DISSERTATION 
 
 
 
 

EVALUATING SOIL MICROBIAL COMMUNITY ASSEMBLY TO UNDERSTAND 

PLANT-SOIL DIVERSITY FEEDBACKS 

 
Submitted by 

 
Shabana Hoosein 

 
Graduate Degree Program in Ecology 

 
 
 
 

In partial fulfillment of the requirements 
 

For the Degree of Doctor of Philosophy 
 

Colorado State University 
 

Fort Collins, Colorado 
 

Spring 2022 
 
 
 
Doctoral Committee: 
 

 Advisor: Mark W. Paschke  

 Co-Advisor: Pankaj Trivedi 
 

 Mary Stromberger 

Ryan R. Busby 
Cameron Egan 

 
  



Copyright by Shabana Hoosein 2022 

All Rights Reserved



 

 

 

ii 

ABSTRACT 
 
 
 

EVALUATING SOIL MICROBIAL COMMUNITY ASSEMBLY TO UNDERSTAND 

PLANT-SOIL DIVERSITY FEEDBACKS 

 

The integral role of soil biological relationships in ecological restoration is widely 

acknowledged as critical for vegetation establishment and primary ecosystem functions. In the 

era of rapid land degradation, soil restoration will likely become a reoccurring need across global 

conservation and restoration efforts. By increasing our understanding of the relationship between 

aboveground plant communities and belowground soil communities, we can begin to include the 

restoration of soil biological communities. Within the rhizosphere, plant roots and arbuscular 

mycorrhizal fungi (AM fungal) form intimate associations, by which their diversity and 

functioning are inherently linked. Increasing our understanding of AM fungal interactions with 

other soil microorganisms in the rhizosphere microbiome can help us explore how changes to 

plant-soil feedbacks contribute to plant community restoration success. To achieve this goal, I 

evaluated how changes in plant diversity impacts AM fungal and bacterial interactions in mixed 

grass prairies using amplicon-based sequencing techniques, diversity metrics, and microbial 

network analyses.  

To understand how plant diversity influences soil microbes, I conducted an observational 

field study using replicated plots across an experimental plant diversity gradient (low, medium, 

high plant diversities). Soils were sampled and processed for amplicon-based sequencing, which 

revealed the coupled nature between aboveground plant communities and soil bacteria, but not 

fungi.  A microbial network analysis of the data showed that high plant diversity had the least 



 

 

 

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nodes and edges, but high modularity and positive interactions. These results suggest that soil 

microbial communities associated with high plant diversity developed complex interactions that 

increased the stability of soil microbial communities and their interactions.  

This led me to create an experimental greenhouse study to evaluate microbial community 

response to changes in plant diversity. Using conditioned soils from the observational field study 

as inoculum (from high and low plant diversity plots), I created mesocosm plant communities of 

high (30 species), medium (15 species), and low (5 species) diversities with prairie species. Pots 

were inoculated with the high and low diversity field-conditioned soils. Inoculated mesocosm 

plant communities grew in the greenhouse for six months. Soils were then sampled and 

processed using amplicon-based sequencing methods as well as diversity metrics and network 

analyses to evaluate how the relationship between AM fungi and bacteria change with shifts in 

plant diversity. Overall, we found that AM fungi dominated in contributions to network 

interactions in all field inoculum treatments. Furthermore, AM fungi also dominated as the hub 

taxa for most treatments. Positive interactions outweighed negative interactions in the high 

greenhouse-established plant diversity, high field-conditioned inoculum treatment. Along with 

the high alpha diversity of AM fungal and bacterial communities in these treatments, the data 

inferred that these networks are self-maintaining and stable. Bacteria played a minor role in the 

stability of microbial interactions in field-conditioned inoculum treatments but became the 

dominant hub taxa in the uninoculated control treatment. 

Lastly, I explored how the microbial community responds to aboveground disturbance. 

To answer this question, I used mesocosm pots from the previous experiment to initiate a 

disturbance on the greenhouse-established plant community by clipping above-ground biomass 

once every two weeks for two months, followed by a one-month recovery period.  Soil samples 



 

 

 

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were then collected and processed using amplicon-based sequencing methods to evaluate soil 

microbial diversity and network analyses post-disturbance.  I found that alpha diversity metrics 

showed little difference across greenhouse-established plant diversity and inoculum treatments. 

When evaluating beta diversity, bacteria showed differences across all treatments and AM fungi 

showed different microbial ordinations across high and low field-conditioned inoculum 

treatments. Under disturbance, negative interactions outweighed positive interactions, which is a 

common finding for stressed systems. In addition, high plant diversity treatments showed greater 

modularity, or stability of interactions, which is likely due to AM fungal contributions to 

network interactions. 

Overall, this research explored some controversial assumptions often made in plant-soil 

feedback studies and addresses the diverse use of methodologies to better understand linkages 

between plant community diversity and soil microbial community dynamics. Plant community 

diversity is not necessarily a direct reflection of soil microbial diversity and was correlated with 

bacterial diversity. This finding indicates that members of the soil microbial community have 

different relationships with the plant community. Despite changes in AM fungal diversity across 

treatments, AM fungi play a major role in interactions within the rhizosphere microbiome, which 

was confirmed through hub taxa analyses. In the face of disturbance to aboveground 

communities, dynamics in the rhizosphere shift based on the composition of the plant 

community, with AM fungi contributing the most in high greenhouse-established plant 

communities. AM fungi and bacteria differentially contributed to plant-soil feedbacks and their 

contributions are likely to shift as plant stressors limit the functioning of plant-soil feedbacks.  

Collectively, this dissertation shows that alpha and beta diversity metrics do not reveal 

much pertaining to soil microbial interactions and stability. However, they are still a valuable 



 

 

 

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tool when used in conjunction with network analyses to understand the complex relationships 

between soil microbial communities and changes in plant community dynamics. 

  



 

 

 

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ACKNOWLEDGEMENTS 

 

I am incredibly grateful to all of those that have contributed to my supportive network 

and that have continuously been in support of my success. I’d like to thank my advisors, Dr. 

Mark Paschke and Dr. Pankaj Trivedi, for giving me the platform to be the best version of a 

scientist that I could be. Mark, thank you for challenging me and for giving me the space to 

nurture and explore my passions. You were right, building my research from the bottom-up was 

difficult, but I grew in ways that I could have never imagined. Pankaj, you’ve supported me, 

valued my intellect and contributions, advocated for my success and have given me so many 

insights into life. Thank you for every golden nugget that you have given me. I will cherish them 

all and hope to give them to others someday. 

Special thanks to my committee members who have supported my growth as a scientist, 

colleague, and friend. Dr. Cameron Egan, thank you for responding to my fan mail. It is an 

absolute privilege to work with you. Thank you for always believing and having confidence in 

me even though I have yet to meet you in-person. Maybe, someday. Dr. Ryan R. Busby, thank 

you for being so actively engaged throughout my whole PhD experience. Your research inspired 

my inquiry into Mark’s lab and has opened a world of possibilities for me. Dr. Mary 

Stromberger, thank you for encouraging me to think broadly and for supporting my ambition 

when I could not see it.  

This research was made possible due to support from the Graduate Degree Program in 

Ecology, SER-Rocky Mountains Chapter, and an endowment from Shell USA, Inc. Special 

thanks to the Trivedi, Cotrufo, and Paschke labs for all your feedback and providing a 

community-based support system that I can only hope to find in my future endeavors. Thank you 



 

 

 

vii 

to Kristen Otto who has seen my highest highs and lowest lows on the lab bench. You’ve been 

my “lab mom” and have taught me nearly everything I know in the lab. Thank you, Kris, for 

your authenticity, reliability, organization, and patience, particularly when I not the fastest or 

most accurate pipette handler. Thanks to Jason Corwin, Chanda Trivedi, and Kylie Bryce for 

your investments in me as a peer, collaborator, and friend. Jayne Jonas-Bratten, thank you for 

your Nebraskan insight, your field work expertise, and for always being available for my 

numerous stats questions. Thank you to Chris Helzer and The Nature Conservancy for making 

everything at the site so simple to navigate as a researcher. I’m hoping that researchers can 

continue to use the Dahm’s Diversity Plots at Platte River Prairies, NE for decades! Jeffrey 

Corbin, for being the reason why my research path began in 2009 at Union College in 

Schenectady, NY. And the largest thank you to my personal and mental support systems — 

friends and family, Kailee Reed, Marie Orton, Sharon Shaughnessy, Leena Vilonen, Bethany 

Avera, Alison Foster, Dan Ott, and of course, Kat Morici. Nicky, thank you for being at my side 

through the steepest of slopes, acknowledging, participating, and having patience for everything 

that I put into my work, for reading my entire dissertation by choice, and for being my biggest 

advocate. And thanks to my Loli girl for reminding me to take better care of her (and myself), for 

being my little nugget, for her sassy patience, and for being the greatest best buddy, a girl could 

ask for.  

  



 

 

 

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DEDICATION 
 
 
 

For my mother and father,  

Jung Ok Chwe and Safir Hoosein. 

Words cannot describe how much you have given me. I am so grateful to be like each of you. 

This is for you, 엄마 and 아빠. 

 

 

And for Lyla and Lucas Egas. 

The most that I can relay is that I wish this gives you hope and a cloud to dream on. 

Love you, 임이. 

  



 

 

 

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TABLE OF CONTENTS 
 
 
 

ABSTRACT .................................................................................................................................... ii 

ACKNOWLEDGEMENTS ........................................................................................................... vi 

DEDICATION………………………………………………………………………………… viii 

CHAPTER 1: LITERATURE REVIEW: The key to the root microbiome: incorporating bacteria 

and arbuscular mycorrhizal fungal community assembly to understand interactions and 

functionality .................................................................................................................................... 1 

CHAPTER 2: Understanding plant and soil microbial community diversity through the lens of 

network interactions ...................................................................................................................... 40 

CHAPTER 3: Influence of plant community diversity and field-conditioned inoculum on soil 

microbial community structure and network dynamics ................................................................ 62 

CHAPTER 4: Influence of plant community diversity imparts stability of microbial network 

dynamics in the face of disturbance ............................................................................................ 111 

CHAPTER 5: SYNTHESIS ........................................................................................................ 140 

APPENDIX……………………………………………………………………………………. 145 



 

 

 

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CHAPTER 1. LITERATURE REVIEW: 

The key to the root microbiome: incorporating bacteria and arbuscular mycorrhizal fungal 

community assembly to understand interactions and functionality 

 

 

1.1.Introduction 

 Arbuscular mycorrhizal (AM) fungi are a valued fundamental taxonomic group in the 

rhizosphere because of their intimate relationship within the plant root, their role in plant nutrient 

acquisition, and their contributions to plant success under unfavorable environmental conditions 

(van der Heijden et al. 1998, Jeffries et al. 2003, Powell and Rillig 2018). As a prospect for 

agricultural and ecological restoration applications, AM fungal community assembly has long 

been debated (Horn et al. 2017) and recent studies have yet to elucidate the interdependent 

complexities behind its composition (Kokkoris et al. 2020). Within the rhizosphere, AM fungi 

interact with many microbes and have collectively adapted beneficial relationships that influence 

the fitness of their host plants (Filion et al. 1999, Artursson et al. 2006). The entire functional 

entity, known as the holobiont (see Box 1. for term glossary), is based on the co-evolutionary 

history between a host plant and its associated microbes, as well as interactions amongst 

microbes (see Box 1. for term glossary) (Vandenkoornhuyse et al. 2015, Sánchez-Cañizares et al. 

2017). The complexity behind AM fungal community assembly begs the question of how soil 

microbes and interactions within the holobiont help shape the function of the rhizosphere 

microbiome and its contributions to ecosystem dynamics. 

1.1.1. Past and current perspectives on AM fungal community assembly 

 Over the past 20 years, there have been ongoing debates about the drivers of mycorrhizal 

community assembly. Historically, scientists have viewed the drivers of AM fungal assembly 

from the plant perspective (the Passenger hypothesis) (Newsham et al. 1995) or the fungal 



 

 

 

2 

perspective (the Driver hypothesis) (Hart et al. 2001). Neither of these hypotheses fully represent 

the determinants of mycorrhizal community assembly because they fail to incorporate a holistic 

view of this symbiotic relationship and the factors that contribute to the holobiont microbial 

community assemblage. More recently, ecologists have developed the Codependency hypothesis 

where community assembly of both host plant and fungus are linked (Horn et al. 2017) and rely 

on each other for the sustainability of their respective community structures (Kokkoris et al. 

2020). Various methodological and experimental approaches have increased our understanding 

of the AM fungal-plant relationship, allowing for the untangling of this complex relationship to 

be achieved from the holobiont perspective. 

 One evolutionary hypothesis that contributes to the holobiont view of community 

assembly is partner fidelity feedback (PFF), by which the evolutionary cooperation of one 

individual contributes to the fitness of the other individual, its partner (Fredrickson 2013, 

Vandenkoornhuyse et al. 2015). This feedback requires the mutualistic coevolution of two 

individuals for the symbiotic partner to receive benefits from this feedback and in turn optimize 

their host partners. Another example of community assembly through the conceptual holobiont 

can be explained at the transcriptional and metabolic levels. The host genome represents the 

persistence of host evolutionary processes, along with the genetic contributions of its 

evolutionarily selected microbiota that benefit from host evolutionary processes (Guerrero et al. 

2013). Therefore, it is imperative that AM fungal scientists consider the holobiont concept as a 

system of microbial interactions to gain insight into how AM fungi influence the function of the 

plant microbiome.  

 The plant holobiont concept considers the coevolutionary history between plant hosts and 

their associated microorganisms, a process that is likely shaped by the composition and assembly 



 

 

 

3 

of AM fungal communities. Throughout this paper, I will refer to the holobiont concept as the 

assemblage of microorganisms that occupy the space in and around the host, influencing host 

fitness and survival through interdependent and complex plant-soil feedback dynamics (Fig 1.1) 

(Guerrero et al. 2013, Vandenkoornhuyse et al. 2015, Sánchez-Cañizares et al. 2017, Hassani et 

al. 2018).  When studying mycorrhizal symbiosis, it is important that we increase our 

understanding of the interactions between AM fungi and bacteria to extend the plant-mycorrhizal 

holobiont concept to include other interacting microbial groups that contribute to the functioning 

of a plant as a whole.  Early ancestors of AM fungi co-evolved with plants, along with other 

plant-associated microbiota, to adapt to historic high carbon levels in the environment (Helgason 

& Fitter 2009). Furthermore, the host genome and its associated microbiome, or the hologenome 

(see Box 1. for term glossary), includes the coevolutionary dynamics that lead to a ‘genomic 

reflection’ that is evident in host or microbial genomes during these interactions (Guerrero et al. 

2013, Tipton et al. 2019). Despite co-evolutionary knowledge behind the processes that have led 

to our present-day plant and AM fungal relationship, the breadth of AM fungal species diversity 

is not as great as its relative, ectomycorrhizal fungi. However, the narrow taxonomic scope of 

AM fungal diversity is a common evolutionary product of other endophytic microorganisms, 

indicative of the functional plasticity across AM fungal taxonomic groups (Vandenkoorhuyse et 

al. 2015, Knapp and Kovács 2016, Powell and Rillig 2018). 

 In this review, I argue that within root microbiome studies, arbuscular mycorrhizal fungi 

should be studied in conjunction with soil microbes due to similarities in scale, niche occupation, 

and plant relationships that can shift coexistence dynamics between microorganisms in the root 

microbiome. I also provide some insights this knowledge gap by delving into pattern-based 

analyses, network analysis and core microbiome research, that have been effective in 



 

 

 

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communicating data trends throughout other disciplines over the past 10+ years. I intend for this 

paper to bring light to interdisciplinary research and cross-disciplinary collaborations that can 

help push AM fungal research toward understanding the potential, functionality (see Box 1. for 

term glossary) and shifts that lead to the assembly of AM fungal communities. Research on 

microbiomes have shown progress in understanding a plant’s associated organisms by focusing 

on how microorganisms from different phyla interact with the host. In this review, I highlight 

how bacteria and arbuscular mycorrhizal fungal associations sustain functionality in the root 

microbiome by incorporating knowledge from multiple disciplines.   

 

1.2.The coevolution and ecology of AM fungal community assembly 

1.2.1. Assembly within the holobiont 

 Researchers have come to recognize that different mycorrhizal types are governed by 

different soil microbial traits based on plant-soil relationships (Neuenkamp et al. 2018). 

However, it has been difficult to discern factors that govern mycorrhizal community assembly 

due to contradicting methodologies, lack of consensus across the literature, and lack of studies 

that go beyond AM fungi’s morphological and physiological intricacies. While environmental 

factors, soil characteristics, host associations and dispersal potential all can influence where and 

how certain communities become established, we have yet to establish why certain AM fungal 

groups persist within certain plant communities. Studying interactions between AM fungi and 

their hosts have further limited our understanding AM fungal interactions in the rhizosphere 

microbiome, but the incorporation of the holobiont concept may lead to insights about biological 

factors that could contribute to community assembly and interactions with other microorganisms 

in the rhizosphere. 



 

 

 

5 

 To understand factors that contribute to AM fungal community assembly, it is helpful to 

reexamine factors that govern community assembly within the broader field of ecology. 

Community assembly is the process that shapes a species’ correspondence to a local community 

of organisms (HilleRisLambers et al. 2012). Thus, the definition of assembly rules must include 

the exploration of patterns that determine the occurrences of species assemblages (Weiher et al. 

1998). A better evaluation of AM fungi would include a more holistic evaluation with the 

consideration of plant-symbiont co-evolution and AM fungi’s role in the holobiont system.  It is 

important to evaluate AM fungi in the context of the holobiont because the plant host is a major 

contributor to the interactions within the rhizosphere. The greatest factor that defines a 

holobiont-influenced system is its interactions between organisms associated with the host (Wipf 

et al. 2019). Therefore, by understanding the plant host and AM fungal interactions in the 

rhizosphere we will be able to gain a better understanding of patterns that contribute to the 

formation of AM fungal communities. 

1.2.2. AM fungal and bacterial cooperation in the rhizosphere 

 At each trophic level of interacting organisms, microbial and symbiotic co-evolutionary 

processes support the establishment and persistence of the plant host and the success of plant 

host microbial communities. These coevolutionary processes are evident in many forms within 

the rhizosphere including the production of antimicrobial and antifungal compounds, the high 

biomass of mycorrhizal hyphae, and host plant exudations that inhibit pathogens that influence 

the root microbiome. 

 While bacteria and AM fungi occupy the similar spatial niches to benefit their host 

plants, the distinct, yet collaborative roles of bacterial-fungal interactions in the rhizosphere have 

only recently been studied (Vályi et al. 2016, Yuan et al. 2021). Bacteria play a protective role in 



 

 

 

6 

the persistence of mycorrhiza through the inhibition of antagonistic AM fungal pathogens, 

promotion of hyphal growth, and the protection of mycorrhizal associations by endophytic 

processes (Igiehon and Babalola 2018). Physiologically, we know that AM fungi grow to occupy 

spaces beyond the rhizosphere by delving into crevices and aggregates through hyphal extension 

to acquire and expose pockets of nutritional hotspots (Leake et al. 2004, Peay 2016). On the 

other hand, bacteria occupy a much smaller space and have evolved various mechanisms for 

movement due to their limited spatial occupation in the rhizosphere. To provide functionally 

different benefits to the plant host under various conditions, bacteria and fungi are most abundant 

in the rhizosphere where metabolites are exuded by plants as a communicative bridge between 

soil microbes and plants (Boer et al. 2005, el Zahar Haichar et al. 2008). Recent research 

suggests that collaborative efforts between fungi and bacteria contribute to plant optimization 

due to their physiological differences which can be advantageous to host plants under different 

environmental conditions (Bonfante and Anca 2009, Bergmann et al. 2020). 

 Due to its intimate association within plant roots, AM fungi have been known to 

influence the development of the soil microbial community (Zhang et al. 2018, Chen et al. 2019).  

AM fungi benefit soil microbes by encouraging the growth of plant beneficial bacteria, such as 

plant-growth-promoting-rhizobacteria (PGPR) and mycorrhiza helper bacteria, which 

synergistically prevent antagonistic prokaryotic infections in the rhizosphere (Arturrsson et al. 

2006, Frey-Klett et al. 2007, Scherlach et al. 2013). Mycorrhiza helper bacteria increase AM 

fungal spore germination and symbiosis establishment with the host plant (Giovannini et al. 

2020). Isolates of actinobacteria (within the genera Streptomyces, Corynebacterium, and 

Pseudomonas, amongst others) have likely co-evolved with AM fungi because of their ability to 

decompose insoluble biopolymers that make up AM fungal spore walls, enhancing AM fungal 



 

 

 

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spore germination under the appropriate conditions (Turini et al. 2018).  In utilizing in vivo and 

in vitro techniques, researchers have found that co-inoculation of AM fungi and bacteria increase 

the success of host plants with bacteria, playing an important role in plant-AM fungal symbioses 

(Kameoka et al. 2019, Emmett et al. 2021). Studies using PGPR have shown that the synergistic 

effect of co-inculation with both AM fungi and Pseudomonas enhances host plant defenses 

(Pérez-de-Luque et al. 2017), increases host plant salinity tolerance (Pan et al. 2020, Moreira et 

al. 2020), and alleviates host plant stress from drought (Ghorchiani et al. 2018, Begum et al. 

2019). In all cases co-inoculation was more effective than inoculation with either microbial 

group alone. Therefore, the interactions between fungi and bacteria provide more for the 

rhizosphere microbiome than each kingdom alone. 

 Not only do fungi benefit bacteria, but fungi also act as a selective force in the 

rhizosphere. Bacteria’s coevolution with fungi is evident in bacteria’s resistance to antibacterial 

products produced by fungi, allowing bacteria to colonize near fungi (de Boer et al. 2005). 

Researchers have found that mycorrhizal-associated bacteria inhibit fungal pathogens through 

the production of antibiotics or by secreting siderophores that outcompete pathogenic bacteria for 

iron (Garbaye 1994, Turrini et al. 2018). There are also specific AM fungal characteristics that 

have coevolved with bacteria. For example, the surface of AM fungal hyphae selects for 

particular bacteria that excrete extracellular polymers to adhere to the hyphal surface (Bianciotto 

et al. 2001, Artursson et al. 2006). On the other hand, the extraradical mycelium of AM fungi 

have shown to play a different role in the relationship between fungi and bacteria. Parts of the 

extraradical mycelium are known to be areas where active nutrient absorption occurs (Kameoka 

et al. 2019).  



 

 

 

8 

 These synergistic interactions between mycorrhizal fungi and bacteria help provide 

necessary nutrients for plant growth, such as phosphorus, which are mobilized by bacteria and 

taken up by AM fungal hyphae (Wang et al. 2019, Sharma et al. 2020), where it is transported to 

the host plant for uptake. From the plant-to-bacteria perspective, plant-assimilated 

photosynthates are transferred indirectly to bacteria by travelling through AM fungal hyphae 

from plant roots (Kaiser et al. 2015). This likely increases the number of nutritional hotspots by 

stimulating bacterial communities with labile C in an environment that contains mostly non-

labile (recalcitrant) forms of carbon (Jansa et al. 2013). There is accumulating evidence that AM 

fungi do not act alone in contributing to the root microbiome. Other than root metabolite 

utilization, bacterial contributions to ecosystem processes are likely to be indirectly (or directly) 

influenced by other biotic factors and interactions that have shown reproducible results (Emmett 

et al. 2021). 

 It is well established that AM fungi act as a major conduit of carbon transfer between 

plants and soil microbial communities (Drigo et al. 2010), which could have a substantial impact 

on nutrients available to the bacterial community, influencing bacterial composition and structure 

(Wang et al. 2019). To access these nutritional hotspots, bacteria adhere to hyphal surfaces 

enabling them to spread throughout the soil environment (Hassani et al. 2018). These mycelial 

networks, or ‘fungal highways’, mobilize bacteria thus increasing their exposure to nutrients that 

are spread out in the bulk soil environment (Kohlmeier et al. 2005, Worrich et al. 2016). AM 

fungal hyphae also recruit specific bacteria that enhance nutrient mineralization and harbor 

distinct bacterial communities near AM fungal hyphae that differ from bulk soil (Zhang et al. 

2018). These interactions between bacteria and mycorrhizal fungi indicate the distinct 



 

 

 

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physiological and ecological advantages that AM fungi contribute to the rhizosphere microbiome 

and how mycorrhizal fungi enhance accessibility of critical nutrients for plants. 

 Other synergistic interactions between bacterial consortia and fungi are evident in the 

formation of biofilms on ectomycorrhizal hyphae. Ectomycorrhizal fungi release exudates such 

as trehalose and organic acids that attract fungal-associated bacteria (de Boer et al. 2005). These 

bacteria utilize fungal exudates, much like their utilization of root exudates, as energy sources. 

While the bacterial biofilms are evident across mycobiomes, some fungi in the Ascomycota class 

are not able to provide an environment conducive to hyphal biofilm formation (Miquel Guennoc 

et al. 2018). The presence of beneficial plant-biofilm associations may also enhance aspects of 

hyphal network resilience to ecological stressors and promote the adhesion and motility of other 

beneficial soil bacteria (Motaung et al. 2020). There is a highly facilitative effect between 

mycorrhizal species and multiple bacterial strains, however, little is understood about plant-

associated fungal biofilms due its non-medical application. 

1.2.3. AM fungal influence on plant molecular processes 

 Beyond physical and biochemical bacteria-fungal interactions, AM fungi influence plant 

metabolic processes that concomitantly have effects on plant-bacterial interactions.  AM fungi 

play a direct role in regulating hormone levels and plant genes via molecular crosstalk with 

plants (Scherlach et al. 2013). AM fungi trigger host plant genes involved in immunity response 

(Aseel et al. 2019), allowing for the priming of plant defenses to be initiated before a threat, or 

disease, has made contact. These shifts in plant gene expression boost host plant immune 

systems and communicate molecular information to multiple plants tapped into the shared AM 

hyphal network.  



 

 

 

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 Associations with AM fungi alter gene expression in host plants, some of which regulate 

symbiosis with AM fungi and others that influence plant metabolic activity via differential gene 

expression. When colonized with AM fungi, 362 plant genes have been shown to be up-

regulated, with most genes being associated with primary and secondary metabolism, response to 

stimuli, and protein modification (Fiorilli et al. 2009). Other studies have found that 80% of plant 

genes were up-regulated with many differentially expressed genes influencing transcription 

factors, which may be involved in the transcriptional regulation during symbiosis (Handa et al. 

2015). More recently, Vangelisti et al. (2018) found that 694 genes were over-expressed in 

Helianthus annuus roots during the late stage of mycorrhizal colonization, with many 

differentially expressed genes influencing plant metabolic processes when associated with AM 

fungi.  

 Due to AM fungi’s influential role in host plant transcriptomes, it is likely that AM 

fungal-influenced plant transcriptional changes leads to changes in the expression of plant 

metabolites. In a study using wheat roots, inoculation with AM fungi stimulated plant root 

exudation and the production of secondary metabolites (Lucini et al. 2019).  The idea of 

microbes stimulating changes in root exudation is a concept that has gained substantial attention 

in the rhizosphere research community as the ‘cry-for-help’ hypothesis (Rolfe et al. 2019). This 

hypothesis suggests that stressed plants release exudates that recruit beneficial microbes while 

simultaneously discouraging the development of plant pathogens in the root zone (Rolfe et al. 

2019). While the ‘cry-for-help’ hypothesis is not highly cited by the AM fungal research 

community, AM fungi influence bacterial recruitment outside of the rhizosphere through 

molecular interactions in the hyphosphere (see Box 1. for term glossary) (Schueblin et al. 2010). 

Signaling communications through the AM fungal hyphosphere confirm that plants influence 



 

 

 

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metabolic activity within the hyphosphere, changing microbial composition within the AM 

fungal hyphosphere (Cabral et al. 2019). The mechanisms behind neighbor-induced triggers to 

increase plant defenses deserves more investigation. Nonetheless, AM fungi are heavily involved 

in processing communications from host plants to the soil environment. This communication 

between AM fungi and other microbes throughout the soil environment warrants more attention 

in AM fungal research. 

 Primary metabolites exuded by plant roots are not likely to be the sole contributor to 

changes in microbial assembly within root microbiomes. Secondary metabolites are often 

induced by biotic stressors and consist of signaling or antimicrobial communications that are less 

likely to be metabolized by microbes and persist longer in the root microbiome (Rolfe et al. 

2019). Transcriptional changes that occur with plant association to mycorrhiza have been found 

to be in both primary (nitrogen, protein, and carbohydrate pathways) and secondary metabolic 

pathways (Sbrana et al. 2014). Therefore, AM fungal interactions with the host plant provide a 

pathway for the indirect regulation of microbial communities (in the hyphosphere) and through 

their direct influence on plant secondary metabolites that are released as exudates, interfering 

with microbe-microbe crosstalk in the rhizosphere. 

 While the effect of plant secondary metabolites on rhizosphere bacteria are obscure, there 

have been several studies that have investigated the production of secondary metabolites in 

plants associated with AM fungi. Associations with AM fungi change the amount of phenolic 

acid exudates released by plants, which contain antimicrobial properties (Wu et al. 2021). 

Specific AM fungal interactions, between two species (Funneliformis geosporum and 

Acaulospora laevis), reduced phenolic acid production in associated host plants while all other 

combinations of mycorrhizal inoculum increased phenolic acid levels (Wu et al. 2021). While 



 

 

 

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AM fungal-induced changes in plant secondary metabolite production could indirectly influence 

bacterial communities, AM fungal-induced increases in plant phenolic acid levels have been 

presumed to attract other bacteria to the rhizosphere, imposing direct competition with the 

existing microbial community (O’Banion et al. 2018).   

 The production of secondary metabolites due to plant associations with mycorrhizal fungi 

may also play a role in metabolic mutualism, or cross-feeding, amongst other microorganisms in 

the rhizosphere (D’Souza et al. 2021). Rhizosphere bacteria have been known to synthesize their 

own secondary metabolites for microbial communications including anti-fungal, anti-bacterial, 

pigments that provide protection, and siderophores involved in scavenging iron (Dror et al. 

2020). While the functionality of AM fungal taxa has yet to be elucidated, it is evident that AM 

fungi indirectly influence host plant function, plant metabolite production, and communication 

with soil bacteria in ways that bacteria do directly. Nonetheless, AM fungi’s high biomass 

throughout the soil environment lend to ecological advantages that increase their interactions 

both within and outside the rhizosphere. 

1.2.4. AM fungal interactions contribute to microbiome functional diversity 

 While the direct effects of AM fungi’s contributions to rhizosphere microbiome function 

are limited, it is likely that AM fungi’s presence and role as a foundational organismal group in 

the rhizosphere have many indirect interactions that increase holobiont functional diversity (see 

Box 1. for term glossary) and resiliency. AM fungal communities likely contribute to increased 

rhizosphere microbiome functional diversity when groups of mixed AM fungal taxa are 

incorporated into inoculum (Ceccarelli et al. 2010). Inoculation with mixed AM fungal 

communities enhance host plant secondary metabolite production and metabolic plasticity 

increasing plant adaptations to environmental stressors (Albrechtova et al. 2012, Hart et al. 2015, 



 

 

 

13 

Avio et al. 2018). Furthermore, plants associated with different AM fungal strains have been 

observed to have different plant metabolic plasticity that enhance plant tolerance to stress 

(Rivero et al. 2018). The contents of these secondary metabolites are thought to play a 

fundamental role in recruitment of plant health-promoting bacteria and increasing functional 

diversity of the rhizosphere microbiome (Agnolucci et al. 2015, Turini et al. 2018, Agnolucci et 

al. 2020).  

 Further work incorporating experimental manipulations of soil microbial communities is 

needed to reach a better understanding of how microbial interactions influence AM fungal 

communities and in turn influence host plant secondary metabolic exudation. For example, 

mock, or synthetic, bacterial communities could be constructed and implemented in the 

rhizosphere with the addition of AM fungi and without. Here we would be interested in how the 

presence of AM fungi influences plant secondary metabolic production and if these metabolites 

change bacterial community structure. By studying how AM fungi influence plant secondary 

metabolic production and indirectly influence bacterial communities, we may begin to 

understand the holobiont system more clearly. 

 

1.3. Exploring AM fungal community assembly through microbiome functionality 

1.3.1. Impact of AM fungal functioning on host plants and ecosystems 

 The functional capacity of AM fungal communities exists on a spectrum based on 

different host plant and ecosystem qualities that determine the functional potential of plant-AM 

fungal relationships. AM fungi benefit from nutrient deficient, or low-phosphorus soil, because 

phosphorus-limitations encourage plants to make associations with AM fungi (Smith et al. 2011, 

Johnson et al. 2015). AM fungi have also been shown to enhance decomposition and acquire 



 

 

 

14 

nitrogen from patches of organic materials in direct competition with other microbes (Hodge et 

al. 2001). AM fungi have been found to alleviate salt stress through a series of molecular, 

proteomic, and biochemical reactions (Evelin et al. 2009, Porcel et al. 2012, Jia et al. 2019). 

Furthermore, Augé et al. have spent over a decade studying how AM fungi are beneficial under 

drought stress to find that AM fungi influence stomatal conductance (Augé 2001, Augé et al. 

2015). While AM fungi have an influence on the plant transcriptome and metabolic pathways 

during drought (Begum et al. 2019), it is likely that the interactions between bacteria (Rubin et 

al. 2017) and fungal network stability (de Vries et al. 2018) promote plant success under drought 

conditions. Current research has only begun to uncover how interactions between fungal 

symbionts and beneficial bacteria contribute to plant survival under stressed conditions. On the 

other hand, plant pathologists have evaluated how AM fungi alter plant-pathogen interactions 

and have found that the presence of AM fungi negatively affects plant-pathogen growth 

(Borowicz 2001, Sikes et al. 2009, Sikes 2010). However, much of the research covered only 

investigates questions directed from one perspective of the entire soil biotic environment within 

the rhizosphere. 

 While many studies have described the beneficial impact that AM fungi have on plants, 

AM fungal contributions to plant fitness are highly variable (Johnson et al. 1997). There are 

likely many abiotic factors that influence AM fungal influence on plant fitness, but the biological 

processes that contribute to plant fitness in associated plant hosts are unclear. Conflicting, 

context-dependent results further convolute the impact that AM fungi have on plant fitness 

(Hoeksema et al. 2010, van der Heyde et al. 2017, Ryan & Graham 2018). Plant association with 

AM fungi does not always contribute to greater plant benefit, but plant benefit is highly 

dependent on the relatedness of the AM fungi shared within plant networks (van’t Padje et al. 



 

 

 

15 

2020). Differences in biotic and abiotic variables also change the strength in symbiotic 

relationships where some AM fungal taxa receive greater recognition and benefits from plant 

partners based on their contributions to plant fitness (Kiers et al. 2011). While many researchers 

have debated how morphology and phylogeny contribute to AM fungal functionality, neither 

incorporate the holobiont perspective and lack the consideration that the relationship between 

AM fungal-plant host relationships are not strictly exclusive.  

 An additional factor that needs to be accounted for in AM fungal studies is the 

interactions between microbes within the biological marketplace, as represented by a series of 

hyphal networks (Kiers et al. 2011, Fellbaum et al. 2012, Noë & Kiers 2018), as hyphal networks 

provide a niche for bacterial establishment. For example, Bahram et al. (2020) found that soils 

dominated by AM fungi experience more nutrient turnover and cycling compared to 

ectomycorrhizal dominated soils suggesting that plant benefits from AM fungal associations are 

reliant on the function of the entire holobiont and its associated microbiota. 

 

1.3.2. AM fungal functionality: why phylogenetic and morphological solutions are inadequate 

 Collections of morphological and phylogenetic data pertaining to AM fungal assembly 

have shown to be insufficient in understanding AM fungal functionality. In addition, the 

relationships between AM fungal morphology and phylogeny are elusive and limited in its 

interpretation of functional roles. AM fungal morphology is rather cryptic with single species 

forming multiple spore morphs, unclear genetic repercussions of anastomosis, and constant 

systematic reconfigurations (Morton & Msiska 2010, Schüßler et al. 2011, Krüger et al. 2012). 

As obligate symbionts with ambiguous reproductive strategies, understanding the life-history 

traits that could indicate functionality have been a challenge. While there is an extensive analysis 



 

 

 

16 

that has provided a phylogenetic basis for the classification of Glomeromycota (Krüger et al. 

2012), linkage of phylogeny to AM fungal functionality has not been extensively confirmed in 

the literature.  

 Many mycorrhizal studies have focused on evaluating AM fungal phylogenetic and 

morphological differences in hopes of bringing insight into functional roles. Life history 

classification of AM fungal traits was also considered in the context of Grime’s C-S-R model 

(Grime 1977) where physiological AM fungal traits, like hyphal growth, were characterized by 

family (Chagnon et al. 2013). While this gave researchers a better idea of the physiological 

attributes that occur in AM fungal families, it could be improved with the incorporation of the 

holobiont (plant and bacterial evolutionary traits) alongside the model. More recently, spore 

morphologies were evaluated (Chaudhary et al. 2020) using life-history traits to understand AM 

fungal dispersal since dispersal is a mechanism that is independent of the holobiont. Nonetheless, 

life-history traits and morphological differences are not sufficient in determining the 

functionality of AM fungi within the holobiont. Since bacteria likely co-evolved along with the 

plant-fungal symbiosis, these inter-kingdom interactions deserve more attention in order to 

develop a fuller understanding of AM fungal functionality and its correlation with AM fungal 

taxonomy.  

 Phylogenetic and taxonomic attributes should not be the only evidence collected when 

studying AM fungal functionality. The complex relationship between AM fungal morphology 

and function suggests that phylogenetic classification is far from being established (Sbrana et al. 

2014). While taxonomy and phylogeny may lead to indications of functionality, neither consider 

plant host associations and interactions that likely define AM fungi these roles. It is still unclear 

how AM fungal functionality and AM fungal families are related. While there have been 



 

 

 

17 

attempts to elucidate these patterns (Chagnon et al. 2013), it is possible that AM fungal 

community diversity cannot solely be used to predict community functionality. As Munkvold et 

al. found in (2004), AM fungal communities with low species diversity may still have 

considerable heterogeneity in their functional representation and contributions to the rhizosphere. 

Therefore, it would be more effective for researchers to evaluate soil bacterial and saprophytic 

fungal interactions with AM fungi as context clues for understanding the metabolic benefits that 

AM fungi indirectly (or directly) influence.  

 While there is extensive evidence in the literature that hypothesize the particular function 

of AM fungal groups, there has been less evidence indicating that AM fungal associations work 

independently from other microbial groups. AM fungi interacts with most organisms in the 

rhizosphere, which indicates its high importance in connecting different microbial communities 

and maintaining the functioning of root systems (Banerjee et al. 2016). While linkages between 

community assembly and metabolic attributes in the root microbiome are unknown, the assembly 

of AM fungal communities has been found to be based on both niche and neutral processes 

(Dumbrell et al. 2010).  AM fungal community assembly may be driven by either neutral or 

niche processes depending on how the filters of assembly rules affects either the plant or AM 

fungal communities (Chagnon et al. 2015). AM fungi communicate and interpret external 

stressors to the plant, dictating their neutral assembly, which may not directly affect the 

community assembly of AM fungal communities. As external stressors are recognized as 

stressors, signaling are translated by the plant host to the soil microbial community through 

molecular communications, dictating niche processes that determine the soil microbial functional 

roles needed by the plant begin to take hold. We may be able to better understand how plant 

communications with AM fungi facilitate further microbial interactions in the rhizosphere by 



 

 

 

18 

understanding how these molecular communications dictate the functional capacity and stability 

of the rhizosphere microbiome. 

1.3.3. The Drawbacks and Feedbacks that Influence AM Fungal Community Assembly Studies  

 Identifying the factors that contribute to AM fungal community assembly remains elusive 

due to lack of research on the contributions of specific taxa to holobiont and ecosystem 

processes, which may have resulted from discipline differences across the AM fungal research 

community. For example, some researchers may think of community assembly from a particular 

perspective (ie. from the plant or fungal perspective). Furthermore, scale plays a factor in many 

of the discrepancies in our communication about AM fungal community assembly, which has 

been acknowledged in other areas of microbial ecology research (Nemergut et al. 2013).  As 

such, microbial assembly processes are distinct due to the biological features and 

biogeographical patterns that make microorganisms unique, ie. size, dormancy, and energy 

acquisition. Thus, we should reconsider how we use the term “community assembly” when 

referring to AM fungi and assure that its use is in the context of microbial and symbiotic 

functioning to make the most of emerging datasets and cross-system meta-analyses. 

 Patterns of AM fungal community composition have been studied using a variety of 

methods and molecular primers. For example, a common method in the early 2000s was using T-

RFLP (terminal restriction fragment-length polymorphism) (Johnson et al. 2004) and 454-

pyrosequencing (Öpik et al. 2009, Dumbrell et al. 2011), which have now been replaced with 

more precise methods like amplicon-based high-throughput sequencing. Throughout soil 

bacterial ecology, the use of standard primers has proven consistent results in capturing the 

conserved regions of bacterial rRNA due to consensus in soil microbiology to use these 

established universal primers (Head et al. 1998). On the other hand, AM fungi have a series of 



 

 

 

19 

primers that have been tested and utilized in a range of studies. Primer pair ITS1-ITS4 were 

created to capture the diversity of all fungal taxa and may skew results with an 

overrepresentation of other fungal groups due to relatively low AM fungal frequencies (Suzuki et 

al. 2020). The most widely used primers for sequencing AM fungi remain to be AM1 and NS31. 

NS31 was designed as a universal eukaryotic primer (Gorzelak et al. 2012) and has been shown 

to amplify non-AM fungi (Helgason et al. 1999). Nonetheless, when considering AM fungal taxa 

at the family and genus levels, it is imperative that there is consensus in primer selection with 

low sequence variability, such as WANDA and AML2 (Egan et al. 2018). The lack of primer 

consensuses poses a major barrier for AM fungal research and for understanding AM 

functionality. If we cannot consistently identify AM fungi using a set of standard primers, then 

patterns pertaining to AM fungal composition and community assembly will remain elusive. 

 Confusion surrounding AM fungal community assembly may also be due to lack of 

distinction between AM fungal studies performed in standard conditions in controlled 

environments versus interactive conditions in field environments. At this point in AM fungal 

ecological research, we still lack an understanding of AM fungal functionality in standard 

conditions within controlled environments, therefore contributing to inconsistent and 

irreproducible results. Novel efforts to produce a trait-based framework incorporating the 

holobiont perspective in mycorrhizal studies are imperative for clarifying the communication of 

these symbioses, but many baseline aspects of AM fungal communities have yet to be explored 

(Dawson et al. 2021). Other fields of study have resolved these discrepancies by developing tools 

to define standard conditions in which traits can be measured (Pérez- Harguindeguy et al. 2013, 

Moretti et al. 2017), allowing for standard measurements of traits to be applied to all AM fungal 

taxa across all biomes. Efforts have been made to provide consensus in data and metadata 



 

 

 

20 

management (Tedersoo et al. 2015), but no consensus has been developed on experimental 

design or data collection methods used to elucidate AM fungal community patterns under 

standard conditions, resulting in further confusion in the interpretation of feedback processes 

(Krause et al. 2014). 

 Plant-AM fungal associations can influence long term plant-soil dynamics. Fungi with 

ruderal traits can influence long term plant-soil feedbacks and later plant successional trajectories 

(Duhamel et al. 2019). Due to the co-evolutionary holobiont nature of the system, changes in 

AM fungal communities have a domino effect on bacterial communities resulting in emergent 

ecosystem properties. The functional complementarity across both soil fungal and bacterial taxa 

promotes different aspects of ecosystem function and stability, where fluctuations in microbial 

richness increase the functional complementarity of the microbiome and the stability of the 

system (Fig 1.1) (Wagg et al. 2021). In addition, multiple studies have found that soil 

conditioning plays a significant role in the recovery of plant communities after disturbance and 

indicate that previous plant community spatial structure (soil conditioning) influence AM 

community assembly (Bittebiere et al. 2020). Therefore, soil conditioning and plant-soil 

feedbacks may play a significant role in influencing assembly of AM fungal communities, but a 

lack of evidence remains on the mechanisms that are responsible for these patterns. A possible 

solution to this lack of knowledge can be found through the incorporation of synthetic 

communities of AM fungi and bacteria under standard conditions to understand how soil 

microbial interactions can infer trait-based microbial functions (de Souza et al. 2020, Toju et al. 

2020). 

 

 



 

 

 

21 

1.3.4. Beyond structure: interactions influence microbiome community assembly 

 Examining community structure can be an important tool in understanding AM fungal 

community assembly by exploring the interactions that lead to rhizosphere microbiome 

formation. The coevolutionary history of AM fungal community assembly show that AM fungi 

and bacteria are influenced by different filters, biotic in AM fungi (Neuenkamp et al. 2018, 

Davison et al. 2020) and abiotic filters in bacterial communities (Fierer & Jackson 2006, 

Delgado-Baquerizo et al. 2018). Understanding interactions between microbes can indicate how 

the microbiome functions as a whole and which taxa influence the structure of the microbiome. 

For example, we can determine if AM fungi provide a supplementary role to bacterial functions 

or if bacterial and fungal roles are independent by developing experiments examining 

interactions between interkingdom microbial groups. 

 Although there has been much controversy in using community assembly data to interpret 

functionality, it has shown to be a helpful tool in identifying repeated and repeatable patterns in 

community structure that can characterize AM fungal taxa (van der Heijden & Scheublin 2007, 

Van Diepen et al. 2011). Nonetheless, AM fungal community structure is an important tool but is 

best used in conjunction with other ‘omic’ based techniques to provide insight into the 

mechanisms contributing to assembly. The incorporation of network interactions and predictive 

tools, like machine learning, have seldom been used in microbiome studies, but have the 

potential to give insight on microbial contributions to ecosystem functionality (Thompson et al. 

2019). Through the coupling of experimental approaches and modeling, we can likely resolve 

many of the methodological and technological challenges that face soil microbial studies and 

translate ‘omics’-based datasets into functional predictions (Trivedi et al. 2020). After 

establishing community-level structural compositions, we may then be able to elucidate patterns 



 

 

 

22 

that contribute to community structural patterns, which can then guide experimental design for 

the elucidation of functional roles. 

 

1.3.5. Quantifying synergistic properties and drawing inferences from networks 

 Microbe-microbe interplay has proven to consist of important selective forces in forming 

complex microbial assemblages impacting resource acquisition for host plants (Hassani et al. 

2018). Much of the research investigating microbial interplay has been the result of careful 

experimental design with synthetic (or mock) communities (Liu et al. 2019). Much of this 

research has ignored AM fungi, which is unfortunate given its keystone role in the rhizosphere 

(Jeffries et al. 2003). Recently, a number of studies analyzed the metabolic facilitation of AM 

fungi and bacterial interactions in acquiring nutrients for the host plant (Nacoon et al. 2021, 

Jansa & Hodge 2021, Jiang et al. 2021). Much more research on AM fungal-bacterial 

interactions are needed to quantify how AM fungi play a keystone role in the rhizosphere and to 

utilize the complementarity between AM fungi and bacteria effectively in an applied setting. At 

the moment, the most efficient way to study these interactions will be through understanding 

patterns in microbial networks and using inferred data to dictate the questions and experiments 

that we design. 

 Network analyses performed on microbial communities are often evaluated as co-

occurrence networks where multiple correlations and models indicate influential taxa, core taxa, 

and the types of relationships (synergistic or antagonistic) predicted amongst microbial consortia.  

In 2018, de Vries et al. found that soil bacterial co-occurrence networks were destabilized by 

drought in grassland systems, whereas fungal networks were more stable. Along with networks, 

de Vries et al. found that shifts in bacterial communities had greater effects on ecosystem 



 

 

 

23 

functioning than fungi. Here, co-occurrence networks are used in conjunction with other analyses 

to understand the stability of microbial communities under stress as well their recoveries.  

Furthermore, networks are used to understand the linkages between taxa. Scientists have found 

that fungal-bacterial networks provide insight into cooperative and competitive interactions 

(Zheng et al. 2018). Therefore, the utilization of network analyses can help scientists understand 

the types of interactions that occur between soil microorganisms, and under which circumstances 

they shift. The culmination of multiple network analyses may lead to changes in the way that we 

think about and evaluate relationships between soil organisms and the spatial scale at which they 

operate. 

 Fungal-bacterial co-occurrence networks also have an important application in the 

understanding of soil functioning. For example, Banerjee et al. (2016) used network analysis to 

find that organic matter decomposition rates were associated with keystone microbial taxa in 

bacterial and fungal communities. Despite being contextually inferential, the application of 

network analyses could elucidate functional roles and potential impacts on ecosystem 

functioning by building predictive models. Mathematical models, such as dynamic network 

modelling, characterize aspects of microbial community interactions and reveal quantitative 

insights into complex dynamics utilized in microbiome studies (Garcia & Kao-Kniffin 2018, 

Garcia & Kao-Kniffin 2020). These particular network models bring important inferences to 

microbial interactions that are crucial to ecosystem functioning (Zhu & Penuelas 2020). 

Nevertheless, there are certain limitations in the use of network analyses and their associated 

models, particularly with sample size. To maximize the robustness of inferred networks, studies 

should have a large number of replicates and should therefore (in all cases) aim to have a large 



 

 

 

24 

collection of samples to improve the predictive power of these models (Barroso-Bergadá et al. 

2020).  

 Due to the complex nature of the rhizosphere, it is difficult to determine to what extent 

host-symbiont interactions or microbe-microbe interactions influence microbial community 

dynamics. Other studies have found that plant-AM fungal networks are stochastic in nature 

(Encinas-Viso et al. 2016) because they lack information pertaining to the holobiont that could 

give insight into AM fungal community assembly (Ryan & Graham 2018, Johnson & Gibson 

2021). An often overlooked, but important aspect to consider is how interactions between AM 

fungi and soil microbes influence rhizosphere microbiome community assembly (Hassani et al. 

2018, Ryan & Graham 2018). Certain functional dynamics can be interpreted from network 

analyses that are useful for modeling or other downstream analyses. For example, the 

quantification of network complexity can indicate how specialized the interactions are in that 

community (Mendes et al. 2014). Furthermore, community abundance distributions can be used 

to discern the factors that contribute to community assembly. Nonetheless, experimental design 

remains an important factor in testing the subjective results of network analyses and community 

abundance datasets. However, the use of large datasets in conjunction with machine learning 

algorithms has proven to be more powerful in creating predictive models than network analyses 

alone (Ramirez et al. 2018). The predictive power of machine learning analyses can be used to 

identify potential ‘indicator’ taxa that have a strong influence on maintaining community 

structure of microbiomes.  

1.3.6. Core microbiomes and hierarchical scales of plant-associated microbes 

 With the advancement of sequencing technology and computationally dense datasets, it 

has become increasingly possible to explore datasets in ways that elucidate microbial processes 



 

 

 

25 

in ecology. The identification of a “core” microbiome is a useful step in reducing the complexity 

within intricate microbial datasets and can be useful in generating novel hypotheses for studies 

that reconstruct aspects of the rhizosphere microbiome (Trivedi et al. 2021).  Nonetheless, the 

context in which core microbiota data is interpreted has resulted in discrepancies across some 

disciplines and consensus among others.  

 While we can define core microbes that are evident in samples and across treatments, it is 

important to understand where the concept of ‘core microbes’ comes from and how it is used to 

make ecological inferences in each field of study. Since hub microbiota in network analyses are 

such an important tool in identifying which organisms provide the structure of network 

interactions, identification of the core microbiota can be used in conjunction with network 

analyses to get a better idea of which taxa has the most influential presence in the studied 

microbiome and where core microbiota contributes to network structure. Nonetheless, it is 

important to recognize that core taxa are not necessarily hub or connector taxa (Stopnisek & 

Shade 2021). Within the context of the plant holobiont, the core microbiota refers to 

microorganisms that are consistent across samples for a given host plant species 

(Vandenkoornhuyse et al. 2015). However, the definition of core microbiota is highly dependent 

on the context of the study (Risely 2020). 

 Core taxa can be used to identify key microorganisms that regulate microbiome structure 

in the rhizosphere. In ecology, taxa that contribute to the maintenance of ecosystem dynamics 

have been coined as ‘keystone organisms’ (Banerjee et al. 2019, Risely 2020). Often, core 

microbiome analyses decipher common taxa across treatments that infer importance to 

microbiome assembly. The incorporation of metagenomic and transcriptomic tools in 

conjunction with the identification of core microbiomes are helpful in assessing microbiome 



 

 

 

26 

functioning and may help decipher which taxa are crucial to host survival under various levels of 

abiotic stressors (Shade & Handelsman 2012). Recent advances in network analyses and 

metagenomics can be useful in reducing the complexity of the microbiome by identifying the 

keystone taxa that contribute to microbiome functions through the deconstruction of the 

microbial community. These communities are then reconstructed with and without proposed 

keystone taxa (through the use of synthetic communities and ‘omic’ techniques in experimentally 

manipulated environments), which will help delineate the hierarchical importance of each taxon 

to microbiome function (Toju et al. 2020, Trivedi et al. 2021).  

 

1.4. Conclusions and Future Directions 

 The field’s latest advances in whole genome sequencing of the AM fungal model 

organism, Rhizophagus irregularis, has been a crucial first step in understanding AM fungal 

gene function (Tisserant et al. 2013). As a result, many more studies have utilized this promising 

work to further our understanding of AM fungal genetics and bacterial contributions to AM 

fungal genetic diversity (Tamayo et al. 2014, Chen et al. 2018, Li et al. 2018, Masclaux et al. 

2019).  By investigating gene expression, we could have a better idea of AM fungal nutrient 

transporter genes, which could then be used to identify AM fungi for use as agricultural 

inoculum (Giovannini et al. 2020). Many avenues could be explored with AM fungi using 

molecular-based techniques that tell us more about function rather than composition. 

Nonetheless, compositional data is fundamental in cataloguing which taxa are associated with 

particular plant species or found in particular systems. While abundance data is useful in relation 

to other data, it is necessary to collect pattern-based information to be used for hypothesis 



 

 

 

27 

generation. These community patterns could be critical in exploring the community assembly of 

AM fungi. 

 Much more inter-disciplinary research is needed in order to develop a better 

understanding of AM fungal community assembly and the role that AM fungi play within the 

rhizosphere microbiome. Some of the challenges that face the field of AM fungal ecology have 

the potential to be overcome by incorporating a combination of techniques (like amplicon-based 

sequencing, transcriptomic, and proteomic methods) from the field of microbiome science and 

other disciplines. Due to similarities in scale and niche occupation, the field of AM fungal and 

soil microbiome research should include and promote research by each other’s fields to broaden 

tools and increase cross-disciplinary collaborations.  

 To better understand AM fungal community assembly and functionality, research that 

includes both field studies and controlled environment studies is an appropriate first step to take. 

Furthermore, pattern-based analyses, like network analyses, could help shed light on the 

interactions and functional roles that allow these microbes to persist in the rhizosphere 

microbiome. By manipulating key taxa that contribute to holobiont function, using synthetic or 

mock communities, we can experimentally tease apart these interactions and build knowledge 

pertaining to microbial function using controlled environment studies (Egan et al. 2018). The use 

of synthetic communities has a great advantage over exclusionary treatments, like fungicide 

because chemical applications may have adversary effects that change the overall chemistry 

within soil microbial communities. By utilizing the technologies, like synthetic communities, and 

bringing tools together from different disciplines, we can overcome many of the obstacles 

pertaining to the study of AM fungal community assembly and ecology. 



 

 

 

28 

 In this dissertation, I aim to discuss how plant community diversity, AM fungal 

community assembly and bacterial interactions contribute to plant-soil feedback dynamics over 

three chapters. My second chapter examines patterns of AM fungal, bacterial and overall fungal 

communities in a mixed-grass prairie to gain an understanding of the microbial communities 

established under low, medium, and high levels of plant community diversity in an observational 

field study. Throughout this chapter, I investigate if aboveground diversity mirrors belowground 

diversity and observe the microbial interactions that sustain these plant communities. In my third 

chapter, I explore how soil conditioning and plant diversity influence microbial community 

composition, structure and interactions in an experimental greenhouse study. In my last chapter, I 

seek to understand if plant stress by clipping contributes to changes in microbial composition, 

structure, and interactions by using the experimental design established in Chapter 3. By using 

network analyses to reduce the complexity of large datasets obtained from genetic sequencing, I 

explore microbial taxa co-occurrence to identify patterns of their community assembly and 

decipher plant-soil feedbacks through experimental manipulation. Through the utilization of 

plant-soil feedback experiments, I aim to understand questions on the community scale that can 

give insights into community assembly and expand the knowledge basis contributions to 

management and restoration practices. 

 
 



 

 

 

29 

 
 

  

Figure 1.1. Feedback dynamics between plant and soil biogeochemical processes that shift soil biological 
network interactions to increase functional complementarity in the rhizosphere microbiome.  Panel A 
represents a structurally and compositionally simple network of microbial interactions with feedbacks that 
sustain its existing microbial root community. Panel B represents a positive plant-microbial feedback loop, 
consisting of structurally and compositionally complex network formations of microbial interactions that 
contribute to higher microbial diversity, increased microbial connections, increased plant biomass, and 
increased diversity of nutrient qualities returning to the soil. 

+ Biomass

+ Litter

Shoot & Root

Litter

Physical, 

Chemical, 

Biological

breakdownMicrobial 

Activity

Microbially 

available nutrients

+ Nutrient 

Quality

Plant 

Available

Nutrients

+ Microbial CommunityA B



 

 

 

30 

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