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Characterizing protistan diversity and quantifying protistan grazing in the North Pacific Subtropical Gyre

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Characterizing protistan diversity and quantifying protistan grazing in the North Pacific Subtropical Gyre

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Content COPYRIGHT 2024 JENNIFER LYNNE BEATTY
CHARACTERIZING PROTISTAN DIVERSITY AND QUANTIFYING PROTISTAN
GRAZING IN THE NORTH PACIFIC SUBTROPICAL GYRE
by
Jennifer Lynne Beatty
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(MARINE BIOLOGY AND BIOLOGICAL OCEANOGRAPHY)
DECEMBER 2024

ii
Dedication
To my family, both those who walked before me and those who walk beside me, who constantly
inspire and guide me. Grandpa Jim, the first to go to college and always the first person in the
ocean and the last one out. Grandma Jean, an educator who taught me to take big risks when it’s
the right move. Abuelita Bea, who finished her college degree at 75 years old. Pop-pop Perry,
whose spirit of adventure lives on in me. My dad, Brian, who passed on a love for the ocean and
a passion for education. My mom Nellie, whose creativity is inspiring. Elizabeth, our fearless
leader. Teresa, my confidant. Katherine, my southern California buddy. Angela, my STEM
buddy. Christopher, my mini me.

iii
Acknowledgements
Dr. David Caron- for the funding, advice, counsel, instruction, and countless edits that
transformed scattered ideas to this manuscript. Committee members: Dr. Naomi Levine, Dr. Jed
Fuhrman, Dr. Seth John, Dr. John Heidelberg, Dr. Cameron Thrash, Dr. Frank Corsetti, and Dr.
Carly Kenkel- for their guidance and thoughtful conversations throughout this process. Caron
Lab members: Dr. Gerid Ollison, Brittany Stewart, Dr. Sam Gleich, Dr. Isha Kalra, Dr. Sarah
Trubovitz, Dr. Julie Hopper, Dr. Allie Li, Dr. Jayme Smith, and Emily Eggleston- for welcoming
me, teaching me, listening to me and guiding me through the twists and turns that constitute a
PhD. UCSD Mentors: Dr. Ali Freibott, Dr. Belli Valencia, and Dr. Jenni Brandon- for sharing
their offices and lives with me. LMC Mentors: Mark Lewis, Brianna McCarthy and Dr. Mindy
Capes- for believing in me and encouraging me to take the next step. My family: Grandpa Jim,
Grandma Jean, Pop-pop Perry, Abuelita Bea, Dr. Brian Beatty, Nellie Beatty, Elizabeth
Blumhorst-Beatty, Nathan Blumhorst, Teresa Beatty, Katherine Beatty, Starla Perez, Angela
Beatty, Christopher Beatty, Courtney Beatty, Tia Susie, and my dozens of cousins- for speaking
truth to me on the days of doubt and bringing much laughter throughout the years. Cruise
friends: Dr. Rachel Kelly, Phil Kong, Brandon Brenes, Daniel Muratore, Syrena Whitner- for
making the long days at sea fly by. MEB friends: Jordan, Kyla, Rae, Delaney, Ryan, Yingqi,
Talya, Meagan, Yiming, Anjali, Catie, Teagan, Katie, Anna, Chuankai, Yiwei, Daniel, Danny,
Justin, Colette, Alexis, Natalie, Nina -for commiserating with and encouraging me in our many
conversations in the MEB hallways. UCSD friends: Mikee, Gabe, Lindsay, Heidi, Anya, Noah,
Natalie, Kevin, Angeline, Holly- for being there from the beginning and sticking through the
end. Los Angeles friends: Avery, Megan, Natalie, Cari, Eitan, Cassie, Amanda, Jesse, Emily,
Lauren, Paloma, Samuel, Rachel, Mike, Isobel, Ariel- for transforming Los Angeles from a place
I wanted to escape to a place I consider home.
Thank you all for being the village that got me here.

iv
TABLE OF CONTENTS
Dedication…………………………………………………………………………………………ii
Acknowledgements………………………………………………………………………………iii
List of Tables…………………………………………………………………………………..….v
List of Figures…………………………………….………………………………………………vi
Abstract……………………………………………………………………………………...…..viii
Introduction…………………………………………………………………………………..……1
Chapter 1: Protistan community structure across an eddy dipole in the North Pacific
Subtropical Gyre…………………………………………………………………………..6
Abstract……………………………………………………………………………………6
Introduction………………………..………………………………………………………7
Materials and Methods………….………………………………………………………..12
Results…………………………..…………………..……………………………………17
Discussion…………………….…………………………….……………………………23
Conclusion…………………….…………………………………………………………30
Figures……………………...………………..…………………………………………..33
Chapter 2: Experimental evaluation of microzooplankton grazing methods: fact or fiction?.......46
Abstract…………………………………………………………………………………..46
Introduction………………………………………………………………………………47
Materials and Methods……………………………………….…………………………..50
Results………………………………………………..……..……………………………55
Discussion…………………………………………………..……………………………59
Conclusion………………………………………………….……………………………66
Tables……………………………………………….……………………………………68
Figures………………………………………..…...……………………………………..70
Chapter 3: Microzooplankton grazing in the euphotic zone of the North Pacific Subtropical
Gyre …………………...…………………………………………………………………75
Abstract…………………………………………………………………………………..75
Introduction………………………………………………………………………………76
Materials and Methods……………………….…………………………………………..78
Results………………………………………..……..……………………………………82
Discussion…………………………………….…………….……………………………85
Conclusion…………………………………….…………………………………………89
Tables……………………………………………….……………………………………92
Figures………………………………………..…...………….…………………………..96
Conclusion……………………………………...………………………………………………101
References……………………………………...……………………………………………….105

v
Table ...Page
Table 2.1 Prochlorococcus mortality rates by experimental technique and treatment……..68
Table 2.S1 Experimental setup conditions…………………………………………………...69
Table 3.1 Environmental and biological conditions of each experimental station…………92
Table 3.2 Mean and standard error grazing rates by experimental type and depth…...……94
Table 3.S1 Grazing rates and phytoplankton growth rates by experiment and incubation
location…………………………………………………..………………..……………...95

vi
Figure ...Page
Figure 1.1 Regional map of the study area north of Oʻahu, Hawai'ian Islands with inset
showing the sampling sites………………………………………………………………33
Figure 1.2 Depth profiles of temperature (a), chlorophyll (b), potential density (c), and
nitrate + nitrite (d), at water column stations across the eddy dipole…………….……...34
Figure 1.3 Taxonomic composition of the protistan community in water samples based
on relative abundance of ASVs determined by sequencing the rRNA transcript
derived from RNA (a) or rRNA gene sequence derived from DNA (b) of the V4
region of small subunit ribosomal RNA genes…………………...……………………...35
Figure 1.4 Taxonomic composition of the protistan community associated with sinking
particles based on relative abundances of ASVs in Trap rRNA gene sequence derived
from DNA (a) and by microscopy (b)……………………………………………………36
Figure 1.5 Examples of organisms observed in sediment trap material……………………..37
Figure 1.6 Mean relative abundances of identifiable organisms observed using the
Scripps Plankton Camera (SPC) in the anticyclonic eddy center (a) and the cyclonic
eddy center (b)………………………………………... ………………………………...38
Figure 1.7 Examples of organisms observed using the Scripps Plankton Camera (SPC)…...39
Figure 1.8 NMDSs from Bray-Curtis dissimilarity of protistan community composition
based on ASVs…………………………………………………………………………...40
Figure 1.9 Shared ASVs in rRNA transcripts derived by RNA, rRNA gene sequencing
derived by DNA, Trap rRNA gene sequencing derived by DNA by sample type………41
Figure 1.10 Flux of sinking cells identified by microscopy from PITs plotted by SLA
for each station……………………………………………………………………………42
Figure 1.S1 Sequencing depth (a) and diversity metrics (b)- ASV richness, (c)-
Shannon’s index, (d)- Inverse Simpson’s index) for protistan assemblages inferred
from sequence data (ASVs) between rRNA transcripts derived by RNA, rRNA gene
sequencing derived by DNA, Trap rRNA gene sequencing derived by DNA…………..43
Figure 1.S2 NMDS from Bray-Curtis dissimilarity of protistan communities inferred
from OTUs formed at 97% sequence similarity from rRNA transcripts derived by
RNA, rRNA gene sequencing derived by DNA, Trap rRNA gene sequencing
derived by DNA……………………………………………………………………..…...44

vii
Figure 1.S3 NMDS from Bray-Curtis dissimilarity of protistan communities inferred
From shared ASVs…………………………………………………………45
Figure 2.1 Generalized changes in population abundances during an experiment………….70
Figure 2.2 Prochlorococcus abundances across time in all treatments……………………...71
Figure 2.3 Prochlorococcus mortality across all treatments for all measurements…………72
Figure 2.4 Percent differences between observed and experimentally derived………………..
Prochlorococcus mortality rates……………………………………………..…73
Figure 2.S1 Prochlorococcus mortality rates and percent differences grouped according
to the light/dark cycle when each experiment was performed………………...…………74
Figure 3.1 Map of study area….……………………………………………….…………….96
Figure 3.2 Phytoplankton mortality rates (a,c) and growth rates (b,d) of total
phytoplankton community (based on chlorophyll a) in 2021 at 25 m (a, b) and
150 m (c, d) determined using the dilution technique………………………………….97
Figure 3.3 Ratio of phytoplankton growth rate to mortality rate (based on chlorophyll a)
in 2021 at 25 m (circles) and 150 m (triangles) determined using the dilution
…technique………………………………….…………………………………………...98
Figure 3.4 Grazing rates determined using the disappearance of fluorescently labeled
algae (FLA; a, c, e) and fluorescently labeled bacteria (FLB; b, d, f)……… ………..99
Figure 3.S1 Dilution technique model I linear regressions…………………...……………..100

viii
Abstract
In the oligotrophic open ocean, microbial eukaryotes (protists) are important grazers that link
picophytoplankton to higher trophic levels and contribute to the sinking of carbon out of the
euphotic zone. The goal of my dissertation was to combine multiple methods to (1) characterize
protistan diversity in the water column and associated with sinking particles, and (2) quantify
protistan grazing on prey throughout the euphotic zone in the North Pacific Subtropical Gyre
(NPSG). I characterized protistan diversity in the water column and on sinking particles using
amplicon sequencing of 18S rRNA gene transcripts and 18S rRNA genes with in-situ automatic
imaging in the water column, and 18S rRNA gene sequencing and light microscopy of sinking
material collected in particle interceptor traps. Each method provided unique perspectives on the
protistan community composition yet supported that a subset of the water column community
contributed to sinking particles. Two commonly employed methods for measuring grazing rates
of protists in nature were assessed in a controlled laboratory experiment. Results indicated that,
under ideal conditions, these methods underestimated picoplankton mortality by 50-80%. These
methods were applied in the field to identify patterns of grazing preference throughout the
euphotic zone. There was an order of magnitude difference between techniques in natural
samples raising questions concerning the validity of the assumptions underlying the techniques.
Overall, this work advances our understanding of protistan role in the carbon cycle and food web
in the oligotrophic open ocean and emphasizes that combining multiple techniques robustly
assesses diversity and function.

1
Introduction
Unicellular eukaryotic organisms (protists) are ubiquitous in marine systems and diverse
in morphology and function (Caron et al. 2012). Large photosynthetic protists, such as diatoms,
are important in nutrient rich regions as the base of the food web, which are consumed by larger
mesozooplankton directly that move carbon to higher trophic levels. In the oligotrophic open
ocean, small picophytoplankton cyanobacteria and photosynthesizing picoeukaryotes form the
base of the food web, which are too small for mesozooplankton to consume directly (Calbet
2008). Instead, protistan microzooplankton consume the small photosynthesizers and allow the
carbon to move to higher trophic levels (Schmoker et al. 2013). Additionally, protists can
contribute to particulate organic matter flux out of the euphotic zone directly by sinking, as
particles in mesozooplankton fecal pellets or by transforming the material that is sinking out of
the euphotic zone by grazing on particles directly or attaching to particles and grazing on the
bacteria associated with the particle (Guidi et al. 2016; Worden et al. 2015). Thus, understanding
the diversity of protists present and quantifying their grazing on picoplankton reveals the role of
protists in a particular marine region.
The methods used to characterize protistan diversity shape the observed diversity and
potentially our understanding of the functionality of protistan assemblages. These methods can
broadly be categorized into imaging techniques based on morphology and molecular techniques
based on genetic sequencing. Traditional light microscopy has historically been used to
characterize the diversity of morphologically distinct protistan groups. Light microscopy been
applied to the water column to identify different protistan assemblages with depth, as well as to
sinking particles which has identified larger organisms present (Beers et al. 1982; Gowing et al.
2001; Michaels et al. 1995; Pasulka et al. 2013; Venrick 1990). Microscopy has been limited to

2
organisms that can survive plankton nets and preservation and can be time consuming and
require expertise in group-specific morphology. Newer in situ imaging platforms have reduced
the time limits by processing large volumes of water quickly, and eliminated the issue of delicate
plankton or plankton that do not survive traditional preservation (Lombard et al. 2019). In situ
imaging has revealed the abundance of large and delicate plankton previously considered not
important due to their low abundance and inability to survive traditional methods of collection
(Biard et al. 2016). Next generation high throughput sequencing (NGS) is revolutionizing our
understanding of eukaryotic taxonomic diversity. Not limited by morphological distinction, NGS
has revealed many species previously undocumented throughout the global ocean (de Vargas et
al. 2015). However, obtaining absolute abundances from amplicon sequencing is still not
possible due to PCR bias and varying copy levels (Santoferrara et al. 2020), and taxonomic
identity for many protsits is presently limited. Each technique provides complementary
information regarding the protistan community reflecting the strengths and limitations of the
method, suggesting that combining multiple techniques provides a robust assessment of protistan
diversity.
Despite the acknowledged global importance of microzooplankton grazing, measuring
microbial eukaryotic grazing rates is difficult due to the diversity in feeding mechanisms that
may impact prey selectivity. Two of the most commonly employed methods for quantifying
trophic activity of microzooplankton were developed in the 1980s and have yet to be carefully
vetted in a laboratory experiment. The dilution technique disrupts the predator and prey
relationship by sequentially relieving phytoplankton from grazing pressure and observing the
phytoplankton growth rates in the absence of the grazing (Landry et al. 1995; Landry and Hassett
1982). Differences in apparent phytoplankton growth rate relative to grazing pressure is

3
attributed to microzooplankton grazing. Tracking the consumption of a fluorescently labeled
prey is another commonly employed method to quantify grazing rates on bacterial and algal
sized particles (Rublee and Gallegos 1989; Sherr et al. 1987). This method assumes that the
predator is consuming the labeled prey or surrogate at the same rate as the natural prey. Both
methods have been extensively used in both marine and freshwater systems for estimating small
plankton mortality due to grazing (Calbet 2001; Medina et al. 2017; Rocke et al. 2015; Schmoker
et al. 2013). These methods are frequently used in the field yet have never been carefully
assessed in the laboratory to evaluate how the grazing rates estimated by these techniques
compare to observed (actual) mortality rates nor have they have been compared in the field to
evaluate how the grazing rates estimated by each technique compare to each other.
Much of this work was carried out in the North Pacific Subtropical Gyre (NPSG), which
is considered the largest contiguous biome on earth. About 100 km north of the Hawaiian Island
of Oahu is station ALOHA, at 22˚45’ N, 158˚00’ W and is the site of the Hawaiian Ocean Time
Series. Near monthly assessments taken from station ALOHA since the 1980s has established the
typical conditions in the NPSG (Karl and Church 2014). The water column there is characterized
by a two-layer euphotic zone where the surface layer demonstrates higher temperature, light, and
low nutrients and is driven by recycled nutrients, and the lower euphotic zone has lower
temperature, light and higher nutrients and a well-defined deep chlorophyll maximum (DCM)
(Karl and Church 2017). There is relatively low seasonality in the NPSG, but mesoscale eddies
are common features that are present ~30% of the time at station ALOHA suggesting that they
may have important impacts on biogeochemical cycles (Barone et al. 2019). Studies on protistan
communities near station ALOHA using both microscopy and NGS have revealed diverse and
distinct protistan communities with depth, and possible eddy influences on protistan community

4
composition and function (Hu et al. 2016; Hu et al. 2018; Ollison et al. 2021; Pasulka et al.
2013). Studies on the prokaryotic communities associated with sinking particles have revealed
that many prokaryotes are eukaryotic associated organisms suggesting that eukaryotes, including
protists, are important determents in sinking particulate matter out of the euphotic zone (Boeuf et
al. 2019; Fontanez et al. 2015; Li et al. 2023; Pelve et al. 2017; Poff et al. 2021). Additionally,
protistan grazing is closely linked to surface picophytoplankton abundance suggesting that
protists are important in determining the fate of carbon in the oligotrophic open ocean (Beckett et
al. 2021; Connell et al. 2020; Ribalet et al. 2015; Rii et al. 2016). Protists are well established
fundamental members of the microbial community that are critically important in determining
the fate of photosynthetic carbon in the NPSG.
Questions remain about the impact of mesoscale eddies on protistan community
composition throughout the water column, the contribution of protists to sinking material out of
the euphotic zone, the nature of grazing below the euphotic zone in the NPSG and assessments
of the methods used to measure microzooplankton grazing rates. The goal of this dissertation
was to combine multiple methods to (1) characterize protistan diversity in the water column and
associated with sinking particles in response to eddy forcing, (2) assess methods commonly used
to measure protistan grazing, and (3) quantify protistan grazing on prey throughout the euphotic
zone in the NPSG. Protistan diversity in the water column and on sinking particles was
characterized using amplicon sequencing of 18S rRNA gene transcripts and 18S rRNA genes
with in-situ automatic imaging in the water column, and 18S rRNA gene sequencing and light
microscopy of sinking material collected in particle interceptor traps. Each method provided
unique perspectives on the protistan community composition yet supported that a subset of the
water column community contributed to sinking particles. Two commonly employed methods

5
for measuring grazing rates of protists in nature were assessed in a controlled laboratory
experiment. Results indicated that, under ideal conditions, these methods underestimated
picoplankton mortality by 50-80%. These methods were applied in the field to identify patterns
of grazing preference throughout the euphotic zone. There was an order of magnitude difference
between techniques in natural samples raising questions concerning the validity of the
assumptions underlying the techniques. Overall, this work advances our understanding of
protistan role in the carbon cycle and food web in the oligotrophic open ocean and emphasizes
that combining multiple techniques robustly assesses diversity and function.

6
Chapter 1: Protistan community structure across an eddy dipole in the North Pacific
Subtropical Gyre
Jennifer L. Beatty*1
, Brittany P. Stewart1
, Lisa Y. Mesrop2
, Edward F. DeLong3
, David M.
Karl3
, David A. Caron1
1 University of Southern California, Los Angeles, CA 90089, USA
2 University of California at Santa Barbara, Santa Barbara, CA 93106, USA
3 University of Hawai'i at Mānoa, Honolulu, HI 96822, USA
Author Contributions: Jennifer L. Beatty- Conceptualization (equal); Data acquisition (lead);
Data curation (lead); Formal analysis (lead); Visualization (lead); writing- original draft (lead)
and editing manuscript, approval of the final submitted manuscript. Brittany Stewart- Data
acquisition (equal); Writing- review and editing (equal). Lisa Mesrop- Data acquisition (equal).
Edward DeLong- Resources (equal); Writing- review and editing (equal). David Karl- Resources
(equal); Writing- review and editing (equal). David Caron- Conceptualization (equal); Funding
acquisition (lead); Resources (lead); Writing-original draft (supporting); Writing- review and
editing (equal).
Abstract
Ocean eddies are common mesoscale features that can extend >100 km and maintain
cohesiveness for months, impacting planktonic community structure and water column
biogeochemical cycles. Standing stocks of protists in the water column and on sinking particles
were investigated using microscopy, in situ imagery and metabarcoding across an anticyclonic to
cyclonic eddy dipole in the North Pacific Subtropical Gyre during July 2017. The water column

7
was sampled from the surface to 500 m and particle interceptor traps were deployed at 150 m.
Protistan assemblage composition varied substantially between sample type and analytical
approach (metabarcoding of 18S rRNA transcripts derived from RNA or rRNA gene sequences
derived from DNA from water samples, metabarcoding of 18S rRNA gene sequences derived
from DNA from trap material or microscopy of trap material) across the eddy dipole. Alveolates
represented 63% of sequences from water samples. Planktonic assemblages characterized using
rRNA gene sequences derived from DNA were strongly affected by depth but less so for
assemblages assessed by rRNA transcripts derived from RNA. In contrast to water samples,
metabarcoding of sediment trap material revealed a dominance of rhizarian protists representing
79% of trap rRNA gene sequences. Microscopy of trap material confirmed the important
contribution of rhizaria to sinking particles, and revealed increased relative abundances of
ciliates in the anticyclonic eddy and diatoms in the cyclonic eddy. Using in situ imagery
confirmed the presence of relatively large but rare rhizaria that were not adequately assessed
from water samples but contributed significantly to particle flux. Together, these data
demonstrate differing perspectives of planktonic protistan community composition and
contributions to sinking particles gained from the application of different sampling and analytic
approaches. Our observations and analyses also indicate that a specific subset of the protistan
community contributed disproportionately to organic matter downward export.
Introduction
Planktonic protists are assemblages of microbial eukaryotes that are diverse in form and
function, contributing to primary and secondary production, decomposition and elemental
cycling; processes strongly influencing the sinking material that eventually reaches the seafloor

8
(Caron et al. 2012; de Vargas et al. 2015; Worden et al. 2015). In the oligotrophic open ocean,
phototrophic protists can at times contribute directly to the transport of carbon to the deep ocean
(Durkin et al. 2016; Poff et al. 2021). Additionally, phagotrophic protists consume minute
autotrophs and heterotrophs thereby playing roles in trophic transfer, nutrient remineralization,
and the production of sinking particles. Characterization of the composition and dynamics of
these protistan assemblages is therefore essential for understanding their various roles in oceanic
ecosystem.
Surveys of protistan community composition are strongly influenced by the
methodological approach(es) employed to gather that information. Each method offers a
different insight into the protistan community, collectively providing a more comprehensive
assessment of presence and activity. Microscopy has provided a wealth of historical background
on the distributions and abundances of morphologically distinct species, albeit limited by
organismal size and morphological complexity. In open ocean ecosystems, microscopical studies
have helped outline the basic structure of communities of morphologically-distinct protists
(Venrick 1990) and the seasonal and interannual variability associated with these communities
(Pasulka et al. 2013). Microscopy has also been instrumental in identifying the organisms
contributing to flux out of the euphotic zone (Gowing et al. 2001; Michaels et al. 1995).
Recent automated imaging platforms have significantly advanced our ability to
characterize morphologically-distinct protists including delicate and rare taxa, as well as
extending our observations to the smallest planktonic microbes. Underwater microimaging
systems provide the ability to automate image analysis, increasing the speed and volume of the
resulting datasets. Flow cytometers quantify picoplankton that are often too small or nondiscript
to obtain taxonomic information using traditional microscopy, thereby providing both

9
abundances and characteristics based on optical properties (Ribalet et al. 2019; Rii et al. 2021;
Sieracki et al. 1998). Imaging flow cytometers take flow cytometry one step further by providing
pictures that can be used to identify nano and microplankton based on morphological details
(Dugenne et al. 2020; Olson and Sosik 2007)The latter two platforms yield greater taxonomic
diversity and resolution than traditional microscopy or macroimaging but are limited to small
volumes and cells less than approximately 100 µm. In contrast, automated in situ macroimaging
enables the analysis of larger volumes of water (up to 100s of liters of water per cast) potentially
capturing larger (>100 µm), rare forms of protists often undetected by methods commonly
employed for identifying and counting small protists (2-100 µm) (Lombard et al. 2019).
However, macroimaging approaches typically lack the magnification to identify protists <100
µm in size. These datasets typically enlist the utilization of machine learning algorithms to
classify taxa and potentially functional traits in the large number of images captured (Dennett et
al. 2002; Orenstein et al. 2022). Leveraging automatic image classification increases the
usefulness of these technologies applied to ocean science.
Complementing the technologies noted above, current methods of high-throughput
sequencing (NGS) approaches as a means of assessing protistan species richness and community
composition are now widely applied to aquatic samples. These approaches have revolutionized
our understanding of protistan diversity by providing taxonomic breadth and resolution that is
not possible using any of the imaging instruments noted above (Santoferrara et al. 2020). NGS
also provides a mechanism to differentiate metabolically active protists from the total protists
present by targeting either rRNA transcripts (RNA) or rRNA gene sequences (DNA) (Giner et al.
2020; Hu et al. 2016). NGS applied to sinking particles can also characterize some components
of the protistan community contributing to vertical flux that are difficult to identify using

10
microscopy (Guidi et al. 2016; Pelve et al. 2017). Unhindered by morphological distinction or
size limitations, NGS offers a taxonomic detail unparalleled by visual assessments.
Station ALOHA (A Long-term Oligotrophic Habitat Assessment), is located within the
North Pacific Subtropical Gyre (NPSG) at 22˚45’ N, 158˚00’ W. This location is representative
of highly oligotrophic oceanic regions, and the oceanographic conditions in the region have been
well characterized (Karl and Church 2014; Karl and Church 2017). Mesoscale eddies are
common oceanic features of the NPSG that alter water column density fields and create unique
habitats marked by light and nutrient conditions distinct from the water surrounding the eddies.
Deflection of water movement from that adjacent to the eddies due to Coriolis effect and
Eckman transport leads to mesoscale eddies; anticyclonic eddies with convergent water masses
marked by higher sea-surface height and downwelling, and cyclonic eddies characterized by
divergent water masses marked by lower sea-surface height and upwelling (McGillicuddy 2016).
Mesoscale eddies are present approximately 30% of the time at Station ALOHA (Barone et al.
2019). Positive sea level anomalies (SLA) have been associated with a depression of the deep
chlorophyll maximum (DCM), relatively high prokaryotic picophytoplankton abundances and
organic matter below the DCM, with converse observations associated with negative SLA
(Barone et al. 2019).
These shifts in physical environment with SLA anomalies present the possibility of
consequent responses of the biological assemblages and in sinking particle fluxes from the
euphotic zone of the NPSG. Planktonic assemblages can be different inside and outside an eddy
(and different between cyclonic and anticyclonic eddies) creating a complex but poorly
understood relationship between SLA and biological assemblages. Cyclonic eddies vertically
transport nutrients upward in the water column into regions of higher light intensity allowing

11
diatoms to dominate in the vicinity of Station ALOHA where they are typically scarce (Barone et
al. 2022; Benitez-Nelson et al. 2007; Harke et al. 2021; Vaillancourt et al. 2003)Barone et al.
2022). However, a community shift towards larger phytoplankton species does not necessarily
equate to increased particle export from surface waters. Benitez-Nelson et al. (2007), for
example, examined phytoplankton assemblage composition and carbon export response to
mesoscale eddy forcing near Station ALOHA. They reported an increase of biogenic silica
export within cyclonic eddies but no increase in carbon export, an observation hypothesized to
be in part due to a tight coupling between primary production and microzooplankton grazing
(Landry et al. 2008).
In contrast, downwelling associated with anticyclonic eddy centers decreases availability
of nutrients in surface waters, which has been hypothesized to lead to increased abundances of
nitrogen fixing microorganisms and increased rates of nitrogen fixation (Dugenne et al. 2023;
Fong et al. 2008; Guidi et al. 2012; Wilson et al. 2017). A study of two eddy dipoles sampled in
spring and summer of different years near Station ALOHA revealed no substantive difference in
protistan gene expression between eddy centers in the surface mixed layer (Harke et al. 2021).
However, another recent study comparing eddy centers indicated that eddy polarity played a
significant but secondary role in controlling protistan community structure and gene expression,
although depth understandably exerted a dominant role (Gleich et al. 2024). Additional studies
are warranted to characterize the effects of eddy polarity on protistan ecology, and their resulting
implications for sinking particle composition.
The goal of this study was to comprehensively assess the diversity of the protistan
assemblage within suspended and particle-associated assemblages throughout the euphotic zone
in the NPSG to (1) compare information gathered using different methodological approaches,

12
and (2) determine if eddy polarity significantly alters community structure and protistan
contribution to organic material flux out of the euphotic zone. Specifically, we examined
protistan community composition along a transect from the centers of adjacent anticyclonic and
cyclonic eddies (an eddy dipole) near Station ALOHA. The impact of eddy polarity and depth on
protistan community composition was assessed using nucleic acid sequencing, microscopy and
in situ imagery of discrete water samples and sinking particles collected in sediment traps. A
significant effect of eddy polarity on community composition was not observed in communities
characterized by molecular analysis, while microscopy of sediment trap material analysis
revealed an increased flux of silicious organisms associated with negative SLA, suggesting a
possible influence of eddy polarity on protistan contribution to particle flux. Communities
examined using molecular analysis revealed that water column communities were dominated by
alveolates while sedimenting material was dominated by rhizarian protists. Microscopy of
sediment material and imagery of the water column supported the observations of large rare
rhizarian protists. We conclude that a subset of the protistan community contributed
disproportionately to organic material flux out of the euphotic zone.
Materials and Methods
Samples were collected along a transect line that spanned an eddy dipole (adjacent
anticyclonic and cyclonic eddies) on the MESO-SCOPE cruise (June 26th-July 15th, 2017), near
Station ALOHA (Figure 1.1). Three types of assessments of protistan community composition
were made in the water column at the study site, as detailed below. Water samples were collected
at four depths at six stations along the transect line that intersected two contrasting eddy dipoles
(cyclonic vs anticyclonic) between June 29th-July 1st, 2017 (labeled S4-S14 on Figure 1.1).

13
Protists on sinking particles were collected using twelve sets of free-floating sediment traps
(Particle Interceptor Traps; PITs) deployed for 9- to 12-days across the eddy dipole from July
2nd-July 14th (labeled P1-P12 on Figure 1.1). In situ imagery was obtained using a Scripps
Plankton Camera (SPC) deployed within each eddy center (July 3nd-July 10th).
Environmental parameters
A CTD (SeaBird, Bellevue, WA) was used at six stations across the eddy dipole transect
to collect vertical profiles of temperature, potential density, and chlorophyll fluorescence.
Concentrations of NO3 + NO2 were quantified as described in Karl et al. (2001), a modification
of Armstrong et al. (1967) at the University of Hawai'i (Armstrong 1967). The SLA was
corrected to adjust for the 30-year average observed at Station ALOHA and referred to as
SLAcorr (Barone et al. 2019).
Amplicon sequencing of water column communities
Seawater was collected at four depths at each of the six stations occupied along the eddy
dipole transect; 15 m, the depth of the deep chlorophyll maximum (DCM) (which was
determined based on the fluorescent profile on the downcast, and ranged between 98 m – 125 m),
175 m, and 500 m using a Niskin® rosette equipped with conductivity, temperature, depth
(CTD), fluorescence and oxygen sensors. Water from the Niskin® bottles was transferred into
clean (5% HCl-washed and DI water rinsed) 20 L carboys using silicone tubing to minimize
turbulence which can damage delicate plankton. 2 L aliquots of whole (unfiltered) seawater
(WSW) from depths <150 m, or 4 L of WSW from depths >150 m, were used. WSW samples
were filtered directly onto 43 mm diameter GF/F filters (nominal porosity 0.7 �m) and filtering
time did not exceed one hour. Duplicate samples were collected for nucleic acid analysis. Filters

14
were placed in 1.5 mL of RLT buffer (Qiagen #79216) fortified with ß-mercaptoethanol and
flash frozen in liquid nitrogen and stored at -80ºC until processed.
Nucleic acids were extracted from filters that were thawed on ice before bead beating
using RNAse free silica beads for 10 minutes. Total DNA and RNA extractions were conducted
using the AllPrep DNA/RNA mini kit (Qiagen #80204). RNA was converted to cDNA using
iscript cDNA synthesis kit (Bio-Rad #1708890). Blank water samples were processed alongside
environmental samples through the PCR steps and subsequent sequencing. The V4 hypervariable
region of the 18S rSSU gene was targeted using the following primers: forward (5′
-
CCAGCASCYGCGGTAATTCC-3′
) and reverse (5′
- ACTTTCGTTCTTGATYRA-3′
) (Hu et al.
2015; Stoeck et al. 2010). Following denaturation at 98ºC for 2 min, the PCR thermal cycle was
completed in two steps due to different annealing temperatures of the forward and reverse
primers. The first step was 10 cycles of denaturation at 98ºC for 10 sec, annealing at 53ºC for 30
sec, and extension at 72ºC for 30 sec. The second step was 15 cycles of denaturation at 98ºC for
10 sec, annealing at 48ºC for 30 sec, and extension at 72ºC for 30 sec, followed by a final
extension at 72ºC for 2 min. PCR products were cleaned with AMPure bead clean up (Beckman
Coulter #A63880). Samples were indexed using Illumina-specific P5 and P7 barcodes before
pooling for sequencing. Pooled samples were sequenced using MiSeq 250 x 250 bp PE
sequencing at Laragen (Culver City, CA, USA).
Sequences are available from NCBI with the accession number PRJNA981211.
Sequences were processed using the DADA2 pipeline in qiime2 (v2018.8), which joins pairedends reads, removes sequence errors (denoising) and dereplicates sequences, filters sequences by
quality and chimeras, and resolves Amplicon Sequence Variants (ASVs) (Bolyen et al. 2019;
Callahan et al. 2016). ASV taxonomy was assigned with a naïve Bayes classifier trained to the

15
Protist Ribosomal Reference (PR2) database at 90% confidence (Guillou et al. 2013). ASVs
were used as they offer high sequence resolution and are comparable between studies (Callahan
et al. 2017). For comparison, however, open-reference operational taxonomic units (OTUs) were
also generated at 97% sequence similarity and taxonomy was assigned using PR2. Blank water
samples run in parallel during DNA/RNA extraction and PCR steps were statistically applied to
remove ASVs that were possible contamination using Decontam (Davis et al. 2018). ASVs were
normalized via trimmed mean of M value (TMM) transformation using edgeR (Robinson et al.
2010). Global singletons were removed from the dataset as well as metazoan sequences prior to
downstream analysis. Statistical analysis was conducted using the R programming language to
evaluate protistan assemblage composition across the horizontal transect and sampling depths
through comparisons of taxonomy, assemblage diversity and dissimilarity (R Core Team 2022).
Characterization of protists associated with sinking particles
Protists on sinking particles were obtained using surface-tethered particle interceptor
traps (PITs) deployed at 150 m at twelve locations along the eddy dipole transect, referred to as
PIT stations. Sediment traps (Buesseler et al. 2007; Martin et al. 1987) were filled with
hypersaline solution to prevent the loss of sinking particles (Knauer et al. 1978). To preserve
sinking genetic material, a collection jar filled with full strength RNAlater was placed at the
bottom of the sediment trap column below a funnel. The saline solution of separate sediment
traps used for microscopical analyses were supplemented with buffered 37% formalin and
strontium chloride in order to preserve acantharian protists which have exoskeletons of strontium
sulfate (Beers and Stewart 1970). Strontium, as strontium chloride, was added at 10x average
ocean concentration for a final concentration of 0.08 g L-1
.

16
The material collected in the sediment traps for molecular analyses was filtered onto 0.2
�m Supor filters and extracted using DNeasy PowerBiofilm Kit (Qiagen #24000-50) (Poff
2021). PCR amplification, library prep, sequencing, and sequence processing were conducted
using the same methods described above for water samples collected in Niskin® bottles. The
removal of metazoan sequences from the resulting dataset removed metazoan carcasses or
portions of dead metazoans that sunk in the traps as well as contamination by “swimmers”,
which are live metazoa that may have actively swum into the sediment traps due to diel vertical
migratory behavior.
Trap material examined by microscopy was stored at 4ºC until analyzed. Depending on
abundances in a sample, 15-30 mL of the concentrated sample was settled for 24-hours in an
Utermöhl sedimentation chamber. Protists were counted on an inverted Olympus microscope.
Morphologically distinguishable organisms were identified to the lowest taxonomic level
possible at 200X magnification, generally either phylum or class level. Major groups identified
included ciliates, diatoms, dinoflagellates, and rhizarian taxa.
Automatic imaging of suspended plankton within eddy centers
A MACRO-SPC (Scripps Plankton Camera) (Jaffe Lab, Scripps Institution of
Oceanography, San Diego, CA) was deployed within the eddy centers to obtain imagery of
larger, delicate plankton in situ. The SPC was deployed twice within the cyclonic eddy center
during the day and once during the evening, and twice in the anticyclonic eddy center during the
day. The camera was lowered to 100 m at a rate of 10 m min-1
. The camera imaged 0.5 L of
water per frame with a 0.137X TC2MHR024‐C (Opto Engineering) lens magnification to
characterize particle sizes >500 �m using darkfield illumination (Orenstein et al. 2020). The SPC
was encased in a cage to eliminate movement of the camera body, and the cage attached to a

17
wire at the top so that on its descent the flow of water to the imaged volume was not interrupted
by the wire or frame. Images were captured at a rate of 20 frames second-1
. Within the camera,
each frame was processed from raw images into regions of interest (ROIs), which were
determined based on light regions upon the black background.
A random forest classifier was used to assign images to classes of interest. Due to the
configuration of the camera and cage, images from the ascent were used as a training set for the
classifier, and the trained classifier was used to analyze images from the descent. The classifier
was trained by manually identifying 48,898 ROIs into classes of 135 ROIs per class. The random
forest classifier was built in Python® using sci-kitlearn on a pretrained ResNet18 model
(Pedregosa et al. 2011; He et al. 2016). Cross-validation was used to test overall classifier
accuracy, with a training to validation set of 80% to 20%. The overall accuracy of the trained
classifier was 89%. For the ROIs classified as identifiable organisms, relative abundance of each
class was calculated, and then the mean of each relative abundance per class was taken for each
eddy center. There was no discernable difference between the two day casts and one evening cast
in the cyclonic eddy center.
Results
Physical features across the eddy dipole transect
The cyclonic and anticyclonic eddy centers generally represented the most extreme and
opposite values across the transect for DCM depth, chlorophyll concentration and temperature,
and for nutrients below the DCM (Figure 1.2). The DCM at the cyclonic eddy center occurred at
98 m with a temperature of 18ºC, potential density of 25.1 kg m-3
, and a chlorophyll
concentration of 1.16 �g L-1
. In contrast, the DCM at the anticyclonic eddy center occurred at

18
125 m with a temperature of 22ºC, potential density of 24.4 kg m-3 and a chlorophyll
concentration of 0.59 �g L-1
. Nitrate + nitrite concentrations ranged from 0.67 to 7.47 �mol kg-1
across the transect at 200 m, with the lowest value within the anticyclonic eddy center and the
highest value within the cyclonic eddy center.
Protistan community composition by methodological approach
Water samples analyzed for protistan community composition by sequencing18S rRNA
transcripts derived from RNA (i.e. free-living protists and those on suspended particles) had an
average sequencing depth of 743,958 ± 340,217 reads yielding 1,906 ± 474 ASVs per sample
(Figure 1.S1 a, b). Community diversity had a Shannon’s Index of 6.04 and Inverse Simpson’s
value of 131 (Figure 1.S1 c, d). All rRNA transcripts derived from RNA communities across
depth and site along the transect were dominated by alveolates, which had a relative contribution
of 18% ciliates, 27% dinoflagellates, and 10% syndiniales amongst the total water column RNA
community (Figure 1.3a). Haptophytes constituted 7% when averaged across all depths, but only
4% at 500 m. Stramenopiles were approximately 19% across all depths. Within the
stramenopiles, there were higher relative abundances of pelagophytes in the samples from the
DCM and 175 m (5% in 15 m, 11% in the DCM, 7% in 175 m, <1% in 500 m). Higher relative
abundances of rhizarians were observed lower in the water column relative to shallow samples
(6% in 15 m, 5% in the DCM, 8% in 175 m, 12% in 500 m).
Water samples analyzed for protistan community composition by sequencing 18S rRNA
genes (i.e. free-living protists and those on suspended particles, henceforth referred to as ‘rRNA
gene sequences derived from DNA’) had a sequencing depth of 782,101 ± 240,926 reads and
1,806 ± 627 ASVs per sample (Figure 1.S1 a, b). The Shannon diversity index was 6.05 and the
Inverse Simpson’s value was 131 (Figure 1.S1 c, d). The communities were dominated by

19
alveolates in all samples across all depths and location along the transect (Figure 1.3b). Among
all water column samples, DNA assemblages of alveolates comprised 2% ciliates, 31%
dinoflagellates, and 39% syndiniales of the whole community. Haptophytes were present at 4%
in the surface and the DCM and were less than 1% below the DCM. Chlorophytes were 2% at
the DCM and less than 1% at all other depths. Relative abundances of rhizarian protists were
greater at deeper depths relative to shallow samples (2% in 15 m, 10% in the DCM, 29% in 175
m, 32% in 500 m), with acantharians contributing greater percentages at 500 m.
Sediment trap samples analyzed for protistan community composition by 18S rRNA gene
sequencing (i.e. sinking protists and those on sinking particles, henceforth referred to as ‘Trap
rRNA gene sequences derived from DNA’) had an average sequencing depth of 1,483,453 ±
1,178,761 reads, yet only 391 ± 144 ASVs per sample were observed, compared to thousands in
the water samples (Figure 1.S1 a, b). Communities observed in sediment traps were much less
diverse, with a Shannon diversity Index of 2.95 and Inverse Simpson’s value of 9 (Figure 1.S1 c,
d) relative to indices observed in both rRNA transcripts derived from RNA or rRNA gene
sequences derived from DNA communities. Sequence datasets from sediment trap samples were
highly dominated by rhizarians (79%) and alveolates (16%) (Figure 4a). The proportion of
rhizarian protists was much higher relative to the contribution of rhizarian protists in the
communities observed in water samples (both DNA and RNA; compare Figure 1.3 and 1.4a).
Additionally, trap sample P8 had a notably different composition compared to all other sediment
traps samples across the transect, with 60% alveolates and 29% rhizarians.
Protistan community composition in trap material determined using compound
microscopy was markedly divergent from community composition inferred from DNA
sequencing of trap material, and from that observed in rRNA gene sequences derived from DNA

20
and rRNA transcripts derived from RNA samples (compare Figure 1.4b with 1.4a and 1.3a, b).
Recognizable cells observed by microscopy yielded generally higher contributions of diatoms
(39%), and to some degree alveolates (36%), and rhizarians (11%), relative to other sample types
(Figure 1.4b). Sedimenting organisms were composed of a variety of morphological types within
the commonly observed major taxonomic groups (Figure 1.5).
Images collected using the SPC were dominated numerically by diatom chains (41%) and
a variety of mesozooplankton (40%) (Figure 1.6). Rhizarian protists comprised 18% of the
Regions of Interests (ROIs); acantharia (9%) and foraminifera (6%) were observed more
frequently than solitary or colonial radiolarians (both contributed 1%). A small proportion of
images were Trichodesmium colonies (1%). Figure 1.7 demonstrates the types of organisms
observed within each category. There were 59,361 ROIs from the upcasts across all
deployments, with 11,872 ± 8,387 per deployment and 42% ± 10% of the ROIs classified as
blurry or did not contain an identifiable organism.
Community differences across nucleic acid and sample type
Non-metric multidimensional scaling (NMDS) based on Bray-Curtis Dissimilarity applied to
the ASV datasets of water samples (RNA or DNA) and trap material (DNA) formed three
distinct groupings according to dataset (Figure 1.8a). The different sample types and
methodological analyses yielded somewhat different characterizations of protistan community
composition. The communities inferred from Trap rRNA gene sequences derived from DNA
samples separated farthest from the protistan communities inferred by rRNA transcripts derived
from RNA, while those inferred by rRNA gene sequences derived from DNA were closer but
still distinct. The protistan community composition of water samples inferred from RNA and
DNA grouped more closely to each other but were still distinct at 95% confidence intervals. As

21
noted above, there were also statistically significant differences between protistan communities
inferred by Trap rRNA gene sequences derived from DNA and rRNA transcripts derived from
RNA or DNA with respect to ASV richness, Shannon’s Index, and Inverse Simpson’s Index
(Dunn test, p < 0.05) (Figure 1.S1 b, c, d). Communities described by ASVs or OTUs revealed
quite similar patterns overall (compare Figure 1.8a and Figure 1.S2), therefore the analysis
presented below is based on ASVs.
Protistan community composition using only the ASVs that were present in both rRNA
transcripts derived from RNA and rRNA gene sequences derived from DNA was examined to
check if filtering the dataset by ASVs shared between RNA and DNA significantly altered the
observed community patterns (Laroche et al. 2017). There were 6,457 ASVs found in both rRNA
transcripts derived from RNA and rRNA gene sequences derived from DNA communities (19%
of the whole dataset) (Figure 1.9). The dataset containing only shared ASVs revealed similar
patterns to the whole dataset (compare Figure 1.8 and Figure 1.S3), therefore the whole dataset
was used for subsequent analyses.
There were 33,367 ASVs across all sample types, stations, and depths; 79% (26,335) were
unique to one of the three datasets, 18% (6,104) were shared by two datasets, and only 3% (928)
were shared by all datasets (Figure 1.9a, and b). The 14,155 unique ASVs within the protistan
communities inferred from rRNA gene sequences derived from DNA were 65% alveolates, 4%
stramenopiles, and 12% rhizarians, the 10,808 unique ASVs within protistan communities
inferred by rRNA transcripts derived from RNA were 47% alveolates, 24% stramenopiles, and
6% rhizarians, while the 1,372 unique ASVs observed in the communities inferred by Trap
rRNA gene sequences derived from DNA were 51% alveolates, and 26% rhizarians (Figure
1.9a). The 5,528 shared ASVs within protistan communities observed in rRNA transcripts

22
derived from RNA and rRNA gene sequences derived from DNA were 61% alveolates, 13%
stramenopiles, and 7% rhizarians (Figure 1.9a). The 929 ASVs shared between all sample types
were 59% alveolates, 23% rhizarians, and 7% stramenopiles (Figure 9a). Although ASVs shared
among all sample types were only 3% of the total ASVs, these shared ASVs constituted 44 ±
14% of the total reads per sample, while the unique ASVs were 15 ± 11% of the total reads per
sample (Figure 1.9c).
Influence of depth on community structure
Depth variable protistan community structure inferred from nucleic acid sequencing,
although the effect of sample type (water samples vs. sediment trap material) had a dominant
effect as noted above (Figure 1.8a). Protistan communities inferred from DNA had distinct
structure with depth, as depth explains the variability described by NMDS axis 1 (Figure 1.8b).
The surface, 175 m and 500 m samples were most distinct from one another, while the surface
and DCM were marginally distinct, and the DCM and 175 m overlapped a small amount. There
were higher relative abundances of chlorophytes (2%) and stramenopiles (4%) at the DCM
compared to other depths with <1% chlorophytes and <3% stramenopiles (Figure 1.3). There
were also higher relative abundances of rhizarian protists below the euphotic zone (2% in 15 m
and 32% in 500 m). In contrast to the protistan community structure in water samples assessed
by DNA, water samples inferred from RNA showed little trend by depth and lacked discrete
clustering by depth (Figure 1.8c). There were higher relative abundances of pelagophytes at the
DCM and 175 m (5% in 15 m, 11% in DCM, 7.5% in 175 m, and < 1% in 500 m) (Figure 1.3),
and slightly higher abundances of rhizarian protists at 500m (6% in 15 m and 12% in 500 m).
Eddy polarity effects on the flux of protistan taxa

23
Eddy polarity impacted the flux of some sinking protistan taxa inferred from sediment
trap material analyzed by microscopy (Figure 1.10), contrasting with the relatively small impact
of eddy polarity on communities inferred by molecular analysis (Figures 1.3, 1.4, 1.8).
Interestingly, communities observed using in situ imaging also showed little differences between
the two eddy centers (compare Figure 1.6 a, b). Diatoms comprised 56% of microscopy counts
within the cyclonic eddy center while they only constituted 39% of counts in the anticyclonic
eddy center (Figure 1.4a). In contrast to diatoms, the anticyclonic eddy center had higher relative
abundances of ciliates within the anticyclonic eddy center (46%) compared to the cyclonic eddy
center (19%). Using SLAcorr as a proxy for eddy polarity across the transect, there were fewer
silicious radiolarians, diatoms, and silicoflagellates associated with higher SLAcorr (higher SLA
was observed in the anticyclonic eddy center), while alveolates and non-silicious rhizarians did
not exhibit a trend with SLAcorr (Figure 1.10).
Discussion
The choice of methodological approach(es) is crucial for data interpretation in any
research project. Combining multiple methods of investigation often provides different insights
to the topic or question, improving the robustness of data acquisition and resulting
interpretations. In the present study, we combined traditional microscopy, nucleic acid
sequencing, and in situ imaging to examine and compare protistan community structure
suspended in the water column and on sinking particles within the context of the effect of
mesoscale eddies in the NPSG. Each of the methods applied yielded substantively different

24
insights as noted below, while collectively they provided an improved perspective that would not
have been attained by any single approach.
Overall, the greatest differences in protistan community structure in this study were
observed between communities occurring in water samples (analyzed by nucleic acid
sequencing) and those sinking into sediment traps (analyzed by nucleic acid sequencing or
microscopy). While some of these differences were undoubtedly due to the inherent
methodological advantages and limitations of the approaches as noted in the Introduction, it is
clear that substantive differences between the sample types existed. Communities observed in
water samples based on sequencing of rRNA genes (derived from DNA) were strongly
dominated by alveolates, while communities in trap material were dominated by rhizarian
protists (Figures 1.3b, 1.4a). This discrepancy between rRNA gene sequence information from
suspended and sinking protistan communities were in part reconciled by in situ imaging which
demonstrated the presence of large, relatively rare protists in the water column that were not
readily captured by CTD water sample collection and subsequent filtration. Comparison of
rRNA transcripts derived from RNA and rRNA gene sequencing derived from DNA of water
samples also yielded somewhat disparate characterizations of protistan community structure in
water samples, differences that apparently relate to the relative abundances of total vs.
metabolically active protists (Figure 1.3). The magnitude of these differences (both real and
methodologically influenced) overshadowed differences in protistan community structure
attributed to sampling depth, and to the influence of eddies of opposite polarities.
Sinking protists were a subset of the water column community
Clear differences in protistan community structure were observed between discrete water
samples (rRNA gene sequences derived from DNA, rRNA transcripts derived from RNA) and

25
communities associated with sinking particles (Trap rRNA gene sequences derived from DNA
and microscopy analysis) (compare Figures 1.3, 1.4, 1.8a). Community dissimilarity analysis of
nucleic acid sequence information revealed distinct communities by dataset, with sample type
(rRNA gene sequences derived from DNA, rRNA transcripts derived from RNA, Trap rRNA
gene sequences derived from DNA) explaining the largest variability. In particular, water
samples separated distinctly from trap samples on an NMDS plot (Figure 1.8a). This finding is
consistent with the fact that many unique ASVs were observed in each of the three nucleic acid
samples types (Figure 1.9a, b). Other studies comparing water column and trap communities
using nucleic acid sequences have found similar differences between the two sample types
(Amacher et al. 2013; Cruz et al. 2021; Duret et al. 2019; Gutierrez-Rodriguez et al. 2019).
Nonetheless, sequences shared between all three sample types (rRNA transcripts derived from
RNA, rRNA sequences derived from DNA, Trap rRNA sequences derived from DNA)
constituted a very significant proportion of the total number of reads for all samples (44%;
Figure 1.9c), and trap protistan communities had the lowest species richness and diversity
compared to communities in the water column (Figure 1.S1). These findings imply that protists
appearing in the traps were predominantly composed of a subset of the water column
community.
Microscopy also confirmed the uniqueness of the protistan communities collected in traps
(Figure 1.4b) relative to communities observed in water samples (rRNA transcripts derived from
RNA or rRNA sequences derived from DNA; Figure 1.3). In part these differences reflect
limitations of microscopy to identify minute and morphologically nondescript protists. However,
community composition based on rDNA sequences from trap material (Figure 1.4a) was also
distinct from community composition based on rDNA sequences from water samples (Figure

26
1.3b) as noted above, documenting substantive differences between protistan communities from
traps and water samples. Rhizaria dominated the relative abundances of protists observed by
Trap rRNA gene sequences derived from DNA while ciliates and diatoms dominated the relative
abundances of protists observed using microscopy. The latter distinction between trap
microscopy and Trap rRNA gene sequences derived from DNA may in part be due to high rDNA
gene copy number in alveolates, and exemplifies the stark differences that can be observed when
directly comparing different analytical approaches for assessing protistan community
composition.
In situ imaging yielded yet a different insight than nucleic acid sequencing into protistan
community composition of the water column, with diatoms and various rhizarians comprising
the bulk of protists that could be categorized from the images (Figure 1.6). These large, relatively
rare protistan taxa observed using the SPC were more commonly recorded from trap samples as
noted above (Trap rRNA gene sequences derived from DNA or microscopy; Figure 1.4) than in
discrete water samples analyzed by nucleic acid sequencing (Figure 1.3). We speculate that these
large, rare protists contributed disproportionately to sinking protists in our study but were not
sufficiently represented in the molecular analyses of relatively small aliquots of water. In
agreement with these findings, diatoms have been previously reported as important contributors
to sinking particles at Station ALOHA in the NPSG (Follett et al. 2018; Karl et al. 1996) and in
situ imaging studies have documented the presence of large rhizarian protists in the water
column of oceanic ecosystems (Biard and Ohman 2020; Dennett et al. 2002; Stukel et al. 2018).
These taxa have been reported as important constituents of sinking particles collected in the deep
ocean (Boeuf et al. 2019; Eloe et al. 2011) and in midwater sediment traps (Gutierrez-Rodriguez
et al. 2019; Valencia et al. 2022). Diatoms and many rhizarians form skeletons rich in silica and

27
opal, respectively. Our application of traditional microscopy and in situ imaging in this study
was able to document these populations of relatively rare but presumably important contributors
to particle flux, highlighting the complementarity of the latter approaches to the nowcommonplace genetic methods used to characterize planktonic microbial community structure.
One obvious caveat for comparing our analyses of discrete water samples with material
collected in sediment traps is the different time frames and locations at which samples were
obtained. The PITs collected temporally-integrated samples of sinking particles at a single depth
over the entire period of deployment of each trap (9-12 days) while discrete multiple water
samples (total of 24 samples) were collected largely suspended particles at four depths across the
eddy transect over a span of three days. However, it is unlikely that the differences between these
sampling designs can explain the dramatic differences in protistan community composition noted
above.
Different insights from rRNA and rRNA genes (i.e. rDNA)
Our comparison of methods above revealed major differences among our
characterizations of protistan community composition in water samples and sediment trap
samples, although a direct analysis of the water samples alone by rRNA and rDNA sequencing
also yielded somewhat disparate assessments of community structure (Figure 1.3). Sequencing of
rRNA and rDNA has been previously conducted in order to provide insight into the total
protistan community (revealed by rDNA) as well as the metabolically active component of the
community (revealed by rRNA) (Blazewicz et al. 2013; Hu et al. 2016).
Characterizations of protistan diversity utilizing 18S sequencing on both a global scale
and locally at Station ALOHA specifically have reported alveolate protists as a dominant
component of the total community (de Vargas et al. 2015; Ollison et al. 2021), consistent with

28
the results of the present study. In particular, syndinian protists were a major component of the
alveolate assemblages within the rRNA gene sequences derived from DNA samples compared to
the rRNA transcripts derived from RNA dataset (Figure 1.3b). The important contribution of
these putative parasites has been previously demonstrated within the protistan community at
Station ALOHA (Ollison et al. 2021). We hypothesize that a large proportion of these taxa may
exist as dormant life stages, because they were observed more prevalently in the rRNA gene
sequences derived from DNA samples in the present study than in the rRNA transcripts derived
from RNA samples (i.e. total vs active components of the community), and a recent study that
noted these taxa were not major contributors to the protistan metatranscriptomes of this
ecosystem (Gleich et al. 2024). In contrast, pelagophytes and haptophytes constituted a larger
proportion of the communities observed in rRNA transcripts derived from RNA samples than
rRNA sequences derived from DNA samples (Figure 1.4). These taxa have been previously
shown to have increased relative abundances in the lower euphotic zone of the water column
near Station ALOHA based on RNA gene sequencing and metatranscriptomic analyses (Gleich
et al. 2024; Ollison et al. 2021; Rii et al. 2021).
Depth as a determinant of protistan community composition
Among the water column samples analyzed, depth was also an important factor affecting
protistan community composition, although not to the degree that sample type (water samples vs
trap material) revealed. Depth as a factor affecting protistan community structure was an
expected finding given that significant proportions of the protistan community are phototrophic
or mixotrophic taxa (i.e. dependent on light availability) and that sampling was conducted to 500
m depth. Distinct groupings of water samples were apparent by sample depth (Figure 1.8b, and
c), particularly for the rDNA sequences (Figure 1.8b), indicating the influence of depth albeit the

29
effect was secondary to the effect of sample type (Figure 1.8a). Increased relative abundances of
protistan groups possessing phototrophic capacity such as some stramenopiles and hacrobids
were apparent in the lighted water column relative to deeper samples, while heterotrophic taxa
such as rhizarians exhibited greater contributions in the deep samples. These depth-related
clustering patterns of protistan assemblages by depth have been previously documented (BlancoBercial et al. 2022; Ollison et al. 2021; Rii et al. 2021).
Eddy type affected protists in sinking particle flux
Mesoscale eddies are known to affect several biological processes in the North Pacific
Subtropical Gyre due to localized effects on water column’s structure, with an uplift of the
isopycnals in the cyclonic eddy center and a depression of the isopycnals in the anticyclonic
eddy centers (Barone et al. 2022; Barone et al. 2019). The most pronounced impact due to eddy
polarity in the present study was observed in the mass flux of specific protistan taxa estimated
from the sediment trap material assessed by microscopy (Figure 1.10). An analysis of 16S rRNA
from the same PIT material as this study also found consistent communities within trap material
across the eddy dipole (Poff 2021). Although molecular analysis of the suspended and sinking
assemblages revealed no strong effect of eddies on protistan assemblages, microscopy of sinking
material revealed a greater flux of diatoms and rhizarians within the cyclonic eddy center relative
to the anticyclonic eddy (Figures 1.4b, 1.10). The differences between the open traps used to
collect microscopy samples and the modified funneled traps that collected molecular samples
may explain a portion of the differences but the stark contrast presumably reflects the differences
in imagery and molecular techniques and not the PIT setup. This finding is consistent with
previous studies that have hypothesized that diatoms in the cyclonic eddy may be responding to
the influx of inorganic nutrients from the uplifted nutricline (Benitez-Nelson et al. 2007; Guidi et

30
al. 2012; Vaillancourt et al. 2003). Other studies from the MESOSCOPE research cruise have
highlighted an increase in particulate silica flux within the cyclonic eddy center with no increase
of particulate carbon flux. Empty diatom frustules could explain the increased particulate silica
flux without an increase in carbon flux (Barone et al. 2022). A large proportion of diatoms were
observed by in situ imaging within the water column, further suggesting that diatoms may be
responsible for the increased particulate silica flux (Figure 1.6). The differences between water
column communities and sinking communities characterized using either traditional
(microscopy) or molecular (sequencing) approaches emphasize how eddies significantly affect
the protistan contribution to material sinking out of the euphotic zone.
Eddy effects were observed within a given water column sample type at specific depths
(Figure 1.8b, c). The communities identified by DNA sequencing had the greatest separation
along the NMDS primary axis at 175 m with the two eddy centers being the end members of the
spread (Figure 1.8b). A study of protistan metabolism within a different eddy dipole at Station
ALOHA revealed a similar pattern of depths below the DCM showing the greatest difference
between the eddy centers (Gleich et al. 2024). Gleich 2024, observed differences in community
function within the eddy centers, with an increase in heterotrophy related transcripts in the
anticyclonic eddy center. Overall, these findings suggest that members of the protistan
community are changing their functional role within eddy centers.
Conclusion
Combining multiple methods to sample protistan communities across an anticyclonic and
cyclonic eddy dipole in the North Pacific Subtropical Gyre revealed a complex response of
protistan assemblages to eddy forcing. Samples did not group by location on the transect, which

31
implies both that the eddy centers did not drive assemblage structure and that the suspended
assemblages were more similar to each other across depths than to the sinking assemblages
across the transect. Sample type (rRNA transcripts derived from RNA, rRNA gene sequences
derived from DNA, and Trap rRNA gene sequences derived from DNA) identified different
protistan assemblages, and within a sample type there was no clear trend relating assemblage
structure to SLAcorr across the dipole. Community differences between rRNA gene sequences
derived from DNA and Trap rRNA gene sequences derived from DNA highlight the importance
of analyzing sinking material to understand which species are contributing to particle flux
outside the euphotic zone. Microscopy of sinking material revealed different community
dynamics across the transect compared to rRNA gene sequencing of sediment trap material
highlighting the disparity between the information provided by these two different methods of
assessment. A higher flux of silicious organisms within the cyclonic eddy center suggested that
these organisms may contribute to higher organic matter observed below the euphotic zone in
cyclonic eddy centers. In situ imaging explained some of the discrepancies between the
sequencing of water column samples and what was observed in the sediment traps. The
differences in methodologies highlight the importance of choosing methods appropriate for the
scientific question, and when possible combining multiple sampling and analytical methods to
capture community dynamics.
Acknowledgements
The authors declare that they have no conflicts of interest. This study was funded by the Simon’s
Foundation Grant P4802 (to DAC) and 721252 (to DMK). The authors would like to
acknowledge the captain and crew of the R/V Kilo Moana during the 2017 MESOSCOPE cruise.

32
The authors would like to thank Benedetto Barone for planning and executing a successful 2017
MESOSCOPE cruise. The authors are grateful to Devin Ratelle and Eric Orenstein for their
support in SPC analysis.

33
Figure 1.1
Regional map of the study area north of Oʻahu, Hawai'ian Islands with inset showing the
sampling sites. The background color depicts SLAcorr (as described in Barone 2019) on July 1,
2017. Filled circles mark the locations of PIT deployments labeled P1-P12, with lines showing
the drift tracts followed during deployment. The six stations at which water column samples
were collected are labeled S4-S14 (S6 was considered the anti-cyclonic eddy center and S12 was
the cyclonic eddy center). Numbers are not consecutive because not every station along the
cruise transect was used for this analysis. The circle above Oʻahu indicates the 6 nmi location of
Station ALOHA.

34
Figure 1.2
Depth profiles of temperature (a), chlorophyll (b), potential density (c), and nitrate + nitrite (d),
at water column stations across the eddy dipole. The color of the line is based on sea level
anomaly (SLAcorr) at each station, with red denoting positive SLAcorr (anticyclonic eddy center)
and lavender denoting negative SLAcorr (cyclonic eddy center). Intermediate SLAcorr are shown
as neutral colors.

35
Figure 1.3
Taxonomic composition of the protistan community in water samples based on relative
abundance of ASVs determined by sequencing the rRNA transcript derived from RNA (a) or
rRNA gene sequence derived from DNA (b) of the V4 region of small subunit ribosomal RNA
genes. Samples are ordered vertically by depth and station across six stations occupied across the
eddy-dipole transect. S6 (red star) was within the anti-cyclonic eddy center, while S12 (blue star)
was within the cyclonic eddy center.

36
Figure 1.4
Taxonomic composition of the protistan community associated with sinking particles based on
relative abundances of ASVs in Trap rRNA gene sequence derived from DNA (a) and by
microscopy (b). The twelve samples are ordered across the eddy transect from the anticyclonic
eddy center (P12) to the cyclonic eddy center (P1).

37
Figure 1.5
Examples of organisms observed in sediment trap material. A-D, Alveolata- Ciliates; A- tintinnid
Parundella sp., B- tintinnid Dictyocysta sp., C & D- unk. E-H Alveolata- Dinoflagellates; EAlexandrium sp., F- Dinophysis sp., G- Ornithocercus sp., H- Pyrocystis sp. I-N, Rhizaria; IAcantharia- unk., J- Acantharia- Diploconus sp. K- juvenile Foraminifera- unk., L-RadiolariaDictyocoryne profunda., M- Radiolaria- Peromelissa Phalacra, N- Radiolaria- Hexacontium sp.,
O- Radiolaria- Theocorythium trachelium. P-S, Stramenopila; P- Diatom- Rhizosolenia sp., QDiatom-Asteromphalus sp., R- Diatom-Planktoniella sol. S- Silicoflagellate- Dictyocha sp. Scale
bars represent 50 μm for all except N (100 μm) and P (200 μm).

38
Figure 1.6
Mean relative abundances of identifiable organisms observed using the Scripps Plankton Camera
(SPC) in the anticyclonic eddy center (a) and the cyclonic eddy center (b). Size of the box is
proportional to the relative abundance, and color is based on the organismal type (rad colony
short for Radiolaria colony).

39
Figure 1.7
Examples of organisms observed using the Scripps Plankton Camera (SPC). A-F, Diatoms; AChain, B- Circle, C-E- Single, F- Rhizosolenia mat (classified as chain). G-H- Trichodesmium. IO, Mesozooplankton; I- Appendicularia, J- Chaetognath, K-M- Gelatinous, N-O- Crustacean. PQ, Radiolaria; P- colonial Radiolarians, Q- solitary Radiolarian, R-S- Foraminifera, T-UAcantharia. Scale bars represent 1 mm for all except F, J, M, P, S (1 cm).

40
Figure 1.8
NMDSs from Bray-Curtis dissimilarity of protistan community composition based on ASVs.
Filled colors of symbols for all figures are based on SLAcorr, which is a proxy for eddy polarity
across the transect. a) Shape of the symbols represents the sample type (rRNA transcripts derived
from RNA, rRNA gene sequence derived from DNA, Trap rRNA gene sequence derived from
DNA), 95% confidence ellipses are based on sample type. b) and c) Shape of the symbols
indicates depth of collection of the discrete water samples, 95% confidence ellipses encompass
each depth. b) Protistan communities in water samples assessed by rRNA transcripts derived
from RNA. c) Protistan communities in water samples assessed by rRNA gene sequencing
derived from DNA.

41
Figure 1.9
Shared ASVs in rRNA transcripts derived by RNA, rRNA gene sequencing derived by DNA,
Trap rRNA gene sequencing derived by DNA by sample type. a) Upset plot with x axis
indicating which samples are included in the histograms above, indicating taxonomic
composition within the groupings; the inset map is a larger version of the ASVs present in all
sample types, b) Venn diagram with color of circles defining sample type, c) the percentages of
the total number of reads per sample of ASVs unique to each sample type, ASVs shared by two
sample types, or ASVs present in all sample types (indicated by box color), grouped by sample
type.

42
Figure 1.10
Flux of sinking cells identified by microscopy from PITs plotted by SLA for each station. Each
plot depicts a different taxon indicated by the color key, with a model two regression of the data.

43
Figure 1.S1
Sequencing depth (a) and diversity metrics (b)- ASV richness, (c)- Shannon’s index, (d)- Inverse
Simpson’s index) for protistan assemblages inferred from sequence data (ASVs) between rRNA
transcripts derived by RNA, rRNA gene sequencing derived by DNA, Trap rRNA gene
sequencing derived by DNA. Colors indicate sample types. Statistical significance is indicated
by ****: p.adjust<=0.0001 (Dunn test).

44
Figure 1.S2
NMDS from Bray-Curtis dissimilarity of protistan communities inferred from OTUs formed at
97% sequence similarity from rRNA transcripts derived by RNA, rRNA gene sequencing
derived by DNA, Trap rRNA gene sequencing derived by DNA. Filled colors of the symbols are
based on SLAcorr. Shape of symbols and the 95% confidence ellipses are based on sample type.

45
Figure 1.S3
NMDS from Bray-Curtis dissimilarity of protistan communities inferred from shared ASVs. The
datasets for the rRNA transcripts and rRNA gene sequences were the ASVs shared between the
water column datasets, for the Trap rRNA gene sequences the ASVs were shared between all
datasets. Filled colors of the symbols are based on SLAcorr. Shape of symbols and the 95%
confidence ellipses are based on sample type. b) and c) Shape of the symbols indicates depth of
collection of water column samples, 95% confidence ellipses encompass each depth. b) Protistan
communities in water column samples assessed by rRNA gene sequencing derived by DNA. c)
Protistan communities in water column samples assessed by rRNA transcripts derived by RNA.

46
Chapter 2: Experimental evaluation of microzooplankton grazing methods: fact or fiction?
Jennifer L. Beatty*1
, Brittany P. Stewart1
, Kendra A. Turk-Kubo2
, Jonathan P. Zehr2
, David A.
Caron1
1 University of Southern California, Los Angeles, CA 90089, USA
2 University of California at Santa Cruz, Santa Cruz, CA 95064, USA
ABSTRACT
Grazing on picoplankton by microbial eukaryotes is a fundamental process within aquatic
food webs, particularly in oligotrophic regions that are typically dominated by
picophytoplankton. Remarkably, classical methods that have been used for decades to measure
this process in the field have yet to be fully evaluated under carefully controlled laboratory
conditions. This study employed two commonly used field techniques, the dilution technique
(DLN) and the disappearance of fluorescently labeled bacteria (FLB), in a laboratory experiment
designed to compare these measurements to picophytoplankton mortality observed directly in
culture based on changes in cell abundance. Both experimental techniques were applied to a
culture of the cyanobacterium, Prochlorococcus, subjected to a nanozooplanktonic grazer in the
presence and absence of a cyanophage that lyses Prochlorococcus. Summed across multiple
treatment types, mortality rates estimated by FLB disappearance displayed high variability and
on average underestimated observed mortality rates by ~80%. The dilution technique also
underestimated observed mortality rates by ~50% but displayed relatively low variance. There
were no statistically significant differences between mortality rates measured or observed in the
presence or absence of viruses, indicating the dominant role of grazer-mediated mortality in the

47
experimental setup. Overall, the DLN and FLB technique provided reasonable, albeit somewhat
underestimated microzooplankton-mediated mortality rates under ‘ideal’ conditions, but caution
should be exercised in interpreting these rates in field studies where the assumptions of the
methods may be difficult to meet.
1. INTRODUCTION
Grazing by microbial eukaryotes is a fundamental process of aquatic food webs,
dominated by phagotrophic protists that consume heterotrophic and autotrophic picoplankton
populations. As such this process is central in the movement of carbon to higher trophic levels as
well as nutrient recycling (Sherr and Sherr 2002). Grazing on picoplankton is a dominant trophic
process in oligotrophic regions of the world oceans where picophytoplankton constitute most of
the biomass of primary producers and are generally too small for effective mesozooplankton
grazing. Phagotrophic protists display a vast array of feeding strategies across a wide spectrum
of heterotrophic and mixotrophic taxa (Caron et al. 2012; Jürgens and Massana 2008; Stoecker et
al. 2017). Microbial eukaryotic grazers also demonstrate varying levels of prey selectivity
relating to size and shape, chemistry, motility, and perhaps other features (Jürgens and Massana
2008; Sherr and Sherr 2002). These features of protistan grazing present significant challenges to
measuring their rates of prey consumption.
Two commonly employed approaches developed to quantify trophic interactions between
microbial consumers and picoplanktonic prey are the dilution technique (DLN), and the use of
fluorescently labeled bacteria (FLB) (Caron 2001; Landry et al. 1995; Landry and Hassett 1982;
Sherr et al. 1987). These methods are the most extensively employed approaches for estimating
microbial grazing rates in natural marine and freshwater samples.

48
The dilution technique (DLN) has generally been employed to measure phytoplankton
growth rates and mortality (grazing) rates by serially diluting the microbial grazers in a water
sample and measuring apparent growth rates of the prey following incubation (usually 24 hr) as a
consequence of grazer dilution (Landry et al. 1995; Landry and Hassett 1982). Phytoplankton
growth rates are determined from changes in cell number or biomass across a series of dilutions
prepared from a natural water sample and water from which all living cells have been removed
by filtration. Nutrients are added to all bottles in the series prior to incubation in order to
compensate for nutrients produced by grazers, and unenriched water is also incubated to
determine phytoplankton growth rates in the absence of nutrient addition (i.e. ‘natural’
phytoplankton growth rates) (Landry et al. 1995). Apparent phytoplankton growth rates in each
bottle of the dilution sequence are plotted against the degree of dilution, and the slope of the line
is calculated to determine the phytoplankton mortality rate attributable to grazing. There are
three basic assumptions with this method: dilution does not impact phytoplankton intrinsic
growth rate, per-cell clearance rates of consumers are unchanged across the dilution series
(although the rate of prey consumption is affected by dilution), and phytoplankton intrinsic
growth rate is exponential and unchanged across the dilution series. The DLN technique has
been used globally since the 1980s, and several limitations and assumptions of the method have
been identified, as well as approaches to compensate for these limitations (Calbet 2008; Landry
et al. 1995; Sandhu et al. 2018; Schmoker et al. 2013).
Fluorescently labeled bacteria (FLB) (Jürgens and Massana 2008; Sherr et al. 1987) have
been employed to estimate the rate of bacteria-sized particle consumption. The approach
assumes that the consumption and digestion of FLB mimic the rate of consumption and digestion
of natural prey of similar size. For experiments examining community-level rates of prey

49
mortality, FLB are added to samples at tracer amounts, incubated, and the ‘disappearance rate’ of
FLB is determined using microscopy or flow cytometry. The basic assumption of the method is
that grazers consume natural prey at the same rates as FLB during the incubation period. Filtered
water samples containing FLB without grazers are also incubated to account for non-grazerrelated changes in FLB abundances. FLB experiments have been applied widely in aquatic
systems to estimate grazing of bacterial size particles including heterotrophic bacteria and
picophytoplankton (Jürgens and Massana 2008; Weisse et al. 2016).
These two methods have been employed for decades to measure microbial grazing rates
across space and time (Calbet 2008; Medina et al. 2017; Rocke et al. 2015; Schmoker et al.
2013). Theoretical papers have explored the challenges associated with the assumptions of the
methods, suggesting that the DLN technique overestimates grazing, that dilution can impact both
grazer and prey populations, and that trophic cascades within dilutions may alter grazing rates
(Agis et al. 2007; Beckett and Weitz 2017; Calbet 2008; Dolan and McKeon 2005). The FLB
method is susceptible to errors associated with the choice of prey surrogate (which can impact
the grazing rate measurements), and the model choice for calculating grazing rates (Fu et al.
2003; Sandhu et al. 2018; Vaqué et al. 1994). Despite these confounding issues, and the
widespread use of both techniques up to the present time, few studies have attempted to quantify
these potential errors in a controlled laboratory setting.
This study provides the first comparison of multiple methods employed to measure
microzooplankton grazing rates within a carefully controlled laboratory experiment. The goal
was to compare mortality rates estimated using the dilution technique and the disappearance of
FLB to mortality rates measured directly from changes in prey abundance in cultures. The simple
experimental system included a picophytoplankton, Prochlorococcus, a heterotrophic

50
nanoplanktonic protist (Paraphysomonas bandaiensis), an ambient heterotrophic bacterial flora,
and a cyanophage capable of lysing Prochlorococcus. Treatments were conducted with either
predator (protist or virus) acting singly, as well as in combination. The DLN technique and FLB
disappearance experiments were conducted during 48-hour experiments, at times when
Prochlorococcus populations were decreasing rapidly due to mortality. Both DLN and FLB
techniques underestimated observed mortality (by ~50% and 80% respectively), and FLB
displayed high variability compared to DLN. The presence or absence of grazers did not impact
the average difference between the experimentally derived and observed mortality rates. These
results highlight that even in ‘ideal’ conditions, the DLN and FLB methods can yield variable
results and suggest that multiple grazing experiments should be conducted whenever possible to
capture a reasonable estimate.
2. MATERIALS & METHODS
2.1 Experimental setup
The format of the experimental approach used in this study is described in detail in
(Lindell et al. in prep). The goal of this aspect of the study was to assess two methodologies
commonly used in the field for estimating rates of microbial mortality mediated by protistan
grazers. The design involved using a controlled laboratory setup consisting of a
picophytoplankter (Prochlorococcus), a nanozooplanktonic grazer capable of consuming the
picophytoplankter, and a cyanophage capable of lysing the picophytoplankter (Fig 2.1).
Experimental treatments were conducted without virus (Fig 2.1a) and with virus (Fig 2.1b) as
part of a larger design to compare viral-mediated and protist mediated mortality. Treatments
containing virus and protists or protists alone were included in the analyses conducted here to

51
determine if the presence of lytic virus might impact the estimation of protist-mediated mortality
rates. An accompanying mixed assemblages of heterotrophic bacteria was present in the initial
cultures of the virus and protist but not a focus of the study. The fundamental approach of the
study was to experimentally estimate mortality rates using the dilution technique (DLN) and the
disappearance of FLB (FLB) and compare those rates to the observed mortality rate of
Prochlorococcus over the same time interval based on changes in cell abundances of the
picophytoplankter in the cultures (Fig 2.1).
Briefly, experimental vessels of 10 L polycarbonate bottles were incubated for 48-hour
periods. All bottles were kept in a walk-in incubator at 21°C on a 14-hr:10-hr light: dark cycle at
50 µmol m-2 s-1
. All treatments relevant to this study contained the picoplanktonic
cyanobacterium Prochlorococcus (a strain of Prochlorococcus MED4) and various combinations
of a protistan grazer capable of consuming Prochlorococcus (Paraphysomonas bandaiensis,
hereto referred to as the ‘grazer’), and a virus (P-SSP7, a T7-like cyanophage hereto referred to
as the ‘virus’) capable of lysing Prochlorococcus. Both the Prochlorococcus and grazer cultures
had mixed attendant bacterial assemblages present. Treatments relevant to this study included a
control vessel containing Prochlorococcus alone (5 vessels), Prochlorococcus + high initial
concentration of the grazer (3 vessels), Prochlorococcus + low initial concentration of the grazer
(3 vessels), Prochlorococcus + low initial concentration of the grazer with virus (9 vessels), or
Prochlorococcus + high concentration of the grazer with virus (9 vessels). Initial abundances in
all treatments are provided in Sup Table 2.1.
Samples were collected every two hours for the measurement of abundances of
Prochlorococcus and the grazer, and every six hours for the measurement of abundances of
heterotrophic bacteria throughout a 48-hour experimental period. Samples were analyzed on an

52
Influx flow cytometer fitted with 457 nm and 488 nm lasers and a small particle detector (BD
Biosciences, San Jose, CA) according to procedures described in Lindell et al. (in prep). Samples
were fixed at a final concentration 0.1% glutaraldehyde, preserved for 15 minutes in the dark,
flash frozen in liquid nitrogen and stored at -80°C. Samples for measurements of grazer and
heterotrophic bacterial abundances were stained at a final concentration with 1X SYBR Green I
and incubated for 15 minutes in the dark. Prochlorococcus was detected from forward scatter
and red fluorescence (692 ± 20), grazers were detected by forward scatter and green fluorescence
(530 ± 20), and heterotrophic bacteria were detected by forward scatter and green fluorescence
(530 ± 40). Samples analyzed for virus abundances were taken every six hours throughout the
48-hour period and were quantified by qPCR for encapsulated virus DNA. Samples were
subjected to DNase treatment immediately after collection to remove free virus DNA, and were
then flash frozen in liquid nitrogen and stored at -80°C (no fixative). Samples were thawed,
diluted 100-fold and processed for qPCR quantification using SYBR Green I qPCR assays in a
LightCycler 480 qPCR machine (#05015278001, Roche Diagnostics) (Baran et al. 2018).
2.2 Prochlorococcus mortality rates estimated using the dilution technique
Dilution technique experiments (Landry et al. 1995; Landry and Hassett 1982) were
conducted to estimate Prochlorococcus mortality due to grazing once or twice for each treatment
bottle for each 48-hour experiment, during the periods of rapid decrease in the abundances of
Prochlorococcus. A subsample from each treatment bottle was collected in a 500 mL flask, and a
dilution series was prepared using 0.2 �m filtered media. The dilution series, run in triplicate,
consisted of 100% (150 mL culture), 80% (120 mL culture, 30 mL filtered media), 40% (60 mL
culture, 90 mL filtered media), and 20% (30 mL culture, 120 mL filtered media). Following
dilution series preparation, initial samples of 1.5 mL were collected and preserved with 10%

53
formalin at a final concentration of 1% formalin and stored at -80ºC until analysis by flow
cytometry. Dilution flasks were incubated in the walk-in incubator for the duration of the
incubation (4-12 hours, depending on the treatment). Incubations were shorter than for traditional
dilution technique experiments because prey abundance decreased rapidly in the treatments.
Final samples of 1.5 mL were collected from each flask and preserved as for initial samples. All
samples (initial and final) were analyzed on an Influx flow cytometer equipped with a 488 nm
laser at the University of California, Santa Cruz (BD Biosciences, San Jose, CA).
Prochlorococcus abundances were detected and quantified using forward scatter and red
fluorescence (692 ± 40).
Phytoplankton growth and mortality rates were calculated using Model I linear regression
of apparent growth rate versus dilution factor. The slope of the regression yielded the mortality
rate (m, units: d-1 )(Landry and Hassett 1982).
2.3 Prochlorococcus mortality rates estimated using the fluorescently labeled bacteria
disappearance
Fluorescently labeled bacteria disappearance (FLB) experiments (Jürgens and Massana
2008; Sherr et al. 1987) were also used to estimate rates of Prochlorococcus due to grazing by P.
bandaiensis, assuming that FLB disappearance mimicked Prochlorococcus consumption. FLB
were prepared from a culture of Dokdonia donghaensis following standard protocol (Sherr et al.
1987, Caron 2001). Briefly, D donghaensis, was grown in ZoBell medium, rinsed and starved in
filtered seawater for two days, stained and heat killed with 5-(4,6-dichlorotriazin-2-yl)
aminofluorescein (DTAF), aliquoted and stored at -80°C until used for experiments.
All FLB experiments were performed in triplicate in 500 mL culture flasks, similar to the
dilution technique experiments. Subsamples from each 10 L culture vessel were added to 500

54
mL culture flasks for a total volume of 150 mL. FLB were added to flasks at approximately 20-
30% of the abundances of Prochlorococcus at the time of subsample removal, determined from
flow cytometer counts two hours prior to initiating the FLB experiments. Controls composed of
FLBs in 0.2 �m filtered media were performed concurrently to characterize any non-grazing
losses of FLB. All flasks were placed in the incubator with the original experimental 10 L culture
vessels for the duration of the experiment. Samples were removed immediately after addition of
FLB (time zero) and again after 3-12 hours of incubation (depending on the trajectory of
population abundances in a treatment), preserved for flow cytometry with 10% formalin at a
final concentration of 1% formalin and kept at -80ºC until analyzed. Sample analyses were
performed using an Influx flow cytometer at the University of California, Santa Cruz (BD
Biosciences, San Jose, CA).
FLB disappearance rates (g, units: d-1
), and by proxy, Prochlorococcus mortality rates,
were determined using the following equation, using the initial FLB abundances (F0) and
abundances at the end of the incubation (Ft) and the length of incubation in days (t) (Marrasé et
al. 1992):
� = ln %
�!
�"
' ∗ (− 1
�
)
Mortality rates were calculated from the change in FLB abundance in control bottles
conducted at six time points to account for changes in FLB abundance due to non-grazing
effects. The mean value of mortality in the control bottle was 1.35 d-1 and was subtracted from
calculated mortality rates determined in the experimental bottles.
2.4 Observed Prochlorococcus mortality rates based on changes in abundances
Mortality rates in the Prochlorococcus cultures were also determined in each
experimental treatment containing grazers (with or without virus) based on changes in

55
Prochlorococcus abundances over the same time interval for which mortality rates were
measured using the dilution technique or the FLB disappearance technique. These ‘observed’
Prochlorococcus mortality rates were calculated using the following equation, where P0 is the
initial Prochlorococcus abundance, Pt is abundance at the end of the incubation, and t is the
length of incubation in days:
� = ln %
�!
�"
' ∗ (− 1
�
)
Data visualization and statistical analysis were completed in R (R Core Team 2022).
3. RESULTS
3.1 Prochlorococcus abundance dynamics were similar across treatments
Across all experimental treatments containing grazers, grazer-mediated mortality reduced
Prochlorococcus abundances by 2-3 orders of magnitude generally within 48 hours compared to
initial conditions and to the controls containing only Prochlorococcus (Fig 2.2a). Treatments
containing only virus (i.e. no grazer) reduced Prochlorococcus abundances generally later and to
a lesser degree compared to treatments with grazers present (Fig 2.2a). Unsurprisingly,
treatments with initially high grazer abundances (with or without virus) reduced
Prochlorococcus abundances more rapidly (generally within 24 hours) than treatments initiated
with low grazer abundances (with or without virus) (Fig 2.2a).
The shapes of all Prochlorococcus abundance curves subjected to protistan grazing
appeared similar across all treatments (except for one replicate of the Low Grazer and Virus
treatment that did not decrease quite as fast as the others; Fig 2.2a). However, the timing of the
exponential decreases in abundances occurred at different times during the 48-hour experiment.
The dynamics of Prochlorococcus abundances in all treatments were therefore compared by

56
superimposing the curves along the x-axis according to the point at which each treatment
attained a Prochlorococcus abundance of log 5.6 (Fig 2.2b). This inflection point was chosen
because it was approximately halfway through the time period over which the decreases in
Prochlorococcus abundances were exponential (i.e. linear on a log-plot of abundances vs. time;
Fig 2.2b). Superimposing the Prochlorococcus abundance curves addressed temporal offsets in
the onset of rapid decreases in Prochlorococcus abundances between treatments yet allowed
comparison of the shape of the abundance curves. The superimposed curves revealed that all the
treatments demonstrated very similar dynamics for changes in Prochlorococcus abundances for
either the Grazer Only treatments or the Grazer and Virus treatments (yellow and blue lines in
Fig 2.2b, respectively).
The comparison of mortality curves (Fig 2.2b) demonstrated largely non-significant
fluctuations in Prochlorococcus abundances from the start of each experiment until
approximately log 6.5, when all treatments underwent rapid decreases in Prochlorococcus
abundances. Prochlorococcus abundances also demonstrated divergence amongst treatments
below log 4.5, presumably due to random or treatment-specific differences in the contribution of
bacterial growth (see Discussion). Therefore, the linear slope of each mortality curve was
calculated for values between log 4.5 to 6.5 (Fig 2.2b). The slopes of the mortality curves in
treatments containing the grazer and virus were highly consistent, as were curves in treatments
containing only grazers, with the grazer and virus treatments exhibiting slightly more rapid
decreases in Prochlorococcus abundances than treatments containing only grazers.
3.2 Comparison of observed and experimentally estimated Prochlorococcus mortality rates
Mortality rates were expected to be strongly affected by the abundances of
Prochlorococcus prey and their predators, therefore rates were interpreted in relation to the

57
average Prochlorococcus abundances at the time of each experiment calculated using the
equation of Heinbokel (Heinbokel 1978):
�$ = (�! − �")
ln(�!) − ln(�")
Where �$ is the average abundances of Prochlorococcus during each experimental time
interval, and �" is the abundances of Prochlorococcus at the initial time point and �! is the
abundances of Prochlorococcus at the final time point.
Prochlorococcus mortality rates estimated or measured in three ways (experimentally
estimated using the dilution technique (DLN) or fluorescently labeled bacteria (FLB)
disappearance and observed directly from changes in Prochlorococcus abundances) were
compared across treatments by comparing rates obtained at similar abundances of
Prochlorococcus (assuming prey abundance is a primary determinant of grazer-mediated
mortality). Mortality rates determined across treatments, method and time intervals ranged
widely, with overall averages of 3.75 ± 0.5 day-1 for the DLN experiments, 5.85 ± 1.8 day-1 for
the FLB experiments, and 7.41 ± 1.1 day-1 when calculated from changes in Prochlorococcus
abundances (Table 2.2). When mortality rates at similar average Prochlorococcus abundances
were compared, there was no apparent relationship between the presence or absence of virus and
the mortality rate estimated or observed, indicating the dominant role of grazer-mediated
mortality in all treatments with grazers (yellow and blue symbols in Fig 2.3).
Mortality rates of Prochlorococcus observed in the cultures based on changes in
Prochlorococcus abundances displayed an increase in mortality at Prochlorococcus abundances
between log 5 and 6, and lower rates at higher and lower Prochlorococcus abundances (Fig
2.3a). In contrast, mortality rates estimated using the DLN technique did not display any clear
relationship to Prochlorococcus abundances at the times that the various experiments were

58
conducted (Fig 2.3c). Mortality rates estimated using the FLB technique were somewhat
intermediate to these latter two trends. Rates estimated using the FLB technique tended to yield
higher mortality rates at lower Prochlorococcus abundances, as well as much higher variability
in the rates estimated (Fig 2.3b). There did not appear to be a relationship between mortality
rates and the light cycle at the time the experiments were conducted (Fig 2.S1a).
3.3 Estimated mortality rates of Prochlorococcus differed from observed rates
Percent difference between the two methods for estimating Prochlorococcus mortality
rates (FLB and DLN) and the observed mortality rates (based on changes in Prochlorococcus
abundances) were determined to explore differences between the experimentally estimated and
observed rates (Fig 2.4a). Outliers were identified per experimental technique (FLB or DLN)
using the interquartile region of percent difference values; the experiments identified as outliers
were removed for further analysis (4 values were removed from the DLN dataset and 15 values
were removed from the FLB dataset). The percent differences in rates of mortality (FLB vs.
observed or DLN vs. observed) were plotted against the log of average Prochlorococcus
abundances at the time of each experiment to consider the dependence of mortality rate on
Prochlorococcus abundance (Fig 2.4a). There was considerable variability in the differences
between estimated and observed mortality rates across the range of Prochlorococcus abundances
at which experiments were conducted, with no obvious trend between treatments with or without
virus present. Comparing averaged results for FLB and DLN experiments, the average mortality
rates estimated using the FLB technique were not significantly different from zero for the
treatments with or without virus, but both exhibited greater ranges than the DLN experiments.
Average mortality rates estimated using the DLN technique for treatments with and without virus
were both less than zero (i.e. on average, the DLN technique underestimated mortality; Fig 2.4b).

59
There was no obvious influence of the light cycle on these relationships (i.e. the high range of
values yielded by the FLB technique and the underestimation of mortality rates by the DLN
technique; Fig 2.S1b).
4. DISCUSSION
The main goal of this study was to use a carefully controlled laboratory experiment to assess
the accuracy and precision of the dilution technique (DLN) and fluorescently labelled bacteria
(FLB) disappearance experiments for measuring grazer-mediated mortality of picoplanktonic
prey. Experiments were conducted with high abundances of a single prey to facilitate
measurements in the lab and to shorten the duration of the experimental incubations for
measuring mortality. These abundances were not representative of natural conditions, but the
trophic interactions investigated were representative of those occurring in natural water samples.
Several of the treatments included viruses as a part of a larger study (Lindell et al. in prep) while
providing insights into the grazer-focused methods examined here.
Grazer-mediated mortality dominated Prochlorococcus mortality rates in all treatments. Viral
lysis appeared to be additive to the impact of grazing, but always contributed a minor amount to
Prochlorococcus mortality. Therefore, we included treatments containing viruses in our
analyses, although we also analyzed the contribution of viral lysis separate from that of grazer
activities (Fig 2.2). Both methods of measuring mortality rates underestimated the observed
(actual) mortality rates determined directly from changes in prey abundance over the same time
intervals examined for the two methods, although the variance among the DLN rates was smaller
than for the FLB experiments. Averaged across all experiments, mortality rates determined by
DLN and FLB were approximately 80% and 50%, respectively, of the mortality rates determined

60
by changes in Prochlorococcus abundances during the experiments (Fig 2.4). However, variance
among the FLB-determined rates was considerably greater than for the DLN-determined rates
(Fig 2.4b). These findings indicate that under relatively ideal conditions (i.e. employing a
carefully controlled and well-characterized culture system to verify actual mortality rates), the
DLN and FLB methods provided underestimates of Prochlorococcus mortality rates, and
relatively high variability among individual experiments. One might therefore expect even lower
accuracy and perhaps greater variability when attempting to quantify grazer-mediated mortality
in a field setting where assumptions of these methods are difficult to ascertain.
4.1 Mortality-mediated changes in Prochlorococcus abundances
Rates of decreases and the magnitude of decreases in the abundances of Prochlorococcus
in the experimental treatments were very different when only viruses were present compared to
treatments where grazers were present (both grazer only and grazer + virus treatments) (Fig
2.2a). The rates of decline of Prochlorococcus abundances were substantively less in the
presence of virus alone compared to the presence of grazers. Additionally, viruses did not reduce
Prochlorococcus populations to the low abundances that grazers did, although these aspects of
virus-mediated mortality presumably are strongly influenced by the specific virus-prey pair
(Fuhrman 1999). In our experimental design, the virus was an inferior (but additive) source of
mortality for Prochlorococcus relative to the protistan grazer.
Rates of decreases in Prochlorococcus abundances were exponential (straight line
portions of curves in Fig 2.2b in the presence of grazers alone or grazers + viruses), albeit with
slightly different slopes owing to the contribution of virus-mediated mortality in the grazer +
virus treatments (Fig 2.2b). The onset of substantive decreases in Prochlorococcus abundances
was also temporally offset among the latter treatments primarily due to whether a treatment

61
began with high or low initial concentrations of grazers. Slight differences in the initiation time
of the treatments may also have contributed to these temporal offsets. Additionally, among the
low or high grazer treatments, decreases of Prochlorococcus abundances were observed earlier
in the presence of viruses. The rapid decrease of Prochlorococcus abundance in the presence of
viruses was expected given the multiple modes of mortality (grazer and virus) compared to a sole
mode of mortality (grazer) (Fig 2.1) (Berdjeb et al. 2011; Simek et al. 2001). These observations
were facilitated by overlapping the mortality curves at a single log abundance of
Prochlorococcus that was approximately half of the initial and final abundances observed in the
treatments (Fig 2.2b).
4.2 Limited accuracy of mortality rates by DLN and FLB methods
Comparison of the accuracy of the DLN and FLB methods (relative to observed mortality
rates) was restricted to the range of Prochlorococcus abundances where direct measurements of
mortality could be verified from changes in Prochlorococcus abundance. Grazer abundances at
the early stages of the 48-hr incubations were too low to significantly affect Prochlorococcus
abundances over short experimental periods. Therefore, accurate measurements of changes in
Prochlorococcus abundance by flow cytometry could not be obtained, and thus no comparisons
of the two methods were performed during this period (log Prochlorococcus abundances > 6.5 in
Fig 2.2b). Conversely, the presence of an attendant bacterial assemblage in all treatments
confounded a comparison of methods during the later portion of the incubations (log
Prochlorococcus abundances < 4.5) in Fig 2.2b. Bacterial abundances at the earlier stages of the
48-hr incubations were minimal, and therefore should not have substantively affected rate
measurements. However, as Prochlorococcus were consumed and organic material produced by
the grazer and/or virus, that material undoubtedly stimulated cryptic growth of the heterotrophic

62
bacterial assemblage during the latter part of the incubations. Consumption of bacteria by the
grazer (in lieu of, or in addition to consumption of Prochlorococcus) would confound the
ingestion of Prochlorococcus by the grazer, at least for the FLB method which does not directly
measure Prochlorococcus ingestion but uses the FLB as a proxy for the consumption of all
particles of appropriate size (in our case, both Prochlorococcus and heterotrophic bacteria).
The light regime is another factor that might have affected a direct comparison of results
from the DLN or FLB methods and the observed rates of mortality. Our experimental
incubations were short relative to the time generally employed in DLN or FLB incubations
carried out in the field (a few hr relative to 24 hr, respectively). Field measurements therefore
generally span a full light cycle, whereas our grazing measurements were conducted completely
during the dark cycle, completely during the light cycle, or a combination of both. We therefore
examined the potential impact of these different light regimes on the resulting accuracy and
precision of the DLN and FLB estimations of mortality rates (Fig 2.S1). However, there were no
observable trends between observed mortality rates and the light regime (Fig 2.S1a), a finding
consistent with at least one other study that found that light did not significantly impact grazing
rates (Marrasé et al. 1992).
Given the caveats noted above, mortality rates estimated using fluorescently labeled
bacteria (FLB) experiments revealed a similar trend to the corresponding observed mortality
rates with a peak at intermediate Prochlorococcus abundances (Fig 2.3). This finding indicates a
general pattern of FLB rates to mirror the observed rates. Also, the overall average among
mortality rates determined using the FLB disappearance technique was reasonably close to the
average observed morality rate (80% overall; Fig 2.4). However, the FLB mortality rates

63
exhibited higher variability throughout the time series than the observed rates (Fig 2.3a vs. b; Fig
2.4).
The FLB technique relies on the uptake and digestion of FLB at the same rates as natural
prey are ingested. Differences in size, chemical composition, motility and other factors would be
expected to affect the ability of FLB to mimic prey ingestion (Florenza and Bertilsson 2023).
Overestimation of mortality rates would occur if the predators preferentially consumed the
surrogate prey, while the method should underestimate mortality rates if the predators selected
against the surrogate prey. In our experiment, the size of FLB was chosen to approximate the
size of Prochlorococcus. We therefore assume that our experimental design yielded results that
might be superior (i.e. more accurate rates) to most FLB disappearance experiments conducted in
the field, because of the presence of our choice of a prey of uniform size and composition
(Prochlorococcus) and the choice of our FLB. Natural prey assemblages are composed of a
mixture of prey sizes, shapes and species, and therefore might be less adequately represented by
a single FLB type, as used in most FLB disappearance experiments. Some field studies have
employed concentrated, labeled natural prey assemblages but the difficulty of obtaining such
fluorescently labeled prey is significant, and their ability to replicate the natural assemblage is
questionable (Cho et al. 2000; Cleven and Weisse 2001; Pachiadaki et al. 2016).
Mortality rates estimated using the dilution technique (DLN) displayed lower accuracy
relative to observed mortality rates (overall average ≈50% of observed rates), but also relatively
low variability of mortality rates across Prochlorococcus abundances. In particular, the DLN
method failed to capture the very high mortality rates that were sometimes observed in the
culture (Fig 2.3a,c). Because the DLN technique measures prey abundance specifically, similar
mortality rates should have been observed if the assumptions of the method were met. A basic

64
assumption of this method is that grazers are always clearing water of prey at maximal rates.
This is not likely the case throughout the wide range of Prochlorococcus abundances in the 48-hr
incubations and thus we conducted the DLN experiments within a relatively narrow range of
Prochlorococcus abundances. Nonetheless, there is no guarantee that this assumption was met
throughout all treatments and at all times. Another assumption of the method is that the growth
rate of prey is similar in all dilutions, which was presumably met by growing all dilution bottles
in nutrient rich medium. Despite these relatively ideal conditions, the DLN yielded lower
mortality rates than observed in the cultures, and consistently failed to yield high mortality rates.
4.3 Large variability in accuracy of techniques for assessing mortality rates
Both the DLN and FLB techniques yielded mortality estimated that often differed
substantively from observed mortality over the same incubation period (Fig 2.4). Variability was
particularly high for rates obtained using the FLB method. These deviations from ‘actual’ rates
of mortality are concerning in part because such experiments are laborious to conduct in the field
(particularly true for the DLN technique), and as a consequence relatively few experiments are
sometimes carried out because of time and labor considerations. The high variances of the DLN
or FLB results from observed mortality rates observed in our study call into question the
accuracy of any particular experiment, and emphasizes the importance of conducting multiple
experiments to more accurately assess this important trophic process in nature.
Variances of the experimentally derived mortality rates (relative to observed rates) were
not strongly affected by when each experiment was conducted within the 48-hr incubation
(shown when plotted against Prochlorococcus abundances; Fig 2.4a). Relatively poor matches
between estimated mortality rates using the DLN or the FLB method and the observed rates for
each experiment occurred across >3 orders of magnitude in Prochlorococcus abundance (Fig

65
2.4a), although the poorest matches were obtained at the lowest and highest Prochlorococcus
abundances (for reasons noted in the previous section). These findings imply that there was
inherent variability in the techniques not attributable to the predator and prey dynamics of
individual experiments. Excluding the experiments conducted at high and low Prochlorococcus
abundances (at the beginning and end of a 48-hr experimental run) would have removed a few
experiments with large percent errors but would not change the overall trends (Fig 2.4b). High
variability of experimentally derived mortality rates was observed even when the experiments
were conducted during the optimal time period identified in Fig 2.2b (i.e. when grazer and
Prochlorococcus interactions strongly dominate population dynamics within the experiment).
Differences between observed and estimated mortality rates were also not attributable to the light
conditions when the experiment was conducted, as noted above (Fig 2.S1b).
FLB appeared to more accurately estimate observed mortality rates than DLN on average
but yielded highly variable results for individual experiments (Fig 4). Although we observed less
variability in treatments with grazers alone compared to the variability when grazers + virus were
present (Fig 2.4b), there was no statistical significance difference in the means. We speculate
that the possible higher variability when grazer and virus were present may reflect a difference in
prey preference due to viral infection, however it is unclear if it might be a preference for or
against the infected prey. Overestimation of mortality rates (values above 0 percent difference,
Fig 2.4a) could have resulted from grazers consuming heterotrophic bacteria (and concurrently
FLB), at a higher rate than grazers were consuming Prochlorococcus. Consuming more
heterotrophic bacteria than Prochlorococcus would have been more likely at lower
Prochlorococcus abundances towards the end of an experiment, yet overestimation of mortality
using the FLB method also occurred at high Prochlorococcus abundances (left side of Fig 2.4a).

66
Underestimations of mortality rates could also be due to grazers selecting against FLB in
preference to living prey (Jürgens and Massana 2008), although we would have expected to see
this consistently across all abundances of Prochlorococcus examined.
Relative to the large deviances from observed mortality rates observed using the FLB
method, DLN experiments more consistently underestimated mortality rates compared to
observed mortality rates in the cultures (Fig 2.4). Only a few experiments yielded DLN rates that
were equivalent to or slightly greater than observed mortality rates (Fig 2.4a). These finding do
not substantiate previous speculations that the dilution technique may consistently overestimate
mortality rates (Dolan and McKeon 2005). It is possible that the highly contrived nature of our
experiment somehow accounts for our different result. DLN mortality rates in the grazer + virus
treatments underestimated the observed rates to a greater extent than in the grazer alone
treatments, presumable an indication that the dilution technique was not capturing mortality due
to virus infection in the treatments with grazers and viruses. Further analysis is needed to
determine if the difference between the observed mortality and estimated mortality in the grazer
+ virus treatment was due to the additive effect of viral lysis or to some synergistic effect when
grazers and viruses are present (Beckett and Weitz 2017; Staniewski and Short 2018). Modified
the dilution techniques have been used to capture viral induced mortality at the same time as
grazer induced mortality (Evans et al. 2003; Staniewski and Short 2018). This modified dilution
technique was not employed in this study due to the already complex study design.
5. CONCLUSION
Quantifying grazer-mediated mortality of picoplanktonic prey is crucial to understanding
and modeling the fate of carbon in the ocean. We tested two widely employed methods, the

67
dilution (DLN) technique and fluorescently labelled bacteria (FLB) disappearance, in a carefully
controlled laboratory experiment to investigate the accuracy and precision of these methods in a
simple microbial food web including a grazer, a phytoplankton prey and a virus. Mortality was
dominated by the grazer in our experimental setup, while viral lysis had a minor additive impact
to that of grazing. Both methods generally underestimated mortality rates relative to the observed
mortality rates, in the presence of grazers alone and in the presence of viruses and grazers.
Mortality rates determined by the dilution technique had smaller variance (relative to observed
mortality rates) than the FLB experiments to observed rates. Our results highlight that caution
should be exercised when interpreting the validity of any single estimate of grazer-mediated
mortality using either the DLN or FLB methods, although the performance of multiple
experiments in a given locale or situation may provide a reasonable estimate (i.e, within 50-80%)
of this fundamental process of aquatic food webs.
Acknowledgements
This study was funded by the Simon’s Foundation Grant P4802 (to DAC). The authors
would like to thank Debbie Lindell and the Lindell Lab at Technion University for hosting the
experiment and providing the Prochlorococcus abundances.

68
Table 2.1
Prochlorococcus mortality rates by experimental technique and treatment. Mean and standard
error of Prochlorococcus mortality rates (day-1
) estimated by the disappearance of fluorescently
labeled bacteria (FLB) or the dilution (DLN) technique for treatments containing grazers with or
without virus. (Grazer Only n=6, Grazer and Virus n=18).
Technique Treatment Experimentally
estimated
Mortality (day-1
)
Observed
Mortality (day-1
)
FLB Grazer Only 5.5 ± 1.4 7.0 ± 0.8
Grazer and Virus 6.0 ± 0.8 5.7 ± 0.5
DLN Grazer Only 3.9 ± 0.3 5.9 ± 1.1
Grazer and Virus 4.2 ± 0.6 11.1 ± 0.8

69
Table 2.S1
Experimental setup conditions. Abundances of Prochlorococcus, grazers, virus and heterotrophic
bacteria at T0, mean and standard error based on treatment type (Low Grazer Only n=3, Low
Grazer and Virus n=9, High Grazer Only n=3, High Grazer and Virus n=9).
Treatment Pro MED4 (cells/ml) Grazer (grazers/ml) Virus (virus/ml) Heterotrophic
Bacteria (bacteria/ml)
Low Grazer Only 1.47E+07 +/- 9.54E+05 6.23E+02 +/- 6.03E+01 0 1.18E+06 +/- 5.24E+04
Low Grazer and Virus 1.69E+07 +/- 3.93E+05 7.85E+02 +/- 1.31E+02 1.55E+07 +/- 2.14E+06 7.98E+05 +/- 9.63E+04
High Grazer Only 1.63E+07 +/- 7.15E+05 4.68E+03 +/- 1.78E+02 0 1.81E+06 +/- 3.86E+04
High Grazer and Virus 1.32E+07 +/- 1.74E+06 7.43E+03 +/- 1.10E+03 1.66E+08 +/- 4.66E+07 9.19E+05 +/- 1.51E+05

70
Figure 2.1
Generalized changes in population abundances during an experiment. Idealized population
changes through time in the experimental treatments: (a) Prochlorococcus and exclusively grazer
present. (b) Prochlorococcus, both grazer and virus present. Solid purple lines indicate the time
span of dilution technique (DLN) experiments, solid green lines indicate the time spans of
fluorescently labeled bacteria (FLB) disappearance experiments. The dashed colored lines
indicate the time and abundance ranges over which Prochlorococcus mortality rates in the
cultures were determined and compared to corresponding DLN and FLB mortality rates.

71
Figure 2.2
Prochlorococcus abundances across time in all treatments. Changes in abundances of
Prochlorococcus across time in all treatments. (a) Temporal changes in the log base 10 of
Prochlorococcus abundances across the time. (b) Log base 10 of Prochlorococcus abundances,
with time adjusted so that all curves converge at Prochlorococcus abundances of log 5.6 (i.e.
approximately midway through the period of rapid prey decline in each treatment), superimposed
is a linear model on a dataset truncated below log 4.5 and above log 6.5. Colors in (a) indicate
treatments with Prochlorococcus only control, exclusively high initial grazer abundances only,
high initial grazer abundances with virus, exclusively low initial grazer abundances only, low
initial grazer abundances with virus, exclusively high initial virus abundances only, and
exclusively low initial virus abundances only. Colors in (b) distinguish treatments containing
grazers only (blue), and those with grazers and virus (yellow) (i.e. treatments with low and high
initial grazer abundances have been grouped).

72
Figure 2.3
Prochlorococcus mortality across all treatments for all measurements. Prochlorococcus
mortality rates across all treatments determined from changes in (a) Prochlorococcus
abundances (Observed) for the same time periods over which mortality rates were also estimated
from (b) the disappearance of fluorescently labeled bacteria (FLB) or (c) using the dilution
(DLN) technique. The x axis is the binned log based 10 of average Prochlorococcus abundances
for the time a FLB or DLN experiment was conducted and the axis have been reversed to reflect
the changes in Prochlorococcus abundances through time from high to low abundance. Outliers
have been removed (see Methods and Materials for details). Color represents exclusively grazer
(blue) or grazer and virus (yellow) treatments.

73
Figure 2.4
Percent differences between observed and experimentally derived Prochlorococcus mortality
rates. Percent differences in Prochlorococcus mortality rates between experimental estimations
(FLB or DLN) and direct measurements (based on changes Prochlorococcus abundances in the
cultures). Outliers were calculated on the FLB and DLN values separately based on the
interquartile region. (a) Percent differences plotted against the average log base 10 abundance of
Prochlorococcus for the time interval over which the experiment was performed. Colors are
based on treatments with exclusively grazers (blue) or grazers and virus (yellow). Symbol shapes
indicate FLB (circle) or DLN (triangle) estimations. (b) Mean and upper and lower quartiles of
the percent differences for treatments with exclusively grazers present, or with grazers and virus.
Outliers have been removed (see Methods and Materials for details). Colors are based on
treatments with exclusively grazer (blue) or grazer and virus (yellow).

74
Figure 2.S1
Prochlorococcus mortality rates and percent differences grouped according to the light/dark
cycle when each experiment was performed. Prochlorococcus mortality rates (a) and percent
differences (b) for FLB or DLN and observed mortality rates (based on changes in
Prochlorococcus abundances), as plotted in figure 3a and 4b, with outliers removed. Colors
reflect the experimental light conditions during each experiment. Dark: experiment was
conducted entirely during the dark period; Dark-Light: experiment began during the dark period
and finished in the light period; Light: experiment was conducted entirely during the light; LightDark: experiment began in the light period and finished in the dark period.

75
Chapter 3: Microzooplankton grazing in the euphotic zone of the North Pacific Subtropical
Gyre
Jennifer L. Beatty*1
, Brittany P. Stewart1
, Samantha P. Gleich, David A. Caron1
1 University of Southern California, Los Angeles, CA 90089, USA
Abstract
Grazing by microeukaryotes is a fundamental process in the lighted waters of
oligotrophic open ocean ecosystems where picophytoplankton typically dominate primary
production. Two commonly-employed methods to measure grazing by microzooplankton in field
studies are the dilution technique, which observes phytoplankton growth in the absence of
grazers, and the disappearance of fluorescently labeled prey, which uses a prey mimic to estimate
prey ingestion. This study utilized the dilution technique and fluorescently labeled bacteria
(FLB) and algae (FLA) disappearance techniques to measure mortality rates of phytoplankton,
bacterial-sized particles, and picoeukaryotic phytoplankton at three depths within the euphotic
zone near Station ALOHA (25 m, 125 m, and 150 m) on cruises conducted in two successive
years (July/August 2021 and August 2022). Experimental bottles were placed on an in situ array
and on deck incubators that mimicked the temperature and light conditions at corresponding
depths. The dilution technique revealed higher mortality rates in 2021 compared to FLB or FLA
disappearance in experiments comparing the two methods. Grazing rates of FLB and FLA
determined in both 2021 and 2022 were comparable to one another across depths. Overall, this
study found an order of magnitude difference between grazer mediated mortality estimated with
the dilution technique compared to fluorescently labeled prey disappearance experiments
suggesting that these methods assumptions are not met in the field and recommending careful

76
consideration for the ecological interpretation of grazing rates obtained from small sample
numbers.
Introduction
Picophytoplankton are primarily responsible for primary production in the oligotrophic
open ocean and are too small for mesozooplankton grazers to consume directly, therefore grazing
by microbial eukaryotes is an important component of these food webs (Calbet 2008; Jürgens
and Massana 2008; Schmoker et al. 2013; Sherr and Sherr 2002). Microzooplankton (technically
defined here as nano- (2-20 µm) and micro- (20-200 µm) zooplankton consume both autotrophic
and heterotrophic bacteria serving as a trophic link between picoplankton and higher trophic
levels from mesozooplankton to fish (Calbet 2008). Additionally, microzooplankton grazing
releases dissolved organic matter and remineralized nutrients into the water, which can provide
energy and nutrients for continued heterotrophic bacterial and phytoplankton growth. Most
grazing studies have focused on the surface ocean while these processes are not well constrained
below the euphotic zone.
The North Pacific Subtropical Gyre (NPSG) is the largest contiguous biome on earth.
This ecosystem has been long-studied, particularly at Station ALOHA (A Long-term
Oligotrophic Habitat Assessment), which is located 22˚45’ N, 158˚00’ W. The oceanographic
conditions in the region have been well characterized, including a dominance of
picophytoplankton (Prochlorococcus, Synechoccocus and photosynthesizing picoeukarotes)
responsible for much of the primary production at Station ALOHA (Karl and Church 2014; Karl
and Church 2017; Rii et al. 2016). Grazing in the surface near Station ALOHA appears to be
tightly coupled with bacterial growth rate (Connell et al. 2020; Liu et al. 1995; Ribalet et al.

77
2015). The system is often characterized as a two-layer euphotic zone, with picoeukaryotes being
more adaptive to the upper and lower euphotic zone and Prochloroccocus dominating the lower
euphotic zone (Rii et al. 2016).
In this study, we combined commonly employed methods to estimate microzooplankton
grazing at multiple depths in the euphotic zone of the North Pacific Subtropical Gyre. Samples
were collected three times in 2021 and incubated on in situ arrays or in on-deck incubators, and
twice in 2022 using on-deck incubators only. We used the dilution technique in 2021 with water
collected at 25 m and 125 m to estimate phytoplankton growth and grader mediated mortality
rates, and to compare those measurements from samples incubated on in situ arrays and on-deck
incubators. Experiments using the disappearance of fluorescently labeled bacteria to estimate
grazing on heterotrophic and autotrophic bacterial populations were conducted on water
collected at 25 m, 125 m, 150 m in 2021 for samples incubated on in situ arrays and in on-deck
incubators. These experiments were also conducted in 2022 using on-deck incubators only.
Fluorescently labeled algae disappearance experiments to estimate grazing on picoeukaryotic
phytoplankton were conducted on water collected at 25 m, 125 m, and 150 m in 2021 and 2022
using on-deck incubators, the in situ array was used for samples at 150 m in 2021 only.
Phytoplankton mortality rates estimated using the dilution technique were higher than grazing
rates estimated using fluorescently labeled bacteria or algae. The phytoplankton growth rate to
grazer mediated mortality rate ratio was significantly different between experimental bottles
incubated on the in situ array or in on deck incubators, with the values from in the in situ array
being closer to a balance between growth rate and grazing rate. Grazing rates estimated by
fluorescently labeled prey were comparable whether bacteria or algae were used across all depths
studied and the incubation location of the experimental bottles. Altogether, these findings

78
suggest that the methodological choices can influence the interpretation of microzooplankton
grazing rates estimated in the field and highlight the importance of carefully deriving
conclusions from few values.
Materials and Methods
Samples were collected on the Simons Collaboration on Ocean Processes and Ecology
(SCOPE) PARAGON cruises (July– August 2021 and August 2022), near Station ALOHA (Fig
3.1). Water samples were collected at three depths at three stations during 2021 (25 July, 29 July
29 and 2 August) and two stations during 2022 (8 August and 11 August). Estimations of
microzooplankton grazing rates were conducted using the dilution (DLN) technique (Landry et
al. 1995; Landry and Hassett 1982) in 2021 only, and the disappearance of fluorescently labeled
bacteria (FLB) (Sherr et al. 1987) and fluorescently labeled algae (FLA) experiments (Rublee
and Gallegos 1989) in 2021 and 2022, as detailed below. Experiments were initiated at night and
conducted for 24 hours, with duplicate sets of samples incubated on an in situ array and in ondeck incubators in 2021, and in on deck incubators in 2022.
Water collection
Seawater was collected at 25 m, 125 m (approximately the depth of the DCM) and 150 m
using a Niskin® rosette equipped with conductivity, temperature, depth (CTD), fluorescence and
oxygen sensors (SeaBird, Bellevue, WA). Water from the Niskin® bottles was transferred into
clean (5% HCl-washed and rinsed with deionized water) 20 L carboys using silicone tubing to
minimize turbulence which can damage delicate plankton.
Phytoplankton growth and mortality rates estimated using the dilution technique

79
Seawater from 25 m and 125 m was used from stations in 2021 to conduct dilution
experiments (Landry et al. 1995; Landry and Hassett 1982) to measure phytoplankton growth
and grazing. Seawater from each depth was filtered through a pre-acid washed 0.2 �m inline
AcroPak (#12686, Pall) filter cartridge (FSW) using silicone tubing. Whole (unfiltered) sea water
(WSW) was added to 500 mL bottles previously rinsed with 5% HCl and deionized water. A
dilution series was conducted in triplicate at concentration of 25% WSW (75% FSW), 50%
WSW (50% FSW) and 100% WSW. Nutrients were added to all treatments to account for
nutrient dilution due to exclusion of grazers (final incubation concentration- 0.1 �M FeCl3, 0.7
�M NaH2PO4·H2O, 1 �M NH4Cl, 10 �M NaNO3) (Connell et al. 2018; Landry et al. 1995). The
dilutions were performed alongside treatments of 100% WSW without nutrients for controls. Six
replicates of 250 mL WSW were filtered onto 25 mm diameter GF/F filters (nominal pore size
0.7 �m, #1825-025, Whatman) taken for chlorophyll analysis. Filters were placed in 2 mL
cryovials and stored at -80ºC until analyzed. Dilution bottles were placed on an in situ array and
incubated at the depth of collection, while another set of dilution bottles were placed in the ondeck incubators. The 25 m experimental bottles were put in an incubator of that maintained
ambient surface water temperature and light attenuation, and the 125 m experimental bottles
were placed in an incubator that maintained ambient temperature at 125 m and maintained light
attenuated to 125 m. The in situ array was retrieved and samples from the bottles processed after
24 hours, and the bottles from the on-deck incubators were processed after 24 hours. Each bottle
was split into duplicate samples by filtering 250 mL onto 25 mm diameter GF/F filters which
were placed in 2 mL cryovials and stored at -80ºC until analyzed.
In the lab, duplicate filters for chlorophyll samples were thawed and extracted in 4 mL of
100% Acetone, wrapped in foil and left in the freezer for 24-hour at -20ºC. Samples were thawed

80
for 30 minutes in the dark before analysis on a Trilogy Laboratory Fluorometer (#7200-000,
Turner Designs, San Jose, CA) using the non-acidification method (Welschmeyer 2003).
Growth rates and grazing rates were calculated using Model I linear regression of
apparent growth rate versus dilution factor. The y-intercept was the net growth rate (nutrientenriched growth) (�n), the slope of the regression was the mortality rate (m), and the growth rate
in the unenriched treatment yielded the intrinsic growth rate in the presence of 100% grazing and
natural nutrient concentration (�0) (Landry et al. 1995). The difference between �n and �0 was
used to estimate the intrinsic growth rate of the phytoplankton community.
Picoplankton grazing rates estimated using fluorescently labeled bacteria disappearance
Fluorescently labeled bacteria (FLB) disappearance experiments (Jürgens and Massana
2008) were used to identify protistan consumption of bacterial-sized particles at three depths
within the water column. FLB were prepared using a culture of Donkdonia donghaensis (Caron
2001; Sherr et al. 1987). The D. donghaensis culture was grown in ZoBell medium, rinsed,
starved in filtered seawater for two days to ensure the size was comparable to the bacteria in
natural seawater. The starved cells were heat killed and stained with 5-(4,6-dichlorotriazin-2-yl)
aminofluorescein (DTAF), aliquoted and stored at -80ºC until used. In 2021 and 2022,
experiments were conducted with seawater collected at 25 m, 125 m and 150 m. WSW from
each depth was aliquoted into triplicate 500 mL bottles. FLB were added to bottles at 5 x 104
FLB cells mL-1 (approximately ≤10% of natural bacterial abundance). Controls (FLB at the same
abundance in 0.2 �m filtered seawater) from each depth were also performed in triplicate to
account for non-grazing impacts on FLB abundance. Initial samples were preserved for flow
cytometry with 10% formalin at a final concentration of 1% formalin and kept at -80ºC until
analysis. In 2021, experimental bottles were incubated on the same in situ array used for the

81
dilution experiments and incubated at the depth of sample collection. A duplicate set of bottles
were placed in on-deck incubators. The 25 m experimental bottles were put in an incubator that
maintained ambient surface water temperature and the 125 m experimental bottles were placed in
an incubator that maintained ambient temperature at 125 m, both incubators maintained light
attenuated to the depth of collection. The 150 m experimental bottles were placed in a trash bag
inside an incubator to mimic light attenuation at the depth of collection and the temperature was
set to ambient temperature at 150 m. In 2022, the in situ array was not available and only an ondeck incubator was used. The in situ array was recovered after 24 hours, and on-deck
experiments were also processed after 24-hours. All bottles were sampled at the end of the
incubations for flow cytometry as noted above.
Flow cytometry samples were performed in the lab on an Accuri flow cytometer (BD
Biosciences, San Jose, CA). Forward scatter and green fluorescence (533 ± 30) were used to
identify FLB. Technical duplicates from each bottle and time point were used. Grazing mortality
rates (g, units: d-1
) for bacterial-sized particles were determined using the following equation,
where F0 is the initial FLB abundance and Ft is the abundance at the end of the incubation in
days (t):
� = ln %
�!
�"
' ∗ (− 1
�
)
Picoeukaryote algae grazing rates estimated using fluorescently labeled algae disappearance
Fluorescently labeled algae (FLA) disappearance experiments (Rublee and Gallegos
1989) were conducted to quantify protistan grazing on picoeukaryotic algae within the euphotic
zone. FLA were prepared as described in Rublee and Gallegos 1989, using Chlorella
stigmatophora (nominal size: 3 �m) grown in LKS-Si media. Briefly, the cells were centrifuged
to concentrate cells, resuspended, heat-killed and stained with DTAF, washed and resuspended

82
before being aliquoted and frozen until use. Whole (unfiltered) seawater in triplicate from each
depth (25 m, 125 m and 150 m) was aliquoted into 500 mL bottles. FLA were added to the
bottles at 5 x 102 FLA cells mL-1 (approximately ≤10% of natural picoeukaryotic algae
abundance). Triplicate controls containing FLA in 0.2 �m filtered seawater from each depth
were also incubated to account for non-grazing impacts on FLA abundance. Initial samples were
preserved for flow cytometry with 10% formalin at a final concentration of 1% formalin and kept
at -80ºC until analysis. Bottles from all depths in both 2021 and 2022 were placed in an on-deck
incubator at in situ temperature and light conditions, as described above for the FLB
experiments. One set of bottles was placed on the in situ array at 150 m during 2021, other
depths were not possible due to space constraints on the arrays and no access to the array in
2022. All experiments were processed after 24-hours at which time all bottles were sampled
again for flow cytometry.
Flow cytometry samples were analyzed in the lab on an Accuri flow cytometer (BD
Biosciences, San Jose, CA). Forward scatter and green fluorescence (533 ± 30) were used to
identify FLA. Technical duplicates from each bottle and time point were used. The
picoeukaryotic phytoplankton grazing mortality (g, units: d-1
) was determined using the
following equation from the initial FLA abundance (A0), abundance at the end of the incubation
(At) and the length of incubation in days (t):
� = ln %
�!
�"
' ∗ (− 1
�
)
Sample analysis was conducted in the R programming language (R Core Team 2022).
Results

83
Environmental conditions during 2021 and 2022
During both years, temperature and salinity patterns observed near station ALOHA were
typical for the region and season, with temperature decreasing and salinity increasing with depth
(Table 3.1). In 2021, the highest chlorophyll values were observed at 125 m and in 2022, the
highest chlorophyll values were observed at 25 m (Table 3.1). Prochlorococcus, Synechococcus
and heterotrophic bacteria were more abundant at 25 m than at 125 m while picoeukaryotic cells,
were more abundant at 125 m than 25 m (Table 3.1). Prochlorococcus and heterotrophic bacteria
were at least one order of magnitude more abundant than Synechococcus and picoeukaryote
cells.
Phytoplankton growth and grazing rates
Grazing mortality rates (m) measured using the dilution technique ranged from 0.2 – 0.6
d-1 with a mean of 0.4 d-1
(Table 3.S1). The mean grazing rate at 25 m (0.48 d-1
) was significantly
different than the mean grazing rate at 125 m (0.31 d-1
) when both incubation locations were
taking into consideration (Kruskal-Wallace, p=0.05, Fig 2a, b, Table 3.2). At 25m, there was
significant difference between the grazing rates measured for experiments incubated in the in situ
array (0.4 d-1
) than in the on-deck incubator (0.6 d-1
) (Dunn test, p <0.05, Fig 2a), but no
significant difference at 125 m (Fig 3.2c). Phytoplankton growth rates (�), adjusted for nutrient
addition, ranged from -0.9 to 0.8 d-1 with a mean of 0.2 d-1
. The mean growth rate at 25 m was
0.1 d-1 (Fig 3.2b, Table 3.2), while the mean growth at 125 m (0.3 d-1
, Fig 3.2d, Table 3.2). There
was no significance between the mean growth rate the in situ array and on-deck incubators (Fig
3.2 b and d). Grazing rates at both depths and treatments yielded less variability than growth
rates, especially from on-deck incubations (compare Fig 3.2 a and c to Fig 3.2 b and d). There

84
were some stations where the intrinsic growth rate unamended for nutrients was higher than the
nutrient amended treatments (Fig 3.S1).
Ratios of growth rates (�) to grazer mediated mortality rates (m) ranged from -1.8 to 2.9
with an overall mean of 0.65 (Fig 3.3). The growth rate to mortality rate ratio was significantly
higher for the experiments conducted on the in situ array (1.5) compared to the on-deck
incubations (-0.16). There was no significance difference between the growth rate to mortality
rate ratio at 25 m (0.4) and 125 m (0.9).
Grazing rates on picoeukaryote phytoplankton
Grazing rates of the picoeukaryotic phytoplankton estimated using fluorescently labeled
algae (FLA) disappearance experiments were generally low, with a range of -0.2 to 0.2 d-1 and a
mean of 0.03 d-1 across depths and years. There were no significant differences between depths,
years or incubations conditions (array vs. on-deck) (Fig 3.4a,c,e; Table 3.S1). At 25 m, the mean
picoeukaryotic phytoplankton grazing rate was 0.01 d-1 across both years (Fig 3.4a, Table 3.2).
Grazing rates of picoeukaryotic phytoplankton across both years had a mean of 0.06 d-1
at 125 m
(Fig 3.4c, Table 3.2). The mean grazing rate of picoeukaryotic phytoplankton at 150 m was 0.02
d-1 (Fig 3.4e, Table 3.2).
Grazing rates on bacterial size particles
Grazing rates of picoplankton estimated using fluorescently labeled bacteria (FLB)
disappearance experiments were generally similar to rates obtained using FLA, with a range of -
0.1 to 0.2 d-1 (with the exclusion of one outlier of 5.9 d-1
) and a mean of 0.03 d-1
. There were no
significant differences between years, depths or locations (Table 3.S1). The mean bacterial
grazing rate at 25 m was 0.003 d-1 (Fig 3.4b, Table 3.2). The mean bacterial grazing observed at

85
125 m was 0.05 d-1 (Fig 3.4d, Table v2). The bacterial grazing rates measured at 150 m had a
mean of 0.04 d-1 (Fig 3.4e, Table 3.2).
Discussion
Grazing on picophytoplankton is a fundamental component of open ocean food webs and
thus quantifying the grazing mediated mortality rate of picoplankton is an essential component of
understanding marine ecosystems. In the North Pacific Subtropical Gyre, picophytoplankton are
responsible for the majority of primary production at Station ALOHA and therefore impact
carbon fluxes despite their small size (Rii et al. 2016; Schmoker et al. 2013; Zoccarato et al.
2016). We measured picophytoplankton grazing rates at multiple depths in the euphotic zone
using an in situ array in the summer of 2021 and on-deck incubations in the summer of both
2021 and 2022. We compared the grazing rates estimated using three commonly employed
techniques: the dilution (DLN) technique to capture total phytoplankton growth rate and
mortality rate from chlorophyll a analysis, fluorescently labeled algae (FLA) disappearance
experiments to measure grazing rates on picoeukaryotic phytoplankton, and fluorescently labeled
bacteria (FLB) disappearance experiments to quantify grazing rates on autotrophic and
heterotrophic bacteria.
The biggest difference observed among grazing mortality rates related to the technique
used to measure them, with minor differences attributed to the mode of incubations (i.e. in situ
array vs. on-deck incubator). The dilution technique yielded mortality rates an order of
magnitude higher than the FLA or FLB disappearance experiments, which may reflect
fundamental differences between the experimental techniques. While fluorescently labeled prey
disappearance experiments estimate a grazing rate of an unknown portion of the community,

86
presumable the portion that is that comparable to the tracer prey, the dilution technique estimates
growth and grazing rates for the whole phytoplankton community. The fluorescently labeled
prey experiments yielded higher variability in grazing rates than the dilution technique, and the
fluorescently labeled algae grazing rates had higher variability than fluorescently labeled bacteria
mortality estimates. Secondarily to differences in grazing rates between the dilution technique
and fluorescently labeled prey experiments, there were differences in the growth rate to grazing
rate captured by the dilution technique in these two locations. There were lower growth rates and
higher grazing rates within dilution bottles placed in the on-deck incubators while the dilution
bottles placed on the in situ array demonstrated a growth rate to grazing rate over one or closer to
a balance between growth rates and grazing rates. Overall, these findings emphasize how
methodological choices can impact the interpretation of microzooplankton rates obtained in the
field leading to different ecological interpretations of the balance of growth rates and grazing
rates. Caution should be taken when interpreting grazing rates in the field when few experiments
are conducted.
Total phytoplankton mortality rates were higher than algal or bacterial grazing rates
Phytoplankton mortality rates due to grazing as estimated by the DLN technique (0.4 d-1
)
was higher than picoplankton grazing rates estimated by both FLA (0.03 d-1
) and FLB (0.03 d-1
)
disappearance techniques. Grazing rates estimated by FLA or FLB were comparable throughout
depths and locations. The grazing mortality rates estimated by the DLN technique while the FLB
and FLA disappearance estimated grazing rates were an order of magnitude lower than the
biomass-normalized production rates of Prochlorococcus (0.3-0.9 d-1
), Synechococcus (0.3-0.8 d1
) and photosynthetic picoeukaryotes (0.2-0.6 d-1
) in the NPSG, (Rii et al. 2018).

87
The difference between the DLN technique estimated grazing mortality and the grazing
rates estimated by FLB or FLA disappearance implies a magnitude difference in the movement
of carbon through microzooplankton grazers between these techniques. As much as possible we
tried to keep experimental conditions the same between each techniques, including conducting
grazing experiments for 24 h to address any diel changes in grazing rates (Connell et al. 2020;
Ribalet et al. 2015; Rii et al. 2016). It has been suggested that the dilution technique can
overestimate grazing (Dolan and McKeon 2005). And estimations by the dilution technique were
based on total chlorophyll a which would include both picophytoplankton and larger organisms
present while the fluorescently labeled prey disappearance only capture picoplankton grazing
rates. However, in a controlled laboratory experiment comparing the Prochlorococcus mortality
rate estimated using the dilution technique and fluorescently labeled bacteria disappearance
experiments, both techniques yielded comparable rates (albeit lower than observed
Prochlorococcus mortality) (Beatty et al. in prep). This suggests that in a highly contrived
laboratory experiment in optimal conditions when the assumptions of the methods are
presumably met, these two techniques (DLN and FLB disappearance) are both able to capture
the grazing rates that are actually occurring in the prey population. The divergence of mortality
rates derived by these two experimental method types (DLN or fluorescently labeled prey
disappearance with FLA or FLB) in the field suggest the possibility that these methods’
assumptions are not met in the field.
These two experimental method types are based on fundamentally different assumptions.
The dilution technique assumes that phytoplankton growth rates and microzooplankton grazing
rates are not impacted by dilution. However, studies have documented that dilution can impact
both grazer and prey communities (Agis et al. 2007; Dolan 2000), trophic cascades can impact

88
the linearity of grazing rates (Calbet 2008), and low grazing rates can be difficult to detect
(Schmoker et al. 2013). It does not seem that nonlinearity or low grazing rates are an issue in our
study, however we cannot rule out the possibility that dilution impacted components of either the
grazer or prey communities. The major assumption of fluorescently labeled prey disappearance
experiments is that the tracer is consumed at the same rate as natural prey. Marine bacterial
assemblages contain many different species of different sizes and shapes. Microzooplankton
grazers primarily select prey based on size but can also distinguish based on motility and
chemical signals (Florenza and Bertilsson 2023). Some studies have concentrated the natural
bacterial assemblage and used it as the tracer prey instead of a monoculture FLB (Cho et al.
2000; Cleven and Weisse 2001; Pachiadaki et al. 2016). However, using natural prey
assemblages can result in uneven staining, and certain species within the natural assemblage may
dominate the culture anyways so it is not necessarily better than a uniform FLB. Additionally,
Prochlorococcus is typically the dominant phytoplankton at Station ALOHA (Rii et al. 2016),
and in a laboratory setting the FLB used in this study captured Prochlorococcus mortality rates
(Beatty et al. in prep) so selection against the FLB is not likely to be causing the low mortality
rates captured by FLB disappearance observed in this study.
Small difference between in situ array and on deck incubations
The only significant difference between grazing rate estimated by incubating samples on
the in situ array versus the on-deck incubators was observed using the dilution technique, there
were differences between grazing mortality rates at 25 m and between the phytoplankton growth
rate to grazing rate ratios (Fig 3.2), and there was higher growth rate and lower grazing mortality
rate observed in experimental bottles incubated on the in situ array compared to on deck
incubations (Fig 3.3). In general, there was no difference between the mortality rates estimated

89
from the in situ array and the on-deck incubators for the experiments conducted with FLA and
FLB. The difference in growth rate to grazing mortality rate ratios suggested that growth and
grazing were more balanced in bottles incubated on the in situ array. A previous study near
Station ALOHA observed higher growth rates to grazing mortality rates on Prochlorococcus and
Synechoccocus in the euphotic zone (Liu et al. 1995). However, it is speculative to say that the in
situ array incubations in this study are more accurate to what is occurring in water column.
The main environmental difference between depths in the water column is light spectrum
and temperature, and while we tried to keep these factors consistent between the in situ array and
the on-deck incubations, differences could drive different predator and prey interactions within
the bottles in these two conditions. The blue screening used by the on-deck incubators to adjust
the light at different depths is chosen to match the light intensity at each depth, but the light
spectrum will be somewhat different between the light spectrum in the water column.
Conclusion
Microzooplankton grazing is a fundamental process of oligotrophic food webs,
particularly in oligotrophic oceanic gyres where picophytoplankton dominate the primary
production. We employed multiple methods to characterize the grazing rates of microbial
consumers on different members of the phytoplankton community in the North Pacific
Subtropical Gyre. Total phytoplankton grazing mortality rates estimated by the dilution
technique was an order of magnitude higher than picoplankton grazing rates estimated from the
disappearance of either fluorescently labeled algae or bacteria, suggesting that assumptions of
one or both methods are not being met in the field. Phytoplankton grazing mortality rates
estimated by the dilution technique were highest at 25 m and picoplankton grazing rates

90
estimated by fluorescently labeled particle disappearance was similar throughout the euphotic
zone. Phytoplankton growth rate to grazing mortality rate ratios were much more in line to one
another in experimental bottles incubated on the in situ array compared to experimental bottles
placed in on-deck incubators. The in situ array incubations may be providing a more accurate
appraisal of phytoplankton dynamics because there was more balanced growth rate to grazing
rate ratio, however this is speculative and requires more studies aimed at resolving this
comparison. Overall, two methods to estimate grazer mediated mortality yielded a magnitude
difference in grazing rates emphasizing the importance of carefully interpreting results from
these methods when their assumptions may be violated.
Acknowledgments
The authors declare that they have no conflicts of interest. This study was funded by the Simon’s
Foundation Grant P4802 (to DAC). The authors would like to acknowledge the captain and crew
of the R/V Kilo Moana during the 2021 and 20222 PARAGON cruises. The authors would like
to thank Angelicque White and Matthew Church for planning and executing successful cruises.
The authors are grateful to Brandon Brenes, Ryan Tabata, and Timothy Burrell for their support
deploying the grazing array. The authors extend gratitude to Cameron Thrash for providing space
to run the flow cytometry samples.
Author Contributions
Jennifer L. Beatty- Conceptualization (equal); Data acquisition (lead); Data curation (lead);
Formal analysis (lead); Visualization (lead); Writing- original draft (lead) and editing
manuscript, approval of the final submitted manuscript. Brittany Stewart- Data acquisition

91
(equal); Writing- review and editing (equal). Samantha Gleich- Data acquisition (equal);
Writing- review and editing (equal). David Caron- Conceptualization (equal); Funding
acquisition (lead); Resources (lead); Writing-original draft (supporting); Writing- review and
editing (equal).
Data availability
The scripts used to process and analyze data are available on GitHub:
https://github.com/Jennifer1Beatty/paragon_grazing.

92
Table 3.1
Environmental and biological conditions of each experimental station. Chlorophyll and temperature were
obtained from the CTD casts when water was collected from experiments. Values for picoplankton
abundances were obtained from flow cytometer and were not available at 150 m in 2021 or any depths in
2022.

93
Date Station Latitude Longitude Depth (m) Chl a (mg L-1) Temperature (ºC ) Prochlorococcus (cells mL-1) Synechococcus (cells mL-1) Picoeukaryote (cells mL-1) Heterotrophic Bacteria (cells mL-1)
25-Jul-21 G1 21.734 155.277
25 0.2 25.7 2.25 x 105 6.29 x 102 9.74 x 102 6.29 x 105
125 0.56 22.5 4.71 x 104 4.09 x 101 1.47 x 103 2.96 x 105
150 0.42 22.2 - - - -
29-Jul-21 G2 21.567 156.003
25 0.12 25.8 2.19 x 105 1.11 x 103 1.11 x 103 6.40 x 105
125 0.46 22.3 3.61 x 104 0.00 1.99 x 103 3.07 x 105
150 0.29 21.7 - - - -
2-Aug-21 G3 22.135 156.262
25 0.13 25.7 2.52 x 105 1.11 x 103 8.07 x 102 6.18 x 105
125 0.74 22.4 9.53 x 104 2.55 x 102 1.78 x 103 3.76 x 105
150 0.45 22.3 - - - -
8-Aug-22 G1 23.386 154.62
25 0.43 25.9 - - - -
125 0.26 22.1 - - - -
150 0.15 21.7 - - - -
11-Aug-22 G2 23.291 155.324
25 0.40 25.8 - - - -
125 0.20 22.1 - - - -
150 0.07 21.6 - - - -

94
Table 3.2
Mean and standard error grazing rates by experimental type and depth. Phytoplankton growth
and grazing mortality estimated by the dilution technique, bacterial grazing by fluorescently
labeled bacteria disappearance, and picoeukaryote grazing by fluorescently labeled algae
disappearance.
Depth (m) Phytoplankton
growth (μ d-1
)
Grazer
mortality
(m d-1
)
Bacterial
grazing (d-1
)
Picoeukaryote
grazing (d-1
)
25 0.1 ± 0.2 0.48 ± 0.06 0.003 ± 0.03 0.001 ± 0.06
125 0.3 ± 0.2 0.31 ± 0.05 0.05 ± 0.03 0.06 ± 0.07
150 - - 0.04 ± 0.02 0.02 ± 0.04

95
Table 3.S1
Grazing rates and phytoplankton growth rates by experiment and incubation location.
Phytoplankton growth and grazing mortality estimated by the dilution technique, bacterial
grazing by fluorescently labeled bacteria disappearance, and picoeukaryote grazing by
fluorescently labeled algae disappearance.
Date Station
Depth
(m)
Incubation
Location
Phytoplankton
growth
(� d − 1)
Grazer
mortality
(m d-1
)
Bacterial
grazing (d-1
)
Picoeukaryote
grazing (d1
)
25-Jul-21 G1
25 Array 0.53 0.23 0.05 -
Deck 0.24 0.58 0.08 0.14
125 Array 0.83 0.34 -0.04 -
Deck 0.65 0.46 -0.02 -0.11
150 Array - - 0.05 0.23
Deck - - 0.03 -0.06
29-Jul-21 G2
25 Array 0.7 0.45 -0.14 -
Deck 0.45 0.61 -0.01 -0.09
125 Array 0.31 0.17 5.86 -
Deck -0.27 0.4 0.14 0.04
150 Array - - 0.05 0.02
Deck - - 0.06 -0.10
2-Aug21 G3
25 Array 0.17 0.45 0.02 -
Deck -0.89 0.56 0.06 0.04
125 Array 0.1 0.18 0.03 -
Deck -0.33 0.33 0.10 0.28
150 Array - - 0.16 0.05
Deck - - 0.02 0.09
8-Aug22 G1
25 Deck - - -0.07 -0.17
125 Deck - - 0.01 0.13
150 Deck - - -0.03 0.06
11-Aug22 G2
25 Deck - - 0.04 0.13
125 Deck - - 0.09 -0.07
150 Deck - - 0.02 -0.11

96
Figure 3.1
Map of study area. Map of study area relative to the Hawaiian Islands and the gray circle
indicates the 6 nmi radius defining the location of the Station ALOHA. The shaded boxes
indicate the location of the subset maps depicting the sampling locations by year, the green box
is 2021 and the orange box is 2022. The background color of the inset maps is based on satellite
chlorophyll a information from the Global Ocean Colour (Copernicus-GlobColour) L4 (doi:
10.48670/moi-00281). The 2021 inset map has satellite chlorophyll information from 20 July 20,
2021. The 2022 inset map has satellite chlorophyll information from August 9, 2022. Both dates
were chosen as approximate middle dates between sampling dates for either cruise.

97
Figure 3.2
Phytoplankton mortality rates (a,c) and growth rates (b,d) of total phytoplankton community
(based on chlorophyll a) in 2021 at 25 m (a, b) and 150 m (c, d) determined using the dilution
technique. Boxes correspond to the first and third quartile, the thick line in the middle is the
median, and dots represent the mean. Experimental bottles were placed on an in situ array (pink)
or in on-deck incubators (blue). Values include rates from each station. Growth rate is the
intrinsic growth rate adjusted for nutrient addition.

98
Figure 3.3
Ratio of phytoplankton growth rate to mortality rate (based on chlorophyll a) in 2021 at 25 m
(circles) and 150 m (triangles) determined using the dilution technique. Experimental bottles
were placed on an in situ array (pink) or in on-deck incubators (blue). Growth rate is the intrinsic
growth rate adjusted for nutrient addition.

99
Figure 3.4
Grazing rates determined using the disappearance of fluorescently labeled algae (FLA; a, c, e)
and fluorescently labeled bacteria (FLB; b, d, f). Boxes correspond to the first and third quartile,
the thick line in the middle is the median, and dots represent the mean. Experimental bottles were
placed on an in situ array (pink) or in on-deck incubators (blue) in 2021 (n=3) and 2022 (n=2).
Values include rates from each station, with one FLB outlier removed.
Supplemental Figure 1. Dilution technique model I linear regressions. Mean value from triplicate
dilution bottles. Apparent phytoplankton growth rates (based on chlorophyll a) in 2021 at 25 m
(a, b) and 150 m (c, d). Experimental bottles were placed on an in situ array (pink) or in on-deck
incubators (blue).

100
Figure 3.S1
Dilution technique model I linear regressions. Mean value from triplicate dilution bottles.
Apparent phytoplankton growth rates (based on chlorophyll a) in 2021 at 25 m (a, b) and 150 m
(c, d). Experimental bottles were placed on an in situ array (pink) or in on-deck incubators
(blue).

101
Conclusion
Microbial eukaryotes are ecologically important in marine systems as primary producers,
grazers, and contributors to carbon flux. At station ALOHA in the North Pacific Subtropical
Gyre, a location representing low nutrient ocean gyres that cover large areas of the ocean, this
dissertation characterized protistan diversity and grazing rates utilizing multiple methods for
additional comparison. Major findings included: (1) a subset of the water column protistan
communits were sinking out of the euphotic zone; (2) while water column protistan communities
displayed little variation between eddy centers of opposite polarity, there may have be an
increase in the sinking of siliceous organisms within the centers of cyclonic eddies; (3) the
dilution technique and fluorescently labeled bacteria disappearance experiments generally
captured picoplankton mortality rates but tended to underestimate picoplankton mortality rates
(by 50% and 80% respectively) in a controlled laboratory setting; and (4) when the dilution
technique and the fluorescently labeled prey disappearance experiments was applied to the field
a divergence of mortality rates was observed suggesting that the assumptions of one or both of
the techniques were not met in the field. Overall, this dissertation furthers our understanding of
protistan communities in oligotrophic aquatic systems and highlights the strengths of utilizing
multiple methods to assess microbial communities.
Mesoscale eddies are present around 30% of the time at station ALOHA, potentially
impacting microbial communities and biogeochemical cycles. Combining imaging and molecular
techniques of the water column and sinking particles to investigate an eddy-dipole of adjacent
anticyclonic and cyclonic eddies revealed that the differences in methodologies was greater than
differences in communities between the eddy centers. The water column communities identified
by sequencing the rRNA gene and the rRNA transcript were both dominated by alveolates, while

102
the sinking communities identified by sequencing the rRNA gene were dominated by rhizarian
protists, which were also observed by in situ imagine of the water column. Microscopy of
sinking material was dominated by diatoms, which were observed by in situ imaging of the water
column. While there was no trend in community composition across the eddy dipole observed in
communities characterized by amplicon sequencing or in situ imaging of the water column,
microscopy of sinking material and the subsequent flux of organisms out of the euphotic zone
displayed an increase in siliceous organisms (eg. diatoms, silicoflagellates and radiolarians) in
the cyclonic eddy center. Altogether, the methods supported that a subset of the water column
community was sinking out of the euphotic zone, and that there was an increase flux of siliceous
organisms in the cyclonic eddy center. Further research directions include investigating changes
in sinking material community composition with depth to characterize how sinking material
changes as it sinks as well as possibly identify where in the water column sinking material
originates.
Microbial eukaryotic grazing is a fundamental component of aquatic microbial food webs
and therefore using techniques that precisely measure grazing rates is important for knowledge
of carbon flow and accurate biogeochemical models. Assessing commonly employed field
methods in a carefully controlled laboratory setting revealed that the dilution technique and
fluorescently labeled bacteria disappearance experiments yielded comparable grazing rates on
picophytoplankton, yet both methods underestimated picophytoplankton mortality. Methods
were assessed by comparing the grazing rate estimated by each technique to the mortality
observed in a culture by differences in cell abundance. The dilution technique displayed less
variance and underestimated grazing by ~50% but is quite intensive to execute. The fluorescently
labeled bacteria disappearance technique is easier to execute but demonstrated larger variance

103
and underestimated grazing by ~80%. These findings reveal that in the controlled laboratory
setting these commonly used field methods do capture grazing rates similar to mortality rates
observed yet suggests that multiple experiments should be conducted to increase the probability
of capturing accurate rates. Future research could target specific assumptions of the methods to
identify where the breakdown of the assumptions occurs, especially in regard to when the
methods are applied in the field.
Oligotrophic aquatic systems, such as the North Pacific Subtropical Gyre, are dominated
by picophytoplankton due to their small size which allows them to outcompete larger
phytoplankton. In these areas, microbial eukaryotes are important components of the food web
making microbial carbon available to higher trophic levels. The dilution technique, which
estimates total phytoplankton growth and mortality rates, and fluorescently labeled prey
disappearance experiments, which estimate the grazing of bacterial and picoeukaryote size
particles, were applied across multiple depths and years in the North Pacific Subtropical Gyre to
compare grazing rates estimated by these two commonly employed techniques. The dilution
technique captured mortality rates that were an order of magnitude greater than grazing rates
estimated using fluorescently labeled prey disappearances experiments. There was a greater
growth to rate to mortality rate observed in experimental bottles placed on in situ array compared
to experimental bottles placed on deck, suggesting that this technical decision may impact
ecological interpretation of results obtained using dilution experiments. There were no
significant differences between depths or prey type using fluorescently labeled bacteria or algae.
The divergence of grazing rates observed in the field between the dilution technique and
fluorescently labeled prey disappearance experiments suggest that the techniques are not meeting
assumptions, because the two techniques revealed similar magnitudes of grazing rates in a

104
controlled laboratory experiment. Future studies should focus on identifying which assumptions
are broken and which technical decisions may minimize these issues.

105
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Abstract (if available)

Abstract In the oligotrophic open ocean, microbial eukaryotes (protists) are important grazers that link picophytoplankton to higher trophic levels and contribute to the sinking of carbon out of the euphotic zone. The goal of my dissertation was to combine multiple methods to (1) characterize protistan diversity in the water column and associated with sinking particles, and (2) quantify protistan grazing on prey throughout the euphotic zone in the North Pacific Subtropical Gyre (NPSG). I characterized protistan diversity in the water column and on sinking particles using amplicon sequencing of 18S rRNA gene transcripts and 18S rRNA genes with in-situ automatic imaging in the water column, and 18S rRNA gene sequencing and light microscopy of sinking material collected in particle interceptor traps. Each method provided unique perspectives on the protistan community composition yet supported that a subset of the water column community contributed to sinking particles. Two commonly employed methods for measuring grazing rates of protists in nature were assessed in a controlled laboratory experiment. Results indicated that, under ideal conditions, these methods underestimated picoplankton mortality by 50-80%. These methods were applied in the field to identify patterns of grazing preference throughout the euphotic zone. There was an order of magnitude difference between techniques in natural samples raising questions concerning the validity of the assumptions underlying the techniques. Overall, this work advances our understanding of protistan role in the carbon cycle and food web in the oligotrophic open ocean and emphasizes that combining multiple techniques robustly assesses diversity and function.

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Identifying and quantifying transcriptional module heterogeneity and genetic co-regulation, with applications in asthma

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Creator Beatty, Jennifer Lynne (author)

Core Title Characterizing protistan diversity and quantifying protistan grazing in the North Pacific Subtropical Gyre

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Biology (Marine Biology and Biological Oceanography)

Degree Conferral Date 2024-12

Publication Date 11/11/2024

Defense Date 09/09/2024

Publisher Los Angeles, California (original), University of Southern California (original), University of Southern California. Libraries (digital)

Tag amplicon sequencing,environmental DNA,euphotic zone,marine microbiology,marine particles,microbial ecology,microzooplankton grazing,OAI-PMH Harvest,open ocean,protistan diversity,protistan ecology,protists

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Caron, David A. (committee chair), Fuhrman, Jed A. (committee member), John, Seth G. (committee member), Levine, Naomi M. (committee member)

Creator Email jbeatty273@gmail.com,jlbeatty@usc.edu

Unique identifier UC11399DI6C

Identifier etd-BeattyJenn-13623.pdf (filename)

Legacy Identifier etd-BeattyJenn-13623

Document Type Dissertation

Format theses (aat)

Rights Beatty, Jennifer Lynne

Internet Media Type application/pdf

Type texts

Source 20241113-usctheses-batch-1222 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu