Nutrient Solution Effects on Plant Health and Microbial Community Composition in Soilless Strawberry Culture

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Winter 2020

NUTRIENT SOLUTION EFFECTS ON PLANT HEALTH AND MICROBIAL COMMUNITY COMPOSITION IN SOILLESS STRAWBERRY CULTURE

William Miller Hardesty-Dyck University of New Hampshire, Durham

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NUTRIENT SOLUTION EFFECTS ON PLANT HEALTH AND MICROBIAL COMMUNITY

COMPOSITION IN SOILLESS STRAWBERRY CULTURE

WILLIAM HARDESTY-DYCK Bachelor of Arts, Williams College, 2016

THESIS

Submitted to the University of New Hampshire

in Partial Fulfillment of

the Requirements for the Degree of

Master of Science

Agricultural Sciences

December, 2020 ii

This thesis was examined and approved in partial fulfillment of the requirements for the degree of

Master of Science in Agricultural Science by:

Thesis Director, Dr. Anissa Poleatewich, Assistant Professor of Plant

Pathology

Dr. Jessica Ernakovich, Assistant Professor of Microbial Ecology

Dr. Todd Guerdat, Civil/Agricultural Engineer, USDA NRCS

On December 1 st , 2020

Approval signatures are on file with the University of New Hampshire Graduate School. iii

ACKNOWLEDGMENTS

This work was supported by the Engineering for Agricultural Production Systems (A1521) grant no. 2018-67022-27844/project accession no. 1015270 from the USDA National Institute of

Food and Agriculture.

I would like to thank my academic advisor, Dr. Anissa Poleatewich, for her guidance and for her dedication to this research and to my development as a scientist. I extend my gratitude to the members of my thesis committee, Drs. Jessica Ernakovich and Todd Guerdat. I thank Dr.

Ernakovich for sharing her knowledge of microbial ecology and bioinformatics. Dr. Guerdat introduced me to the theory and practice of aquaculture and aquaponics and generously allowed me the use of culture water from the recirculating aquaponic systems at Kingman Farm.

I would also like to thank my lab mates, Liza DeGenring, Anna DeVitto, Crysta Harris,

Madeleine Hassett, Isobel Michaud, Haley Nolen, and Ryan Spelman, for their support, advice, labor, and camaraderie. My gratitude is also with the members of the Guerdat (Sean Fogarty, Ashu

Rao, Alex Sitek, and Joe Tetreault) and Ernakovich (Lukas Bernhardt and Stacey Doherty) labs, all of whom contributed to the completion of this thesis. I thank Dr. Tom Davis and the rest of the

Davis lab for graciously sharing their space and equipment with me.

Luke Hydock, the manager of the MacFarlane greenhouses, was instrumental in the design, construction, and maintenance of the growth system. He, Amber Kittle, and the rest of the

MacFarlane staff kept my plants healthy and well-fed throughout this work. Victoria Schacht pruned buds, leaves, and runners. I thank the Hubbard Center for Genome Studies for their sequencing expertise.

Finally, I extend my deep appreciation to my family and friends for their love and support in this, and in all things. iv

TABLE OF CONTENTS

ACKNOWLEDGMENTS ...... iii LIST OF TABLES ...... vi LIST OF FIGURES ...... vi ABSTRACT ...... vii CHAPTER 1 INTRODUCTION ...... 1 1.1. Ecological Limits and Sustainable Food System Intensification ...... 1 1.2. Controlled Environment Agriculture ...... 3 1.2.1. Soilless Culture ...... 5 1.3. Survey of Substrates, Irrigation Systems, and Nutrient Solutions in Soilless CEA ...... 5 1.3.1. Irrigation ...... 6 1.3.2. Substrates ...... 7 1.3.3. Naturally Derived Nutrient Solutions ...... 9 1.4. Pathogens in Soilless CEA ...... 13 1.4.1. Pythium ...... 15 1.5. Microbial Communities in Soilless CEA ...... 20 1.5.1. Organic Solutions and Microbial Communities ...... 23 1.5.2. Aquaponic Microbial Communities ...... 24 1.6. Research Questions ...... 27 1.6.1. Hypotheses ...... 28 CHAPTER 2 METHODS ...... 29 2.1. Experimental Design ...... 29 2.2. Growth Environment ...... 29 2.3. System Design and Construction ...... 30 2.4. Nutrient Solutions ...... 32 2.5. Plants ...... 34 2.6. Pythium Inoculation ...... 35 2.6.1. Inoculum ...... 35 2.6.2. Drench Inoculation ...... 36 2.7. Plant Health Metrics ...... 36 2.7.1. Aboveground Mass ...... 36 2.7.2. Root Assessments ...... 37 2.8. Plant Health Statistical Analyses ...... 38 v

2.9. Sampling ...... 38 2.9.1. Leachate ...... 38 2.9.2. Solution ...... 38 2.9.3. Bulk Substrate ...... 38 2.9.4. Roots and Rhizosphere Substrate ...... 39 2.10. DNA Extraction ...... 39 2.10.1. Leachate and Solution ...... 39 2.10.2. Substrate ...... 39 2.11. Amplicon PCR ...... 40 2.12. Sequencing ...... 40 2.13. Bioinformatics ...... 41 2.13.1. 16S rRNA...... 41 2.13.2. ITS ...... 44 CHAPTER 3 RESULTS ...... 46 3.1. Pythium Root Rot ...... 46 3.2. Plant Growth ...... 46 3.3. Microbial Communities ...... 47 3.3.1. 16S rRNA ...... 47 3.3.2. ITS ...... 52 CHAPTER 4 DISCUSSION ...... 55 4.1. Pythium aphanidermatum inoculation failed to produce significant symptoms ...... 55 4.2. Nutrient solution provenance and sucrose amendment impacted plant growth ...... 56 4.3. Coco coir substrate microbial communities ...... 59 4.3.1. α-Diversity and differences in composition ...... 59 4.3.2. Taxa of interest in coco coir substrate ...... 60 4.4. Limitations and future directions ...... 66 CHAPTER 5 CONCLUSIONS ...... 68 LIST OF REFERENCES ...... 70

LIST OF TABLES

Table 1.1. Common soilless substrates...... 8 Table 2.1. List of pathogen and nutrient solution treatments tested...... 29 Table 2.2. Synthetic “Yamazaki” solution recipe...... 32 Table 2.3. Sucrose amendation...... 33 Table 2.4. Correlations between transformed library sizes and Shannon’s diversity...... 42 Table 3.1. Diversity and richness of substrate microbial communities...... 50 Table 3.2. Unclassified ITS reads as a portion of total rarefied ASVs and abundance...... 52 Table 4.1. Abundant substrate fungal taxa and associated ecologies...... 63 Table 4.2. Abundant substrate bacterial taxa and associated ecologies...... 64

LIST OF FIGURES

Figure 1.1. CEA system health ...... 4 Figure 1.2. Irrigation systems in soilless CEA...... 7 Figure 1.3. The life cycle of pathogenic Pythium spp...... 16 Figure 1.4. Organic substrates and nutrient solutions shape community composition...... 22 Figure 2.1. Arrangement of troughs and treatments in the greenhouse compartment...... 31 Figure 2.2. Root health scale...... 37 Figure 3.1. Pythium root rot severity on strawberry cv. “Albion” 35 days post inoculation ...... 46 Figure 3.2. Above- and belowground plant growth...... 47 Figure 3.3. Rarefied phylum-level substrate bacterial and fungal abundance...... 48 Figure 3.4. CAP biplot of Aqua and Aqua C substrate bacterial communities...... 49 Figure 3.5. Relative class-level substrate bacterial abundance...... 50 Figure 3.6. Abundant bacterial ASVs in coir substrate...... 51 Figure 3.7. Relative order-level substrate fungal abundance...... 53 Figure 3.8. Abundant fungal ASVs in coir substrate...... 54

vii

ABSTRACT

Holding advantages in efficiency and geographical flexibility, soilless Controlled

Environmental Agriculture (CEA) systems have emerged as economically viable and ecologically sound alternatives to field agriculture, especially in high-value fruit and vegetable production.

While commercial soilless operations typically use synthetic, mineral-based nutrient solutions for plant nutrition, there is interest in incorporating more ecologically sustainable naturally derived sources. Aquaponics - the linkage of aquaculture with soilless crop production - represents one such alternative. Aquacultural effluents can provide partial to complete plant nutrition following solids removal and nitrifying biofiltration, while the incorporation of solutes into crop biomass reduces the need to discharge culture water and provides a secondary source of revenue for producers. Bacteria that suppress common soilless root pathogens (e.g. Pythium spp., Fusarium spp., Rhizoctonia solani ) have been isolated from aquaponic culture water, suggesting potential for in vivo suppression. Aquacultural effluent and other naturally derived nutrient solutions contain variable concentrations of dissolved organic carbon (DOC). In recirculating aquaculture system

(RAS) biofilters, high DOC can stimulate a proliferation of opportunistic heterotrophs that outcompete nitrifiers for oxygen, reducing nitrification efficiency. The plant health and microbial community effects of DOC in the crop subsystem of aquaponics are less understood. The objectives of this research were a) to characterize the plant growth effects of synthetic and aquaponic nutrient solutions with and without added DOC, b) to evaluate Pythium root rot severity in plants irrigated with each nutrient solution, and c) to explore the compositions of solution, substrate, and rhizosphere microbial communities associated with each nutrient solution.

Strawberries (Fragaria x ananassa Duch. cv. “Albion”) were grown in drip irrigated coco coir substrate culture using four nutrient solutions: a synthetic mineral solution (Synth), dilute viii

culture water from three recirculating aquaponics systems at Kingman Farm (Aqua), and the same two solutions amended with sucrose to raise DOC concentrations (Synth C and Aqua C). Half of the bags in each nutrient solution treatment were inoculated with the root rot pathogen Pythium aphanidermatum . Five weeks after inoculation, plant above- and belowground health was evaluated; roots were rated for disease; and bulk substrate, solution, and rhizosphere samples were collected for 16S rRNA and ITS amplicon metabarcoding.

P. aphanidermatum inoculation failed to produce significant symptoms, so suppression could not be evaluated. Plants irrigated with sucrose amended solutions were stunted, with smaller root balls and reduced aboveground biomass compared to unamended solutions. Irrigation with Synth produced plants with 40% greater aboveground dry mass than those irrigated with Aqua. Bacterial richness was significantly greater in coir substrate samples from the Synth C treatment than those from Synth. Substrate bacterial community compositions differed significantly between the Aqua and Aqua C treatments. Substrate fungal communities did not significantly differ across nutrient solution. Abundant bacterial and fungal taxa in substrate samples included saprotrophs of refractory organic carbon as well as strains with putative plant growth promoting and phytopathogen suppressive effects.

This work affirmed the importance of keeping labile DOC low in nutrient solutions to maintain crop health. Microbial taxa with putative plant-beneficial activity were identified in coco coir substrate samples, informing future work to elucidate complex relationships among organic carbon, nutrient solutions, microbial communities, and soilless CEA system health. 1

CHAPTER 1

INTRODUCTION

1.1. Ecological Limits and Sustainable Food System Intensification

The global population is projected to reach approximately nine billion by the year 2050, necessitating increases in agricultural output (Tilman et al. 2011; Phalan et al. 2014). Approaches toward meeting this need can be conceptualized as a gradient spanning extensification on one end and intensification on the other. Extensification refers to the geographical expansion of agricultural production, while intensification denotes increased efficiency of production within current geographical boundaries (Tilman et al. 2011). The global agricultural system currently uses ~43% of all ice- and desert-free land (Poore and Nemecek 2018), and suitable land is a dwindling resource (Lambin and Meyfroidt 2011). Agricultural expansion is the key driver of deforestation, which decreases biodiversity and compromises the carbon sequestration capacity of the world’s forests (Geist and Lambin 2001, 2002; Gibbs et al. 2010; Leblois et al. 2017;

Mitchard 2018). Cleared land is predominantly used for the production of export commodities

(Leblois et al. 2017), the global transportation of which represents an important source of greenhouse gas (GHG) emissions (Poore and Nemecek 2018; Vermeulen et al. 2012).

Intensification of agricultural production can increase yields while limiting disruptions to critical ecosystems.

Agricultural intensification over the past half-century has relied on monoculture, external inputs (fertilizers, pesticides, herbicides) and mechanization to improve profitability and yields

(Garibaldi et al. 2017). The social and ecological externalities generated by this process have been profound (Erisman et al. 2013; Cordell and White 2014; Rasmussen et al. 2018; Dawson et al. 2016; Ramankutty et al. 2018). The global food system (including input manufacture, 2

crop/livestock production, and distribution) is estimated to contribute between 19% and 29% of total annual GHG emissions after accounting for plant and microbial carbon fixation (Vermeulen et al. 2012). Methane (a product of ruminant digestion and anaerobic microbial respiration in flooded rice-paddies) and N 2O (a byproduct of microbial nitrification/denitrification in field soils) are major contributors alongside the CO 2 produced during input manufacture, cultivation, and transportation. The production and use of synthetic fertilizers disrupts global nitrogen and phosphorus cycles (Erisman et al. 2013; Cordell and White 2014; Steffen et al. 2015). Phosphate mining, chiefly for synthetic fertilizers, extracts far more phosphate yearly than is deposited by natural means. Continued exploitation will gradually increase prices, exhaust reserves, and threaten sustained agricultural productivity (Cordell and White 2014). Eutrophic algal blooms fueled by nitrate and phosphate runoff from over-fertilized conventional systems produce hypoxic and anoxic “dead zones” in coastal areas across the globe (Rabalais et al. 2010; Howarth et al. 2011; Erisman et al. 2013). Further agricultural intensification must minimize associated GHG emissions and maximize input-use efficiency.

While CO 2, methane, phosphate, and nitrate are destructive pollutants when released to the environment in massive quantities, they also represent potential inputs for useful productive processes, agricultural and otherwise. When captured and recycled, for example, excess nitrate, phosphate, and CO 2 can be harnessed for further crop yields. Approaching a “circular” or closed-loop agricultural economy in which ecologically undesirable outputs are a) minimized and/or b) processed and reintegrated into the production process will be critical to an ecologically sustainable agricultural intensification (Andersen 2007; Jun and Xiang

2011; Campbell et al. 2014; Jurgilevich et al. 2016) .

1.2. Controlled Environment Agriculture

Controlled environment agriculture (CEA) demonstrates the potential to fulfill many of the criteria surveyed above and, therefore, holds promise as a component of sustainable intensification. CEA denotes crop production systems that are, at minimum, protected from the external environment (Dalrymple 1973). Technological complexity and the degree of environmental control can range from rudimentary high-tunnels and cold frames to highly sophisticated indoor production systems (Shamshiri et al. 2018; Graamans et al. 2018). The partial or complete enclosure of the growth environment allows growers control over a wide range of production variables (Fig. 1.1). Simple CEA systems can extend growing seasons, allow production of warmer-climate crops outside of their native range, and provide some pest protection. At the other end of the technological spectrum, state of the art greenhouses and indoor farming operations employ automated systems to precisely control temperature, humidity, CO 2 enrichment, and lighting for year-round production (Benke and Tomkins 2017; Graamans et al.

2018). Stacking crop beds can maximize space and energy efficiency (Graamans et al. 2018).

Nutrient solutions can be optimized to suit particular crops and stages of development. Nutrient runoff can be greatly reduced in CEA by filtering, refreshing, and recirculating nutrient solutions in a closed loop irrigation system (Benke and Tomkins 2017; Rufí-Salís et al. 2020;

Massa et al. 2020). Recirculation and humidity control also contribute to significant reductions in water loss compared to field systems - up to 90% in arid environments (Barbosa et al. 2015).

CEA’s compact geographic footprint and high yields allow operations to produce in proximity to major urban consumer markets, cutting costs and greenhouse gas emissions associated with transport while simplifying supply chains (Weber and Matthews 2008; Wakeland et al. 2012;

Benke and Tomkins 2017; Nixon and Ramaswami 2018). Though heating and supplemental 4

lighting require considerable energy inputs, associated costs and carbon emissions can be reduced by insulation, efficient LED lighting, and the incorporation of alternative energy sources (Cuce et al. 2016; Shamshiri et al. 2018; Graamans et al. 2018). The economic viability and geographical flexibility of high technology models is most clearly attested by the establishment of large-scale indoor farming operations in major metropolitan areas worldwide (Benke and Tomkins 2017;

Shamshiri et al. 2018). Low-input recirculating CEA systems are also prime candidates for use in manned space travel (Paradiso et al. 2020).

Figure 1.1. A holistic approach to system health is critical for efficient production of healthy crops. CEA allows growers to optimize nutrient solution and environmental parameters for crop production and disease control. Produced with BioRender.com.

1.2.1. Soilless Culture

Modern CEA systems predominantly use soilless culture. The degree of overlap between the concepts “ soilless culture” and “ hydroponics” depends on the specificity of the latter’s definition (Savvas and Gruda 2018). This thesis follows Mattson and Lieth (2019):

Soilless Culture denotes any growth system in which plants are grown without soil . This

definition encompasses everything from container culture in potting mixes to fully liquid

culture.

Hydroponics denotes soilless systems in which a) crop nutrition is exclusively provided

by the nutrient solution and b) any substrate used does not impede bulk flow of the nutrient

solution through the root zone.

A key aim of soilless culture is the elimination of root pathogen pressure - substrates are often sterilized, and best practices in plant sourcing, propagation, and management reduce the risk of pathogens entering the system via the crop itself (Alsanius and Wohanka 2019).

1.3. Survey of Irrigation Systems, Substrates, and Nutrient Solutions in Soilless CEA

A diversity of available techniques, nutrient solutions, and substrates allow CEA practitioners to adapt the design of their system to suit the particularities of their operation and capital constraints. The following sections provide a brief survey of common approaches to root zone support and irrigation.

1.3.1. Irrigation

Modern CEA typically uses synthetic nutrient solutions (Savvas and Gruda 2018; Silber and Bar-

Tal 2019). Salts containing the 14 essential elements of plant nutrition are diluted into source water to target concentrations optimized for the particular crop, developmental stage, and growth environment (Rouphael et al. 2008; Silber and Bar-Tal 2019). Multiple stock tanks are often used to prevent precipitation of incompatible salts (Mattson and Lieth 2019). During irrigation events, these stock solutions are dosed into the source water for delivery to the root zone. Irrigation systems can vary to suit particular crops or systems (Fig. 1.2) (Van Os et al. 2019):

a) Drip irrigation uses emitters embedded in a substrate to supply nutrient solution directly

to the root zone of each plant.

b) Nutrient film technique (NFT) maintains a thin film of flowing solution at the base of a

shallow trough. Crop roots grow into this film without substrate support.

c) Deep flow technique (DFT) or deep water culture floats crops atop a deep trough filled

with nutrient solution. As in NFT, crop roots are directly in contact with the solution.

d) Aeroponics employs emitters that aerosolize nutrient solution into a small space below the

crop bed. The resultant mist coats roots, allowing for nutrient uptake.

e) Ebb and flow (EF) periodically floods the production area with nutrient solution, allowing

solution to wick into substrate. Once adequate substrate saturation is achieved, the solution

is drained.

(A) (B)

Figure 1.2. Irrigation systems in soilless CEA. (A) Drip irrigation, (B) NFT, (C) DFT, (D) Ebb and flood in troughs. Not pictured: Aeroponics. Photo credits: A. Poleatewich (A,B,D), A. DeVitto (C).

Following nutrient uptake in the root zone, excess nutrient solution may either be filtered, refreshed, and recycled (closed-loop irrigation) or expelled from the system (open-loop irrigation) (Massa et al. 2020; Rufí-Salís et al. 2020). Recirculation reduces water and nutrient waste, but pathogens and solutes can build up without proper management (Hong and Moorman

2005; Postma, van Os, et al. 2008).

1.3.2. Substrates

Liquid culture systems (NFT, DFT, aeroponics) use minimal substrate, instead supporting plants with buoyant rafts or fixed support structures. Drip irrigation requires a substrate in which to fix emitters. Substrates also provide roots with support and a reservoir for the nutrient solution.

Substrate culture growers can choose from an extensive and growing diversity of materials to support crop root zones. With this diversity in materials comes near infinite variation in physical, chemical, and biological characteristics in the root environment. Mixtures of multiple materials in different proportions further expand the permutations of these characteristics. The agronomically 8

important physical and chemical parameters mirror those found in field soils: density, porosity, particle size, pH and cation-exchange capacity (Wallach 2008; Silber 2019). Substrate materials can be categorized as organic, synthetic, or inorganic (Table 1.1) (Savvas and Gruda 2018):

Organic substrates encompass naturally occurring organic materials. Organic is used here

in the chemical sense of “organic carbon-based” and should not be confused with organic

certification. These materials may be physically processed to varying degrees prior to use

but are not chemically modified.

Inorganic substrates are naturally occurring, unmodified inorganic materials.

Synthetic substrates are products of industrial manufacture or modification, and may be

inorganic or organic.

A full account of the physical, chemical, and biological properties of each substrate is beyond the scope of this thesis, though following sections will address key characteristics relevant to disease control and microbial community structure and function.

Table 1.1. Common soilless substrates. Adapted from Savvas and Gruda (2018). Material Advantages Disadvantages Low nutrient and water holding Sand Cheap, good drainage capacity Inorganic Cheap, long -lasting, environmentally Pumice High transport costs friendly Expensive, low nutrient capacity, Perlite Sterile, neutral pH energy-intensive production Good nutrient and water holding Expensive, compacts when wet, Synthetic Vermiculite capacity, good aeration, pH energy-intensive production buffering High porosity, inert, straightforward Energy -intensive production, Rockwool nutrition management tricky disposal Nonrenewable, removal Good air and water holding capacity, Peat contributes to CO emissions, low porosity 2 pH Organic Good air and water holding capacity, High salt, CO emissions Coco coir 2 porosity, pH 5-7 associated with transport Good air and water holding capacity, Wood fiber Variable composition porosity, long-lasting 9

1.3.3. Naturally Derived Nutrient Solutions

While the use of synthetic nutrient solutions is typical of modern commercial CEA, plant nutrition can also be supplied in part or in full by a variety of naturally derived materials (Mattson and Lieth 2019). “Organic” soilless culture has a history spanning millennia: archaeologically attested pre-industrial examples include the floating chinampas of the Valley of Mexico (Lumsden et al. 1987; Torres-Lima et al. 1994; Frederick 2007; Gonzalez Carmona and Torres Valladares

2014) and the various terraced gardens associated with many post-neolithic cultures of the Near

East and Mediterranean basin (Dalley 1993).1 In the modern context, interest in naturally derived nutrient sources for soilless culture has been spurred by a) the greenhouse gas contributions of synthetic fertilizers, b) an abundance of organic wastes generated by industrial, agricultural, and municipal processes, and c) a desire to “close the circle” of agricultural economies (Stoknes et al.

2016). Growers using naturally derived solutions and substrates could petition for recognition under organic certification regimes, thereby securing a price premium to partially offset the greater expense involved in production (Burnett et al. 2016; Mattson and Lieth 2019). Soilless culture squares awkwardly with soil-centric organic certification regimes in the US and Europe, but novel designations with expanded criteria could be implemented.

Solids and sludges used in field systems, such as compost and manure, do not lend themselves to effective use in most soilless systems. Suspended solids foul irrigation lines, and nutrients are not plant-available without microbial mediation. Passing organic sludges through aerobic (AT) and anaerobic (AD) microbial digestion bioreactors can solubilize nutrients, resulting in solutions capable of providing full or supplementary plant nutrition (Monsees et al. 2017).

Researchers have experimented with digestates from different sources, with varied results

1 Once displaced from the field into a receptacle or atop a structure, “soil” in the vernacular sense (dirt) is no longer soil sensu stricto . These systems thus fall within the broad umbrella of soilless culture. 10

(Tambone et al. 2010; Burnett et al. 2016; Stoknes et al. 2016; Gattullo et al. 2017; Scaglia et al.

2017; Ronga et al. 2019; Guilayn et al. 2020; Takemura et al. 2020). Yields from naturally derived solutions have generally been found comparable to or slightly lower than those achieved with synthetic solutions. In one notable exception, Ronga et al. (2019) found that lettuce grown with synthetic substrate/liquid silage digestate and solid digestate/synthetic solution developed greater shoot biomass than their full-synthetic counterparts. Lettuce, tomato, and cucumber have been successfully grown in solutions derived from fish meal digestate, corn steep liquor, and food waste with comparable yields to synthetic solutions (Shinohara et al. 2011; Chinta et al. 2014;

Kawamura-Aoyama et al. 2014). Bioaccumulation of heavy metals during the composting/digesting process has been a concern (Gattullo et al. 2017), though this may be avoided by a careful selection of ingestate (the raw input to the process). Even human wastewater streams can provide crops with adequate nutrition, following sanitation measures and microbial solubilization (Al-Gheethi et al. 2016). Human waste is plentiful, renewable, and has a long and successful history as a fertilizer (Angelakis and Zheng 2015). The incorporation of modern treatment techniques may revitalize its use in the agricultural arena (Al-Gheethi et al. 2016).

Organic nutrient solutions typically have higher concentrations of dissolved organic carbon (DOC) than their synthetic counterparts (Zheng et al. 2014; Scaglia et al. 2017). This carbon pool, along with microbiota enriched and stabilized during digestion, may have beneficial or detrimental effects on plant health. Scaglia et al. (2017), using a gas chromatographic/mass spectrometric (GC/MS) approach, found auxin-like compounds in digested pig slurry. Japanese research groups have found evidence of plant disease suppression in tomato, lettuce, and cucumber cultivated with a range of digestates (Shinohara et al. 2011; Fujiwara et al.

2012; Chinta et al. 2014, 2015). 11

Aquaponics

Another notable organic nutrient source is aquacultural effluent. Aquaculture denotes the controlled production (“farming”) of aquatic organisms, including fish, crustaceans, and aquatic plants. Rising seafood demand and declining wild populations have driven the expansion of the aquaculture sector, a trend that will almost certainly continue (FAO 2016). While the bulk of aquacultural production has historically been situated in naturally occurring marine, brackish, or freshwater environments, on-shore systems have also been developed and implemented (Martins et al. 2010). In these systems, fish, crustaceans, and other commercialized species are raised in tanks, acquiring nutrients from feed and expelling waste products. On-shore aquaculture and CEA can be understood as products of the same logic of expanded grower control over production variables (Tal et al. 2009). Water quality parameters, feed rates, and filtration systems are closely monitored and adjusted to optimize growth (Zhang et al. 2011). The growth environment is protected from the geographical-climatic conditions of the immediate surroundings, and can be heated or cooled depending on the needs of the species (Heinen et al. 1996).

Further, just as recirculation of nutrient solution increases nutrient and water efficiency in closed-loop CEA, so too can aquacultural culture water be conserved by recirculation (Badiola et al. 2012). Designs which recycle effluent in this way are called recirculating aquaculture systems

(RAS) (Heinen et al. 1996; Losordo et al. 2009; Rakocy 2012). RAS requires proper waste treatment to maintain water quality (Losordo et al. 2009). Fish generate solid and aqueous waste, and proper management of both is critical to maintaining system health (Badiola et al. 2012). Solid waste has a high biochemical oxygen demand (BOD) – heterotrophic bacteria consume oxygen as they digest, causing hypoxia where solids accumulate (Chen et al. 1997). The aqueous fraction contains dissolved and suspended solids as well as ammonia, which is toxic to fish at low 12

concentrations (Masser and Rakocy 1999). To recirculate without compromising fish health via hypoxia or the build-up of solutes, solids, and pathogens, RAS typically include unit processes for solids removal (e.g. drum-screen filters, sand filters, settling tanks), biofiltration (chiefly

+ - nitrification of NH 4 /NH 3 to NO 3 ), pathogen disinfestation (e.g. UV filtration, ozonation), and oxygenation (Losordo et al. 2009; Rakocy 2012; Rurangwa and Verdegem 2015). Concentrated solids are discharged from the system as a nutrient rich sludge. The soluble digestate of this sludge contains plant-available macro- and micronutrients that can supplement soilless culture (Delaide et al. 2019; Tetreault 2020). Recirculating the nitrified aqueous fraction still leads to the build-up of potassium and other solutes to suboptimal concentrations, requiring periodic discharge (Goddek et al. 2016).

Aquaponics, the linkage of soilless plant production with RAS, presents an alternative approach to aquacultural water management and soilless plant nutrition alike (Gichana et al.

2018; Hochman et al. 2018; Palm et al. 2018). The basic form of this linkage is a single closed system in which a) effluent from the fish component is passed through a biofilter into a soilless crop component, b) solutes are taken up in the root zone and incorporated into crop biomass, and c) the (now dilute) culture water is passed back into the fish component (Schmautz et al. 2017). In such systems, culture water is shared by the fish and crops, limiting the degree to which the system can be tailored to maximize the production of each (Goddek et al. 2016). Decoupling the two growth environments – engineering them as two distinct compartments - offers producers the ability to regulate each independently without adversely affecting the other (Goddek et al. 2016).

In any aquaponic system, effluent from rearing tanks must still pass through physical and biological filtration prior to delivery to the crop. Following solids removal and biofiltration, effluent is suitable for plant nutrition. The nutrient balance, EC, and pH of the effluent can be 13

manipulated by the choice and rate of feed and/or supplementation with further inputs to best suit the needs of the crop (Rakocy 2012; Palm et al. 2018). Aquaponic systems are still relatively new in commercial production, and details of system design vary widely from producer to producer

(Love et al. 2015). The full impacts of aquaponic nutrient sources on crop production remain under-characterized, including microbial community and plant pathogen dynamics

(Munguia-Fragozo(C) et al. 2015; Junge et al. 2017; Wielgosz et al. 2017; Sanchez et al. 2019). 1.4. Pathogens in Soilless CEA

A key advantage attributed to soilless CEA is a reduction in pathogen pressure, especially in the root zone. While this is broadly the case, certain root pathogens well-adapted to the soilless environment still pose threats. These pathogens include oomycetes (e.g. Pythium spp. ,

Phytophtora spp. ) and fungi (e.g. Fusarium oxysporum )(Vallance et al. 2010; Redekar et al. 2019).

Especially where nutrient solution is recycled, these pathogens can build-up and cause significant yield losses (Vallance et al. 2010). Consistent with integrated pest management (IPM) principles, disease prevention in CEA begins with preventative cultural and system design considerations that promote overall plant health and keep pathogen density low (Postma, van Os, et al. 2008; Raudales et al. 2014). Biosecurity measures such as antimicrobial foot baths and the use of sterile tools can limit the introduction and propagation of pathogens. General sanitation, including the removal of plant debris and excess substrate, decreases the possible pathogen reservoir within the production environment. Closed loop systems incorporate filtration and disinfestation components that can include pasteurization, UV irradiation, and physical filtration (Zhang and Tu 2000; Raudales et al.

2014). While effective at reducing pathogen load, these approaches are non-specific, removing neutral and potentially plant-beneficial microbes as well (Raudales et al. 2014). Preventative chemical approaches include chlorination, ozonation, and fungicide treatment (Postma, van Os, et 14

al. 2008). While non-target effects are also a potential concern with these approaches, fungicides can be tailored to the biology of the target organism. Additional fungicide applications may be employed to deal with outbreaks. Care must be taken during fungicide application, as overexposure to some compounds can have deleterious health effects for both the crop and the worker.

Overreliance on fungicides also exerts a selective pressure for the development of resistance

(Moorman et al. 2002).

Biological Control

Biological approaches harness other organisms, whether native to the system or introduced by the grower, to protect crops from disease (Bélanger et al. 2012). Biological control rarely eradicates pathogens outright, but can greatly reduce their activity (Vallance et al. 2010). It does this while maintaining the integrity of the broader microbial community, unlike physical methods; and without relying on any single mode of action, unlike targeted chemical methods (Lee and Lee

2015). While biocontrol products have been commercialized, their efficacy can be heavily influenced by growth environment parameters including substrate characteristics, endogenous microbiota, and carbon availability (Hoitink and Boehm 1999; Larkin and Fravel 2002; Degenring

2019). Biocontrols often include only 1 or 2 strains - suppression in field soils and soilless systems has been linked to community level characteristics that emerge from complex meta-metabolic networks (Van Os et al. 2004; Postma et al. 2005; Postma, Schilder, et al. 2008) The relationships between endogenous microbiota and disease suppression in soilless CEA are less understood than in field soil and may provide an additional preventative tool for growers.

1.4.1. Pythium

Phytopathogenic members of the cosmopolitan oomycete genus Pythium are responsible for root-rot and damping off in a wide range of economically important crops, including leafy greens, fruiting crops, and floral crops (Moorman et al. 2002; Li et al. 2014; Miyake et al. 2014;

Redekar et al. 2019). Pythium is a sister genus to Phytophtora and PhytoPythium , which also include many destructive phytopathogens; and is more distantly related to the downy mildews

(Peronospora and Plasmopara ) and Saprolegnia , which includes fish pathogens found in aquacultural systems (Meyer 1991; Ruggiero et al. 2015). Common pathogenic strains in CEA production include P. aphanidermatum (Edson) Fitzp., P. irregulare Buisman, P. ultimum Trow, and P. dissotocum Drechsler (Del Castillo Múnera and Hausbeck 2016; Redekar et al. 2019). These strains are generalists with very broad host ranges (Sutton et al. 2006). Facultative saprotrophy allows Pythium spp. to survive in the absence of living hosts as long as adequate labile carbon sources are present and there are low competitive pressures from other microbes (Sutton et al.

2006). Pythium spp. can enter CEA systems via irrigation water, substrates, infested plant material, or facility staff and equipment (Moorman et al. 2002; Hong and Moorman 2005). Within the CEA environment, fungus gnats ( Bradysia spp.) and shoreflies ( Scatella stagnalis ) may vector propagules from plant to plant (Braun et al. 2012).

The water-rich environment of soilless culture provides conducive conditions for the proliferation of Pythium zoospores, mobile and virulent products of asexual reproduction

(Fig. 1.3) (Van West et al. 2003; Lennard and Leonard 2006; Sutton et al. 2006; Vallance et al.

2010; Sabaratnam 2016) . Once released, biflagellate zoospores chemotactically propel themselves up root exudate gradients. Contact with a host triggers encystment and penetration of root tissue with a germ tube (Van West et al. 2003; Sutton et al. 2006; Sabaratnam 2016). P. aphanidermatum 16

(Edson) Fitzp., P. irregulare Buisman, and P. dissotocum (Drechsler) all propagate via zoospores.

P. ultimum does not produce zoospores - infection instead primarily occurs via hyphal swelling at the rhizomycelial interface (Sutton et al. 2006; Del Castillo Múnera and Hausbeck 2016). Pythium spp. also produce thick-walled, persistent oospores during sexual reproduction (Fig. 1.3.) (Hendrix and Campbell 1973; Sutton et al. 2006; Sabaratnam 2016). These can remain dormant in infested substrate and nutrient solution until exposure to host exudates triggers germination and infection

(Hendrix and Campbell 1973).

Figure 1.3. The life cycle of pathogenic Pythium spp. (van West et al., 2003).

Once hyphae have penetrated the root cortex, an extracellular hyphal network develops, releasing cellulases and pectinases to degrade host cell walls and liberate cytosolic nutrients (van

Buyten and Höfte 2013; Zerillo et al. 2013). Well-established roots offer more resistance, accounting for preferential infection of young, fine roots (Boudjeko et al. 2006). Infection causes symptoms including root browning, necrosis, and cortical sloughing. Cortical sloughing is especially characteristic of Pythium root rot, since the suite of digestive enzymes associated with

Pythium spp. is less potent at disrupting stele tissues than other root rot pathogens (e.g. Rhizoctonia spp., F. oxysporum ) (van Buyten and Höfte 2013). In advanced infections, plants may exhibit above-ground vascular wilt symptoms as water and nutrient uptake capacity declines (Gold and

Stanghellini 1985).

Root stress - especially caused by low dissolved oxygen, waterlogging, physical damage, high salinity, and temperature extremes - facilitates Pythium infection (Sutton et al. 2006). Pythium spp. are tolerant of hypoxia, with growth reductions beginning at oxygen concentrations around 1-

1.5%, well below the ranges typical of commercial soilless CEA (6-8%). When dissolved oxygen

(DO) concentrations dip below 6%, relatively unbothered Pythium spp. can take advantage of hypoxia-stressed roots to establish infection (Chérif et al. 1997). High temperatures and waterlogging can contribute to root-zone hypoxia while simultaneously causing direct plant stress, further simplifying Pythium infection (Sutton et al. 2006). P. aphanidermatum infection of spinach, in particular, is favored at temperatures above 25 o C (Gold and Stanghellini 1985). Proper control of Pythium spp. thus begins with good drainage, irrigation, and temperature management to prevent infection (Stouvenakers et al. 2019).

It is not generally necessary to eradicate Pythium propagules to prevent economically significant losses: Pythium disease incidence is dose dependent, with low propagule density posing little risk to crop health (Menzies et al. 1996; Sauvageau et al. 2019). Once an outbreak is underway, however, lowering inoculum density can mitigate losses. In closed loop systems, physical filtration, pasteurization, and UV irradiation can significantly reduce density (Postma et al. 2008a; Maucieri et al. 2018). Oomycete-specific fungicides with the active ingredients mefenoxam and metalaxyl can also limit proliferation, though mefenoxam resistance has been identified in many Pythium strains (Bates 1984; Moorman et al. 2002).

Biological control offers growers another tool for Pythium suppression. There are several commercial biocontrol products claiming to target Pythium spp. - common active organisms include Bacillus subtilis and Trichoderma spp. (Whipps and Lumsden 1991; Harris et al. 1994;

Berger et al. 1996; Benhamou and Chet 1997; Harris 1999; Ownley et al. 2010; Tchameni et al.

2020). Pythium suppression has also been observed by pseudomonad strains ( Pseudomonas fluorescens, Ps. putida, Ps. chloroaphis ), non-pathogenic Pythium strains ( Py. nunn, Py. oligandrum ), Streptomyces spp., and Candida oleophila (Howell 1980; Loper 1988; Berger et al.

1996; Shafi et al. 2017; Nikolaidis et al. 2020). Predatory nematode ( Steinernema feltiae ) formulations can control fungus gnat populations responsible for Pythium vectoring (Jagdale et al.

2004).

Biocontrol organisms can suppress pathogen populations and pathogenicity through several modes of direct and indirect action including antibiosis, hyperparasitism, predation, competitive exclusion, and induced systemic resistance (ISR) (Köhl et al. 2019). Individual organisms commonly integrate multiple modes of action. The fungal biocontrol species

Trichoderma harzianum , for example, hyperparasitizes Pythium spp. while also inducing host 19

resistance and competing for nutrients (Benhamou and Chet 1997). The combination of complementary modes of action avoids the high selective pressure for resistance generated by metalaxyl, propamocarb, fosetyl, and other Pythium fungicides (Sutton et al. 2006; Köhl et al.

2019).

While most biocontrol products are formulated around a single active organism or compound, similar suppressive activity can also arise from a consortium of organisms or from the entire community. These phenomena are especially well attested in field agriculture. Field soils can exhibit suppressive activity on a spectrum ranging from general to specific: general suppression manifests as crop tolerance to a broad range of pathogens, while specific suppression indicates complete or near-complete resistance to a single strain or group of closely-related strains

(Alabouvette 1986; Postma et al. 2008b; Schlatter et al. 2017). Specific suppression typically involves the presence of a single antagonistic microbe (or a small consortium of microbes) and can be transferred from one soil to another by the application of a small inoculum of suppressive soil

(McSpadden Gardener and Weller 2001; Weller et al. 2002; Kwak and Weller 2013). General suppression, on the other hand, is a more ecologically complex phenomenon involving community-level parameters and cannot easily be transferred from field to field (Schlatter et al.

2017). However, cultural practices can be tailored toward the development of an endogenous microbiota which exhibits either general or specific suppression (Postma et al. 2000; Van Os et al.

2004; Postma et al. 2005; Ryan et al. 2009; Hartmann et al. 2015; Wang et al. 2019). This approach is often more labor intensive but can maintain long-term system health with lower chemical and biological inputs (Busby et al. 2017; Bonanomi et al. 2018). Additionally, the development of an in situ suppressive community avoids logistical concerns (i.e. shelf-life) and inconsistent results which have limited the commercialization of potential biocontrol organisms (Bélanger et al. 2012). 20

There may be analogous opportunities to harness microbial communities for disease suppression and overall plant health in soilless CEA (Alsanius et al. 2017; Wielgosz et al. 2017;

Sanchez et al. 2019) .

1.5. Microbial Communities in Soilless CEA

The importance of rhizosphere microbiota to overall plant health has been well-reviewed.

Alongside disease suppression, rhizosphere communities stimulate plant growth, facilitate nutrient uptake, and mediate resistance to abiotic stresses (Lugtenberg and Kamilova 2009; Mendes et al.

2013; Jacoby et al. 2017; Hartmann et al. 2019; Compant et al. 2019). In marked contrast to field systems, soilless CEA practices typically focus on limiting microbial abundance and activity, an approach justified on the grounds that a) the microbiota may host pathogens and b) microbes compete for and immobilize plant nutrients while consuming root-zone O 2 needed for healthy root function (Alsanius and Wohanka 2019). Precise control of the growth environment also reduces abiotic stressors, diminishing the relative benefit provided by microbes. Nonetheless, a microbiota invariably develops in all but the most sealed systems, sustained by root exudates and nutrients in the solution and substrate (Alsanius and Wohanka 2019). The composition, function, and plant health implications of this microbiota are areas of active research (Munguia-Fragozo et al.

2015; Rurangwa and Verdegem 2015; Grunert et al. 2016a,b; Rud et al. 2016; Wielgosz et al.

2017; Mardanova et al. 2019; Ortiz-Estrada et al. 2019) .

The soilless growth environment can be divided into three distinct microbial compartments: rhizosphere, bulk substrate, and nutrient solution (Vallance et al. 2010;

Alsanius and Wohanka 2019). The boundaries between these compartments may be more or less well-defined depending on system characteristics. For instance, liquid culture has no bulk substrate, and the rapid dilution of root exudates may homogenize the rhizosphere and nutrient 21

solution (Alsanius and Wohanka 2019). The microbial constituents of each compartment are determined by physicochemical properties as well as the local (i.e. root-associated, solution, and substrate) microbiota available for colonization (Fig. 1.4) (Grunert et al. 2016a). The rhizosphere extends from the interior of the root (endorrhizosphere) into the surrounding volume of substrate in which root exudates are highly concentrated. Bulk substrate refers to the remainder of the substrate not significantly impacted by root penetration or exudation (Alsanius and Wohanka

2019).

With the exception of the aquaponic biofilter, soilless microbial communities are primarily heterotrophic, subsisting on nutrients provided by the solution, crop, and substrate (Vallance et al.

2010). The profile of this available nutrient pool plays a key role in driving community composition - particularly as regards organic carbon (Rosberg 2014). Crop root exudates are the prime drivers of rhizosphere community composition, especially in synthetic nutrient solutions and substrates with little to no organic carbon (Grunert et al. 2019). This exudate profile (and the resultant microbial community) varies significantly across crop genotype, age, and health status

(Rosberg 2014; Picot et al. 2020). However, organic substrates, amendments, and solutions provide additional carbon sources and endogenous microbes that may further shape community dynamics (Fig. 1.4) (Vallance et al. 2010; Attramadal et al. 2014; Michaud et al.

2014; Grunert et al. 2016a,b; Schmautz et al. 2017; Sanchez et al. 2019; Navada et al. 2020). 22

Figure 1.4. Organic substrates and nutrient solutions shape microbial community composition by a) determining physicochemical characteristics in the root zone, b) contributing an endogenous microbiota, and c) providing carbon sources for heterotrophs. Heterotrophic respiration consumes oxygen, which is important for healthy root development.

Once irrigated and planted, microbial communities develop in both mineral and organic substrates. There is evidence that robust endogenous substrate communities can confer suppressive and other plant-beneficial effects to the resultant rhizosphere system. For instance, Postma et al.

(2000) and Minuto et al. (2007) observed suppression of Pythium aphanidermatum and Fusarium oxysporum , respectively, in greenhouse cucumbers grown in unsterilized, reused rockwool, 23

demonstrating the development of a suppressive microbial community during cultivation. The ecological vacuum presented by sterile solutions and substrates can encourage the proliferation of facultative saprotrophs like Pythium spp. which are easily outcompeted by other organisms in microbially-rich environments (Postma et al. 2000). Rhizoplane (root surface) niches may similarly be exploited in plant material which has been surface-sterilized prior to planting.

Recent amplicon-based studies have begun to explore soilless communities in more depth:

Grunert et al. (2016a) found that peat-coco coir grow bags and rockwool hosted distinct and stable microbial communities in eggplant production. Higher abundances of families in the phyla

Proteobacteria and Actinobacteria were observed in the peat-coco mixture. These phyla have been associated with suppression in field soil and may exhibit similar functional profiles in soilless substrates (Mendes et al., 2011).

1.5.1. Microbial Communities and Naturally Derived Nutrient Solutions

The use of naturally derived nutrient solutions represents a further driver of soilless microbial community structure and function. The physicochemical characteristics, organic carbon profile, and endogenous microbiota of the solution determine the character and magnitude of its effects. Organic carbon compounds can have a diversity of community-level impacts matching their structural diversity. They can be broadly separated into two fractions based on solubility: particulate organic carbon (POC) includes cell fragments, organic substrate particles, dislodged biofilms, and other insoluble constituents, while dissolved organic carbon (DOC) includes soluble metabolites released from roots and microbes (Masser and Rakocy 1999; Hambly et al.

2015; Fossmark et al. 2020). While nutrient solution DOC profiles remain understudied, organic acids seem to predominate in synthetic solutions (Alsanius and Wohanka 2019). In recirculating systems, POC can be filtered out above a size or density threshold determined by the method of 24

physical filtration employed. Viable methods include membrane filtration, microscreen filtration, canister filtration, and slow sand filtration (Tu and Harwood 2005; Raudales et al. 2014).

Membrane, microscreen, and canister filters have pores that filter out particles above a certain size.

Slow sand filtration involves biological as well as physical mechanisms: a heterotrophic biofilm forms in the top layers of the filter as effluent passes through. The film traps particles while microbial constituents immobilize soluble nutrients including DOC, resulting in lower post-filter concentrations (Linlin et al. 2011). Further, the biofilm traps and neutralizes phytopathogen propagules, though the precise modes of action involved remain unclear (Raudales et al. 2014;

Prenafeta-Boldú et al. 2017).

1.5.2. Aquaponic Microbial Communities

Aquacultural effluent can provide adequate plant nutrition for a wide variety of crops.

However, it also contains variable concentrations and compositional profiles of dissolved organic carbon (DOC) and particulate organic carbon (POC), which are typically higher than those found in pre-crop synthetic hydroponic nutrient solutions. Organic carbon concentrations have been demonstrated to shift bacterial community compositions in the context of RAS (Zhu and Chen

2001; Guerdat et al. 2011; Attramadal et al. 2014; Michaud et al. 2014; Fossmark et al. 2020). The aquaculture literature consensus points to the importance of maintaining low total organic carbon

(TOC) to favor a “mature,” K-selected community which can resist perturbations, exclude fish pathogens, and improve overall fish health (Attramadal et al. 2014; Rurangwa and Verdegem

2015). K-selected taxa have low growth rates but can establish niches in competitive, oligotrophic conditions. r-selection, by contrast, favors fast growing, opportunistic taxa, which may include facultative pathogens. In well-managed aquaponic systems, POC is mechanically filtered from the fish and crop subsystem effluents, removing fecal particles, excess feed, and clumps of bacteria 25

(Guerdat et al. 2011; Fossmark et al. 2020). DOC concentrations can be reduced by biological filtration unit processes including slow sand filters or moving bed biofilm reactors (MBBR) (Van

Os 1999; Goddek et al. 2016). Even with proper filtration in place, small particles and a DOC fraction can still be found in the downstream compartments of coupled aquaponic systems. This carbon is metabolically available to heterotrophic microbes, both in the nitrifying bioreactor and in the plant bed. Where C:N ratios exceed 1:1, bioreactor efficiency is negatively impacted by a proliferation of heterotrophs that outcompete autotrophic nitrifiers for oxygen (Zhu and Chen

2001; Ling and Chen 2005; Guerdat et al. 2011; Michaud et al. 2014).

While it seems likely that a similar heterotrophic proliferation would take place in the aquaponic rhizosphere under high C:N, the plant health implications of this have not been fully established . Shifts in substrate and rhizosphere microbial community composition have also not been characterized. The biochemical oxygen demand (BOD) represented by the carbon pool would likely reduce dissolved oxygen (DO) concentrations in the rhizosphere while simultaneously immobilizing plant nutrients, limiting root growth and nutrient uptake. However, high microbial activity could also limit pathogen proliferation through competitive exclusion and antagonism, as observed in both field and soilless systems (Postma et al. 2000; Mazzola and

Freilich 2017).

The aquacultural microbiota is a possible source of endogenous biocontrol and plant- growth promoting (PGP) organisms. Gravel et al. (2015) observed in vitro reductions in P. ultimum Trow and F. oxysporum f. sp. lycopersici growth when challenged with sludge from a rainbow trout ( Oncorhynchus mykiss ) RAS. This effect was less pronounced using autoclaved sludge, indicating a biological mediation. Sirakov et al. (2016) isolated 42 bacterial strains with in vitro suppressive effects toward both P. ultimum and the oomycete fish pathogen Saprolegnia 26

parasitica from a recirculating tilapia-tomato system, while Sanchez et al. (2019) identified 25 putative PGP bacteria in a tilapia ( Oreochromis niloticus ) RAS including members of Bacillus and

Pseudomonas . In vivo suppressive effects have not been well-established.

As compared with field systems, few studies of hydroponic or aquaponic systems have incorporated amplicon metabarcoding of the microbial communities associated with system components (Munguia-Fragozo et al. 2015). Bacterial communities, which dominate most soilless systems, have been better characterized than fungal or oomycete assemblies. Aquaponic and aquacultural systems have been characterized by a predominance of Proteobacteria and

Bacteroidetes (Schmautz et al. 2017; Eck et al. 2019; Fossmark et al. 2020). However, the rhizosphere, especially in organic substrates such as coco coir, can host significant fungal and oomycete communities, including root pathogens (e.g. Pythium , Phytophtora, Fusarium spp.) (Li et al. 2014; O’Neill et al. 2014; Redekar et al. 2019). Incorporating fungal ITS amplicon metabarcoding into microbial community analyses will deepen our understanding of ecological processes in the crop-substrate environment. Currently, no studies have explicitly investigated the relationship between DOC and microbial community structure in the crop compartments of hydroponic or aquaponic systems. This is an important gap in knowledge to fill since rhizosphere microbial communities can influence plant performance and yield

(Vallance et al. 2010; Mendes et al. 2013).

1.6. Research Questions

The overall goal of this research was to investigate the effects of hydroponic and aquaponic nutrient solutions on plant health, microbial community composition, and Pythium root-rot severity in greenhouse production. Strawberry was used as a model crop in an open-loop drip irrigation system with coco coir grow bag substrate. Four nutrient solutions were evaluated: a synthetic solution (Synth), an aquaponic solution (Aqua), and the same two solutions amended with sucrose to artificially spike labile DOC concentrations (Synth C and Aqua C, respectively).

The research questions were as follows:

a) how do synthetic and aquaponic nutrient solutions with and without added sucrose

affect plant growth ?

b) will an aquaponic nutrient solution or added sucrose result in suppression of

Pythium root rot ?

c) do synthetic and aquaponic nutrient solutions with and without added sucrose host

distinct microbial communities and, if so, do they impact substrate and rhizosphere

community composition ?

1.6.1. Hypotheses

The hypotheses were that

a) plant growth would be lower when irrigated with sucrose-spiked nutrient

solutions due to heterotrophic microbial competition with roots, and that plants

irrigated with unamended aquaponic solution would exhibit lower growth than

those irrigated with the unamended synthetic solution due to the optimization of the

synthetic solution for soilless strawberries and based on previous findings by DeVitto

(2019);

b) Pythium root-rot severity would be lower on plants irrigated with aquaponic

solutions (Aqua, Aqua C) than those irrigated with synthetic solutions (Synth,

Synth C) due to competitive exclusion and/or antagonism by the endogenous microbial

community present in aquaponic effluent. Solutions amended with sucrose (Synth C,

Aqua C) would generate higher root-rot severity than their unamended

counterparts (Synth, Aqua) due to higher concentrations of labile organic carbon;

and

c) richness and diversity would be higher in aquaponic solutions than in synthetic due

to the presence of an endogenous microbiota in the aquaponic effluent and higher in

sucrose amended solutions than unamended due to additional labile organic carbon and

associated proliferation of opportunistic heterotrophs; and

community composition would differ across nutrient solutions given differences in

biochemical characteristics. These differences across solution would result in differences

in composition in bulk substrate and the rhizosphere. 29

CHAPTER 2

METHODS

2.1. Experimental Design

The experiment consisted of a full factorial 4 x 2 split-plot design, with four nutrient solution treatments and two drench inoculation treatments (Table 2.1). Treatments were arranged in a split-plot design in compartment 5A of the McFarlane greenhouses at UNH. Eight troughs were constructed. Each trough held four coco coir growbags (FibreDust, Cromwell, CT) with three plants each for a total of 96 plants. Each nutrient source was replicated across two randomly assigned troughs. Within each trough, Pythium inoculation was randomized by growbag, such that two growbags were inoculated while the remaining two were water controls (Fig. 2.1). The experiment was conducted twice, with some modifications.

Table 2.1. List of pathogen and nutrient solution treatments tested. Each treatment was applied to 4 replicate grow bags containing 3 plants each.

Treatment Pythium Nutrient Solution Growbags (3 plants/bag) 1 Yes Synth 4 2 Yes Synth C 4 3 Yes Aqua 4 4 Yes Aqua C 4 5 No Synth 4 6 No Synth C 4 7 No Aqua 4 8 No Aqua C 4

2.2. Growth Environment

Both experimental replicates were conducted in compartment 5A of the MacFarlane research greenhouses at the University of New Hampshire (Durham, NH). Troughs were constructed parallel to the side walls of the compartment (Fig. 2.1). The outermost troughs were

1.5 m from the side walls, and all troughs were spaced at least 0.76 m apart. An Argus system 30

(Argus Control Systems, Surrey, BC) automated the maintenance of environmental parameters within the compartment. Daytime (8 am to 8 pm) and nighttime (8 pm to 8 am) temperatures were set to 22.2 o C and 14.4 o C, respectively. Temperatures above these setpoints triggered the compartment’s passive cooling system, opening side and roof vents, while temperatures below the setpoints turned on radiant heating along the side walls of the compartment. The sensor responsible for measuring temperature was located at the approximate center of the compartment.

2.3. System Design and Construction

Eight troughs were set up in compartment 5A of the MacFarlane research greenhouses at

UNH. Each trough was constructed using two 3.05 m lengths of galvanized steel roof ridging. The two lengths were stacked such that 1.22 m of the top length overlapped the bottom length, creating a single 4.88 m trough. Two 2.44 m long 2” x 4” pressure treated white pine beams (Home Depot,

Portsmouth, NH) were run beneath the center of each trough to distribute weight and prevent warping and buckling. The troughs were placed on three cinderblock and stone supports of increasing height to achieve an incline of 2.47 o. The lowest support was a single cinder block of

0.56 m height, placed 12 cm from the effluent end of each trough. The second support was a cinder block with a stone slab stacked on top for a total height of 0.66 m, placed equidistant from each end of the trough. The highest support consisted of a cinder block and two stone slabs with a total height of 0.76 m and was placed 12 cm from the influent end of each trough. Small holes were drilled at 0.61 m intervals along one side of each completed trough to suspend irrigation tubing.

Each trough held four coco coir growbags of 70:30 pith:chip ratio, spaced at intervals of

12 cm from one another. Each growbag was elevated above the trough surface on four bricks. This ensured that leachate from upstream bags did not contact downstream bags, preventing cross- contamination. All troughs ran to waste, with a catch basin positioned beneath the effluent end to 31

collect leachate. In the first experimental replicate, each growbag was slit at the bottom four corners to provide drainage. Each slit was approximately 6 cm in length. For the second experiment, larger slits of approximately 10 cm length were made, and a third pair of slits was added at the middle of each bag to improve drainage.

25 1 85 61 7 79 91 19 26 2 86 62 8 80 92 20 27 3 87 63 9 81 93 21

73 49 37 13 10 82 43 67 74 50 38 14 11 83 44 68 75 51 39 15 12 84 45 69

76 4 40 64 55 31 94 22 77 5 41 65 56 32 95 23 78 6 42 66 57 33 96 24

28 52 88 16 58 34 46 70 29 53 89 17 59 35 47 71 30 54 90 18 60 36 48 72

Nutrient Solutions Pythium Inoculation Aqua Yes Synth No Aqua C Synth C Figure 2.1. Arrangement of troughs and treatments in the greenhouse compartment.

2.4. Nutrient Solutions

Two separate nutrient solution delivery systems were constructed using ¾” diameter black plastic tubing. One system mixed and delivered the synthetic nutrient solutions (Synth and Synth

C) and the other delivered the aquaponic solutions (Aqua and Aqua C). The synthetic system was fed by MacFarlane greenhouse clear water. Dosatron injectors (model no. D14MZ5, Dosatron,

Clearwater, FL) injected a two-part synthetic nutrient solution into the clear water at a rate of 1:100 v:v. A and B solutions were mixed in separate covered 20 L plastic containers and brought to final volumes of 18.9 L with MacFarlane clear water following a modified “Yamazaki” recipe from

Anna DeVitto (2019). The Yamazaki recipe is a Japanese formulation suggested by the University of Arizona Hydroponic Strawberry Information Website (Kubota 2015). Table 2.2 shows the A and B tank recipes used during this experiment. These recipes were adjusted to suit the chemistry of MacFarlane clear water. The final electrical conductivity (EC) and pH of the nutrient solution were adjusted to 1.00 dS/cm and 5.7, respectively, as measured with a Hanna HI9813-6 pH meter

(Hanna Instruments, Smithfield, RI).

The second (Aqua) delivery system was Table 2.2. Synthetic “Yamazaki” solution recipe. Adapted from DeVitto (2020) and Kubota (2015) fed by aquaponic culture water sourced from three replicate research-scale coupled Tank A (5 gal) Tank B (5 gal)

KNO 3 574 g Ca(NO 3)2 307 g lettuce/tilapia recirculating aquaponic systems MgSO 4-7H 20 234 g Fe 37.9 g

(RAqS) housed in greenhouses at UNH’s (NH 4)2SO 4 63.0 g HNO 3 16.1 mL

Na 2B4O2 5.68 g Kingman Farm (Route 155, Madbury, NH). Fresh MnSO 4 3.20 g culture water was sourced biweekly. 20 L plastic CuSO 4 0.369 g

Na 2MoO 0.237 g containers were used to collect and transport 18.9 ZnSO 4 2.74 g

L of culture water from the sump compartment of H3PO 4 89.5 mL 33

each RAqS. Culture water in the sump compartment had passed through both a rotating drum microscreen filter and a nitrifying biofilter, and was the nutrient solution pumped into the crop component of the RAqS.

Solutions from all three Kingman greenhouses were pooled into a single covered 120 L black plastic container and diluted with MacFarlane clear water to an EC value between 0.95 and

3- 1.05 dS/cm. Phosphoric acid (85% m:m) was added to provide PO 4 , which was limited in the aquaponic solution, and to lower the pH to a final value within the range 5.5-6.0. Aerobic conditions within the aquaponic container were maintained with a Second Nature Whisper air pump (Tetra, Blacksburg, VA) with attached airstone. During irrigation events, an EBARA

OPTIMA-3MS1 pump (EBARA, Rock Hill, SC) delivered solution through the system.

Both delivery systems split into 2 subsystems, for a total of 4 separate nutrient solutions.

Each of these 4 subsystems fed two troughs. One subsystem delivered the nutrient solutions as described above, unamended. The second subsystem included a Dosatron injector feeding a concentrated sucrose solution to artificially increase DOC concentration. Table 2.3 lists the concentration of carbon in each sucrose tank, dose rates, and the calculated final C:N of each amended nutrient solution. Table 2.3. Sucrose amendation. [C] is the mass ppm of carbon (1 ppm C = 1mg C L -1) in the concentrated sucrose tanks. Final C:N ratios were calculated by adding the initial [C] of each nutrient solution (assumed to be negligible in the synthetic solution) to the [C] provided by the concentrated sucrose solutions and dividing the total by the [N] in each solution. C:N are mass:mass. *The nutrient and carbon profiles and thus C:N of the aquaponic solution varied, and nutrient and carbon analyses were not conducted on the diluted and sucrose-amended solutions. This estimated C:N was calculated using a C:N range for the undiluted aquaponic solution at Kingman Farm provided by Sean Fogarty (personal communication).

Nutrient Solution [C] (ppm) Dose Rate (v:v) C:N Synth C 7000 1:50 1.82:1 Aqua C 5250 1:50 1.65:1 *

Four-way irrigation driplines (Netafilm Irrigation, Fresno, CA) with a maximum flow rate of 2.0 L/hr were plugged into the tubing suspended on each trough to deliver solution to the root zone of each plant. Emitters were seated 5 cm from the crown of each plant to an approximate depth of 4 cm. With 3 plants per bag, one emitter from each four-way dripline was held out of use.

These emitters were cinched off with black plastic zip ties to prevent the passage of solution.

2.5. Plants

Bare root strawberry plants ( Fragaria x ananassa cv. “Albion”) (Nourse Farms, Whately,

MA) were used in the first replicate experiment. Upon delivery, these plants were washed with

MacFarlane clear water and inserted into fully saturated growbags on each trough (3 plants/bag), spaced at 31.8 cm intervals. The root crown of each plant was level with the substrate.

The second experimental replicate began in September, when bare root plants could not be obtained. Instead, runners were harvested from 30 mother plants and the experimental plants from the first experiment. All runners were scouted for symptoms of disease, and diseased runners were discarded. Remaining runners were surface sterilized by 1 minute immersion in 1:100 v:v ZeroTol

2.0 (BioSafe Systems, East Hartford, CT). Runners were rooted on Oasis RootCubes (Oasis

Grower Solutions, Kent, OH) under mist in the propagation compartment of the MacFarlane greenhouses for 3 weeks, until roots were visible on the sides and bottom of the RootCubes. Cubes were then planted at the same spacing and depth as the bare root plants in the first experimental replicate.

2.6. Pythium inoculation

2.6.1. Inoculum

Pythium aphanidermatum (Edson) Fitzp. isolate KOP8 was cultured as a drench following the pond water method of PPath-protocol-019, originally sourced from Dr. Jennifer Parke. Twenty plugs of P. aphanidermatum cultured on V8 agar were transferred to 10 empty petri dishes using a #1 VWR cork borer (cat. No. 72410-010, VWR, Radnor, PA). Each petri dish received 20 mL of a sterile V8 broth (1:4 v:v clarified V8 juice:tap water). The dishes were stored in a dark cabinet for 3 days, at which point mycelial mats had fully colonized each dish. The remaining broth in each dish was emptied, and the mycelial mats were washed three times with 20 mL autoclaved pond water sourced from College Woods at UNH. The dishes then received 20 mL of sterile reverse osmosis (RO) water and were placed under constant fluorescent light for 48 hours to stimulate sporangia production. The RO water was poured off at the end of this period and replaced with chilled (4 o C) sterile RO water. The plates were refrigerated for 1 hr and then allowed to sit under light at room temperature for 1 hr for zoospore release. 10 uL aliquots of the resultant zoospore suspension were enumerated on a hemocytometer (Hausser Scientific, Horsham, PA) under an Olympus CX43RF compound microscope (Olympus, Center Valley, PA) to estimate the zoospore concentration of the resultant suspension. The suspension was diluted with sterile tap water to an estimated final zoospore concentration of 1 x 10 5 zoospore/mL. This suspension was used as the drench inoculum for plants in the Pythium treatment group.

Second Experimental Replicate

In the second experimental replicate, mycelial mats were blended in a sterile food processer following the refrigeration step. The resultant suspension was filtered through three layers of cheesecloth to remove large hyphal fragments. Sporangia and zoospores were enumerated as in 36

the first experiment, and the suspension was diluted to approximately 1 x 10 5 propagules/mL.

Using sporangia as well as zoospores was an attempt to increase the chances of successful infection

– zoospores are short-lived once released from the sporangium.

2.6.2. Drench inoculation of Pythium

An ErgoOne Fast serological pipette (USA Scientific, Ocala, FL) was used to deliver 50 mL of P. aphanidermatum isolate KOP8 zoospore suspension to the 48 plants in the Pythium treatment group. The remaining 48 plants received 50 mL of sterile tap water as a control.

Inoculum and water were expelled in a circle of radius 5 cm from the root crown of each plant to target the growing root zone. Inoculations were performed 3 weeks post-planting.

Second Experimental Replicate

In the second experimental replicate, a second inoculation was performed two weeks following the first. This inoculation was 150 mL per plant. The decision to increase inoculum volume was based on a lack of significant symptom development during the first experimental replicate and in the two weeks following the initial inoculation of the second replicate. The same procedures were used to perform the second inoculation as the first.

2.7. Plant Health Metrics

2.7.1. Aboveground Mass

Plants grew for 5 weeks post-inoculation before final destructive sampling. Aboveground plant material was cut away from the roots at the crown using a sterile stainless-steel pruning shear.

The aboveground fresh mass of each plant was taken using an Ohaus Ranger 3000 digital scale

(Ohaus, Parsippany, NJ). The aboveground portion of each plant was then placed in a brown paper bag, and all aboveground portions were dried in a drying oven at 70 o C for 48 hours. After drying, the same scale was used to obtain dry masses for each plant. 37

0.5 1.0 1.5 2 3 3 4 5

Fig. 2.2. Root health scale. A semi-quantitative 0-5 scale was used to evaluate the size of each root ball. The scale was constructed using representative root balls from the experiment. A score of 0 indicated the absence of any roots, while a 5 indicated a root ball size comparable to the largest root systems found in the experiment.

2.7.2. Root Assessments

Root Size Assessment

The size of each root ball was assessed using a semiquantitative scale (Fig. 2.2) from 0-5.

A lack of any visible root growth was indicated by a 0, while 5 corresponded to the largest root balls in each experimental replicate.

Root Rot Severity and Root Plating

Pythium root rot severity was assessed on a semiquantitative scale from 0-100. A score of 0 indicated no visible root rot symptoms (discoloration, liquefaction, and cortical sloughing), while a score of 100 indicated full colonization and infection of the root system. To confirm the presence of Pythium on diseased roots, sections including transition zone were collected with sterile forceps and transported to the lab in 15 mL Falcon tubes. Under a laminar hood, 1 cm sections of these roots were plated on PARP V8 and Rose-Bengal agars. Mycelial growth and morphology were monitored daily to ascertain the presence of Pythium .

2.8. Plant Health Statistical Analyses

Root-rot severity, root ball size, and aboveground mass were analyzed using a split-plot ANOVA in JMP Pro 14 (SAS Institute, Cary, NC). For the main plot, row replicate and solution type were fixed effects. Row replicate crossed with solution was added to the model as a random effect in the main plot. In the subplot, inoculation and inoculation crossed with solution were the fixed effects. Statistical significance was assessed at α = 0.05 and a Tukey Honest Significant Difference

(HSD) post hoc test was used to separate the means.

2.9. Sampling

2.9.1. Leachate

Sterile 10-inch diameter clear plastic drain trays were placed underneath the slits at the downhill end of each growbag. All nutrient solutions were run until at least 100 mL of leachate was present in each drain tray. Sterile 50 mL Norm-Ject syringes (Air-Tite, Virginia Beach, VA) were used to draw up 50 mL of leachate from each tray, which was then ejected into a 50 mL

Falcon tube. Samples were stored at -80 o C until extraction.

2.9.2. Solution

Two drip emitters were selected at random from each trough to use for solution collection.

Emitters were removed from the substrate, rinsed with tap water, and submerged in 10% bleach for 5 mins to surface sterilize. Each emitter was then placed in a 50 mL Falcon tube. Irrigation was run until 50 mL of each solution had collected in the tubes. MacFarlane greenhouse clear water was also collected. Samples were stored at -80 o C until extraction.

2.9.3. Bulk Substrate

A sterile stainless-steel measuring tablespoon was used to take three substrate samples from each grow bag. The samples were taken approximately 5 cm from the crown of each plant at 39

the surface of the substrate, to an approximate depth of 3 cm. Samples from the same bag were pooled together in 50 mL Falcon tubes and stored at -80 o C until microbial DNA extraction.

2.9.4. Roots and Rhizosphere Substrate

Sterile forceps and scalpels were used to take four root segments with adhering substrate from each root ball. Segments were taken from different sides of the ball. Segments from each root balls were pooled in 50 mL Falcon tubes and stored at -80 o C for microbial DNA extraction.

2.10. DNA Extraction

2.10.1. Leachate and Solution

Leachate and nutrient solution samples were passed through 0.22 µm nitrocellulose filter membranes (cat. N8645, MilliporeSigma, Burlington, MA) via vacuum filtration to concentrate microbial biomass. DNA was extracted from each membrane following Qiagen PowerWater (cat.

14900, Qiagen, Hilden, Germany) manufacturer protocol. Extracts were quantified using a Qubit fluorometer (Thermo Fisher Scientific, Waltham, MA) and stored at -80 o C until PCR.

2.10.2. Substrate

Microbial DNA was extracted from substrate samples using a Qiagen PowerSoil extraction kit (cat. 12888, Qiagen, Hilden, Germany). The manufacturer’s protocol was followed with the following modifications: a) coir samples were pre-ground with sterile mortar and pestle to break up chips and aggregates and b) the first centrifugation time was doubled to achieve adequate pelleting of the relatively low-density coir. Extracts were quantified using a Qubit fluorometer

(Thermo Fisher Scientific, Waltham, MA) and stored at -80 o C until PCR.

2.11. Amplicon PCR

The variable V4-V5 (515F-926R; (Parada et al. 2016) region of the 16S rRNA gene and the ITS1f-ITS2 internal transcribed spacer region (Walters et al. 2016) were used to barcode prokaryotes and fungi, respectively, in each sample. The primers were prepared by the UNH

Hubbard Center for Genome Studies (HCGS):

Forward Reverse 16S V4-V5 5’-GTGYCAGCMGCCGCGGTAA-3’ 5’-CCGYCAATTYMTTTRAGTTT-3’ ITS1f-ITS2 5’-CTTGGTCATTTAGAGGAAGTAA-3’ 5’-GCTGCGTTCTTCATCGATGC-3’

PCR reactions for both amplicons were prepared to a final volume of 12 µL with the following reagent concentrations: 1x DreamTaq Hot Start (Thermo Scientific, Waltham, MA), 3.4 µM each primer, and 1 ng/µL template DNA. The PCR conditions for each reaction were as follows:

16S ITS Hot Start 95˚C – 3 min 94˚C – 3 min Denaturation 95˚C – 30 s 94˚C – 30 s 35x Annealing 55˚C – 30 s 52˚C – 30 s Extension 72˚C – 30 s 65˚C – 30 s Extension 72˚C – 5 min 65˚C – 10 min

Successful PCR was confirmed by 1% agarose gel electrophoresis. No template controls (NTC’s) were run alongside all samples to identify any contamination.

2.12. Sequencing

Library preparation and sequencing were performed by HGCS. A second round of PCR with indexed, multiplexed primers was used to prepare each sample library, and an Illumina Hiseq

2500 (2 x 250 bp) (Illumina, San Diego, CA) was used for sequencing. NTC’s were included along with samples. Reads were demultiplexed and returned for downstream analyses.

2.13. Bioinformatics

2.13.1. 16S rRNA

Denoising, Taxonomy, and Phylogeny

Initial bioinformatics were performed with QIIME 2 2020.2 (Bolyen et al. 2019) on

Premise: a central, shared, high-performance computing (HPC) cluster at UNH. Demultiplexed sequence data were denoised to amplicon sequence variant (ASV) resolution with DADA2 (q2- dada2) with –p-trunc-len-f 230, --p-trunc-len-r 230, --p-trim-left-f 19, and –p-trim-lef-r 20

(Callahan et al. 2016). –p-trunc-len truncates reads to a defined base-pair length to maintain read quality (here, 230), and –p-trim-left removes a defined number of base-pairs from the left side of each read (here, 19 and 20 to remove forward and reverse primers). A taxonomy classifier was trained on the Greengenes 13_8 99% OTUs reference sequences (McDonald et al. 2012) using the naïve Bayes method in q2-feature-classifier classify-sklearn (Bokulich et al. 2018). Taxonomy was assigned to sample ASVs using the trained classifier. Mitochondrial and chloroplast sequences were excluded with q2-taxa filter-table. Sequences were aligned with the MAFFT option of q2- alignment (Katoh et al. 2002) for q2-phylogeny with fasttree2 (Price et al. 2010).

Pre-processing and Normalization

The taxonomy, phylogenetic tree, and feature table generated in QIIME 2 were imported with ‘qiime2R::qza_to_phyloseq’ into a phyloseq R object (McMurdie and Holmes 2013; R Core

Team 2020) along with a metadata file containing experimental factors. The decontam function

‘isContaminant’ (Davis et al. 2018) with the prevalence method and a threshold of 0.5 was used to identify likely contaminant features based on their prevalence in NTC’s. Contaminants and

ASVs with unassigned phylum-level taxonomy were pruned with ‘prune_taxa.’ NTC’s and samples with no remaining reads were removed with ‘prune_samples.’ Coir samples were rarefied 42

without replacement to a depth of 2,505 with ‘rarefy_even_depth,’ the highest value possible while maintaining at least three replicates of each nutrient solution treatment. While rarefaction is discouraged in favor of more sensitive and statistically sophisticated normalization techniques

(McMurdie and Holmes 2014; Callahan et al. 2016; Beule and Karlovsky 2020), approaches must be tailored to the particularities of each experiment (Weiss et al. 2017; Willis 2019). In this case, library sizes varied greatly (5 orders of magnitude) across samples, and this variance was not evenly distributed across nutrient solution treatment. log 2 transformation with pseudocount as implemented in ‘normTransform’ ( DESeq2 ), variance stabilized transformation (VST) as implemented in ‘varianceStabilizingTransformation’ (DESeq2 ) (Huber et al. 2003; Love et al.

2014) and cumulative sum scaling (CSS) as implemented in ‘cumNorm’ (metagenomeSeq )

(Paulson et al. 2013) each resulted in persistent correlations between Shannon diversity and library size that compromised the validity of between-treatment comparisons of α-diversity (Table 2.3).

Rarefaction adequately addresses this concern.

Table 2.4. Positive linear correlations between transformed library size and Shannon’s diversity. Raw count

data was normalized using three approaches: a log 2 transformation with pseudocount of 1, a variance stabilized transformation (VST), and cumulative sum scaling (CSS). Shannon’s diversity was calculated for each transformed sample. R 2 values indicate the goodness of fit for linear regression models of Shannon’s diversity against transformed sample library sizes. p values below 0.05 indicate a statistically significant correlation.

Transformation ( R package ) R2 p

-8 log 2(n + 1) ( DESeq2 ) 0.684 6.31 × 10 VST ( DESeq2 ) 0.813 8.22 × 10 -11 CSS ( metagenomeSeq ) 0.864 1.54 × 10 -12

Community Composition Analysis

A stacked barplot of relative abundance at the class level was generated using tax_glom, psmelt, and ggplot2 (Wickham 2016). Shannon (diversity) and Chao1 (richness) indices for each rarified sample were calculated and plotted with ‘estimate_richness’ and ‘plot_richness.’

Following Shapiro-Wilk tests of normality and Levene tests of homogeneity of variance as implemented in rstatix (Kassambara 2020), differences in mean Shannon and Chao1 indices across nutrient solutions were analyzed for significance via ANOVA and Tukey’s HSD post-hoc test with block as a random effect. The vegan function ‘adonis’ (Oksanen et al. 2019) was used to run a perMANOVA using Bray-Curtis distance with nutrient solution as the factor and 720 permutations, followed by the RVAideMemoire post-hoc function ‘pairwise.perm.manova’ to identify community differences (Hervé 2020), also with 720 permutations. The communities associated with the Aqua and Aqua C nutrient solutions were found to have significant differences in composition (p = 0.050). A second perMANOVA run on the subset of these two nutrient solutions confirmed significance (p = 0.036). Samples and taxa from the Aqua and Aqua C treatments were plotted using a canonical analysis of principal coordinates (CAP) ordination on

Bray-Curtis distance matrices as implemented in the phyloseq function ‘ordinate’ (Anderson and

Willis 2003; Paliy and Shankar 2016). Wilcoxon rank tests were run on each abundant feature to determine whether rarefied abundance differed significantly (α = 0.05) between Aqua and Aqua C samples. Barplots of the 20 most abundant ASVs in each nutrient solution treatment were created using psmelt and ggplot2.

2.13.2. ITS

The same general pipeline was used for analysis of ITS sequence data as 16S. The following sections chiefly highlight differences in parameters.

Denoising, Taxonomy, and Phylogeny

Demultiplexed sequence data was passed through ITSxpress as implemented in QIIME2

(q2-itsxpress trim-pair-output-unmerged) to trim primers and conserved regions flanking the variable ITS1 region (Rivers et al. 2018). This resulted in virtually all reads being thrown out. The reason for this remains unclear; reads had high quality scores. Bypassing ITSxpress and denoising to the ASV level using paired-end DADA2 as implemented in QIIME2 (--p-trim-left-f 20, --p- trim-left-r 20, --p-trunc-len-f 0, --p-trunc-len-r 0) successfully retained and denoised reads. Quality scores were high, so reads were not truncated to maximize overlap for merging. A taxonomic classifier was trained on UNITE 99% (ASV) reference sequences and taxonomy (Nilsson et al.

2019) using q2-feature-classifier fit-classifier-naïve-bayes and used to assign taxonomy to ASVs

(q2-feature-classifier classify-sklearn). A phylogenetic tree was constructed as with 16S sequences.

Pre-Processing and Normalization

Pre-processing with decontam and phyloseq was performed as with 16S data to identify and remove contaminant taxa and NTC’s. One additional ASV (assigned to the agaricomycete wood-rot genus Gymnopilus ) was pruned with subset_taxa, as it was only present as a dominant

(84% relative abundance prior to rarefaction) taxon in one Synth C sample and a less abundant

(4%) taxon in one Aqua C sample, with no reads in any other samples. Coir samples were rarefied without replacement to a depth of 9,153, the highest value possible while maintaining at least 3 replicates of each nutrient solution treatment. 45

Community Composition Analysis

A stacked barplot of relative abundance at the order level was generated using tax_glom, psmelt, and ggplot2. Shannon indices were calculated for each sample and compared across nutrient solutions as with 16S data. Chao1 indices were calculated for each sample as with 16S data. A Shapiro-Wilk test did not support the assumption of normality necessary for a valid

ANOVA, so the non-parametric Welch’s test (as implemented in rstatix ) was used to compare means across nutrient solution. Neither index was significantly different between solutions, so no post-hoc tests were conducted. A perMANOVA with 1000 permutations was run to analyze differences in composition across nutrient solution treatment. The perMANOVA did not find significance, so no post-hoc tests were performed. Wilcoxon rank tests were conducted to evaluate differential abundance of the 25 most abundant taxa across nutrient solution as with 16S data.

Barplots of the 20 most abundant ASVs in each nutrient solution treatment were created as with

16S data.

CHAPTER 3

RESULTS

3.1. Pythium Root Rot

Drench inoculation with P. aphanidermatum resulted in no significant differences in root-rot severity compared to water controls. Root rot symptoms were observed every bag with no significant differences in severity across nutrient solution treatments (Fig. 3.1). Selective root plating on Rose-Bengal and V8 PARP agars confirmed the presence of Pythium spp. in every bag.

100 90 80 70 Pythium Water 60 50 40 30

Root Rot Severity [%] 20 10 0 Synth Synth CSolution Aqua Aqua C Fig. 3.1. Pythium root rot severity on strawberry cv. “Albion” 35 days post inoculation. Pythium root rot severity was measured on a semiquantitative scale based on the percentage of each plant’s root zone exhibiting characteristic symptoms including cortical sloughing, browning, and liquefaction. Severity was not significantly different across solution or inoculation.

3.2. Plant Growth

Plants irrigated with the unamended nutrient solutions (Synth and Aqua) exhibited significantly higher above- and belowground growth than those irrigated with sucrose-amended solutions (Synth C and Aqua C). While root health was comparable between plants irrigated with the unamended synthetic and aquaponic solutions, shoot dry mass was significantly reduced in the

Aqua treatment (p = 2.98 × 10 -4). No significant differences in growth were observed between control and P. aphanidermatum inoculated plants (Fig. 3.2). 47

9 5 8 A p = 9.43 × 10 -8 A p = 2.41 × 10 -5 Pythium A 4.5 7 4 A A A Water 6 B B 3.5 5 3 B B B 4 2.5 B 2 3 C C C C Root Health 1.5 Shoot Dry Mass (g) 2 1 1 0.5 0 0 Synth Synth C Aqua Aqua C Synth Synth C Aqua Aqua C Solution Solution Fig. 3.2. Above- and belowground plant growth is reduced in plants irrigated with sucrose-amended nutrient solutions. Values that do not share a letter are significantly different (p < 0.05) as determined by a post hoc Tukey’s HSD. Plants grown in low DOC nutrient solutions developed a greater root volume and aboveground mass than those grown in high DOC solutions. Inoculation with Pythium had no significant effect. p-values above each plot indicate overall significance of nutrient solution effects as determined by ANOVA.

3.3. Microbial Communities

3.3.1. 16S rRNA

Sequencing, denoising, and filtering of 16S rRNA amplicon products from 32 coir substrate samples (8 biological replicates × 4 nutrient solutions) generated library sizes ranging from 10 to 667,412, with a median count of 9,485. After pruning 2 contaminants, 5,226 ASVs assigned to 32 bacterial phyla and 205 named genera remained. Rarefaction to a depth of 2,505 removed 14 samples and 2,044 ASVs. Rarefied samples included representatives of 30 phyla and

166 named genera. The most abundant phyla were the Proteobacteria, Bacteroidetes,

Verrucomicrobia, and Actinobacteria (Fig. 3.3). 48

Acidobacteria (A) (B) 4.20 %

Bacteroidetes 16.0 %

Proteobacteria Ascomycota 48.0 % 79.1 %

Planctomycetes 4.22 %

Figure 3.3. Rarefied phylum-level substrate bacterial and fungal abundance. Proteobacteria, Bacteroidetes, and Verrumicrobia dominated substrate bacterial communities (A). Fungal communities were overwhelmingly composed of Ascomycetes, with Basidiomycetes, unassigned taxa, and other phyla collectively contributing only 20.9 % of ASVs.

Community Composition Analyses

Shannon diversity did not differ significantly (p = 0.0649) across nutrient solutions. Chao1 indices were significantly higher in Synth C than Synth samples (Table 3.1), indicating increased taxonomic richness in Synth C communities. Aqua and Aqua C substrate bacterial communities were found to have a significant difference (p = 0.050) in composition by perMANOVA with a post-hoc pairwise test on Bray-Curtis distance (Fig. 3.4). A perMANOVA on the subset of Aqua and Aqua C samples confirmed significance (p = 0.036). Wilcoxon rank tests revealed three differentially abundant ASVs across Aqua and Aqua C (Fig. 3.4): an unassigned member of the γ-

Proteobacteria family Sinobacteraceae , another from the Actinobacteria family

Micromonosporaceae , and an Asticcacaulis sp. (α-Proteobacteria). The Sinobacteraceae and 49

Micromonosporaceae were both more abundant in growbags irrigated with sucrose-amended

aquaponic solution, while the Asticcacaulis sp. was more abundant in the unamended treatment.

Samples Micromonosporaceae (p = 0.0381)

Sinobacteraceae (p = 0.0238) Asticcacaulis (p = 0.0269)

Taxa

Figure 3.4. Canonical analysis of principal coordinates (CAP) biplot of Aqua and Aqua C substrate bacterial communities. The x – axis (CAP1) is the product of a linear discriminant analysis (LDA), which maximizes the distance between nutrient solution treatment groups, using linear combinations of rarefied ASV abundance. The y- axis (MDS1) is the first dimension of an MDS ordination on the post-CDA distance matrices. The bracketed percentages indicate the percentage of variation among samples that each axis explains. Bray-Curtis distance matrices were used as the input of the analysis. Samples are plotted in black with shape indicating nutrient solution treatment. Similar samples cluster together. The 10 most abundant ASVs overall are plotted as dots, with color representing genus-level or lowest-assigned taxonomy. The position of each ASV on the plot indicates its correlation with the two axes shown. ASVs labeled in red had significant differences in abundance across nutrient solution as determined by Wilcoxon rank tests.

Figure 3.5. Relative class-level substrate bacterial abundance across nutrient solution. Bars indicate relative abundances of bacterial classes following rarefaction. “Low Abundance” includes all classes with relative abundance below 3%. α-Proteobacteria were the most abundant class in every sample.

Table 3.1. Amendment of a synthetic nutrient solution with sucrose increased substrate bacterial community richness. Standard deviation is provided for each index. Values which do not share a letter are significantly different (ANOVA, Tukey’s HSD, α = 0.05).

16S rRNA ITS Solution Chao1* Shannon Chao1 Shannon Aqua 875 ± 261 ab 6.01 ± 0.247 121 ± 55.1 2.67 ± 0.551 Aqua C 664 ± 383 ab 5.73 ± 0.473 147 ± 132 2.88 ± 0.688 Synth 203 ± 30.5 b 4.95 ± 0.216 137 ± 93.0 2.72 ± 0.144 Synth C 1000 ± 471 a 5.75 ± 0.520 74.5 ± 20.2 2.45 ± 0.423

At the genus and family level, Chitinophagaceae, Opitutaceae, Micromonosporaceae,

Devosia, and Rhodoplanes were abundant under all nutrient solution treatments (Fig. 3.6).

Abundant taxa were mostly conserved across nutrient solution treatment, with some shifts in

order.

No Sucrose Sucrose Synth Aqua

Figure 3.6. Abundant bacterial ASVs in coir substrate irrigated with synthetic and aquaponic nutrient solutions. Bars indicate relative abundances of bacterial ASVs following rarefaction. The 20 most abundant ASVs in each nutrient solution treatment are labeled at the genus level, if assigned, or at the lowest assigned taxonomy. The top plots show abundance data for the synthetic nutrient solution treatments: (A) represents Synth and (B) shows Synth C. The bottom plots represent the aquaponic treatments: (C) is Aqua and (D) is Aqua C. 52

3.3.2. ITS

Sequencing, denoising, and filtering of ITS amplicon products from 32 coir substrate samples generated library sizes ranging from 150 to 260,846 with median 12,577.5. After pruning

1 contaminant, 1,157 ASVs remained across 6 fungal phyla and 94 named genera. Rarefaction to a depth of 9,153 removed 14 samples and 206 ASVs, leaving representatives of 6 phyla and 77 genera. The ascomycete orders Sordariales, Capnodiales, Hypocreales, and Eurotiomycetes were abundant in substrates irrigated with all nutrient solutions, while Wallemiales were the chief classified representatives of the Basidiomycota (Fig. 3.5). However, the majority of ASVs were either unassigned (616/945 post-rarefaction ASVs) or classified as “unidentified” (18/945) at the order level. These ASVs contributed 47, 355 reads after rarefaction, nearly a third of the 155,601 total. Of these unclassified reads at the order level, 12,868 could not be classified at the phylum level (Table 3.2).

Table 3.2. Unclassified ITS reads made up a significant portion of rarefied ASVs and abundance. The majority of these ASVs were either unclassified Ascomycetes or had no classification below kingdom level.

Phylum Class Rarefied Count % of Total Unknown Unknown 12868 8.27% Rozellomycota Unknown 183 0.118% Chytridiomycota Unknown 90 0.0578% Unknown 663 0.426% Basidiomycota Agaricomycetes 158 0.102% Unknown 30022 19.3% Ascomycota Dothideomycetes 1 0.00% Total 155601 28.3%

Community Composition Analysis

No significant community level differences across nutrient solutions were found for substrate fungal data. Fungal communities exhibited lower diversity and richness than their bacterial counterparts despite a deeper rarefaction (Table 3.1.). Wilcoxon rank tests on the 25 most abundant ASVs revealed one differentially abundant taxon, an unidentified member of the

Sordiariales (p = 0.0498). This taxon was 2.5 times as abundant in the aquaponic nutrient solution amended with sucrose (Aqua C) than in the amended synthetic solution (Synth C).

Figure 3.7. Relative order-level substrate fungal abundance across nutrient solution treatment. Bars indicate relative abundances of fungal orders after rarefaction. “Low Abundance” includes all orders with relative abundance below 1%. “Unassigned” includes ASVs with unassigned order-level taxonomy as well as those classified as “unidentified” by a classifier trained on the UNITE database. 54

No Sucrose Sucrose Synth

Aqua

Figure 3.8. Abundant fungal ASVs in coir substrate irrigated with synthetic and aquaponic nutrient solutions. Bars indicate relative abundances of fungal taxa following rarefaction. The 20 most abundant taxa in each nutrient solution treatment are labeled at the genus level, if assigned, or at the lowest assigned taxonomy. The top plots show abundance data for the synthetic nutrient solution treatments: (A) represents Synth and (B) shows Synth C. The bottom plots represent the aquaponic treatments: (C) is Aqua and (D) is Aqua C.

The genera Zopfiella, Penicillium, Simplicillium, Subulicystidium, Aspergillus, Fusarium,

Rhizoctonia, Wallemia, Cladosporium, and Acremonium were abundant in all nutrient solution treatments (Fig. 3.8). While differences were not statistically significant, Penicillium and

Simplicillium spp. were more abundant in substrate irrigated by sucrose-amended solutions than unamended, while Aspergillus ostianum was higher in abundance only in substrate irrigated with sucrose-amended synthetic solution. 55

CHAPTER 4

DISCUSSION

4.1. Pythium aphanidermatum inoculation failed to produce significant symptoms

In both experiments, plants inoculated with P. aphanidermatum (Edson) Fitzp. isolate

KOP8 did not exhibit root rot symptoms significantly different from water controls. In the first experiment, each plant received 50 mL of inoculum at a density of 1 x 10 5 propagules/mL. In the second experiment, two rounds of inocula were applied: the first was 50 mL at 1 x 10 5 propagules/mL, while the second was 150 mL at the same concentration. Inoculum did not generate significant symptoms during either experimental replicate, though there was a low level of Pythium present in all treatments, as confirmed by root symptom diagnostics and selective plating. P. aphanidermatum colonizes and causes symptoms at elevated temperatures (Gold and

Stanghellini 1985; Miyake et al. 2014), so the relatively cool nutrient solution and ambient temperatures in the greenhouse compartment may have restricted proliferation and infection.

While the first experimental replicate was run in the summer, the second was conducted from fall to winter. While the greenhouse compartment had supplemental heating, the difference in outdoor temperature may have also played a role in decreasing P. aphanidermatum pathogenicity.

The presence of Pythium symptoms in plants from every treatment level is in concordance with observations by (Degenring 2019) in the same greenhouse compartment as well as the literature consensus that Pythium spp. are ubiquitous in aquatic environments. This suggests that pathogenic Pythium spp. are endemic to the greenhouse water supply. The greenhouse receives chlorinated water from the UNH Water Treatment plant, which is therefor unlikely to be the source of contamination. MacFarlane plumbing and/or irrigation line are more likely sources. The irrigation line and emitters were surface sterilized with a 24 hr submersion in 10% bleach prior to 56

use, but it is possible that solution did not reach the entire inner surface of the tubing. Any biofilm left within lines from prior use could also protect propagules from contact with active bleach. The lack of significant differences in infection precludes any conclusions regarding suppression mediated by the aquaponic culture water or the addition of sucrose.

4.2. Nutrient solution provenance and sucrose amendment impacted plant growth

Plants irrigated with the unamended synthetic solution (Synth) showed the highest aboveground growth of any nutrient solution treatment. Irrigation with the unamended aquaponic solution (Aqua) did not significantly impact root health, but did result in a reduction of aboveground growth comparable to that observed by DeVitto (2019). Whereas the synthetic nutrient solution had a consistent nutrient composition optimized for strawberry production, the aquaponic solution varied in nutrient profile across the duration of each replicate experiment and came from a system which was being managed for lettuce production. Furthermore, the aquaponic

+ + solution was low in NH 4 as compared to its synthetic counterpart. Root uptake of NH 4 generates an H+ flux into the root zone, decreasing pH (Imler et al. 2019). The root zones of plants irrigated with the aquaponic solution may have become more neutral than those irrigated with the synthetic

+ solution due to the lower NH 4 , limiting the efficiency of nutrient uptake. Finally, the unamended aquaponic solution had a relatively low calculated C:N ratio (~ 0.11:1), but even this small organic carbon source may have stimulated some microbial competition for oxygen and phytonutrients in the root zone. These dynamics are not mutually exclusive - it is very likely that their combined effects are responsible for the decreased aboveground biomass observed in the Aqua treatment group.

Irrigation with either sucrose-amended solution (Synth C and Aqua C) produced severely stunted plants, with no significant differences between the two. This clearly represents an adverse 57

plant health outcome driven, ultimately, by the added sugar. There are two plausible mechanisms through which the sucrose may have directly and indirectly contributed to decreased root growth and shoot biomass: osmotic stress and heterotrophic respiration .

Osmotic stress

Dissolved sucrose contributes to the solute potential of a solution, which in turn affects water dynamics in the root zone. High root zone solute potential can cause osmotic stress - water uptake becomes less energetically favorable as the solute concentration gradient across the root surface moves toward equilibrium (Xiong and Zhu 2002). Plant responses to osmotic stress are metabolically intensive, consuming energy that could otherwise be directed toward growth. Lower water uptake efficiency, together with the activation of stress-related metabolic pathways, results in slower growth. The higher osmolarity of the sucrose-amended solutions relative to unamended may have played a role in the low growth observed in these experiments.

Heterotrophic respiration

Sucrose also represents a pool of labile carbon available for digestion by heterotrophic microbes. Ling and Chen (2005) and Guerdat et al. (2011) found that dosing recirculating aquacultural culture water with sucrose led to the growth of heterotrophic bacteria in biofiltration units, resulting in decreased nitrification efficiency. Aerobic heterotrophy consumes oxygen, and respiring microbes also incorporate nutrients as biomass, rendering them unavailable for plant use.

If diffusion is inadequate to replace the oxygen being consumed (in e.g. stagnant, waterlogged conditions), sustained heterotrophic respiration causes hypoxia and, eventually, anoxia.

In soilless culture, DO is typically held above 6%, with lower concentrations risking plant stress and yield losses (Chérif et al. 1997). While bulk substrate and root zone DO were not directly measured in this study, bags that had been irrigated with sucrose-amended solutions were 58

waterlogged and gave off distinct odors of hydrogen sulfide upon opening at the end of each experiment. Hydrogen sulfide is a product of anaerobic sulfate reduction in waterlogged soils and substrates, so the presence of an odor strongly suggests the presence of anoxic zones (Lamers et al. 2013). Given that the growth differential between sucrose-amended and unamended nutrient solution treatments was visible well prior to the end of each experiment and that irrigation rates were held consistent across nutrient solution treatments, the following chain of causality seems plausible:

1) sucrose stimulated heterotrophic respiration in the Synth C and Aqua C treatment groups

from the beginning of each experiment onward;

2) competition with heterotrophic microbes for dissolved oxygen and nutrients limited root

growth in Synth C and Aqua C plants;

3) the resultant decrease in root surface area, along with high root zone osmolarity from

sucrose and unassimilated ions, decreased the uptake of water and nutrients compared to

the unamended treatments, limiting aboveground growth;

4) fewer and smaller leaves provided a lower area available for a) photosynthesis,

decreasing the carbon pool available for metabolism and growth, and b) evapotranspiration,

further decreasing the efficiency of water uptake;

5) 3 and 4 became two points of a negative feedback loop, diminishing a) water and nutrient

uptake and b) growth rates compared to plants irrigated with the unamended solutions;

6) excess water from the irrigation regime accumulated in the growbags, saturating the

substrate;

7) excess nutrient salts built up, contributing another possible limit to plant growth;

8) saturation prevented adequate oxygen diffusion into the substrate; 59

9) hypoxic and anoxic zones developed with continuing heterotrophic respiration, stressing

roots and further preventing growth.

While the above chain involves multiple mechanisms that interact and build upon themselves and one another, the added sucrose is the initial drop in the cascade.

Plant and substrate characteristics may have further contributed to the severity of the chain: strawberries are known to be particularly sensitive to hypoxic stress (Iwasaki 2008) and the coco coir substrate used had a high water holding capacity and limited drainage. Finally, irrigation was managed to encourage root infection by P. aphanidermatum – saturation and mild hypoxia were by design in the unamended treatment groups. Independently adjusting irrigation rates within each treatment group, which may have lessened the gap in plant size and health across amended and unamended solutions, was impossible due to the system design.

4.3. Coco coir substrate microbial communities

Rhizosphere and solution microbial DNA could not be sequenced and analyzed prior to submission of this thesis. However, the communities associated with the bulk coco coir substrate were sequenced and analyzed for differences in α-diversity and composition across nutrient solution treatments.

4.3.1. α-Diversity and differences in composition

Bags irrigated with Synth C had higher Chao1 indices than those irrigated with Synth, with no difference in Shannon’s diversity. Chao1 is calculated using the following equation: =

+ ( ⁄2), where S 1 is the estimated number of ASVs in the community being sampled, S obs is the observed number in the sample, a is the number of singletons (ASVs with only one read), and b is the number of doubletons (ASVs with two reads) (Chao 1984; Colwell and Coddington

1994). Therefore, a higher Chao1 index can indicate 1) a higher number of observed ASVs and/or 60

2) a higher singleton to doubleton proportion. A community with many taxa of relatively low abundance would thus be expected to have a high Chao1 index. In the Synth C community, the high Chao1 index may indicate instability as many opportunistic strains take advantage of the availability of sucrose. The comparatively oligotrophic environment of the Synth treatment may have limited the community to fewer, niche-adapted strains capable of competing for scarce nutrients.

No significant differences in α-diversity were found between bacterial communities from

Aqua or Aqua C bags. Fungal α-diversity metrics were not significantly different across nutrient solution. Aqua and Aqua C substrate community compositions were significantly different, and

Wilcoxon rank tests identified three differentially abundant taxa between the two treatments.

Without physicochemical parameters or community functional assays, these differences are difficult to assign meaning to. Taken together, these findings provide limited support for the hypothesis that nutrient solution, and particularly labile organic carbon, shape substrate bacterial community composition. Fungal community structure did not show any significant changes. Fungi tend to have slower growth rates than bacteria, so it is possible that the fungal communities did not have time to fully adapt to the nutrient solution.

4.3.2. Taxa of interest in substrate

Fungi

Abundant fungal ASVs included Fusarium proliferatum , one strain of which (BRL1) has been identified as a plant-growth promoting endophyte in the roots of Oxalis corniculata and dwarf rice. F. proliferatum BRL1 secretes gibberellins (GA) and indole acetic acid (IAA), which are growth promoting phytohormones (Bilal et al. 2018). It also upregulates host GA production and 61

downregulates the GA antagonist jasmonic acid (JA), which is involved in abiotic and biotic stress responses and can limit growth at high concentrations (Hou et al. 2013; Bilal et al. 2018).

An unclassified Rhizoctonia ASV was also abundant. While the plant-health significance of this particular strain is unknown, Rhizoctonia solani is an opportunistic phytopathogen with a wide host range that includes strawberry (Ajayi-Oyetunde and Bradley 2018). Acremonium alternata , endophytic isolates of which have been demonstrated to suppress below- and aboveground fungal phytopathogens (Jäschke et al. 2010), was present in all nutrient solution treatments. While statistical significance was not established, A. alternata abundance was higher in substrate irrigated with unamended solutions. ASVs assigned to Penicillium spp. and

Aspergillus ostianus were rich in all treatments and more abundant in sucrose amended treatments

(no statistical signficance). Members of these taxa are recognized for the synthesis of antimicrobial metabolites that can suppress bacterial, fungal, and oomycete phytopathogens (Kito et al. 2008;

Waqas et al. 2015). A Simplicillium lamellicola ASV exhibited a similar, non-significant increase in abundance in sucrose amended treatments. Table 4.1 provides a survey of abundant taxa with associated speculative ecological signficance drawn from the literature.

Bacteria

Bacterial ASVs included Streptomyces spp., Bacillus spp., and an uncultured

Pseudomonas , all genera with plant-beneficial members and/or known suppressive effects.

Nitrogen-fixers were rich, including Rhizobium and Bradyrhizobium . Devosia,

Hyphomicrobiaceae, and Comamonadaceae were abundant in all solution treatments as well.

Members of those three filamentous taxa were found in abundance in the biofilm of a RAS moving bed bioreactor (MBBR) (Rud et al. 2017). Chitinophagaceae taxa were abundant – as the name suggests, members often produce endo- and/or exochitinases, which can have antifungal properties 62

(Swiontek Brzezinska et al. 2014). Finally, Flavobacterium spp. were present, though in low abundance. Flavobacterium includes opportunistic fish pathogens, but the genus has also been correlated with plant-beneficial effects in the rhizosphere (Loch and Faisal 2015; Kolton et al.

2016). Table 4.2 provides a survey of abundant bacterial taxa with associated speculative ecological significance drawn from the literature.

3 Table 4.1. Abundant substrate fungal taxa and associated ecologies. Google Scholar and Web of Science literature searches were used to connect 6 abundant (> 0.5% overall relative abundance following rarefaction) fungal ASVs with assigned family-level taxonomy to possible ecological significance. Asterisks denote ASVs which were not present in all nutrient solution treatments. Precise functional significance is speculative without proteomic and metametabolic information.

Relative Taxon Ecology Abundance

Zopfiella 20.2 % Digests refractory carbon. Some members produce antifungal and antioomycotic compounds (Futagawa et al. 2002; Huang et al. 2015; Blaya et al. 2016).

Fusarium proliferatum 5.83 % Endophytic BRL1 strain secretes gibberellins and indole acetic acid (IAA) and upregulates host gibberellin production (Bilal et al. 2018).

Acremonium alternatum 5.75 % Endophytic strains suppress clubroot ( Plasmodiophora brassicae ) and powdery mildews ( Leveillula taurica and Sphaerotheca fuliginea ) (Malathrakis 1985; Kasselaki et al. 2006; Jäschke et al. 2010).

Simplicillium lamellicola 3.07 % Strain BCP suppresses Botrytis cinerea and Ralstonia solanacearum on tomatoes (Choi et al. 2009; Le Dang et al. 2014). Penicillium 2.95 % Members suppress bacterial, fungal, and oomycotic phytopathogens (Hossain et al. 2007; Yang et al. 2008; Waqas et al. 2015). Subulicystidium brachysporum 2.68 % Saprotroph on woody tissue (Ordynets et al. 2018).

Chaetomiaceae 2.38 % Members are cellulolytic and associated with composts (Habtewold et al. 2020; Zhang et al. 2020). Higher abundance has been found to correlate with Fusarium oxysporum suppression (Zhou et al. 2019). Rhizoctonia 2.14 % R. solani is a phytopathogen with a broad host-range (Ajayi-Oyetunde and Bradley 2018). ASVs in this study were unassigned at the species level. Aspergillus ostianus 1.98 % Secretes antimicrobial and antioxidant metabolites (Namikoshia et al. 2003; Kito et al. 2008).

Cladosporium perangustum 1.55 % Strains cause leaf spot on coconut palm ( Syagrus oleracea ) and wood discoloration on pine ( Pinus spp.) (Lee et al. 2012; Oliveira et al. 2014).

Ramophialophora 1.10 % Saprotroph on woody tissue (Calduch et al. 2004).

Savoryellaceae 0.850 % Saprotroph on woody tissue, including in submerged environments (Dayarathne et al. 2019). Cladosporium velox 0.585 % Endophytic strains produce phenolic compounds with antioxidant activity (Singh et al. 2016). Associated with plant roots in tropics, with commensal endophytic strains (Nonaka et al. 2012; Jauregui et al. Simplicillium aogashimaense 0.542 % 2020).

4 Table 4.2. Abundant substrate bacterial taxa and associated ecologies. Google Scholar and Web of Science literature searches were used to connect 6 abundant (>20 total reads following rarefaction) bacterial ASVs with assigned family-level taxonomy to possible ecological significance. Asterisks denote ASVs which were not present in all nutrient solution treatments. Most ASVs were unassigned below family or genus level. Precise functional significance is speculative without proteomic and metametabolic information.

Relative Taxon Ecology Abundance

Chitinophagaceae 8.15 % Members produce chitinases and exhibit antifungal properties (Sangkhobol and Skerman 1981; McKee et al. 2019). High relative abundances in coco coir substrate (Grunert, Hernandez-Sanabria, et al. 2016).

Sinobacteraceae 5.03 % Members degrade complex organic carbon (Zhou et al. 2014).

Opitutaceae 4.28 % Includes anaerobic methanotrophs as well as strains that digest xylan and pectin (van Passel et al. 2011; Rodrigues and Isanapong 2014).

Rhodoplanes 4.20 % Predominantly photoautotrophic (Hiraishi and Ueda 1994).

Devosia 3.64 % Degradation of lignin and other aromatics (Talwar et al. 2020).

Micromonosporaceae 3.14 % Members are suppressive to Sclerotinia minor (Swiontek Brzezinska et al. 2014).

Cytophagaceae 1.33 % Members produce chitinases and digest cellulose (McBride et al. 2014).

Hyphomicrobium 1.08 % Includes denitrifiers and N-fixers (Kloos et al. 2006; Martineau et al. 2015). Includes opportunistic fish pathogens (Loch and Faisal 2015), produce enzymes capable of degrading refractory carbon Flavobacterium * 0.872 % species (e.g. chitin, pectin, xylan) as well as bacterio- and fungistatic metabolites (Enisoglu-Atalay et al. 2018). May promote plant health in rhizosphere (Kolton et al. 2016). Sphingomonas 0.770 % Degrades refractory organic compounds (Balkwill et al. 2006; Roggo et al. 2013). Bacillus 0.617 % Suppressive members (Shafi et al. 2017). Rhizobiaceae 0.583 % N-fixation (Carrareto Alves et al. 2014).

Bradyrhizobium 0.382 % N-fixation, leguminous root nodulation, some photosynthetic strains (Ormeño-Orrillo and Martínez-Romero 2019).

Members produce antifungal compounds and siderophores, induce plant resistance, and suppress a wide range of plant Pseudomonas 0.364 % pathogens (Loper 1988; Déniel et al. 2004; Polano et al. 2019). Ps. aeruginosa and Ps. syringae , two pathogenic taxa, were not found.

5 Includes strains linked to suppression of Fusarium oxysporum (Cha et al. 2016) and Pythium aphanidermatum (Postma et al. 6 Streptomycetaceae 0.348 % 2005). Members produce diverse antimicrobial metabolites (Olanrewaju and Babalola 2019). Burkholderia 0.317 % Includes putative plant-beneficial as well as phytopathogenic clades. Produce antifungal and phytotoxic metabolites (Eberl and Vandamme 2016). Mucilaginibacter 0.315 % Degrades refractory organic compounds (Cui et al. 2011). composti Stenotrophomonas * 0.288 % Members produce endo- and exochitinases with antifungal activity (Jankiewicz et al. 2012; Swiontek Brzezinska et al. 2014).

Methylobacterium 0.144 % Includes endophytic members with plant-growth promoting potential (Nóbrega Dourado et al. 2015).

Lysobacter 0.102 % Abundant taxon in RAS biofilters (Eck et al. 2019). Produce antimicrobial enzymes and metabolites. Contributes to suppression of Rhizoctonia solani (Gómez Expósito et al. 2015). Includes siderophore producing strains (Radzki et al. 2013) as well as opportunistic fish pathogens (Loch and Faisal 2015). Chryseobacterium * < 0.1 % Increased in abundance as Pseudomonas spp . decreased in wheat colonized by Gaeumannomyces graminis var. tritici (Take- all disease; McSpadden Gardener and Weller 2001). Azospirillum < 0.1 % Includes N-fixing, pectinolytic strains with growth promoting activity in cereals (Khammas et al. 1989; Khammas and Kaiser irakense * 1991; Dobbelaere et al. 2002). Acidovorax * < 0.1 % Acidovorax citrulli is a pathogen of cucurbits (Burdman and Walcott 2012). Includes cellulolytic strains (Pal et al. 2018). 66

4.4. Limitations and future directions

This study was exploratory in nature – only plant yield responses and microbial community composition were collected. Clear conclusions regarding microbial community function cannot be drawn without the integration of functional profiles. Substrate physicochemical parameters were not collected, so conclusive links cannot be drawn between the substrate and the microbiota, either.

Sucrose is not necessarily representative of the organic carbon compounds found in soilless culture, though it does illustrate the dangers of high labile carbon in autotrophic system components.

Rhizosphere sequencing data will be added to the substrate data presented above to provide more information regarding microbial community structure at the root-substrate interface. Solution data will deepen the analysis by providing an idea of the microbiota entering and exiting the substrate. The synthetic and aquaponic nutrient solutions may host distinct microbial communities with implications for plant health. With a full complement of microbial community metabarcodes, it will be possible to draw conclusions regarding how much overlap exists between the communities in each major environ of the substrate culture.

Further work is needed to explicitly address the relationships among microbial community structure, function, plant health, and naturally derived nutrient solutions. Thus far, few studies have addressed soilless microbial community function as an integrated whole – nitrification has received the most attention (Grunert et al. 2016b; Saijai et al. 2016; Wongkiew et al. 2017). In some cases, function has been found to be more a more stable constellation than taxonomic composition (Goss-Souza et al. 2019), and the mobility of prokaryotic functional genes can cloud predictive phylogenetic approaches (Young 2016). Metatranscriptomics, metametabolomics, and proteomics offer directions forward in this area. Microbiome studies should continue to 67

incorporate temporal sampling into their design – tracking microbial community dynamics alongside plant health may reveal fluxes that “snapshot” studies like this one miss.

More granular characterizations of the chemistry of naturally derived substrates and nutrient solutions will be useful – GC/MS and other high-resolution approaches can uncover a diversity of chemistries that are overlooked in the DOC/POC and labile/refractory dichotomies.

Completing our understanding of the factors shaping microbial community structure and function may soon allow soilless CEA producers to extend their mastery over the growth environment into the microbiological dimension. Harnessing microbial function will play a key role in reducing external inputs while maintaining and expanding yields.

CHAPTER 5

CONCLUSIONS

The overall goal of this research was to characterize the plant, pathogen, and microbial community responses to irrigation with different nutrient solutions. A research-scale run-to-waste soilless culture system was set up to test a synthetic nutrient solution, an aquaponic solution, and the two solutions amended with sucrose. Albion strawberries in coco coir growbags were inoculated with Pythium aphanidermatum or a water control to evaluate suppression. Plant above and belowground health was evaluated 5 weeks after inoculation. Roots were evaluated for diagnostic Pythium root rot symptoms, and diagnoses were confirmed by selective plating.

Substrate and nutrient solution samples were collected for analyses of microbial community composition using 16S rRNA and ITS amplicon bar-coding for prokaryotes and fungi, respectively.

Naturally derived solutions and amendments have demonstrated suppressive effects toward

Pythium and other common diseases in soilless culture (Chinta et al. 2014; Minuto et al. 2007;

Chinta et al. 2015; Wang et al. 2019), and potential biocontrol organisms have been isolated from aquaponic solutions (Sirakov et al. 2016). Based on this, it was hypothesized that the aquaponic solution would suppress P. aphanidermatum . However, inoculations failed to produce significant symptoms, so suppressiveness could not be evaluated.

Dissolved organic carbon (DOC) is typically held as low as possible in recirculating aquaculture due to the risk of heterotrophic proliferation in the nitrifying biofilter, which reduces available oxygen for the nitrifiers (Guerdat et al. 2011b; Ling and Chen 2005). Sucrose was dosed into solutions to test whether a similar phenomenon occurs in the hydroponic crop bed. Plants irrigated with the sucrose amended solutions were stunted, and signs of anaerobic respiration were 69

observed in growbags. This pattern strongly suggests that heterotrophic proliferation did occur, putting plants into hypoxic stress and limiting root growth. This finding reinforces the importance of limiting labile dissolved organic carbon in soilless systems.

Recent work by Grunert and colleagues (2016; 2019) has demonstrated the effects of substrate, nutrient source, and crop on the composition of rhizosphere and substrate microbial communities in tomato container culture. While the plant was found to play an outsized role through exudate-mediated recruitment, the use of an organic fertilizer rather than a synthetic one was responsible for significant differences in rhizosphere microbial community structure. In this study, it was hypothesized that the different nutrient solutions would similarly give rise to significantly different substrate communities. Only one significant difference in composition was observed; between the bacterial communities irrigated with unamended aquaponic culture water and their counterparts irrigated with the same solution dosed with sucrose. Without information on function or physicochemical characteristics, it is difficult to link this difference with either cause or effect.

As soilless culture continues to develop, innovations that further decrease waste and increase input-use efficiency should be actively pursued. Organic wastes are plentiful and always being produced. Revalorizing them as agricultural inputs has the potential to close economic loops, dealing with waste products while producing healthy food for local populations. More basic research on the microbiological dimensions of organic nutrient solutions is necessary to unravel the complex webs of interaction.

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