Long-Term N Fertilization Affects Soil Bacterial Communities in Agronomic Fields

LONG-TERM N FERTILIZATION AFFECTS SOIL BACTERIAL COMMUNITIES IN AGRONOMIC FIELDS

RENPENG SUN

THESIS

Submitted in partial fulfillment of the requirements for the degree of Master of Science in Crop Sciences in the Graduate College of the University of Illinois at Urbana-Champaign, 2018

Urbana, Illinois

Adviser:

Associate Professor Maria Villamil

ABSTRACT

Soil microbial communities influence plant productivity, chemistry and structure of soil and nutrient cycling. In turn, long-term nitrogen fertilization changes soil physical and chemical properties and thus influences soil microbiomes. In-depth understanding of the long-term nitrogen fertilization effects on soil microbial communities is critical for improving fertilization practices to optimize the microbiomes for soil health, productivity and sustainability. Our goal was to characterize the microbial community structure under long-term N gradient treatment in continuous corn (CCC) and to investigate the response of different soil microbial groups involved in the N cycle to different N fertilization levels. Field trial in CCC on three N fertilization rates (0, 210, and 280 kg N/ha) is arranged in a randomized complete block design

(RCBD) with three replications. Using 16S rRNA gene-based pyrosequencing analysis of the V4 region and DNA extracted from soil in a 33-year-old agricultural field trial, we characterized the structure, composition and nitrogen cycling function of the bacterial communities involved. The downstream bioinformatics processing and analysis were conducted with QIIME (Quantitative

Insights Into Microbial Ecology). V4 region of 16S rDNA gene sequences were clustered into operational taxonomic units (OTUs) and the bacterial community composition and diversity were analyzed based on the OTUs extracted. We applied principal component analyses (PCA) and canonical discriminant analysis (CDA) to our major bacterial phyla dataset. Our results indicate that high N fertilization level tended to decrease the diversity and richness of soil bacterial community and altered the relative abundance of the major bacterial phyla. There was a significant response of different soil bacterial groups involved in the N cycle to different N fertilization levels.

ii TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION...... 1

CHAPTER 2: MATERIALS AND METHODS……………………...... 5

CHAPTER 3: RESULTS...... 11

CHAPTER 4: DISCUSSION...... 17

TABLES AND FIGURES...... 21

REFERENCES...... 30

iii CHAPTER 1 INTRODUCTION

Nitrogen fertilizers have been a major contributor to the impressive crop yield increases realized since the 1950s (Robertson and Vitousek, 2009) and widely used as a common management practice to maintain soil fertility and crop productivity (Shen et al., 2010). Soil microbial communities provide many benefits including assimilation of nutrients, plant disease resistance, and stabilization of the soil ecosystem (Figueiredo et al; Gupta et al., 2000). In agronomic systems, these functions positively shift the soil quality and productivity capacity of soils over the long term (Lebauer and Treseder, 2008). Therefore, investigating the effects of N fertilizers on soil microbial communities is of vital importance due to the critical part that microbes play in the biochemical processes. Nitrogen additions directly affect plant residue quantity, quality, and rate of decomposition thus shifting soil nutrient content (Zhong et al. 2010) which can influence the size, structure, and function of microbial communities. Though numerous studies examined the influence of the N fertilizer on traditional soil biological properties, such as microbial biomass and its carbon C and N contents (Kirk et al., 2004), few evaluations have included a concurrent assessment of the soil microbiome. Modern approaches, using phylogenetic surveys of bacteria employing universal primers, allow for characterization and comparison of the microbial diversity in different soil environments as well as the comparative analysis of changes in community structure due to management practices (Daniel,

2005; Myrold et al., 2014).

The nitrogen cycle (N cycle) is one of the most important components in the agronomic ecosystem where nitrogen is a common limiting factor to the growth of crops. The N cycle is based on the redox transformation of nitrogen compounds and most of the processes are driven

1 by a diverse range of microorganisms. (Simon and Klotz, 2013) Microbial N cycle consists of four main steps: nitrogen fixation, mineralization, nitrification and denitrification. Functional bacterial communities involved in the N cycle, such as N fixers, ammonia oxidizers, nitrifiers and denitrifiers, exert significant effects on agroecosystem functioning. (Kowalchuk and

Stephen, 2001; Philippot and Hallin, 2005). N fixation is processed by diazotrophs (unique in

+ their ability to fix atmospheric nitrogen) which convert nitrogen (N2) to ammonium (NH4 ).

Studies have found that diazotrophs in the soil are influenced by agriculture practices, including

N addition (Wang et al., 2009), and it was well established that the Rhizobia, Cyanobacteria, and

Frankia are three dominant diazotrophs in the ecosystem (Hsu et al., 2009). The step following N

+ − fixation is nitrification, in which NH4 is oxidized to nitrate (NO3 ). This oxidation is performed only by a few phylogenetically constrained ammonia-oxidizing bacteria (AOB) and ammonia- oxidizing archaea (AOA) (Treusch et al., 2005). Denitrification is the stepwise reduction of

− − NO3 to N2 via nitrite (NO2 ), nitric oxide (NO) and nitrous oxide (N2O) by denitrifiers. AOB is detected in the two classes in Proteobacteria phylum: Betaproteobacteria and

Alphaproteobacteria. (Purkhold et al., 2000) However, compared with AOB, denitrifying bacteria from more than 60 genera have been identified in agricultural soil (Philippot et al.,

2007).

Intensive agricultural management practices, including fertilizer levels are generally considered to lead to simpler, less diverse, soil food webs (Bender et al., 2016) with varied impact on microbial biomass (Geisseler and Scow, 2014; McDaniel et al, 2014). Therefore, the nitrogen enrichment, as shown in many experiments (Suding et al., 2005), leads to declines in microbial community diversity and species richness. Besides diversity, the phylogenetic shifts of the soil microbial community have been observed in response to N fertilization (Fierer et al.,

2 2012). Although the effects of N fertilization on the relative changes in N cycle participants has been investigated, most studies have focused on a single participant of the nitrogen cycle, with a lack of research on the different response of N cycle community to long-term N fertilization in soil with CCC rotation treatment (Bru et al., 2011). Some studies found that N fertilization increased the relative abundance of AOB (Fan et al., 2011; Wakelin et al., 2007) while other studies found that the high N application rate led to soil secondary salinization and acidification, lower N use efficiency, and a decrease in soil microbial functional diversity, including in the population of AOB. (Shen et al., 2016; Zhong et al., 2016) Enhanced nitrification could provide abundant substrate for denitrification, and the increase of oxygen consumption could lead to anoxic conditions in some soil area, which are favorable for denitrifiers thus stimulating denitrification (Miller et al., 2008).

Well maintained long-term field experiments can provide insights into long-term effects of agricultural treatments on soil properties. (Powlson et al. 2014). Field triasl of N fertilization rates on continuous corn in this study were established in 1982, which presents a unique opportunity to characterize the bulk soil microbiome and to test whether microbial shifts under increasing N fertilization levels occur. Improved understanding the microbial community can create various beneficial effects on soil environment such as reducing nitrate runoff and greenhouse gas emission. (Kramer et al. 2006). However, baseline characterization of the soil biological component and microbial shifts under N fertilization is currently lacking in Illinois.

Our hypothesis is that long-term N additions increase the relative abundance of certain bacterial community groups in N cycle related to nitrification and denitrification, and decrease the diversity of the microbial community. Therefore, the objectives of this study were 1) to characterize the microbial community structure under long-term N rates applied to continuous

3 corn plots using DNA sequencing and bioinformatics analyses and 2) to investigate the response of different soil microbial groups involved in the N cycle to different N fertilization levels.

Advancing the understanding of the relationships between soil microbiome with agroecosystem services of carbon and N cycling and crop productivity will enable us to study potential long- term effects of microbial communities on the productivity and sustainability of agricultural soils.

4 CHAPTER 2 MATERIALS AND METHODS

SITE DESCRIPTIONS AND SOIL SAMPLING

The detailed set up for this field trial was described previously (Jagadamma et al., 2007).

This study was established in 1982 at the Northwestern Illinois Agricultural Research and

Demonstration Center in Monmouth, IL (40°90′N, 90°73′W); average elevation of 230 m. The soils are Muscatune series (fine-silty, mixed, superactive, mesic, Aquic Argiudolls) with surface soil being usually dark greyish brown and granular in structure. The experiment was established in 1982 and field trials with three N fertilization rates (0, 210, and 280 kg N/ha) in continuous corn (CCC) is arranged in a randomized complete block design (RCBD) with three replications.

Total experimental units include 3 × 3 = 9 plots. Main plots in this trial are 12m × 9m and thus, subplots of cover crop treatments are 12m × 4.5m each. We continued the agronomic practices that have been in use at each long-term trial (Zuber et al., 2015; Jagadamma et al., 2008). In this trial, tillage consists of primary tillage with a chisel plow 20-25 cm deep in the fall after harvest, and secondary tillage with a field cultivator before planting in the spring. Corn is planted in April or early May each year, in 76-cm rows at a density of about 75,000 seeds/ha. Nitrogen fertilizers rate were applied at or before planting as incorporated urea ammonium nitrate (28% N) and at rates of 246 kg/ha. The N fertilizer at 0, 210, and 280 kg N/ha rates for corn was injected at 10-

15 cm depth before planting using urea ammonium nitrate solution (28% N). Weed control was achieved by using recommended rates and timing of appropriate herbicides along with hand weeding if necessary. Plots were maintained weed free during the study period.

5 Soil sampling of this study was conducted after the harvest in fall 2015, after 34 years with N rates in the same plots each year. Three composite samples (~500 g each) per plot were collected to 10 cm depths using an Eijelkamp grass plot sampler. Subsamples for the later sets of samples were preserved with ice packs in the field and frozen to -20ºC upon arrival to our lab facilities for proper handling and determination of biological properties.

SOIL DNA EXTRACTION, PCR AND SEQUENCING

Total genomic DNA from the soil samples was extracted employing the Power Soil DNA isolation kits (MoBio Inc., Carlsbad, CA) from 0.25 g of the 27 soil samples, with the soils carefully homogenized before subsampling. The extracted DNA quantity and quality were checked with a Nanodrop1000 Spectrophotometer (Thermo Scientific, MA, USA) following the manufacturer’s protocol. The extracted DNA samples were stored at -20ºC until analysis.

A multi-targeted amplicon library with individual barcodes for each sample and compatible with the Illumina HiSeq was constructed. To build the amplicon library, 25 µl PCR reactions were performed using a BioRad T100 thermal cycler in 25 µl volumes with 1x buffer

(GoTaq® Flexi buffer; Promega Corp.), with the following composition: 2.5 mM MgCl2, 200 uM dNTPs, 0.4 µM each primer (forward and reverse), 1.0 µl template DNA (pooled amplicons) and 1.0 unit of GoTaq polymerase. PCR parameters were: initial denaturation of 95 °C for 10 min, followed by 34 cycles of amplification (45 secs at 95 °C; 45 secs at 58 °C; 45 secs at 72

°C), and a final extension of 72 °C for 10 min. PCR products were visualized on a 1.3% agarose gel containing GreenGlo™ Safe DNA dye (Denville Scientific) under UV illumination. The 16S rDNA (V4 region) was amplified using the primer set 515F (GTGYCAGCMGCCGCGGTAA) and 806R (GGACTACNVGGGTATCTAAT) (Fierer, 2005). The primers were designed as 5’-

6 PCR-specific + gene region + 3’-PCR-specific + 10nt barcode and the Fluidigm platform utilized two primer sets simultaneously to create the final DNA amplicon.

The resulting amplicon libraries were quantitated with a Qubit Fluorometer, and run on a bio-analyzer to evaluate the profile of fragment lengths. The barcoded libraries were pooled in equimolar concentrations, and diluted to 10nM. The diluted libraries were sequenced at the Roy

Carver Biotechnology Center Functional Genomics lab at the University of Illinois at Urbana-

Champaign (Urbana, IL, USA) using paired-end sequencing on the Illumina HiSeq2500

(Illumina, CA, USA) in Rapid Mode yielding reads 250nt in length.

BIOINFORMATIC ANALYSIS

The bacterial community composition and diversity were analysed using the V4 region of

16S rDNA gene sequences. Sequences produced from paired-end sequencing included the forward read, the reverse read, and the index read were de-multiplexed and then checked for quality. Quality control of the paired-end sequences included trimming of low-quality bases at the extreme of the sequences and filtering of sequences that have an average PHRED quality score < 20, sequence length < 200nt or > 800nt, presence of ambiguous nucleotides, primer mismatch, and errors in barcode. The primers on both forward and reverse reads were trimmed using the software Trimmomatic-0.36 (Bolger, et al, 2014). The overlapping paired-end reads were joined using “fastq-join” program (Aronesty, 2013). Quality of reads was assessed using

FASTQC (Gonzalez-Pena et al., 2016).

The chimera checking of the sequences was performed with USEARCH v5.2 (Edgar,

2010) using GOLD as a reference database (Reddy et al. 2014) and sequences identified as chimeric were filtered. The high-quality sequences were assigned to samples according to the

7 barcodes. Sequences were clustered into operational taxonomic units (OTUs) using open reference OTU picking at 97% similarity threshold and clustered against the Greengenes 16S rRNA database (Aug 2013 release) (DeSantis et al., 2006). The most representative (most abundant) sequence of each OTU was aligned using the PyNAST algorithm with a minimum

80% identity (Caporaso et al., 2009). Annotation of the taxonomic unit to representative OTU was based on classifications from the Ribosomal Database Project (RDP) and an OTU table was generated. Downstream processing of sequences was completed using the python programs built in QIIME (Quantitative Insights into Microbial Ecology) pipeline v1.9.1 (Caporaso et al., 2010).

The generated OTU table was rarefied to an even depth (135,639 sequences per sample) using the single_rarefaction.py script. Samples with fewer reads than the minimum rarefaction number were removed from further analysis and a rarefaction plot of the three treatments was generated by using Mothur (Schloss et al., 2009). The OTUs with the number of sequences

<0.005% of the total number of sequences were discarded (Bokulich et al., 2013) using filter_otus_from_otu_table.py script. Alpha diversity metrics including chao1 (Chao, 1984),

Shannon’s index, ACE and Fisher’s alpha (Walther, et al., 2005) were extracted using alpha_diversity.py in QIIME pipeline, and observed species were compared between different N fertilization rates. To characterize the changes to the bacterial community structure, nine main phyla (accounts for > 90% in relative abundance) were recorded and analysed. Distance matrices were obtained using unweighted and weighted UniFrac methods to compare the relative abundance and binary detection. (Lozupone and Knight, 2005). Non-parametric testing of beta diversity was performed employing jackknife resampling based on 5000 sequences per sample.

To compare bacterial community structures across all samples with the three treatments, principal coordinate analysis (PCoA) was conducted for the relative abundance of beta diversity.

8 PCoA for beta diversity was conducted employing beta_diversity_through_plot.py script in

QIIME pipeline using the output from weighted UniFrac distances to create data visualization of the relationships between samples from three increasing N fertilization treatments. Furthermore, to examine the dispersion of beta diversity, a permutational multivariate analysis of variance using distance matrices was performed with ADONIS by R built in compare_categories.py in

QIIME pipeline (Warton et al., 2012).

STATISTICAL ANALYSIS

To analyse the soil bacterial community structural changes, a data set was extracted for statistical analysis with SAS (SAS 9.4, SAS Institute Inc., 2012). The data set was comprised of

27 observations on 9 variables which are the nine main phyla: Proteobacteria, Acidobacteria,

Bacteroidetes, Actinobacteria, Gemmatimonadetes, Verrucomicrobia, Chloroflexi,

Planctomycetes and Euryarchaeota. The explanatory variables in the data set included N fertilization levels (0, 210, 280 kg N/ha) and blocks (1, 2, 3).

To avoid the dominance of the variable with the highest variance on the results, all multivariate analyses were conducted on standardized data (mean=0, standard deviation=1) obtained with the STANDARD procedure in SAS. Pearson’s correlation coefficients were calculated using the CORR procedure in SAS to explore the correlation among soil variables

(Table 1). Correlations between variable pairs were found to be |0.3| (moderate to high range) in most cases which indicated the need to deploy a data reduction technique such as principal component analysis (PCA) to avoid problems of multicollinearity by compiling the information into a new smaller set of uncorrelated variables. A principal component analysis (PCA) was performed using PRINCOMP procedure in SAS. PCA creates new uncorrelated variables called

9 principal components (PCs) that are linear combinations of the original raw variables that maximize the variability explained by the set of variables (Johnson and Wichern, 2002). PC scores explained an important proportion of the total variability of each data set and PC scores with eigenvalues 1 were extracted. Three PCs were extracted from the data set and thus, the

PCA reduced the dimensionality of the dataset from 9 (correlated) variables to 3 (uncorrelated)

PCs (PC1 to PC3). Next, linear mixed models were fit to the PCs extracted using PROC

GLIMMIX in SAS to evaluate the effect of N fertilization rates on the relative abundance of the main phyla. Blocks were considered as a random effect and N rates as fixed effects. When appropriate, least square means were separated using the pdiff option of the lsmeans statement, setting the probability of Type I error at 0.05(α). To investigate the variables that contributed to maximized N rate differences, a canonical discriminant analysis (CDA) was performed using the

CANDISC procedure in SAS. Cross-validation of our linear discriminant functions obtained with the CDA was carried out to estimate the probabilities of correct classification of new observations into the location and rotation groups as suggested by Johnson and Wichern (2002).

To investigate further about the relative abundance of soil bacterial groups related to the

N cycle, we used PROC GLIMMIX to conduct an ANOVA respectively on bacterial groups related to nitrogen fixation, nitrification and denitrification. The effects of N rate on nitrogen- fixing bacteria (genus Frankia, class Alphaproteobacteria and class Betaproteobacteria), nitrifying bacteria (class Gammaproteobacteria) and denitrifying bacteria (class Nitrospira) were examined. Blocks were considered as a random effect and N rates as fixed effects.

10 CHAPTER 3 RESULTS

SEQUENCING SUMMARY

After the elimination of low-quality sequences using Trimmomatic and chimeras using

USEARCH, a total of 6,459,949 bacterial rDNA sequences were obtained with a range of

107,802 to 350,409 and a mean of 239,257 sequences per sample. To measure the quality and sufficiency of sequences in the samples of each treatment, a rarefaction plot was generated and shown in Figure.1. Rarefaction plots generally grow rapidly at first, as the most common species were found, but the curves approach a plateau as only the rarest species remain to be sampled

(Gotelli, et al., 2001; Sogin, et al., 2006). In this study, all rarefaction curves tended to approach the saturation plateau indicating that only the rarest species remain to be sampled, and thus the sequences obtained were sufficient to capture the diversity information of the bacterial community within 27 samples.

SOIL BACTERIAL COMMUNITY DIVERSITY

After the upstream bioinformatics analysis, a rarefied and filtered OTU table was generated using QIIME (data not shown). From the OTU table, we found that the mean number of observed OTUs were 2258, 2245 and 2055 respectively with the increasing N fertilization levels (from 0, 210 to 280 kg N/ha) showing a decrease in abundance. Table 2 shows the observed OTUs, phylogenetic diversity and other estimators of alpha diversity and richness of the soil bacterial community. Phylogenetic diversity observed within 0, 210, 280 kg N/ha treatments were 122, 121 and 112 respectively. The estimators of soil bacterial community alpha diversity (Fisher’s alpha and Shannon’s diversity) and richness (Chao1 richness estimate and ACE diversity index) indicated that the increasing N fertilization treatment significantly

11 decreased the alpha diversity and richness. The results of least significant difference (LSD) suggested a significant difference between the 210 and 280 kg N/ha group on all alpha diversity and richness indicators while no significant differences were found between 0 and 210 kg N/ha group for all indicators.

To characterize the overall effect of N rates on the beta diversity of the total bacterial phyla and genera, a principal coordinate analysis (PCoA) was applied to the entire OTU dataset to calculate the pairwise distances between the bacterial community (Yashiro and McManus

2012) at 0, 210, 280 kg N/ha N fertilization levels of the 27 samples. The results of PCoA were shown in Figure. 2 demonstrating a contrasting separation between the three N fertilization level treatments. The three axes of the PCoA respectively explained 26.81%, 5.66% and 4.50% of the total variance. Furthermore, the results of the permutational multivariate analysis of variance using Weighted UniFrac distance matrices showed that the increasing N fertilization levels had a significant effect on the dispersion of beta diversity within the soil bacterial community (R2 =

0.34, p < 0.001). Thus, the bacterial communities in the 0, 210 and 280 kg N/ha N fertilization treatments were different from one another.

SOIL BACTERIAL COMMUNITY TAXONOMIC COMPOSITION

All OTUs in the sequences were annotated from phylum to genus level based on classifications of the Ribosomal Database Project using the scripts in QIIME pipeline. Forty one different phyla including three archaea phyla were identified from the samples. Though all N rate treatments showed similarities in bacterial community composition at the phylum level, the proportion of the groups represented under each N rate level were found to be unequal. The bacterial community structure at the phylum level for N fertilization level treatments is shown in

Table 3. The three N rate treatments were dominated in decreasing order of relative abundance

12 by members of phyla Proteobacteria, Acidobacteria, Bacteroidetes, Actinobacteria,

Gemmatimonadetes, Verrucomicrobia, Chloroflexi, Planctomycetes and Euryarchaeota. These nine groups accounted for over 90% of the OTUs in each N rate treatments. Among these main phyla, an increasing trend in relative abundance under the three increasing N rate treatments was found in Proteobacteria, Gemmatimonadetes and Euryarchaeota while a decreasing trend was found in Acidobacteria, Chloroflexi and Planctomycetes. (Figure. 3) For Bacteroidetes,

Actinobacteria and Verrucomicrobia, there was no continuous increasing or decreasing trend found following the N rate gradient. A decreasing trend between N rates was detected in the remaining 33 phyla, however, these low-abundance (relative abundance < 1%) have less impact on the compositional structure of the soil bacterial community.

To avoid issues of multicollinearity in our results (Table 3), we conducted a principal component analysis (PCA) on the relative abundance of the nine main phyla. The PCA on nine variables rendered a set of three uncorrelated variables (PC 1 to PC 3) with eigenvalues larger than 1 that explained about 75.7% of the variability contained in the main phyla database. The first PC1 had the largest eigenvalue (3.35) and explained about 37% of the bacterial community taxonomic variability while included high positive loadings (> 0.30) for Chloroflexi and

Planctomycetes, and low negative loadings (< -0.30) for Proteobacteria, Gemmatimonadetes and

Euryarchaeota. PC2 had an eigenvalue of 2.06 and explained 22.9% of the variability in soil bacterial community taxonomy dataset; PC2 eigenvalue had negative loadings for

Gemmatimonadetes, Actinobacteria and Chloroflexi, and a high positive loading for

Bacteroidetes and Verrucomicrobia. The eigenvalue for PC3 was 1.40 and explained an additional 15.6% of the total variability. The PC3 eigenvector includes a high positive loading for Actinobacteria and Euryarchaeota. We used each of these new three PCs as as independent

13 variables in follow up analysis of variance (ANOVA). We tested the effect of N fertilization levels on the relative abundance of the nine main bacterial phyla represented by each PC. The probability values and degrees of freedom associated with the ANOVA for the effects of N rates are shown in the lower portion of Table 4 for each extracted PCs. The N rate effect was statistically significant for PC1 and PC2 but not statistically significant for PC3. The 0 kg N/ha and 280 kg N/ha showed contrasting PC1 mean score with a high positive value (1.7331) associated with 0 kg N/ha and a high negative value (-1.8618) associated with 280 kg N/ha. The

210 kg N/ha treatment showed a neutral value (0.1287) for PC1 mean score. Thus, the low N rate is associated with higher relative abundance for Chloroflexi and Planctomycetes while the high

N rate is associated with higher for Proteobacteria, Gemmatimonadetes and Euryarchaeota. The results of PCA are in accord with the trends reflected in Figure. 3.

To supplement the one-by-one investigation of the 3 PC variables obtained from the main soil bacterial phyla data set, we performed a canonical discriminant analysis (CDA) to answer the question on which combination of variables - what PCs – maximized the segregation among groups. The CDA was deployed focusing only on N fertilization level as a grouping factor and the result of the analysis is plotted in Figure. 4. Discriminant analyses indicated that LD1 accounted for 96% of the total variance among the groups. Each LD is a linear combination of the independently measured main bacterial phyla (summarized by our 3PCs) and is orthogonal to the others. Thus, PCs with a high relative weight in the discriminant function contribute more to the discriminant power of the function and therefore, they are the most desirable as indicators of soil bacterial community structure. The loadings or raw coefficients of the PCs on the discriminant functions were as follows:

LD1= 1.17*PC1 + 0.07*PC2 - 0.91*PC3

14 LD2= 0.06*PC1 + 0.71*PC2 + 0.25*PC3

The first discriminant function LD1 was dominated by high positive loadings form PC1 and negative loadings from PC3. The second function LD2 was characterized by high loadings of

PC2. A cross-validation procedure was included to estimate the probabilities of correct classification of new observations as a measure of accuracy of our discriminating rules (Johnson and Wichern, 2002) and the results are shown in Table 5. From Table 5 along with Figure. 4, we can observe that 0 kg N/ha showed high positive loadings for LD1 while 280 kg N/ha showed low negative loadings; 210 kg N/ha clearly situated between the two opposite loading groups.

Our LD1 rule successfully classifies observation 93% of the time (error = 7%).

SOIL BACTERIA RELATED TO NITROGEN CYCLE

In the present study, bacteria affiliated to certain genus or families which were correlated to N cycle was detected at phylum, class and genus level. Among the nitrogen-fixing bacteria, the relative abundance of Alphaproteobacteria showed a significant increase as the N fertilization levels escalate from 8.6% (0 kg N/ha), 11.1% (210 kg N/ha) to 13.3% (280 kg N/ha) in relative abundance (p-value < 0.001). However, there was no significant difference (p-value = 0.503) in

Betaproteobacteria between the N rate treatments with the relative abundance at 8.8% at 0 kg

N/ha, to 9.0% at 210 kg N/ha and 8.5% (280 kg N/ha). For the relative abundance of bacteria affiliated with genus Frankia, no significant difference (p-value = 0.112) was detected though an increase was found from 0.060% (0 kg N/ha), to 0.105% (210 kg N/ha) to 0.112% (280 kg N/ha).

For the nitrifying bacteria, there were 35 genera of Gammaproteobacteria detected and a significant increase (p-value = 0.003) was found as N rate increased a with relative abundance of

4.85%, 5.84% to 7.98% a N rates of 0, 210 and 280 kg/ha. In contrast, the bacteria relative abundance of class Nitrospira which is related to denitrification decreased significantly (p-value

15 < 0.01) as N fertilizer levels increased from 0 kg N/ha (0.85%), 210 kg N/ha (0.45%) to 280 kg

N/ha (0.33%).

16 CHAPTER 4 DISCUSSION

Soil bacterial communities are vital to the environment due to their role in cycling mineral compounds, decomposing organic materials, and various soil biophysical processes

(Critter et al., 2004). In turn, soil bacterial groups exhibit natural dynamics for their diversity and functions are influenced by numerous factors including soil nutrients and agricultural management (Griffiths et al., 2006). Significant effects on soil quality and sustainability have been observed from 135 kg N/ha nitrogen input with continuous wheat in north-eastern China

(Shen et al., 2010). The altered soil quality significantly influences the phylogenetic diversity and abundance of bacterial groups.

While many studies on 25 grassland sites across the world detected changes in soil bacterial communities across the N gradient over the long term (Leff, et al., 2013), research on individual field sites was missing in this regard in Illinois. Using a long-term fertilization field experiments established in 1983, we monitored the structure of soil bacterial communities, dynamics of the relative abundance of soil bacteria and certain OTUs correlated to nitrogen cycling. According to the sequencing results, we found that there were significant differences between the escalating N rate treatments in soil bacterial community diversity, relative abundance of main phyla and N cycle related OTUs.

In a recent meta-analysis, Geisseler and Scow (2014) researched using a dataset based on

64 long-term field trials from across the world. The durations of these trials ranged from 5 to 130 years averaging 37 years and the N rates ranged from 10 to 65 kg N/ha averaging 136 kg N/ha with urea or ammonium salts being the most commonly used fertilizers. They concluded that long-term repeated mineral N applications can alter microbial community composition.

17 For soil bacterial community structure, a few studies found no or only minute effects on microbial communities (Ogilvie et al., 2008; Börjesson et al., 2012). However, according to

Allison and Martiny (2008), 32 of 38 studies with an average length of 8.2 years reported that the diversity of microbial community was sensitive to N fertilization. Our findings are in agreement with these results and reported a significant decrease in soil bacterial alpha diversity.

These contrasting changes are mainly corresponded to the reduced soil pH which is attributed to the long-term application of urea and ammonium fertilizers (Malhi et al., 2000).

With increasing rates of application, the decrease in pH per unit of N fertilizer is more pronounced and this is because some of the acidity produced by nitrification is neutralized when plants take up more nitrate than cations (Barak et al., 1997). Our results demonstrate a similar point in that that there is a significant difference between the intermediate and high N rate group on all alpha diversity and richness indicators while no significant differences were found between control and intermediate N rate group for all indicators (Table 2).

Soil bacterial composition changes in response to the long-term N fertilizer application and we characterize this change by investigating the most abundant nine bacteria phyla. We found that the relative abundance of Proteobacteria and Gemmatimonadetes tend to increase with increasing N rate (Figure. 3) while the relative abundance of Acidobacteria and Chloroflexi tend to decrease. Similar shift changes have been found in other studies investigating bacterial community responses to N addition (Campbell et al., 2010; Nemergut et al., 2008). Campbell reported that Acidobacteria were relatively less abundant in fertilized soil samples from a 15- year ecological research site. In addition, Nemergut also found fertilization increased the relative abundance of the Bacteroidetes and the Gemmatimonadetes at a tundra site. These shifts in the main phyla could be attributed not only to the pH changes but also to the change of C/N ratio due

18 to the fertilization. Some studies reported that excessive N input influenced soil C/N ratio and thus influence soil bacterial communities (Hallin et al., 2009). Results from our ANOVA on

PCA indicate that PC1 has statistically significant differences and has loadings greater than |0.3| in Proteobacteria, Gemmatimonadetes and Chloroflexi (Table 4), which is in line with other studies. However, Acidobacteria has loadings greater than |0.3| even if a decreasing trend was shown. Proteobacteria and Acidobacteria are the two most abundant phyla detected accounting to over 45%. The Proteobacteria are a major group of bacteria and include many species responsible for nitrogen fixation and the Acidobacteria are physiologically diverse and ubiquitous in soil (Barns et al., 2007).

We also monitored structural changes in the bacterial communities involved in the N cycle with increasing N rate. We had anticipated that there was significant response of certain soil microbial groups involved in the N cycle to different N rates based on previous literature.

Wessén et al. (2009) found that N rates had significant effects on microbial communities affiliated with N cycling especially on denitrifying, ammonia oxidizing archaea (AOA) and ammonia oxidizing archaea (AOB) from a long-term trial established in 1956 in Sweden where the soil type was Eutric Cambisol. In the present study, we detected changes in the relative abundance of 5 genera or classes affiliated with each key step in N cycle with the increasing N fertilizer application. We had anticipated that bacterial groups associated with N fixing will be less abundant and the nitrifying as well as denitrifying groups will be more abundant with increasing N rates since there is more available N in soil which is the product of N fixing and substrate of nitrification. We found, instead, that the relative abundance of N fixing groups significantly increased or tended to increase while the relative abundance of denitrifying groups significantly decreased.

19 The main reason for this phenomenon might be that the N addition enhanced soil N availability which is the substrate for N cycling and thus favored N cycle affiliated groups.

However, to test this hypothesis and yield more convincing and comprehensive results, we need future work on quantitative changes of N cycle related groups. Quantitative real time PCR can be used to obtain the abundance of key functional genes coding for enzymes nitrogenase (nifH), ammonia monooxygenase (amoA), nitrite reductase (nirK/nirS), and nitrous oxide reductase

(nosZ).

CONCLUSION

Long-term N fertilization treatments have resulted in differences in the structure and of soil bacterial communities, and also produced differential relative abundance of bacterial groups affiliated with N cycling. However, the reason why the soil bacterial communities were altered remains unclear. A more complete understanding of the long-term interactions and feedback between agronomic practices, soil properties such as pH and C/N ratio and characteristics of bacterial communities is essential for enabling the design of optimal agronomic practices that improve agricultural sustainability.

20 TABLES AND FIGURES

Table 1. Matrix of Pearson’ correlation coefficients for the relative abundance of the main soil bacterial phyla variables in the data set, including the relative abundance of Proteobacteria (Proteo, %), Acidobacteria (Acido, %), Bacteroidetes (Bacteroi, %), Actinobacteria (Actino, %), Gemmatimonadetes (Gemmatimo, %), Verrucomicrobia (Verrucom, %), Chloroflexi (Chloro, %), Planctomycetes (Plancto, %) and Euryarchaeota (Eury, %).

Proteo Acido Bacteroi Gemmatimo Actino Verrucom Chloro Plancto Eury Proteo 1.00 Acido -0.10 1.00 Bacteroi -0.28 -0.25 1.00 Gemmatimo 0.32 -0.02 -0.52 1.00 Actino -0.47 -0.50 -0.20 0.01 1.00 Verrucom -0.38 -0.11 0.63 -0.41 -0.16 1.00 Chloro -0.60 -0.15 -0.08 -0.43 0.66 -0.10 1.00 Plancto 0.62 0.02 0.30 -0.36 0.28 0.49 0.38 1.00 Eury 0.41 -0.19 -0.14 0.51 -0.33 -0.01 -0.41 -0.51 1.00

21 Table 2. Alpha diversity metrics for samples from 0, 210, 280 kg N/ha treatments. Values represent the mean of observed OTUs, phylogenetic diversity (PD), chao1 richness estimate (Chao1), Shannon’s diversity (Shannon’s), ACE diversity index (ACE) and fisher’s alpha diversity (Fisher’s) of nine samples at the same N rate level. Within each column, values followed by the same letter are not significantly different from each other (P < 0.05). a Values represent p-values calculated using a generalized linear mixed model considering N fertilization levels as fixed effect and blocks as random effects.

Treatment Observed OTUs PD Chao1 Shannon's ACE Fisher's

0 kg N/ha 2258 A 122.0 A 2379 A 10.00 A 2340 A 420.3 A

210 kg N/ha 2245 A 120.8 A 2367 A 9.93 A 2336 A 414.0 A 280 kg N/ha 2055 B 111.7 B 2191 B 9.62 B 2151 B 370.3 B

Treatment Effecta 0.0193 0.0034 0.0385 0.0007 0.0274 0.0102

22 Table 3. Bacterial composition at the phylum level. Relative abundance (%) of bacterial phyla within the communities under 0 kg N/ha, 210 kg N/ha and 280 kg N/ha N rate treatments. Sequences that could not be classified into any known group were assigned as unclassified_Bacteria. Phylum 0 kg N/ha 180 kg N/ha 240 kg N/ha Proteobacteria 29.05 31.97 34.91 Acidobacteria 17.44 16.58 15.77 Bacteroidetes 11.81 12.53 10.23 Actinobacteria 9.24 7.57 8.23 Chloroflexi 5.85 4.57 4.07 Verrucomicrobia 5.56 5.98 5.09 Gemmatimonadetes 5.19 6.03 7.23 Planctomycetes 4.26 4.03 3.59 Euryarchaeota 1.86 2.25 2.39 Crenarchaeota 1.74 1.42 1.56 Cyanobacteria 1.44 0.50 0.23 Firmicutes 1.25 1.04 0.92 WS3 0.98 0.64 0.27 Nitrospirae 0.85 0.45 0.33 Tenericutes 0.62 0.44 0.27 Armatimonadetes 0.58 0.79 0.81 unclassified_Bacteria 0.57 0.41 0.43 [Parvarchaeota] 0.34 0.87 1.06 Elusimicrobia 0.29 0.34 0.32 Chlorobi 0.18 0.17 0.22 Aquificae 0.18 0.46 0.70 Spirochaetes 0.14 0.15 0.12 Fibrobacteres 0.08 0.11 0.06 OP3 0.07 0.03 0.03 AD3 0.07 0.21 0.33 [Thermi] 0.07 0.04 0.05 Lentisphaerae 0.07 0.00 0.00 Thermotogae 0.05 0.04 0.01 FCPU426 0.04 0.11 0.14 Synergistetes 0.03 0.01 0.00 WS2 0.02 0.01 0.00 BRC1 0.01 0.01 0.01 EM19 0.01 0.01 0.01 TM6 0.01 0.02 0.02 TM7 0.01 0.05 0.24 Deferribacteres 0.01 0.00 0.01 OD1 0.01 0.02 0.04 WPS-2 0.00 0.11 0.22 Chlamydiae 0.00 0.01 0.04 OP11 0.00 0.01 0.02 OP8 0.00 0.01 0.02

23 Table 4. Principal component analysis of relative abundance variables for 9 main soil bacterial phyla with eigenvalues and cumulative proportion of the data set variability explained by the three principal components (PC) extracted with eigenvalues >1. Component correlation scores (eigenvalues) with loadings greater than |0.3| are bolded. Probability values for the analysis of variance (ANOVA) and degrees of freedom (df) available for the analysis of the effect of N fertilization levels are shown for the three extracted PCs. Estimate values represent the mean of each PC at the same N rate level. Within each column, values followed by the same letter are not significantly different from each other (P < 0.05).

PC1 PC2 PC3 Eigenvalue 3.35 2.06 1.40 Cum. Proportion 0.37 0.60 0.76 Main bacterial phyla Component Correlation Scores Proteobacteria -0.44 0.08 0.08 Acidobacteria -0.06 0.11 -0.80 Bacteroidetes 0.25 0.49 0.26 Gemmatimonadetes -0.37 -0.23 0.11 Actinobacteria 0.27 -0.52 0.31 Verrucomicrobia 0.26 0.49 0.20 Chloroflexi 0.37 -0.39 -0.01 Planctomycetes 0.43 0.07 -0.09 Euryarchaeota -0.36 0.11 0.37 Factor df Probability Values N rate 2 <0.0001 0.0246 0.0840 Treatment Estimate 0 kg N/ha 1.73 A 0.84 A 0.49 A 210 kg N/ha 0.13 B -0.36 B 0.20 AB 280 kg N/ha -1.86 C -0.48 B -0.69 B

24 Table 5. Cross-validation results showing the number of observations (n) and percent of observations (%) correctly classified for N rate groups under study based on the discriminant function obtained with CDA on the selected main phyla in soil bacterial community.

From: Classified into: N rate (kg N/ha) 0 210 280 Total 0 n 9 0 0 9 % 100 0 0 100 210 n 0 8 1 9 % 0 88.9 11.1 100 280 n 0 1 8 9 % 0 11.1 88.9 100 Total n 9 9 9 27 % 33.3 33.3 33.3 100 Error Count Estimates for Location Group BT DK DS Total Error Rate 0.00 0.11 0.11 0.07 Priors 0.33 0.33 0.33

Figure 1. Rarefaction analysis. Rarefaction curves. The sample groups labelled 0, 210, 280 represent samples from the 0 kg/ha, 210kg/ha and 280kg/ha treatments.

Figure 2. Principal coordinate analysis (PCoA) plots based on weighted UniFrac distances generated for N fertilization treatments 0 kg/ha (red squares), 210 kg/ha (blue circles) and 280 kg/ha (yellow triangles).

Figure 3. Relative abundance of main phyla (accounted for >90% of the soil microbial community) of increasing N fertilizer levels. Error bars indicate standard errors.

Nrate 0 180

LD2 (4%) 240

−1

−5 0 5 LD1 (96%)

Figure 4. Plot of the linear discriminant functions LD1 and LD2 obtained with the Canonical Discriminant Analyses of 3 N rate groups. The LD1 and LD2 functions are the same in both CDA, explaining about 96 and 4% of the variability among groups, respectively.

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