Lacey, C - Thesis

SPATIAL AND TEMPORAL VARIABILITY OF KARENIA BREVIS
WITHIN THE CHOCTAWHATCHEE BAY SYSTEM
by
Claire Nichola Lacey
B. Sc., University of Lethbridge, 2009
A thesis submitted to the Department of Earth and Environmental Sciences
College of Science, Engineering and Health
The University of West Florida
In partial fulfillment of the requirements for the degree of
Master of Science
2015

The thesis of Claire Nichola Lacey is approved:
____________________________________________ _________________
Allison Y. Beauregard Schwartz, Ph.D., Committee Member Date
____________________________________________ _________________
Zhiyong Hu, Ph.D., Committee Member Date
____________________________________________ _________________
Matthew C. Schwartz, Ph.D., Committee Chair Date
Accepted for the Department/Division:
____________________________________________ _________________
Matthew C. Schwartz, Ph.D., Chair Date
Accepted for the University:
____________________________________________ _________________
Jay Clune, Ph,D Interim AVP for Academic Programs Date

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ACKNOWLEDGMENTS
To my family and friends for supporting my decision to move across the continent and all
my new people that welcomed a foreigner with open arms. Jolene Friesen for always being there
with love and encouragement. Tanya Gallagher for taking care of me when I was too busy to
take care of myself. Andre Calaminus for being my sounding board, editor and motivator (and
for your contribution to the Starburst necklace). Fritz Langerfeld for the programming and
entertainment. Karen Milne for providing me a sanctuary to focus and write.
Matt Schwartz and Allison Beauregard for providing the groundwork of this study. To all
the amazing and generous people that so willingly gave up their time when I reached out for
help: notably Bob Ulrich, David Laskin, Dr. Tak Fung and Nathan McKinney (but there were
many more).
This project was supported by a grant from the University of West Florida through the
Office of Research and Sponsored Programs. Samples, funding and support were also provided
by the Mattie Kelly Environmental Institute.

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TABLE OF CONTENTS
ACKNOWLEDGMENTS ................................................................................................. iv
TABLE OF CONTENTS.................................................................................................... v
LIST OF TABLES...........................................................................................................viii
LIST OF FIGURES ........................................................................................................... ix
ABSTRACT....................................................................................................................... xi
CHAPTER 1 INTRODUCTION........................................................................................ 1
CHAPTER 2 BACKGROUND .......................................................................................... 3
Bloom Dynamics ............................................................................................................ 4
Nutrient Control.............................................................................................................. 6
Brevetoxins ..................................................................................................................... 9
Climatic Influences ....................................................................................................... 10
Mitigation, Prevention and Control .............................................................................. 13
Study Area .................................................................................................................... 15
CHAPTER 3 METHODS................................................................................................. 18
Field Sampling Techniques........................................................................................... 18
Analytical Methods....................................................................................................... 18
K. brevis Quantification................................................................................................ 18
Standards................................................................................................................... 18

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RNA Extraction ........................................................................................................ 20
Sequencing................................................................................................................ 21
Amplification............................................................................................................ 22
GIS Analysis ................................................................................................................. 26
Statistical Analysis........................................................................................................ 28
CHAPTER 4 RESULTS................................................................................................... 30
GIS Interpolation .......................................................................................................... 32
Statistical Analysis........................................................................................................ 37
Multivariable Analysis.............................................................................................. 38
CHAPTER 5 DISCUSSION............................................................................................. 40
Conclusion .................................................................................................................... 43
WORKS CITED ............................................................................................................... 45
APPENDICES .................................................................................................................. 49
APPENDIX A L1 Medium Components...................................................................... 50
APPENDIX B RT-qPCR Master Mix .......................................................................... 54
APPENDIX C Stock Slide Counting Sheet.................................................................. 56
APPENDIX D PCR Analysis Prep Processing Form................................................... 58
APPENDIX E PCR Supply List ................................................................................... 60
APPENDIX F SPSS Statistical Syntax......................................................................... 62
APPENDIX G Raw Data.............................................................................................. 64

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APPENDIX H PCR Graphs.......................................................................................... 69
APPENDIX I Additional Interploation Maps............................................................... 75
APPENDIX J Things to Consider................................................................................. 82
APPENDIX K Funding................................................................................................ 84

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LIST OF TABLES
Table 2.1 Concentration Classification Levels of K brevis. .......................................................... 5
Table 2.2 Choctawhatchee Bay Sample Site Locations............................................................... 16
Table 3.1 K. brevis Primer and Probe Sequences........................................................................ 22
Table 3.2 RT-qPCR Cycling Sequence ....................................................................................... 24
Table 3.3 Seasonal cutoffs for study period................................................................................. 27
Table 4.1 Parameter averages by sample site location, annually and seasonally ......................... 31
Table 4.2 Wald Chi-Square (χ2
), coefficients (β), and significance (p) for nutrient and
physical water characteristics as a predictor for K. brevis cell abundance................ 38
Table 4.3 Groups created for multivariate analysis ..................................................................... 39
Table 4.4 Test of Multivariable Model Effects............................................................................ 39

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LIST OF FIGURES
Figure 2.1 Magnified Karenia brevis organism. Image credit (FWRI, 2012) ............................... 3
Figure 2.2 Choctawhatchee Bay Watershed. Data obtained from FGDL.................................... 16
Figure 2.3 Study Area with sample site locations........................................................................ 17
Figure 3.1 RNEasy Extraction Procedure.................................................................................... 21
Figure 3.2 Arrangement of Samples and Standards in the Rotor-Gene 3000 – 72-well Ring
Thermocycler............................................................................................................. 23
Figure 3.3 Schematic of RT-qPCR Process Using the TaqMan One-Step RT-PCR Reagents
Kit .............................................................................................................................. 25
Figure 3.4 Workflow Model for Spline with Barriers Interpolation............................................ 27
Figure 3.5 Variable Interpolation Tool Created Using Workflow Model ................................... 27
Figure 4.1 K. brevis Concentrations by Sample Site from Nov. 2011 through Nov. 2012......... 30
Figure 4.2 Results from Interpolation of Average Annual K. brevis Cell Concentration from
Each Sample Site ....................................................................................................... 32
Figure 4.3 Results from Interpolation of Seasonal Averages from Each Sample Site for K.
brevis Cell Concentrations: (a) Average Spring K. brevis, (b) Average Summer K.
brevis, (c) Average Fall K. brevis, (d) Average Winter K. brevis............................. 33
Figure 4.4 Results from Interpolation of Seasonal Averages from Each Sample Site for
Dissolved Inorganic Nitrogen (NO2+3+NH4) (µM)): (a) Average Spring Dissolved
Inorganic Nitrogen, (b) Average Summer Dissolved Inorganic Nitrogen, (c)
Average fall Dissolved Inorganic Nitrogen, (d) Average Winter Dissolved
Inorganic Nitrogen..................................................................................................... 34
Figure 4.5 Results from Interpolation of Seasonal Averages from Each Sample Site for
Phosphorus (PO4) (µM)): (a) Average Spring PO4
3-
, (b) Average Summer PO4
3-
,
(c) Average fall PO4
3-
, (d) Average Winter PO4
3-
. .................................................... 35
Figure 4.6 Proportional Representation Showing Relative Ranking of Seasonal Averages for
K. brevis, Dissolved Inorganic Nitrogen and Phosphorus for Spring 2011 .............. 36
Figure I1 Results from interpolation of seasonal averages from each sample site for salinity:
(a) Average Spring salinity, (b) Average Summer salinity, (c) Average fall
salinity, (d) Average winter salinity. ......................................................................... 76

x
Figure I2 Results from Interpolation of Seasonal Averages from Each Sample Site for
Surface Water Temperature: (a) Average Spring Temperature, (b) Average
Summer Temperature, (c) Average Fall Temperature, (d) Average Winter
Temperature............................................................................................................... 77
Figure I3 Results from interpolation of seasonal averages from each sample site for dissolved
oxygen (% saturation): (a) Average Spring dissolved oxygen, (b) Average
Summer dissolved oxygen, (c) Average fall dissolved oxygen, (d) Average winter
dissolved oxygen. ...................................................................................................... 78
Figure I4 Results from interpolation of seasonal averages from each sample site for dissolved
oxygen (mg/L): (a) Average Spring dissolved oxygen, (b) Average Summer
dissolved oxygen, (c) Average fall dissolved oxygen, (d) Average winter
dissolved oxygen. ...................................................................................................... 79
Figure I5 Results from interpolation of seasonal averages for Chlorophyll a data: (a) Average
Spring Chlorophyll a, (b) No data was available for the summer duration as all
Chlorophyll a data after April 9, 2012 was lost due to a lab oversight, (c) Average
fall Chlorophyll a, (d) Average winter Chlorophyll a. .............................................. 80
Figure I6 Results from Interpolation of Seasonal Averages from Each Sample Site adjusted
for N:P: (a) Average Spring N:P, (b) Average Summer N:P, (c) Average fall N:P,
(d) Average Winter N:P............................................................................................. 81

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ABSTRACT
SPATIAL AND TEMPORAL VARIABILITY OF KARENIA BREVIS
WITHIN THE CHOCTAWHATCHEE BAY SYSTEM
Claire Nichola Lacey
The coastal region of northwest Florida has been the site of red tide harmful algal blooms caused
by the toxic dinoflagellate Karenia brevis. Water samples were collected from six shore
locations, in two bayous in western Choctawhatchee Bay. Surface-water nutrient levels and
chlorophyll a were measured for all samples along with standard physical water characteristics
(dissolved oxygen, temperature, and salinity) to provide relevant biogeochemical framework to
assess the observed spatial and temporal variability in K. brevis. Samples were analyzed using
polymerase chain reaction (PCR) in order to amplified target cDNA segments of K. brevis for
quantification. Spectrophotometric analysis and PCR results were evaluated for spatial and
temporal correlation to expose potential causes for the periodic blooms of K. brevis using SPSS.
K. brevis cell abundance was found to have an inverse correlation with various nitrogen species
as well as N:P ratio, however no correlation was observed with phosphorus.
Keywords: Karenia brevis, algae, HAB, red tide, nutrients, bayous, estuaries, nitrogen,
phosphorus, PCR, cDNA

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CHAPTER 1 INTRODUCTION
Historically, Florida has been host to many harmful algae blooms (HABs) linked to the
ichthyotoxic dinoflagellate Karenia brevis. Recurring blooms of this microscopic organism are
responsible for massive fish, bird and dolphin mortalities, human illness, and accumulation of
toxins in shellfish (Bricelj et al., 2012; FWRI, 2012; K. A. Steidinger & Haddad, 1981; Thorpe,
Sultana, & Stafford, 2002). The lipophilic brevetoxins (PbTxs) produced by K. brevis adversely
affect public health in two ways: (1) risk of neurotoxic shellfish poisoning (NSP) by consuming
contaminated shellfish, and (2) respiratory irritation from aerosolized PbTxs (Abbott et al.,
2009).
Monitoring of HABs has become a significant undertaking in the state of Florida, as well
as many areas around the world, because of the associated negative impacts on public health, the
environment, and the economy. Significant economic impacts include the closure of shellfish
harvesting areas, massive fish kills, declines in tourism and associated service industries, as well
as health related expenditures (FWRI, 2012).
Wide tolerances to environmental changes in nutrients, salinity, temperature, and UV
make pinpointing the causes of K. brevis blooms difficult (Lekan & Tomas, 2010; Karen A.
Steidinger, 2009; Vargo, 2009). Several key biogeochemical processes are recognized as playing
a role in bloom development but many integral factors remain unknown. Previous studies have
focused on benthic fluxes, rainfall, hydrodynamics, species interaction and chemical and
terrestrial influences as possible triggers of K. brevis blooms. It has been suggested these HABs
may develop inshore when resting cysts meet favorable nutrient and hydrologic conditions for
resuspension and/or excystment (K. A. Steidinger & Haddad, 1981; Karen A. Steidinger, 2010;

2
Tester, Stumpf, Vukovich, Fowler, & Turner, 1991). More recent research suggests the blooms
develop in the deeper waters of the Gulf of Mexico (GOM) and are advected inshore where they
meet the conditions necessary for propagation and maintenance (Bronk et al., 2014; Dixon,
Kirkpatrick, Hall, & Nissanka, 2014). A study conducted by Vargo in 2009 suggests that
research should include combinations of biological as well as abiotic factors influencing nutrient
availability that may lead to the support of K. brevis blooms.
The frequency, duration, intensity and spatial extent of HABs have increased
considerably over the past few decades. This project explored the spatial and temporal variability
of K. brevis in the Choctawhatchee Bay system in an attempt to expose potential causes for the
periodic blooms, including nutrient loading from surface and subsurface fluxes. A geographic
information system (GIS) was used to interpolate the results.
Objectives of the research included the following:
(1) Use PCR based detection method to quantify cell concentration of K. brevis
during sampling events.
(2) Compare water chemistry parameters (including dissolved oxygen (DO), specific
conductance and temperature) and nutrient levels with cell concentrations to
examine relationships.
(3) Create a GIS database of K. brevis concentrations, nutrient levels and standard
physical water characteristics for each sample site and interpolate to visualize the
study variables.
(4) Identify potential spatial and/or temporal correlations between K. brevis
concentrations and water characteristics.
(5) Expose potential causes for the periodic blooms.

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CHAPTER 2 BACKGROUND
K. brevis is a small- to medium-sized dinoflagellate, measuring 18 - 45µm wide, found in
the GOM and North Atlantic (Abbott et al., 2009). This single-celled eukaryote is characterized
by a grooved apical carina and two whip-like flagella that it uses to rotate and propel through the
water at a rate of roughly one meter per hour (Figure 2.1) (Karen A. Steidinger, 2009). As such,
this weak swimmer mainly travels via oceanic currents and wind (Abbott et al., 2009; Karen A.
Steidinger, 2009; Tester et al., 1991). The unarmored fragility of K. brevis cells allows PbTxs to
become aerosolized if they are lysed at the sea surface and the resulting sea foam can be more
than 100 times as toxic as the seawater (Abbott et al., 2009).
Figure 2.1 Magnified Karenia brevis organism. Image credit (FWRI, 2012)

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Bloom Dynamics
The earliest recorded red tide event off the west Florida shelf dates back to 1854 but
while reports of massive fish kills in the GOM have been documented as early as 1648, the
potential source was unknown (Magaña, Contreras, & Villareal, 2003; Karen A. Steidinger,
2009). Blooms are typically seen in the winter but as duration and intensity have increased, red
tide HABs have been reported year-round (Dixon et al., 2014; Surge & Lohmann, 2002).
Possibly the worst documented red tide event occurred off the west coast of Florida in
1946-1947 with K. brevis concentrations recorded up to 5.6 x 107
cells L-1
(Karen A. Steidinger,
2009). Subsequently, in 1948, Charles C. Davis initially classified the organism as Gymnodinium
breve but it was reclassified in 2000 when Karenia was identified as its own genus (Karen A.
Steidinger, 2009). The identification of K. brevis as the organism responsible for the fish kills in
Florida’s waters was the first breakthrough in red tide research. Following identification,
scientists were able to culture the organisms in an artificial medium which allowed studies of
various nutrient and environmental conditions that could affect growth and toxicity (Karen A.
Steidinger, 2009).
Until the National Oceanic and Atmospheric Administration (NOAA) and the
Environmental Protection Agency (EPA) established the Ecology and Oceanography of Harmful
Algal Blooms (ECOHAB) and NOAA Monitoring and Event Response of Harmful Algal
Blooms (MERHAB) programs in the late 1990s, previous research of K. brevis and red tide
events had been sporadic based on available funding cycles (Karen A. Steidinger, 2009). From
1954 to 2006, the Florida Fish and Wildlife Conservation Commission’s (FWC) Fish and
Wildlife Research Institution (FWRI) maintained a red tide database containing over 64,000
water samples and their recorded K. brevis concentration levels (FWRI, 2012; Karen A.

5
Steidinger, 2009). It was studies such as these that allowed for the identification of concentration
threshold values (Table 2.1) which are used for bloom classification and public safety.
Table 2.1
Concentration Classification Levels of K brevis.
Data derived from (Anderson, 2009; Gray, Wawrik, Paul, & Casper, 2003; Hu, Muller-Karger, & Swarzenski,
2006; Karen A. Steidinger, 2009)
Population Size K. brevis cells L-1
Notes
Very Low < 1000 Background concentrations
Low 1,000 - 10,000 Bloom, slight risk of respiratory irritation
Moderate
5,000
Bioconcentration in shellfish can occur within 1 day
FL regulations close shellfish harvesting at this stage
50,000 - 100,000 Satellites can detect chlorophyll from K. brevis
High 100,000 - 1,000,000 Fish kills can occur
Very High >1,000,000 Human eye can see water discoloration
The majority of K. brevis blooms initiate in the GOM, roughly 18 – 75 km off the west
coast of Florida but currents occasionally transport red tides north and occurrences have been
seen as far up the Atlantic coast as North Carolina (Schaeffer, Kamykowski, McKay, Sinclair, &
Milligan, 2007; Karen A. Steidinger, 2009; Tester et al., 1991). While a comprehensive
understanding of the complex environmental forcings that produce a K. brevis bloom is still
unknown, it is accepted that these blooms result in the combination of growth and concentration
from accumulation, rather than from simply enhanced growth rates (Vargo, 2009).
Under optimal circumstances, these organisms grow at a rate 2 - 3 times higher than
during non-bloom periods (Vargo, 2009). A study conducted by Brand and Compton (2007)
comparing data from 1954-1963 and 1994-2002 revealed much higher concentrations of K.
brevis nearshore than offshore but a spatial expansion of these concentrations into the GOM.

6
Frequency, intensity and duration were also shown to have increased dramatically since the
1950s.
Nutrient Control
It is widely accepted that HABs have intensified in recent years, but controversy remains
as to the exact cause of this amplification. While these blooms do occur naturally in the
environment, it is reasonable to infer that anthropogenic activities are one probable cause for the
observed increase in HAB frequency around the world (Brand & Compton, 2007). However,
while many blooms have been attributed to an influx of nutrients into coastal water systems in
densely populated areas, some oligotrophic waters have seen an increase in HAB events that
cannot be easily explained by a simple nutrient flux and may be a result of climatic variables
(Anderson, 2009; Anderson et al., 2008; Davidson et al., 2012).
No single nutrient source has been identified as the primary contributor to these
prolonged blooms but the evidence for estuarine and marine eutrophication as a result of
“cultural eutrophication” is unmistakable (Smayda, 2008). Anthropogenic sources of nutrients
include fertilizers, combustion of fossil fuels, as well as human and agricultural effluents (Masó
& Garcés, 2006; Paerl, Valdes, Peierls, Adolf, & Harding, 2006). Coastal waters around the
world are increasingly enriched by these sources and this is altering baseline levels and natural
biogeochemical processes (Paerl, 1997; Smayda, 2008). K. brevis appears to be mixotrophic yet
researchers have identified various nutrient factors thought to influence the life cycle of this
organism and its blooms.
Nutrients, such as nitrogen (N) and phosphorus (P), are necessary for cellular synthesis of
phytoplankton. K. brevis is an efficient consumer of nutrients and can use both inorganic and
organic N and P, making it well adapted for oligotrophic conditions in the GOM open waters

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(Karen A. Steidinger, 2009; Vargo, 2009). Anderson et al. (2009) present consistent findings that
the toxic blooms usually form offshore and move inshore when onshore winds relax. Nutrient
levels measured in freshwater discharge and surface runoff rarely meet the concentrations
thought necessary to initiate and sustain a bloom (Anderson, 2009; Hu et al., 2006). While many
researchers have found it difficult to quantify the mechanisms and processes associated with
cultural eutrophication, a strongly persuasive argument exists regarding the changing
environment being a direct result of exogenous human influence (Paerl, 1997; Smayda, 2008).
Research by Davidson et al. (2012) states there is no clear correlation between the
increased frequency, duration or magnitude of HAB events and changes in N:P ratios. The
authors suggest it is futile to consider traditional use of molar N:P ratios of dissolved inorganic
nutrients for species that are not obligate autotrophs, such as K. brevis. An important caveat also
noted, when considering nutrient ratios: ratios are only important when the concentration of one
nutrient is low enough to limit growth. Therefore, the use of nutrient ratios can be misleading if
the nutrient concentrations are not influencing species competition (Davidson et al., 2012). The
ability to metabolize organic matter could allow K. brevis to proliferate in the presense of low
dissolved inorganic nitrogen (DIN), e.g. during the decompsition process that follows a diatom
population crash.
Davidson et al. (2012) propose that silicate:nitrogen (Si:N) ratios are responsible for
determining the dominant phytoplankton group and that this ratio is intensified by the increasing
N and P levels from anthropogenic effluents and low fluvial Si levels as a result of damming. All
diatoms require Si for cell wall formation and studies have shown that in Si-limited
environments (often after the spring and summer blooms) dinoflagellates begin to dominate over
diatoms (Davidson et al., 2012). It is thought that Si becomes the limiting nutrient for diatom

8
growth, which are subsequently replaced by dinoflagellates. This reveals yet another potential
environmental dynamic that plays a role in the development of a red tide event. Further studies
are needed to elucidate the influences of nutrient cycling and limitation.
Dust storms in both Africa and Asia have become more frequent over the last few
decades, likely due in part to climate change (Taylor, 2002). The iron limitation hypothesis
proposes that aeolian processes transport and deposit iron-rich Sahelien dust into the open waters
of the GOM. This deposit may alleviate the iron-limitation of the nitrogen-fixing cyanobacteria,
Trichodesmium erythraeuma, which in turn may supply enough N to support the growth of a K.
brevis bloom (Brand & Compton, 2007; Taylor, 2002; Walsh & Steidinger, 2001). Once the
blooms are initiated, subsequent fish kills provide a longer-term source of N and P. While some
remain skeptical of this idea, K. brevis is often found in the company of T. erythraeuma and the
hypothesis has yet to be disproven.
Atmospheric carbon (CO2) is also thought to have an influence on HAB production.
There is discussion on the proposed idea of using iron fertilization in the oceans to draw down
CO2 from the atmosphere by stimulating large blooms of diatoms. This practice would likely
have implications in the relationship thought to exist between K. brevis and diazotrophic
cyanophytes and should likely be avoided as a measure of CO2 sequestration (Moore et al.,
2008). In the past, lab experiments on harmful algae have largely focused on the effects of
elevated pH and have generally found a positive relationship between pH and growth or toxin
production but CO2 is known to increase ocean acidification (decrease pH) and thus further
research should be conducted on the effects of acidification on HABs (Moore et al., 2008).

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Brevetoxins
Many dinoflagellates are toxic because of toxins that induces paralytic, diarrheic,
neurotoxic, or azaspiracid shellfish poisoning (PSP, DSP, NSP and AZP) (Abbott et al., 2009;
Anderson, 2009). K. brevis produce neurotoxin that is known to affect vertebrate nervous
systems by interefering with sodium channels (Brand & Compton, 2007; Lekan & Tomas, 2010).
They also pose a threat to people with underlying respiratory issues when aerosolized. This toxin
is essentially “mild” when compared with other HAB toxins, however 3 – 6 hours after exposure,
symptoms may include: chills, headache, diarrhoea, muscle weakness, muscle and joint pain,
nausea and vomiting, paraesthesia, altered perception of hot and cold, difficulty in breathing,
double vision, and difficulty talking and swallowing (Masó & Garcés, 2006).
It is interesting to note that many varieties of harmful algae become more toxic when
cells are 'nutrient-stressed'. At the population level, a relationship has been seen between
abundance and nutrient concentrations but at the cellular level, toxin synthesis may be driven by
nutrient deficiency (Smayda, 2008). According to Smayda (2008), blooms experience an initial
phase of accelerated growth stimulated by high nutrient availability, followed by a reduction in
growth and increase in toxin synthesis. The carbon:nutrient balance hypothesis predicts that
nutrient stresses result in plants diverting more energy towards defenses. K. brevis has
corroborated this by showing increases in PbTxs during N-limitation and P-limitations (Corcoran
et al., 2014; Hardison et al., 2013; C. Heil et al., 2014; Lekan & Tomas, 2010; Hardison et al.,
2012). This hypothesis also predicts that PbTx production will increase most when nutrient
limitation first occurs and growth is initially suppressed.
Some studies have shown that K. brevis cells are likely to become more toxic towards the
end of a bloom when nutrients become increasingly limited and it is thought that toxins are

10
synthesized when biomass synthesis slows (Davidson et al., 2012; Smayda, 2008). However, a
study conducted by Lekan and Tomas (2010) compared the toxicity levels of three different K.
brevis clones when subject to various changes in temperature, salinity and nutrient limitations.
According to the authors, toxicity has been found to vary from bloom to bloom and it is feasible
that small blooms can be highly toxic while blooms with high cell densities may only be slightly
so. The samples were tested during the stationary phase, which should be the peak of nutrient
limitation. The research revealed genetic influence had a greater impact on toxicity than
environmental variables. A complex relationship between N and P concentrations exists with
respect to toxicity of HABs and when coupled with the further variability of population size,
physiological state of the species in bloom and toxicity of the strain, separation of influences
become increasingly difficult (Smayda, 2008).
After a series of reported aquatic wildlife mortalities in January of 2006, the subsequent
investigation by FWRI determined the most likely cause to be PbTxs from the K. brevis
organism. Despite the fact that concentrations of the organism and neurotoxin were measured at
background levels, high concentrations of the PbTxs were found in the internal organs of a
variety of fish (FWRI, 2012). As a result of this and other red tide events in the area, in 2008, the
Mattie Kelly Environmental Institute started collecting samples in two neighboring bayous
(Garnier and Cinco) in western Choctawhatchee Bay.
Climatic Influences
While tolerances vary between laboratory and field studies, a general range of
temperature and salinity thresholds have been established for K. brevis. Suggested optimal field
ranges for temperature and salinity were 20-28°C (68-82.4°F) and salinities of 31-37 respectively
(Karen A. Steidinger, 2009). Live K. brevis cells have been found in environments ranging from

11
5-33°C and at salinities less than 21 and greater than 40; however, cells do not typically fare well
under these circumstances (Lekan & Tomas, 2010; Karen A. Steidinger, 2009; Vargo, 2009).
Temperature strongly influences available habitat and species ranges, rates of
decomposition and nutrient cycling. Rising temperatures resulting from climate change could
have far-reaching implications for HABs. A change in sea surface temperature (SST) may alter
community structure and reduce grazing or competition from other phytoplankton species, but
the opposite can just as easily be predicted (Mulholland et al., 1997). For a thermally tolerant
species, such a K. brevis, a positive or negative change in temperature may provide an advantage
over other organisms. Increasing temperatures may cause primary production to increase to the
point where nutrient levels become so depleted they are unable to support usual ecosystem
functions, however the anthropogenic nutrient supply coupled with increase primary production
may increase eutrophication rates.
Mulholland (1997) predicts an increase in temperature for the SE United States and
eastern Mexico of 3 and 3.5°C for summer and winter, respectively. This increase in temperature
may intensify water column stratification; because K. brevis is capable of vertical migration, this
organism may see a competitive advantage over other species (Errera, Yvon-Lewis, Kessler, &
Campbell, 2014; Moore et al., 2008). Along the same vein, an increase in SST could result in an
increase in hurricane intensity, which would promote greater water column perturbation and
upwelling and thus introduce more inorganic nutrients into the shallower waters.
Salinity also affects habitat and species range. Surge and Lohmann (2002) discovered the
effects that channelization and increased runoff have on estuarine salinity. The migration of the
midpoint of the mixing interface (between fresh and salt water) is a notable topic with regards to
habitat and competition. During times of limited freshwater runoff, the midpoint shifts closer to

12
the fresh source and vice versa during times of increased runoff (Surge & Lohmann, 2002). This
altered midpoint location could affect the habitat range of certain species and might have
implications on the competition for adaptable organisms such as K. brevis.
Early hypotheses of K. brevis HAB growth focused on rainfall and runoff. This is likely
due to the fact that most blooms went unnoticed until reaching fish-kill levels (Table 2.1) and
these events were typically observed near the coast. Some of the earliest reports hypothesized
that flooding wetlands were discharging toxins into the estuaries (Vargo, 2009). More complex
theories have developed which incorporate current understanding of biogeochemical interactions.
Hu et al. (2006) propose that unaccounted nutrients required to sustain a HAB may be
provided by submarine groundwater discharge. Prevalent red tide blooms occur along the west-
central and northern Florida coastlines where many large submarine springs are located (Hu et
al., 2006). Increased frequency of extreme weather events such as hurricanes dramatically affects
the surface and submarine discharge rate, which in turn affects the salinity and temperature of the
receiving estuarine systems. The precipitation regime strongly affects the quality and quantity of
runoff, wetland distribution and saturation, flushing rates and incidence of anoxia, and land-
water interactions in riparian environments. Despite the dilution factor related to the increase in
intensity and duration of rain events, erosion and sedimentation may increase while residence
time for nutrients decreases and as a result, increased runoff will likely amplify nutrient loading
into coastal marine systems.
The Loop Current, which introduces warm tropical waters originating from the Caribbean
Sea into the GOM, dictates much of the hydrological properties and complex interactions found
in these waters. The outermost shelf waters are most influenced by variations in the Loop
Current, while inshore waters are more influenced by wind and land runoff. The inflow from the

13
Loop Current, the evaporation–precipitation budget and North American rivers freshwater
supply all interact to affect hydrologic variables such as sea-surface salinity and temperature
which subsequently influence the thermohaline circulation via the Gulf Stream (Montero-Serrano
et al., 2010).
The upwelling systems of the eastern boundaries of the GOM are susceptible to HABs
because they are highly productive, nutrient-rich environments. The enrichment of surface
waters inshore of the front supports high productivity and an upwelling along eastern ocean
boundaries has been predicted to intensify as a result of climate change (Moore et al., 2008;
Pitcher, Figueiras, Hickey, & Moita, 2010). Shelf circulation patterns influence HAB
development through wind stress on the surface boundary layer and surface mixed-layer
characteristics. The winds that are most favorable for upwelling are strongest during the spring
and summer, reducing thermal stratification during this period (Pitcher et al., 2010). This may be
one of the reasons why diatoms tend to dominate at this time, and why K. brevis may have a
greater advantage later in the year when stratification increases.
Mitigation, Prevention and Control
NOAA defines mitigation as the minimization of HAB impacts on human health, living
resources, and coastal economies (Abbott et al., 2009). These strategies do not attempt to deal
with the organism itself (Sengco, 2009). This is primarily accomplished by continued routine
monitoring programs of toxin levels in shellfish, instituting harvesting bans, removal of dead fish
from beaches and towing fishnet pens away from intense HAB sites (Anderson, 2009; Sengco,
2009).
Prevention strategies attempt to stop blooms from occurring or minimize their frequency
and spatial range (Sengco, 2009). A general consensus exists among scientists that prevention

14
would be the ideal control measure, but currently there is an insufficient understanding of why
HABs occur to successfully employ tactics to prevent blooms from developing and thus
prevention strategies are limited. The best option for prevention is the regulation of nutrient input
from terrestrial effluents and ballast water releases (Abbott et al., 2009). Regulations imposed
on effluent discharge in Japan’s Seto Inland Sea have proven beneficial in preventing certain
type of HABs (Anderson, 2009). It seems reasonable that the best prevention method available
today is to improve water quality and reduce nutrient inputs into aquatic environments.
Control strategies attempt to limit the impact of the bloom by killing or removing the
organisms from the water (Sengco, 2009). There are a variety of control measure being testing
and implemented world-wide in hopes of managing HABs. While research has progressed
rapidly over the past 50 years, many of the strategies currently employed have controversy
surrounding their effectiveness and potential consequences. These control measures can be
broken down into three categories; mechanical, biological and chemical (Abbott et al., 2009;
Anderson, 2009).
One of the biggest problems with control is the limited understanding of bloom
termination. Some of the general mechanisms suggested include cyst formation, cell death, cell
lysis, nutrient limitation, grazing dilution, and disruption of physical concentrations but none of
these have proven absolute and no studies have been conclusive in documenting the role physical
processes play.
No universally successful tactic has been established but some have proven to be a short-
term solution and strategies vary between regions based on species, environmental conditions,
available resources and technology, and regulations. Current strategies endeavor to reduce the
negative effects of HABs rather than totally eradicate the organism, since the role (Abbott et al.,

15
2009). In any event, early warning is essential for any monitoring program in order to
implement precautionary strategies and control efforts to minimize environmental and health
impacts (D. C. Heil, 2009).
Study Area
The Choctawhatchee Bay watershed encompasses over 19,202 km2
of northwest Florida and
contains some of the highest elevations in Florida. (Figure 2.2). The sandy soils, intense rainfalls,
and steep relief leave this area highly susceptible to erosion which may allow greater nutrient
runoff from nearby agricultural and industrial areas. The bay experiences minimal tidal exchange
(~ 0.15m), likely due in part to the single, narrow opening to the Gulf of Mexico located at East
Pass (Ruth & Handley, 1996). This opening is located in close proximity to the sample area and
constitutes the more saline and deeper portion of the bay. Much of the western portion of the bay
drains through urbanized areas with notable waterways occurring through a waste treatment
facility and golf course into Garnier Bayou (Figure 2.3).
A number of red tide events within Choctawhatchee Bay have resulted in mass
mortalities of fish, dolphins and other marine life. Garnier Bayou in particular experienced
significant blooms in 1999/00 and 2005/06 (FWRI, 2012; Thorpe et al., 2002).
The study site is located on the Florida panhandle near Fort Walton Beach and
encompassed an area ~20km2
. Sample locations were chosen to provide a broad overview of the
two bayous, including the confluence site between them while maintaining convenient land-
based access (Table 2.2 and Figure 2.3). As of July 2011, the Lower Garnier site location was
modified due to access permission.

16
Figure 2.2 Choctawhatchee Bay Watershed. Data obtained from FGDL
Table 2.2
Choctawhatchee Bay Sample Site Locations
Site ID Site Description Bayou Location Latitude Longitude
C Confluence Cinco-Garnier Confluence 30°25'47.08"N 86°36'1.64"W
LC Lower Cinco Cinco 30°25'30.81"N 86°36'33.29"W
LG Lower Garnier Garnier 30°26'55.32"N 86°36'3.96"W
MC Mid-Cinco Cinco 30°25'52.50"N 86°37'34.01"W
MG Mid-Garnier Garnier 30°27'40.61"N 86°35'46.33"W
UC Upper Cinco Cinco 30°25'52.15"N 86°38'12.07"W

17
Figure 2.3 Study Area with sample site locations.

18
CHAPTER 3 METHODS
Field Sampling Techniques
As part of a red tide study, the Mattie Kelly Environmental Institute (MKEI) started
collecting water samples in September of 2008 in both Cinco and Garnier Bayous. Water
samples were collected biweekly from each of the six shore locations, in the two bayous located
in western Choctawhatchee Bay from September 2008 through December 2014.
During each sampling event a YSI Model 85 was used to measure physical water
characteristics such as temperature, DO, and salinity. Surface water samples were collected in a
clean Nalgene bottle after the container was rinsed three times with the water to be sampled.
Samples were stored on dry ice immediately after collection and filtered upon arrival at the
laboratory. Filtered samples were stored in a -80°C freezer until processing. Field notes were
transcribed into a Microsoft Excel database along with lab analysis results.
Analytical Methods
Standard colorimetric and fluorometric methods (Sharp, 2001) were used for nutrient and
chlorophyll analysis. Unfortunately, an accidental power outage in the lab led to the loss of most
of the chlorophyll a data for the analysis period. This data is used as a measurement of algal
biomass and would have been useful in providing additional information about the
phytoplankton community.
K. brevis Quantification
Standards
K. brevis stock was obtained from Bill Richardson of the Florida Fish and Wildlife
Conservation Commission’s Fish and Wildlife Research Institute. The stock was incubated in an
L1 Medium seawater solution to sustain the culture. The solution was prepared by adding

19
chemical components to 950 mL of filtered natural seawater (see APPENDIX A for formula).
After the components were added, the final volume of the solution was brought up to 1 liter
using filtered natural seawater and the mixture autoclaved. This medium was then added to K.
brevis stock at a ratio of 3:1, and cultured at room temperature on a 12-hour light/dark cycle.
Care was required to ensure incubated populations did not crash. The incubation
containers were vented in order to provide oxygen to the cells by placing a sterile sponge in the
mouth of the bottle and keeping the lid on but not tightly closed. The containers were kept on a
reciprocating shaker table to prevent stagnation, at the lowest speed setting to prevent lysing.
The stock solution was quantified by diluting a 2mL aliquot in 6mL of filtered seawater
and adding 40µL of Lugol’s solution in order to immobilize the cells. 1mL of this mixture was
pipetted very slowly to prevent lysing onto a 1mm Gridded Sedgewick Rafter phytoplankton
counting slide. Ten squares from five rows of the slide were counted from top to bottom and an
average was calculated for the stock solution (see APPENDIX C for the slide counting sheet
used to record results). This was repeated three times for the mixture. Each square contained 1µL
of solution. The average was multiplied by the dilution factor (DF) and the units converted into
cells/mL. The volume, V, of stock solution to filter to create a set of standards was calculated by
dividing the desired concentration (cells/mL) by the calculated average cells/mL.
V =
Target[ ]
(Average × 𝐷𝐹) (1000)
(3.1)
Equation 3.1. Calculated volume (in mL) to filter in order to obtain desired cell concentration for rt-qPCR
standards.
This stock solution was used as a positive control when running reverse transcription
quantitative real-time polymerase chain reaction (RT-qPCR) analysis, a molecular biological
technique that allows for the quantification of an organism by amplifying a specific genetic

20
sequence across several orders of magnitude. Serial dilutions of the RNA elution were created to
provide quantification cycle threshold standards between which the samples values were
interpolated.
RNA Extraction
Samples from November 2011 through November 2012 were selected and processed in
non-sequential order. Samples from 2010 through 2011 were also processed, however an error
with the internal probe invalidated the results. The lab surface was prepared by cleaning with
RNase AWAY™ to remove any RNase that could potentially degrade the RNA in the samples.
RNA extraction was conducted using the Qiagen RNEasy Extraction Kit (Figure 3.1).
RNA was extracted by first opening the folded filters1
in the tube and lysing the cells by
washing them from the filter with 500µL of a mixture of 70% RLT buffer, 30% ethanol.
Foaming of the lysis buffer during sample disruption was minimized by adding 2µL of Reagent
DX to each tube. The cells were vortexed for 30 seconds, left to incubate at room temperature for
15 minutes and vortexed again for 30 seconds before spinning down and pipetting 500µL into
one of the provided spin columns. The spin column was centrifuged (all centrifuging was
performed at 10,000 rpm) for 1 minute before the column was transferred to a fresh 2mL
collection tube and the flow-through discarded. 700µL of Buffer RW1 was added to the spin
column and centrifuged for 1 minute and the column was transferred to a new collection tube.
Two subsequent washes of Buffer RPE were then performed by adding 500µL of the buffer to
1
Note: Upon consultation with the Knight Oceanographic Research Center at the University of South Florida, it
was discovered that the practice of folding these filters was not necessary and invariably led to further risk of
contamination during lab analysis. In the future, it is recommended to simply place the filter into the collection
tube with the filtered particles/substrate facing in.

21
the spin column and centrifuging for 1 minute (during each wash, a fresh collection tube was
used). After the RPE washes, the column was placed in a new collection tube before being
centrifuged for 2 minutes to ensure all buffers were removed from the frit. The column was
placed into a new 1.5mL collection tube and 50µL of RNAse-free water was pipetted directly
onto the frit before a final centrifuge for 1 minute.
Figure 3.1 RNEasy Extraction Procedure
RNA concentrations in the final elution were determined spectrophotometrically and any
sample over 2ng/µL was diluted. The dilution factor was recorded and applied in the final
concentration calculations.
Sequencing
The amplicon used for the assay was isolated by Gray et al. (2003). Genetic markers for
K brevis were identified on the large-subunit gene (rbcL) of ribulose-1,5-bisphosphate
carboxylase/oxygenase (RuBisCO) (see Table 3.1 for genetic primer and probe sequence). The
isolates found in the northwest region of Florida are Piney Island, Mexico Beach, and
Apalachicola (named for their isolation location) (Schaeffer et al., 2007). Each of these strains

22
were included in the isolation analysis conducted by Gray et al. (2003). The rbcL gene is highly
expressed in mRNA and since RNA rapidly degrades in the environment using an RNA target
provides a better representation of the population than DNA-based methods, which are unable to
clearly distinguish between living and dead cells (Gray et al., 2003).
Table 3.1
K. brevis Primer and Probe Sequences
Primer Sequence (5' to 3')
forward primer TGAAACGTTATTGGGTCTGT
reverse primer AGGTACACACTTTCGTAAACTA
internal probe [6FAM]TTAACCTTAGTCTCGGGTA[BHQ1]
Amplification
Once the RNA extraction procedure was complete, the Master Mix was prepared (see
APPENDIX B for exact constitution) and 23µL of the mixture was pipetted into 0.1 ml Tube and
Cap Strips along with 2µL of the target RNA. Amplification for the project was carried out using
a Rotor-Gene RG-3000 72-well Thermocycler. Each run consisted of a set of serial dilutions of
the standard, a negative control and up to 18 samples, each run in triplicate (Figure 3.2).

23
Figure 3.2 Arrangement of Samples and Standards in the Rotor-Gene 3000 – 72-well Ring Thermocycler
As RNA cannot serve as a template for PCR, the first step in an RT-PCR assay is the
reverse transcription of the mRNA template into cDNA, followed by a series of thermal cycling
events that denature, anneal and extend the original target sample material (Table 3.1 and Figure
3.1). After the DNA strands are denatured, the hydrolysis probe binds itself to a target segment
between the Primer markers that are located along the 3- and 5-prime positions on the target. The
Taq polymerase then allows nucleoside triphosphates (dNTPs) to bind to the denatured strands
between the primers. The 6-FAM fluorophore modifier represents fluorescent molecules in the
probe that re-emit light upon excitation. The Black Hole Quencher (BHQ) dye is paired with the
fluorophore to absorb excitation energy emissions at the same wavelength. When the probe is
intact, the proximity of the fluorophore to the quencher dye results in suppression of the reporter
fluorescence. The fluorescence is suppressed while paired but when the Taq polymerase extends

24
the sequence at the 5’ end, a hairpin loop is extended and then separated from the fluorophore
during the subsequent thermocycle, allowing for the light energy to be emitted as fluorescence.
Accumulation of PCR products is detected directly by monitoring the increase in fluorescence
which is used to quantify the amount of the target sequence in the initial sample.
Table 3.2
RT-qPCR Cycling Sequence
Cycle Cycle Point
Reverse Transcription Precycling Hold at 45°C, 30 min 0 secs
Initial Denaturing Hold at 95°C, 10 min 0 secs
Cycling (50 repeats)
Step 1 - 95°C, hold 60 secs
Step 2 - 55°C, hold 60 secs,
acquiring to Cycling A FAM
Step 3 - 72°C, hold 60 secs

25
Figure 3.3 Schematic of RT-qPCR Process Using the TaqMan One-Step RT-PCR Reagents Kit
The results from the Rotor-Gene 3000 thermocycler were assessed using Rotor-Gene
6.1.93 software. The software interpolated the sample values based on the standards and
calculated the geometric mean of the three replicates. The dilution factor DF and filtration
volumes VF for each sample were accounted for after the fact (Equations 3.2 & 3.3).
𝑉𝐹 =
1000𝑚𝐿
𝑉 𝑚𝐿
(3.2)
𝐾𝐵 = 𝑅𝑒𝑝. 𝐶𝑎𝑙𝑐. 𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 × 𝐷𝐹 × 𝑉𝐹 (3.3)

26
GIS Analysis
Sample data collection can be expensive, time consuming and is often restricted by
access. Surface interpolation tools can be used to create a continuous representation of
phenomena from a selection of sample locations. While often used solely for mapping purposes,
advanced analytical GIS functions can quantify patterns to reveal hidden relationships and
trends.
Seasonal averages were calculated for each site, and a predictive surface was created for
each parameter from the sample points. It should be noted, certain areas of the waterbody
extended beyond the extent of the sample locations resulting in an extrapolation that may be
misleading. The sample locations ended up falling almost exclusively along two Euclidean
planes. This lack of geometric variance among sample site locations may serve to reduce the
effectiveness of the interpolation. Typical to environmental sampling, time restraints prohibited
the inclusion of additional sample locations.
Season durations were established using solstice and equinox dates for the analysis period
(Table 3.3). A workflow model was created to reduce user input and speed the analysis process
(Figure 3.4). ESRI’s ArcGIS 3D Spatial Analyst “Spline with Barriers” interpolation method was
used to generate a smooth surface raster that was restricted to the perimeter of the study area.
The cell size used for analysis was 10m by 10m. This workflow was imbedded into a custom tool
with adjustable parameters (Figure 3.5).

27
Table 3.3
Seasonal cutoffs for study period.
Season Date
Fall Sept. 23, 2011
Winter Dec. 22, 2011
Spring Mar. 20, 2012
Summer June 20, 2012
Fall Sept. 22, 2012
Figure 3.4 Workflow Model for Spline with Barriers Interpolation.
Figure 3.5 Variable Interpolation Tool Created Using Workflow Model

28
A map showing proportional representation of seasonal averages for K. brevis cell counts,
DIN and P was created to illustrate the variation between cell counts and nutrients at each site for
the spring 2011 period.
Statistical Analysis
A Generalized Estimating Equation (GEE) model was used to test for associations
between dependent (K.brevis cell concentrations) and independent variables. This model was
developed by Liang and Zeger in 1986 as a means for analyzing correlated longitudinal data and
is an extension of the generalized linear models (GLMs) which use fixed effects regression
models for normal and non-normal data. GEEs allow for the investigation of interactions
between one or more variables as well as the effects of individual factors by modeling regression
of each parameter’s dependence separately as a linear predictor and allowing for analysis of
repeated measurements. The linear predictor, 𝜂, is based on covariates for the subjects
𝜂𝑖 = 𝜒𝑖 𝛽 (3.4)
Where 𝜒𝑖 is the vector of explanatory variables, or covariates, for subject i with fixed
effects β.
Time lag analysis was conducted for the nutrient variables using one and two sampling
event periods (two weeks and one month, respectively). The purpose of this analysis was to
reveal any potential delayed effects that a change in nutrient levels may have on K. brevis.
After the initial single variable analysis, a multivariable assessment was performed to
investigate the interactions between multiple variables as well as the effects of individual factors.
The multivariate models included a matrix output called the sums-of-squares and cross-products

29
(SSCP) and the estimated marginal means provided estimates of predicted mean values for each
of the variables.

30
CHAPTER 4 RESULTS
The concentration of K. brevis during the study period varied between 0 and 299 cell L-1
,
never reaching bloom levels (Figure 4.1 and Table 4.1). The highest recorded value was located
at the Mid-Garnier site during the spring, however, there was considerable variability throughout
the study with regards to concentrations of K. brevis cells and seasonal variation for each site
(refer to APPENDIX G). Statistical analysis was used to compare study parameters with cell
counts to reveal correlations.
Figure 4.1 K. brevis Concentrations by Sample Site from Nov. 2011 through Nov. 2012

31
Table 4.1
Parameter averages by sample site location, annually and seasonally
Site
Time
Frame
K. brevis
(cells/L)
Temp
(˚C)
DO
(mg/L)
DO Sat.
%
Salinity
(PSS)
Chl a
(µg/L)
NO3
-
(µM)
NO2
-
[µM]
NO3+2
-
[µM]
NH4
+
[µM]
DIN
[µM]
PO4
3-
[µM]
N:P
N:P
(adj)
C Annual 12.45 21.8 7.2 92.5 23.3 1.880 1.3351 0.0429 1.3720 1.1687 2.6148 0.0530 54.6 46.0
C Spring 41.49 26.9 6.3 89.6 24.1 1.578 0.7721 0.0326 0.7875 0.2972 1.1179 0.0532 67.3 23.6
C Summer 4.43 28.8 6.2 89.9 20.2 N/A 2.8795 0.0426 2.9152 0.2742 3.4433 0.0766 56.7 29.6
C Fall 2.52 18.0 7.9 95.2 23.4 0.837 0.7099 0.0241 0.7340 0.1073 0.8581 0.0564 4.7 52.2
C Winter 4.88 14.8 8.2 95.0 24.9 2.676 1.2362 0.0721 1.3084 3.8470 5.1781 0.0298 85.9 75.7
LC Annual 15.93 21.7 6.2 78.7 23.2 2.442 0.7825 0.0378 0.8091 0.5976 1.5296 0.0595 26.4 41.7
LC Spring 20.40 25.6 5.8 79.0 23.3 2.950 0.3395 0.0412 0.3655 0.6270 1.0337 0.0677 16.5 16.1
LC Summer 7.56 28.1 4.8 67.0 22.1 N/A 0.5861 0.0736 0.6403 1.2968 2.3603 0.0727 33.6 27.6
LC Fall 24.55 18.6 6.7 82.1 23.1 1.523 1.1316 0.0185 1.1397 0.3704 1.5286 0.0304 36.9 57.9
LC Winter 11.95 14.6 7.6 86.7 24.3 2.885 1.0727 0.0181 1.0907 0.0961 1.1956 0.0672 13.5 65.1
LG Annual 5.65 21.6 7.1 89.9 20.6 3.227 6.8628 0.0558 6.9184 0.4939 7.5326 0.0536 134.5 77.6
LG Spring 4.97 26.4 6.8 92.8 20.8 3.695 10.4616 0.0773 10.5389 0.7510 11.3672 0.0736 179.3 72.6
LG Summer 0.27 28.5 6.2 88.9 16.4 N/A 6.0253 0.0743 6.0996 0.1532 6.6949 0.0809 132.8 68.8
LG Fall 9.43 18.1 7.0 84.5 20.9 2.055 5.4728 0.0506 5.5222 0.7175 6.2489 0.0596 35.4 67.6
LG Winter 6.00 14.5 8.3 93.1 23.6 3.852 5.3522 0.0242 5.3763 0.2971 5.6799 0.0050 170.8 100.0
MC Annual 3.74 22.2 6.0 77.0 22.1 5.501 3.0454 0.0735 3.1178 1.4330 4.7295 0.0491 97.6 67.2
MC Spring 0.15 26.8 5.6 81.0 23.0 5.160 1.9212 0.0624 1.9789 1.5936 3.6349 0.0668 53.6 49.5
MC Summer 12.62 28.5 4.8 68.5 19.1 N/A 3.5340 0.0799 3.6139 1.3931 5.6050 0.0699 90.0 60.9
MC Fall 1.32 18.8 6.4 77.6 22.7 4.080 2.9459 0.1005 3.0464 1.9294 5.0041 0.0329 170.5 68.2
MC Winter 0.27 14.8 7.1 81.0 23.4 6.562 3.7804 0.0514 3.8318 0.8160 4.6739 0.0270 42.2 90.2
MG Annual 18.72 23.1 7.0 90.9 21.6 2.746 7.7239 0.0441 8.1009 0.1772 8.4092 0.0460 595.2 86.4
MG Spring 60.62 27.3 6.3 87.4 21.0 4.243 8.3745 0.0627 8.4319 0.4657 8.9613 0.0800 112.2 71.2
MG Summer 8.22 29.8 6.5 94.8 18.4 N/A 9.0121 0.0497 9.0617 0.1479 9.5927 0.0714 130.2 82.8
MG Fall 12.81 19.9 7.2 88.5 22.7 1.915 7.3037 0.0183 7.3220 0.0506 7.3838 0.0268 2,136.0 91.6
MG Winter 0.21 15.6 7.9 93.1 24.2 2.800 6.2054 0.0459 7.4854 0.0447 7.5569 0.0060 119.6 100.0
UC Annual 9.45 23.3 5.0 63.9 19.5 4.330 4.2861 0.0991 4.3825 2.1834 6.7258 0.0760 61.7 62.1
UC Spring 1.54 27.7 4.6 66.2 22.4 3.238 2.2105 0.0613 2.2610 2.0077 4.3300 0.0849 46.7 44.9
UC Summer 0.92 29.2 3.6 51.3 16.0 N/A 2.7243 0.1102 2.8345 2.1277 5.4126 0.1297 72.9 45.0
UC Fall 20.58 19.7 5.4 66.1 20.4 3.040 4.8246 0.1248 4.9495 3.0032 7.9921 0.0653 62.0 61.6
UC Winter 12.00 16.7 6.2 72.1 19.0 5.554 7.3849 0.1001 7.4850 1.4774 9.1684 0.0240 83.5 97.0

32
GIS Interpolation
The annual interpolation of K. brevis showed the highest concentrations were located
closest to the mid-Garnier site (Figure 4.2). This sample location included the highest recorded
levels during the study period. The highest concentrations and greatest variability in cell density
was observed during the spring, as evident in Figure 4.3.
Figure 4.2 Results from Interpolation of Average Annual K. brevis Cell Concentration from Each Sample
Site

33
Figure 4.3 Results from Interpolation of Seasonal Averages from Each Sample Site for K. brevis Cell
Concentrations: (a) Average Spring K. brevis, (b) Average Summer K. brevis, (c) Average
Fall K. brevis, (d) Average Winter K. brevis.
a b
c d

34
A comparison of nutrient levels during the study period show that the highest levels of
DIN were observed during the spring, however, these levels were found at the lower-Garnier
site, rather than mid-Garnier site, where highest K. brevis populations were noted (Figure 4.4).
Figure 4.4 Results from Interpolation of Seasonal Averages from Each Sample Site for Dissolved Inorganic
Nitrogen (NO2+3+NH4) (µM)): (a) Average Spring Dissolved Inorganic Nitrogen, (b) Average
Summer Dissolved Inorganic Nitrogen, (c) Average fall Dissolved Inorganic Nitrogen, (d)
Average Winter Dissolved Inorganic Nitrogen.
a b
c d

35
Phosphorus levels were shown to be lowest during the winter. Overall, the highest levels
of PO4 were observed at the upper-Cinco location, located furthest from the confluence in an
urbanized area.
Figure 4.5 Results from Interpolation of Seasonal Averages from Each Sample Site for Phosphorus (PO4)
(µM)): (a) Average Spring PO4
3-
, (b) Average Summer PO4
3-
, (c) Average fall PO4
3-
, (d) Average
Winter PO4
3-
.
a b
c d

36
The proportional ranking of average spring K. brevis and nutrient concentrations shows
the spatial variation between all sites. It can be seen that while the Mid-Garnier site had the
highest levels of Karenia during this period, the highest levels of nitrogen were located further
south at the Lower-Garnier site.
Figure 4.6 Proportional Representation Showing Relative Ranking of Seasonal Averages for K. brevis,
Dissolved Inorganic Nitrogen and Phosphorus for Spring 2011

37
Statistical Analysis
The Generalized Estimating Equation results revealed significant inverse correlations
between cell counts and nitrite (NO2
-
) (p = 0.001), nitrite + nitrate (NO2+3
-
) (p = <0.001),
dissolved ammonium (NH4
+
) (p = 0.032), DIN (p = <0.001) and the nitrogen to phosphorus
ratios (N:P p = <0.001 and N:P(adj) p = 0.009), indicating a decrease in various dissolved
inorganic nitrogen species as K. brevis cell concentrations increased (or vice versa). The Wald-
Chi statistic reflects the relative importance of the independent variable, indicating that N:P ratio
was the principal factor (χ2
= 84.827), followed by NO2+3
-
(χ2
= 29.727), and DIN (χ2
= 19.445).
After a two week (one sampling event) time lag, significant negative correlations were observed
for nitrate (NO3
-
) (p = <0.001), NO2
-
(p = 0.011), NO2+3
-
(p = <0.001), DIN (p = <0.001), and
N:P (p = 0.009). After a 1 month (2 sampling events) time lag, NO2
-
was no longer correlated.

38
Table 4.2
Wald Chi-Square (χ2
), coefficients (β), and significance (p) for nutrient and physical water characteristics as a
predictor for K. brevis cell abundance
Multivariable Analysis
In order to compare the effect multiple variables had in the presence of one another, the
data was grouped into classes based on a logarithmic scale (Table 4.3). The model indicated DO
(% saturation) had a slightly positive significant correlation (p = 0.016) with regards to K. brevis
when controlling for factors DIN and PO4 (Table 4.4)
2
In order to eliminate the ‘divide by zero’ error returned when calculating the N:P ratio, the adjusted N:P ratio was
calculated based on the modification of negative nutrient (N and P) spectrophotometric analysis values being set
to half of the lowest recorded value as opposed to zero. This was in response to the discovery that these were
likely false negatives as a result of potential contamination during lab analysis.
Independent
Variables
No Time Lag 2 Week Time Lag 1 Month Time Lag
df χ 2
β p χ 2
β p χ 2
β p
Season 3 5.706 N/A 0.127
Temperature 1 2.076 0.387 0.150
DO (mg/L) 1 0.458 0.968 0.498
DO (sat) 1 3.521 0.313 0.061
Salinity 1 0.075 -0.134 0.784
NO3
-
1 3.572 -0.667 0.059 19.473 -1.129 <0.001* 7.068 -0.746 0.008*
NO2
-
1 11.222 -88.134 0.001* 6.488 -40.296 0.011* 0.434 21.752 0.510
NO2+3
-
1 29.727 -0.866 <0.001* 29.689 -0.918 <0.001* 4.912 -0.622 0.027*
NH4
+
1 4.599 -1.631 0.032* 2.094 -1.150 0.148 1.591 -1.390 0.207
DIN 1 19.445 -0.994 <0.001* 34.207 -1.310 <0.001* 6.303 -0.772 0.012*
PO4
3-
1 0.729 42.021 0.374 0.318 13.485 0.573 5.998 21.599 0.014
N:P 1 84.827 -0.004 <0.001* 6.769 -0.003 0.009* 0.024 0.000 0.878
N:P (adj)2
1 6.854 -0.001 0.009* 8.501 -0.001 0.004* 10.902 -0.001 0.001*
* significant at <0.05

39
Table 4.3
Groups created for multivariate analysis
Table 4.4
Test of Multivariable Model Effects
Group Cells/L Freq. % Cum. %
1 None 19 14.2 14.2
2 0-1 69 51.5 65.7
3 1-10 23 17.2 82.8
4 10-100 18 13.4 96.3
5 100+ 5 3.7 100.0
Total 134 100.0
Source χ2 β df p
DO % sat. 5.831 0.014 1 0.016*
DIN 2.202 -0.038 1 0.138
PO4 0.034 -0.674 1 0.853
Dependent Variable: Grouped K. brevis Cell [ ]
* significant at <0.05

40
CHAPTER 5 DISCUSSION
This investigation provided a case study to examine the effects of nutrient and physical
water characteristics on cell counts of K. brevis. It is widely accepted within the aquatic science
community that nutrients play a key role in the development and maintenance of HABs caused
by this organism.
It was surmised that, since K. brevis requires nitrogen and phosphorus for development,
nutrient constraints would be a limiting factor in cell growth, however, the results revealed a
negative correlation with nitrogen and N:P ratios, and no correlation with respect to phosphorus
(p = 0.374). The negative correlation between N and K. brevis was somewhat surprising, though
this seems consistent with the negative N:P correlation, as a decrease in total N would result in a
lower N:P value, assuming phosphorus levels stayed the same or decreased at a slower rate than
nitrogen.
These results could be rationalized by the ability of K. brevis to utilize both organic and
inorganic nutrients. Since diatoms are the dominant species in the study area, it is hypothesized
that an increase in K. brevis growth could follow a diatom population crash, perhaps as a result
of silica or DIN limitation. These low levels of inorganic nutrients could allow K. brevis to
exploit the resulting increase in available organic nutrients as the diatoms decompose.
Having chlorophyll data could have helped to corroborate this hypothesis by allowing a
comparison of total biomass in the water during the time of sample to K. brevis levels. K. brevis
inhabits a complex ecosystem that includes, and is likely influenced by, a variety of bacteria,
viruses, fungi and other microbes (Van Dolah et al., 2009). A temporal analysis of nutrient and
biomass structure would help elucidate other microbial influences.

41
Conducting a more comprehensive nutrient cross-section by including carbon, silica and
iron in the assay might elucidate more complex nutrient exchanges, cycling and interspecies
dynamics. Bronk et al. (2014) characterized the N nutrition of phytoplankton on the West Florida
Shelf and used this to determine whether N uptake and regeneration varied in the presence of K.
brevis. This particular study found that the inorganic N forms of ammonium (NH4
+
) and nitrate
were the most important N substrates at all sites. NH4
+
contributed the greatest percentage of
uptake (48.4-76.7%) at all sites and, in the presence of Karenia, was taken up at an even higher
rate (62.6% versus 48.4%). The study also found that rates of absolute NO3
-
uptake was
positively correlated with abundance of K. brevis and abundance closely followed dissolved
organic phosphorus levels. Overall, the study concluded that N is only one component of
complex set of requirements for bloom development.
It is interesting to note that during the spring, while the highest cell counts were found at
the northernmost sample location in Garnier Bayou, the highest nitrogen levels were recorded
farther south, at the lower-Garnier location. Physical controls may place a crucial role in bloom
development. It follows with the research that salinity and temperature were not correlated to cell
counts, as this species is particularly tolerant to a wide range for both variables. However
precipitation could affect nutrient fluxes and spatial lag could be modeled to incorporate the
variability of aquatic environments. Additional studies could include precipitation and tidal
information in an estuary model to assess flow patterns which could be used to determine if
recorded nutrient levels have been altered by hydrologic dynamics.
Improving the statistical and geospatial model outputs would require more thorough
analysis of the study area. Increasing the number of sample locations would improve
interpolation and statistical validity. Including bathymetric data in the interpolation analysis and

42
taking water samples from the benthic zone could improve analysis by representing the study
area more accurately as 3-dimensional rather than as a flat plane.
It is recognized that wind is a primary means of conveyance for this weak swimmer and
wind driven upwelling is responsible for red tide blooms manifesting along the coastline. The
results showed a positive multivariate correlation with DO, when controlling for DIN and PO4 (p
= 0.016).When considering the hypothesis that these blooms would follow a diatom population
crash, an inverse relationship with DO would be expected. If this is the case, there would likely
need to be other controlling forces at work. Winds from the east have potential to increase fetch
which could increase mixing, oxygenate the water and resuspend dormant cysts, resulting in a
positive correlation between cell counts and dissolved oxygen.
Previous studies have shown a wide annual variability for all parameters considered in
this study (Dixon et al., 2014; Weisberg et al., 2014). It would have been valuable to have more
than one year worth of data to analyze however time and financial constraint on this project did
not allow for this. A study conducted by Dixon et al. (2014) comprised a greater breadth of
variables, including a more complex nutrient analyses, a variety of depth measurements as well
as estuary, nearshore, coastal and offshore sampling. The results of this study also failed to
isolate direct linkages between the occurrence or severity of K. brevis and nutrient levels.
An important caveat to acknowledge when assessing these results is the fact there were
no recorded blooms during the duration of the study. All concentrations were well below the
1000 cell L-1
threshold that indicates a bloom. It is difficult to assess and identify controlling
factors of a HAB that was not present during the study.

43
Conclusion
Overall, there was no clear link between cell counts and nutrient concentrations. The low
cell counts of K. brevis observed during this study period may have limited the scope of the
effect nutrient composition has on bloom development and mitigation. The lack of bloom
development brings into question whether these results are indicative of the contributing factors
and scenarios that would result in a bloom of K. brevis. It is possible nutrient levels did not
exceed thresholds where causative relationships could be seen.
It is becoming more apparent, through the varieties of studies that have been conducted
on this organism, that K. brevis employs a diverse nutrient strategy in order to achieve a
competitive advantage in a complex system. The ability of this organism to metabolize inorganic
and organic nutrients could provide a reasonable explanation for seeing an increase in K. brevis
during periods of inorganic nutrient depletion. Future studies should consider nutrient uptake
effects on existing blooms and include a more thorough time-lag analysis that includes overall
phytoplankton community structure. A time-lag relationship between diatoms, Karenia cell
growth and nutrients could very well exist but there would need to be more frequent and
consistent sampling in order to identify a strong relationship.
Incorporating GIS into aquatic studies has potential to vastly improve our understanding
of this complex environment. Isolating and modelling the parameters that influence blooms of
this harmful organism will play a key role in monitoring and mitigation practices in the future.
Non-biogeochemical parameters that might be considered for future studies include precipitation,
tidal and aeolian flows. This information could be used provide a more thorough analysis that
could be used to model spatial and temporal lag.

44
K. brevis continues to have a large economic impact in the American southeast.
Terrestrial nutrient loading is undoubtedly occurring in coastal waters yet a concrete formula for
what is controlling these HABs is yet to be determined. Smaller scale studies, such as the one
conducted for this project, may help to improve the overall understanding of this particular
organism but the results may be influenced by unforeseen parameters. Identifying the variables
controlling bloom development and maintenance of K. brevis will serve to help minimize the
overall impact this organism has on coastal communities as well as the influence anthropogenic
sources have on this organism.

45
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119–126. http://doi.org/10.1016/j.hal.2014.04.010

50
APPENDIX A
L1 Medium Components

51
L1 Medium Components
Guillard and Hargraves (1993)
This enriched seawater medium is based upon f/2 medium (Guillard and Ryther 1962) but has
additional trace metals. It is a general purpose marine medium for growing coastal algae.
To prepare, begin with 950 mL of filtered natural seawater. Add the quantity of each component
as indicated below, and then bring the final volume to 1 liter using filtered natural seawater. The
trace element solution and vitamin solutions are given below. Autoclave. Final pH should be 8.0
to 8.2.
Component Stock Solution Quantity
Molar Concentration in
Final Medium
NaNO3 75.00 g L-1
dH2O 1 mL 8.82 x 10-4 M
NaH2PO4· H2O 5.00 g L-1
dH2O 1 mL 3.62 x 10-5 M
Na2SiO3 · 9 H2O 30.00 g L-1
dH2O 1 mL 1.06 x 10-4 M
trace element solution (see recipe below) 1 mL ---
vitamin solution (see recipe below) 0.5mL ---

52
L1 Trace Element Solution
To 950 mL dH2O add the following components and bring final volume to 1 liter with dH2O.
Autoclave.
Component Stock Solution Quantity
Molar Concentration
in Final Medium
Na2EDTA · 2H2O --- 4.36 g 1.17 x 10-5 M
FeCl3 · 6H2O --- 3.15 g 1.17 x 10-5 M
MnCl2·4 H2O 178.10 g L-1 dH2O 1 mL 9.09 x 10-7 M
ZnSO4 · 7H2O 23.00 g L-1 dH2O 1 mL 8.00 x 10-8 M
CoCl2 · 6H2O 11.90 g L-1 dH2O 1 mL 5.00 x 10-8 M
CuSO4 · 5H2O 2.50 g L-1 dH2O 1 mL 1.00 x 10-8 M
Na2MoO4 · 2H2O 19.9 g L-1 dH2O 1 mL 8.22 x 10-8 M
H2SeO3 1.29 g L-1 dH2O 1 mL 1.00 x 10-8 M
NiSO4 · 6H2O 2.63 g L-1 dH2O 1 mL 1.00 x 10-8 M
Na3VO4 1.84 g L-1 dH2O 1 mL 1.00 x 10-8 M
K2CrO4 1.94 g L-1 dH2O 1 mL 1.00 x 10-8 M

53
f/2 Vitamin Solution
(Guillard and Ryther 1962, Guillard 1975)
First, prepare primary stock solutions. To prepare final vitamin solution, begin with 950 mL of
dH2O, dissolve the thiamine, add the amounts of the primary stocks as indicated in the quantity
column below, and bring final volume to 1 liter with dH2O. Filter sterilize. At the CCMP, we
autoclave to sterilize. Store in refrigerator or freezer.
Component
Primary Stock
Solution
Quantity
Molar Concentration
in Final Medium
thiamine · HCl (vit. B1) --- 200 mg 2.96 x 10-7 M
biotin (vit. H) 0.1g L-1
dH2O 10 mL 2.05 x 10-9 M
cyanocobalamin (vit. B12) 1.0 g L-1
dH2O 1 mL 3.69 x 10-10 M
Guillard, R.R.L. 1975. Culture of phytoplankton for feeding marine invertebrates. pp 26-60. In
Smith W.L. and Chanley M.H (Eds.) Culture of Marine Invertebrate Animals. Plenum
Press, New York, USA.
Guillard, R.R.L. and Hargraves, P.E. 1993. Stichochrysis immobilis is a diatom, not a
chrysophyte. Phycologia 32: 234-236.
Guillard, R.R.L. and Ryther, J.H. 1962. Studies of marine planktonic diatoms. I. Cyclotella
nana Hustedt and Detonula confervacea Cleve. Can. J. Microbiol. 8: 229-239.

54
APPENDIX B
RT-qPCR Master Mix

56
APPENDIX C
Stock Slide Counting Sheet

58
APPENDIX D
PCR Analysis Prep Processing Form

61
PCR Supply List
 Isopore Filters 10um (pore size), 25mm diam. Isopore filter
 10% Lugols
 74104 - RNeasy Mini Kit
 19201 - Collection Tubes (2 ml)
 21402178 - Molecular BioProducts™ RNase AWAY™ Spray Bottle; 475mL
 100% Ethanol
 79216 - Buffer RLT; 220 ml
 125472500 – β MERCAPTOETHANOL 98%; 250ML
 19088 - Reagent DX; 1 ml
 4392938 - TaqMan® RNA-to-CT™ 1-Step Kit
 Primers & Probe
 T319-4N - 0.1 ml Tube and Cap Strips

62
APPENDIX F
SPSS Statistical Syntax

65
Table G1
Results from PCR analysis showing cell counts, volume of seawater filtered and dilution required for RNA
concentration
PCR
Process Date
RT
Cruise
Site
Sample
No
Filtered
Volume
RNA Dilution
Factor
K. brevis
Cells L-1
5/23/2014 41 C 375B 500 7 0.10
6/26/2014 42 C 391B 95 11 2.65
5/19/2014 43 C 407A 385 15 12.32
6/12/2014 44 C 413A 500 9 0.00
6/26/2014 45 C 429A 340 14 0.48
7/2/2014 46 C 445B 430 9 12.05
6/16/2014 47 C 461B 140 8 4.76
5/23/2014 48 C 477A 105 8 9.11
5/19/2014 49 C 493A 385 10 2.56
6/12/2014 50 C 509B 110 2 0.35
5/19/2014 52 C 532A 500 5 0.90
6/16/2014 53 C 547A 305 9 0.64
6/17/2014 54 C 563A 470 6 205.88
5/23/2014 55 C 579A 455 3 0.00
6/25/2014 56 C 595A 144 7 0.02
6/16/2014 58 C 615B 230 15 0.04
6/26/2014 59 C 631A 240 8 0.33
6/17/2014 60 C 637A 120 6 10.23
6/17/2014 61 C 643A 300 8 11.56
6/25/2014 62 C 649A 375 7 0.00
6/25/2014 63 C 665A 500 20 0.00
6/25/2014 64 C 681A 375 18 0.04
6/12/2014 41 LC 378A 457 6 12.19
6/16/2014 42 LC 394A 500 10 10.88
6/26/2014 43 LC 410A 500 6 0.10
5/23/2014 44 LC 415A 500 9 0.39
5/19/2014 45 LC 431A 170 12 3.09
6/12/2014 46 LC 447A 340 7 0.03
7/2/2014 47 LC 463B 285 9 0.00
6/26/2014 48 LC 480A 295 6 60.80
6/26/2014 49 LC 496A 425 21 0.64
5/23/2014 50 LC 511A 420 4 7.16
6/12/2014 52 LC 533A 500 8 0.05
7/2/2014 53 LC 550B 375 12 0.51
7/2/2014 54 LC 566A 500 16 101.05
6/16/2014 55 LC 581A 470 7 0.40
6/12/2014 56 LC 597A 310 20 0.02
5/23/2014 57 LC 612A 500 6 0.80

66
6/17/2014 58 LC 617A 500 10 11.89
6/25/2014 59 LC 633A 405 7 0.00
6/26/2014 60 LC 639A 310 14 0.32
6/17/2014 61 LC 645A 270 15 32.37
6/25/2014 62 LC 651A 265 5 0.01
6/25/2014 63 LC 667A 500 56 0.04
7/2/2014 64 LC 684A 330 24 123.69
6/26/2014 41 LG 377B 500 14 0.17
6/12/2014 42 LG 393A 448 7 6.52
6/26/2014 43 LG 409B 400 10 11.74
6/26/2014 44 LG 416B 340 8 0.28
5/23/2014 45 LG 432A 90 18 11.60
5/19/2014 46 LG 448A 270 10 0.67
6/12/2014 47 LG 464A 180 9 2.91
6/17/2014 48 LG 479A 285 10 19.14
6/16/2014 49 LG 495A 305 16 1.65
6/17/2014 50 LG 512A 395 8 0.42
5/23/2014 52 LG 534A 390 7 0.20
7/2/2014 53 LG 549A 425 14 0.09
6/16/2014 54 LG 565A 500 14 0.00
7/2/2014 55 LG 582A 450 4 0.00
7/2/2014 56 LG 598A 285 10 24.66
5/23/2014 58 LG 618A 310 5 0.02
6/25/2014 59 LG 634A 380 5 0.00
7/2/2014 60 LG 640A 315 7 0.84
7/2/2014 61 LG 646A N/A 9 N/A3
7/2/2014 62 LG 652A 180 18 5.57
7/2/2014 63 LG 668A 195 25 0.06
6/26/2014 64 LG 683A 305 23 37.82
5/14/2014 41 MC 373A 460 8 0.10
5/19/2014 42 MC 389A 500 10 4.71
5/23/2014 43 MC 405A 375 4 0.77
6/16/2014 44 MC 411A 360 6 0.28
7/2/2014 45 MC 427B 215 12 0.88
5/23/2014 46 MC 443A 305 7 0.00
5/19/2014 47 MC 459A 255 8 0.05
6/12/2014 48 MC 475A 250 6 0.11
6/17/2014 49 MC 491A 260 7 0.39
6/26/2014 50 MC 507B 245 6 0.18
6/16/2014 52 MC 529A 280 8 0.01
5/23/2014 53 MC 545A 355 12 0.01
3
Note this value was not computed because the volume of water filtered was not recorded.

67
6/12/2014 54 MC 561A 500 5 0.14
6/17/2014 55 MC 577A 325 10 0.59
6/16/2014 56 MC 593A 356 14 0.00
6/12/2014 57 MC 609A 440 4 0.12
6/25/2014 58 MC 613A 405 7 0.00
6/17/2014 59 MC 629A 380 9 75.28
6/25/2014 60 MC 635A 235 4 0.10
6/26/2014 61 MC 641A 160 14 0.18
6/25/2014 62 MC 647A 175 4 0.03
6/25/2014 63 MC 663A 315 28 2.05
6/25/2014 64 MC 679A 275 18 0.03
6/16/2014 41 MG 376B 392 14 0.26
5/23/2014 42 MG 392A 500 18 2.18
6/16/2014 43 MG 408A 500 14 0.00
5/19/2014 44 MG 414A 415 4 0.07
6/12/2014 45 MG 430A 385 5 0.05
6/16/2014 46 MG 446A 400 8 0.48
6/17/2014 47 MG 462A 240 9 0.45
6/16/2014 48 MG 478A 500 7 0.00
5/23/2014 49 MG 494A 400 7 0.06
5/19/2014 50 MG 510A 330 4 0.25
7/2/2014 52 MG 531A 420 5 299.22
6/17/2014 53 MG 548A 500 7 0.12
6/25/2014 54 MG 564B 500 8 0.00
6/12/2014 55 MG 580A 395 2 0.00
5/23/2014 56 MG 596A 342 3 3.76
6/17/2014 57 MG 611A 390 5 1.09
7/2/2014 58 MG 616A 285 6 3.29
7/2/2014 59 MG 632A 430 4 0.02
6/17/2014 60 MG 638A 280 7 1.95
6/25/2014 61 MG 644A 212 9 0.00
6/26/2014 62 MG 650A 285 9 42.99
6/26/2014 63 MG 666A 310 19 72.56
6/26/2014 64 MG 682A 370 10 1.81
5/19/2014 41 UC 374A 500 15 121.35
5/14/2014 42 UC 390A 265 11 0.12
6/12/2014 43 UC 406A 290 14 0.03
6/26/2014 44 UC 412B 300 20 0.49
6/16/2014 45 UC 428B 80 45 0.00
7/2/2014 46 UC 444B 135 67 64.82
5/23/2014 47 UC 460A 100 20 6.84
5/19/2014 48 UC 476A 225 4 0.33
6/12/2014 49 UC 492A 255 2.5 0.00

68
6/16/2014 50 UC 508A 240 6 0.01
6/17/2014 52 UC 530A 265 7 3.44
6/12/2014 53 UC 546A 235 4 0.05
5/23/2014 54 UC 562A 270 6 2.51
6/25/2014 55 UC 578A 185 10 0.01
6/17/2014 56 UC 594A 325 4 1.70
6/16/2014 57 UC 610A 180 5 0.14
6/26/2014 58 UC 614A 265 13 0.76
6/17/2014 59 UC 630B 185 5 2.74
6/25/2014 60 UC 636A 175 8 0.82
6/26/2014 62 UC 648A 160 14 0.17
6/26/2014 63 UC 664A 150 20 0.95
6/26/2014 64 UC 680A 140 14 0.52

70
Figure H1 Linear regression from RT-qPCR Run on 2014-05-16

71

72

73
Figure H8 Linear regression from RT-qPCR Run on 2014-06-25 (1)

74
Figure H9 Linear regression from RT-qPCR Run on 2014-06-26 (2)

75
APPENDIX I
Additional Interploation Maps

76
Addition Maps
Figure I1 Results from interpolation of seasonal averages from each sample site for salinity: (a) Average
Spring salinity, (b) Average Summer salinity, (c) Average fall salinity, (d) Average winter salinity.
a b
c d

77
Figure I2 Results from Interpolation of Seasonal Averages from Each Sample Site for Surface Water
Temperature: (a) Average Spring Temperature, (b) Average Summer Temperature, (c) Average
Fall Temperature, (d) Average Winter Temperature.
a b
c d

78
Figure I3 Results from interpolation of seasonal averages from each sample site for dissolved oxygen (%
saturation): (a) Average Spring dissolved oxygen, (b) Average Summer dissolved oxygen, (c)
Average fall dissolved oxygen, (d) Average winter dissolved oxygen.
a b
c d

79
Figure I4 Results from interpolation of seasonal averages from each sample site for dissolved oxygen (mg/L):
(a) Average Spring dissolved oxygen, (b) Average Summer dissolved oxygen, (c) Average fall
dissolved oxygen, (d) Average winter dissolved oxygen.
a b
c d

80
Figure I5 Results from interpolation of seasonal averages for Chlorophyll a data: (a) Average Spring
Chlorophyll a, (b) No data was available for the summer duration as all Chlorophyll a data
after April 9, 2012 was lost due to a lab oversight, (c) Average fall Chlorophyll a, (d) Average
winter Chlorophyll a.
No Summer
Chlorophyll a Data
Available
a b
c d

81
Figure I6 Results from Interpolation of Seasonal Averages from Each Sample Site adjusted for N:P: (a)
Average Spring N:P, (b) Average Summer N:P, (c) Average fall N:P, (d) Average Winter N:P.
a b
c d

82
APPENDIX J
Things to Consider

83
Things to Consider
• Using a hemocytometer to count cells did not work. Had to get one specifically for
phytoplankton
• It is recommended that the mouth of the incubation container should be treated with a
flame before transferring the solution in order to kill anything on the mouth of the bottle.
• Lab protocol optimization
• Tests to determine minimum amount if Lugols solution to kill the Kb cells
• Tests were done to optimize lysing – vortexing & homogenizing with beads,
without beads, and for various lengths of time (30s, 1 min, 3 min)
• A typo when ordering primers or probes can result in months of unsuccessful PCR
attempts and a significant amount of wasted money. Double check your order!
• Switched from TAMRA to BHQ1 probe.
• When copying data into SPSS double check values to ensure accuracy in results.
• Making sure that all calculations are correct before completing subsequent calculations.
e.g. thinking a number is a concentration instead of just a count (standards)

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