Stable soil moisture altered the rhizosphere microbial community structure via affecting their host plant

Stable soil moisture altered the rhizosphere microbial community structure via affecting their host plant | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Stable soil moisture altered the rhizosphere microbial community structure via affecting their host plant Dichuan Liu, Zhuan Wang, Guolong Zhu, Renlian Zhang, Ray Bryant, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1966150/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Temporal variation of soil moisture is one of the influencing factors affecting crop water use efficiency (WUE). Compared with fluctuating soil moisture (FSM), stable soil moisture (SSM) with weaker temporal variance has the potential to improve the WUE of crops. However, response of crop rhizosphere microbiome to soil moisture temporal variation remains unclear. In this study, we performed pot experiments on romaine lettuce ( Lactuca sativa L. var. longifolia ) to compare the effects of different soil moisture temporal variation on plant growth, yield, water use efficiency (WUE), and rhizosphere bacterial and fungal community structures, via manual irrigation and negative pressure irrigation to create FSM and SSM conditions, respectively. The results indicate that SSM improved the growth and WUE of romaine lettuce. Moreover, the rhizosphere microbial community composition of romaine lettuce differed under SSM and FSM conditions. Under SSM, bacterial Bacillus , fungal Aspergillus and Chaetomium were enriched in the romaine lettuce rhizosphere, whereas some taxa such as bacterial Devosia , Lysobacter , Blastococus and Bacillus , fungal Alternaria were reduced; these taxa could therefore be biomarkers in future research. Partial least squares path model (PLS-PM) analysis revealed that rhizosphere microbial communities were indirectly affected by the soil moisture temporal variation, as evidenced by the improvement in plant growth. Our results suggest that the rhizosphere microbial communities of romaine lettuce primarily respond to changes in the soil moisture temporal variation through the plant-microbiome interaction but are not directly affected by soil moisture. soil moisture temporal variation romaine lettuce water use efficiency rhizosphere microbiome bacteria fungi Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Romaine lettuce ( Lactuca sativa L. var. longifolia ) is one of the most commonly consumed vegetables of the genus Lactuca owing to its high nutritional value and ease of cultivation. Soil moisture has a considerable impact on the growth and water use efficiency (WUE) of Lactuca , which may be due to its high water demands during growth and resulting sensitivity to different soil moisture regimes [ 1 ]. As such, this genus is a great candidate for use in exploring the WUE of crops, which impacts both resource conservation and agricultural production. The rhizosphere represents the interface between plant roots and soil, through which plants absorb water and nutrients from the soil and release root exudates [ 2 , 3 ]. Bacteria and fungi colonise the rhizosphere and are collectively referred to as rhizosphere microorganisms [ 4 ]. Plant roots and rhizosphere microorganisms interact with each other through their metabolism and secretion [ 5 , 6 ]; this interaction plays a vital role in plant water and nutrient acquisition [ 7 ]. Changes in soil moisture regimes affect this interaction—a plant water absorption and utilisation feedback inevitably develops between the plant and rhizosphere microbiome [ 8 ]. In fact, recent research has shown that rhizosphere microbiome may enhance drought resistance [ 9 ]. For example, Lau and Lennon found that plant adaptability under different soil moisture regimes was related to the response of soil microbiome to moisture control [ 10 ]. Some specific groups of rhizosphere microbiome contribute to plant growth and water conservation under drought stress [ 11 , 12 ]. Nevertheless, our understanding of the mechanism by which rhizosphere microorganisms influence plant behaviour is still very limited, and few studies have explored the relationship between rhizosphere microorganisms and plant responses to different soil moisture regimes. Therefore, further exploration of the interaction between rhizosphere microorganisms and plants under different soil moisture regimes and their effect on plants will provide a theoretical basis for improving crop drought resistance. Soil moisture changes occur via a dynamic process, which can be classified according to its temporal variation as either fluctuating soil moisture (FSM) or stable soil moisture (SSM) [ 13 ]. Traditional irrigation methods and most high WUE irrigation technologies (such as deficit irrigation) are based on FSM conditions. However, some high WUE crops are grown under SSM conditions. For example, Yang et al found that SSM conditions created under negative pressure irrigation (NPI) resulted in better growth and WUE in crown daisy when compared to FSM conditions created by manual irrigation [ 14 ]. Similarly, Souza et al found that a higher frequency of irrigation and more SSM regime led to a higher yield and WUE of beans [ 15 ]. As the most closely related microorganisms to plant, the rhizosphere microbiome will be bound to respond to soil moisture temporal variation. Thus, we ask: Is rhizosphere microbes affected by soil moisture directly? Or indirectly? And whether this effect is related to the efficient use of water by their host plant? To answer the question above, pot experiments were performed under SSM and FSM conditions using NPI and manual irrigation to achieve the respective conditions and romaine lettuce as the experimental plant [ 13 ]. Thereafter, the response of romaine lettuce growth, WUE and its rhizosphere microbial community under different soil moisture regimes were analysed and a theoretical basis for the exploration of rhizosphere microbial resources was explored. Materials And Methods Field site description Pot experiments were conducted from August 2019 to October 2019 at a net house of the Chinese Academy of Agricultural Sciences (39°57'37'' N, 116°20'0.9'' E), Beijing, China. Sandy loam potting soil was collected from the 0–20 cm surface layer of cultivated soil from the International High-tech Industrial Park, Chinese Academy of Agricultural Sciences (39°36'53'' N, 116°36'89'' E), Langfang, Hebei Province. The soil bulk density was 1.43 g cm 3 , the pH was 8.25, the organic matter content was 10.06 g kg − 1 , the available N content was 58.4 mg kg − 1 , the P content was 20.4 mg kg − 1 , and the K content was 112.4 mg kg − 1 . Experimental design Four treatments were designed: three SSM treatments (S3, S2, and S1) and a control FSM treatment (F). Manual irrigation with upper and lower limits (70–90% field capacity) was used for treatment F. Different NPI water pressures of − 3, −6, and − 9 kPa were used to achieve SSM conditions for S3, S2, and S1, respectively. The three SSM treatments were designed to generate a water content gradient that matched the range of soil moisture content in treatment F and explore the trends among different soil moisture content levels under SSM. Three replicates were performed per treatment, where each pot was regarded as a replicate, with a total of 12 pots in the experiment. The NPI device was consists of three parts: a negative pressure generator, a water bucket (inner diameter of 13.1 cm and height of 80 cm), and a capillary water emitter (length 250 mm, outer diameter 19 mm, and inner diameter 10 mm); each part was connected via silica gel pipes (Fig. 1 A). Absorption of water by crops reduces the soil water pressure (SWP). Thus, once the SWP is lower than the negative water pressure set, the soil actively absorbs water from the water bucket to prevent a further decline in SWP, thereby maintaining soil moisture stability [ 16 ]. In the present study, Lactuca sativa L. var. longifolia , which is commonly referred to as Meilijian lettuce, was used as the experimental romaine lettuce. The sowing of seeds in each pot began on August 20, 2019, and six holes per pot and five seeds per hole were used. Before sowing, 8.35 g of urea, 4.57 g of calcium super-phosphate, and 2.56 g and potassium sulphate powder were mixed and applied to each pot. Each pot contained 23 kg of soil and received 5 L of water to fully wet the soil. The initial volumetric soil moisture content (θ v ) was 28.3%. Once seedling grow four leaves, the seedlings were thinned to one per hole, with six plants per pot. The NPI system was used to control pot water from the time at which most plants had grown four leaves (September 16) until harvest (October 12). Plant and soil sampling Plant and pot soil samples were collected when the lettuce was mature. The whole plant (including roots) was removed, and the above-ground and below-ground parts were isolated to keep the roots attached to the soil. Soil removal began with gentle shaking of large clods until a 1–2 mm soil layer was left on the roots; soil that fell off was regarded as bulk soil. The remaining soil was placed into a centrifuge tube filled with phosphate buffered saline and vibrated until the rhizosphere soil fell off. Roots were removed from the centrifuge tube, and the centrifuge tube was placed in a high-speed centrifuge at 4 ℃ and 10,000 rpm for 10 min; any sediment remaining in the tube was classed as rhizosphere soil [ 4 ]. DNA extraction, PCR amplification, and sequencing DNA was extracted from 0.5-g samples of rhizosphere and bulk soil using a Fast DNA SPIN Kit (MP Biomedicals, USA). The concentration and quality of DNA were determined using NanoDrop 2000 (Thermo Scientific, USA) and 1% agarose gel electrophoresis, respectively. The v3–v4 region of the bacterial 16S rRNA gene was amplified by PCR using the upstream primers 338F and downstream primer 806R. The v5–v7 region of the fungal ITS gene was amplified by PCR using the upstream primer ITS1F and the downstream primer ITS2R. Each DNA sample was amplified by PCR with three replications, and PCR-amplified products were tested by agarose gel electrophoresis (2%). The PCR products were purified using an Axyprep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and a Quantus™ Fluorometer (Promega, USA) for quantitative analysis. Purified PCR products were sequenced using high-throughput Illumina MiSeq (Illumina, USA). The obtained sequences were submitted to the NCBI database (accession numbers PRJNA744013 and PRJNA744028 for bacterial and fungal sequences, respectively). Measurements Plant growth and yield Plant growth parameters were measured every 10 days in terms of plant height, number of leaves, and the largest leaf length and width. The above-ground yield was measured after harvest. Water parameters An AZS-100 soil moisture meter (Beijing Aozuo Ecology Instrumentation Ltd.) was used to measure the soil volumetric moisture content every two days during the irrigation period. Soil moisture stability was evaluated using the coefficient of variation (C V ) of soil moisture, which was calculated according to [ 17 ]: C v = σ / µ (1) where σ and µ represent the standard deviation and average value of the soil moisture content, respectively. A weak temporal variation in soil moisture exists when C v ≤ 0.1, a medium temporal variation exists at 0.1 < C v < 1, and strong variation exists when C v ≥ 1. The coefficient of fluctuation (C f ) of the soil moisture was calculated according to [ 16 ]: C f = ∑ [|θ i - θ i−1 | / ( θ i + θ i−1 ) / 2] / ( n-1) (2) where θ i is the observed soil moisture content at time i, θ i−1 is the observed soil moisture content at the previous moment of time i, and n is the number of observations. The coefficient of fluctuation reflects the stability of soil moisture, with smaller values representing a more SSM content. The water consumption of lettuce was calculated according to [ 16 ]: ET k = M k − ΔW = M k − (θm k - θm k−1 ) × m s /ρ w , (3) where ET k is the amount of water consumed in the K th period, M k is the amount of irrigation in the K th period, ΔW is the change in soil water storage, θm k is the soil mass moisture content at the K th moment, θm k−1 is the soil mass moisture content at the previous K th moment, m s is the weight of soil in the pot, and ρ w is the density of water (1 g cm − 3 ). The WUE of a single plant was calculated as: WUE = Y / I (4) where Y is the plant yield (g), and I is the irrigation volume (L) per plant (irrigation volume per pot divided by six). Statistical analyses Raw data of the high-throughput sequences were processed using fastp 0.20.0, flash 1.2.7, uparse 7.1, RDP classifier 2.2, and mothur1.30.2. All other data were processed using Microsoft Excel 2016. All data were analysed via SPSS 17.0 and plotted using origin8.5 and R v3.6.6. Results Dynamic process of soil moisture changes and its temporal variance The results confirmed that two distinct range of soil moisture temporal variance were created, with one treatment under FSM conditions and three levels of SSM conditions. During the controlled irrigation period from the four-leaves stage (September 16) until harvest (October 12), the final cumulative irrigation amount in each treatment was as follows: S3 (26.12 L) > S2 (19.87 L) > S1 (15.65 L) > F (15.42 L), and the water supply rates were similar in treatments S1 and F. The average soil volumetric moisture values showed a similar pattern to those of cumulative irrigation, at S3 (34.84%) > S2 (28.59%) > S1 (22.56%) > F (21.94%) (Fig. 1 C, Table S1). There was a significant difference in the soil moisture content between S3, S2, and S1 ( p < 0.05 ). The difference in soil moisture content between S1 and F was only 0.0062 g/cm 3 , which was not significant. The temporal variation in soil moisture estimated combine C v and C f (Table 1). As for C v , S3, S2, and S1 exhibited weak temporal variation (C v 0.1). Temporal variance estimated by C f for S3, S2, S1, and F were 0.013 (S3), 0.009 (S2), 0.005 (S1), and 0.029 (F), respectively. The C f of F was 2–5 times larger than that of S3, S2, and S1. Growth and WUE of lettuce At the first day after water control, the average plant height, number of leaves, and largest leaf length and width of lettuce across all treatments were 4.90 cm, 4.17 pcs, 6.02 cm, and 1.83 cm, respectively (Fig. 2 ). All growth parameters increased with time; however, this increase tended to gradually slow until harvest (28 days after water control), at which no significant differences were observed among S3, S2, and S1. However, significant differences in plant height and the largest leaf length and width existed between S1 and F at harvest, with values for S1 being 129.4%, 40.4%, 166.6%, and 75.4% greater than those for F. Comparing F with S1, which had approximately the same cumulative irrigation amount and volumetric water content, we found that the water consumption per plant, fresh shoot biomass, and WUE of S1 were 12.0%, 107.5%, and 87.7% higher than those of F, respectively, but due to the high degree of variability these differences were not statistically significant. However, the root/shoot ratio of S1 was significantly lower than that of F by 41.7% ( P < 0.05) (Table 2). Taxonomic profiles of rhizosphere and bulk soil microbiome Among all samples, the rhizosphere bacterial community comprised the phyla Actinobacteria (19.2–34.2%), Proteobacteria (17.9–28.8%), Chloroflexi (9.9–23.7%), Cyanobacteria (0.5–32.5%), Firmicutes (5.1–16.2%), Gemmatimonadetes (1.2–4.3%), Acidobacteria (1.1–4.0%), Patescibacteria (1.2–6.8%), Deinococcus-Thermus (0.6–2.7%), and Bacteroidetes (1.2–2.1%), which collectively accounted for 97.0–99.2% of all bacterial sequences (Fig. 3 A). The overall rhizosphere fungal community was dominated by Ascomycota (91.2–91.8%), unclassified_k_Fungi (0.9–6.1%), Mortierellomycota (0.1–2.0%), and Basidiomycota (0.1–1.3%), which collectively accounted for 97.4–99.5% of all fungal sequences (Fig. 3 B). No significant difference was found between the bulk soil microbial communities under different treatments; however, some significant differences were found between the rhizosphere bacterial and fungal communities. Among treatments S3, S2, and S1, an increase was observed in the relative abundance of Actinobacteria and Gemmatimonadetes in bacteria and unclassified_k_Fungi, Mortierellomycota, and Basidiomycota in fungi with increased soil moisture content but a decrease in the relative abundance of Deinococcus-Thermus in bacteria and Ascomycota in fungi. Community structure of rhizosphere and bulk soil microbiome To clarify the effect of soil moisture on microbial community alpha diversity, we used the Chao1 index to present richness and the Shannon index to represent diversity. The alpha diversity of the bacterial community in the rhizosphere, as indicated by both the Chao 1 index and the Shannon index, was significantly lower than that in the bulk soil ( P < 0.05; Table 3); however, no significant difference was observed in the fungal community α-diversity (Table 4). Moreover, no relationship was observed between the α-diversity of rhizosphere bacterial and fungal communities among S3, S2, and S1. Likewise, no significant difference was found in the α-diversity of rhizosphere bacterial and fungal communities between S1 and F. Beta diversity analyses evaluated the sources of community variation. We use non-metric multidimensional scaling (NMDS) analysis based on weighted UniFrac distances was used to determine whether treatments are associated with changes in the rhizosphere and bulk soil microbial communities. As showed in Fig. 3 C and D, bulk soil samples clustered together in both bacterial and fungal community, and there was no significant difference in bacterial or fungal communities in the bulk soil between treatments. Instead, rhizosphere samples do cluster separately from bulk soil, rhizosphere bacterial and fungal communities of SSM (S3, S2, and S1) differed significantly from those of FSM (F). Linear discriminant analysis To further identify the specific phylotypes between different soil moisture temporal variation (where S1 and F represent SSM and FSM, respectively), the linear discriminant analysis (LDA) was used to determine the features that most likely explain differences between biological groups [ 18 ]. Twelve taxa at the genus level (LDA ≥ 3.5) were identified as biomarkers between SSM and FSM (Fig. 4 ). For 16S rRNA biomarker, eight bacterial taxa including norank_f__norank_o__Saccharimonadales , n orank_f__Saccharimonadaceae , Devosia , unclassified_f__Methylophilaceae , norank_f__Methylophilaceae , Lysobacter , Blastococcus and Pseudomonas were enriched in FSM condition, while one bacterial taxon Bacillus was enriched in SSM condition (LDA ≥ 3.5, P < 0.05; Fig. 4 A). For ITS biomarker, Aspergillus and Chaetomium were significantly more abundant in SSM than FSM, while Alternaria was less (LDA ≥ 3.5, P < 0.05; Fig. 4 B). Partial least squares path model analysis To integrate the complex interrelationships between soil moisture, romaine lettuce, and rhizosphere microbial communities, a partial least squares path model (PLS-PM) was constructed. The results indicate that temporal variations in soil moisture had a direct effect on the growth of lettuce (− 0.68), which then affected the yield (0.99) and rhizosphere bacterial (− 1.03) and fungal (− 1.09) community structures. WUE was largely affected by the soil moisture content (− 0.44) and yield (1.08) (Fig. 5 ). Overall, the soil moisture dynamic process indirectly affected rhizosphere microbial communities by altering the growth of lettuce. Discussion SSM was more beneficial to the growth and WUE of lettuce Soil moisture regimes can be one of the most restrictive factors in agricultural production, with higher soil moisture contents leading to higher crop yield [ 19 ]. Our results indicate that, even at the same average soil moisture content (S1 and F), SSM conditions are more conducive to growth than FSM, as quantified by the biomass accumulation and WUE of lettuce (Fig. 1 C, Table 2). Thus, soil moisture temporal variation plays a vital role in crop production. Previous NPI-related research has shown that SSM can significantly improve the yield and WUE of many crops [ 20 ]. For example, Zhao et al found that the yield, quality, and WUE of rapeseed were significantly improved under SSM created by NPI [ 21 ]. The yield and WUE of maize under SSM were also significantly higher than those under FSM within a specific range (47–78% field capacity) [ 16 ]. The stable soil moisture and fertilizer regimes provided by SSM conditions could affect the soil nutrient availability, spatial distribution, and even soil enzyme activities, and thus improve the nutrient absorption capacity and fertiliser use efficiency of crops [ 22 ]. Moreover, SSM conditions may be beneficial for the synthesis of chlorophyll, which can affect plant growth and yield by influencing photosynthesis [ 23 ]. The link between SSM conditions and a high WUE of plants is not only seen in our NPI results but has also been observed by researchers examining the role of irrigation frequency (cite); that is, a reasonable increase in irrigation frequency is more conducive to efficient and water-saving agricultural production [ 15 ], which may be related to reduced soil moisture fluctuations resulting from more frequent irrigation. Such frequent irrigation can maintain the soil moisture content at close to the FC, increase the crop yield, and improve the WUE [ 24 ]. The PLS-PM results also showed that SSM conditions promoted plant growth, thereby increasing crop yield. However, the mechanism of SSM → plant growth → yield → WUE cannot be clearly explained by our results or those of previous studies. Under SSM conditions, the theory of saving water through crop compensation for water deficits is the theoretical basis of most high-efficiency water-saving irrigation technologies but is not applicable to our results. Therefore, it was hypothesized that there may be another way for SSM conditions to promote growth and crop water use, which may be related to crop photosynthetic transpiration, root signal substances, osmotic adjustment substances, etc. [ 13 ]. Under SSM conditions, lettuce yield decreased in the following order: S3 > S2 > S1, whereas WUE decreased in the following order S2 > S3 > S1 (Table 2). Compared with S2, the yield of S3 was only improved by 9.8%, but the water consumption per plant increased by 32.9%, whereas the WUE decreased by 17.1%, suggesting that the soil moisture content level of S2 was more conducive to improving WUE. Previous research has shown that lettuce crops require a large amount of water, and the closer the soil moisture is to 100% FC, the higher the yield and WUE [ 25 ]. When the soil moisture content reached 125% FC, the increase in yield was small, and the WUE even decreased, which may be due to the soil moisture content exceeding the amount the plant could absorb, resulting in a reduction of WUE. Rhizosphere microbial communities were indirectly affected by the soil moisture temporal variation via plant-microbiome interaction Soil moisture is a critical requirement for soil microbes having direct and indirect effects on plant growth and metabolic processes [ 26 , 27 ]. Soil properties, including respiration or enzyme activities affected by moisture, can alter the soil microbial structure [ 28 ]. Moreover, the effect of soil moisture regimes on rhizosphere microbes may primarily result from changes in plant roots induced by soil moisture [ 29 , 30 ]. Thus, the microbial community structure of the rhizosphere and bulk soil was compared under different soil moisture regimes and found no evident changes in bulk soil microbial communities. In contrast, the microbial communities of the rhizosphere were affected by both the soil moisture content level and the soil moisture temporal variance (Fig. 3 ). Previous studies have shown that the rhizosphere microbial biomass can increase with increasing soil moisture content but only within a certain range (30–50% FC), and the microbial biomass of the bulk soil is hardly affected [ 31 ]. However, when the soil moisture changes dramatically enough to produce drought stress, the number and diversity of both rhizosphere and bulk soil microbes decrease significantly [ 32 ]. Within a suitable range, changes in soil moisture may be counteracted by the drought resistance of soil microbes themselves, such that bulk soil microbes are not affected, whereas rhizosphere microbes may be affected by their close interactions with plants [ 33 , 34 ]. Research supporting our hypothesis has found that compared with the direct effect caused by soil moisture conditions, rhizosphere microorganisms are more indirectly affected by plants [ 7 , 8 ]. At the same soil moisture content level, no significant effect was observed in the α-diversity of the rhizosphere microbial community ( p > 0.05) (Tables 3 and 4); however, the rhizosphere microbial community composition changed significantly (Fig. 3 C and D). This is consistent with the findings of Ochoa-hueso et al, who suggested that changes in soil moisture, such as drought, are more likely to affect the soil microbial community composition than the diversity or richness [ 35 ]. Huang et al also found that the community composition of rhizosphere bacteria and fungi was altered after drought and rehydration, whereas the alpha diversity showed no change [ 36 ]. Some researchers hypothesise that the soil moisture state not only affects the community composition but also significantly promotes diversity [ 21 , 37 ]. Based on previous research [ 10 , 38 , 39 ], the possible response patterns of rhizosphere microbes to soil moisture regimes were divided into two types: 1) where both the diversity and composition of rhizosphere microbial communities changed in response to changes in the soil moisture regime; and 2) where the composition of the rhizosphere microbial community was influenced by soil moisture changes in multiple ways, for example, by altering the soil physicochemical properties (pH, eH, etc.) or plant root activity, while the buffer effect provided by plant roots maintained relatively stable diversity. Because of the small range of soil moisture content variation in our study (78–120% FC), soil microbes may not have been directly affected; thus, the second response pattern is more likely. We applied PLS-PM to further determine the possible rhizosphere microbial community response pathway to the soil moisture temporal variation (Fig. 5 ). Our results show that the altered rhizosphere bacterial and fungal community structures were mainly associated with the growth of lettuce, which was highly negatively correlated with the degree of temporal variation in soil moisture. Plant roots improve growth by altering root secretion patterns to select a favourable rhizosphere microbial community under different growth patterns [ 9 ]. Therefore, it was hypothesised that the soil moisture temporal variation might influence plant growth, water use, or some other plant function, which in turn affects root growth and secretion to ultimately alter the rhizosphere microbial community structure via plant-microbiome interaction [ 30 , 40 ]. Reduced recruitment of rhizosphere probiotic taxa under SSM Plant roots can enrich specific groups of microbes by releasing root exudates during growth, which can regulate the physicochemical properties of rhizosphere microdomains and improve the rhizosphere microhabitat, thereby promoting their own root growth and helping to develop stress resistance [ 41 ]. At the same soil moisture content, some specific taxa were enriched in the rhizosphere of lettuce under SSM and FSM conditions, respectively (Fig. 4 ). These taxa might explain the majority of differences in the rhizosphere microbial community structure under different soil moisture temporal variances [ 18 ]. Several bacteria of the taxa that were highly enriched under FSM condition had been proved to exhibit biocontrol or host plant growth-promoting effects. For example, Lysobacter was shown to inhibit soil − borne pathogens [ 42 ]; Devosia and Pesdomonas are referred as a member of plant growth-promoting rizobacteria (PGPR) [ 43 , 44 ]. Nonetheless, even though romaine lettuce recruits such growth-promoting taxa into rhizosphere, it did not show a corresponding improvement in yield or WUE (Table 2). We therefore hypothesize that the significant enrichment of probiotic taxa in romaine lettuce rhizosphere under FSM condition might be a "water deficit compensation effect" at microbial level, i.e., romaine lettuce gown under FSM experiences more water stress than SSM, in order to resist these water stresses, the root system is selectively recruit with specific taxa with probiotic function or effect [ 45 , 46 ]. In contrast, under SSM condition, romaine lettuce has been exposed to a more stable and suitable soil moisture content, its root system does not receive drought signals may not attract probiotic taxa come to colonize [ 47 ]. Moreover, only one taxon of fungus was significantly enriched under FSM conditions, namely Alternaria, which explained most of the differences in fungal community structure changes under different soil moisture temporal variance. This may suggest that the lettuce rhizosphere under SSM conditions is not suitable for Alternaria survival, which might be due to the release of root exudates from lettuce under SSM conditions that could inhibit activity. Interestingly, Alternaria is a very common and dangerous pathogenic fungus, which has been shown to cause disease in many economic crops such as lettuce and celery [ 48 ]. Therefore, the inhibition of Alternaria growth under SSM conditions in this study may highlight a potential mechanism for disease control and prevention. Conclusion Our results suggest that SSM condition is more beneficial to the growth, yield, and WUE of romaine lettuce than FSM condition. The different soil moisture temporal variance altered the community composition of both rhizosphere bacteria and fungi; however, this effect was not directly caused by soil moisture but via an indirect effect from host plant. In addition, romaine lettuce rhizosphere selectively recruit specific microbial taxa under different soil moisture temporal variance. These results have improved our understanding of soil moisture–crop–rhizosphere interactions, providing a theoretical basis for effectively using microbial resources to improve the WUE of crops. Declarations Ethical approval This study dose not involve animals. Consent to participate This study dose not involve human participants Conflict of interest The authors declare no competing interests. Acknowledgements This work was supported by the National Key Research and Development Program of China (2018YFE0112300) and the Fundamental Research Funds for Central Non-profit Scientific Institution (No. Y2020PT37), and partly supported by the United States Department of Agriculture (USDA) Agricultural Research Service. The findings and conclusions in this manuscript are those of the author(s) and should not be construed to represent any official USDA or United States Government determination or policy. Any mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. The USDA is an equal opportunity provider and employer. References Damerum A, Smith HK, Clarkson G, et al (2021) The genetic basis of water‐use efficiency and yield in lettuce. BMC Plant Biol 21:1–14. https://doi.org/10.1186/s12870-021-02987-7 Hacquard S, Garrido-Oter R, González A, et al (2015) Microbiota and host nutrition across plant and animal kingdoms. Cell Host Microbe 17:603–616. https://doi.org/10.1016/j.chom.2015.04.009 Taiz L, Zieger E (2015) Plant physiology and development Sinauer Associates. Sinauer Associates, Oxford Edwards J, Johnson C, Santos-Medellín C, et al (2015) Structure, variation, and assembly of the root-associated microbiomes of rice. Proc Natl Acad Sci U S A 112:E911–E920. https://doi.org/10.1073/pnas.1414592112 Gu Y, Wang X, Yang T, et al (2020) Chemical structure predicts the effect of plant-derived low-molecular weight compounds on soil microbiome structure and pathogen suppression. Funct Ecol 34:2158–2169. https://doi.org/10.1111/1365-2435.13624 Korenblum E, Dong Y, Szymanski J, et al (2020) Rhizosphere microbiome mediates systemic root metabolite exudation by root-to-root signaling. Proc Natl Acad Sci U S A 117:3874–3883. https://doi.org/10.1073/pnas.1912130117 de Vries FT, Williams A, Stringer F, et al (2019) Changes in root-exudate-induced respiration reveal a novel mechanism through which drought affects ecosystem carbon cycling. New Phytol 224:132–145. https://doi.org/10.1111/nph.16001 Preece C, Peñuelas J (2016) Rhizodeposition under drought and consequences for soil communities and ecosystem resilience. Plant Soil 409:1–17. https://doi.org/10.1007/s11104-016-3090-z de Vries FT, Griffiths RI, Knight CG, et al (2020) Harnessing rhizosphere microbiomes for drought-resilient crop production. Science (80- ) 368:270–274. https://doi.org/10.1126/science.aaz5192 Lau JA, Lennon JT (2012) Rapid responses of soil microorganisms improve plant fitness in novel environments. Proc Natl Acad Sci U S A 109:14058–14062. https://doi.org/10.1073/pnas.1202319109 Rubin RL, van Groenigen KJ, Hungate BA (2017) Plant growth promoting rhizobacteria are more effective under drought: a meta-analysis. Plant Soil 416:309–323. https://doi.org/10.1007/s11104-017-3199-8 Niu X, Song L, Xiao Y, Ge W (2018) Drought-tolerant plant growth-promoting rhizobacteria associated with foxtail millet in a semi-arid and their potential in alleviating drought stress. Front Microbiol 8:1–11. https://doi.org/10.3389/fmicb.2017.02580 Long H, Wu X, Zhang S et al. (2020) Connotation and research progress of crop initiate water drawing technology. Trans Chin Soc Agric Eng 36:139–152. (in Chinese) https://doi.org/10.11975/j.issn.1002-6819.2020.23.017 Yang P, Bian Y, Long HY, Drohan PJ (2020) Comparison of emitters of ceramic tube and polyvinyl formal under negative pressure irrigation on soil water use efficiency and nutrient uptake of crown daisy. Agric Water Manag 228:105830. https://doi.org/10.1016/j.agwat.2019.105830 Souza SA, Vieira JH, dos Santos Farias DB, et al (2020) Impact of irrigation frequency and planting density on bean’s morpho-physiological and productive traits. Water (Switzerland) 12:. https://doi.org/10.3390/w12092468 Wang Z, Zhu G, Long H et al. (2020) Effects of temporal variation of soil moisture on the growth and water use efficiency of maise. J Agric Sci Technol 22:153–164. (in Chinese) https://doi.org/10.13304/j.nykjdb.2019.0648 Hillel D (1980) Applications of soil physics. 10.2307/2403017 Academic Press, Cambridge, MA Segata N, Izard J, Waldron L, et al (2011) Metagenomic biomarker discovery and explanation. Genome Biol 12, R60 (2011). https://doi.org/10.1186/gb-2011-12-6-r60 Moradgholi A, Mobasser H, Ganjali H, et al (2021) WUE, protein and grain yield of wheat under the interaction of biological and chemical fertilizers and different moisture regimes. Cereal Res Commun. https://doi.org/10.1007/s42976-021-00145-1 Li Y, Wang L, Xue X, et al (2017) Comparison of drip fertigation and negative pressure fertigation on soil water dynamics and water use efficiency of greenhouse tomato grown in the North China Plain. Agric Water Manag 184:1–8. https://doi.org/10.1016/j.agwat.2016.12.018 Zhao X, Gao X, Zhang S, Long H (2019) Improving the Growth of Rapeseed (Brassica chinensis L.) and the Composition of Rhizosphere Bacterial Communities through Negative Pressure Irrigation. Water Air Soil Pollut 230:. https://doi.org/10.1007/s11270-018-4061-1 Wang JJ, Long HY, Huang YF, et al (2019) Effects of different irrigation management parameters on cumulative water supply under negative pressure irrigation. Agric Water Manag 224:105743. https://doi.org/10.1016/j.agwat.2019.105743 Wang J, Huang Y, Long H, et al (2017) Simulations of water movement and solute transport through different soil texture configurations under negative-pressure irrigation. Hydrol Process 31:2599–2612. https://doi.org/10.1002/hyp.11209 Puértolas J, Albacete A, Dodd IC (2020) Irrigation frequency transiently alters whole plant gas exchange, water and hormone status, but irrigation volume determines cumulative growth in two herbaceous crops. Environ Exp Bot 176:104101. https://doi.org/10.1016/j.envexpbot.2020.104101 Ouzounidou G, Paschalidis C, Petropoulos D, et al (2013) Interaction of soil moisture and excess of boron and nitrogen on lettuce growth and quality. Hortic Sci 40:119–125. https://doi.org/10.17221/15/2013-hortsci Sawada K, Funakawa S, Kosaki T (2017) Effect of repeated drying–rewetting cycles on microbial biomass carbon in soils with different climatic histories. Appl Soil Ecol 120:1–7. https://doi.org/10.1016/j.apsoil.2017.07.023 Na X, Yu H, Wang P, et al (2019) Vegetation biomass and soil moisture coregulate bacterial community succession under altered precipitation regimes in a desert steppe in northwestern China. Soil Biol Biochem 136:107520. https://doi.org/10.1016/j.soilbio.2019.107520 Preece C, Verbruggen E, Liu L, et al (2019) Effects of past and current drought on the composition and diversity of soil microbial communities. Soil Biol Biochem 131:28–39. https://doi.org/10.1016/j.soilbio.2018.12.022 Dijkstra FA, Cheng W (2007) Moisture modulates rhizosphere effects on C decomposition in two different soil types. Soil Biol Biochem 39:2264–2274. https://doi.org/10.1016/j.soilbio.2007.03.026 Zhalnina K, Louie KB, Hao Z, et al (2018) Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat Microbiol 3:470–480. https://doi.org/10.1038/s41564-018-0129-3 Xue R (2017) Responses and mechanisms of rhizospheric nutrients and microbial activity to water supply patterns in early growth stage of wheat. Lanzhou University. (in Chinese) Carbone MJ, Alaniz S, Mondino P, et al (2021) Drought influences fungal community dynamics in the grapevine rhizosphere and root microbiome. J Fungi 7:. https://doi.org/10.3390/jof7090686 Battistuzzi FU, Hedges SB (2009) A major clade of prokaryotes with ancient adaptations to life on land. Mol Biol Evol 26:335–343. https://doi.org/10.1093/molbev/msn247 Maldonado-Michel MA, Muñiz-Valencia R, Peraza-Campos AL, et al (2021) Antifungal activity of Swietenia humilis (Meliaceae: Sapindales) seed extracts against Curvularia eragrostidis (Ascomycota: Dothideomycetes). J Plant Dis Prot 128:471–479. https://doi.org/10.1007/s41348-020-00410-1 Ochoa-Hueso R, Collins SL, Delgado-Baquerizo M, et al (2018) Drought consistently alters the composition of soil fungal and bacterial communities in grasslands from two continents. Glob Chang Biol 24:2818–2827. https://doi.org/10.1111/gcb.14113 Huang X, Qin L, Huang L et al. (2020) Variation Characteristics of Drought and Rehydration on the Growth of Hibiscus rosa-sinensis Linn.and Soil Microbial Diversity in rhizosphere. Chin J Trop Crops 41:401–408. (in Chinese) Gao X, Zhang S, Zhao X, Long H (2019) Stable water and fertilizer supply by negative pressure irrigation improve tomato production and soil bacterial communities. SN Appl Sci 1:1–8. https://doi.org/10.1007/s42452-019-0719-6 Nannipieri P, Ascher J, Ceccherini MT, et al (2008) Effects of Root Exudates in Microbial Diversity and Activity in Rhizosphere Soils. Soil Biology, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75575-3_14 Chaparro JM, Sheflin AM, Manter DK, Vivanco JM (2012) Manipulating the soil microbiome to increase soil health and plant fertility. Biol Fertil Soils 48:489–499. https://doi.org/10.1007/s00374-012-0691-4 Song F, Han X, Zhu X, Herbert SJ (2012) Response to water stress of soil enzymes and root exudates from drought and non-drought tolerant corn hybrids at different growth stages. Can J Soil Sci 92:501–507. https://doi.org/10.4141/CJSS2010-057 Fitzpatrick CR, Copeland J, Wang PW, et al (2018) Assembly and ecological function of the root microbiome across angiosperm plant species. Proc Natl Acad Sci U S A 115:E1157–E1165. https://doi.org/10.1073/pnas.1717617115 Xiong W, Guo S, Jousset A, et al (2017) Bio-fertilizer application induces soil suppressiveness against Fusarium wilt disease by reshaping the soil microbiome. Soil Biol Biochem 114:238–247. https://doi.org/10.1016/j.soilbio.2017.07.016 Chhetri G, Kim I, Kang M, et al (2022) Devosia rhizoryzae sp. nov., and Devosia oryziradicis sp. nov., novel plant growth promoting members of the genus Devosia, isolated from the rhizosphere of rice plants. J Microbiol 60:1–10. https://doi.org/10.1007/s12275-022-1474-8 Comeau D, Balthazar C, Novinscak A, et al (2021) Interactions Between Bacillus Spp., Pseudomonas Spp. and Cannabis sativa Promote Plant Growth. Front Microbiol 12:. https://doi.org/10.3389/fmicb.2021.715758 Goswami M, Deka S (2020) Plant growth-promoting rhizobacteria—alleviators of abiotic stresses in soil: A review. Pedosphere 30:40–61. https://doi.org/10.1016/S1002-0160(19)60839-8 Pereira LB, Andrade GS, Meneghin SP, et al (2019) Prospecting Plant Growth-Promoting Bacteria Isolated from the Rhizosphere of Sugarcane Under Drought Stress. Curr Microbiol 76:1345–1354. https://doi.org/10.1007/s00284-019-01749-x Munoz-Ucros J, Wilhelm RC, Buckley DH, Bauerle TL (2022) Drought legacy in rhizosphere bacterial communities alters subsequent plant performance. Plant Soil 471:443–461. https://doi.org/10.1007/s11104-021-05227-x Berbee ML, Pirseyedi M, Hubbard S (1999) Cochliobolus phylogenetics and the origin of known, highly virulent pathogens, inferred from ITS and glyceraldehyde-3-phosphate dehydrogenase gene sequences. Mycologia 91:964–977. https://doi.org/10.2307/3761627 Tables Table 1 Soil moisture contents and temporal variance under different treatments Treatment Average volumetric soil moisture content (%) Variation range of soil moisture content (%) C v C f S3 34.84±2.45 a 30.02 ~ 38.05 0.070 0.013 S2 28.59±1.72 b 25.63 ~ 30.40 0.060 0.009 S1 22.56±1.41 c 19.85 ~ 24.48 0.062 0.005 F 21.94±2.31 c 18.47 ~ 24.63 0.105 0.029 Note: Different lowercase letters in the same column indicate significant differences among treatments at p < 0.05. Table 2 Photosynthetic product distribution and water use efficiency of lettuces under different treatments Treatment Water consumption per plant (L·pot -1 ) The fresh biomass of shoot (g) Root/shoot ratio Water-use efficiency (g·kg -1 ) S3 5.38±1.83 a 36.79±18.03 a 0.06±0.02 b 7.89±5.37 a S2 3.61±0.37 a 33.20±3.58 ab 0.06±0.02 b 9.24±1.08 a S1 3.23±0.75 a 27.79±6.27 ab 0.07±0.01 b 8.61±0.78 a F 2.89±0.31 a 13.39±3.58 b 0.12±0.01 a 4.59±0.70 a Note: Different lowercase letters in the same column indicate significant differences among treatments at p < 0.05. Table 3 Alpha diversity index of lettuce rhizosphere (R) and bulk soil (B) bacterial communities under different treatments Soil apartment - treatment OTUs Chao1 index Shannon index Coverage R-S3 2158.00±117.94 b 3138.63±119.02 b 4.68±0.22 c 0.9758 R-S2 2069.67±116.67 b 3048.42±205.13 b 5.36±0.49 b 0.9707 R-S1 1935.00±143.15 b 2946.87±294.70 b 4.83±0.53 c 0.9741 R-F 1856.00±221.28 b 2781.26±322.21 b 4.96±0.48 bc 0.9811 B-S3 2661.67±147.06 a 3617.59±190.37 a 6.41±0.07 a 0.9744 B-S2 2837.67±111.79 a 3835.32±132.73 a 6.50±0.02 a 0.9769 B-S1 2917.00±165.01 a 3814.30±162.28 a 6.48±0.12 a 0.9801 B-F 2766.67±300.31 a 3759.66±183.46 a 6.42±0.06 a 0.9763 Note: R and B of the soil compartment represent the rhizosphere and bulk soil, respectively, and different lowercase letters in the same column indicate significant differences among the treatments at p < 0.05. Table 4 Alpha diversity index of lettuce rhizosphere (R) and bulk soil (B) fungal communities under different treatments Soil apartment - treatment OTUs Chao1 index Shannon index Coverage R-S3 377.33±145.25 ab 511.32±163.58 a 2.75±0.52 bc 0.9983 R-S2 309.67±60.35 b 411.20±74.15 a 2.85±0.39 abc 0.9987 R-S1 386.00±121.45 ab 508.09±86.47 a 2.97±1.03 abc 0.9987 R-F 317.00±29.05 b 423.79±13.62 a 2.26±0.58 c 0.9984 B-S3 392.00±130.40 ab 453.00±137.57 a 2.89±0.62 abc 0.9988 B-S2 486.00±31.32 a 530.60±27.20 a 3.69±0.04 a 0.9992 B-S1 377.33±36.07 ab 416.75±51.81 a 3.30±0.14 ab 0.9991 B-F 450.67±32.01 ab 499.75±38.33 a 3.49±0.16 ab 0.9987 Note: R and B of soil compartment represent rhizosphere and bulk soil, respectively; Different lowercase letters in the same column indicates significant differences among the treatments at P<0.05 level. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1966150","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":131729526,"identity":"441bf91e-bdfb-463c-860d-aff087a64872","order_by":0,"name":"Dichuan Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dichuan","middleName":"","lastName":"Liu","suffix":""},{"id":131729527,"identity":"e111df01-5340-4b2e-90f6-c69202a2e621","order_by":1,"name":"Zhuan Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhuan","middleName":"","lastName":"Wang","suffix":""},{"id":131729528,"identity":"ac312066-37c7-470f-86f3-cea979b69dd8","order_by":2,"name":"Guolong Zhu","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guolong","middleName":"","lastName":"Zhu","suffix":""},{"id":131729529,"identity":"b337e30c-5bfe-4bfe-ac9d-2800dc2f5c2b","order_by":3,"name":"Renlian Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Renlian","middleName":"","lastName":"Zhang","suffix":""},{"id":131729530,"identity":"e6f291b4-72c3-4ea8-a9b6-846eaaee6f0c","order_by":4,"name":"Ray Bryant","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ray","middleName":"","lastName":"Bryant","suffix":""},{"id":131729531,"identity":"07118084-b203-46fd-9610-23ab73aaaa4f","order_by":5,"name":"Patrick J. Drohan","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Patrick","middleName":"J.","lastName":"Drohan","suffix":""},{"id":131729532,"identity":"3355f9ae-382d-4b74-8a62-d164f8b3fef9","order_by":6,"name":"Huaiyu Long","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYBACPiBmBmIeIHWAOC1sCC1sCaRpAekyIFILe+/h14VtdjIGN3I+fvhRU8fAP7uBgBaec2nWM9uSeQxu5G6W7Dl2mEHizgECWiRyzIx525hBWrYx8LAdYDCQSCBKSz1QS84zxj//6ojSYvyYt+0wSAsbM9A6IrTwnDFj5jl3nEfyzDNjadm+wzwSNwho4WfvMf7MU1Ztz3c8+eHHN9/q5PhnENACdhuIVDgA4fEQVA8EzB9ApHwDMWpHwSgYBaNgRAIAXhI7HQhIphsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-1317-9209","institution":"CAAS IARRP: Chinese Academy of Agricultural Sciences Institute of Agricultural Resources and Regional Planning","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Huaiyu","middleName":"","lastName":"Long","suffix":""},{"id":131729533,"identity":"a1058f54-4e48-45e6-856f-abb5312636d0","order_by":7,"name":"Aiguo Xu","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Aiguo","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2022-08-16 06:36:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1966150/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1966150/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":25806510,"identity":"98f30640-e76c-4bea-9927-8a5d40eda06d","added_by":"auto","created_at":"2022-08-29 17:51:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1081508,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the negative pressure irrigation system, a: plotting container, b: capillary water emitter, c: water bucket, d: atmospheric outlet tube, e: negative pressure controller (\u003cstrong\u003eA\u003c/strong\u003e). Picture of working negative pressure irrigation system in pot experiment (\u003cstrong\u003eB\u003c/strong\u003e). Cumulative volume of water applied and dynamic changes in the soil volumetric moisture content under different treatments (\u003cstrong\u003eC\u003c/strong\u003e). \u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-1966150/v1/480218d2eabeeb240bf140e3.png"},{"id":25806511,"identity":"b5d1235c-df53-4e68-8ffb-5ed93d56bd82","added_by":"auto","created_at":"2022-08-29 17:51:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":148517,"visible":true,"origin":"","legend":"\u003cp\u003eDynamic changes of romaine lettuce growth under different treatments. (\u003cstrong\u003eA\u003c/strong\u003e) plant height of lettuces, (\u003cstrong\u003eB\u003c/strong\u003e) number of lettuce leaves, (\u003cstrong\u003eC\u003c/strong\u003e) largest leaf length of lettuces, and (\u003cstrong\u003eD\u003c/strong\u003e) largest leaf width of lettuces.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-1966150/v1/7ab448ac7cf55ddf6a9c41b9.png"},{"id":25806014,"identity":"520ac4b9-a52f-4ee0-94d4-f664b6319b54","added_by":"auto","created_at":"2022-08-29 17:46:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":126017,"visible":true,"origin":"","legend":"\u003cp\u003eTaxonomic profiles (at the phylum level) of the rhizosphere and bulk soil microbial community: (\u003cstrong\u003eA\u003c/strong\u003e) Bacterial community, (\u003cstrong\u003eB\u003c/strong\u003e) fungal community. Non-metric multidimensional scaling representation of romaine lettuce rhizosphere microbial communities under different treatments: (\u003cstrong\u003eC\u003c/strong\u003e) Bacterial community, (\u003cstrong\u003eD\u003c/strong\u003e) fungal community. Ordinations were based on weighted UniFrac distances. Different colour shapes include all plots within a treatment, circle dots represent rhizosphere samples and square dots represent bulk soil samples.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-1966150/v1/2f18ac4db810b6d4cca2297e.png"},{"id":25806015,"identity":"cabe9904-a3a8-4ff2-85a1-a21aedaf78d3","added_by":"auto","created_at":"2022-08-29 17:46:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":638975,"visible":true,"origin":"","legend":"\u003cp\u003eKey phylotypes of rhizosphere microbiota in response to the soil moisture change process identified using the linear discriminant analysis (LDA): (\u003cstrong\u003eA\u003c/strong\u003e) bacterial taxa, (\u003cstrong\u003eB\u003c/strong\u003e) fungal taxa. The hollow triangle shows the LDA scores (≥ 3.5), the histogram shows the relative abundance of genus.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-1966150/v1/5a9fcfe131bf9c5ebd25dcdc.png"},{"id":25806017,"identity":"f69b2d6f-a799-4b78-9588-e881ffdd144a","added_by":"auto","created_at":"2022-08-29 17:46:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":89541,"visible":true,"origin":"","legend":"\u003cp\u003eDirected graph of the partial least squares path model. Each box represents an observed variable (i.e., measured) or latent variable (i.e., constructed). Loading for non-metric multidimensional scaling scores of microbial communities and plant growth (consisting of plant height, number of leaves, and largest length and width of leaves) used to create the latent variables are shown in the dashed rectangles. Path coefficients are calculated after 1,000 bootstraps and reflected by the arrow width, with blue and red indicating positive and negative effects, respectively. Dashed arrows show that coefficients did not differ significantly from zero (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). The model was assessed using the Goodness of Fit (GoF) statistic, and the GoF value was 0.70. \u003cem\u003e***p\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003e**p\u003c/em\u003e \u0026lt; 0.01, \u003cem\u003e*p\u003c/em\u003e \u0026lt; 0.05\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-1966150/v1/386595b375f4663837d04df7.png"},{"id":26942213,"identity":"297dff6b-c346-44a6-b1f3-4f4fac9451de","added_by":"auto","created_at":"2022-09-26 00:35:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1729120,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1966150/v1/9afad4bd-1565-4cb2-afdc-99716dcb3522.pdf"}],"financialInterests":"","formattedTitle":"Stable soil moisture altered the rhizosphere microbial community structure via affecting their host plant","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRomaine lettuce (\u003cem\u003eLactuca sativa L. var. longifolia\u003c/em\u003e) is one of the most commonly consumed vegetables of the genus \u003cem\u003eLactuca\u003c/em\u003e owing to its high nutritional value and ease of cultivation. Soil moisture has a considerable impact on the growth and water use efficiency (WUE) of \u003cem\u003eLactuca\u003c/em\u003e, which may be due to its high water demands during growth and resulting sensitivity to different soil moisture regimes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As such, this genus is a great candidate for use in exploring the WUE of crops, which impacts both resource conservation and agricultural production. The rhizosphere represents the interface between plant roots and soil, through which plants absorb water and nutrients from the soil and release root exudates [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Bacteria and fungi colonise the rhizosphere and are collectively referred to as rhizosphere microorganisms [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Plant roots and rhizosphere microorganisms interact with each other through their metabolism and secretion [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]; this interaction plays a vital role in plant water and nutrient acquisition [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Changes in soil moisture regimes affect this interaction\u0026mdash;a plant water absorption and utilisation feedback inevitably develops between the plant and rhizosphere microbiome [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In fact, recent research has shown that rhizosphere microbiome may enhance drought resistance [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For example, Lau and Lennon found that plant adaptability under different soil moisture regimes was related to the response of soil microbiome to moisture control [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Some specific groups of rhizosphere microbiome contribute to plant growth and water conservation under drought stress [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Nevertheless, our understanding of the mechanism by which rhizosphere microorganisms influence plant behaviour is still very limited, and few studies have explored the relationship between rhizosphere microorganisms and plant responses to different soil moisture regimes. Therefore, further exploration of the interaction between rhizosphere microorganisms and plants under different soil moisture regimes and their effect on plants will provide a theoretical basis for improving crop drought resistance.\u003c/p\u003e \u003cp\u003eSoil moisture changes occur via a dynamic process, which can be classified according to its temporal variation as either fluctuating soil moisture (FSM) or stable soil moisture (SSM) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Traditional irrigation methods and most high WUE irrigation technologies (such as deficit irrigation) are based on FSM conditions. However, some high WUE crops are grown under SSM conditions. For example, Yang et al found that SSM conditions created under negative pressure irrigation (NPI) resulted in better growth and WUE in crown daisy when compared to FSM conditions created by manual irrigation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Similarly, Souza et al found that a higher frequency of irrigation and more SSM regime led to a higher yield and WUE of beans [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. As the most closely related microorganisms to plant, the rhizosphere microbiome will be bound to respond to soil moisture temporal variation. Thus, we ask: Is rhizosphere microbes affected by soil moisture directly? Or indirectly? And whether this effect is related to the efficient use of water by their host plant?\u003c/p\u003e \u003cp\u003eTo answer the question above, pot experiments were performed under SSM and FSM conditions using NPI and manual irrigation to achieve the respective conditions and romaine lettuce as the experimental plant [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Thereafter, the response of romaine lettuce growth, WUE and its rhizosphere microbial community under different soil moisture regimes were analysed and a theoretical basis for the exploration of rhizosphere microbial resources was explored.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eField site description\u003c/h2\u003e \u003cp\u003ePot experiments were conducted from August 2019 to October 2019 at a net house of the Chinese Academy of Agricultural Sciences (39\u0026deg;57'37'' N, 116\u0026deg;20'0.9'' E), Beijing, China. Sandy loam potting soil was collected from the 0\u0026ndash;20 cm surface layer of cultivated soil from the International High-tech Industrial Park, Chinese Academy of Agricultural Sciences (39\u0026deg;36'53'' N, 116\u0026deg;36'89'' E), Langfang, Hebei Province. The soil bulk density was 1.43 g cm\u003csup\u003e3\u003c/sup\u003e, the pH was 8.25, the organic matter content was 10.06 g kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, the available N content was 58.4 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, the P content was 20.4 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and the K content was 112.4 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eExperimental design\u003c/h2\u003e \u003cp\u003eFour treatments were designed: three SSM treatments (S3, S2, and S1) and a control FSM treatment (F). Manual irrigation with upper and lower limits (70\u0026ndash;90% field capacity) was used for treatment F. Different NPI water pressures of \u0026minus;\u0026thinsp;3, \u0026minus;6, and \u0026minus;\u0026thinsp;9 kPa were used to achieve SSM conditions for S3, S2, and S1, respectively. The three SSM treatments were designed to generate a water content gradient that matched the range of soil moisture content in treatment F and explore the trends among different soil moisture content levels under SSM. Three replicates were performed per treatment, where each pot was regarded as a replicate, with a total of 12 pots in the experiment.\u003c/p\u003e \u003cp\u003eThe NPI device was consists of three parts: a negative pressure generator, a water bucket (inner diameter of 13.1 cm and height of 80 cm), and a capillary water emitter (length 250 mm, outer diameter 19 mm, and inner diameter 10 mm); each part was connected via silica gel pipes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Absorption of water by crops reduces the soil water pressure (SWP). Thus, once the SWP is lower than the negative water pressure set, the soil actively absorbs water from the water bucket to prevent a further decline in SWP, thereby maintaining soil moisture stability [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the present study, \u003cem\u003eLactuca sativa\u003c/em\u003e L. \u003cem\u003evar. longifolia\u003c/em\u003e, which is commonly referred to as Meilijian lettuce, was used as the experimental romaine lettuce. The sowing of seeds in each pot began on August 20, 2019, and six holes per pot and five seeds per hole were used. Before sowing, 8.35 g of urea, 4.57 g of calcium super-phosphate, and 2.56 g and potassium sulphate powder were mixed and applied to each pot. Each pot contained 23 kg of soil and received 5 L of water to fully wet the soil. The initial volumetric soil moisture content (θ\u003csub\u003ev\u003c/sub\u003e) was 28.3%. Once seedling grow four leaves, the seedlings were thinned to one per hole, with six plants per pot. The NPI system was used to control pot water from the time at which most plants had grown four leaves (September 16) until harvest (October 12).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePlant and soil sampling\u003c/h2\u003e \u003cp\u003ePlant and pot soil samples were collected when the lettuce was mature. The whole plant (including roots) was removed, and the above-ground and below-ground parts were isolated to keep the roots attached to the soil. Soil removal began with gentle shaking of large clods until a 1\u0026ndash;2 mm soil layer was left on the roots; soil that fell off was regarded as bulk soil. The remaining soil was placed into a centrifuge tube filled with phosphate buffered saline and vibrated until the rhizosphere soil fell off. Roots were removed from the centrifuge tube, and the centrifuge tube was placed in a high-speed centrifuge at 4 ℃ and 10,000 rpm for 10 min; any sediment remaining in the tube was classed as rhizosphere soil [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction, PCR amplification, and sequencing\u003c/h2\u003e \u003cp\u003eDNA was extracted from 0.5-g samples of rhizosphere and bulk soil using a Fast DNA SPIN Kit (MP Biomedicals, USA). The concentration and quality of DNA were determined using NanoDrop 2000 (Thermo Scientific, USA) and 1% agarose gel electrophoresis, respectively. The v3\u0026ndash;v4 region of the bacterial 16S rRNA gene was amplified by PCR using the upstream primers 338F and downstream primer 806R. The v5\u0026ndash;v7 region of the fungal ITS gene was amplified by PCR using the upstream primer ITS1F and the downstream primer ITS2R. Each DNA sample was amplified by PCR with three replications, and PCR-amplified products were tested by agarose gel electrophoresis (2%). The PCR products were purified using an Axyprep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and a Quantus\u0026trade; Fluorometer (Promega, USA) for quantitative analysis. Purified PCR products were sequenced using high-throughput Illumina MiSeq (Illumina, USA). The obtained sequences were submitted to the NCBI database (accession numbers PRJNA744013 and PRJNA744028 for bacterial and fungal sequences, respectively).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMeasurements\u003c/h2\u003e \u003cp\u003e \u003cb\u003ePlant growth and yield\u003c/b\u003e Plant growth parameters were measured every 10 days in terms of plant height, number of leaves, and the largest leaf length and width. The above-ground yield was measured after harvest.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWater parameters\u003c/b\u003e An AZS-100 soil moisture meter (Beijing Aozuo Ecology Instrumentation Ltd.) was used to measure the soil volumetric moisture content every two days during the irrigation period. Soil moisture stability was evaluated using the coefficient of variation (C\u003csub\u003eV\u003c/sub\u003e) of soil moisture, which was calculated according to [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003eC\u003csub\u003ev\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;σ / \u0026micro; (1)\u003c/p\u003e \u003cp\u003ewhere σ and \u0026micro; represent the standard deviation and average value of the soil moisture content, respectively. A weak temporal variation in soil moisture exists when C\u003csub\u003ev\u003c/sub\u003e \u0026le; 0.1, a medium temporal variation exists at 0.1\u0026thinsp;\u0026lt;\u0026thinsp;C\u003csub\u003ev\u003c/sub\u003e \u0026lt; 1, and strong variation exists when C\u003csub\u003ev\u003c/sub\u003e \u0026ge; 1.\u003c/p\u003e \u003cp\u003eThe coefficient of fluctuation (C\u003csub\u003ef\u003c/sub\u003e) of the soil moisture was calculated according to [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003eC\u003csub\u003ef\u003c/sub\u003e = \u0026sum; [|θ\u003csub\u003ei\u003c/sub\u003e - θ\u003csub\u003ei\u0026minus;1\u003c/sub\u003e| / ( θ\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;θ\u003csub\u003ei\u0026minus;1\u003c/sub\u003e ) / 2] / ( n-1) (2)\u003c/p\u003e \u003cp\u003ewhere θ\u003csub\u003ei\u003c/sub\u003e is the observed soil moisture content at time i, θ\u003csub\u003ei\u0026minus;1\u003c/sub\u003e is the observed soil moisture content at the previous moment of time i, and n is the number of observations. The coefficient of fluctuation reflects the stability of soil moisture, with smaller values representing a more SSM content.\u003c/p\u003e \u003cp\u003eThe water consumption of lettuce was calculated according to [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003eET\u003csub\u003ek\u003c/sub\u003e = M\u003csub\u003ek\u003c/sub\u003e\u0026thinsp;\u0026minus;\u0026thinsp;ΔW\u0026thinsp;=\u0026thinsp;M\u003csub\u003ek\u003c/sub\u003e \u0026minus; (θm\u003csub\u003ek\u003c/sub\u003e - θm\u003csub\u003ek\u0026minus;1\u003c/sub\u003e) \u0026times; m\u003csub\u003es\u003c/sub\u003e/ρ\u003csub\u003ew\u003c/sub\u003e, (3)\u003c/p\u003e \u003cp\u003ewhere ET\u003csub\u003ek\u003c/sub\u003e is the amount of water consumed in the K\u003csup\u003eth\u003c/sup\u003e period, M\u003csub\u003ek\u003c/sub\u003e is the amount of irrigation in the K\u003csup\u003eth\u003c/sup\u003e period, ΔW is the change in soil water storage, θm\u003csub\u003ek\u003c/sub\u003e is the soil mass moisture content at the K\u003csup\u003eth\u003c/sup\u003e moment, θm\u003csub\u003ek\u0026minus;1\u003c/sub\u003e is the soil mass moisture content at the previous K\u003csup\u003eth\u003c/sup\u003e moment, m\u003csub\u003es\u003c/sub\u003e is the weight of soil in the pot, and ρ\u003csub\u003ew\u003c/sub\u003e is the density of water (1 g cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eThe WUE of a single plant was calculated as:\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003cp\u003eWUE\u0026thinsp;=\u0026thinsp;Y / I (4)\u003c/p\u003e \u003cp\u003ewhere Y is the plant yield (g), and I is the irrigation volume (L) per plant (irrigation volume per pot divided by six).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eRaw data of the high-throughput sequences were processed using fastp 0.20.0, flash 1.2.7, uparse 7.1, RDP classifier 2.2, and mothur1.30.2. All other data were processed using Microsoft Excel 2016. All data were analysed via SPSS 17.0 and plotted using origin8.5 and R v3.6.6.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDynamic process of soil moisture changes and its temporal variance\u003c/h2\u003e \u003cp\u003eThe results confirmed that two distinct range of soil moisture temporal variance were created, with one treatment under FSM conditions and three levels of SSM conditions. During the controlled irrigation period from the four-leaves stage (September 16) until harvest (October 12), the final cumulative irrigation amount in each treatment was as follows: S3 (26.12 L)\u0026thinsp;\u0026gt;\u0026thinsp;S2 (19.87 L)\u0026thinsp;\u0026gt;\u0026thinsp;S1 (15.65 L)\u0026thinsp;\u0026gt;\u0026thinsp;F (15.42 L), and the water supply rates were similar in treatments S1 and F. The average soil volumetric moisture values showed a similar pattern to those of cumulative irrigation, at S3 (34.84%)\u0026thinsp;\u0026gt;\u0026thinsp;S2 (28.59%)\u0026thinsp;\u0026gt;\u0026thinsp;S1 (22.56%)\u0026thinsp;\u0026gt;\u0026thinsp;F (21.94%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, Table S1). There was a significant difference in the soil moisture content between S3, S2, and S1 (\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e). The difference in soil moisture content between S1 and F was only 0.0062 g/cm\u003csup\u003e3\u003c/sup\u003e, which was not significant. The temporal variation in soil moisture estimated combine C\u003csub\u003ev\u003c/sub\u003e and C\u003csub\u003ef\u003c/sub\u003e (Table\u0026nbsp;1). As for C\u003csub\u003ev\u003c/sub\u003e, S3, S2, and S1 exhibited weak temporal variation (C\u003csub\u003ev\u003c/sub\u003e \u0026lt; 0.1), and F exhibited medium temporal variation (C\u003csub\u003ev\u003c/sub\u003e \u0026gt; 0.1). Temporal variance estimated by C\u003csub\u003ef\u003c/sub\u003e for S3, S2, S1, and F were 0.013 (S3), 0.009 (S2), 0.005 (S1), and 0.029 (F), respectively. The C\u003csub\u003ef\u003c/sub\u003e of F was 2\u0026ndash;5 times larger than that of S3, S2, and S1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGrowth and WUE of lettuce\u003c/h2\u003e \u003cp\u003eAt the first day after water control, the average plant height, number of leaves, and largest leaf length and width of lettuce across all treatments were 4.90 cm, 4.17 pcs, 6.02 cm, and 1.83 cm, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All growth parameters increased with time; however, this increase tended to gradually slow until harvest (28 days after water control), at which no significant differences were observed among S3, S2, and S1. However, significant differences in plant height and the largest leaf length and width existed between S1 and F at harvest, with values for S1 being 129.4%, 40.4%, 166.6%, and 75.4% greater than those for F. Comparing F with S1, which had approximately the same cumulative irrigation amount and volumetric water content, we found that the water consumption per plant, fresh shoot biomass, and WUE of S1 were 12.0%, 107.5%, and 87.7% higher than those of F, respectively, but due to the high degree of variability these differences were not statistically significant. However, the root/shoot ratio of S1 was significantly lower than that of F by 41.7% (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05) (Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTaxonomic profiles of rhizosphere and bulk soil microbiome\u003c/h2\u003e \u003cp\u003eAmong all samples, the rhizosphere bacterial community comprised the phyla Actinobacteria (19.2\u0026ndash;34.2%), Proteobacteria (17.9\u0026ndash;28.8%), Chloroflexi (9.9\u0026ndash;23.7%), Cyanobacteria (0.5\u0026ndash;32.5%), Firmicutes (5.1\u0026ndash;16.2%), Gemmatimonadetes (1.2\u0026ndash;4.3%), Acidobacteria (1.1\u0026ndash;4.0%), Patescibacteria (1.2\u0026ndash;6.8%), Deinococcus-Thermus (0.6\u0026ndash;2.7%), and Bacteroidetes (1.2\u0026ndash;2.1%), which collectively accounted for 97.0\u0026ndash;99.2% of all bacterial sequences (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The overall rhizosphere fungal community was dominated by Ascomycota (91.2\u0026ndash;91.8%), unclassified_k_Fungi (0.9\u0026ndash;6.1%), Mortierellomycota (0.1\u0026ndash;2.0%), and Basidiomycota (0.1\u0026ndash;1.3%), which collectively accounted for 97.4\u0026ndash;99.5% of all fungal sequences (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). No significant difference was found between the bulk soil microbial communities under different treatments; however, some significant differences were found between the rhizosphere bacterial and fungal communities. Among treatments S3, S2, and S1, an increase was observed in the relative abundance of Actinobacteria and Gemmatimonadetes in bacteria and unclassified_k_Fungi, Mortierellomycota, and Basidiomycota in fungi with increased soil moisture content but a decrease in the relative abundance of Deinococcus-Thermus in bacteria and Ascomycota in fungi.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCommunity structure of rhizosphere and bulk soil microbiome\u003c/h2\u003e \u003cp\u003eTo clarify the effect of soil moisture on microbial community alpha diversity, we used the Chao1 index to present richness and the Shannon index to represent diversity. The alpha diversity of the bacterial community in the rhizosphere, as indicated by both the Chao 1 index and the Shannon index, was significantly lower than that in the bulk soil (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table\u0026nbsp;3); however, no significant difference was observed in the fungal community α-diversity (Table\u0026nbsp;4). Moreover, no relationship was observed between the α-diversity of rhizosphere bacterial and fungal communities among S3, S2, and S1. Likewise, no significant difference was found in the α-diversity of rhizosphere bacterial and fungal communities between S1 and F.\u003c/p\u003e \u003cp\u003eBeta diversity analyses evaluated the sources of community variation. We use non-metric multidimensional scaling (NMDS) analysis based on weighted UniFrac distances was used to determine whether treatments are associated with changes in the rhizosphere and bulk soil microbial communities. As showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and D, bulk soil samples clustered together in both bacterial and fungal community, and there was no significant difference in bacterial or fungal communities in the bulk soil between treatments. Instead, rhizosphere samples do cluster separately from bulk soil, rhizosphere bacterial and fungal communities of SSM (S3, S2, and S1) differed significantly from those of FSM (F).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLinear discriminant analysis\u003c/h2\u003e \u003cp\u003eTo further identify the specific phylotypes between different soil moisture temporal variation (where S1 and F represent SSM and FSM, respectively), the linear discriminant analysis (LDA) was used to determine the features that most likely explain differences between biological groups [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Twelve taxa at the genus level (LDA\u0026thinsp;\u0026ge;\u0026thinsp;3.5) were identified as biomarkers between SSM and FSM (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For 16S rRNA biomarker, eight bacterial taxa including \u003cem\u003enorank_f__norank_o__Saccharimonadales\u003c/em\u003e, n\u003cem\u003eorank_f__Saccharimonadaceae\u003c/em\u003e, \u003cem\u003eDevosia\u003c/em\u003e, \u003cem\u003eunclassified_f__Methylophilaceae\u003c/em\u003e, \u003cem\u003enorank_f__Methylophilaceae\u003c/em\u003e, \u003cem\u003eLysobacter\u003c/em\u003e, \u003cem\u003eBlastococcus\u003c/em\u003e and Pseudomonas were enriched in FSM condition, while one bacterial taxon \u003cem\u003eBacillus\u003c/em\u003e was enriched in SSM condition (LDA\u0026thinsp;\u0026ge;\u0026thinsp;3.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). For ITS biomarker, Aspergillus and Chaetomium were significantly more abundant in SSM than FSM, while \u003cem\u003eAlternaria\u003c/em\u003e was less (LDA\u0026thinsp;\u0026ge;\u0026thinsp;3.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePartial least squares path model analysis\u003c/h2\u003e \u003cp\u003eTo integrate the complex interrelationships between soil moisture, romaine lettuce, and rhizosphere microbial communities, a partial least squares path model (PLS-PM) was constructed. The results indicate that temporal variations in soil moisture had a direct effect on the growth of lettuce (\u0026minus;\u0026thinsp;0.68), which then affected the yield (0.99) and rhizosphere bacterial (\u0026minus;\u0026thinsp;1.03) and fungal (\u0026minus;\u0026thinsp;1.09) community structures. WUE was largely affected by the soil moisture content (\u0026minus;\u0026thinsp;0.44) and yield (1.08) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Overall, the soil moisture dynamic process indirectly affected rhizosphere microbial communities by altering the growth of lettuce.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSSM was more beneficial to the growth and WUE of lettuce\u003c/h2\u003e \u003cp\u003eSoil moisture regimes can be one of the most restrictive factors in agricultural production, with higher soil moisture contents leading to higher crop yield [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Our results indicate that, even at the same average soil moisture content (S1 and F), SSM conditions are more conducive to growth than FSM, as quantified by the biomass accumulation and WUE of lettuce (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, Table\u0026nbsp;2). Thus, soil moisture temporal variation plays a vital role in crop production. Previous NPI-related research has shown that SSM can significantly improve the yield and WUE of many crops [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For example, Zhao et al found that the yield, quality, and WUE of rapeseed were significantly improved under SSM created by NPI [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The yield and WUE of maize under SSM were also significantly higher than those under FSM within a specific range (47\u0026ndash;78% field capacity) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The stable soil moisture and fertilizer regimes provided by SSM conditions could affect the soil nutrient availability, spatial distribution, and even soil enzyme activities, and thus improve the nutrient absorption capacity and fertiliser use efficiency of crops [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Moreover, SSM conditions may be beneficial for the synthesis of chlorophyll, which can affect plant growth and yield by influencing photosynthesis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The link between SSM conditions and a high WUE of plants is not only seen in our NPI results but has also been observed by researchers examining the role of irrigation frequency (cite); that is, a reasonable increase in irrigation frequency is more conducive to efficient and water-saving agricultural production [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], which may be related to reduced soil moisture fluctuations resulting from more frequent irrigation. Such frequent irrigation can maintain the soil moisture content at close to the FC, increase the crop yield, and improve the WUE [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The PLS-PM results also showed that SSM conditions promoted plant growth, thereby increasing crop yield. However, the mechanism of SSM \u0026rarr; plant growth \u0026rarr; yield \u0026rarr; WUE cannot be clearly explained by our results or those of previous studies. Under SSM conditions, the theory of saving water through crop compensation for water deficits is the theoretical basis of most high-efficiency water-saving irrigation technologies but is not applicable to our results. Therefore, it was hypothesized that there may be another way for SSM conditions to promote growth and crop water use, which may be related to crop photosynthetic transpiration, root signal substances, osmotic adjustment substances, etc. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnder SSM conditions, lettuce yield decreased in the following order: S3\u0026thinsp;\u0026gt;\u0026thinsp;S2\u0026thinsp;\u0026gt;\u0026thinsp;S1, whereas WUE decreased in the following order S2\u0026thinsp;\u0026gt;\u0026thinsp;S3\u0026thinsp;\u0026gt;\u0026thinsp;S1 (Table\u0026nbsp;2). Compared with S2, the yield of S3 was only improved by 9.8%, but the water consumption per plant increased by 32.9%, whereas the WUE decreased by 17.1%, suggesting that the soil moisture content level of S2 was more conducive to improving WUE. Previous research has shown that lettuce crops require a large amount of water, and the closer the soil moisture is to 100% FC, the higher the yield and WUE [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. When the soil moisture content reached 125% FC, the increase in yield was small, and the WUE even decreased, which may be due to the soil moisture content exceeding the amount the plant could absorb, resulting in a reduction of WUE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eRhizosphere microbial communities were indirectly affected by the soil moisture temporal variation via plant-microbiome interaction\u003c/h2\u003e \u003cp\u003eSoil moisture is a critical requirement for soil microbes having direct and indirect effects on plant growth and metabolic processes [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Soil properties, including respiration or enzyme activities affected by moisture, can alter the soil microbial structure [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Moreover, the effect of soil moisture regimes on rhizosphere microbes may primarily result from changes in plant roots induced by soil moisture [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Thus, the microbial community structure of the rhizosphere and bulk soil was compared under different soil moisture regimes and found no evident changes in bulk soil microbial communities. In contrast, the microbial communities of the rhizosphere were affected by both the soil moisture content level and the soil moisture temporal variance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Previous studies have shown that the rhizosphere microbial biomass can increase with increasing soil moisture content but only within a certain range (30\u0026ndash;50% FC), and the microbial biomass of the bulk soil is hardly affected [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. However, when the soil moisture changes dramatically enough to produce drought stress, the number and diversity of both rhizosphere and bulk soil microbes decrease significantly [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Within a suitable range, changes in soil moisture may be counteracted by the drought resistance of soil microbes themselves, such that bulk soil microbes are not affected, whereas rhizosphere microbes may be affected by their close interactions with plants [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Research supporting our hypothesis has found that compared with the direct effect caused by soil moisture conditions, rhizosphere microorganisms are more indirectly affected by plants [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the same soil moisture content level, no significant effect was observed in the α-diversity of the rhizosphere microbial community (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Tables\u0026nbsp;3 and 4); however, the rhizosphere microbial community composition changed significantly (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and D). This is consistent with the findings of Ochoa-hueso et al, who suggested that changes in soil moisture, such as drought, are more likely to affect the soil microbial community composition than the diversity or richness [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Huang et al also found that the community composition of rhizosphere bacteria and fungi was altered after drought and rehydration, whereas the alpha diversity showed no change [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Some researchers hypothesise that the soil moisture state not only affects the community composition but also significantly promotes diversity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Based on previous research [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], the possible response patterns of rhizosphere microbes to soil moisture regimes were divided into two types: 1) where both the diversity and composition of rhizosphere microbial communities changed in response to changes in the soil moisture regime; and 2) where the composition of the rhizosphere microbial community was influenced by soil moisture changes in multiple ways, for example, by altering the soil physicochemical properties (pH, eH, etc.) or plant root activity, while the buffer effect provided by plant roots maintained relatively stable diversity. Because of the small range of soil moisture content variation in our study (78\u0026ndash;120% FC), soil microbes may not have been directly affected; thus, the second response pattern is more likely. We applied PLS-PM to further determine the possible rhizosphere microbial community response pathway to the soil moisture temporal variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Our results show that the altered rhizosphere bacterial and fungal community structures were mainly associated with the growth of lettuce, which was highly negatively correlated with the degree of temporal variation in soil moisture. Plant roots improve growth by altering root secretion patterns to select a favourable rhizosphere microbial community under different growth patterns [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, it was hypothesised that the soil moisture temporal variation might influence plant growth, water use, or some other plant function, which in turn affects root growth and secretion to ultimately alter the rhizosphere microbial community structure via plant-microbiome interaction [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eReduced recruitment of rhizosphere probiotic taxa under SSM\u003c/h2\u003e \u003cp\u003ePlant roots can enrich specific groups of microbes by releasing root exudates during growth, which can regulate the physicochemical properties of rhizosphere microdomains and improve the rhizosphere microhabitat, thereby promoting their own root growth and helping to develop stress resistance [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. At the same soil moisture content, some specific taxa were enriched in the rhizosphere of lettuce under SSM and FSM conditions, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These taxa might explain the majority of differences in the rhizosphere microbial community structure under different soil moisture temporal variances [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Several bacteria of the taxa that were highly enriched under FSM condition had been proved to exhibit biocontrol or host plant growth-promoting effects. For example, \u003cem\u003eLysobacter\u003c/em\u003e was shown to inhibit soil\u0026thinsp;\u0026minus;\u0026thinsp;borne pathogens [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]; \u003cem\u003eDevosia\u003c/em\u003e and \u003cem\u003ePesdomonas\u003c/em\u003e are referred as a member of plant growth-promoting rizobacteria (PGPR) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Nonetheless, even though romaine lettuce recruits such growth-promoting taxa into rhizosphere, it did not show a corresponding improvement in yield or WUE (Table\u0026nbsp;2). We therefore hypothesize that the significant enrichment of probiotic taxa in romaine lettuce rhizosphere under FSM condition might be a \"water deficit compensation effect\" at microbial level, i.e., romaine lettuce gown under FSM experiences more water stress than SSM, in order to resist these water stresses, the root system is selectively recruit with specific taxa with probiotic function or effect [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In contrast, under SSM condition, romaine lettuce has been exposed to a more stable and suitable soil moisture content, its root system does not receive drought signals may not attract probiotic taxa come to colonize [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, only one taxon of fungus was significantly enriched under FSM conditions, namely Alternaria, which explained most of the differences in fungal community structure changes under different soil moisture temporal variance. This may suggest that the lettuce rhizosphere under SSM conditions is not suitable for Alternaria survival, which might be due to the release of root exudates from lettuce under SSM conditions that could inhibit activity. Interestingly, Alternaria is a very common and dangerous pathogenic fungus, which has been shown to cause disease in many economic crops such as lettuce and celery [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Therefore, the inhibition of Alternaria growth under SSM conditions in this study may highlight a potential mechanism for disease control and prevention.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur results suggest that SSM condition is more beneficial to the growth, yield, and WUE of romaine lettuce than FSM condition. The different soil moisture temporal variance altered the community composition of both rhizosphere bacteria and fungi; however, this effect was not directly caused by soil moisture but via an indirect effect from host plant. In addition, romaine lettuce rhizosphere selectively recruit specific microbial taxa under different soil moisture temporal variance. These results have improved our understanding of soil moisture\u0026ndash;crop\u0026ndash;rhizosphere interactions, providing a theoretical basis for effectively using microbial resources to improve the WUE of crops.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e This study dose not involve animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;This study dose not involve human participants\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key Research and Development Program of China (2018YFE0112300) and the Fundamental Research Funds for Central Non-profit Scientific Institution (No. Y2020PT37), and partly supported by the United States Department of Agriculture (USDA) Agricultural Research Service. The findings and conclusions in this manuscript are those of the author(s) and should not be construed to represent any official USDA or United States Government determination or policy. Any mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. The USDA is an equal opportunity provider and employer.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDamerum A, Smith HK, Clarkson G, et al (2021) The genetic basis of water‐use efficiency and yield in lettuce. BMC Plant Biol 21:1\u0026ndash;14. https://doi.org/10.1186/s12870-021-02987-7\u003c/li\u003e\n\u003cli\u003eHacquard S, Garrido-Oter R, Gonz\u0026aacute;lez A, et al (2015) Microbiota and host nutrition across plant and animal kingdoms. Cell Host Microbe 17:603\u0026ndash;616. https://doi.org/10.1016/j.chom.2015.04.009\u003c/li\u003e\n\u003cli\u003eTaiz L, Zieger E (2015) Plant physiology and development Sinauer Associates. Sinauer Associates, Oxford\u003c/li\u003e\n\u003cli\u003eEdwards J, Johnson C, Santos-Medell\u0026iacute;n C, et al (2015) Structure, variation, and assembly of the root-associated microbiomes of rice. Proc Natl Acad Sci U S A 112:E911\u0026ndash;E920. https://doi.org/10.1073/pnas.1414592112\u003c/li\u003e\n\u003cli\u003eGu Y, Wang X, Yang T, et al (2020) Chemical structure predicts the effect of plant-derived low-molecular weight compounds on soil microbiome structure and pathogen suppression. Funct Ecol 34:2158\u0026ndash;2169. https://doi.org/10.1111/1365-2435.13624\u003c/li\u003e\n\u003cli\u003eKorenblum E, Dong Y, Szymanski J, et al (2020) Rhizosphere microbiome mediates systemic root metabolite exudation by root-to-root signaling. Proc Natl Acad Sci U S A 117:3874\u0026ndash;3883. https://doi.org/10.1073/pnas.1912130117\u003c/li\u003e\n\u003cli\u003ede Vries FT, Williams A, Stringer F, et al (2019) Changes in root-exudate-induced respiration reveal a novel mechanism through which drought affects ecosystem carbon cycling. New Phytol 224:132\u0026ndash;145. https://doi.org/10.1111/nph.16001\u003c/li\u003e\n\u003cli\u003ePreece C, Pe\u0026ntilde;uelas J (2016) Rhizodeposition under drought and consequences for soil communities and ecosystem resilience. Plant Soil 409:1\u0026ndash;17. https://doi.org/10.1007/s11104-016-3090-z\u003c/li\u003e\n\u003cli\u003ede Vries FT, Griffiths RI, Knight CG, et al (2020) Harnessing rhizosphere microbiomes for drought-resilient crop production. Science (80- ) 368:270\u0026ndash;274. https://doi.org/10.1126/science.aaz5192\u003c/li\u003e\n\u003cli\u003eLau JA, Lennon JT (2012) Rapid responses of soil microorganisms improve plant fitness in novel environments. Proc Natl Acad Sci U S A 109:14058\u0026ndash;14062. https://doi.org/10.1073/pnas.1202319109\u003c/li\u003e\n\u003cli\u003eRubin RL, van Groenigen KJ, Hungate BA (2017) Plant growth promoting rhizobacteria are more effective under drought: a meta-analysis. Plant Soil 416:309\u0026ndash;323. https://doi.org/10.1007/s11104-017-3199-8\u003c/li\u003e\n\u003cli\u003eNiu X, Song L, Xiao Y, Ge W (2018) Drought-tolerant plant growth-promoting rhizobacteria associated with foxtail millet in a semi-arid and their potential in alleviating drought stress. Front Microbiol 8:1\u0026ndash;11. https://doi.org/10.3389/fmicb.2017.02580\u003c/li\u003e\n\u003cli\u003eLong H, Wu X, Zhang S et al. (2020) Connotation and research progress of crop initiate water drawing technology. Trans Chin Soc Agric Eng 36:139\u0026ndash;152. (in Chinese) https://doi.org/10.11975/j.issn.1002-6819.2020.23.017 \u003c/li\u003e\n\u003cli\u003eYang P, Bian Y, Long HY, Drohan PJ (2020) Comparison of emitters of ceramic tube and polyvinyl formal under negative pressure irrigation on soil water use efficiency and nutrient uptake of crown daisy. Agric Water Manag 228:105830. https://doi.org/10.1016/j.agwat.2019.105830\u003c/li\u003e\n\u003cli\u003eSouza SA, Vieira JH, dos Santos Farias DB, et al (2020) Impact of irrigation frequency and planting density on bean\u0026rsquo;s morpho-physiological and productive traits. Water (Switzerland) 12:. https://doi.org/10.3390/w12092468\u003c/li\u003e\n\u003cli\u003eWang Z, Zhu G, Long H et al. (2020) Effects of temporal variation of soil moisture on the growth and water use efficiency of maise. J Agric Sci Technol 22:153\u0026ndash;164. (in Chinese) https://doi.org/10.13304/j.nykjdb.2019.0648\u003c/li\u003e\n\u003cli\u003eHillel D (1980) Applications of soil physics. 10.2307/2403017 Academic Press, Cambridge, MA\u003c/li\u003e\n\u003cli\u003eSegata N, Izard J, Waldron L, et al (2011) Metagenomic biomarker discovery and explanation. Genome Biol 12, R60 (2011). https://doi.org/10.1186/gb-2011-12-6-r60\u003c/li\u003e\n\u003cli\u003eMoradgholi A, Mobasser H, Ganjali H, et al (2021) WUE, protein and grain yield of wheat under the interaction of biological and chemical fertilizers and different moisture regimes. Cereal Res Commun. https://doi.org/10.1007/s42976-021-00145-1\u003c/li\u003e\n\u003cli\u003eLi Y, Wang L, Xue X, et al (2017) Comparison of drip fertigation and negative pressure fertigation on soil water dynamics and water use efficiency of greenhouse tomato grown in the North China Plain. Agric Water Manag 184:1\u0026ndash;8. https://doi.org/10.1016/j.agwat.2016.12.018\u003c/li\u003e\n\u003cli\u003eZhao X, Gao X, Zhang S, Long H (2019) Improving the Growth of Rapeseed (Brassica chinensis L.) and the Composition of Rhizosphere Bacterial Communities through Negative Pressure Irrigation. Water Air Soil Pollut 230:. https://doi.org/10.1007/s11270-018-4061-1\u003c/li\u003e\n\u003cli\u003eWang JJ, Long HY, Huang YF, et al (2019) Effects of different irrigation management parameters on cumulative water supply under negative pressure irrigation. Agric Water Manag 224:105743. https://doi.org/10.1016/j.agwat.2019.105743\u003c/li\u003e\n\u003cli\u003eWang J, Huang Y, Long H, et al (2017) Simulations of water movement and solute transport through different soil texture configurations under negative-pressure irrigation. Hydrol Process 31:2599\u0026ndash;2612. https://doi.org/10.1002/hyp.11209\u003c/li\u003e\n\u003cli\u003ePu\u0026eacute;rtolas J, Albacete A, Dodd IC (2020) Irrigation frequency transiently alters whole plant gas exchange, water and hormone status, but irrigation volume determines cumulative growth in two herbaceous crops. Environ Exp Bot 176:104101. https://doi.org/10.1016/j.envexpbot.2020.104101\u003c/li\u003e\n\u003cli\u003eOuzounidou G, Paschalidis C, Petropoulos D, et al (2013) Interaction of soil moisture and excess of boron and nitrogen on lettuce growth and quality. Hortic Sci 40:119\u0026ndash;125. https://doi.org/10.17221/15/2013-hortsci\u003c/li\u003e\n\u003cli\u003eSawada K, Funakawa S, Kosaki T (2017) Effect of repeated drying\u0026ndash;rewetting cycles on microbial biomass carbon in soils with different climatic histories. Appl Soil Ecol 120:1\u0026ndash;7. https://doi.org/10.1016/j.apsoil.2017.07.023\u003c/li\u003e\n\u003cli\u003eNa X, Yu H, Wang P, et al (2019) Vegetation biomass and soil moisture coregulate bacterial community succession under altered precipitation regimes in a desert steppe in northwestern China. Soil Biol Biochem 136:107520. https://doi.org/10.1016/j.soilbio.2019.107520\u003c/li\u003e\n\u003cli\u003ePreece C, Verbruggen E, Liu L, et al (2019) Effects of past and current drought on the composition and diversity of soil microbial communities. Soil Biol Biochem 131:28\u0026ndash;39. https://doi.org/10.1016/j.soilbio.2018.12.022\u003c/li\u003e\n\u003cli\u003eDijkstra FA, Cheng W (2007) Moisture modulates rhizosphere effects on C decomposition in two different soil types. Soil Biol Biochem 39:2264\u0026ndash;2274. https://doi.org/10.1016/j.soilbio.2007.03.026\u003c/li\u003e\n\u003cli\u003eZhalnina K, Louie KB, Hao Z, et al (2018) Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat Microbiol 3:470\u0026ndash;480. https://doi.org/10.1038/s41564-018-0129-3\u003c/li\u003e\n\u003cli\u003eXue R (2017) Responses and mechanisms of rhizospheric nutrients and microbial activity to water supply patterns in early growth stage of wheat. Lanzhou University. (in Chinese)\u003c/li\u003e\n\u003cli\u003eCarbone MJ, Alaniz S, Mondino P, et al (2021) Drought influences fungal community dynamics in the grapevine rhizosphere and root microbiome. J Fungi 7:. https://doi.org/10.3390/jof7090686\u003c/li\u003e\n\u003cli\u003eBattistuzzi FU, Hedges SB (2009) A major clade of prokaryotes with ancient adaptations to life on land. Mol Biol Evol 26:335\u0026ndash;343. https://doi.org/10.1093/molbev/msn247\u003c/li\u003e\n\u003cli\u003eMaldonado-Michel MA, Mu\u0026ntilde;iz-Valencia R, Peraza-Campos AL, et al (2021) Antifungal activity of Swietenia humilis (Meliaceae: Sapindales) seed extracts against Curvularia eragrostidis (Ascomycota: Dothideomycetes). J Plant Dis Prot 128:471\u0026ndash;479. https://doi.org/10.1007/s41348-020-00410-1\u003c/li\u003e\n\u003cli\u003eOchoa-Hueso R, Collins SL, Delgado-Baquerizo M, et al (2018) Drought consistently alters the composition of soil fungal and bacterial communities in grasslands from two continents. Glob Chang Biol 24:2818\u0026ndash;2827. https://doi.org/10.1111/gcb.14113\u003c/li\u003e\n\u003cli\u003eHuang X, Qin L, Huang L et al. (2020) Variation Characteristics of Drought and Rehydration on the Growth of Hibiscus rosa-sinensis Linn.and Soil Microbial Diversity in rhizosphere. Chin J Trop Crops 41:401\u0026ndash;408. (in Chinese)\u003c/li\u003e\n\u003cli\u003eGao X, Zhang S, Zhao X, Long H (2019) Stable water and fertilizer supply by negative pressure irrigation improve tomato production and soil bacterial communities. SN Appl Sci 1:1\u0026ndash;8. https://doi.org/10.1007/s42452-019-0719-6\u003c/li\u003e\n\u003cli\u003eNannipieri P, Ascher J, Ceccherini MT, et al (2008) Effects of Root Exudates in Microbial Diversity and Activity in Rhizosphere Soils. Soil Biology, vol 15. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75575-3_14\u003c/li\u003e\n\u003cli\u003eChaparro JM, Sheflin AM, Manter DK, Vivanco JM (2012) Manipulating the soil microbiome to increase soil health and plant fertility. Biol Fertil Soils 48:489\u0026ndash;499. https://doi.org/10.1007/s00374-012-0691-4\u003c/li\u003e\n\u003cli\u003eSong F, Han X, Zhu X, Herbert SJ (2012) Response to water stress of soil enzymes and root exudates from drought and non-drought tolerant corn hybrids at different growth stages. Can J Soil Sci 92:501\u0026ndash;507. https://doi.org/10.4141/CJSS2010-057\u003c/li\u003e\n\u003cli\u003eFitzpatrick CR, Copeland J, Wang PW, et al (2018) Assembly and ecological function of the root microbiome across angiosperm plant species. Proc Natl Acad Sci U S A 115:E1157\u0026ndash;E1165. https://doi.org/10.1073/pnas.1717617115\u003c/li\u003e\n\u003cli\u003eXiong W, Guo S, Jousset A, et al (2017) Bio-fertilizer application induces soil suppressiveness against Fusarium wilt disease by reshaping the soil microbiome. Soil Biol Biochem 114:238\u0026ndash;247. https://doi.org/10.1016/j.soilbio.2017.07.016\u003c/li\u003e\n\u003cli\u003eChhetri G, Kim I, Kang M, et al (2022) Devosia rhizoryzae sp. nov., and Devosia oryziradicis sp. nov., novel plant growth promoting members of the genus Devosia, isolated from the rhizosphere of rice plants. J Microbiol 60:1\u0026ndash;10. https://doi.org/10.1007/s12275-022-1474-8\u003c/li\u003e\n\u003cli\u003eComeau D, Balthazar C, Novinscak A, et al (2021) Interactions Between Bacillus Spp., Pseudomonas Spp. and Cannabis sativa Promote Plant Growth. Front Microbiol 12:. https://doi.org/10.3389/fmicb.2021.715758\u003c/li\u003e\n\u003cli\u003eGoswami M, Deka S (2020) Plant growth-promoting rhizobacteria\u0026mdash;alleviators of abiotic stresses in soil: A review. Pedosphere 30:40\u0026ndash;61. https://doi.org/10.1016/S1002-0160(19)60839-8\u003c/li\u003e\n\u003cli\u003ePereira LB, Andrade GS, Meneghin SP, et al (2019) Prospecting Plant Growth-Promoting Bacteria Isolated from the Rhizosphere of Sugarcane Under Drought Stress. Curr Microbiol 76:1345\u0026ndash;1354. https://doi.org/10.1007/s00284-019-01749-x\u003c/li\u003e\n\u003cli\u003eMunoz-Ucros J, Wilhelm RC, Buckley DH, Bauerle TL (2022) Drought legacy in rhizosphere bacterial communities alters subsequent plant performance. Plant Soil 471:443\u0026ndash;461. https://doi.org/10.1007/s11104-021-05227-x\u003c/li\u003e\n\u003cli\u003eBerbee ML, Pirseyedi M, Hubbard S (1999) Cochliobolus phylogenetics and the origin of known, highly virulent pathogens, inferred from ITS and glyceraldehyde-3-phosphate dehydrogenase gene sequences. Mycologia 91:964\u0026ndash;977. https://doi.org/10.2307/3761627\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eSoil moisture contents and temporal variance under different treatments\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003eAverage volumetric soil moisture content\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eVariation range of soil moisture content\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eC\u003csub\u003ev\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003eC\u003csub\u003ef\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e34.84\u0026plusmn;2.45 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003e30.02 ~ 38.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e28.59\u0026plusmn;1.72 b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003e25.63 ~ 30.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e22.56\u0026plusmn;1.41 c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003e19.85 ~ 24.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.11111111111111%\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e21.94\u0026plusmn;2.31 c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003e18.47 ~ 24.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.2020202020202%\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Different lowercase letters in the same column indicate significant differences among treatments at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003ePhotosynthetic product distribution and water use efficiency of lettuces under different treatments\u003c/p\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eTreatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003eWater consumption per plant\u003c/p\u003e\n \u003cp\u003e(L\u0026middot;pot\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003eThe fresh biomass of shoot\u003c/p\u003e\n \u003cp\u003e(g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eRoot/shoot ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003eWater-use efficiency\u003c/p\u003e\n \u003cp\u003e(g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eS3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e5.38\u0026plusmn;1.83\u0026nbsp;a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e36.79\u0026plusmn;18.03\u0026nbsp;a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.06\u0026plusmn;0.02\u0026nbsp;b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e7.89\u0026plusmn;5.37\u0026nbsp;a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e3.61\u0026plusmn;0.37\u0026nbsp;a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e33.20\u0026plusmn;3.58\u0026nbsp;ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.06\u0026plusmn;0.02\u0026nbsp;b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e9.24\u0026plusmn;1.08\u0026nbsp;a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e3.23\u0026plusmn;0.75\u0026nbsp;a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e27.79\u0026plusmn;6.27\u0026nbsp;ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.07\u0026plusmn;0.01\u0026nbsp;b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e8.61\u0026plusmn;0.78\u0026nbsp;a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.43298969072165%\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e2.89\u0026plusmn;0.31\u0026nbsp;a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e13.39\u0026plusmn;3.58\u0026nbsp;b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.12\u0026plusmn;0.01\u0026nbsp;a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.649484536082475%\"\u003e\n \u003cp\u003e4.59\u0026plusmn;0.70 a\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: Different lowercase letters in the same column indicate significant differences among treatments at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eAlpha\u0026nbsp;diversity index of lettuce rhizosphere (R) and bulk soil (B) bacterial communities under different treatments\u003c/p\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"98%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003eSoil apartment - treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eOTUs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eChao1 index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003eShannon index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eCoverage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003eR-S3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e2158.00\u0026plusmn;117.94\u0026nbsp;b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e3138.63\u0026plusmn;119.02\u0026nbsp;b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e4.68\u0026plusmn;0.22\u0026nbsp;c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.9758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003eR-S2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e2069.67\u0026plusmn;116.67\u0026nbsp;b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e3048.42\u0026plusmn;205.13 b\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e5.36\u0026plusmn;0.49 b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.9707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003eR-S1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e1935.00\u0026plusmn;143.15 b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e2946.87\u0026plusmn;294.70\u0026nbsp;b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e4.83\u0026plusmn;0.53 c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.9741\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003eR-F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e1856.00\u0026plusmn;221.28\u0026nbsp;b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e2781.26\u0026plusmn;322.21\u0026nbsp;b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e4.96\u0026plusmn;0.48\u0026nbsp;bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.9811\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003eB-S3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e2661.67\u0026plusmn;147.06 a\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e3617.59\u0026plusmn;190.37\u0026nbsp;a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e6.41\u0026plusmn;0.07 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.9744\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003eB-S2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e2837.67\u0026plusmn;111.79 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e3835.32\u0026plusmn;132.73 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e6.50\u0026plusmn;0.02 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.9769\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003eB-S1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e2917.00\u0026plusmn;165.01 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e3814.30\u0026plusmn;162.28 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e6.48\u0026plusmn;0.12 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.9801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003eB-F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e2766.67\u0026plusmn;300.31 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e3759.66\u0026plusmn;183.46 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e6.42\u0026plusmn;0.06\u0026nbsp;a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.9763\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: R and B of the soil compartment represent the rhizosphere and bulk soil, respectively, and different lowercase letters in the same column indicate significant differences among the treatments at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003eAlpha\u0026nbsp;diversity index of lettuce rhizosphere (R) and bulk soil (B) fungal communities under different treatments\u003c/p\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"98%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eSoil apartment - treatment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003eOTUs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\"\u003e\n \u003cp\u003eChao1 index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eShannon index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003eCoverage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eR-S3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e377.33\u0026plusmn;145.25\u0026nbsp;ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\"\u003e\n \u003cp\u003e511.32\u0026plusmn;163.58\u0026nbsp;a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2.75\u0026plusmn;0.52\u0026nbsp;bc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.9983\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eR-S2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e309.67\u0026plusmn;60.35\u0026nbsp;b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\"\u003e\n \u003cp\u003e411.20\u0026plusmn;74.15\u0026nbsp;a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2.85\u0026plusmn;0.39\u0026nbsp;abc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.9987\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eR-S1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e386.00\u0026plusmn;121.45\u0026nbsp;ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\"\u003e\n \u003cp\u003e508.09\u0026plusmn;86.47\u0026nbsp;a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2.97\u0026plusmn;1.03\u0026nbsp;abc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.9987\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eR-F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e317.00\u0026plusmn;29.05\u0026nbsp;b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\"\u003e\n \u003cp\u003e423.79\u0026plusmn;13.62\u0026nbsp;a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2.26\u0026plusmn;0.58\u0026nbsp;c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.9984\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eB-S3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e392.00\u0026plusmn;130.40\u0026nbsp;ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\"\u003e\n \u003cp\u003e453.00\u0026plusmn;137.57 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e2.89\u0026plusmn;0.62 abc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.9988\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eB-S2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e486.00\u0026plusmn;31.32\u0026nbsp;a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\"\u003e\n \u003cp\u003e530.60\u0026plusmn;27.20 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e3.69\u0026plusmn;0.04 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.9992\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eB-S1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e377.33\u0026plusmn;36.07\u0026nbsp;ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\"\u003e\n \u003cp\u003e416.75\u0026plusmn;51.81 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e3.30\u0026plusmn;0.14 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.9991\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003eB-F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\"\u003e\n \u003cp\u003e450.67\u0026plusmn;32.01 ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\"\u003e\n \u003cp\u003e499.75\u0026plusmn;38.33 a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e3.49\u0026plusmn;0.16\u0026nbsp;ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.9987\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: R and B of soil compartment represent rhizosphere and bulk soil, respectively;\u0026nbsp;Different lowercase letters in the same column indicates significant differences among the treatments at P\u0026lt;0.05 level.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"soil moisture temporal variation, romaine lettuce, water use efficiency, rhizosphere microbiome, bacteria, fungi","lastPublishedDoi":"10.21203/rs.3.rs-1966150/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1966150/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTemporal variation of soil moisture is one of the influencing factors affecting crop water use efficiency (WUE). Compared with fluctuating soil moisture (FSM), stable soil moisture (SSM) with weaker temporal variance has the potential to improve the WUE of crops. However, response of crop rhizosphere microbiome to soil moisture temporal variation remains unclear. In this study, we performed pot experiments on romaine lettuce (\u003cem\u003eLactuca sativa L. var. longifolia\u003c/em\u003e) to compare the effects of different soil moisture temporal variation on plant growth, yield, water use efficiency (WUE), and rhizosphere bacterial and fungal community structures, via manual irrigation and negative pressure irrigation to create FSM and SSM conditions, respectively. The results indicate that SSM improved the growth and WUE of romaine lettuce. Moreover, the rhizosphere microbial community composition of romaine lettuce differed under SSM and FSM conditions. Under SSM, bacterial \u003cem\u003eBacillus\u003c/em\u003e, fungal \u003cem\u003eAspergillus\u003c/em\u003e and \u003cem\u003eChaetomium\u003c/em\u003e were enriched in the romaine lettuce rhizosphere, whereas some taxa such as bacterial \u003cem\u003eDevosia\u003c/em\u003e, \u003cem\u003eLysobacter\u003c/em\u003e, \u003cem\u003eBlastococus\u003c/em\u003e and \u003cem\u003eBacillus\u003c/em\u003e, fungal \u003cem\u003eAlternaria\u003c/em\u003e were reduced; these taxa could therefore be biomarkers in future research. Partial least squares path model (PLS-PM) analysis revealed that rhizosphere microbial communities were indirectly affected by the soil moisture temporal variation, as evidenced by the improvement in plant growth. Our results suggest that the rhizosphere microbial communities of romaine lettuce primarily respond to changes in the soil moisture temporal variation through the plant-microbiome interaction but are not directly affected by soil moisture.\u003c/p\u003e","manuscriptTitle":"Stable soil moisture altered the rhizosphere microbial community structure via affecting their host plant","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-29 17:46:31","doi":"10.21203/rs.3.rs-1966150/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8094f597-1913-49cc-9ed7-fb5e509888a0","owner":[],"postedDate":"August 29th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-09-26T00:35:32+00:00","versionOfRecord":[],"versionCreatedAt":"2022-08-29 17:46:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1966150","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1966150","identity":"rs-1966150","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}