ABSTRACT XIA, QING. Defining the Soil Texture-Based Microbial Community

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ABSTRACT

XIA, QING. Defining the Soil Texture-Based Microbial Community. (Under the direction of Dr. Wei Shi).

Soil texture is an essential component of soil survey for estimating potentials and limitations of land use and management. It has been appreciated as an important predictor for numerous soil processes. However, its connections with the diversity and composition of the soil microbial community at the heart of ecosystem services remain less understood. To help achieve adjustable and directable soil microbial functions via agricultural management, this work aimed to specify soil texture-based diversity, composition, and taxon interactions of the soil microbial community using marker-gene high throughput sequencing approaches. Soils of different textural classes were collected from variously managed turfgrass landscapes and crop fields and further subjected to laboratory manipulations to attain a wide spectrum of soil particle content, pore size distribution, water connectivity, and/or organic substance chemistry. Data supported that coarse-textured soil promoted fungal species richness and yet fine-textured soil boosted more even distribution of bacterial taxa. Filamentous bacteria, e.g., Actinobacteria were more associated with silt and clay fractions, whereas Betaproteobacteria was relatively more abundant in the sand fraction. Fusarium, a large genus of soil fungi with most species as saprophytes and some as pathogens, was preferred in the clay fraction. These texture-based bacterial and fungal distribution patterns were attributed to soil particle-directed locality of organic substance chemistry and pore size-mediated resource (e.g., water) connectivity. In addition, intra- and inter-kingdom associations of microbes appeared to be less in soils with more micropores (< 30

µm) than soils with more macropores (> 75 µm). Given that interactions, e.g., synergy and cooperation, are vital for microbes to secrete costly extracellular enzymes in a stoichiometric manner, texture-based soil enzyme activities and organic matter degradation were further elaborated. Results showed that both bacterial and fungal community structure was texture- dependent, and the detailed pattern relied on texture-based substrate distribution and reaction conditions. This dissertation provided insights on texture-based microbial community structure and functions and proposed possible mechanisms. So far, predictions on soil microbial diversity and functions are still challenging and require more investigations on microbes in diverse ecosystems.

by Qing Xia

A dissertation submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy

Soil Science

Raleigh, North Carolina 2021

APPROVED BY:

______Dr. Wei Shi Dr. Joshua L. Heitman Committee Chair

______Dr. Dean L. Hesterberg Dr. Brian J. Reich

DEDICATION

To my beloved family, for making me smile, successful, and strong.

BIOGRAPHY

Qing Xia was born and raised in Shandong, China. She got a bachelor’s degree in biological sciences in 2016 at Zhejiang University where she developed her interest in environmental microbiology. She came to NC State University in the same year to pursue her

Ph.D. degree under the direction of Dr. Wei Shi.

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ACKNOWLEDGMENTS

I would first like to thank my supervisor, Dr. Wei Shi, for her patient support and guidance in the past five years. None of the work would be possible without her teaching and encouraging me to think critically and professionally from the beginning of my research life. I would also like to thank my committee members, Dr. Joshua Heitman, Dr. Dean Hesterberg, and

Dr. Brian Reich for any of the help that you provided and for witnessing my growth through this long journey.

I would also like to thank anyone in the Department of Crop and Soil Sciences and also my labmates in the Shi’s lab for your great help in my experiments. I would like to especially thank my previous labmate Dr. Huaihai Chen for teaching me molecular techniques of microbiology studies when I joined the lab and thank Dr. Thomas Rufty and his technician for the help in the site selection and the extremely laborious soil sampling of my first project.

Without all your help, I could never start my research in such a smooth and successful way.

TABLE OF CONTENTS

LIST OF TABLES ...... viii LIST OF FIGURES ...... ix Chapter 1: Introduction ...... 1 1.1. The soil microbiome and microscale heterogeneity ...... 1 1.2. Morphological and physiological differences between bacteria and fungi ...... 2 1.3. Soil environmental properties and the association with the soil microbial community ...... 5 1.3.1. Soil texture and pores – the soil physical skeleton and microhabitats ...... 5 1.3.2. Soil water, pore connectivity, and the dynamic aqueous phase ...... 8 1.3.3. Soil aggregation and organic matter ...... 10 1.4. The scope of this dissertation ...... 13 1.5. References ...... 14

Chapter 2: Soil Microbial Diversity and Composition: Links to Soil Texture and Associated Properties...... 27 Abstract ...... 27 2.1. Introduction ...... 28 2.2. Materials and Methods ...... 30 2.2.1. Soil sampling and preparation ...... 30 2.2.2. Soil physical and chemical properties ...... 31 2.2.3. DNA extraction, amplification, and sequencing ...... 31 2.2.4. Bioinformatics analysis ...... 33 2.2.5. Statistical analysis ...... 34 2.3. Results ...... 34 2.3.1. Soil physicochemical properties ...... 34 2.3.2. Microbial alpha diversity and relationships with soil properties ...... 35 2.3.3. Microbial beta diversity and relationships with soil properties ...... 35 2.3.4. Associations between individual taxa and soil properties ...... 36 2.3.5. Functional genes and relationships with soil properties ...... 38 2.4. Discussion ...... 40 2.4.1. Soil texture effects on microbial alpha diversity ...... 40 2.4.2. Taxa-dependent responses to soil texture and other soil properties ...... 43 2.4.3. Texture-based soil C and N cycling potentials ...... 45 2.5. Conclusions ...... 48 2.6. Acknowledgements ...... 49 2.7. References ...... 50

Chapter 3: Soil Pore Size Distribution Shaped not only Compositions but also Networking of the Soil Microbial Community ...... 68 Abstract ...... 68 3.1. Introduction ...... 69 3.2. Materials and Methods ...... 71 3.2.1. Soil sampling, microcosm construction, and incubation ...... 71 3.2.2. Soil physicochemical properties ...... 72 3.2.3. Nucleic acids extraction, amplification, and sequencing ...... 73

3.2.4. Bioinformatics analysis ...... 74 3.2.5. Statistical and network analysis ...... 75 3.3. Results ...... 76 3.3.1. Soil texture, water retention, and biochemical properties ...... 76 3.3.2. Soil microbial alpha diversity and relationships with soil properties ...... 77 3.3.3. Microbial beta diversity and taxa preference for soil properties ...... 78 3.3.4. Microbial taxon co-occurrence patterns ...... 80 3.4. Discussion ...... 81 3.4.1. PSD effects on microbial alpha diversity...... 81 3.4.2. PSD-based microbial preferences ...... 83 3.4.3. Microbial co-occurrence and networking in soils of different PSD ...... 86 3.5. Conclusions ...... 88 3.6. Acknowledgements ...... 88 3.7. References ...... 89

Chapter 4: Soil Texture Influences on Organic Decomposition: Perspectives of Microbial Diversity, Composition, and Networking ...... 107 Abstract ...... 107 4.1. Introduction ...... 108 4.2. Materials and Methods ...... 110 4.2.1. Microcosm construction and soil texture manipulation ...... 110 4.2.2. Soil physicochemical properties and enzyme activities ...... 111 4.2.3. Microbial DNA extraction, amplification, and sequencing ...... 112 4.2.4. Bioinformatics and statistical analysis ...... 113 4.3. Results ...... 114 4.3.1. Soil texture-associated pore size distribution, pH, enzyme activity, and CO2 emission ...... 114 4.3.2. Microbial diversity, composition, and preferences for textural classes ...... 116 4.3.3. Intra- and interkingdom associations of bacterial and fungal taxonomic groups 118 4.3.4. Texture-based associations between the relative abundance of microbial taxa, enzyme activities and CO2 emissions ...... 119 4.4. Discussion ...... 120 4.4.1. Soil enzyme activities and associations with microbial taxa ...... 120 4.4.2. CO2 emission rate of different stages and associations with soil texture ...... 122 4.4.3. Soil texture-based microbial alpha diversity...... 124 4.4.4. Microbial community structure and the interaction of soil texture and SOM ..... 126 4.4.5. Microbial interactions and possible illustrations ...... 128 4.5. Conclusions ...... 129 4.6. References ...... 130

Chapter 5: Conclusions, Limitations, and Future Work ...... 145 5.1. The overall conclusions ...... 145 5.2. Limitations and future work...... 146 5.3. Reference ...... 148

Appendices ...... 149

Appendix A ...... 150 Appendix B ...... 156 Appendix C ...... 163

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LIST OF TABLES

Table 1.1 Morphological and physiological differences between bacteria and fungi...... 25

Table 2.1 Descriptive statistics of physicochemical properties of all the soil samples...... 56

Table 2.2 Descriptive statistics of microbial alpha diversity metrics. Metrics of both bacteria and fungi were estimated at a sequence depth of 12000...... 57

Table 2.3 Results of marginal tests by distLM (distance-based linear model) for the Bray-Curtis dissimilarity matrices of all the soil samples...... 58

Table 2.4 Results of sequential tests by distLM (distance-based linear model) for the Bray-Curtis dissimilarity matrices of all the soil samples...... 59

Table 3.1 Descriptive statistics of physicochemical properties of all the soil samples...... 96

Table 3.2 Structural parameters of networks by soil pore size groups (PSD-1: IA, GA, and TG13; PSD-2: TG22, TG31, and TN). Bacterial and fungal ASVs for making network were ≥ 0.05% relative abundance and present in over the half of soil samples. Spearman’s rank correlation coefficients between ASVs were ≥ 0.7 for bacteria and ≥ 0.5 for fungi in intrakingdom microbial networks and were ≥ 0.5 for both bacteria and fungi in interkingdom networks...... 97

Table 4.1 Percentage of effective soil pore size relative to the respective total pore volume, estimated from soil water retention curves. Target and actual bulk density represent bulk densities at the beginning and end of water retention curve measurement, respectively...... 136

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LIST OF FIGURES

Figure 1.1 The soil texture-based conceptual model of microscale heterogeneity and mechanistic impacts on microbial distribution and diversity. Dashed lines represent direct impacts on soil microbes. SOM, soil organic matter...... 26

Figure 2.1 Distribution of the 36 intact soil cores (0-10 cm depth) collected from bermudagrass golf courses on soil texture triangle. Dots represent individual soil cores...... 60

Figure 2.2 Heatmaps of Spearman’s rank correlation coefficients of bacterial and fungal alpha-diversity metrics (observed OTUs, Shannon index, evenness, and Faith’s PD) with soil physicochemical properties for the entire set of soil samples (All) and subsets of different soil depths (Top and Bottom) and moistures (Dry, Moist, and Wet). The square size and color represent the magnitude and direction of correlation coefficient, respectively. Moisture, gravimetric moisture content; WFPS, water-filled pore space; TC, total soil carbon; InorgN, inorganic N; Sand, Silt, and Clay, the percentage of sand, silt, and clay particles, respectively...... 61

Figure 2.3 The heatmap of Spearman’s rank correlation coefficients between relative abundances of major bacterial taxa from phyla to genera (> 2% of the relative abundance on average) and soil properties. The square size and color represent the magnitude and direction of the correlation coefficient, respectively. Horizontal box plots show the relative abundances of individual taxa. If a taxon and its sublevels had the same Spearman’s rank correlation coefficient as well as the relative abundance (e.g., Acidimicrobiia and Acidimicrobiales), they were merged into one line/row on the heatmap. Moisture, gravimetric moisture content; WFPS, water-filled pore space; TC, total soil carbon; InorgN, inorganic N; Sand, Silt, and Clay, the percentage of sand, silt, and clay particles, respectively...... 62

Figure 2.4 The heatmap of Spearman’s rank correlation coefficients between relative abundances of major fungal taxa from phyla to genera (> 1% of the relative abundance on average) and soil properties. The square size and color represent the magnitude and direction of the correlation coefficient, respectively. Horizontal box plots show the relative abundances of individual taxa. If a taxon and its sublevels had the same Spearman’s rank correlation coefficient as well as the relative abundance (e.g., Mortierellacea and Moretierella), they were merged into one line/row on the heatmap. Moisture, gravimetric moisture content; WFPS, water-filled pore space; TC, total soil carbon; InorgN, inorganic N; Sand, Silt, and Clay, the percentage of sand, silt, and clay particles, respectively...... 64

Figure 2.5 The heatmap of Spearman’s rank correlation coefficients between the copy numbers of putative genes for N cycling (or C degradation) and soil properties

at a sequence depth of 12,000. The square size and color represent the magnitude and direction of the correlation coefficient, respectively. Horizontal box plots show copy numbers of the putative genes. Moisture, gravimetric moisture content; WFPS, water-filled pore space; TC, total soil carbon; InorgN, inorganic N; Sand, Silt, and Clay, the percentage of sand, silt, and clay particles, respectively...... 66

Figure 3.1 Soil water retention curves at the high (A) and low (B) bulk density treatments. Dashed lines separate the groups of major effective soil pore sizes by which water was considered to be depleted at the corresponding matric suction. Error bars represent standard error...... 98

Figure 3.2 Principal coordinate analyses (PCoA) of soil bacterial (A, B) and fungal (C, D) communities based on Bray-Curtis dissimilarity matrices of both DNA (triangle) and cDNA (square). Samples are labeled as IA, blue; GA, cyan; TG13, light blue; TG22, salmon; TG31, red; TN, dark red. Sequence depths were 7,000 and 6,000 for bacterial and fungal communities, respectively...... 99

Figure 3.3 Preferences of major taxa of the total (A) and active (B) bacterial communities in terms of pore size distribution (PSD-1: IA, GA, and TG13; PSD-2: TG22, TG31, and TN; black circle, IA not included; grey circle, all samples included) and other soil properties (IA, greater total C and pH versus soils having relatively low total C and pH, black triangle). Horizontal box plots show the relative abundance of each taxa. Only taxa with the relative abundance ≥ 0.5% and point-biserial correlation coefficient ≥ 0.5 were included. Taxon names are given staring from the phylum level...... 100

Figure 3.4 Comparisons of bacterial preference over soil moisture status between the two groups of soil pore size distribution (PSD-1: IA, GA, and TG13; PSD-2: TG22, TG31, and TN). Only taxa with the relative abundance ≥ 0.5% and point- biserial correlation coefficient ≥ 0.5 were included. Taxon names are given staring from the phylum level...... 102

Figure 3.5 Preferences of major taxa of the total fungal communities (A) in terms of pore size distribution (PSD-1: IA, GA, and TG13; PSD-2: TG22, TG31, and TN; black circle, IA not included; grey circle, all samples included) and other soil properties (IA, greater total C and pH versus soils having relatively low total C and pH black triangle). Taxon preferences of 12 cDNA samples over different soil moisture and bulk density treatments are also reported (B). Horizontal box plots show the relative abundance of each taxa. Only taxa with the relative abundance of ≥ 0.1% and point-biserial correlation coefficient ≥ 0.5 were included. Taxon names are given staring from the phylum level...... 103

Figure 3.6 Microbial association networks by soil pore size groups based on Spearman’s rank correlation coefficients of bacterial and fungal ASVs. Criteria for ASVs to be included for the network construction are ≥ 0.05% relative abundance,

presence in ≥ the half of total samples, and ≥ 0.7 and 0.5 Spearman’s coefficients for bacterial DNA/cDNA and fungal DNA, respectively. Nodes are colored by modularity class and node sizes are proportional to node degree. Corresponding adjacency matrixes at bacterial (sub)phyla level and fungal class level with relative abundance ≥ 0.1% are also given as correlograms. Taxon names are spell out in the first correlogram and thereafter abbreviations are used...... 104

Figure 3.7 Interkingdom microbial association networks by soil pore size groups based on Spearman’s rank correlation coefficients of bacterial and fungal ASVs (red, bacteria; green, fungi). Criteria for ASVs to be included for the network construction are: ≥ 0.05% relative abundance, presence in ≥ the half of total samples, and ≥ 0.7 Spearman’s coefficients for both bacterial and fungal DNA, respectively. Node sizes are proportional to node degree. Corresponding adjacency matrixes at bacterial (sub)phyla and fungal class levels with relative abundance ≥ 0.1% are also given as correlograms with the interkingdom interactions marked in a black rectangle. Taxon names are spell out in the first correlogram and thereafter abbreviations are used...... 106

Figure 4.1 Soil enzyme activities (A, 1:100 microcosm; B, 1:1 microcosm). Error bars represent standard error. Different letters within individual substrate groups indicate significant effects of soil texture (P < 0.05)...... 137

Figure 4.2 Soil CO2 emission rates of 1:100 microcosm (A) and 1:1 microcosm (B); cumulative CO2 emission at the end of incubation for TSB, CA, and BS in 1:100 and 1:1 microcosms (C). Error bars represent standard errors. Asterisks indicate significant effects of soil texture under respective substrates (P < 0.05)...... 138

Figure 4.3 Principal coordinate analyses (PCoA) of soil bacterial (A) and fungal (B) communities based on Bray-Curtis dissimilarity matrices at a sequencing depth of 12,000 and 7,000, respectively (unshaded symbols, 1:100 microcosm; half- shaded symbols, 1:1 microcosm; green: TSB; red, CA; cyan, BS; square, SL; diamond, SiL; hexagon, Cl; circle, ClL)...... 139

Figure 4.4 The heatmap of normalized relative abundance of major bacterial taxa (>1%) in soil microcosm 1:100 at a sequencing depth of 12,000. Only taxa that were significant by soil texture, substrate, or their interactions were included. For each substrate treatment, abundance with significant or marginally significant texture effects (P < 0.1) was given after being normalized by subtracting the mean of different textural classes and then divided by the standard deviation. The horizontal scatter plot shows the average relative abundance of each taxon in every substrate treatment. Error bars represent standard errors...... 140

Figure 4.5 The heatmap of normalized relative abundance of major bacterial taxa (> 1%) in soil microcosm 1:1 at a sequencing depth of 12,000. Only taxa that were

significant by soil texture, substrate, or their interactions were included. For each substrate treatment, abundance with significant or marginally significant texture effects (P < 0.1) was given after being normalized by subtracting the mean of different textural classes and then divided by the standard deviation. The horizontal scatter plot shows the average relative abundance of each taxon in every substrate treatment. Error bars represent standard errors...... 141

Figure 4.6 The heatmap of normalized relative abundance of major fungal taxa (> 1%) in soil microcosm 1:100 at a sequencing depth of 7,000. Only taxa that were significant by soil texture, substrate, or their interactions were included. For each substrate treatment, abundance with significant or marginally significant texture effects (P < 0.1) was given after being normalized by subtracting the mean of different textural classes and then divided by the standard deviation. The horizontal scatter plot shows the average relative abundance of each taxon in every substrate treatment. Error bars represent standard errors...... 142

Figure 4.7 The heatmap of normalized relative abundance of major fungal taxa (>1%) in soil microcosm 1:1 at a sequencing depth of 7,000. Only taxa that were significant by soil texture, substrate, or their interactions were included. For each substrate treatment, abundance with significant or marginally significant texture effects (P < 0.1) was given after being normalized by subtracting the mean of different textural classes and then divided by the standard deviation. The horizontal scatter plot shows the average relative abundance of each taxon in every substrate treatment. Error bars represent standard errors...... 143

Figure 4.8 Correlograms of Spearman’s correlations at bacterial (sub)phyla and fungal class levels with relative abundance ≥ 0.1% and detected in over two-thirds of samples in individual soil textural classes. Interkingdom interactions are marked in a black rectangle. Taxon names are spell out in their first appearance and thereafter abbreviations are used...... 144

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CHAPTER 1: Introduction

1.1. The soil microbiome and microscale heterogeneity

Soil is one of the largest microbial habitats on Earth with one gram accommodating up to

1010 of bacterial cells and 106 of fungal cells of thousands of species (Raynaud and Nunan, 2014;

Rosselló-Mora and Amann, 2001; Trevors, 2010). These tiny and unseen soil residents play important roles in terrestrial ecosystems, including but not limited to carbon sequestration, nutrient cycling, bioremediation, and plant production (Coleman et al., 1983; Gadd, 2010; Finkel et al., 2017). As such, it has long been a direction of research to understand microbial responses to the soil environment, so that microbial/soil functions can be better managed for ecosystem services.

Soil is a porous three-phase (solid, liquid, and gas) media characterized by its heterogeneity. Heterogeneity at the microscale has been deemed vital to support and maintain enormous microbial diversity in soil (Curd et al., 2018; Vos et al., 2013). In general, the microscale heterogeneity can manifest in numerous soil properties, such as pH, nutrients, water, and oxygen. Over decades, our knowledge on microbial diversity has substantially advanced, in particular pertaining to the impacts of soil pH, moisture, nutrients, and also management practices (Rousk et al., 2009; Orchard and Cook, 1983; Bradford et al., 2008; Doran, 1980;

Fontaine et al., 2003). And yet, ecological interpretations are primarily from the perspectives of field-scale gradients of abiotic and biotic factors and are seldom deliberated at the microscale level.

Many soil properties estimated at a bulk soil level only represent the static sum of microsite properties. They cannot capture dynamics of carbon, nutrients, water, oxygen, and other microbial growth essentials nor mosaic locality patterns of resource in microsites.

Accordingly, they are not always reliable and/or effective in predicting soil processes, in particular biological ones. Of all the soil properties gauged often at a bulk soil level, soil texture is perhaps the most suitable factor to infer microscale dynamics and heterogeneity. This is because soil texture helps partition other properties into two major microscale features: nutrient accessibility, and pore environment for bioreaction. As known, soil microbes are unevenly distributed in pores, which are fragmented by the soil particle skeleton and thus partially connected (or completely disconnected). Microbial heterogeneous distribution in pores is further tied up with texture-associated soil moisture characteristics, emphasizing the importance of soil texture for understanding soil microscale heterogeneity and thus microbial distribution and diversity.

This chapter summarizes how soil texture regulates the microscale heterogeneity in microbial growth essentials, such as water and soil organic matter (SOM) from the perspectives of mineral surface chemistry and pore size distribution. Here, soil texture is considered as an indicator manifesting soil microscale heterogeneity (Fig. 1.1). It superimposes other soil properties by modifying soil pore size and thus the pore-associated environment and carbon and nutrient accessibility. Hence, soil texture helps integrate multiple factors at the microscale level to influence microbial distribution and diversity. The major soil microbes discussed in the

Chapter are bacteria and fungi, and Archaea and virus are beyond the scope of the dissertation.

Given that texture effects are likely bacteria- and fungi-dependent, this Chapter briefly illustrates morphological and physiological differences between bacteria and fungi. Then, it discusses how soil properties at the microscale affect microbial distribution and diversity, and further how soil texture regulates soil properties at the microscale.

1.2. Morphological and physiological differences between bacteria and fungi

As dominant organisms belowground, bacteria and fungi differ largely in morphology and physiology (Table 1.1), suggesting possible divergence in ecological niches as well as their sensitivity to environmental change. Bacteria rely on aqueous environments and tend to reside near or on soil particles for better exchange of carbon and nutrients across the particle-solution interface (Mills, 2003). Peptidoglycan, the main component of bacterial cell walls, consists of a significant amount of carboxyl and amino groups, resulting in overall negative charges on cell surfaces (Silhavy et al., 2010). The intensity of negative charges is related to types and quantity of functional groups, which can be taxa/species specific (Salton and Kim, 1996). For bacteria of low charges, hydrophobic forces will push attachment to soil particles causing low diffusion of the cells in water (van Loosdrecht et al., 1987). By contrast, for highly and negatively charged bacteria, an electrical double layer will be built around cells in the soil solution (Chandraprabha and Natarajan, 2009). Thickness of the layer can directly influence bacterial association with soil particles; and it is related not only to the amount of charge on the bacterial cell wall but also to types and concentration of surrounding minerals. Typically, an increase in the ionic strength can promote bacterial adhesion to soil particles.

Active motility is a trait of many bacteria and includes three major motions (flagella- dependent, twitching, and gliding) according to bacterial use of appendages (Kaiser, 2000;

Mitchell and Kogure, 2006; Hobot, 2014). Flagella-dependent motion and twitching take place in the aqueous phase, whereas gliding is on solids. Bacteria cells have a tendency of chemotaxis, meaning that they move towards chemicals for resources (e.g., carbon, nutrient, O2) or move away from harmful substances (e.g., antibiotics, pesticides) (Scharf et al., 2016; Marshall, 1975).

Chemotaxis also serves as the basis of bacterial quorum sensing (Hegde et al., 2011). As a

special group of bacteria, most members of Actinobacteria cannot move (Li et al., 2016), and only a few species are imparted via production of flagellated and mobile spores (Phillips et al.,

2002; Makkar and Cross, 1982). Growing with filamentous and branching structure,

Actinobacteria more closely resemble fungi in the way of acquiring resources. Actinobacteria may produce spores as one strategy of self-protections from the harsh environment. Some other bacteria like Firmicutes can also produce endospores for self-protection when facing extremely stressed environmental conditions (Driks, 2002). However, different from the spores of

Actinobacteria, endospores are more dormant with little metabolism (Nicholson et al., 2002).

Dimension is one of the significant features that distinguish fungi from bacteria.

Although bacterial density in soils can be three to four orders higher than the fungal density, fungal biomass is often comparable to, if not greater than, the bacterial biomass (Frey et al.,

1999). Well-grown fungi can expand into multicellular and mycelial entities, which are able to bridge spatial gaps that bacteria cannot. Therefore, despite no mobility, filamentous mycelia allow fungi to better acquire growth essentials from far distance through pores larger than their dimensions. However, mycelial structure depends on the location and quality of resources, energy cost of hyphal exploration, and the potential stress of competition and predation (Heaton et al., 2016; Bebber et al., 2007; Fricker et al., 2007). Fungi can also explore resources through passive transports of spores; a blow of wind may send fungal spores more than 2 km away

(Norros et al., 2014).

Fungi can be classified into saprotrophs, symbiotrophs, and pathotrophs by trophic modes; and saprotrophs often account for > 40% of the total abundance (Tedersoo et al., 2014).

Fungi are among the most efficient decomposers of SOM, including the complex ones such as lignin and humic substances (Blanchette, 1991; Stevenson, 1994). The complete decomposition

of SOM requires extracellularly enzymatic broken-down reactions (Burns, 2011) to generate soluble compounds for microbial absorption and further intracellular degradation, and this general scenario of SOM decomposition applies to both fungi and saprotrophic bacteria.

1.3. Soil environmental properties and the association with the soil microbial community

1.3.1. Soil texture and pores – the soil physical skeleton and microhabitats

Soil texture describes the proportion of soil sand, silt, and clay fractions, and yet each particle category itself varies greatly in shape, size, as well as mineral composition. It is estimated that the specific surface area of one gram of soils can range from 0.1 m2 of sandy soils to 100 m2 of clayey soils (Tecon and Or, 2017). Small soil particles, especially clay are formed mainly through long-term chemical weathering of rocks. The phyllosilicate minerals are usually negatively charged because of the isomorphic substitution of Al and/or Si by Fe, Mg, or other elements (Seyama and Soma, 1985). The negatively charged substitution sites, together with the positively charged exposed crystal lattice and the hydroxyl group on the surface of various metal oxides can affect the adsorption or attachment of bacteria as well as nutrients. Typically, bacteria can attach to soil particles of any sizes, but the adhesion is more in fine-textured soils than in coarse-textured soils and also adhesion mechanisms vary with textural classes (Huysman and

Verstraete, 1993). Bacterial adhesion in clay loam likely results from ionic interactions with soil surface and can be influenced by the soil cation exchange capacity and surface area. In contrast, the adhesion in sandy soils is primarily due to the hydrophobicity of bacterial surface and can be easily disrupted by water flow. Perhaps, phylogenetically different bacteria are preferentially associated with different soil particles, given that the composition and characteristics of bacteria cell envelopes are taxa dependent (Schleifer and Kandler, 1972). If so, bacterial distribution and diversity may be soil texture dependent.

Despite the branching structure, fungal preference of varied soil particles was sporadically reported (Bond and Harris, 1964; Wösten et al., 1994). Different from bacteria, however, fungal attachment appears to be mainly influenced by the chemical nature of soil particle-associated SOM, nutrients, and environment in the vicinity but not by physical characters of soil particles (Rosling et al., 2004). Therefore, understanding feature associations between chemicals and soil particles will help to estimate fungal preference for soil particles.

Fungal secretion of polysaccharides may also help bind hyphae to soil particles, but this process is unselective for particles. It is worth mentioning that care should be taken when interpreting results of particle-preferential fungi due to pitfalls or limitations of techniques. In the case of soil particle fractionation by sonication, fungi have been found to be preferentially associated with clay fraction due to co-sedimentation of fungal spores with clay particles (Hemkemeyer et al.,

2019). By contrast, fungal hyphae associated with larger particles can be underestimated due to disruption and washing up, especially when adapting the fractionation method from a study of soil prokaryotes (Hemkemeyer et al., 2018). Hence, non-destructive methods need to be considered for reliably examining particle-associated fungi.

Besides particle attachment, soil pore size can also affect microbial distribution.

Generally, soil pores are classified into five size categories for convenience of discussion

(Cameron and Buchan, 2006): macropores (> 75 μm), mesopores (30 – 75 μm), micropores (5 –

30 μm), ultramicropores (0.1 – 5 μm), and cryptopores (< 0.1 μm). Soil pore size classes are built upon soil texture (Nimmo, 2005), although they can vary with other biotic and abiotic features, such as plant root systems, fungal mycelia, and anthropogenic activities (Burr-Hersey et al.,

2020; Ritz and Young, 2004; Hill et al., 1985). Typically, coarse-textured soils have more percentage of large pores as there are fewer small particles to fill the voids between larger ones.

Compaction of soils decreases the fraction of macropores but may keep a higher percentage of mesopores (Otten et al., 2001; Sleutel et al., 2012). Micro- and ultramicropores are thought to be the preferable microbial habitats; however, differences exist between bacteria and fungi (Hassink et al., 1993; Sleutel et al., 2012). Fungi are more positively correlated with pore sizes of 15 – 30

μm while bacteria are more enriched in smaller ones. Such a difference in habitat preference has been attributed to the size of microbial cells as well as prey-predator relationships especially for bacteria. Bacteria in small pores can efficiently avoid predation of protozoa such as ciliates

(Wright et al., 1995; Hassink et al., 1993). Besides, the shape and tortuosity of pore networks also influence bacterial migration and access to microhabitats (Olson et al., 2005; Ebrahimi and

Or, 2014). Soil pores connected by thin necks cannot be colonized by bacteria, although environmental conditions, including growth resources, e.g., SOM may be suitable. The estimated average distance between non-colonized individual bacterial cells in soil is ~ 13 μm, within the definition of micropores and comparable to preferred dimension of microhabitats (Raynaud and

Nunan, 2014). On one hand, this indicates largely uneven distribution of bacteria in soil at the microscale because bacteria have a tendency of agglomeration as colonies and biofilms. On the other hand, it may also suggest possibly low interactions between bacterial species as pore size classes get smaller. This warrants further investigations on bacterial distribution and diversity from relationships with pore size classes.

Fungi are thought to proliferate in soil pores of various sizes, but they tend to enrich in large soil pores and at low bulk density (Harris et al., 2003). Low percentage of large pores may increase the tortuosity of exploration paths and restrict fungal hyphal expansion (Otten et al.,

2001). Generally, fungi differ largely from bacteria in occupying preferential pores and voids, and therefore a pore size change may have divergent impacts on fungi and bacteria. Still, it is

largely unknown how fungi and bacteria interactively explore the environment for acquiring carbon and nutrients through the soil pore network.

1.3.2. Soil water, pore connectivity, and the dynamic aqueous phase

Water is essential for life, including microbes for the sake of multifaceted involvements in biochemical reactions, diffusion and transport of carbon and nutrients, and cell turgor maintenance. Soil water availability and moisture characteristics, however, are highly associated with soil textural classes, although different soils share similar physical processes for water drainage following precipitation. At first, water will drain and eventually be depleted from large pores due to gravity or evaporation forces. Smaller pores will keep saturated until the head pressure reaches the radii of small pores (Young et al., 2008). Due to capillary forces, water will be kept in a form of water film on particle surfaces or the neck between pores in unsaturated soils. It is without doubt that soil pore geometry and/or microporosity dictates water availability and activity. Being porous, soils contain pores of different sizes and shapes and therefore may possess both saturated and unsaturated pores at a given time and in close proximity. Besides, pores with lots of hydrophobic organic matter can be dry even in saturated soils (Culligan et al.,

2004). Therefore, microbial responses to water will be largely intervened by soil microporosity and associated characteristics.

Activities of soil bacteria are generally restricted to water films (Wong and Griffin,

1976). Under drier microhabitats, spore-forming bacteria will produce spores and get into dormant states (Driks, 2002) and non-spore-forming bacteria need to reduce the metabolism considerably to keep alive. Activity reduction will cut off bacterial interactions, enable coexistence of different species, and thereby contribute to the overall diversity of soils. When moisture contents of microhabitats are adequate, bacteria will proliferate if they are able to

successfully compete for resources, such as dissolved and/or particle-adsorbed carbon and nutrients. Consequently, bacteria of best fit to the microhabitats will overpower others and become dominant. Oxygen, the most effective oxidant for organismal energy harvest is inversely related to pore water content. When pores are closed and water occluded, oxygen will be eventually depleted due to microbial proliferation (Harris et al., 2003). As a result, dominant bacteria will shift from aerobes to anaerobes. Although bacteria can move actively or passively, e.g., migration through fungal hyphal extension and continuous water film (Ingham et al., 2011;

Kohlmeier et al., 2005), they are still non-uniformly distributed across soil pores and mainly inhabit the fragmented water film, which may limit interspecies competition (Long and Or, 2005;

Treves et al., 2003). A decline in soil moisture content has been found to promote soil bacterial diversity (Carson et al., 2010), likely because of an increase in fragmented water film and/or a decline in predation stress as the sliding motion of predators (e.g., protozoa) depends on water

(Bamforth, 1988). Soil texture can affect soil water film connectivity via controls on pore size and distribution. Therefore, observed relationships have been reported sporadically between bacterial diversity and percentage of sand (Chau et al., 2011). Compared to bacteria, fungi are less dependent on soil moisture. The mode of apical growth enables fungi to explore resource over air-filled pores and therefore allow fungi to adapt better in drier soils (Yuste et al., 2011).

Fungal growth has been reported to be positively associated with air-filled pores, and hyphae expansion can be inhibited when the water film blocks soil pores (Otten et al., 1999).

Soil moisture is dynamic due to precipitation, drainage, and evaporation (or evapotranspiration). As a result, large pores may be subjected to more frequently drying and wetting events than small pores where moisture keeps relatively stable. This likely generates consequences on microbial distribution and diversity because water flow through large pores can

make bacteria or fungal spores detach or relocate (Huysman and Verstraete, 1993; Germann et al., 1987). Wetting and drying events can also change microbial distribution and diversity by reshaping soil macroaggregates and thus leading to soil structural alteration and release of SOM previously occluded (Najera De Ferrari et al., 2020; Xu et al., 2017). In addition, wetting and drying events can change soil osmotic pressures almost instantaneously. A perpendicular increase of osmotic pressure may cause cell dehydration of bacteria while a rapid decline of osmotic pressure likely causes water influx and cell swelling (Wood, 2015). So bacterial cells must adapt to the moisture fluctuation by changing the intracellular concentration of ions or osmolytes (i.e., low molecule weight organic compounds) to avoid over dehydration or cell lysis

(Halverson et al., 2000). This may also lead to changes in dissolved nutrients in soil solution and further affect microbial distribution and diversity. Moreover, hysteresis should not be overlooked when considering the impacts of wetting and drying events on microbial distribution and diversity. To the same moisture content, for example, wetting may be more favorable to the expansion of fungi and soil aeration than drying due to hysteresis, as changes in air-filled pores lag behind wetting and drying events (Culligan et al., 2004). However, fungi appear to be more sensitive to non-extreme moisture variations than bacteria (Kaisermann et al., 2015). In summary, soil moisture can directly and indirectly affect the soil microbial community through processes at the microporosity level.

1.3.3. Soil aggregation and organic matter

Soil aggregates and SOM are in nature two different types of properties, and yet reciprocally influenced. Aggregates are secondary “soil particles” that are formed by interactions of primary particles (i.e., sand, silt, and clay) via the chemical binding of organic substances and the physical entanglement of fungal hyphae and plant fine roots. SOM, including microbial

mucilage and necromass, serves as the binding agent to adhere soil primary particles into microaggregates (Ramesh et al., 2008). Macroaggregates are further formed and stabilized by the transient binding agent (e.g., polysaccharide derived from microbes or plants) and also temporary binding agents (e.g., fungal hyphae, plant roots, or bacteria) (Lugato et al., 2010;

Tisdall and Oades, 1982). Different roles that fungi and bacteria play in soil aggregation are associated with their distinct distributions in aggregates, where fungal hyphae tend to grow along aggregate surface and bacteria reside within aggregates. Different distributions are also in accordance with morphological and ecological differences between fungi and bacteria, with fungi being large in dimension and generally requiring aerobic conditions but bacteria being small and more palatable to predators (e.g., protozoa). Large pores between aggregates enable better extension of fungal hyphae and mycelia, whereas microporous heterogeneity of aggregates in surface chemistry, OM and moisture may allow coexistence of diverse bacteria. Therefore, aggregates of well-structured soils can be considered as important microscale habitats that regulate pore connections and redistribute SOM and as such shape the soil microbial community.

SOM is made of organic substances of different chemical compositions that originate from plant litter, root secretions, microbial necromass and metabolites, and live microbes (Liang et al., 2019; Adamczyk et al., 2018; Karlovsky, 2008). Generally, microbes can directly assimilate organic compounds of low molecular weight (e.g., amino acids and sugars) and then degrade compounds intracellularly. For compounds of complex structure and high molecular weight (e.g., cellulose), extracellular enzymes-catalyzed degradation needs to take place before microbial assimilation of compounds and intracellular degradation. Nonetheless, microbial decomposition is an important process that transfers external organic substances into SOM.

Aromatic moieties, long chain n-alkanes, and lignin contribute to the recalcitrance of SOM

(Ramesh et al., 2008; Kleber, 2010), meaning that they are generally resistant to biological degradation. Still, some microbes are able to decompose recalcitrant organic substances. For instance, the white rot fungi of Basidiomycetes may decompose lignin completely to carbon dioxide under aerobic conditions (Robertson et al., 2008). Soil bacteria like Actinobacteria can also be involved in the decomposition of lignin so that polysaccharides can be released from lignocellulosic materials and thereafter decomposed (Dignac et al., 2005). Even the extractable portion of humic substances may be readily degradable (Stevenson, 1994). All together, these suggest that besides biochemical recalcitrance, there must exist other protections from the biological degradation of organic substances.

Physicochemical protections are imperative for effective soil carbon sequestration and stability (Six et al., 2002). Organo-mineral interactions between organics and mineral surface via various forces, such as hydrogen bonds, van der Waals forces, ionic bonds, and ligand exchange help preserve and stabilize organics. Occlusions within aggregates and/or pores also control microbial access to organics (Horwath, 2015; Stamati et al., 2013). Further, other soil properties may spatially and temporarily limit the accessibility and/or availability of organics to microbes and therefore decomposition. After all, microbial decomposition requires a close proximity between microbes/extracellular enzymes and organics; and water, for example, can largely affect this process through diffusion and transport (Sparks and Banwart, 2017). Biotic interactions may also affect the accessibility and/or availability of organics due to resource competitions as well as synergism in which, for example, degradation initiated by one population may lead to the production of substrates favorable for another population. Taken together, microbial decomposition of organics is a complex process that requires a better understanding of microbial distribution and diversity with regards to microscale habitats.

1.4. The scope of this dissertation

The overall goal of the dissertation was to develop soil texture-based knowledge on microbial diversity, composition, and species associations. Chapter 2 aimed to uncover microbial distribution patterns with regards to soil sand, silt and clay fractions and also possible mechanistic insights. Chapter 3 was to explore feature linkages between the soil microbial community and pore size distribution. Especially, microbial interactions were elaborated from the perspective of pore size distribution. To gain insight into ecological consequences of soil texture-based microbial distribution, Chapter 4 focused on substrate-induced microbial community assembly across different soil textural classes in mediating production of extracellular enzymes and biological decomposition. I expected that data generated from individual chapters would consecutively add more details to explain soil texture-based microbial distribution and diversity from the viewpoints of texture controls on organic matter accessibility of microsites, pore habitable conditions, and physical isolations of microbes. I anticipated that the outcomes of this dissertation research would improve our understanding of soil texture-based microbial assemblage and thus-obtained knowledge would help management towards desired microbial/soil functions.

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Table 1.1 Morphological and physiological differences between bacteria and fungi.

Bacteria Fungi Cell type Prokaryotic Eukaryotic

Morphology Mostly unicellular of various Mostly multicellular (branching shapes (coccus, bacillus, spiral, structure) filamentous, etc.) (Lyons and Kolter, 2015)

Size ∼0.4–3 µm3 of volume 2-10 μm in diameter; 5-50 μm in (Levin and Angert, 2015) length averagely (hyphae) (Barnes-Svarney and Svarney, 2015)

Carbon metabolism Mostly heterotrophic Heterotrophic

Energy type category Mostly chemotrophic Chemotrophic

Oxygen requirements Aerobic/anaerobic/facultative Aerobic

Unique components Peptidoglycan Chitin in cell wall (van Heijenoort, 2001) (Bowman and Free, 2006)

Motility Many are motile (flagella- or Mostly no active motility pilus-facilitated; gliding) pH preference 5.5 – 9.0 for non-extremophiles Higher acid tolerance than (Padan, et al., 2005) bacteria (Rousk, et al., 2010; Matthies, et al., 1997)

CHAPTER 2: Soil Microbial Diversity and Composition:

Links to Soil Texture and Associated Properties

The contents of this chapter have been previously published in Soil Biology and Biochemistry

149 (2020) 107953. DOI: 10.1016/j.soilbio.2020.107953

Abstract

4.4 – 53.0%. These soil cores were arranged into three sets of equal numbers, and each set of 12 was subjected to three moisture regimes (dry spell, field moisture, and saturation-field capacity), respectively, for 15 days. Soil cores were further stratified into top and bottom sections, leading to a total of 72 samples with varying soil physical and chemical properties. Our data revealed that fungal alpha diversity was more strongly related to soil texture than the bacterial one, with fungal species richness and Shannon diversity being positively correlated with the sand fraction.

Soil texture was the second most important factor after soil pH in shaping the soil microbial community. Relative abundances of some fungi (Basidiomycota and Eurotiomycetes) and filamentous bacteria (Actinobacteria, Chloroflexi) significantly increased with silt and/or clay content. The genetic potential for the degradation of organic compounds also appeared to be

higher in finer textured soils than the coarse-textured soils. By identifying sand, silt or clay- preferred microbial taxa and characterizing mineral particle-dependent genetic potential of organic carbon degradation and nitrogen cycling, this work highlighted the significance of soil texture and further discussed texture-associated pores and resource locality in regulating microbial diversity and composition.

Keywords: Soil texture; Microbial community; 16S; ITS; Organic C degradation; N cycling

2.1. Introduction

Soil texture refers to the relative proportions of sand, silt, and clay particles in soils and is an essential component of soil survey for estimating the potentials and limitations of land use and management. To date, soil texture has been appreciated as an important control for a number of soil processes, including but not limited to structure development, carbon sequestration, nutrient (e.g., N) retention, and water infiltration and storage (Fernandez-Illescas et al., 2001; Six et al., 2002; Bronick and Lal, 2005). However, its impacts on the diversity and composition of the soil microbial community still remain less understood.

Micro-scale soil heterogeneity is the foundation for the coexistence of diverse microbes in soil (Vos et al., 2013; Curd et al., 2018). The level and degree of soil heterogeneity may, to some extent, hinge on soil texture, given that soil texture is associated with the distribution of pores of different morphologies and also the locality of chemicals due to surface adsorption of mineral particles (Lilly and Lin, 2004; Plante et al., 2006). Pore-based water arrangement and resource transportation have been given more attention to substantiating soil bacterial diversity

(Tecon and Or, 2017). Based on the T-RFLP fingerprinting of the soil bacterial community,

Chau et al. (2011) hypothesized that water film fragmentation of macropores led to greater species richness in coarser textured soils. Using the same technique, however, Sessitch et al.

(2001) documented that bacterial diversity was greater in silt and clay fractions than in the sand fraction due to fine particle-associated high nutrient availability as well as associated small pore protection from predation. Such inconsistent findings indicate the complex regulations of texture on bacterial diversity and raise a question about the reliability of the fingerprinting technique for estimating the incredible diversity of the bacterial community.

Mineral particle-associated C and nutrients have been considered to be robust forces for structuring the bacterial community (Sessitsch et al., 2001; Hemkemeyer et al., 2018). Some bacteria are able to colonize in coarse particle-associated habitats that are presumably heterogeneous, less-protected, and oligotrophic (Karimi et al., 2018). For example, sand fraction was found to be a favorable ecological niche for the survival and growth of Bacteroidetes and

Alphaproteobacteria (Hemkemeyer et al., 2018). Sessitch et al. (2001) also showed that

Sphingomonas, a genus of Alphaproteobacteria was more abundant in the sand fraction. By contrast, Actinobacteria seems to better colonize the finer particle fraction (Hemkemeyer et al.,

2018; Karimi et al., 2018). Despite incomplete information on the linkage between soil texture and the bacterial community composition, all of these studies support the presence of texture- sensitive/responsive taxa. Certainly, more work is needed to complete the list of texture- dependent microbial taxa so that we can better develop a texture-based prediction model for managing soil use.

Besides a prevailing notion that fungi may better colonize sand-associated large pores, much less is known on texture regulation of fungal distribution and abundance. Compared to their prokaryotic counterpart, fungi generally grow at a slower rate but are able to use more recalcitrant compounds and maximize yields under nutrient-poorer conditions. As such, impacts and mechanisms by which soil texture modulate the fungal community is expected to be not the

same as those for the bacterial community. The overall goal of this work was to examine soil texture effects on the diversity and composition of both fungal and bacterial communities using

16S rRNA genes and ITS sequencing approaches. Specifically, we would identify sand, silt or clay-preferred microbial taxa and further characterize the genetic potentials of sand, silt, or clay- associated soil C degradation and N cycling. To minimize any confounding factors from aboveground vegetation, a number of bermudagrass golf courses with similar professional management practices were used as the study sites. However, soil stratification and moisture treatments were imposed to promote pore- and/or mineral particle-associated habitat differences.

Further, for better assessment of texture effects on the soil microbial community, several other edaphic properties that are known to affect the soil microbial community, such as pH, total soil C and available N were surveyed in this work (Kowalchuk et al., 2002; Hartman et al., 2008; Cruz-

Martinez et al., 2009; Lauber et al., 2009; Fierer et al., 2012).

2.2. Materials and Methods

2.2.1. Soil sampling and preparation

Intact soil cores (10 cm height x 5 cm diameter) were collected in February/March 2018 from 10 professionally managed bermudagrass golf courses, North Carolina, USA (Table A.1).

Each golf course had three cores taken randomly from fairways of a single hole (~1.5 ha), except for two golf courses with six from two holes, resulting in a total of 36 soil cores from 12 sampling sites (1 hole x 8 golf courses + 2 holes x 2 golf courses). Cores from the same hole were respectively subjected to three moisture treatments (Dry, Moist, and Wet) for 15 days. The dry spell (Dry) was uncapped and let air-dried; the field moisture (Moist) had soil cores capped to maintain moisture content; and the saturation-field capacity (Wet) was attained by saturating soil cores, allowing free drainage until no percolating, and then cores were capped to maintain

the obtained moisture content. After the incubation, cores were cut at a depth of 5 cm into two halves (Top: 0-5 cm; Bottom: 5-10 cm). Such preparation resulted in a total of 72 testable soil samples (2 soil depths x 3 moisture treatments x 12 sampling sites), which would allow a better understanding of soil texture effects on the soil microbial community by teasing apart effects of soil nutrient status and moisture content/pore network. These soil samples were sieved (< 2 mm) and had plant roots and cobbles removed; an aliquot of each soil sample was stored at -20 ℃ before soil DNA extraction and the rest was kept at 4 ℃ for the analysis of soil properties.

2.2.2. Soil physical and chemical properties

All soil physiochemical properties except for soil texture were measured for each individual soil sample. We presumed that soil texture was uniform throughout the 10-cm sampling depth, and measured it by the hydrometer method for only Bottom samples or mixtures of Top and Bottom if the Bottom ones were not enough. Soil gravimetric moisture content was measured by oven-drying soil samples at 105 ℃ for 24 h. Soil bulk density was estimated from the weight and volume of soil cores after deducting those of rocks and plant roots, and then soil porosity was calculated with an assumed soil particle density of 2.65 g cm-3. Soil pH was determined at a soil/water mass ratio of 1:2.5. Soil total C and N were analyzed using a dry combustion method with a Perkin-Elmer 2400 CHN analyzer (Perkin-Elmer Corporation,

+ - Norwalk, CT, USA). Soil inorganic N (NH4 -N and NO3 -N) was extracted with 0.5M K2SO4 at a soil (g)/solution (ml) ratio of 1:5, filtered through a Whatman #42 filter paper, and determined by FIA QuikChem 8000 autoanalyzer (Lachat Instruments, Loveland, CO, USA).

2.2.3. DNA extraction, amplification, and sequencing

Soil DNA was extracted from ~ 0.5 g of each soil sample using FastDNA Spin Kit for

Soil (MP Bio, Solon, OH, USA), with the concentration (> 100 ng μL-1) measured by a

NanoDrop Spectrophotometer (Thermo Scientific, Wilmington, DE, USA). Bacterial 16S rRNA genes and fungal ITS regions were PCR-amplified with primer pairs targeting V3-V4 (F319: 5’-

ACTCCTACGGGAGGCAGCAG-3’ and R806: 5’- GGACTACHVGGGTWTCTAAT-3’) and

ITS1-ITS2 (F_KYO2: 5’-162 TAGAGGAAGTAAAAGTCGTAA-3’ and R_KYO2: 5’-

TTYRCTRCGTTCTTCATC-3’), respectively (Toju et al., 2012; Klindworth et al., 2013), and with Illumina MiSeq overhang adapters in a 25 μL reaction, consisting of 12.5 μL KAPA HiFi

HotStart ReadyMix (KAPA Biosystems, Wilmington, MA, USA), 2.5 μL DNA template (5 ng

μL-1), 1 μL of each primer (5 mM μL-1), and 8 μL nuclease-free water. Thermal cycling conditions for PCR amplification were: initial denaturation at 95 ℃ for 3 min; 30 cycles of 95 ℃ for 30 sec, 55 ℃ for 30 sec, and 72 ℃ for 30 sec for bacteria and 98 ℃ for 30 sec, 51 ℃ for 15 sec, and 72 ℃ for 30 sec for fungi, followed by a final elongation at 72 ℃ for 5 min. A negative control of no DNA template was also included. The amplified DNA was then cleaned up with

AMPure XP beads (Beckman Coulter Genomics, Danvers, MA, USA), eluted in 10 mM Tris buffer (pH 8.5), and added with unique index (barcode) sequences using the Nextera XT Index

Kit (Illumina, San Diego, CA, USA) in a 50 μL reaction of 25 μL KAPA HiFi HotStart

ReadyMix, 5 μL of cleaned-up amplified PCR products, 5 μL of each index primer, and 10 μL of nuclease-free water. Thermal cycling conditions for both 16S rRNA genes and ITS barcoding

PCR were initial denaturation at 95 ℃ for 3 min, 8 cycles of 95 ℃ for 30 sec, 55 ℃ for 30 sec,

72 ℃ for 30 sec, and final elongation at 72 ℃ for 5 min. A second clean-up was performed after barcoding PCR. The purified 16S rRNA genes and ITS fragments were diluted to 20 nM, mixed equimolarly, and paired-end sequenced on Illumina MiSeq platform (300×2 PE, v3 chemistry)

(Illumina, San Diego, CA, USA). The Miseq sequences were deposited in GenBank with the

BioProject accession number PRJNA606949.

2.2.4. Bioinformatics analysis

Primers and adapters were removed from demultiplexed sequencing reads by cutadapt (v

1.18) (Martin, 2011), and then reads were processed using DADA2 (v 1.10) pipeline in R (3.5.1)

(Callahan et al., 2016; R Core Team, 2018). In brief, the forward and reverse reads of 16S rRNA genes were trimmed to a length of 270 and 220 bp, respectively, to discard base pairs with average Phred quality score less than 20. Due to the high variation of ITS regions, the primer- free ITS reads were not trimmed to fixed lengths based on Phred quality scores, but a minimal length of 50 bp was enforced to filter spurious short reads. The trimmed/filtered reads were dereplicated and then processed with the core sample inferences algorithm of DADA2 and trained error models. The full denoised sequences were obtained by paired-end reads merging and chimeras removal. The end product of the pipeline was a table of amplicon sequence variants

(ASVs, a higher-resolution analogue of operational taxonomic units (OTUs)) and their copy numbers. The resulted ASV table was imported into QIIME2 (v 2018.11) for downstream analysis (Caporaso et al., 2010). Singletons (ASVs with a total frequency of one) were removed before bacterial and fungal taxonomy classification using Greengenes database (13_8) and

UNITE database (7.2), respectively (DeSantis et al., 2006; Kõljalg et al., 2013). Alpha diversity metrics including observed OTUs, Shannon index, evenness, and Faith’s phylogenetic diversity were then estimated in QIIME2. Bray-Curtis dissimilarity matrixes were used for both bacterial and fungal beta diversity analyses. Putative functional genes involved in C and N processes were predicted using PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of

Unobserved States) by matching bacterial ASVs normalized at a sequence depth of 12,000 with reference genomes (Douglas et al., 2019). Genes and copy numbers were estimated using hsp.py script based on Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog (KO) database.

2.2.5. Statistical analysis

One-way ANOVA and post-hoc Tukey’s test (SAS 9.4, SAS Institute Inc. Cary, NC,

USA) were performed for the multiple comparisons of soil properties among soil depths and moisture treatments. DistLM (distance-based linear models) was used to determine the relationships of soil bacterial and fungal communities with soil properties in PRIMER (Plymouth

Routines in Multivariate Ecological Research Statistical Software, version 7.0.13, PRIMER-E

Ltd). Spearman’s rank-order correlation was used to analyze the relationships of microbial alpha diversity metrics and relative abundances of individual taxa with soil properties. Due to tight associations between soil total C and N and also between sand, silt, and clay, we did not include soil total N and the silt fraction into all the correlation analyses. A significance level at P < 0.05 was used unless otherwise specified.

2.3. Results

2.3.1. Soil physicochemical properties

Seventy-two soil samples varied most in total C and N, inorganic N, clay content, and moisture (70-80% CV, the coefficient of variation), moderately in porosity and sand and silt contents (20-30% CV), and least in pH (10% CV) (Table 2.1). These samples distributed across seven soil texture groups: loamy sand, sandy loam, sandy clay loam, loam, sandy clay, clay loam, and clay (Fig. 2.1), with sand fraction varying from 30.3 – 83.4%, clay fraction from 4.4 –

53.0 %, and silt fraction from 11.7 – 38.8%. Large variations in soil C and N, inorganic N, and moisture were partially attributed to soil core manipulations. Depth stratification separated nutrient-rich and poor soils, with total soil C and N and inorganic N in the Top tripling that of

Bottom. Dry spell treatment led to a ~ 75% reduction in the moisture of fresh soil samples (Table

A.2). Wetting procedure increased water-filled soil pore space (WFPS) by ~20%, although soil

moisture was not statistically different between the Wet and Moist treatments. Soil samples were slight acidic, with pH in the range of 4.2-7.2; and soil pH was unaffected by soil core manipulations.

2.3.2. Microbial alpha diversity and relationships with soil properties

Bacterial and fungal alpha diversities were assessed by observed OTUs, Shannon diversity index, evenness, and Faith’s phylogenetic diversity (PD). These metrics varied moderately among soil samples, with 24-37% CV for observed OTUs and Faith’s PD and 3-13%

CV for Shannon diversity index and evenness (Table 2.2). Spearman’s rank correlation coefficients (ρ) were categorized into four groups, representing strong (|ρ| > 0.7), moderate (0.7

< |ρ| < 0.5), weak (0.5 < |ρ| <0.3), and negligible (|ρ| < 0.3) associations between alpha diversity metrics and soil physicochemical properties (Fig. 2.2) (Hinkle et al., 2003). For the bacterial community, evenness was the most pronounced metric in associating with soil properties and increased primarily with soil pH when all the soil samples were included for analysis. Evenness estimated from a subset of soil samples (e.g., Top and Wet) was also moderately affected by soil texture, which declined with sand content but increased with clay content. For the fungal community, observed OTUs appeared to be the most significant metric in associating with soil properties and was mainly affected by soil texture (Fig. 2.2). Regardless of sampling sizes (e.g.,

All, Bottom, and Moist), observed OTUs was associated negatively with clay content and positively with sand content. Observed OTUs estimated from a subset of soil samples (e.g.,

Bottom and Dry) was also negatively affected by soil pH.

2.3.3. Microbial beta diversity and relationships with soil properties

Seventy-two soil samples spread widely on ordination PCoA plots due to considerable variations in soil properties (Fig. A.1). However, these samples did cluster by individual soil

properties. Soil samples with high percentages of sand content were well separated from those with lower sand content. Also, soil samples of lower pH diverged from those of neutral pH.

Distance-based linear models (distLM) confirmed that soil pH, the percentage of sand or clay, soil total C, and inorganic N were all significantly correlated with bacterial and fungal community compositions (Table 2.3). Soil bacterial community compositions were also associated with soil porosity and moisture. However, no individual soil properties explained >

8% of variations in bacterial and fungal communities. As such, these properties together only explained ~ 22% and 17% of variations in bacterial and fungal communities, respectively (Table

2.4). The relative importance of soil properties in explaining the microbial beta diversity appeared to vary with soil moisture and stratification treatments. Soil pH explained more variations in both bacterial and fungal communities than other soil properties under the Dry treatment (Table A.3). By contrast, under Wet or Moist treatments, the percentage of sand was most important. It also showed that the percentage of sand was more important in explaining the microbial community composition in Bottom soils than Top soils.

2.3.4. Associations between individual taxa and soil properties

A total of 48 bacterial phyla were detected in soil samples. The most abundant phylum was Proteobacteria, accounting for ~ 34% on average, followed by Acidobacteria (~ 19%),

Actinobacteria (~ 17%), Chloroflexi (~ 10%), Bacteroidetes (~ 4%), Planctomycetes (~ 2%),

Nitrospirae (~ 2%), and Firmicutes (~ 2%) (Fig. 2.3). Of 46 abundant bacterial taxa (> 2% relative abundance) from the phylum to genus level, ~ 89% showed significant correlations with one or more soil properties. Twenty-three taxa were able to correlate with soil texture (sand, silt, or clay), 19 with pH, 15 with water/aeration conditions (porosity, moisture, or WFPS), and 11 with soil nutrients (total C or inorganic N).

Actinobacteria and its sub-level taxa were mainly affected by soil texture; their relative abundances except for the class Acidimicrobiia declined moderately as the sand content increased. Generally, relationships of bacterial taxa with sand versus with clay were opposite because sand and clay were negatively correlated (Spearman’s rank correlation ρ = -0.91) due to the relatively lower fraction of silt in most soil samples (~ 20% on average). Proteobacteria and some of its sub-level taxa also showed weak and yet significant associations with soil texture, but directions were taxa specific, being positive with sand for the class Betaproteobacteria and

Deltaproteobacteria and negative for the family Bradyrhizobiaceae. Unlike Actinobacteria and

Proteobacteria, Acidobacteria and its sub-level taxa were primarily correlated with soil pH, showing either positive (e.g., the class [Chloracidobacteria] and Acidobacteria-6) or negative

(e.g., the class Acidobacteriia and Solibacteres) relationships. Planctomycetes were also negatively related to soil pH. Some bacterial taxa, including Bacteroidetes,

Gammaproteobacteria, and their sub-level taxa were predominately and positively related to soil nutrient status (e.g., total soil C and inorganic N) and water/aeration conditions (e.g., porosity and moisture). The phylum Nitrospirae and its sub-level taxa were associated negatively with sand content and positively with pH. Cholorflexi was the only phylum correlated with almost all the soil properties examined. WFPS only affected the class Solibacteres and its sub-level taxa. It is worth mentioning that magnitudes of correlation between soil texture and relative abundance of bacterial taxa varied with the soil stratification treatment (i.e., Top vs. Bottom), although correlation directions were generally similar between the two (Fig. A.2). For example, Alpha-,

Beta, and Gamma-proteobacteria were found to be more strongly correlated with soil texture in the Bottom than Top. We also noticed that aeration and water status were less important in the

Bottom than Top for affecting the relative abundance of bacterial taxa. However, there were

more bacterial taxa in the Bottom than Top showing significant and positive associations with soil total C or N.

The fungal community consisted of 14 phyla and was dominated by Ascomycota (51%), followed by Mortierellomycota (9.3%) and Basidiomycota (5.6%) (Fig. 2.4). Sixteen out of 40 abundant taxa (> 1% relative abundance) showed moderate (or weak) and yet significant correlations with soil properties. However, half of the associations, mainly for Basidiomycota and members of Ascomycota, were with soil texture (sand, silt or clay), being negative with the sand content and positive with silt/clay content. Fusarium, a large genus of soil fungi showed the strongest positive association with clay fraction (Spearman’s rank correlation ρ = 0.52) (Fig.

2.4). The family Pleosporaceae and its members were mainly associated with soil pH and nutrient status, being negative for soil pH and positive for total soil C and inorganic N. As in the case of bacteria, the soil stratification treatment also primarily modified the magnitude of correlation between fungal taxa and soil texture (Fig. A.3). Associations of Dothideomycetes and its sublevel taxa with clay were opposite between the Bottom and Top, but such a directional difference was likely due to confounding impacts of aeration/water status and nutrients. In contrast to bacteria, there were more fungal taxa in the Top than Bottom showing significant and negative correlations with total soil C or N.

2.3.5. Functional genes and relationships with soil properties

We examined the relative abundances of 32 bacterial genes putative for the N

+ metabolism, including NH4 assimilation, nitrification, denitrification, N fixation, dissimilatory

- + - + NO3 reduction to NH4 (DNRA), assimilatory NO3 reduction to NH4 , and mineralization (Fig.

2.5). The glutamate synthase gene (glnA) was most abundant, and genes for nitrification were generally least abundant. Twenty-five genes were found to be significantly associated with soil

properties, of which 56% correlated with soil texture (sand, silt, or clay), 48% with soil pH, and

48% with soil nutrient status and water/aeration conditions.

- - Genes for the NO3 reduction, including denitrification, DNRA, and assimilatory NO3

+ reduction to NH4 were, to a large extent, associated with soil texture. Out of the 10 examined genes for denitrification, one was significantly and positively correlated with the clay content, and five were negative with silt. One (nrfH) of the DNRA genes was also significantly and negatively related to the silt content. In contrast, genes (nasA and nasB) involved in assimilatory

- NO3 reduction were significantly and negatively correlated with sand (ρ < -0.7), but positively related to silt or clay. Apparently, relationships of these genes with silt were dependent on whether sand was a significant factor. When sand was a not significant factor, silt showed a negative effect; otherwise, silt showed a positive effect. Genes for denitrification and DNRA

- + were also positively related to pH, but those for assimilatory NO3 reduction to NH4 were negatively related to pH.

Instead of soil texture, genes responsible for N fixation and nitrification were correlated with soil nutrient status, water/aeration conditions, and/or soil pH. Genes for N fixation were weakly and negatively related to total soil C or porosity, while those for nitrification were moderately and positively related to total soil C and porosity. Further, a negative trend (-0.5 < ρ

< -0.3) was found between soil pH and nitrification genes. Genes for N mineralization and extracellular enzymes showed weak or moderate associations with soil texture, pH, nutrient status, and water/aeration conditions.

Genes for the C metabolism except for those encoding beta-glucosidase and glucoamylase were found to be either negatively correlated with sand fraction or positively with silt or clay fraction. An increase in soil pH hindered starch degradation genes and the beta-

glucosidase gene bglX. Two genes for pectin degradation showed opposite relations with soil pH.

However, all the C metabolism genes were related to neither the soil total nutrient status nor moisture/aeration conditions.

2.4. Discussion

2.4.1. Soil texture effects on microbial alpha diversity

Both bacterial and fungal alpha diversity were, to some extent, correlated with soil texture, but conditions for observed relationships were in sharp contrast between the two. The pronouncing effect of texture on fungi occurred in Bottom and Dry/Moist soil treatments but in

Top and Wet treatments for bacteria, suggesting that texture regulated bacterial and fungal diversity through different processes. Bacteria are simple in morphology and yet most divergent in demands for resource and environment. Not only do they differ in the source of energy

(phototrophs versus chemotrophs), carbon (autotrophs versus heterotrophs), and reducing power

(lithotrophs versus organotrophs), but also they require distinct living conditions (e.g., oxic versus anoxic conditions, neutral versus acidic/alkaline conditions). Therefore, assorted niches are needed for the co-existence of bacterial species having different fitness values. Given the porous nature and remarkable pore-scale variations in resource and environment (e.g., carbon, nutrients, aeration, proton concentration, and water availability), pore-scale heterogeneity has been accepted as the determinant for stunningly diverse microbes in soil (Tilman and Kareiva,

1997; Ettema and Wardle, 2002; Amarasekare, 2003; Vos et al., 2013; Stein et al., 2014).

Therefore, any environmental factors that modify pore-scale niche differences can generate tandem effects on microbial diversity. Compared to the Dry/Moist Treatment, Wet treatment might help the diffusion of growth essentials as well as passive and/or active movement of bacteria through the pore network; and this effect was likely texture dependent. By offering a

great proportion of large pores, coarse-textured soils held less water than finer textured soils and led to a lower rate of resource diffusion and/or bacterial migration through pores. The distribution of resources (e.g., O2) was expected to be more heterogeneous in coarse-textured soils than fine-textured soils under the Wet treatment, and also the competition between bacterial species was likely less severe in coarse-textured soils due to more physical confinement of bacterial movement. This texture-hydraulic interaction-enhanced niche difference might explain the greater bacterial diversity (observed OTUs and Faith’s PD) in coarse-textured soil. It was previously reported that coarse-textured soils promoted bacterial richness under field moisture conditions (Chau et al., 2011); and authors interpreted that poor hydraulic connectivity in coarse- textured soils created more different niches for bacterial coexistence. However, it is worth mentioning that in that study, means of species richness of sample replicates were used to infer the relationship between texture and bacterial diversity. Due to extremely large standard deviations within replicates, we doubt the reliability of the observed relationship under field moisture conditions. Nonetheless, conditional and sporadic relationships between texture and diversity, together with inconsistency between diversity metrics (richness evenness and diversity index) suggest that texture effects on bacterial diversity were limited, although texture could modulate pore-scale hydraulic connectivity and thus microhabitat heterogeneity. Out of all the soil properties examined, pH varied least (~ 12% CV) among soil samples and yet showed a significant and positive association with the bacterial diversity at the entire set of soil samples, supporting its dominance in dictating bacterial diversity (Fierer and Jackson, 2006).

Compared to bacteria, fungi are more morphologically complex but physiologically similar. Most fungi need aerobic conditions and organic matter as the source of carbon and energy to grow and proliferate, thus competitions within the fungal community may be more

severe. Although fungi can acquire and redistribute nutrients from a far distance via hyphal elongation and branching, specifically through large pores (Otten et al., 2001; Cairney, 2005;

Guhr et al., 2015), this trait may not efficiently alleviate species competition due to the high energy cost. As a consequence, pore-scale physicochemical modulations to reduce fungal species competition would be the key to improving fungal diversity. Regardless of the metrics used, fungal diversity was consistently promoted in coarse-textured soils. It is known that fungi prefer to reside and migrate in large (e.g., > 15 µm) pores due to the filamentous nature (Kandeler et al.,

2000; Harris et al., 2003; Strong et al., 2004; Kravchenko et al., 2011). Large pores are also prone to rapid and ephemeral fluctuation in resources compared to fine pores. For instance, plant debris can be quickly transported into large pores and then rapidly decomposed (Negassa et al.,

2015; Quigley et al., 2018). Water is depleted more rapidly from large pores than smaller ones due to gravitational drainage. As a result, large pores may be more frequently subjected to drought and resource deprivation, the conditions suboptimal or even stressful for fungal growth and activity. These environmental constraints on fungal activity will lower their competitive abilities, enhancing the co-existence of different fungal species. This might explain why texture effects on fungal diversity were mainly observed in soils with lower organic matter content (the

Bottom treatment) and lower water availability (Dry/Moist Treatments). The fact that soil texture was associated with the fungal species richness at the entire set of soil samples indicated stronger impacts of texture on fungal than bacterial alpha diversity. This was likely due to a shift in dominant processes, i.e., fitness differences versus niche differences, by which texture affected fungal and bacterial diversity, respectively.

2.4.2. Taxa-dependent responses to soil texture and other soil properties

Multivariate analyses ranked soil texture as the second most important factor in structuring both bacterial and fungal communities. Although the sand (or clay) content only explained a small portion of the total variance of the microbial community, it was of the same order of magnitude as the effect of soil pH, the strongest known factor for modulating the microbial community structure (Fierer and Jackson, 2006; Lauber et al., 2009; Rousk et al.,

2010). A nationwide survey of French soil bacteria also showed that soil texture (clay or silt content) was a major driver for bacterial distribution at the phylum level despite no more than

6% of the variance explained (Karimi et al., 2018). This agreement among investigations at different ecosystems as well as at different geographic scales indicates that soil texture is a universal driver for the soil microbial community. However, the relatively low capability of interpreting the variance at the whole community level suggests that texture effects are highly taxon-dependent.

Texture was the main driver to regulate the relative abundances of Actinobacteria and its sub-level taxa. Their positive relationships with clay were independent of the soil stratification treatment and aligned well with several other studies (Karimi et al., 2018, Sessitsch et al., 2001;

Hemkemeyer et al., 2018). A positive and soil stratification-independent relationship with clay was also found for another bacterial phylum Chloroflexi in this work and that of Karimi et al.

(2018). While Actinobacteria and Chloroflexi are in contrast in the cytoplastic membrane structure (diderm versus monoderm) and thus the Gram-stain feature (positive versus negative), they both are typically filamentous and can get nutrients by apical branching and filamentous growth through the soil pore network (Wolf et al., 2013). Given the fungus-like strategy of nutrient acquisition, filamentous bacteria may interact more strongly with fungi than coccus or

rod-shaped bacteria. A recent study has demonstrated that Streptomycetes, a genus of

Actinobacteria can enhance their capability of resource exploration by interacting with fungi

(Jones et al., 2017). Along with Actinobacteria and Chloroflexi, some fungi, including members of Eurotiomycetes, Sordariomycetes, and Basidiomycota also showed their preferences of fine- textured soils. The concerted responses to texture between fungus-like bacteria and fungi were perhaps due to the need for mining finer particle-associated resources (Anderson et al., 1981;

Baldock et al., 1992; Six et al., 2002). Out of the nine fungal taxa showing significant associations with soil texture, most were positively associated with the silt content, perhaps due to divergent distributions of organic matter across soil particles.

In contrast to fungi and filamentous bacteria, non-filamentous bacteria, e.g., members of

Acidobacteria and Betaproteobacteria were positively associated with the sand content. A few studies (Sessitsch et al., 2001; Hemkemeyer et al., 2018) also showed that some bacteria, e.g., members of Alphaproteobacteria and Bacteroidetes were enriched in the fraction of large particles, due to the presence of particulate organic matter, a readily available C and energy source in that fraction. However, a lack of consistency among studies about which bacterial taxa were positively related to the sand content (Sessitsch et al., 2001; Hemkemeyer et al., 2018;

Karimi et al., 2018, this work) suggested that texture effects on non-filamentous microbes were not as strong as on their filamentous counterparts. This is perhaps because that the relative abundance and degradability of particulate organic matter in the fraction of large particles are temporally more heterogeneous than organic substances in the fine particle size fraction.

Fungi were little affected by soil pH and water availability due to their great capacity to adapt drought and acidity (Nevarez et al., 2009; Rousk et al., 2010). By contrast, a number of bacterial taxa were well correlated with soil pH and nutrient availability. Our results were

consistent with a bulk of publications, showing that the majority of Acidobacteria and

Planctomycetes were acidophilic (Barns et al., 1999; Chan et al., 2006; Sait et al., 2006; Dedysh and Kulichevskaya, 2013; Kielak et al., 2016; Fuerst, 2017) and Nitrospirae were neutrophilic or alkaliphilic (Ehrich et al., 1995; Lebedeva et al., 2008). Nonetheless, associations of the fungal community with a fewer array of soil properties than the bacterial community emphasizes a more important role of soil texture in structuring the fungal community.

Relationships between microbial taxon abundance and total C (or N) were soil stratification-dependent, but association trends were opposite between bacteria and fungi.

Bacteria, so-called r-strategy organisms, generally prefer a nutrient-rich environment and may be highly sensitive to nutritional changes at a nutrient-limited environment. This perhaps explains why more bacterial taxa in Bottom than Top showed significant and positive relationship with soil total C (or N). In contrast, fungi of K strategy are generally inferior competitors at a nutrient rich environment. Thus, interkingdom competitions for resources in Top may favor bacteria over fungi, leading to more “apparent” negative associations between fungal taxa and soil total C (or

N). Such stratification-dependent resource impacts, together with no significant correlations between soil texture and total C (or N, data not shown) further stress our hypothetical notion that texture modifies the soil microbial community likely via particle-scale heterogenous distributions of organic substances. Further investigations are needed in this regard.

2.4.3. Texture-based soil C and N cycling potentials

Relative abundances of genes for degrading organic compounds increased with silt and/or clay content. Given that soil organic matter generally dominates in silt and/or clay fractions (Six et al., 2002), such a positive association indicates that organic C-degrading genes are mainly controlled by the distribution of soil organic C across sand, silt and clay fractions. The

relationships between C-degradation genes and texture also aligned well with the relationships between Actinobacteria and texture, suggesting the critical role of fungus-like Actinobacteria in degrading organic polymers (e.g., starch, hemicellulose, and cellulose) and aromatic substances

(e.g., vanillin). Although the genetic potential of organic C degradation in soil was predicted based on the bacterial community, the similarity in relationships of Actinobacteria and fungi with the soil texture inferred the significance of fungi in the degradation of fine soil particle-protected organic substances. This inference was further supported by the finding that the relative abundance of gene encoding chitinase increased with the silt content, since this enzyme is often induced by the presence of chitin, a major component of fungal cell walls. By contrast, the genetic potential of organic N mineralization was greater in the sand fraction than in the silt or clay fraction, although hydrolyzable total N and amino acid N are often greater in fine soil particles (Schnitzer and Ivarson, 1982; Moni et al., 2012). The discrepancy between genetic potentials of organic N versus organic C was likely attributed to the specific genes examined and their sources of N. Lysozyme is a hydrolytic enzyme catalyzing the cleavage of beta-1,4- glycosidic bond between N-acetylmuramic acid and N-acetylglucosamine in peptidoglycan of the bacterial cell wall and is important to bacterial cell lysis and turnover (Vermassen et al.,

2019). The negative association between the clay fraction and the relative gene abundance of lysozyme may suggest the relatively lower bacterial activity in this fraction perhaps due to organic compound recalcitrance as well as the inferior competitive ability of coccus- and rod- shaped bacteria for energy and C source compared to fungi. Genes encoding glutamate dehydrogenases, enzymes for deamination were also negatively associated with the silt or clay fraction, but positively with the sand fraction. This further supported that enzyme-mediated biochemical N mineralization might be stronger in the fraction of large particle size due to the

presence of plant debris and particulate organic matter than in the fraction of finer particle size.

Texture-based distribution of organic substances also controlled the genetic potential of

- + microbial N assimilation. Genes for assimilatory NO3 reduction to NH4 and glutamate synthases were more abundant in the clay fraction, implying the microbial demand of N as a result of greater organic C degradation in this fraction. On the other hand, genes encoding

- + dissimilatory NO3 reduction to NH4 were mainly negatively associated with the silt fraction. A possible explanation may be that facultative denitrifiers were not stronger competitors for energy and C compared to other aerobic heterotrophs under oxic and sub-oxic conditions.

Genetic potentials of both N fixation and nitrification could not be predicted from the soil texture. Nitrogen fixation was mainly and negatively associated with total soil C content.

Although we did not include total soil N in the analysis, the tight coupling between total soil C and N (ρ = 0.99) could be translated into a negative correlation between genes for N fixation and total soil N. It is known that the N fixation is inhibited by soil available N, but there was no apparent association between soil inorganic N and the relative abundances of genes for N fixation. This suggested that the population size of N fixers stemmed from the legacy of a constant supply of available N through mineralization rather than the snapshot of inorganic N at the sampling. The genetic potentials of autotrophic nitrification were moderately and positively associated with total soil C and therefore total soil N but weakly with soil inorganic N. This further confirms the dominant role of available N supply via mineralization in regulating both N fixation and nitrification, despite their contrasting relationships with soil available N. A moderate and positive relationship of nitrifiers with the total porosity might stem from the need of aerobic conditions for nitrification. However, the weak and negative association between pH and the genetic potential of nitrification was contradictory to the general notion that nitrifiers prefer

neutral and alkaline conditions. We considered that this might not be authentic due to the relatively smaller variation of soil pH among 72 soil samples compared to other soil properties as well as the possibility of covariations among soil properties.

2.5. Conclusions

Our work highlighted the significance of soil texture in regulating the abundance and distribution of soil microbes and demonstrated the divergent effects of soil texture on fungal versus bacterial communities. First, soil texture was found to play a more important role in affecting fungal than bacterial alpha diversity, with fungal species richness and thus Shannon diversity index being enhanced in coarse-textured soils. Second, the soil texture effects on microbial community structure were species-dependent. Silt and/or clay content promoted the relative abundance of filamentous bacteria (Actinobacteria and Chloroflexi) and a few fungal taxa (Basidiomycota and Eurotiomycetes). Third, a fine-textured soil promoted the abundance of bacterial genes putatively involved in the degradation of numerous organic substances.

Particle/pore size-dependent distributions of specific microbial groups would likely be drivers for associations of microbial community structure and function with the soil texture.

Texture-independent microbial taxa and functions were also identified. Bacteroidetes appeared to be more resource-philic, whereas Acidobacteria were more pH-reliant. Unlike N

- + mineralization, assimilatory NO3 reduction to NH4 , denitrification, and DNRA, the potentials of nitrification and N fixation were completely unrelated to soil texture. Nonetheless, soil texture was the second most important factor after soil pH in structuring microbial community structure and compositions. Our data suggests that texture effects on the soil microbial community structure and function were likely through pore and particle-scale organic matter distributions.

2.6. Acknowledgements

The authors would like to thank the Environmental and Agricultural Testing Service

Laboratory, NCSU for their help in analyses of inorganic N and total soil C and N. We would also like to thank Christopher Niewoehner for his guidance in soil texture measurement.

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Table 2.1 Descriptive statistics of physicochemical properties of all the soil samples#.

Moisture Porosity WFPS pH Sand Silt Clay Inorganic N TC TN (%) (%) (µg g-1 soil) (mg g-1 soil) Min 2.4 24.7 8.0 4.2 30.3 11.7 4.4 13.2 1.4 0.2 Mean 22.7 51.9 49.4 5.9 60.2 20.3 19.5 49.4 21.8 1.8 Max 84.7 79.1 100.0 7.2 83.4 38.8 53.0 148.0 97.2 7.2 SE 1.9 1.5 3.1 0.1 1.7 0.7 1.6 3.8 2.1 0.2 CV 72.9 24.1 53.9 12.3 24.0 29.7 68.4 65.4 82.2 80.2 Skewness 1.2 0.0 0.0 -0.4 -0.3 0.9 1.0 1.2 1.5 1.3 Kurtosis 1.9 -0.5 1.2 0.1 -0.7 1.2 0.3 0.9 3.3 1.8 #Moisture, gravimetric moisture content; WFPS, water-filled pore space; Sand, Silt, and Clay, the percentage of sand, silt, and clay particles respectively; TC, total soil carbon; TN, total soil nitrogen; SE, standard error; CV, the coefficient of variation (%).

Table 2.2 Descriptive statistics of microbial alpha diversity metrics. Metrics of both bacteria and fungi were estimated at a sequence depth of 12000#.

Observed OTUs Shannon index Evenness Faith's PD Bacteria Min 687 8.17 0.82 70 Mean 1988 9.91 0.91 136 Max 3245 10.79 0.95 202 SE 86 0.08 0.00 4 CV 36.6 6.4 2.8 24.3 Skewness -0.30 -0.81 -1.78 -0.38 Kurtosis -1.15 -0.26 3.26 -0.90

Fungi Min 231 3.89 0.45 79 Mean 757 6.69 0.70 224 Max 1430 8.68 0.86 414 SE 29 0.10 0.01 8 CV 32.9 13.1 10.0 29.2 Skewness 0.40 -0.72 -1.06 0.54 Kurtosis -0.20 1.18 2.49 0.55 #SE, standard error; CV, the coefficient of variation (%).

Table 2.3 Results of marginal tests by distLM (distance-based linear model) for the Bray-Curtis dissimilarity matrices of all the soil samples.

Variable SS(trace)# Pseudo-F P Prop. Bacteria pH 1.914 5.259 0.001 0.071 Sand 1.638 4.450 0.001 0.061 Clay 1.530 4.141 0.001 0.057 TC 1.011 2.682 0.002 0.037 Porosity 0.887 2.340 0.001 0.033 InorgN 0.841 2.216 0.001 0.031 Moisture 0.627 1.640 0.009 0.023 WFPS 0.327 0.845 0.775 0.012

Fungi pH 1.409 3.894 0.001 0.053 Sand 1.118 3.055 0.001 0.042 Clay 1.119 3.059 0.001 0.042 InorgN 0.780 2.103 0.004 0.029 TC 0.705 1.895 0.011 0.026 Porosity 0.546 1.458 0.057 0.020 Moisture 0.463 1.235 0.167 0.017 WFPS 0.343 0.910 0.567 0.013 #SS, sum of squares; F, F distribution; P, P value; Prop., the proportion of explained variation;

Sand, sand percentage; Clay, clay percentage; InorgN, inorganic N; TC, total soil carbon;

Moisture, gravimetric water content; WFPS, water-filled pore space.

Table 2.4 Results of sequential tests by distLM (distance-based linear model) for the Bray-Curtis dissimilarity matrices of all the soil samples.

Variable Adjusted R2 SS(trace)# Pseudo-F P Prop. Cumul. Bacteria pH 0.057 1.914 5.259 0.001 0.071 0.071 Sand 0.093 1.311 3.746 0.001 0.049 0.119 TC 0.112 0.817 2.382 0.001 0.030 0.150 Clay 0.127 0.735 2.179 0.001 0.027 0.177 Porosity 0.132 0.466 1.391 0.033 0.017 0.194 InorgN 0.135 0.402 1.204 0.132 0.015 0.209 WFPS 0.135 0.343 1.027 0.403 0.013 0.221

Fungi pH 0.039 1.409 3.894 0.001 0.053 0.053 Sand 0.060 0.899 2.540 0.002 0.034 0.086 Clay 0.073 0.698 1.999 0.004 0.026 0.112 InorgN 0.086 0.661 1.921 0.010 0.025 0.137 TC 0.088 0.413 1.203 0.196 0.015 0.153 Moisture 0.090 0.377 1.099 0.282 0.014 0.167 #SS, sum of squares; F, F distribution; P, P value; Prop., the proportion of explained variation;

Cumul., the cumulative proportion of explained variation; WFPS, water-filled pore space; Sand, sand percentage; Clay, clay percentage; InorgN, inorganic N; TC, total soil carbon; Moisture, gravimetric water content.

Figure 2.1 Distribution of the 36 intact soil cores (0-10 cm depth) collected from bermudagrass golf courses on soil texture triangle. Dots represent individual soil cores.

Figure 2.2 Heatmaps of Spearman’s rank correlation coefficients of bacterial and fungal alpha- diversity metrics (observed OTUs, Shannon index, evenness, and Faith’s PD) with soil physicochemical properties for the entire set of soil samples (All) and subsets of different soil depths (Top and Bottom) and moistures (Dry, Moist, and Wet). The square size and color represent the magnitude and direction of correlation coefficient, respectively. Moisture, gravimetric moisture content; WFPS, water-filled pore space; TC, total soil carbon; InorgN, inorganic N; Sand, Silt, and Clay, the percentage of sand, silt, and clay particles, respectively.

Figure 2.3 The heatmap of Spearman’s rank correlation coefficients between relative

abundances of major bacterial taxa from phyla to genera (> 2% of the relative abundance on average) and soil properties. The square size and color represent the magnitude and direction of

the correlation coefficient, respectively. Horizontal box plots show the relative abundances of individual taxa. If a taxon and its sublevels had the same Spearman’s rank correlation coefficient as well as the relative abundance (e.g., Acidimicrobiia and Acidimicrobiales), they were merged

into one line/row on the heatmap. Moisture, gravimetric moisture content; WFPS, water-filled

pore space; TC, total soil carbon; InorgN, inorganic N; Sand, Silt, and Clay, the percentage of

sand, silt, and clay particles, respectively.

Figure 2.4 The heatmap of Spearman’s rank correlation coefficients between relative

abundances of major fungal taxa from phyla to genera (> 1% of the relative abundance on average) and soil properties. The square size and color represent the magnitude and direction of

the correlation coefficient, respectively. Horizontal box plots show the relative abundances of individual taxa. If a taxon and its sublevels had the same Spearman’s rank correlation coefficient as well as the relative abundance (e.g., Mortierellacea and Moretierella), they were merged into

one line/row on the heatmap. Moisture, gravimetric moisture content; WFPS, water-filled pore

space; TC, total soil carbon; InorgN, inorganic N; Sand, Silt, and Clay, the percentage of sand,

silt, and clay particles, respectively.

Figure 2.5 The heatmap of Spearman’s rank correlation coefficients between the copy numbers

of putative genes for N cycling (or C degradation) and soil properties at a sequence depth of

12,000. The square size and color represent the magnitude and direction of the correlation

coefficient, respectively. Horizontal box plots show copy numbers of the putative genes.

Moisture, gravimetric moisture content; WFPS, water-filled pore space; TC, total soil carbon;

InorgN, inorganic N; Sand, Silt, and Clay, the percentage of sand, silt, and clay particles,

respectively.

CHAPTER 3: Soil Pore Size Distribution Shaped not only Compositions

but also Networking of the Soil Microbial Community

Abstract

Soil pore size and arrangement control the heterogeneous distribution of nutrients, water, and air and, therefore, are a superimposed and integrated factor dictating the soil microbial community structure. It is known that soil with more large pores can potentially harbor more diverse microbes under low hydraulic connectivity. Still, understanding on how soil pore size distribution (PSD) governs the composition and moreover the association of the microbial community is insufficient. This work examined the PSD effects on microbial community compositions and networking via marker gene high-throughput sequencing of both DNA and cDNA. Three soils with a large variation in silt content (~ 11 – 73%) and their combinations at different mass ratios were used to enhance the continuity in silt content and thus PSD.

Investigations were made under different levels of total pore volume and pore hydraulic connectivity by incubating soils at varied bulk densities and moisture contents for 50 days. The total of six soils was dichotomized into two PSD groups based on soil water retention curves, with PSD-1 of more macro- and mesopores (> 30 μm) and PSD-2 of more micropores (< 30 μm).

Effects of moisture treatments on both fungal and bacterial evenness and Shannon diversity index were pore size group specific, supporting the importance of pore hydraulic connectivity in regulating microbial diversity. PSD-1 soils promoted the proliferation of Betaproteobacteria,

Bacteroidetes, and Eurotiales, whereas PSD-2 soils favored Alphaproteobacteria,

Sordariomycetes, and Chaetothyriales. Pore hydraulic connectivity slightly and yet significantly affected the microbial relative abundance of PSD-2 soils, with Actinobacteria being more abundant under drier conditions. There were less intra- and interkingdom associations in PSD-2

than PSD-1 soils, and such differences were little affected by pore volume and pore hydraulic connectivity. Our work highlighted PSD-dependent soil microbial distributions and associations, but ecological consequences need to be further examined.

Keywords: Soil pore size distribution; Pore hydraulic connectivity; Microbial diversity;

Community composition; Microbial networking

3.1. Introduction

Soil pore heterogeneity has been recognized as an important control of the soil microbial community. Not only do pore size and arrangement regulate the distribution of essential elements and compounds for microbial growth, including organic carbon, nutrients, protons, water, and air, but also constrain the stochastic dispersal and chemotaxis of microbes (Curd et al., 2018; Vos et al., 2013). The prominence of pore heterogeneity in maintaining the co-existence of diverse microbes in soil has long been acknowledged, in particular pertaining to species richness, i.e., the number of different species in a community (Heijnen and Veen, 1991). Studies revealed that soils with a great proportion of large pores were apt to have more disconnected water films and therefore favored species richness (Carson et al., 2010; Chau et al., 2011). We consider that soil pore size distribution (PSD) is superimposed upon many other soil properties and thus represents an integrated factor in characterizing the soil microbial community. However, knowledge in this regard is only cursory.

Our understanding on soil pore-associated microbial distribution is mainly from the perspective of microbial morphology and predator-prey relationships (Hassink et al., 1993;

Wright et al., 1995; Sleutel et al., 2012). Generally, bacteria were found to mainly colonize in micropores while fungi were more abundant in macropores. Such pore size-based preferences in distributions are, on the one hand, attributed to interkingdom morphological differences, with

bacteria tending to attach to the surface of soil particles but fungi growing along aggregates or through voids (Huang et al., 2002; Sessitsch et al., 2001). Besides, filamentous fungi are also less affected by environmental fluctuations compared with coccus- and rod-shaped bacteria (Nevarez et al., 2009; Rousk et al., 2010; Sleutel et al., 2012). On the other hand, predators, e.g., protozoa in large pores of > 30 μm may exert a strong top-down control on bacteria, leading to an overall reduction in bacterial abundance and yet an increase in juvenile bacteria of greater activity

(Hassink et al., 1993; Wright et al., 1995). Pore size-based microbial distribution may also be inferred from texture and aggregate-associated microbial distribution patterns, in that both properties are somewhat related to PSD (Hemkemeyer et al., 2018; Xia et al., 2020). For example, Betaproteobacteria was considered to be more abundant in large pores due to the observed positive associations with sand content (Xia et al., 2020). Bacteroidetes and

Alphaproteobacteria were also considered to be ecologically favored in large pores because of their positive associations with soil particles/aggregates of large size (Hemkemeyer et al., 2018).

Nonetheless, more explicit information on PSD-based microbial distribution is needed given that texture and aggregates cannot offer the integration and comprehensiveness imparted by PSD.

Ecological services of microbial diversity depend on microbial interactions, e.g., competition, synergy, and cooperation (Deveau et al., 2018; McCully et al., 2020). For instance, decomposition of complex organic material may require diverse microbes to cooperatively secrete extracellular enzymes for the depolymerization of organics of different biochemistries

(Folse III et al., 2012; Wright et al., 2019). It is known that pores of different sizes vary in the ability of protecting organic matter from microbial decomposition (Negassa et al., 2015; Quigley et al., 2018). Still, rarely are inter-taxa correlations explored for a more integrated understanding of PSD-based microbial decomposition. Hence, the objective of this work was to investigate

PSD-associated soil microbial diversity, composition and co-occurrence. Both 16S/18S DNA and cDNA sequencing approaches were used, because total and active communities might provide different aspects of microbial responses to environmental cues. Given interactive effects of PSD and water on microbes (Uikman et al., 1991; Young and Ritz, 2000; Focht, 1992;

Heijnen and Veen, 1991), investigations were conducted under different soil moisture contents.

Further, different bulk densities were also included because soil compaction can significantly limit fungal hyphal expansion (Harris et al., 2003; Otten et al., 2001).

3.2. Materials and Methods

3.2.1. Soil sampling, microcosm construction, and incubation

Three soils with a large variation in silt content were used, the Greenville soil series from

Georgia (GA) (~ 11% silt), the Drummer soil series from Indiana (IA) (~ 49% silt), and a soil series from Tennessee (TN) (~ 73% silt). All the soil samples were collected from the surface (0-

20 cm depth) of agricultural land used for row crop production, air dried in bulk for over a year, and sieved (< 2 mm). To increase the continuity of silt content among soil samples, stock soils

TN and GA were mixed at 1:3, 2:2, and 3:1 mass ratios for generation of additional soil samples

TG13, TG22, and TG31, respectively, resulting in a total of six soil samples for the following microcosm experiment.

The microcosm experiment was set up in a 2 × 2 factorial design with two moisture (Dry and Wet) and two bulk density (Low and High) levels, leading to four treatments that were arranged completely in three blocks (i.e., lab bench locations). Given the large variation in soil texture, packing and maintaining all the soil samples at the same low or high bulk density was not possible. Therefore, each of the six soil samples was packed into its own low and high bulk densities with a fixed difference of 0.3 g cm-3 between the two. Samples were then maintained at

respective dry and wet water contents so that water-filled pore space was the same for all the soil samples within an individual treatment (Table B.1). Briefly, soil samples in plastic bags were moistened to the target water content, mixed, and left for two days to allow for moisture redistribution and homogenization. Then, soils with 80 g dry-weight equivalent were packed into amber jars (5 cm height × 5.5 cm diameter) to the respective bulk density, capped, and incubated at room temperature for about 50 days. Every seven days, the headspace CO2 concentration was measured using a LI-820 CO2 Gas Analyzer (LI-COR, Lincoln, NE, USA) and then headspace was refreshed. Soil water content was maintained by mass balance-based water addition. At the end of incubation, soils were sampled from microcosms block by block over two weeks for soil

DNA and RNA extraction. Aliquots of soil samples were also kept at 4℃ for the later analysis of soil physicochemical properties.

3.2.2. Soil physicochemical properties

Stock soils IA, GA, and TN were measured for total C and N by dry combustion, cation exchange capacity (CEC) by NH4OAc at pH 7.0, and percent of base saturation (BS) before the experimental setup. This helped ascertain that soils chosen for combination (GA and TN) had similar properties other than texture. After incubation, all soil samples were measured for soil texture by the hydrometer method, soil pH by a pH meter at a soil/water (w/w) ratio of 1:2.5, and

+ - inorganic N (NH4 -N and NO3 -N) by a FIA QuikChem 8000 autoanalyzer (Lachat Instruments,

Loveland, CO, USA) following extraction with 1.0 M KCl at a soil/solution (w/v) ratio of 1:3.

Soil water retention curves were determined twice by desorbing the packed soils at respective target bulk densities from saturation, using pressure plate extractors (Klute, 1986). To verify if a bulk density was maintained over the measurement, soil bulk densities were also measured at the end, hereafter referred to as actual bulk densities.

3.2.3. Nucleic acids extraction, amplification, and sequencing

Soil RNA was extracted from ~ 2 g of each soil sample using RNeasy PowerSoil Total

RNA Kit (QIAGEN, Germantown, MD, USA) with genomic DNA removed using DNase Max

Kit (QIAGEN, Germantown, MD, USA). Purified RNA was ~ 100 – 1500 ng per sample, estimated using a NanoDrop Spectrophotometer (Thermo Scientific, Wilmington, DE, USA).

The first-strand cDNA was synthesized using M-MLV Reverse Transcriptase (Invitrogen,

Carlsbad, CA, USA) and oligo (dT)12-18 primers (Invitrogen, Carlsbad, CA, USA) following manufacturer’s instructions. Soil DNA (~ 50 – 200 ng μL-1) was extracted from ~ 500 mg of each soil sample using FastDNA Spin Kit for Soil (MP Bio, Solon, OH, USA).

Soil bacterial 16S DNA and cDNA and fungal 18S DNA and cDNA were amplified with

Illumina adapter-appended primer pairs targeting 16S V3-V4 region (F319: 5’-

ACTCCTACGGGAGGCAGCAG-3’ and R806: 5’- GGACTACHVGGGTWTCTAAT-3’) and

18S V7-V8 region (FF390: 5’-CGATAACGAACGAGACCT-3’ and FR-1: 5’-

ANCCATTCAATCGGTANT-3’), respectively (Klindworth et al., 2013; Banos et al., 2018). A

25 μL PCR reaction included 12.5 μL KAPA HiFi HotStart ReadyMix (KAPA Biosystems,

Wilmington, MA, USA), 5 mmol of each primer, and 12.5 – 50 ng DNA or cDNA templates.

The thermal conditions for amplification were as follows: initial denaturation at 95 ℃ for 3 min;

30 cycles at 98 ℃ for 30 s, 55 ℃ for 15 s for bacteria (or 51 ℃ for 15 s for fungi), and 72 ℃ for

30 s; and final elongation at 72 ℃ for 5 min. Two negative controls were included by replacing

DNA and cDNA templates with nuclease-free water and a randomly selected RNA template, respectively. Agarose gel electrophoresis did not show any bands in all negative controls, indicating no exogenous or genomic DNA contamination in PCR amplification. After cleanup by

AMPure XP beads (Beckman Coulter Genomics, Danvers, MA, USA), the amplified DNA had

unique adapter sequences (Nextera XT Index Kit, Illumina, San Diego, CA, USA) added in a 50

μL reaction of 25 μL KAPA HiFi HotStart ReadyMix, 5 μL of cleaned-up PCR amplicons, 5 μL of each index primer, and 10 μL of nuclease-free water. The thermal cycling conditions for both

16S and 18S DNA and cDNA were: denaturation at 95 ℃ for 3 min; 8 cycles at 95 ℃ for 30 s,

55 ℃ for 30 s, and 72 ℃ for 30 s; final elongation at 72 ℃ for 5 min. After a second cleanup on

PCR products, the barcoded 16S and 18S DNA and cDNA amplicons were diluted to 10 nM, mixed equimolarly, and pair-end sequenced on an Illumina MiSeq platform (300bp×2, v3 chemistry). Sequences were deposited on the NCBI Sequence Read Archive (SRA) database under the BioProject accession number of PRJNA680171.

3.2.4. Bioinformatics analysis

Demultiplexed sequencing reads had primers and adapters removed by cutadapt (v1.18)

(Martin, 2011) and were then processed in R (3.6.1) using DADA2 (v1.12.1) pipeline (Callahan et al., 2016; R Core Team, 2020). The forward and reverse reads of 16S DNA and cDNA were truncated to a length of 270 and 220 bp respectively; corresponding 18S DNA (or cDNA) reads were truncated to a length of 260 and 190 bp, respectively. The core sample inference algorithm of DADA2 was applied to the truncated/filtered reads following dereplication and error model training. After pair-end merging and chimeras removal, a table of the full denoised amplicon sequence variants (ASVs, a higher-resolution analogue of the traditional operational taxonomic units (OTUs)) and their copy numbers was exported as the end product of the pipeline and was then processed in QIIME2 (2019.7) for downstream analysis (Caporaso et al., 2010). Singletons were removed before diversity analysis and taxonomy classification. Due to poor fungal RNA purification and amplification, only 12 samples from TG31 and TN soils were retained after quality check and filtration. The bacterial and fungal ASVs were annotated using Greengenes

database (v13.8) and SILVA database (v132), respectively (DeSantis et al., 2006; Quast et al.,

2013), and reads that were not classified as bacteria or fungi were discarded. Microbial alpha diversity metrics (observed ASVs, Shannon index, evenness, and Chao1) were estimated, and the

Bray-Curtis dissimilarity matrixes were used for both bacterial and fungal beta diversity analyses.

3.2.5. Statistical and network analysis

Data normality was examined by the Shapiro-Wilk test, with P ≤ 0.05 for most data, indicating non-normal distribution. Therefore, non-parametric Kruskal-Wallis test and paired

Wilcoxon test were used to evaluate differences in microbial alpha diversity metrics and soil respiration between soil samples, pore size distribution groups, and treatment factors.

Differences in beta diversity was assessed by the non-parametric permutational multivariate analysis of variance (PERMANOVA). Taxon preferences in terms of pore size distribution and respective water or bulk density treatments were estimated based upon point-biserial correlation coefficients from R package indicspecies (v1.7.9, Cáceres and Legendre, 2009). Microbial co- occurrence patterns were evaluated via microbial network construction and analysis (R packages igraph v1.2.5 and qgraph v1.6.5; Csardi and Nepusz, 2006; Epskamp et al., 2012) and were graphed using Gephi (Bastian et al., 2009). Briefly, matrices of Spearman’s rank correlation coefficient (ρ) were constructed from bacterial and fungal ASVs. Only ASVs with abundance ≥

0.05% and appeared in ≥ half of the samples were included to generate correlation matrices.

After filtering out correlations with ρ < 0.7 for bacteria and < 0.5 for fungi, adjacency matrices of binary data were created for constructing both intra- and interkingdom microbial networks and then analyzed for modularity, clustering coefficient, hub species, and other small world properties. Co-occurrence patterns were also analyzed at bacterial and fungal high taxonomic

levels via adjacency matrices made by the Correlograms in R packages (corrplot v0.84, Wei and

Simko, 2017).

3.3. Results

3.3.1. Soil texture, water retention, and biochemical properties

The six soil samples belonged to four soil texture classes; GA and TG13 as sandy clay loam, TG22 as loam, TG31 and TN as silty loam, and IA as clay loam, with sand fraction ranging from 17.2 – 67.0%, silt fraction from 10.5 – 73.2%, and clay fraction from 9.6 – 27.1%

(Table 3.1). Inorganic N was the most varied property among soil samples, with ~ 60% coefficient of variation, whereas pH and base saturation were the least (Table 3.1). There were clear-cut differences between IA and the other soils. Compared to the other soils, IA was slightly alkaline and had ~ two-time greater total soil C, ~ three-time greater CEC and ~ 30% lower base saturation. Despite differences in total soil N, IA and GA had similar inorganic N content, which was two to six-fold lower than the inorganic N in the other soils (P < 0.05).

Water retention curves appeared to differ among the six soil samples, in particular for the high bulk density treatment, showing greater water loss in IA, GA and TG13 than in the other soil at low water suction pressures (Fig. 3.1). For the low bulk density treatment, saturation and drying processes of water retention curve measurement led to soil compaction (Table B.2), suggesting less reliability for estimating water retention characteristics. Nonetheless, relative fractions of effective soil pore classes (i.e., macro-, meso-, and micropores) estimated from water retention curves (Banwart et al., 2017; Brewer, 1964) still showed a similar trend between the two bulk density treatments, with more macro- and mesopores presenting in IA, GA, and TG13 than the other soils (Table B.2). Therefore, the six soil samples were dichotomized into groups of relatively large pore size (PSD-1: IA, GA, and TG13) and small pore size (PSD-2: TG22, TG31,

and TN) for evaluating soil structural impacts on microbial diversity, composition, and co- occurrence.

Soil CO2 emission rates decreased over the incubation and varied largely among the six soil samples (Fig. B.1). Emission rates were not affected by PSD or moisture treatments but enhanced by soil compaction. On average, cumulative CO2 emissions over the incubation was ~

43% greater in the high than the low bulk density treatments. This was likely due to soil compaction-associated changes in microbial biomass and gas exchange between soil and air and, therefore, would not be discussed further.

3.3.2. Soil microbial alpha diversity and relationships with soil properties

Bacterial and fungal alpha diversity metrics (observed ASVs, evenness, Shannon diversity index, and Chao1) varied largely with soil samples, presumably attributed to differences in soil geographical location, management, as well as soil physicochemical properties. Consistently across all soil samples, the alpha diversity metrics of active bacteria were significantly lower than those of total bacteria (Fig. B.2). Compared to the total fungal community in TG31 and TN samples, the activity fungal community also showed lower alpha diversity metrics.

Evaluation on significant differences in diversity metrics between PSD groups hinged on whether or not IA was included for the analysis. Without IA, bacterial diversity metrics were generally greater in PSD-2 than PSD-1, particularly for the active community. However, when

IA was included, most differences were erased. Significant differences in fungal diversity also depended on the exclusion of IA from PSD-1, and yet differences mainly appeared in species richness, but not in evenness or Shannon diversity index.

Of the six soil samples, IA had the greatest diversity metrics for both bacteria and fungi

(Fig. B.3). Bulk density treatments did not affect alpha diversity metrics in bacterial and fungal communities, and neither did water manipulations when all the soil samples were included for the analysis (data not shown). However, slight and yet significant effects were found in diversity metrics of the total bacterial community when soil PSD groups were analyzed separately, with

Wet treatment promoting evenness (P = 0.009) and marginally increasing Shannon diversity index (P = 0.054) in PSD-1 but reducing evenness in PSD-2 (P < 0.001). Wet treatment also marginally promoted fungal Shannon diversity index in PSD-1 (P = 0.099).

3.3.3. Microbial beta diversity and taxa preference for soil properties

Bacterial and fungal communities primarily clustered by soil samples, with stock soils

IA, GA, and TN well separated (Fig. 3.2). PERMANOVA of distance matrix further identified several soil properties-based clusters. The PSD-based grouping explained 21.6%, 17.4%, and

23.1% of the variance in the total bacterial, active bacterial and total fungal community, respectively. Separating IA from other soils also helped explain the variance, especially for the total fungal community (34.8%). Silt content-based grouping (Low: GA, TG13, and TG22; High:

IA, TN, and TG31) contributed 8.9%, 5.8%, and 5.7% of the variance in the total bacterial, active bacterial, and fungal community, respectively. In addition, active communities were also separated from the total communities, with 8.9% of variance for bacteria in all samples and

16.9% for fungi in TN and TG31 samples.

A total of 48 phyla were detected from active and total bacterial communities in soil samples. The major phyla that contributed to the separation of active bacterial community from the total bacterial community (biserial point correlation coefficient ≥ 0.5) were Acidobacteria,

Bacteroidetes, Chloroflexi, Firmicutes, Gemmatimonadetes, and Verrucomicrobia. Acidobacteria

and Bacteroidetes were more abundant in the active community, whereas the rest were relatively richer in the total community. Many taxa of lower taxonomic ranks contributed to PSD-based grouping of soil samples, and their preferences over different PSD were little affected by IA exclusion from PSD-1 (Fig. 3.3A; Fig. B.4). For the total bacterial community, PSD-1 was generally more abundant in members of Betaproteobacteria (e.g., Burkholderiales),

Acidobacteria, Actinobacteria, and Planctomycetes, whereas PSD-2 was relatively more copious in members of Alpha-, Delta-, and Gammaproteobacteria. Both groups showed mixed results in terms of preference over Bacteroidetes, Chloroflexi, and Gemmatimonadetes. Preference patterns over PSD groups remained for the Alpha-, Beta-, and Gammaproteobacteria and Planctomycetes of the active bacterial community, but were inconsistent for members of Acidobacteria and

Actinobacteria (Fig. 3.3B). Of the taxa showing PSD preferences, only a few responded to soil total C/pH status (Fig. 3.3A, B). Similarly, of the taxa showing preferences for soil total C/pH, many were independent of PSD. For example, Nitrospirae was more abundant in IA of the highest total C/pH, and Firmicutes proliferated better in other soils with lower total C and pH; however, the relative abundance of both was little affected by PSD (data not shown). Bacterial responses to soil moisture treatments mainly occurred in PSD-2 (Fig. 3.4). Dry treatment increased the relative abundance of Actinobacteria by up to 4-fold compared to the Wet treatment. Dry treatment also promoted Acidobacteria, but reduced the relative abundance of

Bacteroidetes, Chlamydiae, OD1, and Proteobacteria. However, bacteria did not respond to soil bulk density treatments.

The total fungal community consisted of 11 identified phyla, dominated by Ascomycota

(51.3%), Mortierellomycota (8.0%), and Chytridiomycota (1.3%). In general, PSD-1 was more abundant in the order Eurotiales, whereas PSD-2 was more abundant in the order Hypocreales

and Chaetothyriales (Fig. 3.5A). Fusarium, a large genus of soil fungi was more favored in PSD-

2 than PSD-1. Unlike bacteria, more fungal taxa showed responses to both PSD and soil total

C/pH status and yet these responses appeared to be contradictory in IA (Fig. 3.5A; Fig. B.5).

Based on the grouping of total C and pH, for example, IA harbored more Sordariomycetes and

Cryptomycota but less Eurotiales, which were contradictory to PSD-based expectation on IA.

Both moisture and bulk density treatments had little influence on the total fungal community composition. For the identified active fungi in PSD-2, a few fungal taxa showed responses to soil moisture treatments (Fig. 3.5B). Dry treatment promoted Eurotiomycetes and its members; Wet treatments favored the proliferation of Fusarium. Ambispora, a genus belonging to

Glomeromycota, was the only taxon showing a response to soil bulk density treatments and was more abundant by compaction.

3.3.4. Microbial taxon co-occurrence patterns

Distinct microbial correlation patterns were detected between soil PSD groups (Fig. 3.6).

PSD-1 showed more linkages between microbes than PSD-2 for both active and total bacterial communities, as verified by greater clustering coefficient, average degree, and edge density but a lower number of isolated components (Table 3.2). Topology differences in the total bacterial community between the two PSD groups generally remained following removal of IA from

PSD-1. But for the active bacterial community, removing IA from PSD-1 led to a large reduction in the average degree and yet an increased in modularity. However, moisture and bulk density manipulations did not affect bacterial network topologies except that modularity of the active bacteria in PSD-1 was slightly increased (Data not shown). Similar to their counterparts, fungi had more connections in PSD-1 than PSD-2 (Fig. 3.6; Table 3.2). Topology differences in the

fungal community between the two PSD groups were also very clear at the class level. Removal of soil IA from PSD-1 had little influence on topology parameters except for modularity.

Interkingdom connections between bacteria and fungi were also denser in PSD-1 than

PSD-2, as shown by greater average degree and edge density but lower number of disconnected components (Fig. 3.7; Table 3.2). Some fungi, e.g., Dothideomycetes and Endogonomycetes were less connected with bacteria, but others, e.g., Eurotiomycetes, Sordariomycetes,

Mortierellomycetes, Agaricomycetes, and Mucoromycetes showed more strongly positive or negative associations with bacteria (Fig. 3.7). Again, removal of IA led to alterations in topology parameters, but topology differences between the two PSD groups remained.

3.4. Discussion

3.4.1. PSD effects on microbial alpha diversity

Microbial diversity is sensitive to many soil properties and management practices.

Therefore, it is not surprising to observe significant differences in diversity metrics (i.e., species richness, evenness, Shannon index, or chao1) among three stock soils that were from geographically different agricultural lands and possessed different soil properties. However, disparities in diversity in terms of active versus total community, bacteria versus fungi, and contrasting PSD were still salient. Active microbes produce transcript RNAs at a given environment and normally account for a portion of the total microbial community. As expected, observed and estimated species richness (ASVs and chao1) were lower in the active than the total community. Species evenness and Shannon diversity index were also lower in the activity community, suggesting stronger inter-species competitions compared to the total community.

This was particularly true for fungi, shown by the 31-65% reduction in fungal diversity metrics compared to the 7-17% reduction in bacteria.

Our observations that regardless of PSD, moisture treatments did not affect bacterial richness is contradictory to the notion that moisture could modify pore hydraulic connectivity and therefore change species richness (Carson et al., 2010; Chau et al., 2011). Instead, moisture treatments significantly altered species evenness or Shannon diversity index. Our previous work that examined the relationship between soil texture and microbial diversity also showed that bacterial species evenness rather than richness was more responsive to soil properties (Xia et al.,

2020). Nevertheless, both bacterial and fungal diversity metrics (i.e., species evenness, Shannon diversity index) responded to moisture treatments in PSD-dependent manners, being promoted by wetting in PSD-1 soils and drying in PSD-2 soils. Water influences microbial diversity in three major ways: control on osmotic stress and thus microbial activity (Csonka, 1989); dissolving and diffusion of soluble substrates and other resources (e.g. O2) (Killham et al., 1993;

Schjønning et al., 2003); and reallocation of soil resources by free water across soil pores (Zhou et al., 2002). We considered that microbial species evenness resulted from the combination of these pathways under pore environments. PSD-2 soils contained over two thirds of micropores

(< 30 μm) where water was typically deemed immobile (Soil Science Glossary Terms

Committee, 2008). Therefore, resource allocation would be constrained even when soil was relatively wet; but local dissolution of nutrients was still allowed, leading to a reduction of microbial evenness. In contrast, PSD-1 soils had more macropores where C and nutrient availability also depended on water flow and diffusion besides local dissolution. By increasing soil water content in large pores, resource availability in microsites would be more similar, leading to an improved species evenness. Here, moisture effects were apparently double-edged.

On one hand, dry conditions helped create more disconnected water films to increase the coexistence of different taxa. On the other hand, wet conditions promoted equal opportunities for

different microbes to react on substrates. This seems to suggest that the highest microbial diversity would be associated with an intermediate water activity. By examining bacterial diversity across biomes, Bickel and Or (2020) also concluded that bacterial diversity is mediated by pore-scale aqueous phase processes and is highest at intermediate water contents. It is worth mentioning that the relationship between moisture content and water activity vary with soils due to differences in soil physical and chemical nature. A mathematical model that incorporates PSD, water content, texture, pH, and organic matter would be better to predict microbial diversity.

Interestingly, moisture effects on bacterial diversity were pronounced in the total, but not the active, community level. This was likely because RNA profile, a snapshot of the community, was determined at the end of 50-day incubation when effects of water activity were limited by available substrates. However, moisture effects on the active community should not be ignored because, after all, observed effects on the total community were translated from effects on the activity community. Moisture effects on fungal diversity metrics were not as strong as on bacterial ones likely because fungi are generally considered as K-strategists and are less affected by environmental fluctuations (Nevarez et al., 2009; Rousk et al., 2010). In addition, fungal filamentous growth can help overcome water constrains.

3.4.2. PSD-based microbial preferences

PSD appeared to be an important factor in structuring the bacterial community, because the two soils, IA and GA that were similar in PSD and yet differed largely in soil total C and pH were closer along the PCoA-1. Moisture and bulk density manipulations did not change the overall structure of the microbial community, suggesting that resources were mainly local in pores where individual microbes resided. In other words, pore-scale heterogeneity of resources, e.g., carbon and nutrients, was the primary factor for the observed PSD-based patterns of the

microbial community structure. Of all the detected taxa, the relative abundance of

Burkholderiales, an order of Betaproteobacteria differed most between PSD groups, being more abundant in soils with a greater proportion of macro and mesopores. This was in contrast to the

PSD-based patterns of Alphaproteobacteria because Alphaproteobacteria was more abundant in soils having a great proportion of micropores. These findings were in good agreement with survey results about soil texture-microbial taxa relationships (Xia et al., 2020), assuming that coarse-textured soils contained more macro- and mesopores. PSD-based patterns were also found for the members of Gammaproteobacteria and other taxa. However, cautions need to be taken because the two PSD soil groups also differed in inorganic N. Several studies reported that as soil available N was increased, the relative abundance of the oligotrophic Acidobacteria decreased but Protobacteria increased (Zhou et al., 2017; Wang et al., 2018). Our results substantiated those findings, showing that Acidobacteria were more abundant in PSD-1 soils with low inorganic N and Gammaproteobacteria were more abundant in PSD-2 soils with high inorganic N. Our previous work also showed that the relative abundance of

Gammaproteobacteria was positively related to soil inorganic N but not related to soil texture

(Xia et al., 2020). Therefore, some of the apparent relationships between PSD and bacterial taxa were likely caused by inorganic N rather than PSD.

PSD effects on the fungal community were not as strong as on the bacterial community, as shown by the fact that PSD-1 soils (e.g., IA and GA) were not clustered. Therefore, many apparent PSD effects on fungal community compositions likely stemmed from confounded impacts of other soil properties. Texture is one of such factors that could affect fungal community compositions. As an example, the greater finer particles a soil has, the more relative abundance of Fusarium would be (Xia et al., 2020). This work also showed that the relative

abundance of Fusarium was greater in PSD-2 soils with greater silt content, supporting that fine soil particles promoted Fusarium. As another example, inorganic N boosted the relative abundance of Dothideomycetes (Xia et al. 2020). Therefore, greater relative abundance of

Dothideomycetes in PSD-2 soils might not be caused by PSD, but rather inorganic N. An interesting phenomenon was the concerted responses to PSD between Actinobacteria, the filamentous bacteria, and a fungal order, Eurotiales; both were more abundant in PSD-1 soils with relatively lower silt content. This appeared to be contradictory to their preference to silt content (Xia et al., 2020), suggesting PSD might be an overriding factor to act on the relative abundance of Actinobacteria and Eurotiales. Actinobacteria grow by apical branching and share similar morphology with fungi (Wolf et al., 2013). The mycelial lifestyle enabled Actinobacteria to bridge gaps between soil particles or aggregates and explore resources over relatively long distance (Chater, 2011). Actinobacteria is known to be more tolerant to low water activity than many non-filamentous bacteria and will become dominant as soil water content decreases.

Compared to other fungi, some members of Eurotiomycetes were also more drought tolerant

(Meisner et al., 2018; Geiser e al., 2006). In this work, two thirds of soil treatments had water content of < 32% water-filled pore space, likely a stress condition to microbes, in particular for soils with a great proportion of large pores. Nevertheless, the concerted actions of Actinomycetes and Eurotiales found in this work and others indicated that they may share similar ecological niches and also function similarly, such as mining organic matter for decomposition.

Despite slight impacts of water manipulations on the overall soil microbial community, several bacterial taxa showed significant responses. As expected, drying promoted

Actinobacteria, specifically in the PSD-2 soils. In contrast, many non-filamentous Gram- negative bacteria with thin cell walls and low ability of acquiring distantly located resources, e.g.

Bacteroidetes, were less tolerant to dry conditions and were therefore favored by wetting. Again, we noticed an “apparent” contradiction in terms of the niche preference of Bacteroidetes.

Bacteroidetes were relatively more abundant in PSD-1 soils where large pores were more likely subjected to dry conditions, and yet they preferred a wetter condition in PSD-2. This was perhaps beyond PSD-associated abiotic controls. Trophic interactions between prey and predators mainly occur in predator-dwelled macropores. Rod-shaped bacteria such as Bacteroidetes may be less

“palatable” to protozoa (Sessitsch et al., 2011) and therefore have a better chance to survive from grazing than coccus-shaped bacteria. If grazing is strong, it may cause “apparent” dominance of microbial species even under unfavorable conditions. Compared to bacteria, fungi were less responsive to moisture manipulation partially due to hyphal expansion towards resource and the

K strategy for survival (Harris et al., 2003; Wilpiszeski et al., 2019). Given that bulk densities mainly affect the total pore volume instead of soil PSD, little influence of bulk densities on bacteria or fungal community compositions were reasonable, despite some reports of significant effects (Otten et al., 2001; Harris et al., 2003). It was also possible that other soil factors such as soil nutrients and texture weighed more importantly in shaping the microbial community than the soil compaction.

3.4.3. Microbial co-occurrence and networking in soils of different PSD

The most pronounced impacts of PSD were on intra- and inter-kingdom associations of the microbial community. The complexity of associations was little dependent on the community compositions since differences in microbial networking between PSD groups were generally irrelevant of whether IA, whose community compositions differed significantly from those of the other soils, was included for the analysis. The more micropores a soil had, the less apparent associations there were among intra- or interkingdom taxa. One possible explanation of this

finding was that an increase in soil microporosity led to more physical isolation of microbes and thereby less direct contact among them. For instance, fungal expansion was restricted and became slower when soil microporosity increased (Otten et al., 2001). An increase in microporosity also reduced water flow, gas diffusion, and nutrient exchange (Neira et al., 2015;

Clifford and Hillel, 1986) and, might further restrict microbial associations. Another explanation was that large pores might hold more readily available carbon, e.g., particulate organic matter, than small pores (Quigley et al., 2018; Semenov et al., 2020), likely resulting in stronger microbial agglomeration and thus more associations. Differences in apparent microbial networking patterns between PSD groups raise a question on whether complex associations promote the decomposition of organic substances.

There were no significant differences in soil CO2 emission rates between two PSD groups. However, this did not indicate no linkage between organic matter decomposition and microbial networking because organic matter biochemistry also affected the rate of decomposition. Soils used in this work were from different agricultural lands, varying in not only total amount but also likely the biochemistry of organic C in soil. Decomposition of organic matter in macropores largely contributes to the consumption and/or formation of organic matter in soil and may be crucial to the overall soil carbon sequestration (Deurer et al., 2012). As such, microbial co-occurrence patterns in terms of PSD need to be further explored for the better understanding of microbial carbon use efficiency and soil organic C accrual. Besides networking complexity, Actinobacteria and Eurotiomycetes were found to be significantly and positively associated in PSD-2 soils but not in PSD-1 soils. As described above and in our previous work

(Xia et al., 2020), these taxa preferred silt fraction and might work together to mine recalcitrant organic matter associated with soil fine particles. We considered that understanding PSD-

associated microbial co-occurrence could help advance our knowledge of soil organic matter dynamics and thus carbon sequestration.

3.5. Conclusions

This work highlighted the significance of soil pore size distribution and water-filled pore space in structuring the soil bacterial and fungal communities and demonstrated divergent effects of different pore size distributions. First, soil pore size distribution and water-filled pore space were found to play a more important role in influencing bacteria rather than fungi. Soil wetting promoted alpha diversity of the total bacterial community especially evenness in soils with more macropores and yet suppressed those in soils with more micropores. Second, effects of soil pore size distribution and pore connectivity depended on microbial morphology. The fungus-like

Actinobacteria and a few fungi (Ascomycota subtaxa) were more abundant under drier conditions for soils with more micropores while other bacteria were more abundant by wetting.

Third, there were less microbial associations in soils with more micropores. This was likely due to micropore-imposed physical isolations and restrictions of nutrient diffusion.

3.6. Acknowledgements

This work was financially supported by the NC turfgrass center for research and teaching and the USDA regional project, turfgrass phytobiome. We greatly appreciated Mr. Adam

Howard for helping determine soil water retention curves and the Environmental and

Agricultural Testing Service lab of NC State University for characterizing soil properties.

3.7. References

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Table 3.1 Descriptive statistics of physicochemical properties of all the soil samples#.

Sand Silt Clay pH Inorganic N TC TN CEC BS -1 -1 -1 % μg N g soil mg g soil cmolc kg soil % IA 24.1 48.8 27.1 7.7 52.7 16.4 1.3 20.3 69 GA 67.0 10.5 22.5 5.8 47.4 6.7 0.6 5.6 97 TG13 54.8 25.1 20.1 5.7 137.5 - - - - TG22 43.3 39.9 16.8 5.7 232.7 - - - - TG31 30.8 55.6 13.6 5.8 318.6 - - - - TN 17.2 73.2 9.6 5.9 326.8 7.9 1.2 7.7 100 SE 2.1 2.4 0.7 0.1 13.8 3.1 0.2 4.6 10 CV 44.3 48.5 31.6 12.1 62.7 29.5 21.2 40.1 11 #Sand, Silt, and Clay, sand, silt, and clay particles respectively; TC, total soil C; TN, total soil N;

CEC, cation exchange capacity; BS, base saturation; SE, standard error; CV, the coefficient of variation (%); Symbol “-”, not measured.

Table 3.2 Structural parameters of networks by soil pore size groups (PSD-1: IA, GA, and TG13; PSD-2: TG22, TG31, and TN).

Bacterial and fungal ASVs for making network were ≥ 0.05% relative abundance and present in over the half of soil samples.

Spearman’s rank correlation coefficients between ASVs were ≥ 0.7 for bacteria and ≥ 0.5 for fungi in intrakingdom microbial networks and were ≥ 0.5 for both bacteria and fungi in interkingdom networks#.

Node no. Avg. degree Modularity Clustering Component no. Edge density Bacterial DNA PSD-1 265 97.4 0.094 0.732 18 0.369 PSD-1-IA 238 12.1 0.266 0.621 86 0.051 PSD-2 378 2.3 0.473 0.544 260 0.006 Bacterial cDNA PSD-1 154 36.3 0.120 0.693 18 0.237 PSD-1-IA 132 4.5 0.575 0.493 32 0.034 PSD-2 309 6.8 0.124 0.609 165 0.022 Fungal DNA PSD-1 69 23.8 0.296 0.828 4 0.350 PSD-1-IA 57 18.0 0.261 0.721 5 0.321 PSD-2 80 3.7 0.134 0.627 40 0.051 All DNA PSD-1 334 99.5 0.104 0.718 27 0.299 PSD-1-IA 294 13.8 0.262 0.590 98 0.047 PSD-2 458 3.1 0.313 0.604 323 0.007 # PSD-1-IA, PSD-1 without IA soils; Node no., number of nodes; Avg degree, average degree; Clustering, clustering coefficient;

Component no., number of components.

Figure 3.1 Soil water retention curves at the high (A) and low (B) bulk density treatments.

Dashed lines separate the groups of major effective soil pore sizes by which water was considered to be depleted at the corresponding matric suction. Error bars represent standard error.

Figure 3.2 Principal coordinate analyses (PCoA) of soil bacterial (A, B) and fungal (C, D) communities based on Bray-Curtis dissimilarity matrices of both DNA (triangle) and cDNA

(square). Samples are labeled as IA, blue; GA, cyan; TG13, light blue; TG22, salmon; TG31, red; TN, dark red. Sequence depths were 7,000 and 6,000 for bacterial and fungal communities, respectively.

Figure 3.3 Preferences of major taxa of the total (A) and active (B) bacterial communities in terms of pore size distribution (PSD-1: IA, GA, and TG13; PSD-2: TG22, TG31, and TN; black

circle, IA not included; grey circle, all samples included) and other soil properties (IA, greater total C and pH versus soils having relatively low total C and pH, black triangle). Horizontal box

plots show the relative abundance of each taxa. Only taxa with the relative abundance ≥ 0.5%

and point-biserial correlation coefficient ≥ 0.5 were included. Taxon names are given staring

from the phylum level.

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Figure 3.4 Comparisons of bacterial preference over soil moisture status between the two groups of soil pore size distribution (PSD-1: IA, GA, and TG13; PSD-2: TG22, TG31, and TN). Only taxa with the relative abundance ≥ 0.5% and point-biserial correlation coefficient ≥ 0.5 were included. Taxon names are given staring from the phylum level.

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0.1% and point-biserial correlation coefficient ≥ 0.5 were included. Taxon names are given staring from the phylum level.

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Figure 3.6 Microbial association networks by soil pore size groups based on Spearman’s rank

correlation coefficients of bacterial and fungal ASVs. Criteria for ASVs to be included for the network construction are ≥ 0.05% relative abundance, presence in ≥ the half of total samples, and

≥ 0.7 and 0.5 Spearman’s coefficients for bacterial DNA/cDNA and fungal DNA, respectively.

Nodes are colored by modularity class and node sizes are proportional to node degree.

Corresponding adjacency matrixes at bacterial (sub)phyla level and fungal class level with relative abundance ≥ 0.1% are also given as correlograms. Taxon names are spell out in the first

correlogram and thereafter abbreviations are used.

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Figure 3.7 Interkingdom microbial association networks by soil pore size groups based on

Spearman’s rank correlation coefficients of bacterial and fungal ASVs (red, bacteria; green, fungi). Criteria for ASVs to be included for the network construction are: ≥ 0.05% relative abundance, presence in ≥ the half of total samples, and ≥ 0.7 Spearman’s coefficients for both bacterial and fungal DNA, respectively. Node sizes are proportional to node degree.

Corresponding adjacency matrixes at bacterial (sub)phyla and fungal class levels with relative abundance ≥ 0.1% are also given as correlograms with the interkingdom interactions marked in a black rectangle. Taxon names are spell out in the first correlogram and thereafter abbreviations are used.

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CHAPTER 4: Soil Texture Influences on Organic Decomposition: Perspectives of

Microbial Diversity, Composition, and Networking

Abstract

Global change and increasing food demand call for more sustainable and ecological soil management efforts, including directing soil microbial communities toward improved C sequestration. However, the heterogeneous nature of soils as well as microbial preferences to specific soil texture suggest that directing microbial roles in soil C sequestration will be challenging. This work investigated the combined effects of soil texture and substrate types on the soil microbial community structure and their C degradation abilities. Two sets of soil microcosms (1:100 and 1:1) of a 4×3 factorial design were constructed, with 4 types of manipulated soil texture (sandy loam; silt loam; clay; and clay loam) and 3 sources of organics

(TSB, tryptic soy broth; CA, a mixture of cellulose and humic/fulvic acids; and BS, barley straw). The two microcosms differed in that the 1:100 was created by inoculating a natural soil into artificially mixed pure soil particles at 1% rate and the other was by mixing the natural soil with the particles at 1:1 ratio. Soil enzyme activities, CO2 emission rate, and water retention curves were examined, and the bacterial and fungal community were investigated with MiSeq sequencing of marker genes. An interkingdom cooperation of complex C degradation of

Streptomyces and fungal genus Cephalotrichum was found in the 1:100 microcosm, where activities by fungi was promoted in large-pored and/or soils with more clay fraction and those by bacteria promoted in high-clay and fine-pored soils. In the 1:1 microcosm where SOM condition was more complicated, mainly fungi like the species Chrysosporium merdarium was involved in

C degradation. Actinobacteria were mainly negatively correlated with those of the other bacteria like Proteobacteria, and the weakest interactions were observed in the interkingdom interaction

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of clay in the 1:1 microcosm. The abundance of an endophyte Firmicutes species and several fungal species (e.g., Fusarium solani, Actinomucor elegans, Aspergillus protuberus) did not follow the patterns of the major fungal taxa which might be important indicators of soil health and are worthy of further investigation.

Keywords: Soil texture; Soil pore size distribution; C degradation; Microbial diversity; Microbial networking

4.1. Introduction

The issues of global change and increasing food demand are calling attention to a more sustainable and ecological soil management (Tilman et al., 2011; Komatsuzaki and Ohta, 2007;

Powlson et al., 2011). An idea of altering structure and function of the soil microbiome through managing soil habitats has become one of the promising solutions to improve the ecological services of soils (Lal, 2002; Bender et al., 2016). Studies over years found that the soil microbial community could be influenced by multiple environmental factors included but not limited to soil pH, moisture content, and nutrients (Lauber et al., 2009; Borowik and Wyszkowska, 2016;

Miransari, 2013), and there have been attempts to manipulate those properties for restoration management (Heneghan et al., 2008). Recently, more and more research pointed out that soil physical structure (e.g., pore size distribution and soil texture) also acted as a significant driver in shaping the soil microbial community (Chau et al., 2011; Carson et al., 2010). Due to the physiological and morphological differences between the single-cell bacteria and branched fungi, fungi could extend over large soil gaps for distant nutrient and oxygen while bacteria were mainly associated with mineral surfaces or within small aggregates (Huang et al., 2002; Sessitsch et al., 2001). Microbes belonging to the same kingdom but of different taxa also showed varied preference of distinct soil structure (Xia et al., 2020). Since phylogenetically different microbes

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have various usage of soil resources, the active existence or dominance of some specific taxa may have significant influences on soil function such as C sequestration, nitrogen cycling, and bioremediation. Therefore, there would be a question worth asking: is it possible to force an existing soil microbial community to develop towards a specific direction by changing the soil physical structure artificially (e.g., amending soil texture amending or soil compaction)? And if so, would this development alter soil function, and further influence soil ecological services?

However, these questions have been poorly addressed.

Current understanding of the soil texture or pore size effects on soil microbes are mainly based on natural soil survey. Due to other uncontrollable and/or unknown soil properties, soil physical structure has been shown to have significant, moderate, or trivial influence on the soil microbial community (Hassink et al., 1993; Chau et al., 2011; Karimi et al., 2018). There has been an increasing trend of using artificial soil incubation system to allow control and manipulation on soil properties. The artificial soils were either completely “created” by mixing soil particles and/or minerals at various combination, or by altering the texture of natural soils by adding different soil particles (Wei et al., 2014; Sleutel et al., 2012; Wolf et al., 2013). Through these approaches, the soil microbial community structure was found to be greatly affected by soil pore size distribution over substrates or even pH (Sleutel et al., 2012), and high clay content promoted the degradation of fresh soil organic matter (Wei et al., 2014). However, while many were able to identify the linkage between the soil structure and soil microbes or soil function, the interkingdom microbial associations and the relationship between the soil microbial community structure and their ecological functions were less addressed. As the overall detected soil function are in fact an additive result of all the microbial activity, understanding what microbes primarily function in the community would help better adjust the soil condition.

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The aim of this work tries to answer the question: to what extent does manipulation of soil texture and pore size distribution affect the soil microbial community structure and microbial ability in C degradation of organic matter with different complexity. Considering the possible confounding and extraneous factors, investigations were conducted with two artificial soil setups: one by altering the texture of natural soil with pure soil particles and other by inoculating small amount of natural soil into pure artificial ones.

4.2. Materials and Methods

4.2.1. Microcosm construction and soil texture manipulation

Two sets of three replicated microcosms of a 4×3 factorial design were constructed, with

4 types of manipulated soil texture (SL, sandy loam; SiL, silt loam; Cl, clay; and ClL, clay loam) and 3 sources of organics (TSB, tryptic soy broth; CA, a mixture of cellulose and humic/fulvic acids; and BS, barley straw). An agricultural soil (a mesic Typic Endoaquoll of clay loam; 24.1% sand, 48.8% silt, and 27.1% clay) was collected from the surface (20 cm) of a row crop production field in Indiana, air-dried, stored at room temperature for over a year, and then sieved

(2 mm) before use. Pure sand, silt, and clay particles (Fisher Scientific (Hampton, NH, USA) and

VWR Science (Radnor, PA, USA)), for soil texture manipulation were prepared for removal of particles-attached organic material and microbes by a sequential procedure of 3% H2O2 washing, supernatant removal, distilled H2O washing, and 24 h oven-drying at 105℃ (Soil Survey Staff,

2014; McDonnell, 1983).The first set of microcosms (1:1) was created by mixing the agricultural soil at the mass ratio of 1:1 with the pure sand, silt, clay, or their mixture equivalent to the particle fractions of the soil used, resulting in four types of manipulated soil texture, SL, SiL, Cl, and ClL, respectively (Table C.1; Fig. C.1). The second set of microcosms (1:100) was created

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by inoculating four artificial soils (i.e., pure sand, silt, and clay mixtures equivalent to the four types of manipulated soil texture as described above) with 1% of the agricultural soil.

Each type of manipulated soil texture was amended with TSB, CA (the mixture of cellulose and DS-80 Humic/Fulvic acid (Wallace Organic Wonder, Greene, RI, USA) at a mass ratio of 3:1), or BS (chopped and ground to < 0.1 cm), representing easily, intermediately, or difficultly degradable organic substances, respectively. By assuming 45% C of all the three sources of organics, we added TSB at 1.5 mg C g-1 soil, CA at 9 mg C g-1 soil, and BS at 9 mg C g-1 soil. Given large differences in four types of manipulated texture, it would be difficult to pack and maintain all these samples to the same bulk density. Instead, samples of four types of manipulated texture were packed at slightly different bulk densities based on ease of packing from some preliminary tests, but the water-filled pore space was kept similar (~ 46%). Briefly, prepared soil samples (50 g dry weight equivalent) were packed into amber jars (5 cm height ×

5.5 cm diameter), capped, and incubated at room temperature. The headspace CO2 concentration was measured daily during incubation using a LI-820 CO2 Gas Analyzer (LI-COR, Lincoln, NE,

USA) and air was refreshed every time after the measurement. Mass balance-based water addition was performed every week to maintain the soil moisture content. TSB treatments were terminated after two-week incubation; but CA and BS treatments were terminated after one- month incubation when headspace CO2 concentration was found to be relatively low and stable.

Aliquots of soils were stored at -20 ℃ and 4 ℃ for DNA extraction and soil physicochemical properties measurement, respectively.

4.2.2. Soil physicochemical properties and enzyme activities

The soil pH was determined at a soil/water (w/v) ratio of 1:2.5 by a pH meter. Soil water retention curves were determined by desorbing packed soils at target bulk densities from

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saturation with pressure plate extractors (Klute, 1986) with triplicates for each soil texture treatment. Effective pore sizes were estimated from the water retention curves and were classified into three groups: macropores (> 75 µm), mesopores (30 – 75 µm), and micropores (<

30 µm) (Cameron and Buchan, 2006; Banwart, et al., 2017).

Soil enzyme activities were measured, according to the methods described by Tian and

Shi (2014). Briefly, soil slurries were prepared by adding 50 mM sodium acetate buffer at pH 5.0 to soil at a soil/solution (w/v) ratio of 1:2.5 and shaking at 200 rpm for 1 h. Thereafter, 0.8 mL soil slurries were mixed with 0.2 mL of sodium acetate buffered substrates, i.e., 2 mM p- nitrophenyl-β-D-cellobioside, 10 mM p-nitrophenyl-β-D-glucopyranoside, and 2 mM p- nitrophenyl N-acetyl-β-D-glucosaminidase for exoglucanase, β-glucosidase, and β- glucosaminidase, respectively, and were incubated at 37 ℃ for 1 h, except for 2 h for exoglucanase. After centrifugation at 11,700 × g for 4 min, optical density of the supernatant was measured at 410 nm. The activity of the three enzymes was estimated based on the standard curve of p-nitrophenol and expressed as μmol of product g-1 soil h-1.

For the measurement of phenol oxidase, 0.8 mL soil slurries were mixed with 0.8 mL 5 mM 3,4-Dihydroxy-L-phenylalanine made by 50 mM sodium acetate buffer at pH 5.0 and incubated in dark for 1 h. After centrifugation, optical density of the supernatant was measured at

460 nm and the activity of phenol oxidase was expressed as the optical density differences (△

OD460) after deducting the soil alone control (no substrate) and the negative control (sodium acetate buffer alone) from the soil sample readings.

4.2.3. Microbial DNA extraction, amplification, and sequencing

Soil DNA was extracted from ~0.5 g soil of each sample using FastDNA Spin Kit for

Soil (MP Bio, Solon, OH, USA) and concentration was determined with a Nanodrop

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Spectrophotometer (Thermo Scientific, Wilmington, DE, USA). The V3-V4 region of bacterial

16S rRNA genes and the ITS1-ITS2 region of fungal ITS were PCR-amplified with Illumina adapter-appended primer pairs F319: 5’ -ACTCCTACGGGAGGCAGCAG-3’ and R806: 5’-

GGACTACHVGGGTWTCTAAT-3’ and F_KYO2: 5’-TAGAGGAAGTAAAAGTCGTAA-3’ and R_KYO2: 5’-TTYRCTRCGTTCTTCATC-3’, respectively (Toju et al., 2012; Klindworth et al., 2013). The amplification PCR was carried out in a 25 μL reaction of 12.5 μL KAPA HiFi

HotStart ReadyMix (Roche, Basel, Switzerland), 2.5 μL of DNA template (5 ng μL-1), and 5 μL of each primer (1 mM μL-1). Thermal cycling conditions for amplification PCR were: initial denaturation at 95℃ for 3 min; 30 cycles of 95℃ for 30 s, 55 ℃ for 30 s for bacteria and 51 ℃ for 30 s for fungi, and 72 ℃ for 30 seconds; final elongation at 72 ℃ for 5 min. The amplified

DNA was cleaned up with AMPure XP beads (Beckman Coulter Genomics, Danvers, MA, USA) and eluted in 10 mM Tris buffer (pH 8.5). The cleaned amplicons then had the unique index sequences added using the Nextera XT Index Kit (Illumina, San Diego, CA, USA). The index

PCR was a 25 μL reaction of 12.5 μL KAPA HiFi HotStart ReadyMix, 2.5 μL of cleaned-up amplicons, 2.5 μL of each index primer, and 5 μL of nuclease-free water. Thermal cycling conditions for index PCR were: initial denaturation at 95℃ for 3 min; 8 cycles of 95℃ for 30 s,

55 ℃ for 30 s, and 72 ℃ for 30 seconds; final elongation at 72 ℃ for 5 min. After a second cleanup on the index PCR products, the barcoded 16S and ITS amplicons were diluted to 10 nM, pooled in one library, and pair-end sequenced (300bp×2, v3 chemistry) on the Illumina MiSeq platform.

4.2.4. Bioinformatics and statistical analysis

Demultiplexed sequencing data had primers and adapters removed by cutadapt (v 1.18)

(Martin, 2011) and then processed using DADA2 (1.12) pipeline in R (3.5.3) (Callahan et al.,

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2016; R Core Team, 2018). In brief, the forward and reverse reads of 16S DNA and ITS regions were truncated and/or filtered. A table of amplicon sequence variants (ASVs) was generated after dereplication, error model training, pair-end merging, and chimeras removal. Singletons were then filtered out in QIIME 2 (2020.11) (Bolyen et al., 2019) before taxonomy classification and microbial diversity analysis. The ASV tables of bacteria and fungi were then rarefied to 12,000 and 7,000 respectively for downstream analysis. Alpha diversity metrics including observed

ASVs, Chao1, Shannon diversity index, and evenness were estimated; and the Bray-Curtis dissimilarity matrixes were used for both bacterial and fungal beta diversity analyses. Data normality was check by the Shapiro-Wilk test, with P value < 0.05 for most of CO2 data, indicating non-normal distribution. Therefore, the Kruskal-Wallis test by ranks was used for analysis of CO2 emission rate; two-way ANOVA and post-hoc Tukey’s test were performed in R for the multiple comparisons of other measured properties. The adonis permutation-based statistical test (Anderson, 2001; Oksanen, et al., 2018) was performed for group significance test of beta diversity. Co-occurrence patterns were analyzed at bacterial (sub)phyla and fungal class levels by making adjacency matrices from Spearman’s rank correlation coefficients of microbial abundances using the correlogram package in R (corrplot v 0.84, Wei and Simko, 2017).

Spearman’s rank correlation was also performed for evaluating associations of relative abundance of microbial taxa with soil particle fractions (i.e., sand, silt, and clay).

4.3. Results

4.3.1. Soil texture-associated pore size distribution, pH, enzyme activity, and CO2 emission

Due to slight changes in soil structure during soil saturation, the actual soil bulk density in soil water retention measurement were slightly different from the target bulk density (Table

4.1). However, as bulk density remained constant for all triplicates of the same texture groups,

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the water retention data could still reflect the overall water characteristics of the treatments. Soil texture-associated pore size distribution was estimated via water retention curves (Table 4.1; Fig.

C.1). The proportion of macropores (> 75 µm) was greatest in SL, followed by Cl, ClL, and SiL.

By contrast, the proportion of micropores (< 30 µm) was lower in SL than others. Based on the coarse-level pore size distribution, the four textural classes could be divided into three groups:

SL alone with the greatest macroporosity, SiL and ClL with the least macroporosity, and Cl with the medium macroporosity and yet a high fraction of micropores.

All the soils were alkaline (8 - 9) with the soil pH of 1:100 microcosm ~ 0.3 - 0.5 higher than those of 1:1 microcosm (Table C.2). Soil enzyme activities varied with soil texture and yet variations were enzyme and substrate dependent (Fig. 4.1). The activities of exoglucanase and β- glucosidase were generally lower in SL and Cl of lower silt content and greater macropores than in SiL and ClL when TSB was the substrate. When CA or BS was the substrate, activities of exoglucanase and β-glucosidase were the highest in Cl except for β-glucosidase in the 1:100 microcosm where the highest activity occurred in ClL. Compared to hydrolases, phenol oxidase activity responded to substrates more inconstantly between 1:100 and 1:1 microcosms, although in most cases, phenol oxidase activity was relatively lower in Cl. Besides, the differences in optical density of phenol oxidase were in fact much lower than blank alone control. It is worth mentioning that all enzyme activities differed significantly among soil types regardless of microcosm setups (P < 0.05), but the activities of exoglucanase and β-glucosidase varied the greatest, with the activities in CA and BS nearly two to five times greater than those in TSB.

Daily CO2 emission rates appeared to be higher in the 1:100 microcosm than the 1:1 microcosm (Fig. 4.2). Regardless of microcosm setups and substrate types, SiL was the soil that had the most fluctuation and often ranked the first in CO2 emission rates in the first half of the

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incubation. ClL had CO2 emission rates fluctuated in only the 1:100 microcosms but may be much higher than those in SiL when CA and BS were the substrate. In the second half of the incubation, for CA, the highest CO2 emission rates were more most often associated with SL especially in the last four days of incubation; for BS, sporadic significantly higher emission rates were found in the 1:1 microcosm (P < 0.05) but the trend associated with soil texture was mostly not significant. For the cumulative CO2 emission, significant differences were found only in the

CA treatment where ClL rank the first and SiL the second in the 1:100 microcosm and SiL the first in the 1:1 microcosm (P < 0.05).

4.3.2. Microbial diversity, composition, and preferences for textural classes

Soil texture influenced microbial alpha diversity, but effects were not reflected on all metrics (observed ASVs, Chao1, Shannon diversity index, and evenness) (Figure C.3). For bacteria, effects were more on Shannon diversity and/or evenness. Generally, the greatest diversity metrics were associated with Cl, especially for CA and BS in the 1:100 microcosm. For fungi, texture effects were also found on species richness in the 1:1 microcosm, besides Shannon diversity and evenness. In most cases, fungal diversity metrics were higher in SL and/or Cl than

SiL and ClL when TSB was the substrate. When CA was the substrate, however, fungal diversity metrics were greater in SiL than in others. When BS was the substrate, fungal evenness and

Shannon diversity were lower in either ClL or Cl than in other textural classes. It should be noted that diversity metrics were generally greater in the 1:1 microcosm than 1:100 microcosm and also in TSB than in other substrates.

Ordination analysis showed that bacterial and fungal communities were primarily separated by microcosm setups (1:100 vs. 1:1), followed by types of substrate and textural classes (Fig. 4.3). Within individual microcosm setups, substrates explained ~ 37 – 46% of

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bacterial variations and ~ 60 – 71% of fungal variations (Table C.3). Textural classes and their interactions with substrates also explained significant portions of bacterial (12 – 25%) and fungal variations (5 – 18%). It is worth noticing that except for one Cl sample of the BS treatment in 1:1 microcosm, all the bacterial communities in Cl were well separated from the others.

The bacterial community consisted of 17 and 24 phyla in 1:100 and 1:1 microcosms, respectively. Actinobacteria were dominant (~ 50 – 60%), followed by Proteobacteria (~ 13 –

23%), Firmicutes (~ 9 – 22%) and Bacteroidetes (~ 4 – 8%). Relative abundances of

Actinobacteria and Chloroflexi were promoted in CA or BS treatments, whereas Firmicutes and

Proteobacteria were enhanced in TSB (Figs. 4.4 and 4.5). In the 1:100 microcosm,

Actinobacteria were generally the most abundant in either Cl or ClL. By contrast, Firmicutes and

Proteobacteria were found to be more associated with SL and Cl, with the pattern more consistent when CA was the substrate (Fig. 4.4). Bacterial preferences on textural classes were linked to their associations with soil particle fractions (Fig. C.4), being positive between members of Actinobacteria and clay fraction and negative for members of Proteobacteria.

Bacterial preferences on textural classes in the 1:1 microcosm followed generally similar patterns in the 1:100 microcosm when TSB or CA was the substrates (Fig. 4.5). However, there were much fewer (marginally) significant texture effects in the CA or BS treatments. Besides, bacterial taxa correlations with soil particle fractions or pore size distribution in TSB and CA were similar between the two microcosm setups, but correlations in BS were different (Fig. C.4).

A total of 5 and 13 fungal phyla were identified in the 1:100 and 1:1 microcosms, respectively, with Ascomycota being most abundant (~72 – 92%), followed by

Mortierellomycota (~ 3 – 7%) and Mucoromycota (~ 2 – 19%) (Figs. 4.6 and 4.7). Nearly 45% of the Ascomycota abundance was contributed by a single genus Cephalotrichum of

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Sordariomycetes in the 1:100 microcosm and by a single species Chrysosporium merdarium of

Eurotiomycetes in the 1:1 microcosm. Though not completely the same, the distribution of

Ascomycota abundance across textural classes in both microcosm setups resembled those of its two major members, being more abundant in Cl and ClL of more clay or SiL and ClL of less macropores in the TSB treatments, or being more abundant in SL and Cl of more large pores in

CA and/or BS treatments. There were several other families that had only one or two abundant species or genus identified, such as Aspergillaceae (Aspergillus protuberus and Penicillium),

Nectriaceae (Fusarium solani), Cunninghamellaceae (Absidia caatinguensis), and Mucoraceae

(Actinomucor elegans), and consistent texture-based patterns were found in many of these genera or species especially in CA and BS. For example, when CA and BS were the substrates, they were generally the least abundant in Cl of the 1:100 microcosm, being negatively associated with soil clay percentages (Fig. C.5). In the CA and BS treatments of 1:1 microcosm, they were the most abundant in SiL or Cl, respectively, being positively or negatively associated with soil silt fraction. Preferences of other major fungal taxa on textual classes were also mirrored on correlations with soil particle fractions and pore size distribution and yet the results were less significant. For example, the association of Ascomycota and Cephalotrichum in the 1: 100 microcosm was similar but were much more significant in Ascomycota, being positively correlated with clay for TSB and negatively with silt for CA and BS. In the 1:1 microcosm, the association of Ascomycota less resembled those of its abundant sublevel taxa, being positively associated with silt for TSB, negatively with silt or clay for CA or BS, respectively.

4.3.3. Intra- and interkingdom associations of bacterial and fungal taxonomic groups

Distinct patterns of microbial taxonomic associations were found between soil textural classes (Fig. 4.8). In the 1:100 microcosm, positive bacterial associations (Spearman’s ρ > 0.3) in

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SL were ~ two times of those in SiL, Cl and ClL. Besides, more positive and/or negative interkingdom interactions (|ρ| > 0.3) were found in SL and SiL than Cl and ClL. Texture-specific intra- and inter-kingdom associations were stronger in the 1:1 microcosm than in the 1:100 microcosm. Similarly, more positive bacterial associations were found in SL than in SiL, Cl and

ClL. However, inter-kingdom associations were much weaker in Cl than SL, SiL and ClL.

Compared to CA and BS across soil textural classes, TSB resulted in more inter-kingdom associations (Fig. C.6).

4.3.4. Texture-based associations between the relative abundance of microbial taxa, enzyme activities and CO2 emissions

In general, texture-based patterns of cumulative CO2 emissions were not aligned with the texture-based patterns of soil enzyme activities, except for β-glucosidase in CA of the 1:100 microcosm. Relatively greater CO2 emissions in SiL and ClL of the 1:100 microcosm appeared to parallel with greater β-glucosidase activity in SiL and ClL. Besides, relative abundance of some dominant microbial taxa also aligned with the activities of several enzymes (Fig. C.7). In the 1:100 microcosm when CA and BS were the substrate, the highest exoglucanase activity and the highest abundance of Cephalotrichum occurred both in Cl, where the Cephalotrichum abundance accounted for ~51% and ~38% in CA and BS, respectively. When TSB was the substrate, Mucorales, a highly promoted order of Mucoromycota, had abundance aligned better with exoglucanase activity. Regardless of substrate types, abundance of Streptomyces aligned well with the activity of β-glucosidase in the 1:100 microcosm, however, this correlation remained only in the TSB treatment of the 1:1 microcosm. In the 1:1 microcosm, more but weaker linkages were found between microbial abundance and the two cellulose degradation enzymes. For example, Sordariomycetes, accounting for ~57% of the total fungal community in

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TSB, had the highest abundance in SiL and ClL, in which exoglucanase and β-glucosidase also had the highest activity. The species Chrysporium merdarium of Eurotiomycetes had abundance in accordance with both exoglucanase and β-glucosidase in the CA treatment. This association was weaker when BS was the substrate (not shown in the figure). However, no significant alignments of the abundance-activity pattern were found in the BS treatments of 1:1 microcosm.

These abundance preferences were also mirrored in the interkingdom correlations, with

Actinobacteria consistently and positively correlated with Sordariomycetes in all textural classes of the 1:100 microcosm, but less correlated or even negatively correlated in the 1:1 microcosm.

As for β-glucosaminidase, the enzyme activity aligned mostly with the abundance of

Actinobacteria except for in the BS treatment of the 1:1 microcosm where it aligned better with

Alphaproteobacteria. No microbial taxonomy could align with phenol oxidase activity.

4.4. Discussion

4.4.1. Soil enzyme activities and associations with microbial taxa

The activity of extracellular enzymes in soils are in nature a combined result of substrate availability, enzyme concentration, and the conditions for degradation reactions, among which the enzyme concentration may be translated to the abundance of microbial decomposers. With most members of Actinobacteria and fungi being able to utilize SOM, discussion on extracellular enzyme activities with every single taxon would be unrealistic. Here, comparisons were only made on enzyme activities with the most abundant taxa for that these microbes may be the major decomposer in various combination of soil texture and substrates.

The greatest contrast of exoglucanase and β-glucosidase activities among substrate groups in both microcosm setups might be due to substrate limitation, as cellulose, either in the form of pure chemical or components of straw, was only provided in CA and BS treatments.

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Degradable cellulose in the TSB treatment may mainly came from necromass especially those of bacteria as they were r-strategist that could quickly proliferate with simple organic nutrients.

Because of bacterial preference in micropores (Hassink et al., 1993), necromass accumulation was expected in soil SiL and ClL when TSB was the treatment, which explained the activity patterns of both exoglucanase and β-glucosidase. Therefore, the distribution of the major fungal decomposers (or the enzyme producer) would depend on substrate distribution. On the other hand, for soils without substrate limitation (CA and BS in the 1:100 microcosm), reaction condition would be a more important factor that mattered. The genus Cephalotrichum that shared the same texture-based abundance pattern with exoglucanase was previously identified as an efficient cellulose decomposer (Woudenberg et al., 2017), and it was highly possible that

Cephalotrichum was the major microbes that contributed to exoglucanase activity. As oxygen was required in the process of enzyme production and substrate consumption by fungi and that extracellular enzyme function could be enhanced by clay surfaces, soils with more macropore fractions as well as clay fractions (Cl) would have higher exoglucanase activities.

This was also the case for exoglucanase and β-glucosidase in the 1:1 microcosm when

CA and BS were the substrate. In contrast, due to the exactly cooccurred pattern of β-glucosidase activity and Streptomyces abundance among textural classes in the 1:100 microcosm, the enzyme activity could mainly depend on the abundance of this bacteria, which might prefer an environment of more clay fractions. Recent research reported that nutrient exploration of

Streptomyces could be triggered by fungal signals (Jones et al., 2017). Our results may be an evidence on microbial interkingdom cooperation in soil C degradation. However, interkingdom interactions in C degradation may be much weaker in soils with more complexed SOM, i.e., the

1:1 microcosm which carried the original SOM in agricultural soils. In this situation, C mining in

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recalcitrant C compounds may rely mainly on fungi and intrakingdom corporation. When the C complexity further increased (BS treatment), more taxa would be involved in C degradation.

The activity pattern of β-glucosaminidase was another case of decomposer limitation as it was of very similar activity levels among substrate types as well as textural classes. Bacteria were previously recognized as the major mediator of chitin degradation (Beier and Bertilsson,

2013) and the members included but were not limited to Actinobacteria and Alphaproteobacteria

(Krsek and Wellington, 2001; Cottrell et al., 2000; Raimundo et al., 2021). The cooccurrence pattern of the enzyme activity with Actinobacteria in most soils suggested soil clay fraction was the most important factor that determined β-glucosaminidase activity as well as the abundance of

Actinobacteria.

However, the activity of phenol oxidase could not be explained with the described mechanisms. With only the CA treatments having phenolic compounds provided, the phenol oxidase activities among substrate treatments remained at very close levels, suggesting that all the activities were in fact very low and phenolic compounds were not the major C source in the microcosm. A possible explanation was that although microbes were able to utilize phenolics

(Robertson, et al., 2008), recalcitrant C was not the first choice for microbes especially when other forms of C presented (cellulose).

4.4.2. CO2 emission rate of different stages and associations with soil texture

The higher values of both daily and cumulative CO2 emission rates in the 1:100 microcosm over the other suggested that microbes in the former microcosm setups were of more active status. This was a reasonable pattern as the microbes in the 1:1 microcosm had partially adapted to the soils whereas the 1% inoculation stimulated microbial adaptation to a new environment of the 1:100 setup.

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During the first two-week incubation, CO2 emission pulse existed merely in the textural classes with least macropore fraction. The similar pattern of cumulative CO2 emission in the

1:100 microcosm when CA and TSB were the substrate with Streptomyces abundance suggested that the increased soil respiration during early incubation may mainly result from the thrive of

Actinobacteria decomposer in small pores at the early stage of degradation. Another possible reason for CO2 pulse was that the percentage of air-filled pore space in micropores could be limited and soil aeration was poor. In the microcosms, the produced CO2 may be restricted in soils at first, and then quickly released when microbial C degradation caused soil aggregation and tiny changes in pore networks as reported previously (Rabbi et al., 2020), and when the hyphae of Actinobacteria reached soil surface.

The noticeable divergence of texture-differentiated CO2 emission trends after two-week incubation suggested that SOM degradation could be a long-term reaction with several stages.

Typically, sandy soils were recognized as the one with least C storage due to the lack of enough surface area for SOM attachment or adsorption (Tecon and Or, 2017) as well as the best soil aeration and water permeability (Al-Jibury, 1961). Although the total microbial activity may not be the highest, C of SOM in sandy soils may be released by relatively low but continuous degradation. In contrast, in soils with smaller pores or more clay fraction, microbes especially bacteria would quickly proliferate at the initial community establishment due to better contact with SOM and other nutrients. However, a fraction of SOM may remain as that the heterogeneity of soil water condition and pore sizes may not allow microbial degradation (Stamati, et al., 2013) and this divergence could be more significant in soils with simpler SOM combination like the

CA treatment. As for the more complicated SOM like the straw, the degradation process may involve the cooperation of a well-organized microbial community (Zhan et al.). As less than 50%

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of straw could be degraded within a month (Chu et al., 2021), our results of the daily and cumulative CO2 may be an early trend of the reaction and a more significant and different pattern may emerge during long-term degradation.

4.4.3. Soil texture-based microbial alpha diversity

It is not surprising that both bacterial and fungal species richness were much lower in the

1:100 microcosm than in the 1:1 microcosm, given that “microbial inoculum” was 10-fold diluted in the 1:100 microcosm and thus might only contain a small set of taxonomic groups present in the 1:1 microcosm. However, the advantage of the 1:100 microcosm over the 1:1 microcosm lay in reduced confounding and extraneous factors, such as indigenous soil organic matter, thereby being better for understanding mechanics of texture influences on microbial diversity. The observation that the clay-rich soil (Cl) promoted bacterial evenness and Shannon diversity index in CA and BS was in agreement with our previous work (Xia et al., 2020).

However, there were no (discernible) differences in diversity metrics among textural classes in

TSB; and the removal of differences was mainly attributed to the stimulation by TSB in clay- poor soils. A possible explanation for inconsistent soil texture effects between substrates was that sufficient carbon supply via TSB might relieve competitions for limited resource in clay-poor more than in clay-rich soils, favoring co-existence and co-development of diverse bacteria.

Indeed, clay-poor soils (SL, SiL, and ClL) were found to have more negative inter-kingdom associations than the clay-rich soil (Cl). Perhaps, the clay-rich soil improved microbial isolations via fine pores and thus limited inter-kingdom competitions for resource to promote diversity.

While pores of < 30 µm are preferable microsites for microbes, fungi may be more populated in pores of 15 – 30 μm and bacteria more enriched in smaller pores (Hassink et al., 1993; Sleutel et

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al., 2012). Unfortunately, data sets of water retention were insufficient for characterizing pores of < 30 μm.

Fungal evenness and Shannon diversity index also showed substrate-based texture responses, being enhanced in the clay-rich soil (Cl) when TSB was the substrate, no difference between textural classes when CA was the substrate and enhanced in the clay-poor soils (SL,

SiL, and ClL) when BS was the substrate. Compared to bacteria, fungi are generally not fast- growing organisms for occupying territory and therefore they are “apparently” inferior competitors for abundant and readily available carbon as in the case of TSB. As such, the more they were separated from bacteria, the better they could proliferate. The clay-rich soil (Cl) likely provided more microsites to separate fungi from bacteria and thus promoted fungal diversity. On the contrary, fungal diversity might be promoted in clay-poor soils (SL, SiL, and ClL), provided that fast-growing bacteria were not well stimulated as in the case of BS. No texture-based differences in diversity metrics when CA was the substrate was likely because the substrates favored fungi over bacteria, resulting in less competitive pressure from bacteria and thus less reliance on physical protection for promoting diversity.

Texture effects on diversity metrics became more complicated in the 1:1 microcosm. Of all the soils, SL of the 1:1 microcosm had the greatest proportion of macropores (~ 50%), exacerbating uneven distribution of water in macro-, meso-, and micropores. As discussed previously (Chapter 3), large pores of low water activity likely promoted fungal diversity metrics, particularly species richness. Surface organic chemistry of soil particles might also be part of the equation in controlling microbial selection, competition, and thus diversity. Humic and fulvic acids are known to be clay- and silt-bound, and their concentrations and chemical

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compositions could be altered by soil mineralogy. Perhaps, this was the cause for greater fungal diversity in the silt-rich soil (SiL) in the 1:1 microcosm.

4.4.4. Microbial community structure and the interaction of soil texture and SOM

The relative abundance of the bacterial community in the 1:100 microcosm aligned well with our previous report that Actinobacteria favored clayey soils and some members of

Proteobacteria favored sandy soils (Xia et al., 2020). The inclusion of different substrate types in this work might contribute with some new explanation. Different texture-based abundance distribution occurred in Actinobacteria when CA was the substrate where the relative abundance was much higher in SL, Cl, and ClL. Due to the highest relative abundance of Actinobacteria in the CA treatments, Actinobacteria may be at the most active status in this treatment compared to

TSB and CA. Considering the possible cooperation of the most abundant sublevel taxon

Streptomyces with fungal decomposers, proliferation of Actinobacteria in SL may benefit from the combined cellulose degradation by both bacterial and fungi. This was also supported by the result that interkingdom interaction of Actinobacteria and Sordariales was positive in all texture groups and in all substrate groups of the 1:100 microcosm. While the interaction grouped by substrate types were weakened or even negative in the 1:1 microcosm, the slightly positive correlation of Actinobacteria with Eurotiomycetes suggested that Actinobacteria were still able to acquire benefits from fungi despite changes in the Ascomycota composition. However, this effect may be weaker when soil SOM condition was more complex and when fungi played a more important role in C degradation. It was possible that when nutrient types and amount were limited, interkingdom cooperation was more important in C mining whereas bacteria and fungi hardly cooperated and utilize different nutrients when SOM and soil pore networks became complicated. This mechanism was supported by the weakest interkingdom interaction when BS

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was the substrate and in soil Cl of the 1:1 microcosm and could partially explain why the abundance of Actinobacteria in the 1:1 microcosm did not align with those of fungi when BS was the substrate.

Different from Actinobacteria, other major bacterial taxa showed a general preference for soil SL and SiL, but more texture-based variations occurred when TSB or BS was provided in the 1:100 microcosm. This pattern might not be a true preference of these bacteria in non-clayey soils but instead as a reflect of better proliferation of Actinobacteria in clayey soils as

Actinobacteria was extremely dominant (80 - 90%) in the CA treatment. Besides, abundance of

Firmicutes and Proteobacteria were the highest when TSB was the substrate, and the

“preference” of non-clayey soils was the weakest. However, explanation of the texture-based bacterial abundance in the 1:1 microcosm would be much more challenging. As less bacterial taxa were (marginally) significantly affected by texture groups in the CA and BS treatments, it might be possible that when substrates other than readily available C were provided, bacterial abundance in the 1:1 microcosm may be more influenced by the diverse nutrients which came from the agricultural soils.

The abundance of Firmicutes was one of the trickiest patterns to explain as regardless of soil microcosm setups, over 50% of the phylum abundance belonged to a plant endophyte (Wang et al., 2017), Bacillus flexus that with no reported association with soil texture. It was ever found to be active in very alkaline environment (pH of 9 - 12) other than soils (Toldra and Kim, 2017), and it might explain why the species was very abundant in our soils and mostly abundant in non- clayey soils except for the CA treatment of the 1:1 microcosm where its abundance was the lowest. Future investigation may be needed in studying the mechanism of B. flexus responses to soil environment changes and it may help as a plant-beneficial indicator.

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In the fungal community of the 1:100 microcosm, the preference of Ascomycota in Cl and also in SL with CA and BS as the substrate were exactly in alignment with the most abundant class Sordariomycetes, and also the most abundant genus Cephalotrichum. As described earlier, this might be a reflection of the aerobic cellulose degradation. In contrast, in the 1:1 microcosm, only the CA treatment showed the similar texture-based pattern but with a stronger correlation to the abundant cellulose decomposer C. merdarium. The unique texture- based abundance pattern of F. solani, Aspergillus, Penicillum, A. elegans and B. atrogriseum was not in accordance with our previous report and no perfect mechanism could be proposed for their distributions based on current knowledge. As their abundance pattern were nearly against those of majority fungal members, explanation for their distribution based on only the soil texture and substrates information would be adventurous.

4.4.5. Microbial interactions and possible illustrations

The intra- and interkingdom interactions of bacteria and fungi were an integrated reflection of the entire soil microbial community. The consistently negative association of

Actinobacteria with sublevel taxa of Proteobacteria suggested that the two microbial taxa with completely different morphology and physiology occupied distinct niches in soils and that

Actinobacteria may resemble fungi in acquiring nutrients. The positive correlation of

Actinobacteria with most fungal classes in the 1:100 microcosm and yet its negative or weak associations with most of fungal classes in the 1:1 microcosm suggested that in soils with SOM of less amount and less complicated compositions, bacteria and fungi may collaborate in mining soil C, but they may explore organic matter of different composition and complexity when soil

SOM was more complicated. The interkingdom correlations could also be restricted by soil pore size distributions when the habitats for both bacteria and fungi were not limited. Therefore, the

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Cl soil of the 1:1 microcosm became the one with the least interkingdom correlations as it contained not only a large fraction of macropores but also the finest sizes of micropores. As for the stronger intrakingdom correlations in the 1:1 over the 1:100 microcosms, a possible reason could be that with more microbial species and more complicated soil physicochemical environments, competition pressure would be much stronger in the 1:1 microcosm.

4.5. Conclusions

This work highlighted the combined effects of soil texture and substrate types on the soil microbial community as well as their activities. An interkingdom cooperation of complex C degradation of Streptomyces and fungal members was found in soils, where activities by fungi was promoted in large-pored soils and those by bacteria promoted in clayey soils. When the

SOM condition became complicated, more microbes especially fungal species were involved in

C degradation and soil pore size would be the main factor that restricted the reaction if substrates were sufficient. The abundance of both bacteria and fungi were taxa-specific and texture-based where the mechanism lied in mainly the reaction requirements of C degradation or synthetic processes. The abundance patterns of the plant beneficial Firmicutes sublevel taxa and several fungal species may be affected by other unknown factors other than the described texture or substrate effects and are worth further investigations.

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Soil Target ρb Actual ρb Total porosity Macropore Mesopore Micropore (g cm-3) (%) (%) Microcosm 1:1 SL 1.40 1.33 47.3 52.3 8.5 39.2 SiL 1.24 1.18 53.6 11.1 15.0 73.9 Cl 1.24 1.18 52.4 24.9 8.2 66.9 ClL 1.32 1.25 50.3 15.8 12.8 71.4

Microcosm 1:100 SL 1.50 1.51 41.6 21.2 21.3 57.5 SiL 1.24 1.24 54.7 9.7 12.6 77.7 Cl 1.24 1.25 51.0 15.6 10.2 74.2 ClL 1.40 1.40 47.4 9.1 10.3 80.6

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Figure 4.2 Soil CO2 emission rates of 1:100 microcosm (A) and 1:1 microcosm (B); cumulative

CO2 emission at the end of incubation for TSB, CA, and BS in 1:100 and 1:1 microcosms (C).

Error bars represent standard errors. Asterisks indicate significant effects of soil texture under respective substrates (P < 0.05).

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Figure 4.3 Principal coordinate analyses (PCoA) of soil bacterial (A) and fungal (B) communities based on Bray-Curtis dissimilarity matrices at a sequencing depth of 12,000 and

7,000, respectively (unshaded symbols, 1:100 microcosm; half-shaded symbols, 1:1 microcosm; green: TSB; red, CA; cyan, BS; square, SL; diamond, SiL; hexagon, Cl; circle, ClL).

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Figure 4.5 The heatmap of normalized relative abundance of major bacterial taxa (> 1%) in soil microcosm 1:1 at a sequencing depth of 12,000. Only taxa that were significant by soil texture, substrate, or their interactions were included. For each substrate treatment, abundance with significant or marginally significant texture effects (P < 0.1) was given after being normalized by subtracting the mean of different textural classes and then divided by the standard deviation. The horizontal scatter plot shows the average relative abundance of each taxon in every substrate treatment. Error bars represent standard errors.

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CHAPTER 5: Conclusions, Limitations, and Future Work

5.1. The overall conclusions

This dissertation provided insights into soil texture-based patterns of the soil microbial community structure and functions as well as provided explanations for the possible mechanisms. Briefly, in Chapter 2, through a survey of the microbial community across various soil environments, soil texture was found to have taxa-dependent effects on the soil microbial community. The enhancement of Actinobacteria, Chloroflexi, Basidiomycota, and

Eurotiomycetes in soils with more silt/clay content suggested that the soil texture effects may be via soil pore size distribution and perform differently based on microbial morphology. The promoted abundance of bacterial genes involved in organic matter degradation by fine-textured soils suggested the possibility of predicting soil microbial function with soil texture information.

In Chapter 3, the effects of soil pore size distribution and water-filled pore space were specifically investigated, with the two properties found to play a more important role in influencing bacteria rather than fungi and the effects depending on microbial morphology. The results of the preference of Actinobacteria and some fungal taxa for dry and small pore environments echoed the findings in Chapter 2. Besides, less microbial associations were observed in soil with micropores. The results further emphasized the importance of soil pore sizes and connectivity in structuring the soil microbial community. In Chapter 4, with manipulation on both soil texture and substrate types, soil enzyme activities also showed texture- based patterns which aligned with the abundance of several major bacterial (e.g., Streptomyces) and/or fungal (e.g., Sordariomycetes and Eurotiomycetes) taxa. These results suggested the possibility of adjusting the soil microbial community and functions with soil texture and other

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associated properties. However, more research together with other techniques are needed for a better understanding of the detailed mechanism.

5.2. Limitations and future work

Due to limited time and facilities, there were several limitations of the study. The main conclusions of the dissertation were mainly based on the relative abundance of microbial taxa.

Chances were that the changes of some rare species/taxa may be the consequence of the active changes by some major microbes. Although there have been attempts trying to eliminate these effects (Morton, et al., 2017), the tools were not very effective on environmental microbes especially when the studied properties (soil particle fractions and pore size distribution) were continuous variable and could not be arbitrarily divided into groups. Better ways like quantitative analysis (e.g., quantitative PCR) and/or analysis of traits of some specific microbes could be involved to facilitate the discussion of the community data. However, as not all the soil microbes were well studied currently, there is still a long way to go for investigations with assistance of other techniques.

The correlation analysis of microbial abundance with soil particle fractions suggested a taxa-specific distribution pattern of various microbes. However, microbial preference of soil particles was not directly verified by examining the community composition within each particle class separately. Soil fractionation and wet sieving are traditional methods for investigating particle-associated microbes and yet application on the vast number of samples in this work would be unrealistic. As for future work, these techniques could be applied to small-scale soil microcosms with more accurate control on soil properties and with comparison of sieved and non-sieved soils. Besides, through direct visualization or quantification of soil pore networks and resource distribution at microscale, techniques like X-ray topography as well as in situ probing

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of heterogeneous soil properties could also facilitate the understanding of the detailed mechanism of microbial distributions.

Besides, all soil properties were analyzed, and the microbial DNA was sequenced at the end of soil incubation. While some microbes may behave as critical species in the initial establishment of the microbial community at various soil environments and varied mechanisms may be involved, this could not be reflected in this dissertation. Due to the dynamic characteristic of soil microbes, the results of this work were a snapshot of the soil environments and could not reflect the long-term development of the microbial community. Because that microbial DNA extraction had to be done with destructive sampling and that Miseq sequencing had limitation on sample numbers to be examined, time-series analysis on soil microbes could not be realized. For future work, if the detailed association of soil functions and enzyme activities were to be investigated, experiments could focus on only one or two soil functions/enzymes with regular sampling during soil incubation which would allow for time- based analysis and revealing the dynamic microbial changes with soil properties.

One final goal of the soil microbiology study is to estimate the status of soil function/health or to provide advice on soil management based on metrics of soil microbes.

However, this goal could only be achieved with more data input and more investigations on soil microbes in different environments because climate and other continent-scale properties could all restrict or modify the potential of microbial activities. It would be promising if metadata analysis can be carried out to build mathematical models with data from enough and related studies.

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5.3. Reference

Morton, J.T., Sanders, J., Quinn, R.A., McDonald, D., Gonzalez, A., Vázquez-Baeza, Y., et al.,

2017. Balance trees reveal microbial niche differentiation. MSystems 2(1): e00162–16.

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APPENDICES

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Appendix A

Table A.1 GPS locations and years of establishment of 10 bermudagrass golf courses in North

Carolina, USA.

Golf course GPS§ Year of establishment Bayer Research Facility, Clayton NC 35°38'47.4"N 2010 78°25'42.4"W Lonnie Poole Golf Course, Raleigh NC# 35°45'41.8"N 2010 78°40'26.9"W NC State Practice Facility, Raleigh NC 35°48'06.0"N 2001 78°41'42.2"W The Revival Golf Course at the Crescent, 35°42'16.3"N 2000 Salisbury NC 80°30'00.9"W The Neuse Golf Club, Clayton NC 35°38'13.1"N 1993 78°23'56.6"W Treyburn Country Club, Durham NC 36°07'14.0"N 1988 78°51'27.6"W North Ridge Country Club, Raleigh NC 35°52'33.2"N 1967 78°37'04.4"W Raleigh Country Club, Raleigh NC 35°46'26.7"N 1948 78°35'38.1"W Country Club of Salisbury, Salisbury NC# 35°41'31.4"N 1927 80°28'04.6"W Hope Valley Country Club, Durham NC 35°56'46.3"N 1926 78°56'42.0"W #Six intact cores were taken randomly from these golf courses, whereas only three were from other courses.

§GPS coordinates were recorded for the first intact soil cores that were sampled from respective golf courses.

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Table A.2 Soil physicochemical properties by soil depths and moisture treatments. Different letters within each column indicate significant differences (P < 0.05). Values without letter labeling were not statistically different.

Moisture# Porosity WFPS pH Sand Silt Clay Inorganic N TC TN (%) (%) (µg g-1 soil) (mg g-1 soil) Moisture treatment Dry 7.2 b 49.0 0.18 c 6.0 59.2 21.0 19.8 44.1 21.2 1.7 Moist 28.6 a 54.5 0.59 b 5.8 60.7 20.5 18.8 53.5 23.0 1.9 Wet 32.1 a 52.0 0.71 a 5.9 60.7 19.4 19.9 50.4 21.4 1.7 Soil depth Top 30.0 a 60.3 a 0.49 5.8 60.2 20.3 19.5 70.9 a 34.2 a 2.8 a Bottom 15.4 b 43.5 b 0.50 6.0 60.2 20.3 19.5 27.8 b 9.5 b 0.8 b #Moisture, gravimetric moisture content; WFPS, water-filled pore space; Sand, Silt, and Clay, the percentage of sand, silt, and clay particles, respectively; TC, total soil carbon; and TN, total soil nitrogen.

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Table A.3 Marginal and sequential tests by distLM (distance-based linear model) to show the proportions of sand percentage in explaining variations of the bacterial and fungal communities under different moisture and soil depth treatments.

Marginal Test Sequential Test P# Prop. P Prop. Rank Bacteria Dry 0.002 0.081 0.022 0.062 2 (pH) Moist 0.002 0.078 0.002 0.079 2 (pH) Wet 0.001 0.095 0.001 0.095 1 Top 0.001 0.079 0.006 0.041 4 (pH, TC, and porosity) Bottom 0.001 0.081 0.001 0.073 2 (pH)

Fungi Dry 0.033 0.063 0.061 0.062 2 (pH) Moist 0.002 0.095 0.001 0.095 1 Wet 0.012 0.088 0.011 0.088 1 Top 0.016 0.051 0.013 0.048 3 (TC, and pH) Bottom 0.007 0.053 0.037 0.044 2 (pH) #P, P value; Prop., the proportion of sand percentage-explained variation; Rank, the rank of sand percentage into the regressor set by a forward-selection sequential test; higher rank regressors are given in parentheses; TC, total soil carbon.

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Figure A.1 Principal coordinate analyses (PCoA) of soil bacterial (A, B) and fungal (C, D) communities based on Bray-Curtis dissimilarity matrices of the entire set of soil samples (empty circles). Samples are also grouped by the percentage of sand (or pH) into three classes of equal size (means ± standard errors for n = 24): Low (sand = 30.3 – 52.2%, pH = 4.2-5.6, grey),

Medium (sand = 53.9 – 71.3%, pH = 5.7-6.1, dark grey), and High (sand = 71.8 – 83.4%, pH =

6.2-7.2, black).

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Figure A.2 The heatmap of Spearman’s rank correlation coefficients between soil properties and relative abundances of major bacterial taxa from phyla to genera (>2% of the relative abundance on average) grouped by Bottom (left) or Top soils (right). The square size and color represent the magnitude and direction of the correlation coefficient, respectively. If a taxon and its sublevels had the same Spearman’s rank correlation coefficient as well as the relative abundance (e.g.,

Acidimicrobiia and Acidimicrobiales), they were merged into one line/row on the heatmap.

Moisture, gravimetric moisture content; WFPS, water-filled pore space; TC, total soil carbon;

InorgN, inorganic N; Sand, Silt, and Clay, the percentage of sand, silt, and clay particles, respectively.

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Figure A.3 The heatmap of Spearman’s rank correlation coefficients between soil properties and relative abundances of major fungal taxa from phyla to genera (>1% of the relative abundance on average) grouped by Bottom (left) or Top soils (right). The square size and color represent the magnitude and direction of the correlation coefficient, respectively. If a taxon and its sublevels had the same Spearman’s rank correlation coefficient as well as the relative abundance (e.g.,

Mortierellacea and Moretierella), they were merged into one line/row on the heatmap. Moisture, gravimetric moisture content; WFPS, water-filled pore space; TC, total soil carbon; InorgN, inorganic N; Sand, Silt, and Clay, the percentage of sand, silt, and clay particles, respectively.

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Appendix B

Table B.1 Treatment design of moisture content and bulk density and the corresponding water- filled pore space.

Soil Treatment Gravimetric Bulk density Water-filled -3 moisture content (ρb, g cm ) pore space (θg, %) (%) IA/TN/TG31 Low θg; Low ρb 14.67 0.9 20 Low θg; High ρb 14.67 1.2 32 High θg; Low ρb 23.62 0.9 32 High θg; High ρb 23.62 1.2 52

TG13/TG22 Low θg; Low ρb 10.63 1.1 20 Low θg; High ρb 10.63 1.4 32 High θg; Low ρb 16.78 1.1 32 High θg; High ρb 16.78 1.4 50

GA Low θg; Low ρb 9.12 1.2 20 Low θg; High ρb 9.12 1.5 32 High θg; Low ρb 14.37 1.2 32 High θg; High ρb 14.37 1.5 50

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Table B.2 Percentage of effective soil pore size relative to the respective total pore volume, estimated from water retention curves of the six soils at high and low bulk density (ρb). Target and actual bulk density represent bulk densities at the beginning and end of water retention curve measurement, respectively.

Soil Target ρb Actual ρb Total porosity Macropore Mesopore Micropore g cm-3 % % High ρb treatment IA 1.2 1.2 54.9 27.5 11.1 61.4 GA 1.5 1.5 37.5 16.8 22.4 60.8 TG13 1.4 1.4 43.4 15.0 13.6 71.5 TG22 1.4 1.4 44.7 7.2 7.6 85.2 TG31 1.2 1.3 55.6 14.0 8.5 77.5 TN 1.2 1.2 53.1 9.2 5.1 85.7 Low ρb treatment IA 0.9 1.1 67.0 49.9 6.1 44.0 GA 1.2 1.3 50.8 48.0 15.9 36.0 TG13 1.1 1.3 56.0 35.9 15.0 49.1 TG22 1.1 1.4 58.9 31.1 5.4 63.5 TG31 0.9 1.4 64.6 28.3 4.0 67.6 TN 0.9 1.1 63.2 29.3 5.5 65.2

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Figure B.1 Soil CO2 respiration rates over seven weeks averaged by soils (A), soil moisture content (B), and bulk density treatments (C).

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Figure B.2 Alpha diversity metrics of the total (DNA) and active (cDNA) bacterial and fungal communities.

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Figure B.3 Average alpha diversity metrics of the total bacterial and fungal communities by soils. Different letters within individual diversity metrics indicate significant differences between soils.

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Figure B.4 The heatmap of normalized relative abundance of major bacterial taxa shown in

Figure 3.3A. Normalization was made by subtracting the mean and then divided by the standard deviation of the six soils.

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Figure B.5 The heatmap of normalized relative abundance of major fungal taxa shown in Figure

3.5A. Normalization was made by subtracting the mean and then divided by the standard deviation of the six soils.

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Appendix C

Table C.1 Settings of soil physical properties in microcosm design#.

Sand Silt Clay ρb WFPS % g cm-3 % 1:100 microcosm SL 62.0 24.4 13.6 1.50 45.2 SiL 12.5 74.4 13.6 1.24 47.7 Cl 12.5 24.4 63.6 1.24 47.7 ClL 24.1 48.8 27.1 1.40 45.2

1:1 microcosm SL 62.0 24.4 13.6 1.40 45.2 SiL 12.0 74.4 13.6 1.24 47.7 Cl 12.0 24.4 63.6 1.24 47.7 ClL 24.1 48.8 27.1 1.32 46.1 # Sand, Silt, and Clay, sand, silt, and clay particles respectively; ρb, bulk density; WFPS, water- filled pore space.

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Table C.2 Soil pH measured at the end of incubation experiment. Different letters indicate significant differences of soil texture effects under specific substrate conditions.

Soil pH 1:100 microcosm 1:1 microcosm TSB treatments SL 9.04 a 8.68 ab SiL 8.96 ab 8.73 a Cl 8.87 b 8.52 c ClL 9.02 ab 8.65 b

CA treatments SL 8.68 a 8.32 a SiL 8.60 a 8.29 ab Cl 8.37 b 8.04 c ClL 8.59 a 8.20 b

BS treatments SL 8.77 a 8.22 b SiL 8.75 a 8.37 a Cl 8.45 b 8.04 c ClL 8.75 a 8.26 ab

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Table C.3 Adonis test of bacterial and fungal beta diversity.

Df SumsOfSqs MeanSqs F.Model R2 Pr(>F) Bacteria of 1:100 microcosm Substrate 2 2.630 1.315 39.347 0.461 0.001 Texture 3 1.425 0.475 14.215 0.250 0.001 Substrate*Texture 6 0.886 0.48 4.417 0.155 0.001 Residuals 23 0.769 0.033 NA 0.135 NA Total 34 5.709 NA NA 1.000 NA

Bacteria of 1:1 microcosm Substrate 2 2.242 1.121 17.301 0.369 0.001 Texture 3 0.752 0.251 3.866 0.124 0.001 Substrate*Texture 6 1.531 0.255 3.939 0.252 0.001 Residuals 24 1.555 0.065 NA 0.256 NA Total 35 6.080 NA NA 1.000 NA

Fungi of 1:100 microcosm Substrate 2 4.154 2.077 36.189 0.603 0.001 Texture 3 0.708 0.236 4.114 0.103 0.002 Substrate*Texture 6 0.703 0.117 2.042 0.102 0.021 Residuals 23 1.320 0.057 NA 0.192 NA Total 34 6.886 NA NA 1.000 NA

Fungi of 1:1 microcosm Substrate 2 6.108 3.054 156.059 0.710 0.001 Texture 3 0.445 0.148 7.581 0.052 0.001 Substrate*Texture 6 1.585 0.264 13.496 0.184 0.001 Residuals 24 0.470 0.020 NA 0.055 NA Total 35 8.607 NA NA 1.000 NA

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Figure C.1 Respective textural classes of artificial soil samples marked on the soil texture triangle.

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Figure C.2 Soil water retention curves of the two microcosms (A, 1:100 microcosm; B, 1:1 microcosm). Error bars represent standard errors. Dashed lines separate the groups of major effective soil pore sizes by which water was considered to be depleted at the corresponding matric suction.

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Figure C.3 Alpha diversity metrics of bacteria (A) and fungi (B) rarefied at the depth of 12,000 and 7,000, respectively. Error bars represent standard errors. Different letters indicate significant texture effects within individual substrates.

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Figure C.4 Spearman’s correlations of major bacterial taxa (>1%) with soil sand, silt, or clay fractions. Only taxa that were significant by substrate, soil texture, or their interactions are included. Significance at P < 0.001, P < 0.01, and P < 0.05 is indicated by ***, **, and *, respectively.

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Figure C.5 Spearman’s correlations of major fungal taxa (>1%) with soil sand, silt, or clay fractions. Only taxa that were significant by substrate, soil texture, or their interactions are included. Significance at P < 0.001, P < 0.01, and P < 0.05 is indicated by ***, **, and *, respectively.

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Figure C.6 Correlograms of Spearman’s correlations at bacterial (sub)phyla and fungal class levels with relative abundance ≥ 0.1% and detected in over two-thirds of samples in individual substrates. Interkingdom interactions are marked in a black rectangle. Abbreviations of taxon names are used, and the corresponding full names are the same as in Figure 4.10.

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Figure C.7 Activities of cellulose enzymes and the most promoted microbial taxa in each substrate treatment (A, 1:100 microcosm; B, 1:1 microcosm).

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