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Article

Afforestation of Taxodium Hybrid Zhongshanshan Influences Soil Bacterial Community Structure by Altering Soil Properties in the Yangtze River Basin, China

by
Qin Shi
1,
Zhidong Zhou
1,
Ziyang Wang
1,
Zhiguo Lu
1,
Jiangang Han
2,
Jianhui Xue
1,
David Creech
3,
Yunlong Yin
1 and
Jianfeng Hua
1,*
1
Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences (Nanjing Botanical Garden Mem. Sun Yat-Sen), Nanjing 210014, China
2
College of Biology and Environment, Nanjing Forestry University, Nanjing 210037, China
3
Arthur Temple College of Forestry and Agriculture, Stephen F. Austin State University, Nacogdoches, TX 75962-3000, USA
*
Author to whom correspondence should be addressed.
Plants 2022, 11(24), 3456; https://0-doi-org.brum.beds.ac.uk/10.3390/plants11243456
Submission received: 1 November 2022 / Revised: 25 November 2022 / Accepted: 7 December 2022 / Published: 9 December 2022
(This article belongs to the Section Plant Ecology)
Browse Figures
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Abstract

:
Taxodium hybrid Zhongshanshan has been widely planted in the Yangtze River Basin (YRB) for soil and carbon conservation, with quantities over 50 million. The objective of this study was to determine how T. hybrid Zhongshanshan plantations affected soil physicochemical properties and bacterial community structure in the YRB, and to examine the consistency of changes by afforestation. Soils under T. Zhongshanshan plantations across six sites of the YRB were compared with soils of adjacent non-forested sites. Soil physicochemical properties and bacterial community structure were determined to clarify edaphic driving factors and reveal the effects of afforestation on bacteria. The results indicated that most soil attributes manifested improvements, e.g., total nitrogen in Jiangxi and Shanghai; available phosphorus in Hubei, Chongqing and Yunnan, exhibited the potential to maintain or ameliorate soil quality. A decrease in soil bulk density caused by plantation was also observed at the expense of soil macro-aggregates augment. Afforestation of T. Zhongshanshan plantation has habitually improved Shannon diversity and Chao1 richness, of which dominant phyla were Proteobacteria, Acidobacteria, and Actinobacteria, and increased the relative abundance of the phyla Proteobacteria and Nitrospirae, and the classes Flavobacteriia, Acidobacteria_Gp5, and Bacilli. We concluded that T. Zhongshanshan plantation can be employed to facilitate soil nutrient accumulation in the YRB, but that the degree, rate and direction of changes in soil attributes are sites dependent. It is recommended that afforestation of nutrient-depleted and less productive lands in the YRB should utilize this fast-growing species in combination with proper fertilization.

1. Introduction

The Yangtze River Basin (YRB) originates from the east of Qinghai-Tibet Plateau, traverses three major economic zones of eastern, central, and western of China, accounting for 18.8% of China’s land area [1]. Due to the dual effects of climate change and anthropogenic activities, the vegetation in the YRB has degenerated to some extent. In terms of soil and water conservation, a series of large-scale and long-standing afforestation projects have been implemented in this area, such as the Yangtze River Basin Shelterbelt Construction Project, which has greatly increased forest area by 6.02 × 105 km2 from 1998 to 2014 [2]. Individual trees are known to alter soil properties, creating a section of influence under the tree canopy [3]. Issues related to the alteration of soil condition caused by tree plantations have aroused concerns for many years [4,5,6]. It was reported that afforestation could affect most soil properties and biological activities: water-retention characteristics, bulk density (BD), particle-size distribution, content of nutrients and microbial properties [7,8]. Practice of afforestation in Xiong’an New Area suggested that mixing of different species helped trees to make the utmost of nutrients and increased soil bacterial diversity [9]. Studying with five common native tree species, Diao et al. [10] proclaimed that native broad-leaved tree species played pivotal roles in maintaining soil chemical and microbial properties in a temperate secondary forest. These changes are owing to understory vegetation existing and the effects of trees themselves.
Taxodium hybrid Zhongshanshan is a superior interspecies hybrid of T. distichum and T. mucronatum. Due to its impressive water tolerance, fast growth, and adaptability to degraded sites, it has been widely planted in southeastern China and is considered potentially as a universally accepted tree species for building forest cities and realizing carbon neutrality. As one of the main afforestation tree species, T. Zhongshanshan was introduced to Chongqing in 2002 and immediately became the eligible species on account of its innate superiorities. The experiment in the Three Gorges Reservoir exhibited a more than 90% survival rate of the T. hybrids after they were wholly submerged for an utmost of 122 d under water depth to 12 m [11]. Hitherto, large YRB areas of T. Zhongshanshan plantation have been artificially established as ecological forests, with quantities over 50 million. The current T. Zhongshanshan stands in the YRB area can store up to 217 t ha−1 carbon, and have a substantial potential for carbon sequestration as the total land area under these plantations is consecutively expanding, most of which are still immature [12]. The demand for landscape plantations and timber for this species still outstrips supply.
In recent years, intensive studies concerning the water resistance mechanism of T. Zhongshanshan have been carried out [13,14,15], while few studies have quantified the influence of T. Zhongshanshan on soil characteristics. To date, no field plantations studies have included microbiology aspects of T. Zhongshanshan. A better understanding of the soils of T. Zhongshanshan plantations in the YRB differ, and the underlying mechanisms will improve our competence to predict the effects of T. Zhongshanshan plantation on the YRB ecosystems, and then guide the further popularization with evidence. Moreover, it is the required first step toward understanding the consequences faced by terrestrial ecosystems and soil biogeochemical cycling. The aims of this paper, which are part of a larger study on afforestation ecology of T. Zhongshanshan in the YRB, are as follows: (i) to compare soil physicochemical properties and bacterial community structure under afforested and non-afforested sites; (ii) to examine the consistency of changes in soil properties by afforestation, if any, across sites of the YRB.

2. Materials and Methods

2.1. Study Location and Site Description

As the largest basin in China, the YRB locates between 90°33′−122°25′ E and 24°30′−35°45′ N, characterized by a subtropical monsoon climate. We selected afforested plantation areas in 5 Provinces and 1 Municipality with aspects from west to east (102°42′−121°32′ E), and from 1891 to 3 m above sea level, representing the general status of the YRB. The annual average temperature ranges from 12.6 to 18.0 °C and annual average precipitation is about 1086 mm. Details of afforestation sites were given in Figure 1.

2.2. Experiment Design and Soil Sampling

T. Zhongshanshan plantations aged sequence of 6, 8, 9, 11, 13, and 14 years in YRB were selected as experimental stands, and all were >10 ha. The vegetation in the plantation areas and adjacent non-afforested sites was composed of Erigeron annuus (L.) Pers., Chenopodium album L., Rubus hirsutus Thunb., Angelica sieboldin (F.) hirsutum, Mazus japonicus (Thunb.) Kuntze, Plantago depressa Willd., Setaria viridis (L.) Beauv. and Solidago canadensis L., with sparsely scattered woods (mainly Morus alba L. and Ulmus pumila L.). Basic information for the different stands including coordinate, soil color, tree height, diameter at breast height (DBH), crown diameter (CD) and tree density were recorded in Table 1. Aboveground biomass (AGB) was calculated according to the allometric growth equation elaborated by Shi et al. [12]. Both plantations and the adjacent non-afforested soils had not received any fertilization or weed control treatment. Therefore, the adjacent non-afforested soils could be considered as a control (CK) for this study. Any soil differences here could reflect the effects of the plantation establishment process. The experiment began in June 2021, and lasted for two months. We established large sites with a size of 1 ha covering minor local heterogeneity in soil conditions, which allowed us to differentiate 3 fixed plots of 20 × 30 m in each stand and were spaced 50 m from each other. Therefore, a total of 36 plots were established for soil sample collection.
Litter horizons were removed before soil sampling. Three soil profiles were set and diagonally distributed in each of the 36 plots. A total of 15 soil profiles were manually dug by a shovel from each plot at different arbitrary distances within 0.3–1 m from the tree trunks, in which the vertically projected crown of each T. Zhongshanshan was covered. To ensure the sampling points keeping identical from the trunks, we chose 0.5 m away from the trunk as the sampling point. At each plot, soil samples at the depths of 0–15, 15–30, and 30–45 cm were collected from the T. Zhongshanshan plantations. Soils within the same depth of three profiles were mixed together as one sample which containing approximately 500 g of soils, and thus a total of 108 samples (3 plots × 3 soil depths respectively in 6 afforested and non-afforested plots) were obtained. Another set of soil samples from 0–15, 15–30, and 30–45 cm depths were separately and intactly collected by inserting a steel cylinder of known volume (5 cm high and with a 5 cm inner diameter) for BD determination. All samples were transported to the laboratory and divided into three parts. Before being air-dried, approximately 10 g of fresh soil samples were disengaged and stored in 4 °C for microbial assays. Part of the remaining air-dried samples in the same profile was mixed thoroughly for soil aggregate distribution determination, and another part was sieved passed through a 2-mm screen for further analyses.

2.3. Laboratory Analysis

Soil pH and electrical conductivity (EC) were measured in a soil-water suspension (1:5 soil-water ratio) by a pH (Yueping PHS-3CU, Shanghai, China) and conductivity meter (Yueping DDS-307, Shanghai, China). After being sieved < 0.25 mm, a total of 1.00 g soils were digested in H2SO4 with mixed catalyst (K2SO4:CuSO4:Se = 5:1:1, w:w:w) and heated at 400 °C until the contents became colorless. The extracting solution was analyzed using the Kjeldahl method to determine total nitrogen (TN) according to Wu et al. [16]. Available phosphorus (AP) was determined by the molybdenum antimony resistance colorimetry. Samples of 5.0 g soil were extracted in 100 mL of non-phosphorus sodium bicarbonate, and then soil AP was determined using Mo-Sb colorimetric analysis. For soil available potassium (AK) determination, 5.0 g soils were extracted in ammonium acetate and then measured with flame atomic absorption spectrometry (Agilent 240 AA, Santa Clara, CA, USA). After they were sieved to 0.15 mm, 5.0 g soils were oxidized by potassium dichromate to measure soil organic matter (SOM) content by calculating the absorbance recorded at 590 nm with a spectrophotometry instrument (Jasco UV-visible, Tokyo, Japan). The aggregate distribution was determined using an auto sieve shaker (Orto Alresa Oass 203, Madrid, Spain) with a wet sieving method by Shi et al. [17]. Briefly, 3 sieves were applied to obtain aggregate size fractions: large macro-aggregates (>2000 μm), small macro-aggregates (250–2000 μm), micro-aggregates (53–250 μm) and silt + clay factions (<53 μm). The program was set to 500 rpm for 20 min at 10 s intervals. After the water flowing from the bottom became colorless, soils at different particle diameters were collected and dried at 80 °C until they reached a constant weight. Thereafter, soils of different particle diameters were weighed accurately.

2.4. Soil DNA Extraction, PCR and Processing of the High-Throughput Sequencing Data

DNA was extracted from 0.50 g of soil using the FastDNA SPIN Kit for soil (MP Biomedicals, Irvine, CA, USA) according to the manufacturer’s instructions. The DNA extracts were dissolved in 50 mL of TE buffer, and were qualified by gel electrophoresis. After that, the extracted DNA was evaluated by a micro-spectrophotometer (NanoDropND-2000, Thermo Fisher Scientific, Waltham, MA, USA) for quantity and quality and stored at −20 °C until further usage.
PCR amplification was conducted with primer set 519F (3′-CAGCMGCCGCGGTAATWC-5′) and 907R (3′-CCGTCAATTCMTTTRAGTTT-5′) targeting the V4-V5 region of the bacterial 16S rRNA gene. The 5-bp bar-code oligonucleotides were fused to the forward primer. Each PCR 50 uL reaction mixture contained 1.25 μM of deoxynucleosidetriphosphate, 2 μL (15 μM) of forward and reverse primers, 2 μM of Taq DNA polymerase (TaKaRa, Kusatsu, Japan), and 1 μL (50 ng) of genomic community DNA as a template. Each reaction was performed in triplicate to counter biases generated during amplification. The negative control was always run with sterile water as the template instead of soil DNA extract. Amplifications were carried out using the thermal conditions as follows: 94 °C for 5 min, 30 cycles (94 °C for 30 s, 55 °C for 30 s, and 72 °C for 45 s), and a final extension at 72 °C for 10 min. Reaction products for each sample were pooled and purified using the QIA quick PCR Purification Kit (Qiagen, Germany), and quantified using a NanoDrop ND-2000 micro-spectrophotometer.
The sequences were quality-filtered and clustered into operational taxonomic units (OTUs) at 97% shared identity with consensus taxonomy. The taxonomic classification was based on the Silva 119 database (http://www.arb-silva.de/, accessed on 16 March 2022).

2.5. Statistical Analyses

Differences in soil properties between afforested and non-afforested plots were compared using T-test (p < 0.05) using mean values by SPSS 19.0 (IBM Corporation, Somers, NY, USA). Figures of soil physicochemical properties were conducted with Origin 2021 (Origin Lab, Northampton, MA, USA). The diversity and richness indices were estimated with normalized OTU data using Mothur software. Microbial community data and the edaphic variable data (pH, BD, EC, AK, TN, AP, and SOM) were Hellinger standardized and log (x + 1)-transformed, respectively, for further multivariate analysis. A cluster analysis was used to analyze the soil microbial community structure based on Bray-Curtis similarities. A detrended correspondence analysis was conducted to identify the community data distribution model. Due to the gradient length value of <3.0 (linear model), we used a redundancy analysis (RDA), with a Monte Carlo permutation test (number of permutations: 999) for the comparison of the community structure and the correlation of the explanatory variables (R: vegan and ggplot2). Network analysis was conducted with the Molecular Ecological Network Analysis Pipeline (MENAP, http://ieg4.rccc.ou.edu/mena, accessed on 24 March 2022) [18,19]. A similarity matrix of the Pearson correlation coefficient was generated based on the OTU abundance data. Random matrix theory was then applied to automatically identify a similarity threshold for network construction. The networks were visualized with Cytoscape software. The module hubs, network hubs, and connectors were defined according to Deng et al. [18].

3. Results

3.1. Changes of Soil Attributes following T. Zhongshanshan Afforestation

Concerning the plantations’ span, soil pH showed large divergences in the YRB. Soils in JX are acidic, and the others remain neutral or slightly alkaline (Figure 2). According to the soil nutrient grading criterion of the second national soil census of China [20], TN and SOM of HB were the highest among all sites, which was divided into grade I-II (>1.50 g kg−1) and IV (10–20 g kg−1), indicating a nutrient-rich soil condition. Conversely, CQ soils exhibited a lower TN (grade VI, <0.5 g kg−1) than all the other sites. AP of JX and JS was divided into grade VI (<3.00 mg kg−1), which denoted that P contents and supplies were poor. AK of SH (grade I, > 200 mg kg−1) was higher than all the other sites, and the rest ranged as CQ > HB > JX > JS > YN.
For soil pH, significant differences (p < 0.05) between CK and forested soils (F) were observed in 0–15 cm and 15–30 cm of JX as well as HB, and 30–45 cm of JS (Figure 2a). BD of all soil depths differed significantly between CK and F in JS, YN and SH. In HB, BD of 0–15 and 15–30 cm also decreased significantly because of T. Zhongshanshan plantation (Figure 2b). Increase in soil EC was most evident in all depths in JX, JS, and SH (Figure 2c). TN in 0–15 and 15–30 cm of T. Zhongshanshan plantation in JX was 1.8 and 1.5 times higher than that in CK. JS and YN soils followed the opposite pattern as JX, exhibiting a reduction of TN due to T. Zhongshanshan growth (Figure 2d). In HB and CQ, a marked decrease was found in soil AK after T. Zhongshanshan growth in all soil depths. In YN, soil AK in the 0–15 and 15–30 cm increased by 1.7 and 1.3 times than that in CK, but decreased 37.7% in 30–45 cm. AK in SH soils followed opposite trends as compared to YN, since soil AK in the top 0–15 cm significantly increased but decreased by 30.4% and 25.4% in 15–30 and 30–45 cm than that in the corresponding depths in CK (Figure 2e).
Significant (p < 0.05) differences of SOM between CK and F were observed in JX, HB, YN and SH. Among the four above sites, T. Zhongshanshan plantation significantly (p < 0.05) increased SOM in JX and SH. However, in all depths in HB and YN, soils of T. Zhongshanshan plantation had lower (p < 0.05) SOM than non-forested soils (Figure 2f). In all plantation sites (except for JS), T. Zhongshanshan resulted in an enrichment of soil AP (Figure 2g). Generally, with regards to the effects of T. Zhongshanshan plantation, wide ranges of soil property divergences were observed in comparison with CK.
Soils were composed most of silt + clay, with the highest levels recorded in JX in both CK (580 g kg−1) and F (557 g kg−1). In all plantation sites (except for HB and YN), the establishment of T. Zhongshanshan plantation resulted in a reduction (p < 0.05) of silt + clay compared with CK, with the highest 40.4% in SH, and then 34.2% in CQ. Conversely, soils under T. Zhongshanshan plantation had higher (p < 0.05) large macro-aggregates in HB, CQ and YN compared with CK (Figure 3).

3.2. Changes of Soil Bacterial Community Structure following T. Zhongshanshan Afforestation

The heatmap analysis for the top 30 most abundant bacterial classes showed that soils of the same site clustered in one group, and T. Zhongshanshan plantation separated from that of non-forested sites (Figure 4a). This finding indicated that afforestation had a different bacterial community composition. Overall, the 3 most abundant classes in CK were Flavobacteriia, Acidobacteria_Gp25 and Sphingobacteriia, while afforestation of T. Zhongshanshan significantly (p < 0.05) increased the relative abundance of the classes Flavobacteriia, Acidobacteria_Gp5, and Bacilli. The dominant phyla (more than 25% of the gene sequences) were Proteobacteria, Acidobacteria, and Actinobacteria in all soils (Figure 4b). The hierarchical clustering results showed that the composition of the soil bacterial communities in T. Zhongshanshan plantation separated from that of non-forested sites. T. Zhongshanshan plantation significantly increased (p < 0.05) the relative abundance of the phyla Proteobacteria and Nitrospirae, on the contrary, decreased the abundance of Acidobacteria and Gemmatimonadetes (p < 0.05, Figure 4b).
The soil microbial network of the T. Zhongshanshan plantation sites had more modules (31 vs. 19) and modularity (0.765 versus 0.516) than the non-forested soils, which reflected the more microbial structure complexity of the former. The non-forested sites had the connectors while they disappeared after the T. Zhongshanshan plantation, maybe resulting in the increase of average path distance in the T. Zhongshanshan plantation. In addition, the module hubs changed from groups of Acidobacteria, Alphaproteobacteria, Deltaproteobacteria and Gamaproteobacteria in non-forested sites to Acidobacteria, Planctomycetes and Bacteroidetes in T. Zhongshanshan plantation sites. In the T. Zhongshanshan plantation network, module 1, module 2, and module 3 had the same module hub Acidobacteria (Figure 5a). In the non-forested network, modules 1 and 2 had the same hub Acidobacteria (Figure 5b).
The Shannon diversity indices decreased with increasing soil depth among all plantation sites. Except for SH and YN, soils under T. Zhongshanshan plantation showed higher (p < 0.05) Shannon diversity than those of CK, especially in JX, JS, and HB, where each soil depth all exhibited significant (p < 0.05) differences. Compared with the middle reaches (HB and JX, Shannon: 9.02 and 8.84) and the lower reaches (JS and SH, Shannon: 8.82 and 9.06), T. Zhongshanshan plantations in the upper reaches (YN and CQ, Shannon: 9.10 and 9.10) of the Yangtze River had a relatively higher microbial biodiversity. The Simpson diversity indices among all sites exhibited no significant differences. In all sites but SH, planting T. Zhongshanshan trees significantly (p < 0.05) increased the Chao1 richness (Table 2).

3.3. Correlations between the Soil Properties and the Microbial Community Composition

The redundancy analysis (RDA) model based on soil bacterial class and phylum genera level satisfactorily distinguished the T. Zhongshanshan plantation sites from each other and from the CK (Figure 6a,b). RDA also showed that JS, and JX group together along the main RDA1 axis according to their microbial community. Permutation tests for the RDA model manifested that the RDA was enough to explain the correlation between the microbial community structure and the soil edaphic variables (p < 0.01). In class level, AK, pH and EC were positively correlated with RDA1 axis (r = 0.46, p < 0.01; r = 0.46, p < 0.001; r = 0.99, p < 0.001), while AP, TN and SOM exhibited negative correlation (r = −0.52, p < 0.01; r = −0.51, p < 0.001; r = −0.42, p < 0.001) with RDA1 explained the 58.77% of variance (Figure 6a). Soil pH, AK and EC were positively correlated with class Anaerolineae, Deltaproteobacteria, Gammaproteobacteria, Planctomycetia, Betaproteobacteria, Flavobacteriia, and Cytophagia (Figure 6a).
In phylum level, pH and EC were positively correlated with RDA1 axis (r = 0.95, p < 0.001; r = 0.54, p < 0.001), while AK, AP, TN, and SOM exhibited negative correlation (r = −0.10, p < 0.01; r = −0.92, p < 0.001; r = −0.91, p < 0.001; r = −0.84, p < 0.001) with 44.0% of variance explained (Figure 6b). Except for BD, soil properties could significantly influence the bacterial community structure both phylum and genera level (p < 0.05). At the phylum level, soil EC, and pH were positively correlated with Actinobacteria, Bacteroidetes, Planctomycetes, and Proteobacteria (Figure 6b).

4. Discussion

4.1. Effect of T. Zhongshanshan Plantation Establishment on Soil Attributes

Compared to the soils in non-forested sites, our study proved that T. Zhongshanshan plantation ameliorated most of soil attributes, e.g., TN in JX and SH and AP in HB, CQ and YN. This meant that T. Zhongshanshan plantations had relatively replenished the nutrient pools in soils of the YRB. With increasing plantation age, the development of understory vegetation formed an important component of net primary productivity, and their life cycle provided an important input of carbon (C), N, and other nutrients. The significant increase in AP content was directly related with the SOM. Reports on changes in the chemical properties of soils caused by the conversion of former agricultural lands into forests were diverse, both positive and negative. Forest plantations in southern Rwanda proved that soil C and N accumulated over time in the Eucalyptus saligna afforested sites [21]. Likewise, Mishra et al. [22] observed an increase in total soil C and N during 9 years of E. tereticornis plantation. In a chronosequence study of Pinus plantation in Australia, Turner et al. [23] observed a 35% loss in SOM after 10 years of establishment. Cross-country studies of Pinus showed either a decline or increase in soil SOM in tropical regions [24]. However, the soils in JS and YN under T. Zhongshanshan plantations had relatively lower TN compared to CK. According to our previous pot experiment, T. Zhongshanshan plants exhibited soil-specific characteristics, preferring soils rich in N, P, and K [17]. The initial decline of soil TN in T. Zhongshanshan plantation might result from a restrained supply of K, since plants demand more nutrients to satisfy photosynthesis, mainly through adenosine triphosphate (ATP) synthesis. In the 1–10 year range of plantation ages, fast-growing T. Zhongshanshan plants in JS and YN could compete for soil resources with other plants and soil organisms. This phenomenon would be especially noticeable in the YRB if the soil elements, like P and K, were in relatively inadequate supply. SOM in HB and YN belonging to T. Zhongshanshan plantations also showed a tendency of reduction. Same tree species or species within a functional group can differ in their effects on soils. In the cases where decreases in soil C of plantations were significant, Hailu [25] attributed the differences to impacts of plantation density. In this study, T. Zhongshanshan in HB and YN was initially planted at 1111 stem·ha−1 (3 m spacing between and within rows) and had not yet been thinned. As forest ages, a larger initial tree density may gradually decrease the light transmittance for understory plants and lead to fierce competition, which in turn is not conducive to C availability to soils. Silvicultural interventions in T. Zhongshanshan plantation can create a more spacious canopy, and then soils may be more coupled with light penetration to increase temperatures, which is instrumental in litter decomposition.
In this study, BD of the T. Zhongshanshan plantation was significantly lower than of the non-forested sites (except for JX). This may be attributed to a dense root system that is capable of efficiently penetrating the soil substrate, thus increasing soil porosity and reducing BD. However, T. Zhongshanshan plants in JX were sparse and relatively small, whose root penetration was not as extensive as that of the > 6-year-old plantations, resulting in no significant differences of BD as compared to adjacent non-forested soils. The influence of forest cover on increasing soil porosity, by reducing BD and increasing capillary and gravitational pores is well evidenced [26]. The shift from relatively small aggregate sizes to large, afforestation of T. Zhongshanshan had a positive influence on physical characteristics principally at the expense of soil macro-aggregates augment. Concordant with our finding, a conspicuous increase in large macro-aggregates of the Robinia pseudoacacia afforestation in the Loess Plateau was also recorded [27], highlighting the crucial role of afforestation in improving soil stability. Wei et al. [28] reported a marked decline in macro-aggregate amounts within 50 years of converting forest to cropland, compared with a notable increase after long-time afforestation. Soil structures may be re-established to pre-deforestation standards by consecutive vegetation restoration. Afforestation over time increases root biomass, thus favoring the formation of macro-aggregates.

4.2. Effect of T. Zhongshanshan Plantation Establishment on Soil Bacterial Community

Afforestation can affect soil nutrients by changing the nature of litter and root exudates, both of which can alter the growth of soil microbes and ultimately transform soil bacterial diversity [29]. Afforestation of T. Zhongshanshan significantly increased the relative abundance of the class Flavobacteriia, Acidobacteria_Gp5, and Bacilli. This implied that the community composition of Flavobacteriia, Acidobacteria_Gp25 and Sphingobacteriia changed during the transformation from non-forested soils to forested soils, which was owing to changes of soil physicochemical properties in T. Zhongshanshan plantations. The influence of plants on the microbiome class had been previously reported. Long-term rice cultivation orderly changed the succession of the bacterial class community by increasing relative abundances of Anaerolineae, Deltaproteobacteria, Betaproteobacteria, and Gammaproteobacteria, which were capable of decomposing carbohydrates [30]. Furthermore, Proteobacteria and Acidobacteria were the most 2 abundant phyla in YRB soils, akin to the findings in other soils and environments [31,32]. Our results suggested that the establishment and development of T. Zhongshanshan plantation will lead to alteration in the understory microhabitat, acting as the driving force to increase the relative abundance of the phyla Proteobacteria and Nitrospirae, on the contrary decrease the abundance of Acidobacteria and Gemmatimonadetes. Proteobacteria belong to copiotrophic groups and are considered to be r-strategists that preferentially utilize easily accessible sources of C or alternatively closely associated with the soil C cycle. Acidobacteria belong to oligotrophic groups and prefer nutrient-poor environments. The arresting shift in soil bacterial community compositions following T. Zhongshanshan afforestation suggested that these below ground communities transitioned from slow-growing oligotrophic groups to fast-growing copiotrophic groups. Similar to what we perceived, Liu et al. [33] concluded that the R. pseudoacacia plantation improved the soil nutrient conditions according to higher Proteobacteria abundance.
Shannon index and Chao1 richness of soil bacteria were higher for afforested sites, this can be attributed to changes in the relative abundance of Proteobacteria, which were reported to represent the most abundant bacterial phylum. This increase in diversity could result from higher above-ground biomass in T. Zhongshanshan plantation soil as compared to CK, and understory microhabitats further mediate the diversity and composition of the soil microbial community. Diverge from Zhao et al. [34], whose study manifested that soil bacterial diversity increased sharply in the initial stages of R. pseudoacacia plantations, our results did not exhibit such a clear tendency. The research of R. pseudoacacia plantations was carried out in the Wuliwan Watershed, where is semi-arid and the soil is deposited by wind erosion. This discrepancy suggested that not only afforestation species, but also variable factors may influence soil bacterial diversity, including soil types and climate.
Afforestation played a momentous role in the soil bacterial community and potential functional composition due to substantial contributions to the soil system. In our study, the soil properties explained 85.21% and 82.69% of the respective variation in the soil bacterial phylum and class level and could significantly influence the bacterial community structure. The relationship of afforestation on the soil microbial community has been well documented in a variety of field-based studies [35,36]. In Horqin Desert afforested with Pinus sylvestris, Cao et al. [36] demonstrated that soil properties were regarded as indicators of the bacterial community and function. SOM is notably the primary factor influencing the soil microbial community at the phylum level. Liu et al. [37] reported that R. pseudoacacia afforestation shaped the soil bacterial community composition via enhancement of soil physicochemical properties, of which soil AP was the predominant factor. Moreover, soil bacterial diversity in non-forested lands was proved highly susceptible when compared with that in afforested lands, suggesting that afforestation is expected to increase biodiversity stability by enriching the soil nutrient pool, mainly N, SOM and P [38]. These findings confirmed our inference that the soil bacterial composition and diversity were affected by soil properties following T. Zhongshanshan afforestation (Figure 7).

5. Conclusions

Afforestation of T. Zhongshanshan plantation had greatly improved soil bacterial abundance and diversity, which had been accompanied by increases in soil nutrients, and a decrease in soil bulk density. It forecasted that T. Zhongshanshan plants established a new forest ecological balance through changing the soil environment and indicated that the metabolic pattern changed from resource acquisition to resource utilization. With the extension of forest areas in the YRB, we suggested that measures such as thinning or pruning should be implemented by forestry sectors with increasing plantation age, which could maximize soil C and nutrient contents. In nutrient-depleted soils, proper fertilization is recommended for this fast-growing species to achieve a high biomass production and replenishment of the nutrient pools. Potential risks of T. Zhongshanshan plantation for reduced soil fertility over time, such as leaching of nutrients from the soil, immobilization of nutrients in the organic layer and biomass should be further studied.

Author Contributions

Q.S.: Methodology, Validation, Investigation, Formal analysis, Writing-Original Draft. Z.Z.: Software, Formal analysis, Visualization, Writing-Review and Editing. Z.W.: Investigation and Formal analysis. Z.L.: Investigation. J.H. (Jiangang Han): Funding Acquisition. J.X.: Funding Acquisition. D.C.: Supervision. Y.Y.: Writing-Review and Editing. J.H. (Jianfeng Hua): Data curation, Conceptualization, Writing-Review and Editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the Jiangsu Special Fund on Technology Innovation of Carbon Dioxide Peaking and Carbon Neutrality (BK20220016), Innovation Project of Plant Germplasm Resources of Chinese Academy of Sciences (kfj-brsn-2018-6-003), Jiangsu Long-term Scientific Research Base for Taxodium Rich. Breeding and Cultivation (LYKJ [2021] 05) and Forestry Technology Innovation and Promotion Project of Jiangsu Province (LYKJ[2020]08).

Acknowledgments

We would like to thank the many colleagues of Jingzhou Hubei Academy of Forestry Sciences, Institute of Wanzhou Chongqing Forestry Science, and Jiangxi Academy of Forestry Sciences for the assistance with sample collection.

Conflicts of Interest

The authors declare no competing interests.

References

  1. Qu, S.; Wang, L.; Lin, A.; Zhu, H.; Yuan, M. What drives the vegetation restoration in Yangtze River Basin, China: Climate change or anthropogenic factors? Ecol. Indic. 2018, 90, 438–450. [Google Scholar] [CrossRef]
  2. Bryan, B.A.; Gao, L.; Ye, Y.; Sun, X.; Hou, X. China’s response to a national land-system sustainability emergency. Nature 2018, 559, 193–204. [Google Scholar] [CrossRef] [PubMed]
  3. Bargués Tobella, A.; Reese, H.; Almaw, A.; Bayala, J.; Malmer, A.; Laudon, H.; Ilstedt, U. The effect of trees on preferential flow and soil infiltrability in an agroforestry parkland in semiarid Burkina Faso. Water Resour. Res. 2014, 50, 3342–3354. [Google Scholar] [CrossRef] [Green Version]
  4. Paul, K.I.; Polglase, P.J.; Nyakuengama, J.; Khanna, P. Change in soil carbon following afforestation. For. Ecol. Manag. 2002, 168, 241–257. [Google Scholar] [CrossRef]
  5. Hong, S.; Piao, S.; Chen, A.; Liu, Y.; Liu, L.; Peng, S.; Sardans, J.; Sun, Y.; Peñuelas, J.; Zeng, H. Afforestation neutralizes soil pH. Nat. Commun. 2018, 9, 520. [Google Scholar] [CrossRef] [Green Version]
  6. Friggens, N.L.; Hester, A.J.; Mitchell, R.J.; Parker, T.C.; Subke, J.; Wookey, P.A. Tree planting in organic soils does not result in net carbon sequestration on decadal timescales. Glob. Chang. Biol. 2020, 26, 5178–5188. [Google Scholar] [CrossRef]
  7. Lima, A.; Silva, I.R.; Neves, J.; Novais, R.F.; Barros, N.F.; MendonçA, E.S.; Smyth, T.J.; Moreira, M.S.; Leite, F.P. Soil organic carbon dynamics following afforestation of degraded pastures with Eucalyptus in southeastern Brazil. For. Ecol. Manag. 2006, 235, 219–231. [Google Scholar] [CrossRef]
  8. Zhang, D.; Jian, Z.; Yang, W.; Wu, F. Effects of afforestation with Eucalyptus grandis on soil physicochemical and microbiological properties. Soil Res. 2012, 50, 167. [Google Scholar] [CrossRef]
  9. Wang, K.; Qiu, Z.; Zhang, M.; Li, X.; Fang, X.; Zhao, M.; Shi, F. Effect of afforestation mode on rhizosphere soil physicochemical properties and bacterial community structure of two major tree species in Xiong’an New Area. For. Ecol. Manag. 2022, 520, 120361. [Google Scholar] [CrossRef]
  10. Diao, M.; Yang, K.; Zhu, J.; Li, M.; Xu, S. Native broad-leaved tree species play key roles on maintaining soil chemical and microbial properties in a temperate secondary forest, Northeast China. For. Ecol. Manag. 2020, 462, 117971. [Google Scholar] [CrossRef]
  11. Hua, J.; Han, L.; Wang, Z.; Gu, C.; Yin, Y. Morpho-anatomical and photosynthetic responses of Taxodium hybrid Zhongshanshan 406 to prolonged flooding. Flora 2017, 231, 29–37. [Google Scholar] [CrossRef]
  12. Shi, Q.; Hua, J.; Creech, D.; Yin, Y. Biomass estimation and carbon storage of Taxodium hybrid Zhongshanshan plantations in the Yangtze River Basin. Forests 2022, 13, 1725. [Google Scholar] [CrossRef]
  13. Qi, B.; Yang, Y.; Yin, Y.; Xu, M.; Li, H. De novo sequencing, assembly, and analysis of the Taxodium hybrid Zhongshanshan roots and shoots transcriptome in response to short-term waterlogging. BMC Plant Biol. 2014, 14, 201. [Google Scholar] [CrossRef] [Green Version]
  14. Wang, Z.; Gu, C.; Xuan, L.; Hua, J.; Shi, Q.; Fan, W.; Yin, Y.; Yu, F. Identification of suitable reference genes in Taxodium hybrid Zhongshanshan under abiotic stresses. Trees 2017, 31, 1519–1530. [Google Scholar] [CrossRef]
  15. Xuan, L.; Hua, J.; Zhang, F.; Wang, Z.; Pei, X.; Yang, Y.; Yin, Y.; Creech, D.L. Identification and functional analysis of ThADH1 and ThADH4 genes involved in tolerance to waterlogging stress in Taxodium hybrid Zhongshanshan 406. Genes 2021, 12, 225. [Google Scholar] [CrossRef]
  16. Wu, G.; Zhang, Z.F.; Song, F.; Liu, X.; Ma, C.H.; Pang, X.M.; Zhang, J.L.; Liu, Y.Y. Determination of total nitrogen in plant fruits by graphite digestion apparatus and automatic azotometer. Rock Min. Anal. 2020, 39, 311–317. [Google Scholar]
  17. Shi, Q.; Yin, Y.; Wang, Z.; Creech, D.; Hua, J. Influence of soil properties on the performance of the Taxodium hybrid Zhongshanshan 407 in a short-term pot experiment. Soil Sci. Plant Nutr. 2017, 63, 145–152. [Google Scholar] [CrossRef] [Green Version]
  18. Deng, Y.; Jiang, Y.; Yang, Y.; He, Z.; Luo, F.; Zhou, J. Molecular ecological network analyses. BMC Bioinf. 2012, 13, 1–20. [Google Scholar] [CrossRef] [Green Version]
  19. Zhou, J.; Deng, Y.; Luo, F.; He, Z.; Tu, Q.; Zhi, X. Functional molecular ecological networks. MBio 2010, 1, e110–e169. [Google Scholar] [CrossRef] [Green Version]
  20. Shi, Y.Y.; Deng, M.J.; Jian, T.; Song, X.C.; Wang, H.L.; Qin, Q.Y. Forest soil nutrient classification method based on spatial analysis. Pratacult. Sci. 2016, 33, 1112–1117. [Google Scholar]
  21. Nsabimana, D.; Klemedtson, L.; Kaplin, B.; Wallin, G. Soil carbon and nutrient accumulation under forest plantations in southern Rwanda. Afr. J. Environ. Sci. Technol. 2008, 2, 142–149. [Google Scholar]
  22. Mishra, A.; Sharma, S.; Khan, G. Improvement in physical and chemical properties of sodic soil by 3, 6 and 9 years old plantation of Eucalyptus tereticornis: Biorejuvenation of sodic soil. For. Ecol. Manag. 2003, 184, 115–124. [Google Scholar] [CrossRef]
  23. Turner, J.; Lambert, M.J.; Johnson, D.W. Experience with patterns of change in soil carbon resulting from forest plantation establishment in eastern Australia. For. Ecol. Manag. 2005, 220, 259–269. [Google Scholar] [CrossRef]
  24. Hartemink, A.E. Assessing soil fertility decline in the tropics using soil chemical data. Adv. Agron. 2006, 89, 179–225. [Google Scholar]
  25. Hailu, Z. Ecological Impact Evaluation of Eucalyptus Plantations in Comparison with Agricultural and Grazing Land-Use Types in the Highlands of Ethiopia; Institute of Forest Ecology, Vienna University of Agricultural Sciences: Vienna, Austria, 2002. [Google Scholar]
  26. Podrázský, V.; Holubík, O.; Vopravil, J.; Khel, T.; Moser, W.; Prknová, H. Effects of afforestation on soil structure formation in two climatic regions of the Czech Republic. J. For. Sci. 2015, 61, 225–234. [Google Scholar] [CrossRef] [Green Version]
  27. Wang, X.; Zhong, Z.; Li, W.; Liu, W.; Han, X. Effects of Robinia pseudoacacia afforestation on aggregate size distribution and organic C dynamics in the central Loess Plateau of China: A chronosequence approach. J. Environ. Manag. 2020, 268, 110558. [Google Scholar] [CrossRef]
  28. Wei, X.; Shao, M.; Gale, W.J.; Zhang, X.; Li, L. Dynamics of aggregate-associated organic carbon following conversion of forest to cropland. Soil Biol. Biochem. 2013, 57, 876–883. [Google Scholar] [CrossRef]
  29. Ren, C.; Chen, J.; Deng, J.; Zhao, F.; Han, X.; Yang, G.; Tong, X.; Feng, Y.; Shelton, S.; Ren, G. Response of microbial diversity to C: N: P stoichiometry in fine root and microbial biomass following afforestation. Biol. Fertil. Soils 2017, 53, 457–468. [Google Scholar] [CrossRef]
  30. Zhang, Y.; Li, Q.; Chen, Y.L.; Dai, Q.G.; Hu, J. Dynamic change in enzyme activity and bacterial community with long-term rice cultivation in mudflats. Curr. Microbiol. 2019, 76, 361–369. [Google Scholar] [CrossRef]
  31. Feng, W.; Zhang, Y.; Yan, R.; Lai, Z.; Qin, S.; Sun, Y.; She, W.; Liu, Z. Dominant soil bacteria and their ecological attributes across the deserts in northern China. Eur. J. Soil Sci. 2020, 71, 524–535. [Google Scholar] [CrossRef]
  32. Su, Y.G.; Jie, L.; Zhang, B.C.; Zhao, H.M.; Gang, H. Habitat-specific environmental factors regulate spatial variability of soil bacterial communities in biocrusts across northern China’s drylands. Sci. Total Environ. 2020, 719, 137479. [Google Scholar] [CrossRef] [PubMed]
  33. Liu, J.; Dang, P.; Gao, Y.; Zhu, H.; Zhu, H.; Zhao, F.; Zhao, Z. Effects of tree species and soil properties on the composition and diversity of the soil bacterial community following afforestation. For. Ecol. Manag. 2018, 427, 342–349. [Google Scholar] [CrossRef]
  34. Zhao, F.Z.; Fan, X.D.; Ren, C.J.; Zhang, L.; Han, X.H.; Yang, G.H.; Wang, J.; Doughty, R. Changes of the organic carbon content and stability of soil aggregates affected by soil bacterial community after afforestation. Catena 2018, 171, 622–631. [Google Scholar] [CrossRef]
  35. Sun, X.; Zhang, L.; Pei, J.; Huang, L.F. Regulatory relationship between quality variation and environment of Cistanche deserticola in three ecotypes based on soil microbiome analysis. Sci. Rep. 2020, 10, 6662. [Google Scholar] [CrossRef]
  36. Cao, H.; Du, Y.; Gao, G.; Rao, L.; Zhang, Y. Afforestation with Pinus sylvestris var. mongolica remodelled soil bacterial community and potential metabolic function in the Horqin Desert. Glob. Ecol. Conserv. 2021, 29, e01716. [Google Scholar]
  37. Liu, J.; Yang, Z.; Dang, P.; Zhu, H.; Zhao, Z. Response of soil microbial community dynamics to Robinia pseudoacacia L. afforestation in the Loess Plateau: A chronosequence approach. Plant Soil 2018, 423, 327–338. [Google Scholar] [CrossRef]
  38. Lozano, Y.M.; Hortal, S.; Armas, C.; Pugnaire, F.I. Interactions among soil, plants, and microorganisms drive secondary succession in a dry environment. Soil Biol. Biochem. 2014, 78, 298–306. [Google Scholar] [CrossRef]
Plants 11 03456 g001 550
Figure 1. Spatial distribution of 6 T. Zhongshanshan stands in the Yangtze River Basin (blue area in the lower right corner).
Figure 1. Spatial distribution of 6 T. Zhongshanshan stands in the Yangtze River Basin (blue area in the lower right corner).
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Plants 11 03456 g002 550
Figure 2. Soil properties in different T. Zhongshanshan plantations with increased age. Lowercase letters in the upper left corner represent pH (a), bulk density (b), electrical conductivity (c), total nitrogen (d), available potassium (e), soil organic matter (f), available phosphorus (g), respectively. The asterisks on the column indicate significant differences (p < 0.05) between CK and F of the same layer in each site. JX = Jiangxi; JS = Jiangsu; HB = Hubei; CQ = Chongqing; YN = Yunnan; SH = Shanghai; CK = non-forested soils; F = forested soils.
Figure 2. Soil properties in different T. Zhongshanshan plantations with increased age. Lowercase letters in the upper left corner represent pH (a), bulk density (b), electrical conductivity (c), total nitrogen (d), available potassium (e), soil organic matter (f), available phosphorus (g), respectively. The asterisks on the column indicate significant differences (p < 0.05) between CK and F of the same layer in each site. JX = Jiangxi; JS = Jiangsu; HB = Hubei; CQ = Chongqing; YN = Yunnan; SH = Shanghai; CK = non-forested soils; F = forested soils.
Plants 11 03456 g002
Plants 11 03456 g003 550
Figure 3. Amounts of aggregates in different T. Zhongshanshan plantations with increased age. The asterisks on the column indicate significant differences (p < 0.05) between CK and F in each site. JX = Jiangxi; JS = Jiangsu; HB = Hubei; CQ = Chongqing; YN = Yunnan; SH = Shanghai; CK = non-forested soils; F = forested soils. The asterisks on the column indicate significant differences (p < 0.05) between CK and F in each site.
Figure 3. Amounts of aggregates in different T. Zhongshanshan plantations with increased age. The asterisks on the column indicate significant differences (p < 0.05) between CK and F in each site. JX = Jiangxi; JS = Jiangsu; HB = Hubei; CQ = Chongqing; YN = Yunnan; SH = Shanghai; CK = non-forested soils; F = forested soils. The asterisks on the column indicate significant differences (p < 0.05) between CK and F in each site.
Plants 11 03456 g003
Plants 11 03456 g004 550
Figure 4. Cluster analysis and relative abundance of bacterial class (a) phylum (b) based on Bray-Curtis similarities in forested and non-forested soils. JX = Jiangxi; JS = Jiangsu; HB = Hubei; CQ = Chongqing; YN = Yunnan; SH = Shanghai; CK = non-forested soils; F = forested soils. Arabic numbers of 1, 2 and 3 after abbreviations indicated soil depth at 0–15, 15–30 and 30–45 cm, respectively.
Figure 4. Cluster analysis and relative abundance of bacterial class (a) phylum (b) based on Bray-Curtis similarities in forested and non-forested soils. JX = Jiangxi; JS = Jiangsu; HB = Hubei; CQ = Chongqing; YN = Yunnan; SH = Shanghai; CK = non-forested soils; F = forested soils. Arabic numbers of 1, 2 and 3 after abbreviations indicated soil depth at 0–15, 15–30 and 30–45 cm, respectively.
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Plants 11 03456 g005 550
Figure 5. The structure of F (a) network and CK (b) network. CK = non-forested soils; F = forested soils. Modules with fewer than 5 members are not displayed. Circle colors represent the OTU node-affiliated taxa at the bacterial phylum level (Proteobacteria are at the class level) and the circle size represents the connectivity degree of the nodes. Grey lines represent positive correlations and red lines represent negative correlations. The numbers in the parentheses are the number of module hubs or connectors.
Figure 5. The structure of F (a) network and CK (b) network. CK = non-forested soils; F = forested soils. Modules with fewer than 5 members are not displayed. Circle colors represent the OTU node-affiliated taxa at the bacterial phylum level (Proteobacteria are at the class level) and the circle size represents the connectivity degree of the nodes. Grey lines represent positive correlations and red lines represent negative correlations. The numbers in the parentheses are the number of module hubs or connectors.
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Plants 11 03456 g006 550
Figure 6. Redundancy analysis (RDA) based on the bacterial class (a) and phylum (b) data. JX = Jiangxi; JS = Jiangsu; HB = Hubei; CQ = Chongqing; YN = Yunnan; SH = Shanghai; CK = non-forested soils; F = forested soils; BD = bulk density; EC = electrical conductivity; AK = available potassium; TN = total nitrogen; AP = available phosphorus; SOM = soil organic matter.
Figure 6. Redundancy analysis (RDA) based on the bacterial class (a) and phylum (b) data. JX = Jiangxi; JS = Jiangsu; HB = Hubei; CQ = Chongqing; YN = Yunnan; SH = Shanghai; CK = non-forested soils; F = forested soils; BD = bulk density; EC = electrical conductivity; AK = available potassium; TN = total nitrogen; AP = available phosphorus; SOM = soil organic matter.
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Plants 11 03456 g007 550
Figure 7. Afforestation of Taxodium hybrid Zhongshanshan influences soil bacterial community structure by altering soil properties.
Figure 7. Afforestation of Taxodium hybrid Zhongshanshan influences soil bacterial community structure by altering soil properties.
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Table
Table 1. General information of T. Zhongshanshan plantations.
Table 1. General information of T. Zhongshanshan plantations.
SitesAgeSoil ColourCoordinateMean Height (m)Mean DBH (cm)CD
(m × m)		Density
(Stems ha−1)		AGB (t ha−1)
JX 6red116°7′4″ E
28°30′49″ N		7.710.71.6 × 2.2111120.3
JS 8yellow-brown118°49′35″ E
32°10′59″ N		8.912.61.9 × 2.6111134.8
HB 9yellow-brown112°13′37″ E
30°19′34″ N		10.414.12.9 × 4.0111161.9
CQ 11purple108°27′3″ E
30°45′58″ N		13.417.14.4 × 6.1920131.7
YN 13yellow-red102°46′41″ E
24°49′43″ N		13.723.35.6 × 6.11111180.1
SH 14yellow121°38′29″ E
31°34′57″ N		14.222.76.4 × 7.2830152.6
JX = Jiangxi; JS = Jiangsu; HB = Hubei; CQ = Chongqing; YN = Yunnan; SH = Shanghai; DBH = diameter at breast height; CD = Crown diameter; AGB = aboveground biomass.
Table
Table 2. Shannon, Simpson and Chao1 indexes of soil bacteria in different T. Zhongshanshan plantations with increased age.
Table 2. Shannon, Simpson and Chao1 indexes of soil bacteria in different T. Zhongshanshan plantations with increased age.
SitesTreatmentsDepth (cm)ShannonSimpsonChao1
JXCK0–158.22b0.99a1513b
15–308.14b0.99a1646b
30–458.06b0.99a1639a
F0–158.92a0.99a1723a
15–308.98a0.99a1966a
30–458.64a0.99a1570a
JSCK0–158.34b0.99a1957a
15–308.19b0.99a1721b
30–457.80b0.99a1329b
F0–158.90a1.00a1869a
15–308.87a1.00a2063a
30–458.71a0.99a1957a
HBCK0–158.67b0.99a2261b
15–308.60b0.99a2036b
30–458.40b0.99a1879a
F0–159.04a1.00a2534a
15–309.08a1.00a2524a
30–458.96a1.00a2316a
CQCK0–158.92a0.99a2296a
15–308.64b0.99a2328a
30–458.42b0.99a2009b
F0–159.13a1.00a2298a
15–309.10a1.00a2342a
30–459.07a1.00a2382a
YNCK0–159.01a1.00a1922b
15–309.01a1.00a2157b
30–458.98a1.00a2194b
F0–159.20a0.99a2395a
15–309.15a0.99a2565a
30–458.96a0.97a2476a
SHCK0–159.03a1.00a2210a
15–309.06a1.00a2205a
30–459.05a1.00a2194a
F0–159.11a1.00a2262a
15–309.09a0.99a2327a
30–458.99a0.99a2102a
JX = Jiangxi; JS = Jiangsu; HB = Hubei; CQ = Chongqing; YN = Yunnan; SH = Shanghai; CK = non-forested soils; F = forested soils. Different lowercase letters in each column indicate significant differences (p < 0.05) between CK and F in the same layer.
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Shi, Q.; Zhou, Z.; Wang, Z.; Lu, Z.; Han, J.; Xue, J.; Creech, D.; Yin, Y.; Hua, J. Afforestation of Taxodium Hybrid Zhongshanshan Influences Soil Bacterial Community Structure by Altering Soil Properties in the Yangtze River Basin, China. Plants 2022, 11, 3456. https://0-doi-org.brum.beds.ac.uk/10.3390/plants11243456

AMA Style

Shi Q, Zhou Z, Wang Z, Lu Z, Han J, Xue J, Creech D, Yin Y, Hua J. Afforestation of Taxodium Hybrid Zhongshanshan Influences Soil Bacterial Community Structure by Altering Soil Properties in the Yangtze River Basin, China. Plants. 2022; 11(24):3456. https://0-doi-org.brum.beds.ac.uk/10.3390/plants11243456

Chicago/Turabian Style

Shi, Qin, Zhidong Zhou, Ziyang Wang, Zhiguo Lu, Jiangang Han, Jianhui Xue, David Creech, Yunlong Yin, and Jianfeng Hua. 2022. "Afforestation of Taxodium Hybrid Zhongshanshan Influences Soil Bacterial Community Structure by Altering Soil Properties in the Yangtze River Basin, China" Plants 11, no. 24: 3456. https://0-doi-org.brum.beds.ac.uk/10.3390/plants11243456

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