Novel Insight into the Change of Physicochemical Properties and Microbial Characteristics of Coastal Saline-alkali land after the construction of raised fields

Novel Insight into the Change of Physicochemical Properties and Microbial Characteristics of Coastal Saline-alkali land after the construction of raised fields | 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 Novel Insight into the Change of Physicochemical Properties and Microbial Characteristics of Coastal Saline-alkali land after the construction of raised fields Tingting Niu, Nan Ma, Jiangtao Jin, Yanjie Lu, Yi Cui, Kaichun Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5541868/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 Background To address the issue of land degradation caused by coastal salinization, the saline-alkali soil in the coastal area of Huanghua, Hebei, China, was selected as a pilot study to evaluate the effects of different approaches to soil improvement, especially the construction of raised fields (RFs). Methods The microbiota of various saline-alkali soils (mild, moderate, severe, and saline levels) and their adaptation to growth-limiting factors were studied. DGGE and multivariate statistical analyses were combined to analyze the structure and dominant flora of different soil bacterial communities and the correlation between soil physicochemical indexes and soil microbiota. Key Findings : A shortage of soil microbes was found in this area, with the bacterial community being dominant (more than 50% relative abundance), while the fungal community showed higher tolerance, stability, and resilience to various saline-alkali soils than the bacterial and actinomycetes communities. The contents of organic matter, available N, total salt, and Na + in the soil each exerted a great influence on the abundance of soil microbes. Notably, some mineral ions (especially orthophosphate) in the saline-alkali land could be directly utilized in the available forms by the plants grown in RFs through the symbiosis between mycorrhizal fungi and plants. Conclusion The construction of RFs and the identification of keystone bacteria that have potential adaptability to various saline-alkali environments, therefore, may be effective strategies for coastal saline-alkali land utilization as well as ecological restoration. saline-alkali soil soil microbe arbuscular mycorrhiza raised field Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The global land area affected by saline-alkali is about 1 billion hm 2 [ 1 ], while the area of saline-alkali land in China is nearly 100 million hm 2 [ 2 ]. The salinization of cultivated land is intensifying in China. It was estimated that about half of the world's arable land will be affected by salinization by 2050 [ 3 , 4 ]. Although soil salinization is mainly caused by inappropriate irrigation practices and drought [ 5 , 6 ], rising sea levels and changing precipitation patterns due to climate change can intensify coastal land degradation processes. High salinity, low terrain, poor drainage, and the characteristic of enhanced capillary action of groundwater in coastal areas can further intensify the degree of soil salinization [ 7 , 8 ]. Therefore, it is crucial to develop strategies for soil improvement to adapt to the impacts of climate change, such as rising sea levels and altered precipitation patterns. The coastal area of Tianjin, which is located in the northeast of the North China Plain, has 493,000 hm 2 of saline-alkali land, accounting for 42.3% of the total land area of ​​Tianjin’s municipal area [ 9 ]. Saline-alkali soil can reduce the nutrients needed by plants and affect plant growth through osmotic stress and alterations of soil properties, particularly as a result of high salt concentrations [ 10 ]. High concentrations of salt in the soil ause ion imbalance, oxidative damage, water deficit, nutrient deficiency, and alkali stress. High alkali stress can cause ion imbalance and pH instability, and the combined effects can lead to molecular damage, growth retardation, and even the death of plants. Saline-alkali soil hampers plant growth and reduces crop productivity because of osmotic stress, ion imbalance, and the impact of high salt concentrations on soil microorganisms. To mitigate the adverse effects of saline-alkali soil, various strategies have been tried to enhance soil fertility and support plant growth in such challenging environments. These strategies include soil amendment and proper irrigation practices. However, typical saline-alkali land improvement methods that have been tried all have their limitations. For example, the leaching methods apply excess water to the soil to flush out the salts and reduce their concentration in the soil, but care should be taken to avoid waterlogging as well as water wastage. Chemical methods and the installation of subsurface drainage systems have also been used to help remove excess water and salts from the root zone, preventing waterlogging and salt buildup. Unfortunately, these methods require a large investment [ 11 ]. Planting deep-rooted plants and salt-tolerant plants in high-salinity soil is another approach to soil amelioration, but this approach limits the variety of planted crops. Compared with these typical saline-alkali land improvement methods, the construction of raised fields (RFs) is simple and does not require a lot of manpower or the management of material resources in the later stage. Of particular importance in building RFs are: 1) increasing altitude and the promotion of salt infiltration by excavating soil and stacking fields [ 12 ]; 2) reduction of salinity brought to the ground surface with the evaporation of the groundwater since the accumulated RFs are farther away from the groundwater; and 3) reduction of the salinity in the RFs due to irrigation or rainfall. The RFs system can, therefore, make the conditions for growing crops more favorable by improving water management, maintaining soil fertility, and regulating soil conditions, thereby increasing crop yields. On the other hand, by converting drainage ditches into fish ponds and integrating raised field cultivation with aquaculture, the RF model could also transform saline-alkali wastelands into efficient composite agricultural ecosystems. Furthermore, the RF structure, characterized by alternating water and land surfaces and undulating microtopography, creates heterogeneous underlying surface features that help regulate the local microclimate. The water surfaces of fish ponds increase air humidity, especially during the summer, forming a relatively humid microclimate that improves conditions for crop growth. Additionally, RFs also serve as a distinctive agricultural landscape in saline-alkali areas, offering certain tourism and aesthetic value. So far, research on saline-alkali soil in RFs has mainly focused on the physical and chemical indicators, pH, and other aspects of the soil [ 13 ]. Soil also contains important microorganisms that can affect soil quality because these microorganisms are involved in various nutrient cycling activities in addition to providing plants with minerals and nutrients. Through such processes, microorganisms play a crucial role in soil nutrients. However, few researchers have focused on the microbial population structure, dominant microbiota, and their relationship with the physicochemical properties of saline-alkali soil. Microorganisms also participate in the processes of the terrestrial geochemical cycle since they are a component of the terrestrial ecosystem. Therefore, studying the structure and composition of microorganisms after soil improvement and their relationship with soil environmental factors would help to understand the coupling relationship between microorganisms and soil. However, there are few reports on the changes in soil microbial flora and structural properties in saline-alkali land in this area, as well as the relationship between soil microbial ecological distribution and soil salinity. In this study, we explored the impact of the RF model on the physicochemical properties of coastal saline-alkali soil after improvement, as well as its impact on soil microorganisms, especially on AM. The performance of RFs was compared with that of farmland and wasteland, all of which were located within the same stretch of coastal land with saline-alkali soil adjacent to the Tianjin seaside area, China. The investigation specifically explored the changes in the structure and composition of microorganisms in the soil of RFs with different ages and analyzed the correlation of these changes with soil environmental factors as well as the outcome of some vegetation planted in RFs. The results were then compared with those obtained for farmland and wasteland. The valuable information on the dominant bacteria and microbial community composition and community structure in the soil obtained after the construction of RFs would provide the basis for the effective utilization of coastal saline soils in the future. 2. Materials and methods 2.1 Study site The experiment was conducted in Huanghua, Hebei Province, China (38°22′N, 117°21′E). This study site is close to the Bohai Sea and belongs to a warm temperate semi-humid climate with an annual average temperature of 12.3°C, an annual rainfall of 590.6 mm, and a southwesterly wind with an average wind speed of 4.2 m/s. The land chosen as the study site was divided into three different configurations: farmland and RF, which were subjected to artificial interference, and wasteland, which received no interference and served as control land (Fig. 1 ). A total of 15 sampling sites were established. The status of each sampling site and the main vegetation grown on it are shown in Fig. 2 . The farmland sites consisted of corn fields, soybean fields, and cotton fields. The wasteland sites consisted of salt-tolerant wild plants such as reeds and Suaeda salsa. As for the RF sites, the soil of the cultivated layer was relatively raised while the groundwater level was openly lowered to achieve the effect of soil leaching and desalination. Four RFs were selected according to the time that the RFs had been constructed. The RFs consisted of those constructed over 4 years (4Y-RF), 3 years (3Y-RF), and 2 years (2Y-RF), as well as newly constructed RFs (N-RF). The time over which the RFs were constructed will henceforth be referred to as the age of the RFs. These RFs were constructed using shallow pond soil. 2.2 Sample collection In different types of saline-alkali soils, representative plants were selected for sampling. Three plants of the same species were randomly selected at each sampling site. At the same time, 2 cm of the topsoil was removed, and 1 kg of bulk soil spanning 2–20 cm in depth (rhizosphere soil) was then collected into a sealable bag and stored at 4°C for soil physicochemical analysis and fungal spore density determination. The collected root samples were also brought back to the laboratory and then stored at 4°C. The fresh roots were cut into 1 cm segments and mixed for the determination of root arbuscular mycorrhiza (AM) infection rate. 2. 3 Determination of soil physicochemical properties Soil samples were air-dried to analyze their physicochemical properties. Organic matter in the soil was measured using a potassium dichromate oxidation method [ 14 ]. The available nitrogen (AN) in the soil was measured by an alkali N-proliferation method [ 15 ]. The available phosphorus (AP) and potassium (AK) in the soil were measured according to the method described by Olsen and Sommers and by ammonium acetate flame photometry, respectively [ 16 ]. Soil pH was measured with a combination electrode pH meter, and total salinity in the soil was measured by a gravimetric method [ 17 ]. The contents of sodium and potassium ions in the soil were measured by ICP-AES (JY2000-2‌‌1, HORIBA Jobin Yvon‌). 2.4 Determination of arbuscular mycorrhiza (AM) The harvested roots of each plant were cut into 1 cm-long root segments and stained with Trypan Blue. The structural features such as arbuscules, vesicles, and hyphae were observed with a microscope. Infection of the plants by Arbuscular mycorrhizal fungi (AMF) was determined by the Philips and Hayman method [ 18 ]. To determine the AMF spore density, 25 g of air-dried soil was taken from each sample, and the AMF spores were isolated by wet sieving and decanting. The number of spores was recorded by a stereomicroscope to obtain the total number of spores. The spore content per 100 g of dry soil was calculated as shown below to give the spore density. Soil moisture (%) = (soil wet weight − soil dry weight) / soil wet weight × 100% The number of spores per gram of dry soil = total number of spores / [soil wet weight × (1 − soil moisture)] Each experiment was conducted with three biological replicates 2.5 Separation and quantification of soil microorganisms The number of bacteria, fungi, or actinomycetes in the soil samples was measured by the dilution-plating method [ 19 ], and its respective percentage with respect to the total number of microbes in the sample was then calculated (number of bacteria, actinomycetes, or fungi / total number of microorganisms × 100%). Beef extract peptone medium, Gao’s No. 1 medium, and PDA medium were used to cultivate the bacteria, fungi, and actinomycetes, respectively. Data statistics were carried out according to the specification requirements for microbial enumeration. The number of bacterial and fungal colony-forming units (CFUs) was calculated per gram of dry soil, and the calculation formula is as follows: Soil microbial biomass (cfu/g) = MD/W where M is the average number of effective colonies of parallel samples, D is the dilution factor, and W is the mass of dried soil (g). 2.6 DNA extraction and 16S rRNA gene amplification Total DNA was extracted from the soil samples using a PowerSoil DNA Isolation Kit (Mobio, CA, USA) according to the manufacturer’s protocol. Prokaryotic communities were amplified using a 16S rRNA gene with the primer set 341F-GC: 5′- CGCCCGCCGCGCCCCGCGCCCGGCCCGCCGCCCCCGCCCC -CCTACGGGAGGCAGCAG-3′ (GC-rich sequences were integrated into the 5'-end of the primer) and 534R: 5′-ATTACCGCGGCTGCTGG-3′. DNA amplification was performed in an Eppendorf Mastercycler (Eppendorf, Hamburg, Germany), and the sample consisted of 1 µL Taq DNA polymerase (Takara, Dalian, China), 5 µL PCR buffer, 4 µL dNTP, 1.5 µL of each forward and reverse primer, and 10 ng of template DNA in a total sample volume of 50 µL. The conditions of the amplification for the 16S rRNA gene are as follows: 94°C for 5 min followed by 30 cycles of 94°C (30 s), 55°C (30 s), and 72°C (90 s), with each cycle reduced by 0.2°C, and a final extension at 72°C for 10 min. The PCR products were then separated by DGGE electrophoresis. 2.7 Denaturing Gradient Gel Electrophoresis (DGGE) analysis of PCR products The PCR products were analyzed by the DGGE technique using a D-Code system apparatus (BioRad, Hercules, CA, USA). The denaturant concentration (formamide:urea) in the gel was prepared, ranging from 40–60%. The gels were run under a voltage of 150 V and a temperature of 60°C using Tris-acetate-EDTA buffer (1X TAE) for 9 h. The DNA bands in the gel were visualized by silver staining performed with a Bio-Rad silver stain kit. The image of the gel was scanned and analyzed using the Gel Pro. Analyzer (Media Cybernetics, Inc., Rockville, MD, USA) to score for the presence or absence of bands at different positions in each lane. The SPSS software (SPSS Inc., Chicago, IL) was used to cluster the microbial communities indicated by the DNA bands in the gel. 2.8 DGGE strip recovery and phylogenetic analysis The recovery of DGGE strips was carried out as described by Schwieger F et al . [ 20 ]. The DNA bands were each eluted from the gel with 50 µL of water and then stored at 4°C. overnight. These extracted DNA samples were amplified again with non-GC-clamp primers and sequenced by an ABI377 sequencer. The identification of the different organisms at the species level was performed by comparing the obtained sequences with those deposited in the NCBI database. The sequences were aligned using ClustalW, and a phylogenetic tree was constructed using MEGA 7.0 software with 1000 bootstrap replicates [ 3 , 4 ]. The neighbor-joining method was used to construct a phylogenetic dendrogram, and tree topology was evaluated by performing a bootstrap analysis of 1,000 data sets using MEGA 7.0 software [ 21 ]. The NJ tree was visualized with iTOL [ 22 ]. The phylogenetic tree of the bacteria was inferred by using the Maximum Likelihood method[ 23 ]. The initial tree(s) for the heuristic search were obtained automatically by applying Neighbor Joining method algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach and then selecting the topology with a superior log likelihood value. The tree was drawn to scale with branch lengths measured in the number of substitutions per site. 2.9 Data analysis ArcGIS 10.2 was used to map the study site, whereas BioRender was used to determine the type of sampling site. The heatmap was performed using Origin 2021. 3. Results 3.1 Soil physicochemical properties from different types of sampling sites The soil from the Huanghua area is classified as coastal saline soil, and the degree of salinization is extremely high in the wasteland. The salinity content of the salt-spotted land without vegetation was 29.2‰, while the soil with vegetation, which exhibited severe salinization, had a salt content of 6‰ (Table 1 ). The farmland was mildly salinized because of perennial tillage (many years of cultivation), and it had a salt content of 0.9‰~2.1‰, with a sodium ion content of 47 mg/kg ~ 386 mg/kg. The soil of RFs was moderately salinized, and its total salt content was 2.3‰~2.9‰, with a sodium ion content of 274.5–853 mg/kg. The shallow pool soil, which was the original soil used for constructing the RFs, was severely salinized, with a salt content of 5.7‰. Therefore, the degree of salinization in the soil was significantly reduced after it was converted into RF. Through good drainage, salt was effectively removed to prevent its excessive accumulation on the surface of the soil. Compared with the salinization degree of saline-alkali wasteland filled with reed and Suaede salsa, reed appeared to have a stronger alleviation effect on salinization, indicating that different plants might have different effects on the salinization degree of the soil in the long run. Little spatial variation in soil pH and organic matter content was found in each soil type in the Huanghua area. Interestingly, the contents of AP and AK in RFs were higher than those of the farmland, with AP content being 22–577% higher and AK content being 19–136% higher, indicating the construction of RFs may help to maintain nutrients and organic matter in the soil, thereby providing more AP and AK for the crops. Table 1 Basic physicochemical properties of soil in the Huanghua area Types of sampling sites Vegetation types pH SOM AP AK AN TSC K + Na + (g/kg) (mg/kg) (mg/kg) (mg/kg) (g/kg) (mg/kg) (mg/kg) Raised Fields 4Y-RF 9.2 10.1 13.7 313.5 40.6 2.5 39.8 853 3Y-RF 9.3 14.2 14.9 406 58.1 2.9 69.8 716 2Y-RF 9.4 15.2 7 286.5 54 2.3 26.9 274.5 N-RF 8.9 9.6 6.5 260.5 44.4 2.6 32.6 1250 SPS 9.2 7.6 9.6 422 22.1 5.7 44.1 2400 Farmland ZM 8.8 13.2 5.3 199.5 40.9 2.1 26.5 386 S 8.7 15.3 2.8 219 65.4 0.9 54.9 47.6 C 8.9 13 2.2 172 44.9 1 40.6 197.4 Saline alkali wasteland R 9 18.3 2.4 520.5 54.3 2.8 57 874.5 SS 8.9 12.5 11.1 268 47.5 6.1 50.1 2340 NV 8.6 8.5 10.3 428 27.1 29.2 141 4330 4Y-RF, 4-year-old RF; 3Y-RF, 3-year-old RF; 2Y-RF, 2-year-old RF; SPS, shallow pool soil; ZM, Zea mays; S, soybean; C, cotton; R, reed; SS; Suaede salsa; NV, no vegetation. SOM, soil organic matter; AP, available phosphorus; AK, available potassium; AN, available nitrogen; TSC, total salt content; K + , potassium ion; Na, sodium ion. 3.2 Number of microorganisms in plots with different degrees of salinization A total of 15 soil samples in the study site were graded according to the grading standard for the degree of salinity in coastal saline land in China [ 9 ]. The number and composition of microorganisms in this area were classified and statistically analyzed (Fig. 3 ). Differences were found in the distribution of bacteria among the different sampling sites, with a maximum difference of 12-fold. While in the case of fungi, the maximum difference among different sampling sites (including 3Y-RF, 4Y-RF, ZM, S, and C) is approximately 24.7% (Fig. 3 C). However, over 1–2 years, the fungal population gradually reached a certain level, indicating the adaptation of these fungal species in RFs to the soil environment, manifested as a stable population (Fig. 3 C). Overall, the numbers of bacteria and fungi were abundant in the soils with mild and moderate salinity compared with those in the soils with severe salinity. With decreased salinity in the soil, the number of actinomycetes increased, even in the vegetation-free N-RF, the actinomycete population is still approximately 454% higher than that in SPS. But the magnitude (change range) was not as large as for the bacteria and fungi because actinomycetes typically have a strong stress-resistance characteristic (Fig. 3 B). The number of bacteria and fungi in RFs increased from N-RF to 3Y-RF and decreased from 3Y-RF to 4Y-RF with the age of the RFs, but the number of fungi decreased with an increased Na + concentration in the soil of the RFs. As shown in Fig. 3 D, the soils of the studied sites were dominated by bacteria (more than 50%), followed by actinomycetes and fungi (1 ~ 5%). Less change was observed for the fungal population in response to increased soil salinity. As the degree of soil salinization increased, the number of microorganisms in the soil of the studied sites decreased, except for those from the farmlands (Fig. 3 A-C). Furthermore, non-vegetation plots (NV, SPS, 0aD) had fewer bacteria, actinomycetes, and fungi than the vegetation plots, and different crops had a greater effect on the number of bacteria and actinomycetes than on the number of fungi. As shown in Fig. 3 B, the number of bacteria in the soil of the 2-year-old and 3-year-old RFs increased significantly but decreased in the soil of the 4-year-old RFs, becoming similar to that of the farmland [ 24 ]. The number of actinomycetes did not change much between RFs of different ages, with the 4Y-RF increasing by only 27.9% compared to the 2Y-RF. The number of actinomycetes increased with the age of the RFs, reaching a maximum increase of approximately 28.4% (Fig. 3 B). The number of fungi in the farmland soil planted with different crops shows relatively small variations, with a maximum variation of approximately 18.2%. It should be noted that the soil in the 3-year-old and 4-year-old RFs basically had a similar number of fungi to that found in the farmland soil, indicating that different crops and the construction of RFs might have little effect on the soil fungal population in the long run (Fig. 3 C). 3.3 Infection rate of AMF Vesicular Infection Rate (VIR), Arbuscular Infection Rate (AIR), Hyphal Infection Rate (HIR), and Total Infection Rate (TIR) are terms often used in the context of mycorrhizal symbiosis, specifically in the study of plant-mycorrhizal interactions involving AMF. VIR refers to the percentage of root segments that have vesicles. AIR refers to the percentage of root segments that have arbuscules present. Hyphae are the thread-like structures that make up the fungal network. HIR refers to the percentage of root segments that have hyphae present. TIR is the sum of all the different infection rates, including vesicular, arbuscular, and hyphal infection rates. Table 2 compares the rates of AMF infection of different types of crops planted on different types of land. For example, in farmland, the infection rate was 71% for maize roots but only 20.8% for cotton roots, suggesting that the rate of AMF infection found in a particular type of land was related to the types of crops planted (since they were all planted under the same conditions. In the case of RF, TIR for maize root negatively correlated with salinity, providing new insight into the exchange of nutritional benefits between the symbiotic partners in the reduction of soil erosion and nutrient leaching by AM. Table 2 Infection rate of AM on plant rhizosphere in different sampling sites Types of sampling sites Vegetation types VIR AIR HIR TIR % % % % 4Y-RF 3Y-RF 2Y-RF ZM 66.8 1.1 80.3 81.9 ZM 36.5 14.5 78.3 79 ZM 20.2 79.5 94.5 95.1 Farmland ZM 4.7 55.7 67.7 71 S 45.6 5.5 56.9 57.2 C 11 0 20.8 20.8 Saline alkali wasteland R 19.7 4.1 25.2 28.2 SS 21.5 3.4 25.3 27 4Y-RF: 4-year-old RF; 3Y-RF: 3-year-old RF; 2Y-RF: 2-year-old RF; ZM: zea mays; S: soybean; C: cotton; R: Reed; SS: suaeda salsa. VIR: vesicular infection rate; AIR: arbuscular infection rate; HIR: hyphal infection rate; TIR: total infection rate. 3.4 Relationship between soil factors and soil microorganisms Spearman correlation analysis (Fig. 4 A) of the relationship between different soil factors revealed a significant and positive correlation between TSC and AP and AK and between TSC and sodium ions. Sodium ions had a significant positive correlation with AK and a significant negative correlation with AN and SOM. Moreover, there was an extremely significant (p ≤ 0.001) positive correlation between AN and SOM. Correlation analysis between the microbial population and soil factors (Fig. 4 B) revealed a significant positive correlation between the number of bacteria and both SOM and AN. The number of actinomycetes had a significant (P ≤ 0.01) positive correlation with TSC and sodium ions. The number of fungi showed a significant and positive correlation with SOM and AN and a significant but negative correlation with TSC and sodium ions. It could be concluded that the distribution of the soil microbial population was greatly influenced by SOM, AN, TSC, and sodium ions. TSC and sodium ions restricted the richness of fungi and actinomycetes. 3.5 Diversity and phylogenetic analysis of bacterial communities Since only slight changes in the number of fungi in the different sampling sites were observed and large differences were found between the number of actinomycetes in RFs and those in the farmland, the changes in bacterial community structure and diversity were specifically analyzed. Fragments of DNA of about 200 bp were obtained from the DGGE analysis when total DNA isolated from the bacteria of different soil samples was used as templates and 341F and 534R as primers in the amplification process. As many as 27 different bands corresponding to bacterial DGGE analysis were obtained (Fig. 5 ). In the DGGE map, each band represents at least one population. From the data in Fig. 5 A, the structure of the microbial community in the soil appeared to be complex, with an average of about 20 dominant bands in each lane, especially in severe saline-alkali soil. This may suggest an increased diversity of bacteria in the soil samples with severe saline-alkali conditions. Such a condition may be caused by an increase in halophilic and basophilic bacteria that adapt to the salinized environment[ 25 ]. The cluster analysis result for the bacterial communities is shown in Fig. 5 B. Each community could be divided into two groups, in which R1, SS1, SS2, and NV belong to the same group, with a similarity of more than 85%, indicating that the bacterial populations in the saline-alkali soil with and without vegetation belong to the same group. The communities with more than 70% similarity belong to the other group. The similarity between each of the two different types of salinized soil (4Y-RF1, S1, C, 3Y-RF, S2, ZM) was more than 86%, indicating that the soil bacterial communities of the 3- or 4-year-old RF were similar to that of the farmland. For the older RFs, the diversity of the bacterial communities in the RF gradually became similar to that in the farmland. 3.6 Phylogenetic tree analysis of sequences based on DGGE profiles To determine the bacterial phylogenetic relationships and identify key bacterial genera involved in the microbial changes in different soil samples, the phylogenetic tree for the DNA sequences derived from the DGGE bands was constructed using the MEGA 7 software. As shown in Fig. 6 , almost all the matched bacteria sequences were assigned to four phyla, with 1 sequence for Synergistetes, 8 sequences for Proteobacteria, 12 sequences for Firmicutes, and 6 sequences for Bacteroides. It was found that the dominant bacteria in the severe saline-alkali soil were the Escherichia genus of Enterobacteriaceae in Proteobacteria and the Clostridium genus in Firmicutes. In the case of moderately saline-alkali soil, in addition to the genus Escherichia of the Proteobacteria, the genus Pseudomonas was also dominant. In the mild saline-alkali soil, similar to the previous two cases, except for Escherichia , the dominant bacteria were of the genus Bacteroides for Bacteroides and the genus Enterococcus for Firmicutes. 4. Discussion The global soil area affected by salt is approximately 833 million hectares, accounting for 8.7% of the Earth's surface, mainly distributed in natural arid and semi-arid areas of Africa, Asia, and Latin America. In this study, the soil types in the coastal saline alkali soil survey area were divided into four types based on the degree of salinization: mild, moderate, severe, and saline soil. The total salt content and sodium ion content of the non-vegetated saline-alkali wasteland were significantly higher than those of other sample plots, indicating that the ecology of the study site was relatively fragile. On the other hand, the salt components are not conducive to plant growth because of their high salt content, and most of the minerals are not used by plants. Nevertheless, saline-alkali land is usually rich in mineral components, and some of these can still be directly absorbed and utilized by plants under moderate conditions [ 26 , 27 ]. These mineral components include potassium, calcium, magnesium, phosphorus, sulfur, and some trace elements. Interestingly, analysis of the correlation between AMF and soil factors (Fig. 4 C) showed that AM fungal vesicle infection, AM fungal hyphal infection, and total infection were all positively correlated with AP. Some mineral ions, especially orthophosphate, in saline-alkali soil can be directly absorbed by RFs' plants as available phosphorus (AP) through their symbiosis with mycorrhizal fungi. This interaction helps reduce the need for excessive fertilization, as plowing and high phosphorus application may disrupt AMF abundance and infection. [ 28 – 31 ]. Arbuscular mycorrhiza is the most common microbe that forms a symbiotic relationship with plants. The biological structure of the plants can be deeply affected by AMF in the process of biome restoration. In addition, the revegetation of the damaged habitats can be accelerated artificially by AM fungal manure or AM inoculum[ 32 ]. The content of bacteria, actinomycetes, and fungi in farmland, RFs, and wasteland showed an increasing trend, indicating that the salinity in the soil, which acts as a stress factor, could negatively affect the number of microorganisms [ 33 ], consistent with the results obtained for the coastal tidal flat soil in Lake O'Connor in Western Australia [ 34 ]. The number of fungi and actinomycetes was significantly negatively correlated with TSC, and the number of bacteria and fungi was significantly positively correlated with SOM. Overall, the numbers of bacteria and fungi were abundant in the soils with mild and moderate salinity compared with those in the soils with severe salinity. With increased salinity in the soil, the number of actinomycetes decreased, but the magnitude (change range) was not as large as for the bacteria and fungi because actinomycetes typically have a strong stress-resistance characteristic[ 35 ]. The number of bacteria and fungi in RFs first increased and then decreased with the age of the RFs, but the number of fungi decreased with increased Na + concentration in the soil of the RFs. The content of fungi in the different plant regions of the farmland or in the saline-alkali wasteland was different from each other, indicating that different plants in the salt marsh zone also affected the number of fungi in this zone. The fact that the percentages of bacteria and fungi in the 4-year-old RFs planted with maize were similar to those found in the farmland planted with maize indicated that the microbial community structure of the 4-year-old RFs was similar to that of the farmland, making it possible for large-scale corn cultivation in RFs. The correlation analysis of soil microbial quantity and soil physical and chemical factors showed that the SOM, AN, TSC, and sodium ion content in soil had a strong influence on the distribution of soil microbial quantity. The total salt content in the soil has a very significant positive correlation with Na + because the soluble salt in saline-alkali soil in the coastal area of the Bohai Sea is mainly sodium chloride. It could be concluded that TSC had a significant effect on Na+, AP, and AK, and there was a strong dependent relationship between AN and SOM[ 7 , 9 ]. However, compared with the newly constructed RFs, the soil of the other RFs demonstrated an improvement in terms of salinization reduction, as after a few years of crop planting, the content of Na + in the soil was relatively reduced, while the contents of SOM, AP, and AK in the soil were significantly increased, and the number of soil microorganisms also increased significantly. In addition, because the soil of N-RF was taken from the shallow pond soil, the content of Na + was high, which might have caused the structure of the soil to become less compacted. After 1–2 years of rainwater infiltration as well as improved drainage and increased organic matter content, the soil structure of the 2-year-old or older RFs (2Y-RF, 3Y-RF, 4Y-RF) appeared more similar to the structure of the soil in the deep layer. The loose and well-drained soil would gradually become suitable for crop cultivation. Another important feature uncovered in this study is that the number of microorganisms in different communities was limited by different physical and chemical factors of the soil. During the successive planting of vegetation with severe, moderate, and mild salt tolerance, the soil salinity decreased continuously, and nutrients such as AP continued to accumulate so that the entire micro-ecological system could develop in the direction of a virtuous cycle, consistent with what earlier studies have shown [ 36 , 37 ]. Keystone bacteria with adaptability to saline-alkali environments were identified, and the common dominant flora found in the saline-alkali soil was Clostridium from the phylum Firmicutes, and it may confer benefits by restoring saline-alkali soils or assisting the plants by forming a symbiotic relationship with them. In saline-alkali soils, specific bacteria such as Pseudomonas aeruginosa play crucial roles in nutrient cycling and organic matter decomposition through enzymatic breakdown of complex materials, thereby releasing plant-available nutrients. These bacteria additionally mitigate high pH stress via H⁺ secretion and organic acid release, enhancing nutrient availability and plant resilience under saline conditions [ 38 – 40 ]. Similarly, the dominant microbial phyla in the soil around Lake Aibi, a salt lake in Xinjiang, China, and the dominant microbial phyla in the Gurbantunggut Desert in Xinjiang all include Firmicutes, Proteobacteria, and Bacteroidetes [ 41 , 42 ]. Many Firmicutes can produce spores, which are resistant to dehydration and thus readily survive in environments with low soil nutrients [ 43 ]. It is worth noting that these are general guidelines, and soil conditions and climates may vary in each region. Before carrying out saline soil amendment, it is best to conduct a soil test that can precisely measure the salt content of the soil. Exploring the correlation between environmental microorganisms and soil factors would enable the selection of appropriate types and quantities of microbial agents based on local conditions. However, the dominant flora might differ for soils with different salinity levels. Therefore, the alleviation of the salt stress might be achieved by measuring the salt content of the soil intended for planting and then adding the corresponding dominant flora suitable for a particular salt content. Declarations Author contributions Niu Tingting, Ma Nan and He Jianwei: Conceptualization, Methodology, Writing- Original draft. Ma Nan, Jin Jiangtao and Li Hui: Formal analysis, Investigation. Lu Yanjie and Niu Tingting: Visualization, Software. Wang kaichun and Cui Yi: Data curation, Validation. He Jianwei, Li Hui and Ma Nan: Project administration, Supervision and Funding acquisition. Li Hui: Resources. He Jianwei: Writing- Reviewing and Editing. Acknowledgments We thank all staff from Hebei Huanghua Experimental Base for their kind help and expert assistance. We also thank Dr. Alan K. Chang (Wenzhou University) for helpful discussion and for his kind effort in revising the language of the manuscript. Funding Declaration This work was supported by grants from the National Natural Science Foundation of China (No. 31670103) and General Project from the Educational Department of Liaoning Province (LJKMZ20220448). Ethics, Consent to Participate, and Consent to Publish declarations ： Not applicable. Competing Interest declaration ： The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability statement All data supporting the findings of this study are available within the manuscript. References B. Wicke, E. Smeets, V. Dornburg, B. Vashev, T. Gaiser, W. Turkenburg, A. Faaij, The global technical and economic potential of bioenergy from salt-affected soils (vol 4, pg 2669, 2011), Energy Environ. Sci., 13 (2020) 2585-2585 DOI: 10.1039/d0ee90035d. J.F. Zhu, Z.R. Cui, C.H. Wu, D. C., J.H. Chen, H.X. Zhang, Research Advances and Prospect of Saline and Alkali Land Greening in China, World Forestry Research, 31 (2018) 70-75 DOI: 10.13348/j.cnki.sjlyyj.2018.0034.y. M. Iqbal, S. Irshad, M. Nadeem, T. Fatima, A.B. 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Saad, Unveiling the bacterial diversity and potential of the Avicennia marina ecosystem for enhancing plant resilience to saline conditions, Environmental Microbiome, 19 (2024) DOI: 10.1186/s40793-024-00642-w. K.P. Zhang, Y. Shi, X.Q. Cui, P. Yue, K.H. Li, X.J. Liu, B.M. Tripathi, H.Y. Chu, Salinity Is a Key Determinant for Soil Microbial Communities in a Desert Ecosystem, Msystems, 4 (2019) 11 DOI: 10.1128/mSystems.00225-18. S. Zhao, J.J. Liu, S. Banerjee, N. Zhou, Z.Y. Zhao, K. Zhang, C.Y. Tian, Soil pH is equally important as salinity in shaping bacterial communities in saline soils under halophytic vegetation, Scientific Reports, 8 (2018) 11 DOI: 10.1038/s41598-018-22788-7. W.M. Sattley, M.T. Madigan, W.D. Swingley, P.C. Cheung, K.M. Clocksin, A.L. Conrad, L.C. Dejesa, B.M. Honchak, D.O. Jung, L.E. Karbach, A. Kurdoglu, S. Lahiri, S.D. Mastrian, L.E. Page, H.L. Taylor, Z.T. Wang, J. Raymond, M. Chen, R.E. Blankenship, J.W. Touchman, The genome of Heliobacterium modesticaldum, a phototrophic representative of the Firmicutes containing the simplest photosynthetic apparatus, Journal of bacteriology, 190 (2008) 4687-4696 DOI: 10.1128/jb.00299-08. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5541868","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":446804667,"identity":"d59124c1-a08e-4938-972d-4669526ac927","order_by":0,"name":"Tingting Niu","email":"","orcid":"","institution":"Liaoning University","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Niu","suffix":""},{"id":446804668,"identity":"16fa06b3-46e2-41a9-a195-7b1229637d70","order_by":1,"name":"Nan Ma","email":"","orcid":"","institution":"China Academy of Transportation Sciences","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Ma","suffix":""},{"id":446804670,"identity":"2c68e880-c03a-4eed-af82-8ccfe14ea211","order_by":2,"name":"Jiangtao Jin","email":"","orcid":"","institution":"Liaoning University","correspondingAuthor":false,"prefix":"","firstName":"Jiangtao","middleName":"","lastName":"Jin","suffix":""},{"id":446804673,"identity":"7717a994-de46-4306-9c32-92e8bcd13626","order_by":3,"name":"Yanjie Lu","email":"","orcid":"","institution":"Liaoning University","correspondingAuthor":false,"prefix":"","firstName":"Yanjie","middleName":"","lastName":"Lu","suffix":""},{"id":446804674,"identity":"63e48500-bc34-49a9-a05f-589bbbec6d82","order_by":4,"name":"Yi Cui","email":"","orcid":"","institution":"Liaoning University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Cui","suffix":""},{"id":446804675,"identity":"d569e4c1-e084-42db-b398-faa4d5e606e7","order_by":5,"name":"Kaichun Wang","email":"","orcid":"","institution":"Liaoning University","correspondingAuthor":false,"prefix":"","firstName":"Kaichun","middleName":"","lastName":"Wang","suffix":""},{"id":446804676,"identity":"1a285317-e4eb-4b4f-8d18-9999a44aeec1","order_by":6,"name":"Hui Li","email":"","orcid":"","institution":"Liaoning University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Li","suffix":""},{"id":446804677,"identity":"4ffb8a08-2e7c-4d93-8949-5e39b60793cd","order_by":7,"name":"Jianwei He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYBACxgYeBoYPBWC2AfFaGGcYkKKFgYGHgZmHJC3MM3IPPrYxuJPYwN68TYKh5g4RDpuRl2ycY/AssYHnWJkEw7FnRGiZnWMmnWNwOLFBIsdMgrHhMFFazH9bgLTIvyFeixkzA9gWHmK1zH9jLNljcNi4jSet2CLhGBFaDHvOGH74UXFYtp/98MYbH2qI0dIAZbCBiATCGhgY5IlRNApGwSgYBSMcAADxIjZkFE0cKwAAAABJRU5ErkJggg==","orcid":"","institution":"Liaoning University","correspondingAuthor":true,"prefix":"","firstName":"Jianwei","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2024-11-28 10:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5541868/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5541868/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81266333,"identity":"15b3eaa6-2813-409a-aade-8bcceb94a02a","added_by":"auto","created_at":"2025-04-24 07:37:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":687750,"visible":true,"origin":"","legend":"\u003cp\u003eDifferent land configurations within the same site chosen for this study. Farmland (left), RF (middle) and wasteland (right)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5541868/v1/d0b4d40188307635be61271c.png"},{"id":81267590,"identity":"b18c318c-6317-4396-9807-532608b196a2","added_by":"auto","created_at":"2025-04-24 07:45:28","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45643,"visible":true,"origin":"","legend":"\u003cp\u003eVegetation status of the sampling sites.Vegetation symbols: ZM, zea mays; S, soybean; C, cotton. The RFs consisted of those constructed over 4 years (4Y-RF), 3 years (3Y-RF), and 2 years (2Y-RF), as well as newly constructed RFs (N-RF). R: Reed; SS: Suaeda salsa; NV: No vegetation. This image was drawn by BioRender.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5541868/v1/668662cfbf98a73b92ede177.jpeg"},{"id":81267889,"identity":"2600c53f-fd45-4740-918e-2e49fc0e388f","added_by":"auto","created_at":"2025-04-24 07:53:28","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":114520,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of soil microorganisms in different types of salinized soil. (A) Number of bacteria in different types of salinized soil; (B) Number of actinomycetes in different types of salinized soil; (C) Number of fungi in different types of salinized soil; (D) Relative abundance of bacteria, actinomycetes, and fungi in different types of salinized soil. 4Y-RF, 4-year-old RF; 3Y-RF, 3-year-old RF; 2Y-RF, 2-year-old RF; N-RF, newly constructed RF; SPS, shallow pool soil; ZM, zea mays; S, soybean; C: cotton; R, reed; SS, Suaede salsa; NV, no vegetation. NB, number of bacteria; NA, number of Actinomycetes; NF, number of fungi; PF, percentage of fungi; PA, percentage of actinomycetes; PB, percentage of bacteria; Mild saline-alkali soil: ZM, S, C, moderate saline-alkali soil: 4Y-RF, 3Y-RF, 2Y-RF, N-RF, R, severe saline-alkali soil: SPS, SS; Saline-alkali soil NV.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5541868/v1/eae315d1723d0219e61ed9b0.jpeg"},{"id":81267591,"identity":"c4addb90-6620-4c3c-9c74-dd8d56347973","added_by":"auto","created_at":"2025-04-24 07:45:28","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":54673,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between soil factors and soil microorganisms. (A) Correlations among different soil factors; (B) Correlations between microbial quantity and soil factors; (C) Correlations between AMF and soil factors. NF, number of fungi; NA, number of Actinomycetes; NB, number of bacteria; TIR, total fungal infection rate; HIR, hyphal infection rate; VIR, vesicular infection rate; SOM, soil organic matter; AP, available phosphorus; AK, available potassium; AN, available nitrogen; TSC, total salt content; Statistical analysis was visualized with Origin 2021. *p ≤ 0.05, **p ≤ 0.01, *** p ≤ 0.001.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5541868/v1/7e75d48ce9ab1aaf2cab28fd.jpeg"},{"id":81266353,"identity":"abf350b3-6167-404e-a167-99f209b86dbd","added_by":"auto","created_at":"2025-04-24 07:37:28","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":122312,"visible":true,"origin":"","legend":"\u003cp\u003eDGGE spectrum (A) and cluster analysis plot (B) of bacterial community structure and diversity in different soil samples. 4Y-RF1: 4-year-old RF sample 1; S1: Soybean sample 1; R0: reed field; 4Y-RF2: 4-year-old RF sample 2; C: cotton; 3Y-RF: 3-year-old RF; S2: Soybean sample 2; 2Y-RF: 2-year-old RF. ZM: Corn; N-RF: Newly constructed RF; R1: Saline reed sample 1; SS1: suaede salsa sample 1; SS2: suaede salsa sample 2; NV: No vegetation; R2: Saline reed sample 2. Band 1~27 in the gel were cut and sequenced. The scale bar on the figure is the degree of separation (%).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5541868/v1/0deb3a48560839a898eeb83d.jpeg"},{"id":81266340,"identity":"b331400f-b4af-4575-bf1d-a47169e57930","added_by":"auto","created_at":"2025-04-24 07:37:28","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":65478,"visible":true,"origin":"","legend":"\u003cp\u003eN-J phylogenetic tree of DGGE bands sequence of bacteria (reported as percentages of 1000 replications). Statistical analysis was performed using iTOL.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5541868/v1/5f72de76aee0ba54a024f456.jpeg"},{"id":82413083,"identity":"08f3abb8-9cda-4679-9e2d-1efed42c9409","added_by":"auto","created_at":"2025-05-10 07:31:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2166593,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5541868/v1/f2d65e30-7499-481c-9ac7-d2af09264651.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Novel Insight into the Change of Physicochemical Properties and Microbial Characteristics of Coastal Saline-alkali land after the construction of raised fields","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe global land area affected by saline-alkali is about 1\u0026nbsp;billion hm\u003csup\u003e2\u003c/sup\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], while the area of saline-alkali land in China is nearly 100\u0026nbsp;million hm\u003csup\u003e2\u003c/sup\u003e [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The salinization of cultivated land is intensifying in China. It was estimated that about half of the world's arable land will be affected by salinization by 2050 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although soil salinization is mainly caused by inappropriate irrigation practices and drought [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], rising sea levels and changing precipitation patterns due to climate change can intensify coastal land degradation processes. High salinity, low terrain, poor drainage, and the characteristic of enhanced capillary action of groundwater in coastal areas can further intensify the degree of soil salinization [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, it is crucial to develop strategies for soil improvement to adapt to the impacts of climate change, such as rising sea levels and altered precipitation patterns.\u003c/p\u003e \u003cp\u003eThe coastal area of Tianjin, which is located in the northeast of the North China Plain, has 493,000 hm\u003csup\u003e2\u003c/sup\u003e of saline-alkali land, accounting for 42.3% of the total land area of ​​Tianjin\u0026rsquo;s municipal area [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Saline-alkali soil can reduce the nutrients needed by plants and affect plant growth through osmotic stress and alterations of soil properties, particularly as a result of high salt concentrations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. High concentrations of salt in the soil ause ion imbalance, oxidative damage, water deficit, nutrient deficiency, and alkali stress. High alkali stress can cause ion imbalance and pH instability, and the combined effects can lead to molecular damage, growth retardation, and even the death of plants. Saline-alkali soil hampers plant growth and reduces crop productivity because of osmotic stress, ion imbalance, and the impact of high salt concentrations on soil microorganisms. To mitigate the adverse effects of saline-alkali soil, various strategies have been tried to enhance soil fertility and support plant growth in such challenging environments. These strategies include soil amendment and proper irrigation practices. However, typical saline-alkali land improvement methods that have been tried all have their limitations. For example, the leaching methods apply excess water to the soil to flush out the salts and reduce their concentration in the soil, but care should be taken to avoid waterlogging as well as water wastage. Chemical methods and the installation of subsurface drainage systems have also been used to help remove excess water and salts from the root zone, preventing waterlogging and salt buildup. Unfortunately, these methods require a large investment [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Planting deep-rooted plants and salt-tolerant plants in high-salinity soil is another approach to soil amelioration, but this approach limits the variety of planted crops.\u003c/p\u003e \u003cp\u003eCompared with these typical saline-alkali land improvement methods, the construction of raised fields (RFs) is simple and does not require a lot of manpower or the management of material resources in the later stage. Of particular importance in building RFs are: 1) increasing altitude and the promotion of salt infiltration by excavating soil and stacking fields [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]; 2) reduction of salinity brought to the ground surface with the evaporation of the groundwater since the accumulated RFs are farther away from the groundwater; and 3) reduction of the salinity in the RFs due to irrigation or rainfall. The RFs system can, therefore, make the conditions for growing crops more favorable by improving water management, maintaining soil fertility, and regulating soil conditions, thereby increasing crop yields.\u003c/p\u003e \u003cp\u003eOn the other hand, by converting drainage ditches into fish ponds and integrating raised field cultivation with aquaculture, the RF model could also transform saline-alkali wastelands into efficient composite agricultural ecosystems. Furthermore, the RF structure, characterized by alternating water and land surfaces and undulating microtopography, creates heterogeneous underlying surface features that help regulate the local microclimate. The water surfaces of fish ponds increase air humidity, especially during the summer, forming a relatively humid microclimate that improves conditions for crop growth. Additionally, RFs also serve as a distinctive agricultural landscape in saline-alkali areas, offering certain tourism and aesthetic value.\u003c/p\u003e \u003cp\u003eSo far, research on saline-alkali soil in RFs has mainly focused on the physical and chemical indicators, pH, and other aspects of the soil [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Soil also contains important microorganisms that can affect soil quality because these microorganisms are involved in various nutrient cycling activities in addition to providing plants with minerals and nutrients. Through such processes, microorganisms play a crucial role in soil nutrients. However, few researchers have focused on the microbial population structure, dominant microbiota, and their relationship with the physicochemical properties of saline-alkali soil. Microorganisms also participate in the processes of the terrestrial geochemical cycle since they are a component of the terrestrial ecosystem. Therefore, studying the structure and composition of microorganisms after soil improvement and their relationship with soil environmental factors would help to understand the coupling relationship between microorganisms and soil. However, there are few reports on the changes in soil microbial flora and structural properties in saline-alkali land in this area, as well as the relationship between soil microbial ecological distribution and soil salinity.\u003c/p\u003e \u003cp\u003eIn this study, we explored the impact of the RF model on the physicochemical properties of coastal saline-alkali soil after improvement, as well as its impact on soil microorganisms, especially on AM. The performance of RFs was compared with that of farmland and wasteland, all of which were located within the same stretch of coastal land with saline-alkali soil adjacent to the Tianjin seaside area, China. The investigation specifically explored the changes in the structure and composition of microorganisms in the soil of RFs with different ages and analyzed the correlation of these changes with soil environmental factors as well as the outcome of some vegetation planted in RFs. The results were then compared with those obtained for farmland and wasteland. The valuable information on the dominant bacteria and microbial community composition and community structure in the soil obtained after the construction of RFs would provide the basis for the effective utilization of coastal saline soils in the future.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study site\u003c/h2\u003e \u003cp\u003eThe experiment was conducted in Huanghua, Hebei Province, China (38\u0026deg;22\u0026prime;N, 117\u0026deg;21\u0026prime;E). This study site is close to the Bohai Sea and belongs to a warm temperate semi-humid climate with an annual average temperature of 12.3\u0026deg;C, an annual rainfall of 590.6 mm, and a southwesterly wind with an average wind speed of 4.2 m/s.\u003c/p\u003e \u003cp\u003eThe land chosen as the study site was divided into three different configurations: farmland and RF, which were subjected to artificial interference, and wasteland, which received no interference and served as control land (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A total of 15 sampling sites were established. The status of each sampling site and the main vegetation grown on it are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The farmland sites consisted of corn fields, soybean fields, and cotton fields. The wasteland sites consisted of salt-tolerant wild plants such as reeds and Suaeda salsa. As for the RF sites, the soil of the cultivated layer was relatively raised while the groundwater level was openly lowered to achieve the effect of soil leaching and desalination. Four RFs were selected according to the time that the RFs had been constructed. The RFs consisted of those constructed over 4 years (4Y-RF), 3 years (3Y-RF), and 2 years (2Y-RF), as well as newly constructed RFs (N-RF). The time over which the RFs were constructed will henceforth be referred to as the age of the RFs. These RFs were constructed using shallow pond soil.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample collection\u003c/h2\u003e \u003cp\u003eIn different types of saline-alkali soils, representative plants were selected for sampling. Three plants of the same species were randomly selected at each sampling site. At the same time, 2 cm of the topsoil was removed, and 1 kg of bulk soil spanning 2\u0026ndash;20 cm in depth (rhizosphere soil) was then collected into a sealable bag and stored at 4\u0026deg;C for soil physicochemical analysis and fungal spore density determination. The collected root samples were also brought back to the laboratory and then stored at 4\u0026deg;C. The fresh roots were cut into 1 cm segments and mixed for the determination of root arbuscular mycorrhiza (AM) infection rate.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2. 3 Determination of soil physicochemical properties\u003c/h3\u003e\n\u003cp\u003eSoil samples were air-dried to analyze their physicochemical properties. Organic matter in the soil was measured using a potassium dichromate oxidation method [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The available nitrogen (AN) in the soil was measured by an alkali N-proliferation method [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The available phosphorus (AP) and potassium (AK) in the soil were measured according to the method described by Olsen and Sommers and by ammonium acetate flame photometry, respectively [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Soil pH was measured with a combination electrode pH meter, and total salinity in the soil was measured by a gravimetric method [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The contents of sodium and potassium ions in the soil were measured by ICP-AES (JY2000-2\u0026zwnj;\u0026zwnj;1, HORIBA Jobin Yvon\u0026zwnj;).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Determination of arbuscular mycorrhiza (AM)\u003c/h2\u003e \u003cp\u003eThe harvested roots of each plant were cut into 1 cm-long root segments and stained with Trypan Blue. The structural features such as arbuscules, vesicles, and hyphae were observed with a microscope. Infection of the plants by Arbuscular mycorrhizal fungi (AMF) was determined by the Philips and Hayman method [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. To determine the AMF spore density, 25 g of air-dried soil was taken from each sample, and the AMF spores were isolated by wet sieving and decanting. The number of spores was recorded by a stereomicroscope to obtain the total number of spores. The spore content per 100 g of dry soil was calculated as shown below to give the spore density.\u003c/p\u003e \u003cp\u003eSoil moisture (%) = (soil wet weight \u0026minus; soil dry weight) / soil wet weight \u0026times; 100%\u003c/p\u003e \u003cp\u003eThe number of spores per gram of dry soil\u0026thinsp;=\u0026thinsp;total number of spores / [soil wet weight \u0026times; (1 \u0026minus; soil moisture)]\u003c/p\u003e \u003cp\u003eEach experiment was conducted with three biological replicates\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Separation and quantification of soil microorganisms\u003c/h2\u003e \u003cp\u003eThe number of bacteria, fungi, or actinomycetes in the soil samples was measured by the dilution-plating method [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and its respective percentage with respect to the total number of microbes in the sample was then calculated (number of bacteria, actinomycetes, or fungi / total number of microorganisms \u0026times; 100%). Beef extract peptone medium, Gao\u0026rsquo;s No. 1 medium, and PDA medium were used to cultivate the bacteria, fungi, and actinomycetes, respectively. Data statistics were carried out according to the specification requirements for microbial enumeration.\u003c/p\u003e \u003cp\u003eThe number of bacterial and fungal colony-forming units (CFUs) was calculated per gram of dry soil, and the calculation formula is as follows:\u003c/p\u003e \u003cp\u003eSoil microbial biomass (cfu/g)\u0026thinsp;=\u0026thinsp;MD/W\u003c/p\u003e \u003cp\u003ewhere M is the average number of effective colonies of parallel samples, D is the dilution factor, and W is the mass of dried soil (g).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 DNA extraction and 16S rRNA gene amplification\u003c/h2\u003e \u003cp\u003eTotal DNA was extracted from the soil samples using a PowerSoil DNA Isolation Kit (Mobio, CA, USA) according to the manufacturer\u0026rsquo;s protocol. Prokaryotic communities were amplified using a 16S rRNA gene with the primer set 341F-GC: 5\u0026prime;-\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCGCCCGCCGCGCCCCGCGCCCGGCCCGCCGCCCCCGCCCC\u003c/span\u003e-CCTACGGGAGGCAGCAG-3\u0026prime; (GC-rich sequences were integrated into the 5'-end of the primer) and 534R: 5\u0026prime;-ATTACCGCGGCTGCTGG-3\u0026prime;. DNA amplification was performed in an Eppendorf Mastercycler (Eppendorf, Hamburg, Germany), and the sample consisted of 1 \u0026micro;L Taq DNA polymerase (Takara, Dalian, China), 5 \u0026micro;L PCR buffer, 4 \u0026micro;L dNTP, 1.5 \u0026micro;L of each forward and reverse primer, and 10 ng of template DNA in a total sample volume of 50 \u0026micro;L. The conditions of the amplification for the 16S rRNA gene are as follows: 94\u0026deg;C for 5 min followed by 30 cycles of 94\u0026deg;C (30 s), 55\u0026deg;C (30 s), and 72\u0026deg;C (90 s), with each cycle reduced by 0.2\u0026deg;C, and a final extension at 72\u0026deg;C for 10 min. The PCR products were then separated by DGGE electrophoresis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Denaturing Gradient Gel Electrophoresis (DGGE) analysis of PCR products\u003c/h2\u003e \u003cp\u003eThe PCR products were analyzed by the DGGE technique using a D-Code system apparatus (BioRad, Hercules, CA, USA). The denaturant concentration (formamide:urea) in the gel was prepared, ranging from 40\u0026ndash;60%. The gels were run under a voltage of 150 V and a temperature of 60\u0026deg;C using Tris-acetate-EDTA buffer (1X TAE) for 9 h. The DNA bands in the gel were visualized by silver staining performed with a Bio-Rad silver stain kit. The image of the gel was scanned and analyzed using the Gel Pro. Analyzer (Media Cybernetics, Inc., Rockville, MD, USA) to score for the presence or absence of bands at different positions in each lane. The SPSS software (SPSS Inc., Chicago, IL) was used to cluster the microbial communities indicated by the DNA bands in the gel.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 DGGE strip recovery and phylogenetic analysis\u003c/h2\u003e \u003cp\u003eThe recovery of DGGE strips was carried out as described by Schwieger F \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The DNA bands were each eluted from the gel with 50 \u0026micro;L of water and then stored at 4\u0026deg;C. overnight. These extracted DNA samples were amplified again with non-GC-clamp primers and sequenced by an ABI377 sequencer.\u003c/p\u003e \u003cp\u003eThe identification of the different organisms at the species level was performed by comparing the obtained sequences with those deposited in the NCBI database. The sequences were aligned using ClustalW, and a phylogenetic tree was constructed using MEGA 7.0 software with 1000 bootstrap replicates [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The neighbor-joining method was used to construct a phylogenetic dendrogram, and tree topology was evaluated by performing a bootstrap analysis of 1,000 data sets using MEGA 7.0 software [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The NJ tree was visualized with iTOL [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe phylogenetic tree of the bacteria was inferred by using the Maximum Likelihood method[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The initial tree(s) for the heuristic search were obtained automatically by applying Neighbor Joining method algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach and then selecting the topology with a superior log likelihood value. The tree was drawn to scale with branch lengths measured in the number of substitutions per site.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Data analysis\u003c/h2\u003e \u003cp\u003eArcGIS 10.2 was used to map the study site, whereas BioRender was used to determine the type of sampling site. The heatmap was performed using Origin 2021.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Soil physicochemical properties from different types of sampling sites\u003c/h2\u003e \u003cp\u003eThe soil from the Huanghua area is classified as coastal saline soil, and the degree of salinization is extremely high in the wasteland. The salinity content of the salt-spotted land without vegetation was 29.2\u0026permil;, while the soil with vegetation, which exhibited severe salinization, had a salt content of 6\u0026permil; (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The farmland was mildly salinized because of perennial tillage (many years of cultivation), and it had a salt content of 0.9\u0026permil;~2.1\u0026permil;, with a sodium ion content of 47 mg/kg\u0026thinsp;~\u0026thinsp;386 mg/kg. The soil of RFs was moderately salinized, and its total salt content was 2.3\u0026permil;~2.9\u0026permil;, with a sodium ion content of 274.5\u0026ndash;853 mg/kg. The shallow pool soil, which was the original soil used for constructing the RFs, was severely salinized, with a salt content of 5.7\u0026permil;. Therefore, the degree of salinization in the soil was significantly reduced after it was converted into RF. Through good drainage, salt was effectively removed to prevent its excessive accumulation on the surface of the soil. Compared with the salinization degree of saline-alkali wasteland filled with reed and Suaede salsa, reed appeared to have a stronger alleviation effect on salinization, indicating that different plants might have different effects on the salinization degree of the soil in the long run.\u003c/p\u003e \u003cp\u003eLittle spatial variation in soil pH and organic matter content was found in each soil type in the Huanghua area. Interestingly, the contents of AP and AK in RFs were higher than those of the farmland, with AP content being 22\u0026ndash;577% higher and AK content being 19\u0026ndash;136% higher, indicating the construction of RFs may help to maintain nutrients and organic matter in the soil, thereby providing more AP and AK for the crops.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic physicochemical properties of soil in the Huanghua area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTypes of sampling sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVegetation types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSOM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(mg/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(mg/kg)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eRaised Fields\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4Y-RF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e313.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3Y-RF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e58.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e69.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2Y-RF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e286.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e274.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN-RF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e260.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSPS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFarmland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e199.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e386\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e54.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e47.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e197.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSaline alkali wasteland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e520.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e874.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e50.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2340\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e4Y-RF, 4-year-old RF; 3Y-RF, 3-year-old RF; 2Y-RF, 2-year-old RF; SPS, shallow pool soil; ZM, Zea mays; S, soybean; C, cotton; R, reed; SS; Suaede salsa; NV, no vegetation. SOM, soil organic matter; AP, available phosphorus; AK, available potassium; AN, available nitrogen; TSC, total salt content; K\u003csup\u003e+\u003c/sup\u003e, potassium ion; Na, sodium ion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Number of microorganisms in plots with different degrees of salinization\u003c/h2\u003e \u003cp\u003eA total of 15 soil samples in the study site were graded according to the grading standard for the degree of salinity in coastal saline land in China [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The number and composition of microorganisms in this area were classified and statistically analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Differences were found in the distribution of bacteria among the different sampling sites, with a maximum difference of 12-fold. While in the case of fungi, the maximum difference among different sampling sites (including 3Y-RF, 4Y-RF, ZM, S, and C) is approximately 24.7% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). However, over 1\u0026ndash;2 years, the fungal population gradually reached a certain level, indicating the adaptation of these fungal species in RFs to the soil environment, manifested as a stable population (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Overall, the numbers of bacteria and fungi were abundant in the soils with mild and moderate salinity compared with those in the soils with severe salinity. With decreased salinity in the soil, the number of actinomycetes increased, even in the vegetation-free N-RF, the actinomycete population is still approximately 454% higher than that in SPS. But the magnitude (change range) was not as large as for the bacteria and fungi because actinomycetes typically have a strong stress-resistance characteristic (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The number of bacteria and fungi in RFs increased from N-RF to 3Y-RF and decreased from 3Y-RF to 4Y-RF with the age of the RFs, but the number of fungi decreased with an increased Na\u003csup\u003e+\u003c/sup\u003e concentration in the soil of the RFs.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, the soils of the studied sites were dominated by bacteria (more than 50%), followed by actinomycetes and fungi (1\u0026thinsp;~\u0026thinsp;5%). Less change was observed for the fungal population in response to increased soil salinity. As the degree of soil salinization increased, the number of microorganisms in the soil of the studied sites decreased, except for those from the farmlands (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C). Furthermore, non-vegetation plots (NV, SPS, 0aD) had fewer bacteria, actinomycetes, and fungi than the vegetation plots, and different crops had a greater effect on the number of bacteria and actinomycetes than on the number of fungi.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, the number of bacteria in the soil of the 2-year-old and 3-year-old RFs increased significantly but decreased in the soil of the 4-year-old RFs, becoming similar to that of the farmland [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The number of actinomycetes did not change much between RFs of different ages, with the 4Y-RF increasing by only 27.9% compared to the 2Y-RF. The number of actinomycetes increased with the age of the RFs, reaching a maximum increase of approximately 28.4% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The number of fungi in the farmland soil planted with different crops shows relatively small variations, with a maximum variation of approximately 18.2%. It should be noted that the soil in the 3-year-old and 4-year-old RFs basically had a similar number of fungi to that found in the farmland soil, indicating that different crops and the construction of RFs might have little effect on the soil fungal population in the long run (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Infection rate of AMF\u003c/h2\u003e \u003cp\u003eVesicular Infection Rate (VIR), Arbuscular Infection Rate (AIR), Hyphal Infection Rate (HIR), and Total Infection Rate (TIR) are terms often used in the context of mycorrhizal symbiosis, specifically in the study of plant-mycorrhizal interactions involving AMF. VIR refers to the percentage of root segments that have vesicles. AIR refers to the percentage of root segments that have arbuscules present. Hyphae are the thread-like structures that make up the fungal network. HIR refers to the percentage of root segments that have hyphae present. TIR is the sum of all the different infection rates, including vesicular, arbuscular, and hyphal infection rates.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e compares the rates of AMF infection of different types of crops planted on different types of land. For example, in farmland, the infection rate was 71% for maize roots but only 20.8% for cotton roots, suggesting that the rate of AMF infection found in a particular type of land was related to the types of crops planted (since they were all planted under the same conditions. In the case of RF, TIR for maize root negatively correlated with salinity, providing new insight into the exchange of nutritional benefits between the symbiotic partners in the reduction of soil erosion and nutrient leaching by AM.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInfection rate of AM on plant rhizosphere in different sampling sites\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTypes of sampling sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVegetation types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTIR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4Y-RF\u003c/p\u003e \u003cp\u003e3Y-RF\u003c/p\u003e \u003cp\u003e2Y-RF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFarmland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSaline alkali wasteland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e4Y-RF: 4-year-old RF; 3Y-RF: 3-year-old RF; 2Y-RF: 2-year-old RF; ZM: zea mays; S: soybean; C: cotton; R: Reed; SS: suaeda salsa. VIR: vesicular infection rate; AIR: arbuscular infection rate; HIR: hyphal infection rate; TIR: total infection rate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Relationship between soil factors and soil microorganisms\u003c/h2\u003e \u003cp\u003eSpearman correlation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) of the relationship between different soil factors revealed a significant and positive correlation between TSC and AP and AK and between TSC and sodium ions. Sodium ions had a significant positive correlation with AK and a significant negative correlation with AN and SOM. Moreover, there was an extremely significant (p\u0026thinsp;\u0026le;\u0026thinsp;0.001) positive correlation between AN and SOM.\u003c/p\u003e \u003cp\u003eCorrelation analysis between the microbial population and soil factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) revealed a significant positive correlation between the number of bacteria and both SOM and AN. The number of actinomycetes had a significant (P\u0026thinsp;\u0026le;\u0026thinsp;0.01) positive correlation with TSC and sodium ions. The number of fungi showed a significant and positive correlation with SOM and AN and a significant but negative correlation with TSC and sodium ions. It could be concluded that the distribution of the soil microbial population was greatly influenced by SOM, AN, TSC, and sodium ions. TSC and sodium ions restricted the richness of fungi and actinomycetes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Diversity and phylogenetic analysis of bacterial communities\u003c/h2\u003e \u003cp\u003eSince only slight changes in the number of fungi in the different sampling sites were observed and large differences were found between the number of actinomycetes in RFs and those in the farmland, the changes in bacterial community structure and diversity were specifically analyzed. Fragments of DNA of about 200 bp were obtained from the DGGE analysis when total DNA isolated from the bacteria of different soil samples was used as templates and 341F and 534R as primers in the amplification process. As many as 27 different bands corresponding to bacterial DGGE analysis were obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In the DGGE map, each band represents at least one population. From the data in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, the structure of the microbial community in the soil appeared to be complex, with an average of about 20 dominant bands in each lane, especially in severe saline-alkali soil. This may suggest an increased diversity of bacteria in the soil samples with severe saline-alkali conditions. Such a condition may be caused by an increase in halophilic and basophilic bacteria that adapt to the salinized environment[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe cluster analysis result for the bacterial communities is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB. Each community could be divided into two groups, in which R1, SS1, SS2, and NV belong to the same group, with a similarity of more than 85%, indicating that the bacterial populations in the saline-alkali soil with and without vegetation belong to the same group. The communities with more than 70% similarity belong to the other group. The similarity between each of the two different types of salinized soil (4Y-RF1, S1, C, 3Y-RF, S2, ZM) was more than 86%, indicating that the soil bacterial communities of the 3- or 4-year-old RF were similar to that of the farmland. For the older RFs, the diversity of the bacterial communities in the RF gradually became similar to that in the farmland.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Phylogenetic tree analysis of sequences based on DGGE profiles\u003c/h2\u003e \u003cp\u003eTo determine the bacterial phylogenetic relationships and identify key bacterial genera involved in the microbial changes in different soil samples, the phylogenetic tree for the DNA sequences derived from the DGGE bands was constructed using the MEGA 7 software. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, almost all the matched bacteria sequences were assigned to four phyla, with 1 sequence for Synergistetes, 8 sequences for Proteobacteria, 12 sequences for Firmicutes, and 6 sequences for Bacteroides. It was found that the dominant bacteria in the severe saline-alkali soil were the \u003cem\u003eEscherichia\u003c/em\u003e genus of Enterobacteriaceae in Proteobacteria and the \u003cem\u003eClostridium\u003c/em\u003e genus in Firmicutes. In the case of moderately saline-alkali soil, in addition to the genus \u003cem\u003eEscherichia\u003c/em\u003e of the Proteobacteria, the genus \u003cem\u003ePseudomonas\u003c/em\u003e was also dominant. In the mild saline-alkali soil, similar to the previous two cases, except for \u003cem\u003eEscherichia\u003c/em\u003e, the dominant bacteria were of the genus \u003cem\u003eBacteroides\u003c/em\u003e for Bacteroides and the genus \u003cem\u003eEnterococcus\u003c/em\u003e for Firmicutes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe global soil area affected by salt is approximately 833\u0026nbsp;million hectares, accounting for 8.7% of the Earth's surface, mainly distributed in natural arid and semi-arid areas of Africa, Asia, and Latin America. In this study, the soil types in the coastal saline alkali soil survey area were divided into four types based on the degree of salinization: mild, moderate, severe, and saline soil. The total salt content and sodium ion content of the non-vegetated saline-alkali wasteland were significantly higher than those of other sample plots, indicating that the ecology of the study site was relatively fragile. On the other hand, the salt components are not conducive to plant growth because of their high salt content, and most of the minerals are not used by plants. Nevertheless, saline-alkali land is usually rich in mineral components, and some of these can still be directly absorbed and utilized by plants under moderate conditions [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These mineral components include potassium, calcium, magnesium, phosphorus, sulfur, and some trace elements. Interestingly, analysis of the correlation between AMF and soil factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) showed that AM fungal vesicle infection, AM fungal hyphal infection, and total infection were all positively correlated with AP. Some mineral ions, especially orthophosphate, in saline-alkali soil can be directly absorbed by RFs' plants as available phosphorus (AP) through their symbiosis with mycorrhizal fungi. This interaction helps reduce the need for excessive fertilization, as plowing and high phosphorus application may disrupt AMF abundance and infection. [\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Arbuscular mycorrhiza is the most common microbe that forms a symbiotic relationship with plants. The biological structure of the plants can be deeply affected by AMF in the process of biome restoration. In addition, the revegetation of the damaged habitats can be accelerated artificially by AM fungal manure or AM inoculum[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe content of bacteria, actinomycetes, and fungi in farmland, RFs, and wasteland showed an increasing trend, indicating that the salinity in the soil, which acts as a stress factor, could negatively affect the number of microorganisms [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], consistent with the results obtained for the coastal tidal flat soil in Lake O'Connor in Western Australia [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The number of fungi and actinomycetes was significantly negatively correlated with TSC, and the number of bacteria and fungi was significantly positively correlated with SOM. Overall, the numbers of bacteria and fungi were abundant in the soils with mild and moderate salinity compared with those in the soils with severe salinity. With increased salinity in the soil, the number of actinomycetes decreased, but the magnitude (change range) was not as large as for the bacteria and fungi because actinomycetes typically have a strong stress-resistance characteristic[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The number of bacteria and fungi in RFs first increased and then decreased with the age of the RFs, but the number of fungi decreased with increased Na\u0026thinsp;+\u0026thinsp;concentration in the soil of the RFs. The content of fungi in the different plant regions of the farmland or in the saline-alkali wasteland was different from each other, indicating that different plants in the salt marsh zone also affected the number of fungi in this zone. The fact that the percentages of bacteria and fungi in the 4-year-old RFs planted with maize were similar to those found in the farmland planted with maize indicated that the microbial community structure of the 4-year-old RFs was similar to that of the farmland, making it possible for large-scale corn cultivation in RFs.\u003c/p\u003e \u003cp\u003eThe correlation analysis of soil microbial quantity and soil physical and chemical factors showed that the SOM, AN, TSC, and sodium ion content in soil had a strong influence on the distribution of soil microbial quantity. The total salt content in the soil has a very significant positive correlation with Na\u003csup\u003e+\u003c/sup\u003e because the soluble salt in saline-alkali soil in the coastal area of the Bohai Sea is mainly sodium chloride. It could be concluded that TSC had a significant effect on Na+, AP, and AK, and there was a strong dependent relationship between AN and SOM[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, compared with the newly constructed RFs, the soil of the other RFs demonstrated an improvement in terms of salinization reduction, as after a few years of crop planting, the content of Na\u003csup\u003e+\u003c/sup\u003e in the soil was relatively reduced, while the contents of SOM, AP, and AK in the soil were significantly increased, and the number of soil microorganisms also increased significantly. In addition, because the soil of N-RF was taken from the shallow pond soil, the content of Na\u003csup\u003e+\u003c/sup\u003e was high, which might have caused the structure of the soil to become less compacted. After 1\u0026ndash;2 years of rainwater infiltration as well as improved drainage and increased organic matter content, the soil structure of the 2-year-old or older RFs (2Y-RF, 3Y-RF, 4Y-RF) appeared more similar to the structure of the soil in the deep layer. The loose and well-drained soil would gradually become suitable for crop cultivation.\u003c/p\u003e \u003cp\u003eAnother important feature uncovered in this study is that the number of microorganisms in different communities was limited by different physical and chemical factors of the soil. During the successive planting of vegetation with severe, moderate, and mild salt tolerance, the soil salinity decreased continuously, and nutrients such as AP continued to accumulate so that the entire micro-ecological system could develop in the direction of a virtuous cycle, consistent with what earlier studies have shown [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKeystone bacteria with adaptability to saline-alkali environments were identified, and the common dominant flora found in the saline-alkali soil was Clostridium from the phylum Firmicutes, and it may confer benefits by restoring saline-alkali soils or assisting the plants by forming a symbiotic relationship with them. In saline-alkali soils, specific bacteria such as \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e play crucial roles in nutrient cycling and organic matter decomposition through enzymatic breakdown of complex materials, thereby releasing plant-available nutrients. These bacteria additionally mitigate high pH stress via H⁺ secretion and organic acid release, enhancing nutrient availability and plant resilience under saline conditions [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Similarly, the dominant microbial phyla in the soil around Lake Aibi, a salt lake in Xinjiang, China, and the dominant microbial phyla in the Gurbantunggut Desert in Xinjiang all include Firmicutes, Proteobacteria, and Bacteroidetes [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Many Firmicutes can produce spores, which are resistant to dehydration and thus readily survive in environments with low soil nutrients [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is worth noting that these are general guidelines, and soil conditions and climates may vary in each region. Before carrying out saline soil amendment, it is best to conduct a soil test that can precisely measure the salt content of the soil. Exploring the correlation between environmental microorganisms and soil factors would enable the selection of appropriate types and quantities of microbial agents based on local conditions. However, the dominant flora might differ for soils with different salinity levels. Therefore, the alleviation of the salt stress might be achieved by measuring the salt content of the soil intended for planting and then adding the corresponding dominant flora suitable for a particular salt content.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNiu Tingting, Ma Nan and He Jianwei: Conceptualization, Methodology, Writing- Original draft. Ma Nan, Jin Jiangtao and Li Hui: Formal analysis, Investigation. Lu Yanjie and Niu Tingting: Visualization, Software. Wang kaichun and Cui Yi: Data curation, Validation. He Jianwei, Li Hui and Ma Nan: Project administration, Supervision and Funding acquisition. Li Hui: Resources. He Jianwei: Writing- Reviewing and Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all staff from Hebei Huanghua Experimental Base for their kind help and expert assistance. We also thank Dr. Alan K. Chang (Wenzhou University) for helpful discussion and for his kind effort in revising the language of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Declaration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Natural Science Foundation of China (No. 31670103) and General Project from the Educational Department of Liaoning Province (LJKMZ20220448).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest declaration\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are available within the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eB. 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Touchman, The genome of Heliobacterium modesticaldum, a phototrophic representative of the Firmicutes containing the simplest photosynthetic apparatus, Journal of bacteriology, 190 (2008) 4687-4696 DOI: 10.1128/jb.00299-08.\u003c/li\u003e\n\u003c/ol\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":"saline-alkali soil, soil microbe, arbuscular mycorrhiza, raised field","lastPublishedDoi":"10.21203/rs.3.rs-5541868/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5541868/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo address the issue of land degradation caused by coastal salinization, the saline-alkali soil in the coastal area of Huanghua, Hebei, China, was selected as a pilot study to evaluate the effects of different approaches to soil improvement, especially the construction of raised fields (RFs).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe microbiota of various saline-alkali soils (mild, moderate, severe, and saline levels) and their adaptation to growth-limiting factors were studied. DGGE and multivariate statistical analyses were combined to analyze the structure and dominant flora of different soil bacterial communities and the correlation between soil physicochemical indexes and soil microbiota.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKey Findings\u003c/strong\u003e: A shortage of soil microbes was found in this area, with the bacterial community being dominant (more than 50% relative abundance), while the fungal community showed higher tolerance, stability, and resilience to various saline-alkali soils than the bacterial and actinomycetes communities. The contents of organic matter, available N, total salt, and Na\u003csup\u003e+\u003c/sup\u003e in the soil each exerted a great influence on the abundance of soil microbes. Notably, some mineral ions (especially orthophosphate) in the saline-alkali land could be directly utilized in the available forms by the plants grown in RFs through the symbiosis between mycorrhizal fungi and plants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe construction of RFs and the identification of keystone bacteria that have potential adaptability to various saline-alkali environments, therefore, may be effective strategies for coastal saline-alkali land utilization as well as ecological restoration.\u003c/p\u003e","manuscriptTitle":"Novel Insight into the Change of Physicochemical Properties and Microbial Characteristics of Coastal Saline-alkali land after the construction of raised fields","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-24 07:37:23","doi":"10.21203/rs.3.rs-5541868/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":"93a64157-9c67-4723-964a-79155ff78499","owner":[],"postedDate":"April 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-10T07:23:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-24 07:37:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5541868","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5541868","identity":"rs-5541868","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}