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. 2021 Oct 14;87(21):e01366-21. doi: 10.1128/AEM.01366-21

High Salinity Inhibits Soil Bacterial Community Mediating Nitrogen Cycling

Xiang Li a, Achen Wang a, Wenjie Wan a, Xuesong Luo a, Liuxia Zheng a, Guangwen He a, Daqing Huang a, Wenli Chen a,✉, Qiaoyun Huang a,b
Editor: Ning-Yi Zhouc
PMCID: PMC8516042  PMID: 34406835

ABSTRACT

Salinization is considered a major threat to soil fertility and agricultural productivity throughout the world. Soil microbes play a crucial role in maintaining ecosystem stability and function (e.g., nitrogen cycling). However, the response of bacterial community composition and community-level function to soil salinity remains uncertain. Here, we used multiple statistical analyses to assess the effect of high salinity on bacterial community composition and potential metabolism function in the agricultural ecosystem. Results showed that high salinity significantly altered both bacterial alpha (Shannon-Wiener index and phylogenetic diversity) and beta diversity. Salinity, total nitrogen (TN), and soil organic matter (SOM) were the vital environmental factors shaping bacterial community composition. The relative abundance of Actinobacteria, Chloroflexi, Acidobacteria, and Planctomycetes decreased with salinity, whereas Proteobacteria and Bacteroidetes increased with salinity. The modularity and the ratio of negative to positive links remarkedly decreased, indicating that high salinity destabilized bacterial networks. Variable selection, which belongs to deterministic processes, mediated bacterial community assembly within the saline soils. Function prediction results showed that the key nitrogen metabolism (e.g., ammonification, nitrogen fixation, nitrification, and denitrification processes) was inhibited in high salinity habitats. MiSeq sequencing of 16S rRNA genes revealed that the abundance and composition of the nitrifying community were influenced by high salinity. The consistency of function prediction and experimental verification demonstrated that high salinity inhibited soil bacterial community mediating nitrogen cycling. Our study provides strong evidence for a salinity effect on the bacterial community composition and key metabolism function, which could help us understand how soil microbes respond to ongoing environment perturbation.

IMPORTANCE Revealing the response of the soil bacterial community to external environmental disturbances is an important but poorly understood topic in microbial ecology. In this study, we evaluated the effect of high salinity on the bacterial community composition and key biogeochemical processes in salinized agricultural soils (0.22 to 19.98 dS m−1). Our results showed that high salinity significantly decreased bacterial diversity, altered bacterial community composition, and destabilized the bacterial network. Moreover, variable selection (61% to 66%) mediated bacterial community assembly within the saline soils. Functional prediction combined with microbiological verification proved that high salinity inhibited soil bacterial community mediating nitrogen turnover. Understanding the impact of salinity on soil bacterial community is of great significance for managing saline soils and maintaining a healthy ecosystem.

KEYWORDS: soil salinization, bacterial community, network stability, community assembly, functional prediction, nitrogen cycling

INTRODUCTION

Salinization is a global problem that affects soil health and environmental quality. Over 900 million hectares of soil have been contaminated with different salinity in the world (1). Salinization causes soil compaction and influences soil fertility, reducing the crop yield and threatening food security (2–4). Saline soils are generally accompanied by high pH and electrical conductivity (EC) values. Soil with an EC value greater than 4 dS/m is defined as a saline soil (5). There are approximately 36 billion hectares of soil in China with various degrees of salinization, accounting for 4.9% of the country’s available farmland (6).

The soil microbial community plays a vital part in regulating global ecosystem functions, such as nutrient cycling, soil structure maintenance, greenhouse gas production, and environmental pollutant purification (7–10). The composition and diversity of microbial communities are under the influence of different biotic/abiotic factors (11). Salinity is recognized as a critical factor in determining bacterial communities in multiple ecosystems (12–14). Salinity could influence bacterial growth and decrease microbial biomass (15). The alpha diversity of the bacterial community decreased with increasing soil salinity, and salinity is a vital environment variable shaping bacterial community structure (16–18). High salinity in soil limits the activity of some bacteria and thereby directional selection of the salt-tolerant community (13). However, there are some inconsistent reports on the influence of salinity on soil bacterial communities. For instance, a prior study reported that the diversity of the soil bacterial community was independent of the salinity (19). Soil salinity increased the metabolic activity and functional diversity of the bacterial community (20). These conflicting results may be due to different sources of soil samples with various degrees of salinity. Hence, it is essential to assess how salinity affects soil bacterial community composition and diversity in agricultural ecosystems.

The correlation interaction and assembly processes of the microbial community are key topics in microecology (21). Unfortunately, many studies on microbial communities focus mainly on diversity and composition. The specific microbial interaction and assembly processes of the bacterial community in different salinity levels have been largely ignored. Microbial interactions within a complex environment are investigated by network analysis (22). The complexity and topology features of the network provide a new perspective for microbial composition (23–25). Besides, network analysis can offer information on how bacterial communities respond to environmental change (26). For example, climate warming increases microbial network complexity in grassland soils (27). Salinity reduces bacterial diversity but enhances network complexity in Tibetan Plateau lakes (28). Microbial community assembly processes consist of deterministic and stochastic processes (29, 30). Deterministic processes are imposed by biotic and abiotic factors, while stochastic processes are associated with drift, dispersal, birth, and death events (31). It has been identified that environmental variables (e.g., available sulfur, pH value, and soil organic matter) are the critical factors that drive microbial community assembly in the terrestrial ecosystem (32–34). Yet, it is still unclear the difference of microbial interaction and ecological assembly between low and high salinity agricultural soils.

Microbial communities are responsible for various services and functions of the ecosystem, such as carbon fixation and degradation, nitrogen cycling, phosphorus transformation, and sulfur metabolism (35–37). Nitrogen is an essential element of all living organisms and the main limiting factor of productivity in terrestrial ecosystems. Nitrogen cycling mainly includes nitrogen fixation, assimilation, ammonification, nitrification, denitrification, and anammox processes. These nitrogen transformation processes were carried out by nitrogen cycling microorganisms (38). In nature, microorganisms from the various phyla of the microbial community participated in nitrogen transformation reactions (39). Changes in the populations of nitrogen transformation microorganisms could alter nitrogen availability to crops and nitrogen loss from the ecosystem (40). Many studies have evaluated the effect of environmental variables on microbial community composition in different ecosystems (41–43). However, we still lack a predictive understanding of salinity effects on the bacterial community and specific nitrogen transformation microorganisms in salinized soils. Besides, whether the changes in bacterial community composition would influence potential ecosystem function (e.g., nitrogen transformation reactions) is still uncertain.

The Yellow River Delta faces Bohai and is the main activity area for land-sea interactions (44). Seawater erosion and seasonal flooding have led to the salinization of farmland soil in this area (45). Saline soil limits crop yields and affects the sustainable development of agriculture (46–48). The above information motivates and drives us to evaluate how salinity affects bacterial community composition and ecosystem function. We hypothesized that high salinity could affect the diversity, composition, and assembly of bacterial communities, thereby affecting the function of the ecosystem. To test our hypothesis, we collected 40 soil samples from the agricultural region of the Yellow River Delta. The abundance of nitrogen cycling functional genes was detected by quantitative PCR, while the bacterial composition was measured by high-throughput sequencing. Two ecological models and a network analysis were used to explore the assembly and interaction of the bacterial communities within the lowly and highly saline soils. Phylogenetic investigation of communities by reconstruction of unobserved states 2 (PICRUSt2) was used to predict microbial community metabolism functions. This work aimed to (i) compare the bacterial community diversity and composition within low and high salinity soils; (ii) explore how high salinity affects the association interactions and ecological assembly of the bacterial community; and (iii) assess whether high salinity will affect the metabolic function of the bacterial community, especially the key function of participating in ecosystem nutrient cycling.

RESULTS

Influence of salinity on community diversity and taxonomic composition.

A total 1,859,232 high-quality sequences were obtained from 40 soil samples within the Yellow River Delta and grouped into 12,380 operational taxonomic units (OTUs). Our results showed that the alpha diversity (Shannon-Wiener index and phylogenetic diversity) of the bacterial community was significantly lower in soils with an EC of >4 dS m−1 soils compared with those with an EC of <4 dS m−1 (Fig. 1A and B) (Duncan’s test, P < 0.01). In addition, the Shannon-Wiener index (R2 = 0.521, P < 0.001) and Faith’s phylogenetic diversity (PD) (R2 = 0.123, P < 0.01) had a negative correlation with increasing salinity (Fig. 1C and D). The Mantel test showed that EC (r = 0.366, P < 0.001), SOM (r = 0.197, P < 0.01), and TP (r = 0.165, P < 0.05) were related markedly to alpha diversity indices (see Fig. S2 in the supplemental material). Furthermore, we estimated bacterial beta diversity based on Bray-Curtis distance. Results showed that high salinity soils had significantly lower community similarity than low salinity soils (Fig. 1E). Nonmetric multidimensional scaling (NMDS) ordination showed that the composition of the soil bacterial communities differed significantly between low and high salinity levels (analysis of similarities [ANOSIM], R = 0.491, P < 0.001) (Fig. 1F).

FIG 1.

FIG 1

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Bacterial community alpha/beta diversity in low salinity (LS) and high salinity (HS) soils. The Shannon-Wiener index (A) and phylogenetic diversity (B) in LS and HS soils. The relationship between soil salinity and Shannon-Wiener index (C) and phylogenetic diversity (D). (E) Bacterial community similarity (Bray-Curtis distance) in LS and HS soils. (F) Nonmetric multidimensional scaling (NMDS) ordination showing the variation of soil bacterial communities across two salinity levels. Black asterisks indicate that the alpha/beta diversity index was significantly higher in LS soils (*, P < 0.05; **, P < 0.01; ***, P < 0.001).

We found 3,298 OTUs in soils with an EC of <4 dS m−1 and 2,621 OTUs in soils with an EC of >4 dS m−1, and they shared 6,461 OTUs (Fig. 2A). Altogether, 19 bacterial phyla were detected from the 40 soil samples (see Fig. S3 in the supplemental material). Proteobacteria (19.5% to 56.7%) was the predominant phylum among soil samples, followed by Bacteroidetes (3.1% to 42.9%), Actinobacteria (1.1% to 21.3%), Chloroflexi (0.6% to 16.1%), Planctomycetes (1.0% to 14.0%), Acidobacteria (0.1% to 18.2%), and Gemmatimonadetes (0.2% to 10.1%). Proteobacteria and Bacteroidetes had enhanced relative abundances, whereas Actinobacteria, Chloroflexi, Planctomycetes, and Acidobacteria decreased with salinity (see Fig. S4 in the supplemental material). Besides, the relative abundance of taxa assigned to Cyanobacteria and Patescibacteria was unchanged with increasing salinity. Soil EC values and SOM showed a close correlation with the dominant bacterial phyla (see Table S2 in the supplemental material). The microbial community composition in high salinity soils was clearly separate from the low salinity soils (ANOSIM, R = 0.491, P < 0.001). The CAP result demonstrated that soil EC value (12.78%; F = 5.27, P < 0.001), pH (10.35%; F = 4.39, P < 0.001), SOM (8.91%; F = 3.72, P < 0.001), and TN (8.76%; F = 3.65, P < 0.001) markedly impacted the structure of the bacterial community (Fig. 2B). Of the diverse variables, EC most significantly affected the bacterial community. From the Mantel test, we also found that EC values (r = 0.519, P < 0.001) emerged as the dominant environmental factor related to bacterial community composition compared with other physicochemical variables (Fig. S2).

FIG 2.

FIG 2

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Bacterial community structure and co-occurrence network in LS and HS soils. (A) Venn diagrams of operational taxonomic unit (OTU) richness in two treatments. (B) Constrained analysis of principal coordinates (CAPSCALE) derived from Bray-Curtis dissimilarities of the community composition of sampling points based on 16S rRNA gene amplicon sequencing. Network of co-occurring bacterial genera based on Spearman correlation analysis sorted in color by phylum. A connection stands for a strong (Spearman’s r > 0.6) and significant (P < 0.01) correlation. The size of each node is proportional to the degree of the OTUs. (C) Network in soils with an EC of <4 dS m−1. (D) Network in soils with an EC of >4 dS m−1.

Impact of salinity on network topology properties and community assembly.

High salinity significantly decreased co-occurrence network complexity. The network in soils with an EC of <4 dS m−1 consisted of 131 nodes and 815 edges, while there were 56 nodes and 265 edges in soils with an EC of >4 dS m−1 (Fig. 2C and D). Besides, high salinity destroyed the network stability. The ratio of negative to positive links decreased from 0.391 in low salinity habitats to 0.053 in high salinity habitats. The modularity decreased from 1.152 in low salinity soils to 0.139 in high salinity soils, accompanying the increase of average path length from 2.785 to 3.449 (see Table S3 and Fig. S5 in the supplemental material). The network was assigned to 9 and 8 phyla within lowly and highly saline soil samples, separately. Among them, nodes in the low salinity network were mostly from Proteobacteria (49.6%), Actinobacterium (27.5%), Chloroflexi (10.7%), and Verrucomicrobia (4.6%) and in the high salinity network mostly from Proteobacteria (73.2%), Actinobacterium (10.7%), Bacteroidetes (7.1%). Pseudomonas, Sulfurimonas, Salinimicrobium, Marinobacter, and Halomonas genera were determined to be the keystone taxa according to the value of betweenness centrality.

The null model was applied to quantify the contribution of ecological assembly processes to bacterial community assembly in two salinity levels. Variable selection influenced the low salinity community (66%) more than the high salinity community (61%). Dispersal limitation occupied a larger proportion in the assembly of the high salinity community (26%) than that of the low salinity community (18%) (Fig. 3A). Consequently, deterministic rather than stochastic processes dominated bacterial community assembly both in low (64%) and high (68%) salinity soils. Meanwhile, the contributions of differentiating processes (low salinity, 87%; high salinity, 84%) imposed greater influences on bacterial community assembly than homogenizing processes (Fig. 3B). The Mantel test showed that the differences in soil salinity were significantly positively associated with β nearest taxon index (βNTI) of the bacterial community in low salinity soils (R2 = 0.100, P < 0.001) and bacterial community in high salinity soils (R2 = 0.185, P < 0.001) (Fig. 3C and D; Table 1). Based on the null model analysis, the ratio of sorting to dispersal limitation was higher in the high salinity community (3.778) than that in the low salinity community (2.461) (see Fig. S6A in the supplemental material). The neutral model further demonstrated the results with a lower community immigration rate (m value) in the high salinity bacterial community (m = 0.035) than in the low salinity bacterial community (m = 0.139) (Fig. 3E and F and Fig. S6B). The metacommunity size times immigration (Nm) value was higher for bacterial community in the low salinity soils (Nm = 4,142) than that in the high salinity soils (Nm = 1,178). The null model and neutral model together revealed that the high salinity bacterial community was more environmentally constrained.

FIG 3.

FIG 3

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The bacterial community assembly processes in saline soils. (A and B) The percentage of turnover in soil bacterial community assembly. Deterministic processes, homogeneous + variable selection; stochastic processes, dispersal limitation + homogenizing dispersal + undominated processes; homogenizing, homogeneous selection + homogenizing dispersal; differentiating, variable selection + dispersal limitation. (C and D) Relationships between β-nearest taxon index (βNTI) and differences in soil salinity. (E and F) Fit of Sloan’s neutral model for analysis of microbial community assembly. The solid blue lines indicate the best fit to the neutral model, and the dashed blue lines represent 95% confidence intervals around the model prediction. OTUs that occur more or less frequently than predicted by the neutral community model are shown in different colors. R2 indicates the fit to this model, while the m value indicates community immigration rate.

TABLE 1.

Mantel test of environment factors against the βNTI of low salinity and high salinity bacterial communities in agricultural soils

Factora Mantel test result byb:
Low salinity High salinity
TS −0.157 0.092
TC −0.029 0.197
TN 0.194 0.280
C/N 0.312* −0.042
pH 0.493** 0.158
EC 0.524*** 0.590***
SOM 0.103 0.204
TP 0.131 0.303*
AP 0.227 −0.116
TK −0.127 0.144
AK −0.079 0.211
NH4 −0.152 0.104
NO3 0.086 −0.216
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a

TC, total carbon; TN, total nitrogen; TS, total sulfur; EC, electrical conductivity; TP, total phosphorus; AP, available phosphorus; TK, total potassium; AK, available potassium; SOM, soil organic matter; NH4, NH4+-N content; NO3, NO3-N content.

b

*, P < 0.05; **, P < 0.01; ***, P < 0.001.

Effect of salinity on bacterial community metabolism functions and nitrogen cycling.

To test our question of whether or not high salinity will affect the bacterial community metabolism function, we adopted the PICRUSt2 functional prediction analysis. At KEGG level 2, we found nitrogen metabolism and sulfur metabolism were significantly higher in low salinity soils than high salinity soils (Fig. 4A). Considering that nitrogen cycling occupies a central position in the biogeochemical process of global ecosystems, we first paid attention to nitrogen metabolism. A total of 23 nitrogen metabolism functions were collected at KEGG level 3, and we found most of them were affected by high salinity (Fig. 4B). The relative abundance of key functions that participate in ammonification (e.g., urease [EC 3.5.1.5]), nitrogen fixation (e.g., nitrogenase [EC 1.18.6.1]), and nitrification (e.g., ammonia monooxygenase [EC 1.13.12.-] and hydroxylamine reductase [EC 1.7.99.1]) was significantly lower in high salinity soils (Duncan’s test, P < 0.05). Only one metabolism function involved in denitrification (e.g., nitrite reductase [NO forming] [EC 1.7.2.1]) was significantly inhibited by high salinity. Besides, the relative abundance of arginase (EC 3.5.3.1) and beta-N-acetylhexosaminidase (EC 3.2.1.52) was lower in high salinity soils than that of low salinity soils (Duncan’s test, P < 0.05).

FIG 4.

FIG 4

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Function differences predicted by PICRUSt2 according to the 16S rRNA gene sequencing data in LS and HS soils. (A) Function differences associated with multinutrient metabolism in LS and HS soils at KEGG level 2. (B) Function differences associated with nitrogen metabolism in LS and HS soils at KEGG level 3. Black asterisks indicate that the relative abundance was significantly higher in LS soils (*, P < 0.05; **, P < 0.01; ***, P < 0.001).

Based on high-throughput sequencing of the 16S rRNA gene, nitrifiers were picked out to evaluate the response of the nitrifying community to soil salinity (49, 50). The relative abundance of ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) decreased from 0.015% and 0.479% in low salinity soils to 0.012% and 0.081% in high salinity soils, respectively (Fig. 5A and B). Within the AOB, the Nitrosomonas (74.39 to 83.53%), Nitrosospira (2.35 to 9.76%), and Nitrosococcus (14.12 to 15.85%) were detected across all soil samples (Fig. 5C). As for NOB, the Nitrospira, Nitrococcus, Nitrotoga, and Nitrospina were detected in saline soils. The relative abundance of Nitrospira occupied 93.67% and 76.04% of all the NOB phylotypes in low and high salinity soils, separately. On the contrary, the relative abundance of Nitrococcus increased from 2.42% in low salinity soils to 17.66% in high salinity soils (Fig. 5D). The CAP results also showed that salinity was the crucial factor shaping nitrifying community composition. Furthermore, we observed a significant negative correlation between soil salinity differences and nitrifier community similarity (r = −0.409, P < 0.001) (Fig. 5E and F).

FIG 5.

FIG 5

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The abundance and composition of nitrifying community in LS and HS soils. Proportional changes of nitrifying populations of AOB (A) and NOB (B) based on differences in 16S rRNA genes as identified by high-throughput sequencing of total microbial communities. Proportional changes of nitrifying phylotypes of AOB (C) and NOB (D) in LS and HS soils. (E) Constrained analysis of principal coordinates (CAPSCALE) derived from Bray-Curtis dissimilarities of the nitrifying community composition. (F) The relationship between salinity differences and nitrifiers community similarity (Bray-Curtis distance).

Subsequently, we selected some functional genes and related enzyme activities for verification. The quantitative PCR (qPCR) results showed that the abundance of key nitrogen function genes, such as ammonia-oxidizing archaea (AOA) amoA, AOB amoA, Nitrospira nxrB, and nirK, was significantly affected by high salinity (Fig. 6). Moreover, the potential ammonia oxidation activity was decreased from 0.315 in low salinity soils to 0.245 μg NO2-N g−1 soil h−1 in high salinity soils. The potential nitrite oxidation activity decreased from 0.303 in low salinity soils to 0.103 μg NO2-N g−1 soil h−1 in high salinity soils. Finally, we drew a schematic diagram of the effect of salinity on the metabolic function of nitrogen cycling (Fig. 6M). The results showed that the urease, nitrogenase, ammonia monooxygenase, hydroxylamine reductase, and nitrite reductase were significantly inhibited by high salinity (Duncan’s test, P < 0.05). High salinity also slightly decreased other metabolism functions (e.g., hydroxylamine oxidase, nitric-oxide synthase, and nitric-oxide reductase), but the statistics were not significant (Duncan’s test, P > 0.05).

FIG 6.

FIG 6

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Salinity effects on functional genes involved in nitrogen cycling. (A to J) The absolute abundance of key nitrogen cycling function genes in LS and HS soils (qPCR data). The nxrA and nxrB indicated Nitrobacter nxrA and Nitrospira nxrB genes, respectively. (K and L) Potential ammonia oxidation activity and nitrite oxidation activity in LS and HS soils. (M) The percentage changes in the normalized relative abundance associated with nitrogen cycling genes compared with low salinity soils (function prediction data at KEGG level 3). The percentage change value in blue means the decrease by comparison between LS and HS soils. The N in nirK-N, norB-N, and nosZ-N indicated denitrification function genes carried by nitrifying microorganisms (nitrifier denitrification). Black asterisks indicate that the relative abundance was significantly higher in LS soils (*, P < 0.05; **, P < 0.01; ***, P < 0.001).

DISCUSSION

Salinity shapes bacterial community composition, interaction, and assembly.

In this study, we collected soil samples from natural agricultural saline areas to explore the effect of salinity on bacterial community composition, interaction, and assembly. Our results showed that the alpha diversity (e.g., Shannon-Wiener index and phylogenetic diversity) of the bacterial community showed a reduced trend as salinity increased (0.22 to 19.98 dS m−1). This result might be attributed to the fact that some bacterial species were not well adapted to a high salinity environment. Selection pressure from high salinity made them become dormant or die, thus decreasing bacterial community alpha diversity (12, 51). Besides, high salinity remarkably reduced soil nutrient availability, including TN, SOM, AP, and NH4+-N, which in turn affected the alpha diversity of the bacterial community (52). We also found that salinity variation induced beta diversity changes of the soil bacterial community. High salinity soils had lower bacterial community similarity that indicated that salinity strongly shifts the bacterial community composition. Proteobacteria, Bacteroidetes, Actinobacteria, Chloroflexi, Planctomycetes, Acidobacteria, and Gemmatimonadetes are the most dominant phylum in the study area, which agreed with prior reports in black soils (53), in agroforestry systems (54), and in saline agricultural soil (55). The relative abundance of Actinobacteria, Chloroflexi, Gemmatimonadetes, and Acidobacteria decreased with salinity, whereas Proteobacteria and Bacteroidetes bacteria displayed the opposite trend. Most of the Proteobacteria members were well adapted to the saline agriculture soils, which explained their wide existence and metabolic diversity. Gammaproteobacteria can use various organic matter types as energy at very low levels of nutrients to survive in a high salinity environment (56, 57). Within Gammaproteobacteria, some Pseudomonas members are likely resistant to high salinity (58). Bacteroidetes are aerobic chemoorganotrophic organisms and could cooperate with other bacteria to use the substrates in saline soil (59, 60). An earlier study revealed that salinity-tolerant Bacteroidetes had been widely located in brackish water and freshwater sediment (61). Therefore, these results confirmed the widespread distribution of Proteobacteria and Bacteroidetes in hypersaline habitats.

Previous studies discovered that high salinity increased the complexity of the microbial community network in cadmium (Cd)-contaminated soils (62) and in Tibetan Plateau lakes (28). However, our network analysis showed that high salinity obviously weakened the bacterial association interaction in agricultural soils. High salinity altered microbial network topological properties (nodes, edges, modularity, and the ratio of negative to positive links), indicating that high salinity filtered the bacterial community and decreased the network stability (63). Interestingly, we found the Proteobacteria phylum accounted for a considerable proportion in a high salinity network (73.2%) compared with that in a low salinity network (50.4%). The result might be explained by the fact that some Pseudomonas bacteria are halophilic or halotolerant (64, 65). According to Spearman’s correlation and betweenness centrality scores, the Pseudomonas, Sulfurimonas, Salinimicrobium, Marinobacter, and Halomonas genera were identified as keystone taxa. These keystone taxa might play an essential role in maintaining the structure links, information transmission, and ecological functions of the entire ecological communities (66, 67).

Estimating the contribution of stochastic and deterministic processes to microbial community assembly is important in microecology (29, 32, 34). We observed that variable selection, which belongs to the deterministic processes, played a key role in shaping the assembly of the bacterial community in saline soils. The result in accordance with previous research revealed that deterministic assembly governed the microbial assembly in desert ecosystems (43) and in saline-sodic soils (68). Stochastic assembly processes corresponded to high soil nutrient conditions, while deterministic assembly processes occurred in lower nutrient conditions. High salinity significantly decreased resource availability, and the remarkable alterations in resource availability imposed variable selection (32). According to the null model analysis, the dispersal limitation fraction increased within the highly saline habitats. The neutral model analysis further revealed that the bacterial community immigration rate (m value) in high salinity soils was lower than that in low salinity soils, indicating that high salinity limited the bacterial community dispersal ability. This result might be attributed to high salinity increasing soil bulk density, decreasing soil porosity, and destroying soil structure (69). Changes in soil physical properties might prevent bacterial communities from migrating to new locations (70, 71). We found a positive correlation between βNTI and salinity differences. However, we cannot exclude the influence of spatial factors on the assembly processes because many studies have shown that spatial variation plays an important role in the microbial community assembly (43, 72). Further research should focus on the influence of spatial factors on the assembly of the microbial community in salinized soils. In conclusion, our results revealed that deterministic processes mediate bacterial community ecological assembly within lowly and highly saline soils. Our findings showed a vital connection between agricultural soil salinization and bacterial community assembly, which might affect bacterial diversity and agriculture ecosystem processes.

High salinity stress inhibited bacterial community mediating nitrogen cycling.

Linking microbial community composition to function potentials is important to improve ecosystem productivity. Here, we attempted to provide insights into the response of key metabolism function to soil salinization. A prior study using a meta-analysis reported that soil salinization promoted nitrogen mineralization but had no effect on the nitrification, denitrification, and dissimilatory nitrate reduction to ammonium in coastal ecosystems (73). On the contrary, our function prediction data showed that high salinity significantly decreased nitrogen metabolism, such as ammonification, nitrogen fixation, nitrification, and denitrification processes, in agricultural soils. Phylogenetic analysis of 16S rRNA genes showed that the abundance and composition of the nitrifying community were significantly impacted by soil salinity. This result is in line with previous studies showing that salinity is a key factor driving the nitrogen cycling in the mangrove sediments (48). Indeed, we observed that the abundance of key nitrogen cycling functional genes, such as AOA amoA, AOB amoA, Nitrospira nxrB, and nirK, was remarkedly reduced in high salinity soils. Moreover, both the potential ammonia oxidation activity and nitrite oxidation activity were inhibited by high salinity. These experimental verification results could support the function prediction points. At least three possibilities might explain this phenomenon, as follows: (i) high salinity significantly decreased the soil nutrient availability, (ii) high salinity soils harbored higher iron concentration and induced osmotic effects, or (iii) high salinity remarkedly reduced bacterial community diversity and altered community composition. First, soil nutrients, such as TN, AP, and SOM availability, play an important role in the growth and metabolism of nitrogen-transforming microorganisms. At the same time, diverse nitrogen-transforming processes (e.g., nitrification and denitrification) depend on ammonia/nitrate-nitrogen availability (38). Second, high salinity soils contain higher iron concentrations (e.g., sodium, chloride, and sulfate), which lead to damage to proteins and nucleic acids of nitrogen cycling microorganisms (74, 75). Finally, soil microbial diversity loss significantly decreased in specialized functional capacity, such as potential nitrification and denitrification processes (76, 77). Previous publications reported that Nitrospirae and Nitrospinae play an important role in nitrite oxidation. Bacteroidetes and Chloroflexi were shown to play important roles in denitrification and Planctomycetes contributed mostly to dissimilatory nitrate reduction (38, 40). Shifts in the taxonomic composition of microbial community were associated with changes in community-level function (42, 78, 79). Moreover, high salinity soils are generally accompanied by higher pH and lower oxygen concentrations (12). Oxygen availability is essential for the reproduction and growth of key nitrogen cycling microorganisms. Nitrifying microorganisms use oxygen for nitrification, while denitrifying microorganisms need anaerobic conditions to perform denitrification. Therefore, high salinity reduced resource availability and shaped the bacterial community diversity and composition, thereby affecting the potential metabolism function of the bacterial community.

The present study revealed a strong link between bacterial community composition and potential function (80–82). The consistency of function prediction and experimental verification showed that high salinity inhibited the bacterial community mediating nitrogen cycling. Therefore, our results proved that high salinity stress limited nitrogen turnover in agricultural soils. Considering the importance of soil nitrogen cycling, future research should use multiomics (e.g., metagenomic and metatranscriptomic) to reveal the effects of salinization on soil nitrogen turnover in agricultural ecosystems with a broader salinity range.

In conclusion, in this study, we investigated the response of bacterial community composition and metabolism function to soil salinity in agricultural ecosystems. Based on amplicon sequencing and multiple statistical analyses, we found that salinity significantly decreased bacterial diversity and altered bacterial community composition. High salinity weakened the correlation interaction and destabilized bacterial networks. Variable selection plays a predominant role in bacterial community assembly within lowly and highly saline agricultural soils. Function prediction and microbiological verification results demonstrated that high salinity inhibited soil bacterial community mediating nitrogen turnover. These findings will provide novel information for understanding bacterial community response to salinity change in the agricultural ecosystem.

MATERIALS AND METHODS

Soil collection and analysis.

Shengli country, located in the city of Dongying (Shandong Province, China; 37°30′N, 118°15′E), is listed as a “forbidden area” for plant cultivation (see Fig. S1 in the supplemental material). Due to its proximity to the Yellow River Delta, seawater erosion and flooding frequently occur in this area. Local farmers have long endured extremely low crop productivity. In the past few decades, the local government has adopted a series of ecological protection measures, such as “Top Agriculture Bottom Fishing” and “Using Hidden Pipe to Drain Alkaline,” to improve soil salinization. These ecological projects aimed to reduce soil salinity and improve regional environmental quality.

For the study area, its annual average precipitation and temperature were 634 mm and 14.2°C, respectively. The main local crop rotation methods are winter wheat and summer maize. We speculated that agricultural soils closer to the bank of the Yellow River had higher salinity. As a result, soils farther from the Yellow River show reduced salinity. Detailed information about soil sample collection can be found in reference 77. After sampling, the harvested soils were put on ice before being delivered to the laboratory in 24 h. Later, all samples were classified into three parts to analyze the physical and chemical properties of soil and the abundance of nitrogen cycling functional genes, along with the composition of the bacterial community. To compare the community composition and metabolism functions of bacterial communities within lowly and highly saline habitats, all samples were classified as two subsamples according to the salinity level, namely, lowly saline (EC, <4 dS m−1; n = 20) and highly saline group (EC, >4 dS m−1; n = 20). This separation corresponds generally to the classification criteria of saline and nonsaline soils (5, 13).

Soil pH and electronic conductivity (EC) were detected within the soil-water extracts of 1:5 (wt:vol) by using the pH meter and conductivity meter, respectively. The soil total sulfur (TS) content was determined using a Vario Max element analyzer (Elementar, Hanau, Germany). HF-HClO4 was added to digested total potassium (TK) and total phosphorus (TP) according to previous methods (83). A total of 0.5 mol liter−1 NaHCO3 (pH 8.5) was used to extract available phosphorus (AP), whereas 1.0 mol liter−1 NH4OAc (pH 7.0) was used to determine available potassium (AK) (84, 85). TP and AP were measured using the molybdenum-blue method, and TK and AK were detected by a flame photometer. The determination of the soil total nitrogen (TN), total carbon (TC), soil organic matter (SOM), nitrate-nitrogen (NO3-N), ammonium-nitrogen (NH4+-N), potential ammonia oxidation rate (PAO), and potential nitrite oxidation rate (PNO) was conducted according to reference 86.

DNA extraction, quantitative PCR (qPCR), and 16S rRNA sequencing.

The soil DNA kit (MP Biomedicals, CA, USA) was utilized to extract community DNA from a 0.5-g freeze-dried soil sample. Thereafter, spectrophotometry (NanoDrop 2000) and 1% agarose gel electrophoresis were conducted to determine genomic DNA content and quality. The absolute abundances of nitrogen cycling genes (including AOA amoA, AOB amoA, Nitrobacter nxrA, Nitrospira nxrB, narG, napA, nirK, nirS, qnorB, and nosZ) were quantified by qPCR using an ABI VIIA 7 real-time PCR system (Applied Biosystems, Foster City, CA, USA). Each 20-μl qPCR mixture contained 1 μl of template DNA or standard plasmid, 10 μl SYBR qPCR master mix (TaKaRa, Dalian, China), 1.0 μl of a 10 mM solution of each primer, and 7 μl nuclease-free water. The standard curve was generated using 10-fold serial dilutions of a plasmid harboring target gene fragments. A negative control was included in each run using water instead of soil DNA. Melting curve and standard agarose gel electrophoresis analyses were performed to confirm amplification specificity. Table 2 summarizes the appropriate primers and corresponding amplification efficiency.

TABLE 2.

Primers used for amplification of nitrogen cycling functional genes

Gene (organism) Primer name Amplicon size (bp) Amplification efficiency (%) Reference
amoA (AOAa) Arch-amoAF/Arch-amoAR 635 91 93
amoA (AOBb) amoA-1F/amoA-2R 491 92 94
nxrA (Nitrobacter) F1norA/R1norA 322 89 95
nxrB (Nitrospira) nxrB169f/nxrB638r 485 95 96
narG narGf/narGr 173 90 97
napA V17m/napA4r 152 92 97
nirK F1aCu/R3Cu 473 102 98
nirS cd3aF/R3cd 425 90 99
qnorB qnorB2F/qnorB5R 262 93 100
nosZ nosZ1F/nosZ1R 259 97 101
Open in a new tab
a

AOA, ammonia-oxidizing archaea.

b

AOB, ammonia-oxidizing bacteria.

PCR amplification of the 16S rRNA gene V3-V4 region was conducted using the primers Bakt_341F (5′-CCT ACG GGN GGC WGC AG-3′) and Bakt_805R (5′-GAC TAC HVG GGT ATC TAA TCC-3′) (33, 87). After purification, we sequenced PCR products on the Illumina MiSeq PE300 platform (Shanghai Personal Biotechnology, Co., Ltd., Shanghai, China). Thereafter, the raw sequence data of the 16S rRNA gene were analyzed by applying the Quantitative Insights into Microbial Ecology (QIIME) pipeline (88). Using a dissimilarity level of 3%, we clustered operational taxonomic units (OTUs) into the UPARSE pipeline.

Statistical analysis.

Bacterial alpha diversity was compared between lowly saline and highly saline soils by one-way analysis of variance (ANOVA) using SPSS 23. Spearman correlation between physicochemical properties and their correlation to species richness, diversity, and community composition were analyzed at a P value of <0.05 using the “corrplot” and “magrittr” packages in R. Bacterial community composition was visualized using nonmetric multidimensional scaling (NMDS) based on Bray-Curtis dissimilarities. The distinct and intersected OTUs of the two salinity levels were compared by building the Venn diagrams. In the meantime, a constrained analysis of principal coordinates (CAPSCALE) was conducted to explain how environmental factors affected the structure of the bacterial community. Analysis of similarities (ANOSIM) was used to test the differences in bacterial community composition between low and high salinity soils. The salinity difference was calculated by the Euclidean distance using the vegan package in R.

The co-occurrence network was constructed to show the correlation interaction of bacterial communities between lowly and highly saline soils. Moreover, OTUs occurring at a 90% frequency in one set of samples (n ≥ 18) were selected for network construction. Between two OTUs, the following thresholds were selected: a P value of <0.01 and the Spearman’s correlation coefficient (ρ) of ≥0.6 (89). The network topology properties were determined by the nodes, edges, ratio of negative to positive associations, average path length, graph density, and modularity. The co-occurrence network was visualized using Gephi 0.9.2 (https://gephi.org/). In order to evaluate how stochastic and deterministic processes contributed to community assembly, we adopted the null model approach to calculate the β nearest taxon index (βNTI) and Bray-Curtis-based Raup-Crick (RCbray) (29, 90). In brief, a βNTI of <−2 indicated a bacterial community governed by homogeneous selection, whereas a βNTI of >2 indicated that by variable selection. A |βNTI| of <2 and RCbray between >+0.95 and <−0.95 indicate dispersal limitation and homogenizing dispersal-driven bacterial community, respectively. Furthermore, we determined that a |βNTI| of <2 and |RCbray| of <0.95 indicated “undominated” assembly processes. The βNTI and RCbray were determined by the “comdistnt” function of the R “picante” package (R 4.1.0). The explanatory variables of environmental factors to microbial community assembly were assessed using the Mantel test. To further reveal the importance of stochastic processes on community assembly, we selected the neutral model to predict the relationship between taxonomic occurrence frequency and the abundance. In this model, the m represents migration rate and R2 indicates the fit to the neutral model (91). Functions were predicted by phylogenetic investigation of communities by reconstruction of unobserved states 2 (PICRUSt2) according to 16S rRNA sequence data. PISRUSt2 analysis can provide the difference of metabolism functions between low and high salinity habitats (92).

Data availability.

The raw amplicon sequence data sets for 16S rRNA genes have been deposited in the NCBI (http://www.ncbi.nlm.nih.gov) Sequence Read Archive (SRA) database under the accession number PRJNA739396.

ACKNOWLEDGMENTS

This work was supported by the National Key Research and Development Program of China (2018YFE0105600) and the National Natural Science Foundation of China (41830756).

Sequencing service was provided by Personal Biotechnology Co., Ltd., Shanghai, China.

We declare no conflicts of interest.

Footnotes

Supplemental material is available online only.

Supplemental file 1
Tables S1 to S3, Fig. S1 to S6. Download AEM.01366-21-s0001.pdf, PDF file, 0.8 MB (868.3KB, pdf)

Contributor Information

Wenli Chen, Email: wlchen@mail.hzau.edu.cn.

Ning-Yi Zhou, Shanghai Jiao Tong University.

REFERENCES

  • 1.Szabolcs I. 1989. Salt-affected soils. CRC Press, Inc. Boca Raton, FL. [Google Scholar]
  • 2.Paul D. 2013. Osmotic stress adaptations in rhizobacteria. J Basic Microbiol 53:101–110. 10.1002/jobm.201100288. [DOI] [PubMed] [Google Scholar]
  • 3.Setia R, Rengasamy P, Marschner P. 2013. Effect of exchangeable cation concentration on sorption and desorption of dissolved organic carbon in saline soils. Sci Total Environ 465:226–232. 10.1016/j.scitotenv.2013.01.010. [DOI] [PubMed] [Google Scholar]
  • 4.Nagler K, Moeller R. 2015. Systematic investigation of germination responses of Bacillus subtilis spores in different high-salinity environments. FEMS Microbiol Ecol 91:fiv023. 10.1093/femsec/fiv023. [DOI] [PubMed] [Google Scholar]
  • 5.Richards LA. 1954. Diagnosis and improvement of saline and alkali soils. United States Department of Agriculture, Washington, DC. [Google Scholar]
  • 6.National Soil Mass Screening Office. 1998. Chinese soil. China Agriculture Press, Beijing, China. [Google Scholar]
  • 7.Chaparro JM, Badri DV, Vivanco JM. 2014. Rhizosphere microbiome assemblage is affected by plant development. ISME J 8:790–803. 10.1038/ismej.2013.196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Xie Y, Wang J, Wu Y, Ren C, Song C, Yang J, Yu H, Giesy JP, Zhang X. 2016. Using in situ bacterial communities to monitor contaminants in river sediments. Environ Pollut 212:348–357. 10.1016/j.envpol.2016.01.031. [DOI] [PubMed] [Google Scholar]
  • 9.Kuzyakov Y, Horwath WR, Dorodnikov M, Blagodatskaya E. 2019. Review and synthesis of the effects of elevated atmospheric CO2 on soil processes: no changes in pools, but increased fluxes and accelerated cycles. Soil Biol Biochem 128:66–78. 10.1016/j.soilbio.2018.10.005. [DOI] [Google Scholar]
  • 10.Li Y, Sun Y, Zhang HL, Wang LF, Zhang WL, Niu LH, Wang PF, Wang C. 2019. The responses of bacterial community and N2O emission to nitrogen input in lake sediment: estrogen as a co-pollutant. Environ Res 179:108769. 10.1016/j.envres.2019.108769. [DOI] [PubMed] [Google Scholar]
  • 11.Fierer N. 2017. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat Rev Microbiol 15:579–590. 10.1038/nrmicro.2017.87. [DOI] [PubMed] [Google Scholar]
  • 12.Rath KM, Rousk J. 2015. Salt effects on the soil microbial decomposer community and their role in organic carbon cycling: a review. Soil Biol Biochem 81:108–123. 10.1016/j.soilbio.2014.11.001. [DOI] [Google Scholar]
  • 13.Rath KM, Fierer N, Murphy DV, Rousk J. 2019. Linking bacterial community composition to soil salinity along environmental gradients. ISME J 13:836–846. 10.1038/s41396-018-0313-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Gavrichkova O, Brykova RA, Brugnoli E, Calfapietra C, Cheng ZQ, Kuzyakov Y, Liberati D, Moscatelli MC, Pallozzi E, Vasenev VI. 2020. Secondary soil salinization in urban lawns: microbial functioning, vegetation state, and implications for carbon balance. Land Degrad Dev 31:2591–2604. 10.1002/ldr.3627. [DOI] [Google Scholar]
  • 15.Egamberdieva D, Renella G, Wirth S, Islam R. 2010. Secondary salinity effects on soil microbial biomass. Biol Fertil Soils 46:445–449. 10.1007/s00374-010-0452-1. [DOI] [Google Scholar]
  • 16.Héry M, Volant A, Garing C, Luquot L, Elbaz F, Gouze P. 2014. Diversity and geochemical structuring of bacterial communities along a salinity gradient in a carbonate aquifer subject to seawater intrusion. FEMS Microbiol Ecol 90:922–934. 10.1111/1574-6941.12445. [DOI] [PubMed] [Google Scholar]
  • 17.Shen WS, Ni YY, Gao N, Bian BY, Zheng SN, Lin XG, Chu HY. 2016. Bacterial community composition is shaped by soil secondary salinization and acidification brought on by high nitrogen fertilization rates. Appl Soil Ecol 108:76–83. 10.1016/j.apsoil.2016.08.005. [DOI] [Google Scholar]
  • 18.Zhao QQ, Bai JH, Gao YC, Zhao HX, Zhang GL, Cui BS. 2020. Shifts in the soil bacterial community along a salinity gradient in the Yellow River Delta. Land Degrad Dev 31:2255–2267. 10.1002/ldr.3594. [DOI] [Google Scholar]
  • 19.Zhang XY, Qi LL, Li WM, Hu BX, Dai ZX. 2021. Bacterial community variations with salinity in the saltwater-intruded estuarine aquifer. Sci Total Environ 755:142423. 10.1016/j.scitotenv.2020.142423. [DOI] [PubMed] [Google Scholar]
  • 20.Marinari S, Carbone S, Vittori Antisari L, Grego S, Vianello G. 2012. Microbial activity and functional diversity in Psamment soils in a forested coastal dune-swale system. Geoderma 173–174:249–257. 10.1016/j.geoderma.2011.12.023. [DOI] [Google Scholar]
  • 21.Mori AS, Isbell F, Seidl R. 2018. β-Diversity, community assembly, and ecosystem functioning. Trends Ecol Evol 33:549–564. 10.1016/j.tree.2018.04.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Wood SA, Gilbert JA, Leff JW, Fierer N, D'Angelo H, Bateman C, Gedallovich SM, Gillikin CM, Gradoville MR, Mansor P, Massmann A, Yang N, Turner BL, Brearley FQ, McGuire KL. 2017. Consequence of tropical forest conversion to oil palm on soil bacterial community and network structure. Soil Biol Biochem 112:258–268. 10.1016/j.soilbio.2017.05.019. [DOI] [Google Scholar]
  • 23.Shi S, Nuccio EE, Shi ZJ, He J, Zhou J, Firestone MK. 2016. The interconnected rhizosphere: high network complexity dominates rhizosphere assemblages. Ecol Lett 19:926–936. 10.1111/ele.12630. [DOI] [PubMed] [Google Scholar]
  • 24.Khan MAW, Bohannan BJM, Nüsslein K, Tiedje JM, Tringe SG, Parlade E, Barberán A, Rodrigues JLM. 2019. Deforestation impacts network co-occurrence patterns of microbial communities in Amazon soils. FEMS Microbiol Ecol 95:fiy230. 10.1093/femsec/fiy230. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Lu YM, Zhang WL, Li Y, Zhang C, Wang LF, Niu LH, Zhang HJ. 2020. Insights into the key components of bacterial assemblages in typical process units of oily wastewater treatment plants. Environ Res 180:108889. 10.1016/j.envres.2019.108889. [DOI] [PubMed] [Google Scholar]
  • 26.Feng K, Zhang ZJ, Cai WW, Liu WZ, Xu MY, Yin HQ, Wang AJ, He ZL, Deng Y. 2017. Biodiversity and species competition regulated the resilience of microbial biofilm community. Mol Ecol 26:6170–6182. 10.1111/mec.14356. [DOI] [PubMed] [Google Scholar]
  • 27.Yuan MT, Guo X, Wu LW, Zhang Y, Xiao NJ, Ning DL, Shi Z, Zhou XS, Wu LY, Ynag YF, Tiedje JM, Zhou JZ. 2021. Climate warming enhances microbial network complexity and stability. Nat Clim Chang 11:341–348. 10.1038/s41558-021-00989-9. [DOI] [Google Scholar]
  • 28.Ji M, Kong W, Yue L, Wang J, Deng Y, Zhu L. 2019. Salinity reduces bacterial diversity, but increases network complexity in Tibetan plateau lakes. FEMS Microbiol Ecol 95:fiz190. 10.1111/1462-2920.15281. [DOI] [PubMed] [Google Scholar]
  • 29.Stegen JC, Lin XG, Fredrickson JK, Chen X, Kennedy DW, Murray CJ, Rockhold ML, Konopka A. 2013. Quantifying community assembly processes and identifying features that impose them. ISME J 7:2069–2079. 10.1038/ismej.2013.93. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Dini-Andreote F, Stegen JC, van Elsas JD, Salles JF. 2015. Disentangling mechanisms that medicate the balance between stochastic and deterministic processes in microbial succession. Proc Natl Acad Sci USA 112:E1326–E1332. 10.1073/pnas.1414261112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Zhou JZ, Ning DL. 2017. Stochastic community assembly: does it matter in microbial ecology? Microbiol Mol Biol Rev 81:e00002-17. 10.1128/MMBR.00002-17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Feng YZ, Chen RR, Stegen JC, Guo ZY, Zhang JW, Li ZP, Lin XG. 2018. Two key features influencing community assembly processes at regional scale: initial state and degree of change in environmental conditions. Mol Ecol 27:5238–5251. 10.1111/mec.14914. [DOI] [PubMed] [Google Scholar]
  • 33.Tripathi BM, Stegen JC, Kim M, Dong K, Adams JM, Lee YK. 2018. Soil pH mediates the balance between stochastic and deterministic assembly of bacteria. ISME J 12:1072–1083. 10.1038/s41396-018-0082-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Jiao S, Lu YH. 2020. Abundant fungi adapt to broader environmental gradients than rare fungi in agricultural fields. Glob Chang Biol 26:4506–4520. 10.1111/gcb.15130. [DOI] [PubMed] [Google Scholar]
  • 35.Cardinale BJ, Duffy JE, Gonzalez A, Hooper DU, Perrings C, Venail P, Narwani A, Mace GM, Tilman D, Wardle DA, Kinzig AP, Daily GC, Loreau M, Grace JB, Larigauderie A, Srivastava DS, Naeem S. 2012. Biodiversity loss and its impact on humanity. Nature 486:59–67. 10.1038/nature11148. [DOI] [PubMed] [Google Scholar]
  • 36.Jiao S, Xu YQ, Zhang J, Hao X, Lu YH. 2019. Core microbiota in agricultural soils and their potential associations with nutrient cycling. mSystems 4:e00313-18. 10.1128/mSystems.00313-18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Widdig M, Schleuss P-M, Biederman LA, Borer ET, Crawley MJ, Kirkman KP, Seabloom EW, Wragg PD, Spohn M. 2020. Microbial carbon use efficiency in grassland soils subjected to nitrogen and phosphorus additions. Soil Biol Biochem 146:107815. 10.1016/j.soilbio.2020.107815. [DOI] [Google Scholar]
  • 38.Kuypers MMM, Marchant HK, Kartal B. 2018. The microbial nitrogen-cycling network. Nat Rev Microbiol 16:263–276. 10.1038/nrmicro.2018.9. [DOI] [PubMed] [Google Scholar]
  • 39.Daims H, Lücker S, Wagner M. 2016. A new perspective on microbes formerly known as nitrite-oxidizing bacteria. Trends Microbiol 24:699–712. 10.1016/j.tim.2016.05.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Ren M, Zhang ZF, Wang XL, Zhou ZW, Chen D, Zeng H, Zhao SM, Chen LL, Hu YL, Zhang CY, Liang YX, She QX, Zhang Y, Peng N. 2018. Diversity and contributions to nitrogen cycling and carbon fixation of soil salinity shaped microbial communities in Tarim Basin. Front Microbiol 9:431. 10.3389/fmicb.2018.00431. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Xun WB, Li W, Xiong W, Ren Y, Liu YP, Miao YZ, Xu ZH, Zhang N, Shen QR, Zhang RF. 2019. Diversity-triggered deterministic bacterial assembly constrains community functions. Nat Commun 10:3833. 10.1038/s41467-019-11787-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Jiao S, Peng ZH, Qi JJ, Gao JM, Wei GH. 2021. Linking bacterial-fungal relationships to microbial diversity and soil nutrient cycling. mSystems 6:e01052-20. 10.1128/mSystems.01052-20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Zhang KP, Shi Y, Cui XQ, Yue P, Li KH, Liu XJ, Tripathi BM, Chu HY. 2019. Salinity is a key determinant for soil microbial communities in a desert ecosystem. mSystems 4:e00225-18. 10.1128/mSystems.00225-18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Gao ZQ, Bai JH, Jia J, Wen XJ, Zhang GL, Zhao QQ. 2015. Spatial and temporal changes of phosphorus in coastal wetland soils as affected by a tidal creek in the Yellow River Estuary. China Phys Chem Earth 89–90:114–120. 10.1016/j.pce.2015.08.003. [DOI] [Google Scholar]
  • 45.Bai JH, Xiao R, Zhao QQ, Lu QQ, Wang JJ, Reddy KR. 2014. Seasonal dynamics of trace elements in tidal salt marsh soils as affected by the flow-sediment regulation regime. PLoS One 9:e107738. 10.1371/journal.pone.0107738. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Cui X, Hu J, Wang L, Yang J, Lin X. 2016. Reclamation negatively influences arbuscular mycorrhizal fungal community structure and diversity in coastal saline-alkaline land in Eastern China as revealed by Illumina sequencing. Appl Soil Ecol 98:140–149. 10.1016/j.apsoil.2015.10.008. [DOI] [Google Scholar]
  • 47.Hu Y, Wang L, Xi X, Hu J, Hou Y, Le Y, Fu X. 2016. Effects of salinity on soil bacterial and archaeal community in estuarine wetlands and its implications for carbon sequestration: verification in the Yellow River Delta. Chem Ecol 32:669–683. 10.1080/02757540.2016.1177519. [DOI] [Google Scholar]
  • 48.Wang HT, Gilbert JA, Zhu YG, Yang XR. 2018. Salinity is a key factor driving the nitrogen cycling in the mangrove sediment. Sci Total Environ 631–632:1342–1349. 10.1016/j.scitotenv.2018.03.102. [DOI] [PubMed] [Google Scholar]
  • 49.Liu HY, Li J, Zhao Y, Xie KX, Tang XJ, Wang SX, Li ZP, Liao YL, Xu JM, Di HJ, Li Y. 2018. Ammonia oxidizers and nitrite-oxidizing bacteria respond differently to long-term manure application in four paddy soils of south of China. Sci Total Environ 633:641–648. 10.1016/j.scitotenv.2018.03.108. [DOI] [PubMed] [Google Scholar]
  • 50.Xia WW, Zhao J, Zheng Y, Zhang HM, Zhang JB, Chen RR, Lin XG, Jia ZJ. 2020. Active soil nitrifying communities revealed by in situ transcriptomics and microcosm-based stable-isotope probing. Appl Environ Microbiol 86:e01807-20. 10.1128/AEM.01807-20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Oren A. 2011. Thermodynamic limits to microbial life at high salt concentrations. Environ Microbiol 13:1908–1923. 10.1111/j.1462-2920.2010.02365.x. [DOI] [PubMed] [Google Scholar]
  • 52.Francioli D, Schulz E, Buscot F, Reitz T. 2018. Dynamics of soil bacterial communities over a vegetation season relate to both soil nutrient status and plant growth phenology. Microb Ecol 75:216–227. 10.1007/s00248-017-1012-0. [DOI] [PubMed] [Google Scholar]
  • 53.Liu JJ, Sui YY, Yu ZH, Shi Y, Chu HY, Jin J, Liu XB, Wang GH. 2014. High throughput sequencing analysis of biogeographical distribution of bacterial communities in the black soils of northeast China. Soil Biol Biochem 70:113–122. 10.1016/j.soilbio.2013.12.014. [DOI] [Google Scholar]
  • 54.Banerjee S, Baah-Acheamfour M, Carlyle CN, Bissett A, Richardson AE, Siddique T, Bork EW, Chang SX. 2016. Determinants of bacterial communities in Canadian agroforestry systems. Environ Microbiol 18:1805–1816. 10.1111/1462-2920.12986. [DOI] [PubMed] [Google Scholar]
  • 55.Zhao S, Liu JJ, Banerjee S, Zhou N, Zhao ZY, Zhang K, Hu MF, Tian CY. 2020. Biogeographical distribution of bacterial communities in saline agricultural soil. Geoderma 361:114095. 10.1016/j.geoderma.2019.114095. [DOI] [Google Scholar]
  • 56.Bouskill NJ, Barker-Finkel J, Galloway TS, Handy RD, Ford TE. 2010. Temporal bacterial diversity associated with metal-contaminated river sediments. Ecotoxicology 19:317–328. 10.1007/s10646-009-0414-2. [DOI] [PubMed] [Google Scholar]
  • 57.Li X, Meng D, Li J, Yin H, Liu H, Liu X, Cheng C, Xiao Y, Liu Z, Yan M. 2017. Response of soil microbial communities and microbial interactions to long-term heavy metal contamination. Environ Pollut 231:908–917. 10.1016/j.envpol.2017.08.057. [DOI] [PubMed] [Google Scholar]
  • 58.Samaei-Nouroozi A, Rezaei S, Khoshnevis N, Doosti M, Hajihoseini R, Khoshayand MR, Faramarzi MA. 2015. Medium-based optimization of an organic solvent-tolerant extracellular lipase from the isolated halophilic Alkalibacillus salilacus. Extremophiles 19:933–947. 10.1007/s00792-015-0769-7. [DOI] [PubMed] [Google Scholar]
  • 59.Kirchman DL. 2002. The ecology of Cytophaga-Flavobacteria in aquatic environments. FEMS Microbiol Ecol 39:91–100. 10.1111/j.1574-6941.2002.tb00910.x. [DOI] [PubMed] [Google Scholar]
  • 60.Fierer N, Bradford MA, Jackson RB. 2007. Toward an ecological classification of soil bacteria. Ecology 88:1354–1364. 10.1890/05-1839. [DOI] [PubMed] [Google Scholar]
  • 61.Bai YC, Mei LJ, Zuo WG, Zhang Y, Gu CH, Shan YH, Hu J, Dai QG. 2019. Response of bacterial communities in coastal mudflat saline soil to sewage sludge amendment. Appl Soil Ecol 144:107–111. 10.1016/j.apsoil.2019.07.007. [DOI] [Google Scholar]
  • 62.Wang M, Chen SB, Chen L, Wang D. 2019. Responses of soil microbial communities and their network interactions to saline-alkaline stress in Cd-contaminated soils. Environ Pollut 252:1609–1621. 10.1016/j.envpol.2019.06.082. [DOI] [PubMed] [Google Scholar]
  • 63.Hernandez DJ, David AS, Menges ES, Searcy CA, Afkhami ME. 2021. Environmental stress destabilizes microbial networks. ISME J 15:1722–1734. 10.1038/s41396-020-00882-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Egamberdieva D, Kamilova F, Validov S, Gafurova L, Kucharova Z, Lugtenberg B. 2008. High incidence of plant growth-stimulating bacteria associated with the rhizosphere of wheat grown on salinated soil in Uzbekistan. Environ Microbiol 10:1–9. 10.1111/j.1462-2920.2007.01424.x. [DOI] [PubMed] [Google Scholar]
  • 65.Samaei MR, Mortazavi SB, Bakhshi B, Jafari AJ. 2013. Isolation genetic identification, and degradation characteristics of n-hexadecane degrading bacteria from tropical areas in Iran. Fresenius Environ Bull 22:1304–1312. [Google Scholar]
  • 66.Lupatini M, Suleiman AKA, Jacques RJS, Antoniolli ZI, de Siqueira Ferreira A, Kuramae EE, Roesch LW. 2014. Network topology reveals high connectance levels and few key microbial genera within soils. Front Environ Sci 2:10–10. 10.3389/fenvs.2014.00010. [DOI] [Google Scholar]
  • 67.Ma B, Wang H, Dsouza M, Lou J, He Y, Dai Z, Brookes PC, Xu J, Gilbert JA. 2016. Geographic patterns of co-occurrence network topological features for soil microbiota at continental scale in eastern China. ISME J 10:1891–1901. 10.1038/ismej.2015.261. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Xu JS, Gao W, Zhao BZ, Chen MQ, Ma L, Jia ZJ, Zhang JB. 2021. Bacterial community composition and assembly along a natural sodicity/salinity gradient in surface and subsurface soils. Appl Soil Ecol 157:103731. 10.1016/j.apsoil.2020.103731. [DOI] [Google Scholar]
  • 69.Singh K, Singh B, Singh RR. 2013. Effect of land rehabilitation on physicochemical and microbial properties of a sodic soil. Catena 109:49–57. 10.1016/j.catena.2013.05.006. [DOI] [Google Scholar]
  • 70.Olff H, Alonso D, Berg MP, Eriksson BK, Loreau M, Piersma T, Rooney N. 2009. Parallel ecological networks in ecosystems. Philos Trans R Soc Lond B Biol Sci 364:1755–1779. 10.1098/rstb.2008.0222. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Qadir M, Oster JD, Schubert S, Noble AD, Sahrawat KL. 2007. Phytoremediation of sodic and saline-sodic soils. Adv Agron 96:197–247. 10.1016/S0065-2113(07)96006-X. [DOI] [Google Scholar]
  • 72.Liu LM, Yang J, Yu Z, Wilkinson DM. 2015. The biogeography of abundant and rare bacterioplankton in the lakes and reservoirs of China. ISME J 9:2068–2077. 10.1038/ismej.2015.29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Zhou MH, Butterbach-Bahl K, Vereecken H, Bruggemann N. 2017. A meta-analysis of soil salinization effects on nitrogen pools, cycles and fluxes in coastal ecosystems. Glob Chang Biol 23:1338–1352. 10.1111/gcb.13430. [DOI] [PubMed] [Google Scholar]
  • 74.Butchart SHM, Walpole M, Collen B, van Strien A, Scharlemann JPW, Almond REA, Baillie JEM, Bomhard B, Brown C, Bruno J, Carpenter KE, Carr GM, Chanson J, Chenery AM, Csirke J, Davidson NC, Dentener F, Foster M, Galli A, Galloway JN, Genovesi P, Gregory RD, Hockings M, Kapos V, Lamarque J-F, Leverington F, Loh J, McGeoch MA, McRae L, Minasyan A, Hernández Morcillo M, Oldfield TEE, Pauly D, Quader S, Revenga C, Sauer JR, Skolnik B, Spear D, Stanwell-Smith D, Stuart SN, Symes A, Tierney M, Tyrrell TD, Vié J-C, Watson R. 2010. Global biodiversity: indicators of recent declines. Science 328:1164–1168. 10.1126/science.1187512. [DOI] [PubMed] [Google Scholar]
  • 75.Yan Y, Kuramae EE, Hollander M, Klinkhamer PGL, Veen JA. 2017. Functional traits dominate the diversity-related selection of bacterial communities in the rhizosphere. ISME J 11:56–66. 10.1038/ismej.2016.108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Philippot L, Spor A, Hénault C, Bru D, Bizouard F, Jones CM, Sarr A, Maron P-A. 2013. Loss in microbial diversity affects nitrogen cycling in soil. ISME J 7:1609–1619. 10.1038/ismej.2013.34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Li X, Wan WJ, Zheng LX, Wang AC, Luo XS, Huang QY, Chen WL. 2021. Community assembly mechanisms and co-occurrence patterns of nitrite-oxidizing bacteria communities in saline soils. Sci Total Environ 772:145472. 10.1016/j.scitotenv.2021.145472. [DOI] [PubMed] [Google Scholar]
  • 78.Galand PE, Pereira O, Hochart C, Auguet JC, Debroas D. 2018. A strong link between marine microbial community composition and function challenges the idea of functional redundancy. ISME J 12:2470–2478. 10.1038/s41396-018-0158-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79.Zhang GL, Bai JH, Tebbe CC, Zhao QQ, Jia J, Wang W, Wang X, Yu L. 2021. Salinity controls soil microbial community structure and function in coastal estuarine wetlands. Environ Microbiol 23:1020–1037. 10.1111/1462-2920.15281. [DOI] [PubMed] [Google Scholar]
  • 80.Hector A, Bagchi R. 2007. Biodiversity and ecosystem multifunctionality. Nature 448:188–190. 10.1038/nature05947. [DOI] [PubMed] [Google Scholar]
  • 81.Fierer N, Barberán A, Laughlin DC. 2014. Seeing the forest for the genes: using metagenomics to infer the aggregated traits of microbial communities. Front Microbiol 5:614. 10.3389/fmicb.2014.00614. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82.Giovannoni SJ, Cameron Thrash J, Temperton B. 2014. Implications of streamlining theory for microbial ecology. ISME J 8:1553–1565. 10.1038/ismej.2014.60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 83.Jackson ML. 1958. Soil chemical analysis, p 111–133. Prentice-Hall, Inc., Englewood Cliffs, NJ. [Google Scholar]
  • 84.Olsen SR, Cole C, Watanabe FS, Dean L. 1954. Estimation of available phosphorus in soils by extraction with sodium bicarbonate. Circular (No. 939), p 1–19. USDA Press, Washington, DC. [Google Scholar]
  • 85.Kanehiro Y, Sherman GD. 1965. Fusion with sodium carbonate for total elemental analysis, p 952–958. In Black CA, editor. Methods of soil analysis, part 2-agronomy 9. America Society of Agronomy, Inc., Madison, WI. [Google Scholar]
  • 86.Li X, Han S, Wan WJ, Zheng LX, Chen WL, Huang QY. 2020. Manure fertilizes alter the nitrite oxidizer and comammox community composition and increase nitrification rates. Soil Till Res 204:104701. 10.1016/j.still.2020.104701. [DOI] [Google Scholar]
  • 87.Herlemann DP, Labrenz M, Jürgens K, Bertilsson S, Waniek JJ, Andersson AF. 2011. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J 5:1571–1579. 10.1038/ismej.2011.41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 88.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters BWA, Widmann J, Yatsunenko T, Zaneveld J, Knight R. 2010. QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335–336. 10.1038/nmeth.f.303. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 89.Jiao S, Liu ZS, Lin YB, Yang J, Chen WM, Wei GH. 2016. Bacterial communities in oil contaminated soils: biogeography and co-occurrence patterns. Soil Biol Biochem 98:64–73. 10.1016/j.soilbio.2016.04.005. [DOI] [Google Scholar]
  • 90.Stegen JC, Lin XG, Konopka AE, Fredrickson JK. 2012. Stochastic and deterministic assembly processes in subsurface microbial communities. ISME J 6:1653–1664. 10.1038/ismej.2012.22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 91.Sloan WT, Lunn M, Woodcock S, Head IM, Nee S, Curtis TP. 2006. Quantifying the roles of immigration and chance in shaping prokaryote community structure. Environ Microbiol 8:732–740. 10.1111/j.1462-2920.2005.00956.x. [DOI] [PubMed] [Google Scholar]
  • 92.Langille MGI, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega RL, Thurber Knight R, Beiko RG, Huttenhower C. 2013. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol 31:814–821. 10.1038/nbt.2676. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 93.Francis CA, Roberts KJJ, Beman M, Santoro AE, Oakley BB. 2005. Ubiquity and diversity of ammonia-oxidizing archaea in water columns and sediments of the ocean. Proc Natl Acad Sci USA 102:14683–14688. 10.1073/pnas.0506625102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 94.Rotthauwe JH, Witzel KP, Liesack W. 1997. The ammonia monooxygenase structural gene amoA as a functional marker: molecular fine-scale analysis of natural ammonia-oxidizing populations. Appl Environ Microbiol 63:4704–4712. 10.1128/aem.63.12.4704-4712.1997. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 95.Poly F, Wertz S, Brothier E, Degrange V. 2008. First exploration of Nitrobacter diversity in soils by a PCR cloning-sequencing approach targeting functional gene nxrA. FEMS Microbiol Ecol 63:132–140. 10.1111/j.1574-6941.2007.00404.x. [DOI] [PubMed] [Google Scholar]
  • 96.Pester M, Maixner F, Berry D, Rattei T, Koch H, Lücker S, Nowka B, Richter A, Spieck E, Lebedeva E, Loy A, Wagner M, Daims H. 2014. NxrB encoding the beta subunit of nitrite oxidoreductase as functional and phylogenetic marker for nitrite-oxidizing Nitrospira. Environ Microbiol 16:3055–3071. 10.1111/1462-2920.12300. [DOI] [PubMed] [Google Scholar]
  • 97.Bru D, Sarr A, Philippot L. 2007. Relative abundances of proteobacterial membrane-bound and periplasmic nitrate reductases in selected environments. Appl Environ Microbiol 73:5971–5974. 10.1128/AEM.00643-07. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 98.Hallin S, Lindgren PE. 1999. PCR detection of genes encoding nitrile reductase in denitrifying bacteria. Appl Environ Microbiol 65:1652–1657. 10.1128/AEM.65.4.1652-1657.1999. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 99.Throbäck IN, Enwall K, Jarvis A, Hallin S. 2004. Reassessing PCR primers targeting nirS, nirK and nosZ genes for community surveys of denitrifying bacteria with DGGE. FEMS Microbiol Ecol 49:401–417. 10.1016/j.femsec.2004.04.011. [DOI] [PubMed] [Google Scholar]
  • 100.Braker G, Tiedje JM. 2003. Nitric oxide reductase (norB) genes from pure cultures and environmental samples. Appl Environ Microbiol 69:3476–3483. 10.1128/AEM.69.6.3476-3483.2003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 101.Henry S, Bru D, Stres B, Hallet S, Philippot L. 2006. Quantitative detection of the nosZ gene, encoding nitrous oxide reductase, and comparison of the abundances of 16S rRNA, narG, nirK, and nosZ genes in soils. Appl Environ Microbiol 72:5181–5189. 10.1128/AEM.00231-06. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental file 1

Tables S1 to S3, Fig. S1 to S6. Download AEM.01366-21-s0001.pdf, PDF file, 0.8 MB (868.3KB, pdf)

Data Availability Statement

The raw amplicon sequence data sets for 16S rRNA genes have been deposited in the NCBI (http://www.ncbi.nlm.nih.gov) Sequence Read Archive (SRA) database under the accession number PRJNA739396.

Articles from Applied and Environmental Microbiology are provided here courtesy of American Society for Microbiology (ASM)

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