Assembly, network and functional compensation of specialists and generalists in poplar rhizosphere under salt stress

Introduction

Soil salinization significantly hampers plant growth and crop productivity worldwide¹. The rhizosphere microbiome, pivotal in aiding plant tolerance to salt stress, is often considered as the plant’s ‘second genome’^2,3. Numerous studies have shown when faced with high salinity conditions, plants can attract specific beneficial soil bacteria to their rhizosphere, fostering growth^3,4,5,6,7,8. Populus euphratica, renowned for its ability to thrive in saline environments, possesses a distinct soil microbiome that may underpin its stress resistance⁹. Despite the critical interplay between P. euphratica and its rhizosphere microorganisms, research in this area remains sparse.

Traditional studies have generally classified microorganisms as either abundant or rare taxa, neglecting their ecological niche. In response to diverse environmental conditions, species are often identified as specialists, generalists, or neutral taxa based on their niche breadth, which reflects the range of resources, habitats, or environments a species utilized¹⁰. This approach offers a novel perspective in microbial classification. Previous research indicated that microbial generalists had a stronger ability to evolve toward specialists¹¹, although the underlying reasons for this evolutionary process remain unclear. Xu et al.¹² observed that the impact of generalists and specialists on soil microbial diversity in farmland varies, depending on network perspective, community assembly, and biogeographic patterns¹². Specialists are more governed by deterministic processes, whereas generalists are swayed by stochastic processes. Liao et al.¹³ found that while stochastic processes predominantly influenced the distribution of generalists in plateau lakes of China, deterministic processes played a more significant role in the assembly of specialists¹³. However, a study on Tibetan lake sediment microorganisms found that stochastic processes significantly affected both generalists and specialists¹⁴, with specialists maintaining robust connections within the network and exhibiting high modularity. Nevertheless, there is a lack of research on the assembly mechanisms and networks of specialists and generalists, underscoring the need for more exploration into their assembly and network characterization in the rhizosphere under salt stress. This will help uncover the various adaptive dynamics and evolutionary reasons behind the existence of specialists and generalists.

Plants can modify the composition of the rhizosphere microbiome to better adapt to various soil conditions. There is evidence that plants may recruit beneficial microorganisms to aid growth under stressful and nutrient-limited environments¹⁵. Ren et al.¹⁶ explored rhizosphere function across different soil environments, uncovering an effect for functional compensation¹⁶. The availability of nutrients likely influenced plant rhizosphere microbial communities, triggering functional compensation to boost host fitness. For instance, in nutrient-rich soil, nutrient cycling functions in the rhizosphere bacterial community might be downregulated, whereas nutrient cycling might become more crucial in nutrient-poor soil¹⁶. The question of whether functional compensation is a widespread phenomenon remains open for exploration, particularly regarding the roles of microbial generalists and specialists.

This study aimed to (i) examine the composition and characteristics of specialist and generalist microbiomes in the rhizosphere of P. euphratica; (ii) understand the community assembly mechanisms and network characterization of specialists and generalists under salt stress; (iii) decipher the functional potential of rhizosphere microorganisms in P. euphratica under salt stress. By elucidating the assembly patterns, network characterization, and functional roles of specialists and generalists in P. euphratica, we aspire to deepen our understanding of the ecological processes of rhizosphere microorganisms under salt stress. This knowledge may pave the way for novel strategies to manipulate microorganisms and enhance ecosystem functions in the P. euphratica rhizosphere community.

Results

Diversity of bacterial specialists exhibited a positive correlation with salinity

We collected 251 rhizosphere soil samples from P. euphratica trees (Fig. 1A) and proceeded to analyze the soil using amplicon sequencing. Following the filtering process, our analysis yielded 5524 bacterial ASVs and 1298 fungal ASVs. Examination of bacterial species categorized 1979 (35.8%) as specialist species and 901 (16.3%) as generalists (Fig. 1B). Among fungi, 847 (65.8%) were identified as specialists and 31 (2.4%) as generalists. At the phylum level, bacterial specialists were dominated by Firmicutes, Proteobacteria, and Actinomycetes, whereas generalists were predominantly Proteobacteria, Actinomycetes, and Bacteroidota (Fig. 1C). As for fungi, the specialists were primarily composed of Sordariomycetes and Dothideomycetes, with generalists largely from Tremellomycetes, Sordariomycetes, Dothideomycetes, and Eurotiomycetes. Bacterial generalists exhibited higher α diversity than specialists (Fig. 1D and Supplementary Fig. 1A). Correlation analysis between microbial diversity of environmental factors (pH, AK, AP, salt, and OM) showed a significant positive correlation between bacterial specialists’ α diversity with salinity, unlike the negative correlation observed for generalists (Fig. 1E). This trend was exclusive to bacteria, as only the fungi generalists showed a negative correlation with salinity (Fig. 1F and Supplementary Fig. 1B). By dividing bacterial communities according to salinity levels, we can observe the trends more clearly as described above (Supplementary Fig. 1C). Additionally, species composition analysis under salinity gradients showed an increased abundance of Planococcus, Planomicrobium, among bacterial specialists (Fig. 1G and Supplementary Fig. 2). Furthermore, specialists showed significant structural variations (F = 7.289, P = 0.01) across salinity rather than generalists (F = 1.313, P = 0.3) as indicated by PCoA analysis (Fig. 1H). Further analysis revealed that salinity had the most significant impact on bacterial specialists (Supplementary Fig. 3, F = 9.119, P = 0.01).

Assembly, network and functional compensation of specialists and generalists in poplar rhizosphere under salt stress — **Fig. 1: Diversity and community structure of the specialists and generalists in the *P. euphratica* rhizosphere.**

Assembly mechanisms and environmental adaptation of bacterial specialists and generalists

In this study, we integrated the neutral model with the null model¹⁷, revealed a high degree of fit for both specialists and generalists (Supplementary Fig. 4). The null model indicated that stochastic processes predominantly governed the assembly of both groups, accounting for 80.52 and 81.95% for specialists and generalists, respectively (Fig. 2A). Specialists were more controlled by deterministic assembly compared to generalists. A regression analysis of the Euclidean distance of salinity and βNTI demonstrated a strong correlation (P < 0.001) between the pairwise comparisons of βNTI values for bacterial specialists and generalists with salinity changes, as shown in Fig. 2B. Bacterial specialists and generalists exhibited greater certainty (mainly homogeneous selection) in high salinity stress and extremely low salinity conditions (Fig. 2C), while heterogeneous selection showed the opposite pattern in these situations. The assembly process of fungal specialists is less impacted by salinity (Supplementary Fig. 5). While fungal generalists might be somewhat affected by salinity, they were primarily controlled by undominant processes (Supplementary Fig. 5), the proportion of undominant processes decreased under high salt conditions. Undominant processes had a greater impact on fungal generalists compared to bacterial generalists. Homogeneous dispersal only accounted for a relatively high proportion in bacterial specialists with low salinity (0–2 g/kg), while the proportion of others was very small. Furthermore, dispersal limitation had a greater impact on specialists than generalists, and undominated had a greater impact on generalists than specialists. An increase in dispersal limitation, mainly for bacterial specialists was significantly correlated with rising salinity levels (Fig. 2D), which was opposite to the situation of fungal specialists (Supplementary Fig. 5). Therefore, Specialists, especially bacterial specialists, played a pivotal role in shaping community diversity through deterministic processes and dispersal limitation.

Fig. 2: The construction process of microbial communities and relative influences of deterministic and stochastic assembly processes in shaping bacteria specialists and generalists over the salinity gradients.

To identify the environmental thresholds for the specialists and generalists in response to various variables, we evaluated the cumulating z− and z+ change points using threshold indicator taxa analysis (Supplementary Fig. 6). The results showed that, apart from AP, specialists possess broader environmental threshold than generalists for AK, OM, pH, and salt (Fig. 4E).

Positive correlation between key microbe and network complexity under salt stress

We constructed five bacterial co-occurrence networks under varying salinity gradients and analyzed the evolution of these networks as salinity increased (Fig. 3A). The analysis revealed that both the quantity of the nodes and links, as well as the degree and weighted degree of networks, increased up to a salinity concentration of 20 g/kg (Fig. 3A, C). Moreover, an analysis of the eigenvalues related to the networks’ closeness centrality suggested an increase in network complexity with rising salinity levels (Fig. 3B, E). Further investigation confirmed a significant positive correlation between network complexity and salinity (Fig. 3E). Within these networks, 31 module hubs and one connector were identified, with the absence of generalists being noteworthy (Fig. 3B and Supplementary Fig. 7A). Keystone species, comprising of bacterial specialists and neutral taxa, demonstrated a strong positive correlation with closeness centrality (Fig. 3E, F). Phylogenetic analysis of these keystone species (module hubs and connectors) identified them predominantly within the Proteobacteria, Chloroflexi, and Actinobacteria phyla (Fig. 3B). Correlation analysis between keystone species and five physicochemical properties (pH, AK, AP, salt, and OM) identified a positive relationship of keystone species abundance and rising salinity. The key microbes are those keystone species that are correlated with salinity and rank in the top 1% of IVI values. A significant positive correlation was also observed between key microbes and network complexity (Fig. 3E, G, H).

**Fig. 3: Co-occurrence network of the *P. euphratica* rhizosphere.**

Node-level topological features across different sub-communities were examined, revealing that specialists exhibited higher values in degree, weighted degree, closeness, harmonic closeness, clustering, and eigenvector centrality compared to generalists (Supplementary Fig. 7B). The most dominant phylum within the network was Proteobacteria, followed by Actinobacteria, Chloroflexi, Firmicutes, Acidobacteriota, Gemmatimonadota, and Ascomycota (Supplementary Fig. 7C). Proteobacteria, Actinomycetes, Chloroflexi, Firmicutes were found to make up a large proportion of the bacteria in the network, while Ascomycota and Myxomycetes were the most prominent fungi. Network analysis showed that most nodes were organized into nine major modules, representing 55.47% of the total nodes (Supplementary Fig. 7D). Examination of these modules revealed that eight predominantly consisted of specialists and neutral groups, whereas generalists were less represented. The analysis underscored the prominence of bacterial specialists and neutral taxa within the network (Supplementary Fig. 7E).

Salinity stress triggers functional compensation in the P. euphratica rhizosphere

To elucidate the functional characteristics within different salinity gradients, we constructed co-occurrence networks for ASVs across these different salinity gradients and ranked them based on node degree. This allowed us to categorize the ASVs into clusters for functional prediction. By comparing the predicted functions with theoretical expectations, we identified functions that were either enriched or depleted across varying salinity levels.

The analysis of the rhizosphere bacterial community’s functional profiles revealed distinct variations across different salinity conditions (Fig. 4). Notably, the shift in functional enrichment between varying levels of salinity was significant. Under conditions of low salinity (2–5 g/kg, Fig. 4B), ASVs that were less dominant exhibited an enrichment in a broader range of metabolic functions. Conversely, in conditions of higher salinity (5–10 g/kg, 10–20 g/kg, Fig. 4C, D), more dominant ASVs demonstrated enrichment in specific metabolic functions including “Ascorbate and aldarate metabolism”, “Arachidonic acid metabolism”, “Ubiquinone and other terpenoid−quinone biosynthesis”, and “Terpenoid backbone biosynthesis” and “Fructose and mannose metabolism”. Functional changes did not exhibit any specific pattern under extremely low salinity (0–2 g/kg, Fig. 4A) and extremely high salinity stress (>20 g/kg, Fig. 4E). Hence, within certain boundaries, metabolic functions were more greatly suppressed in low salt conditions, while they were enhanced in high salt conditions. This suggests that certain metabolic functions become more critical under high salinity conditions, possibly as a response to the increased stress, thereby enhancing the microbial community’s requirement for metabolic function associated with stress resistance. The noteworthy observation is that 50% of the dominant ASVs with enriched function are specialists, while only 20% are generalists. In the context of bacterial communities, specialists exhibited a higher proficiency in metabolic functions including those related to ascorbate, arachidonic acid, terpenoids, carbon, nitrogen, and methane. while generalists played a more significant role in pathways such as the metabolism, phosphonate, and phosphonate metabolism (Supplementary Fig. 8). The insights suggest that specialists contribute more significantly to functional compensation within the rhizosphere of P. euphratica under salt stress conditions, potentially explaining the observed increase in their abundance and diversity.

**Fig. 4: Functional compensation in the *P. euphratica* rhizosphere.**

Discussion

Specific root-associated bacteria can be recruited by plants when confronted with salinity stress⁴. This adaptive strategy is notably evident in halophytes, which leverage root-associated microorganisms to enhance their resilience to salt stress⁷. Our research indicated that the rhizosphere microbiome of P. euphratica demonstrated functional compensation, with specialists in the rhizosphere adapting and performing necessary functions to aid the plant in surviving in salty conditions. We postulated that the harsh saline environment might have driven the evolution of these specialists, uniquely adapted to their specific ecological niche^11,18. Conversely, it might also be interpreted as a strategic response by the plant, a “cry for help” to attract these specialist microorganisms, thereby fortifying its defense against the challenges posed by salinity^15,19.

Our findings revealed a positive correlation between soil salinity levels and the α diversity of bacterial specialists within the rhizosphere. The influence of salinity on the differentiation between specialist and generalist bacteria is notable. This distinction underscores the critical role of salt resistance among bacterial specialists. Our results showed that Bacteria had a stronger reaction to changes in salinity during the salt stress period than fungi. Bacterial Specialists demonstrated a positive response to salt stress, resulting in increased diversity. Although the community structure of fungi changed, there was no notable increase in fungal diversity. It has been reported that bacterial generalists are vital for maintaining community and functional stability in dynamic environments due to their broad ecological resistance and diversification¹². However, in more static environments, specialists are key contributors to community diversity and function. Our research supports this paradigm within the relatively stable rhizosphere of P. euphratica. Further supporting our findings, we observed an increased abundance of bacterial specialists, particularly from the genera Planocucus and Planomicronium, in environments with elevated salinity. This aligns with existing research indicating these microorganisms’ potential for salt tolerance^20,21. For instance, Planococcus rifietoensis, known for its moderate halotolerance²⁰, has been shown to facilitate wheat growth under salinity conditions by converting ammonia into nitrogen, thereby enhancing soil fertilization. Additionally, this bacterium’s ability to metabolize potassium contributes to maintaining ion balance within plant cells²¹. Similarly, the discovery of Planomicrobium iranicum sp. nov. highlights the emergence of slightly halophilic bacteria adapted to saline environments²². Moreover, Li et al. suggest that the broader capability of soil bacteria to mitigate salt stress in plants, extending beyond the microorganisms’ own salinity tolerance levels⁴. This trend suggests that these salt-tolerant species may colonize the rhizosphere to compensate for functional deficits induced by salt stress.

We found that specialists exhibit a more deterministic assembly process, this is consistent with the results of previous studies^12,13,18. Heterogeneous selection was found to play a more substantial role in the assembly of specialists compared to generalists²³. Bacterial specialists and generalists exhibit greater certainty in high salinity stress and extremely low salinity conditions. A previous report had shown that environmental filters are more pronounced under extreme, such as highly variable soil pH²⁴, indicating that challenging environmental conditions may amplify the deterministic processes governing microbial community assembly mechanisms. With increasing salinity, especially bacterial specialists experienced enhanced diffusion limitations. This finding suggests that salinity may act as a deterministic force influencing the diffusion process of microorganisms. This is consistent with the results of previous studies, stochastic processes, particularly dispersal limitation, played critical roles even under high-stress conditions^25,26. Previous results have also found that bacteria are more affected by diffusion limitations than fungi²⁷. However, the stronger stochastic exhibited by fungi may be due to the better stability of fungal communities under stress^23,28. The assembly mechanism of fungi seemed to be more influenced by randomness, indicating that salinity factors had a smaller impact on fungi in comparison to bacteria. In summary, within the rhizosphere of P. euphratica, specialists play a pivotal role in shaping community diversity through deterministic processes and dispersal limitation.

Our study revealed that specialist organisms in the rhizosphere of P. euphratica exhibited a greater environmental threshold compared to their generalist counterparts. Despite their narrower ecological niche, specialists demonstrated an ability to thrive across a broader spectrum of environmental factors within specific habitats. This finding aligned with the ecological principles of categorizing species as specialists or generalists based on Levins’ niche breadth. Generalists, despite their adaptability to a wide range of environments, are at a disadvantage in specialized habitats. On the other hand, specialists, with their narrow ecological niche, demonstrate superior competitiveness and adaptability in specialized environments when compared to generalists. The rhizosphere of P. euphratica was characterized by reduced fluctuation, providing specialists with a survival advantage. While generalists were capable of adapting to diverse ecological settings, they tended to be outcompeted by specialists within certain niche ranges. It has been noticed that in stable environments, specialists are more likely to contribute to enhancing community diversity than generalists¹².

Furthermore, an increase in salinity had been observed to complicate the network dynamics within rhizosphere communities, which exhibited distinct network interactions in response to external disturbances²⁹. Despite these complexities, specialists constituted a significant portion of the network across various salinity levels. Our analysis of the microbial co-occurrence network indicated a closer association between bacterial specialists and neutral groups, with specialists and neutral groups dominating in eight observed cases. Network eigenvalues revealed that specialists generally had higher values than generalists, except betweenness centrality and eccentricity. This could be attributed to the prominent centrality and eccentricity of specific ASV mediators among generalists, suggesting that specialists occupied central roles within the network and maintained strong connections with neutral groups, thereby exhibiting high modularity¹⁴. The composition of most modules primarily included specialists, neutral taxa, and a few generalists, apart from one module where generalists predominated, implying a significant interconnection and functional exchange among specialists and neutral tax¹². Keystone species predominantly consisted of bacterial specialists and neutral taxa. Fungi had only two keystone species. This underscored the pivotal role of bacterial specialists over generalists within the network. In a previous study, researchers identified the influential microbial players in a network, using IVI and some other centrality measures³⁰. In our research, we also used the IVI to identify key microbes. We obtained a significant positive correlation between the key microbes and the network complexity and salinity. Many of these key microbes are bacterial specialists and neutral groups, highlighting the significance of bacterial specialists in high salt stress conditions rather than fungi. These microorganisms play a crucial role in regulating microbial interactions under high salt stress, potentially aiding in functional compensation.

Salt stress can significantly alter the metabolic and ecological functions of root-associated bacteria⁴. Rhizosphere microbes play a crucial role in enhancing plant salt stress tolerance by re-establishing ion and osmotic homeostasis, thereby preventing damage to plant cells and facilitating the resumption of plant growth under salt stress conditions². Our research demonstrated that in high salinity environments, the functions of the rhizosphere microorganisms, particularly those that bolster plant tolerance to abiotic and biotic stress-are increasingly valued, leading to functional compensation in the P. euphratica rhizosphere. Specifically, metabolic pathways such as “Ascorbate and aldarate metabolism”, “Arachidonic acid metabolism”, “Ubiquinone and other terpenoid−quinone biosynthesis”, and “Terpenoid backbone biosynthesis”, “glycan biosynthesis, are generally involved in enhancing plant stress tolerance¹⁶. For instance, l-ascorbic acid (AsA) was a plentiful metabolite in plants, playing crucial roles in stress physiology as well as growth and development³¹. Similarly, arachidonic acid has been identified as a signaling molecule that can attract beneficial microbiota to the rhizosphere, thus promoting plant growth and facilitating nutrient turnover in the soil³². Additionally, the triterpenoid compound cucurbitacin has been found to improve plant disease resistance by regulating the rhizosphere flora³³. However, there are numerous metabolic functions related to stress resistance that remain unexplored. Our observations indicated that while many of these functions were marginalized in low salt conditions, they gained prominence in high salt soil environments. Further investigation into the roles of these metabolisms revealed significant implications for plant health. These findings highlight the multifaceted roles of bacterial specialists in supporting plant resilience and health in saline conditions.

In summary, our study elucidates the significant impact of salinity on the formation and function of specialist versus generalist bacteria within the rhizosphere, highlighting the adaptive strategies that enable certain bacteria to thrive under saline stress. Our findings also confirmed that the P. euphratica rhizosphere microbiome also employed a functional compensation in response to salt stress, highlighting the pivotal role of bacterial specialists in this process. This adaptive response may be attributed to the recruitment of more salt-resistant and microorganism specialists by P. euphratica as salinity levels increase. Previous studies have also found that generalist-to-specialist transformations occur three times more frequently than the reverse transformations¹¹. It is hypothesized that in the rhizosphere of P. euphratica, the increase in salinity may trigger functional compensation, leading to the shift from generalists to specialists. This insight not only advances our understanding of microbial ecology in saline environments but also points to potential avenues for leveraging these microbial adaptations under salinity stress aimed at exploring the formation causes of specialists and generalists and enhancing crop resilience to salinity stress.

Moving forward, we aim to identify and further investigate salt-tolerant strains among these specialists, exploring their function and the potential for creating synthetic microbial communities (SynCom). The development of artificially selected microbiomes that confer salt tolerance represents a promising strategy to enhance agricultural productivity³⁴. The engineering of the desert microbiome into SynCom capable of protecting plants in natural soils from abiotic stress opens new avenues for agricultural innovation³⁵. Quite a few studies have demonstrated that root endophytes also have the ability to help plants withstand stress tolerance^36,37,38,39. By integrating the findings on endophytes with the importance of rhizosphere microorganisms in salt tolerance, we anticipate uncovering novel and intriguing insights in further studies. Our findings not only shed light on the dynamics of the P. euphratica rhizosphere microbiome under salt stress but also provide a valuable framework for the selection of salt-resistant strains. This research lays the foundation for future studies on the interplay between specialists’ and generalists’ microorganisms in the P. euphratica rhizosphere, offering insights that could lead to the development of resilient agricultural systems in arid and saline environments.

Research on the rhizosphere microorganisms of P. euphratica has revealed that an increase in salinity will lead to an increase in the α diversity of bacterial specialists and alterations of structure. Changes in salinity levels have an effect on the assembly of bacterial specialists and generalists, with the former being more characterized by deterministic processes and exhibiting wider adaptation. Furthermore, bacterial specialists are found to play a more significant role in the microbial community. The relationship between key microbes, particularly bacterial specialists, and network complexity is strongly positive. As salinity levels increase, the metabolic function of microorganisms becomes more crucial, shaping the assembly of plant rhizosphere microbial communities under stress. This stress prompts a functional compensation that enhances plant health, as P. euphratica recruits specialized rhizosphere microorganisms. This research highlights the importance of the plant-microbe interaction in promoting resilience and adaptability in the face of environmental challenges, shedding light on the diversity, assembly, network characterization, and functions of bacterial specialists and generalists in the rhizosphere of P. euphratica.

Methods

Sample collection

The 251 rhizosphere soil samples were collected from P. euphratica trees located in the Tarim River Basin of Yuli County, Xinjiang Uygur Autonomous Region, China (41°00–41°20N, 86°00–86°20E) in September, 2021. Using a five-point sampling method, we collected rhizosphere microorganisms around each tree, maintaining a distance of 0.5 m from the trunk. By using a soil drill, we obtained fine roots with a diameter of ≤2 mm. Each fine root was shaken carefully to remove the bulk soil. The soil still adhering to the fine roots was defined as rhizosphere soil. The rhizosphere soil was separated from the fine roots by agitating it in 50 ml of sterile 0.9% NaCl solution for 5 min and then centrifuging it at 8000×g for 10 min.

Soil physicochemical measurement

According to the Environmental Monitoring Method Standards of the Ministry of Ecology and Environment of the People’s Republic of China, the pH, AK, AP, salinity, and OM of the P. euphratica rhizosphere soil were determined.

DNA extraction and sequencing

We utilized the E Z. N.A ® Instructions for the Soil DNA Kit (Omega BioTek, Norcross, GA, USA) to extract total microbial DNA from rhizosphere samples. The structure of rhizosphere bacterial communities was analyzed using the V4-V5 region-targeting primers 515 F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 907R (5′-CCGTCAATTCMTTTRAGTTT-3′)⁴⁰. Rhizosphere fungi were assessed using the ITS1F-ITS2R primers ITS1F (5′-barcode CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2R (5′-GCTGGTTCTTCATCGATGC-3′)⁴¹. The PCR reaction was set up as follows: initial 5 min at 95 °C, 30 cycles of 30 s at 95 °C, 30 s at 55 °C and 30 s at 72 °C, then followed by 5 min extension at 72 °C at the end of amplification. Purified PCR products were sequenced on an Illumina MiSeq platform at Shanghai Biozeron Biological Technology.

The raw data obtained from sequencing is distinguished by barcodes and primers at the beginning and end of the sequence, and the sequence direction is adjusted. After data splitting, data impurities are removed. Analysis of the sequencing data using the ASV-based pipeline was performed using the DADA2 pipeline. Finally, the table of ASV and the species information of each ASV at various taxonomic levels is obtained, and the microbial community composition of each sample at each taxonomic level is statistically analyzed.

Community structure and diversity analysis

To test whether diversity patterns of the microbiome in the rhizosphere of Populus euphratica, we evaluated α diversity indices of bacteria and fungi with the vegan package in R.4.3.0. Principal coordinates analysis (PCoA) based on Bray–Curtis dissimilarity was applied to explore the pattern of the community. Statistical associations between variables were inferred using the Pearson correlation test. Statistical differences between groups were inferred using ANOVA followed. These statistical analyses were operated in an R environment.

Analysis of the habitat specialists and generalists

To determine the habitat specialization of microorganisms in the P. euphratica rhizosphere, we employed Levin’s niche breadth¹⁴. Levin’s niche breadth is a statistical concept utilized in ecology for assessing a species’ ecological niche breadth. Niche width indicates the variety of environmental conditions in which a species can thrive and reproduce. Species with higher Levins’ niche breadth are typically more generalized and tend to be generalists. On the contrary, species with lower Levins’ niche breadth exhibit stronger specialization and are more inclined towards specialists. The EcolUtils package is a tool in the R including functions that can be used to calculate Levins’ niche breadth. The EcolUtils package was used to evaluate the statistical significance of each specialist index, with 1000 permutations. If the habitat specialization values surpass the upper 95% confidence interval or fall below the lower 95% confidence interval of the 1000 permutations, they are labeled as generalists or specialists⁴².

Quantification of community assembly

We employed Null model-based β diversity metrics (βNTI and RC_Bray) to value various community assembly processes²⁶. To estimate the relative influences of stochastic and deterministic processes, we calculate the βNTI and RC_Bray values. In brief, βNTI <−2 or >+2 indicates that βMNTD_obs deviates from the mean βMNTD_null by more than two standard deviations. Thus, the model considers βNTI <−2 or >+2 to indicate significantly less than or greater than expected phylogenetic turnover, respectively, for a given pairwise comparison. In this case, βNTI <−2 indicates the dominance of deterministic processes and low turnover (i.e., homogeneous selection); βNTI >2 indicates the dominance of deterministic processes and high turnover (i.e., variable selection); and −2 < βNTI <2 indicates the lack of deviation and the dominance of stochastic processes. In addition, we calculated the Bray–Curtis-based Raup–Crick metric (RC_Bray) to further partition the relative influences of non-selection processes, i.e., dispersal limitation (RC_Bray >0.95), homogenizing dispersal (RC_Bray <−0.95) and undominated processes (−0.95 >RC_Bray >0.95). Dispersal limitation constrains the movement of species and led to higher levels of community dissimilarity; on the contrary, homogenizing dispersal, defined as high levels of species movement, led to a decrease in community dissimilarities²⁶. The neutral model is also used by us to evaluate the mechanism of community assembly^43,44.

Phylogenetic distance, environmental breadth, and phylogenetic signal analysis

To determine the threshold value of habitat specialists and generalists in response to each environment variable, threshold indicator taxa analysis (TITAN) was carried out using the “TITAN2” package of R⁴⁵. Briefly, we used the sums of taxa scores for ASVs to determine upper and lower thresholds of difference in the habitat specialists and generalists based on environmental variables⁴⁶. TITAN categorizes the community into two groups: Z⁻ taxa that negatively respond to increased environmental gradient, and Z⁺ taxa that positively respond to increased gradient. Taxa without any response to the environmental gradient were not considered. TITAN then tracks the cumulative responses of declining taxa (sum(Z⁻)) and increasing taxa (sum(Z⁺)) in the community. Ecological thresholds are defined as the points where the maximum aggregate change in the frequency and relative abundance of responding taxa occurs. When the environmental values reach and exceed the ecological thresholds, the abundance and occurrence frequency of species will decrease in the Z⁻ group while increasing in the Z⁺ group. Therefore, the range of niche optima for the community is defined as the gradient below sum(Z⁻) and above sum(Z⁺).

Co-occurrence network construction

The network analysis was conducted to identify co-occurrence patterns of generalist and specialist species. Correlations with Spearman’s correlation coefficients (ρ) greater than 0.6 and corresponding P values less than 0.01 were considered significant⁴⁷. The “rcorr” function from the Hmisc package was used to perform pairwise comparisons based on ASVs, with p values adjusted accordingly⁴⁸. Co-occurrence networks were constructed using the igraph package, where each node represented one ASV and each edge represented a strong and significant correlation^49,50. The resulting networks were visualized using the interactive platform Gephi (0.9.2).

Furthermore, using the Gephi software, we performed calculations on the node-level topological features, such as degree, betweenness, closeness, and eigenvector centrality. To identify statistical differences in these features, we conducted the Wilcoxon test. High values of the topological features suggest a core position of a node in the network, while low values suggest a peripheral position^51,52. Subsequently, we categorized the nodes into four groups based on their within-module connectivity (Zi) and among-module connectivity (Pi) to assess the topological roles of taxa in the networks. These groups consisted of module hubs (Zi >2.5), network hubs (Zi >2.5 and Pi >0.62), connectors (Pi >0.62), and peripherals (Zi <2.5 and Pi <0.62). All statistical analyses were performed using R version 4.3.0. The integrated value of influence (IVI) is a novel influential node detection method, and the IVI algorithm is the synergistic product of Hubness and spreading values³⁰. We used an influential package to calculate the IVI value of each node in the network to evaluate its importance using R version 4.3.0.

Functional prediction and compensation effect

The composition of the rhizosphere bacterial community was measured by high-throughput DNA sequencing of the 16S rRNA gene, and the rhizosphere functional traits were predicted using PICRUSt2 software. The PICRUSt2 method consists of phylogenetic placement, hidden-state prediction, and sample-wise gene and pathway abundance tabulation. ASV sequences and abundances are taken as input, and gene family and pathway abundances are output. All necessary reference trees and trait databases for the default workflow are included in the PICRUSt2 implementation⁵³. The PICRUSt2 software⁵⁴ was applied to predict KEGG ortholog (KO) functional profiles⁵⁵ of microbial communities using the 16S rRNA gene sequences. To analyze the segmented functions, we assessed the importance of each ASV in the co-occurrence network by examining its degree of correlation with other ASVs. The ASVs were then organized into functional clusters, with the most important ASVs forming the first cluster and the least important forming the last. The PICRUSt2 software was used to predict the “segmented predicted function” of each cluster, while the “segmented theoretical function” was calculated using the relative abundance of each segmented ASV cluster¹⁶.