Drylands soil bacterial community is affected by land use change and different irrigation practices in the Mezquital Valley, Mexico

Dryland agriculture nourishes one third of global population, although crop irrigation is often mandatory. As freshwater sources are scarce, treated and untreated wastewater is increasingly used for irrigation. Here, we investigated how the transformation of semiarid shrubland into rainfed farming or irrigated agriculture with freshwater, dam-stored or untreated wastewater affects the total (DNA-based) and active (RNA-based) soil bacterial community composition, diversity, and functionality. To do this we collected soil samples during the dry and rainy seasons and isolated DNA and RNA. Soil moisture, sodium content and pH were the strongest drivers of the bacterial community composition. We found lineage-specific adaptations to drought and sodium content in specific land use systems. Predicted functionality profiles revealed gene abundances involved in nitrogen, carbon and phosphorous cycles differed among land use systems and season. Freshwater irrigated bacterial community is taxonomically and functionally susceptible to seasonal environmental changes, while wastewater irrigated ones are taxonomically susceptible but functionally resistant to them. Additionally, we identified potentially harmful human and phytopathogens. The analyses of 16 S rRNA genes, its transcripts and deduced functional profiles provided extensive understanding of the short-term and long-term responses of bacterial communities associated to land use, seasonality, and water quality used for irrigation in drylands.

Drylands are defined as regions with arid, semi-arid, and dry sub humid climate with an annual precipitation/ evapotranspiration potential ratio (P/PET) 1 ranging from 0.05 to 0.65 2 . This ecosystem covers 40% of the world's land surface 3 and its extension could increase as the global surface temperature rises and temporal as well as spatial patterns of precipitation are modified due to climate change 4,5 . The crop production in drylands is important for maintaining the food security regionally, supporting one third of the global population 3 . The climatic conditions in these regions limit soil moisture and constrain farming to the rainy season (1-179 days) 2 if irrigation is not feasible. As world population grows, crop demand raises and fresh water resources become scarce. Thus, the only way to maintain agricultural productivity in these areas is by using treated or untreated wastewater for field irrigation 3,6-8 . In recent years, the knowledge on soil microbial communities in drylands has increased as they participate in critical processes needed to maintain soil quality and ecosystem functioning. For instance, soil microbes are important for organic matter decomposition and depend on the quality and amount of the plant residues that are incorporated into the soil. They are also responsible for cycling essential elements, such as nitrogen (N), carbon (C), phosphorus (P) and sulphur (S), which are converted to their inorganic forms 9,10 . The structure and diversity of soil microbial communities is driven by the soil physicochemical characteristics, such as pH, soil moisture, organic C and nutrient content; which are influenced by environmental parameters, as climate, land use type, and management [11][12][13][14] .
The land use change with agricultural purposes in drylands, as in many other ecosystems, modifies the quantity and quality of organic matter, diminishing soil carbon stocks 15, 16 . In addition, the use of wastewater for crop

Results and Discussion
Land use change and water quality use for irrigation alter soil properties. We analyzed soil under five land use systems: xerophytic shrubland (S), agricultural fields under rainfed (R), fresh water irrigation (FW), dam-stored wastewater irrigation (DWW) and untreated wastewater irrigation (UTWW) (Fig. 1). The soils are classified as Phaeozems with high clay content (on average 44.7%). The soil properties differed significantly among land use systems and some properties such as moisture, electric conductivity and P content also between seasons ( Fig. 2 and Supplementary material Fig. S1). Soil moisture was higher in soils irrigated with DWW and UTWW with more than 30%, while the rest of the land use systems had less than 19%. Rainfed and FW showed higher moisture during the rainy season. The pH ranged from 7.1 to 7.8, being the lowest in shrubland and the highest in rainfed and FW. The increase and decrease of soil pH due to irrigation with different types of wastewater has been reported earlier 8,37 . The increase is caused by additional input of exchangeable cations, mostly sodium 38 , and the decrease by large ammonium-nitrogen input. In the rainfed, soil N and C content was lowest (1.6 and 12 g kg −1 , respectively) compared to the other systems (on average 2.1 and 25 g kg −1 , respectively). The concentration of calcium in the ion exchange complex of the soil decreased in soil irrigated with untreated wastewater, as the high input of sodium displaces calcium from soil 6 . Several soil properties showed an increasing tendency from rainfed to UTWW, such as electrical conductivity, and the amount of sodium, potassium, magnesium and phosphorous. It has previously been reported that flood irrigation and the use of different types of wastewater increase soil salinity 8,39 . The electrical conductivity was significantly lower during the rainy season General characteristics of the 16S rRNA datasets. To analyze the bacterial community structure and diversity, DNA and RNA were isolated from a total of 80 soil samples derived from all analyzed systems. We isolated DNA from all 80 samples and RNA from 52. Despite several attempts with modified conditions, the isolation of high quality RNA or cDNA synthesis in sufficient amount from four shrubland, 13 rainfed, two FW, eight DWW and one UTWW samples failed. After removal of low-quality sequences and singletons amplicon-based analysis of the V3-V4 region of the 16S rRNA resulted in 5,040,696 (DNA) and 3,370,420 (RNA) high-quality sequences. The number of sequences per sample ranged from 18,976 to 299,941 (DNA) and from 19,618 to 487,021 (RNA). After rarefaction analysis with the minimal amount of sequences per sample (18,900 DNA, 19,600 RNA) we obtained 12,854 OTUs from DNA (2,544 ± 238 per sample) and 12,182 from RNA (2,590 ± 337 Land use systems impact bacterial diversity. Bacterial diversity (Shannon index H' and Faith's phylogenetic diversity index PD) responded to land use change and water used for irrigation (p ≤ 0.05, Fig. S3). Total (DNA-based) and potentially active communities (RNA-based) did not show the same pattern. The diversity of the total bacterial community was higher in FW (H' 9.67, PD 143) than the corresponding active communities (H' 9.3, PD 135). The opposite was recorded for UTWW (total: H' 9.4, PD 134; active: H' 9.5, PD 145.5). In contrast to our hypothesis (c), the diversity of the natural dryland community was only higher compared to that of the rainfed system but not than those of the other systems. These results partially agree with previous studies that have reported higher diversity in agricultural soil than in natural drylands 24 . Additionally, wastewater irrigated soil has previously shown higher bacterial and enzymatic activity as a result of the nutrients and organic matter provided by wastewater 29 . A previous work studying Mezquital Valley soil 19 reported no significant differences in the total bacterial richness and diversity between rainfed and UTWW, but a richer soil bacterial community during the dry season. Our results indicate that only FW exhibited a richer bacterial diversity during the dry season, which was related to the available P (r s = −0.24, p ≤ 0.05). The differences between both studies might be due to a lower survey size and lower amount of analyzed samples in the other study.
Impact of soil parameters on bacterial community composition and diversity. As indicated by ANOSIM test (p = 0.01), the composition of the total and active bacterial communities differed by land use system. The (dis)similarities are driven by conversion of shrubland to agricultural land use systems, especially from those under monthly irrigation. We could observe that the communities from shrubland and rainfed systems are similar, while the communities irrigated with wastewater (DWW and UTWW) are even more similar to each other, as they cluster nearby (Fig. 3). The FW community distributed between these two groups, representing an intermediate land use system, as we hypothesized. Despite RNA clustering was less condense, the general pattern remained the same for DNA and RNA. It is important to notice that the crop type or vegetation cover is the consequence of the land use system and the season. Shrubland soil presents the same vegetation cover trough out the year, while the agricultural systems are cropped with maize during the rainy season and during the dry season the plots were left fallow or cropped with oat, grass or alfalfa. Hence, after evaluating the structure of the community differentiating by season, we observed a stronger effect by the crop type/vegetation cover during the dry season than in the rainy season (Supplementary material Fig. S4). We observed differences between seasons (ANOSIM test, p = 0.01) in the structure of the total and potentially active community of the FW system and in the potentially active community of the UTWW system. These results were influenced by different soil properties, and in the case of the potentially active communities by the crop type present in the different seasons (Supplementary material Fig. S5). Cover crops have shown to modify the soil microbial community 28 .
We tested the correlation of soil characteristics and fitted them onto the ordination to determine what properties were correlated to the bacterial community compositions. Exchangeable sodium content exhibited the strongest impact on the total bacterial community (R 2 = 0.504, p = 0.001), followed by soil moisture (R 2 = 0.416, p = 0.001; Fig. 3a). The structure of the potentially active community was influenced by soil moisture and pH (R 2 = 0.557, 0.553, respectively, p = 0.001), followed by sodium content (R 2 = 0.48, p = 0.001; Fig. 3b). These results are consistent with previous reports of soil microbial communities in drylands 24,40 , in which water content was identified as the limiting factor for bacterial growth and activity 41 . In addition, water content changed drastically by introducing crop irrigation, excess soil water content results in limited O 2 diffusion, which reduces microbial aerobic activity, but could increase the activity of anaerobes 42 . Soil pH has been widely reported to be the strongest driver of bacterial community structure [43][44][45] . In the soils of the Mezquital Valley this trend is preserved despite the small pH range (7.1 to 7.8). The pH is directly or indirectly related to many soil characteristics including soil moisture, salinity and nutrient availability 43 . Previous studies 42 have shown that high concentrations of soil soluble salts, such as Na, affect microbial community structure, as they reduce the osmotic potential of microbial cells. Hence, they can adapt to low osmotic potentials by accumulating osmolyts, which is energetically costly 42 . Higher sodium content in soil irrigated with wastewater has been reported to have negative effects on basal respiration and adenylate energy charge (AEC), while the denitrification capacity increased 29 . These demonstrate the ability some bacterial taxa have to adapt to or tolerate stress cause by Na soil content.
Shannon index was positively correlated to soil pH at the entire community level (r s = 0.34, p ≤ 0.05; Supplementary material Table S2). This effect of soil pH on bacterial diversity has been reported earlier in a multiple ecosystem type study 43 and in forest and grasslands soil 45 ; the latter study showed higher diversity under slight alkaline conditions, similar to what we observed. To our knowledge, the effect of soil pH has not been shown for irrigation systems before. Interestingly, the diversity of the active community was positively correlated to soil moisture and sodium content (r s = 0.36 and 0.41, respectively; p ≤ 0.05; Supplementary material Table S2).
Predominant proteobacterial orders, such as Caulobacterales, Rhizobiales, Bdellovibrionales, Desulfobacterales and Myxococcales; and the verrucomicrobial order Opitutales and group OPB35 showed higher relative abundances at RNA level than at DNA level. Acidobacteria (subgroups 4 and 6), Chloroflexi, Nitrospira and Planctomycetes (candidate group WD2101 and Planctomycetales) showed the opposite trend. This is possibly related to the different life strategies of these taxa. Proteobacteria show copiotrophic activity and high grow rates when resources are abundant and preference for labile organic C consumption 47 . Acidobacteria, Chloroflexi, Nitrospirae and Planctomycetes have been categorized as oligotrophs with slow growth rates and thriving capacity in nutrient-poor environments 26,47,48 . Verrucomicrobia follow oligotrophic strategies 49 , however, it is indicated that their water-related opportunistic behaviour 35 has a strong influence on their activity, as in our study they are more active in wastewater irrigated soil.
Land use systems shape bacterial community. To identify orders that were significantly associated with one, two, or more land use systems, we performed a correlation-based association analysis. All bacterial orders were included in the analysis, 362 at DNA level and 371 at RNA level. On average 56% of the orders were not significantly different (p ≤ 0.05) in relative abundance with respect to land use system.
The correlation-based association analysis (Fig. 6) is consistent with the multivariate analysis (Fig. 3). Bacterial communities from shrubland sites and rainfed systems showed similarities, they shared on average 3.2% of bacterial orders. Even more similar were soil communities irrigated with wastewater (DWW and UTWW), they shared 7.5% bacterial orders. The FW clustered between these two groups, sharing 3.8% bacterial orders with the drier systems (shrubland and rainfed) and 5.5% with the systems under monthly irrigation.
The groups Chthoniobacterales and subsection III (Microcoleus) were associated uniquely to shrubland soil. Microcoleus species are desiccation tolerant and photosynthetic and are found in arid soils worldwide 50 . Only two alphaproteobacterial taxa (Rhizobiales and Sphingomonodales), as well as Pseudonocardiales, Micromonosporales, Euzebyales and Kallotenuales were associated to shrubland and rainfed soil. The last two seem to be only active in rainfed soil. Rhizobiales and Sphingomonadales have previously been associated with arid soils 51 . The wastewater free systems, which are also the driest (shrubland, rainfed and FW) shared 23 bacterial orders, most of them are actinobacterial (Rubrobacterales, Frankiales, Pseudonocardiales, Streptosporangiales, Streptomycetales and Solirubrobacterales). In general, we observed that irrigation and predominantly irrigation with wastewater diminished the abundance of Actinobacteria. This phylum is known to harbour drought resistant groups, specifically, class Rubrobacteridae increase in abundance during desiccation 35 . Groups associated uniquely to FW were Planctomycetales and Order II (Cytophagia). Tepidisphaerales (formerly WD2101) was one of the most abundant orders but only showed activity in FW. Land use systems under monthly irrigation (FW, DWW and UTWW) shared 27 orders only few of them were active, among those were Cytophagales, Nitrosomonadales and two Gemmatimonadetes candidate groups (SO134 and BD2.11). Finally, the systems under wastewater  irrigation shared 30 orders, most of them were active in one or both systems. These orders belonged mainly to Proteobacteria (Xanthomonadales, Burkholderiales, Synthrophobacterales, Anaeroliniales, Rickettsiales) and Bacteroidetes (Sphingobacteriales, Flavobacteriales), as well as, Chlamydiales and Phycisphaerales. Several of these orders are known for comprising pathogenic representatives. Wastewater clearly transfers members of Bacteroidetes and Proteobacteria into the soil (Supplementary Material Fig. S6), orders such as Flavobacteriales, Rhodospirillales, Burkholderiales, Syntrophobacterales and Pseudomonadales were detectable in the corresponding wastewater irrigated soils. Bacteroidetes have been shown to be more abundant in agricultural systems than in natural ones, and their abundance is also associated to fertilized soil 52 . The Xanthomonadaceae family has been previously reported to exhibit higher abundance in treated wastewater irrigated soil 25 . In general, we observed that irrigation enhanced the abundance of Planctomycetes, Proteobacteria, Bacteroidetes and Gemmatimonadetes. Additionally, wastewater irrigation increased the abundance of Chlamydia. These results are consistent with previous studies on dryland soil 5 and on treated 25 and untreated 19 wastewater irrigated soils.
Finally, the correlation of diversity indices to soil properties suggests lineage-specific adaptations, bacterial taxa associated to wastewater irrigated soil are adapted to higher concentrations of sodium ions and the corresponding salt stress provided by the wastewater. The taxa associated to wastewater irrigated soil might be also adapted to surfactants and heavy metals present in wastewater 29 , that were not monitored in this study. The bacterial orders associated to shrubland, rainfed and partially FW are adapted to drought, hence sensible to sodium content and probably other contaminants.
Land use systems and seasonality defines the bacterial functional profile. Bacterial community functions participate in critical processes needed to maintain soil quality and ecosystem functioning. We predicted the functional capabilities of the potentially active bacterial (RNA-based) communities in relation to the C, N, S, and P cycles by employing Tax4Fun 53 . Abundances of important enzyme-encoding genes participating in different pathways within these cycles differed among land use systems and seasons (p ≤ 0.05; Fig. 7). Genes involved in nitrification and denitrification had higher abundances in wastewater irrigated soil (DWW and UTWW). This effect might be due to the high N supply by the wastewater (ammonium and organic N) 37 , and the higher abundance of genera like Nitrosospira, Nitrosomonas (Nitrosomonadales), Nitrospira (Nitrospirae), Nitratireductor (Rhizobiales) and Massilia (Burkholderia). Friedel and colleagues 29 reported a higher denitrification capacity in wastewater irrigated soil than in rainfed soil of the Mezquital Valley. Further, nitrous oxide emissions from wastewater irrigated fields cropped with maize are 10-fold larger than from rainfed fields 54 . Methane metabolism genes were also more abundant in soil under wastewater irrigation, this might be associated to the high methane content of wastewater 55 and the higher abundance of Verrucomicrobia and Gammaproteobacteria in wastewater irrigated soils, as they comprise methanotrophic taxa, such as Methylacidiphilum and Methylococcacea, respectively 56 . The abundance of genes related to dissimilatory sulphate reduction was also higher in wastewater irrigated systems, probably due to the higher amount of sulphate present in the untreated wastewater. Genes related to nitrogen fixation were less abundant in FW compared to wastewater irrigated systems, contrary to the general trend that high nitrate contents decrease the efficiency of the N fixation and the unfavourable effects of salinity for nodule development 39 . Furthermore, an increase in genes related to lignin and chitin breakdown was observed in shrubland and rainfed soils, probably caused by the higher abundance of genera like Arthrobacter (Micrococcales) and Streptomyces (Streptomycetales), which are known to degrade single ring aromatic substrates 57 , and contain larger numbers of chitinase encoding genes per genome 58 . These results are in accordance with soil organic matter quality analyses performed in the same land use systems 59 , which evidenced a much stronger microbial degradation of lignin in the nutrient-limited shrubland than in wastewater irrigated fields. The abundance of genes related to dissimilatory nitrate reduction and annamox were also higher in shrubland soil and lower in UTWW. Dissimilatory nitrate-reducing bacteria are generally found in zones with low or limited nitrate availability 60 , as semiarid shrubland and rainfed soil. Phosphatase genes (alkaline phosphatases and acid phosphatase) showed lowest abundances in shrubland and in FW, respectively. We assume that this is related to the difference in soil pH, as FW exhibited the highest pH (7.8) and shrubland the lowest pH (7.1).
We observed seasonal effects on the bacterial functional profile between the dry and the rainy season. As shown before, FW and UTWW communities differed significantly in composition between seasons (p ≤ 0.01) and the DWW community showed seasonal differences in diversity. As expected, several pathways (hemicellulose and chitin breakdown, nitrification, dissimilatory nitrate and sulphate reduction, and acid and alkaline phosphatases) were affected in FW suggesting this community is susceptible taxonomically and functionally to environmental changes. However, no seasonal effect was recorded for the functional traits in the soil communities influenced by DWW and UTWW. This suggests that wastewater irrigated soil bacterial communities are functionally redundant. Redundancy concerning functional diversity enables soil bacterial communities to adapt to changing environmental parameters 61 . Our results indicate that land use change and the irrigation with different water quantity and quality does not only influences bacterial composition but also affects their functional capabilities. Each community reveals a different response towards changing parameters, which might have consequences for soil quality and ecosystem functioning.

Detection of pathogenic bacteria in wastewater irrigated soils.
To determine the potential risk on human and plant health 8 of introducing pathogenic bacteria in soils by using wastewater for crop irrigation, especially untreated wastewater, we analyzed the occurrence of several common bacterial genera containing pathogenic members in soil (DNA and RNA-based) and wastewater samples (DNA-based  Table S4). Different species of these genera are commonly present in wastewater and are considered waterborne pathogens 62,63 . In the soils of the Mezquital Valley we found eight species with an increased abundance in the DWW and UTWW (p ≤ 0.05) and for seven of them activity was indicated by occurrence at RNA level: Acinetobacter baumanni, A. soli, A. junii, A. haemolytic, A. schindleri, Bacillus thuringiensis/anthracis/cereus and Nocardia flavorosea (Supplementary Material Fig. S7). These microorganisms have been previously reported in connection with treatment and use of wastewater for irrigation [63][64][65] . B. anthracis, B. cereus, and B. thuringiensis are members of the Bacillus cereus group; B. anthracis causes the fatal disease anthrax, B. thuringiensis produces toxic proteins to some insect larvae, therefore it is commonly used as a biological pesticide. B. cereus is an ubiquitous soil bacterium and an opportunistic pathogen, which commonly causes food poisoning 66 . Acinetobacter and Nocardia species are frequently found in soil, water and decaying fecal deposits, and are recognized as opportunistic pathogens mainly in immunocompromised patients 67,68 . In the wastewater irrigated soil of the Mezquital Valley, Broszat and colleagues 19 obtained isolates of Acinetobacter and Bacillus spp. resistant to antibiotics. Our results indicate that only few waterborne pathogens survive under soil conditions, and Acinetobacter, Bacillus and Norcadia spp. are in low abundances, yet they are potentially active and harmful, representing a potential health issue mainly in Mezquital Valley areas were wastewater is used for crop irrigation.
The phytopathogens Acidovorax valerianellae and Rhizorhabdus argentea were detected in the inlet wastewater; species of these genera have been previously found in wastewater treatment plants 8 . Within soil, we detected nine species of plant pathogens from the genera Pseudomonas, Streptomyces, Pantoea, Ralstonia and Rhodococcus, from which only R. fascians was potentially active. Rhodococcus fascians provokes abnormal growth in a broad range of plants 69 . Members of the genera Pseudomonas, Ralstonia and Xanthomonas have been previously detected in irrigation systems 70 . These results suggest that wastewater irrigation of crops can have detrimental effects on the growth of specific plant species. However, in this area the main crops are maize and alfalfa, which are not targeted by the detected phytopathogens.

Conclusion
In line with our general hypothesis dryland transformation to agricultural land under irrigation with different water quantity and quality exhibits a significant effect on the soil bacterial composition, diversity and functionality at entire and active community level. In agreement with our hypothesis (b), we observed shrubland and rainfed soil bacterial communities had similar structure and even closer was the structure of the dam and untreated wastewater irrigated soil communities. The structure of the freshwater irrigated soil shares taxa with both, the driest and the wastewater irrigated soil, as an intermediate land use systems. In contrast to our hypothesis (c), the bacterial diversity of the natural dryland community was only higher compared to that from the rainfed system but not to those of the other systems. In accordance with our hypothesis (a) soil moisture in addition with sodium content and soil pH were the strongest drivers of bacterial community structure. Our results indicate lineage-specific adaptations to drought and sensitivity to sodium content for taxa associated to shrubland, rainfed and freshwater irrigated soil; taxa associated to freshwater, dam wastewater and untreated irrigated systems show the opposite trend. The seasonal difference in the community structure was only evident at active community level in freshwater and untreated wastewater irrigated systems. The freshwater-irrigated soil communities appear sensitive to environmental changes since their composition and functionality are severely affected. The soil community under wastewater irrigated systems varies in diversity and composition, but it seems that their high functional redundancy favours them to resist changing conditions as their functionality is not altered. These emphasizes the advantage of using rRNA-based analyzes to evaluate short term effects on the soil bacterial community. We could identify potentially harmful human and phytopathogens that might be a health risk for the population but not for the predominant crops in the area. In the future, we will focus on the analysis of seasonal changes of the soil microbial community structures and their functions along the different land use systems in drylands. Interactions between different prokaryotic groups and other soil microorganisms including fungi will be investigated to expand our understanding of how dryland transformation influences soil quality and ecosystem functions.

Methods
Study sites and sample collection. This survey was performed in the Mezquital Valley (Fig. 1), located 100 km north of Mexico City in the state of Hidalgo (20°7′44′′N and 99°12′54′′O). The valley has a semi-arid climate with average annual temperatures from 16 to 18 °C and an average annual rainfall from 400 to 600 mm 71 . The natural vegetation in the area is classified as xerophytic shrubs with mesquite (Prosopis juliflora) as the dominant tree species. Shrubland areas are located in the mountains and upper piedmont areas, where no irrigation infrastructure exists. The main crop is maize (Zea mays), which is produced in the rainy season (June-October), or alfalfa (Medicago sativa), a perennial fodder crop under irrigated agriculture. Alfalfa is rotated with maize (three years of alfalfa and two years of maize). The fields under rainfed agriculture are abandoned the rest of the year. The lowest areas of the valley are irrigated periodically (every 30 days on average) with fresh water pumped from deep wells, untreated wastewater coming from Mexico City or wastewater temporally stored in the Endhó dam where a sedimentation process occurs during 3 months of storage. Irrigation is performed by flooding, with 200 mm per irrigation 71 . Alfalfa receives 10 irrigations per year and maize 6 irrigations during the growing cycle.
The sampling included four plots × five land use systems × four samplings = 80 soil samples. The land use systems included sites with natural dryland vegetation (shrubland plots S1-S4), rainfed plots (RF1 to RF4), plots irrigated with freshwater (FW1 to FW4), plots irrigated with wastewater coming from the Endhó dam (DWW1 to DWW4) and plots irrigated with untreated wastewater (UTWW1 to UTWW4) (Fig. 1). The size of the plots ranged among one to two ha. The sampling was performed four times in the same 20 plots, twice during the rainy season (June -October) 2014, and twice during the dry season (November -May) in 2015). The plots used for agriculture were cropped with maize during the rainy season, during the dry season the plots were fallow or SCIENtIfIC REPORtS | (2018) 8:1413 | DOI:10.1038/s41598-018-19743-x cropped with alfalfa, oat or grass (irrigated sites). At each sampling plot, 20 bulk soil cores (6 cm diameter) from the upper 10 cm were sampled in a regular systematic grid; the cores were homogenized and pooled into one composite sample per plot. Plant debris and stones were removed from the homogenized composite samples. One part of the soil samples was frozen in liquid nitrogen and kept at −80 °C and the other part was air dried for soil characterization.
Characterization of soil parameters. The soils of all plots were classified as Haplic Phaeozem 72 (IUSS, 2014) with clay to silty clay loam texture. The soil samples were air-dried for 24 h, homogenized and sieved using a metallic mesh (2 mm). The physical and chemical properties were determined using standard procedures 73 . The soil pH was measured in a 1:2 soil:CaCl 2 10 mM suspension using a sensION156 HACH pH meter (Conductronic pH 120, Puebla, Mexico) and the electric conductivity was determined in a 1:2 soil:distilled water suspension with a conductivity meter (Hanna HL4321, Rhode Island, USA). Soil organic C content and total N was determined using a Perkin Elmer 2400 CHNS/O elemental analyzer (Massachusetts, USA). Available P was determined by spectrophotometry (Genesis 20, Massachusetts, USA) based on the methodology developed by Olsen 74 . Soil particle size distribution was determined using the Bouyoucos hydrometer method. Exchangeable base cations (Ca 2+ , Mg 2+ , Na + and K + ) were extracted with a 1 N solution of ammonium acetate (NH 4 OAc) at pH 7, and the determination of Ca 2+ and Mg 2+ was done by atomic absorption spectrophotometry (Perkin Elmer 3110, Massachusetts, USA), and the one of Na + and K + by flame emission spectrometry (Sherwood Scientific 36, Cambridge, UK), according to van Reeuwijk (1992) 74 . The gravimetric soil water content (%) was calculated from oven-dried subsamples. The soil properties and crop type of each sample and plot are shown in Table S1 of the Supplementary Material. Nucleic acid extraction, cDNA synthesis and amplification of 16 S rRNA genes and transcripts. Soil samples for microbiological analyses were freeze-dried with liquid nitrogen at the field, they lasted 3-4 days in liquid nitrogen (during field campaign) and were then transferred to −80 °C, where they remained until nucleic acid extraction. DNA was extracted from approximately 0.25 g soil by employing the MoBio PowerSoil DNA isolation kit (MoBio Laboratories, Carlsbad, CA, USA) following the instructions of the manufacturer. Total RNA was extracted from approximately 2 g of soil per sample by employing the PowerSoil total RNA isolation kit (MoBio Laboratories, Carlsbad, CA, USA) according to the manufacturer's instruction. Nucleic acid concentrations were quantified using a NanoDrop ND-1000 Spectrophotometer (Thermo Scientific, Schwerte, Germany). Subsequently, total RNA extracts were treated with Turbo DNase to remove remaining DNA and purified by using the Qiagen RNeasy MinElute Cleanup kit (Qiagen, Hilden, Germany). The presence of remaining DNA was tested by PCR as described by Wemheuer et al. 75 . Purified RNA (30-100 ng) was converted to cDNA by using the SuperScript III as recommended by the manufacturer (Thermo Scientific, Schwerte, Germany). Bacterial 16S rRNA gene and transcript amplicons were generated using the bacterial primers targeting the V3-V4 region described by Klindworth et al. 76 with adapters for Illumina MiSeq sequencing. The PCR reaction mixture (25 µl) contained 5-fold Phusion GC buffer, 200 µM of each of the four deoxynucleoside triphosphates, 5% DMSO, 0.4 µM of each primer, 1 U of Phusion HF DNA polymerase (Fisher Scientific GmbH, Schwerte, Germany), and 25 ng of isolated DNA or cDNA as template. For DNA amplification, the following cycling scheme was used: initial denaturation at 98 °C for 5 min and 25 cycles of denaturation at 98 °C for 45 s, annealing at 60 °C for 30 s and extension at 72 °C for 30 s, followed by a final extension at 72 °C for 10 min. PCR reactions were performed in triplicate for each sample. The resulting PCR products were pooled in equal amounts and purified using the QIAquick Gel Extraction kit (Qiagen, Hilden, Germany) as recommended by the manufacturer. Quantification of the PCR products was performed using the Quant-iT dsDNA HS assay kit and a Qubit fluorometer as recommended by the manufacturer (Invitrogen GmbH, Karlsruhe, Germany). Indexing of the PCR products was performed with Nextera XT Index kit as described by the supplier (Illumina, San Diego, CA, USA). Sequencing of 16 S rRNA was performed using the dual index paired-end approach (2 × 300 bp) with v3 chemistry for the Illumina MiSeq platform.
Sequence processing and analyses. Demultiplexing of raw sequences was performed by CASAVA data analysis software (Illumina). Paired-end sequences were merged using PEAR v0.9.10 (64 bit) with default parameters 77 . Sequences with average quality score lower than 20 or containing unresolved nucleotides were removed from the dataset with the split_libraries_fastq.py script from QIIME 1.9.1 78 . We additionally removed remaining unclipped reverse and forward primer sequences by employing cutadapt v1.10 79 with default settings. For operational taxonomic unit (OTU) clustering, we used USEARCH (8.1.1861) with the UPARSE algorithm 80 to dereplicate, remove singletons, and sort all quality-filtered sequences by length (400 bp). Subsequently, OTUs were clustered at 97% sequence identity using USEARCH. Chimeric sequences were removed using UCHIME 81 in reference mode against RDP trainset15_092015.fasta (https://sourceforge.net/projects/rdp-classifier/files/RDP_ Classifier_TrainingData/). All quality-filtered sequences were mapped to chimera-free OTUs and an OTU table was created using USEARCH. Taxonomic classification of the picked reference sequences (OTUs) was performed with parallel_assign_taxonomy_blast.py against the SILVA SSU database release 123.1 82 . Extrinsic domain OTUs, chloroplasts, and unclassified OTUs were removed from the dataset by employing filter_otu_table.py. Sample comparisons were performed at the same surveying effort, utilizing the lowest number of sequences by random selection (DNA level 18,900 and RNA level 19,600). Species richness, alpha and beta diversity estimates and rarefaction curves were determined using the QIIME 1.9.1 script alpha_rarefaction.py.  Table S3). Mean abundances of genes in each land use system were used for statistical analyses (relative to mean abundance in the complete datasets). We  Clavibacter, Arthrobacter, Leifsonia, Rhodococcus, Streptomyces, Pseudomonas, Xanthomonas, and Herbaspirillum using QIIME 1.9.1 script filter_taxa_from_otu_table.py. The corresponding sequences were double checked against the nucleotide collection of the NCBI using BLASTn. The OTUs that exhibited > 98% identities to known members of the genus were further analyzed. The pathogenic nature of the thereby identified taxa was confirmed by literature searches. Taxa for which no pathogenic features were described were discarded.

Identification of human-and phyto-pathogens.
Statistical analyses. All statistical analyses were conducted employing R version 3.3.1 83 . The results of all statistical tests were regarded significant with p ≤ 0.05. For all statistical analysis, the OTU table clustered at 97% sequence identity was used. The statistical analyses employed depended on the normality of the data in each variable. For those variables that followed a normal distribution we used ANOVA followed by Tukey's multiple comparison to test for differences among land use systems and T-test for differences between seasons (bacterial diversity and richness). For those variables following a non-normal distribution we used Kruskal-Wallis analysis of variance followed by Dunn's multiple comparison to test for differences among land use systems and permutation test (package "perm") 84 for differences between seasons (soil properties, potential bacterial pathogens and key genes). To evaluate the diversity indexes relation with the soil parameters Spearman's rank correlation test was employed (r s ). To identify the bacterial orders associated with the different land use systems, an analysis based on the point biserial correlation coeficient was performed using multipatt (package "indicSpecies") 85 . For visualization, a network was generated using the land use systems as source nodes, and the bacterial orders as target nodes. All taxa with significant (p ≤ 0.05) associations were visualized in the networks. Network generation was performed using the edge-weighted spring embedded layout algorithm in Cytoscape 86 , with the edge weight corresponding to the association strength of each order with each land use system. To assess the (dis) similarity of bacterial communities between land use systems and season ANOSIM test was performed in Qiime 78 on weighted Unifrac distances 87 . To visualize the multivariate dispersion of community composition a non-metric multidimensional scaling (NMDS) was performed, standard deviation ellipses by land use system were projected onto the ordination, utilizing the function ordiellipse. The effects of environmental parameters on the bacterial community were analyzed using the envfit function and projected into the ordination with arrows. Ordiellipse and evfit functions are contained in the "vegan" package 88 .
Sequence data deposition. The 16S rRNA gene and transcript sequences were deposited in the National Centre for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under bioproject number PRJNA386070.