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Article

Mixed Plantations Improve Soil Bacterial Similarity by Reducing Heterogeneous Environmental Selection

by
Handan Dai
1,†,
Biao Dong
1,†,
Zhu Yang
1,
Yidan Yuan
1,
Yuhua Tan
1,
Yongtao Huang
1,* and
Xiao Zhang
2,3,*
1
Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, The College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
2
College of Forestry, Northwest A&F University, Xianyang 712100, China
3
Qinling National Forest Research Station, Huoditang, Ningshan, Ankang 711600, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2023, 14(7), 1341; https://doi.org/10.3390/f14071341
Submission received: 29 April 2023 / Revised: 1 June 2023 / Accepted: 27 June 2023 / Published: 29 June 2023
(This article belongs to the Section Forest Soil)
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Abstract

:
Monocultures and mixed plantations have long been applied in forestry and landscape restoration to maximize the benefits of plantations. These different plantations can have unpredictable effects on the forest ecosystem. Monocultures and mixed plantations may influence soil bacterial communities, yet the underlying mechanisms of the soil bacterial community similarity response to monocultures and mixed plantations are still unknown. This study aimed to unravel how the community assembly processes and their associated factors mediate soil bacterial community similarity among monocultures and mixed plantations. We present a conceptual model to understand the mechanisms controlling soil bacterial community similarity among monocultures and mixed plantations. We tested these conceptual model hypotheses and the underlying mechanisms by conducting experiments in three plantation forest regions in southern China. We found that different monocultures led to a highly dissimilar environment, which increased heterogeneous selection and resulted in a high dissimilarity of soil bacterial communities among monocultures. Compared with monocultures, mixed plantations afford more similar environmental conditions for soil bacterial communities and decrease the heterogeneous selection process, leading to a higher soil bacterial similarity among mixed plantations. In addition, we demonstrate that stochastic processes are also the dominant driver in determining the soil bacterial community similarity among mixed plantations. Overall, the conversion from monocultures to mixed plantations affects the community assembly process by altering environmental similarity and edaphic factors, subsequently determining the similarity of soil bacterial communities. Our study can provide scientific guidance for exploring the role of mixed plantations in forest management.

1. Introduction

A major goal in microbial ecology is to reveal how ecological processes and environmental factors govern the assembly of microbial communities [1]. There are two alternative views on what influences community assembly processes [2]. On the one hand, the traditional niche-based theory states that deterministic processes (e.g., ecological selection) influence organismal fitness and further change the species composition and their relative abundance [3]. On the other hand, the neutral theory states that community structure is governed by stochastic processes of drift, speciation and dispersal events, which leads to species composition patterns that are indistinguishable from those produced by random chance alone [4]. When deterministic processes prevail, microbial communities can largely reflect local environmental conditions, which affect species fitness and consequently shape the composition of communities [3,5]. On the contrary, stochastic processes prevail when selection is relatively weak [2]. Based on the assumption of species equivalence, stochastic processes (e.g., random birth-and-death processes) can result in unpredictable changes in community composition [6]. From the viewpoint of ecological processes, both deterministic and stochastic processes occur simultaneously in community assembly, with different dominant processes found in different cases [7].
Debates over deterministic and stochastic processes as theoretical explanations are continuous, whereas, in practice, the similarity and assembly of microbial communities could be explained by integrating both roles of the two different community assembly processes [8]. Due to variations in fitness between species, selection is referred to as a deterministic process driving the local community composition [9]. Selection resulting from homogeneous or heterogeneous environmental conditions can result in more similar or dissimilar structures among communities [10]. On the basis of environmental conditions, there are two main categories into which selection can be classified. If environmental conditions are homogeneous, selection leads to increased similarity between communities. This is known as homogeneous selection [9]. In contrast, heterogeneous selection is the main cause of high species turnover or dissimilarity due to a shift in selection pressure that is caused by variational environment conditions [11]. There is increasing evidence that the relative importance of stochastic processes in structuring microbial community similarities may be greater than previously thought [5,9]. Because environmental filters are not powerful enough to operate as selection forces that impose species sorting, this seems to lead to stochastic processes being more relevant in systems with low environmental heterogeneity [12]. In particular, species with low dispersal rates associated with drift or weak selection may lead to high community turnover or dissimilarity between communities [10].
The quality of root and leaf litter differs strikingly among forest species (e.g., Eucalyptus robusta and Pinus massoniana), creating specific plant-induced environments that directly or indirectly determine soil chemical and physical properties [13]. The heterogeneous environment caused by such different forest plants is known to strongly contribute to the maintenance of the local similarity of soil microbial communities [14]. Although several studies have presumed that the influence on the soil bacterial community structure relies on the diversity of the tree species and stand type [15,16] and that the compositional dissimilarity of soil microbes is associated with plant diversity [17], these influences are often ignored when assessing the impact of single and mixed tree species. Recent studies have shown that monoculture plantations can affect microbial communities by altering abiotic environmental variables, such as the soil C:N ratio and pH [18]. Further studies also showed that tree species growing in different monocultures can rapidly select different soil microbial communities with distinct functions [19]. By contrast, mixed plantations could induce higher soil microbial diversity than those in monocultures [20]. Given the objective differences in the environment between forest plantations, soil bacterial community similarity between monocultures and mixed plantations could face different environmental constraints [21]. Compared with monocultures, mixed plantations often share more balanced resources [22]. Therefore, the conversion from monoculture forests to mixed forests is closely related to the change in soil microbial dissimilarity. Nevertheless, the mechanisms by which monocultures or mixed plantations influence soil bacterial similarity are still poorly understood.
To maximize the benefits of plantations, foresters have explored a variety of plantation strategies, one of which is to plant both single-species (monocultures) and multispecies stands (mixtures or mixed plantations) [23]. Compared with monocultures, the advantages and differences of mixed plantations have been widely recognized [24], but the mechanisms underlying their differences, especially the community assembly processes of soil bacterial communities, are still unclear. Here, we explore soil bacterial community similarity among monocultures and mixed plantations at three sites: Gonghe, Heshan and Pingxiang, which were chosen because they have served as models for studying the forest ecosystem function of monocultures and mixed plantations in recent decades [25,26]. Our study focused on investigating the mechanism underlying the similarity of soil bacterial communities during the conversion from monocultures to mixed plantations by addressing the following questions:
Is the degree of species similarity of soil bacterial communities among mixed plantations higher than that among monocultures? We hypothesize that soil bacterial communities are more similar among mixed plantations than among monocultures (Figure 1).
If so, what assembly mechanism drives the high similarity of soil bacterial communities among mixed plantations? We hypothesize that mixtures will decrease the impact of heterogeneous selection, and homogeneous selection may be more important under certain conditions (Figure 1).

2. Methods

2.1. Study Site

Three plantation forest regions in South China were used for this study, which were the experimental sites in Gonghe (22°34′ N, 112°50′ E), Heshan (22°40′ N, 112°54′ E) and Pingxiang (22°10′ N,106°50′ E). Gonghe and Heshan are located in Guangdong Province’s Heshan National Field Research Station of Forest Ecosystem, and the Pingxiang study site is located at the Experimental Center of Tropical Forestry, Chinese Academy of Forestry, in the south of Guangxi Zhuang Autonomous Region. The three regions are characterized by a typical subtropical monsoon climate, with a distinct rainy (a hot and humid summer from April to September) and dry season (a cool and dry winter from October to March) [24]. The annual average temperatures in Gonghe, Heshan and Pingxiang are 21.7 °C, 22.6 °C and 21 °C, respectively, and the mean annual precipitation is 1688 mm, 1700 mm and 1400 mm, respectively. The soil type is classified as red or lateritic red soil in the Chinese soil classification system, which is equivalent to an Oxisol in the USDA Soil Taxonomy [27,28].
The plantation in Gonghe was established in 2005 because of an ecological restoration project. Before afforestation, burning treatments were carried out, which made the soil conditions of different forest types basically the same at the initial stage of afforestation. Then, six experimental plantations were planted in the same period: the three monoculture plantations included Acacia crassicarpa (AC), Castanopsis hystrix (CH) and Eucalyptus robusta (ER); the three mixtures were Eucalyptus robusta (MER), Castanopsis hystrix (MCH) and Castanopsis-Liquidambar (MCL). The age of the plantations was 15 years.
To restore the degraded ecosystems, a variety of experimental plantations were established at the Heshan Station on homogeneous hilly land in 1984 [23]. Historically, the site in Heshan had a typical subtropical monsoon evergreen broad-leaved forest. Six plantations were selected as research objects at the site in Heshan, which included three monoculture plantations of Eucalyptus robusta (ER), Pinus massoniana (PM) and Acacia mangium (AM) and three mixed plantations of Eucalyptus robusta (MER), local tree (MLT) and Coniferous mixed (MCM). The age of the plantations was 30 years.
The soils were homogeneous within the study site in Pingxiang before the establishment of different tree species. Specifically, the site in Pingxiang was covered by subtropical evergreen forest, and it was deforested in the 1950s and then planted with a C. lanceolata plantation. Subsequently, monocultures and mixed plantations were established in 1983 after the clearcutting of the C. lanceolata plantation. As a result of the planting measure, monocultures and mixtures were randomly located across the site. The three monoculture plantations of Betula alnoides (BA), Castanopsis hystrix (CH) and Erythrophleum fordii (EF) were included, and the three mixed plantations were Pinus-Erythrophleum (MPE), Castanopsis-Betula (MCB) and Castanopsis-Cunninghamia (MCC). The age of the plantations was 30 years. In all mixed plantations, different forest species exhibited alternate distribution patterns, and mixed 1:1. In addition, there was the same number in each monoculture and mixed plantation.

2.2. Experimental Design

Except for the monoculture plantations of CH in Gonghe, which was set up with 4 replicate forest quadrats, 5 replicate forest quadrats were selected per plantation type for each of the 11 plantation types in the sites in Gonghe and Heshan, and 8 replicate quadrats were established in each of the 6 plantation types selected in the site in Pingxiang, with a total of 107 sampling plots in our study. Sampling plots were randomly chosen for each type of plantation based on similar topographies, soil compositions, stand ages, and management histories. In addition, the understory vegetation was exiguous due to the closed canopy. In each plantation type, each 20 m × 20 m plot was built at least 25 m away from a plantation boundary. The distances and distribution of all quadrats in the sites in Gonghe, Heshan and Pingxiang are consistent and similar. Additionally, the fact that all quadrats were designed to be within a short (<10 km) distance ensured that differences in the size of species pools between quadrats could only be caused by environmental selection, thus eliminating the possibility of dispersal limitation as a causal factor.

2.3. Soil Sampling and Biogeochemical Analyses

Soil samples were taken between June and July 2015. Before collecting soil samples, the litter on the surface was carefully removed. In each plot, six soil cores (2.5 cm in diameter and 10 cm in depth) were obtained at random using sterile blades and then combined to form a single sample. Soil samples were stored in disposable sterile bags and taken to the laboratory in an ice box. Then, 2 mm mesh sieves were used to pass fresh soil samples to remove debris, such as coarse living roots and gravel, to make them completely homogenized. Each sample was subdivided into two subsamples: one for measuring soil properties stored at 4 °C and another subsample for extracting DNA stored at −40 °C.
Soil pH was determined in a 1:5 soil–water slurry using a digital pH meter. The moisture content of the soil samples (SM) was calculated using the gravimetric method after drying at 60 °C for 48 h. The total nitrogen (TN) concentration in the soil was measured with combustion (CN-2000). After filtering 50 mL of a 2 M KCL solution, ammonium nitrogen (NH4+-N) and nitrate nitrogen (NO3−-N) concentrations were determined using indophenol blue colorimetry and the cadmium reduction method using a modified Berthelot reaction, respectively [29]. Utilizing the ultraviolet (UV) spectrophotometry method, the amount of available and total phosphorus (TP) in the soil was quantified. Soil organic carbon (SOC) was analyzed by performing K2Cr2O7 titration after digestion.

2.4. DNA Extraction and MiSeq Sequencing of 16S rRNA Gene Amplicons

DNA was isolated from 0.3 g of well-homogenized soil using the MoBio Power Soil DNA isolation kit (MoBio Laboratories, Carlsbad, CA, USA) following the manufacturer’s instructions. The NanoDrop Spectrophotometer (NanoDrop Technologies Inc., Wilmington, DE, USA) was used to assess the quality and concentrations of the extracted DNA, and then each sample was stored at −20 °C until further use. Polymerase chain reaction (PCR) amplification was conducted with universal primers 515F (5′-GTGYCAGCMGCCGCGGTA-3′) with 12 nt unique barcodes at the 5′-end and 909R (5′-CCCCGYCAATTCMTTTRAGT-3′) for the V4−V5 hypervariable regions of bacterial 16S rRNA genes [30]. The 25 μL PCR reaction volume comprised 1 μL of each primer (10 μM), 10 ng of template DNA and 0.5 units of Accuprime high-fidelity Taq polymerase. Each sample was amplified in triplicate using the following conditions: 28 cycles of denaturation at 94 °C for 30 s, followed by annealing at 55 °C for 45 s, and then extension at 72 °C for 45 s, with a final extension of 5 min at 72 °C. The PCR products for each sample were pooled and purified using the Gel Extraction Kit (Omega Bio-Tek). The PCR products from all samples were mixed in equimolar amounts for library construction and then sequenced with the Illumina TruSeq DNA kit according to the manufacturer’s instructions and mixed with PhiX in amounts equal to 30% of the total DNA. The sequencing library was prepared using Truseq DNA PCR-Free Library Preparation Kits and sequenced with 2 × 250 bp V2 Kits on the Illumina Miseq platform.

2.5. Sequence Analyses

The processing of raw sequencing data was mainly carried out using QIIME-Version 1.7.0 (http://qiime.org/, accessed on 12 March 2020) [31]. According to the unique barcodes, the sequences were assigned to the corresponding samples, which were denoised and trimmed to avoid diversity overestimation. Sequences longer than 200 base pairs and an average base quality score of at least 30 were required for use in downstream analyses. The number of primer mismatches allowed was set at two, and ambiguous bases and barcode errors were disallowed [32]. All singletons and potential chimeras were removed using the Uchime algorithm [33]. Additionally, “unassigned” and “archaea” sequences were eliminated. Resampling was performed using the Perl script to make the sequence depth the same (15,000 bacterial sequences per sample) [34]. The resulting high-quality sequences were clustered into operational taxonomic units (OTUs) using a closed-reference OTU-picking technique in QIIME according to a 97% sequence similarity threshold. The Ribosomal Database Project (RDP) 16S Classifier program of QIIME was used to analyze the representative sequences of each OTU.

2.6. Statistical Analyses

To evaluate the effect of mixed plantations on the overall change in bacterial communities and environmental factors, principal coordinate analysis (PCoA) based on Bray–Curtis distance matrices and Euclidean distance matrices was used to visualize the bacterial community structure and environmental characteristics among monocultures and mixtures. Based on the Bray–Curtis dissimilarity matrix, PerMANOVA was carried out to identify whether there were statistical differences among community structures. Boxplots illustrate the similarity among communities between monocultures and mixed plantations, which was calculated using the Bray–Curtis distance based on bacterial OTU tables. The shared and unique OTUs of soil bacterial communities among monocultures and mixed plantations were calculated, and their distributions were shown in a Venn diagram using the “VennDiagram” package in R (version v4.0.5). The coefficient of variation (CV) (standard deviation divided by mean) of soil bacterial communities among monocultures and mixed plantations at the phylum, class and genus levels was tested. Independent-samples t-tests were used to evaluate the differences in environmental factors between monocultures and mixed plantations.
We used the β-deviation from the null model to examine how community assembly processes influence bacterial community similarity. First, according to the Bray–Curtis metric, we measured the observed β-diversity as the distance from a single plot to the center point of all plots in a site (centroid distance). Then, we calculated the β-deviation as the difference between the observed β-diversity and expected dissimilarity from 999 iterations of the null model, divided by the standard deviation of expected values. The average β-deviation is zero, which means that the stochastic process plays a dominant role in shaping bacterial community similarity; a positive average beta deviation (significantly greater than zero) indicates that the observed species are farther away from the expected relationship, indicating that heterogeneous selection is dominant in shaping bacterial community similarity; the negative mean β-deviation (significantly less than zero) indicates that species are closer than expected, indicating that homogeneous selection is dominant in shaping the similarity of bacterial communities.
Piecewise structural equation modeling (pSEM) was performed to further discern the potential direct and indirect effects of environmental variables, environmental similarity and local assembly processes on bacterial similarity. pSEM was implemented with the piecewiseSEM package [35]. This approach allowed the path diagram to be translated into a set of linear mixed-effects models and then evaluated individually to test the hypothetical directed acyclic graph model. The chi-square test on Fisher’s C (p > 0.05) was used to assess the fit of the model [36].

3. Results

3.1. Effects of Monocultures and Mixed Plantations on Bacterial Community Similarity

Proteobacteria, Acidobacteria and Actinobacteria dominated the soil bacterial communities in all samples (Figure 2). The coefficients of variation (CVs) at the phylum level among monocultures and mixed plantations were significantly different across all sites, in which the coefficient-of-variation values were lower among mixed plantations compared to monocultures (Table S1). Principal coordinate analysis (PCoA) showed significant differences in soil bacterial community structure among monocultures and mixed plantations at the three sites (Figure 3). The first two components of PCoA explained 41.9%, 56.3%, and 44.7% of the soil bacteria variation (at the sites in Gonghe, Heshan and Pingxiang, respectively). Bacterial communities of mixed plantation species were more clustered than monoculture plantations (PerMANOVA tests, p < 0.05, Table S2). In addition, the result was confirmed by the analysis of community similarity (Figure S2). These results suggest that the similarity among communities in mixed plantations is higher than that in monocultures.

3.2. Different Community Assembly Processes of Bacterial Communities among Monocultures and Mixed Plantations

We calculated the β-deviation to explore the relative contributions of deterministic versus stochastic processes to bacterial community similarity. Our results indicate that monocultures exhibit a consistent heterogeneous selection process, while community assembly processes in mixed plantations were different between the three sites. The β-deviation gradually shifted from heterogeneous selection among monocultures (β-deviation values > 0) to stochastic processes among mixed plantations at the site in Gonghe (Figure 4A). At the site in Heshan, heterogeneous environmental selection gradually weakened with the conversion of monocultures into mixed plantations (Figure 4B). In addition, we found that homogeneous environmental selection shaped bacterial communities in the MER monoculture plantation. At the site in Pingxiang, although heterogeneous environmental selection was dominant in both monocultures and mixed plantations, its role gradually weakened (Figure 4C). In summary, with the shift of monocultures to mixed plantations, soil bacterial communities showed that the relative importance of heterogeneous environmental selection decreased.

3.3. Mechanism of the Influence of Mixed Plantations on Soil Bacterial Community Similarity

The PCoA based on the Euclidean distance metric showed that monoculture and mixed plantations significantly changed the environmental similarities (Figure 5). Compared with monocultures, the environments in mixed plantations were more clustered, which showed a higher degree of environmental similarity. In addition, the soil variables in most mixed plantations were significantly higher than those in monocultures (Figure S4). pSEM was used to explore the potential mechanisms of different soil bacterial communities in monocultures and mixed plantations. After removing the independence requirement, which had no statistical significance, the model for the three sites resulted in good model data (Figure 6). pSEM showed that the indirect influence of environmental factors and environmental similarities regulated the assembly process and further explained soil bacterial community similarity (Figure 6). We found that available phosphorus and nitrate nitrogen had a consistent and significant response to the stand conversion from monocultures into mixed plantations at all sites. Overall, our results revealed that the conversion from monocultures to mixed plantations affects the community assembly process by altering environmental similarity and subsequently impacting the similarity of soil bacterial communities.

4. Discussion

4.1. Tree Species Mixture Result in More Similar Soil Bacterial Communities Than Monocultures

Our results revealed that different species significantly changed the soil bacterial community composition and structure at all experimental sites (Figure 2, Figure S3, Table S2). Most prior studies found similar results showing that the soil bacterial community structures among monocultures and mixed plantations are significantly different. These results indicated the important effects of planted forests on soil microbial composition [37,38,39]. Further analysis illustrated that the soil bacterial communities of mixed plantations were more clustered than in monocultures (Figure 3), indicating that the tree species mixture improved the similarity of soil bacterial communities (Figure S2), which is in concordance with previous studies [40,41]. Additionally, we found that the number of shared OTUs among mixed plantations was higher than that among monocultures, and the coefficients of variation were less than those among monoculture plantations. These findings provide extensive evidence for highly similar soil bacterial communities among mixed plantations. Different tree species living in different monocultures can rapidly select microbial communities characterized by unique habitats. Compared with monocultures, mixed plantations likely create a more similar environment and provide more opportunities for soil microbes to survive in a shared environment [42], which may result in more similar microbiomes from the mixture soil. Overall, our results confirm our first hypothesis that soil bacterial communities among mixed plantations are more similar than those among monocultures.

4.2. Soil Bacterial Similarity among Monoculture and Mixed Plantations Is Driven by Different Community Assembly Processes

Although many studies have demonstrated that plant richness clearly affects soil microbial diversity [17,43,44], few of these studies have been able to elucidate the underlying assembly mechanisms. We further explored the community assembly processes underlying bacterial community similarity among monocultures and mixed plantations. Our study revealed that deterministic processes (heterogeneous selection) in monocultures are more important than stochastic processes at all sites (Figure 4). However, we observed evidence of different processes that characterize mixed plantations in different sites: stochastic effects (Figure 4A), a decrease in heterogeneous environmental selection (Figure 4B,C), and an increase in homogeneous environmental selection (Figure 4B). We interpret this as a legacy effect of plant species, which only partly supports our second hypothesis. Regional drivers include site-level changes in species and variations in environmental conditions [45], which could influence the similarity between monocultures and mixed plantations. It has been demonstrated that plant diversity influences bacterial abundance and structure in soil [46]. Changes in environmental conditions can further shift species turnover in the community [11]. Long-term single-litter input and the effect of tree species characteristics in monoculture plantations result in relatively specialized habits, which lead to more heterogeneous environmental conditions among monoculture plantations [26,47]. Changes in heterogeneous environmental conditions result in high variations in community structure, as it can enhance the turnover in community compositions [48]. Thus, species living in different monocultures led to highly dissimilar environments in our study, which increases heterogeneous selection and results in a high dissimilarity of monoculture soil bacterial communities. In summary, the deterministic process contributed more to soil bacterial communities than the stochastic process in monocultures, confirming the predictions of the niche theory that species can reach all locations where environments are suitable, and this habitat specialization process was a classic deterministic process mainly influenced by environmental heterogeneity [49]; thus, the degree of the environmental difference among monocultures served as the driver of species similarity.
Our results also show that the degree of heterogeneous selection among mixed plantations was less than that among monocultures in Heshan and Pingxiang (Figure 4B,C). We speculated that this may be because environmental conditions among mixed plantations are relatively similar (Figure 5), which leads to a decrease in heterogeneous selection. Macroecological theory suggests that deterministic assembly processes may be the result of homogeneous or heterogeneous selection due to environmental characteristics. Different sites that have dissimilar environmental conditions may lead to heterogeneous selection and select different taxa [9]. In contrast, homogeneous environmental selection is expected in the same environmental conditions, which increases the similarity between communities [50,51]. That is, homogeneous selection plays an important role when similar environmental characteristics result in the convergence of community composition across a range of sites [4]. Microecology researchers also found evidence that selection plays a key role in driving bacterial community structure in different environments, and its relative importance decreases as their environmental conditions become more homogeneous [10,52,53]. Our study supports these studies, which provide empirical evidence that selection is the predominant factor in shaping the soil bacterial dissimilarity among monocultures. When converting monocultures to mixed plantations, the decrease in heterogeneous selection and the increase in homogeneous selection both increased the soil bacterial community similarity among mixed plantations. In addition, it is noteworthy that during this conversion process, stochastic processes may also play a leading role among mixed plantations.
The β-deviation values close to zero suggest that stochastic processes are important factors leading to highly similar soil bacterial communities among mixed plantations in Gonghe (Figure 4A). There are three underlying mechanisms to explain why stochastic processes are more influential in mixed plantations. First, when environmental factors do not impose overwhelming selection pressure [3] or are balanced between heterogeneous and homogeneous selection, stochastic processes have a stronger impact on community assembly [54]. Second, the mixed plantations received a greater nutrient supply than monoculture plantations (Figure S4), which may introduce more stochastic effects on the microbial community to counteract the effects of deterministic processes [55,56]. Finally, dispersal processes between mixed plantations may be higher than between monoculture plantations as a result of more similar environments [57,58], and dispersal can even outweigh the influences of deterministic processes at high dispersal rates [11]. Overall, our results indicate that the effects of heterogeneous selection on soil bacterial community similarity among monoculture plantations are consistently the strongest but suggest that stochastic effects are also critical in mixed plantations. These findings confirm that the effects of deterministic and stochastic processes on soil microbial community assembly in mixed plantations are not isolated but exert a concerted influence.

4.3. Driving Mechanism of Soil Bacterial Similarity among Monocultures and Mixed Plantations

Many studies have found that plant species diversity alters soil factors and directly affects soil bacterial communities [14,59], but few of these studies have addressed the underlying community assembly processes. Our pSEM analysis revealed that the conversion from monocultures to mixed plantations affects the community assembly process by altering environmental similarity and subsequently impacting soil bacterial community similarity (Figure 6). These findings showed that most soil factors did not directly affect soil bacterial similarity, but rather changed the environmental selection pressure by increasing environmental similarities. The positive correlation between the transformation process from monocultures to mixed plantations and environmental similarity indicates the important role of mixed forests in driving environmental similarity (Figure 6). Soil resource concentration differences and the selectivity of soil bacteria to the environment may explain this phenomenon [60]. Mixed plantations can provide a more even environment by enhancing the resource (e.g., litter) and microhabitat (e.g., soil nutrients) diversity [42,43], which could improve the similarity of soil environments among mixed plantations. The negative correlation between environmental similarity and community assembly processes indicated that changes in soil environments decrease the importance of heterogeneous selection (Figure 6). Some studies have confirmed that the selection driving the bacterial community structure in heterogeneous environments and its relative importance decrease as the environmental conditions become more homogeneous [10,61]. It is noteworthy that weak environmental selection causes increased community similarity in many terrestrial ecosystems [62,63]. Weak environmental selection could reduce deterministic processes and improve dispersal and drift [64]. Wang, Shen, et al. [12] also found that the assembly of bacterial communities was governed by deterministic processes, whereas the effects of stochastic processes increased as environmental heterogeneity decreased. As a result, in the process of stand conversion from monocultures to mixed plantations, heterogeneous selection pressures acting on microbial communities consistently decreased, which increased the similarity of bacterial communities.
Our results indicate that different soil variables drove environmental similarity and soil bacterial community assembly processes, which subsequently determined soil bacterial community similarity. Among different influencing factors, only soil nitrate N and available P were significantly shared soil determinants at all sites (Figure 6). Many studies have found that species diversity or identity affects soil N accumulation through litter and root input in forest ecology systems [65,66]. The mixed plantations exhibited higher soil N contents, suggesting that soil N accumulation in mixed plantations was higher than in monocultures (Figure S4). This is probably because mixed plantations can provide higher root turnover, which results from higher root production, thereby increasing the soil nitrogen content of mixed plantations [67]. Moreover, mixed forests have stronger resistance to environmental pressures, have stronger nitrogen retention ability, and can prevent nitrogen leaching in soil [68]. In terrestrial ecosystems, especially in tropical and subtropical forests, soil P has been considered an important limiting nutrient [69], and therefore, strong selection pressure is exerted to drive differences in microbial community composition. Several studies have found higher P availability in mixed plantations compared to monocultures [70,71], which is consistent with our studies. Interactions between species in mixed plantations may lead to shifts in the impact of individual species on soil nutrient mineralization, thereby affecting soil nutrient availability [71]. As a high N supply can promote AP activity by transferring the microbial stoichiometric demand from N to P [72], the higher AP in our study could also be a result of high N in mixed plantations [73]. Previous studies have shown that stochastic processes correspond to higher nutrient levels, whereas deterministic assembly processes increase with decreasing soil nutrient levels [56,74]. Therefore, the significant improvement of soil N and P in mixed plantations may be an important reason for the decrease in environmental heterogeneity in mixed plantations. Taken together, with the conversion from monocultures to mixed plantations, the soil environment becomes less harsh or heterogeneous, and homogeneous selection or neutral processes become more and more important in shaping bacterial community similarity. Although the role of environmental factors in soil microbial community similarity was demonstrated by pSEM, some residual variation (18%–41%) was not explained. This may be because of unmeasured environmental variables, species interactions or other historical events that we did not take into account.

5. Conclusions

This study provides a markedly improved understanding of soil bacterial community similarity and the underlying mechanisms among monocultures and mixed plantations. Our study revealed that species living in different monocultures led to highly dissimilar environments, which increased heterogeneous selection and resulted in a high dissimilarity of soil bacterial communities among monocultures. Compared to monocultures, mixed plantations afford more similar environmental conditions for soil bacterial communities and decrease the heterogeneous environmental selection process, leading to a higher soil bacterial similarity among mixed plantations. In addition, we demonstrate that stochastic processes also dominate in determining soil bacterial similarity among mixed plantations. Overall, the conversion from monocultures to mixed plantations affects the community assembly process by altering environmental similarity and subsequently impacting the similarity of soil bacterial communities. Our study provides scientific guidance for forest plantation strategies considering soil bacterial community similarity, emphasizing the importance of selecting mixed plantations.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f14071341/s1, Table S1: The coefficient of variation (CV) of soil bacterial communities among monocultures and mixtures at the phylum (a) class (b) and genus (c) levels; Table S2: PerMANOVA test of the differences in bacterial community structures based on Bray−Curtis distances measures (permutation: 9999); Table S3: Summary of environmental factors in monocultures and mixtures; Figure S1: Relative abundance (% of total reads) of soil bacterial communities among monocultures and mixtures at the class (A) and genus (B) levels; Figure S2: Boxplot of Bray−Curtis distances illustrates the similarity among communities of monocultures and mixtures; Figure S3: Venn diagrams demonstrating the shared and unique OTUs of soil bacterial communities among monocultures and mixtures in the site of Gonghe (a), Heshan (b), and Pingxiang (c); Figure S4: Independent samples t−test is used to compare the significant difference of environmental factors between monoculture and mixture.

Author Contributions

Y.H. and X.Z. designed the study. X.Z. sampled soil samples, Y.H. and H.D. measured soil chemistry and were responsible for measures of bacterial community OTUs data and provided data. H.D., B.D., Z.Y., Y.Y., Y.T., X.Z. and Y.H. wrote the first draft of the manuscript, All authors have read and agreed to the published version of the manuscript.

Funding

We gratefully acknowledge the joint support of the Natural Science Foundation of China (32071556, 31800375, 31700383, and U1904204) and the Natural Science Foundation of Henan Province (222300420036).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no competing interest.

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Figure 1. Hypotheses were tested to illuminate the underlying mechanisms of soil bacterial community similarity during the conversion from monocultures to mixed plantations. The top half of the figure shows above-ground vegetation, with three different monoculture plantations on the left and different mixtures on the right. The bottom half of the figure shows soil bacterial communities, with different shapes representing different hypothetical bacterial types. The short lines with arrows represent the environmental selection process: three short lines with different colors represent heterogeneous selection in monocultures, and three short lines with yellow represent homogeneous selection in mixed plantations. The yellow curve represents the change in the selection process. The height of the column at the bottom of the figure represents the numerical value of similarity among communities.
Figure 1. Hypotheses were tested to illuminate the underlying mechanisms of soil bacterial community similarity during the conversion from monocultures to mixed plantations. The top half of the figure shows above-ground vegetation, with three different monoculture plantations on the left and different mixtures on the right. The bottom half of the figure shows soil bacterial communities, with different shapes representing different hypothetical bacterial types. The short lines with arrows represent the environmental selection process: three short lines with different colors represent heterogeneous selection in monocultures, and three short lines with yellow represent homogeneous selection in mixed plantations. The yellow curve represents the change in the selection process. The height of the column at the bottom of the figure represents the numerical value of similarity among communities.
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Figure 2. Relative abundance (% of total reads) of soil bacterial communities among monocultures and mixtures at the phylum level. The letters (A–C) stand for the sites in Gonghe, Heshan and Pingxiang, respectively.
Figure 2. Relative abundance (% of total reads) of soil bacterial communities among monocultures and mixtures at the phylum level. The letters (A–C) stand for the sites in Gonghe, Heshan and Pingxiang, respectively.
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Figure 3. Principal coordinate analysis (PCoA) based on Bray–Curtis distance of soil bacterial communities among monocultures and mixed plantations. The letters (A–C) stand for the sites in Gonghe, Heshan and Pingxiang, respectively. Orange dots represent monocultures, and blue dots represent mixed forest plantations.
Figure 3. Principal coordinate analysis (PCoA) based on Bray–Curtis distance of soil bacterial communities among monocultures and mixed plantations. The letters (A–C) stand for the sites in Gonghe, Heshan and Pingxiang, respectively. Orange dots represent monocultures, and blue dots represent mixed forest plantations.
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Figure 4. The null deviations were calculated to separate bacterial community assembly into deterministic and stochastic processes. Deviations that are greater than zero represent pairs of samples where deterministic processes dominated community assembly (deviation < 0: homogeneous selection; deviation > 0: heterogeneous selection). Deviations that are close to zero represent pairs of samples where stochastic processes dominated community assembly. Boxes represent the median and 25th/75th percentiles, and whiskers extend to 1.5 times the interquartile range. The letters (A–C) stand for the sites in Gonghe, Heshan and Pingxiang, respectively. Orange boxes represent monocultures, and blue boxes represent mixed forest plantations.
Figure 4. The null deviations were calculated to separate bacterial community assembly into deterministic and stochastic processes. Deviations that are greater than zero represent pairs of samples where deterministic processes dominated community assembly (deviation < 0: homogeneous selection; deviation > 0: heterogeneous selection). Deviations that are close to zero represent pairs of samples where stochastic processes dominated community assembly. Boxes represent the median and 25th/75th percentiles, and whiskers extend to 1.5 times the interquartile range. The letters (A–C) stand for the sites in Gonghe, Heshan and Pingxiang, respectively. Orange boxes represent monocultures, and blue boxes represent mixed forest plantations.
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Figure 5. Principal coordinate analysis (PCoA) of environment based on Euclidean distance among monocultures and mixed plantations. The Euclidean distance matrix was logarithmically transformed, and then the result was inverted. The environmental parameters used include soil pH, soil moisture, soil organic carbon, total nitrogen, ammonia nitrogen, nitrate nitrogen, total phosphorus and available phosphorus. The letters (A–C) stand for the sites in Gonghe, Heshan and Pingxiang, respectively. Orange dots represent monocultures, and blue dots represent mixed forest plantations.
Figure 5. Principal coordinate analysis (PCoA) of environment based on Euclidean distance among monocultures and mixed plantations. The Euclidean distance matrix was logarithmically transformed, and then the result was inverted. The environmental parameters used include soil pH, soil moisture, soil organic carbon, total nitrogen, ammonia nitrogen, nitrate nitrogen, total phosphorus and available phosphorus. The letters (A–C) stand for the sites in Gonghe, Heshan and Pingxiang, respectively. Orange dots represent monocultures, and blue dots represent mixed forest plantations.
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Figure 6. Piecewise structural equation modeling (pSEM) quantifying the relative importance of the directional causal relationships between tree mixture, soil parameters, community assembly process and soil bacterial community similarity. Both models were well supported by our data: (A) Gonghe: Fisher’s C = 62.97; df = 60; p = 0.38; (B) Heshan: Fisher’s C = 63.54; df = 56; p = 0.23; (C); Pingxiang: Fisher’s C = 52.70; df = 54; p = 0.53). None of the independent claims implied by the model was statistically significant at p > 0.05. Green and red arrows indicate positive and negative relationships, respectively. Gray dashed arrows represent nonsignificant (p > 0.05) relationships. Arrow width is proportional to the standardized path coefficient and can be interpreted as the relative importance of each factor, which is shown above each arrow. R2 values are included for each endogenous (response) variable. Significance: * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001. Abbreviations: Ammonia N, ammonia nitrogen; Nitrate N, nitrate nitrogen; Total P, total phosphorus; Available P, available phosphorus.
Figure 6. Piecewise structural equation modeling (pSEM) quantifying the relative importance of the directional causal relationships between tree mixture, soil parameters, community assembly process and soil bacterial community similarity. Both models were well supported by our data: (A) Gonghe: Fisher’s C = 62.97; df = 60; p = 0.38; (B) Heshan: Fisher’s C = 63.54; df = 56; p = 0.23; (C); Pingxiang: Fisher’s C = 52.70; df = 54; p = 0.53). None of the independent claims implied by the model was statistically significant at p > 0.05. Green and red arrows indicate positive and negative relationships, respectively. Gray dashed arrows represent nonsignificant (p > 0.05) relationships. Arrow width is proportional to the standardized path coefficient and can be interpreted as the relative importance of each factor, which is shown above each arrow. R2 values are included for each endogenous (response) variable. Significance: * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001. Abbreviations: Ammonia N, ammonia nitrogen; Nitrate N, nitrate nitrogen; Total P, total phosphorus; Available P, available phosphorus.
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Dai, H.; Dong, B.; Yang, Z.; Yuan, Y.; Tan, Y.; Huang, Y.; Zhang, X. Mixed Plantations Improve Soil Bacterial Similarity by Reducing Heterogeneous Environmental Selection. Forests 2023, 14, 1341. https://doi.org/10.3390/f14071341

AMA Style

Dai H, Dong B, Yang Z, Yuan Y, Tan Y, Huang Y, Zhang X. Mixed Plantations Improve Soil Bacterial Similarity by Reducing Heterogeneous Environmental Selection. Forests. 2023; 14(7):1341. https://doi.org/10.3390/f14071341

Chicago/Turabian Style

Dai, Handan, Biao Dong, Zhu Yang, Yidan Yuan, Yuhua Tan, Yongtao Huang, and Xiao Zhang. 2023. "Mixed Plantations Improve Soil Bacterial Similarity by Reducing Heterogeneous Environmental Selection" Forests 14, no. 7: 1341. https://doi.org/10.3390/f14071341

APA Style

Dai, H., Dong, B., Yang, Z., Yuan, Y., Tan, Y., Huang, Y., & Zhang, X. (2023). Mixed Plantations Improve Soil Bacterial Similarity by Reducing Heterogeneous Environmental Selection. Forests, 14(7), 1341. https://doi.org/10.3390/f14071341

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