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Article

Planting Trees on Sandy Saline Soil Increases Soil Carbon and Nitrogen Content by Altering the Composition of the Microbial Community

by
Tianyun Shao
1,†,
Xiao Yan
1,†,
Kenan Ji
1,
Zhuoting Li
1,
Xiaohua Long
2,*,
Yu Zhang
2 and
Zhaosheng Zhou
1,*
1
Jiangsu Provincial Key Laboratory of Coastal Saline Soil Resources Utilization and Ecological Conservation, College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, China
2
Institute of Crop Sciences, Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010031, China
*
Authors to whom correspondence should be addressed.
†
These authors are the co-first author.
Agronomy 2024, 14(10), 2331; https://doi.org/10.3390/agronomy14102331
Submission received: 19 August 2024 / Revised: 13 September 2024 / Accepted: 17 September 2024 / Published: 10 October 2024
(This article belongs to the Section Soil and Plant Nutrition)
Browse Figures
Versions Notes

Abstract

:
The remediation and exploitation of sandy saline soils, an underutilized resource, can be enhanced by a greater comprehension of the impact of plants and microorganisms on nutrient cycling. However, there is scant research information on the capacity of different trees and shrubs to improve carbon and nitrogen cycling in saline soils at different depth layers. This study investigated the effect of the trees Zelkova serrata (ZS) and Ligustrum lucidum (LL) and shrub Hibiscus syriacus (HS) on the carbon and nitrogen fractions, soil enzyme activities and microbial communities in sandy saline soils. Planting ZS, LL or HS improved soil quality, increased soil carbon and nitrogen content, changed rhizosphere soil metabolites and enhanced soil enzyme activities and microbial abundance and diversity. Compared to values in the bare soil, the highest reduction in soil salinity was noticed under Zelkova serrata (49%) followed by Ligustrum lucidum (48%). The highest increase in total soil organic carbon (SOC) was noted under Ligustrum lucidum and Hibiscus syriacus (62% each), followed by Zelkova serrata (43%), as compared to levels in the bare soil. In the 0–10 cm soil layer, the total N in bare soil was 298 ± 1.48 mg/kg, but after planting LL, ZS or HS, the soil total N increased by 101%, 56% and 40%, respectively. Compared with that of the bare soil, cbbL sequencing showed that the relative abundance of Bradyrhizobium increased and that of Bacillus decreased due to planting. Similarly, the nifH sequencing results indicated that the relative abundance of Bradyrhizobium and Motiliproteu increased and that of Desulfuromonas and Geoalkalibacter decreased. These findings suggested that soil microorganisms could play a pivotal role in the carbon and nitrogen cycle of saline soils by influencing the content of soil carbon and nitrogen.

1. Introduction

Soil salinization refers to the accumulation of soluble salt in the surface soil due to the joint action of some natural factors such as climate, hydrology and topography; destructive human factors; and a fragile ecological environment [1]. Salt-affected soils are a unique type of land resource, with a total global area of approximately 1.10 × 109 hm2 [2]. The total area of saline–alkali land in China has reached 3.69 × 107 hm2 [3]. China’s coastal beach area is extensive, with a considerable distribution of saline soil. Consequently, coastal saline soil constitutes a considerable proportion of China’s saline soil [4].
Saline soil exhibits a number of characteristics that are detrimental to plant growth. These include poor soil structure and permeability, inadequate soil fertility, slow organic matter conversion and low biological activity [5]. The presence of soil salinity not only adversely affects plant growth but also significantly impacts the composition and functionality of the soil microbial community [6]. Some studies have indicated that soil salinity affects microbial community composition [7] and has an adverse effect on microbial survival, resulting in a reduction in community richness and diversity [8].
The most commonly used methods for improving saline–alkali land include physical or engineering improvement, chemical improvement and biological improvement [9]. Among these methods, physical improvement is expensive and short-lived, while chemical improvement is susceptible to secondary pollution [10]. Biological improvement stands out as one of the most ecologically and economically beneficial technical measures. Plant restoration falls under the category of biological improvement technology, which not only enhances the physicochemical properties of saline soil but also plays a crucial role in greening saline soil [11,12]. The greening of saline soil can contribute to the stability of ecosystems and enhance the environmental carrying capacity [13]. The cultivation of salt-tolerant plants not only reduces soil salinity and enhances enzyme activity [14] but also augments soil organic carbon [15], soil nutrient content [16] and microbial abundance [17,18]. Trees and shrubs, as woody plants, exhibit greater resistance to harsh growing conditions and are well-suited for adapting to salt stress, making them valuable for improving saline soil [19].
The activities of microorganisms have a pivotal impact upon the transformation of soil nutrients, particularly with regards to carbon and nitrogen [20,21,22]. Autotrophic microorganisms absorb atmospheric carbon dioxide through the Calvin–Benson–Bassham (CBB) pathway, in which ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is the key enzyme [23]. The cbbL gene encodes RuBisCO, making it a key gene for the study of autotrophs [24,25]. On the other hand, nitrogen-fixing bacteria convert atmospheric N2 into ammonium that can be used by plants. The nifH gene exists widely in nitrogen-fixing bacteria [26,27]. The cbbL and nifH genes are commonly used for quantitative analysis of the soil microbiome [28].
The efficient storage of soil carbon and nitrogen in saline soils is essential for improving plant productivity and for mitigating greenhouse gas emissions [29]. Plants affect the soil microbiome composition, with different plant species having different soil microbial communities [30]. However, there is little information on (i) the capacity of trees and shrubs to adapt to and colonize sandy saline soils and (ii) their effects on the microecology of different soil layers. We selected the trees Zelkova serrata (ZS) and Ligustrum lucidum (LL) and shrub Hibiscus syriacus (HS) to study their effects on soil carbon and nitrogen dynamics. We hypothesized that (1) planting ZS, LL and HS can significantly increase the content of carbon and nitrogen, especially active carbon and nitrogen, in coastal sandy saline soil; (2) the effect of trees on soil carbon and nitrogen is greater than that of shrubs; and (3) plants can alter the content of carbon and nitrogen in coastal saline soil by modifying the composition of the rhizosphere microbial community.

2. Materials and Methods

2.1. Site Description and Experimental Design

The experimental field was situated in the Rudong experimental area (32°34′ N, 121°0′ E), Nantong City, Jiangsu Province (coastal region with sandy saline soils). The climatic conditions of the area may be defined as northern subtropical oceanic monsoon. As of the year 2022, the average annual temperature in this region was 16 °C, with an average annual precipitation of 1032 mm. The frost-free period had an average duration of 222 days per year.
We set up four treatments, consisting of three planted plots containing one species each [Zelkova serrata (ZS), Ligustrum lucidum (LL) or Hibiscus syriacus (HS)] and a bare soil (BS) area, with three field replicates for each treatment. Each plot was 6.0 × 7.5 m. The planting (row and intra-row spacing 1.5 × 1.5 m) was performed in September 2020, preceded by adding organic fertilizer (600 kg/ha) and soil conditioner (ZL 201310386417.1, 120 kg/ha) and then ploughing the experimental site to a depth of 45 cm. The management of the field was consistent across all plots. The diameter of the ZS and LL seedlings was 4–5 cm, with a height of 2.5–3.0 m, while the HS seedlings had a diameter of 4–5 cm and a height of 1.5–2.0 m. Subsequently, soil samples were taken, at which point ZS and LL had reached a diameter of 6–7 cm and a height of 3.0–3.5 m, while HS had reached a diameter of 5–6 cm and a height of 2.0–2.5 m.

2.2. Sample Collection and Processing

In October 2022, samples of soil were collected from bare soil (no plant) and plant soil (ZS, LL and HS). The bare soil was collected from bare soil plot following the five-point sampling method (depth of 0–10, 10–20, 20–30 and 30–40 cm) using a soil auger (6 cm diameter). The samples were mixed and divided equally into three parts. The plant soil samples (containing three plants with similar growth) were obtained using a soil auger at four depths (0–10, 10–20, 20–30 and 30–40 cm) and at two distances from the main trunks (30 and 60 cm). A portion of each soil sample was stored in a self-sealing bag and subsequently air dried, ground and sieved through 0.15 mm mesh for chemical analysis. The remaining portion underwent snap freezing and storage at −80 °C for subsequent soil DNA extraction. In addition, shovels were used to obtain a mixture of soil and roots. Rhizosphere soil (attached to the root within 1 mm) was used to determine soil metabolites.

2.3. Methods

Soil salinity (Fe38-Standard, Mettler Toledo, Greifensee, Switzerland) and pH (PHS-3C, Leici, Shanghai, China) were evaluated using the conductivity method [31]. Soil available phosphorus was determined using the molybdenum–antimony colorimetric method [32].
An ICP-OES instrument (Agilent 710, Waltham, MA, USA) was used to determine soil available potassium (1 M NH4OAc) and water-soluble K+, Ca2+, Mg2+ and Na+ contents [33]. Soil aggregate distribution and stability were determined using the wet sieving method [34,35].
Soil organic carbon (SOC) content was determined using K2Cr2O7 [36]. Dissolved organic carbon (DOC) was measured by cold-water extraction using a total organic carbon analyzer (Multi N/C 3100, Jena, Germany) [37]. The readily oxidizable carbon (ROC) was determined using the 333 mM KMnO4 oxidation procedure [38].
The soil total nitrogen (TN) was then measured using an AA3 AutoAnalyzer (Seal, Norderstedt, Germany). Ammonium and nitrate were extracted in 2 M KCl and then analyzed using the auto analyzer AA3. Soil alkali-hydrolyzable nitrogen (AN) was measured according to Roberts [39]. The microbial biomass nitrogen (MBN) was determined using the chloroform fumigation–extraction method [40].
The activities of soil enzymes were determined using the respective Solaibio (Beijing, China) soil kits. Soil catalase activity was assessed by measuring the change in absorbance at 240 nm [41]. Soil alkaline phosphatase activity was determined by measuring absorbance at 660 nm [42]. Soil urease activity was assessed by measuring the amount of NH3-N at 630 nm [43]. Soil sucrase activity was measured by colorimetry at 540 nm [44]. Soil cellulase activity was determined using the 3,5-dinitrosalicylic acid method [45]. The activity of soil alkaline protease was determined by tyrosine [46]. The determination of azo compounds in soil at 520 nm was performed to assess nitrate reductase activity [47].
Soil metabolites was measured by GC-MS (Thermo Fisher Scientific Co., Ltd., Shanghai, China) [31].
Total genomic DNA was extracted from soil samples using a FastBeat SoilPure Soil DNA Isolation Kit (Bolaz, Nanjing, China). The cbbL fragment was amplified using primers cbbL2F (5′-CATCATGTTCGACCAGGACT-3′) and cbbL2R (5′-TCGAACTTGATTTCTTTCCA-3′). The nifH fragment was amplified using primers nifH-F (5′-AAAGGYGGWATCGGYAARTCCACCAC-3′) and nifH-R (5′-TTGTTSGCSGCRTACATSGCCATCAT-3′). There were 3 replicates per sample. The PCR products of the same sample were mixed for product purification and detection quantification. Purified PCR products were employed for library construction, with the original data subsequently uploaded to the NCBI SRA database (serial number: PRJNA1120518). The raw data were quality filtered, merged, rarefied and annotated for taxonomic classification.
Detailed methods of microbial sequencing analysis and other indicators are shown in the Supplementary Materials File S1.

2.4. Statistical Analysis

The statistical analysis was conducted utilizing Microsoft Excel 2007 and SPSS Statistics 26 software packages. The mean values of the data were calculated from three replications, and standard errors were derived. One-way analysis of variance (ANOVA) with Tukey’s new multiple range test was employed in order to determine the differences between the treatment groups at the 95% confidence level (p ≤ 0.05). The normality and homogeneity of variance were tested before ANOVA. The differences in the soil layers were compared by the independent samples t-test. Alpha diversity indices were calculated with QIIME (Version 1.7.0) and displayed with R software (Version 4.3.1). Alpha diversity box plotting, Wayne plotting and LEfSe analyses were performed using R software (Version 4.3.1) with default LDA score filtering set to a value of 4.0. The ranking analysis for RDA utilized the CCA and RDA functions from the vegan package in R. Correlation analyses were conducted using the Mantel test and Pearson test with the ggplot2, ggcor and dplyr packages in R.

3. Results

3.1. Soil Physical and Chemical Characteristics

At distances of 30 and 60 cm from the main trunk, all soil parameters considered showed significant differences among plant types and soil depths (Figure 1). Soil pH values, salinity and Na+ significantly increased with soil depth, while available P, available K and the extractable contents of Ca2+, K+ and Mg2+ significantly decreased with soil depth (p ≤ 0.05).
At a distance of 30 cm from the main trunk, planting ZS and LL reduced the soil pH value at a depth of 20–40 cm compared with that of BS. At a distance of 60 cm from the main trunks, planting LL reduced the soil pH value at a depth of 10–40 cm compared with that of BS. At 30 cm from the main trunk, planting LL and ZS significantly reduced soil salinity (by 26–49%) compared to that of BS, whereas at 60 cm from the main trunk, only LL significantly reduced salinity (by 12–33%) compared to that of BS at soil depths of 10–40 cm. At 30 cm away from the main trunk, the extractable Na+ contents in soils with ZS and LL decreased significantly (by 8.0–72 and 35–87%, respectively) compared with levels in BS samples. Compared with those of BS, soil available K and P contents increased significantly after planting ZS, LL and HS. At a depth of 0–20 cm, the soil available P in plots with ZS, LL and HS increased significantly (by 2.32–0.46 times, 4.44–2.73 times and 1.35–0.51 times, respectively) compared with that of BS. Compared to that of BS, the amount of available potassium in the surface soil (0–10 cm) of the planting area increased the most. With LL, available potassium increased by 1.78 times (at a distance of 30 cm from the main trunk), while with ZS and HS, it increased by 0.70 times and 1.51 times (at a distance of 60 cm from the main trunk), respectively. For all soil layers, the extractable contents of Ca2+ and Mg2+ significantly increased in plots with ZS, LL and HS (by 0.39–4.28, 0.89–3.50 and 0.13–0.83 times respectively) compared to those in BS plots. The extractable K+ content under LL planting was significantly (by 0.39–2.37 times) higher than that in BS.
The distribution and stability of soil aggregates were found to be significantly affected by plant types and soil depths at a distance of 30 cm from the main trunk (p ≤ 0.05, Table S1). Soil aggregates ≤0.25 mm in diameter dominated (74.6–97.6%) in all samples. The WSA content exhibited a notable decline with increasing soil depth (Table S1). At 0–40 cm soil depth, the WSA content increased by 1.71–14.8% with the three plant types compared with that of BS. In the 0–20 layers, the WSA content of LL soil was 13.8–14.8% higher compared with that of BS (Table S1). At a soil depth of 0–20 cm, the mean weight diameter and geometric mean diameter of the LL soil were significantly greater than those of BS soil.

3.2. Soil Carbon in Different Treatments

At 30 and 60 cm from the main trunk, the plant types and soil depths significantly affected soil carbon contents (SOC, DOC and ROC) (p ≤ 0.05, Figure 2). A significant decrease in soil carbon (SOC, DOC and ROC) content was observed with increasing soil depth. Compared to BS values, planting ZS, LL and HS significantly increased the content of SOC, DOC and ROC (p ≤0.05). At depths of 0–20 cm, the order of decreasing SOC content was as follows: LL > HS = ZS > BS. At 30–40 cm deep and 30 cm away from the main trunk, SOC was found to be significantly higher in the HS treatment than in the other treatments. The maximum SOC increased by 62%, 30% and 50% following the conversion of BS to LL, ZS and HS plantings, respectively. At a distance of 30 cm from the main trunk, the decreasing order of DOC content was as follows: HS > LL > ZS > BS; the DOC content of the HS plot was 118% higher compared to that of the bare soil. At a distance of 60 cm, the order was LL > HS > ZS > BS; the LL soil had a 134% higher DOC content than the bare soil. At 0–20 cm deep, the ROC content of LL soil increased by 155–255% compared to that of BS, which was significantly higher than that of ZS and HS soils. In the 30–40 cm soil layers, the ROC content of HS soil was significantly higher than that of BS (30 cm away from the trunk), while the ROC content of the LL treatment was significantly higher than that of BS (60 cm away from the trunk).

3.3. Nitrogen Content in Soils in Different Treatments

According to the previous results, the distance from the main stem had the same influence on the soil. Therefore, the topsoil 30 cm away from the main stem was selected for further investigation. Planting significantly increased soil nitrogen content (p ≤ 0.05, Figure 3). At a depth of 0–10 cm, the greatest increase in TN, DON, MBN and AN was observed. The TN content of the ZS, LL and HS soils increased by 56.5%, 101% and 39.6%, respectively, compared to that of BS (p ≤ 0.05, Figure 3A). The DON content in ZS, LL and HS plots increased by 3.50, 3.37 and 3.14 times, respectively, compared to that of BS (p ≤ 0.05, Figure 3B). The MBN content of ZS, LL and HS soils increased by 5.02, 1.11 and 0.84 times, respectively, compared to that of BS (p ≤ 0.05, Figure 3C). The AN content in ZS, LL and HS samples increased by 1.04, 0.56 and 0.72 times, respectively, compared to that of BS (p ≤ 0.05, Figure 3D). At 0–20 cm, only the NH4+-N of HS soil was significantly lower than that of BS (p ≤ 0.05, Figure 3E). In the 0–10 cm and 10–20 cm soil layers, LL and ZS samples had the highest NO3−-N, respectively (p ≤ 0.05, Figure 3F).

3.4. Soil Enzyme Activities and Rhizosphere Soil Metabolites

Plant types significantly affected soil enzyme activities and rhizosphere metabolites (p ≤ 0.05, Figure 4). At a 10–20 cm depth, the catalase activity in the LL and ZS treatments were 16.1 and 15.7% lower compared to that of the bare soil. At 0–20 cm in depth, soil alkaline phosphatase enzyme activities were 1.46–4.5, 2.30–12.4 and 0.82–5.33 times higher in the ZS, LL and HS treatments, respectively, compared with those in the bare soil. At 0–20 cm in depth, soil urease enzyme activities were 2.13–3.09, 2.07–6.64 and 0.51–2.28 times higher in the ZS, LL and HS treatments, respectively, compared with those in the BS. At 0–10 cm in depth, the sucrase activity in ZS and HS soil increased by 73.5% and 37.0% compared with that of BS. At 0–10 cm in soil depth, the ZS, LL and HS soil cellulase activities increased by 32.4%, 38.0% and 29.6% compared with those of BS. The cellulase activity in soil with a depth of 10–20 cm was significantly higher in LL samples than in BS and increased by 31.8% compared with that in BS. At a 0–20 cm soil depth, the soil alkaline protease activity of ZS, LL and HS soils was significantly higher than that of BS (p ≤ 0.05), and the highest increase was 1.11 times, 0.76 times and 1.13 times higher compared with that of BS, respectively. At 0–10 cm in depth, the nitrate reductase activity in ZS and LL soils was significantly higher than that in BS and increased respectively compared with BS.
A total of seven rhizosphere soil metabolites were detected (Figure S1). Alcohol was detected only in the ZS soils. Acids/esters accounted for 47–50% of ZS, LL and BS metabolites but only 25% of HS soil metabolites. Aromatic hydrocarbons in HS soil represented up to 53% of metabolites, which was higher than in LL (15%) and ZS (11%) soils. The hydrocarbons levels in ZS soils (19%) were higher than in other samples, while oxide levels were higher in LL samples (2.5%) than in others. The highest concentrations of ZS, LL and HS soil metabolites were observed for bis (2-ethylhexyl) phthalate, dibutyl phthalate and 1,3-dimethyl-benzene, respectively (Table S2).

3.5. Soil Microorganisms

3.5.1. Soil Autotrophic Microorganism Diversity and Richness Analysis

The number of OTUs detected by cbbL sequencing decreased as follows: ZS (798) > HS (665) > LL (597) > BS (434) (Figure 5A). Compared with those in BS, the numbers of OTUs in soil increased significantly in all plant types (p ≤ 0.05) (Figure 5A). The order of Chao 1, ACE and Shannon index was ZS > HS = LL > BS (Figure 6B–D). Only four phyla and nine genera were identified (Figure 5F,G). The most abundant phylum was Proteobacteria, with a relative abundance of more than 94% in BS and about 84% in the ZS soil (Figure 5F). The four most dominant genera were Bradyrhizobium, Methylibium, Thiomonas and Symbiodinium (Figure 5G). Compared with BS samples, those from LL, ZS and HS had a higher percentage of Bradyrhizobium. Conversely, Thiomonas had a higher relative abundance in bare soil than in ZS, LL and HS soils. The LEfSe analysis (Figure 5H) identified nine taxa with differential abundance in the ZS soil, five in the HS soil and three in the bare soil, but none in the LL soil. The most abundant in the ZS soil was s_Bradyrhizobium guangdongense, whereas c_Alphaproteobacteria, o_Rhizobiales, f-Bradyrhizobiaceae and g_Bradyrhizobium dominated in the HS soil.

3.5.2. Nitrogen-Fixing Bacteria Community Richness and Diversity in Different Treatments

Plant types significantly affected soil nitrogen-fixing bacteria community OTUs, richness and diversity indices (p ≤ 0.05, Figure 6). The ZS soil bacterial richness was found to be significantly greater than that of LL, HS and BS samples (p ≤ 0.05). However, the bacterial diversity of LL soil was found to be significantly lower than that of the others (p ≤ 0.05). The OTUs were ranked in the following order: HS > ZS > LL > BS, with values of about 1188, 1183, 1037 and 920, respectively. At the phylum level, ZS, LL, HS and BS samples exhibited a total of 12 nitrogen-fixing bacteria (Figure 6). The proportion of Pseudomonas in all treatments was the highest, accounting for more than 50% of bacteria in the LL and HS soils. After planting ZS, LL and HS, the abundances of Pseudomonadota and Bacillota were increased, while the abundances of Thermodesulfobacteriota and Nitrospirota were decreased. At the genus level, planting ZS, LL and HS increased the relative abundance of Bradyrhizobium, Bacillus and Vitreoscilla and decreased the relative abundance of Geoalkalibacter and Desulforhopalus. The LL soil relative abundance of Motiliproteus was higher than that of ZS and HS soils, while the relative abundance of Paraburkholderia in HS and Geomonas in ZS samples were higher than in the others. The impact of significantly different species is shown in Figure 6. The LEfSe analysis identified three abundantly differential clades in the ZS soil, eight in the LL soil, nine in the HS soil and ten in the bare soil. The most abundant taxa at the genus level were Geomonas in ZS, Bradyrhizobium and Motiliproteus in LL, Bacillus and Paraburkholderia in HS and Geoalkalibacter and Desulfuromonas in BS.

3.6. Correlation and Redundancy Analysis

3.6.1. RDA

For autotrophic microbes (Figure 7A), Thiomonas in BS; Symbiodinium, Sinorhizobium and Synechococcus in ZS soil; and Bradyrhizobium in LL and HS soils play an important role. Bradyrhizobium was positively correlated with SOC, DOC, ROC, NO3− and TN. For nitrogen-fixing microbes (Figure 7B), Desulfuromonas and Sulfurivermis in BS, Paraburkholderia and Geomonas in ZS and Motiliproteus in LL samples play an important role. Bacillus, Vitreoscilla and Bradyrhizobium showed a highly significant correlation with SOC, DOC, ROC, DON, NO3−, AN and TN.

3.6.2. Correlation Analysis

The soil pH and salinity were significantly positively correlated with Na+ content (p ≤ 0.05, Figure 7C). Significant positive correlations were found between WSA, SOC, DON, ROC, TN, AN, NO3−-N, DON, AKP, UE, SC and ALPT activity (p ≤ 0.001). The richness and diversity of autotrophic microbes were significantly correlated with WSA, SOC, DON, ROC, TN, AN, NO3−-N, DON, AKP, UE, SC and ALPT activity (p ≤ 0.05). The richness of nitrogen-fixing microbes showed a significant correlation with SOC, DON, ROC, AN, DON and ALPT activity. The diversity of nitrogen-fixing microbes were significantly correlated with ALPT activity.
The analysis showed that rhizosphere soil metabolites had a significant impact on the soil environment (Figure S2). Specifically, hydrocarbons had a positive correlation with the OTUs of cbbL and nifH, while aromatic hydrocarbons had a negative correlation. The oxide, aromatic hydrocarbon and amide contents were positively correlated with soil carbon, while hydrocarbons and alcohol were negatively correlated with soil carbon. Aromatic hydrocarbons were positively correlated with TN, while acids/esters and ketones were negatively correlated with TN.

4. Discussion

4.1. Analysis of Soil Physical and Chemical Characteristics

Studies have shown that plants have different ameliorating effects on soils at different depths [48,49,50]. The concentration of soil carbon and nitrogen decreased with increasing soil depth. The majority of soil carbon and nitrogen are stored in the surface layer of soils with shrubs and trees (0–20 cm). This mainly occurs for the following reasons: on the one hand, the accumulation of soil litter and root growth promote the accumulation of organic matter in the surface soil [51,52]. On the other hand, the above-ground litter input is only partially transferred to the deep soil, and the decomposition rate decreases with depth [53].
In our study, we found that planting trees and shrubs significantly improved soil quality. The accumulation of organic matter in litter and the decomposition of roots release weak acid, which results in a decrease in soil pH value [54]. The increase in AP and AK content in the soil was related to plant litter. The decomposition of plant litter promotes an increase in phosphorus and potassium in the soil [55,56]. Planting ZS and LL can significantly reduce the salt and sodium content in the soil (Figure 1). On the one hand, the growth of plant roots cuts the capillaries in the soil, preventing salt from rising from deeper in the soil and reducing salt accumulation in the topsoil [35]. On the other hand, plants absorb a portion of the toxic salt ions, especially Na ions, to maintain osmotic balance [57].
Soil organic carbon is the most extensive organic carbon reservoir and plays a vital function in the worldwide carbon cycle [58]. Mixed trees and shrubs can hold soil water, improve vegetation growth and increase soil organic carbon storage [59]. In our study, planting ZS, LL and HS increased soil carbon and nitrogen content (Figure 2 and Figure 3). Plant biomass and litter increased, resulting in the accumulation of soil C and N inputs [60,61]. In addition, the increase in soil organic carbon was related to active organic carbon (ROC and DOC) and soil aggregates (Figure 7C). As a part of soil organic carbon, active organic carbon can be directly utilized by plants and microorganisms. Increasing the content of active organic carbon can enhance the carbon supply capacity of soil [62]. After planting LL, the stability of soil aggregates was significantly improved. Plants can reduce surface erosion by mulching with litter [63], and root exudates acting as binding agents [64] to promote aggregate stability. Soil macroaggregate content and aggregate stability are conducive to the physical protection of soil organic carbon and the reduction in carbon loss [65].
The composition and content of soil nitrogen fractions are closely related to the potential of the soil to supply nitrogen [66,67,68]. The amount of DON and AN increased significantly after planting. The addition of litter significantly increased the soil DON and AN pool and enhanced the soil nitrogen supply capacity [61,69]. Compared with that in the bare soil, the content of NH4+-N decreased but that of NO3−-N increased in the planted soil (Figure 3). On the one hand, plant selective uptake leads to a decline in soil ammonium nitrogen content [70]. On the other hand, the decrease in soil pH will lead to the escape of soil ammonium nitrogen in the state of ammonia and reduce the content of ammonium nitrogen in soil [71].

4.2. Analysis of Soil Enzyme Activity and Autotrophic and Nitrogen Fixation Microorganisms

Soil enzyme activity can be used for assessing soil biodiversity, productivity and microbial potential, which influence the efficiency of soil nutrient cycling and utilization [72]. Sucrase converts soil carbohydrates [73], urease acts on carbon–nitrogen bonds in organic matter [74], and catalase is involved in the redox ability of the soil [75]. The activity of these enzymes is essential for the carbon cycle in soil ecosystems [76]. The conversion of mineral nitrogen components in the soil is facilitated by nitrate reductase [77]. Higher nitrate reductase enzyme activity indicates that the tree continues to assimilate the nitrogen inputs [78] and suggests higher nitrogen acquisition abilities [79]. Correlation analysis showed that protease activity was positively correlated with TN (Figure 7C). Protease activity is the rate-limiting step in SON mineralization [80,81]. The activity of the PT enzyme increased, indicating that planting trees and shrubs improved soil nitrogen mineralization. Root exudates can serve as a source of soil organic carbon (SOC) and be stabilized through various mechanisms, resulting in long-term storage [82]. A past study revealed that root exudate C can decrease the likelihood of nitrogen loss by stimulating microbial nitrogen fixation and slightly reducing nitrification [83].
The abundance and diversity of autotrophic and nitrogen-fixing soil microorganisms increased, and the composition of the microbial community changed after planting (Figure 5 and Figure 6). Plants shape the surrounding microbiome by secreting exudates [84]. Soil nutrients are a key parameter affecting the nitrogen-fixing bacterial community, and the increase in soil nutrient content will lead to a substantial increase in nifH gene abundance [85]. Bradyrhizobium and Bacillus abundance increased after planting trees and shrubs (Figure 5F and Figure 6F). Bradyrhizobium is a facultative chemoautotroph that utilizes CO2 as its primary carbon source [86] and has the potential to mitigate the effects of salt stress on plants [87]. Using Bacillus inoculants on farmland reduces nitrogen and ammonia gas emissions [88]. Desulfuromonas and Geoalkalibacter were found to be suitable for living in high-salt environments [89,90]. Planting reduces the salt content of the soil and is not suitable for the survival of these bacteria. This may be the reason why the relative abundance of Desulfuromonas and Geoalkalibacter was reduced after planting (Figure 5F and Figure 6F). RDA revealed the critical role of microorganisms in regulating the carbon and nitrogen cycles (Figure 7A,B). The abundance of autotrophs and nitrogen-fixing microorganisms was found to be significantly correlated with DOC, AN, DON and nitrate nitrogen. This may be due to the fact that these activated carbon and nitrogen compounds are derived mainly from microbial metabolic decomposition [91]. In conclusion, microorganisms play an important role in regulating soil carbon and nitrogen content.

4.3. Comparison Analysis of the Difference of the Effects of Three Species on Soil

Soil aggregate stability and sucrase activity were observed to be significantly greater in LL than in ZS and HS soils (Table S1 and Figure 4). Plant roots can enhance the stability of soil aggregates by secreting exudates, metabolites and organic inputs, thereby providing physical protection for SOC [92]. Soil enzyme activity is controlled by soil pH [93]. The planting of LL results in a decrease in soil pH, which subsequently improves the soil environment and enhances soil enzyme activity. The increase in soil enzyme activity had a positive effect on the increase in soil carbon and nitrogen content (Figure 7C). This may be the reason why the SOC content of the LL surface layer (0–20 cm) soil was significantly higher than that of ZS and HS soils (Figure 2). Soil DOC is a potential source of C fixation in deep soil [94]. The DOC of HS soil was significantly higher than that of LL and ZS at depths of 20–40 cm (Figure 2). Compared with ZS and LL, HS has a longer taproot, which has a greater impact on deep soil, and the secretion of root metabolites increases the content of DOC in soil. Plants provide the main pathway for nitrogen into the soil, and the type of plant that regulate soil nitrogen plays a vital role. In this study, the LL surface soil total nitrogen content was significantly higher than that of ZS and HS soils (Figure 3). Compared with evergreen trees such as LL, deciduous trees (ZS and HS) require more nitrogen, resulting in a decrease in soil nitrogen content [95]. The correlation analysis revealed a significant positive correlation between soil nitrogen components and soil organic carbon (Figure 7C). The content of organic carbon in soil is a significant factor influencing the growth of nitrogen-fixing bacteria in soil [96]. The increase in soil organic carbon promotes the increase in soil nitrogen, which may be another reason for the high total nitrogen content of LL soil.
Deciduous trees (ZS) require a higher input of nitrogen and meet the nitrogen requirements of plants by increasing the abundance of nitrogen-fixing microorganisms [95]. This may be the reason for the highest richness and diversity of soil nitrogen-fixing microorganisms in ZS. This study showed that the soil autotrophic and nitrogen-fixing microbial communities differed among ZS, LL and HS (Figure 5 and Figure 6). In nifH sequencing, the dominant genera for each sample were Geomonas in ZS, Bradyrhizobium and Motiliproteus in LL and Bacillus and Paraburkholderia in HS (Figure 6). Soil organic matter derived from plant exudates and fine root secretions can function as a nutrient and energy source for microbes, promoting the growth and development of specific microbial communities [97,98]. Bis (2-ethylhexyl) phthalate (DEHP) was the most abundant metabolite in the ZS rhizosphere. It has been demonstrated to inhibit the microbial utilization of carbon sources and to affect the activity of key enzymes, thus negatively affecting the rate-limiting step of nitrification [99]. Dibutyl phthalate was the most abundant metabolite in the LL rhizosphere, which can reduce microbial community abundance and change community composition in salinized soil, making the community related to carbon cycling the key community [100]. Furthermore, the soil environment, including pH, salt content and nutrient content, also exerts an influence on the composition of the soil microbial community [101].

5. Conclusions

This study demonstrated that the planting of ZS, LL and HS had a positive impact on soil quality. This was evidenced by an increase in soil carbon and nitrogen content, as well as enhanced soil enzyme activity and microbial abundance and diversity. The observed increase in soil aggregate stability and enzyme activity contributed to the higher levels of soil carbon and nitrogen. Furthermore, the rise in soil organic carbon was associated with an increase in dissolved organic carbon and resistant organic carbon. Correlation analysis revealed a significant positive relationship between soil nitrogen components and soil organic carbon, indicating a coupling between them, which may be related to plants and soil microorganisms. Additionally, RDA analysis indicated that soil microorganisms were significant factors affecting both soil carbon and nitrogen content. Therefore, it can be concluded that soil microorganisms play a crucial role in the carbon and nitrogen cycle of saline soils by influencing their content levels.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy14102331/s1, Figure S1: Soil metabolites in the rhizosphere. Note: ZS: Zelkova serrata, LL: Ligustrum lucidum, HS: Hibiscus syriacus; Figure S2: Correlation analysis of soil metabolites in the rhizosphere. Note: ZS: Zelkova serrata, LL: Ligustrum lucidum, HS: Hibiscus syriacus, BS: bare soil, Ca2+, Mg2+, K+ and Na+: extractable ion contents, CAT: catalase enzyme activity, AKP: alkaline phosphatase enzyme activity, UE: urease enzyme activity, SC: sucrase enzyme activity, CL: cellulase enzyme activity, NR: nitrate reductase enzyme activity, ALPT: alkaline protease enzyme activity; Table S1: Soil aggregate size distribution (%) and stability across different plant species and soil depths 30 cm from main trunk; Table S2: Composition and content of root metabolites in different plant types. File S1: Detailed methods of microbial sequencing analysis and other indicators.

Author Contributions

T.S., X.Y. and X.L. designed the study. X.Y., Z.L., K.J. and T.S. participated in the experiment. X.Y. and T.S. drafted the original manuscript. Y.Z. and Z.Z. proofread the article. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by funding from the following projects: the National Natural Science Foundation of China (32301433), Natural Science Foundation of Jiangsu Province Programs funded by Jiangsu Provincial Department of Science & Technology (BK20230987), China Postdoctoral Science Foundation Programs funded by China Postdoctoral Science Foundation (2023M741739), Excellent postdoctoral program of Jiangsu province Programs funded by Jiangsu Provincial Department of Human Resources & Social Security (2023ZB509), Jiangsu Province Science and Technology Special Projects for Key Social Development Programs (BE2023824) and National Key Project of Scientific and Technical Supporting Programs funded by Jiangsu Provincial Department of Science & Technology (BE2022304).

Data Availability Statement

Data are contained within the article and supplementary materials.

Conflicts of Interest

There are no competing financial interests associated with the publication of this article.

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Figure 1. Soil characteristics across different plant species, soil depths and horizontal distances from the plant main trunks (d). Soil pH (A), salinity (B), soil available potassium (C) and phosphorus (D); water-extractable soil concentrations of Ca2+ (E), K+ (F), Mg2+ (G) and Na+ (H). ZS: Zelkova serrata, LL: Ligustrum lucidum, HS: Hibiscus syriacus, BS: bare soil. Different lowercase letters above the bars indicate significant differences among plant species at the same soil depth (p ≤ 0.05), whereas capital letters indicate significant differences among soil depths for the same plant species (p ≤ 0.05). Data are means ± standard error (n = 3).
Figure 1. Soil characteristics across different plant species, soil depths and horizontal distances from the plant main trunks (d). Soil pH (A), salinity (B), soil available potassium (C) and phosphorus (D); water-extractable soil concentrations of Ca2+ (E), K+ (F), Mg2+ (G) and Na+ (H). ZS: Zelkova serrata, LL: Ligustrum lucidum, HS: Hibiscus syriacus, BS: bare soil. Different lowercase letters above the bars indicate significant differences among plant species at the same soil depth (p ≤ 0.05), whereas capital letters indicate significant differences among soil depths for the same plant species (p ≤ 0.05). Data are means ± standard error (n = 3).
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Figure 2. Total soil organic carbon (SOC) (A) and active organic carbon components (DOC and ROC) (B,C) across different plant species, soil depths and horizontal distances (d) from the plant main trunks. DOC: dissolved organic carbon, ROC: readily oxidizable organic carbon, ZS: Zelkova serrata, LL: Ligustrum lucidum, HS: Hibiscus syriacus, BS: bare soil; d: horizontal distance from the plant main trunk. Different lowercase letters indicate significant differences among vegetation types at the same soil depths (p ≤ 0.05), whereas capital letters indicate significant differences among soil depths within the same vegetation types (p ≤ 0.05). Data are shown as means ± standard error (n = 3).
Figure 2. Total soil organic carbon (SOC) (A) and active organic carbon components (DOC and ROC) (B,C) across different plant species, soil depths and horizontal distances (d) from the plant main trunks. DOC: dissolved organic carbon, ROC: readily oxidizable organic carbon, ZS: Zelkova serrata, LL: Ligustrum lucidum, HS: Hibiscus syriacus, BS: bare soil; d: horizontal distance from the plant main trunk. Different lowercase letters indicate significant differences among vegetation types at the same soil depths (p ≤ 0.05), whereas capital letters indicate significant differences among soil depths within the same vegetation types (p ≤ 0.05). Data are shown as means ± standard error (n = 3).
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Figure 3. Comparison of carbon and nitrogen components in bare soil and planted soil. TN: total nitrogen (A), DON: organic nitrogen components (B), MBN: microbial biomass nitrogen (C), AN: alkali-hydrolyzable nitrogen (D), NH4+-N: ammonium nitrogen (E), NO3−-N: nitrate nitrogen (F). ZS: Zelkova serrata soil, LL: Ligustrum lucidum soil, HS: Hibiscus syriacus soil, BS: bare soil. Different lowercase letters indicate significant differences among vegetation types at the same soil layers (p ≤ 0.05), and the analysis of significant differences was performed using the t-test (*, p ≤ 0.05; **, p ≤ 0.01). Data are presented as means ± standard error (n = 3).
Figure 3. Comparison of carbon and nitrogen components in bare soil and planted soil. TN: total nitrogen (A), DON: organic nitrogen components (B), MBN: microbial biomass nitrogen (C), AN: alkali-hydrolyzable nitrogen (D), NH4+-N: ammonium nitrogen (E), NO3−-N: nitrate nitrogen (F). ZS: Zelkova serrata soil, LL: Ligustrum lucidum soil, HS: Hibiscus syriacus soil, BS: bare soil. Different lowercase letters indicate significant differences among vegetation types at the same soil layers (p ≤ 0.05), and the analysis of significant differences was performed using the t-test (*, p ≤ 0.05; **, p ≤ 0.01). Data are presented as means ± standard error (n = 3).
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Figure 4. Soil enzyme (catalase (A), alkaline phosphatase (B), urease (C), sucrase (D), cellulase (E), nitrate reductase (F), and alkaline protease (G)) activities across different vegetation types. ZS: Zelkova serrata, LL: Ligustrum lucidum, HS: Hibiscus syriacus, BS: bare soil. Different lowercase letters indicate significant differences among vegetation types at the same soil depths (p ≤ 0.05), and the analysis of significant differences was performed using the t-test (*, p ≤ 0.05; **, p ≤ 0.01). Data are presented as means ± standard error (n = 3).
Figure 4. Soil enzyme (catalase (A), alkaline phosphatase (B), urease (C), sucrase (D), cellulase (E), nitrate reductase (F), and alkaline protease (G)) activities across different vegetation types. ZS: Zelkova serrata, LL: Ligustrum lucidum, HS: Hibiscus syriacus, BS: bare soil. Different lowercase letters indicate significant differences among vegetation types at the same soil depths (p ≤ 0.05), and the analysis of significant differences was performed using the t-test (*, p ≤ 0.05; **, p ≤ 0.01). Data are presented as means ± standard error (n = 3).
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Figure 5. The results of cbbL sequence analysis. Panels (A–C) show bacterial richness indices, and panel (D) shows the bacterial diversity index. For each parameter, different lowercase letters indicate significant differences (p ≤ 0.05). Panel (E) shows a Venn diagram. Panel (F) shows the relative abundance of cbbL-carrying bacterial phyla, and panel (G) shows the relative abundance of cbbL-carrying bacterial taxa. Panel (H) displays a histogram of the LDA distribution based on LEfSe analysis of classification information. ZS: Zelkova serrata, LL: Ligustrum lucidum, HS: Hibiscus syriacus, BS: bare soil.
Figure 5. The results of cbbL sequence analysis. Panels (A–C) show bacterial richness indices, and panel (D) shows the bacterial diversity index. For each parameter, different lowercase letters indicate significant differences (p ≤ 0.05). Panel (E) shows a Venn diagram. Panel (F) shows the relative abundance of cbbL-carrying bacterial phyla, and panel (G) shows the relative abundance of cbbL-carrying bacterial taxa. Panel (H) displays a histogram of the LDA distribution based on LEfSe analysis of classification information. ZS: Zelkova serrata, LL: Ligustrum lucidum, HS: Hibiscus syriacus, BS: bare soil.
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Figure 6. The results of nifH sequence analysis. Panels (A–C) show bacterial richness indices, and panel (D) shows the bacterial diversity index. For each parameter, different lowercase letters indicate significant differences (p ≤ 0.05). Panel (E) displays a Venn diagram. Panel (F) shows relative abundance of nifH-carrying bacterial phyla and genera. Panel (G) displays a histogram of LDA distribution based on LEfSe analysis of classification information. The bar chart in panel represents the magnitude of these effects, with a threshold value of 4. The linear discriminant analysis effect size (LEfSe, panel (H)) identified the significant difference in taxa abundance. The taxa with significantly different relative abundance are represented by colored dots (p < 0.05); the non-significantly affected taxa are not shown in the cladogram. The dots from the center to the outer sphere represent the phylum, class, order, family and genus levels. Each dot has an effect size LDA score > 4.0.
Figure 6. The results of nifH sequence analysis. Panels (A–C) show bacterial richness indices, and panel (D) shows the bacterial diversity index. For each parameter, different lowercase letters indicate significant differences (p ≤ 0.05). Panel (E) displays a Venn diagram. Panel (F) shows relative abundance of nifH-carrying bacterial phyla and genera. Panel (G) displays a histogram of LDA distribution based on LEfSe analysis of classification information. The bar chart in panel represents the magnitude of these effects, with a threshold value of 4. The linear discriminant analysis effect size (LEfSe, panel (H)) identified the significant difference in taxa abundance. The taxa with significantly different relative abundance are represented by colored dots (p < 0.05); the non-significantly affected taxa are not shown in the cladogram. The dots from the center to the outer sphere represent the phylum, class, order, family and genus levels. Each dot has an effect size LDA score > 4.0.
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Figure 7. Redundancy analysis and correlation analysis between bacterial genera and physicochemical soil properties across different plant species. (A) Redundancy analysis depicting the correlation between autotrophic bacterial genera and soil physicochemical properties. (B) Redundancy analysis depicting the correlation between nitrogen-fixing bacterial genera and soil physicochemical properties. Arrow lengths represent the strength of correlation between environmental factors and bacterial genus distribution. (C) Correlation analysis of soil physicochemical properties and microbiota across different vegetation types. The Mantel edge width represents the Mantel r-value, and edge color indicates the statistical significance. The color gradient of the Pearson correlation coefficient “r” illustrates the pairwise correlation of variables (*, p ≤ 0.05; **, p ≤ 0.01; ***, p ≤ 0.001). ZS: Zelkova serrata; LL: Ligustrum lucidum; HS: Hibiscus syriacus; BS: bare soil; Ca2+, Mg2+, K+ and Na+: extractable ion contents; CAT: catalase enzyme activity; AKP: alkaline phosphatase enzyme activity; UE: urease enzyme activity; SC: sucrase enzyme activity; CL: cellulase enzyme activity; NR: nitrate reductase enzyme activity; ALPT: alkaline protease enzyme activity. The analysis of significant differences was performed using the t-test (*, p ≤ 0.05; **, p ≤ 0.01).
Figure 7. Redundancy analysis and correlation analysis between bacterial genera and physicochemical soil properties across different plant species. (A) Redundancy analysis depicting the correlation between autotrophic bacterial genera and soil physicochemical properties. (B) Redundancy analysis depicting the correlation between nitrogen-fixing bacterial genera and soil physicochemical properties. Arrow lengths represent the strength of correlation between environmental factors and bacterial genus distribution. (C) Correlation analysis of soil physicochemical properties and microbiota across different vegetation types. The Mantel edge width represents the Mantel r-value, and edge color indicates the statistical significance. The color gradient of the Pearson correlation coefficient “r” illustrates the pairwise correlation of variables (*, p ≤ 0.05; **, p ≤ 0.01; ***, p ≤ 0.001). ZS: Zelkova serrata; LL: Ligustrum lucidum; HS: Hibiscus syriacus; BS: bare soil; Ca2+, Mg2+, K+ and Na+: extractable ion contents; CAT: catalase enzyme activity; AKP: alkaline phosphatase enzyme activity; UE: urease enzyme activity; SC: sucrase enzyme activity; CL: cellulase enzyme activity; NR: nitrate reductase enzyme activity; ALPT: alkaline protease enzyme activity. The analysis of significant differences was performed using the t-test (*, p ≤ 0.05; **, p ≤ 0.01).
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Shao, T.; Yan, X.; Ji, K.; Li, Z.; Long, X.; Zhang, Y.; Zhou, Z. Planting Trees on Sandy Saline Soil Increases Soil Carbon and Nitrogen Content by Altering the Composition of the Microbial Community. Agronomy 2024, 14, 2331. https://doi.org/10.3390/agronomy14102331

AMA Style

Shao T, Yan X, Ji K, Li Z, Long X, Zhang Y, Zhou Z. Planting Trees on Sandy Saline Soil Increases Soil Carbon and Nitrogen Content by Altering the Composition of the Microbial Community. Agronomy. 2024; 14(10):2331. https://doi.org/10.3390/agronomy14102331

Chicago/Turabian Style

Shao, Tianyun, Xiao Yan, Kenan Ji, Zhuoting Li, Xiaohua Long, Yu Zhang, and Zhaosheng Zhou. 2024. "Planting Trees on Sandy Saline Soil Increases Soil Carbon and Nitrogen Content by Altering the Composition of the Microbial Community" Agronomy 14, no. 10: 2331. https://doi.org/10.3390/agronomy14102331

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