80 90

15

### Figure 2.6

Vaucheria s p. percen t c over in the f a ll.



## Vaucheri a % cover

<10 20 30 40 50 60 70 80 90 100

16

### Figure 2.7

Macroalgae dry m a ss (DM) and ash f r ee dry m a ss (AFDM) at 59 sites in th e f a ll.



## DM mg/cm 2



### <50 50 100 150 >150



## AFDM mg/cm 2



### <50 50 100 >100

17

### Figure 2.8

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



## Chla mg/cm 2



### <2 2 4 6 8 10 >10



## Pheophytin mg/cm 2



### <0.5 0.5 1 1.5 >2

18

### Percent cover and biomass

The percen tage of m acroalgae cov e r varied from 0% in Cypress Spring to 94% in

Manatee Spring and was higher for southern site s than for northern sites (F igure 2.3, Table 2.4,

and Appendix A.7).

The average m acroalgae m a t thicknes s varied from 0 c m for Cypress Spring to 55 cm at

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

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

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

Other m acroalgal taxa w e re also common, but their percent cover was low e r than that of

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

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

Macroalgae, water TN and TP, and environmen tal variables

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

There was n o relation between m acroalgae percen t cover and m acroalgal m a t thickness, and TN

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

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

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

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

2.13). Below 0.6 m g TN/L , Vaucheria accrual was constrained to a bout 10% cover or less. The

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

2.13), but th ese relations hips were no t evalu a ted with probab i lity s t atis tics due to tim e

constraints. Vaucheria s p. percen t cover was po sitiv ely co rrel ated with alkalinity (Table 2.4).

Mat TN and TP content was not correlated with the m acroalgae percent cover and m a t thickness

for all taxa com b ined or for Lyngbya and Vaucheria percent cover (Table 2.4) . W a ter alkalinity

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

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

Sim ilarities in the m acroalgal assem b lages be tween sites we re id entified using DCA of

m acroalgal percent cover data. Axis 1 expl ained 14.4% and axis 2 explained 11% of the

variance in the species data (F igure 2.15). Th e first axis grouped sites based on conductivity.

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

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

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

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

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

by Cladophora sp., Hydrodictyon sp. and Spirog y ra sp.

19

### Figure 2.9

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



## TP mg/L

<0.05 0.05 0.10 0.15 >0.15



## TN mg/L

<1 1 2 3 4 5 6

20

### Table 2.5

Pearson cor r e lation m a trix f o r Total P and Total N in the water and environ m ental varia b les in

the fall (p<0.05 in bold).

T O T A L P TOT A L N

ALK A L I N I T Y 0

. 19 7 0. 41 4

AM M O NI AN

- 0 .1 4 3 0. 30 7

C A LC I U M 0

. 13 9 0. 39 3

CH LO R I D E

- 0 .034

- 0 .166

IRO N

- 0 .0 0 1 -0 .0 1 4

N_ KJEL _T OT 0

. 22 0 0. 11 5

NO 2 N O 3 N 0

. 17 3 0. 99 8

PH OSP H ATE 0. 89 4 0. 15 4

SIL I CA 0. 28 8

- 0 .3 1 0

SOD I U M

- 0 .033

- 0 .169

SULF AT E 0

. 09 6 -0 .1 3 7

TOT A L P 0. 18 6

TOT A L N 0. 18 6

M A G N ES IU M 0

. 05 0 -0 .1 5 5

STR O NTI U M 0

. 13 9 -0 .2 0 1

LAT I TUDE

- 0 .286 0. 11 3

LONGITUDE 0. 45 2

- 0 .059

PH

- 0 .320

- 0 .466

CO ND U C TIVI TY

- 0 .017

- 0 .096

DO

- 0 .339 0. 00 4

TEMPERATURE 0. 30 0 0. 06 7

CA NO PY

- 0 .109

- 0 .143

B U FFER 0

. 09 3

- 0 .307

Transect Le ngt h -0.062 0. 32 5

TR E E S 0

. 23 6 -0 .1 7 8

SH RU B S

- 0 .018

- 0 .193

H E R B S

- 0 .036

- 0 .119

B A R E

- 0

. 0 2 0 0. 26 8

L D I 0

. 06 5 0. 16 8

DISC H A R G E 0

. 11 9 0. 13 5



### 21

### Figure 2.10

Macroalgae percen t cover (PCCOVER) as a functi on of water TN and TP for all sites (s catter

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

0. 04 0. 08 0. 12 0. 16

TO TA L P 0 10 20 30 40 50 60 70 80 90 100

P C C O V E R

1 2 3 4 5 6

TO TA L N 0 10 20 30 40 50 60 70 80 90 100

P C C O V E R

0. 0 4 0. 08 0. 12 0. 16

TO TA L P 20 40 60 80 10 0

P C C O V E R

1 2 3 4 5 6

TO T A L N 20 40 60 80 10 0

P C C O V E R

<0

. 0 2 5

. 025-

. 0 50 > 0

. 050

To t a l P 0 10 20 30 40 50 60 70 80 90 100

P r o p o r t i o n o f S i t e s

<0

. 5 0

. 50- 1

. 0 >1

. 0

To t a l N 0 10 20 30 40 50 60 70 80 90 10

0

PC C O V5 0

_ 1 0 0 PC C O V2 5

_ 5 0 PC C O V0

_ 2 5



### 22

### Figure 2.11

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

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

the f a ll.

0. 0 4 0. 0 8 0. 1 2 0. 1 6

TO TA L P 0 10 20 30 40 50 60

A V G C O V T H K

1 2 3 4 5 6

TO TA L N 0 10 20 30 40 50 60

A V G C O V T H K

0. 04 0. 0 8 0. 12 0. 16

TO T A L P 1. 0 10. 0

A V G C O V T H K

1 2 3 4 5 6

TO T A L N 1. 0 10

. 0

A V G C O V T H K

< 0

. 025

. 025

-

. 050 > 0

. 050

To t a l P 0 10 20 30 40 50 60 70 80 90 10

0

P r o p o r t i o n o f S i t e s

<0 .5 0 .5 0

- 1

. 0 >1

. 0

To t a l N 0 10 20 30 40 50 60 70 80 90 10

0

AC T G T 8 AC T 3

_ 8 AC T 0

_ 3



### 23

### Figure 2.12

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

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

0. 0 4 0. 0 8 0. 1 2 0. 1 6

TO TA L P 0 10 20 30 40 50 60 70 80 90

L P C C O V E R

1 2 3 4 5 6

TO TA L N 0 10 20 30 40 50 60 70 80 90

L P C C O V E R

0. 0 4 0.08 0. 1 2 0. 1 6

TO T A L P 0. 1 1. 0 10

. 0

L P C C O V E R

1 2 3 4 5 6

TO T A L N 0. 1 1. 0 10.0

L P C C O V E R

<0

. 0 2 5

. 025

-

. 050 >0

. 0 5 0

To t a l P 0 10 20 30 40 50 60 70 80 90 100

P r o p o r t i o n o f S i t e s

<0

. 5 0

. 50- 1

. 0 >1

. 0

To t a l N 0 10 20 30 40 50 60 70 80 90 100

LP C C O V G T 10 LP C C O V 0_1 0 LP C C O V 0



### 24

### Figure 2.13

Vaucheria sp. percent cover as a functi on of water TN and TP for a ll sites (scatter plots) and

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

0. 04 0. 08 0. 12 0. 16

TO T A L P 0 10 20 30 40 50 60 70 80 90 10

0

V P C C O V E R

1 2 3 4 5 6

TO T A L N 0 10 20 30 40 50 60 70 80 90 100

V P C C O V E R

0. 0 4 0. 08 0. 12 0. 16

TO T A L P 0. 1 1. 0 10

. 0 10 0

. 0

V P C C O V E R

1 2 3 4 5 6

TO T A L N 0. 1 1. 0 10

. 0 10 0

. 0

V P C C O V E R

<0

. 0 2 5

. 025

-

. 050 >0

. 0 5 0

To t a l P 0 10 20 30 40 50 60 70 80 90 100

P r o p o r t i o n o f S i t e s

<0 .5 0 .50- 1

. 0 >1 .0

To t a l N 0 10 20 30 40 50 60 70 80 90 100

VP C C O V G T 1 0 V P C C O V 0_ 10 VP C C O V 0



### 25

### Figure 2.14

Alkalinity of spring water at 60 sites in the fall.



## CaCO 3 

## mg/L

Figure 2.15

Ordination diagram based on DCA of m acroalgal percent cover (60 s ites and 20 species of

m acroalgae) displaying 25% of variance. Eigenv alues for the first thr ee axes are 0.363, 0.277 and

.0173. First and second axis were w e ll correlated with environm en tal variables (r=0.872 for the

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

-1. 0 + 7.0-2.0+2.5

CHA

- 0 3

HO M

- 0 1

CHA

- 0 2 J UN- 0 4 SL V- 0 5

SL V- 0 2 SL V

- 0 4

SL V- 0 1 SL V- 0 3

TU T- 0 1 MN T

- 0 1

WI L

- 0 1 I CH- 0 2

AL E- 0 1 RA I

- 0 3

PO N

- 0 1 J UN- 0 3 RA I

- 0 4

RA I

- 0 2

WG T

- 0 1 Ch

E

U

O

D

V

B

Hd L

Ap

S C

Rh Ls 

### <50 50 100 150 200

26

### Figure 2.16

Correlation triplot based on CCA of the m acroalgal percent co ver (20 m acroalgae sp ecies, 60

sites and 16 environm ental variable s). Num b er of sites and specie s presented were restricted by

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

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

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

-2 .5 +2 .0-2.0+2.0

Alka linity pH

Su lf at e

Cano py Con ductiv i ty

S ilica TO TALN

Buffe r Magne sium

O

- P h os ph LDI

TO TALP Disc h a re DO

MN T

- 0 1

TU T- 0 1 SL V

- 0 5

VO L

- 0 1 W AK- 0 1 SL V- 0 4

IN D

- 0 1

VO L

- 0 2 PO N

- 0 1 SL V- 0 3 SL V- 0 2

SL V- 0 1

IC H

- 0 1

WG T

- 0 1 AL E- 0 1

RA I

- 0 1 RA I

- 0 4

IC H

- 0 6 RA I

- 0 3

JA C

- 0 2 RA I

- 0 2

S G L- 01

IC H

- 0 4 Hd Ap

V S

Rh O

Ls C

B L



### 27

### Figure 2.17

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

only species and environm ental variables are shown).

-2.5 + 2.5-2.0+2.5

Ca l c i u m

A l k

a lin it y Di s c h a r e g e

St r

o n t i u m pH

Su l f a t e

Ca n o p y TO TA L N

S ili c a

Bu f f e r DO

So d i u m Ch l o r i d e Am m o n i a

M agn es i u m LD I

TO TA L P

O- P h os ph Hd Ap

V

Rh S

O C L



### 28

### Figure 2.18

Relation between m acroalgae m a t thickness and di stan ce fro m the boil. Macroalgae thickness

was m easured as up to 0.5cm

, 0.5 – 2 cm indicated as 2 cm , 2 -5 cm indicated as 5 cm, 5-20 cm

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

.

130m



## 1 10 100 1000 10000

Distance from the boil (m) 0 20 40 60 80 100 120

M a c r o a l g a e m a t t h i c k n e s s ( c m )

RAI04



### 29

### Figure 2.19

Relation b e tween m acroalgae m a t th ickness a nd water depth. Macr oa lgae thickne ss was

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

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

.

0. 10 1. 0 0 10. 00

D e pt h ( m ) 0 20 40 60 80 100 120

M a c r o a l g a e m a t t h i c k n e s s ( c m )



### Figure 2.20

Relation b e tween m acroalgae m a t th ickness and estim ated cu rrent v e loc i ty (0 – curr e n t veloc ity

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

velocity above 50 cm /s). Macroalgae thickn ess was m easured as up to 0.5 cm , 0.5 – 2 cm

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

50 cm and over 50 cm indicated as 100 cm

.

-1 0 1 2 3 4

C u r r ent V e l o c i t y 0 20 40 60 80 10 0 12 0

M a c r o a l g a e m a t t h i c k n e s s ( c m )



### 30

### Since high conductivity sites (HOM -01 and CHA-03) and taxa ( Enteromo rpha sp. and

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

analysis. Th e first CCA axis grouped sam p les acco rding to n u trient levels, alkalinity and pH

(Figure 2.16 ), while the second axis grouped samp les according to sprin g discharg e, DO, silica

and m a gnesium

. Lyngbya majuscula was associated with high nut rient and high discharge sites,

and Vaucheria sp. was associated with high alkalinity si tes. D i atom s were associated w ith high

silica s ites, a nd Hydrodictyon sp. and Spirogyra s p. were asso ciated w ith high pH sites (Figure

2.17).

Macroalgae mat th ickness, water depth, di stance from the boil and current velocity

Analysis of m acroalgae cover thickness base d on RPHA data collected for 4851 points in

the fall indicated that the thicke st m acroalgal m a ts (over 50 cm thick) were found only within

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

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

This site is affected by an old phosphate m i ne pit called Blue Cove. Thicker m acroalgal m a ts

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

m acroalgal m a t thickness (Figure 2.20).

Macroalgal mat TN and TP

Macroalgae m a t TN and TP content was the highest for m a croalgae from Ichetucknee

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

water phosphate and was not affected by water T N and TP (Table 2.6). T h e m o lar TN:TP ratio

in m a ts was greater than the 16: 1 Redfield ratio for m o st of the study sites ((Borchardt, 1996),

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

TN/TP ratio below 16:1, indicati ng the phosphate lim itation of m acr oalgal growth if nutrient

concentrations were low e nough to regulate algal growth.

Algal Limiting Nutrients (ALN) and Algal Growth Potentia l (AGP) bioassays

Phosphorus was the lim i ting nutrient at 15 and nitrogen at 6 out of 27 sites in the AL N

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

growth pote n tia l was hig h est f o r the water sam p le f r om W K W

- 01 and lowest f o r the water

sam p le from MNT-01 (Appendix A.11).

Epiphytes

Epiphyte D M , AFDM, and chlorophyll a varied am ong study sites (Figure 2.23,

Appendix A.9) and were as high as 7 m g /cm 2, 6 mg/cm 2 

### , and 40 μ g/cm 2 

### , respectively. Epiphyte

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

2.24, Table 2.7). DM was correlated with ch loride, iron, sodium , m a gnesium , sulfate and

conductivity (Table 2.7 ). Epiphyte thickness estim ated in RHPA varied from slim y to over 5

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

(Appendix A.13; pictures of dom i na nt taxa can be found in Appendi x A.14). Epiphytic algal cell

density p e r unit plant leaf surface area can be fo und in Appendix A.15.

31

### Table 2.6

Pearson correlation m a trix for m acroalgal m a t TN and TP content and environm ental variables.

Mat TN and TP m easured in FM m g / K g (p<0.05 in bold).

Mat TN Mat TP Mat m o lar TN/ T P

ALK A L I N I T Y 0

. 18 7 0. 17 4 -0 .2 5 0

A MMON I A N

- 0 .109

- 0 .112 0

. 0 9 7

C A LC I U M 0

. 06 2 0. 04 4 -0 .2 4 2

CH LO R I D E

- 0 .142

- 0 .141 0

. 0 1 7

IRO N

- 0 .1 5 4 -0 .1 2 7 0. 03 0

N

_ K J EL_ T O T

- 0 .202

- 0 .161

- 0 .238

N O 2N O3N

- 0 .155

- 0 .145 0

. 0 9 6

PH OSP H ATE 0. 28 4 0

. 38 4 -0 .4 2 0

SIL I C A 0

. 21 0 0. 24 4 -0 .1 5 0

SOD I U M

- 0 .141

- 0 .140 0

. 0 1 6

SU LFA T E

- 0 .150

- 0 .130

- 0 .115

TOT A L P 0. 10 8 0. 25 1

- 0 .440

TO TA L N

- 0 .166

- 0 .154 0

. 0 8 0

MA GN ESI U M

- 0 .138

- 0 .126

- 0 .036

STRON T IU M

- 0 .135

- 0 .123

- 0 .195

LAT I TU D E 0

. 17 0 0. 09 8 0. 25 4

LO NGI TUD E

- 0 .112

- 0 .151 0. 38 5

P H 0

. 05 9 0. 05 3 0. 30 1

CO ND U C TIVI TY

- 0 .133

- 0 .132

- 0 .007

D O

- 0 .213

- 0 .209 0. 28 2

TEMPERA T U R E

- 0 .180

- 0 .112

- 0 .150

CANOPY 0. 32 0 0

. 33 4 0. 11 0

BUFFER 0. 40 5 0

. 38 0

- 0 .130

Tran sect Le ngt h -0 .2 2 2 -0 .2 3 5 0. 17 6

T E E S 0

. 0 7 8 0. 0 7 0 0

. 0 7 1

SHRUB S 0. 35 1 0

. 32 0

- 0 .078

HER B S 0

. 16 9 0. 18 1 0. 09 7

BA R E

- 0 .224

- 0 .188

- 0 .120

LD I

- 0 .133

- 0 .099

- 0 .084

D I SC HA RG E

- 0 .193

- 0 .180

- 0 .031



### 32

### Table 2.7

Pearson correlation m a trix for epiphyte DM, AFDM (m g/c m 2 

### ), epiphyte chlorophyll a and

pheophytin (μg/cm 2 

### ) per leaf surface area and env i ronm enta l v a riab les in the fall (p<0. 05 in

bold).

D M AF DM C h l a Ph eo ph ytin

A L KA LI NI TY -0 .0 7 7

- 0 .1 7 0 0. 07 8 0. 01 7

AM M O NI AN 0. 32 4 0. 10 2

- 0 .0 5 6 -0 .0 7 4

CALCIUM 0. 05 5

- 0 .1 1 5 0. 05 2

- 0 .0 2 6

CHLOR I DE 0. 43 5 0. 19 4

- 0 .0 5 5 -0 .0 6 5

IRO N 0. 36 9 0. 18 5

- 0 .1 0 3 0

. 01 5

N_ KJEL _T OT 0. 28 6 0. 13 9

- 0 .0 5 8 -0 .0 5 4

NO 2 N O 3 N

- 0 .017

- 0 .095

- 0 .072

- 0 .072

PH OSP H ATE -0 .0 6 2 0

. 04 0 0. 20 1 -0 .0 4 0

SIL I CA 0. 16 7 0

. 16 2 0. 18 1 0

. 17 6

SO DI UM 0. 43 4 0. 19 6

- 0 .0 5 3 -0 .0 6 3

SULF AT E 0. 30 1 0. 11 8

- 0 .0 4 5 -0 .0 5 2

TOT A L P -0 .0 2 4 0

. 05 8 0. 08 0 -0 .0 5 0

TOT A L N 0

. 0 0 1

- 0 .085

- 0 .076

- 0 .075

MA GN ESI U M 0. 40 6 0. 17 0

- 0 .0 6 0 -0 .0 4 1

STRONTIUM 0. 19 8 0

. 06 9 -0 .0 1 8 -0 .0 5 4

LAT I TUDE -0 .0 0 6

- 0 .0 7 1 0. 07 9 0. 19 1

LONGITUDE 0. 07 3 0

. 16 8 0. 10 5 -0 .0 6 7

PH 0. 07 1 0

. 14 5 0. 04 2 -0 .0 1 3

CON DUCT I V I TY 0. 40 7 0. 15 6

- 0 .0 6 5 -0 .0 8 3

DO 0. 16 9 0

. 07 6 -0 .1 2 8 -0 .0 7 1

TEMPERATURE 0. 11 7 0

. 03 5 -0 .0 7 7

- 0 .260

CA NO PY

- 0 .222

- 0 .039 0

. 0 7 3

- 0 .157

B U FFER 0. 08 5 0

. 02 2 0. 20 4 0

. 00 6

Transect Le ngt h 0. 31 4 0. 09 2

- 0 .0 5 6 0

. 09 2

T E E S 0

. 1 6 4 0.10 0 0

. 0 7 2 0

. 0 3 7

SHR U B S 0. 15 2 0

. 23 5 0. 23 4 0

. 06 5

HER B S 0. 10 9 0

. 15 1 0. 15 7 0

. 19 6

BA R E

- 0 .212

- 0 .199

- 0 .146

- 0 .055

LD I 0

. 0 8 0

- 0 .072

- 0 .106

- 0 .187

D I SC HA RG E

- 0 .046

- 0 .114

- 0 .076 0

. 0 0 3



### 33

### Table 2.8

Pearson correlation m a trix for epipelon DM, AFDM (m g/c m 2 

### ), epipelon chlorophyll a and

pheophytin (μg/cm 2 

### ) and environm ental variables for spri ng and fall data com b ined (p<0.05 in

bold).

D M AF DM C h l a Ph eo ph ytin

A L KA LI NI T Y

- 0

. 026 0. 26 9 -0 .0 6 0

. 04 5

AM M O NI AN

- 0 .0 2 6 0. 02 6 0. 06 4 0. 0 1

C A LC I U M

- 0 .1 1 0. 14 7 -0 .0 9 1 -0 .0 0 2

C H LOR I D E 0

. 05 5 -0 .0 4 7 0. 04 7 0. 01 9

IR O N 0

. 08 5 0. 07 6 -0 .0 9 5 -0 .0 6 7

N_ KJEL _T OT

- 0 .0 1 3 -0 .1 2 1 0. 14 6 0. 09 7

N O 2N O3N

- 0 .127

- 0 .052 0

. 0 1 1

- 0 .031

PHO S PHA T E

- 0 .113

- 0 .103 0

. 0 6 5

- 0 .099

SIL I CA 0. 32 5 0. 33 9

- 0 .1 6 3 -0 .0 1 9

SO DI UM 0. 05 3 -0 .0 5 2 0

. 03 3

- 0 .0 0 1

SU LFA T E

- 0 .026

- 0 .033

- 0 .063

- 0 .042

TO TA L P

- 0 .068

- 0 .035 0

. 0 8

- 0 .091

TO TA L N

- 0 .127

- 0 .059 0

. 0 2

- 0 .025

M A G N ES IU M 0

. 09 8 0. 03 9 0. 00 6 0. 01 2

STRON T IU M

- 0 .053

- 0 .027

- 0 .036

- 0 .047

LAT I TU D E

- 0 .0 1 9 0. 15 8

- 0 .305

- 0 .149

LON G I TU DE

- 0 .0 2 -0 .1 2 3 0. 14 6 0. 01 7

P H 0. 07 8 -0 .0 5 9 0

. 04 3

- 0 .0 3 5

C O N DUC T I V I T Y 0

. 04 1 -0 .0 2 4 0. 04 5 0. 02 6

D O 0

. 23 5 0. 25 5 0. 12 9 0. 11 3

TEMPERA T U R E

- 0 .045

- 0 .094 0. 29 7 0. 14 4

CA NO PY

- 0 .252

- 0 .259

- 0 .231

- 0 .137

B U FFER 0

. 04 5 0. 28 4 -0 .1 1 2 0

. 01 9

Tran sect Le ngt h 0. 17 2 0. 23 4 0. 08 9 0. 09 4

TREES 0. 38 5 0

. 45 3 0. 12 0 0

. 09 7

SH RU B S

- 0 .027

- 0 .136 0

. 0 5 7

- 0 .042

HER B S 0

. 04 3 -0 .1 5 0 0. 27 7 0

. 26 0

B A R E

- 0 .1 8 5 -0 .2 3 7 0. 13 5 0. 12 3

L D I

- 0 .0 1 4 0. 03 1 0. 39 4 0

. 49 5

DISC H A R G E 0

. 22 2 0. 39 8 0. 18 6

- 0 .0 0 2



### 34

### Figure 2.21

Macroalgal m a t TN and TP content in FM m g /Kg.



## Mat TN FM mg/Kg

<10000 100000 200000 300000 400000 500000 600000 700000



## Mat TP FM mg/Kg

<10000 10000 20000 30000 40000 50000 60000 70000

35

### Figure 2.22

Macroa lgal m a t m o lar TN/TP ratio.

Mat TN/TP

<10 10 20 30 40 50 60 70 80 90

36

### Figure 2.23

Epiphyte D M and AFDM expressed per surface of leaf surface are at 58 sites in th e fall.



# 0 1 2 3 4 5 6 7 E p i p h y te D M m g /c m 2

0 1 2 3 4 5 6 E p i p h y te A F D M m g /c m 2

37

### Figure 2.24

Epiphyte ch lorophyll a and pheophytin expressed per surface of leaf surface ar e at 58 sites in th e

f

a ll.



# 0 10 20 30 40 Ep i phy te C h l a μg/cm 2



# 0 10 20 30 40 Ep i phy t e Ph eop hy ti n μg /cm 2



### 38

### Figure 2.25

Epipelon D M and AFDM express e d per bottom su rface area at 59 sites (b ased on spring and fall

samp ling).



# 0 200 400 600 800 100 0 120 0 Epipelon DM mg/cm 2



# 0 10 20 30 40 50 60 70 Epipelon AFDM mg/cm 2



### 39

### Figure 2.26

Epipelon ch lorophyll a and pheophytin expressed per bottom surface area at 59 sites (b ased on

spring and f a ll sam p ling).



# 0 20 40 60 80 100 120 Epip elo n Ch l a μg/cm 2



# 0 100 200 300 400 500 600 Epip elo n ph eo ph ytin μ g /cm 2



### 40

### Table 2.9

Aquatic plant taxa found in Florida Springs a nd codes for aquatic plan ts used in this study.

Taxa Aq uat i c pl a n t c ode

Attern an th erea ph ilo x e ero i d e s A

Cerato ph yllu m sp

. Ce

Chara sp. Ch

Cladium ja m a icense Cj

C obam b a sp

. C o

C r i num am eri canum Sl

Cycu ta sp

. Ct

Egeri a de nsa Ed

Eichh o r n ia c r a ssiper W

Eleocha r is s p

. Sg

Hyd r illa sp

. H

Hyd r o c o til sp

. Hy

Hyg r op h ila po lysp erm a Hp

Lem n a sp. Le

Lu dwi g i a bs p

. L u

m o s s M o

M y ri op hy l l u m sp. M

Naja s gua dal u pen s i s N

Naja s s p

. N2

N uph ar sp. N u

Ny m phoi des s p

. Y

Pani cum repe n s Tr

Pistia stratio tes Pi

Ponteda r ia sp. Pn

Po tam o g e to n pectin atu s Pp

Po tam o g e to n pu sillu s Pu

Po tam o g e to n sp p. Ps

Riccia sp. Rc

Ro ripp a sp. R

Sag ittaria kurzian a Sk

Sag ittaria latifo lia St

Salv in ia m i n i ma Sm

Scirpus s p

. Br

Typha sp. T

Utricularia sp

. U

Vallisn e ria sp

. Val

Zizania sp. Z



### 41

### Epipelon



### Epipelon A F DM was correlated with large spri ngs with high discha rge (like SLV, WAK

and W A C), indicating th at a high er flow can support a larger bi om ass and greater dep o sition of

organic m a teria l in to the sedim e nts. It was al so p o sitiv ely co r r ela t ed with tree cover (Figure

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

### and pheophytin were po sitiv ely

correlated with the Land Developm ent Intensit y index (LDI) (Figure 2.26). Epipelon thickness

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

Aquatic pla n ts



### In IND-01 and LAF-01, no aquatic plants were found. The rem a ining sites had aquatic

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

taxa of aquatic plants were found (Table 2.9). The m o st common were Vallisn e ria 

### sp. and

Sagittar i a k u rziana 

### (Appendix A.17). At m a ny s ites both were present, and when sampling was

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

Seasonal changes



### AFDM and pheophytin m easure m ents did not differ between spring and f a ll (repeated

m easures ANOVA for tim e e ffect AFDM p=0.8641, pheophytin p=0.4579). DM and

chlorophyll a 

### m easurements differed between spring and fall (repeated m easures ANOVA for

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

Stats/AFDM_ChlaRMANOVA.doc).

Discussion



### Macroalgae were found at alm o st all of the studied sites and on average, covered about

half of the bottom area of the spring reaches stud ied. Lyngbya 

### and Vaucheria 

### were the m o st

commonly observed m acroalgae. Many of the m a croa lgal taxa observed in this study were also

reported in previous studies (Davis and Gwo rek, 1972; Odum, 1957; W h itford, 1956). However,

a larg e Lyngbya 

### taxon, which we identified as L. majuscula 

### , was not reported in any of these past

studies. This indicates that it was not comm on or was not present at thes e sites in th e past.

Nutrients were not the most im portant f actors affecting geographic distribution of

different m a croalgal taxa. Conduc tivity, alkalinity, pH, and factor s related to the size of springs

were indicated by ordination statistics as the m o st im portant determ inants. This is one of the

first surveys of m acroalgal biod iversity and relationships to e nvironm ental variables. Although

regulation of diatom biodivers ity by conductivity, alkalinity, a nd pH is well known (e.g., Pan et

al., 1996), the regulation of m acroalgae had not been docum e n ted before this study.

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

not dir ectly rela ted to th e tota l alg a l m a t cover or cover by Lyngbya majuscula 

### . However,

Vaucheria 

### cover was clearly related to NO 2 

### -NO 3, 

### tota l N conc entra tions and two indicators of

hum a n distu r bance: % b a re area of the riparian zone along th e reach and LD I. The threshold in

Vaucheria 

### cover response at 0.6 m g TN/L was sim ilar to observed responses in Cladophora 

### in

stream s (Dodds et al., 1997; Stevenson et al., accepted). The response of Vaucheria 

### to TP is not

42

### as certain, although a few sites had low TP concen trations. Of those sites with low TP (<0.025

mg/L), less than 10% of the sites had a Vaucheria 

### cover greater than 10% of the spring bottom

.

Lyngbya majuscula 

### is possibly an exotic sp ecies in a phase of expans ion. It is a nitrogen

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

light levels (Rossi et al., 1997). It form ed thick mats only in the proxim ity to the boil, which

could indicate that very thick m a ts of Lyngbya majuscula 

### are lim ited by carbon, and that CO 2



### -

satura ted wa ter d i scha rg ed f r om the boil f acilitated the deve lopm ent of thick m a ts.

Alternatively, unm easured nutrients, such as m i cr onutrients, m a y have be en m o re available near

the boil and depleted rapidly downstream . Southe rn sites had a higher m acroalg al percent cover

than northern sites.

The taxonom i c status of Lyngbya majuscula 

### is not clear. It wa s suggested that it is

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

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

### Recent m o lecular

work by (Joyner, 2004) indicated that Lyngbya wollei 

### is not rela ted to L y ngbya majuscula. 

### It i s

possible that there are m a ny speci es within what is te rm ed Ly ngbya majuscule. 

### This would also

explain why there were no str ong relationships between the L y ngbya 

### percent cover and the

environm ental variables in our study. The percent cover of Vaucheria 

### was correlated with TN,

and in Manatee Spring, Vaucheria 

### covered the bottom com p l e tely.

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

Bay/Crysta l River es tuar ine syst em , also did not find a relatio n between water nutrients and

Lyngbya 

### biom ass. Cowe ll and Botts suggested that a downstream drift of the Lyngbya 

### ma t mi g h t

contribute to error and might result in the underestim ation of the tr ue algal biom ass. They also

perform e d a series of laboratory experim e nts a nd did not find a direct re lation between nutrients

and growth of Lyngbya. 

### In their study, the addition of calcium to the gro w th m e dia stim ulated

the growth of Lyngbya, 

### but only in low and m e dium phosphate and nitrate concentrations.

The results of this survey of m acroalgae in Florida springs do not provide the inform ation

needed to determ ine conclusively whether anthro pogenic increases in nutr ients in spring waters

have caused higher algal biom ass than naturally o ccurs. No q u antitatively and system atic ally

collected data is availab l e th at can be used to characteri ze the algal biom ass and nutrient

concentrations in Florida springs bef o re hum ans altered these ecosystem s. Modern hum a n

activity was likely preceded by disturbance by N a tive Am ericans. Nativ e Am erican activ ity

dates back m a ny years. Although th ere are records of increasing ni trogen levels (Rosenau et al.,

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

absolute certainty that th ey caused an increase in algal biom ass.

The results of this study do i ndicate that regulating nutrients could constrain m acroalgal

cover, but m o re work is necessary before such an investm e nt is m a de. Control of nitrogen could

constrain nuisance growths of m acr oalgae, either directly or indi rectly. T h e positive correlation

between Vaucheria 

### abundance in springs and T N concentra tions indicate that TN regulates its

abundance. Although this correlation between Vaucheria 

### and TN does not prove causation,

evidence fro m experim e ntal s t ream s in Chapte r 3 does indicate that th ere is a cause-effect

relationship between N and Vaucheria 

### growth rates. However ,TN:TP molar ratios were usually

43

### greater th an the 16:1 N:P in the m acroalgae, indi c a ting P would be lim iting if nutrien ts were low

enough. The N supply m a y li m it the growth of al gae in stream s, alt hough this has not been

docum ented widely. N m a y be li m iting even wh en water co lum n N:P ratios are g r eater than

16:1. P recy cling in algal m a ts is con s idered to be high becaus e of the luxu ry consum ption of P

by algae, and is probably m o re efficient than N r ecycling. Benthic algae m a y not be able to hold

onto N as effectively as P. N: P ratios in benthic diatom s in e xperim e nts decrease as biom ass

increases during colonization, even though N and P concentrations rem a ined the sam e in the

water during that period (Hum phrey and Stevens on 1992). N:P ratios in periphyton of Kentucky

and Michigan stream s were less than 16:1 despite higher ratios in overlying waters (S tevenson et

al., unpublished data).

Even though, in this study, Lyngbya 

### biom ass and growth (Chapter 3) did not show a

relationship to nutrients, the contro l of nutrients could constrain pr oliferations of this important

nuisance alga for several reasons. First, rese archers m a y not have been able to m easure Lyngbya



### responses precisely enough. However, results indicate Lyngbya 

### is adap ted well to g r owth in low

nutrient conditions. S e cond, Lyngbya 

### proliferations could be c ontrolled indirectly by reducing

the proliferation of other benthic m acroalg ae that do respond to nutrient reductions. L y ngbya 

### has

no direct m e thod to hold onto surfaces, except by becom i ng entangled with rough surfaces or

objects attac h ed to the b o ttom

. Norm ally, Lyngbya 

### was found attached to Vaucheria 

### and aquatic

plants. Proliferation of both Vaucheria 

### and aquatic plants could be constrained by nutrient

regulation. Cowell ant Botts (1994) suggested that Lyngbya 

### colonizes n e w areas after a

disturbance (prescribed rem oval of Hydrilla 

### or after hurricane dam a ge to beds of aquatic plants).

Possibly the rem a ins of aquatic plants for m a good attachm e nt point for L y ngbya, 

### and since

Lyngbya 

### grows fast and shades the rem a ins of aquatic plants, the m acrophyte beds cannot

recover.

Many factors affecting algal cover w e re not considered for the analyses b e cause th ere

was insufficient inform a tion. Disturbance from flooding m a y affect m acroalgae. Som e sites

never flood and som e are flooded regularly. Si tes along Suwannee River are flooded regularly

with dark w a ter that lim its ligh t availability for macroalgae an d can resu lt in m acroalgal biom ass

depletion. Direct d i stu r bance by hu m a n recreati on in the sp rings m a y open space for m acroalgal

growth during periods when hum a n activ ity le vels ar e low. Hum a n activity m a y have also

reduced grazing by fish and m a natees. All of these factors could have profound i m pacts on

m acroalgal accrual on substrata. Th ey should be considered as factors th at m a y affect nuisan ce

growths of m acroalgae and developm en t of nutr i ent-response relationships.

Macroalgal m a ts of the size observed in the Florida springs m u st have unique nutrient

dynam i cs inside the m a t. The response of these m a ts to water nutrients must also be different

from the response of m i croalgal biof ilm s. Macroa lgal m a ts were attached to the bottom and to

aquatic plants. Usually at least part of the m a t was attached di rec tly to th e bottom and was in

contac t with the sed i m e nt. Vaucheria 

### m a ts develop rhizoids that grow dir ectly into th e sedim e nt.

This indicates that also nutrients from sedim e nt s could be very im portant to m acroalgal growth

(as has been observed for aquatic plants) and co uld contribute to the l ack of relationships

between m a croalgal biom ass and wa ter nutrients.

44

### Overall, this study indicates that m acroalg ae are very comm on and diverse in Florida

springs. Only a few taxa ( L. majuscula, Vaucheria 

### sp., Dichotomosiphon 

### sp. and Compsopogon



### sp.) form ed very thick mats. Lyngbya majuscula 

### could be a n e w species that was recently

introduced in the springs or a species that was historically present, but no t sufficiently common

to be listed. The apparent adaptation of this tax on to low nutrient conc e n tra tions ind i cates its

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

### , and

indirectly Lyngbya 

### , m a y be possible if low levels of nutrients constrain the growth of Vaucheria



### .

45

## CHAPTER 3. EXPERIMENTAL CO NFIRMATION O F LIMITI NG

NUTRI E NTS USING EXPERIM E NTAL STREAMS

Introductio n

Enrichm e nt of surface waters with n u trients can result in increased growth of benthic

algae (Stockner and Shortree d, 1978; Biggs, 2000). Nitrogen a nd phosphorus are the two m a in

nutrients affecting benthic algal growth (B orchardt, 1996). Nitrat e and ammonia are the

bioavailable for m s of nitrogen. Bioavailable phosphorus comes in the form of phosphates.

Bioavailable for m s of nutrients are rapidly taken up by autotrophic a nd heterotrophic m i crobes

and plants in surface waters and trans f or m e d in to organic form. Particulate and dissolved

organic N and P can be recycled to bioava ilable for m s by microbial and photochem i cal

processes. All of these for m s of nut rients originate from both natu ral and anthropogenic sources.

Im portant natural sources include nitrogen-fixing bacte r ia in soils and w a ter and dissolution of

m i nerals, such as the phosphate-rich rocks found in som e regions of Central Florida. Im portant

anthropogenic sources include septic tanks, wast e water treatm ent plants, and fertilizers.

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

Rainbow River), m a y also leak phosphate after rains and high water levels.

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

nutrient con centr ation s observed in f i eld survey s of m u ltiple sites is com p lica t ed. Fir s t, N and P

concentratio ns covary among sites (Dodds and Welch, 2000; Stelzer and Lam b ertii, 2002;

Stevenson, 1 996), because m a ny human activitie s produce both (Arbuckle and Downing, 2001).

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

biom ass increases. TN and TP are more comm on ly re lated to the benth i c a l gal b i om ass in

stream s than to dissolved inorganic nutrients, bu t this is likely the re sult of the sloughing of

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

indicators of bioavailable N and P in field survey s. The level of hum a n a c tivities in a watershed

that gen e rates nutrients, estim ated lo ading rates of nutrients, and diatom indicators of nutrients

can also b e used to ind i cate th e bioavailable nutrients in stream s.

Experim e nts provide a valuable complem e nt to field surveys by docum enting the

independent and interactive effects of contam ina n ts and natural environm ental factors (Pan, et al.

2000). Although the researchers w ould argue that the results of experim e nts should not take

preceden ce over the results of field surveys to es tablish env i ronm ental cr iteria (Stevenson, et al.

2004), experim e nts do indicate the range of nutrient concentrations. W ithout the clear effects of

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

experim e nts could confirm the re la tiv e sensitiv ity of these two m a croalgae to nutrients and the

likely range of nutrient concentrations that would affect their growth.

The objective of this study was to in vestig ate the ef f

ect of an experim e ntally m a nipulated

range of nitrate and phosphate concentra tions on the growth of the m acroalgae Lyngbya

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

most common algae in springs and the m o st likely to cau se p r oblem s. The researchers

46

### independently m a nipulated nitr ate and phosphate concentration in two experim e nts. They

created a range of phosphate concentrations in artificial stream s w ith elevated nitrate

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

m acroalgae. Then the research ers created a ra nge of nitrate concentr ation s with elevated

phosphate to determ ine the nitrate con centrations that constrain growth.

Methods

Experimental setup

Experim e nts were conducted in S ilver River State Park (N 29.20020 W -82.03518) in

Ocala, Florida, in November and Decem be r 2003. Running experim e ntal stream s required

access to electricity, well or str eam water, acces s to spring water low in N and P, and a secure

site with sufficient exposure to sunlight. Expe rim e ntal strea m s (ES) we r e built from 2” PVC,

were 1 m long and 30 cm wide, and were f illed with f r om Juniper Spr i ng (Figure 3.1 )

.

Figure 3.1

Experim e ntal setup show ing single experim e ntal stream

.

Juniper Spring water was judged to have the lo west nutrient concentrat ions in the region.

An air pum p produced bubbles at the bottom of t h e upstream vertical tube that lifted and

circu l ated water in the E S . Current v e locity wa s regulated at 25 cm /s (equivalent to m e dium

high current velocity observed in the field) with air flow.

Fresh spring water used in the experim e nt s was pum ped into a large tank from Juniper

Spring in Ocala Fores t an d transpo r ted three tim es a week to Silver Riv e r State Park. Juniper

Spring has a low dissolv ed solids co ncentration com p ared with m o st springs in Florida, and its

m ean nutrient concentrations, based on m easurem ents from 1984 to 2000, were 30 μg SRP/L, 30

μg TP/L and 80 μg NO

x 

### /L (St. Johns River W a ter Managem e nt District, 2002). On March 27,

2003, water from Juniper Spring had 29 μg SRP/ L, 60 μg TP /L, 58 μg NO

x 

### /L and 120 μg TN/L. Well

water

Well water DI water Spring

w i t h nu tri ents Macr oalga e water

Air pump Spring water Spring w a ter

w i t h nutri ents

Well

wa t e r

overflo w overflo w



### 47

### A peristaltic pum p continuously delivered spring water through large tubing and distilled water

with dissolved nutrients through sm all tubing to the ES at the targ et rate of 12 m L / m in for spring

water and 0.68 m L / m in for water with nutrients. Excess water in the stream s flowed through an

opening at the downstream end of each experimental stream . The lower 2/3 of the ex perim e ntal

stream s were subm erged in well water in a pool to reduce the high diurnal variability in water

tem p eratures that could result in exp o sed str eams and to be tter sim u late the relativ ely stable

tem p eratures of springs receiving groundwater.

Before the experim e nts were started, the ES ’s were power washed with a hose and well

water. The large peristaltic pum p tubing and expe rim e ntal stream s were th en clean ed with a 5%

bleach so lution for several hours, rin s ed with well water for at least 48 ho urs, and rinsed with

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

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

Experim e ntal stream s we re covered with a sc reen m a de of 6 m il clear vinyl to protect

them from rain and dust, and to shad e them slightly (35 % lig h t reduc tion ) . The ES were lo cated

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

. The

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

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

added to experim e ntal stream s on day 0. The phosphate experim e nt (21 stream s) was conducted

first and was finished on day 8. The nitrate ex perim e nt (20 stream s) was conducted second and

was finished on day 10. Based on the growth ra tes observed in the phosphate experim e nt, the

researchers decided to extend the duration of this experim e nt. Ev ery 2-3 days actual flow rates

were tested from each piece of peristaltic pum p t ubing. Pump tubing th at did not m eet expected

flows was replaced.

Nutrient dosing

Two experim e nts, nitrate additions and phos phate additions, were conducted. Sodium

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

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

concentrations, was established am ong experim e nt channels. In the n itrate dosing experim e nt,

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

was added in all stream s to attain a target con centration of 0.240 m g P /L, which was expected to

saturate P dem a nd. Mass -based Redfield ratios we re less than 7 for all treatm ents except the 5

mg N/L treatm ent. In the phosphate experim e nt phosphate concentrations were varied am ong

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

level, which was targeted at 1 m g N/ L. Here the m a ss-based Redfield ratios were m o r e than 7

for all treatm ents, except the 0.240 mg P/L. Als o, due to an error, treatm e nt 0 m g P/ L had no

nitrate add e d.

48

### Table 3.1

Target concentration of P and N in the experi m e ntal stream s and in the holding bottles from

which nutrients were being dosed.

N itr ate do sing Pho s ph ate do sin g

Targ et add itio n

o f

N m g /L in

ES C once n t r at i o n of

N m g /L in d o s i n g

water n Targ et add itio n

o f

P m g /L in ES C once n t r at i o n of

P m g /L in

dosi n g w a t e r n

0. 00 0

0. 00 5

0. 02 5

0. 10 0

0. 50 0

1. 00 0

5. 00 0 0. 00 0

0. 09 4

0. 46 9

1. 87 6

9. 38 0

1 8 .760

9 3 .800 3

3

3

2

3

3

3 0. 00 0

0. 00 3

0. 00 9

0. 02 7

0. 06 0

0. 12 0

0. 24 0 0. 00 0

0. 05 6

0. 16 9

0. 50 7

1. 12 6

2. 25 1

4. 50 2 3

3

3

3

3

3

3



### Chemical and physical analyses of water and algae

In each stream, the researchers m easured the temperatu r e, pH , dissolved o xygen and

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

collected fro m each stream for water chem istry an alyses (alk alin ity, ammonia, calciu m, chloride,

iron, m a gnesium , Kjeldahl N, nitrates, phosphate, s ilica, sodium , sulfate and TP). Samples for

nitrogen and phosphorus content in L. majuscula and Vaucheria sp. were c o llec t ed in tr iplicate at

the beginn in g of the experim e nt from the origin al alg a l m a t and from one sam p le from each

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

confirm nutrient av ailability and m e a s ure physio l ogical respo n se to nutrient m a nipulations.

Water nutrient analyses and algal m a t nitrogen and phosphorus content were analyzed by FDEP.

Algal m a t Kjeldah l N an d TP content are exp r ess e d per unit of m acroalgae fresh m a ss . W h en the

com pound c oncentration was below the detection lim it, the m e thod detection lim it was reported

(f

or detec tio n lim its, refer to Table 2.2).

Algae

The experim e nt was conducted on two taxa of m acroalgae: Lyngbya majuscula collected

from Juniper Spring and Vaucheria sp. collected from Alexander Spring. Fragm e nts of algal

m a ts were gently patted dry between Nitex sc reens and weighed. The initial fresh m a ss of

individual macroalgae fragm ents wa s 0.200±0.010g for both algal taxa. Algae were attached to

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

experim e ntal stream s. Four tiles with L. majuscula and four tiles with V aucheria sp. were plac ed

in each s t ream. One of each was used for al gae N and P content an alyses, and the rem a ining

three of each were used to determ ine growth rate s with m easures of fresh m a ss (FM) and ash free

dry m a ss (AFDM)). The biom ass of three n e sted replicates within each s t ream was combined

and constituted one com posite biomass per one str eam (one experim e ntal unit). In addition, at

the beginn in g of experiment 10, alg a l sam p les fr om each taxo n were weig hed for FM and frozen

to be processed for AFDM to m easure initial FM and initial AFDM for algae plac ed in the

stream s. A lgae were weighed for FM and frozen at the end of the experim e nt. In the laboratory,

49

### all frozen samples were processed for AFDM (Cle sceri et al., 1998). Alga e relative growth rate

was calculated as follows (Hunt, 1990):

RGR= [ln(final m a ss)-ln(initial m a ss)]/# of days

Statis tical a nalyses

The experim e nts had a factorial design with 7 nutrien t leve ls ( p lus initial m a t nutrient

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

treatm ent is listed in Tab l e 3.1). The biom ass of 3 nested replicates within each stream was

com b ined by summation and thereby constitute d one com p osite initial and com posite final

biom ass assessm ent per stream (1 experim e ntal unit). ANOVA (glm mod e l) was used to analyze

data. Differences were consid ered significant if p<0.05 (SAS, 2000) . Tukey’s or L S m eans test

was used to test d i fferences between m eans. A ll graphs show m e ans and standard error (SE).

Results

Nitrate dosing experiment

Nutrient dosing and nutrient concentration in the ES water

The average actual flow rate of spring water from large tubing was 10.631 m l/m in

(SD=0.689, SE=0.089). For sm all tubing with distil led water with nutrients, the average flow

rate was 0.621 m l/m in (SD=0.032, SE=0.004). Nitr ate and phosphate dosing resulted in the

expected nitrate concentration gr adient and high TP concentration in all treatm e nts (T able 3.2).

W ith control NO

x 

### averaging 0.075 mg N/L on day 1, nut rient additions resulted in NO

x 

### -N

concentrations varying from 0.075 to 5.467 m g /L am ong treatm e nts and TN varying from 0.135

to 5.527 m g / L . Ammonia concentrations were mo stly below the detect ion lim it of 0.010 m g /L.

NO

x 

### concentrations in the recirculating stream s decreased about 0.05 m g / L in m o st treatm ents

during the experim e nt, despite constant loading rate s. This was likely due to increases in algal

biom ass and nutrient uptake by algae during the experim e nt.

ES water temperature, pH, conductivity and DO

Water tem p erature averaged 17.0°C (SD=2.1, SE=0.21, m i n=13.3, m a x= 19.1). There

were no differences between experim e ntal st ream s (ANOVA, F=0.01, p=1). The pH averaged

7.77 (SD=0.098, SE=0.010, m i n=7.58, m a x=7.94), and th ere were no differences between

experim e ntal stream s (ANOVA, F=0.47, p=0.967) . DO was saturating and averaged 9.61 m g /L

(SD=0.7, SE=0.07, m i n= 8.2, m a x=10.72), and there were no differences between experim e ntal

stream s (ANOVA, F=0.12, p=1). Conductivity w a s hi gher in high nitrate dosing treatm e nts (0.5,

1 and 5 m g N/L; Table 3.3) than the control treatm e nt.

50Table 3.2Average nutrient concentrations in the streamwater in nitrate dosing experimentTargetNdosingmg/LDayTNmg/LTPmg/LAmmoniaNmg/LKjeldahlNmg/LNOxmg/LCalciummg/LChloridemg/LIronmg/LMagnesiummg/LSodiummg/LSulfatemg/L0.00010.1350.2570.0100.0600.07511.6004.3330.0104.2002.5775.7330.00050.1070.2670.0100.0600.04712.1334.2670.0104.1672.4405.3330.000100.0810.2600.0120.0600.02112.6674.1670.0104.2332.4805.0330.00510.1560.2630.0100.0800.07611.7334.2330.0104.1672.6305.4330.00550.1170.2670.0100.0600.05712.5674.7330.0104.3002.5307.7330.005100.0870.2670.0100.0600.02712.7334.2000.0104.2332.4875.2000.02510.1490.2570.0100.0600.08911.8674.2330.0104.1672.5775.2330.02550.1280.2670.0100.0600.06812.1674.3330.0104.1672.4705.6330.025100.1020.2730.0100.0600.04212.7004.2000.0104.2332.5075.1000.10010.2700.2750.0100.0950.17511.9504.2000.0104.2502.7855.2500.10050.2050.2750.0100.0600.14512.4005.2500.0104.3002.63511.5500.100100.1750.2900.0110.0600.11512.5004.2000.0104.2002.6305.0500.50010.7170.2770.0100.0600.65711.5334.1330.0104.1003.5705.2000.50050.6430.2730.0100.0600.58312.5334.2000.0104.2673.3935.1330.500100.6170.2770.0110.0600.55712.7334.1670.0114.2333.3935.0331.00011.2600.2670.0100.0601.20011.5004.2000.0104.1334.4805.3001.00051.2330.2730.0100.1001.13312.5004.2670.0104.2674.3175.1001.000101.1930.2700.0110.0601.13312.8334.2000.0104.3334.3275.0005.00015.5270.2600.0100.0605.46711.8004.2000.0104.20011.9805.2335.00055.8600.2730.0100.0605.80012.5335.7000.0104.30011.83314.5675.000105.8930.2930.0130.0605.83312.6004.1670.0104.23312.1675.06751

### Table 3.3

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

sharing the s a m e superscript letter did not differ (Tukey’s test).

N itr ate do sing

Mg /L M e a n S D S E M I N M A X

0. 00 0

0. 00 5

0. 02 5

0. 10 0

0. 50 0

1. 00 0

5. 00 0 10 7. 2 6 a

10 7. 6 0 a

10 7. 3 3 a

10 7. 5 0 a

11 1. 6 0 b

11 6. 0 0 c

15 0. 6 0 d 0. 79

0. 63

1. 05

1. 08

0. 91

0. 53

7. 48 0. 21

0. 16

0. 27

0. 34

0. 24

0. 14

1. 93 10 5

10 6

10 4

10 5

11 0

11 5

13 6 10 8

10 8

10 8

10 8

11 4

11 7

16 2



### Table 3.4

Results of ANOVA for m a t Kjeldahl Nitrogen a nd TP content at the end of nitrate dosing

experim e nt for Lyngbya majuscula and Vaucheria sp. Data were log (x+1) transform e d to m e et

the assum p tion of norm a lity and var i ance hom ogeneity.

Kjel dahl Ni t r o g en

Source of Vari ation DF SS MS F p

Ni t r at e 6 1. 3 2 0. 2 2 0. 4 6 0. 83 2

Al g a e 1 0. 7 8 0. 7 8 1. 6 1 0. 21 7

Ni t r at e* Al gae 6 1. 4 4 0. 2 4 0. 5 0 0. 80 3

TP

Source of Vari ation DF SS MS F p

Ni t r at e 6 1. 0 7 0. 1 8 0. 3 7 0. 89 1

Al g a e 1 0. 3 0 0. 3 0 0. 6 1 0. 44 4

Ni t r at e* Al gae 6 1. 9 3 0. 3 2 0. 6 6 0. 68 0



### 52

### Figure 3.2

Algal m a t TP and Kjeldahl Nitroge n content in nitrate dosing experi m e nt. Bars sharing the sam e

letter do not differ (LSmeans test).

V a uche r i a

0 50 100 150 200 250 300

I n i t i a l 0 5 2 5 100 500 1000 5000

N i t r a t e dos i n g μ g / LPg/KgFM

ac ac

b ac c

a a a

Va u c h e r i a

0 100 200 300 400 500 600 700 800

I n i t i a l 0 5 2 5 100 500 1000 5000

N i t r a t e d o s i ng μ g / LN g/KgFM Ly ng by a

0 50 100 150 200 250 300

I n i t i a l 0 5 2 5 100 500 1000 5000

N i t r a t e d o s i ng μ g / LPg/KgFM

Ly

n gby a

0 100 200 300 400 500 600 700 800

I n it ia l 0 5 2 5 1 0 0 5 0 0 1 0 0 0 5 0 0 0

N i t r a t e dos i n g μ g / LgN/KgFM



### Nitrogen and phosphorus content in algal mats

The water nitrate concentration gradient did no t a f f

ect the f i nal m a t Kjeldahl Nitrogen

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

The Kjeldah l Nitrog en c ontent in the m a t at th e end of the experim e nt did not differ from the

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

experim e nt also did not differ from the ini tial content (ANOVA, F= 1.93, p=0.140). However,

the f i nal m a t TP content in the Vaucheria sp. m a t was higher than in the initial m a t (ANOVA,

F=5.54, p=0.005), indicating a possible P lim itation in the m a t collected from the field. The

molar m a t TN/TP ratio a t the end of the exper i m e nt for both taxa did not differ from the initial

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

except the in itial r a tio in the Lyngbya majuscula m a t, were below 16, indicating a nitrogen

lim itation ev en in the h i g h est nitra t e dosing tr eatm e nt.

53

### Table 3.5

Mat m o lar TN/TP ratio in nitr ate dos ing exper i me nt in the algal m a t collected from the field

(Initia l) and algal m a t at the e nd of experim e nt from different nitrate dosing concentrations.

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

p=0.477).

Treat m e nt ( m g/ L) Lyn gby a sp. Va ucheri a s p

.

In i t i a l 2 2 .66±0.5 1 4 .3±1 .6

0 1 0 .7 ± 8

. 9 5. 4± 3. 7

0. 00 5 8

. 2± 4. 5 4. 5± 2. 8

0. 02 5 8

. 3± 5. 1 5. 0± 3. 2

0. 10 0 9

. 6± 6. 6 6. 5± 4. 1

0. 50 0 4

. 3± 0. 6 1. 9± 0. 5

1. 00 0 8

. 2± 3. 2 5. 5± 3. 7

5. 00 0 4

. 3± 0. 4 5. 2± 2. 5



### Macroalgae growth rates

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

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

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

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

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

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

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

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

54

### Figure 3.3

Algal relative growth rate (RGR) in nitrate dos ing experim e nt based on fresh m a ss and ash free

dry m a ss.

Ly ng by a

0 0. 01 0. 02 0. 03 0. 04 0. 05 0. 06 0. 07 0. 08

0 5 25 1 0 0 5 00 1000 5000

N i t r a t e do s i ng μ g / LRGR forFM(g/g/day) Ly ng by a

0 0. 01 0. 02 0. 03 0. 04 0. 05

0 5 25 1 0 0 5 00 1000 5 000

N i t r at e d o s i n g μ g / LRGR for AFDM(g/g/day)

V a uche r i a

0 0. 01 0. 02 0. 03 0. 04 0. 05 0. 06 0. 07 0. 08

0 5 25 1 0 0 5 00 1000 5 000

N i t r at e d o s i n g μ g / LRGR forFM(g/g/day) V a uche r i a

0 0. 01 0. 02 0. 03 0. 04 0. 05

0 5 25 1 0 0 5 0 0 1000 5 000

N i t r a t e do s i ng μ g / LRGR forAFDM(g/g/day)



### Phosphate dosing experiment

Nutrient dosing and nutrient concentration in the ES water

The average actual flow rate of spring water from the large tubing was 11.386 m l/m i n

(SD=0.653, SE=0.069), and the flow rate for sm all tu bing with distilled water with nutrients was

0.603 m l/m i n (SD=0.077, SE=0.009). Phosphate and nitr ate dosing resulted in the expected TP

concentration gradient and high nitrogen concentra tion in all treatm ents (Table 3.6). There was

an error in phosphate dosing in 240μ g/L treatm ent on days 1, 2 and 3 that resulted in a low TP

concentration on day 1 (Table 3.6). W ith TP av e r aging 0.021 m g P/L in the control treatm e nt

during th e whole experim e nt, phosphate add itio ns re sulted in TP concentrations vary ing from

0.021 to 0.268 m g /L a m ong treatm e n ts. TP and NO

x 

### concentrations in the recirculating stream s

did not decrease during the experim e nt, as NO

x 

### did in the nitrate experim e nt.

ES water temperature, pH, conductivity and DO

Water tem p erature averaged 18.3°C (SD=2.1, SE=0.16, m i n=13.8, m a x= 23.0), and there

were no differences between experim e ntal st ream s (ANOVA, F=0.02,p=1). The pH averaged

7.72 (SD=0.166, SE=0.012, m i n=7.31, m a x=8.15), and th ere were no differences between

experim e ntal stream s (ANOVA, F=0.15, p=1) . Conductivity averaged 114.1 μS/cm ( S D=2.93,

SE=0.21, m i n=109, m a x=120), and there were no di fferences between experim e ntal stream s

(ANOVA, F=0.78, p=0.730). DO was saturati ng and averaged 9.59 m g /L (SD=0.573,

SE=0.044, m i n=8.60, m a x=10.96) , and there were no di fferences between experim e ntal stream s

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

55Table 3.6Average nutrient concentrationsin the streamwater inphosphate dosing experiment.TargetPdosingmg/LDayTNmg/LTPmg/LAmmoniaNmg/LKjeldahlNmg/LNOxmg/LCalciummg/LChloridemg/LIronmg/LMagnesiummg/LSodiummg/LSulfatemg/L0.00010.1360.0250.0100.1000.03613.7334.3670.0104.5672.6335.4670.00050.1100.0160.0100.0800.03013.6334.5670.0104.4332.6636.1000.00080.0930.0230.0100.0600.03313.2674.5000.0104.5002.6405.5670.00311.0200.0280.0100.0800.94012.5674.1000.0104.1674.0905.2670.00351.1100.0180.0100.0801.03012.6004.4000.0104.1674.1637.2000.00381.0730.0210.0100.0601.01312.3004.2670.0104.2004.2075.3000.00911.0500.0330.0100.1000.95012.7004.1000.0104.2334.1135.2330.00951.1330.0230.0100.1001.03312.2004.7330.0104.1004.10315.6670.00981.0430.0310.0100.0600.98312.4004.3000.0104.2334.2405.3000.02711.2400.0580.0100.0801.16012.7334.0330.0104.2004.5275.1670.02751.1800.0430.0100.1001.08012.8004.7000.0104.2674.23714.7670.02781.1100.0380.0100.0601.05012.4674.3000.0104.2334.2835.3000.06011.0300.0780.0100.1000.93012.8674.1000.0104.3334.4005.2330.06051.1400.0730.0100.1001.04012.5004.5670.0104.1674.13010.2670.06081.2130.0770.0100.0601.15312.2674.2330.0104.1674.4705.2670.12011.0770.1400.0100.1000.97712.6674.1000.0104.2334.1675.3000.12051.1670.1430.0100.1001.06712.2674.6330.0124.1334.21012.5330.12081.0930.1430.0100.0601.03312.4004.2330.0104.2004.2805.2670.24011.0100.0480.0100.0800.93012.7674.1670.0104.2004.2135.2330.24051.1430.2500.0100.1001.04312.3674.4330.0104.1674.1838.5670.24081.3350.2870.0100.0651.27012.0004.2670.0104.1004.5735.13356

### Table 3.7

Results of ANOVA for m a t Kjeldahl Nitrogen and TP content at the end of phosphate

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

transf orm e d to m eet the assum p tion of norm a lity and hom ogeneity of var i ance.

Kjel dahl Ni t r o g en

Source of

Variation D F S S M S F p

Ph osp h at e 6 1. 1 4 0. 1 9 0. 4 1 0. 86 7

Al g a e 1 8. 5 6 8. 5 6 18 .3 8 0. 00 0

Ph osp h at e * A l g ae 6 0. 52 0. 09 0. 18 0. 97 9

TP

Source of

Variation D F S S M S F p

Ph osp h at e 6 0. 4 1 0. 0 7 0. 2 1 0. 97 1

Al g a e 1 1. 3 1 1. 3 1 3. 9 3 0. 05 7

Ph osp h at e * A l g ae 6 0. 51 0. 08 0. 25 0. 95 4



### Figure 3.4

Algal m a t TP and Kjeldahl Nitrogen c ontent in phosphate dosing experim e nt.

V a uche r i a

0 10 20 30 40 50 60 70 80 90 100

I n i t i a l 0 3 9 27 60 120 240

P h os pha t e d o s i ng μ g / LP g/KgFM

V

a uche r i a

0 100 200 300 400 500 600 700 800

I n i t i a l 0 3 9 27 60 1 2 0 240

P h os pha t e d o s i ng μ g / LN g/KgFM Ly n gby a

0 10 20 30 40 50 60 70 80 90 100

I n i t i a l 0 3 9 27 60 1 2 0 240

P h os pha t e d o s i ng μ g / LPg/KgFM ab

a b ab ab ab ab b

Ly

ng by a

0 100 200 300 400 500 600 700 800

I n i t i a l 0 3 9 27 60 1 2 0 240

P h os pha t e d o s i ng μ g / LN g/KgFM



### 57

### Nitrogen and phosphorus content in algal mats

The water phosphate concentr ation gradient did not a ffect final m a t Kjeldahl

Nitrogen and TP content (Figure 3.4, Tabl e 3.7). At the end of the experim e nt, Lyngbya

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

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

for Algae effect F=3.93, p=0.057). The Kjeldahl Nitrogen cont ent in the m a t at the end

of the experim e nt did not differ fro m th e initial content for both taxa (ANOVA, F=0.68,

p=0.780). The TP content in the Lyngbya ma t at the end of the experim e nt was higher

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

m a t collected from the field. The fin a l TP conten t in th e Vaucheria sp. m a t did not differ

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

The m o lar m a t TN/TP ratio a t the e nd of the experim e nt for both taxa did not

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

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

indicating a phosphorus lim itation even in th e high phosphate treatm ent. All TN/TP

ratios in Vaucheria sp. m a t were below 16, indica ting a nitrogen lim itation.

Table 3.8

Mat m o lar TN/TP ratio in phosphate dosing expe rim e nt in the algal m a t collected from

the f i eld (Initia l) and a l g a l m a t at the end of experim e nt from different phosphate dosing

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

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

Treat m e nt Lyngbya sp

. V aucheria sp

.

I n i t i a l 2 2

. 3 ± 1

. 1 1 2

. 8 ± 0

. 1

0 2 0

. 2±1. 5 13. 5±1. 6

0. 0 0 3 2 3

. 4±1. 7 12. 5±0. 9

0. 0 0 9 1 9

. 9±1. 1 12. 1±1. 1

0. 0 2 7 2 0

. 7±1. 2 10. 9±2. 7

0. 0 6 0 2 1

. 8±0. 9 13. 1±1. 2

0. 1 2 0 1 9

. 1±2. 8 9. 2±0. 9

0. 2 4 0 2 1

. 5±0. 9 14. 9±2. 2



### Macroalgae growth rates

Macroalgal growth rates were not affect ed by the increased co ncentration s of

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

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

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

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

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

g AFDM/g AFDM/d.

Lyngbya majuscula had a significantly hi gher growth rate than Vaucheria sp

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

did not differ in their RG R (ANO VA for Algae effect, F=3.53.19, p=0.071).

58

### Figure 3.5

Algal relative growth rate (RGR) in phospha te dosing experim e nt based on fresh m a s s

and ash free dry m a ss.

Ly

ng by a

0 0. 01 0. 02 0. 03 0. 04 0. 05 0. 06 0. 07 0. 08

0 3 9 2 7 6 0 1 2 0 240

P h o s pha t e do s i n g μ g / LRGRforFM(g/g/day) Ly ng by a

0 0. 01 0. 02 0. 03 0. 04 0. 05

0 3 9 2 7 6 0 1 2 0 240

P hos p h a t e do s i n g μ g / LRGR forAFDM(g/g/day)

V a uche r i a

0 0. 01 0. 02 0. 03 0. 04 0. 05 0. 06 0. 07 0. 08

0 3 9 2 7 6 0 1 2 0 240

P hos p h a t e do s i n g μ g / LRGRforFM(g/g/day) V a uc he r i a

0 0. 01 0. 02 0. 03 0. 04 0. 05

0 3 9 2 7 6 0 120 2 4 0

P h o s ph a t e d o s i ng μ g / LRGR for AFDM(g/g/day)



### Discussion

No increas e in m acroalgal growth w a s observed with increased nutrient

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

short duration of experiments, artifacts of the experim e ntal environm ent, growth

lim itation b y m i croelem e nts ( like calcium or iron), unfavorable light conditions or the

ability of these algae to efficiently utilize the r e la tively low nutrient conc e n tra tions in

Juniper Spring water.

The short duration of experim e nts can be a problem if algae have not had tim e to

respond or h a ve not beco m e acclim at ed to enviro nm ental con d itions. Bo th m acroalgae

did have m easurable growth rates, so they li kely did have time to respond. The growth

rates we re r e latively s l o w com p ared to the g r owth rates of diatom s in other experim e nts,

but within the range of rates observed in other studies (Stevenson, 1984; Rier et al.,

accepted). Algal respo n ses to experim e ntal treatm ents have been observ e d in other,

short- term experim e nts within th is s hort duration, which has the advantage of reducing

artifacts due to rem oval from natural condi tions and confinement in an experim e ntal

setting. Macroalgal responses in 1-week experim e nts were observed in the conductivity

experim e nt (Chapter 3, this report). Algae can store luxury quantities of nutrients in their

cells, and can use these to support growth duri ng periods of low nutrien t availability. The

Vaucheria from Alexander Spring and Lyngbya from Juniper Spring m a y have had

luxury quantities of nutrients when taken fr om their natural set tings; however, these

settings were chosen because of their re latively low nutrient s upplies. In addition,

59

### exposure to elevated P concentrations in both experim e nts resulted in a significant

incre a se in the P content of algal ce lls rela tiv e to their initia l P c ontent. T h e sam e was

true for N in the P experim e nt, but not in th e N experim e nt. Therefore, acclim ation to

experim e ntal treatm e nts was probably not an issue.

Artifacts of the experim e ntal environm en t m a y have constrained algal responses

to nutrients. Spring water close to the boil had high quantities of dissolved carbon

dioxide that “bubbles” o u t to th e atm o sphere as soon as water reaches the surface due to a

decrease in pressure as water em erges fr om the ground. Since the water used in the

experim e nts was transported, the dissolved CO 2 

### concentration had to have been m u ch

lower than that observ e d in the sp rin g s, where th e thick e st a l gal m a ts were observed o n ly

close to boils. Nutrient uptake by algae in experim e ntal settings m a y constrain growth

rates. Although N and P were added, the m a ny micronutrients that al gae require were not

added. However, the m a croalgae did grow, indi c a ting that an y lim itation, if present, was

not severe. The growth rates were low, but this was probably due to the binding of

m a sses of macroalgae rather th an limitations due to a lo w m i cronutrient supply. Masses

of m acroalgae were bound in tight balls, and gro w th patte rns indica ted th at the surf ac e

algae were the only algae that grew. Algae deep in the bound m a sses probably had little

exposure to light and nutrients.

Lyngbya majuscula growth m i ght have been restrained by very low iron

concentration in the experim e ntal stream water, which was below 0.01 mg/L. The

optim um iron concentration for the growth of L. majuscula is 1.4-6.1 m g / L (Gross and

Martin, 1996). Also, the calcium concen tration in Juniper Spring water was low

com p ared to other springs and in this study averaged 12.5 m g / L

. Lyngbya majuscula ( L.

wollei ) had higher grow th rates when calcium wa s added to the growth media (Cowell

and Botts, 1994). Lyngbya majuscula ( L. wollei ) is a nitrogen fixer, and optim um

conditions for that process are low light and low oxygen concentration (Philips et al.,

1992), which is the opposite of the conditions in the experim e ntal stream s in this study.

The accrual of thick m a ts of L. majuscula could facilitate nitrogen fixation by the

filam e nts located deeper in the m a t. The e xperim e ntal algal m a ts were very sm all, and

m a y have ha d good oxygen penetration in the m a t.

Algae in this experim e nt were exposed to relatively high light intensity.

Vaucheria bursata grow th in laboratory conditions was saturated at very low levels (20-

50 μm ol/m 2 

### / s ) (Leukart and Hanelt, 1995). L. majuscula ’ s ab ility to f i x nitrogen was

reduced when exposed to light.

The water used in the experim e nt, from Juniper Spring, the spring in the Silver

River area with the lowest P, had higher TP th an expected. So target concentrations from

even control and low P treatm e nts were higher than planned. Little is known about the

nutrient con centration s that constrain the growth of Vaucheria and Lyngbya , however,

there is considerable evidence that phosphate availabil ity in stream s with 0.030 m g TP/L

is sufficient to support nuisance growths of Cladophora (Dodds et al., 1997; Stevenson et

al., accepted )

. Cladophora growth seem s to be constrai ned in stream s with less 0.600 mg

TN/L (Dodds et al. 1997). These nutrient c oncentrations constrai n relatively dense

60

### growths of Cladophora , not the thin layer of m acroalg ae exp o sed on the surface of th e

bound m a sses in the experim e nt. Rapid nutrient uptake and slow m i xing in dense m a ts of

algae cause com p etition for nutrien ts in periphyt o n, which increases in in tensity with the

thickness of algal m a ts and algal density on substrata (Stevenson and Glover 1993;

Dodds, 1991). The 0.020 m g TP/L and 0.100 m g TN/L found in Juniper Spring water

should surely constrain the growth of dens e m a ts of m acroalgae, but perhaps not the

relatively thin, active out er layer of bound m a sses used in th e experim e nt. The accrual of

thick benthic m a sses of m acroalgae in Florid a springs m a y be constrained by naturally

low nutrient concentrations, but that hypothesi s was not tested in the experim e nts that

were conducted.

The results of this study and previous studies on Lyngbya majuscula ( L. wollei)

(Cowell and Botts, 1994) suggest that growth of this m acroalga is probably controlled by

a com b ination of factors: concentration of m acro and m i cronutrients, flow, dissolved

oxygen and CO2 concentrations, and light availa bility. Less is known about what affects

the growth of Vaucheria sp.

61

## CHAPTER 4. M A CROALGAL GR OWTH IN HIGH AND LOW

CONDUCTIVI TY SPRING WATER

Introductio n

Many factors m a y regulate the ability of algae to respond to nutrients. Tem p erature,

light, pH, grazers, and conductiv ity are im portan t factors. C onductiv ity is one of the most

im portant variables regulating th e changes of algal species co mposition in aquatic ecosystem s

(Pan et al., 1999; Chapter 6, this report). In the field survey, th e researchers observed that sites

with high conductivity often had th icker algal m a ts and a higher percentage of the spring bottom

was covered by m acroalgae. Great inter-specific dif f

e rences in spec ies ab ilitie s to utilize

nutrients and even differences am ong populations w ithin species were expected (Carpenter and

Guilla rd, 19 71).

To test the h ypothesis th at water con duc tiv ity an d algal g r owth rates are positiv ely

correlated, the growth rates were compared of algae in m e socosm s using water from 3 springs

with sim ilar nutrient co ncentrations but differing water c onductivity. In addition, the hypotheses

that growth rates of species and populations w ithin species differ were tested by com p a r ing

growth rates of a Vaucheria population and two Lyngbya majuscula populations.

Methods

Experimental setup

This experim e nt had 6 treatm e nts with 3 repli cates. A factorial design w a s used, with two

factors: water conductivity (low, m e dium and high) and water nutrients (no nutrients added and

nutrient enrichm e nt with addition of 240 μg P/L as phosphate and 1000μg N/L as nitrate). Three

kinds of alg ae were gro w n in each m e socosm : L. majuscula from 2 springs and Vaucheria sp.

Eighteen Glad ® containers were used as m e socosm s. Each held 1.5 L of water. W a ter was

m i xed with aeratorsin each m e socos m to generate current. T h e m e socosm s were placed in a

water ba th to m oderate diurna l var i a b ility in water tem p eratu r es. The wa ter ba th was a sm all

swimming pool filled with well water and heated with 3 aquarium heaters

. This pool was

covered with a screen (as described in Chapte r 3). W a ter with low conductivity (110μS/cm ) was

collected from Juniper Spring, water with m e dium conductivity (1100 μS /cm ) was collected

from Alexander Spring, and water w ith high conductivity (2000 μS/cm ) was collected from

Silver Glen Spring. It w a s decided not to am e nd low conductivity water with salts so as to

reflect the conditions of natural sprin g s as m u ch as possib l e. Water in m e socosm s wa s replaced

with new water every tw o days. The water te m p erature, conductivity, pH and DO also were

m easured every two days. Fresh water was coll ected from the springs three tim es during the

course of the experim e nt. This experi m e nt started on day 0 and ended on day 7.

Algae

The experim e nt was conducted on two taxa of m acroalgae. Lyngbya majuscula was collected

from Juniper Spring and from Silver Glen Spring, and Vaucheria sp. was collected from

Alexander S p ring. Algae were prepared and atta ched to tiles as described in the previous

experim e nts (Chapter 3). On da y 0 algae were introduced to th e m e socosm s. Three tiles with L.

majuscula from Juniper Spring, three tiles with L. majuscula from Silver Glen Spring and two

tiles with Vaucheria sp. f r om Alexander Spring were placed in each m e socosm . One tile with L.

62

### majuscula from each of two springs was used for N a nd P content an alys es and the rem a ining

tiles were used for FM and AFDM a n alyses. Tw o tiles of each kind we re trea ted as n e sted

replicates, and the FM m a ss from e ach m e socosm was summ ed up. In a ddition, at the beginning

of the experim e nt, 10 algal sam p les from each tax on were weighed for FM and frozen to be

processed f o r AFDM to aid in c a lcu l ating the in it ial AFDM f r om the initia l FM f o r alg a e plac ed

in the m e socosm s. At the end of experim e nt, algae were weighed for FM and frozen. In the

laboratory, all frozen samples were processe d for AFDM (Clesceri et al., 1998). Biom assspecific g r o w th rates of each alg a l taxon were calculated as in Chapter 3.

Chemical and physical analyses of water and algae

The tem p erature, pH, dissolved oxygen and cond uctivity were m easured in each m e socosm

every two days. On day 1, day 5 and the last day of the experim e nt (day 7) sam p les were

collected fro m each m e s o cosm for water analyses (alkalin ity, ammonia, calcium , chloride, iron,

m a gnesium , Kjeldahl N, nitrates, phosphate, si lica, sodium , s u lfate and T P ). Sam p les for

nitrogen and phosphorus content in L. majuscula from Juniper Spring and L. majuscula fro m

Silver Glen Spring were colle cted in trip lic ate at the beginning of the experim e nt from the

origin al algal m a t and one sam p le from each m e socosm was collected at the end of the

experim e nt. Sa m p les for nitrogen and phosphorus in Vaucheria sp. were not collected due to

funding restrictions. Samples were an alyzed as described in Chapter 3.

Statis tical a nalyses

Biom asses for the two pseudoreplicates from each m e socosm were summed on respective dates

and constituted one true replicat e used for statistical analyses. ANOVA ( g lm model) was used to

analyze data. Differences were considered sign ificant for p<0.05 (SAS, 2000). The least squares

(LS) m eans test was used to com p are individual m eans. All graphs show m eans and standard

error (SE).

Results

Nutrient con centra tion in mesocosm water

Nutrient addition resulted in high TP , TN and nitrate concentrati ons (Table 4.1). Juniper Spring

water (low conductivity) had the low e st concentr ation of calcium , chloride, m a gnesium, sodium

and sulfate. Alexander S p ring water (m edium conductivity) had higher con centrations of these

salts, and Silver Glen Spring water (high conducti vity) had the highest concentrations of these

salts (Table 4.1).

63Table 4.1Average nutrient concentrationsin the conductivity experiment.SpringwaterfromNutrientsDayTNmg/LTPmg/LAmmonia Nmg/LKjeldahlNmg/LNOxmg/LCalciummg/LChloridemg/LIronmg/LMagnesiummg/LSodiummg/LSulfatemg/LJuniper010.1270.0410.0100.0600.06713.4674.6330.0104.6672.6905.567Juniper050.0720.0240.0100.0680.00413.4675.2330.0104.2332.8005.867Juniper070.0820.0280.0110.0770.00513.9335.8330.0104.3333.0506.300Juniper111.1630.2600.0100.0631.10013.6674.6330.0104.7004.4735.567Juniper150.9760.2270.0120.0800.89713.5675.5000.0104.2674.6505.867Juniper170.9170.2400.0110.0770.84013.4336.2330.0104.2004.6536.200Alexander010.1750.0560.0100.1370.03849.300270.0000.01022.367148.33369.000Alexander050.1240.0330.0100.1200.00448.233270.0000.01019.933136.00068.000Alexander070.1910.0410.0100.1870.00448.800276.6670.01020.033133.33371.333Alexander111.1830.2700.0100.1831.00049.367273.3330.01022.433149.66768.667Alexander150.9430.2370.0220.1830.76048.233263.3330.01019.967138.00068.000Alexander171.0600.2600.0100.1800.88048.300280.0000.01019.767133.33370.333SilverGlen010.1600.0370.0100.1300.03073.200443.3330.01035.933255.000166.667Silver Glen050.1370.0240.0150.1330.00473.533466.6670.01033.367244.667166.667SilverGlen070.1880.0330.0100.1830.00573.633476.6670.01033.233242.000173.333SilverGlen111.1930.2500.0100.1601.03372.800443.3330.01035.667254.667166.667Silver Glen150.8800.2200.0120.1600.72074.400473.3330.01033.833248.667173.333SilverGlen170.9830.2430.0100.1830.80074.867483.3330.01033.533244.333173.33364

### Water conductivity, tem p erature, pH and DO

The water temperature averaged 17. 9°C (SD=2.4, SE=0.29, m i n=14.0, m a x=21.4), and

there were no differences between mesocosm s (ANOVA, F=0.02, p=0.999). The pH

averaged 8.31 (SD=0.16, SE=0.019, m i n=7.99, m a x= 8.60), and there were no differences

between m e socosm s (ANOVA, F=0.01, p=1). DO was close to saturating and averaged

7.43 (SD=0.69, SE=0.082, m i n=6.06, m a x=8.44), and th ere were no differences between

m e socos m s (ANOVA, F=0.72, p=0.610). W a te r conductivity had a steep gradient

between treatm ents (Table 4.2). Addition of nutrients slightly increased w a ter

conductivity in the low c onductivity treatm e nt (Juniper Spring water).

Table 4.2

Water conductivity (μS/cm ) in m e so cosm s in the conductivity experim e nt (ANOVA,

F=12293.3, p<0.0001)

Spri n g w a ter

fr om Nut r i e n t s M e a n S D S E M I N M A X

0 1 1 8. 9 1 4. 8 7 1. 4 1 11 4 13 0 Juni per 1 1 2 8. 5 0 6. 4 5 1. 8 6 12 2 14 3

0 1 1 5 6

. 8 3 19 .1 7 5. 5 3 11 3 8 11 9 7 Al exa nde r 1 1 1 6 8

. 8 3 12 .1 4 3. 5 1 11 5 0 11 9 1

0 1 9 0 3

. 4 2 40 .2 6 11 .6 2 18 5 2 19 6 1 Silver Glen 1 1 9 2 4

. 5 8 39 .8 2 11 .5 0 18 7 1 19 7 6



### Nitrogen and phosphorus content in algal mats

The final algal m a t Kjeldahl Nitrogen and TP content, for Lyngbya majuscula from

Juniper Spring and L. majuscula from Silver Glen Spring, was not affected by water

conductiv ity and by the addition of n u trients. It n e ither differed from the initial content

nor between the two strains of L. majuscula (Figure 4.1, ANOVA for Kjeldahl N,

F=0.28, p=0.989; ANOVA for TP, F=1.94, p=0.070). The molar m a t TN/TP ratio was

not affected by the experim e ntal treatm ent but differed between the two strains of

Lyngbya (Table 4.3). All TN/TP ratios were above 16 (Redfield ratio) indicating

phosphorus lim itation even in the treatm ents with nutrients (Table 4.4).

65

### Table 4.3

Results of ANOVA for m a t TN/TP r a tio in conductivity experim e nt for Lyngbya

majuscula from Silver Glen and Juniper Springs. Da ta were 1/(x+1) transf orm e d to m e et

the assum p tion of norm a lity and var i ance hom ogeneity.

TN/TP

Source of Vari ation DF SS MS F p

Treat m e nt 6 0. 00 0 3 4 0. 00 0 0 6 2. 2 6 0. 06 6

Al g a e 1 0. 00 2 9 3 0. 00 2 9 3 11 6. 1 4 <0 .0 001

Treat m e nt *Al g a e 6 0. 00 0 1 1 0. 00 0 0 2 0. 7 5 0. 61 8



### Table 4.4

Mat m o lar TN/TP ratio in conductivity experim e nt in the algal m a t collected from the

f i eld (In itial) and alg a l m a t at the e nd of experim e nt for two strains of Lyngbya . Mean

±SE. (ANOVA for Silver Glen Lyngbya F=1.61, p=0.216; ANOVA for J uniper Lyngbya

F=1.45, p=0.263).

Treatm e nt Silver Glen

Lyn gby a Juni per

Lyn gby a

Initial 36 .7 ± 1

. 1 23.1 ±1. 0

Alexand e r 32 .3 ± 5

. 1 19.1 ±2. 1

Alexand e r with nutrients 29 .2 ± 1

. 9 22.3 ±0. 9

Juniper 31 .3 ± 1

. 4 19.4 ±1. 6

Juniper with nu trients 27 .8 ± 1

. 0 18.7 ±1. 3

Silver Glen 31 .5 ± 1

. 7 20.5 ±0. 6

Silver Glen with nutrients 28 .3 ± 0

. 3 19.9 ±0. 1



### Macroalgae growth rates

Algae growth rates based on FM and AFDM differed am ong taxa (Tables 4.5 and 4.6).

Lyngbya majuscula from Juniper Spring had a hi g h er FM gro w th rate tha n L. majuscula

from Silver Glen Spring, but AFDM grow th rates did not differ (LS m eans p<0.05;

Figure 4.2). Both Lyngbya majuscula populations had a higher growth rates than

Vaucheria (LS m eans p<0.05).

The source of spring water or conductivity affected the FM and AFDM growth of

Vaucheria sp. (Table 4.7). Vaucheria sp. grew fastest in Alexander Spring water, which

had interm e d iate conductivity levels (LS m eans p<0.05). The addition of nutrients did

stim ulate Vaucheria growth, especially in the m o derate conductivity waters of Alexander

Spring. Nutrients and spring water conductivity had no effect on the growth of either

strain of Lyngbya

.

66

### Figure 4.1

Algal m a t TP and Kjeldahl Nitrogen content in the conductivity experim e nt. Algae grown

in water from different springs without (0) and w ith (1) addition of nutrients.

J unipe r L y ng by a

0 5 10 15 20 25 30 35

I n it ia l 0 1 0 1 0 1

J uni pe r A l e x a n d e r S i lv e r G l e nPg/KgFM

J unipe r Ly ngby a

0 50 10 0 15 0 20 0 25 0 30 0 35 0

In i t i a l 0 1 0 1 0 1

J u n i p e r A l e x a n d e r S i l v e r G l e nN g/KgFM Si lv e r G l e n Ly ngby a

0 5 10 15 20 25 30 35

I n it ia l 0 1 0 1 0 1

J u n i p e r A l e x a n d e r Silv e r G l e nP g/KgFM

S ilv e r G l e n Ly ngby a

0 50 100 150 200 250 300 350

I n i t i a l 01010 1

J u n i p e r A l e x a n d e r Silv e r G l e nN g/KgFM



### Table 4.5

Results of ANOVA for a l gae RGR based on al gal FM in the conduc tivity experim e nt.

Source of Vari ation DF SS MS F p

C o n d u ct i v i t y ( S pri n g) 2 0. 00 0. 00 6. 13 0. 00 5

Nut r i e nt s 1 0. 0 0 0. 0 0 2. 4 7 0. 12 5

Al g a e 2 0. 0 5 0. 0 3 80 .8 1 <.00 01

C o n d u ct i v i t y *Nut ri ent s 2 0. 0 0 0. 0 0 1. 5 7 0. 22 1

C o n d u ct i v i t y *Al g a e 4 0. 0 0 0. 0 0 1. 7 9 0. 15 1

Nut r i e nt s* Al g a e 2 0. 0 0 0. 0 0 4. 0 5 0. 02 6

C o n d u ct i v i t y *Nut ri ent s * A l g a e 4 0. 0 0 0. 0 0 1. 0 4 0. 40 3



### Table 4.6

Results of ANOVA for a l gae RGR based on algal AFDM in the conductivity experim e nt.

Source of Vari ation DF SS MS F p

C o n d u ct i v i t y ( S pri n g) 2 0. 00 0. 00 6. 59 0. 00 4

Nut r i e nt s 1 0. 0 0 0. 0 0 2. 0 6 0. 16 0

Al g a e 2 0. 0 1 0. 0 1 25 .8 7 <.00 01

C o n d u ct i v i t y *Nut ri ent s 2 0. 0 0 0. 0 0 2. 0 6 0. 14 3

C o n d u ct i v i t y *Al g a e 4 0. 0 0 0. 0 0 2. 5 4 0. 05 7

Nut r i e nt s* Al g a e 2 0. 0 0 0. 0 0 0. 1 0 0. 90 2

C o n d u ct i v i t y *Nut ri ent s * A l g a e 4 0. 0 0 0. 0 0 1. 1 6 0. 34 5



### 67

### Table 4.7

Results of ANOVA for RGR o f Vaucheria sp. based on FM and AFDM.

FM

Source of Vari ation DF SS MS F p

Spri n g 2 0. 00 5 5 0. 00 2 7 6. 5 5 0. 01 2

Nut r i e nt s 1 0. 00 2 3 0. 00 2 3 5. 3 8 0. 03 9

Spri n g * N ut ri ent s 2 0. 00 1 8 0. 00 0 9 2. 1 7 0. 15 7

AF DM

Source of Vari ation DF SS MS F p

Spri n g 2 0. 00 2 1 0. 00 1 1 5. 0 2 0. 02 6

Nut r i e nt s 1 0. 00 0 1 0. 00 0 1 0. 2 6 0. 62 0

Spri n g * N ut ri ent s 2 0. 00 1 1 0. 00 0 6 2. 6 6 0. 11 1



### 68

### Figure 4.2

Algal relative growth rate (RGR) in the c onductivity experim e nt based on fresh m a ss and

ash free dry m a ss. Algae grown in water from different springs wit hout (0) and with (1)

addition of nutrients.

J u ni pe r Ly n gby a

0 0. 02 0. 04 0. 06 0. 08 0. 1 0. 12

010 101

J u n i p e r A l ex a n d e r S ilv er

G l e nRGRforFM(g/g/day) Juni p e r L y ngb y a

0 0. 01 0. 02 0. 03 0. 04 0. 05 0. 06

0 1 0 1 0 1

J u n i p e r A l e x a n d e r S ilv er

G l enRGR forAFDM(g/g/day)

S ilv e r Gle n L y n g b y a

0 0. 02 0. 04 0. 06 0. 08 0. 1 0. 12

0 1 0 1 0 1

J u n i p e r A l e x a n d e r S ilv er

G l enRGRforFM(g/g/day) S i lv e r G l e n L y n g b y a

0 0. 01 0. 02 0. 03 0. 04 0. 05 0. 06

0 1 0 1 0 1

J u n i p e r A l e x a n d e r S ilv e r G l e nRGR forAFDM(g/g/day)

Va u c h e r i a

0 0. 02 0. 04 0. 06 0. 08 0. 1 0. 12

0 1 0 1 0 1

J u n i p



### Discussion

This study was designed to addr ess the role of c onductivity on m acroalgal growth. S i nce

natural spring water was used, other f actors in add ition to cond uctiv ity were m a nipulated.

Micronutrients as well as toxic salts m a y vary among springs. However, all salt

concentrations m easured increased in concen tration with increases in conductivity am ong

springs. If it is assum e d that responses of m acroalgae to spring waters were due to

differences in conductivity and associated salts alone, then Vaucheria sp. grew best at

interm ediate conductivity levels . Salinity and conductivity ca n affect the distribution of

Vaucheria sp. in stream s (Moreno et al., 2001). In chapter 2 a positive correlation was

reported between alkalinity and the distribution of Vaucheria sp. Freshwater species of

Vaucheria sp. are known to adapt to different sali nity levels (Schneider et al., 1996). e r A l ex a n d e r S ilv er

G l enRGRforFM(g/g/day) V a uche r i a

0 0. 01 0. 02 0. 03 0. 04 0. 05 0. 06

0 1 0 1 0 1

J u n i p e r A l e x a n d e r S ilv e r

G l e nRGR forAFDM(g/g/day)

b

a

a

a a a d a c ad

bcd bd a a

a



### 69

### Nutrient enrichm e nt had no effect on the Lyngbya growth rate, as in Chapter 2 (this

report). Thus, these results confirm t h at Lyngbya is adapted to grow in very low nutrient

concentrations. Vaucheria growth rates were stimulated by nutrient con c entration s when

higher conductivity conditions were created in the conductiv ity experim e nt than in

recirculating stream s. This resu lt d i f f

e rs f r om the results ob tained in the recirculating

stream experim e nt, which used low conduc tivity water from J uniper Spring. The

response of Vaucheria to nutrients in the conductivit y experim e nt indicates that

Vaucheria proliferations in springs with conductivity sim ilar to Alexander Spring m a y be

m a naged by reductions in nutrient concentrations.

The differences in the growth rates of two populations of Lyngbya majuscula indicated

that genetically different Lyngbya taxa m a y be present. Thus, a distinction m a y not have

been m a de between two different taxa, or phenotypes of populations m a y vary am ong

springs. Both of these factors have implicat ions for future work and m a nagem e nt of

Lyngbya

. Lyngbya majuscula is kn own to adapt well to dif f

e rent environm ents (Burja et

al., 2002; R o ssi et al., 1997); it also requires iron for grow th (Gross and Martin, 1996).

In cultures, L. majuscula grows well in low light. In nut rien t rich m e dia its growth is

carbon-lim ited (Burja et al., 2002). Also, the taxonom i c position of Lyngbya majuscula

inhabiting fresh waters of Flor ida is not clear, possibly becau se it repr ese n ts m o re than

one species (Joyner, 2004; Shannon et al., 1992; Speziale and Dyck, 1992).

70

## CHAPTER 5. NUTRIENT DIFFU SING SUBSTRATA ASSESSMENT

OF NUTRIENT LIMIATION OF MI CROALGAE

Introductio n

Many approaches exist f o r the experim e nt al assessm e nt of nut rient lim itation.

Laboratory assays, field m e so cosm s, and nutrient diffusing substrata (NDS) are th ree

such approaches. NDS is an establishe d m e thod of assessing potential nutrient

lim itations of m i croalgae in field settings (Fairchild and Lowe, 1984; Fairchild et al.,

1985; Fairchild et al., 1989; Francoeur et al., 1999; Marks and Lowe, 1993; Mosisch et

al., 2001; Pillsbury et al., 2002; Pringle, 1990; Snyder et al., 2002). For this report,

researchers investigated whether nitrogen a nd/or phosphorus lim its periphyton growth in

16 springs in northern and central Florida. Nu trient diffusing substrata w e re exposed to

algal colonization for three w eeks and periphyton chlorophyll a was m e a s ured. This

study com p lem e nted the other m e thods, discussed in Chapters 1 and 2, that were used to

assess the nutrients regulating alga l growth in Fl orida springs.

Methods

NDS were prepared from clear polystyrene snap-cap vials (13 dr, 46 m l I.D. x H:

30x53mm ) with a 21 mm diam eter hole cut out in the cap and a Nitex screen (30 μm

m e sh) spread over the rim and held in place by the cut out cap. The Nitex screen s e rved

as a colonization surface for periphyton. The Nitex screen approach was m odified from

Biggs et al. (1998) and also Francoeu r et al. (1999 ). Each vial was filled with a 25 m l

agar (BactoAgarTM) and nutrient solution. Th ere was a 3 cm clearance between solidified

agar and the Nitex screen, which provided space for algal g r o w th with exclusion of

potential grazers. Potassium phosphate m onobasic (KH 2 

### PO 4 

### ) was used as a source of

phosphorus and sodium nitrate (NaNO 3 

### ) was used as a source of nitrogen. Four

treatm ents were used: (C) control (2% ag ar), (N) nitrate enriched (2% agar, 0.5 M

NaNO 3 

### ), (P) phosphate enriched (2% agar and 0.5 M KH 2 

### PO 4 

### ) and (NP) nitrate and

phosphate com b ined (2% agar, 0.5 M NaNO 3 

### and 0.5 M KH 2 

### PO 4 

### ). Vials filled with agar

were refrig erated p r io r to placem ent in the fiel d. At the site, v i als we re filled with site

water, and the opening was covered with the N itex screen pulled tight ly o v er the rim and

secured with snap-cap. F our vials (C, N, P and NP) were glued with a craft hot glue gun

inside a Glad® sandwich box (16.5x16.5x5cm d eep, 739 m l volum e). Each sandwich

box was filled with sand collected at the si te and the box was placed on th e bottom of the

spring. The upper 3 cm of each vial was pos ition e d above the sand and was exposed to

light. S i x boxes containing 4 NDS (C, N, P a nd NP) were deployed at each site. W h en

the initial N D S were deployed, they were plac ed in a sunny location close to the boil at

the depth of approxim a tely 1 m ; however, m o st of the boxes placed in su ch a location

were m i ssing or were dam a ged when the sites we re revisited 7 days later. Consequently,

boxes were placed in areas not easily accessibl e to people and hidden from view. NDS

were left in the field for 3 weeks. In the in itial trials NDS were lef t in the f i eld f o r 2

weeks. However, there was very little alga l colonization after 2 w eeks and an additional

week of exposure was added. In all, 96 boxes (384 NDS) we re placed at16 sites.

Although 278 NDS were recovered, only 148 NDS were not dam a ged and rem a ined

71

### exposed to the water colum n. Conse quently, representative data from NDS recovered in

good condition consisting of 3 replicates (at l east 3 NDS recovered from e a ch treatm ent)

were collected from only 7 sites ( GAI-02, ICH-01, JUN-02, RAI-01, SLV-01, VOL- 01,

WAC-02). See Table 5.1 for m o re co m p lete statistics on recovery.

Table 5.1. Num b er of NDS recovered from each site; num ber of NDS r ecovered in good

condition and their exposure to light.

Spri n g S i t e c o d e ND S

reco vere d ND S

reco vere d i n

g ood co nd itio n Li ght e x pos u r e

Al exa nde r A L E

- 0 1 1 3 8 sun n y / sha d e

C h assa ho wi t z k a C H A

- 0 1 1 8 0 sha d e

Gai n e r G A I- 0 2 2 4 2 0 sun n y / sha d e

Hom o s a s s a H O M

- 0 1 2 0 8 sunny/sha d e

Ichet u ck n e e I C H- 0 1 2 4 1 6 sun n y / sha d e

Jacks o n B l ue JAC

- 01 12 0 sun n y

Juni per/ Fe r n H a m m ock JUN

- 02 20 16 sha d e

Po nce de Le o n PO N- 01 16 0 sha d e

Rain bo w Sp r i ng RA I

- 01 2 4 2 4 su nn y

Silv er G l en SG L- 01 8 0 su nn y

Si l v er R i ve r SLV

- 0 1 16 12 sun n y / sha d e

Vol u si a B l ue VOL

- 01 20 20 sha d e

W a c i s s a W A C

- 0 2 2 0 2 0 sunny/sha d e

W a kku l l a W A K

- 01 1 6 0 su nn y

Weeki Wac h ee WE K- 01 16 0 sun n y

Weki w a W K W- 0 1 1 1 4 sha d e

To tal recov e red 27 8 14 8



### Nitex screens were rem oved from the r ecovered substrata a nd discs colonized by

algae were cut out and frozen imm e diately. In the laboratory, algae were rinsed off from

the Nitex d i s c s with DI water, com b ined to f o rm 3 replicates of each trea tm ent per site (if

there were enough sam p les), and analy zed fluorom etrically f o r chlorophyll a following

extraction in 90% ethano l (Clesceri et al., 1998).

Results

The algal biom ass on nutrient diffusing substr ata varied am ong sites, but nutrient

treatm ent effects were not significant (Tab le 5.2, Figure 5.1). The algal biom ass was

usually v e ry low in all tr eatm e nts, ≤ 1 μ g chl a/cm 2



### .

72

### Table 5.2. Results of ANOVA for n u trient di ffusing substrata experim e n t for 7 sites

(GAI-02, ICH-01, JUN-02, RAI- 01, SLV-01, VOL-01, W A C-02).

S o u r c e S S df MS F P

Nut r i e nt t r eat m e nt 1. 03 1 3 0. 34 4 1

. 0 3 9 0. 38 2

S i t e 2 1 .953 6 3 .6 59 1 1 .062 0. 00 0

Nut r i e nt t r eat m e nt * Si t e 2. 75 8 1 8 0

. 1 5 3 0. 46 3 0. 96 3

Err o r 1 8 .5 2 2 56 0. 33 1



### Figure 5.1. Algal biom ass on nutrient diffusi ng substrata m easured as chl a (Mean

±SE).

GA I

C N P NP

TR E

A T M E N T 0 1 2 3 4

C h l a ( u g / c m 2 ) IC H

C N P NP

TR E

A T M E N T 0 1 2 3 4

C h l a ( u g / c m 2 ) JU N

C N P NP

TR E

A T M E N T 0 1 2 3 4

C h l a ( u g / c m 2 )

RA I

C N P NP

TR E

A T M E N T 0 1 2 3 4

C h l a ( u g / c m 2 ) SL V

C N P NP

TR E

A T M E N T 0 1 2 3 4

C h l a ( u g / c m 2 ) VO L

C N P NP

TR E

A T M E N T 0 1 2 3 4

C h l a ( u g / c m 2 )

WA C

C N P NP

TR E

A T M E N T 0 1 2 3 4

C h l a ( u g / c m 2 )



### 73

### Discussion

Nutrien t diffusing substrata are an ex celle nt tool in assessing lim iting nutrients

directly at the study site, but they are easily dam a ged. Th e recovery rate of NDS was

very poor. Substrata were dam a ged by hum a ns and anim als and were covered with

m acroalgal m a ts, plants, and sedim e nts. Th is indicates that springs are very dynam i c

environm ents with a lot of di sturbance and with m a ny factor s affecting algal growth and

distribution. Although a sm all grazer effect on the substrata was reduced by allowing

algae to colonize under Nitex, sites did have different exposures to light and current

velocity. Although the effects of light and cu rrent velocity m a y have m a sked the effects

of nutrient enrichm e nt, the low algal biom a sses obtained indicated that another factor

m a y be i m portant. W ith grazing m i nim i zed a nd saturating nutrient concentrations, high

densities of algae shou ld have accum u lated du rin g a 3-week period (e.g., Hum phrey and

Stevenson, 1992; Rier et al., acc epted). The low colonization rates m a y have been due to

the unique nature of springs, which likely ha ve very low densities of suspended algae

because they arise from groundwater. Suspe nded algae in m o st shallow stream s with

short residence tim e accum ulate fro m the downstream transport of benthic algae

(Stevenson and Peterson, 1993). Suspended algae provide the in itial colonists for

community developm ent on artificial substrat a (Stevenson, 1983). Low suspended algal

densities would lim it im m i gration, growth, a nd comm unity developm ent on any artificial

substratum . Use of artificial substrata in springs m a y require either inoculation with

algae by special m eans or m u ch longer exposure tim es.

74

## NUTRI E NT DIFFUSING S U BS T RATA ( NDS) AND M A CROALGAE

Nutrient diffusing substrata were m odifi ed to exp o se m acroalgae to in creased

nitrogen and phosphorus levels. The snap-cap vials nutrient diffusi ng substrata described

above were modified by attaching Glad round 125m l cups with a hol e cut in the bottom

and a lid rep l aced by ny lon m e sh. A weighed sample of m a croalgae was placed in the

cup. In the initial 5-day trials m acroalgae were found to be doing well and som e growt h

was observed. In addition to nitrate and phosphate treatm ents, calcium and calcium

carbonate treatm e nts were added. T h ere were 6 treatm ents (C, N, P, NP, NPCa and

NPCaC O 3 

### ). Agar nutrient solutions were pr epared as described above. The NPCa

treatm ent was prepared by adding CaHPO 4 

### (0.1M) and keeping N and P concentrations

as in th e NP trea tm ent. The NPCaCO 3 

### treatm ent was prepared by adding CaCO 3 

### (0.1M)

to solution, as in th e NP treatm ent. Ther e were 6 replicates of each treatm ent (36

modified NDS per site). Approxim a tely 1 g fres h m a ss of macroalg ae w a s placed in each

cup. Prepared substrata were filled with site water and pr ess e d into the b o ttom at the

study site. Modified NDS were placed at 3 sites with restricted access (no swimm i ng

area): JUN-02 (cups with Lyngbya majuscula collected from JUN-01), WAK-01 (cups

with Vaucheria sp. collected at the site) and W E K-01 (cups filled with L. majuscula

collected at the site). A total of 108 substrata were left in the field for two weeks. After

two weeks at JUN-01, the substrata were dam a ged (they appeared to have been chopped

by an alligator), ripped out of the bottom

, or covered by sedim e nts. Less than half of the

substrata placed were recove red. At WAK-01, the substrat a were com p letely covered

with Vaucheria sp., which had grown into the contai ners. At WEK-01, th e substrata were

covered with a L. majuscula m a t or m i ssing.

75

## CHAPTER 6. DIATOM INDICATORS OF NUTRIENT CONDITIONS IN

FLORIDA SPRINGS

Introductio n

Using the sp ecies com p osition of diatom s can be sensitive, precise, and therefore, usef ul

indicators of biological and nutri ent conditions in stream s (Pan et al., 1996; Chetelat et al., 1999;

Wang et al., in press ) . Diatom indicators of nut rient conditio ns m a y be e v en m o re sensitiv e than

chlorophyll and biom ass m easurem e n ts and m o re precise than a one-t im e m easurem e n t of

nutrients (Stevenson and Bahls, 1999; Stevens on and Sm ol, 2003). Diatom s should be better

indicators of biological responses to nutrient enri chm e nt than m a croinvertebrates and fish, because

they respond directly to changes in nutrient concentration. A lthough th e latter hypothesis has not

been rigorously tested, that rati onale and past results with diatom indicators have been used to

support the potential va lue of this study.

Florida sp rings water va ries g r ea tly in solute con centr ation s , and water ag e and orig in

am ong sites (Rosenau et al. 1977; Katz et al. 1999, 2001). Conductivity is recognized as one of the

m a jor factors affecting diatom assemblages (Pan et al. 1996). It is expected that differentiating the

effects of nutrients from conductiv ity will be m o re challeng in g for nutrient indicator d e velopm ent

in Florida springs than in other regions.

The overall objective of this study w a s to develop diatom indicat ors of nutrient conditions

for Florida springs. To do this, first the relations hips were determ ined between nutrients and other

environm ental variables in Florida springs. Seco n d, the re latio n ship were d e term ined between

nutrients and diatom assem b lages in Florida springs . Finally, indicator species were determ ined

f o r dif f e rent nutrient con d itions in th e springs an d m u ltispec i es indicato r s of these nutrien t

conditions were tested.

Materials and Methods

Study sites

Twenty-eight springs located in northern and cen tral Florida were selected for this study.

Sa m p les were collected twice: first in the spring of 2003 (March and April) and second in the fall

of 2003 (Septem b er to Nove m b er). In the spring 48 sites within 16 springs were sampled, and in

the f a ll 60 s i tes with in 2 8 springs we re sam p led. (F or a description of study sites, refer to Chapter

2.)

Diatom sampling

A detailed description of sam p ling protocol can be found in Chapter 2. A com posite

quantitative sam p le of epiphytes was m a de with al gae brushed off and rinsed from the mid-section

of plant fragm e nts of known surf ace area collected at the site. Di atom subsam ples wer e collec t ed

by hom ogenizing sam p les on a m a gne tic stirre r, a nd colle ctin g aliquo ts of each sam p le with a

pipette for diatom subsamples. Multiple aliquots we re collected to redu ce error du e to patchin e ss.

Diatom s were also sam p led from the surface of m acroalgae. Pinches of m acroalgae were

collected fro m 9 transects at each site to produce one com posi t e m acroalgal sam p le per site. Since

76

### it was im possible to homogenize m acroalgae with a blender or Biospec biohom ogenizer, a

subsam ple for diatom analysis was taken by grouping pinches of different taxa of m acroalgae into

separate piles and cutting out 1/4 of each pile wi th a razor blade. Diatom s were separated from

m acroalgae by oxidation and digestio n of organic m a terial with nitr ic ac id, leaving just the gla ss

cell walls of diatom s for identification. Only m acroalgal diatom subsa m ple DM was determ ined.

The surface area of m acroalg ae was not determ ined.

A com posite quantitative epipelon sa mple consis ted of m u ltiple sed i m e nt cores

(approxim a tely 0.3 cm deep and 5.2 cm dia m eter) that were collected at each transect. Algae were

collected through swirling and rins ing the sam p le 10 tim es with distilled water (Stevenson and

Stoerm er, 1983). Diatom subsa m ples were co llected by homogenizing sam p les on a m a gnetic

stirrer, and collecting aliquots of each sam p le with a pipette. Multip le aliquots were collected to

reduce error due to patchiness.

Diatom s we re digested with boiling nitric acid and by a dding a catalyst, potassium

dichrom a te. Diatom s we re m ounted in Naphrax® on m i croscope slides a nd identified to the

lowest possible leve l, which was usua lly spec ies. Identification of diatom s was prim arily based on

Krammer and Lange-Bertalot (1986, 1988, 1991a, 1991b) ; Patrick and Reim er (1966, 1975) were

used as supplem e ntal sources of inform ation.

Water analyses

At each site, on transect 1 and 9 water tem p erature, pH, conductiv ity and d i ssolved oxy gen

(DO) were m easured. From the m o st upstream tran sect water sam p les were co llected for assays of

alkalinity, ammonia nitrogen, calcium, chlori de, iron, m a gnesium , Kjel dahl nitrogen (KN),

nitrates, orthophosphate, silica, sodium, strontium and sulfate. W a ter chemistry sam p les were

analyzed by FDEP. (For a description of wa ter sam p le analyses, refer to Chapter 2.)

Data Analysis

Only data from research during th e s p ring of the year were us ed in analy s es for this ch apter,

because fall sam p les were still being counted at the tim e that data analysis began. The

relationships a m ong environm ental variables were an alyzed first with Pearson correlation analyses

to determ ine the covariation am ong environm ental at tributes. After initial exploration, a new data

set was formed by rem o ving high con ductiv ity sites (> 2SD + m ean), because conductivity is an

im portant factor regulating diatom comm unity st ructure. Sites with highly unusual species

com position can greatly reduce p r ecision when th ey are used in a dataset to develop indicators.

Canonical correspondence analyses (CCA) were executed with the full dataset and then

with a redu c e d datas e t. For th e redu ced data set, sites with un usual chem istry were removed to

assess the relationship between diatom species composition and environm ental variables and the

scale at which diatom responses to nutrients could be observed. Ep iphytes were analyzed first as

an exam ple for epipelon and epi-m acroalgae. Nutrients and potassium were square-root

transform e d; the other environm ental va riab les we re natu ra l-log transform e d.

Species com position of diatom assemblages on pl ants was use d f

o r testing nutrient ind i ces.

It was decided to lim it analysis and presentation of data to one habitat and during one season

because of tim e constrain t s. Analys is of plants was chosen, because pas t experien ce in dicated that

77

### epiphytic diatom s are more sensitiv e to changes in nutrients than diatom s in other habitats (W ang,

unpublished data). Nutrient indicator values were extracted from the literature, past analysis of

Florida s t ream s data, and recen t resu lts from th e Mid-Atlantic Integ r ated Assessm ent (MAIA)

conducted by the Environm ental Monitoring and A ssessm ent Program of t h e USEPA. Nutrient

indices (NI) were calculated as a weighted average of species nutrient indicator values ( θ i 

### ) based

on species relative abundance in samples (p i 

### ). Thus, NI i 

### = Σ (p i 

### θ i 

### )/ Σ p i 

### for i=1,S, where S is the

num b er of s p ecies for which a nutrient indicator value is known. The corre lations between values

of nutrient indices and nutrie nt concentrations w e re eval uated by Pearson correlation.

In add ition to use of o l d auteco logica l cha r ac ter i z a tions of tax a , new cha r a c ter i za tions were

developed w ith the epiphytic algal data sam p le d during the spring. Standard weighted average

regression techniques (ter Braak a nd van Da m

, 1989) were used to develop and test indicators of

TN and TP using the C A LIBRATE ® 

### program (Juggins and ter Braak, 1992).

Species tole rances to lo w nutrien t c onditions and requirem e nts for high nutrients were

further analyzed with indicator species analys is (ISA) (Dufrene and Legendre 1997). TP was

catego r ized accord ing to its concentration rang e in the sam p ling sites. TP < 20 μg/L was assigned

as 1, 20 ≤ TP < 30 μg/L was assigned as 2, 30 ≤ TP < 40 μg/ L was assigned as 3, and TP ≥ 40 μg/ L

was assigned as 4. A TP category of 5 was cr eated for a few sites that had very high TP

concentratio ns. TN was also catego rized accordin g to its conc entra tion an d range of sites sam p led.

One was assigned to TN < 0.5 m g /L, 2 was assigned to 0.5 ≤ T N < 1.0 m g / L , 3 was assigned to 1.0

≤ TN < 1.5 mg/L, and 4 was assign ed to TN ≥ 1.5 m g /L. Indicator speci es analysis was conducted

with datasets that included all sites and with sites with unusual chem istry rem oved, as with m o st

other analyses, to isolate the com p licating fact or, conductivity, from nutri ents. Species were

selected as indicator species if their p < 0.1 in the ISA. TN or TP categories were assigned

accord ing to results of ISA.

Results

When all sites were included in the analysis, N and P concentrations were not correlated.

TN was positive l y cor r elated with N O x, alkalin ity, and Ca and negativ ely corre lated with Mg, Si,

and S (Table 6.1). KN was positively correlated with NH 4 

### -N, Cl, Na, K, and conductivity. NOx

was positive l y highly cor r ela t ed with TN and alkalinit y and negative l y corr elated with Si, Na, Sr, S,

and pH. NH 4 

### -N was positive l y corr elated with KN and negatively corr ela t ed with PO 4 

### and TP. TP

was positive l y corre lated with PO 4 

### , Cl, Mg, Si, Na , Sr, S, te m p erat ure, conductivity, and K but was

negatively correlated with DO and NH 4 

### . PO 4 

### wa s also negatively correlated with DO, pH, and

NH 4 

### but was positiv ely c o rrelated with TP, Si, and S.

Alkalinity w a s highly and positiv ely correlated with Ca, but ne gativ ely co rrelated with pH

(Table 6.1). Both Sr and conductiv ity were highly correlated with cations, Cl, and S. T h e pH data

were negatively correlated with cations, S, and c onductivity, but positively corre lated with DO.

78Table 6.1.Correlations among nutrients and environmental variables at allsites during spring. Only correlations with P<0.05 are recorded. *represents 0.01< p < 0.05, ** represents 0.01 < p < 0.001, *** represents p < 0.001.AlkNH4CaClMgTNKj-NNOxPO4TPKSiNaSrSDOpHTempNH4RPCaR82P**Clr42p*gr590p****p*O4r4p0.*0.*M0.20.**TNr0.40*0.30*-0.33*Kjet-Nr0.510.490.32p*******NOxr0.44-0.370.99p*******P-0.**0TPr-0.340.350.450.86p*******Kr0.360.940.890.410.30p**********Sir0.410.340.44-0.31-0.290.490.47p*************Nar0.441.000.920.47-0.310.360.93p****************Srr0.610.750.80-0.290.430.650.340.77p*******************Sr0.550.780.88-0.31-0.350.310.510.710.400.800.9679AlkNH4CaClMgTNKj-NNOxPO4TPKSiNaSrSDOpHTempp**************************DOr3544-0.2-0.9-0.8-0.7p*********pHr-0.76-0.71-0.41-0.30-0.32-0.32-0.30-0.35-0.390.38p****************Tempr0.470.310.310.310.360.440.510.47p*************Condr0.630.940.910.480.330.930.940.830.84-0.410.35p****************************80

### Four ordination axes exp l ained 29.9 % of the variance in species com position of epip hytic

diatom s with CCA when all s ite s were in cluded in the analysis (Table 6.2). The first and all

canonical axes were statistically significant (F = 4.2, p = 0.005; F = 2.3, p = 0.005). The four axes

explained 62.9% of species-environm ent variance. The first CCA axis was highly correlated with

Cl and conductivity (r = 0.82, p < 0.001; r = 0.75, p < 0.001); the second CCA axis was positively

correlated with Sr (r = 0.62, p < 0.001) and negative ly correlated with alkalinity (r = -0.32, p = 0.03)

(Table 6.3). A CCA biplot indi cated that m o st sites clustere d around the m i d-point of the CCA

with a few, high-conductivity sites strongly affec ting the analysis (Figur e 6.1). A table with

species scores on the C C A axes can be found in Appendix B (Table A).

Table 6. 2.

The percentages of variance explained in the CCA . The CCA was analyzed with epiph y tic d i atom s

and environm ental variables and data from all sites during spring.

A x e s 1 2 3 4 To tal

in ertia

Ei gen v al ues ( λ ) 0. 60 4 0

. 39 6 0

. 28 5 0. 23 0 5. 06 3

Speci es-e n v i r o n m e nt correl a t i o n 0. 95 3 0

. 86 5 0

. 9 3 0. 84 9

Cum u lative % varia n ce of:

Speci e s dat a 11

. 9 1 9

. 8 2 5

. 4 29

. 9

Speci es-e n v i r o m ent rel a t i ons hi p 25

. 1 4 1

. 6 5 3

. 4 62

. 9

Test of sign ifican ce o f first c a n o n i cal axi s F=4. 2, p= 0. 0 0 5

Test of sign ifican ce o f all can oni cal a x es F=2. 3, p= 0. 0 0 5

Sum of al l unc onst r ai ne d λ 5. 06 3

Sum of all ca nonical λ 2. 40 6



### Table 6.3.

The correlation between CCA and environm ental vari ab les on the f i rst two axes. Epiph y tic diatom

data and env i ronm ental v a riab les f r om all sites c o llected during the spring we r e us e d . * i n di c a t e s p

< 0.05, ** indicates p < 0 .01, *** indicates p < 0.001.

En vi ro nm ent a l va ri abl e s C C A 1 C C A 2

Alk a lin ity

NH 4 -N

Cl

TN

Kjel dahl N

PO 4

TP

Si

Sr

DO

pH

Tem p erature

Co ndu ctiv ity 0. 82 ***

0. 75 *** -0 .3 2 *

0. 62 ***



### 81

### Figure 6.1.

A CCA biplot of the first 2 axes. The CCA was analyzed with epiphytic diatom s and

environm ental variables.

-5.0 +15.0-5.0+10.0

Cl Cond Sr

NH4 pH

Temp

TN

Alk TP

DO KN

Si CHA-03

HOM-01

WEK-04 SLV-05 SLV-04

JAC-03 ICH-07 SLV-03

ICH-08

WKW-01

CHA-02 GAi-01 ICH-06

ICH-05

ICH-04 RAI-01

JAC-02

RAI-04 GAi-02

RAI-03 JAC-01 RAI-02 SGL-01

VOL-01 CHA-01

WKW-02

WAC-01 ICH-03 ALE-01

WEK-03 Jun-03

Jun-01

ALE-02

SLV-01

ICH-01

PON-01

WAK-01

WAK-03 SLV-02

WAK-02 VOL-02 JUN-04

WAC-02

WAC-03



### After rem o ving high conductivity site s (ALE-01, ALE-02, CHA-03, HOM-01, JUN-04,

SGL-01, VOL-01, and VOL-02), a second CCA was execu ted (Figure 6.2). Four ordination axes

explained 29.3% of the variance in epiphytic diatom assem b l a ge with CCA (Table 6.4). The first

and all canonical axes w e re statistically signifi cant (F = 2.62, p = 0.01; F = 1.83, p = 0.005). Four

axes explained 59.9% of species-environm ent va riance. The f i rst CCA axis was positively

correlated with Cl (p = 0.79, r < 0.001), but negati vely correlated with NH4-N and TN (r = -0.38, p

= 0.008; r = -0.39, p = 0.009, respectively). The sec ond CCA axis was negatively correlated with

TP and Si (r = -0.32, p = 0.06; r = -0.32, p = 0.06), but positively correlated with NH4-N, Cl, TN,

KN, and DO (r = 0.72, p < 0.001; r = 0.38, p = 0.02; r = 0.48, p = 0.006; r = 0.80, p < 0.001; r = 0.55,

p < 0.00; respectively) (Table 6.5). A table with species scores on the C C A axes can be found in

the Appendix B (Table A).

82

### Figure 6.2.

A CCA biplot of the first 2 axes. The CCA anal ysis was conducted using epiphytic diatom s and

environm ental variables. High conductivity s ites were rem oved from this analysis.

-4.0 +8.0-2.5+5.5

Cl TN NH4

Cond Temp

Si pH

Alk

DO

TP

Sr KN

CHA-02 WEK-04 JAC-03

CHA-01

JAC-02

ICH-08

JAC-01

ICH-07

ICH-06

SLV-05

WKW-02 GAi-01 RAI-04

SLV-02

SLV-03 WAC-01

RAI-01 RAI-03 WAK-03

SLV-04

ICH-03 GAi-02

PON-01 WAC-03

Jun-03 WKW-01 ICH-01 Jun-01

WAK-01 ICH-05 WEK-03

ICH-04 WAC-02

WAK-02 SLV-01 RAI-02



### Table 6.4.

The variance explained by CCA axe s . The CCA an alysis was conducted using epiphytic diatom s

and environm ental variables. High conductivity sites were removed fr om this analysis.

A x e s 1 2 3 4 To tal

in ertia

Ei gen v al ues ( λ ) 0. 35 1 0

. 29 3 0

. 20 5 0. 15 5 3. 42 8

Speci es-e n v i r o n m e nt correl a t i o n 0. 93 6 0

. 93 3 0

. 89 3 0. 8 5

Cum u lative % varia n ce of:

Speci e s dat a 10

. 2 1 8

. 8 2 4

. 8 29

. 3

Speci es-e n v i r o m ent rel a t i ons hi p 21 38 .4 50 .7 59 .9

Test of sign ifican ce o f first c a n o n i cal axi s F=2. 6 2

, p= 0. 0 1

Test of sign ifican ce o f all can oni cal a x es F=1. 8 3

, p= 0. 0 05

Sum of al l unc onst r ai ne d λ 3. 42 8

Sum of all ca nonical λ 1. 67 5



### 83

### Table 6.5.

The correlation between CCA on the first two axes and environm ental variables. Epiphytic diatom

data and environm ental variable s collected during th e spring were used. High conductivity sites

were excluded from this analysis. * indicat es p < 0.05, ** indicates p < 0.01, *** indicates p <

0.001.

En vi ro nm ent a l va ri abl e s C C A 1 C C A 2

Alk a lin ity

NH 4 -N

Cl

TN

Kjel dahl N

TP

Si

Sr

DO

pH

Tem p erature

Co ndu ctiv ity -0 .3 8 *

0. 79 ***

- 0 .39* 0. 72 ***

0. 38 *

0. 48 **

0. 80 ***

-0 .3 2

-0 .3 2

0. 59 *



### Conductivity was the do m i nant environm ental co nstraint on s p ecies com p osition of diatom s

on m acroalgae and in epipelon, but nutrients were al so im portant. Four or dination axes explained

between 23. 2 and 30.1 % of the v a ria tion in d i atom species com positi on am ong sites, eith er with or

without including high conduc tivity sites (Tables B, C, D and E in the Appendix B). Four axes

explained between 52.1 and 60.4% of species-environm ent variance. Significant relationships

between diatom species com position on m acroalg ae and nutrients were only observed after

rem oving high conductivity sites from the data set (Figures A and B in the Appendix B).

Relation s hip s between e p ipelic dia t o m species composition and nutrients were eviden t with both

the full dataset and dataset withou t high conductivity sites, but rela tionships with nutrients in the

later dataset were m o re important (F igures C and D in the Appendix B). In all cases, TN or NO

x 

### were correlated to diato m species composition in different directions than TP or SRP. Table with

species scores on the C C A axes can be found in the Appendix B (Tables G and J).

Diatom indicators of nutrient conditions were co mplexly correlated to nutrient concentrations,

and not correlated as predicted (Table 6.6). Th e MAIA trophic index was correlated to m o re

m easures of nutrient con centration s than other indicators. The FL

avg 

### and FL

new 

### trophic status

indicators w e re correlated to 3 of 8 nutrient m easurem ents. Mo st of these indicators were

negativ ely correlated with N m easurem ents, excep t that the F L TP indicator was positively

correlated with TN and NOx. All indicators were positively corre lated with P m e asurem ents in

springs.

Diatom infe rence m odels of TP and TN deve loped with weighted averaging using diatom s

on plants were highly correlated to TP and TN concentrations (0.59 < r 2 

### < 0.71) before testing with

jackknifing, but were not highly correlated afterw ards (0.15 < r 2 

### < 0.26). Models for TP had

higher r 2 

### values than m odels for TN before jackknif ing, but the opposite was tr ue after jackknifing

(Figure 6.3). Litt le difference in r 2 

### values was observed between m odels using datasets with all

sites and without high conduc tivity sites. Indicator values for diatom taxa are provided in Tables

K and L. Indicato r valu es for epiph y tes using th e dataset with all sites are provided b ecause th ese

84

### m e trics with all sites perfor m ed as well as m e trics based on the dataset w ithout high conductivity

sites.

Table 6.6.

Correlations between nutri ent concentrations and previously developed diatom indicators of

nutrient conditions using species composition of diatom s on plants in Flo r ida Spr i ngs. Only

correlations with P<0.05 are indicated.

Am m o n i a T N Kje

- N T N s NO x PO 4 T P T P s

FL TP Pears o n Correl a tion

Si g. ( 2 -t ai l e d) 0. 35 5

0. 02 0 0. 34 5

0. 02 3

FL T N Pears o n Correl a tion

Si g. ( 2 -t ai l e d)

MA I Pears o n Correl a tion

Si g. ( 2 -t ai l e d) 0. 55 5

0. 00 0 - 0 .481

0. 00 1 0. 39 2

0. 00 9 0. 36 1

0. 01 6 0. 34 8

0. 02 1

Slate SRP Pears o n Correl a tion

Si g. ( 2 -t ai l e d)

Slate TP Pears o n Correl a tion

Si g. ( 2 -t ai l e d) 0. 30 6

0. 04 4

WH ITM O R E Pears o n Correl a tion

Si g. ( 2 -t ai l e d) 0. 43 5

0. 00 3

VD N Pears o n Correl a tion

Si g. ( 2 -t ai l e d) - 0 .450

0. 00 2

VD Salin ity Pears o n Correl a tion

Si g. ( 2 -t ai l e d)

VD Tr o ph Pears o n Correl a tion

Si g. ( 2 -t ai l e d)

FL a vg Pears o n Correl a tion

Si g. ( 2 -t ai l e d) - 0 .362

0. 01 6 0. 31 2

0. 03 9 0. 30 4

0. 04 5

FL new Pears o n Correl a tion

Si g. ( 2 -t ai l e d) - 0 .399

0. 00 7 - 0 .349

0. 02 0 - 0 .377

0. 01 2



### ISA showed lists of indicator species with respect to different groups of nutrients. T h e

results from ISA generally supported the obser vations from CCA. Under low conductivity

conditions, species such as Achnanthes exigua

, N itzsch i a inconspicua

, Navicula a r ven sis

, Nitzschia subacicularis

, Navicula viridula var. ro strata

, Fragilaria bre v is triata

, Sellophora

pupula preferred a high TP environm ent. Species such as Cymbella microcephala and Fragilaria

construens p r eferred low TP conditio ns (Table 6. 7). Under lo w conductiv ity cond ition s

, Achanthes minutissima

, Cymbella affinis

, Melosira varians

, Cymbella microcephala

, Fragilaria

construens , and Fragila ria capucina were grouped as category 4 in TN preference, w h ile

Nitzschia dissipata was grouped as category 1 in TN pr eference (Table 6.8). Under high

conductivity conditions, species such as Navicula tenelloides

, Bacilla ria paradoxa

, Fragilaria

brevis t ria t a

, Navicula gregaria

, Frag ilaria capucina

, Gomphonema clevei

, Navicula

s anctaecrucis

, Synedra fasciculata

, Nitzschia gracilis , and G o mphonema mexicana were

categorized as group 3 or 4 of TP and group 1 or 2 of TN; thus they had a high TP and low TN

85

### preferences. In addi tion, species such as Navicula menusculus

, Nitzschia r ecta

, Fragilaria

pinnata

, Eu notia subarcutoides

, Gomphonema angustum

, Gomphonema dichotumus , and

Navicula co ntenta preferred both category 1 of bot h TP and TN (Table 6.9).

Figure 6.3.

Plots of observed nutrient concentrations (TN mg/L; TP μg/L) vs. weighted average inferred

nutrient concentrations. A and C included all sites; B and D ex cluded high conductivity sites.

A C

C

W e i g ht ed a v er ag e i n f e r r ed T P .1 6 .1 4 .1 2 .1 0 .0 8 .0 6 .0 4 .0 2 0. 00Observed TP .1 6



### B D .1 4

.1 2

.1 0

.0 8

.0 6

.0 4

.0 2

0. 00 RMS E = 0

. 015

6 7

a c kkn i f ed RM SE = 0. 02 4

J a c kkn i f ed r

2 = 0. 21 r 2 = 0.

J

W e i g ht ed a v er ag e i n f e r r ed T P .1 6 .1 4 .1 2 .1 0 .0 8 .0 6 .0 4 .0 2 0. 00Observed TP .1 6

4

.1 2

.1 0

.0 8

.0 6

.0 4

.0 2

0. 00

W e i g ht ed av er age i n f e r r ed T N 3.5 3.0 2.5 2.0 1.5 1.0 .5 0.0

-. 5 -1. 0Observed TN 3.5

3.0

2.5

2.0

1.5

1.0

.5

0.0

-. 5

-1

. 0 RM SE = 0.47

r 2 = 0. 5 1

J a c kkn i f ed RM SE = 0. 59

J a c kkn i f ed r

2 = 0. 25

W e i g ht ed av er age i n f e r r ed T N 3.5 3.0 2.5 2.0 1.5 1.0 .5 0.0

-. 5 -1. 0Observed TN 3.5

3.0

2.5

2.0

1.5

1.0

.5

0.0

-. 5

-1

. 0 RM SE = 0.47

r 2 = 0. 5 1

J a c kkn i f ed RM SE = 0. 59

J a c kkn i f ed r

2 = 0. 25 RMS E = 0

. 015

6 7

a c kkn i f ed RM SE = 0. 02 4

J a c kkn i f ed r

2 = 0. 21 r 2 = 0.

J .1

W e i g ht ed av er ag e i n f e r r ed T N 3.5 3.0 2.5 2. 0 1. 5 1. 0 .5 0.0Observed TN 3.5

3.0

2.5

2.0

1.5

1.0

.5

0.0 RM SE = 0.45

r 2 = 0. 5 9

J a c kkn i f ed RM SE = 0. 62

J a c kkn i f ed r

2 = 0. 26

W e i g ht ed av er ag e i n f e r r ed T N 3.5 3.0 2.5 2. 0 1. 5 1. 0 .5 0.0Observed TN 3.5

3.0

2.5

2.0

1.5

1.0

.5

0.0 RM SE = 0.45

r 2 = 0. 5 9

J a c kkn i f ed RM SE = 0. 62

J a c kkn i f ed r

2 = 0. 26

W e i g h t ed a v er ag e i n f e r r e d T P .1 6 .1 4 .1 2 .1 0 .0 8 .0 6 .0 4 .0 2 0. 00Observed TP .1 6

.1 4

.1 2

.1 0

.0 8

.0 6

.0 4

.0 2

0. 00 RMS E = 0

. 014

r 2 = 0. 7 1

J a c kkn i f ed RM SE = 0. 02 5

J a c kkn i f ed r

2 = 0. 15

W e i g h t ed a v er ag e i n f e r r e d T P .1 6 .1 4 .1 2 .1 0 .0 8 .0 6 .0 4 .0 2 0. 00Observed TP .1 6

.1 4

.1 2

.1 0

.0 8

.0 6

.0 4

.0 2

0. 00 RMS E = 0

. 014

r 2 = 0. 7 1

J a c kkn i f ed RM SE = 0. 02 5

J a c kkn i f ed r

2 = 0. 15



### 86

### Table 6.7.

Species TP preference in diffe rent environm ental conditions assessed by indicator species

analys is.

All sites Selected sites

Species I V p Gr ou p Species I V p Gr ou p

NAte nde s 74

. 1 < 0

. 0 0 1 3 AC exi g u a 8 2 <0. 0 0 1 5

ACdelica 44

. 5 0

. 04 0 3 NIi n co n s 10 0 0. 00 1 5

ROcurvat 38

. 3 0

. 06 8 3 NAa rv e n s 87

. 2 0. 00 2 5

PTlance o 35

. 9 0

. 07 1 3 NIs u baci 9 1 0. 00 4 5

NIs p p 4 1 0

. 09 2 3 GO par v ul 54

. 8 0. 00 8 4

BAp a rado 92

. 4 < 0

. 0 0 1 4 NA vi r r o s 60

. 7 0. 00 8 5

FRbre v is 83

. 9 < 0

. 0 0 1 4 FR capm e s 56

. 7 0. 01 1 4

N A g r eg ar 9 8 .6 < 0 .0 01 4 CAb a c i l l 4 7 .6 0

. 0 2 6 3

FRcapuci 7 8 0

. 00 5 4 NA rhy g e r 54

. 5 0. 02 6 4

GOclev e i 48

. 7 0

. 01 0 4 AM ve net a 54

. 4 0. 03 8 3

N A s an cr u 5 0 0

. 01 2 4 FR co nst u 55

. 9 0. 04 4 1

SYfa scic 5 0 0

. 01 2 4 C M affi n s 51

. 3 0. 04 6 1

NIg r acil 62

. 1 0

. 01 8 4 FR bre v i s 53

. 3 0. 04 6 5

GOm exica 59

. 1 0

. 02 1 4 SFp u pul a 43

. 6 0. 04 7 5

AMin arie 47

. 9 0

. 02 2 4 C C n eo di m 62

. 7 0. 04 8 3

FRellip t 65

. 9 0

. 03 0 4 AC cl evei 49

. 9 0. 05 4 3

PTlanfre 46

. 4 0

. 04 1 4 PTl a nr o s 53

. 7 0. 05 5 5

EPadnata 37

. 4 0

. 04 9 4 GOcl e vei 5 0 0. 05 9 5

GOg r acil 43

. 4 0

. 08 7 4 NA g o ep p e 5 0 0. 05 9 5

NAca p ita 31

. 6 0

. 09 2 4 NAi nge n u 5 0 0. 05 9 5

NAm ol est 5 0 0. 05 9 5

NI graci l 44

. 2 0. 06 4 4

SY ul n a 52

. 3 0. 07 2 2

AUc re nul 44

. 1 0. 07 8 4

AC m i nut i 53

. 2 0. 08 2 1

GO p u m i l u 46 .9 0. 09 2 2



### 87

### Table 6.8.

Species TN preference in different environm ental conditions assesse d by indicator species

analys is.

TN (H i g h cond) TN w itho u t outliers

Species I V p Gr ou p Species I V p Gr ou p

AMlib yca 74

. 7 < 0

. 0 0 1 2 AC m i nut i 66

. 6 0. 00 5 4

NAca p ita 72

. 5 < 0

. 0 0 1 2 C M affi n s 61

. 1 0. 00 5 4

EPadnata 7 5 0

. 00 1 1 AC cl evei 6 3 0. 00 6 3

NIs p p 74

. 7 0

. 00 2 1 FR bre v i s 6 2 0. 00 6 3

ACdelica 6 7

. 7 0

. 0 0 4 2 CCpl a c e n 5 3

. 4 0

. 0 0 8 2

GOa ffi ne 5 0 0

. 01 3 1 M E vari a n 59

. 1 0. 01 1 4

RPb r eb is 5 0 0

. 01 3 1 C M m i c r o c 58

. 1 0. 01 2 4

GOclev e i 48

. 7 0

. 01 6 2 FR co nst u 59

. 9 0. 01 2 4

N A s an cr u 5 0 0

. 01 6 2 FR cap uci 61

. 5 0. 01 5 4

SYfa scic 5 0 0

. 01 6 2 NA rece n s 42

. 2 0. 04 2 2

FRcapuci 73

. 7 0

. 02 1 1 NIs u baci 36

. 4 0. 04 3 3

AMin arie 47

. 9 0

. 02 3 2 NI di ssi p 41

. 7 0. 04 9 1

NAte nde s 43

. 8 0

. 03 2 2 AC r u p d e s 52

. 7 0. 06 5 3

PTlanfre 46

. 4 0

. 03 7 1 NA p h y l l e 27

. 8 0. 07 0 3

N A g r eg ar 45

. 9 0

. 05 6 2 NAa rv e n s 2 3 0. 07 6 3

PTlance o 35

. 9 0

. 07 2 1 SY ul n a 56

. 1 0. 07 9 2

ROcurvat 3 8 .3 0

. 0 8 2 1 CAb a c i l l 3 3 .4 0

. 0 8 3 3

NI graci l 39 .7 0. 08 9 1



### 88

### Table 6.9.

Species with preference f o r low NP conditi ons assessed by indicator species analys is.

Species I V p Gr ou p

NAm enscl 95

. 9 0

. 00 1 1

NIrecta 64

. 4 0

. 00 7 1

FRpinnat 82

. 1 0

. 05 8 1

EUs uba rc 33

. 3 0

. 06 5 1

GOa n gust 33

. 3 0

. 06 5 1

GO di ch ot 33

. 3 0

. 06 5 1

GOs p p 33

. 3 0

. 06 5 1

NAc o n ten 33

. 3 0

. 06 5 1

CM affins 4 9 0

. 07 5 1

FR bi de ns 30

. 8 0

. 08 4 1

PTlance o 31

. 5 0

. 08 4 1

CMsilesi 30

. 2 0

. 08 6 1

PTl a nr os 72

. 5 0

. 09 9 1



### 89

### Discussion

The lack of correlation among TN, TP, and c onductivity in F l orida sp rings is unusual.

After rem o ving high conductivity sites, we found that TN and TP were negatively correlated

am ong most sites in FL springs. In most studies, TN and TP are positivel y correlated, and both are

usually correlated with conductiv ity or Cl and hum a n disturban ce in watersheds (e.g., Johnson et

al., 1997; Herlihy et al., 1998; Stevenson et al., acc epted). In low conductivity waters, increases in

conductivity can be related to lands cape disturbance (Bolstad & Sw ank, 1997) and can be used as

indicators of increased nutrient supply (Biggs, 1988). Variati on in water chem istry among Florida

springs represents a special case of nutrient relations in aquatic system s and likely ref l ects the

diversity in sources of water and contam ination among spring system s in Florida.

The m a gnitude of correlations between diat om species com position and environm ental

variab les in Florida sp rings was simila r to va lues observed in other studies. CCA analyses

commonly explain between 20 and 40% of variat ion in species com position am ong stream s and

lakes in a region (Hall and Sm ol, 1992; Pan et al., 1996). CC A anal yses m a y explai n less variation

in diatom species com position when great physical-c hem i cal diversity occurs am ong sites, such as

wetlands (S tevenson et al., 1999).

Conductivity explain e d more variation in diatom species composition among Florida

springs than nutrients did. C onductivity, pH, or other factors i ndicating ionic/osm o tic gradients

are comm only a dom inant attribute regulating variation in diatom species com position am ong

stream s, wetlands, and lakes (H all and Sm ol, 1992; Pan et al. 1996; Stevenson et al., 1999).

Florida springs were unusual, because the variat ion in conductivity was gr eat and the effects of

other environm ental variables on species com position were masked unless the variation in

conductiv ity was accoun ted for. A simple approach was used in this study to accoun t for

conductivity related va riables. A limited num ber of site s with the highest conductivity were

rem oved from the dataset. Resu lts suggested that nutri ents becom e i m portant variables affecting

species composition if the range in conductivity was constrained.

CCA analyses indicated that diatom s responded differently to TN a nd TP variation am ong

Florida sp rin g sites. Ind i cator spe c ie s analyses a l s o indica ted that dif f e rent species wer e rela tive l y

more abundant in h i gh N and high P environm ents, as well as low NP conditions. From ISA, in

general, high TP species belonged to the low TN s p ecies group and low TP species belonged to the

high TN group. In high conductivity s ites, species listed in the table belonged to the low TN and

high TP gro up. Another group of sp ecies preferre d both low TN and low TP conditio ns. A sim ilar

pattern can be observed from the CCA. Although it is know n that different species occur in low

nutrient conditions than in high nutrient conditions, usually high nutrient conditions include both

high N and P. Few cases exist in which we can distinguish diatom s that thrive in low N/high P

conditions and high N/lo w P conditio ns.

Existing diatom indicators of nut rient conditions need to be re fined before application to

Florida sp rin g system s. Existing d i atom m e trics indicating nutrient conditi ons were unpredictably

correlated to N and P concentratio ns in springs. Most existi ng trophic status m e trics were

positiv ely correlated to TP, but unpredictably co rre lated to T N . Diatom i ndicato r s of nutrient

conditions from other ecosystem s we re develope d in conditions where N and P concentrations

90

### were positiv ely correlated, and usually where P is hypothesized to be the most lim itin g factor

(Stevenson, unpublished data and repor ts). If diatom taxa respond differently to independent N

and P enrich m e nt, as indicated by CCA, then, with the ex isting m e trics, a m o re positive respon se

would be expected to the TP concentration than to TN.

Refined diatom indicators of nutrient condi tions based on autecological inf o rm ation

developed w ith data from diatom s a nd environm en tal conditions in Florid a springs show prom ise

for application, but they should be tested m o re completely. Weighted av erage regression m odels

developed separately to indicate TN or TP were relatively prec ise before testing by jackknifing.

Correla tions with r 2 

### greater than 0.5 usually indicate a good fit (Pan et al. 1996). However, poor

correlations between diatom -inferred a nd m easured nutrient co ncentration s (r 2 

### < 0.26) after

jackknifing suggested caution should be exercised with use. Th is reduction in goodness of fit of

nutrient m o dels after jackknifing is comm on fo r nutrient m o dels (see Pan et al., 1996, for

discussion). In addition, the issue of whether N or P m odels should be used to im ply trophic status

should be explored. Thus, the N and P regulati on of productivity in F l or ida springs and stream s

should be better understood. If springs or streams are P lim ited, then the TP m odel should be

applicable. However, high TN m a y also be a problem

.

It is also recomm ended that weighted average m o dels be used to com p are the trophic status

between ref e rence and test sites and not be used to inf e r spec if ic nutrient c oncentr ation s unless

calibrated more thoroughly to conditions in th ose system s. Weighted average m odels are

transferable am ong system s when the sam e nutrien ts regulate species composition, ho wever, they

m a y be biased and over- or underestim ate actua l nutrient concentratio ns. In a study of

transferability of nutrient indica tors from stream s to wetlands, Wa ng et al. (in preparation) found

that the rank of trophic status wa s correctly identified, but the act ual nutrient concentrations were

underestim ated.

In conclusion, nutrient indicators developed in this study show significant prom ise.

Further analysis of the data co llected and com p arison to ex is ting data on F l orida stream s is

warranted. Use of categorical au tecological characterization s of taxa is recomm ende d, like those

used by van Da m et al. (1994), which will enab le th e rankin g of sites. Indicato r m o dels using

catego r ical autecolog i cal inform ation can be calib ra ted to nutr i ent con centrations to inf e r spec if ic

nutrient con centration s if com p lem e nts to nutrient m easurem ents are needed.

91

## CHAPTER 7. DATA DESCRIPTION

Data sets can be found in Appendix C. Data are organized in fold er with corresponding

chapter number. Data fo r each ch apter can be fo und in the E x cel files an d in the Access

databases. Excel files include th e o r igin al da ta and the f i nal data af te r c a lculation s were

perform e d. Access databases show calcu lations f o r the data sets. Summary of all data files can

be found in Table 7.1.

Chapter 2

The original data from RPHA and fie l d m easurem ents can be found in

RPHA_field_data_fall and in RPHA _field_data_spring Excel files. These two datasets were

im ported in parts to Access database - Fl orida Springs 20040531 for further analyses.

Descriptions of codes and abbreviations used can be found in the Metadata for Chapter 2 Excel

file. All tables in the Florida Sprigs 20040531 database can be linked by Site_code or

Long_site_code.

Epiphytes soft counts data can be found in Access database - Epiphytes_soft_counts.

Final results for epiphytes soft counts can be found in Epiphytes_Cells_m m2 Excel file.

Chapter 3 and 4

The original data and results of calculations for nitrate a nd phosphate dosing experim e nts

can be found in Nitrate_and_Phos phate_dosing_experim e nts Excel f ile. The original data and

results of calculations for nitrate and phos phate dosing experim e nt can be found in

Conductivity_experim e nts. Calcu l ations for th is data sets can be found in Access database

Experim e ntal_stream s. Description of codes and abbreviations used can be found in the

Metadata for Chapter 3 and 4 Excel file.

Chapter 5

The original data and results of calculations for NDS can be found in NDS_data Excel

file. Calculations for N D S data can be found in NDS_chla Access database.

Chapter 6

Results of diatom counts for all sam p les fr om spring and fall are in the Diatom _counts

Excel file. Description of code s for diatom taxa can be found in Diatom _taxa_codes Excel file.

Im ages of c o mm on diatom taxa (contributing at least 5% to any of the diatom counts) can be

found in a PowerPoint file Florida S p rings Common Diatoms.

92

### Table 7.1

Summ ary of data description.

C h a p t e r D a t a de scripti o n E x c e l A c c e s s

2 - d a ta sh eet u s ed in th e

field Datash eet fall 2 003

2 - RP HA

- Chem ical analyses of

wat e r a n d al gal m a t s RPHA_field _data_ f all

RPHA_field _data_ s pring Fl ori d a S p ri ng s

2 004 053 1

2 - Ep iph y tes soft co un ts Ep iph y tes_ Cel l s_ mm 2 Ep iph y tes_ so ft

_ c oun ts

3 - Nitrate dosing

expe ri m e nt

- Ph osph ate d o sin g

expe ri m e nt Ni t r at e_a n d

_ P hos p h at e_ d o si ng _e xpe ri m e n t s E x peri m e nt al _st r eam s

4 - Cond u c tiv ity

expe ri m e nt C o n d u ct i v i t y _expe ri m e nt s E x peri m e nt al _st r eam s

5

- N D S N D S

_ d a t a NDS_c h l a

6 - Di at om count s Di at om _cou nt s

Di at om _t axa_c ode s



### 93

## CHAPTER 8. REFERENCES

Arbuckle, K. E., and J. A. Downing. 2001. The influence of watershed land use on lake n : P in a

predom inantly agricultural land scape. Lim nology and Oceano g raphy 46: 9 70-975.

Biggs, B. J. F. 1988. Artificial substrate expos ure tim es for periphyton biom ass estim a t es in

rivers. New Zealand Journal of Marine and Freshwater Research 22: 507-515.

Biggs, B. J. F.; Kilroy, C

. ; Lowe, R. L. 1998. Pe riphyton developm ent in three valley segm ents

of a New Ze aland grassland river: T e st of a habitat m a trix co nceptua l m o del with in a

catchm e nt. Archiv Fur Hydrobiologie 143 : 147-177.

Biggs, B. J. F. 2000. Eutrophication of stream s and rivers: dissolved nutrient-chlorophyll

relationships for benthic algae. Journal of th e North Am erican Benthol ogical Society 19: 17-31.

Bolstad, P. V., and W

. T. Sw ank. 1997. Cum u lative im pacts of landuse on water quality in a

Southern Appalachian watershed. Journal of the Am erican Water Reso urces Assoc i ation

33: 519-533.

Bonn, M. A., and F. W

. Bell. 2003. Econom ic imp act of selected Florida springs on surrounding

local areas, Florida Dep a rtm e nt of Environm ental Protec tion, Tallahassee, Florida.

Borchardt, M. A. 1996. Nutrients. In: R. J. Stev enson, M. L. Bothwell and R. L. Lowe (eds.)

Algal ecology. Aquatic eco logy. p 183-227. Academ ic Press, Sand Diego.

Burja, A. M. et al. 2002. Culture of the m a rine cyanobacterium, Lyngbya majuscula

(Oscillato riaceae), for b i oprocess intensified pro duction of cyclic and lin ear lipopeptides.

Journal of Microbiologi cal Methods 48: 207-219.

Carpenter, E. J., and R. R. Guillard. 1970. Intras pecific differences in nitrate h a lf-saturation

constants for 3 species of m a irne phytoplankton. Ecology 52: 183-185.

Chetelat, J., F., R. Pick, A. Morin, and P. B. Ha m ilton. 1999. Periphyton biom ass and

community com position in rivers of differe nt nutrient status. Ca nadian Journal of

Fisheries and Aquatic S c iences 56: 560-569.

Clesceri, L. S., A. E. Gr eenberg, and A. D. Eaton (Editors). 1998. Standard m e thods for the

exam ination of water and wastewater. Am eri can Public Health A ssociation, W a shington,

DC.

Cowell, B. C., and P. S. Botts. 1994. Factors in fluencing the distributi on, abundance and growth

of lyngbya wollei in central flor ida. Aquatic Botany 49: 1-17.

van Da m

, H., A. Mertens, and J. Sinkeldam

. 1994. A coded checklist and ecological indicator

values of freshwater diatom s from The Ne therlands. Netherlands Journal of Aquatic

Ecology 28: 117-133.

94

### Davis, J. S., and W

. F. Gworek. 1972. Dichot om ospihon in Florida Springs. Journal of

Phycology 8: 130-131.

Dillard, G. E. 1989. Freshwater algae of the Sout heastern United States. P a rt 1. Chlo ro phyceae:

Volvocales, Tetraspo rales and Ch loro coccales. J. Cram er, Berlin.

Dodds, W

. K. 1991a. Comm unity interac tions between the filam e ntous alga Cladophoraglomerata (L) Kuetzing, its epiphytes, and ep iphyte grazers. Oecologia 85: 572-580.

Dodds, W

. K. 1991b. Factors associated with dom i nance of t h e filam e ntous green-alga

Cladophora glomerata . Water Research 25: 1325-1332.

Dodds, W

. K., V. H. Sm ith, and B. Zander. 1997. Developing nutrient targets to control benthic

chlorophyll levels in stream s: A case study of the Clark Fork River. W a ter Research 31:

1738-1750.

Dodds, W

. K., and E. B. W e lch. 2000. Establishing nutrient criteria in stream s. Journal of the

North Am erican Benthol ogical Society 19: 186-196.

Dressler, R. L. 1991. Identification m a nual for we tland plant species of Florida: Used by the

Florida Departm e nt of Environm ental Regulatio n in dete rm ining the land ward exten t of

waters of th e sta t e. Univ ersi ty of Florida, Vascular Plant Herbarium

, Gainesvill, FL.

Dufrene, M., and P. Legendre. 1997. Species asse m b lages and indicator species: the need for a

flexible asymmetrical approac h. Ecological Monographs 67 :345-366.

Fairchild, G. W

. ; Lowe, R. L. 1984. Artificial substrates which release nutrients - effects on

periphyton and invertebrate su ccession. Hydrobiologia 114: 29-37.

Fairchild, G. W

. ; Lowe, R. L.; Richardson, W

. B. 1985. Algal periphyt on growth on nutrientdiffusing substrates: an in s itu bioassay. Ecology 66: 465-472.

Fairchild, G. W

. ; Sherm a n, J. W

. ; Acker, F. W

. 1989. Effect of nutrient (N, P, C) enrichm e nt,

grazing and depth upon litto ral p e rip hyton of a softwater lak e . Hydrobiologia 173: 69 -83.

Falkowski, P. G. 2000. Rationalizing elem ental ratios in unicellu lar algae. Journal of Phycology

36: 3-6.

Florida Springs Task Force. 2000. Florida’s Spri ngs. Strategies for prot ection and restoration.

Report for the Florida Departm e nt of Envi ronm e n tal Protection, Tallahassee, Florida.

Francoeur, S

. N.; Biggs, B. J. F.; Sm ith, R. A. ; Lowe, R. L. 1999. Nutrient lim itation of algal

biom ass accrual in stream s: seasonal patterns and a com p arison of m e thods. Journal of

the North A m erican Benthological S o ciety 18: 242-260.

95

### Gross, E. D., and D. F. Martin. 1996. Iron dependence of Lyngbya majuscula . Journal of Aquatic

Plant Managem e nt 34: 12-20.

Hall, R. I., and J. P. Sm ol. 1992. A weighted averaging regression and calibration m o del for

inferring total phosphorus concentration from diatom s in British Colum b ia (Canada)

lakes. Freshwater Biology 27: 417-434.

Herlihy, A. T., J. L. Stodda rd, and C. B. Johnson. 1998. The relationship between stream

chem istry and watershed land cover data in the Mid-Atlantic regi on, US . W a ter Air and

Soil Pollution 105: 377-386.

H um phrey, K. P., and R. J. Stevenson. 1992. Responses of benthic algae to pulses in current and

nutrients during sim u lations of subscouri ng spates. Journal of the North A m erican

Benthological Society 11: 37-48.

Hunt, R. 1990. Basic growth analysis. Plant gr owth analysis for beginners. Unwin Hym a n,

London.

Johnson, R. E., N. C. Tuchm a n, and C. G. Peterson. 1997. Changes in the vertical

m i crodistribution of diatom s within a develo ping periphyton m a t. Journal of the North

Am erican Benthological Society 16: 503-519.

Joyner, A. J. J. 2004. Molecular techniques applie d to environm ental sam p les of the freshwater

nitrogen-fixing cyanobacterium

, Lyngbya wollei . In: ASLO Summer Meeting Abstracts.

Katz, B. G., J. K. Bohlke, and H. D. Horns by. 2001. Tim e scales for nitrate contam ination of

spring waters, Northern Florida, US A. Che m ical Geology 179: 167-186.

Katz, B. G., H. D. Hor m sby, J. F. Bohlke, a nd M. F. Mokary. 1999. Sources and chronology of

nitr ate con t a m ination in spring wate r s , Suwa nnee River Basin, Florida. W a ter-Resources

Investigations Report 99-4252, U.S. Ge ological Survey, Tallahassee, FL.

Kom a rek, J., and K. Anagnostidis. 19 99. Cya noprokaryota. 1. Teil: Chroo c occales. Gustav

Fischer, Jena.

Kom a rek, O. 2003. Spatial autocorrelation and fracta l dim e nsion of alga species assemblage in a

gravel stream of Central Europe. Intern ational R e view of Hydrobiology 88: 385-396.

Krammer

, K., and H. Lange-Bertalot. 1986. B acillariophyceae, T e il 1: Nav i culaceae. G u stav

Fischer V e rlag, S t uttgart, Germ any

.

Krammer

, K., and H. Lange-Bertalot. 1988. Bacillariophyceae, T e il 2: Bacillariaceae,

Epithem iaceae, and Surirellaceae. Gu stav Fisch e r V e rlag, S t uttgart, Germ any

.

Krammer

, K., and H. Lange-Bertalot. 1991a. Bacill ariophy ceae, T e il 3: C e nt rales, Fragilariaceae,

and Eunotiaceae. Gustav Fischer V e rlag, S t uttgart, Germ any

.

96

### Krammer

, K., and H. Lange-Bertalot. 1991b. Bac illariophyceae, T e il 4: A c hnanthaceae. Gustav

Fischer V e rlag, S t uttgart, Germ any

.

Leps, J., and P. Sm ilauer. 2003. Multivariate analysis of ecological data using Canoco.

Ca m b ridge University Press, Cam b ridge.

Leukart, P., and D. Hanelt. 1995. Light requirem en ts for photosynthesis and growth in several

m acroalgae from a s m all soft-water str eam in the Spessart Mountains, Germ any.

Phycologia 34: 528-532.

Lundgren, P., K. Bauer, C. Lugom e la, E. Sode rback, and B. Bergm a n. 2003. Reevaluation of the

nitrogen fixation behavior in the m a rine non-heterocystous cyanobacterium Lyngbya

majuscula . Journal of Phycology 39: 310-314.

Marks, J. C.; Lowe, R. L. 1993. Interactive effect s of nutrient availabil ity and light levels on

periphyton com position of a large oligotrophic lake. Canadian Journal of Fisheries and

Aquatic Science 50: 1270-1278.

Moreno, J. L., M. Aboal, M. R. Vidal-Ab arca, and M. L. Suarez. 2001. Macroalgae and

subm erged m acrophytes from fresh and sali ne waterbodies of ephe m e ral stream s

(' ra m b las' ) in sem i arid South-Eastern Spai n. Marine and Freshwater Research 52: 891-

905.

Mosisch, T. D.; Bunn, S. E.; Davies, P. M. 2001. The relative im portance of shading and

nutrients on algal production in subtropical stream s. Fr eshwater Biology 46: 1269-1278.

Odum , H. T. 1957. Trophic structur e and productivity of Silver Springs, Florida. Ecological

Monographs 27: 55-112.

Pan, Y ., R. J. S t evenson, B. Hill, A. Herlihy

, and G

. Collins. 19 96. Using diatom s as indicato r s of

ecological conditions in lotic systems: a reg i onal assessm ent. Journal of North Am erican

Benthological Society 15: 481-495.

Pan, Y., R. J

. Stevenson, P. Vaithiyanathan, J. Slate, and C. J. Richardson. 2000. Changes in

algal assem b lages along observed and experim e ntal phosphorus gradients in a subtropical

wetland, U.S.A. Freshwater Biology 43: 1-15.

Pan, Y. D., R. J. Stevenson, B. H. Hill, P. R. Kaufm a nn, and A. T. Herlihy. 1999. Spatial

patterns and ecological determ inants of be nthic algal assem b lages in Mid-Atlantic

stream s, USA. Journal of Phycology 35: 460-468.

Patrick, R., and C. W

. Reim er. 1966. The diatom s of the United States, Volum e 1. The Academ y

of Natural Sciences of Philadelphia, Philadelphia, Pennsylvania, US.

97

### Patrick, R., and C. W

. Reim er. 1975. The diatom s of the United States, Volum e 2. The Academ y

of Natural Sciences of Philadelphi a, Philadelphia, Pennsylvania, US.

Peterson, C. G., and R. J. Stevenson. 1992. Resist ance and res ilience of lotic alg a l com m unities -

im portance of disturbance tim ing and current. E c ology 73: 1445-1461.

Pillsbu ry, R. W

. ; Lowe, R. L.; Pan, Y. D.; Gr eenwood, J. L. 2002. Changes in th e benthic algal

community and nutrient lim itation in Sagina w Bay, Lake Huron, during the invasion of

the zeb ra mussel ( Dreissena polymorpha ). Journal of the North Am erican Benthological

Society 21: 238-252.

Phlips, E. J., J. Ihnat, and M. Conroy. 1992. Nitrogen-fixation by the benthic fresh-water

cyanobacterium Lyngbya wollei . Hydrobiologia 234: 59-64.

Prescott, G. W. 1982. Al gae of the Western Great Lakes area. Otto Koeltz Science P ublishers,

Koenigstein.

Pringle, C. M. 1990. Nutrient spatial hetero geneity: effects on community structure,

physiognomy, and diversity of stre am algae. Ecology 71: 905-920.

Ra m e y, V. 1995. Aquatic plant identification dec k. Center for Aquatic Palnts, University of

Florida, Gainesville, FL.

Rasband, W. 2003. Im agej v 1.61. National Inst itute of Health, Bethesda, Maryland.

Rosenau, J. C., G. L. Fa ulkner, J. Hendry, C. W

. , and R. W

. Hull. 1977. Springs of Florida.

Geological bulletin v. 31, re v. Bureau of Geology, Florida Departm e nt of Natural

Resources, Tallahassee.

Rossi, J. V., M. A. Roberts, H. Yoo, and W

. H. Gerwick. 1997. Pilot scale culture of the m a rine

cyanobacterium Lyngbya majuscula for its ph arm aceutically-useful natural m e tabolite

curacin a. Journal of A pplied Phycology 9: 195-204.

Saint Johns River W a ter Managem e nt District . 2002. Springs of the St. Johns River Water

Managem e nt District. Technica l Repoart SJ2002-5. Palatka, F L

.

SAS. 2000. SAS/stat. S A S Institu te, Cary, N.C.

Schneider, C. W ., M. K. Riley, and B. M. St ockton. 1996. Stability of antheridial m o rphology in

freshwater North Am erican Vaucheria compacta var dulcis J. Sim ons (Tribophyceae,

Chrysophyta) grown under different sa linities. Aquatic Botany 52: 301-311.

Shannon, K., E. D. Gross, and D. F. Martin. 1992. Variati on of growth of Lyngbya majuscula as

a function of salinity. Biom edical Letters 47: 29-33.

98

### Sheath, R. G. 2003. Red algae. In: J. D. W e hr and R. G. Shea th (eds.) Freshwater algae of North

Am erica. p 197-224. Academ ic Press, Am sterdam.

Snyder, E. B.; Robinson, C. T.; Minshall, G. W

. ; Rushforth, S. R. 2002. Re gional patterns in

periphyton accrual and diatom assemblage structure in a heterogeneous nutrient

landscape. Canadian Journal of Fisher ies and Aquatatic Science 59: 564-577.

Southwest Florid a W a ter Managem e nt District . 2 003. Rainbow River surface water improvem e nt

and m a nage m e nt (SW I M) plan - draft, Tam p a, Florida.

Speziale, B. J., and L. A. Dyck. 1992. Lyngbya infestations: C o m p arative taxonom y of Lyngbya

wollei com b. nov. (Cyanobacteria). Journal of Phycology 28: 693-706.

Starm ach, K. 1963. Cyanophyta-sinice (Eng. Cy anopyta-blue green algae). Panstwowe

Wydawnictwo Naukowe, W a rszawa.

Stelzer, R. S., and G. A. Lam b ertii. 2002. Ecological s t oich io m e try in running waters :

Periphyton chem ical composition and snail g r o w th. Ecolog y 83: 1039-1 051.

Stevenson, R. J. 1983. Effects of current and co nditions sim u lating auto genically ch anging

m i crohabitats on benthic diatom i mmigration. Ecology 64: 1514-1524.

Stevenson, R. J. 1996. Patterns of benthic algae in aquatic ecosystem s . In: R. J. Stevenson, M. L.

Bothwell and R. L. Lowe (eds.) Algal eco logy. Freshwater benthi c ecosystem s. p 3-30.

Academ ic Press, San Diego.

Stevenson, R. J., J. Alba-Tercedor, B. Bailey, M. Barbour, C. Couch, S. Dyer, F. Fulk, J.

Harrington, M. Harass, C. J. Hawkins, C. Hunsaker, R. Johnson, and K. Thornton. 2004.

Designing data collection for ecological assessm ents. In: M. Barbour, K. Thornton and R.

Preston (eds.) Ecological assessm ent of aqua tic resources: Linking science to decisionm a king. Soc i ety of Environm ental Toxi cology and Contam ination Publication.

Stevenson, R. J., and R. Glover. 1993. Effects of algal density and current on ion-transport

through periphyton communities. Limnology and Oceanography 38: 1276-1281.

Stevenson, R. J. and L. L. Bahls. 1999. Periphyton protocols. In M. T. Barbour, J. G e rritsen, &

B. D. Snyde r, eds. Rapid Bioassessm ent Protocols for Use in Wadeable Stream s and

Rivers: Periphyton, Benthic Macroinvertebra tes, and Fish. Second Edition. EPA 841-B99-002 United States Environm ental Prot ection A g ency, W a s h ington. pp 6-1 through 6-

22.

Stevenson, R. J., P. R. Sweets, Y. Pan, and R. E. Schultz. 1999. Algal community patterns in

wetlands and their use as indicators of ecological conditions. In: Proceedings of

INTECOL' s Vth Internationa l W e tland Conference, Adelaide, Australia. p 517-527.

99

### Stevenson, R. J., and J. P. Sm ol. 2003. Applications for the environm ent and earth science. In: J.

D. W e hr and R. G. Shea th (eds.) Freshw ater algae of North Am erica, ecology and

classification. p 775-797. Academ ic Press, Am es terdam

.

Stockner, J. G., and K. R. S. Shortreed. 1978. Enhancem ent of autotrophic production by nutrient

addition in a coastal rainforest stream on Vancouver-Island. Journa l of the Fisheries

Research Board of Canada 35: 28-34

USEPA. 1999. Nutrient criteria technical guidance m a nual: rive rs and stream s. EPA-822-D-99-

003, United States Environm ental Protection Agency, W a shington, D.C.

Wang, Y-K, R. J. Stevenson, and L. Metzm e ier. In press. Developm en t and evaluation of a

diatom -based index of biotic integrity for the Interior P l ateau Ecoregion, Kentucky, USA.

Journal of the North Americ an Benthological Society

Whitford, L. A. 1956. The communities of algae in the springs and spring stream s of Florida.

Ecology 37: 433-442.

Whitford, L. A., and G. J. Schum acher. 1984. A ma nual of fresh-water algae. Sparks Press,

Raleigh, N.C.

100

## CHAPTER 9. APPENDICES

APPENDIX A. Additional tables for Chapter 2.

APPENDIX B. Additio nal tab l es an d f i gures f o r Chapter 6

APPENDIX C. Project orig inal data and calculations

101