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Diversity and dynamics of giant kelp “seed-bank” microbiomes: Applications for the future of seaweed farming

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Diversity and dynamics of giant kelp “seed-bank” microbiomes: Applications for the future of seaweed farming

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Diversity and dynamics of giant kelp “seed-bank” microbiomes:
Applications for the future of seaweed farming

by

Melisa Gürakar Osborne

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(MOLECULAR BIOLOGY)

May 2023

Copyright 2023 Melisa Osborne
ii

Dedication

To my parents, for all their love and support.

iii
Acknowledgements
This work would not have been possible without the professional support of my mentors,
peers, and colleagues. Thanks to my committee (Dr. Sergey Nuzhdin, Dr. Matthew Dean, Dr. Ian
Ehrenreich, Dr. Steven Finkel, Dr. Carly Kenkel, and Dr. Naomi Levine) for sharing helpful
scientific feedback over the years. Thanks to Dr. Steven Finkel for providing my first introduction
to the wonderful world of microbes. And, of course, a special thanks to Dr. Sergey Nuzhdin for
providing several years of academic support and guidance, believing in me, and helping me
develop this exciting research path. Thanks to collaborators at the University of California, Santa
Barbara (UCSB) including Dr. Daniel Reed and Dr. Robert Miller, and at the University of
Wisconsin-Milwaukee (UWM) including Dr. Filipe Alberto. Thanks to the teams from UCSB,
USC, and UWM involved in the 2019 harvest of the kelp farm in Santa Barbara. This work was
funded by: Advanced Research Project Agency-Energy (Grant Number: GR1022773), National
Institute of General Medical Sciences Fellowship (Award Number: T32-GM118289), Rose Hills
Foundation Fellowship, and Environmental Protection Agency (Grant Number: GR1022753).
On a more personal note, I am incredibly thankful to all the wonderful people in my life
who have been part of this expedition. Evan, for being my ray of sunshine, my safe space, and my
person. Kelly and José, for sticking together since day one in lab and making the office a fun place
to be. Gary and Levi, for being great mentors and friends. My parents, Arzu and Jim, and my
brother, Eren, for always rooting for me, being my role models, and providing the infinite love,
support, and guidance that shaped who I am and inspire me to shoot for the stars. And finally, all
my friends (old and new) who have shared the highs and lows and continue to make my life so
colorful. You all have helped me discover so much of myself and the world around me. Thank
you, and cheers!
iv
Table of Contents
Dedication ....................................................................................................................................... ii
Acknowledgements ....................................................................................................................... iii
List of Tables .................................................................................................................................. vi
List of Figures ............................................................................................................................... vii
Abstract ........................................................................................................................................ viii
Chapter 1: Understanding the metabolome and metagenome as extended phenotypes: The next
frontier in macroalgae domestication and improvement ................................................................. 1
Preface ......................................................................................................................................... 1
Abstract ........................................................................................................................................ 1
1 Introduction .............................................................................................................................. 2
1.1 Brief review of marine macroalgae aquaculture ................................................................... 3
1.2 Modern farming in the United States and targeted breeding ................................................. 4
2 Application of metabolomics to macroalga domestication ...................................................... 7
2.1 A need to adapt: global warming and resilient organisms .................................................... 8
2.2 The metabolome of marine macroalgae ................................................................................ 9
2.3 Metabolomic analysis through a structural equation modeling framework .......................... 9
2.4 The macroalgae holobiont: combining metabolomics and metagenomics to probe host–
symbiont interactions ................................................................................................................. 11
3 Application of metagenomics to macroalga domestication .................................................... 12
3.1 Understanding the microbiome ........................................................................................... 13
3.2 Using metagenomic data to understand community assembly patterns of the microbiome 16
3.3 Identifying beneficial bacteria using metagenomics and physiological crop traits ............. 18
3.4 Characterizing impact of host genotype on microbial community ..................................... 19
3.5 Technical challenges of analyzing host genome impact on microbiome ............................ 20
4 Conclusions ............................................................................................................................ 22
Figures ....................................................................................................................................... 24
Chapter 2: Natural variation of Macrocystis pyrifera gametophyte germplasm culture
microbiomes and applications for improving yield in offshore farms .......................................... 27
Preface ....................................................................................................................................... 27
Abstract ...................................................................................................................................... 27
Introduction ............................................................................................................................... 28
Materials and Methods .............................................................................................................. 32
Results ....................................................................................................................................... 39
Discussion .................................................................................................................................. 44
Tables ......................................................................................................................................... 50
Figures ....................................................................................................................................... 57
v
Chapter 3: Investigating the relationship between microbial network features of giant kelp
“seedbank” cultures and subsequent farm performance ................................................................ 66
Preface ....................................................................................................................................... 66
Abstract ...................................................................................................................................... 66
Introduction ............................................................................................................................... 67
Materials and Methods .............................................................................................................. 71
Results ....................................................................................................................................... 78
Discussion .................................................................................................................................. 81
Tables ......................................................................................................................................... 85
Figures ....................................................................................................................................... 90
References ................................................................................................................................... 100

vi
List of Tables
Table 2.1. M. pyrifera sampling sites in Southern California……………………………………..50
Table 2.2. Taxa differentially abundant between sporophyte biomass groups……………………50
Table 2.3. Taxa that have significant correlations with sporophyte biomass……………………..51
Table S2.1. M. pyrifera biomass quantiles………………………………………………………..52
Table S2.2. Distribution of classified and unclassified taxa across taxonomic levels…………….52
Table S2.3. Distribution of taxa across M. pyrifera populations………………………………….53
Table S2.4. Relative abundance of taxa across M. pyrifera populations………………………….54
Table S2.5. GLMs used to model microbe abundance and sporophyte biomass………………….55
Table S2.6. Coefficient estimates of GLM modeling…………………………………………….55
Table S2.7. Existing knowledge regarding taxa of interest (non-exhaustive)…………………….56
Table 3.1. Odds ratio values for network topology factors used in POLR models………………..85
Table 3.2. Hub taxa by biomass group……………………………………………………………86
Table S3.1. Summary of network topology factors……………………………………………….87
Table S3.2. Summary of p-value and odds ratio values…………………………………………..88
Table S3.3. Hub taxa by population………………………………………………………………89

vii
List of Figures
Figure 1.1 Domestication of marine macroalgae species…………………………………………24
Figure 1.2 Use of omics techniques to investigate kelp adaptation……………………………….25
Figure 1.3 Overview of omics techniques for macroalgae domestication………………………...26
Figure 2.1. Relative abundance of bacteria across M. pyrifera populations………………………57
Figure 2.2. Taxonomic richness and Shannon diversity of bacterial species……………………...58
Figure 2.3. PCA of microbial communities across M. pyrifera populations……………………...59
Figure 2.4. PCA of microbial communities across biomass quantiles……………………………60
Figure S2.1. Venn diagram of species level core microbes……………………………………….61
Figure S2.2. Richness and diversity of higher-level taxa across populations……………………..62
Figure S2.3. Richness and diversity of bacterial species across biomass quantiles……………….63
Figure S2.4. PCA of higher-level microbial communities across M. pyrifera populations……….64
Figure S2.5. PCA of higher-level microbial communities across biomass quantiles……………..65
Figure 3.1. Workflow for data collection and network construction……………………………...90
Figure 3.2. Microbial network features of gametophytes vary with sporophyte biomass………...91
Figure 3.3. Co-occurrence networks of the microbial community classified at the genus level…..92
Figure 3.4. Co-occurrence networks of the microbial community classified at the family level….93
Figure S3.1. Co-occurrence networks of the microbial community from LC gametophytes……..94
Figure S3.2. Zeta diversity graphs for zeta order 3, 5, 10, 20, and 50 at the species level…………95
Figure S3.3. Zeta diversity graphs for zeta order 50 at order, family, genus, and species levels…..96
Figure S3.4. Box plots of network topology factors at the order level…………………………….97
Figure S3.5. Box plots of network topology factors at the family level…………………………...98
Figure S3.6. Co-occurrence networks of the microbial community classified at the order level….99
viii
Abstract

The need for eco-friendly practices has prompted interest in seaweed-based biofuels as a
sustainable alternative to fossil fuels. Macrocystis pyrifera, a brown macroalgae known as “giant
kelp” is native to Southern California and a prime candidate for domestic biofuel production. The
poor fitness of M. pyrifera in offshore farms is a critical barrier to large-scale cultivation and
commercial applications; however, given the tight associations between seaweeds and their native
microbiota, there’s an opportunity to utilize seaweed-microbe interactions and optimize growth in
offshore farms. Here, I explore the use of microbial tools to improve seaweed cultivars: 1) I review
existing knowledge of seaweed-microbe associations and discuss opportunities to apply
metagenomic research to seaweed aquaculture, 2) I characterize the microbial community of giant
kelp “seed-bank” cultures, identify taxa correlated with increased biomass yield, and propose these
as candidates for a growth-promoting inoculant, and 3) I investigate the network topology and
community dynamics of those same microbial communities and determine which features are
unique to giant kelp cultivars that become high-biomass individuals. Together, this work provides
a valuable knowledge base for the development of microbial tools for seaweed aquaculture and an
exciting step forward in the commercialization of seaweed as a biofuel feedstock.
1
Chapter 1: Understanding the metabolome and metagenome as extended phenotypes: The
next frontier in macroalgae domestication and improvement

As published in the Journal of the World Aquaculture Society,
Co-authored with Kelly J. DeWeese.

Preface
The review presented in this chapter was produced as a collaborative effort between myself
and Kelly J. DeWeese. The abstract, introduction, and conclusion sections were contributed to
equally. The remaining sections were divided as follows: MGO authored “1.1 Brief review of
marine macroalgae aquaculture”, “3 Application of metagenomics to macroalga domestication”
and all its subsections (3.1-3.5). KJD authored “1.2 Modern farming in the United States and
targeted breeding”, “2 Application of metabolomics to macroalga domestication” and all its
subsections (2.1-2.4). KJD created all figures. Sections by KJD should not be evaluated as part of
this dissertation. Minor edits from the published version of this chapter were made to ensure
consistent formatting throughout this dissertation.
Abstract
“Omics” techniques (including genomics, transcriptomics, metabolomics, proteomics, and
metagenomics) have been employed with huge success in the improvement of agricultural crops.
As marine aquaculture of macroalgae expands globally, biologists are working to domesticate
2
species of macroalgae by applying these techniques tested in agriculture to wild macroalgae
species. Metabolomics has revealed metabolites and pathways that influence agriculturally
relevant traits in crops, allowing for informed crop crossing schemes and genomic improvement
strategies that would be pivotal to inform selection on macroalgae for domestication. Advances in
metagenomics have improved understanding of host–symbiont interactions and the potential for
microbial organisms to improve crop outcomes. There is much room in the field of macroalgal
biology for further research toward improvement of macroalgae cultivars in aquaculture using
metabolomic and metagenomic analyses. To this end, this review discusses the application and
necessary expansion of the omics tool kit for macroalgae domestication as we move to enhance
seaweed farming worldwide.
1 Introduction
The farming of marine macroalgae (seaweed) contributes to the international food,
cosmetic, science, and pharmaceutical industries (Buschmann et al., 2017; FAO, 2020).
Aquaculture of macroalgae has historically been almost exclusively an Asian economic
enterprise—over 99% of annual production is based in Asia (Ferdouse et al., 2018). With increased
globalization and the search for ecofriendly alternatives in the food and biofuel industries,
aquaculture has grown outside of Asia as well. Recently, Europe and the Americas have seen an
emergence of aquaculture farms, companies, and research facilities specializing in algae (Kim et
al., 2017). Over the last two decades, world production of marine macroalgae has more than tripled,
up from 10.6 million tons in 2000 to 32.4 million tons in 2018 (FAO, 2020). Although there are
hundreds of known marine macroalgae species, 81% of algae production is composed of only a
handful of brown and red macroalgae species (FAO, 2018). Aquaculture, particularly that of
3
marine macroalgae, has much potential to expand globally onto different coastlines and harness
new species. Furthermore, there are large economic and environmental gaps to fill, specifically in
the realms of biofuel and food production (Duarte et al., 2017; Guzinski et al., 2018; Pereira &
Yarish, 2008; Sudhakar et al., 2018). Macroalgae are an especially attractive species for food and
biofuel crops as they do not compete with agriculture for land or freshwater resources (Kim et al.,
2019). From the growing pressure that climate change presents to agriculture, aquaculture has
quickly gained global attention and many species of macroalgae are positioned to become
significant marine crops.
1.1 Brief review of marine macroalgae aquaculture
Seaweed farming, and its impact on aquaculture, has evolved over hundreds of years
(Pereira & Yarish, 2008). It began as a practice of coastal harvesting, and the increasing use and
value of seaweed in human food products has led to more direct farming of seaweed as a crop. In
2018, farmed seaweeds represented 97.1% by volume of the total of 32.4 million tons of wild-
collected and cultivated aquatic algae combined (FAO, 2020). Although global production of
farmed seaweed has experienced relatively slow growth in recent years, a renewed interest in
sustainable foods, feeds, phycocolloids, and biofuel production has ignited groups in academia and
industry to focus efforts on developing several species of macroalgae as commercial crops (Kim
et al., 2019). In fact, over the last 30 years, several US federal agencies, including the Department
of Energy (DOE), Department of Agriculture (USDA), and Department of Commerce's National
Oceanic and Atmospheric Administration (NOAA), have invested nearly 1 billion dollars into
developing the United States as a leader in aquaculture (Kim et al., 2019; Love et al., 2017).
Seaweeds are extractive crops, meaning that they benefit the environment by removing
waste materials such as nitrogen, phosphorus, and carbon (FAO, 2018; Kim et al., 2015). High
4
levels of these materials can have negative consequences on coastal ecosystems by triggering, for
example, harmful microalgae blooms. However, these materials can be absorbed by several species
of seaweed and are in fact important nutrients to support proper growth and development (Buck et
al., 2017; Rose et al., 2015). In addition to being an environmentally friendly crop, seaweeds are
increasingly being recognized for their abundance of nutrients including, but not limited to, iodine,
iron, and vitamin A (FAO, 2020; Tanna & Mishra, 2019). Seaweeds contain micronutrient
minerals (e.g., iron, calcium, iodine, potassium and selenium) and vitamins (particularly A, C, and
B12) and are the only non-fish sources of natural omega-3 long-chain fatty acids (FAO, 2020).
Beyond their applications in the human food industry, seaweeds have a diverse range of
commercial applications such as additives in feeds, fertilizers, and cosmetics; production of
alginate, agar, and carrageenan; pharmaceuticals; and biofuels (Buschmann et al., 2017; Kim et
al., 2015; Kim et al., 2017; Pereira & Yarish, 2008).
1.2 Modern farming in the United States and targeted breeding
In the 50 years between 1950 and 2000, the human population more than doubled,
increasing in an unprecedented manner from 2.5 billion to 6 billion (Bongaarts, 2009). At the head
of the population boom, the 1960s were fraught with concern about the ratio of food to population
(Khush, 2001). This sentiment contrasts starkly with our reality today, where we struggle to
minimize food waste in developed countries (Walia & Sanders, 2019). Agriculture was able to
keep pace with the human population with its own productivity boom now called the “green
revolution,” a period in which advanced breeding crop techniques were developed and crops were
first genetically modified to increase yields (Khush, 2001). Since the green revolution changed the
face of agriculture, entire fields of science have developed to further investigate the genes, variants,
expression profiles, and metabolites involved in creating high-yield, superior crops.
5
The past two decades have seen genomics and other omics disciplines begin to dominate
the study of agriculture. Many significant agricultural crops have had their genomes sequenced,
allowing scientists to gain an understanding of the gene networks and pathways that determine
crop success for targeted breeding approaches (Van Emon, 2016). Modern genomics techniques
for improving agricultural crops include quantitative trait loci (QTL) mapping and genome-wide
association studies (GWASs) (Huang & Han, 2014). QTL mapping identifies genetic loci that
cause or contribute to specific phenotypes in crops, while GWAS identifies single-nucleotide
polymorphisms (SNPs) associated with traits of interest (Huang et al., 2015; Murray et al., 2008).
QTL mapping has been applied to crop improvement in agricultural breeding programs through
marker-assisted selection (MAS), which utilizes polymorphic regions linked to specific alleles to
detect and select for desirable alleles and traits in crop cultivars (Collard & Mackill, 2008).
However, MAS is best able to detect high-effect genes or monogenic traits (Heffner et al., 2009;
Xu & Crouch, 2008). The limitations of MAS can be overcome in part using advanced genetic
methods that incorporate GWAS models into breeding, in a process known as genomic selection
(Goecke et al., 2020). In genomic selection, SNPs are used as markers across the entire genome to
estimate breeding values, a technique that has a far higher resolution than MAS, can help to
detangle tightly linked traits, and is starting to model polygenic traits (Heffner et al., 2009).
Microarray and RNA sequencing data, used to build transcriptomes, and expression profiles, as
well as to conduct differential expression analyses, have also been instrumental in identifying
significant genes and variants in agricultural crops (Ashikari et al., 2005; Van Emon, 2016). To
review the use of molecular markers and genomic breeding programs in seaweed aquaculture, we
refer the reader to reviews by Yong et al. (2016) and Goecke et al. (2020) (Goecke et al., 2020;
6
Yong et al., 2016). For a more recent review of genomic approaches for improving agriculture
more broadly, we also point the reader to the review by Bohra et al. (2020) (Bohra et al., 2020).
Even more recently, metabolomics and metagenomics data have been incorporated into
analyses for crop improvement (Dixon et al., 2006; Gastauer et al., 2019; Lin et al., 2018;
O'Donnell et al., 2019; Van Emon, 2016; Walker et al., 2016). Metabolomics provides insight into
pathways involved in growth and other desirable phenotypes, while metagenomics reveals
associated microbial species and putative functions that they could be performing for their crop
hosts such as nitrogen fixation.
Although humans have utilized seaweed as a food source for hundreds of years, its presence
in Western diets is concentrated in scattered coastal areas including Maine, Hawaii, and Alaska
(Hunter, 1975; Kim et al., 2019; Mouritsen et al., 2013). The vast majority of global seaweed
production is grown in Asia (FAO, 2018), and aquaculture is one of the fastest growing maritime
industries in the United States, particularly around New England (Kim et al., 2019). With the
growing accessibility of and technology for multi-omics applications (O'Donnell et al., 2019), this
presents a timely opportunity for domestication and improvement of relevant seaweed species
through omics.
Despite recent growth and investment in the U.S. aquaculture industry, several challenges
still exist. Primarily, securing permits for coastal domestication is a significant bottleneck for those
wishing to enter the industry (Kim et al., 2019). Offshore farming is considered a more suitable
route for aquaculture as it presents fewer permitting conflicts (Buck et al., 2004; Cicin-Sain et al.,
2001; Kim et al., 2019; Tiller et al., 2013). However, offshore farms are recognized as more
stressful environments for macroalga due to stronger currents, limited nutrients, and cooler
temperatures (Charrier et al., 2018; Roleda & Hurd, 2019; Solan & Whiteley, 2016).
7
Breeding programs can and have been developed to improve the productivity of seaweeds
in certain environments or tailor crops to enhance specific characteristics of interest (Figure 1.1)
(Hwang et al., 2019; Walker, 2018; Zhang et al., 2016; Zhang et al., 2018). Species of seaweeds
targeted for this type of work include, but are not limited to, Saccharina, Laminaria,
Porphyra, Pyropia, and Macrocystis (Kim et al., 2019). For example, in Saccharina japonica, QTL
mapping has been used to identify QTLs responsible for higher yield by increasing blade size
(Wang et al., 2018), and in Saccharina latissima, QTLs for stipe length were similarly identified
(Mao et al., 2020). Macroalgal targeted breeding programs can be enhanced by more extensive
incorporation of recent advances in multi-omics technology. This review makes the case for the
inclusion of metabolomic and metagenomic data and analyses in continued efforts to domesticate
and improve marine macroalgae for aquaculture.
2 Application of metabolomics to macroalga domestication
Implicitly or explicitly, the metabolome—the suite of biological molecules present in a
cell, tissue or organism—plays a large role in the domestication and improvement of crops
(Burgess et al., 2014; Dixon et al., 2006). Selecting for certain desired phenotypes is a necessary
step in domestication to increase yield and cultivate optimal morphological characteristics.
Investigating the genetics of cultivated crops has allowed the field of agriculture to create and
employ improved crop varieties that have a higher yield (Murray et al., 2008; Wang et al., 2018)
and better nutrient profiles or flavor (Gao et al., 2014), and to determine alleles involved in
heterosis, allowing for the creation of crossing schemes in which offsprings perform far superior
with respect to their parents (Huang et al., 2015). When combined with genomics and
transcriptomics, metabolomics has further benefited agricultural plant domestication and
8
improvement research in many areas including pesticide resistance (Aliferis & Chrysayi-
Tokousbalides, 2010); microbial associations (Han & Micallef, 2016); crop flavor and nutrition
profiles (Fernie & Schauer, 2009; Hall et al., 2008); stress and heat tolerance (Che-Othman et al.,
2020; Sung et al., 2015); and growth and yield (Obata et al., 2015).
2.1 A need to adapt: global warming and resilient organisms
The projected global demand for food, livestock feeds, and bio-energy by 2050 will force
the increased farming of low-carbon and carbon-sequestering marine resources, such as kelp and
shellfish—especially in the face of large-scale environmental changes in climate and land use
(Froehlich et al., 2018; Kim et al., 2019; Waite et al., 2014). The application of the suite of modern
omics techniques, specifically genomics, transcriptomics, and metabolomics, to study and improve
macroalgae is not only relevant to aquaculture seaweed production for human use; however, native
macroalgae species are often habitat-forming and are integral species in coastal ecosystems
(Teagle et al., 2017). Wild temperate kelps have been increasingly threatened by the effects of
global warming. Although globally marine macroalgae populations have been steadier than
expected, when essential, habitat-forming macroalgae are displaced or lost in an environment, it
can be extremely detrimental to the marine ecosystem (Krumhansl et al., 2016); the loss
of Macrocystis pyrifera beds on Australian coastlines is one example (Mabin et al., 2019).
Several metabolomic studies to identify climate change-related responses have been
conducted in plant species (Ahuja et al., 2010; Arbona et al., 2013; Park et al., 2014; Peñuelas et
al., 2013). Recently, the use of metabolomics alongside other functional genomics techniques to
identify metabolic responses in climate change-tolerant and construct conservation plans has been
frequently suggested (De Ollas et al., 2019; Ouborg et al., 2010; Rilov et al., 2019). Climate
change-resilient marine macroalgae (e.g., heat-, acidification-, low nutrient-tolerant) can be
9
identified with analogous metabolomic studies to those proposed for terrestrial plants (Figure 1.2).
The future of wild marine macroalgae populations as well as domesticated macroalgae cultivars
will depend on biologically informed conservation and improvement efforts, which can be made
more effective with metabolomic analyses.
2.2 The metabolome of marine macroalgae
Metabolite profiling and analysis has a long history in marine macroalgae (Gupta et al.,
2014), but has focused on metabolites that are bioactive, pharmaceutically relevant compounds
(Davis & Vasanthi, 2011; Greff et al., 2017); seasonal variation (Surget et al., 2017); delineating
biochemical differences of green, red, and brown algae (Belghit et al., 2017); profiling during
reproductive fragmentation (He et al., 2019); and stress, defense, and environmental responses
(Gaubert et al., 2019; Gaubert et al., 2020; La Barre et al., 2004; Ritter et al., 2014). Domestication-
and improvement-based metabolomic studies in macroalgae are sparse, even for model organisms.
A promising step forward in metabolomics of marine macroalgae was the development of
a metabolomic “atlas” for brown macroalgae, called EctoGEM, generated through a genome-scale
analysis of the metabolome of the model organism Ectocarpus siliculosus (Prigent et al., 2014).
The state of omics research in marine macroalgae is advancing rapidly (Cock et al., 2010; Collén
et al., 2013; Liu et al., 2019; Michel et al., 2010; Ritter et al., 2014), and it is rising to meet more
advanced functional genomics techniques to improve and optimize macroalgal species for
aquaculture applications.
2.3 Metabolomic analysis through a structural equation modeling framework
Metabolomic data sets are constructed using mass spectrometry (MS) and nuclear magnetic
resonance (NMR) on tissues of interest (Emwas et al., 2019; Zacharias et al., 2018). The specific
classes of primary and secondary metabolites captured by varying MS and NMR analyses have
10
been reviewed extensively elsewhere (Bingol, 2018; Emwas et al., 2019; Emwas, 2015; Gupta et
al., 2014; Kumar et al., 2016; Nemkov et al., 2019). Metabolites are the intermediate and final
molecules modified and consumed by protein activity. The type and abundance of metabolites
present are intimately tied to gene expression (Burgess et al., 2014; Nelson & Cox, 2017).
In an effort to investigate this relationship between metabolic flow and gene expression,
metabolomic data sets are often complemented with RNA sequencing and phenotypic data (Fernie
& Schauer, 2009). To model metabolic pathways with metabolomic and gene expression data,
structural equation modeling (SEM), also known as confirmatory factor analysis, is a powerful
tool. Modeling the metabolism provides a deeper understanding of the underlying pathways
determining phenotypes of interest and how specific genetic variants perturb these metabolic
pathways (Karns et al., 2013). SEM is a supervised approach based on geneticist Sewell Wright's
path analysis, where the order and direction of the relationships between genes and metabolites are
intrinsic parts of the structural model (Igolkina & Samsonova, 2018; Wright, 1918, 1921).
Previously, SEM has been used to model gene regulatory networks (GRNs) in both animals (Fear
et al., 2015) and plants (Igolkina et al., 2019). Recent applications of SEM include annotations of
underlying pathways for biomass development in rice (Momen et al., 2019), grain yield in wheat
(Vargas et al., 2007), and body mass index in humans (Kaakinen et al., 2010). They have also been
used to construct local GRNs based on patterns of differential gene expression (Aburatani, 2012;
Tarone et al., 2012). The SEM framework can validate results using a variance × covariance
structure of metabolites and transcripts across genotypes. This allows for the expansion of
metabolic and transcriptomic networks. Using existing knowledge about a given GRN as a
baseline model, geneticists can systematically scan macroalgae genomes for additional
components and improve their understanding of existing networks. To validate constructed
11
metabolic networks, flux balance analysis, a network reconstruction-based approach, is often used
to model the flow of small molecules through known reactions (Orth et al., 2010).
Many macroalgal systems, such as the model brown macroalga E. siliculosus, model red
alga Chondrus crispus, or the commercial kelp S. japonica, have been characterized with genomic
and transcriptomic methods (Cock et al., 2010; Collén et al., 2013; Liu et al., 2019). Expanding
upon these data to develop full metabolomic models for organisms can reveal which genetic
variants affect phenotype, which can be probed to maximize beneficial (e.g., growth and yield)
phenotypes and would particularly benefit macroalgae crops in aquaculture.
As more studies harnessing modern omics methods are conducted in macroalgae,
domestication and improvement of macroalgae species will become more efficient and be able to
accomplish phenotypic changes with informed crossing schemes and genetic manipulation, which
have taken thousands of years for traditional crop domestication in agriculture (Zsögön et al.,
2018).
2.4 The macroalgae holobiont: combining metabolomics and metagenomics to probe host–
symbiont interactions
The term “holobiont” emerged in the context of holistic biology as early as the 1940s with
Dr. Adolf Meyer-Abich (Baedke et al., 2020). Modern usage of the term to describe the
relationship between organisms and their closely associated microbiota has been traced back to
work by Dr. Lynn Margulis in 1991. Initially described as the relationship between a host and a
single symbiont, the term has since evolved to describe a host organism and its associated biome,
specifically including symbionts without which the holobiont would be able to perform its
functions (Margulis & Fester, 1991). To learn more about the evolution of this term, we refer the
reader to a recent review by Baedke et al. (Baedke et al., 2020). The holobiont concept has
12
transformed the way scientists think about host organisms and their associated microbiota
(collectively termed the microbiome). Specifically, it promotes the symbiotic relationship between
host and microbiome by considering the microbiome and extended phenotype of the host and
recognizing how both entities are influenced and shaped by the other. This frame of thought has
been applied to microbial studies of many eukaryotes, including plants and mammals.
Applying this concept to seaweeds is a particularly strong example of holobionts in action,
as microorganisms (specifically bacteria) are recognized to play an essential role in the health and
fitness of aquatic plants and algae. As previously reviewed in Egan et al. (2013), many species of
macroalgae rely on their associated microbes to provide essential nutrients (such as CO2 and fixed
nitrogen) required for proper growth and development (Egan et al., 2013). To that end, several
models have been proposed on how macroalgae may go so far as to “recruit” beneficial microbes
by creating a desirable habitat by, for example, metabolite or chemical mediation (Saha &
Weinberger, 2019). Given these tight associations, it is reasonable that macroalgal species and
their microbial communities may have co-evolved to rely on each other's biological mechanisms
and lose redundant gene pathways. Indeed, there exists an exciting opportunity to leverage existing
knowledge of the macroalgal holobiont and apply tools from metabolomics, metagenomics, and
transcriptomics to understand what genes are present and being expressed in the host and microbial
metagenome (Egan et al., 2008). Exploiting the fruits of these discoveries—which may include
identifying microbe species that serve essential functions for the macroalgae holobiont,
determining growth-promoting nutrients at specific macroalgae life stages, and creating beneficial
or protective microbial inoculants—will be essential for future genomic strategies for
domestication and improvement of economically relevant macroalgae species.
3 Application of metagenomics to macroalga domestication
13
High-throughput sequencing, such as shotgun metagenomics, is a powerful resource for
understanding microbial communities (Olson et al., 2019; Sieber et al., 2018; Wilkins et al., 2019).
Advances in this field have enabled the discovery of novel genes and pathways that contribute to
overall microbiome function. By combining metagenomics data from the native microbiome with
physiological and genomic data from the host, we can investigate how the microbiome can be
utilized to optimize host growth. There are several review papers on the use of metagenomic data
to understand taxonomic composition and functional profile of the microbiome (Alves et al., 2018;
Chiu & Miller, 2019; Gilbert & Dupont, 2011; Quince et al., 2017), and how these methods can
benefit aquaculture (Martínez-Córdova et al., 2015; Tello et al., 2019). Specifically, functions of
the microbiome can be exploited to improve crop productivity by providing essential nutrients or
increasing disease resistance (Caruso, 2013; Ezemonye et al., 2009; Martínez-Córdova et al.,
2015).
Application of these methods to macroalgae domestication is a unique opportunity and a
powerful tool for research in agriculture and microbiomes because macroalgae is a fast-growing
organism and its overall fitness is tightly intertwined with the native microbiome (Busetti et al.,
2017; Egan et al., 2013; Florez et al., 2017; Laycock, 1974; Tello et al., 2019).
3.1 Understanding the microbiome
One of the challenges around developing macroalgae as a commercial crop is that they are
highly adapted to their local environment and do not demonstrate resilience or consistent
physiological traits when farmed in new locations (Grebe et al., 2019; Minich et al., 2018; Z. Qiu
et al., 2019; Zacher et al., 2019). An example of this is giant kelp (M. pyrifera), a brown
macroalgae that thrives in coastal environments and is a promising resource for future domestic
biofuel production. Although giant kelp is one of the fastest growing organisms, farms along the
14
US coast would not produce enough harvest to support giant kelp as a viable biofuel feedstock
(Kim et al., 2019). Domestication of giant kelp in offshore farms would provide ample space for
necessary production rates, but current practices of farming still remain uncompetitive to wild
harvested macroalga. Similar challenges arise with the domestication of other species of
macroalgae that are native to coastal environments. Offshore farms present a significant challenge
in that they experience lower nutrient concentrations and are a more stressful environment for
macroalgae compared to coastlines. Engineering solutions to overcome issues of containment,
nutrient availability, and protection will be critical for offshore farming (Duarte et al., 2017;
Roesijadi et al., 2008). Some individuals may be successful in this environment by chance, yet the
unpredictable growth of seaweeds in offshore farms is a critical barrier to large-scale cultivation
and commercial applications. Although breeding programs can be developed to optimize the gene
content of macroalgae and promote genotypes that are resilient in low nutrient conditions, there
remain significant challenges. However, limitations of breeding, such as trade-offs in yield and
stress resistance, can be overcome by microbial treatments that improve crop fitness and
predictable growth (Tack et al., 2016). Development of these treatments requires better
understanding of seaweed–bacterial associations, which are necessary for proper growth,
recruitment, and development of seaweeds (Fitzpatrick et al., 2018; Morris et al., 2016).
The surface microbiome of seaweeds is composed of a diverse community of
microorganisms that contribute to host health and form a biofilm across kelp blades. Macroalgal
hosts rely on resident surface-associated microbes for proper growth and development. In addition
to providing growth-benefitting compounds, epiphytic microbes provide a number of additional,
beneficial services such as nutrient acquisition and protection from pathogens (Crawford & Clardy,
2011; Dubilier et al., 2008; Egan et al., 2013; Egan et al., 2008; Naragund et al., 2020; Wahl et al.,
15
2012). For example, certain seaweed-associated bacterial isolates are capable of fixing
atmospheric nitrogen, which is often a limiting nutrient for kelp growth and development (Singh
& Reddy, 2014).
Given that many species of macroalgae are not natively found offshore, this provides a
unique challenge for open-ocean farming. The open-ocean environment constitutes a low nutrient
and stressful environment for macroalgae (Hawkes & Connor, 2017). For field applications,
microbial inoculations face an additional challenge as native members of the kelp microbiome may
outcompete introduced microbes or their interactions may prove to be detrimental to kelp growth
(Gause, 1934; Hutchinson, 1957; van Veen et al., 1997). By investigating community assembly
patterns, we can navigate ecological niches to reduce competition and build a predictive
understanding of how microbial treatments will impact the microbial community and overall
growth of seaweed in aquaculture. Understanding functional traits across the community is also
vital to developing successful microbial treatments for aquaculture. Although it remains a
challenge to characterize function across the community, metagenomic sequencing summed across
taxa provides sufficient representation of overall function compared to species-specific
characteristics (Fierer et al., 2014; Jiang et al., 2016). Furthermore, host genotype also plays a key
role in recruiting and sustaining a beneficial microbial community (Horton et al., 2014). By
treating the microbiome as an extended phenotype of the host, one can perform a GWAS to identify
genotypes or specific genes that are better suited to supporting a beneficial microbial community,
which can guide future breeding design to compound the growth benefit of fast-growing genotypes
and beneficial microbes (Awany et al., 2018).
Few studies have attempted to rigorously interrogate macroalgal host–microbe
associations, assess the impact of host genotype on microbial community recruitment, or utilize
16
these interactions to support macroalgae as a commercial crop. Amplicon sequencing, such as that
of 16S rRNA, is a great resource for understanding species diversity in microbial communities.
However, the added benefit of using metagenomic shotgun sequencing for studying the
microbiome is that the greater genomic coverage and data output give insight into overall
functional diversity, in addition to identifying unique and novel members of the community.
Utilizing these types of data would enable us to characterize not only community assembly patterns
and identify beneficial species of the microbiome, but also the impact of genetic diversity on
seaweed-associated microbial communities to find host genes promoting or restricting recruitment
of different symbionts. Although several studies have investigated the microbial community of
several macroalgal species (Hawkes & Connor, 2017; Minich et al., 2018; Morris et al., 2016;
Smith et al., 2018), this field would benefit from a large-scale study in a single well-controlled
farm environment to understand microbiome structure and functional diversity in the context of a
wide range of macroalgae genotypes. Understanding the mechanisms and functions of the
microbial community, and the interaction between macroalgae and bacteria in offshore farms, is
essential for ensuring success of the rising macroalgae aquaculture industry.
3.2 Using metagenomic data to understand community assembly patterns of the
microbiome
It is well recognized that targeted metagenomic sequencing, such as 16S rRNA sequencing,
is frequently insufficient to characterize taxonomic and functional variation in microbial
communities. Shotgun sequence metagenomic data can be used to fully annotate the species and
functional diversity of microbial communities across a wide range of macroalgal species.
Taxonomic identification and analysis of the microbial community with shotgun data requires the
construction of Metagenomic Assembled Genomes. An example of a program to assist with this
17
step is Anvi'o, an advanced analysis and visualization platform for omics data (Eren et al., 2015).
To create consensus taxonomy for each unique genome, Anvi'o uses both single-copy core genes
(SCGs) and the taxonomy determined by The Genome Taxonomy Database (Parks et al., 2020;
Parks et al., 2018). Established species abundance tables then enable one to investigate community
assembly patterns and develop a predictive understanding of how introduction of novel species
will interact with the native community. A challenge often associated with analysis of
metagenomic data is low coverage organisms and closely related taxa. To correct for this, there
exist programs such as BinSanity, which uses an algorithm that clusters assemblies using coverage
with compositional-based refinement to optimize bins containing multiple source organisms
(Graham et al., 2017).
Previous studies have analyzed microbial and benthic macroinvertebrates using
community ecology tools such as beta (β) and zeta (ζ) diversity (Doane et al., 2017; Simons et al.,
2019). These tools, which are established methods for measuring compositional change across
ecological communities, can be used to analyze community assembly patterns in the context of
macroalgal surface microbiomes (Hui & McGeoch, 2014; McGeoch et al., 2019). These tools
traditionally require that microbiome samples be collected from separate sites and use distance
between sites to measure assembly patterns across a larger area. However, in macroalgal farm
settings, by treating each individual as an independent community, and having information on the
genome of the host, genetic distance between individuals can be used to establish the foundation
for community assembly patterns. Moving from individual phenotypes (microbial loads) to
functional groups, exploratory and confirmatory factor analyses, such as structural equation
models described earlier, combined with QTL mapping can be used to determine how kelp genes
affect community structures. These computational tools are innovative and have recently been
18
applied to analyze the effects of human genetic variation on the metabolome (Igolkina &
Samsonova, 2018), but have not yet been considered for microbial communities.
3.3 Identifying beneficial bacteria using metagenomics and physiological crop traits
Metagenomic analysis in macroalgal farm settings provides a powerful tool when
combined with physiological trait data of crops. By focusing on desired physiological traits, such
as blade weight or nutrient composition, one can establish a metric to assess “successful” growth
and consequently identify beneficial microbial species or groups of species. Co-occurrence
networks can be used to determine microbial species most often associated with successful growth.
These species can be considered “beneficial” and used to tailor future aspects of the analysis
pipeline, such as analysis of functional traits. Understanding functional traits across the community
is vital to developing successful microbial treatments for aquaculture. One trait of particular
interest is nitrogen fixation. Nitrogen is an important resource for macroalgae growth (Hanisak,
1983; Harrison & Hurd, 2001; Zehr et al., 1996). Metagenomic sequence data would enable one
to characterize symbiotic relationships between seaweed and nitrogen-fixing bacteria that have not
yet been described. Preliminary studies have demonstrated a pattern of strong, and potentially
vertical, co-transmission of Mesorhizobium spp. and Sinorhizobium spp. with giant kelp (Minich
et al., 2018). This points to an opportunity to enhance nitrogen fixation in brown macroalgae and
optimize growth since more than half of nitrogen fixation in Sargassum is thought to be derived
from associated microbes (Phlips & Zeman, 1990; Raut et al., 2018) and is known to be a limiting
factor in M. pyrifera (Minich et al., 2018; Raut et al., 2018).
For this type of analysis, one can consider each genotype as an independent experimental
observation with a unique effect on its associated microbiome. Specifically, metagenomic shotgun
and 16S rRNA sequencing of the microbiome can be used in a quantitative model to infer kelp
19
genes affecting recruitment and diversity of the native microbial communities. Community
profiling, including species abundance and functional niches, can be assessed using established
methods for taxonomy assignment and gene annotation (Eren et al., 2015; Ortiz-Estrada et al.,
2019; Sieber et al., 2018; Vollmers et al., 2017; Woloszynek et al., 2019).
3.4 Characterizing impact of host genotype on microbial community
Similar to work done in other organisms, structural equation models and QTL mapping can
be used to determine how kelp genes affect overall community structure of the native microbiome
(C. Chen et al., 2018; Horton et al., 2014; Jones et al., 2019). There have been a number of studies
investigating the microbiome of several species of seaweeds such as Porphyra and Pyropia
(Aydlett, 2019; Miranda et al., 2013; Quigley et al., 2018; Yan et al., 2019). The value of these
studies, and overall impact on the macroalgae aquaculture industry, can be enhanced by further
considering the impact of the most (macroalga) genotype on the recruitment of the native
microbiome community structure.
For every group of kelp genotype with significant microbiome difference, one can generate
co-occurrence networks to illustrate the likeliest associations between kelp genes of interest and
particular members of their microbiome. In farm settings, unique kelp genotypes can be considered
as independent experimental observations, each with a unique effect on its associated microbes.
Similar work has been applied in humans (Igolkina et al., 2018), agriculture (Shin et al., 2019),
and other aquaculture crops (Simons et al., 2018). Metagenomic work for large numbers of
samples can be cost prohibitive. Although a restricted number of metagenomic samples can be
limiting for GWAS, this can be overcome by identifying strongly diverged populations of
macroalgae and focusing on genotypes collected by hybrid zones. By having a sufficient number
of samples come from hybrid zone lineages and focusing on QTL mapping, it becomes more
20
feasible to have sufficient statistical power and replication to determine correlations between kelp
genotypes and their microbiomes. To further focus this type of analysis, genotypes represented in
this work, and microbiome samples to be analyzed, can be chosen based on whole genome
sequencing (WGS) data from macroalgal individuals.
3.5 Technical challenges of analyzing host genome impact on microbiome
To accomplish GWAS and QTL analysis, the microbial community should be treated as a
multi-trait extended phenotype, with groups of community members or functions considered as
different traits. For sparse data sets, pairwise distance matrices using beta-diversity can be used as
the quantified host phenotype. Several approaches for multi-trait models have been proposed, but
analysis can be challenging with correlated traits such as species abundance (Hackinger & Zeggini,
2017; Yang & Wang, 2012). One way to cope with correlated traits is to model the inter-trait
covariance with random effect in linear mixed-effects models (Laird & Ware, 1982). Until
recently, this model could use only a pair of correlated traits at a time due to the computational
intensity (Korte et al., 2012). To reduce this load, variable reduction techniques have been
suggested to replace several phenotypic traits with new independent constructs. These constructs
play the role of new traits and can be obtained with a standard principal component analysis of
traits, various principal components of heritability (Baltimore, 1986; Lange et al., 2004; Wang et
al., 2007) or pseudo-principal components (Gao et al., 2014). Another challenge in association
studies is to develop a powerful multi-locus model. Testing SNP by SNP, single-locus models
require correction for multiple testing afterward, which can eliminate important quantitative trait
variants. To avoid this problem, multi-locus models, that consider all markers simultaneously, can
be applied. Due to the “large p (number of SNPs), small n (sample size)” problem, many multi-
locus models are based on regularization/penalized techniques including LASSO (Wu et al., 2009),
21
elastic net (Cho et al., 2009), Bayesian LASSO (Yi & Xu, 2008), and adaptive mixed LASSO
(Wang et al., 2011). Other multi-locus methods (incorporated in the mrMLM package) involve
two-step algorithms, which first selects candidate variants in single-locus design and then
examines them together in a multi-locus manner (Wen et al., 2018).
Despite the broad spectrum of multi-trait and multi-locus models in GWAS and trait
prediction studies, only a few of them simultaneously incorporate correlated traits and several
associated variants (Dutta et al., 2018; Lippert et al., 2014; Liu et al., 2016; Weighill et al., 2019;
Zhan et al., 2017). In principle, multi-trait and multi-locus models have a potential to reveal
complex and important types of associations, for instance, a single variant might have a direct
effect on one trait, and an indirect impact on the other trait; a SNP may act on a single trait or its
effect might be pleiotropic affecting several traits. However, none of these trait–variant
associations are explicitly embedded into known models, but they can be directly accounted for
with the previously described method SEM, a multivariate statistical analysis technique first
introduced for path analysis by geneticist Sewell Wright (Wright, 1918, 1921). SEM has been
widely used in the fields of genetics, econometrics, and sociology, and current SEM applications
are gradually shifting to molecular biology (Igolkina et al., 2018). SEM models have also been
applied in the association studies in both multi-trait and multi-locus designs. For example, the GW-
SEM method was developed to test the association of a SNP with multiple phenotypes through a
latent construct (Verhulst et al., 2017). It was demonstrated that in comparison with the existing
multi-trait single-locus GWAS software package GEMMA (Zhou & Stephens, 2012), GW-SEM
provides for more accurate estimates of associations; however, GEMMA was almost three times
faster than GW-SEM. Another SEM-based model that can be used in association studies was
proposed for multi-trait QTL mapping (Mi et al., 2010). This method proposes that phenotypes are
22
causally related, forming a core structure without latent constructs, and that QTLs play the roles
of exogenous variables to the structure. This approach allows the model to decompose QTL effects
into direct, indirect, and total effects.
Addressing these challenges is critical to understanding and properly analyzing the impact
of the host genome on the recruitment of the native microbiome. Applications described here have
not been applied to the seaweed microbiome and should be explored in more detail to have an
impact on the aquaculture industry.
4 Conclusions
Advances in modern omics technology, including in sequencing and metabolite profiling,
have created powerful tools and analysis pipelines for understanding the metagenome and
metabolome of living organisms. Established pipelines from functional genomics research in
agriculture are beginning to be applied to seaweed systems but must be expanded to improve the
development of seaweed aquaculture. Metabolomic analysis has been used to fine-tune
domestication and crop improvement strategies in agriculture by revealing pathways involved in
crop nutrition, flavor, and stress response. Applying metabolomics analyses—alongside other
functional genomics analyses—more broadly in marine macroalgae toward improvement of
cultivars for aquaculture will allow scientists to identify significant pathways for macroalgae
domestication more efficiently.
The microbiome plays an important role in the overall health of several macroalgal species
and it is imperative to further understand microbial communities in the context of host genotypes
to ensure the success of the rising macroalgae industry in the United States. Development and
understanding of host–microbe associations in aquaculture can lead to microbial inoculations to
23
boost crop yields, and future breeding programs should focus on the compounded benefit of
seaweed genotypes and optimized microbial communities. Further investigation of the
microbiome and metabolome in seaweeds has the potential to greatly improve current methods of
US macroalgae domestication.
By treating the metabolome and microbiome as extended phenotypes, pathways for
domestication and optimized breeding go beyond the traditional desired physiological traits in
crops, such as size and nutrient load. Specifically, functional traits of the microbiome, such as its
ability to fix nitrogen or prevent disease, can be exploited to improve overall crop health and
fitness. Application of these tools in macroalgae is a unique opportunity because it is a fast-
growing organism that heavily relies on an optimal, environment-adapted metabolic profile, and
beneficial microbial interactions.
With the rise of marine macroalgae in the aquaculture industry, there exists a unique
opportunity to utilize modern developed omics tools to domesticate macroalgae species rapidly,
including by optimizing their metabolomic and metagenomic profiles (Figure 1.3). As future
breeding programs are developed to establish the United States as a world leader in aquaculture,
we must incorporate the powerful tools and recent advances in modern omics techniques to guide
our breeding protocol and turn to functional genomics to greatly improve upon traditional breeding
programs. Metagenomics and metabolomics analyses provide important contributions that
deserves future consideration as scientists continue to explore omics in aquaculture.

24
Figures
Figure 1.1. Domestication of marine macroalgae species.

Domestication of marine macroalgae species. Many wild marine macroalgae populations settle on
rocky substrates, providing important habitats for hundreds of marine species. There is much
variation in rate of growth and chemical composition among wild macroalgae populations. Similar
to agricultural crops, this natural phenotypic variation is reduced in domesticated macroalgae
cultivars as the phenotype is optimized.

25
Figure 1.2. Use of omics techniques to investigate temperate kelp adaptation.

Use of omics techniques to investigate temperate kelp adaptation to climate change. (a) Kelps
grown in cool water (blue, high [nitrate]) are expected to perform better overall than kelps grown
in warmer water (red, low [nitrate]). The blue star denotes the hypothetical fittest cool-water
individuals, while the red star is fittest in warm water. (b)–(d) Analyses of individual kelps in setup
of (a). (b) Population genetics analysis of SNPs segregating between fittest kelps in cool versus
warm water. (c) Differential expression analysis between fittest individuals in cool versus warm
water. (d). Identification of pathways involved in kelp adaptation to warming oceans with genetic,
transcriptomic, and metabolomic data.

26
Figure 1.3. Overview of omics techniques for macroalgae domestication.

Overview of omics techniques currently used and proposed for use in macroalgae domestication
and improvement. Proposed schema for the application of DNA and RNA sequencing,
metagenomics to efficiently improve and domesticate macroalgae, emphasizing how each analysis
can complement and build upon others. The information gained through performing metagenomic
and metabolomic analyses on top of conventional sequencing analyses—including the
identification of potential microbial inoculants for macroalgae cultivars, essential nutrients for
macroalgae growth, and the mechanism by which genetic variants of macroalgae species produce
desirable phenotypes—makes a case for the increased collection and utilization of these important
data sets and analyses.

27
Chapter 2: Natural variation of Macrocystis pyrifera gametophyte germplasm culture
microbiomes and applications for improving yield in offshore farms

As published in the Journal of Phycology,
Co-authored with Gary Molano, Ariel Levi Simons, Valerie Dao, Brandon Ong, Brandon Vong,
Anupam Singh, Gabriel J. Montecinos Arismendi, Filipe Alberto, and Sergey V. Nuzhdin.

Preface
The work presented in this chapter was produced with the support of co-authors listed
above. All content within this chapter should be evaluated as part of this dissertation. The
contribution of each co-author is as follows: MGO designed and performed research, analyzed
data, and wrote paper. GM and ALS guided research design and edited paper. VD and BO
performed literature review. BV performed literature review and edited paper. AS guided research
design and edited paper. GJMA contributed to germplasm sequencing and maintenance. FA guided
research design and edited paper. SVN guided research design and edited paper. Minor edits from
the published version of this chapter were made to ensure consistent formatting throughout this
dissertation.
Abstract
With national interest in seaweed-based biofuels as a sustainable alternative to fossil fuels,
there is a need for tools that produce high-yield seaweed cultivars and increase the efficiency of
offshore farms. Several agricultural studies have demonstrated that the application of microbial
28
inoculants at an early life stage can improve crop yield and there is an opportunity to use similar
techniques in seaweed aquaculture. However, there is a critical knowledge gap regarding host-
microbiome associations of macroalgae gametophytes in germplasm cultures. Here, we investigate
the microbial community of Macrocystis pyrifera gametophyte germplasm cultures that were used
to cultivate an offshore farm in Santa Barbara, California and identify key taxa correlated with
increased biomass of mature sporophytes. This work provides a valuable knowledge base for the
development of microbial inoculants that produce high-biomass M. pyrifera cultivars to ultimately
be used as biofuel feedstocks.
Introduction
The presence of bacteria is crucial for plants and widely understood to impact the growth
and development of plants across different life stages (Lata Rana et al., 2020). Host-microbe
associations are highly specialized to individual hosts and can impact plant phenotypes and core
functions through direct or indirect mechanisms, such as the supply of nitrogen or disease
repression (Awany et al., 2018; Zhiguang Qiu et al., 2019). For this reason, manipulating the
microbial composition of plants can increase their resilience in challenging environments (Trivedi
et al., 2022). Although there is a wealth of research on the exploitation of host-microbe associations
to improve plant fitness, most work has focused on land plants and agricultural crops. Building off
the knowledge that host-microbe associations in aquatic plants, such as seaweeds, similarly impact
fitness, there is an exciting opportunity to extend existing research into seaweed aquaculture
(Osborne & DeWeese, 2021).
Seaweed aquaculture, the harvest of wild and farmed seaweeds, is a global industry that in
2018 valued over $13 billion USD (FAO, 2020). Seaweed farming not only produces biomass to
29
support commercial interests (such as the pharmaceutical, food, and cosmetic industries), but also
improves ocean conditions by sequestering excess nutrients and carbon to reduce eutrophication
and ocean acidification (García-Poza et al., 2020; Kang et al., 2021; Kübler et al., 2021; Park et
al., 2021; Racine et al., 2021). This industry is now being targeted in the U.S. as a viable and
sustainable resource for biofuel feedstocks that can replace the use fossil fuels and reduce carbon
emissions. In fact, the Department of Energy Advanced Research Projects Agency-Energy has
invested over $60 million through programs such as the Macroalgae Research Inspiring Novel
Energy Resources to fund projects and research that advance seaweed aquaculture in the U.S. for
this purpose (ARPA-E, 2021). In conjunction with this effort, the National Oceanic and
Atmospheric Administration has identified Southern California as a prime location for developing
the aquaculture industry (ARPA-E, 2021; NOAA, 2020).
Macrocystis pyrifera, a brown macroalgae commonly known as “giant kelp”, is a key
candidate for biofuel production in Southern California. It is native to the California coastline and
has the potential to double its biomass every 34 days (Navarrete et al., 2021; Reed et al., 2008). A
majority of biofuel consumed in the U.S. is produced by blending petroleum gasoline with ethanol
extracted from renewable resources, such as seaweeds, and is typically used for transportation.
The high carbohydrate and low lignin content of M. pyrifera make it an ideal feedstock for
producing ethanol through fermentation, with an ideal yield estimated to be ~0.281 weight (wt)
ethanol/wt biomass (Bellona, 2017; Camus et al., 2016). Anaerobic digestion can also be
performed on M. pyrifera to produce biogas, which can be used as a replacement for natural gas
for heating and electricity (Bellona, 2017; EIA, 2022).
M. pyrifera sporophytes (adults) propagate by releasing spores that develop into male and
female gametophytes. This haplodiplontic life cycle of M. pyrifera enables individual genotypes
30
to be clonally and vegetatively propagated and maintained in a gametophyte germplasm bank,
which provides a library of genetic diversity that can support robust crop lines and serve as a
reliable source material for farms. Targeted crosses of specific genotypes in this germplasm can
be repeatedly crossed towards specific phenotypic targets, such as increased biomass. We used our
germplasm to create a breeding program for M. pyrifera that would result in high-biomass
individuals that can thrive in an offshore farm off the coast of Santa Barbara. The work presented
in this study will ultimately improve the efficiency of offshore farms by creating higher yields per
acre and increasing the biomass available for biofuel feedstocks through the use of microbial tools.
Offshore farms in Southern California can provide the necessary light (~25 μmol photons
m
-2
s
-1
), nutrients (>1 μM nitrate), and ambient temperature (11-19 ºC) for M. pyrifera to thrive
(Deysher & Dean, 1986; Neushul & Haxo, 1963). However, as demonstrated by our offshore test
farm in Santa Barbara, seaweed grown in the open ocean can have highly variable biomass
outcomes (Table S2.1). Host phenotype can be impacted by several factors including the local
environment, host genetics, host gene regulation, and the resident microbiome (Awany et al.,
2018). Given that this study was performed in the same farm, and assuming that this environment
is relatively homogenous, the effect of environmental variance on M. pyrifera phenotypes in this
study is minimized. Our group is separately investigating how the genetic factors of M. pyrifera
impact phenotypic outcomes in this farm (unpublished data). Therefore, with the knowledge that
microbial community composition can impact plant phenotypes, and the growing popularity of
microbial inoculants in agriculture, here we sought to explore the impact of the microbiome on
host phenotype and identify bacteria associated with high-biomass individuals that could be
candidates for a growth-promoting microbial inoculant to be applied to future seaweed crops.
31
Microbial inoculation, the application or introduction of beneficial bacteria to plants, has
been explored to improve the fitness of crops in agriculture (Santos et al., 2019). There are several
successful examples of inoculants increasing the yield of agricultural crops including rice, peanut,
maize, cotton, lavender, wheat, tomato, and spinach (Bakhshandeh et al., 2015; Dey et al., 2004;
Hameeda et al., 2008; Kaur & Reddy, 2015; Mengual et al., 2014; Qureshi et al., 2012; Song et
al., 2015). In fact, previous research has shown that inoculating seeds with growth-promoting
bacteria can increase farm productivity, and that the composition of seed microbiomes can be an
early predictor of final yield (Bisen et al., 2015; O’Callaghan, 2016; Zhiguang Qiu et al., 2019;
Rocha et al., 2019; Santos et al., 2019; Singh et al., 2016). Seed microbiota ultimately impact plant
performance through priority effects, meaning that the first taxa to colonize a host can influence
successive community structure, function, and host-microbe associations throughout the rest of the
plant life cycle (Shade et al., 2017). In addition to this work on early life-stage microbial
inoculants, this study builds off previous investigations of the microbial diversity and host-microbe
associations of wild M. pyrifera sporophytes and lab-grown gametophytes (Lin et al., 2018; Minich
et al., 2018; Morris et al., 2016; Pfister et al., 2019; Ramírez-Puebla et al., 2022; Weigel & Pfister,
2019, 2021). The overall fitness and resilience of seaweeds are partly regulated by resident
microbial communities that provide essential services for normal growth and development, such
as scavenging for growth-limiting nutrients or providing secondary metabolites (Egan et al., 2013).
Of particular interest for this study, a previous study found that altering the microbial community
abundance of lab-grown gametophyte cultures can increase growth rate (Morris et al., 2016). To
the best of our knowledge, there is no published work investigating the microbiome of early life
stage farmed seaweeds (i.e., gametophyte cultivars) and its impact on final biomass yield.
However, the existing knowledge of host-microbe associations in various seaweeds and early life
32
stage inoculants in agriculture provides sufficient evidence to suggest that gametophyte microbiota
can be manipulated to ultimately impact sporophyte yield.
Here, we investigate the microbial community of M. pyrifera gametophyte germplasm
cultures collected from distinct natural populations across Southern California. This germplasm
culture was used to produce sporophytes grown on an offshore farm in Santa Barbara, California.
We compare microbial community differences across populations and biomass outcomes of
mature sporophytes to identify key taxa that may be used in growth-promoting inoculants. We
hypothesize that the microbial composition of gametophyte germplasm cultures will significantly
vary across populations, and that there will be unique microbial taxa whose abundances will be
significantly associated with high-biomass M. pyrifera sporophytes at the gametophyte stage.
Microbial taxa associated with increased biomass will be prime candidates for the development of
a growth-promoting inoculant. Overall, this work provides a valuable knowledge base for host-
microbe associations in seaweed aquaculture and the development of early stage microbial
inoculants that produce high-biomass M. pyrifera cultivars to ultimately be used as biofuel
feedstocks. As the industry moves toward large-scale production, this work will complement other
tools being developed to improve seaweed farming techniques and revolutionize the production of
seaweed-based biofuels (Gao et al., 2020; Leyton et al., 2020; Navarrete et al., 2021; Osborne &
DeWeese, 2021; Purcell-Meyerink et al., 2021).
Materials and Methods
Sporophyte collection. Sporophylls, the reproductive blades of M. pyrifera that contain
spores, were collected from natural kelp beds in the fall of 2018 (early December) from four
Southern California regions: (1) the Santa Barbara Channel (Arroyo Quemado, n = 60), (2) the
33
Channel Islands (Catalina Island at the Wrigley Marine Science Center, n = 60), (3) the South
(Camp Pendleton, n = 60), and (4) a hybrid zone (Leo Carrillo, n = 370) (Table 2.1). These
locations represent four distinct populations that correspond to areas of genetic divergence in the
region (Johansson et al., 2015) and were sampled to start a germplasm collection of gametophytes.
Sporophylls were shipped overnight to the University of Wisconsin-Milwaukee for gametophyte
isolation and DNA extraction.
Gametophyte isolation and DNA extraction. One day after sampling, each sporophyll
was cleaned and spore release was induced following the Oppliger method (Oppliger et al., 2011).
Briefly, spores were released in sterile Provasoli-Enriched Seawater (PES) (Provasoli, 1968) made
with Instant Ocean Sea Salt and ultrapure water (Symplicity water system) at a salinity of 34 PSU.
Spores were then inoculated in 60 mm x 15mm Petri dishes with 10 mL of PES. For each
sporophyte donor we used two dilutions to obtain final densities of 10 spore mm
-2
and 100 spore
mm
-2
that allowed for single gametophyte isolation. Petri dishes were placed in a plant growth
chamber under red light (fluorescent tubes wrapped with red cellophane paper) with light intensity
20 μmol photons m
-2
s
-1
(checked with Quantum/Radiometer/Photometer LI-COR 185A) and a
12:12 (Light:Dark) photoperiod at 12º C. PES medium was replaced every two weeks while the
spores were in this low density phase. Once gametophytes had grown to approximately 100μm, a
Pasteur pipette that had the tip thinned out by a Bunsen burner was used under an inverted
microscope to isolate and place single gametophytes in a 24-well plate. Gametophyte sex was
identified based on cell size. Dimorphism between female and male gametophytes is characteristic
in Laminareales; in M. pyrifera, female gametophyte cells are 5 to 7 times larger than male cells
(Luthringer et al., 2014; Schiel & Foster, 2015). All gametophyte cultures were grown under red
light (30 μmol photons m
-2
s
-1
), at 12ºC temperature. Higher light intensity at this stage was used
34
to induce faster vegetative growth and bulk up the biomass of each gametophyte culture, since
gametophytes had grown larger and adapted to increased light. PES medium was replaced every
week while cultures were in the 24-well plates. After two months of growth the isolated
gametophytes were approximately 2 mm in diameter. At this stage, to promote exponential growth
of clone biomass, selected clones were fragmented mechanically by repeated pipetting in the plate
well. After an additional month of growth, selected clones were transferred into 75 mL cell culture
horizontal bottles. PES medium was replaced every week and periodically fragmented using a
portable mini drink mixer inside the culture bottle. Prior to DNA extraction, aliquots of each
gametophyte were gently centrifuged to the bottom of an Eppendorf tube and the supernatant
medium was discarded to obtain 50 to 100 mg of gametophyte biomass. The gametophyte tissue
was then pulverized using liquid nitrogen. High quality genomic DNA from a total of 500 female
and 100 male gametophyte lines was extracted using the NucleoSpin 96 Plant Kit (Macherey-
Nagel, Duren, Germany). There was no antibiotic treatment prior to DNA extraction; therefore,
we co-extracted the haploid DNA of each giant kelp gametophyte culture with its associated
microbiota.
Shotgun sequencing of gametophytes and quality filtering of reads. Extracted DNA
was sent to the BGI North America NGS lab for library preparation and whole-genome re-
sequencing. After library preparation, samples were sequenced using an Illumina S4 Novaseq
platform, generating 11.2 GB of 150 base pair reads per sample. 562 samples were sequenced in
total. Raw fastq files were processed using the ‘fastp’ program, which filters out low-quality reads,
cuts low-quality bases, and trims off adapter sequences (S. Chen et al., 2018). Due to the lack of a
published M. pyrifera genome at the time of analysis, and evidence of bacterial contamination in
previously published brown macroalgae genomes, we included all reads in the classification
35
pipeline (See: ‘Classification and normalization of quality filtered and trimmed reads’) to ensure
that all candidate bacterial reads were analyzed (Dittami & Corre, 2017).
Sporophyte production, farm design, and phenotyping. A total of 2,500 sporophytes
(500 unique genotypes with five replicates each) were grown in an offshore farm 1-mile off the
coast of Santa Barbara, California. Sporophytes were produced from the cross of 500 unique
female gametophytes (370 from Leo Carillo, and 60 each from Arroyo Quemado, Catalina Island,
and Camp Pendleton) with a single male from Leo Carillo. Gametophyte cell cultures were
fragmented into filaments approximately 5-10 cells long by placing culture samples in 1.5mL
Eppendorf tubes and gently rotating with a pellet pestle. Female gametophyte fragments were then
seeded on polyvinyl lines (6cm long, 2 mm diameter), followed by male fragments the next day.
The lines were then exposed to white light, which was increased gradually from 15, 22, 35, to 60
μmol photons m
-2
s
-1
on the 1
st
, 2
nd
, 3
rd
, and 4
th
days following inoculation, respectively. Crosses
grew for one month until sporophytes developed and were shipped in acrylic vials with culture
medium overnight to a marine laboratory at the University of California, Santa Barbara (UCSB).
Upon arrival to UCSB the vials were placed in tanks with running seawater to maintain
temperature and white light exposure at 100 μmol m
-2
s
-1
. To facilitate out planting to the offshore
farm long-lines, we attached the sporophyte strings to a 1⁄4 inch nylon seeding line (seeding line)
at 0.5 m intervals in the laboratory the day before out planting. This was done by inserting the
sporophyte strings through the weave of the seeding line. Ten 25m seedling lines, each containing
50 sporophyte strings (described above) were fastened in series to the backbone of 1” diameter
long-line by divers resulting in 250 genotypes per longline. One replicate set of the 500 genotypes
was out planted to two adjacent longlines on the farm on the first week of May 2019. The position
of the ten 25m seeding lines that made up one replicate of the 500 genotypes was stratified along
36
the five pairs of long-lines to minimize the potential for positioning on the long-line to effect
phenotypic expression. All out planted kelp was harvested between Sept 7-12, 2019 using Santa
Barbara Mariculture’s vessel Perseverance. A total of 1,677 plants were harvested, representing
an outplant survivorship of 67% for the four month grow out period. All plants were returned to
the UCSB marine laboratory in mesh bags on the day they were harvested and stored overnight in
shaded tanks equipped with filtered flow-through seawater. The following morning, plants were
damp dried by spinning for 20 seconds, then weighed to obtain total biomass. The total biomass
for individual replicates was averaged to obtain a single value for each unique genotype (n = 500).
Of the 562 gametophyte samples sequenced, 457 samples had corresponding phenotype data and
were used in analysis for this study. The other 105 samples did not have phenotype data because
of premature loss during or death before harvest. For some of the analysis described below, unique
genotypes were either binned by population (Arroyo Quemado, Catalina Island, Camp Pendleton,
and Leo Carillo) or into biomass quantiles for the entire dataset and each population individually
(Table S2.1).
Classification and normalization of quality filtered and trimmed reads. Partial 16S
rRNA sequences were extracted from raw shotgun sequence reads and classified to the finest
possible resolution using the ‘metaxa2’ package (version 2.2.2) (Bengtsson-Palme et al., 2015).
Classified reads were then conglomerated to broader taxonomic ranks using the ‘phyloseq’
(McMurdie & Holmes, 2013) package in R (R, 2020; RStudio, 2019). The BLAST+ search option
and SSU_SILVA128 reference database were used. The metaxa2 traversal tool and data collector
were used to organize the identified SSU sequences into a taxonomy table and a raw abundance
table, which were further analyzed and processed with the ‘phyloseq’ package in R (McMurdie &
Holmes, 2013; R, 2020; RStudio, 2019). In alignment with recommended treatment of sequenced
37
datasets, we used a compositional approach and did not rarefy read counts (Gloor et al., 2017;
McMurdie & Holmes, 2014). Raw abundance counts were processed by removing singletons and
doubletons, normalizing counts by sequencer, averaging counts for samples that were sequenced
over multiple runs, and again removing any remaining singletons and doubletons. Only taxa from
the bacterial domain were kept for analysis. Unless stated otherwise, for all steps described below
we ran analyses with bacterial classifications conglomerated to five levels (class, order, family,
genus, and species) for all gametophyte samples (n = 457). Analysis at the class, order, family,
genus, and species level was done to address the challenge of taxonomic resolution and
classification uncertainty at higher levels (i.e. genus and species) and consider lower levels (i.e.
class, order, and family) as proxies for ecological function (Langille et al., 2013; Ramond et al.,
2019). For kelp population comparisons, gametophytes were grouped according to the geographic
region of their parent sporophyte: Arroyo Quemado (n = 52), Catalina Islands (n = 44), Camp
Pendleton (n = 53), and Leo Carillo (n = 308). As described above, we also ran analyses with
gametophytes grouped by quantile of adult sporophytes’ biomass instead of by population (Table
S2.1).
Fraction unclassified and taxonomic richness (alpha diversity). Metaxa2 attempts to
classify reads to the highest resolution. As a result, some reads are identified as “unclassified” at
various taxonomic levels. To calculate the fraction of unclassified taxa, we first conglomerated
absolute abundance counts of bacteria to the class, order, family, genus, or species level. Taxa that
were not present for subsets of the gametophyte data (described above) were removed. The number
of taxa that included “Unclassified” in the name were also removed and counted. The number of
unclassified taxa was then divided by the sum of unclassified taxa plus the remaining taxa in the
taxonomy table (Tables S2.2 & S2.3). Observed taxonomic richness and Shannon diversity for
38
classified species and all species including unclassified was calculated using the
‘estimate_richness’ command from the ‘phyloseq’ package. Here, we define taxonomic richness
as the number of unique taxa observed in an individual sample. Shannon diversity takes into
account taxonomic richness and evenness. Reported measures represent the average alpha
diversity metric (taxonomic richness or Shannon diversity) per individual. The significance with
which the alpha diversity of microbial communities differed between groups was evaluated using
a Kruskal-Wallis test (a = 0.05), which accounts for unequal sample sizes.
Comparison of relative abundance for each taxa across gametophyte populations. We
calculated the relative abundance of classified taxa (>1%) at the class, order, family, genus, and
species level for all populations together and each population individually (Table S2.4). The
“overall” relative abundance for each taxa was calculated using the entire gametophyte dataset
(i.e., all populations together). We identified differences in microbial composition between
gametophyte populations by subtracting the overall relative abundance from that of each
population for every classified taxa. For each taxa, we recorded which population had the largest
difference from overall relative abundance.
Compositional normalization and variance-based Principal Component Analysis
(beta diversity). We normalized and transformed absolute abundance counts of bacteria using a
compositional approach (Gloor et al., 2017). Briefly, absolute abundance counts were centered
log-ratio (‘clr’) transformed using the ‘microbiome’ package in R (Lahti & Shetty, 2017). Then,
the Aitchison distance, which is the Euclidean distance of clr-transformed counts, for each sample
was calculated then a PCA ordination was performed and visualized with the ‘phyloseq’ package
(Aitchison, 1986; Gloor et al., 2017). To identify whether there were significant differences
between group centroids we did a PERMANOVA (a = 0.05) using the ‘adonis’ function from the
39
‘vegan’ package in R (Oksanen et al., 2020). Significant differences in beta-dispersion was first
tested with PERMDISP2 (a = 0.05) using the ‘vegan::betadisper’ function and then further
investigated with ANOSIM (a = 0.05) using the ‘vegan::anosim’ function if a significant
difference was indicated with the PERMDISP2 test.
Differential abundance of individual taxa and correlation with sporophyte biomass.
Differential (relative) abundance and correlation tests were done using the ‘aldex’ command
within the ‘ALDEx2’ package in R, which clr-transforms the data (Fernandes et al., 2014). Only
bacteria that had a relative abundance count >1% were included. We ran this analysis on a subset
of M. pyrifera individuals with the highest and lowest sporophyte biomass from the top and bottom
biomass quantiles (Table S2.1). We compared the differential abundance of taxa between the two
biomass groups using a Wilcoxon t-test (a = 0.1), which was run with the base R function
wilcox.test using default settings. Highest and lowest biomass comparisons for differential
abundance testing were made for each population separately and all together. For correlation
testing, we used the continuous biomass data for all individuals and estimated the Spearman
correlation (a = 0.1) using ALDEx2 between the sporophyte biomass and individual taxa
abundance. Q-values, which are the Benjamini Hochberg multiple testing corrected p-values, were
reported for the Wilcoxon t-test and Spearman correlation (a = 0.1). Taxa of interest (q < 0.1) from
differential abundance and correlation testing were used to build a linear regression model
predicting sporophyte biomass as a factor of clr-transformed relative abundance. The ‘glm’ and
‘stepAIC’ functions within the ‘MASS’ package was used to determine best model fit and
significance (t-test, a = 0.1) (Table S2.5) (Venables & Ripley, 2002).
Results
40
Classification of bacterial community reveals evidence of a core microbiome across
all four populations of M. pyrifera gametophytes. Shotgun sequence data from a total of 457 M.
pyrifera gametophytes was used to characterize the microbial community across all samples. A
total of 1,594 species-level taxa were identified across the collective set of M. pyrifera
gametophytes, of which 1,106 species-level taxa were classifiable with the SILVA 128 database
(Quast et al., 2013) (Tables S2.2 & S2.3). We recorded taxa with >1% relative abundance in each
individual population and overall (Table S2.4). Across all populations, the most abundant taxa
belonged to the classes Alphaproteobacteria, Flavobacteriia, Cytophagia, and
Gammaproteobacteria (Figure 2.1A, Table S2.4). Highly abundant orders include Rhizobiales,
Sphingomonadales, Flavobacteriales, Cytophagales, Rhodobacterales, and Alteromonadales
(Figure 2.1B, Table S2.4). Highly abundant families include Phyllobacteriaceae,
Sphingomonadaceae, Flavobacteriaceae, Flammeovirgaceae, Rhodobacteraceae, and
Alteromonadaceae (Figure 2.1C, Table S2.4). At the species level, highly abundant taxa include
Lentilitoribacter donghaensis, Sphingopyxis sp., Nonlabens ulvanivorans, Marinobacter sp., and
Ochrobactrum sp. (Table S2.4). Analysis of species-level taxa with >0.1% relative abundance and
present in at least 75% of samples revealed that Flavobacterium sp., Lentilitoribacter donghaensis,
Marinobacter sp., Nonlabens ulvanivorans, Ochrobactrum sp. and Sphingopyxis sp. constitute a
core microbiome shared across all populations (Figure S2.1).
Alpha diversity varies by population. To investigate the complexity of gametophyte
microbial communities, we compared taxonomic richness and Shannon diversity of gametophytes
between populations and biomass quantiles. For population comparisons, we grouped
gametophytes into one of four populations based on where the parent sporophyll was collected:
AQ, CI, CP, and LC. When comparing gametophytes from all four populations, taxonomic
41
richness of bacterial species significantly differed (Kruskal-Wallis, p = 0.0016) across all
populations (Figure 2.2). For pairwise comparisons between LC and the remaining populations
(AQ, CP, and CI), LC had higher observed richness counts. This trend is similar for higher
taxonomic levels (class, order, family, and genus); however, pairwise differences varied in
significance (Figure S2.2). Shannon diversity also significantly differed (Kruskal-Wallis, p =
1.2x10
-12
) across all populations (Figure 2.2). Pairwise comparisons of Shannon diversity were all
significant except for Arroyo Quemado to Camp Pendleton and Catalina to Leo Carillo. For
biomass comparisons, we considered the range of biomass outcomes for adult sporophytes at the
time of harvest and created four quantile bins: Quantile 1 (x ≤ 75g, n = 116), Quantile 2 (75g > x
≤ 140.67g, n = 113), Quantile 3 (140.67g > x ≤ 235g, n = 114), and Quantile 4 (x > 235g, n = 114).
For taxonomic richness between biomass quantiles, there were no significant differences overall
regardless of taxonomic level. However, there were some significant differences of taxonomic
richness and Shannon diversity for pairwise comparisons between biomass quantiles at the class,
order, family, and genus levels (Figure S2.3).
Community composition significantly differs across populations and biomass
quantiles. We found that the relative abundance of classified bacteria varied across populations
(Table S2.4). Here, we report the taxa from each population that had the largest difference from
overall relative abundance. AQ had higher relative abundance of Flavobacteriia, Flavobacteriales,
Phyllobacteriaceae, Flavobacteriaceae, and Nonlabens, and lower relative abundance of
Alteromonadales, Alteromonadaceae, Brucellaceae, Marinobacter, and Ochrobactrum. CP had
higher relative abundance of Alphaproteobacteria, Rhizobiales, Sphingomonadales,
Sphingomonadaceae, Erythrobacteraceae, Hoeflea, Sphingorhabdus, and
Lentilitoribacter donghaensis, and Sphingopyxis sp., and lower than average relative abundance
42
of Cytophagia, Gammaproteobacteria, Cytophagales, Rhodobacterales, Flammeovirgaceae, and
Rhodobacteraceae. LC gametophytes had higher relative abundance of Nonlabens ulvanivorans,
Marinobacter sp., and Ochrobactrum sp. (Table S2.4).
Variance-based compositional principal component analysis (PCA) was performed to
visualize differences between microbial community samples (beta diversity). PERMANOVA of
gametophyte microbial communities revealed that there was a significant difference
(PERMANOVA, p < 0.001) in microbial composition between gametophyte populations at the
species level, with PC1 and PC2 accounting for 18.3% of the variance explained (Figure 2.3). This
finding is conserved when the microbial communities are classified at the class, order, family, and
genus levels (PERMANOVA, p < 0.001) with PC1 and PC2 accounting for 35.3%, 24.3%, 20.1%,
and 19.4% of the variance explained, respectively (Figure S2.4).
Within-group dispersion was significantly different between populations at all taxonomic
levels, with LC having the greatest distance to the centroid at the order, family, genus, and species
levels. Distance between groups was not significantly greater than within groups except for class-
level microbial community analysis. With LC as the largest sample group, these results are
considered conservative (Anderson & Walsh, 2013). The exception is for analysis done at the class
level, in which the smallest group, CI, had the greatest distance to the centroid and results are
consequently considered liberal. Looking at a subset of gametophytes that became low and high
biomass sporophytes (Quantile 1 (<75g) and Quantile 4 (>235g), n = 230), there was a significant
difference (PERMANOVA, p < 0.01) in microbial community composition at the order, family,
genus (Figure S2.5) and species levels (Figure 2.4). The variance explained at these levels was
24.6%, 20.6%, 19.4%, and 19.1%, respectively. There were no significant differences found for
within-group dispersion when comparing biomass quantiles.
43
Key taxa are differentially abundant between quantile groups and correlate with
biomass. When comparing bacterial taxa between the top and bottom biomass quantiles (Quantile
1 (<75g) and Quantile 4 (>235g), n = 230), several taxa at the class, order, family, genus, and
species levels were found to be differentially abundant (Wilcoxon t-test, q < 0.1) (Table 2.2). The
following taxa were found to have increased relative abundance in gametophytes that became high-
biomass sporophytes: Clostridia, Rhizobiales, Sphingomonadales, Thermoanaerobacterales,
Family III, Mesorhizobium, Caldicellulosiruptor, Nitratireductor sp., Zoogloea ramigera,
Mesorhizobium genosp., Aquamicrobium sp., and Mesorhizobium sp.. The only taxa that was
found to have increased relative abundance in gametophytes that became low-biomass sporophytes
was Labrenzia sp..
Using a Spearman correlation test on the continuous biomass data and microbial clr-
transformed relative abundance for all gametophyte samples (n = 457), we similarly found that a
number of taxa that correlate (Spearman, q < 0.1) with sporophyte biomass (Table 2.3).
Verrucomicrobiae, Clostridia, Rhizobiales, Sphingomonadales, Thermoanaerobacterales,
Erythrobacteraceae, Phyllobacteriaceae, Brucellaceae, Family III, Caldicellulosiruptor,
Mesorhizobium, Zoogloea ramigera, Nitratireductor sp., Mesorhizobium genosp.,
Aquamicrobium sp., and Mesorhizobium sp. were found to each have a positive correlation with
sporophyte biomass. Cytophagia and Labrenzia sp. each had a negative correlation with
sporophyte biomass.
Select taxa from the gametophyte microbiome are significant predictors of
sporophyte biomass. To determine which taxa are the best predictors of biomass, linear regression
models were built and iterated for best fit with the taxa identified in differential abundance and
correlation testing (Tables 2.2 & 2.3, Table S2.5). Several taxa from the gametophyte germplasm
44
cultures were shown to be significant predictors (GLM t-test, p < 0.1) of sporophyte biomass
(Table S2.6). Verrucomicrobiae, Thermoanaerobacterales, Sphingomonadales, Brucellaceae,
Mesorhizobium, and Mesorhizobium sp. were predictors of increased sporophyte biomass while
Cytophagia and Labrenzia sp. were predictors of decreased biomass. Coefficient estimates ranged
from -6.110 to 28.859, with Brucellaceae having the largest value.
Discussion
Analysis of the most abundant taxa revealed evidence of a core microbiome of M. pyrifera
gametophyte germplasm cultures shared across populations (Table S2.4, Figure S2.1). Bacteria
that constitute this core microbiome may contribute to a set of essential functions, ecological roles,
or symbiotic associations with M. pyrifera gametophytes that are necessary for normal growth and
development. Encouragingly, Alphaproteobacteria, Gammaproteobacteria, Flavobacteriaceae,
Rhodobacteraceae, and Alteromonadaceae are also highly abundant in wild M. pyrifera
sporophytes collected from Arroyo Quemado (James et al., 2020), suggesting that bacteria from
these classes and families found in our gametophyte germplasm may represent a group of essential
microbes that are conserved across life stages and growth conditions of Southern California giant
kelps. Furthermore, Alphaproteobacteria, Flavobacteriia, Gammaproteobacteria, Rhizobiales,
Sphingomonadales, Flavobacteriales, Rhodobacteriales, and Alternomonadales are also highly
abundant in wild kelp forests with co-occuring M. pyrifera and Nereocystis luetkeana sampled in
Washington, suggesting that these taxa are conserved in giant kelp populations across the west
coast (Weigel & Pfister, 2019). Of interest, it appears that Cytophagia, Cytophagales,
Phyllobacteriaceae, Sphingomonadaceae, Brucellaceae, and Erythrobacteraceae were uniquely
more abundant in our dataset; however, it is unclear if this is a feature of lab-grown M. pyrifera or
45
gametophytes more broadly. Further investigation of the microbial communities from our farmed
sporophytes, or wild gametophytes, would provide insight to this question. Future studies will also
be required to investigate the functional role of core microbes identified in this study and determine
how they impact M. pyrifera hosts.
Taxonomic richness and Shannon diversity significantly varied by population, with Leo
Carillo often having higher values (Figure 2.2). Leo Carillo is a putative genetic hybrid zone
(Johansson et al., 2015) and it is possible that increased microbial species richness and variation
is a product of shared ancestry between the kelp populations. The observed richness per individual
only represents a small fraction of the total microbial diversity from the full dataset (Table S2.2),
which suggests that there may be a small set of essential taxa present in M. pyrifera gametophyte
cultures and a relatively large number of non-essential taxa that vary between microbial
communities. However, it is important to note that our sequencing and classification methods do
not account for unobserved taxa that are likely present in this environment. Therefore, additional
studies will be required to further investigate essential versus non-essential taxa.
We found evidence of a core microbiome by identifying highly abundant taxa present in
our gametophyte germplasm that represents several M. pyrifera populations in Southern California
and comparing our findings to previous studies of M. pyrifera sporophytes collected across the
West Coast. Although there is a group of common taxa shared between M. pyrifera individuals,
we found significant differences in the composition of our gametophyte dataset when comparing
populations and biomass quantiles (Figures 2.3 & 2.4). We similarly found that the relative
abundance of individual taxa varied by population (Table S2.4). Together, these results suggest
that the geographic origin of the parent sporophyte plays a role in microbial community
composition, perhaps by vertical transmission of microbes (Ghaderiardakani et al., 2020; Theis et
46
al., 2016). The greater dispersion in Leo Carillo may be a feature of inherited recruitment of
microbes, briefly mentioned above, resulting in greater variation of microbial communities.
Significant community differences between gametophytes that become high- or low-biomass
sporophytes suggests that the presence of certain taxa or the overall composition plays a role in
the final yield of cultivated M. pyrifera. Low-biomass sporophytes were also more often fouled
and covered with visible epiphytes, and it is possible that the gametophyte microbial communities
from these sporophytes impacted susceptibility to disease. While the effect of M. pyrifera genetic
variation on sporophyte biomass cannot be ignored, the fact that community differences accounts
for at least 19.1% of the total variation in biomass suggests that there is ample opportunity for
microbial inoculants in germplasm cultures to increase the yield of M. pyrifera cultivars and
perhaps provide disease-resistance.
Developing a microbial inoculant to be introduced at the gametophyte stage and increase
sporophyte biomass requires the identification of individual species associated with high-biomass
individuals. Differential abundance and correlation models identified Mesorhizobium,
Caldicellulosiruptor, Nitratireductor, Aquamicrobium, and Zoogloea ramigera as promising
candidates and existing literature has explored functions of these taxa (Table S2.7). Indeed, further
analysis with a generalized linear model revealed that species within the Mesorhizobium genus are
the most significant predictors of increased sporophyte biomass (Tables S2.5 & S2.6), suggesting
that these would be excellent candidates to explore as growth-promoting inoculants in future
studies. While Brucellaceae had the highest coefficient estimate associated with increased
biomass, several species within this family cause disease in humans (Kämpfer et al., 2014) and
future studies should be performed with appropriate caution.
47
Regarding their potential as a growth-promoting inoculant, Mesorhizobium spp. are known
for their nitrogen fixation capabilities. Nitrogen is an essential, limiting compound for plant growth
and can be increased through the presence of nitrogen fixing bacteria. Nitrogen fixation converts
atmospheric nitrogen into a usable and readily available form for plant uptake (Laranjo et al.,
2014). Inoculation with Meorihzobium is an established practice in several crops including
legumes, wheat, and chickpea, and has shown growth-promoting properties (Benjelloun et al.,
2021; Imran et al., 2015; Ullah et al., 2017). Although the most well-studied mechanism of this
phenomena is nitrogen fixation, other factors (such as increased access to nutrients, secretion of
plant hormones, and disease resistance) may contribute to the growth-promoting effect (Pankievicz
et al., 2019).
While the method of bacterial classification used in this study has limited resolution at the
species level, this can be overcome by culturing gametophyte microbiota, isolating colonies, and
doing inoculation trials with species that are classified within the appropriate genus. As the catalog
of microbial marine species improves with time, the dataset used for this study can also be
reanalyzed with updated databases. Importantly, this dataset was created using shotgun
metagenomics, meaning that there is an exciting opportunity to revisit these samples and focus on
analyzing functional genes or building metagenome-assembled genomes (MAGs). Due to the large
amount of computational power required to do this work, and the lack of a foundational study
investigating host-microbe associations in M. pyrifera gametophyte germplasm cultures, we
decided it was appropriate to first investigate these associations with the 16S classification pipeline
used in this study. Investigating the functional genes of candidate microbial inoculants would
provide insight into what mechanisms are being used to promote growth in M. pyrifera individuals
48
and illuminate potential avenues of editing bacterial genomes to make them more efficient at this
task.
It is important to acknowledge that culturing marine bacteria in lab-settings is challenging,
and may be a barrier to development of growth-promoting inoculants. In this case, one could
explore how variations in the kelp genome impact presence or absence of specific microbes by
performing a genome-wide association study and design kelp cultivars genetically inclined to
recruit beneficial bacteria. In addition to the development of growth-promoting inoculants,
removal of taxa associated with lower biomass (i.e. Cytophagia and Labrenzia sp.) of cultivated
seaweeds may be considered to prevent an inhibiting effect. Further study on whether or not those
taxa are essential for proper development would be needed. The current lack of species-specific
antibiotic presents a clear obstacle for removing individual taxa. Potential solutions could include
inoculating gametophytes with taxa that compete and outgrow those associated with lower
biomass; however, further study is needed on how this would impact the microbial community and
host fitness overall.
Farmed seaweed cultivars are raised in different environments throughout their lifecycle
(i.e., gametophytes first raised in lab-cultures then transferred to offshore farms as juvenile
sporophytes), which can impact microbial community composition, and its impact on the seaweed
host, over time. In the context of this study, this suggests that inoculating microbes at the
gametophyte stage will not necessarily result in a beneficial impact to sporophytes. However,
assuming that a microbial inoculant at the gametophyte stage successfully colonizes and persists
through the sporophyte stage, the inoculant may result in a growth-promoting effect with minimal
changes to the rest of the community or promote recruitment of other organisms through microbial
succession. To fully explore the range of potential impacts either of these scenarios would have on
49
crop outcomes, it will be necessary to perform inoculation trials on M. pyrifera gametophytes and
track changes in growth rate and microbial community composition throughout the cultivar’s life
cycle.
In conclusion, we characterized the microbiome of M. pyrifera gametophyte germplasm
cultures collected from distinct natural populations across Southern California. These
gametophytes were cultivated in an offshore farm in Santa Barbara, California, which enabled us
to analyze how the microbial composition of gametophyte cultures varied with the final biomass
yield of adult sporophytes. With our novel approach, which leverages recent developments in
seaweed genomics resources, we identified a set of taxa associated with increased biomass
production of kelp cultivars. These candidate bacterial taxa should be tested using a manipulative
approach for their capacity as growth-promoting inoculants in future studies. In particular, the use
of nitrogen-fixing Mesorhizobium species is a promising avenue. Further study is needed to
determine whether an inoculant design would be more effective by introducing individual bacteria
or a multi-species community. The efficacy of these inoculants may also be improved by ample
consideration of community dynamics and network topology of the existing microbiome to reduce
risk of niche competition and ensure that inoculants can be integrated on a large scale. Finally,
genetic variation in M. pyrifera cultivars can impact the recruitment of microbes and should be
considered when designing inoculants. If successful, the development of a growth-promoting
inoculant for M. pyrifera cultivars has the potential to revolutionize kelp farming for biofuels. By
increasing the biomass yield of individual cultivars, growth-promoting inoculants could increase
the efficiency of offshore farms and ultimately increase the amount of biomass available per
harvest for biofuel feedstocks. Overall, this work is an exciting step forward in understanding the
50
microbial community of M. pyrifera gametophyte germplasm cultures and improving the
productivity of offshore farms.
Tables
Table 2.1. M. pyrifera sampling sites in Southern California.

We selected one site per genetic region identified in the Johansson et al. (2015) genetic
differentiation study. We additionally sampled Leo Carrillo, which demonstrated an admixture
pattern from all other regions. Spores released from sampled sporophytes were used to start the
gametophyte germplasm collection.

Table 2.2. Taxa differentially abundant between sporophyte biomass groups.

Table of taxa (>1% relative abundance) that are differentially abundant (q < 0.1) between the top
and bottom biomass quantiles (Quantile 1 (<75g) and Quantile 4 (>235g), n = 230) for
gametophyte germplasm cultures. A positive effect size indicates that the taxa is more abundant in
gametophytes that became high-biomass sporophytes. A negative effect size indicates that the taxa
is more abundant in those that became low-biomass sporophytes. Q-values are the Benjamini
Hochberg corrected p-values for multiple testing.

Population Genetic group Latitude Longitude n
Arroyo Quemado Santa Barbara channel 34.468783° N -120.121417° W 60
Catalina Island Channel Islands 33.446882° N -118.485067° W 60
Camp Pendleton Southern group 33.290911° N -117.499969° W 60
Leo Carrillo Hybrid region 34.042933° N -118.934500° W 370

Taxonomic Level Taxa Effect Size Q-Value
Class Clostridia 0.2 0.1
Order Rhizobiales 0.19 0.1
Order Sphingomonadales 0.22 0.05
Order Thermoanaerobacterales 0.27 0.02
Family Family III (Thermoanaerobacterales) 0.26 0.04
Genus Mesorhizobium 0.23 0.01
Genus Caldicellulosiruptor 0.24 0.07
Species Labrenzia sp. -0.28 0.01
Species Nitratireductor sp. 0.15 0.1
Species Zoogloea ramigera 0.15 0.06
Species Mesorhizobium genosp. 0.19 0.02
Species Aquamicrobium sp. 0.2 0.02
Species Mesorhizobium sp. 0.22 0.01

51
Table 2.3. Taxa that have significant correlations with sporophyte biomass.

Table of taxa (>1% relative abundance) that significantly correlate with sporophyte biomass. A
Spearman correlation was used to model the association between individual taxa at the
gametophyte stage with sporophyte biomass. All gametophyte samples (n = 457) and their
continuous biomass data were used. Q-values are the Benjamini Hochberg corrected p-values for
multiple testing. Only results with q < 0.1 are shown.

Taxonomic Level Taxa
Spearman
Rho Q-Value
Class Cytophagia -0.1 0.09
Class Verrucomicrobiae 0.09 0.1
Class Clostridia 0.12 0.06
Order Rhizobiales 0.11 0.1
Order Sphingomonadales 0.13 0.07
Order Thermoanaerobacterales 0.17 0.01
Family Erythrobacteraceae 0.11 0.1
Family Phyllobacteriaceae 0.11 0.1
Family Brucellaceae 0.12 0.1
Family Family III (Thermoanaerobacterales) 0.17 0.01
Genus Caldicellulosiruptor 0.16 0.02
Genus Mesorhizobium 0.19 0.004
Species Labrenzia sp. -0.16 0.01
Species Zoogloea ramigera 0.14 0.03
Species Nitratireductor sp. 0.15 0.02
Species Mesorhizobium genosp. 0.16 0.01
Species Aquamicrobium sp. 0.17 0.01
Species Mesorhizobium sp. 0.18 0.004

52
Table S2.1. M. pyrifera biomass quantiles.

Description of M. pyrifera biomass quantiles for all populations and each population individually.
Average total biomass was calculated for all surviving sporophyte replicates and divided into
quantile bins to investigate group differences in microbial community composition of
corresponding gametophytes. AQ = Arroyo Quemado, CI = Channel Islands, CP = Camp
Pendleton, and LC = Leo Carillo.

Table S2.2. Distribution of classified and unclassified taxa across taxonomic levels.

Distribution of unique taxa across all 457 M. pyrifera gametophyte germplasm cultures within the
Bacteria domain classified at five taxonomic ranks: class, order, family, genus, and species. Total
taxa was estimated using the number of classified taxa and the fraction of unclassified taxa. Four
populations of M. pyrifera are represented in this gametophyte set and are named according to
their geographic region: Arroyo Quemado (n = 52), Channel Islands (n = 44), Camp Pendleton (n
= 53), and Leo Carillo (n = 308).

Quantile 1 Quantile 2 Quantile 3 Quantile 4
Population Sample
Size
Weight
(Size)
Weight
(Size)
Weight
(Size)
Weight
(Size)
All 457 < 75g
(n = 116)
75g to 140.67g
(n = 113)
140.67g to 235g
(n = 114)
> 235g
(n = 114)
AQ 52 < 70g
(n = 13)
70g to 124g
(n = 13)
124g to 240.83g
(n = 13)
> 240.83g
(n = 13)
CI 44 < 128.42g
(n = 11)
128.42g to
182.67g
(n = 11)
182.67g to 246g
(n = 11)
> 246g
(n = 11)
CP 53 < 160 g
(n = 14)
160g to 236.33g
(n = 13)
236.33g to 323g
(n = 13)
> 323g
(n = 13)
LC 308 < 63.92g
(n = 77)
63.92g to 125g
(n = 78)
125g to 211g
(n = 76)
> 211g
(n = 77)

Taxonomic Rank Total Taxa (est.) Classified Taxa Fraction Unclassified
Class 54 44 18.5%
Order 111 97 12.6%
Family 250 205 18%
Genus 759 626 17.5%
Species 1594 1106 30.6%

53
Table S2.3. Distribution of taxa across M. pyrifera populations.

Distribution of unique taxa within the Bacteria domain for each population classified at five
taxonomic ranks: class, order, family, genus, and species. Four populations of M. pyrifera are
represented in this gametophyte set and are named according to their geographic region: Leo
Carillo (n = 308), Camp Pendleton (n = 53), Channel Islands (n = 44), and Arroyo Quemado (n =
52).

Population Taxonomic
Rank
Total Taxa
(est.)
Classified Taxa Fraction
Unclassified
Arroyo Quemado Species 823 571 30.6%
Arroyo Quemado Genus 460 365 20.7%
Arroyo Quemado Family 173 147 15%
Arroyo Quemado Order 81 70 13.6%
Arroyo Quemado Class 38 32 15.8%
Channel Islands Species 841 593 29.5%
Channel Islands Genus 447 360 19.5%
Channel Islands Family 164 136 17.1%
Channel Islands Order 69 61 11.6%
Channel Islands Class 34 29 14.7%
Camp Pendleton Species 844 569 32.6%
Camp Pendleton Genus 482 386 19.9%
Camp Pendleton Family 176 149 15.3%
Camp Pendleton Order 78 69 11.5%
Camp Pendleton Class 37 31 16.2%
Leo Carillo Species 1480 1013 31.6%
Leo Carillo Genus 724 598 17.4%
Leo Carillo Family 238 197 17.2%
Leo Carillo Order 109 95 12.8%
Leo Carillo Class 53 43 18.9%

54
Table S2.4. Relative abundance of taxa across M. pyrifera populations.

Relative abundance of taxa (>1%) for each population and overall. LC = Leo Carillo, CP = Camp
Pendleton, CI = Channel Islands, AQ = Arroyo Quemado.

Taxonomic
Level Taxa Overall AQ CI CP LC
Class Alphaproteobacteria 71.1 73.6 71.4 84.5 68.4
Class Flavobacteriia 12.2 15.4 9.4 9.3 12.5
Class Cytophagia 7.8 7.1 12.9 2 8.3
Class Gammaproteobacteria 7.5 3.7 5.2 3.6 9
Order Rhizobiales 38.9 39.2 37.7 40.8 38.7
Order Sphingomonadales 22.3 30.3 23.1 38.2 18.3
Order Flavobacteriales 12.2 15.4 9.4 9.3 12.5
Order Cytophagales 7.8 7.1 12.9 2 8.3
Order Rhodobacterales 7.4 3.7 8.9 3.3 8.5
Order Alteromonadales 4.4 2.2 2.9 2.4 5.3
Family Phyllobacteriaceae 31.3 35 31.1 36.3 29.9
Family Sphingomonadaceae 19.1 25.5 21.4 27.3 16.4
Family Flavobacteriaceae 11.8 15.2 12.9 9.3 11.9
Family Flammeovirgaceae 7.7 7 9.4 2 8.3
Family Rhodobacteraceae 7.4 3.7 8.9 3.3 8.5
Family Alteromonadaceae 4.2 1.9 2.7 2.2 5.1
Family Brucellaceae 3.3 2.6 3.8 3.3 3.4
Family Erythrobacteraceae 3.3 4.9 1.7 10.9 1.9
Genus Hoeflea 29.7 32.9 27.5 34.7 28.6
Genus Sphingorhabdus 18.6 24.2 21 26.1 16.2
Genus Nonlabens 5.5 10 4 7.9 4.5
Genus Marinobacter 3.9 1.8 2.7 2 4.7
Genus Ochrobactrum 3.3 2.6 3.7 3.2 3.4
Species Lentilitoribacter donghaensis 29.6 32.8 27.3 34.6 28.5
Species Sphingopyxis sp. 18.1 24 20.2 25.9 15.7
Species Nonlabens ulvanivorans 5.3 9.6 3.9 7.5 15.7
Species Marinobacter sp. 3.8 1.7 2.6 1.9 15.7
Species Ochrobactrum sp. 3.3 2.6 3.7 3.2 15.7

55
Table S2.5. GLMs used to model microbe abundance and sporophyte biomass.

Linear regression models at the class, order, family, genus, and species levels. Models were first
built using the list of taxa identified in differential abundance and correlation testing (main text,
Tables 2-3), then the predictor variables shown above were chosen using the stepAIC method to
improve model fit.

Table S2.6. Coefficient estimates of GLM modeling.

Table of taxa, coefficient estimates, standard errors, and p-values of predictor variables (taxa of
interest) for sporophyte biomass. A general linear model was used (see Table S5). Coefficient
estimates represent the average change in the log odds value of increased sporophyte biomass with
a one unit increase in the clr-transformed abundance of select taxa.

Taxonomic
Level
Model
Class Biomass ~ Cytophagia + Verrucomicrobiae
Order Biomass ~ Thermanaerobacterales + Sphingomonadales
Family Biomass ~ Family III (Thermanaerobacterales) + Brucellaceae
Genus Biomass ~ Mesorhizobium
Species Biomass ~ Mesorhizobium sp. + Labrenzia sp.

Taxonomic
Level
Taxa Coefficient
Estimate
Std.
Error
P-Value
Class Cytophagia -5.275 2.412 0.0293
Class Verrucomicrobiae 9.648 5.825 0.0984
Order Thermoanaerobacterales 6.658 3.064 0.0303
Order Sphingomonadales 19.335 7.830 0.0139
Family Brucellaceae 28.859 13.072 0.0278
Genus Mesorhizobium 14.009 3.798 0.000253
Species Mesorhizobium sp. 13.428 3.511 0.000149
Species Labrenzia sp. - 6.110 2.180 0.005283

56
Table S2.7. Existing knowledge regarding taxa of interest (non-exhaustive).

Column descriptions (left to right): (1) taxonomic level for taxa of interest, (2) taxa identified in
this study that are associated with M. pyrifera sporophyte biomass, (3) positive or negative
association with biomass as modeled in this study, (4) general associations with plant growth
described in previous studies, (5) brief review of known functions, and (6) select studies and
reviews used as sources for columns 4-5.

Taxonomic
Level Taxa
Association w/
M. pyrifera
biomass
Association
w/ plant
growth Functional highlights References
Class Cytophagia Positive Unknown
Remineralize organic
compounds into
micronutrients Mayrberger (2011)
Class Clostridia Positive Positive
Fermentation of
polysaccharides
Boutard et al (2014);
Zeiler et al (2015)
Class Verrucomicrobiae Positive Positive
Carbon and nitrogen
cycling, polysaccharide
degradation Bunger et al (2020)
Order Rhizobiales Positive Positive
Nitrogen fixation,
provide nutrients,
phytohormones, and
pre-metabolites Erlacher et al (2015)
Order Sphingomonadales Positive Positive
Degrade aromatics,
environmental
remediation Luo et al (2019)
Order Thermoanaerobacterales Positive Positive
Biohydrogen
production
Lee et al (1993); Ashton
(1981); Wright & Lima
(2021); Singh et al (2016)
Family Brucellaceae Positive Unknown Pathogenic in mammals Kampfer et al (2014)
Family Erythrobacteraceae Positive Positive
Carbon cycling and
energy metabolism Tang et al (2019)
Family
Family III
(Thermoanaerobacterales) Positive Positive
Biohydrogen
production
Fontana et al (2019);
Atasoy (2021); Rittman
& Herwig (2012); Garrity
et al (2009); Wright &
Lima (2021)
Family Phyllobacteriaceae Positive Positive Nitrogen fixation Willems (2014)
Genus Aquamicrobium Positive Unknown
Oxidase and catalase
activity
Kampfer et al (2009);
Bambauer et al (1998);
Jin et al (2013); Xu et al
(2017)
Genus Caldicellulosiruptor Positive Unknown
Lignocellulose
degradation
Hamilton-Brehm et al
(2010); VanFossen et al
(2011); Lee et al (2018);
Straub et al (2020);
Chung et al (2014)
Genus Mesorhizobium Positive Positive Nitrogen fixation
Ferguson (2017); Ramsay
et al (2017); Vessey et al
(2004)
Genus Nitratireductor Positive Unknown Nitrate reduction
Labbé et al (2004); Ou et
al (2017); Jiang et al
(2020); Manickam et al
(2011)
Species Labrenzia sp. Negative Unknown Antibacterial activity
Camacho et al (2016); de
Castro et al (2010);
Sharma et al (2019);
Diaz-Cardenas et al
(2020)
Species Zoogloea ramigera Positive Unknown
Biosorption of metals,
formation of activated
sludge flocs
Rosselló-Mora et al
(1995)

57
Figures
Figure 2.1. Relative abundance of bacteria across M. pyrifera populations.

Relative abundance of bacterial community for four M. pyrifera populations (AQ = Arroyo
Quemado, CP = Camp Pendleton, CI = Channel Islands, LC = Leo Carillo). Taxa were classified
and conglomerated to (A) class, (B) order, and (C) family.

58
Figure 2.2. Taxonomic richness and Shannon diversity of bacterial species.

Taxonomic richness and Shannon diversity based on (A) classified species and (B) classified and
unclassified species. Taxa were observed in bacterial populations of M. pyrifera gametophytes
from all four kelp populations: Arroyo Quemado (AQ, pink), Channel Islands (CI, blue), Camp
Pendleton (CP, yellow), and Leo Carillo (LC, green). Pairwise significance was tested with the
Kruskal-Wallis test: ns: not significant, *: p <= 0.05, **: p <= 0.01, ***: p<=0.001, ****: p <=
0.0001.

59
Figure 2.3. PCA of microbial communities across M. pyrifera populations.

Principal Component Analysis (PCA) demonstrating compositional difference of microbial
communities classified at the species level between gametophyte samples (n = 457) with 95%
confidence ellipses. Each dot represents a unique female gametophyte from one of four
populations: Arroyo Quemado (AQ, pink), Channel Islands (CI, blue), Camp Pendleton (CP,
yellow), and Leo Carillo (LC, green). Distance between points represents how compositionally
similar or distinct the microbial communities of gametophytes are.

60
Figure 2.4. PCA of microbial communities across biomass quantiles.

Principal Component Analysis (PCA) demonstrating compositional difference of microbial
communities classified at the species level between gametophyte samples from bottom and top
sporophyte biomass quantiles (<75g and >235g, n = 230) with 95% confidence ellipses. Each dot
represents a unique female gametophyte from one of four populations. Distance between points
represents how compositionally similar or distinct the microbial communities of gametophytes are.

61
Figure S2.1. Venn diagram of species level core microbes.

Venn diagram of species level core microbes (>0.1% relative abundance and present in at least
75% of samples) shared between all four kelp populations. (a) classified species. Six shared species
are Flavobacterium sp., Lentilitoribacter donghaensis, Marinobacter sp., Nonlabens
ulvanivorans, Ochrobactrum sp. and Sphingopyxis sp. (b) classified and unclassified species.
Sixteen shared species are Lentilitoribacter donghaensis, Nonlabens ulvanivorans, Ochrobactrum
sp., Sphingopyxis sp. and twelve unclassified taxa.

62
Figure S2.2. Richness and diversity of higher-level taxa across populations.

Taxonomic richness and Shannon diversity for bacterial populations of M. pyrifera gametophytes
at the (A) class, (B) order, (C) family, and (D) genus levels. Gametophytes are representative of
four populations: Arroyo Quemado (AQ, pink), Channel Islands (CI, blue), Camp Pendleton (CP,
yellow), and Leo Carillo (LC, pink). Pairwise significance was tested with the Kruskal-Wallis test:
ns: not significant, *: p <= 0.05, **: p <= 0.01, ***: p<=0.001, ****: p <= 0.0001.

63
Figure S2.3. Richness and diversity of bacterial species across biomass quantiles.

Taxonomic richness and Shannon diversity for bacterial populations of M. pyrifera gametophytes
at the (a) class, (b) order, (c) family, and (d) genus levels. Here, all 457 gametophytes were binned
into four quantiles according to their wet biomass weight at harvest: Quantile 1 (<75g), Quantile
2 (75g to 140.67g), Quantile 3 (140.67g to 235g), and Quantile 4 (>235g). Pairwise significance
was tested with the Kruskal-Wallis test: ns: not significant, *: p <= 0.05, **: p <= 0.01.

64
Figure S2.4. PCA of higher-level microbial communities across M. pyrifera populations.

Principal Component Analysis (PCA) demonstrating compositional difference of microbial
communities between gametophyte samples (n = 457) with 95% confidence ellipses at the (A)
class, (B) order, (C) family, and (D) genus taxonomic levels. (A-D) Each dot represents a unique
female gametophyte from one of four populations: Arroyo Quemado (AQ, pink), Channel Islands
(CI, blue), Camp Pendleton (CP, yellow), and Leo Carillo (LC, green). Distance between points
represents how compositionally similar or distinct the microbial communities of gametophytes are.

65
Figure S2.5. PCA of higher-level microbial communities across biomass quantiles.

PCA demonstrating compositional difference of microbial communities between gametophyte
samples from bottom and top biomass quantiles (<75g and >235g, n = 230) with 95% confidence
ellipses at the (A) order, (B) family, and (C) genus taxonomic levels. (A-C) Each dot represents a
unique female gametophyte from one of four populations. Distance between points represents how
compositionally similar or distinct the microbial communities of gametophytes are.
66
Chapter 3: Investigating the relationship between microbial network features of giant kelp
“seedbank” cultures and subsequent farm performance

Preface
The work presented in this chapter was produced with the support of: Ariel Levi Simons,
Gary Molano, Bernadeth Tolentino, Anupam Singh, Gabriel J. Montecinos Arismendi, Filipe
Alberto, and Sergey V. Nuzhdin. With the exception of Figures 1 and 2, all content within this
chapter should be evaluated as part of this dissertation. The contribution of each co-author is as
follows: MGO designed and performed research, analyzed data, and wrote chapter. ALS and GM
guided research design and edited. BT created Figures 1 and 2. AS guided research design. GJMA
contributed to germplasm sequencing and maintenance. FA guided research design and edited.
SVN guided research design and edited.
Abstract
Microbial inoculants can increase the yield of cultivated crops and are successful in
independent trials; however, efficacy drops in large-scale applications due to insufficient
consideration of microbial community dynamics. The structure of microbiomes, in addition to the
impact of individual taxa, is an important factor to consider when designing growth-promoting
inoculants. Here, we investigate the microbial network and community assembly patterns of
Macrocystis pyrifera gametophyte germplasm cultures (collectively referred to as a “seed-bank”)
used to cultivate an offshore farm in Santa Barbara, California, and identify network features
associated with increased biomass of mature sporophytes. We found that (1) several network
67
features, such as clustering coefficient and edge ratios, significantly vary with biomass outcomes;
(2) gametophytes that become low- or high-biomass sporophytes have different hub taxa; and (3)
microbial community assembly of gametophyte germplasm cultures is niche-driven. Overall, this
study describes microbial community dynamics in M. pyrifera germplasm cultures and ultimately
supports the development of early life stage inoculants that can be used on seaweed cultivars to
increase biomass yield.
Introduction
Building off the knowledge that microbes have a significant impact on plant health, there
has been a wealth of research on the use of microbial inoculants (i.e., the introduction or addition
of beneficial bacteria to a host) in agriculture (Bakhshandeh et al., 2015; Bisen et al., 2015;
Hameeda et al., 2008; O’Callaghan, 2016; Zhiguang Qiu et al., 2019; Qureshi et al., 2012; Rocha
et al., 2019; Santos et al., 2019; Singh et al., 2016). Previous work has shown that addition of
growth-promoting bacteria can increase the fitness and yield of several agricultural crops including
rice, maize, and cotton (Bakhshandeh et al., 2015; Hameeda et al., 2008; Qureshi et al., 2012). In
particular, the use of these inoculants at an early life stage in plant hosts can increase farm
productivity and be used to predict yield (Bisen et al., 2015; O’Callaghan, 2016; Zhiguang Qiu et
al., 2019; Rocha et al., 2019; Santos et al., 2019; Singh et al., 2016). Many studies focus on the
impact that individual microbes have on host health rather than the indirect role that microbe-
microbe interactions and microbial community dynamics can hold. While useful, this approach
can be limited because the variability of microbe-microbe and microbe-plant interactions across
hosts means that that an inoculant with the desired effect on one plant host will not necessarily
have the same effect on all (Morales Moreira et al., 2022). Indeed, the fact that host microbiomes
68
are not a collection of isolated individuals, but rather an interdependent group with complex
functional and metabolic pathways, lends itself to this point (Layeghifard et al., 2017). Microbial
inoculants can compete with native species, preventing successful colonization of the inoculant or
causing negative impacts on crop performance (Cornell et al., 2021). Inoculants may also prompt
microbial succession, thereby altering community structure and function (Imam et al., 2021;
Vacher et al., 2016). As a result, insufficient consideration of the existing microbial network and
community dynamics when designing inoculants can contribute to low efficacy during large-scale
applications in agriculture (Zhiguang Qiu et al., 2019; Timmusk et al., 2017). Therefore, in order
to fully harness the beneficial impact of microbes and establish a strong framework for growth-
promoting inoculants, it is critical to develop an understanding of microbial community dynamics
in the context of crop outcomes.
Microbial community dynamics of host-associated microbiomes may be better understood
by analyzing co-occurrence networks, hub microbes, and community assembly patterns. Co-
occurrence networks represent the likely patterns of spatial co-occurrence (i.e., being present
together in an environment), which can be used to infer potential relationships between individual
taxa. These networks can be visually represented as a collection of nodes and edges. In the context
of this study, nodes represent unique taxa and edges represent the links or co-occurrence patterns
between them. Co-occurrence patterns can be quantified with measures of network topology such
as the clustering coefficient, modularity, and edge ratios. Clustering coefficient and modularity
describe the division of a network into sub-networks and the density of connections between nodes,
respectively. Identification of these sub-communities provides insight on potential local
interactions and their contribution to the overall structure and function of the network (Layeghifard
et al., 2017). The ratio of positive to negative edges, which represent significant patterns of spatial
69
co-occurrence or exclusion, can also indicate the degree to which the community has potentially
synergistic or competitive interactions. By investigating how microbial co-occurrence networks at
the early life stage of crops varies with crop performance we may use this insight to predict crop
yield and develop agricultural inoculants that synergize with network features of high-performing
crops (Imam et al., 2021; Layeghifard et al., 2017).
Hub microbes are central to the process of microbiome recruitment and have several
associations across the microbial network (Agler et al., 2016; Bulgarelli et al., 2013; Toju et al.,
2018). They are identified with network topology data and defined by having a disproportionate
number of links with other taxa in the network. Hub microbes are key drivers of the overall
microbial community because of their intrinsic ability to recruit and support the introduction of
other bacteria that directly benefit the host, particularly at the early life stage of crops (Zhiguang
Qiu et al., 2019). The impact hub microbes have on the diversity of host microbiomes can occur
directly (i.e., by impacting the colonization of other microbes) or indirectly (i.e., through the host)
(Agler et al., 2016). While hub microbes are not typically the focus of agricultural inoculants, by
identifying those of high-performing crops they can be used in tandem with growth-promoting
bacteria to improve crop fitness by increasing native recruitment of beneficial bacteria and
supporting synergistic interactions (Zhiguang Qiu et al., 2019). Furthermore, the use of inoculants
that do not compete with hub taxa can also improve long-term success and facilitate predictable
changes in the overall community (O’Callaghan, 2016).
While co-occurrence network and hub microbe analysis, as described above, can be used
to understand representative microbiomes for a group of hosts, community assembly patterns
provide insight into what mechanisms drive the variability of microbiomes across hosts. The two
most common forms of community assembly follow a stochastic or niche assembly process (Hui
70
& McGeoch, 2014). During stochastic assembly, microbes are randomly incorporated from the
environment into a community. During niche assembly, the likelihood of species being
incorporated is dependent on their ecological role and those of existing community members. Here,
we investigate the relative likelihood of these two assembly processes using the zeta diversity
framework, a method for calculating the number of shared species across an arbitrarily large
number of sample sites (Hui & McGeoch, 2014; McGeoch et al., 2019). As the number of sites
being compared increases, zeta diversity typically decays following an exponential or power-law
form (Bannar-Martin et al., 2018; Leibold et al., 2017). An exponential decay suggests that
communities are more likely to be assembled stochastically, while a power-law decay suggests
they are more likely to be assembled via niche-differentiation (McGeoch et al., 2019). In the
context of this study, understanding whether microbial communities assemble in a stochastic or
niche-driven manner can help improve inoculant design. If the assembly is niche-driven, for
example, inoculants can be designed to avoid competition with established niches and increase the
likelihood of establishment.
Analysis of microbial community dynamics, using the methods described above, will allow
for a more precise development of microbial inoculants that can increase crop yield (Imam et al.,
2021). Here, we pursue this work with giant kelp (Macrocystis pyrifera) gametophyte germplasm
cultures, collectively referred to as a “seedbank”, that were used to cultivate an offshore farm in
Santa Barbara, California (Figure 3.1). Our group has previously determined that there is a
significant difference in microbial community composition between gametophytes that become
high- versus low-biomass sporophytes, and that bacteria within the Mesorhizobium genus are key
candidates for creating a growth-promoting inoculant (Osborne et al., 2023). This study builds
upon that work by investigating both the topology of microbiome co-occurrence networks, as well
71
as the relative likelihoods of two common community assembly processes for giant kelp seedbank
cultures, and the relationships of these network features with the final biomass yield of mature
sporophytes. Given that microbe-microbe interactions impact overall microbiome function and,
consequently, host condition (Layeghifard et al., 2017), we hypothesize that the final yield of M.
pyrifera adult sporophytes is in part influenced by differences in microbial community dynamics
during the gametophyte stage. Specifically, that the microbiomes of gametophytes that become
high-biomass sporophytes will have co-occurrence patterns that support synergistic interactions
and have hub microbes that are distinct from those of gametophytes that become low-biomass
sporophytes. We further hypothesize that given the tight ecological interactions between microbes
and their seaweed hosts (Egan et al., 2013), that seedbank microbial communities will assemble
through niche-differentiation. The primary goal of this study is to identify microbial network
characteristics of early stage gametophytes that become high-biomass sporophytes (Figure 3.2).
Accomplishing this will support the development of growth-promoting inoculants which,
following successful introduction during the gametophyte stage, will help increase the biomass of
resulting sporophytes. Overall, this work provides a valuable knowledge base for the development
of microbial inoculants that could be used in the development of gametophyte germplasm
banks for the production of high-biomass M. pyrifera cultivars that can be used as biofuel
feedstocks.
Materials and Methods
Production of gametophytes and cultivation of sporophytes. Sporophyte collection,
spore release, sequencing, and classification followed protocol reported in Osborne et al (Osborne
et al., 2023) and is briefly described here. Reproductive blades of M. pyrifera were collected from
72
Southern California regions in December 2018 representing four genetically distinct natural
populations (Johansson et al., 2015): Arroyo Quemado (AQ), Catalina Island (CI), Camp
Pendleton (CP), and Leo Carillo (LC). Blades were shipped overnight to University of Wisconsin-
Milwaukee for spore release in sterile Provasoli enriched seawater medium (PES) (Provasoli,
1968) at 34 PSU salinity following the Oppliger method (Oppliger et al., 2011). Spores were
raised to the gametophyte stage, then isolated and vegetatively grown to create genetically unique
germplasm cultures. From this germplasm, a total of 2,500 sporophytes (500 unique genotypes
with five replicates each) were produced from the cross of 500 unique female gametophytes (370
from LC, and 60 each from AQ, CI, and CP) with a single male from LC. Gametophyte crosses
were seeded on polyvinyl lines and grown in lab conditions for one month before being shipped
overnight to a marine laboratory at the University of California, Santa Barbara (UCSB).
Sporophytes were adjacently planted on ten longlines in an offshore farm 1-mile off the coast of
Santa Barbara in May 2019. All surviving sporophytes were harvested between September 7-12,
2019 using Santa Barbara Mariculture’s vessel Perseverance. Harvested sporophytes were
weighed to record total wet biomass, which includes stipe and blades. The average biomass of all
surviving genetic replicates was calculated and used in this study. A number of individuals were
lost due to issues during harvest or premature loss. Due to smaller sample size and restricted
availability of phenotype data for the AQ, CI, and CP populations, we only report network analysis
across biomass outcomes for the LC population (see: ‘Grouping gametophytes and taxonomy
levels for biomass and population comparisons’).
DNA extraction, microbial shotgun sequence data, and classification. For DNA
extraction, aliquots of each culture were centrifuged and gametophyte tissue was pulverized using
liquid nitrogen. Kelp genome and microbial DNA were co-extracted and sequenced from female
73
and male gametophytes using the NucleoSpin 96 Plant Kit (Macherey-Nagel, Duren, Germany).
Due to the nature of this extraction technique, the microbial DNA of both exogenous and
endogenous species was extracted. For sequencing, 11.2GB of 150bp reads per sample was
generated at BGI North American NGS lab using an Illumina S4 Novaseq platform. Raw fastq
files were processed with the ‘fastp’ program (S. Chen et al., 2018). Due to evidence of bacterial
contamination in existing brown macroalgae genomes (Dittami & Corre, 2017), all reads were
included in the bacterial classification pipeline to ensure that all candidate sequences were
analyzed. Reads were classified using the ‘metaxa2’ package (version 2.2.2) which extracts and
classifies partial rRNA sequences against the SSU_SILVA128 database (Bengtsson-Palme et al.,
2015; Quast et al., 2013). The resulting abundance table was further processed and analyzed with
the ‘phyloseq’ package in R (McMurdie & Holmes, 2013; R, 2020). Abundance counts were
processed by removing singletons and doubletons, normalizing counts by sequencer, averaging
counts for samples that were sequenced over multiple runs, and again removing any remaining
singletons and doubletons. Only taxa from the bacterial domain were kept for analysis.
Grouping gametophytes and taxonomy levels for biomass and population
comparisons. Due to the smaller number of individuals within the AQ, CI, and CP populations,
the comparison of network analysis across biomass outcomes was only performed with individuals
from the LC population. A total of 308 individuals from the LC population were divided into one
of four quantile groups based on their wet biomass weight at the time of harvest: Quantile 1 (<
63.92g, n = 77), Quantile 2 (> 63.92g and < 125g, n = 78), Quantile 3 (> 125g and < 211g, n =
76), and Quantile 4 (> 211g, n = 77). These biomass values represent that of diploid sporophytes
grown on the farm. Recall that the crossing scheme used in this study crossed a single male
gametophyte from the LC population with 500 female gametophytes across the AQ, CI, CP, and
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LC populations (see: ‘Production of gametophytes and cultivation of sporophytes’). Consequently,
the microbial community of the corresponding female gametophyte for each sporophyte was
analyzed. After running the bacterial classification pipeline described above, we conglomerated
bacterial reads to four taxonomic levels (order, family, genus, and species) for all LC individuals.
Analysis at several taxonomic levels was done to address the challenge of taxonomic resolution
and classification uncertainty at higher levels (i.e. genus and species) and consider lower levels
(i.e. order, and family) as proxies for ecological function (Langille et al., 2013; Ramond et al.,
2019). For population comparisons, individuals were grouped according to the geographic region
(natural population) in which their parent sporophyte was collected: AQ (n = 64, 12 males and 52
females), CI (n = 57, 12 males and 45 females), CP (n = 69, 16 males and 53 females), and LC (n
= 369, 54 males and 315 females). Because population comparisons do not involve biomass data,
we were able to source a larger number of available individuals, including a number of males that
were not used in the crossing scheme for the farm. However, due to the complexity of the microbial
communities for these samples, we were only able to run network analysis at the order and family
levels.
Quantification and visualization of co-occurrence network and hub taxa. Starting with
network analysis across biomass outcomes, LC gametophytes (n = 308) were divided into four
quantiles as described above. For each quantile, we randomly selected 50 individuals 100 times
and constructed networks using the R package ‘SpiecEasi’, which infers ecological associations in
microbial communities (Kurtz et al., 2021). The default settings for SpiecEasi with neighborhood
selection (the Meinshausen and Bühlmann or “MB” method) were used (Meinshausen &
Bühlmann, 2006). The resulting representative network models were analyzed and graphed with
the ‘igraph’ package in R (Csardi & Nepusz, 2006). For each network, the following network
75
topology features were recorded: total nodes, total edges, number of positive edges, number of
negative edges, ratio of positive to negative edges, average path length, heterogeneity, modularity,
average degree per node, clustering coefficient, and hub score. Nodes represent unique taxa and
edges are the significant co-occurrences between them. Positive edges indicate that connected taxa
tend to be present together and negative edges indicate the opposite (i.e., if one is present in a
community, the other is absent). Positive and negative edge information was also used to infer
whether taxa of interest had competitive interactions with other taxa. The average path length
considers the shortest edge path connecting each pair of nodes. Heterogeneity, the distribution of
degrees or connections from each node, was calculated as described in Jacob et al (Jacob et al.,
2017). Modularity, the density of node connections compared to a randomly structured network,
was measured with the Louvain method that maximizes the score for each community (Blondel et
al., 2008). Hub score was calculated for the whole network without subsampling using Kleinberg’s
centrality score, which ranges from 0 to 1 (Kleinberg, 1999). This pipeline was repeated with
microbial networks classified at the order, family, genus, and species levels. For network analysis
across gametophyte populations, we used the same pipeline and randomly selected 50 individuals
100 times from each population: AQ (n = 64), CI (n = 57), CP (n = 69), and LC (n = 369). Due to
the higher complexity of these microbial networks and subsampling regime, we were unable to
construct networks for the LC population at the genus and species levels. Therefore, we report
only the network analysis done at the order and family levels across all four populations.
Identifying network topology factors that predict sporophyte biomass. For network
comparisons across biomass quantiles, we used gametophytes from the LC population (n = 308)
and divided them into one of four quantile groups based on their wet biomass weight at the time
of harvest: Quantile 1 (< 63.92g, n = 77), Quantile 2 (> 63.92g and < 125g, n = 78), Quantile 3 (>
76
125g and < 211g, n = 76), and Quantile 4 (> 211g, n = 77). We repeated analysis with bacteria
conglomerated to the order, family, genus, and species levels. We constructed networks (described
above) by randomly selecting 50 individuals 100 times from each quantile group. An ordered
logistic regression model was estimated using the ‘polr’ command from the ‘MASS’ package in
R (Venables & Ripley, 2002). The model was first run using all non-multicollinear factors: total
nodes, total edges, positive to negative edge ratio, average path length, modularity, average degree,
heterogeneity, and clustering coefficient. Using the ‘regsubsets’ command from the ‘leaps’
package in R we determined the best predictors for host biomass using co-occurrence networks
generated from microbiomes classified at the following taxonomic levels: order, family, genus,
and species. As stated earlier, analysis at several taxonomic levels was done to address the
challenge of taxonomic resolution and classification uncertainty at higher levels (i.e. genus and
species) and consider lower levels (i.e. order, and family) as proxies for ecological function
(Langille et al., 2013; Ramond et al., 2019). Models were additionally confirmed for best fit factors
using the ‘stepAIC’ command from MASS. In the case of a mismatch, which only occurred at the
genus and species level, the simpler model was chosen. Log likelihoods were converted to odds
ratios for ease of interpretation.
Comparing network topology measured between populations. For network
comparisons across populations, we analyzed gametophytes from four populations: AQ (n = 64),
CI (n = 57), CP (n = 69), and LC (n = 369). Networks were constructed by randomly selecting 50
individuals 100 times from each population. Analysis was repeated with bacteria conglomerated
to the order and family levels. We used a Kruskal Wallis test to determine if there was a significant
difference overall across populations for the following topology measures: total nodes, total edges,
ratio of positive to negative edges, average path length, modularity, average degree, heterogeneity,
77
and clustering coefficient. Pairwise comparisons were tested for significant differences using a
Wilcoxon test.
Modeling community assembly patterns using the zeta diversity metric. To determine
whether community assembly patterns differed between low- and high-biomass outcomes, we used
zeta diversity to help determine the relative likelihoods of niche differentiated (non-random) and
stochastic (random) processes of community assembly for kelp microbiomes found using either
low- or high-biomass individuals. Due to the reduced number of individuals in the AQ, CI, and
CP populations, this analysis was run on the biomass quantiles from the LC population alone. In
order to model community assembly patterns and determine the degree to which microbial
communities are randomly structured, we used the zeta diversity metric. This metric quantifies the
number of species shared between any number of sites (Hui & McGeoch, 2014). Zeta order refers
to the number of sites being considered at a time when calculating their compositional overlap. As
zeta order increases in size, the value of zeta diversity becomes increasingly influenced by more
common species and the decline in the number of shared species can be modeled as an exponential
or power-law regression. It has been found that the relative likelihoods of an exponential versus
power-law model of zeta diversity is associated with the respective relatively likelihoods of a
stochastic (random) versus niche-differentiated (non-random) model of community assembly (Hui
& McGeoch, 2014). For this study, the microbiome of each unique gametophyte is considered a
“site”. Abundance counts were first converted to presence (1) and absence (0) scores. Zeta decline
was modeled using the ‘zetadiv’ package in R (Latombe et al., 2020). Comparison of AIC scores
was used to determine best fitting model (exponential versus power-law regression) and more
likely method of community assembly. Common species are shared between a higher number of
sites while rare species are shared between fewer. Consequently, analysis was done for zeta orders
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3, 5, 10, 20, and 50 at the species level to investigate the contribution of rare (lower zeta orders)
versus common (higher zeta orders) species to compositional change. Analysis was also done at
the class, order, family, and genus levels for zeta order 50 to determine if community assembly
patterns differ between taxonomic levels.
Results
Network topology is a predictive measure of sporophyte biomass. Using gametophytes
from the Leo Carillo (LC) population (n = 308), we constructed co-occurrence networks of the
microbial community with taxa classified at the order, family, genus, and species level (Figure 3.3,
Figure S3.1). LC gametophytes were binned into one of four biomass quantile groups based on
their sporophyte weight at the time of harvest. Network analysis was performed for each biomass
group and topological measures of the co-occurrence networks of their associated microbiomes
were recorded (Table S3.1). To identify network topology factors that vary with biomass, we used
a proportional odds logistic regression model. The best fit model for each taxonomic level included
different topology factors (Table 3.1, Table S3.2). Clustering coefficient was a strong predictor of
biomass across the order, family, genus, and species levels; however, its association with increased
biomass changed across levels. At the order and species level, with a one unit increase in the
clustering coefficient the odds of higher biomass was 3.52x10
3
and 1.60x10
37
more likely,
respectively. At the family and genus levels, there was an opposite trend with higher biomass being
4.30x10
5
and 4.07x10
7
times less likely, respectively. Positive to negative edge ratio was also a
predictive factor for biomass at the order, family, and genus levels: with each one unit increase
(i.e. a higher proportion of positive associations between taxa) the odds of higher biomass was
1.04, 1.22, and 1.22 times more likely, respectively. Increased heterogeneity and lower modularity
79
were predictive of higher biomass at the order and family levels. For each one unit increase in
heterogeneity, increased biomass is 1.00x10
9
and 6.34x10
9
times more likely. For each one unit
increase in modularity, the odds of increased biomass was 1.97x10
7
and 4.50x10
9
times less likely.
Finally, for average path length at the order level with each one unit increase the odds of increased
biomass was 1.75 times less likely.
Microbial communities from gametophytes that become low- or high-biomass
sporophytes have unique hub taxa. From the network analysis described above we also
calculated hub scores for each taxa and identified those with the highest scores (Figure 3.3, Table
3.2). Here we define hub taxa as those that had a score of at least 0.5 and we report those from the
order, family, genus, and species levels (Table 3.2). Microbial communities from gametophytes
that became low-biomass sporophytes (<63.92g) had the following hub taxa (score followed in
parentheses): orders Frankiales (1) and Kineosporiales (0.89); families Veillonellaceae (1),
Archangiaceae (0.99), Burkholderiaceae (0.89), and Clostridiaceae 1 (0.85); genera
Marixanthomonas (1), Magnetococcus (0.91), Epibacterium (0.84), alpha proteobacterium
PWB3(0.57), and Collinsella (0.53); species Kordiimonas lacus (1), Methylosinus trichosporium
(0.87), alpha proteobacterium SAORIC-651 (0.74), Stappia taiwanensis (0.62), and marine
bacterium VA011 (0.55). In general, high-biomass sporophytes (>211g) had fewer hub taxa in the
gametophyte microbial communities. High-biomass hub taxa were orders Desulfovibrionales (1),
Nitrospinales (0.99); families Magnetococcaceae (1), Beijerinckiaceae (0.95), and Holosporaceae
(0.81); genera Wenyingzhuangia (1) and Pedobacter (0.89); and species mucus bacterium 80 (1)
and Marinomonas brasilensis (0.59).
Candidate growth-promoting taxa co-occurs with hub microbes of gametophytes that
become high-biomass sporophytes. In a previous study, we found that bacteria from the genus
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Mesorhizobium is associated with increased biomass of M. pyrifera and therefore a prime
candidate for a growth-promoting inoculant (Osborne et al., 2023). Using the representative
networks constructed for this study, we investigated the positive and negative associations
Mesorhizobium has with other taxa in the microbial community of M. pyrifera gametophyte
germplasm cultures. We found that Mesorhizobium co-occurs with Wenyingzhuangia and
Pedobacter, which had the two highest hub scores for gametophytes that become high-biomass
sporophytes. We also found that Mesorhizobium has negative co-occurrence values with
Aquamarina, Sneathiella, Pseudohaliea, and Saccharospirillum.
Network topology and hub taxa differ between gametophyte populations. Using
gametophytes from all four populations (AQ, CI, CP, and LC), we constructed co-occurrence
networks of the microbial community with taxa classified at the order and family levels (Figure
3.4, Figure S3.6). We investigated whether there was a significant difference across populations
for the following network topology measures: total nodes, total edges, ratio of positive to negative
edges, average path length, modularity, average degree, heterogeneity, and clustering coefficient
(Figures S3.4 & S3.5). We found that there was a significant difference overall for all topology
measures. Pairwise comparisons were significant for all combinations for total nodes, total edges,
average degree, and clustering coefficient. For the remaining measures, most combinations were
significantly different except for the following: ratio of positive to negative edges and modularity
for the AQ and CI populations, average path length for the CI and CP populations, and
heterogeneity for the AQ and CP populations. In addition to differences among network topology
measures, we also found that the four populations had distinct hub taxa. In general, the LC
population had the greatest number of hub taxa with a score over 0.5. Of those identified, only two
taxa overlapped between populations: Chthoniobacterales was shared between the AQ and CP
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populations and Cryptosporangiaceae was shared between the AQ and LC populations (Table
S3.3).
Community assembly of gametophyte microbial community is niche-driven across
biomass outcomes. Zeta diversity, the number of shared species between three or more sites, was
used to model community assembly patterns and determine whether they are driven by stochastic
(random) or niche-driven (non-random) mechanisms. Zeta order refers to the number of sites
included in this measure. Here, sites refer to gametophyte microbiome samples. To understand the
contribution of rare and common taxa to compositional change we ran zeta diversity analysis at
zeta orders 3, 5, 10, 20, and 50. We found that for gametophytes that became high-biomass
sporophytes, zeta diversity decline of microbial communities follow a power-law regression of
zeta diversity decline for all zeta orders (Figure S3.2). A similar pattern was found for all
gametophyte microbial communities regardless of biomass outcome (data not shown). To
determine whether community assembly patterns vary across taxonomic levels, we additionally
ran analysis with zeta order 50 at the class, order, family, and genus levels (Figure S3.3). All
taxonomic levels demonstrated niche-driven assembly patterns across biomass outcomes.
Discussion
Analysis of the microbial co-occurrence network topology in gametophytes cultures across
biomass outcomes revealed that several features can be used to predict sporophyte yield.
Clustering coefficient and the ratio of positive to negative edges were identified as significant
factors for the predictive modeling of sporophyte biomass when looking at gametophyte microbial
networks classified at the class, order, family, and genus levels. At the species level, larger
clustering coefficient values, which are associated with highly complex communities and strong
82
microbe-microbe interactions (Guo et al., 2022), have a profoundly high likelihood of increased
biomass. This suggests that densely connected subnetworks are associated with improved growth
in M. pyrifera. Although topological analysis does not offer insight on the mechanisms behind this
impact, higher clustering coefficients may suggest greater cooperation (Li & O'Riordan, 2013) that
can benefit the host. Likewise, a higher ratio of positive to negative edges associated with increased
biomass suggests less competition between taxa that could detract from host health and
performance. At the order and family levels, increased heterogeneity, indicating more variation in
the number of connecting edges per node, suggests that when the edge connections of a network
are concentrated on a small number of taxa there is a growth benefit to the host. In other words,
this may indicate that having few hub taxa (with dense connections to other members of the
community) that dominate associations across the network is beneficial.
We confirmed that hub taxa are different for M. pyrifera gametophytes that become low-
and high-biomass sporophytes. Previous work has shown that hub microbes impact the
colonization and abundance of other bacteria (Agler et al., 2016). They may also impact host
physiology, including host metabolism, which indirectly impacts what microbial species are
present (Agler et al., 2016). It is possible that hub microbes from low-biomass hosts may be
inefficient at recruiting bacteria that provide the greatest growth benefit to the host. Consequently,
future studies should investigate whether this relationship may be exploited to recruit beneficial
microbes at the early stage of seaweeds and increase growth. In particular, the addition of taxa
from the genera Wenyingzhuangia and Pedobacter or the addition of species mucus bacterium 80
and Marinomonas brasilensis are promising directions to test whether inoculation at the early life
stage of M. pyrifera will recruit other beneficial bacteria and induce a growth-promoting benefit.
Future work may also focus on isolating and sequencing these taxa to gain insight on their
83
functional capability and mechanisms for regulating microbe-microbe and microbe-host
interactions overall (Zhiguang Qiu et al., 2019). Upon further investigation, we discovered that
Mesorhizobium, which is a prime candidate for growth-promoting inoculants in M. pyrifera, does
not have negative associations with Wenyingzhuangia nor Pedobacter. This suggests that if
bacteria from these three genera were included in a growth-promoting inoculant they would not
compete with each other and perhaps even provide a synergistic effect. This is a promising finding
given that the perturbance and removal of hub taxa can have negative cascading effects throughout
a microbial community and decrease stability overall. The genera that Mesorhizobium does not
co-occur with (Aquamarina, Sneathiella, Pseudohaliea, and Saccharospirillum) are not hub taxa;
further investigation is needed to determine if taxa from these genera would directly compete with,
or disrupt the efficacy of, a Mesorhizobium inoculation.
In line with previous findings that microbial community diversity significantly differs
across populations (Osborne et al., 2023), we found that network dynamics similarly vary by
population. This is likely a consequence of diverse taxa inhabiting M. pyrifera individuals from
different populations, perhaps driven by genetic diversity of kelp gametophytes. Of particular
interest, even though network variations across biomass outcomes were only analyzed in the LC
population, it is possible that gametophytes from other populations will respond positively to
inoculation with hub microbes of high-biomass LC gametophytes given that a previous study
demonstrated that M. pyrifera gametophytes from San Diego had increased length and abundance
when grown in different microbial treatments of seawater from Catalina (Morris et al., 2016).
While network topology analysis increases our understanding of the structural traits associated
with increased biomass, it will be more insightful to layer this work with other data types including
84
those from genomics and metabolomics to infer functional mechanisms impacting host growth
(Layeghifard et al., 2017).
Zeta diversity analysis revealed that the microbial community assembles in a niche-driven
manner when conglomerated to the class, order, family, genus, and species levels and that this is
consistent across all biomass outcomes. At the species level, rare and common species similarly
contribute to this assembly pattern. Together, this suggests that the community is competitively
structured and that assembly patterns are not a driving factor in the difference between biomass
yields for M. pyrifera cultivars. This may make the design and introduction of growth-promoting
inoculants more challenging. Inoculants will have to be designed in a way that does not compete
with established niches so that it can persist in the context of the native microbial community.
In conclusion, we analyzed the network dynamics and community assembly patterns of
microbial communities for cultivated M. pyrifera gametophytes and compared these
characteristics with sporophyte performance to ultimately identify features associated with
increased biomass. We found that the network dynamics and hub taxa of microbial communities
at the gametophyte stage may be a driving force in biomass outcomes at the sporophyte stage. In
addition, we found that microbial communities assemble in a niche-driven manner across all
biomass outcomes. When designing inoculants to increase the biomass yield of M. pyrifera
cultivars, avoiding competition with hub taxa identified here may increase long-term efficacy.
Introduction of desired hub taxa at the gametophyte stage can also induce the recruitment of other
beneficial bacteria and shape the overall community in a more precise manner. There are several
exciting opportunities for future research to help us better understand microbe-microbe
interactions and their impact on the host, such as genome sequencing of hub taxa to elucidate
functional pathways and genome-wide association studies to identify genetic factors of M. pyrifera
85
that impact recruitment of these taxa. Incorporating analysis of the host genome is particularly
exciting for growth-promoting applications discussed here as the impact that host genotype can
have on the overall microbial community are strongest if focused on hub microbes (Agler et al.,
2016). Finally, inoculation trials will need to be performed to track long-term efficacy, change in
biomass outcomes, and impact on network structure. Altogether, this is a helpful study that will
support the use of growth-promoting microbial inoculants in M. pyrifera cultivars and the
seaweed-based biofuel industry.
Tables
Table 3.1. Odds ratio values for network topology factors used in POLR models.

Clustering
Coefficient
Positive to
Negative
Edge Ratio
Heterogeneity Modularity Average
Path
Length
Order 3.52x10
3
1.04 1.00x10
9
5.07x10
-8 a
5.73x10
-1 a

Family 2.33x10
-6 a
1.22 6.34x10
9
2.22x10
-10 a

Genus 2.46x10
-8 a
4.65 NS
Species 1.60x10
37

Summary of odds ratio values for network topology factors (p < 0.01) used in proportional odds
logistic regression (POLR) models. Separate models were built for the order, family, genus, and
species taxonomic levels. Factors to include for each model was determined by best fit and blanks
indicate that a factor was not used in the model. (For example, at the species level: Biomass
Quantile ~ Clustering Coefficient.) ‘NS’ signifies that although used in the model, the factor was
not a significant predictor of biomass.
a
The odds ratio values, which are recorded in this table, can be challenging to interpret. For ease
of interpretation, the reciprocal for values with negative exponents is calculated to represent how
“less likely” the odds of increased biomass is with each one unit increase in the corresponding
network topology factor and is referenced this way in the main text. Values with positive exponents
are interpreted as that much “more likely” to have increased biomass with each one unit increase
in the corresponding network topology factor.

86
Table 3.2. Hub taxa by biomass group.
Hub taxa with a Kleinberg’s centrality score of over 0.5. M. pyrifera gametophytes from the Leo
Carillo population were binned into biomass groups based on their sporophyte weight at the time
of harvest. Representative networks were generated for the microbial communities of each biomass
group. Taxa were then given a score to quantify their role as a hub taxa. Taxa from the genus and
species levels that scored over 0.5 are recorded here. Biomass groups: Low (<63.92g, n = 77) and
High (>211g, n = 77). Taxa names are listed as the direct outputs from the metaxa2 classification
pipeline with the SILVA 128 database.
a
The SILVA taxonomy database is manually curated and shown to have guide tree errors (Edgar,
2018). This species appears to have been incorrectly classified as a genus.

Taxonomic Level Taxa Hub Score Biomass Group
Order Desulfovibrionales 1 High
Order Nitrospinales 0.99 High
Family Magnetococcaceae 1 High
Family Beijerinckiaceae 0.95 High
Family Holosporaceae 0.81 High
Genus Wenyingzhuangia 1.00 High
Genus Pedobacter 0.89 High
Species mucus bacterium 80 1.00 High
Species Marinomonas brasilensis 0.59 High
Order Frankiales 1 Low
Order Kineosporiales 0.89 Low
Family Veillonellaceae 1 Low
Family Archangiaceae 0.99 Low
Family Burkholderiaceae 0.89 Low
Family Clostridiaceae 1 0.85 Low
Genus Marixanthomonas 1.00 Low
Genus Magnetococcus 0.91 Low
Genus Epibacterium 0.84 Low
Genus alpha proteobacterium PWB3
a
0.57 Low
Genus Collinsella 0.53 Low
Species Kordiimonas lacus 1.00 Low
Species Methylosinus trichosporium 0.87 Low
Species alpha proteobacterium SAORIC-651 0.74 Low
Species Stappia taiwanensis 0.62 Low
Species marine bacterium VA011 0.55 Low

87
Table S3.1. Summary of network topology factors.

Taxonomic Level Network Topology Factor Minimum Maximum Mean ± SD
Order Total Nodes 34 74 58 ± 11.32
Order Total Edges 30 120 82 ± 25.28
Order Positive Edges 28 104 74 ± 20.65
Order Negative Edges 1 21 8 ± 5.12
Order Positive / Negative Edge Ratio* 3.75 49 10 ± 11.88
Order Positive / Total Edge Ratio 0.79 0.98 0.91 ± 0.04
Order Negative / Total Edge Ratio 0.02 0.21 0.09 ± 0.04
Order Average Path Length* 1.72 7.5 3.63 ± 0.98
Order Modularity* 0.62 0.88 0.76 ± 0.04
Order Average Degree 1.58 3.43 2.68 ± 0.48
Order Heterogeneity* 0.23 0.39 0.31 ± 0.03
Order Clustering Coefficient* 0 0.31 0.16 ± 0.08
Family Total Nodes 130 163 144 ± 8.76
Family Total Edges 189 424 224 ± 65.89
Family Positive Edges 178 357 206.5 ± 47.03
Family Negative Edges 5 89 19 ± 19.87
Family Positive / Negative Edge Ratio* 3.61 38.8 10.95 ± 4.74
Family Positive / Total Edge Ratio 0.78 0.97 0.92 ± 0.04
Family Negative / Total Edge Ratio 0.03 0.22 0.08 ± 0.04
Family Average Path Length 3.57 7.36 5.2 ± 0.84
Family Modularity* 0.76 0.9 0.83 ± 0.03
Family Average Degree 2.74 5.2 3.14 ± 0.68
Family Heterogeneity* 0.2 0.3 0.25 ± 0.02
Family Clustering Coefficient* 0.1 0.25 0.17 ± 0.03
Genus Total Nodes 461 484 473.5 ± 8.35
Genus Total Edges 1884 3070 2819.5 ± 435.28
Genus Positive Edges 1560 2396 2181.5 ± 270.84
Genus Negative Edges 254 731 623.5 ± 169.43
Genus Positive / Negative Edge Ratio* 3 7.55 3.62 ± 1.09
Genus Positive / Total Edge Ratio 0.75 0.88 0.78 ± 0.04
Genus Negative / Total Edge Ratio 0.12 0.25 0.22 ± 0.04
Genus Average Path Length 2.86 3.54 3.07 ± 0.19
Genus Modularity 0.65 0.77 0.68 ± 0.04
Genus Average Degree 8.05 12.94 11.89 ± 1.75
Genus Heterogeneity* 0.19 0.26 0.23 ± 0.02
Genus Clustering Coefficient* 0.12 0.24 0.15 ± 0.02
Species Total Nodes 745 790 749.5 ± 18.39
Species Total Edges 5533 8284 6104.5 ± 701.76
Species Positive Edges 4308 5847 4692.5 ± 350.63
Species Negative Edges 1214 2537 1425.5 ± 361.44
Species Positive / Negative Edge Ratio 2.2 3.74 3.26 ± 0.41
Species Positive / Total Edge Ratio 0.69 0.79 0.77 ± 0.03
Species Negative / Total Edge Ratio 0.21 0.31 0.23 ± 0.03
Species Average Path Length 2.75 3.17 2.94 ± 0.08
Species Modularity 0.62 0.7 0.66 ± 0.01
Species Average Degree 14.57 20.97 16.3 ± 1.5
Species Heterogeneity 0.22 0.27 0.24 ± 0.01
Species Clustering Coefficient* 0.12 0.18 0.14 ± 0.01

Network topology factors recorded for LC gametophytes (n = 308) with bacteria classified at four
taxonomic levels: order, family, genus, and species. LC gametophytes were divided into four
biomass quantiles and a summary of all data is presented here. For each taxonomic level, we
randomly sampled 50 individuals from each quantile 100 times to create representative networks.
(*) Used in regression model.
88
Table S3.2. Summary of p-value and odds ratio values.

Taxonomic Level Network Topology Factor P-Value Odds Ratio
Order Clustering Coefficient 2.64x10
-4
3.52x10
3

Order Positive to Negative Edge Ratio 3.10x10
-3
1.04
Order Heterogeneity 2.54x10
-5
1.00x10
9

Order Modularity 7.40x10
-9
5.07x10
-8

Order Average Path Length 2.16x10
-4
5.73x10
-1

Family Clustering Coefficient 3.13x10
-3
2.33x10
-6

Family Positive to Negative Edge Ratio 4.67x10
-10
1.22
Family Heterogeneity 5.69x10
-3
6.34x10
9

Family Modularity 3.92x10
-5
2.22x10
-10

Genus Clustering Coefficient 8.08x10
-3
2.46x10
-8

Genus Positive to Negative Edge Ratio 3.30x10
-10
4.65
Species Clustering Coefficient 1.84x10
-10
1.60x10
37

P-value and odds ratio values for network topology factors used in proportional odds logistic
regression (POLR) model. Models were as follows: (Order) Biomass Quantile ~ Positive to
Negative Edge Ratio + Average Path Length + Modularity + Heterogeneity + Clustering
Coefficient, (Family) Biomass Quantile ~ Positive to Negative Edge Ratio + Modularity +
Heterogeneity + Clustering Coefficient, (Genus) Biomass Quantile ~ Positive to Negative Edge
Ratio + Heterogeneity + Clustering Coefficient, and (Species) Biomass Quantile ~ Clustering
Coefficient.

89
Table S3.3. Hub taxa by population.

Taxonomic
Level Taxa Hub Score Population
Order NIASMIV (Gammaproteobacteria) 1 AQ
Order Chthoniobacterales
+
0.94 AQ
Order Chlorobiales 1 CI
Order Ignavibacteriales 0.56 CI
Order Bifidobacteriales 1 CP
Order Myxococcales 0.95 CP
Order Chthoniobacterales
+
0.83 CP
Order Frankiales 1 LC
Order Subsection III (Cyanobacteria) 0.95 LC
Order Kineosporiales 0.87 LC
Order Subsection IV (Cyanobacteria) 0.79 LC
Order Micromonosporales 0.62 LC
Order Solirubrobacterales 0.56 LC
Family Iamiaceae 1 AQ
Family Cryptosporangiaceae* 0.91 AQ
Family Micrococcaceae 0.77 AQ
Family Chlorobiaceae 1 CI
Family Ignavibacteriales Incertae Sedis 0.99 CI
Family Sphingomonadaceae 0.54 CI
Family Ectothiorhodospiraceae 1 CP
Family Granulosicoccaceae 0.92 CP
Family Family I (Cyanobacteria, Subsection III) 1 LC
Family Kineosporiaceae 0.97 LC
Family Cryptosporangiaceae* 0.96 LC
Family Family I (Cyanobacteria, Subsection IV) 0.92 LC
Family Micromonosporaceae 0.72 LC
Family Moritellaceae 0.70 LC
Family Bdellovibrionaceae 0.69 LC
Family Caedibacter caryophilus group (Rickettsiales) 0.63 LC
Family Mycobacteriaceae 0.62 LC
Family Staphylococcaceae 0.61 LC

Hub taxa with a Kleinberg’s centrality score of over 0.5. M. pyrifera gametophytes from all four
populations (AQ, CI, CP, and LC). Representative networks were generated for the microbial
communities of each population. Taxa were then given a score to quantify their role as a hub taxa.
Taxa from the order and family levels that scored over 0.5 are recorded here. *,+ denotes hub taxa
found in more than one population with a score over 0.5.

90
Figures
Figure 3.1. Workflow for data collection and network construction.

1. Wild M. pyrifera sporophylls were collected from four natural populations across Southern
California: Arroyo Quemado (AQ), Catalina Island (CI), Camp Pendleton (CP), and Leo Carillo
(LC). 2. Reproductive blades were surface sterilized and prepared for spore release. 3. Spores were
released in sterile Provasoli enriched seawater medium (PES). 4. Spores were raised to
gametophyte stage in petri dishes. Single, genetically unique gametophytes (green) were isolated
and used to establish a giant kelp seedbank. No antibiotic treatment was applied, and resident
microbes (purple) persisted. 5. Genetically unique gametophyte germplasm cultures were grown
vegetatively in sterile PES. 6. M. pyrifera (green) and microbial (purple) DNA of each genetically
unique gametophyte culture were co-extracted, followed by shotgun sequencing using an Illumina
S4 Novaseq platform. 7. Microbial DNA was filtered and characterized using the ‘metaxa2’
program with the SILVA128 database. 8. Microbial networks were constructed and analyzed using
the ‘SpiecEasi’ and ‘igraph’ programs.

91
Figure 3.2. Microbial network features of gametophytes vary with sporophyte biomass.

The network features of genetically unique gametophytes (green, labelled A-C) were analyzed to
identify whether any characteristics of early stage gametophyte microbiomes can be used to predict
which individuals will become high-biomass sporophytes.

92
Figure 3.3. Co-occurrence networks of the microbial community classified at the genus level.

Each node represents a unique genus. Node size represents the hub score and node color represents
phylum membership. Edge opacity represents the strength of the link and edge color represents a
positive (green) or negative (magenta) co-occurrence pattern. Microbial networks sampled from
(A) low-biomass gametophytes (<63.92g, n = 77) and (B) high-biomass gametophytes (>211g, n
= 77).
93

Figure 3.4. Co-occurrence networks of the microbial community classified at the family level.

Each node represents a unique family. Node size represents the hub score and node color represents
phylum membership. Edge opacity represents the strength of the link and edge color represents a
positive (green) or negative (magenta) co-occurrence pattern. Microbial networks sampled from
four populations: (A) AQ, (B) CI, (C) CP, and (D) LC.

94
Figure S3.1. Co-occurrence networks of the microbial community sampled from LC
gametophytes.

Co-occurrence networks classified at the (A-B) order, (C-D) family, and (E-F) species levels. Each
node represents a unique taxa. Node size represents the hub score and node color represents
phylum membership. Edge opacity represents the strength of the link and edge color represents a
positive (green) or negative (magenta) co-occurrence pattern. (A, C, E) Microbial network sampled
from low-biomass gametophytes (<63.92g, n = 77). (B, D, F) Microbial network sampled from
high-biomass gametophytes (>211g, n = 77).

95
Figure S3.2. Zeta diversity graphs for zeta order 3, 5, 10, 20, and 50 at the species level.

Zeta diversity decline, decline ratio, exponential and power-law regression graphs. For zeta orders
(A) 3, (B) 5, (C) 10, (D) 20, and (E) 50. Results shown are for gametophytes that became high-
biomass sporophytes. Columns from left to right: Zeta diversity decline representing the number
of shared species (Zeta diversity, y-axis) against zeta order; Ratio of zeta diversity decline, also
called the “retention rate curve” that plots the zeta ratios (Zi+1/ Zi) against Zi; zeta decline curves
fitted against exponential and power-law regressions. AIC scores of the two models confirmed that
power-law regression is a better fit for all variations.
96

Figure S3.3. Zeta diversity graphs for zeta order 50 at order, family, genus, and species levels.

Zeta diversity decline, decline ratio, exponential and power-law regression graphs. For zeta order
50 at taxonomic levels (A) order, (B) family, (C) genus, and (D) species. Results shown are for
gametophytes that became high-biomass sporophytes. Columns from left to right: Zeta diversity
decline representing the number of shared species (Zeta diversity, y-axis) against zeta order; Ratio
of zeta diversity decline, also called the “retention rate curve” that plots the zeta ratios (Zi+1/ Zi)
against Zi; zeta decline curves fitted against exponential and power-law regressions. AIC scores
of the two models confirmed that power-law regression is a better fit for all variations.
97
Figure S3.4. Box plots of network topology factors at the order level.

Box plots of (A) total nodes, (B) total edges, (C) positive to negative edge ratio, (D) average path
length, (E) modularity, (F) average degree, (G) heterogeneity, and (H) clustering coefficient for
all populations (AQ, CI, CP, and LC) with bacteria classified at the order level.
98
Figure S3.5. Box plots of network topology factors at the family level.

Box plots of (A) total nodes, (B) total edges, (C) positive to negative edge ratio, (D) average path
length, (E) modularity, (F) average degree, (G) heterogeneity, and (H) clustering coefficient for
all populations (AQ, CI, CP, and LC) with bacteria classified at the family level.
99
Figure S3.6. Co-occurrence networks of the microbial community classified at the order level.

Each node represents a unique taxa. Node size represents the hub score and node color represents
phylum membership. Edge opacity represents the strength of the link and edge color represents a
positive (green) or negative (magenta) co-occurrence pattern. Microbial networks sampled from
four populations: (A) AQ, (B) CI, (C) CP, and (D) LC.

100
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Abstract (if available)

Abstract The need for eco-friendly practices has prompted interest in seaweed-based biofuels as a sustainable alternative to fossil fuels. Macrocystis pyrifera, a brown macroalgae known as “giant kelp” is native to Southern California and a prime candidate for domestic biofuel production. The poor fitness of M. pyrifera in offshore farms is a critical barrier to large-scale cultivation and commercial applications; however, given the tight associations between seaweeds and their native microbiota, there’s an opportunity to utilize seaweed-microbe interactions and optimize growth in offshore farms. Here, I explore the use of microbial tools to improve seaweed cultivars: 1) I review existing knowledge of seaweed-microbe associations and discuss opportunities to apply metagenomic research to seaweed aquaculture, 2) I characterize the microbial community of giant kelp “seed-bank” cultures, identify taxa correlated with increased biomass yield, and propose these as candidates for a growth-promoting inoculant, and 3) I investigate the network topology and community dynamics of those same microbial communities and determine which features are unique to giant kelp cultivars that become high-biomass individuals. Together, this work provides a valuable knowledge base for the development of microbial tools for seaweed aquaculture and an exciting step forward in the commercialization of seaweed as a biofuel feedstock.

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Creator Osborne, Melisa Gürakar (author)

Core Title Diversity and dynamics of giant kelp “seed-bank” microbiomes: Applications for the future of seaweed farming

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Molecular Biology

Degree Conferral Date 2023-05

Publication Date 05/01/2023

Defense Date 03/13/2023

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

Tag aquaculture,biofuel,cultivars,giant kelp,growth promoting inoculant,macroalgae,Macrocystis pyrifera,metagenomics,microbial tools,Microbiology,microbiome,network analysis,OAI-PMH Harvest,offshore farms,phycology,seaweed,seed bank

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Advisor Nuzhdin, Sergey (committee chair), Dean, Matthew (committee member), Ehrenreich, Ian (committee member), Finkel, Steven (committee member), Kenkel, Carly (committee member), Levine, Naomi (committee member)

Creator Email melisa.g.osborne@gmail.com,mgosborn@usc.edu

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