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Title
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Implications of Climate Patterns on Harmful Algal Blooms and the Subsequent Marine Biotoxins
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Creator
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Mangus, Zach
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Identifier
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Thesis_MES_2023_MangusZ
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extracted text
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IMPLICATIONS OF PACIFIC NORTHWEST CLIMATE PATTERNS
ON HARMFUL ALGAL BLOOMS AND THE
SUBSEQUENT MARINE BIOTOXINS
By
Zach Mangus
A Thesis
Submitted in partial fulfillment
Of the requirements for the degree
Master of Environmental Studies
The Evergreen State College
June 2023
© 2023 by Zach Mangus. All rights reserved.
This Thesis for the Master of Environmental Studies Degree
by
Zach Mangus
has been approved for
The Evergreen State College by
_______________________________
Dr. Erin Martin
Member of the Faculty
_______________________________
Date
ABSTRACT
Implications of Pacific Northwest Climate Patterns on Harmful Algal Blooms
and the Subsequent Marine Biotoxins
Zach Mangus
Washington state Department of Health records of Paralytic Shellfish Poisoning (PSP)
concentrations in shellfish from Sequim Bay in the Pacific Northwest were used to investigate
the influence of climate patterns on Alexandrium catenella and shellfish toxicity. Sea surface
temperatures (SSTs) were regressed against years 1957-2022 and found to be significantly
increasing over time. SSTs over 13℃ are known to increase shellfish toxicity so with SST
increasing over time, PSP could be expected to increase as well. The Pacific Decadal Oscillation
(PDO) and El Nino Southern Oscillation (ENSO) are both positively and significantly correlated
with PSP. This is likely because warmer SSTs increase A. catenella blooms thus subsequently
increasing toxin accumulation in shellfish (PSP). The PDO and ENSO both contribute to SST
anomalies and during warm phases this could increase shellfish toxicity.
Table of Contents
Table of Contents ...................................................................................................................... iv
List of Figures ............................................................................................................................ v
Acknowledgements .................................................................................................................... vi
Introduction ...............................................................................................................................1
Literature Review .......................................................................................................................3
Problem Description ..............................................................................................................4
Alexandrium catenella – Paralytic Shellfish Poisoning .........................................................5
Alexandrium catenella Life Cycle ..........................................................................................7
Environmental Conditions and Drivers .............................................................................. 10
Climate Patterns – ENSO and PDO ......................................................................................... 13
El Nino Southern Oscillation ............................................................................................... 13
Pacific Decadal Oscillation .................................................................................................. 14
ENSO, PDO, and Shellfish Toxicity .................................................................................... 16
Thesis Statement – Research Question..................................................................................... 20
Methods .................................................................................................................................... 21
Data Sources......................................................................................................................... 21
El Nino Southern Oscillation (ENSO) Index....................................................................... 21
Pacific Decadal Oscillation .................................................................................................. 24
Sea Surface Temperatures (SSTs) ....................................................................................... 25
Shellfish Toxin Concentrations ........................................................................................... 26
Results ...................................................................................................................................... 29
Discussion ................................................................................................................................ 41
Conclusion ............................................................................................................................... 45
References ................................................................................................................................ 46
iv
List of Figures
FIGURE 1. ALEXANDRIUM CATENELLA------------------------------------------------------------- 5
FIGURE 2. LIFE CYCLE OF ALEXADNRIUM SP. --------------------------------------------------- 8
FIGURE 3. HARMFUL ALGAL BLOOMS (1970 VERSUS 1999) -------------------------------- 9
FIGURE 4. PDO INDEX --------------------------------------------------------------------------------- 15
FIGURE 5. ENSO-DRIVEN PDO ---------------------------------------------------------------------- 16
FIGURE 6. SHELLFISH TOXICITY AND CLOSURE DAYS ------------------------------------ 17
FIGURE 7. SHELLFISH TOXICITY AND SST------------------------------------------------------ 18
FIGURE 8. EL NINO SOUTHERN OSCILLATION 3.4 REGION -------------------------------- 23
FIGURE 9. ENSO INDEX ------------------------------------------------------------------------------- 24
FIGURE 10. HISTORICAL BIOTOXIN SAMPLE SITES ------------------------------------------ 26
FIGURE 11. CURRENT BIOTOXIN SITES ---------------------------------------------------------- 27
FIGURE 12.A SST AND SST ANOMALIES --------------------------------------------------------- 29
FIGURE 12.B SST AND SST ANOMALIES --------------------------------------------------------- 30
FIGURE 13. PDO INDEX ------------------------------------------------------------------------------- 31
FIGURE 14. ENSO INDEX------------------------------------------------------------------------------ 32
FIGURE 15. PSP OVER TIME-------------------------------------------------------------------------- 33
FIGURE 16. PSP INDEX -------------------------------------------------------------------------------- 34
FIGURE 17. SEASONAL PSP -------------------------------------------------------------------------- 35
FIGURE 18. PSP VERSUS TEMPERATURE -------------------------------------------------------- 36
FIGURE 19. PDO AND SST ---------------------------------------------------------------------------- 37
FIGURE 20. ENSO AND SST--------------------------------------------------------------------------- 38
FIGURE 21. PDO AND PSP ---------------------------------------------------------------------------- 39
FIGURE 22. ENSO AND PSP --------------------------------------------------------------------------- 40
v
Acknowledgements
I want to thank my friends and family for supporting me through the process of this thesis.
Specifically, my parents, Sharon and Jeff, and my partner, Noelle. Many responsibilities were
put aside while I pursued this thesis, and I couldn’t have done it without them. I want to thank
Jerry Borchert and Tracie Barry from the Department of Health for their support and invaluable
knowledge on biotoxins. Their insight and shared thoughts helped me form my research
question. Thank you to David Shambley, my current supervisor at the Department of Health, for
supporting me and my research while starting a new position. Thank you to the Evergreen State
College faculty for all their help throughout my time there. I especially want to thank Erin
Martin, my thesis reader, for supporting me through this whole process as well as Averi Azar for
her additional help on formatting. I am not purposely omitting anyone that helped me so thank
you to anyone else I may have forgotten to mention.
vi
Introduction
Washington state is the country’s lead producer of farmed bivalves (e.g., clams,
oysters, and geoducks) with an estimated annual harvest of 270 million dollars (Cooley et al.
2017). The shellfish industry brings jobs to over 32,00 people, primarily in rural coastal
communities where other industry (and jobs) are lacking (King 2020). The Pacific Northwest is
known for its abundance of diverse shellfish and serves as an icon for the region.
An issue that has afflicted the shellfish industry and its stakeholders is the presence of
harmful algal blooms (HABs) (Anderson, Cembella, and Hallegraeff 2012). HABs are blooms of
phytoplankton that naturally release toxins into the surrounding waters. These toxins can harm
marine life or become concentrated in shellfish which poisons and can even kill humans after
consumption. When HABs are present it results in the closure of commercial and recreational
beaches resulting in less seafood for coastal communities and economic loss for all stakeholders
(DOH 2022).
Sea surface Temperature (SST) is known to influence HABs and the subsequent
biotoxins (Moore et al. 2010). Investigating what influences SSTs can help us better monitor and
even predict HABs. The Pacific Decadal Oscillation (PDO) and El Nino Southern Oscillation
(ENSO) are two climate patterns known to influence SST in the Pacific Northwest. These ENSO
and PDO-driven changes in SSTs could be influencing HABs and the subsequent shellfish
toxicity. Previous research has suggested that PDO does influence shellfish toxicity, but ENSO is
still up for debate (Moore et al. 2010).
Understanding HAB dynamics and the factors impacting it can help organizations
better monitor and regulate shellfish to ensure public safety. This thesis research studies the
impacts of the climate patterns, El Nino Southern Oscillation (ENSO) and Pacific Decadal
1
Oscillation (PDO) on a specific HAB species (i.e., Alexandrium catenella) to provide more
insight and predictive capabilities for public health monitoring agencies (e.g., Department of
Health). Specifically, this thesis examines the following research question; To what extent does
the Pacific Decadal Oscillation (PDO) and El Nino southern Oscillation (ENSO) correlate with
shellfish toxicity (Paralytic Shellfish Poisoning) in Washington?
2
Literature Review
This literature review will provide key background information needed to understand
the dynamic of harmful algal blooms and the mechanisms behind the Pacific Decadal Oscillation
(PDO) and the El Nino Southern Oscillation (ENSO). First, we’ll start with information on
phytoplankton and then more specifically, Alexandrium catenella and the subsequent shellfish
toxicity (Paralytic Shellfish Poisoning). Then factors influencing A. catenella, and the climate
patterns (i.e., ENSO and PDO) influencing SSTs. Lastly, we’ll look at relevant literature
regarding PDO, ENSO and PSP.
Phytoplankton are the most abundant living organism in the ocean and can be found
in even a single drop of seawater. They are primary producers that provide more than 45% of the
earth’s oxygen (Simon et al. 2009). As primary producers, they serve as the basis of all marine
food chains. Phytoplankton provides sustenance for everything from microbes and other
microscopic organisms to large fish species making them vitally important to the success of all
marine ecosystems (NOAA, 2021).
The term phytoplankton refers to photosynthesizers unable to propel themselves
against a current (planktos is Greek for “drifter”) (NOAA, 2021). There are two main types of
phytoplankton, dinoflagellates, and diatoms. Dinoflagellates are defined as a single-celled
eukaryote with flagella, most commonly occurring as marine plankton (Simon et al. 2009).
While dinoflagellates do have flagella to swim, it’s mostly for vertical swimming through the
water column (Ralston and Moore 2020). Diatoms are defined as single-celled algae that contain
siliceous skeletons (i.e., frustules), which are commonly found in fresh and marine water (Simon
et al. 2009). An important distinction is that dinoflagellates are motile while diatoms are not,
allowing dinoflagellates to swim in search of nutrients, light, etc. which can be a major
3
advantage in certain environments throughout the year (Moore et al. 2010). For example, once
the surface layer is depleted of nutrients, dinoflagellates can move lower in the photic zone
where more nutrients are available. There they can continue to photosynthesize utilizing the
nutrients and space where there is less competition (Ralston and Moore 2020). This leads to a
cyclical nature of diatom or dinoflagellate dominated times of the year due to the seasonality of
environmental conditions (Alpine and Cloern 1992).
Problem Description
While phytoplankton species provide many benefits for marine ecosystems, there are
also negative consequences of phytoplankton. Some phytoplankton such as Alexandrium
catenella (i.e., a common dinoflagellate in PNW marine waters) are known as harmful algal
bloom (HAB) species because they naturally release biotoxins that are harmful to other life (Fig.
1) (Trainer et al. 2003). There are dozens of dinoflagellate and diatom species that release
biotoxins posing threats to different types of life. These marine biotoxins are directly related to
the success of harmful algal blooms (Moore et al. 2010). HAB biotoxins naturally bioaccumulate
in filter-feeding organisms such as shellfish since they are directly consuming the algal species
(Trainer et al. 2003). These biotoxins do not harm shellfish, but when consumed by other
animals such as mammals and birds, it can have harmful impacts on them (Anderson, Cembella,
and Hallegraeff 2012). A. catenella releases a toxin known as saxitoxin which is a neurotoxin
that accumulates in shellfish throughout the year (mostly in summer and fall in the PNW). This
can be extremely harmful to animals when consumed if the shellfish have accumulated high
concentrations.
Marine biotoxins have been in the Pacific Northwest (PNW) as far back as the 18th
century where written records were found describing shellfish poisoning (Anderson 1998).
4
Anecdotal evidence suggests marine biotoxins and shellfish toxicity has been around much
longer and well known to tribes within Washington State (DOH 2022). The Washington State
Department of Health has been testing for shellfish toxicity since the 1950s (DOH 2022). Several
biotoxins have appeared in Washington’s waters over time that were once not here (Fig. 2). This
is attributed to warming and anthropogenic nutrient input allowing for phytoplankton to thrive
(Van Dolah 2000).
FIGURE 1.
ALEXANDRIUM CATENELLA
Note. Alexandrium catenella cell chain under microscope (Mantua et al. 1997).
Alexandrium catenella – Paralytic Shellfish Poisoning
A. catenella is a dinoflagellate with a unique life cycle (Fig. 3). A. catenella can occur
as a single cell or as a chain of cells (Fig. 1). It is the main HAB species associated with Paralytic
Shellfish Poisoning (PSP) via its release of saxitoxin (Trainer et al. 2003). While saxitoxin is the
main neurotoxin responsible for PSP, there are many chemically similar structures that also
contribute. Collectively, this group of neurotoxins is referred to as saxitoxins (STXs).
Neurotoxins are described as poisons that act on the nervous system and disrupt the normal
5
function of nerve cells. STXs bind strongly to site 1 on the voltage-dependent sodium channel,
inhibiting channel conductance which blocks neuronal activity (Van Dolah 2000). The main area
of STXs action in humans is the peripheral nervous system which leads to the rapid onset of
symptoms (within ~1 hour) (Van Dolah 2000). Symptoms of Paralytic Shellfish Poisoning
include tingling of the fingers and lips, difficulty breathing, loss of muscle control, and
respiratory muscle paralysis leading to death (Van Dolah 2000). It is important to note that
biotoxins cannot be destroyed or cooked out of shellfish and are structurally stable (DOH 2022).
These toxins released by A. catenella can be directly accumulated by algal-feeding
fish or via consumption of phytoplankton-consumers (e.g., consumption of shellfish) (Dyhrman
et al. 2010). PSP is known to affect humans, marine mammals, fish, and birds. There are also
PSP cases that resulted in the death of Humpback whales. (Van Dolah 2000). Globally, almost
2,000 cases of PSP in humans per year are reported. Of those 2,000 cases, roughly 300 are lethal
(Van Dolah 2000). This highlights the intensity of the illness and the importance of monitoring
it. Lethal cases have dropped dramatically since and continue to drop as regulatory bodies
expand and monitor biotoxins like PSP.
A. catenella releases saxitoxin consistently no matter the environment which is
important to recognize as other species don’t always release their respective biotoxins. As noted
previously, the release of toxins is directly related to the concentration of HAB, but there are
many factors that contribute to the success of HAB and their relative toxicity. It is important to
understand these factors contributing to A. catenella blooms and their relative toxicity to better
monitor the presence and concentration of biotoxins in shellfish. In doing so, protection of
humans and animals from PSP exposure can be more effective.
6
Alexandrium catenella Life Cycle
Environmental drivers impact each stage of the A. catenella life cycle so
understanding its life cycle is necessary in understanding how environmental conditions will
impact their population dynamics (Anderson 1998).
Dormancy is the resting state and initial stage of the life cycle where A. catenella can
remain for hours to months or even years depending on the environmental conditions (Fig. 2)
(Anderson, Estrada, and Pitcher 2005; Anderson 1998). The cysts will remain in the sediment
until conditions are ideal for germination. Germination is dependent on certain environmental
factors, so it is usually constrained to certain times of the year. Late spring through fall when
there are warmer temperatures and increased sunlight, the cysts will germinate. If the conditions
are optimal for growth, then the cells will reproduce exponentially. Cell reproduction is via
simple cell division which can occur rapidly. This is when harmful algal blooms and subsequent
toxicity in shellfish occurs (Anderson 1998; Anderson, Estrada, and Pitcher 2005; Anderson,
Cembella, and Hallegraeff 2012). These vegetative cells will continue to grow and divide until
nutrients are gone. Once the nutrients have been depleted, A. catenella cells will form gametes.
Gametes will combine to form zygote cells and then cysts. The cysts then fall onto the ocean
floor and await germination once again (Anderson 1998). In the PNW, A. catenella typically
blooms in early summer and early fall, but there is some variability from year to year (Anderson
1998). In late fall and winter, A. catenella tend to lay as dormant cysts until the warmer seasons
arrive. Each stage of the life cycle is influenced by the surrounding physical properties. The
environmental conditions dictate whether A. catenella cells will continue to proliferate or fall off
into the deep as dormant cysts.
7
FIGURE 2.
LIFE CYCLE OF ALEXADNRIUM SP.
Note. Life cycle of Alexandrium sp. (Anderson, Estrada, and Pitcher 2005).
8
FIGURE 3.
HARMFUL ALGAL BLOOMS (1970 VERSUS 1999)
Note. Comparison of harmful algal bloom (HAB) toxin levels around the world between 1970
and 2000. The encircled areas indicated where toxin levels were concentrated enough to have
negative impacts on human life (Van Dolah 2000).
9
Environmental Conditions and Drivers
Many factors influence the overall success of algal blooms. The main factors
involved are oxygen, nutrient availability, sunlight, temperature, turbulence, and salinity.
Climate and weather patterns (e.g., El Nino Southern Oscillation and Pacific Decadal
Oscillation) also contribute to and alter these factors influencing algal bloom dynamics (see
pages 13-18). For now, we will look at the impact of environmental and physical conditions on
A. catenella so that we can then extrapolate to larger scale climate patterns.
Oxygen must be present for A. catenella cysts to germinate. Cysts can stay in
sediment for years if conditions are not optimal (i.e., no oxygen) unless disturbed by physical or
natural forces (Anderson 1998). Nutrient availability has been a main concern in recent years due
to the excess nutrient input from urban and agricultural runoff (Fig. 3). This excess of nutrients
allows for larger algal blooms resulting in eutrophication and release of toxins (Fig. 3) (Garneau
et al. 2011). Phytoplankton are photosynthesizers so sunlight intensity can limit or induce
growth. Along with sunlight, temperature is a major factor affecting phytoplankton growth. In
general, warmer temperatures correlate with larger and more rapid algal blooms (Wells et al.
2015). The toxicity of A. catenella is also connected to water temperature. Between temperatures
10°C and 12°C, A. catenella produces the most saxitoxins (STXs). These SSTs are usually
during spring and fall in the PNW. When below or above this range, toxin production is
significantly lower in A. catenella (Navarro, Muñoz, and Contreras 2006). It is important to note
that A. catenella still releases saxitoxin in any environment it can survive. This trend has been
shown in other toxic algal species as well (Navarro, Muñoz, and Contreras 2006; Tatters et al.
2013).
A. catenella is known to thrive in stratified bodies of water where winds are weak,
10
and turbulence is low. As mentioned earlier, this is partly because the cells can swim through the
thermocline in search of nutrients and sunlight giving it an advantage over non-motile cells (i.e.,
diatoms) (Ralston and Moore 2020). Turbulence is a major component known to negatively
affect dinoflagellate blooms (Smayda 1997). The three main mechanisms impacting
dinoflagellate blooms via turbulence are physical damage, physiological impairment, and
behavioral modification (Smayda 1997). Turbulence may affect A. catenella through these
mechanisms in a variety of ways. Physical damage (torn or ripped cells) may happen during
intense periods of wind typically during late fall and winter (Sullivan and Swift 2003). A.
catenella cells may be transported lower in the photic zone (where less light penetrates) or
completely below the photic zone via turbulence killing off any viable cells since photosynthesis
would not be possible (Sullivan and Swift 2003). This would be an example of physiological
impairment. Turbulence could also have potentially positive effects on A. catenella via
turbulence mechanisms resulting in behavioral modification. For example, nutrients could be
transported above the thermocline via turbulence negating the need for A. catenella cells to swim
in search of nutrients thus saving energy that could be used for cell division. Turbulence could
also help suspend A. catenella in the photic zone negating the need for it to swim if conditions
are optimal thus saving energy (Sullivan and Swift 2003).
Climate change is predicted to cause further warming of the ocean’s temperatures and
elevated anthropogenic carbon dioxide concentrations is expected to increase ocean acidification
(Cooley et al. 2017). Warmer temperatures and higher CO2 levels are expected to impact A.
catenella in a variety of ways. Generally, warmer temperatures and higher CO2 levels are
expected to increase opportunities for photosynthesis thus an increase in algal blooms (Navarro,
Muñoz, and Contreras 2006). This increase in algal blooms could have negative impacts via
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eutrophication and/or toxin production. An increase in HAB toxins has already been shown, but
the causation is still up for debate (Fig. 3) (Van Dolah 2000). The most notable attributions to
explain the increase of biotoxins over time are the influx of anthropogenic nutrients and warmer
sea surface temperatures (SST).
Increased CO2 levels have been found to increase the cellular toxicity of A. catenella
(Tatters et al. 2013). This is likely due to an increase in the cells photosynthetic rates. The most
toxic scenario in the Tatters et al. experiment was an environment with high CO2, “low”
temperature (15℃), and low phosphate levels further highlighting the complexity of HAB
dynamics (Tatters et al. 2013). While increasing SSTs will likely lead to an increase in HABs,
the warmer temperatures could potentially offset added cellular toxicity from CO2. Continuing to
investigate how these environmental drivers impact A. catenella, we can extrapolate to larger
climate scale patterns and the implications involved with A. catenella and HAB dynamics. For
example, we can investigate the impact of El Nino Southern Oscillation or Pacific Decadal
Oscillation on A. catenella. In doing this, we can then better predict potential shellfish toxicity
and better manage shellfish safety and public health.
12
Climate Patterns – ENSO and PDO
El Nino Southern Oscillation
The El Nino-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) are
climate patterns that result in variable weather conditions in the Pacific Northwest (Moore et al.
2010). They produce changes in the local surface winds, air temperatures, and precipitation
patterns (Moore et al. 2010). There are three phases of the ENSO: 1) warmer than average
tropical Pacific Ocean temperatures (El Nino), 2) colder than average tropical Pacific Ocean
temperatures (La Nina), and 3) a neutral phase where conditions are typical or near the average.
Essentially, a change in the trade winds leads to the different phases of ENSO in the PNW. For
example, stronger than average Eastern Trade Winds will push warm surface water farther
allowing for more upwelling of deep colder water (Newman, Compo, and Alexander 2003). This
would be an example of the La Nina phase associated with colder SSTs. Weak Trade Winds
result in warmer SSTs since less upwelling can occur. This would be the El Nino phase
associated with the warmer SSTs. The different phases of ENSO can last up to 3 years, but
typically are 1 to 2 years (Newman, Compo, and Alexander 2003).
The Pacific Decadal Oscillation is known to be influenced by the phases of ENSO
along with changes in atmospheric pressure (Moore et al. 2010). Since ENSO and PDO are
known to cause variabilities in weather (especially SSTs), it’s important to look at how these
changes impact HAB dynamics like A. catenella. Understanding the factors that will lead to A.
catenella blooms could essentially predict shellfish toxicity, which would be invaluable for the
regulation and safety of the shellfish industry.
13
Pacific Decadal Oscillation
The Pacific Decadal Oscillation (PDO) is known as the most consistent pattern of
monthly North Pacific SST variability (Mantua and Hare 2002). The mechanisms responsible for
this SST variability are still under ongoing research, but some climate cycles and causes are
becoming more clear (Newman et al. 2016). The observed effects of the PDO are not via a single
phenomenon, but instead the combination of many physical processes. The main physical
processes known to force the different PDO phases (i.e., warm and cold) are variability in the
Aleutian low, El Nino-Southern Oscillation (ENSO), and oceanic zonal advection anomalies in
the Kuroshio-Oyashio Extension (Newman, Compo, and Alexander 2003). Low atmospheric
pressure over the Aleutian Islands results in warm waters being transported to the coast of North
America via ocean currents. These warmer waters contribute to higher SSTs in the PNW and are
an example of a warm phase of the PDO. Wind-forced changes in the Kuroshio-Oyashio current
system result in oceanic waves moving westward leading to SST anomalies as well(Newman et
al. 2016). These separate mechanisms and their contribution to SST variability and anomalies in
the PNW are what make up the PDO. The impact of these physical processes on PDO variance is
frequency dependent. At decadal time scales, all three physical processes account for relatively
similar amount of variance in the PDO index (Newman, Compo, and Alexander 2003). The
similar impact on PDO variance from the different physical processes further supports this idea
of multiple physical processes combining into the observed SST anomalies collectively
categorized as the different PDO phases.
The PDO is most easily described as long-lived ENSO-like patterns of climate
variability (Zhang et al. 1997). Extremes of the PDO phases are noted by variations in the Pacific
Basin and the North American climate. These phases are known as warm or cold denoted by the
14
warm or cold anomalies in sea surface temperatures (SST) along the Pacific Coast (Mantua and
Hare 2002). When SSTs are warmer than average along the Pacific Coast and sea level pressures
are below average over the North Pacific, the PDO is in a positive phase denoted by a positive
value (red line) using the PDO index (Fig. 4). When the SSTs are cooler than average along the
Pacific Coast and sea level pressures are above average over the North Pacific, the PDO is in a
negative phase denoted by a negative value (blue line) using the PDO index (Fig. 4).
FIGURE 4.
PDO INDEX
Note. PDO index overtime provided by NOAA. PDO on the x-axis, time on the y-axis from 1854
to current. (NOAA 2022.)
ENSO-forced variability of the PDO phases has been shown over the years (Fig. 5).
La Nina (i.e., cold phase of the ENSO) can exacerbate the cold phase of the PDO and El Nino
(i.e., warm phase of the ENSO) can exacerbate the warm phase of the PDO. The warmer (or
cooler) than average ocean temperatures as result of the Trade Winds moving northward
influencing the atmosphere and sea surface temperatures (Mantua and Hare 2002).
15
FIGURE 5.
ENSO - DRIVEN PDO
Note. PDO index on the y-axis, time in years on the x-axis. Red shading represents El Nino
phase, blue shading represents La Nina phase (NOAA 2022.)
ENSO, PDO, and Shellfish Toxicity
As mentioned, the ENSO and PDO are most known as the source of consistent and
cyclical sea surface temperature anomalies here in the PNW (Mantua and Hare 2002). This can
have major impacts on A. catenella populations as they are sensitive to these kind of fluctuations
(Schneider and Cornuelle 2005). The influence of warm and cold phases of the PDO and ENSO
on A. catenella could provide some insight on future PSP concentrations in shellfish (Moore et
al. 2010; Newman, Compo, and Alexander 2003; Newman et al. 2016). For example, a positive
relationship was found between Saxidomus giganteus (Butter Clam) toxicity and PDO index,
although it was not significant (p = 0.12, Fig. 6) (Moore et al. 2010). This relationship does
follow the trend of warmer SSTs allowing for larger HABs, thus subsequent biotoxin production
and assimilation. A significant and positive relationship was found between number of days SST
16
> 13 ℃ and S. giganteus toxicity, further supporting this trend (Fig. 7) (Moore et al. 2010).
However, there was no significant relationship found between Mytilus edulis (Blue Mussel)
toxicity in the Moore study. This could be due to S. giganteus’ ability to store toxins for longer
providing a more representative sample of the toxins present (Moore et al. 2010).
FIGURE 6.
SHELLFISH TOXICITY AND CLOSURE DAYS
Note. (A) PDO index plotted against Saxidomus giganteus (Butter Clam) toxicity and Mytilus
edulis (Blue Mussel) closure days from 1957-2007. (B) ENSO index plotted against Blue Mussel
closure days from 1957-2007 (Figures taken from Moore et al. 2010).
17
Shellfish toxicity is thought to vary with the Pacific Decadal Oscillation along with
number of days over 13 ℃ (a known threshold for increases in shellfish toxicity in the PNW),
but ENSO is still not considered a causing factor (Moore et al. 2010). ENSO is known to
influence the phases of the PDO. As such, further research exploring the relationship between the
ENSO index and shellfish toxicity is warranted.
FIGURE 7.
SHELLFISH TOXICITY AND SST
Note. Number of days sea surface temperature (SST)>13 ℃ plotted against Saxidomus
giganteus (Butter Clam) toxicity and Mytilus edulis (Blue Mussel) closure days (Figures taken
from Moore et al. 2010).
The SST warm anomalies via ENSO tend to be during winter in the PNW and do not
persist into summer and fall when shellfish typically accumulate toxins. In contrast, SST warm
anomalies via positive PDO phases in the winter and spring typically do persist into the summer
18
and fall when shellfish are accumulating toxins (Moore et al. 2010). This difference in
temperature seasonality is likely the main explanation for the implied relationship between the
PDO index and shellfish toxicity, and the non-relationship between ENSO index and shellfish
toxicity (Moore et al. 2010).
19
Thesis Statement – Research Question
It is documented that the PDO influences SST and SST variability influences shellfish
toxicity (Fig. 6 and 7). ENSO also influences the PDO, so it is possible that ENSO influences
SST and the subsequent shellfish toxicity. This thesis examines the following research question;
To what extent do the Pacific Decadal Oscillation (PDO) and El Nino southern Oscillation
(ENSO) correlate with marine biotoxins (i.e., paralytic shellfish poisoning causing saxitoxin) in
the Pacific Ocean along the Washington coast?
20
Methods
This thesis research involves the analysis of El Nino Southern Oscillation and Pacific
Decadal Oscillation influence on Paralytic Shellfish Poisoning (P.S.P) concentrations in shellfish
in Sequim, WA from 1957 through 2022. Descriptive statistics and data visualizations were also
produced for sea surface temperatures (SSTs) over time, PDO over time, ENSO over time, and
P.S.P over time. Statistical Analyses were performed to determine if there is a relationship
between P.S.P concentration in shellfish and Pacific Decadal Oscillation and/or El Nino
Southern Oscillation. Pearson’s correlations and simple linear regression were the analyses used
in this study following the methods of Moore et al. 2010 to examine the relationship between
PDO, ENSO, and shellfish toxicity (Moore et al. 2010). Moore et al. used the same dataset
presented in this study to examine these relationships between 1957-2007, and this study
reanalyzes this data, extending it through 2022 to understand the impacts of recent climate
patterns on PSP concentrations in Sequim, WA.
Data Sources
All data used for this thesis research was gathered from public databases. Such as the
Department of Health, National Oceanic and Atmospheric Administration, Department of
Fisheries and Oceans Canada, and National Centers for Environmental Information. Each dataset
is described in further detail below.
El Nino Southern Oscillation (ENSO) Index
The ENSO index used in this thesis research is from NOAA’s Physical Science
Laboratory database (Rayner 2003). The index is calculated from SST anomalies averaged over
the NINO 3.4 region (5°North-5°South;170-120°West) (Fig. 8). The SST anomalies used to
21
calculate the ENSO index are from the monthly NOAA Extended Reconstructed SST (ERSST)
v5 at NOAA’s Physical Science Laboratory (NOAA 2023). NINO 3.4 (Fig. 8) region is known
to correlate well with teleconnections to the west coast of North America (Rayner 2003) .
Essentially, the ENSO Index value is calculated by comparing the three-month running mean of
ERSST anomalies against the 30-year average of SSTs.
ERSST data calculations explained below (courtesy of NOAA and NCEI 2023).
“The Extended Reconstructed Sea Surface Temperature (ERSST) dataset is a global
monthly analysis of SST data derived from the International Comprehensive Ocean–Atmosphere
Dataset (ICOADS). The dataset can be used for long-term global and basin-wide studies and
incorporates smoothed local and short-term variations. The NOAA Global Surface Temperature
(NOAAGlobalTemp) product integrates ERSST data with land surface air temperature from
the Global Historical Climatology Network-Monthly dataset to create integrated surface
temperature analyses.” (NOAA and NCEI 2023).
The SST anomalies are calculated with respect to 1971-2000 climatology. The data
sources are from ICOADS 3.0, which combines SST from Argo floats (above 5 meters), Hadley
Center Ice-SST version 2 (HadISST2) ice concentration (1854-2015), and NCEP ice
concentration (2016-present) (NOAA 2023). ENSO Index data has been collected since 1870
(Fig. 9).
22
FIGURE 8.
EL NINO SOUTHERN O SCILLATION 3.4 REGION
Note. Nino 3.4 region (5°North-5°South;170-120°West) depicted in the figure is where SST
anomalies are gathered to calculate the ENSO index used in this research (NOAA 2023).
https://www.ncei.noaa.gov/access/monitoring/enso/sst
23
FIGURE 9.
ENSO INDEX
Note. Nino 3.4 Index timeseries; degrees Celsius on the y-axis. (NOAA 2023).
https://psl.noaa.gov/enso/dashboard.html
Pacific Decadal Oscillation
The PDO index data used in this thesis research is from the National Centers for
Environmental Information (NCEI). The exact method of calculation is explained below
(courtesy of NOAA and NCEI 2023).
“The NCEI PDO index is based on NOAA's extended reconstruction of SSTs (see
ERSST section above). It is constructed by regressing the ERSST anomalies against the Mantua
PDO index for their overlap period, to compute a PDO regression map for the North Pacific
ERSST anomalies. The ERSST anomalies are then projected onto that map to compute the NCEI
index. The NCEI PDO index closely follows the Mantua PDO index.” (Mantua 1999).
The SST anomalies for the PDO index values are obtained via Empirical Orthogonal
Function (EOF) analysis. Essentially, the orthogonal functions are used to examine variability
within the SST data and to analyze relationships with other variables (e.g., ENSO or PDO index)
over time. The SST anomalies are departures from the climatological annual cycle from which
24
global mean SST anomalies have been subtracted. This helps remove external influences that
could be due to climate change (Mantua and Hare 2002).
Sea Surface Temperatures (SSTs)
Sea surface temperature (SST) measurements just north of Sequim Bay were
collected and utilized to track temperature averages to compare shellfish toxicity, PDO index,
and ENSO index. SST data was collected north of Sequim Bay at Race Rocks (Fig. 10) which is
a small rock island located in the Strait of Juan de Fuca with SST measurements that span as far
back as 1914 (Moore et al. 2010). Data is collected by the Department of Fisheries and Oceans
Canada (DFO) which is responsible for developing and implementing policies and programs in
support of Canada’s economic and ecological interests in oceans and other waters (Government
of Canada 2008). The SST data was transformed to represent Sequim Bay SST by regressing
Race Rocks SST to a three-year span (1997-2000) of SST data from Sequim (Moore et al. 2010).
The equation for the relationship derived from linear regression analysis is shown below (Moore
et al. 2010). Sequim Bay SST anomalies were calculated with respect to the average SST
spanning 1971-2001.
SSTSequim= -11.5 + 2.3 X SSTRace Rocks
25
FIGURE 10.
HISTORICAL BIOTOXIN SAMPLE SITES
Note. Shellfish sample sites for Department of Health from 1957 to current in Sequim Bay,
Washington. Sea surface temperatures taken from Race Rocks (circled red) shown in top right
portion of the map (Moore et al. 2010).
Shellfish Toxin Concentrations
Paralytic Shellfish Poisoning concentrations are monitored by the Washington State
Department of Health (DOH). Commercial and recreational shellfish are regularly monitored
throughout the year via mouse bioassay to test for saxitoxin concentration in shellfish
(henceforth referred to as P.S.P concentration) (DOH 2022). The PSP concentration data has
been recorded since 1957 in the DOH database. Concentrations are in micrograms/100grams of
shellfish tissue and recorded every week to 2 weeks depending on saxitoxin levels in the area.
Shellfish toxicity, PSP concentration, and saxitoxin concentration are all used interchangeably. A
PSP index was calculated to use in all analyses in this study. It was calculated by averaging
26
monthly maximums of PSP concentration each year. This was done to better represent average
maximum PSP concentrations each year.
The PS concentrations are from sample areas within Sequim Bay located in Sequim,
Washington (Fig. 10 and 11). The two sites on the map (Fig. 11) are current sample sites for the
Department of Health, but there have been many sites in Sequim Bay since 1957. Previous sites
that were once used to collect PSP concentration are shown in Fig. 10 (Moore et al. 2010). All
sites were treated as one variable to represent shellfish toxicity from Sequim Bay.
FIGURE 11.
CURRENT BIOTOXIN SITES
Note. GIS Map of current DOH biotoxin sites in Sequim Bay (created by Zach Mangus 2022)
27
Microsoft Excel was utilized to manage and compile data from separate databases.
Statistical tests were then run via importing data into R studio (R version 4.2.0). Normality was
assessed using the Shapiro Wilks test for normality. All variables were found to be normally
distributed except PSP index. The PSP index was log transformed and then found to be normally
distributed. Simple linear regression was used to test Sequim Bay SST over time and PSP over
time. Pearson’s correlation was run for PDO index and shellfish toxicity (maximum average
monthly PSP concentrations), ENSO index and PSP concentration, temperature and PSP
concentration. Time, ENSO index, and PDO index were the independent variables. The PSP
index was consistently the dependent variable.
28
Results
Average annual values of SSTs in Sequim Bay are shown in Fig. 12a. There is a positive
trend as shown in fig. 12a. Simple line regression for time (years) and temperature (°C) was
calculated (r2 = 0.31) indicating a modest positive relationship that was statistically significant (p
= < 0.001). Sequim SST anomalies were calculated from 1957-2022 (Fig. 12b).
FIGURE 12.A
SST AND SST ANOMALIES
(a)
13
Average Annual Temperature (°C)
12.5
12
11.5
11
10.5
10
9.5
9
8.5
8
1957
1967
1977
1987
Year
29
1997
2007
2017
FIGURE 13.B
(b)
3
2.5
2
Temperature (°C)
1.5
1
0.5
0
-0.5
-1
-1.5
-2
-2.5
1957
1962
1967
1972
1977
1982
1987
1992
1997
2002
2007
2012
2017
2022
Note. (a) Projected Sequim Bay Sea surface temperatures over time from 1957-2022. The y-axis
represents years, x-axis represents temperature (°C). The data from 1957 through 2007 was
originally published by Moore et al. 2010. (b) Sequim Bay SST anomalies calculated each year
from 1957-2022.
The Pacific Decadal Oscillation (PDO) index is shown for each year from 1957 to
2022 (Fig. 13). Cool phases of the PDO are indicated by negative values while warm phases are
indicated by positive numbers. Warm or cool phases of the PDO historically persist for decades.
For example, a cool phase lasted from 1947 to 1976, and then a warm phase occurred from 1977
to 1998. The longer phases appear to have ceased and shorter more variable phases have taken
30
place since 1998. As shown in fig. 13, we entered a warm phase from 2002 to 2005, a neutral
phase from 2006-2007, then a cold phase from 2008 to 2013, and then back to a warm phase
from 2014 to 2020. The most recent and current phase has been cold again as depicted with
negative values (Fig. 13).
FIGURE 14.
PDO INDEX
1.5
1
PDO index (°C)
0.5
0
-0.5
-1
-1.5
-2
1957
1959
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
2019
2021
-2.5
Note. The annual Pacific Decadal Oscillation index graphed for each year from 1957-2022. The
y-axis displays PDO index in units of °C. The data from 1957 through 2007 was originally
published by Moore et al. 2010.
31
The El Nino Southern Oscillation Index is shown for each year from 1957 to 2022 (Fig.
14). The negative values represent the La Nina phase and positive values represent the El Nino
phase of ENSO. Notice the frequency in ENSO phases compared to the PDO. This variability
remains relatively consistent over the 65-year time series. An ENSO phase typically only lasts 12 years (Fig. 14).
FIGURE 15.
ENSO INDEX
2
1.5
ENSO index (°C)
1
0.5
0
-0.5
-1
1957
1959
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
2019
2021
-1.5
Note. The annual El Nino Southern Oscillation Index graphed for each year from 1957-20022.
The y-axis is ENSO Index (°C). The data from 1957 through 2007 was originally published by
Moore et al. 2010.
32
Annual average maximum paralytic shellfish poisoning (PSP) was taken for each year
and graphed over time (Fig. 15). There is high variability in this time series with a range from 54
µg/100g to 6007 µg/100g and a standard deviation of ~1090. The mean PSP max is ~855 and
the median is 456.
FIGURE 16.
DAILY PSP OVER TIME
Annual Average Maximum PSP Concentration (µg/100g)
7000
6000
5000
4000
3000
2000
1000
1957
1959
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
2019
2021
0
Note. The maximum paralytic shellfish poisoning (PSP) toxin concentration for each year from
1957-2022. The y-axis is PSP concentration (µg/100g).
A PSP index was calculated to account for all the variation and graphed over time
(Fig. 16). The annual maximum PSP index was calculated by averaging the monthly maximums
of PSP for each year.
33
Daily PSP concentrations were graphed over time from 2015 to 2018 to show the
seasonality of A. catenella and the subsequent PSP concentration (Fig. 17). As mentioned
previously, phytoplankton blooms are dependent on the right circumstances (e.g., temperature,
sunlight, nutrients, etc.) and therefore have an innate seasonality. Figure 17 depicts this
seasonality as the PSP concentrations spike in spring, summer, and fall each year when A.
catenella can proliferate due to the ideal environmental factors.
FIGURE 17.
PSP INDEX
800
700
PSP Index (µg/100g)
600
500
400
300
200
100
1957
1959
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
2019
2021
0
Note. Annual averaged maximum paralytic shellfish poisoning (PSP) index graphed for each
year from 1957-2022. The annual maximum PSP index was calculated by averaging maximum
concentrations each month of the year.
34
35
11/12/2018
9/12/2018
7/12/2018
5/12/2018
3/12/2018
1/12/2018
11/12/2017
9/12/2017
7/12/2017
5/12/2017
3/12/2017
1/12/2017
11/12/2016
9/12/2016
7/12/2016
5/12/2016
3/12/2016
1/12/2016
11/12/2015
9/12/2015
7/12/2015
5/12/2015
3/12/2015
1/12/2015
Daily PSP Concentration (µg/100g)
FIGURE 18.
SEASONAL PSP
600
500
400
300
200
100
0
Note. Daily paralytic shellfish poisoning (PSP) concentrations graphed over time from 20152018. The x-axis is time (day/month/year), the y-axis is PSP concentrations (µg/100g).
Temperature was positively correlated with average annual PSP concentration (Fig.
18), although the relationship was not significant (r = 0.13, p = 0.28).
FIGURE 19.
PSP VERSUS TEMPERATURE
800
700
PSP Index (µg/100g)
600
500
400
300
200
100
0
8
8.5
9
9.5
10
10.5
11
11.5
12
12.5
13
Average Annual Temperature (°C)
Note. The paralytic shellfish poisoning (PSP) index plotted against average annual temperature
from each year between 1957 and 2022.
As we can see in Figure 19, Sequim average annual SST follows the average monthly
PDO index closely over time. A positive and significant association between Sequim SST and
PDO index was observed (r = 0.35, p = 0.003). Sequim SST also follows the ENSO index over
time as shown in Figure 20. A positive and significant association was observed (r = 0.36, p =
0.002).
36
FIGURE 20.
PDO AND SST
12.5
1.8
12
1.3
0.8
11
0.3
10.5
-0.2
10
-0.7
9.5
-1.2
9
-1.7
8.5
8
1957
-2.2
1967
1977
1987
1997
2007
2017
Year
Annual SST
PDO
Note. Annual sea surface temperatures (SST) plotted against the Pacific Decadal Oscillation
(PDO) over time from 1957-2022.
37
PDO Index (°C)
Sequim SST (°C)
11.5
FIGURE 21.
ENSO AND SST
12.5
1.7
12
1.2
11.5
0.7
10.5
0.2
10
9.5
ENSO INDEX
SEQUIM SST
11
-0.3
9
-0.8
8.5
-1.3
1957
1959
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
2019
2021
8
YEAR
Annual SST
ENSO 3.4
Note. Annual sea surface temperatures (SST) plotted against the El Nino Southern Oscillation
(ENSO) over time from 1957-2022.
There is a significant and positive relationship between PDO index and PSP
concentration (Fig. 21) (r= 0.36, p= 0.003). The relationship between ENSO index and PSP
concentration looks similar (Fig. 22), showing a significant and positive relationship (r = 0.27, p
= 0.026).
38
FIGURE 22.
PDO AND PSP
800
700
PSP Index (µg/100g)
600
500
400
300
200
100
0
-2
-1.5
-1
-0.5
0
0.5
1
1.5
PDO index (°C)
Note. The paralytic shellfish poisoning (PSP) index plotted against the Pacific Decadal
Oscillation (PDO) from 1957-2022. The data from 1957 through 2007 was originally published
by Moore et al. 2010.
39
FIGURE 23.
ENSO AND PSP
800
700
PSP Index (µg/100g)
600
500
400
300
200
100
0
-1.2
-0.7
-0.2
0.3
0.8
1.3
ENSO index (°C)
Note. The paralytic shellfish poisoning (PSP) index plotted against the El Nino Southern
Oscillation index (ENSO) from 1957-2022. The data from 1957 through 2007 was originally
published by Moore et al. 2010.
40
Discussion
This research suggests that sea surface temperatures (SSTs) in Sequim Bay have
increased slightly over the last 65 years (Fig. 12). Higher average sea surface temperatures could
contribute to more frequent A. catenella blooms and subsequently more shellfish toxicity
(paralytic shellfish poisoning). Daily SSTs at or above 13°C have been shown to increase A.
catenella and paralytic shellfish poisoning (Nishitani and Chew 1984; Moore et al. 2010). The
optimum temperature range is known to be 13-17°C for A. catenella blooms, so continued SST
increases may contribute to more paralytic shellfish poisoning in the future (Tatters et al. 2013).
A limitation of this study is that it employed annual mean SST and correlated it with the
PSP index, which utilized annual monthly maximum PSP concentrations. This was done to better
represent SST warming trends over time. As such, the annual SSTs used here (~ 8 through 12°C)
were below the optimal conditions for A. catenella blooms. Mean annual SST still significantly
positively correlated with the PSP index, but a more representative SST variable (e.g., number of
days SST was greater than 13°C) would likely have statistical significance (Moore et al. 2010).
Number of days over 13°C was the temperature variable Moore et al. (2010) used to represent
SSTs in Sequim Bay which did result in statistical significance when correlated with PSP
concentrations (Moore et al. 2010).
As warming continues, temperatures in the future could persist past the thermal threshold
of A. catenella, potentially lowering PSP concentrations during certain times of the year (i.e.,
primarily in the summer). Sequim Bay SSTs typically range between 12°C and 17°C during the
summer months. SST maximums in Sequim Bay in the summer don’t typically surpass 17°C, but
this could change as warming continues. It would likely lead to more seasonality of A. catenella
and the subsequent shellfish toxicity. This will have to be monitored and studied as SST and PSP
41
concentration data are gathered over time. Monitoring Daily SSTs could prove more beneficial in
determining potential PSP concentrations versus using yearly SST means as this study did.
This work aimed to explore the relationship between the Pacific Decadal Oscillation
(PDO) and the El Nino Southern Oscillation (ENSO) and their impacts on PSP concentrations.
The PDO and ENSO index have previously been shown to be positively correlated with SST as
did this study (Moore et al. 2010; Mantua and Hare 2002). Further, previous research has shown
that SST anomalies via PDO cycles are strongest from May through August, whereas ENSO
tends to influence SST the most in March (Moore et al. 2010). Previous studies have attributed
this difference to why PDO has been shown to influence shellfish toxicity, but not ENSO (Moore
et al. 2008; 2010).
In this study, PDO and ENSO were both found to have positive and significant
relationships with shellfish toxicity (PSP concentration) which is unique compared to the
scientific literature (Moore et al. 2010). This difference could be due to the added years in the
time series (2010-2022) containing a considerable amount of variability and warmer than
average temperatures overall. The years 2014-2016 are especially notable as this was when the
PNW experienced a marine heat wave leading to very warm SST later to be named “The Blob”
(Shanks et al. 2020). The SSTs during the blob were 16% higher compared to the mean SST of
all years (1957-2022). The average SST from the years 1957-2007 compared to the years 20082022 were 5% lower. These warmer than average temperatures would have given the opportunity
for more A. catenella blooms, thus more opportunity for paralytic shellfish poisoning. This could
have been the result of the extreme El Nino phase observed in 2015.
ENSO is known to influence SST and SST is known to influence A. catenella as
previously mentioned. Since SSTs have been increasingly warmer, the ENSO-driven SST
42
anomalies likely lead to more windows of opportunity for HABS and subsequent shellfish
toxicity (PSP concentration) due to the increased baseline of warmer SSTs. This could be
another explanation for the significant relationship between ENSO and PSP concentration found
in this study.
This study also shows the significant relationship between ENSO and PDO as have other
studies in the past (Moore et al. 2010; Newman et al. 2016). ENSO is known as one of the main
forcing factors on PDO and the subsequent SST anomalies. Since ENSO influences PDO and
PDO influences SST and shellfish toxicity, one could deduce that ENSO also influences shellfish
toxicity as shown in this study. One could also surmise that since ENSO is known as one of the
main forcing variables contributing to the PDO phenomenon then at the very least, ENSO would
be an indirect influence of shellfish toxicity knowing that PDO does influence shellfish toxicity.
A. catenella is a dinoflagellate meaning it is motile and can swim in search of nutrients,
light, or other favorable environmental factors. Since the mechanisms of ENSO phases (e.g., El
Nino, La Nina, neutral) impact the environment differently, A. catenella bloom dynamics will
respond accordingly. For example, during an El Nino phase when Eastern Trade Winds are weak
and there is minimal upwelling, less nutrients would be available, but SSTs would be warmer
and more ideal for A. catenella. Due to their motility, these dinoflagellates could also be able
seek out warmer waters where nutrients aren’t depleted thus giving them a competitive
advantage over other phytoplankton. This is a scenario that would lead to a bloom and
subsequently, more shellfish toxicity (PSP concentration).
While this study suggests a positive association between ENSO index and PSP
concentration and PDO index and PSP concentration, it’s important to note that the correlations
were modest. This is likely due to the large number of variables to consider when investigating
43
these relationships among SST, PDO, ENSO, and PSP concentration. Variables like sunlight,
nutrient availability, turbidity, salinity, and many other unnamed or unknown variables could not
be accounted for in this study. These variables impact A. catenella bloom dynamics and the
subsequent PSP concentrations, their exact influence is unknown, but they certainly play a role.
Including more of these variables (i.e., sunlight, salinity, turbidity, etc.) in future analyses could
provide more insight on A. catenella bloom dynamics and the subsequent shellfish toxicity.
44
Conclusion
Harmful algal blooms and the subsequent toxins have increased over time and continue to
expand their presence throughout the world (Van Dolah 2000). Paralytic Shellfish Poisoning is
no exception and is a public health issue that will continue to need effective monitoring and
regulation. The Pacific Decadal Oscillation (PDO) and El Nino Southern Oscillation (ENSO) are
known to cause sea surface temperature (SST) anomalies in the northern Pacific Ocean (Mantua
et al. 1997). Warm PDO phases is associated with warmer SSTs and more days of SST >13 ℃
which results in more shellfish toxicity in the Pacific Northwest (Moore et al. 2010). This work
suggests a significant relationship between PDO and shellfish toxicity (PSP concentration) and
ENSO and shellfish toxicity (PSP concentration).
This calls for more research to further investigate weather patterns and anomalies
associated with ENSO that could be impacting A. catenella and other HAB dynamics. Future
research could also focus on A. catenella cell counts instead of PSP concentrations to better
represent HAB dynamics. This could provide real-time insights on the number of A. catenella
cells. A. catenella cell counts could be used to estimate the subsequent PSP concentrations in
surrounding areas once a threshold was determined for cell counts and its connection to PSP
concentrations.
More research on climate change and the continued warming of SSTs associated with
climate patterns like ENSO or PDO could also shed more light on this complex story.
Understanding the environmental drivers and variabilities that affect A. catenella bloom
dynamics will allow us to monitor shellfish toxicity more effectively. In doing this, shellfish can
continue to be a safe and healthy source of food for all communities.
45
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