Soil texture and properties rather than irrigation water type shape the diversity and composition of soil microbial communities

Applied Soil Ecology xxx (xxxx) xxx

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Soil texture and properties rather than irrigation water type shape the diversity and composition of soil microbial communities Olabiyi Obayomi a, Mitiku Mihiret Seyoum a, Lusine Ghazaryan a, Christoph C. Tebbe b, Jun Murase c, Nirit Bernstein d, Osnat Gillor a, * a

Zuckerberg Institute for Water Research, J. Blaustein Institutes for Desert Research, Ben Gurion University, Midreshet Ben Gurion 84990, Israel Thünen Institute of Biodiversity, Federal Research Center for Rural Areas, Forestry and Fischeries, Bundesalle 65, Braunschweig, Germany Graduate School of Bioagricultural Science, Nagoya University, Chikusa, Nagoya 464-8601, Japan d Institute of Soil Water and Environmental Sciences, Volcani Center, POB 6, Bet-Dagan 50-250, Israel b c

A R T I C L E I N F O

A B S T R A C T

Keywords: Treated wastewater Drip irrigation Vegetable Soil Illumina 16S 18S rRNA Next generation sequencing

Around the world, water scarcity is advocating for treated wastewater (TWW) reuse, especially for agricultural irrigation. However, TWW contains inorganic substances, dissolved organic matter and microorganisms that may alter soil health and fertility. In this study, it was hypothesized that irrigation would differently alter the soil microbial communities in accordance with soil types and properties, but independent of water quality. It was further predicted that the differences in soil community would be mediated by clay content due to its physical properties and effect on organic matter content. To test these predictions, TWW and potable water (PW) were used to irrigate different soil types (clay, loam and loamy sand) for growing two Cucumis species during two cultivation seasons. The abundance, diversity and function of the soil microbial communities (bacteria and protists) were monitored in 248 samples of water and soil. The results demonstrate that the microbial com­ munities significantly differed between water qualities, yet these differences did not carry to the irrigated soils. However, soil types significantly altered the microbial communities, particularly clay content, that correlated with a decrease in diversity indices, and changes in the composition of members of the Proteobacteria, Acti­ nobacteria and SAR taxa. Accordingly, myriad organic compounds degradation pathways were identified in the clay soil samples. The results suggest that soils with lower clay content may be spatially segregated at the microscale, resulting in niche partitioning and higher bacterial diversity regardless of irrigation water type. Therefore, soil texture and properties shape agricultural soils’ microbial communities.

1. Introduction Irrigation with treated municipal effluents is common in drylands and is expected to spread as the availability of potable water (PW) fails to keep up with the growing demand for food production (UNEP, 2002; WHO, 2006). Water stress is further exasebated by climate change events that necessitate improved wastewater management (Singh and Tiwari, 2019). Treated wastewater (TWW) is often proposed as a major water resource (L. Chen et al., 2017; C. Chen et al., 2017) that could provide a cheap, continuous and sustainable source of irrigation water (Pedrero et al., 2010). However, the practice of TWW irrigation entails potential abiotic and biotic risks such as increased salinity, heavy metals, decreased pH, (Gupta et al., 2010; Khan et al., 2008) and the

introduction of pathogens and exogenous microorganisms (Campbell et al., 2001; Fattal et al., 1986; Nygård et al., 2008). The introduced microbiota together with changes in soil chemical and physical prop­ erties (Jueschke et al., 2008; Lado et al., 2012) may affect the soil microbiome and as a result, reduce soil fertility and health. The soil microbiome is pivotal to ecosystem functions (Chaparro et al., 2012) and was shown to influence, and even predict, crops productivity both directly and indirectly (Bender et al., 2016). Moreover, soil health was tightly linked with the diversity and function of the microbial commu­ nity (Wagg et al., 2014), yet most studies to date focused on the bacterial and fungal communities neglecting the protists (Thakur and Geisen, 2019). The impacts of TWW irrigation on soil microbial communities are

* Corresponding author at: Zuckerberg Institute for Water Research, J. Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, 84990, Israel. E-mail addresses: [email protected] (O. Obayomi), [email protected] (M.M. Seyoum), [email protected] (L. Ghazaryan), christoph.tebbe@ thuenen.de (C.C. Tebbe), [email protected] (J. Murase), [email protected] (N. Bernstein), [email protected] (O. Gillor). https://doi.org/10.1016/j.apsoil.2020.103834 Received 15 May 2020; Received in revised form 7 September 2020; Accepted 11 November 2020 0929-1393/© 2020 Elsevier B.V. All rights reserved.

Please cite this article as: Olabiyi Obayomi, Applied Soil Ecology, https://doi.org/10.1016/j.apsoil.2020.103834

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controversial (reviewed by Becerra-Castro et al., 2015). Irrigation with TWW was shown to increase soil microbial abundance, promote the establishment of unique and persistent microbial communities (Bastida et al., 2017; García-Orenes et al., 2015; Hidri et al., 2010; Krause et al., 2020), and increase microbial-mediated nutrient turnover (Bastida et al., 2018; Frenk et al., 2017). Likewise, TWW irrigation of orchards increased soil bacterial activity and changed the community composi­ tion during the dry season, while rain irrigation re-established the original community (Frenk et al., 2015). In contrast, other studies re­ ported no differences in soil microbial biomass, activity or composition between soil irrigated with PW or TWW (Frenk et al., 2014; Guo et al., 2017; Bastida et al., 2018), not even after 40 years (Li et al., 2019). This discrepancy in reports is puzzling, and we wondered whether the adverse effects reported may have aroused from a differential response of microbial communities specific to soil types. Soil texture was shown to differ based on particle size distribution, pH, cation exchange capacity, soil organic matter (SOM) content, or water holding capacity. All these parameters could diversify the soil microbial communities either directly, through niche partitioning, each providing a different set of conditions; or indirectly, by affecting cell-cell interactions (Tecon and Or, 2017). However, the effect of soil texture on soil microbial diversity is still debated. It was suggested that in hydrated finer-textured soil, the aqueous phase would be mostly continuous, while equal conditions in coarser soils would result in spatial segrega­ tion at the micro-scale leading to niche partitioning and higher biodi­ versity (L. Chen et al., 2017; C. Chen et al., 2017). Segregation of the microbial communities would lead to diversification and restrict the competition between the unconnected niches. However, a continuous aqueous phase in soil matrices would equalize conditions and enable connectivity, interactions and competition (Tecon et al., 2018). There­ fore, it was suggested that a decrease in connectivity due to changes in water potential (Treves et al., 2003) and soil texture, will entail an in­ crease in microbial diversity (Tecon and Or, 2017). Indeed, it was re­ ported that bacterial richness increased significantly with soil coarseness, (quantified by sand percentage) although the evenness and diversity did not differ (Chau et al., 2011). Moreover, sand content was shown to positively correlate with diversity and richness, while clay displayed a reverse trend (Ma et al., 2016). Soil textures, along with pH, SOM, salinity and hydration, were shown to be important determinants of the microbial diversity (Escalas et al., 2019; Samba-Louaka et al., 2019). Yet, the impact of irrigation with different water types on the microbial communities of soils types, are not clear (Becerra-Castro et al., 2015). Therefore, the aim of this study was to elucidate the impact of irrigation with different water qualities (TWW and PW) on the microbial communities of various soil textures (sand, silt and clay). We hypothesized that the spatial hetero­ geneity of different soil textures provides various micro-niches that support microbial diversification that would resist irrigation-mediated modifications. We further predicted that the clay and SOM play a major role in shaping the microbial community, regardless of the irri­ gation water quality. To test our hypotheses, we packed lysimeters with three soil types (clay, loam or loamy-sand), irrigated them with two water qualities (either PW or secondary TWW) to cultivate model crops, Cucumis plants (cucumbers or melons), for two consecutive growing seasons. The water and soil samples were analyzed during the cultiva­ tion seasons to investigate the dynamics in the microbial communities (bacteria and protists) and their links to the physico-chemical parameters.

loamy-sand) differing in the composition of their primary particles (Table 1), were packed in lysimeters (diameter 44 cm; height 55 cm; volume 60 L), at eight replicates for each soil type. The lysimeters were buried in a field at the Research Farm of Neve Ya’ar (Supplementary Fig. 1), and holes were drilled at the base of the lysimeters to allow water leaching. The soils were drip irrigated with either PW or TWW culti­ vating cucumbers (Cucumis sativus L. cv. Drby) (A.B. Seeds, Israel) and melons (C. melo L. cv. Segev) (Hazera-Seeds, Israel) that served as model vegetable crops. Altogether, 24 lysimeters were used (= 3 soil types × 2 water qualities × 4 replicates) over two growing seasons from April until July of 2015 and 2016. The soils used in this study had no known history of TWW irrigation. The experiment was conducted in an open field for two cultivation seasons. At each cultivation season, 2 kg of N and 2 kg of P were applied via the PW irrigation as NH4NO3 (18%) and H3PO4 (85%) as previously described (Edelstein et al., 2011). For TWW irrigation, N and P were evaluated and the irrigation water was amended to the concentrations mentioned above. For cucumbers, fertigation was performed nine times throughout the growing season at seven to ten days intervals, while for the melons, fertigation was carried out three times throughout the growing season. Each lysimeter was irrigated with 300 and 250 l of water during the first and second growing seasons, respectively. 2.2. TWW source and treatment The water source and treatment has been previously described (Obayomi et al., 2019). Briefly, we used secondary TWW originating from the municipal WW treatment plant HaSoleleim that uses activated sludge. After treatment, the effluent water was chlorinated with 0.5 mg hypochloride L− 1 (Sigma, Darmstadt, Germany), stored in a reservoir then transferred to the field for irrigation. 2.3. Analyses of water samples 2.3.1. Sampling PW and TWW were sampled in the field as previously described (Orlofsky et al., 2015). Briefly, 100 L of each water type was collected and transported to the laboratory for physico-chemical and molecular analyses and stored at 4 ◦ C until processed for up to 24 h after collection. Altogether 18 water samples were collected during both cultivation seasons. 2.3.2. Processing The collected water samples were concentrated by ultrafiltration as previously described (Benami et al., 2013). Briefly, 100 L was concen­ trated into 250 mL using 35 Da cut-off filters (Fresenius, Bud Hamburg, Germany). Particles were eluted from the membrane with PBS con­ taining 0.1% Tween80 (Sigma), 19% glycerol (Sigma) and 1% TrisEDTA buffer (Sigma) stored at − 80 ◦ C for later microbial analyses. For analysis, sub-samples (40 mL) were centrifuged at 5000 rpm (Labofuge 400, Kendro, Germany) for 1 h at room temperature, the supernatant was then removed by aspiration and DNA was extracted from the pellets using the gram-positive bacteria genome extraction protocol of GeneJet DNA extraction kit (ThermoFisher Scientific, Waltham, MA, USA) ac­ cording to the manufacturer’s instructions. 2.3.3. Physico-chemical analyses pH was measured with a pH-meter WTW pH 3110 (Weilheim, Ger­ many), and electrical conductivity (EC) with conductivity meter HI98303, Hanna Instruments (Woonsocket, RI, USA). Ammonium, ni­ trate and phosphorus were analyzed by QuikChem8500 auto-analyzer (Lachat Instruments, Milwaukee, WI, USA) while K+ and Na+ were analyzed by a flame photometer 410 (Sherwood Scientific, Cambridge, UK).

2. Material and methods 2.1. Experimental design The field experiment has been previously described (Obayomi et al., 2019). Briefly, to simulate the effect of TWW and PW irrigation on mi­ crobial communities in different soils, three soil types (clay, loam and 2

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Table 1 Texture and physicochemical characteristics of the three soil types used in this study. Soil were classified as defined by the USDA method (Burt, 2009). CEC = cation exchange capacity; ESP = exchangeable sodium percentage; SAR = sodium absorption ratio; OM = organic matter. Porosity was not measured because it changes very quickly in the lysimeter with irrigation and crop growth. Soil type

Sand (%)

Silt (%)

Clay (%)

pH

CEC (cmolc/kg)

ESP (%)

SAR

CaCO3 (%)

OM (%)

Clay Loam Loamy sand

1.3 43.2 80.6

44.9 41.9 13.5

53.8 15 6

7.5 7.7 7.6

36.5 9.1 5.9

1.3 2.8 1.1

0.57 0.12 0.23

14.8 14.3 3.9

2.9 0.7 0.5

2.4. Analyses of soil samples

(Klindworth et al., 2012) that amplifies the V3–V4 hypervariable re­ gions of bacterial 16S rRNA encoding gene (16S rDNA). In addition, the eukaryotic primer set Euk20F (5′ -ACACTGACGACATGGTTCTA­ CATGCCAGTAGTCATATGCTTGT-3′ ) and 519R (5′ -TACGGTAGCAGA­ GACTTGGTCTACCAGACTTGYCCTCCAAT-3′ ) (Euringer and Lueders, 2008) amplified the V1–V3 regions of the eukaryotic 18S rRNA encoding gene (18S rDNA). Both sets of primers were adapted with the Illumina Cs1/Cs2 adaptors. To mitigate reaction level biases, triplicate amplifi­ cation reactions were carried out, then pooled and barcoded for illumina sequencing. Protocols, conditions, library preparation and sequencing strategy are detailed in the Supplementary File. To characterize the microbiome in the soils, 108 and 104 16S and 18S rDNA amplicon soil samples were sequenced, respectively, in addition to 36 water samples. Sequencing generated a total of 24.4 Gb and details about the outcome of sequencing is presented in Supplementary Table 1.

2.4.1. Sampling The soil samples were collected and analyzed as previously described (Obayomi et al., 2019). Soils from non-irrigated plots were sampled to serve as a reference. Samples of irrigated soil (500 g) were collected from the top 10 cm at five sampling points from each lysimeter using ethanol sterilized scoops. The subsamples from each lysimeter were combined, placed in a sterile plastic bag, transported to the laboratory and stored at 4 ◦ C until the time for processing, for no more than 24 h. 2.4.2. Processing The soil samples were homogenized by sieving through sieves with a mesh size of 0.2 cm to remove debris, gravel and plant material. For each sample, 4 g was frozen at − 80 ◦ C for DNA extraction and the rest was oven-dried at 65 ◦ C for physico-chemical analyses. DNA was extracted from 0.5 g of soil (wet weight) using Exgene soil extraction kit (GeneAll, Seoul, S. Korea) following the manufacturer’s instructions. Phenol chloroform extraction (Angel, 2012) was performed on a subset of clay soil samples that proved to be challenging.

2.7. Bioinformatics analysis Bacteria (16S rDNA) and protist (18S rDNA) sequences were collated and processed using the open reference OTU picking pipeline in QIIME 1.9.1 (Caporaso et al., 2010) in which non-chimeric sequences were clustered to OTUs using SortMeRNA v2.0 (Kopylova et al., 2012) and SUMACLUST v1.0.00 (Mercier et al., 2013) at 97% genetic similarity. Representative OTU sequences were assigned taxonomy using Silva database (Quast et al., 2013) for QIIME release 128 and the RDP clas­ sifier (Cole et al., 2005). Prior to clustering, the 18S rDNA reads failed to merge, based on the set filtering criteria, hence single-end forward reads were used for downstream analyses. Non-target OTUs were filtered from each rDNA dataset based on taxonomic assignments. Sequences that were assigned to mitochondria, archaea and chloroplast were filtered out of the 16S rDNA dataset. Likewise, sequences assigned to Fungi, plants within Archaeplastida (Caryophyllales, Fabales, Malvales, Aster­ ales, Solanales, Capsicum, uncultured plants, Asparagales, Brassicales, Cupressales, Malpighiales, Embryophyta, Pinales, Jatropha, Poales, Rosales, Tracheophyta, Bryophyta, Spermatophyta and Arecales) and Metazoa (Animalia) were removed from the 18S rDNA dataset. Rare OTUs, i.e., OTUs with a number of sequences less than 0.005% of the total number of sequences (Navas-Molina et al., 2013) were excluded from the ana­ lyses. The OTU tables were randomly rarefied at the highest possible depth for subsequent analyses to avoid bias from unequal sampling depth (Supplementary Table 2).

2.4.3. Physico-chemical analyses the oven-dried soil samples were grinded such that samples could pass through 2 mm sieves before analyses. The values of pH and EC were determined for filtered [through a 0.45 μm PTFE syringe (Membrane Solutions, WA, USA)] saturated paste solution (1:5 soil-to-water ratio) using a pH-meter GLP 21 (Crison Instruments, Barcelona, Spain) and an EC-meter 525-A (Crison Instruments), respectively. Ammonium and nitrate were extracted with 1 M KCl and analyzed by an auto-analyzer (Lachat Instruments). Available P was extracted with sodium carbon­ ate and was measured spectrophotometrically (Olsen et al., 1954). For the determination of K+ and Na+, soil samples were extracted with ammonium acetate and the extracts were analyzed by a flame photometer (Sherwood Scientific). 2.5. 16S rDNA gene copy number estimation The 16S rDNA was amplified using the primer set Bac341F (5′ CCTACGGGAGGCAGCAG3′ ) and Bac518R (5′ ATTACCGCGGCT GCTGG3′ ) (Klindworth et al., 2012) together with SYBR Green® to determine the copy number in each sample. The estimations were extrapolated from a standard curve of 10-fold serial dilutions using known amounts of template Escherichia coli genomic DNA. Reactions were performed using the following settings: 95 ◦ C for 3 min (1 cycle) followed by 95 ◦ C for 15 s and 60 ◦ C for 1 min (35 cycles) on a CFX96 (Bio-Rad, Hercules, CA, USA). Triplicates of each reaction were used and reactions without template (n = 3) were included in each run as negative controls.

2.8. Functional and metabolic profiles of the bacterial community The functional profiles of the bacterial community based on the 16S rRNA gene sequences of the water and soil were predicted using PIC­ RUSt2 v2.3.0-b (Douglas et al., 2020). The PICRUST2 pipeline was applied on the unrarefied OTU abundance table and representative se­ quences using default settings. We analyzed the genes present in the KEGG pathway for acid, sodium and potassium stress in order to test if TWW irrigation induced stress in the soil. In addition, to test the effect of TWW irrigation on nutrients and their biogeochemical cycles, we screened for genes involved in general nitrogen, potassium and sodium metabolism and genes involved in nitrogen fixation and denitrification in the soil and water. The counts generated by PICRUST2 were rounded up to integers as required by DESeq2 (Love et al., 2014), underwent

2.6. Sequencing 2.6.1. Amplification of bacterial and protists sequences Amplifications were carried as previously described (Obayomi et al., 2019). In short, we used the bacterial primer set Bac341F (5′ ACACT­ GACGACATGGTTCTACACCTACGGGAGGCAGCAG-3′ ) and Bac806R (5TACGGTAGCAGAGACTTGGTCTGGACTACHVGGGTWTCTAAT-3) 3

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variance stabilizing transformation using DESeq2’s variance Stabilizing Transformation function and were subjected to principal component analysis (PCA) in order to visualize the structure of the genes (KEGG IDs) and pathways (MetaCyc pathways). The obtained KEGG IDs were upload to the KEGG mapper server at https://www.genome.jp/kegg/too l/map_pathway2.html to identify the categories of the IDs and to iden­ tify the top functional categories. We note that the results from predicted functional profile based on 16S rRNA data are limited and could differ from metagenomics profiling due to the limited links between taxo­ nomic identification and the presence of functional genes.

pathways in any of the contrasts was constructed (Supplementary File 2). Hierarchical clustering of the genes or pathways after z-scoring of their variance stabilized counts, was carried out using pheatmap v1.0.12 (Kolde, 2019). The non-parametric permutational multivariate analysis of variance (PERMANOVA) was used to determine the overall changes in functional and pathway composition between samples and group clus­ tering with 999 permutations based on Bray-Curtis distance matrices (McArdle and Anderson, 2001) generated from the variance transformed counts using the adonis2 function in vegan package (Oksanen et al., 2018).

2.9. Statistical analyses

2.10. Accession numbers

Statistical analyses and graphical displays were conducted and generated using R (R Core Team, 2018), and R packages namely ggpubR (Kassambara, 2017), and ggplots2 (Wickham, 2009), respec­ tively. OTUs generated from the data processing were used to determine alpha and beta diversities. To estimate alpha-diversity, we calculated the total number of OTUs, Chao1, Faith’s PD (phylogenetic diversity) tree, Shannon’s diversity index (H′ ) and made rarefaction curves using the alpha_rarefaction.py script in QIIME. Indices of observed OTUs and Chao1 estimated the communities’ richness while PD tree and Shannon are estimates of diversity. Significant differences between the alphadiversity samples metrices were verified using Wilcoxon’s rank-sum test. Rarefaction curves were generated for collated alpha diversity output files from QIIME with the R package qiimer (Bittinger, 2015). For beta-diversity, we calculated the Bray-Curtis distance between samples using the beta_diversity_through_plots.py (QIIME) and visualized using principal coordinate analysis (PCoA) in R. Permutational multivariate analysis of variance (PERMANOVA) with 999 permutations on the BrayCurtis distance matrices generated with QIIME (McArdle and Anderson, 2001) was used to determine the overall changes in microbial commu­ nities between samples. We identified genus-level taxa that contributed to the dissimilarity between water and soil types using the similarity percentage analysis (SIMPER) in R. We also draw Venn diagrams to characterize the OTUs unique to each soil or water types (Chen, 2018). Relationships between environmental variables and microbial OTUs composition were analyzed via Bray-Curtis distance-based redundancy analysis (dbRDA) using the dbrda (Oksanen et al., 2018). The signifi­ cance of the overall model, individual axes and terms were determined through ANOVA-like permutation tests (with 999 permutations) avail­ able as the anova.cca() function. Mantel test was employed to assess the effect of environmental variables on bacterial and protists communities, P < 0.05 was considered significant. Spearman rank correlation analysis was performed in R to investi­ gate the correlation between the richness (observed OTUs) and diversity (Shannon), and the environmental variables (pH, EC, NH4, NO3, TN, P, K and Na). Statistically significant differences in the environmental vari­ ables, microbial differential abundance and copy number were esti­ mated using the non-parametric Wilcoxon rank-sum or Kruskal Wallis tests to compare between two or multiple treatment groups. To account for potential false positives, p-values for differentially abundant taxa were adjusted to q-values via the Benjamini and Hochberg false dis­ covery rate (FDR) correction for multiple hypotheses testing (Benjamini and Hochberg, 1995). Taxa were considered to have significantly different abundance if their q-values were below 0.05 (5% false dis­ covery rate). Statistical testing for differential expression of genes and pathways in the water and soil was carried out using DESeq2 (Love et al., 2014). This method is specifically tailored for count data by the use of a negative binomial generalized linear model. All potential contrasts between PW and TWW and between all irrigated and non-irrigated soil treatments were assessed. Genes or pathways were considered differentially expressed (DE) in a certain contrast if they had FDR adjusted p-value < 0.05 and linear fold change >1.3 or <− 1.3, where a minus sign denotes down-regulation. For each matrix, a unified list of DE genes and

All sequencing data have been deposited to NCBI’s (https://ncbi.nl m.nih.gov) sequence read archive and is accessible under Bioproject number PRJNA561420. 3. Results 3.1. Sequencing and initial processing Supplementary Table 1 illustrates the outcome of sequencing and initial processing for soil and water. Sequencing yielded in total 38,218,922 reads for water and soil samples (Supplementary Table 1). Rarefaction curves based on OTUs at 97% similarity (Supplementary Fig. 2) showed that with the increase in sequence number, all curves tended to saturate. This indicates that the presented gene sequences reasonably present the bacterial and protists communities in the soils and waters, at the sequencing depth used in this study. Clustering yiel­ ded the highest number of OTUs in the soil rather than the water (Supplementary Table 1), and more OTUs were derived after clustering of the bacteria rather than protists (Supplementary Table 1). 3.2. Physico-chemical properties of water and soil The analysis of physico-chemical properties in the irrigation water revealed that nutrients and salinity were significantly higher (p < 0.05, Wilcoxon test) in TWW than PW while pH was similar (p > 0.05, Wil­ coxon test; Table 2). Generally, when these waters were applied to the soil they did not result in significant changes (p > 0.05; Wilcoxon) in physico-chemical properties. However, values were higher in TWWcompared to PW-irrigated soils for salinity, potassium and sodium (Table 3). In clay soil, salinity and potassium were significantly higher (p < 0.05; Wilcoxon) in TWW- than PW- irrigated soils (Table 3). Interestingly, salinity, ammonium and sodium were significantly higher (p > 0.05; Kruskal Wallis) in clay compared to loam or loamy sand soils, while the phosphorus and potassium were significantly higher in the loam and loamy sand soils. The concentration of potassium was two to four times higher (p < 0.05; Kruskal Wallis) in loam and loamy sand than in clay soils (Table 3). Table 2 Chemical properties of the irrigation water types used in this study. Values are means ± SE; n = 9 of the measured parameter throughout the two growing seasons.

4

Parameters

PW

TWW

pH EC (μS cm− 1) NH4 (mg kg− 1) NO3 (mg kg− 1) TN (mg kg− 1) P (mg kg− 1) K (mg kg− 1) Na (mg kg− 1)

7.7 ± 0.13a 865.8 ± 121.57b 0.2 ± 0.07a 0.7 ± 0.23b 0.9 ± 0.19b 0.2 ± 0.06b 4. 5 ± 1.25b 62.0 ± 18.36b

7.6 ± 0.13a 1633.9 ± 17.79a 2.0 ± 0.93a 7.6 ± 1.35a 9.6 ± 1.24a 4.4 ± 0.80a 33.0 ± 1.75a 184.0 ± 2.56a

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0.05; Kruskal Wallis) in clay soil than in loam and loamy sand soils (Fig. 1B vs. C and D).

Table 3 Soil properties analyzed throughout both cultivation seasons. The values correspond to means ± standard error throughout the two growing seasons. The differences between PW and TWW irrigated soil were compared by Wilcoxon pairwise comparison. Different letters signify statistical significant difference at p value < 0.05. EC = Electrical conductivity; TN = total nitrogen; K = potassium concentration; Na = sodium concentration; NH4 = ammonium; NO3 = Nitrate. Parameters

pH EC (μS cm− 1) NH4 (mg kg− 1) NO3 (mg kg− 1) TN (mg kg− 1) P (mg kg− 1) K (mg kg− 1) Na (mg kg− 1)

Clay

Loam

3.3.2. Alpha-diversity Bacterial richness and diversity were significantly higher (Supple­ mentary Table 3; p < 0.05, t-test) in TWW than in PW (Fig. 2A and B; Supplementary Table 2), yet, it was inconclusive for protists (Fig. 2C and D; Supplementary Table 3) as the observed OTUs was not significantly different (t = − 1.424, p = 0.175, t-test), while Chao1 was marginally higher (t = − 2.200, p = 0.044; t-test) in TWW than in PW (Supple­ mentary Table 3). Protist PD tree diversity estimates were surprising (Supplementary Table 3) as they indicated that diversity was signifi­ cantly higher in PW than TWW (t = 2.412, p = 0.028; t-test), while the Shannon estimates were similar (t = − 1.284, p = 0.219; t-test). The indices of richness and diversity in the soil followed a similar pattern as in the irrigation water, bacteria richness and diversity were significantly affected by irrigation regardless of the water quality (p < 0.05; Wilcoxon) with few exceptions (Fig. 3; Supplementary Table 2). While, protist richness and diversity were not significantly affected by irrigation with either water (p > 0.05; Wilcoxon) with few exceptions (Fig. 4; Supplementary Table 3) and even showed a slight decrease in the alpha diversity indices. Interestingly, richness and diversity significantly differed (p < 0.05; Kruskal Wallis) between soil textures (Figs. 3 and 4; Supplementary Tables 2 and 3). The richness and diversity in soils with lower clay content (loam and loamy-sand) were significantly higher (p < 0.05; Wilcoxon) than clay soils (Figs. 3 and 4; Supplementary Tables 2 and 3).

Loamy sand

PW (n = 41)

TWW (n = 40)

PW (n = 39)

TWW (n = 40)

PW (n = 40)

TWW (n = 36)

6.7 ± 0.01a 405.4 ± 4.97ab 28.8 ± 0.72c 20.0 ± 0.46a 47.5 ± 1.00c 38.0 ± 0.59a 11.28 ± 0.07b 168.2 ± 1.91a

6.8 ± 0.01a 577.2 ± 8.46c 22.4 ± 0.56 cd 18.4 ± 0.47a 41.6 ± 0.81 ac 34.3 ± 0.34a 19.6 ± 0.20d 300.1 ± 3.93c

6.9 ± 0.01ab 398.2 ± 6.81ab 10.5 ± 0.14ab 17.2 ± 0.50a 29.1 ± 0.60ab 38.8 ± 0.60a 33.0 ± 0.47a 193.6 ± 3.10ab

6.9 ± 0.01b 449.2 ± 6.68a

6.8 ± 0.01ab 356.6 ± 6.39b 9.6 ± 0.20b 14.9 ± 0.34a 25.7 ± 0.46b 70.8 ± 1.22b 32.4 ± 0.40a 169.6 ± 3.23a

6. 9 ± 0.01ab 499.9 ± 9.32 ac

21.8 ± 0.72ad 25.3 ± 0.89a 50.7 ± 1.46abc 39.0 ± 0.42a 55.1 ± 0.69c 279.7 ± 3.54c

15.1 ± 0.37ad 30.7 ± 1.33a 45.3 ± 1.35abc 52.1 ± 0.69b 47.2 ± 0.58c 263.2 ± 5.53bc

3.3. Microbial abundance and diversity in the waters and soils 3.4. Microbial community composition in the waters and soils

108

A

106

104

PW 1010

B

108

106

NON

1010

C

108

106

TWW

Abundance (copy no. / gr soil)

Abundance (copy no. / gr soil)

Analyses of beta diversity in the water revealed distinct clustering of TWW from PW samples (Pseudo-F = 8.753, p = 0.001; Protist: Pseudo-F = 6.027, p = 0.001; PERMANOVA) as shown in the PCoA plots (Fig. 5; Supplementary Table 4). Pseudahrensia and Limnobacter as well as Ver­ mamoeba and Chlorella sp. AN 1-3 were the top contributors to the dissimilarity between PW and TWW (Supplementary Table 5). However,

Abundance (copy no. / gr soil)

Abundance (copy no. / L water)

3.3.1. Total abundance Fig. 1 depicts the total bacteria community abundance revealing significant differences between the irrigation waters (Fig. 1A; p < 0.05; Wilcoxon), soils (Fig. 1B, C and D; p < 0.05; Kruskal Wallis), and their combination (p < 0.05; Kruskal Wallis). Yet, the irrigation waters did not impact the bacterial abundance in the soil types (p > 0.05; Wil­ coxon). In addition, bacterial abundance was significantly lower (p <

PW

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Fig. 1. Bacterial abundance in water and non-irrigated soil (NON) potable water (PW) and treated wastewater (TWW) irrigated soils. 16S rRNA gene copy number detected in the irrigation water (A) and the different soil types: Clay (B), Loam (C) and Loamy Sand (D) assessed by qPCR. Boxes represent 25–75% of the data, solid lines the median, dots in the box mean, the tips represent the minimum and maximum values excluding the outliers (1.5 times lesser or greater than the lower or upper quantiles) represented by dots outside of the boxes. 5

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Fig. 2. Alpha diversity measures of the bacteria (A and B) and protists (C and D) communities in potable water (PW) and treated wastewater (TWW). Richness (observed OTUs) and diversity (Shannon-Wiener) indices were evaluated in irrigation waters (n = 9). Boxes represent 25–75% of the data, solid lines the median, dots in the box mean, the tips represent the minimum and maximum values excluding the outliers (1.5 times lesser or greater than the lower or upper quantiles) rep­ resented by dots outside of the boxes.

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Fig. 3. Alpha diversity measures of bacterial communities in non-irrigated (NON), potable water (PW) and treated wastewater (TWW) irrigated soils. Richness (observed OTUs) and diversity (Shannon-Winer) indices were evaluated in clay (A and B), loam (C and D) and loamy sand (E and F) soils. The figures present nonirrigated soils (n = 5) and irrigated Clay (n = 12), Loam (n = 15) and Loamy Sand (n = 15). Boxes represent 25–75% of the data, solid lines the median, dots in the box mean, the tips represent the minimum and maximum values excluding the outliers (1.5 times lesser or greater than the lower or upper quantiles) represented by dots outside of the boxes.

when these irrigation water qualities were applied to the soil (Fig. 6; Supplementary Table 4), this dissimilarity did not result in separate clustering of the soil samples based on irrigation water quality (p > 0.05; PERMANOVA) but rather on soil type (Bacteria: Pseudo-F = 16.963, p =

0.001; Protist: Pseudo-F = 4.693, p = 0.001; PERMANOVA). Sphingo­ monas and Halomonas (Supplementary Table 6) as well as Chloroplastida, Chlorophyceae and Hypotrichia (Supplementary Table 7) were the major contributors to the dissimilarity between clay and the other soil types. 6

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Fig. 4. Alpha diversity measures of protists communities in non-irrigated (NON), potable water (PW) and treated wastewater (TWW) irrigated soils. Richness (observed OTUs) and diversity (Shannon) indices were evaluated in clay (A and B), loam (C and D) and loamy sand (E and F) soils. The figures present non-irrigated soils (n = 5) and irrigated Clay (n = 12), Loam (n = 15) and Loamy Sand (n = 15). Boxes represent 25–75% of the data, solid lines the median, dots in the box mean, the tips represent the minimum and maximum values excluding the outliers (1.5 times lesser or greater than the lower or upper quantiles) represented by dots outside of the boxes.

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Fig. 5. Beta diversity of the water microbial communities. Principal coordinates analysis (PCoA) plot on the bray-curtis distance matrix generated from rarefied OTU abundances and depicting patterns of beta diversity for bacteria (A) and protists (B) communities in the waters. Points that are closer together on the ordination have communities that are more similar. Permutational multivariate analysis of variance indicated highly significant (P < 0.001) differences between water types communities.

Flavobacterium and Pseudarthrobacter (Supplementary Table 6) as well as Chloroplastida and Chlorophyceae (Supplementary Table 7) were the major contributors to the dissimilarity between loam and loamy-sand soils.

3.5. Correlating alpha and beta diversity with soil physico-chemical properties and crops Distance based redundancy analysis was used to visualize the 7

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Fig. 6. Beta diversity of the soil microbial communities. Principal coordinates analysis (PCoA) plot on the bray-curtis distance matrix generated from rarefied OTU abundances and depicting patterns of beta diversity for bacteria (A) and protists (B) communities in the soils. Points that are closer together on the ordination have communities that are more similar. Permutational multivariate analysis of variance indicated highly significant (P < 0.001) differences between soil types communities.

correlations between samples OTUs structure and physico-chemical properties (Supplementary Fig. 3). The dbRDA models that included pH, EC, ammonium, nitrate, total nitrogen, phosphorus, potassium and sodium, were statistically significant (Bacteria: F8,73 = 3.504, p = 0.001;

Protist: F8,72 = 2.638, p = 0.001; Permutation test). Bacterial commu­ nities in the different soil types strongly correlated with the physicochemical properties, while only weak correlations were detected with the protist’s communities. Ammonium and phosphorus had strong positive correlation with the bacteria communities of loam and loamy-

Relative abundance (%) 80 60 40 20 0

Fig. 7. Relative abundance of the thirty most abundant bacterial classes in the irrigation water and soil arranged according to phylum. PW = potable water; TWW = treated wastewater; NON = non-irrigated soil. 8

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sand soils, respectively, while potassium had a strong negative corre­ lation with clay bacterial communities (Supplementary Fig. 3A). These chemical variables could explain 19.4% and 16.6% of the total com­ munity variance between treatments for bacteria (Supplementary Fig. 3A) and protists (Supplementary Fig. 3B) communities, respec­ tively. We further evaluated the correlations of the alpha and beta di­ versity with the soil physico-chemical properties (Supplementary Tables 8 and 9). Bacterial richness in the PW irrigated clay soil showed strong significant negative correlation with EC, nitrate and total nitro­ gen and a significant positive correlation with sodium (Supplementary Table 8). Generally, there were no strong correlations of the protists community’s alpha- (p > 0.05, Spearman) and beta- (p > 0.05, Mantel test) diversities with soil environmental variables with minor exceptions (Supplementary Table 9).

loam and loamy-sand (FDR q < 0.0001, Wilcoxon). At the temporal scale, the relative abundance of the most abundant bacterial taxa were different between temporal and soil types scales (Supplementary Figs. 7 and 8). However, only the Bacilli significantly increased at the end of the first and second seasons in both clay and loamy-sand soils but remained low in the loam soil (Supplementary Figs. 7 and 8). Other taxa fluctuated in different soil types over the growing seasons. For instance, in clay soil, Betaproteobacteria increased at the end of the first season but decreased in the next season, while Actinobacteria and Acidobacteria significantly decreased at the end of the first season then increased at the second season (Supplementary Figs. 7 and 8). In loam and loamy sand, Fla­ vobacteria significantly increased after irrigation but was either very low or absent throughout the second season, while Actinobacteria decreased after irrigation and remained low throughout the cultivation seasons (Supplementary Figs. 7 and 8).

3.6. Microbial community composition

3.7. Protists

3.6.1. Bacteria TWW was dominated by Betaproteobacteria, Actinobacteria, and Acidimicrobiia, while PW was dominated by Alphaproteobacteria (Fig. 7 and Supplementary Fig. 4). Bacterial taxa significantly differed (FDR q < 0.05, Wilcoxon) between PW and TWW (Supplementary Table 10). Yet, these differences were not carried to the irrigated soil (FDR q > 0.05, Wilcoxon) but differed between soil types (Fig. 7; FDR q < 0.05; Kruskal Wallis) and the combination of soils, waters and the nonirrigated soils (FDR q < 0.05; Kruskal Wallis; Supplementary Ta­ bles 11 and 12). A total of 568 OTUs were shared by both water types, while 264 and 576 were unique to PW and TWW, respectively (Sup­ plementary Fig. 5A). In the soil, a total of 2386 OTUs were shared by all soil types (Supplementary Fig. 6A), while 29, 6 and 5 OTUs were exclusive to clay, loam and loamy-sand, respectively (Supplementary Fig. 6A). In fact, loam and loamy-sand soils shared more OTUs with each other than with the clay soil (Supplementary Fig. 6A). Members of Proteobacteria and Actinobacteria dominated all soil types (Fig. 7; Supplementary Fig. 4), with Gammaproteobacteria significantly higher in clay soils (FDR q < 0.0001; Wilcoxon), and Bacteriodetes in

A total of 215 OTUs were shared by both water types, while 79 and 77 were exclusively found in PW and TWW, respectively (Supplemen­ tary Fig. 5B). TWW was dominated by the classes Chloroplastida and Aveolata, while PW was dominated by members of SAR and Amoebozoa (Fig. 8; Supplementary Fig. 9). Within the soil types, TWW irrigation did not significantly affect (FDR q > 0.05, Wilcoxon) the relative abundance of the soil protists (Fig. 8; Supplementary Fig. 9). A total of 521 OTUs were shared by all soil types, while 190 were shared by loam and loamy-sand soil and 3 by clay and either loam or loamy sand (Supplementary Fig. 6B). The soil protists communities were all dominated by Chloroplastida, Aveolata and Rhizaria (Fig. 8; Supple­ mentary Fig. 9). However, irrigation, independent of the water quality, differentially changed the communities’ structure (Supplementary Table 13). Aveolata increased while Chloroplastida decreased with time in all soil types (Supplementary Fig. 10). Specifically, in clay, Hypo­ trichia, Colpodida and Ciliophora were significantly increased (FDR q < 0.0001; Wilcoxon) at the end of both grown seasons, whereas in loam and loamy sand soils irrigation resulted in a significant increase (FDR q Relative abundance (%) 80 60 40 20 0

Fig. 8. Relative abundance of the thirty most abundant protist classes in the irrigation water and soil arranged according to phylum. PW = potable water; TWW = treated wastewater; NON = non-irrigated soil. 9

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< 0.05, Kruskal Wallis) of members of Archaeplastida and Euplotes (Supplementary Fig. 11).

in TWW than in PW though the pH did not differ (Table 2). Yet, these differences were not reflected in the irrigated soils; TWW irrigation did not significantly change the soil physico-chemical properties compared to PW irrigation with few exceptions (Table 3). TWW irrigation resulted in increase in the soils’ salinity, K and Na concentrations (Table 3), while previous studies reported that TWW-irrigated soil showed pronounced increase in N and P that were suggested to have a positive effect on crops yield (Elliott and Jaiswal, 2012; Pedrero et al., 2014). Alterations in bacterial diversity in agricultural soil were associated with various physico-chemical variables including soil texture (Chau et al., 2011), N content (Zhang et al., 2018), water quality (Frenk et al., 2015), soil organic matter (Adrover et al., 2012) and soil pH (Rousk et al., 2010). Yet in our study, no significant differences were detected (Supplementary Tables 8 and 9), which may primarily be due to the small variations in the physico-chemical variables (Table 3). Moreover, it was previously shown that although TWW contain higher loads of organic matter (Jueschke et al., 2008), the effects on carbon concen­ trations in the irrigated soil are inconsistent. Some reported increase in organic carbon in TWW irrigated soil (Jueschke et al., 2008), while others reported a marginal effect (Lado et al., 2012) or even depletion due to priming of the soil microbial activity (Adrover et al., 2012; Ibekwe et al., 2018). Here, although the water qualities significantly differed in physico-chemical composition (Table 2), TWW irrigation had negligible impact on the soil microbial diversity and community composition compared to PW irrigation (Figs. 3, 4, 7 and 8).

3.8. Functional characteristics of water and soil bacterial community We hypothesized that the composition of bacterial function profile in the water and soil is shaped in a similar manner to the bacterial com­ munity composition. PICRUSt2-derived functional profiles and path­ ways were tested by PCA based on Euclidean distances generated from transformed (variance stabilized) abundance counts (Supplementary Fig. 12). The results were like those based on the overall community composition. All function categories and pathways significantly differed (p < 0.05, PERMANAOVA; Supplementary Fig. 12 A and B) between PW and TWW except denitrification genes (F1,16 = 1.332, p = 0.235, PER­ MANAOVA; data not shown). However, no differences were detected in the soil (p > 0.05, PERMANAOVA; Supplementary Fig. 12C and D) based on water quality but significant differences were detected be­ tween soil types (p < 0.05, PERMANAOVA; Supplementary Fig. 12C and D). In total, 6439 and 7104 KEGG orthologs (KOs) comprising 359 and 391 KEGG pathways, 302 and 326 KEGG modules were identified encompassing the water and soil samples, respectively (Supplementary File 2). The top KEGG functional categories included metabolic path­ ways, microbial metabolism in diverse environments and biosynthesis of secondary metabolites (Supplementary File 2). In addition, ATP binding proteins, dehydrogenases, reductases, transcription regulators, and transport proteins were the most dominant predicted genes in both water and soil indicating the occurrence of active metabolic processes in both matrices (Supplementary File 2). Significant differences in the abundance of metabolic profiles in soil and water were detected (Supplementary Fig. 13; Supplementary Tables 14–16 and Supplementary File 2). it was detected that 181 and 200 pathways significantly differed in the water and soil, respectively (Supplementary Tables 14–16). In addition, expressed genes’ patterns together with pathways differed between PW and TWW but were similar between the corresponding irrigated soil types (Supplementary Fig. 13). In addition, different patterns were detected among soil types, particu­ larly between clay and loamy-sand and sand (Supplementary Fig. 13B). Differentially expressed functions and pathways were detected for all tested categories between PW and TWW except for genes related to ni­ trogen fixation, phosphorus and potassium stress (Supplementary Table 14). TWW expressed different genes and pathways compared to PW (Supplementary File 2), while in the soil, the largest amount of differ­ entially expressed genes and pathways were detected between TWW irrigated clay and Loam soils, regardless of irrigation water quality. However, little to no differences were detected within soil types, regardless of water quality, though significant differences were detected between irrigated and non-irrigated soils (Supplementary Tables 15 and 16). The degradation of some organic compounds was mostly up regu­ lated in clay soil samples, while biosynthesis related pathways were mostly up regulated in soils with low clay content. Degradation of aro­ matic and other organic compounds, as well as sugars, the biosynthesis of vitamins and organic compounds as well as pathways involved in TCA cycle, were all up regulated in clay soils and down regulated in soils with low clay content (Supplementary File 2). In contrast, biosynthesis pathways of L-glutamine, thiazole, nitric oxide reductase, peptido­ glycan, and glycogen as well as the degradation pathways of gallate, glycine and vanillin were up regulated in soils with low clay content and down regulated in clay soil samples (Supplementary File 2).

4.2. Effect of soil types and TWW irrigation on the diversity of soil microbial communities The microbial diversity and community structure significantly differed between water qualities (Figs. 2 and 5), yet these differences were not reflected in the irrigated soil types. However, irrigation, regardless of irrigation water quality, triggered an increase in soil di­ versity (Figs. 3, 4, 6 and 7). Inert effect of municipal TWW irrigation on the diversity of soil microbial communities were similarly reported in both short- and long- term studies (Frenk et al., 2015; Li et al., 2019; Dang et al., 2019). However, irrigation with industrial TWW (Dang et al., 2019) or raw WW (Shen et al., 2019) were shown to significantly alter the soil microbial communities. These reports suggest that irriga­ tion with municipal secondary effluent may have negligible effect on soil diversity and health both in the short- and long- terms, yet alternative effluents should be evaluated prior to use. Irrigation, regardless of the water quality, increased soil microbial abundance (Fig. 1), richness and diversity (Fig. 3; Supplementary Ta­ bles 2 and 3) when clay content was low. The microbial abundance seems to increase with decreasing particle size, an observation that was justified by the relative increase in the particle surface area allowing colonization (Sessitsch et al., 2001; Neumann et al., 2013). This discrepancy in results may be attributed to water content and retention capacity because higher clay content results in higher water retention capacity (Rawls et al., 1991). In the loam and loamy-sand, partial hy­ dration (below maximum field capacity) ensued lower connectivity resulting in spatial isolation and niche partitioning that may prevent interactions and promote diversification of the soil microbial commu­ nities (Chau et al., 2011; Kogbara et al., 2015; Ma et al., 2016). More­ over, the spatial isolation could also result in higher heterogeneity of carbon resources that further augment niche variation and microbial diversification (Torsvik and Øvreås, 2002). In contrast, increased clay content due to the higher SOM and fine sized particles enhances water retention capacity in the soil, thus promoting connectiveness that may decrease heterogeneity at the micro-scale, mitigating niche variation and enhance competition that result in lower diversity (Terrat et al., 2017). In addition, clay is characterized by high loads of organic matter, high cation exchange capacity and fine sized particles (Table 1). TWW irrigation was shown to decreased the soil stability and alter the pore architecture, as pores are clogged with suspended solids, thus retaining

4. Discussion 4.1. The physico-chemical properties of TWW and PW irrigated soils The concentration of nutrients and salinity were significantly higher 10

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excess moisture and nutrients, and reducing the hydraulic conductivity (Bardhan et al., 2016). These changes may lead to lower microbial di­ versity, selecting for microbes that could cope with these conditions.

toward predation with steady irrigation. 4.5. Bacterial function profiles in the soil and water

4.3. The correlations of soil chemical properties with the microbial alphaand beta- diversity

The major functional categories predicted in the water and soil communities were metabolic pathways, microbial metabolism in diverse environments and biosynthesis of secondary metabolites. Yet, the changes in bacterial functions and pathways did not reflect changes in the soil bacterial community diversity based on water quality reflected by high abundance of putative functions related to energy metabolism and transporters. This imply an exchange of nutrients and signals sug­ gesting that the bacteria can apply an active mechanism to cope with stressful conditions such as TWW irrigation. Yet, Na, K, and acid stressrelated genes were either undetected or did not differ between soils, indicating that the treatments did not invoke stress within the bacterial community. However, the genes and pathways significantly differed between the soil types particularly between clay and sand and loamysand (Supplementary Tables 15–16 and Supplementary Fig. 12C and D). Genes and pathways involved in degradation of organic compounds were up regulated in clay soils while genes and pathways involved in biosynthesis were mostly up regulated in soils with low clay content (Supplementary File 2). This may be attributed to the high OM in the clay soil (Jueschke et al., 2008), which may have selected for microbes involved in OM degradation thus reducing the overall diversity in clay. However, the analysis performed is limited in scope as it is based on 16S rRNA sequences. Therefore, further studies using shot-gun meta­ genomics or metatranscriptomics would provide a better understanding of the effects of TWW irrigation on the function of the soils’ communities.

Potassium, phosphorus, sodium, ammonium and salinity proved to be important factors driving bacterial communities’ structure, while the protists communities were unaffected (Supplementary Fig. 3). The dif­ ferences in soil properties including scaling proportions of sand, silt, and clay, as well as the associated SOM content (Table 1), may further explain the difference in the microbial diversity (Figs. 3 and 4) and community composition (Figs. 5 and 6). It was reported that specific sorption capacity and different soil organic sources may select for different microbial communities (Hemkemeyer et al., 2015, 2014). Moreover, the silt and clay particles were shown to bind organic carbon and trace elements that are only available to certain members of the microbial community, thus promoting diversification (Banerjee and Siciliano, 2012). Interestingly, the variations in the bacterial commu­ nities did not always entail diversification of the protists (Fig. 8), which may imply that the detected protists are generalist predators (Hirakata et al., 2016). 4.4. Effect of soil types and TWW irrigation on the microbial communities’ composition As expected, bacterial abundance (Fig. 1) and diversity (Fig. 2) were higher in TWW than PW. In contrast, the protists community was equally diverse in the TWW and PW samples (Fig. 2). In fact, PW was shown to be dominated by the heterotrophic Dinoflagellates (Supple­ mentary Fig. 9) known to thrive in oligotrophic environments (Bates et al., 2013; Hu et al., 2011; Jeong, 1999). In clay soil, the relative abundance of Gammaproteobacteria was significantly higher than in loam and loamy-sand soils (Fig. 7; Supple­ mentary Figs. 4 and 7). The broad niche members of the Gammapro­ teobacteria taxa Sphingomonas (Aislabie and Deslippe, 2013) and Pseudomonas (Glick, 2012; Bhattacharyya and Jha, 2012) were more abundant in clay soil (Supplementary Figs. 4 and 8). This result could correspond with the higher connectiveness of the clay soil that would promote bacteria utilizing a wide array of nutrients (Wang and Or, 2013). In contrast, the Bacteriodetes taxa was more abundant in low clay soils (Fig. 7; Supplementary Fig. 4), and was dominated by Fla­ vobacterium, known to favor highly specific organic compounds (Li et al., 2017). In our study, the microbial communities differed among soil types (Fig. 6), yet some key OTUs were shared across treatments (Supple­ mentary Fig. 6A) suggesting a strong influence of soil type, while the Cucumis species exhibit smaller influence on the soil microbiota. Our results correspond with reports suggesting that soil types rather that rhizosphere shape the diversity of soil microbial communities in rice (Xu et al., 2020) or cucumber (Krause et al., 2020). In contrast, soybean rhizosphere was shown to control the communities regardless of soil types (Chang et al., 2019). Soil samples, regardless of treatment, were dominated by the green algae Chloroplastida in clay and Chlorophyta in loam and loamy sand. The predatory ciliates Aveolates (Hypotricha and Colipodida), and members of another predator, the Acanthameoba spp. (Fig. 8; Supplementary Fig. 9) also dominated the irrigated soils. Both ciliates and amoeba play an important role in soil ecosystems as predators of bacteria, fungi and other protists, and their grazing activity enhances nutrient flow in soil thus increasing nutrient uptake by plants (Griffiths, 1986; Geisen et al., 2014). Following irrigation (regardless of the water quality), the relative abundance of green algae decreased, while the relative abundance of the bacteriovorous flagellates increased (Fig. 8; Supplementary Fig. 11), mostly in loam and Loamy-sand. These results may suggest a shift

5. Conclusions Soil texture properties, especially clay and SOM, appear to shape the bacterial and protists communities, while the irrigation water or culti­ vated crop had negligible impact on the diversity and community composition. As we predicted, clay content and SOM were important drivers of soil microbial communities’ structure, and therefore may affect soil health and resilience. The higher connectiveness in clay soils may result in spatial connections and niche homogeneity, enhancing interactions and preventing diversification of the microbial commu­ nities thus sustaining richness and diversity. Interestingly, the alterna­ tions in microbial functions did not reflect the changes in bacterial community composition. High OM levels in the clay soil may have selected for microbes mostly involved in the degradation of organic matter thus reducing the overall diversity compared to low clay soils. These results reinforce our claim that soil type, especially, soil texture and organic matter, are the major predictors of the soil microbial com­ munities rather than water quality. However, the effects of soil texture on the soil food web and microbial function needs further attention in order to develop efficient and beneficial agricultural practices that will sustain the soil health and ensure its fertility. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This research was supported by the Israel Ministry of Agriculture Grants No 857-0723-14 and 16-38-0018. We would like to thank Dr. Uri Dicken, Shani Shushan, Tenaw Haymero and Fabian Baumkoler for constructing the experimental setup and conducting the water and soil chemical analyses. 11

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Appendix A. Supplementary data

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