ERROR LOADING HTML FROM SOURCE (http://ncf.sobek.ufl.edu//design/skins/UFDC/html/header_item.html)

Cyanobacteria as a biological indicator for the celery fields storm water mitigation area in Sarasota

Permanent Link: http://ncf.sobek.ufl.edu/NCFE004361/00001

Material Information

Title: Cyanobacteria as a biological indicator for the celery fields storm water mitigation area in Sarasota
Physical Description: Book
Language: English
Creator: Bedi de Silva, Anamica
Publisher: New College of Florida
Place of Publication: Sarasota, Fla.
Creation Date: 2011
Publication Date: 2011

Subjects

Subjects / Keywords: Cyanobacteria
phytoplankton
algae
HAB
eutroplatication, celery fields
Genre: bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Cyanobacteria are opportunistic phytoplankton that respond to changes in excess nutrients such as nitrogen and phosphorous. Their presence in ecosystems is particularly important due to their role as primary producers but also in their potential to accumulate to toxic levels. The relationship between limiting ammonia, nitrate and phosphate and the size of cyanobacteria populations was investigated in the Celery Fields Storm Water Mitigation Area in Sarasota, FL, USA. Water samples were collected weekly for eight weeks from each of the three ponds that make up the Celery Fields. Regression analysis from the study showed no relationship between nutrient concentration and cyanobacteria cell densities. Major improvements to the study include increasing the study period to at least one hydrological cycle, using molecular data to identify species, and incorporating new cyanobacteria cell enumeration methods.
Statement of Responsibility: by Anamica Bedi de Silva
Thesis: Thesis (B.A.) -- New College of Florida, 2011
Electronic Access: RESTRICTED TO NCF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE
Bibliography: Includes bibliographical references.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The New College of Florida, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Local: Faculty Sponsor: McCord, Elzie

Record Information

Source Institution: New College of Florida
Holding Location: New College of Florida
Rights Management: Applicable rights reserved.
Classification: local - S.T. 2011 B4
System ID: NCFE004361:00001

Permanent Link: http://ncf.sobek.ufl.edu/NCFE004361/00001

Material Information

Title: Cyanobacteria as a biological indicator for the celery fields storm water mitigation area in Sarasota
Physical Description: Book
Language: English
Creator: Bedi de Silva, Anamica
Publisher: New College of Florida
Place of Publication: Sarasota, Fla.
Creation Date: 2011
Publication Date: 2011

Subjects

Subjects / Keywords: Cyanobacteria
phytoplankton
algae
HAB
eutroplatication, celery fields
Genre: bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Cyanobacteria are opportunistic phytoplankton that respond to changes in excess nutrients such as nitrogen and phosphorous. Their presence in ecosystems is particularly important due to their role as primary producers but also in their potential to accumulate to toxic levels. The relationship between limiting ammonia, nitrate and phosphate and the size of cyanobacteria populations was investigated in the Celery Fields Storm Water Mitigation Area in Sarasota, FL, USA. Water samples were collected weekly for eight weeks from each of the three ponds that make up the Celery Fields. Regression analysis from the study showed no relationship between nutrient concentration and cyanobacteria cell densities. Major improvements to the study include increasing the study period to at least one hydrological cycle, using molecular data to identify species, and incorporating new cyanobacteria cell enumeration methods.
Statement of Responsibility: by Anamica Bedi de Silva
Thesis: Thesis (B.A.) -- New College of Florida, 2011
Electronic Access: RESTRICTED TO NCF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE
Bibliography: Includes bibliographical references.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The New College of Florida, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Local: Faculty Sponsor: McCord, Elzie

Record Information

Source Institution: New College of Florida
Holding Location: New College of Florida
Rights Management: Applicable rights reserved.
Classification: local - S.T. 2011 B4
System ID: NCFE004361:00001


This item is only available as the following downloads:


Full Text

PAGE 1

CYANOBACTERIA AS A BIOLOGICAL INDICATOR FOR TH E CELERY FIELDS STORM WATER MITIGATION AREA IN SARASOTA, FL, USA BY ANAMICA BEDI DE SILVA A THESIS Submitted to the Division of Natural Sciences New College of Florida in partial fulfillment of the requirements for the degree Bachelor of Arts in Biology Under the sponsorship of Dr. Elzie McCord, Jr. May 2011 Sarasota, FL

PAGE 2

Para m inha av Maria de Jesus da Silva, que me ajuda voar. Barn swallows swoop to catch gnats on the morning of March 7, 2011 as Anamica takes notes on the shore of the South Pond. Sandpipers can be seen in the weeds enjoying their first meal of the day. ii

PAGE 3

Acknowledgments A very special thank you to Dr. Vinc e Lovko of Mote Marine Aquarium and Laboratory. You did not have to take me on as a student, but you did, and for that I am forever grateful. This project would not have succeeded without your support. Thank you to thesis committee member Dr. Sandra Gilchrist of New College of Florida for having faith in this project fr om the start. Thank you, again, for giving me advice for obtaining an AOC in biology. Thank you to thesis committee member Dr. Diana Weber of New College of Florida for asking me questi ons that made me re-think the goals of my project. Your advice helped me conceptualize my study and I am happy to have received it. Thank you to Anamari Boyes of Mote Marine Aquarium and Laboratory for showing me the ropes in pigment extr action and for her s upport throughout the process. Thank you to Joel Beaver of the Pritzk er Marine Laboratory for absolutely everything. I thank you once more for di stracting me during long hours of cell counts. Thank you to Carmela French for the use of her car during collection days. More importantly, thank you for the use of your cat. To Dr. Duff Cooper of New College of Florida, thank you for giving me tenminute lessons in statistics. I promise to make you proud once I take my biostats course. Thank you to Dr. Rosildo Paiva of O Universisdade Federal do Para, for inviting me into his laboratory and introducing me to the world of phytoplankton. Of course, I would like to thank my fa mily. They have given me absolutely everything in life. Lastly, a huge thank to my thesis spons or, Dr. Elzie McCord, Jr. of New College of Florida. You have supported me from the beginning. Believe me when I say that my time with you has enriched my life. Thank you, from the bottom of my heart. iii

PAGE 4

Contents Dedication------------------------------------------------------------------ii Acknowledgements------------------------------------------------------iii List of Figures--------------------------------------------------------vi-vii List of Tables-----------------------------------------------------------viii Abstract--------------------------------------------------------------------ix 1 Project Background---------------------------------------------1-5 Summary I. Phytoplankton as Biological Indicators--------------------1 II. Ilha do Mos quiero Project-----------------------------------1 III. The Curren t Study-------------------------------------------4 2 Introduction to the Cyanophyta------------------------------6-14 Summary I. Cyanobacteria--------------------------------------------------6 II. Unique Pigments---------------------------------------------6 III. Endosymbiotic Theory and Relation to Mode rn Chloroplasts------------------------------------7 IV. Extremophili c Adaptations--------------------------------8 V. Specializati on of Ce lls---------------------------------------9 VI. Cyanobacteria in the Trophic System------------------10 VII. Toxicity of Cyanobacteria------------------------------11 VIII. Government al Regulations-----------------------------12 3 The Ecology of Cyanobacteria1------------------------------5-22 Summary I. Limiting Factor s of Algae Growth------------------------15 II. Principles of Eutrophi cation------------------------------15 III. Human Influence in HAB Formation-------------------17 IV. Noteworthy Eutr ophication Events---------------------19 V. Dominance of Cyanobacteria in Blooms----------------20 4 Materials and Methodology-------------------------------23-34 Summary I. Study Site Information-------------------------------------23 II. A Note on the Improved Methodology------------------26 III. Improving the Enumeration Method--------------------27 IV. Additions to the Procedure-------------------------------28 V. Methodology------------------------------------------------29 VI. Determination of Cyanobacteria -------------------------Density------------------------------------------------------30 VII. Identification of Cyanobacteria Species-----------------------------------------------------31 VIII. Nutrie nt Analysis----------------------------------------30 IX. Pigmen t Analysis-----------------------------------------32 iv

PAGE 5

X. Data Treatment---------------------------------------------34 5 Results and Discussion------------------------------------35-70 Summary I. Analysis of Relations Between Cell Densities and Measured Variables------------------39 II. Regression Analysis---------------------------------------38 III. Identifi ed Species-----------------------------------------43 IV. Pigmen t Analysis------------------------------------------50 V. Non-represen tative Samples------------------------------58 VI. Difficulty in Species Identification---------------------59 VII. Error in Cell Counts-------------------------------------60 6 Conclusions ----------------------------------------------64-66 Appendix ----------------------------------------------67-70 References ----------------------------------------------71-75 v

PAGE 6

List of Figures Figure 1.1Ilha do Mosqui ero, collection sites in dicated by pushpins. Red line indicates 10km. Reprinted from Google Earth April 10, 2011.-------2 Figure 2.1Specialized cells in a ge neric filamentous cyanobacteria. ---------------------10 Figure 3.1Nutrient input and eutr ophication. Image retrieved from http://commons.wikimedia.org/wiki/Media: Scheme_eutrophi cation-e n.svg.------------------------------------------------------6 Figure 4.1Figure 4.1The Celery Fields with co llection sites labeled. Red line indicates 2km. Photo generated using Google Earth on 02/09/2 011-------------------------------------------------24 Figure 4.2 A sandhill crane feeding in the southern pond. Photo courtesy of Danielle Fasig.--------------------------------------------------25 Figure 4.3Samples were obtained using a simple grab method during the 3/7/2011 collec tion------------------------------------------------------29 Figure 4.4Diagram of microtiter plate used for enumeration.------------------------------30 Figure 4.5 Figure 4.5 The HPLC appara tus at Mote Marine Aquarium and Laboratory, Department of Phytoplankton Ecology.----------------------------33 Figure 5.1Weekly estimated cell densities for the North pond over the eight week study peri od.------------------------------------------------------------36 Figure 5.2Weekly estimated cell densities for the Mid pond over the eight week study pe riod.-------------------------------------------------------37 Figure 5.3Weekly estimated cell densities for the South pond over the eight week study pe riod.-------------------------------------------------------38 Figure 5.4Spirulina spp ., a commonly-encountered cyanobacteria species in the North and Mid ponds. Image from Cyanosite, retrieved on 4/14/ 2011.--------------------------------------------------------------49 Figure 5.5Microcystis aeruginosa the most common species in found in the study sample. Image from Florida Department of Environmental Protection we bsite, retrieved on 4/14/2011.--------------------50 Figure 5.6Anabaena circinalis a heterocystic speci es found mainly in the North and Mid ponds. Image from Cyanosite, retrieved on 4/14/ 2011.--------------------------------------------------------------50 vi

PAGE 7

Figure 5.7Chrom atograms and spectr um of 3/7/2011 North Pond sample with 15 identified peaks. Screenshot taken of Shimadzu software on 4/14/2011.-----------------------------------------------------------------------------52 Figure 5.8Chromatograms and spectrum of 3/7/2011 Mid Pond sample with 15 identified peaks. Screenshot taken of Shimadzu software on 4/14/ 2011.-------------------------------------------------------------------------53 Figure 5.9Chromatographs and spectrum of 3/7/2011 South Pond sample with 15 identified peaks. Screenshot taken of Shimadzu softwa re on 4/14/2011.-------------------------------------------------54 Figure 5.10Relative abundance of four phytoplankton groups for five weeks of North pond samples------------------------------------------------55 Figure 5.11Relative abudance of f our phytoplankton groups for five weeks of Mi d pond samp les.-------------------------------------------------------56 Figure 5.12Relative abudance of f our phytoplankton groups for five weeks of Sout h pond samp les.-----------------------------------------------------57 Figure5.13North pond sample from 1/ 24/2011 containing a typical suite of cyanobacteria species. A Microcystis aeruginosa (A) is seen in the center with worm-like Spirulina spp. (B) caught in its mucilage. Anabaena circinalis (C) is seen to the top right of M. aeruginosa.------------------------61 Figure5.14This figure represents the diversity found commonly in the Mid pond. Merismopedia tenuissima (A), Synnechococcus spp (B), Microcystis aeruginosa (C), and Spirulina spp (D) can all be seen. -----------------------61 Figure 5.15This sparse view is typical of the South pond. A single Microcystis aeruginosa (A) colony is seen at the center. -------------------------------------62 Figure 5.16Possible processes for future projects involving cyanobacteria as a biological indicator for the Celery Fields. ----------------------------------63 vii

PAGE 8

List of Tables Table 5.1Cyanobacteria specie s found in 1/17/2011 samples with estimated cell counts. ---------------------------------------------------------------44 Table 5.2Cyanobacteria species found in 1/24/2011 samples with estimated cell counts.----------------------------------------------------------------------------44 Table 5.3Cyanobacteria species found in 1/31/2011 samples with estimated cell counts----------------------------------------------------------------------------------45 Table 5.4Cyanobacteria species found in 2/7/2011 samples with estimated cell counts.----------------------------------------------------------------------------45 Table 5.5Cyanobacteria species found in 2/14/2011 samples with estimated cell counts. 46 Table 5.6Cyanobacteria species found in 2/21/2011 samples with estimated cell counts. ---------------------------------------------------------------46 Table 5.7Cyanobacteria species found in 2/28/2011 samples with estimated cell counts-----------------------------------------------------------------------------47 Table 5.8Cyanobacteria species found in 3/7/2011 samples with estimated cell counts-----------------------------------------------------------------------------47 Table 5.9Species compositions of each of Norht, Mid, and South ponds.---------------48 Table 1Raw numerical data used in cell density and regression analyses.---------------67 Table 2Pigment concentrations found through ChemTax (continued on next page). ------------------------------------------------------68-69 Table 3Relative abundance of phytoplankton groups found through ChemTax. ----------------------------------------------------------------------------70 viii

PAGE 9

ix CYANOBACTERIA AS A BIOLOGICAL INDICATOR FOR TH E CELERY FIELDS STORM WATER MITIGATION AREA IN SARASOTA, FL, USA Anamica Bedi New College of Florida, 2011 ABSTRACT Cyanobacteria are opportunistic phytopla nkton that respond to changes in excess nutrients such as nitrogen and phosphorous. Thei r presence in ecosystems is particularly important due to their role as primary producer s but also in their po tential to accumulate to toxic levels. The relationship between lim iting ammonia, nitrate and phosphate and the size of cyanobacteria populati ons was investigated in the Celery Fields Storm Water Mitigation Area in Sarasota, FL, USA. Water samples were collected weekly for eight weeks from each of the three ponds that make up the Celery Fields. Regression analysis from the study showed no relationship between nutrient concentration and cyanobacteria cell densities. Major improvements to the study include increasing the study period to at least one hydrological cycle, us ing molecular data to identi fy species, and incorporating new cyanobacteria cell enumeration methods. ______________________ Dr. Elzie McCord, Jr. Division of Natural Sciences

PAGE 10

Chapter 1 Project Background Phytoplankton as Biol ogical Indicators Throughout history, humans have ex erted increasing influence on the environment around them (Topping 1976). Howeve r, modern scientists have begun to quantify the effects of negative human im pact on the Earth (Johnston 1976). The development of monitoring techniques has pr ogressed largely within the last century. Biological indicators are tools that environmental scientists have employed to measure the health of ecosystems. Biological indicators, or bioindicators, are organisms that reside in the habitat under examination whose popul ations may change in number, size, physiology or behavior in res ponse to fluctuations in the environment (United States Environmental Protection Agency 2011). In measuring the health of waterways, phytoplankton has proven to be a very useful bioindicator. Phytoplankton, photosynthetic organisms, are opportunistic and respond quickly to changes in nutri ent loads. One group of phytoplankton in particular, the cyanobacteria, has a prof ound effect on the health of wildlife and human populations (Chorus and Bartram 1999). Ilha do Mosqueiro Project This study is a continuation of a project conducted in the eastern Amazonian Island of Mosquiero. The purpose of the Mosqui ero project was to determine if the level of human influence on an aquatic system coul d be determined by determining the density and species composition of cyanobacteria communities. Samples were collected on May 8, 2010, from the littoral zone of five Island of Mosquiero estuary beaches. A map of the 1

PAGE 11

study sites is seen in Figure 1. Two sam ples were collected from each beach. One sample was used to estimate cell de nsities and the other used fo r species identification. The physical-chemical parameters of pH, temperat ure, dissolved oxygen content, electrical conductivity and concentration of dissolved solids were measured and noted with each sample. Figure 1.1Ilha do Mosquiero, collection sites indicated by pushpins. Red line indicates 10km. Reprinted from G oogle Earth April 10, 2011. Cyanobacteria densities found during th e study did seem to follow a pattern. Fewer colonies were found on Farl Beach, the beach located closest to the downtown area of the island. Highest densities were found on So Francisco beach, which had 2

PAGE 12

rela tively fewer buildings on its shores. All of these buildings were residential as opposed to resorts and restaurants found near other st udy sites. Higher cyanob acteria diversity was also found in the waters of So Francisco. However, the results of the project were largely inconclusive, due to a number of factors. No measure of human influence was clea rly established. This portion of the study was largely speculative as relative number of buildings around each collection point was used to measure human activity. The projec t was based on samples from one day of collection, which did not allow a standard refe rence point of ecological equilibrium to be established. Patterns between cyanobacteria and the measured physical-chemical parameters were either non-existent or misleading. The samples were not collected simultaneously, and as the day progressed, the water became hotte r. Other variables were likely to have fluctuated throughout the day. This means that differences in cell densities could be due to variations in the normal daily water cycle rather than human influence (Oliver and Ganf 2000). Another major study obstacle was the e numeration technique. The Utermhl sedimentation method has been used since the 1930s to calculate the density of phytoplankton cells in a sample (Utermohl 1931). This method is generally accepted as an accurate procedure to count cells. However, the Utermhl method can be quite cumbersome if a large sample volume is a llowed to sediment. Traditional Utermhl chambers are available in various sizes, but are often large. The current study adapted traditional tools to avoid Utermhl limitations. The current procedure also used a new 3

PAGE 13

estim ation technique to average cell densities in stead of counting all cells within a sample individually. The Current Study This study aims to improve on the methods of the Amazonian project in measuring human influence and in specifi c techniques of enumeration and species identification. The chosen study site for the curr ent project is far more practical as it is divided into three gradient zones of human impact. The Celery Fields Strom Water Mitigation Area, Sarasota, Florida, USA, is made up of several ponds connected by small channels. A map of the Celery is seen in Figure 4.2 of Chapter 4. Northern ponds of the study site receive storm water from nearby resi dential areas. Water then flows to a central zone with long grasses along the shoreline. A third zone is made of shallow ponds where the water flows to Sarasota Bay. Theoretical ly, human influence will be highest in the northern pond, which receives storm water, a nd lowest in the southern pond, which is farthest away from the storm water source. Human influence will be analyzed by examining the availability of limiting nutrients. As will be explained, humans can be a major source of nutrients in aquatic systems. In this study, runoff from human settlements play a major role in providing nutrients to this aquatic system. This study is concerned mainly with finding a connection between the size and composition of cyanobacteria with human inputs i.e.; excess nutrients. Project conclusions will be based largely on the natu re of cyanobacteria. General cyanobacteria features will be covered along with ecologi cal principles of these microorganisms. 4

PAGE 14

Im provements to the methodology will also be included. Justifications for choosing these new techniques as opposed those used in th e Mosquiero project wi ll also be covered. 5

PAGE 15

Chapter 2 Introduction to the Cyanophyta Cyanobacteria Phytoplankton play a crucial role in both aquatic and terrestrial ecosystems. These microorganisms aid researchers in determini ng the health of a water body (Granli and Turner 2006; Stevenson 1997). One phytoplankt on group, the cyanobacteria, is of great interest to phycologists and public health officials because the sudden growth of populations can lead to epidemiological consequences (Chorus and Bartram 1999; National Toxicology Program 2004). All cyanobacteria belong to the phylum Cyanophyta in the domain of Bacteria (Stanier and Cavalier-Smith 2009). Members of this phylum are prokaryotic and exist either as single cells or as colonies. Cell sizes range from 0.1-6.0 M. Cyanobacteria are easily viewed with a light microscope (Mauseth 2009). Unique Pigments Like modern plants, cyanobacteria u tilize photosystem II to produce reducing agents and ATP molecules. This is accomplished by chlorophylla as in higher plants. Cyanobacteria lack chlorophyllb but utilize several accessory pigments to collect light. Carotenoids and phycobillins are often accessory pigments. Cyanobacteria are known for the phycobillin pigment phycocyanin which a ggregates in granul es throughout cells (Fogg and others 1973; Mauseth 2009). Th ese granules give cyanobacteria a characteristic blue-green co lor (Fogg and others 1973; Mauseth 2009). This color is the namesake of the group, as the prefix cyano is Greek for blue-green. Coloration also gives cyanobacteria the nickname blue-gr een algae(Fogg and others 1973; Mauseth 6

PAGE 16

2009). Phycocyanin absorbs light at 620-640 nm allowing blue-greens to utilize wavelengths not absorbed by other phot osynthetic organisms containing only chlorophylla and chlorophyllb. (Fogg and others 1973; Maus eth 2009; Oliver and Ganf 2000). This pigment allows cyanobacteria to be easily distinguished from other algae in laboratory analysis. Endosymbiotic Theory and Relation to Modern Chloroplasts Shared characteristics with modern plants, such as photosystem II and chlorophylla, have lead to the postulation of a sh ared evolutionary ancestor that was engulfed by a eukaryotic cell (Campbell and Reece 2002c; Mauseth 2009). This is known as the Endosymbiotic Theory. A ccording to this theory, precursors to modern organelles existed as free living organisms that were engulfed, or endocytosed, by the ancestor of the eukaryotic cell. Their union formed a symbiotic relationship as the eukaryotic ancestor obtained nutrients and the chloroplast predecessor gained a safe microenvironment habitat (Campbell and Reece 2002a; Mauseth 2009). Support for this theory is quite solid. Cyanobacteria share si milar structural components to chloroplasts, the photosynthetic organelles of higher plants. Cyanobacteria contain membranous folds, synonymous with thylakoid membranes in ch loroplasts, which allow for the production of proton gradients necessary for photosynthesi s. Cyanobacteria and chloroplasts contain genomes made of circular DNA, which incl ude several shared genes. Similar 70s ribosome proteins are also found in both. Lastly, chloroplasts are of similar size to many cyanobacteria. The discovery of a cyanob acteria group that c ontained chlorophyllb, prochlorophytes, was thought to support the endosymbiotic relationship between eukaryotic plant cells and cyanobacteria (Campbell and Reece 2002c; Mauseth 2009). 7

PAGE 17

Advances in genetics have shown that prochl orophytes are not relate d to chloroplasts but branched fro m cyanobacteria at a different point in evolutionary history (Palenik and Haselkorn 1992). Chloroplasts and cyanobacteria are also not directly related, instead sharing a common ancestor. The role of cyanobacteria in chloropl ast evolutionary history provides a window to their relative age. Cyanobacteria are the oldest known photosynthetic organisms to utilize water as an electron source, as oppos ed to earlier autotrophs which utilized hydrogen sulfide (Mauseth 2009). This is evidenced by fossilized cyanobacterial mats in Australia dating to 3.5 billion years ago. The by-product of using water in photosynthesis is oxygen. For this reason, early cyanobacter ia are credited with the oxygenation of Earths atmosphere 2.7 billi on years ago (Mauseth 2009). Extremophilic adaptations The evolutionary age of cyanobacteria may help to explai n their ubiquitous nature. Blue-green algae are found in nearly every aquatic environment as well as moist soils and in Azolla plants, living symbiotically (M auseth 2009). Perhaps the most interesting cyanobacteria species are t hose that reside in extreme conditions. Cyanobacteria have been found in hot springs, geysers, salin e lakes, alkaline lakes, as well as in environments of intense acidity (Campbell and Reece 2002b; Fogg and others 1973; Oliver and Ganf 2000). Examples of extremophilic blue-greens include Aphanotece halophytica Frmy in Hof et Frmy, a species found in the Great Salt Lake. A. halophytica grows optimally in 2 M sodium chloride concentration in the la boratory (Fogg and others 1973). The genera Calothrix, Phormidium and Synechococcus have been found growi ng abundantly in the 8

PAGE 18

geysers of North Island, New Zealand, whic h reach boiling temperatures (Jones and others 2003). Synechococuss species have been recorded at temperatures around 74 C in Hunters Ho t Spring, Oregon (Ward and Cast enholtz 2000). Thermophilic species also occur at Yellowstone Nationa l Park, providing a tourist at traction by forming colorful mats around geyser formations (Yellows ton Media 2011). Other genera, such as Ocillatoria and Nostoc are found in the Antarctic (Mora and others 2011). Cyanobacteria are found living in environm ents at both ends of the pH scale. Gloeothece samoensis var. major Wille, is found at pH 2.8 (Dominic and Madhusoodanan 1999). Arthrospira a genus widespread in Africa, Asia and South America, is found in waters averaging a pH of 10.2 (Fogg and others 1973). Specialization of Cells Extremophilic cyanobacteria become the dominant life form in times of environmental distress (Oliver and Ganf 2000). This ability is especially present in colonial species that form specialized cells that enable them to survive in unfavorable conditions. An example of advantageous cell diff erentiation are nitroge n-fixing cells called heterocysts. Heterocysts are able to fix diva lent nitrogen into a usable form for other colony members. Plants from the genus Azolla take advantage of heterocystic Anabaenas nitrogen fixing ability. Azolla form root nodules for Anabaena to reside in, while Anabaena produces fixed nitrogen which the pl ant can utilize (Mauseth 2009). A similar situation involves an endocytobiotic relationship between Geosiphon fungus and members of the Nostoc genus of cyanobacteria (Kluge and others 2003). 9

PAGE 19

Akinetes are another example of how cya nobacteria utilize ce ll specialization to proliferate. Some phycologists de scribe akinetes as the repro ductive structure of colonial species. They take the form of vegetative cells that become active after periods of environmental hardship. Akinetes contribute to the ability of cyanobacteria to proliferate suddenly as a result of environmental fluctua tions, a concept that will be covered in more detail in the next chapter. Akinetes Non-specialized cell Heterocyst Figure 2.1Specialized cells in a generic filamentous cyanobacteria. Role of Cyanobacteria in the Trophic System The abundance of cyanobacteria thro ughout the planet dem onstrates their im portance as a prim ary food source in aquatic ecosystem s. Aquatic trophic system s often begin with cyanobacteria, as well as other phytoplankton, being consum ed by zooplankton grazers or lesser aquatic anim als, such as m o llusks. Higher anim als, including fishes or m a rine m a mmals, in tu rn eat these m ollusks. This sam p le trophic system can ultim ately end with human seafood consum ption. The placem ent of cyanobacteria at th e center of the tro phic web m a kes them crucial p l aye r s in the hea lth of ecosystem s and, in turn, the plants and anim als that re ly on them Therefore, a disturbance in cyanobacteria populations radi ates throughout the trophic system ( W orld Health Organization 1998). 10

PAGE 20

Toxicity of Cyanobacteria One reason blue-green algae is now an eco logical indicator is the toxic nature of several genera (Chorus and Bartram 1999; Fogg and others 1973). The freshwater family Nostocacae is the most notorious (Fogg a nd others 1973; National Toxicology Program 2004). Within the last century, a myriad of intoxication events in cities around the world have lead to the increased interest of the global community to prevent the entrance of cyanotoxins into public water supplies. The toxic effects of consuming water containing cyanobacteria have been known for a thousand years. Soldiers in China drank what was described as green-colored water and shortly after became sick with symptoms similar to gastroenteritis (National Toxi cology Program 2004). The next major recorded cyanotoxic event was recorded in an 1878 issue of Nature when an increase in Nodularia spumigena (Mertens ex Bornet et Flahault 1888) in a lake caused the death of livestock (Francis 1878). Instances such as these made cyanobacteria monitoring a concern for governmental agencies. In 1958, the World Health Organization (WHO) published guidelines in their first drinking water report, International Standards for DrinkingWater, for the monitoring of cyanotoxins in water (Chorus and Bartram 1999). The cyanotoxins of main concern are the microcystins, named for the most commonly noted toxic species, Mycrocystis aeruginosa ((Ktzing) Ktzing 1846). Microcystins are produced by se veral genera of the Nostocac ae family. These genera are commonly found in freshwater and include Anabaena, Nostoc, Oscillatoriam and Anabaenopsis (Chorus and Bartram 1999). Microcystins damage liver cells by inhibiting protein phosphatases I and II (Y oshizawa and others 1990). He patotoxic effects cause the liver to enlarge, which leads to hemorrhag ing. In severe cases, hemorrhaging can be 11

PAGE 21

fatal. (Daws on 1998) Large quant ities of mircocystins lead to fish kills and death for those animals reliant on contaminated water sources (Paerl and Fulton III 2006). Once an animal has ingested microcystins it is rendered inedible. Livestock poisoned by the cyanotoxins cannot be sold for meat nor can infected seafood be marketed. For these reasons, monitoring cyanobacteria populations for density and species composition is important in maintaining the health of local ecosystems as well as local economies (World Health Organization 1998). Governmental Regulations World Health Organization guidelines were first published after a number of occurrences showed the large impact toxic cyanobacteria can have on human populations (Chorus and Bartram 1999). A noteworthy intoxication event took place in 1988 on the island of Itaparica in Brazil during a large cyanobacteria growth in which eighty-eight residents, mostly children, died after drinking from the contaminated public water supply (Cavalli and others 2005). Two hundred ot hers became ill from the same event. Cyanobacteria communities have allegedly le d to two cases of hepatotoxicosis among dialysis patients in Brazil a nd Portugal (Azevedo and others 2002; Cavalli, Cidral, Nilson 2005; Dawson 1998). The Brazilian event occurr ed in Caruru, Pernambuco involved 116 patients. The water had been processed through a treatment facility but cyanotoxins were able to pass through undetected. Patient symp toms were used to diagnose Portuguese patients suffering from cyanotoxin intoxication post factum Several intoxication events have also taken place within the United Stat es. Increases in cyanobacteria populations in the Northeastern United States have corres ponded to large bouts of gastroenteritis among 12

PAGE 22

communitie s in Pennsylvania, New Jersey, Washington D.C. and Virginia (National Toxicology Program 2004). The WHO recommends monitoring program s to predict cyanobacteria blooms (World Health Organization 1998). Some officials believe that monitoring for blooms is not enough (National Toxicology Program 2004). The reasoning for this is that blooms are not the only cause of intoxication from blue-green algae. There is evidence that prolonged exposure to small amounts of cyanotoxins can still be fatal in the form of liver cancer. Ueno and others (1996) found that an increase in liver cancer in Haimen City, China, coincided with elevated, but small, le vels of cyanobacteria in local potable water sources (Ueno and others 1996). Bulera and others (2003) and Yang and others. (2010) have shown that microcystins can also affect gene expression in aquatic animals and mice, adding to the evidence of carcinogenicity of these chemicals (Bulera and others 2003; Yang and others 2010). Evidence has incited certain governmental organizations to claim that even one part per billion is too hi gh a concentration of cy anobacteria if such levels persist in public waterway s (National Toxicology Program 2004). The removal of cyanotoxins from wate rways can be complicated and costly. Ultraviolet radiation is the most common treatme nt for potable water, as it can break up microcystin within hours. Treatment with UV on a large scale is undesirable and the best option for communities is prevention of large quantities of cyanobacteria from forming (Tsuji and others 1995). This would not onl y protect human communities from falling ill but would also positively influence wildli fe populations. The means to controlling cyanobacteria populations are discussed at length within the next chapter. 13

PAGE 23

Cyanobacteria are ubiquitous organism s that show an amazing tenacity for life. The ease of obtaining cyanobacteria samples as well as their size, color, and tendency to be preserved well in laboratory environments ma ke these prokaryotes id eal as a biological indicator. Though their extrem ophilic tendencies and evolutionary age make them quite novel, the real concern of blue -green algae stems from their ability to affect human and animal health. Intoxication events around the globe have influenced governmental organizations to recommend monitori ng programs to protect the public. This study involves the monito ring of a small public area, the Celery Fields, in an attempt to better understand the process by which cyanobacteria becomes a public nuisance. Namely, the project addresses the relationship between cy anobacteria diversity and density with human influence. Can cyanobacteria be used to measure human influence in the Celery Fields system? The an swer to this question begins with the basic ecology of blue-green algae. 14

PAGE 24

Chapter 3 The Ecology of Cyanobacteria Limiting Factors of Algae Growth All algae are reliant on the same basic nut rients plants need for their metabolic processes. The most important nutrients include sulfur, phosphorous, carbon and organic nitrogen (Chorus and Bartram 1999). In a ge neric aquatic environments, all of these nutrients, except nitrogen and phosphorous, are available in ex cess to the needs of algae (Chorus and Bartram 1999; Khan and Ansari 2005). These compounds play a role in limiting the growth of alg ae within water bodies. Predation also plays a key role in lim iting algae growth (Dodds and others 2002; Stevenson 1997). Stevenson discusses predatory regulation of phytoplankton communities. Zooplankton and invertebrate graz ers are able to reduce larger colonies before they grow to a size where there are inedible. Another factor that impedes algal growth is the availability of substr ate and space. Regulation of phytoplankton communities can also take th e form of undesirable physical -chemical properties of the water, including hydrogen ion concentration, te mperature, salinity and the presence of toxic substances (Stevenson 1997). An imbalance of any of these regulating factors can cause drastic changes in the size and properties of an algal community. Principles of Eutrophication When an aquatic environment is stable an excess of limiting nutrients can cause phytoplankton to proliferate at a rate faster than can be controlled by grazers (Stevenson 1997). Algae will continue to reproduce until sp atial limits or outside factors, such as 15

PAGE 25

algaecides, act on them This sudden prolifer ation of phytoplankton often manifests itself on the waters surface, forming what is called a bloom. The formation of blooms as a result of an excess in limiting nutrients is a process called eu trophication (Paerl and Fulton III 2006). Figure 3.1 depict s the eutrophication process. Eutrophication has become one of the greate st waterway health concerns in recent history (Johnston 1976). Algal bl ooms cause several devasta ting effects, many of which are environmental hazards. Less serious issues arise from the blooms unsightly physical appearance, which can cause problems for tour ism. The odor of the bloom can also be deleterious to this industry. If the bloom occu rs in a potable water source, the taste of the water can be affected. As a bloom persists, boat machinery can be damaged as long filamentous species can become tangles in propellers and motors. Algae can also cause corrosion through oxidative processes (Oliver and Ganf 2000; Paerl and Fulton III 2006; Topping 1976). Figure 3.1Nutrient input and eutr ophication. Image retrieved from http://commons.wikimedia.org/wiki/Media:Scheme_eutrophication-en.svg. 16

PAGE 26

More severe consequences result when eutrophicati on begins to affect the mortality of other organisms. The physical presence of the algal mat decreases light penetration and photosynthetic organisms that are lower in the wa ter column are unable to perform metabolic processes at a normal ra te, which stunts their growth and decreases oxygen levels. This hypoxic effect increases as individuals of the bloom complete their life cycles simultaneously and begin to d ecompose. As decomposition requires oxygen, the surrounding environment becomes anaerobi c. In turn, animals may die from hypoxia (Oliver and Ganf 2000; Paerl a nd Fulton III 2006; Topping 1976). The negative effects of eutrophication have caused environmental and economic damage around the world. These effects give rise to the common term Harmful Algal Bloom or HAB (Granli and Turner 2006). HABs can become even more problematic if the species involved in the bloom ar e toxin-producing. However, toxin-producing species are commonplace in phyt oplankton communities and do not become a threat until they form a bloom (Paerl and Fulton III 2006). Human Influence in HAB Formation The process of eutrophication occurs naturally in the wild. In deed, eutrophication is part of the natural aging, or successi on, of a water body (Khan and Ansari 2005). In this case, natural succession is described as the means by which a water body becomes a drier terrestrial body by passing first thr ough the phases of a marsh and then a bog. Through succession, eutrophication ca n take thousands of years. Natural events can speed up the HAB process. Phenomenon such as se vere weather, animal migrations and volcanic eruptions can lead to the increase of available nutrients in aquatic systems. 17

PAGE 27

Hum ans, however, have become the mo st common causes of HABs (Granli and Turner 2006; Johnston 1976; Khan and Ansa ri 2005). Anthropogeni c eutrophication is a result of the increasing human influence on the natural world. Destructive business practices, population growth and global cl imate change contribut e to eutrophication (Paerl and Scott 2010). Human activities ge nerate limiting nutrients that can be introduced to natural waterways. Large-scal e industrial agriculture, poorly maintained sewage and drainage systems, as well as the creation of exhaust from industry and automobiles all play roles in HAB formati on (Topping 1976). Effluent from these actions can feed directly into waterways or run-off can wash nutrient-rich products into nearby basins (Granli and Turner 2006; Jo hnston 1976; Khan and Ansari 2005). Humans add to the likelihood of HABs through manipulation of the physicalchemical properties of a water body. This can take the form of thermal pollution, which may be caused by hot water formed as a by-product of industr ial activities (Topping 1976). Chemicals introduced by humans into th e environment can have a profound affect on hydrogen ion concentration, allowing for the proliferation of tole rant species and a decrease in overall biodiversity. Erosion can lead to a wide ning of a water basin, creating a shallower water body that has greater light penetration, more surface area and an increased mean water temperat ure (Khan and Ansari 2005). Pollution of water bodies in the United States is a significant issue. The United States Environmental Protection Agency (USE PA) reports that as of 2002, 47% of lakes, 32% of bays and estuaries and 45% of streams studied within the United States are unfit for fishing and swimming (U SEPA Office of Water 2002). 18

PAGE 28

Noteworthy Eutrophication Events Examples of anthropogenic eutrophication ar e not hard to find. Perhaps one of the biggest instances occurred in the 1960s-1970s when human development around Lake Erie influenced the formation of severa l large HABs. According to Sharma (1998), approximately eighty tons of phosphorous was added to Lake Erie each day in 1965. The blooms created washed up on shore, formi ng mats along beaches (Khan and Ansari 2005). This reduced the tourist value of the lake, as fishi ng was largely unfruitful and beaches were unfit for leisure (Sharma 1998). The consequences of eutrophication are quite real to the ecosystems of Florida. The subtropical climate allows for much warm er waters, further promoting the growth of algae once nutrients are made available in excess. Two Florida lakes rank among the most not able waterways devastated by human activities (Khan and Ansari 2005). Lake Okeech obee is the eighth largest lake in the continental United States and the largest la ke in Florida. Humans have used Lake Okeechobee for thousands of years as a potable water source as well as a source of fish. Okeechobee also plays a significant role in th e ecology of the Florida Everglades. Today, the lake is used for irrigation systems as well as for drinking water (Lake Okeechobee Restoration Plan 2003). The shores of Lake Okeechobee became developed for agricultural use during the 1930s (Florida Department of En vironmental Protection 2009; Lake Okeechobee Restoration Plan 2003) Until the beginnings of governmental regulations in the1980s, runoff containing fertilizer and animal waste have added significant amounts of nitrogen and phosphorous to the lake. This resulted in HAB events, leading to large fish kills and the impairment of the lake for recreational use 19

PAGE 29

(British Broadcasting Com pany 2009; Lake Okeechobee Restoration Plan 2003). In 2000, the Florida legislature passed the Lake Ok eechobee Protection Act to prevent further damage to the lake. In 2005 the Lake Okeec hobee & Estuary Recovery (LOER) plan was passed to restore the lake to former conditi ons (Florida Department of Environmental Protection 2009; Lake Okeec hobee Restoration Plan 2003). The second of Floridas most eutrophic la kes is Lake Apopka. This lake is located near the center of state. This lake has also had a long history of human influence (Coveney and others 2002; Friends of La ke Apopka 2010). Lake Apopka is now known for a number of environmental hazards, including toxic contamination and mutation of wildlife from heavy pesticid e use (Casarett and Doull 2008) For centuries, Apopka was a bass fishing lake. In the early 1900s, several fishing camps were located along its shores. In 1941, the southern shore was de veloped for farming. Vegetable and citrus crops were cultivated extensively, leeching fertilizers into the lake. The resulting algal blooms were severe enough to close down a ll fishing camps as only a few species of hardy fish could survive such conditions. As such, Lake Apopka has become known as the most polluted lake in Florida (Clary 1996). In 1996 the Florida legislator passed the Lake Apopka Restoration Act, but the lake is cu rrently still in need of much repair (Clary 1996). Dominance of Cyanobacteria in Blooms Cyanobacteria can dominate algae blooms within freshwater ecosystems (Oliver and Ganf 2000). The toxicity of the species involved makes this dominance of great concern (Calijuri and others 2006; Chorus and Bartram 1999; Paerl and Fulton III 2006). There are quite a few adaptations that allow cyanobacteria to out-compete other 20

PAGE 30

phytoplankton. As previously m entioned, several genera are capable of nitrogen fixation. This feature is only found in heterocystic sp ecies (Fogg and others 1973). Other species, however, are capable of storing nitrogen in the form of cyanophycin and in the pigment phycocyanin (Oliver and Ganf 2000). The abil ity of cyanobacteria to fix and store nitrogen makes phosphorous more limiting, allowing cyanobacteria to grow in systems slightly more deficient in n itrogen than in phosphorous. Some cyanobacteria species have adap tations to overcome phosphorous-deficient environments in the form of gas-vacuoles Phosphorous goes through a cycle of storage and availability within aqua tic ecosystems (Khan and Ansa ri 2005; Oliver and Ganf 2000). Upon entering the aquatic system, phosphorous is stored at the bottom of the water body. Phosphorous slowly leaks out of the sedi ment, a process that accelerates under anoxic conditions formed during the summer. This situation creates a separation of resources for phytoplankton communities. Light and carbon dioxide are available at the surface of the water while nutrients are stored near the bottom. Several genera contain gas-vacuoles, the contents of which can be regulated to control buoyancy. These species are able to move vertically within the wate r column. This adaptation can also explain the sudden nature of algal blooms. Blooms often a ppear in the morning. This is because bluegreens proliferate below the surface and come up in a unifi ed mat (Oliver and Ganf 2000). The toxic nature of cyanobacteria is also thought to be an evolutionary adaptation to out compete other algal species (Fogg and others 1973; Paerl and Fulton III 2006). These toxic species are quite common among algal blooms, perhaps demonstrating that this adaptation is quite effective during times of environmental fluctuations. However, it 21

PAGE 31

is popularly accepted that this is a secondary resu lt of toxin-producing species. The phycological community generally agrees that toxins are used primarily to deter grazing by zooplankton. Certain fluctuations ar e more favorable for some species than others. A phycologist can deduce water quality by identify ing the species presen t within an area. For example, Anabaena species tend to favor saline water. Microcystis species require large concentrations of ni trogenous compounds and phosphorous. Nostoc species prefer non-polluted water (Fogg and others 1973; Ko mrek and Hauer 2011). The identification of species can be a useful tool in underst anding the overall state of an aquatic system. 22

PAGE 32

Chapter 4 Materials and Methodology Study Site Information The Celery Fields are located in Sarasota, Florida, off Interstate 75 and Fruitville Road [27.17N, 82.41W], as seen in Figure 4.1. The 300-acre complex is comprised of five retention ponds connect ed by channels and a man-made creek. Northern ponds are the deepest and receive storm water directly are surrounded by tall marsh grasses. Ponds in the mid zone are sim ilar in depth to those in the north zone and are bordered by dry brush. Ponds in the south zone are shallow enough to walk in. These ponds contain wetland plants a nd are bordered by sandy shores. 23

PAGE 33

Figure 4.1The Celery Fields with collection sites labeled. Red line indicates 2km. Photo generate d using Google Earth on 02/09/2011. 24

PAGE 34

The fields were used as agricu ltural land for celery production until 1995 when Sarasota County bought the area for purposes of storm water drainage (Federal Emergency Management Agency 2007; Levey-Baker 2006; Rawson 2009). The Celery Fields also serves as a recrea tional destination for cyclists, hikers and birders as the ponds attract thousands of birds each year with the Sarasota Audubon Society hosting monthly counts (Rawson 2009). Figure 4.2 shows a sandhill crane, one of the many distinct bird species that attract community member to the Celery Fields. Figure 4.2 A sandhill crane feeding in the so uthern pond. Photo courtesy of Danielle Fasig. Past flooding in residentia l areas around the Celery Fi elds prompted Sarasota County to buy the land from celery farmers in order to reroute storm water to the area. This was largely due to the low elevation of the complex, allowing runoff to naturally flow into the area (Federal Emergenc y Management Agency 2007; Rawson 2009). 25

PAGE 35

The northern portion of the Celery Fields receives excess s torm water, as seen in Figure 4.1. This zone is comprised of the d eepest ponds. From there, the water follows channels to a central pond area containing we tland plants. The water then enters the shallow southern ponds and flows into Philippi Creek, which proceeds to Sarasota Bay. The presence of wetland plants in the southern zone mitigates the problem of excess nutrients, as plants can use the nutrients for growth and development. However, the shallow nature of the southern ponds could promote bloom formation. During the time of study, Sarasota County was performing major earth-moving projects to better drain the area (Federal Emergency Management Agency 2007; Trojak 2010). This activity should be considered during analysis of this studys results. The recreational importance of the Celery Fields has the potential to suffer from HAB formation. As a destination for bird enthusiasts, the area could suffer if the waters no longer support the food source for these bi rds. A toxic bloom could also lead to devastating effects for the avifauna of the area. The Celery Fields is an open system, as the water from the retention ponds eventually empty into Sarasota Bay. The formation of a HAB could lead to negative consequences for the Bay in the form of an intoxication event. Regulation of nutrient loads into the Celery Fields would prev ent HAB formation on a broader scale. A Note on the Improved Methodology Several new features were considered in creating the current methodology. One main issue with the Mosquiero study was that data were drawn from one single day of collection. To gain enough data for statistical analysis, the study needed to expand to a larger time frame. The current study is ei ght weeks long with weekly collections. 26

PAGE 36

Although eight weeks would not be as ideal as gaining inform ation from an entire hydrological cycle of one year, this time frame functions w ithin the constraints of the research period granted. Improving the Enumeration Method The eight-fold increase in the number of collections poses a problem. The five samples of the Mosqueiro study took five w eeks for density analyses using traditional Utermhl chambers of 5-7 mL A modification of the previo us method was used in an attempt to speed up the enumeration process. A 24-well microtiter plat e was used in place of larger Utermhl chambers. Plate wells prov ided a smaller volume, which was easier to work with. Multiple samples could be obser ved on the same plate, allowing for the comparison of several zones simultaneously. Additions to The Procedure Two new analyses were added to the current study in addition to the identification and enumeration techniques used in the Mos quiero study. The first new analysis involved the determination of nitrogen and phosphorous concentrations of each sample. These nutrients act as an easily-measurable proxy to human influence, a topic poorly addressed in the Mosquiero study. A high concentration of these nutrients coinciding with a high density of cyanobacteria would support th e overall hypothesis (Dodds and others 2002). A procedure to analyze pigment composition was included. Data from these analyses could help explain th e overall role of cyanobacteria within the phytoplankton community of the Celery Fields. Speci fically, it could determine whether the cyanobacteria community is large enough to be considered significant to the Celery Fields ecosystem. A low concentration of cy anobacteria-specific pigments is evidence 27

PAGE 37

that the cyan obacteria are not necessarily a major player in this ecosystem, and are, therefore, a poor choice as a biological indicator. Methodology Weekly water samples were collected from each of the three pond areas during each collection. Collection sites are labeled as N for the North site, M for the Mid site and S for the South site in Figure 4.1. Samples were obtained from the shore using the grab method, demonstrated in Figure 4.3 Analyses for nutrient content, cyanobacteria density, species identification and pigment composition were performed for each weekly collection. The following me thodologies followed the recommendations of the WHO, found in Chorus and Bartrams Toxic Cyanobacteria in Water: A guide to their public health consequences, monitoring and management (Chorus and Bartram 1999) as well as Kemp and others (1993) Handbook of Methods in Microbial Biology (MacIsaac and Stockner 1993). Water temperature, pH and weather conditions were measured alongside each sample and recorded. 28

PAGE 38

Figure 4.3Samples were obt ained using a simple grab method during the 3/7/2011 collection. Determination of Cyanobacteria Density One-hundred and twenty-five milliliter IC HEM amber glass vials were filled with sample water. Each vial wa s pre-filled with 0.68 mL of 37% formaldehyde solution for a final concentration of 0.2% once the vial was filled with sample. Vials were refrigerated for up to two weeks until prepared for enumeration. Cell counts were made by filling wells in 24-well microtiter plates with 2.5mL of sample, as seen in Figure 4.4. Samples were allowed to settle for a minimum of 24 hours. The top row of the plate was left unstained, so that the organisms in these wells could retain their original colorati on. Unstained specimens acted as a reference in determining the legitimacy of counting an object whos e identity as a cyanobacteria was under 29

PAGE 39

question. The other rows of sa mple were stained with lugol. The lugol stain provided contrast between cyanobacteria, non-organic material and the background of the plate. Week 1 | Week 2 North Mid South | North Mid South Unstained Stained with lugol Figure 4.4Diagram of microtiter plate used for enumeration. An Accu-scope 3030 Microscope Series I nversion Microscope was used to view specimens. The first one-hundred object en countered were counted. Objects included cyanobacteria individuals or colonies. The num ber of cells found was then multiplied by the number of fields needed to fill the entire area of the well (Chorus and Bartram 1999). Cells of filamentous species were co unted individually. The number of cells inside non-filamentous colonial species, such as Microcystis was estimated by assuming a rectangular shape. The number of cells of th e vertical axis was multiplied by the number of cells on the horizontal axis for an estimat e of the total number of cells within the colony. Densities from each of the three stai ned wells for each zone were calculated and then averaged to find the fina l cell density of the sample. Identification of Cyanobacteria Species A 50 mL sterile polyethylene bottle was f illed with sample water and treated with formaldehyde to a concentration of 2%. Water from these samples were allowed to settle 30

PAGE 40

in a well plate overnight and then viewed us ing th e inverted microscope. Identification guides included Kormreks on-line cyanobact eria database, (Komrek and Hauer 2011) Purdue Universitys Cyanosite (Schneegurt 1999) the WHOs guideline for toxic cyanobacteria and (Chorus and Bartram 1999) Foggs book on blue-green algae (Fogg and others 1973). Relative abundance of each species was noted. Nutrient Analysis A one-liter amber glass bottle, rinsed with methanol, was used for nutrient analyses of water samples. Nutrient analys es were performed the same day of sample collection. Samples for nutrient analysis we re immediately put on ice after collection. Samples remained unfixed throughout this proc ess. Each zone was tested for nitrate, ammonia and phosphorous concentrations. The average of three runs for each nutrient was determined and used in data analyses. All nutrient concentrations were found using Palintest nutrient test kits and a YSI 9500 Photometer. Ammonia Ammonia concentrations were found by reacting sample water with alkaline salicylate in chloride, a process which forms a blue-green indophenol complex. The concentration of the indophenol complex was determined using a YSI photometer. Nitrate The reaction to determine nitrate levels includes reducing ni trate to nitrite, reacting the nitrite with diaz onium and finally reacting diazo nium with a dye to form a reddish compound. Nitrate concentration wa s determined with a YSI photometer. 31

PAGE 41

Phosphate Phosphate was determ ined by a reacti on with ammonium molybdate forming phospho-molybdic acid that was then reduced by ascorbic acid to form a blue molybdenum complex. A YSI photometer was then used to determine phosphate concentration. Pigment analysis The method used for pigment analysis is based on the Wright and others (1991) protocol for High Performance Liquid Chro matography (HPLC). Pigment analysis was carried out in the Phytoplankton Ecology De partment of Mote Marine Aquarium. The same water collected for nutrient analysis was used for pigment analysis. The samples were filtered through 25 mm Whatma n GF/F filters until the filter was substantially covered with algae. The filters were then stored in liquid nitrogen until the day of pigment extraction. Extraction of pigments was carried out in a closed room with green lighting. Filters were placed in 15 mL centrifuge tubes that were then placed in 50 mL centrifuge tubes. The samples were sonicated for 20-30 seconds to break up th e filter and release pigment into the Pigment Extraction Solv ent (PES) a 98:2 Methanol: Ammonia mix. Pigment was then extracted through centrifugation. The extrac ted liquid was then placed in an amber vial and analyzed with a Shim adzu HPLC with a di ode array detector. 32

PAGE 42

Figure 4.5 The HPLC apparatus at Mote Ma rine Aquarium and Laboratory, Department of Phytoplankton Ecology. Components of the HPLC include a SIL-10A auto injector and a column with a diameter of 5 um, 4.6x250 mm length. Three mo bile phases were used (Wright et al. 1991) : Solvent A: 80:20 methanol : 0.5M a mmonium acetate (aq.; pH 7.2 v/v) Solvent B: 90:10 acetonitrile (210 nm UV cut-off grade) : water (v/v) Solvent C: Ethyl acetate Methanol, acetonitrile, ethyl acetate and water were all HPLC grade. Ammonia acetate was A.R. grade. Flow rate was 1 mL min-1. The first four minutes of the analytical system used it outlined in Wright and others(1999). Chromatograms were generated and anal yzed using Shimadzus HPLC software. Identification of pigments, labeling of peaks, and determination of pigment volumes were done with this interface and UNESCOs Phytoplankton Pigments in Oceanography 33

PAGE 43

(Jeffery and others 1997). The data w as th en imported into ChemTax, where pigment concentrations were determined and composition of phytoplankton communities was determined using the pigment signa tures of seven phytoplankton groups. Data Treatment Regression analysis was performed usi ng Microsoft Excels Analysis Toolkit. Cell densities for each pond were compared to all other measure variables: collection date, pH of water, water temperature, amm onia concentration, nitr ate concentration, and phosphate concentration. Data were log-transformed before analysis to ensure non-zero values. Raw data can be found in the Appendix. Species data was organized into tables for each collection data. Estimated cell count was included for each spec ies alongside their binomial name. Data gathered from pigment analysis wa s made into bar graphs. Only data from the four most common phytoplankton groups for each collection date were used. Raw data from pigment analysis can be found in the Appendix. 34

PAGE 44

Chapter 5 Results and Discussion Numerical data used in these analyses can be found in Table 1, Table 2 and Table 3 of Appendix. Cell Densities The following three pages contain Figur es 5.1-5.3, graphs of the estimated cyanobacteria cell densities found for each pond during the eight weeks of study. 35

PAGE 45

North Densities3872.37 2995.60 11342.00 8895.60 3282.00 7400.60 7235.33 7564.00 0.00 2000.00 4000.00 6000.00 8000.00 10000.00 12000.00 12345678 Week of CollectionCell Densit mL ) / cells ( y Figure 5.1Weekly estimated cell densities for the North pond over the eight week study period. 36

PAGE 46

Mid Densities4492.33 8212.80 11343.73 8759.20 5155.47 3818.67 7390.13 7175.15 0.00 2000.00 4000.00 6000.00 8000.00 10000.00 12000.00 12345678 Week of CollectionCell densit L m / cells ( y Fi g ure 5.2Weekl y estimated cell densities for the Mid p ond over the ei g ht week stud y p eriod. 37

PAGE 47

South Densities3385.97 5611.63 1709.01 3180.80 2555.79 4607.73 2798.45 702.16 0 1000 2000 3000 4000 5000 6000 12345678 Week of CollectionCell Densit y ( cells / mL ) Fi g ure 5.3Weekl y estimated cell densities for the South p ond over the ei g ht week stud y p eriod. 38

PAGE 48

Densities in the three figures do not correspond with each other. Peaks in the density of one pond do not tend to coincide wi th a peak in any other pond. One point of interest, however, a peak in cell density for the North and Mid pond during week five. This was followed by a peak in cell density for the South pond during week 6. Weather events that could have caused an increase in nutrients did not o ccur during this time. North and Mid densities were often simila r, while South densities were always at least 1000 points lower for any given wee k. This supports the hypothesis that cell densities are lowest when fart hest from the storm water source During the enumeration process, only the first one hundred objects encountered were counted. This means that the number of fields of view investigated related to the number of objects in each field. In other word, lower cyanobacteria density leads to a higher number of fields counted. This should be kept in mind when looking at species data, as the South will seem to have more cells present in comparison to the other ponds. This is because five fields were often needed to encounter one hundred objects in comparison to the one or two fields needed for the North and Mid pond. Analysis of Relations Between Ce ll Densities and Measured Variables Regression analysis was used to gather information on the nature of relationships between measured variables of the study and average cell densities. However, these relationships proved to be non-li near, meaning that regression analysis is invalid for this set of data. The results of regr ession analysis are still used in this section to support key arguments regarding these relationships. Relationships between cell densities and time were not found fo r any of the three ponds. R-squared values from these analyses are weak: R2=0.003 for the North pond, 39

PAGE 49

R2=0.0255 for the Mid pond, R2=0.2804 for the South. This is strong evidence that data from this study cannot be used to predict fu ture cell densities. One suggestion to improve these results is to expand the collection period to at least one hydr ological cycle of one year. This would provide more data points, which could lead to a better trend line. An expanded study period would also account for normal fluctuations that coincide with seasonal changes, e.g. wet months lead to more run-off into the Celery Fields system, which could lead to larger cyanobacteria cell counts. Regression analyses of cyanobacteria densities and ammonia for all three ponds provide strong evidence that no relationship between these two variables exist. R-squared values are well below one: R2=0.2179 for the North pond, R2=0.0126 in the Mid pond and R2=0.0017 for the South pond. Increased sample size over longer periods would be beneficial to measure ammonia concentra tions to obtain a larger data set. Factors that could have affected th e relationship between ammonia and cell densities include threshold con centrations and the presence of other organisms within the Celery Fields system. Dodds, et al. (2002) and Stevenson (1997) present evidence that blooms occur above a certain point, specific to the ecosystem in question, in the concentration of total nitrogen and total phosphorous in streams (Dodds, Smith, Lohman 2002; Stevenson 1997). Roselli, et al. (2009) discussed the role of nitrogen threshold in algae populations for a pond (Roselli and ot hers 2009). Perhaps the concentration of ammonia in these ponds did not fluctuate ar ound the systems threshold. It is possible that below this threshold, cyanobacteria are relatively unaffected by changes in ammonia concentration. 40

PAGE 50

As will be discussed in the Pigment Analysis section, several other phytoplankton groups were present in the Celery Fields sy stem during this study. Other organisms that were observed included various zooplankton and fishes. Different organisms will have a different affinities for ammonia than others. The metabolic processes of these organisms can include nutrients such as ammonia into other nitrogenous molecules, such as urea (Sekar and others 2002). As a result, fluctu ations in ammonia may be explained by its uptake by organisms other than cyanobacteria and its subsequent biotransformation. Cyanobacteria may not be able to compete fo r ammonia as well as other organisms in the Celery Fields system It must also be noted that ammonia ma y be a poor indicator of cyanobacteria density because the blue-green algae of the system simply do not utilize it as much as other nitrogenous compounds. Perhaps a better indicator would be to tal nitrogen, rather than ammonia. Even then, nitrogen-fixing cap abilities of some cyanobacteria may make any form of nitrogen a poor indi cator of cyanobacteria densities. Regression analyses of cell densitie s and nitrate levels also provide R2 values below one: R2=0.2365 for North, R2=0.1316 for Mid, R2=0.2693. However, all nitrate R2 values are on the order of 10-1 while R2 values from previous analyses were usually around 10-2. Expanding the data for nitrate may yi eld better results in comparison to previous analyses as the R2 values from nitrate provide evidence that a relationship between densities and nitrate is more likely than previous variables. For the moment, it is not possible to say that there is no re lationship between these two variables. Similar explanations for the results of am monia analyses can be made for nitrate. Nitrate levels may not have fluctuated around threshold values, allowing for no 41

PAGE 51

observable difference in cell densities between o ne nitrate concentr ation and another. Celery Fields resident organisms may out-compete cyanobacteria for nitrate and transform it into another substance. Lastly, nitr ate may not be the correct form of nitrogen that the resident cy anobacteria utilize. Results from phosphate analysis are simila r to those found for previous variables. It can be said with confidence that no relationship between cell density and phosphate exist with the given data set. It would be pa rticularly interesting to gather more data points for the South pond, as its R2 is 0.5851, which is larger than any R2 value so far. Until more data are collected, this value remains as evidence that no relationship was found. Reasons for the lack of a relationship between cell densities and phosphate levels are similar for ammonia and nitrate. Phos phate could be the wrong form of phosphorous to which cyanobacteria are sensitive. Ot her organisms may also transform phosphorous into non-usable forms. Further study into this topic and a survey of phosphorous compound within the Celery Fields is needed to substantiate this da ta. Threshold levels may not have been reached during the study period. As a result, cyanobacteria remain free from the influence of phosphate levels.. Relationships were not found between pH and cyanobacteria densities. It is possible that pH values did not change significantly enough in the eight-week period to elicit a response in cyanobacteria. More data points are required to confirm a relationship between cell densities with pH. 42

PAGE 52

The R2 values of density and temperatur e regression analysis, 0.1298 for North, 0.2009 for Mid, and 0.0002 for South, do not suppor t a relationship between temperature and cell densities. The eight-week period ma y not have long enough for cyanobacteria populations to respond to temperature cha nges. The bulk of cyanobacteria production occurs farther from shore, where temperature is more or less constant due to the depth of the ponds in central areas. This means that temperatures from the shores, where samples were collected, have less influe nce on cyanobacteria populations. Identified Species The following tables contain the binomial names of all species identified over the eight-week study period. The estimated number of cell found for each species is listed for each pond. Table 5.9 is a summation of species found in each pond. 43

PAGE 53

Table 5.1C yanobacteria species found in 1/17/2011 samples with estimated cell counts. Collection Date 1/17/2011 Zone Species North Mid South 74 Anabaena circinalis Rabe nhorst Anabaena perturbata var. tumida (Nygaard) Cronberg & Komrek Anabaenopsis elenkinii Mil ler Aphanocapsa rivularis (Car michael) Rabenhorst Eucapsis spp. Cl ements et Shantz 17 11 Gloeocapsa granosa (B erkely) Ktzing Merismopedia punctata Meye n 24 Merismopedia spp. Me yen 56 304 Merismopedia tenuissima Le mmermann 5808 2887 6306.7 Microcystis aeruginosa (K tzing) Ktzing Microcystis wesenbergii (Kor mrek) Kormrek in Kondrateva 25 Oscillatoria spp. Vauc her ex Gomont Phormidium spp. K tzing ex Gomont 184 195 Spirulina spp. Turpi n ex Gomont 4 42 Synechococcus spp. Nge li 5 Table 5.2C yanobacteria species found in 1/24/2011 samples with estimated cell counts. Collection Date 1/24/2011 Zone Species North Mid South 15 Anabaena circinalis Rabe nhorst Anabaenopsis elenkinii Miller Aphanocapsa rivularis (Car michael) Rabenhorst Eucapsis spp. Clements et Shantz Gloeocapsa granosa (B erkely) Ktzing Merismopedia punctata Meyen 24 Merismopedia spp. Meyen Merismopedia tenuissima Le mmermann 50 128 44 1918.4 1103.3 10278.5 Microcystis aeruginosa (K tzing) Ktzing Microcystis wesenbergii (Kormrek) Kormrek in Kondrateva 1405 Oscillatoria spp. Vauc her ex Gomont Phormidium spp. Ktzing ex Gomont 313 201 58 Spirulina spp. Turpi n ex Gomont Synechococcus spp. Ngeli 64 5 8 44

PAGE 54

Table 5.3C yanobacteria species found in 1/31/2011 samples with estimated cell counts. Collection Date 1/31/2011 Zone Species North Mid South 5 10 Anabaena circinalis Rabe nhorst Anabaenopsis elenkinii Miller 5 Aphanocapsa rivularis (Car michael) Rabenhorst Eucapsis spp. Clements et Shantz 5 Gloeocapsa granosa (B erkely) Ktzing Merismopedia punctata Meyen 40 Merismopedia spp. Meyen Merismopedia tenuissima Le mmermann 135.2 148 4654 2254.9 2874.7 Microcystis aeruginosa (K tzing) Ktzing Microcystis wesenbergii (Kormrek) Kormrek in Kondrateva 208.6 10 Oscillatoria spp. Vauc her ex Gomont Phormidium spp. Ktzing ex Gomont 187 419 156 Spirulina spp. Turpi n ex Gomont Synechococcus spp. Ngeli 13 15 6 Table 5.4C yanobacteria species found in 2/7/2011 samples with estimated cell counts. Collection Date 2/ 7/2011 Zone Species North Mid South 12 Anabaena circinalis Rabe nhorst Anabaenopsis elenkinii Miller Aphanocapsa rivularis (Car michael) Rabenhorst Eucapsis spp. Clements et Shantz 6 Gloeocapsa granosa (B erkely) Ktzing Merismopedia punctata Meyen Merismopedia spp. Meyen Merismopedia tenuissima Le mmermann 152 16 2856.6 2581.8 6285.6 Microcystis aeruginosa (K tzing) Ktzing Microcystis wesenbergii (Kormrek) Kormrek in Kondrateva 6 Oscillatoria spp. Vauc her ex Gomont Phormidium spp. Ktzing ex Gomont 311 322 15 Spirulina spp. Turpi n ex Gomont Synechococcus spp. Ngeli 22 6 34 45

PAGE 55

Table 5.5C yanobacteria species found in 2/14/2011 samples with estimated cell counts. Collection Date 2/14/2011 Zone Species North Mid South 19 25 Anabaena circinalis Rabe nhorst Anabaenopsis elenkinii Miller 19 27 Aphanocapsa rivularis (Car michael) Rabenhorst Eucapsis spp. Clements et Shantz 6 5 Gloeocapsa granosa (B erkely) Ktzing Merismopedia punctata Meyen Merismopedia spp. Meyen Merismopedia tenuissima Le mmermann 176 92 8084.4 8033.8 4495.1 Microcystis aeruginosa (K tzing) Ktzing Microcystis wesenbergii (Kormrek) Kormrek in Kondrateva 156 Oscillatoria spp. Vauc her ex Gomont Phormidium spp. Ktzing ex Gomont 146 316 Spirulina spp. Turpi n ex Gomont Synechococcus spp. Ngeli 10 18 Table 5.6C yanobacteria species found in 2/21/2011 samples with estimated cell counts. Collection Date 2/21/2011 Zone Species North Mid South 46 Anabaena circinalis Rabe nhorst Anabaenopsis elenkinii Miller 12 Aphanocapsa rivularis (Car michael) Rabenhorst Eucapsis spp. Clements et Shantz 22 5 Gloeocapsa granosa (B erkely) Ktzing Merismopedia punctata Meyen 16 Merismopedia spp. Meyen Merismopedia tenuissima Le mmermann 204 164 16 Microcystis aeruginosa (Ktzing) Ktzing 2543 2885.67 8023.9 Microcystis wesenbergii (Kormrek) Kormrek in Kondrateva 1666.7 205 Oscillatoria spp. Vaucher ex Gomont Phormidium spp. Ktzing ex Gomont Spirulina spp. Turpin ex Gomont 178 208 2 Synechococcus spp. Ngeli 3 24 8 46

PAGE 56

Table 5.7C yanobacteria species found in 2/28/2011 samples with estimated cell counts. Collection Date 2/28/2011 Zone Species North Mid South Anabaena circinalis Rabenhorst 83 31 Anabaenopsis elenkinii Miller 74 28 Aphanocapsa rivularis (Carmichael) Rabenhorst 44 Eucapsis spp. Clements et Shantz 8 4 Gloeocapsa granosa (Berkely) Ktzing 8 2 Merismopedia punctata Meyen Table 5.8C yanobacteria species found in 3/7/2011 samples with estimated cell counts. Merismopedia spp. Meyen 16 Merismopedia tenuissima Lemmermann 164 Microcystis aeruginosa (Ktzing) Ktzing 700 1569.5 5196 Microcystis wesenbergii (Kormrek) Kormrek in Kondrateva 1093.7 196 Oscillatoria spp. Vaucher ex Gomont Phormidium spp. Ktzing ex Gomont Spirulina spp. Turpin ex Gomont 257 185 8 Synechococcus spp. Ngeli 52 17 Collection Date 3/7/2011 Zone Species North Mid South Anabaena circinalis Rabenhorst 123 31 Anabaenopsis elenkinii Miller 126 24 Aphanocapsa rivularis (Carmichael) Rabenhorst Eucapsis spp. Clements et Shantz Gloeocapsa granosa (Berkely) Ktzing 5 Merismopedia punctata Meyen Merismopedia spp. Meyen 48 44 60 Merismopedia tenuissima Lemmermann 186 40 Microcystis aeruginosa (Ktzing) Ktzing 397 618 870.5 Microcystis wesenbergii (Kormrek) Kormrek in Kondrateva 233 70 Oscillatoria spp. Vaucher ex Gomont Phormidium spp. Ktzing ex Gomont Spirulina spp. Turpin ex Gomont 154 154 3 Synechococcus spp. Ngeli 10 12 13 47

PAGE 57

Table 5.9S pecies compositions of North, Mid, and South ponds. North 11 species Mid 11 species South 9 species Anabaena circinalis Rabenhorst Anabaenopsis elenkinii Miller Aphanocapsa rivularis (Carmichael) Rabenhorst Gleocapsa granosa (Berkely) Kutzing Merismopedia tenuissima Lemmerman Merismopedia spp. Meyen Microcystis aeruginosa (Kutzing) Kutzing Microcystis wesenbergii (Kormarek) Kormarek in Kondrateva Spirulina spp. (Turpin ex Gomont) Synechococcus spp. Nageli Anabaena circinalis Rabenhorst Anabaenopsis elenkinii Miller Eucapsis spp. Clements et Shantz Gleocapsa granosa (Berkely) Kutzing Merismopedia tenuissima Lemmerman Merismopedia spp. Meyen Microcystis aeruginosa (Kutzing) Kutzing Microcystis wesenbergii (Kormarek) Kormarek in Kondrateva Oscillatoria spp. Vaucher ex Gomont Spirulina spp. (Turpin ex Gomont) Synechococcus spp. Nageli Anabaena circinalis Rabenhorst Gleocapsa granosa (Berkely) Kutzing Merismopedia spp. Meyen Merismopedia tenuiss ima Lemmerman Microcystis aeruginosa (Kutzing) Kutzing Microcystis wesenbergii (Kormarek) Kormarek in Kondrateva Spirulina spp. (Turpin ex Gomont) Synechococcus spp. Nageli As seen in Table 5.9, species diversit y was highest in the North and Mid ponds, with eleven species identif ied during the study period while the South pond contained only nine. Note that it was rare for the Sout h pond to have more than 4-5 species at any 48

PAGE 58

given tim e. These findings do not fit with th e hypothesis that the pond nearest the storm water source would have the fewest species. However, it is possible that the South pond does have more species, but in such low c oncentrations that they were not observed during the identific ation process. Microcystis aeruginosa was prevalent in all ponds and was found in all samples. M. aeruginosa played a significant role in the South pond as cell counts from other species were relatively small. Spirulina spp. was always found in North and Mid ponds. This species was especially prominent in the Mid pond. Anabaena circilanis and Anabaenopsis elenkinii were common members in North and Mid ponds. The profile of all of these species is quite similar. All are found in brackish waters of streams or ponds (Komrek and Hauer 2011). Spirulina spp. and Anabaenopsis do especially well around submerged plants (K omrek and Hauer 2011). Three of the genera encountered, Spirulina, Microcystis, and Anabaena, seen in Figures 5.22-5.24, produce toxins (Komrek and Hauer 2011). Th e presence of these genera and their repeated role in the pond system is enough to endorse a monitoring program for the area. The extent of this monitoring program re quires more study to obtain an accurate the baseline cell densities. Figure 5.4Spirulina spp ., a commonly-encountered cyanob acteria species in the North and Mid ponds. Image from Cyanosite, retrieved on 4/14/2011. 49

PAGE 59

Figure 5.5Microcystis aeruginosa the most common species found in the study sample. Image from Florida Department of Envi ronmental Protection website, retrieved on 4/14/2011. Figure 5.6Anabaena circinalis a heterocystic species found mainly in the North and Mid ponds. Image from Cyanosite, retrieved on 4/14/2011. Pigment Analysis Week 5 samples from the Mid pond, week 6 samples from the South pond, and week 7 samples from the North did not produce chlorophylla peaks after High Pressure Liquid Chromatography (HPLC) analyses. A second HPLC run was done to eliminate the possibility of a procedural error. The second run confirmed first results. This is evidence that pigment degradation occurred, due to over-exposure to light or heat. ChemTax will not calculate relative abundance values fo r chromatographs without 50

PAGE 60

chlorophylla peaks. Consequently, no inform ation for relative phytoplankton group abundance is available for these samples. Sa mples from week 1 and 2 were not analyzed due to time constraints. Fifteen pigments were identifie d by the HPLC. Figures 5.25-5.27 show chromatographs from the week 8 collection. 51

PAGE 61

Figure 5.7Chromatograms and spectrum of 3/7/2011 North Pond sample with 15 iden tified peaks. Screenshot taken of Shimadzu software on 4/14/2011. 52

PAGE 62

Figure 5.8Chromatograms and spectrum of 3/7/2011 Mid Pond sample with 15 identified peaks. Screenshot taken of Shimadzu software on 4/14/2011. 53

PAGE 63

54 Figure 5.9Chromatographs and spectrum of 3/7/2011 South Pond sample with 15 iden tified peaks. Screenshot taken of Shimadzu software on 4/14/2011. 54

PAGE 64

Relative abundance of four major phytoplankton groups were found from the ChemTax software: diatoms, cryptophytes, ch lorophytes and cyanobacteria. Figures 5.255.27 show the percent composition of each pond of these four phytoplankton groups. Relative Abundance of Four Major Phytoplankton Groups in North Pond56.1 61.8 67.4 39.1 57.2 24.9 24.3 23.0 25.6 21.0 7.0 3.8 17.9 7.0 11.2 9.3 17.5 14.0 0.2 8.9 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 34568 Week of Collection Chlorophytes Cyanobacteria Cryptophytes Diatoms Figure 5.10Relative abundance of four phytopl ankton groups for five weeks of North pond samples. Diatoms were the most prolific phytoplankton group in all North samples, as seen in Figure 5.28. This is as expected, as diatoms are the most abundant phytoplankton group on the planet and often pl ay a large role in an ecosy stem at equilibrium (Round and others 2007). Cyanobacteria play the smallest role in these samples (Figure 5.28). This does not mean, however, that they are a poor biol ogical indicator in the North pond. A sudden influx of excess nutrients could change th e composition of the pond, as cyanobacteria could out-compete other phytopl ankton in this situation. 55

PAGE 65

Cryptophytes are the second most a bundant phytoplankton group in the North pond. Interestingly, cryptophytes though opportunistic like th e cyanobacteria, perform very well in nutrient-poor environments (John an d others 2002). Their large role in this pond suggests that nutrients are indeed below a threshold level, as discussed in the Regression Analysis section. Relative Abundance of Four Major Phytoplankton Groups in Mid Pond55.9 55.3 10.6 47.7 18.2 21.9 23.7 45.5 14.7 21.6 7.1 6.4 12.6 26.2 8.0 14.4 14.0 31.3 40.9 18.6 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 34678 Week of Collection Chlorophytes Cyanobacteria Cryptophytes Diatoms Figure 5.11Relative abudance of four phytopl ankton groups for five weeks of Mid pond samples. Composition of the Mid pond is similar to that of the North pond (Figure 5.29). Diatoms played a large role, as before, but cryptophytes dominate during week 6. Week 6 is noteworthy because diatoms are reduced to one-fifth of their previous percentage and cryptophytes account for nearly half the phytoplankton community. Cyanobacteria and chlorophytes also grew in population. This is evidence that an influx of excess nutrients occurred, providing non-diatom populations and opportunity to compete for these 56

PAGE 66

resources and proliferate. Figure 5.29 show evidence that an event occurred, as diatoms are reduced and the other three groups have higher relative abundan ce than the previous week. Relative Abundance of Four Major Phytoplankton Groups in South Pond35.7 25.6 9.4 0.3 17.1 35.2 59.3 69.9 79.0 55.8 12.3 2.8 5.5 5.6 8.7 16.4 9.3 15.1 15.0 12.0 0% 20% 40% 60% 80% 100% 34578 Week of Collection Chlorophytes Cyanobacteria Cryptophytes Diatoms Figure 5.12Relative abudance of four phytopl ankton groups for five weeks of South pond samples. Cryptophytes are much more prominent in the South pond with diatoms being the second most prominent (Figure 5.20). Week 7 showed the most dramatic growth of cryptophytes, one week after the growth of non-diatom groups of week 6 in the North and Mid ponds. Cell densities data for North and Mi d ponds show a peak during week 5, with a peak in cell density in the South pond a w eek later. As the South pond is the last to receive storm water, this weeks delay is logical. The fact that these peaks occurred a week before the changes in phytoplankton composition for each pond shows that some event took place that allowed non-diatom populations to out-compete diatoms and 57

PAGE 67

proliferate. Interes tingly, a cold snap occu rred during the collection of week 4. Cooler water formed at the top of the ponds may have moved towards the bottom of each pond, causing a stirring of water and perhaps disturbi ng sediments of the basin. This may have released limiting nutrients into the aqua tic system, allowing for the growth of phytoplankton communities. Non-representative Samples Cell density estimates were not represen tative of their resp ective ponds in this study. Samples were collected at only one spot at each pond. Distribution of cyanobacteria throughout each pond was not ta ken into consideration. To better understand the distribution of cyanobacteria densities, the study needed to extend to the entirety of each pond. An increase in the numbe r of collection sites, as well as samples, would also be beneficial dur ing regression analysis. One way to expand the study is to divide each pond into transects. Samples woul d then be collected from at least three randomly chosen transects for each pond each week. Randomization would allow for a better understanding of the entire ponds cell density by combining data from cyanobacteria-poor and cya nobacteria-rich areas. Resources limited the possibility of genera ting representative sa mples. Transect in central portions of the north ern and middle zones were only accessible through the use of a boat, because of the size and depth of ponds. Cell counts can be quite time-consuming and an increase in the number of samples would also require more than one researcher in order to complete analyses in a reasonable time frame. 58

PAGE 68

Difficulty in Speci es Iden tification Expertise in cyanobacteria identificati on takes years to develop. However, an experienced researcher can sti ll have trouble identifying cyanob acteria species. Individual species can take on various morphologies as a result of ecological conditions. Cyanobacteria cells are largely featureless, and they can be difficult to separate from similar species using light microscopy alone. During the current study, there were two major instances of confusion during cyanobacteria species identification. One species, identified temporarily as Unknown (a) was determined to be Microcystis aeruginosa upon further investigation. Unknown (a) was more spherical and compact than other M. aeruginosa colonies. The colonies of Unknown (a), however, had the same characteris tic mucilage, spherical cells, and refraction pattern as M. aeruginosa. A similar misunderstanding occurred between an unknown filamentous species and Synechococcus spp Investigation into the nature of Synechococcus showed that many species divide a nd form chains that appear to be filamentous colonies. Cyanobacteria phylogenies have been revi sed several times in the last century. Classification of cyanobacteria was based largely on morphology (F ogg and others 1973). Current phylogenic data are re liant on molecular components of individual species. An improvement to the methodology of this proj ect would be to is olate cyanobacteria species, extract and amplify DNA, then send the data to a laboratory for species identification (Fogg et al. 1973). 59

PAGE 69

Error in Cell Counts Averaging methods used in this project s cell counts expedited the process in comparison the Mosquiero study. However, th e averaging technique s did introduce error into the calculations. Homogeneity throughout each well was assumed, but aggregation of cells in one location is possible, especially with mucilaginous species that attract other cells. Rectangular shapes of col onies was also assumed, although Microcystis species form globular shapes. This approximation also ignored the three-dimensional shape of the colony. Confusion is mitigated somewhat by allowing sample to settle overnight, allowing gravity to flatten colonies. Diminishing error in cell counts could be done by enumerating a larger number of samples while using the modified Utermhl method. A microscope with higher resolution would also help to distinguish individual cells from their nei ghbor, eliminating the possibility of this error source. MacIsaac (1993) presented an enumerat ion method using autofluoresence of cyanobacteria (MacIsaac and Stockner 1993). This method would require a fluorescence microscope that is capable of emitting in the range that would excite cyano-specific pigments. The benefit of this technique is that it would avoi d confusion between cyanobacteria and other phytoplankton. Researchers would also be able to distinguish cyanobacteria covered by debris. This method was used brie fly at the beginning of the project. However, more expertise was n eeded in order to successfully count cyanobacteria. Figures5.31-5.33 are photographs taken of sample from the second week 60

PAGE 70

of collection using MacIsaac's autofluorescen ce technique. The photos were taken at 40X on a Leitz Orthoplan fluorescence microscope with a Leica rhodamine filter cube. Figure5.13North pond sample from 1/ 24/2011 containing a typical suite of cyanobacteria species. A Microcystis aeruginosa (A) is seen in the center with worm-like Spirulina spp.(B) caught in its mucilage. Anabaena circinalis (C) is seen to the top right of M. aeruginosa. Figure5.14This figure represents the diversity found commonly in the Mid pond. Merismopedia tenuissima (A), Synnechococcus spp (B), Microcystis aeruginosa (C), and Spirulina spp (D) can all be seen. A B C A D C B 61

PAGE 71

A Figure 5.15This sparse view is typical of the South pond. A single Micro cystis aeruginosa (A) colony is seen at the center. The Centre for Cyanobacteria and Their Toxins (CTT) in The Czech Republic provides resources for enumeration techni ques using flow cytometry (nbl 2008). Fluorescence of cyanobacteria is again utili zed, in conjunction with forward and side light scatter. Such a methodology would reduc e human error because a researcher would not have to actively decide whether or not to count a ce ll based on its morphology (nbl 2008). Figure 5.16 is a schematic view of possibl e processes for future projects using cyanobacteria as a biological indicator for the Celery Fields. The flow chart can be applied to a similar pond system, as th e analyses indicated are universal. 62

PAGE 72

Celery Fields Pigment Analysis HPLC ChemTax Statistical Data Relative Abundance of Phytoplankton Groups South Zone (Least human impact) Mid Zone North Zone (Moderate human impact) (Direct hum an impact) Flow Cytometry Autofluoresence Method Utermhl Method Genetic Information Morphological Approach Species Profile of Each Pond Cell Enumeration Species Identification Randomized Weekly Water Identification of Native Organisms Record Weather and W ater Nutrient Analysis Palintest and Spectrometer Figure 5.16Possible processes for future proj ects involving cyanobacteria as a biological indicato r for the Celery Fields. 63

PAGE 73

Chapter 6 Conclusions Results from this study did not conform to assumptions made at the beginning of the project. Expectations were that cyanobact eria densities and nutri ent data would form a relationship, cyanobacteria densities would be lower the farther from the storm water source, and diversity of cya nobacteria species would increase as human influence became lower. Regression analysis data from this projec t provided evidence to confidently state that no relationship was found between ammonia, phosphate, n itrate and cell densities. This does not mean that these nutrients do not play a role in the Celery Fields system. A better understanding of biotransformation of nu trients in the ponds is needed to make a definitive conclusion. More data, from at least one year of sampling, must also be collected in order to understand the ro le of nutrients in this pond system. Cell densities data, found in Figures 5.15.3, did show that cell densities are significantly lower in the South pond, which is the pond furthest from the storm water source. North and Mid densities (Figure 5.1 a nd 5.2, respectively) were similar. Further study is needed to conclude why the North and Mid ponds often acted as a unit while the South pond had independent patterns in cell de nsities. Speculation refers to the similar depth and size of North and Mid ponds. Species diversity behaved opposite of what was expected. Eleven species were found in the North and Mid ponds while onl y nine were found in the South pond. However, densities of certain species may have been so low that they were not observed during identification of South pond samples. If the numbers do indeed represent the 64

PAGE 74

ponds, then the increased diversity of the North and Mid ponds could be explained sim ply. Favorable conditions in terms of temperature, pH, salinity, etc. may have been better met by the North and Mid ponds, given th eir size. In this case, the pond with the least human influence may be the most extr eme in physical-chemical parameters, making it an unfavorable habita t for a number of cya nobacteria species. The toxic nature of three genera does ju stify the suggestion for a cyanobacteria monitoring program for the Celery Fields. Further study, however, is needed before resources are dedicated to such a program. Indeed, a longer study is needed to provi de any conclusive evidence. Studies lasting at least one hydrologica l cycle would establish baseli nes for cyanobacteria cell densities, nutrient concentrations, an d relative abundance of phytoplankton groups. Surveys of resident organisms would also be beneficial in unders tanding the role of biotransformation in nutrient cycling. Results from the current study were similar to those found in the Ilha do Mosquiero study. Cell densities were higher in the least influenced areas for the Mosquiero study and higher in areas of greater influe nce in the current study. Relationships between physical-chemical parameters and cell densitie s were not found in either study. However, diversity in this study was higher in areas of higher human activity. The Celery Fields project did introduce elements that aided in conceptualizing changes in the cyanobacteria community. Pi gment analysis, in particular, provided information that was valuable in determining the importance of cyanobacteria as a biological indicator. Though cha nges in relative abundance of cyanobacteria suggested a 65

PAGE 75

66 nutrient-influx event, it appeared that cryptophytes responded more drastically to these changes. Cryptophytes, however, can be difficu lt to identify and may not be the best biological indicator for this reason. Overall, the project was successful in providing cyanobacteria densities and identifying toxic species. Improvements must be made in order for future results to provide concrete evidence regarding the re lationship between human influence and cyanobacteria.

PAGE 76

Appendix Table 1Raw numerical data used in cell density and regression analyses. Week Pond Ave_Den Ammonia Nitrate Phosphate pH Temp 1 North 7235.33 0 0 1.45 8.38 16.39 2 North 3872.37 0 0.37 0.75 8.15 15.39 3 North 7400.60 0 0.80 0.52 8.12 17.39 4 North 2995.60 0 0.38 0.59 7.65 21.17 5 North 11342.00 0.22 0.15 0.17 8.17 15.39 6 North 8895.60 0.05 0.39 0.18 8.11 16.78 7 North 7564.00 0.11 0.11 0.06 8.23 23.17 8 North 3282.00 0.09 1.07 0.35 8.10 20.00 1 Mid 4492.33 0.11 0.069666667 1.3 8.37 16.55556 2 Mid 7390.13 0 0.817333333 1.3 8.38 15.11111 3 Mid 7175.15 0.003333333 0.335333333 0 8.58 17.55556 4 Mid 8212.80 0 0.166666667 0.40333333 8.21 20.88889 5 Mid 11343.73 0.16 0.005 0.35333333 8.32 15.05556 6 Mid 8759.20 0.02 0.811 0.28333333 8.36 16.44444 7 Mid 5155.47 0.04 0.878666667 0.21666667 8.44 22.55556 8 Mid 3818.67 0.03 0.881333333 0.30666667 8.24 19.55556 1 South 3385.973333 0.085 0.120333333 2.1 7.7 16.05556 2 South 5611.626667 0.003333333 0.404666667 1.47666667 8.17 14.55556 3 South 1709.006667 0 1.033666667 0.59333333 7.92 15.88889 4 South 3180.8 0.05 0.164 1.1 7.7 19.77778 5 South 2555.786667 0.963 0.963 0.82666667 7.71 10.88889 6 South 4607.733333 0.093333333 1.525 1.06666667 7.88 18.66667 7 South 2798.453333 0.033333333 1.516333333 1.71666667 7.98 19.55556 8 South 702.16 0.076666667 1.997 0.37333333 7.96 17 67

PAGE 77

Table 2Pigm ent concentrations found through ChemTax (continued on next page) Sample Date Sample chl c3 chlorophilide chl c1+c2 fuco 9-cisneo viola diadino anther allo diato lutein zea 1/31/2011 NO 28 SOUTH 0.40435 1.20344 0.00000 0.46016 0.04277 0.10769 0.39140 0.05372 0.33891 0.05552 0.19164 0.36560 1/31/2011 NO 30 MID B 1.56826 14.39523 0.00000 7.49951 0.58519 0.68951 4.46036 0.00000 1.98883 0.65091 1.64283 2.29916 1/31/2011 NO 31 MID A 1.83424 14.85175 0.00000 7.79981 0.61426 0.54234 4.43545 0.37039 1.95368 0.60058 1.77245 2.28536 1/31/2011 NO 33 NORTH 1.39219 11.67541 0.04282 6.51383 0.47164 0.56273 3.94665 0.00000 2.01576 0.53994 1.12114 2.10591 2/7/2011 NO 36 SOUTH 0.00000 6.04666 0.01408 1.14091 0.17863 0.14281 0.53836 0.00000 2.34220 0.11005 0.35239 0.35100 2/7/2011 NO 39 MID 1.10910 10.75034 0.00000 5.78465 0.57577 0.67454 2.52215 0.28158 1.79559 0.28050 1.58827 2.01579 2/7/2011 NO 42 NORTH A 2.20136 19.99215 0.00000 12.35205 0.84410 0.47099 4.92416 0.00000 3.20889 0.75354 1.13573 1.16170 2/7/2011 NO 42 NORTH B 1.34131 15.39673 0.00000 9.91539 0.66500 0.47548 4.09181 0.00000 2.61847 0.71898 1.08977 1.58451 2/14/2011 NO 45 SOUTH A 0.00000 0.85011 0.00000 0.28392 0.04108 0.06055 0.17896 0.00000 0.50221 0.03980 0.10906 0.11600 2/14/2011 NO 45 SOUTH B 0.00000 0.81261 0.00000 0.26169 0.03049 0.02990 0.16267 0.01371 0.47966 0.04595 0.09598 0.09343 2/14/2011 NO 48 MID 0.00000 6.57681 0.00000 2.72019 0.43438 0.43183 1.61606 0.17387 0.75250 0.23450 0.46590 0.00000 2/14/2011 NO 51 NORTH 0.00000 17.28648 0.00000 10.22573 0.57025 0.08887 6.17295 0.29420 2.14046 0.85182 0.79454 0.00000 2/21/2011 NO 54 SOUTH 0.00000 0.95286 0.00000 0.51856 0.09896 0.09254 0.26365 0.03283 0.21556 0.00000 0.12497 0.20775 2/21/2011 NO 57 MID A 0.00000 5.07030 0.00000 2.26575 0.37161 0.23347 1.13714 0.13499 0.62016 0.09832 0.60131 0.59155 2/21/2011 NO 57 MID B 1.73830 6.50325 0.00000 2.78667 0.42435 0.35494 1.84667 0.22696 1.75222 0.36715 1.12074 1.13933 2/21/2011 NO 60 NORTH 2.37756 0.00000 0.00000 5.10099 0.09295 0.28754 2.12707 0.32578 1.38394 0.26910 0.84480 1.56673 2/28/2011 NO 63 SOUTH 0.00000 1.08972 0.00000 0.21072 0.07580 0.04503 0.24436 0.00000 0.71219 0.05266 0.11728 0.14847 2/28/2011 NO 66 MID 0.00000 4.77438 0.00000 1.98245 0.37870 0.23775 1.26297 0.26673 0.52890 0.20055 0.76418 1.21305 2/28/2011 NO 69 NORTH A 2.11846 3.52550 0.00000 1.73766 0.31788 0.15522 1.03874 0.08785 0.25486 0.13719 0.35455 1.25356 2/28/2011 NO 69 NORTH B 0.00000 2.85327 0.00000 1.86020 0.32722 0.12527 1.02765 0.06893 0.21639 0.13223 0.40768 1.36073 3/7/2011 NO 72 SOUTH A1 0.00000 1.98143 0.00000 0.48351 0.12150 0.13977 0.40355 0.04909 1.46328 0.07597 0.35115 0.68817 3/7/2011 NO 72 SOUTH B 0.00000 1.74553 0.00000 0.40325 0.10329 0.10635 0.33028 0.00000 1.20935 0.06135 0.28140 0.57951 3/7/2011 NO 75 M 0.93777 5.93393 0.00000 2.68451 0.59061 0.62655 1.73436 0.26224 1.04731 0.26124 1.63267 1.99086 3/7/2011 NO 78 N 0.75167 5.06864 0.00000 2.61097 0.46882 0.31692 2.58843 0.29900 0.67674 0.49137 0.79639 1.40086 68

PAGE 78

Sample Date Sample chl b chl a @440nm monovinyl chl a divinyl chl a chl a epimer betaeta betabeta 1/31/2011 NO 28 SOUTH 0.29809 2.18772 0.00000 0.00000 0.24872 0.02316 0.19900 1/31/2011 NO 30 MID B 4.12278 26.79380 0.00000 0.00000 0.00000 0.00000 1.89237 1/31/2011 NO 31 MID A 4.29568 24.81461 0.00000 0.00000 0.00000 0.00000 2.16171 1/31/2011 NO 33 NORTH 2.78777 26.91623 0.00000 0.00000 0.00000 0.13780 1.70396 2/7/2011 NO 36 SOUTH 0.91267 10.05230 10.77256 0.15764 0.00000 0.22912 0.41956 2/7/2011 NO 39 MID 4.09414 28.84530 31.36213 -0.08758 0.00000 0.13004 1.87908 2/7/2011 NO 42 NORTH A 6.78353 50.76780 57.22607 -0.91246 0.00000 0.29994 1.98773 2/7/2011 NO 42 NORTH B 5.07818 42.35510 46.41940 -0.07901 0.00000 0.27304 1.92806 2/14/2011 NO 45 SOUTH A 0.14312 1.28290 1.33342 -0.03247 0.00000 0.00000 0.00000 2/14/2011 NO 45 SOUTH B 0.15397 1.04524 1.09225 -0.00602 0.05959 0.00000 0.00000 2/14/2011 NO 48 MID 0.29113 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 2/14/2011 NO 51 NORTH 4.74973 29.34582 32.66418 -0.23782 0.62333 0.00000 0.06061 2/21/2011 NO 54 SOUTH 0.08689 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 2/21/2011 NO 57 MID A 0.19421 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 2/21/2011 NO 57 MID B 1.15181 1.98025 2.04652 -0.04077 0.29531 0.00000 0.00000 2/21/2011 NO 60 NORTH 0.65248 0.70206 0.00000 0.00000 0.00000 0.00000 0.00000 2/28/2011 NO 63 SOUTH 0.25160 1.59754 1.64288 -0.07789 0.08896 0.00000 0.00000 2/28/2011 NO 66 MID 0.35964 0.29420 0.00000 0.00000 0.00000 0.00000 0.00000 2/28/2011 NO 69 NORTH A 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 2/28/2011 NO 69 NORTH B 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 3/7/2011 NO 72 SOUTH A1 0.80530 6.99487 7.27835 0.25038 0.49428 0.09958 0.33006 3/7/2011 NO 72 SOUTH B 0.64800 4.93398 5.04348 0.21035 0.40818 0.10684 0.28553 3/7/2011 NO 75 M 4.21823 20.75995 21.92031 0.38126 2.26550 0.00000 1.66125 3/7/2011 NO 78 N 3.67200 20.28596 22.38391 -0.16399 2.29863 0.00000 1.32426 69

PAGE 79

70 Table 3Relative abundance of phytopl ankton groups found through ChemTax. Sample Date Sample Diatoms Cyanobacteria Chlorophytes Dinoflagellates Haptophytes_S Cryptophytes Prochlorophytes 1/31/2011 NO 28 SOUTH 35.7331 12.29502501 16.4264905 0.08150566 0.15540655 35.1500821 0.1584378071/31/2011 NO 30 MID B 55.8111 6.955588661 13.9241986 0.15810294 0.30410393 22.5257029 0.3212013511/31/2011 NO 31 MID A 55.931 7.075954213 14.4424044 0.134232107 0.261941252 21.8624751 0.2920396951/31/2011 NO 33 NORTH 56.0533 6.964853117 11.1824518 0 .181732958 0.344645456 24.9401556 0.3328555322/7/2011 NO 36 SOUTH 25.6077 2.842857297 9.32879755 0.074194222 0.134024905 59.3310254 2.6814397882/7/2011 NO 39 MID 55.2842 6.377870855 14.0031052 0.226709471 0.427473052 23.6773824 0.003238312/7/2011 NO 42 NORTH A 63.08832.5026301519.188883660.223162411 0.4230795824.573901902/7/2011 NO 42 NORTH B 61.84573.8006121229.296695410.227300069 0.43081066324.26223520.1366456812/14/2011 NO 45 SOUTH A 9.440175.53571632915.13036210 069.893747202/14/2011 NO 45 SOUTH B 6.334254.99742757913.16008860 075.508235402/14/2011 NO 51 NORTH 67.3863 0.238784515 8.94260892 0.157500634 0.308191573 22.9666572 02/21/2011 NO 57 MID B 10.6159 12.59019258 31.3419094 0 0 45.4519842 02/21/2011 NO 60 NORTH 39.0822 17.85203687 17.5130609 0 0 25.5526784 02/28/2011 NO 63 SOUTH 0.33753 5.628339257 15.0059776 0 0 79.0281497 02/28/2011 NO 66 MID 18.205 26.18333536 40.8898068 0 0 14.7218369 03/7/2011 NO 72 SOUTH A1 21.87487.86018715811.42069270.104284134 0.18984417652.79956455.7506523453/7/2011 NO 72 SOUTH B 17.06248.66853232812.0181470.052955306 0.09563788355.7651616.3372116013/7/2011 NO 75 M 47.72 8.033974617 18.6290582 0.259522309 0.486686223 21.5527874 3.3179377953/7/2011 NO 78 N 57.1834 6.951946232 13.961007 0.326356503 0.61192018 20.9653321 0

PAGE 80

References Azevedo, S. M. F. O., Carmichael, W. W., Jochimsen, E. M., Rinehart, K. L., Lau, S., Shaw, G. R. & Eaglesham, G. K. 2002. Human intoxica tion by microcystins during renal dialysis treatment in CaruaruBrazil. Toxicology 181-182:441-6. British Broadcasting Company. 2009. Pre ssure grows on madagascar coup. Bulera, S., Eddy, S. M., Ferguson, E., Jatkoe, T. A., Reindel, J. F., Bleavins, M. R. & De La Iglesia, F. A. 2003. RNA expression in the early characterization of hepatotoxicants in wistar rats by high-density DNA microarrays. Hepatology 33:1239. Calijuri, M. C. C., Alves, M. S. A. & Al ves dos Santos, A. C. 2006. Cianobactria e Cianotoxinas Em guas Con tinentais. RiMa Editora, Sao Carlos, SP, Brazil, Campbell, N. A. & Reece, J. B. 2002a. Nutritional and metabolic diversity. In Nutritional and metabolic diversity. Biology. Benjamn Cummings, San Francisco, pp. 532-535. Campbell, N. A. & Reece, J. B. 2002b. The ecosystem approach to ecology. In The ecosystem approach to ecology. Biology. Benjamin Cummings, San Francisco, pp. 1199--1205. Campbell, N. A. & Reece, J. B. 2002c. The orig in and early diversifi cation of eukaryotes. In The origin and early divers ification of eukaryotes. Biology. Benjamin Cummings, San Francisco, pp. 548-72. Casarett, L. J. & Doull, J. 2008. Toxicology: The Basic Science of Poisons, Seventh ed. Macmillan, New York, Cavalli, V., Cidral, R. J. & Nilson, R. 2005. Contagem de cianobactrias do gnero microcystis e determinao de microcistinas pelo mtodo de imunoensaio competitivo no controle de tratamento de gua para abastecimento. Chorus, I. & Bartram, J. 1999. Toxic Cyanobacteria in Water: A Guide to their Public Health Consequences, Monitoring and Management World Health Organization, 362 pp. Clary, M. 1996. Florida seeks fountain of youth for polluted lake : Officials hope to turn back years of neglect and restore lake apopka's game fish and plants .. Los Angeles Times American Album. 71

PAGE 81

Coveney, M. F., Stites, D. L., Lowe, E. F., Battoe, L. E. & Conrow, R. 2002. Nutrien t removal from eutrophic lake water by wetland ltration. Ecol. Eng. 19:141-59. Dawson, R. M. 1998. The toxico logy of microcystins. Toxicon 36:953. Dodds, W. K., Smith, V. H. & Lohman, K. 2002. Nitrogen and phosphorous relationships to benthic algal biomass in temperate streams. Can. J. Fish. Aquat. Sci. 59:865-874. Dominic, T. K. & Madhusoodanan, P. V. 1999. Cyanobacteria from extreme acidic environments. Curr. Sci. Federal Emergency Management Agency. 2007. Sarasota county drainage project protects celery fields: Full mitigation best practice story sarasota county, florida. Florida Department of Environmental Protection. 2009. History of lake okeechobee. 2011. Fogg, G. E., Stewart, W. D. P., Fay, P. & Walsby, A. E. 1973. The Blue-Green Algae, Second ed. Academic Press, London, 459 pp. Francis, G. 1878. Poisonous australian lake. Nature 18:11. Friends of Lake Apopka. 2010. Lake apopka timeline. 2011. Granli, E. & Turner, J. T. 2006. An introduction to harmful algae. In Granli, E. & Turner, J. T. [Eds.] Ecology of Harmful Algae. Springer-Verlag, Berlin, pp. 3-7. Jeffery, S. W., Mantoura, R. E. C. & Wri ght, S. W. 1997. Phytoplankton Pigments in Oceanography, UNESCO, Paris, France, 456 pp. John, D. M., Whitton, B. A. & Brook, A. J. 2002. The Freshwater Algal Flora of the British Isles, First ed. Cambridge Univer sity Press, Cambride, United Kingdom, 686 pp. Johnston, R. 1976. Mechanisms and problems of marine pollution in relation to commercial fisheries. In Johnston, R. [Ed.] Marine Pollution. Academic Press, London, pp. 3-156. Jones, B., Renaut, R. W. & Rosen, M. R. 2003. Silicified microbes in a geyser mound: The enigma of low-temperature cyanob acteria in a high-temperature setting SEPM 18:87--109. 72

PAGE 82

Karp, G. 2010. Interactions of cells with extracellular m aterials. In Interactions of cells with extracellular materials. Cell Biology: International Student Edition. John Wiley & Sons, Inc., Singapore, pp. 420-6. Khan, F. A. & Ansari, A. A. 2005. Eu trophication: An eco logical vision. The Botanical Review 71:449--482. Kluge, M., Mollenhauer, D., Wolf, E. & Schler, A. 2003. The nostoc-geosiphon endocytobiosis. In Rai, A. N., Bergman, B. & Rasmussen, U. [Eds.] Cyanobacteria in Symbiosis. Kluwer Academic Publishers, New York, pp. 1930. Komrek, J. & Hauer, T. 2011. CyanoDB.cz online database of cyanobacterial genera. 2011. Lake Okeechobee Restoration Plan. 2003. Introduction. 2011. Levey-Baker, C. 2006. Celery fields forever: Stalking the remnants of an old sarasota industry. Creative Loafing Urban Explorer. MacIsaac, E. A. & Stockner, J. G. 1993. Enumeration of phototrophic picoplankton by autofluorescence microscopy. In Kemp, P. F., Sherr, B. F., Sherr, E. B. & Cole, J. J. [Eds.] Handbook of Methods in Aquatic Microbial Ecology. Lewis Publishers, Boca Raton, pp. 187--197. Mauseth, J. 2009. Botany: An Introduction to Plant Biology, Fourth e d. Jones and Bartlett Publishers, Boston, 624 pp. Mora, M., McKnight, D. & Lubinski, D. J. 2011. Antarctic cyanobacteria web site: Taxa and samples from the dry valleys. McMurdo dry valleys LTER. 2011. National Toxicology Program. 2004. I. microcystin toxicity. Oliver, R. L. & Ganf, G. G. 2000. Freshwater blooms. In Whitton, B. A. & Potts, M. [Eds.] The Ecology of Cyanobacteria: Thei r Diversity in Time and Space. Kluwer Academic Publishers, Dordrecht, pp. 149--194. Paerl, H. W. & Scott, J. T. 2010. Throwing fuel on the fire: Syne rgistic effects of excessive nitrogen inputs and global warming on harmful algal blooms. Environ. Sci. Technol. 44:7756--7758. Paerl, H. W. & Fulton III, R. S. 2006. Ecology of harmful cyanobacteria. In Granli, E. & Turner, J. T. [Eds.] Ecology of Harmful Algae. Springer-Verlag, Berlin, pp. 95-109. 73

PAGE 83

Palenik, B. & Haselkorn, R. 1992. Multip le evol utionary origins of prochlorophytes, the chlorophyllb -containing prokaryotes. Nature 355:265-7. Rawson, A. 2009. Celery fields. 2011. Roselli, L., Fabbrocini, A., Manzo, C. & D'Adamo, R. 2009. Hydrological heterogeneity, nutrient dynamics and water quality of a non-tidal le ntic ecosystem (lesina lagoon, italy). Estuar. Coast. Shelf Sci. 84:539-52. Round, F. E., Crawford, R. M. & Mann, D. G. 2007. The Diatoms: Biology and Morphology of the Genera, Cambridge University Press, Cambridge, United Kingdom, 760 pp. Schneegurt, M. A. 1999. Cyanosite for cyanobacteria, blue-green algae and prochlorophytes. 2011. Sekar, R., Nair, K. V. K., Rao, V. N. R. & Venugopalan, V. P. 2002. Nutrient dynamics and successional changes in a lentic freshwater biofilm. Freshwater Biol. 47:1893-907. Sharma, P. D. 1998. Ecology and Environment Rastogi Publications, Meerut, India, nbl, I. 2008. Flow cytometry. 2011. Stanier & Cavalier-Smith. 2009. The taxonomicon. 2011. Stevenson, R. J. 1997. Resource thresholds and stream ecosystem sustainability. J. N. Am. Benthol. Soc. 16:410--424. Topping, G. 1976. Sewage and the sea. In Johnston, R. [Ed.] Marine Pollution. Academic Press, London, pp. 303-51. Trojak, L. 2010. Celery fields forever. Siteprep May/June:20--22. Tsuji, K., Watanuki, T., Kondo, F., Watanabe, M. F., Suzuki, S., Nakazawa, H., Suzuki, M., Uchida, H. & Harada, K. -. 1995. Stability of microcystins from cyanobacteria. II. effect of UV light on decomposition and isomerization. Toxicon 33:1619. Ueno, Y., Nagata, S., Tsutsumi, T., Hasegawa, A., Watanabe, M. F., Park, H. -., Chen, G. -., Chen, G. & Yu, S. -. 1996. Detection of microcystins, a blue-green algal hepatotoxin, in drinking water sampled in haimen and fusui, endemic areas of primary liver cancer in china, by highly sensitive immunoassay. Carcinogenesis 17:1317. 74

PAGE 84

75 United States Environmental Protection Agency. 2011. Biological indicators of watershed health. 2011. USEPA Office of Water. 2002. The national wate r quality inventory: Report to congress for the 2002 reporting cycleA profile. EPA 841-F-07-003. Utermohl, v. H. 1931. Neue wege in de quant itaven erfassung des planktons. (miter besondere berlicksichtigung de ultraplanktons.). Ver. Int. Verein. Angew. Limnol. 5:567--595. Ward, D. M. & Castenholtz, R. W. 2000. Cyanobacteria in geothermal habitats. In Whitton, B. A. & Potts, M. [Eds.] The Ecology of Cyanobacteria: Their Diversity in Time and Space. Kluwer Academic Publis hers, Dordrecht, pp. 3660. World Health Organization. 1998. Cyanobacterial toxins: Microcysti n-LR in drinkingwater: Background document for developm ent of WHO guidelines for drinkingwater quality Wright, S. W., Jeffrey, S. W., Matoura, R. F. C., Llewellyn, C. A., Bjrnland, T., Repeta, D. & Welschmeyer, N. 1991. Improved HPLC method for the analysis of chlorophylls and carotenoids from marine phytoplankton. Mar. Ecol. Prog. Ser. 77:183--196. Yang, Z., Wu, H. & Li, Y. 2010. Toxic effect on tissues and differentially expressed genes in hepatopancreas identified by suppression subtractive hybridi zation of freshwater pearl mussel (hyriopsis cumingii) following microcystin-LR challenge. Environmental Toxicology Yellowstone Media. 2011. Yellowstone geysers-mammoth hot springs. 2011. Yoshizawa, S., Matsushima, R., Watanabe, M. F., Harada, K. -., Ichihara, A., Carmichael, W. W. & Fujiki, H. 1990. Inhibition of prot ein phosphatases by microcystis and nodularin associ ated with hepatotoxicity. Journal of Cancer Research and Clinical Oncology 116:609.


ERROR LOADING HTML FROM SOURCE (http://ncf.sobek.ufl.edu//design/skins/UFDC/html/footer_item.html)