University of Southern California Dissertations and Theses

Marine protistan diversity, spatiotemporal dynamics, and physiological responses to environmental cues

(USC Thesis Other)

Marine protistan diversity, spatiotemporal dynamics, and physiological responses to environmental cues

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content Marine Protistan Diversity, Spatiotemporal Dynamics, and
Physiological Responses to Environmental Cues
by
Gerid A. Ollison
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfilment of the
Requirement for the Degree
DOCTOR OF PHILOSOPHY
BIOLOGY (MARINE BIOLOGY AND
BIOLOGICAL OCEANOGRAPHY)
December 2023

Approved by Advisory Committee:
David A. Caron (Chair)
Jed Fuhrman
Carly Kenkel
Seth John

ii
DEDICATION:
“The marathon continues.”
—Nipsey Hussle
I left home for the Navy after high school, Certain that My
future did not involve a college classroom. My father sent me
off with a final thought, a spark that ignited a fire. “I never
had a silver spoon for any of y’all.” He started, cool
conviction, referring to my brothers and me. “All I have is a
conversation: there ain’t nothing that you can’t damn do.” I
believed him, and am still striving for my highest potential.
The culmination of this work stems from my father’s words,
and my mother’s ride-or-die love and logistical support at every
step of my journey. I dedicate this small milestone to them:
Donna Jo-Ann Ollison John Westley Ollison III

iii
ACKNOWLEDGEMENTS:
Dr. Julie Victoria Hopper-Ollison: you had unwavering confidence
in me, you allowed me to dedicate myself to my work with
effortless ease. G: my best friend and protector, you showed me
unconditional love in its purest form, you helped me find my
center, you kept me outside communing with nature, and during
those last hours you finally taught me to slow down. My mom and
pops, Donna Jo-Ann Ollison and John Westley Ollison: you created
me as formless as you knew how, you gave me wings, and the
freedom to express myself. To my brothers, John W. Ollison IV &
Jason T. Ollison: soldiers. Thanks for reminding me of where I
come from. Dr. Anita K. Hopper: I was convinced that you
believed in me more than I did, and that’s not easy. Jim E.
Hopper, thanks for the example to follow. Scott W. Roy: thank
you for introducing me to the weird beauty that lurks with the
genomes of eukaryotes. Without you, there is no Gerid A.
Ollison, the biologist. Sarah Hu: you welcomed me to this space
with open arms, and continue to show me the way. I am forever
indebted for your gracious support, tough love, example, and
friendship. The many personalities in my cohort and student
mentors: Melody Aleman, Emily Aguirre, Wyatt, Nina Yang, Colette
Fletcher-Hoppe, Yubin Raut, Justin Gaffney, Teagan and Katie,
Inessa Chandra, Anjali Bhatnagar, Daniel Olivares-Zambrano,

iv
Danny Osorio, Tina Nguyen, Jordan Coelho, Mike Lee, Nathan
Walworth, Cesar Ignacio-Espinosa, Ben Tully. Even Josh, Ben, and
Kenny. I used everything you all gave me. Thanks. My lab: Jay
Liu, Jayme Smith, Alle Lie, Avery Tatters, Lisa Mesrop, Brittany
Stewart. Jennifer Beatty: It was a true honor sharing an office
and exchanging so much perspective. Thanks for teaching me
balance. Samantha Gleich, Sarah Trubovitz, Isha Kalra. My
experience as a graduate student was so positive, and most of
this was a result of the support network I formed in you. The
Professors, David Hutchins and Suzanne Edmunds; Carly Kenkel:
you will forever stand out as an example of excellence in the
broadest sense—research, mentorship, service; Cameron Thrash,
Jed Fuhrman, Jill Sohm, Scott Applebaum; Eric Webb and John
Heidelberg: I’m forever grateful for the opportunity you two
gave me, and how much I learned from you two during GEM 2018 and
2019; Seth John, Naomi Levine, and Matthew Dean; Karla
Heidelberg: I wouldn’t have come to USC if it weren’t for that
pen. Sergio Sanudo-Wihelmy and Jim Moffett; Myrna Jacobson: I
won’t soon forget the many insightful hallway conversations we
shared during my time at SC. Thank you all for your open doors,
encouragement, and insightful conversations. The logistics:
Douglas Burelson, Adolfo Dela Rosa, Linda Bazilian. Don Bingham:
your consistency and reliability made this ride smooth from the
first interview.

v
I am forever indebted to my advisor, Dr. David A. Caron,
who didn’t attempt to press and mold me. I’ll never be able to
adequately thank you for all of the resources, both money and
time—the latter of which you can never get back—that you spent
without hesitation. Thank you for pushing me to do my absolute
best work and for helping me reach my highest scholarly
potential.
This body of work was made possible by the R/V Kilo Moana,
SCOPE and HOT ops team; the R/V Yellowfin with Troy, Dennis,
Captain Dennis, and Adriana; the staff of the Heal The Bay
Aquarium (Santa Monica Pier) for logistical support of Chapters
II and III; and my funding sources: Chapter I was supported by
the Simons foundation [Grants P49802 to DAC; 329108 and 721223
to EFD] and NSF [Grant 1737409 to DAC], and Chapters II –
chapter IV were supported by the NSF [Grant 1136818 to DAC and
JF].

vi
TABLE OF CONTENTS:
Dedication..................................................ii
Acknowledgements...........................................iii
List of Tables.............................................vii
List of Figures...........................................viii
Abstract.....................................................x
Introduction.................................................1
Introduction References......................................5
Chapter I: Come Rain or Shine: Depth, Not Season, Shapes
Protistan Communities in the NPSG...............................8
Abstract................................................9
Introduction...........................................10
Materials and Methods..................................15
Results................................................20
Discussion.............................................31
Tables and Figures.....................................38
References.............................................63
Chapter II: Daily dynamics of contrasting spring blooms in Santa
Monica Bay (central Southern California Bight)................71
Abstract...............................................72
Introduction...........................................73
Materials and Methods..................................77
Results................................................84
Discussion.............................................93
Tables and Figures....................................108
References............................................128
Chapter III: Physiological dynamics of contrasting spring blooms
in Santa Monica Bay (central Southern California Bight).....139
Abstract..............................................140
Introduction..........................................141
Materials and Methods.................................144
Results...............................................152
Discussion............................................168
Figures...............................................182
References............................................198
Chapter IV: Microscopy vs high-throughput sequencing methods for
investigating protistan diversity.............................205
Abstract..............................................206
Introduction..........................................207
Materials and Methods.................................213
Results...............................................217
Discussion............................................221
Figures...............................................230
References............................................239

vii
LIST OF TABLES:
Chapter I:
Table 1: Table S1.............................................39
Table 2: Table S2.............................................39
Chapter II:
Table 1: Table 1.............................................109
Table 2: Table S1............................................109
Table 3: Table S2............................................109
Table 4: Table S3............................................110
Table 5: Table S4............................................110

viii
LIST OF FIGURES:
Chapter I:
Figure 1: Figure 1............................................44
Figure 2: Figure 2............................................45
Figure 3: Figure 3............................................46
Figure 4: Figure 4............................................47
Figure 5: Figure 5............................................48
Figure 7: Figure 6............................................49
Figure 8: Figure S1...........................................50
Figure 9: Figure S2...........................................51
Figure 10: Figure S3..........................................52
Figure 11: Figure S4..........................................53
Figure 12: Figure S5..........................................53
Figure 13: Figure S6..........................................54
Chapter II:
Figure 1: Figure 1...........................................115
Figure 2: Figure 2...........................................116
Figure 3: Figure 3...........................................117
Figure 4: Figure 4...........................................118
Figure 5: Figure 5...........................................119
Figure 6: Figure 6...........................................120
Figure 7: Figure 7...........................................121
Figure 8: Figure S1..........................................122
Figure 9: Figure S2..........................................123
Figure 10: Figure S3.........................................124
Figure 11: Figure S4.........................................125
Figure 12: Figure S5.........................................126
Figure 13: Figure S6.........................................127
Chapter III:
Figure 1: Figure 1...........................................186
Figure 2: Figure 2...........................................187
Figure 3: Figure 3...........................................188
Figure 4: Figure 4...........................................189
Figure 5: Figure 5...........................................190
Figure 6: Figure S1..........................................191
Figure 7: Figure S2..........................................192
Figure 8: Figure S3..........................................193
Figure 9: Figure S4..........................................194
Figure 10: Figure S5.........................................195
Figure 11: Figure S6.........................................196
Figure 12: Figure S7.........................................196
Figure 13: Figure S8.........................................197
Chapter IV:
Figure 1: Figure 1...........................................233
Figure 2: Figure 2...........................................234
Figure 3: Figure 3...........................................235
Figure 4: Figure 4...........................................236

ix
Figure 5: Figure S1..........................................237
Figure 6: Figure S2..........................................238

x
ABSTRACT:
Unicellular eukaryotes (protists) are integral to all known
marine microbial assemblages. Phagotrophic species are
responsible for the majority of bacterial mortality and
remineralization with few exceptions; photosynthetic species
contribute approximately half of oceanic primary production and
constitute important sinks for atmospheric CO2. The immense
diversity within protistan assemblages——size, morphology, and
trophic function——enables them to serve many functions across
multiple trophic levels at the base of natural food webs.
Characterizing protistan diversity and their community responses
to environmental cues across geographic and temporal scales will
improve our understanding of anthropogenic impacts on ecosystem
health and better enable us to model marine food web functioning
in future climate scenarios.
The four chapters of this dissertation aims to improve our
understanding of spatiotemporal variations in protistan
diversity, and how both physical and chemical environmental
factors (abiotic factors) intersect with species interactions
(biotic factors) to shape protistan assemblages in two marine
ecosystems: coastal and open ocean ecosystems.
In chapter I, I examined the vertical and seasonal
distribution of metabolically active protists in the North
Pacific Subtropical Gyre, an oceanic desert off the coast of

xi
Hawai’i. In Chapter II and Chapter III I examined the temporal
dynamics and the physiological responses of the protistan
community to environmental cues during upwelling blooms
dominated by diatoms (2018) and dinoflagellates (2019) in the
Southern California coastal upwelling regime (Southern
California Bight region), a highly productive ecosystem.
Finally, in Chapter IV I employed time series data from
Santa Monica Bay to contrast four common methodological
approaches for assessing protistan diversity: 1) compound light
microscopy, 2) PCR-amplified 18S-V4 rRNA gene transcripts
grouped into amplicon sequence variants (ASVs), and
metatranscriptome-derived 3) 18S rRNA gene transcripts and 4)
non-rRNA mRNA. At the time of writing this dissertation,
morphology still represents the gold standard for protistan
classification, albeit laborious. At the same time, rapid
advances in sequencing technology and expanding taxonomic and
functional databases have enabled a variety of high-throughput
sequencing methodological approaches for examining protistan
free-living protistan assemblages. This chapter aims to examine
how the strengths of these common methodologies might be
leveraged in a complimentary manner in future studies.

1
INTRODUCTION:
Microbes conduct the elemental transformations that drive
biogeochemically important cycles throughout the global ocean.
Unicellular eukaryotes (protists) serve integral roles in all
known marine microbial assemblages as both producers and
consumers(Pomeroy, 1974; Azam et al., 1983; Caron et al., 2012;
Worden et al., 2015). Heterotrophic species are integral
intermediates in marine food webs and are responsible for the
majority of bacterial mortality and elemental remineralization
that support major biogeochemical cycles (C, N, Fe, P) with few
exceptions (Caron, 1994; Sherr and Sherr, 2002) and
heterotrophic flagellates in the nano- (2-20 µm), and parasites
impact community structure and elemental cycling in ways that
have yet to be fully defined. Photosynthetic species contribute
approximately half of oceanic primary production (Field et al.,
1998), support some of the world’s most important
fisheries(Ryther, 1969), and large species with carbon
exoskeletons constitute important sinks for atmospheric
CO2(Buesseler, 1998; Smetacek, 1999; Gutierrez-Rodriguez et al.,
2018). Mixotrophy, broadly defined as the combined use of
heterotrophic and phototrophic nutrition in a single organism
(Mitra et al., 2016; Stoecker et al., 2017), is prevalent in
protistan assemblages and has important consequences to food web
structure, the efficiency of carbon transfer to higher trophic

2
levels and sinking to the deep ocean (Ward and Follows, 2016).
The immense diversity of size classes and morphology of species
across all trophic modes (hetero-, photo-, and mixotrophic
modes) enables them to serve many integral functions at multiple
levels at the base of all known marine food webs (Caron et al.,
2012; de Vargas et al., 2015; Worden et al., 2015).
Characterizing protistan diversity and their community
responses to environmental cues across geographic and temporal
scales will improve our understanding of anthropogenic impacts
on ecosystem health and better enable us to model marine food
web functioning in future climate scenarios. Historically,
protistan species were characterized using microscopy; however,
morphology-based classification is very laborious and requires
specialized equipment. Recent advances in high throughput
sequencing have enabled the study of protists in natural
environments. Many studies based on high-throughput sequencing
methods have begun to reshape our conception of protistan
diversity and distribution (de Vargas et al., 2015; Lima-Mendez
et al., 2015; Massana et al., 2015), and provide insight into
their adaptive physiological responses to environmental cues
(Bender et al., 2014; Liu et al., 2016; Harke et al., 2017; Lie
et al., 2017; Wurch et al., 2019) and trophic interactions
(Alexander et al., 2015; Liu et al., 2019; Massana et al.,
2020).

3
The four chapters of this dissertation utilized highthroughput sequencing methodologies to improve our understanding
of spatiotemporal variations in protistan diversity, and how
both physical and chemical environmental factors (abiotic
factors) intersect with species interactions (biotic factors) to
shape protistan assemblages and the physiological processes
undergirding them in two marine ecosystems: coastal and open
ocean ecosystems.
In chapter I, I examined the vertical and seasonal
distribution of metabolically active protists in the North
Pacific Subtropical Gyre, an oceanic desert off the coast of
Hawai’i. In Chapter II and Chapter III I examined the temporal
dynamics and the physiological responses of the protistan
community to environmental cues during upwelling blooms
dominated by diatoms (2018) and dinoflagellates (2019) in the
Southern California coastal upwelling regime (Southern
California Bight region), a highly productive ecosystem.
Finally, in Chapter IV I employed time series data from
Santa Monica Bay to contrast four common methodological
approaches for assessing protistan diversity: 1) compound light
microscopy, 2) PCR-amplified 18S-V4 rRNA gene transcripts
grouped into amplicon sequence variants (ASVs), and
metatranscriptome-derived 3) 18S rRNA gene transcripts and 4)
non-rRNA mRNA. At the time of writing this dissertation,

4
morphology still represents the gold standard for protistan
classification, albeit laborious. At the same time, rapid
advances in sequencing technology and expanding taxonomic and
functional databases have enabled a variety of high-throughput
sequencing methodological approaches for examining protistan
free-living protistan assemblages. Chapter IV aims to examine
how the strengths of these common methodologies might be
leveraged in a complimentary manner in future studies.

5
REFERENCES:
Alexander, H., Jenkins, B.D., Rynearson, T.A., and Dyhrman, S.T.
(2015) Metatranscriptome analyses indicate resource partitioning
between diatoms in the field. Proc Natl Acad Sci U S A 112:
E2182-2190.
Azam, F., Fenchel, T., Field, J.G., Gray, J.S., Meyer-Reil,
L.A., and Thingstad, F. (1983) The Ecological Role of WaterColumn Microbes in the Sea. Marine Ecology Progress Series 10:
257-263.
Bender, S.J., Durkin, C.A., Berthiaume, C.T., Morales, R.L., and
Armbrust, E.V. (2014) Transcriptional responses of three model
diatoms to nitrate limitation of growth. Frontiers in Marine
Science 1.
Buesseler, K.O. (1998) The decoupling of production and
particulate export in the surface ocean. Global Biogeochemical
Cycles 12: 297-310.
Caron, D.A. (1994) Inorganic Nutrients, Bacteria, and the
Microbial Loop. Microbial Ecology 28: 295-298.
Caron, D.A., Countway, P.D., Jones, A.C., Kim, D.Y., and
Schnetzer, A. (2012) Marine protistan diversity. Ann Rev Mar Sci
4: 467-493.
Caron, D.A., Alexander, H., Allen, A.E., Archibald, J.M.,
Armbrust, E.V., Bachy, C. et al. (2017) Probing the evolution,
ecology and physiology of marine protists using transcriptomics.
Nat Rev Microbiol 15: 6-20.
Carradec, Q., Pelletier, E., Da Silva, C., Alberti, A.,
Seeleuthner, Y., Blanc-Mathieu, R. et al. (2018) A global ocean
atlas of eukaryotic genes. Nature Communications 9.
de Vargas, C., Audic, S., Henry, N., Decelle, J., Mahe, F.,
Logares, R. et al. (2015) Eukaryotic plankton diversity in the
sunlit ocean. Science 348: 1261605-1261601.
Field, C.B., Behrenfeld, M.J., James, T., Randerson, J.T., and
Falkowski, P.G. (1998) Primary Production of the Biosphere:
Integrating Terrestrial and Oceanic Components. Science 281:
237-240.
Gutierrez-Rodriguez, A., Stukel, M.R., Lopes Dos Santos, A.,
Biard, T., Scharek, R., Vaulot, D. et al. (2018) High
contribution of Rhizaria (Radiolaria) to vertical export in the

6
California Current Ecosystem revealed by DNA metabarcoding. ISME
J.
Harke, M.J., Juhl, A.R., Haley, S.T., Alexander, H., and
Dyhrman, S.T. (2017) Conserved Transcriptional Responses to
Nutrient Stress in Bloom-Forming Algae. Frontiers in
Microbiology 8.
Lie, A.A., Liu, Z., Terrado, R., Tatters, A.O., Heidelberg,
K.B., and Caron, D.A. (2017) Effect of light and prey
availability on gene expression of the mixotrophic chrysophyte,
Ochromonas sp. BMC Genomics 18: 163.
Lima-Mendez, G., Faust, K., Henry, N., Decelle, J., Colin, S.,
Carcillo, F. et al. (2015) Determinants of community structure
in the global plankton interactome. Science 348.
Liu, Z., Campbell, V., Heidelberg, K.B., and Caron, D.A. (2016)
Gene expression characterizes different nutritional strategies
among three mixotrophic protists. FEMS Microbiol Ecol 92.
Liu, Z., Mesrop, L.Y., Hu, S.K., and Caron, D.A. (2019)
Transcriptome of Thalassicolla nucleata Holobiont Reveals
Details of a Radiolarian Symbiotic Relationship. Frontiers in
Marine Science 6.
Massana, R., Labarre, A., López-Escardó, D., Obiol, A.,
Bucchini, F., Hackl, T. et al. (2020) Gene expression during
bacterivorous growth of a widespread marine heterotrophic
flagellate. The ISME Journal.
Massana, R., Gobet, A., Audic, S., Bass, D., Bittner, L.,
Boutte, C. et al. (2015) Marine protist diversity in European
coastal waters and sediments as revealed by high-throughput
sequencing. Environ Microbiol 17: 4035-4049.
Mitra, A., Flynn, K.J., Tillmann, U., Raven, J.A., Caron, D.,
Stoecker, D.K. et al. (2016) Defining Planktonic Protist
Functional Groups on Mechanisms for Energy and Nutrient
Acquisition: Incorporation of Diverse Mixotrophic Strategies.
Protist 167: 106-120.
Pomeroy, L.R. (1974) The Ocean's Food Web, A Changing Paradigm.
BioScience 24: 499-504.
Ryther, J.H. (1969) Photosynthesis and Fish Production in the
Sea. Science 166: 72-76.

7
Sherr, E.B., and Sherr, B.F. (2002) Significance of predation by
protists in aquatic microbial food webs. Antonie van Leeuwenhoek
81: 293-308.
Smetacek, V. (1999) Diatoms and the Ocean Carbon Cycle. Protist
150: 25-32.
Stoecker, D.K., Hansen, P.J., Caron, D.A., and Mitra, A. (2017)
Mixotrophy in the Marine Plankton. Ann Rev Mar Sci 9: 311-335.
Ward, B.A., and Follows, M.J. (2016) Marine mixotrophy increases
trophic transfer efficiency, mean organism size, and vertical
carbon flux. Proc Natl Acad Sci U S A 113: 2958-2963.
Worden, A.Z., Follows, M.J., Giovannoni, S.J., Wilken, S.,
Zimmerman, A.E., and Keeling, P.J. (2015) Environmental science.
Rethinking the marine carbon cycle: factoring in the
multifarious lifestyles of microbes. Science 347: 1257594.
Wurch, L.L., Alexander, H., Frischkorn, K.R., Haley, S.T.,
Gobler, C.J., and Dyhrman, S.T. (2019) Transcriptional Shifts
Highlight the Role of Nutrients in Harmful Brown Tide Dynamics.
Frontiers in Microbiology 10.

8
Chapter I: Come Rain or Shine: Depth, Not Season, Shapes the
Protistan Community in the NPSG
Gerid A. Ollison1
, Sarah K. Hu2
, Lisa Mesrop3
, Edward DeLong4
,
David A. Caron1
1
Department of Biological Sciences, University of Southern
California, 3616 Trousdale Parkway, Los Angeles, CA 90089-0371
USA.
2
Woods Hole Oceanographic Institution, Marine Chemistry and
Geochemistry, MS 51, Woods Hole, MA, 02543 USA.
3
Department of Ecology, Evolution and Marine Biology, University
of California Santa Barbara, Santa Barbara, CA, 93106, USA.
4
Department of Oceanography, University of Hawaii, Honolulu, 1000
Pope Road, Honolulu, HI 96822, USA.

9
ABSTRACT:
Protists are extremely diverse morphologically and
physiologically, and they play important ecological roles at
multiple trophic levels as primary producers and consumers in
nearly all microbial communities. In spite of their fundamental
importance in marine ecosystems, protistan diversity and
distribution has yet to be comprehensively characterized in much
of the world ocean, particularly in the vast expanses below the
euphotic zone. We examined protistan community structure and
species diversity in the oligotrophic North Pacific Subtropical
Gyre. Our primary goal was to better characterize the breadth of
metabolically active protistan species throughout the water column
spanning 12 depths from 5m to 770m (~600m below the euphotic zone)
across three seasons using 18S rRNA gene V4 amplicon sequencing of
RNA. Protistan community structure changed markedly across
relatively narrow ranges of increasing depths between 75m-125m,
and again between 175m-300m. In this permanently stratified water
column, changes were driven by depth-specific distributions among
major protistan taxa associated with the upper mixed layer, deep
chlorophyll maximum and subeuphotic zone, respectively. Diatoms
and some heterotrophic protists (MAST, Choanoflagellates) were
important contributors in the upper mixed layer, while haptophytes
and pelagophytes increased in relative abundances in the lower
euphotic zone. Radiolaria, ciliates and Syndiniales increased in

10
relative abundances below the euphotic zone. Overall, the highest
taxonomic richness of amplicon sequence variants (ASVs) was
observed in aphotic samples. Additionally, the dominant ASVs
within some taxonomic groups that were observed in deep water were
different than those observed in the upper water column, implying
adaptations to specific depth strata rather than passive transport
of surface-dwelling species. In contrast to depth-related changes,
seasonal changes in protistan community structure were not
significant (p < 0.01).
INTRODUCTION:
Unicellular eukaryotes (protists) are fundamental components
of nearly every microbial assemblage. The immense ranges of
sizes, forms, and trophic lifestyles of protists enables them to
influence energy and carbon flow across marine food webs through
primary production, phagotrophic consumption, and parasitic
associations (Caron et al., 2012; Worden et al., 2015).
Protistan primary producers are ubiquitous in the euphotic zone
of the global ocean (de Vargas et al., 2015), and account for
nearly half of the annual pelagic production (Field et al.,
1998). Episodic algal blooms can also be a significant source of
carbon flux to the deep ocean (Scharek et al., 1999).
Processes that remineralize organic carbon or transfer it to
higher trophic levels in surface waters, or export it out of the

11
photic zone, are directly mediated by diverse assemblages of
heterotrophic protists (Caron, 1994; Sherr and Sherr, 2002;
Calbet and Saiz, 2005) including phagotrophic, mixotrophic, and
parasitic species. Parasites and species that combine
phototrophic and heterotrophic lifestyles (mixotrophs) can have
a profound impact on carbon cycling and ecosystem function
through positive and negative, multi-trophic interactions
(Lafferty et al., 2006; Poulin, 2010) and by acting as a
significant source of bacterial mortality in low nutrient
environments (Hartmann et al., 2012; Unrein et al., 2014),
respectively. Although we are gaining a clearer picture of the
diversity and distribution of protists thanks to recent advances
in high-throughput sequencing technology, reference databases,
and global oceanographic surveys (Guillou et al., 2008; Guillou
et al., 2013; Lima-Mendez et al., 2015), species richness is not
fully characterized in most free-living communities, especially
below the euphotic zone in the open ocean. Understanding these
aspects of community ecology are essential for modeling
ecosystem function and response to environmental change (Ward et
al., 2012; Worden et al., 2015; Ward and Follows, 2016).
Protistan species richness and diversity have traditionally
been documented using microscopy, and the primary taxonomy of
these species is still based on morphological descriptions.
However, genetic studies of diversity that have become

12
commonplace over the past two decades have altered our
comprehension of the species richness and community structure of
free-living protistan communities (Stoeck et al., 2009; Edgcomb
and Pachiadaki, 2014; de Vargas et al., 2015; Hu et al., 2016b;
Giner et al., 2020). Considerable effort is now being expended
on merging genetic, morphological and physiological information
into a single species concept (Berney et al., 2017). Such a
merged species concept will greatly facilitate the
interpretation of environmental surveys of protistan genetic
diversity in an ecological context.
Most studies of marine protistan diversity have been conducted
from specific geographical locations and/or with limited
temporal coverage, although a few studies have been carried out
across large expanses of the global ocean or examining
seasonality. The latter studies have begun to establish the
broader spatiotemporal distributions of protists (de Vargas et
al., 2015; Lima-Mendez et al., 2015; Pernice et al., 2015; Hu et
al., 2016a; Giner et al., 2019). However, relatively few studies
dedicated to the study of protistan assemblages of the deep
ocean have been conducted (Countway et al., 2007; Edgcomb et
al., 2011; Orsi et al., 2011; Pernice et al., 2016; Giner et
al., 2016; Xu et al., 2017; Giner et al., 2020; Obiol et al.,
2020; Canals et al., 2020),
, some of which have been targeted at
specific benthic features such as hydrothermal vents (Coyne et

13
al., 2013; Edgcomb, 2016; Pasulka et al., 2019; Mars Brisbin et
al., 2020).
The North Pacific Subtropical gyre (NPSG) is the largest
contiguous biome on the planet and accounts for a significant
amount of organic carbon production and biogeochemical cycling.
The biological, chemical and physical aspects of the NPSG have
been studied regularly and continuously since 1988 at station
ALOHA through the Hawai’i Ocean Time series program (HOT) (Karl
and Church, 2014). A broad range of habitats characterize
station ALOHA extending from the persistently warm, nutrientstarved, light-saturated upper mixed layer to the cold,
nutrient-rich, light-limited regions of the lower euphotic and
below. We have gained a lot of insight about bacterial diversity
and vertical stratification in this oligotrophic regime via
high-throughput sequencing (HTS) analyses (metagenomic,
metatranscriptomic, and metabarcode) (Konstantinidis et al.,
2009; Malmstrom et al., 2010; Mende et al., 2017; Boeuf et al.,
2019), and other molecular and phylogenetic-based methodologies
(Karner et al., 2001; Karl and Church, 2014), however the extent
of protistan species richness and vertical distribution below
the euphotic zone at station ALOHA has not been comprehensively
characterized (Pasulka et al., 2013; Rii et al., 2016b).
We employed 18S rRNA gene V4 amplicon sequencing of cDNA (RNA)
to characterize the breadth of metabolically active protistan

14
taxa and their vertical distribution from 12 depths spanning 5m770m across three seasons at station ALOHA. The protistan
community changed markedly across two narrow ranges of
increasing depth (between 75m-100m and 175m-300m), forming three
community zones encompassing the upper euphotic (5m-75m), the
lower euphotic (100m-175m), and the aphotic depths (300m-770m).
Differences between the communities were a consequence of
distinct distributional patterns of major protistan groups. A
photosynthetic community composed of dinoflagellates,
stramenopiles and haptophytes collectively occupied the euphotic
zone, however, specific groups were endemic to either the lightsaturated upper mixed layers (≤75m) or the deep chlorophyll
maximum (DCM; 100m-150m). Heterotrophic protists, primarily
alveolates and rhizarians, colonized the entire water column but
increased in relative abundance and changed in taxonomic
composition with depth. The exceptions, small heterotrophic
flagellates (MArine STramenopiles and choanoflagellates), were
preferential to shallower depths. Parasites colonized the entire
water column, increased markedly with depth, and illustrated
punctuated species-specific vertical distributions.
Collectively, the aphotic depths harbored the most diversity and
uncharacterized ASVs. Compared to depth dependent changes,
changes associated with season were insignificant (p < 0.05)
with few exceptions.

15
MATERIALS AND METHODS:
Overview:
Sequencing of environmental 18S rRNA gene transcripts (RNA)
were carried out on samples collected at 12 depths from 5m-770m
in the North Pacific Subtropical Gyre (NPSG) to assess protistan
species richness and diversity across seasons and depths
spanning the euphotic and aphotic zones. Sampling was carried
out on four dates spanning a one-year period.
Sample collection:
Samples were collected during four expeditions at station
ALOHA (22.75˚N, 158˚W) as part of the Hawai’i Ocean Time-series
(HOT) program from December 2014 to December 2015 (HOT 268-
121614, HOT 271- 042415, HOT 275- 081115, HOT 279- 120815) (Karl
and Church, 2014). Water was collected from twelve depths
spanning euphotic and aphotic depths: 5m, 25m, 45m, 75m, 100m,
125m, 150m, 175m, 300m, 400m, 500m, 770m. A sample at 45m was
not taken in the month of April 2015. Each of the 12 depths were
collected using a 24-place rosette equipped with 12-L Niskin
bottles. Real-time nitrate, temperature, salinity, dissolved
oxygen, and chlorophyll a concentrations were collected with a
vertically mounted SBE-9/11plus CTD equipped with dual C, T, DO

16
sensors. All CTD data are available at
hahana.soest.hawaii.edu/hot/hot-dogs/interface.html.
Seawater was prefiltered through an 80 µm Nitex mesh to
minimize the contribution of metazoa, and 3.5L aliquots of each
sample were vacuum filtered (< 15 psi) onto 25mm 0.2 μm Supor PES
Membrane Disc filters (Pall, USA). Samples were stored in 300 µl
or RNAlater (Sigma-Aldrich, #R0901) and immediately flash frozen
in liquid nitrogen for later RNA extraction.
Nucleic acid extraction and sequencing:
Samples were thawed on ice, centrifuged for 1 minute at 10,000
rpm, and RNALater was removed before subsequent extraction
steps. Total RNA was extracted using the All Prep DNA/RNA Mini
kit (Qiagen, Valencia, CA #80204). Genomic DNA was removed
during the RNA extraction using an RNase-Free Qiagen DNase
(Qiagen, #79254). DNA removal was confirmed by lack of
amplification of extracted DNA in downstream PCR reactions. RNA
was reverse transcribed to cDNA using the iScript Reverse
Transcription Supermix with random hexamers (Bio-Rad
Laboratories, Hercules #170-8840).
Samples were PCR amplified with Q5 High-Fidelity 2x Master Mix
(NEB, #M0492S) 18S 565F (5’-CCAGCASCYGCGGTAATTCC-3’) and 948R
(5’-ACTTTCGTTCTTGATYRA-3’) primers (Stoeck et al., 2010). PCR
reactions were carried out in two steps due to the differences

17
in annealing temperature between the primer pair. Reactions
consisted of a 98˚C denaturation step for 2 min, followed by 10
cycles of 98˚C for 10s, 53˚C for 30s and 72˚C for 30s. Next, 15
cycles of 98˚C for 10s, 48˚C for 30s and 72˚C for 30s, followed
by a final elongation step at 72˚C for 2min. PCR products were
purified using Agencourt AMPure XP beads per our established lab
protocol (dx.doi.org/10.17504/protocols.io.hdmb246), and indexed
using Illumina-specific P5 and P7 indices. Samples were
quantified using a Qubit 2.0 fluorometer (ThermoFisher
Scientific, #Q32866), quality checked on an Agilent Bioanalyzer
2100 (Agilent Technologies, Santa Clara), and normalized prior
to sequencing (dx.doi.org/10.17504/protocols.io.rfvd3n6).
Paired-end sequencing (250 X 250 bp) was performed via Illumina
MiSeq (Laragen Inc. Culver City, CA).
Sequence analysis:
Amplicon Sequence Variants (ASVs) (Callahan et al., 2017)
were generated using DADA2 (Callahan et al., 2016) via Qiime2
v2018.11 (Bolyen et al., 2019) from rRNA transcripts (cDNA)
reads to investigate the metabolically active taxa within the
protistan community (Blazewicz et al., 2013). Briefly, reads
from each sample were demultiplexed with the Qiime2 demux
function. Barcodes, primers, and low-quality bases at the ends
of both forward and reverse reads were removed using the trim

18
and trunc options of the Qiime2 DADA 2 plugin. Sequences were
quality filtered, denoised, merged, dereplicated, chimera
checked, and clustered into amplicon sequence variants (ASV)
using the Qiime2 DADA 2 plugin default parameters. Taxonomic
classifications were assigned at 90% confidence using the Qiime2
pre-fitted sklearn-based taxonomy classification and the latest
iteration of the PR2 database (Guillou et al., 2013) (v.12.1;
https://github.com/pr2database/pr2database ). When necessary,
ASVs lacking taxonomic identification via PR2 were further
crosschecked using the SILVA 18S rRNA gene database (v. 132)
(Pruesse et al., 2007), and individual abundant representative
sequences of interest lacking taxonomic information within
either database were further investigated using NCBI BLAST.
Further quality assessment and final sequence analysis was
conducted in R (R_Core_Team, 2020) v3.5.1 “Feather Spray.” ASVs
that contained one sequence within the entire dataset were
removed, and raw ASV sequence counts were subsequently
normalized using the Trimmed Mean by M-value transformation
(TMM) prior to all downstream analysis using edgeR v.3.30. TMM
normalization was chosen because it allowed the retention of all
sequenced reads, and has been shown to be the a robust method of
normalization when employed with Bray-Curtis dissimilarities
(Weiss et al., 2017). Taxonomic group names were manually
curated at approximately phylum level for easier ecological

19
interpretation (Figure 1), and manually assigned at higher
levels of resolution when necessary (Figures S2, S3).
Alpha diversity statistics (Shannon and inverse Simpson) were
calculated in R using the diversity function (Vegan v2.4-2).
Total species richness was estimated by totaling the number of
unique ASVs. Non-metric multidimensional scaling with BrayCurtis dissimilarity was calculated in R using the metaMDS
function (Vegan v2.4-2). UpSet plots were generated from a
binary ASV table (constructed from the raw ASV table;
Supplemental table 1) where an ASV with greater or less than 10
reads in a sample was given a 1 for presence in a sample or 0
for absence, respectively. The UpSet R package was used to
generate upset plots (Conway et al., 2017). Cluster dendrograms
were generated by combining pairwise Euclidean distances with
the average method of clustering. Briefly, after quantifying
distances between samples (Euclidean), pairwise averages were
used as the distances between other pairs and clusters. All R
scripts for plots, data quality filtering and analysis can be
found at https://github.com/theOlligist/ALOHA-SpatioTemporal.
Data Accessibility:
Raw sequence data are available on NCBI under accession number
SRP110191. The quality-filtered ASV table used in this study is
available as a comma-separated table (Supplementary Table 1).

20
RESULTS:
Environmental conditions at the study site:
Physical and chemical properties of the water column at
station ALOHA were generally consistent with long-term
measurements conducted as part of the HOT program. Surface
temperatures varied by less than 4˚ C across the three sampled
seasons, which basically encompassed the full seasonal range
generally observed at station ALOHA (Figure S1a). The depth of
the seasonal thermocline was approximately 45m in August,
deepening to approximately 120m in December (Figure S1a;
http://hahana.soest.hawaii.edu/hot/hot-dogs/interface.html).
August and December were the only months with significant
seasonal variation in temperature and chlorophyll a values.
Nitrate values in the upper euphotic were between 1-5µM/L
throughout the year with the highest recorded values in the
December months and the shallowest nitricline (~120m) in the
month of August (Figure S1b). Oxygen depth profiles were not
noticeably variable across seasons, with the exception of a
spike at 65m in August. Oxygen values ranged from 180 - 210µM/kg
from the surface to the oxycline. The latter feature began at
approximately 300m, with oxygen values decreasing to <50µM/kg by
600m (Figure S1c). Chlorophyll a concentrations at the DCM were

21
seasonally variable with the highest concentrations observed in
August (0.75 µg/l) (Figure S1d).
Protistan community richness and diversity varied across depths
and seasons:
The primary aim of this study was to characterize protistan
diversity within the sunlit and aphotic zones at station ALOHA,
and compare the influences of depth and seasonal changes. A
total of 2.6 million reads of rRNA transcripts were obtained
from 47 samples after quality filtration and the removal of
chimeric sequences (Table S2). The quality filtered reads were
clustered into a total of 11,538 Amplicon Sequence Variants
(ASVs) representing species-level to strain-level distinctions
for most protistan taxa (Caron and Hu, 2018) (Table S2). ASVs
were assigned species-level taxonomic classifications by
alignment to the PR2 database (v. 4.12.1).
The Stramenopila, Alveolata, and Rhizaria (SAR) supergroup
constituted more than 50% of the community throughout the entire
water column during every season (Figure 1). MAST was the only
classifiable stramenopile group that averaged more than a few
percent of the community at all depths and across all seasons
(12%). Ciliates, dinoflagellates, and Syndiniales were the most
abundant alveolates. Dinoflagellates were the most abundant
major taxonomic group within the protistan community, accounting

22
for > 20% of the community at every depth and during every
month. There were 24 classifiable dinoflagellate taxa (1449
ASVs) distributed throughout the water column, with 3 genera
(Gyrodinium, Gymnodinium, Prorocentrum) accounting for over 30%
of the dinoflagellate community (Figure S2). The majority of
dinoflagellate ASVs lacked higher levels of taxonomic
classification due to limited information in PR2 and SILVA 18S
rRNA sequence databases. Ciliates and Syndiniales ASVs also
consistently averaged more than 5% of the protistan community
throughout the water column in all seasons, 14% and 9%
respectively (Figure 1). The majority of the ciliate diversity
fell within six phylogenetic groups of which spirotrichs were
the most abundant (Figure S3). The other ciliate groups in order
of relative abundances were oligohymenopheran, phyllopharyngean,
litostome, colpodean and prostomastean ciliates. Similar to
dinoflagellates, most Syndiniales ASVs were not classifiable
with the PR2 database (v.12.1) or NCBI BLAST.
Radiolarian ASVs (1269 ASVs) were the only rhizarian clade
present at more than 1% in any sample, and ranged from 4-14% of
the community across all seasons (Figure 1). Five subgroups
accounted for the classifiable radiolarian assemblage, of which
RAD-B accounted for the majority of sequence reads (331K reads)
(Figure S3). Acantharian taxa were the second most abundant
group within the radiolarian clade (286K reads). Other less

23
abundant taxa, in order of sequence abundance, included RAD-C
(180K reads), polycystines (80K reads), and RAD-A (40K reads).
Three out of the four relatively abundant stramenopile groups
that were observed in the study were found primarily above 175m.
Both diatoms and pelagophytes were most abundant during August
(Figure 1, Table S1). The diatom assemblage was composed of 16
classifiable taxa (322 ASVs) (Figure S2), the majority of which
exhibited maximal abundances between 5m and 75m, and were 4-fold
more abundant in August than any other month sampled.
Unclassifiable ochrophyte ASVs mirrored diatom spatiotemporal
distributions, implying that there were diatoms that were not
taxonomically resolved (Figure 1). In contrast, pelagophytes
exhibited higher relative abundances at the DCM and near the
base of the euphotic zone (100m-150m) (Figure 1).
Beyond SAR taxa, several other protistan groups were observed
at low relative abundances with the exception of hacrobian taxa,
which were comparable to ciliate abundances in the total
community (30%; Figure 1). More than half of the hacrobian taxa
were taxonomically classified as haptophytes. 8 of 20
classifiable haptophyte taxa (423 ASVs) were well represented,
of which Chrysochromulina species were the most abundant (Figure
1, Figure S3). Choanoflagellates (opisthokonts) constituted a
minor but significant fraction of the total protistan community,
with relative abundances greater than 5% occurring in the upper

24
water column across all seasons (Figure 1). Other opisthokonts
(fungi, metazoa, ‘other’) were present at very minor relative
abundances in part due to pre-filtration of the water samples
which would have removed many of these taxa. All other taxa were
observed at low relative abundances. Green algae
(archaeplastids) occurred at low abundances in the community
(generally <1% of total reads), but were present at >2% at or
near the DCM with no clear seasonal variation. Amoebozoan and
Excavata ASVs collectively accounted for less than 1% of the
combined dataset.
The protistan community is vertically partitioned into three
distinct communities:
Non-metric multidimensional scaling (Bray-Curtis) including
all 47 samples formed three well-defined clusters revealing that
the protistan community structure from the surface (5m) to 770m
was divided into three distinct zones according to depth: upper
euphotic, lower euphotic and aphotic zones (Figure 2a). Major
transitions in community composition and richness occurred
between the upper euphotic zone (5m-75m) and lower euphotic and
shallow aphotic zone (100m-175m) and deep aphotic zone (300m700m). The influence of depth on sample dissimilarity was most
apparent within the aphotic cluster (300m-770m), with each depth
forming somewhat distinct groups. The lower euphotic cluster was

25
clearly distinguishable, but lacked strong seasonal or depthrelated sub-groupings. Samples collected in the upper euphotic
zone (5m-75m) encompassed sub-groupings that were related to
sampling season (samples from April and August grouped
separately from samples collected in the December samplings
conducted one year apart). Hierarchical clustering analysis
further supported that the majority of observed similarity
between samples was on the basis of depth (Figure S4). The
results of both statistical analyses were consistent with the
separation between samples and clusters being the result of
depth-dependent factors.
Measures of species richness and diversity (total number of
ASVs and Simpson Index) were greatest for the 300m-770m aphotic
cluster (1920 ASVs and 209 respectively) (Figure 2b and c).
Species richness was similar between the clusters associated
with the upper euphotic zone (5m-75m) and the lower euphotic or
shallow aphotic zone (1720 and 1740 ASVs respectively; Figure
2b). However, diversity based on the Simpson index was 3-fold
greater in the upper euphotic zone relative to the lower
euphotic or shallow aphotic zone (180 and 45.9, respectively;
Figure 2c).
Protistan community composition of three well-defined,
vertically distributed clusters:

26
Our statistical analysis of sample dissimilarity suggested
that differences in taxonomic composition through seasons and
down the water column was largely attributable to depth.
Estimates of diversity also implied that each cluster had
differing levels of species richness and/or evenness (Figure
2b,c). Accordingly, the spatial distribution of dominant taxa
was investigated using ASVs averaged for the four seasonal
samples at each depth. An UpSet plot color-coded for the three
different zones revealed that a large number of ASVs were unique
to each depth (Figure 3; 12 columns on left side) but also that
there was more similarity among communities (in terms of ASV
richness and ASVs shared across depths (intersections) within
each of the depth zones, than between communities from different
zones (Figure 3). Intersections between different depth clusters
were rare and occurred mostly on the borders of adjacent zones.
There were no intersections between depths within the upper
euphotic and aphotic zone, highlighting the difference in
community structure between these two pelagic biomes.
Photosynthetic stramenopiles (diatoms, pelagophytes and other
ochrophytes) were a significant fraction of the upper euphotic
zone samples (13% total), and unclassified stramenopiles
comprised an additional 7% (Figure 4a). Diatoms and other
ochrophytes were minor components in the lower euphotic zone
(both at 1%), while pelagophytes were particularly abundant in

27
the lower euphotic zone samples (18% of reads versus 2% in the
upper euphotic zone; Figure 4b). Haptophytes accounted for 7%
and 9% of total reads in the upper euphotic and lower euphotic
zones, respectively. Archaeplastids also accounted for 1% of
reads within the lower euphotic zone samples (Figure 4b).
Among heterotrophic protistan groups, MAST ASVs were
significant contributors to sun-lit depths, accounting for 17%
of sequences in the upper euphotic zone and 11% in the lower
euphotic zone (Figure 4a,b). Choanoflagellates were 6% of the
community in the upper euphotic, substantially greater than this
group’s relative abundance in the lower euphotic and deep
aphotic zones (≤1%) (Figure 4a,b). In contrast, radiolarian
sequences in the upper euphotic and lower euphotic zone
communities were approximately one third the relative abundances
of this group in samples from the deep aphotic zone (5% and 4%,
respectively; Figure 4a,b). Dinoflagellate, ciliate and
Syndiniales sequences were present at similar abundances in the
two euphotic zones at approximately 21%, 10% and 7%,
respectively. Relative abundances of ciliates and Syndiniales in
the two euphotic zone clusters were approximately half their
contributions in the deep aphotic zone, while the classifiable
dinoflagellate contributions were little changed by depth
(Figure 4a,b versus 4c).

28
Alveolate sequences comprised the greatest portion of the
aphotic zone community composition (57% of sequenced reads)
(Figure 4c versus 4a,b). Dinoflagellates and ciliates accounted
for more of this component of the community than any other
single phylum (24% and 20%, respectively). Globally distributed
parasitic alveolates, Syndiniales, were 13% of the deep aphotic
community. Radiolaria were the second most abundant taxa in the
deep aphotic zone (14% of reads). These same samples were
relatively devoid of classifiable photosynthetic stramenopiles
(less than 1%), although heterotrophic stramenopiles (MAST)
accounted for approximately 8% of the aphotic community (Figure
4c). Haptophytes, which contain numerous mixotrophic species,
accounted for 2% of the aphotic community. Unicellular
opisthokonts (Choanoflagellates) also constituted less than 1%
of total reads in the samples from the deep aphotic zone, while
metazoa were approximately 8% of reads.
Group-specific vertical distributions of dominant protists:
Patterns of the vertical distributions observed in this study
were unique for several of the dominant protistan groups,
contributing to the observed depth-related differences in
community structure. Diatoms (composed largely of
photoautotrophic species) were most abundant in the upper
euphotic zone (5m-75m) at relatively unchanging abundances,

29
followed by a steady decrease in abundances beginning at 75m, to
less than 2% of total reads below 100m (Figure 5a).
Pelagophytes, on the other hand, were less abundant in the upper
euphotic zone compared to their abundances in the lower euphotic
zone (Figure 5b). In particular, peak average abundances of
pelagophytes (about 15% of sequence reads) occurred at 125m and
declined to <1% below 175m. Haptophytes had the broadest
distribution range, accounting for nearly 10% of the community
from 45m-125m, and averaged nearly 2% of the community below
300m (Figure 5c).
The heterotrophic taxa within the Choanoflagellates and MAST
groups were most abundant within the upper euphotic zone and
decreased gradually with depth (Figure 5d,e). Conversely, the
heterotrophic taxa within the ciliate and radiolarian groups
both increased gradually in relative abundances down the water
column, and reached peak abundances at or near 770m (Figure
5f,g). The vertical distribution of Syndiniales ASVs was
strikingly similar to the ciliate and radiolarian distribution
patterns (Figure 5h), and dinoflagellates (Dinophyceae) also
increased gradually in relative abundances down the water
column, reaching peak abundances near the base of the lower
euphotic/ shallow aphotic zone (175m) (Figure 5i). Perhaps
unsurprisingly, the number of unclassifiable taxa increased with
depth (Figure 5j).

30
ASV-specific vertical distributions among protists:
MAST, haptophytes, radiolarians, and ciliates contained
several taxonomic classifications that were used to investigate
putative species-specific vertical distributions. Dominant
Syndiniales ASVs were used due to lack of higher taxonomic
resolution via PR2 and Silva databases. As noted above, overall
major group-specific distribution patterns were similar for
radiolarians, ciliates and Syndiniales ASVs such that relative
abundances generally increased with depth (Figure 5f,g,h). In
contrast, relative abundances of MAST were greatest in the upper
euphotic zone and gradually decreased with depth (Figure 5a),
while haptophytes had subsurface maximal relative abundances
between 45 and 175m (Figure 5d). However, species-specific
vertical distributions within each of these groups did not
always conform to these general patterns (Figures 6, S3).
Dominant Syndiniales ASVs, containing taxa from dino-groups I,
III, and IV, exhibited distinct vertical distributions. Among
eleven abundant ASVs in the overall dataset, three had maximal
relative abundances between 5 and 175m, with very few sequences
of these ASVs observed below 175m (ASVs 1-3; Figure 6).
Conversely, five ASVs had greater relative abundances at depth
than in the upper water column (ASVs 5, 7, 9-11; Figure 6) while
two appeared to have maximal abundances at intermediate depths

31
(ASVs 6, 8; Figure 6). One ASV (ASV 4) had nearly constant
abundances at all depths (Figure 6).
Many of the ciliate taxa were present throughout the water
column with little or no change in relative abundances (Figure
S3). The vast majority of ciliate ASVs were identified as
spirotrich ciliates. Spirotrichs displayed fairly constant
abundances throughout the water column, along with
phyllopharyngean and colpodean ciliates, both of which were
present at much lower relative abundances than spirotrichs
(Figure S3). However, ASVs attributable to oligohymenophoreans
exhibited a pronounced increase in relative abundances at depths
≥300m, as did litostome ASVs to a lesser degree (Figure S3).
These latter two ciliate groups were largely responsible for the
overall trend of increasing ciliate sequence abundances in the
deepest samples (Figure 5f).
Some radiolarian ASVs also exhibited distinct vertical
patterns. ASVs identified as acantharians, RAD-A, polycystines
and other radiolarian taxa lacking higher classification
(Rad.Other) had relative abundances that did not change
appreciably with depth (Figure S3). However, RAD-B and RAD-C
were marginally present in the upper euphotic zone and gradually
increased to high relative abundances in the deepest samples.
Relative abundances of acantharian taxa were 10-fold greater
than all other radiolarian taxa combined at depths above 100m.

32
The haptophyte assemblage had an overall vertical distribution
with highest abundances between 45m and 175m (Figure 5c) that
was mirrored by the vertical distributions of most of the
haptophyte ASVs observed in the study, although the depth of
maximal abundances varied among the taxa from 45m to 175m
(Figure S3). Similarly, the abundant MAST ASVs were most
abundant in the upper euphotic zone and decreased with depth
(Figure S3), as did the overall distribution of the MAST group
(Figure 5e).
DISCUSSION:
We characterized the vertical distribution and diversity of
the protistan community across three seasons and twelve depths
from 5m to 770m via sequencing of the V4 hypervariable region of
the 18S rRNA gene in which more than 11,000 ASVs were recovered
from 2.5 million rRNA reads across 47 samples. The most striking
feature of our results was the marked changes in community
structure across three depth ranges driven by distribution
patterns between, and in some cases within, major taxonomic
groups. In stark contrast to the influence of depth on the
overall protistan community structure, seasonal changes were not
significant (p < 0.05).

33
Three distinct protistan communities occupy the water column at
station ALOHA:
Non-metric multidimensional scaling (NMDS) of Bray-Curtis
dissimilarity between all 47 samples revealed that the protistan
community from the upper euphotic to 770 meters formed three
distinct clusters according to depth (Figure 2). In contrast,
seasonality had a relatively small influence on community
structure, albeit some groups like diatoms did exhibit summer
maxima (Figure 1). Hierarchical clustering of all 47 samples
also indicated statistically significant differences on the
basis of depth (Figure S4). Moreover, large numbers of unique
ASVs were observed at every depth sampled in this study (Figure
3). Communities from depths below 175m formed one cluster
corresponding to the deep aphotic zone (300m-770m), whereas
communities from near the ocean surface down to the shallow
aphotic zone formed two well-defined clusters corresponding to
the upper euphotic zone (5m-45m) and the lower euphotic to
shallow aphotic zones (75m-175m; Figure 2a). The latter cluster
was co-located with the deep chlorophyll maximum, a permanent
feature of this portion of the NPSG (Karl and Church, 2014)
(Figure S1).
A minor influence of seasonality on community structure was
apparent only among samples collected in the upper euphotic zone
(Figure 2a). These findings are not surprising, as the greatest

34
influence of seasonality would be anticipated near the ocean
surface where seasonal ranges in temperature, light and other
factors are greatest. Samples collected during August and April
in the upper euphotic zone were separated (albeit marginally)
from each other on the NMDS plot, and from samples collected
during December. Samples collected on the two December dates
clustered together (and away from August and April) despite the
fact that the December sampling events were one year apart.
Seasonality was not apparent (i.e. their locations on the NMDS
plot overlapped in Figure 2a) among protistan communities
collected from the lower euphotic zone (75m-150m). No clear
groupings by season were observed in the lower euphotic despite
reported seasonal changes in light energy and the position of
the nutricline (Letelier et al., 2004) (Figure 2a). Differences
in the relative influence of depth and season on protistan
community composition between the upper euphotic and the lower
euphotic were presumably a consequence of the strong gradients
of light and nutrients, and wind-induced vertical mixing in the
upper euphotic zone, establishing the “two-layered” euphotic
zone typically observed in the NPSG (Coale and Bruland, 1987;
Bryant et al., 2016).
In contrast to samples from the upper and lower euphotic
zones, samples from the deep aphotic zone separated neatly by
depth, not season. Communities from each sample collected at

35
depths ≥300m formed groupings unrelated to sampling season, which
is not surprising, but it was unanticipated that samples from
300m to 770m would be distinct from one another. For example,
all samples across seasons associated with 770m grouped on the
second axis, and did not overlap with other samples in the
aphotic cluster (Figure 2a). A similar grouping pattern was
observed for samples collected from 300m, 400m and 500m. The
dissimilarity of communities observed at the various sampling
depths ≥300m implies considerable, depth-specific community
complexity in the deep ocean. Interestingly, Boeuf et al. (Boeuf
et al., 2019) observed significant contributions of
oligohymenophorean ciliates in sediment traps deployed at 4,000m
near station ALOHA, based on metatranscriptome analyses. High
relative abundances of these ciliates were also observed in
water samples collected at depths ≥300m in the present study
(Figure S3), and may indicate an important role for these
heterotrophic protists in food web structure and organic matter
processing in the deep ocean.
Estimates of total protistan species richness (total ASVs)
observed in our study were greatest for the deep aphotic zone
community (Figure 2b), perhaps indicating the presence of
endemic protistan species together with the presence of surfacedwelling protistan assemblages transported to the deep ocean.
Species richness estimates between the upper and lower euphotic

36
zones were somewhat comparable to each other, however both were
substantially lower than species richness estimates in the deep
aphotic zone. The Simpson diversity index, taking into account
the evenness of the community, was quite high for the deep
aphotic and the upper euphotic zones, while the Simpson index
was drastically lower for the lower euphotic zone (Figure 2c).
We speculate that the strong dominance of the protistan
community at and near the DCM by a relatively small number of
phytoplankton species (e.g. pelagophytes) capable of exploiting
this highly stable, selective ecosystem may explain the low
diversity index observed for the protistan community in the
lower euphotic zone (Figure 4b).
Regardless of the specific causes of the vertical zonation of
distinct protistan communities observed in our study, these
findings have implications for food web structure, and
presumably energy flow and elemental transformation in the water
column at station ALOHA. Transitions in the vertical
distribution of phytoplankton species in the euphotic zone might
be expected due to strong gradients in light and nutrient
availability, and will influence trophic structure as a
consequence of differences in the size and composition of the
dominant primary producers (Rii et al., 2016a; Rii et al.,
2016b; Rii et al., 2018). More significantly, wholesale changes
in community structure below the euphotic zone (Figures 2a, 4),

37
and depth-specific distributions of heterotrophic protist ASVs
within some of the major protistan groups (Figures 5, 6, S3)
indicate that the protistan taxa carrying out important
ecological processes are largely distinct at various depths
throughout the water column in this oceanic ecosystem.
Taxonomic and trophic diversity of the metabolically active
protistan community at station ALOHA:
We have shown that the metabolically active protistan
community involved in ecosystem function and resource cycling at
station ALOHA are extremely diverse and that the taxa exhibited
specific vertical distributions at all levels of taxonomic
resolution from phylum to ASV. Phylogenetically distinct alga
from three phyla (Dinoflagellate, Stramenopile, Haptophytes)
having specific nutritional requirements and growth rates
dominated the sun lit depths (Figure 1, 4, S2, S3). An equally
expansive pool of morphologically distinct heterotrophs with
unique trophic strategies and metabolic capacities collectively
colonized the entire water column (Figure 1, 4, S3). Parasites
(Syndiniales spp.) were also prevalent throughout the water
column. Interestingly, the dominant Syndiniales ASVs exhibited
punctuated vertical distributions that differed from one another
throughout the water column including those that appeared only
in the euphotic, lower euphotic or aphotic zone (Figure 6). The

38
ubiquity with which diverse lineages of parasites are found in
this study and throughout the global ocean (de Vargas et al.,
2015; Pernice et al., 2016; Clarke et al., 2018), including this
study, suggests that future ecosystem models should consider
this potentially important but presently overlooked trophic
interaction.
The expansive pool of taxonomic and trophic diversity found in
this study has implications for improved ecosystem models in
which bulk rates are used for photosynthetic and heterotrophic
activity. Improving the resolution of ecosystem models in
addition to the incorporation of less appreciated trophic modes
such as mixotrophy (Ward and Follows, 2016) and parasitism
(Lafferty et al., 2006) may improve our understanding of
ecosystem function and our ability to predict community level
responses to environmental change.
TABLES AND FIGURES:
Table legends:
Table S1: Raw ASV table containing ASV cunts produced from DADA2
and PR2 v12.1 taxonomy assigned at 90% confidence.
Table S2: Total rRNA sequences, ASVs, and singletons per sample.
Tables:

39
Table S1:
https://github.com/theOlligist/ALOHA-SpatioTemporal
Table S2:
samples sequence_total ASV_count ASV_singleton
5m.Dec2014 86504 768 2
25m.Dec2014 64988 682 0
45m.Dec2014 55678 608 0
75m.Dec2014 87424 780 0
100m.Dec2014 53931 746 2
125m.Dec2014 50649 658 3
150m.Dec2014 68535 817 6
175m.Dec2014 54520 552 1
300m.Dec2014 132084 1173 3
400m.Dec2014 80535 935 1
500m.Dec2014 78374 817 2
770m.Dec2014 101613 878 2
5m.Apr 57199 648 0
25m.Apr 62103 665 0
75m.Apr 48195 747 2
100m.Apr 50921 848 1
125m.Apr 55271 727 1
150m.Apr 53244 768 4
175m.Apr 43776 686 2
300m.Apr 44903 744 0
400m.Apr 85980 977 2
500m.Apr 74359 1045 2
770m.Apr 79572 817 0
5m.Aug 97523 787 0
25m.Aug 83069 907 1
45m.Aug 76894 904 0
75m.Aug 104836 818 1
100m.Aug 50420 713 1
125m.Aug 24246 523 1
150m.Aug 30023 636 0
175m.Aug 32852 734 2
300m.Aug 26720 584 0
400m.Aug 33464 697 1
500m.Aug 29631 668 1
770m.Aug 32917 618 0
5m.Dec2015 44278 639 0
25m.Dec2015 60138 800 0
45m.Dec2015 33859 647 1
75m.Dec2015 32924 702 1
100m.Dec2015 31289 542 2
125m.Dec2015 23897 469 4
150m.Dec2015 27516 560 3

40
175m.Dec2015 29800 593 2
300m.Dec2015 30907 658 2
400m.Dec2015 23215 554 1
500m.Dec2015 24524 480 0
770m.Dec2015 42638 665 1
Figure Legends:
Figure 1: Relative abundance of rRNA-based ASVs across all
depths (5m - 770m) from three seasons at station ALOHA; December
(A-B), April (C), and August (D) are shown. Colors represent
major protistan taxonomic groups. No samples were taken at 45m
during the month of April 2015.
Figure 2: Relationship between samples using cDNA-based ASV
abundances across depth and season. (A) NMDS plot (Bray-Curtis)
of ASV abundance and composition across all 47 samples: 12
depths, 3 seasons; Each point represents one sample. Vertical
colored bars represent zone cluster membership (Blue: Upper
euphotic zone, Orange: Lower euphotic zone, Green: Aphotic
zone); shapes represent the sampled months (Circle-Square:
December, Triangle:April, Diamond: August). Measures of ASV
richness and diversity for each zone is illustrated using total
number of ASV (B) and Simpson’s diversity index calculated (C)
for each zone, respectively. Boxes illustrate the inter-quantile
range (IQR) with the line indicating the median. Whiskers extend
from the box to the furthest data within 1.5 X IQR.

41
Figure 3: UpSet plot illustrating shared ASVs (intersections)
between depths averaged across the four seasons. Horizontal bars
(Set Size) represent the number of ASVs within each depth,
vertical bars represent the number of ASVs found within a sample
set, i.e. the intersections between depths illustrated by shaded
balls and sticks within the grid. Colored shading indicates the
zone membership of the corresponding depths. i.e. blue, orange,
green shading, correspond to upper, lower and aphotic zones,
respectively.
Figure 4: Community composition of (A) upper euphotic, (B) lower
euphotic, and (C) aphotic zones shown by krona plot.
Figure 5: Vertical distribution patterns of protistan groups
accounting for 5% or more of the total protistan community
composition. Points indicate average percent abundance from
samples taken in December 2014, April, August, and December
2015. Error bars standard deviation between samples. The
protistan groups represented are: (A) Diatoms, (B) Pelagophytes,
(C) Haptophytes, (D) Choanoflagellates, (E) MAST, (F) Ciliates,
(G) Radiolaria, (H) Syndiniales, (I) Dinoflagellates and (J)
Unclassified eukaryotes.

42
Figure 6: Spatial distribution of the 11 most abundant
Syndiniales ASVs. Balloon size is proportional to total read
abundance. Some ASVS (1-3) are distributed throughout the entire
euphotic zone or specific to the lower euphotic zone (6 & 8),
while ASVs (7, 9-11) are preferential to the aphotic. ASVs 4 and
5 are shown distributed throughout the water column. Taxonomic
information for each ASV is shown above each row.
Figure S1: Vertical (A) temperature, (B) nitrate, (C) oxygen,
and (D) chlorophyll a profiles taken in December 2014 (red),
April 2015 (green), August 2015 (blue), and December 2015
(purple) by CTD cast at station ALOHA. All data can be found at
http://hahana.soest.hawaii.edu/hot/hot-dogs/interface.html.
Figure S2: Balloon plot illustrating the diversity and
distribution of (A) dinoflagellate and (B) diatom taxa (Pnitz =
Pseudo-nitzschia). Dinoflagellates and diatoms totaled 24 and 16
classifiable taxa, respectively. Taxonomic classifications
corresponded approximately to genus level across 1449 and 322
ASVs, respectively.
Figure S3: Balloon plot illustrating the distribution of
subgroups within Radiolaria, ciliates, MAST, and haptophytes.
Circle radii are proportional to the number of reads per depth.

43
Figure S4: Hierarchical clustering dendrogram illustrating the
groupings of depth and season among all 47 samples. Distances
between samples were quantified using Euclidean distances and
pairwise averages for clustering (see methods).
Figure S5: Zonal clustering persists after merging depths across
seasons. Hierarchical clustering dendrogram illustrating the
groupings of depth after averaging ASV counts for each depth
across the four sampled months. Distances between samples were
quantified using Euclidean distances and clustered using
pairwise averages (see methods).
Figure S6: Pelagophyte abundances drive dissimilarity of the
lower euphotic zone. Hierarchical clustering dendrogram
illustrating the groupings of depth and season among all 47
samples after the removal of pelagophyte ASVs, which were
disproportionately abundant in the lower euphotic zone.
Distances between samples were quantified using Euclidean
distances and clustered using pairwise averages (see methods).
Figures:

44
Figure 1

45
Figure 2

46
Figure 3

47
Figure 4

48
Figure 5

49
Figure 6

50
Figure
S
1
10
20
0 250 500 750 1000
Depth (m)
Temperature (C)
December 2014
April 2015
August 2015
December 2015
0
10
20
30
40
Nitrate (uM/L) 50
100
150
200
Oxygen (uM/L) 0.00
0.25
0.50
0.75
1.00
Chl−a (ug/L)
A. B. C. D.

51
Figure
S
2
0
10000
20000
30000
A
B
Read Count
Read Count
0
50000
100000
150000
Unk. diatom Chaetoceros Pnitz Mediophyceae pennate Pleurosigma Hemiaulus Stellarima Corethron Thalassiosira Guinardia Cylindrotheca Nitzschia Minutocellus Actinocyclus Navicula Cerataulina
m5
m25
m45
m75
m100
m125
m150
m175
m300
m400
m500
m770
Depth
Unk.dino Blastodinium Heterocapsa Takayama Woloszynskia Lingulodinium Archaeperidinium Gyrodinium Alexandrium Noctiluca Gonyaulax Abedinium Symbiodinium Prorocentrum Gymnodinium Kofoidinium Lepidodinium Suessiales Protoperidinium Pyrodinium Cochlodinium Phalacroma Warnowia Neoceratium Protoceratium
m5
m25
m45
m75
m100
m125
m150
m175
m300
m400
m500
m770
Depth

52
Figure
S
3

53
Figure S4
Figure S5

54
Figure S6
Supplemental discussion: Dominant metabolically active protistan
taxa at station ALOHA:
Alveolate ASVs were the most abundant taxa in the present
study, accounting for over 45% of all ASVs (Figure 1).
Dinoflagellates were the most abundant alveolates across all
samples, and throughout the water column, and harbored the most
extensive pool of (rare) taxa, defined here as taxa comprising
less than .5% of the community (Figure 1, Figure S2a, Table S1).
Alveolate (dinoflagellate) dominance (x%) is consistent with

55
expectations, given the high copy number of genes within these
species, and the functional diversity among dinoflagellates
which includes heterotrophic, photosynthetic, and mixotrophic
taxa. The physiological flexibility of dinoflagellates as a
group may also help explain their dominance at all depths,
although the constrained vertical distributions observed for
some species implies that different species are adapted to
conditions at specific depth strata. For example, Neoceratium
and Gonyaulax appeared to be restricted to the upper euphotic,
while Abedinium was restricted to the depths near the DCM.
Prorocentrum and Gymnodinium were found throughout the water
column. Interestingly, most dinoflagellate ASVs in our samples
were not identifiable using existing taxonomy databases (PR2 and
Silva), and lacked positive results from BLAST searches,
indicating that the ecological roles of many of these species
are yet to be characterized.
Ciliates were the second most abundant alveolates, and some
taxa exhibited clear depth-specific vertical distributions.
Spirotrich ciliates made up the majority of ciliate reads and
were abundant throughout the water column, while
oligohymenophoreans were also abundant but had relative
abundances that were much higher in the deepest samples (Figure
S3). This and two other studies investigating protistan
distributions in mesopelagic waters might imply that the

56
ecological and /physiological flexibility on the part of some A
study of the vertical distribution of metabolically active
protists in the Western Pacific Ocean reported vertical
distribution and relative abundances of the active ciliate
population via amplified RNA (cDNA) that were nearly identical
to those found in this study (Zhao et al., 2017). Similarities
to this study were also reported for the mesopelagic ciliate
community in the Mediterranean, in which spirotrich species were
observed to be both surface dwelling or endemic to the deep sea
(Dolan et al., 2019). These findings perhaps imply
ecological/physiological flexibility on the part of some ciliate
taxa that occurred at significant abundances throughout the
water column (e.g. spirotrichs and phyllopharyngeans).
Spirotrichs, in particular, are morphologically complex and are
found in almost every microhabitat in which ciliated protozoa
have been found. Their distribution throughout the water column
is likely explained by the group’s vast morphospecies and
physiological diversity. Additionally, some ciliate classes
appear particularly well suited for environmental conditions
and/or prey availability in the deep sea (e.g.
oligohymenophoreans and litostomes). As noted above, for
example, the presence of oligohymenophoreans in material
collecting in sediment traps suspended at 4,000m at station

57
ALOHA may indicate a role for these ciliates in sinking particle
colonization and/or decomposition (Boeuf et al., 2019).
Protistan parasites are phylogenetically diverse, and have
been found in abundance throughout the world ocean (Guillou et
al., 2008; Siano et al., 2011; de Vargas et al., 2015).
Syndiniales ASVs were the dominant parasites in our samples,
accounting for almost a third of the Alveolate sequences (Figure
1, 4, Table S1). The dominant ASVs exhibited vertical
distributions that differed from one another throughout the
water column including those that appeared only in the euphotic,
lower euphotic or aphotic zone (Figure 6). All known species in
this group are obligate parasites that kill their hosts
(parasitoids) and release hundreds of free-living spores (2µm to
10µm) (Coats, 1999; Coats and Park, 2002). It is presently
unclear if the Syndiniales species observed in this study were
in the spore life stage or in hospite, but their high relative
abundances throughout the water column raises questions
regarding their impact on host populations. The ubiquity with
which diverse lineages of parasites are found throughout the
global ocean (de Vargas et al., 2015; Pernice et al., 2016;
Clarke et al., 2018), including this study, suggests that future
ecosystem models should consider this potentially important but
presently overlooked trophic interaction.

58
Rhizarian groups in this study (mostly radiolarian taxa)
accounted for more than 1% of the protistan community at all
depths, but much larger fractions of the relative abundances
were found in the deepest samples (Figure 1). Low radiolarian
representation in shallow water samples and increased relative
abundances with depth been reported from studies investigating
the spatiotemporal distribution and diversity of active protists
using RNA:DNA ratios in the San Pedro Basin (Hu et al., 2016a)
and the South China Sea (Xu et al., 2017), but contrasted with
the higher abundances that were found in shallow depths using
DNA alone throughout the global ocean (de Vargas et al., 2015).
Most knowledge on the biology of radiolaria is based on
surface-dwelling forms (Anderson, 1983). Their large, fragile
forms make them difficult to collect, and only relatively
recently has the use of underwater imagery begun to characterize
the degree to which their abundances have been underestimated
(Dennett et al., 2002; Biard et al., 2016). Additionally,
radiolarians have resisted culture attempts, and they are
therefore poorly represented in 18S rRNA gene and transcriptomic
databases (Keeling et al., 2014; Del Campo et al., 2018). Our
findings draw attention to the fact that the vertical
distributions and abundances of many radiolarian species remain
poorly characterized at this time. Notably, many of the
radiolarian species in this study exhibited higher abundances in

59
the deep ocean (Figure S3), revealing our poor understanding of
the ecology of many of these species. A few radiolarian groups
were present throughout the water column, particularly RAD-A and
acantharians.
Photosynthetic stramenopiles were composed of diatoms,
pelagophytes, and ‘other ochrophytes’, the latter of which were
not assigned more resolved taxonomic identity (Figures 1, 4).
Not surprisingly, these taxa had highest relative abundances in
the euphotic zone (Figure 5c,e, Figure S4b), but the depth
ranges for the maximal abundances of these groups differed. For
example, diatom abundances were highest in the upper euphotic
zone (≤75m), while pelagophytes were most abundant throughout the
year in the lower euphotic (100m-150m) (Figure 1, Table S1).
Both of these groups exhibited maximum abundances during the
summer (August) (Figure 1, Figure S1).
Marine stramenopile (MAST) lineages, which are believed to be
mostly uncultured, bacterivorous, heterotrophic flagellates that
have been reported throughout the global ocean (Massana et al.,
2009; Rodriguez-Martinez et al., 2013; Wang et al., 2018) were
the only stramenopiles found throughout the water column at
substantial abundances in this study, with their largest
abundances in the upper euphotic (Figures 1, 5e). MAST,
dominated by MAST-3, decreased from 17% of the cumulative upper
euphotic community to 8% in the deep aphotic zone (Figure 4),

60
and have been reported to be virtually undetected in
bathypelagic waters of the global ocean (Pernice et al., 2016).
Declines in MAST abundance with depth were likely related to
depth-dependent prey abundances. Prochlorococcus (the dominant
picocyanobacterium at station ALOHA), Synechococcus and
heterotrophic bacterial abundances all decrease significantly
with depth (Rabouille et al., 2007; Al-Otaibi et al., 2020)
(also see http://hahana.soest.hawaii.edu/hot/hotdogs/interface.html). Like MAST, Choanoflagellates are primarily
bacterivores, and presumably attain higher abundances in the
upper water column where picoplanktonic prey abundances are
greatest (Figure 5).
Beyond SAR taxa, two hacrobian groups (haptophytes and
unclassified hacrobian taxa) were found in abundance throughout
the water column (Figures 1, S3). Haptophytes are mostly
photosynthetic, however the prevalence of heterotrophic
capabilities (mixotrophy) among haptophyte lineages may explain
the occurrence of several haptophyte taxa below the euphotic
zone in this study, including ambiguously classified haptophyte
ASVs, Clade EX, Hap4, and other haptophytes lacking further
classification (Figure S3). Although mixotrophy has not been
reported for the Phaeocystis, ASVs corresponding to this widely
distributed marine alga were found at low relative abundances in
the deep aphotic zone, in addition to unsurprising prevalence in

61
euphotic zones (Figure S3). A possible explanation for
metabolically active Phaeocystis spp. in aphotic depths may be
their roles as algal symbionts of acantharian species (Decelle
et al., 2012; Hu et al., 2018).
Amoebae and excavates collectively accounted for less than 1%
of the protistan community. The rarity of Excavates in this
study may have been a consequence of V4 amplicon primer bias
(Pawlowski 2011) or mean that they are truly rare in this
ecosystem.
By characterizing the protistan community along a vertical
profile extending below the euphotic zone across three seasons
we have shown that the protists involved in ecosystem function
and resource cycling are extremely diverse. The expansive pool
of taxonomic and trophic diversity found during this study has
implications for improved ecosystem models in which bulk rates
are used for photosynthetic and heterotrophic activity. Three
phylogenetically distinct algal lineages (Dinoflagellate,
Stramenopile, Haptophytes) with specific growth requirements and
rates dominated the sun lit depths. An equally expansive pool of
morphologically distinct heterotrophs with unique trophic
strategies and metabolic capacities colonized the entire water
column. Notably, we have shown that mixotrophic and parasitic
taxa are prevalent among the protistan community. Improving the
resolution of ecosystem models in addition to the incorporation

62
of less appreciated trophic modes such as mixotrophy (Ward and
Follows, 2016) and parasitism (Lafferty et al., 2006) may
improve our understanding of ecosystem function and our ability
to predict community level responses to environmental change.

63
REFERENCES:
Berney, C., Ciuprina, A., Bender, S., Brodie, J., Edgcomb, V.,
Kim, E., Rajan, J., Parfrey, L.W., Adl, S., Audic, S.,
Bass, D., Caron, D.A., Cochrane, G., Czech, L., Dunthorn,
M., Geisen, S., Glockner, F.O., Mahe, F., Quast, C., Kaye,
J.Z., Simpson, A.G.B., Stamatakis, A., Del Campo, J.,
Yilmaz, P., de Vargas, C., 2017. UniEuk: Time to Speak a
Common Language in Protistology! J Eukaryot Microbiol 64
(3), 407-411.
Blazewicz, S.J., Barnard, R.L., Daly, R.A., Firestone, M.K.,
2013. Evaluating rRNA as an indicator of microbial activity
in environmental communities: limitations and uses. ISME J
7 (11), 2061-2068.
Boeuf, D., Edwards, B.R., Eppley, J.M., Hu, S.K., Poff, K.E.,
Romano, A.E., Caron, D.A., Karl, D.M., DeLong, E.F., 2019.
Biological composition and microbial dynamics of sinking
particulate organic matter at abyssal depths in the
oligotrophic open ocean. Proc Natl Acad Sci U S A 116 (24),
11824-11832.
Bolyen, E., Rideout, J.R., Dillon, M.R., Bokulich, N.A., Abnet,
C.C., Al-Ghalith, G.A., Alexander, H., Alm, E.J., Arumugam,
M., Asnicar, F., Bai, Y., Bisanz, J.E., Bittinger, K.,
Brejnrod, A., Brislawn, C.J., Brown, C.T., Callahan, B.J.,
Caraballo-Rodríguez, A.M., Chase, J., Cope, E.K., Da Silva,
R., Diener, C., Dorrestein, P.C., Douglas, G.M., Durall,
D.M., Duvallet, C., Edwardson, C.F., Ernst, M., Estaki, M.,
Fouquier, J., Gauglitz, J.M., Gibbons, S.M., Gibson, D.L.,
Gonzalez, A., Gorlick, K., Guo, J., Hillmann, B., Holmes,
S., Holste, H., Huttenhower, C., Huttley, G.A., Janssen,
S., Jarmusch, A.K., Jiang, L., Kaehler, B.D., Kang, K.B.,
Keefe, C.R., Keim, P., Kelley, S.T., Knights, D., Koester,
I., Kosciolek, T., Kreps, J., Langille, M.G.I., Lee, J.,
Ley, R., Liu, Y.-X., Loftfield, E., Lozupone, C., Maher,
M., Marotz, C., Martin, B.D., McDonald, D., McIver, L.J.,
Melnik, A.V., Metcalf, J.L., Morgan, S.C., Morton, J.T.,
Naimey, A.T., Navas-Molina, J.A., Nothias, L.F., Orchanian,
S.B., Pearson, T., Peoples, S.L., Petras, D., Preuss, M.L.,
Pruesse, E., Rasmussen, L.B., Rivers, A., Robeson, M.S.,
Rosenthal, P., Segata, N., Shaffer, M., Shiffer, A., Sinha,
R., Song, S.J., Spear, J.R., Swafford, A.D., Thompson,
L.R., Torres, P.J., Trinh, P., Tripathi, A., Turnbaugh,
P.J., Ul-Hasan, S., van der Hooft, J.J.J., Vargas, F.,
Vázquez-Baeza, Y., Vogtmann, E., von Hippel, M., Walters,
W., Wan, Y., Wang, M., Warren, J., Weber, K.C., Williamson,

64
C.H.D., Willis, A.D., Xu, Z.Z., Zaneveld, J.R., Zhang, Y.,
Zhu, Q., Knight, R., Caporaso, J.G., 2019. Reproducible,
interactive, scalable and extensible microbiome data
science using QIIME 2. Nature Biotechnology 10.1038/s41587-
019-0209-9.
Bryant, J.A., Aylward, F.O., Eppley, J.M., Karl, D.M., Church,
M.J., DeLong, E.F., 2016. Wind and sunlight shape microbial
diversity in surface waters of the North Pacific
Subtropical Gyre. ISME J 10 (6), 1308-1322.
Calbet, A., Saiz, E., 2005. The ciliate-copepod link in marine
ecosystems. Aquatic Microbial Ecology 38, 157-167.
Callahan, B.J., McMurdie, P.J., Holmes, S.P., 2017. Exact
sequence variants should replace operational taxonomic
units in marker-gene data analysis. ISME J 11 (12), 2639-
2643.
Callahan, B.J., McMurdie, P.J., Rosen, M.J., Han, A.W., Johnson,
A.J., Holmes, S.P., 2016. DADA2: High-resolution sample
inference from Illumina amplicon data. Nat Methods 13 (7),
581-583.
Canals, O., Obiol, A., Muhovic, I., Vaque, D., Massana, R.,
2020. Ciliate diversity and distribution across horizontal
and vertical scales in the open ocean. Mol Ecol 2020,
00:01–16.
Caron, D.A., 1994. Inorganic Nutrients, Bacteria, and the
Microbial Loop. Microbial Ecology 28 (2), 295-298.
Caron, D.A., Countway, P.D., Jones, A.C., Kim, D.Y., Schnetzer,
A., 2012. Marine protistan diversity. Ann Rev Mar Sci 4,
467-493.
Caron, D.A., Hu, S.K., 2018. Are We Overestimating Protistan
Diversity in Nature? Trends in Microbiology 27 (3), 197-
205.
Clarke, L.J., Bestley, S., Bissett, A., Deagle, B.E., 2018. A
globally distributed Syndiniales parasite dominates the
Southern Ocean micro-eukaryote community near the sea-ice
edge. ISME J 13 (3), 734-737.
Coale, K.H., Bruland, K.W., 1987. Oceanic stratified euphotic
zone as elucidated by 234Th: 238U disequilibria. Limnol.
Oceanogr 32 (1), 189-200.

65
Conway, J.R., Lex, A., Gehlenborg, N., 2017. UpSetR: an R
package for the visualization of intersecting sets and
their properties. Bioinformatics 33 (18), 2938-2940.
Countway, P.D., Gast, R.J., Dennett, M.R., Savai, P., Rose,
J.M., Caron, D.A., 2007. Distinct protistan assemblages
characterize the euphotic zone and deep sea (2500 m) of the
western North Atlantic (Sargasso Sea and Gulf Stream).
Environ Microbiol 9 (5), 1219-1232.
Coyne, K.J., Countway, P.D., Pilditch, C.A., Lee, C.K., Caron,
D.A., Cary, S.C., 2013. Diversity and distributional
patterns of ciliates in Guaymas Basin hydrothermal vent
sediments. J Eukaryot Microbiol 60 (5), 433-447.
de Vargas, C., S. Audic, N. Henry, J. Decelle, F. Mahe, R.
Logares, E. Lara, C. Berney, N. Le Bescot, I., Probert,
M.C., J. Poulain, S. Romac, S. Colin, J. M. Aury, L.
Bittner, S. Chaffron, M., Dunthorn, S.E., O. Flegontova, L.
Guidi, A. Horak, O. Jaillon, G. Lima-Mendez, J. Lukes,, S.
Malviya, R.M., M. Mulot, E. Scalco, R. Siano, F. Vincent,
A. Zingone, C. Dimier, M., Picheral, S.S., S. KandelsLewis, C. Tara Oceans, S. G. Acinas, P. Bork, C. Bowler,
G., Gorsky, N.G., P. Hingamp, D. Iudicone, F. Not, H.
Ogata, S. Pesant, J. Raes, M. E., Sieracki, S.S., L.
Stemmann, S. Sunagawa, J. Weissenbach, P. Wincker, and E.
Karsenti., 2015. Eukaryotic plankton diversity in the
sunlit ocean. Science 348 (6237), 1261605-1261601.
Edgcomb, V., Orsi, W., Taylor, G.T., Vdacny, P., Taylor, C.,
Suarez, P., Epstein, S., 2011. Accessing marine protists
from the anoxic Cariaco Basin. ISME J 5 (8), 1237-1241.
Edgcomb, V.P., 2016. Marine protist associations and
environmental impacts across trophic levels in the twilight
zone and below. Curr Opin Microbiol 31, 169-175.
Edgcomb, V.P., Pachiadaki, M., 2014. Ciliates along oxyclines of
permanently stratified marine water columns. J Eukaryot
Microbiol 61 (4), 434-445.
Field, C.B., Behrenfeld, M.J., T., J., Randerson, J.T.,
Falkowski, P.G., 1998. Primary Production of the Biosphere:
Integrating Terrestrial and Oceanic Components. Science
281(5374), 237-240.
Giner, C.R., Balague, V., Krabberod, A.K., Ferrera, I., Rene,
A., Garces, E., Gasol, J.M., Logares, R., Massana, R.,

66
2019. Quantifying long-term recurrence in planktonic
microbial eukaryotes. Mol Ecol 28 (5), 923-935.
Giner, C.R., Forn, I., Romac, S., Logares, R., de Vargas, C.,
Massana, R., 2016. Environmental Sequencing Provides
Reasonable Estimates of the Relative Abundance of Specific
Picoeukaryotes. Appl Environ Microbiol 82 (15), 4757-4766.
Giner, C.R., Pernice, M.C., Balague, V., Duarte, C.M., Gasol,
J.M., Logares, R., Massana, R., 2020. Marked changes in
diversity and relative activity of picoeukaryotes with
depth in the world ocean. ISME J 14 (2), 437-449.
Guillou, L., Bachar, D., Audic, S., Bass, D., Berney, C.,
Bittner, L., Boutte, C., Burgaud, G., de Vargas, C.,
Decelle, J., Del Campo, J., Dolan, J.R., Dunthorn, M.,
Edvardsen, B., Holzmann, M., Kooistra, W.H., Lara, E., Le
Bescot, N., Logares, R., Mahe, F., Massana, R., Montresor,
M., Morard, R., Not, F., Pawlowski, J., Probert, I.,
Sauvadet, A.L., Siano, R., Stoeck, T., Vaulot, D.,
Zimmermann, P., Christen, R., 2013. The Protist Ribosomal
Reference database (PR2): a catalog of unicellular
eukaryote small sub-unit rRNA sequences with curated
taxonomy. Nucleic Acids Res 41, D597-604.
Guillou, L., Viprey, M., Chambouvet, A., Welsh, R.M., Kirkham,
A.R., Massana, R., Scanlan, D.J., Worden, A.Z., 2008.
Widespread occurrence and genetic diversity of marine
parasitoids belonging to Syndiniales (Alveolata). Environ
Microbiol 10 (12), 3349-3365.
Hartmann, M., Grob, C., Tarran, G.A., Martin, A.P., Burkill,
P.H.S., D.J., Zubkov, M.V., 2012. Mixotrophic basis of
Atlantic oligotrophic ecosystems. PNAS 109 (15), 5756-5760.
Hu, S.K., Campbell, V., Connell, P., Gellene, A.G., Liu, Z.,
Terrado, R., Caron, D.A., 2016a. Protistan diversity and
activity inferred from RNA and DNA at a coastal ocean site
in the eastern North Pacific. FEMS Microbiology Ecology
10.1093/femsec/.
Hu, S.K., Campbell, V., Connell, P.E., Gellene, A.G., Liu, Z.,
Terrado, R., Caron, D.A., 2016b. Protistan diversity and
activity inferred from RNA and DNA at a coastal ocean site
in the eastern North Pacific. FEMS Microbiol Ecol 92 (4),
fiw050.

67
Karl, D.M., Church, M.J., 2014. Microbial oceanography and the
Hawaii Ocean Time-series programme. Nat Rev Microbiol 12
(10), 699-713.
Karner, M.B., DeLong, E.F., Karl, D.M., 2001. Archael dominance
in the mesopelagic zone of the Pacific Ocean. Nature 409,
507-510.
Konstantinidis, K.T., Braff, J., Karl, D.M., DeLong, E.F., 2009.
Comparative metagenomic analysis of a microbial community
residing at a depth of 4,000 meters at station ALOHA in the
North Pacific subtropical gyre. Appl Environ Microbiol 75
(16), 5345-5355.
Lafferty, K.D., Dobson, A.P., Kuris, A.M., 2006. Parasites
dominate food web links. Proc Natl Acad Sci U S A 103 (30),
11211-11216.
Letelier, R.M., Karl, D.M., Abbot, M.R.B., R. R., 2004. Light
driven seasonal patterns of chlorophyll and nitrate in the
lower euphotic zone of the North Pacific Subtropical Gyre.
Limnology and Oceanography 49 (2), 508-519.
Lima-Mendez, G., Faust, K., Henry, N., Decelle, J., Colin, S.,
Carcillo, F., Chaffron, J.S., Ignacio-Espinosa, C., Roux,
S., Vincent, F., Bittner, L., Darzi, Y., Wang, J., Audic,
S., Berline, L., Bontempi, G., Cabello, A.M.C., L.,
Cornejo-Castillo, F.M., d'Ovidio, F., Meester, L.D.,
Ferrera, I., Garnet-Delmas, M., Guidi, L., Lara, E.,
Pesant, S., Royo-Llonch, M., Salazar, G., Sanchez, P.,
Sebastian, M., Souffreau, C., Dimier, C., Picheral, M.,
Searson, S., Kandels-Lewis, S., Gorsky, G., Not, F., Ogata,
H., Speich, S., Stemmann, L., Weissenbach, J., Wincker, P.,
Acinas, S.G., Suanagawa, S., Bork, P., Sullivan, M.B.,
Karsenti, E., Bowler, C., de Vargas, C., Raes, J., 2015.
Determinants of community structure in the global plankton
interactome. Science 348 (6237).
Malmstrom, R.R., Coe, A., Kettler, G.C., Martiny, A.C., FriasLopez, J., Zinser, E.R., Chisholm, S.W., 2010. Temporal
dynamics of Prochlorococcus ecotypes in the Atlantic and
Pacific oceans. ISME J 4 (10), 1252-1264.
Mars Brisbin, M., Conover, A.E., Mitarai, S., 2020. Influence of
Regional Oceanography and Hydrothermal Activity on Protist
Diversity and Community Structure in the Okinawa Trough.
Microbial Ecology 80 (4), 746-761.

68
Mende, D.R., Bryant, J.A., Aylward, F.O., Eppley, J.M., Nielsen,
T., Karl, D.M., DeLong, E.F., 2017. Environmental drivers
of a microbial genomic transition zone in the ocean's
interior. Nat Microbiol 2, 1367-1373.
Obiol, A., Giner, C.R., Sanchez, P., Duarte, C.M., Acinas, S.G.,
Massana, R., 2020. A metagenomic assessment of microbial
eukaryotic diversity in the global ocean. Mol Ecol Resour
20 (3).
Orsi, W., Edgcomb, V., Jeon, S., Leslin, C., Bunge, J., Taylor,
G.T., Varela, R., Epstein, S., 2011. Protistan microbial
observatory in the Cariaco Basin, Caribbean. II. Habitat
specialization. ISME J 5 (8), 1357-1373.
Pasulka, A., Hu, S.K., Countway, P.D., Coyne, K.J., Cary, S.C.,
Heidelberg, K.B., Caron, D.A., 2019. SSU-rRNA Gene
Sequencing Survey of Benthic Microbial Eukaryotes from
Guaymas Basin Hydrothermal Vent. J Eukaryot Microbiol
10.1111/jeu.12711.
Pasulka, A.L., Landry, M.R., Taniguchi, D.A.A., G., T.A.,
Church, M.J., 2013. Temporal dynamics of phytoplankton and
heterotrophic protists at station ALOHA. Deep Sea Research
Part II: Topical Studies in Oceanography 93, 44-57.
Pernice, M.C., Forn, I., Gomes, A., Lara, E., Alonso-Saez, L.,
Arrieta, J.M., del Carmen Garcia, F., Hernando-Morales, V.,
MacKenzie, R., Mestre, M., Sintes, E., Teira, E., Valencia,
J., Varela, M.M., Vaque, D., Duarte, C.M., Gasol, J.M.,
Massana, R., 2015. Global abundance of planktonic
heterotrophic protists in the deep ocean. ISME J 9 (3),
782-792.
Pernice, M.C., Giner, C.R., Logares, R., Perera-Bel, J., Acinas,
S.G., Duarte, C.M., Gasol, J.M., Massana, R., 2016. Large
variability of bathypelagic microbial eukaryotic
communities across the world's oceans. ISME J 10 (4), 945-
958.
Poulin, R., 2010. Network analysis shining light on parasite
ecology and diversity. Trends Parasitol 26 (10), 492-498.
Pruesse, E., Quast, C., Knittel, K., Fuchs, B.M., Ludwig, W.,
Peplies, J., Glockner, F.O., 2007. SILVA: a comprehensive
online resource for quality checked and aligned ribosomal
RNA sequence data compatible with ARB. Nucleic Acids Res 35
(21), 7188-7196.

69
R_Core_Team, 2020. R: A language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna,
Austria.
Rii, Y.M., Bidigare, R.R., Church, M.J., 2018. Differential
Responses of Eukaryotic Phytoplankton to Nitrogenous
Nutrients in the North Pacific Subtropical Gyre. Frontiers
in Marine Science 5 (92).
Rii, Y.M., Duhamel, S., Bidigare, R.R., Karl, D.M., Repeta,
D.J., Church, M.J., 2016a. Diversity and productivity of
photosynthetic picoeukaryotes in biogeochemically distinct
regions of the South East Pacific Ocean. Limnology and
Oceanography 61 (3), 806-824.
Rii, Y.M., Karl, D.M., Church, M.J., 2016b. Temporal and
vertical variability in picophytoplankton primary
productivity in the North Pacific Subtropical Gyre. Marine
Ecology Progress Series 562, 1-18.
Scharek, R., Tupas, L.M., Karl, D.M., 1999. Diatom fluxes to the
deep sea in the oligotrophic North Pacific gyre at Station
ALOHA. Marine Ecology Progress Series 182, 55-67.
Sherr, E.B., Sherr, B.F., 2002. Significance of predation by
protists in aquatic microbial food webs. Antonie van
Leeuwenhoek 81, 293-308.
Stoeck, T., Bass, D., Nebel, M., Christen, R., Jones, M.D.,
Breiner, H.W., Richards, T.A., 2010. Multiple marker
parallel tag environmental DNA sequencing reveals a highly
complex eukaryotic community in marine anoxic water. Mol
Ecol 19 (1), 21-31.
Stoeck, T., Behnke, A., Christen, R., Amaral-Zettler, L.,
Rodriguez-Mora, M.J., Chistoserdov, A., Orsi, W., Edgcomb,
V.P., 2009. Massively parallel tag sequencing reveals the
complexity of anaerobic marine protistan communities. BMC
Biol 7 (72).
Unrein, F., Gasol, J.M., Not, F., Forn, I., Massana, R., 2014.
Mixotrophic haptophytes are key bacterial grazers in
oligotrophic coastal waters. ISME J 8 (1), 164-176.
Ward, B.A., Dutkiewicz, S., Jahn, O., Follows, M.J., 2012. A
size-structured food-web model for the global ocean.
Limnol. Oceanogr 57 (6), 1877-1891.

70
Ward, B.A., Follows, M.J., 2016. Marine mixotrophy increases
trophic transfer efficiency, mean organism size, and
vertical carbon flux. Proc Natl Acad Sci U S A 113 (11),
2958-2963.
Weiss, S., Xu, Z.Z., Peddada, S., Amir, A., Bittinger, K.,
Gonzalez, A., Lozupone, C., Zaneveld, J.R., Vázquez-Baeza,
Y., Birmingham, A., Hyde, E.R., Knight, R., 2017.
Normalization and microbial differential abundance
strategies depend upon data characteristics. Microbiome 5
(1).
Worden, A.Z., Follows, M.J., Giovannoni, S.J., Wilken, S.,
Zimmerman, A.E., Keeling, P.J., 2015. Environmental
science. Rethinking the marine carbon cycle: factoring in
the multifarious lifestyles of microbes. Science 347
(6223), 1257594.
Xu, D., Li, R., Hu, C., Sun, P., Jiao, N., Warren, A., 2017.
Microbial Eukaryote Diversity and Activity in the Water
Column of the South China Sea Based on DNA and RNA High
Throughput Sequencing. Front Microbiol 8 (1121).

71
Chapter II: Daily Dynamics of Contrasting Spring Algal Blooms in
Santa Monica Bay (central Southern California Bight)
Gerid A. Ollison1
, Sarah K. Hu2
, Julie V. Hopper1
, Brittany P.
Stewart1
, Jayme Smith3
, Jennifer L. Beatty1
, Laura K. Rink4
, David
A. Caron1
1
Department of Biological Sciences, University of Southern
California, 3616 Trousdale Parkway, Los Angeles, CA 90089-0371
USA.
2
Woods Hole Oceanographic Institution, Marine Chemistry and
Geochemistry, MS 51, Woods Hole, MA, 02543 USA.
3
Southern California Coastal Water Research Project, 3535 Harbor
Blvd., Suite 110, Costa Mesa, CA 92626 USA.
4
Heal the Bay Aquarium, 1600 Ocean Front Walk, Santa Monica, CA
90401 USA.

72
ABSTRACT:
Protistan algae (phytoplankton) dominate coastal upwelling
ecosystems where they form massive blooms that support the
world’s most important fisheries, and constitute an important
sink for atmospheric CO2. Bloom initiation is well understood but
the biotic and abiotic forces that shape short-term dynamics in
community composition are still poorly characterized. Here, high
frequency (daily) changes in relative abundance dynamics of the
metabolically active protistan community were followed via
expressed 18S V4 rRNA genes (RNA) throughout two algal blooms
during the spring of 2018 and 2019 in Santa Monica Bay (central
Southern California Bight).
A diatom bloom formed after wind-driven, nutrient upwelling
events in both years, but different taxa dominated each year.
Whereas diatoms bloomed following elevated nutrients and
declined after depletion each year, a massive dinoflagellate
bloom manifested under relatively low inorganic nitrogen
conditions following diatom bloom senescence in 2019 but not
2018. Network analysis revealed associations between diatoms and
cercozoan putative parasitic taxa and syndinean parasites during
2019 that may have influenced the demise of the diatoms, and the
transition to a dinoflagellate-dominated bloom.

73
INTRODUCTION:
Numerous studies have firmly established the role of marine
protists as integral to primary production, heterotrophic
processes and global biogeochemical cycling (Pomeroy, 1974;
Azam et al., 1983; Caron et al., 2012; Worden et al., 2015).
Photosynthetic protists conduct nearly half of all oceanic
primary production and are particularly important in coastal
upwelling regimes located on the eastern boundaries of oceans;
the so-called ‘new production factories of the global ocean’
(Falkowski et al., 1998; Field et al., 1998). The rapid
proliferation of algae (algal blooms) in upwelling regimes that
outpaces mortality caused by viral lysis, parasitic infection,
or grazing results in short food chains that support the world’s
most important fisheries, and may account for a disproportionate
amount of export flux of atmospheric CO2 to the deep ocean
(Ryther, 1969; Buesseler, 1998; Smetacek, 1999; GutierrezRodriguez et al., 2018).
Algal blooms in eastern boundary ecosystems are generally the
result of wind-driven upwelling of new nutrients (Kudela et al.,
2005; Chavez and Messié, 2009). The characteristic equatorward
winds along the west coasts of continents result in the net
offshore transport of surface water, and the upwelling of
nutrient-laden water, fueling rapid algal proliferation during
seasons of sufficient sunlight (Chavez and Messié, 2009; Capone

74
and Hutchins, 2013). The timing of blooms along the Southern
California Bight Region–part of the California Current upwelling
regime–is fairly consistent such that blooms are especially
pronounced in the spring months when the strongest wind-driven
upwelling intersects with ample sunlight and shallow mixing in
the euphotic zone (Checkley and Barth, 2009; Smith et al.,
2018).
A generality based on the physiological responses of different
algal taxonomic groups to abiotic environmental forcing factors
indicates that diatom species tend to dominate under the highnutrient, cool, shallow turbulent mixing conditions resulting
from upwelling along the California coast. Dinoflagellates, by
contrast, are predicted under warm, stratified, nutrient-poor
conditions that are typical in the summer (Margalef, 1978;
Reynolds, 2001; Shipe et al., 2008). However, variability within
and from that generality are common, and the specific taxa that
will dominate a particular bloom are difficult to predict solely
on the basis of measurable abiotic parameters. This situation
implies that other factors dictate the specific community
composition of a spring bloom.
Trophic interactions (biotic factors) are increasingly
acknowledged as working synergistically with abiotic factors to
shape community structure. Predation and viral lysis influence
community structure in ways that are not easily attributable to

75
abiotic conditions (Suttle, 2007; Kranzler et al., 2019). The
role of symbiosis is also not well understood, but can have a
demonstrable impact on community structure. For example,
parasitic protists that kill their hosts and release hundreds of
free living zoospores (zoosporic parasites) are globally
distributed and have been shown to be an important source of
mortality and community succession of microbial eukaryotes
(Myung G. Park, 2002; Chambouvet et al., 2008; Guillou et al.,
2008; Lima-Mendez et al., 2015; Scholz et al., 2016; Kim et al.,
2017). Recognition and studies of the ecological significance of
these biotic factors for controlling community composition in
free-living plankton assemblages have been made possible in part
by advances in molecular and computational biology.
High-throughput sequencing (HTS)-based studies have enabled
previously unattainable taxonomic breadth and resolution in the
study of free-living protistan communities (de Vargas et al.,
2015; Berdjeb et al., 2018; Hu et al., 2018a). Daily
measurements are especially appealing for understanding the
factors controlling community composition and turnover of
protists because one can account for both photosynthetic and
heterotrophic community turnover on time scales that are
relevant to growth and mortality processes (Field et al., 1998;
Fuhrman et al., 2015). Moreover, recent applications of RNAbased (cDNA) HTS have been particularly instrumental in

76
distinguishing metabolically active members of the community
from senesced or dead cells, extracellular DNA, as well as
reducing bias resulting from highly variable gene copy number in
protists (Charvet et al., 2014; Massana et al., 2015; Hu et al.,
2016; Gong and Marchetti, 2019; Ollison et al., 2021).
Robust sequence datasets generated in HTS studies are also
amenable to networking analyses that can aid in the
identification of trophic interactions among the many taxonomic
units (‘species’ or ‘strains’; OTUs or ASVs) generated by HTS
studies (Newman, 2004; Proulx et al., 2005; Poulin, 2010; Faust
and Raes, 2012; Layeghifard et al., 2017; Wang et al., 2017;
Muller et al., 2018; Rottjers and Faust, 2018). Pairwise
association calculation, such as Pearson’s and Spearman’s rank
correlation coefficients have been widely adopted (Xia et al.,
2011; Faust et al., 2015; Needham et al., 2017; Jones et al.,
2018). Recent graphical lasso-based approaches assume sparsity
in HTS datasets (most species pairs are assumed not to interact)
and account for interdependence of species (compositional bias),
while employing the concept of conditional independence; two
nodes (i.e. species) in the graphical model are conditionally
independent if they provide no information about the state of
the other given the states of all other species in the dataset.
As a result, each link produced by the model implies a direct
connection, as opposed to correlated but indirectly connected

77
ASVs (Friedman et al., 2007; Kurtz et al., 2015; Tackmann et
al., 2019; Yoon et al., 2019).
In this study, changes in the protistan community and
corresponding environmental conditions were tracked daily during
two spring blooms in April 2018 and 2019 in Santa Monica Bay,
California (Hickey, 1979, 1992). 18S rRNA gene transcript (RNA)
sequencing and chemical/physical measurements were performed to
examine the biotic and abiotic factors that may shape protistan
community structure during the blooms.
MATERIALS AND METHODS:
Sample collection:
Algal blooms along the coast in the Southern California
Bight (SCB) region typically follow significant, seasonal
upwelling events driven by episodic, seasonal equatorward
(northerly) winds (Kudela et al., 2005; Kim et al., 2009; Kudela
et al., 2010; Seubert et al., 2013). Blooms were targeted in
Santa Monica Bay during spring 2018 and 2019 using local
meteorological information to anticipate coastal upwelling
events (Figure S1). Sampling from the Santa Monica Pier (SMP)
was conducted daily at 0900 from the same location and
orientation on the SMP from the 16th through the 30th in April 2018
(15 days), and in 2019 from the 13th April through 6th May (22
days; no sample was collected on the 14th April 2019). Sampling

78
periods are henceforth referred to as 2018 and 2019,
respectively.
An RBR Concerto (https://rbr-global.com) was deployed in
surface water for 15 minutes at the time of each sample
collection to obtain temperature, conductivity, chlorophyll a
fluorescence, and dissolved oxygen concentrations (Table S1). A
20 µm mesh plankton net was drift towed from the pier (15 min),
and samples examined via light microscopy to identify the
dominant planktonic taxa and their relative abundances. Surface
water was collected via bucket tosses from the pier and was used
for 18SrRNA transcript sequencing, nutrient analyses, extracted
chlorophyll concentrations, and domoic acid concentrations; an
extended funnel was used to gently fill a single acid-washed-3xrinsed 20 L carboy per established lab protocol
(https://www.protocols.io/view/sample-collection-from-the-fieldfor-downstream-mo-hisb4ee). The carboy was protected from the
light and immediately transported approximately 300 meters to
the Heal the Bay Aquarium located at the SMP for sample
processing.
Sample Processing
Microscopic determinations of dominant plankton was made
from 90 ml samples of seawater preserved with 10 ml of acid
Lugol’s solution (1%) (Auinger et al., 2008), analyzed following

79
Utermöhl methodology (Utermohl, 1958) using a using a Leica DM
IRBE inverted light microscope. Molecular community analyses
were performed in triplicate on 2 L seawater samples prefiltered with nitex mesh (80 µm) and collected on 45 mm GF/F
filters (nominal pore size 0.7 µm, Whatman, International Ltd.
Florham Park, NJ) to capture the unicellular eukaryote community
while excluding most metazoa from the samples. The filters were
placed in a 15 ml RNAse-free Falcon tube containing 1.5 ml of
RLT buffer + betamercaptoethanol, immediately flash frozen in
liquid nitrogen, and subsequently stored at -80 ˚C until RNA
extraction. Chlorophyll a (hereafter referred to as chlorophyll)
was used as a proxy for biomass and bloom magnitude. Major
blooms were categorized using two standard deviations (SD = 4.6)
added to the 15-year average (~2.8 µg/L; 2008 through 2020, minus
year 2015) at the Santa Monica Pier (Kim et al., 2009; Seubert
et al., 2013). Extracted chlorophyll and particulate domoic acid
concentrations (pDA in 2018 only) were determined by filtering
up to 300 ml of seawater onto 25 mm GF/F filters in duplicate.
Less than 300 ml of seawater was collected for chlorophyll
samples in 2019 when the filters clogged during periods of high
biomass. Filters collected for chlorophyll analysis were
extracted in 100% acetone at -20 °C in the dark for 24 hours and
then analyzed fluorometrically via the non-acidification method
using a Trilogy Turner Designs fluorometer. Samples collected

80
for domoic acid quantification were analyzed via Mercury
Science, Inc., DA Enzyme-Linked ImmunoSorbant Assay (ELISA:
Mercury Science, Durham, NC) according to the methods described
in (Seubert et al., 2013). Samples for nitrite+nitrate and
phosphate were measured from 0.02 µm filtered seawater via flow
injection analysis on a Quickchem 8500 at the Marine Science
Institute Analytical Lab, UCSB. Meteorological data was
collected from NOAA buoy station 46025
(https://www.ndbc.noaa.gov/).
RNA extraction and sequencing:
Total RNA was extracted per a previously established
protocol (https://www.protocols.io/view/rna-and-optional-dnaextraction-from-environmental-hk3b4yn)(Ollison et al., 2021).
Briefly, each GF/F filter was shredded by vortexing after the
addition of silica beads to each tube containing a GF/F filter
and lysis buffer. The mixture was transferred to a syringe that
was used to obtain the lysate from the filter/water slurry. RNA
was extracted from the lysate via Qiagen All Prep DNA/RNA Mini
Kit (Qiagen, #80204) per manufacturer instructions. Genomic DNA
was removed prior to RNA extraction using an RNase-Free Qiagen
DNase (Qiagen, #79254). RNA was reverse transcribed to cDNA
using the Bio-Rad iScript Reverse Transcription Supermix with
random hexamers (Bio-RAD, #170-8840).

81
The 18S rRNA V4 region in each sample was PCR amplified
using 18S 565F (5’-CCAGCASCYGCGGTAATTCC-3’) and 948R (5’-
ACTTTCGTTCTTGATYRA-3’) primers (Stoeck et al., 2010) via Q5
High-Fidelity 2x Master Mix (NEB, #M0492S). PCR reactions were
carried out in two steps due to the difference in annealing
temperature between primer pairs. The PCR reaction consisted of
an initial 98 ˚C denaturation for 2 min, and 10 cycles of 98 ˚C
for 10s, 53 ˚C for 30s, and 72 ˚C for 30s. The final 15 cycles
consisted of 98 ˚C for 10s, 48 ˚C for 30s and 72 ˚C for 30s. A
final extension at 72 ˚C for 2 min was performed after both
steps. PCR products were subsequently purified using Agencourt
AMPure XP beads (Beckman Coulter #A63881), indexed using
Illumina-specific P5 and P7 indices, quantified using a QuBit
2.0 fluorometer (ThermoFisher, #Q32866), and normalized to 10 µM
prior to sequencing. Normalized samples were quality checked on
an Agilent Bioanalyzer 2100 and paired-end sequenced (250X250)
on an Illumina MiSeq (Laragen Inc. Culver City, CA).
Sequence analysis:
18S rRNA V4 amplicon sequences were demultiplexed, quality
filtered, denoised, merged, chimera checked, dereplicated, and
grouped into amplicon sequence variants (ASVs) via the DADA2
plugin of Qiime2 (v2020.11) (Bolyen et al., 2019). Barcodes,
primers, and low-quality bases at the ends of both forward and

82
reverse reads were assessed with the interactive quality plot
and removed using the trim and trunc options of the DADA2 plugin
(--p-trim-left-f 12 --p-trim-left-r 12 --p-trunc-len-f 250 --ptrunc-len-r 250). ASVs were assigned taxonomic classifications
at 90% identity using the PR2 database (v12; Guillou et al.,
2013)(Table S2).
ASVs occurring 50 times or less throughout the entire
dataset were removed to attenuate background noise; sequences
suspected of being contaminant sequence were removed using the
decontam R package (Davis et al. 2017). Samples were
subsequently normalized using the trimmed mean by M value method
(edgeR) before further analysis (Robinson et al., 2010).
Taxonomic identification was assigned at approximately phylum
level for easier interpretation, and manually assigned higher
classification for diatom and dinoflagellate genera. UpSet plots
were generated from TMM-normalized, binary ASV tables using the
UpSetR package (Conway et al., 2017). A detection limit for ASV
presence and absence was defined as ASVs with ≥ 10 reads in a
sample were given a 1 for presence; those with < 10 were
assigned 0 for absence. The most abundant diatom and
dinoflagellate ASVs were investigated by removing ASVs below a
1% cutoff score within their respective assemblage. The
individual ASVs that accounted for ≥ 1% of total diatom or
dinoflagellate reads were considered abundant ASVs in subsequent

83
analyses. Abundant ASVs were aggregated at approximately genus
level, and the dominant taxa, containing multiple ASVs, were
those that accounted for 10% or more read abundance of the
sampling period.
Inverse Simpson index was calculated in R using the
diversity function (Vegan v2.4–2). Total species richness was
estimated by tallying the number of unique ASVs (after the
removal of singletons). Cluster dendrograms were generated by
combining pairwise Euclidean distances with the average method
of clustering. After quantifying Euclidean distances between
individual samples, the pairwise averages were used as the
distances between other samples, pairs and clusters.
Co-association networks for the 2018 and 2019 sampling
periods were constructed using the SPIEC-EASI R package (Kurtz
et al., 2015). SPIEC-EASI uses a centered log ratio
transformation and a graphical lasso approach to identify direct
associations between ASVs while simultaneously addressing the
sparsity and composition bias inherent in all rRNA-based
datasets (Kurtz et al., 2015). Nodes (also called vertices)
represent ASVs, and links (also called edges) represent
correlations (positive associations) and anti-correlations
(negative associations) between ASVs. Links with a weight below
|0.01| and all disconnected nodes were removed before downstream analysis. Network structural properties (clustering

84
coefficient, summary statistics of network degree and
betweenness-centrality, network diameter, and average path
length) were calculated using the igraph package (Csardi and
Nepusz, 2005).
Raw sequence data can be found on NCBI SRA under project
number PRJNA480318, and all R scripts used for data analysis can
be found at https://github.com/theOlligist/Daily_dynamics-SMP.
RESULTS:
Community responses to physical and chemical dynamics:
Sampling was conducted for 15 and 22 consecutive days in
Santa Monica Bay the spring of 2018 and 2019, respectively, to
investigate protistan community dynamics associated with spring
upwelling algal blooms. Short periods of elevated, down-coast
(northerly) winds just prior to the initiation of sampling at
the pier during these years were associated with lowered surface
water temperatures and higher salinity values, characteristic of
upwelled deep water in the region (Figure 1; Figure S1B, C). The
lowest recorded surface water temperature during 2018 was 12.8
˚C on Day 3 with a concomitant rise in salinity that is
characteristic of upwelled water in the region (Figure 1A).
Temperature gradually warmed to approximately 16 ˚C during the
remainder of the sampling period while salinity decreased during
the first four days following wind stress and then increased

85
gradually for the remainder of the study period. Low surface
water temperatures were less pronounced and more sporadic during
2019, with the lowest value (13.8 ˚C) observed on Day 1 and
modest minima also observed on Day 5 (14.5 ˚C) and Day 10 (14.3
˚C), with a gradual increase to 16-17 ˚C during the remainder of
the sampling period (Figure 1B). Salinity increased on Day 4
during 2019, and remained relatively high until Day 11,
decreased again Day 12-14, and then gradually increased again
for the remainder of the sampling period. It is unclear if the
variability was a consequence of weak wind events that occurred
during the sampling period, the advective movement of water at
the pier, or both (Figure 1B; Figure S1C).
Inorganic nutrient concentrations (nitrite+nitrate and
phosphate) were elevated coincident with low temperatures and
high salinity values observed at the beginning of each sampling
period (Figures 1, 2; Table S1). Nitrite+nitrate concentrations
increased from approximately 3 µM to 10 µM during the first 3
days of sampling in 2018, and declined rapidly (Figure 2A; Table
S1). Nitrite+nitrate concentration was high on Day 1 during 2019
(8 µM), and declined rapidly to < 1 µM between Day 1 and Day 4
(Figure 2B; Table S1). The latter finding implies that bloom
initiation, and consequent nutrient drawdown, may have already
begun by the time sampling began in 2019. Phosphate values were
substantial at the beginning of the sampling periods in both

86
years, and remained variable during the duration of each
sampling period (Figure 2).
Extracted chlorophyll concentrations increased markedly on
Day 5 during 2018, two days after maximal nitrite+nitrate
concentration was observed, with a gradual decline for the
remainder of the sampling period (Figure 2A; Table S1).
Chlorophyll concentrations were generally higher in 2019
relative to 2018, but the initial response of chlorophyll (≈18
µg/L on Day 4) during 2019 was comparable to the maximal value
observed on Day 5 during 2018. Beyond that day, however,
chlorophyll values in 2019 remained relatively high throughout
the sampling period, with exceptionally high concentrations
observed between Day 16 and Day 21. This period of elevated
chlorophyll values had a maximal concentration > 60 µg/L recorded
on Day 18 (Figure 2B; Table S1). Blooms during both years were
characterized as major blooms for the region according to
chlorophyll concentration (see materials and methods).
Dominant taxa and community dynamics differed between 2018 and
2019:
Daily changes in community composition and diversity of the
metabolically active protistan community were characterized by
sequencing the V4 hypervariable region of 18S rRNA gene
transcripts (RNA) during algal blooms in 2018 and 2019. A total

87
27,442,839 sequenced reads with an average of 241,975 sequences
per sample formed 29,643 total ASVs. 1,102 ASVs remained after
filtering of possible contaminant sequences and (rare) ASVs that
accounted for ≤ 50 reads throughout the entire dataset. Large
differences were observed between the two years in quantitative
estimates of species richness, diversity, as well as taxonomic
composition. Hierarchical clustering of all samples illustrated
that Year (2018 vs. 2019) was the primary difference in
community composition (among all 37 samples; Figure S2A). The
exceptions were four samples taken before Day 7 in 2019 that
clustered more closely with samples from 2018.
Diatoms were the most abundant taxa observed from Day 2
through Day 10 during 2018 (Figure 3A). Diatoms reached peak
relative abundances on Days 4 and 5 in that year, and gradually
declined over the remaining 10 days of sampling. Haptophytes and
archaeplastids accounted for significant fractions of the
phototrophic assemblage with relatively unchanging relative
abundances throughout the sampling period in 2018. Cryptophytes
and pelagophytes constituted minor components of the
phytoplankton community throughout 2018, whereas dinoflagellate
relative abundance was lower at the beginning of the study and
gradually increased coincident with the decline of diatom
relative abundance.

88
In contrast to 2018, dinoflagellates were consistently the
dominant taxa during 2019, and accounted for approximately 90%
of the total community during the period of highest recorded
chlorophyll concentrations (Figures 2B, 3B). Other algae with
PR2 classifications (diatoms, archaeplastids, cryptophytes, and
haptophytes) collectively accounted for approximately 20% of the
total community at the beginning of our sampling period, but
were observed at low relative abundances by Day 13 in 2019.
Changes in the relative abundances of well-defined
heterotrophic protists during 2018 and 2019 were similar despite
differences in phytoplankton community composition, and
generally constituted minor components of the total read count.
The largest increase in MArine STramenopile lineages (MAST) and
choanoflagellates, which are globally distributed bacterivorous
protists, occurred after the decline of diatoms in both years.
Cercozoan and syndinean taxa also exhibited higher relative
abundances following the diatom decline in both years; however,
increases in cercozoan relative abundances were more pronounced
during 2019 (Figure 3). The cercozoan genus is diverse and
contains both parasitic and non-parasitic taxa, whereas
syndinean taxa are believed to be globally-distributed parasitic
protists. Ciliates were present throughout both sampling periods
at notable relative abundances, but were more abundant and
diverse during 2019 (Days 18 to 19 in Figure 3B). Several

89
unclassifiable ochrophytes were present throughout both sampling
periods, whereas non-cercozoan rhizarian taxa were nearly
undetectable throughout the sampling periods of both years.
Shared and unique diatom and dinoflagellate ASVs:
The diversity and daily changes in relative abundances of
the dominant taxa (approximately genus-level groupings) within
the diatom and dinoflagellate assemblages during 2018 and 2019
blooms revealed that 6 of the 60 total classified diatoms (10%)
and 5 of the 66 total classified dinoflagellates (8%) accounted
for the majority of total reads in their respective assemblages.
3 of 6 abundant diatom taxa dominated in 2018 (Figure S3).
Thalassiosira, Rhizosolenia, and Pseudo-nitzschia, (in order of
decreasing total relative abundance) increased markedly to high
proportions over 4 days, and gradually declined with the
pronounced rise and fall in chlorophyll values during the 2018
bloom (Figure 4A). Three different diatom taxa, Guinardia,
Chaetoceros, and Leptocylindrus, accounted for the highest
proportion of the diatom assemblage in 2019, although fewer
total diatom reads were observed that year (Figures 4C vs 4A,
S4). The three dominant taxa during 2019 gradually declined from
their highest proportions observed on Day 1 with the exception
of Day 10, and all 6 diatom taxa were nearly undetectable by Day
13 (Figures 3B, 4C).

90
The five most abundant dinoflagellate taxa observed in 2018
and 2019 were also taxonomically distinct (Figure 4B, D). There
were gradual increases in the relative abundances of many
dinoflagellate taxa throughout the sampling periods during both
years (Figures S3, S4). Prorocentrum and Gyrodinium became the
most relatively abundant dinoflagellate taxa after the decline
of diatoms in 2018 (Figure 4B), and also contained the most ASVs
of the dinoflagellate assemblage in 2018. Margalefidinium
(formerly Cochlodinium) and Akashiwo (in order of decreasing
relative abundance) were the dominant dinoflagellate taxa during
2019 (Figure 4D). Margalefidinium was the most relatively
abundant taxa when sequenced reads were summed throughout the
22-day sampling period. We identified this species as
Margalefidinium fulvescens via light microscopy (Figure 5F, S).
Akashiwo was relatively rare until Day 12, but subsequently
increased more than 10-fold. Margalefidinium and Akashiwo
reached their peak relative abundances coincident with the
highest recorded chlorophyll values (~60 µg/L; Figure 4D).
The diatom taxa with the greatest relative read abundances
were generally not the taxa containing the most unique ASVs
(richness). For example, Chaetoceros was one of the least
abundant taxa in 2018, but accounted for a larger fraction of
ASVs than all other dominant groups with the exception of
Thalassiosira (Figure S5A). Conversely, dinoflagellate taxa

91
containing the greatest number of ASVs generally accounted for
greatest relative abundance in both years (Figure S5C).
An examination of the number of diatom and dinoflagellate
ASVs that were observed only in 2018 or 2019, versus those that
were shared across both years, revealed that the majority of
diatom ASVs (103) and dinoflagellate ASVs (129) were shared
across both years, whereas comparatively few ASVs from both
assemblages were unique to 2018 or 2019 sampling periods (Figure
6A, C). Examination of the unique ASVs that accounted for more
than 1% of the total reads from the diatom or dinoflagellate
assemblage in either year revealed that nearly all (> 95%) of
the most abundant diatom and dinoflagellate ASVs were shared
across both years; 33 of the 34 diatom, and 30 of the 31
dinoflagellate ASVs greater than 1% were shared in both years.
The percent abundances of each varied between the two years
(Figure 6B, D). One diatom ASV classified as Thalassiosira was
found during 2018 but not 2019, and one dinoflagellate ASV
classified as Proterythropsis was found during 2019 but not
2018.
Network analysis of ecologically significant associations:
A sparse co-association network of positive and negative
associations was constructed using ASVs from the 2018 and 2019
datasets to examine community-level connectivity and species

92
interactions that may have influenced the temporal changes in
community structure of the protistan assemblage (Figure 7).
Structural features of both the 2018 and 2019 association
networks exhibited modularity and “small-world” characteristics
(Watts et al. 1998) (Table 1). The degree distribution, which
provides the probability that a randomly selected node in the
network has n number of associations, decreased asymptotically;
the majority of nodes had relatively few associations; the rare
exceptions–hub nodes–contained up to 91 and 115 associations in
2018 and 2019, respectively (Table 1).
Diatoms accounted for the majority of positive and negative
associations in 2018 (23% and 21%, respectively) and in 2019
(20% and 18%; Figures 7, S6). The majority of the positive
diatom associations found in both years occurred with other
diatom nodes, and nodes corresponding to ciliates in 2019. The
majority of negative diatom associations in 2018 were with
dinoflagellates (Figure 7B), whereas the majority of negative
diatom associations in 2019 were with syndinean and cercozoan
taxa (some possible cercozoan parasites; Figure 7D).
Dinoflagellates accounted for 6-fold fewer associations than
diatoms in both 2018 and 2019 (Figures 7, S6; Table S4), and no
associations with Margalefidinium nodes were identified despite
it being the dominant taxa in 2019.

93
DISCUSSION:
Our study examining the abiotic and biotic controls of
protistan community composition during spring blooms in two
consecutive years in Santa Monica Bay revealed that diatoms
dominated the community following both wind-driven upwelling
events. Consistent with previous findings, diatoms in the region
are highly successful in response to the general
physical/chemical conditions brought about by upwelling.
However, substantially different diatom taxa dominated the two
blooms in our two-year study despite somewhat similar
environmental forcing factors and conditions (Figures 1, 2;
Figure S1), and despite strong overlap in the identity of the
abundant ASVs in both years (Figure 6). Also significant was our
finding that, while both diatom blooms declined following the
depletion of inorganic nutrients (primarily nitrogen) in the
days following both upwelling events, the phytoplankton
assemblage during 2019, but not 2018, transitioned to a massive,
long-lived dinoflagellate bloom. Minor differences in the
magnitude, timing, and frequency of wind events (and consequent
differences in water chemistry and physics) may explain some of
the community differences between the two years. Our network
analysis and microscopy also revealed interactions between
parasites and diatoms that may have played a pivotal role in

94
diatom bloom senescence and dinoflagellate succession during
2019.
Wind-driven upwelling shaped overall community composition, but
subtleties controlled species dominance:
Environmental parameters characteristic of upwelling were
captured during both 2018 and 2019 (decreased temperature,
increased salinity, elevated nutrient concentrations), and
therefore do not immediately explain the distinct diatom
communities observed during the early portion of the sampling
period during each year. Elevated nutrient concentrations
observed at the beginning of both sampling periods were
indicative of strong upwelling events in the region (Howard et
al., 2014). Marginally lower minimal temperature (12.6 vs 13.6
˚C), higher maximal salinity (33.64 vs 33.62 PSU), higher
maximal nitrite+nitrate concentration (10.10 vs 8.06 µM), and
higher maximal phosphate concentration (1.16 vs 0.80 µM) were
observed in 2018 relative to 2019 (Figures 1, 2), although
sampling during 2019 appears to have begun during nutrient
drawdown rather than at peak nutrient concentrations (Figure
2B).
Phytoplankton bloomed to comparable magnitudes (i.e.
chlorophyll concentrations of 17-18 µg/L) following upwelling
events on Days 4 or 5 of sampling in both years, and both blooms

95
were classified as major blooms for the region (Figure 2; Table
S1)(Seubert et al., 2013). Both assemblages were dominated by
diatoms, as predicted by general paradigm regarding their rapid
response to upwelling conditions. However, community analysis of
the blooms via 18S rRNA gene V4 sequencing revealed very
different dominant taxonomic composition each year. Diatoms
identified as Thalassiosira, Rhizosolenia, and Pseudo-nitzschia
dominated in 2018, whereas Chaetoceros, Leptocylindrus, and
Guinardia dominated the diatom assemblage in 2019 (Figure 4A vs
4C).
It is unknown if minor differences in the initial upwelling
conditions led to different bloom trajectories in 2018 and 2019
with respect to diatom community composition. Cell abundances
catalogued by the Southern California Coastal Ocean Observing
System (SCCOOS) illustrated that cell abundances of Pseudonitzschia spp. were almost four-fold higher in March 2018–prior
to sampling–than in 2019
(https://erddap.sccoos.org/erddap/tabledap/HABsSantaMonicaPier.html). Cell abundance information for the other
numerically dominant diatom taxa from this study were likely
aggregated with “Other Diatoms”. However, upwelling strength
appears to have been stronger and temporally less clearly
defined as a single event during the study period in 2018
relative to 2019 (Figures 1; Figure S1B vs C). Physical forcing

96
but also resting periods thought to be important for the
spawning of coastal blooms (Smayda, 2010; Trainer et al., 2010).
Strong equatorward winds in this study with short resting
periods that may have facilitated the algal response were
recorded three times in April of 2018 (once prior to and twice
during the sampling period) while a single strong event was
recorded prior to sampling during 2019 (Figure S1).
Additionally, modest peaks in nutrient concentrations were
observed multiple times throughout the 15-day observation period
in 2018, while increases in nutrient concentrations were muted
throughout the 23-day observation period in 2019 after Day 4. We
speculate that periodic, minor reinforcement of upwelling
conditions may have significantly prolonged dominance of diatoms
in 2018, but cannot immediately explain compositional
differences of the diatom assemblages observed in the two years.
A massive dinoflagellate bloom succeeded the diatom bloom in
2019 but not 2018:
Another striking finding of our study was the formation of
a dinoflagellate bloom after the diatom bloom senescence, which
was comprised of the potentially harmful species,
Margalefidinium fulvescens in 2019, but not in 2018 (Figures 3B,
4D, 5F). In contrast to 2019 where dinoflagellates were
relatively abundant throughout the sampling period, they were

97
only a minor component of the phytoplankton assemblage during
the 2018 study period (Figure 3A). Chlorophyll concentrations
also differed markedly between the two study periods.
Chlorophyll attained similar values in the early portion of the
blooms in both years when diatoms dominated (~17-18 µg
chlorophyll/L; Figure 2) but decreased slowly during 2018
following a maximum on Day 5 (Figure 2A), although diatoms
constituted a substantial fraction of the phytoplankton
community for approximately 10 days (Figure 3A). Chlorophyll
during 2019 decreased for approximately one week but then rose
again following the decline of the diatom assemblage that peaked
on Day 4 (Figure 2B). The subsequent dinoflagellate bloom
attained very high chlorophyll values (up to > 60 µg/L) from Day
12 through Day 2, albeit day-to-day variability was substantial
presumably due to patchy distribution of dinoflagellates.
Pre-bloom cell abundances don’t immediately explain the
succession to or community composition of the dinoflagellate
bloom. Although dinoflagellates appeared to be more abundant in
March 2019 prior to the bloom, Margalefidinium (Cochlodinium)
was not counted and diatoms also appeared to have greater
abundances compared with 2018
(https://erddap.sccoos.org/erddap/tabledap/HABsSantaMonicaPier.html).

98
It is possible that differences in the physical forcing
factors during each year may explain the succession of
dinoflagellates following the diatom bloom during 2019 but not
2018. Multiple wind events were observed prior to, and during
our study in 2018 (Apr 12, 16-17, 19-20), while extended
quiescent wind conditions, warmer water temperatures, and a
presumed stabler water column followed the single notable
upwelling event just prior to our study period in 2019 (April 9-
12; Figure S1C). As noted above, continued dominance of diatoms
during the more turbulent 2018 study period and succession of
dinoflagellates during the warmer, quiescent period of 2019 are
consistent with expectations of the general conditions promoting
either type of bloom (Margalef, 1978; Reynolds, 2001; Smayda,
2010).
Additionally, while differences in community composition
during the latter portions of the observational periods in 2018
and 2019 may be related to subtle differences in physical
forcing between the two years (specifically the success of
dinoflagellates in 2019 but not 2018), the magnitude of the
dinoflagellate-dominated bloom is more difficult to explain.
Chlorophyll concentrations observed from Day 16 through 21 in
2019 were approximately two to four-fold higher than the values
observed during the peak diatom bloom (Day 4), despite the
absence of significant wind forcing or nutrient increases after

99
diatom senescence (Day 4 in Figure 2B; Figure S1C). The source
of nutrients that supported such a bloom is therefore not
obvious. However, mixotrophy (Here, the consumption of prey by
photosynthetic dinoflagellates) is a common strategy among many
dinoflagellate species to acquire nutrients not readily
available to purely photosynthetic taxa (Stoecker, 1999; Hansen,
2011; Stoecker et al., 2017).
Vertical migration is also a long-known behavior among
dinoflagellates in nearshore coastal communities for acquiring
nutrients sequestered in deeper waters or sediments (Eppley et
al., 1968; Koizumi et al., 1996). Interestingly, Park et al.
(2019) documented a 15 m diel vertical migration in a red tide
event near Namhae Island, Korea by Margalefidinium
(Cochlodinium) polykrikoides (Park et al., 2019).
Margalefidinium was a dominant taxon in the dinoflagellate bloom
that appeared at the SMP in 2019 (Figures 4D, 5F). We speculate
that mixotrophy and/or vertical migration coupled to the lack of
physical forcing events may explain–in part–the massive bloom of
dinoflagellates observed during 2019.
Parasitic protists may have contributed to diatom bloom
senescence and community succession:
Changes in the relative abundances of diatoms and putative
parasites captured with 18S rRNA (cDNA) sequencing, in

100
conjunction with associations catalogued via network analysis
and microscopy, indicate that parasites may have played a
significant role during the diatom blooms in both years, but a
particularly important role in bringing about diatom bloom
senescence during 2019. Cercozoan and syndinean ASVs exhibited
increases in relative abundance coincident with the decline of
the diatom bloom in 2019, in which the cercozoan increase was
particularly noteworthy (Figure 3B). Network analysis during
2019 revealed strong associations between both cercozoan and
syndinean taxa with diatoms (Figure 7C, D). Cercozoan
associations with diatoms outnumbered syndinean associations
with diatoms; however, both taxa exhibited more negative
associations with diatoms than positive (Table S4). Similar
temporal dynamics and cercozoan associations with diatoms were
observed during a spring bloom in the San Pedro Channel,
California (Berdjeb et al., 2018). Conversely, in 2018 we
observed slight increases in cercozoan and syndinean ASVs during
the decline of the diatom bloom, but the majority of the
associations captured between both cercozoan and syndinean
groups with diatoms during that year were positive (Figure 7,
Table S4).
Parasite associations have been characterized as both
positive and negative in ecological association networks.
Positive associations have been thought to represent cases where

101
the parasites did not produce enough mortality to produce a
negative relationship (Faust and Raes, 2012; Fuhrman et al.,
2015; Berdjeb et al., 2018). We speculate that the mortality
caused by parasites in our 2019 study may have been sufficient
to yield the observed negative associations between cercozoan
and syndinean parasites with diatoms.
Cryothecomonas sp., a known cercozoan parasite, was
identified feeding on Guinardia spp., one of the numerically
dominant diatom genera in 2019, via light microscopy (Figure
5A). Cryothecomonas contains many specialist and generalist
species that are capable of forming infective and virulent
propagules (zoospores) with specialized structures for
penetrating the frustules of diatom hosts (Drebes et al., 1996).
Species of Cryothecomonas have been found in plankton samples at
temperatures ranging from 14 to 17 ˚C, consistent with the
present study (Drebes et al., 1996; Tillmann et al., 1999;
Schnepf and Kühn, 2000; Peacock et al., 2014; Scholz et al.,
2016; Markussen Bjorbækmo et al., 2019). No parasites were
observed by microscopy during 2018, but the dynamics of
cercozoan and syndinean taxa during the decline of the diatom
bloom during that year, and their generally positive
associations with diatoms, appears to indicate a low level of
infection during 2018 compared to 2019.

102
Parasitic infection and lysis have biogeochemical
implications due to the physical nature and chemical properties
of material released by parasitized cells. It is probable that
lysis debris not consumed by parasites contains labile dissolved
organic matter, as well as the host’s empty silica frustule.
Parasitism may therefore be more analogous to DOM and POM
release resulting from lytic infection by marine viruses
(Fuhrman, 1999), rather than feeding by micro- or mesozooplankton. Free-living parasites and zoospores are also
smaller than their hosts, and thereby vulnerable to predation by
a diverse range of phagotrophic protists significantly smaller
than the host (Sherr and Sherr, 2002; Massana et al., 2009;
Scholz et al., 2016; Piwosz et al., 2021). Such overall
reduction in particle size may reduce the amount of carbon and
energy transferred up plankton food chains.
Dissolved nutrients released from infected cells may have
contributed to the success of dinoflagellates after diatom
senescence in 2019. The possible utilization of the DON and DOP
released from infected hosts by dinoflagellate taxa is
consistent with recent studies that have illustrated the
utilization of a wide variety of dissolved organic nitrogen and
phosphorus distinct from diatoms, (Alexander et al., 2015).
Targeted metatranscriptomic assays during such regime changes
may shed light on the adaptive strategies of dinoflagellates by

103
identifying the differentially expressed genes related to
nutrient uptake and metabolism (Hu et al., 2018b). Indeed, a
recent study investigating gene expression during the shift from
a diatom-dominated bloom to dinoflagellate dominance revealed
differences in nutrient uptake, energy acquisition, and
catabolic strategies between the dominant diatom taxa and the
dominant dinoflagellate, Prorocentrum donghaiense (Zhang et al.,
2019).
Zoosporic parasites are diverse and may have several
impacts in plankton communities (Guillou et al., 2008; Scholz et
al., 2016). They may increase diversity in food webs (Dunne et
al., 2013), dominate food web links in ecological networks
(Lafferty et al., 2006; Lima-Mendez et al., 2015), they are
globally distributed throughout the water column (de Vargas et
al., 2015; Ollison et al., 2021), and can have a demonstrable
impact on species succession during algal blooms (Chambouvet et
al., 2008). Although the magnitude of their impact is unclear,
parasites likely played an ecological role in diatom bloom
dynamics during both 2018 and 2019, and should be considered in
future modeling efforts of upwelling bloom dynamics.
Heterotrophy by tiny predators (MAST) was elevated during bloom
senescence:

104
We observed an increase in the relative abundance of MAST
lineages coincident with the decline of both diatom blooms, and
substantial direct associations between MAST and diatoms during
both 2018 and 2019. Comparable amounts of negative and positive
associations were captured in 2018, whereas only negative
associations were captured in 2019.
Results from environmental surveys have indicated that many
heterotrophic nano-flagellates, which are the most important
consumers and remineralizers of heterotrophic bacteria and
picophytoplankton in marine ecosystems, are novel MAST lineages
(Massana et al., 2006; Massana et al., 2009; Lin et al., 2012).
Several of the twelve known lineages have been found throughout
the global ocean, but the specific trophic roles of each is
unknown. For example, MAST-3 is thought to contain parasitic
lineages based on sequence similarity to Solenicola setigera, a
known diatom parasite (Gómez, 2007; Gómez et al., 2011).
Moreover, heterotrophic bacteria as consumers of labile DOM and
POM are known to mobilize and proliferate during algal blooms in
decline (Teeling et al., 2012; Buchan et al., 2014; Needham and
Fuhrman, 2016). The increase in the relative abundance of MAST
during the demise of diatom blooms in 2018 and 2019 was likely
linked to possible increases in prey availability (the
mobilization of bacterial prey and/or the release of free-living
zoosporic parasites; Figure 3). However, the possibility of MAST

105
lineages acting as diatom parasites during either year cannot be
excluded.
Protistan community structure and the core community of Santa
Monica Bay:
Molecular methods for studying protistan diversity and
community composition throughout the global ocean have resulted
in estimates of community richness that have greatly surpassed
earlier estimates based on traditional methods (i.e. microscopy)
of species identification and quantification (Caron et al.,
2012; de Vargas et al., 2015; Pedros-Alio et al., 2018). Recent
genetic studies have shown that the vast pool of (mostly rare)
protistan species enables communities to vary greatly over large
and fine spatiotemporal gradients (Caron and Countway, 2009;
Balzano et al., 2015; de Vargas et al., 2015; Hu et al., 2016;
Ollison et al., 2021).
In this study, thousands of unique ASVs were catalogued in
Santa Monica Bay during the spring upwelling season in two
different years (Figure S2B; Table S2;). However, the same
diatom (33) and dinoflagellate (30) ASVs each consistently
accounted for at least 1% or more of the community across both
years (Figure 6B, D). The repeated appearance of the most
abundant ASVs, which likely represent subspecies-level diversity
(Caron and Hu, 2018), despite the vast amounts of species

106
richness and spatiotemporal fluctuation associated with the
annual cycle in the California Current (Hickey, 1979) is
consistent with a core community of diatom and dinoflagellate
taxa in Santa Monica Bay that constitute a seed bank for blooms
during spring upwelling (Table S3). The core ASVs fell within
relatively few unique taxonomic groupings at approximately
genus-level, many of which have been previously identified from
water samples collected along the Southern California coast,
including well-known HAB taxa (Shipe et al., 2008; Kim et al.,
2009; Venrick, 2012; Barth et al., 2020). It is not yet clear
how the aforementioned abiotic and biotic forces work in concert
with other physical and chemical factors not characterized in
this study to select the dominant, blooming taxa from this core
during a particular spring bloom season, or what anticipated
climate-induced changes (wind, temperature, and chemical
properties) portend for bloom composition, magnitude, duration,
and phenology (Fu et al., 2012; Capone and Hutchins, 2013; Howes
et al., 2015; Hutchins and Boyd, 2016; Weatherdon et al., 2016;
Hutchins and Fu, 2017; Tatters et al., 2018; Griffith et al.,
2019; Hutchins et al., 2019; Vikebø et al., 2019). We
hypothesize that the ecological redundancy represented by the
taxonomic identities of the core community indicates an
adaptable community that may be able to maintain biogeochemical

107
processes in spite of small or dramatic changes in environmental
factors (Caron and Countway, 2009).
Conclusions:
This work extends the findings of DNA-based high-frequency
sampling metabarcoding studies of phytoplankton blooms by
sequencing 18S rRNA gene transcripts (RNA) daily throughout two
blooms in the Southern California Coastal upwelling regime. This
approach enabled us to examine metabolically active taxa and
contrast changes in relative abundances that are not distorted
by genomic variation in 18S rRNA gene copies or chloroplasts per
cell. In this study as well as other metabarcoding-based
studies, diatoms bloomed to numerical dominance immediately
following upwelling; smaller flagellated taxa were in greater
relative abundance following diatom senescence; taxa within both
guilds exhibited intra-guild successions on short time scales
(Needham and Fuhrman, 2016; Needham et al., 2018; Trefault et
al., 2021). Accordingly, and consistent with other findings, we
have shown that microbial communities can exhibit rapid
community turnover with multiple taxa exhibiting many
differential responses within the community (Wilson et al.,
2021). The traditional view of the impact of nutrients and even
small differences in physical-forcing was augmented in this
study; however, similar to other studies, we have illustrated

108
that species interactions are likely an important driver of
community succession (Fuhrman et al., 2015; Needham and Fuhrman,
2016; Berdjeb et al., 2018; Trombetta et al., 2020).
TABLES AND FIGURES:
Table Legends:
Table 1: Quantitative properties of networks produced using the
2018 and 2019 sample sets. Nodes and links represent the number
of taxa and associations in each network, respectively. Average
Path length (Avg Path) represents the average number of nodes in
the shortest path for all possible pairs of nodes. Network
diameter (Diamet) represents the shortest distance between the
two most distant nodes in the network. Degree is defined as the
number of links (associations) connected to a node. AD, MD, and
MaxD represent the average, median, and maximum degrees,
respectively. The clustering coefficient (CC) value is a measure
the modularity of a typical neighborhood of highly connected
nodes, and accounts for the number of triangles in the network.
Table S1: Temperature, salinity, nitrite+nitrate, chlorophyll,
dissolved oxygen, and domoic acid values recorded in 2018 and
2019. Domoic acid concentrations were not measured in 2019.

109
Table S2: Raw ASV table produced via QIIME 2 (v12) with PR2
taxonomy (v. 12).
Table S3: Core community of diatom and dinoflagellate ASVs in
Santa Monica Bay. Table contains the taxonomic classification
(PR2 v.12) and unique 18S-V4 sequence for each ASV in the core
diatom and dinoflagellate community of Santa Monica Bay.
Table S4: Associations between ASVs in 2018 and 2019 networks
with corresponding ASV ID, taxonomic classification, edges
weight, and interaction type (positive vs negative).
Tables:
Table 1
Year Nodes Links Avg
Path
Diamet AD MD MaxD CC
2018 783 7383 4.13 0.61 19 9 90 0.45
2019 733 7993 4.09 0.55 22 10 114 0.48
Table S1
https://github.com/theOlligist/Daily_dynamics-SMP
Table S2
https://github.com/theOlligist/Daily_dynamics-SMP

110
Table S3
https://github.com/theOlligist/Daily_dynamics-SMP
Table S4
https://github.com/theOlligist/Daily_dynamics-SMP
Figure Legends:
Figure 1: Temperature (triangles with dashed line) and salinity
(filled circles with solid line) dynamics during 2018 (A) and
2019 (B) at the Santa Monica Pier. Black horizontal bars below
graph A represent periods of relatively high amplitude winds.
Sampling from the Santa Monica Pier (SMP) was conducted daily at
0900 from the same location and orientation on the SMP from the
16th through the 30th in April 2018 (15 days), and in 2019 from
the 13th April through 6th May (22 days; no sample was collected
on the 14th April 2019).
Figure 2: Concentrations of extracted chlorophyll (filled
circles with solid line), nitrite+nitrate (triangle with dashed
line), and phosphate (square with dotted line) during the 2018
(A) and 2019 (B) sampling periods at the Santa Monica Pier.
Values larger than the figure scale on some sampling dates in B
are shown at the top of the graph. Black horizontal bars below

111
graph A represent periods of relatively high amplitude winds.
Sampling from the Santa Monica Pier (SMP) was conducted daily at
0900 from the same location and orientation on the SMP from the
16th through the 30th in April 2018 (15 days), and in 2019 from
the 13th April through 6th May (22 days; no sample was collected
on the 14th April 2019).
Figure 3: Changes in the relative proportion of major protistan
taxonomic groupings during the sampling periods in 2018 (A) and
2019 (B). Black horizontal bars below figure A represent periods
of relatively high amplitude winds. Sampling from the Santa
Monica Pier (SMP) was conducted daily at 0900 from the same
location and orientation on the SMP from the 16th through the
30th in April 2018 (15 days), and in 2019 from the 13th April
through 6th May (22 days; no sample was collected on the 14th
April 2019).
Figure 4: Changes in the proportion of the 6 diatom taxa that
accounted for > 10% of the diatom assemblage during 2018 (A) and
2019 (C). Changes in the proportion of the 5 dinoflagellate taxa
that accounted for > 10% of the dinoflagellate assemblage during
the sampling periods in 2018 (B) and 2019 (D) where Dinophyceae
represents the dinoflagellates that lacked genus-level
classification in the PR2 database (v. 12). Sampling from the

112
Santa Monica Pier (SMP) was conducted daily at 0900 from the
same location and orientation on the SMP from the 16th through
the 30th in April 2018 (15 days), and in 2019 from the 13th
April through 6th May (22 days; no sample was collected on the
14th April 2019).
Figure 5: Images taken of the dominant protistan taxa from the
2018 and 2019 blooms via light microscopy. Black scale bar = 20
µm. A) Cryothecomonas sp. (Cercozoa) infecting Guinardia sp., B)
Pseudo-nitzschia sp., C) Rhizosolenia sp., D) Akashiwo sp., E)
Thalassiosira eccentrica, F) Margalefidinium fulvescens.
Figure 6: Upset plots (left column) illustrating the number of
unique and shared diatom (A) and dinoflagellate (C) ASVs
observed in 2018 and 2019. Vertical bars represent the number of
ASVs present in both years (left), only 2018 (middle), or only
2019 (right); horizontal bars represent the total number of ASVs
counted for each year. Pyramid diagrams (right column)
illustrating the percent overlap and taxonomic classification of
the diatom (B) or dinoflagellate (D) ASVs that accounted for at
least 1% relative (read) abundance of their respective
assemblages.

113
Figure 7: Negative (A and C) and positive (B and D)
associations among major taxonomic groups during the 2018 (top)
and 2019 (bottom) sampling periods. Ribbon width is proportional
to the number of associations between and among species within
the major taxonomic groups.
Figure S1: Map illustrating the geographical location of the
study site, the Santa Monica Pier represented by red star in
(A), and April wind speed in Santa Monica Bay in 2018 (B) and
2019 (C). Vertical dashed lines in graphs B and C indicate the
start of sampling during each year.
Figure S2: Cluster dendrogram (A) depicting the similarity of
samples on the basis of community composition (Euclidean
distances – Average clustering). Inverse Simpson score (invsimp)
and number of ASVs (ASV_count; B) describing quantitative
estimates of diversity and richness, respectively.
Figure S3: Daily changes in the number of reads for diatom and
dinoflagellate genera during 2018 sampling period. Sampling from
the Santa Monica Pier (SMP) was conducted daily at 0900 from the
same location and orientation on the SMP from the 16th through
the 30th in April 2018 (15 days), and in 2019 from the 13th

114
April through 6th May (22 days; no sample was collected on the
14th April 2019).
Figure S4: Daily changes in the number of reads for diatom and
dinoflagellate genera during the 2019 sampling period. Sampling
from the Santa Monica Pier (SMP) was conducted daily at 0900
from the same location and orientation on the SMP from the 16th
through the 30th in April 2018 (15 days), and in 2019 from the
13th April through 6th May (22 days; no sample was collected on
the 14th April 2019).
Figure S5: The number of diatom and dinoflagellate ASVs and
relative proportions of dominant taxa from the 2018 and 2019
sampling periods. Left panels: Diatom ASV counts (A) and
dinoflagellate ASV counts (B) during 2018 (salmon) and 2019
(turquoise). Right panel: Relative (read) proportions of diatom
(C) and dinoflagellate (D) taxa accounting for 10% or more of
their respective assemblage during 2018 (left) and 2019 (right).
Figure S6: Pyramid diagram illustrating the percent of positive
(turquoise) and negative (salmon) associations of nodes within
major taxonomic groupings during the sampling periods in 2018
(A) and 2019 (B).

115
Figures:
Figure 1

116
Figure 2

117
Figure 3

118
Figure 4

119
Figure 5

120
Figure 6

121
Figure 7

122
Figure S1

123
Figure S2

124
Figure S3

125
Figure S4

126
Figure S5

127
Figure S6

128
REFERENCES:
Alexander, H., Rouco, M., Haley, S.T., Wilson, S.T., Karl, D.M.,
and Dyhrman, S.T. (2015) Functional group-specific traits drive
phytoplankton dynamics in the oligotrophic ocean. Proc Natl Acad
Sci U S A 112: E5972-5979.
Auinger, B.M., Pfandl, K., and Boenigk, J. (2008) Improved
Methodology for Identification of Protists and Microalgae from
Plankton Samples Preserved in Lugol's Iodine Solution: Combining
Microscopic Analysis with Single-Cell PCR. Applied and
Environmental Microbiology 74: 2505-2510.
Azam, F., Fenchel, T., Field, J.G., Gray, J.S., Meyer-Reil,
L.A., and Thingstad, F. (1983) The Ecological Role of WaterColumn Microbes in the Sea. Marine Ecology Progress Series 10:
257-263.
Balzano, S., Abs, E., and Leterme, S.C. (2015) Protist diversity
along a salinity gradient in a coastal lagoon. Aquatic Microbial
Ecology 74: 263-277.
Barth, A., Walter, R.K., Robbins, I., and Pasulka, A. (2020)
Seasonal and interannual variability of phytoplankton abundance
and community composition on the Central Coast of California.
Marine Ecology Progress Series 637: 29-43.
Berdjeb, L., Parada, A., Needham, D.M., and Fuhrman, J.A. (2018)
Short-term dynamics and interactions of marine protist
communities during the spring-summer transition. ISME J.
Buchan, A., LeCleir, G.R., Gulvik, C.A., and Gonzalez, J.M.
(2014) Master recyclers: features and functions of bacteria
associated with phytoplankton blooms. Nat Rev Microbiol 12: 686-
698.
Buesseler, K.O. (1998) The decoupling of production and
particulate export in the surface ocean. Global Biogeochemical
Cycles 12: 297-310.
Capone, D.G., and Hutchins, D.A. (2013) Microbial
biogeochemistry of coastal upwelling regimes in a changing
ocean. Nature Geoscience 6: 711-717.

129
Caron, D.A., and Countway, P.D. (2009) Hypotheses on the role of
the protistan rare biosphere in a changing world. Aquatic
Microbial Ecology 57: 227-238.
Caron, D.A., and Hu, S.K. (2018) Are We Overestimating Protistan
Diversity in Nature? Trends in Microbiology 27: 197-205.
Caron, D.A., Countway, P.D., Jones, A.C., Kim, D.Y., and
Schnetzer, A. (2012) Marine protistan diversity. Ann Rev Mar Sci
4: 467-493.
Chambouvet, A., Morin, P., Marie, D., and Guillou, L. (2008)
Control of Toxic Marine Dinoflagellate Blooms by Serial
Parasitic Killers. Science 322: 1254-1257.
Charvet, S., Vincent, W.F., and Lovejoy, C. (2014) Effects of
light and prey availability on Arctic freshwater protist
communities examined by high-throughput DNA and RNA sequencing.
FEMS Microbiology Ecology 88: 550-564.
Chavez, F.P., and Messié, M. (2009) A comparison of Eastern
Boundary Upwelling Ecosystems. Progress in Oceanography 83: 80-
96.
Checkley, D.M., and Barth, J.A. (2009) Patterns and processes in
the California Current System. Progress in Oceanography 83: 49-
64.
Csardi, G., and Nepusz, T. (2005) The igraph software package
for complex network research. InterJournal Complex Systems.
de Vargas, C., Audic, S., Henry, N., Decelle, J., Mahe, F.,
Logares, R. et al. (2015) Eukaryotic plankton diversity in the
sunlit ocean. Science 348: 1261605-1261601.
Drebes, G., Kühn, S.F., Gmelch, A., and Schnepf, E. (1996)
Cryothecomonas aestivalis sp. nov., a colourless nanoflagellate
feeding on the marine centric diatom Guinardia delicatula
(Cleve) Hasle. Helgoland Wiss Meeresunters 50: 497-515.
Dunne, J.A., Lafferty, K.D., Dobson, A.P., Hechinger, R.F.,
Kuris, A.M., Martinez, N.D. et al. (2013) Parasites Affect Food
Web Structure Primarily through Increased Diversity and
Complexity. PLoS Biology 11: e1001579.
Eppley, R.W., Holm-Hansen, O., and Strickland, J.D.H. (1968)
Some Observations on the Vertical Migration of Dinoflagellates.
J Phycol 4: 333-340.

130
Falkowski, P.G., Barber, R.T., and Victor Smetacek, V. (1998)
Biogeochemical Controls and Feedbacks on Ocean Primary
Production. Science 281: 200-206.
Faust, K., and Raes, J. (2012) Microbial interactions: from
networks to models. Nat Rev Microbiol 10: 538-550.
Faust, K., Lima-Mendez, G., Lerat, J.S., Sathirapongsasuti,
J.F., Knight, R., Huttenhower, C. et al. (2015) Cross-biome
comparison of microbial association networks. Front Microbiol 6:
1200.
Field, C.B., Behrenfeld, M.J., James, T., Randerson, J.T., and
Falkowski, P.G. (1998) Primary Production of the Biosphere:
Integrating Terrestrial and Oceanic Components. Science 281:
237-240.
Friedman, J., Hastie, T., and Tibshirani, R. (2007) Sparse
inverse covariance estimation with the graphical lasso.
Biostatistics 9: 432-441.
Fu, F.X., Tatters, A.O., and Hutchins, D.A. (2012) Global change
and the future of harmful algal blooms in the ocean. Marine
Ecology Progress Series 470: 207-233.
Fuhrman, J.A. (1999) Marine viruses and their biogeochemical and
ecological effects. Nature 399: 541-548.
Fuhrman, J.A., Cram, J.A., and Needham, D.M. (2015) Marine
microbial community dynamics and their ecological
interpretation. Nat Rev Microbiol 13: 133-146.
Gómez, F. (2007) The Consortium of the Protozoan Solenicola
setigera and the Diatom Leptocylindurs mediterraneus in the
Pacific Ocean. Acta Protozoologica 46: 15-24.
Gómez, F., Moreira, D., Benzerara, K., and López-García, P.
(2011) Solenicola setigera is the first characterized member of
the abundant and cosmopolitan uncultured marine stramenopile
group MAST-3. Environmental Microbiology 13: 193-202.
Gong, W., and Marchetti, A. (2019) Estimation of 18S Gene Copy
Number in Marine Eukaryotic Plankton Using a Next-Generation
Sequencing Approach. Frontiers in Marine Science 6.
Griffith, A.W., Doherty, O.M., and Gobler, C.J. (2019) Ocean
warming along temperate western boundaries of the Northern
Hemisphere promotes an expansion of Cochlodinium polykrikoides

131
blooms. Proceedings of the Royal Society B: Biological Sciences
286: 20190340.
Guillou, L., Viprey, M., Chambouvet, A., Welsh, R.M., Kirkham,
A.R., Massana, R. et al. (2008) Widespread occurrence and
genetic diversity of marine parasitoids belonging to Syndiniales
(Alveolata). Environ Microbiol 10: 3349-3365.
Gutierrez-Rodriguez, A., Stukel, M.R., Lopes Dos Santos, A.,
Biard, T., Scharek, R., Vaulot, D. et al. (2018) High
contribution of Rhizaria (Radiolaria) to vertical export in the
California Current Ecosystem revealed by DNA metabarcoding. ISME
J.
Hansen, P.J. (2011) The Role of Photosynthesis and Food Uptake
for the Growth of Marine Mixotrophic Dinoflagellates1. Journal
of Eukaryotic Microbiology 58: 203-214.
Hickey, B.M. (1979) The California current system—hypotheses and
facts. Progress in Oceanography 8: 191-279.
Hickey, B.M. (1992) Circulation over the Santa Monica-San Pedro
Basin and Shelf. Progress in Oceanography 30: 37-115.
Howard, M.D.A., Sutula, M., Caron, D.A., Chao, Y., Farrara,
J.D., Frenzel, H. et al. (2014) Anthropogenic nutrient sources
rival natural sources on small scales in the coastal waters of
the Southern California Bight. Limnology and Oceanography 59:
285-297.
Howes, E.L., Joos, F., Eakin, C.M., and Gattuso, J.-P. (2015) An
updated synthesis of the observed and projected impacts of
climate change on the chemical, physical and biological
processes in the oceans. Frontiers in Marine Science 2.
Hu, S.K., Connell, P.E., Mesrop, L.Y., and Caron, D.A. (2018a) A
Hard Day's Night: Diel Shifts in Microbial Eukaryotic Activity
in the North Pacific Subtropical Gyre. Frontiers in Marine
Science 5.
Hu, S.K., Campbell, V., Connell, P.E., Gellene, A.G., Liu, Z.,
Terrado, R., and Caron, D.A. (2016) Protistan diversity and
activity inferred from RNA and DNA at a coastal ocean site in
the eastern North Pacific. FEMS Microbiol Ecol 92: fiw050.
Hu, S.K., Liu, Z., Alexander, H., Campbell, V., Connell, P.E.,
Dyhrman, S.T. et al. (2018b) Shifting metabolic priorities among
key protistan taxa within and below the euphotic zone. Environ
Microbiol 20: 2865-2879.

132
Hutchins, D.A., and Boyd, P.W. (2016) Marine phytoplankton and
the changing ocean iron cycle. Nature Climate Change 6: 1072-
1079.
Hutchins, D.A., and Fu, F. (2017) Microorganisms and ocean
global change. Nat Microbiol 2: 17058.
Hutchins, D.A., Jansson, J.K., Remais, J.V., Rich, V.I., Singh,
B.K., and Trivedi, P. (2019) Climate change microbiology -
problems and perspectives. Nat Rev Microbiol 17: 391-396.
Jones, A.C., Hambright, K.D., and Caron, D.A. (2018) Ecological
Patterns Among Bacteria and Microbial Eukaryotes Derived from
Network Analyses in a Low-Salinity Lake. Microb Ecol 75: 917-
929.
Kim, H.-J., Miller, A.J., McGowan, J., and Carter, M.L. (2009)
Coastal phytoplankton blooms in the Southern California Bight.
Progress in Oceanography 82: 137-147.
Kim, S., Jeon, C.B., and Park, M.G. (2017) Morphological
observations and phylogenetic position of the parasitoid
nanoflagellate Pseudopirsonia sp. (Cercozoa) infecting the
marine diatom Coscinodiscus wailesii (Bacillariophyta). Algae
32: 181-187.
Koizumi, Y., Uchida, T., and Honjo, T. (1996) Diurnal vertical
migration of Gymnodinium mikimotoi during a red tide in Hoketsu
Bay, Japan. Journal of Plankton Research 18: 289-294.
Kranzler, C.F., Krause, J.W., Brzezinski, M.A., Edwards, B.R.,
Biggs, W.P., Maniscalco, M. et al. (2019) Silicon limitation
facilitates virus infection and mortality of marine diatoms.
Nature Microbiology 4: 1790-1797.
Kudela, R.M., Seeyave, S., and Cochlan, W.P. (2010) The role of
nutrients in regulation and promotion of harmful algal blooms in
upwelling systems. Progress in Oceanography 85: 122-135.
Kudela, R.M., Pitcher, G.C., Probyn, T., Figueiras, F., Moita,
T., and Trainer, V.L. (2005) Harmful Algal Blooms in Coastal
Upwelling Systems. Oceanography 18: 184-197.
Kurtz, Z.D., Muller, C.L., Miraldi, E.R., Littman, D.R., Blaser,
M.J., and Bonneau, R.A. (2015) Sparse and compositionally robust
inference of microbial ecological networks. PLoS Comput Biol 11:
e1004226.

133
Lafferty, K.D., Dobson, A.P., and Kuris, A.M. (2006) Parasites
dominate food web links. Proc Natl Acad Sci U S A 103: 11211-
11216.
Layeghifard, M., Hwang, D.M., and Guttman, D.S. (2017)
Disentangling Interactions in the Microbiome: A Network
Perspective. Trends Microbiol 25: 217-228.
Lima-Mendez, G., Faust, K., Henry, N., Decelle, J., Colin, S.,
Carcillo, F. et al. (2015) Determinants of community structure
in the global plankton interactome. Science 348.
Lin, Y.C., Campbell, T., Chung, C.C., Gong, G.C., Chiang, K.P.,
and Worden, A.Z. (2012) Distribution patterns and phylogeny of
marine stramenopiles in the north pacific ocean. Appl Environ
Microbiol 78: 3387-3399.
Margalef, R. (1978) Life-forms of phytoplankton as survival
alternatives in an unstable environment. Oceanologica ACTA 1:
493-509.
Markussen Bjorbækmo, M.F., Evenstad, A., Lieblein Røsæg, L.,
Krabberød, A.K., and Logares, R. (2019) The planktonic protist
interactome: where do we stand after a century of research? ISME
14: 544-559.
Massana, R., Terrado, R., Forn, I., Lovejoy, C., and PedrosAlio, C. (2006) Distribution and abundance of uncultured
heterotrophic flagellates in the world oceans. Environmental
Microbiology 8: 1515-1522.
Massana, R., Unrein, F., Rodriguez-Martinez, R., Forn, I.,
Lefort, T., Pinhassi, J., and Not, F. (2009) Grazing rates and
functional diversity of uncultured heterotrophic flagellates.
ISME J 3: 588-596.
Massana, R., Gobet, A., Audic, S., Bass, D., Bittner, L.,
Boutte, C. et al. (2015) Marine protist diversity in European
coastal waters and sediments as revealed by high-throughput
sequencing. Environ Microbiol 17: 4035-4049.
Muller, E.E.L., Faust, K., Widder, S., Herold, M., Martínez
Arbas, S., and Wilmes, P. (2018) Using metabolic networks to
resolve ecological properties of microbiomes. Current Opinion in
Systems Biology 8: 73-80.

134
Myung G. Park, S.K.C., Wonho Yih, D. Wayne Coats (2002) Effects
of two strains of the parasitic dinoflagellate Amoebophrya on
growth, photosynthesis, light absorption, and quantum yield of
bloom-forming dinoflagellates. Marine Ecology 227: 281-292.
Needham, D.M., and Fuhrman, J.A. (2016) Pronounced daily
succession of phytoplankton, archaea and bacteria following a
spring bloom. Nat Microbiol 1: 16005.
Needham, D.M., Sachdeva, R., and Fuhrman, J.A. (2017) Ecological
dynamics and co-occurrence among marine phytoplankton, bacteria
and myoviruses shows microdiversity matters. ISME J 11: 1614-
1629.
Needham, D.M., Fichot, E.B., Wang, E., Berdjeb, L., Cram, J.A.,
Fichot, C.G., and Fuhrman, J.A. (2018) Dynamics and interactions
of highly resolved marine plankton via automated high-frequency
sampling. ISME J 12: 2417-2432.
Newman, M.E.J. (2004) Detecting community structure in networks.
The European Physical Journal B - Condensed Matter 38: 321-330.
Ollison, G.A., Hu, S.K., Mesrop, L.Y., DeLong, E.F., and Caron,
D.A. (2021) Come rain or shine: Depth not season shapes the
active protistan community at station ALOHA in the North Pacific
Subtropical Gyre. Deep Sea Research Part I: Oceanographic
Research Papers 170: 103494.
Park, J.G., Jeong, M.K., Lee, J.A., Cho, K.-J., and Kwon, O.S.
(2019) Diurnal vertical migration of a harmful dinoflagellate,
Cochlodinium polykrikoides (Dinophyceae), during a red tide in
coastal waters of Namhae Island, Korea. Phycologia 40: 292-297.
Peacock, E.E., Olson, R.J., and Sosik, H.M. (2014) Parasitic
infection of the diatom Guinardia delicatula, a recurrent and
ecologically important phenomenon on the New England Shelf.
Marine Ecology Progress Series 503: 1-10.
Pedros-Alio, C., Acinas, S.G., Logares, R., and Massana, R.
(2018) Marine Microbial Diversity As Seen By High-Throughput
Sequencing. In Microbial Ecology of the Oceans. Gasol, J.M., and
Kirchman, D.L. (eds). Hoboken, NJ: John Wiley & Sons, Inc.
Piwosz, K., Mukherjee, I., Salcher, M.M., Grujčić, V., and
Šimek, K. (2021) CARD-FISH in the Sequencing Era: Opening a New
Universe of Protistan Ecology. Frontiers in Microbiology 12.

135
Pomeroy, L.R. (1974) The Ocean's Food Web, A Changing Paradigm.
BioScience 24: 499-504.
Poulin, R. (2010) Network analysis shining light on parasite
ecology and diversity. Trends Parasitol 26: 492-498.
Proulx, S.R., Promislow, D.E., and Phillips, P.C. (2005) Network
thinking in ecology and evolution. Trends Ecol Evol 20: 345-353.
Reynolds, T.J.S.a.C.S. (2001) Community assembly in marine
phytoplankton: application of recent models to harmful
dinoflagellate blooms. Journal of Plankton Research 23: 447-461.
Robinson, M.D., McCarthy, D.J., and Smyth, G.K. (2010) edgeR: a
Bioconductor package for differential expression analysis of
digital gene expression data. Bioinformatics 26: 139-140.
Rottjers, L., and Faust, K. (2018) From hairballs to hypothesesbiological insights from microbial networks. FEMS Microbiol Rev
42: 761-780.
Ryther, J.H. (1969) Photosynthesis and Fish Production in the
Sea. Science 166: 72-76.
Schnepf, E., and Kühn, S.F. (2000) Food uptake and fine
structure of Cryothecomonas longipes sp. nov., a marine
nanoflagellate incertae sedis feeding phagotrophically on large
diatoms.
Scholz, B., Guillou, L., Marano, A.V., Neuhauser, S., Sullivan,
B.K., Karsten, U. et al. (2016) Zoosporic parasites infecting
marine diatoms - A black box that needs to be opened. Fungal
Ecol 19: 59-76.
Seubert, E.L., Gellene, A.G., Howard, M.D., Connell, P., Ragan,
M., Jones, B.H. et al. (2013) Seasonal and annual dynamics of
harmful algae and algal toxins revealed through weekly
monitoring at two coastal ocean sites off southern California,
USA. Environ Sci Pollut Res Int 20: 6878-6895.
Sherr, E.B., and Sherr, B.F. (2002) Significance of predation by
protists in aquatic microbial food webs. Antonie van Leeuwenhoek
81: 293-308.
Shipe, R.F., Leinweber, A., and Gruber, N. (2008) Abiotic
controls of potentially harmful algal blooms in Santa Monica
Bay, California. Continental Shelf Research 28: 2584-2593.

136
Smayda, T.J. (2010) Adaptations and selection of harmful and
other dinoflagellate species in upwelling systems 1. Morphology
and adaptive polymorphism. Progress in Oceanography 85: 53-70.
Smetacek, V. (1999) Diatoms and the Ocean Carbon Cycle. Protist
150: 25-32.
Smith, J., Connell, P., Evans, R.H., Gellene, A.G., Howard,
M.D.A., Jones, B.H. et al. (2018) A decade and a half of Pseudonitzschia spp. and domoic acid along the coast of southern
California. Harmful Algae 79: 87-104.
Stoecker, D.K. (1999) Mixotrophy among Dinoflagellates. Journal
of Eukaryotic Microbiology 46: 397-401.
Stoecker, D.K., Hansen, P.J., Caron, D.A., and Mitra, A. (2017)
Mixotrophy in the Marine Plankton. Ann Rev Mar Sci 9: 311-335.
Suttle, C.A. (2007) Marine viruses--major players in the global
ecosystem. Nat Rev Microbiol 5: 801-812.
Tackmann, J., Matias Rodrigues, J.F., and von Mering, C. (2019)
Rapid Inference of Direct Interactions in Large-Scale Ecological
Networks from Heterogeneous Microbial Sequencing Data. Cell
Systems 9: 286-296.e288.
Tatters, A.O., Schnetzer, A., Xu, K., Walworth, N.G., Fu, F.,
Spackeen, J.L. et al. (2018) Interactive effects of temperature,
CO2 and nitrogen source on a coastal California diatom
assemblage. Journal of Plankton Research 40: 151-164.
Teeling, H., Fuchs, B.M., Becher, D., Klockow, C., Gardebrecht,
A., Bennke, C.M. et al. (2012) Substrate-Controlled Succession
of Marine Bacterioplankton Populations Induced by a
Phytoplankton Bloom. Science 336: 608-611.
Tillmann, U., Hesse, K., and Tillman, A. (1999) Large-scale
parasitic infection of diatoms in the Northfrisian Wadden Sea.
Journal of Sea Research 42: 255-261.
Trainer, V.L., Pitcher, G.C., Reguera, B., and Smayda, T.J.
(2010) The distribution and impacts of harmful algal bloom
species in eastern boundary upwelling systems. Progress in
Oceanography 85: 33-52.
Trefault, N., De la Iglesia, R., Moreno-Pino, M., Lopes dos
Santos, A., Gérikas Ribeiro, C., Parada-Pozo, G. et al. (2021)

137
Annual phytoplankton dynamics in coastal waters from Fildes Bay,
Western Antarctic Peninsula. Scientific Reports 11.
Trombetta, T., Vidussi, F., Roques, C., Scotti, M., and
Mostajir, B. (2020) Marine Microbial Food Web Networks During
Phytoplankton Bloom and Non-bloom Periods: Warming Favors
Smaller Organism Interactions and Intensifies Trophic Cascade.
Frontiers in Microbiology 11.
Utermohl, H. (1958) Zur vervollkommnung der quantitativen
phytoplankton-methodik. Mitt int Ver theor angew Limnol 9: 1-38.
Venrick, E.L. (2012) Phytoplankton in the California Current
system off southern California: Changes in a changing
environment. Progress in Oceanography 104: 46-58.
Vikebø, F.B., Strand, K.O., and Sundby, S. (2019) Wind Intensity
Is Key to Phytoplankton Spring Bloom Under Climate Change.
Frontiers in Marine Science 6.
Wang, H., Wei, Z., Mei, L., Gu, J., Yin, S., Faust, K. et al.
(2017) Combined use of network inference tools identifies
ecologically meaningful bacterial associations in a paddy soil.
Soil Biology and Biochemistry 105: 227-235.
Weatherdon, L.V., Magnan, A.K., Rogers, A.D., Sumaila, U.R., and
Cheung, W.W.L. (2016) Observed and Projected Impacts of Climate
Change on Marine Fisheries, Aquaculture, Coastal Tourism, and
Human Health: An Update. Frontiers in Marine Science 3.
Wilson, J.M., Chamberlain, E.J., Erazo, N., Carter, M.L., and
Bowman, J.S. (2021) Recurrent microbial community types driven
by nearshore and seasonal processes in coastal Southern
California. Environmental Microbiology.
Worden, A.Z., Follows, M.J., Giovannoni, S.J., Wilken, S.,
Zimmerman, A.E., and Keeling, P.J. (2015) Environmental science.
Rethinking the marine carbon cycle: factoring in the
multifarious lifestyles of microbes. Science 347: 1257594.
Xia, L.C., Steele, J.A., Cram, J.A., Cardon, Z.G., Simmons,
S.L., Vallino, J.J. et al. (2011) Extended local similarity
analysis (eLSA) of microbial community and other time series
data with replicates. BMC Syst Biol 5 Suppl 2: S15.
Yoon, G., Gaynanova, I., and Müller, C.L. (2019) Microbial
Networks in SPRING - Semi-parametric Rank-Based Correlation and

138
Partial Correlation Estimation for Quantitative Microbiome Data.
Frontiers in Genetics 10.
Zhang, Y., Lin, X., Shi, X., Lin, L., Luo, H., Li, L., and Lin,
S. (2019) Metatranscriptomic Signatures Associated With
Phytoplankton Regime Shift From Diatom Dominance to a
Dinoflagellate Bloom. Front Microbiol 10: 590.

139
Chapter III: To Live and Die in L.A.: Physiological
Underpinnings of Diatom vs Dinoflagellate Dominance and Decline
During Two Coastal Algal Blooms in Santa Monica Bay
Gerid A. Ollison1
, Sarah K. Hu2
, Julie V. Hopper3
, Brittany P.
Stewart1
, Jennifer L. Beatty1
, David A. Caron1
1
Department of Biological Sciences, University of Southern
California, 3616 Trousdale Parkway, Los Angeles, CA 90089-0371
USA.
2
Department of Oceanography, Texas A&M University, 3146 TAMU,
College Station, TX 77843-3146 USA.
3
Office of Sustainability, University of Southern California,
3434 S. Grand Ave, Los Angeles, CA 90089-3915 USA.

140
ABSTRACT:
Algal blooms on the Southern California coast are typically
dominated by diatom and dinoflagellate taxa, and are governed by
their physiological responses to environmental cues; however, we
lack a predictive understanding of the environmental controls
underlying the establishment and persistence of these distinct
bloom events.
In this study, we examined gene expression among the numerically
dominant diatom and dinoflagellate taxa during spring upwelling
bloom events to compare the physiological underpinnings of
diatom vs dinoflagellate bloom dynamics. Diatoms, which bloomed
following upwelling events, expressed genes related to dissolved
inorganic nitrogen utilization, and genes related to the
catabolism of chitin that may have prolonged their bloom
duration following nitrogen depletion. Conversely,
dinoflagellates bloomed under depleted inorganic nitrogen
conditions, exhibited less variation in transcriptional
activity, and expressed few genes associated with dissolved
inorganic nutrients during their bloom. Dinoflagellate profiles
exhibited evidence of proteolysis and heterotrophy that may have
enabled them to bloom to high abundances under depleted
inorganic nutrients. Taken together, diatom and dinoflagellate
transcriptional profiles illustrated guild-specific physiologies

141
that are tuned to respond to and thrive under distinct
environmental “windows of opportunity”.
INTRODUCTION:
Unicellular eukaryotes (protists) conduct nearly half of
oceanic primary production, and are particularly important in
coastal upwelling regimes located on the eastern boundaries of
oceans, high-nutrient ecosystems that have been called the “new
production factories of the global ocean” (Field et al., 1998;
Capone and Hutchins, 2013). Seasonal algal blooms of large
protistan taxa in coastal upwelling regimes feed into short food
chains that support the world’s most important fisheries, and
constitute an important sink of atmospheric CO2. However, some
species can form harmful algal blooms (HABs) as a result of high
biomass accumulations, or produce toxins that are detrimental to
ecosystem and human health (Ryther, 1969; Smith et al., 2018).
The timing of blooms along the southern California coast is
fairly consistent such that blooms are especially pronounced
during the spring months when wind-driven upwelling of new
nutrients occurs during periods of sufficient light and shallow
mixing (Checkley and Barth, 2009). However, we still lack a
predictive understanding of the specific environmental controls
that govern taxonomic composition or bloom magnitude and
duration.

142
A recent study examined the biotic and abiotic controls of
bloom dynamics by tracking daily changes in the protistan
community and corresponding physicochemical conditions during
two contrasting (diatom vs dinoflagellate) blooms in Santa
Monica Bay off the coast of Southern California (Ollison et al.,
2022). A diatom bloom dominated by Thalassiosira and Pseudonitzschia formed following wind-driven upwelling and elevated
inorganic nutrient concentrations during 2018, whereas two
mixotrophic dinoflagellates, Margalefidinium and Akashiwo,
formed a massive bloom under lower inorganic nutrient
concentrations in 2019 (Ollison et al., 2022). Network analysis
and direct observations via microscopy revealed that parasites
were actively infecting diatom taxa in 2019 (Ollison et al.,
2022). The authors speculated that parasite attack on diatoms
during 2019, but not in 2018, may have suppressed diatoms or
produced alternative sources of nutrients that influenced the
subsequent dinoflagellate bloom success.
Algal bloom dynamics are governed by physiological
responses to environmental cues and many studies have
illustrated that diatoms and dinoflagellates have distinct
ecological niches (Irwin et al., 2012). Diatoms are thought to
thrive under cool nutrient-rich conditions as a result of their
size and high capacity for utilizing dissolved inorganic
nitrogen and thus typically dominate under nutrient upwelling

143
conditions during the spring (Margalef, 1978; Paul G. Falkowski,
2004; Armbrust, 2009). Conversely, dinoflagellate blooms
typically occur under warm, nutrient poor, stratified
conditions. Additionally, many dinoflagellate taxa such as
Margalefidinium and Akashiwo are mixotrophs, having the ability
to consume prey in addition to conducting photosynthesis to
support their nutritional demands (Smayda, 2010; Caron, 2016;
López-Cortés et al., 2019; Yang et al., 2020). However,
relatively few studies have examined the physiological processes
that underpin natural blooms of these algal groups at the level
of gene transcription (Alexander et al., 2015b; Wurch et al.,
2019; Zhang et al., 2019; Metegnier et al., 2020).
Metatranscriptomic sequencing has become a tractable
approach for examining the physiological activity of whole
protistan communities in situ, and is largely a result of
technological, computational, and database advances. For
example, the Marine Microbial Eukaryotic Transcriptome
Sequencing Project (MMETSP) has produced over 650 assembled,
annotated, and publically available transcriptomes of non-model
protists (Keeling et al., 2014). Metatranscriptomic
investigations of natural communities have begun to improve our
understanding of physiological adaptations that underpin species
responses to diverse environmental stimuli (Wurch et al., 2014;
Alexander et al., 2015a; Alexander et al., 2015b; Geisen et al.,

144
2015; Cohen, 2017; Hu et al., 2018; Zhang et al., 2019; Harke et
al., 2021; Cohen et al., 2022). For example, Wurch et al. (2019)
examined the physiological underpinnings of an Aureococcus bloom
and reported the unexpected importance of phosphorus in
controlling bloom dynamics (Wurch et al., 2019).
This current study extends findings from Ollison et al.
(2022) by examining the gene expression patterns associated with
temporal changes in relative abundances of dominant diatom and
dinoflagellate taxa. Ordination of diatom and dinoflagellate
transcriptomes revealed guild-specific physiologies, while gene
co-expression network analysis and differential expression
analysis illustrated periodic shifts in physiological priorities
that may have influenced bloom persistence under nutrient
deficiency. Our analyses indicate that while diatoms responded
to inorganic nutrient enrichment from upwelling, and exhibited
alternative strategies for inorganic nutrient acquisition that
may have prolonged bloom duration following nutrient drawdown,
dinoflagellates gene expression indicated that they may exploit
organic sources of nutrients or prey (i.e. mixotrophy) to reach
bloom proportions under depleted inorganic nutrient conditions.
MATERIALS AND METHODS:
The physiological underpinnings of two distinct algal
blooms in waters off the coast of southern California — a

145
diatom-dominated bloom in spring 2018 and a dinoflagellatedominated bloom during spring 2019 — were examined using
eukaryotic metatranscriptomic datasets. Gene expression of
dominant diatom and dinoflagellate taxa were filtered and
examined from whole community gene expression during four
periods of growth: pre-bloom; a period of rapid increases in
chlorophyll concentration and relative abundance; a period of
sustained abundances (determined from chlorophyll concentrations
and maximum relative abundances); and bloom decline. The
dominant diatom and dinoflagellate taxa during each year were
identified previously via light microscopy and 18S rRNA gene
transcript sequencing (Ollison et al., 2022). The diatom genera
Thalassiosira and Pseudo-nitzschia were the numerically dominant
taxa during 2018, whereas two gymnodiniacean dinoflagellate
genera, Margalefidinium and Akashiwo were dominant during 2019.
In the present study, gene expression from these dominant taxa
were examined on five days during each year that spanned the
aforementioned bloom periods. Sequences of these dominant taxa
were filtered from whole community gene expression, assigned
KEGG functional annotations, and examined using multi-variate,
differential expression, and weighted gene co-expression network
analyses to compare the physiological processes that governed
shifts in biomass during each year.

146
Sample collection:
Protistan communities and physicochemical parameters in
Santa Monica Bay, California, were sampled daily throughout
blooms dominated by diatoms and dinoflagellates during spring
2018 and 2019, respectively (Ollison et al., 2022). Briefly,
sampling from the Santa Monica Pier (SMP) was conducted daily,
April 16th through the 30th in 2018 (15 consecutive days), and
April 13th through May 6th in 2019 (22 days; no sample was
collected on the 14th April 2019). The taxonomic composition of
metabolically active protists was assessed during each bloom
using 18S rRNA transcript sequencing and light microscopy,
chlorophyll a assessed from whole seawater (chlorophyll) via
fluorometry of acetone extracted chlorophyll, and
nitrate+nitrite and phosphate concentrations were measured from
0.2 µm filtered seawater via flow injection (see Ollison et al
2022). Sampling periods are hereafter referred to as 2018 and
2019, and are synonymous with diatom and dinoflagellate
dominated blooms, respectively.
Temporal changes in taxonomic abundances and
physicochemical parameters discussed in Ollison et al. (2022)
informed our metatranscriptomic sequencing approach to assess
the four distinct bloom phases from each year: 1) Pre-bloom, 2)
onset of increasing relative abundances (based on 18S-V4 rRNA
gene transcript sequencing, see below) and chlorophyll

147
concentration, 3) sustained relative abundances (maximum
relative abundances and chlorophyll concentration), 4) and bloom
decline (decline in relative abundances and chlorophyll
concentration).
Sample Processing:
Eukaryotic metatranscriptomic community analyses on each
sampling date were performed in triplicate on 2 L seawater
samples, which were pre-filtered through Nitex mesh (80 µm) to
exclude most metazoa, and collected onto 47 mm GF/F glass fiber
filters (nominal pore size 0.7 µm; Whatman, International Ltd.
Florham Park, NJ) to capture the unicellular eukaryote community
while excluding most metazoa. The filters were placed in 15 ml
RNAse-free Falcon tubes containing 1.5 ml of RLT buffer (Qiagen,
#79216) + betamercaptoethanol (ThermoFisher, #21985023 ),
immediately flash frozen in liquid nitrogen, and subsequently
stored at -80 ˚C until RNA extraction.
RNA extraction and sequencing:
Total RNA was extracted per a previously established
protocol (Ollison et al., 2021). Briefly, each GF/F filter was
shredded by vortexing after the addition of silica beads to each
tube containing a GF/F filter and lysis buffer. The mixture was
transferred to a syringe that was used to obtain the lysate from

148
the filter/water slurry. RNA was extracted from the lysate via
Qiagen All Prep DNA/RNA Mini Kit (Qiagen, #80204) per
manufacturer instructions. Genomic DNA was removed prior to RNA
extraction using an RNase-Free Qiagen DNase (Qiagen, #79254).
RNA was reverse transcribed to cDNA using the Bio-Rad iScript
Reverse Transcription Supermix with random hexamers (Bio-RAD,
#170-8840).
Sequence library preparation was performed at the
University of Southern California’s UPC Genome Core facility
using Kapas Stranded mRNA library preparation kit with poly-A
tail selection beads to concentrate eukaryotic mRNA (Kapa
Biosystems, Inc., Wilmington, MA #KK8420). RNA libraries were
quality checked (Agilent bioanalyzer 2100) prior to sequencing.
Sequencing was conducted on four NextSeq High Output PE 150
runs.
Sequence processing:
Sequence adapters, low quality bases (phred score below 10
within a 25 bp sliding window), and sequences shorter than 50 bp
were removed using Trimmomatic v.0.32 (Bolger et al., 2014).
rRNA and mRNA were sorted from quality-filtered reads using
Sortmerna (v2.1)(Kopylova et al., 2012). Taxonomic
classification was subsequently assigned to sorted rRNA reads
using the Protist Ribosomal Reference (v.11) database via uclust

149
at 97% identity (Guillou et al., 2013).
Messenger RNA sequences from combined replicate samples
were co-assembled to contiguous sequences (contigs) using
MEGAHIT v. 1.0.3 (Li et al., 2015) with default parameters.
Contigs were assigned taxonomic identities and gene function IDs
(Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology IDs)
using diamond BLASTX on sensitive mode using an e-value cutoff
of 0.001 to a customized cDNA reference database that augmented
the Marine Microbial Eukaryote Transcriptome Sequencing Project
(MMETSP) database with other publicly available genomes and
transcriptomes (EukZoo) (Keeling et al., 2014; Buchfink et al.,
2015). Hits with bit scores within the top 95% were assigned
taxonomic and functional annotations, and only transcripts with
functional annotations were used in downstream analysis of
physiological activity. More information about the EukZoo is
available with download at the Zenodo repository:
http://doi.org/10.5281/zenodo.1476236. Transcript abundances
were quantified using Salmon v. 0.11.3 (kmer size = 31)(Patro et
al., 2017).
Sequence analysis:
Relative abundances of taxa were characterized via
metatranscriptome-derived rRNA during this study, and augmented
with 18S-V4 gene transcript sequencing-based characterization

150
from Ollison et. al. (2022). Genes expressed by the numerically
dominant diatom taxa—i.e. those with the greatest relative
abundances—during 2018 were bioinformatically isolated from
total community gene expression during both years. Genes
expressed by the numerically dominant dinoflagellates during
2019 were isolated from whole community gene expression during
both years. Two samples—one technical replicate from Day 1 and
Day 12 of 2019—were excluded from further analysis due to low
read abundances after filtration for gymnodiniacean
dinoflagellates. Each diatom and dinoflagellate taxon was
normalized independently prior to differential expression
analysis using the trimmed mean of m-value (TMM) method via
edgeR v3.30.3 (Robinson et al., 2010) in order to account for
changes in relative proportions during the bloom.
Differential expression analysis was conducted by pairing
algorithms for calculating differential expression via edgeR
(Robinson et al., 2010) and DESeq2 (Love et al., 2014), where an
estimate of common dispersion of normalized counts (edgeR) and
raw counts (DESeq2) was calculated across the three technical
replicates. Only transcripts that met three criteria were
considered differentially expressed and retained for downstream
analysis: one, determined to be significantly differentially
expressed by both algorithms (adjusted p-values < 0.01); two,
differentially expressed in the same direction (positive vs

151
negative log2 fold change) in both algorithms; three, the
difference in log fold change predicted by both algorithms less
than |1|.
Weighted gene co-expression networks were also constructed
to examine the association of expression modules, which are
clusters of genes with similar expression patterns, with bloom
periods and their correlation with environmental parameters
during both blooms using the WGCNA package (Langfelder and
Horvath, 2008). Co-expression networks were constructed using
powers of 7 and 8 for diatom and dinoflagellate network
construction, respectively. Genes with similar expression
patterns were grouped into modules using the cutreeDynamic
function with deepsplit, pamRespectsDendro, and minClusterSize
flags set to 2, FALSE, and 10, respectively. The cutheight flag
was left at the default parameter. Modules were clustered
(average method) using Pearson correlations of module
eigenvalues (first principal component of the expression
matrix), and highly similar modules were manually merged for
subsequent analyses. Functional enrichment of KEGG terms within
WGCNA modules was conducted using Cluster Profiler, which
queries current KEGG databases for functional enrichment
annotation (Wu et al., 2021). Enrichments were manually curated
when necessary.
Ordination was executed using the vegan package, v.2.7

152
(Dixon, 2003). All artwork was visualized in R studio, and most
data wrangling and visualization was executed using tools from
the Tidyverse. All custom scripts used in data analysis are
available at https://github.com/theOlligist/SMP-metaT-PairingedgeR-and-DESeq-differential-expression.
RESULTS:
Protistan community composition and dynamics:
The protistan community was sampled daily following two
upwelling events that resulted in major phytoplankton blooms
(both > 12 µg/L chlorophyll), where diatoms were dominant during
the spring of 2018 and dinoflagellates during the spring of 2019
(Ollison et al., 2022). 18S rRNA transcript sequencing and light
microscopy illustrated that Thalassiosira and Pseudo-nitzschia
were the numerically dominant taxa during 2018 and two
gymnodiniacean dinoflagellates, Margalefidinium and Akashiwo,
dominated during 2019 (Ollison et al., 2022). During each bloom,
whole community gene expression was examined during four
distinct periods: 1) Pre-bloom, 2) onset of increasing biomass
(chlorophyll) and relative abundances of 18S-V4 rRNA gene
transcripts, 3) sustained growth (maximum relative abundances
and chlorophyll concentrations), 4) bloom decline. An average of
27.2 million quality filtered reads per sample produced an
average of 2.5 million co-assembled contiguous sequences per

153
sampling day, where 50% of sequence reads were assigned taxonomy
and 30% were assigned KEGG orthology annotations (Table S1).
Consistent with dynamics characterized by 18S-V4 gene
transcripts reported by Ollison et al. (2022),
Metatranscriptome-derived rRNA illustrated that during 2018,
diatoms accounted for approximately 10% of the protistan
community on Day 1, which increased to 25% on Day 3, and reached
maximum relative abundances on Day 5 (45%), followed by a
gradual declined through Day 9 (30%) and Day 11 (20%; Figure
1A,C). Chlorophytes and haptophytes accounted for approximately
30% of the community during Day 1 and reduced to approximately
15% on Day 9 and Day 11. Dinoflagellates comprised approximately
25% of the community on average and accounted for slightly more
of the community during advanced bloom stages during the 2018
study period (Figure 1A,C).
For most of the 2019 sampling period, dinoflagellates
accounted for greater than 50% of the protistan community.
Dinoflagellates accounted for approximately 20% on Day 1,
rapidly increased in relative abundances through Day 4 (50%) and
Day 12 (75%), reached peak relative abundances (85%) on Day 18,
and declined to 70% of the community on Day 20 (Figure 1B, D).
Transcripts assigned to other photosynthetic taxa (chlorophytes,
diatoms, haptophytes) greatly declined in relative abundance
after Day 1, and collectively constituted approximately 10% of

154
the protistan community throughout the remainder of sampling
period during 2019. Changes in chlorophyll concentrations
reported by Ollison et al. (2022) recapitulated increases,
maxima, and declines of both diatom and dinoflagellate relative
abundances during 2018 and 2019, respectively (Figure 1B of
Ollison et al., 2022).
Accordingly, mostly diatom reads were recovered during
2018, whereas mostly dinoflagellate reads were recovered during
2019 (Figure 1C, D). Approximately 60% of diatom reads were
classifiable during 2018, and Thalassiosira and Pseudo-nitzschia
constituted the largest fraction of classifiable diatom
transcripts. Both taxa also accounted for a substantial fraction
of diatom reads during 2019, although diatoms cumulatively
accounted for only 4% of total reads during that year compared
with 28% during 2018. Approximately 30% of dinoflagellate reads
were classifiable during 2019, and Gymnodiniaceae, the
dinoflagellate family that encompasses both Margalefidinium spp.
and Akashiwo spp., accounted for approximately 30% of
classifiable dinoflagellate reads on average during both years.
Hereafter we refer to the dominant dinoflagellates examined in
this study (Margalefidinium and Akashiwo) as gymnodiniacean
dinoflagellates.
Contrasting diatom and dinoflagellate physiological profiles:

155
Gene expression of Thalassiosira, Pseudo-nitzschia and
gymnodiniacean dinoflagellates was examined during
aforementioned growth phases of their respective bloom and nonbloom conditions to contrast the physiological underpinnings of
diatom vs dinoflagellate growth dynamics associated with algal
blooms in the two years.
The functional composition of diatom and dinoflagellate
expression profiles revealed that approximately 2.2 million
diatom transcripts corresponded to 22,231 KEGG orthology (KO)
IDs during their bloom year (2018), and approximately 3.4
million diatom transcripts corresponded to 12,573 KO IDs during
2019. Gymnodiniacean dinoflagellates accounted for approximately
226,000 transcripts that corresponded to 2402 KO IDs during
their bloom year (2019) and approximately 33,200 transcripts
that corresponded to 2,388 KO IDs during 2018.
Non-metric multidimensional scaling (Bray-Curtis) of five
daily samples during the two years for the three algal taxa (two
diatom genera and one dinoflagellate family) based on their
composition of KO IDs (30 total functional profiles) produced
three broad groupings (Figure 2). There was a clear separation
between the functional profiles of dinoflagellates (Figure 2,
purple symbols) and diatoms. Additionally, Thalassiosira from
both years grouped with Pseudo-nitzschia profiles obtained
during 2018, while Pseudo-nitzschia during 2019 formed a

156
distinct, relatively loose grouping (Figure 2, gray diamonds).
Samples for all three taxa formed secondary groupings according
to year, where Pseudo-nitzschia profiles exhibited the greatest
dissimilarity between years.
KO IDs were assigned to functional category groupings, and
the functional profiles (based on these groupings) of
Thalassiosira and Pseudo-nitzschia were combined to further
compare diatom vs dinoflagellate physiology. Diatom functional
profiles from 2018 and 2019 contained the same functional
categories although proportions varied between years and
somewhat between sampling days, particularly during 2019 (Figure
3A, B). During 2018, the proportions of functional categories
were relatively stable across all stages, but during 2019 they
exhibited a pronounced shift in proportions after Day 4. The
shift was mostly due to the sharp increase in transcripts
associated with the genetic information processing category,
which increased in proportion from ~10% on Day 1 to ~60% on Day
12 during 2019. Approximately 90% and 98% of transcripts
associated with this category coded for ribosomal proteins in
Thalassiosira and Pseudo-nitzschia, respectively (Figure 3A vs
B; Table S1). Conversely, during 2018 the genetic information
processing category consistently accounted for approximately 25%
of diatom profiles. Transcripts associated with energy and
carbon metabolism (photosynthesis, Calvin cycle, glycolysis, TCA

157
cycle, and fatty acid metabolism) accounted for approximately
half of diatom expression profiles during 2018 and early stages
of 2019, but only accounted for approximately 25% during
advanced stages of 2019 (Figure 3A, B; Figure S1A, B).
Transcripts associated with inorganic nitrogen uptake and
assimilation accounted for approximately 5% diatom profiles on
average, where the proportion of transcripts associated with
nitrogen assimilation (GS/GOGAT) was greater than those
associated with dissolved inorganic nitrogen uptake (AMT and
NRT) during both years (Figure S2A, B). The overall proportion
of transcripts associated with nitrogen assimilation was stable
across 2018, but decreased from approximately 8% on Day 1 to
less than 1% after Day 4 during 2019 (Figure 3A, B). In contrast
to inorganic nitrogen, transcripts associated with phosphate and
the urea cycle accounted for less than 1% of the diatom profiles
during both years.
Transcripts associated with catabolic processes accounted
for approximately 5% of diatom profiles throughout 2018 and the
majority of 2019, and lysosomal processes accounted for most of
this category (Figure 3A, B; Figure S1A, B). Transcripts
associated with the catabolism of chitin, which is a nitrogenrich polymer produced by diatoms and diverse eukaryotic
lineages, and endocytosis also accounted for approximately 1% of
the diatom profiles on average during both years, a feature that

158
was unique to Thalassiosira (Figure S3).
Transcripts associated with nucleotide and amino acid
metabolism, and environmental information processing categories
collectively accounted for approximately 10% of diatom profiles
throughout 2018 and prior to Day 12 of 2019. The majority
nucleotide and amino acid metabolism-associated transcripts were
represented by nucleoside-diphosphate kinase and aspartate
kinase, which accounted for 32% and 13% of this category,
respectively, on average (Figure 3A, B).
Dinoflagellate profiles observed during 2018 and 2019 also
varied in proportion of categories as well as composition (i.e.
presence/ absence), where dinoflagellate profiles during 2018
contained fewer categories than 2019 (Figure 3C, D). Transcripts
associated with the genetic information processing category
accounted for the majority of dinoflagellate profiles (~50%)
during both years with relatively low variability compared to
diatoms during 2019. The majority of transcripts in this
category coded for snRNPs (~23%) and ribosomal proteins (~71%).
The proportion of transcripts associated with energy and
carbon metabolism accounted for ~20% of dinoflagellate profiles
during both years, less than half of the average proportion
observed in diatoms (Figure 3C, D). Notably, dinoflagellate
profiles during 2019 contained several additional energy and
carbon metabolism-related categories that were not present

159
during 2018. For example, transcripts associated with fatty acid
metabolism were unique to dinoflagellate profiles during 2019,
with the largest proportions observed following Day 4, during
the latter stages of the bloom (Figure S3D).
Transcripts associated with dissolved nitrogen uptake were
highly variable between years in dinoflagellate profiles. During
2018, transcripts associated with nitrogen uptake (NRT)
increased from 3% on Day 1 to greater than 25% by Day 11;
however, during 2019, transcripts associated with nitrogen
uptake decreased to less than 1% of dinoflagellate profiles
after Day 1 (Figure 3C, D; Figure S1C, D). Interestingly,
transcripts associated with nitrogen assimilation (GS/GOGAT)
remained approximately 5% of dinoflagellate profiles during both
years. There were no transcripts associated with the urea cycle
during 2018, but during 2019 they accounted for approximately 1%
of dinoflagellate profiles on average, with maximum expression
(~3%) on Day 12. The majority of urea cycle transcripts were for
arginase, a nitrogen-rich intermediate for ornithine and urea
biosynthesis. Similar to diatoms, transcripts associated with
phosphate were a minor component of dinoflagellate profiles
during both years, with maximum transcription (~1%) during 2019
on Day 20.
Transcripts associated with catabolism and heterotrophy
accounted for ~6% of dinoflagellate profiles on Day 1 and

160
decreased to less than 1% by Day 11 during 2018; the majority of
transcripts in this category during 2018 were associated with
SNARE complex and phagosome maturation. Conversely, throughout
2019, catabolism and heterotrophy accounted for ~10% of
dinoflagellate profiles, and the majority transcripts in this
category were associated with lysosomal processing and phagosome
maturation (Figure 3D, S1D).
The proportion of transcripts associated with
nucleotide/amino acid metabolism accounted for approximately 12%
of dinoflagellate profiles on average during both 2018 and 2019,
and the majority of these transcripts were associated with GDPD-mannose (~50%), and uroporphyrinogen decarboxylase (~11%).
Patterns of differential gene expression during diatom and
dinoflagellate blooms:
Analysis of genes that exhibited significant differential
expression by diatoms and dinoflagellates was conducted using
Day 1 from each sampling period as the pre-bloom baseline from
which to contrast gene expression levels during subsequent
growth phases of their respective blooms (diatoms in 2018 and
dinoflagellates in 2019). Examination of genes that exhibited
significant differential expression (DEGs) by diatoms and
dinoflagellates revealed that diatoms exhibited 1008 total DEGs
where approximately 60% exhibited negative log2 fold changes

161
relative to baseline gene expression (under-expressed; Figure
S4), and dinoflagellates exhibited only 225 DEGs with the
majority exhibited positive log2 fold changes relative to
baseline gene expression (over-expressed; ~72%; Figure S5, Table
S2).
The number of DEGs was unequally distributed across either
study period for both taxa. For diatoms, bloom development (Day
3) exhibited the fewest proportion of DEGs (~5%) whereas Day 5,
which coincided with maximum chlorophyll concentrations,
accounted for approximately 50% of total DEGs; most were underexpressed (Figure S4A, B). Dinoflagellates also expressed the
majority of DEGs during the period of maximum chlorophyll
concentrations on Day 18 of 2019(~30%), although most were overexpressed, and the early phase of bloom establishment (Day 4)
also accounted for the fewest total DEGs as was also the case
for diatoms (~18%; Figure S5A, B).
Diatom DEGs corresponded to 28 KO categories across four
broad scale physiological groupings, and the average log2 fold
change of most categories exhibited shifts in direction
(positive vs negative) and magnitude across bloom stages (Figure
4A). Most genes exhibiting differential expression in diatoms
were associated with energy/carbon metabolism, a category that
also accounted for the majority of diatom transcription profiles
(Figure 3A). Whereas enzymes functioning in both glycolysis and

162
the Calvin cycle (glycolysis-Calvin), and other metabolismrelated transcripts lacking further annotation remained underexpressed throughout the 2018 sampling period, the majority of
the other energy/carbon metabolism-related categories changed
magnitude and direction across bloom phases relative to Day 1
(Figure 4A). Generally, DEGs in this category were underexpressed during 2018; however, fatty acid metabolism was overexpressed for the majority of 2018, and the most over-expression
of various energy/carbon metabolism categories was observed
during Day 5, the period of maximum chlorophyll.
During Day 3, which contained the fewest diatom DEGs,
transcripts associated with the TCA cycle maintained
constitutive expression, and the majority of DEGs related to
energy/carbon metabolism were under-expressed (i.e. the average
log2 fold change of each DEG category), where DEGs associated
with the Calvin cycle were the exception, and the fatty acid
metabolism category exhibited the strongest under-expression
(Figure 4A). During Day 5, which coincided with maximum relative
abundances of transcripts, all metabolism-related categories
exhibited differential expression of which half were overexpressed. Notably, DEGs associated with photosynthesis and
glycolysis DEG categories were over-expressed relative to Day 1,
along with fatty acid metabolism which also exhibited increasing
log2 fold changes during subsequent periods. DEGs associated with

163
primary production were subsequently under-expressed through Day
9 and Day 11, a period when only fatty acid metabolism (Day 9)
and/or cellular respiration (glycolysis and TCA cycles; Day 11)
was over-expressed.
Relatively few categories related to nutrient processing (3
of 8 categories) and catabolism/heterotrophy (2 of 6 categories)
were differentially expressed on Day 3, the period that
coincided with rapid increases in diatom relative abundances
(Figure 4A, 1A). All three nutrient processing categories
exhibiting differential expression (GS/GOGAT, NRT, and Urea
cycle) were over-expressed and contained categories associated
with both dissolved inorganic nitrogen uptake (NRT),
assimilation (GS/GOGAT), and dissolved organic nitrogen
utilization (urea cycle; Figure 4A). NRT, GS/GOGAT and DEGs
associated with assimilatory nitrate reduction (ferredoxinnitrite reductase) maintained over-expression throughout the
majority of 2018, although categories related to nitrogen
assimilation decreased in magnitude of differential expression
with time (Figure 4A). The urea cycle category was overexpressed throughout the 2018 sampling period by diatoms with
the exception of Day 5.
During Day 5, all catabolism and nutrient-related
categories expressed by diatoms were differentially expressed,
and nearly half were under-expressed in both groupings, a

164
pattern similar to the energy/carbon metabolism grouping.
Notably categories associated with nitrate assimilation
(GS/GOGAT, NR, assimilatory nitrate reduction (ferredoxinnitrite reductase), and sulfite oxidase) were over-expressed,
whereas only one category associated with nitrogen uptake (NRT)
was over-expressed; AMT, urea cycle, and P metabolism were all
under-expressed on Day 5 (Figure 4A). Additionally, whereas most
DEGs associated with catabolism and heterotrophy remained underexpressed throughout 2018, chitinase and endocytosis were only
over-expressed when found significantly differentially expressed
on Day 5 and Day 11. Most categories associated with other
cellular processes were under-expressed after Day 3.
Dinoflagellates during 2019 exhibited differential
expression (relative to Day 1) across 17 categories, a number
smaller than what was observed in diatoms (28 categories; Figure
4B). Six categories associated with energy/carbon metabolism
exhibited differential expression during 2019. On Day 4,
photosynthesis, fatty acid metabolism, and DEGs associated with
energy/carbon metabolism but lacking further functional
classification (“Other energy/carbon metabolism” category) were
the only categories exhibiting differential expression.
Transcripts associated with fatty acid metabolism were only
under-expressed on average on Day 4 (2019), but those associated
with glycolysis-gluconeogenesis were under-expressed for the

165
majority of the sampling period. Conversely, transcripts
associated with photosynthesis and other carb/energy metabolism
were over-expressed on average during Day 4 and during other
advanced bloom phases (Figure 4B).
Four functional categories associated with nutrient
processing exhibited differential expression. Whereas GS/GOGAT
and urea cycle were over-expressed throughout the 2019 sampling
period after Day 1, NRT were under-expressed after Day 4. DEGs
associated phosphate metabolism (P metabolism) were overexpressed on Day 20, a period of bloom decline during 2019
(Figure 4B).
The majority of differentially expressed categories
associated with catabolism/heterotrophy were over-expressed in
dinoflagellates relative to Day 1 expression levels (Figure 4B).
V-type ATPase, which acidifies lysosomes, was over-expressed
during periods of increased relative abundances, but was
subsequently under-expressed on Day 18, a day that coincided
with maximum chlorophyll concentrations during 2019. Lysosomal
processing was over-expressed on Day 12 and subsequently underexpressed throughout Day 18 and Day 20. Phagosome maturation
DEGs were over-expressed following Day 4, and DEGs associated
with motility were over-expressed on Day 20.
The majority of categories associated with other cellular
processes were over-expressed; however, DEGs associated with

166
nucleotide and amino acid metabolism were over-expressed during
periods of increasing relative abundances (Day 2 and Day 12) and
under-expressed during periods coincident with sustained growth
(Day 18) and decline (Day 20). Notably, por was also overexpressed at the onset of bloom decline (Day 20), a pattern
similar to observations in diatoms during 2018 (Figure 4).
Contrasting diatom and dinoflagellate gene expression modules:
Weighted gene co-expression network analysis (WGCNA) was
conducted using diatom (Thalassiosira and Pseudo-nitzschia) and
gymnodiniacean dinoflagellate gene expression to further
investigate patterns of gene expression that were unique to each
growth period and to identify correlations with environmental
parameters. WGCNA is a technique for identifying clusters of
genes with similar expression dynamics (co-expression modules)
and correlating the module eigengenes, which represent the first
principal component of the expression matrix, with environmental
parameters (Langfelder and Horvath, 2008).
Approximately 26,000 diatom KEGG IDs during 2018 (15,781,
Thalassiosira; 10,278 Pseudo-nitzschia) and 2,400 gymnodiniacean
dinoflagellate KEGG IDs during 2019 were clustered into thirtyone and twenty-two modules, respectively, of varying sizes
(Figure S6A and S7A). Nine and five diatom and dinoflagellate
modules, respectively, exhibited strong correlations with

167
sampling periods that were consistent across all replicates and
were kept for further analysis (Figure S6B and S7B).
Further investigation of module eigengenes revealed that 2
diatom modules (1 and 5) were highly expressed during periods of
increases in chlorophyll and high relative abundance of rRNA
transcripts (Day1 and Day 3) and were strongly correlated with
phosphate and nitrite+nitrate concentrations (p < 0.01; Figure
S6B). Only module 46, a dinoflagellate module, was strongly
correlated with nutrient concentrations (p < 0.01; Figure, S7B).
Functional enrichment analysis for both algal groups was mostly
inconclusive due to lack of annotations, a shortcoming extremely
pronounced in dinoflagellates. In diatoms however, whereas
enrichments in module 1 contained mostly biosynthesis processes,
proteolysis enrichments were a consistent feature of the modules
that correlated with advanced bloom stages. Additionally,
enrichments associated with human diseases were most enriched in
modules 1 and 31, which correlated with peak bloom and decline
during 2018 (Figure S8). There was marginal overlap between
diatom and dinoflagellate modules, and the largest overlapping
set of KEGG IDs was between modules that were highly expressed
during bloom dominance (Figure 5).

168
DISCUSSION:
The daily dynamics of the protistan community during two
contrasting algal blooms were recently characterized via 18S
rRNA gene transcript sequencing (Ollison et al., 2022). This
study expands on the findings of Ollison et al. (2022) by
examining expression patterns of KEGG annotated transcripts
associated with changes in the relative abundances of
numerically dominant diatom (2018) and dinoflagellate (2019)
taxa during key phases of their respective blooms. The KEGG
framework enabled broad comparisons between these
phylogenetically distant algal guilds.
Diatoms Thalassiosira and Pseud-nitzschia were found to be
the numerically dominant diatom taxa during 2018, while
Margalefidinium and Akashiwo were the numerically dominant
dinoflagellate taxa during 2019. Whereas Thalassiosira and
Pseudo-nitzschia belong to phylogenetically distinct families,
Margalefidinium and Akashiwo are both gymnodiniacean
dinoflagellates. Analysis of gymnodiniacean dinoflagellate gene
expression at the genus level was limited by the availability of
dinoflagellate annotations, especially for Margalefidinium.
Physiologies underpinning diatom and dinoflagellate assemblages
were distinct:
Diatoms and dinoflagellates exhibited distinct

169
transcriptional makeups regardless of year as exhibited by nonmetric multi-dimensional scaling (Bray-Curtis) of gene
expression profiles of Thalassiosira, Pseudo-nitzschia and
gymnodiniacean dinoflagellates (combined Akashiwo and
Margalefidinium transcripts) during both 2018 and 2019 blooms.
This NMDS resulted in three groupings where the primary grouping
(NMDS1) was according to guild (diatom vs dinoflagellate), a
finding consistent with results of other recent comparative
studies (Figure 2) (Alexander et al., 2015b; Harke et al., 2017;
Zhang et al., 2019; Metegnier et al., 2020). For example, Harke
et al. (2017) examined the transcriptional responses of species
from phylogenetically distinct algal guilds to nutrient stress
and found little variation between the responses of two
distantly related mixotrophic dinoflagellate species, but in
contrast found distinct physiological responses of these
dinoflagellates compared to diatoms and haptophytes.
Additionally, during the current study, WGCNA of Thalassiosira
and Pseudo-nitzschia gene expression during 2018 and
gymnodiniacean dinoflagellate gene expression during 2019
produced clusters of genes with similar expression dynamics
(modules) that corresponded with each aforementioned growth
phases during their respective bloom. Few KEGG IDs were shared
between diatom and dinoflagellate modules despite their
association with analogous growth phases, which underscores

170
their physiological distinction (Figure 5).
Shifting physiological priorities of diatoms during bloom
succession:
The majority of diatom gene expression was dedicated to
carbohydrate and fatty acid metabolism, especially during the
2018 bloom event when they were numerically dominant (Figure 3A,
B), a finding that has been previously reported (Alexander et
al., 2015a; Zhang et al., 2019). Additionally, energy/carbon
metabolism categories that were differentially expressed by
diatoms (here, Thalassiosira and Pseudo-nitzschia) were
generally under-expressed during 2018. Most over-expression of
categories within this grouping during 2018 was observed on Day
5 (max chlorophyll), the period of maximum relative abundances
and chlorophyll concentrations (Figure 4A). All energy/carbon
metabolism categories were subsequently under-expressed at the
time of bloom decline (Day 9) with the exception of fatty acid
metabolism, which were over-expressed throughout most of 2018.
Genes associated with glycolysis and the TCA cycle were both
over-expressed on Day 11.
Most nutrient processing categories that exhibited
differential expression in diatoms during 2018 were overexpressed (Figure 4A). Few categories were differentially
expressed on Day 3, which was a day that coincided with

171
increased relative abundance of diatoms during 2018, but all
categories were differentially expressed on Day 5. Notably all
genes associated with nitrogen assimilation (GS/GOGAT, NR,
Sulfite Oxidase, and other genes involved in assimilatory
Nitrate reduction) were over-expressed during that period, and
genes associated with phosphate, organic nitrogen (urea cycle),
and extracellular ammonium uptake were under-expressed (Figure
4A). Fewer categories associated with nitrogen assimilation were
differentially expressed by Day 11, but all categories
associated with ammonium and nitrate uptake (AMT, NRT) as well
as with the degradation of chitin were over-expressed.
Most of the seven categories associated with other cellular
processes were under-expressed. However, perhaps unsurprisingly,
the nucleotide and amino acid metabolism category was overexpressed during the increase of diatom relative abundances (Day
3), and subsequently under-expressed during the periods of
maximum abundance and decline.
Over-expression of genes associated with nucleotide and
amino acid biosynthesis during a period of increasing relative
abundances is consistent with diatom cell division and
proliferation following nutrient upwelling as noted by Ollison
et al. (Figure 1A of Ollison et. al. 2022). Consistent with
dissolved inorganic nitrogen drawdown and subsequent
assimilation, all categories associated with nitrate and

172
ammonium assimilation were subsequently over-expressed, and
those associated with phosphate and organic nitrogen (urea
cycle) were under-expressed on Day 5 (Figure 4A).
On Day 5, there was also a general over-expression of
energy/carbon metabolism genes in diatoms, including genes
involved in photosynthesis, the Calvin cycle, glycolysis, and
fatty acid metabolism. The increase in organic carbon production
at the height of the bloom presumably reflects the uptake and
utilization of new, upwelled nitrogen for primary production,
and the necessity of organic carbon skeletons for nitrogen
assimilation (Hockin et al., 2012). All energy/carbon metabolism
categories were under-expressed following depleted inorganic
nitrogen concentrations (Ollison et al. 2022), and the number of
differentially expressed categories associated with nitrogen
assimilation decreased, and categories associated with ammonium
and nitrate uptake were over-expressed. Differential expression
patterns of both nutrient processing and energy/carbon
metabolism categories during 2018 for diatoms mirrored
proportional changes during 2019, where the overall proportion
of energy/carbon metabolism categories were depressed and
ammonium transporter was elevated during diatom decline under
nutrient depleted conditions observed during the advanced stages
of 2019 (Figure 3B, S1, S3). Studies examining the
transcriptional response of diatoms to nitrogen limitation in

173
culture have reported similar under-expression of genes
associated with photosynthesis and central carbohydrate
metabolism (Bender et al., 2014; Harke et al., 2017).
Nitrogen is an essential nutrient that is required for the
biosynthesis of chlorophyll. Down-regulation of photosynthesis
under nitrogen limitation is universal across distantly related
photosynthetic organisms as chlorophyll is a nitrogenous
compound, and decreasing its synthesis might lower nitrogen
demand. Therefore, the under-expression of transcripts
associated with photosynthesis and the Calvin cycle in this
study is not surprising, and is in agreement with other studies
(Mock et al., 2008; Hockin et al., 2012; Schmollinger et al.,
2014). Likewise, upregulation of genes associated with nutrient
uptake and catabolism of nitrogenous compounds such as chitin
and amino acids has been reported under nutrient limitation
across an array of phylogenetically distinct phytoplankton
(Frischkorn et al., 2014; Liu et al., 2015; Cooper et al., 2016;
Alexander et al., 2020).
Genes associated with the degradation of chitin were overexpressed in diatoms during Day 11, a period of decline under
depleted inorganic nitrogen (Figure 4A). The proportion of
chitinase transcripts were also greatly elevated during the
advanced, low inorganic nitrogen stages of 2019 (Figure S2B;
S3B). Chitin is a nitrogenous compound produced by some diatoms

174
as a means of controlling buoyancy by modulating the length of
chitin spicules through the activity of chitinase (Durkin et
al., 2009). Thalassiosira cells produce chitin fibers that
extend from the theca through specialized pores (Blackwell et
al., 1967; Herth and Schnepf, 1982), and chitinase has been
identified in the genome of T. pseudonana; however, chitinase
has not be identified in Pseudo-nitzschia (Armbrust, 2004; Di
Dato et al., 2015). Chitin degradation may produce intracellular
nitrogen that can be reallocated during periods of deficiency.
Accordingly, the expression of chitinase by Thalassiosira during
periods of nitrogen deficiency during both years of this study
is consistent with recycled nitrogen from chitin degradation as
an alternative means of nutrient acquisition for sustained
growth under deficient extracellular inorganic nitrogen.
Interestingly, genes putatively associated with endocytosis were
over-expressed with each instance of chitinase over-expression.
Diatoms are believed to be purely phototrophic, although they
evolved from phagotrophic origins (Paul G. Falkowski, 2004;
Armbrust, 2009). The genes putatively associated with
endocytosis may be deeply conserved and have been coopted for
different purposes in the phagotrophic relatives of diatoms
while serving in non-phagotrophic, catabolic pathways for
assimilating intracellular nutrients in diatoms.

175
Diatoms are physiologically divergent under stress:
Thalassiosira and Pseudo-nitzschia were the numerically
dominant diatom taxa following upwelling during 2018. Ordination
of their gene expression profiles grouped all Thalassiosira
expression profiles (both 2018 and 2019) with Pseudo-nitzschia
expression profiles observed during 2018 (Figure 2); however,
Pseudo-nitzschia gene expression observed during 2019 formed an
independent grouping (Figure 2). The proportion of functional
categories within gene expression profiles of each diatom taxa
illustrated that their profiles were mostly indistinguishable
during 2018; however, their profiles were divergent during the
later portion of the algal bloom in 2019 (Figure S3). The most
salient differences were chitinase expression by Thalassiosira,
and the spike in the proportion of transcripts associated with
the genetic information processing category by Pseudo-nitzschia
during advanced stages of 2019, most of which coded for
ribosomal proteins and tRNA synthetases. As noted in the
previous section, Thalassiosira is a known producer of chitin
and contains genes for the synthesis of chitinase within its
genome (Armbrust, 2004; Durkin et al., 2009). We speculate that
this species persisted more effectively than Pseudo-nitzschia
under deficient inorganic nitrogen during advanced stages of
2019 through the use of recycled nitrogen from chitin
degradation.

176
Thalassiosira and Pseudo-nitzschia evolved from
phylogenetically distinct families whose last common ancestor
may have diverged nearly 200 MYA (Paul G. Falkowski, 2004;
Strassert et al., 2021). The grouping of their expression
profiles during 2018 and their dissimilarity during 2019
indicate that these two deeply diverged diatom taxa exhibited
similar physiology under the upwelling conditions observed
during 2018, while their physiologies diverged under more
pronounced nutrient depletion observed during 2019. Studies
contrasting genomes and transcriptomes among phylogenetically
distinct diatoms have illustrated conserved traits that likely
underpins their ability to thrive under high-nutrient settings
such as costal upwelling regimes (Armbrust, 2004; Bowler et al.,
2008; Bender et al., 2014; Alexander et al., 2015a; Di Dato et
al., 2015). For example, Bender et al. (2014) found similarities
in the regulation of carbohydrate and nutrient utilization among
three phylogenetically distant diatoms—Thalassiosira, Pseudonitzschia, and Fragilariopsis—that may indicate pathway-level
similarities governing diatom responses to environmental cues
despite evolved differences in morphology and physicochemical
requirements (Bender et al., 2014). However, chitinase
expression by Thalassiosira during nutrient deficiency was one
distinguishing feature identified in this study through
contrasting diatom gene expression patterns (Figure S3). Our

177
results reveal that while the physiological strategies of
Thalassiosira and Pseudo-nitzschia may be similar under
favorable growth conditions, subtle differences in their
physiologies may result in differential success under nutrient
stress.
Shifting physiological priorities of dinoflagellates during
their bloom:
Ollison et al. (2022) reported stronger upwelling
conditions during the diatom bloom in 2018, and prolonged
nitrogen depletion during the dinoflagellate bloom in 2019.
Gymnodiniacean dinoflagellates Akashiwo and Margalefidinium were
minor components of the protistan community during 2018, but
during 2019 they bloomed to high cell abundances and the maximum
recorded chlorophyll concentration during their bloom was 60 µg/L
chlorophyll, a concentration approximately six times the major
bloom threshold for Santa Monica Bay (Seubert et al., 2013).
Surprisingly, comparatively few dinoflagellate transcripts
exhibited differential expression (225 dinoflagellate vs 1,138
diatom DEGs), and the majority were over-expressed relative to
Day 1 during 2019 (Figure 4B). The exceptions were genes
involved in glycolysis and nitrate uptake (NRT), both of which
maintained under-expression throughout advanced bloom stages.
The observed low levels of transcriptional activity in

178
dinoflagellates has been previously reported in other studies
that have examined the transcriptional responses of
dinoflagellates to environmental cues (Alexander et al., 2015b;
Murray et al., 2016; Harke et al., 2017; Cohen et al., 2021).
Dinoflagellates exhibited increases in relative abundances
coincident with increases in chlorophyll concentrations on Day
4. The observed over-expression of genes associated with
photosynthesis, nucleotide and amino acid biosynthesis, and
several ribosomal proteins on this day is expected under
conditions of population growth (Figure 1B, 4B).
Genes associated with inorganic nitrate uptake were a
relatively minor component of dinoflagellate profiles during
2019, and were under-expressed throughout advanced bloom stages;
however, genes associated with ammonium assimilation (GS/GOGAT)
and the urea cycle (arginase) were over-expressed throughout
2019 (Figure 3D, 4B). Arginine, an amino acid containing a
nitrogenous side chain, is produced in the urea cycle as a
precursor to ornithine and urea, and therefore may represent a
possible nitrogen storage molecule (Allen et al., 2011). Overexpression of arginase may represent the catabolic breakdown of
arginine and subsequent assimilation of organic ammonium (Hockin
et al., 2012). The lack of gene expression associated with
dissolved inorganic nitrogen uptake during 2019 may further

179
indicate a preference for organic nitrogen sources during the
2019 bloom.
We speculate, based on their transcriptional patterns, that
dinoflagellates during the 2019 bloom may have acquired
significant nutrients for growth via heterotrophy (i.e.
mixotrophically). Mixotrophy, which is the combination of
heterotrophy and phototrophy, has been reported in Akashiwo and
Margalefidinium (Nielsen and Kiørboe, 2015; López-Cortés et al.,
2019; Yang et al., 2020). Margalefidinium in particular is
thought to reach high abundances in natural ecosystems only when
dissolved nutrients are supplemented with phagotrophy (Hofmann
et al., 2021). The elevated proportion of transcripts putatively
associated with phagotrophic consumption in dinoflagellate
expression profiles in addition to their over-expression during
their bloom under depleted inorganic nitrogen is consistent with
phagotrophy as a means of acquiring organic carbon and nitrogen
(Figure 4; Figure S1).
The same categories associated with heterotrophy and the
utilization of organic nitrogen were less prominent in
dinoflagellate expression profiles during 2018, and the elevated
proportion of dinoflagellate transcripts associated with
dissolved inorganic nitrogen uptake (NRT) and assimilation (NR)
during upwelling conditions of 2018 are consistent with greater
reliance on dissolved inorganic nitrogen during that year

180
(Figure 3C vs D; Figure S2C, D). Diatoms typically out compete
dinoflagellates under high-nutrient upwelling conditions due to
their faster intrinsic growth rates, consistent with our
observation of diatom dominance following the strong upwelling
in 2018 (Margalef, 1978).
Nonetheless, it is unclear why these mixotrophic
dinoflagellates appeared to prioritize dissolved inorganic
nitrogen during this year, based on their transcriptional
profiles, whereas organic and heterotrophically acquired
nitrogen was prioritized during 2019. One possibility is the
lack of suitable prey during 2018 relative to 2019. Ollison et
al. (2022) reported that parasites were particularly pronounced
during 2019 and increased in relative proportion coincident with
the decline of non-dinoflagellate algae (Figure 3B of Ollison
2022)(Ollison et al., 2022). They hypothesized that zoosporic
cercozoan parasites may have facilitated the bloom of
dinoflagellates either indirectly through the production of
dissolved organic nutrients from diatom lysis, or directly as
prey for phagotrophic consumption. Free-swimming zoosporic
parasites are known to be grazed by microzooplankton and are
thought to be an excellent food source in terms of shape, size
and content (Kagami et al., 2007; Thieltges et al., 2013; Kagami
et al., 2014). Although free-swimming parasites acting as prey
for mixotrophic dinoflagellates has yet to be demonstrated, gene

181
expression measured during this study is consistent with these
dinoflagellates consuming prey during their bloom in 2019,
perhaps due to higher abundances of parasites in that year but
not 2018. More work examining these important agents is a
necessary next step towards elucidating this possibility.
Categories associated with genetic information processing
were over-expressed throughout all phases of the dinoflagellate
bloom during 2019, and accounted for nearly half of
dinoflagellate expression profiles (Figure 3C,D, 4B). The
majority of transcripts in this category were genes that coded
for snRNPs and ribosomal proteins, which are proteins involved
in pre-mRNA splicing and translation, respectively, and is
indicative of highly active splicing machinery coupled with
protein translation in these species. The majority of
dinoflagellate genes are thought to be post-transcriptionally
regulated with pre-mRNA transcripts processed through spliced
leader trans-splicing (Zhang et al., 2009; Murray et al., 2016).
Post-transcriptional regulation may explain the low differential
expression of genes in dinoflagellates here and across many
studies. Alternative splicing is a hallmark of eukaryotic posttranscriptional regulation in which alternatively spliced premRNA transcripts (i.e. introns and exons) result in multiple
distinct mRNA transcript isoforms with distinct, and sometimes
antagonistic fates. Expressed isoforms may also be down-

182
regulated through the splicing of poison exons (Gilbert, 1978;
Lareau et al., 2007; Wang and Burge, 2008). Additionally, high
numbers of introns per gene have been reported dinoflagellates,
and a recent study that reconstructed intron evolution in five
dinoflagellate genomes found evidence for recently active
Introners, which are a type of genetic element that creates
copies of itself that insert into many genes across the genome
(Roy et al., 2023). The large genomes of dinoflagellates present
formidable challenges in the study of their gene regulation.
Accordingly, over 50% of dinoflagellate transcripts in this
study lacked gene function or deeper taxonomic classification.
However, sequencing of more non-model dinoflagellate genomes
would improve our ability to investigate post-transcriptional
gene regulation in this ecologically important guild.
FIGURES:
Figure Legends:
Figure 1: Gene expression dynamics of protistan communities at
the Santa Monica Pier on five days during spring 2018 (A) and
spring 2019 (B). The proportion of genes transcribed by the
diatom assemblage (solid line) and dinoflagellate assemblage
(dashed line) during 2018 (C) and 2019 (D).

183
Figure 2: Non-metric multidimensional scaling (Bray-Curtis) of
gymnodiniacean dinoflagellate (Gymnodiniaceae; purple) and
diatom (Thalassiosira in yellow and Pseudo-nitzschia in grey)
KEGG ID’d expression profiles during 2018 (circles) and 2019
(diamonds).
Figure 3: Relative proportion of KEGG ID’d transcripts on five
days during 2018 (A,C) and 2019 (B,D) grouped as functional
categories that were expressed by diatoms (A,B) and
dinoflagellates (C,D) during 2018 and 2019. Arrows indicate
years of numerical dominance of diatoms (A) and dinoflagellates
(D).
Figure 4: Differential expression (average log2 fold change
relative to Day 1) of 36 functional categories grouped into four
broad physiological processes of numerically dominant diatoms
(A) vs dinoflagellates (B) during periods (x-axis) of their
bloom in 2018 vs 2019, respectively. Nitrate+nitrite (Nitrogen),
phosphate, and chlorophyll concentrations are shown in the top
three rows. The four physiological processes are color-coded in
the left-hand vertical column.
Figure 5: UpSet plot illustrating the overlap of KEGG IDs within
diatom (orange) and dinoflagellate (black) expression modules.

184
Horizontal bars (left) indicate the number of KEGG IDs within a
module (based on WGCNA), and vertical bars indicate the number
of KEGG IDs within the set indicated by the dots and connecting
lines in the lower panel: single dots indicate single modules,
and multiple connected dots indicate overlapping sets.
Figure S1: High resolution relative proportion of KEGG ID’d
transcripts on five days during 2018 (A,C) and 2019 (B,D)
grouped as categories that were expressed by diatoms (A,B) and
dinoflagellates (C,D) during 2018 and 2019. Arrows indicate
years of numerical dominance of diatoms (A) and dinoflagellates
(D).
Figure S2: Relative proportion of diatom (A,B) and
dinoflagellate (C,D) functional categories associated with
nutrient utilization.
Figure S3: High resolution relative proportion of KEGG ID’d
transcripts on five days during 2018 (A,C) and 2019 (B,D)
grouped as categories that were expressed by Thalassiosira (A,B)
and Pseudo-nitzschia (C,D) during 2018 and 2019.
Figure S4: Number and proportion of differentially expressed
diatom genes across sampling days (A), in addition to the

185
fraction (B) and distribution (C) of differentially expressed
genes with positive (purple) vs negative (yellow) log2 fold
change values relative to Day 1 during 2018.
Figure S5: Number and proportion of differentially expressed
dinoflagellate genes across sampling days (A), in addition to
the fraction (B) and distribution (C) of differentially
expressed genes with positive (purple) vs negative (yellow) log2
fold change values relative to Day 1 during 2019.
Figure S6: Module gene expression across all replicates
collected on each sampling day (A) and correlations between
module eigengenes and nutrient concentrations (B) during 2018.
Only module eigengene – environment correlations with p-values <
0.01 are shown. Modules were constructed from Thalassiosira and
Pseudo-nitzschia gene expression during 2018.
Figure S7: Module gene expression across all replicates
collected on each sampling day (A) and correlations between
module eigengenes and nutrient concentrations (B) during 2019.
Only module eigengene – environment correlations with p-values <
0.01 are shown. Modules were constructed from gymnodiniacean
dinoflagellate gene expression during 2019.

186
Figure S8: Functional terms that were significantly enriched in
diatom modules (Thalassiosira and Pseudo-nitzschia) during 2018
(left panel) and gymnodiniacean dinoflagellate modules during
2019 (right panel; GeneRatio > 0.025 and p-value < 0.01).
Figures:
Figure 1

187
Figure 2

188
Figure 3

189
Figure 4

190
Figure 5

191
Figure S1

192
Figure S2

193
Figure S3

194
Figure S4

195
Figure S5

196
Figure S6
Figure S7

197
Figure S8

198
REFERENCES:
Alexander, H., Jenkins, B.D., Rynearson, T.A., and Dyhrman, S.T.
(2015a) Metatranscriptome analyses indicate resource
partitioning between diatoms in the field. Proc Natl Acad Sci U
S A 112: E2182-2190.
Alexander, H., Rouco, M., Haley, S.T., and Dyhrman, S.T. (2020)
Transcriptional response of Emiliania huxleyi under changing
nutrient environments in the North Pacific Subtropical Gyre.
Environmental Microbiology 22: 1847-1860.
Alexander, H., Rouco, M., Haley, S.T., Wilson, S.T., Karl, D.M.,
and Dyhrman, S.T. (2015b) Functional group-specific traits drive
phytoplankton dynamics in the oligotrophic ocean. Proc Natl Acad
Sci U S A 112: E5972-5979.
Allen, A.E., Dupont, C.L., Oborník, M., Horák, A., Nunes-Nesi,
A., McCrow, J.P. et al. (2011) Evolution and metabolic
significance of the urea cycle in photosynthetic diatoms. Nature
473: 203-207.
Armbrust, E.V. (2009) The life of diatoms in the world's oceans.
Nature 459: 185-192.
Armbrust, V.E. (2004) The Genome of the Diatom Thalassiosira
Pseudonana: Ecology, Evolution, and Metabolism.
Bender, S.J., Durkin, C.A., Berthiaume, C.T., Morales, R.L., and
Armbrust, E.V. (2014) Transcriptional responses of three model
diatoms to nitrate limitation of growth. Frontiers in Marine
Science 1.
Blackwell, J., Parker, K.D., and Rudall, K.M. (1967) Chitin
Fibres of the Diatoms Thalassiosira fluviatilis and Cyclotella
cryptica. Journal of Molecular Biology 28: 383-385.
Bolger, A.M., Lohse, M., and Usadel, B. (2014) Trimmomatic: a
flexible trimmer for Illumina sequence data. Bioinformatics 30:
2114-2120.
Bowler, C., Allen, A.E., Badger, J.H., Grimwood, J., Jabbari,
K., Kuo, A. et al. (2008) The Phaeodactylum genome reveals the
evolutionary history of diatom genomes. Nature 456: 239-244.
Buchfink, B., Xie, C., and Huson, D. (2015) Fast and sensitive
protein alignment using DIAMOND. Nature Methods 12: 59-69.

199
Capone, D.G., and Hutchins, D.A. (2013) Microbial
biogeochemistry of coastal upwelling regimes in a changing
ocean. Nature Geoscience 6: 711-717.
Caron, D.A. (2016) Mixotrophy stirs up our understanding of
marine food webs. Proc Natl Acad Sci U S A 113: 2806-2808.
Checkley, D.M., and Barth, J.A. (2009) Patterns and processes in
the California Current System. Progress in Oceanography 83: 49-
64.
Cohen, N.R. (2017) Diatom Transcriptional and Physiological
Responses to Changes in Iron Bioavailability across Ocean
Provinces. Frontiers in marine biology.
Cohen, N.R., Alexander, H., Krinos, A.I., Hu, S.K., and Lampe,
R.H. (2022) Marine Microeukaryote Metatranscriptomics: Sample
Processing and Bioinformatic Workflow Recommendations for
Ecological Applications. Frontiers in Marine Science 9.
Cohen, N.R., McIlvin, M.R., Moran, D.M., Held, N.A., Saunders,
J.K., Hawco, N.J. et al. (2021) Dinoflagellates alter their
carbon and nutrient metabolic strategies across environmental
gradients in the central Pacific Ocean. Nature Microbiology 6:
173-186.
Cooper, J.T., Sinclair, G.A., and Wawrik, B. (2016)
Transcriptome Analysis of Scrippsiella trochoidea CCMP 3099
Reveals Physiological Changes Related to Nitrate Depletion.
Front Microbiol 7: 639.
Di Dato, V., Musacchia, F., Petrosino, G., Patil, S., Montresor,
M., Sanges, R., and Ferrante, M.I. (2015) Transcriptome
sequencing of three Pseudo-nitzschia species reveals comparable
gene sets and the presence of Nitric Oxide Synthase genes in
diatoms. Sci Rep 5: 12329.
Dixon, P. (2003) VEGAN, a package of R functions for community
ecology. Journal of Vegetation Science 14: 927-930.
Durkin, C.A., Mock, T., and Armbrust, E.V. (2009) Chitin in
Diatoms and Its Association with the Cell Wall. Eukaryotic Cell
8: 1038-1050.
Field, C.B., Behrenfeld, M.J., James, T., Randerson, J.T., and
Falkowski, P.G. (1998) Primary Production of the Biosphere:
Integrating Terrestrial and Oceanic Components. Science 281:
237-240.

200
Frischkorn, K.R., Harke, M.J., Gobler, C.J., and Dyhrman, S.T.
(2014) De novo assembly of Aureococcus anophagefferens
transcriptomes reveals diverse responses to the low nutrient and
low light conditions present during blooms. Front Microbiol 5:
375.
Geisen, S., Tveit, A.T., Clark, I.M., Richter, A., Svenning,
M.M., Bonkowski, M., and Urich, T. (2015) Metatranscriptomic
census of active protists in soils. ISME J 9: 2178-2190.
Gilbert, W. (1978) Why genes in pieces. Nature.
Guillou, L., Bachar, D., Audic, S., Bass, D., Berney, C.,
Bittner, L. et al. (2013) The Protist Ribosomal Reference
database (PR2): a catalog of unicellular eukaryote small subunit rRNA sequences with curated taxonomy. Nucleic Acids Res 41:
D597-604.
Harke, M.J., Juhl, A.R., Haley, S.T., Alexander, H., and
Dyhrman, S.T. (2017) Conserved Transcriptional Responses to
Nutrient Stress in Bloom-Forming Algae. Frontiers in
Microbiology 8.
Harke, M.J., Frischkorn, K.R., Hennon, G.M.M., Haley, S.T.,
Barone, B., Karl, D.M., and Dyhrman, S.T. (2021) Microbial
community transcriptional patterns vary in response to mesoscale
forcing in the North Pacific Subtropical Gyre. Environmental
Microbiology 23: 4807-4822.
Herth, W., and Schnepf, E. (1982) Chitin-Fibril Formation in
Algae. In Cellulose and Other Natural Polymer Systems. Brown,
R.M. (ed). Boston, MA.: Springer.
Hockin, N.L., Mock, T., Mulholland, F., Kopriva, S., and Malin,
G. (2012) The Response of Diatom Central Carbon Metabolism to
Nitrogen Starvation Is Different from That of Green Algae and
Higher Plants Plant Physiology 158: 299-312.
Hofmann, E.E., Klinck, J.M., Filippino, K.C., Egerton, T.,
Davis, L.B., Echevarría, M. et al. (2021) Understanding controls
on Margalefidinium polykrikoides blooms in the lower Chesapeake
Bay. Harmful Algae 107: 102064.
Hu, S.K., Liu, Z., Alexander, H., Campbell, V., Connell, P.E.,
Dyhrman, S.T. et al. (2018) Shifting metabolic priorities among
key protistan taxa within and below the euphotic zone. Environ
Microbiol 20: 2865-2879.

201
Irwin, A.J., Nelles, A.M., and Finkel, Z.V. (2012) Phytoplankton
niches estimated from field data. Limnology and Oceanography 57:
787-797.
Kagami, M., Miki, T., and Takimoto, G. (2014) Mycoloop: chytrids
in aquatic food webs. Front Microbiol 5: 166.
Kagami, M., de Bruin, A., Ibelings, B.W., and Van Donk, E.
(2007) Parasitic chytrids: their effects on phytoplankton
communities and food-web dynamics. Hydrobiologia 578: 113-129.
Keeling, P.J., Burki, F., Wilcox, H.M., Allam, B., Allen, E.E.,
Amaral-Zettler, L.A. et al. (2014) The Marine Microbial
Eukaryote Transcriptome Sequencing Project (MMETSP):
illuminating the functional diversity of eukaryotic life in the
oceans through transcriptome sequencing. PLoS Biol 12: e1001889.
Kopylova, E., Noé, L., and Touzet, H. (2012) SortMeRNA: fast and
accurate filtering of ribosomal RNAs in metatranscriptomic data.
Bioinformatics 28: 3211-3217.
Langfelder, P., and Horvath, S. (2008) WGCNA: an R package for
weighted correlation network analysis. BMC Bioinformatics 9:
559.
Lareau, L.F., Inada, M., Green, R.E., Wengrod, J.C., and
Brenner, S.E. (2007) Unproductive splicing of SR genes
associated with highly conserved and ultraconserved DNA
elements. Nature 446: 926-929.
Li, D., Liu, C.M., Luo, R., Sadakane, K., and Lam, T.W. (2015)
MEGAHIT: an ultra-fast single-node solution for large and
complex metagenomics assembly via succinct de Bruijn graph.
Bioinformatics 31: 1674-1676.
Liu, Z., Koid, A.E., Terrado, R., Campbell, V., Caron, D.A., and
Heidelberg, K.B. (2015) Changes in gene expression of Prymnesium
parvum induced by nitrogen and phosphorus limitation. Front
Microbiol 6: 631.
López-Cortés, D.J., Núñez Vázquez, E.J., Dorantes-Aranda, J.J.,
Band-Schmidt, C.J., Hernández-Sandoval, F.E., Bustillos-Guzmán,
J.J. et al. (2019) The State of Knowledge of Harmful Algal
Blooms of Margalefidinium polykrikoides (a.k.a. Cochlodinium
polykrikoides) in Latin America. Frontiers in Marine Science 6.

202
Love, M.I., Huber, W., and Anders, S. (2014) Moderated
estimation of fold change and dispersion for RNA-seq data with
DESeq2. Genome Biology 15.
Margalef, R. (1978) Life-forms of phytoplankton as survival
alternatives in an unstable environment. Oceanologica ACTA 1:
493-509.
Metegnier, G., Paulino, S., Ramond, P., Siano, R., Sourisseau,
M., Destombe, C., and Le Gac, M. (2020) Species specific gene
expression dynamics during harmful algal blooms. Scientific
Reports 10.
Mock, T., Samanta, M.P., Iverson, V., Berthiaume, C., Robison,
M., Holtermann, K. et al. (2008) Whole-genome expression
profiling of the marine diatom Thalassiosira pseudonana
identifies genes involved in silicon bioprocesses. Proceedings
of the National Academy of Sciences 105: 1579-1584.
Murray, S.A., Suggett, D.J., Doblin, M.A., Kohli, G.S., Seymour,
J.R., Fabris, M., and Ralph, P.J. (2016) Unravelling the
functional genetics of dinoflagellates: a review of approaches
and opportunities. Perspectives in Phycology 3: 37-52.
Nielsen, L.T., and Kiørboe, T. (2015) Feeding currents
facilitate a mixotrophic way of life. The ISME Journal 9: 2117-
2127.
Ollison, G.A., Hu, S.K., Mesrop, L.Y., DeLong, E.F., and Caron,
D.A. (2021) Come rain or shine: Depth not season shapes the
active protistan community at station ALOHA in the North Pacific
Subtropical Gyre. Deep Sea Research Part I: Oceanographic
Research Papers 170: 103494.
Ollison, G.A., Hu, S.K., Hopper, J.V., Stewart, B.P., Smith, J.,
Beatty, J.L. et al. (2022) Daily dynamics of contrasting spring
algal blooms in Santa Monica Bay (central Southern California
Bight). Environmental Microbiology.
Patro, R., Duggal, G., Love, M.I., Irizarry, R.A., and
Kingsford, C. (2017) Salmon provides fast and bias-aware
quantification of transcript expression. Nat Methods 14: 417-
419.
Paul G. Falkowski, M.E.K., Andrew H. Knoll, Antonietta Quigg,
John A. Raven, Oscar Scholfield, F. J. R. Taylor (2004) The
Evolution of Modern Eukaryotic Phytoplankton. Science 305: 354-
360.

203
Robinson, M.D., McCarthy, D.J., and Smyth, G.K. (2010) edgeR: a
Bioconductor package for differential expression analysis of
digital gene expression data. Bioinformatics 26: 139-140.
Roy, S.W., Gozashti, L., Bowser, B.A., Weinstein, B.N., Larue,
G.E., and Corbett-Detig, R. (2023) Intron-rich dinoflagellate
genomes driven by Introner transposable elements of
unprecedented diversity. Current Biology 33: 189-196.e184.
Ryther, J.H. (1969) Photosynthesis and Fish Production in the
Sea. Science 166: 72-76.
Schmollinger, S., Mühlhaus, T., Boyle, N.R., Blaby, I.K.,
Casero, D., Mettler, T. et al. (2014) Nitrogen-Sparing
Mechanisms in Chlamydomonas Affect the Transcriptome, the
Proteome, and Photosynthetic Metabolism. The Plant Cell 26:
1410-1435.
Seubert, E.L., Gellene, A.G., Howard, M.D., Connell, P., Ragan,
M., Jones, B.H. et al. (2013) Seasonal and annual dynamics of
harmful algae and algal toxins revealed through weekly
monitoring at two coastal ocean sites off southern California,
USA. Environ Sci Pollut Res Int 20: 6878-6895.
Smayda, T.J. (2010) Adaptations and selection of harmful and
other dinoflagellate species in upwelling systems 1. Morphology
and adaptive polymorphism. Progress in Oceanography 85: 53-70.
Smith, J., Connell, P., Evans, R.H., Gellene, A.G., Howard,
M.D.A., Jones, B.H. et al. (2018) A decade and a half of Pseudonitzschia spp. and domoic acid along the coast of southern
California. Harmful Algae 79: 87-104.
Strassert, J.F.H., Irisarri, I., Williams, T.A., and Burki, F.
(2021) A molecular timescale for eukaryote evolution with
implications for the origin of red algal-derived plastids.
Nature Communications 12.
Thieltges, D.W., Amundensen, P.-A., Hechinger, R.F., Johnson,
P., Lafferty, K.D., Mouritsen, K.N. et al. (2013) Parasites as
prey in aquatic food webs: implications for predator infection
and parasite transmission. Oikos 122: 1473-1482.
Wang, Z., and Burge, C.B. (2008) Splicing regulation: from a
parts list of regulatory elements to an integrated splicing
code. RNA 14: 802-813.

204
Wu, T., Hu, E., Xu, S., Chen, M., Guo, P., Dai, Z. et al. (2021)
clusterProfiler 4.0: A universal enrichment tool for
interpreting omics data. The Innovation 2: 100141.
Wurch, L.L., Gobler, C.J., and Dyhrman, S.T. (2014) Expression
of a xanthine permease and phosphate transporter in cultures and
field populations of the harmful alga Aureococcus
anophagefferens: tracking nutritional deficiency during brown
tides. Environ Microbiol 16: 2444-2457.
Wurch, L.L., Alexander, H., Frischkorn, K.R., Haley, S.T.,
Gobler, C.J., and Dyhrman, S.T. (2019) Transcriptional Shifts
Highlight the Role of Nutrients in Harmful Brown Tide Dynamics.
Frontiers in Microbiology 10.
Yang, Y., Huang, B., Tang, Y., and Xu, N. (2020) Allelopathic
effects of mixotrophic dinoflagellate Akashiwo sanguinea on cooccurring phytoplankton: the significance of nutritional
ecology. Journal of Oceanology and Limnology 39: 903-917.
Zhang, H., Campbell, D.A., Sturm, N.R., and Lin, S. (2009)
Dinoflagellate Spliced Leader RNA Genes Display a Variety of
Sequences and Genomic Arrangements. Molecular Biology and
Evolution 26: 1757-1771.
Zhang, Y., Lin, X., Shi, X., Lin, L., Luo, H., Li, L., and Lin,
S. (2019) Metatranscriptomic Signatures Associated With
Phytoplankton Regime Shift From Diatom Dominance to a
Dinoflagellate Bloom. Front Microbiol 10: 590.

205
Chapter IV: Acknowledging and incorporating differences between
methodological approaches for assessing free-living protistan
community diversity: HTS vs Microscopy
Gerid A. Ollison1
, Shengwei Hou2
, Jayme Smith3
, Jed Fuhrman1
,
David A. Caron1
1
Department of Biological Sciences, University of Southern
California, 3616 Trousdale Parkway, Los Angeles, CA 90089-0371
USA.
3
Southern University of Science and Technology, 1088 Xueyuan
Ave., Shenzhen 518055, P.R. China.
3
Southern California Coastal Water Research Project, 3535 Harbor
Blvd., Suite 110, Costa Mesa, CA 92626 USA.

206
ABSTRACT:
Unicellular eukaryotes (protists) are integral to primary
production, nutrient remineralization and elemental
transformation in marine microbial communities. Therefore,
characterizing their diversity and dynamics in natural marine
ecosystems is central to understanding carbon production and
energy flow. Traditional microscopy-based characterizations of
protistan assemblages provide direct measures of species (or
group) abundances, but tradeoffs (low taxonomic resolution,
difficulty of classifying morphologically cryptic species,
specialized training and expertise, reagents and equipment, and
labor intensiveness) make large scale microscopy-based studies
of free-living protists daunting tasks. Surveying the diversity
and identity of metabolically active protists via highthroughput sequencing methodologies (metabarcoding and
metatranscriptomics) have become attractive alternatives to
microscopy owing to technological advances that have boosted
sequencing power while cutting costs, and the rapid expansion of
expertly curated 18S rRNA gene and transcriptome databases have
greatly improved such taxonomic classifications. However, the
relationship between HTS and microscopy-based approaches is
still poorly characterized.
To directly address this issue, we employed samples of natural
plankton communities throughout an algal bloom to characterize

207
and compare the taxonomic composition and daily changes in
abundances (dynamics) of the protistan assemblages using four
common methods: 1) compound light microscopy, 2) PCR-amplified
18S-V4 rRNA gene transcripts (ASVs), and metatranscriptomederived 3) 18S rRNA gene transcripts and 4) non-rRNA mRNA.
Community compositions appeared most similar among HTS methods
than between any single HTS method and microscopy. Additionally,
overall proportions of communities reported by HTS methods were
distinct from those reported by microscopy. Community dynamics
were more comparable between microscopy and 18S rRNA-based
methods; however, community dynamics based on mRNA profiles
exhibited the lowest variation in the proportion of the diatoms
throughout the sampling period, a finding that illustrated that
diatom dynamics derived from mRNA may have been skewed by the
level of transcriptional activity across periods of changing
environmental conditions.
INTRODUCTION:
Unicellular eukaryotes (protists) are integral to all marine
microbial communities. Their assemblages consist of diverse
heterotrophic forms that dominate bacterial and phytoplankton
consumption in aquatic ecosystems, and phototrophic forms that
collectively constitute half of oceanic primary production while
serving as important sinks for atmospheric CO2 (Field et al.,

208
1998; Sherr and Sherr, 2002). The immense diversity of sizes,
morphologies, and trophic functions of protists enable them to
influence food web function at multiple trophic levels (Caron et
al., 2012; Worden et al., 2015). Improving our ability to
characterize protistan diversity and spatiotemporal dynamics
provides insight into how species richness influences ecosystem
function which will better enable us to model future
environmental scenarios in marine ecosystems.
Protistan species have traditionally been defined based on
observable morphological features, primarily using light
microscopy augmented by ultrastructural details revealed by
electron microscopy. Species classification and enumeration via
light microscopy over decades have served as the foundation for
our understanding of protistan diversity and distribution (Caron
et al., 2012). Microscopy-based assessments of protistan
diversity provide precise measures of cell abundances. However,
microscopy is laborious. Low taxonomic resolution can often be
achieved by light microscopy of natural seawater; however,
species-level classification may require specialized microscopes
(ultrastructural), reagents (dyes, stains, fixatives), and
training (taxonomy and procedural). Cataloguing the number
species in a natural community via microscopy is further
complicated by the inability to count small amorphous,
synonymous, and/or otherwise cryptic species, and present

209
limitations on assessing physiological status (Caron and Hu,
2018).
High-throughput sequencing (HTS) of unique gene sequences to
examine protistan diversity in natural assemblages has become a
widespread alternative to microscopy due in part to advances in
sequencing technology and computational biology that have
facilitated large-scale surveys and robust statistical analysis
(ordination, network analysis, and large prediction models).
Additionally, rapidly expanding 18S rRNA gene and transcriptome
databases (Pruesse et al., 2007; Guillou et al., 2013; Keeling
et al., 2014), and the availability of more sequenced genomes of
ecologically important protists have facilitated improved
taxonomic classification via the two most routinely used types
of HTS methodologies for examining protistan assemblages:
metabarcoding of 18S rRNA genes and metatranscriptome
sequencing.
In this regard, examining polymorphisms in short hypervariable
regions (amplicons) of the 18S rRNA gene (grouped as ASVs or
OTUs) collected from natural assemblages has become a
cornerstone of microbial diversity studies over the past few
decades (Caron et al., 2009; Balzano et al., 2015; Priest et
al., 2020; Yan et al., 2023). Many such metabarcoding studies
conducted across space and time have revealed novel taxa and
estimates of diversity that have far exceeded those from

210
observational studies (Massana et al., 2004; Shalchian-Tabrizi
et al., 2006; de Vargas et al., 2015; Locey and Lennon, 2016).
Although the relationship between species diversity vs sequence
diversity is not yet clearly defined, metabarcoding studies are
continuing to modify our conception of protistan diversity
(Caron and Hu, 2018).
One short coming of metabarcoding—in addition to nucleic acid
extraction biases that affect all HTS methods: some cells are
more difficult to lyse than others—is its vulnerability to
biases that can arise from PCR amplification. Mismatches between
primers and the conserved regions that flank the target
hypervariable regions can result in large underestimation of
specific taxa especially in complex samples (Parada et al.,
2016; Yeh et al., 2018). Another challenge is the length of some
hypervariable regions that can exceed the limits of current
short-read sequencing platforms preclude detection of some
protistan species using “universal” primers (Pawlowski et al.,
2014). Another issue is the existence of multiple 18S rRNA gene
copies in some protistan cells that can inflate abundance
estimates of some taxonomic groups. Although sequencing 18S rRNA
transcripts (RNA, not DNA) is thought to attenuate the effects
of copy number variation while capturing only metabolically
active taxa, shortcomings arising from PCR-induced bias
challenges the ability of metabarcoding to be a quantitative

211
methodology (Eisenstein, 2018; Gong and Marchetti, 2019).
Finally, PCR-based HTS methodologies report proportional
abundances, not absolute cell abundances. Although this
limitation may pass in time (and methodological improvements),
it is presently a major detractor of this sequencing approach.
PCR independent techniques such as “shotgun meta-omics” are
another alternative.
Metatranscriptomic approaches—in which total mRNA content of a
sample is fragmented, sequenced, reassembled, and classified—
requires deeper sequencing and has become feasible due to
advances in sequencing technology. Additionally, the efforts of
the Marine Microbial Eukaryotic Transcriptome Sequencing Project
(MMETSP), an initiative that produced a database containing over
650 assembled, annotated and publically available transcriptomes
of non-model protists, have greatly improved our ability to
classify taxonomy to non-rRNA, mRNA (Keeling et al., 2014).
However, mRNA databases are far less complete than 18S gene
databases at present (Pruesse et al., 2007; Guillou et al.,
2013), and as a result, classification using non-rRNA mRNA is
more coarsely resolved than classification using 18S rRNA gene
transcripts.
Metatranscriptome sequencing also enables classification using
rRNA transcripts. rRNA accounts for >85% of total mRNA in a
given protistan cell (Kraus et al., 2019). Although conventional

212
metatranscriptome sequencing pipelines utilize affinity
techniques to attenuate the concentration of rRNA transcripts in
a sample prior to sequencing in order to boost the signal of
mRNA, a significant amount of rRNA transcripts are still
sequenced (Kraus et al., 2019). As a result, tools have been
created to separate rRNA and non-rRNA transcripts in order to
assigned taxonomy using 18S gene databases, which are far more
complete than those dedicated to non-rRNA transcripts such as
the MMETSP database. However, the rRNA that ‘bleeds through’
affinity filtration may be biased towards some taxa due to
affinity binding. And computational tools that sort mRNA and
rRNA in silico using 18S gene databases can possibly compound
the issue (Kopylova et al., 2012).
In this study, taxonomic identities, daily changes in relative
proportions (henceforth community dynamics), were characterized
during a diatom bloom in Santa Monica Bay (Ollison 2022) using
(1) microscopy, (2) metabarcoding of PCR amplified 18S rRNA V4
transcripts (ASVs), and metatranscriptome-derived (3) rRNA and
(4) mRNA to assess how our interpretation of community dynamics
illustrated from these methods compare. The overall proportions
of communities reported by HTS methods were distinct from
microscopy; however, community dynamics were similar between
microscopy and 18S rRNA-based HTS methods. Diatom dynamics based
on mRNA profiles exhibited the lowest variation of all methods

213
and may have been skewed in the direction of transcriptional
activity.
MATERIALS AND METHODS:
Sample collection and processing:
Samples from a previous study that collected characterized the
protistan community via microscopy and 18S-V4 rRNA gene
transcript sequencing for fifteen consecutive days during a
spring bloom dominated by diatom species in Santa Monica Bay,
California were employed to compare community compositions and
dynamics profiled by Microscopy and three HTS methodologies
(Ollison et al., 2022).
Seawater samples employed for microscopy were collected and
preserved with 10% acid Lugol’s solution (final concentration)
for the enumeration of the taxonomic composition of the
protistan assemblage (cells >10 μm in size). Preserved samples
were stored in the dark at 4 °C until analysis. Prior to
analysis, samples were de-stained using 40 µL of a 60 g L-1
thiosulfate solution and sample aliquots of 5 mL to 25 mL were
settled in Utermöhl chambers for approximately 24 hours
(Utermöhl, 1958). The volume settled varied based on plankton
abundance. All protists were then enumerated using a Leica DM
IRBE inverted light microscope (Leica Microsystems, Buffalo
Grove, IL) at 400x. The counting method as applied yielded a

214
limit of detection ranging from 3.0 x 103 cells L-1 to 8.0 x 103
cells L-1
, dependent on cell abundances and the volume
concentrated. For the purpose of this analysis, cells were
categorized and grouped according to four groupings: diatoms,
dinoflagellates, and other, although organisms within each group
were identified to genus where possible.
Nucleic Acid extraction and sequencing:
Molecular analyses for each sample were conducted from two
liters of pre-filtered (80 µm) surface seawater collected onto 45
mm GF/F filters (0.7 µm nominal pore size, Whatman, International
Ltd. Florham Park, NJ). Sequences of the V4 region of 18S rRNA
genes were obtained from a previous study (Ollison et al.,
2022). Briefly, sequencing was performed on daily samples, in
triplicate, from 2 L of pre-filtered (80 µm) seawater collected
onto 45 mm GF/F filters (nominal pore size 0.7 µm; Whatman,
International Ltd. Florham Park, NJ) to capture the protistan
community while excluding most metazoan and bacterial organisms.
RNA was extracted per previously established protocol using the
Qiagen All Prep DNA/ RNA Mini Kit (Ollison et al., 2021) and
reverse transcribed to cDNA using iScript Reverse Transcription
Supermix with random hexameters (Bio-RAD, #170-8840). The 18S
rRNA V4 amplicon was PCR amplified using 18S 565F (5’ -

215
CCAGCASCYGCGGTTAATTCC - 3’) and 948R (5’ - ACTTTCGTTCTTGATYRA -
3’) primers (Stoeck et al., 2010).
Fifteen samples for metatranscriptome sequencing were
collected daily. Daily metatranscriptomes were prepared from 10
L of pre-filtered (80 µm) water filtered onto 0.2 µm Sterivex
filters. RNA was extracted using the AllPrep DNA/RNA/Protein
Mini Kit (Qiagen #80004) and converted to cDNA using NEBnext
Ultra E7771L and E7550L. Illumina libraries were prepared using
the NuGen ultralow v2 kit and sequenced on NovaSeq 6000, PE
2X150.
Data analysis:
18S rRNA V4 amplicon sequences were demultiplexed, quality
filtered, denoised, merged, chimera checked, dereplicated and
grouped into amplicon sequence variants (ASVs) with the DADA2
plugin of Qiime2 (Callahan et al., 2016; Bolyen et al., 2019)
and classified with taxonomic identification using a PR2
classifier trained on the 18S rRNA V4 amplicon.
Metatranscriptome sequence adapters, low quality bases (phred
score below 10 within a 25 bp sliding window), and sequences
shorter 50 bp were removed using Trimmomatic v.0.32 (Bolger et
al., 2014). rRNA and mRNA were sorted from quality filtered
reads using Sortmerna (v2.1). Taxonomic classification was
subsequently assigned to sorted rRNA reads using the full-length

216
Protist Ribosomal Reference (v.11) database via uclust at 97%
identity (Guillou et al., 2013).
mRNA sequences were co-assembled into contiguous sequences
(contigs) using MEGAHIT v. 1.0.3 with default parameters(Li et
al., 2015). Transcript abundances were quantified as transcripts
per million (TPM) using Salmon v0.11.3 (kmer size = 31)(Patro et
al., 2017). Contigs were assigned taxonomic identities using
diamond BLASTX on sensitive mode using an e-value cutoff of
0.001 to a customized cDNA reference database that augmented the
Marine Microbial Eukaryote Transcriptome Sequencing Project
(MMETSP) database with other publicly available genomes and
transcriptomes (EukZoo). Hits with bit scores within the top 95%
were assigned taxonomic and functional annotations. More
information and download the EukZoo at the Zenodo repository:
http://doi.org/10.5281/zenodo.1476236.
Transcript counts were normalized across all samples using the
trimmed mean of m-value (TMM) method via, and differential
expression analysis was conducted using edgeR (v.3.30) (Robinson
2010). All artwork was visualized in R studio, and most data
wrangling and visualization was executed using tools from the
Tidyverse. All custom scripts used in data analysis are
available at
https://github.com/theOlligist/ProtistanDiversityMethodCompariso
n.

217
RESULTS:
Community dynamics and diversity via microscopy:
The protistan community was characterized for fifteen
consecutive days during the full cycle of a diatom bloom that
caught the inception and demise of the bloom via metabarcoding
of 18S-V4 rRNA gene transcripts (ASVs) (Ollison et al., 2022).
This study employed samples collected during Ollison et al. 2022
to compare communities characterized by four common
methodological approaches (Table 1): one, light microscopy (with
enumeration of cell abundances); two, metabarcoding of 18S-V4
rRNA gene transcripts; three, metatranscriptome-derived rRNA;
four, metatranscriptome-derived mRNA. Blooms along the Southern
California Coast are typically dominated by diatom and
dinoflagellate taxa; therefore, diatom and dinoflagellate taxa
were classified at genus-level via microscopy, whereas
approximately order-level classifications were provided for the
six taxa from other taxonomic groups identified during the
sampling period.
Microscopy revealed changes in diatom abundances during the
sampling period that were characteristic of an algal bloom
dominated by diatoms. Fifteen diatom genera collectively
accounted for 235 cells mL-1 on Day 1, bloomed to 2,034 cells mL-1
between Day 5 and Day 8, and gradually declined to approximately

218
150 cells mL-1 by Day 15 (Figure 1). Pseudo-nitzschia,
Thalassiosira, and Guinardia (in order of decreasing abundance)
were the numerically dominant diatoms of the 15 genera
identified.
Twelve dinoflagellate genera identified via microscopy
constituted a minor component of the protistan community
throughout the 15-day study, reaching a cumulative maximum of
326 cells mL-1 on Day 15 (Figure 1). However, Prorocentrum
persisted at low abundances throughout the bloom, and increased
to numerical dominance (269 cells mL-1
) after the diatoms
declined. Other identifiable species from five taxonomic groups
(one rhizarian, one euglenoid, and three distinct ciliate
groups) were also identified and collectively accounted for a
very small fraction of the community throughout the sampling
period and exhibited only minor fluctuations in abundance.
Molecular characterization of community dynamics:
Two 18S rRNA gene transcript sequencing methodologies were
employed to examine protistan diversity and community dynamics:
first, metabarcoding of PCR-amplified 18S-V4 amplicon
transcripts grouped into ASVs with each unique ASV assigned
taxonomic classification; and second, metatranscriptome-derived
rRNA transcripts with each sequence assigned taxonomy by local
alignment. Both methods reported that diatoms accounted for the

219
greatest proportion of total reads, exhibited a spike in
relative abundance after Day 1, and gradually declined after Day
9, a trend similar to that observed by microscopy (Figure 2).
Dinoflagellate proportions in communities characterized by
metatranscriptome-derived rRNA signatures were much greater and
exhibited a more pronounced increase in relative abundance after
Day 9 relative to microscopy (Figure 2B). The increase in
dinoflagellate proportions and coincident decrease in diatoms
appeared to be most pronounced in the communities characterized
with 18S rRNA gene transcripts compared to other methods.
Dinoflagellates reported by 18S rRNA gene transcripts were the
numerically dominant group by Day 10, and, as a result, diatoms
accounted for less than 7% of the community by Day 15 (Figure
2A). Comparatively in communities characterized by
metatranscriptome-derived rRNA, dinoflagellates increased during
the diatom decline more slowly than those characterized by ASVs,
and they were not numerically dominant until Day 14, a day where
diatoms still accounted for approximately 15% of the rRNA-based
community (Figure 2B).
The ability to characterize amorphous, small, and uncommon
taxa using expertly curated databases is another strength of 18S
rRNA sequencing-based analyses relative to traditional
microscopy. Several taxonomic groups that were present at
substantial proportions in rRNA-based communities, but not

220
distinguishable (or observed at all) using light microscopy
accounted for a substantial proportion of the 18S rRNA from both
methods. For example, haptophytes and green algae together
accounted for 10% - 25% substantial proportions of communities
characterized by rRNA signatures, taxa that were not readily
distinguished by light microscopy (Figure 2 vs 1).
The protistan community was also characterized using
metatranscriptome-derived mRNA (non-rRNA), and classified using
a custom transcriptome database that augmented the MMETSP
database (Keeling et al., 2014). Taxonomic classifications of
mRNA transcripts are typically more coarsely resolved than rRNAbased methods because of differences in database completeness.
As a result, taxa classified from mRNA were occasionally grouped
differently from rRNA methods (Figures 3). Similar to other
methods examined in this study, diatoms accounted for the
majority of mRNA reads during the 15-day sampling period;
however, whereas diatom mRNA increased in proportion after Day 1
to its acme near Day 4 (no sample taken Day 5), the peak
relative abundance during the fifteen-day time series was less
than was reported by other methods.
Comparison of community compositions:
Taxonomic proportions of the protistan community determined by
microscopy differed from those derived using HTS methods (Figure

221
S1). Day to day community compositions characterized by
microscopy revealed a strong dominance of diatoms over most of
the 15-day study period, resulting in community structure based
on that analysis forming a distinct (yet widespread) cluster
from all other analyses. Shifts in the proportion of diatoms
versus dinoflagellates over the course of the 15-day study
resulted in the dispersion among the microscopy samples.
Diatom dominance generally gave way to dinoflagellates during
the last 4 days of the study in the microscopy analysis, but
that analysis also substantially underestimated the contribution
of other taxonomic groups (e.g. haptophytes) compared to HTS
methods (Figures 2 and 3). As a result, the overall proportions
of diatoms, dinoflagellates, and other taxa (“Others”) were
closer to a 1:1 ratio among HTS methods; however,
dinoflagellates were most abundant in ASV-based communities
(Figure 4). Dinoflagellate proportions determined by rRNA were
closer to a 1:1 ratio with those determined using microscopy.
DISCUSSION:
We used four methodological approaches to characterize species
composition and diversity of protistan assemblages during an
algal bloom dominated by diatoms resulting from a seasonal
upwelling event (Ollison et al., 2022). The punctuated change in
community structure catalyzed by a nutrient upwelling event was

222
useful for assessing and comparing the taxonomic composition of
the protistan assemblage (what taxa are present and at what
proportions), and community dynamics (how the community
proportions changed with time) as revealed by the different
methods.
Community compositions characterized by microscopy were not
influenced by nucleic acid extraction efficiency or primer and
PCR biases, and community dynamics characterized by microscopy
were not influenced by interdependence of taxonomic abundances
as revealed by the HTS-based methodologies (abundances are
always proportional). Both approaches, however, have
limitations. Microscopy precludes the detection of a large
fraction of the protistan community relative to HTS methods, yet
it can yield absolute abundances of those species that are
identified. Conversely, HTS methods captured a substantial
amount of species that were not classifiable or even detectable
by the microscope-assisted human eye but at present yields on
only relative abundance measurements (Figure 1 vs 2). This study
and results from others underscore the superior ability of HTS
methods to plumb the depths of protistan diversity compared to
microscopy (Figures 1,2,3)(Locey and Lennon, 2016); however, the
ecological interpretation of sequence diversity (e.g. 18S rRNA
gene polymorphisms) and its relationship with species diversity
is still in need of clarification due largely to the limitations

223
of existing databases for species identifications (Caron and Hu
2018). This study provides insight into how protistan community
dynamics characterized using common methodologies compare, and
how their strengths may be leveraged in a complementary manner.
Different methodologies yielded different perspectives on
community composition (Microscopy vs HTS):
Community compositions were distinguished by methodological
approach (Microscopy vs HTS), where the taxonomic proportions
were most similar among HTS methods, and HTS communities were
distinct from microscopy-based communities (Figure S2). However,
the proportions of communities characterized by
metatranscriptome-derived rRNA were more similar to microscopy
than other HTS methods were to microscopy (Figure 4A, B).
Communities characterized by microscopy contained the highest
proportion of diatoms, and the least non-diatom/ nondinoflagellate (Other) taxa (black circles in Figure 4A, B) due
to the limitations of microscopy-based counting methods to
accurately identify many taxa (see methods). Size being a major
factor for detectability, it’s not surprising that diatoms were
the largest contributor to microscopy-based communities. Most
diatom species are large (20->200µm) and morphologically distinct
(Tomas, 1997). Another important factor was the density of the
samples taken during the algal bloom and the necessity of

224
dilution. Dilution is necessary to resolve single cells with the
human eye, but dilution decreases sensitivity for assessing taxa
present at low abundances. Additionally, cells may have been
lost due to structural differences among protists and the
differential success of the preservation method to retain intact cells for enumerating. Some taxa don’t preserve as well as
others and taxa with calcium carbonate exoskeletons can dissolve
in the acid Lugols solution. Also, protistan communities contain
many microscopic, amorphous, and cryptic taxa—morphologically
indistinguishable; reproductively isolated and ecologically
distinct—that may only be classified with more sensitive methods
such as HTS techniques. Accordingly, non-diatom/ nondinoflagellate taxa accounted for 15-50% of all HTS-based
communities (Figure 2).
ASV-based communities are presumably influenced by rRNA copy
number variation:
The number of rRNA copies per cell varies across protistan
clades ranging from single copies to thousands, and
dinoflagellates are known to have hundreds to thousands more
copies than diatoms (Gong and Marchetti, 2019; Lavrinienko et
al., 2021). Although transcript sequencing likely attenuates the
influence of DNA copies by capturing only transcribed rRNA,
dinoflagellate cells (and other taxa with high copy numbers) may

225
contain more transcribed rRNA owing to the activity of multiple
genomic copies. Furthermore, multiple genomic rRNA copies with
variations in sequence and physiological activities (ribosome
heterogeneity) has been characterized in metazoan cells (Shi et
al., 2017; Hopes et al., 2022). Its currently unknown if
heterogeneous ribosomes exist in the genomes of marine protists,
or if ribosome heterogeneity is a prerequisite for transcription
of multiple rRNA gene copies. Communities based on ASVs involves
exponential amplification of barcodes via PCR. Taxa with more
genomic copies such as dinoflagellates will likely have greater
proportions in ASV-based communities owing to PCR bias, and may
also explain the proportional similarity of this taxon in
communities reported by metatranscriptome-derived rRNA and
microscopy compared to communities characterized by ASVs (Figure
4A vs B).
Community dynamics based on microscopy were more similar to
rRNA-based dynamics:
Single samples provided snapshots of community structure
during the bloom, but the time series illustrated temporal
community dynamics: how the community changed over time. The
broad community dynamics reported by microscopy and rRNA
signatures (ASVs and metatranscriptome-derived rRNA) were
similar (Figure S2). Temporal changes in cell abundances via

226
microscopy illustrated the rapid proliferation of diatoms on Day
5, sustained peak abundances, and a gradual decline a few days
later (Figure 1). Similarly, the proportion of diatom ASVs and
rRNA transcripts exhibited a sharp increase and sustained
maximum proportions of approximately 50% of the community
between Day 2 and Day 8, and a subsequent gradual decline
(Figure 2).
Microscopy also revealed that diatom cell abundances declined
after Day 8 with a simultaneous increase in dinoflagellate cell
abundances, a pattern that may be exaggerated in both HTS
datasets where abundances are proportional, and where
dinoflagellate abundances can be inflated by the presence of
multiple transcribed 18S rRNA gene copies per cell (Godhe et
al., 2008; Galluzi et al., 2009).
All methods reported an increase in dinoflagellates coincident
with diatom decline; however, the increase in dinoflagellates—
and coincident decline in diatoms—was most pronounced in
communities as assessed by ASVs likely owing to the interaction
of PCR amplification with copy number variance (there are likely
multiple transcribed rRNA copies in dinoflagellate cells as
detailed in the previous section) and the influence of
proportionality: abundances are not independent in communities
characterized by HTS-based methodologies. It is possible that
the effect of proportionality and copy number variance can

227
further conflate relative abundance estimates in time series
data when a decrease in one taxonomic group is simultaneous with
the increase in another. For example, in situations where
diatoms (which are believed to contain single copies of 18S rRNA
genes) senescence and dinoflagellate (which contain varying
numbers of 18S rRNA gene copies) proliferation occur
simultaneously. In that situation, the disappearance of one
diatom 18S rRNA sequence would be accompanied by the addition of
multiple dinoflagellate 18S rRNA gene sequences with an equal
increase/decrease in cell abundances. Accordingly, the increase
in dinoflagellate proportions in communities profiled by ASVs
appeared more pronounced than their increase reported in
communities characterized by metatranscriptome-derived rRNA,
while the later methodology appeared to be more similar to
microscopy than ASVs (Figure S1).
mRNA diverged from other methods:
Community dynamics illustrated by mRNA failed to recapitulate
the magnitude of changes in diatom relative abundances that was
illustrated by all other methodologies (Figure S2). The variance
in diatom proportions was the smallest for mRNA, and the
difference between maximum and minimum diatom proportions for
mRNA (~23%) was smaller than communities reported by rRNA (~37%)
and ASVs (~44%).

228
An important difference of mRNA and rRNA-based community
characterization is that whereas the pool of rRNA is derived
from a single-function gene, the pool of mRNA contains many
transcribed genes whose expression dynamics reflect their
distinct functional roles in the cell. An increase in mRNA
levels of a taxon may not correlate with changes in cell
abundance if environmental cues stimulate an elevated (or
depressed) multifaceted transcriptional response; i.e. if a
population is in physiological flux.
Many culture studies of protistan transcriptional responses to
environmental cues have demonstrated the under-expression of
suites of genes during bountiful conditions, and an adaptive
response to environmental stress that often involves their overexpression (Marchetti et al., 2011; Dyhrman et al., 2012; Bender
et al., 2014; Liu et al., 2015; Cooper et al., 2016; Haley et
al., 2017). For example, genes associated with nutrient
acquisition (e.g. uptake and catabolysis) in multiple
phylogenetically distinct algal guilds appear to be overexpressed under nutrient limited conditions relative to replete
conditions (Frischkorn et al., 2014; Harke et al., 2017;
Alexander et al., 2020). Accordingly, the physiological activity
of the numerically dominant diatoms during this study were
recently examined. Ollison et al. 2023 reported during advanced
stages of the 2018 bloom signatures of nutrient stress in

229
diatoms, and showed that the number of differentially expressed
genes (DEGs; relative to pre-bloom gene expression) and the
ratio of DEGs with positive log2 fold changes to those with
negative log2 fold changes (i.e. over-expressed : underexpressed) both increased with time (Ollison 2023, Figure S4 of
that study). Thus, diatom proportions in communities reported by
mRNA may have been skewed in the direction of transcriptional
activity.
Conclusion:
We compared community profiles and dynamics characterized by
microscopy and three HTS methodologies in order to examine how
their strengths might be leveraged in a complementary manner.
The microscopy assessment revealed the major contributors and
broad community dynamics with true cell abundances uninfluenced
by proportionality. However, microscopy failed to detect a large
swath of taxa—some major contributors to community structure—
that were detected by all high-throughput sequencing methods
methodologies.
Characterization of the protistan community with HTS methods
catalogued much higher taxonomic richness, but abundance
estimates were proportional and PCR bias may have inflated
estimates of dinoflagellate taxa reported by ASVs.
Metatranscriptome-derived mRNA produced community compositions

230
that were similar to metatranscriptome-derived rRNA. However,
communities profiled by mRNA signatures were classified at lower
taxonomic resolution than 18S rRNA HTS methodologies (due to
present limitations of existing reference databases) and
exhibited smaller variation in diatom proportions compared to
all other methodologies. Diatom proportions in communities
profiled by mRNA may have been influenced by their intracellular
transcriptional activity during the rapid environmental changes
occurring over the study period. Understanding how and why
community profiles differ across common methodologies may
improve our ability to compare across traditional microscopy
based assessments and HTS-based methods.
FIGURES:
Figure Legends:
Figure 1: Dot plot illustrating genus-level dynamics of diatom
(blue circles) dinoflagellate (slate circles) and other (pink
circles) taxa determined via light microscopy. Circle diameter
is proportional to number of cells mL-1
.
Figure 2: Characterization of the protistan community determined
via rRNA methods: 18S-V4 ASVs (A) and metatranscriptome-derived
rRNA (B). Taxa were grouped at varying levels of taxonomic
resolution enabled by existing rRNA databases.

231
Figure 3: Characterization of the protistan community determined
via metatranscriptome-derived mRNA. Taxa grouped at varying
levels of taxonomic resolution enabled by existing mRNA
databases.
Figure 4: Ratio (1:1 = dashed line) of the relative abundance of
diatoms (steel blue triangles), dinoflagellates (light grey
squares), and all other taxa (“Others”; black circles) in
protistan communities characterized by microscopy, metabarcoding
of 18S-V4 rRNA gene transcripts grouped into ASVs, and
metatranscriptome-derived rRNA and mRNA: A) Microscopy vs
metatranscriptomic rRNA, B) Microscopy vs 18S-V4 ASVs, C)
metatranscriptomic rRNA vs 18S-V4 ASVs, D) metatranscriptomic
rRNA vs metatranscriptomic mRNA. All Axis represent community
proportions of the taxon indicated by the color and shape of the
point for the method indicated in the parentheses.
Figure S1: Non-metric multi-dimensional scaling of Bray-Curtis
dissimilarity of protistan communities characterized via
microscopy (red squares), 18S-V4 ASVs (grey circles),
metatranscriptome-derived mRNA (blue diamonds) and rRNA (black
triangles).

232
Figure S2: Daily changes in relative and cell abundances of
diatom (steel blue), dinoflagellate (light grey), and all others
(black) cells via (A) light microscopy, (B) metabarcoding of
18S-V4 ASVs, and metatranscriptome-derived (C) 18S rRNA, and (D)
mRNA. Abundance via microscopy is in cells mL-1
; molecular
methods are in relative abundance.
Figure S3: Box plot illustrating the distribution of log fold
changes (y-axis) of 5104 differentially expressed mRNA
transcripts across five time points (x-axis). The four shown
time points were contrasted against the sample collected prior
to upwelling and bloom (Day 1).
Figures:

233
Figure 1

234
Figure 2

235
Figure 3

236
Figure 4

237
Figure S1

238
Figure S2

239
REFERENCES:
Alexander, H., Rouco, M., Haley, S.T., Dyhrman, S.T., 2020.
Transcriptional response of Emiliania huxleyi under
changing nutrient environments in the North Pacific
Subtropical Gyre. Environmental Microbiology 22 (5), 1847-
1860.
Balzano, S., Abs, E., Leterme, S.C., 2015. Protist diversity
along a salinity gradient in a coastal lagoon. Aquatic
Microbial Ecology 74 (3), 263-277.
Bender, S.J., Durkin, C.A., Berthiaume, C.T., Morales, R.L.,
Armbrust, E.V., 2014. Transcriptional responses of three
model diatoms to nitrate limitation of growth. Frontiers in
Marine Science 1.
Bolger, A.M., Lohse, M., Usadel, B., 2014. Trimmomatic: a
flexible trimmer for Illumina sequence data. Bioinformatics
30 (15), 2114-2120.
Bolyen, E., Rideout, J.R., Dillon, M.R., Bokulich, N.A., Abnet,
C.C., Al-Ghalith, G.A., Alexander, H., Alm, E.J., Arumugam,
M., Asnicar, F., Bai, Y., Bisanz, J.E., Bittinger, K.,
Brejnrod, A., Brislawn, C.J., Brown, C.T., Callahan, B.J.,
Caraballo-Rodríguez, A.M., Chase, J., Cope, E.K., Da Silva,
R., Diener, C., Dorrestein, P.C., Douglas, G.M., Durall,
D.M., Duvallet, C., Edwardson, C.F., Ernst, M., Estaki, M.,
Fouquier, J., Gauglitz, J.M., Gibbons, S.M., Gibson, D.L.,
Gonzalez, A., Gorlick, K., Guo, J., Hillmann, B., Holmes,
S., Holste, H., Huttenhower, C., Huttley, G.A., Janssen,
S., Jarmusch, A.K., Jiang, L., Kaehler, B.D., Kang, K.B.,
Keefe, C.R., Keim, P., Kelley, S.T., Knights, D., Koester,
I., Kosciolek, T., Kreps, J., Langille, M.G.I., Lee, J.,
Ley, R., Liu, Y.-X., Loftfield, E., Lozupone, C., Maher,
M., Marotz, C., Martin, B.D., McDonald, D., McIver, L.J.,
Melnik, A.V., Metcalf, J.L., Morgan, S.C., Morton, J.T.,
Naimey, A.T., Navas-Molina, J.A., Nothias, L.F., Orchanian,
S.B., Pearson, T., Peoples, S.L., Petras, D., Preuss, M.L.,
Pruesse, E., Rasmussen, L.B., Rivers, A., Robeson, M.S.,
Rosenthal, P., Segata, N., Shaffer, M., Shiffer, A., Sinha,
R., Song, S.J., Spear, J.R., Swafford, A.D., Thompson,
L.R., Torres, P.J., Trinh, P., Tripathi, A., Turnbaugh,
P.J., Ul-Hasan, S., van der Hooft, J.J.J., Vargas, F.,
Vázquez-Baeza, Y., Vogtmann, E., von Hippel, M., Walters,
W., Wan, Y., Wang, M., Warren, J., Weber, K.C., Williamson,
C.H.D., Willis, A.D., Xu, Z.Z., Zaneveld, J.R., Zhang, Y.,
Zhu, Q., Knight, R., Caporaso, J.G., 2019. Reproducible,

240
interactive, scalable and extensible microbiome data
science using QIIME 2. Nature Biotechnology 37 (8), 852-
857.
Callahan, B.J., McMurdie, P.J., Rosen, M.J., Han, A.W., Johnson,
A.J., Holmes, S.P., 2016. DADA2: High-resolution sample
inference from Illumina amplicon data. Nat Methods 13 (7),
581-583.
Caron, D.A., Countway, P.D., Jones, A.C., Kim, D.Y., Schnetzer,
A., 2012. Marine protistan diversity. Ann Rev Mar Sci 4,
467-493.
Caron, D.A., Countway, P.D., Savai, P., Gast, R.J., Schnetzer,
A., Moorthi, S.D., Dennett, M.R., Moran, D.M., Jones, A.C.,
2009. Defining DNA-based operational taxonomic units for
microbial-eukaryote ecology. Appl Environ Microbiol 75
(18), 5797-5808.
Caron, D.A., Hu, S.K., 2018. Are We Overestimating Protistan
Diversity in Nature? Trends in Microbiology 27 (3), 197-
205.
Cooper, J.T., Sinclair, G.A., Wawrik, B., 2016. Transcriptome
Analysis of Scrippsiella trochoidea CCMP 3099 Reveals
Physiological Changes Related to Nitrate Depletion. Front
Microbiol 7, 639.
de Vargas, C., Audic, S., Henry, N., Decelle, J., Mahe, F.,
Logares, R., Lara, E., Berney, C., Bescot, N., Probert, I.,
Carmichael, M., Poulain, J., Romac, S., Colin, S., Aury,
J., Bittner, L., Chaffron, S., Dunthorn, M., Engelen, S.,
Flegontova, O., Guidi, L., Horak, A., Jaillon, O., LimaMendez, G., Lukes, J., Malviya, S., Morard, R., Mulot, M.,
Scalco, E., Siano, R., Vincent, F., Zingone, A., Dimier,
C., Picheral, M., Searson, S., Kandels-Lewis, S.,
Coordinators., T.O., Acinas, S.G., Bork, P., Bowler, C.,
Gorsky, G., Grimsley, N., Hingamp, P., Iudicone, D., Not,
F., Ogata, H., Pesant, S., Raes, J., Sieracki, M.E.,
Speich, S., Stemmann, L., Sunagawa, S., Weissenbach, J.,
Wincker, P., E., K., 2015. Eukaryotic plankton diversity in
the sunlit ocean. Science 348 (6237), 1261605-1261601.
Dyhrman, S.T., Jenkins, B.D., Rynearson, T.A., Saito, M.A.,
Mercier, M.L., Alexander, H., Whitney, L.P., Drzewianowski,
A., Bulygin, V.V., Bertrand, E.M., Wu, Z., Benitez-Nelson,
C., Heithoff, A., 2012. The transcriptome and proteome of

241
the diatom Thalassiosira pseudonana reveal a diverse
phosphorus stress response. Plos One 7 (3), e33768.
Eisenstein, M., 2018. Microbiology: making the best of PCR bias.
Nat Methods 15 (5), 317-320.
Field, C.B., Behrenfeld, M.J., James, T., Randerson, J.T.,
Falkowski, P.G., 1998. Primary Production of the Biosphere:
Integrating Terrestrial and Oceanic Components. Science 281
(5374), 237-240.
Frischkorn, K.R., Harke, M.J., Gobler, C.J., Dyhrman, S.T.,
2014. De novo assembly of Aureococcus anophagefferens
transcriptomes reveals diverse responses to the low
nutrient and low light conditions present during blooms.
Front Microbiol 5, 375.
Galluzi, L., Bertozzini, E., Penna, A., Perini, F., Garcés, E.,
Magnani, M., 2009. Analysis of rRNA gene content in the
Mediterranean dinoflagellate Alexandrium catenella and
Alexandrium taylori: implications for the quantitative
real-time PCR-based monitoring methods. Journal of Applied
Phycology 22, 1-9.
Godhe, A., Asplund, M.E., Harnstrom, K., Saravanan, V., Tyagi,
A., Karunasagar, I., 2008. Quantification of diatom and
dinoflagellate biomasses in coastal marine seawater samples
by real-time PCR. Appl Environ Microbiol 74 (23), 7174-
7182.
Gong, W., Marchetti, A., 2019. Estimation of 18S Gene Copy
Number in Marine Eukaryotic Plankton Using a NextGeneration Sequencing Approach. Frontiers in Marine Science
6.
Guillou, L., Bachar, D., Audic, S., Bass, D., Berney, C.,
Bittner, L., Boutte, C., Burgaud, G., de Vargas, C.,
Decelle, J., Del Campo, J., Dolan, J.R., Dunthorn, M.,
Edvardsen, B., Holzmann, M., Kooistra, W.H., Lara, E., Le
Bescot, N., Logares, R., Mahe, F., Massana, R., Montresor,
M., Morard, R., Not, F., Pawlowski, J., Probert, I.,
Sauvadet, A.L., Siano, R., Stoeck, T., Vaulot, D.,
Zimmermann, P., Christen, R., 2013. The Protist Ribosomal
Reference database (PR2): a catalog of unicellular
eukaryote small sub-unit rRNA sequences with curated
taxonomy. Nucleic Acids Res 41, D597-604.

242
Haley, S.T., Alexander, H., Juhl, A.R., Dyhrman, S.T., 2017.
Transcriptional response of the harmful raphidophyte
Heterosigma akashiwo to nitrate and phosphate stress.
Harmful Algae 68, 258-270.
Harke, M.J., Juhl, A.R., Haley, S.T., Alexander, H., Dyhrman,
S.T., 2017. Conserved Transcriptional Responses to Nutrient
Stress in Bloom-Forming Algae. Frontiers in Microbiology 8.
Hopes, T., Norris, K., Agapiou, M., McCarthy, C.G.P., Lewis,
Philip A., O’Connell, Mary J., Fontana, J., Aspden,
Julie L., 2022. Ribosome heterogeneity in Drosophila
melanogaster gonads through paralog-switching. Nucleic
Acids Research 50 (4), 2240-2257.
Keeling, P.J., Burki, F., Wilcox, H.M., Allam, B., Allen, E.E.,
Amaral-Zettler, L.A., Armbrust, E.V., Archibald, J.M.,
Bharti, A.K., Bell, C.J., Beszteri, B., Bidle, K.D.,
Cameron, C.T., Campbell, L., Caron, D.A., Cattolico, R.A.,
Collier, J.L., Coyne, K., Davy, S.K., Deschamps, P.,
Dyhrman, S.T., Edvardsen, B., Gates, R.D., Gobler, C.J.,
Greenwood, S.J., Guida, S.M., Jacobi, J.L., Jakobsen, K.S.,
James, E.R., Jenkins, B., John, U., Johnson, M.D., Juhl,
A.R., Kamp, A., Katz, L.A., Kiene, R., Kudryavtsev, A.,
Leander, B.S., Lin, S., Lovejoy, C., Lynn, D., Marchetti,
A., McManus, G., Nedelcu, A.M., Menden-Deuer, S., Miceli,
C., Mock, T., Montresor, M., Moran, M.A., Murray, S.,
Nadathur, G., Nagai, S., Ngam, P.B., Palenik, B.,
Pawlowski, J., Petroni, G., Piganeau, G., Posewitz, M.C.,
Rengefors, K., Romano, G., Rumpho, M.E., Rynearson, T.,
Schilling, K.B., Schroeder, D.C., Simpson, A.G., Slamovits,
C.H., Smith, D.R., Smith, G.J., Smith, S.R., Sosik, H.M.,
Stief, P., Theriot, E., Twary, S.N., Umale, P.E., Vaulot,
D., Wawrik, B., Wheeler, G.L., Wilson, W.H., Xu, Y.,
Zingone, A., Worden, A.Z., 2014. The Marine Microbial
Eukaryote Transcriptome Sequencing Project (MMETSP):
illuminating the functional diversity of eukaryotic life in
the oceans through transcriptome sequencing. PLoS Biol 12
(6), e1001889.
Kopylova, E., Noé, L., Touzet, H., 2012. SortMeRNA: fast and
accurate filtering of ribosomal RNAs in metatranscriptomic
data. Bioinformatics 28 (24), 3211-3217.
Kraus, A.J., Brink, B.G., Siegel, T.N., 2019. Efficient and
specific oligo-based depletion of rRNA. Scientific Reports
9 (1).

243
Lavrinienko, A., Jernfors, T., Koskimäki, J.J., Pirttilä, A.M.,
Watts, P.C., 2021. Does Intraspecific Variation in rDNA
Copy Number Affect Analysis of Microbial Communities?
Trends in Microbiology 29 (1), 19-27.
Li, D., Liu, C.M., Luo, R., Sadakane, K., Lam, T.W., 2015.
MEGAHIT: an ultra-fast single-node solution for large and
complex metagenomics assembly via succinct de Bruijn graph.
Bioinformatics 31 (10), 1674-1676.
Liu, Z., Koid, A.E., Terrado, R., Campbell, V., Caron, D.A.,
Heidelberg, K.B., 2015. Changes in gene expression of
Prymnesium parvum induced by nitrogen and phosphorus
limitation. Front Microbiol 6, 631.
Locey, K.J., Lennon, J.T., 2016. Scaling laws predict global
microbial diversity. Proceedings of the National Academy of
Sciences 113 (21), 5970-5975.
Marchetti, A., Schruth, D.M., Durkin, C.A., Parker, M.S.,
Kodner, R.B., Berthiaume, C.T., Morales, R.L., 2011.
Comparative metatranscriptomics identifies molecular bases
for the physiological responses of phytoplanton to varying
iron availability. Proceedings of the National Academy 109
(6), E317-E325.
Massana, R., Castresana, J., Balagué, V., Guillou, L., Romari,
K., Groisillier, A., Valentin, K., Pedrós-Alió, C., 2004.
Phylogenetic and Ecological Analysis of Novel Marine
Stramenopiles. Applied and Environmental Microbiology 70
(6), 3528-3534.
Ollison, G.A., Hu, S.K., Hopper, J.V., Stewart, B.P., Smith, J.,
Beatty, J.L., Rink, L.K., Caron, D.A., 2022. Daily dynamics
of contrasting spring algal blooms in Santa Monica Bay
(central Southern California Bight). Environmental
Microbiology 10.1111/1462-2920.16137.
Ollison, G.A., Hu, S.K., Mesrop, L.Y., DeLong, E.F., Caron,
D.A., 2021. Come rain or shine: Depth not season shapes the
active protistan community at station ALOHA in the North
Pacific Subtropical Gyre. Deep Sea Research Part I:
Oceanographic Research Papers 170, 103494.
Parada, A.E., Needham, D.M., Fuhrman, J.A., 2016. Every base
matters: assessing small subunit rRNA primers for marine
microbiomes with mock communities, time series and global
field samples. Environ Microbiol 18 (5), 1403-1414.

244
Patro, R., Duggal, G., Love, M.I., Irizarry, R.A., Kingsford,
C., 2017. Salmon provides fast and bias-aware
quantification of transcript expression. Nat Methods 14
(4), 417-419.
Pawlowski, J., Lejzerowicz, F., Esling, P., 2014. NextGeneration Environmental Diversity Surveys of Foramnifera:
Preparing the Future. Biological Bulletin 277 (2), 91-209.
Priest, T., Fuchs, B., Amann, R., Reich, M., 2020. Diversity and
biomass dynamics of unicellular marine fungi during a
spring phytoplankton bloom. Environmental Microbiology 23
(1), 448-463.
Pruesse, E., Quast, C., Knittel, K., Fuchs, B.M., Ludwig, W.,
Peplies, J., Glockner, F.O., 2007. SILVA: a comprehensive
online resource for quality checked and aligned ribosomal
RNA sequence data compatible with ARB. Nucleic Acids Res 35
(21), 7188-7196.
Shalchian-Tabrizi, K., Eikrem, W., Klaveness, D., Vaulot, D.,
Minge, M.A., Le Gall, F., Romari, K., Throndsen, J.,
Botnen, A., Massana, R., Thomsen, H.A., Jakobsen, K.S.,
2006. Telonemia, a new protist phylum with affinity to
chromist lineages. Proceedings of the Royal Society B:
Biological Sciences 273 (1595), 1833-1842.
Sherr, E.B., Sherr, B.F., 2002. Significance of predation by
protists in aquatic microbial food webs. Antonie van
Leeuwenhoek 81, 293-308.
Shi, Z., Fujii, K., Kovary, K.M., Genuth, N.R., Röst, H.L.,
Teruel, M.N., Barna, M., 2017. Heterogeneous Ribosomes
Preferentially Translate Distinct Subpools of mRNAs Genomewide. Molecular Cell 67 (1), 71-83.e77.
Stoeck, T., Bass, D., Nebel, M., Christen, R., Jones, M.D.,
Breiner, H.W., Richards, T.A., 2010. Multiple marker
parallel tag environmental DNA sequencing reveals a highly
complex eukaryotic community in marine anoxic water. Mol
Ecol 19 (1), 21-31.
Tomas, C., 1997. Identifying marine phytoplankton. Academic
Press, San Diego.

245
Utermöhl, H., 1958. Zur vervollkommnung der quantitativen
phytoplankton-methodik. Mitt. int. Ver. theor. angew.
Limnol. 9, 1-38.
Worden, A.Z., Follows, M.J., Giovannoni, S.J., Wilken, S.,
Zimmerman, A.E., Keeling, P.J., 2015. Environmental
science. Rethinking the marine carbon cycle: factoring in
the multifarious lifestyles of microbes. Science 347
(6223), 1257594.
Yan, Y., Lin, T., Xie, W., Zhang, D., Jiang, Z., Han, Q., Zhu,
X., Zhang, H., 2023. Contrasting Mechanisms Determine the
Microeukaryotic and Syndiniales Community Assembly in a
Eutrophic bay. Microbial Ecology 10.1007/s00248-023-02175-
0.
Yeh, Y.C., Needham, D.M., Sieradzki, E.T., Fuhrman, J.A., 2018.
Taxon Disappearance from Microbiome Analysis Reinforces the
Value of Mock Communities as a Standard in Every Sequencing
Run. mSystems 3 (3).
END.

Abstract (if available)

Abstract Unicellular eukaryotes (protists) are integral to all known marine microbial assemblages. Phagotrophic species are responsible for the majority of bacterial mortality and remineralization with few exceptions; photosynthetic species contribute approximately half of oceanic primary production and constitute important sinks for atmospheric CO2. The immense diversity within protistan assemblages——size, morphology, and trophic function——enables them to serve many functions across multiple trophic levels at the base of natural food webs. Characterizing protistan diversity and their community responses to environmental cues across geographic and temporal scales will improve our understanding of anthropogenic impacts on ecosystem health and better enable us to model marine food web functioning in future climate scenarios.

The four chapters of this dissertation aims to improve our understanding of spatiotemporal variations in protistan diversity, and how both physical and chemical environmental factors (abiotic factors) intersect with species interactions (biotic factors) to shape protistan assemblages in two marine ecosystems: coastal and open ocean ecosystems.

In chapter I, I examined the vertical and seasonal distribution of metabolically active protists in the North Pacific Subtropical Gyre, an oceanic desert off the coast of Hawai’i. In Chapter II and Chapter III I examined the temporal dynamics and the physiological responses of the protistan community to environmental cues during upwelling blooms dominated by diatoms (2018) and dinoflagellates (2019) in the Southern California coastal upwelling regime (Southern California Bight region), a highly productive ecosystem.

Finally, in Chapter IV I employed time series data from Santa Monica Bay to contrast four common methodological approaches for assessing protistan diversity: 1) compound light microscopy, 2) PCR-amplified 18S-V4 rRNA gene transcripts grouped into amplicon sequence variants (ASVs), and metatranscriptome-derived 3) 18S rRNA gene transcripts and 4) non-rRNA mRNA. At the time of writing this dissertation, morphology still represents the gold standard for protistan classification, albeit laborious. At the same time, rapid advances in sequencing technology and expanding taxonomic and functional databases have enabled a variety of high-throughput sequencing methodological approaches for examining protistan free-living protistan assemblages. This chapter aims to examine how the strengths of these common methodologies might be leveraged in a complimentary manner in future studies.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

PDF

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

PDF

Genetic characterization of microbial eukaryotic diversity and metabolic potential

PDF

Spatial and temporal investigations of protistan grazing impact on microbial communities in marine ecosystems

PDF

The development of novel measures of landscape diversity in assessing the biotic integrity of lotic communities

PDF

Physiological and ecological consequences of environmental temperature on Antarctic protists

PDF

Using molecular techniques to explore the diversity, ecology and physiology of important protistan species, with an emphasis on the Prymnesiophyceae

PDF

Changes in the community composition of marine microbial eukaryotes across multiple temporal scales of measurement

PDF

Examining potential triggers of algal blooms and harmful algae in the Southern California bight region

PDF

Patterns of molecular microbial activity across time and biomes

PDF

Thermal diversity within marine phytoplankton communities

PDF

Spatial and temporal dynamics of marine microbial communities and their diazotrophs in the Southern California Bight

PDF

Ecological implications of daily-to-weekly dynamics of marine bacteria, archaea, viruses, and phytoplankton

PDF

Marine bacterioplankton biogeography over short to medium spatio-temporal scales

PDF

Temporal variability of marine archaea across the water column at SPOT

PDF

Enhancing recovery of understudied and uncultured lineages from metagenomes

PDF

Future impacts of warming and other global change variables on phytoplankton communities of coastal Antarctica and California

PDF

Ecological patterns of free-living and particle-associated prokaryotes, protists, and viruses at the San Pedro Ocean Time-series between 2005 and 2018

PDF

Disentangling the ecology of bacterial communities in cnidarian holobionts

PDF

Dynamics of marine bacterial communities from surface to bottom and the factors controling them

PDF

Transgenerational inheritance of thermal tolerance in two coral species in the Florida Keys

Asset Metadata

Creator Ollison, Gerid Alexander (author)

Core Title Marine protistan diversity, spatiotemporal dynamics, and physiological responses to environmental cues

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Biology (Marine Biology and Biological Oceanography)

Degree Conferral Date 2023-12

Publication Date 11/08/2023

Defense Date 11/08/2023

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

Tag algal blooms,metabarcoding,metatranscriptomics,OAI-PMH Harvest,protistan diversity,protistan ecology

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Caron, David (committee chair), Caron, David A. (committee chair), Fuhrman, Jed (committee member), John, Seth (committee member), Kenkel, Carly (committee member)

Creator Email docolli13@yahoo.com,gollison@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113766182

Unique identifier UC113766182

Identifier etd-OllisonGer-12463.pdf (filename)

Legacy Identifier etd-OllisonGer-12463

Document Type Dissertation

Format theses (aat)

Rights Ollison, Gerid Alexander

Internet Media Type application/pdf

Type texts

Source 20231114-usctheses-batch-1106 (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