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Article

Composition of Soil Bacterial and Nematode Communities within Soil Aggregates in a Kiwifruit Orchard under Cover Crop Treatment

by
Qingmei Li
1,2,3,
Xiaoxu Qi
1,2,3,
Lingling Zhang
1,2,3,
Yanjun Zhang
1,2,3,
Haifang Zhang
1,2,3,
Hongmei Liu
1,2,3,
Dianlin Yang
1,2,3 and
Hui Wang
1,2,3,*
1
Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
2
Key Laboratory of Original Agro-Environmental Pollution Prevention and Control, MARA, Tianjin 300191, China
3
Tianjin Key Laboratory of Agro-Environment and Agro-Product Safety, Tianjin 300191, China
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(5), 1377; https://doi.org/10.3390/agronomy13051377
Submission received: 20 March 2023 / Revised: 10 April 2023 / Accepted: 1 May 2023 / Published: 15 May 2023
(This article belongs to the Section Soil and Plant Nutrition)
Browse Figures
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Abstract

:
Soil, which exhibits difference in nutrient contents and aggregate sizes, provides spatially distinct habitats for biota. Cover crops influence the compositions of soil organism communities, playing an indispensable role in regulation of underground food webs and ecosystem functions. However, the effect of cover crops on soil microbes and nematodes distribution within different aggregate sizes remains unknown. Thus, a field experiment in a kiwifruit orchard with cover crops was conducted to estimate the distribution of soil nematodes and bacteria with different soil aggregate sizes (mega-aggregate (>2 mm, LMA), macro-aggregate (0.25–2 mm, SMA), and micro-aggregate (<0.25 mm, MA)) and cover crop treatments (four cover crop species (CC) and no cover crop as control (CK)). The results showed that bacterial compositions varied with both aggregate sizes and cover crop treatments. The composition of bacterial community was significantly different between mega-aggregate and micro-aggregate, and bacterial community diversity was significantly higher in micro-aggregate compared with mega-aggregate. Moreover, cover crop treatment dramatically changed the compositions of bacterial communities. However, the nematode communities were mainly impacted by soil aggregate sizes. Larger aggregates (mega- and macro-aggregates) contained higher abundance of omnivores/predators and lower abundance of fungivores. In contrast to bacterial community, the richness of nematode community was lower in micro-aggregates compared with larger aggregates (mega- and macro-aggregates). Redundancy analysis (RDA) and structural equation model (SEM) showed soil organic carbon (SOC) was the main soil factor that directly and indirectly affected both bacterial and nematode communities. The investigations of both bacterial and nematode communities could provide a better understanding on carbon and nutrient cycling across aggregate size fractions.

1. Introduction

As the basic unit of soil physical structures, soil aggregates are formed by mineral and organic matters [1] and affect soil water retention, nutrient conservation, and nutrient supply [2]. The distribution and stability of aggregates influence the composition of soil biotic communities [3]. In addition, highly heterogeneous soil structures affect the distribution, quantity, diversity and function of soil organisms [4,5]. Soil properties, nutrient characteristics and microbial activity exhibit obvious difference among aggregates with different sizes [6]. Previous studies reported that soil carbon and nitrogen contents increased as the aggregate sizes decreased [7], and organic carbon contents are less susceptible to change in the micro-aggregates than those in the larger aggregates [8]. In addition, there is an obvious contract in community and diversity of soil organisms in different-sized aggregates [3]. The change in soil nutrients and organisms in different-sized aggregates can impact the conversion process of key nutrients, such as carbon and nitrogen, and then affect soil ecosystem function [9,10,11].
Soil microbes form critical links between detritus and soil fauna, and compose the most important resource base for soil food web [3]. In addition, soil microbes are involved in organic matter decomposition, aggregate formation, as well as aggregate stability [12,13]. Therefore, maintenance of the diversity and composition of the soil microbial is vital for sustaining soil ecosystem function [14]. However, there are contradictory results about the relationship between microbial abundance and aggregate sizes from different studies. Some studies demonstrated that microbes mainly concentrate in aggregates with the size >0.25 mm [3,15,16]. Other studies showed that microbes enrich in the aggregates with the size <0.25 mm [10,17]. Therefore, it is necessary to systemically investigate the distribution of soil bacterial community in the aggregates with different sizes, which will provide new information for future studies in soil ecology.
Soil nematodes are prevalent inhabitants of soil ecosystems [18] which occupy a major position in energy flow and material cycle [19,20] and play a central role in soil micro-food webs [21,22]. The distribution of soil nematodes with specific body sizes and feeding habits may be determined by resource availability and soil aggregation related to different soil structures [15,23]. Assessment of nematode community composition in different-sized aggregates can provide insights into micro-scale soil biochemical process in soil due to various feeding groups that have unique preferences for their food sources [24,25,26]. Nematode communities, together with microbial communities, vary with soil management practices in agroecosystem. Exploring the distribution characteristics of soil nematodes and microbes in different-sized aggregates under soil management practices contribute to supplement our understanding of the dynamics of soil food web and enhance the effectiveness of soil management practices [3,27]. However, it is insufficient to investigate the distribution characteristics of soil microbes and nematode in different-sized aggregates.
Soil management measures, e.g., fertilization, crop rotation and cover crops, are known to improve soil aggregate structure and distribution [7,28] and to regulate the community and diversity of soil organisms [7,29,30]. Especially cover crops may increase the proportion and the stability of large aggregates, enhance soil nutrients, and regulate structures of soil organism communities [31,32,33]. Compared with a single cover crop, multiple cover crops can input more organic residues to soil, which enter detritus food web for decomposition of soil organisms [34]. In addition, plant diversity has an important impact on soil fauna that greatly contributes to soil ecosystem functions [35]. Increasing crop species diversity in rotation system enhances soil microbial biomass and changes structures of microbial community, which promote nutrient circulation and organic matter degradation [30]. Meanwhile, plant diversity increases soil nematode community diversity, which has a significant influence on soil nitrogen mineralization [36]. While some outcomes of cover crop diversity have been documented, the distribution of soil microbe and nematode under cover crop treatment in different-sized aggregates is still unknown.
In the present study, we explored the effect of aggregates with different sizes under cover crop treatment (CC) and no cover crop as control (CK) on the composition of nematode and bacterial communities in a kiwifruit orchard. Soil samples were collected and separated into three aggregate size levels (>2, 0.25–2, <0.25 mm). The soil organic carbon (SOC), total nitrogen (TN), soil organic carbon/total nitrogen ratio (C/N), and the bacterial and nematode compositions were determined in different-sized aggregates. We aimed to (i) determine the distribution characteristics of soil bacterial and nematode communities in different-sized aggregates and in cover crop treatments, (ii) clarify the main drivers for the variation in the communities of microbial and nematode among different-sized aggregates.

2. Materials and Methods

2.1. Site Description and Experimental Design

The study was established in a kiwifruit planting base of the Economic Crop Research Institute, Shiyan Academy of Agricultural Sciences, Shiyan City, Hubei Province, China (32°50′ N, 110°60′ E). The annual average precipitation was 950 mm, with a mean annual temperature of 16 °C and a frost-free period of 248 d. Prior to the experiment, the soil had the following chemical properties: soil organic matter: 6.67 g kg−1, pH 8.14, total phosphorus: 0.49 g kg−1, total nitrogen: 0.44 g kg−1.
The experiment was conducted in a kiwifruit (Actinidia chinensis Planch.) orchard. Kiwifruit trees were planted in 2015 with a spacing of 5 m × 3 m. In 2016, the cover crop experiments were set up with randomized block with three replications. The whole plots were divided into two sub-plots and were randomly assigned as cover crop treatment (CC) and no cover crop as control (CK). These two treatments resulted in 6 plots (2 m × 20 m).
Before sowing the seeds, all plots were ploughed by a small rotary cultivator. For CK, no cover crops were planted, the weeds were mowed regularly and the weed residues were removed to leave bare soil throughout the trial process. For CC, four cover crops were sprinkled in inter-row of kiwifruit trees by hand. The cover crops were mowed down three or four times every year by mowers, and the cover crop residues were left on the ground as mulch. Cover crops were selected to represent diverse plant families (Gramineae and Leguminosae) in accordance with different plant functional groups. Cover crops used in this study included ryegrass (Lolium perenne) 9 g m−2, bluegrass (Poa pratensis) 12 g m−2, white clover (Trifolium repens) 6 g m−2, and red clover (Trifolium pratense) 3.6 g m−2. Field management measures were kept consistent in all the plots.

2.2. Soil Sampling and Aggregate Separation

Ten soil cores with 20 cm depth were randomly sampled in each of the 6 plots on 2 November 2018 after harvest. The ten soil cores were mixed into one composite sample per plot and put into septic plastic bags. Then, the fresh soil samples were brought to the laboratory and stored at 4 °C before the processing and analyses.
We classified the aggregates into three size classes through the dry-sieving method, as follows: >2 mm (mega-aggregates, LMA), 0.25–2 mm (macro-aggregates, SMA), and <0.25 mm (micro-aggregates, MA). The composite samples were divided along the natural profiles, and the moist field soils were dried at 4 °C until they reached a gravimetric water of about 100 g H2O kg−1 [11,37]. The soil samples were then sieved through a 5 mm sieve to remove the roots, large stones, and macrofauna. The aggregates were divided by putting 100 g of soil (<5 mm) every time on a series of 2 sieve nests (2 mm and 0.25 mm) mounted on a dry soil aggregate analyser (DM185, 1400 times per min; Soil Dry Aggregate Analytics, Shanghai, China) for 10 min, repeating the above steps until all the soil were sieved. Same aggregate levels in each plot were mixed into on sample and divided into three parts. The first part was air-dried for SOC and TN determination, the second part was stored at 4 °C for nematode isolation and identification, and the third part was stored at −80 °C for bacterial community composition and high-throughput sequencing analyses.

2.3. Soil Physicochemical Parameters

TN content was determined by acid digestion and measured with a continuous-flow analyser (AutoAnalyzer 3; Bran + Luebbe Analytics, Germany), the SOC content was determined by the K2CrO7-H2SO4 oxidation method, and the carbon/nitrogen ratio (C/N) was also calculated [33].

2.4. Soil DNA Extraction and Illumina Sequencing

Soil DNA was extracted from the 0.3 g soil samples using the Power Soil Kit. DNA concentration was determined by Nanodrop 2000 (Thermo Scientific, Waltham, MA, USA) and the purity was checked using 1% agarose gels. V4 region of the 16S rRNA gene was amplified using the specific primer (515F/806R) with the barcodes [38]. Then, we performed the PCR reaction, and the PCR products were purified with GeneJET Gel Extraction Kit (Thermo Scientific). All sequences were performed by the Illumina HiSeq platform and 250 bp paired-end reads were generated. All of the aforementioned steps were finished by the Novogene Company (Beijing, China).
Sequences were analysed using QIIME software package. Operational taxonomic units (OTUs) with 97% similarity cut off were clustered [39]. Then, we used pick_de_novo_otus.py to pick OTUs by making OTU table. The taxonomy of each OTU representative sequence was performed by RDP Classifier version 2.2 [40] at the 0.7 confidence threshold. We rarified the OTU table and used it for further analysis. The rarefaction curves of Sobs and Shannon index of 18 samples are shown (Figure S1). The bacterial sequences raw data were uploaded to NCBI Sequence Read Archive (SRA) database following the accession numbers: SRR23312864-23316881.

2.5. Soil Nematode Composition

Nematodes were extracted from 50 g fresh soil using the shallow dish methodology [41]. With 100 nematodes randomly selected for identification from each sample using a fluorescent inverted microscope after counting the total number of nematodes. Nematode was identified at the genus level and divided into four trophic groups: omnivores/predators (Op), bacterivores (Ba), fungivores (Fu) as well as plant parasites (Pp) [26]. Richness index and nematode channel ratio (NCR) were then calculated. The Richness index was calculated as (n − 1)/LN (100), where n is the number of taxa, 100 is the number of identified nematodes. The nematode channel ratio (NCR) was calculated as B/(B + F); B and F were the relative abundance of bacterivores and fungivores, respectively.

2.6. Statistical Analyses

T-test was used for determining the differences in the soil chemical properties, bacterial diversity, and nematode abundance between CC and CK at p < 0.05 level. One-way ANOVA was applied to define the varies of the soil chemical properties, bacterial diversity, and nematode abundance among different-sized aggregates at p < 0.05 level. Pearson correlation analysis between soil chemical properties and nematode abundance was performed. SPSS 22.0 was performed for these analyses. Permutational multivariate analysis of variance (PERMAVONA) was used for testing the effects of cover crop treatments and soil aggregations on soil nematode and bacterial community.
Principal component analysis (PCA) was conducted to determine variations in the composition of bacterial and nematode community. Redundancy analysis (RDA) was used to determine the associations between soil chemical factor, bacterial and nematode communities. PERMAVONA, PCA, and RDA were performed by R (version 3.3.1) coupled with the vegan package. Heatmap figures were created using the corrplot package in the R software (version 3.3.1). Bacterial community diversity was calculated using Shannon–Weiner index and sobs, respectively. Shannon–Weiner index was calculated as −∑(Pi × ln Pi), where Pi is the relative abundance of ith taxa in a sample. Sobs richness was the actual observed richness.
Structural equation model (SEM) was performed to assess the direct and indirect impacts of soil aggregate fractions and cover crops, SOC, C/N, on the soil bacterial and nematode communities using IBM SPSS AMOS 21. The first principal component (PC1) of bacterial and nematode compositions was used in the subsequent SEM analysis to express soil bacterial community and nematode community, respectively. The chi-square test, root mean square error of approximation, and Akaike information criterion were used to assessed the goodness of fit of the model.

3. Results

3.1. Soil Aggregate Distribution and Chemical Properties

The distribution of soil aggregate in both CC and CK treatments evidenced that macro-aggregates (49.59~50.58%) were the dominant fractions followed by the mega-aggregates (47.12~47.63%), while the micro-aggregates had the lowest proportion with values of 2.55~2.90% (Table 1). In addition, TN (p < 0.001), SOC (p < 0.001), and C/N ratio (p = 0.016) exhibited significant difference among different-sized aggregates and were higher in micro-aggregates than that in mega- and macro-aggregates, respectively (Table 1 and Table S1). SOC and TN in different aggregate fractions obviously increased within cover crop treatment, except for SOC in the micro-aggregates (Table 1 and Table S2). CC had lower C/N ratio compared with CK in the micro-aggregates.

3.2. Bacterial Community Composition

In the present study, the ten dominant bacterial phyla across all samples were Proteobacteria (41.65%), Actinobacteriota (30.58%), Bacteroidota (9.10%), Myxococcota (6.22%), Acidobacteriota (5.21%), Gemmatimonadota (1.63%), Firmicutes (1.09%), Verrucomicrobiota (1.06%), Patescibacteria (0.85%), and Cyanobacteria (0.55%) according to the sequencing date (Figure 1A). The relative abundance of bacterial taxa represented obvious variations among different aggregate fractions. We observed significantly higher relative abundance of Acidobacteriota, Myxococcota, Nitrospirota, and Chloroflexi but lower relative abundance of Patescibacteria in mega- and macro-aggregates compared with micro-aggregates (Figure S2). Moreover, the relative abundance of Actinobacteria, Gemmatimonadota, and Nitrospirota was obviously greater in the CC treatment compared with that in the CK treatment within micro-aggregate (p < 0.05) (Figure S3). We also observed a notable increase in the Shannon index in macro- and micro-aggregates compared with mega-aggregates (p < 0.01) (Table S3). However, we did not detect influences of CC on the bacterial community diversity (data not shown).
Principal component analysis (PCA) of taxa revealed that the bacterial community of CC obviously differed from those of CK along axis 1 within different-sized aggregates (Figure 1B). In addition, bacterial community compositions in mega-aggregates were evidently isolated from those in micro-aggregates along axis 2. PERMANOVA results revealed the structure of bacterial community was dramatically influenced by cover crop (20.37%, p < 0.001) and aggregation (30.73%, p < 0.001) (Table S4).

3.3. Soil Nematode Composition

A total of 34 nematode genera were identified (Table S5), and the dominant trophic groups were fungivores (41.15%), bacterivores (37.16%). Aphelenchoides, Aphelenchus, Acrobeles, Eucephalobus and Rhabditis were the dominant genera, which together account for 60% of all nematodes identified (Figure 2A). The abundance of omnivore predators enriched in mega- and macro-aggregates was greater than that in micro-aggregates (p < 0.001), while fungivore abundance was greater in macro- as well as micro-aggregates than that in mega-aggregates (p = 0.026) (Table 2 and Table S1). The abundance of total nematode, bacterivore, and plant parasite in mega- and macro-aggregates was greater than that in micro-aggregates, but no significant differences among different aggregate fractions was observed (Table 2 and Table S1). Moreover, we observed significant effects of the cover crop treatments on the abundance of total nematodes as well as different trophic groups in the different-sized aggregates. In the mega-aggregates, the abundance of total nematodes (p = 0.008), bacterivores (p = 0.004), fungivores (p = 0.011), and plant parasites (p = 0.028) in the CC was evidently greater than in the CK (Table 2 and Table S2). In micro-aggregates, total nematode abundance (p = 0.032) and bacterivore abundance (p = 0.002) of CC treatment was significantly higher than that of the CK treatment (Table 2 and Table S2). The abundance of total nematodes as well as different trophic groups increased under CC treatment compared with that of CK, but displayed no obvious differences between CC and CK in macro-aggregates (Table 2 and Table S2).
Principal component analysis (PCA) of genera indicated that the nematode community was significantly varied among aggregates and formed different clusters in the ordination plot (Figure 2B). Specifically, nematode community compositions in mega-aggregates dramatically differed from those in micro-aggregates along axis 1. In addition, we observed that the nematode communities of CC obviously differed compared to those of CK along axis 2 only in micro-aggregate (Figure 2B). PERMANOVA suggested that the nematode community is mainly influenced by soil aggregation (53.49%, p < 0.001) (Table S4).
Mega-aggregates and macro-aggregates were associated with the higher richness of nematodes, and the lower richness was obtained in the micro-aggregates irrespective of cover crop treatments (p < 0.001, Table 2 and Table S1). Moreover, mega-aggregates were associated with the highest NCR followed by macro-aggregates, and the micro-aggregates had the lowest NCR regardless of CC treatments (p < 0.001, Table 2 and Table S1). Moreover, nematode richness (p = 0.002) as well as NCR (p = 0.013) showed significant differences between CC and CK treatments only in micro-aggregates (Table 2 and Table S2).

3.4. Correlations between Soil Chemical Factors and Bacterial and Nematode Communities

RDA was used to analyse the association between bacterial and nematode communities and the soil chemical properties (Figure 3, Table S6). The results revealed that bacterial community was mainly influenced by SOC (r2 = 0.8721, p = 0.001) and TN (r2 = 0.8861, p = 0.001), while nematode community was mainly affected by SOC (r2 = 0.6312, p = 0.001) and C/N (r2 = 0.6683, p = 0.001). The SEM also assessed the relations between aggregate fractions, cover crops, soil chemical properties, bacterial, and nematode community (Figure 4 and Table S7). The results suggested that the aggregate fractions had direct and great negative impacts on SOC (r = −3.73, p < 0.001) and bacterial community (r = −0.04, p < 0.001), while the aggregate fractions had no direct impacts on the C/N, the abundance of bacterivore and omnivore predators, and nematode community. We determined that the SOC (r = −0.26, p < 0.001) influenced the omnivore predator abundance directly. In addition, bacterial community was negatively and closely linked with bacterivore (r = −1.83, p < 0.001) and omnivore predators (r = −4.91, p < 0.01) abundance, and the abundance of bacterivore (r = 0.88, p < 0.05) and omnivore predators (r = 0.30, p < 0.01) was positively and closely linked with soil nematode community. Cover crop also had effects on the communities of soil bacterial and nematode directly and indirectly. Cover crop had obvious and direct positive influence on SOC (r = 1.22, p < 0.01), bacterivore abundance (r = 0.09, p < 0.01), and great and negative influence on C/N (r = −0.23, p < 0.05). However, the cover crop treatments had no direct effects on the bacterial community. We determined that the SOC (r = 0.01, p < 0.001) impacted the bacterial community directly. In addition, C/N (r = −0.15, p < 0.01) was negatively and closely linked with s bacterivore abundance, which had positive and obvious effects on nematode community.

4. Discussion

4.1. Influence of Cover Crop Treatments on Bacterial Community in Soil Aggregates

Soil aggregates provide different habitats and resources for the colonization of various microorganisms [7]. The distribution of microbes in the different aggregates demonstrated their effects on the microorganism diversity and biogeochemical processes at small-scale spatial [42,43]. In our study, bacterial communities clearly differ among different-sized aggregates [6,33]. The diversity of bacteria community increased in the macro- and micro-aggregates, which was mainly contributing to the higher concentration of TN and SOC at the macro- and micro-aggregates compared with mega-aggregates [7], translating directly into food availability for the bacterial community [3,29]. In this study, SOC was significantly and positively linked with bacterial community (Figure 4). Mega- and macro-aggregates were regarded as possessing oligotrophic-rich communities because of the high concentration of labile and coarser particulate organic carbon [7,44]. Our findings are also associated with the copiotrophic/oligotrophs hypothesis, which explains that copiotrophs (e.g., Bacteroidota), r-strategists, grow faster in nutrient-rich environments, while oligotrophs (e.g., Acidobacteriota and Chloroflexi), k-strategists, grow faster in nutrient-poor soil habitats [7,45,46]. As expected, the relative abundances of Chloroflexi and Acidobacteriota increased in the mega- and macro-aggregates, which may be because of the soil nutrient deficiency in mega- and macro-aggregates (Figure S2), while the relative abundance of Bacteroidota enhanced in the micro-aggregates in comparison with mega-aggregates owing to the high soil nutrient concentration in micro-aggregates (Figure S2). In addition, Myxococcota and Nitrospirota were enriched in the macro- and mega-aggregates, while Patescibacteria was higher in micro-aggregates. We further determined that oligotroph microbes (Acidobacteriota and Chloroflexi) showed negative associations with SOC and TN, while copiotrophic microbes (Bacteroidota) showed positive associations with SOC and TN (Figure S4).
Community analysis also revealed that cover crop treatment exhibit obviously influences on the bacterial community structure within the different sized aggregates, while the bacterial diversity showed no remarkable distinction between cover crop treatment and CK treatment in the different sized aggregates. It was reported that microbial function, for example, decomposition, extracellular enzyme production, is dominated by alterations in the community structure [30,47,48]. Cover crop enhances the growth of different microbes by increasing the number and diversity of substrates as well as root exudates and changing habitat conditions of microbial [49,50]. In this study, only Nitrospirota was more abundance for cover crop treatment compared with CK (Figure S5). This result indicated that cover crop treatment improved nitrogen cycling, mainly owing to the higher TN concentration of the cover crop treatments [51]. In addition, cover crop treatment influenced bacterial community by increasing SOC, which has significant effects on soil bacterial community [29].

4.2. Influence of Cover Crop Treatments on Nematode Community in Soil Aggregates

There are differences in soil pores between different-sized aggregates, resulting in a change in the distribution of soil nematodes in aggregates [11,52]. The soil aggregate size was one of the primary factors affecting the soil nematode community in our study. In contrast to bacterial abundance, the richness of nematode increased in mega- and macro-aggregates in comparison with those in the micro-aggregates. The abundance of omnivore predators was higher in the mega- and macro-aggregates compared with those in the micro-aggregates [14], while the fungivore abundance in different-sized aggregates showed opposite variation tendencies [5]. The reason might be that omnivore predators have large body sizes, while microbivorous nematodes have small body sizes. A nematode with large-sized body (>30 µm) was confined to enter the small pores within these aggregate fractions [11]. In addition, the NCR decreased with the aggregates decreasing, indicating that the decomposition channel changed from bacteria to fungi with the aggregate size decreased. This is mainly because in mega-aggregates, there was a higher content of unstable carbon, which was rapidly increased mainly by bacteria. While, micro-aggregates hold higher content of total carbon and recalcitrant carbon, which mainly decomposes by fungi [7,12,53].
In the present study, the impacts of cover crop treatment on the nematode abundance as well as the four trophic groups were estimated. Those results documented that cover crop treatment caused an obvious increase in the total nematode abundance in the mega- and micro-aggregates, mainly resulting from an obvious growth in bacterivores within cover crop treatment. Similar to our study, previous research has proved that the nematode abundance enhanced in the cover crop treatments, suggesting that the various residues, root biomass as well as exudates of cover crops could provide sufficient resources for nematodes [54], which could transform nutrients from low trophic level to high trophic level and promote nutrient cycling [31]. In this study, bacterivore abundance was positively associated with TN in mega- and micro-aggregates, and positively correlated to SOC in macro-aggregates (Table S8). In addition, there is a predator-prey relationship between nematodes and microorganisms [36]; cover crop changed the microbial community, which influenced the community of nematodes. In our study, cover crop influenced bacterial community by increased SOC, and bacterial community directly affected bacterivores and omnivore predator abundance, which finally affected nematode community.

4.3. Relationships between Soil Chemical Factors and Community of Bacterial and Nematode

In this study, both aggregate fractions and cover crops exhibit significant effects on bacterial and nematode communities (Table S4). We determined dramatic associations between soil chemical factors, bacterial and nematode communities. The evident difference in microenvironment caused by management practices and spatial heterogeneity heavily regulates the distribution of soil nematode and bacteria [11,29]. However, there are obvious contrasts between the effects of soil chemical properties on the bacterial and nematode communities. The bacterial community was affected by both cover crops and aggregate fractions (Table S4), and was closely related to SOC (Figure 3A and Figure 4). The cropping of legume crops caused an increase in soil nitrogen which can be an important cause of the variety in the community of soil bacterial [54]. In our study, we also discovered that TN also dramatically influenced soil bacterial community. In addition, both aggregate fractions and cover crops affect the nematode community, which was closely related to C/N (Figure 3B and Figure 4). In addition, we observed that cover crop residues increased SOC [55], which had significant and indirect effect on nematode community by influencing omnivore predators and bacterial community. The various responses of bacterial and nematode communities to the changes in soil chemical factors (TN, SOC, and C/N) indicate that the mechanisms in regulating the assembly of soil bacterial and nematode communities were different at the aggregate scale. In turn, the interaction between bacterial and nematode communities influences soil carbon and nitrogen cycling [9,15]. In future studies, we will reveal the relationship between soil biota community (soil microbial and nematode community) and soil carbon and nitrogen cycle within different-sized aggregates.

5. Conclusions

The data we presented here demonstrate the important effects of soil aggregates and cover crop treatment on soil bacterial and nematode community. The bacterial community composition is affected by both soil aggregates and cover crop treatment. SEM showed that aggregate fractions had directly negative impact on bacterial community, while cover crop indirectly impacted bacterial community mainly by increasing SOC. Bacterial community diversity also increased with the aggregates decreasing mainly due to the higher content of carbon and nitrogen. However, the composition of nematode community is mainly driven by different-sized aggregates. The abundance of omnivores/predators, nematode richness and NCR decreased with the aggregates decreasing mainly because of the small pore size of microaggregates that prevent larger body sizes nematodes from entering. SEM suggested that aggregate fractions indirectly affected nematode community by influencing SOC and bacterial community, while cover crop indirectly affected nematode community by influencing SOC and C/N. These responses of the bacterial community, nematode community and abundance of total nematodes as well as the different feeding groups for cover crop treatment in different aggregates might have consequences for the carbon and nutrient cycles in orchard ecosystem. For future studies, we will validate whether bacterial and nematode community interaction follows our predictions by setting up controlled manipulative experiments.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13051377/s1, Table S1 Results of one-way ANOVAs of the effects of the aggregate sizes on aggregate distribution, SOC, TN, C/N, total nematode abundance, bacterivores, fungivores, plant parasites, omnivores-predators abundance, richness and NCR of nematode; Table S2 Results of t-test of the effects of cover crop treatment on aggregate distribution, SOC, TN, C/N, total nematode abundance, bacterivores, fungivores, plant parasites, omnivores predators abundance, richness and NCR of nematode under different size aggregates; Table S3 Wilcox test about bacterial alpha diversity among different aggregate sizes; Table S4 Results of PERMANOVA analysis showing the contribution (%) of cover crop treatments and aggregates to variations in the compositions of bacterial and nematode community; Table S5 Effects of cover crop treatments on the relative abundance (%) of soil nematode among different sized aggregates; Table S6 Correlation between the composition of bacterial and nematode communities and soil geochemical factors; Table S7 Results of structural equation modeling of aggregates fractions and cover crop effects on soil bacterial and nematode communiry through all plausible interaction pathways; Table S8 Correlation between bacterivores abundance and soil chemical factors; Figure S1. Multisample Sobs rarefaction curves (A) and multisample Shannon-Wiener curves (B); Figure S2 Abundance of the dominant bacteria at phylum level in the soil between mega- and micro-aggregate (A), macro- and micro-aggregate (B); Figure S3 Effects of cover crop treatments on soil bacteria abundance at phylum level within micro-aggregates; Figure S4 Pearson correlation between soil chemical factors and the abundance of bacteria at phylum level; Figure S5 Effects of cover crop treatments on soil bacteria abundance at phylum level.

Author Contributions

Q.L.: methodology, software, formal analysis, investigation, data curation, writing—original draft preparation, writing—review and editing, visualization. X.Q. and L.Z.: investigation. H.Z. and H.L.: resources. Y.Z.: project administration and funding acquisition. D.Y.: Conceptualization, methodology, supervision, project administration and funding acquisition. H.W.: Conceptualization, methodology, investigation, writing—review and editing, supervision, project administration and funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

The study was funded by the Science and Technology Innovation Project from the Chinese Academy of Agricultural Sciences (CAAS-XTCX2016015) and Central Public-interest Scientific Institution Basal Research Fund (2021-jbkyywf-wh; 2020-jbkyywf-zyj).

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

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Agronomy 13 01377 g001 550
Figure 1. The abundances of total bacterial community are based on the proportional frequencies of 16S rRNA sequences at phylum level (A) and principal component analysis (PCA) ordinations plots dissimilarity of the bacterial communities in the different aggregate fractions with the cover crop treatments (B). LMA: mega-aggregates, SMA: macro-aggregates, MA: micro-aggregates. Individual points represent soil samples from the different cover crop treatments within different-sized aggregates. CK: no cover crop, CC: cover crop treatment.
Figure 1. The abundances of total bacterial community are based on the proportional frequencies of 16S rRNA sequences at phylum level (A) and principal component analysis (PCA) ordinations plots dissimilarity of the bacterial communities in the different aggregate fractions with the cover crop treatments (B). LMA: mega-aggregates, SMA: macro-aggregates, MA: micro-aggregates. Individual points represent soil samples from the different cover crop treatments within different-sized aggregates. CK: no cover crop, CC: cover crop treatment.
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Figure 2. The abundances of total nematode community (A) and principal component analysis (PCA) ordination plot dissimilarity of the nematode communities in the different aggregate fractions with the cover crop treatments (B). LMA: mega-aggregates, SMA: macro-aggregates, MA: micro-aggregates. Individual points represent soil samples from the different cover crop treatments within different-sized aggregates. CK: no cover crop, CC: cover crop treatment.
Figure 2. The abundances of total nematode community (A) and principal component analysis (PCA) ordination plot dissimilarity of the nematode communities in the different aggregate fractions with the cover crop treatments (B). LMA: mega-aggregates, SMA: macro-aggregates, MA: micro-aggregates. Individual points represent soil samples from the different cover crop treatments within different-sized aggregates. CK: no cover crop, CC: cover crop treatment.
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Figure 3. Redundancy analysis (RDA) of bacterial (A) and nematode (B) community changes with soil chemical factors. LMA: mega-aggregates, SMA: macro-aggregates, MA: micro-aggregates. Individual points represent soil samples from the different cover crop treatments within different-sized aggregates. CK: no cover crop, CC: cover crop treatment. SOC, soil organic carbon; TN, total nitrogen; C/N, soil organic carbon/total nitrogen, Aggregate: aggregate distribution.
Figure 3. Redundancy analysis (RDA) of bacterial (A) and nematode (B) community changes with soil chemical factors. LMA: mega-aggregates, SMA: macro-aggregates, MA: micro-aggregates. Individual points represent soil samples from the different cover crop treatments within different-sized aggregates. CK: no cover crop, CC: cover crop treatment. SOC, soil organic carbon; TN, total nitrogen; C/N, soil organic carbon/total nitrogen, Aggregate: aggregate distribution.
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Figure 4. Structural equation modelling for the direct and indirect relationships of aggregate fractions and cover crops on soil bacterial and nematode communities. SOC, soil organic carbon; C/N, soil organic carbon/total nitrogen ratio. Red and green lines indicate significant negative and positive path coefficients, respectively: * p < 0.05, ** p < 0.01, *** p < 0.001. χ2/df = 0.457, p = 0.930, RMSEA = 0.000.
Figure 4. Structural equation modelling for the direct and indirect relationships of aggregate fractions and cover crops on soil bacterial and nematode communities. SOC, soil organic carbon; C/N, soil organic carbon/total nitrogen ratio. Red and green lines indicate significant negative and positive path coefficients, respectively: * p < 0.05, ** p < 0.01, *** p < 0.001. χ2/df = 0.457, p = 0.930, RMSEA = 0.000.
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Table
Table 1. Effects of cover crop treatment on soil characteristics within different-sized aggregates.
Table 1. Effects of cover crop treatment on soil characteristics within different-sized aggregates.
Soil VariablesTreatment Aggregate Sizes
Mega-AggregateMacro-AggregateMicro-Aggregate
Aggregate proportions (%)CK47.63 ± 0.90 a49.59 ± 0.53 a2.90 ± 0.23 a
CC47.12 ± 1.02 a50.58 ± 1.13 a2.55 ± 0.20 a
BAC
SOC (g kg−1)CK4.28 ± 0.06 b5.44 ± 0.08 b18.47 ± 0.59 a
CC4.63 ± 0.09 a5.79 ± 0.04 a21.30 ± 1.52 a
BBA
TN (g kg−1)CK0.61 ± 0.00 b0.78 ± 0.01 b2.29 ± 0.02 b
CC0.67 ± 0.01 a0.84 ± 0.01 a3.00 ± 0.13 a
BBA
C/N ratioCK6.97 ± 0.07 a6.94 ± 0.02 a8.06 ± 0.24 a
CC6.95 ± 0.06 a6.87 ± 0.05 a7.08 ± 0.24 b
BBA
Note: CK, no cover crop; CC, cover crop treatment. TN: total nitrogen, SOC: soil organic carbon, C/N ratio: soil organic carbon/total nitrogen. Different lowercase letters (ab) indicate significant differences (p < 0.05) between cover crop and no cover crop treatments within the same aggregate fraction and different uppercase letters (ABC) indicate significant differences (p < 0.05) among aggregate fractions. Values are means of three replicates ± SD.
Table
Table 2. Effects of cover crop treatment on soil nematode abundance (individuals per 100 g dry soil) and ecological indices (richness and NCR) within different-sized aggregates.
Table 2. Effects of cover crop treatment on soil nematode abundance (individuals per 100 g dry soil) and ecological indices (richness and NCR) within different-sized aggregates.
Nematode Abundance and Ecological IndicesTreatment Aggregates
Mega-AggregateMacro-AggregateMicro-Aggregate
Total nematode abundanceCK1207.02 ± 84.06 b1408.02 ± 242.54 a1066.42 ± 145.51 b
CC1982.72 ± 140.55 a2102.60 ± 291.01 a1774.17 ± 164.50 a
AAA
Bacterivore abundanceCK423.41 ± 36.17 b554.03 ± 99.82 a256.12 ± 14.77 b
CC860.78 ± 72.02 a836.74 ± 51.16 a708.25 ± 98.11 a
AAA
Fungivore abundanceCK273.90 ± 37.79 b593.88 ± 148.34 a676.74 ± 102.87 a
CC591.41 ± 64.56 a856.52 ± 181.07 a886.18 ± 82.24 a
BAA
Plant parasite abundanceCK136.89 ± 10.73 b119.31 ± 3.82 a126.00 ± 26.02 a
CC233.97 ± 29.68 a229.23 ± 62.61 a149.08 ± 46.93 a
AAA
Omnivore predator abundanceCK372.82 ± 31.43 a140.80 ± 24.25 a7.56 ± 3.99 a
CC296.56 ± 17.63 a180.12 ± 51.92 a30.66 ± 8.10 a
AAB
RichnessCK4.49 ± 0.19 a 3.55 ± 0.19 a 2.05 ± 0.05 b
CC3.84 ± 0.19 a 3.91 ± 0.25 a 2.41 ± 0.01 a
AAB
NCRCK0.61 ± 0.03 a 0.49 ± 0.05 a0.28 ± 0.02 b
CC0.59 ± 0.01 a 0.50 ± 0.04 a 0.44 ± 0.03 a
ABC
Note: CK, no cover crop; CC, cover crop treatment. NCR: Nematode channel ratio. Different lowercase letters (ab) indicate significant differences (p < 0.05) between cover crop and no cover crop treatments within the same aggregate fraction and different uppercase letters (ABC) indicate significant differences (p < 0.05) among aggregate fractions. Values are means of three replicates ± SD.
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Li, Q.; Qi, X.; Zhang, L.; Zhang, Y.; Zhang, H.; Liu, H.; Yang, D.; Wang, H. Composition of Soil Bacterial and Nematode Communities within Soil Aggregates in a Kiwifruit Orchard under Cover Crop Treatment. Agronomy 2023, 13, 1377. https://doi.org/10.3390/agronomy13051377

AMA Style

Li Q, Qi X, Zhang L, Zhang Y, Zhang H, Liu H, Yang D, Wang H. Composition of Soil Bacterial and Nematode Communities within Soil Aggregates in a Kiwifruit Orchard under Cover Crop Treatment. Agronomy. 2023; 13(5):1377. https://doi.org/10.3390/agronomy13051377

Chicago/Turabian Style

Li, Qingmei, Xiaoxu Qi, Lingling Zhang, Yanjun Zhang, Haifang Zhang, Hongmei Liu, Dianlin Yang, and Hui Wang. 2023. "Composition of Soil Bacterial and Nematode Communities within Soil Aggregates in a Kiwifruit Orchard under Cover Crop Treatment" Agronomy 13, no. 5: 1377. https://doi.org/10.3390/agronomy13051377

APA Style

Li, Q., Qi, X., Zhang, L., Zhang, Y., Zhang, H., Liu, H., Yang, D., & Wang, H. (2023). Composition of Soil Bacterial and Nematode Communities within Soil Aggregates in a Kiwifruit Orchard under Cover Crop Treatment. Agronomy, 13(5), 1377. https://doi.org/10.3390/agronomy13051377

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